The main specifications for IRFP4668PbF MOSFET.
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
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Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"3072",leadTitle:null,fullTitle:"Mass Transfer - Advances in Sustainable Energy and Environment Oriented Numerical Modeling",title:"Mass Transfer",subtitle:"Advances in Sustainable Energy and Environment Oriented Numerical Modeling",reviewType:"peer-reviewed",abstract:"Our knowledge of mass transfer processes has been extended and applied to various fields of science and engineering including industrial processes in recent years. Since mass transfer is primordial phenomenon, it plays a key role in the scientific researches and fields of mechanical, energy, environmental, materials, bio, and chemical engineering. In this book, energetic authors especially provide advances in scientific findings and technologies, and develop new theoretical models concerning mass transfer for sustainable energy and environment. This book brings valuable references for research engineers working in the variety of mass transfer sciences and related fields. 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Prior to isotope studies, it was believed that the protein pool in the body was in a static state without any dynamic changes [1–3]. The pioneering work of Schoenheimer and his colleagues investigated the metabolic activities of body proteins using amino acid tracers and therein established the dynamic nature of the protein pool [4, 5]. Subsequent experiments thoroughly studied the protein balance in the body and revealed that the diet only provides 60–80 g of proteins (per day) as a source of amino acids building blocks for protein synthesis, while the human body synthesizes 300–500 g of protein every day [6]. This discrepancy between dietary protein supply and synthesis suggests that the majority of the newly made proteins are synthesized from amino acids which are derived from degradation of preexisting proteins [7]. In addition, the
Sources of intracellular amino acids for protein synthesis.
The total protein synthesis rates in whole body and different organs have been measured using radioactive (14C, 35S, and 3H) and stable (13C, 2H, and 15N) isotope labeled amino acids in a tissue using the labeling ratio between the precursor amino acids and the protein products [10]. Because of the simplicity, radioactive isotopes dominated early protein turnover studies until gas chromatography-mass spectrometry (GC-MS) became commonly available for stable isotope-based tracer studies [11]. Radioactive amino acids were widely used in pulse-chase experiments that enabled quantification of both protein synthesis and degradation. However, due to safety concerns, radioactive isotopes found limited application in human studies. With the advancement in mass spectrometry instrumentation, the stable isotope-based amino acids found widespread use in clinical research. Similar to radioactive isotopes, two major designs, i.e., flooding dose or primed infusion of the stable isotope labeled amino acids are utilized to study protein turnover in human studies. Multiple studies investigated advantages and disadvantages of both methods [12, 13]. With a different degree of success, both methods enhanced our understanding of total protein dynamics in different tissues and circulation. However, both methods have been associated with several problems related to the assessment of true precursor enrichment and its impact on data interpretation; in addition, experimental design typically requires inpatient tracer administration. As discussed below, this is particularly critical for the short-term labeling protocol that is based on a precursor and product relationship. The “true precursor” for protein synthesis is the intracellular tRNA-bound amino acids which are usually not accessible, particularly in human studies. Therefore, several extracellular surrogate markers of the “true precursor” have been used for calculation of the kinetic parameters with varying success. Finally, these methods generally require a large amount of expensive tracers, and in the case of stable isotopes, infusion of labeled amino acids elevates amino acid levels and perturbs normal protein metabolism. Until recently, all of these methods were only applicable in studies of total protein kinetics (i.e., consisting of a mixture of proteins) without giving any knowledge about the turnover rates of individual proteins. This shortcoming has particular relevance to health and disease, since it is recognized that proteins differentially respond to stress and the averaging of individual protein fluxes may result in a cancellation of changes in their kinetics. This point can be easily illustrated in the case of acute-phase response (APR) proteins. Due to the distinct dynamics of positive and negative APRs, they are differently affected in conditions associated with inflammation [14] or fasting [15]. Although advancement in methods surrounding protein isolation and sample preparation allowed the analysis of purified (individual) proteins, these methods are in general cumbersome, labor-intensive and, in many cases, it is difficult to purify proteins (specifically low abundant ones) from other contaminants.
Over the last 25 years, the development of novel analytical proteomics methods has provided a major advancement in medical research by allowing investigators to quickly identify and measure the relative amount of a large number of proteins in a plasma or tissue sample. On the other hand, like Western blots, these methods only provide static data on protein levels, and no information on the temporal changes on a given protein. By contrast, coupling of static proteomics with stable isotope-based metabolic labeling approaches enables the study of temporal protein dynamics on a proteome scale. Stable isotope labeled amino acids in cell culture (SILAC) [16] and 15N-labeled algae feeding [17] were successfully applied to study protein turnover in cell culture and then
Among all other tracers, 2H2O and H218O have been used to study the protein turnover [22, 23]. The ubiquitous presence of H and O atoms in amino acids allowed investigators to consider both 2H2O and H218O as unique tracers for the synthesis of virtually all proteins [2, 7, 24]. Since 18O (M+2) isotope adds at least 2Da to each amino acid, the utilization of H218O results in a larger mass shift that improves the sensitivity of the assay as compared to 2H2O. However, H218O is a relatively expensive tracer and is not necessarily affordable for use in humans.
A simplified scheme of 2H-labeling of alanine and proteins.
By contrast, 2H2O is a low-cost tracer which makes it practical for human application [25]. Similar to H218O, 2H2O is safe and it easily equilibrates with total body water (TBW) and 2H2O also rapidly labels all amino acids (e.g., ~10–20 min in rodents and 1 h in humans) [15, 26]. Thus, the quick steady-state labeling of non-exchangeable H atoms in free amino acids after 2H2O administration demonstrates that the rate limiting step of 2H incorporation into proteins is protein synthesis from amino acids (Fig. 2). Although the use of 2H2O in metabolic studies has a long history [24, 27], recently the 2H2O-metabolic labeling experienced a renaissance, for assessing DNA synthesis [28], gluconeogenesis [29], and lipid turnover [30, 31]. Previously, the 2H2O-metabolic labeling approach has been used by us and others to measure the average synthesis rate of mixed tissue proteins [32–35]. We, and others, recently pioneered 2H2O to study the synthesis rates of individual proteins using advanced mass spectrometry-assisted proteomics
By giving 2H2O in the drinking water, one can enrich the precursor amino acid pool with 2H and sustain it indefinitely without affecting the total concentration of precursor amino acids. The rationale is based on the observation that in the presence of 2H2O, cells generate 2H-labeled amino acids via transamination and/or
The incorporation of multiple copies of 2H atoms into nonessential amino acids increases tryptic peptides 2H labeling and improves the assay sensitivity. As a safe, nonradioactive tracer, 2H2O can be administered in the drinking water to free living organisms without interfering with their lifestyle routines. These valuable characteristics of 2H2O-metabolic labeling make it a unique tracer to study the synthesis rates of all proteins in different species, including humans.
Essentially, all tracer-based protein turnover studies rely on establishing precursor (amino acid) and product (protein) relationships. When using a pre-labeled amino acid, one of the major challenges in protein turnover studies is determination of intracellular true precursor enrichment for the kinetic calculations. The true precursor in protein synthesis is an intracellular tRNA-bound amino acid which is in low quantities, and it is not accessible in extracellular fluids [38]. Therefore, the intracellular labeling of free amino acids has been used as the substitute for true precursor enrichment. Although this can be easily done in animal studies, the invasive tissue analysis is not suitable for human studies. In many experiments, only extracellular amino acids are accessible for the precursor enrichment measurements. Since amino acid movement through the cell membrane is a tightly regulated transporter-mediated process, there is an enrichment and concentration gradient of amino acids across the extracellular and intracellular space. To circumvent this issue, several approaches have been proposed to assess true precursor enrichment. For instance, the labeling of an extracellular α-ketoisocaproate (KIC), a metabolite of leucine, was used as a surrogate of intracellular leucine enrichment [39], while intracellular glycine enrichment was assessed based on urinary hippurate metabolite of glycine [40]. In other studies, intracellular amino acids labeling was assessed based on the analysis of protein-bound amino acid in a fast turnover protein like apoB100 [41]. Several studies have demonstrated that different surrogate precursors result in substantially different kinetic calculations and therefore defining the true precursor and data interpretation are key issues in protein turnover studies [42–44].
In contrast to amino acids, 2H2O freely and rapidly equilibrates with the total body water in all organs and cell compartments and transfers 2H to intracellular amino acids [15, 36]. This underlying assumption has been validated in multiple studies through analysis of total body water and intracellular amino acids labeling at different time points [15, 26, 45]. For the kinetic calculations, we assume that protein levels do not change during the 2H2O-metabolic labeling study period, and that there is steady-state flux of all proteins. We have validated this assumption through quantification of plasma proteins abundance using synthetic stable isotope-labeled peptides [31]. In addition, other investigators have performed a direct comparison of the heavy water method with a primed infusion of [2H3] leucine [45] and/or a flooding dose of [2H5]-phenylalanine [46]; these efforts suggest the validity and the reliability of the 2H2O-metabolic labeling approach.
Flow scheme for experimental design and analysis of proteome dynamics with 2H2O. After bolus load of 2H2O (0.3 ml/kg body weight), human subjects consume 0.5% in drinking water for 1 week and blood samples are collected at different time points.
These experimental results allow investigators to consider 2H2O as a precursor of 2H tracer for proteins synthesis. Recently, we developed an algorithm (details discussed below) for calculating the enrichment of intracellular amino acid based on body water enrichment analysis (from accessible body fluids by simple headspace GC-MS analysis) [37]. This overcomes the issue related to true precursor enrichment. Furthermore, oral administration of heavy water after a bolus load easily maintains a steady-state labeling of total body water and amino acids that result in a substantial enrichment of analyzed proteins. When applied to plasma or serum proteins, the experimental design for 2H2O-metabolic labeling is as follows:
2H2O is given in a bolus dose followed by low intake in the drinking water to maintain a constant steady-state enrichment of 2H2O in body water (Fig. 3).
Body water and tissue amino acids are rapidly labeled and attain a steady state without perturbing the normal concentration of amino acids. The 2H labeling of free amino acids is directly proportional to the number of carbon-bound exchangeable hydrogen atoms and the 2H2O enrichment of body water, and is independent of the rate of protein synthesis.
Blood is obtained and proteins are isolated at preselected time points.
Tryptic peptides are analyzed by LC-MS/MS, which identifies several peptides from each protein of interest, both the endogenous mass and heavier peptides that are enriched with 2H-labeled amino acids. Proteins are identified using online MASCOT or alternative software packages.
The high-resolution full scan data analysis is performed using in-house-developed software allows quantification of mass isotopomer distribution of peptides.
The time-course labeling of unique peptides represents the rate of synthesis of a protein that they originate from.
Protein life spans (or half-lives) range from minutes to more than 1 month. Although the heavy water-based metabolic labeling approach may not be suitable for the kinetic studies of very short-lived regulatory proteins such as glucagon, insulin, leptin, and adiponectin, it can capture the kinetics of thousands of proteins with the half-lives that are longer than the distribution and equilibration of 2H2O with amino acids.
This method has major advantages over other stable isotope methods that utilized amino acids pre-labeled with 2H, 13C, or 15N, namely: (1) it enriches all proteogenic amino acids and thus increases the enrichment of newly synthesized proteins to a far greater extent than that can be achieved by infusion or feeding labeled amino acids or proteins, (2) it can be given to humans by multiple oral doses over the course of a day in drinking water and does not require IV infusion, and (3) it is relatively inexpensive (~$350/person) compared to traditional amino acid tracers ($1,000-$4,000/person).
For the most accurate calculation of protein kinetics, two different short-term and long-term experimental designs with heavy water have been employed.
