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Abstract Invasive species such as Xanthium strumarium L., can disrupt ecosystems, reduce crop yields, and degrade pastures, leading to economic losses and jeopardizing food security and biodiversity. To address the challenges posed by invasive species such as X. strumarium, this study uses species distribution modeling (SDM) to map its potential distribution in Pakistan and assess how it might respond to climate change. This addresses the urgent need for proactive conservation and management strategies amidst escalating ecological threats. SDM forecasts a species’ potential dispersion across various geographies in both space and time by correlating known species occurrences to environmental variables. SDMs have the potential to help address the challenges posed by invasive species by predicting the future habitat suitability of species distributions and identifying the environmental factors influencing these distributions. Our study shows that seasonal temperature dependence, mean temperature of wettest quarter and total nitrogen content of soil are important climatic factors influencing habitat suitability of X. strumarium. The potential habitat of this invasive species is likely to expand beyond the areas it currently colonizes, with a notable presence in the Punjab and Khyber Pakhtunkhwa regions. These areas are particularly vulnerable due to threats to agriculture and biodiversity. Under current conditions, an estimated 21% of Pakistan’s land area is infested by X. strumarium, mainly in upper Punjab, central Punjab and Khyber Pakhtunkhwa. The range is expected to expand in most regions except Sindh. The central and northeastern parts of the country are proving to be particularly suitable habitats for X. strumarium. Effective strategies are crucial to contain the spread of X. strumarium. The MaxEnt modeling approach generates invasion risk maps by identifying potential risk zones based on a species’ climate adaptability. These maps can aid in early detection, allowing authorities to prioritize surveillance and management strategies for controlling the spread of invasive species in suitable habitats. However, further research is recommended to understand the adaptability of species to unexplored environments.

Abstract Statin drugs lower blood cholesterol levels for cardiovascular disease prevention. Women are more likely than men to experience adverse statin effects, particularly new-onset diabetes (NOD) and muscle weakness. Here we find that impaired glucose homeostasis and muscle weakness in statin-treated female mice are associated with reduced levels of the omega-3 fatty acid, docosahexaenoic acid (DHA), impaired redox tone, and reduced mitochondrial respiration. Statin adverse effects are prevented in females by administering fish oil as a source of DHA, by reducing dosage of the X chromosome or the Kdm5c gene, which escapes X chromosome inactivation and is normally expressed at higher levels in females than males. As seen in female mice, we find that women experience more severe reductions than men in DHA levels after statin administration, and that DHA levels are inversely correlated with glucose levels. Furthermore, induced pluripotent stem cells from women who developed NOD exhibit impaired mitochondrial function when treated with statin, whereas cells from men do not. These studies identify X chromosome dosage as a genetic risk factor for statin adverse effects and suggest DHA supplementation as a preventive co-therapy.

Abstract Background Moderately severe or major trauma (injury severity score (ISS) > 8) is common, often resulting in physical and psychological problems and leading to difficulties in returning to work. Vocational rehabilitation (VR) can improve return to work/education in some injuries (e.g. traumatic brain and spinal cord injury), but evidence is lacking for other moderately severe or major trauma. Methods ROWTATE is an individually randomised controlled multicentre pragmatic trial of early VR and psychological support in trauma patients. It includes an internal pilot, economic evaluation, a process evaluation and an implementation study. Participants will be screened for eligibility and recruited within 12 weeks of admission to eight major trauma centres in England. A total of 722 participants with ISS > 8 will be randomised 1:1 to VR and psychological support (where needed, following psychological screening) plus usual care or to usual care alone. The ROWTATE VR intervention will be provided within 2 weeks of study recruitment by occupational therapists and where needed, by clinical psychologists. It will be individually tailored and provided for ≤ 12 months, dependent on participant need. Baseline assessment will collect data on demographics, injury details, work/education status, cognitive impairment, anxiety, depression, post-traumatic distress, disability, recovery expectations, financial stress and health-related quality of life. Participants will be followed up by postal/telephone/online questionnaires at 3, 6 and 12 months post-randomisation. The primary objective is to establish whether the ROWTATE VR intervention plus usual care is more effective than usual care alone for improving participants’ self-reported return to work/education for at least 80% of pre-injury hours at 12 months post-randomisation. Secondary outcomes include other work outcomes (e.g. hours of work/education, time to return to work/education, sickness absence), depression, anxiety, post-traumatic distress, work self-efficacy, financial stress, purpose in life, health-related quality of life and healthcare/personal resource use. The process evaluation and implementation study will be described elsewhere. Discussion This trial will provide robust evidence regarding a VR intervention for a major trauma population. Evidence of a clinically and cost-effective VR intervention will be important for commissioners and providers to enable adoption of VR services for this large and important group of patients within the NHS. Trial registration ISRCTN: 43115471. Registered 27/07/2021.

Cellular plasticity is enhanced by dedifferentiation processes such as epithelial-mesenchymal transition (EMT). The dynamic and transient nature of EMT-like processes challenges the investigation of cell plasticity in patient-derived breast cancer models. Here, we utilized patient-derived organoids (PDOs) as a model to study the susceptibility of primary breast cancer cells to EMT. Upon induction with TGF-β, PDOs exhibited EMT-like features, including morphological changes, E-cadherin downregulation and cytoskeletal reorganization, leading to an invasive phenotype. Image analysis and the integration of deep learning algorithms enabled the implantation of microscopy-based quantifications demonstrating repetitive results between organoid lines from different breast cancer patients. Interestingly, epithelial plasticity was also expressed in terms of alterations in luminal and myoepithelial distribution upon TGF-β induction. The effective modeling of dynamic processes such as EMT in organoids and their characteristic spatial diversity highlight their potential to advance research on cancer cell plasticity in cancer patients.

There is an ongoing debate about the potential of localized conservation efforts to address the ecological challenges brought about by global climate shifts. Among these challenges, biological invasion negatively affects ecosystem health and biodiversity. In this study, we assessed the potential impact of global climate changes and human activities on the spread of the invasive species Broussonetia papyrifera in Pakistan's subtropical region. Jackknife analysis indicated that, among the climatic variables studied, the maximum temperature of the warmest month (bio05) made the most significant contribution, accounting for 25.6 % of the model's predictive capacity. Potential highly invaded regions are located in the territory of Islamabad, Rawalpindi, Attock, Nowshera, Swabi, Haripur, and Mardan. Some patches were also found to be invaded in Khyber Pakhtunkhwa (Abbottabad, Buner, Tor Ghar, and Peshawar) and Punjab (Narowal, Sialkot, Gujrat, Jhelum, and Chakwal). Specifically, based on all four Shared Socioeconomic Pathways (SSPs) scenarios for the 2050 s and 2070 s, the highly invaded regions of B. papyrifera are predicted to shift Northern Punjab, Eastern Khyber Pakhtunkhwa, and Northern Kashmir. Based on the SSPs 245 and 585 scenarios, the majority of highly invaded regions are projected to expand by 2050. Similarly, within these regions, the potentially invaded land area is estimated to increase to 2.74 % under SSPs 585 in the 2050 s, 5.29 % under SSPs 245 in the 2070 s, and 6.27 % under SSPs 585 in the 2070 s. Applying circuit theory and ecological resistance, a surface connectivity map of the species revealed that Rawalpindi and Sargodha in Punjab had the most prominent corridors, with rates being highest where ecological patches continually merged. This study is the first to predict B. papyrifera distribution in Pakistan under current and future climate change scenarios. Implementing nationwide guidelines comprehensively may curb the spread of invasive species and protect native plant diversity from extinction. The findings of this study will aid in timely monitoring and surveillance of B. papyrifera and in formulating integrated management plans at the national level to prevent its invasion, spread, and potential harm to newly projected regions.

Background and aimsUltrasound derived carotid intima-media thickness (cIMT) is valuable for cardiovascular risk stratification. We assessed the relative importance of traditional atherosclerosis risk factors and plasma proteins in predicting cIMT measured nearly a decade later.MethodWe examined 6,136 UK Biobank participants with 1,461 proteins profiled using the proximity extension assay applied to their baseline blood draw who subsequently underwent a cIMT measurement. We implemented linear regression, stepwise Akaike Information Criterion-based, and the least absolute shrinkage and selection operator (LASSO) models to identify potential proteomic as well as non-proteomic predictors. We evaluated our model performance using the proportion variance explained (R2).ResultThe mean time from baseline assessment to cIMT measurement was 9.2 years. Age, blood pressure, and anthropometric related variables were the strongest predictors of cIMT with fat-free mass index of the truncal region being the strongest predictor among adiposity measurements. A LASSO model incorporating variables including age, assessment center, genetic risk factors, smoking, blood pressure, trunk fat-free mass index, apolipoprotein B, and Townsend deprivation index combined with 97 proteins achieved the highest R2 (0.308, 95% C.I. 0.274, 0.341). In contrast, models built with proteins alone or non-proteomic variables alone explained a notably lower R2 (0.261, 0.228–0.294 and 0.260, 0.226–0.293, respectively). Chromogranin b (CHGB), Cystatin-M/E (CST6), leptin (LEP), and prolargin (PRELP) were the proteins consistently selected across all models.ConclusionPlasma proteins add to the clinical and genetic risk factors in predicting a cIMT measurement. Our findings implicate blood pressure and extracellular matrix-related proteins in cIMT pathophysiology.

