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The solid-state field-effect transistor, FET, and its theories were paramount in the discovery and studies of graphene. In the past two decades another transistor based on conducting polymers, called organic electrochemical transistor (ECT), has been developed and largely studied. The main difference between organic ECTs and FETs is the mode and extent of channel doping; while in FETs the channel only has surface doping through dipoles, the mixed ionic-electronic conductivity of the channel material in organic ECTs enables bulk electrochemical doping. As a result, organic ECTs maximize conductance modulation at the expense of speed. To date ECTs have been based on conducting polymers, but here we show that MXenes, a class of 2D materials beyond graphene, enable the realization of electrochemical transistors (ECTs). We show that the formulas for organic ECTs can be applied to these 2D ECTs and used to extract parameters like mobility. These MXene ECTs have high transconductance values but low on-off ratios. We further show that conductance switching data measured using ECT, in combination with other in situ-ex situ electrochemical measurements, is a powerful tool for correlating the change in conductance to that of the redox state, to our knowledge, this is the first report of this important correlation for MXene films. 2D ECTs can draw great inspiration and theoretical tools from the field of organic ECTs and have the potential to considerably extend the capabilities of transistors beyond those of conducting polymer ECTs, with added properties such as extreme heat resistance, tolerance for solvents, and higher conductivity for both electrons and ions than conducting polymers. Here we show that not only conducting polymers, but also 2D MXenes can be used as materials for electrochemical transistors ECTs. MXene extend the capabilities of ECTs with properties such as extreme heat resistance, and higher conductivity/speeds.

Supervised machine learning (ML) techniques have been widely used in various geotechnical applications. While much attention is given to the ML techniques and the specific geotechnical problem being addressed, the influence of sampling methods on ML performance has received relatively less scrutiny. This study applies supervised ML to the strain-dependent slope stability (SDSS) method for the prediction of the factor of safety (FoS) using hypoplasticity. It delves into different sampling strategies for training the ML model, emphasizing predictions of soil behavior in lower stress ranges. A novel sampling method is introduced to ensure a more representative distribution of samples in these ranges, which is challenging to achieve through traditional sampling approaches. The ML models were trained using traditional and modified sampling methods. Subsequently, slope stability analyses using SDSS were conducted with ML models trained from six different sampling methods. The results illustrate the impact of sampling methods on the FoS. Besides a noticeable improvement in predictions of shear stresses within the lower stress ranges, a decisive effect on the overall FoS was observed as well.

Introduction:: Gastrointestinal (GI) parasites are major health concerns in both humans and domestic animals. Livestock farming is one of the common livelihood practices in rural Nepal. The proximity at human and domestic animal interface increases the chances of dissemination of enteric parasites, especially those of zoonotic importance. This study was aimed at finding the parasite prevalence and risk factors in both humans and their domestic animals in Jirel community. Materials and Methods:: A field survey was conducted on the Jirel ethnic people and their domestic animals in Dolakha district, where a total of 152 fresh fecal samples from humans and domestic animals (cow, pigs, goats, chickens, ducks, and pigeons) were collected. The feces were examined by wet mounts and concentration techniques. A structured questionnaire survey was carried out among the local people and owners of the domestic animals to gather sociodemographic information, awareness, and hygienic practices in relation to parasite transmission Results:: The enteric parasite prevalence was found to be highest in goats (80.0%;12/15), followed by pigs (55.55%;5/9), cows (45.45%;6/11), chickens (11.7%;4/34), and humans (1.41%;1/71), while the fecal samples of ducks and pigeons did not contain any parasites. The only parasite identified in humans was Ascaris lumbricoides. Similarly, three genera of GI parasites (Eimeria sp., Strongyloides sp, and Trichuris sp.) from goats, two genera each from cow (Eimeria sp. and Strongyloides sp.), pigs (Entamoeba sp. and A. suum), and chickens (Eimeria sp. and Ascaridia galli), were detected Conclusions:: Based on the direct field observation, questionnaire survey and laboratory analysis, it is concluded that the Jirel community people are aware of health and hygiene; however, intervention measures are necessary to prevent parasitic infection in their domestic animals.

Ayurveda is one of the oldest system of healthcare in the world. In  Ayurveda,‘Amavata’ was mentioned for the first time by AcharyaMadhavakara as a special  disease entity in which both‘Ama’as well as ‘Vata’play a predominant role in the  pathogenesis of this disease.‘Amavata’ is one of the crippling diseases causingthe maximum  loss of human power.It is not only a joint disorder but is also a systemic disease and is named  after it’s chief pathogenic constituent which are ‘Ama’ and ‘Vata’.The disease ‘Amavata’ is  very similar to Rheumatoid Arthritis. It is chronic in nature and effect mostly the middle age  group. Theonset of Rheumatoid arthritis is most frequent during the fourth and fifth decades of life, with 80% of all patients developing the disease between the age of 35 and 50. The incidence of RA is more than six times as great in 60-to-64-year-old women compared to18- to29-year-old women. Soneed of time is to go for cost effective, safe and efficient treatment  of Amavata.Although, there are many drugs available in modern medical science for the  treatment of Rheumatoid arthritis (Amavata) but they are associated with adverse effects, so  there is need of the hour to look into the safe herbal remedy which not only treat the disease  but also helps in reducing the relapse of the disease.Keeping all above points in mind  theAmrita Gugguluhas beenselected to know the effect in treating Amavata (Rheumatoid  arthritis).Amrita Gugguluconsists of6 main contents which have different properties it  consist of Guduchi, Shuddha Guggulu ,Haritaki, Vibhitaka, Aamalaki and Prakshepa  Dravya Danti, Shunthi, Pippali, Maricha, Vidanga ,Guduchi, Haritaki,Vibhitaka,  Aamalaki, Twaka, Trivrit.&nbsp

