A probe of a patient, seeking help in an emergency ward of a French hospital in late December 2019 because of Influenza like symptoms, was retrospectively tested positive to COVID-19. Despite the early appearance of the virus in Europe, the prevalence and virulence appeared to be low for several weeks, before the spread and severity of symptoms increased exponentially, yet with marked spatial and temporal differences. Here, we compare the possible linkages between peaks of fine particulate matter (PM2.5) and the sudden, explosive increase of hospitalizations and mortality rates in the Swiss Canton of Ticino, and the Greater Paris and London regions. We also discuss the influence of Saharan dust intrusions on the COVID-19 outbreak observed in early 2020 on the Canary Islands. We find that high PM2.5 concentrations – possibly favored by air temperature inversions or Saharan dust intrusions – are not only modulating but even more so boosting severe outbreaks of COVID-19. We conclude that the overburdening of the health services and hospitals as well as the high over-mortality observed in various regions of Europe in spring 2020 can be linked to peaks of PM2.5 and likely particular weather situations that have favored the spread and enhanced the virulence of the virus. In the future, we recommend to monitor not only the prevalence of the virus, but also to consider the occurrence of weather situations that can lead to sudden, very explosive COVID-19 outbreaks.

Global climate change requires urgent solutions. Ambitious tree-planting initiatives, many already underway, aim to sequester enormous quantities of carbon, partly compensating for the anthropogenic CO₂ emissions that are a major cause of rising global temperatures. However, poorly planned and executed tree-planting could actually increase CO₂ emissions and have long-term, deleterious impacts on biodiversity, landscapes and livelihoods. Here, we highlight the main environmental risks of large-scale tree planting and propose ten golden rules, based on some of the most recent ecological research, to implement forest ecosystem restoration that maximizes rates of both carbon sequestration and biodiversity recovery, while simultaneously improving livelihoods. These are: i) Protect existing forest first; ii) Work together (involving all stakeholders); iii) Maximize biodiversity recovery to meet multiple goals; iv) Select appropriate areas; v) Use natural regeneration wherever possible; vi) Select species to maximise biodiversity; vii) Use resilient plant material (with appropriate genetic variability and provenance); viii) Plan ahead for infrastructure, capacity and seed supply; ix) Learn by doing (using an adaptive management approach); and x) Make it pay (ensuring the economic sustainability of the project). We focus on the design of long-term strategies to tackle the climate and biodiversity crises and support livelihood needs. We emphasize the role of local communities and their dependence on benefits from successful reforestation programmes that restore ecosystem functioning and deliver a diverse range of forest products and services. While there is no simple and universal recipe for forest restoration, it is now crucial to build on the public and private interest in this topic to ensure interventions provide effective, long-term carbon sinks and maximise benefits for biodiversity and people.

Piscirickettsia salmonis is an intracellular bacterial fish pathogen that causes piscirickettsiosis, a disease with numerous negative impacts in the Chilean salmon farming industry. Although transcriptomic studies of P. salmonis and its host have been performed, dual host-pathogen proteomic approaches during infection are still missing. Considering that gene expression not always corresponds with observed phenotype, and bacteriological culture studies inadequately reflect infection conditions, to improve the existing knowledge for the pathogenicity of P. salmonis we present here a global proteomic profiling of Salmon salar macrophage-like cell cultures infected with P. salmonis LF-89. The proteomic analyses identified several P. salmonis proteins from two temporally different stages of macrophages infection; some of them related to key functions for bacterial survival in other intracellular pathogens. Metabolic differences were observed in early-stage infection bacteria, compared to late-stage infections. Virulence factors related to membrane, LPS and surface component modifications, cell motility, toxins and secretion systems also varied between the infection stages. Pilus proteins, beta-hemolysin and the T6SS were characteristic of the early-infection stage, while fimbria, upregulation of 10 toxins or effector proteins, and the Dot/Icm T4SS were representative of the late-infection stage bacteria. Previously described virulence-related genes in P. salmonis plasmids were identified by proteomic assays during infection in SHK-1 cells, accompanied by an increase of mobile-related elements. By comparing the infected and un-infected proteome of SHK-1 cells, we observed changes in cellular and ROS homeostasis, innate immune response, microtubules and actin cytoskeleton organization and dynamics, alteration in phagosome components, iron transport and metabolism, and amino acids, nucleoside and nucleotide metabolism, together with an overall energy and ATP production alteration. Our global proteomic profiling and the current knowledge of the P. salmonis infection process allowed us to propose a model of the macrophage-P. salmonis interaction.

Tropical forests constitute prolific sanctuary of unique floral diversity and potential medicinal sources, however, many of them remains unexplored. Herein, seven Mascarene endemic plants leaves were extracted and evaluated for their in vitro antioxidant properties and antiproliferative effects on a panel of cancer cell lines using MTT and clonogenic cell survival assay. Flow cytometry and comet assay were used to investigate the cell cycle and DNA damaging effects, respectively. Bioassay guided-fractionation coupled with LC-Mass spectrometry (MS), gas chromatography-MS, and nuclear magnetic resonance spectroscopy analysis were used to identify the bioactive compounds. Among the seven plants tested, Terminalia bentzoë was comparatively the most potent antioxidant extract with significantly (p < 0.05) higher cytotoxic activities. T. bentzoë extract further selectively suppressed the growth of human hepatocellular carcinoma cells and significantly halted the cell cycle progression in G0/G1 phase, decreased the cells replicative potential and induced significant DNA damage. Ten phenolic compounds including punicalagin and ellagic acid were identified and likely contributed to the extract potent antioxidant and cytotoxic activities. These results established a promising basis for further in-depth investigations on the potential use of T. bentzoë as supportive therapy in cancer management.

The CoDRIVE solution builds on R&amp;D in the development of connected and autonomous vehicles (CAVs). The mainstay of the system is a low-cost GNSS receiver integrated with a MEMS grade IMU powered with CoDRIVE algorithms and high precision data processing software. The solution integrates RFID (radio-frequency identification) localisation information derived from tags installed in the roads around the University of Nottingham. This aids the positioning solution by correcting the long-term drift of inertial navigation technology in the absence of GNSS. The solution is informed of obscuration of GNSS through city models of skyview and elevation masks derived from 360-degree photography. The results show that predictive intelligence of the denial of GNSS and RFID aiding realises significant benefits compared to the inertial only solution. According to the validation, inertial only solutions drift over time, with an overall RMS accuracy over a 300 metres section of GNSS outage of 10 to 20 metres. After deploying the RFID tags on the road, experiments show that the RFID aided algorithm is able to constrain the maximum error to within 3.76 metres, and with 93.9% of points constrained to 2 metres accuracy overall.

Aim: Abnormal laboratory findings have been shown to be associated with severe COVID-19. However, all aspects of this association have not been reviewed systematically. Therefore, the aim of this meta-analysis was to explore crucial laboratory parameters in severe COVID-19 infection. Methods: We performed the literature review of scientific articles indexed in electronic databases. Scientific search engines were used to perform the electronic literature search. After the removal of duplicates and selection of articles of interest, 30 studies were eligible to include. If heterogeneity was high (I²>50%), a random-effects model was applied to combine the data. Otherwise, a fixed-effects model was used.Results: A total of 5586 individuals were assessed (1555 patients with severe COVID-19 infection and 3452 with non-severe infection). Platelets, lymphocytes and serum albumin were significantly lower in severe patients while other biochemical and immunological parameters including prothrombin time, ALT, AST, total bilirubin, LDH, procalcitonin, CRP, IL-6, and IgA were significantly higher in patients with severe infection. Neutrophil and monocyte counts as well as hemoglobin level, D-dimer, hypersensitive troponin I, IL-2R, IgG and IgM levels were different between two groups; however, the difference was not statistically significant (All P-values >0.05). Conclusions: Lymphopenia, elevated liver enzymes, and high levels of inflammatory biomarkers are associated with severe COVID-19 infection.

Sea cucumbers (Holothuroidea; Echinodermata) are ecologically significant constituents of benthic marine habitats. We surveilled RNA viruses inhabiting 8 species (representing 4 families) of holothurian collected from four geographically distinct locations by viral metagenomics, including a single specimen of Apostichopus californicus affected by a hitherto undocumented wasting disease. The RNA virome comprised genome fragments of both single-stranded positive sense and double stranded RNA viruses, including those assigned to the Picornavirales, Ghabrivirales, and Amarillovirales. We discovered an unconventional flavivirus genome fragment which was most similar to a shark virus. Ghabivirales-like genome fragments were most similar to fungal totiviruses in both genome architecture and homology, and likely infected mycobiome constituents. Picornavirales, which are commonly retrieved in host-associated viral metagenomes, were similar to invertebrate transcriptome-derived picorna-like viruses. Sequence reads recruited from the grossly normal A. californicus metavirome to nearly all viral genome fragments recovered from the wasting-affected A. californicus. The greatest number of viral genome fragments was recovered from wasting A. californicus compared to any other surveyed holothurian, including the grossly normal A. californicus, which reflects a pattern observed in viral metagenomics study of sea star wasting. These results expand the known host range of flaviviruses, and suggest that fungi and their viruses may play roles in holothurian ecology.

This study was to investigate and assess salivary biomarkers as a means of diagnosing periodontitis. A total of 121 subjects were included: 28 periodontally healthy subjects, 24 with stage I, 24 with stage II, 23 with stage III, and 22 with stage IV periodontitis. Salivary proteins including active matrix metalloproteinase-8 (MMP-8), pro-MMP-8, total MMP-8, C-reactive protein, secretory immunoglobulin A and planktonic bacteria including Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola, Fusobacterium nucleatum, Prevotella intermedia, Porphyromonas nigrescens, Parvimonas micra, Campylobacter rectus, Eubacterium nodatum, Eikenella corrodens, Streptococcus mitis, Streptococcus mutans, Staphylococcus aureus, Enterococcus faecalis, and Actinomyces viscosus were measured from salivary samples. The performance of the diagnostic models was assessed by receiver operating characteristics (ROC) and area under the ROC curve (AUC) analysis. The diagnostic models were constructed based on the subjects’ proteins and/or microbial profiles, resulting in two potential diagnosis models, which achieved better diagnostic powers with an AUC value &gt; 0.750 for the diagnosis of stage II, III, and IV periodontitis (Model PC-I; AUC: 0.796, sensitivity: 0.754, specificity: 0.712) and for the diagnosis of stage III and IV periodontitis (Model PC-II; AUC: 0.796, sensitivity: 0.756, specificity: 0.868). This study can contribute to screening for periodontitis based on salivary biomarkers.

Coronavirus that is also known as COVID-19 disease is produced by SARSCoV-2. This causative agent is highly contagious and can cause potentially fatal pneumonia worldwide with serious public health concerns. In the beginning among infected individuals, most of them were those who were mainly shown to the wet animal market in a big city of China known as Wuhan. So, it was suggested that this was almost certainly the zoonotic source of COVID-19illness. The transitional source of origin and their mode of transmission to humans were not known obviously. Conversely, from human to human rapidly transformation have been confirmed generally. Currently, there is no availability of FDA approved clinically antiviral drugs and/or vaccines to be used against the COVID-19. Afterward, SARS-CoV and MERS-CoV, the occurrence of SARS-CoV-2 has been manifested as the third sketch of an enormously pathogenic coronavirus into human population globally. In this review, we provide a brief overview of the history of COVID-19 in Pakistan up-to 18^th weeks after beginning, current situation, epidemiology, and its impact on the human population. Moreover, we focused on physiological variation during the incubation period, genome analysis of SARS-CoV2, supportive treatment approaches, and safety measures in the Pakistani population, which may be supportive for combating the risk of COVID-19 epidemic. We also reviewed the future approaches for the development of therapeutic interventions and vaccines to cope with the COVID-19 epidemic.

Exercise induced bronchoconstriction (EIB) is a common complication of athletes and individuals who exercise regularly. It is estimated that about 90% of patients with underlying asthma experience EIB. Sex differences in the prevalence of asthma have been widely reported, with higher rates in boys vs. girls before puberty, and higher rates in women than men after puberty. Because atopy has been reported to occur at higher rates in athletes than in non-athletes, in this study we investigated sex differences in EIB and atopy in athletes. A systematic literature review identified 60 studies evaluating EIB and/or atopy in post-pubertal adult athletes (n=7501). Collectively, these studies reported: 1) a 23% prevalence of EIB in athletes; 2) a higher prevalence of atopy in male athletes vs. females; 3) a higher prevalence of atopy in athletes with EIB; and 4) a significantly higher rate of atopic EIB in male vs. female athletes. Our analysis indicates that the physiological changes that occur during exercise may differentially affect male and female athletes, and suggest an interaction between male sex, exercise, and atopic status in the course of EIB. Understanding these sex differences is important to provide personalized management plans to athletes with underlying asthma and/or atopy.

The solute carrier family 25 (SLC25) participates in the transport of metabolites and cofactors across the membranes of mitochondria, plastids, peroxisomes, and endoplasmic reticulum. By calling for genomic blocks involved in adjacent metabolic reactions, this report introduces gene clusters of the Slc25 subfamily 44, stilbene and chalcone synthases, and subunits of the mitochondrial electron transfer complexes. The Slc25A44 gene was found ubiquitously expressed and transcriptionally co-regulated with energy metabolism genes in human, mouse, and Arabidopsis thaliana. The Slc25A44s also had no homozygous missense mutation and were highly conserved at intra-species level with the majority of polymorphism present in the non-coding regions. When expressed in oocytes, AdSlc25A44 from Arachis duranensis showed transport activity for the common precursors of flavonoids, stilbenoids, and ubiquinone. Accordingly, AdSLC25A44 and its human orthologue HsSLC25A44 elevated the production of para-coumaric, 4-aminobenzoic, and 4-hydroxybenzoic acids in Saccharomyces cerevisiae strains designed to produce para-coumaric acid via different pathways. Moreover, the engineered SLC25 subfamily specific signature, i.e., AdSLC25A44^LWW206IQF, had a stronger effect on para-coumaric acid secretion than the native variant. Importantly, the aerobic growth-rate of S. cerevisiae was significantly higher when expressing the AdSLC25A44, HsSLC25A44, or AdSLC25A44^LWW206IQF. These results suggest that SLC25A44 is an essential mitochondrion-ER-nucleus zone transporter associated with metabolism of secondary metabolites and energy.

