How various stakeholder pressure influences mega-project sustainable performance through corporate social responsibility and green competitive advantage.

Although numerous research studies have revealed that mega-projects are closely linked to globalization and civilization, few researchers have performed logical assessments of mega-projects using stakeholder theory. As stakeholders demand greater sustainability in the construction industry, sustainable development (SD) has become a priority. However, corporate social responsibility (CSR) and green competitive advantage (GCA) have often been overlooked. This study aims to fill this knowledge gap by creating a comprehensive model to predict mega-project sustainable performance (MSP). Data was collected from 289 respondents in Pakistan's construction industry, and hypotheses were tested using partial least squares structural equation modeling and fuzzy set qualitative comparative analysis. The results indicate that both secondary and primary stakeholders' pressure positively impacts CSR and MSP. Moreover, CSR significantly affects GCA and MSP, with CSR and GCA partially mediating these relationships. Thus, the proposed model could offer innovative insights for decision-makers and engineering managers, recommend adopting social and green practices to enhance MSP with the support of concerned stakeholders' pressure, and help achieve SD objectives.

Impact of corporate motives for sustainable sourcing: key moderating role of regulatory pressure.

Organizational decisions and their motivations are crucial for successfully implementing sustainable sourcing practices (SSP). Still, there is scant research on how SSPs are impacted by corporate motives (CM). To fill this research gap, we formed a three-tiered stakeholder theory (ST) based paradigm that accounts for the moderating impact of regulatory pressure (RP) while examining the relationship between different types of corporate motives (instrumental, relational, and moral) and SSP. Partial least squares structural equation modeling (PLS-SEM) was used to examine data collected from 248 respondents in the Pakistani manufacturing industry. The outputs of SEM disclosed that all CMs affect SSP. RP also confoundedly moderated these targeted relationships. Importance performance map analysis (IPMA) showed that regulatory pressure (0.319) and relational motives (67.38) are more important and perform better than all other exogenous variables. This study sheds light on corporate strategies and decision-making in multi ways. All dimensions of CM greatly enhance SSP directly and through RP, as RP firmly moderates these associations, indicating the relevance of ST. Finally, this empirical investigation ends with a framework of testable assertions and many future research endeavors on environmental sustainability.

FRMD7 Gene Alterations in a Pakistani Family Associated with Congenital Idiopathic Nystagmus.

Congenital idiopathic nystagmus (CIN) is an oculomotor disorder characterized by repetitive and rapid involuntary movement of the eye that usually develops in the first six months after birth. Unlike other forms of nystagmus, CIN is widely associated with mutations in the FRMD7 gene. This study involves the molecular genetic analysis of a consanguineous Pakistani family with individuals suffering from CIN to undermine any potential pathogenic mutations. Blood samples were taken from affected and normal individuals of the family. Genomic DNA was extracted using an in-organic method. Whole Exome Sequencing (WES) and analysis were performed to find any mutations in the causative gene. To validate the existence and co-segregation of the FRMD7 gene variant found using WES, sanger sequencing was also carried out using primers that targeted all of the FRMD7 coding exons. Additionally, the pathogenicity of the identified variant was assessed using different bioinformatic tools. The WES results identified a novel nonsense mutation in the FRMD7 (c.443T>A; p. Leu148 *) gene in affected individuals from the Pakistani family, with CIN resulting in a premature termination codon, further resulting in the formation of a destabilized protein structure that was incomplete. Co-segregation analysis revealed that affected males are hemizygous for the mutated allele c.443T>A; p. Leu148 * and the affected mother is heterozygous. Overall, such molecular genetic studies expand our current knowledge of the mutations associated with the FRMD7 gene in Pakistani families with CIN and significantly enhance our understanding of the molecular mechanisms involved in genetic disorders.

The impact of public infrastructure project delays on sustainable community development.

