Koalas (Phascolarctos cinereus) are native Australian marsupials whose populations are in decline from a range of threats. Infectious diseases caused by the bacterium Chlamydia pecorum and other pathogens are of particular concern. We analysed 26 poly-A selected RNA-sequencing libraries from a data set designed to study the immune response of koalas to ocular chlamydial infection. Using virus discovery techniques, we identified the coding-complete genome sequence of a novel picorna-like virus, denoted Burpengary virus, that was most common in south-east Queensland. Notably, abundance measurements of the virus across all 26 libraries revealed an inverse relationship in prevalence with ocular disease in Koalas, suggesting that co-infection between Burpengary virus and Chlamydia pecorum is inhibited.

Without protective and/or therapeutic agents the SARS-CoV-2 infection known as coronavirus disease 2019 (COVID-19) is quickly spreading worldwide. It has surprising transmissibility potential, since it could infect all ages, gender, and human sectors. It attacks respiratory, gastrointestinal, urinary, hepatic, and endovascular systems and can reach the peripheral nervous system (PNS) and central nervous system (CNS) through known and unknown mechanisms. The reports on the neurological manifestations and complications of the SARS-CoV-2 infection are increasing exponentially. Herein, we enumerate seven candidate routes, which the mature or immature SARS-CoV-2 components could use to reach the CNS and PNS, utilizing the within-body crosstalk between organs. The majority of SARS-CoV-2 infected patients suffer from some neurological manifestations (e.g., confusion, anosmia, and ageusia). It seems that although the mature virus did not reach the CNS or PNS of the majority of patients, its unassembled components and/or the accompanying immune-mediated responses may be responsible for the observed neurological symptoms. The viral particles and/or its components have been specifically documented in endothelial cells of lung, kidney, skin, and CNS. This means that the blood-endothelial-barrier may be considered as the main route for SARS-CoV-2 entry into the nervous system, with the barrier disruption being more logical than barrier permeability, as evidenced by postmortem analyses.

Tilapia lake virus (TiLV) is the main tilapia-infecting virus worldwide, causing serious economic losses. However, there is no vaccine for this viral disease. Here, TiLV ORF10 (TiLV-ORF10) encoding a protein with abundant epitopes was constructed into the eukaryotic expression vector pcDNA3.1, and used to evaluate the immune protective effects in Nile tilapia (Oreochromis niloticus). RT-PCR and western blot analyses confirmed vaccine plasmid expression in tilapia muscle tissues. Moreover, the transcription levels of immunoglobulin M, toll-like receptor 2, myeloid differentiation factor 88, interleukin 8, tumor necrosis factor alpha, gamma-IFN, and nuclear factor κB immune-related genes were statistically significantly upregulated in the spleen, liver, and kidney of vaccinated tilapias (P < 0.05). TiLV challenge experiments showed that relative percent survival (RPS) was significantly enhanced in fish by this DNA vaccine. Moreover, RPS was enhanced further when using a higher amount of the DNA vaccine (85.72% RPS at a DNA dose of 45 μg pcDNA3.1–ORF10). Vaccination with pcDNA3.1–ORF10 significantly reduced virus replication, as evidenced by the low amount of virus in the spleen, liver, and kidney of vaccinated tilapias after TiLV challenge. Thus, pcDNA3.1–ORF10 could induce protective immunity in tilapia and may be a potential vaccine candidate for controlling diseases caused by TiLV.

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.

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.

Polydiacetylene (PDA) inserted in films or in vesicles have received increasing attention due to PDA property to undergo a blue-to-red colorimetric transition along with a change from non-fluorescent to fluorescent upon application of various stimuli. In this review paper, the principle of the detection of various microorganisms (bacteria: directly detected or detected through the emitted toxins or through their DNA, and viruses) and of antibacterial and antiviral peptides based on these responsive PDA vesicles are detailed. The obtained analytical performances, when vesicles are in suspension or immobilized, are given and compared to those of the responsive vesicles mainly based on the vesicle encapsulation method. Many future challenges are then discussed.

Background:Although highly strict social distancing and viral spread protection guidelines are in force, the reported numbers of COVID-19 cases across the world are still increasing. This indicates that we are still unable to completely understand the transmission routes of SARS-CoV-2. One of the possible routes that can play a significant role is the fecal-oral transmission since SARS-CoV-2 can replicate in the intestines as demonstrated by isolation of infectious virus from fecal samples of COVID-19 cases. Scope and approach:In this review, we compare the characteristics of SARS-CoV-2 with the distinctive characteristics of enteric foodborne viruses. We also discuss and respond to the arguments given in some reports that downplay the importance of foodborne transmission route of SARS-CoV-2. Key findings and conclusions:Enteric viruses such as human noroviruses (HuNoVs) and hepatitis A virus (HAV) are known to transmit through foods such as fresh produce and berries, leading to frequent multistate foodborne disease outbreaks all over the world. SARS-CoV-2 was found to share four distinctive characteristics of foodborne viruses that allow them to transmit through foods. This similarity in characteristics, recent report of detecting SARS-CoV-2 particles from frozen food packages in China, and recent suspected foodborne COVID-19 case in New Zealand, indicate that foodborne transmission of SARS-CoV-2 is more evident than previously thought possible. To support or deny this route of transmission, urgent research needs to be undertaken to answer two primary questions and many secondary ones as described in this review.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the etiological agent of the current pandemic worldwide. The pathological condition induced by this pathogen is known as COVID-19 disease. SARS-CoV-2 associated pandemic has been defined as a “public health emergency of international concern” by the International Health Regulation Emergency Committee of the World Health Organization. To date, considerable efforts are in progress to develop more advanced strategies against SARS-CoV-2. Despite the numerous scientific studies published, our knowledge regarding this pathogen is still incomplete, as this virus has been identified only recently. Therefore, scientific investigation of the SARS-CoV-2 has been possible only for a short period of time and effective management of the serious forms of this disease is still lacking. Considerable efforts are in progress worldwide with the purpose to develop more advanced strategies against this pathogen. In this review, we have analyzed the structural and the biological SARS-CoV-2 characteristics and those of other well-known RNA viruses, with the aim to identify possible similarities and analogies between all these pathogens, may be a very useful approach. These infectious agents have been widely studied since several years ago and, a large series of scientific reports are available in the literature regarding this topic. Therefore, focusing on the Human Immunodeficiency Virus (HIV), Hepatitis C Virus (HCV) and Influenza viruses (IVs), we have collected their historical data, clinical manifestations, pathogenetic mechanisms and related infections. Taking advantage of the results of our research, we have assembled this narrative review, with the aim to get useful insights and lessons from HIV, HCV and IVs characteristics and, consequently, to transfer the obtained knowledge to the study of SARS-CoV-2 biology. There are well known differences between all these pathogens. In particular, they present a distinct mode of transmission, as SARS-CoV-2 and Influenza viruses are airborne pathogens, whereas HIV and HCV are bloodborne infectious agents. However, these viruses exhibit some potential common clinical manifestations and pathogenetic mechanisms and their understanding may contribute to establishing preventive measures and new therapies against SARS-CoV-2. Accordingly, we have analysed and discussed the following points: 1) the biology, the pathogenesis and the clinical manifestations of SARS-CoV-2, HIV, HCV and IVs in mankind; 2) the onset and spreading of pandemics caused by respiratory viruses according to a perspective historical point of view; 3) the possible development of a persistent SARS-CoV-2 reservoir worldwide; 4) the possibility of SARS-CoV-2 reinfection/reactivation; 5) the possible involvement and impact of climatic factors in increasing the risk of SARS-CoV-2 spreading.

BK polyomavirus (BKPyV) causes a lifelong chronic infection and is associated with debilitating disease in kidney transplant recipients. Despite its importance, aspects of the virus life cycle remain poorly understood. In addition to the structural proteins, the late region of the BK genome encodes for an auxiliary protein called agnoprotein. Studies on other polyomavirus agnoproteins have suggested that the protein may contribute to virion infectivity. Here, we demonstrate an essential role for agnoprotein in BK virus release. Viruses lacking agnoprotein do not propagate to wild-type levels and fail to release from host cells. Despite this, loss of agnoprotein does not impair virion infectivity or morphogenesis. Instead, agnoprotein expression correlates with nuclear egress of BK virions. We demonstrate that the agnoprotein binding partner α-SNAP is necessary for BK virion release, and siRNA knockdown of α-SNAP prevents nuclear release of wild-type BK virions. These data highlight a novel role for agnoprotein and begin to reveal the mechanism by which polyomaviruses leave an infected cell.

Interferon regulatory factor 5 (IRF5) is known to be involved in the innate immune response and pro-inflammatory cytokines. However, the roles of IRF5 in immune responses in Malabar grouper (Epinephelus malabaricus) have not been extensively explored. In this study, IRF5 gene was identified and characterized from M. grouper. The full-length IRF5 cDNA consisted of a 5’ terminal untranslated region (5’-UTR) of 289 bp and a 3’-UTR of 542 bp, an open reading frame (ORF) of 1500 bp encoding a polypeptide of 499 amino acids with a predicted molecular mass of 56.28 kDa and isoelectric point (pI) of 5.2. The putative MgIRF5 protein consists of four important conserved domains: a helix DNA-binding domain (DBD) at the N-terminus, a middle region, an IRF association domain (IAD) and a virus activated domain (VAD) at the C-terminus. Sequence alignment and phylogenetic analysis showed that highest sequence similarity of IRF5 was observed between the IRF5 genes from Oplegnathus fasciatus and Miichthys miiuy. The mRNA transcripts of IRF5 were detected in a wide range of tissues types from healthy M. grouper with highest expression in muscle, liver and skin. After treatment with poly (I: C), it was significantly up-regulated in spleen and liver tissues. When infected with NNV, the expression level of MgIRF5 was up-regulated in spleen and head kidney and their transcriptional responses to IRF5 increased in the grouper kidney cells. This approach suggests that MgIRF5 is important in the underlying mechanism of the innate immune responses against antiviral response.