The short-term protocol requires the bolus load of heavy water and the measurement of peptide enrichment during the semilinear increase segment of 2H-labeling time-course curve [15, 22]. The optimal design for the short-term heavy water protocol requires multiple time points in the early period of protein synthesis, although a single time-point sampling after 2H2O administration is also possible [47]. For the kinetic calculations, we assume that protein levels do not change during the 2H2O-metabolic labeling study period, and that there is a steady-state flux of all proteins. We have validated this assumption through quantification of plasma protein abundance using synthetic stable isotope-labeled peptides. Thus, at a steady state, the rate constant represents both the fractional synthesis rate (FSR) and the fractional catabolic rate (FCR). In this case, the fractional synthesis rate (FSR) of a protein could be calculated based on the slope of the labeling of the tryptic peptide and precursor amino acid enrichment using the formula [15]:
where the slope of
The FSR calculation using equation (1) necessitates the analysis of amino acids labeling in specific tissues in order to determine true precursor labeling. As mentioned above, invasive tissue analysis limits the application of this technique mainly to animal studies and complicates its translation to clinical research. In order to circumvent the problems related to the measurement of intracellular amino acid labeling, we developed an algorithm for estimation of the precursor enrichment based on accessible body fluids [37]. The rationale is similar to those used for heavy water-based lipid turnover studies and based on the fact that the 2H-labeling of body water represents the precursor enrichment. Thus, the precursor amino acid enrichment in equation (1) could be replaced with the total body water enrichment. However, since a product (analyzed peptide) incorporates multiple copies of 2H, the denominator in equation (1) should take into account the asymptotic number of deuterium (
where
We demonstrated the utility of this approach by quantifying the effect of the nutritional status on the synthesis of albumin and other acute-phase response proteins in rats [15]. With this approach, protein turnover could be determined in a few hours with the total body water (TBW) enrichment of ~2.5%. For the plateau labeling of analyzed plasma proteins, we used the data from our 10-day 2H2O experiment. Since the half-life of rat albumin is ~1.8 day, the number of incorporated deuterium atoms from 10-day labeling experiment (i.e., 5 half-lives of albumin) is close to the maximum possible 2H incorporation. This short-term 7-h 2H2O labeling protocol allows measurement of the kinetics of proteins with a wide range of rate constants (~1%/h for albumin and ~16%/h for ApoB100). Calculated half-lives of different plasma proteins observed using this approach agree with their known biological functions. For example, rapid FSRs were observed for the acute-phase response proteins haptoglobin and fibrinogen. Hemoglobin, albumin, and ApoAI which are involved in oxygen delivery, fatty acid transport, and reverse cholesterol transport, respectively, have the longest half-lives from all the studied plasma proteins. The observed half-lives are also in agreement with the
A short-term (e.g., 7-h) 2H2O-labeling experiment in rats also allows assessing the effect of nutritional status on the synthesis of plasma proteins, including albumin. Using this approach, it was determined that fasting has a divergent effect on protein synthesis in accordance with the biological function of the protein. In agreement with previous studies using amino acid tracers, it was found that fasting increases the synthesis rate of ApoB100 while reducing the synthesis rates of albumin and fibrinogen. Stimulated synthesis of ApoB100, the principal protein of very-low-density lipoprotein (VLDL), suggests increased secretion of VLDL, a well-known phenomenon in fasting. However, the synthesis rate of albumin, the most abundant plasma protein, was reduced ~twofold in the fasting state as compared to the fed state. Presumably, this was related to the regulation of albumin synthesis by amino acid substrate availability.
Although the short-term experimental design enables one to assess the turnover rates of plasma proteins in several hours, it requires the knowledge of the precursor enrichment. Alternatively, a long-term labeling protocol allows one to measure protein turnover based on modeling of the time-course labeling of analyzed peptides without knowledge of precursor enrichment; note that this is often based on the assumption of a single compartment [15, 22]. The drawback of this design is that it requires the collection of multiple samples for the curve fitting. The FSR in a long-term experiment is calculated by fitting the time-course total labeling of a peptide (
where
Total labeling of a peptide will be calculated using the formula:
where MPE
Similar to other tracer experiments, there is a time delay between 2H2O administration and the effective onset of a protein labeling. Such delays most likely reflect a lag between ribosomal protein synthesis and export. Secretory proteins are synthesized on polysomes bound to rough endoplasmic reticulum (ER) and are transported to the lumen of the ER. Before secretion, proteins are transported from the ER to the Golgi apparatus and there is a temporal delay in the transfer from the ER. This delay is especially important in calculation of FSR for relatively fast turnover proteins, such as ApoB100 [50]. It takes ~30 min for newly synthesized ApoB100 to be packaged and released into the circulation; thus, there is a time lag between protein synthesis and appearance in the plasma. To take the delay into account, the expression of
where
In both short-term and long-term heavy water metabolic labeling experiments, the production rates (PR) for a protein is calculated as the product of FSR and the respective pool size of a given protein:
where the pool size is an absolute content of a protein. In the case of plasma proteins, the pool size is the product of a protein concentration and plasma volume, estimated as 45 ml/kg body weight. Plasma concentration of a protein can be measured using a standard enzyme-linked immunosorbent assay (ELISA) techniques or the isotope dilution method by mass spectrometry [51].
Although the low dose of 2H2O (~0.5% TBW enrichment) is well tolerated in humans, the transient dizziness has been observed in some subjects with the higher bolus aiming to bring TBW enrichment 1.5–2% [52]. To reach this high level of 2H2O, according to the original study designs, human subjects ingested 4–5 smaller doses of 2H2O over 4–5 h. Recently, instead of a primed bolus, the gradual increase of 2H2O of TBW enrichment was proposed. According to this protocol, 2H2O enrichment of TBW exponentially increases and reaches the plateau value [25, 53]. The gradual increase of 2H2O in body fluids prevents any side effects related to 2H-isotope effect. This nonsteady-state labeling of TBW increases the study duration and somewhat complicates the calculation. We applied this approach, i.e., slow increase of 2H2O enrichment of TBW, to study mitochondrial proteome dynamics in a rat model of heart failure [54]. We also constructed a new algorithm to calculate the time-dependent changes in heavy mass isotopomers of newly synthesized peptides. To account for the relatively slower increase in body water labeling, we fit the measured body water enrichment into an exponential curve that yields the body water turnover curve. Then, the modeled continuous body water curve was used for estimation of kinetically relevant body water enrichment required for accurate calculation of synthesis rates. We demonstrated that the calculated turnover rate constants for mitochondrial proteins using this nonsteady-state labeling protocol are very similar to those based on the steady-state bolus labeling of TBW [55]. Thus, this data analysis approach allows accurate quantification of the rate constants to analyze a protein turnover when 2H2O is administered without a priming bolus. This is of particular importance for human studies when it is preferable to increase the TBW enrichment gradually in order to eliminate concerns related to occasional transient dizziness observed with a high bolus dose of heavy water [52]. This also simplifies the study design, since small amounts of heavy water can be consumed outside of the clinical research unit without interference with the daily lifestyle of study subjects.
Like other stable isotope-based turnover studies, heavy water metabolic labeling requires sensitive and reproducible measurements of isotope labeling of proteins. This necessitates an accurate quantification of isotopomer distribution of protein-bound amino acids or tryptic peptides unique to a specific protein. Accurate and precise estimates of the isotopic ratio are critical when one aims to quantify subtle changes in protein synthesis due to diseases or an intervention.
Classical studies of protein turnover studies with heavy water utilized GC-MS to measure 2H-incorporation into protein-bound amino acids after the hydrolysis of protein(s). Because of the low cost of GC-MS instruments, they have traditionally been more accessible than LC-MS instruments. In addition, until recent developments in high-resolution ion detections, many LC-MS instruments had lower accuracy in isotope ratio measurements compared to simple GC-MS instruments. A gas chromatography inlet enables separation of individual amino acids and quadrupole mass analyzer allows accurate measurement of isotope enrichment with ± 0.3%. In the case of 2H-labeled compounds, heavy isotopomers enriched with 2H are slightly shifted and eluted in front of the monoisotopic signal (M0). This chromatographic fractionation was used for the accurate quantification of low 2H enrichment in amino acids and other molecules. With this approach, as low as 0.01% 2H could be accurately measured using a simple quadrupole GC-MS instrument [56]. The majority of early studies with GC-MS were focused on total body or tissue-specific mixed protein turnover without giving knowledge about individual proteins. Later on, this approach was extended to the analysis of purified individual proteins. This requires labor-intensive purification of individual proteins and permits only analysis of one protein at a time. In addition to being time consuming, these protocols suffer from potential contamination associated with protein isolation. The development of isotope-ratio mass spectrometry (IRMS) systems adds more than 100-fold increase in sensitivity for measuring of 2H enrichment compared with GC-MS [57]. However, similar to GC-MS, IRMS instruments are limited to analysis of protein-bound amino acids.
Recently, a proteomics-based approach was applied to assess the protein turnover in a mixture of proteins [15, 25, 47]. In contrast to static proteomics, the dynamic proteomics method requires accurate quantification of the isotope distribution of peptides that requires high-resolution mass analysis. Studies by Anderson’s group evaluated the utility of different type of electron spray ionization (ESI) and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry for the isotope distribution analysis [58, 59]. A Finnigan TSQ 700 or Micromas Quatto II, Thermo-Finnigan linear trap quadrupole (LTQ) linear ion-trap and Applied Biosysytems Q-STAR XL hybrid quadrupole-TOF, and Bruker BiFlex III MALDI-TOF were tested [59]. Tandem spectra on the ion-trap instrument were collected in either a zoom scan or profile mode while the quadrupole instrument was operated in the selected ion monitoring (SIM) mode. It has been determined that the signal intensity is the key parameter for accurate characterization of isotope distribution. For instance, the quantification of M1 with precision better than 5% requires intensities of the base peak ≥20,000 counts in a MALDI-TOF instrument. Based on our experience, similar precision on LTQ linear ion-trap instrument can be achieved with an ion intensity of 104 relative to the background signal. It has been noted that MALDI-TOF slightly overestimates M1.When the ESI trap and quadrupole instruments were tested for the accuracy and precision of isotope distribution, the ion-trap MS performed better than the SIM quadrupole MS. Interestingly, the quadrupole instrument in SIM mode had greater precision than MALDI-TOF MS and the accuracy of the quadrupole measurement was improved when it was operated in a profile scan mode.
When applied to protein turnover studies, the high resolution of MALDI-TOF MS allows accurate quantification of 2H enrichment of tryptic peptides [22]. The traditional proteomics methods coupled with MALDI-TOF MS-assisted isotope distribution analysis greatly advanced protein turnover studies in a mixture of proteins. However, the absence of a chromatographic inlet enables the analysis of only the most abundant proteins and therein compromises a broader application to low abundant proteins. Also, regardless of the peptide abundance, the presence of interfering signals compromises the utility of MALDI-TOF MS for turnover studies in a complex mixture of proteins. To avoid this issue, we also evaluated the utility of the linear ion-trap LTQ instrument for measurement of the fractional synthesis rates of plasma proteins [15]. The high sensitivity of LTQ MS in zoom scan mode allows accurate measurement of the kinetics of proteins and the assessment of changes in plasma proteins synthesis rates related to animal nutritional status. One of the limitations of this instrument is that only a limited number of peptides can be targeted in each duty cycle which is limited by the scan speed. To increase the number of analyzed proteins in a single run, the chromatogram can be divided into several time segments. In this case, several peptides are analyzed in each time segment. Still, this approach allows only the quantification of isotope distribution using 10–15 peptides from 3–6 proteins using 2-h high-performance liquid chromatography (HPLC) gradient and well-designed MS method. Although several mass spectrometer platforms with liquid chromatographic inlets allow accurate quantification of the isotope distribution, only the high-resolution mass spectrometers permit measuring protein turnover on a truly proteome-wide scale.
It has been shown that quadrupole time-of-flight (Q-TOF) MS instruments have a good reproducibility and can accurately measure isotope ratios [60], and it was utilized to study the lipoprotein turnover in mice. However, Q-TOF instruments have relatively lower resolution (~30,000) that limits the isotope ratio accuracy of the isotope ratio analysis. By contrast, the hybrid Fourier transform ion cyclotron (FT-ICR) and Orbitrap mass spectrometers are characterized by unsurpassed resolution (>100,000), high mass accuracy, and sensitivity [61, 62]. The high mass accuracy of these instruments improves identification and characterization of peptides, while high resolution provides additional information for the characterization of the molecular formula based on natural enrichment. Importantly, the high resolution of these instruments, coupled with the increased scan rates, allows accurate isotope distribution analysis that enables measurement of metabolic labeling of all analyzed peptides. Recently, we demonstrated that isotopic ratios between the monoisotopic and heavy isotopic peaks are consistently lower than predicted values and the magnitude of the spectral error in the FT-ICR MS is proportional to the scan duration of the ion clouds (i.e., resolution setting) [63]. It has been shown that the logarithm of the measured isotopic ratio linearly decreases with the acquisition time, and this phenomenon has previously been used to improve the accuracy of the isotopic distribution analyses [64]. However, even at the lowest resolution setting (e.g., 7,500) a significant error (~5%) was observed with FT-ICR MS analysis. Mass accuracy and isotopic ratios may be affected by the static Coulomb repulsion of ions, so fewer ion numbers could reduce the error. However, accurate quantifications of isotopomers require a sufficient number of ions. We found that ion intensities could be accurately measured with ion counts ranging from ~10,000 to 100,000. In this range, the isotopic ratios are approximately the same, while higher ion counts leads to greater error in isotope ratio measurements. To obtain accurate isotopic ratio measurements of peptides, multiple scans with different durations were performed, and the data were extrapolated to the initial moment of the ion rotation. This approach minimizes the absolute isotopic ratio error to within ~1–0.5%. In addition, we found that monitoring the parent ions in the SIM mode (mass interval is 10 Da), and the collision-induced dissociation (CID) fragments in the single reaction monitoring (SRM) mode, improves the specificity of the assay and allows selective identification of peptide and its fragments for isotopomer analysis. Using SIM and SRM experiments in the same acquisition allows reliable simultaneous quantification of the isotopic distribution of both the parent peptide and its fragment ions [37]. An accurate measurement of two consecutive peptide fragments allows one to calculate the labeling of protein-bound amino acids, including alanine, glutamine, and glutamate [37].
