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Oat husks are produced during the milling process of oats. Oat husks are a lignocellulosic material that have the potential for valorization thereby improving the circular economy of agricultural by-products. However, due to the high lignocellulosic content, there are limited valorization pathways for oat husks. To improve the anaerobic digestibility of oat husks, pre-treatment was investigated as a method to aid valorization. A novel extrusion process was used in an attempt to fragment the lignocellulosic structure of oat husks prior to anaerobic digestion. The extrusion pre-treatment was investigated to determine the effect it may have on altering the methane yield and digestibility of oat husks. Biochemical methane potential assays were undertaken using oat husks with no pre-treatment and extruded oat husks. These assays demonstrated that extruded oat husks produced a significantly higher methane yield of 264 ml/gVS fed, which was 27% greater than the methane yield produced from the untreated oat husks. Similarly, the total solids degradation was also significantly higher for extruded oat husks treatment compared to the untreated oat husks. Overall, the extrusion process demonstrated an increased methane yield for oat husks compared to previously published data. The biomethane potential tests suggest that extruded oat husks would be a feedstock suitable for anaerobic digestion.

The aim of this work focuses on the relationship among the Dark Triad (psychopathy, narcissism, and Machiavellianism), perceptions of organizational politics, political skills, and counterproductive work behavior. This study empirically tests the mediating role of perceptions of organizational politics in the relationship between the Dark Triad and counterproductive work behavior. Furthermore, the study investigates the moderating role of political skills in strengthening the link between the Dark Triad and the perceptions of organizational politics. A sample of 149 participants was randomly selected. To analyze the data of the present work, we employed a structural equation model using partial least square and PROCESS. From empirical findings, we imply an inference that perception of organizational politics partially mediates the Dark Triad's influence on the counterproductive work behavior. Moreover, the results identify the moderating role of political skills in strengthening the link between the Dark Triad and the perceptions of organizational politics. Empirical findings suggest important policy implications for the hospitality industry.

Oat husks are produced during the milling process of oats. Oat husks are a lignocellulosic material that have the potential for valorization thereby improving the circular economy of agricultural by-products. However, due to the high lignocellulosic content, there are limited valorization pathways for oat husks. To improve the anaerobic digestibility of oat husks, pretreatment was investigated as a method to aid valorization. A novel extrusion process was used in an attempt to fragment the lignocellulosic structure of oat husks prior to anaerobic digestion. The extrusion pre-treatment was investigated to determine the effect it may have on altering the methane yield and digestibility of oat husks. Biochemical methane potential assays were undertaken using oat husks with no pre-treatment and extruded oat husks. These assays demonstrated that extruded oat husks produced a significantly higher methane yield of 264ml/gVS fed, which was 27% greater than the methane yield produced from the untreated oat husks. Similarly, the total solids degradation was also significantly higher for extruded oat husks treatment compared to the untreated oat husks. Overall, the extrusion process demonstrated an increased methane yield for oat husks compared to previously published data. The biomethane potential tests suggest that extruded oat husks would be a feedstock suitable for anaerobic digestion. [ABSTRACT FROM AUTHOR]

Understanding the cycle of carbon emissions resulting from agricultural practices is critical for evaluating their effect on environmental quality. This study investigates the influence of corn production on environmental quality across six major corn producing provinces in China: Hebei, Heilongjiang, Henan, Hubei, Shandong, and Sichuan, using panel datasets spanning from 1990 to 2022. Utilizing a robust methodological framework and advanced econometric techniques such as the Panel Mean Group Autoregressive Distributed Lag model (PMG-ARDL), Panel Quantile Regressions (PQR), Panel Least Square regression (PLSR), this study offers a comprehensive analysis of both short-term and long-term impacts of several agricultural inputs, agricultural GDP, and temperature on environmental quality. The findings reveal significant long-term contributions to carbon emissions from the use of agricultural water, agricultural credit, and fertilizers use, indicating the environmental costs associated with intensive agricultural practices. The study shows carbon emissions have a long-term negative relationship with corn production. The results from the PMG-ARDL model are consistent with those obtained from the PQR, and PLSQR analyses, demonstrating strong positive correlations between agricultural loans, fertilizer use, agricultural water usage, and carbon emissions. The Dumitrescu and Hurlin results show unidirectional causation of carbon emissions from pesticide use, temperature, and agricultural GDP, and bidirectional causal relationship between carbon emissions, corn production, fertilizer use, agricultural water usage, and agricultural credit. The study underscores the critical need for policies that balance agricultural productivity with environmental quality, suggesting directions for future research to explore diverse agricultural systems and incorporate more dynamic modeling approaches to better understand and mitigate the environmental impacts of agriculture.

Concentration of trace elements (CTEs) is a significant environmental concern worldwide. This study assessed CTE levels in the Lower Indus River (LIR) by analyzing CTEs in water, sediments, tissues of fish (Cirrhinus mrigala), and macrophytes using electrothermal atomic absorption spectrometry (ETAAS) and flame atomic absorption spectrometry (FAAS). The study shows that CTE range—arsenic (As, 58.7–112.1 μg/L), lead (Pb, 59.9–95.6 μg/L), cadmium (Cd, 3.8–8.1 μg/L), nickel (Ni, 40.9–63.4 μg/L), and zinc (Zn, 590.7–847.6 μg/L)—and water parameters (temperature, pH, COD, BOD, turbidity, and alkalinity) exceeded WHO (World Health Organization) acceptable limits. The CTEs (mg/kg dried basis) were analyzed in fish tissues, and As, Cd, Cr, Cu, Hg, Ni, and Zn have the following accumulation order: liver > gill > muscle. In contrast, Ag and Pb were present in higher amounts in gills than in the liver (gill > liver > muscle). Prediction of bioavailability of CTEs, with the extraction of sediment load with EDTA, revealed that As, Cd, and Zn were among the most bioavailable elements in the LIR. Health risk assessment indicated that the presence of CTEs in the fish could pose potential adverse health effects on humans. The study emphasizes significant ecological and health concerns due to fish consumption in the affected region, noting high risks of non-carcinogenic effects. These insights are essential for policymakers and stakeholders in Sindh Province to manage and reduce trace element pollution.

IntroductionHepatic artery pseudoaneurysm (HAP) is a rare and potentially life-threatening condition associated with high mortality. This study aims to review the etiology, clinical manifestations, management, and outcomes of patients diagnosed and treated for HAP at the Mayo Clinic.MethodologyThis study was a retrospective chart review of medical records for patients diagnosed and treated for hepatic artery pseudoaneurysm (HAP) at the Mayo Clinic (Florida, Minnesota, and Arizona) between September 1, 1998, and June 30, 2022. A total of 27 patients with HAP were identified, and their demographics, presenting symptoms, location of HAP, etiology, associated liver pathology, type of intervention, and outcomes were analyzed.ResultsThe majority of patients with hepatic artery pseudoaneurysm (HAP) were male (63%), with a median age of 57 years (range: 25–87 years). HAP was predominantly intrahepatic (85.2%) and most commonly located on the right hepatic artery (RHA) (70.4%). In 89.9% of cases, the condition was attributable to hepatobiliary procedures or trauma, while only 10.1% occurred spontaneously. Presenting symptoms at the time of HAP diagnosis varied, including gastrointestinal (GI) bleeding (29.6%), abdominal pain (14.81%), non-GI bleeding (11.1%), traumatic bodily injury (11.1%), and other symptoms (14.81%). Asymptomatic or incidental findings of HAP were observed in 18% of cases. Malignancy was identified in 52% of patients, and 26% were liver transplant recipients. Statistical analysis revealed that factors such as prior knowledge of HAP (p = 0.381), HAP rupture (p = 0.382), anticoagulation therapy (p = 0.856), hemorrhagic shock (p = 0.25), liver cirrhosis (p = 0.143), gastrointestinal bleeding (p = 0.879), hepatobiliary abscess (p = 0.079), liver transplantation (p = 0.738), spontaneous HAP (p = 0.381), and malignancy (p = 0.163) were not significantly associated with increased mortality. In contrast, the need for transfusions (p = 0.021), tumor invasion (p = 0.023), portal vein thrombosis (PVT) (p = 0.02), and liver necrosis (p = 0.02) were significantly associated with higher mortality. The overall infection rate was 3%, while the mortality rate was 18.5%.DiscussionHepatic artery pseudoaneurysm (HAP) is a rare but serious condition often associated with hepatobiliary procedures, trauma, or liver transplants, though it can also occur spontaneously. While HAP is commonly detected incidentally, its diagnosis is frequently linked to complications such as rupture and gastrointestinal bleeding. However, our study suggests that these complications do not necessarily increase mortality. Key factors associated with higher mortality include the need for blood transfusions, tumor invasion, portal vein thrombosis, and liver necrosis at the time of diagnosis. The overall infection rate was low, but the mortality rate was 18.5%, highlighting the importance of early detection and management.

