bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Exploratory study for characterIzing and predicting prostate abNormalities uSing MRI-based radiomics and arTificial intEllIgeNce (EINSTEIN)
Authors: Bouchareb Yassine , Delanerolle Gayathri , Al-Bulushi Yarab , Al-Khudhuri Ali , Sirsangandla Srinivasa Rao , Al Badaai Ghalib , Cavalini Heitor , Phiri Peter , Shetty Ashish , Shi Jian Qing 
Published: November 2023

AbstractIntroductionProstate cancer (PCa) is the fourth most prevalent cancer globally, and the most common among men. Most PCa patients in Oman are presented during the advanced stages of the disease with widespread metastatic disease reducing their overall rates of survival. Characterisation of the Omanis PCa population could be beneficial to develop a clinical profile demonstrating specific characteristics to better classify and derive radiomics signatures. These could help in developing artificial intelligence methods to assist with earlier and quicker diagnosis of possible prostate lesions.MethodsA retrospective, cross-sectional study has been designed to determine the pathological and radiological characteristics based on multi-sequence 3-dimensional Magnetic Resonance Imaging (MRI). The MRI records are maintained within the existing electronic healthcare records of the Sultan Qaboos University Hospital’s Department of Radiology and Molecular Imaging. Data will be extracted based on a confirmed diagnosis reported between the 1stof January 2010 and October 2023. All patients included within the study will be aged between 18-99 years. A study specific data extraction template has been devised to gather demographic details, clinical parameters and radiological findings based on existing imaging reports within the HIS and PACS systems.Ethics approvalResearch Ethics approval reference for this study is (MREC #3176 REF. NO. SQU-EC/ 283\2023)ConclusionThe data analysis will be conducted using statistical software to conduct a Joinpoint regression analysis and linear regression modelling. We will also conduct a descriptive analysis.
Multi-trait genome-wide association study in 34,394 Chinese women reveals the genetic architecture of plasma metabolites during pregnancy
Authors: Liu Siyang , Yao Jilong , Lin Liang , Lan Xianmei , Wu Linlin , Kong Nannan , Deng Yuqing , Li Yan , Xie Jiansheng , Zhu Huanhuan , Wu Xiaoxia , Li Zilong , Xiong Likuan , Wang Yuan , Ren Jinghui , Qiu Xuemei , Zhao Weihua , Gao Ya , Chen Yuanqing , Su Fengxia , Zhou Yun , Rao Weiqiao , Zhang Jing , Hou Guixue , Huang Liping , Li Linxuan , Liu Xinhong , Nie Chao , Luo Liqiong , Liu Zengyou , Chen Fang , Lin Shengmou , Zhao Lijian , Fu Qingmei , Jiang Dan , Yin Ye , Xu Xun , Wang Jian , Yang Huanming , Wang Rong , Niu Jianmin , Wei Fengxiang , Jin Xin , Liu Siqi 
Published: November 2023

SummaryMetabolites are important indicators of individual health and can serve as crucial targets for therapy. However, the genetic basis of many metabolites remains largely unexplored, especially among underrepresented East Asians and during critical periods such as pregnancy. In this study, we utilized genetic information obtained from non-invasive prenatal testing to conduct a genome-wide association analysis of 84 metabolites, including 37 amino acids, 10 vitamins, 24 metal elements, and 13 hormones, among 34,394 Chinese pregnant women. Of these metabolites, 52 and 11 had not previously been studied in East Asians or globally. We identified 30 novel metabolite-gene associations. We also observed substantial differences in the genetic effects on hormones between pregnancy and non-pregnancy periods, suggesting effect modifications in response to physiological changes. Furthermore, we uncovered pervasive pleiotropic effects for 50.94% of the genetic associations among metabolites, as well as between six metabolites and eight pregnancy biomarkers. Using mendelian randomization, we identified potential causal relationships between plasma folate and ischemic stroke, vitamin D3 and Graves’ disease, copper and open-angle glaucoma, and androstenedione and rheumatoid arthritis. These discoveries provide invaluable genetic insights into human metabolism, laying the foundation for future mechanistic studies and the development of new therapeutic targets, particularly for underrepresented East Asians.
Changes in the physical behaviour of older adults during the 13 weeks GOTO intervention explain a boost in immuno-metabolic health
Authors: Paraschiakos Stylianos , Bogaards Fatih A. , Knobbe Arno , Slagboom P. Eline , Beekman Marian 
Published: November 2023

