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Faiqa Arshad,1 Sidrah Saleem,1 Romeeza Tahir,2 Shah Jahan,2 Kokab Jabeen,3 Aneela Khawaja,4 Zille Huma,1 Muhammad Roman,1 Faheem Shahzad2 1Department of Microbiology, University of Health Sciences, Lahore, Punjab, Pakistan; 2Department of Immunology, University of Health Sciences, Lahore, Punjab, Pakistan; 3Department of Pathology, Ammer ud Din Medical College, PGMI, Lahore, Punjab, Pakistan; 4Department of Pathology, Rahbar Medical & Dental College, Lahore, Punjab, PakistanCorrespondence: Faiqa Arshad, Department of Microbiology, University of Health Sciences, Khayaban-e-Jamia, Block D Muslim Town, Lahore, Punjab, 54600, Pakistan, Email drfaiqaimran@gmail.comBackground: The association of treatment failure and mortality with vancomycin minimum inhibitory concentration creep (MIC) is a matter of serious concern in patients with severe methicillin resistant Staphylococcus aureus (MRSA) infections. The purpose of the study was to identify and characterize staphylococcal cassette chromosome mec (SCCmec) and clonal types of MRSA strains, exhibiting the vancomycin MIC creep phenomenon.Methods: A total of 3305 S. aureus strains were isolated from various clinical samples of Lahore General Hospital, Lahore, Pakistan. MRSA strains were identified by cefoxitin resistant (≤ 21mm) followed by mecA and mecC gene genotyping. Vancomycin MIC creep was determined by E-test. Isolates having MIC values > 1.5 μg/mL were further subjected for SCCmec typing (I–V and XI) and multiple-locus variable number tandem repeat analysis (MLVA) by amplification of spa, sspA, clfA, clfB, and sdrCDE genes. A dendrogram was created based on the similarity index using bioneumerics software.Results: About 13.3% (440/3305) isolates were MRSA with 99.3% (437/440) and 0.7% (3/440) carried mecA and mecC genes, respectively. In 120 MRSA isolates, the MIC of vancomycin was > 1.5μg/mL. In MRSA isolates with high vancomycin MIC (> 1.5μg/mL), the most common SCCmec type was SCCmec III (38.3%), followed by SCCmec IVa (15.8%), SCCmec IIIa (13.3%,), SCCmec IVc (7.5%), SCCmec IVe (5.8%), SCCmec IVd (5.8%), SCCmec IVb (4.2%), SCCmec II (2.5%), SCCmec V (1.7%), SCCmec I (1.7%) and SCCmec XI (1.7%). MLVA revealed 60 genotypic groups of MRSA isolates having a 92% similarity index.Conclusion: SCCmec III was the most common type in genetically related MRSA isolates showing vancomycin MIC creep. The presence of SCCmec XI may further add burden to infection control measures.Keywords: methicillin resistant Staphylococcus aureus, staphylococcal chromosomal cassette, multilocus variable number tandem repeat analysis

The elimination of insignificant predictors and the combination of predictors with indistinguishable coefficients are the two issues raised in searching for the true model. Pairwise Absolute Clustering and Sparsity (PACS) achieves both goals. Unfortunately, PACS is sensitive to outliers due to its dependency on the least-squares loss function which is known to be very sensitive to unusual data. In this article, the sensitivity of PACS to outliers has been studied. Robust versions of PACS (RPACS) have been proposed by replacing the least squares and nonrobust weights in PACS with MM-estimation and robust weights depending on robust correlations instead of person correlation, respectively. A simulation study and two real data applications have been used to assess the effectiveness of the proposed methods.

Dengue is an acute arboviral infection common in tropical and subtropical countries. Dengue has been highlighted as a public health concern in the last five decades, affecting almost 50% of the population in developing nations. Dengue infection results in a complex symptomatic disease that ranges from headache, fever, and skin rash to extreme hemorrhage fever and liver dysfunction. The diagnosis of the disease is essential for effective treatment. The early onset of the infection can be detected through viral structural peptides that act as markers for detection, including Pre‐Membrane (Pre‐M) protein. In the currently proposed research, the structural gene obtained from local isolates was targeted for studies. For this purpose, recombinant structural protein Pre‐M was amplified, cloned, and expressed in the bacterial expression system. The expression of the structural protein (Pre‐M) was scrutinized by Sodium Dodecyl Sulphate‐Polyacrylamide Gel Electrophoresis (SDS‐PAGE) and validated by western blot and dot blot, and afterwards, the antigen was purified. The purified Pre‐M protein carries the potential for the development of in‐house diagnostic assay as well as for vaccine production. This study aimed to develop a highly specific, sensitive, and cost‐effective in‐house enzyme‐linked immunoassay (ELISA) for the detection of antibodies of Pakistani most prevalent dengue virus serotype 2 (DENV‐2). The success of this research would also pave the way toward developing novel vaccines for the future prevention of dengue infection. [ABSTRACT FROM AUTHOR]

Baya weaver is found in floodplains of Pakistan and is famous for building retort shaped hanging nests. For this study, 24 samples of baya weaver (twelve from both sexes) were collected from the wild habitat to analyze morphometry, haematology, and gut contents. Morphometric parameters e.g., body weight was measured with the help of a weight balance while body weight, body length, tail length, wingspan, wing length, longest primary feather, tarsus, central toe length, head length with and without bill, bill length, and chest circumference were measured with measuring tape. Feeding analysis was carried out through a study of complete gastrointestinal tracts (GITs). For that purpose, birds were anaesthetized with a combination of ketamine HCL (10 mg/kg) and diazepam (0.2 mg/kg). GITs were removed through dissection and kept in 10% formalin and analyzed in an ornithological lab with the help of dissecting microscopes. About 5 µL blood was taken from the wing vein and stored in the EDTA tube for haematological analysis. The body length (male 18.43±0.58cm, 15.93±0.26cm) and chest circumference (male 12.53±0.05 cm, female 12.00±0.22 cm) of both sexes were significantly different between male and female baya weavers. Both sexes mostly feed on wheat and rice. However, pin red grass (Saccharum munja), beetles, moths, sunflower seeds, and plant materials were also recorded in the study. Haematological parameters were recorded for the first time for baya weaver in this study. This study concludes that male baya weaver has a longer body length and chest circumference as compared to female baya weaver. The preferred food of male baya weaver was beetles, while female's preferred food was rice. [ABSTRACT FROM AUTHOR]