In the present work, we evaluate the prediction capability of six Pedotransfer functions (PTFs), reported in the literature, for the saturated hydraulic conductivity estimations (Ks). We used a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. Additionally, six new PTFs were construct for Ks from clay percentage, bulk density and saturation water content data. The results show, for the evaluated models, that one model present an overestimation for Ks>0.5 cm h-1 values, three models have a underestimation for Ks>1.0 cm h-1 and two models have a good correlation (R2>0.98) but are necessary more than three parameters. Nevertheless, the last two models requires from three to four parameters in order to get the optimization. By other hand, the models proposed in this work have a similar correlation with a less number of parameters: the fit is seen to be much better than using the existing ones, achieving a correlation of R2 = 0.9822 with only one variable and a R2 = 0.9901 when we use two.

Solid state sensors having timing capabilities are becoming an absolute need in particle tracking techniques of future experiments at colliders. In this sense, silicon sensors having 3D structure are becoming an interesting solution, due to their intrinsic speed and radiation resistance. A characteristics of such devices is the strict dependence of their performance on their geometric structure, which can be widely optimised by design, thus requiring suitable tools for an accurate modeling of their behaviour. This paper illustrates the development, performance and use of the TCoDe simulator, specifically dedicated to the fast simulation of carrier transportation phenomena in solid state sensors. Some examples of its effectiveness in the design and analysis of 3D sensors is also given.

The targeted application of plant growth promoting rhizobacteria (PGPR) provides the key for a future sustainable agriculture with reduced pesticide application. PGPR interaction with the indigenous microbiota is poorly understood but essential to develop reliable applications. Therefore, Stenotrophomonas rhizophila SPA-P69 was applied as seed coating and in combination with a fungicide based on the active ingredients fludioxonil, metalaxyl-M, captan and ziram. Plant performance and rhizosphere composition of treated and non-treated maize plants of two field trials were analyzed. Plant health was significantly increased by treatment; however overall corn yield was not changed. By applying high-throughput amplicon sequencing of the 16S rRNA and the ITS genes, the bacterial and fungal changes in the rhizosphere due to different treatments were determined. Despite treatments had a significant impact on the rhizosphere microbiota (9- 12%), the field site was identified as main driver (27- 37%). Soil microbiota composition from each site was significantly different, which explains the site-specific effects. In this study we were able to show first indications how PGPR treatments increase plant health via microbiome shifts in a site-specific manner. This way first steps towards a detailed understanding of PGPRs and developments of consistently efficient applications in diverse environments are set.

Rheumatoid arthritis (RA) is a complex disease triggered by the interaction between genetics and environment, especially through the shared epitope (SE) and cell surface calreticulin (CSC) theory. However, the available evidence shows that genetic diversity and environmental exposure cannot explain all the clinical characteristics and heterogeneity of RA. In contrast, recent studies demonstrate that epigenetics play important roles in the pathogenesis of RA, especially DNA methylation and histone modification. DNA methylation and histone methylation are involved in innate and adaptive immune cell differentiation, and migration, proliferation, apoptosis, and mesenchymal characteristics of fibroblast-like synoviocytes (FLS). Epigenetic-mediated regulation of immune-related genes and inflammation pathways explains the dynamic expression network of RA. In this review, we summarized the comprehensive evidence to show that methylation of DNA and histones is significantly involved in the pathogenesis of RA and could be applied as a promising biomarker in the disease progression and drug response prediction. We also explained the advantages and challenges of the current epigenetics research in RA. In summary, epigenetic modules provide a possible interface, through which genetic and environmental risk factors connect to contribute to the susceptibility and pathogenesis of RA. Additionally, epigenetic regulators provide promising drug targets to develop novel therapeutic drugs for RA. Finally, DNA methylation and histone modifications could be important features for providing a better RA subtype identification, to accelerate personalized treatment and precision medicine.

Organizational skills are a set of cognitive abilities responsible for goal-directed behaviors. While they are moderately studied in clinical settings, the assessment of organizational skills in the general population remains under-studied. This paper presents the new Durand Organizational Skills Questionnaire (DOSQ), which was developed to examine the factors associated with organizational abilities in the general population. Exploratory factor analysis, validated by a confirmatory factor analysis, suggests eight factors: Work Organization, Communication Clarity, Punctuality, Goal-Oriented Behavior, Assiduity, Workspace Organization, Strategies, and Attentiveness. Three studies using samples from the general population provided evidence for the reliability and validity of the DOSQ’s scores. Overall, the results suggest that the DOSQ offers a valid approach to measuring organizational skills in the general population.

Gold nanoparticles, were deposited in titanium oxide (TiO2) Degussa-P25 with the Photodeposition method in the presence of UV light at different concentrations. It was determined by diffuse reflectance (DF), Scanning electron microscopy (SEM), that the Photodeposition method is effective for the inclusion of gold particles on the surface. The catalyst band gap showed a reduction to 2.9 e.V, as well as it was observed that the gold-doped catalyst shows absorption in the visible light range around 500 to 600 nm. The percentage of deposited gold nanoparticles was determined by energy dispersive spectroscopy (EDS). The experimental data were analyzed using different analytical techniques (UV-Vis spectrophotometry, TOC total organic carbon), with these results a carbon-based mass balance and reaction kinetics were generated using the Langmuir-Hinshelwood (LH-HW) heterogeneous catalysis model. For the estimation of the kinetic constants, the non-linear regression of the Levengerd Marquad algorithm was used, with these results, kinetic models of the degradation of the molecule and the generation and consumption of Organic Intermediate Products (OIP) were generated.

The outbreak of novel Coronavirus (SARS-COV-2 ) disease (COVID-19) in Wuhan has attracted worldwide attention. SARS-COV-2 known to share a similar clinical manifestation that includes various symptoms such as pneumonia, fever, breathing difficulty, and in particular, SARS-COV-2 also causes a severe in ammation state that leads to death. Consequently, massive and rapid research growth has been observed across the globe to elucidate the mechanisms of infections and disease progression in genotype and phenotype scale. Data Science is playing a pivotal role in in-silico analysis to draw hidden and novel insights about the SARS-COV-2 origin, pathogenesis, COVID-19 outbreak forecasting, medical diagnosis, and drug discovery. With the availability of multi-omics, radiological, biomolecular, and medical data urges to develop novel exploratory and predictive models or customise exiting learning models to t the current problem domain. The presence of many approaches generates the need for the systematic surveys to guide both data scientists and medical practitioners. We perform an elaborate study on the state-of-the-art data science method ologies in action to tackle the current pandemic scenario. We consider various active COVID-19 data analytics domains such as phylogeny analysis, SARS-COV-2 genome identication, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological analysis, and most importantly (existing) drug discovery. We highlight types of data, their generation pipeline, and the data science models in use. We believe that the current study will give a detailed sketch of the road map towards handling COVID-19 like situation by leveraging data science in the future. We summarise our review focusing on prime challenges and possible future research directions .

One of the most important component of a Geographic Information Retrieval (GIR) is the geoparser, which performs toponym recognition, disambiguation, and geographic coordinate resolution from unstructured text domain. However, news articles which report several events across many place references mentioned in the document is not yet adequately modeled by regular geoparser types where the scope of resolution is either on toponym-level or document-level. The capacity to detect multiple events, geolocate its true locations and coordinates along with their numerical arguments are still missing from modern geoparsers, much less in Indonesian news corpora domain. We propose a novel type event geoparser which integrates an ACE-based event extraction model and provides precise event-level scope resolution. The geoparser casts the geotagging and event extraction as sequence labeling and uses Conditional Random Field with keywords feature obtained using Aggregated Topic Model as a semantic exploration from large corpus, which eventually increases the generalizability of the model. The geoparser also use Smallest Administrative Level feature along with Spatial Minimality-derived algorithm to improve the identification of Pseudo-location entities, resulting 19.4% increase for weighted F1 score. As a side effect of event extraction, the geoparser also extracts various numerical arguments and able to generate thematic choropleth map from a single news story.

Leishmania infantum is causative of visceral leishmaniasis (kala-azar), the most severe form of leishmaniasis, lethal if untreated. Few years ago, re-sequencing and de novo assembling of the L. infantum (strain JPCM5) genome was accomplished, and now we aimed to describe and characterize the experimental proteome of this species. In this work, we have performed a proteomic analysis from axenic cultured promastigotes and carried out a detailed comparison with other Leishmania experimental proteomes published to date. We identified 2,352 proteins based on the search of mass spectrometry data against a database built from the six-frame translated genome sequence of L. infantum. We have detected many proteins belonging to organelles such as glycosomes, mitochondria or flagellum, as well as many metabolic enzymes, and a large number of putative RNA binding proteins and molecular chaperones. Moreover, we listed the proteins presenting post-translational modifications, such as phosphorylations, acetylations and methylations, among others. On the other hand, the identification of peptides mapping to genomic regions previously annotated as non-coding has allowed to correct annotations, leading to N-terminal extension of protein sequences, and the uncovering of eight novel protein-coding genes. The alliance of proteomics, genomics and transcriptomics has resulted in a powerful combination for improving the L. infantum reference genome annotation.

The Study proposes possible solution to enhance Password Authentication using Association Technique based on the Ecological theory of memory and data at the Presbyterian university College Ghana. The study used a deductive research approach and employed two empirical Studies using the non-probability sampling technique where few respondents were selected in categories out of the populace by means of openness in other to get similar categories of respondents with different age groups and education background. The two Empirical study also used a quasi-experimental approach which structure incorporate observation, experimental treatment and timing. The First empirical study carried out an investigation to identify existing Password authentication Technique used by End Users, as well as their behavior in password utilization through a self-completed questionnaires which was analyzed using SPSS version 21 . The Second empirical study was an experiment to compared three kinds of password constructions that is own set, modified dictionary, and association against one another to see which of them would be best meet the ecological theory of memory and data which aims at creating a secured password that is easy to recall. The computation and evaluation of password construction was done using My1login Password meter whiles Levenshtein Distance String Edit Software was also used to compute the memorability of all given password. Across-tabulation was then employed out of the experiment using SPSS version 21. The result from the analysis revealed that the majority of the respondents do have weak passwords and also have few passwords which they even end up sharing with families and friends and reuse it. This confirms statements made by researchers that human being is the weakest connection in information system securities. To maintain Confidentiality, Availability and Integrity of data, the study therefore recommended the use of the Association Password technique which makes it easier to develop a much secured password that is difficult to crack but easy remember.

The fact is quite transparent that almost everybody around the world is using android apps. Half of the population of this planet is associated with messaging, social media, gaming, and browsers. This online marketplace provides free and paid access to users. On the Google Play store, users are encouraged to download countless of applications belonging to predefined categories. In this research paper, we have scrapped thousands of users reviews and app ratings. We have scrapped 148 apps’ reviews from 14 categories. We have collected 506259 reviews from Google play store and subsequently checked the semantics of reviews about some applications form users to determine whether reviews are positive, negative, or neutral. We have evaluated the results by using different machine learning algorithms like Naïve Bayes, Random Forest, and Logistic Regression algorithm. we have calculated Term Frequency (TF) and Inverse Document Frequency (IDF) with different parameters like accuracy, precision, recall, and F1 and compared the statistical result of these algorithms. We have visualized these statistical results in the form of a bar chart. In this paper, the analysis of each algorithm is performed one by one, and the results have been compared. Eventually, We've discovered that Logistic Regression is the best algorithm for a review-analysis of all Google play store. We have proved that Logistic Regression gets the speed of precision, accuracy, recall, and F1 in both after preprocessing and data collection of this dataset.

The aim of this study was to assess the best timing to perform arthrocentesis in the management of temporomandibular disorders with regards to conservative treatment. A systematic search based on PRISMA guidelines, including a computer search with specific keywords, reference list search, and manual search was done. Relevant articles were selected after 3 search rounds for final review based on 6 predefined inclusion criteria, followed by a round of critical appraisal. Eleven publications, including 5 randomized controlled trials and 6 prospective clinical studies informed this review. The studies were divided into 3 groups based on the timing of arthrocentesis: 1). Arthrocentesis as the initial treatment, 2). Early arthrocentesis, and 3). Late arthrocentesis. Meta-analyses compared the efficacy of improvement in mouth opening and pain reduction in the 3 groups. The results were statistically significant and favoured early arthrocentesis, followed by late arthrocentesis in terms of both improvements in mouth opening and pain reduction. All 3 groups showed improvement in mouth opening and pain reduction. We conclude that early arthrocentesis may be able to produce the best clinical results, while arthrocentesis before attempting conservative treatment may produce less favourable outcomes.

Knowledge about the formation energies of compounds is essential to derive phase diagrams of multi-component phases with respect to elemental reservoirs. The determination of formation energies using common (semi-)local exchange-correlation approximations of density functional theory (DFT) exhibits well-known systematic errors if applied to oxide compounds containing transition metal elements. In this work, we generalize, reevaluate and discuss a set of approaches proposed and widely applied in the literature to correct for errors arising from the over-binding of the O2 molecule and from correlation effects of electrons in localized transition-metal orbitals. The DFT+U method is exemplarily applied to iron oxide compounds, and a procedure is presented to obtain U values, which lead to formation energies and electronic band gaps comparable to experimental values. Using such corrected formation energies, we derive the phase diagrams for LaFeO3, Li5FeO4 and NaFeO2, which are promising materials for energy conversion and storage devices. A scheme is presented to transform the variables of the phase diagrams from the chemical potentials of elemental phases to those of precursor compounds of a solid-state reaction, which represents the experimental synthesis process more appropriately. The discussed workflow of methods can directly be applied to other transition metal oxides.

