The global pandemic of COVID-19 accounts for more than 14,000 deaths worldwide. However, little is known about the host genetics interaction with infection and COVID-19 progression. To better understand the role of host genetics, we review the current literature, aggregate readily available genetic resources, and provide some updated analysis relevant to COVID-19 and associated phenotypes. Using the unrelated individuals in UK Biobank (total n = 337,579 across 5 populations), we aggregate human leukocyte antigen and ABO blood type frequencies. We find significant and consistent risk reduction of blood group O reported in Zhao et al. and encourage broad sharing of ABO blood type frequencies that are readily accessible across COVID-19 with mild, moderate, and severe/critical symptoms for robust inferences at https://tinyurl.com/abo-covid19. In addition, we generate polygenic risk scores (PRSs) weights for 29 blood measurements, including clinically relevant haematological measurements for COVID-19, such as lymphocyte count and percentage. Focusing on the 8 most COVID-19 clinically relevant blood measurements, we performed PRS-PheWAS analysis across 44 disease antigen measurements (n = 6,643 unrelated individuals in White British group), infectious diseases and acute respiratory infections (n = 20,928 cases and 349,000 controls across 3 population groups) and deaths (n = 1,846 cases and 368,082 controls), recorded in hospital inpatient record and death registry data, respectively, in UK Biobank, and find host genetic PRS associations with disease risk. Taken together, we anticipate these resources (https://github.com/rivas-lab/covid19) will aid in improving our understanding of host genetic risk factors playing a role in SARS-CoV-2 infection and COVID-19 disease severity.

The impact of the accurate estimated time of arrival (ETA) is often overlooked by bus operators. By providing accurate ETA to riders, it gives them the impression of bus services is efficient and reliable and this promotes higher ridership in the long run. This research project aims to predict bus arrival time by using the Support Vector Regression (SVR) model which is based on the same theory as the Support Vector Machine (SVM). Urban City Bus data covering part of the Petaling Jaya area (route name PJ03) is used in this research work. Features related to traffic such as travel duration, a distance of the road, weather and operation at peak or non-peak hour have been used as input in the training of the SVR model. By using kernel trick and specifying optimum parameters, all the features in higher dimensions are efficiently calculated and the SVR model achieves convergence. The model is evaluated with the test set of data split from the original dataset. The experimental result indicates the SVR model displays good prediction ability with its low average error on the prediction result. However, weather data has not been influential to the prediction model as the results of the model trained with and without weather data show a negligible difference.

Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among the renewable energies. In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the inputs variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced approaches and evaluate their performance. The proposed LSSVM model outperformed ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.

The study uses annual time series data from the South Africa Reverse Bank (SARB) from 1980 to 2020 to examine the effectiveness of fiscal policy on economic growth in South Africa. The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests, as well as the Johansen Co-integration test, Granger causality test, and Vector Auto-Regression (VAR) method, were used in the study. Real GDP per capita (RGDP) is used as proxy of economic growth and gross fixed capital formation (GFCF), government expenditure (GEXP) and government deficit (GOVD) as the proxies of fiscal policy. The ADF test results show that all variables are stationary at the first difference, with the exception of GFCF and GEXP, which are stationary at I(0), whereas the PP test results show that all variables are stationary at I(1), with the exception of GEXP, which is stationary at I(0). At Maximum Eigenvalue, the four variables are not cointegrated. The findings of the Granger causality test demonstrated a unidirectional causation from GOVD to RGDP, as well as a bidirectional causality from RGDP to GFCF and GEXP. Error Correction Model Estimated using VAR shows that GFCF, GEXP have positive effect on RGDP whereas GOVD has a negative effect on RGDP in the short run. The findings also presented that the VAR's residuals are homoscedastic, which means they are normally distributed and have no serial correlation.

The novel coronavirus, COVID-19 is now officially declared as a pandemic by the World Health Organization (WHO), and most parts of the world are taking drastic measures to restrict human movements to contain the infection. Like millions of others around the world, I am wondering, is there anything that could be done, other than keeping high personal hygiene, and be vigilant of symptoms, to reduce the chances of infection, or at least to reduce the burden of the disease. So far, the National and International health agencies, including the National Institutes of Health (NIH), the Centers for Disease Control and Prevention (CDC), and the WHO have provided clear guidelines for both preventive and treatment suggestions. In this opinion-based article, I want to discuss, why keeping the adequate micronutrient balance might enhance the host response and be protective of viral infections. A detailed in-depth discussion of various micronutrients is not the purpose of this article, I will mostly emphasize on the role of zinc in viral infection.

Oil immersed paper insulation condition is a crucial aspect of power transformer’s life condition diagnostic. The measurement testing database collected over the years made it possible for researchers to implement classification analysis to in-service power transformer. This article presents classification analysis of transformer oil-immersed paper insulation condition. The measurements data (dielectric characteristics, dissolved gas analysis, and furanic compounds) of 149 transformers with primary voltage of 150 kV had been gathered and analyzed. The algorithm used for developing classification model is Support Vector Machine (SVM). The model has been trained and tested using different datasets. Different models have been created and the best chosen, resulting in 90.63% accuracy in predicting the oil-immersed paper insulation condition. Further implementation was executed to classify oil-paper condition of 19 Transformers which Furan data is not available. The classification results combined, reviewed, and compared to conventional assessment methods and standards, confirming that the model developed has the ability to do classification of current oil-paper condition based on Dissolved Gasses and Dielectric Characteristics.

Dayaoshan, as an important metal ore producing area in China, is faced with the dilemma of resource depletion due to long-term exploitation. In this paper, remote sensing method is used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, vegetation interference bas been removed by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; then, altered mineral distribution information has been extracted based on principal component analysis (PCA) and support vector machine (SVM) method; Thirdly, the favorable areas of gold mining in Gulong has been delineated by using ant colony algorithm (ACA) optimization SVM model to remove false altered minerals; Lastly, field survey verified that the extracted alteration mineralization information is correct and effective. The results show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing.

“You should know the words by the company they keep!” has been one of the most famous slogans attributed to John Rubert Firth, 1957. This has ignited a whole school in linguistic research known as the British empiricist contextualism. Sixty years later, many un- or semi-supervised machine learning algorithms have been successfully designed and implemented aiming at extracting word meaning from within the context of a text corpus. These algorithms treat words, more or less, as vectors of real numbers representing frequencies of word occurrences within context and word meaning as positions of words in a high-dimensional vector space model. Word associations, in turn, are treated as calculated distances among them. With the rise of Deep Learning (DL) and other artificial neural networks based architectures, learning the positioning of words and extracting word associations as measured by their distances has further improved. In this paper, however, we revisited the main stream of algorithmic approaches and set the stage for a partly cross-disciplinary evaluation framework to judge about the nature of the extracted word associations by state-of-the-art machine learning algorithms. Our preliminary results are based on word associations extracted from the application of DL framework on a Google News text corpus, as well as on comparisons with human created word association lists such as word collocation dictionaries and psycholinguistic experiments. The results and conclusions provide some insights into the inherited limitations in interpreting the type of word associations and underpinning relations between words with inevitable consequences in other areas, such as extraction of knowledge graphs or image understanding.

Coronavirus Disease 2019 (COVID-19), caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome - Coronavirus-2) of the family Coronaviridae, appeared in Wuhan, Hubei province, China being its epicenter in December 2019. This disease was declared as posing Public Health International Emergency by World Health Organization on January 30, 2020, attained the status of a very high-risk category on February 29, and now having a pandemic status (March 11, 2020). COVID-19 has presently spread to more than 195 countries/territories while killing nearly 19,600 humans out of cumulative confirmed cases accounting to more than 430,000 within a short period of just a few weeks. The majority of deaths have been reported in Italy and China. Researchers worldwide are pacing with high efforts to counter the spread of this virus and to design effective vaccines and therapeutics/drugs. Few of the studies have shown the potential of animal-human interface and zoonotic links in the origin of SARS-CoV-2. Exploring the possible zoonosis and revealing the factors responsible for its initial transmission from animals to humans will pave ways to design and implement effective preventive and control strategies to counter COVID-19. The present review presents an overview of COVID-19 and the causative virus SARS-CoV-2, with particular emphasis on the role of animals and their jumping the cross-species barriers, experiences learned from SARS- and MERS-CoVs, zoonotic links, and spillover events, transmission to humans and rapid spread., highlighting in very brief the preventive and control measures along with a few of the recent research developments to counter this pandemic virus/disease.

Convalescent blood product therapy has been introduced since early 1900s to treat emerging infectious disease based on the evidence that polyclonal neutralizing antibodies can reduce duration of viremia. Recent large outbreaks of viral diseases for whom effective antivirals or vaccines are still lacking has revamped the interest in convalescent plasma as life-saving treatments. This review summarizes historical settings of application, and surveys current technologies for collection, manufacturing, pathogen inactivation, and banking, with a focus on COVID-19.

Research within the field of hydrology often focuses on comparing stochastic to machine learning (ML) forecasting methods. The comparisons performed are all based on case studies, while an extensive study aiming to provide generalized results on the subject is missing. Herein, we compare 11 stochastic and 9 ML methods regarding their multi-step ahead forecasting properties by conducting 12 large-scale computational experiments based on simulations. Each of these experiments uses 2 000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 100 values and the second time using time series of 300 values. Additionally, we conduct a real-world experiment using 405 mean annual river discharge time series of 100 values. We quantify the performance of the methods using 18 metrics. The results indicate that stochastic and ML methods perform equally well.

Hosting FIFA World Cup obviously produce immense socio-economic impacts to a nation. Proximal non-host city perception to mega event was frequently explored, while distal non-host city perception has less investigated. This study assessed the socio-economic impacts of FIFA World Cup Qatar 2022 in Chon Buri Sports City residents. Participants were 422 Chon Buri Sports City residents from eleven districts. Perception on socio-economic impacts of hosting FWC Qatar 2022 was measured from the translated Scale of Perceived Social Impacts. Extracted seven dimensions by confirmatory factor analysis is consistent with empirical data. Positive impact dimensions are Community Development, Community Pride, Economic Benefits. Negative impact dimensions are Traffic Problems, Security Risks, Economic Costs, and Environmental Concerns. Chon Buri Sports City residents perceived fewer negative impacts than other non-host city residents. Positive impacts of hosting FWC Qatar 2022 has more influenced on Chon Buri Sports City residents’ perception than negative impacts. These findings advantage for sport stakeholders to deeper understand the impacts of mega event hosting.

Supervised land-use/land-cover (LULC) classifications are typically conducted using class assignment rules derived from a set of multiclass training samples. Consequently, classification accuracy varies with the training data set and is thus associated with uncertainty. In this study, we propose a bootstrap resampling and reclassification approach that can be applied for assessing not only the uncertainty in classification results of the bootstrap-training data sets, but also the classification uncertainty of individual pixels in the study area. Two measures of pixel-specific classification uncertainty, namely the maximum class probability and Shannon entropy, were derived from the class probability vector of individual pixels and used for the identification of unclassified pixels. Unclassified pixels that are identified using the traditional chi-square threshold technique represent outliers of individual LULC classes, but they are not necessarily associated with higher classification uncertainty. By contrast, unclassified pixels identified using the equal-likelihood technique are associated with higher classification uncertainty and they mostly occur on or near the borders of different land-cover.