AbstractThe liver is an important and multifunctional human organ. Early and accurate diagnosis of a liver tumor can save lives. Computed Tomography (CT) images provide comprehensive information for liver tumor diagnosis using feature extraction techniques. These extracted features help classify liver tumors using Machine Learning (ML) and Deep Learning (DL) methods. For this research, twelve 1hundred CT images were acquired from the Radiology Department of Nishter Medical University & Hospital. The noise was removed using Gabor Filter after converting CT images into grayscale. Image quality was enhanced by adopting Histogram Equalization (HE), and finally, the Image’s edges and boundaries were improved using a smoothening and sharpening algorithm. Preprocessed images were forwarded to extract six features: Histogram, Run-length, Co-occurrence, Autogressive, Gradient, and Wavelet Transform. A major focus of this research is to evaluate that ML methods produced good accuracy using already extracted features while DL Algorithms could not produce better results. Firstly, ML methods such as Decision Tree (DT), Random Forest (RF), Boost, and Support Vector Machine (SVM) are deployed using an already extracted feature list containing all six features. It was observed that DT, RF, Boost, and SVM produced 96.5%, 99.6%, 99.7%, and 98.0% classification accuracy. After that, DL Algorithms such as Neural Networks (NN), Long-Short Term Memory (LSTM), Bi-Directional Long Short Term Memory (Bi-LSTM), and Convolutional Neural Networks (CNN) were deployed. The results showed that NN, LSTM, Bi-LSTM, and CNN produced 50.0%, 53.0%, 54.0%, and 54.0% accuracy respectively. To validate the major focus of this research, finally, Pre-trained DL Algorithms such as Residual Network 50 (Resnet50), Visual Geometry Group 16 (VGG16) and LSTM + CNN were deployed. Results showed that Resnet50, VGG16, and LSTM + CNN attained 78.0%, 88.0%, and 97.0% accuracy respectively. Hence, ML methods performed better using already extracted features, while DL Algorithms could not produce promising results on these extracted features.

The purpose of this paper is to determine the improved system, reliability by introducing the redundancy of components. This paper considers one warm, stand-by unit with, one main unit. We study the reliability of such systems using probability models in terms of Sedyakin's model. A parametric family of Birnbaum-Saunders distributions is considered as the distribution of failure times both of main and stand-by units. Parametric point and interval estimation is obtained from censored data. Bibliography: 17 titles., 1. INTRODUCTION Recently, a series of papers on redundant systems was published by Bagdonavicius et al. [4-7]. We treat the reliability of a redundant system based on a parametric Birnbaum-Saunders [...]

Crowdsourcing Software Development (CSD) platforms, i.e., TopCoder, function as intermediaries connecting clients with developers. Despite employing systematic methodologies, these platforms frequently encounter high task abandonment rates, with approximately 19% of projects failing to meet satisfactory outcomes. Although existing research has focused on task scheduling, developer recommendations, and reward mechanisms, there has been insufficient attention to the support of platform moderators, or copilots, who are essential to project success. A critical responsibility of copilots is estimating project duration; however, manual predictions often lead to inconsistencies and delays. This paper introduces an innovative machine learning approach designed to automate the prediction of project duration on CSD platforms. Utilizing historical data from TopCoder, the proposed method extracts pertinent project attributes and preprocesses textual data through Natural Language Processing (NLP). Bidirectional Encoder Representations from Transformers (BERT) are employed to convert textual information into vectors, which are then analyzed using various machine learning algorithms. Zero-shot learning algorithms exhibit superior performance, with an average accuracy of 92.76%, precision of 92.76%, recall of 99.33%, and an f-measure of 95.93%. The implementation of the proposed automated duration prediction model is crucial for enhancing the success rate of crowdsourcing projects, optimizing resource allocation, managing budgets effectively, and improving stakeholder satisfaction.

Listeriosis, caused by Listeria monocytogenes (Lm), has zoonotic implications and has a wide host range. It has unique potential to cross blood brain barrier and placental barrier which results in encephalitis, meningitis and abortions. Initially only animals were considered as its victim but later on it emerged as an important food borne human pathogen and so far, it has been isolated from various food samples and environmental samples too. The current study is based upon the determination of genome-based distribution of L. monocytogenes (hly gene) in various soil samples collected from selected districts of Punjab province, Pakistan. The selection of the districts was based on historical information about simultaneous prevalence of the soil borne human and animal diseases... A total of 970 soil samples were processed independently by real-time PCR and an association between occurrences in soil was correlated to several categorical variables. The genome was detected in 17 samples (1.75%, 95 % CI: ±0.5), each originating from different location across districts under study. A greater number of positive soil samples for Lm genome were detected in district Attock followed by Sheikhupura, Faisalabad, Sargodha, Sahiwal and Lahore. The odds for occurrence of Lm were more at places near to water sources [2.85, CI:95% (0.9256, 8.831) p=0.05] and having more than 1000 animals per village [3.32, CI:95% (1.077, 10.27) p=0.02]. In conclusion, DNA of Lm is distributed in the soil of several districts of Punjab and fodder grown on such contaminated soil could be a potential risk factor to health of both animals and humans. Future studies are required to identify a causal relationship between presence of bacterium in soil to seroconversion in animal and human and prevalent serotypes across a wide geographical range across Pakistan. [ABSTRACT FROM AUTHOR]