Aim. The purpose of the work was the development of a machine learning model for diagnosing the stage of liver fibrosis in patients with chronic viral hepatitis C according to the data of routine clinical examination. Materials and methods. A total of 1240 patients with chronic viral hepatitis C was examined. A set of data obtained from 689 patients balancing by the stage of liver fibrosis was used for developing and testing machine learning models. 9 routine clinical parameters were selected as the most important predictors for determining the likelihood of liver fibrosis the 3–4 stages presence: age, height, weight and body mass index of the patient, the number of platelets in the clinical blood test, levels of alanine transaminase, aspartate transaminase, gamma-glutamyltransferase, and total bilirubin in a biochemical blood test. Results. The accuracy of the developed method for determining the 3–4 stages of liver fibrosis in patients with chronic viral hepatitis C in comparison with the «gold standard» of diagnosis (liver biopsy) was 80.56% (95% CI: 69.53–88.94%), sensitivity — 66.67%, specificity — 94.44%. Conclusion. The developed method is an alternative to more expensive and geographically inaccessible studies. The method does not require the purchase of additional equipment or software, as well as additional laboratory tests, when used in real clinical practice. The introduction of the method into clinical practice can help to solve the problem of low material and territorial availability of diagnostic tests and allow determining the stage of liver fibrosis in patients with chronic viral hepatitis C.

Background: Zika virus (ZIKV) infection has significantly affected Latin America in 2015–2017. Most studies have been reported from Brazil and Colombia, and only a few from Central America. For these reasons we analyzed the incidence, incidence rates and evolution of cases in Honduras from 2016–2017. Methods: Using epidemiological weeks (EW) surveillance data on the ZIKV epidemics in Honduras, we estimated incidence rates (cases/100,000 population), and developed maps at national, departmental and municipal levels. Results: From 1 January 2016 to 31 December 2017, a total of 32,607 cases of ZIKV were reported (98.5% in 2016 for an incidence rate of 36.85 cases/100,000 pop; 1% confirmed by RT-PCR). The highest peak was reached on the EW 6°, 2016 (2,559 cases; 29.34 cases/100,000 pop). The department with the highest number of cases and incidence rate was Cortés (13,128 cases, 791.08 cases/100,000 pop in 2016). Discussion: The pattern and evolution of ZIKV infection in Honduras has been similar to that which occurred for chikungunya in 2015. As previously reported, infection with chikungunya involved predominantly the central and capital area of the country, reaching incidences there >750 cases/100,000 pop. Studies using geographical information systems linked with clinical disease characteristics are necessary to attain accurate epidemiological data for public health systems. Such information is also useful for assessment of risk for travelers who visit specific areas in a destination country.

This paper proposes a method of extracting energy from zero-point energy and evaluates the amount of energy gained. In addition, this electric circuit-based approach exhibits the Meissner effect, suggesting a new type of superconductivity that does not require refrigeration. The proposed method can provide extremely large amounts of energy, which is more than a conventional power station, without consuming fossil fuels or emitting radiation. Thus, it has the potential to solve the global energy problem. It involves preparing two electric loops containing diodes and connecting the loops together with current sources. The diodes are oriented in the same direction within each loop but in opposite directions in different loops. With this setup, the currents from the current sources build iteratively within the loops, resulting in large output currents. Our numerical analysis indicates that extremely large electric potentials are produced, which in turn yield large output currents. In addition, we confirm numerically that the voltage is zero around a loop and show analytically that the Meissner effect is present, proving the existence of a new type of superconductivity. Furthermore, when we introduce induction coils to not break the loop’s symmetry, they store extremely large amounts of energy and we can thus obtain energy from them via discharge currents.

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.

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.

The simple sequence repeats (SSRs) are small 1-6bp tandem repeat elements present across diverse genomes and involved in gene regulation and evolution. Presently we analyzed SSRs in genomes of 98 species of family Polyomaviridae across four genera. The genome size ranged from 3962bp (BM87) to 7369bp (BM85) but maximum genomes were in the range of 5 to 5.5 kb. The GC% had an average of 42% and ranged between 34.69 (BM95) to 52.35 (BM81). A total of 3036 SSRs and 223 cSSRs were extracted using IMEx with incident frequency from 18 to 56 and 0 to 7 respectively. The most prevalent mono-nucleotide repeat motif was “T” (48.95%) followed by “A” (33.48%). “AT/TA” was the most prevalent dinucleotide motif closely followed by “CT/TC”. The distribution was expectedly more in coding region with 77.6% SSRs of which nearly half were in Large T Antigen (LTA) gene. Notably, most viruses with humans, apes and related species as host exhibited exclusivity of mono-nucleotide repeats in AT region, a proposed predictive marker for determination of humans as host in virus in course of its evolution. Each genome has a unique SSR signature which is pivotal for viral evolution particularly in terms of host divergence.

The biosynthesis of DNA inherently competes with RNA synthesis because it depends on the reduction of ribonucleotides (RNA precursors) to 2’-deoxyribonucleotides by ribonucleotide reductase (RNR). Hence, RNA viruses can increase viral RNA production in cells by partially blocking the synthesis of DNA, e.g. by downregulating the mammalian selenoprotein thioredoxin reductase (TR), which normally acts to sustain DNA synthesis by regenerating reduced thioredoxin, a hydrogen donor for RNR. Computational and preliminary experimental evidence supports the hypothesis that a number of pathogenic RNA viruses, including HIV-1, Ebola, Zika, some flu viruses, and SARS-CoV-2, target TR isoforms by antisense. TR knockdown would create a host antioxidant defect that could be partially rectified by increased selenium intake, or be exacerbated by selenium deficiency, contributing to viral pathogenesis. There are several non-selenium-dependent means that viruses might also exploit to slow DNA synthesis, such as targeting RNR itself, or components of the glutaredoxin system, which serves as a backup redox system for RNR. HIV-1 substantially downregulates glutathione synthesis, so it interferes with both the thioredoxin and glutaredoxin systems. Computational results suggest that, like Ebola, SARS-CoV-2 targets TR3 by antisense. TR3 is the only TR isoform that includes an N-terminal glutaredoxin domain, so antisense knockdown of TR3 may also affect both redox systems, favoring RNA synthesis. In contrast, some DNA viruses encode their own glutaredoxins, thioredoxin-like proteins and even RNR homologues – so they are doing just the opposite, favoring DNA synthesis. This is clear evidence that viruses can benefit from shifting the RNA:DNA balance to their advantage.

Abstract: Background: Our goal was to evaluate whether wearing personal protective equipment (PPE) such as an N95, or a surgical mask during the (COVID-19) pandemic had really reduced the cases of influenza in the state of Wisconsin. Methods: Respiratory Virus Surveillance Reports from the Wisconsin Department of Health Services (DHS) and the Centers for Disease Control and Prevention (CDC) were used to compare the rates of Influenza during the seasons of 2018-2019 and 2020-2021. Results: The number of cases, hospitalizations, and mortality rates with Influenza had significantly decreased in the 2020-2021 season compared to the Influenza season of 2018-2019. Discussion: Reducing the burden of influenza illnesses, hospitalizations, and deaths on the health care system is imperative. Wearing masks should be addressed for the most vulnerable.

The 2019 Coronavirus Disease (COVID-19) is a major cause of morbidity and mortality worldwide. Since late November 2021, Omicron variant has emerged as the primary cause of COVID-19 and caused a huge increase in the reported incidence around the world. To date, 32-34 spike mutations have been reported, 15 of which were located in the receptor-binding domain that interacts with the cell surface of the host cells, while the rest were located in the N-terminal domain and around the Furin cleavage site. At present, both the genomic and clinical profiles of this novel variant are being investigated. Here, we aim to discuss the recent reports on the transmissibility and severity of Omicron variant from both the genetic and clinical perspectives. Afterward, we also take the chance to deliver our personal view on the topic.

The pandemic history of pathogenic SARS-CoV-2 associated COVID-19 infection began in December 2019, with its emergence in Wuhan, China. Pertaining to its high transmissibility and wide host adaptability, this new and unique human coronavirus spread across the planet affecting almost every country, inflicting 91 million people and causing 1.9 million deaths (as of January 17th, 2021). Limited or negligible pre-existing immunity to multiple SARS-CoV-2 variants has resulted in severe morbidity and mortality worldwide, as well as a record-breaking surge in the use of medical-surgical supplies and personal protective equipments. In response to the global need for effective sterilization techniques, this study evaluated the virucidal efficacy of FATHHOME’s self-contained, ozone-based dry-sanitizing device, by dose and time response assessment. We tested inactivation of human coronavirus, HCoV-OC43, a close genetic model of SARS-CoV-2, on porous (N95 filtering facepiece respirator/FFR) and nonporous (glass) surfaces. We started our assays with 20 ppm of ozone for 10 min exposure, which was able to effectively reduce, 99.8% and 99.9% of virus from glass and N95 FFR surfaces, respectively. Importantly, the virus was completely inactivated, below the detection limit (over 6-log10 reduction) with 25 ppm ozone for 15 mins on both tested surfaces. As expected, a higher ozone concentration (50 ppm) resulted in faster inactivation of HCoV-OC43 with 100% inactivation in 10 mins from both the surfaces, with no residual ozone present after completion of the 5-minute post exposure recapture cycle and no measurable increase in ambient ozone levels. These results confirmed that FATHHOME’s device may provide a safe and viable solution for rapid decontamination of SARS-CoV-2- from worn items, frequently touched items, and PPE including N95 FFRs, face shields and other personal items.