Next, we tested the utilities of hybrid Orbitrap Velos and Orbitrap Elite instruments for the 2H-based metabolic labeling studies [54, 55]. To evaluate the utility of the newer generation Orbitrap Elite instrument for isotope distribution analysis, a calibration curve was constructed by adding an increasing amount of [2-2H]alanyl-YLYEIAR to a constant amount of unlabeled YLYEIAR. Interestingly, similar but lower magnitude error in isotope ratios was observed in both Orbitrap instruments. Consistent with previous studies [65], the Orbitrap also yields higher error at higher resolution setting. The Orbitrap Elite displayed the highest accurate isotope ratio measurements. We found consistent underestimation of the isotope ratio measurement when lower 2H enrichment was measured, while overestimation was observed at the higher 2H enrichment (Fig. 4). Interestingly, the error was less when doubly charged ions of the same peptide were analyzed.
Calibration curves of [2-2H]alanyl-YLYEIAR enrichment (0–97%) measured at different resolutions in Orbitrap Elite instrument. More than 5% error was observed at higher resolution (100 K) for a singly charged peptide ion. Measured enrichment of doubly charged peptide ion is similar to simulated theoretical values.
The accuracy and precision of molar percent enrichment (MPE) determinations, calculated as the fraction of the total intensity, depends on the number of isotopomers that are used in the calculation. This is due to a lower abundance of heavy isotopomers which introduce more error in MPE calculation. To circumvent this problem, an alternative approach, i.e., M1/M0 ratio, was proposed to assess 2H-induced changes in an isotope distribution [59]. Although this approach is useful for the modeling of the labeling data in a long-term experiment, it does not allow one to assess the total labeling of an analyzed peptide and asymptotical number of 2H, the critical step in calculation of the FSR in a short-term experiment.
The high-resolution mass spectrometers allow one to analyze isotopic distributions of virtually all peptides, thus enabling measurement of global proteome dynamics. The bottleneck in these experiments is the data processing. Therefore, high-throughput and robust bioinformatics tools are required to extract the relative isotopomer information from time-course data for the calculation of protein turnover rate constants based on large volume and complex data sets generated by high-resolution mass spectrometers.
Several software solutions have been proposed for the tracer-specific protein turnover studies. SILACtor has been successfully used for protein turnover SILAC experiments in cell culture [66]. SILACtor is useful for
The heavy water-metabolic labeling approach poses further specific challenges to data analysis software [68]. In contrast to protein turnover studies with pre-labeled amino acids that lead to substantial average mass shifts in newly synthesized proteins, the labeling with heavy water mainly affects the relative isotopomer distribution without a measurable mass shift (maximum ~0.2–0.4 Da in an average mass of tryptic peptides). Thus, the partial labeling of proteins with the overlapping isotope profiles of labeled and unlabeled species complicates routine data analysis with 2H2O-labeling approach.
Therefore, the successful implementation of the heavy water labeling experiment, in addition to improvements in mass spectrometry, sample preparation, and fractionation, depends on the efficiency of robust software for data processing. It is also preferable that the software could handle the data generated by different high-resolution instruments. Recently, several high-resolution mass spectrometer platforms have been used to study protein turnover using stable isotopes, including 2H2O. Q-TOF mass spectrometer (Agilent) was applied to assess the proteome dynamics in plasma and different tissues [25]. For the data analysis, the authors used MassHunter software package (B0.4) from Agilent (Santa Clara, CA) specially designed for the isotopic distribution analysis of peptides processed in Agilent 6520 Q-TOF mass spectrometers. As Agilent’s proprietary software, the MassHunter software package is not freely available to the public. Although this software facilitates the analysis of data generated in Agilent Q-TOF MS, for accurate isotopomer profiling, each sample is analyzed twice: during the first injection, MS/MS spectra are collected for protein identification and a second injection was performed for high-resolution full scan acquisition which doubles the instrument time per sample and limits high-throughput analysis. In addition, unlike high-resolution FT mass spectrometers, Q-TOF instruments have relatively lower resolution (~30,000 compared to 120,000 in Orbitrap Elite) that limits the accuracy of isotope ratio-based turnover measurement in this instrument.
Although currently available FT LTQ-ICR and LTQ-Orbitrap hybrid instruments allow both MS/MS scans and full scan analysis in a single acquisition with unsurpassed high resolution, in contrast to an Agilent Q-TOF instrument, they are not supported with software that could automatically extract the data from high-resolution full scan spectra. Thus, specialized software for automated high-resolution data analysis is critically needed. To advance 2H2O-metabolic labeling for
In the original version, the software used a mass accuracy of 100 ppm and resolution of 15,000, which increases the likelihood of contamination of mass isotopomers by co-eluting signals. To avoid the complexity caused by co-elution, the mass spectrometers were operated at lower resolution (15,000) and mass accuracy (100 ppm) [69] which simply masks the interfering signals due to low resolution. A later version of the software included more stringent filtering parameters: a mass window of 75 ppm is recommended for 30,000 or 60,000 resolution (http:/www.heartproteome.org/proturn/index.html). However, this software is not freely available to the public, and the raw data from outside investigators could be processed only with the assistance of a web administrator. So far, to the best of our knowledge, no study from outside investigators has been reported using this software.
To aid our heavy water-based proteome dynamics studies, we recently developed an alternative software [55] which is available at the University of Texas Medical Branch (UTMB) website, https://ispace.utmb.edu/users/rgsadygo/Proteomics/HeavyWater/Version.1.0. Although this semiautomated software still requires a skilled operator for the data analysis, to the best of our knowledge, this is the only freely available software for quantification of proteome dynamics using heavy water-based metabolic labeling approach. With this software, a routine data analysis workflow for the heavy water labeled samples starts with the peptide/protein identifications from tandem mass spectra using protein sequence databases. Thus, the software reads all peptide IDs from the MASCOT mzIdentML files and confirms each ID based on the stored MS/MS scans at every time point. The initial step is to overlay the chromatographic profiles for each LC-MS run from all time points. Then, the software generates extracted ion chromatograms for each isotopomer for positively identified peptides from the high-resolution full MS scans within the elution time window of the corresponding MS/MS scan. In addition to peptide selection based on an exact mass and retention time, the software also filters unlabeled peptides at the baseline (
Next, the mass isotopic distributions for all selected peptides are quantified as a function of time. Peaks intensities are extracted from the averaged full scan by searching for an intensity that is maximum within the theoretical mass window (±10 ppm). We then use separate software to compute FSR, and the values for the same proteins are averaged. Examination of large data sets reveals that even with using these stringent criteria, contaminating signals may result in inaccurate rate constant calculations. This could be related to contributions from minor overlapping unresolved peaks that may not be easily filtered during isotope distribution analysis by the software. Therefore, a second-line filtering step of “contaminated peptides” involves the elimination of outlier peptides based on the coefficient of variation in the protein turnover rate constant relative to the average of the other peptides. Thus, the extracted data from only those peptides that could be modeled with the regression coefficient cutoff of 0.95 for nonlinear curve fitting and coefficient variation less than 30% relative to average of other peptides are selected for final quantification of the rate constants. These stringent selection criteria combined with precise isotope distribution analysis results in accurate quantification of protein synthesis rates.
Recent technological advancements in bioanalytical instrumentation and their application to systems biology are starting to significantly advance our understanding of integrative physiology. These achievements would not be possible without progresses made in genomics, proteomics, and metabolomics, that is, “omics” technologies that enable comprehensive screening of the genome, proteome, and metabolome [70, 71], respectively. The immense information collected using these “omics” sciences helps to understand the diseases mechanisms and facilitates early diagnosis of the disease, along with implementation and evaluation of personalized therapy [72]. Utilization of genomics in particular enabled the discovery of several genetic diseases. Although multiple protein biomarkers have been identified using quantitative proteomics, compared to genomics, proteomics is still lagging behind as a clinical test method. This is partly related to the complexity of the human proteome. In addition, profiling of proteins may not be sufficient to understand physiological changes in a living organism, because they have inherent limitations associated with the low sensitivity of static measurements which are the end result of the changes in dynamic flux. In general, stress-induced changes in a biological system first affect the flux of a protein(s) that may lead to more drastic changes in their pool sizes. Only an uncompensated response to stress would result in the nonsteady-state changes in the synthesis or degradation of proteins leading to alterations of their pool sizes. Importantly, the magnitude of changes in flux measured with a small amount of tracer often exceeds the changes in large pool sizes. This is why the kinetic measurements are usually more sensitive than static measurements. In addition, if the stress equally increases or decreases both synthesis and degradation, then the pool size may not change at all. Isotope-based technologies allow investigators to measure changes in flux, and recently, “fluxomics” joined the “omics” sciences. Stable isotope-assisted dynamic metabolomics helped discover previously unknown metabolic pathways [73]. While fluxomics measures large numbers of metabolite turnover, a stable isotope-assisted protein turnover investigates the dynamic genome expression through the temporal changes in a protein flux. Thus, the traditional static proteomics, coupled with a metabolic labeling approach and high-resolution mass spectrometry, is expected to provide a means for simultaneous measurements of proteome dynamics. From the tracer selection point of view, a heavy water-based metabolic labeling approach is of particular interest. For example, H is the ubiquitous element of all biological molecules, and as a universal tracer, 2H2O labels DNA, RNA, proteins, and metabolites and provides the wealth of information in integrated comprehensive “omics.” Because of our focus on proteomics, we will mainly highlight the biological application of 2H2O-based proteome dynamics.
Since proteins are indispensable to life activity and involved in multiple structural functions, enzymatic, activities, signal transduction, growth, and repair functions, only minor alterations in a protein homeostasis can lead to genetic and acquired diseases. Mass spectrometry-based protein turnover studies enables the analysis of perturbations in the protein metabolism in different diseases. Recently, 2H2O-based metabolic labeling approach was applied to study proteome dynamics in whole blood, blood cell fractions, plasma, whole tissue samples, and cell organelles. Here we will focus mainly on
Early studies with the 2H2O-based labeling approach were focused on plasma albumin and mixed tissue proteins synthesis. Using a rat model, it was found that plasma protein synthesis is very sensitive to nutrient availability and ~50% of plasma albumin that was synthesized over a 24-h period was produced within ~5 h after the meal [74]. Furthermore, this study demonstrated that the heavy water approach also permits the analysis of plasma albumin synthesis during metabolic “steady-state” and “nonsteady-state” conditions corresponding to fasted and fed states. Consistent with these results, using a proteomic approach, we demonstrated albumin synthesis in rats was significantly reduced after 22-h fasting [15].
The effects of dietary factors on tissue protein synthesis were investigated in acute fasting (20 h) vs. chronic food restriction (7 days), and feeding (a single meal) conditions in rats. Both acute and chronic fasting significantly reduced mixed tissue protein synthesis in the liver and gastrocnemius muscle, while it did not affect protein synthesis in the left ventricle of the heart [32], indicating that cardiac protein synthesis is preserved in conditions of nutritional perturbations. The follow-up studies demonstrated that diet-induced obesity in mice did not affect the skeletal muscle protein synthesis; however, it did impair the response of muscle protein synthesis to nutrient supply [34].
