Main characteristics on the methods used for the determination of GLY and its main metabolite in soil matrices.
\r\n\t
",isbn:"978-1-83768-117-4",printIsbn:"978-1-83768-116-7",pdfIsbn:"978-1-83768-118-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"a15f5d35a75d3dfee7d27e19238306b0",bookSignature:"Dr. Rakhab Mehta",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/12014.jpg",keywords:"Baseball, Volleyball, Soccer Ball, Tennis Ball, Cricket Ball, Golf Ball, Fluid Mechanics, Forces and Moments, Flight Trajectory, Control and Stability, Aerodynamic Coefficients, Atmospheric Conditions",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 19th 2022",dateEndSecondStepPublish:"June 16th 2022",dateEndThirdStepPublish:"August 15th 2022",dateEndFourthStepPublish:"November 3rd 2022",dateEndFifthStepPublish:"January 2nd 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"21 days",secondStepPassed:!1,areRegistrationsClosed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Participated in the aerodynamic design of launch and reentry vehicles. The post-flight analysis is carried out to evaluate the vehicle's aerodynamic performance. An inverse heat conduction algorithm was developed to predict the convective heat transfer in a rocket nozzle.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"56358",title:"Dr.",name:"Rakhab",middleName:null,surname:"Mehta",slug:"rakhab-mehta",fullName:"Rakhab Mehta",profilePictureURL:"https://mts.intechopen.com/storage/users/56358/images/system/56358.jpeg",biography:"R. C. Mehta obtained his Ph.D. from the Indian Institute of Technology, Madras. He has worked as the Head of Aerodynamics\r\nDivision of Vikram Sarabhai Space Centre/Indian Space Research Organization and has participated in the design of launch and reentry vehicles. He has served as a Senior Fellow in the School of Mechanical and Aerospace Engineering at Nanyang Technological University, Singapore. He is the recipient of the Lifetime Achievement Award from the Flow Physics Society of India. He is a senior member of AIAA, has published over 120 papers in peer-reviewed national and international journals, five book chapters, and co-authored two books. He is a reviewer for many international journals and is presently Dean in the Noorul Islam Centre for Higher Education, Kumaracoil, India.",institutionString:"Noorul Islam University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"5",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Noorul Islam University",institutionURL:null,country:{name:"India"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"20",title:"Physics",slug:"physics"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"453624",firstName:"Martina",lastName:"Scerbe",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/453624/images/20399_n.jpg",email:"martina.s@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"8356",title:"Metastable, Spintronics Materials and Mechanics of Deformable Bodies",subtitle:"Recent Progress",isOpenForSubmission:!1,hash:"1550f1986ce9bcc0db87d407a8b47078",slug:"solid-state-physics-metastable-spintronics-materials-and-mechanics-of-deformable-bodies-recent-progress",bookSignature:"Subbarayan Sivasankaran, Pramoda Kumar Nayak and Ezgi Günay",coverURL:"https://cdn.intechopen.com/books/images_new/8356.jpg",editedByType:"Edited by",editors:[{id:"190989",title:"Dr.",name:"Subbarayan",surname:"Sivasankaran",slug:"subbarayan-sivasankaran",fullName:"Subbarayan Sivasankaran"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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This review focuses on the degenerative pathways but essentially incorporates cues from proliferative mechanisms to develop a holistic approach towards understanding the disease progression. In case of neurodegeneration such as that in Alzheimer’s disease [AD], Huntington disease [HD], Fronto temporal Dementia [FTD] and Parkinson’s disease [PD], insulin dysregulation has been reported [1]. Therapies have been successfully developed encompassing the insulin pathway in ADwhere intranasal administration of insulin assists in recuperation, however resistance towards insulin and mode of administration remains an elusive matter [2]. Similar strategies are gradually being developed for FTD as well using Novolin-R insulin [3]. Insulin shock therapy for the schizophrenic patients was one of the initial approaches towards tackling the disease, but with time insulin resistance or insensitivity to higher dosages led to search for better ways of ameliorating the disease on relapse [4]. Insulin like growth factor 1 [IGF1] therapy has been implemented in ADbut due to the lack of conclusive evidence, resistance and contentious results from experimental models, the attempt did not stand the test of time [5]. It is thus impending to further investigate the modes of regulation and pathways which could lead the therapeutic development.
Insulin receptor family is a subset of the broader Receptor Tyrosine kinase [RTK] family comprised of 20 precise receptor sub-families further sub divided into more families based on ligand and domains of the receptors that play varied roles in neurodegeneration [6]. They include ErbB, PDGF, Ins, VEGF, FGF, Trk, PTK7, Ror, MuSK, Met, Axl, Tie, Eph, Ret, Ryk, DDR, ROS, LMR, STYK1 and ALK [7]. Many of these families have been shown to be involved in AD, PD as well as proliferative diseases such as cancer. The alterations in expression as well as activity has been documented which clearly elucidates the importance of understanding the roles of these receptors in disease pathology. With respect to neurodegeneration however, the roles played by RTKs are gradually being explored and understood since the complexities both on the membrane front and intracellular pathways are numerous. Insulin receptor family composed of Insulin Receptor [INSR], Insulin like growth factor receptor [IGF1R, IGF2R], and Insulin receptor related receptor [INSRR] [8] forms a common bridge for understanding neurodegenerative pathways as they are implicated in almost all diseases and relatively well studied yet poorly understood. It is crucial to mention that unlike other members of the insulin family, INSRR is an orphan receptor with no known ligand. Recent studies have shown that it is pH sensitive and the receptor is activated by alkaline pH [9]. IGF2R unlike IGF1R is non-mitogenic and involved in targeting IGF2 to lysosomes for degradation. It basically functions in signal attenuation and on overexpression has been reported to increase the amyloid beta generation [10].
Thus we begin with an overview of the hallmarks of neurodegeneration, their underlying mechanisms in brief and then delve into the possibility of therapeutics encompassing insulin pathway as a future prospect for palliation of neurodegeneration. Insulin pathway involves mainly insulin andIGF1 which elicit different roles in the cell despite being structurally similar with common pathways that had been studied for decades but is still poorly understood in the context of neurodegeneration. Insulin pathway being a metabolic pathway primarily, is capable of modulating several downstream important signaling molecules and influences metabolism, growth and survival through P13K pathway and MAPK mediated pathway that determines cellular fate [11]. IGF1 additionally engages in the Jak–stat pathway [12] and uses several components of the GPCR pathways and in turn get regulated by them as well [13].
Neurodegenerative diseases like Alzheimer’s and Parkinson have been long studied and the key proteins identified have been tried and tested for targeting in order to ameliorate the disease. However most of it has failed [14]. Several mutations have been identified for both such as
Patients with Huntington often develop diabetes, whereas those with diabetes are more prone to developing AD [18]. The impairment of insulin pathways is common across patients suffering from different neurodegenerative diseases. Studies with transfected striatal nerve cells in vitro, showed that IGF1 can block the mutant protein huntingtin-induced cellular death and decreases formation of intranuclear inclusions [19]. Reduction in apoptosis was perhaps not the reason for this observation since BDNF which does the same, did not prohibit formation of such inclusions. The mitochondrial dysfunction in these striatal cell lines derived from huntingtin Knock-in mice is perhaps ameliorated by insulin and IGF1. The roles of these factors have further been observed in several studies where reduced energy metabolism in lymphoblasts derived from HD patients was shown to be associated with downregulation of Akt and Erk activation which can be helped with IGF1 and insulin [20].
ALS and FTD are different diseases but both elicit a degeneration of neurons, their clinical as well as pathological manifestations are similar. Interestingly both of these display alterations in Growth hormone and IGF1 secretions [21]. ALS was initially characterized by the mutation in a gene, Superoxide dismutase [SOD1] with a 10–20% incidence in patients and since then more than a hundred different types of SOD1 mutations that cause ALS has been discovered [22]. The trigger for ALS and proposed mechanism though could be through growth hormones anomaly, could as well be through glutamate-induced neurotoxicity with an aberrant increase in glutamate concentration in CSF [23].
The aggregated proteins form intra cellular inclusions or extra cellular aggregates in different brain areas. These proteins usually have a beta -sheet structure that allows aggregation and fibril formation as part of the misfolding process [24]. Misfolding of protein aggregates is one of the key underlying cause of neurodegeneration. Amyloid fibrils form plaques found in AD, Phosphorylated tau leads to neurofibrillary tangles, prions mediate in neurodegeneration and alpha synuclein aggregates in PD are also common [25].
In case of PD, ALS and AD, upto10% cases are inherited. However, in HD almost every case has a familial history [26]. The common disease/common variant [CD/CV] hypothesis explains that common disorders are governed by common DNA variants which elevates risks but are usually not causative factors and might add to the understanding of genetic involvement in phenotypic manifestation of disease [27]. For example, the Apolipoprotein E [APOE] encodes a 299 amino acid long glycoprotein and is estimated to be a major contributing factor in AD development. It has also been reported in PD [27]. This similarity further elicits that neurodegenerative disorders might have a common underlying protein–protein interaction network (Figure 1). Also, intervention for neurodegenerative disorders could be facilitated by exploring the genes and its regulatory components including ncRNAs that might govern the progression and allow scope of regulating the protein–protein network downstream [28, 29]. Studies have focused on individual disorders but rarely generated a common platform that allows better understanding of the network by taking varied disorders into perspective.
Commonalities across neurodegenerative disorders, two or more are often shared.
Amongst the hallmarks of neurodegeneration that significantly contributes towards the progression is oxidative stress. It has been implicated in several diseases, including AD, PD and ALS [30]. Extensive oxidation of lipids, DNA and proteins leads to deactivation of major processes or upregulation of toxic cellular cascades. The imbalance in the scales of Reactive oxygen species [ROS] generation damages the cells [31]. Amyloid beta which is originally generated by neurons in AD in response to insults and cellular damage in pursuit of protection, in turn coordinates iron and copper to generate peroxide that accelerates ROS generation by Fenton chemistry [14]. Dopamine buildup in cytoplasm in PD coordinates iron and induces ROS formation. Active site destabilization of SOD also allows further oxidation. Such unregulated ROS further affects calcium regulation which leads to excitotoxicity [32]. The current drugs encompassing ROS generation affect the rate of progression at late stages and thus it is increasingly important to understand the growth hormone axis that changes early in the disease cycle and determines the final outcome, ROS generation and toxic misfolding of proteins aggregates amongst other catastrophic events that lay ahead of the domino like cascade of neurodegenerative pathways.
The growth factors bind to receptors and they no longer respond to the ligand binding when resistance develops, which would have otherwise triggered a cascade of downstream signaling. Several studies have attempted to evaluate the total IGF1 or Insulin levels that are responsible for the resistance to overcome it. Dosage up to 100 nM Insulin even instead of physiologically relevant 1 nM have been unsuccessful in reinstating the sensitivity. This further drives attention towards the receptor [33]. The anomalies in the reports pertaining to the receptor stimulation particularly in AD clearly elucidate a faulty signaling cascade operating at different stages of the disease [2]. The receptors of the Insulin family, vis-à-vis INSR and IGF1R are elusive and bind to both ligands, Insulin and IGFs. Their diverse intracellular domains allows them to bind several other adaptor proteins other than the conventional mediators of signaling cascade, IRS1 and IRS2 [34]. There are numerous astounding facts about these receptors which make them unique targets and add to their therapeutic value. INSR and IGF1R forms hybrids that has a higher affinity for IGF1 [35, 36, 37] but their activity if its varied from individual dimers and their respective localization after stimulation is unknown. The insulin resistance poses a major setback to its therapeutic value and correcting the axis by identifying other players in the cascade both downstream and at the membrane front could thus help in re-sensitizing the receptors.
Both Insulin and IGF1R has been shown to enter the nucleus when activated recently and it is speculated that they perform physiological roles which might be altered in different disease situations [38]. It has been experimentally illustrated as an orchestrated event that occurs physiologically in non-cancerous cell lines along with different cancers, in which this behavior of nuclear migration was first found. It however remains due to illustrate the proportions of the nuclear and cytoplasmic amount of IGF1R in different disease conditions where these metabolic signaling pathways are known to be altered. Their phosphorylation status too remains to be explored since IGF1R has multiple phosphorylation sites [39] and they could be important in understanding their role in neurodegeneration.
Recent studies show that phosphoINSR can be translocated into the nucleus in a clathrin mediated manner. It forms a complex with RNA pol-II, HCF 1 and DNA binding transcription factors like THAP 11. Mass-spectrometry data shows the translocation involves KPNA 2 and HSP 70 [40].
IGF-1R has been observed in the nucleus in case of prostate cancer and breast cancer cells. Full length IGF-1R alpha and beta chains were reported in the nuclear extract of prostate cancer cells. This is the only example of a receptor which traffics as individual sub unit to the nucleus [41]. Other RTKs such EGFR, FGFR has also been previously been observed in the nucleus. The endocytosis is here both clathrin and caveolin mediated. The nuclear transport here is not mediated by adaptor proteins like IRS 1 or an inherent NLS but by SUMOylation [42].
The cause and effect relationship for the ever so complex pathway and its involvement in AD or PD remains unclear and further experimental studies are required to investigate the connection of this underlying nuclear migration with disease progression. The ligands and receptors need to betreated an individual elements instead of a holistic component in the cascade, since there remains the possibility that Insulin and IGF1 both can stimulate other receptors [43]. The concerned receptors could be activated in diseases like AD by Amyloid beta fragments [44, 45] and behave differently in terms of interacting partners and localization, thus altering the signaling cascade majorly.
Insulin production in the body was assumed to primarily happen in the pancreas and circulated throughout, however production in the CNS of both insulin and IGF1 is now proven [46, 47, 48]. Insulin production in CNS appears to be important for the lower organisms than that in higher organism like humans. However further research in the last decade has yielded results that clearly indicate that insulin is secreted in the CNS and might play important roles in physiology. The amount of the same is presumed to be lower compared to the pancreas derived insulin which is transported into the brain through receptor mediated transcytosis. However it can also be independent of the receptor as illustrated by [49]. Insulin circulating in the bloodstream binds to receptors present on the endothelial cells at the blood brain barrier which is further moved into the interstitial fluid. There it binds to insulin receptors distributed throughout the cerebral cortex, olfactory bulb, hippocampus, hypothalamus, amygdala and septum [48]. IGF1 on the other hand, binds to one of its 6 binding proteins and remains in the inactive form in the bloodstream and in local tissues. The entrance into the brain occurs in a similar fashion as in the case of insulin [50].