Robotic probe manipulator for echocardography (echo) can potentially reduce cardiac radiologists’ physical burden. Echo procedure with industrial robots has wide Range of Motion (RoM) but poses safety risks because the robot may clamp the patient against the bed. Conversely, a soft robotic manipulator for echo has safe contact force but suffers from a limited RoM. Due to COVID-19, cardiac radiologists explored performing echo in the prone-positioned patients, which yielded good-quality images but was difficult to perform manually. From robot design perspective, prone position allows safer robot without clamping issue because all actuators are under the patient with minimal RoM to reach the cardiac windows. In this work, we propose a robotic probe manipulator for echo in the prone position employing a combination of a delta 3D printer and a soft end-effector and investigate its feasibility in a clinical setting. We implemented the robot as a scanner type device in which the probe manipulator scans from under a bed with an opening around the chest area. The doctor controls the robot with a joystick and a keypad while looking at a camera view of the chest area and the ultrasound display as feedback. For the experiments, three doctors and three medical students scanned the parasternal window of the same healthy subject with the robot and then manually. Two expert cardiologists evaluated the captured ultrasound images. All medical personnel could obtain all the required views with the robot, but the scanning time was considerably longer than the manual one. The ultrasound image quality scores of the doctors’ group remained constant between manual and robotic scans. However, the image scores of the robotic scan were lower in the students’ group. In summary, this work verified the ability to obtain clinically sufficient images in echocardiography in the prone position by expert medical doctors using the proposed robotic probe manipulator. Our robot can be further developed with semi automatic procedure to serve as a platform for safe and ergonomic echocardiography.

Haemonchus contortus poses a global challenge as a parasite affecting small ruminants, yet the problem of absence of an effective vaccine against H. contortus infection still exists. This investigation sought to appraise the immunological reaction induced by recombinant H. contortus excretory/secretory-24 (rHcES-24) in combination with complete Freund’s adjuvant (CFA) and bio-polymeric nanoparticles (NPs) within a murine model. In this study, rHcES-24 was encapsulated in poly(d, l-lactide-co-glycolide) (PLGA) and chitosan (CS) NPs, administered subcutaneously to mice. Researchers analyzed the NPs using scanning electron microscope (SEM) and assessed lymphocyte proliferation, specific antibodies, cytokines, T cell proliferation (CD3e+CD4+, CD3e+CD8a+), and phenotypic alteration in splenocytes (CD11c+CD83+, CD11c+CD86+) through flow cytometry to understand the immune response. The results demonstrated that the administration of nanovaccines (NVs) prompted immune responses towards Th1 pathway. This was indicated by notable enhancements in the production of specific antibodies, heightened cytokine levels, and a robust proliferation of lymphocytes observed in mice that received the NVs compared to control groups. Remarkably, mice vaccinated with the antigen-loaded NPs formulations exhibited considerably higher proportions of splenic dendritic cells (DCs) and T cells in comparison to those receiving the traditional adjuvant or the control groups. Incorporating HcES-24 protein into NPs effectively conferred immunity against H. contortus, paving the way for developing a targeted and commercial vaccine.

IntroductionThe coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed extraordinary challenges to global health systems and economies. The virus’s rapid evolution has resulted in several variants of concern (VOCs), including the highly transmissible Omicron variant, characterized by extensive mutations. In this study, we investigated the genetic diversity, population differentiation, and evolutionary dynamics of the Omicron VOC during the fifth wave of COVID-19 in Pakistan.MethodsA total of 954 Omicron genomes sequenced during the fifth wave of COVID-19 in Pakistan were analyzed. A Bayesian framework was employed for phylogenetic reconstructions, molecular dating, and population dynamics analysis.ResultsUsing a population genomics approach, we analyzed Pakistani Omicron samples, revealing low within-population genetic diversity and significant structural variation in the spike (S) protein. Phylogenetic analysis showed that the Omicron variant in Pakistan originated from two distinct lineages, BA.1 and BA.2, which were introduced from South Africa, Thailand, Spain, and Belgium. Omicron-specific mutations, including those in the receptor-binding domain, were identified. The estimated molecular evolutionary rate was 2.562E-3 mutations per site per year (95% HPD interval: 8.8067E-4 to 4.1462E-3). Bayesian skyline plot analysis indicated a significant population expansion at the end of 2021, coinciding with the global Omicron outbreak. Comparative analysis with other VOCs showed Omicron as a highly divergent, monophyletic group, suggesting a unique evolutionary pathway.ConclusionsThis study provides a comprehensive overview of Omicron’s genetic diversity, genomic epidemiology, and evolutionary dynamics in Pakistan, emphasizing the need for global collaboration in monitoring variants and enhancing pandemic preparedness.

IntroductionRice is a vital staple food for many countries, and its yield is known to be significantly affected by various abiotic stresses, which are expected to intensify with climate change, posing a threat to global food security. Abscisic acid (ABA), a crucial plant growth regulator, plays a crucial role in plant responses to these abiotic stresses. It influences several processes, such as seed dormancy, leaf gas exchange, reactive oxygen species (ROS) scavenging, ion toxicity reduction, and root elongation, all of which contribute to enhancing plant survival under stress.MethodsThis article reviews recent research on ABA-mediated gene responses and expressions involved in rice plant architecture and its response to abiotic stress.Results and discussionAbscisic acid responses were primarily driven by changes in gene expression. Expression analyses of the gene related to ABA biosynthesis or catabolism indicated several changes in plant architecture, such as changes in leaf angle, delayed flowering, and modifications in growth regulators. Additionally, tolerance-related mechanisms, such as increased ROS scavenging, reduced membrane leakage, and vacuolar compartmentation of toxic radicals, were activated under single or multiple stress conditions. While these adaptations may improve plant survival and yield sustainability under stress, they may not necessarily enhance yield potential in environments affected by drought, salinity, or heat stress. ABA expression was also associated with improved pollen viability, grain-filling potential, and seed setting under abiotic stresses such as heat, which could enhance seed yield in such challenging environments.

