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Background: Guselkumab is an interleukin (IL)‐23 inhibitor with demonstrated efficacy in patients with psoriasis. Objectives: Evaluate the impact of early disease intervention on clinical responses following 28 weeks of guselkumab treatment in patients with moderate‐to‐severe plaque psoriasis. Correlate clinical response and disease duration data with serum biomarker data. Methods: GUIDE is a phase IIIb randomized, double‐blind, parallel‐group, multicentre study of adults with moderate‐to‐severe plaque psoriasis. In study part 1, patients with a short disease duration (SDD [≤2 years]) or a long disease duration (LDD [>2 years]) received guselkumab 100 mg at Week (W) 0, 4, 12, and 20. Those achieving complete skin clearance at W20 and W28 were defined as a super responder (SRe). A multivariable logistic regression analysed the association between baseline factors and the likelihood of becoming an SRe. The relationship between clinical response, disease duration and serum biomarker data was assessed at W0 and 4. Results: In total, 880 patients were enrolled (SDD/LDD = 40.6%/59.4% of patients). More SDD than LDD patients achieved absolute Psoriasis Area and Severity Index (PASI) = 0 at W28 (51.8% vs. 39.4%) and were SRes (43.7% vs. 28.1% [overall 34.4%]). SDD patients also achieved PASI = 0 quicker than LDD patients (median 141 vs. 200 days). Disease duration and prior biologic use had the greatest impact on becoming an SRe, with no strong association among these independent variables. At baseline, there were no significant differences in the serum biomarker levels of IL‐17A, IL‐17F, IL‐22 and β‐defensin 2 between SDD and LDD patients, or between SRe and non‐SRe patients. Guselkumab rapidly decreased these markers of systemic inflammation across all patient groups analysed at W4. Guselkumab was well tolerated. Conclusions: Guselkumab efficacy was consistent across subpopulations, on the skin and systemically. The proportion of SRes was higher in SDD than LDD patients, indicating early treatment intervention may improve clinical outcomes. [ABSTRACT FROM AUTHOR]

While plant diseases continue to have a severe impact on food production, farmers face a formidable challenge in trying to meet the escalating demands of a population that is expanding quickly for agricultural items like potatoes. Despite spending billions on disease management, farmers frequently struggle to effectively control disease without the aid of cutting-edge technology. The paper examines a disease diagnosis method based on deep learning. To be more precise, it uses a Convolutional Neural Network (CNN) method for the disease's detection and classification. This study examines the impact of data augmentation while conducting an extensive performance evaluation of the hyper-parameter in the setting of detecting plant diseases with a focus on potatoes. The experimental findings demonstrate the effectiveness of the suggested model's 98% accuracy. Considering growing global issues, this research aims to open new pathways for more efficient plant disease management and, eventually, increase agricultural output.

Background: PERSIST was a prospective, non‐interventional, long‐term, German multicentre study of patients with moderate‐to‐severe psoriasis receiving guselkumab, an approved monoclonal antibody that binds to the p19 subunit of interleukin (IL)‐23, in a real‐world setting. Objectives: Evaluation of the efficacy and safety of guselkumab, and its effect on health‐related quality of life (HRQoL), in patients with moderate‐to‐severe psoriasis who have received 52 weeks of treatment. Methods: Patients (≥18 years old) were prescribed guselkumab as per routine clinical practice. End points assessed include Psoriasis Area and Severity Index (PASI), Physician's Global Assessment (PGA), target Nail Psoriasis Severity Index (NAPSI), and the Dermatology Life Quality Index (DLQI). Results: Overall, 303 patients were enrolled and treated with guselkumab. Mean disease duration was 21.0 years, and 77.2% and 51.2% of patients had received ≥1 prior conventional systemic or ≥1 prior biologic therapy, respectively. Mean PASI score decreased from 16.4 at baseline to 3.0 by Week (W) 28, and further decreased to 2.4 by W52, while the proportion of patients achieving an absolute PASI score of ≤1 increased from 1.3% at baseline, to 50.8% at W28 and to 58.4% by W52. PASI90 and PASI100 responses also showed marked improvements between W28 and W52, regardless of biologic treatment history. Clearance of psoriatic skin was observed in difficult‐to‐treat areas, with the percentage of patients achieving a PGA score ≤1 increasing between W28 and W52. Guselkumab improved HRQoL; mean DLQI score decreased from 13.7 at baseline to 2.8 by W28, and further decreased to 2.4 by W52. At W52, 64.6% of patients achieved a DLQI score ≤1. The cumulative probability of drug survival was 92.4% at W52. Conclusions: Guselkumab is efficacious and well tolerated regardless of previous biologic therapies, comorbidities or psoriasis manifestation in difficult‐to‐treat areas. No new safety signals were observed. [ABSTRACT FROM AUTHOR]