Several parts of the brain are sensitive towards insulin and they have a different relationship associated with the alterations of the levels. Neuroimaging studies have shed light on the insulin induced brain responses in the fusiform gyrus, hippocampus, pre-frontal cortex, striatum, hypothalamus and insular cortex. Thus healthy insulin signaling controls brain networks implicated in reward processing, memory retrieval, homeostatic control and cognitive control in general [51]. These wide involvements of insulin in regulation of distinct parts of the brain responsible for different activities leads to the marked impact of a mild dysregulation and thus indicate an alteration could infact be an initial event in the cascade of neurodegeneration.
Insulin is known to activate cell growth, cell repair, mitochondrial activity, gene expression, energy utilization and protein synthesis for decades. In both AD and PD, insulin signaling pathway and downstream regulators contribute significantly to the pathology of the disease. Insulin signaling in the brain of these patients is desensitized and while analyzing post-mortem brains, it appears that they have inactivated receptors and downstream IRS1 and 2 as well. The key secondary messengers of this signaling pathway, Akt and mTOR also appears to be inactivated in these patients as it is observed diabetes [14, 20, 27]. Thus, AD was termed as Type 3 diabetes where a systemic resistance to the pathway occurs [52]. However, unlike diabetes the reasons could be very different. Insulin desensitization occurring in the brain could be part and parcel of the inflammatory response in the brain. In case of AD, where amyloid beta aggregates lead to plaque formation, the oxidative stress and cytokines involvement in the long run could restrict the supply of the insulin and IGF essential for growth and repair of the neurons [53]. Pro-inflammatory cytokines like Tumor necrosis factor [TNF] could possibly block the signaling pathways of insulin and IGF1.
Saenger et al. [54] investigated into the SOD1-G93A mouse lines that elicit ALS like pathology, both mild and severe phenotype form. The results indicated IGF1 therapy in the early stages can be effective but in case of severe cases, the functional outcomes were no better. Despite increase signaling in brain, at high doses, survival chances did not improve. Clinical trials that evaluated the role of Growth hormones in patients with ALS yielded mixed results. Researchers back in 1993 employed a very small dosage [0.1 mg/day] which impacted the IGF1 levels after therapy. In another study recently in 2012, 2.8 mg/day was used, but that further led to a reduction in the IGF1 and IGF1-BP3 demonstrating the effectiveness of the therapy further [46].
The brain receives its IGF1 supply through both autocrine and paracrine pathways. IGF1 is secreted by liver, in response to binding of growth hormone [GH] to their respective GH receptors, which leads to increase in the circulating IGF1 levels. The IGF1 thus secreted by liver then binds to their receptors IGF1R in the pituitary and hypothalamus, which in turn inhibits Growth hormone releasing hormone [GHRH] and Growth hormone [GH] production [46].
The hepatic IGF1 production makes up for 70% of the total circulating ligand pool and caters to the brain by passing through the blood brain barrier at choroid plexus directly into the Cerebrospinal fluid [CSF] with the assistance of IGF1R and Megalin, a low density lipoprotein receptor related protein 2 transporter [46]. There is a clear feedback loop for the hepatic production regulation, but not for the autocrine production in the brain (Figure 2). Studies show mutations that manifest in GH deficiency or resistance present normal cognitive functionality [55] however when IGF1 production is globally eradicated or insensitivity is induced, that leads to microcephaly and cognitive deficits in children [56]. This suggests the autocrine brain production might be preserved in GH mutated scenarios and a separate feedback loop exists for that regulation. Adding to the complexity, the circulated IGF1 is bound to IGF binding proteins [IGFBPs] mainly IGFBP-3 being the most abundant, making them unavailable for receptor stimulation [57].
Feedback loop for hepatic and autocrine production of IGF1 in the brain.
The autocrine production though expected to be independent of the hepatic IGF1 production, appears to decline with age similarly. The endocrine decline in IGF1 levels has been related to the diminished GH pulse frequency and amplitude, observed in case of aging. It is partly due to the decrease in ghrelin binding to GH secretagogue receptor [GHSR] [57]. Aging and lowering of cognitive abilities is observed to be associated with lower levels of IGF1, where the receptor levels increase to compensate for the lower availability perhaps. The increase in the receptor levels in aged individuals could also be a coping mechanism for combating insults and stress induced due to the breach in blood brain barrier. Nevertheless, it is evident that IGF1 plays a major neutrophic role within PNS and CNS and is strongly involved in neurogenesis, anti-apoptotic, synaptogenesis and anti-inflammatory effects at cortical, sensory and motor levels and hence further investigation into the puzzling characteristics of the receptor shall shed light on the definitive involvement in neurodegeneration.
IGF1 other than being implicated in AD and other neurodegenerative diseases is also a major risk factor for cancer. Its upregulation is a major implication of proliferation in several cancers. Modulation of the cell cycle, apoptosis and cell survival through interaction with IRS1 and IRS2 and downstream effectors like PI3K/AKT/mTOR allows IGF1 to drive the cell towards proliferation [58]. Some pre-clinical studies state that mutations in genes that control the GF/INS/IGF axis can increase the lifespan even in invertebrate and vertebrate animal models [59].
Aging and IGF1 are intertwined on several levels, adding to the complexity of the insulin pathway. Research shows IGF1 deficiency could slower aging [60, 61, 62, 63] and thus is a separate concern for therapy development for patients who develop Late onset AD [LOAD]. However combinatorial treatment with other membrane receptor antagonists or agonists for that matter which are implicated in the diseases could offer better options for prophylaxis.
The amyloid hypothesis that focuses primarily on the protein misfolding that occurs in AD and aggregation associated with it largely fails at analyzing the actual neuronal pathophysiological developments in the brain. Inflammatory mediators like cytokines can promote the state in CNS through several mechanisms, crossing the BBB or entering by circumventricular organs, communication transmitted via the vagal nerve, and signaling through the cerebral endothelium [51]. These pathways allow insinuation and perpetuation of pro-inflammatory responses within the brain. Amyloid beta oligomerisation and tau phosphorylation which are hallmarks of AD can also be promoted through such changes [51].
The impact of growth factors comes into play since important cellular phenomena like inflammation and underlying reasons for neuronal loss are in turn corrected with insulin based therapies. The problems with such therapies persist, and have been long known, as progressive resistance. Key growth factors present in the brain such as BDNF, NGF, GDNF, IGF1 and insulin all lose their capacity of reversing or controlling the damage over time [64]. However, the improvements are often long lasting and disease progression is halted effectively by them through receptors in the glia initially [51]. Neurodegenerationis a complex process andfactors like GLP1, GIP1, and insulin cross the blood brain barrier in order to provide protection on several levels including ROS generation. In response to these, synaptic activity as well as plasticity is restored, brain functionality and memory retention is improved, and mitogenesis and mitochondrial function which dysregulates the energy utilization is also corrected [65]. Autophagy occurs normally and apoptosis rates are reduced as well.
In order to improve the capabilities of the insulin therapy and circumvent the issues with hyperinsulinemia, it is important to understand the crosstalk possibilities for this important axis. The goal to re-sensitize the cells towards treatment or induce a similar cascade by receptor stimulation through other ligands or adaptors could pave the way for combinatorial therapy. Thus, understanding the cross talk possibilities for neurodegenerationis impending. In case of heart diseases a crosstalk between insulin receptors and beta 2 adrenergic receptors [β2AR] is found which paved the path towards understanding the exploitation of GPCR signaling pathways by RTKs [66]. RTKs can use Beta arrestin, G protein receptor kinases, insulin to directly induce tissue RAS activation, regulate beta-adrenergic catecholamine stimulation and even to attenuate contractile response to β2AR stimulation in myocardial ischemia [67].
Angiotensin II [AII] acts on the cell by virtue of its receptor and since 1996, the direct connection between the two pathways on the phosphorylation and the downstream P13K activity has been known. Stimulation with AII inhibited both basal and insulin stimulated PI3K activity in rats [68].
Amongst interesting findings, IGF1R has been found to exist in association with GABAB, which offers neuroprotection to cerebellar granule neurons from low potassium induced apoptosis. This process involves Akt recruitment and activation of IGF1R with the assistance of Gi/o- protein and FAK1 [69, 70]. Antidepressants can potentially trans-activate RTKs like EGFR by inducing activation of LPA receptors [71]. Reports show that acute MOR agonists can induce beta arrestin dependent and src-dependent IGF1R transactivation through subsequent Erk phosphorylation, prolonged treatment with the agonist however leads to heterologous desensitization of IGF1R based cascade [72]. The studies corroboratively indicate insulin GPCR heterocomplex plays important roles in different tissues and several of such associations could be involved in neurons in physiological and disease scenarios as well.
Studies show IGF1 receptor signaling and anti-apoptotic activity in cortical neurons is partly due to the Src dependent PACAP type I receptor which is transactivated [73]. Non-canonical insulin pathway receptors like TrkA has also been observed in such complexes with another receptor LPA1 that allows for constitutive activation of the cascade involving ERK1/2 in response to NGF [71]. IGF1 can also mediate G protein dependent ERK1/2 activation through transactivation of sphingosine 1 phosphate receptors [73].
Dopamine and Insulin signaling pathways are also intertwined as they elicit a reciprocal relationship. Antagonism of D2 receptors for a short duration leads to upregulation of insulin secretion [74]. Insulin can also enhance reuptake of dopamine, which has been visualized with respect to mental health and metabolic syndromes.
Recent studies on ncRNAs are also evolving and shows that several lnc RNAs and miRs that are involved in controlling key phenomena in neurodegenerative diseases like AD, PD, HD and ALS. Long non-coding RNAs like BACE1-AS, XIST are upregulated in AD [75, 76]. Neat1 and MALAT1 are upregulated in FTD as well as ALS, where they form paraspeckles with TDP-43 and FUS proteins. UCHL1-AS1 leads to perturbation of ubiquitin-proteasome system that and is upregulated in PD. HTT-AS, HAR1 and BDNF-AS were reported to be dysregulated in HD. Interestingly, insulin responsiveness of these genes have not been explored in neuronal perspective. Some lncRNAs such as H19, lncASIR have been reported by several groups [29, 77] but their implications and involvement, interaction with other proteins or ncRNAs shall open up avenues for therapy oriented research. Furthermore, Lnc RNAs that are known to interact with these receptors such as IRAIN, GAS5, NNT-AS1 [78] needs to be studied in the neurodegenerative landscape to allow translational medicine development.
Insulin resistance remains a major challenge towards drug development and meanwhile alternative strategies encompassing hormones are being tested and developed forneurodegenerative diseases. Incretin hormones like GLP1 and GIP show similar therapeutic roles and do not lead to insulin desensitization, as they do not activate the receptors however they lend similar effects [79]. Furthermore, analogues of the peptide hormones do not affect the blood glucose levels in non-diabetics with normoglycemic index. The side effects are mild loss of appetite and nausea. Detemir study led to this important realization that drugs for non-diabetic with AD or PD who require intervention with hormones needs to be developed with caution. Those with higher peripheral insulin resistance performed better with the drug, however those with lower peripheral resistance suffered from worsening of memory formation. Though there is plenty to understand and explore about the insulin pathway and its role in complex multifactorial neurodegenerative diseases, the treatments encompassing these factors that appears to be effective must be discussed.
GLP1 is part of a peptide based growth factor family that activates a glucagon type GPCR, expressed in primates, rodents and human neurons. Other receptor agonists such as lixisenatidemliraglutide and semaglutide available for treating Type II diabetes are also being tested for eeffectiveness in AD and PD [1]. Some of them can traverse the blood brain barriers and are thus prospective game changers for therapeutics. GLP 1 mimetic have shown promising results in animal models of AD, they exhibit fascinating reduction of chronic inflammation which is a major driver for progression of disease.
GIP is another sister incretin that bind to a GPCR on the membrane and its receptor is abundant in a wide range of cells including pyramidal neurons, Purkinje cells in cerebellum and basal brain areas. It was capable of offering neuroprotection to APP/PS1 mice, reduced loss of synapses and recreated synaptic plasticity [79]. Furthermore, the amyloid plaque load was also reduced along with oxidative stress and DNA damage.
Substantial advancement in the field of growth hormone, RTKs and their involvement in neurodegeneration has been made in the last two decades. The development of peptide based therapies involving incretins that can mitigate the degenerative processes in the brain is a major feat that shows promise. Controlling this major InsR and IGF1R, which are prominent and one of the most important albeit in age reversal [61, 80] is yet to be achieved but picking up cues from diseases like cancer that elicits an alternate pathway [81], in terms of therapy could accelerate the process of developing therapies (Figure 3). The ability of growth factors to modulate cellular events such as ROS generation, energy utilization and others are remarkable and thus developing more sophisticated approaches using the knowledge thus gathered to invoke the right set of signals for slowing the cycle and early detection are important. Though possibilities involving the insulin pathway have been only explored on the protein level, regulation on the RNA level could be utilized yet to enhance sensitivity.
Therapies that could exploit the unified approach and yield therapeutic benefits.
Pre-clinical studies from growth hormone therapies often leave out important aspects like multiple binding partners, transactivation and cross talks that leads to different results when applied to humans. Research on analogues with no resistance, compounds that re-instate sensitivity and alternative drugs such as mAbs against RTKs altered are the need for the hour. The current peptide based drugs on the market are promising since they can potentially reverse a range of pathophysiological parameters of neurodegeneration. However understanding the hormonal axis that led to the death is important for further biomarker development and therapy development as well. The growth hormone axis could indeed be an underlying cause amongst the plethora of factors already known for neurodegeneration. Studies on their involvement in determining cellular fate and their tuning in accordance with progression of disease are required for developing a better understanding about stages of the progressive disorders discussed holistically. The crosstalk with other pathways and gradual involvement of several miR and Lnc RNA which are crucial are complicating the story and yet simplifying it in terms of the puzzling and contentious results.