Time series is very important branch in Statistical analysis. In recent years, the fractional difference has been studied because the fractional difference studies the difference between zero and one. In this paper, we will estimate the fractional difference using Bayesian and non-Bayesian methods. Our aim is to show the best method to estimate the fractional parameter using simulation and real data of BMW dataset and inflation rate USA index. [ABSTRACT FROM AUTHOR]

Transformational leaders must constantly monitor their environments to identify and develop significant internal and external exposure to multiple points of view. Transformational leaders serve as role models for bridging organizational boundaries and proactive outreach to external stakeholders to communicate information, develop inter-organizational cooperation, establish coalitions, and harness resources. The major goal of this article is to examine the dynamics that drive organizational project success, specifically the impact of transformational leadership on external and internal social capital in management teams. This research analyzed transformational leadership and project success relationships in Pakistan’s construction industry setting along with the moderating role of the project manager subordinate’s social capital and the mediating role of the project manager subordinate’s self-efficacy. The data were collected from196 subordinates of project managers of different private organizations. Data were examined through correlation, regression, and the Hayes model through SPSS. According to the results, transformational leadership is positively associated with project success and substantially associated with social capital. Additionally, self-efficacy is positively associated with transformational leadership and project success, and it mediates the relationship between transformational leadership and project success. Social capital moderates the relationship between transformational leadership and project success, according to additional findings. This study sheds light on the relationship between transformational leadership and self-efficacy in the context of project success.

In this paper, the suggested method for solving the problems of outliers and the correlations among groups of variables facing time series and affecting them negatively was explained. Initially, the elastic method was used to deal with the problem of correlations, but this method is nonrobust to the outliers. Therefore, the loss function was replaced by the Robust function. As for the applied side, real medical data was used for lung cancer patients as these data were collected from Al Diwaniyah Hospital. Through the application of the statistical program R, it was concluded that the MM_lag weighted elastic net estimator is best based on the smallest value of RPEW. [ABSTRACT FROM AUTHOR]

Coronavirus 2019 (COVID-19) is a global pandemic. It has recently swept the world. Statistics indicate an increase in the number of confirmed cases with this virus. The researchers have used an advanced statistical method called Singular Spectrum Analysis (SSA) to study the numbers of the confirmed cases in Iraq and compared them with some countries, like China, America, Spain and Italy during the period 21 January 2020 up to 26 April 2020. The results show that there is a trend, so there is an increase in confirmed cases with slight seasonality and periodicity in the time series of USA, Spain and Italy, but decrease in confirmed cases with slight seasonality and periodicity in the time series. [ABSTRACT FROM AUTHOR]

Objective:Nocturnal leg cramps are painful, sleep related involuntary muscle contractions of the lower limb. The objective was to determine the frequency of nocturnal leg cramp symptoms and to compare stress, sleep disturbances, and physical activity between males and females in middle-aged adults having nocturnal leg cramps.Materials and Methods:An analytical cross-sectional survey was conducted on 220 participants in twin cities of Pakistan. Patients between the ages of 35-55 years who experienced nocturnal leg cramps in the previous three months were included, those with any comorbidities were excluded from the study. A diagnostic criterion from a previous systematic review was used to determine and find the frequency of nocturnal leg crampsymptoms. The perceived stress scale, Pittsburgh sleep quality index, and global physical activity questionnaire were used to analyze stress, sleep, and physical activity, respectively.Results:The mean age of all participants was 44.01±6.74 years. The most reported symptom by participants was pain duration lasting from seconds to 10 minutes 219 (99.55%), whereas pain/cramps in thigh/hamstrings 162 (73.64%) was least reported symptom. The mean age of participants in the male and female groups was 44.27±7.65 years, 44.72±6.56 years. Group comparison of gender showed a significant difference with stress (p0.05).Conclusion:The study concludes that majority of the participants experienced all symptoms of the nocturnal leg cramps, whereas leg cramps were the most frequent symptom. Furthermore, females suffering from nocturnal leg cramp reported higher levels of stress compared with males.

Competitive Crowdsourcing Software Development (CCSD) is popular among academics and industries because of its cost-effectiveness, reliability, and quality. However, CCSD is in its early stages and does not resolve major issues, including having a low solution submission rate and high project failure risk. Software development wastes stakeholders’ time and effort as they cannot find a suitable solution in a highly dynamic and competitive marketplace. It is, therefore, crucial to automatically predict the success of an upcoming software project before crowdsourcing it. This will save stakeholders’ and co-pilots’ time and effort. To this end, this paper proposes a well-known deep learning model called Bidirectional Encoder Representations from Transformers (BERT) for the success prediction of Crowdsourced Software Projects (CSPs). The proposed model is trained and tested using the history data of CSPs collected from TopCoder using its REST API. The outcomes of hold-out validation indicate a notable enhancement in the proposed approach compared to existing methods, with increases of 13.46%, 8.83%, and 11.13% in precision, recall, and F1 score, respectively.

