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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.