Understanding the mitochondrial proteome is a new emerging area in proteomics analysis which is largely aimed at targeting over one thousand proteins that are critical in adenosine triphosphate (ATP) synthesis and cell signaling [75]. Mitochondrial dysfunction plays a key role in aging and different diseases associated with oxidative stress and impaired energy metabolism [54, 76]. Therefore, recent attention toward mitochondrial biogenesis [77, 78] and proteome dynamics [55, 69] became an intense area of research in mitochondrial biology. The wide range of concentration of mitochondrial proteins poses a great challenge for comprehensive analysis of the mitochondrial proteome. Nevertheless, several fractionation and enrichment methods have been used to map mitochondrial proteomes. Different labeling approaches have been applied to measure the turnover rates of mitochondrial proteins. For example, [2H3]-leucine was used to assess the
Previously, it has been shown that the turnover rate of the total mixed mitochondrial brain proteins are slower than those of cardiac proteins [82]. When we compared the turnover rates of individual proteins in the rat brain and heart mitochondria, we found that in the brain, the turnover rate of superoxide dismutase is indeed slower than in the heart (Fig. 5). By contrast, ATP synthase F1β has a much faster turnover rate in the brain than the heart, suggesting that the kinetics of individual proteins in each organ is determined by their functions. Consistent with previous studies [82], we found that similar to the heart, the turnover rates of all analyzed mitochondrial brain proteins had much slower turnover rates compared to those in the liver.
Comparison of half-lives of brain and heart mitochondrial proteins.
To test the effect of heart failure on the stability of cardiac mitochondrial proteins, we utilized our 2H2O approach to measure mitochondrial proteome dynamics in a well-established rat model of heart failure induced by chronic transverse aortic constriction (TAC) [54]. Decreased mitochondrial ATP generating capacity in myocardium is a hallmark of heart failure; however, the underlying mechanisms contributing to mitochondrial dysfunction in heart failure are not yet fully understood. Rats with TAC develop moderate heart failure after 22 weeks, which results in left ventricular remodeling, dysfunction, and reduced oxidative capacity in mitochondria. Heart failure caused a decrease of mitochondrial proteins and respiratory capacity in IFM, but not in SSM. We used a heavy water method to determine whether the decreased synthesis of mitochondrial proteins contribute to the respiratory dysfunction in heart failure. Although the synthesis rates of proteins in IFM tend to be higher than those in SSM, it only started to reach modest significance (
Thus, the kinetic measurements of mitochondrial proteins may help understand the mechanisms responsible for mitochondrial alterations in the failing heart. Taken together, utilization of the 2H2O method for mitochondrial proteome studies demonstrated that this method is robust and can distinguish subtle differences in synthetic rates between subcellular populations of mitochondria. In addition, measuring the kinetics of individual proteins enables one to uncover changes in the mitochondrial proteome due to heart disease that cannot be obtained by simply measuring their static expression at any given time point.
In a follow-up study, Lam and coworkers applied the heavy water method to determine protein kinetic signatures of β-adregengic-induced cardiac remodeling in a mouse model [47]. Several kinetic markers of calcium signaling, energy metabolism, proteostasis, and mitochondrial dynamics were identified. Although large set of data was generated, the biological relevance of these results requires further evaluation based on protein properties and pathways that they are involved.
Hellerstein and coworkers used 2H2O labeling-based dynamic proteomics combined with the stable isotope labeling in mammals (SILAM) quantitative proteomics to explain the effect of long-term calorie restriction on longevity [83]. Through assessment of both catabolic rate and absolute synthesis of hepatic proteins, the authors demonstrated that calorie restriction reduces the turnover of most (~80%) hepatic proteins, including mitochondrial proteins. Thus, long-term calorie restriction increases the stability of proteins and reduces global protein synthetic burden that is associated with decreased mitochondrial biogenesis and mitophagy. The pathway analysis revealed that proteins with related functions display coordinated changes.
The 2H2O-labeling methods were also applied to identify kinetic biomarkers of neuronal dysfunction in mouse models of neurodegeneration [84]. After a bolus administration of 2H2O, appearance of 2H-labeled neuronal proteins with transport and cargo functions in cerebrospinal fluid was quantified. Compared to controls, the appearance of proteins in mice with neurodegeneration was delayed, which was normalized after microtubule-modulating pharmacotherapy, suggesting that the transport kinetics may provide a test method for monitoring disease progression and therapy for neurodegenerative diseases.
We applied 2H2O-based metabolic labeling approach to assess the high-density lipoprotein (HDL) proteome dynamics in a diet-induced mouse model of nonalcoholic fatty liver disease (NAFLD) [85]. HDL displays multiple functions that include reverse cholesterol transport (RCT), preventing inflammation, oxidation, platelet activation, and maintaining endothelial function. In metabolic diseases associated with insulin resistance, HDL may lose these protective functions and become dysfunctional. The reasons for these changes are not fully understood and may be attributed to alterations of the HDL particle composition and modifications of HDL proteins. In addition to ApoAI and ApoAII (which account ~65% and ~15% of HDL protein mass, respectively), recently more than 50 less abundant HDL proteins have been identified. These HDL proteins involved in lipid metabolism, acute-phase response, innate immunity, protease inhibition, and regulation of endothelial cell apoptosis that determines HDL’s anti-inflammatory, anti-atherogenic, and cell survival properties. Thus, alterations in the HDL proteome composition may be a key factor involved in HDL dysfunction.
It is well known that a Western diet (WD, high-fat diet containing cholesterol) for 12 weeks leads to insulin resistance, NAFLD (hepatic steatosis, oxidative stress, and inflammation), and atherosclerosis (aortic root lesion) in low-density lipoprotein receptor (LDLR-/-) mice. Proteomics analysis of ApoB-depleted plasma revealed that a WD also altered the levels of multiple proteins known to be associated with HDL. The kinetics of 60 previously identified HDL proteins involved in lipid metabolism, thrombosis, protease inhibition, complement regulation, and acute-phase response were quantified. The analyzed HDL proteins exhibited a wide range of half-lives varying from a few hours to days. For instance, in a standard chow diet-fed LDLR-/- mice, ApoE, ApoAI, and PON1 have half-lives 5, 15, and 64 h, respectively. A WD has differential effects on the turnover rates of proteins with different functions. We found that a WD results in decreased levels and increased catabolism of PON1 which is responsible for the antioxidant function of HDL. Interestingly, a WD also resulted in increased levels and turnover of phospholipid transfer protein (PLTP), which is responsible for promoting HDL remodeling through phospholipid transfer from ApoB-containing particles to HDL. Mice deficient in PLTP are protected from atherosclerosis, while HDL from mice over expressing PLTP is dysfunctional in promoting cholesterol efflux, and these mice developed higher atherosclerotic lesion compared to control mice. Thus, 2H2O labeling allows to measure HDL proteome flux that is relevant to HDL functionality.
Since the RCT function of HDL represents the dynamic flux of cholesterol from peripheral tissues, including macrophage transfer to liver for clearance, we next applied our 2H2O-metabolic labeling approach to assess HDL flux as an
2H2O labeling-based HDL turnover method also was applied to assess the effect of different isoforms of apoAI and gender on
Although 2H2O has been used for more than 60 years in animal studies to measure a proteins’ renewal rate, only in 2004 it was introduced to study protein synthesis rates in humans [87]. This first human study validated the basic underlying assumptions of 2H2O use in humans, i.e., equilibrium with total body water and amino acids is rapid and body water enrichment can be maintained constant for a long period of time. With ~0.4% TBW enrichment, the FSR of albumin based on albumin-bound alanine enrichment was determined to be ~4%/day in renal patients.
A recent study evaluated the long-term safety and hemodynamic effects of higher levels of heavy water ingestion in healthy young human subjects [53]. Subjects consumed 70% enriched 2H2O in 4 boluses of 0.51 ml/kg body weight daily during the first week of labeling. During the second week, the subjects consumed 4 boluses of 0.56 ml/kg. This protocol resulted in gradual increase of body water enrichment up to ~2% during the 14 days of heavy water exposure. The subjects’ vital signs were monitored during 2H2O administration, and these subjects were followed up to an 8-month period. Total body water enrichment during exposure and subsequent physiological clearance from body fluids were determined during the following 2 weeks. No signs of discomfort and physiological effect were reported in these healthy young adults. After depletion of 14 of the most abundant proteins by multiple affinity columns, the tryptic digest from remaining proteins was fractionated using two-dimensional liquid chromatography separations and analyzed by the LTQ Orbitrap instrument. The turnover rates of hundreds of proteins were then determined. There was no correlation between protein turnover rates and protein abundance. Although many proteins involved in cardiovascular disease were also quantified, this proof of the concept study did not evaluate any link between protein turnover rates and disease. It was concluded that 2H2O is safe and effective tracer for large-scale human studies.
Several human studies utilized low-dose heavy water to assess the effect of exercise and cachexia on muscle protein synthesis. Gaiser and colleagues applied 2H2O (~0.3% TBW 2H2O enrichment) with a single biopsy protocol to test the effect of short-term (24-h) exercise on mixed muscle protein synthesis [46]. With this approach, the effect of acute resistance exercise on integrative myofibrillar protein synthesis in healthy young subjects was determined. Subjects performed unilateral exercise using one leg while the other leg served as a control. Interestingly, exercise did not have any effect on the FSR of mixed muscle proteins. The high-intensity resistance exercise increased myofibrillar protein synthesis in the exercising leg (0.94±0.16%/h) compared to the control leg (0.75±0.08%/h,
Recently, Wilkinson and colleagues expanded on these studies and investigated the effect of long-term (8-day) exercise on mixed muscle protein synthesis with heavy water for monitoring day-to-day changes in muscle subfractions (myofibrillar, collagen, sarcoplasmic) synthesis [88]. Similar to the study by Gaiser and colleagues, the authors employed a one-legged resistance exercise that allows use of the second leg as an internal control. The longer period of exercise and heavy water administration with multiple muscle biopsies at different time points in this study allowed them assess the changes in muscle protein synthesis in response to the temporal and cumulative successive bouts of exercise. By using the highly sensitive IRMS instrument, this study validated the utility of low dose (0.16–0.24% enrichment of TBW) heavy water for quantification of diurnal changes in muscle protein synthesis and for the assessment of short-term changes in protein turnover. It was demonstrated that protein synthesis in myofibrillar and collagen fractions was increased due to both short-term and long-term exercise; however, sarcoplasmic protein synthesis remained unchanged.
Scalzo and colleagues applied heavy water-based dynamic proteomics to assess integrated and individual muscle protein synthesis response and mitochondrial biogenesis for endurance exercise in males and females after 3 weeks of sprint interval training [89]. This study utilized 3 weeks of 2H2O-labeling protocol to achieve 1–2% TBW enrichment. It was demonstrated that due to exercise, muscle protein synthesis increased and the magnitude of change was higher in males compared with females. The increase in integrative muscle protein synthesis was associated with increased mitochondrial biogenesis assessed based on the synthesis rates of individual mitochondrial proteins and mitochondrial biogenesis signaling. It is important to note that it is unfeasible to use pre-labeled amino acid tracers for this kind of long-term studies of muscle protein synthesis, because this would require inpatient tracer infusion for several days.
Recently, a few studies utilized the heavy water method to assess the protein turnover in different diseases. A single oral dose of heavy water was applied to assess muscle protein synthesis in patients undergoing surgery for upper gastrointestinal cancer [90]. It was demonstrated that the mixed muscle protein synthesis was not decreased, rather, it was marginally increased as compared to healthy controls (
Studies from Hellerstein’s group tested the utility of the heavy water method as a diagnostic tool in patients with psoriasis diseases [91]. The epidermal kinetics was determined in patients with psoriasis using twice-daily doses of 2H2O for 16–38 days. Keratin turnover was significantly accelerated in psoriatic lesions, suggesting that keratin synthesis could be used as a kinetic biomarker of psoriasis and other skin diseases.
These studies demonstrated that the heavy water method has a great potential for human studies.
Since 2H2O can be administered to humans, the dynamic proteomics approach could be widely used for clinical studies. Proteomics centers and infrastructure, which are equipped with state-of-the-art instrumentations and bioinformatics, exist in many areas in the USA and around the world. Static quantitative proteomics is already making highlights in clinical research and patient care. It is expected that in the near future, 2H2O will complement the traditional proteomics and expand to different areas of clinical research. The most obvious application of the heavy water method would be its utilization for the assessment of dynamics of circulatory proteins. Because of the high sensitivity of existing mass spectrometers, dynamic proteome analysis using small-tissue biopsy samples is also feasible. Thus, there is a great potential of using “dynamic markers” of health and disease. However, despite the wide-range potential for use in clinical settings, the heavy water method is still lagging behind as a diagnostic tool in patient care. This is partly related to several unmet methodological, instrumental, and bioinformatics challenges associated with studies of heavy water-based proteome dynamics. Unresolved issues related to the patient-oriented test design, user-friendly software development, and challenges centered around the data interpretation currently impede the routine clinical application of this technology.
