The list of ENSO years as found in Lupo et al. [35] and references therein.
\r\n\tAn important component of this book must be dedicated to the more recent treatments namely with biologic therapies but focusing also on new small molecule inhibitors and experimental therapies.
",isbn:"978-1-80356-264-3",printIsbn:"978-1-80356-263-6",pdfIsbn:"978-1-80356-265-0",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,hash:"949e69c6b2120eab3174d0a7e6b1c655",bookSignature:"Dr. Sulaiman Wadi Harun",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11495.jpg",keywords:"Mode-Locking, Ultrafast Lasers, Fiber Lasers, Chirped Pulse Amplification, Charged Particles Acceleration, Nonlinear Process, Soliton, Supercontinuum Generation, Ultrafast Nonlinear Pulses, Laser Material Processing, 3-D Micromachining, Quantum Optics",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 10th 2022",dateEndSecondStepPublish:"April 13th 2022",dateEndThirdStepPublish:"June 12th 2022",dateEndFourthStepPublish:"August 31st 2022",dateEndFifthStepPublish:"October 30th 2022",remainingDaysToSecondStep:"a month",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"A prominent Malaysian Professor in Photonics, appointed Fellow of Malaysian Academic of Science, founder and honorary advisor for the Optical Society of Malaysia.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"14201",title:"Dr.",name:"Sulaiman Wadi",middleName:null,surname:"Harun",slug:"sulaiman-wadi-harun",fullName:"Sulaiman Wadi Harun",profilePictureURL:"https://mts.intechopen.com/storage/users/14201/images/system/14201.png",biography:"Sulaiman Wadi Harun received his bachelor’s degree in Electrical and Electronics System Engineering from Nagaoka University of Technology, Japan, in 1996, and his master’s and doctoral degrees in Photonics Technology from the University of Malaya in 2001 and 2004, respectively. 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Predicting clinical response to vaccines and drugs remains a challenge in any viral treatment such as COVID-19 and SARS. COVID-19, a sort of corona disease, created a pandemic danger situation caused due to newly raised virus. Predicting novel virus biology lies majorly in vitro models, as it permits viral replication. Animal and human organoids had raised their value in the experimental virology platform. COVID-19 is a killing disease that increased the death rate of human beings higher than influenza. The number of reported cases is more or less equal to the number of deaths. Similarly, the infection mortality rate tends to increase deaths to 1%. This disease was most leniently spread from person to person through contacts or even through the respiratory system. There are different approaches taken to solve this dreadful disease which took away the lives and livelihood of millions worldwide. One such research approach is by gearing towards the direction of 3D organoids and ex-vivo platforms. 3D organoid models and ex-vivo platforms are known to recreate the disease-specific and organ-specific microenvironment in the lab. These models are well studied in cancer research [1].
Studies indicated that using 3D organoid and ex vivo models at an early stage of virus research might help them from failure. Most organoids can be established from induced pluripotent stem cells (IPSC), commonly containing 3D structure. Also, it consists of cell types for the specific organ, and multipotent adult tissue stem cells can create an organoid. To predict tissue tropism of this emerged COVID-19, various research groups ought to get the help of an organoid approach for preventing gastric tract and kidney failure. COVID-19 directly infected capillary organoids and kidney organoids. This chapter describes 3D ex-vivo organoid enabled stem cell culture for prediction, helping to combat COVID-19. Using ex vivo platforms to predict the response for vaccines is important before it reaches clinical trials. Subsequently, treatment for tumors with an inhabitation induced activation, but all tumors failed to induce an antitumour response in a subset; likewise, COVID-19 drugs fail to give antiviral activity. Thus, it is important to use precision ex vivo models [2].
While new infection disease emerged, virologists expose indicator cell lines panel. From a typical human origin or monkey origin to patient materials and find for viral replication signs. Species barriers get complicated due to this trial-and-error approach. In the indicator panel, it was also done by the potential absence of a target. Recently severe acute respiratory syndrome due to coronavirus, organoids termed research emphasizes the value of more physiological in vitro models. In Wuhan city novel coronavirus disease 2019 (COVID-19) outbreak began that quickly transmitted everywhere china and to various parts of the domain in all countries. This spread of COVID-19 resulted in an extensive pandemic. Numerous treatment methods have been established for these diseases. As stated, earlier vaccines for this disease were not yet developed. Organoids are newly generated patterns developed from human stem cells, ex vivo duplicating process drug, and viral screening process for infection models. Self-categorized tissues are commonly seen in human organoids, which contain the different structure of cells. Cells that contain the same structure of cell functions as real organs for a human being. This pattern permitted viral infection efficiency and resulted in experimentation.
Areas unmet with COVID 19 research about leveraging this organoid method tends to help us predict the effect of this disease in tissues concerning organs. It will be an efficient tool for assisting researchers in predicting disease in the lab and helping to improve in development of a drug for COVID 19. It deciphers pathways for biomarker by keeping in mind that coronavirus is the fastest spreading disease, particularly for those with a weak immune system. Studying and understanding the effect of COVID 19 with the help of this platform in this increasing population is essential for regular therapeutics improvement in this field. They are focusing on miniature organ research at the lab to study coronavirus’s progress in the human body. These organoids suggested that virus versatility for organs invading gut, kidney, and lungs to the liver. Experiments are conducted using drugs with the help of these mini tissues for identifying the working of these therapies would be candidates for treating people. Experts understand that SARS-Cov-2 might have a side effect on organs in the human body from residing infected persons. Unfortunately, there is no proper statement about this damage directly affected by the virus to secondary complications of infection. Various groups with the help of organoid to represent in body virus, cell infects that cause damages [3, 4]. Splender of 3D ex vivo organoids recreates human microenvironment in true lab morphology of tissues. An overview of COVID-19 diagnosis and management is shown in Figure 1.
COVID-19 diagnosis.
3D structures of organoids are placed from induced pluripotent stem cells, and also multipotent adult tissue stem cells are substituted. Organize themselves via spatially restricted for containing specific-organ cell types and cell sorting lineage commitment to creating cell assembly with permanent tissue architectural and functional characteristics. Typically, it consists of complement varied cell types that are present in interesting organs. Standardized organoid models since designed from cell sources that is extended from a wide amount of time. Organoids that depend on ipsc initially during cell population that permits certain extension. For generating organoids, given that ipsc permitted to form a human body and signals that takes through an execution developed to duplicate continuous improvement, brain or kidney organs are interested terms followed in vivo. Fully identified stem cells that taken straight from tissue, so the Establishment of ASC extracted organoids is complex. Smoothly design whole organoid structure several growth factors like tissues such as culturing and mouse gut in one cocktail. It involves active stem cells of relevant kinds of cells. Mainly organoids in other tissues are expanded by the proliferation of the progenitor cells and the ASCs in rich growth factors. The cell structure necessary to reduce the growth factor levels can be driven to adopt them [5].
Some low-limit nations with similarly powerless wellbeing frameworks and restricted ability to balance the monetary and social expenses of populace level physical removal, incorporating a few nations with wellbeing framework delicacy and very weak populaces, presently announce inconsistent cases, bunches of cases, and network transmission. The window for control at the subnational and public level might be shut in many of these nations. Individuals living in aggregate destinations are powerless against COVID-19 to a limited extent as a result of the wellbeing hazards related to development or uprooting, stuffing, expanded climatic presentation because of the unacceptable sanctuary, and poor nourishing and wellbeing status among influenced populaces. Albeit, a few variations of site plans may not be achievable. Augmenting site getting ready for better removing among occupants and group the executives, adherence to contamination counteraction and control principles, solid danger correspondence and network commitment, and a decent observation framework to distinguish starting cases early can significantly decrease the inclination for COVID-19 to spread inside such settings. Proper case, the board can lessen mortality among those tainted with the infection. The Interim Guidance plots the essential strides to guarantee these limits are set up.
Samples of saliva often included more SARS-CoV-2 copies than swab samples, and up to 10 days after initial diagnosis, a larger fraction of Saliva samples were positive. And in 495 health care professionals’ testing, two more asymptomatic cases were recognized as Swab’s. The scientists concluded in their letter that these data confirm the potential for detecting SARS-CoV-2 infection are used saliva specimens. At least saliva seems comparable with nasopharyngeal swabs in regulated health care environments. However, COVID-19 is a global epidemic, with rural, disadvantaged or otherwise under deployed communities most afflicted. And such situations can affect the way saliva-based tests perform [6].
PCR tests were also taken while affected by the Covid-19 virus. PCR accurately discover the occurrence of an antigen instead of bodily presence antibodies or immune responses. PCR test will find out at the initial stage of health patients has affected by virus based on recognizing the viral RNA, before antibodies generation, which will be available in the body illness signs occur. The general welfare authorities might better understand the transmission of disease, like Covid-19, among a population by using PCR testing to monitor huge bundles of Nasopharyngeal swab tests from within a populace.
After Covid-19, how long immunity retro will be staying in the body is indeterminate. Previous numerous researches have illustrated, who survived the epidemic of SARS (Acute Rapid Air Syndrome) those peoples had antibodies in their body for long eons afterwards retrieval. In several cases, after affected diseases, immunity will be increased naturally. Still, if Covid-19 causes a comparable immunological reaction, it would be untimely to say that coronaviruses produce Covid-19 and SARS. Nowadays, specific patients again affected by covid-19. This represents that these patients did not get immunity in their body naturally, which was displayed in several kinds of research. For antibody tests, such as the PCR tests, which usually use swabs to identify Covid-19, blood samples are normally needed. There seems to be little coronavirus but substantial and quantifiable antibody presence, circulating throughout the blood linked with the respiratory tract.