Over the years, public infrastructure projects have generated substantial attention as they take the initiative to enrich sustainable community development. This paper looks to identify if the delays in public projects significantly impact the sustainable community development. A questionnaire survey approach is used to collect data. This study employed the partial least square structural equation modeling to examine the hypothesized model. Data obtained from 325 project experts in the Pakistani construction industry provided empirical support for the study. The outcome of the statistical analysis showed that project delay significantly influences sustainable community development. Based on the findings, this study suggests valuable insights to project management planners and executors to improve strategic planning for project executions through proper sustainability approaches.

Assessing key factors for sporting industry sustainable development through multilayer artificial perceptron neural network approach.

Sustainable development (SD) has recently emerged as a critical axis in industrial strategic management and debate. Apart from the scarcity of resources, the influence of economic difficulties on human life and society has increased the relevance of SD. However, estimating and analyzing the SD of any business are still in its infancy. Thus, the SD of the sports goods industry is assessed using a multilayer artificial perceptron neural network (MLP) technique employing eight distinct characteristics connected to production efficiency, international and internal environment, export, and domestic development by using Statistica software. To discover an ideal model for SD in the sports goods business, stepwise models with varied input parameters were built. The identity activation function was used to create and train an error propagation algorithm-based network. The results of various eight network models revealed that, among other models, the accuracy of our fifth model, which incorporates production efficiency, international and internal environment, strategic management, and domestic distribution organization, gives the best answer. This model has the lowest root-mean-square error (RMSE) = 0.002 and the greatest R square = 0.922, demonstrating its efficacy and effectiveness in terms of SD. Based on these findings, it is possible to conclude that using artificial neural networks is beneficial in determining an optimal solution for SD and other similar problems since this tool is important and would be utilized for forecasting and decision-making in any business.

Genomic analysis of Chryseobacterium indologenes and conformational dynamics of the selected DD-peptidase.

Chrysobacterium indologenes is an emerging MDR pathogen that belongs to the family Flavobacteriaceae. The genome of the C. indologenes, isolated from the nephrotic patient, was sequenced through Illumina MiSeq. The pangenomics of available 56 C. indologenes strains using BPGA revealed an open pangenome (n=5553 CDS), core genome (2141), and accessory genome (2013). The CEG/DEG database identified 662 essential genes that drastically reduced to 68 genes after non-homology analyses towards human and gut microbiome. Further filtering the data for other drug target prioritizing parameters resulted in 32 putative targets. Keeping in view the crucial role played in cell wall biosynthesis, dacB was selected as the final target that encodes D-alanyl-d-alanine carboxypeptidase/endopeptidase (DD-peptidase). The 3D structure of dacB was modelled and rendered to docking analyses against two compound libraries of African plants (n=6842) and Tibetan medicines (n=52). The ADMET profiling exhibited the physicochemical properties of final compounds. The MD simulations showed the stability of inhibitor-DD-peptidase complex and interactions in terms of RMSD, RMSF, binding free energy calculation and H-bonding. We propose that the novel compounds Leptopene and ZINC95486338 from our findings might be potent DD-peptidase inhibitors that could aid in the development of new antibiotic-resistant therapy for the emerging MDR C. indologenes.

Green effectual orientations to shape environmental performance through green innovation and environmental management initiatives under the influence of CSR commitment.

This study elaborates the concept of green effectuation orientations (GEOs) to enhance the environmental performance of entrepreneurial SMEs based in the Jiangsu province of China. For this purpose, we collected the data from 328 employees of entrepreneurial SMEs via a survey and analyzed it through SEM. We evaluated the GEO concept on mindset and action-based views to operationalize the construct with all subdimensions fully. Our study illustrates some unexplored domains of entrepreneurship research and comes up with the findings that (GEOs) are positively correlated with environmental performance (EP), green innovation performance (GIP), and environmental management initiatives (EMI). Additionally, the findings of this research also unveil that EMI and GIP pave the way for EP, as both mediate the relationship between GEO and EP. Last but not the least, this research study also explained the effective mechanism through which CSR commitment as a moderator can affect the relationship between GEO and EP. The findings of this study expand the spheres of effectuation theory and come up with several exciting theoretical and managerial implications. Besides, it is the first of its kind to demonstrate that GEO could enhance GIP, EMI, and EP.