Highly efficient pure photocatalysts for water-splitting applications have long been plagued by structure imperfectness, narrowband light absorption, rapid charge recombination, and sluggish surface reaction kinetics. Herein we report a Z-scheme heterojunction photocatalyst made of Fe2O3, CN, and a conductive carbon layer (C) at the interface of the two materials (Fe2O3-C/CN). The structure has been characterized using a range of physicochemical and photo-electrochemical techniques. Compared to pristine Fe2O3, the Fe2O3-C/CN photocatalyst revealed superior photogenerated charge carriers, transport efficiency, and suppressed recombination process along with the conductive carbon layer acting as a mediator. The optimum composite of (5 wt% Fe2O3-C/CN) shows excellent activity towards pure water splitting, which reached 408 and 199 μmol/g.h for H2 and O2 evolution respectively, and a solar-to-hydrogen conversion efficiency of approximately 0.29 % when used for the pure water splitting process. Such a superior efficiency and production rate offer great potential for pure water splitting, and provide an alternative solution to future green energy production processes.

يهدف البحث إلى بيان تأثير مستخلصات نبات الطيون على البكتريا المسببة للالتهابات المعوية النزفية عند الدواجن، تمت هذه الدراسة في مخبر الأحياء الدقيقة في كلية الهندسة الزراعية بجامعة تشرين ومخبر العقاقير والأحياء الدقيقة في كلية الصيدلة بجامعة طرطوس، حيث تم جمع أوراق نبات الطيون من منطقة صافيتا في شهري تشرين الأول وتشرين الثاني من العام 2021، وبعدها تم تجفيف الأوراق وحفظها لحين الاستخدام. تم استخدام أربع مستخلصات لأوراق نبات الطّيون وهي: ايتل الأسيتات والميتانول والكلوروفورم وثنائي كلور الميتان بثلاث تراكيز 20-40-80 لكل مستخلص، ودراسة تأثيرها على ثلاث أنواع من البكتريا المسببة للالتهابات المعوية عند الدواجن وهي Escherichia coliو Streptococcus aureusو Clostridium Perfringes. تم إجراء اختبارات الحساسية للجراثيم الثلاثة المدروسة باستخدام المستخلصات الأربعة السابقة، وأظهرت الدراسة أن جراثيم E. coli و S. aureus و C. Perfringes تحسست تجاه المستخلصات الأربعة المستخدمة باستثناء جراثيم E. coli التي أبدت مقاومة عند المستخلص الايتانولي والكلوروفورم و ثنائي كلور الميتان وذلك عند التركيزين 20 و 40 ميكروليتر وبدون ظهور أي هالة تثبيط، في حين اعطى مستخلص إيتل الأسيتات تأثير تثبيطي تجاه الجراثيم المستخدمة عند التراكيز الثلاث المضافة، وكان اعلى قطر حلقة تثبيط عند التركيز 80 ميكروليتر حيث وصل متوسط قطر حلقة التثبيط الى ( 18.11 و 25.52 و 23.14 ملم)، على التوالي، مقارنة مع الشاهد DMSO 5% بقطر حلقة التثبيط وصلت الى 6 ملم عند E. coli و 6.6 عند S. aureus و 6.3 عند C. Perfringes. كما أن الجراثيم Streptococcus aureus و Clostridium Perfringes لم تبدي أي مقاومة تجاه المستخلصات المستخدمة وبالتراكيز الثلاثة المطبقة، واعطت اعلى متوسط قطر حلقة تثبيط النمو الجرثومي عند التركيز 80 ميكروليتر بالنسبة لمستخلص إيتل الأسيتات (25.52 و23.14 ملم) والميتانول (33.55 و30.74 ملم) والكلوروفورم (27.91 و 26.18 ملم) وثنائي كلور الميتان (25.93 و 24.64 ملم)، على التوالي، مقارنة مع الشاهد ( 5% DMSO) الذي بلغ فيه متوسط قطر التثبيط 6.6 ملم عند الجراثيم Streptococcus aureus و 6.3 ملم عند الجراثيم Clostridium Perfringes. تشير النتائج أن المستخلصات أوراق نبات الطيون تمتلك فعالية مضادة تجاه الأنواع الجرثومية الممرضة المستخدمة، وبذلك يمكن أن تكون مصدراً للمضادات الحيوية الطبيعية في المستقبل.

Lack of appropriate early diagnostic tools for drug-resistant tuberculosis (DR-TB) and their incomplete drug susceptibility testing (DST) profiling is concerning for TB disease control. Existing methods, such as phenotypic DST (pDST), are time-consuming, while Xpert MTB/RIF (Xpert) and line probe assay (LPA) are limited to detecting resistance to few drugs. Targeted next-generation sequencing (tNGS) has been recently approved by WHO as an alternative approach for rapid and comprehensive DST. We aimed to investigate the performance and feasibility of tNGS for detecting DR-TB directly from clinical samples in Bangladesh. pDST, LPA and tNGS were performed among 264 sputum samples, either rifampicin-resistant (RR) or rifampicin-sensitive (RS) TB cases confirmed by Xpert assay. Resistotypes of tNGS were compared with pDST, LPA and composite reference standard (CRS, resistant if either pDST or LPA showed a resistant result). tNGS results revealed higher sensitivities for rifampicin (RIF) (99.3%), isoniazid (INH) (96.3%), fluoroquinolones (FQs) (94.4%), and aminoglycosides (AMGs) (100%) but comparatively lower for ethambutol (76.6%), streptomycin (68.7%), ethionamide (56.0%) and pyrazinamide (50.7%) when compared with pDST. The sensitivities of tNGS for INH, RIF, FQs and AMGs were 93.0%, 96.6%, 90.9%, and 100%, respectively and the specificities ranged from 91.3 to 100% when compared with CRS. This proof of concept study, conducted in a high-burden setting demonstrated that tNGS is a valuable tool for identifying DR-TB directly from the clinical specimens. Its feasibility in our laboratory suggests potential implementation and moving tNGS from research settings into clinical settings.

Introduction: Antimicrobial resistance (AMR) has been a very concerning issue with consistent rise on a global scale. The rapid spread of drug-resistant pathogenic bacteria is a serious public health concern in both developed and developing countries, including Bangladesh. This study aimed to determine the prevalence of antibiotic resistant pathogenic bacteria in pus samples and their multidrug resistance (MDR) rate. Methods: A total of 891 pus positive samples were collected from the Lab Zone and Hormone center in Tangail, Bangladesh between January 2018 to March 2022. The standard microbiology cultural method such as Nutrient agar, Blood sheep agar, and MacConkey agar media plates were used to process the samples following aseptic techniques and the resulting bacterial isolates were confirmed by biochemical tests and gram staining to classify bacterial species into two large groups. Antimicrobial susceptibility tests were performed for the identified bacterial isolates using the disk diffusion method. Results: Among the Gram-negative bacterial isolates, Pseudomonas spp. was the most predominant (n=368, 58.69%), followed by Proteus spp. (n=95, 15.15%), E. coli (n=93, 14.83%), Klebsiella spp. (n=64, 10.21%), Acinetobacter spp. (n=6, 0.96%) and Enterobacter (n=1, 0.16%). On the other hand, in Gram-positive bacterial isolates, Enterococcus faecalis constitutes 39.02% of the total (n=103) which is the majority followed by Staphylococcus aureus (n=81, 30.68%), Staphylococcus spp. (n=77, 29.16%) and Streptococcus spp. (n=3, 1.14%). In this study, an examination of the multidrug resistance (MDR) pattern among Gram-negative bacteria revealed notable findings. Escherichia coli exhibited the highest resistance level, with 98.92% (n = 92) of isolates demonstrating resistance. Following closely, Pseudomonas displayed a substantial resistance pattern at 92.66% (n = 341), Proteus at 91.58% (n = 87), Klebsiella at 87.5% (n = 56), and Acinetobacter exhibiting complete resistance of 100% (n =6). The cumulative MDR trend for Gram-negative bacteria was significant, reaching 92.98% (n = 583). Conversely, Gram-positive bacteria demonstrated a robust resistance pattern as well. Streptococcus spp. displayed resistance in 66.66% (n = 2) of cases, and Enterococcus faecalis exhibited resistance in 92.23% (n = 95) of instances. While, Staphylococcus aureus showed a high resistance level with 95.06% (n = 77) of isolates. The overall drug-resistant pattern for Gram-positive bacteria was substantial, reaching 87.5% (n = 231). Conclusion: As per the findings of this study, bacteria frequently encountered demonstrate a concerning prevalence of multidrug resistance (MDR), posing a significant challenge to public health. The outcomes of this research may contribute valuable insights for formulating evidence-based treatment strategies and underscore the critical need for early identification of drug-resistant bacteria. This imperative step holds the potential to mitigate the disease burden effectively. [ J Adv Biotechnol Exp Ther 2024; 7(1.000): 178-189]