A SARS-CoV-2 alteration that enables the virus to avoid detection using different COVID-19 therapies produced antibodies which are proved by much research. Modern medicines termed monoclonal antibodies are shown with immunological molecules that are naturally produced. Jesse Bloom and his team mapped all probable SARS-CoV-2 mutations at the Fred Hutchinson Cancer Research Laboratory in Seattle, Washington, that were likely to impede binding with three monoclonal antibodies. One was made by Indiana, Eli Lilly in Indianapolis, and the second was made in a cocktail prepared in Tarrytown, New York by Regeneron. The receptor-binding element is the Alterations that impair a protein fragment interacting and entering cells by the virus. The researchers detected an alteration that caused the virus to elude the identification of one of the three antibodies with Regeneron’s antibodies cocktail. Not many of these changes are generally circling in tainted individuals. In every case, one is frequent in Europe and one in Denmark and Netherlands, where cases of mink and individuals working in mink ranching have been reported in SARS-CoV-2. The discoveries have not yet been peer-inspected.
Fluvoxamine, Umifenovir, camostat, ritonavir, Famotidine, Nafamostat, Lopinavir, hydroxychloroquine and chloroquine are drugs that are testing for medication of COVID-19. The treatment for SARS-CoV-1 and the Covid, which induce respiratory disorders for the Middle East, was successfully proved in vitro tests. However, there was no testing that confirmed that equal SARS-CoV-2 activity component. Nafamostat and camostat are antagonists of serine proteases. Camostat was already reported to prevent SARS-CoV entrance by acting as a researcher and serine protease inhibitor TMPRSS2 suggest together camostat, and nafamostat may inhibit SARS-CoV-2. Russia and China have licensed for use only as Umifenovir, and this is a tiny prophylactic indole derivative compound for influenza A and B viruses. Thiazolidine is Nitazzoxanide used in parasitic, bacterial and viral contamination as a viable enemy of an infectious disease. Several bio-informatics approaches can be used in detecting the sequence of the virus [7]. Scientists are also working to counteract potential “cytokine storms” in some patients that cause lung harm and severe respiratory discomfort. As stated early, for covid-19, there is no vaccines or drugs for the treatment. Hence some methods may predict this disease and take treatments to reduce its severeness and stop spreading to other persons. Some of them are discussed below.
Interferons are anti-inflammatory proteins and natural broad-spectrum antiviral that induce signaling pathway followed by transformation of IFN-stimulated genes and bind to their receptors on the surface of various cells [8]. It includes an antiviral enzyme and also pro-inflammatory components. These are a group of cytokines and are primarily developed from infected cells and immune cells. These activated immune cells perform the killing of infected cells and also deactivate movement of the virus in the human body. This method is helpful during the early prediction of diseases, and hence treatment can happen with ease. While implementing interferon at the early-stage amount of infections in the beginning stage prompts decreases, disease duration is short. However, as mentioned earlier, there are no vaccines for Covid-19. Hence prevention is the only way to safeguard. Covering mouth during cough, wearing mask, washing hands rapidly, and a safe distance from each other might help. However, this method helps an average person with a better immune system and does not favor elderly persons and infants.
A human ex-vivo model affirmed the significance of NK cells in medication prompted demise under pressure in formerly led tests [9]. These discoveries focussed on an intermingling between drug-initiated obstruction and tumor-resistant contexture. Be that as it may, sympathetic to the science behindhand this viral reproduction, infection system medication disclosure endeavors are restricted because of the absence of an appropriate test model. Previously, single-cell RNA sequencing data of human organoids to explore explanations of ACE2 and TMPRSS2, despite an assortment of RNA receptors to investigate their capacity in SARS-CoV-2 pathogenesis, were used. ACE2 is abundant in all organoids, besides the prostate and brain, and TMPRSS2 is omnipresent [1]. Natural, secure pathways in all organoids with the exception of the lungs are expanded in ACE 2(+) cells. Inquisitively, ACE2 (+) of the digestive tract, lung and retinal organoids have an extending low-thickness lipoprotein receptor with a more prominent joint in lung organoids. This investigation uncovers that organoids could be utilized for the review of this new pollution component and for the improvement of medications as a logical stage. General organoid application is shown in Figure 2.
Applications of organoids.
For acute gastroenteritis, human norovirus is the main cause. The primary drawback for the development of efficient therapy for norovirus is the absence of a powerful in vitro contamination model. Co-workers of Estes find the transfer of virus to gut enterocytes and its cell type is unavailable from cell lines intestine. Rather it was found in organoids. In ASC inferred enterocytes, little intestinal societies allowed development with different human norovirus strains. It ended up being an extra mind boggling factor. Mechanism of pathogenesis find organoids, where ipsc based organoids techniques are unique, and also it permits key aspect modeling for human brain development. While it happened during the Zika virus epidemic, it notably had an association between severe abnormalities and ZIKV infections. It does not affect the brain, and sequential studies used human cerebral organoids handled over causation proof. Replication of ZIKV in brain development and preferably affects and killed neural precursors.
It caused obstacles to microcephaly4 and cortical extension4. Similarly, organoids may be employed to reveal explicit species powerlessness differences. Pig H1N1 and Avian H7N2 flu infections mostly contaminate winged animals and pigs, separately, yet purported ‘reassortant’ flu infections. H1N1 virus (H1N1pdm) spread out speedily through human populaces. In the last periods, robust in vitro models are not available. The uses of ex vivo bronchus manipulating organizations to evaluate the seasonal infection of humans. The specimens of excision established by these transitory bronchus explant cultures. Hui et al. [10] the expected replication capacity, tropism in the tissue and the generation of cytokines inducing bronchi- and human-aviation pathways organoids from human and avian strains of flu. These tests provide appropriate findings by using organoids and explants. Considering organoids may be enlarged over the eons and removed from airways and frozen, organoids were considered to be beneficial for assessing the pandemic risk of animal flu infections. This end was confirmed by an equal report. Zhou et al. [11] human aviation ways organoids generate wide tug cultures, here it involves a few considerable aviation ways epithelial cells categories such as club cells, basal cells, ciliated cells and flagon cells. Two sets of infections with a clearly identified infectiveness in individuals were provided to these organoids. The coordinated contagions that ineffectually infectious in peoples by imitated two infections of people were further comprehensive.
Multiple organs are joined to generate the spinal tract, which arises from a rudimentary general tube that is elaborated between the mouth and the anus. The gut tube [10] has endodermal layer pillage, which has a three-region subdivision. Hindgut, midgut and Foreskin, where each location in the development of the embryonal layer produces certain organs throughout specific intervals. The Pancreas, stomach, liver, esophagus, Pharynx and part of the duodenum have also been raised by foregust. This portion is generated as 3D organoids from a mouse that depicts intestinal SC, which arise in a rich lamina madrigal from the structures of crypt-villus and all the primary cell types of the gut [12]. The biomedical platform uses Intestinal organoid technology for forskolin-induced human organoid, which permits the cellular structure of drug investigation. By isolating the colon, organoids grow superficially into a single-layer epithelium with notions of auto surgery that have injured the mouse colon [13, 14].
The liver comes from the extension of the foregut ventral wall that has been conceived to the structure of the liver bud. Hypoblasts described as hepatic endoderm cells formed from this bud, and both sinusoidal endothelial cells provide an estimate of the surrounding environment and mesenchyme. Hepatic vasculature is developed in conjunction with the development of liver bud, which forms a vasculogenesis and angiogenesis combination. It turns into a primary hematopoietic foetal organ. Therefore, the liver is designed based on the sensitive orchestration of signals between mesenchymal, endothelial and endodermal signals before the perfusion of the blood. 3D liver organoids designed for the treatment of liver failure are produced and assisted. The development of 3D organoids contributes to the development of epithelial liver bodies [15, 16, 17].
Mesodermal origins are mostly in the metanephric kidney and generate the back of the trunk. The organogenesis starts with the defined intermediate mesoderm of the kidney precursor tissue, leading to mesonephric and epithelial mesenchyme and monitoring the rostral-caudal direction. Uteric bud is a new epithelial protrusion in the development of a branched collecting tube structure that invades and interacts with the neighboring MM. In the same way, mature nephrons and other MM derivatives cause MM differentiation, epithelization and condensation through progressive MM. The signals and branches are regulated. It is obvious before the other organs that the kidney tissue can be self-organized from previous research with reaggregation.
The neural ectoderm formed a neural plate system, which generates a flat lamina of ectodermal cells positioned in close contact with the embryo and gradually builds a cylindrical epithelial structure called a neural tube [11]. The stringent spatial–temporal gradient of morphogens allows for the epithelial tube to be separated into four prosencephalons, mesencephalon and rhombencephalon areas with rostral-caudal axes and ventral-dorsal axes. Here prosencephalon emerges from secondary vesicles and creates the diencephalon and telencephalon. Neural stem cells that are followed by organoids generate glia and CNS neurons. Rostro caudally resides in the neural tube, and symmetrical simulation also asymmetrical simulations are continually initialized. NSC also provides more segmented cell structures to self-renewing progenitors, which include interim progenitors and neurons. Mostly distinct cells move outside the NSC domain, creating multi-layered structures such as the cerebral cortex, optic tectum and the medulla [18, 19, 20].