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is characterized by a high mortality of elderly men with age-related comorbidities. In most of these patients, uncontrolled local and systemic hyperinflammation induces severe and often lethal outcomes. The aging process is characterized by the gradual development of a chronic subclinical systemic inflammation (inflamm-aging) and by acquired immune system impairment (immune senescence). Here, we advance the hypothesis that some key features of aging contribute to the disproportionate SARS-CoV-2 mortality suffered by elderly men. At least four well-recognized aging-related characteristics that are strongly expressed in older men go some way towards explaining why these patients account for the vast majority of fatalities: i. the presence of subclinical systemic inflammation without overt disease, ii. a blunted acquired immune system and type I interferon response due to the chronic inflammation; iii. the downregulation of ACE2 (SARS-CoV-2 receptor), which triggers inflammation, particularly in patients with age-related comorbid diseases such as type II diabetes; and iv. accelerated biological aging, as measured by epigenetic and senescence markers (e.g. telomere shortening) associated to the chronic inflammatory state. Though typical of the aged, especially of elderly men, it is conceivable that these features are also shared by some subsets of the younger population. The high mortality rate of SARS-CoV-2 infection suggests that clarification of the mechanisms of inflamm-aging and immune senescence can help combat not only age-related disorders but also SARS-CoV-2 infection.

Despite the implementation of control strategies, the invasive parasitic mite Varroa destructor remains one of the principal causes of honey bee (Apis mellifera) colony losses in numerous countries. For this reason, the parasite represents a serious threat to beekeeping and to agro-ecosystems that benefit from the pollination services provided by honey bees. Numerous selection programmes have been initiated over the last three decades with the aim of promoting the establishment of balance in the host–parasite relationship and, thus, helping European honey bees to survive in the presence of the parasite without the need for acaricide treatments. Such programmes have focused on either selective breeding for putative resistance traits or natural selection. To date, no clear overview of these attempts has been available, which has prevented building on past successes or failures and, therefore, hindered the development of a sustainable strategy for solving the V. destructor problem. In the present study, we review past and current selection strategies, report on their outcomes and discuss their limitations. Based on this state-of-the-art knowledge, we propose a strategy for increasing response to selection and colony survival against V. destructor infestations. Developing in-depth knowledge regarding the selected traits, optimising selection programmes and communicating their outcomes are all crucial to our efforts to establish a balanced relationship between the invasive parasite and its new host.

The parasitic mite, Varroa destructor, has shaken the beekeeping and pollination industries since its spread from its native host, the Asian honey bee (Apis cerana), to the naïve European honey bee (A. mellifera) used commercially for pollination and honey production around the globe. Varroa is the greatest threat to honey bee health. Worrying observations include increasing acaricide resistance in the varroa population and sinking economic treatment thresholds, suggesting that the mites or their vectored viruses are becoming more virulent. Highly infested weak colonies facilitate mite dispersal and disease transmission to stronger and healthier colonies. Here, we review recent developments in the biology, pathology and management of varroa, and integrate older knowledge that is less well known.

Since general relativity (GR) has already established that matter can simultaneously have two different values of mass depending on its context, we argue that the missing mass attributed to non-baryonic dark matter (DM) actually obtains because there are two different values of mass for the baryonic matter involved. The globally obtained "dynamical mass'' of baryonic matter can be understood as a small perturbation to a background spacetime metric even though it's much larger than the locally obtained "proper mass". Having successfully fit the SCP Union2.1 SN Ia data without accelerating expansion or a cosmological constant, we employ the same ansatz to compute dynamical mass from proper mass and explain galactic rotation curves (THINGS data), the mass profiles of X-ray clusters (ROSAT and ASCA data) and the angular power spectrum of the cosmic microwave background (Planck 2015 data) without DM. We compare our fits to modified Newtonian dynamics (MOND), metric skew-tensor gravity (MSTG) and scalar-tensor-vector gravity (STVG) for each data set, respectively, since these modified gravity programs are known to generate good fits to these data. Overall, we find our fits to be comparable to those of MOND, MSTG and STVG. While this favorable comparison does not establish the validity of our proposition, it does provide confidence in using the fits to pursue an underlying action. Indeed, the functional form of our ansatz reveals an interesting structure in these fits.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision as compared to various RBFNNs.

Exploring the biological significance of mutations in SARS-CoV-2 coronavirus, causing the COVID–19 pandemic, has recently become an area of paramount interest for many researchers, who are pouring their tremendous efforts, in cracking the COVID–19 pandemic code. One of many such mutations that have occurred in the viral genome is V483A mutation, which is a part of the receptor-binding motif (RBM), present in the S1 domain of the spike protein. V483A mutant virus is becoming popular in North America with 36 cases so far, due to its frequent occurrences in recent days. In this review, we have assembled all information, currently available on V483A mutation, and have made a critical analysis based on the perspectives of many researchers all around the world. Comparison is made between the wild type and the V483A mutants to analyze certain factors like the type of interaction between the virus and host cell interface, binding affinity, stability, partition energy, hydrophobicity, occurrence rate, and transmissibility. Insilico dynamic analysis shows minimal alteration in the receptor-binding domain (RBD) of V483A mutant protein in free-state and no significant change of mutant tertiary structure of RBM upon binding to the ACE2 receptor. Comprehensive details about infectivity and evasion of the immune system by the virus are discussed. This information can in turn be of monumental importance in the field of vaccine and drug development because the mutants are becoming resistant to the vaccines and monoclonal antibodies.

The question of the relevance of human and natural capital, as well as the potential adverse effect of natural capital on economic growth, has gained increased attention in development economics. The aim of this paper is to theoretically and empirically assess the relevance of several forms of capital on economic growth in small economies that are dependent upon tourism or natural resources. The empirical framework is based on Impulse Response Functions obtained from Vector Autoregressive models in which we focus on the model where economic growth is the dependent variable for ten small economies that are dependent upon either tourism or natural resources. We find that there is evidence of the ‘’natural resource curse’’, especially in the economies that have a strong dependence on resources that are easily substitutable and whose prices constantly fluctuate. We further find that in the majority of observed cases the type of capital these small economies are most dependent on for their economic growth causes negative impulses in the majority of the observed periods. The main policy recommendation should be to assure that even these small economies should strive towards further diversification and avoid dependence on only one segment of their economy.

The most widespread intracellular bacteria in the animal kingdom are maternally-inherited endosymbionts of the genus Wolbachia. Their prevalence in arthropods and nematodes worldwide and a stunning arsenal of parasitic and mutualistic adaptations make these bacteria a biological archetype for basic studies of symbiosis and applied outcomes for curbing human and agricultural diseases. Here, we conduct a summative, centennial analysis of living in the Wolbachia world. We synthesize literature on Wolbachia’s host range, phylogenetic diversity, genomics, cell biology, and applications to filarial, arboviral, and agricultural diseases. We also review the mobilome of Wolbachia including phage WO and its essentiality to hallmark phenotypes in arthropods. Finally, the Wolbachia system is an exemplar for discovery-based science education using biodiversity, biotechnology, and bioinformatics lessons. As we approach a century of Wolbachia research, applications, and education, the interdisciplinary science and knowledge from this symbiosis stand as a model for consolidating and teaching the integrative rules of endosymbiotic life.

A novel coronavirus (2019-nCoV) that is initially found to trigger human severe respiratory illness in Wuhan City of China, 2019, has been recognized as a public health emergency of international concern. In the past two months, this deadly agent has caused 77,785 cases with 2,666 deaths via rapid person-to-person transmission and reached at least 25 countries. However, its evolutionary origin is poorly understood. Here we show integrative evidence that 2019-nCoV is a possible progenitor for SARS-CoV with bat origin. Our finding underscores the importance of tracing origin in the efficient monitoring, and effectively preventing the interspecies transmission of such emerging/re-emerging coronaviruses.

To solve large scale linear equations involved in the Fast Multipole Boundary Element Method (FM-BEM) efficiently, an iterative method named the generalized minimal residual method (GMRES)(m)algorithm with Variable Restart Parameter (VRP-GMRES(m) algorithm) is proposed. By properly changing a variable restart parameter for the GMRES(m) algorithm, the iteration stagnation problem resulting from improper selection of the parameter is resolved efficiently. Based on the framework of the VRP-GMRES(m) algorithm and the relevant properties of generalized inverse matrix, the projection of the error vector r_m+1 on r_m is deduced. The result proves that the proposed algorithm is not only rapidly convergent but also highly accurate. Numerical experiments further show that the new algorithm can significantly improve the computational efficiency and accuracy. Its superiorities will be much more remarkable when it is used to solve larger scale problems. Therefore, it has extensive prospects in the FM-BEM field and other scientific and engineering computing.

Biosensors are measurement devices that can sense several biomolecules, and are widely used for the detection of relevant clinical pathogens such as bacteria and viruses, showing outstanding results. Because of the latent existing risk of facing another pandemic like the one we are living due to COVID-19, researchers are constantly looking forward to developing new technologies for diagnosis and treatment of infections caused by different bacteria and viruses. Regarding that, nanotechnology has improved biosensors design and performance through the development of materials and nanoparticles that enhance their affinity, selectivity, and efficacy in detecting these pathogens, such as employing nanoparticles, graphene quantum dots, and electrospun nanofibers. Therefore, this work aims to present a comprehensive review that exposes how biosensors work in terms of bacterial and viral detection, and the nanotechnological features that are contributing to achieving a faster yet still efficient COVID-19 diagnosis at the point-of-care.

In this paper, a new approach to retrieve semantic images based on shape and geometric features of image in conjunction with multi-class support vector machine is proposed. Zernike moment as shape feature is to verify the invariance of objects for silhouette image. In addition, a set of geometrical features is to explore the objects shape using two features of rectangularity and circularity. Then the extracted features are normalized and employed for multi-class support vector machine either for learning or retrieving processes. The retrieving process relies on three main tasks which namely Query Engine, Matching Module and Ontology Manger, respectively. Query Engine is to build the input text or image query using SPARQL language. The matching module extracts the shape and geometric features of image’s objects and employ them to Ontology Manger which in turn inserts them in ontology knowledge base. Benchmark mammals have been conducted to empirically conclude the outcome of proposed approach. Our experiment on text and image retrieval yields efficient results to problematic phenomena than previously reported.