Abstract This work develops an efficient parameter estimation technique, based on manufacturer datasheet, to obtain unknown parameter of solar photovoltaic (PV), precisely. Firstly, a nonlinear least square objective function, in terms of variables given in manufacturer datasheet, has been developed. Then, two optimization techniques, namely the Particle Swarn Optimization (PSO) and Harmony Search (HS) are applied on the developed objective function to achieve the optimized result. Further, the correctness of the developed technique is tested by estimating the performance indices, namely percentage maximum power deviation index (%MPDI) and overall model deviation index (OMDI), of two different solar PV, viz., Kyocera KD210GH-2PU (poly-crystalline), and Shell SQ85 (mono-crystalline). It is shown that developed method with PSO outperforms the HS. The developed method with PSO gives the values of %MPDI and OMDI of 0.0214% and 0.213, only. Also, the existing methods, based on hybrid, multi-objective function, numerical method, have been considered for the comparative analysis. It is revealed through the comparative studies that the developed method with PSO has smaller value of MPDI (= 0.0041%) and OMDI (0.005) than the other existing methods. Further, the convergence of the developed method has also been estimated to check the speed of estimation. It is shown that the developed technique converges only in 5 s. In addition, the developed technique avoids the need of extensive data as it is based on manufacturer datasheet.

Soil salinity is the primary factor restricting plant growth and development. In this study, the morpho-physiological, biochemical, and molecular responses of turnip seedlings to three distinct salt stress treatments were investigated: no salinity 0 mM, moderate salinity at 80 mM, and severe salinity at 160 mM. Gibberellic acid treatments were applied in three different concentrations: control, 1 mM, and 2 mM. The dry and fresh weights, shoot and root lengths, carotenoid and chlorophyll levels, K+ ion accumulation, and antioxidant enzyme activity were all decreased by salt stress, particularly at 200 mM. However, it accelerated the buildup of Na+ ions and oxidative damage. Application of gibberellic acid enhanced turnip development under salt stress. Compared with the control, the gibberellic acid-2mM recorded the highest increase in roots and shoots length (16.79–10.80%), shoots fresh and dry weights (15.21–28.39%), chlorophyll a and b (7.49–15.94%), SOD and POD (16.92–10.85%), yield weight and diameter (18.33–10.11%) during different salinity levels. This treatment also reduced the MDA and H2O2 content (15.12–10.58%), and Na+ ions in roots and leaves (20.20–5.44%). Based on these findings, gibberellic acid at a concentration of 2 mM may be a useful tool for enhancing turnip development and growth.

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.

The surge in demand for hydrogen (H2) across diverse sectors, including clean energy transportation and chemical synthesis, underscores the need for a thorough investigation into H2 production dynamics and the development of effective controllers for industrial applications. This paper focuses on an electrically heated steam methane reforming (SMR) process for H2 production, offering advantages such as enhanced environmental sustainability, compactness, efficiency, and controllability compared to conventional reforming methods. Electric heating of the entire system allows for adjustments in current to control reactor temperature, thereby impacting hydrogen production rates. However, accurately modeling hydrogen production dynamics presents a formidable challenge, as complex models with high precision are computationally unsuitable for real-time control integration. Considering these factors, an accurate and efficient first-principles-based lumped-parameter model is developed to provide a dependable estimation of hydrogen production in an electrically-heated steam methane reformer. This model is validated experimentally and then utilized in a model predictive controller (MPC). To obtain the necessary state estimate information for the MPC, an extended Luenberger observer (ELO) method is employed to estimate state variables from limited, infrequent and delayed measurements of gas-phase reactor outlet stream and frequent measurements of the reactor temperature. Simulation comparisons with a proportional-integral (PI) controller reveal a much faster response in achieving the desired H2 production rate under the estimation-based MPC. Additionally, the simulations demonstrate the robustness of the controller to process variability such as a decrease in catalyst activation energy, commonly encountered in the SMR process, highlighting its effectiveness in maintaining stable operation under varying process conditions.

The Internet of Things (IoT) is an emerging technology that allow smart devices to communicate through various heterogeneous channels (wired or wireless). However, for conventional networks, it has become a challenging task to efficiently control and manage the data flows of a huge number of devices. Software-defined networking (SDN) is a new way of thinking about networking. Because it is programmable, flexible, agile, and gives you a big picture of the network, it has tried to solve some IoT problems, like scalability, heterogeneity, and complexity. In large-scale SDN-IoT networks, there is a requirement for routing protocols that are both efficient and secure in order to ensure a superior level of quality of service (QoS) and quality of experience (QoE). To address the above stated challenges, a novel deep reinforcement learning (DRL) known as DQQS model is proposed. The aim is to achieve QoS and QoE while also ensuring the security of the SDN-IoT network. The proposed DQQS model dynamically extracts patterns from the past network history by interacting with the underlying network and generating optimized routing policies. This article employs three network metrics—throughput, latency, and the probability of avoiding malicious nodes—to measure the performance of DQQS. Simulations reveal that the proposed framework outperforms four state-of-the-art routing algorithms: OSPF, L-L Routing, Sailfish Routing, and RL-Routing in terms of both throughput and latency. For instance, in an attacked environment, the proposed DQQS model achieved the highest throughput value of 14.5 Mbps, surpassing OSPF at 8 Mbps, L-L at 8.2 Mbps, Sailfish at 9 Mbps, and RL at 9.5 Mbps. Similarly, this model exhibited superior performance in latency, recording the lowest latency value of 52 ms, compared to OSPF 88 ms, L-L 85 ms, Sailfish 72 ms, and RL 75 ms routing algorithms. The experimental results demonstrate that this new DQQS model is a pioneering deep reinforcement learning-based technique that optimally addresses secure routing in the SDN-IoT environment, ensuring enhanced quality of service and experience, and outperforming state-of-the art DL methodologies in both security and network performance metrics.

Learning is an activity carried out by educators and learners in an environment that aims to achieve a learning goal. The fact is that in schools, there are still many educators who still need to achieve learning goals, especially learning maharah kalam. Therefore, the problem in schools is the low value of students in learning maharah kalam. This is due to an educator's need for more initiative in using several components that support the achievement of learning goals. One of these components is the learning method. Thus, the researchers used the picture-and-picture Learning method to develop students' Maharah Kalam. This study aimed to determine how the picture and picture method has influenced the development of Arabic speaking skills among students in MAN 2 Situbondo. In this study, researchers used quantitative approaches and types of experiments. This study's experiments and data collection methods use tests, questionnaires, observation, and documentary. Analysis of the data using the formula of the product of moments: r = nΣxy – (Σx)(Σy) /√{nΣx² – (Σx)²} {nΣy2 – (Σy)2}. The results of this study showed that the effect of the picture-and-picture method on the development of students ' speaking skills at an error rate of 5% is powerful. The average test result in the experimental class was 86.7, and in the observation class, 69.9. The data analysis results for the t-test calculation are 8.71 and T-schedule 1.67 or 8.71>1.67.

Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial Measurement Units, a pressure sensitive display floor, and machine learning techniques to automatically detect relevant behaviours and provides the user with the appropriate information. While working with trainers and amateur athletes, we also explore potential applications that are driven by automatic action recognition, that contribute various requirements to the platform. The first application is an automatic video-tagging protocol that marks key events (captured on video) based on the automatic recognition of volleyball-specific actions with an unweighted average recall of 78.71% in the 10-fold cross-validation setting with convolution neural network and 73.84% in leave-one-subject-out cross-validation setting with active data representation method using wearable sensors, as an exemplification of how dashboard and retrieval systems would work with the platform. In the context of action recognition, we have evaluated statistical functions and their transformation using active data representation besides raw signal of IMUs sensor. The second application is the “bump-set-spike” trainer, which uses automatic action recognition to provide real-time feedback about performance to steer player behaviour in volleyball, as an example of rich learning environments enabled by live action detection. In addition to describing these applications, we detail the system components and architecture and discuss the implications that our system might have for sports in general and for volleyball in particular.

Steam methane reforming (SMR) is one of the most widely used hydrogen (H2) production processes. In addition to its extensive utilization in industrial sectors, hydrogen is expanding it share as a clean energy carrier, and more sustainable and efficient H2 production methods are continuously being explored and developed. One method replaces conventional fossil fuel-based heating with electrical heating through the flow of electrons across the reformer. At UCLA, an experimental setup was built of an electrically heated steam methane reforming process. This paper describes the system components, explains the digitalization of the experimental setup and introduces methods for building a first-principles-based dynamic process model using parameters estimated via data-driven methods from process experimental data. The modeling approach uses a lumped parameter approximation and employs algebraic equations to solve for gas-phase variables. The reaction parameters are calculated from steady-state experimental data, and the temperature change is modeled with respect to change in electric current using a first-order dynamic model. The overall dynamic process model is then used in a computational model predictive control (MPC) scheme to drive the process to a new H2 production set-point under unperturbed and steam flowrate disturbance cases. The performance and robustness of the proposed MPC scheme are compared to the ones of a classical proportional–integral (PI) controller and are demonstrated to be superior in terms of closed-loop response, robustness, and constraint handling.