This paper is focused on the analysis of methods used to assess effectiveness and efficiency of the occupational safety and health management system (OSHMS) in a company. It reviews current Russian and international occupational safety and health standards. The paper details the method outlined in International Standard GOST 12.0.230.3-2016 “Occupational safety standards system. Management systems for occupational safety. Evaluation of effectiveness and efficiency” based on the data collected during the external audit of OSHMS function efficiency in a Russian mining company. Effectiveness and efficiency indicators were determined, and a generalized desirability coefficient was calculated. Weaknesses of the method proposed in GOST 12.0.230.3-2016 were identified and mathematical and linguistic solutions were proposed to improve it. Harrington function was used to determine a numerical and linguistic score. The performed calculation demonstrated that the company needed strategic management decisions to improve the current situation. Practical approaches were offered to enhance the company's systems-based occupational safety and health performance..

The montane subtropical broadleaved humid forests of Meghalaya (Northeast India) are highly diverse and situated at the transition zone between the Eastern Himalayas and Indo-Burma biodiversity hotspots. Fagaceae family are the keystone species forms an important component of these forests. These forests in Meghalaya are highly degraded and fragmented due to anthropogenic disturbances (e.g., mining, unsustainable forest utilization, shifting cultivation, browsing, etc.). In this study, we assessed for the first time, the restoration potential (i.e. capacity to naturally regenerate and sustain desired forest structure) of Fagaceae species (2 Lithocarpus, 4 Castanopsis, and 4 Quercus species) in Meghalaya and how the biotic and abiotic factors, as well as anthropogenic disturbances, influence the restoration potential of these species. We selected fragmented forest patches in six locations on an elevational gradient on south-facing slopes in the Khasi Hills, Meghalaya. Fagaceae was the most dominant family in all sites except one site (Laitkynsew) where Fagaceae was co-dominant with Lauraceae. The family also had high natural regeneration (i.e., a high number of seedlings and saplings) but low recruitment to adult trees (DBH ≥ 10cm) at all sites. This study provides a means for assessing regeneration and a basis for forest management strategies in degraded and fragmented forests of Meghalaya.

The role of the immune response to SARS-CoV-2 infection is not yet well known, in particular about the persistence of circulating antibodies. The aim of the study is to compare the results of two automated systems for the determination of IgG antibodies against SARS CoV-2 and to assess the time course of the IgG response after the onset of symptoms for a period longer than that evaluated to date. IgG were measured in 98 specimens of 55 subjects with COVID-19 (time from the onset of symptoms from 3 to 109 days) using the automated tests "Abbott SARS-COV-2 IgG" and the "MAGLUMI 2019-nCoV IgG". The two methods had a concordance of 91.8%, but the quantitative correlation showed very dispersed results. All the specimens resulted positive after 17 days from the onset of the synptoms. However, the median concentrations of IgG, after a rapid increase up to about 20 days, quickly decrease to about 15% of the maximum for Maglumi. The same samples measured by Architect showed a quite constant trend up to 80 day, and then an only moderate decline. The titer of IgG against SARS-CoV-2 in patients exposed to COVID-19 may significantly and rapidly decrease, with a different time-course depending on the method used for the determination.

Chronic rhinosinusitis is a common inflammatory disease of paranasal sinuses, which causes rhinorrhea, nasal congestion and hyposmia. The genetic predisposition or the exposure to irritants can sustain the inflammatory response and the development of nasal polyposis. Nasal polyps are benign and teardrop-shaped growths that project in the nasal cavities and originate from the ethmoid sinuses. This inflammatory process is associated with high expression of IL-5 cytokine and infiltration of eosinophils. Humanized monoclonal antibodies targeting IL-5 or its receptor, represent a therapeutic strategy in the treatment of nasal polyposis in combination with corticosteroids. The molecular pathogenesis of nasal polyps in CRS patients is associated to the epithelial-mesenchymal transition (EMT), a process in which epithelial cells lose their typical phenotype acquiring a mesenchymal phenotype. TGFβ/SMAD, ERK, and Wnt/β-catenin pathways are altered in EMT during the nasal tissue remodeling. miRNA and inhibitor molecules targeting these altered signaling pathways are able to interfere with EMT; which could lead to alternative therapies. Nasal polyps are an alternative source of mesenchymal stem cells which can be easily isolated from surgical biopsies. A molecular understanding of the biology of PO-MSCs will contribute to delineating inflammatory process underlying the development of nasal polyps.

Coronavirus disease is the current cause of global concern. The massive outbreak of COVID-19 has led the World Health Organization (WHO) to declare this as a pandemic situation. The Severe Acute Respiratory Syndrome Coronavirus-2 (SARSCoV-2) is responsible for COVID-19 leading to acute respiratory distress and substantial mortality in humans. However, the first laboratory confirmation of SARS-CoV-2 in a pet dog in Hong Kong has shown the possibility of human-to-animal transmission (zooanthroponotic) of the virus. Thereafter, many animals including cat, tiger, lion and mink have also been reported to acquire the virus in several countries. In this situation the role of veterinarian assumes important in treating the animals, helping in food security, disease diagnosis, surveillance and boosting the economy of livestock stakeholders at the grassroot level. In the absence of any selective vaccine or drug against SARS-CoV-2, the world is anticipated to triumph over this pandemic with collaborative, multisectoral, and transdisciplinary approach linking human, animal and environmental health. This article gives an insight into the confirmed SARS-CoV-2 outbreaks in animals, including the factors behind the shuffling of the virus among variety of species and also emphasizes on the role of veterinarian in managing and safeguarding public health so as to pave the way for adopting one health approach in order to conserve biodiversity.

In this paper, the design of a low cost portable ventilator with performance analysis have been done to solve the scarcity of respiratory ventilators for COVID-19 patients. The materials used to build the system are: DC motor, rotating disc and pneumatic piston. The system input is the patient heart beat and the output is volume of air to the patient lung with adjusted breathing rate. This ventilator adjusts the breathing rate to the patient depending on his heart beat rate. The performance analysis of this system have been done using Proportional Integral Derivative (PID) and Full State Feedback H2 controllers. Comparison of the system with the proposed controllers have been done using a step change and a random change of the patient heart beat and a promising result have been analyzed successfully.

Publically available (own) transcriptomic data were re-analyzed to quantify the alteration of functional pathways in the thyroid cancer, establish the gene hierarchy, identify potential gene targets and predict the effects of their manipulation. The expression data were generated from one case of papillary thyroid carcinoma (PTC) and from genetically manipulated BCPAP (papillary) and 8505C (anaplastic) human thyroid cancer cell lines. The study used the genomic fabric perspective that considers the transcriptome as a multi-dimensional mathematical object based on the three independent characteristics that can be derived for each gene from the expression data. We found remarkable remodeling of the thyroid hormone synthesis, cell cycle, oxidative phosphorylation and apoptosis pathways. Serine peptidase inhibitor, Kunitz type, 2 (SPINT2) was identified as the Gene Master Regulator of the investigated PTC. The substantial increase of the expression synergism of SPINT2 with apoptosis genes in the cancer nodule with respect to the surrounding normal tissue (NOR) suggests that its experimental overexpression may force the PTC cells into apoptosis with negligible effect on the NOR cells. The predictive value of the expression coordination for the expression regulation was validated with data from 8505C and BCPAP cells before and after lentiviral transfection with DDX19B.

Loss of muscle mass and strength are progressing with aging. Exercise is a beneficial method to prevent physical disfunction and habitual exercise improve the muscle quality. Therefore, we evaluated the effects of a long-term habitual exercise on the senescence-accelerated mice prone8 (SAMP8). 27wk SAMP8 were used in this study. Mice were classified into 28 (28w) and 44 weeks old. The 44-week group was divided into the sedentary group (44w) and a group exercising for 16 weeks (44w+Ex). The 44w+Ex performed habitual exercise from 28 to 44 weeks. Additionally, grip strength tests were performed with mice aged 28 and 44 weeks. Mice were dissected and collected muscle samples and measured muscle weight at 44w. Gastrocnemius was decreased in 44w but were unchanged in 44w+Ex. Grip strength in 44w was lower trend, but there was no change in 44w+Ex. The phosphorylation levels of Akt and p70S6K as a protein synthesis marker were decreased in 44w. Cytochrome c oxidase subunit IV(COXIV) mRNA and protein levels decreased in 44w. These results suggested that long-term habitual exercise attenuated muscle mass and strength decline through improving muscle protein synthesis and mitochondrial function. In conclusion, long-term habitual exercise attenuated muscle mass and strength decline.

Within the scope of observed materialistic physical systems, without addressing the roots of H-Bar and big G, it is impossible to construct a workable model of final unification. With reference to our earlier proposed three large atomic gravitational constants, it is possible to reform and compactify the 10 dimensional String theory to 3+1 dimensions

Objectives: To investigate the value of early lactate dynamic monitoring index in predicting prognosis of patients with sepsis and septic shock. Methods: We performed our test on 50 patients. Out of 50 patients, 28 are male, and 22 are female. Prospectively studied pediatric patients with septic shock were performed. Vital signs, Lactate clearance, were obtained at presentation 6 h, 12 h, 24 h over the first 48 h of hospitalization. The therapy received, outcome parameters of mortality and duration of hospitalization were recorded. Results: The statistical data and comparative analysis showed that an average of 16.88 days after admission, 5 patients have died, 17 patients are poorly prognosis leaves the hospital, and the remaining 28 are recovered and discharged. The primary outcome variable of mean 16 days hospitalization mortality rate was 10%. Poor prognosis 34% and fully recovery 56 % were observed. In this retrospective cohort study, a lactate level of more than 2.5mmol/L was the best threshold to predict 28-day mortality among severe sepsis and septic shock patients. In our research, we found mean LC 6 h 3.08mmol/L, and after 48 h mean it is 1.79mmol/L. Significant LC 6 h found, which is 8.08mmol/L in the death group patient where 48 h mmol/L shows significant high. Poor prognosis also presents a clinical increase of lactate level high in the LC 6 h analysis, which is 3.32mmol/L. Recovered patients showed a significant improvement after administering treatment depending on the patient organ involvement and good decrease of lactate reports achieved, which is 1.20mmol/L, where admission reports show it was 1.91mmol/L in LC 6 h. Mean Heart rate 94/51mmhg, pulse 119, temperature 39℃, respiratory rate 32.26, and urine output 456 ml recorded during our study. Death patient shows a remarkable detonation of those reports but has a significant clinical report with the recovered patients. Conclusion: The early lactate dynamic monitoring index has a high value in predicting sepsis and septic shock patients' prognosis, thus worth popularizing.

Background: The aim of the present work is the elaboration of a systematic review of existing research on physical fitness, self-efficacy for physical exercise, and quality of life in adulthood. Method: Using the PRISMA statement guidelines, and based on the findings in 396 articles, the final sample was composed of 31 articles, which were reviewed to show whether self-efficacy has previously been studied as a mediator in the relationship between physical fitness and quality of life in adulthood. Results: The results indicate that little research exists in relation to healthy, populations with the majority being people with pathology. Physical fitness should be considered as a fundamental aspect in determining the functional capacity of the person. Conclusions: We discuss the need to investigate the mediation of self-efficacy in relation to the value of physical activity on quality of life and well-being in the healthy adult population in adult life.

Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require a large computation power and processing time. In this work, we evaluate the speed-accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed-accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113 what is very promising for the forensic field.

The availability of high quality surface observations of precipitation and volume observations by polarimetric operational radars make it possible to constrain, evaluate and validate numerical models with a wide variety of microphysical schemes. In this article, a novel particle-based Monte-Carlo microphysical model (called “McSnow”) is used to simulate the outer rain bands of Hurricane Dorian which traversed the densely instrumented precipitation research facility operated by NASA at Wallops Island, Virginia. The rain bands showed steady stratiform vertical profiles with radar signature of dendritic growth layers near −15 °C and peak reflectivity in the bright band of 55 dBZ along with polarimetric signatures of wet snow with sizes inferred to exceed 15 mm. A 2D-video disdrometer measured frequent occurrences of large drops &gt;5 mm and combined with an optical array probe the drop size distribution was well-documented in spite of uncertainty for drops &lt;0.5 mm due to high wind gusts and turbulence. The 1D McSnow control run and four numerical “experiments” were conducted and compared with observations. One of the main findings is that even at the moderate rain rate of 10 mm/h collisional breakup is essential for the shape of the drop size distribution.

Marine renewable energy technologies have a great potential in supplying clean electricity to millions of people across the globe, if technical and economic conditions are in right. So far, ocean energy projects are commonly started by SMEs or educational institutions with limited budgets. Therefore, any effort to reduce expenses is of great value. One of the areas involving substantial expenses are the inevitable seabed inspection prior to deployment of marine renewable energy device. Detailed seabed inspections can also reduce the risk of associated with deployment of structures on uneven seabed, especially marine renewable energy devices with gravity foundations. By reducing the costs and risks of such surveys prior and during the installation phases, the feasibility of marine renewable energy projects can be more favoured and competitive. In this perspective, this study proposes a cost and time effective technique for seabed surveys. The proposed technique involves the use of high precision and inexpensive sonar systems and underwater optical cameras integrated into a versatile and compact subsea monitoring platform. It also involves simple and practical data acquisition and processing protocols that do not requires hi expertise for operation. The results obtained showed that high resolution bathymetric maps and detailed seabed inspections imagery can be acquired. This study concludes that a simple and inexpensive subsea monitoring platform comprising a multibeam, dual beam and video cameras can be effective for high resolution seabed inspection and bathymetric measurements for marine energy applications.