A Machine-Learning-Based Robust Classification Method for PV Panel Faults.

Renewable energy resources have gained considerable attention in recent years due to their efficiency and economic benefits. Their proportion of total energy use continues to grow over time. Photovoltaic (PV) cell and wind energy generation are the least-expensive new energy sources in most countries. Renewable energy technologies significantly contribute to climate mitigation and provide economic benefits. Apart from these advantages, renewable energy sources, particularly solar energy, have drawbacks, for instance restricted energy supply, reliance on weather conditions, and being affected by several kinds of faults, which cause a high power loss. Usually, the local PV plants are small in size, and it is easy to trace any fault and defect; however, there are many PV cells in the grid-connected PV system where it is difficult to find a fault. Keeping in view the aforedescribed facts, this paper presents an intelligent model to detect faults in the PV panels. The proposed model utilizes the Convolutional Neural Network (CNN), which is trained on historic data. The dataset was preprocessed before being fed to the CNN. The dataset contained different parameters, such as current, voltage, temperature, and irradiance, for five different classes. The simulation results showed that the proposed CNN model achieved a training accuracy of 97.64% and a testing accuracy of 95.20%, which are much better than the previous research performed on this dataset.

Next-generation whole exome sequencing to delineate the genetic basis of primary congenital glaucoma.

To delineate the genetic bases of primary congenital glaucoma (PCG), we ascertained a large cohort consisting of 48 consanguineous families. Of these, we previously reported 26 families with mutations in CYP1B1 and six families with LTBP2, whereas the genetic bases responsible for PCG in 16 families remained elusive. We employed next-generation whole exome sequencing to delineate the genetic basis of PCG in four of these 16 familial cases. Exclusion of linkage to reported PCG loci was established followed by next-generation whole exome sequencing, which was performed on 10 affected individuals manifesting cardinal systems of PCG belonging to four unresolved families along with four control samples consisting of genomic DNAs of individuals harboring mutations in CYP1B1 and LTBP2. The analyses of sequencing datasets failed to identify potential causal alleles in the 10 exomes whereas c.1169G > A (p. Arg390His) in CYP1B1 and c.3427delC (p.Gln1143Argfs*35) in LTBP2 were identified in the control samples. Taken together, next-generation whole exome sequencing failed to delineate the genetic basis of PCG in familial cases excluded from mutations in CYP1B1 and LTBP2. These data strengthen the notion that compound heterozygous coding variants or non-coding variants might contribute to PCG.

Improvement in the Tracking Performance of a Maneuvering Target in the Presence of Clutter.

The proposed work uses fixed lag smoothing on the interactive multiple model-integrated probabilistic data association algorithm (IMM-IPDA) to enhance its performance. This approach makes use of the advantages of the fixed lag smoothing algorithm to track the motion of a maneuvering target while it is surrounded by clutter. The suggested method provides a new mathematical foundation in terms of smoothing for mode probabilities in addition to the target trajectory state and target existence state by including the smoothing advantages. The suggested fixed lag smoothing IMM-IPDA (FLs IMM-IPDA) method's root mean square error (RMSE), true track rate (TTR), and mode probabilities are compared to those of other recent algorithms in the literature in this study. The results clearly show that the proposed algorithm outperformed the already-known methods in the literature in terms of these above parameters of interest.

Genome sequencing and analysis of genomic diversity in the locally transmitted SARS-CoV-2 in Pakistan.