This paper presents a symmetrical topology for the design of solid-state transformer, made up of power switching converters, to replace conventional bulky transformers. The proposed circuitry not only reduces the overall size but also provides power flow control with the ability to be interfaced with renewable energy resources (RESs) to fulfill the future grid requirements at consumer end. Solid state transformer provides bidirectional power flow with variable voltage and frequency operation and has the ability to maintain unity power factor, and current total harmonic distortion (THD) for any type of load within defined limits of IEEE standard. Solid State Transformer offers much smaller size as compared to that of the conventional iron core transformer. MATLAB/Simulink platform is adopted to test the validity of the proposed circuit for different scenarios by providing the simulation results evaluated at 25 kHz switching frequency.

Vimentin is an intermediate filament protein that plays key roles in integration of cytoskeletal functions, and therefore in basic cellular processes such as cell division and migration. Consequently, vimentin has complex implications in pathophysiology. Vimentin is required for a proper immune response, but it can also act as an autoantigen in autoimmune diseases or as a damage signal. Although vimentin is a predominantly cytoplasmic protein, it can also appear at extracellular locations, either in a secreted form or at the surface of numerous cell types, often in relation to cell activation, inflammation, injury or senescence. Cell surface targeting of vimentin appears to associate with the occurrence of certain posttranslational modifications, such as phosphorylation and/or oxidative damage. At the cell surface, vimentin can act as a receptor for bacterial and viral pathogens. Indeed, vimentin has been shown to play important roles in virus attachment and entry of severe acute respiratory syndrome-related coronavirus (SARS-CoV), dengue and encephalitis viruses, among others. Moreover, the presence of vimentin in specific virus-targeted cells and its induction by proinflammatory cytokines and tissue damage contribute to its implication in viral infection. Here, we recapitulate some of the pathophysiological implications of vimentin, including the involvement of cell surface vimentin in interaction with pathogens, with a special focus on its role as a cellular receptor or co-receptor for viruses. In addition, we provide a perspective on approaches to target vimentin, including antibodies or chemical agents that could modulate these interactions to potentially interfere with viral pathogenesis, which could be useful when multi-target antiviral strategies are needed .

Infection with Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) causes Coronavirus Disease 2019 (COVID-19), which has reached pandemic proportions. A number of effective vaccines have been produced, including mRNA vaccines and viral vector vaccines, which are now being implemented on a large scale in order to control the pandemic. The mRNA vaccines are composed of the Spike S1 protein encoding mRNA, incorporated in a lipid nanoparticle, stabilized by polyethylene glycol (PEG). mRNA vaccines are novel in many respects, including cellular uptake, the intracellular routing, processing, and secretion of the viral protein. Viral vector vaccines have incorporated DNA sequences encoding the SARS-CoV-2 Spike S1 protein into (attenuated) adenoviruses. The antigen presentation routes in MHC class I and class II, in relation to induction of virus neutralizing antibodies and cytotoxic T-lymphocytes will be reviewed. In rare cases, mRNA vaccines induce unwanted immune mediated side effects. mRNA based vaccines may lead to an anaphylactic reaction. This reaction may be triggered by PEG. The intracellular routing of PEG, and potential presentation in the context of CD1 will be discussed. Adenovirus vector based vaccines have been associated with thrombocytopenic thrombosis events. The anti-platelet factor 4 antibodies found in these patients could be generated due to conformational changes of relevant epitopes presented to the immune system.

By providing useful tools to study host-pathogen interactions, next-generation omics has recently enabled the study of gene expression changes in both pathogen and infected host simultaneously. However, since great discriminative power is required to study pathogen and host simultaneously throughout the infection process, the depth of quantitative gene expression profiling has proven to be unsatisfactory when focusing on bacterial pathogens, thus preferentially requiring specific strategies or the development of novel methodologies based on complementary omics approaches. In this review, we focus on the difficulties encountered when making use of omics approaches to study bacterial pathogenesis. Besides, we review different omics strategies (i.e. transcriptomics, proteomics and secretomics) and their applications for studying interactions of pathogens with their host.

Humans may be exposed to microbial pathogens at recreational beaches via environmental sources, such as water, sand, and aerosols. Although infectious disease risk from exposure to waterborne pathogens has been an active area of research for decades, sand is a relatively unexplored reservoir of pathogens and fecal indicator bacteria (FIB). Beach sand and water habitats provide unique advantages and challenges to pathogen introduction, growth, and persistence, as well as continuous exchange between habitats. Models of FIB and pathogen fate and transport in sandy beach habitats can help predict the risk of infectious disease from recreational water use, but filling knowledge gaps such as decay rates and potential for microbial growth in beach habitats is necessary for accurate modeling. Climatic variability, whether natural or anthropogenically-induced, adds complexity to predictive modeling, but may increase human exposure to waterborne pathogens via extreme weather events, warming of water bodies and sea level rise in many regions. The popularity of human recreational beach activities, combined with predicted climate change scenarios, could amplify the risk of human exposure to pathogens and related illnesses. Other global change trends such as increased population growth and urbanization are expected to exacerbate contamination events and the predicted impacts of increasing levels of waterborne pathogens on human health. Such changes will alter microbial population dynamics in beach habitats, and will consequently affect the assumptions and relationships used in population models and quantitative microbial risk assessment (QMRA). Here, we discuss the literature on microbial population and transport dynamics in sand-water continuum habitats at beaches, how these dynamics can be modeled, and how climate change and other anthropogenic influences (e.g., land use, urbanization) should be considered when using and developing more holistic, beachshed-based models.

Human influenza A viruses (IAVs) cause global pandemics and epidemics, which remain serious threats to public health because of the shortage of effective means of control. To combat the surge of viral outbreaks, new treatments are urgently needed. Developing new virus control modalities requires better understanding of virus-host interactions. Here we describe how IAV infection triggers cellular apoptosis, and how this process can be exploited towards development of new therapeutics, which might be more effective than the currently available anti-influenza drugs.

The ectoparasitic mite Varroa destructor is the most significant pathological threat to the western honey bee, Apis mellifera, leading to the death of most colonies if left untreated. An alternative approach to chemical treatments is to selectively enhance heritable honey bee traits of resistance or tolerance to the mite through breeding programs, or select for naturally surviving untreated colonies. We conducted a literature review of all studies documenting traits of A. mellifera populations either selectively bred or naturally selected for resistance and tolerance to mite parasitism. This allowed us to conduct an analysis of the diversity, distribution and importance of the traits in different honey bee populations that can survive V. destructor throughout the globe. In a second analysis, we investigated the genetic bases of these different phenotypes by comparing ’omics studies (genomics, transcriptomics, and proteomics) of A. mellifera resistance and tolerance to the parasite. Altogether, this review provides a detailed overview of the current state of the research projects and breeding efforts against the most devastating parasite of A. mellifera. By highlighting the most promising traits of varroa-surviving bees and our current knowledge on their genetic bases, this work will help direct future research efforts and selection programs to control this pest. Additionally, by comparing the diverse populations of honey bees that exhibit the traits, this review highlights the consequences of anthropogenic and natural selection on the interactions between hosts and parasites.

This research is aimed at evaluating two different scenarios, firstly, appraising the impacts of employing the concepts of Total Quality Management (TQM) to the construction projects in Saudi Arabia. The results of the study were obtained through utilization of a descriptive analytical approach, where 300 questionnaires were distributed to engineering firms and companies with a response rate of 200 questionnaires, hence achieving the study sample for this research. The data gathered was analyzed by applying the Statistical Package for Social Science (SPSS) program and calculating the Relative importance index (RII) and the mean values. From the research conducted, the outcomes showed that the management’s ability to commit using TQM while applying BIM obtained a relative importance of (0.717), while the relative importance for the management’s ability to commit using TQM without the application of BIM is (0.552). The results showed that construction projects in Saudi Arabia still sustain setbacks from applying TQM concepts and suffer from the lack of administrative, scientific and technical applications. In a second scenario, a hybridized support vector regression (SVR) Harris-hawks optimization (HHO) (i.e., SVR-HHO) were used to predict the TQM. The performance accuracy of the models was checked through three different evaluation metrics namely; mean square error (MSE), correlation co-efficient (CC) and Nash-Sutcliffe efficiency (NSE). the hybridized emerging SVR-HHO outperformed the other two data driven approaches in both the training and testing stages based on the employed evaluation metrics. Overall, the obtained results showed that both the machine learning and metaheuristic approaches were capable of predicting TQM.

Public health agencies are increasingly using pathogen whole genome sequencing (WGS) to support surveillance and epidemiologic investigations. As access to WGS has grown, greater amounts of molecular data have helped improve our ability to detect outbreaks, investigate transmission chains, and explore large-scale population dynamics, such as the spread of antibiotic resistance. However, the wide adoption of WGS also poses challenges due to the amount of data generated and the need to transform raw data prior to analysis. This complexity means that public health agencies may need more advanced computational infrastructure, a broader technical workforce, and new approaches to data management and stewardship. As both a guide for how this development could occur, and a place to initiate discussion, we describe ten proposals for developing and supporting an informatics infrastructure for public health.

Inherent in every Active Magnetic Bearing (AMB) are differences between the expected geometric axes and the actual magnetic axes due to a combination of discrepancies, including physical variation from manufacturing tolerances and misalignment from mechanical assembly, fringing and leakage effects, as well as variations in magnetic material properties within a single AMB. A method is presented here for locating the magnetic axes of an AMB that will facilitate the accurate characterization of the bearing air gaps for potential improvement in field tuning, performance analyses and certain shaft force measurement techniques. This paper presents an extension of the application of the bias current perturbation method for the determination of the magnetic center [4] to the determination of magnetic axes for the further development of accurate current-based force measurement techniques [1].

Although the parasite Toxoplasma gondii is one of the most pervasive neurotropic pathogens in the world, the host-parasite interactions during CNS infection and consequences of neurological infection are just beginning to be unraveled. The chronic stages of infection have been considered dormant, although several studies have found correlations of infection with an array of host behavioral changes. These may facilitate parasite transmission and impact neurological diseases. During infection, in addition to the presence of the parasites within neurons, host-mediated neuroimmune and hormonal responses to infection are also present. T. gondii induces numerous changes to host neurons during infection and globally alters host neurological signaling pathways, as discussed in this review. Understanding the neurophysiological changes in the host brain is imperative to understanding the parasitic mechanisms and to delineate the effects of this single-celled parasite on health and its contribution to neurological disease.

: On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.