Objective: To identify the types and frequency of pre-analytical errors in the haematology laboratory and to reduce these preanalytical errors by taking corrective measures. Study Design: Cross-sectional study. Place and Duration of Study: Haematology Laboratory of Pak Emirates Military Hospital, Rawalpindi Pakistan, from Mar to Apr 2022 after applying corrective measures, pre-analytical errors were re-analysed from Jun to Jul 2022. Methodology: The study included all samples received in the haematology laboratory for complete blood count, peripheral blood smear, coagulation profile, and blood malarial parasite. Blood samples were checked for clotted samples, insufficient quantity, diluted samples, hemolysed samples, lipemic samples, and degenerated samples. After that, corrective measures were taken to reduce the pre-analytical errors. Results: The total number of samples received in the first session was 30,169, and pre-analytical errors were present in 508(0.016%) samples. The most frequent pre-analytical errors were of clotted samples 300(59.1%) followed by quantity not sufficient 168(33.1%), hemolysed samples 68(13.4%), diluted samples 49(9.6%), degenerated samples 22(4.3%) and lipemic samples 18(3.5%). After applying corrective measures, pre-analytical errors were reduced in clotted samples 78(52.7%), quantity not sufficient 39(26.4%), hemolysed samples 14(9.5%), diluted samples 11(7.4%), degenerated samples 5(3.4%) and lipemic samples 4(2.7%) respectively. Conclusion: This study showed the relative frequency of various pre-analytical errors in a haematology laboratory. It also signifies the implementation of corrective measures for reducing these pre-analytical errors.

Introduction: Iron deficiency anemia (IDA) is a significant public health issue in Pakistan, affecting up to 63% of children, as revealed by the Pakistan National Nutritional Survey 2018. While oral iron remains the primary choice, challenges in compliance have led to the consideration of intravenous (IV) iron therapy, particularly iron sucrose. The study discusses the safety and efficacy of IV iron therapy in Pakistani children and aims to broaden our understanding of treating IDA in resource-constrained regions. Aims & Objectives: To determine the average hemoglobin (Hb) increase in iron-deficient-anemic children aged 01 to 12 years after treatment with intravenous iron sucrose. Place and Duration of Study: A quasi-experimental study was undertaken at Fauji Foundation Hospital in Rawalpindi, spanning from September 2021 to September 2022. Material & Methods: The study focused on a cohort of pediatric patients aged between 1 and 12 years, all of whom were diagnosed with iron deficiency anemia and had previously encountered ineffective outcomes with oral iron therapy. In this investigation, the patients received treatment involving intravenous iron sucrose. Subsequently, their progress was diligently monitored after a period of 8 weeks. Improvement in hemoglobin level was assessed at the end of treatment to evaluate its efficacy.Data processing and statistical analysis were carried out using SPSS version 23,p-value of

The fresh and mechanical properties of concrete made with brick aggregates of eight different maximum aggregate sizes (MAS), i.e., 10 mm, 12.5 mm, 19 mm, 25 mm, 37.5 mm, 50 mm, 63 mm, and 75 mm, were investigated. The other parameters studied were sand-to-aggregate volume ratio (s/a) (0.40 and 0.45), W/C (0.45, 0.50, and 0.55), and cement content (375 kg/m3 and 400 kg/m3). In total, 80 different concrete mixes were studied; the perimeter of the interfacial transition zone (ITZ) along the brick aggregates was quantified with an image-analysis software and the microstructure along the ITZ was investigated using a scanning-electron microscope (SEM) to corroborate the hardened properties of the concrete. Although larger MAS leads to greater slump in concrete, its effect on hardened properties is linked to other design parameters. For a cement content of 375 kg/m3 and W/C of 0.45 and 0.50, the compressive strength of concrete increases (by up to 5%–15%) with increases in MAS of up to 37.5 mm irrespective of s/a (0.40 and 0.45) and then reduces gradually. For all other cases, the compressive strength of concrete is reduced with increases in MAS. The SEM imaging confirmed the presence of weak and porous ITZ and the deposition of ettringite in the voids left by entrapped bleed water under large aggregates. The compressive strength also increased with increases in s/a from 0.40 to 0.45, predominantly for smaller MAS. Correlations between mechanical properties of concrete and stress–strain curves are proposed for different MAS.

Abstract Background A new type of viral pneumonia, which has been named Coronavirus disease (COVID-19) began in Wuhan, China in late 2019 and has spread across the world since then. It has claimed more than 370 million confirmed cases and over 5.6 million deaths have been reported globally by the end of January 2022. This study aimed to analyze the trends, highly-nuanced patterns, and related key results relative to COVID-19 epidemiology in Sri Lanka. Methods Data on COVID-19 from March 2020 to January 2022 were obtained from published databases maintained by the Epidemiology Unit of the Ministry of Health in Sri Lanka and information regarding populations in administrative districts was obtained from the Department of Census and Statistics, Sri Lanka. Descriptive spatiotemporal analysis and autocorrelations were analyzed using SPSS statistical software. Results In Sri Lanka, the first case of COVID-19 was a Chinese national and the first local case was identified in the second week of March. As of 31st of January 2022, a total of 610,103 COVID-19 cases had been recorded in the country, and 15,420 patients had died. At the beginning, the disease was mainly concentrated in the Western province and with time, it spread to other provinces. However, very low numbers of patients were identified in the North, Eastern, North Central, and Uva provinces until April 2021. The peak of COVID-19 occurred in August and September 2021 in all provinces in Sri Lanka. Then a decreasing trend of COVID-19 cases showed after September 2021. Conclusions COVID-19 is an emerging public health problem in Western and Southern Sri Lanka where the population density is high. A decreasing trend of COVID-19 cases showed in all provinces after September 2021. Public awareness programs for the prevention and control of the disease in endemic regions are essential to reduce the incidence of this infection.

Abstract Background Traumatic brain injury (TBI) is a global public health challenge, affecting about 69 million individuals annually and being one of the leading causes of mortality. It has adverse consequences in terms of cognitive and physical functioning, which makes rehabilitation interventions an integral part of its management. Early neuro-rehabilitation guidelines for traumatic brain injury have not yet been developed and implemented in most of Africa especially Sub-Saharan Africa. Body We aimed with this Opinion to propose a collective reflection on the development and implementation of early neuro-rehabilitation guidelines as an integral part of the care in traumatic brain injury. The different aspects to be considered for reflection have been highlighted: Traumatic brain injury severity to be considered in early neuro-rehabilitation; who should be assessed and receive early neurorehabilitation, barriers to be considered for early neurorehabilitation; what early neurorehabilitation to be considered; the different phases involved in rehabilitation after mild, moderate, and severe TBI; and lastly, what perspective for the creation of neurorehabilitation teams. In conclusion, neuro-rehabilitation should start at the time of admission and should continue from the intensive care unit through the community for the moderate-to-severe traumatic brain injury population. However, mild TBI should also be considered for long-term follow-up in the community due to the fact that some mild traumatic brain injury patients might develop chronic cognitive problems or fatigue with time. Conclusion Neurorehabilitation should start at the time of admission and continue from the intensive care unit through the community for the moderate-to-severe traumatic brain injury population. There is a need to develop, agree on, and implement guidelines on early neuro-rehabilitation interventions for patients with moderate to severe traumatic brain injury in the African region, where disparities in care are common reality.

Modern stealth application systems require integrated meta-devices to operate effectively and have gained significant attention recently. This research paper proposes a 1-bit coding metasurface (CM) design. The fundamental component of the proposed CM is integrated to convert linearly polarized incoming electromagnetic waves into their orthogonal counterpart within frequency bands of 12.37–13.03 GHz and 18.96–32.37 GHz, achieving a polarization conversion ratio exceeding 99%. Furthermore, it enables linear-to-circular polarization conversion from 11.80 to 12.29, 13.17 to 18.44, and 33.33 to 40.35 GHz. A second element is produced by rotating a fundamental component by 90°, introducing a phase difference of π (pi) between them. Both elements are arranged in an array using a random aperiodic coding sequence to create a 1-bit CM for reducing the radar cross-section (RCS). The planar structure achieved over 10 dB RCS reduction for polarized waves in the frequency bands of 13.1–13.8 GHz and 20.4–30.9 GHz. A prototype was fabricated and tested, with the experimental results showing a good agreement with the simulated outcomes. The proposed design holds potential applications in radar systems, reflector antennas, stealth technologies, and satellite communication.