A striking aspect of COVID-19 is the difference in the outcome of the infection between different countries, and different ethnic groups within one country. We surveyed the literature on SARS-CoV-2 complemented with comparative publications on SARS-CoV and other coronaviruses to capture the current understanding of virus – host interactions including virus subtypes, transmission, zoonotic aspects, and potential host determinants.

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.

Covid-19 pandemic lockdown has slow down the world economic system. The pandemic has cleared the roads, close factories and grounded planes causing severe economic challenges. The damaging impact of the pandemic amid lockdown has been a blessing in guise for the environment because of significant drop in pollution level as transport and industrial sectors shutdown. Transport and industrial sectors are major contributors to environmental degradation through various emissions as a result of fossil fuel consumption. Energy consumed by transport and industrial sectors will have to shift to viable, readily available, economically and environmentally friendly with no carbon build up post Covid-19 pandemic. Hydrogen energy remains the best alternative option technologies containing green house gas emission and pollutions of several forms. Hydrogen holds the potential to provide a clean, reliable, renewable and economical source of energy for meeting the growing and unending global energy needs post pandemic. The present paper explores the economic feasibility and potential of hydrogen to serve as a competitive fuel option post pandemic. In this paper, the role of hydrogen as an energy carrier hydrogen economy structure, potential of hydrogen economy, hydrogen production methods, hydrogen application and the economic and environmental importance of hydrogen as a viable fuel option post covid-19 pandemic were discussed. There will be a surge in demand and investment for hydrogen economy post Covid-19.

With an estimated 3 to 5 million human cases annually and the potential to infect domestic and wild animal populations, influenza viruses are one of the greatest health and economic burdens to our society [1] and pose an ongoing threat of large-scale pandemics. Despite our knowledge of many important aspects of influenza virus biology, there is still much to learn about how influenza viruses replicate in infected cells, for instance how they use entry receptors or exploit host cell trafficking pathways. These gaps in our knowledge are due, in part, to the difficulty of directly observing viruses in living cells. In recent years, advances in light microscopy, including super-resolution microscopy and single-molecule imaging, have enabled many viral replication steps to be visualised dynamically in living cells. In particular, the ability to track single virions and their components, in real time, now allows specific pathways to be interrogated providing new insights to various aspects of the virus-host cell interaction. In this review, we discuss how state-of-the-art imaging technologies, notably quantitative live-cell and super-resolution microscopy, are shedding new nanoscale and molecular insights into influenza virus replication and revealing new opportunities for developing antiviral strategies.

Aim: The aim of this study was to use a multi target approach to testing with both serological tests and an in-house real-time molecular test to investigate the prevalence of the caprine arthritis-encephalitis virus (CAEV) in goats from three hobbyist farms in the Republic of Tatarstan, Russia. Materials and Methods: We have approached the detection of using a multi target approach testing with both ELISA and an in-house real-time PCR test to investigate the prevalence of CAEV in goats. Animals from three hobbyist farms were used in this study. The animals from two farms (n=13 for F1 and n=8 for F2) had clinical signs of arthritis and mastitis. In the third farm (n=15 for F3), all goats were homebred and had no contact with imported animals. Results: CAEV antibodies (ELISA targets TM env and gag genes) were detected in serum samples from two farms (F1 and F2), indicating a seroprevalence 87.50-92.31%. Specific CAEV antibodies were also detected in milk samples. CAEV proviral DNA was detected in 53.85-62.50%. Results from all tests performed in the third farm (F3) were negative, indicting all tests were 100% specific.Conclusion: Results of this work show that CAEV is circulating and present in small hobbyist goat farms in Russia. Serological and molecular tests could be of importance for CAEV control and eradication programs in Russia for hobbyist goat farms.

We have analysed the death and recovery rate of Covid-19 disease progression. From the analysis, we have argued that the pandemic is over in certain countries (labelled as group-A) and for other countries (labelled as group-B) the disease appears to remain as endemic. Taking into account the serological survey (sero-survey) test results obtained by certain groups and comparing it with herd immunity threshold value one can infer that the low number of infection for group-B is either due to acquired immunity by some previous infection by other coronavirus or due to innate immunity towards this infection. This effect is stronger for group-B to slow the progress of the disease to such an extent resulting in flattening of the disease progression curve compared to group-A.

As revealed by the recent resurgence of yellow fever virus (YFV) activity in the tropical regions of Africa and South America, YFV control measures need urgent rethinking. Over the last decade, most reported outbreaks occurred in, or eventually reached, areas of low vaccination coverage but suitable for virus transmission, with an unprecedented risk of expansion to densely populated territories in Africa, South America and Asia. As reflected in the World Health Organization’s initiative launched in 2017, it is high time to strengthen epidemiological surveillance to monitor accurately, viral dissemination and redefine vaccination recommendation areas. Vector-control and immunisation measures need to be adapted and vaccine manufacturing must be reconciled with an increasing demand. We will have to face more YF cases in the upcoming years hence, improving disease management through the development of efficient treatments will prove most beneficial. Undoubtedly, these developments will require in-depth descriptions of YFV biology at molecular, physiological and ecological levels. This second section of the two-part review describes the current state of knowledge and gaps regarding the molecular biology of YFV, along with an overview of the tools that can be used to manage the disease at the individual, local and global levels.

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.

In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

Viruses are the major causes of acute and chronic infectious diseases in the world. According to the World Health Organization, there is an urgent need for better control of viral diseases. Re-purposing existing antiviral agents from one viral disease to another could play a pivotal role in this process. Here we identified novel activities of obatoclax and emetine against herpes simplex virus type 2 (HSV-2), human immunodeficiency virus 1 (HIV-1), echovirus 1 (EV1), human metapneumovirus (HMPV) and Rift Valley fever virus (RVFV) in cell cultures. Moreover, we demonstrated novel activities of emetine against influenza A virus (FluAV), niclosamide against HSV-2, brequinar against HIV-1, and homoharringtonine against EV1. Our findings may expand the spectrum of indications of these safe-in-man agents and reinforce the arsenal of available antiviral therapeutics pending the results of further in vivo tests.

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.

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.

: 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.

Introduction Most cases of COVID-19 coronavirus infection occurred in the Chinese city of Wuhan at the end of December 2019 in the form of acute pneumonia. COVID-19 virus infects people of all ages. The most affected elderly people with underlying medical conditions. This may cause various symptoms such as fever, difficulty breathing, lung infection, coughing and sneezing. Aim The aim of the present study to highlight the COVID-19 pandemic in Saudi Arabia. Method A search was conducted using the relevant keywords to retrieve the studies conducted in Saudi Arabia regarding COVID-19. The search was by Google Scholar, Pub MED, and Twitter. Conclusion The COVID-19 pandemic carries multiple-risk, and public health groups, such as the United States' Centres for Disease Control and Prevention (CDC) and the World Health Organisation (WHO), are monitoring the pandemic and posting updates on their website. These groups have also made recommendations on disease prevention and treatment, and according to the Saudi Centres for Disease Control, they have also included the necessary public health measures for mandatory reporting by calling a certain number and through the e-health monitoring network.

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.

Aedes aegypti is inherently susceptible to arboviruses. The geographical expansion of this vector host species has led to the persistence of Dengue, Zika and Chikungunya human infections. These viruses take advantage of the mosquito’s cell to create an environment conducive for their growth. Arboviral infection triggers transcriptomic and protein dysregulation in Ae. aegypti and in effect, host antiviral mechanisms are compromised. Currently, there are no existing vaccines able to protect human hosts from these infections and thus, vector control strategies such as Wolbachia mass release program is regarded as a viable option. Considerable evidence demonstrates how the presence of Wolbachia interferes with arboviruses by decreasing cellular components vital for the pathogen and strengthening antiviral host responses. However, variation in the magnitude of Wolbachia’s viral inhibition that is neither due to strain nor density has been observed. Furthermore, the cellular mechanisms involved in the endosymbiont’s pathogen-blocking differs among hosts. This prompts the need to explore the cellular interactions between Ae. aegypti-arboviruses-Wolbachia and how these interactions overall affect the mosquito’s cell. Understanding what happens at the cellular and molecular level will provide evidence on the sustainability of Wolbachia vector control.

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.

Autophagy is an evolutionarily conserved central process in host metabolism. Among its major functions are conservation of energy during starvation, recycling organelles, and turnover of long-lived proteins. Besides, autophagy plays a critical role in removing intracellular pathogens and very likely represents a primordial intrinsic cellular defence mechanism. More recent findings indicate that it has not only retained its ability to degrade intracellular pathogens, but also functions to augment and fine tune antiviral immune responses. Interestingly, viruses have also co-evolved strategies to manipulate this pathway and use it to their advantage. Particularly intriguing is infection-dependent activation of autophagy with positive stranded (+)RNA virus infections, which benefit from the pathway without succumbing to lysosomal degradation. In this review we summarise recent data on viral manipulation of autophagy, with a particular emphasis on +RNA viruses and highlight key unanswered questions in the field that we believe merit further attention.