Aashish Bhattarai, Rajani Shakya, Durga Bista Department of Pharmacy, School of Science, Kathmandu University, Dhulikhel, NepalCorrespondence: Aashish Bhattarai, Department of Pharmacy, School of Science, Kathmandu University, Dhulikhel, 44600, Nepal, Tel +9779807902444, Email bhattarai.aashish75@gmail.comBackground: Asthma and Chronic obstructive pulmonary disease (COPD) are chronic respiratory conditions characterized by airflow obstruction and respiratory symptoms. Adherence to prescribed inhaler therapy and correct inhalation technique are essential for effective disease management and optimal disease control. However, non-adherence and incorrect inhalation technique are common challenges faced by patients with asthma and COPD, leading to suboptimal treatment outcomes and increased healthcare burden.Purpose: To study the impact of a pharmacist-led intervention on inhaler adherence, inhalation technique, and disease control among patients with asthma and COPD.Patients and Methods: A pre-post interventional design assessed the effects of pharmacist-led intervention on inhaler adherence, inhalation techniques, and disease control in asthma and COPD patients at Dhulikhel Hospital in Nepal. Inclusion criteria: adult patient clinically diagnosed with asthma or COPD patients of all genders. The intervention comprised counseling patients with aids like videos, and informational leaflets. Impact was measured using checklist method for inhalation technique, the Test of Adherence to Inhaler (TAI) questionnaire for adherence to inhaler, and “Asthma Control Test (ACT)” or “COPD Assessment Test (CAT)” for disease control.Results: The pharmacist-led intervention significantly increased adherence to inhalers, evidenced by a notable rise in the proportion of patients with good adherence (P< 0.001). Sporadic, deliberate, and unwitting noncompliance pattern also improved significantly after the intervention (P< 0.001, P< 0.001 and P=0.001). Inhalation technique ex

Granular materials are ubiquitous in nature and industry; their mechanical behavior has been a subject of academic and engineering interest for centuries. One of the reasons for their rather complex mechanical behavior is that stresses exerted on a granular material propagate only through contacts between the grains. These contacts can change as the packing evolves. This makes any deformation and mechanical response from a granular packing a function of the nature of contacts between the grains and the material response of the material the grains are made of. We present a study in which we isolate the role of the grain material in the contact forces acting between two particles sliding past each other. By using hydrogel particles, we find that a viscoelastic material model, in which the shear modulus decays with time, coupled with a simple Coulomb friction model, captures the experimental results. The results suggest that particle material evolution itself may play a role in the collective behavior of granular materials.

BACKGROUND: Enteric fever is a serious public health concern. The causative agents, Salmonella enterica serovars Typhi and Paratyphi A, frequently have antimicrobial resistance (AMR), leading to limited treatment options and poorer clinical outcomes. We investigated the genomic epidemiology, resistance mechanisms, and transmission dynamics of these pathogens at three urban sites in Africa and Asia. METHODS: S Typhi and S Paratyphi A bacteria isolated from blood cultures of febrile children and adults at study sites in Dhaka (Bangladesh), Kathmandu (Nepal), and Blantyre (Malawi) during STRATAA surveillance were sequenced. Isolates were charactered in terms of their serotypes, genotypes (according to GenoTyphi and Paratype), molecular determinants of AMR, and population structure. We used phylogenomic analyses incorporating globally representative genomic data from previously published surveillance studies and ancestral state reconstruction to differentiate locally circulating from imported pathogen AMR variants. Clusters of sequences without any single-nucleotide variants in their core genome were identified and used to explore spatiotemporal patterns and transmission dynamics. FINDINGS: We sequenced 731 genomes from isolates obtained during surveillance across the three sites between Oct 1, 2016, and Aug 31, 2019 (24 months in Dhaka and Kathmandu and 34 months in Blantyre). S Paratyphi A was present in Dhaka and Kathmandu but not Blantyre. S Typhi genotype 4.3.1 (H58) was common in all sites, but with different dominant variants (4.3.1.1.EA1 in Blantyre, 4.3.1.1 in Dhaka, and 4.3.1.2 in Kathmandu). Multidrug resistance (ie, resistance to chloramphenicol, co-trimoxazole, and ampicillin) was common in Blantyre (138 [98%] of 141 cases) and Dhaka (143 [32%] of 452), but absent from Kathmandu. Quinolone-resistance mutations were common in Dhaka (451 [>99%] of 452) and Kathmandu (123 [89%] of 138), but not in Blantyre (three [2%] of 141). Azithromycin-resistance mutations

This research investigates the impact of attributes of line managers based on the Abilities, Motivation, and Opportunity (AMO) model in relation to effective HRM implementation. Conducted within an organization in the battery industry, the study measures HRM implementation through a performance appraisal system called Leapsome. A self-administered questionnaire, informed by previous literature on AMO and HRM implementation effectiveness factors derived from interviews with three HR department employees involved in developing Leapsome, was utilized. The questionnaire was distributed as per census data collection method, to 258 line managers via Google Forms, yielding 103 complete responses for analysis. Regression and correlation analyses were performed, focusing on three main dependent variables: effort, frequency of use, and alignment with deadlines. The results indicated positive relationships between all independent variables and the dependent variables, except for the relationship between ability and effort, which was not supported. Consequently, the hypotheses were accepted, except for the hypothesis stating that ability positively correlates with effort. The study concludes with managerial and theoretical implications and provides recommendations for future research.

Left bundle branch block (LBBB) is generally associated with a poorer prognosis in comparison to normal intraventricular conduction. LBBB may be the first manifestation of a more diffuse myocardial disease. The typical surface ECG feature of LBBB is a prolongation of QRS above 0.11s in combination with a delay of the intrinsic deflection in leads V5 and V6 of more than 60 ms and no septal q waves in leads I, V5, and V6 due to the abnormal septal activation from right to left. LBBB may induce abnormalities in left ventricular performance due to abnormal asynchronous contraction patterns. Asynchronous electrical activation of the ventricles causes regional differences in workload which may lead to asymmetric hypertrophy and left ventricular dilatation, especially due to increased wall mass in late-activated regions, which may aggravate preexisting left ventricular pumping performance or even induce it.