To increase the agricultural production, nano fertilizers have allowed the development of new technologies. In this study, zinc oxide nanoparticles (ZnO-NPs), iron oxide nanoparticles (FeO-NPs) and magnesium oxide nanoparticles (MgO-NPs) have been prepared and applied on the medicinal plants (Caesalpinia bonducella) to increase the yield, nutrient and chlorophyll contents. This study intended to compare the impacts of synthesised NPs with the control plants and their conventional fertilizers such as ZnSO4·7H2O (Zn-bulk), FeSO4·7H2O (Fe-bulk) and MgSO4·7H2O (Mg-bulk). All fertilizers were applied in a completely randomized design including six treatments and three replications of each treatment by using a combination of seed priming method and soil mixing process. Moreover, the NPs were also compared for the best gaining of results. The concentrations selected for applications were 10, 20, 30, 40 ppm for iron and magnesium fertilizers and 25, 50, 75, 100 ppm for zinc-based fertilizers. The results indicated the increase in morphological parameters of the plants of 28–50% by the application of conventional fertilizers while 50–93% by NPs application as compared to control plants. Furthermore, the increase in chlorophyll contents was 5–28% with conventional fertilizers while 30–80% in case of NPs application than control plants. The obtained results indicate that the best increase in growth, nutrient and chlorophyll contents of plants were achieved at the dose concentrations of 40 ppm for Mg and Fe fertilizers and 100 ppm for Zn fertilizers. The comparative study of NPs showed that the highest growth and yield was obtained by the use of ZnO-NPs in comparison to others. Finally, it is worth highlighting that NPs have a huge potential to increase the productivity of crops if applied in optimal concentrations.

Aim: To investigate the reliability of atherogenic index of plasma (AIP) as a marker of cardiovascular disease risk and its correlation with CVD risk factors. Methodology: It is a sub-study of the second National Diabetes Survey of Pakistan (NDSP) 2016–2017, an epidemiological survey including both genders with >20 years of age in all four provinces of Pakistan. Out of 213 clusters, twenty-seven clusters were selected and households were identified from each cluster. Selected members of every 10th household were counseled for a visit to the campsite after observing an overnight fast. Standardized techniques were used for measuring height, weight, BMI, waist circumference, and blood pressure. Biochemical parameters including oral glucose tolerance test, lipid profile, and various lipid-derived parameter/ratios were also analyzed via standardized methods. Results: A total of 7351 individuals were selected for this sub-analysis after fulfilling the inclusion criteria. Mean AIP score was calculated as 0.38 ± 0.31 with statistically significant difference among all groups (p

Background: We previously showed enrichments of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) in broiler chicks fed defatted microalgae.Objectives: The aims of this study were to determine 1) if the enrichments affected meat texture and were enhanced by manipulating dietary corn oil, selenium, and vitamin E concentrations and 2) how the enrichments corroborated with hepatic gene expression involved in biosynthesis and oxidation of EPA and DHA.Methods: Day-old hatching Cornish Giant cockerels (n = 216) were divided into 6 groups (6 cages/group and 6 chicks/cage). Chicks were fed 1 of the 6 diets: a control diet containing 4% corn oil, 25 IU vitamin E/kg, and 0.2 mg Se/kg (4CO), 4CO + 10% microalgae (defatted Nannochloropsis oceanica; 4CO+ MA), 4CO+ MA - 2% corn oil (2CO+MA), 2CO+MA + 75 IU vitamin E/kg (2CO+MA+E), 2CO+MA + 0.3 mg Se/kg (2CO+MA+Se), and 2CO+MA+E + 0.3 mg Se/kg (2CO+MA+E+Se). After 6 wk, fatty acid profiles, DHA and EPA biosynthesis and oxidation, gene expression, lipid peroxidation, antioxidant status, and meat texture were measured in liver, muscles, or both.Results: Compared with the control diet, defatted microalgae (4CO+MA) enriched (P < 0.05) DHA and EPA by ≤116 and 24 mg/100 g tissue in the liver and muscles, respectively, and downregulated (41-76%, P < 0.01) hepatic mRNA abundance of 4 cytochrome P450 (CYP) enzymes (CYP2C23b, CYP2D6, CYP3A5, CYP4V2). Supplemental microalgae decreased (50-82%, P < 0.05) lipid peroxidation and improved (16-28%, P < 0.05) antioxidant status in the liver, muscles, or both. However, the microalgae-mediated enrichments in the muscles were not elevated by altering dietary corn oil, vitamin E, or selenium and did not affect meat texture.Conclusion: The microalgae-mediated enrichments of DHA and EPA in the chicken muscles were associated with decreased hepatic gene expression of their oxidation, but were not further enhanced by altering dietary corn oil, vitamin E, or selenium. [ABSTRACT FROM AUTHOR]

The elimination of insignificant predictors and the combination of predictors with indistinguishable coefficients are the two issues raised in searching for the true model. Pairwise Absolute Clustering and Sparsity (PACS) achieves both goals. Unfortunately, PACS is sensitive to outliers due to its dependency on the least-squares loss function which is known to be very sensitive to unusual data. In this article, the sensitivity of PACS to outliers has been studied. Robust versions of PACS (RPACS) have been proposed by replacing the least squares and nonrobust weights in PACS with MM-estimation and robust weights depending on robust correlations instead of person correlation, respectively. A simulation study and two real data applications have been used to assess the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

The current study evaluated the use of pumice, a volcanic mineral and common sand, in treating reverse osmosis membrane reject water (ROR) using a novel combined adsorption distillation (CAD) method. The CAD method is developed to separate the dissolved solids through adsorption distillation, i.e., leaving the vaporized distillate as freshwater and concentrated brine. The adsorption potential of pumice and sand was investigated at different adsorbent doses, i.e., 2, 5, and 10 g, and consecutive CAD adsorbent backwashing cycles. The improved results were achieved at a 10 g pumice dose. However, its adsorption efficiency declined in longer CAD cycles, i.e., due to the separated deposition of solids. After backwashing, the adsorbed and accumulated salts were slightly removed, and pumice adsorption capacity was maintained for up to 20 cycles of CAD. The properties of the pumice, i.e., before and after five CAD cycles and after backwashing, were characterized with scanning electron microscopic (SEM), elemental disruptive spectroscopy (EDS), and X-ray diffraction (XRD), which revealed that the porous structure of the pumice was completely accumulated with deposits of ionic salts, which were slightly washed away after backwashing, but accumulation remained continued in post-CAD cycles. The explored method revealed a high potential of pumice in water filtration.