In particular, a simple study design with a minimal number of short-term samples is very critical. This also requires creation of a reference database with human protein half-lives for implementation of a simple test for the proteins of interest based on their expected half-life ranges. In terms of methodological issues, still there are no published study on the effect of posttranslational modification and damage-induced aggregation of proteins on protein turnover and stability.
Although the mass spectrometry-based hardware tools are developing very fast, the cost of existing instruments is not easily affordable for many clinical laboratories which drives the cost of any proteomics test. Therefore, the cost reduction in this direction would facilitate the dynamic proteomics application as a clinical test method.
Some additional challenges are related to data interpretation and software issues. To advance
Our current software allows assessing the fractional catabolic and synthesis rates of a protein in a steady state. However, it is also critically important to know the absolute production rate of a protein and to determine whether protein abundance is regulated by the changes in a protein degradation or production. These types of measurements require simultaneous quantification of isotopic distribution and protein abundance. Also, currently used regression analysis for calculation of a rate constant(s) is based on a single compartmental model that relies on a steady-state assumption. However, amino acids and protein levels are in a nonsteady state during growth, aging, and diseases [92]. The nonsteady-state calculations of protein turnover necessitate kinetic models, including data on both protein abundance and relative isotopomer distribution. The future bioinformatics tools based on multi-compartmental kinetic analysis and the quantification of absolute protein production rate in nonsteady-state condition would greatly advance proteome dynamics studies. In addition, there is currently a gap between dynamic proteomics and pathway analysis. Although several software are available for the functional analysis of data based on static proteomics data, currently there are no bioinformatics tools for system biology flux analysis using the proteome dynamics data.
Finally, clinical application of the heavy water method would necessitate fully automated data analysis. So far, existing software solutions are unconnected applications that require multiple format conversion for the input and analysis. Improvement in software cross talk between raw data inputs and data analysis applications would integrate data analysis pipelines with data acquisition and search engines. This would require software engineering development that could transform the existing algorithms to robust user-friendly software packages.
In addition, to the technical limitations highlighted above, the heavy water-based metabolic labeling approach is applicable to analysis of dynamics of proteins with a half-life of greater than ~2 h. This is because it takes approximately 1 h to reach the steady-state enrichment in the amino acid pool, thus it cannot be used for rapidly secreted fast turnover peptides. On the other hand, it is ideally suited to assess proteins that have a more constant rate of secretion and relative stable plasma concentrations, and a half-life of >2 h. It is also not appropriate in short-term experiments (less than 1 week) to measure proteins in plasma that are slowly synthesized constituents of cells, such as troponin or creatine kinase, released in response to tissue injury or necrosis.
Thus, routine and widespread utilization of 2H2O as a diagnostic tool in patient care requires future advancement in several areas. As we discussed above, robust study designs complemented with facile sample preparation, multiplexed analysis, and user-friendly software package allowing high-throughput data processing and interpretation are required. As a universal tracer, heavy water could be used to measure other metabolic fluxes along with proteome dynamics. Thus, as a comprehensive diagnostic tool, the heavy water method could revolutionize personalized medicine, provided there are certain future technological advancements in this field.
We thank Dr. Vernon E. Anderson for his insight and efforts in developing early stages of our proteome dynamic studies. This work was funded by National Institutes of Health Grants 5R21RR025346 (SP, TK), R21 HL-114407 (TK), NLBIHHSN268201000037C (RGS), 1RO1GM112044-01A1 (RGS, TK), American Heart Association 131RG14700011, and 15GRNNT25500004 (TK), and was supported in part by the National Institutes of Health, Clinical Translational Science Collaborative of Cleveland UL1TR000439 (TK).
The Thermo Orbitrap Elite mass spectrometer used in this study was purchased using NIH shared instrument grant 1S10RR031537-01 (BW).
Electrification of urban transportation services, from high power solutions, such as busses and trains, to low power ones, such as small cargo delivery vehicles became highly important. The pressure of developing such solutions is continuously increasing due to the highly crowded urban scenarios in which merchandise and goods must be delivered as fast as possible. Investigating, researching and creating such solutions challenges many universities and OEM engineers. Nowadays, taking advantage of the achievements in the field of computer aided design, any serious study requires advanced simulations, fusing measured data, estimated and computed ones into powerful computation models, able to replicate behavior of the actual hardware. It is important to mention that at a certain level of complexity, studies can become rather difficult by means of large required computation time, powerful computers to perform and many model’s implementations time.
In order to achieve high precision and accurate phenomenon simulation, one must use several software interconnected, each performing analysis in their field of expertise. For example, when simulation of an urban electric vehicle (UEV), all the mechanical, aerodynamical and road conditioning factors can be simulated in Amesim Software, while all the power electronics, electrical drive assemblies and their advanced control can be simulated in Matlab/Simulink. Such a solution ensures high fidelity and correct analysis approach of each assembly of the UEV. However, a co-simulation of this magnitude becomes rather complicated, time and resource demanding, returning a non-real-time behavioral screening. Hence, one solution is to engage a third software, National Instruments VeriStand. This was created to integrate exported model from many simulation software and create one complex simulation solution. Another hands-on advantage is that using a PXIe embedded controller, VeriStand can upload the model and perform the simulation in real-time, allowing the user to observe the system behavior just like in the real life. The simulation time can be from seconds elongated to tens of hours. The data recording can be streamed and decimated as desired. Such a solution will be presented in this chapter.
Another important feature of this tool is given by the real-time target’s nature. This allows external communication via analog and digital I/O s simplifying and facilitating the transition from model-based analysis to hardware in the loop (HIL) based analysis.
In order to offer the possibility of selecting the accuracy and the magnitude of the model, the authors considered developing multilevel complexity model, all organized in Energetic Macroscopic Representation (EMR). The latter ensures the user with the correct understanding of the system’s power flow, creates a direct path when designing the control loops and facilitates the model exchange when passing from one level of complexity to another. It has to be mentioned that all the models presented in the following sections are designed in EMR philosophy. However, the interest of this chapter is not focused towards this special organization method, hence the reader is encouraged to lecture references [1, 2, 3].
The multilevel approach for developing simulation models for an electric vehicle’s propulsion system must be broken down into its main assemblies: the power supply (batteries), the electronic converter, the electrical machine and the general system controller (electronic control unit-ECU). To have a comprehensive analysis, each of these assemblies will be addressed separately in the following sub-sections. For each model, the corresponding energetic macroscopic representation (EMR) block will be presented, separately from its content. The latter will be in fact the modeling approach of each assembly. EMR is an energy flow based graphical organization philosophy easing the building process of complex electromechanical systems. The connection between blocks is always based on action-reaction principle according to the physical causality. In Figure 1, the basic elements used in EMR organization are depicted. These will be used in the following sections as some assemblies, for example, an electrical machine will include more than one such block in their graphical description.
Main EMR pictograms.
Before diving in the presentation of the multilevel models, it is lucrative to explain the philosophy of building a control scheme using EMR. This is necessary because, in the present chapter, the control units will be also under discussion and their multilevel attribute will come from the level of compromises chosen when building its EMR representation. The control loop can be simply generated by inverting all the model blocks. Hence, the blue pictograms (the control) are a reversed model of the orange ones (the model). The main difference is that in the control loop, only the action path is reversed, while the reaction is neglected, as the latter one is necessary only for the model description and not for the control. In the above indicated references, more details about building EMR control loops are presented, hence the reader in encouraged to visit those.
When it comes to model a power supply for an electric vehicle, one can choose between ideal or non-ideal approaches. The latter category can be divided into less or more complex mathematical description. In general, besides the ideal approach the non-ideal one include first and second order battery modeling [4, 5]. A source or a sink in EMR is represented by a green oval, outputting voltage, and inputting current or vice versa.
In Figure 2 the pictogram used for the battery as electrical source in a simulation is depicted. The content of this block however, can be adjusted to the designer’s needs. In Figure 2(a)–(c), the ideal battery model is depicted (where the voltage is imposed invariant of any changes in the system), the first and second order models (where the battery voltage is dependent on the sourced/sinked current and the state of charge (SOC)). The latter models are called first and second order ones due to the number of parallel RC branches that compound their circuit.
First (a) and second (b) order equivalent battery circuits and their EMR pictogram (c).
The mathematical translation of the circuits is detailed in Eq. (1) and Eq. (2). It is important to mention that the circuits parameter’s values for such models are identified using laboratory experiments and actual battery cells. Such approaches were carried out by the authors on a LG (LGABD11865) battery with a rated capacity of 3000 mAh, 3.75 V rated, 4.2 V maximum over charge voltage, 2.7 V minimum discharge voltage, 0.5–1 A charging current and 0.2–0.5 A discharging current.
The complete process described step-by-step to identify the parameters of the battery cell needed to run the correct model is presented in detail in [4]. Even more, not only the experimental process itself is presented, but also very important post processing steps mandatory to reach high accuracy results.
The open circuit voltage (VOC) is identical for both models, being measured for the same conditions. The recorded function of the SOC is depicted in Figure 3.
The open circuit cell voltage vs. SOC.
To help the reader emphasize an image about the precision of these models, a comparative analysis was carried out, superimposing their results for the same testing conditions. After the parameter’s identification process [5, 6, 7, 8], both models presented in Eq. (1) and Eq. (2) were built in Matlab/Simulink, as depicted in Figure 4-left.
The Matlab/Simulink cell models (left) and the comparative analysis (right).
The considered test was to apply equal length current pulses with equal length relaxation time between them, discharging the cell from 100–10%. Initially, this test was applied to the actual cell, the results being also used to identify the necessary data for the simulation models. Imposing the same current variation to the two models, and comparing their voltage calculations with the measured one, one can conclude that the second order model reaches higher accuracy than the first order one. In the zoomed plot from Figure 4-right, it can be observed that the second order and measured voltages are in best agreement, while the first order’s one marks a slight deviation.
To quantify this difference to a certain palpable value, the root mean square error (RMSE) is computed using Eq. (3) and the instantaneous error between the two signals.
The comparative results of the RMSE and the instantaneous error are depicted in Figure 5 (left and right). In both cases one can observe that the second order model yells lower disturbance compared to the first order one. More, in Figure 5-left shows that during the ongoing discharging process, the RMSE for the first order model increases gradually faster than the one of the second order one.
The RMSE (left) and the instantaneous error (right) of the battery models.
To conclude this section, one can understand that increasing the rank of the battery model, the gap between real and simulated results is mitigated. The cost of this is the increased complexity of models, time-consuming parameters identification and the necessity of more data post processing operations. On the contrary, the first order model is quite simple to design, its parameters are simple to identify with classical calculations and there is no need for values post processing. If the compromise of accepting a small error in the battery model with the gain of simplicity, the first order model is the hands-on solution.
The general approach when dealing simulation approaches of electronic converters regard conventional ideal ones, with or without switching, so called smooth or switched models. However, none of these consider the losses that occur in a non-ideal converter. The largest losses are due to the switching components (power transistors and/or diodes), while the rest of the losses inside a converter can be neglected (those on the driver and measuring circuits). One reason why the losses are generally neglected overall is because their actual measurement is rather difficult and requires dedicated equipment. In doing so, one simple method is to consider a general efficiency of the converter and add it into its calculations. So, already some straightforward solutions for modeling electronic converters were mentioned, solutions that are available in nearly any reference the reader would search. To add value to the research and to the present chapter, the authors introduce 2 different approaches to compute the losses that occur on one MOSFET transistor. Of course, these can be then extrapolated towards the total number of power switches.
In Figure 6-bottom the EMR pictogram used for any electronic converter is presented. It inputs the DC link voltage (Vdc), the load currents (i_load) and the duty cycle (m_s). After the internal calculations, the converter outputs the resulting voltage (u) to be supplied to the load and its output currents (i_conv). The inputs and outputs do not change with the chosen method of internal calculations. Either is a smooth or switched model, these remain the same and valid even if we consider DC or AC converters.
Smooth (a) and switched (b) converter architectures and their EMR pictogram (c).
In addition to the above depicted models, this block can include also the calculus for the converter losses. Even if in the standard pictogram there are no input/output ports considered for this, the information, being additional, can be manipulated to/from the block via wireless labels.
Taking into consideration of these losses is mandatory when one is interested in reaching optimal converter architectures [9, 10]. The following calculus can be adapted to any power transistor, the authors using the IRFP4668PBF MOSFET, having its specifications listed in Table 1.