AbstractBackgroundThe aging population faces numerous health challenges, with sedentary behavior and decreased physical activity being paramount. We explore the physical behaviour of older adults in the GOTO combined lifestyle intervention study and its related immuno-metabolic health effects.MethodsThe research utilized accelerometers and machine learning to assess physical activity behaviours during a 13-week program of increased physical activity and decreased calorie intake. Subsequently, the association of variation in physical behaviour with immuno-metabolic health parameters is investigated cross-sectionally at baseline and longitudinally using sex-stratified linear regression and linear mixed regression respectively.ResultsParticipants exhibited physical behaviors similar to their age-matched peers from the UK-Biobank. Interestingly, gender-based differences were evident, with men and women showing distinct daily physical behavioural patterns. At baseline, a positive correlation was found between higher physical behavior and a healthier immune-metabolic profile, particularly in men. The longitudinal changes depict an overall boost in activity levels, predominantly among women. While increasing general activity and engaging in intense exercises proved advantageous for physical health, the immune-metabolic health benefits were more pronounced in men.ConclusionThe short-term GOTO intervention underscores the significance of regular physical activity in promoting healthy aging even in middle to older age. Gender differences in behavior and health benefits deserve much more attention though. Our results advocate the broader implementation of such programs and emphasize the utility of technology, like accelerometers and machine learning, in both monitoring and promoting active lifestyles among older adults.
The Effects of Different Fatigue Types on Action Anticipation and Physical Performance in High-level Volleyball Players
Authors: Yu Ying , Zhang Liqing , Cheng Ming-Yang , Liang Zhijun , Zhang Ming , Qi Fengxue 
Published: November 2023

AbstractThis study examined the effects of different fatigue types on action anticipation and physical performance in high-level volleyball players. Twenty-four participants underwent four counterbalanced conditions: 60-min of cycling at 60% peak power output, 60-min Stroop task, 60-min cycling at 60% peak power output with Stroop task and 60-min neutral documentary to induce physical fatigue (PF), mental fatigue (MF), dual fatigue (DF) and control group (CG), respectively. Action anticipation (anticipation test and visual search test) and physical performance (countermovement jump, T-test, and spike test) were conducted at baseline, immediately after (Post1), and 40-min after fatigue intervention (Post2). DF and PF led to significantly lower jump height, T-test completion time, and spiking speed at Post1 and Post2 compared with CG and MF. Compared with PF, DF led to a significant decline in jumping, agility, and spiking performance at Post1 and decreased jumping performance at Post2. MF significantly decreased reaction time in the anticipation test at Post1 compared with CG. Reaction times in the visual search test were significantly lower with DF and MF at Post2 compared with CG. PF led to a greater decline in physical performance and this was exacerbated in DF, yet anticipation performance remained lesser impact.
Large-scale proteogenomics characterization of theMycobacterium tuberculosishidden microproteome
Authors: de Souza Eduardo V. , Dalberto Pedro F. , Miranda Adriana C. , Saghatelian Alan , Pinto Antonio Michel , Basso Luiz A. , Machado Pablo , Bizarro Cristiano V. 
Published: November 2023

ABSTRACTTuberculosis remains a burden to this day, due to the rise of multi and extensively drug-resistant bacterial strains. The genome ofMycobacterium tuberculosis (Mtb)underwent an annotation process that excluded small Open Reading Frames (smORFs), which encode a class of peptides and small proteins collectively known as microproteins. As a result, there is an overlooked part of its proteome that is a rich source of potentially essential, druggable molecular targets. Here, we employed our recently developed proteogenomics pipeline to identify novel microproteins encoded by smORFs in the genome ofMtbusings hundreds of mass spectrometry experiments in a large-scale approach. We found protein evidence for hundreds of novel microproteins and identified smORFs potentially involved in bacterial growth and virulence. Moreover, many smORFs are co-expressed or share operons with a myriad of biologically relevant genes and may play a role in antibiotic response. Together, our data presents a resource of unknown genes that play a role in the success ofMtbas a widespread pathogen.
Development and Outcomes of an International Certificate Program in Science Policy and Advocacy for STEM PhD Students and Postdocs
Authors: Bankston Adriana , Ralston Amy , Ly Joanne , Singh Harinder 
Published: November 2023