Cadmium (Cd) is a well-known pollutant in agricultural soil, affecting human health through the food chain. To combat this issue, Ca + Mg (25 mg L−1) nanocomposite and Bacillus pumilus, either alone or combined, were applied to rice plants under Cd (5 mg kg−1, 10 mg kg−1) contamination. In our study, growth and yield traits demonstrated the beneficial influence of Ca + Mg and B. pumilus application in improving rice defense mechanism by reducing Cd stress. Combined Ca + Mg and B. pumilus application increased SPAD (15), total chlorophyll (18), chlorophyll a (11), chlorophyll b (22), and carotenoids (21%) with Cd (10 mg kg−1), compared to the application alone. Combined Ca + Mg and B. pumilus application significantly regulated MDA (15), H2O2 (13), EL (10), and O2•– (24%) in shoots under Cd (10 mg kg−1), compared to the application alone. Cd (10 mg kg−1) increased the POD (22), SOD (21), APX (12), and CAT (13%) in shoots with combined Ca + Mg and B. pumilus application, compared to the application alone. Combined Ca + Mg and B. pumilus application significantly reduced Cd accumulation in roots (22), shoots (13), and grains (20%) under Cd (10 mg kg−1), compared to the application alone. Consequently, the combined application of Ca + Mg and B. pumilus is a sustainable solution to enhance crop production under Cd stress.

Arsenic (As) toxicity is a serious hazard to agricultural land due to growing industrialization, which has a negative effect on wheat crop yields. To address this issue, using seaweed extract and Azospirillum brasilense has emerged as an effective strategy for improving yield under stress conditions. However, the combined application of A. brasilense and seaweed extract in wheat crops under As toxicity has not been fully explored. The effectiveness of combining A. brasilense and seaweed extract in reducing As toxicity in wheat production was examined in this study through a 2-year pot experiment with nine treatments. These treatments included a control with no additives and two As concentrations (50 and 70 μM). At 50 and 70 μM, As was tested alone, with seaweed extract, with A. brasilense, and both. Significant results were achieved in reducing As toxicity in wheat crops. Arsenic at 70 μM proved more harmful than at 50 μM. The application of A. brasilense and seaweed extract was more effective in improving crop growth rates, chlorophyll levels, and stomatal conductance. The combined application notably decreased As concentration in wheat plants. It was concluded that applying A. brasilense and seaweed extract not only improves wheat growth but can also improve soil parameters under As toxicity conditions by increasing organic matter contents, boosting nutrient availability, and increasing the production of antioxidant enzymes.

IntroductionBreast cancer is an extremely common and potentially fatal illness that impacts millions of women worldwide. Multiple criteria and inclinations must be taken into account when selecting the optimal treatment option for each patient.MethodsThe selection of breast cancer treatments can be modeled as a multi-attribute group decision-making (MAGDM) problem, in which a group of experts evaluate and rank alternative treatments based on multiple attributes. MAGDM methods can aid in enhancing the quality and efficacy of breast cancer treatment selection decisions. For this purpose, we introduce the concept of a 2-tuple linguistic interval-valued q-rung orthopair fuzzy set (2TLIVq-ROFS), a new development in fuzzy set theory that incorporates the characteristics of interval-valued q-rung orthopair fuzzy set (IVq-ROFS) and 2-tuple linguistic terms. It can express the quantitative and qualitative aspects of uncertain information, as well as the decision-makers' level of satisfaction and dissatisfaction.ResultsThen, the 2TLIVq-ROF weighted average (2TLIVq-ROFWA) operator and the 2TLIVq-ROF weighted geometric (2TLIVq-ROFWJ) operator are introduced as two new aggregation operators. In addition, the multi-attribute border approximation area comparison (MABAC) method is extended to solve the MAGDM problem with 2TLIVq-ROF information.DiscussionTo demonstrate the efficacy and applicability of the suggested model, a case study of selecting the optimal breast cancer treatment is presented. The results of the computations show that the suggested MAGDM model is able to handle imprecision and subjectivity in complicated decision-making scenarios and opens new research scenarios for scholars.

Steel bearings have been commonly used to counteract induced loading from thermal and traffic conditions in numerous bridges. However, their effectiveness has been compromised due to aging and maintenance limitations, potentially impacting the overall bridge system performance. Existing monitoring techniques for detecting malfunctioning steel bearings lack automation and precision, making them inadequate for long-term and real-time bridge dynamics assessment. This study proposes a response-based approach to identify bearing malfunction by analyzing the traffic-induced response in the bearing vicinity. To implement this approach, laser displacement sensors and wireless acceleration sensors were employed to monitor both malfunctioning and well-functioning steel bridge bearings. Significant differences in bearing performance were observed through response analysis and comparison. Laser sensor measurements revealed larger vertical deflections in the girder at malfunctioned bearing under traffic loading. Moreover, the investigation of the acceleration response in the bearing locality indicated that bearing malfunction could alter the vibrational characteristics of the vicinity, significantly affecting Cross Power Spectral Density (CPSD) and cross-correlation. To quantitatively evaluate the performance of steel bearings, a Condition Score (CS) was introduced. The CS exhibited a strong correlation with bearing damage, providing valuable insights for maintenance and decision-making processes in bridge asset management. This study offers a comprehensive and automated method for identifying steel bridge bearing malfunction by utilizing advanced monitoring techniques and introducing the CS for assessment. The results obtained from this approach can enhance bridge maintenance strategies and contribute to effective bridge asset management.

Forest vegetation is an important component of forest ecosystems, contributing to terrestrial plant diversity while also providing a variety of ecological services. In managed landscapes, plantations emerge as dominant kinds after stand-replacing disturbances. However, the dynamics of vegetation cover, diversity, and composition in plantation forests remains poorly understood in the subtropical region. Our study recorded a rich floral diversity with 173 angiosperm species, characterized by varying life forms and distinct flowering phenology. The uneven distribution of species across families demonstrated the complexity of the ecosystem, with Poaceae being dominant. Diversity patterns among different plantation types varied, with Dalbergia sissoo and Populus nigra plantations exhibiting higher species richness and diversity. Conversely, Eucalyptus camaldulensis and Morus alba plantations displayed lower diversity, emphasizing the influence of plantation type on biodiversity. Non-metric multidimensional scaling (nMDS) and PERMANOVA analyses revealed significant dissimilarity patterns of vegetation composition. Indicator species analysis identified unique compositions within each plantation type, emphasizing the importance of conserving specific types to protect indicator species and maintained ecological distinctiveness. Canonical Correspondence Analysis (CCA) demonstrated that road accessibility, stem cutting, and fire significantly influenced plant distribution patterns. The present research underscored the importance of considering plantation type in forest management for biodiversity conservation and highlighted the environmental variables’ influence on the formation of plant communities. These results provided major implications for sustainable forest management and conservation efforts in tropical regions.

Shigella dysenteriae has been recognized as the second most prevalent pathogen associated with diarrhea that contains blood, contributing to 12.9% of reported cases, and it is additionally responsible for approximately 200,000 deaths each year. Currently, there is no S. dysenteriae licensed vaccine. Multidrug resistance in all Shigella spp. is a growing concern. Current vaccines, such as O-polysaccharide (OPS) conjugates, are in clinical trials but are ineffective in children but protective in adults. Thus, innovative treatments and vaccines are needed to combat antibiotic resistance. In this study, we used immuno-informatics to design a new multiepitope vaccine and identified S. dysenteriae strain SD197’s membrane protein targets using in-silico methods. The target protein was prioritized using membrane protein topology analysis to find membrane proteins. B and T-cell epitopes were predicted for vaccine formulation. The epitopes were shortlisted based on an IC50 value

This research examines the link between social anxiety disorder (SAD), psychological distance (PD), and burnout using survey data from 463 software development workers who are currently working remotely. According to the results of the study, SAD was associated with higher PD, but, in contrast to what had been shown in earlier studies, this higher PD had no effect on the participants' reported levels of burnout. Both psychological safety and workplace attachment orientation (WAO) were tested for their moderating effects in this study. According to the study's findings, the link between SAD and PD was moderated by WAO but not by psychological safety. The findings of this study underscore the importance of conducting additional research on the challenges faced by people with social anxiety in the workplace and the steps that can be taken by employers to better accommodate them.

