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Abstract Upon stimulation of membrane receptors, nicotinic acid adenine dinucleotide phosphate (NAADP) is formed as second messenger within seconds and evokes Ca2+ signaling in many different cell types. Here, to directly stimulate NAADP signaling, MASTER-NAADP, a Membrane permeAble, STabilized, bio-rEversibly pRotected precursor of NAADP is synthesized and release of its active NAADP mimetic, benzoic acid C-nucleoside, 2’-phospho-3’F-adenosine-diphosphate, by esterase digestion is confirmed. In the presence of NAADP receptor HN1L/JPT2 (hematological and neurological expressed 1-like protein, HN1L, also known as Jupiter microtubule-associated homolog 2, JPT2), this active NAADP mimetic releases Ca2+ and increases the open probability of type 1 ryanodine receptor. When added to intact cells, MASTER-NAADP initially evokes single local Ca2+ signals of low amplitude. Subsequently, also global Ca2+ signaling is observed in T cells, natural killer cells, and Neuro2A cells. In contrast, control compound MASTER-NADP does not stimulate Ca2+ signaling. Likewise, in cells devoid of HN1L/JPT2, MASTER-NAADP does not affect Ca2+ signaling, confirming that the product released from MASTER-NAADP is a bona fide NAADP mimetic.

Abstract Herbs have been used as medicines since antiquity, and it has been discovered that the human body responds well to herbal remedies. Research on the effect of butin was conducted in the current study in the alloxan-induced diabetic rat paradigm. A total of 30 Wistar rats were randomly assigned into the following groups (n = 6): I-Normal; II-Alloxan-induced (50 mg/kg); III-Alloxan + butin 25 mg/kg; IV-Alloxan + butin 50 mg/kg; V-Butin per se 50 mg/kg. Various diabetic parameters (blood glucose, insulin, HbA1c), lipid profile, inflammatory (TNF-α, IL-1β, IL-6 and NF-κB), antioxidant enzymes (CAT, SOD and GSH), oxidative stress indicators (MDA), apoptosis marker (caspase-3), hepatic markers (ALT and AST), and histopathological changes were assessed. Additionally, molecular docking and dynamics were performed to evaluate the interaction of butin with target proteins. Butin treatment, at both doses, significantly restored biochemical parameters and preserved pancreatic histopathology in diabetic rats. It effectively modulated blood parameters, lipid profiles, inflammatory markers, apoptosis, antioxidant enzyme activity, oxidative stress, and hepatic markers. Molecular docking revealed that butin binds to proteins such as caspase-3 (1NME), NF-κB (1SVC), and serum insulin (4IBM) with binding affinities of − 7.4, − 6.5, and − 8.2 kcal/mol, respectively. Molecular dynamics simulations further suggested that butin induces significant conformational changes in these proteins. Butin exhibits potential effects against alloxan-induced diabetic rats by restoring biochemical balance, reducing inflammation, and protecting pancreatic tissue. Its binding to key proteins involved in apoptosis and inflammation highlights its therapeutic potential in diabetes management.

Abstract The essence of quantum machine learning is to optimize problem-solving by executing machine learning algorithms on quantum computers and exploiting potent laws such as superposition and entanglement. Support vector machine (SVM) is widely recognized as one of the most effective classification machine learning techniques currently available. Since, in conventional systems, the SVM kernel technique tends to sluggish down and even fail as datasets become increasingly complex or jumbled. To compare the execution time and accuracy of conventional SVM classification to that of quantum SVM classification, the appropriate quantum features for mapping need to be selected. As the dataset grows complex, the importance of selecting an appropriate feature map that outperforms or performs as well as the classification grows. This paper utilizes conventional SVM to select an optimal feature map and benchmark dataset for predicting air quality. Experimental evidence demonstrates that the precision of quantum SVM surpasses that of classical SVM for air quality assessment. Using quantum labs from IBM’s quantum computer cloud, conventional and quantum computing have been compared. When applied to the same dataset, the conventional SVM achieved an accuracy of 91% and 87% respectively, whereas the quantum SVM demonstrated an accuracy of 97% and 94% respectively for air quality prediction. The study introduces the use of quantum Support Vector Machines (SVM) for predicting air quality. It emphasizes the novel method of choosing the best quantum feature maps. Through the utilization of quantum-enhanced feature mapping, our objective is to exceed the constraints of classical SVM and achieve unparalleled levels of precision and effectiveness. We conduct precise experiments utilizing IBM’s state-of-the-art quantum computer cloud to compare the performance of conventional and quantum SVM algorithms on a shared dataset.

Abstract Breast cancer (BC) has emerged as the most common malignancy among females. The genomic profile of BC is diverse in nature and complex due to heterogeneity among various geographically different ethnic groups. The primary objective of this study was to carry out a comprehensive mutational analysis of Indian BC cases by performing whole exome sequencing. The cohort included patients with a median age of 48 years. TTN, TP53, MUC16, SYNE1, and OBSCN were the frequently altered genes found in our cohort. The PIK3CA and KLC3 genes are driver genes implicated in various cellular functions and cargo transportation through microtubules, respectively. Except for CCDC168 and PIK3CA, several gene pairings were found to be significantly linked with co-occurrence. Irrespective of their hormonal receptor status, RTK/RAS was observed with frequently altered signaling pathways. Further analysis of the mutational signature revealed that SBS13, SBS6, and SBS29 were mainly observed in our cohort. This study supplements the discovery of diagnostic biomarkers and provides new therapeutic options for the improved management of BC.

Abstract Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is being increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying gene regulatory programs in complex diseases. In response, MOCHA (Model-based single cell Open CHromatin Analysis) presents major advances over existing analysis tools, including: 1) improving identification of sample-specific open chromatin, 2) statistical modeling of technical drop-out with zero-inflated methods, 3) mitigation of false positives in single cell analysis, 4) identification of alternative transcription-starting-site regulation, and 5) modules for inferring temporal gene regulatory networks from longitudinal data. These advances, in addition to open chromatin analyses, provide a robust framework after quality control and cell labeling to study gene regulatory programs in human disease. We benchmark MOCHA with four state-of-the-art tools to demonstrate its advances. We also construct cross-sectional and longitudinal gene regulatory networks, identifying potential mechanisms of COVID-19 response. MOCHA provides researchers with a robust analytical tool for functional genomic inference from scATAC-seq data.

Abstract Therapeutic apheresis aims to selectively remove pathogenic substances, such as antibodies that trigger various symptoms and diseases. Unfortunately, current apheresis devices cannot handle small blood volumes in infants or small animals, hindering the testing of animal model advancements. This limitation restricts our ability to provide treatment options for particularly susceptible infants and children with limited therapeutic alternatives. Here, we report our solution to these challenges through an acoustofluidic-based therapeutic apheresis system designed for processing small blood volumes. Our design integrates an acoustofluidic device with a fluidic stabilizer array on a chip, separating blood components from minimal extracorporeal volumes. We carried out plasma apheresis in mouse models, each with a blood volume of just 280 μL. Additionally, we achieved successful plasmapheresis in a sensitized mouse, significantly lowering preformed donor-specific antibodies and enabling desensitization in a transplantation model. Our system offers a new solution for small-sized subjects, filling a critical gap in existing technologies and providing potential benefits for a wide range of patients.