Surveillance of genetic diversity of the SARS-CoV-2 is extremely important to detect the emergence of more infectious and deadly strains of the virus. In this study, we evaluated mutational events in the SARS-CoV-2 genomes through whole genome sequencing. The samples were collected from COVID-19 patients in different major cities of Pakistan during the four waves of the pandemic (May 2020 to July 2021) and subjected to whole genome sequencing. Using in silico and machine learning tools, the viral mutational events were analyzed, and variants of concern and of interest were identified during each of the four waves. The overall mutation frequency (mutations per genome) increased during the course of the pandemic from 12.19 to 23.63, 31.03, and 41.22 in the first, second, third, and fourth waves, respectively. We determined that the viral strains rose to higher frequencies in local transmission. The first wave had three most common strains B.1.36, B.1.160, and B.1.255, the second wave comprised B.1.36 and B.1.247 strains, the third wave had B.1.1.7 (Alpha variant) and B.1.36 strains, and the fourth waves comprised B.1.617.2 (Delta). Intriguingly, the B.1.36 variants were found in all the waves of the infection indicating their survival fitness. Through phylogenetic analysis, the probable routes of transmission of various strains in the country were determined. Collectively, our study provided an insight into the evolution of SARS-CoV-2 lineages in the spatiotemporal local transmission during different waves of the pandemic, which aided the state institutions in implementing adequate preventive measures.

Towards Automatic License Plate Detection.

Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles' license plates in images is a critical step that has a substantial impact on any ALPD system's recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications.

Environmental administrative penalty, environmental disclosures, and the firm's cash flow: evidence from manufacturing firms in China.

Environmental administrative penalty is a strong tool for the government to regulate environmental pollution. The influence of an environmental administrative penalty on a firm's cash flow has become a hot topic in academics and industry. By employing ordinary least square and the bootstrap method, it is established that the penalty significantly reduces the firm's cash flow in the succeeding year through incremental disclosures which play a mediating role. Further, we distinguished environmental information's nature and then measured it from negative, sensitive (hard) information and positive (soft) information aspects. However, it is found that the mediating effect of the two types of disclosure increment did not exist, respectively. These results help us to comprehend the significance of environmental management to the stability of corporate cash flow and have practical illumination to corporate environmental management and environmental pollution control.

In silico screening, SAR and kinetic studies of naturally occurring flavonoids against SARS CoV-2 main protease.

The Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) pandemic has become a global challenge based on its replication within the host cells that relies on non-structural proteins, protease (Mpro). Flavonoids, an important class of naturally occurring compounds with medicinal importance, are frequently available within fruits and vegetables. Herein, we report the in silico studies on naturally occurring flavonoids consisting of molecular docking studies and evaluation of theoretical kinetics. In this study, we prepared a library of nine different classes of naturally occurring flavonoids and screened them on Autodock and Autodockvina. The pharmacokinetic properties of most promising compounds have been predicted through ADMET SAR, inhibition constants, ligand efficiency and ligand fit quality have been worked out theoretically. The results revealed that naturally occurring flavonoids could fit well in the receptor's catalytic pocket, interact with essential amino acid residues and could be useful for future drug candidates through in vitro and in vivo studies. Moreover, MD simulation studies were conducted for two most promising flavonoids and the protein-ligand complexes were found quite stable. The selected natural flavonoids are free from any toxic effects and can be consumed as a preventive measure against SARS CoV-2.

De novo and bi-allelic variants in AP1G1 cause neurodevelopmental disorder with developmental delay, intellectual disability, and epilepsy.

Adaptor protein (AP) complexes mediate selective intracellular vesicular trafficking and polarized localization of somatodendritic proteins in neurons. Disease-causing alleles of various subunits of AP complexes have been implicated in several heritable human disorders, including intellectual disabilities (IDs). Here, we report two bi-allelic (c.737C>A [p.Pro246His] and c.1105A>G [p.Met369Val]) and eight de novo heterozygous variants (c.44G>A [p.Arg15Gln], c.103C>T [p.Arg35Trp], c.104G>A [p.Arg35Gln], c.229delC [p.Gln77Lys∗11], c.399_400del [p.Glu133Aspfs∗37], c.747G>T [p.Gln249His], c.928-2A>C [p.?], and c.2459C>G [p.Pro820Arg]) in AP1G1, encoding gamma-1 subunit of adaptor-related protein complex 1 (AP1γ1), associated with a neurodevelopmental disorder (NDD) characterized by mild to severe ID, epilepsy, and developmental delay in eleven families from different ethnicities. The AP1γ1-mediated adaptor complex is essential for the formation of clathrin-coated intracellular vesicles. In silico analysis and 3D protein modeling simulation predicted alteration of AP1γ1 protein folding for missense variants, which was consistent with the observed altered AP1γ1 levels in heterologous cells. Functional studies of the recessively inherited missense variants revealed no apparent impact on the interaction of AP1γ1 with other subunits of the AP-1 complex but rather showed to affect the endosome recycling pathway. Knocking out ap1g1 in zebrafish leads to severe morphological defect and lethality, which was significantly rescued by injection of wild-type AP1G1 mRNA and not by transcripts encoding the missense variants. Furthermore, microinjection of mRNAs with de novo missense variants in wild-type zebrafish resulted in severe developmental abnormalities and increased lethality. We conclude that de novo and bi-allelic variants in AP1G1 are associated with neurodevelopmental disorder in diverse populations.