Topological descriptors are numerical results generated from the structure of a chemical graph that are useful in identifying the physicochemical characteristics of a wide range of drugs. The introduction of molecular descriptors advances quantitative structure-property relationship research. This article focuses on the nine degree-based topological indices and the linear regression model of the eye infection drugs. We introduced two new indices, namely, the “first revised Randic index” and the ”second revised Randic index, for the analysis of eye infection drugs. Topological indices are calculated by using edge partitioning, vertex degree counting, and vertex degree labeling. This analysis is done with a scientific calculator and then authenticated with Matlab, a potent tool for examining data. The experimental data and results of the topological indices serve as inputs for the statistical computations and provide the values of intercepts, slopes, and correlation coefficients. All the correlations for the eye-infection drugs are positive, indicating a direct relationship between the experimental and estimated results of the drugs. There are significant results of the p-test for all of the characteristics of eye infection, such as molecular weight, boiling point, enthalpy, flash point, molar refraction, and molar volume, that validate the accuracy of the computations. A significant link was determined in this study between the defined indices with two properties: molar weight and molar refraction. The molar weight and molar refraction have a correlation coefficient ranging from 0.9. These results demonstrate a strong association between the indices and the properties under investigation. The linear regression approach is a valuable tool for chemists and pharmacists to obtain data about different medicines quickly and cost-effectively.

A topological index is a real number calculated from the structure of a chemical compound to describe its topology. The use of molecular descriptors has been increasing in recent years, helping to determine the physicochemical and biological properties of drugs. The main purpose of this article is to investigate the properties of the octane isomers using the theoretical method. To study the structures of octane isomers, we have introduced a new approach called “neighborhood product degree” to calculate all the classical degree-based topological indices. The np-degree approach is applied to approximate eight properties of octane isomers, such as the acentric factor, density, refractive index, critical volume, molar volume, radius of curvature, critical pressure, and LogP. The np-degree-based topological indices are the estimated values of the properties of octane structures, so the linear and quadratic regression models and correlation coefficients are applied to check the validity of the estimated results. The quantitative structure property relation are obtained by using the linear, quadratic, exponential, logarithmic and sinusoidal regression methods with the help of SPSS. Two models are applied to all the compuations and three regression models are applied to the np-degree Randic index. The computation showed that quadratic regression model is suitable for study octane isomers and np-degree based graph invariants. If the values of the correlation coefficient r ⩾ 0.7, p-values ⩽ 0.05, and F-values ⩾ 2.5, then the results are significant. The results of np-degree-based topological indices satisfy all the criteria for being significant, so these newly introduced indices are valid to study octane isomers. The information determined in this article is beneficial for chemists and pharmacists.

Mycobacterium tuberculosis is an organism that causes tuberculosis (TB), a common infectious disease that has a high death and morbidity rate. Topological indices are mathematical tools used to describe the structural properties of molecules or networks. They provide a quantitative measure of the connectivity and complexity of a system, and play a crucial role in numerous area such as biochemistry and bioinformatics. The purpose of topological indices is to simplify complex structures into numerical values that can be easily analyzed and compared. QSPR modeling is a technique in chemistry that relates the structure of a chemical compound to its physical or chemical properties. It is used to predict properties like boiling points, solubilities, toxicities, and even biological activities of compounds. This saves time, resources, and enables researchers to make informed decisions in drug discovery, material science, and many other areas. In this study, we conducted an analysis of several drugs used for the treatment of tuberculosis. We focused on computing the reducible topological indices based on their degrees. Several techniques and approaches are employed. To perform calculations, we used edge partition methodology, analytical techniques, theoretical graph utilities, and degree counting method. Additionally, we examined six physicochemical properties of these drugs. To establish quantitative structure-property relationship models and evaluate their effectiveness, we employed linear, quadratic, and logarithmic regression analysis. By analyzing the reducible topological indices and physicochemical properties, we aimed to gain a deeper understanding of the drugs’ characteristics and their potential impact on tuberculosis treatment. This study established a significant relationship between the defined indices with two key properties: molar mass and collision cross section. The correlation coefficients for molar mass range from 0.7 to 0.9, while the collision cross section range from 0.8 to 0.9. These results demonstrate a strong association between the indices and the properties under investigation. Furthermore, it is worth noting that both molar mass and collision cross section satisfy the requirements for p-value and F-test value across all indices. This indicates the statistical significance of the observed correlations and the reliability of our findings.

In this article, a quantitative structure-property relationship is performed for the prediction of six physico-chemical properties of 16 alkaloid structures using three different types of degree-based topological indices. Chemical structures are considered as graphs, where elements are taken as vertices and bounds between them are taken as edges. We defined two new degree-based topological indices, namely, the “modified harmonic index” and the “advanced harmonic index,” to analyze and examine the properties of alkaloids. The topological indices and experimental values act as the inputs of linear and quadratic regression models. The correlation values and p-values for all the indices are significant which describe the validity and usefulness of the results. The outcomes determined in this article assist pharmacists and chemists in studying the structures of alkaloids for use in daily life, agriculture, pharmacy, and industries. The study of chemical structures with the help of molecular descriptors and regression models is a theoretical method that saves time and money.

The impact of artificial intelligence (AI) in education can be viewed as a multi-attribute group decision-making (MAGDM) problem, in which several stakeholders evaluate the advantages and disadvantages of AI applications in educational settings according to distinct preferences and criteria. A MAGDM framework can assist in providing transparent and logical recommendations for implementing AI in education by methodically analyzing the trade-offs and conflicts among many components, including ethical, social, pedagogical, and technical concerns. A novel development in fuzzy set theory is the 2-tuple linguistic q-rung orthopair fuzzy set (2TLq-ROFS), which is not only a generalized form but also can integrate decision-makers quantitative evaluation ideas and qualitative evaluation information. The 2TLq-ROF Schweizer-Sklar weighted power average operator (2TLq-ROFSSWPA) and the 2TLq-ROF Schweizer-Sklar weighted power geometric (2TLq-ROFSSWPG) operator are two of the aggregation operators we create in this article. We also investigate some of the unique instances and features of the proposed operators. Next, a new Entropy model is built based on 2TLq-ROFS, which may exploit the preferences of the decision-makers to obtain the ideal objective weights for attributes. Next, we extend the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique to the 2TLq-ROF version, which provides decision-makers with a greater space to represent their decisions, while also accounting for the uncertainty inherent in human cognition. Finally, a case study of how artificial intelligence has impacted education is given to show the applicability and value of the established methodology. A comparative study is carried out to examine the benefits and improvements of the developed approach.

Multidrug-resistant organisms are bacteria that are no longer controlled or killed by specific drugs. One of two methods causes bacteria multidrug resistance (MDR); first, these bacteria may disguise multiple cell genes coding for drug resistance to a single treatment on resistance (R) plasmids. Second, increased expression of genes coding for multidrug efflux pumps, which extrude many drugs, can cause MDR. Antibiotic resistance is a big issue since some bacteria may withstand almost all antibiotics. These bacteria can cause serious sickness, making them a public health threat. Methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), Multidrug resistant Mycobacterium tuberculosis (TB), and CRE are gut bacteria that resist antibiotics. Antimicrobial resistance is rising worldwide, increasing clinical and community morbidity and mortality. Superbugs have made antibiotic resistance in some environmental niches even harder to control. This study introduces new medicinal plants, gene-editing methods, nanomaterials, and bacterial vaccines that will fight MDR bacteria in the future.