In conclusion, it is apparent that the neurodegenerative disorders have an underlying insulin pathway abnormality and growth hormone axis plays a major role the CNS and in turn affects progression of neurodegeneration. The Insulin receptor family amongst the RTKs is an important set that could lead the path towards therapies for degenerative disorders in a non-invasive manner if understood in their entirety and the regulation though complex could be a common network of protein–protein interaction that would simplify prognosis and prophylaxis.
We would like to acknowledge Dr. DebdattoMookherjee, Universität Basel for his constant motivation and constructive feedback during manuscript preparation.
The authors declare no conflict of interest.
After World War II, the world was in the need to overcome food scarcity. Therefore, several pest and weed management techniques were adopted by farmers all over the world using various synthetic herbicides. The invention of glyphosate (GLY; N-(phosphonomethyl) glycine) was a big breakthrough in that era. GLY with CAS No. 1071-83-6 is a broad-spectrum, postemergent, nonselective, and synthetic universal herbicide, whose commercial formulations are referred to as glyphosate-based herbicides (GBHs) [1, 2]. Glyphosate was first synthesized in 1950 by Swiss chemist Henry Martin, who worked for the Swiss company Cilag. The work was never published. Its herbicidal activity was not discovered until GBHs were resynthesized and tested in 1970, being used for this purpose since 1974. It was the Monsanto Corporation in 1974 that introduced and made commercially available the herbicidal formulation Roundup containing GLY as active substance. Farmers quickly adopted glyphosate for agricultural weed control, gaining the potential to kill weeds without killing their crops. Indeed, glyphosate proved able to kill weeds without killing their crops, especially annual broadleaf weeds and grasses known to compete with commercial crops grown around the globe by interfering with the synthesis of the aromatic amino acids phenylalanine, tyrosine, and tryptophan [3].
\nSince then, its use in agricultural and nonagricultural settings has steadily increased from a total of 0.6 Mg applied in 1974 to a total of 125.5 Mg applied in 2014, and it is currently the most widely used herbicide in the United States and throughout the world [4, 5]. Monsanto’s last commercially relevant US patent expired in 2000. Nowadays, GLY formulations that are used as a broad-spectrum systemic herbicide have been widely applied in agronomic crops and orchards. Furthermore, GLY formulations are currently approved by regulatory bodies and marketed worldwide by many agrochemical companies, such as Bayer, Dow AgroSciences, and Monsanto, in different solution strengths and with various adjuvants.
\nGLY approval is renewed in the European Union (EU) on 16 December 2017, while its approval expires on 15 December 2022. Therefore, GLY can be used as an active substance in plant protection products (PPPs), until 15 December 2022. GLY has been thoroughly assessed, under an intense debate due to a concern about its effects on the environment and human health, by the Member States, the European Chemicals Agency (ECHA), and the European Food Safety Authority (EFSA) in recent years [6, 7]. An important prerequisite for GLY upcoming renewal as an ingredient in PPPs is that GLY should not adversely affect the environment and human and animal health as delineated by European regulation [8].
\nGiven the widespread use of glyphosate, the investigation of the relationship between glyphosate and soil ecosystem is critical and has great significance for its valid application and environmental safety evaluation. Although herbicides containing glyphosate are not intentionally applied directly to the soil, they may contaminate soils in and around the treated areas, via spray drift during their application and after being washed off from leaf surfaces with rainfall.
\nThe fate of glyphosate in soil is complex and attributed to mineralization, degradation, immobilization, and leaching. Several studies trying to identify and understand the mechanisms that control the fate of chemicals as a source of environmental contamination have been published in previous years, especially in soils and water. Some were conducted with the acid form of glyphosate and others with formulated products, since glyphosate is not introduced into the environment as pure active ingredients but as formulated products containing co-formulant chemicals (adjuvants) and other additives. In a recent review, Mesnage et al. presented an overview of the most common surfactants containing co-formulants in glyphosate-based herbicides and explained whether the presence of such surfactant (e.g., Triton CG-110) has the potential to affect adsorption, leaching, and mineralization of glyphosate in the soil [9].
\nThe fate of glyphosate depends on soil composition, its physicochemical properties (texture, organic matter content, pH), its biological properties (microbial community, climatic conditions), the chemical properties of the specific pesticide, as well as the timing between precipitation and pesticide application [10, 11, 12, 13]. A recent study by Muskus et al. showed that temperature, pH, and total organic carbon (TOC) variations influenced the mineralization kinetics of glyphosate as well as the amount of extractable glyphosate and the extent of bio-NER formation over time in a German soil [14].
\nGlyphosate degrades at a relatively rapid rate in most soils, with a half-life estimated to be between 7 and 60 days. The relatively rapid degradation of glyphosate has the advantage of limiting its role in polluting the environment, especially soil and water resources. However, its degradation could increase the pollution risk by its metabolites: aminomethylphosphonic acid (AMPA) and/or sarcosine. The degradation of the herbicide molecule as described in the literature (Figure 1) can follow two paths: the first is based on the breakdown of the carbon-nitrogen bond and leads to the formation of AMPA (main metabolite of glyphosate) via glyphosate oxidoreductase which is further degraded to carbon dioxide, while the second way is based on the splitting of the carbon-phosphorus (C-P) bond that is mediated by C-P lyase enzyme and results in the formation of sarcosine and glycine [15, 16, 17, 18, 19, 20]. However, AMPA also exists in the environment as a photodegradation product of aminopolyphosphonates in water [21].
\nMain glyphosate biodegradation pathways in the environment [
Glyphosate is a small, amphoteric molecule characterized by three polar functional groups. These are the phosphonomethyl, amine, and carboxymethyl groups arranged in a linear manner. As a result of the presence of those groups in its structure, glyphosate is an ionic compound (log KOW = −3.20), highly polar and soluble in water (10.5 g L−1 at 20°C). GPS is a polyprotic acid with four pKa values, 0.7, 2.2, 5.9, and 10.6, 8 meaning that the speciation of the molecule is dependent upon the pH value of the solution. Three pKa values, 0.9, 5.6, and 10.2, characterize AMPA. Over the pH values commonly found in soils, mono- and divalent anions are the predominant species present [6, 22].
\nGlyphosate is soluble in water, but it also binds onto soil particles under certain conditions, particularly in clays. Numerous laboratory studies have shown that the absorption constant of the molecule in the soil varies between 8 and 377 dm3/kg. This coefficient value indicates a high absorption in the soil. Glyphosate adsorption to soil, and later release from soil, varies depending on the characteristics and composition of the soil (clay, sand, or gravel), temperature, and soil moisture. So it may quickly wash out of sandy soils or last for more than a year in soils with a high clay content. Even when bound to soil particles, it may dissolve back into soil water later on, for example, in the presence of phosphates. Glyphosate can also form complexes with metal ions, potentially affecting the availability of nutrients in the soil.
\nThe mechanism of glyphosate sorption to soil is similar to that of phosphate fertilizers, the presence of which can reduce glyphosate sorption [23]. Glyphosate compared to most other pesticides strongly absorbs to soil and is not expected to move vertically below the six-inch soil layer, exception made of a colloid-facilitated transport. Its soluble residues are expected to be poorly mobile in the free pore water of soils. The mobility of glyphosate in soil is very low because, as a strong chelating agent through the carboxyl, phosphonate, and amino groups, it creates the complexes that immobilize the mineral micronutrients of the soil (calcium, iron, magnesium, manganese, nickel, zinc, etc.) making them unavailable to plants [11, 24]. Similar to glyphosate, AMPA accumulates in soil and adsorbs in soils with high mineralization rates. Where strong sorption is demonstrated, glyphosate accumulation in soils can be expected. The interaction of pesticide-soil and the diffusion process lead to the formation of non-extractable residues trapped in areas not accessible to water flowing through the soil. The contamination of the environment is therefore considered to be relatively limited.
\nNevertheless, this adsorption is not permanent because glyphosate can also be found in lower soil layers. Many studies suggest the possibility of a slow remobilization of these residues, which could explain the low pollution level of groundwater by some pesticides at a long term. Glyphosate does have the potential to contaminate surface waters through erosion, as it adsorbs to soil particles suspended in runoff. Rain events can trigger dissolved glyphosate loss in transport-prone soils [25, 26].
\nThe increase of glyphosate-based herbicides has raised concerns about the occurrence of GLY and AMPA in the environment. Reports of GLY presence in the environment from other parts of the world are numerous. A considerable attention has been given to Argentina [27, 28, 29, 30], Canada [31], across the United States [32], Mexico [33], and Portugal [34] as well to Spain [35], New Zealand [36], Austria [37], and French [38].
\nHowever, although GLY is the most sold herbicide in Europe, a combined approach on the occurrence and levels of glyphosate residues in European soils and air, in conjunction with analytical methods used for this scope, is still scarce, compared to the magnitude of its use though some research articles and reviews (not only focusing on soil) started to appear (indicatively see [39, 40, 41]).
\nThe first large-scale assessment of distribution of GLY and AMPA in soils from agricultural topsoils of the European Union was recently published by Silva, where glyphosate and its metabolite AMPA were tested in 317 EU agricultural topsoils; 21% of the tested EU topsoils contained glyphosate and 42% contained AMPA, while both glyphosate and AMPA displayed a maximum concentration in soil of 2 mg kg−1. Both compounds were present at higher frequencies in northern soils, while eastern and southern regions generally had the most glyphosate- and AMPA-free soils (<0.05 mg kg−1), respectively. In addition, some contaminated soils were observed in areas highly susceptible to water and wind erosion [42]. Therefore, residue threshold values in soils are urgently needed to define potential risks for soil health and off-site effects related to export by wind and water erosion.
\nIn order to detect the presence and quantity of GLY dispersed in the environment, various laboratory analyses are performed on samples taken in situ.
\nOne of the key problems for obtaining reliable results from field samples is the use of the best suitable extraction solution, since sorption and desorption of glyphosate in soils are extremely pH dependent. Some reports showed that humic substances (substances and heterogenic mixtures dispersed and abundant in soils and sediments) adsorb glyphosate strongly due to the hydrogen bonding interactions between the two matrices. Another important aspect is that GLY is a highly polar herbicide, very soluble in water and insoluble in most organic solvents, which does not allow extraction with organic solvents and makes the extraction difficult and the preconcentration step quite lengthy. However, due to the amphoteric character of GLY and AMPA, both anionic and cationic resins have been used for preconcentration and cleanup purposes (commented in the below sections).
\nAs already mentioned, GLY has been shown to bind strongly to soils, especially to soils with high amounts of organic matter, iron, and aluminum [43, 44]. There is also evidence that glyphosate binds to clay minerals in a manner similar to inorganic phosphate [44, 45, 46]. The strength of the interactions of the phosphonate, carboxyl, and amino groups with iron oxides, silica, alumina, and organic matter depends on factors such as pH, metal cations, phosphate from fertilizers, etc. Therefore, it is hard to detect GLY without a pretreatment method [47].
\nThe choice of the best suitable extraction solution remains a problem that must be addressed accordingly.
\nSeveral authors in the past reported different extraction methods of these compounds from soil, mainly using alkaline solutions with different recovery rates [48, 49, 50, 51] and most times applicable for one type of soil. In 1980 the FDA’s “Pesticide Analytical Manual” (PAM) including a procedure for the analysis of glyphosate residues in soil is published. However low and irreproducible recoveries in soil samples have been reported using this method. Later, Glass in 1983–1984 analyzed soils by alkaline extraction, followed by cleanup using flocculation with CaCl2 and anion exchange [52, 53, 54]. Yet, recoveries were still remained poor and ranged from 19 to 55%. Many extractants for soil have been tested in the years that followed with the most commonly used being aqueous bases KOH or NaOH, aqueous NH4OH or NH3, or triethylamine. Other extractants include NaHCO3, KH2PO4, mixed solutions of KH2PO4 and NH3 or NH4OH and HPO4, sodium borate buffers [55, 56, 57, 58, 59, 60], or even weak acids such as 10% phosphoric acid buffers [13, 61].
\nMoreover, it is vital to adjust the concentration of the extraction media in such a way that high recovery rates can be obtained while avoiding matrix problems provoked by excessively aggressive alkaline media, which may enrich the dissolved humic substances in the extraction solution [49]. Humic acids interfere, for example, with the derivatization and suppress the ionization in ESI-MS/MS detectors.
\nAlthough GLY is the most widely used agrochemical in the world, it is also the most cumbersome in its determination in analytical methods, a fact known as the “glyphosate paradox.” The challenge to detect GLY using a simple analytical method is an outcome of its ionic character, low volatility and low mass, high polarity and solubility in water, poor solubility in common organic solvents, high boiling points, difficult evaporation, and poor retention on traditional analysis columns. The quantitative and qualitative analyses of GLY (and AMPA) are extremely difficult due to the absence of fluorophores or chromophores in their structure. Furthermore, its determination at the low concentration levels required for residue analysis in different matrices is very difficult. In soil its determination is even more difficult due to the complexity of this matrix and subsequent matrix effects. The derivatization process using different derivatization reagents has been extensively used to overcome some of the above problems [62].
\nPrior to any attempt, it is important that all analysts to work with a glass that is not silanized to avoid the typical pitfall of GLY analysis. GLY has a profound affinity to glass, and any analytical solution prepared by this way will deviate substantially from its nominal concentration.
\nChromatography is the most used and powerful method for the determination of GLY and its main metabolite AMPA, utilizing gas chromatography (GC) and liquid chromatography (LC) after derivatization or directly and capillary electrophoresis (CE). Conventional detectors are difficult to be used (especially for a straightforward analysis) due to the lack of chromophore and fluorophore groups in GLY. Usually, the limits of detection for GLY in soil vary between 0.01 and 0.3 mg/kg.