Purpose/Objectives: Definitive chemoradiation is an accepted alternative treatment in the management of esophageal cancer in patients who are not candidates for surgery. The location of esophagus poses a challenge because of its close proximity to the critical organs. Various modern techniques of radiation planning offer the advantage of better dose conformality and increased sparing of organs at risk. This study aims to compare the dosimetric parameters of Intensity Modulated Radiotherapy (IMRT) and Helical Tomotherapy (HT) planning techniques in radical radiotherapy for esophageal cancer patients. Materials/Methods: A total of 38 patients of esophageal squamous cell carcinoma with clinical staging cT2-T4/N0-N2/M0 planned for definitive chemoradiation were enrolled from June 2021 till November 2022. CT simulation was done with intravenous and oral contrast, and contouring of tumor volumes and organs at risk were done according to RTOG contouring guidelines. All patients were planned for a total dose of 50.4Gy in 28 fractions. Radiation plans of IMRT and HT were generated for each patient. Dosimetric parameters of PTV D98, D2, Dmean, V95, V110, Homogeneity Index (HI) and Conformity Index (CI) were noted for both the plans. Organs at risk (OAR) parameters such as Lung (V20, V30, V5, Dmean), Heart (V25, V30, Dmean), Spinal Cord Dmax, Larynx (Dmax, Dmean) and Liver Dmean were noted. All parameters of the two different planning techniques were compared and analyzed using SPSS software v23 with paired t-test. Results: The coverage of the PTV in IMRT and HT was similar in terms of PTV V95 and PTV D98. The regions receiving more than 107% and 110% of the prescribed dose was significantly higher in IMRT compared to HT, on assessing V107, V110 and D2 (p=0.001). HI was similar in both the groups, but the CI was significantly better in HT compared to IMRT (0.9 ± 0.02 vs 1.03 ± 0.01; p=0.002). Doses to the lungs, heart and liver were similar between the two groups. IMRT had higher spinal cord (39.1 ± 3.2 vs 34.2 ± 5.5; p=0.001) and laryngeal dose (9.6 ± 5.5 vs 5.6 ± 3.7; p=0.009) compared to HT. Conclusion: HT offers better dose conformality and uniformity when compared to IMRT with lesser dose to spinal cord and larynx. As this is a dosimetric study, a larger sample size and patient follow up is essential for clinical correlation and assessment of benefit of different radiation modalities.

Healthcare is delivered in a very complicated environment where numerous variables affect the patient related outcomes. A multidisciplinary team that places a high priority on patient satisfaction and healthcare quality can help to simplify and manage the environment of complex healthcare delivery. The foundation of rehabilitation treatments is a multidisciplinary team approach to provide interventions and care for the patients. Regardless of the population studied, multidisciplinary rehabilitation has been shown to be more effective than therapy delivered by individual therapists in a number of different populations. Due to the patient’s complex impairments, a team-based approach is necessary for assessments and facilitates coordinated care planning in the rehabilitation settings. The aim of this paper is to offer a narrative review of the advantages and challenges of multidisciplinary team working. The key components of teamwork in rehabilitation are the individualization of patient care and effective team communication. Keywords: Multidisciplinary; Teams; Rehabilitation; Outcomes; Challenges.

This research focuses on encrypted distributed control architectures, aimed at enhancing the operational safety, cybersecurity and computational efficiency of large-scale nonlinear systems, where only partial state measurements are available. In this setup, a distributed model predictive controller (DMPC) is utilized to partition the process into multiple subsystems, each controlled by a distinct Lyapunov-based MPC (LMPC). To consider the interactions among different subsystems, each controller receives and shares with the other controllers control inputs computed for its particular subsystem. As full state feedback is not available, we integrate an extended Luenberger observer with each LMPC, initializing the LMPC model with complete state estimate information provided by the observer. Furthermore, to enhance cybersecurity, wireless signals received and transmitted by the controllers are encrypted. Guidelines are established to implement this proposed control structure in any large-scale nonlinear chemical process network. Simulation results, conducted on a specific nonlinear chemical process network, demonstrate the effective closed-loop performance of the encrypted DMPC with state estimation, utilizing partial state feedback with sensor noise. This is followed by a comprehensive comparison of the closed-loop performance, control input computational time, and suitability of encrypted centralized, decentralized, and distributed MPC frameworks.

Background: Cardiovascular disease (CVD) is one of the most common causes of death among men and women worldwide. It is predicted that by 2030 around 23.3 million people will die as a consequence of CVD. There are numerous risk factors of CVD. The goal of this study is to examine the cardiovascular clinical and biochemical parameters of hospitalized CVD patients, as well as to assess the most common risk factors. Methods: 72 known healthy individuals were randomly allocated to the control group (male 47 and female 25). We collected data through a questionnaire from 154 CVD patients as a study group (male 108 and female 46). Cardiovascular status was assessed using clinical parameters like hypertension, chest pain, shortness of breathing, pain in the arm, and biochemical parameters like lipid profile, RBS, Creatinine, and an electrolyte panel. Inter- and intra-group comparison was performed using SPSS and Microsoft excel. Results: A total of 154 hospitalized CVD patients were analyzed (male 108 female 46) The most significant age group for males was observed 56- 65 years (mean 60) and female was 38-48 (43) years. A highly statistically significant increase was observed in total cholesterol and LDL in males than females, but a decrease in TG than females. Changes were also observed in other cardiovascular biochemical and clinical Parameters. Conclusion: There was a significant difference in smoking status, physical activity, lipid panel, and other biochemical parameters among males and females. [ J Adv Biotechnol Exp Ther 2023; 6(1.000): 25-34]

Maha Fathy Elmasry,1 Moemena Maher Fathi Khalil,2 Ashraf Badawi,3,4 Naglaa Sameh Zaki,1 Aya Fahim1 1Department of Dermatology, Faculty of Medicine, Cairo University, Cairo, Egypt; 2Department of Dermatology, Quesna Central Hospital, Menofia, Egypt; 3Department of Medical Laser Applications, National Institute of Laser Enhanced Sciences, Cairo University, Cairo, Egypt; 4Department of Dermatology and Allergology, Faculty of Medicine, University of Szeged, Szeged, HungaryCorrespondence: Aya Fahim, Dermatology Department, Faculty of Medicine, Cairo University, Post office Box 109, El-Malek El-Saleh, Cairo, 11562, Egypt, Tel +202 01020279628, Email ayafahim2011@cu.edu.egBackground: Acanthosis nigricans (AN) is a common chronic skin disorder clinically presents by velvety hyperpigmented lesions mainly at the flexural areas. Fractional photothermolysis has been reported to improve both pigmentary and textural changes by removing thin layers of skin with minimal thermal damage. Other options are the Q-switched (Qs) Nd:YAG (1064 nm) and Qs KTP (532 nm) lasers. Both can induce collagen remodeling by dermal photo-mechanical microdamage.Aim of the Work: The aim of this study was to assess the clinical efficacy and the safety of fractional CO2 laser versus Qs Nd:YAG and KTP lasers in the treatment of acanthosis nigricans.Methods: This randomized-controlled split neck study was conducted on 23 patients suffering from AN. For each patient, one side of the neck was randomly assigned to fractional CO2 laser and the other side to Qs Nd:YAG and KTP lasers every four weeks for four months followed by 4 monthly follow-up assessment. Acanthosis Nigricans Area and Severity Index (ANASI) score, melanin and erythema indices as well as Patient Satisfaction Scale (PSS) were used to assess improvement on each side separately.Results: There was no statistically significant difference regarding the clinical improvement between the side treated with Fractional CO2 laser and the side treated with Qs Nd:YAG and KTP lasers (P value > 0.05). In most patients, both sides showed improvement during different sessions of therapy, as regards ANASI scores, melanin indices, patient satisfaction scores, and side effects.Conclusion: In this study, we concluded that both fractional CO2 and Q-switched lasers proved to be a safe and effective line of treatment of acanthosis nigricans.Keywords: acanthosis nigricans, fractional CO2 laser, Q-switched Nd:YAG laser, ANASI score, Q-switched KTP laser

Service life is often used by state transportation agencies to evaluate alternative flexible pavement designs. The design that keeps the pavement in acceptable condition longest before it needs another maintenance treatment is the preferred design. For a given rehabilitation design, this study evaluates the pavement service life using AASHTO 1972 pavement design guide and mechanistic-empirical (M-E) pavement design guide. The service life here is referred as the duration from last completed rehabilitation to the next maintenance treatment. The goal is not to determine which method is better, but rather, it is to understand how their estimated service lives differ and what factors contribute to their differences. To perform the evaluation, rehabilitation designs developed by the SCDOT for a research project are used. These designs take into account existing pavement distresses obtained through field investigation of 28 actual rehabilitation projects on primary routes. Service life is determined using MAP-21 criteria for good and fair pavements. Analysis results indicated that (1) the predicted service life using the M-E method has lower variation than the AASHTO 1972 method and is affected by the location (rural vs. urban) of the routes, (2) the predicted service life using the AASHTO 1972 method is generally lower than that of the M-E method and is affected by the difference between the design structural number (SN) and required SN, (3) the M-E service life for good pavement is generally longer for routes that include CMRB design and the percentage of trucks lower than 10%, and (4) the AASHTO 1972 service life is higher if the existing SN is already exceeding the required SN from the previous maintenance.