WNT10B belongs to the family of WNT proteins that are implicated in a range of phenomena that are affected by the Wnt signaling pathway. Recent studies have shown that WNT10B plays a role in colorectal cancer. WNTs have been found to directly affect the stemness of the tumor cells via regulation of ASCL2. Switching off the ASCL2 literally blocks the stemness process of the tumor cells and vice versa. Furthermore, recent findings suggest BVES to be highly suppressed in malignancies and in vitro deletions of BVES show higher Wnt signaling activity to induce stem- ness. WNT10B was found to be highly expressed in such cases. Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. For example, WNT10B-ASCL2 is one such 2^nd order combination whose relation needs to be tested under the influence of recently developed porcupine-WNT inhibitor ETC-1922159. The inhibitor is known to suppress PORCN (porcupine) and thus inhibit a range of oncogenes known to be directly or indirectly affected by the Wnts. In a recent unpublished work in bioRxiv, Sinha 1, we had the opportunity to rank these unknown biological hypotheses for down regulated genes at 2^nd order level after the drug was administered. The in silico observations showed that the combination of WNT10B-ASCL2 was assigned a relatively lower rank, thus validating the pipeline’s efficacy with the confirmed wet lab experiment that indicate that both WNT10B and ASCL2 were down regulated after treatment in cancer cells. Here, we take one step further by in silico analysis of the 3rd order combinations of WNT10B-X-X (X can be known or unknown factor), from a range of 100 randomly picked down regulated genes after ETC-1922159 treatment. The pipeline uses the density based HSIC (Hilbert Schmidt Information Criterion) sensitivity index with an rbf (ra- dial basis function) kernel, which is known to be highly effective in sensitivity analysis. Various unknown/unexplored/untested 3^rd order biological hypotheses emerge some of which are con- firmed in wet lab, while others need to be tested. The potential of such ranking is indispensable in the current era of search in a vast combinatorial forest.

The knowledge of protein function is essential for the study of biological processes, the understanding of disease mechanism and the exploration of novel therapeutic target. Apart from experimental methods, a number of in-silico approaches have been developed and extensively used for protein function prediction. Among these approaches, BLAST predicts functions based on protein sequence similarity, and machine learning predicts functional families from protein sequences irrespective of their similarity, which complements BLAST and other methods in predicting diverse classes of proteins including distantly related proteins and homologous proteins of different functions. However, their identification accuracies and the false discovery rate have not yet been assessed so far, which greatly limits the usage of these prediction algorithms. Herein, a comprehensive comparison of the performances among four popular functional prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these algorithms were systematically assessed by four metrics (sensitivity, specificity, accuracy and Matthews correlation coefficient) based on the independent test datasets generated from 93 protein families defined by UniProtKB Keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model species (homo sapiens, arabidopsis thaliana, saccharomyces cerevisiae and mycobacterium tuberculosis). As a result, the substantially higher sensitivity and stability of BLAST and SVM were observed compared with that of PNN and KNN. But the machine learning algorithms (PNN, KNN and SVM) were found capable of significantly reducing the false discovery rate (SVM < PNN ≈ KNN). In summary, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research.

Legionella pneumophila RpoS (LpRpoS) is an alternative sigma factor of RNA polymerase (RNAP) essential for virulence and stress resistance. To investigate the mechanism of RpoS in the intracellular pathogen L. pneumophila, we determined the high-resolution crystal structure of the LpRpoS (residues 95-194) containing a partial region 1.2 and region 2. The structure of LpRpoS (residues 95-194) reveals that the conserved residues are critical for promoter melting, DNA and core RNAP binding. The differences in regulatory factor binding site between Escherichia coli RpoS and LpRpoS suggest that LpRpoS may employ a distinct mechanism to recruit alternative regulatory factors controlling transcription initiation.

The matrix converter converts the input line voltage into a variable voltage with an unrestricted output frequency without using an intermediate circuit, dc link circuit. A pure sine in and pure sine out is the unique feature of the matrix converter. This research paper also analyzes the basic operating principle and the simulation modeling of the direct matrix converter, which is controlled by the Space Vector Pulse Width Modulation technique by using the software which is known as MATLAB/Simulink. The most desirable features in the power frequency changes can be fulfilled by using the matrix converters, and this is the reason for the tremendous interest in the topology. Since the power electronic circuits which is known as the motor drives are used to operate the AC motors at the frequencies other than that of the supply.

Defect detection and identification from fruits and vegetables are particularly challenging for Indian agriculture. Defect Detection is a process to identify the defects or damages in vegetables and fruits, based on the shapes, colors and textures. The local market finds it difficult to cope with the defects and other infections in fruits and vegetables as quality evaluations and classification of vegetables and fruits have become tedious process. Recently, several approaches based on Image processing, Machine Learning and Artificial Intelligence methods have been proposed for the purpose of defect detection. On the basis of classifying the types of defects, related pathogens, and physical and morphological characteristics descriptors, we review the different approaches based on a corpus of 57 articles between 2016 and 2021. In the process of describing the defect analysis, steps from the target articles, algorithms, and methods including qualitative and quantitative evaluation are mainly summarized. The aim of this current review work is to present-day novel images and collects recent defective area calculation methods to detect surface defects of fruits and vegetables using RGB images and to classify whether the fruit is defected or fresh. A rigorous evaluation of many new algorithms provided for quality assurance by researcher’s probes of vegetables and fruits have been conducted in this work. This review work conveys that using the recent identification features will help to decrease the disadvantages in fruit storeroom owing to storage of the affected vegetables and fruits, ie. Preventing the spread of defects and other infections from the infected fruits and vegetables to the fresh ones.

In this paper we give the improved and new analytical and semi-analytical expression for calcu-lating the magnetic vector potential, magnetic field, magnetic force, mutual inductance, torque, and stiffness between two inclined current-carrying arc segments in air. The expressions are ob-tained either in the analytical form over the incomplete elliptic integrals of the first and the sec-ond time or by the single numerical integration of some elliptical integrals of the first and the second kind. The validity of the presented formulas is proved from the special cases when the inclined circular loops are treated. We mention that all formulas are obtain by the integral ap-proach except the stiffness which is found by the derivative of the magnetic force.

Zika virus (ZIKV), a mosquito-borne flavivirus, was an almost neglected pathogen until its introduction in the Americas in 2015, where it has been responsible for a threat to global health, causing a great social and sanitary alarm due to its increased virulence, rapid spread, and an association with severe neurological and ophthalmological complications. Currently, no specific antiviral therapy against ZIKV is available, and treatments are palliative and mainly directed to symptoms relief, such as fever and rash, by administering antipyretics, anti-histamines, and fluids for dehydration. Nevertheless, lately, a great effort has been made to search for antiviral candidates using different approaches and methodologies, ranging from repurposing of specific compounds with known antiviral activity to the screening of libraries and of natural compounds. The identified antiviral candidates include drugs targeting viral components (structural proteins and enzymes), as well as cellular ones. Here, we present an updated review of current knowledge about anti-ZIKV strategies, focusing on host-directed antivirals as a realistic alternative to combat ZIKV infection.

During the last two decades, ecological speciation has been a major research theme in evolutionary biology. Ecological speciation occurs when reproductive isolation between populations evolves as a result of niche differentiation. Phytophagous insects represent model systems for the study of this evolutionary process. The host-plants on which these insects feed and often spend parts of their life cycle constitute ideal agents of divergent selection for these organisms. Adaptation to feeding on different host-plant species can potentially lead to ecological specialization of populations and subsequent speciation. This process is thought to have given birth to the astonishing diversity of phytophagous insects and is often put forward in macroevolutionary scenarios of insect diversification. Consequently, numerous phylogenetic studies on phytophagous insects have aimed at testing whether speciation driven by host-plant adaptation is the main pathway for the diversification of the groups under investigation. The increasing availability of comprehensive and well-resolved phylogenies and the recent developments in phylogenetic comparative methods are offering an unprecedented opportunity to test hypotheses on insect diversification at a macroevolutionary scale, in a robust phylogenetic framework. Our purpose here is to review the contribution of phylogenetic analyses to investigate the importance of plant-mediated speciation in the diversification of phytophagous insects and to present suggestions for future developments in this field.

A novel coronavirus has been reported as the causative pathogen of the Coronavirus disease 2019 (COVID-19) outbreak Wuhan city, China in December 2019. Due to the rapid spreading of COVID-19 worldwide, it has been announced as a pandemic by the World Health Organization (WHO). Hospitalized patients in Wuhan are associated with the Huanan seafood wholesale market where live animals, such as poultry, bats, snakes, frogs, rabbits, marmots, and hedgehogs are sold in that market which suggests a possible zoonotic infection. Therefore, it is essential to identify the potential animal reservoir, and the possibility of infection for other animal species. This short review aims to provide an overview on the relation between severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) infection and animals.

Various factors and constraints should be considered when developing a manufacturing production schedule, and such a schedule is often based on rules. This paper develops a composite dispatching rule based on heuristic rules that comprehensively consider various factors in a semiconductor production line. The composite rule is obtained by exploring various states of a semiconductor production line (machine status, queue size, etc.), where such indicators as makespan and equipment efficiency are used to judge performance. A model of the response surface, as a function of key variables, is then developed to find the optimized parameters of a composite rule for various production states. Further, dynamic scheduling of semiconductor manufacturing is studied based on support vector regression (SVR). This approach dynamically obtains a composite dispatching rule (i.e. parameters of the composite dispatching rule) that can be used to optimize production performance according to real-time production line state. Following optimization, the proposed dynamic scheduling approach is tested in a real semiconductor production line to validate the effectiveness of the proposed composite rule set.

Proteases are integral enzymes of the plant immune system. Multiple aspects of defence are regulated by proteases, including the hypersensitive response, pathogen recognition, priming and peptide hormone release. These processes are regulated by unrelated proteases residing at different subcellular locations. In this review we discuss ten prominent plant proteases contributing to the plant immune system, highlighting the diversity of roles they perform in plant defence.

Breast cancer is a significant health issue across the world. Breast cancer is the most widely-diagnosed cancer in women; early-stage diagnosis of disease and therapies increase patient safety. This paper proposes a synthetic model set of features focused on the optimization of the genetic algorithm (CHFS-BOGA) to forecast breast cancer. This hybrid feature selection approach combines the advantages of three filter feature selection approaches with an optimize Genetic Algorithm (OGA) to select the best features to improve the performance of the classification process and scalability. We propose OGA by improving the initial population generating and genetic operators using the results of filter approaches as some prior information with using the C4.5 decision tree classifier as a fitness function instead of probability and random selection. The authors collected available updated data from Wisconsin UCI machine learning with a total of 569 rows and 32 columns. The dataset evaluated using an explorer set of weka data mining open-source software for the analysis purpose. The results show that the proposed hybrid feature selection approach significantly outperforms the single filter approaches and principal component analysis (PCA) for optimum feature selection. These characteristics are good indicators for the return prediction. The highest accuracy achieved with the proposed system before (CHFS-BOGA) using the support vector machine (SVM) classifiers was 97.3%. The highest accuracy after (CHFS-BOGA-SVM) was 98.25% on split 70.0% train, remainder test, and 100% on the full training set. Moreover, the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed (CHFS-BOGA-SVM) system was able to accurately classify the type of breast tumor, whether malignant or benign.