Abstract This research aims to investigate the effects of seawater parameters like salinity, pH, and temperature on the external corrosion behaviour and microhardness of offshore oil and gas carbon steel pipes. The immersion tests were performed for 28 days following ASTM G-1 standards, simulating controlled artificial marine environments with varying pH levels, salinities, and temperatures. Besides, Field emission scanning electron microscopy (FESEM) analysis is performed to study the corrosion morphology. Additionally, a Vickers microhardness tester was used for microhardness analysis. The results revealed that an increase in salinity from 33.18 to 61.10 ppt can reduce the corrosion rate by 28%. In contrast, variations in seawater pH have a significant effect on corrosion rate, with a pH decrease from 8.50 to 7 causing a 42.54% increase in corrosion rate. However, the temperature of seawater was found to be the most prominent parameter, resulting in a 76.13% increase in corrosion rate and a 10.99% reduction in the microhardness of offshore pipelines. Moreover, the response surface methodology (RSM) modelling is used to determine the optimal seawater parameters for carbon steel pipes. Furthermore, the desirability factor for these parameters was 0.999, and the experimental validation displays a good agreement with predicted model values, with around 4.65% error for corrosion rate and 1.36% error for microhardness.

Abstract Blood cancer has emerged as a growing concern over the past decade, necessitating early diagnosis for timely and effective treatment. The present diagnostic method, which involves a battery of tests and medical experts, is costly and time-consuming. For this reason, it is crucial to establish an automated diagnostic system for accurate predictions. A particular field of focus in medical research is the use of machine learning and leukemia microarray gene data for blood cancer diagnosis. Even with a great deal of research, more improvements are needed to reach the appropriate levels of accuracy and efficacy. This work presents a supervised machine-learning algorithm for blood cancer prediction. This work makes use of the 22,283-gene leukemia microarray gene data. Chi-squared (Chi2) feature selection methods and the synthetic minority oversampling technique (SMOTE)-Tomek resampling is used to overcome issues with imbalanced and high-dimensional datasets. To balance the dataset for each target class, SMOTE-Tomek creates synthetic data, and Chi2 chooses the most important features to train the learning models from 22,283 genes. A novel weighted convolutional neural network (CNN) model is proposed for classification, utilizing the support of three separate CNN models. To determine the importance of the proposed approach, extensive experiments are carried out on the datasets, including a performance comparison with the most advanced techniques. Weighted CNN demonstrates superior performance over other models when coupled with SMOTE-Tomek and Chi2 techniques, achieving a remarkable 99.9% accuracy. Results from k-fold cross-validation further affirm the supremacy of the proposed model.

Abstract Accurate screening of COVID-19 infection status for symptomatic patients is a critical public health task. Although molecular and antigen tests now exist for COVID-19, in resource-limited settings, screening tests are often not available. Furthermore, during the early stages of the pandemic tests were not available in any capacity. We utilized an automated machine learning (ML) approach to train and evaluate thousands of models on a clinical dataset consisting of commonly available clinical and laboratory data, along with cytokine profiles for patients (n = 150). These models were then further tested for generalizability on an out-of-sample secondary dataset (n = 120). We were able to develop a ML model for rapid and reliable screening of patients as COVID-19 positive or negative using three approaches: commonly available clinical and laboratory data, a cytokine profile, and a combination of the common data and cytokine profile. Of the tens of thousands of models automatically tested for the three approaches, all three approaches demonstrated > 92% sensitivity and > 88 specificity while our highest performing model achieved 95.6% sensitivity and 98.1% specificity. These models represent a potential effective deployable solution for COVID-19 status classification for symptomatic patients in resource-limited settings and provide proof-of-concept for rapid development of screening tools for novel emerging infectious diseases.

Abstract The development of technology and the processing speed of computing machines have facilitated the evaluation of advanced pharmacokinetic (PK) models, making modeling processes simple and faster. The present model aims to analyze the PK of brivaracetam (BRV) in healthy and diseased populations. A comprehensive literature review was conducted to incorporate the BRV plasma concentration data and its input parameters into PK-Sim software, leading to the creation of intravenous (IV) and oral models for both populations. The developed physiologically based pharmacokinetic (PBPK) model of BRV was then assessed using the visual predictive checks, mean observed/predicted ratios (Robs/pre), and average fold error for PK parameters including the maximum systemic concentration (Cmax), the area under the curve at time 0 to t (AUC0–∞), and drug clearance (CL). The PBPK model of BRV demonstrated that mean Robs/pre ratios of the PK parameters remained within the acceptable limits when assessed against a twofold error margin. Furthermore, model predictions were carried out to assess how AUC0–∞ is affected following the administration of BRV in individuals with varying degrees of liver cirrhosis, ranging from different child–pugh (CP) scores like A, B, and C. Moreover, dose adjustments were recommended by considering the variations in Cmax and CL in various kidney disease stages (mild to severe).

Abstract Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff.

Abstract Cotton (Gossypium hirsutum) is an economically potent crop in many countries including Pakistan, India, and China. For the last three decades, cotton production is under the constant stress of cotton leaf curl disease (CLCuD) caused by begomoviruses/satellites complex that is transmitted through the insect pest, whitefly (Bemisia tabaci). In 2018, we identified a highly recombinant strain; Cotton leaf curl Multan virus-Rajasthan (CLCuMuV-Raj), associated with the Cotton leaf curl Multan betasatellite-Vehari (CLCuMuBVeh). This strain is dominant in cotton-growing hub areas of central Punjab, Pakistan, causing the third epidemic of CLCuD. In the present study, we have explored the CLCuD diversity from central to southern districts of Punjab (Faisalabad, Lodhran, Bahawalpur, Rahimyar Khan) and the major cotton-growing region of Sindh (Tandojam), Pakistan for 2 years (2020–2021). Interestingly, we found same virus (CLCuMuV-Raj) and associated betasatellite (CLCuMuBVeh) strain that was previously reported with the third epidemic in the central Punjab region. Furthermore, we found minor mutations in two genes of CLCuMuV-Raj C4 and C1 in 2020 and 2021 respectively as compared to its isolates in 2018, which exhibited virus evolution. Surprisingly, we did not find these mutations in CLCuMuV-Raj isolates identified from Sindh province. The findings of the current study represent the stability of CLCuMuV-Raj and its spread toward the Sindh province where previously Cotton leaf curl Kokhran virus (CLCuKoV) and Cotton leaf curl Shahdadpur virus (CLCuShV) have been reported. The findings of the current study demand future research on CLCuD complex to explore the possible reasons for prevalence in the field and how the virus-host-vector compatible interaction can be broken to develop resistant cultivars.

Abstract Nonlinear microscopy has become an invaluable tool for biological imaging, offering high-resolution visualization of biological specimens. In this manuscript, we present the application of a spectral phase measurement technique, i $$^2$$ 2 PIE, to compress broad-bandwidth supercontinuum pulses for two-photon excitation fluorescence light-sheet fluorescence microscopy. The results demonstrated a significant improvement in the two-photon excitation response achieved. We also showed that the implementation of i $$^2$$ 2 PIE allowed for enhanced image contrasts when compared to conventional compression techniques, with i $$^2$$ 2 PIE producing an image contrast improvement over conventional methods by over 50%.