\nIn all cases, the analytical methodology is practically exclusive for this analyte, since the working conditions cannot be applied to the determination of pesticides different from glyphosate, except for some organophosphorus, such as glufosinate and other polar compounds, and this chemical is difficult to incorporate in the vast majority of multiresidue methods. However, many of the methods published for the determination of GLY are also suitable and report results for the determination of AMPA. The majority of developed analytical methods concerned a single matrix (most often water) and may not be suitable for other matrices. Therefore, the last decade, numerous revised methods have been published on the analysis of glyphosate and AMPA in different matrices such as water, plants, or soils. Many of them just modify several parameters of previously published methods, as the pH of the water in the extraction, cleanup procedure, and derivatization step (volume and/or concentration of the samples or reagents). Other modifications include the use of different separation techniques or detection systems or even new matrices. Fewer new methods have been reported in the past 5 years for more complex matrices such as soil. Very few articles have been published on multimatrix methods.
\nIn Table 1 numerous analytical methods that have been used for the determination of GLY and AMPA in soil matrices are summarized. Based on the given information, at present LC is the most used method since it is considered the most suitable technique for the detection of phosphonic and amino acid-type herbicides at low concentrations. Hence, the lack of chromophore or fluorophore groups makes it difficult to use conventional detection methods such as ultraviolet (UV) absorption or fluorimetry. LC–MS/MS is currently the method of choice for polar analytes due to its high selectivity and sensitivity.
\nYear | \nSample preparation (extraction/cleanup) | \nDerivatization (pre- or post-column) | \nAnalytical method | \nLOD/LOQ μg/g | \nOther information | \nReference | \n
---|---|---|---|---|---|---|
1986 | \n0.1 M (C2H5)3 N/SAX cleanup | \nFDNB | \nHPLC-UV (405 nm) | \n0.05 GLY 0.1 AMPA | \n\n | [63] | \n
1988 | \n0.1 M KH2PO4 or 0.2 M KOH | \nFMOC-CI | \nHPLC-FLD (λex = 270 nm, λem = 315 nm) | \n0.5–1.0 GLY | \nMinutes till some days of analysis time 0.1 M KH2PO4 (sandy soils)/0.2 M KOH (high clay soils) | \n[50] | \n
1989 | \n0.1 M KH2PO4\n | \nTFAA-TFE | \nGC-NPD | \n0.01/0.05 | \nRecoveries 66–75% | \n[64] | \n
1991, 1999 | \n\n | TsCl | \nHPLC-UV (240 nm or 280 nm) | \n8 mg/L GLY 10 mg/L AMPA | \n\n | [64, 65, 66] | \n
1994 | \n0.25 M NH4OH and 0.1 M KH2PO4\n | \nHFB/TFAA (1:2) | \nGC–MS (EI-SIM) | \n0.01/0.05 GLU, 0.01/0.05 AMPA | \nRecoveries 84–97% | \n[67] | \n
1996 | \n0.6 M KOH | \nFMOC-CI | \nLC–LC/FLD (263 nm λex = excitation, λem = 317 nm) | \n0.01/0.05 GLY 0.01/0.05 AMPA | \nConcerning soil organic matter and clay contents, the LOQ can reach 0.01 μg/g for both analytes for sandy samples, and for soil samples with a high organic matter and clay contents, LOQ is of 0.04 μg/g for glyphosate and 0.1 μg/g for AMPA | \n[68] | \n
1996 | \nNaOH 0.2 M | \niso-PCF | \nGC-FPD | \n0.8/8.0 GLY 1.2/12 AMPA | \nRecoveries 91–106% | \n[69] | \n
2000 | \n1 M NaOH | \nTFAA/TFE (2:1) | \nGC–MS (EI) | \n0.003/0.006 GLY 0.003/0.006 AMPA | \nRecoveries 75–78% | \n[48] | \n
2002 | \n\n | \n | Cyan sensor | \n0.45 GLY | \nPhosphonomethyl glycine Inhibits amino acid biosynthesis | \n[70] | \n
2005 | \n0.6 M KOH | \nFMOC-CI | \nLC-ESI-MS/MS | \n0.005/0.05 GLY 0.005/0.05 AMPA | \nRecoveries 88–92% | \n[49] | \n
2007 | \n0.1 N NaOH/SAX-SPE | \nFMOC-CI | \nHPLC-ESI-MS/MS | \n0.02/0.035 GLY 0.03/0.05 AMPA | \nMean recovery values were 70% (7%) for GLU and 63% (3%) for AMPA | \n[71] | \n
2008 | \n2 M NH4OH | \nTFAA/TFE | \nGC-NPD | \n0.01 GLY | \nRecoveries 88–94% | \n[72] | \n
2009 | \n0.1 M KOH/CAX cleaned up | \nFMOC-CI | \nHPLC-FLD | \n0.025 | \n\n | [55] | \n
2009 | \n2 M NH4OH | \n1. CS2 2. Ammonical solution of Cu(II) | \nUV 435 nm | \n1.1./3.7 μg/mL GLY | \n80–87% | \n[73] | \n
2010 | \nWater | \n\n | CE/MS (negative with amino capillary) | \n20 GLY 40 AMPA | \nRecoveries 76–121% (20/200/2000) for GLY and 83–89% (40/400/4000) for AMPA | \n[74] | \n
2011 | \nWater | \n1. Ca(ClO)2\n 2.OPA/ME | \nSIA-FLD (λex =270 nm, λem =315 nm) | \n0.08/0.25 mmol/L GLY | \nPre-column conversion: 1. of glyphosate to glycine by Ca(ClO)2; 2. followed by reaction with OPA/ME in borate buffer (pH 9.5) to produce the fluorescent 1-(2′-hydroxyethylthio)-2-N-alkylisoindole | \n[75] | \n
2012 | \n0.2 M KOH | \n\n | SWV using CFME | \n25/83 μg/L GLY | \nRecoveries 89–102% | \n[76] | \n
2013 | \n0.1 Μ ΚΟΗ/SPE | \nFMOC-CI | \nHPLC-ESI-MS/MS using reversed-phase C18 | \n0.02/0.05 GLY 0.01/0.03 AMPA | \nRecoveries 79–117% | \n[77] | \n
2014 | \n0.6 M KOH | \nFMOC-CI | \nHPLC-FLD (λex = 267, λem =317 nm) using reversed-phase C18 | \n0.5 GLY 0.5 AMPA | \n\n | [57] | \n
2014 | \n10% H3PO4\n | \n\n | SPE-HPLC-ESI-MS/MS (using HILIC) | \n0.37 GLY 0.61 AMPA | \nRecoveries 85–126% | \n[61] | \n
2014 | \n0.01 M | \nFMOC-CI | \nSPE-HPLC-FLD (λex =263 nm and λem =317 nm) | \n0.6/2.0 ng/mL GLY 0.4/1.3 ng/mL AMPA | \n\n | [78] | \n
2014 | \nWater | \nHCl/NaNO2\n | \nDIPN-GNPs-PGE | \n0.35 ng/mL GLY | \n1.0 g soil was suspended in a 30 mL water Recoveries 98.6–102.8% | \n[79] | \n
2016 | \nWater | \n\n | IS-FLD using lgG-CDs | \n0.35 ng/mL GLY | \nRecoveries 87.4–105.5% | \n[80] | \n
2015 | \n1 M Na2B4O7\n | \nFMOC-Cl | \nHPLC-PDA (206 nm) | \n0.01/0.1 GLY 0.01/0.1 AMPA | \nRecoveries 70–76% Confirmation with QTOF MS | \n[16] | \n
2015 | \nNaOH | \nFMOC-Cl | \nHPLC-UV (254 nm) | \n— | \n\n | [3] | \n
2015, 2016, 2018 | \n0.6 M KOH | \nFMOC-CI | \nHPLCMS/MS/using reversed-phase C18 | \n0.02/0.05 GLY 0.03/0.05 AMPA | \nRecoveries 77–87% | \n[81, 82, 83] | \n
2016 | \nNaOH | \nFMOC-CI | \nSPE-HPLC–MS | \n0.02 mg/L GLY 0.05 mg/L AMPA | \n\n | [84] | \n
2018 | \nNH4Cl | \nFMOC-CI | \nUV–Vis (264 nm) | \n20 GLY | \n\n | [85] | \n
2018 | \nKOH | \nFMOC-CI | \nSPE-HPLC–MS | \n\n | 0.001 GLY 0.001 AMPA | \n[86] | \n
2018 | \n0.1 M K2PO4\n | \nFMOC-CI | \nHPLC-MS (negative ionization) | \n0.002 GLY 0.005 AMPA | \nRecovery 80% | \n[27] | \n
2018 | \n40 mM Na2B4O7 or NaHCO3 0.5 M | \n2% succinic anhydride IN DMSO | \nL’ELISA (via microtiter plate reader at 450 nm) | \n0.8 10–3/0.1 GLY | \nRecoveries 87.4–97.2% (0.1–10 μg/g) confirmed with HPLC-FLD (by Ibanez) | \n[87] | \n
2019 | \n0.6 M KOH | \nFMOC-CI | \nLC-ESI-MS/MS using reversed-phase C18 | \n\n
| \nRecoveries from 89.6 to 118.8% for GLY and from 68 to 94.6% for AMPA | \n[88] | \n
2019 | \nWater | \nHFBA/FBA | \nSPE-GC-FPD | \n0.10/0.37 ng/mL GLY 0.22/0.81 ng/mL AMPA | \nRecoveries 94–110% | \n[89] | \n
2019 | \n\n | 1. Ca(ClO)2\n 2.OPA/ME | \nSIC-FLD | \n0.03/0.10 GLY | \nPre-column conversion: 1. of glyphosate to glycine by Ca(ClO)2; 2. followed by reaction with OPA/ME in borate buffer (pH 9.5) to produce the fluorescent 1-(2′-hydroxyethylthio)-2-N-alkylisoindole | \n[90] | \n
Main characteristics on the methods used for the determination of GLY and its main metabolite in soil matrices.
Solid-phase extraction = SPE; anion exchange = SAX; cation exchange = CAX; Flame photometric detector = FPD; nitrogen-phosphorus detector = NPD; fluorescence detector = FLD (λex = excitation; λem = emission); capillary electrophoresis = CE; immunosensor = IS; square wave voltammetry = SWV; carbon-fiber microelectrode = CFME; double template imprinted polymer film-gold nanoparticle-modified pencil graphite electrode = DIPN-GNPs-PGE; carbon dot-labeled antibodies = lgG-CDs; sequential injection chromatography = SIC; sequential injection analysis = SIA; linker-assisted enzyme-linked immunosorbent assay = L’ELISA; laser-induced fluorescence detection = LIF; Fluorenylmethylchloroformate = FMOC-Cl; 1-fluoro-2,4-dinitrobenzene = FDNB; trifluoroethanol = TFE; N-methyl-N-(tert-butyldimethylsilyl)-trifluoroacetamide = MTBSTFA; 2,2,3,3,4,4,4-heptafluoro-1-butanol = HFB; trifluoroacetic anhydride = TFAA; heptafluorobutyric anhydride = HFBA; isopropyl chloroformate = iso-PCF; o-phthalaldehyde/2-mercaptoethanol = OPA/ME; 4-toluenesulfonyl chloride = TsCl.
Gas chromatography methods are used after derivatization by simultaneous acylation, esterification, or trialkylsilylation reactions to convert the analytes into volatile compounds [69, 91, 92]. Typically used derivatization reagents are the mixture of trifluoroacetic anhydride (TFAA) and trifluoroethanol (TFE) or N-methyl-N-(tert-butyldimethylsilyl)-trifluoroacetamide (MTBSTFA) containing 1% tertbutyldimethylchlorosilane (TBDMCS) in excess producing sufficiently volatile derivatives for GC analysis. These derivatization approaches can be applied not only to soil (or sediment) but to other commodities as well [91].
\nGLY as a compound permits its detection (in conjunction with GC) by several detectors such as the flame photometric detector (FPD), flame ionization detector (FID), electron capture detector (ECD), nitrogen-phosphorus detector (NPD),and also the more sensitive and selective mass spectrometer detector (MSD). The quantification of GLY in the soil through NPD has reached an limit of quantification (LOQ) equivalent of 0.02 mg/kg [93, 94]. GC analytical methods are reliable, sensitive, and selective, but the sample preparation is very time-consuming, complicated, and tedious as all ionic groups must be derivatized. In addition, they involve anhydrous conditions, extensive cleanup using solvent partitioning steps, charcoal elimination of pigments, and a large-volume anion of cation exchange.
\nBorjesson and Torstensson using GC-MS managed to sensitively detect GLY and AMPA in soil [48]. One point that should have been addressed was the content of humic acids a problem mentioned above as well. Extraction was tedious, involving extraction under basic conditions, adjustment of the pH to acidic, and then subjected to column purifications to achieve ligand-anion exchanges. To derivatize GLY and AMPA TFE and TFAA were used. By this way the respective ester and acetyl derivative are formed suitable for GC analysis.
\nUtilizing the S/N approach, they presented one of the lower LOQs of the bibliography for GLY, established at 0.006 mg/kg. The application of the method in soils collected from Swedish embankments after being treated with GLY revealed the gradual degradation of GLY along with the presence of AMPA.
\nBergstrom et al. investigated at laboratory level GLY and AMPA in sand and clay soils [13]. More specifically, its degradation was monitored using a GC-MS method, after derivatization with TFE and TFAA. The extraction of the soil was accomplished using an alkaline solution. Selected ion monitoring was utilized to enhance selectivity and optimize sensitivity of the method. The LOQ of the method was established at 0.01 mg/kg. The studied kinetics demonstrated that GLY had a very slow degradation rate in the clay soil. Concerning AMPA, though it is more tenacious than GLY (when derived from GLY), it degrades faster than GLY.
\nA 2019 study on GLY and AMPA analysis in soil showed that still GC-FPD can function as an adequate tool for such demanding analyses [89]. This research was intrigued by the obstacles observed in derivatization in connection with the by-product interferences in high-performance liquid chromatography (HPLC) analysis (when MS/MS mode is not utilized), which lead to inferior selectivity. Consequently, the soil samples were mixed, turned to powder, and then ultrasonic extracted using water assisted by a solid-phase extraction (SPE). After derivatization, the samples were subjected to chemical analysis. A breakthrough of this process was the three-cross derivatization, and the elaborate investigation of its optimization, aided by the orthogonal experimental design. Such design is fundamental in the selection of the optimum conditions, in this case, reaction temperature, time, and ratio of the derivatizing-coupling reagents.