Genetic Causes of Oculocutaneous Albinism in Pakistani Population.

Melanin pigment helps protect our body from broad wavelength solar radiation and skin cancer. Among other pigmentation disorders in humans, albinism is reported to manifest in both syndromic and nonsyndromic forms as well as with varying inheritance patterns. Oculocutaneous albinism (OCA), an autosomal recessive nonsyndromic form of albinism, presents as partial to complete loss of melanin in the skin, hair, and iris. OCA has been known to be caused by pathogenic variants in seven different genes, so far, according to all the currently published population studies. However, the detection rate of alleles causing OCA varies from 50% to 90%. One of the significant challenges of uncovering the pathological variant underlying disease etiology is inter- and intra-familial locus heterogeneity. This problem is especially pertinent in highly inbred populations. As examples of such familial locus heterogeneity, we present nine consanguineous Pakistani families with segregating OCA due to variants in one or two different known albinism-associated genes. All of the identified variants are predicted to be pathogenic, which was corroborated by several in silico algorithms and association with diverse clinical phenotypes. We report an individual affected with OCA carries heterozygous, likely pathogenic variants in TYR and OCA2, raising the question of a possible digenic inheritance. Altogether, our study highlights the significance of exome sequencing for the complete genetic diagnosis of inbred families and provides the ramifications of potential genetic interaction and digenic inheritance of variants in the TYR and OCA2 genes.

Biallelic variants in TMEM222 cause a new autosomal recessive neurodevelopmental disorder.

PURPOSE: To elucidate the novel molecular cause in families with a new autosomal recessive neurodevelopmental disorder. METHODS: A combination of exome sequencing and gene matching tools was used to identify pathogenic variants in 17 individuals. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) and subcellular localization studies were used to characterize gene expression profile and localization. RESULTS: Biallelic variants in the TMEM222 gene were identified in 17 individuals from nine unrelated families, presenting with intellectual disability and variable other features, such as aggressive behavior, shy character, body tremors, decreased muscle mass in the lower extremities, and mild hypotonia. We found relatively high TMEM222 expression levels in the human brain, especially in the parietal and occipital cortex. Additionally, subcellular localization analysis in human neurons derived from induced pluripotent stem cells (iPSCs) revealed that TMEM222 localizes to early endosomes in the synapses of mature iPSC-derived neurons. CONCLUSION: Our findings support a role for TMEM222 in brain development and function and adds variants in the gene TMEM222 as a novel underlying cause of an autosomal recessive neurodevelopmental disorder.

A missense allele of PEX5 is responsible for the defective import of PTS2 cargo proteins into peroxisomes.