The popular motor drive systems with a single two-level voltage source inverter (VSI) have one main problem that is the occurrence of the common-mode voltage (CMV), which is an effect of the electromagnetic interference, shaft voltage, bearing currents, leakage current. These cause the high stress, increasung temperature and early mechanical failure in machine. To overcome this problem, the technology of the dual two-level VSI fed open-end three-phase ac loads is now available to eliminate the CMV at the ac/induction motor load with the 120-degree modulation technique for controlling each inverter. In this paper, the discontinuous space vector modulation (DSVM) schemes are proposed and applied for the dual two-level VSI fed open-end load. It is based on the 120-degree modulation technique by using only 12 active voltage vectors and the 10 zero voltage vectors from the total 64 voltage vectors along with the different five-segment swicthing sequence designs with centralizing pulse width modulation technqiue in order to not only cancel the CMV in the ac load, but also reduce the switching number/switching loss of the conversion system. Among the various DSVM schemes, their performances are compared in this paper, such as the number of the switching, the step and peak value of the CMV in each inverter, and the quality of the output waveform, etc. The details of the verfication and comparison are carried out by simulation using Matlab/Simulink software.

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on inflammation, nutrition, and homeostasis. Although driven by a complex combination of intrinsic and extrinsic factors, many chronic diseases such as diabetes, cancer, Inflammatory Bowel Disease among others are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites etc have recently been elucidated, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to pragmatic limitations with respect to parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a systems and community level. In the past decade, various computational biology and machine learning methodologies and approaches have been developed with an aim to fill these existing gaps. Due to the agnostic nature of the tools, they have been applied in various disease contexts to analyze and infer the interactions between the microbiome and host molecular components, and in the case of a few selected tools, on downstream host processes. Generally, most of the tools are enabled by frameworks to statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic microbiome-host interactions and their potential benefit for basic and clinical research. These could include but are not limited to the development of activity and mechanism based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

DNA repair helps in maintaining the proper and healthy functioning for the cells in the human body. Failure in DNA repair process can lead to aberrations as well as tumorous stages. There are various types of damages that a DNA can go through, one of which is the DNA double strand breaks (DSB) that can be repaired via homologous recombination (HR). RAD51 plays a central role in HR and has been implicated as a negative/poor prognostic marker for colorectal adenocarcinoma, with high expression in colorectal cancer. Mechanistically, RAD51AP1 facilitates RAD51 during the repairing process by binding with RAD51 via two DNA binding sites, thus helping in the D-loop formation in the HR process. Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. For example, RAD51AP1-XRCC2 is one such 2^nd order combination whose relation needs to be tested under the influence recently developed Porcupine-WNT inhibitor ETC-1922159. The x-ray repair cross complementing XRCC family is known to work as a mediator or stabilizer for RAD51 during the HR process. The inhibitor is known to suppress Porcupine and thus inhibit a range of oncogenes known to be directly or indirectly affected by the Wnts. In a recent unpublished work in bioRxiv, we had the opportunity to rank these unknown biological hypotheses for down regulated genes at 2^nd order level after the drug was administered. The in silico observations showed that the combination of RAD51AP1-XRCC2 was assigned a relatively lower rank, thus validating the pipeline's efficacy with the confirmed wet lab experiment that indicate that both RAD51AP1 and XRCC2 were down regulated after treatment in cancer cells. Here, we take one step further by in silico analysis of the 3^rd order combinations of RAD51-X-X & RAD51AP1-X-X (X can be known or unknown factor), from a range of 100 randomly picked down regulated genes after ETC-1922159 treatment. The pipeline uses the density based HSIC (Hibert Schmidth Information Criterion) sensitivity index with an rbf (radial basis function) kernel, which is known to be highly effective in sensitivity analysis. Various unknown/unexplored/untested RAD51/RAD51AP1 related 3^rd order biological hypotheses emerge some of which are confirmed in wet lab, while others need to be tested.

The enzyme-coupled transient receptor potential channel subfamily M member 7, TRPM7, has been associated with immunity and immune cell signalling. Here, we review the role of this remarkable signalling protein in lymphocyte proliferation, differentiation, activation and survival. We also discuss its role in mast cell, neutrophil and macrophage function and highlight the potential of TRPM7 to regulate immune system homeostasis. Further, we shed light on how the cellular signalling cascades involving TRPM7 channel and/or kinase activity culminate in pathologies as diverse as allergic hypersensitivity, arterial thrombosis, and graft versus host disease (GVHD), stressing the need for TRPM7 specific pharmacological modulators.

For evaluating probabilities of arbitrary random events with respect to a given multivariate probability distribution, specific techniques are of great interest. An important two-dimensional high risk limit law is the Gauss-exponential distribution whose probabilities can be dealt with based upon the Gauss-Laplace law. The latter will be considered here as an element of the newly introduced family of (p,q)-spherical distributions. Based upon a suitably defined non-Euclidean arc-length measure on (p,q)-circles we prove geometric and stochastic representations of these distributions and correspondingly distributed random vectors, respectively. These representations allow dealing with the new probability measures similarly like with elliptically contoured distributions and more general homogeneous star-shaped ones. This is demonstrated at hand of a generalization of the Box-Muller simulation method. En passant, we prove an extension of the sector and circle number functions.

Recognition of the human interaction on the unconstrained videos taken from cameras and remote sensing platforms like a drone is a challenging problem. This study presents a method to resolve issues of motion blur, poor quality of videos, occlusions, the difference in body structure or size, and high computation or memory requirement. This study contributes to the improvement of recognition of human interaction during disasters such as an earthquake and flood utilizing drone videos for rescue and emergency management. We used Support Vector Machine (SVM) to classify the high-level and stationary features obtained from Convolutional Neural Network (CNN) in key-frames from videos. We extracted conceptual features by employing CNN to recognize objects from first and last images from a video. The proposed method demonstrated the context of a scene, which is significant in determining the behaviour of human in the videos. In this method, we do not require person detection, tracking, and many instances of images. The proposed method was tested for the University of Central Florida (UCF Sports Action), Olympic Sports videos. These videos were taken from the ground platform. Besides, camera drone video was captured from Southwest Jiaotong University (SWJTU) Sports Centre and incorporated to test the developed method in this study. This study accomplished an acceptable performance with an accuracy of 90.42%, which has indicated improvement of more than 4.92% as compared to the existing methods.

The pennate diatom Phaeodactylum tricornutum is a model organism able to synthesize industrially-relevant molecules. Commercial-scale cultivation currently requires large monocultures, prone to bio-contamination. However, little is known about the identity of the invading organisms. To reduce the complexity of natural systems, we systematically investigated the microbiome of non-axenic P. tricornutum cultures from a culture collection in reproducible experiments. The results revealed a dynamic bacterial community that developed differently in “complete” and “minimal” media conditions. In complete media, we observed an accelerated “culture crash”, indicating a more stable culture in minimal media. The identification of only four bacterial families as major players within the microbiome suggests specific roles depending on environmental conditions. From our results we propose a network of putative interactions between P. tricornutum and these main bacterial factions. We demonstrate that, even with rather sparse data, a mathematical model can be reconstructed that qualitatively reproduces the observed population dynamics, thus indicating that our hypotheses regarding the molecular interactions are in agreement with experimental data. Whereas the model in its current state is only qualitative, we argue that it serves as a starting point to develop quantitative and predictive mathematical models, which may guide experimental efforts to synthetically construct and monitor stable communities required for robust upscaling strategies.

Metagenomic sequencing has led to a recent and rapid expansion in the animal virome. It has uncovered a multitude of new virus lineages from under-sampled host lineages, including many that break up long branches among previously known clades, and many with genomes that display unexpected sizes and structures. Although there are challenges to inferring the existence of a virus from a virus-like sequence, the analysis of nucleic acid (including small RNAs) and sequence data can give us considerable confidence in the absence of an isolate. As a consequence, this period of ‘molecular natural history’ is helping to reshape our views of deep virus evolution. Nevertheless, there is a limit to what metagenomic discovery alone can tell us, and some open questions will require experimental isolates.

The aim of this study is to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area. i) Using different classification algorithms: Maximum Likelihood, Random Forest, Support Vector Machines and Sequential Maximum a Posteriori, with parameter optimisation in the second and third cases; ii) using different feature sets: spectral features, spectral and textural features, and spectral, textural and terrain features; and iii) using different image-sets: winter, spring, summer, autumn, winter+summer, winter+ spring+summer; and a four seasons combination. A 3-way ANOVA is used to discern which of these approaches and their interactions significantly increases accuracy. Tukey-Kramer contrast using a heteroscedasticity-consistent estimation of the kappa covariances matrix was used to check for significant differences in accuracy. The experiment was carried out with Landsat TM, ETM, and OLI images corresponding to the period 2000-2015. A combination of four images was the best way to improve accuracy. Maximum Likelihood, Random Forest and Support Vector Machines do not significantly increase accuracy when textural information is added, but do so when terrain features are taken into account. On the other hand, Sequential Maximum a Posteriori increases accuracy when textural features are used, but reduces accuracy substantially when terrain features are included. Random Forest using the three feature subsets and Sequential Maximum a Posteriori with spectral and textural features had the largest kappa values, around 0.9.

The alien invasive insect pest Spodoptera frugiperda Smith (Lepidoptera: Noctuidae), commonly referred to as Fall Armyworm (FAW), is a polyphagous insect feeding on more than 350 host plants in addition to maize in its native habitat. Due to the voracious nature of FAW, significant yield losses on maize production were reported across the African continent since its detection in 2016. Despite being a polyphagous insect, little is known about its alternative host plants in the new habitat including Mozambique. This study aimed to assess the host range of FAW in the central province of Manica, Mozambique. A field survey was conducted from May to August 2019 (dry season of 2018/2019 cropping season) and in December 2019 and January 2020 (rainy season of 2019/2020 cropping season) in maize fields and crops often mixed with maize or located in the vicinity of maize fields. A total of 1291 fields were surveyed. In each field, 20 plants were selected in a “W” pattern and checked for the presence of FAW egg masses and/or larvae. At the time of the sampling, no evidence was found suggesting that in Manica province FAW feeds in crops other than maize because out of 35 different crops surveyed, FAW was only recorded on maize. Results from this study suggest that the strain of FAW occurring in Manica province might be the one specialized in maize or the continuous availability of maize fields throughout the year is influencing the choices of the host plants of this invasive insect pest.

Dynamic measurement error correction is an effective method to improve the sensor precision. Dynamic measurement error prediction is an important part of error correction, support vector machine (SVM) is often used to predicting the dynamic measurement error of sensors. Traditionally, the parameters of SVM were always set by manual, which can not ensure the model’s performance. In this paper, a method of SVM based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement error of sensors. Natural selection and Simulated annealing are added in PSO to raise the ability to avoid local optimum. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters, they are the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absoluter percentage error are employed to evaluate the prediction models’ performances. The experiment results show that the NAPSO-SVM has a better prediction precision and a less prediction errors among the three algorithms, and it is an effective method in predicting dynamic measurement errors of sensors.