\nGC-NPD was utilized by Hu and coworkers to analyze GLY in soil, using GC-MS for verification [72]. Extraction was performed in alkaline environment, followed by acidification in the dry extract. The authors stated that NH4OH was the most adequate extractant due to lesser extracted interferences than other alkaline agents, though in other works other agents are selected [48]. Derivatization was accomplished by the use of TFE and TFAA, followed by a liquid extraction using methylene chloride. The method verified the degradation of GLY in soil in apple orchards.
\nThe availability of derivatization techniques compatible with an aqueous extract or sample and the chromatographic separation makes LC a more attractive pre-column derivatization [91]. Derivatization approach is used to produce fluorescent derivatives and to enhance their retention in hydrophobic stationary phases prior to detection by fluorescence detection (FLD), UV detection, electrochemical detection (ECD), or tandem mass spectrometry (MS/MS). In post-column procedures, the most known reactions are ninhydrin derivatization accompanied by UV detection and fluorogenic labeling with o-phthalaldehyde (OPA) in mercaptoethanol or N,N-dimethyl-2-mercaptoethylamine after oxidation of glyphosate to glycine.
\nAlthough GLY and its derivatives show high sensitivity in LC determination, a laborious cleanup procedure such as ion-exchange column chromatography is required which may result in some sample loss and lower reproducibility, or many laboratories do not have the facilities required for this type of pre- or post-column fluorogenic labeling. The use of either hydrophilic/weak exchange or reversed-phase/weak exchange mixed-mode chromatography without any derivatization, followed by diverse detection techniques including tandem mass spectrometry detection, is gaining interest [77, 95]. HPLC methods are highly sensitive with fluorogenic labeling, but they lack specificity and usually require a laborious cleanup procedure such as ion-exchange column chromatography, which may result in some sample loss and lower reproducibility.
\nPre-column procedures are a good alternative to post-column ones, and this has gradually come to play an important role in the analysis of glyphosate. The easier, less demanding and more current popular method to analyze these compounds is derivatization with 9-fluorenylmethylchloroformate (FMOC-Cl) followed by HPLC with FLD or MS/MS. A factorial experimental design was applied by a Chilean group in a critical analysis of this derivatization reaction [78]. The design was studied in aqueous soil extracts, unveiling the proper equilibrium between agents for the successful completion of the reaction. For example, excess of FMOC-Cl is required since there are also other active centers (amine-hydroxy groups) with which FMOC-Cl can react. Isotherm data verified the broad applicability of this method.
\nBack in the 1990s, Sancho et al. established a method for the analysis of GLY in soil samples that involved a pre-column derivatization step with FMOC-Cl and subsequent estimation by coupled-column liquid chromatography with fluorescence detection (LC–LC/FLD) [68]. However, for the determination of glyphosate in soils based on FMOC derivatization analytics, an extraction procedure including an SPE cleanup step has been used in many studies and considered more efficient [37]. In particular, Todorovic et al. extracted soils using sodium tetraborate. Once again, a group devoted substantial time in the extraction of GLY and AMPA from soil due to the complex sorption and desorption in soil which is also pH dependent. The sodium tetraborate performed better in terms of chromatographic efficiency than KOH extraction (more matrix interferences, more humic substances, etc.). The authors after derivatizing GLY and AMPA with FMOC-Cl cleaned up-enriched the extract with a polymeric SPE cartridge. Overall, the method was fit for purpose based on the analytical results on three different types of soils.
\nBotero-Coy et al. have established a method based on LC–MS/MS, which was successfully applied to soil samples from Colombia and Argentina [77]. This work was an improvement of the previous work in the same domain [49]. In that work, the soil samples were extracted with potassium hydroxide solution and purified with SPE Oasis HLB cartridges. A pre-column derivatization step was also required in this method for which 9-fluorenylmethylchloroformate (FMOC-Cl) was used and the purification method using SPE cartridges was troublesome and expensive. Despite these difficulties, the analysis was conducted in Spanish soils with success. But, when soil samples from the mentioned countries were analyzed by the specific protocol, their high organic content proved an obstacle in the analysis. For this reason the authors introduced a dilution step of the extract assisted by pH adjustment to 9, before the SPE step. For SPE the polymeric reversed-phase Oasis HLB cartridges proved better in retaining-releasing the FMOC derivative than Oasis MAX used with good results.
\nInternal standard’s use compensated possible downsides during sample preparation and corrected matrix effects. An additional tool in this work was the use of high-resolution mass spectrometry exploiting the time-of-flight technology. By this way additional interferences that would affect the analysis were further elucidated using the accurate mass full-acquisition data. It is noteworthy that the authors investigated MS ions used in the MS/MS mode. Interestingly, MRM transition containing the m/z 179 was problematic since it is related to FMOC and lacks specificity. In our work (see below), the specific ion was monitored only in AMPA transition, solving this issue [88]. Overall, the analysis verified the presence of GLY and AMPA in the majority of samples.
\nAnother work in the field of GLY analysis in soil/sludge using FMOC-Cl as a derivatizing agent was presented by Sun and coworkers [96]. In this context, an optimized sample preparation protocol was developed, applying extraction with sodium phosphate and trisodium citrate solutions (aqueous) and a purification step using hexane in acidified soil. The rationale behind the use of trisodium citrate was to counteract the effect of other metal ion complexing agents (such as Mg2+, Ca2+, etc.), in which GLY binds. The method was validated in three types of soils (and sludge samples) verifying that it was fit for purpose. The demonstrated LOQ was determined at 0.04 mg/kg.
\nA pre-column derivatization was applied by Druart and coworkers, embracing glufosinate also in their portfolio [60]. A detailed study was conducted on the parameters governing the extraction of the analytes from the matrix. Accelerated solvent extraction, ultrasonic extraction, and magnetic stirring agitation were tested to achieve optimum conditions. In the end agitation was selected. The group also optimized derivatization by selecting water as the solvent of the reaction, though the previous study showed that an equivalent mixture of H2O:ACN would compromise the solubility of both GLY and FMOC-Cl reagents [97]. In the same study, it was demonstrated that a C18 column of 30 cm superseded other columns tested, even a respective NH2 column broadly used for such separations.
\nIn addition our group has developed a methodology for GLY and AMPA detection in topsoils originating from Greece [88]. The sample preparation was envisaged by previous works (one of our group) [49, 98]. The LC–MS/MS method developed was adequate for the analysis of both active substances, showing that GLY and AMPA were detected in 37 and 45%, respectively, of the samples investigated. A breakthrough of this work was the association of the results with the land use utilizing geographical information system (GIS) databases.
\nGLY in soil is studied for registering not only its residual prevalence (including AMPA’s) but also its degradation dynamics. With this in view, Zhang et al. investigated its dynamics using an HPLC-FD method, utilizing FMOC derivatization [3]. Results of this study showed that the degradation is dependent on the physicochemical parameters of the soil, exemplified by the pH. The behavior of GLY and AMPA was investigated in compost-amended soils by Erban and colleagues [86]. Soil depth was disclosed as a key factor on the concentrations detected. GLY and AMPA though showed a different behavior when moisture and saturated hydraulic conductivity are considered. More specifically, GLY was affected principally by moisture, whereas AMPA was impacted by this conductivity.
\nOliveira-Pereira and colleagues, in the context of adsorption studies, determined GLY and AMPA using a low-cost reversed-phase sequential injection chromatography method [90]. More specifically, GLY was converted (pre-column) to glycine (using hypochlorite). Then, by reaction with o-phthaldialdehyde, the respective fluorescent indole was formed. Expectedly, this reaction reduces the polarity of the indole derivative making it adequate for analysis under reversed-phase conditions (e.g., C18).
\nDirect analysis of GLY and AMPA, avoiding the derivatization step, is still a challenge for the analysts. In this context, Marek and Koskinen developed a method for the straightforward analysis of GLY and AMPA in soil using for separation a Bio-Rad cation H exchange column coupled to LC–MS/MS [61]. The sample preparation involved mixing of soil with phosphoric acid solutions and sequential extractions advancing from a specific SPE technology. The combined extracts were purified using IC-Chelate cartridges known for their ability to exchange transition metals and divalent cations. A portion of the end extract was reacidified and passed through an IC-RP SPE cartridge to eliminate hydrophobic interferences prior to analysis. This work managed to provide very high recoveries for both substances regardless of the type of soil, which is a clear advantage.
\nDue to the chemical nature of GLY, its analysis can be pursued under normal phase conditions, utilizing the same framework, the golden standard—HILIC. The latter is used in the efficient separation of a plethora of polar compounds, including pesticides. Despite its application for the separation of challenging polar pesticides, including GLY, in a variety of commodities [99], seldom are the reports for GLY analysis in soil. Marek reported a poor chromatographic performance when HILIC conditions (only one HILIC column was used; data were not shown) were used in the determination of GLY in soil and other matrices [61]. Hence, efforts need to be made in this direction, considering the inherent advantages of analyses of polar compounds under these conditions.
\nCapillary electrophoresis methods have been reported in recent years using detection systems such as contactless conductivity, electrochemiluminescence [100], and laser-induced fluorescence [101, 102], as reviewed by Gauglitz et al. [103]. Ion chromatography [104], electrochemical method, surface resonance-enhanced spectrometry, enzyme-linked immunosorbent assay also called ELISA methods [87], spectrophotometry [73, 85], and fluorescent spectrometry [50, 55, 57, 75, 78, 80, 90, 96] were also reported to detect GLY in current literatures. However, the selectivity of ion chromatography was limited. Unlike other pesticides, the application of immunoanalytical techniques for glyphosate determination has been troublesome, although they have made some improvements.
\nIndicatively, El-Gendy and coworkers studied GLY in Egyptian soil samples using an optimized and sensitive linker-assisted enzyme-linked immunosorbent assay (L’ELISA) [87]. To derivatize GLY succinic anhydride was used. The method was well correlated with an HPLC-FD method that used sodium tetraborate for the extraction.
\nThe advances in cutting-edge technologies can further hyphen such methods with modern mass spectrometers to provide solutions that currently are disregarded or seem problematic.
\nThe environmental pollution instigated by the use of plant protection products, commonly referred to as pesticides, is one of the most serious problems that facing the world due to their potential toxicity, high persistence, and slow degradation. Pesticide fate in the environment is characterized by a number of complex processes occurring in different environmental compartments, such as air, soils, and plants [105]. A wide variety of pesticides has been detected in different environmental media, including water bodies, soil, and the atmosphere. The extended use of pesticides containing persistent active ingredients can lead to raised concentrations due to the accumulation in the environment and long-term exposure to nontarget organisms.
\nSince the last decades, there has been an increasing global concern over the human health impacts attributed to the environmental pollution and specifically to air pollution. During applications, a noteworthy segment of applied pesticides ranged from 15 to 40% is dispersed in the atmosphere and can travel with long-range atmospheric transport [106]. Thus, the atmosphere has been considered as an important spread vector at local, regional, and global scales. It has been reported in the international literature that air pesticide contamination was observed both in urban and rural areas with concentration levels ranging from some picograms to several nanograms per cubic meter [107]. However, the contamination of air by pesticides is an aspect of atmospheric pollution that remains less documented than that of other environments.
\nWorry over the transport of pesticides in air started in the 1960s with the detection of persistent and volatile substances such as DDT, dieldrin, and aldrin far from their application sites. The first legislation to consider air as an exposure route was in the United States in 1971. Since then the issue of pesticides in air has been subject to sporadic regulatory concern, especially in Europe [108].
\nMilestone legislation in Europe concerning pesticides in the atmosphere occurred in 1996 with the Stockholm convention on persistent organic pollutants (POPs). This regulation covers all chemicals, including pesticides, and lays down principles to identify substances for which aerial transport may be noteworthy [108].
\nLong-range transport in air and water can result in the exposure of remote and particularly vulnerable ecosystems such as the Arctic [109, 110].
\nPesticides enter into the atmosphere, and their residues can move away from the application sites resulting in accidental exposure for humans, animals, and plants, close or distant the treated sites. It is well recognized that the exposure and effect assessment of pesticides should not be constrained to the target area, and its close zone because this does not adequately cover possible hazards associated with their use.
\nThe most common routes of pesticide entry into the atmosphere could be the drift during their application, volatilization from the soil, surface water or crop foliage, as well as wind erosion of deposited residues [111, 112, 113]. Once they enter in the atmosphere, pesticides are distributed between the gaseous and particulate phases depending on parameters such as:
Octanol-air partition coefficient
Vapor pressure
Henry’s law constant
Water solubility
Total suspended particulate matter
Weather conditions [114]
In the atmosphere, pesticides are distributed between particle and vapor phases based on their vapor pressure, the ambient temperature, and the concentration of suspended particulate matter. Taking into account the low volatility of the majority of the most commonly used pesticides; it could be considered that they are often absorbed on the surface of atmospheric particles. In that way they may incur transformation processes resulting in the formation of secondary metabolites which could be even more hazardous than the parent released compounds [106]. Pesticides released into the atmosphere can settle to the ground, be broken down by sunlight and water, or dissipate into the surrounding air.
\nDuring and after the application of a pesticide, a considerable portion of the amount applied may enter into the atmosphere through many different routes (the most important will be briefly discussed) and consequently may be transported over shorter and longer distance.
\nThrough spray application of pesticides, a fraction of the spray would exist as pesticides in the gas phase and as small droplets or particles. The latter do not reach their target due to their extremely small size and cannot be captured by drift collectors. This fraction that exists in the gas phase and as aerosol should be taken into account along with drift.
\nVolatilization is defined as the transfer of pesticide residues into the gas phase after application. Volatilization from treated areas is a constant process and could be the main dissipative route for numerous pesticides [115]. Its extent is governed by the physical and chemical properties of the pesticide such as vapor pressure and Henry’s law constant; the application parameters such as the droplet size and the water volume; and finally the climatic conditions during and after application [108, 116]. Volatilization may be swayed by relative humidity, the atmospheric pressure, and the wind velocity [117]. The compound’s volatility with medium vapor pressure values is significantly influenced by environmental and application factors, whereas substances with high vapor pressure values present high volatilization which does not depend on other factors. It is broadly established in the literature that vapor pressure can be used to categorize pesticides with a very high or with no volatilization potential. Vapor pressure also rules the partitioning of a semi-volatile constituent between the gas and the airborne particle phases. According to Bidleman substances with a vapor pressure value higher than 10−2 Pa are mainly expected in the vapor phase, while those with vapor pressure value lower 10−5 Pa solely exist in the particle-adsorbed phase [118]. Pesticides with vapor pressure between 10−2 and 10−5 Pa values partition between these phases.