Abstract Nucleotide-binding site (NBS) domain genes are one of the superfamily of resistance genes involved in plant responses to pathogens. The current study identified 12,820 NBS-domain-containing genes across 34 species covering from mosses to monocots and dicots. These identified genes are classified into 168 classes with several novel domain architecture patterns encompassing significant diversity among plant species. Several classical (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR, etc.) and species-specific structural patterns (TIR-NBS-TIR-Cupin_1-Cupin_1, TIR-NBS-Prenyltransf, Sugar_tr-NBS etc.) were discovered. We observed 603 orthogroups (OGs) with some core (most common orthogroups; OG0, OG1, OG2, etc.) and unique (highly specific to species; OG80, OG82, etc.) OGs with tandem duplications. The expression profiling presented the putative upregulation of OG2, OG6, and OG15 in different tissues under various biotic and abiotic stresses in susceptible and tolerant plants to cotton leaf curl disease (CLCuD). The genetic variation between susceptible (Coker 312) and tolerant (Mac7) Gossypium hirsutum accessions identified several unique variants in NBS genes of Mac7 (6583 variants) and Coker312 (5173 variants). The protein–ligand and proteins-protein interaction showed a strong interaction of some putative NBS proteins with ADP/ATP and different core proteins of the cotton leaf curl disease virus. The silencing of GaNBS (OG2) in resistant cotton through virus-induced gene silencing (VIGS) demonstrated its putative role in virus tittering. The presented study will be further helpful in understanding the plant adaptation mechanism.

Abstract We study the generation and control of genuine tripartite entanglement among quantum emitters (QEs) that are side-coupled to one-dimensional spin-momentum locked (or chiral) waveguides. By applying the machinery of Fock state master equations along with the recently proposed concurrence fill measure of tripartite entanglement [S. Xie and J. H. Eberly, Phys. Rev. Lett. 127, 040403 (2021)], we analyze how three-photon Gaussian wavepackets can distribute entanglement among two and three QEs. We show that with a five times larger waveguide decay rate in the right direction as compared to the left direction, the maximum value of tripartite entanglement can be elevated by $$35\%$$ 35 % as compared to the symmetric scenario where both left, and right direction decay rates are equal. Additionally, chirality can maintain the tripartite entanglement for longer than the corresponding symmetric decay rate. Finally, we study the influence of detunings and spontaneous emission on the resulting entanglement. We envision quantum networking and long-distance quantum communication as two main areas of applications of this work.

Abstract Among the actinomycetes in the rare genera, Micromonospora is of great interest since it has been shown to produce novel therapeutic compounds. Particular emphasis is now on its isolation from plants since its population from soil has been extensively explored. The strain CR3 was isolated as an endophyte from the roots of Hieracium canadense, and it was identified as Micromonospora chokoriensis through 16S gene sequencing and phylogenetic analysis. The in-vitro analysis of its extract revealed it to be active against the clinical isolates of methicillin-resistant Staphylococcus aureus (MRSA) and Candida tropicalis (15 mm). No bioactivity was observed against Gram-negative bacteria, Escherichia coli ATCC 25922, and Klebsiella pneumoniae ATCC 706003. The Micromonospora chokoriensis CR3 extract was also analyzed through the HPLC-DAD-UV–VIS resident database, and it gave a maximum match factor of 997.334 with the specialized metabolite BagremycinA (BagA). The in-silico analysis indicated that BagA strongly interacted with the active site residues of the sterol 14-α demethylase and thymidylate kinase enzymes, with the lowest binding energies of − 9.7 and − 8.3 kcal/mol, respectively. Furthermore, the normal mode analysis indicated that the interaction between these proteins and BagA was stable. The DFT quantum chemical properties depicted BagA to be reasonably reactive with a HOMO-LUMO gap of (ΔE) of 4.390 eV. BagA also passed the drug-likeness test with a synthetic accessibility score of 2.06, whereas Protox-II classified it as a class V toxicity compound with high LD50 of 2644 mg/kg. The current study reports an endophytic actinomycete, M. chokoriensis, associated with H. canadense producing the bioactive metabolite BagA with promising antimicrobial activity, which can be further modified and developed into a safe antimicrobial drug.

This service activity aims to increase the professionalism of teachers at schools on the Indonesia-Malaysia border in preparing independent curriculum teaching materials. The method of implementing this service uses participatory-based training. The partners involved are teachers at SMAN 1 Jagoi Babang on the Indonesia-Malaysia border. This service evaluation instrument includes participant satisfaction surveys, knowledge tests before and after the workshop, as well as direct observation of participant participation and skills. Data analysis was analyzed using descriptive statistical methods to describe the success rate of the workshop, as well as comparative analysis to see changes before and after the workshop. The results of this service show an increase in teachers' understanding of the preparation of teaching materials in the independent curriculum, as well as an increase in enthusiasm and skills in creating teaching modules. In addition, participants showed high interest in implementing differentiated learning and adapting to curriculum changes. It shows that there is a positive impact of service activities on teacher professionalism and readiness to face the demands of the new curriculum.

Abstract Differentiation of stem and progenitor cells is a highly regulated process that involves the coordinated action of multiple layers of regulation. Here we show how the post-transcriptional regulatory layer instructs the level of chromatin regulation via miR-144 and its targets to orchestrate chromatin condensation during erythropoiesis. The loss of miR-144 leads to impaired chromatin condensation during erythrocyte maturation. Among the several targets of miR-144 that influence chromatin organization, the miR-144-dependent regulation of Hmgn2 is conserved from fish to humans. Our genetic probing of the miR-144/Hmgn2 regulatory axis establish that intact miR-144 target sites in the Hmgn2 3’UTR are necessary for the proper maturation of erythrocytes in both zebrafish and human iPSC-derived erythroid cells while loss of Hmgn2 rescues in part the miR-144 null phenotype. Altogether, our results uncover miR-144 and its target Hmgn2 as the backbone of the genetic regulatory circuit that controls the terminal differentiation of erythrocytes in vertebrates.

Abstract The transverse momentum (p T) spectra of protons (p), deuterons (d), and tritons (t) in 200 GeV gold–gold (Au + Au) collisions at RHIC are examined across a range of centrality bins using the Levy Tsallis (TS) statistical model. The model's predictions closely match the experimental results from PHENIX (p) and STAR (d and t) Collaborations. Kinetic freeze-out temperatures of hadrons are obtained from particle spectra. The results showed that the kinetic freeze-out temperature decreases as collisions move from center to the periphery. This work found mass-dependent kinetic freeze-out temperatures, heavier particles arrive to the freeze-out phase before lighter ones. Comparison with same data fitted by blast wave function with Tsallis statistics (BWTS) showed that T 0 values are increasing from central to peripheral collisions, while in case of TS function (current analysis) it decreases. This behavior puts a question mark on the reliability of using such functions for temperature extraction.

Abstract Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson’s patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson’s dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson’s disease analysis.