We study nonthermal production of heavy dark matter from the dynamics of the background scalar field during a first-order phase transition, predominantly from bubble collisions. In scenarios where bubble walls achieve runaway behavior and get boosted to very high energies, we find that it is possible to produce dark matter with mass several orders of magnitude above the symmetry breaking scale or the highest temperature ever reached by the thermal plasma. We also demonstrate that the existing formalism for calculating particle production from bubble dynamics in a first-order phase transition is not gauge invariant, and can lead to spurious results. While a rigorous and complete resolution of this problem is still lacking, we provide a practical prescription for the computation that avoids unphysical contributions and should provide reliable order-of-magnitude estimates of this effect. Furthermore, we point out the importance of three-body decays of the background field excitations into scalars and gauge bosons, which provide the dominant contributions at energy scales above the scale of symmetry breaking. Using our improved results, we find that scalar, fermion, and vector dark matter are all viable across a large range of mass scales, from O(10) TeV to a few orders of magnitude below the Planck scale, and the corresponding phase transitions can be probed with current and future gravitational wave experiments., Comment: 41 pages, 3 figures

Visual Inspection with Acetic Acid (VIA) remains the most feasible cervical cancer screening test in resource-constrained settings of low- and middle-income countries (LMICs), which are often performed screening camps or primary/community health centers by nurses instead of the preferred but unavailable expert Gynecologist. To address the highly subjective nature of the test, various handheld devices integrating cameras or smartphones have been recently explored to capture cervical images during VIA and aid decision-making via telemedicine or AI models. Most studies proposing AI models retrospectively use a relatively small number of already collected images from specific devices, digital cameras, or smartphones; the challenges and protocol for quality image acquisition during VIA in resource-constrained camp settings, challenges in getting gold standard, data imbalance, etc. are often overlooked. We present a novel approach and describe the end-to-end design process to build a robust smartphone-based AI-assisted system that does not require buying a separate integrated device: the proposed protocol for quality image acquisition in resource-constrained settings, dataset collected from 1,430 women during VIA performed by nurses in screening camps, preprocessing pipeline, and training and evaluation of a deep-learning-based classification model aimed to identify (pre)cancerous lesions. Our work shows that the readily available smartphones and a suitable protocol can capture the cervix images with the required details for the VIA test well; the deep-learning-based classification model provides promising results to assist nurses in VIA screening; and provides a direction for large-scale data collection and validation in resource-constrained settings.

We propose a novel nonorthogonal multiple access (NOMA) scheme referred as adaptive constellation multiple access (ACMA) which addresses key limitations of existing NOMA schemes for beyond 5G wireless systems. Unlike the latter, that are often constrained in choices of allocation of power, modulations and phases to allow enough separation of clusters from users combined signals, ACMA is power, modulation and phase agnostic forming unified constellations instead where distances of all possible neighbouring points are optimized. It includes an algorithm at basestation (BS) calculating phase offsets for users signals such that, when combined, it gives best minimum Euclidean distance of points from all possibilities. The BS adaptively changes the phase offsets whenever system parameters change. We also propose an enhanced receiver using a modified maximum likelihood (MML) method that dynamically exploits information from the BS to blindly estimate correct phase offsets and exploit them to enhance data rate and error performances. Superiority of this scheme, which may also be referred to as AC NOMA, is verified through extensive analyses and simulations., Comment: 5 pages, 6 figures, Submission to an IEEE Journal

Predicting the strategy (sequence of concepts) that a student is likely to use in problem-solving helps Adaptive Instructional Systems (AISs) better adapt themselves to different types of learners based on their learning abilities. This can lead to a more dynamic, engaging, and personalized experience for students. To scale up training a prediction model (such as LSTMs) over large-scale education datasets, we develop a non-parametric approach to cluster symmetric instances in the data. Specifically, we learn a representation based on Node2Vec that encodes symmetries over mastery or skill level since, to solve a problem, it is natural that a student's strategy is likely to involve concepts in which they have gained mastery. Using this representation, we use DP-Means to group symmetric instances through a coarse-to-fine refinement of the clusters. We apply our model to learn strategies for Math learning from large-scale datasets from MATHia, a leading AIS for middle-school math learning. Our results illustrate that our approach can consistently achieve high accuracy using a small sample that is representative of the full dataset. Further, we show that this approach helps us learn strategies with high accuracy for students at different skill levels, i.e., leveraging symmetries improves fairness in the prediction model., Comment: Proceedings of 37th AAAI Conference on Artificial Intelligence Artificial Intelligence for Education. arXiv admin note: substantial text overlap with arXiv:2308.03892

Privacy poses a significant obstacle to the progress of learning analytics (LA), presenting challenges like inadequate anonymization and data misuse that current solutions struggle to address. Synthetic data emerges as a potential remedy, offering robust privacy protection. However, prior LA research on synthetic data lacks thorough evaluation, essential for assessing the delicate balance between privacy and data utility. Synthetic data must not only enhance privacy but also remain practical for data analytics. Moreover, diverse LA scenarios come with varying privacy and utility needs, making the selection of an appropriate synthetic data approach a pressing challenge. To address these gaps, we propose a comprehensive evaluation of synthetic data, which encompasses three dimensions of synthetic data quality, namely resemblance, utility, and privacy. We apply this evaluation to three distinct LA datasets, using three different synthetic data generation methods. Our results show that synthetic data can maintain similar utility (i.e., predictive performance) as real data, while preserving privacy. Furthermore, considering different privacy and data utility requirements in different LA scenarios, we make customized recommendations for synthetic data generation. This paper not only presents a comprehensive evaluation of synthetic data but also illustrates its potential in mitigating privacy concerns within the field of LA, thus contributing to a wider application of synthetic data in LA and promoting a better practice for open science.