Background: Bacterial metabolites play a crucial role in human health and have proven effective in treating various diseases. In this study, the 16S rRNA method and streaking were employed to isolate and molecularly identify a bacterial strain, with the goal of characterizing bioactive volatile metabolites extracted using nonpolar and polar solvents.Methods: Gas chromatography–mass spectrometry (GC–MS) analysis was conducted to identify 29 compounds in the bacterial metabolites, including key compounds associated with Bacillus spp. The main compounds identified included 2-propanone, 4,4-ethylenedioxy-1-pentylamine, 1,2-benzenedicarboxylic acid, 1,1-butoxy-1-isobutoxy-butane, and 3,3-ethoxycarbonyl-5-hydroxytetrahydropyran-2-one.Results: The literature indicates the diverse biological and pharmacological applications of these compounds. Different concentrations of the metabolites from Bacillus species were tested for biological activities, revealing significant inhibitory effects on anti-diabetic activity (84.66%), anti-inflammatory activity (99%), antioxidant activity (99.8%), and anti-hemolytic activity (90%). Disc diffusion method testing also demonstrated a noteworthy inhibitory effect against tested strains.Conclusion:In silico screening revealed that 1,2-benzenedicarboxylic acid exhibited anticancer activity and promising drug-designing properties against epithelial glioblastoma cancer genes. The study highlights the potential of Bacillus spp. as a valuable target for drug research, emphasizing the significance of bacterial metabolites in the production of biological antibacterial agents.

The demand for clean and sustainable alternative energy resources is linearly increasing day by day due to the prevailing electricity crisis. Small-scale energy harvesting is considered a sustainable way to generate clean energy. Advanced energy solar cells, mainly dye-sensitized solar cells use solar energy and convert it into electrical energy. Similarly, MEMS-based piezoelectric materials are used to convert mechanical energy into electrical energy. For these applications, zinc oxide is considered one of the most suitable materials with high conductive, tunable band gap, and piezoelectric properties. However, altering these properties can be carried out by the addition of metal and other materials. Various research work has been carried out to study the addition of conductive metal as a dopant to alter the properties of zinc oxide. In this study, Strontium has been doped in ZnO to form a nanostructure for application in DSSC and microelectromechanical systems (MEMS) energy harvesters. Analysis has been conducted using the simulation and fabrication method. The results show that the doping and the pore size of the substrate (Anodic Aluminum oxide membrane) largely affect the output voltage and current. The difference between the simulated and experimental results was less than 1%, which shows the accuracy of the simulation. Tuning of the band gap can be observed by the addition of Sr in the ZnO nanostructure. For microelectromechanical systems energy harvesters, Sr-doped ZnO nanostructures deposited on anodic aluminum oxide show 7.10 mV of voltage and 1.11 uA of current output. The addition of Sr doping in ZnO shows the improvement in the generated current and voltage for the energy harvester and the improvement in overall power conversion efficiency for dye-sensitized solar cells. MEMS-based energy harvesting devices and low-cost advanced solar cells are promising to improve the efficiency of energy generation at a small scale.

BackgroundMost cavernous malformations (CM) usually involve the parenchyma and rarely occur in cranial nerves. Recurrence of CM associated with cranial nerves after surgical resection has not been previously reported.Case descriptionThis paper describes the case of an 11-year-old girl who presented with left otalgia and headache because of a left trigeminal cavernous malformation. She underwent radical resection via a left retrosigmoid approach while sparing the trigeminal nerve. Surveillance imaging at 18 months demonstrated recurrence along the length of the trigeminal nerve into Meckel's cave with significant extension into the middle cerebellar peduncle. Subsequent re-operation via an extended middle fossa approach with anterior petrosectomy enabled complete resection with division of the trigeminal nerve. Postoperatively, she had a transient left facial paresis, and right hemiparesis that resolved within 48 h.ConclusionThis case highlights the importance of close postoperative surveillance in CM associated with cranial nerves as recurrence after nerve-sparing resection is possible. Surgical treatment due to the morphology of significant recurrence required the use of a complex skull base approach through a new corridor to achieve optimal clinical outcome.

Pulmonary Fibrosis (PF) is an immedicable respiratory condition distinguished by permanent fibrotic alterations in the pulmonary tissue for which there is no cure. Hence, it is crucial to diagnose PF swiftly and precisely. The existing research on deep learning-based pulmonary fibrosis detection methods has limitations, including dataset sample sizes and a lack of standardization in data preprocessing and evaluation metrics. This study presents a comparative analysis of four vision transformers regarding their efficacy in accurately detecting and classifying patients with Pulmonary Fibrosis and their ability to localize abnormalities within Images obtained from Computerized Tomography (CT) scans. The dataset consisted of 13,486 samples selected out of 24647 from the Pulmonary Fibrosis dataset, which included both PF-positive CT and normal images that underwent preprocessing. The preprocessed images were divided into three sets: the training set, which accounted for 80% of the total pictures; the validation set, which comprised 10%; and the test set, which also consisted of 10%. The vision transformer models, including ViT, MobileViT2, ViTMSN, and BEiT were subjected to training and validation procedures, during which hyperparameters like the learning rate and batch size were fine-tuned. The overall performance of the optimized architectures has been assessed using various performance metrics to showcase the consistent performance of the fine-tuned model. Regarding performance, ViT has shown superior performance in validation and testing accuracy and loss minimization, specifically for CT images when trained at a single epoch with a tuned learning rate of 0.0001. The results were as follows: validation accuracy of 99.85%, testing accuracy of 100%, training loss of 0.0075, and validation loss of 0.0047. The experimental evaluation of the independently collected data gives empirical evidence that the optimized Vision Transformer (ViT) architecture exhibited superior performance compared to all other optimized architectures. It achieved a flawless score of 1.0 in various standard performance metrics, including Sensitivity, Specificity, Accuracy, F1-score, Precision, Recall, Mathew Correlation Coefficient (MCC), Precision-Recall Area under the Curve (AUC PR), Receiver Operating Characteristic and Area Under the Curve (ROC-AUC). Therefore, the optimized Vision Transformer (ViT) functions as a reliable diagnostic tool for the automated categorization of individuals with pulmonary fibrosis (PF) using chest computed tomography (CT) scans.

Sugarcane (Saccharum spp. hybrids) is a worldwide acclaimed important agricultural crop used primarily for sugar production and biofuel. Sugarcane’s genetic complexity, aneuploidy, and extreme heterozygosity make it a challenging crop in developing improved varieties. The molecular breeding programs promise to develop nutritionally improved varieties for both direct consumption and commercial application. Therefore, to address these challenges, the development of simple sequence repeats (SSRs) has been proven to be a powerful molecular tool in sugarcane. This study involved the collection of 285216 expressed sequence tags (ESTs) from sugarcane, resulting in 23666 unigenes, including 4547 contigs. Our analysis identified 4120 unigenes containing a total of 4960 SSRs, with the most abundant repeat types being monomeric (44.33%), dimeric (13.10%), and trimeric (39.68%). We further chose 173 primers to analyze the banding pattern in 10 sugarcane accessions by PAGE analysis. Additionally, functional annotation analysis showed that 71.07%, 53.6%, and 10.3% unigenes were annotated by Uniport, GO, and KEGG, respectively. GO annotations and KEGG pathways were distributed across three functional categories: molecular (46.46%), cellular (33.94%), and biological pathways (19.6%). The cluster analysis indicated the formation of four distinct clusters among selected sugarcane accessions, with maximum genetic distance observed among the varieties. We believe that these EST-SSR markers will serve as valuable references for future genetic characterization, species identification, and breeding efforts in sugarcane.