Background: After serious epidemics of chikungunya (CHIKV) and Zika (ZIKV) in the Americas, dengue (DENV) have reemerged in most countries. We analyzed the incidence, incidence rates, and evolution of DENV cases in Honduras from 2015-2018 and the ongoing 2019 epidemic. Methods: Using epidemiological weeks (EW) surveillance data on the DENV in Honduras, we estimated incidence rates (cases/100,000 population), and developed maps at national, departmental, and municipal levels. Results: From 1 January 2016 to 21 July 2019, a total of 109,557 cases of DENV were reported, 28,603 in 2019, for an incidence rate of 312.32 cases/100,000 pop this year; 0.13% laboratory-confirmed. The highest peak was reached on the EW 28°, 2019 (5,299 cases; 57.89 cases/100,000 pop). The department with the highest number of cases and incidence rate was Cortes (8,404 cases, 479.68 cases/100,000 pop in 2019). Discussion: The pattern and evolution of DENV epidemic in 2019 in Honduras has been similar to that which occurred for in 2015. As previously reported, this epidemic involved the north and central areas of the country predominantly, reaching municipality incidences there >1,000 cases/100,000 pop (1%). Studies using geographical information systems linked with clinical disease characteristics are necessary to attain accurate epidemiological data for public health systems. Such information is also useful for assessment of risk for travelers who visit specific areas in a destination country.

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.

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.

It is shown that a Mueller quaternion can be formulated as a quaternion with matrix coefficients, and this Mueller quaternion transforms the Stokes quaternion for depolarizing process as well as for nondepolarizing process. Mueller quaternion keeps track of the phase acquired by the Stokes quaternion and, in general, quaternion states of optical media comprises all properties of Jones, Mueller-Jones and Mueller matrices.

We introduce a modelling framework for the investigation of on-line machine learning processes in non-stationary environments. We exemplify the approach in terms of two specific model situations: In the first, we consider the learning of a classification scheme from clustered data by means of prototype-based Learning Vector Quantization (LVQ). In the second, we study the training of layered neural networks with sigmoidal activations for the purpose of regression. In both cases, the target, i.e. the classification or regression scheme, is considered to change continuously while the system is trained from a stream of labeled data. We extend and apply methods borrowed from statistical physics which have been used frequently for the exact description of training dynamics in stationary environments. Extensions of the approach allow for the computation of typical learning curves in the presence of concept drift in a variety of model situations. First results are presented and discussed for stochastic drift processes in classification and regression problems. They indicate that LVQ is capable of tracking a classification scheme under drift to a non-trivial extent. Furthermore, we show that concept drift can cause the persistence of sub-optimal plateau states in gradient based training of layered neural networks for regression.

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.

ClinVar is a web platform that stores around 774k curated entries, which allows exploring genetic variants and their associations with complex phenotypes. A partial set of ClinVar’s genetic associations were reported with conflict of interpretation or uncertain clinical impact significance, which currently challenges clinicians and geneticists. Here, we evaluate the performance of data pre-processing methods combined with classical prediction methods, such as Naive Bayes, Random Forest, and Support Vector Machine to build a meta-prediction model aiming to improve genetic pathogenicity interpretation. Models were trained with ClinVar data (September 2020), and genetic variants were annotated with eight functional impact predictors catalogued with SnpEff/SnpSift (v4.3). A 10-fold cross-validation strategy was performed for evaluation by accuracy, F1-Score, Receiver Operating Characteristic, Area Under Curve. The best meta-prediction model raises by combining one-hot encoding with tree-based classifiers as Random Forest, which shows Area Under Curve ≥ 0,93. We predict pathogenicity for 109k genetic variants, which were found labeled as uncertain significance or conflict of interpretation. Additionally, we implemented AmazonForest (https://www.lghm.ufpa.br/amazonforest), a web tool to query data for a set of 5k variants that were predicted with high pathogenic probability (RFprob >= 0.9).

The lumpy skin disease virus (LSDV) is an animal virus and a member of the Poxviridae family, which causes lumpy skin disease (LSD) in livestock animals like cows and buffaloes. LSD is an important transboundary disease of economic importance that was first discovered in 1929 in Zambia. LSDV has been prevalent in African countries, where several outbreaks have been reported previously. However, the virus has spread rapidly across the Middle East in the past two decades, reaching Russia and, recently, the Asian subcontinent. With the unprecedented cluster outbreaks reported across Asian countries, LSDV is certainly undergoing an epidemiological shift and expanding its geographical footprint globally. The recent LSD outbreaks have gained attention from global regulatory authorities and raised serious concerns among epidemiologists and veterinary researchers. Although there is no dearth of knowledge about LSDV, the disease lacks networked global surveillance and management, consequently making the current statistics deficient, fragmented, and unreliable. Hence, recurrent LSD outbreaks seriously threaten the global livestock industry. This review provides recent insights into LSDV by augmenting latest literature associated with its epidemiology, pathogenesis, transmission, currently-available intervention strategies, and economic implications on the dairy industries. The review also critically examines the changing epidemiological footprint of LSD and speculates on the possible reasons contributing to the ongoing multi-country LSD outbreak.

The rapid development of 2019-2020 Wuhan seafood market pneumonia currently posed a major public health concern in China. Genome sequencing identified a novel beta-coronavirus closely related to SARS-CoV, named 2019-nCoV by WHO, as the cause of this pandemic disease. Viruses with single stranded RNA genome are prone to evolve quickly by accumulation of mutations, such as SNV, INDEL and cross viral recombination, aiding fast transmission among hosts and cross species. Here we collected related genome sequences and investigated variations shared by different strains of 2019-nCoV, identified reoccurrence of SNV mutations in clusters of patients, an indication of rapid evolution of 2019-nCoV at the transmission from animal host to human. The information collected herein would help to understand the dynamics of current pandemic.

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.

Influenza is a disease that poses a significant health burden worldwide. Vaccination is the best way to prevent influenza virus infections. However, conventional vaccines are only effective for a short period of time due to the propensity of influenza viruses to undergo antigenic drift and antigenic shift. The efficacy of these vaccines is uncertain from year-to-year due to potential mismatching between the circulating viruses and vaccine strains, mutations arising due to egg adaptation, and potential contamination of the vaccine virus stock. Subsequently, the inability to store these vaccines long-term and vaccine shortages are challenges that need to be overcome. Conventional vaccines are also less effective for certain populations, including the young, old, and immunocompromised. This warrants for diverse efficacious vaccine developmental approaches, involving both active and passive immunization. As opposed to active immunization platforms (requiring the use of whole or portions of pathogens as vaccines), the rapidly developing passive immunization involves administration of either pathogen-specific or broadly acting antibodies against a kind or class of pathogens as a prophylactic treatment to corresponding acute infection. Several antibodies with broadly acting capacities have been discovered that may serve as means to suppress influenza viral infection and allow the process of natural immunity to engage opsonized pathogens whilst boosting immune system by antibody-dependent mechanisms that bridge the innate and adaptive arms. By that, passive immunotherapeutics approach assumes a robust tool that could aid control of influenza viruses. In this review, we comment on some improvements in influenza management and promising vaccine development platforms, with emphasis on the capacity of passive immunotherapeutics especially when coupled with the use of antivirals in the management of influenza infection.

Avian infectious bronchitis virus (IBV) is the causative agent of infectious bronchitis, which causes considerable economic losses to the poultry industry worldwide. It is imperative to develop safe and efficient candidate vaccines to control IBV infection. In the current study, recombinant baculoviruses co-expressing S1 and N proteins, mono-expressing S1 or N proteins alone of IBV were constructed and prepared into subunit vaccines rHBM-S1-N, rHBM-S1 and rHBM-N. The levels of immune protection of these subunit vaccines were evaluated by inoculating specific pathogen-free (SPF) chickens at 14 days of age, boosting with the same dose 14 days later, and following challenge with a virulent GX-YL5 strain of IBV 14 days post-booster (dpb). The commercial vaccine strain H120 was used as a control. The IBV-specific antibody levels as well as the percentages of CD4+ and CD8+ T lymphocytes were detected within 28 days post-vaccination (dpv). The morbidity, mortality, and re-isolation of virus from the tracheas and kidneys of challenged birds were evaluated at 5 days post-challenge (dpc). The results showed that the IBV-specific antibody levels and the percentages of CD4+ and CD8+ T lymphocyte in rHBM-S1-N group were higher than those of rHBM-S1 and rHBM-N groups, especially the cellular immunity response. At 5 dpc, the mortality, morbidity and virus re-isolation rate of rHBM-S1-N were slightly higher than those of H120 group, but were lower than those of rHBM-S1 group and rHBM-N group. The present study demonstrated that the protection of recombinant baculovirus co-expressing S1 and N proteins was better than that of recombinant baculoviruses mono-expressing S1 or N protein alone. Thus, the recombinant baculovirus co-expressing S1 and N proteins could serve as a potential IBV vaccine and this demonstrates that the bivalent subunit vaccine including the S1 and N proteins might be a strategy for the development of an IBV subunit vaccine.

Primary varicella-zoster virus (VZV) infection causes varicella (chickenpox) and the establishment of a lifelong latent infection in ganglionic neurons. VZV reactivates in about one-third of infected individuals to cause herpes zoster, often accompanied by neurological complications. The restricted host range of VZV and, until recently, the lack of suitable in vitro models to study VZV latency have seriously hampered molecular studies of viral latency. Nevertheless, recent technological advances facilitated a series of exciting studies that resulted in the discovery of a VZV latency-associated transcript (VLT) and have redefined our understanding of VZV latency and factors that initiate reactivation. Together, these findings pave the way for a new era of research that may finally unravel the precise molecular mechanisms that govern latency. In this review, we will summarize the implications of recent discoveries in the VZV latency field from both a virus and host perspective and provide a roadmap for future studies.