The recent Zika virus (ZIKV) outbreak in Americas surprised all of us because of its rapid spread and association with neurologic disorders including fetal microcephaly, brain and ocular anomalies and Guillain-Barré syndrome. In responding to this global health outcry, unprecedented and world-wide efforts are taking place to study the ZIKV etiology. Much have been learned about this virus in the areas of epidemiology, clinical manifestation, viral sequences and protein structures, as well as effects of ZIKV infection on fetal brain development and microcephaly. However, the molecular mechanism underlying ZIKV-mediated neurologic disorders remains elusive. Some critical questions include: 1) what type of virologic changes has taken place that increased the viral virulence? 2) which ZIKV protein(s) is responsible for the enhanced viral pathogenicity? And 3) how the newly adapted and pathogenic ZIKV strains alter their interactions with host cells leading to neurologic disorders? The goal of this review is to explore the molecular insights into the ZIKV-host interactions with special focuses on host cell receptor usage for viral entry, host cellular and immune antiviral responses, ZIKV counteraction and ZIKV-induced cytopathic effects. Our hope with this literature review is to inspire additional studies focusing on molecular studies of ZIKV-host Interactions.

This paper is devoted to the study of reproducing kernels on 2-inner product Hilbert spaces. We focus on a new structure to produce reproducing kernel Hilbert and Banach spaces. According to multi-variable computing, this structures can be useful in electrocardiographs, machine learning and economy

An analysis is presented of the possible existence of a second anomalous dipole moment of Dirac’s particle next to the angular one. It includes a discussion why, in spite of his own derivation, Dirac has doubted about its relevancy. It is shown why since then it has been overlooked and why it has vanished from leading textbooks. A critical survey is given on the reasons of its reject, including the failure of attempts to measure and the perceived violations of time reversal symmetry and chargeparity symmetry. It is emphasized that the anomalous electric dipole moment of the pointlike electron (AEDM) is fundamentally different from the quantum field type electric dipole moment of an electron (eEDM) as defined in the standard model of particle physics and that its measurement requires different instrumentation. A proposal has been described how to prove or disprove its existence by experiment. Moreover, by reference from literature, the possible impact is discussed in the nuclear domain and in the gravitational domain.

NaoMaiTong (NMT: Radix et Rhi-zoma Rhei, Radix Ginseng, Radix Puerariae, and Rhizoma Ligustici Chuanxiong as 9: 9: 6: 6) is a traditional Chinese medicine prescription for treating ischemia cerebral apoplexy. In this work, four cell injury models of ischemic stroke were establish, namely hypoxic injury, glutamate damage, injury of potassium chloride and hydrogen peroxide damage model. The protective effects of NMT and its single herbs-containing sera of different time points on PC12 cell damage were evaluated respectively, and the corresponding efficacy-time curves were drawn. Cell viability was measured by MTT (3-(4,5)-Dimethylthiahiazo(-z-y1)-3,5-di-phenytetrazoliumromide) assay. Furthermore, an statistical methods of support vector machine (SVM) were used to establish the correlation between concentration-time-efficacy for the first time. These results revealed that NMT-containing serum has obvious protective effect on the four injury models and can significantly improve cell viability. The PK-PD correlation between fifteen ingredients in the NMT compound with four model efficacy indexes indicated that rhein, puerarin, and 3'-methoxy puerarin might be the most important constituents controlling the pharmacological effects of NMT. The study suggested that these fifteen components are likely to be the material basis of NMT and recommended to increase the amounts of Pueraria in the NMT compound. That provided the scientific basis and demonstration for the research of efficacy material base of Traditional Chinese Medicine (TCM).

There is growing interest in characterizing the viromes of diverse mammalian species, particularly in the context of disease emergence. However, little is known about virome diversity in aquatic mammals, in part due to difficulties in sampling. We characterized the virome of the exhaled breath (or blow) of the Eastern Australian humpback whale (Megaptera novaeangliae). To achieve an unbiased survey of virome diversity a meta-transcriptomic analysis was performed on 19 pooled whale blow samples collected via a purpose-built Unmanned Aerial Vehicle (UAV, or drone) approximately 3km off the coast of Sydney, Australia during the 2017 winter annual northward migration from Antarctica to northern Australia. To our knowledge, this is the first time that UAVs have been used to sample viruses. Despite the relatively small number of animals surveyed in this initial study, we identified six novel virus species from five viral families. This work demonstrates the potential of UAVs in studies of virus disease, diversity, and evolution.

Factors affecting the host-virus interaction must be understood for the effective application of bacteriophages to combat bacterial pathogens. Two novel E. coli phages, the T1-like siphophage LL5 and the rV5-like myophage LL12, were subjected to forward genetic screens against the Keio collection, a library of single non-essential gene deletions in E.coli str. BW25113. These genome-wide screens and subsequent experiments identified eight genes required for efficient propagation of phage LL5 and six genes required for propagation of LL12. The majority of the genes identified were involved in production of the phage receptors. E. coli mutants deficient in heptose II and the phosphoryl substituent of heptose I of the inner core lipopolysaccharide (LPS) were unable to propagate phage LL5, as were mutants deficient in the outer membrane protein TolC. Mutants lacking glucose I of the LPS outer core failed to propagate LL12. Two cytoplasmic chaperones, PpiB and SecB, were found to be required for efficient propagation of phage LL5 but not LL12. This approach may be useful for identifying phage receptors and required host factors in other phages, which would provide valuable information for their potential use as therapeutics and for phage engineering.

This paper introduces an approach that chooses the fault detection by calculating probabilities using probability mass function (pmf) and cumulative distribution function (CDF). This work used a method for multiple stuck-at faults by producing a new test pattern in combinational circuits. We assumed that existence of all multiple faults is only because of one single component that is faulty. A complete test set can be created by all possible single stuck-at faults in a combinational circuit using some combination of gates. The test set generation fault detection method is applied on two different 3-bit input variable and 4-bit input variable circuits. The probability of error occurrence is calculated at both 3-bit and 4-bit input variable circuits. The resulting feature is used to obtain maximum error occurrence probability to detect faults by the logic used that the complexity of the circuit is inversely proportional to the fault occurrence probability. Then again, undetectability is directly proportional to the complexity of the circuit. Therefore, finest feasible circuit should have large input variable components with less complexity to reduce the fault occurrence probability.

The new emergence and re-emergence of arbovirus infections vectored by Aedes mosquitoes have been spread across Southeast Asia, Central Africa, United States, tropical Oceania and become a major of public health concerns. These arbovirus diseases found to have a similar vector, symptoms of the diseases and environments. The situation has become complicated without any specific vaccine or treatment for the diseases. As far as we concern, vector control is the best defense with many challenges, a scattered breeding site and biological behavioral sometimes, more difficult to control. Herein, we present a review of studies on current techniques proposed to combat arbovirus infections vectored by Aedes mosquitoes. A crucial gap in a vector control program is the inability to eliminate and destroy the cryptic breeding site by using conventional control methods. The idea and proper concept of using mosquitoes to bring insecticide to their oviposition site had been getting more interest in people to explore. Autodissemination is a self-delivery technique by manipulating the behavior of mosquitoes, carrying the insecticide and disseminate to cryptic breeding sites. This technique has shown a promising result in some countries and can be considered as additional tools in a vector control program

The genus Aphanommata in the Old World is reviewed. Aphanommata kuscheli sp. nov. from São Nicolau and A. strakai sp. nov. from Fogo (both Cape Verde islands) are described. Aphanommata euphorbiarum (Wollaston, 1867) from Santo Antão in the Cape Verde islands is redescribed and its lectotype is designated. All three Aphanommata species from the Cape Verde islands as well as A. filum (Mulsant & Rey, 1859) from Old World are diagnosed, illustrated, and keyed. Mature larva of A. kuscheli sp. nov. is described, larval morphology is discussed and the current state of knowledge about immature stages of Cossoninae is summarized. Vertical and inter-insular distributional pattern of Cape Verde Aphanommata and Pselactus is reviewed and discussed.

Although the existing paradigm states that CMV reactivation is under control of cellular immune response, the role of humoral counterpart is underestimated. Anti-CMV positive woman after conditioning with Bu-Flu-ATG underwent stem cell transplantation from fully matched, seronegative sibling donor. In immunodeficient recipient fast and significant decrease of specific immune response was observed but reconstitution of marrow-derived B and NK cells was prior thymic origin T cells. The lowest CMV-IgG(89 U/ml) was observed just before CMV viremia. Noteworthy, the sole and exclusive factor of CMV-specific immune response is a residual recipient antibody class IgG. The CMV-quantiferon increase was observed later, but in the first phase immune reconstitution of the PHA-induced IFNγ release was significantly lower than that CMV-induced. It corresponds with NK cells increase at the top of lymphocyte reconstitution and undetected CMV-specific CD8 cells by pentamer technique. Most of NK cells are CD16+, thus are stimulated by residual IgG. In immunocompetent donor the cellular and humoral immune response increases in parallel manner but symptoms of CMV mononucleosis were observed till the increase of specific IgG. Our observations shed light on a unique host-CMV interaction: indicate that significant decrease of CMV-IgG is a good predictor for CMV reactivation during secondary immunodeficiency.

A Pogorelov polytope is a combinatorial simple 3-polytope realizable in the Lobachevsky (hyperbolic) space as a bounded right-angled polytope. It has no 3- and 4-gons and may have any prescribed numbers of k-gons, k ≥ 7. Any polytope with only 5-, 6- and at most one 7-gon is Pogorelov. For any other prescribed numbers of k-gons, k ≥ 7, we give an explicit construction of a Pogorelov and a non-Pogorelov polytopes. Any Pogorelov polytope different from Löbel polytopes can be constructed from the 5- or the 6-barrel by cuttings off pairs of adjacent edges and connected sums with the 5-barrel along a 5-gon with the intermediate polytopes being Pogorelov. For fullerenes there is a stronger result. Any fullerene different from the 5-barrel and the (5, 0)-nanotubes can be constructed by only cuttings off adjacent edges from the 6-barrel with all the intermediate polytopes having 5-, 6- and at most one additional 7-gon adjacent to a 5-gon. This result can not be literally extended to the latter class of polytopes. We prove that it becomes valid if we additionally allow connected sums with the 5-barrel and 3 new operations, which are compositions of cuttings off adjacent edges. We generalize this result to the case when the 7-gon may be isolated from 5-gons.

The global COVID-19 pandemic claiming global spread continues to evolve, now to the verge of a third wave of outbreak possibly caused by the novel variants of concern of severe acute respiratory syndrome corona virus-2 (SARS-CoV-2). The test positivity rate (TPR) and case fatal-ity rate (CFR) have increased steeply in the second wave of COVID-19 compared to the first. From the example of Kerala, a state in southern India, positivity increased from 1.33% at the peak of wave one in 10th June 2020 to 13.45% during 10th June 2021 in the second wave of pandemic. SARS-CoV-2 is an enveloped single-stranded RNA virus. Angiotensin-Converting Enzyme-2 (ACE-2) is a trans membrane surface protein present on multiple types of cells in the human body to which the viral spike protein attaches. Genetic variations in the SARS-CoV-2 and ACE2 receptor can affect the transmission, clinical manifestations, mortality and the efficacy of drugs and vaccines for COVID-19. Mutations are the primary cause of genetic variations. Given the high TPR and CFR, it is necessary to understand the variations of SARS-CoV-2 and cellular receptors of SARS-CoV-2 at the molecular level. In this review, we summarize the impact of genetic and ep-igenetic variations in determining COVID-19 pathogenesis and disease outcome.