Explanations on relational data are hard to verify since the explanation structures are more complex (e.g. graphs). To verify interpretable explanations (e.g. explanations of predictions made in images, text, etc.), typically human subjects are used since it does not necessarily require a lot of expertise. However, to verify the quality of a relational explanation requires expertise and is hard to scale-up. GNNExplainer is arguably one of the most popular explanation methods for Graph Neural Networks. In this paper, we develop an approach where we assess the uncertainty in explanations generated by GNNExplainer. Specifically, we ask the explainer to generate explanations for several counterfactual examples. We generate these examples as symmetric approximations of the relational structure in the original data. From these explanations, we learn a factor graph model to quantify uncertainty in an explanation. Our results on several datasets show that our approach can help verify explanations from GNNExplainer by reliably estimating the uncertainty of a relation specified in the explanation., Comment: Published in Proceedings of 2023 IEEE Conference on Big Data

NRF2's intricate role within cancer is continually developing, with its function as a transcription factor extending to the regulation of genes responsible for maintaining redox balance, protein homeostasis, lipid and iron metabolism, proliferation, and survival. Herein, we highlight NRF2's pivotal involvement in cellular responses to several downstream stressful pathways, its regulatory mechanisms, and its impact on health and disease, notably in cancer. Complementary to this, the study uncovers NRF2-mediated p97 upregulation, shedding light on its implications for protein homeostasis and redox balance. Furthermore, investigations into NRF2's influence on chronic arsenic-induced carcinogenesis provide crucial insights. The second facet of the research confronts these mechanisms further, proposing innovative strategies such as NRF2 and p97 co-inhibition as a therapeutic approach. Additionally, the exploration of NRF2's involvement in modulating ferroptosis, a form of regulated cell death characterized by iron-dependent lipid peroxidation, unveils novel insights into NRF2's broader impact beyond classical antioxidant responses. Furthermore, the study extends its scope to development of targeted synthetic nanobodies against SLC7A11, a crucial component of the ferroptosis pathway. Meanwhile, leveraging advanced techniques such as the NRF2 crystal structure analysis of the Neh1-MafG-ARE complex provides valuable insights for NRF2 targeted precision drug design. Taken together, these findings expand our understanding of NRF2-related pathways in mechanisms of cancer resistance and offer promising avenues for innovative therapeutic strategies harnessing the NRF2 axis against cancer and drug resistance challenges.

This paper introduces Waste Factor (W), also denoted as Waste Figure (WF) in dB, a promising new metric for quantifying energy efficiency in a wide range of circuits and systems applications, including data centers and RANs. Also, the networks used to connect data centers and AI computing engines with users for ML applications must become more power efficient. This paper illustrates the limitations of existing energy efficiency metrics that inadequately capture the intricate energy dynamics of RAN components. We delineate the methodology for applying W across various network configurations, including MISO, SIMO, and MIMO systems, and demonstrate the effectiveness of W in identifying energy optimization opportunities. Our findings reveal that W not only offers nuanced insights into the energy performance of RANs but also facilitates informed decision-making for network design and operational efficiency. Furthermore, we show how W can be integrated with other KPIs to guide the development of optimal strategies for enhancing network energy efficiency under different operational conditions. Additionally, we present simulation results for a distributed multi-user MIMO system at 3.5, 17, and 28 GHz, demonstrating overall network power efficiency on a per square kilometer basis, and show how overall W decreases with an increasing number of base stations and increasing carrier frequency. This paper shows that adopting W as a figure of merit can significantly contribute to the sustainability and energy optimization of next-generation wireless communication networks, paving the way for greener and more sustainable, energy-efficient 5G and 6G technologies., Comment: 26 pages, 21 figures, 5 tables

This paper introduces Waste Factor (W) and Waste Figure (WF) to assess power efficiency in any multiple-input multiple-output (MIMO) or single-input multiple-output (SIMO) or multiple-input single-output (MISO) cascaded communication system. This paper builds upon the new theory of Waste Factor, which systematically models added wasted power in any cascade for parallel systems such as MISO, SIMO, and MIMO systems, which are prevalent in current wireless networks. Here, we also show the advantage of W compared to conventional metrics for quantifying and analyzing energy efficiency. This work explores the utility of W in assessing energy efficiency in communication channels, within Radio Access Networks (RANs)., Comment: 6 pages, 6 figures

The task of Spell Correction(SC) in low-resource languages presents a significant challenge due to the availability of only a limited corpus of data and no annotated spelling correction datasets. To tackle these challenges a small-scale word-based transformer LM is trained to provide the SC model with contextual understanding. Further, the probabilistic error rules are extracted from the corpus in an unsupervised way to model the tendency of error happening(error model). Then the combination of LM and error model is used to develop the SC model through the well-known noisy channel framework. The effectiveness of this approach is demonstrated through experiments on the Nepali language where there is access to just an unprocessed corpus of textual data., Comment: 8 pages

Recent language models use subwording mechanisms to handle Out-of-Vocabulary(OOV) words seen during test time and, their generation capacity is generally measured using perplexity, an intrinsic metric. It is known that increasing the subword granularity results in a decrease of perplexity value. However, the study of how subwording affects the understanding capacity of language models has been very few and only limited to a handful of languages. To reduce this gap we used 6 different tokenization schemes to pretrain relatively small language models in Nepali and used the representations learned to finetune on several downstream tasks. Although byte-level BPE algorithm has been used in recent models like GPT, RoBERTa we show that on average they are sub-optimal in comparison to algorithms such as SentencePiece in finetuning performances for Nepali. Additionally, similar recent studies have focused on the Bert-based language model. We, however, pretrain and finetune sequential transformer-based language models., Comment: 11 pages