ABSTRACTSpecies distribution modeling and niche dynamics of alien plants can aid in understanding the existing and future invasion potential in the wake of climate change. We describe the niche dynamics of S. viarum Dunal, and report on its present and predicted future distribution in Pakistan under scenarios of climate change. The results revealed that S. viarum had an extensive range of prospective distribution zones in Pakistan, with Central Punjab, Eastern Khyber Pakhtunkhwa, and Southern Balochistan being the most suitable. According to ROC curve data, the MaxEnt model has a great prediction precision and credible outcomes. The most important variables influencing S. viarum latent distribution may be precipitation and temperature. The current potential area of S. viarum is approximately 105,750 km2 (12%). Under climate change regimes, the highly suitable area for S. viarum increases by 2–9% under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). The outcomes of this study could help to develop control of regional invasions and climate change approaches in Pakistan. More importantly, the findings of niche dynamics in native and invaded S. viarum areas contribute to a better understanding of the nature of niche modifications in invasive species and the potential for invasion under climate change.

The effects of electric vehicle (EV) integration in three different urban residential neighborhoods of Bangladesh are assessed in this work. A Monte Carlo simulation-based stochastic load flow algorithm is developed to analyze system parameters such as aggregated power demand, voltage profile, energy losses, and voltage stability margin (VSMsys). Two scenarios for EV penetration (20% and 30%) were investigated, and the implications of seasonal load variation were assessed. The Monte Carlo results indicate that one of the areas is susceptible to sustaining EVs in the present system due to the potential of voltage collapse. The results also suggest that the other two areas would be able to accommodate EVs in the future. Finally, a slime mould algorithm (SMA)-based optimization approach is developed and applied to the relevant networks to identify their optimal charging strategies based on the outcomes of the Monte-Carlo simulation. A constrained objective function with an optimal amount of load, (VSMsys) index, and total active power loss was formulated to complete the assessment. The results indicate that the proposed optimal charging approach improves the grid’s capacity to accommodate a more significant proportion of the EV load while maintaining ideal system voltage and reducing power loss.

Chemerin is a chemokine/adipokine, regulating inflammation, adipogenesis and energy metabolism whose activity depends on successive proteolytic cleavages at its C-terminus. Chemerin levels and processing are correlated with insulin resistance. We hypothesized that chemerin processing would be higher in individuals with type 2 diabetes (T2D) and in those who are insulin resistant (IR). This hypothesis was tested by characterizing different chemerin forms by specific ELISA in the plasma of 18 participants with T2D and 116 without T2D who also had their insulin resistance measured by steady-state plasma glucose (SSPG) concentration during an insulin suppression test. This approach enabled us to analyze the association of chemerin levels with a direct measure of insulin resistance (SSPG concentration). Participants were divided into groups based on their degree of insulin resistance using SSPG concentration tertiles: insulin sensitive (IS, SSPG ≤ 91 mg/dL), intermediate IR (IM, SSPG 92–199 mg/dL), and IR (SSPG ≥ 200 mg/dL). Levels of different chemerin forms were highest in patients with T2D, second highest in individuals without T2D who were IR, and lowest in persons without T2D who were IM or IS. In the whole group, chemerin levels positively correlated with both degree of insulin resistance (SSPG concentration) and adiposity (BMI). Participants with T2D and those without T2D who were IR had the most proteolytic processing of chemerin, resulting in higher levels of both cleaved and degraded chemerin. This suggests that increased inflammation in individuals who have T2D or are IR causes more chemerin processing.

Multifunctional integrated meta-devices are the demand of modern communication systems and are given a lot of attention nowadays. Most of the research has focused on either cross-polarization conversion (CPC) or linear-to-circular (LP–CP) conversion. However, simultaneously realizing multiple bands with good conversion efficiency remains crucial. This paper proposes a multiband and multifunctional dual reflective polarization converter surface capable of converting a linearly polarized (LP) wave into a circularly polarized (CP) wave, in frequency bands of 12.29–12.63 GHz, 16.08–24.16 GHz, 27.82–32.21 GHz, 33.75–38.74 GHz, and 39.70–39.79 GHz, with 3 dB axial ratio bandwidths of 2.7%, 40.15%, 14.6%, 13.76%, and 0.2%, respectively. Moreover, the converter is capable of achieving CPC with a polarization conversion ratio (PCR) that exceeds 95%, within the frequency ranges of 13.10–14.72 GHz, 25.43–26.00, 32.46–32.56 GHz, and 39.14–39.59 GHz. In addition, to identify the fundamental cause of the CPC and LP–CP conversion, a comprehensive theoretical investigation is provided. Furthermore, the surface current distribution patterns at different frequencies are investigated to analyze the conversion phenomena. A sample prototype consisting of 20 × 20 unit cells was fabricated and measured, verifying our design and the simulated results. The proposed structure has potential applications in satellite communications, radar, stealth technologies, and reflector antennas.

Wild fruits and vegetables (WFVs) have been vital to local communities for centuries and make an important contribution to daily life and income. However, traditional knowledge of the use of wild fruits is at risk of being lost due to inadequate documentation. This study aimed to secure this knowledge through intermittent field visits and a semi-structured questionnaire. Using various ethnobotanical data analysis tools and SPSS (IBM 25), this study identified 65 WFV species (52 genera and 29 families). These species, mostly consumed as vegetables (49%) or fruits (43%), were predominantly herbaceous (48%) in wild and semi-wild habitats (67%). 20 WFVs were known to local communities (highest RFC), Phoenix sylvestris stood out as the most utilized species (highest UV). Surprisingly, only 23% of the WFVs were sold at markets. The survey identified 21 unique WFVs that are rarely documented for human consumption in Pakistan (e.g., Ehretia obtusifolia, Euploca strigosa, Brassica juncea, Cleome brachycarpa, Gymnosporia royleana, Cucumis maderaspatanus, Croton bonplandianus, Euphorbia prostrata, Vachellia nilotica, Pongamia pinnata, Grewia asiatica, Malvastrum coromandelianum, Morus serrata, Argemone mexicana, Bambusa vulgaris, Echinochloa colonum, Solanum virginianum, Physalis angulata, Withania somnifera, Zygophyllum creticum, and Peganum harmala), as well as 14 novel uses and five novel edible parts. Despite their ecological importance, the use of WFVs has declined because local people are unaware of their cultural and economic value. Preservation of traditional knowledge through education on conservation and utilization could boost economies and livelihoods in this and similar areas worldwide.

The reliance on Online Social Networks (OSN) for both formal and informal social interactions has dramatically changed the way people communicate. In this paper, a novel Social Behavioral Biometric (SBB), human micro-expression, is introduced for person identification. An emotion detection model is developed to extract emotion probability scores from person’s writing samples posted on Twitter. The corresponding emotion-progression features are extracted using an original technique that turns users’ microblogs into emotion-progression signals. Finally, a novel social behavioral biometric system that leverages rank-level weighted majority voting to achieve an accurate person identification is implemented. The proposed system is validated on a proprietary benchmark dataset consisting of 250 Twitter users. The experimental results convincingly demonstrate that the proposed social behavioral biometric, human micro-expression, possesses a strong distinguishable ability and can be used for person identification. The study further reveals that the proposed social behavioral biometric outperforms all the original SBB traits.

Emotion is a natural intrinsic state of mind that drives human behavior, social interaction, and decision-making. Due to the rapid expansion in the current era of the Internet, online social media (OSM) platforms have become popular means of expressing opinions and communicating emotions. With the emergence of natural language processing (NLP) techniques powered by artificial intelligence (AI) algorithms, emotion detection (ED) from user-generated OSM data has become a prolific research domain. However, it is challenging to extract meaningful features for identifying discernible patterns from the short, informal, and unstructured texts that are common on micro-blogging platforms like Twitter. In this paper, we introduce a novel representation of features extracted from user-generated Twitter data that can capture users’ emotional states. An advanced approach based on Genetic Algorithm (GA) is used to construct the input representation which is composed of stylistic, sentiment, and linguistic features extracted from tweets. A voting ensemble classifier with weights optimized by a GA is introduced to increase the accuracy of emotion detection using the novel feature representation. The proposed classifier is trained and tested on a benchmark Twitter emotion detection dataset where each sample is labeled with either of the six classes: sadness, joy, love, anger, fear, and surprise. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art classical machine learning-based emotion detection techniques, achieving the highest level of precision (96.49%), recall (96.49%), F1-score (96.49%), and accuracy (96.49%).