Abstract The relationship between two variables is an essential factor in statistics, and the accuracy of the results depends on the data collected. However, the data collected for statistical analysis can be unclear and difficult to interpret. One way to predict how one variable will change about another is by using the correlation coefficient (CC), but this method is not commonly used in interval-valued Pythagorean fuzzy hypersoft set (IVPFHSS). The IVPFHSS is a more advanced and generalized form of the Pythagorean fuzzy hypersoft set (PFHSS), which allows for more precise and accurate analysis. In this research, we introduce the correlation coefficient (CC) and weighted correlation coefficient (WCC) for IVPFHSS and their essential properties. To demonstrate the applicability of these measures, we use the COVID-19 pandemic as an example and establish a prioritization technique for order preference by similarity to the ideal solution (TOPSIS) model. The technique is used to study the problem of optimizing the allocation of hospital beds during the pandemic. This study provides insights into the importance of utilizing correlation measures for decision-making in uncertain and complex situations like the COVID-19 pandemic. It is a robust multi-attribute decision-making (MADM) methodology with significant importance. Subsequently, it is planned to increase a dynamic bed allocation algorithm based on biogeography to accomplish the superlative decision-making system. Moreover, numerical investigations deliberate the best decision structures and deliver sensitivity analyses. The efficiency of our encouraged algorithm is more consistent than prevalent models, and it can effectively control and determine the optimal configurations for the study.

Abstract Substantial global attention is focused on how to reduce the risk of future pandemics. Reducing this risk requires investment in prevention, preparedness, and response. Although preparedness and response have received significant focus, prevention, especially the prevention of zoonotic spillover, remains largely absent from global conversations. This oversight is due in part to the lack of a clear definition of prevention and lack of guidance on how to achieve it. To address this gap, we elucidate the mechanisms linking environmental change and zoonotic spillover using spillover of viruses from bats as a case study. We identify ecological interventions that can disrupt these spillover mechanisms and propose policy frameworks for their implementation. Recognizing that pandemics originate in ecological systems, we advocate for integrating ecological approaches alongside biomedical approaches in a comprehensive and balanced pandemic prevention strategy.

Abstract Rising temperatures are leading to increased prevalence of warm-affinity species in ecosystems, known as thermophilisation. However, factors influencing variation in thermophilisation rates among taxa and ecosystems, particularly freshwater communities with high diversity and high population decline, remain unclear. We analysed compositional change over time in 7123 freshwater and 6201 terrestrial, mostly temperate communities from multiple taxonomic groups. Overall, temperature change was positively linked to thermophilisation in both realms. Extirpated species had lower thermal affinities in terrestrial communities but higher affinities in freshwater communities compared to those persisting over time. Temperature change’s impact on thermophilisation varied with community body size, thermal niche breadth, species richness and baseline temperature; these interactive effects were idiosyncratic in the direction and magnitude of their impacts on thermophilisation, both across realms and taxonomic groups. While our findings emphasise the challenges in predicting the consequences of temperature change across communities, conservation strategies should consider these variable responses when attempting to mitigate climate-induced biodiversity loss.

Abstract White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.

This paper aims to strengthen composites by treated and untreated date palm fibers (PDF), with sodium hydroxide (NaOH), for light applications. With 75% cellulose content and a density of 1.2 g/cm3, the palm fibers were exposed to a preparatory treatment with 1.5% NaOH for 24 h prior to integration into a polyester. Four polyester samples comprising 30% of palm fiber were manufactured. Additionally, the palm fiber interface was evaluated using scanning electron microscopy (SEM) and optical microscopy. The specimens underwent mechanical testing and it shows that tensile (18% increase in stress and 1.2% increase in Young’s modulus) and flexural properties (20% increase in strength and 10% increase in Young’s modulus) of treated composites as compared with untreated fibers. A MATLAB-based Artificial Neural Network (ANN) model was applied to estimate stress and strain at break as well as the Young’s modulus, based on three input characteristics: section, sample length, and chemical treatment. It was obtained that the polyester reinforced by NaOH-treated palm fibers increased the mechanical characteristics relative to the untreated fibers. The coefficient of determination R2 in the ANN models is 0.87. These results suggest that the ANN model is a useful tool for predicting mechanical properties.

Lower limb fragility fractures require long-term rehabilitation and are also very expensive to treat. Clinically, early weight bearing and walking stability were reported as key measures of fracture restoration. This study introduces methods to numerically quantify these performance indices for a range of ankle and knee joint fractures. As a follow-up of initial treatment, experimental data was collected using force plates from 367 subjects divided into seven groups: ankle fracture (AF), lower leg ankle fracture (AL), calcaneus foot fracture (CF), knee tibia fracture (KF), knee patella fracture (KP), kneecap rupture (KR), and normal limb (NL). For each joint, data was analysed to evaluate intralimb and interlimb weight-bearing and walking stability for all fracture conditions. These thresholds were statistically compared with normal subjects. Some advantages of evaluating fracture restoration indices over the others include: • to quantify fracture restoration (weight-bearing, walking stability, and gait symmetry) using minimum setup and signal requirements. • to provide comprehensive tools to assess and overcome fracture-associated complications through a detailed preview of fractured limb functionality during subphases of a gait cycle. • in clinical research, such assessments are important as a reference to evaluate existing or new rehabilitative interventions.

Background Measures of hemiparetic ankle dorsiflexor muscle strength and rate of force development (RFD) are often used to determine the efficacy of rehabilitation interventions after stroke. However, evidence supporting the reliability of these measures is limited. This brief report provides a secondary analysis investigating the between-session reliability of isometric ankle dorsiflexor muscle strength, rate of force development (RFD), and tibialis anterior electromyography (TA EMG), in people with chronic stroke. Method Participants (n=15) completed three maximal isometric contractions of the ankle dorsiflexor muscles as fast as possible using a rigid dynamometer. Tests were repeated seven days later. Outcomes included ankle dorsiflexor isometric maximal voluntary contraction (MVC), RFD in the first 200ms (RFD200ms), time to reach 90% MVC, and peak TA EMG. Data were analysed for 13 participants using intra-class correlation coefficients (ICC) and standard error of the measure percentage (SEM%). Results Reliability was higher when analysing the mean of three trials rather than the best of three trials. There was excellent reliability for isometric dorsiflexor MVC (ICC 0.97 [95% CI 0.92, 0.99], SEM% 7%). However, for other outcomes, while the ICC indicated good reliability, the lower bound of the 95% confidence interval of the ICC fell in the moderate range for TA EMG (ICC 0.86 [95% CI 0.60, 0.96], SEM% 25%) and time to reach 90% MVC (ICC 0.8 [95% CI 0.53, 0.93], SEM% 23%) and in the poor range for dorsiflexor RFD200ms (ICC 0.79 [95% CI 0.48, 0.92], SEM% 24%). Conclusion The findings raise concerns about the reliability of measures of rapid force production in the dorsiflexor muscles after stroke. Given the functional significance of the ankle dorsiflexors, larger studies should be conducted to further investigate these concerns and explore reliable methods for measuring rapid force production in the hemiparetic dorsiflexor muscles.