INTRODUCTION: Salmonella Typhi and Salmonella Paratyphi, fecal-oral transmitted bacterium, have temporally and geographically heterogeneous pathways of transmission. Previous work in Kathmandu, Nepal implicated stone waterspouts as a dominant transmission pathway after 77% of samples tested positive for Salmonella Typhi and 70% for Salmonella Paratyphi. Due to a falling water table, these spouts no longer provide drinking water, but typhoid fever persists, and the question of the diseases dominant pathway of transmission remains unanswered. METHODS: We used environmental surveillance to detect Salmonella Typhi and Salmonella Paratyphi A DNA from potential sources of transmission. We collected 370, 1L drinking water samples from a population-based random sample of households in the Kathmandu and Kavre Districts of Nepal between February and October 2019. Between November 2019 and July 2021, we collected 380, 50mL river water samples from 19 sentinel sites on a monthly interval along the rivers leading through the Kathmandu and Kavre Districts. We processed drinking water samples using a single qPCR and processed river water samples using differential centrifugation and qPCR at 0 and after 16 hours of liquid culture enrichment. A 3-cycle threshold (Ct) decrease of Salmonella Typhi or Salmonella Paratyphi, pre- and post-enrichment, was used as evidence of growth. We also performed structured observations of human-environment interactions to understand pathways of potential exposure. RESULTS: Among 370 drinking water samples, Salmonella Typhi was detected in 7 samples (1.8%) and Salmonella Paratyphi A was detected in 4 (1.0%) samples. Among 380 river water samples, Salmonella Typhi was detected in 171 (45%) and Salmonella Paratyphi A was detected in 152 (42%) samples. Samples located upstream of the Kathmandu city center were positive for Salmonella Typhi 12% of the time while samples from locations in and downstream were positive 58% and 67% of the time respectively. I

BackgroundPrior cross-sectional research suggests that both men's and women's attitudes towards intimate partner violence (IPV) are predictive of women's IPV experience, although this can vary greatly by context. In general, women who have experienced IPV are likely to report attitudes accepting of it. Men who perpetrate IPV may also report attitudes accepting of it, although some research has found that there is not always an association. Studies that investigate these dynamics often conflate attitudes with social norms, or use attitudes as a proxy for social norms, given that valid measures on social norms are usually lacking. Here we conduct a secondary data analysis to ask how are men's and women's IPV-related attitudes associated with women's reports of IPV and how are men's and women's perceived social norms associated with women's reports of IPV.MethodsDyadic data were collected from a representative sample of married adolescent girls and their husbands in 48 rural villages of the Dosso region of Niger (N = 1010). Assessments included logistic regression analyses of husbands' and wives' reports of individual attitudes towards IPV, and social norms based on husbands' and wives' perceptions of their communities' beliefs related to gender roles and acceptability of IPV.ResultsEight percent of women in this sample reported IPV. We found that, consistent with other research, wives who have reported IPV are more likely to report attitudes in support of IPV, while for husbands whose wives report IPV, that relationship is insignificant. On the other hand, husbands who report that people in their community believe there are times when a woman deserves to be beaten are more likely to have perpetrated IPV, while for wives there is no association between the community norm and IPV reporting. Finally, wives who report that people in their community hold inequitable gender norms in general are more likely to have experienced IPV, while for husbands, community gender norms

The unique properties of hydrogels enable the design of life-like soft intelligent systems. However, stimuli-responsive hydrogels still suffer from limited actuation control. Direct electronic control of electronically conductive hydrogels can solve this challenge and allow direct integration with modern electronic systems. An electrochemically controlled nanowire composite hydrogel with high in-plane conductivity that stimulates a uniaxial electrochemical osmotic expansion is demonstrated. This materials system allows precisely controlled shape-morphing at only -1 V, where capacitive charging of the hydrogel bulk leads to a large uniaxial expansion of up to 300%, caused by the ingress of & AP;700 water molecules per electron-ion pair. The material retains its state when turned off, which is ideal for electrotunable membranes as the inherent coupling between the expansion and mesoporosity enables electronic control of permeability for adaptive separation, fractionation, and distribution. Used as electrochemical osmotic hydrogel actuators, they achieve an electroactive pressure of up to 0.7 MPa (1.4 MPa vs dry) and a work density of & AP;150 kJ m-3 (2 MJ m-3 vs dry). This new materials system paves the way to integrate actuation, sensing, and controlled permeation into advanced soft intelligent systems. The unique properties of hydrogels enable the design of life-like soft intelligent systems. This work demonstrates how the swelling of hydrogels from cellulose nanofibrils and carbon nanotubes can be electrochemically controlled to achieve electrochemical osmotic actuation. This new materials system paves the way for integrated actuation, sensing, and controlled permeation in electrotunable separation membranes or soft actuators.image, Funding Agencies|Stora Enso AB; Digital Cellulose Centre (DCC) - Swedish Innovation Agency VINNOVA; Knut and Alice Wallenberg Foundation; Olle Engkvists Stiftelse; Max Planck Society, Germany; [201605193]

We outline the unique opportunities and challenges in the search for "ultraheavy" dark matter candidates with masses between roughly 10 TeV and the Planck scale mpl≈1016 TeV. This mass range presents a wide and relatively unexplored dark matter parameter space, with a rich space of possible models and cosmic histories. We emphasize that both current detectors and new, targeted search techniques, via both direct and indirect detection, are poised to contribute to searches for ultraheavy particle dark matter in the coming decade. We highlight the need for new developments in this space, including new analyses of current and imminent direct and indirect experiments targeting ultraheavy dark matter and development of new, ultra-sensitive detector technologies like next-generation liquid noble detectors, neutrino experiments, and specialized quantum sensing techniques.

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.