Since 1998, when Jim van Etten’s team initiated its characterization, Paramecium bursaria Chlorella virus 1 (PBCV-1) had been the largest known DNA virus, both in terms particle size and genome complexity. In 2003, The Acanthamoeba-infecting Mimivirus unexpectedly superseded PBCV-1, opening the era of giant viruses, i.e. with virions large enough to be visible by light microscopy and genomes encoding more proteins than many bacteria. During the 15 following years, the isolation of many Mimivirus-relatives, have made the Mimiviridae one of the largest and most diverse family of eukaryotic viruses isolated from aquatic environments. Metagenomic studies keep suggesting that many more remain to be isolated. As Mimiviridae members are found to infect an increasing range of phytoplanckton species, their taxonomic position compared to the traditional Phycodnaviridae (i.e. etymologically “algal viruses”) became a source of confusion in the literature. Following a rapid history of the key discoveries that established the Mimiviridae family, we describe its current taxonomic structure and propose a set of operational criteria to help in the classification of future isolates.

The Covid-19 coronavirus, SARS-CoV-2, is inactivated much faster on paper (3h) than on plastic (7d). By classifying materials according to virus stability on their surface, the following list is obtained (from long to short stability): polypropylene (mask), plastic, glass, stainless steel, pig skin, cardboard, banknote, cotton, wood, paper, tissue, copper. These observations and other studies suggest that SARS-CoV-2 may be inactivated by dryness on water absorbent porous materials but sheltered by long-persisting micro-droplets of water on waterproof surfaces. If such physical phenomenons were confirmed by direct evidence, the persistence of the virus on any surface could be predicted, and new porous objects could be designed to eliminate the virus faster.

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.

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.

The ASCE-EWRI reference evapotranspiration (ETo) equation is recommended as a standardized method for reference crop ETo estimation. However, various climate data as input variables to the standardized ETo method are considered limiting factors in most cases and restrict the ETo estimation. This paper assessed the potential of different machine learning (ML) models for ETo estimation using limited meteorological data. The ML models used to estimate daily ETo included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF). Three input combinations of daily maximum and minimum temperature (Tmax and Tmin), wind speed (W) with Tmax and Tmin, and solar radiation (Rs) with Tmax and Tmin were considered using meteorological data during 2003–2016 from six weather stations in the Red River Valley. To understand the performance of the applied models with the various combinations, station, and yearly based tests were assessed with local and spatial approaches. Considering the local and spatial approaches analysis, the LR and RF models illustrated the lowest rate of improvement compared to GEP and SVM. The spatial RF and SVM approaches showed the lowest and highest values of the scatter index as 0.333 and 0.457, respectively. As a result, the radiation-based combination and the RF model showed the best performance with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among models and approaches.

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.

We present the detailed calculations of social distancing requirement. A comparative study of the growth pattern and death tolls in different communities indicates that the growth pattern of infected patients and death rate follow the similar distribution with different parametrizations. Every distribution follows an exponential growth pattern curve, like other microbes, then reaches the saturation point an d eventually decay s However, the argument for the exponential function depends on several parameters unbeknownst, as of yet. However, the slope varies different ial ly for various epicenter s and seems to have a relationship with parameters such as accessibi lity to healthcare facilities, pre existing medical conditions socio economic conditions and lifestyle. The mismatch of the growth pattern is also linked with the impact of various other factors and a premature interpretation of limit ed data. Novel behavi or of the virus brought many surprises, opened up new venues for medical research, and the need for the more detailed study of pathogens in the light of the interaction of RNA and DNA The adaptability to diverse ecological condition s and the relevant modification in the structure is also worth investigation The genetic modification can be studied using quantum mechanical probabilistic approach.

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.

Machine learning (ML) methodologies have been looked upon recently as a potential candidate for mitigating nonlinearity issues in optical communications. In this paper, we experimentally demonstrate a 40-Gb/s 256-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator. By utilizing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.

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 success of many viruses depends upon cooperative interactions between viral genomes. For example, viruses that coinfect the same cell can share essential gene products, such as replicase, the enzyme that replicates the viral genome. However, when cooperation occurs, there is the potential for ‘cheats’ to exploit that cooperation. We suggest that: (1) the biology of viruses makes viral cooperation particularly susceptible to cheating; (2) cheats are common across a wide range of viruses, including viral entities that are already well studied, such as defective interfering genomes, and satellite viruses. Consequently, evolutionary theory developed to explain cheating offers a conceptual framework for understanding and manipulating viral dynamics. At the same time, viruses offer unique opportunities to study how cheats evolve, because cheating is relatively common in viruses, compared with taxa where cooperation is more usually studied, such as animals.

Feline immunodeficiency virus (FIV) is a naturally-occurring retrovirus that infects domestic and non-domestic feline species, producing progressive immune depletion that results in an acquired immunodeficiency syndrome (AIDS). Much has been learned about FIV since it was first described in 1987, particularly in regard to its application as a model to study the closely related lentivirus, human immunodeficiency virus (HIV). In particular, FIV and HIV share remarkable structure and sequence organization, utilize parallel modes of receptor-mediated entry, and result in a similar spectrum of immunodeficiency-related diseases due to analogous modes of immune dysfunction. This review summarizes current knowledge of FIV infection kinetics and mechanisms of immune dysfunction in relation to opportunistic disease, specifically in regard to studying HIV pathogenesis. Furthermore, we present data which highlight changes in the oral microbiota and oral immune system during FIV infection, and outline the potential for the feline model of oral AIDS manifestations to elucidate pathogenic mechanisms of HIV-induced oral disease. Finally, we discuss advances in molecular biology, vaccine development, neurologic dysfunction, and the ability to apply pharmacologic interventions and sophisticated imaging technologies to study experimental and naturally occurring FIV, which provide an excellent, but often overlooked resource for advancing therapies and management of HIV/AIDS.

Survival following Ebola virus (EBOV) infection correlates with the ability to mount an early and robust interferon (IFN) response. The host IFN-induced proteins that contribute to controlling EBOV replication are not fully known. Among the top genes with the strongest early increases in expression after infection in vivo is IFN-induced HERC5. Using a transcription- and replication-competent VLP system, we showed that HERC5 inhibits EBOV virus-like particle (VLP) replication by depleting EBOV mRNAs. The HERC5 RCC1-like domain was necessary and sufficient for this inhibition and did not require zinc finger antiviral protein (ZAP). Moreover, we showed that EBOV (Zaire) glycoprotein (GP) but not Marburg virus GP antagonized HERC5 early during infection. Our data identifies a novel ‘protagonist-antagonistic’ relationship between HERC5 and GP in the early stages of EBOV infection that could be exploited for the development of novel antiviral therapeutics.

The recent resurgence of yellow fever virus (YFV) activity in the tropical regions of Africa and South America has sparked renewed interest in this infamous arboviral disease. YFV had been a human plague for centuries prior to the identification of its urban transmission vector, the Aedes aegypti mosquito species, and the development of an efficient live-attenuated vaccine, the YF-17D strain. The combination of vector-control measures and vaccination campaigns drastically reduced YFV incidence in humans on many occasions, but the virus never ceased to circulate in the forest, through its sylvatic invertebrate vector(s) and vertebrate host(s). Outbreaks recently reported in Central Africa (2015-2016) and Brazil (since late 2016), reached considerable proportions in terms of spatial distribution and total numbers of cases, with multiple exports, including to China. In turn, questions regarding the likeliness of occurrence of large urban YFV outbreaks in the Americas or of a successful import of YFV to Asia are currently resurfacing. This two-part review describes the current state of knowledge and gaps regarding the molecular biology and transmission dynamics of YFV, along with an overview of the tools that can be used to manage the disease at the individual, local and global levels.

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.

Based on comprehensible non-parametric methods, estimates of crucial parameters that characterise the COVID-19 pandemic with a focus on the German epidemic are presented. Where appropriate, the estimates for Germany are compared with the results for seven other countries (FR, IT, US, UK, ES, CH, BR) to get an idea of the breadth of applicability and a relational understanding. Thereby, only prevalence data of daily reported new counts of diagnosed cases and fatalities provided by the Johns Hopkins University are used. Drawing on uncertain a priori knowledge is avoided. Specifically, we present estimates resulting from delay-time correlations for the duration from diagnosis to death being 13 days for Germany and Switzerland. The delay-time correlation applied to time series from other countries exhibit less pronounced peaks suggesting high variabilities for the corresponding time-to-death durations. With respect to the German data, the two time series of new cases and fatalities exhibit a strong coherence within the frequency range of interest, which backs our findings. Furthermore, based on the knowledge of this time lag between diagnoses and deaths, properly delayed asymptotic as well as instantaneous fatality-case ratios are calculated having superiority compared to the commonly published case-fatality rate. The temporal median of the instantaneous fatality-case ratio with proper delay of 13-days between cases and deaths for Germany turns out to be 0.02. Time courses of asymptotic fatality-case ratios are presented for other countries which substantially differ during the first half of the pandemic, however, converge to a narrow range with standard deviation 0.57% and mean 2.4%. Additionally, the time courses of instantaneous fatality-case ratios with optimal delay for the 8 exemplarily chosen countries are calculated and compared by means of the temporal medians. Similarly to the asymptotic fatality-case ratios, the differences are much smaller than expected from mass media reports. The basic reproduction number, R₀, for Germany is estimated to be between 2.4 and 3.4. The uncertainty stems from uncertain knowledge of the generation time. A delay autocorrelation shows resonances at about 4 days and 7 days, where the latter resonance is at least partially attributable to the sampling process with weekly periodicity. The calculation of the basic reproduction number is based on an evaluation of cumulative numbers of cases yielding time-dependent doubling times as an intermediate step. This allows to infer to the reproduction number during the early phase of onset of the epidemic. In a second approach, the instantaneous reproduction number is derived from the incident (counts of new) cases and allows, in contrast to the first version, to infer to the temporal behaviour of the reproduction number during the later epidemic course. The time course of the reproduction number is compared to an alternative control measure given by the per capita growth, which largely confirms the conclusions drawn from the reproduction number. To conclude, by avoiding complicated parametric models we provide insights into basic features of the COVID-19 epidemic in an utmost transparent and comprehensible way. The perhaps most striking insight is that the fatality-case ratios do not differ between countries as much as previously suspected.