Academic writing is a salient skill for medical students all over the world. Academic writing starts with composing effective paragraphs, laying the foundation for students' advancement in writing during college. This study investigated common mistakes in English paragraph writing committed by the first-year medical students of the Medicine Faculty at Kabul University of Medical Sciences. Additionally, it measured the challenges that Medicine Faculty students tackled in paragraph writing. This quantitative study was conducted through an action research design. The data were collected from 52 medical students, and their work was analyzed using the developed rubrics. The data were analyzed in the SPPS 26 version, and the descriptive statistics were reported. The findings revealed that participants obtained the highest means in the mechanics of paragraph writing (22 out of 25), and they received the lowest means (12 out of 25) in the contents of a paragraph. In English for Medical Purposes (EMP), students should improve their content finding, paragraphing, summarizing, and referencing skills for their assignments and research writings. Medical students should give detailed attention to every characteristic of academic paragraph writing because they give readers a full grasp of the contents of their written works.

Objective: To compare the outcome of inhaled Salbutamol (Albuterol) with normal saline (Placebo) in terms of mean duration of oxygen therapy and mean hospital stay in newborns with transient tachypnea of the newborn. Study Design: Quasi-experimental study. Place and Duration of Study: Department of Paediatrics, Islamic International Medical College, Trust Pakistan Railway General Hospital, Rawalpindi Pakistan, from Jan to Dec 2019. Methodology: One hundred newborns meeting the inclusion criteria were enrolled. All newborns included in this study had X-ray chests. Neonates were divided into Groups A and B by lottery method. Group-A received inhaled Salbutamol plus 2 ml normal saline 0.9% by nebulizer, and Group-B received 0.9% normal saline by nebulizer six hourly and outcomes were recorded. Results: In this study, 32(32%) patients were female newborns, and 68(68%) were male babies. 97(97%) babies were delivered by Caesarean section. In Salbutamol-Group, the mean duration of oxygen inhalation was 20.86±10.79 hours, while in PlaceboGroup, the mean was 40.42±16.64 hours (p-value

The purpose of this work is to study machine-learning-based model predictive control of nonlinear systems with time-delays. The proposed approach involves initially building a machine learning model (i.e., Long Short Term Memory (LSTM)) to capture the process dynamics in the absence of time delays. Then, an LSTM-based model predictive controller (MPC) is designed to stabilize the nonlinear system without time delays. Closed-loop stability results are then presented, establishing robustness of this LSTM-based MPC towards small time-delays in the states. To handle input delays, we design an LSTM-based MPC with an LSTM-based predictor that compensates for the effect of input delays. The predictor is used to predict future states using the process measurement, and then the predicted states are used to initialize the LSTM-based MPC. Stabilization of the time-delay system with both state and input delays around the steady state is achieved through the featured design. The approach is applied to a chemical process example, and its performance and robustness properties are evaluated via simulations.

BackgroundCore outcome sets (COSs) are important and necessary as they help standardize reporting in research studies. Cranioplasty following traumatic brain injury (TBI) or stroke is becoming increasingly common, leading to an ever-growing clinical and research interest, especially regarding the optimal material, cost-effectiveness, and timing of cranioplasty concerning neurological recovery and complications. Consequently, heterogeneous reporting of outcomes from such diverse studies has led to limited meta-analysis ability and an ongoing risk of outcome reporting bias. This study aims to define a standardized COS for reporting in all future TBI and stroke cranioplasty studies. ObjectiveThis study has four aims: (1) undertake a systematic review to collate the most current outcome measures used within the cranioplasty literature; (2) undertake a qualitative study to understand better the views of clinicians, patients' relatives, and allied health professionals regarding clinical outcomes following cranioplasty; (3) undertake a Delphi survey as part of the process of gaining consensus for the COS; and (4) finalize consensus through a consensus meeting resulting in the COS. MethodsAn international steering committee has been formed to guide the development of the COS. In addition, recommendations from other clinical initiatives such as COMET (Core Outcomes and Effectiveness Trials) and OMERACT (Outcome Measures in Rheumatology) have been adhered to. Phase 1 is data collection through a systematic review and qualitative study. Phase 2 is the COS development through a Delphi survey and consensus meetings with consensus definitions decided and agreed upon before the Delphi survey begins to avoid bias. ResultsPhase 1 started at the end of 2019, following ethical approval in December 2019, and the project completion date is planned for the end of 2022 or beginning of 2023. ConclusionsThis study should result in a consensus on a COS for cranioplasty, following TBI or stroke, to help standardize outcome reporting for future studies, which can be applied to future research and clinical services, help align future studies, build an increased understanding of cranioplasty and its impact on a patient’s function and recovery, and help standardize the evidence base. International Registered Report Identifier (IRRID)DERR1-10.2196/37442

Objective: To compare the mean post-treatment bilirubin level with light emitting diode (LED) phototherapy versus compact fluorescent lamp (CFL) phototherapy in neonates presenting with hyperbilirubinemia Study Design: Comparative prospective study Place and Duration of Study: Department of Paediatrics, Pakistan Railway Hospital, Rawalpindi Pakistan, from Jan to Dec 2019. Methodology: Hundred children fulfilling the inclusion criteria were recuited in the study from Emergency Department. Then neonates were divided into two groups. In Group-A, neonates underwent phototherapy with LED; in Group-B, neonates underwent phototherapy with conventional phototherapy. Then neonates were followed up in the neonatal intensive care unit for 24 hours. Pre and post-treatment reports were assessed, and bilirubin level was noted. Results: In Group-A, the mean age of neonates was 49.12±23.42 hours, while in Group-B was 38.70±15.42 hours. In Group-A, the mean Bilirubin level at baseline was 13.64±5.98 gm/dl and in Group-B was 15.88±5.44 gm/dl. In Group-A, the mean Bilirubin level after 24 hours was 8.47±4.96 gm/dl and in Group-B was 9.538±5.35 gm/dl. In both groups, no significant difference in the mean bilirubin level was observed after 24 hours (p-value=0.30). Conclusion: Light-emitting diode phototherapy and compact fluorescent lamp phototherapy are equally effective in a mean reduction in the total serum bilirubin after 24 hours.

The evaluation of alien invasive plants (AIPs) environmental and ecological distribution patterns can provide important data as well as a theoretical approach for management methods such as invasion prevention, eradication, and control of established AIPs, and recognition of ecosystems at high threat of invasion. Twenty 100 m2 transects were randomly established at each of the ten sample sites to collect data on the impact of edaphic properties on the spread of alien species in the semi-arid region. After computing the Important Value Index for each recorded species, ordination and indicator species analyses were carried out. In the study area, 104 alien plants from 30 families and 72 genera were found. Poaceae was the largest family, with 18 taxa (17%), followed by Fabaceae (14 taxa; 13%) and Asteraceae (11 taxa; 10%). Herbs made up 51% of these species, grasses 17%, and shrubs 12%. In terms of life form, Therophytes (56%) were dominant, followed by Chamaephytes (12%). The leaf size spectrum of alien plants was dominated by microphylls (48%), followed by mesophylls (23%), and nanophylls (20%). The CCA ordination analysis clearly showed that environmental variables like pH, moisture and soil texture (silt, sand, and clay) significantly (p ≤ 0.002) influenced how species are distributed in the region. In total, twenty indicator alien species were detected across five different semi-arid ecosystems. Parthenium hysterophorus recorded from all ecotypes except wetland with highest IVI from agroecosystem. Prosopis juliflora was observed in urban ecosystems, while Stellaria media was in both agroecosystems and forests, and Lantana camara was only observed in forests. Our findings reveal patterns and processes of alien invasive plant in different ecotypes, but only remediation and continuous monitoring might affect the situation in the near future. In this regard, the baseline data gathered during the current study would be significant in guiding future scientific monitoring of such ecological changes, as well as in landscape ecology management and planning.

Objective: Insulin resistance (IR) increases risk of type 2 diabetes and atherosclerotic cardiovascular disease and is associated with lipid and lipoprotein abnormalities including high triglycerides (TG) and low high-density lipoprotein cholesterol (HDL-C). Lipoprotein size and lipoprotein subfractions (LS) have also been used to assist in identifying persons with IR. Associations of LS and IR have not been validated using both direct measures of IR and direct measures of LS. We assessed the usefulness of fasting lipoprotein subfractions (LS) by ion mobility to identify individuals with IR. Methods: Lipid panel, LS by ion mobility (LS-IM), and IR by steady-state plasma glucose (SSPG) concentration were assessed in 526 adult volunteers without diabetes. IR was defined as being in the highest tertile of SSPG concentration. LS-IM score was calculated by linear combination of regression coefficients from a stepwise regression analysis with SSPG concentration as the dependent variable. Improvement in prediction of IR was evaluated after combining LS-IM score with TG/HDL-C, TG/HDL-C and BMI as well as with TG/HDL-C, BMI, sex, race and ethnicity. IR prediction was evaluated by area under the receiver operating characteristic curve (AUC) and positive predictive value (PPV) considering the highest 5% of scores as positive test. Results: Prediction of IR was similar by LS-IM score and TG/HDL-C (AUC=0.68; PPV=0.59 and AUC=0.70; PPV=0.59, respectively) and prediction was improved when LS-IM was combined with TG/HDL-C (AUC=0.73; PPV=0.70), TG/HDL-C and BMI (AUC=0.82; PPV=0.81) and with TG/HDL-C, BMI, sex, race and ethnicity (AUC=0.84; PPV=0.89). Conclusion: For identifying individuals with IR, LS-IM score and TG/HDL-C are comparable and their combination further improves IR prediction by TG/HDL-C alone. Among patients who have undergone IM testing, the LS-IM score may assist prioritization of subjects for further evaluation and interventions to reduce IR.