Natural resistance-associated macrophage proteins (NRAMPs) are a class of metal transporters found in plants that exhibit diverse functions across different species. Transporter proteins facilitate the absorption, distribution, and sequestration of metallic elements within various plant tissues. Despite the extensive identification of NRAMP family genes in various species, a full analysis of these genes in tree species is still necessary. Genome-wide identification and bioinformatics analysis were performed to understand the roles of NRAMP genes in copper (CuCl2) stress in Kandelia obovata (Ko). In Arachis hypogaea L., Populus trichocarpa, Vitis vinifera, Phaseolus vulgaris L., Camellia sinensis, Spirodela polyrhiza, Glycine max L. and Solanum lycopersicum, a genome-wide study of the NRAMP gene family was performed earlier. The domain and 3D structural variation, phylogenetic tree, chromosomal distributions, gene structure, motif analysis, subcellular localization, cis-regulatory elements, synteny and duplication analysis, and expression profiles in leaves and CuCl2 were all investigated in this research. In order to comprehend the notable functions of the NRAMP gene family in Kandelia obovata, a comprehensive investigation was conducted at the genomic level. This study successfully found five NRAMP genes, encompassing one gene pair resulting from whole-genome duplication and a gene that had undergone segmental duplication. The examination of chromosomal position revealed an unequal distribution of the KoNRAMP genes across chromosomes 1, 2, 5, 7, and 18. The KoNRAMPs can be classified into three subgroups (I, II, and SLC) based on phylogeny and synteny analyses, similar to Solanum lycopersicum. Examining cis-regulatory elements in the promoters revealed five hormone-correlated responsive elements and four stress-related responsive elements. The genomic architecture and properties of 10 highly conserved motifs are similar among members of the NRAMP gene family. The conducted investigations demonstrated that the expression levels of all five genes exhibited alterations in response to different levels of CuCl2 stress. The results of this study offer crucial insights into the roles of KoNRAMPs in the response of Kandelia obovata to CuCl2 stress.

Computer viruses, malicious, and other hostile attacks can affect a computer network. Intrusion detection is a key component of network security as an active defence technology. Traditional intrusion detection systems struggle with issues like poor accuracy, ineffective detection, a high percentage of false positives, and an inability to handle new types of intrusions. To address these issues, we propose a deep learning-based novel method to detect cybersecurity vulnerabilities and breaches in cyber-physical systems. The proposed framework contrasts the unsupervised and deep learning-based discriminative approaches. This paper presents a generative adversarial network to detect cyber threats in IoT-driven IICs networks. The results demonstrate a performance increase of approximately 95% to 97% in terms of accuracy, reliability, and efficiency in detecting all types of attacks with a dropout value of 0.2 and an epoch value of 25. The output of well-known state-of-the-art DL classifiers achieved the highest true rate (TNR) and highest detection rate (HDR) when detecting the following attacks: (BruteForceXXS, BruteForceWEB, DoS_Hulk_Attack, and DOS_LOIC_HTTP_Attack) on the NSL-KDD, KDDCup99, and UNSW-NB15 datasets. It also maintained the confidentiality and integrity of users’ and systems’ sensitive information during the training and testing phases.

Green Intellectual Capital (GIC) refers to environmentally friendly management styles for gaining a competitive advantage. In this study, the impact of GIC was assessed on the project’s sustainability while big data was taken as a moderating variable. Data was collected from the manufacturing industry and analyzed using structural equation modeling. Results indicated that GIC has a significant effect on project sustainability. In the same way, big data moderates the relationship between the GIC and project sustainability, except for the relational capital construct. The finding of the study recommends that GIC, with the support of big data, can be helpful for the completion of sustainable manufacturing projects. Moreover, the study recommends that elements of the relational capital construct need to be explored in-depth to assess their role in project sustainability. In the same way, the GIC may also need to be tested and applied in other industries and countries for the delivery of value to customers.