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

We present the generation and nonlinear frequency conversion of nanosecond cylindrical vector beams (CVBs) in two-mode fiber (TMF). Based on the polarized dependence vector mode coupling characteristic and the precise wavelength tunability of an acoustically-induced fiber grating (AIFG), the nanosecond cylindrical vector beams (CVBs, 1064 nm, 10 ns) is directly generated in a TMF with convenient switching characteristics between the radial and azimuthal vector beams. Furthermore, the stimulated Raman scattering (SRS) is produced based on the transmission of the nanosecond CVBs in the 100-meters long TMF, and the spatial intensity and polarization distributions characteristics the 1st-order Stokes shifted component is consistent with the nanosecond CVBs pump pulse. This work provides a method for achieving wavelength conversion of the CVBs in optical fiber.

The emergence of proteins in the prebiotic world was a watershed event at the origin of life. With their astonishing versatility, the protein enzymes catalyzed crucial biochemical reactions within protocells into more complex biomolecules in diverse metabolic pathways, whereas structural proteins provided strength and permeability in the cell membrane. Five major biochemical innovations followed in succession after availability of various kinds of protein molecules during decoding and translation of mRNAs. These are: (1) the modification of the phospholipid membrane into the plasma membrane; (2) the origin of primitive cytoplasm; (3) primitive gene regulation; (4) the beginnings of the virus world; and (5) the advent of DNA. The creative role of viruses during prebiotic synthesis led to the origin of the DNA world, when DNA replaced mRNA as the major genome of the protocells. With the advent of DNA, replication of information was entirely dissociated from its expression. Because DNA is much more stable than mRNA with more storage capacity, it is a superb archive for information systems in the form of base sequences. DNA progressively took over the replicative storage function of mRNA, leaving the latter for protein synthesis. Genetic information began to flow from DNA to mRNA to protein in a two-step process involving transcription and translation. In the biological stage, DNA replication was central to the binary fission of the first cell, orchestrated by the duplication of genomes and then the division of the parent cell into two identical daughter cells. With the onset of binary fission, the population of primitive cells grew rapidly in the hydrothermal vent environment, undergoing Darwinian evolution and diversification by mutation. The habitat of the earliest fossil record (≥ 3.5 Ga) from the Archean sedimentary rocks of Canada, Greenland, Australia, South Africa, and India offers a new window on the early radiation of microbial life. The development of anoxygenic and then oxygenic photosynthesis from early hyperthermophiles would have allowed life to escape the hydrothermal setting to the mesophilic global ocean.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that causes the new Coronavirus disease (COVID-19). The symptoms range from mild to severe with a higher incidence of severe cases seen in patients with risk factors such as older age and comorbidities. COVID-19 is mainly spread through the inhalation of respiratory droplets from coughing or sneezing or via contact with droplet-contaminated surfaces. Paramedics should be aware that some aerosol generating procedures (AGPs) may put them at a higher risk of contracting the virus via possible airborne transmission. The use of remote triage clinical assessment is likely to increase as a result of the pandemic. There is no curative drug treatment for the virus and some medications may exacerbate its effects or make patients more susceptible to it. Paramedics should accept that feeling stressed by the pandemic is a natural response. Official guidelines and advice are evolving continually as the evidence on SARS-CoV-2 and COVID-19 grows. Paramedics should keep up to date with the latest clinical guidance from their employers.

Dengue virus infections are a serious public health problem worldwide. Aedes aegypti is the primary vector of dengue in Cuba. Since there is no vaccine or specific treatment, the control efforts are directed to reduce mosquito populations. The indiscriminate use of pesticides can lead to increase insecticide resistance as well as adverse effects on human health. The sterile insect technique is a species-specific and environmental friendly method of insect control based on the release of large numbers of sterile males. The success of this technique in sustainable control of agricultural pests has encouraged its evaluation for mosquito control. Here, we describe an open field trial to evaluate the effect of the release of irradiated males on a wild population of Aedes aegypti. The case-control study was performed in a suburb of Havana, and compared the mosquito population before and after the intervention, in both control and treated areas. The wild population was monitored by an ovitrap network, recording frequency and density of eggs as well as their hatch rate. A significant induced sterility was observed in the field population, compared to the control. The ovitrap index and the mean eggs/ trap declined dramatically after an expected lag period of twelve and five weeks, respectively. For the last three weeks, no egg was collected in the treated area, evidencing a significant suppression of the wild population. We conclude that the sterile males released competed successfully, and induced enough sterility to suppress the local Aedes aegypti population.

Although phylogenetic analysis shows coronaviruses (CoV) share similar genome sequences, CoVs encode different number of proteins (5 to 14), which has implication on viral pathogenicity and infection. Here, we aimed to identify (in-silico) the similarities between different members of coronavirus family. The analysis included 50 coronavirus proteomes, including SARS-CoV-2 (COVID-19), to find the variation of the number of protein functional motifs and domain in each coronavirus. For this role, we used the experimentally validated domain (motif) that known to be crucial for viral infection. Although human CoVs are classified within one genus, we found variations among them. SARS-CoV-1, SARS-CoV-2 and MERS-CoV encode different type of domains, which has implications on the molecular interactions triggered by each virus within human cells. Secondly, we used functional motifs to reconstruct the potential molecular pathways or interactions triggered by SARS-CoV-2 proteins within human cell.

Evaporation is one of the main processes in the hydrological cycle, and it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, the evaporation is a complex and nonlinear phenomenon; therefore, the data-based methods can be used to have precise estimations of it. In this regard, in the present study, Gaussian Process Regression (GPR), Nearest-Neighbor (IBK), Random Forest (RF) and Support Vector Regression (SVR) were used to estimate the pan evaporation (PE) in the meteorological stations of Golestan Province, Iran. For this purpose, meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W) and sunny hours (S) collected from the Gonbad-e Kavus, Gorgan and Bandar Torkman stations from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The outcome indicates that the optimum state of Gonbad-e Kavus, Gorgan and Bandar Torkman stations, Gaussian Process Regression (GPR) with the error values of 1.521, 1.244, and 1.254, the Nearest-Neighbor (IBK) with error values of 1.991, 1.775, and 1.577, Random Forest (RF) with error values of 1.614, 1.337, and 1.316, and Support Vector Regression (SVR) with error values of 1.55, 1.262, and 1.275, respectively, have more appropriate performances in estimating PE. It found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W, and S had the most accurate performances and proposed for precise estimation of PE. Due to the high rate of evaporation in Iran and the lack of measurement instruments, the findings of the current study indicated that the PE values might be estimated with few easily measured meteorological parameters accurately.

Remaining service life (RSL) of pavement, as a sign of future pavement performance, has always received growing attention from pavement engineers. The RSL describes the time from the moment of pavement inspection until such a time when a major repair or reconstruction is required. The conventional approach to determining RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise users' safety. In this paper, surface distresses of pavement have been used to estimate the pavement’s RSL in order to eliminate the aforementioned problems and challenges. To implement the proposed theory, 105 flexible pavement segments were taken from Shahrood-Damghan Highway (Highway 44) in Iran. For each pavement segment, the type, severity, and extent of surface damage and pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include Support Vector Regression (SVR), Support Vector Regression Optimized by Fruit Fly Optimization Algorithm (SVR-FOA), and Gene Expression Programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The Correlation Coefficient (CC), Nash-Sutcliffe efficiency (NSE), Scattered Index (SI), and Willmott’s Index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.

During the last decades West Nile Virus (WNV) outbreaks have continuously occurred in the Mediterranean area. In August 2020 a new WNV outbreak affected 71 people with meningoencephalitis in Andalusia and 6 more cases in Extremadura (south-west of Spain), causing a total of eight deaths. The whole genomes of four viral isolates were obtained and phylogenetically analyzed in the context of recent outbreaks. The Andalusian viral samples belonged to the lineage 1 and were relatively similar to previous outbreaks occurred in the Mediterranean region. Here we present a detailed analysis of the outbreak, including an extensive phylogenetic study.