Drinking water is the main source of fluoride intake for the human body and its regulated consumption helps in decreasing dental caries. However, excessive fluoride consumption over a prolonged time period causes fluorosis disease which adversely affects many tissues and organs of the body. This paper describes the evaluation of chronic intoxication of fluoride on human serum metabolome. The untargeted metabolomics approach using UPLC-QTOF-MS/MS is applied for metabolomic profiling, whereas the estimation of fluoride in serum samples was carried out using the ion-selective electrode (ISE). Fluoride concentration was found to be 0.16–1.25 mg/L in serum samples of 39 fluorosis patients and 0.008–0.045 mg/L in 20 healthy samples. A total of 47 metabolites were identified based on the high-resolution mass spectrometry analysis. A volcano plot was generated to discriminate features that are significantly different between the fluorosis and healthy groups at the probability of 0.05 and fold change ≥ 2. Among all identified metabolites, intensities of ten differential identified metabolites including inosine, α-linolenic acid, guanosine, octanoyl-L-carnitine, His-Trp, phytosphingosine, lauroyl-L-carnitine, hydrocortisone, deoxyinosine and dodecanedioic acid have been found altered in disease samples compared to healthy controls. Major pathways identified based on these metabolites include energy metabolism, fatty acid oxidation, purine degradation pathway, elevated protein degradation, and increased ω-6 fatty acid linoleate signatures were observed.

The recent emergence of novel coronavirus disease (COVID-19) triggered by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has resulted in substantial mortality worldwide. Presently, there is no approved treatment for COVID-19. Consequently, the clinical, scientific, and regulatory authorities have joint efforts to reduce the severe impact of COVID-19. To date, there is minimal arsenal with no definite curative drugs, licensed-vaccines, or therapeutic conducts to combat the COVID-19 infections. Keeping in view the threats of this pandemic, various global organizations, physicians, researchers, and scientists, are trying to recognize the epidemiological characteristics and pathogenic mechanisms of COVID-19 to discover potential treatment regimens, vaccines, and therapeutic modes for future anticipation. Herein, we summarize a contemporary overview of curative invasions and vaccines for COVID-19 based on the earlier information and considerate of similar earlier RNA coronaviruses. The information reviewed here establishes a paramount intellectual basis to promote ongoing research to develop vaccines and curative agents. Thus, this review suggests the furthermost accessible frontiers in the vaccine development to tackle or combat the COVID-19/SARS-CoV-2.

Coronaviruses are single-stranded RNA viruses that cause severe respiratory, enteric, and systemic infections in a vast range of hosts, including man, fish, mammals, and avian. Scientific interest has heightened on coronaviruses after the emergence of the 2019 novel Coronavirus (SARS-CoV-2). This review provides current perspectives on morphology, genetic diversity, transmission characteristics, replication cycle, diagnostic approaches, epidemiological assessment, and prevention strategies against the SARS-CoV-2. Moreover, different potential biotherapeutics such as small drug molecules, different vaccines, and immunotherapies to control severe acute respiratory infections caused by 2019 novel coronavirus (SARS-CoV-2) are repurposed and discussed with different mechanistic approaches. The current growth trends of the SARS-CoV-2/COVID-19 outbreak globally and preventive measures are briefly discussed. Furthermore, the lessons learned from the COVID-19 outbreak, so far, concluding remarks and future directions for controlling for COVID-19, are also recommended for a safer tomorrow.

Presents a study which focused on absorption edge measurements in chemically deposited pyrite iron sulphur thin layers. Experimental technique; Results; Discussion.

Amina Nazir,1,2,* Yachao Zhao,2,* Manli Li,2 Rakia Manzoor,3,4 Rana Adnan Tahir,1 Xianglilan Zhang,2 Hong Qing,1 Yigang Tong2 1Key Laboratory of Molecular Medicine and Biotherapy in the Ministry of Industry and Information Technology, Department of Biology, School of Life Sciences, Beijing Institute of Technology, Beijing, People’s Republic of China; 2State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, People’s Republic of China; 3State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, People’s Republic of China; 4School of Life Science, University of Chinese Academy of Sciences, Beijing 100101, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hong QingKey Laboratory of Molecular Medicine and Biotherapy in the Ministry of Industry and Information Technology, Department of Biology, School of Life Sciences, Beijing Institute of Technology, Beijing, People’s Republic of ChinaTel +8613264286266Email hqing@bit.edu.cnYigang TongState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, People’s Republic of ChinaEmail tong.yigang@gmail.comBackground: Multidrug-resistant plasmids carrying replication genes have been widely present in various strains of Klebsiella pneumoniae. RepA and repB1 were found in plasmids belong to the IncFIB, but their detailed structural and genomic characterization was not reported yet. This is the first study that delivers structural and functional insights of repA- and repB1-carrying IncFIB plasmids.Methods: Klebsiella pneumoniae strains A1705, 911021, and 1642 were isolated from the human urine samples and bronchoalveolar fluids collected from different hospitals of China. Antibacterial susceptibility and plasmid transfer ability were tested to characterize the resistant phenotypes mediated by the pA1705-qnrS, p911021-tetA, and p1642-tetA. The complete nucleotide sequences of these plasmids were determined through high-throughput sequencing technology and comparative genomic analyses of plasmids belong to the same incompatibility group were executed to extract the genomic variations and features.Results: The pA1705-qnrS, p911021-tetA, and p1642-tetA are defined as non-conjugative plasmids, having two replication genes, repA and repB1 associated with IncFIB family, and unknown incompatible group, respectively. Comparative genomic analysis revealed that relatively small backbones of IncFIB plasmids integrated massive accessory module at one “hotspot” that was located between orf312 and repB1. These IncFIB plasmids exhibited the distinct profiles of accessory modules including one or two multidrug-resistant regions, many complete and remnant mobile elements comprising integrons, transposons and insertion sequences. The clusters of resistant genes were recognized in this study against different classes of antibiotics including β-lactam, phenicol, aminoglycoside, tetracycline, quinolone, trimethoprim, sulfonamide, tunicamycin, and macrolide. It has been observed that all resistant genes were located in multidrug resistance regions.Conclusion: It is concluded that multidrug-resistant repA and repB1-carrying IncFIB plasmids are a key source to mediate the resistance through mobile elements among Klebsiella pneumoniae. Current findings provide a deep understanding of horizontal gene transfer among plasmids of the IncFIB family via mobile elements that will be utilized in further in vitro studies.Keywords: plasmids, repA, repB1, multidrug resistance, structural genomics, bioinformatics