AbstractScientists must play a significant role in enacting societal change by educating and advising policymakers on relevant policy topics. To fill the identified gap in science policy and advocacy training, during the pandemic years, we piloted and offered the online Science Policy & Advocacy for STEM Scientists Certificate Program starting in 2020. Over three cohorts, the program was focused on practical skills and concepts, networking, and career development opportunities, and providing pathways for PhD students and postdoctoral researchers to learn about and in many cases fully transition into science policy and advocacy roles and careers. Program participants were exposed to many important aspects of what it means to be involved in science policy and advocacy, and many chose to enter the field through subsequent opportunities facilitated by the program training. We sought to reduce a number of barriers to program participation, and additionally provided resources for others to develop similar programs at their university. We believe this training model is innovative and can be recapitulated, and sincerely hope to see more universities create similar programs in the US and internationally in order to serve trainees interested in science policy and advocacy and facilitate their building impactful careers in the field for years to come.
A novel vitamin C analog acts as a potent bio-enhancer to augment the activities of anti-tuberculosis drugs againstMycobacterium tuberculosis
Authors: Bokolia Naveen Prakash , Bag Kingsuk , Sarkar Biplab , Jhawar Ruchi , Chatterji Dipankar , Jayaraman Narayanaswamy , Ghosh Anirban 
Published: November 2023

AbstractMycobacterium tuberculosisis a deadly pathogen that claims millions of lives every year. Current research focuses on finding new anti-tuberculosis drugs that are safe and effective, with lesser side effects and toxicity. One important approach is to identify bio-enhancers that can improve the effectiveness of anti-tuberculosis drugs, resulting in reduced doses and shortened treatment times. We investigated the use of vitamin C-derived isotetrones as bio- enhancer agents. In this context, our results revealed that the lead compound C11 inhibits growth, improves MIC/MBC, and enhances the killing ofM. tuberculosisH37Rv strain when used in combination with first-line and injectable anti-TB drugs in a dose-dependent manner. The combination of C11 and rifampicin also reduced the generation of spontaneous mutants against rifampicin and reached a mutation prevention concentration (MPC) with moderate rifampicin concentrations. The identified compounds were proven to be effective against the MDR strain ofM. tuberculosisand non-cytotoxic in HepG2 cells. We also found that C11 induced the generation of reactive oxygen species (ROS) inside macrophages and within bacteria, resulting in better efficacy.
Making predictions using poorly identified mathematical models
Authors: Simpson Matthew J. , Maclaren Oliver J. 
Published: November 2023

AbstractMany commonly used mathematical models in the field of mathematical biology involve challenges of parameter non-identifiability. Practical non-identifiability, where the quality and quantity of data does not provide sufficiently precise parameter estimates is often encountered, even with relatively simple models. In particular, the situation where some parameters are identifiable and others are not is often encountered. In this work we apply a recent likelihood-based workflow, calledProfile-Wise Analysis(PWA), to non-identifiable models for the first time. The PWA workflow addresses identifiability, parameter estimation, and prediction in a unified framework that is simple to implement and interpret. Previous implementations of the workflow have dealt with idealised identifiable problems only. In this study we illustrate how the PWA workflow can be applied to both structurally non-identifiable and practically non-identifiable models in the context of simple population growth models. Dealing with simple mathematical models allows us to present the PWA workflow in a didactic, self–contained document that can be studied together with relatively straightforward Julia code provided onGitHub. Working with simple mathematical models allows the PWA workflow prediction intervals to be compared withgold standardfull likelihood prediction intervals. Together, our examples illustrate how the PWA workflow provides us with a systematic way of dealing with non-identifiability, especially compared to other approaches, such as seeking ad hoc parameter combinations, or simply setting parameter values to some arbitrary default value. Importantly, we show that the PWA workflow provides insight into the commonly–encountered situation where some parameters are identifiable and others are not, allowing us to explore how uncertainty in some parameters, and combinations of some parameters, regardless of their identifiability status, influences model predictions in a way that is insightful and interpretable.
A Multi-Granularity Approach to Similarity Search in Multiplexed Immunofluorescence Images
Authors: Yu Jennifer , Wu Zhenqin , Mayer Aaron T. , Trevino Alexandro , Zou James 
Published: November 2023