Peroxisomes, single-membrane intracellular organelles, play an important role in various metabolic pathways. The translocation of proteins from the cytosol to peroxisomes depends on peroxisome import receptor proteins and defects in peroxisome transport result in a wide spectrum of peroxisomal disorders. Here, we report a large consanguineous family with autosomal recessive congenital cataracts and developmental defects. Genome-wide linkage analysis localized the critical interval to chromosome 12p with a maximum two-point LOD score of 4.2 (θ = 0). Next-generation exome sequencing identified a novel homozygous missense variant (c.653 T > C; p.F218S) in peroxisomal biogenesis factor 5 (PEX5), a peroxisome import receptor protein. This missense mutation was confirmed by bidirectional Sanger sequencing. It segregated with the disease phenotype in the family and was absent in ethnically matched control chromosomes. The lens-specific knockout mice of Pex5 recapitulated the cataractous phenotype. In vitro import assays revealed a normal capacity of the mutant PEX5 to enter the peroxisomal Docking/Translocation Module (DTM) in the presence of peroxisome targeting signal 1 (PTS1) cargo protein, be monoubiquitinated and exported back into the cytosol. Importantly, the mutant PEX5 protein was unable to form a stable trimeric complex with peroxisomal biogenesis factor 7 (PEX7) and a peroxisome targeting signal 2 (PTS2) cargo protein and, therefore, failed to promote the import of PTS2 cargo proteins into peroxisomes. In conclusion, we report a novel missense mutation in PEX5 responsible for the defective import of PTS2 cargo proteins into peroxisomes resulting in congenital cataracts and developmental defects.

Mouse Models of Human Pathogenic Variants of TBC1D24 Associated with Non-Syndromic Deafness DFNB86 and DFNA65 and Syndromes Involving Deafness.

Human pathogenic variants of TBC1D24 are associated with clinically heterogeneous phenotypes, including recessive nonsyndromic deafness DFNB86, dominant nonsyndromic deafness DFNA65, seizure accompanied by deafness, a variety of isolated seizure phenotypes and DOORS syndrome, characterized by deafness, onychodystrophy, osteodystrophy, intellectual disability and seizures. Thirty-five pathogenic variants of human TBC1D24 associated with deafness have been reported. However, functions of TBC1D24 in the inner ear and the pathophysiology of TBC1D24-related deafness are unknown. In this study, a novel splice-site variant of TBC1D24 c.965 + 1G > A in compound heterozygosity with c.641G > A p.(Arg214His) was found to be segregating in a Pakistani family. Affected individuals exhibited, either a deafness-seizure syndrome or nonsyndromic deafness. In human temporal bones, TBC1D24 immunolocalized in hair cells and spiral ganglion neurons, whereas in mouse cochlea, Tbc1d24 expression was detected only in spiral ganglion neurons. We engineered mouse models of DFNB86 p.(Asp70Tyr) and DFNA65 p.(Ser178Leu) nonsyndromic deafness and syndromic forms of deafness p.(His336Glnfs*12) that have the same pathogenic variants that were reported for human TBC1D24. Unexpectedly, no auditory dysfunction was detected in Tbc1d24 mutant mice, although homozygosity for some of the variants caused seizures or lethality. We provide some insightful supporting data to explain the phenotypic differences resulting from equivalent pathogenic variants of mouse Tbc1d24 and human TBC1D24.

Novel Mutations in CLPP, LARS2, CDH23, and COL4A5 Identified in Familial Cases of Prelingual Hearing Loss.

We report the underlying genetic causes of prelingual hearing loss (HL) segregating in eight large consanguineous families, ascertained from the Punjab province of Pakistan. Exome sequencing followed by segregation analysis revealed seven potentially pathogenic variants, including four novel alleles c.257G>A, c.6083A>C, c.89A>G, and c.1249A>G of CLPP, CDH23, COL4A5, and LARS2, respectively. We also identified three previously reported HL-causing variants (c.4528C>T, c.35delG, and c.1219T>C) of MYO15A, GJB2, and TMPRSS3 segregating in four families. All identified variants were either absent or had very low frequencies in the control databases. Our in silico analyses and 3-dimensional (3D) molecular modeling support the deleterious impact of these variants on the encoded proteins. Variants identified in MYO15A, GJB2, TMPRSS3, and CDH23 were classified as "pathogenic" or "likely pathogenic", while the variants in CLPP and LARS2 fall in the category of "uncertain significance" based on the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) variant pathogenicity guidelines. This paper highlights the genetic diversity of hearing disorders in the Pakistani population and reports the identification of four novel mutations in four HL families.