Parameter | Symbol | Value | Unit |
---|---|---|---|
Forward voltage | V | 120 | V |
Drain to source resistance | Rds | 0.008 | Ω |
Switching Frequency | fsw | 10000 | Hz |
Output Capacitance | Coss | 810e-12 | F |
Turn off time | Toff | 64e-9 | s |
Turn on time | Ton | 41e-9 | s |
Forward voltage drop | Vf | 1.3 | V |
Reverse Leakage current | Ir | 0.00025 | A |
Reverse Recovery Time | Trr | 135e-9 | |
Max Recovery Current | Irrm | 8.7 | A |
Gate Resistance | Rg | 11 | Ω |
Plateau Voltage | Uplateau | 6.1 | V |
Gate Capacitance | CGD1 | 200e-12 | F |
Gate Capacitance | CGD2 | 1200e-12 | F |
Reverse recovery charge | Qrr | 700e-9 | C |
Rise Time | tr | 105e-9 | s |
Fall time | tf | 74e-9 | s |
Reverse Recovery Current | IDoff | 8.7 | A |
Average diode current | IFav | 20 | A |
Diode on-state zero-current voltage | ud0 | 0.5 | V |
The main specifications for IRFP4668PbF MOSFET.
The losses computed in any active switching electronic component can be broken down into: conduction losses, switching losses and blocking (leakage) losses, the later normally being neglected, as expressed in Eq. (4).
As mentioned before, two approaches will be presented in detail. The first one computes the power losses during conduction and switching the MOSFET while the second one computes losses during switch-on and switch-off transients.
In the first approach, the conduction and switching losses are computed based on Eqs. (5) and (6), with regards to the semiconductor’s parameters and its operation. The latter is described via the duty cycle (D), the switching frequency (fsw) and the transistor’s output capacitance (Coss)
On the other hand, the losses occurring in the diode attached to the transistor are broken down into conduction, reverse and switching ones. The total losses due to the diode’s operation are:
Each of the eq.’s (7) components are formulated in expressions (8)–(10), with parameters valued and explained in Table 1:
This model is quite simple and represents the hands-on solution for quantifying the converter losses without engaging too many mathematical tools.
The more complex model, the conduction losses are computed function of the on-state resistance (Rds) and the current passing through the component (id):
The next component of the total losses, the switching one, is broken down into two states, according to the on and off transients. There are different opinions and mathematical interpretations on how to compute the voltage fall-time (tfu) to gain accuracy. Details are presented in Ref. [10]. Engaging a straight forward approach, the fall-time is considered to be composed of two parts in accordance with the variation of the gate capacitance:
In Eq. (12), the terms (Udrv) and (UDD) refer to the driver voltage that excites the transistor’s gate and the converter’s DC link voltage, respectively. The rest of the terms are to be found in Table 1.
The complementary model to compute the switch-off losses differ just by subtracting from Eq. (12) the driver voltage.
The required energy to turn on and off the transistor is calculated in Eq. (14) and (15).
With the above computed ingredients, one can calculate the power losses on the transistor, Eq. (16), and diode, Eq. (17).
The losses in the MOSFET and the free-wheeling diode are computed as the sum of the conduction and switching losses.
It has to be mentioned that during operation, in one transistor or diode, the currents can be positive or negative. In an analytical approach, this would lead to decreased losses, hence, to correctly calculate those, the absolute current’s value is used.
The instantaneous converter losses for the simple and complex models.
Comparing the results of the two approaches (Figure 7) one can conclude that the complex model reaches higher calculated values. This is more realistic as it contains more loss sources compared to the simple model. However, the simple model proves to offer a quite good quantification of the losses despite its ease in approach, reaching satisfactory results.
The general approach when modeling a permanent magnet synchronous machine (PMSM) is to use its mathematical representation in dq0 reference frame, fixed to the rotor.
The variables in Eqs. (20) and (21) are: the phase resistance (Rs), the direct and quadrature inductances (Ld and Lq) and the cross-coupling inductances (Ldq and Lqd). The number of pole pairs is denoted with (p), while the rotational speed is marked by (ω) and the friction coefficient in marked by (B). The dq currents (Ids and Iqs) are computed by integration of the voltage expressions.
For a PMSM, the EMR model is constructed by coupling 3 pictograms, one accumulation element (for the windings) described by Eq. (20), one electromechanical conversion (for the torque generation), described by Eq. (21) and another accumulation element (for the mechanical equilibrium), described by Eq. (22), (visit Table 1 for clarifications). Such a model is depicted in Figure 8.
The EMR for a PMSM.
Returning to Eq. (21), the dq magnetic flux equations give the opportunity to choose the level of modeling complexity. In this section, 3 different such levels will be detailed: based on constant dq inductances, based on variable dq inductances and a flux-linkage based model. The latter is the most complex and precise one.
To have a comprehensive analysis, like for the battery and the power inverter, for the PMSM the model’s parameters are listed in Table 2.
Electric and geometric parameters | |||
---|---|---|---|
Dimension | Value | Unit | |
1 | Pole pairs | 3 | — |
2 | Rotor speed | 2000 | [rot/min] |
3 | Rated torque | 12 | [Nm] |
4 | Rated current per phase | 21 | [A] |
5 | Mechanical power | 2500 | [W] |
6 | DC Voltage link | 120 | [V] |
The PMSM under analysis.
The first level of complexity consists in using constant values for the d and q axes inductances. The identification of these is a very simple process. It can be carried out in finite element analysis (FEA) or on the real machine (or in both environments). It requires only fixing the rotor in 0 and 90 electrical degrees positions and applying in both one controlled voltage pulse. The time dependent slope of the current will return after some simple calculations the global machine inductance (L). Having this value, the d and q components are easy to be computed, using Eq. (23). Details about the identification process can be selected from references [11, 12].
The second level model regards detailed information about the machine under testing. The latter needs to be simulated in a FEA environment in order to compute the dq inductance’s variations function of the machine current. This is generally ranged from ±10 × In, where In is the machine’s rated current. The method itself that allows preliminary identification of the 3-phase magnetic fluxes is the so called “frozen permeability” approach. Applying it demands to set to 0 the permanent magnet’s remanent flux density, freezing its permeability. More details about this method are to be found in Ref.s [12, 13]. The resulted phase magnetic fluxes will be used to compute the dq inductances versus the machine current, applying Eq. (24).
The outcome of these calculations is stored in look up tables (LUT) and introduced in the machine model. It is clear that such a procedure is impossible to be implemented in a real testbench or it would require complex and expensive hardware. One can observe that using the calculus from Eq. (23), the direct as well as the cross-coupling inductances are computed. Their variation function of the ranged current is depicted in Figure 9.
The direct and cross-coupling inductances calculated with frozen permeabilities.
The last and altogether the most complex model is based on using instead of inductances, the variation of the d and q currents function of the d and q magnetic fluxes. These are 2D LUTs as depicted in Figure 10.
The 2D LUTs of id(Ψd, Ψq) and iq(Ψd, Ψq).
It is important to mention that building the 2D LUTs from Figure 10 is a quite complex and challenging process. Here to the designer needs to have the FEA model of the machine under study. Several simulations need to be accomplished, fetching initially the Ψd (id, iq) and Ψq (id, iq). From this information mathematical inversion methods need to be applied to get id(Ψd, Ψq) and iq(Ψd, Ψq). The complete process is explained in detail in [14].
In Figure 11 a simple comparative analysis of the results obtained when simulating all the 3 models for the same conditions are depicted. Focusing the attention on the variation of the q axis magnetic flux, it can be noticed that flux linkage and frozen permeability models do saturate, while the one based on constant inductances reaches large values. The latter is ideal; hence the machine will never saturate. In this condition, one can perform simulations within rated operation. However, when attempting to go in overload region the results will not reflect the real machine operation.
The PMSM torque and Ψq during benchmarking the simulation levels.
The flux linkage and frozen permeabilities models are behaving quite in the same manner. However, the complexity of the flux linkage model is high, both in data post processing and design. The ultimate conclusion is that using the frozen permeabilities model will lead to quite satisfactory results reflecting closely the actual machine behavior.
Energetic Macroscopic Representation is one organization concept that besides helping the designer to build simulation programs that reflect the actual power flow of a real testbench, it also proves to be a very lucrative solution when it comes to building control loops. To do so, one has just to inverse the simulation model and keep only the action path, discarding the reaction one.
In Figure 12 the control loop can be observed being highlighted in light blue. Its inversed character compared to the model (in orange) is noticeable and each block contains the mathematical description of the inversed action.
The complete PMSM simulation model in EMR organization.
The reversed accumulation block contains a regulator that outputs the reference torque to be developed function of the error between the actual and the imposed speed (see Eq. (25)).
The inversed block without controller computes the reference q axis current function of the refence torque above expressed. In the same time, the d axis current is imposed to 0, this for torque maximization purposes.
This reference d and q currents are compared with the actual ones. The resulting errors are sent to regulators that output the reference d and q voltages. This process is carried out in the third control block (see Figure 12 from right to left).
The resulted d and q voltage refences are converter to 3-phase quantities and sent to the last control block, that computes the duty-cycle (m) for each phase. The duty-cycle is sent to the PWM generator (in orange) closing the model’s loop. More details about generating the process model are indicated in Ref. [15].
To develop an analysis tool able to replicate as close as possible the behavior of a real electric urban vehicle, it is clear that using complex simulation developments for the electrical assemblies will not be enough. However, building the mechanical models using advanced mathematical tools is highly time consuming and would require a lot of expertise and education in this direction. Hence, a wise solution is to engage a software created for such simulations, like Simcenter Amesim from Siemens.
In order to highlight the previously mentioned application with a practical example, an urban vehicle’s mechanical model is developed. The vehicle is an electrical tricycle used for cargo delivery purposes. Its main specifications are presented in Table 3.
Parameter | Symbol | Value |
---|---|---|
Maximum mass | m | 250 kg |
Maximum speed | v | 10 m/s |
Wheel radius | Rw | 12″ |
Maximum road slope | α | 15% |
Rolling resistance | f | 0.017 |
Front area | A | 1.2 m2 |
Air drag coefficient | Cs | 0.36 |
Gear ratio | Gr | 5 |
Tricycle specifications.
In Figure 13, the Amesim model is depicted, observing that none electrical assemblies are included in the model. Those are modeled in Matlab/Simulink as it will be presented in the following sections. The link between the two models will be performed using National Instruments VeriStand software, able to create an integrated project that runs a co-simulation on a real-time processor for the complete vehicle.
The mechanical assemblies’ Amesim model.
The Matlab/Simulink model for the electrical assemblies is built putting together all the components detailed in Section 2 based on the hierarchy depicted in Figure 12. It has to be mentioned that the pictograms cannot be exactly replicated in Simulink as the inputs are on the left and the outputs always on the right. However, the organization is the same as it can be observed in Figure 14.
The urban vehicle’s EMR in Matlab/Simulink.
Having all the models above presented, the goal is to create a virtual simulation platform that allows simple transition hardware in the loop (HiL) testing. The latter is the ultimate goal of testing any electromechanical system. It allows users to test certain physical assemblies while the rest of the system is virtual, running as simulation on a real-time target. This must ensure enough computation power and speed to cope with the demands of the actual hardware. National Instruments have in their portfolio hardware (NI PXIe embedded controller) and software (NI VeriStand) that are able to integrate into one real-time simulation all the above detailed models. The PXI computer, via its onboard field programable gate array (FPGA) enables the connection of the virtual model with the testbench using analog and digital IOs.
In Figure 15, the main components of the analysis platform are depicted. One can observe that VeriStand software that integrates the Amesim and Simulink models runs on the PXI and using the FPGA channels communicates with the actual hardware. The user has the possibility to run the virtual model and in parallel the actual hardware in a bidirectional communication network, performing as an entire entity. The real-time processor also features the possibility to run only the simulation of the entire vehicle, without any hardware connected to it. The difference between such a simulation and one running on a PC is that the latter would take tens of hours for a road cycle of 30 minutes while on the real-time computer it will take the elapsed 30 minutes.
The platform’s hardware/software architecture.
Both scenarios will be presented in detail in the following section, comparing the results and discussing them.
Taking advantage of the flexibility of the above presented analysis platform one can perform a study using only simulation, hence a virtual vehicle or can combine virtual elements (mechanical assemblies) with real ones (the propulsion motor). For a robust simulation model, it is important to ensure that using it as reference will return results that mimic closely the actual hardware. In doing so, the platform depicted in Figure 15 was initially tested only for simulation, the complete system being entirely virtual and running on the PXIe under VeriStand software. The latter ensured the continuous communication of the Simulink model (running all electrical assemblies) with Amesim (running all mechanical systems).