A cell wall determines the mechanical properties of a cell, serves as a barrier against plant stresses, and allows cell division and growth processes. The COBRA-Like (COBL) gene family encodes a putative glycosylphosphatidylinositol (GPI)-anchored protein that controls cellulose deposition and cell progression in plants by contributing to the microfibril orientation of a cell wall. Despite being studied in different plant species, there is a dearth of the comprehensive global analysis of COBL genes in poplar. Poplar is employed as a model woody plant to study abiotic stresses and biomass production in tree research. Improved genome resequencing has enabled the comprehensive exploration of the evolution and functional capacities of PtrCOBLs (Poplar COBRA-Like genes) in poplar. Phylogeny analysis has discerned and classified PtrCOBLs into two groups resembling the Arabidopsis COBL family, and group I genes possess longer proteins but have fewer exons than group II. Analysis of gene structure and motifs revealed PtrCOBLs maintained a rather stable motif and exon–intron pattern across members of the same group. Synteny and collinearity analyses exhibited that the evolution of the COBL gene family was heavily influenced by gene duplication events. PtrCOBL genes have undergone both segmental duplication and tandem duplication, followed by purifying selection. Promotor analysis flaunted various phytohormone-, growth- and stress-related cis-elements (e.g., MYB, ABA, MeJA, SA, AuxR, and ATBP1). Likewise, 29 Ptr-miRNAs of 20 families were found targeting 11 PtrCOBL genes. PtrCOBLs were found localized at the plasma membrane and extracellular matrix, while gene ontology analysis showed their involvement in plant development, plant growth, stress response, cellulose biosynthesis, and cell wall biogenesis. RNA-seq datasets depicted the bulk of PtrCOBL genes expression being found in plant stem tissues and leaves, rendering mechanical strength and rejoinders to environmental cues. PtrCOBL2, 3, 10, and 11 manifested the highest expression in vasculature and abiotic stress, and resemblant expression trends were upheld by qRT-PCR. Co-expression network analysis identified PtrCOBL2 and PtrCOBL3 as hub genes across all abiotic stresses and wood developing tissues. The current study reports regulating roles of PtrCOBLs in xylem differentiating tissues, tension wood formation, and abiotic stress latency that lay the groundwork for future functional studies of the PtrCOBL genes in poplar breeding.

Salinity stress, a significant global abiotic stress, is caused by various factors such as irrigation with saline water, fertilizer overuse, and drought conditions, resulting in reduced agricultural production and sustainability. In this study, we investigated the use of halotolerant bacteria from coastal regions characterized by high salinity as a solution to address the major environmental challenge of salinity stress. To identify effective microbial strains, we isolated and characterized 81 halophilic bacteria from various sources, such as plants, rhizosphere, algae, lichen, sea sediments, and sea water. We screened these bacterial strains for their plant growth-promoting activities, such as indole acetic acid (IAA), phosphate solubilization, and siderophore production. Similarly, the evaluation of bacterial isolates through bioassay revealed that approximately 22% of the endophytic isolates and 14% of rhizospheric isolates exhibited a favorable influence on seed germination and seedling growth. Among the tested isolates, GREB3, GRRB3, and SPSB2 displayed a significant improvement in all growth parameters compared to the control. As a result, these three isolates were utilized to evaluate their efficacy in alleviating the negative impacts of salt stress (150 mM, 300 mM, and seawater (SW)) on the growth of wheat plants. The result showed that shoot length significantly increased in plants inoculated with bacterial isolates up to 15% (GREB3), 16% (GRRB3), and 24% (SPSB2), respectively, compared to the control. The SPSB2 strain was particularly effective in promoting plant growth and alleviating salt stress. All the isolates exhibited a more promotory effect on root length than shoot length. Under salt stress conditions, the GRRB3 strain significantly impacted root length, leading to a boost of up to 6%, 5%, and 3.8% at 150 mM, 300 mM, and seawater stress levels, respectively. The bacterial isolates also positively impacted the plant’s secondary metabolites and antioxidant enzymes. The study also identified the WDREB2 gene as highly upregulated under salt stress, whereas DREB6 was downregulated. These findings demonstrate the potential of beneficial microbes as a sustainable approach to mitigate salinity stress in agriculture.

Probiotics, like lactic acid bacteria, are non-pathogenic microbes that exert health benefits to the host when administered in adequate quantity. Currently, research is being conducted on the molecular events and applications of probiotics. The suggested mechanisms by which probiotics exert their action include; competitive exclusion of pathogens for adhesion sites, improvement of the intestinal mucosal barrier, gut immunomodulation, and neurotransmitter synthesis. This review emphasizes the recent advances in the health benefits of probiotics and the emerging applications of probiotics in the food industry. Due to their capability to modulate gut microbiota and attenuate the immune system, probiotics could be used as an adjuvant in hypertension, hypercholesterolemia, cancer, and gastrointestinal diseases. Considering the functional properties, probiotics are being used in the dairy, beverage, and baking industries. After developing the latest techniques by researchers, probiotics can now survive within harsh processing conditions and withstand GI stresses quite effectively. Thus, the potential of probiotics can efficiently be utilized on a commercial scale in food processing industries.

IntroductionScientific documentation of the qualitative forest vegetation parameters of a biogeographical area provides baseline information to guide conservation strategies and design policies for biodiversity management regulations.MethodsWe present one of the most comprehensive qualitative vegetation analyses to evaluate the entire structure and function of an ecosystem in the remote northern part of the Kashmir Himalaya, India. Several multivariate ecological community analyses were conducted after determining the presence of plant species in the various habitats using a random sampling technique.ResultsIn total, 155 plant species belonging to 120 genera and 49 families occurred in the area. Asteraceae was the largest family (12% of plant species) followed by Rosaceae (11%). The patterns of species distribution across families were uneven, with 50% of the species belonging to only 7 families, and 23 families being monotypic. In terms of functional groups, the herbaceous growth form dominated. Therophytes were the dominant life form, indicating that the vegetation was disturbed. According to the phytogeographical research, 65% of the species documented in the study area were native, 15% were invasive, 14% naturalized, and 5% being casual. The majority (30%) of exotic species were reported along roadsides. Of all the species found, 39% grew in their natural habitats, such as forests, and 11% were scattered along roadsides. Plant species were grouped in five different clusters based on their floristic similarity. According to the estimated diversity indices, natural forest has the greatest values for Shannon’s and Simpson’s index. We found that the study area serves as the natural habitat for several significant, endangered medicinal plants, including Arnebia benthamii, Bergenia ciliata, Delphinium roylei, Gentiana kurroo, Phytolacca acinosa, Saussurea costus, and Trillium govanianum. Therefore, we recommend that human intervention in natural regeneration efforts be prioritized in these habitats to increase the population of these species.ConclusionExamining species features from the perspective of functional groups contributes to our understanding of the ecological aspects of the flora. It may also be useful in developing management plans to ensure long-term management of forest landscapes in this remote Himalayan region.

There has been progressive improvement in immunoinformatics approaches for epitope-based peptide design. Computational-based immune-informatics approaches were applied to identify the epitopes of SARS-CoV-2 to develop vaccines. The accessibility of the SARS-CoV-2 protein surface was analyzed, and hexa-peptide sequences (KTPKYK) were observed having a maximum score of 8.254, located between amino acids 97 and 102, whereas the FSVLAC at amino acids 112 to 117 showed the lowest score of 0.114. The surface flexibility of the target protein ranged from 0.864 to 1.099 having amino acid ranges of 159 to 165 and 118 to 124, respectively, harboring the FCYMHHM and YNGSPSG hepta-peptide sequences. The surface flexibility was predicted, and a 0.864 score was observed from amino acids 159 to 165 with the hepta-peptide (FCYMHHM) sequence. Moreover, the highest score of 1.099 was observed between amino acids 118 and 124 against YNGSPSG. B-cell epitopes and cytotoxic T-lymphocyte (CTL) epitopes were also identified against SARS-CoV-2. In molecular docking analyses, -0.54 to -26.21 kcal/mol global energy was observed against the selected CTL epitopes, exhibiting binding solid energies of -3.33 to -26.36 kcal/mol. Based on optimization, eight epitopes (SEDMLNPNY, GSVGFNIDY, LLEDEFTPF, DYDCVSFCY, GTDLEGNFY, QTFSVLACY, TVNVLAWLY, and TANPKTPKY) showed reliable findings. The study calculated the associated HLA alleles with MHC-I and MHC-II and found that MHC-I epitopes had higher population coverage (0.9019% and 0.5639%) than MHC-II epitopes, which ranged from 58.49% to 34.71% in Italy and China, respectively. The CTL epitopes were docked with antigenic sites and analyzed with MHC-I HLA protein. In addition, virtual screening was conducted using the ZINC database library, which contained 3,447 compounds. The 10 top-ranked scrutinized molecules (ZINC222731806, ZINC077293241, ZINC014880001, ZINC003830427, ZINC030731133, ZINC003932831, ZINC003816514, ZINC004245650, ZINC000057255, and ZINC011592639) exhibited the least binding energy (-8.8 to -7.5 kcal/mol). The molecular dynamics (MD) and immune simulation data suggest that these epitopes could be used to design an effective SARS-CoV-2 vaccine in the form of a peptide-based vaccine. Our identified CTL epitopes have the potential to inhibit SARS-CoV-2 replication.