\nA significant amount of pesticides entering into the atmosphere for several days or weeks after pesticide application comprises volatilization from the soil and plant surfaces as well as wind erosion of soil particles containing sorbed pesticides [119, 120]. Many parameters such as the physicochemical properties of the pesticide (vapor pressure, solubility, adsorption coefficient, molecular mass, and chemical nature), the soil properties (water content, soil density, soil organic matter content, clay content/texture, soil pH), the weather conditions (air temperature, solar radiation, rain, air humidity, and wind), and the agricultural practices used (application date and rate and formulation type) may influence the volatilization process [111].
\nVolatilization from plants is considered up to three times higher than soil volatilization under similar meteorological conditions. The vapor pressure and Henry’s law constant are the physicochemical characteristics of the compound that seem to be related with the degree of volatilization. Additionally, application methods and weather conditions may also play an important role in the volatilization process from plants [121].
\nThe Focus Air group has deemed that vapor pressure is the most significant factor affecting volatilization and deemed that active ingredients applied to soil with vapor pressure values higher than 10−4 Pa and active ingredients applied to plants with vapor pressure values higher than 10−5 Pa have a high possibility to enter in the air and for that reason require a risk assessment evaluation before authorization [108].
\nPesticides existing in the aerial phase could be carried by wind and deposited accidentally in untreated areas by dry (gas and particle) and wet (rain and snow) deposition [122].
\nThe atmosphere could be efficiently cleaned of suspended particulate matter to which pesticides might be sorbed by rainfall, and thus gas-phase pesticides can partition directly into a falling raindrop [122].
\nHigh pesticide concentrations in the air could be considered seasonal and often associated with local use and thus occur during the spraying months [123]. The physical and chemical properties of each pesticide also play a significant role in determining if a pesticide converts airborne, whether it then exists primarily in the gaseous or particle phase, and how efficiently rainfall removes it from the atmosphere. The period of time that a pesticide is applied, its amount, and the cultivated area play also significant roles in whether a pesticide exists in the atmosphere and at which concentration [113].
\nGlyphosate (N-[phosphonomethyl] glycine), a broad-spectrum, nonselective, and post emergence herbicide, is the most widely used pesticide worldwide.
\nAlthough numerous laboratory and field studies have been carried out for the determination of glyphosate and AMPA in the aquatic environment, there are limited studies in field soils. Furthermore, atmospheric concentrations of glyphosate and AMPA are shabbily documented as very few studies have monitored them in the atmosphere [124].
\nThe first report about the atmospheric concentrations of glyphosate and AMPA had been published in 1991 in order to present the results of a study that had been conducted in 1988 in northeastern Finland for measuring the workers’ exposure to glyphosate when they used sprayers connected to brush saws. In that study glyphosate was determined from the breathing zone and from urine samples. Based on the results of this study and at the end of the spraying week, two air samples were found to have measurable levels of glyphosate at concentrations 2.8 and 15.7 μg m−3. AMPA had not been detected in any of the air samples [125].
\nIn 2002, Humphries et al. examined the atmospheric samples at three different sites in east-central Alberta. For the purposes of the study, air samples were collected before the application of glyphosate and after its application and for 24 h time period at regular intervals. Glyphosate was not detected in any of the collected air samples at levels above the method LOQ; however, it was detected in few particulate samples [126]. The nonexistence of glyphosate in the polyurethane foam indicates that glyphosate is not released as the vapor forms into the atmosphere but rather is carried by a particulate matter.
\nIn 2004, glyphosate was examined in 59 atmospheric samples in Hauts-de-France Region in France, with a detection occurrence of 14% and a maximum concentration of 0.19 ng m−3 [124, 127].
\nChang et al. reported that both glyphosate and AMPA had been detected in the ambient air of Iowa, Indiana, and Mississippi during two growing seasons of the years 2007 and 2008. Atmospheric concentrations of glyphosate reached 9.1 and 5.4 ngm−3 in Mississippi and Iowa agricultural areas, respectively; however atmospheric concentrations of AMPA touched 0.49 and 0.97 ngm−3 in Mississippi and Iowa, correspondingly. It had been concluded that the existence of glyphosate in air is due to spray drift or wind erosion as it is not a volatile compound whereas AMPA presence is due to wind erosion as it is a glyphosate degradation product and it is formed in soil [128]. The authors provided also measurements in rainwater and estimated that 97% of glyphosate existing in the atmosphere could be removed by weekly rainfall greater than 30 mm [129].
\nMorshed et al. determined the atmospheric concentrations of glyphosate in treated fields in Malaysia during spray applications by a mist blower [129]. The maximum concentration of 42.96 μgm−3 was measured for glyphosate, and additionally a first modeling attempt for the estimation of glyphosate emission to the atmosphere at regional level was done; however, there were no measurements to confirm the model output.
\nIn 2014, and specifically from July to November, Sousa et al. performed a study in northeastern Brazil, in the municipality of Limoeiro do Norte-Ceará, in urban and rural areas, for the determination of the atmospheric concentrations of glyphosate. Glyphosate detected at concentrations ranged between 0.313 and 2.939 μg m3 in all collected atmospheric samples [130].
\nDuring the years 2015–2016, glyphosate and AMPA were searched in 142 air samples during a 2-year field campaign in France. Samples were taken from both nonagricultural and agricultural areas, while atmospheric concentrations of glyphosate were detected at an overall frequency of 7%. AMPA was not detected in any sample. The maximum concentration of 1.04 ng m−3 was measured for glyphosate in the rural site of Cavaillon. As regards the temporal distribution of glyphosate, it had been pointed out that there was no reproducible detection pattern from 2015 to 2016 [125].
\nGenerally, a few number of monitoring studies have been conducted for the determination of pesticide residues in atmospheric samples. These studies could not provide consistent results due to the variability in experimental conditions, the lack of consistency in sampling methodologies, the variation in collection time and duration, the analytes selected, the analytical methods used [131], as well as the method detection limits. Most of the studies have been performed at the national level, they are short-term as they lasted from 1 to 2 years, and for that reason, the overall conclusion on the long-term trends and the atmospheric movements of pesticides could not been reached [108].
\nPesticides existing in the atmosphere are usually at very low concentrations, and thus appropriate sampling and techniques are necessary. The most common sampling techniques used for pesticides in the ambient air could be separated into two categories: the active and the passive or diffuse samplers [132].
\nActive samplers allow the pesticides existing in gaseous and particulate phases to be trapped by pumping air through a filter followed by a solid adsorbent. Thus, pesticides standing in the gas phase are stacked by the solid adsorbent, whereas pesticides in the particulate phase are maintained in the filter.
\nPesticides present in the atmosphere could be sampled through low-volume or high-volume samplers. As pesticide residues in the atmosphere are at very low concentrations, high-volume samplers are usually used [121].
\nFor sampling of semi-volatile pesticides, the use of diffusion denuder systems, which consist of a series of coaxial glass tubes coated with an appropriate adsorbent through which the air flows, is proposed [121].
\nPassive air samplers are devices that collect pesticides from the air without the use of pump, and they are comprised of an accumulating intermediate which has a high retention capacity for the target analytes. Passive samplers are able to gather only the free gaseous phase pesticides, while the length of sampling range from few weeks to several months, considerably larger than the usual time required using the active ones [121].
\nIn 1991, Jauhiainen et al. collected air samples for the determination of glyphosate from the breathing zone through a portable pump onto an absorption liquid [125]. The air samples collected were first evaporated to dryness and then dissolved with trifluoroethanol and trifluoroacetic anhydrite.
\nChang et al. used high-volume active samplers for collecting air samples for the determination of glyphosate. The glass fiber filters used were baked at 550°C, cooled to the room temperature, and enfolded in aluminum foil before sampling [128, 130]. The glass fiber filters after sampling were slowly grounded in a polypropylene tube and then extracted with hydrochloric acid (pH 2) and further with a potassium hydroxide solution (pH 11). Cellulose nitrate filters were used under vacuum for filtration [128].
\nRavier et al. used also high-volume samplers, and the particulate samples were collected on quartz microfiber filters. The filters after sampling were protected from the light and stored at −20°C [124]. Field air blank samples were also collected for the determination of the background contamination through handling and storage. The extraction of all the samples was performed in polytetrafluoroethylene or polypropylene vessels in order to avoid loss of the studied compounds via wall adsorption. According to Ravier et al., filters were extracted with ultrahigh quality water with the addition of appropriate quantities of Borax (0.05 M) and EDTA solutions. Polyethersulfone membranes were used for sample filtration. FMOC-Cl was used as a derivatization agent.
\nMorshed et al. performed a study for the determination of glyphosate in the atmosphere by using both active and passive sampling methods. For the purposes of the study, three different air samplers were used. Cellulose filter patches and polyurethane foam were used for passive samplers. Active samplers were also used for sampling and were connected to polyurethane foam plug for the determination of glyphosate existing in the vapor phase and a quartz fiber filter for the particulate phase of airborne glyphosate [129]. Sample extraction for both active and passive extraction methods was performed with borate buffer. FMOC-Cl was used as a derivatizing agent.
\nHigh-volume air samplers were used to collect suspended, airborne particulates and trap airborne glyphosate vapors in a study conducted in Alberta’s area. A volatile glyphosate was collected on a polyurethane foam plug and particulate glyphosate on a filter paper [126, 133].
\nSousa et al. used a glass sample holder in which a polyurethane foam (adsorbent medium) was placed. The particulate material was collected from the glass fiber filters. Glyphosate was determined in the atmosphere after extraction from polyurethane foams with a solution comprising of monobasic potassium phosphate and methanol in ultrapure water while the pH of the solution was maintained at 2 using concentrated phosphoric acid. The samples were concentrated in a C18 solid-phase extraction cartridge.
\nThe chromatographic analysis of glyphosate and AMPA is considered tough in trace analysis. Due to their low molecular weight, low volatility, thermal lability, and excellent water solubility, their extraction and determination are complex.
\nThe main analytical techniques used for the analysis of glyphosate in atmospheric samples are liquid chromatography equipped with diode array or fluorescence detectors and liquid chromatography interfaced with a quadrupole-time-of-flight mass spectrometer or mass spectrometry. However, gas chromatographic technique with ECD has also been used.
\nIn 1991 Jauhiainen et al. reported that a gas chromatographic system equipped with ECD and fused silica has been used for glyphosate determination in air samples. Additionally a triple-quadrupole mass spectrometer equipped with fused silica was used for identification purposes.
\nIn 2011 a liquid chromatographic method for the determination of glyphosate in air samples was reported [129]. The analytical standards (stock and working) were prepared in a 0.025 M sodium borate buffer (pH 9) solution. Prior to HPLC chromatographic analysis, working standards were pre-column derivatized with a derivatizing agent (0.002 M FMOC-Cl). The liquid chromatographic system consisted of a florescence detector and a Hypersil NH2 chromatographic column, while the mobile phase comprised of 50% phosphate buffer (0.05 M potassium phosphate monobasic KH2PO4 adjusted to pH 6.0 with 7 N KOH). The glyphosate retention time was 5.6 min and the total run time was 10 min. The LOD of the method was 0.015 μg ml−1, while the LOQ was 0.05 μg ml−1 and determined through the linear calibration curve.
\nChang et al. reported another method for the determination of glyphosate and AMPA by using a liquid chromatography tandem mass spectrometer. Both glyphosate and AMPA were derivatized with 9-fluorenylmethylchloroformate before analysis. A gradient elution system comprised of 95% of 5 mM ammonium acetate in HPLC-MS-grade water to 100% HPLC-grade acetonitrile was used. The molecular ion and the fragment ion for glyphosate were 390 and 168. In the case of AMPA the molecular ion and the fragment ions were 332, 110, and 136 [128].
\nZhang et al. performed the analyses for the determination of glyphosate in the air samples of workplaces by ion chromatography using a conductivity detector. The limit of detection was found to be 0.003 mg/m3. The recovery ranged between 94.8 and 97.4% [134].
\nAccording to Maria Gizeuda de F. Sousa et al., glyphosate was determined by liquid chromatography equipped with a diode array detector and a C-18 chromatographic column at 195 nm. The mobile phase consisted of 0.006 mM KH2PO4, and the flow rate set at 1.0 mL/min. Under these conditions glyphosate is eluted at 2.97 min, whereas the total analysis time was 7 min. The analytical method LOD was 0.09 μg mL−1, whereas the LOQ was 0.27 μg mL−1 [130].
\nFor the determination of glyphosate and its major metabolite AMPA, Ravier et al. used an ultra-performance liquid chromatographic (UPLC) system interfaced with a quadrupole-time-of-flight mass spectrometer and equipped with an electrospray ion source and a C18 UPLC column. The elution system consisted of water with 5 mM ammonium formate and acetonitrile. The analyses are performed in the negative ionization mode. Both the LOD and the LOQ were determined by the calibration curve and were 0.05 and 0.14 ngm−3, respectively, for glyphosate and 0.30 and 0.90 ngm−3, respectively, for AMPA [124].
\nHPLC methods are highly sensitive especially with fluorogenic labeling, but they lack specificity and usually require a laborious cleanup procedure such as ion-exchange column chromatography, which may result in some sample loss and lower reproducibility. At present LC-MS in tandem mode (MS/MS) is considered the most suitable technique for the detection of phosphoric and amino acid-type herbicides at low concentrations. Derivatization is the most common way to analyze GLY and AMPA using LC-ESI-MS/MS systems, a procedure that is described in soil matrix as well.
\nThe maximum concentrations of glyphosate in atmospheric samples correspond to the time of its application. Due to the limited number of monitoring studies for monitoring pesticides and specifically glyphosate in the air, a reliable conclusion about its fate could not be reached.