The retina is a light open area of the eye and digests from neural ectoderm, and retinal primordia emerge from the diencephalon that evaginates along the side. This produces pseudo-stratified neuro-epithelia, called optical vesicles, which turns into a sensory neural retina in its distant section. Proximal segment offers to begin to cash layered tissue and melanin delivered retinal shade epithelium, and OVs go through invagination at their distal part that frames an optic cup with RPE and NR as its external and internal dividers as for one another. NR comprises PC that is isolated from ganglion cell design, cones and bars alongside strong cell types. This vertebrate retina is created as 3D organoids and utilized as a most remarkable reaggregation model in tissue designing for examining the neural layer at its fundamental. This examination was led in chick embryos and archived that supplanted retina performed with a surprising limit with respect to reassembling them as different round types with the entire plan [21, 22, 23].
Since COVID-19 has an extremely rapidly distributed worldwide channel for infection, it is still not favorable for pathogenic mechanisms. A disease model is essential for the study of the pathological characteristics of virus infection or drug prediction. In viral infection research, 2D models are most frequently utilized. However, in an ex vivo context, it was unable to imitate and search reliability was limited. The large distance between human beings and primates is a disadvantage for these species. Broncho-alveolar resections using lavage material are extracted from airway organoids. The procedure was helpful and can be modified for drug screening for beyond 1 year. The system includes mesenchymal cells, human-derived and endothelial. In the human liver environment, it was highly stimulated. For a consistent structure, intercellular interactions such as tight connections are needed. Additional organoids are notified that not only microvilli, bile capillaries and lipid droplets in hepatic cells are dedicated to more human organoids in LO. It is substantially more distinct from LO than in cells like the liver generated by human beings [24].
A precondition for suspect ability due to viral infection without considering as in vivo or in vitro. A prerequisite for questionable ability to develop in vivo or in vitro due to viral infection. Human pluripotent stem cell reveals lung markers, extracted from 3D LBO’s
These LBOs showed growing, detachment and wretchedness tantamount to human lungs when the respiratory syncytial infection is contaminated. Contrasted with essential human hepatocytes, Na + −taurocholate co-shipping polypeptide, an HBV section receptor, was higher in human iPSC-LOs, which showed high helplessness to HBV contamination. Factors also increase the efficiency of infection, such as
During previous periods the improvement of organoids has shown as a revolution. Lung organoids, the invention of human intestinal organoids and human organs, are assent with the help of adult stem cell (ASC). The ASC also proved by the above-indicated organs. Once the separated intestinal organoids are produced, the multi-cellular structure and efficient complication of human intestine epithet are precisely mimicked for more than a year. The human gastrointestinal system is the widely used path of microbial attack. In vitro models for the study of intestinal illnesses have been shown to be popular in humans. In past studies, many studies were done to show an ASC culture of an intestine organoid epithelial bat. The possible source of SARS-CoV-2 is empirically linked to suggested bat organoids [26, 27]. The likelihood of enteric disease is investigated using SARS-CoV-2 in human intestinal organoids. The use of Crypts separated from the intestines in R Sinicus bats has been explored in SARS-CoV-2 and SARSr-breakout CoV’s in fecal horseshoe bat species to evaluate high distinguishing features of SARS-CoV and SARSr-CoVs. It developed bat small bowel organoids (enteroids), which use the methodology to make human bowel organoids. In the environment expanding and in a ratio of 1:2 every seven days, the indistinguishable bat entereroids are grown. In order to facilitate separation, the expansion medium was converted into a differentiation media during which enteroids are incubated for 4 days. The developed enteroids in bat replicate the multicellular structure of the native bat’s small intestinal epithelium. Employing electrical transmission microscopy, cells with typical characteristics of four important bat-enteroid intestinal cell kinds such as, enteroendocrine (EE) cells, paneth (P) cells, goblet (G), and including enterocytes (E) were found. Although one line of bat entereroids has been spread sequent for 12 weeks, the other lines, unlike human intestine organoids, stopped active production for at least 1 year following the passage for 4 or 5 weeks. It recognized the first bat intestinal organoid to imitate the bat intestinal epithelium cellular makeup [28].
The non-tumor lung tissue generated from patients in resection with pulmonary fluids was extracted from human lung stems. Human spherical organoids of 50 to 200 μm have been produced from pulmonary stem cells and lung parenchymatic cells. Rganoid and ex-vivo cultures belonged to the same 3 donors aged between 55 and 69 years. Every single donor was produced from a single line. Scalpel lung tissue with a laundry of 10 ml of DMEM, 10 mm HEPES and 1% of Glutamax media and F12 media penicillin–streptomycin solution. The ling tissue type 2 mg/mL ling tissue has been digested for one hour at 37°C on a shaking platform (St. Louis, MO, USA, Sigma-Aldrich).
The residual tissue parts and the Filter suspension were repeatedly sheared with Glutamax, 10 mM HEPES, and 1% penicillin streptomycin solution using a 100 μm filter with 10 mL complete DMEM and F12 media. The filtrate was then collected in a 50 mL bottle with 2% foetal serum bovine. Then the remaining volume is centrifuged for 5 minutes at 4°C at 600rcf. Lysis buffer Red blood cells lysed at room temperature for 5 min (Roche, Basel, Switzerland). Cut the whole DMEM/F12 media into a 10 mL cell pellet and centrifugate the pellet at 600rcf for 5 min. Matrigel will obtain human airway organoids during 14 days using cultivated. The Cultrex growth factor of cellular membrane type 2 matrix Culturex cell membrane (Gaithersburg, Trevigen, MD, United States) is reduced by the 40 μL droplet from cell membranic membrane extract cell suspension at 35°C for 15–30 min to solidify pre-heated 24-well suspension platforms with a 10 mg/mL lung cell. Each well was filled with 500 μL of organoid media and incubators with 5 percent CO2 at 37°C. The new organoid medium has taken on mechanical cutting with a 1000uL pipette and flamed Pasteur pipettes every four days. Every two weeks, the organoids were transported. The entire DMEM/F12 medium was added 10 mL, and organoids were centrifuged for 5 min at 450 rcf. The fragments are seeded in 1:1–1:6 proportions, and the fragmented organic fragmentation is replaced in a cold matrigel [29, 30, 31].
Human airway organoids are ready for infection at 37°C after 14 days at 5 percent CO2. Organoid matrigel with organoids comprising a number of organoids of the growth agent. Reactive substances and concentration for each growth factor. While less common than respiratory symptoms, gastrointestinal disorders have occurred in a considerable proportion of people with COVID-19. For a group of 73 COVID-19 patients, 53% had SARS-CoV-2 RNA in stool, with stool remaining positive even if breathing trials were negative with RNA viruses in 23% of patients. Viral NP-positive cells have been found in the gastrointestinal epithelial cells of these biopsy patients’ tissues. Persistent fecal disposal was especially prominent in pediatric patients. Taken together, these clinical findings show that COVID-19 patients may get enteric infections. This shows, however, that a new pathway for the virus can be the human digestive system [32, 33].
Does not need animal care after operation in an ex-vivo instrument is valuable, permits more duplicability between lesions and gives the regeneration experiment a rigorously regulated artificial air [26]. Ex vivo models of the spinal cord include cultivation for up to three weeks of several hundred micron-sized crosspieces. Ex vivo methods specifically secluded perfused lung enjoys a benefit of generally controlled dosing complex multicellular reaction physiological openness productive utilization of material. It additionally shows certain restrictions. For example, it was, in fact, requesting, and it has a short perception time. Another method, to be specific accuracy cut lung cut, enjoys the benefit of controlled cell portion, complex multicellular reaction, and effective utilization of material. It additionally has certain weaknesses of non-physiological openness and short perception time. Impediments of ex vivo treatment incorporate incidental unite mass of numerous cell types like fibroblasts and astrocytes. One more prevalent constraint of ex vivo is that hereditary change is proper for secret modules that can digest cellularly information and does not help for remedial modules that assisted with entering objective cells. It is comparably huge not to make sham longings, and moreover to address two indispensable issues that swarm the majority of the current organoid systems: a shortfall of reproducibility and, coupled to this, our shortfall of cognizance of the cycles that deal with their development.
In its earliest phases, the creation of 3D organoid products from human PSCs has now advanced rapidly. Soon human organoids can be produced for organisms already developed in the mouse or when re-aggregation investigations have already demonstrated a source of self-orientation. The skin, mammary gland, muscle and bone are part of it [27]. Organ expansion models since organoids show a model approach that is plainly available and allow them to open up doors to increasingly complicated or unachievable issues that have been resolved through conventional approaches. This applies in particular to biological concepts specific to people. For particular, the particular class approach of human neural stem cells has already been studied with human brain organoids. Retinal organoids have also been used for testing changes between morphogenesis and timing of human and rodent tissue. In addition, GI tract organoids can also be employed to investigate the organized promotion of GI bodies, a method that shows crucial human change combined with animal laboratory. Organoids are also promising to model homosexuality for adults. The relevance of the crypt niche in stem-cell self-renovation and differentiation was previously studied by intestinal organoids. This applies primarily to organoids from adult progenitors such as the liver and stomach, which closely recreate regeneration processes seen in the adult organ. Although if numerous options of organoids are clear, it is important to remember their existing limitations. In the recapitulation of in vivo development, all organoid approaches that have been shown so far remain meticulously defined. For example, whereas retinal organoids finely show classical laminar composition, external parts do not shape; photoreceptors, for example, are short of being entirely developed to become light sensitive.