An outbreak of a virus-like disease has caused severe damage to noni plants (Morinda citrifolia L.) in Xishuangbanna area of China's southwestern Yunnan province since 2015. The diseased plants displayed typical mosaic symptom with light and dark green patches on leaves. Flexuous filamentous virus particles of about 800 nm in length were observed from the leaf saps by transmission electron microscope. Illumina transcriptomic sequencing further revealed the presence of a potyvirus and its near complete genome was obtained from de novo assembly. The complete genome of 9,659 nts was obtained by Sanger sequencing of eight amplicons generate by RT-PCR and 5’ and 3’ RACE. BLASTp analysis of the polyprotein sequence showed that the virus was most closely related to Tobacco vein banding mosaic virus (TVBMV), but these two viruses only shared 50.7% amino acid sequence similarity. Both phylogenetic analyses of the polyprotein and CP amino acid sequences indicated that this virus is a member of genus Potyvirus. However, the low sequence homology with all known potyviruses established this virus as a new species in the genus, tentatively named as Noni mosaic virus (NoMV). Our field surveys showed that 100% of the symptomatic samples and 28.57% of the asymptomatic samples were infected with this novel potyvirus. Aphids collected from diseased leaves were also detected carrying the virus. In summary, our data indicated that a novel species of potyvirus, NoMV, is prevalent in Yunnan, China and is associated with an emerging mosaic disease on M. citrifolia.

Porcine reproductive and respiratory syndrome virus (PRRSV) infections cause significant economic losses to swine producers every year. Aerosols containing infectious PRRSV are an important route of transmission, and proper treatment of air could mitigate the airborne spread of the virus within and between barns. Previous bioaerosol studies focused on the microbiology of PRRSV aerosols; thus, the current study addressed the engineering aspects of virus aerosolization and collection. Specific objectives were to (1) build and test a virus aerosolization system, (2) achieve a uniform and repeatable aerosol generation and collection throughout all replicates, (3) identify and minimize sources of variation, (4) verify that the collection system (impingers) performed similarly. The system for virus aerosolization was built and tested (Obj. 1). The uniform airflow distribution was confirmed using a physical tracer (<12% relative standard deviation) for all treatments and sound engineering control of flow rates (Obj. 2). Theoretical uncertainty analyses and mass balance calculations showed <3% loss of air mass flow rate between the inlet and outlet (Obj. 3). A comparison of TCID₅₀ values among impinger fluids showed no statistical difference between any two of the three trials (p-value = 0.148, 0.357, 0.846) (Obj. 4). These results showed that the readiness of the system for research on virus aerosolization and treatment (e.g., by ultraviolet light), as well as its potential use for research on other types of airborne pathogens and their mitigation on a laboratory scale.

The current SARS- CoV-2 pandemic underscores the importance of understanding the evolution of RNA genomes. While RNA is subject to the formation of similar lesions as DNA, the evolutionary and physiological impacts RNA lesions have on viral genomes are yet to be characterized. Lesions that may drive the evolution of RNA genomes can induce breaks that are repaired by recombination or can cause base substitution mutagenesis, also known as base editing. Over the past decade or so, base editing mutagenesis of DNA genomes has been subject to many studies, revealing that exposure of ssDNA is subject to hypermutation that is involved in the etiology of cancer. However, base editing of RNA genomes has not been studied to the same extent. Recently hypermutation of single-stranded RNA viral genomes have also been documented though its role in evolution and population dynamics. Here, we will summarize the current knowledge of key mechanisms and causes of RNA genome instability covering areas from the RNA world theory to the SARS- CoV-2 pandemic of today. We will also highlight the key questions that remain as it pertains to RNA genome instability, mutations accumulation, and experimental strategies for addressing these questions.

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.

A case report of the diagnosis of Long Term Sequelae of West Nile Neuroinvasive Disease in a patient with 9 years history. Empirical data of symptoms and test reports has been presented and based on the available data likely pathogenesis of the disease has been discussed. The empirical data has been compared to the published literature to reach a highly confident diagnosis.

Viruses were classically named after the very same Latin word virus, originally meaning poison or venom. Public understanding of viruses reinforces their “malign” aspects, especially nowadays under the COVID-19 global pandemic. It is our aim here to propose a new way to view viruses and understand their origins and evolution. First, viruses are the most abundant biological systems found on Earth. They can be found almost everywhere and form a subtle biological layer named virosphere. Second, viruses are probably the most important drivers of molecular evolution and they are active agents of ecosystems maintenance and homeostasis, allowing and driving their dynamic modification. A significant number of eukaryotic genomes are composed by genome elements similar to viruses and these endogenous viruses are continuously acting for our equilibrium and fitness. They are responsible for the origin of species-specific orphan genes that allow adaptation through the development of specific traits in separate lineages of eukaryotes. Accumulated evidence indicate that a viral infection was responsible to create the eukaryotic nucleus and, also, it is a syncytium structure caused by viral replication that allows the formation of the placenta. Therefore, viruses were fundamental for the evolutionary fate of eukaryotes and mammals. The presence of virus-specific genes that are absent in cellular organisms indicates that viruses existed before cells. Besides, such as progenotes, viruses are simply ribonucleoproteic entities and their capsids are orders of magnitude simpler than proteolipidic membranes. Here, we (i) propose a complete scenario to describe the major transitions in prebiotic evolution, (ii) present the possibility that viruses emerged before LUCA, and (iii) suggest that viruses originated at the age of progenotes. However, viruses do not form a monophyletic clade. They should be seen as an evolutionary stable strategy recurrently achieved by biological systems to survive. We propose that the word “VIRUS”, known as venom, is historically mistaken and introduce a new interpretation for their name as an acronym for “Very Important Replicator Unit and Symbiont”. But more than being “very important”, viruses are of “Utmost” relevance for the maintenance of life in biosphere, by which reason we suggest referring to them as “UIRUS” to reinforce their incredible role in symbiosis and their beneficial characteristics over the infectious ones.

There are many types of coronavirus on the bases of important hosts including human, rat, turkey, rabbit, etc. The virus looks like a crown or corona of the sun with its round projection, spike. Now a day, newly emerged coronavirus disease (COVID 19) was first detected at Wuhan, China in December 2019 and it became a public health emergency international concern. Although a couple of researches has been conducting, much secretes of the virus and disease is still not understood and not reached a common understanding yet. However, sharing basic information is crucial based on the existing published research articles and updated information. Therefore, this review aimed to draw attention to the COVID 19 pandemic facts, opportunities, and challenges based on up to date information on cases in Ethiopia. Accordingly, the coronavirus is a single-stranded, non-segmented RNA genome virus. Two third of the genome (5'end) consists of two genes that code nonstructural proteins and the other 2-7 genes (3' end) code structural proteins including spike, envelope, membrane, and nucleocapsid. The virus transmits from animal to human and suggested as it might be originated from a bat and/or seafood. Coronavirus transmit human to human by direct contact and droplets during coughing and sneezing and common symptoms like fever, dry cough, and tiredness, short breathing, etc. can be seen from the patient. So that stay at home and social distancing are the most practicing pre-prevention methods. The disease causes high economic loss, face to face education closure, community cultural practice, and mass gathering activities are prohibited. However, various charity associations and creativities found increase than before. To this end, peoples should accept and follow governmental advice and instructions to escape from the pandemic.

We refute a recently proposed thought experiment namely the Dead-Alive Physicist (DAP) whose authors claim it to be the falsification of the von Neumann interpretation in Quantum Mechanics. We discover major flaws in their model and our justification serves the purpose of disregarding further claims and assertions made in the paper at various stages. Thus, in a yet another situation, it is proven that the formalism advanced by von Neumann is non-trivial and non-falsifiable.

Blackcurrant leaf chlorosis associated virus (BCLCaV) was detected recently by next-generation sequencing (NGS) and proposed as a new and distinct species in the genus Idaeovirus. Genomic components of BCLCaV that were detected and confirmed include: 1) RNA-1 that is monocistronic and encodes the replicase complex; 2) a bicistronic RNA-2 that encodes a movement protein (MP) and the coat protein (CP) of the virus, with open reading frames (ORF) that overlap by a single adenine (A) nucleotide (nt) representing the third position of an opal stop codon of the MP ORF2a and the first position of the start codon of the CP ORF2b; 3) a subgenomic form of RNA-2 (RNA-3) that contains ORF2b; and 4) a concatenated form of RNA-2 that consists of a complementary and inverted RNA-3 conjoined to the full-length RNA-2. Analysis of NGS-derived paired-end reads revealed the existence of bridge reads encompassing the 3’-terminus and 5’-terminus of RNA-2 or RNA-3 of BCLCaV. The full RNA-2 or RNA-3 could be amplified using outward facing or abutting primers; also RNA-2/RNA-3 could be detected even after three consecutive RNase R enzyme treatments with denaturation at 95 ^oC preceding each digestion. Evidence was obtained indicating that there are circular forms of BCLCaV RNA-2 and RNA-3.

Ebola Virus Disease (EVD) is one of the most lethal transmissible infections characterized by a high fatality rate, and caused by members of the Filoviridae family. The recent large outbreak of EVD in West Africa (2013-2016), highlighted the worldwide danger of this disease and its impact on global public health and economy. The development of highly needed anti-Filoviridae antivirals has been so far hampered by the shortage of tools to study their life cycle in vitro, and therefore screen for potential active compounds outside a biosafety level-4 (BSL-4) containment. Importantly, the development of surrogate models to in vitro study of Filoviridae entry in a BSL-2 setting, such as viral pseudotypes and Ebola virus like particles, tremendously boosted both our knowledge on viral life cycle and the identification of promising anti-Filoviridae compounds interfering with viral entry. In this context, the combination of such surrogate systems with large-scale small molecule compounds and haploid genetic screenings, as well as rational drug design and drug repurposing approaches will prove priceless in our quest for the development of a treatment for EVD.

