bluuebunny/crossref_metadata_2025_split huggingface dataset:
Caste and Untouchability
Authors: Rodrigues Valerian 
Published: June 2024

AbstractThe caste system and the institution of untouchability are social constructs that inform the social ensemble of India. Caste is socially exclusionary, marks a person from birth to his death, locates him in a division of labour, and bears a name. The caste system upholds the principle of graded inequality, characterized by submission from below and aversion from above. Untouchability denotes a polluting social layer, excluded from the social fold, but encompassed at the same time within the caste order as its lowest rung. These institutions and their practices are considered by a vast majority of its members as religiously ordained. The caste system and untouchability have a palpable bearing on religions other than Hinduism and inform class relations as well. Untouchability is by far the worst instance of human subjection ever known, and the path of emancipation from it will invariably have a wider bearing on combatting human servitude everywhere.
The Idea of Democracy
Authors: Rodrigues Valerian 
Published: June 2024

AbstractIn Ambedkar’s view, democracy is a mode of rule that provides fair access and reach to its members to tap the diverse resources of sociality. He distinguished between radical democracy, that is, a self-governing community of equal and free citizens, and a constitutional democracy. There are no restraints on the scope of the former except those cognized by itself. It is akin to fraternity with a rich inner life of its own. Constitutional democracy ensures a mode of rule freely endorsed by a political community through universal franchise and periodic elections and exercised through institutions and processes appropriate for the purpose. It aims to further the prospects of radical democracy and not perpetuate itself. Constitutional democracy may take the form of parliamentary or presidential democracy. While Ambedkar distinguished between democracy and republicanism, he thought the latter can be a great resource to strengthen the former.
Ideas of State and Power
Authors: Rodrigues Valerian 
Published: June 2024

AbstractAmbedkar draws attention to the pervasive presence of power at all levels of a social ensemble. Power shapes our idea of the human, informs social institutions and practices, and directs modes of representation and constitutional and legal order. It is important to diagnose and contend against the complex and overladen working of power to make social relations be in sync with justice and democracy. However, the state being the nodal anchor and ultimate site of power, there cannot be a decisive alternative to the prevailing entwinement of power without decisively shifting state power in this direction. Democracy, representation, and constitutionalism are precisely geared to subserve this end. Given the way Ambedkar constructs the idea of state as the power of the political community as a whole, he squarely rejects such notions as a withering away of the state. He also proposes a distinct genealogy of the Indian state.
Effect of microfilaments produced by eggs of Dalbulus maidis (Hemiptera: Cicadellidae), against egg parasitoids
Authors: Moya-Raygoza Gustavo 
Published: June 2024

Abstract
Insect eggs, once oviposited, defend against abiotic and biotic factors using thickness, secretions, or other defenses. The eggs of most insect species are attacked by parasitoid wasps, which are often their most significant mortality factors. The present study is the first investigation of microfilaments as defense from parasitoids. Specifically, the effect of the corn leafhopper Dalbulus maidis DeLong (Hemiptera: Cicadellidae) egg microfilaments against parasitism by Anagrus virlai Triapitsyn (Hymenoptera: Mymaridae) and Paracentrobia subflava (Girault) (Hymenoptera: Trichogrammatidae) was evaluated. Field and laboratory experiments were conducted to evaluate several biological traits related to parasitism in D. maidis eggs with and without microfilaments. An initial field experiment found no difference in parasitism by A. virlai and P. subflava of eggs with vs. without microfilaments after 5 days of exposure of healthy D. maidis eggs to parasitism. A second field experiment then looked at exposure for 1 day, and this treatment found greater parasitism of healthy D. maidis eggs by each parasitoid species in eggs without microfilaments vs. in eggs with microfilaments. Laboratory experiments conducted separately for A. virlai and P. subflava parasitism after 1 day of exposure to healthy D. maidis eggs confirmed a higher percentage of parasitoid emergence in the eggs without microfilaments (both young and mature eggs) vs. mature eggs with microfilaments. These results suggest that eggs without microfilaments are more parasitized than eggs with microfilaments during a 1-day exposure, but that this difference disappears as egg microfilaments regrow over a 5-day period.
Statistical approaches for the integration of external controls in a cystic fibrosis clinical trial: a simulation and an application
Authors: Warden Mark N , Heltshe Sonya L , Simon Noah , Mooney Stephen J , Mayer-Hamblett Nicole , Magaret Amalia S 
Published: June 2024