In this work, the structural, magnetic, and mechanical properties of Nd16Fe76-xCoxB8 alloys varying the Co content at x = 0, 10, 20 and 25, were experimentally investigated by X-Ray Diffraction (XRD), Mössbauer Spectrometry (MS), Vibrating Sample Magnetometry (VSM) at room temperature (RT), and microhardness test were performed too. The system presents the hard Nd2Fe14B and the Nd1.1Fe4B4 phases for samples with x = 0 and 10. When concentration increases to x= 20 and 25, the CoO phase appears. All MS show the ferromagnetic behavior (eight sextets: sites 16k1, 16k2, 8j1, 8j2, 4c, 4e, sb) associated to the hard and soft magnetic phases, and one paramagnetic component (doublet: site d) associated to the minority Nd1.1Fe4B4 phase, which was not identified by XRD. All samples are magnetically hard present a hard magnetic behavior. The increase of Co content in these samples did not improve the hard magnetic behavior, but increased the critical temperature of the system and decrease the crystallite size of the hard phase. The hysteresis loop showed that predominates the Nd2Fe14B hard magnetic phase. There is a general tendency to increase microhardness with cobalt content, attributable to cobalt doping reduces the lattice parameters and porosities in the sample improving it hardness.

Aerated concrete, which is manufactured from binding material, sand, foaming agent and water, is currently being utilized in the construction industry because of its lightweight and durability. The binding material, cement, along with other materials used in the concrete produces huge carbon footprints during its fabrication. The utilization of natural aggregates name as coarse aggregates depletes the natural resources of the country. Therefore, huge amounts of agricultural wastes have led scholars to investigate the effectiveness of replacing conventional materials used in concrete with agricultural wastes. In the current study, rice husk ash (RHA) was used as supplementary cementing material, thereby reducing the amount of cement used in aerated concrete (AC) mixture will reduce carbon footprints. The experimental and numerical analysis were conducted to investigate structural behavior of reinforced RAC- B beams subjected to flexural load. Parametric study on structural performance of RAC- B beam under flexure were conducted using finite element analysis (FEA). From the experiment and FEA. Results from the parametric study showed that RAC-10%RHA-B with higher depth structurally performed better compared to RAC-B under flexure with greater load carrying capacity, lesser maximum deflection, and less cracks developing in the tension area.

Brucellosis is one of the most common etiologies of laboratory-acquired infections worldwide, and handling of living brucellae should be performed in a Class II biological safety cabinet. The low infecting dose, multiple portals of entry to the body, the great variety of potentially contaminated specimens, and the unspecific clinical manifestations of human infections facilitate the unintentional transmission of brucellae to laboratory personnel. Work accidents such as spillage of culture media cause only a small minority of exposures, whereas >80% of events result from unfamiliarity with the phenotypic features of the genus, misidentification of isolates, and unsafe laboratory practices such as aerosolization of bacteria and working on an open bench without protective goggles or gloves. Although the bacteriological diagnosis of brucellae by traditional methods is simple, the Gram stain and the biochemical profile of the organism, as determined by commercial kits, can be misleading, resulting in inadvertent exposure and contagion. The use of novel identification technologies is not hazard-free. The MALDI-TOF technology requires an initial bacterial inactivation step, while the instruments’ reference database may misidentify Brucella as belonging to other Gram-negative species. The rapid identification by the FISH method mistakes brucellar isolates for members of the closely related Ochrobactrum genus.

Colorectal cancer (CRC) is considered as one of the leading types of cancer in the world. CD133, as a cancer stem cell marker, has a pivotal role in the development of drug resistance, migration, and stemness properties of CRC cells. This study designed to check the combined effect of CD133siRNA and Oxaliplatin on proliferation, migration, apoptosis, and stemness properties of CRC cells in HT-29 cell line.MTT assay was performed to define the combined effect of CD133siRNA and Oxaliplatin on the viability of HT-29 cells. In order to figure out the effect of this combination therapy on CD133 expression at the gene and protein level, qRT-PCR and western blot were exploited, respectively. The ability of cell migration was tested by wound healing assay as well. Also, colony formation and sphere formation were conducted to assess the stemness properties in the combination group. Flow cytometry was conducted to investigate the apoptosis, cell cycle, and surface expression of CD133 in different groups. Finally, the expression of migration-, and stemness-associated genes were measured by qRT-PCR. We indicated that silencing of CD133 reduces the migration and stemness properties of colorectal cancerous cells. This suppression makes HT-29 cells more sensitive to Oxaliplatin and reduces the effective dose of this chemical drug. Therefore, the suppression of CD133 in combination with Oxaliplatin treatment might be a promising therapeutic approach in the treatment of colorectal cancer.

Inclusions in steels and alloys are known as factors that could lower deformation, mechanical, corrosion and other properties. Study of inclusions in nickel based alloys is important since these materials could suffer from corrosion degradation in harsh operational conditions, where inclusions could lead to a pitting initiation. For estimation of a harmful effect of different inclusions on corrosion resistance of Ni-based alloys, inclusion characteristics (such as composition, morphology, size, number and location) on film filter and dissolution of metal matrix around different inclusions on surfaces of metal samples after electrolytic extraction (EE) were investigated in two samples of industrial Ni-based alloys (alloy 718 and EP718). It was found that both Ni-based alloys have various inclusions: carbides (large size NbTi-C and small multicomponent carbides), nitrides TiNb-N and sulfides (TiNb-S in EP718 alloy). The higher harmful effect on corrosion resistance of metal was detected around sulfides and small carbides containing Mo, W, Cr. Dissolution effect was also observed around large carbides and nitrides, especially around inclusions having size more than 10 µm. Moreover, the dissolution of metal matrix around inclusions and clusters located on the grain boundaries is 2.1-2.7 times larger compared to that for those inclusions inside of grains of the given alloy samples.

Severe COVID-19 disease is characterised by an exaggerated inflammatory response, called cytokine storm, accompanied by a condition of immune depression. Even sepsis is characterised by an exaggerated inflammatory response, called SIRS (Systemic Inflammatory Response Syndrome), accompanied by a condition of immune depression called CARS (compensatory anti-inflammatory response syndrome). Clinical studies reveal that most sepsis patients who did not die during the hyper inflammatory response (SIRS) subsequently succumbed to the condition of immune depression (CARS). Severe acute pancreatitis begins with local inflammation that induces systemic inflammatory response syndrome (SIRS), accompanied and followed by a compensatory anti-inflammatory response (CARS). In COVID-19 disease, the male response to SARS CoV-2 virus is typically characterised by a robust inflammatory response. Instead, a cell-mediated immune response is dominant in women. This means that the male sex tends to have a more robust hyper inflammatory response than the female one. Furthermore, in women the condition of immune depression is less represented, therefore they are more protected. Sepsis, severe acute pancreatitis and COVID-19 disease evolve between two fundamental aspects: hyper inflammation and immunodepression. The experience gained over years of studies of sepsis and severe acute pancreatitis suggests that therapies should be differentiated according to the evolutionary stage of the disease. The goal is to save the lives of most patients with COVID-19 disease. The identification of critical points, suitable for designing the windows of therapeutic opportunity, may allow the use of therapeutic interventions, in the COVID-19 disease, which are effective (there are no approved drugs yet), safe (without significant side effects), targeted (based on the evolutionary phase of the disease) personalized, (based on sex, co-morbidities, age, etc.) and timely (based on signs, symptoms, laboratory parameters and instrumental investigations).

Abstract: A large number of collecting expeditions were launched in regions of ‘centers of diversity’ and hundreds of thousands of sample have been collected and stored in gene banks as ‘genetic resources’. So far, only a small number of the samples have been evaluated for their biotic and abiotic stress tolerance. Now, their time to become useful has come. A new global phenomenon has arisen – climate change. The crop genetic resources and their wild progenitors that have survived countless years of changing environment during the last 11,000 years could harbor genes that may be useful under the new growing conditions and environmental factors thrown up by climate change and global warming. With the deployment of modern bio-engineering techniques selected genes or gene fragments can be transferred from genetic resources to modern varieties of crop plants to make them well-prepared to mitigate the effects of global warming and climate change. The latter is the most serious issue facing plant breeders today. New pests and diseases could affect crop production. These review paper discusses various impacts and issues as a result of this phenomenon and suggest ways to safeguard our most important crops through better management of crop plant genetic resources in the near future.

Coronavirus disease of 2019 (COVID-19) is the clinical manifestation of the respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While primarily recognized as a respiratory disease, it is clear that COVID-19 is systemic illness impacting multiple organ systems. One defining clinical feature of COVID-19 has been the high incidence of thrombotic events. The underlying processes and risk factors for the occurrence of thrombotic events in COVID-19 remain inadequately understood. While severe bacterial, viral or fungal infections are well recognized to activate the coagulation system, COVID-19 associated coagulopathy is likely to have unique mechanistic features. Inflammatory-driven processes are likely primary drivers of coagulopathy in COVID-19, but the exact mechanisms linking inflammation to dysregulated hemostasis and thrombosis are yet to be delineated. Cumulative findings of microvascular thrombosis has raised question if the endothelium and microvasculature should be a point of investigative focus. Von Willebrand Factor (VWF) and its protease, ADAMTS13 play important role in the maintenance of microvascular hemostasis. In inflammatory conditions, imbalanced VWF-ADAMTS13 characterized by elevated VWF levels and inhibited and/or reduced activity of ADAMTS13 has been reported. Also, an imbalance between ADAMTS13 activity and VWF antigen is associated with organ dysfunction and death in patients with systemic inflammation. A thorough understanding of VWF-ADAMTS13 interactions during early and advanced phases of COVID-19 could help better define the pathophysiology, guide thromboprophylaxis and treatment and improve clinical prognosis.

We present a Machian model of Quantum Cosmology with full dark matter and light speed expansion and rotation. During galaxy formation and evolution, fraction of dark matter transforms to visual matter with a relation of the form, m_vis = constant * (m_dark)^2/3. Using this relation and replacing MOND’s ‘critical acceleration’ with “current cosmic maximum angular acceleration”, galactic flat rotation speed range of (50 to 500) km/sec can be fitted well. Estimated flat rotation speeds of DD168, Milky Way and UGC12591 are 49.96 km/sec, 199.66 km/sec and 521.75 km/sec respectively. Based on these striking coincidences, it is possible to say that, MOND’s approach is implicitly connected with cosmological estimation of 95% invisible matter. Considering galactic total matter and current cosmic maximum angular acceleration, galactic working radii, angular velocity and visual matter density can be estimated. Even though, this model is free from ‘big bang’, ‘inflation’, ‘dark energy’, ‘flatness’ and ‘red shift’ issues, at 2.722 K, estimated present Hubble parameter is 66.24 km/sec/Mpc, cosmic radius is 146.3 times the Hubble radius, angular velocity is 146.3 times lower than the Hubble parameter and cosmic age is 146.3 times the Hubble age. With future observations and advanced telescopes, it may be possible to see far distant galaxies and very old stars far beyond the current observable cosmic radius.

Coal-fired thermal plants (CTPP) are known to pollute the atmosphere with emission of many greenhouse gases and particulate matter. The power generation from these thermal plants cannot be stopped completely because it forms the backbone of the grid power supply. It is necessary to study the dispersion patterns of pollutants that affect the health of the people. The dispersion patterns are location specific since they depend on local meteorological conditions. In this study, the dispersion of particulate matter (PM) and sulphur dioxide (SO₂) from a CTPP with 275 m high stack are studied under different atmospheric boundary layer (ABL) of neutral, stable and unstable conditions up to a distance of 30 km from the stack. The plume of the PM spreads under all conditions. During some parts of the day, PM settles around the stack while at other times PM keeps suspending in the air for the full distance under study. Sulphur dioxide dilutes to concentrations below the detection limits in about 12-13 km from the stack for neutral and unstable ABL whereas for the stable ABL, the dispersion is up to 30 km. The 24 h weighted average concentration of sulphur dioxide, at 10 m height from the ground, is 14.2 mg/m³ at a distance of 25 kms from the CTPP, which is comparable with the value of 9.2 mg/m³ measured at the Air Quality Stations located around the same distance. Based on the results policy changes that need to be implemented are suggested.

Budget deficits, economic crisis and competing demands for lean state resources are clear reasons why governments, especially in sub-Saharan Africa are now inclined towards the public-private partnership model of infrastructure finance. This paper comparatively examines the regulation of public-private partnership in Nigeria and South Africa. The aim is to highlight areas where both countries can learn from their experiences. The paper finds that beyond the problem of overlapping laws, weak institutional mechanisms and the need to check the arbitrariness of public officials as some of the problems that need to be addressed to build strong public-private partnership regimes in sub-Saharan Africa. The paper recommends among others, holistic strategies for strengthening the framework and practice in both countries and the need to make the public-private partnership process less cumbersome.

In this paper we provide an ``expository overview" of classic epidemic projection models. Starting with the simple case of an epidemic that grows exponentially we then investigate ``compartmental" models. These assume that the growth of an infected population is limited endogenously by the size of the underlying pool of susceptibles. We then describe a new family of so-called "Exo-r" statistical models, which hinge on an exogenously driven growth rate of the infected population. This family, which can be used to model both infections and deaths, captures parsimoniously both the depletion of susceptibles and the effect of interventions such as lockdowns and ``social distancing". The model is used to fit numbers of Covid-19 infections in China. It is also used to model and project deaths in the United States. Results are used to inform a discussion on i) the challenges at hand and ii) the extent to which epidemic projection models may be useful despite being wrong.

Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. In this research, we argue that unlike general-purpose classification problems, medical applications, such as chronic kidney disease (CKD) diagnosis, require special treatment. In the case of CKD, apart from model performance, other factors such as the cost of data acquisition may also be taken into account to enhance the applicability of the automated diagnosis system. In this research, we have proposed two techniques for cost-sensitive feature ranking. An ensemble of decision tree models is employed in both the techniques for computing the worth of a feature in the CKD dataset. An automatic threshold selection heuristic is also introduced which is based on the intersection of features’ worth and their accumulated cost. A set of experiments are conducted to evaluate the efficacy of the proposed techniques on both tree-based and non-tree based classification models. The proposed approaches are also evaluated against several comparative techniques. Furthermore, it is demonstrated that the proposed techniques select around 1/4^th of the original CKD features while reducing the cost by a factor of 7.42 of the original feature set. Based on the extensive experimentation it is concluded that the proposed techniques employing feature-cost interaction heuristic tend to select feature subsets that are both useful and cost-effective.

Cold brew coffee has emerged as a new trend during the last decade. However, “cold brew” is an extraction style of ground roasted coffee with water at less than body temperature (typically 8°C or room temperature) rather than a beverage per se. Cold brew extraction poses several challenges including the need of specific optimization depending on the multivariate influences of coffee variety and processing, roast degree, grinding, dosage, water composition, turbulence, brew system (drip, immersion etc.), time and temperature. While cold brew is typically characterized by a floral sweetness, over-extraction may lead to abundant acidity and bitterness. To avoid this, an extraction degree of 70% was suggested using rather shorter times (i.e. 2 h at 15°C with 80 g/L coffee with optimized medium roast profiles). Due to the lack of sterilizing temperatures during preparation, cold brew is special in the coffee sector because hygiene and food safety aspects pose specific challenges for food industry. To avoid microbiological contamination and spoilage, cold brew should be as freshly prepared as possible and shelf-life should be minimized.

In this study, a monoethanolamine aerosol growth model was developed to investigate the aerosol growth factor. Interactions among the internal conditions in an absorber were considered in this aerosol model. Additionally, an experiment was conducted to measure aerosol particle size, for collecting in-house validation data. Sucrose was used as the aerosol nuclei instead of sulfuric acid to prevent the corrosion of equipment used in the experiment. Experimental results showed that the outlet aerosol sizes increased to the same size regardless of the sucrose concentrations. The aerosol growth model was validated using the in-house experimental data. The aerosol growth model efficiently predicted the aerosol size. For investigating aerosol growth effects, particle number concentration was determined to be the primary factor affecting aerosol growth and amine emissions. When the particle number concentration increased, the aerosol size decreased, whereas the MEA emission increased.

Among the many approaches to COVID-19 prevention, the possible role of diet has so far been somewhat marginal. Nutrition is very rich in substances with a potential beneficial effect on health and some of these could have an antiviral action or in any case be important in modulating the immune system and in defending cells from the oxidative stress associated with infection. This short review draws the attention on some components of Citrus fruits and especially of the orange (Citrus sinensis), well known for its vitamin content, but less for the function of its flavonoids. Among the latter, hesperidin has recently attracted the attention of researchers, because it binds to the key proteins of the SARS-CoV-2 virus. Several computational methods, independently applied by different researchers, showed that hesperidin has a low binding energy both with the coronavirus "spike" protein, and with the main protease that transforms the early proteins of the virus (pp1a and ppa1b) into the complex responsible for viral replication. The affinity of hesperidin for these proteins is comparable if not superior to that of common chemical antivirals. The preventive efficacy of vitamin C, at dosage attainable by diet, against viral infections is controversial, but recent reviews suggest that this substance may be useful in case of increased stress on the immune system. Finally, the reasons that suggest undertaking appropriate research on the Citrus fruits addition in the diet, as a complementary prevention and treatment of COVID-19, are discussed.

Consumption of milk contaminated with antibiotic residues above the maximum residue limit (MRL) causes direct toxicity to humans and the development of superbugs that leads to the failure of antibiotic therapy and threatens human life. Besides, long-duration exposure might alter the nature of gut microflora results in the enhancement of many diseases. Therefore, we examined 300 raw and processed packet milk samples using thin layer chromatography (TLC) and ultra high performance liquid chromatography (UHPLC) method against five veterinary antibiotics and assessed the risk for consumers in Chattogram, Bangladesh. Risk analysis was calculated by using hazard quotient on the basis of 165 ml per capita milk consumption. We found a total of 7% prevalence of antibiotic residues in raw milk samples which were higher (8%) in individual samples than the pooled samples (4%). However, we did not find any processed packet milk samples as positive. The mean concentration of oxytetracycline residue was detected 61.29 µg/l and amoxicillin was 124 µg/l in individual milk samples. Risk analysis showed the hazard quotient values of 0.0056 for oxytetracycline and 0.0017 for amoxicillin residue which was confirmed that, no significant health risk associated with the consumption of milk produced and marketed in the study area. Our study might fill the gap of knowledge to measure the safety status of milk regarding public health issues.

Background: Sickle cell disease is a major public health issue in the DRC while it is still poorly understood by health professionals. The objective of this study was to assess the knowledge and practices of Congolese physicians treating sickle cell disease (SCD) in order to identify areas for improvement in clinical care. Methods: This is a descriptive observational study conducted on Congolese physicians using a questionnaire. Participants were evaluated on a pre-established answer grid. Results: A total of 460 physicians participated, including 81 women (18%) with an average age of 35 years (range 25–60 years). Most physicians were general practitioners. Although self-assessment of their level of knowledge on SCD was estimated as average to good, less than half of the participants (n= 460; 46%) reported adequate management of vaso-occlusive crisis but only 1% of them had received specific training on SCD. Most physicians reported difficulties both in terms of diagnostic (65%) and management (79%) options of SCD patients. This study showed also that 85% of them did not have access to diagnostic tools for SCD. Conclusions: Insufficient knowledge on SCD, poor diagnostic and treatment options may contribute to morbidity and mortality of patients living in the DRC. Interventions aiming at improving physician’s knowledge, patient’s follow-up and treatment access are needed. Specific training alongside existing programs (HIV, malaria), early diagnosis of the disease, and creation of patients’ advocacy groups should improve SCD patient’s care.

Nonwoven fabrics have been widely used in textile manufacturing industry as a sheet or web structure because of soft, water-repellent, recycle, ecological and resilient functions. Ultrasonic welding method has been applied for bonding nonwoven fabrics due to clean, fast and reliable approach. In this work, the ultrasonic stepped horn is designed to generate uniform amplitudes on the working surface by using finite element analysis (FEA) simulation. Chromium carbon steels are utilized to produce ultrasonic horns due to high wear resistant and hardness. Isotactic polypropylene nonwoven fabrics fabricated by spunbond process were bonded by continuous ultrasonic sewing machine. Ultrasonic horn with 70 mm in diameter working at 20 kHz, polypropylene (PP) nonwoven density of 80 gsm and various design of welding joints were applied. A typical image in the case of number one was investigated by the scanning electron microscope (SEM) images of inter-facial micro-structure. However, welding joints of totally eight roller patterns was test the tensile strength of the ultrasonic welding joints on PP nonwoven fabrics. The tensile strength of the welding joints is proportional to the area ratio between the welding area and cycling area. The results showed that the melted zone without welding defects such as crack or blowhole can be seen. Furthermore, the tensile strength of welding joints in eight cases of roller patterns (No.1-No.8) was described in details. The ultrasonic welding joints with brick structures give higher tensile strength while the solid line in the pattern gave less strength.

French bean (Phaseolus vulgaris L.) a member of family Leguminosae is a useful source of protein (∼22%), minerals (folate), vitamins and fibre. Abiotic and biotic stresses are the constraints to high yield and production of French bean. Varieties reluctant to diseases as well as abiotic stresses is among the top breeding objectives for the French bean. Mendelian ratios could know the genetically reliable forms of resistance, whereas it's more robust to understand the intricate kinds, often referred to as quantitative trait loci (QTL). Here, we review and compile the information from the studies related to the identification of QTLs for critical biofortification traits, biotic and abiotic stresses in French bean. Successful map-based cloning requires QTLs represent single genes which could be isolated in near-isogenic lines, and also the genotypes could be unambiguously inferred by progeny testing. Overall, this information will be useful for directing the French bean breeders to select a suitable method for the inheritance evaluation of quantitative traits and determining the novel genes in germplasm resources to ensure that much more potential of genetic information may be uncovered.

Boolean functions and networks are commonly used in the modeling and analysis of complex biological systems, and this paradigm is highly relevant in other important areas in data science and decision making, such as in the medical field and in the finance industry. In a Boolean model, the truth state of a variable is either 0 or 1 at a given time. Despite its apparent simplicity, Boolean networks are surprisingly relevant in many areas of application such as in bioinformatics to model gene expressions and interactions. In the latter case, a gene is either ``on" or ``off" depending on its expression level. Despite the promising utility of Boolean modeling, in most practical applications the Boolean network is not known. Automated learning of a Boolean network and Boolean functions, from data, is a challenging task due in part to the large number of unknowns (including both the structure of the network and the functions) to be estimated, for which a brute force approach would be exponentially complex. In this paper we develop a new information theoretic methodology that we show to be significantly more efficient than previous approaches. Building on the recently developed optimal causation entropy principle (oCSE), that we proved can correctly infer networks distinguishing between direct versus indirect connections, we develop here an efficient algorithm that furthermore infers a Boolean network (including both its structure and function) based on data observed from the evolving states at nodes. We call this new inference method, Boolean optimal causation entropy (BoCSE), which we will show that our method is both computationally efficient and also resilient to noise. Furthermore, it allows for selection of a set of features that best explains the process, a statement that can be described as a networked Boolean function reduced order model. We highlight our method to the feature selection in several real-world examples: (1) diagnosis of urinary diseases, (2) Cardiac SPECT diagnosis, (3) informative positions in the game Tic-Tac-Toe, and (4) risk causality analysis of loans in default status. Our proposed method is effective and efficient in all examples.

Introduction: Lowered thiol or sulfhydryl (-SH) groups and glutathione (GSH) metabolism may be associated with prostate cancer (PCa) and benign prostatic hyperplasia (BPH). The main objectives of this study were to systematically review and meta-analyze the evidence on the associations among -SH groups, GSH, GSH peroxidase (GPx), GSH reductase (GR) and GSH transferase (GST) and PCa/BPH. Methods: Four electronic databases were searched for studies that reported -SH and GSH variables in PCa/BPH and healthy controls (HC) and the data were meta-analyzed by calculating Hedges’s g with 95% confidence intervals. Results: Twenty studies were included in this meta-analysis including 800 PCa and 791 BPH patients and 592 HC. Total -SH (g=-1.750, -2.341/-1.159), GPx (g=-0.789, -1.234/-0.344), GSH (g=-2.219, -4.132/-0.305), and the combination of -SH, GPx, and GSH (g=-1.271, -1.271/-0.800) were significantly lower in PCa patients than in HC. -SH (g=-1.752, -3.123/-0.381) and the combination of -SH, GPx, and GSH (g=-0.813, -1.298/-0.327) were significantly lower in BPH patients than in HC. GPx was significantly lower in PCa than in BPH patients (g=-0.455, -0.896/-0.014). Heterogeneity levels were very high, but Egger’s test showed that none of the biomarkers showed significant publication bias. Conclusion: Thiol/GPx antioxidant defenses are significantly attenuated in patients with PCa while patients with BPH occupy an intermediate risk group position between PCa patients and HC. Those results suggest that reduced thiol-related antioxidant mechanisms play a crucial role in prostatic disorders, especially in PCa. Future research should examine whether thiols/GPx antioxidants are associated with the onset of PCa/BHP.

Neurodegenerative diseases of protein misfolding affect humans and animals. In humans, these diseases include Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and Western Pacific amyotrophic lateral sclerosis and Parkinsonism-dementia complex (ALS/PDC). Mineral exposure may be important in the pathogenesis of protein misfolding cascades. The possible association of bentonite, montmorillonite, and mineral risk factors with Alzheimer’s disease, Parkinson’s disease, ALS, and Western Pacific ALS/PDC is analyzed and discussed.

Background: Lung CT provides an effective modality to evaluate patients with suspected COVID-19. However, overlapping imaging findings with cardiogenic pulmonary oedema have been reported. Reports comparing lung CT features of these diseases have not been elaborated. Thus, we aimed to investigate these gaps in the knowledge regarding low-dose lung CT features of patients with COVID-19 pneumonia with those with acute heart failure (HF). Methods: This retrospective analysis enrolled hospitalized patients with COVID-19 (n=10) and acute heart failure (n=9) that exclusively underwent low-dose lung CT scans within 24-hours of admission. Clinical and lung CT characteristics were collected and analysed. Results: Ground-glass-opacities (GGO) appearance has been recorded in all subjects in HF and COVID-19 group. There was no significant statistical difference between the two groups for rounded morphology, consolidation, crazy paving pattern, lesion distribution, parenchymal band (P> 0.05). However, diffuse lesions were more frequent in HF cases (55.6% vs. 0%) than in COVID-19 pneumonia, which had predominantly multifocal pattern. Notably, CT images in HF patients were more likely to have signs of interstitial tissue thickening such as the interlobular septums, fissures and peribronchovascular interstitium (55.6% vs 0%, 88.9% vs 20% and 44.4% vs 0%,respectively), as well as cardiomegaly (77.8% vs 0%), increased artery to bronchus ratio (55.6% vs 0%), and pleural effusions (77.8% vs 0%). Conclusions: Major overlaps of lung CT imaging features existed between COVID-19 pneumonia and acute HF cases. However, signs of fluid redistribution are clues that favour HF over COVID-19 pneumonia.