The underlying risks and complexities in government infrastructure projects have increased the importance of external stakeholder management in contemporary project management. In developing countries, it is also important for policymaking and planning of infrastructure programs due to the varying nature of stakeholders and their expectations from the government. Few studies have looked at how external stakeholders are involved in public infrastructure projects and how they work together to achieve common project goals by overcoming communication and decision-making barriers. The internal stakeholders and project managers also need to properly liaise with the external stakeholders without compromising the project goals. Thus, there is a need to strategize the stakeholder management process to improve public sector infrastructure projects, especially from a developing nation's perspective. Therefore, the scope of this research has evaluated the prevalence of external stakeholder management in public sector infrastructure construction projects in Pakistan by developing and validating its five core dimensions. Among the constructs were identification and classification, communication, engagement, empowerment, and risk control. Besides this, twenty-seven sub-variables of stakeholder management have also been identified in the context of public sector projects. The results of the factor loading show that "Risk Control" is the most contributing dimension of stakeholder management, and "Empowerment" is the least concern in the current practices. The study emphasizes the importance of establishing a systematic and comprehensive framework for empowering external stakeholders, which will strengthen and improve performance and project outcomes. This study reveals insights that will assist project organizations in integrating external stakeholders into their government-sponsored projects with their effective empowerment and sufficient engagement.

Objective: The objective of the study was to evaluate the clinical, laboratory manifestations, and associations of macrophage activation syndrome (MAS) in a cohort of Pakistani patients with systemic lupus erythematous and to compare it with a well-characterized cohort of active systemic lupus erythematosus (SLE) without MAS. Materials and Methods: Patients with a diagnosis of SLE admitted from January 2017 to July 2019 were retrospectively reviewed. MAS ascertained by either clinical criteria or bone marrow biopsy were studied. Demographics, clinical, and laboratory parameters of MAS patients were evaluated and compared with a cohort of non-MAS active SLE. Results: A total of 305 patients of SLE were evaluated, out of these 18 patients had a diagnosis of MAS, making a MAS prevalence of 5.9%. Mean age of the patients was 27.25 ± 10.16 years. Female comprised 13 (72%) of MAS group. Fever was the most common clinical presentation (100%), followed by splenomegaly (38.9%) and lymphadenopathy (33.3%). At least 5 (27.8%0 patients had MAS as their initial manifestation. Anemia (88.9%), thrombocytopenia (88.9%), leukopenia (72%), and pancytopenia (55.6%) were prominent laboratory parameters. Mean serum ferritin of 1584 ng/ml and mean triglyceride of 270 mg/dl were reported. On comparing with non-MAS cohort; younger age, fever, lymphadenopathy, splenomegaly, mucocutaneous features, any type of cytopenia, high aspartate aminotransferase (AST), low albumin, and lactate dehydrogenase, all showed significant association with MAS (P < 0.05). Conclusion: Fever and associated cytopenias coupled with raised AST are strong predictors of MAS in patients with SLE.

Objective The main objective of the study is to empirically investigate the influence of lean manufacturing (LM) practices on the operational and business performance of manufacturing companies in Oman. Methods Empirical data on LM practices and performance were collected using a self-administered structured survey questionnaire and the sampling frame was manufacturing companies in Oman. In total 300 questionnaires were distributed among 185 companies and a total of 107 with a response rate of 35.6 percent. Findings The statistical analysis obtained from structural equation modeling found that lean manufacturing practices can explain operational performance, however, were unable to benefit overall business performance. Out of eight LM practices considered, small-lot production and quick setups were found to be the most adopted practices in manufacturing companies. Novelty Even though LM has become a fundamental aspect of industrial manufacturing processes; little is known about its impact on performance. This study adds value to the literature by examining the key LM practices-performance relationships within the manufacturing companies in Oman. These findings have significant implications for improving manufacturing organizations' operational and business performance through lean manufacturing strategies.

This research was conducted to study the association between job satisfaction and the wellbeing of secondary school teachers from Gilgit, Pakistan. A total of 167 teachers (89 males and 78 females) from both private and public schools were recruited for this study. A job satisfaction survey by Spector was administered to participants along with World Health Organization (WHO) wellbeing index. Results showed that teachers’ job satisfaction (pay, promotion, fringe benefits, contingent rewards, operating conditions, coworkers, nature of work, and communication) was significantly and positively related to their wellbeing. Teachers from public schools have higher job satisfaction and wellbeing as compared to teachers from private schools. Stepwise regression analysis indicated that after controlling the job sector their level of job satisfaction in terms of communication independently predicted their wellbeing.