BACKGROUND: The Global Typhoid Genomics Consortium was established to bring together the typhoid research community to aggregate and analyse Salmonella enterica serovar Typhi (Typhi) genomic data to inform public health action. This analysis, which marks 22 years since the publication of the first Typhi genome, represents the largest Typhi genome sequence collection to date (n=13,000). METHODS: This is a meta-analysis of global genotype and antimicrobial resistance (AMR) determinants extracted from previously sequenced genome data and analysed using consistent methods implemented in open analysis platforms GenoTyphi and Pathogenwatch. RESULTS: Compared with previous global snapshots, the data highlight that genotype 4.3.1 (H58) has not spread beyond Asia and Eastern/Southern Africa; in other regions, distinct genotypes dominate and have independently evolved AMR. Data gaps remain in many parts of the world, and we show the potential of travel-associated sequences to provide informal 'sentinel' surveillance for such locations. The data indicate that ciprofloxacin non-susceptibility (>1 resistance determinant) is widespread across geographies and genotypes, with high-level ciprofloxacin resistance (≥3 determinants) reaching 20% prevalence in South Asia. Extensively drug-resistant (XDR) typhoid has become dominant in Pakistan (70% in 2020) but has not yet become established elsewhere. Ceftriaxone resistance has emerged in eight non-XDR genotypes, including a ciprofloxacin-resistant lineage (4.3.1.2.1) in India. Azithromycin resistance mutations were detected at low prevalence in South Asia, including in two common ciprofloxacin-resistant genotypes. CONCLUSIONS: The consortium's aim is to encourage continued data sharing and collaboration to monitor the emergence and global spread of AMR Typhi, and to inform decision-making around the introduction of typhoid conjugate vaccines (TCVs) and other prevention and control strategies. FUNDING: No specific funding was awarded f

Background: A needs-based approach is desirable for the transformation of pharmaceutical education, and to link pharmaceutical education with the health needs of populations and national priorities. There are varying levels of data in the literature on the status of pharmaceutical education in all six World Health Organization (WHO) regions, especially in the context of needs identification and evidence-based policy interventions. The framework for this study was the FIP Development Goals. Objectives: The aim of the study was to develop evidence-based policies through a needs-based approach for pharmaceutical education transformation nationally, regionally and globally by addressing the following objectives: 1. Identify global and regional needs in pharmaceutical education, through a regional SWOT analysis and prioritization of FIP development goals; 2. Develop valid and credible regional roadmaps for pharmaceutical education advancement according to the identified prioritized goals and 3. Develop a global call to action as a policy intervention for advancing pharmaceutical education. Methods: This study was conducted between 2020 and 2021 using a mixed methods approach. Surveys of higher education institutions and a series of qualitative interviews were conducted with national professional leadership organizations, with further regional workshops having 284 participants recruited from the International Pharmaceutical Federation (FIP) membership base, spanning all six WHO regions. Results: Eleven out of 21 FIP DGs were identified as priorities for regional roadmaps and FIP DG 1 (Academic capacity) was identified as a priority in four regions. All regions had distinctive results with an area of commonality between them. There were common weaknesses in the adoption of competency-based education and inter-professional education. Conclusions: It is critical for every country and region to develop needs- and evidence-based policies for the transformation of pharmaceut

This study aimed to estimate the socio-economic impacts of landslides and identify mitigation techniques being applied for landslide control in Bagnaskali rural municipality, Palpa. Purposive sampling was conducted to focus the research on plots with frequent landslide occurrences. Out of the 20 identified landslide-prone sites, a random sampling method was used to ensure representative data collection with a 40% sampling intensity. Data were collected through direct observation, transect walks, and household surveys conducted in areas prone to landslides. Additionally, discussions were held with key informants, the technical team, and experts in the field to validate the collected data. In Bagnaskali rural municipality in the year 2020, six households reported damage, two cases of death occurred, 24 livestock fatalities were recorded, and there was damage to cultural heritage due to landslides, resulting in social losses. Affected communities also incurred compensatory losses ranging from NRs 10 to 50 lakhs due to the destruction of water resources, road blockages, and agricultural land damage. As mitigation measures for landslides, the municipality applied civil engineering techniques such as gabion walls, masonry, culverts, check dams, and reinforced concrete (RCC), as well as bioengineering techniques including plantation, wattling, and brush-layering. Civil engineering techniques were prioritized for mitigating landslides, with bioengineering serving as a supporting base. Plots where both civil and bioengineering techniques were employed together exhibited greater slope stability and reduced vulnerability. Therefore, it is recommended to use mitigation techniques that stabilize slopes and reinforce the soil to control landslide hazards effectively. To successfully reduce landslides and protect vulnerable communities, comprehensive policies, laws, and investments are required. Technical and financial assistance from governmental and non-governmental sectors is e

The Standard Model Higgs becomes tachyonic at high energy scales according to current measurements. This unstable regime of the Higgs potential can be realized in the early Universe during high scale inflation, potentially with catastrophic consequences. This letter highlights a crucial inherent feature of such configurations that has so far remained ignored: Higgs particle production out of vacuum induced by the rapidly evolving Higgs field, which gets exponentially enhanced due to the tachyonic instability. Such explosive particle production can rapidly drain energy away from the Higgs field, sustaining a significant density of Higgs particles even during inflation, and could initiate a qualitatively different form of preheating in parts of the post-inflationary Universe. Any study of the Higgs field in its tachyonic phase, either during or after inflation, must therefore take this substantial particle energy density into account, which could significantly affect the subsequent evolution of such systems. This could carry important implications for high scale inflation, post-inflationary preheating, observable signals in the cosmic microwave background, gravitational waves, and primordial black holes, as well as deeper concepts ranging from eternal inflation to the metastability of the electroweak vacuum., Comment: 8 pages, 2 figures