\nThe authors declare no conflict of interest.
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First, a dynamic/measurement model is defined for the tracking systems, assuming both position-only and position-velocity measurements. Then, problems with the Kalman filter design in tracking systems are summarized, and an efficient steady-state performance index proposed by the author [termed the root-mean-squared error index (the RMS index)] is introduced to resolve these concerns. The analytical relationship between the proposed RMS index and the covariance matrix of the process noise is shown, leading to a proposed design strategy that is based on this relationship. Theoretical performance analysis is conducted using the performance indices to show the optimality of the design strategy. Numerical simulations show the validity of the theoretical analyses and effectiveness of the proposed strategy in realistic situations. In addition, the optimal performance of the position-only-measured and position-velocity-measured systems is analyzed and compared. This comparison shows that the position-velocity-measured Kalman filter tracking is accurate when compared with the position-only-measured filter.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Kenshi Saho",authors:[{id:"209334",title:"Associate Prof.",name:"Kenshi",middleName:null,surname:"Saho",slug:"kenshi-saho",fullName:"Kenshi Saho"}]},{id:"57977",doi:"10.5772/intechopen.71900",title:"Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics",slug:"unscented-kalman-filter-for-state-and-parameter-estimation-in-vehicle-dynamics",totalDownloads:1962,totalCrossrefCites:10,totalDimensionsCites:11,abstract:"Automotive research and development passed through a vast evolution during past decades. Many passive and active driver assistance systems were developed, increasing the passengers’ safety and comfort. This ongoing process is a main focus in current research and offers great potential for further systems, especially focusing on the task of autonomous and cooperative driving in the future. For that reason, information about the current stability in terms of dynamic behavior and vehicle environment are necessary for the systems to perform properly. Thus, model-based online state and parameter estimation have become important throughout the last years using a detailed vehicle model and standard sensors, gathering this information. In this chapter, state and parameter estimation in vehicle dynamics utilizing the unscented Kalman filter is presented. The estimation runs in real time based on a detailed vehicle model and standard measurements taken within the car. The results are validated using a Volkswagen Golf GTE Plug-In Hybrid for various dynamic test maneuvers and a Genesys Automotive Dynamic Motion Analyzer (ADMA) measurement unit for high-precision measurements of the vehicle’s states. Online parameter estimation is shown for friction coefficient estimation performing maneuvers on different road surfaces.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Mark Wielitzka, Alexander Busch, Matthias Dagen and Tobias\nOrtmaier",authors:[{id:"122290",title:"Dr.",name:"Tobias",middleName:null,surname:"Ortmaier",slug:"tobias-ortmaier",fullName:"Tobias Ortmaier"},{id:"201140",title:"M.Sc.",name:"Mark",middleName:null,surname:"Wielitzka",slug:"mark-wielitzka",fullName:"Mark Wielitzka"},{id:"202801",title:"M.Sc.",name:"Matthias",middleName:null,surname:"Dagen",slug:"matthias-dagen",fullName:"Matthias Dagen"},{id:"222868",title:"MSc.",name:"Alexander",middleName:null,surname:"Busch",slug:"alexander-busch",fullName:"Alexander Busch"}]},{id:"57804",doi:"10.5772/intechopen.71138",title:"Consensus-Based Distributed Filtering for GNSS",slug:"consensus-based-distributed-filtering-for-gnss",totalDownloads:1377,totalCrossrefCites:4,totalDimensionsCites:5,abstract:"Kalman filtering in its distributed information form is reviewed and applied to a network of receivers tracking Global Navigation Satellite Systems (GNSS). We show, by employing consensus-based data-fusion rules between GNSS receivers, how the consensus-based Kalman filter (CKF) of individual receivers can deliver GNSS parameter solutions that have a comparable precision performance as their network-derived, fusion center dependent counterparts. This is relevant as in the near future the proliferation of low-cost receivers will give rise to a significant increase in the number of GNSS users. With the CKF or other distributed filtering techniques, GNSS users can therefore achieve high-precision solutions without the need of relying on a centralized computing center.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Amir Khodabandeh, Peter J.G. Teunissen and Safoora Zaminpardaz",authors:[{id:"116970",title:"Prof.",name:"Peter",middleName:null,surname:"Teunissen",slug:"peter-teunissen",fullName:"Peter Teunissen"},{id:"210691",title:"Dr.",name:"Amir",middleName:null,surname:"Khodabandeh",slug:"amir-khodabandeh",fullName:"Amir Khodabandeh"},{id:"210714",title:"Dr.",name:"Safoora",middleName:null,surname:"Zaminpardaz",slug:"safoora-zaminpardaz",fullName:"Safoora Zaminpardaz"}]},{id:"57455",doi:"10.5772/intechopen.71205",title:"Kalman Filter Models for the Prediction of Individualised Thermal Work Strain",slug:"kalman-filter-models-for-the-prediction-of-individualised-thermal-work-strain",totalDownloads:1205,totalCrossrefCites:0,totalDimensionsCites:3,abstract:"It is important to monitor and assess the physiological strain of individuals working in hot environments to avoid heat illness and performance degradation. The body core temperature (Tc) is a reliable indicator of thermal work strain. However, measuring Tc is invasive and often inconvenient and impractical for real-time monitoring of workers in high heat strain environments. Seeking a better solution, the main aim of the present study was to investigate the Kalman filter method to enable the estimation of heat strain from non-invasive measurements (heart rate (HR) and chest skin temperature (ST)) obtained ‘online’ via wearable body sensors. In particular, we developed two Kalman filter models. First, an extended Kalman filter (EFK) was implemented in a cubic state space modelling framework (HR versus Tc) with a stage-wise, autoregressive exogenous model (incorporating HR and ST) as the time update model. Under the second model, the online Kalman filter (OFK) approach builds up the time update equation depending only on the initial value of Tc and the latest value of the exogenous variables. Both models were trained and validated using data from laboratory- and outfield-based heat strain profiling studies in which subjects performed a high intensity military foot march. While both the EKF and OKF models provided satisfactory estimates of Tc, the results showed an overall superior performance of the OKF model (overall root mean square error, RMSE = 0.31°C) compared to the EKF model (RMSE = 0.45°C).",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Jia Guo, Ying Chen, Weiping Priscilla Fan, Si Hui Maureen Lee,\nJunxian Ong, Poh Ling Tan, Yu Li Lydia Law, Kai Wei Jason Lee and\nKok-Yong Seng",authors:[{id:"171298",title:"Dr.",name:"Kok-Yong",middleName:null,surname:"Seng",slug:"kok-yong-seng",fullName:"Kok-Yong Seng"},{id:"209402",title:"Dr.",name:"Ying",middleName:null,surname:"Chen",slug:"ying-chen",fullName:"Ying Chen"},{id:"209404",title:"Dr.",name:"Jia",middleName:null,surname:"Guo",slug:"jia-guo",fullName:"Jia Guo"},{id:"220688",title:"Ms.",name:"Weiping Priscilla",middleName:null,surname:"Fan",slug:"weiping-priscilla-fan",fullName:"Weiping Priscilla Fan"},{id:"220689",title:"Ms.",name:"Si Hui Maureen",middleName:null,surname:"Lee",slug:"si-hui-maureen-lee",fullName:"Si Hui Maureen Lee"},{id:"220690",title:"Mr.",name:"Junxian",middleName:null,surname:"Ong",slug:"junxian-ong",fullName:"Junxian Ong"},{id:"220691",title:"Ms.",name:"Poh Ling",middleName:null,surname:"Tan",slug:"poh-ling-tan",fullName:"Poh Ling Tan"},{id:"220692",title:"Ms.",name:"Yu Li Lydia",middleName:null,surname:"Law",slug:"yu-li-lydia-law",fullName:"Yu Li Lydia Law"},{id:"220693",title:"Dr.",name:"Kai Wei Jason",middleName:null,surname:"Lee",slug:"kai-wei-jason-lee",fullName:"Kai Wei Jason Lee"}]},{id:"57768",doi:"10.5772/intechopen.71874",title:"Kalman Filters for Parameter Estimation of Nonstationary Signals",slug:"kalman-filters-for-parameter-estimation-of-nonstationary-signals",totalDownloads:1544,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"An adaptive Taylor-Kalman filter with PSO tuning for tracking nonstationary signal parameters in a noisy environment with primary focus on time-varying power signals has been presented in this piece of work. In order to deal with the dynamic envelope of the power signal, second-order Taylor expansion has been used such that the Taylor coefficients are updated with the PSO-tuned Taylor-Kalman Filter algorithm. In addition to this, for fast convergence, a self-adaptive particle swarm optimization technique has been used for obtaining the optimal values of model and measurement error covariances of the Kalman filter. The proposed algorithm is linear and therefore has less computational burden, which is easier to be implemented on a hardware platform like DSP processor or FPGA. The proposed PSO-tuned Taylor-Kalman filter exhibits robust tracking capabilities even under changing signal dynamics, immune to critical noise conditions, harmonic contaminations, and also reveals excellent convergence properties.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Sarita Nanda",authors:[{id:"209587",title:"Dr.",name:"Sarita",middleName:null,surname:"Nanda",slug:"sarita-nanda",fullName:"Sarita Nanda"}]}],mostDownloadedChaptersLast30Days:[{id:"57673",title:"Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design",slug:"kalman-filter-for-moving-object-tracking-performance-analysis-and-filter-design",totalDownloads:3149,totalCrossrefCites:13,totalDimensionsCites:21,abstract:"This chapter presents Kalman filters for tracking moving objects and their efficient design strategy based on steady-state performance analysis. First, a dynamic/measurement model is defined for the tracking systems, assuming both position-only and position-velocity measurements. Then, problems with the Kalman filter design in tracking systems are summarized, and an efficient steady-state performance index proposed by the author [termed the root-mean-squared error index (the RMS index)] is introduced to resolve these concerns. The analytical relationship between the proposed RMS index and the covariance matrix of the process noise is shown, leading to a proposed design strategy that is based on this relationship. Theoretical performance analysis is conducted using the performance indices to show the optimality of the design strategy. Numerical simulations show the validity of the theoretical analyses and effectiveness of the proposed strategy in realistic situations. In addition, the optimal performance of the position-only-measured and position-velocity-measured systems is analyzed and compared. This comparison shows that the position-velocity-measured Kalman filter tracking is accurate when compared with the position-only-measured filter.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Kenshi Saho",authors:[{id:"209334",title:"Associate Prof.",name:"Kenshi",middleName:null,surname:"Saho",slug:"kenshi-saho",fullName:"Kenshi Saho"}]},{id:"57977",title:"Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics",slug:"unscented-kalman-filter-for-state-and-parameter-estimation-in-vehicle-dynamics",totalDownloads:1962,totalCrossrefCites:10,totalDimensionsCites:11,abstract:"Automotive research and development passed through a vast evolution during past decades. Many passive and active driver assistance systems were developed, increasing the passengers’ safety and comfort. This ongoing process is a main focus in current research and offers great potential for further systems, especially focusing on the task of autonomous and cooperative driving in the future. For that reason, information about the current stability in terms of dynamic behavior and vehicle environment are necessary for the systems to perform properly. Thus, model-based online state and parameter estimation have become important throughout the last years using a detailed vehicle model and standard sensors, gathering this information. In this chapter, state and parameter estimation in vehicle dynamics utilizing the unscented Kalman filter is presented. The estimation runs in real time based on a detailed vehicle model and standard measurements taken within the car. The results are validated using a Volkswagen Golf GTE Plug-In Hybrid for various dynamic test maneuvers and a Genesys Automotive Dynamic Motion Analyzer (ADMA) measurement unit for high-precision measurements of the vehicle’s states. Online parameter estimation is shown for friction coefficient estimation performing maneuvers on different road surfaces.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Mark Wielitzka, Alexander Busch, Matthias Dagen and Tobias\nOrtmaier",authors:[{id:"122290",title:"Dr.",name:"Tobias",middleName:null,surname:"Ortmaier",slug:"tobias-ortmaier",fullName:"Tobias Ortmaier"},{id:"201140",title:"M.Sc.",name:"Mark",middleName:null,surname:"Wielitzka",slug:"mark-wielitzka",fullName:"Mark Wielitzka"},{id:"202801",title:"M.Sc.",name:"Matthias",middleName:null,surname:"Dagen",slug:"matthias-dagen",fullName:"Matthias Dagen"},{id:"222868",title:"MSc.",name:"Alexander",middleName:null,surname:"Busch",slug:"alexander-busch",fullName:"Alexander Busch"}]},{id:"57455",title:"Kalman Filter Models for the Prediction of Individualised Thermal Work Strain",slug:"kalman-filter-models-for-the-prediction-of-individualised-thermal-work-strain",totalDownloads:1205,totalCrossrefCites:0,totalDimensionsCites:3,abstract:"It is important to monitor and assess the physiological strain of individuals working in hot environments to avoid heat illness and performance degradation. The body core temperature (Tc) is a reliable indicator of thermal work strain. However, measuring Tc is invasive and often inconvenient and impractical for real-time monitoring of workers in high heat strain environments. Seeking a better solution, the main aim of the present study was to investigate the Kalman filter method to enable the estimation of heat strain from non-invasive measurements (heart rate (HR) and chest skin temperature (ST)) obtained ‘online’ via wearable body sensors. In particular, we developed two Kalman filter models. First, an extended Kalman filter (EFK) was implemented in a cubic state space modelling framework (HR versus Tc) with a stage-wise, autoregressive exogenous model (incorporating HR and ST) as the time update model. Under the second model, the online Kalman filter (OFK) approach builds up the time update equation depending only on the initial value of Tc and the latest value of the exogenous variables. Both models were trained and validated using data from laboratory- and outfield-based heat strain profiling studies in which subjects performed a high intensity military foot march. While both the EKF and OKF models provided satisfactory estimates of Tc, the results showed an overall superior performance of the OKF model (overall root mean square error, RMSE = 0.31°C) compared to the EKF model (RMSE = 