We present an innovative approach for 2D person pose estimation by developing a convolutional neural network for human 2-channel mask prediction and human 2D pose estimation. Conceptually our idea is simple, inspired by prior image segmentation research in general. We establish a perception that explicitly encoded mask data can be served as a critical feature for person pose estimation. We propose a convolution neural network model by combining the image segmentation technique with the bottom-up approach for human pose estimation. We observe that the construction of a two stage-network for training in an end-to-end manner is beneficial to one another: for person mask prediction and 2D person pose estimation. At the pose estimation stage, we detect heat-maps against the person keypoints location from the mask information and their mutual connection relations. They are then used to estimate an ultimate pose in a way to remove the unwanted or occluded keypoints, as those keypoints may propagate across the network and lead to redundant pose estimation. We train and test our system on the MS-COCO dataset, and the experimental results validate the superior efficiency of the proposed methodology.

The ecological system is being threatened by the environmental risks related to soil and water pollution. The ecological integrity and environmental quality mandate the approaches and efforts to ensure safer monitoring of health system. A number of technologically advanced methodologies have been ameliorating the impact of toxic chemicals and heavy metals pollutants in the environment. In this regard, the colorimetric monitoring of heavy metal based analytes have extensively been investigated in a variety of fields including, environmental science biomedicine and food industry. Their wide range of applications owes from their ability to offer detection of these analytes with bare eye, high selectivity and sensitivity, facile synthesis and easy operating conditions. Herein, the recent advancement in the field of colorimetric detection based on chemically and biologically synthesized nanomaterials has been reviewed. Emerging biologically synthesized nanomaterials via eco-friendlier green routes and chemically synthesized nanomaterials offers a number of desirable features for the detection of target analyte.

Small vertebrate surveys were carried out in the Sungai Brok and Sungai Betis Forest Reserves. The Central Forest Spine (CFS) initiative has recognised these forest reserves as part of Kelantan's important ecological corridors, known as D-PL1. The surveys were conducted from July to September 2019. This study aims to identify and update the checklist of small vertebrates in the selected forest reserves. One hectare plot was established in each forest reserve for this study. Traps were used to capture and document these vertebrates, including 100 collapsible cage traps, three harp traps, 10 mist nets, and 25 pitfall traps deployed at each sampling site. In addition, the surveys also carried out active searches and direct observations. Overall, this study recorded 83 species from 38 families of small vertebrates. With the total, small mammals comprise of 13 spp. from six families, birds (53 spp. from 24 families), and herpetofauna (17 spp. from eight families) were recorded during this study. Ten small mammal species are recorded as additional species for D-PL1. Birds and herpetofauna recorded 38 and eight additional species to the existing checklist of D-PL1, respectively. Based on the IUCN Red List of Threatened Species (Version 3.1), this study recorded three threatened species namely Great Argus (Argusianus argus), Great Slaty Woodpecker (Mulleripicus pulverulentus), and Grey-cheeked Bulbul (Alophoixus tephrogenys) in D-PL1. This information is likely to support stakeholders in formulating a management strategy for the forest mosaics in the corridor, which will continue to serve as an essential habitat for small vertebrates.

Conductive nanomaterials have recently gained a lot of interest due to their excellent physical, chemical, and electrical properties, as well as their numerous nanoscale morphologies, which enable them to be fabricated into a wide range of modern chemical and biological sensors. This study focuses mainly on current applications based on conductive nanostructured materials. They are the key elements in preparing wearable electrochemical Biosensors, including electrochemical immunosensors and DNA biosensors. Conductive nanomaterials such as carbon (Carbon Nanotubes, Graphene), metals and conductive polymers, which provide a large effective surface area, fast electron transfer rate and high electrical conductivity, are summarized in detail. Conductive polymer nanocomposites in combination with carbon and metal nanoparticles have also been addressed to increase sensor performance. In conclusion, a section on current challenges and opportunities in this growing field is forecasted at the end.

The comparative utilization of solar thermal or photovoltaic systems has significantly increased to fulfill the requirement of electricity and heat since few decades. These hybrid systems produce both thermal and electrical energy simultaneously. In recent times, increasing interest is being redirected by researchers in exploiting variety of nanoparticles mixed with miscellaneous base fluids (hybrid nanofluid) for these hybrid systems. This new class of colloidal suspensions has many fascinating advantages as compared to conventional types of nanofluids because of their modified and superior rheological and thermophysical properties which makes them appealing for solar energy devices. Here, we have attempted to deliver an extensive overview of the synthetic methodologies of hybrid nanofluids and their potential in PV/T and solar thermal energy systems. A detailed comparison between conventional types of nanofluids and hybrid nanofluids has been carried out to present in-depth understanding of the advantages of the hybrid nanofluids. The documented reports reveal that enhanced thermal properties of hybrid nanofluids promise the increased performance of solar thermal PV/T systems. Additionally, the unique properties such as nanoparticles concentration and type of base fluid, etc. greatly influence the behavior of hybrid nanofluidic systems. Finally, the outlook, suggestions, and challenges for future research directions are discussed.