AbstractDue to the rapid increase and importance of multiplexed immunofluorescence (mIF) imaging data in spatial biology, there is a pressing need to develop efficient image-to-image search pipelines for both diagnostic and research purposes. While several image search methods have been introduced for conventional images and digital pathology, mIF images present three main challenges: (1) high dimension-ality, (2) domain-specificity, and (3) complex additional molecular information. To address this gap, we introduce the MIISS framework, aMulti-granularity mIFImageSimilaritySearch pipeline that employs self-supervised learning models to extract features from mIF image patches and an entropy-based aggregation method to enable similarity searches at higher, multi-granular levels. We then benchmarked various feature generation approaches to handle high dimensional images and tested them on various foundation models. We conducted evaluations using datasets from different tissues on both patch- and patient-level, which demonstrate the frame-work’s effectiveness and generalizability. Notably, we found that domain-specific models consistently outperformed other models, further showing their robustness and generalizability across different datasets. The MIISS framework offers an effective solution for navigating the growing landscape of mIF images, providing tangible clinical benefits and opening new avenues for pathology research.
Salt-dependent self-association of trinucleotide repeat RNA sequences
Authors: Maity Hiranmay , Nguyen Hung T. , Hori Naoto , Thirumalai D. 
Published: November 2023

AbstractLow complexity repeat RNA sequences self-associate by homotypic interactions to form condensates. Using simulations of a coarse grained Single-Interaction Site model for (CAG)n(n= 30 and 31), we show that the salt-dependent free energy gap, ∆GS, between the ground (perfect hairpin) and the excited state (slipped hairpin (SH) with one CAG overhang) of monomer (neven) is the primary factor that determines the rates and yield of self-assembly. For oddn, the SH ground state free energy (GS) is used to predict self-association kinetics. As the monovalent salt concentration,CS, increases ∆GSandGSincreases, which in turn decreases the self-association rates. In contrast, ∆GSfor scrambled sequences, with the same length and sequence composition as (CAG)31but with higher complexity, is larger which greatly suppresses the propensities to aggregate. Although demonstrated explicitly for (CAG)30and (CAG)31polymers, the finding that there is an inverse correlation betweenCS-dependent ∆GSorGSand RNA aggregation is general. Our predictions are amenable to experimental tests.
scGeneRythm: Using Neural Networks and Fourier Transformation to Cluster Genes by Time-Frequency Patterns in Single-Cell Data
Authors: Jia Yiming , Wu Hao , Ding Jun 
Published: November 2023

AbstractClustering genes in single-cell RNA sequencing plays a pivotal role in unraveling a plethora of biological processes, from cell differentiation to disease progression and metabolic pathways. Traditional time-domain methods are instrumental in certain analyses, yet they may overlook intricate relationships. For instance, genes that appear distinct in the time domain might exhibit striking similarities in the frequency domain. Recognizing this, we present scGeneRhythm, an innovative deep learning technique that employs Fourier transformation. This approach captures the rich tapestry of gene expression from both the time and frequency domains. When evaluated across a spectrum of single-cell datasets, scGeneRhythm consistently outperforms conventional approaches. The gene clusters it identifies not only demonstrate heightened statistical significance in enriched pathways but also bring to light underlying gene relationships previously obscured. Through integrating frequency-domain data, scGeneRhythm not only refines gene grouping but also uncovers pivotal biological insights, such as nuanced gene rhythmicity. By deploying scGeneRhythm, we foster a richer, multi-dimensional understanding of gene expression dynamics, enriching the potential avenues of cellular and molecular biology research.
Alternative splicing variants of Arabidopsis G protein β subunit AGB1 function in plant development and endoplasmic reticulum stress response
Authors: Cho Yueh 
Published: November 2023