Abstract Increasing evidence suggests that a high-velocity, low-amplitude (HVLA) thrust directed at a dysfunctional vertebral segment in people with subclinical spinal pain alters various neurophysiological measures, including somatosensory evoked potentials (SEPs). We hypothesized that an HVLA thrust applied to a clinician chosen vertebral segment based on clinical indicators of vertebral dysfunction, in short, segment considered as “relevant” would significantly reduce the N30 amplitude compared to an HVLA thrust applied to a predetermined vertebral segment not based on clinical indicators of vertebral dysfunction or segment considered as “non-relevant”. In this double-blinded, active-controlled, parallel-design study, 96 adults with recurrent mild neck pain, ache, or stiffness were randomly allocated to receiving a single thrust directed at either a segment considered as “relevant” or a segment considered as “non-relevant" in their upper cervical spine. SEPs of median nerve stimulation were recorded before and immediately after a single HVLA application delivered using an adjusting instrument (Activator). A linear mixed model was used to assess changes in the N30 amplitude. A significant interaction between the site of thrust delivery and session was found (F1,840 = 9.89, p

Abstract Deep neural networks (DNNs) have demonstrated higher performance results when compared to traditional approaches for implementing robust myoelectric control (MEC) systems. However, the delay induced by optimising a MEC remains a concern for real-time applications. As a result, an optimised DNN architecture based on fine-tuned hyperparameters is required. This study investigates the optimal configuration of convolutional neural network (CNN)-based MEC by proposing an effective data segmentation technique and a generalised set of hyperparameters. Firstly, two segmentation strategies (disjoint and overlap) and various segment and overlap sizes were studied to optimise segmentation parameters. Secondly, to address the challenge of optimising the hyperparameters of a DNN-based MEC system, the problem has been abstracted as an optimisation problem, and Bayesian optimisation has been used to solve it. From 20 healthy people, ten surface electromyography (sEMG) grasping movements abstracted from daily life were chosen as the target gesture set. With an ideal segment size of 200 ms and an overlap size of 80%, the results show that the overlap segmentation technique outperforms the disjoint segmentation technique (p-value

Abstract There is no FDA-approved drug for neurological disorders like spinocerebellar ataxia type 3. CAG repeats mutation in the ATXN3 gene, causing spinocerebellar ataxia type 3 disease. Symptoms include sleep cycle disturbance, neurophysiological abnormalities, autonomic dysfunctions, and depression. This research focuses on drug discovery against ATXN3 using phytochemicals of different plants. Three phytochemical compounds (flavonoids, diterpenoids, and alkaloids) were used as potential drug candidates and screened against the ATXN3 protein. The 3D structure of ATXN3 protein and phytochemicals were retrieved and validation of the protein was 98.1% Rama favored. The protein binding sites were identified for the interaction by CASTp. ADMET was utilized for the pre-clinical analysis, including solubility, permeability, drug likeliness and toxicity, and chamanetin passed all the ADMET properties to become a lead drug candidate. Boiled egg analysis attested that the ligand could cross the gastrointestinal tract. Pharmacophore analysis showed that chamanetin has many hydrogen acceptors and donors which can form interaction bonds with the receptor proteins. Chamanetin passed all the screening analyses, having good absorption, no violation of Lipinski’s rule, nontoxic properties, and good pharmacophore properties. Chamanetin was one of the lead compounds with a − 7.2 kcal/mol binding affinity after screening the phytochemicals. The stimulation of ATXN3 showed stability after 20 ns of interaction in an overall 50 ns MD simulation. Chamanetin (Flavonoid) was predicted to be highly active against ATXN3 with good drug-like properties. In-silico active drug against ATXN3 from a plant source and good pharmacokinetics parameters would be excellent drug therapy for SC3, such as flavonoids (Chamanetin).

Abstract The analysis of peristaltic-ciliary transport in the human female fallopian tube, specifically in relation to the growing embryo, is a matter of considerable physiological importance. This paper proposes a biomechanical model that incorporates a finite permeable tube consisting of two layers, where the Jeffrey fluid model characterizes the viscoelastic properties of the growing embryo and continuously secreting fluid. Jeffrey fluid entering with some negative pressure gradient forms the core fluid layer while continuously secreting Jeffrey fluid forms the peripheral fluid layer. The resulting partial differential equations are solved for closed-form solutions after employing the assumption of long wavelength. The analysis delineated that increasing the constant secretion velocity, Darcy number, and Reynolds number leads to a decrease in the appropriate residue time of the core fluid layer and a reduction in the size of the secreting fluid bolus in the peripheral fluid layer. Eventually, the boluses completely disappear when the constant secretion velocity exceeds 3.0 Progesterone ( $$P_4$$ P 4 ) and estradiol ( $$E_2$$ E 2 ) directly regulate the transportation of the growing embryo, while luteinizing hormone (LH) and follicle-stimulating hormone (FSH), prolactin, anti-mullerian hormone (AMH), and thyroid-stimulating hormone (TSH) have an indirect effects. Based on the number and size of blastomeres, the percentage of fragmentation, and the presence of multinucleated blastomeres two groups were formed in an in vitro experiment. Out of 50 patients, 26 (76.5%) were pregnant in a group of the good quality embryos, and only 8 (23.5%) were in a group of the bad quality embryos. The transport of growing embryo in the human fallopian tube and preimplantation development of human embryos in in vitro are constraint by baseline hormones FSH, LH, prolactin, $$E_{2}$$ E 2 , AMH, and TSH.

Abstract Drought stress is a worldwide threat to the productivity of crops, especially in arid and semi-arid zones of the world. In the present study, the effect of selenium (Se) seed priming on the yield of quinoa under normal and drought conditions was investigated. A pot trial was executed to enhance the drought tolerance in quinoa by Se seed priming (0, 3, 6, and 9 mg Se L−1). The plants were exposed to water stress at three different growth stages of quinoa, viz. multiple leaf, flowering, and seed filling. It was noticed that drought significantly affected the yield components of quinoa, however, Se priming improved the drought tolerance potential and yield of quinoa by maintaining the plant water status. Se priming significantly increased main panicle length (20.29%), main panicle weight (26.43%), and thousand grain weight (15.41%) as well as the gas exchange parameters (transpiration rate (29.74%), stomatal conductance (35.29%), and photosynthetic rate (28.79%), total phenolics (29.36%), leaf chlorophyll contents (35.97%), water relations (leaf relative water contents (14.55%), osmotic potential (10.32%), water potential (38.35%), and turgor potential (31.37%), and economic yield (35.99%) under drought stress. Moreover, Se priming markedly improved grain quality parameters i.e., phosphorus, potassium, and protein contents by 21.28%, 18.92%, and 15.04%, respectively. The principal component analysis connected the various study scales and showed the ability of physio-biochemical factors to describe yield fluctuations in response to Se seed priming under drought conditions. In conclusion, a drought at the seed-filling stage has a far more deleterious impact among other critical growth stages and seed priming with Se (6 mg L−1) was found more effective in alleviating the detrimental effects of drought on the grain yield of quinoa.

Abstract Pregnancy-associated anemia is a significant health issue that poses negative consequences for both the mother and the developing fetus. This study explores the triggering factors of anemia among pregnant females in India, utilizing data from the Demographic and Health Survey 2019–21. Chi-squared and gamma tests were conducted to find out the relationship between anemia and various socioeconomic and sociodemographic elements. Furthermore, ordinal logistic regression and multinomial logistic regression were used to gain deeper insight into the factors that affect anemia among pregnant women in India. According to these findings, anemia affects about 50% of pregnant women in India. Anemia is significantly associated with various factors such as geographical location, level of education, and wealth index. The results of our study indicate that enhancing education and socioeconomic status may serve as viable approaches for mitigating the prevalence of anemia disease developed in pregnant females in India. Employing both Ordinal and Multinominal logistic regression provides a more comprehensive understanding of the risk factors associated with anemia, enabling the development of targeted interventions to prevent and manage this health condition. This paper aims to enhance the efficacy of anemia prevention and management strategies for pregnant women in India by offering an in-depth understanding of the causative factors of anemia.