Consequently, brain organoids [34] recapitulate fast brain growth outcomes, but future features, for example, in cortical platelets, are not fully formed. The development issue appears to be a common impediment to organoid technologies and whether this will limit their research and therapeutic opportunities greatly is still to be explored. In the end, the lack of vascularization is usually an in vitro problem for organoids. Organoids have limited growth capacity, which may also impact their development because of nutrition supply restrictions. A whole subject of tissue engineering that has been tackled by the various techniques of vascularization. Spinners can provide healthier nutritional swaps with a size of up to a few millimeters in this example of organoids. Instead, endothelial cell co-culture can create systems like vascular systems. However, the transplantation of these tissues is possibly the most hopeful problem-solve, as was done with liver buds and kidney organoids that fosters hosts invasion [35, 36].
Organoids have significant potential as a way of drug testing and therapy for development and disease modeling. Potential initiatives will certainly get them closer to this prospect.
In this chapter, a discussion about COVID-19 treatment methods based on 3D techniques was briefly analyzed. In the first section, platforms that are available for cell culture and its working characteristics were discussed. Later, it continuously discussed organoids, their definition, and their uses in different applications. Further, its use as different organs is described with its use in different stages. Prediction, as well as treating COVID-19, was found to be crucial and also, research plays a major role to put forth hybridizing of any two methods for accurate curing of COVID-19 from a human body. This method utilized 3D exvivo cell culture method to develop organoids and replace them over infected tissues. 3D disease models are previously available invitro and invivo technologies. However, they showed certain limitations and hence, treating viral infection using any stem cell culture and 3D technologies are quite helpful. Summarizing this chapter is based on the demonstration of active replication of human organoids culture system of lungs are found to be more helpful in the treatment of COVID-19. An organoid culture system is previously proposed and then used in a variety of applications. Rather it implemented 3D technologies for the development of cell culture and then replaced defective cells for an effective cure of viral infection.
A decade ago, Lupo [1] found no statistically significant long-term trends in global tropical cyclone (TC) activity or in many of the regional basins, although detailed records for some parts of the globe (e.g., the Southern Hemisphere) have only been available since about 1980. This study looked at time series of varying lengths within each ocean basin. This same work showed that there was interannual variability in TC occurrences and intensities found in most ocean basins. However, there was little statistically significant interannual TC variability during the negative or cold phase of the Pacific Decadal Oscillation (PDO), but interannual TC variability with respect to El Nino and Southern Oscillation (ENSO) was enhanced during the positive or warm phase of the PDO. Lupo [1] also showed some interannual variability in the length of the TC season in different basins as well.
\nOthers (e.g., [2], and references therein) found significant interdecadal TC variability in the Atlantic Region as related to teleconnections such as the North Atlantic Oscillation (NAO) or the Atlantic Multidecadal Oscillation (AMO), and relate these to a relative minimum in this region’s TC activity in the late twentieth century and a sharp increase in TC activity for the early twenty-first century. These results were consistent with the results of Lupo [1]. Camargo et al. [3] examine the climatological character of TC including long- and short-range variability in each ocean basin as well. This work is a comprehensive review of those that relate TC activity to intraseasonal phenomena such as the Madden Julian Oscillation (MJO), ENSO, the Quasi-biennial Oscillation (QBO), and others.
\nSince Lupo [1], others have published results showing that there have been more recent increases in the number of stronger storms in both the Atlantic (e.g., [4, 5]) and the West Pacific basin [6]. The latter showed this trend has been occurring since 1998, but others have demonstrated that the trend has been present in the West Pacific since the 1970s (e.g., [7]). Globally, several studies (e.g., [8, 9]) have demonstrated an increase in the most intense storms and/or the associated precipitation rates [10]. The latest published study [11] examined the global frequency of intense TC from 1979 to 2017 and found statistically significant increases as well. Some have noted that these increases in intense TC are associated with basin-wide changes in the sea surface temperature patterns (e.g., [6]). Others (e.g., [12]) examined the rapid intensification of TC over the Atlantic Region during the latter part of the 20th century as related to climate variability and trends. Additionally, the IPCC [4] fifth assessment report demonstrated no general agreement about the relative contribution of natural and anthropogenic forcing to changes in TC intensity.
\nThe focus on the most intense TC during the last decade is likely due to the fact that many climatological studies have established well the general character of TC climatologies in the world’s ocean basins. Additionally, the contributing dynamics to TC formation, development, and decay are well known (e.g. [3, 13, 14], and references therein). At the turn of the twenty-first century, tropical cyclone (TC) activity and how this may change in the future were of great interest to the atmospheric science community (e.g., [15, 16]). Furthermore, there is interest in the observed and potential increase in rainfall rates [10]. Increases in intensity and rainfall rates could threaten vulnerable coastal areas.
\nThe consensus of several global and regional scenarios for TC activity continues to project that the annual frequency will remain similar to today or decrease throughout the twenty-first century, but the intensity will increase (e.g., [4]). This may be due to projected decreases in strong tropical convection, although the confidence in this particular projection is lower. Additionally, these TC projections have been identified as uncertain since high-resolution simulations struggle to adequately capture TC occurrence and intensity [17, 18]. Also, the actual count of TCs is dependent on the different detection methods [19].
\nHowever, as discussed above with reference to Lupo [1] and further in that publication, the available time series across each region of the globe is uneven, and the ability to observe TC has continuously improved. There have even been changes in the instrumentation during satellite era and some studies (e.g., [20]) were able to homogenize the most recent satellite-derived data in order to analyze trends in TC occurrence and intensity. Also, changes in the techniques used to determine TC character and intensity have occurred as well (e.g., [21, 22, 23]).
\nThe goal of this work is two-fold. The first is to examine the latest decade in TC activity in order to determine if there have been any major changes in global, regional, or subregional TC frequency since Lupo [1]. The activity of the latest decade will be placed into the context of previous activity going back to 1980 and recent studies where available. By going back to 1980, this work will present the occurrence and intensity of TC in every ocean basin (and sub-basin) where they occur over four decades and this work is one of only a few thus far (e.g., [11]). TC intensity was not available for all ocean basins until approximately 1980 (e.g., [1]). The techniques used here are the same as those found in Lupo [1] and earlier studies in order to facilitate comparisons to these older studies published by this group. The second will examine TC activity with respect to interdecadal variability, and in particular the PDO, in each region in order to determine whether the results of Lupo and Johnston [16] and Lupo [1] remained intact. While the examination of interannual and interdecadal variability of TC is not unique, the study of these quantities over the entire globe and in each TC basin and sub-basin for this recent 40-year period is the first as far as the authors are aware.
\nThe data and methods are similar to those used in Lupo [1] and references therein, and more detail regarding some of these subjects can be found there. This study will examine all the globe’s ocean basins and includes tropical storm occurrences as well. The global ocean basins (Figure 1) are as follows: the North Atlantic, East Pacific, West Pacific, North Indian, Southern Hemisphere (includes South Indian and the South Pacific), and the South Atlantic. Following Lupo and Johnston [16] and Lupo [1], the North Atlantic was divided into west and east along 45oW. The East Pacific is divided along 125oW and 20oN as in Collins [24, 25], while the West Pacific is divided up into 140oE and 20oN following Lupo [1]. The Indian Ocean in the Northern and Southern Hemisphere is divided into west and east along 75oE. The southwest and southeast Pacific are divided by 180o longitude. Both the Indian and SH sub-basin divisions followed Lupo [1]. TCs were assigned to the basin and sub-basin in which they first reached tropical storm status. A study of the background atmospheric and oceanic variables contributing to TC formation is not performed here as it is beyond the scope of this work.
\nThe globe with the borders of each subregion for the North Atlantic (G = Gulf of Mexico, and C = Caribbean), East Pacific (125oW 20oN), West Pacific (140oE 20oN), northern Indian (75oE), and southern hemisphere (75oE in the Indian and 180o in the Pacific).
The TC occurrence and intensity data for all basins since 1980 were downloaded via the UNISYS website (http://weather.unisys.com), although these data can also be found in the National Hurricane Center (NHC) or the Joint Typhoon Warning Center (JTWC) archives (e.g., [21]). The TC data since at least 1900 can be found in the International Best Track Archive for Climate Stewardship (IBTrACS) or the best track data archive [26, 27]. A description of these data sets and their reliability can be found in references, such as Landsea [28], Knapp et al. [26], or Kossin et al. [20]. Here, we use the term TC to include both hurricanes and tropical storms (TSs) following Lupo et al. [29] and references therein. TS refers only to those entities that obtained maximum wind speeds between 35 and 64 kt. The year 1980 was chosen for this study in order that time series of the same length can be compared across the globe since TCs were not categorized in the Southern Hemisphere until that year (see [1]). Also, TC data sets from before the satellite era may be missing TC occurrences that went undetected by ship or aircraft (e.g., [30]). Additionally, this study will compare and contrast briefly the most recent four decades with those previous to 1980 (see [16]) where those data exist (Atlantic Region and West Pacific Region). Hurricane intensity was rendered using the maximum wind speed attained during the lifetime of the storm. However, since wind speed data have relatively large measurement error, the Saffir-Simpson [31] hurricane intensity scale values were used here. In order to further eliminate problems with some of the data as discussed in Lupo and Johnston [16] and later studies, we combined hurricane intensity categories (Category 1 and 2—weak; Category 3, 4, and 5—intense) following Landsea [28].