0.45°C).",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Jia Guo, Ying Chen, Weiping Priscilla Fan, Si Hui Maureen Lee,\nJunxian Ong, Poh Ling Tan, Yu Li Lydia Law, Kai Wei Jason Lee and\nKok-Yong Seng",authors:[{id:"171298",title:"Dr.",name:"Kok-Yong",middleName:null,surname:"Seng",slug:"kok-yong-seng",fullName:"Kok-Yong Seng"},{id:"209402",title:"Dr.",name:"Ying",middleName:null,surname:"Chen",slug:"ying-chen",fullName:"Ying Chen"},{id:"209404",title:"Dr.",name:"Jia",middleName:null,surname:"Guo",slug:"jia-guo",fullName:"Jia Guo"},{id:"220688",title:"Ms.",name:"Weiping Priscilla",middleName:null,surname:"Fan",slug:"weiping-priscilla-fan",fullName:"Weiping Priscilla Fan"},{id:"220689",title:"Ms.",name:"Si Hui Maureen",middleName:null,surname:"Lee",slug:"si-hui-maureen-lee",fullName:"Si Hui Maureen Lee"},{id:"220690",title:"Mr.",name:"Junxian",middleName:null,surname:"Ong",slug:"junxian-ong",fullName:"Junxian Ong"},{id:"220691",title:"Ms.",name:"Poh Ling",middleName:null,surname:"Tan",slug:"poh-ling-tan",fullName:"Poh Ling Tan"},{id:"220692",title:"Ms.",name:"Yu Li Lydia",middleName:null,surname:"Law",slug:"yu-li-lydia-law",fullName:"Yu Li Lydia Law"},{id:"220693",title:"Dr.",name:"Kai Wei Jason",middleName:null,surname:"Lee",slug:"kai-wei-jason-lee",fullName:"Kai Wei Jason Lee"}]},{id:"57692",title:"Applications of Kalman Filters for Coherent Optical Communication Systems",slug:"applications-of-kalman-filters-for-coherent-optical-communication-systems",totalDownloads:1480,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In this chapter, we review various applications of Kalman filtering for coherent optical communication systems. First, we briefly discuss the principles of Kalman filter and its variations including extended Kalman filter (EKF) and adaptive Kalman filter (AKF). Later on, we illustrate the applicability of Kalman filters for joint tracking of several optical transmission impairments, simultaneously, by formulating the state space model (SSM) and detailing the principles. A detailed methodology is presented for the joint tracking of linear and nonlinear phase noise along with amplitude noise using EKF. Also, approaches to enhance the performance obtained by EKF by combining with other existing digital signal processing (DSP) techniques are presented. Frequency and phase offset estimation using a two stage linear Kalman filter (LKF)/EKF is also discussed. A cascaded structure of LKF and EKF by splitting the SSM to jointly mitigate the effects of polarization, phase and amplitude noise is also presented. The numerical analysis concludes that the Kalman filter based approaches outperform the conventional methods with better tracking capability and faster convergence besides offering more feasibility for real-time implementations.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Lalitha Pakala and Bernhard Schmauss",authors:[{id:"210340",title:"M.Sc.",name:"Lalitha",middleName:null,surname:"Pakala",slug:"lalitha-pakala",fullName:"Lalitha Pakala"},{id:"210654",title:"Prof.",name:"Bernhard",middleName:null,surname:"Schmauss",slug:"bernhard-schmauss",fullName:"Bernhard Schmauss"}]},{id:"58292",title:"Sensitivity-Based Adaptive SRUKF for State, Parameter, and Covariance Estimation on Mechatronic Systems",slug:"sensitivity-based-adaptive-srukf-for-state-parameter-and-covariance-estimation-on-mechatronic-system",totalDownloads:1345,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Since the initial developments in the state-space theory in the 1950s and 1960s, the state estimation has become an extensively researched and applied discipline. All systems that can be modelled mathematically are candidates for state estimators. The state estimators reconstruct the states that represent internal conditions and status of a system at a specific instant of time using a mathematical model and the information received from the system sensors. Moreover, the estimator can be extended for system parameter estimation. The resulting Kalman filter (KF) derivatives for state and parameter estimation also require knowledge about the noise statistics of measurements and the uncertainties of the system model. These are often unknown, and an inaccurate parameterization may lead to decreased filter performance or even divergence. Additionally, insufficient system excitation can cause parameter estimation drifts. In this chapter, a sensitivity-based adaptive square-root unscented KF (SRUKF) is presented. This filter combines a SRUKF and the recursive prediction-error method to estimate system states, parameters and covariances online. Moreover, local sensitivity analysis is performed to prevent parameter estimation drifts, while the system is not sufficiently excited. The filter is evaluated on two testbeds based on an axis serial mechanism and compared with the joint state and parameter UKF.",book:{id:"6129",slug:"kalman-filters-theory-for-advanced-applications",title:"Kalman Filters",fullTitle:"Kalman Filters - Theory for Advanced Applications"},signatures:"Mauro Hernán Riva, Mark Wielitzka and Tobias Ortmaier",authors:[{id:"122290",title:"Dr.",name:"Tobias",middleName:null,surname:"Ortmaier",slug:"tobias-ortmaier",fullName:"Tobias Ortmaier"},{id:"201140",title:"M.Sc.",name:"Mark",middleName:null,surname:"Wielitzka",slug:"mark-wielitzka",fullName:"Mark Wielitzka"},{id:"210832",title:"M.Sc.",name:"Mauro",middleName:"Hernán",surname:"Riva",slug:"mauro-riva",fullName:"Mauro Riva"}]}],onlineFirstChaptersFilter:{topicId:"1401",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},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:99,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:288,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:11,numberOfPublishedChapters:104,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:"2753-894X",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:11,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{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"}}}},{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"}}}}]},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. 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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:8,paginationItems:[{id:"91",title:"Sustainable Economy and Fair Society",coverUrl:"https://cdn.intechopen.com/series_topics/covers/91.jpg",editor:{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo",profilePictureURL:"https://mts.intechopen.com/storage/users/181603/images/system/181603.jpg",biography:"Antonella Petrillo is a Professor at the Department of Engineering of the University of Naples “Parthenope”, Italy. She received her Ph.D. in Mechanical Engineering from the University of Cassino. Her research interests include multi-criteria decision analysis, industrial plant, logistics, manufacturing and safety. She serves as an Associate Editor for the International Journal of the Analytic Hierarchy Process. She is a member of AHP Academy and a member of several editorial boards. She has over 160 Scientific Publications in International Journals and Conferences and she is the author of 5 books on Innovation and Decision Making in Industrial Applications and Engineering.",institutionString:null,institution:{name:"Parthenope University of Naples",institutionURL:null,country:{name:"Italy"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"179628",title:"Prof.",name:"Dima",middleName:null,surname:"Jamali",slug:"dima-jamali",fullName:"Dima Jamali",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSAIlQAO/Profile_Picture_2022-03-07T08:52:23.jpg",institutionString:null,institution:{name:"University of Sharjah",institutionURL:null,country:{name:"United Arab Emirates"}}},{id:"170206",title:"Prof.",name:"Dr. Orhan",middleName:null,surname:"Özçatalbaş",slug:"dr.-orhan-ozcatalbas",fullName:"Dr. Orhan Özçatalbaş",profilePictureURL:"https://mts.intechopen.com/storage/users/170206/images/system/170206.png",institutionString:null,institution:{name:"Akdeniz University",institutionURL:null,country:{name:"Turkey"}}},{id:"250347",title:"Associate Prof.",name:"Isaac",middleName:null,surname:"Oluwatayo",slug:"isaac-oluwatayo",fullName:"Isaac Oluwatayo",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRVIVQA4/Profile_Picture_2022-03-17T13:25:32.jpg",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},{id:"141386",title:"Prof.",name:"Jesús",middleName:null,surname:"López-Rodríguez",slug:"jesus-lopez-rodriguez",fullName:"Jesús López-Rodríguez",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRBNIQA4/Profile_Picture_2022-03-21T08:24:16.jpg",institutionString:null,institution:{name:"University of A Coruña",institutionURL:null,country:{name:"Spain"}}},{id:"208657",title:"Dr.",name:"Mara",middleName:null,surname:"Del Baldo",slug:"mara-del-baldo",fullName:"Mara Del Baldo",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRLMUQA4/Profile_Picture_2022-05-18T08:19:24.png",institutionString:"University of Urbino Carlo Bo",institution:null}]},{id:"92",title:"Health and Wellbeing",coverUrl:"https://cdn.intechopen.com/series_topics/covers/92.jpg",editor:{id:"348225",title:"Prof.",name:"Ann",middleName:null,surname:"Hemingway",slug:"ann-hemingway",fullName:"Ann Hemingway",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035LZFoQAO/Profile_Picture_2022-04-11T14:55:40.jpg",biography:"Professor Hemingway is a public health researcher, Bournemouth University, undertaking international and UK research focused on reducing inequalities in health outcomes for marginalised and excluded populations and more recently focused on equine assisted interventions.",institutionString:null,institution:{name:"Bournemouth University",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"169536",title:"Dr.",name:"David",middleName:null,surname:"Claborn",slug:"david-claborn",fullName:"David Claborn",profilePictureURL:"https://mts.intechopen.com/storage/users/169536/images/system/169536.jpeg",institutionString:null,institution:{name:"Missouri State University",institutionURL:null,country:{name:"United States of America"}}},{id:"248594",title:"Ph.D.",name:"Jasneth",middleName:null,surname:"Mullings",slug:"jasneth-mullings",fullName:"Jasneth Mullings",profilePictureURL:"https://mts.intechopen.com/storage/users/248594/images/system/248594.jpeg",institutionString:"The University Of The West Indies - Mona Campus, Jamaica",institution:null},{id:"331299",title:"Prof.",name:"Pei-Shan",middleName:null,surname:"Liao",slug:"pei-shan-liao",fullName:"Pei-Shan Liao",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000032Fh2FQAS/Profile_Picture_2022-03-18T09:39:41.jpg",institutionString:"Research Center for Humanities and Social Sciences, Academia Sinica, Taiwan",institution:null}]},{id:"93",title:"Inclusivity and Social Equity",coverUrl:"https://cdn.intechopen.com/series_topics/covers/93.jpg",editor:{id:"210060",title:"Prof. Dr.",name:"Ebba",middleName:null,surname:"Ossiannilsson",slug:"ebba-ossiannilsson",fullName:"Ebba Ossiannilsson",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6LkBQAU/Profile_Picture_2022-02-28T13:31:48.png",biography:'Professor Dr. Ebba Ossiannilsson is an independent researcher, expert, consultant, quality auditor and influencer in the fields of open, flexible online and distance learning (OFDL) and the "new normal". Her focus is on quality, innovation, leadership, and personalised learning. She works primarily at the strategic and policy levels, both nationally and internationally, and with key international organisations. She is committed to promoting and improving OFDL in the context of SDG4 and the future of education. Ossiannilsson has more than 20 years of experience in her current field, but more than 40 years in the education sector. She works as a reviewer and expert for the European Commission and collaborates with the Joint Research Centre for Quality in Open Education. Ossiannilsson also collaborates with ITCILO and ICoBC (International Council on Badges and Credentials). She is a member of the ICDE Board of Directors and has previously served on the boards of EDEN and EUCEN. Ossiannilsson is a quality expert and reviewer for ICDE, EDEN and the EADTU. She chairs the ICDE OER Advocacy Committee and is a member of the ICDE Quality Network. She is regularly invited as a keynote speaker at conferences. She is a guest editor for several special issues and a member of the editorial board of several scientific journals. She has published more than 200 articles and is currently working on book projects in the field of OFDL. Ossiannilsson is a visiting professor at several international universities and was recently appointed Professor and Research Fellow at Victoria University of Wellington, NZ. Ossiannilsson has been awarded the following fellowships: EDEN Fellows, EDEN Council of Fellows, and Open Education Europe. She is a ICDE OER Ambassador, Open Education Europe Ambassador, GIZ Ambassador for Quality in Digital Learning, and part of the Globe-Community of Digital Learning and Champion of SPARC Europe. On a national level, she is a quality developer at the Swedish Institute for Standards (SIS) and for ISO. She is a member of the Digital Skills and Jobs Coalition Sweden and Vice President of the Swedish Association for Distance Education. She is currently working on a government initiative on quality in distance education at the National Council for Higher Education. She holds a Ph.D. from the University of Oulu, Finland.',institutionString:"Swedish Association for Distance Education, Sweden",institution:null},editorTwo:null,editorThree:null,editorialBoard:[{id:"320585",title:"Ph.D.",name:"Deborah",middleName:null,surname:"Young",slug:"deborah-young",fullName:"Deborah Young",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002vZLcTQAW/Profile_Picture_2022-05-10T08:30:47.jpg",institutionString:"Empowering Communities Globally",institution:null},{id:"348038",title:"Associate Prof.",name:"Feyza",middleName:null,surname:"Bhatti",slug:"feyza-bhatti",fullName:"Feyza Bhatti",profilePictureURL:"https://mts.intechopen.com/storage/users/348038/images/system/348038.jpg",institutionString:"Girne American University",institution:{name:"Girne American University",institutionURL:null,country:{name:"Cyprus"}}},{id:"128665",title:"Prof.",name:"Man-Chung",middleName:null,surname:"Chiu",slug:"man-chung-chiu",fullName:"Man-Chung Chiu",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bR9OrQAK/Profile_Picture_2022-03-09T08:36:59.JPG",institutionString:null,institution:{name:"Beijing Normal University",institutionURL:null,country:{name:"China"}}}]},{id:"95",title:"Urban Planning and Environmental Management",coverUrl:"https://cdn.intechopen.com/series_topics/covers/95.jpg",editor:{id:"181079",title:"Dr.",name:"Christoph",middleName:null,surname:"Lüthi",slug:"christoph-luthi",fullName:"Christoph Lüthi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRHSqQAO/Profile_Picture_2022-04-12T15:51:33.png",biography:"Dr. Christoph Lüthi is an urban infrastructure planner with over 25 years of experience in planning and design of urban infrastructure in middle and low-income countries. 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He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRqB9QAK/Profile_Picture_1626163237970",institutionString:null,institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/195006",hash:"",query:{},params:{id:"195006"},fullPath:"/profiles/195006",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()