Abstract Anal cancer incidence is significantly higher in people living with HIV as HIV increases the oncogenic potential of human papillomavirus. The incidence of anal cancer in the United States has recently increased, with diagnosis and treatment hampered by high loss-to-follow-up rates. Novel methods for the automated, real-time diagnosis of AIN 2+ could enable "see and treat" strategies, reducing loss-to-follow-up rates. A previous retrospective study demonstrated that the accuracy of a high-resolution microendoscope (HRME) coupled with a deep learning model was comparable to expert clinical impression for diagnosis of AIN 2+ (sensitivity 0.92 [P = 0.68] and specificity 0.60 [P = 0.48]). However, motion artifacts and noise led to many images failing quality control (17%). Here, we present a high frame rate HRME (HF-HRME) with improved image quality, deployed in the clinic alongside a deep learning model and evaluated prospectively for detection of AIN 2+ in real-time. The HF-HRME reduced the fraction of images failing quality control to 4.6% by employing a high frame rate camera that enhances contrast and limits motion artifacts. The HF-HRME outperformed the previous HRME (P

Abstract This study reports the prevalence and antibiotics resistance status of Salmonella detected in raw meat from Lahore, Pakistan. Overall, N = 111 meat samples, were collected from local markets. Salmonella was recovered from 57 (51.35%) samples, including 45.83% of poultry, 60% of buffalo, 64.28% of cow, and 60% of goat meat samples. The predominant Salmonella strains were Salmonella enterica serovars; Typhimurium (45.4%), Typhi (27.2%), and Enteritidis (18.1%), identified by VITEK system and 16S rRNA gene sequencing. The isolates exhibited high resistance to Erythromycin (100%), Cefepime (98.24%), Colistin (94.73%), Azithromycin (92.98%), Tetracycline (87.71%), Polymyxin B (84.21%), Ciprofloxacin (84.21%), Trimethoprim-Sulfamethoxazole (80.70%), Nalidixic Acid (80.70%), Kanamycin (78.94%), Chloramphenicol (77.19%), Streptomycin (71.92%) and Ampicillin (64.91%). While the isolates exhibited more susceptibility to Meropenem (75.43%) and Amikacin (73.68%). N = 8 strains were designated as Multidrug Resistant (MDR) and N = 3 as Extensively Drug-Resistant (XDR) Salmonella. The PCR-based detection of resistance genes revealed the presence of bla TEM-1 gene (100%), catA1 gene (64%), and gyrA gene (18%). The whole genome sequencing (WGS) of two selected strains and subsequent downstream analysis confirmed the strains as MDR and XDR Salmonella enterica serovar Typhi. The study showed that raw meat consumed in Lahore carries a significantly high number of drug-resistant Salmonella.

Abstract The present investigation aims to use entropy analysis to analyze the unsteady Magnetohydrodynamic (MHD) flow in a second-grade fuzzy hybrid $$\left( {{\text{Al}}_{{2}} {\text{O}}_{{3}} - {\text{Cu/SA}}} \right)$$ Al 2 O 3 - Cu/SA nanofluid over an exponentially shrinking/stretching surface. The model for hybridization of the mixture of alumina $$\left( {{\text{Al}}_{{2}} {\text{O}}_{{3}} } \right)$$ Al 2 O 3 and copper (Cu) nanoparticles in the sodium alginate (SA) base fluid under heat source/sink, nonlinear thermal radiation, and viscous dissipation. The fundamental partial differential equations (PDEs) are simplified using an appropriate similarity conversion to generate the ordinary differential equations (ODEs). The analytical computation occurs in the MATHEMATICA program implementing the homotopy analysis method (HAM). In terms of code validity, our results are preferable to previous findings. The features of several parameters against the velocity, surface friction coefficient, entropy, temperature, and Nusselt number are described through graphs. According to our findings, the rise in the Brinkman and Reynolds numbers enhanced the total entropy of the system. Furthermore, the nanoparticle volume fraction and viscus dissipation magnifies the fluid temperature while retards the flow profile throughout the domain. Fluid velocity declined due to the Lorentz force using magnetic impact applications. The imprecision of nanofluid and hybrid nanofluid volume fractions was modelled as a triangular fuzzy number (TFN) [0%, 1%, 2%] for comparison. The double parametric approach was applied to deal with the fuzziness of the associated fuzzy parameters. The nonlinear ODEs convert into fuzzy differential equations (FDEs) and use HAM for the fuzzy solution. From our observation, the hybrid nanofluid displays the maximum heat transfer compared to nanofluids. This important contribution will support industrial growth, particularly in the processing and manufacturing sectors. The percentage increase in skin friction factor is 18.3 and 15.0 when $$\alpha$$ α and $$\beta$$ β take input in the ranges of 0 ≤ $$\alpha$$ α ≤ 0.8 and 0 ≤ $$\beta$$ β ≤ 1, respectively.

Abstract Niacin had long been understood as an antioxidant. There were reports that high fat diet (HFD) may cause psychological and physical impairments. The present study was aimed to experience the effect of Niacin on % growth rate, cumulative food intake, motor activity and anxiety profile, redox status, 5-HT metabolism and brain histopathology in rats. Rats were administered with Niacin at a dose of 50 mg/ml/kg body weight for 4 weeks following normal diet (ND) and HFD. Behavioral tests were performed after 4 weeks. Animals were sacrificed to collect brain samples. Biochemical, neurochemical and histopathological studies were performed. HFD increased food intake and body weight. The exploratory activity was reduced and anxiety like behavior was observed in HFD treated animals. Activity of antioxidant enzymes was decreased while oxidative stress marker and serotonin metabolism in the brain of rat were increased in HFD treated animals than ND fed rats. Morphology of the brain was also altered by HFD administration. Conversely, Niacin treated animals decreased food intake and % growth rate, increased exploratory activity, produced anxiolytic effects, decreased oxidative stress and increased antioxidant enzyme and 5-HT levels following HFD. Morphology of brain is also normalized by the treatment of Niacin following HFD. In-silico studies showed that Niacin has a potential binding affinity with degradative enzyme of 5-HT i.e. monoamine oxidase (MAO) A and B with an energy of ~ − 4.5 and − 5.0 kcal/mol respectively. In conclusion, the present study showed that Niacin enhanced motor activity, produced anxiolytic effect, and reduced oxidative stress, appetite, growth rate, increased antioxidant enzymes and normalized serotonin system and brain morphology following HFD intake. In-silico studies suggested that increase 5-HT was associated with the binding of MAO with Niacin subsequentially an inhibition of the degradation of monoamine. It is suggested that Niacin has a great antioxidant potential and could be a good therapy for the treatment of HFD induced obesity.