\nArithmetic means and correlations were analyzed, and means were tested for statistical significance using a two-tailed z-score test, assuming the null hypothesis (e.g., [32, 33]). Intensity distributions were also tested using a χ2 statistical test. These distributions were tested using the total sample climatology as the expected frequency and a subperiod as the observed frequency. The χ2 test was used to test the intensity distributions (TS and Category 1–5) of the most current decade against those of the previous 30 years as well as to examine the interannual or interdecadal variability of intensities. It has been hypothesized that using the climatological frequency as the “expected” frequency is more appropriate than using an approximated distribution since such analytical distributions (e.g., Poisson distribution) may not adequately represent real-world distributions (e.g., [34]). It should be cautioned that while statistical significance reveals strong relationships between two variables, it does not imply cause and effect. Conversely, relationships that are found to be strong, but not statistically significant may still have underlying causes due to some atmospheric forcing process or mechanism (e.g., [34]). The long-term trends were tested for statistical significance using analysis of variance techniques (ANOVA) and in particular the F-test [32, 33].
\nThe following descriptions can be found also in Lupo and Johnston [16]. The data were stratified by calendar year in the Northern Hemisphere (NH). In the Southern Hemisphere (SH), the tropical cyclone year is defined as the period beginning on July 1 and ending on June 30. For example, July 1, 2018 to July 1, 2019 was defined as 2019 since the majority of the SH TC season takes place from approximately December through April. We then analyzed the annual and monthly distributions of TC occurrence in order to find trends in TC season length or both the total sample and each intensity category.
\nThe Japan Meteorological Agency (JMA) El Nino and Southern Oscillation (ENSO) Index was used in this study. A list of El Niño (EN), La Niña (LN), and Neutral (NEU) years used here are shown in Table 1. A description of the JMA ENSO Index can be found on the Center for Oceanic and Atmospheric Prediction Studies website (http://coaps.fsu.edu/jma.shtml) hosted by Florida State University. In summary, this index is widely used (see [35]) and is defined by the long-term running mean sea surface temperature (SST) anomalies from the Niño 3 and 3.4 regions in the central and eastern tropical Pacific (e.g., [36]). The SST anomaly thresholds used to define EN years are those greater than +0.5oC, less than − = 0.5oC for LN years, and NEU otherwise. The JMA ENSO criterion defined the EN year as beginning on October 1 and ending on September 30. For example, ENSO year 1982 began on October 1, 1982 and ended on September 30, 1983. This definition, however, was modified here so that the EN year commenced with the initiation of the NH hurricane season (approximately June 1) following Lupo and Johnston [16] and used in Lupo [1]. This modification was necessary since EI Nino conditions typically begin to set in well before October 1, and the period August to October is close to the climatological peak of the hurricane season for the NH. No modification was needed for the SH. Additionally, while the JMA ENSO Index is more sensitive with the definition of LN than other indexes, it is less sensitive overall [37].
\nEN | \nNEU | \nLN | \n
---|---|---|
1982 | \n1979–1981 | \n1988 | \n
1986–1987 | \n1983–1985 | \n1998–1999 | \n
1991 | \n1989–1990 | \n2007 | \n
1997 | \n1992–1996 | \n2010 | \n
2002 | \n2000–2001 | \n2017 | \n
2006 | \n2003–2005 | \n\n |
2009 | \n2008 | \n\n |
2014–2015 | \n2011–2013 | \n\n |
2018 | \n2016 | \n\n |
\n | 2019 | \n\n |
The list of ENSO years as found in Lupo et al. [35] and references therein.
The Pacific Decadal Oscillation (PDO) is a 50- to 70-year oscillation described in the late twentieth century (e.g., [38, 39]) within the Pacific Ocean basin. We define the epochs of the PDO as found in Lupo et al. [35] and these are also cataloged at COAPS. The positive phase persisted from 1977 to 1998, while the negative phase has persisted since 1999. The most recent negative phase encompasses the most recent two decades, while the decades of the 1980s and 1990s are largely characterized by the positive phase. Where the data exist before 1980 (the Atlantic and western Pacific Regions), we can use the results of Lupo and Johnston [16] to characterize the negative PDO years from 1947 to 1976.
\nAn in-depth discussion is found in Lupo [1] describing why these two teleconnections were used primarily to define interannual and interdecadal variability, in spite of the fact that many studies (e.g., [2, 3], and references therein) have shown for example that variability in the Atlantic Ocean Basin can be linked to teleconnections there. While the NAO-related variations in TC activity can make interpretation of PDO-related hurricane variability more difficult, there is substantial overlap between the PDO and the interdecadal modes of the North Atlantic Oscillation (NAO) [35]. Nonetheless, ENSO is a main driver of interannual TC activity in many ocean basins as demonstrated by many studies (e.g., [3]), and since PDO can be shown to modulate ENSO behavior, the focus here will be on these teleconnections.
\nIn Lupo [1], tropical cyclone activity was examined within each ocean basin over different time periods. Here tropical cyclone activity since 1980 only was examined for each ocean basin and globally (Table A1). Globally, there has been no statistically significant trend in overall TC activity over the last 40 years and this is consistent with recent studies (e.g., [4]) (Tables A1f and 2, Figure 2a and b). There was also no significant difference in the TC intensity distributions when comparing those of the most recent decade versus the 1980–2009 period. A noteworthy change implied in the global data set was an increase in tropical storm activity since 2000 at the expense of weaker (Category 1 and 2) hurricanes. However, the most recent decade (2010–2019) did not show appreciable changes worldwide when compared with the previous decade (2000–2009) or with the 1980–2009 period. There was, however, a significant upward trend in the number of Category 3–5 and 4–5 storms significant at the 99% confidence level (Table 2) consistent with Elsner [6, 10], or Kossin et al. [11].
\n\n | ATL | \nEPAC | \nWPAC | \nNIND | \nSHEMI | \nGlobe | \n
---|---|---|---|---|---|---|
TS + Hur | \n−0.003 | \n−0.044 | \n− | \n0.129 | \n||
Tot Hur | \n−0.023 | \n− | \n−0.044 | \n−0.085 | \n||
Cat 3–5 | \n0.007 | \n−0.007 | \n||||
Cat4–5 | \n0.034 | \n0.030 | \n
A summary of the statistical significance for trends within each ocean basin.
The value given is the slope of the trend line. Statistically significant values are bold, while those marked with a *, ** are significant at the 95%, 99% confidence level, respectively.
The annual occurrence of (a) and (b) global, (c) and (d) Atlantic, (e) and (f) East Pacific, (g) and (h) West Pacific, (i) and (j) North Indian, and (k) and (l) Southern hemisphere tropical cyclones (left) hurricanes (right) from 1980 to 2019. The orange line is the linear trend line in each figure. The abscissa is years and the ordinate is annual occurrence.
An examination of each ocean basin demonstrates that only the ATL (Table A1a and Figure 2c and d) and NIND (Table A1d and Figure 2i and j). Regions experienced statistically significant increases for the trend in hurricane activity (at the 95% and 99% confidence levels, respectively) and total TC activity (at the 99% confidence level in both regions) (Table 2). Both regions showed slightly more activity in the most recent decade (2010–2019). Testing the distribution of TC intensities in both regions showed no statistically significant difference between the distribution of these for the most recent decade versus 1980–2009 using the χ2 test. However, the ATL increases are most notable in the tropical storm category (Table A1a), but with little change in the weak hurricanes. In the NIND Region, however, these increases were noteworthy only for the number of hurricanes, especially major hurricanes (Category 3 and 4). In both of these regions, the increase in the trend for major hurricanes categories was significant at the 99% confidence level. A comparison to Klotzbach and Gray [2] or Lupo [1] showed that the ATL Region trends found here are consistent with those found for the late twentieth or early twenty-first century identified in those publications. Thus, this region has a longer history of increasing activity. In Lupo [1], the NIND Region showed little trend in TC activity. The upward trends in all categories for the NIND noted here (Table 2) could be a real phenomenon or a function of better detection and classification.
\nThe increases were offset by overall decreases in the WPAC (Table A1c and Figure 2g and h) and SHEMI Regions (Table A1e and Figure 2l and k), which would show decreases, but only the decrease in WPAC hurricanes and SHEMI total TC were significant at the 99% confidence level (Table 2). Both regions were less active in the most recent decade (2010–2019). In the WPAC (Table A1c), the results found here were complex but contradict the results cited in section one. Examining the major hurricanes, the trend was downward for the Category 3–5 TC, but upward for the Category 4–5 results. Neither trend was statistically significant (Table 2), and the significant downward trend was noteworthy in TC Category 1–2 (not shown). While this does not agree with studies like Zhao et al. [6], who have found an increase in intense TC over the WPAC, the decrease in weaker TC means that a greater percentage of WPAC TC was in the major category. During the past two decades, about 60% of TC were classified as major compared to 50% in the two decades prior to those (see also [1]). In spite of a greater ratio of more intense TC in the WPAC, the intensity distributions were not significantly different in either region when testing the intensity distributions.
\nIn the SHEMI, the number of total TCs has decreased significantly, but the number of Category 3–5 and Category 4–5 TCs increased and these trends were significant at the 95% and 99% confidence level respectively (Table 2). The overall decrease was driven by decreases in the number of TS and a decrease in Category 1–2 storms (Table A1e) significant at the 99% confidence level (not shown). The 2010–2019 decade showed decreases overall and in the number of hurricanes from the previous decade (2000–2009), and this decade was less active than the last decades of the twentieth century (Table A1e). The most recent decrease continued the overall decrease found in Lupo [1]. As for the WPAC however, the percentage of major hurricanes was higher (55%) for the early twenty-first century compared to the late twentieth century (43%).