An emerging picture in cancer biology is that, paradoxically, chemotherapy can actively induce changes that favor cancer progression. These pro-cancer changes can be either inside (intrinsic) or outside (extrinsic) the cancer cells. In this review, we will discuss the extrinsic pro-cancer effect of chemotherapy; that is, the effect of chemotherapy on the non-cancer host cells to promote cancer progression. We will focus on metastasis, and will first discuss recent data from mouse models of breast cancer. Intriguingly, despite reducing the size of primary tumors, chemotherapy changes the tumor microenvironment, resulting in an increased escape of cancer cells into the blood stream. Furthermore, chemotherapry changes the tissue microenvironment at the distant sites, making it more hospitable to cancer cells upon their arrival. We will then discuss the idea and evidence that these devastating pro-metastatic effects of chemotherapy can be explained in the context of stress response. At the end, we will discuss the potential relevance of these mouse data to human breast cancer and their implication on chemotherapy in the clinic.

Deforestation and associated changing landscapes are major components of environmental changes, with important implications for ecosystem functioning and biodiversity conservation. Tropical forests are hot spots of biodiversity and provide multiple goods and ecosystem services which benefit people in many ways Forest also play an important role in health-related legends, myths, and fairy tales from all over the world, and are important sources of new potential emerging microbial threats to human. Although plausibly numerous abundant microbial forms with a forest origin may exist, our systematic literature review shows that forest-derived infection studies are relatively unexplored, and both taxonomically and geographically biased. Since biodiversity has been associated with emergence of novel infectious diseases at macro-scale, we describe the main biogeographical patterns in the emerging infection-biodiversity-forest loss nexus. Then, we illustrate four fine-scale case studies to decipher the underlying processes of increased infection risk in changing forest clearing landscapes. Finally, we identify scientific challenges and regional management measures required to mitigate these important new emerging threats.

The newly established virus family Phenuiviridae in Bunyavirales harbors viruses infecting three kingdoms of host organisms (animals, plants, and fungi), which is rare in known virus families. Many phenuiviruses are arboviruses and replicate in two distinct hosts (e.g., insects and humans or rice). Multiple phenuiviruses, such as Dabie bandavirus, Rift Valley fever phlebovirus, and Rice stripe tenuivirus, are highly pathogenic to humans, animals, or plants. They impose heavy global burdens on human health, livestock industry, and agriculture and are research hotspots. In recent years the taxonomy of Phenuiviridae has been expanded greatly, and researches on phenuiviruses have made significant progress. With these advances, this review drew a novel panorama regarding the biomedical significance, distribution, morphology, genomics, taxonomy, evolution, replication, transmission, pathogenesis, and control of phenuiviruses, to aid researchers in various fields to recognize this highly adaptive and very important virus family.

The present reality about the Nigerian economy calls for investment and development in the non-oil sector. This becomes necessary as a result of fall in the oil price in the global market. This paper examined the Bayesian Vector Autoregression (BVAR) modeling and forecasting of the dynamic interrelationship between Economic growth and revenue from the oil and non-oil sectors in Nigeria. To achieve this, annual data on Gross Domestic Product (GDP), revenue from oil and non-oil sectors were collected from Central Bank of Nigeria (CBN) bulletin, the sample from 1981 to 2008 was used for analysis, while sample from 2009 to 2014 was used for model validation. Six (6) versions of Sims-Zha BVAR models were compared for out-of-sample forecast, the result revealed the superiority of the BVAR6 model over the other BVAR models. Lastly, evidence from the decomposition forecast errors revealed that revenue of oil sector contributed 7.69% to GDP while revenue from non-oil sector contributed 0.12% to GDP in Nigeria. This paper therefore recommended that the present government should encourage investment that is geared toward development in the non-oil sector, of which it has the capacity to improve the Economic growth of the Nigerian economy.

\textsc{Background} Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. Currently, a major problem in biology is to cherry pick the combinations based on expert advice, literature survey or guesses for investigation. The search and wet lab testing of these combinations costs a lot in terms of time, investment and energy. In a recent development of the PORCN-WNT inhibitor ETC-1922159 for colorectal cancer, a list of down-regulated genes were recorded in a time buffer after the administration of the drug. The regulation of the genes were recorded individually but for a majority, it is still not known which higher ($\geq 2$) order combinations might be playing a greater role in the pathway. \textsc{Results} The pipeline provides a prioritised list of important $2^{nd}$ order combinations of a range of family of genes involved in the Wnt pathway. More specifically, it reveals the various unexplored FZD-WNT combinations that have been untested till now in the pathway. In relation to ETC-1922159 affected combinations, the down-regulation of LGR-RNF family after the drug treatment is evident in these rankings as it takes bottom priorities for LGR5-RNF43 combination. The LGR6-RNF43 takes higher ranking than LGR5-RNF43, indicating that it might not be playing a greater role as LGR5 during the Wnt enhancing signals. These rankings confirm the efficacy of the proposed search engine design. \textsc{Conclusion} A pipeline has been developed to prioritise an $n^{th}$ order combination of factors that affect a signaling pathway. It takes into account the sensitivity indices computed from variance based (SOBOL) and density-kernel based (HSIC) methods to estimate the influence of each factor or combination of factors. These are then fed as feature vectors into a powerful support vector ranking algorithm that produces a ranked list of the interactions/combinations.

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

Although the need to assess hydration is well recognized, laboratory tests and clinical impressions are impractical and lack sensitivity to be clinically meaningful. Different approaches use bioelectrical impedance measurements to overcome some of these limitations and aid in classification of hydration status. One indirect approach utilizes single or multiple frequency bioimpedance in regression equations and theoretical models, respectively, with anthropometric measurements to predict fluid volumes (bioelectrical impedance spectroscopy; BIS) and estimate fluid overload based on deviation of calculated to reference extracellular fluid volume. Alternatively, bioimpedance vector analysis (BIVA) uses direct phase-sensitive measurements of resistance and reactance, measured at 50 kHz, normalized for height, then plotted on a bivariate graph, resulting in a vector with length related to fluid content normalized by standing height, and direction with phase angle that indexes hydration status. Comparison with healthy population norms enables BIVA to classify (normal, under- and over-) and rank (change relative to pre-treatment) hydration independent of body weight. Each approach has wide-ranging uses in evaluation and management of clinical groups with overhydration with an evolving emphasis on prognosis. This review discusses the advantages and limitations of BIS and BIVA for hydration assessment with comments on future applications.

We propose a novel credit risk measurement model for Corporate Default Swap spreads, that combines vector autoregressive regression with correlation networks. We focus on the sovereign CDS spreads of a collection of countries, that can be regarded as idiosyncratic measures of credit risk. We model them by means of a vector autoregressive regression model, composed by a time dependent country specific component, and by a contemporaneous component that describes contagion effects among countries. To disentangle the two components, we employ correlation networks, derived from the correlation matrix between the reduced form residuals. The proposed model is applied to ten countries that are representative of the recent financial crisis: top borrowing/lending countries, and peripheral European countries. The empirical findings show that the proposed model is a good predictor of CDS spreads movements, and that the contemporaneous component decreases prediction errors with respect to a simpler autoregressive model. From an applied viewpoint, core countries appear to import risk, as contagion increases their CDS spread, whereas peripheral countries appear as exporters of risk. Greece is an unfortunate exception, as its spreads seem to increase for both idiosyncratic factors and contagion effects.

The integrated stress response (ISR) is an adaptational signaling pathway that is induced in response to different stimuli, such as accumulation of unfolded and misfolded protein, hypoxia, amino acid deprivation, viral infection and ultraviolet light. It has been known that viral infection can activate ISR, but the role of ISR during viral infection is still unclear. In some cases, ISR is a protective mechanism of host cell against infection with virus whilst ISR may be hijacked by viruses for facilitating its replication. In this review, we highlighted recent advances on induction of ISR upon viral infection and the downstream responses involved such as autophagy, apoptosis, formation of stress granules and innate immunity response. We then discussed the molecular mechanism of ISR regulating viral replication and how viruses antagonize this cellular stress response resulting from ISR.

The goal of VAR or BVAR is the characterization of the dynamics and endogenous relationships among time series. Also the VAR models are known for their applications to forecasting and policy analysis. This paper compare the performance of VAR and Sims-Zha Bayesian VAR models when the multiple time series are jointly influenced by different levels of collinearity and autocorrelation in the short term (T=16, 32, 64 and 128). Five levels (-0.9,-0.5, 0,+0.5,+0.9) of collinearity and autocorrelation were considered and the results from the simulation study revealed that VAR(2) model dominated for no and moderate levels of autocorrelation (-0.5, 0, +0.5) irrespective of the collinearity level except in few cases when T=16. While the BVAR models dominated for high autocorrelation levels (-0.9 and +0.9) irrespective of the collinearity level except in few cases when T=128. The performance of the models varies at different levels of the collinearity and autocorrelated error, and also varies with the short term periods. Furthermore, the values of the RMSE and MAE criteria decrease as a result of increase in the time series length. In conclusion, the performance of the forecasting models depend on the time series data structure and the time series length. It is therefore recommended that the data structure and series length should be considered in using an appropriate model for forecasting.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is causing pandemic of coronavirus disease 2019 (COVID-19). The worldwide transmission of COVID-19 from human to human is spreading like wildfire, affecting almost every country in the world. In the past 100 years, the globe did not face microbial pandemic similar in scale to COVID-19. Taken together, both previous outbreaks of other members of the coronavirus family (SARS-CoV and MERS-CoV) did not produce even 1% of the global harm already inflicted by COVID-19. There are also four other CoVs capable of infecting humans (HCoVs), which circulate continuously in the human population, but their phenotypes are generally mild, and these HCoVs received relatively little attention. These dramatic differences between infection with HCoVs, SARS-CoV, MERS-CoV, and SARS-CoV-2 raise many questions, such as: Why is COVID-19 transmitted so quickly? Is it due to the some specific features of the viral structure? Are there some specific human (host) factors? Are there some environmental factors? The aim of this review is to collect and concisely summaries the possible and logic answers to these questions.

Accurate and stable cost forecasting of substation projects is of great significance to ensure the economic construction and sustainable operation of power engineering projects. In this paper, a forecasting model based on the improved least squares support vector machine (ILSSVM) optimized by wolf pack algorithm(WPA) is proposed to improve the accuracy and stability of the cost forecasting of substation projects. Firstly, the optimal features are selected through the data inconsistency rate (DIR), which helps reduce redundant input vectors. Secondly, the wolf pack algorithm is used to optimize the parameters of the improved least square support vector machine. Lastly, the cost forecasting method of WPA-DIR-ILSSVM is established. In this paper, 88 substation projects in different regions from 2015 to 2017 are chosen to conduct the training tests to verify the validity of the model. The results indicate that the new hybrid WPA-DIR-ILSSVM model presents better accuracy, robustness and generality in cost forecasting of substation projects.