AbstractThe Arabidopsis heterotrimeric G protein β subunit, GTP BINDING PROTEIN BETA 1 (AGB1), has multiple functions in plant development and response to various environmental stimuli including endoplasmic reticulum (ER) stress. However, how the single gene produces the pleiotropic effect remains elusive. Here, we show that AGB1 has 4 alternative splice isoforms with isoform-specific features. AGB1.2 failed to rescue theagb1-3mutant defects and thus was considered a non-functional isoform. Although AGB1.1 and AGB1.4 were both localized at the plasma membrane and the ER, AGB1.1 fully rescued the defects ofagb1-3,and AGB1.4 only partially rescued the defects even though its transcript level was higher than that of AGB1.1. Intriguingly, AGB1.3 was localized at the nucleus and further enhanced the leaf-shape phenotype ofagb1-3. The protein structure of AGB1.3 is unique because of the termination of translation in the 7th-WD40 motif by alternative splicing, which produced an incomplete propeller structure. AGB1.1 and AGB1.4 but not AGB1.3 interacted with the Gγ subunits, AGG1, AGG2, and AGG3, possibly because of the lack of the 7th-WD40 motif in AGB1.3. AGB1 may produce its multifaceted functions in plant development and ER stress tolerance via its alternative splice isoforms with distinct structural features and subcellular localization.
The effects of community treatment orders (CTOs) on readmission to hospital using electronic health records
Authors: Yang Justin C , Thygesen Johan H , Werbeloff Nomi , Kelsey David , Merlande Dominique , Hayes Joseph , Osborn David PJ 
Published: November 2023

AbstractBackgroundCommunity treatment orders (CTOs) are used to manage community-based care for individuals with severe mental health conditions who have been discharged from inpatient care. Evidence examining whether CTOs are successful at reducing rehospitalisation has been mixed.MethodsUsing deidentified electronic health records from 2009-21, we compared patients who had ever been placed on a CTO (n=836) and two other groups of patients who had never been placed on CTO: patients admitted under Section 3 of the Mental Health Act (n=1,182) and outpatients with severe mental health issues (n=7,651). We examined the association between CTOs and rehospitalisation using within-individual stratified multivariable Cox regression.ResultsPatients on CTO were more likely to be male, single, of Black or Mixed ethnicity, and have a severe mental illness diagnosis than patients in the comparison groups. Time spent on CTO was associated with a lower risk of hospitalisation compared to time spent off CTO for the same individual (HR 0.60; 95% CI 0.56-0.64). This decreased risk of hospitalisation remained when we restricted analysis to individuals with a single CTO episode (HR 0.05; 95% CI 0.02-0.11) and when we restricted follow-up time to a patient’s first CTO episode (HR 0.20; 95% CI 0.17-0.25). However, there was no difference in re-hospitalisations when we observed patients starting from the first CTO (HR 1.07; 95% CI 1.00-1.16).ConclusionsWe found that patients on CTO were at lower risk of hospitalisation, though this pattern was not observed when we excluded time prior to the first CTO. Further research should consider whether CTOs provide genuine clinical benefit.
Impact of interest subvention scheme (ISS) on the behaviour of farm households: a case of Andhra Pradesh and Madhya Pradesh
Authors: Sehgal Shubham Kumar 
Published: November 2023

PurposeCredit is an essential element in the production process in agriculture. There are two sources from which farm households can access credit: institutional sources and non-institutional or informal sources of credit. The informal sources of credit, such as moneylenders, charge exorbitant rates of interest, which further puts a financial burden on the farmers. Hence, to increase the flow of credit from institutional sources, a policy known as the interest subvention scheme (ISS) was introduced in the year 2006. This paper aims to find the effect of the ISS on the behaviour of farm households.Design/methodology/approachThe author has used difference-in-difference analysis for estimation. In the analysis, the author has taken Madhya Pradesh as the treatment state and Andhra Pradesh as the controlled state. The author has used the Village Dynamics in South Asia (VDSA) dataset of ICRISAT for analysis. The author has used data from 2009 to 2014 for the two states.FindingsThe author has found that the difference between the average interest rate of Andhra Pradesh and Madhya Pradesh is significant for both pre-treatment and post-treatment periods and this gap has increased after the intervention period. The results suggest that the share of informal sector borrowings has reduced in the treatment group (Madhya Pradesh) as compared to the control group (Andhra Pradesh) in the post-treatment period.Originality/valueThis paper is particularly important because of the dearth of literature on the impact of this scheme in India and may shed light on the much-needed policy implications of this particular policy.