Abstract The thin needle is viewed as a revolutionary object since it has a thinner thickness than a boundary layer. As a consequence, scientific and engineering applications for instance electrical equipment, hot wire anemometers and geothermal power generation are significantly impacted by the flow deformed by a thin moving needle. MHD Eyring–Powell fluid flow over a thin needle perceiving heat source, chemical reaction and nonlinear thermal radiation is the subject of the current investigation. In addition, the present study utilizes the Buongiorno model to examine the special effects of the fluid's Brownian and thermophoretic forces. The solution of the dimensionless form of ODEs is produced by applying exact renovations to the given problem, which is determined by the structure of PDEs. The bvp4c algorithm, based on the finite difference approach is utilized to numerically solve such modified ODEs. For validation, the results obtained indicate good agreement when compared to the literature. Finally, a detailed graphical analysis of key parameters is shown and explained while keeping in mind the physical significance of flow parameters. The results show that as magnetic and fluid parameter values improve, the velocity gradient falls. Increasing heat source and radiation parameters optimises heat transfer rate. The augmentation of the Lewis number and chemical reaction accelerates the rate of mass transfer on the surface. Brownian motion and thermophoresis provide enhanced thermal performance for the fluid temperature. Growing the thermophoresis parameter from 0.1 to 0.3 upsurges the Nusselt number by 5.47% and the Sherwood number by 12.26%.

Abstract There is significant clinical interest in targeting adenosine-mediated immunosuppression, with several small molecule inhibitors having been developed for targeting the A2AR receptor. Understanding of the mechanism by which A2AR is regulated has been hindered by difficulty in identifying the cell types that express A2AR due to a lack of robust antibodies for these receptors. To overcome this limitation, here an A2AR eGFP reporter mouse is developed, enabling the expression of A2AR during ongoing anti-tumor immune responses to be assessed. This reveals that A2AR is highly expressed on all tumor-infiltrating lymphocyte subsets including Natural Killer (NK) cells, NKT cells, γδ T cells, conventional CD4+ and CD8+ T lymphocytes and on a MHCIIhiCD86hi subset of type 2 conventional dendritic cells. In response to PD-L1 blockade, the emergence of PD-1+A2AR- cells correlates with successful therapeutic responses, whilst IL-18 is identified as a cytokine that potently upregulates A2AR and synergizes with A2AR deficiency to improve anti-tumor immunity. These studies provide insight into the biology of A2AR in the context of anti-tumor immunity and reveals potential combination immunotherapy approaches.

Abstract Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.

Abstract Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.

Application of organic amendments and N fertilizer can affect C and N sequestration, however, the degree to which diverse organic amendments and optimal N fertilization for matching demand for high crop productivity contributes to soil organic matter (SOM) of Cambisol in the North China Plain is not fully known. The objective was to evaluate the combined effects of annual maize straw-derived organic amendments (straw, manure, compost, biogas residue, or biochar) and in-season mineral N fertilizer amendment on soil organic carbon (SOC) and total N (TN) accumulation and thermal stability in bulk soils and physically isolated soil aggregate fractions in a long-term field experiment on the North China Plain. Application of organic amendments either alone or with N increased the proportion of macroaggregates (+62 to 137 %), aggregate mean weight diameter (+50 to 106 %), SOC (+38 to 206 %) and TN (+13 to 52 %) contents in bulk soil. The organic amendments and N fertilizer additions also increased SOC and TN contents in each of the isolated fractions: macroaggregates (+119 to 851 % for SOC, +109 to 237 % for TN) and microaggregates (+42 to 192 % for SOC and +30 to 145 % for TN), total fine particulate organic matter (T fPOM, +143 to 891 % for T fPOM-SOC, +129 to 549 % for T fPOM-TN) and total silt + clay within aggregates (53–139 % for SOC and 20–106 % for TN). Biochar resulted in the largest increase in SOC contents and improved soil aggregation without N fertilization, but fertilization greatly decreased these responses. The majority of the accumulated C and N occurred in the total silt + clay fractions, making up 50–90 % of total SOC and 79–96 % of total TN. Total fine POM and total silt + clay within aggregates contributed to 40–78 % and 10–56 % of SOC, 28–60 % and 37–71 % of TN increase between soil with and without organic amendments. Our results suggested that C and N retained in T fPOM and silt + clay fractions within aggregates were important mechanisms for C and N stabilization. Substitution of annual above-ground litter with biochar with or without mineral N fertilizer was the most effective way for SOM build up and stabilization under a wheat/maize system in the North China Plain, while manure, compost and biogas residue resulted in little to no increases of SOM in bulk soils and physically isolated aggregate fractions compared to straw amendment. Besides the effects of each amendment type on SOC, the different recovery rates of the various amendments, which determines the total quantity of each amendment that can be produced, should be taken into consideration in decisions about maize residue management at the regional scale in the North China Plain.

Aldehydes, being an integral part of carbon metabolism, energy generation, and signalling pathways, are ingrained in plant physiology. Land plants have developed intricate metabolic pathways which involve production of reactive aldehydes and its detoxification to survive harsh terrestrial environments. Here, we show that physiologically produced aldehydes, i.e., formaldehyde and methylglyoxal in addition to acetaldehyde, generate adducts with aminoacyl-tRNAs, a substrate for protein synthesis. Plants are unique in possessing two distinct chiral proofreading systems, D-aminoacyl-tRNA deacylase1 (DTD1) and DTD2, of bacterial and archaeal origins, respectively. Extensive biochemical analysis revealed that only archaeal DTD2 can remove the stable D-aminoacyl adducts on tRNA thereby shielding archaea and plants from these system-generated aldehydes. Using Arabidopsis as a model system, we have shown that the loss of DTD2 gene renders plants susceptible to these toxic aldehydes as they generate stable alkyl modification on D-aminoacyl-tRNAs, which are recycled only by DTD2. Bioinformatic analysis identifies the expansion of aldehyde metabolising repertoire in land plant ancestors which strongly correlates with the recruitment of archaeal DTD2. Finally, we demonstrate that the overexpression of DTD2 offers better protection against aldehydes than in wild type Arabidopsis highlighting its role as a multi-aldehyde detoxifier that can be explored as a transgenic crop development strategy.

Abstract This contribution aims to optimize nonlinear thermal flow for Darcy-Forchheimer Maxwell fuzzy $$\left( {{\text{Al}}_{{2}} {\text{O}}_{{3}} + {\text{Cu }} + {\text{TiO}}_{{2}} {\text{/EO}}} \right)$$ Al 2 O 3 + Cu + TiO 2 /EO tri-hybrid nanofluid flow across a Riga wedge in the context of boundary slip. Three types of nanomaterials, $${\text{Al}}_{{2}} {\text{O}}_{{3}} ,$$ Al 2 O 3 , Cu and $${\text{TiO}}_{2}$$ TiO 2 have been mixed into the basic fluid known as engine oil. Thermal properties with the effects of porous surface and nonlinear convection have been established for the particular combination $$\left( {{\text{Al}}_{{2}} {\text{O}}_{{3}} + {\text{Cu}} + {\text{TiO}}_{{2}} {\text{/EO}}} \right){.}$$ Al 2 O 3 + Cu + TiO 2 /EO . Applying a set of appropriate variables, the set of equations that evaluated the energy and flow equations was transferred to the dimensionless form. For numerical computing, the MATLAB software's bvp4c function is used. The graphical display is used to demonstrate the influence of several influential parameters. It has been observed that flow rate decay with expansion in porosity parameter and nanoparticles volumetric fractions. In contrast, it rises with wedge angle, Grashof numbers, Darcy-Forchheimer, nonlinear Grashof numbers, and Maxwell fluid parameter. Thermal profiles increase with progress in the heat source, nanoparticles volumetric fractions, viscous dissipation, and nonlinear thermal radiation. The percentage increases in drag force for ternary hybrid nanofluid are 13.2 and 8.44 when the Modified Hartmann number takes input in the range $$0.1 \le {\text{Mh}} \le 0.3$$ 0.1 ≤ Mh ≤ 0.3 and wedge angle parameters $$0.1 \le m \le 0.3$$ 0.1 ≤ m ≤ 0.3 . For fuzzy analysis, dimensionless ODEs transformed into fuzzy differential equations and employed symmetrical triangular fuzzy numbers (TFNs). The TFN makes a triangular membership function (M.F.) that describes the fuzziness and comparison. This study compared nanofluids, hybrid nanofluids, and ternary nanofluids through triangular M.F. The boundary layer flow caused by a wedge surface plays a crucial role in heat exchanger systems and geothermal.