Abstract
Development of new therapeutics for a rare disease such as cystic fibrosis is hindered by challenges in accruing enough patients for clinical trials. Use of external controls from well-matched historical trials can reduce prospective trial sizes, and this approach has supported regulatory approval of new interventions for other rare diseases. Here we consider 3 statistical methods that incorporate external controls into a hypothetical clinical trial of a new treatment to reduce pulmonary exacerbations in cystic fibrosis patients: (1) inverse probability weighting, (2) bayesian modeling with propensity-score–based power priors, and (3) hierarchical bayesian modeling with commensurate priors. We compare the methods via simulation study and in a real clinical-trial data setting. Simulations showed that bias in the treatment effect was less than 4% using any of the methods, with type I error (or in the bayesian cases, posterior probability of the null hypothesis) usually less than 5%. Inverse probability weighting was sensitive to similarity in prevalence of the covariates between historical and prospective trial populations. The commensurate prior method performed best with real clinical trial data. Using external controls to reduce trial size in future clinical trials holds promise and can advance the therapeutic pipeline for rare diseases.
This article is part of a Special Collection on Pharmacoepidemiology.
Ambient exposure to fine particulate matter with oxidative potential affects oxidative stress biomarkers in pregnancy
Authors: Meng Qi , Liu Jonathan , Shen Jiaqi , Del Rosario Irish , Janzen Carla , Devaskar Sherin U , Lakey Pascale S J , Shiraiwa Manabu , Weichenthal Scott , Zhu Yifang , Oroumiyeh Farzan , Banerjee Sudipto , Paulson Suzanne E , Jerrett Michael , Paul Kimberly C , Ritz Beate 
Published: June 2024

Abstract
Prenatal exposures to ambient particulate matter (PM2.5) from traffic may generate oxidative stress and thus contribute to adverse birth outcomes. We investigated whether PM2.5 constituents from brake and tire wear affect levels of oxidative stress biomarkers (malondialdehyde [MDA], 8-hydroxy-2′-deoxyguanosine [8-OHdG]) using urine samples collected up to 3 times during pregnancy in 156 women recruited from antenatal clinics at the University of California Los Angeles. Land use regression models with co-kriging were employed to estimate average residential outdoor concentrations of black carbon (BC), PM2.5 mass, PM2.5 metal components, and 3 PM2.5 oxidative potential metrics during the 4 weeks prior to urine sample collection. The 8-OHdG concentrations in mid-pregnancy increased by 24.8% (95% confidence interval [CI], 9.0-42.8) and 14.3% (95% CI, 0.4%-30.0%) per interquartile range (IQR) increase in PM2.5 mass and BC, respectively. The brake wear marker (barium) and the oxidative potential metrics were associated with increased MDA concentration in the first sample collected (10-17 gestational week), but 95% CIs included the null. Traffic-related air pollution contributed in early to mid-pregnancy to oxidative stress generation previously linked to adverse birth outcomes.
This article is part of a Special Collection on Environmental Epidemiology.
A Rare Infection Post Hyaluronic Acid Injection in the Trochanter: Case Report and Literature Review
Authors: Ahmed Mohamed Badie , Al-Mohannadi Fatima Saoud , Bensaoua Amina , Binfayed Mansour , Alsherawi Abeer 
Published: June 2024