Skin cancer is the most common cancer, and its incidence is increasing each year. Its diagnosis is based on a histopathological inspection by pathologists. Other imaging techniques are currently being actively researched to assist pathologists in improving diagnostic accuracy. Fourier Transform Infrared (FTIR) spectroscopy is gaining in popularity among researchers due to its ability to provide spatially resolved biochemical and biological information while also being compatible with histopathological samples. Furthermore, facilitating FTIR spectroscopic data with various machine learning methodologies for automating classification has become a popular practice. These methodologies have achieved high accuracy in discriminating cancerous samples from healthy ones, but limited work has been done to determine the metastatic potential of cells and optimize the FTIR spectroscopy protocol for achieving the best classification result. This thesis reviewed all skin cancer studies that used FTIR spectroscopy as a potential diagnostic tool to assess its effectiveness in various aspects of skin cancer research and to analyse the performance of different methods used for differentiating diseased samples from healthy ones. In addition, a model was developed to examine whether different malignant melanoma cell lines (primary and metastatic) can be discriminated using the spectral data acquired from the samples. Furthermore, the effects of different modes (transmission and transflection) and sample processing methods (deparaffinized and paraffinized) on the acquired spectra were investigated, and the combination providing the best classification result was determined. According to the results of this thesis, FTIR spectroscopy can differentiate different types of skin cancers from healthy samples as well as between different skin cancers (melanoma and non-melanoma) and the most common non-melanoma cancers. Melanoma has received the most attention in skin cancer research du, Tiivistelmä Ihosyövät ovat maailmanlaajuisesti yleisimpiä syöpiä, ja niiden ilmaantuvuus kasvaa joka vuosi. Ihosyöpien diagnoosi perustuu patologin tekemään histopatologiseen tutkimukseen. Kuvantamistekniikoita tutkitaan aktiivisesti, jotta ihosyöpien diagnostiikkaa voitaisiin parantaa. Infrapuna (IR)-spektroskopia on kasvattanut suosiota tutkijoiden keskuudessa, koska sillä voidaan tutkia kudosleikkeistä alueellisia eroja kudoksen koostumuksessa. Kudosten IR-spektrien luokittamisessa käytetään usein koneoppimiseen perustuvia automaattisia menetelmiä. Näillä menetelmillä on kyetty erottamaan tarkasti syöpäsolut terveistä soluista. Toisaalta syöpäsolujen metastasoimispotentiaalin määrittämiseksi IR-spektreistä on tehty vain vähän tutkimuksia, ja mittausprotokollia ei ole vielä optimoitu parhaimman luokitustuloksen saavuttamiseksi. Tässä väitöskirjatyössä tehtiin kirjallisuuskatsaus kaikista IR-spektroskopiaa hyödyntävistä ihosyöpätutkimuksista. Katsauksessa arvioitiin IR-spektroskopian soveltuvuutta ihosyöpätutkimuksen eri osa-alueilla ja analysoitiin eri mittaus- ja data-analyysimenetelmien kykyä erottaa sairaat ja terveet ihonäytteet toisistaan. Lisäksi tässä työssä kehitettiin malli, jolla pyrittiin luokittelemaan pahanlaatuisia melanoomasolulinjoja niiden IR-spektrien perusteella. Työssä tutkittiin myös eri mittausmoodien (transmissio ja transflektio) ja näytteenkäsittelymenetelmien (ei-parafinoitu ja parafinoitu) vaikutuksia mitattuihin IR-spektreihin ja määritettiin näiden yhdistelmä, jolla saadaan paras tulos melanoomasolujen luokittelemisessa. Väitöskirjan tulosten perusteella voidaan sanoa, että IR-spektroskopialla pystytään erottamaan eri ihosyövät terveestä ihokudoksesta ja tunnistamaan ihosyövän tyyppi. Melanooma on saanut eniten huomiota ihosyöpätutkimuksessa, koska se pystyy muodostamaan etäpesäkkeitä lähellä oleviin imusolmukkeisiin ja muihin elintärkeisiin elimiin lyhyessä ajassa. IR-spektroskopialla voidaan erottaa eri pahanlaatuiset melanoomasolulin

First order phase transitions (FOPTs) constitute an active area of contemporary research as a promising cosmological source of observable gravitational waves. The spacetime dynamics of the background scalar field undergoing the phase transition can also directly produce quanta of particles that couple to the scalar, which has not been studied as extensively in the literature. This paper provides the first careful examination of various important aspects of this phenomenon. In particular, the contributions from various stages of FOPTs (bubble nucleation, expansion, collision) are disentangled. It is demonstrated that heavy particles primarily originate from the relative motion of bubble walls at distances comparable to the Compton wavelength of the particle rather than from the bubble collision itself. Subtleties related to non-universality of particle interactions and masses in different vacua are discussed, and a prescription to choose the correct vacuum for the calculation is provided. The suppression of non-perturbative effects such as tachyonic instability and parametric resonance due to the inhomegeneous nature of the process is examined., Comment: 20 pages, 2 figures

While first order phase transitions (FOPTs) have been extensively studied as promising cosmological sources of gravitational waves, the phenomenon of particle production from the dynamics of the background field during FOPTs has received relatively little attention in the literature, where it has only been studied with semi-analytic estimates in some simplified settings. This paper provides improved numerical studies of this effect in more realistic frameworks, revealing important qualitative details that have been missed in the literature. We also provide easy to use analytic formulae that can be used to calculate particle production in generic FOPT setups., Comment: 18 pages, 6 figures

Among all cereals, oat is considered to be one of the goods consumed in functional grain, nutraceutical, and pharmaceutical components. The review article aims to explore plant profile, phytochemical constituents present in different parts, and potential therapeutic agent which prevents people from different diseases with their respective mechanism of action it also includes toxicity studies, dosage form, marketed formulation, and industrial application. The information on Avena sativa is collected from the different databases available in Science Direct, PubMed, Scopus, and Google Scholar to search for research papers for literature and data. Reviewed information suggests that the A. Sativa is a good source of a range of phytochemicals including lignans, saponin, anthocyanidins, and avenanthramides, which might be directly and/or indirectly linked with beneficial nutraceutical and/or pharmacological effect(s). The review states, that A. sativa is a promising candidate as a functional and/or non-functional food because of various phytoconstituents. The clinical report has described that the utilization of oats can provide several health benefits. However, systematic rational investigations for such indications and safety profiles are yet to be explored.