\nThe EPAC Region showed very little trend throughout the period (Table A1b, Figure 2e and f) in any category, including no statistically significant trend in the major hurricane categories (Table 2). This result is similar to that of Lupo [1]. However, it was clear that the 2010–2019 period in the EPAC was more active than the previous two decades suggesting interdecadal variability. This will be studied below. Additionally, testing the distribution of TC intensities for this region shows that the distribution of TC from 2010 to 2019 was similar to that for the period 1980–2009 at the 95% confidence level when using the χ2 test (Figure 3). This is the only region in which the distributions were similar to an acceptable degree of confidence.
\nThe TC intensity distributions in the EPAC region for (a) 1980–2009 and (b) 2010–2019. The abscissa is TC intensity and the ordinate is annual occurrence.
In this section, the 2010–2019 results are partitioned by ENSO phase in order to compare these results to those of Lupo [1] and Lupo and Johnston [16]. As shown in Lupo et al. [35], this most recent decade was still classified as a negative PDO. Thus, to examine interannual variability, a comparison was made to the previous decade and interdecadal variability was examined by comparing to the decades of the 1980s and 1990s (Table A2). These decades were primarily positive PDO years (1977–1998). This study also provides an opportunity in some ocean basins to compare to the previous negative PDO epoch in order to determine whether the current negative PDO epoch is comparable or if there are differences that may be due to enhanced satellite coverage or if these differences could be physical. The results here were also compared by sub-basin within each global region.
\nAn examination of the Atlantic Region activity (Table A2a) demonstrates that there were more TCs observed during LN and NEU years during the latest decade, and this activity was consistent with that of the previous decade. A comparison to the activity during the 1980s and 1990s demonstrated that while there were more TCs overall (significant at the 95% confidence interval when testing the means), the ENSO variability was similar. During each decade, EL years were 30% (or greater) less active than during other years. Thus, there was no significant difference between ENSO variability across the positive phase of the PDO and the current negative phase in spite of a more active negative PDO phase when testing the means in Table A2a. Previous studies (e.g., [1]) showed similar results, with the exception that the negative PDO phase showed weaker ENSO-related variability. Additionally, the ENSO variability with respect to TC intensity distributions was similar in that the comparison of the EN years to all years in each phase of the PDO (Figure 4) and these were similar at the 90% confidence level. The LN year distributions were different from either the EN years or those overall, but not at standard levels of significance.
\nThe TC intensity distributions for (a) all PDO+ TC, (b) all PDO+ EN TC, (c) all PDO − TC, and (d) all PDO − EN TC in the ATL region.
A comparison of Table A2a to the results of Lupo and Johnston [16] demonstrated that both the current (since 1999) and the previous (1947–1976) negative PDO epoch were more active than the positive PDO epoch (1977–1998). This result is similar to that of Klotzbach and Gray [2], who also show the mid-twentieth century and early twenty-first century were more active times for TC occurrence in the Atlantic Region compared to the latter twentieth century. This also supports the contention of overlap between multi-decadal epochs of the PDO and Atlantic Region teleconnections described in Section 2.3. However, in Lupo and Johnston [16], the number of TCs reaching hurricane strength did not vary at all with respect to ENSO from 1947 to 1976. Their study did not include tropical storms. Thus, it would be difficult to state with certainty that the difference between the results above and the Lupo [1] study are real as they may be a result of not counting TS in the earlier study or the lack of satellite observations. The non-count of TS is supported since if TSs are not included in the current negative PDO period, the ENSO variability in this phase is much weaker.
\nAn examination of the regional occurrence of TC within the Atlantic over the latest decade (Table A2a) demonstrates that the western Atlantic is the most active sub-basin and that the ENSO variability within this region is minor. The Gulf and Caribbean sub-basin TC activity was also unchanged as EL years are much less active in these two areas. These results agree with the previous studies from this group and others (see [3]). The only substantial difference between the results presented here and the previous results was that the eastern Atlantic was significantly more active (at the 99% confidence level) even when considering the small sample size. This may be due to increased SSTs over this part of the Atlantic during the last decade (e.g., [40]). A comparison of the length of the TC season (not shown here) to previous results [1] would demonstrate that the Atlantic Region TC season may be beginning about 2 weeks earlier than June 1 as TCs were observed in May for 5 of the 10 years during this decade.
\nIn the East Pacific Region (Table A2b), the most recent decade shows ENSO variability that is opposite of the Atlantic Region, in that there are more TCs during EL years than during LN years due to the warmer sea surface temperatures there. This is similar to Collins [24] or Lupo [1]. There was also little difference in TC numbers across the positive and current negative PDO epoch, and their intensity distributions were similar, a result significant at the 95% confidence level (as in Figure 3). When taken together, the first two decades of the 40-year period show ENSO variability similar to the latter two decades in that there were about 30–35% fewer TC in LN years. When testing the means, this result was statistically significant at the 95% confidence level. Testing the TC intensity distributions demonstrates that LN years were similar to the overall distributions at the 95% confidence level during both phases of the PDO in a manner similar to Figure 3. Even the EN year TC intensity distributions are similar to the overall intensity distribution in the positive PDO phase at the 90% confidence level. Only the EN years TC intensity distribution during the negative phase of the PDO was different from the overall distribution, but not at statistically significant levels.
\nIn Table A2b, it is apparent that the East Pacific Region is dominated strongly by activity in the southeast quadrant and this has not changed across any decade or PDO epoch. TC occasionally form further up the Central American and North American coast in the northeast quadrant, but only during LN and NEU years, while TC formed rarely in the northwest quadrant. The southwest quadrant TC activity did account for about 16% of the East Pacific Region activity and the ENSO variability in this quadrant was similar to the previous decades and also similar to that of the southeast quadrant (e.g., Camargo et al. [3] and references therein). The only difference is that the most current decade showed stronger ENSO variability, but this was not statistically significant. Finally, there was no appreciable change in the length of the East Pacific TC season.
\nAs shown above, there has been a decrease in West Pacific hurricanes. Table A2c confirms that the TC activity of the most recent decades is less than that of the previous three decades, which can be assumed to be real since satellite coverage has been comprehensive since 1980. However, it is difficult to attribute this decrease to interdecadal variability when comparing to Lupo [1] since the TC activity from the 1940s through the 1970s occurred during an era with less satellite coverage. This same study concluded that there was no significant West Pacific Region interdecadal or interannual variability. Overall, LN years were 20% less active than EN years from 1980 to 2019. For this period and region, this is significant at the 95% confidence level. Thus, there is a strong correlation between the interannual variability within this region and the East Pacific Region (e.g., [3]). An examination of the TC intensity distributions (Figure 5) shows that the distribution of negative and positive PDO is similar at the 90% confidence level using the χ2 test. This is also true for the EN year TC distributions in relation to the intensity distributions for the positive or negative phase of the PDO (Figure 5).
\nAs in
\nTable A2c also demonstrates that the occurrence of TC by quadrant in the West Pacific over the most recent decade was similar to that found in the earlier decades and Lupo [1]. In short, the southwest quadrant is the most active and shows only marginal (insignificant) interannual and interdecadal variability. The southeast quadrant is associated with 30% less TC activity than the southwest, but very strong (statistically significant at the 95% confidence level) ENSO variability. There were two to four times more TCs in the southeast quadrant during EN years, a result that agrees with many studies (e.g., [1, 3]). The recent decreases noted above for the West Pacific Region overall was distributed among the four sub-basins, though as a percentage, the decrease was largest (approximately 35% less) for the northeast quadrant. Additionally, the active southeast quadrant in the West Pacific during EN years combined with the active southwest quadrant for the East Pacific supports the conclusions of Camargo et al. [3], Lupo [1] and others in that during El Nino years, the Pacific is active across the basin for EN years, while during LN years activity was centered closer to their respective quadrants for both regions. Like the East Pacific, there was no significant change noted in the length of the TC season here (not shown).
\nIn the North Indian Ocean Basin, there was an increase in the number of TC occurrences as shown above, and Table A2d suggests that this was driven primarily by increases in the western Indian Ocean Region including the Arabian Sea. This includes the number of major storms. Since the regional classification for the intensity of these storms began in 1977, there is no need to compare this region or the Southern Hemisphere results (this region began reporting intensity in 1980) to earlier results. Table A2d also demonstrated that EN years were slightly more active than other years, and this is opposite that of the previous three decades. Thus, there are no conclusions that can be drawn about ENSO variability, nor about the interdecadal variability. However, Ng and Chan [27] showed that there was strong variability on the 5-year timescale in this region linked to the Indian Ocean Dipole (IOD).
\nAn examination of TC intensity distributions (not shown) shows that regardless of how the results are stratified in the North Indian Ocean Region, the distributions are similar to the overall distribution at the 95% confidence level or higher. The only exception was the distribution of TC intensities in LN years during the positive phase of the PDO were different, but not at standard levels of significance. The reason for the lack of variability in TC intensities in this region may be the less frequent occurrence of storms in this region. Finally, there was no change in the TC season here (not shown) and this was identical to the results of Lupo [1] and references therein who showed that this region possessed a double peak in activity (May–June and October–December), which is associated with the annual migration of the Intertropical Convergence Zone.