High-density lipoproteins (HDLs) are promising targets for predicting and treating atherosclerotic cardiovascular disease (ASCVD), as they mediate removal of excess cholesterol from lipid-laden macrophages that accumulate in the vasculature. This functional property of HDLs, termed cholesterol efflux capacity (CEC), is inversely associated with ASCVD. HDLs are compositionally diverse, associating with >250 different proteins, but their relative contribution to CEC remains poorly understood. Our goal was to identify and define key HDL-associated proteins that modulate CEC in humans. The proteomic signature of plasma HDL was quantified in 36 individuals in the multi-ethnic population-based Dallas Heart Study (DHS) cohort that exhibited persistent extremely high (>=90th%) or extremely low CEC (p < 0.05 for heterogeneity). These findings suggest that enrichment of specific proteins on HDLs and, thus, different subspecies of HDLs, differentially modulate the removal of cholesterol from the vasculature.

The lungs are critical components of the respiratory system because they allow for the exchange of oxygen and carbon dioxide within our bodies. However, a variety of conditions can affect the lungs, resulting in serious health consequences. Lung disease treatment aims to control its severity, which is usually irrevocable. The fundamental objective of this endeavor is to build a consistent and automated approach for establishing the intensity of lung illness. This paper describes MixNet-LD, a unique automated approach aimed at identifying and categorizing the severity of lung illnesses using an upgraded pre-trained MixNet model. One of the first steps in developing the MixNet-LD system was to build a pre-processing strategy that uses Grad-Cam to decrease noise, highlight irregularities, and eventually improve the classification performance of lung illnesses. Data augmentation strategies were used to rectify the dataset’s unbalanced distribution of classes and prevent overfitting. Furthermore, dense blocks were used to improve classification outcomes across the four severity categories of lung disorders. In practice, the MixNet-LD model achieves cutting-edge performance while maintaining model size and manageable complexity. The proposed approach was tested using a variety of datasets gathered from credible internet sources as well as a novel private dataset known as Pak-Lungs. A pre-trained model was used on the dataset to obtain important characteristics from lung disease images. The pictures were then categorized into categories such as normal, COVID-19, pneumonia, tuberculosis, and lung cancer using a linear layer of the SVM classifier with a linear activation function. The MixNet-LD system underwent testing in four distinct tests and achieved a remarkable accuracy of 98.5% on the difficult lung disease dataset. The acquired findings and comparisons demonstrate the MixNet-LD system’s improved performance and learning capabilities. These findings show that the proposed approach may effectively increase the accuracy of classification models in medicinal image investigations. This research helps to develop new strategies for effective medical image processing in clinical settings.

In this study, we present a general form of nonlinear two-time-scale systems, where singular perturbation analysis is used to separate the dynamics of the slow and fast subsystems. Machine learning techniques are utilized to approximate the dynamics of both subsystems. Specifically, a recurrent neural network (RNN) and a feedforward neural network (FNN) are used to predict the slow and fast state vectors, respectively. Moreover, we investigate the generalization error bounds for these machine learning models approximating the dynamics of two-time-scale systems. Next, under the assumption that the fast states are asymptotically stable, our focus shifts toward designing a Lyapunov-based model predictive control (LMPC) scheme that exclusively employs the RNN to predict the dynamics of the slow states. Additionally, we derive sufficient conditions to guarantee the closed-loop stability of the system under the sample-and-hold implementation of the controller. A nonlinear chemical process example is used to demonstrate the theory. In particular, two RNN models are constructed: one to model the full two-time-scale system and the other to predict solely the slow state vector. Both models are integrated within the LMPC scheme, and we compare their closed-loop performance while assessing the computational time required to execute the LMPC optimization problem.

BACKGROUND: This study aimed to determine the association of single-nucleotide polymorphisms (SNPs) of interleukin-6 (IL-6) gene with the survival and disease status of patients after allogeneic hematopoietic stem cell transplantation. MATERIALS AND METHODS: It was a prospective cohort study of total 102 participants, 51 patients along with their donors who had human leukocyte antigen-identical-matched allogeneic hematopoietic stem cell transplantation (aHSCT). Their pretransplant and posttransplant blood samples were collected for extraction of DNA for genotyping by sequence-specific primers polymerase chain reaction and gel electrophoresis followed by sequencing. The pre- and posttransplant levels of IL-6 were measured using enzyme-linked immunosorbent assay technique. RESULTS: The mean age of our patients was 19.83 ± 12.5 years having males 65 (63.7%) and females 37 (36.3%). The frequency of −174G/C SNP among acute graft-versus-host disease (aGVHD) group was GG = 53.8%, GC = 34.6%, and CC = 11.5%, and for −597G/A SNP, it was GG = 69.2%, GA = 15.4%, and AA = 15.4%. Our results showed that the presence of G allele in both homozygous and heterozygous forms was associated with increased aGVHD incidence, while the homozygous CC and AA mutant genotypes correlated with the lowest number of cases of aGVHD. The polymorphism −597G/A was significantly associated with the incidence of aGVHD with P = 0.04. The GG genotype in −174G/C and −597G/A was found to be significantly associated with aGVHD with P < 0.0001. High serum levels of IL-6 pre- and posttransplant were found to be significantly associated with the incidence of aGVHD with P < 0.0001. CONCLUSION: We concluded that the two SNPs − 174G/C and − 597G/A in the promoter region of IL-6 gene present either in donors or recipients predisposed to increase the risk of development of aGVHD following aHSCT. Their evaluation in our transplant setting may help in the risk stratification of transplant recipients early in time and predict the onset of aGVHD and mortality.

Introduction: The epidemiology and prognosis of the isolated traumatic brain injury (TBI) and spinal cord injury (SCI) are well studied. However, the knowledge of the impact of concurrent neurotrauma is very limited. Research questions: To characterize the longitudinal incidence of concurrent TBI and SCI and to investigate their combined impact on clinical care and outcomes, compared to a comparative but isolated SCI or TBI. Materials and methods: Data from 167,793 patients in the Trauma Audit and Research Network (TARN) registry collected in England and Wales between 2008 and 2018 were analysed. Tandem neurotrauma was defined as patients with concurrent TBI and SCI. The patient with isolated TBI or SCI was matched to the patient with tandem neurotrauma using propensity scores. Results: The incidence of tandem neurotrauma increased tenfold between 2008 and 2018, from 0.21 to 2.21 per 100,000 person-years. Patients in the tandem neurotrauma group were more likely to require multiple surgeries, ICU admission, longer ICU and hospital LOS, higher 30-day mortality, and were more likely to be transferred to acute hospitals and rehabilitation or suffer death at discharge, compared to patients with isolated TBI. Likewise, individuals with tandem neurotrauma compared to those with isolated SCI had a higher tendency to receive more than one surgery, ICU admission, longer LOS for ICU and higher mortality either at 30-day follow-up or at discharge. Discussion and conclusions: The incidence of tandem neurotrauma has increased steadily during the past decade. Its occurrence leads to greater mortality and care requirements, particularly when compared to TBI alone. Further investigations are warranted to improve outcomes in tandem neurotrauma.

Antibiotic resistance is a severe threat to the world's public health, which has increased the need to discover novel antibacterial molecules. In this context, an emerging class of naturally occurring short peptide molecules called antimicrobial peptides (AMPs) has been considered potent antibacterial agents. Amphibians are one of the significant sources of AMPs, which have been extensively studied for the last few decades. Most amphibian AMPs are cationic, and several of these cationic AMPs adopt a well-defined alpha-helical structure in the presence of bacterial membranes. These cationic alpha-helical amphibian AMPs (CαAMPs) can selectively and preferentially bind with the negatively charged surfaces of Gram-positive and Gram-negative bacteria through electrostatic interaction, considered the main reason for their antibacterial activities. Here, we categorized these CαAMPs according to their charge, and to calculate the charge density; we divided the charge of each peptide by its corresponding length. To investigate the effect of charge among these categories, charge or charge density under each charge category was plotted against their corresponding minimum inhibitory concentration (MIC). Moreover, the effect of charge modification of some CαAMPs under specific charge categories in the context of MIC and hemolysis was also discussed. The information in this review will help us understand the antibacterial activity of accessible CαAMPs depending on each charge category across species. Additionally, this study suggests that designing novel functional antibacterial agents requires charge modification optimally.