Nipah virus (NiV) is responsible to cause various outbreaks in Asian countries, with latest from Kerala state of India. Till date there is no drug available despite its urgent requirement. In the current study, we have provided a computational one-stop solution for NiV inhibitors. We have developed “anti-Nipah” web resource, which comprised of a data repository, prediction method, and data visualization modules. The database comprised of 313 (181 unique) inhibitors from different strains and outbreaks of NiV extracted from research articles and patents. However, the quantitative structure–activity relationship (QSAR) based predictors were accomplished using classification approach employing 10-fold cross validation through support vector machine with 120 (68p + 52n) inhibitors. The overall predictor showed the accuracy and Matthew’s correlation coefficient of 88.89% and 0.77 on training/testing dataset respectively. The independent validation dataset also performed equally well. The data visualization modules from chemical clustering and principal component analyses displayed the diversity in the NiV inhibitors. Therefore, our web platform would be of immense help to the researchers working in developing effective inhibitors against NiV. The user-friendly webserver is freely available on URL: http://bioinfo.imtech.res.in/manojk/antinipah/

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.

A novel virus with distinct genome features was discovered by high throughput sequencing in a symptomatic blackcurrant plant. The virus tentatively named as blackcurrant virus A (BCVA) has distinct genome organization and molecular features bridging genera in the order Tymovirales. The genome consists of 7106 nucleotides excluding the poly(A) tail. Five open reading frames were identified with the first encoding a putative viral replicase with methyl transferase (MTR), AlkB, helicase and RNA dependent RNA polymerase (RdRp) domains. The other four putative proteins exhibit no significant homology to other virus proteins. The genome organization downstream of the replicase resembles that of members of the order Tymovirales with an unconventional triple gene block (TGB) movement protein arrangement. Phylogenetic analysis using replicase conserved motifs loosely placed BCVA within the Betaflexiviridae whereas it was evolutionarily distant to existing members of the family when using the putative TGBp 1-like and coat protein sequences. Our analysis strongly suggests that BCVA is a novel virus that should be classified as a species in a new genus in the Betaflexiviridae or a new family in the order Tymovirales.

There is an ongoing global pandemic of human respiratory syncytial virus (RSV) infection that results in substantial annual morbidity and mortality. In Australia, RSV is the major cause of acute lower respiratory tract infections (ALRI). Nevertheless, little is known about the extent and origins of genetic diversity of RSV in Australia, nor the factors that shape this diversity. We conducted a genome-scale analysis of RSV infections in New South Wales (NSW). RSV genomes were successfully sequenced for 144 specimens collected between 2010-2016. Of these, 64 belonged to the RSVA and 80 to the RSVB subtype. Phylogenetic analysis revealed a wide diversity of RSV lineages within NSW and that both subtypes evolved rapidly in a strongly clock-like manner, with mean rates of approximately 6-8 x 10^-4 nucleotide substitutions per site per year. There was only weak evidence for geographic clustering of sequences, indicative of fluid patterns of transmission within the infected population, and no evidence of any clustering by patient age such that viruses in the same lineages circulate through the entire host population. Importantly, we show that both subtypes circulated concurrently in NSW with multiple introductions into the Australian population in each year, and only limited evidence for multi-year persistence.

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.

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.

The establishment of a well characterized non-human primate model of Zika virus (ZIKV) infection is critical for the development of medical interventions. In this study, challenging Indian rhesus macaques (IRMs) with ZIKV strains of the Asian lineage resulted in dose dependent peak viral loads between days 2 and 5 post infection; and a robust immune response which protected the animals from homologous and heterologous re-challenge. In contrast, viremia in IRMs challenged with an African lineage strain was below the assays lower limit of quantitation and the immune response was insufficient to protect from re-challenge. These results corroborate previous observations but are contrary to reports using other African strains obviating the need for additional studies to elucidate the variables contributing to the disparities. Nonetheless, the utility of an Asian lineage ZIKV IRM model for countermeasures development was verified by vaccinating animals with a formalin inactivated reference vaccine and demonstrating sterilizing immunity against a subsequent subcutaneous challenge.

Epithelial characteristics underlying the differential susceptibility of chronic asthma to SARS-CoV-2 (COVID-19) and other viral infections are currently unclear. By revisiting transcriptomic data from patients with Th2 low versus Th2 high asthma, as well as mild, moderate and severe asthmatics, we characterized the changes in expression of human coronavirus and influenza viral entry genes relative to sex, airway location and disease endotype. We found sexual dimorphism in expression of COVID-19 genes ACE2, TMPRSS2, TMPRSS4, and SLC6A19. ACE2 receptor downregulation occurred specifically in females in Th2 high asthma, while proteases broadly assisting coronavirus and influenza viral entry, TMPRSS2 and TMPRSS4, were highly upregulated in both sexes. Overall, changes in COVID-19 gene expression were specific to Th2 high molecular endotype of asthma, and different by asthma severity and airway location. The downregulation of ACE2 (COVID-19, SARS) and ANPEP (HCoV-229E) viral receptors correlated with loss of club and ciliated cells in Th2 high asthma, while the increase in DPP4 (MERS-CoV), ST3GAL4, and ST6GAL1 (influenza) associated with an increase in goblet and basal activated cells. Overall, this study elucidates sex, airway location, disease endotype and changes in epithelial heterogeneity as factors underlying asthmatic susceptibility, or lack thereof, to COVID-19.

The development of compartmentalized neuron culture systems has been invaluable in the study of neuroinvasive viruses, including the alpha herpesviruses Herpes Simplex Virus 1 (HSV-1) and Pseudorabies Virus (PRV). This chapter provides updated protocols for assembling and culturing rodent embryonic superior cervical ganglion (SCG) and dorsal root ganglion (DRG) neurons in Campenot trichamber cultures. In addition, we provide several illustrative examples of the types of experiments that are enabled by Campenot cultures: 1. Using fluorescence microscopy to investigate axonal outgrowth/extension through the chambers, and alpha herpesvirus infection, intracellular trafficking, and cell-cell spread via axons. 2. Using correlative fluorescence microscopy and cryo electron tomography to investigate the ultrastructure of virus particles trafficking in axons.

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.

Over 250 million people are infected chronically with hepatitis B virus (HBV), the leading cause of liver cancer worldwide. HBV persists due in part to its compact, stable minichromosome, the covalently-closed, circular DNA (cccDNA), which resides in the hepatocytes’ nuclei. Current therapies target downstream replication products, however, a true virological cure will require targeting the cccDNA. Finding targets on such a small, compact genome is challenging. For HBV, to remain replication-competent, it needs to maintain nucleotide fidelity in key regions, such as the promoter regions, to ensure that it can continue to utilize the necessary host proteins. HBVdb (HBV database) is a repository of HBV sequences spanning all genotypes (A-H) amplified from clinical samples, and hence implying an extensive collection of replication-competent viruses. Here, we analyzed the HBV sequences from HBVdb using bioinformatics tools to comprehensively assess the HBV core and X promoter regions amongst the nearly 70,000 HBV sequences for highly-conserved nucleotides and variant frequencies. Notably, there is a high degree of nucleotide conservation within specific segments of these promoter regions highlighting their importance in potential host protein-viral interactions and thus the virus’ viability. Such findings may have key implications for designing antivirals to target these areas.

Dewetting transition - a concept borrowed from fluid mechanics - is a physiological process which takes place inside the hydrophobic pores of ion channels. This transient phenomenon causes a metastable state which forbids water molecules to cross the microscopic receptors’ cavities. This leads to a decrease of conductance, a closure of the hole and, subsequently, severe impairment of cellular performance. We suggest that artificially-provoked dewetting transition in ion channels’ hydrophobic pores could stand for a molecular candidate to erase detrimental organisms, such as viruses, bacteria and cancer cells. We describe a novel type of high-affinity monoclonal antibody, which: a) targets specific trans-membrane receptor structures of harmful or redundant cells; b) is equipped with lipophilic and/or hydrophobic fragments that prevent physiological water flows inside ion channels. Therefore, we achieve an artificial dewetting transition inside receptors’ cavities which causes transmembrane ionic flows discontinuity, channel blockage and subsequent damage of morbid cells. As an example, we describe dewetting monoclonal antibodies targeting the M2 channel of the Influenza A virus: they might prevent water to enter the pores, thus leading to virion impairment.

Crimean-Congo hemorrhagic fever virus is one the most important and wide spread tick-borne viruses. Very little is known about the transmission from the tick and the early aspects of pathogenesis. Here, we generate human cutaneous antigen presenting cells: dermal dendritic cells and Langerhans cells, from umbilical cord progenitor cells. In order to mimic the environment created during tick feeding, tick salivary gland extract was generated from semi-engorged Hyalomma marginatum ticks. Our findings indicate that human dermal dendritic cells and Langerhans cells are susceptible and permissive to Crimean-Congo hemorrhagic fever virus infection, however, to different degrees. Infection leads to cell activation and cytokine/chemokine secretion, although these responses vary between the different cell types. Hyalomma marginatum salivary gland extract had minimal effect on cell responses, with some synergy with viral infection with respect to cytokine secretion. However, salivary gland extract appeared to inhibit antigen presenting cell (APC) migration. Based on the findings here we hypothesize that human dermal dendritic cells and Langerhans cells serve as early target cells. Rather affecting Crimean-Congo hemorrhagic fever virus replication, tick saliva likely immunomodulates and inhibits migration of these APC from the feeding site.

COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial global threat. This study presents a novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic networks and information of the viral structure and its genome sequence. For this purpose, we implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions and predict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primary bronchial epithelial cells infected with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targets in all known variants of concern, verifying our targets’ inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types.