Benefit management is a valuable approach, promoted and supported by strategic management for maximizing organizational benefits. It has also been recognized as a means of improving project performance, though more research is needed to understand how it works. This research adds to the contemporary literature on public sector project management by studying the links between project governance, benefit management, and project performance from a developing country perspective. This study provides insights for project professionals who are working in government organizations for optimizing the benefits of investment. The study provides a reference to formulate strategies for managing and governing the performance of government-funded projects in developing countries like Pakistan. The research uses a positivist approach and quantitative design. The quantitative technique is employed to address all the aspects of the study. The structural equation modeling revealed that benefit management acted as a mediating factor in the relationship between project governance dimensions and public sector project performance. This study highlights the importance of incorporating a comprehensive benefit management approach to streamline the benefit management process by aligning projects with the organizational strategy for sustaining project performance. These findings stress the need for a comprehensive benefit management approach in improving the project performance of government-sponsored projects. The study attempts to improve the understanding of project professionals about the role and significance of benefit management, which will be helpful to get organizational support for the employees who are striving to improve the project performance in Pakistan.

The consistent use of synthetic fertilizers and chemicals in traditional agriculture has not only compromised the fragile agroecosystems but has also adversely affected human, aquatic, and terrestrial life. The use of phytostimulants is an alternative eco-friendly approach that eliminates ecosystem disruption while maintaining agricultural productivity. Phytostimulants include living entities and materials, such as microorganisms and nanomaterials, which when applied to plants or to the rhizosphere, stimulate plant growth and induce tolerance to plants against biotic and abiotic stresses. In this review, we focus on plant growth-promoting rhizobacteria (PGPR), beneficial fungi, such as arbuscular mycorrhizal fungi (AMF) and plant growth-promoting fungi (PGPF), actinomycetes, cyanobacteria, azolla, and lichens, and their potential benefits in the crop improvement, and mitigation of abiotic and biotic stresses either alone or in combination. PGPR, AMF, and PGPF are plant beneficial microbes that can release phytohormones, such as indole acetic acid (IAA), gibberellic acid (GA), and cytokinins, promoting plant growth and improving soil health, and in addition, they also produce many secondary metabolites, antibiotics, and antioxidant compounds and help to combat biotic and abiotic stresses. Their ability to act as phytostimulator and a supplement of inorganic fertilizers is considered promising in practicing sustainable agriculture and organic farming. Glomalin is a proteinaceous product, produced by AMF, involved in soil aggregation and elevation of soil water holding capacity under stressed and unstressed conditions. The negative effects of continuous cropping can be mitigated by AMF biofertilization. The synergistic effects of PGPR and PGPF may be more effective. The mechanisms of control exercised by PGPF either direct or indirect to suppress plant diseases viz. by competing for space and nutrients, mycoparasitism, antibiosis, mycovirus-mediated cross-protection, and induced systemic resistance (ISR) have been discussed. The emerging role of cyanobacterial metabolites and the implication of nanofertilizers have been highlighted in sustainable agriculture.

Objective: To determine the clinical and laboratory features during the disease course in patients of anti cytoplasmic antibody (ANCA) associated vasculitis in Pakistani patients presenting to a tertiary care center. Study Design: Case series. Place and Duration of Study: Fatima Memorial Hospital Shadman Lahore, from Dec 2018 to Mar 2019. Methodology: A collection of 20 patients regarding demographic data, constitutional symptoms, mucocutaneous symptoms and signs, upper respiratory symptoms, lower respiratory symptoms, orbital and ocular manifestation, cardiovascular, peripheral vascular manifestation, central and peripheral neurologic manifestation, abdominal manifestation and renal manifestation Results: Sixteen patients (80%) had a diagnosis of Granulomatosis with Polyangiitis, and 4 patients (20%) were of microscopic polyangiitis. The most common systemic involvement in descending order were constitutional symptoms (75%), ear nose and throat symptoms (50%), renal (50%), respiratory (45%), ocular (40%) and neurologic (40%). Most common laboratory abnormalities in our patients included leukocytosis (45%), anemia (35%), hematuria (50%), proteinuria (45%), and elevated serum creatinine (45%). Cytoplasmic-anti cytoplasmic antibody (C-ANCA) was positive in 11 (55%), all cases were of granulomatosis with polyangiitis, P-ANCA was positive in 5 (25%) of all patients, with 4 (100%) in Microscopic polyangiitis. Analysis of Granulomatosis with Polyangiitis according to gender and cytoplasmic-anti cytoplasmic antibody status showed correlation of renal involvement with cytoplasmic-anti cytoplasmic antibody status with statistical significance of p=0.036. Plain chest X-rays showed infiltrates in 2 (10%), nodularity 2 (10%), cavitation in 2 (10%), effusion in 1 (5%), and reticulonodular showing in 1 (5%) patients. High-resolution computed tomography findings included ground-glass opacification in 5............