The stele concept is one of the oldest enduring concepts in plant biology. This paper reviews the concept and its foundations, and builds an argument for an updated view of steles and their evolution. The history of studies of stelar organization has generated a widely ranging array of definitions of the stele that determine the way we classify steles and construct scenarios about the evolution of stelar architecture. Because at the level of the organism biological evolution proceeds by, and is reflected in, changes in development, concepts of structure need to be grounded in development in order to be relevant in an evolutionary perspective. For the stele, most of the traditional definitions that incorporate development have viewed it as the totality of tissues that either originate from procambium – currently the prevailing view – or are bordered by a boundary layer (e.g., endodermis). A definition of the stele that would bring consensus between these perspectives recasts the stele as a structural entity of dual nature. Here, I review briefly the history of the stele concept, basic terminology related to stelar organization, and traditional classifications of the steles. I then revisit boundary layers from the perspective of histogenesis as a dynamic mosaic of developmental domains. I use classic and recent anatomical and molecular data to reaffirm and explore the importance of boundary layers for stelar organization. Drawing on data from comparative anatomy, developmental regulation, and the fossil record, I offer a model for a stele concept that integrates both the boundary layer and the procambial perspective, consistent with a dual nature of the stele. The dual stele model posits that stelar architecture is determined in the apical meristem by two major cell fate specification events: a first one that specifies a provascular domain and its boundaries, and a second event that specifies a procambial domain (which will mature into conducting tissues) from cell subpopulations of the provascular domain. If the position and extent of the developmental domains defined by the two events are determined by different concentrations of the same morphogen (most likely auxin), then the distribution of this organizer factor in the shoot apical meristem, as modulated by changes in axis size and the effect of lateral organs, can explain the different stelar configurations documented among tracheophytes. This model provides a set of working hypotheses that incorporate assumptions and generate implications that can be tested empirically. The model also offers criteria for an updated classification of steles that is in line with current understanding of plant development. In this classification, steles fall into two major categories determined by the configuration of boundary layers – boundary protosteles and boundary siphonosteles, each with subtypes defined by the architecture of the vascular tissues. Validation the dual stele model and, more generally, in-depth understanding of the regulation of stelar architecture, will necessitate targeted efforts in two areas: (i) the regulation of procambium, vascular tissue, and boundary layer specification in all extant vascular plants, considering that most of the diversity in stelar architecture is hosted by seed-free plants, which are the least explored in terms of developmental regulation; (ii) the configuration of vascular tissues and, especially, boundary layers, in as many extinct species and lineages as possible.

We put forward a co-axial pump(optical)-probe(X-rays) experimental concept and show performance of the optical component. A Bessel beam generator with a central 100 micrometers-diameter hole (on the optical axis) was fabricated using femtosecond (fs) laser structuring inside a silica plate. This flat-axicon optical element produces a needle-like axial intensity distribution which can be used for the optical pump pulse. The fs-X-ray free electron laser (X-FEL) beam of sub-1 micrometer diameter can be introduced through the central hole along the optical axis onto a target as a probe. Different realisations of optical pump are discussed. Such optical elements facilitate alignment of ultra-short fs-pulses in space and time and can be used in light-matter interaction experiments at extreme energy densities on the surface and in the volume of targets. Full advantage of ultra-short 10 fs X-FEL probe pulses with fs-pump(optical) opens an unexplored temporal dimension of phase transitions and the fastest laser-induced rates of material heating and quenching. A wider field of applications of fs-laser-enabled structuring of materials and design of specific optical elements for astrophotonics is presented.

Non-pharmacological interventions in the fight against COVID 19 include: a) suppression, which facilitates its extinction; and b) mitigation, which reduces its speed of spread. Left unmitigated, the intensive care unit bed capacity (ICU) is exceeded over its maximum supply, resulting in increased deaths. Suppression has shown in simulation models the potential for decreasing ICU occupation below its surge limit, effectively decreasing mortality. However, for avoiding a rebound in transmission, suppression must be maintained intermittently until a vaccine is available (which may take up to 2 years). The objective of this paper was to describe the mortality patterns observed in Spain, Italy and South Korea for discussing a hypothetical combined public health policy and socioeconomic model that could potentially reduce mortality while reducing the economic impact of this pandemic in Spain. The plan is based on a progressive-voluntary reinstatement to work of the population exposed to the lowest risks (healthy non-immune family units <50 y/o and immune population) and it depends on having sufficiently available ICU beds for providing adequate support. This model, if proven correct for Spain, could eventually be followed by other countries facing a similar impact of the present pandemic.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of CoV disease 2019 (COVID-19) is a highly pathogenic and transmissible CoV that is presently plaguing the global human population and economy. No proven effective anti-viral therapy or vaccine currently exist, and supportive care remains to be the cornerstone treatment. Through previous lessons learned from SARS-CoV-1 and MERS-CoV studies, scientific groups worldwide have rapidly expanded the knowledge pertaining to SARS-CoV-2 virology that included in vitro and in vivo models for testing of anti-viral therapies, and randomized clinical trials. In the present narrative, we review SARS-CoV-2 virology, clinical features, pathophysiology, and animal models with a specific focus on anti-viral and adjunctive therapies currently being tested or require testing in animal models and randomized clinical trials.

The time series of daily new cases of Covid-19 infection in different zones of Italy, several European countries and New York City were analyzed in order to characterize the large fluctuations contained in them. The comparison shows that the time series share a similar evolution, although with delays of one or two weeks, with Italy that precedes the other countries. Correlation analysis indicates a strict resemblance between the time series of the first differences, which emphasize accelerations and slowdowns of the epidemic (e.g., correlation coefficient r=0.70 for Italian vs. worldwide data). According to Fourier analysis the signals have a dominant oscillatory component at a period of seven days, with minima located near the weekends. Some of the possible causes of this time modulation are discussed. In particular, it is hypothesized a delayed or missed recognition of a significant fraction of the new cases during the weekend, with possible consequences for the evolution of the epidemic.

Accurate detection of players and teams in ice hockey games is crucial to the tracking of individual players on court and team tactical decisions, which is therefore becoming an important task for coaches and other analysts. However, hockey is a fluid sport due to complex dynamics and frequent substitutions by both teams, resulting in various body positions of players. Few traditional player detection models from other team sports take these characteristics into account, especially for the detection of teams without prior annotations. Here, we design a two-phase cascaded Convolutional Neural Network (CNN) model coupling between individual players position information and team uniform colors to hierarchically detect players in ice hockey games. Our model filters most of disturbing information, such as audience and sideline advertising bars, in Phase I, and refines the detection of targeted players in Phase II, which is efficient and accurate with a precision rate of 91.30% and a recall rate of 95.60% for individual players detection, and an average accuracy of 93.05% in team classification from a self-built dataset of collected images in the 2018 Winter Olympics. Meanwhile, we also present results based on the images and real-time detection from broadcasting videos of 2019-20 NHL regular games covering all 31 teams to show the robustness of our model.

On March 11, 2020, coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO). This review focuses on where the body mass index (BMI) value can be used as a tool to evaluate the risk of development and/or aggravation of this disease. Databases were used to search studies published up to April 18, 2020. In total, 4285 articles and other scientific literature were found, and twelve articles were included in this systematic review. The mean BMI value of severe COVID-19 patients ranged from 24.5 to 33.4 kg/m², versus 22.0 to 24.3 kg/m² for non-severe patients Articles using the terms obesity or overweight, without indicating the BMI value, in these patients were common, but this is not useful as the nutritional status, when not defined by this index, is confusing due to the classification being different in the West compared to among,, Asian and Korean criteria-based adults. Furthermore, the use of BMI is important during this pandemic, as it should be applied to nutritional support therapy during hospitalization of infected patients, as well as being considered in the home confinement population.

Amid the covid-19 pandemic, other diseases, including viruses, are still acting to the detriment of their seasonality and risk factors for contagion. For this reason, it is interesting to know the degree of impact of other viruses, mainly respiratory, in which they have similar symptoms, in diagnoses for contamination by the new coronavirus based on epidemiological surveys, via epidemiological weeks, in Brazil. To what extent there may be a hypothesis of confusion of contaminated data, harming the health system, with regard to the need for intensive care units and control of viruses, and negatively or positively implying in the control or uncontrolling of viruses in general.

Background: Administrative healthcare databases are widespread and are often standardized with regard to their content and data coding, thus they can be used also as data sources for surveillance and epidemiological research. Chronic dialysis requires patients to frequently access hospital and clinic services, causing a heavy burden to healthcare providers. This also means that these patients are routinely tracked on administrative databases, yet very few case definitions for their identification are currently available. The aim of this study was to develop two algorithms derived from administrative data for identifying incident chronic dialysis patients and test their validity compared to the reference standard of the regional dialysis registry. Methods: The algorithms are based on data retrieved from hospital discharge records (HDR) and ambulatory specialty visits (ASV) to identify incident chronic dialysis patients in an Italian region. Subjects are included if they have at least one event in the HDR or ASV databases based on the ICD9-CM dialysis-related diagnosis or procedure codes in the study period. Exclusion criteria comprise non-residents, prevalent cases, or patients undergoing temporary dialysis, and are evaluated only on ASV data by the first algorithm, on both ASV and HDR data by the second algorithm. We validated the algorithms against the Emilia-Romagna regional dialysis registry by searching for incident patients in 2014. Results: Algorithm 1 identified 680 patients and algorithm 2 identified 676 initiating dialysis in 2014, compared to 625 patients included in the regional dialysis registry. Sensitivity for the two algorithms was respectively 90.8% and 88.4%, positive predictive value 84.0% and 82.0%, and percentage agreement was 77.4% and 74.1%. Conclusions: These results suggest that administrative data have high sensitivity and positive predictive value for the identification of incident chronic dialysis patients. Algorithm 1, which showed the higher accuracy and has a simpler case definition, can be used in place of regional dialysis registries when they are not present or sufficiently developed in a region, or to improve the accuracy and timeliness of existing registries.

Calcium carbonate cements have been synthesized by mixing amorphous calcium carbonate and vaterite powders with water to form a cement paste and study how mechanical strength is created during the setting reaction. In-situ XRD was used to monitor the transformation of ACC and vaterite phases into calcite and a rotational rheometer was used to monitor the strength evolution. There are two characteristic time scales of the strengthening of the cement paste. The short timescale of the order 1 hour is controlled by smoothening of the vaterite grains, allowing closer and therefore adhesive contacts between the grains. The long timescale of the order 10-50 hours is controlled by the phase transformation of vaterite into calcite. This transformation is, unlike in previous studies using stirred reactors, found to be mainly controlled by diffusion in the liquid phase. The evolution of shear strength with solid volume fraction is best explained by a fractal model of the paste structure.

The application of Virtual Reality in a driving simulation is not novel, yet little is known about the use of this technology by senior populations. The effects of age, sex, control device (joystick or handlebar), and task type on wayfinding proficiency using a virtual reality (VR) driving simulation were explored. The driving experimental model involved 96 randomly recruited participants, including 48 young people and 48 seniors (split evenly by gender in each group). The experimental results and statistical analyses indicate that in a VR driving scenario task type significantly affected VR driving performance. Navigational scores were significantly higher for the straight (easy) task than for the curved (difficult) task. The aging effect was the main reason for significant and interacting effects of sex and control device. It was found that interactions between age and sex difference indicated that the young group exhibited better wayfinding performance than the senior group, and in the young group males had better performance than females. Similarly, interactions between age and control device indicated that the handlebar control device type resulted in better performance than the joystick device in the young group, but no difference was found in the senior group due to age or learning effects. Findings provide an understanding of the evaluation of the interface designs of navigational support systems, taking into consideration any effects of age, sex, control device, and task type within three-dimensional VR games and driving systems. With a VR driving simulator, seniors can test drive inaccessible products, such as electric bicycles or cars, using a computer at home.

Solid tumors display complex biology and most therapies including chemotherapy cannot prevent therapy resistance and relapse. Most therapeutics target cancer cells, but recent data suggest the presence of cancer stem cells as cells with self-renewal and tumorigenic abilities. Cancer stem cell markers have been suggested to have prognostic value and can be targeted during cancer treatment and in resistant disease. CSCs have been postulated to play significant contextual roles in tumor initiation, progression, therapy resistance and metastasis. CSCs have thus been targeted by new generation cancer drugs. The transcriptional expression of several CSC markers in different cancers was evaluated by searching publicly available The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) databases. We report here new findings on expression and prognostic significance of CSC markers in several cancers by examining the expression of CSCs markers in tumor tissues versus the adjacent normal tissues. We found that CSC markers were mostly highly expressed various tumors such as colon, lung, pancreatic and esophageal cancers. No CSC marker is expressed in the same pattern in all cancers and individual CSC marker expression was not linked to patient survival. This analysis calls for continued research on CSCs and clinical evaluation of the CSC markers in relation to prognosis of cancers in large population samples. Novel cancer drugs ought to target CSCs, cancer cells and tumor microenvironment variations.

In this paper, we use a new asymptotically flat and spherically symmetric solution in the generalized Einstein-Cartan-Kibble-Sciama (ECKS) theory of gravity to study the weak gravitational lensing and its shadow cast. To this end, we first compute the weak deflection angle of generalized ECKS black hole using the Gauss–Bonnet theorem in plasma medium and in vacuum. Next by using the Newman-Janis algorithm without complexification, we derive the rotating generalized ECKS black hole and in the sequel study its shadow. Then, we discuss the effect of the ECKS parameter on the shadow of the black hole and weak deflection angle. In short, the goal of this paper is to give contribution to the ECKS theory and look for evidences to understand how the ECKS parameter effects the gravitational lensing.

Photogrammetry involves aerial photography of the earth’s surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It’s used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep Learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our system applies grammar guided genetic programming to the search of deep neural network architectures. In this kind of evolutive algorithm all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g. Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state of the art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan.

In Ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight~(LOS), non-line-of-sight~(NLOS), and multi-path (MP) conditions are important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS or MP). However, the major contributions in literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. Though, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the mentioned three classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental data-set. The data-set was collected in different conditions at different scenarios in indoor environments. Using the collected real measurement data, we compare three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results show that applying ML methods in UWB ranging systems are effective in identification of the above-mentioned three classes. In specific, the overall accuracy reaches up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it is 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we (will) provide the publicly accessible experimental research data discussed in this paper at PUB - Publications at Bielefeld University. The evaluations of the three classifiers are conducted using the open-source python machine learning library scikit-learn.

There were warnings before; nevertheless the current CoVID-19 pandemic took the world by surprise: within just four month, it conquered the globe and claimed over 200'000 lives. Unprecedented governmental actions put about half of the population under curfew or lock-down. The resulting economic meltdown is expected to eliminate globally 9’000’000’000’000 (9 trillion) USD in 2020 and 2021 alone, a value roughly the size of the yearly GDP of the world’s 150 smallest economies. The resulting crises might cause mass-unemployment and a hunger pandemic later this year. This Essay analyses current statistical data of the CoVID-19 pandemic to develop a guideline for a path through the crisis, minimizing both loss of lives and economic costs. Part 1 details the current situation; part 2 develops a small set of measures, allowing a near normal life until a future vaccination campaign has reached sufficient numbers of people; and part 3 provides some important lessons for the future beyond SARS-CoV-2. The Essay leads to the following key-messages: 1) The CoVID-19 pandemic will stay for at least two more years. This is the minimum time required for a vaccination campaign to reach sufficient numbers of people. 2) The crucial element to control the pandemic is keeping case numbers under the threshold required for a functional tracing, testing & isolation (TTI) strategy. That threshold differs from country to country and strongly depends on culture and the applied tracing technology as well as available testing capacities. 3) The economic burden of a TTI strategy is moderate while fatalities are also reduced. Hence, such an approach is strongly recommended. Its implementation requires a set of simple and cost-effective measures (see figure below), which in combination seem to be sufficient to keep CoVID-2’s reproductive rate at or below 1. 4) Implementing international coordination of actions will be necessary for effective infection-chain tracing5) If case numbers are above the TTI threshold, shutdown measures remain the only option until tracing of infection chains becomes feasible again.6) In the future, neglected pandemic-related research requires a funding boost. Just 1% of the bill of the current crisis could support the research of 45’000 scientist for 20 years.

The microbiota of human breast milk (HBM) contributes to infant gut colonization; however, whether bacterial extracellular vesicles (EVs) are present in HBM or might contribute to this process remains unknown. In the present study, we characterized the HBM microbiota of healthy Korean mothers and measured the key bacteria likely affecting infant gut colonization by analyzing both the microbiota and bacterial EVs. A total of 22 HBM samples were collected from lactating mothers. The DNA of bacteria and bacteria-derived EVs was extracted from each sample. Gene analysis was performed using Illumina MiSeq. Firmicutes accounted for the largest portion among the phyla, followed by Proteobacteria, Bacteroides, and Actinobacteria in both bacteria and bacterial EV samples. At the genus level, Streptococcus (25.1%) and Staphylococcus (10.7%) were predominant in bacterial samples, whereas Bacteroides (9.1%), Acinetobacter (6.9%), and Lactobacillaceae(f) (5.5%) were prevalent in bacterial EV samples. Several genera including Bifidobacterium were significantly positively correlated between the two samples. Our findings reveal the diverse bacterial communities in HBM of healthy lactating mothers and suggest the presence of key bacteria with metabolic activity in HBM and that EVs derived from these bacteria may contribute to the vertical transfer of gut microbiota from mother to infant.

The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner. The coronavirus spread so quickly between people and approaches 100,000 people worldwide. In this consequence, it is very much essential to identify the infected people so that prevention of spread can be taken. In this paper, the deep feature plus support vector machine (SVM) based methodology is suggested for detection of coronavirus infected patient using X-ray images. For classification, SVM is used instead of deep learning based classifier, as the later one need a large dataset for training and validation. The deep features from the fully connected layer of CNN model are extracted and fed to SVM for classification purpose. The SVM classifies the corona affected X-ray images from others. The methodology consists of three categories of Xray images, i.e., COVID-19, pneumonia and normal. The method is beneficial for the medical practitioner to classify among the COVID-19 patient, pneumonia patient and healthy people. SVM is evaluated for detection of COVID-19 using the deep features of different 13 number of CNN models. The SVM produced the best results using the deep feature of ResNet50. The classification model, i.e. ResNet50 plus SVM achieved accuracy, sensitivity, FPR and F1 score of 95.33%,95.33%,2.33% and 95.34% respectively for detection of COVID-19 (ignoring SARS, MERS and ARDS). Again, the highest accuracy achieved by ResNet50 plus SVM is 98.66%. The result is based on the Xray images available in the repository of GitHub and Kaggle. As the data set is in hundreds, the classification based on SVM is more robust compared to the transfer learning approach. Also, a comparison analysis of other traditional classification method is carried out. The traditional methods are local binary patterns (LBP) plus SVM, histogram of oriented gradients (HOG) plus SVM and Gray Level Co-occurrence Matrix (GLCM) plus SVM. In traditional image classification method, LBP plus SVM achieved 93.4% of accuracy.

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. On the one hand, blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. On the other hand, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating coronavirus (COVID-19) epidemics based on the rapidly emerging literature. First, we introduce a new conceptual architecture which integrates blockchain and AI specific for COVID-19 fighting. Particularly, we highlight the key solutions that blockchain and AI can provide to combat the COVID-19 outbreak. Then, we survey the latest research efforts on the use of blockchain and AI for COVID-19 fighting in a wide range of applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

Retinal pigment epithelial (RPE) cells are the major cell type in the epi- or sub-retinal membranes in the pathogenesis of proliferative vitreoretinopathy (PVR), which is a blinding fibrotic eye disease and still short of effective medicine. The purpose of this study is to demonstrate if Chalocomoracin (CMR), a novel purified compound from fungus-infected mulberry leaves, is able to inhibit vitreous-induced signaling events and cellular responses intrinsic to PVR. Our studies have revealed that the CMR IC50 for ARPE-19 cells is 35.5 μM at 72 hours, and that 5 μM CMR inhibits vitreous-induced Akt activation and p53 suppression; in addition we have discovered that this chemical effectively blocks vitreous-stimulated proliferation, migration and contraction of ARPE-19 cells, suggesting that CMR is a promising PVR prophylactic.

Inflammation is considered an important target for stroke therapy because it induces secondary brain damage after the initial ischemic insult. Peripheral monocytes migrate to the brain parenchyma after a central insult. They then differentiate to macrophages in a positive feedback fashion contributing to damage instead of ischemic resolution and inflammation control. A cyclic diterpenoid, (1S,2E,4R,6R,7E,11E)-cembra-2,7,11-triene-4,6-diol (4R), decreases neurodegeneration after ischemia with central anti-inflammatory activity. This study aims to determine whether the central anti-inflammatory effect of 4R is effective against peripheral inflammation triggered by brain ischemia. To investigate the anti-inflammatory effect of 4R, we treated macrophages with lipopolysaccharide (LPS) as an inflammatory model, followed by treatment with 4R. Microarray transcriptome analysis of over 30,000 genes identified the differential expression of 393 genes. Genes related to inflammation, cell adhesion, and transcription were validated with qPCR, and reduced expression was determined. Quantification of NF-kB phosphorylation served as a marker for the modulation of inflammation through gene transcription. Our results show that 4R was associated with a reduction in NFKB1 and ITGB5 gene expression, increased phosphorylation of NF-kB, and a decrease in macrophage adhesion in a blood-brain barrier model. These results indicate that 4R can partially modulate the peripheral immune response, making 4R a potential drug against post-ischemic inflammation.

SARS-CoV-2 (CoV-2) is a coronavirus which is causing the actual COVID-19 pandemic. The disease caused by 2019 new coronavirus (2019-nCoV) was named coronavirus disease-19 (COVID-19) by the World Health Organization in February 2020. Primary non-specific reported symptoms of 2019-nCoV infection at the prodromal phase are malaise, fever, and dry cough. The most commonly reported signs and symptoms are fever (98%), cough (76%), dyspnea (55%), and myalgia or fatigue (44%). Nonetheless, recent reports suggest an association between COVID-19 and altered olfactory and taste functions, although smell seems to be more affected than taste. These associations of smell and taste dysfunctions and CoV-2 are consistent with case reports describing a patient with SARS with long term anosmia after recovery from respiratory distress, with the observation that olfactory function is commonly altered after infection with endemic coronaviruses, and with data demonstrating that intentional experimental infection of humans with CoV-299 raises the thresholds at which odors can be detected. Post-viral anosmia and is one of the leading causes of loss of sense of smell in adults, accounting for up to 40% cases of anosmia. Viruses that give rise to the common cold are well known to cause post-infectious loss, and over 200 different viruses are known to cause upper respiratory tract infections. I reviewed the possible mechanisms of smell and taste loss in COVID-19. I concluded that since the existence of such a relationship is likely, it is highly recommended that those patients who experience complications such as smell and/or taste loss, even as unique symptoms, should be considered as potential SARS-CoV-2 virus carriers.

Pneumonia is a severe lower respiratory tract infection that is a common complication and a major cause of mortality of the vitamin C-deficiency disease scurvy. This suggests an important link between vitamin C status and lower respiratory tract infections. Due to the paucity of information on the vitamin C status of patients with pneumonia, we assessed the vitamin C status of 50 patients with community-acquired pneumonia and compared these with 50 healthy community controls. The pneumonia cohort comprised 44 patients recruited through the Acute Medical Assessment Unit (AMAU) and 6 patients recruited through the intensive care unit (ICU); mean age 68 ± 17 years, 54% male. Clinical, microbiological and haematological parameters were recorded. Blood samples were tested for vitamin C status using HPLC with electrochemical detection and protein carbonyl concentrations, a marker of oxidative stress, using ELISA. Patients with pneumonia had depleted vitamin C status compared with healthy controls (23 ± 14 µmol/L vs 56 ± 24 µmol/L, P <0.001). The more severe patients in the ICU had significantly lower vitamin C status than those recruited through AMAU (11 ± 3 µmol/L vs 24 ± 14 µmol/L, P = 0.02). The total pneumonia cohort comprised 62% with hypovitaminosis C and 22% with deficiency, compared with only 8% hypovitaminosis C and no cases of deficiency in the healthy controls. The pneumonia cohort also exhibited significantly elevated protein carbonyl concentrations compared with the healthy controls (P < 0.001), indicating enhanced oxidative stress in the patients. We were able to collect subsequent samples form 28% of the cohort (mean 2.7 ± 1.7 days; range 1-7 days). These showed no significant differences in vitamin C status or protein carbonyl concentrations compared with baseline values (P = 0.6). Overall, the depleted vitamin C status and elevated oxidative stress observed in the patients with pneumonia indicates an enhanced requirement for the vitamin during their illness. Due to the important roles that vitamin C plays in the immune system, low vitamin C status is possibly both a cause and a consequence of the disease. Therefore, these patients would likely benefit from additional vitamin C supplementation to restore their blood and tissue levels to optimal. This may decrease oxidative stress and aid in their recovery.

India is one of the large sources of the anthropogenic pollutants and their increasing emission due to the recent economic growth in India. In this study we analyzed the annual and seasonal behaviors of ozone (O₃) gas using satellite remote sensing dataset from the sources Ozone Monitoring Instrument (OMI) over India region from 2006-2015. The study focuses on the seasonal behaviors of O₃ gas i.e., monthly, seasonal, annual mean variations of trace gas and also trend analysis of O₃ gas and comparison of the seasonal behavior of the ozone gas by trend analysis were assessed. In this study we also taken eleven cities to show the increment and decrement in four seasons of O₃ gas by taking 2006 as a base year and investigate the behaviors of gases during (2007-2015) years. Higher concentrations of O₃ south-to-north gradient, indicating the variations due to the impact of emissions and local meteorology. Ozone concentrations were higher during the warmer months. However, in winter season lowest concentration of O₃ seen due to the less amount of heat and due to cold days and ozone holes in the stratosphere. Instead, total O₃ concentrations rises over Delhi, Lucknow and Kolkata due to large population density, high traffic emission, highly polluted air and larger industrial activities.

Although decision making strategy based on clinico-histopathological criteria is well established, renal cell carcinoma (RCC) represents a spectrum of biological ecosystems characterized by distinct genetic and molecular alterations, diverse clinical courses and potential specific therapeutic vulnerabilities. Given the plethora of drugs available, the subtype-tailored treatment to RCC subtype holds the potential to improve patient outcome, shrinking treatment-related morbidity and cost. The emerging knowledge of the molecular taxonomy of RCC is evolving, whilst the antiangiogenic and immunotherapy landscape maintained and reinforced their potential. Although several prognostic factors of survival in patients with RCC have been described, no reliable predictive biomarkers of treatment individual sensitivity or resistance have been identified. In this review, we summarize the available evidence able to prompt more precise and individualized patient selection in well-designed clinical trials, covering the unmet need of medical choices in the era of next-generation anti-angiogenesis and immunotherapy.