Abstract

Filler injection has become a commonplace aesthetic procedure. Although the incidence of infection following filler injections is typically low, ranging between 0.04% and 0.2%, the potential consequences can be serious. In this manuscript, we present the case of a 29-year-old female patient who presented to our emergency department after receiving a hyaluronic acid filler injection in the trochanteric area at a private center. She developed signs and symptoms shortly after the procedure, including bilateral hip pain and a fever that persisted for 3 days. Despite initial antibiotic and pain medication treatment, the patient’s condition continued to worsen. Incision and drainage were performed, and pus culture revealed profuse Extended Spectrum Beta-Lactamase (ESBL)-producing Serratia marcescens. The patient experienced clinical improvement following the incision and drainage procedure and initiation of antibiotics sensitive to the pathogen. Although rare, S. marcescens infection following filler injections has been reported in 2 previous cases. Therefore, early recognition of infection signs and symptoms is crucial to mitigate the severity of consequences and improve outcomes. Aggressive surgical and medical interventions, such as incision and drainage, debridement, and appropriate antibiotic therapy, may be necessary to achieve a successful outcome.

Level of Evidence: 5
Role of a novel endoplasmic reticulum–resident glycoprotein Mtc6/Ehg2 in high-pressure growth: stability of tryptophan permease Tat2 in Saccharomyces cerevisiae
Authors: Kato Yusuke , Mioka Tetsuo , Uemura Satoshi , Abe Fumiyoshi 
Published: June 2024

ABSTRACT
Deep-sea organisms are subjected to extreme conditions; therefore, understanding their adaptive strategies is crucial. We utilize Saccharomyces cerevisiae as a model to investigate pressure-dependent protein regulation and piezo-adaptation. Using yeast deletion library analysis, we identified 6 poorly characterized genes that are crucial for high-pressure growth, forming novel functional modules associated with cell growth. In this study, we aimed to unravel the molecular mechanisms of high-pressure adaptation in S. cerevisiae, focusing on the role of MTC6. MTC6, the gene encoding the novel glycoprotein Mtc6/Ehg2, was found to stabilize tryptophan permease Tat2, ensuring efficient tryptophan uptake and growth under high pressure at 25 MPa. The loss of MTC6 led to promoted vacuolar degradation of Tat2, depending on the Rsp5-Bul1 ubiquitin ligase complex. These findings enhance our understanding of deep-sea adaptations and stress biology, with broad implications for biotechnology, environmental microbiology, and evolutionary insights across species.
Image-based molecular representation learning for drug development: a survey
Authors: Li Yue , Liu Bingyan , Deng Jinyan , Guo Yi , Du Hongbo 
Published: June 2024

AbstractArtificial intelligence (AI) powered drug development has received remarkable attention in recent years. It addresses the limitations of traditional experimental methods that are costly and time-consuming. While there have been many surveys attempting to summarize related research, they only focus on general AI or specific aspects such as natural language processing and graph neural network. Considering the rapid advance on computer vision, using the molecular image to enable AI appears to be a more intuitive and effective approach since each chemical substance has a unique visual representation. In this paper, we provide the first survey on image-based molecular representation for drug development. The survey proposes a taxonomy based on the learning paradigms in computer vision and reviews a large number of corresponding papers, highlighting the contributions of molecular visual representation in drug development. Besides, we discuss the applications, limitations and future directions in the field. We hope this survey could offer valuable insight into the use of image-based molecular representation learning in the context of drug development.
Comparing full variation profile analysis with the conventional consensus method in SARS-CoV-2 phylogeny
Authors: Fiam Regina Nóra , István Csabai , Norbert Solymosi 
Published: June 2024

Abstract
This study proposes a novel approach to studying severe acute respiratory syndrome coronavirus 2 virus mutations through sequencing data comparison. Traditional consensus-based methods, which focus on the most common nucleotide at each position, might overlook or obscure the presence of low-frequency variants. Our method, in contrast, retains all sequenced nucleotides at each position, forming a genomic matrix. Utilizing simulated short reads from genomes with specified mutations, we contrasted our genomic matrix approach with the consensus sequence method. Our matrix methodology, across multiple simulated datasets, accurately reflected the known mutations with an average accuracy improvement of 20% over the consensus method. In real-world tests using data from GISAID and NCBI-SRA, our approach demonstrated an increase in reliability by reducing the error margin by approximately 15%. The genomic matrix approach offers a more accurate representation of the viral genomic diversity, thereby providing superior insights into virus evolution and epidemiology.
TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation
Authors: Chen Nanjun , Yu Jixiang , Zhe Liu , Wang Fuzhou , Li Xiangtao , Wong Ka-Chun 
Published: June 2024