It has to be mentioned that the chosen simulation used the most complex model assemblies from those presented in Section 2. In order to avoid redundancy, results for the rest of the levels are not presented in the chapter, however during the presentations in Section 2, the main differences between the models were already presented.
The second step was to keep in the virtual level the mechanical assemblies (in Amesim) and the battery model (in Simulink). The rest of the virtual electrical assemblies (the inverter, the motor and the controller) were replaced with their laboratory homologs. These remained however connected to the simulator via the analog/digital IOs presented earlier.
The outcome of this comparative analysis was more than satisfactory. Firstly, the reference torque was plotted versus the simulated and the measured ones. It can be observed that the agreement is very good proving the accuracy of the simulator. Knowing that in a PMSM the q current component is responsible for the torque production, for both measured and simulated cases this was recorded and depicted in Figure 16-right. The same conclusions as for the comparative analysis of the torque characteristics can be considered. In order to avoid redundancy, the d currents were not depicted as those values are forced to 0 at all times.
The comparative analysis of the torque (right) and q current component (left).
The slight differences between the values plotted in Figure 16 are more due to measurements error and noise. No filtering was used what’s so ever in order to avoid any unnecessary postprocessing (Figure 17).
The comparative analysis of the speed (right) and its zoomed section (left).
The slight differences between the values plotted in Figure 16 are more due to measurements error and noise. No filtering was used what’s so ever in order to avoid any possible influence over the quality results.
Smoother results can be reached if certain data sampling time or other such procedure is considered. However, the interest in this entire work was to prove that one can reach very accurate results when building responsible simulation programs. Also using a real-time processor can benefit the study with fast analysis and reliable results as well.
When it comes to develop simulation tools wise enough to reach the expectations of the fast-growing industry in the field of engineering, one needs to consider solutions that become hands-on. These must be flexible to changes, simple to implement and accurate when it comes to results. Using real-time processors to run these simulations offers reduced analysis time, accurate results and close to real study. The same processors when engaged, ease the transition from simulation to HiL testing, simply by replacing virtual components with real ones. Choosing between the complexity levels of the virtual models, allows the user to select the accuracy and the necessary time invested in the development. The system under analysis being compound of several assemblies, more complex models can be considered for those that are of interest, while the rest can be ideal ones.
In the present chapter the main assemblies of an electrical urban vehicle’s traction system are presented, offering the choice of complexity, mathematical description and EMR organization. The latter is introduced as graphical method for representing the elements of any simulation program by respecting the actual action/reaction physical and natural laws.
It has to be mentioned that the nature of the present chapter is more towards a review than of an academic lecture, hence the reader is encouraged to consult the indicated references that are guidance and complementary information to the one described in the previous pages.
This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS/CCCDI – UEFISCDI, project number PN-III-P2-2.1-PED-2019-4384, within PNCDI III.
This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS/CCCDI – UEFISCDI, project number PN-III-P1-1.1-TE-2019-0411, within PNCDI III.
IntechOpen implements a robust policy to minimize and deal with instances of fraud or misconduct. As part of our general commitment to transparency and openness, and in order to maintain high scientific standards, we have a well-defined editorial policy regarding Retractions and Corrections.
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\\n\\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
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\\n\\n3.1. ERRATUM
\\n\\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\\n\\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n3.2. CORRIGENDUM
\\n\\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n4. FINAL REMARKS
\\n\\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\\n\\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\\n\\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
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\\n\\nPolicy last updated: 2017-09-11
\\n"}]'},components:[{type:"htmlEditorComponent",content:'IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
\n\n1. RETRACTIONS
\n\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\n\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\n\nPublishing of a Retraction Notice will adhere to the following guidelines:
\n\n1.2. REMOVALS AND CANCELLATIONS
\n\n2. STATEMENTS OF CONCERN
\n\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\n\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\n\n3. CORRECTIONS
\n\nA Correction will be issued by the Academic Editor when:
\n\n3.1. ERRATUM
\n\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\n\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n3.2. CORRIGENDUM
\n\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n4. FINAL REMARKS
\n\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\n\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\n\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
\n\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\n\nPolicy last updated: 2017-09-11
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We describe the marker-less technologies in the area of AR, indoor marker-less AR, outdoor marker-less AR, real-time solutions to the tracking problem, real-time registration, cultural heritage in AR, 3D remonstration techniques, as well as presenting the problems in each research.",book:{id:"7699",slug:"advanced-methods-and-new-materials-for-cultural-heritage-preservation",title:"Advanced Methods and New Materials for Cultural Heritage Preservation",fullTitle:"Advanced Methods and New Materials for Cultural Heritage Preservation"},signatures:"Hoshang Kolivand, Abdennour El Rhalibi, Mostafa Tajdini, Sarmad Abdulazeez\nand Pisit Praiwattana",authors:[{id:"151219",title:"Prof.",name:"Abdennour",middleName:null,surname:"El Rhalibi",slug:"abdennour-el-rhalibi",fullName:"Abdennour El Rhalibi"},{id:"225824",title:"Dr.",name:"Hoshang",middleName:null,surname:"Kolivand",slug:"hoshang-kolivand",fullName:"Hoshang Kolivand"},{id:"256916",title:"Dr.",name:"Sarmad",middleName:null,surname:"Abdulazeez",slug:"sarmad-abdulazeez",fullName:"Sarmad Abdulazeez"},{id:"256917",title:"Dr.",name:"Pisit",middleName:null,surname:"Praiwattana",slug:"pisit-praiwattana",fullName:"Pisit Praiwattana"},{id:"289071",title:"Dr.",name:"Mostafa",middleName:null,surname:"Tajdini",slug:"mostafa-tajdini",fullName:"Mostafa Tajdini"}]},{id:"36570",doi:"10.5772/45619",title:"Archaeological Geophysics - From Basics to New Perspectives",slug:"archaeological-geophysics-from-basics-to-new-perspectives",totalDownloads:6563,totalCrossrefCites:4,totalDimensionsCites:8,abstract:null,book:{id:"1999",slug:"archaeology-new-approaches-in-theory-and-techniques",title:"Archaeology",fullTitle:"Archaeology, New Approaches in Theory and Techniques"},signatures:"Roger Sala, Ekhine Garcia and Robert Tamba",authors:[{id:"131865",title:"Dr.",name:"Roger",middleName:null,surname:"Sala",slug:"roger-sala",fullName:"Roger Sala"}]},{id:"36574",doi:"10.5772/37679",title:"The Study of Shell Object Manufacturing Techniques from the Perspective of Experimental Archaeology and Work Traces",slug:"the-study-of-shell-object-manufacturing-techniques-from-the-perspective-of-experimental-archaeology-",totalDownloads:3119,totalCrossrefCites:1,totalDimensionsCites:5,abstract:null,book:{id:"1999",slug:"archaeology-new-approaches-in-theory-and-techniques",title:"Archaeology",fullTitle:"Archaeology, New Approaches in Theory and Techniques"},signatures:"Adrián Velázquez-Castro",authors:[{id:"113840",title:"Dr.",name:"Adrian",middleName:null,surname:"Velazquez",slug:"adrian-velazquez",fullName:"Adrian Velazquez"}]},{id:"70612",doi:"10.5772/intechopen.89154",title:"The Technological Diversity of Lithic Industries in Eastern South America during the Late Pleistocene-Holocene Transition",slug:"the-technological-diversity-of-lithic-industries-in-eastern-south-america-during-the-late-pleistocen",totalDownloads:684,totalCrossrefCites:1,totalDimensionsCites:4,abstract:"Brazilian archaeological literature has insisted for decades upon associating hunter-gatherer sites dated to the Pleistocene–Holocene transition either to the Itaparica tradition, if located in central or northeastern Brazil, or to the Umbu tradition and Humaitá tradition, if located in southern Brazil, Uruguay, or any other adjacent part of Paraguay and Argentina. These associations have been based almost entirely on the presence or absence of lesmas and “projectile points,” regardless of their morphological and technological features. In the Uruguayan archaeological literature, three other cultures are recognised: Fell industry, Catalanense industry, and Tigre tradition, all in the Uruguayan region. However, the last 10 years of systematic studies on the lithic assemblages from these sites have shown that Paleoindian societies from Eastern South America are more culturally diverse than expected and that previously defined archaeological cultures present several issues in their definition, suggesting that many of these “traditions” are not valid and should no longer be used. Instead, new lithic industries and archaeological cultures should be defined only when cultural patterns are observable through systematic analyses.",book:{id:"9251",slug:"pleistocene-archaeology-migration-technology-and-adaptation",title:"Pleistocene Archaeology",fullTitle:"Pleistocene Archaeology - Migration, Technology, and Adaptation"},signatures:"João Carlos Moreno De Sousa",authors:[{id:"303361",title:"Dr.",name:"João Carlos",middleName:null,surname:"Moreno De Sousa",slug:"joao-carlos-moreno-de-sousa",fullName:"João Carlos Moreno De Sousa"}]}],mostDownloadedChaptersLast30Days:[{id:"36570",title:"Archaeological Geophysics - From Basics to New Perspectives",slug:"archaeological-geophysics-from-basics-to-new-perspectives",totalDownloads:6552,totalCrossrefCites:4,totalDimensionsCites:8,abstract:null,book:{id:"1999",slug:"archaeology-new-approaches-in-theory-and-techniques",title:"Archaeology",fullTitle:"Archaeology, New Approaches in Theory and Techniques"},signatures:"Roger Sala, Ekhine Garcia and Robert Tamba",authors:[{id:"131865",title:"Dr.",name:"Roger",middleName:null,surname:"Sala",slug:"roger-sala",fullName:"Roger Sala"}]},{id:"36576",title:"Homage to Marcel Proust - Aspects of Dissemination and Didactic in a Museum and a Science Centre: Science Communication Visions for the Third Generation Museums",slug:"generations-of-ancient-history-dissemination-towards-the-public-at-the-university-museum-in-trondhei",totalDownloads:2644,totalCrossrefCites:1,totalDimensionsCites:1,abstract:null,book:{id:"1999",slug:"archaeology-new-approaches-in-theory-and-techniques",title:"Archaeology",fullTitle:"Archaeology, New Approaches in Theory and Techniques"},signatures:"Kistian Overskaug",authors:[{id:"117119",title:"Dr.",name:"Kristian",middleName:null,surname:"Overskaug",slug:"kristian-overskaug",fullName:"Kristian Overskaug"}]},{id:"63772",title:"Cultural Heritage in Marker-Less Augmented Reality: A Survey",slug:"cultural-heritage-in-marker-less-augmented-reality-a-survey",totalDownloads:1628,totalCrossrefCites:6,totalDimensionsCites:9,abstract:"Augmented reality (AR) is considered as one of the most significant technologies in the field of computer graphics and is utilised in many applications. In this chapter, we have presented a brief comprehensive survey of cultural heritage using augmented reality systems. This survey describes the main objectives and characteristics of marker-less augmented reality systems through presenting up-to-date research results in this area. We describe the marker-less technologies in the area of AR, indoor marker-less AR, outdoor marker-less AR, real-time solutions to the tracking problem, real-time registration, cultural heritage in AR, 3D remonstration techniques, as well as presenting the problems in each research.",book:{id:"7699",slug:"advanced-methods-and-new-materials-for-cultural-heritage-preservation",title:"Advanced Methods and New Materials for Cultural Heritage Preservation",fullTitle:"Advanced Methods and New Materials for Cultural Heritage Preservation"},signatures:"Hoshang Kolivand, Abdennour El Rhalibi, Mostafa Tajdini, Sarmad Abdulazeez\nand Pisit Praiwattana",authors:[{id:"151219",title:"Prof.",name:"Abdennour",middleName:null,surname:"El Rhalibi",slug:"abdennour-el-rhalibi",fullName:"Abdennour El Rhalibi"},{id:"225824",title:"Dr.",name:"Hoshang",middleName:null,surname:"Kolivand",slug:"hoshang-kolivand",fullName:"Hoshang Kolivand"},{id:"256916",title:"Dr.",name:"Sarmad",middleName:null,surname:"Abdulazeez",slug:"sarmad-abdulazeez",fullName:"Sarmad Abdulazeez"},{id:"256917",title:"Dr.",name:"Pisit",middleName:null,surname:"Praiwattana",slug:"pisit-praiwattana",fullName:"Pisit Praiwattana"},{id:"289071",title:"Dr.",name:"Mostafa",middleName:null,surname:"Tajdini",slug:"mostafa-tajdini",fullName:"Mostafa Tajdini"}]},{id:"73769",title:"Human Evolution in the Center of the Old World: An Updated Review of the South Asian Paleolithic",slug:"human-evolution-in-the-center-of-the-old-world-an-updated-review-of-the-south-asian-paleolithic",totalDownloads:847,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"The