Mankind faces a coronavirus pandemic originating from a seafood market in Wuhan, China since December 2019. The pathogen was named novel coronavirus (n-CoV) and bats are the identified key reservoir. The coronavirus disease 2019 (COVID-19) quickly spread over China across the globe, turned into a pandemic with exponentially increasing numbers of cases and significant mortality rate. China reacted with lockdowns and strict control measurements to prevent spreading the virus. The treatment of severe cases was hampered by lack of specific vaccines. Vaccine-development and production is a painstaking process and can only be enforced by international cooperation. Different supportive treatment options surfaced due to combinations of antiviral agents with antibiotic drugs. Elderly, male, immune-suppressed patients with co-morbidities showed a high mortality rate. Health literacy, strong immune system, adequate serum 25-hydroxyvitamin D (25(OH)D) concentrations and healthy life style choices can support fast recovery. Antibiotic resistance needs to be addressed by development of new generation antimicrobials against nosocomial infections in preparation for future outbreaks. Plant-biosynthesis of nanomaterials and antiseptics may help in prevention and recovery rate. Prevalence of COVID-19 maybe inversely related to BCG vaccination, endemicity of malaria, humidy and temperature but directly with latitude. Recommendations to prevent the spread of COVID-19 should be followed strictly.

The Planning and implementation of construction projects are difficult processes and are burdened with many risk elements. The budget spread over time, which is developed on the basis of the schedule, presents the expected distribution of costs throughout the duration of the works, which during the implementation of the project is subject to constant changes resulting from time, cost and organizational factors. Managing construction contracts requires managers to be able to analyze on an ongoing basis the variances of production costs - from the values calculated in the offer cost estimate and assumed in the Budgeted Cost of Work Scheduled. The article attempts to analyze the emerging time and cost deviations using proprietary T/S and T/C monitoring, based on simple indicators of the EVM method. An example of construction of a multi-family housing development was used to study the variances of planned and incurred costs.

More than 1,000,000 confirmed cases of COVID-19 have been registered worldwide since the beginning of the pandemic in Wuhan on December 2019. The high mortality rate of COVID-19 is associated with age, gender and the presence of comorbidities. Biochemical data have shown that COVID-19 patients develop a local and systemic hyper-inflammatory response associated with poor outcome. Therefore, the understanding of the biological mechanisms underlying SARS-CoV-2-induced inflammation is a compelling need. Following this reasoning, here we will focus on the importance of the progressive age-related development of a pro-inflammatory state (aka inflamm-aging) in the understanding of the unbalanced inflammatory response against SARS-CoV-2 in aged people. In particular, we underpin the role of mitochondrial DNA and genomic DNA telomeric sequences in local and systemic mechanisms of inflammation. Indeed, the leakage of mtDNA out of its natural compartment (i.e. the mitochondrion), into the cytoplasm and in the extracellular environment is a powerful trigger of innate immunity and inflammation, as part of an evolutionary-conserved signaling mechanism of cellular damage (e.g. viral infection). High levels of circulating mtDNA are increased in aged people and set up as inflammatory markers of poor prognosis in intensive care unit patients. In turn, telomeric DNA, which can be released into the cytoplasm and in the extracellular environment upon cell damage, has been proven to exert potent anti-inflammatory activity. Since that aged people (particularly those affected by co-morbidity) are equipped with shortened telomeres, we posit that, in aged people affected by COVID-19 the release of mtDNA, coupled with insufficient telomeric DNA favors the onset of a detrimental inflammatory response. In this regard, we highlight that the mechanism of action of some currently used drugs, as well as potential new ones may be better understood under the light of the above-depicted theoretical framework thus explaining how studies on inflamm-aging may help to understand and combat COVID-19 pandemic.

This study evaluates spatial analyses of groundwater quality and environmental changes to obtain information on the groundwater contamination in the Permian Basin, Texas. Coupled with the U.S. government’s open data, these analyses can identify regions where environmental change could have potentially effected groundwater quality. A total of thirty-six wells were selected within the six counties: Andrews, Martin, Ector, Midland, Crane, and Upton. Spatial distribution maps were created for six different parameters: pH, total dissolved solids (TDS), chloride, fluoride, nitrate, and arsenic. Total groundwater quality maps incorporate all the contaminants and denote regions of poor, medium, and optimum conditions. To identify spatial changes in groundwater quality, maps were separated into two different time intervals, 1992-2005 and 2006-2019. We found that groundwater contamination resulted primarily from the mobilization of the contaminant from natural sources or anthropogenic activities such as chemical fertilizers. Overall, groundwater quality decreased during the study period from 1992 to 2019 as population and urban growth began to develop in the Permian Basin. This study contributes on understanding of the response of groundwater quality associated with environmental change in the Permian Basin. Therefore, this research provides important information for groundwater managements in developing plans for the use of water resource in the future for Texas.

The SARS-CoV-2 is a recently identified positive sense single stranded RNA virus and member of the coronavirus family of viruses. It is thought to be the etiological factor for the ongoing COVID-19 pandemic. This virus is thought to have originated from bats and acquired ability of human-to-human transmission. While SARS-CoV-2 is relatively benign, it has infected more than half a million people (as of March 29^th 2020) worldwide and the number of infected people continues to rise. More than 170 countries have reported COVID-19 positive cases. With a mortality rate of less than both the previous coronavirus outbreaks, COVID-19 has (conversely) caused the death of over 33,980 (as of 29^th March, 2020 at 22.00 hours EDT) people worldwide and the number is increasing. Given the enormous impact of this virus on human health and wellbeing and consequent devastating impacts on world trade, economics and quality of life, it is important to understand this virus better and get insight into its pathogenic mechanisms which will aid in devising effective measure to curb its spread and predict future pattern of its interaction with humans. Though very little is known about this SARS-CoV-2 but its mechanisms and patterns of spread can be speculated (with caution, nevertheless) from what we know about its closest relatives SARS-CoV-1 (responsible for SARS-2002 epidemic) and MERS-CoV (responsible for MERS-2012 epidemic). In the present review, we aim at bringing together the coherent and peer reviewed literature about the SARS-CoV-2 and its close relatives and try to understand its infection patterns and reconstruct its pathogenic mechanisms with anecdotes on diagnosis and future directions. We hope that this paper will serve the purpose of being a reliable source of information to scientists, clinicians and general public.

Background: Coronavirus Disease 2019 (COVID-19) has been demonstrated to be the cause of pneumonia. Nevertheless, it has not been reported as the cause of acute myocarditis or fulminant myocarditis. Case presentation: A 63-year-old male was admitted with pneumonia and cardiac symptoms. He was genetically confirmed as having COVID-19 according to sputum testing on the day of admission. He also had elevated troponin I (Trop I) level (up to 11.37 g/L) and diffuse myocardial dyskinesia along with a decreased left ventricular ejection fraction (LVEF) on echocardiography. The highest level of interleukin-6 was 272.40 pg/ml. Bedside chest radiographs showed typical ground-glass changes indicative of viral pneumonia. Laboratory test results for viruses that cause myocarditis were all negative. The patient conformed to the diagnostic criteria of the Chinese expert consensus statement for fulminant myocarditis. After receiving antiviral therapy and mechanical life support, Trop I was reduced to 0.10 g/L, and interleukin-6 was reduced to 7.63 pg/ml. Moreover, the LVEF of the patient gradually recovered to 68%. The patient died of aggravation of secondary infection on the 33^rd day of hospitalization. Conclusion: COVID-19 patients may develop severe cardiac complications such as myocarditis and heart failure. This is the first report of COVID-19 complicated with fulminant myocarditis. The mechanism of cardiac pathology caused by COVID-19 needs further study.

An explanation of the origin of dark matter is suggested in this work. The argument is based on symmetry considerations about the concept of mass. In the Wigner's view, the rest mass and the spin of a free elementary particle in flat space-time are the two invariants that characterize the associated unitary irreducible representation of the Poincar\'e group. The Poincar\'e group has two and only two deformations with maximal symmetry. They describe respectively the de Sitter (dS) and Anti de Sitter (AdS) kinematic symmetries. Analogously to their shared flat space-time limit, two invariants, spin and energy scale for de Sitter and rest energy for Anti de Sitter, characterize the unitary irreducible representation associated with dS and AdS elementary systems. While the dS energy scale is a simple deformation of the Poincaré rest energy and so has a purely mass nature, AdS rest energy is the sum of a purely mass component and a kind of zero-point energy derived from the curvature. An analysis based on recent estimates on the chemical freeze-out temperature marking in Early Universe the phase transition quark-gluon plasma epoch to the hadron epoch supports the guess that dark matter energy might originate from an effective AdS curvature energy.

New coronavirus (SARS-CoV-2) treatments and vaccines are under development to combat the COVID-19 disease. Several approaches are being used by scientists for investigation including 1) various small molecule approaches targeting RNA polymerase, 3C-like protease, and RNA endonuclease and 2) exploration of antibodies obtained from convalescent plasma from patients who have recovered from COVID-19. The coronavirus genome is highly prone to mutations that lead to genetic drift and escape from immune recognition; thus, it is imperative that sub-strains with different mutations are also accounted for during vaccine development. As the disease has grown to become a pandemic, new B-cell and T-cell epitopes predicted from SARS coronavirus have been reported. Using the epitope information along with variants of the virus, we have found several variants which might cause drifts. Among such variants, 23403A>G variant (p.D614G) in spike protein B-cell epitope is observed frequently in European countries such as the Netherlands, Switzerland and France.

Nearly three months have passed since the emergence of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), which caused the rapidly spreading Coronavirus Disease 2019 (COVID-19) pandemic. To date, there have been more than 550,000 confirmed cases and more than 25,000 deaths globally caused by COVID-19. Chinese health authorities, where the virus emerged, have taken prompt strict public health measures to control and prevent the spread of the outbreak. In the kingdom of Saudi Arabia, unprecedented precautionary strict measures were applied to slow virus entry and to mitigate the risk of the outbreak. Here, we review the experience learned during the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) epidemic in Saudi Arabia, which has been in the country since 2012, and is expected to have helped the country to be well prepared for the current COVID-19 pandemic. We also discuss the country readiness, improvement in research and development, and the unprecedented rapid precautionary measures that have been taken by the Saudi government thus far.

Coronaviruse disease (COVID-19) outbreak has created an emergency globally, and social distancing and isolation is the only solution to prevent its spread. Several countries have announced fully locked on to tackle this pandemic. The recent COVID-2019 has shaken the globe with incidence cases of more than half-million cases, and a mortality toll of more than twenty thousand to date. The coronavirus family is inclusive of pathogen of both – animal species and humans, encapsulating the isolated severe acute respiratory syndrome coronavirus (SARS-CoV). Researchers round the globe have been dexterously working to decode this lethal virus. Many mathematical frameworks have also been depicted which have helped to understand the dynamics of the COVID-19. Research on coronaviruses continues to explore various aspects of viral replication and pathogenesis to understanding the predilection of these viruses to switch between species, to develop an infection in a new host, and to identify significant reservoirs of coronaviruses will dramatically aid in our potential to prophesize when and where potential epidemics may occur. Many of the non-structural and accessory proteins encoded by the viruses remain unclear and unknown. This systematic review highlights the current situation of the pandemic, virus genomic composition, pathogenesis, symptomatology, diagnosis, and prognosis along with mathematical models of disease transmission and dynamics.

Celiac disease (CeD) is a complex immune-mediated inflammatory condition triggered by ingestion of gluten in genetically predisposed individuals. Literature suggests that alterations in gut microbiota composition and function precede the onset of CeD. Considering that microbiota is partly determined by host genetics, we speculate that the genetic makeup of CeD patients could elicit disease development through alterations in the intestinal microbiota. To evaluate potential causal relationships between gut microbiota and CeD, we performed a Two-Sample Mendelian Randomization analysis (2SMR). Exposure data were obtained from the raw results of a previous Genome Wide Association Study (GWAS) of gut microbiota, and outcome data from summary statistics of CeD GWAS and Immunochip studies. We have identified a number of putative associations between gut microbiota SNPs associated with CeD. Regarding bacterial composition, most of the associated SNPs are related to Firmicutes phylum, whose relative abundance has been previously reported to be altered in CeD patients. In terms of functional units, we have linked a number of SNPs to several bacterial metabolic pathways that seem to be related to CeD. Overall, this study represents the first 2SMR approach to elucidate the relationship between microbiome and CeD.

The absorption of medium-chain fatty acids (MCFA) depends on the solubility of these components in the gastric fluid. Parameters such as the total MCFA concentration, carboxyl ionization level, and carbon chain length affect the solubility of these molecules. Moreover, the enzymatic lipolysis of solubilized triacylglycerol (TAG) molecules may depend on the carbon chain length of the fatty acids (FAs) components and their positions on the glycerol backbone. This present study aimed at investigating the effect of electrolyte usually formed during the gastric digestion phase on the solubility of MCFA, and evaluating the influence of the FA carbon chain length on the lipolysis rate during the in vitro digestion simulation. The results obtained here showed that the increasing of electrolyte concentrations tend to decrease the mutual solubility of systems composed by the caproic and caprylic fatty acids + sodium chloride, sodium bicarbonate, and potassium chloride solutions. We also observed that a conventional version of the thermodynamic UNIQUAC model was able to correlate the liquid-liquid phase behavior of the electrolyte solutions. Regarding the in vitro digestion simulation, the experimental data indicated that the action of the pancreatic enzyme occurred preferentially in TAG molecules comprised of short and medium-chain fatty acids.

This review has been realized within the agribusiness sector and experiments the Transaction Cost Theory a branch of the New Institutional Economy which explain market failure caused by many factors. Transaction costs are associated with carrying a transaction between buyers and sellers. This study has been conducted between 2014 and 2017; and has collected data from 11 broiler producers in Jezzine, Lebanon, about: Production costs, capital investment, revenues, land tenure, access to infrastructure, and information about the contract. The propensity score matching method is used to compare the effect of participating in contract farming and to solve the hypotheses, which say: There is a positive relationship between contract farming and the economic benefits of broiler producers and the development of the broiler sector in Jezzine District. Findings from farmer’s interviews indicated that sustainability, guaranteed price, risk reduction, credit facilities and technical aids are the main reasons for signing a contract. In contrast, Farmers have expressed problems concerning the contractors’ responsibilities such as delay in payment and delivery. Also, when prices are high, it was argued that farmers were selling the products in the open market.