Abstract For the purpose of understanding, the governing system of partial differential equations for synovial fluid flow velocity and temperature distribution in the knee joint has been successfully solved for the first time. Therefore, such an article is shedding light on the convective diffusion of the viscous flow along the articular surfaces of the joints through the introduction of power-law fluids with different features of permeability, and stagnation point flow along a magnetic field. Henceforth, the frictional energy causes the knee joint’s temperature to increase. By way of filtration, heated synovial fluid reaches the articular cartilage and provides heat to the bone and cartilage. The lubricant in the joint cavity is properly mixed with this cooled fluid. A rectangular region flow and diffusion model is used to define the issue, thermal diffusion and flow inside the intra-articular gap, as well as flow and thermal diffusion within the porous matrix covering the approaching bones at the joint. Using the similarity solution approach, the linked mixed boundary value problem is addressed. The fluid has been shown to resist moving into or out of the cartilage in certain sick and/or aging synovial joints, causing the temperature to increase. By changing the values of the parameters from their usual levels, it is observed that the temperature did increase in aged and sick joints which impact cartilage and/or synovial fluid degradation.

Abstract Cereal/legume intercropping is becoming a popular production strategy for higher crop yields and net profits with reduced inputs and environmental impact. However, the effects of different spatial arrangements on the growth, grain yield, nitrogen uptake, and land-use advantage of wheat/soybean relay intercropping are still unclear, particularly under arid irrigated conditions. Therefore, in a three-year field study from 2018 to 2021, soybean was relay intercropped with wheat in different crop configurations (0.9 m, narrow strips; 1.8 m, medium strips; and 2.7 m, wide strips), and the results of intercropping systems were compared with their sole systems. Results revealed that intercrops with wide strips outperformed the narrow and medium strips, when the objective was to obtain higher total leaf area, dry matter, nitrogen uptake, and grain yield on a given land area due to reduced interspecific competition between intercrops. Specifically, at maturity, wide strips increased the dry matter accumulation (37% and 58%) and its distribution in roots (37% and 55%), straw (40% and 61%), and grains (30% and 46%) of wheat and soybean, respectively, compared to narrow strips. This enhanced dry matter in wide strips improved the soybean’s competitive ability (by 17%) but reduced the wheat’s competitive ability (by 12%) compared with narrow strips. Noticeably, all intercropping systems accumulated a significantly higher amount of nitrogen than sole systems, revealing that wheat/soybean relay intercropping requires fewer anthropogenic inputs (nitrogen) and exerts less pressure on the ecosystem than sole systems. Overall, in wide strips, intercropped wheat and soybean achieved 62% and 71% of sole wheat and soybean yield, respectively, which increased the greater total system yield (by 19%), total land equivalent ratio (by 24%), and net profit (by 34%) of wide strips compared to narrow strips. Our study, therefore, implies that the growth parameters, grain yields, nutrient accumulation, and land-use advantage of intercrop species could be improved with the proper spatial arrangement in cereal/legume intercropping systems.

Abstract The significance of fuzzy volume percentage on the unsteady flow of MHD tangent hyperbolic fuzzy hybrid nanofluid towards an exponentially stretched surface is scrutinized. The heat transport mechanism is classified by Joule heating, nonlinear thermal radiation, boundary slippage, and convective circumstances. Ethylene glycol (EG) as a host fluid along with the nanomaterial’s Cu and $${\text{Al}}_{{2}} {\text{O}}_{{3}}$$ Al 2 O 3 are used for heat transfer analysis is also considered in this investigation. The nonlinear governing PDEs are meant to be converted into ODEs employing appropriate renovations. Then, a built-in MATLAB program bvp4c is employed to acquire the outcome of the given problem. The variation of flow rate, thermal heat, drag force and Nusselt number and their influence on fluid flow with heat transfer have been scrutinized through graphs. An increase in thermal radiation, power law index and nanoparticle volume friction heightens the heat transmission rate. Skin friction is diminished by swelling the power-law index, Weissenberg number, and ratio parameters, whereas it is increased by enhancing the magnetic parameter. The heat transfer rate upsurges with an increase in Weissenberg number and nanoparticle volume fraction. Also, the nanoparticle volume percentage is expressed as a triangular fuzzy number (TFN). The triangular membership function (MF) and TFN are regulated by the $$\chi - {\text{cut}}$$ χ - cut parameter, which has a range of 0 to 1. In comparison to nanofluids, hybrid nanofluids have a higher heat transmission rate, according to the fuzzy analysis. This investigation has applications in the areas of paper manufacturing, metal sheet cooling and crystal growth.

Progress in treatment for glioblastoma is hindered by the blood-brain barrier (BBB). In genetic mouse models recapitulating brain invasion and abnormal angiogenesis of human glioblastoma, Cai and colleagues demonstrate that optical modulation of the BBB with nanoparticles boosts intratumoural chemotherapy concentration, prolonging survival. We discuss prospects for clinical translation of exemplary innovative techniques.

Abstract The modelling of electricity production and demand requires highly specific and comprehensive meteorological data. One challenge is the high temporal frequency as electricity production and demand modelling typically is done with hourly data. On the other side the European electricity market is highly connected, so that a pure country-based modelling is not expedient and at least the whole European Union (EU) area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed the aggregated European wide climate data set SECURES-Met that has a temporal resolution of one hour, covers the whole EU area and other selected European countries, has a reasonable size but considers the high spatial variability.

Abstract Due to high-ultra thermic significances, the nanosize materials are used in various chemical and mechanical engineering, modern technology and thermic engineering eras. For industrial growth of a country, one of the biggest challenges for engineers and scientists is improvement in thermal production and resources. In this study we analyzed the momentum and thermic aspects of MHD Ellis ternary nano material embedded with dust particles via stretchable Riga plate including volume concentration of dust material. The flow generating PDE’s for two phase models are minimized into dimensionless nonlinear ODE’s by using the right modification. To acquire the graphical results the BVP4c method was adopted in MATLAB software. Fundamental aspects affecting velocity and temperature have investigated through graphs. Additionally Nusselt number and skin friction have also been evaluated. Compared it with previous literature to check the validity of results. Finding reveals that as compared to dusty phase the performance of trihybrid nano phase thermal transport is improved. Moreover, the temperature profile increases for rotational and volume fraction dust particles parameter. Dusty fluids are used in numerous manufacturing and engineering sectors, like petroleum transport, car smoke emissions, caustic granules in mining and power plant pipes.