Novel therapeutic agents discovery have become crucial because humankind is experiencing issues regarding health and the environment. The present study is focusing on thrombolytic agents, which are therapeutics for cardiovascular diseases, eco-friendly, safe and can be used in comparison to synthetic agents that are associated with some side effects and their production is not environment friendly. The main objective of the present research was to evaluate the thrombolytic and cytotoxic potential of different extracts of Escherchia coli-ATCC35218. Soxhlet extraction method with ethanol was used for extraction from cells. Crude protein was extracted from the cell mass with protein extraction buffer. Crude isoprenoid quinone and polar lipids were extracted by using petroleum ether with methanol and chloroform as extraction solvent. In another method, the extracts were prepared using methanol as well as n-hexane, chloroform, ethyl acetate and water in serial extraction. These organic and aqueous extracts of E. coli were dried, weighed, dissolved in DMSO and tested for their hemolytic and thrombolytic potential. Mild to moderate percentage of thrombolytic activity was shown by these extracts and compared with citric acid (standard). Chloroform, ethyl acetate and methanol soluble extracts showed significant thrombolytic activities that were 43.5% ​± ​0.06, 41.8% ​± ​0.16 and 40.5% ​± ​0.07 respectively, while citric acid showed 73.7% ​± ​0.05. Maximum hemolytic activities were shown by polar lipids, crude methanol extract and n-hexane that are 87.20% ​± ​0.08, 82.5% ​± ​0.06 and 73.46%± ​0.08 respectively as compared with 0.1% Triton X-100 (positive control) with 94.75% ​± ​0.15 hemolysis. Finally, results were analyzed statistically through analysis of variance (ANOVA). Further studies on isolation and identification of bioactive thrombolytic agents from each bioactive thrombolytic extract will help us in reporting natural thrombolytic agents from E.coli that could benefit human medically. This is one of the few reports of thrombolytics available from natural sources.

Hybrid electric vehicles (HEV) can provide a same power as gasoline and diesel-powered vehicles while also reducing fuel consumption by 40–50%. However, due to a complex system of HEV, the power control strategies are used to improve the work efficiency, fuel economy and to reduce exhaust emissions. In the present work, four parameters are studied with the main focus on the optimization of power system of a parallel hybrid vehicle. These parameters are the influence of fuzzy logic algorithm on hybrid electric vehicle power train that is acceleration performance, climbing performance, engine efficiency and exhaust gas emissions. First, the vehicle’s power train is optimized using the Advanced Vehicle Simulator (ADVISOR). Secondly, by ensuring acceleration and climbing performance, the vehicle manufacturing cost is reduced by reducing the weight of the vehicle. Thirdly, two driving road test conditions, Extra-Urban Driving Cycle (EUDC) and Urban Dynamometer Driving Schedule (UDDS) are used to enhance the fuel consumption efficiency of the vehicle. Using a fuzzy logic algorithm for random operations, the operating range of an internal combustion engine is controlled as much as possible in the most efficient range. Through the control of 121 different algorithms, the operating efficiency of the powertrain is optimized to reduce exhaust emissions and improve fuel efficiency. The results showed that the proposed strategy can reduce fuel consumption by 5%, CO by 50% and improved the operating efficiency of engine by 15%. Not only this control strategy optimizes the efficiency of powertrain, but also the efficiency of the internal combustion engine. Moreover, the motor and battery pack itself can be optimized. These hybrid vehicles can minimize the fuel demand and are the best substitute for the classic internal combustion engines.

This is case report of 40 years female who presented at term and was co-infected with COVID-19. She had history of previous 3 C-sections so another C-section was performed due to fear of uterine rupture. Her surgery went uneventful. She recovered after surgery from COVID-19. Her infant also tested negative for COVID-19.

In recent years, metal–organic frameworks (MOFs) have received accelerating research attention as a versatile carrier and promising bio-immobilization support materials for enzyme immobilization. This is particularly due to their extraordinary structural properties and multi-functionalities, such as surface area, high porosity, tunable topography, crystallinity, electronic and optical properties, thermal/chemical stability and multiple affinities (hydrophobicity and hydrophilicity). Excellent biocatalytic performance, improved stability and repeatability, high loading ability, and greater accessibility to catalytic sites are the key attributes associated with the use of novel MOF–enzyme bio-composites. This review discusses the recent developments in the use of MOFs as immobilization support materials as a platform to engineer different kinds of enzymes with requisite functionalities for biocatalysis applications in different sectors of the modern world. The second part of the review mainly focuses on MOFs-assisted immobilization strategies including surface immobilization, covalent binding, cage inclusion and in situ MOF formation and enzyme immobilization to develop enzyme–MOF bio-catalytic system. The characteristic properties rendering MOFs as interesting matrices for bio-immobilization are also presented following applications of MOFs-immobilized bio-catalyst for catalysis, sensing and detection, and protein digestion. Lastly, the review is wrapped up with conclusions and an outlook in terms of upcoming challenges and prospects for their scale-up applications. Keywords: Metal–organic frameworks, Biocatalytic system, Support materials, Immobilization strategies, Catalytic performance, Biosensing and detection