Abstract
Bioactive peptide therapeutics has been a long-standing research topic. Notably, the antimicrobial peptides (AMPs) have been extensively studied for its therapeutic potential. Meanwhile, the demand for annotating other therapeutic peptides, such as antiviral peptides (AVPs) and anticancer peptides (ACPs), also witnessed an increase in recent years. However, we conceive that the structure of peptide chains and the intrinsic information between the amino acids is not fully investigated among the existing protocols. Therefore, we develop a new graph deep learning model, namely TP-LMMSG, which offers lightweight and easy-to-deploy advantages while improving the annotation performance in a generalizable manner. The results indicate that our model can accurately predict the properties of different peptides. The model surpasses the other state-of-the-art models on AMP, AVP and ACP prediction across multiple experimental validated datasets. Moreover, TP-LMMSG also addresses the challenges of time-consuming pre-processing in graph neural network frameworks. With its flexibility in integrating heterogeneous peptide features, our model can provide substantial impacts on the screening and discovery of therapeutic peptides. The source code is available at https://github.com/NanjunChen37/TP_LMMSG.
Assessing citation integrity in biomedical publications: corpus annotation and NLP models
Authors: Sarol Maria Janina , Ming Shufan , Radhakrishna Shruthan , Schneider Jodi , Kilicoglu Halil 
Published: June 2024

Abstract

Motivation
Citations have a fundamental role in scholarly communication and assessment. Citation accuracy and transparency is crucial for the integrity of scientific evidence. In this work, we focus on quotation errors, errors in citation content that can distort the scientific evidence and that are hard to detect for humans. We construct a corpus and propose natural language processing (NLP) methods to identify such errors in biomedical publications.

Results
We manually annotated 100 highly-cited biomedical publications (reference articles) and citations to them. The annotation involved labeling citation context in the citing article, relevant evidence sentences in the reference article, and the accuracy of the citation. A total of 3063 citation instances were annotated (39.18% with accuracy errors). For NLP, we combined a sentence retriever with a fine-tuned claim verification model to label citations as ACCURATE, NOT_ACCURATE, or IRRELEVANT. We also explored few-shot in-context learning with generative large language models. The best performing model—which uses citation sentences as citation context, the BM25 model with MonoT5 reranker for retrieving top-20 sentences, and a fine-tuned MultiVerS model for accuracy label classification—yielded 0.59 micro-F1 and 0.52 macro-F1 score. GPT-4 in-context learning performed better in identifying accurate citations, but it lagged for erroneous citations (0.65 micro-F1, 0.45 macro-F1). Citation quotation errors are often subtle, and it is currently challenging for NLP models to identify erroneous citations. With further improvements, the models could serve to improve citation quality and accuracy.

Availability and implementation
We make the corpus and the best-performing NLP model publicly available at https://github.com/ScienceNLP-Lab/Citation-Integrity/.
PredGCN: a Pruning-enabled Gene-Cell Net for automatic cell annotation of single cell transcriptome data
Authors: Qi Qi , Wang Yunhe , Huang Yujian , Fan Yi , Li Xiangtao 
Published: June 2024

Abstract

Motivation
The annotation of cell types from single-cell transcriptomics is essential for understanding the biological identity and functionality of cellular populations. Although manual annotation remains the gold standard, the advent of automatic pipelines has become crucial for scalable, unbiased, and cost-effective annotations. Nonetheless, the effectiveness of these automatic methods, particularly those employing deep learning, significantly depends on the architecture of the classifier and the quality and diversity of the training datasets.