The current study aims to examine the economically viable biomass feedstocks for bioenergy generation and their export potential. The Product Space Model (PSM) is the primary tool used to achieve the aim by accomplishing certain objectives. The study’s findings show that Pakistan has abundant biomass resources for energy production. Canola oil, leather flesh wastes, and poultry fattening show the highest PRODY values, 46,735, 44,438, and 41,791, respectively. These have high-income potential and are considered feasible for export after meeting local energy demand. While goat manure, cashew nutshell, and cotton stalk show lower income potential having values of 3,641, 4,225, and 4,421, respectively. The biowastes having low-income potential are more beneficial to utilize in energy generation plants within the country. The United States is observed to make the most sophisticated products, indicated by an EXPY value of 36296.89. While the minimum level of sophistication is observed for Indonesia, as revealed by its EXPY value of 22235.41 among all considered countries. The PSM policy map analysis of the current study shows that Pakistan and Argentina are located in the Parsimonious Policy quadrant, suggesting shifting toward unexploited products closely related to the existing export baskets. Although the United States, China, India, Indonesia, and Brazil are found in the most desired Let-it-be Policy quadrant. They have more room to diversify their industries and enhance their export potential. The study has practical applications in economic, social, and environmental perspectives, focusing on economic, clean, and sufficient energy. Furthermore, exportable biomass feedstocks are identified to strengthen the economy. Further research must be conducted to evaluate other indicators of the PSM to explore the proximity aspect of PSM, as it would provide a clearer picture of bioenergy and biomass export prospects.

Crop diversification and livestock production is an important strategy to enhance nutrition, sustainable food production, and improve food security, especially at the smallholder household level. However, existing evidences are mixed and there are limited information about the relationship among agriculture crops, household income diversification (HID), and household dietary diversity (HDD) among smallholder farmers in developing country setting like Pakistan. Therefore, this study aims to understand the role of crop diversification (CD) on HDD, nutrition, and sustainable agriculture and food production in the context of smallholder households in Punjab, Pakistan. The study employed ordered probit regression and cross-sectional data from 450 households collected using 24-h recall method. Regression results indicate that crop diversity and intra-household dietary diversity are positively associated across adults, adolescents, and children in all the study districts. Moreover, annual income, key crops grown by the household and family education are also the significant drivers of dietary diversity. Greater travel distance between markets was the most crucial factor in all regions which significantly affect dietary diversity. The overall research findings indicated that crop diversification and livestock production in the selected areas is significantly contributing to improve nutrition and sustainable food production. Therefore this study recommends for promoting crop diversification and livestock production for sustainable agricultural development and improving nutrition in the context of developing countries like Pakistan.

Climate change poses a significant risk to food security. Recent floods in Pakistan could serve as an example. In the current climate change scenario, there is a dire need to develop methods that increase crop productivity and reduce the threat of food insecurity in areas with low crop production. A detailed field experiment was conducted to check the effects of intercropping and straw mulching under conventional tillage (CT) and no tillage (NT) systems on soil health indicators and cotton productivity at the experimental area of Khwaja Fareed University of Engineering and Information Technology (KFUEIT), Rahim Yar Khan, Pakistan. The main plot treatments comprised CT and NT. The subplot treatments were sole cotton (C1), cotton + mung-bean intercropping (C2), cotton + mung-bean + straw mulching (C3) and cotton + straw mulching (C4) under CT, while sole cotton (N1), cotton + mung-bean intercropping (N2), cotton + mung-bean + straw mulching (N3) and cotton + straw mulching (N4) were the NT subplot treatments. Overall, NT increased plant height by 18.4 %, chlorophyll a and b contents by 28.2 and 21.1%, respectively, mean boll weight by 17.9%, and seed yield by 20.9% compared to CT (P < 0.05). The interaction of tillage and mulching increased plant height by 7.0% under CT and 21.8% under NT in comparison with no mulching. Similarly, straw mulching under NT increased chlorophyll a and b contents by 41.9 and 28.5%, respectively, mean boll weight by 26.9%, and cotton seed yield by 23.0% in comparison with no mulching under NT. Intercropping decreased crop yield without straw mulching but increased it under straw mulching. Further, straw mulching increased soil physicochemical properties under NT, which contributed to increasing crop productivity. We concluded that straw mulching under NT might be a promising practice for enhancing cotton yield, productivity, and soil health in low-productivity areas.

IntroductionOphthalmic diseases are approaching an alarming count across the globe. Typically, ophthalmologists depend on manual methods for the analysis of different ophthalmic diseases such as glaucoma, Sickle cell retinopathy (SCR), diabetic retinopathy, and hypertensive retinopathy. All these manual assessments are not reliable, time-consuming, tedious, and prone to error. Therefore, automatic methods are desirable to replace conventional approaches. The accuracy of this segmentation of these vessels using automated approaches directly depends on the quality of fundus images. Retinal vessels are assumed as a potential biomarker for the diagnosis of many ophthalmic diseases. Mostly newly developed ophthalmic diseases contain minor changes in vasculature which is a critical job for the early detection and analysis of disease.MethodSeveral artificial intelligence-based methods suggested intelligent solutions for automated retinal vessel detection. However, existing methods exhibited significant limitations in segmentation performance, complexity, and computational efficiency. Specifically, most of the existing methods failed in detecting small vessels owing to vanishing gradient problems. To overcome the stated problems, an intelligence-based automated shallow network with high performance and low cost is designed named Feature Preserving Mesh Network (FPM-Net) for the accurate segmentation of retinal vessels. FPM-Net employs a feature-preserving block that preserves the spatial features and helps in maintaining a better segmentation performance. Similarly, FPM-Net architecture uses a series of feature concatenation that also boosts the overall segmentation performance. Finally, preserved features, low-level input image information, and up-sampled spatial features are aggregated at the final concatenation stage for improved pixel prediction accuracy. The technique is reliable since it performs better on the DRIVE database, CHASE-DB1 database, and STARE dataset.Results and discussionExperimental outcomes confirm that FPM-Net outperforms state-of-the-art techniques with superior computational efficiency. In addition, presented results are achieved without using any preprocessing or postprocessing scheme. Our proposed method FPM-Net gives improvement results which can be observed with DRIVE datasets, it gives Se, Sp, and Acc as 0.8285, 0.98270, 0.92920, for CHASE-DB1 dataset 0.8219, 0.9840, 0.9728 and STARE datasets it produces 0.8618, 0.9819 and 0.9727 respectively. Which is a remarkable difference and enhancement as compared to the conventional methods using only 2.45 million trainable parameters.