\nThe decrease in Southern Hemisphere variability shown in Table A2e for the most recent decade (2010–2019) continues the trend identified when comparing 2000–2009 or the previous two decades. Like the NIND Region, there are too few years to attribute these decreases to interdecadal variability as of yet. When examining the sub-basins, the decreases over the past two decades were primarily the result of fewer TC in the East Indian Ocean and to a lesser extent over the southwest Pacific. Additionally, during this decade, the TC season extends from October to April, which is similar to the previous decades [1]. Lastly, an examination of the distribution of TC intensities for the positive versus negative PDO demonstrated the distributions were different, but not at standard levels of significance.
\nThe overall interannual variability during the most recent decade showed more TC during EL years, which was counter to the results of the previous three decades (Table A2e). This variability, however, was not significant at acceptable levels of confidence. Lupo et al. (2011) found weak ENSO-related variability, which was marginally significant with more TC occurring during LN years. Examining the sub-basins exposed an error in assigning the ENSO year in Lupo [1] (see their Table 17) for these values only. The overall results were consistent between this study and the Lupo’s [1] study. The distribution of TC intensities during EN and LN years compared to the negative PDO years showed these distributions were similar at the 99% and 95% confidence level, respectively. During PDO positive years, the same comparison showed similarity at the 95% and 90% confidence level, respectively (not shown).
\nA discussion of the SHEMI sub-basin results (Table A2e) demonstrates that TC numbers in the West Indian Ocean Basin demonstrate the most current decade was slightly more active than the previous 30 years. EL years were more active over all decades than LN years observing nearly double the TC activity. This is then opposite to what was reported by Lupo [1] as that study reported more TC in LN years. The East Indian Ocean Basin saw the largest decreases as in the previous 30 years, as 11.3 TC events per year were reported. Here, the results show that for the latest decade only 7.7 TCs per year were observed, representing a decrease of about 33%. This was nearly equal to the total decrease in SEMI TC overall. More importantly, in the previous 30 years, LN season TC outnumbered EN season TC by more than two to one. For the latest decade, LN years experienced only 20% more TCs per season. This preference for LN years as in the West Indian Ocean Basin was of the opposite sense reported in Lupo [1]. However, the results presented here now agree with results for the East Indian and West Pacific numbers reported for these regions in earlier studies (e.g., [3], and references therein).
\nOnly in the southwest Pacific were the observed TCs and their interannual variability in the current decade consistent with those of the previous 30 years, showing a slight preference for LN years. Thus, the coding error of Lupo [1] did not have a major impact on the results reported for this sub-basin only. The southeast Pacific was the least active TC region of the SHEMI outside of the South Atlantic, and the occurrences of TC in the latest decade were consistent with the previous three. The latest decade showed only a slight preference for TC occurrences in EL years, and this was consistent with the three previous decades except that the previous decades saw stronger disparities between annual TC occurrences in EL years versus LA years. The ENSO variability in this sub-basin was opposite to what was reported in Lupo [1].
\nGlobally, there were 79.5, 90, and 92 TCs that occurred during LN, NEU, and EN years, respectively, during the last decade. This compares to 82, 91.3, and 85.7 TCs occurring during these years over the previous decade, respectively. The comparative numbers for the 1980–1999 period revealed there were 83.3, 88.8, and 85.6 TCs that occurred per LN, NEU, and EL year, respectively. Thus, the most recent decade demonstrates slightly different ENSO variability from that of the previous three decades, but this difference is not statistically significant. Over the entire 40-year period, these TC occurrence numbers were 81.8, 89.7, and 87.4, during LN, NEU, and EN years respectively.
\nIn this chapter, the global tropical cyclone activity for 2010–2019 was examined and compared firstly to the TC activity of the previous decade (2000–2009) and then to those occurring from 1980 to 1999. By doing so, we compared the results here to the previous results reported in works such as Camargo et al. [3] or Lupo [1]. The data sources used here were the same as those used in that study. The definitions for the TC season, basins, sub-basins, and internannual and internannual variability were identical to those used in Lupo and Johnston [16] and Lupo [1]. The statistical tests used here can be found in standard statistics text books.
\nGlobal TC activity in general during the latest decade was very similar to that of the previous decade and within most sub-basins there were broad similarities as well. However, this study found some key differences from Lupo [1]. The following results are new here.
\nThese are:
Globally, there were no statistically significant increases or decreases in overall global TC activity although the trend in the number of storms has shown increases. The number of intense storms (Category 3–5) showed a statistically significant increase over the 40-year period similar to IPCC [4], Kossin et al. [11], and others. The number of TSs also increased, but this was not statistically significant. These increases in these TCs were found in most global basins. Only the number of Category 1 and 2 storms decreased, especially since 2000.
In the ATL Region, the number of TCs during 2010–2019 was similar to 2000–2009. The overall 40-year trend was upward in the total number of TCs, hurricanes only, and intense hurricanes. These were all statistically significant. The interannual variability over the latest four decades was similar in that there were more TCs during LN years (about 30% more). Additionally, the ATL TC season during the 2010–2019 period started about 2 weeks earlier than the previous decades, while the eastern Atlantic observed an increase in TC activity.
While the intensity distributions were different when comparing negative and positive phases of the PDO, this result was not statistically significant. Also, the distributions of LN and EN TC intensities were compared to the total sample within each phase of the PDO, and the EN intensity distributions were similar at the 90% confidence level.
In the EPAC, few differences in the climatological character of TC were noted when compared to Collins [24, 25] or Lupo [1]. When comparing the TC intensity, distributions for each phase of the PDO or with respect to ENSO showed that these distributions were similar at standard levels of significance except when comparing the distribution of EN year TC intensities to the distribution of positive PDO TC.
Other studies showed significant increases in the number of intense TCs within the WPAC. Such an increase was not found here, but significant decreases in the number of Category 1 and 2 storms resulted in an increase in the proportion of WPAC TCs classified as intense. The decrease in the number of TC basin-wide was distributed approximately evenly across each quadrant. In this region, the TC intensity distributions were similar for each phase of the PDO at the 90% confidence level. This same result was found when comparing EN year TC intensities to the total distribution in each PDO phase.
Within the IND Region, there were significant increases in TC for the latest decade and over the entire 40-year period for total TC occurrence, Category 1 and 2 storms, and intense TCs and all these trends were statistically significant. These increases were especially evident within the western Indian Ocean Basin and Arabian Sea. All TC intensity distributions tested for interannual and interdecadal variability were similar to each other at standard levels of significance.
In the SHEMI, the 40-year trends showed significant decreases in TC frequency overall including the number of TSs and Category 1 and 2 hurricanes. But there was a significant increase in the number of intense storms. The number of TCs observed over the latest decade was the lowest in the 40-year period and proportion of TCs reaching Category 3 or higher increased. In this region, the positive and negative PDO TC intensity distributions were different, but not at standard levels of significance. The EN and LN year TC intensity distributions in each phase of the PDO were similar to the total sample for that PDO phase.
There was no significant SHEMI interannual variability overall, but the latest decade showed more TCs in EN years as compared to LN years. This was different from the previous 30 years. A coding error found in the Lupo [1] results showed that the variability associated with ENSO was opposite that reported in Lupo [1] for three of the four sub-basins.
The authors are grateful to the anonymous reviewers for their comments on this chapter. Their comments made this work stronger.
\nSee Appendix Tables A1 and A2.