Results
To address these limitations, we present a Pruning-enabled Gene-Cell Net (PredGCN) incorporating a Coupled Gene-Cell Net (CGCN) to enable representation learning and information storage. PredGCN integrates a Gene Splicing Net (GSN) and a Cell Stratification Net (CSN), employing a pruning operation (PrO) to dynamically tackle the complexity of heterogeneous cell identification. Among them, GSN leverages multiple statistical and hypothesis-driven feature extraction methods to selectively assemble genes with specificity for scRNA-seq data while CSN unifies elements based on diverse region demarcation principles, exploiting the representations from GSN and precise identification from different regional homogeneity perspectives. Furthermore, we develop a multi-objective Pareto pruning operation (Pareto PrO) to expand the dynamic capabilities of CGCN, optimizing the sub-network structure for accurate cell type annotation. Multiple comparison experiments on real scRNA-seq datasets from various species have demonstrated that PredGCN surpasses existing state-of-the-art methods, including its scalability to cross-species datasets. Moreover, PredGCN can uncover unknown cell types and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into cell type identification and characterizing scRNA-seq data from different perspectives.

Availability and implementation
The source code is available at https://github.com/IrisQi7/PredGCN and test data is available at https://figshare.com/articles/dataset/PredGCN/25251163.
The downregulation of NSUN5 may contribute to preeclampsia
Authors: Zhang Tianying , Li Hua , Jiang Enhui , Liu Lisheng , Zhang Cong 
Published: June 2024

Abstract
Preeclampsia (PE) is a complication of pregnancy characterized by the new onset of hypertension after 20 weeks of gestation. The incidence of PE is steadily rising, posing a significant threat to the lives of both the pregnant woman and the fetus. Most studies on PE pathogenesis currently focus on the placenta, but maternal decidualization forms the foundation for placental growth and development. Recent studies have shown that impaired decidualization is also a cause of PE. Decidualization is a process where endometrial stromal cells gradually transform into secretory decidual cells during early pregnancy. While NSUN5 encodes a member of a conserved family of proteins, its role in pregnancy remains unknown. In this study, we conducted experiments and observed a significant downregulation of NSUN5 expression in severe PE decidual tissues compared to those of normal pregnant women. When inducing decidualization in vitro, we found an increase in NSUN5 expression. However, when we used siRNA to knockdown NSUN5 expression, the process of decidualization was prevented. Moreover, we observed a decrease in ATP content during both cell decidualization and after knockdown of NSUN5. Finally, through immunoprecipitation combined with mass spectrometry, we discovered that the protein ATP5B interacts with NSUN5. Furthermore, after knocking down ATP5B using siRNA, we observed impaired decidualization. Moreover, transfection with siRNA to suppress NSUN5 resulted in a decrease in ATP5B expression. These significant findings provide strong evidence that NSUN5 plays a crucial role in decidualization and is closely associated with the development of PE through its interaction with ATP5B.
‘Where Have I Found Myself’? Exploring How Black Social Work Students Navigate Identities in School-Based Practice Learning
Authors: Rafter Jenny , Sangha Jas , Mano Irine 
Published: June 2024

Abstract
In 2020, the social work in schools (SWIS) project was implemented with an initial cohort of fifty-four social work students from an Eastern English university. With the aim of exploring the student experience, we undertook an initial focus group. One unanticipated aspect of these discussions was how black students reflected on their experience of being placed in predominantly white schools, which merited a follow-up focus group to explore this in more depth. Intersectionality underpinned this study as it offered an analytical approach to understand the interconnected identities of black students within the context of schools. Constructivist grounded theory was used to analyse the data, and three categories were identified in understanding experiences of participants: Race and Experiences of Being Placed in Schools; Intersections of Race, Racism, and Acceptance; and Experiences of Learning in Schools. Concepts of belonging, acceptance and racial identity underpin these categories and help us understand black students’ experiences of undertaking SWIS placements. Black students developed their skills, knowledge, self-concept and confidence in schools. Conclusions are drawn highlighting the centrality of harnessing black students’ cultural wealth and knowledge to facilitate positive relationships for learning.