Indian Subcontinent was an important geographic region for faunal and hominin evolution in Asia. While the Oldowan as the earliest technocomplex continues to be elusive, the oldest Acheulean is dated to ~1.5 Ma and the early Middle Paleolithic is ~385 ka (from the same site). New Late Pleistocene dates have been reported for the Middle Paleolithic which continues up to 38 Ka in southern India. The Upper Paleolithic remains ambiguous and requires critically multidisciplinary investigations. The microlithic evidence appears to spread rapidly across the subcontinent soon after its emergence at ~48 Ka (though its origin is debated) and continues into the Iron Age. The timeline of the initial arrival of Homo sapiens continues to be debated based on the archaeology (advanced Middle Paleolithic vs. microlithic) and genetic studies on indigenous groups. Other issues that need consideration are: interactions between archaics and arriving moderns, the marginal occurrence of symbolic behavior, the absolute dating of rock art and the potential role of hominins in specific animal extinctions and ecological marginalization. The region does not appear to have been a corridor for dispersals towards Southeast Asia (although gene flow may have occurred). Instead, once various prehistoric technologies appeared in the Subcontinent, they possibly followed complex trajectories within relative isolation.",book:{id:"9251",slug:"pleistocene-archaeology-migration-technology-and-adaptation",title:"Pleistocene Archaeology",fullTitle:"Pleistocene Archaeology - Migration, Technology, and Adaptation"},signatures:"Parth R. Chauhan",authors:[{id:"307040",title:"Dr.",name:"Parth",middleName:null,surname:"Chauhan",slug:"parth-chauhan",fullName:"Parth Chauhan"}]},{id:"73386",title:"Island Migration, Resource Use, and Lithic Technology by Anatomically Modern Humans in Wallacea",slug:"island-migration-resource-use-and-lithic-technology-by-anatomically-modern-humans-in-wallacea",totalDownloads:725,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Island migration and adaptation including both marine and terrestrial resource use and technological development by anatomically modern humans (AMH) are among the most significant issues for Pleistocene archaeology in Southeast Asia and Oceania, and directly related to the behavioral and technological advancements by AMH. This paper discusses such cases in the Wallacean islands, located between the past Sundaland and the Sahul continent during the Pleistocene. The Pleistocene open sea gaps between the Wallacean islands and both landmasses are very likely the major factor for the relative scarcity of animal species originating from Asia and Oceania and the high diversity of endemic species in Wallacea. They were also a barrier for hominin migration into the Wallacean islands and Sahul continent. We summarize three recent excavation results on the Talaud Islands, Sulawesi Island and Mindoro Island in Wallacea region and discuss the evidence and timeline for migrations of early modern humans into the Wallacean islands and their adaptation to island environments during the Pleistocene.",book:{id:"9251",slug:"pleistocene-archaeology-migration-technology-and-adaptation",title:"Pleistocene Archaeology",fullTitle:"Pleistocene Archaeology - Migration, Technology, and Adaptation"},signatures:"Rintaro Ono, Alfred Pawlik and Riczar Fuentes",authors:[{id:"177123",title:"Dr.",name:"Rintaro",middleName:null,surname:"Ono",slug:"rintaro-ono",fullName:"Rintaro Ono"},{id:"300616",title:"Dr.",name:"Alfred",middleName:null,surname:"Pawlik",slug:"alfred-pawlik",fullName:"Alfred Pawlik"},{id:"330591",title:"Dr.",name:"Riczar",middleName:null,surname:"Fuentes",slug:"riczar-fuentes",fullName:"Riczar Fuentes"}]}],onlineFirstChaptersFilter:{topicId:"263",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:287,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517",scope:"Paralleling similar advances in the medical field, astounding advances occurred in Veterinary Medicine and Science in recent decades. These advances have helped foster better support for animal health, more humane animal production, and a better understanding of the physiology of endangered species to improve the assisted reproductive technologies or the pathogenesis of certain diseases, where animals can be used as models for human diseases (like cancer, degenerative diseases or fertility), and even as a guarantee of public health. Bridging Human, Animal, and Environmental health, the holistic and integrative “One Health” concept intimately associates the developments within those fields, projecting its advancements into practice. This book series aims to tackle various animal-related medicine and sciences fields, providing thematic volumes consisting of high-quality significant research directed to researchers and postgraduates. It aims to give us a glimpse into the new accomplishments in the Veterinary Medicine and Science field. By addressing hot topics in veterinary sciences, we aim to gather authoritative texts within each issue of this series, providing in-depth overviews and analysis for graduates, academics, and practitioners and foreseeing a deeper understanding of the subject. Forthcoming texts, written and edited by experienced researchers from both industry and academia, will also discuss scientific challenges faced today in Veterinary Medicine and Science. In brief, we hope that books in this series will provide accessible references for those interested or working in this field and encourage learning in a range of different topics.",coverUrl:"https://cdn.intechopen.com/series/covers/13.jpg",latestPublicationDate:"May 18th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:10,editor:{id:"38652",title:"Dr.",name:"Rita",middleName:null,surname:"Payan-Carreira",slug:"rita-payan-carreira",fullName:"Rita Payan-Carreira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRiFPQA0/Profile_Picture_1614601496313",biography:"Rita Payan Carreira earned her Veterinary Degree from the Faculty of Veterinary Medicine in Lisbon, Portugal, in 1985. She obtained her Ph.D. in Veterinary Sciences from the University of Trás-os-Montes e Alto Douro, Portugal. After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",institutionURL:null,country:{name:"Portugal"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:3,paginationItems:[{id:"19",title:"Animal Science",coverUrl:"https://cdn.intechopen.com/series_topics/covers/19.jpg",isOpenForSubmission:!0,editor:{id:"259298",title:"Dr.",name:"Edward",middleName:null,surname:"Narayan",slug:"edward-narayan",fullName:"Edward Narayan",profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",biography:"Dr. Edward Narayan graduated with Ph.D. degree in Biology from the University of the South Pacific and pioneered non-invasive reproductive and stress endocrinology tools for amphibians - the novel development and validation of non-invasive enzyme immunoassays for the evaluation of reproductive hormonal cycle and stress hormone responses to environmental stressors. \nDr. Narayan leads the Stress Lab (Comparative Physiology and Endocrinology) at the University of Queensland. A dynamic career research platform which is based on the thematic areas of comparative vertebrate physiology, stress endocrinology, reproductive endocrinology, animal health and welfare, and conservation biology. \nEdward has supervised 40 research students and published over 60 peer reviewed research.",institutionString:null,institution:{name:"University of Queensland",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null},{id:"20",title:"Animal Nutrition",coverUrl:"https://cdn.intechopen.com/series_topics/covers/20.jpg",isOpenForSubmission:!0,editor:{id:"175967",title:"Dr.",name:"Manuel",middleName:null,surname:"Gonzalez Ronquillo",slug:"manuel-gonzalez-ronquillo",fullName:"Manuel Gonzalez Ronquillo",profilePictureURL:"https://mts.intechopen.com/storage/users/175967/images/system/175967.png",biography:"Dr. Manuel González Ronquillo obtained his doctorate degree from the University of Zaragoza, Spain, in 2001. He is a research professor at the Faculty of Veterinary Medicine and Animal Husbandry, Autonomous University of the State of Mexico. He is also a level-2 researcher. He received a Fulbright-Garcia Robles fellowship for a postdoctoral stay at the US Dairy Forage Research Center, Madison, Wisconsin, USA in 2008–2009. He received grants from Alianza del Pacifico for a stay at the University of Magallanes, Chile, in 2014, and from Consejo Nacional de Ciencia y Tecnología (CONACyT) to work in the Food and Agriculture Organization’s Animal Production and Health Division (AGA), Rome, Italy, in 2014–2015. He has collaborated with researchers from different countries and published ninety-eight journal articles. He teaches various degree courses in zootechnics, sheep production, and agricultural sciences and natural resources.\n\nDr. Ronquillo’s research focuses on the evaluation of sustainable animal diets (StAnD), using native resources of the region, decreasing carbon footprint, and applying meta-analysis and mathematical models for a better understanding of animal production.",institutionString:null,institution:{name:"Universidad Autónoma del Estado de México",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"28",title:"Animal Reproductive Biology and Technology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/28.jpg",isOpenForSubmission:!0,editor:{id:"177225",title:"Prof.",name:"Rosa Maria Lino Neto",middleName:null,surname:"Pereira",slug:"rosa-maria-lino-neto-pereira",fullName:"Rosa Maria Lino Neto Pereira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9wkQAC/Profile_Picture_1624519982291",biography:"Rosa Maria Lino Neto Pereira (DVM, MsC, PhD and) is currently a researcher at the Genetic Resources and Biotechnology Unit of the National Institute of Agrarian and Veterinarian Research (INIAV, Portugal). She is the head of the Reproduction and Embryology Laboratories and was lecturer of Reproduction and Reproductive Biotechnologies at Veterinary Medicine Faculty. She has over 25 years of experience working in reproductive biology and biotechnology areas with a special emphasis on embryo and gamete cryopreservation, for research and animal genetic resources conservation, leading research projects with several peer-reviewed papers. Rosa Pereira is member of the ERFP-FAO Ex situ Working Group and of the Management Commission of the Portuguese Animal Germplasm Bank.",institutionString:"The National Institute for Agricultural and Veterinary Research. Portugal",institution:null},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:19,paginationItems:[{id:"81793",title:"Canine parvovirus-2: An Emerging Threat to Young Pets",doi:"10.5772/intechopen.104846",signatures:"Mithilesh Singh, Rajendran Manikandan, Ujjwal Kumar De, Vishal Chander, Babul Rudra Paul, Saravanan Ramakrishnan and Darshini Maramreddy",slug:"canine-parvovirus-2-an-emerging-threat-to-young-pets",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Recent Advances in Canine Medicine",coverURL:"https://cdn.intechopen.com/books/images_new/11580.jpg",subseries:{id:"19",title:"Animal Science"}}},{id:"81271",title:"The Diversity of Parvovirus Telomeres",doi:"10.5772/intechopen.102684",signatures:"Marianne Laugel, Emilie Lecomte, Eduard Ayuso, Oumeya Adjali, Mathieu Mével and Magalie Penaud-Budloo",slug:"the-diversity-of-parvovirus-telomeres",totalDownloads:23,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Recent Advances in Canine Medicine",coverURL:"https://cdn.intechopen.com/books/images_new/11580.jpg",subseries:{id:"19",title:"Animal Science"}}},{id:"79909",title:"Cryopreservation Methods and Frontiers in the Art of Freezing Life in Animal Models",doi:"10.5772/intechopen.101750",signatures:"Feda S. 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She obtained her Ph.D. in Veterinary Sciences from the University of Trás-os-Montes e Alto Douro, Portugal. After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. 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Also he received Masters Degree and PhD from Córdoba University, Spain. He is currently a Professor at the Catholic University of Valencia San Vicente Mártir, at the Department of Medicine and Animal Surgery. He teaches diverse courses in the field of Animal Reproduction and he is the Director of the Veterinary Farm. He also participates in academic postgraduate activities at the Veterinary Faculty of Murcia University, Spain. His research areas include animal physiology, physiology and biotechnology of reproduction either in males or females, the study of gametes under in vitro conditions and the use of ultrasound as a complement to physiological studies and development of applied biotechnologies. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. 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Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}},{id:"351158",title:"Prof.",name:"David W.",middleName:null,surname:"Anderson",slug:"david-w.-anderson",fullName:"David W. Anderson",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Calgary",country:{name:"Canada"}}}]}},subseries:{item:{id:"92",type:"subseries",title:"Health and Wellbeing",keywords:"Ecology, Ecological, Nature, Health, Wellbeing, Health production",scope:"\r\n\tSustainable approaches to health and wellbeing in our COVID 19 recovery needs to focus on ecological approaches that prioritize our relationships with each other, and include engagement with nature, the arts and our heritage. This will ensure that we discover ways to live in our world that allows us and other beings to flourish. We can no longer rely on medicalized approaches to health that wait for people to become ill before attempting to treat them. We need to live in harmony with nature and rediscover the beauty and balance in our everyday lives and surroundings, which contribute to our well-being and that of all other creatures on the planet. This topic will provide insights and knowledge into how to achieve this change in health care that is based on ecologically sustainable practices.
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