Objective: To determine the performance of patient reported outcome measurement information system in patients with rheumatoid arthritis in our setup. Study Design: Cross sectional study. Place and Duration of Study: Division of Rheumatology, Fatima memorial hospital, Lahore Pakistan, from May 2019 to Jul 2019. Methodology: A total of 191 patients of either gender, aged more than 16 years with sero +ve rheumatoid arthritis were included in the study. Results: Majority of patients 156 (81.67%) were female and male 35 (18.32%) with mean age 38 ± 12.19 years. Diagnosed as rheumatoid arthritis and compliant with csDMARDs. Out of all these, majority were in low disease activity (LDA) 72 (38%), remission 62 (32%), moderate 42 (22%) and high disease activity 16 (8%). Mean T-score of all measures showed variations of scores as disease progresses. For all measures, mild change was noted between low disease activity and moderate disease activity whereas high difference was seen in remission and high disease activity. Highest correlations were seen among similar constructs of physical health, mental health, and social health. Fatigue was strongly correlated with social role and social activity. Conclusion: There was a considerable impact of rheumatoid arthritis on physical, social and mental health calculated with patient reported outcomes measurement information system-29 (PROMIS-29).

Lithium-Ion batteries require step-ahead information to apply contingency plans to prevent them from operating beyond their safe operation thresholds in grid storage and electric vehicle applications. Recently, machine learning techniques have been increasingly applied to forecast one such battery information metric, State-of-Charge % (SOC). Conventional standalone machine learning techniques applied in recent works suffer from an accuracy standpoint and thus have been replaced by high-fidelity hybrid machine learning techniques. Existing works on hybrid techniques either perform in-sample predictions, provide limited information of the underlying model, or do not consider varying charging-discharging rate (C-rate) dynamics of the battery. To address this issue, this article presents unified SOC forecasting models using a Minimized Akaike Information Criterion ( ${m}$ -AIC) algorithm. Initially, the ${m}$ -AIC algorithm is used in a standalone manner to precisely tune and search ARIMA (Autoregressive Integrated Moving Average) terms’ order automatically to accurately forecast the battery’s current, voltage, and SOC parameters in a univariate manner at a lower C-rate from given C-rates. The ${m}$ -AIC algorithm based univariate models’ (m-AIC) accuracy is further enhanced by first modeling and unifying with Multilayer Perceptron (MLP) and then with Nonlinear Autoregressive Neural Network with external input (NARX) neural networks respectively using the previously forecasted parameters (from m-AIC). This results in unified-MLP (u-MLP) and unified-NARX (u-NARX) models which provide higher accuracy out-of-sample SOC forecasting at the lower C-rate for different optimizer variants. Results show that the proposed u-MLP and u-NARX models reduce the mean squared error to 0.1048% and 0.0175% in comparison to their standalone counterparts which show the lowest corresponding error values of 0.271% and 0.0236% respectively. Furthermore, an additional reference univariate model, Holt-Winters Exponential Smoothing (HWES), is analyzed (by replacing m-AIC) for a comparative performance evaluation in both standalone and unified manners. Recommendations for usage of the preferred respective models in either manner for SOC forecasting are also presented based on epochs and error values.

Increasing distributed topology design implementations, uncertainties due to solar photovoltaic systems generation intermittencies, and decreasing battery costs, have shifted the direction towards integration of battery energy storage systems (BESSs) with photovoltaic systems to form renewable microgrids (MGs). Specific benefits include, but are not limited to, seamless switching and islanding operations during outages and ancillary grid services. The evolution of battery chemistries and other components has also further enhanced practicality; however, developing these multifaceted MGs involves complexity in the design process. Consequently, stakeholders rely on connection standards and operational requirements to guarantee reliable and safe grid-connected operations. This paper presents a technical overview of battery system architecture variations, benchmark requirements, integration challenges, guidelines for BESS design and interconnection, grid codes and standards, power conversion topologies, and operational grid services. In addition, a comprehensive review of the control strategies for battery equalization, energy management systems, communication, control of multiple BESSs, as well as a discussion on protection blinding and intentional islanding using BESSs is also provided. Finally, a discussion of the islanded and black start operation results for time-based analysis and standard validation of a 3MW/9MWh BESS in a grid-connected MG at the Florida International University (FIU) Engineering Campus is presented.