\nCategory | \n1980–1989 | \n1990–1999 | \n2000–2009 | \n2010–2019 | \n1980–2019 | \n
---|---|---|---|---|---|
a. Atlantic | \n|||||
TS | \n4.1 | \n4.6 | \n7.7 | \n8.1 | \n6.1 | \n
Cat. 1,2 | \n3.5 | \n3.9 | \n3.7 | \n4.4 | \n3.9 | \n
Cat. 3–5 | \n1.7 | \n2.5 | \n3.5 | \n2.7 | \n2.7 | \n
Cat. 4,5 | \n1.0 | \n1.4 | \n2.1 | \n1.7 | \n1.6 | \n
Tot Hur | \n5.2 | \n6.4 | \n7.2 | \n7.1 | \n6.5 | \n
TS + Hur | \n9.3 | \n11.0 | \n14.9 | \n15.2 | \n12.6 | \n
b. East Pacific | \n|||||
TS | \n8.6 | \n5.6 | \n9.1 | \n7.8 | \n7.8 | \n
Cat. 1,2 | \n5.4 | \n4.5 | \n4.4 | \n4.7 | \n4.7 | \n
Cat. 3–5 | \n4.6 | \n5.5 | \n2.8 | \n5.7 | \n4.7 | \n
Cat. 4,5 | \n2.3 | \n3.9 | \n1.8 | \n4.0 | \n3.0 | \n
Tot Hur | \n10.0 | \n10.0 | \n7.2 | \n10.4 | \n9.4 | \n
TS + Hur | \n18.6 | \n15.6 | \n16.3 | \n18.2 | \n17.2 | \n
c. West Pacific | \n|||||
TS | \n9.7 | \n10.3 | \n10.4 | \n11.3 | \n10.4 | \n
Cat. 1,2 | \n8.1 | \n9.1 | \n6.5 | \n5.1 | \n7.2 | \n
Cat. 3–5 | \n8.4 | \n9.0 | \n9.5 | \n8.3 | \n8.8 | \n
Cat. 4,5 | \n5.4 | \n7.3 | \n8.0 | \n6.3 | \n6.8 | \n
Tot Hur | \n16.5 | \n18.1 | \n16.0 | \n13.4 | \n16.0 | \n
TS + Hur | \n26.2 | \n28.4 | \n26.4 | \n24.7 | \n26.4 | \n
d. North Indian Ocean | \n|||||
TS | \n3.7 | \n2.9 | \n4.0 | \n3.2 | \n3.4 | \n
Cat. 1,2 | \n0.5 | \n1.3 | \n0.6 | \n1.1 | \n0.9 | \n
Cat. 3–5 | \n0.2 | \n1.0 | \n0.5 | \n1.2 | \n0.7 | \n
Cat. 4,5 | \n0.1 | \n0.7 | \n0.4 | \n0.8 | \n0.5 | \n
Tot Hur | \n0.7 | \n2.3 | \n1.1 | \n2.3 | \n1.6 | \n
TS + Hur | \n4.4 | \n5.2 | \n5.1 | \n5.5 | \n5.0 | \n
e. Southern Hemisphere | \n|||||
TS | \n14.6 | \n12.8 | \n13.0 | \n12.0 | \n13.1 | \n
Cat. 1,2 | \n8.0 | \n7.8 | \n5.6 | \n5.3 | \n6.7 | \n
Cat. 3–5 | \n5.0 | \n7.3 | \n7.1 | \n6.3 | \n6.4 | \n
Cat. 4,5 | \n1.6 | \n4.8 | \n4.5 | \n4.8 | \n3.9 | \n
Tot Hur | \n13.0 | \n15.1 | \n12.8 | \n11.6 | \n13.1 | \n
TS + Hur | \n27.6 | \n27.9 | \n25.8 | \n23.6 | \n26.2 | \n
f. Global | \n|||||
TS | \n40.7 | \n37.3 | \n44.4 | \n43.7 | \n41.3 | \n
Cat. 1,2 | \n25.5 | \n26.6 | \n21.0 | \n20.9 | \n23.4 | \n
Cat. 3–5 | \n19.8 | \n25.3 | \n23.2 | \n24.2 | \n23.2 | \n
Cat. 4,5 | \n10.4 | \n18.1 | \n16.7 | \n17.6 | \n15.6 | \n
Tot Hur | \n45.4 | \n51.9 | \n44.3 | \n44.8 | \n46.6 | \n
TS + Hur | \n86.1 | \n88.2 | \n88.7 | \n88.5 | \n87.9 | \n
The decadal mean number of tropical storm (TS), category 1–2, category 3–5, category 4–5, total hurricanes, and total TC for each decade from the 1980s to the 2010s and for the entire period within each global ocean basin and over the entire globe.
1980–1999/2000–2009 | \n|||||
LN (3/1) | \n12.7/15.0 | \n2.3/2.0 | \n3.0/4.0 | \n3.7/7.0 | \n3.7/2.0 | \n
NEU (12/6) | \n10.1/17.3 | \n1.0/3.3 | \n1.9/3.5 | \n5.8/7.0 | \n2.2/3.7 | \n
EN (5/3) | \n7.0/10.0 | \n0.4/1.3 | \n1.4/1.7 | \n4.0/6.3 | \n1.2/1.3 | \n
Total | \n10.2/14.9 | \n1.1/2.6 | \n1.9/3.0 | \n5.1/6.8 | \n2.2/2.8 | \n
2010–2019 | \n|||||
LN (2) | \n18.0 | \n4.0 | \n2.0 | \n6.5 | \n5.5 | \n
NEU (5) | \n16.2 | \n1.6 | \n3.8 | \n6.5 | \n4.4 | \n
EN (3) | \n11.3 | \n1.3 | \n0.7 | \n5.3 | \n4.0 | \n
Total | \n15.2 | \n2.0 | \n2.5 | \n6.2 | \n4.5 | \n
1980–1999/2000–2009 | \n|||||
LN (3/1) | \n12.3/14.0 | \n0.0/0.0 | \n1.7/1.0 | \n1.7/3.0 | \n9.0/10.0 | \n
NEU (12/6) | \n17.9/15.7 | \n0.1/0.2 | \n1.1/0.8 | \n2.8/2.2 | \n13.8/12.5 | \n
EN (5/3) | \n18.4/18.3 | \n0.0/0.0 | \n0.4/0.3 | \n3.2/4.0 | \n14.6/14.2 | \n
Total | \n17.1/16.3 | \n0.1/0.1 | \n1.0/0.7 | \n2.7/2.8 | \n13.3/12.7 | \n
2010–2019 | \n|||||
LN (2) | \n13.0 | \n0.5 | \n0.5 | \n0.5 | \n11.5 | \n
NEU (5) | \n17.2 | \n0.0 | \n0.4 | \n2.0 | \n14.8 | \n
EN (3) | \n23.3 | \n0.0 | \n0.0 | \n5.7 | \n17.7 | \n
Total | \n18.2 | \n0.1 | \n0.3 | \n2.8 | \n15.0 | \n
1980–1999/2000–2009 | \n|||||
LN (3/1) | \n22.7/25.0 | \n3.7/4.0 | \n3.3/6.0 | \n13.0/11.0 | \n2.7/4.0 | \n
NEU (12/6) | \n28.4/26.7 | \n2.6/3.0 | \n3.7/3.3 | \n13.0/13.2 | \n9.2/7.2 | \n
EN (5/3) | \n27.4/26.3 | \n2.2/3.3 | \n1.6/3.0 | \n10.8/9.3 | \n12.8/10.7 | \n
Total | \n27.3/26.4 | \n2.7/3.2 | \n3.1/3.6 | \n12.5/11.8 | \n9.1/7.9 | \n
2010–2019 | \n|||||
LN (2) | \n20.1 | \n4.0 | \n1.0 | \n11.0 | \n4.0 | \n
NEU (5) | \n26.2 | \n2.6 | \n3.4 | \n12.6 | \n7.6 | \n
EN (3) | \n25.0 | \n2.7 | \n1.0 | \n10.3 | \n11.0 | \n
Total | \n24.7 | \n2.9 | \n2.2 | \n11.6 | \n8.0 | \n
\n | \n | ||||
1980–1999/2000–2009 | \n|||||
LN (3/1) | \n6.0/6.0 | \n2.3/2.0 | \n3.7/4.0 | \n\n | \n |
NEU (12/6) | \n4.5/5.0 | \n1.2/1.5 | \n3.3/3.5 | \n\n | \n |
EN (5/3) | \n4.8/5.0 | \n0.8/1.3 | \n4.0/3.7 | \n\n | \n |
Total | \n4.8/5.1 | \n1.3/1.5 | \n3.6/3.6 | \n\n | \n |
2010–2019 | \n|||||
LN (2) | \n4.5 | \n1.5 | \n3.0 | \n\n | \n |
NEU (5) | \n5.8 | \n2.6 | \n3.2 | \n\n | \n |
EN (3) | \n5.7 | \n3.0 | \n2.7 | \n\n | \n |
Total | \n5.5 | \n2.5 | \n3.0 | \n\n | \n |
1980–1999/2000–2009 | \n|||||
LN (2/2) | \n30.5/28.0 | \n4.0/5.5 | \n15.0/14.5 | \n8.0/5.5 | \n3.5/2.5 | \n
NEU (13/6) | \n27.8/25.0 | \n6.1/5.7 | \n12.5/11.8 | \n7.2/4.7 | \n1.9/2.8 | \n
EN (5/2) | \n26.6/26.0 | \n6.6/9.5 | \n6.8/6.5 | \n6.6/6.5 | \n6.6/3.5 | \n
Total | \n27.8/25.8 | \n6.0/6.4 | \n11.4/11.3 | \n7.1/5.2 | \n3.3/2.9 | \n
2010–2019 | \n|||||
LN (2) | \n22.5 | \n4.0 | \n8.5 | \n7.5 | \n2.5 | \n
NEU (4) | \n22.8 | \n6.5 | \n8.3 | \n4.8 | \n3.3 | \n
EN (4) | \n25.2 | \n8.5 | \n6.8 | \n6.8 | \n3.0 | \n
Total | \n23.6 | \n6.8 | \n7.7 | \n6.1 | \n3.0 | \n
The mean annual TC occurrence stratified by ENSO phase and sub-basin for the (a) ATL, (b) EPAC, (c) WPAC, (d) NIND, and (e) SHEMI.
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\n\nAll these rules apply to BOTH online and offline use.
\n\nParts of the Rules of Attribution are based on Work Attributing Creative Commons Materials published by the Australian Research Council Centre of Excellence for Creative Industries and Innovation, in partnership with Creative Commons Australia, which can be found at creativecommons.org.au licensed under Creative Commons Attribution 2.5 Australia license, and Best practices for attribution published by Creative Commons, which can be found at wiki.creativecommons.org under the Creative Commons Attribution 4.0 license.
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He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. 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He was the Secretary of the Brazilian Society of Biomedical Engineering (SBEB) from 2015 to 2016, President of SBEB (2017-2018) and Vice-President of SBEB (2019-2020). He was the head of the undergraduate program in Biomedical Engineering of the Federal University of Uberlândia (2015 - June/2019) and the head of the Centre for Innovation and Technology Assessment in Health (NIATS/UFU) since 2010. He is the head of the Postgraduate Program in Biomedical Engineering (UFU, July/2019 - to date). He was the secretary of the Parkinson's Disease Association of Uberlândia (2018-2019). Dr. Andrade's primary area of research is focused towards getting information from the neuromuscular system to understand its strategies of organization, adaptation and controlling in the context of motor neuron diseases. 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