The widespread occurrence and adverse environmental and health-related impacts of various types of emerging contaminants (ECs) have become an issue of high concern. With ever increasing scientific knowledge, socio-economic awareness, health-related problems and ecological apprehensions, people are more concerned about the widespread ECs, around the globe. Among ECs, biologically active compounds from pharmaceutical, cosmeceutical, biomedical, personal care products (PPCPs), endocrine-disrupting chemicals (EDCs), and flame-retardants are of paramount concern. The presence and persistence of ECs in water bodies are of continued and burning interest, worldwide. Various types of ECs are being discharged knowingly/unknowingly with/without partial treatments into the aquatic environments that pose serious health issues and affects the entire living ecosystem. So far, various approaches have been developed for ECs degradation and removal to diminish their adverse impact. Many previous and/or ongoing studies have focused on contaminants degradation and efficient removal via numerous treatment strategies, i.e. (1) physical, (2) chemical and (3) biological. However, the experimental evidence is lacking to enable specific predictions about ECs mechanistic degradation and removal fate across various in-practice systems. In this context, the deployment oxidoreductases such as peroxidases (lignin peroxidases, manganese-dependent peroxidases, and horseradish peroxidase), aromatic dioxygenases, various oxygenases, laccases, and tyrosinases have received considerable research attention. Immobilization is highlighted as a promising approach to improve enzyme catalytic performance and stabilization, as well as, to protect the three-dimensional structure of the enzyme against the undesirable consequences of harsh reaction environment. This work overviews the current and state-of-the-art critical aspect related to hazardous pollutants at large and ECs in particular by the immobilized oxidoreductase enzymes. The first part of the review focuses on the occurrence, physiochemical behavior, potent sources and significant routes of ECs. Following that, environmentally-related adverse impacts and health-related issues of ECs are discussed in the second part. In the third part, biodegradation and removal strategies with a comparative overview of several conventional vs. non-conventional methods are presented briefly. The fourth part majorly focuses on operational modes of different oxidoreductase enzyme-based biocatalytic processes for the biodegradation and biotransformation of a wide array of harmful environmental contaminants. Finally, the left behind research gaps, concluding remarks as well as future trends and recommendations in the use of carrier-immobilized oxidoreductases for environmental perspective are also discussed. Keywords: Emerging contaminants, Environmental impact, Oxidoreductases, Enzyme immobilization, Biodegradation, Removal strategies, Environmental management

Mercury is a toxic heavy element, which contaminates air, land, and water, thus posing environment and human health-related threats to the ecological system. Considering the adverse ecological effects, there is an urgent need to design and develop strategic tools to detect a broader spectrum of toxic elements in different environments. The development of point-of-care tools, e.g., sensor-based devices offers a noteworthy solution to detect and monitor real-time generation or release of environmentally-related toxic elements from different sectors, even with or without partial treatments. For a first-hand estimate, a qualitative method (colorimetric) for detection of mercury could suffice. Benefiting from the colorimetric recognition methodology, herein, we developed a new system (2-(5-bromothiazol-2-yl)-3', 6'-bis(diethylamino)spiro[isoindoline-1, 9' xanthen]-3-one) for the detection of mercury ions. The newly developed chemical sensor is composed of a fluorescent part (rhodamine b) and a binding site (2-amino-5-bromothiazole). A highly selective and sensitive response accompanied by visual color change (colorless to pink) towards Hg2+ was observed among miscellaneous metal cations. This colorimetric change confirmed that the coordination complex exists as spirocyclic ring opened derivative of rhodamine moiety. Furthermore, the binding affinity and detection limit was also calculated from the absorbance and emission data. The calculated values are in the order of 4.72 × 104 M-1 and 6.9 μM, respectively. In addition, the results reveal that the complex between the chemical sensor (S) and Hg2+ is reversible in the presence of ethylenediaminetetraacetate (EDTA2-). Finally, the newly developed sensor S was employed to detect Hg2+ in the wastewater. The fluorescence intensity was measured at 583 nm with S followed by spiking with Hg2+ at different concentrations and related linearly. In summary, taken together all the properties suggest that the newly developed sensor might display great potential in the field of environmental monitoring of toxic elements.

In recent years, emerging contaminants (ECs) of high concern are broadly distributed throughout the environmental matrices because of various industrial practices and anthropogenic inputs, i.e., human-made activities. With ever increasing scientific knowledge, technological advancement, socio-economic awareness, people are now more concern about the widespread distribution of environmentally related ECs of high concern. As, ECs possess serious ecological threats and potential risks to human health and aquatic life, even at minor concentrations. The controlled or uncontrolled discharge and long-term persistence of ECs that includes micro-pollutants, endocrine disruptors (EDs), pesticides, pharmaceuticals, hormones, toxins, and industrially-related synthetic dyes and dyes-containing hazardous pollutants, etc. pose a significant challenge to policy regulators, engineers, and scientific community. The conventional treatment technologies are proved ineffective for the complete elimination and removal of an array of contaminants of emerging environmental concern in various biological and environmental samples. In order to overcome the aforementioned ecological threats, tremendous research efforts have been made to boost the efficiency of remediation techniques or develop new modalities to detect, quantify and treat the samples efficiently. The boom in biotechnology and environmental engineering offers potential opportunities to develop advanced and innovative remediation techniques in the field of water treatment. This review discusses the environmental and health hazards associated with a widespread distribution of micro-pollutants, pesticides, pharmaceuticals, hormones, and industrially-related synthetic dyes and dyes-containing hazardous pollutants, etc. in the water bodies, i.e., surface water, groundwater, and industrial wastewater streams. Life-cycle distribution of emerging (micro)-pollutants with suitable examples from various industrial sources viewpoints is also discussed. The later part of the review focuses on innovative and cost-effective remediation (removal) approaches from phase-changing treatment technologies for these ECs of high concern. Keywords: Environmental pollution, Emerging contaminants, Endocrine disruptors, Pesticides, Pharmaceuticals, Toxins, Wastewater, Treatment technologies, Health risks