BackgroundMinority ethnic groups are at increased risk of COVID-19 related mortality or morbidity yet continue to have a disproportionally lower uptake of the vaccine. The importance of adherence to prevention and control measures to keep vulnerable populations and their families safe therefore remains crucial. This research sought to examine the knowledge, perceived risk, and attitudes toward COVID-19 among an ethnically diverse community.MethodsA cross-sectional self-administered questionnaire was implemented to survey ethnic minority participants purposefully recruited from Luton, an ethnically diverse town in the southeast of England. The questionnaire was structured to assess participants knowledge, perceived risk, attitudes toward protective measures as well as the sources of information about COVID-19. The questionnaire was administered online via Qualtrics with the link shared through social media platforms such as Facebook, Twitter, and WhatsApp. Questionnaires were also printed into brochures and disseminated via community researchers and community links to individuals alongside religious, community and outreach organisations. Data were analysed using appropriate statistical techniques, with the significance threshold for all analyses assumed at p = 0.05.Findings1,058 participants (634; 60% females) with a median age of 38 (IQR, 22) completed the survey. National TV and social networks were the most frequently accessed sources of COVID-19 related information; however, healthcare professionals, whilst not widely accessed, were viewed as the most trusted. Knowledge of transmission routes and perceived susceptibility were significant predictors of attitudes toward health-protective practises.Conclusion/recommendationImproving the local information provision, including using tailored communication strategies that draw on trusted sources, including healthcare professionals, could facilitate understanding of risk and promote adherence to health-protective actions.

Mango (Mangifera indica) fruit is known for its taste, health benefits, and drought tolerance. Potassium (K+) is one of the most abundant ions in a plant cell. It is important for various biological functions related to plant growth, development, and flowering/fruiting. It significantly contributes to fruit yield, quality, and drought tolerance in plants. However, molecular mechanisms comprising K+ transport in mango are least known. In the present study, 37 members of K+ transport-related genes (PTGs) were identified in mango, which include 22 K+ transporters (16 HAKs, 1 HKT, and 6 KEAs) and 15 K+ channels (6 TPKs and 8 Shakers). All PTGs were predicted to be expressed at the plasma membrane and possess characteristic motifs and domains. Phylogenetic analysis identified a strong kinship of PTGs among Oryza sativa, Arabidopsis thaliana, Cicer arietinum, Malus domestica, and M. indica. The promoter analysis identified 60 types of cis-elements related to various biological processes. RNA-seq-based expression profiling identified that MiTPK1.2, MiHAK1, MiHAK2.1, HAK6.1, and MiAKT1.1 were most upregulated in roots and that MiKEA2, MiAKT2, and MiAKT1 were upregulated in leaves. Moreover, MiAKT6, MiHAK1.1, MiKAT2, MiKAT2.1, MiHKT1, MiTPK1.1, MiHAK7, and MiHAK12 were highly expressed during the five growth stages of mango fruit. The current study is the first comprehensive report on K+ transport system in tropical fruits. Therefore, it will provide the foundation knowledge for the functional characterization of K+ genes in mango and related plants.

IntroductionThe current monkeypox (MPX) outbreak, caused by the monkeypox virus (MPXV), has turned into a global concern, with over 59,000 infection cases and 23 deaths worldwide.ObjectivesHerein, we aimed to exploit robust immunoinformatics approach, targeting membrane-bound, enveloped, and extracellular proteins of MPXV to formulate a chimeric antigen. Such a strategy could similarly be applied for identifying immunodominant epitopes and designing multi-epitope vaccine ensembles in other pathogens responsible for chronic pathologies that are difficult to intervene against.MethodsA reverse vaccinology pipeline was used to select 11 potential vaccine candidates, which were screened and mapped to predict immunodominant B-cell and T-cell epitopes. The finalized epitopes were merged with the aid of suitable linkers, an adjuvant (Resuscitation-promoting factor), a PADRE sequence (13 aa), and an HIV TAT sequence (11 aa) to formulate a multivalent epitope vaccine. Bioinformatics tools were employed to carry out codon adaptation and computational cloning. The tertiary structure of the chimeric vaccine construct was modeled via I-TASSER, and its interaction with Toll-like receptor 4 (TLR4) was evaluated using molecular docking and molecular dynamics simulation. C-ImmSim server was implemented to examine the immune response against the designed multi-epitope antigen.Results and discussionThe designed chimeric vaccine construct included 21 immunodominant epitopes (six B-cell, eight cytotoxic T lymphocyte, and seven helper T-lymphocyte) and is predicted non-allergen, antigenic, soluble, with suitable physicochemical features, that can promote cross-protection among the MPXV strains. The selected epitopes indicated a wide global population coverage (93.62%). Most finalized epitopes have 70%–100% sequence similarity with the experimentally validated immune epitopes of the vaccinia virus, which can be helpful in the speedy progression of vaccine design. Lastly, molecular docking and molecular dynamics simulation computed stable and energetically favourable interaction between the putative antigen and TLR4.ConclusionOur results show that the multi-epitope vaccine might elicit cellular and humoral immune responses and could be a potential vaccine candidate against the MPXV infection. Further experimental testing of the proposed vaccine is warranted to validate its safety and efficacy profile.

The extensive use of antibiotics and vaccines against microbial infections can result in long-term negative effects on humans and the environment. However, there are a number of plants that have antimicrobial effects against various disease-causing microbes such as bacteria, viruses, and fungi without negative side effects or harm to the environment. In this regard, four particular plants- Capsicum, Nigella sativa, Musa paradisiaca L., and Citrus limetta have been widely considered due to their excellent antimicrobial effect and ample availability. In this review, we discuss their antimicrobial effects due to the presence of thymoquinone, p-cymene, pinene, alkaloids, limonene, camphene, and melanin. These antimicrobial compounds disrupt the cell membrane of microbes, inhibit cellular division, and form biofilm in bacterial species, eventually reducing the number of microbes. Extraction of these compounds from the respective plants is carried out by different methods such as soxhlet, hydro-distillation, liquid-liquid extraction (LLE), pressurized liquid extraction (PLE), solid-phase extraction (SPE), supercritical fluid extraction (SFE), pulsed electric field (PEF), microwave-assisted extraction (MAE), enzyme-assisted extraction (EAE), ultrasound-assisted extraction (UAE), and high-voltage electrical discharge. Suitable selection of the extraction technique highly depends upon the associated advantages and disadvantages. In order to aid future study in this field, this review paper summarizes the advantages and disadvantages of each of these approaches. Additionally, the discussion covers how antimicrobial agents destroy harmful bacteria. Thus, this review offers in-depth knowledge to researchers on the antibacterial properties of Capsicum, Nigella sativa, Musa paradisiaca L. peels, and Citrus limetta.

In Saudi Arabia, air conditioning is the main consumer of electricity, and increasing its energy efficiency is of great importance for energy conservation and carbon footprint reduction. This study presents the evaluation of a hybrid indirect evaporative cooling-mechanical vapor compression (IEC-MVC) cycle for cooling applications in Saudi Arabia. Most cities in this country are characterized by a high sensible cooling demand, and a few cities near the coasts of the Red sea and the Persian Gulf also need dehumidification. By employing the hybrid system, IEC can undertake about 60% of the cooling load in the summer of arid cities, and energy consumption can be reduced by up to 50%. The contribution of IEC and energy saving are less significant in humid cities because the latent loads have to be handled by MVC. Over the whole year, IEC contributes 50% of the total cooling capacity and reduces energy consumption by 40% in dry cities, while the saving is lower at 15%–25% in humid cities like Mecca and Jeddah. The average water consumption of the IEC is in the range of 4–12 L/hr. The water consumption can be replenished by the condensate collected from the MVC evaporator if the ambient humidity is high. Based on the annual performance, the cost of the IEC-MVC process is calculated, and it is 15%–35% lower than the standalone MVC. The results demonstrate the great potential of the hybrid IEC-MVC cycle in Saudi Arabia.