Besides external high-energy photon or proton beam therapy, targeted radionuclide therapy (TRNT) is an alternative approach to deliver radiation to cancer cells. TRNT is distributed within the body by the vascular system and allows targeted irradiation of a primary tumor and all its metastases, resulting in substantially less collateral damage to normal tissues as compared to ex-ternal beam radiotherapy (EBRT). It is a systemic cancer therapy, tackling systemic spread of the disease, which is the cause of death in most cancer patients. The α-emitting radionuclide bis-muth-213 (213Bi) has interesting properties and can be considered as a magic bullet for TRNT. The benefits and drawbacks of targeted alpha therapy with 213Bi are discussed in this review, covering the entire chain from radionuclide production to bedside. First, the radionuclide properties and production of 225Ac and its daughter 213Bi are discussed, followed by the fundamental chemical properties of bismuth. Next, an overview of available acyclic and macrocyclic bifunctional chelators for bismuth, and general considerations for designing a 213Bi-radiopharmaceutical are provided. Finally, we will provide an overview of preclinical and clinical studies involving 213Bi-radiopharmaceuticals, as well as the future perspectives of this promising cancer treatment option.

Bartonella spp. bacteria can be found around the globe and are the causative agents of multiple human diseases. The most well-known infection is called cat-scratch disease, which causes mild lymphadenopathy and fever. As our knowledge of these bacteria grows, new presentations of disease have been recognized with serious manifestations. Not only has more severe disease been associated with these bacteria, but Bartonella species have also been discovered in a wide range of mammals and the pathogens’ DNA can be found in multiple vectors. This review will focus on some common mammalian reservoirs as well as the suspected vectors in relation to disease transmission and prevalence. Understanding the complex interactions between these bacteria, their vectors, and reservoirs as well as the breadth of infection by Bartonella around the world will help toassess the impact of Bartonellosis on public health.

P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood-brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites. A nonlinear quantitative structure-activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 50, r² = 0.96, q²_CV = 0.94, RMSE = 0.10, s = 0.10) and test set (n = 13, q² = 0.80–0.87, RMSE = 0.21, s = 0.22). When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

The recent 2019-nCoV outbreak, spreading infection around the globe is jeopardizing the public health and global economy. The virus was reported to have emerged from an animal market in Wuhan, China at the end of 2019 and presumed to have originated from bats and eventually transmitted in humans. The entry of the virus into human cells is triggered by a series of molecular events initiated with the binding of a receptor-binding domain of viral spike protein to human Ace2 cell surface receptor. Based on the comparative sequence analysis of the well-known binding hotspots of human Ace2, cross-interacting potential of 2019-nCoV was predicted, which suggests Ace2 of wild animals like tiger, bear, orangutan, etc.; aquatic mammals like whale and dolphins; and domestic animals like cat, horse, goat, sheep, dog etc. as potential target. However, the recognition of Ace2 of bats, rats and mice by the 2019-nCoV spike protein remains under question. The study indicates that 2019-nCoV might have broad host range and may thus intensify the gravity of 2019-nCoV outbreak

Prothrombotic thrombocytopathy mimicking heparin-induced thrombocytopenia has been observed in patients with severe COVID-19 and after immunisation with the Vaxzevria vaccine. Herein, we discuss the pathogenesis of this disorder focusing on the possible involvement of anti-platelet factor 4 (PF4) autoantibodies.

The amphibian chytrid fungus, Batrachochytrium dendrobatidis (Bd), is an infectious disease responsible for the worldwide decline of amphibian species. To mitigate these declines, it is necessary to identify the various vectors by which the fungus can be transmitted between individuals and populations. The objective of this study was to determine whether adult female mosquitoes can carry and transfer Bd fungal cells. Mosquitoes were exposed to net soaked in a live Bd zoospore suspension to determine whether they are able to externally acquire the fungus. Another group was placed into containers with a sterile and Bd-inoculated agar plate to determine whether mosquitoes could transfer Bd between these surfaces. Bd DNA was found to be present on mosquito legs exposed to inoculated netting and agar plates suggesting that Bd can be transmitted by the mosquito over short distances This is the first study to demonstrate that an insect host may be a mechanical vector of Bd and suggests that we should begin to consider the role of mosquitoes in the dissemination and control of the fungus.

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter $\mathsf{\gamma }$—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

Three-phase to single-phase matrix converter (TSMC) is used as the transmitter for transient electromagnetic method (TEM) instrument, instead of an AC-DC converter and an H-bridge inverter. The transmitter produces excitation current for primary field. The output current has a waveform with a bipolar square of half duty cycle, which is required by TEM. The space vector PWM based on input currents is discussed in detail. The control method of input power factor is proposed for TSMC. The simulation model of TSMC is established and the simulations of different conditions are completed. The simulation results show the controllability of the input power factor is illustrated.

Microbiome research has gained considerable interest due to the emerging evidence of its impact on human and animal health. Similar to higher organisms, the gut-associated microbiota of mosquitoes affect host fitness and other phenotypes. It is now well established that microbes can alter pathogen transmission in mosquitoes, either positively or negatively, and avenues are being explored to exploit microbes for vector control. However, less attention has been paid to how microbiota affect phenotypes that impact vectorial capacity. Several mosquito and pathogen components, such as vector density, biting rate, survival, vector competence and pathogen extrinsic incubation period all influence pathogen transmission. Interestingly, the mosquito gut-associated microbes can impact each of these components, and therefore ultimately modulate vectorial capacity. Promisingly, this expands the options available to exploit microbes for vector control by also targeting parameters that affect vectorial capacity. However, there are still many knowledge gaps in the biology of the mosquito – microbe symbiosis that need to be addressed in order to understand these interactions more thoroughly and exploit them efficiently. Here, we review current evidence of the impacts of the microbiome on aspects of vectorial capacity highlighting opportunities for novel vector control strategies and areas where further studies are required.

Doubly Fed Induction Generator (DFIG) has a stator winding directly coupled with grid. Whereas, rotor winding is connected via a fault-prone back to back power converters. DFIG is known to be vulnerable to the grid faults. In early times, when a fault occurred, these generators were required to disconnect from the grid to secure the generator and power converters. However, due to the increased penetration of wind turbines into the power system, grid operators demanded that the wind turbines remain connected to the grid, as disconnecting them would further disrupt the grid. When a fault at the grid terminal occur, a high stator current is induced which further result in high rotor current. This current will trigger the DC-link voltage to rise. This high currents and DC-link voltage will cause harm to the converters. Thus, in this paper work, the crowbar protection system is employed for protecting the converters against excess energy. Furthermore, the analysis of DFIG is rendered by integrating the crowbar protection with the Battery Energy Storage System (BESS) for a much effective outcome in enhancing machine to drive the fault. MATLAB-Simulink software is used for modeling and simulation. All system parameters are obtained from ADAMA-II Wind Farm.

The holistic approach of One Health, which sees human, animal, plant, and environmental health as a unit, rather than discrete parts, requires not only interdisciplinary cooperation, but standardized methods for communicating and archiving data, enabling participants to easily share what they have learned and allow others to build upon their findings.Ongoing work by NCBI and the GenomeTrakr project illustrates how open data platforms can help meet the needs of federal and state regulators, public health laboratories, departments of agriculture, and universities. Here we describe how microbial pathogen surveillance can be transformed by having an open access database along with Best Practices for contributors to follow. First, we describe the open pathogen surveillance framework, hosted on the NCBI platform. We cover the current community standards for WGS quality, provide an SOP for assessing your own sequence quality and recommend QC thresholds for all submitters to follow. We then provide an overview of NCBI data submission along with step by step details. And finally, we provide curation guidance and an SOP for keeping your public data current within the database. These Best Practices can be models for other open data projects, thereby advancing the One Health goals of Findable, Accessible, Interoperable and Re-usable (FAIR) data.

Respiratory transmission is the primary route of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Angiotensin I converting enzyme 2 (ACE2) is the known receptor of SARS-CoV-2 surface spike glycoprotein for entry into human cells. A recent study reported absent to low expression of ACE2 in a variety of human lung epithelial cell samples. Three bioprojects (PRJEB4337, PRJNA270632 and PRJNA280600) invariably found abundant expression of ACE1 (a homolog of ACE2 and also known as ACE) in human lungs compared to very low expression of ACE2. In fact, ACE1 has a wider and more abundant tissue distribution compared to ACE2. Although it is not obvious from the primary sequence alignment of ACE1 and ACE2, comparison of X-ray crystallographic structures show striking similarities in the regions of the peptidase domains (PD) of these proteins, which is known (for ACE2) to interact with the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. Critical amino acids in ACE2 that mediate interaction with the viral spike protein are present and organized in the same order in the PD of ACE1. In silico analysis predicts comparable interaction of SARS-CoV-2 spike protein with ACE1 and ACE2. In addition, this study predicts from a list of 1263 already approved drugs that may interact with ACE2 and/or ACE1, potentially interfere with the entry of SARS-CoV-2 inside the host cells and alleviate the symptoms of Coronavirus disease (COVID-19).

With the rapid progress of genetic engineering and gene therapy, World Anti-Doping Agency has alerted to gene doping and prohibited its use in sports. However, there is no standard method available yet for detection of transgenes delivered by recombinant adenoviral (rAdV) vectors. Here we aimed to develop a detection method for transgenes delivered by rAdV vectors in a mouse model that mimics gene doping. rAdV vectors containing mCherry gene was delivered in mice through intravenous injection or local muscular injection. After five days, stool and whole blood samples were collected, and total DNA was extracted. As additional experiments, whole blood was also collected from mouse tail tip until 15 days from injection of the rAdv vector. Transgene fragments from different DNA samples were analyzed using semi-quantitative PCR (sqPCR), quantitative PCR (qPCR), and droplet digital PCR (ddPCR). In the results, transgene fragments could directly be detected from blood cell fraction-DNA, plasma-cell free DNA and stool-DNA by qPCR and ddPCR, depending on specimen type and injection methods. We observed that a combination of blood cell fraction-DNA and ddPCR was more sensitive than other combinations used in this model. These results could accelerate the development of detection methods for gene doping.

Solar energy is a renewable resources of energy which is broadly utilized and have the least pollution impact between the available alternatives of fossil fuels. In this investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support vector machine (LSSVM) are used to build the models for prediction of the thermal performance of a photovoltaic-thermal solar collector (PV/T) by estimating its efficiency as an output of the model while inlet temperature, flow rate, heat, solar radiation, and heat of sun are input of the designed model. Experimental measurements was prepared by designing a solar collector system and 100 data extracted. Different analyses are also performed to examine the credibility of the introduced approaches revealing great performance. The suggested LSSVM model represented the best performance regarding the mean squared error (MSE) of 0.003 and correlation coefficient (R²) value of 0.99, respectively.