Campylobacter spp. is often underreported and underrated bacteria that present real health risks to both humans and animals, including non-human primates. It is a commensal microorganism of gastrointestinal tract known to cause gastroenteritis in humans. Commonly found in many wild animals including non-human primates (monkeys- Rhesus macaques) these pathogens are known to be a common cause of diarrhea in humans in many parts of developing and under developed countries. Rhesus macaques from the two holy sites in Kathmandu (Pashupati and Swoyambhu) were included in this cross-sectional study. Diarrheal samples of monkeys were analyzed to detect and characterize the pathogen using 16S rRNA-based PCR screening, followed by DNA sequencing and phylogenetic analysis. Out of a total 67 collected diarrheal samples, Campylobacter spp. were detected in the majority of the samples (n = 64; 96%). DNA sequences of the amplified PCR products were successfully obtained from 13 samples. Phylogenetic analysis identified Candidatus Campylobacter infans (n = 10, Kimura-2 parameter (K2P) pairwise distance values of 0.002287). Remaining three sequences might potentially belong to a novel Campylobacter species/sub-species- closely relating to known species of C. helviticus (K2P pairwise distance of 0.0267). Both Candidatus Campylobacter infans and C. helvitucus are known to infect humans and animals. Additionally, we also detected the bacteria in water and soil samples from the sites. Campylobacter spp. caused the 2018 diarrhea outbreak in Rhesus macaques in the Kathmandu valley. Campylobacter might be one of the important contributing pathogens in diarrheal outbreaks-both in humans and animals (monkeys) in Nepal. Due to close interactions of these animals with humans and other animals, One Health approach might be the most effective way to prevent and mitigate the threat posed by this pathogen.

One of the main objectives in wastewater treatment and sustainable energy production is to find photocatalysts that are favorably efficient and cost-effective. Transition-metal dichalcogenides (TMDs) are promising photocatalytic materials; out of all, MoS2 is extensively studied as a cocatalyst in the TMD library due to its exceptional photocatalytic activity for the degradation of organic dyes due to its distinctive morphology, adequate optical absorption, and rich active sites. However, sulfur ions on the active edges facilitate the catalytic activity of MoS2. On the basal planes, sulfur ions are catalytically inactive. Injecting metal atoms into the MoS2 lattice is a handy approach for triggering the surface of the basal planes and enriching catalytically active sites. Effective band gap engineering, sulfur edges, and improved optical absorption of Mn-doped MoS2 nanostructures are promising for improving their charge separation and photostimulated dye degradation activity. The percentage of dye degradation of MB under visible-light irradiations was found to be 89.87 and 100% for pristine and 20% Mn-doped MoS2 in 150 and 90 min, respectively. However, the degradation of MB dye was increased when the doping concentration in MoS2 increased from 5 to 20%. The kinetic study showed that the first-order kinetic model described the photodegradation mechanism well. After four cycles, the 20% Mn-doped MoS2 catalysts maintained comparable catalytic efficacy, indicating its excellent stability. The results demonstrated that the Mn-doped MoS2 nanostructures exhibit exceptional visible-light-driven photocatalytic activity and could perform well as a catalyst for industrial wastewater treatment., QC 20230711

Background: The Global Typhoid Genomics Consortium was established to bring together the typhoid research community to aggregate and analyse Salmonella enterica serovar Typhi (Typhi) genomic data to inform public health action. This analysis, which marks 22 years since the publication of the first Typhi genome, represents the largest Typhi genome sequence collection to date (n=13,000). Methods: This is a meta-analysis of global genotype and antimicrobial resistance (AMR) determinants extracted from previously sequenced genome data and analysed using consistent methods implemented in open analysis platforms GenoTyphi and Pathogenwatch. Results: Compared with previous global snapshots, the data highlight that genotype 4.3.1 (H58) has not spread beyond Asia and Eastern/Southern Africa; in other regions, distinct genotypes dominate and have independently evolved AMR. Data gaps remain in many parts of the world, and we show the potential of travel-associated sequences to provide informal 'sentinel' surveillance for such locations. The data indicate that ciprofloxacin non-susceptibility (>1 resistance determinant) is widespread across geographies and genotypes, with high-level ciprofloxacin resistance (≥3 determinants) reaching 20% prevalence in South Asia. Extensively drug-resistant (XDR) typhoid has become dominant in Pakistan (70% in 2020) but has not yet become established elsewhere. Ceftriaxone resistance has emerged in eight non-XDR genotypes, including a ciprofloxacin-resistant lineage (4.3.1.2.1) in India. Azithromycin resistance mutations were detected at low prevalence in South Asia, including in two common ciprofloxacin-resistant genotypes. Conclusions: The consortium's aim is to encourage continued data sharing and collaboration to monitor the emergence and global spread of AMR Typhi, and to inform decision-making around the introduction of typhoid conjugate vaccines (TCVs) and other prevention and control s

Fifty-six desi chickpea (Cicer arietinum L.) advance breeding lines were evaluated in order to explore the possibility of genetic divergence for yield and its contributing traits using Mahalanobis’s D2 Statistics and Principal Component Analysis. High estimates of heritability, genetic advance, GCV and PCV were recorded for seed yield per plant (92.2%, 12.4%, 37.1% and 38.7%), biological yield per plant (88.1%, 21.9%, 29.1% and 31.0%) and harvest index (87.3%, 25.0%, 22.7% and 24.3%). All the test genotypes were sort into five discrete clusters. Biological yield/plant (23.5%), days to maturity (17.3%), harvest index (14.6%), seed yield/plant (11.3%), total number of pods/plant (7.4%) and 100 seed weight (6.49%) were found to have highest percentage contributions to genetic diversity in the present research. The first six principal components (PC1 19.7%, PC 16.2%, PC3 11.2%, PC4 9.69%, PC5 7.2% and PC6 6.69%) could explain 70.68% of the total of the interaction variation and have Eigen value more than one. Genotypes JG 2016-1411, JG 2016-9605, JG 2017-46, ICCV 16105, ICCV 16109, ICCV 16112 and ICCV 16116 were present in more than one PCs hence contributed maximum towards yield and can be used in various breeding programmes for yield improvement.

Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on the lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a large set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed Timed Up & Go (TUG) tasks to contribute highest-yield predictive features, with only minor decrease in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.