bluuebunny/crossref_metadata_2025_split huggingface dataset:
The association between lifestyle factors and the composition of the vaginal microbiota: a review
Authors: Morsli Madjid , Gimenez Elise , Magnan Chloé , Salipante Florian , Huberlant Stéphanie , Letouzey Vincent , Lavigne Jean-Philippe 
Published: August 2024

Abstract
Purpose
The vaginal microbiota offers valuable insights into women’s sexual health and the risk of developing sexually transmitted infections (STIs) and bacterial vaginosis. Despite the public health implications of changes in the vaginal environment, existing data on this topic remain sparse.

Methods
Following the PRISMA statement guidelines, we consulted five bibliographic databases, focusing on five main daily habits and behaviors. We included only studies published up to October 2023, investigating the influence of personal hygiene, sexual behaviors, hormonal contraception, smoking, alcohol consumption, and psychosocial stress on the vaginal microbiota using next-generation sequencing.

Results
Based on our inclusion criteria, we incorporated 37 studies into this review. Hormonal contraception and personal hygiene were found to promote eubiosis of the vaginal microbiota. In contrast, sexual behaviors, smoking, alcohol consumption, and psychosocial stress were associated with an increased susceptibility to bacterial vaginosis, STIs, and severe pelvic inflammatory diseases due to a modified vaginal microbiota. Black ethnicity emerged as a confounding factor, with this population showing unstable vaginal microbiota. Oral contraception and a stable male sexual partner were found to favor Lactobacillus colonization, acting as a protective factor. Conversely, non-hormonal contraception and unprotected or non-penile/vaginal sexual activity increased the incidence of vaginal inflammation and bacterial vaginosis by disturbing the vaginal microbiota and reducing Lactobacillus abundance.

Conclusion
Daily habits and lifestyle can influence the composition of the vaginal microbiota, thereby affecting vaginal health. Disturbances in the vaginal microbiota could be associated factors for STIs and vaginosis. Therefore, prioritizing more appropriate management of the vaginal microbiota is crucial.
Predicting Knee Joint Contact Force Peaks During Gait Using a Video Camera or Wearable Sensors
Authors: Lavikainen Jere , Stenroth Lauri , Vartiainen Paavo , Alkjær Tine , Karjalainen Pasi A. , Henriksen Marius , Korhonen Rami K. , Liukkonen Mimmi , Mononen Mika E. 
Published: August 2024

Abstract
Purpose
Estimating loading of the knee joint may be helpful in managing degenerative joint diseases. Contemporary methods to estimate loading involve calculating knee joint contact forces using musculoskeletal modeling and simulation from motion capture (MOCAP) data, which must be collected in a specialized environment and analyzed by a trained expert. To make the estimation of knee joint loading more accessible, simple input predictors should be used for predicting knee joint loading using artificial neural networks.

Methods
We trained feedforward artificial neural networks (ANNs) to predict knee joint loading peaks from the mass, height, age, sex, walking speed, and knee flexion angle (KFA) of subjects using their existing MOCAP data. We also collected an independent MOCAP dataset while recording walking with a video camera (VC) and inertial measurement units (IMUs). We quantified the prediction accuracy of the ANNs using walking speed and KFA estimates from (1) MOCAP data, (2) VC data, and (3) IMU data separately (i.e., we quantified three sets of prediction accuracy metrics).

Results
Using portable modalities, we achieved prediction accuracies between 0.13 and 0.37 root mean square error normalized to the mean of the musculoskeletal analysis-based reference values. The correlation between the predicted and reference loading peaks varied between 0.65 and 0.91. This was comparable to the prediction accuracies obtained when obtaining predictors from motion capture data.

Discussion
The prediction results show that both VCs and IMUs can be used to estimate predictors that can be used in estimating knee joint loading outside the motion laboratory. Future studies should investigate the usability of the methods in an out-of-laboratory setting.
Mitochondrial control of hypoxia-induced pathological retinal angiogenesis
Authors: Yagi Hitomi , Boeck Myriam , Nian Shen , Neilsen Katherine , Wang Chaomei , Lee Jeff , Zeng Yan , Grumbine Matthew , Sweet Ian R. , Kasai Taku , Negishi Kazuno , Singh Sasha A. , Aikawa Masanori , Hellström Ann , Smith Lois E. H. , Fu Zhongjie 
Published: August 2024

Abstract
Objective
Pathological retinal neovascularization is vision-threatening. In mouse oxygen-induced retinopathy (OIR) we sought to define mitochondrial respiration changes longitudinally during hyperoxia-induced vessel loss and hypoxia-induced neovascularization, and to test interventions addressing those changes to prevent neovascularization.

Methods
OIR was induced in C57BL/6J mice and retinal vasculature was examined at maximum neovessel formation. We assessed total proteome changes and the ratio of mitochondrial to nuclear DNA copy numbers (mtDNA/nDNA) of OIR vs. control retinas, and mitochondrial oxygen consumption rates (OCR) in ex vivo OIR vs. control retinas (BaroFuse). Pyruvate vs. vehicle control was supplemented to OIR mice either prior to or during neovessel formation.

Results
In OIR vs. control retinas, global proteomics showed decreased retinal mitochondrial respiration at peak neovascularization. OCR and mtDNA/nDNA were also decreased at peak neovascularization suggesting impaired mitochondrial respiration. In vivo pyruvate administration during but not prior to neovessel formation (in line with mitochondrial activity time course) suppressed NV.

Conclusions
Mitochondrial energetics were suppressed during retinal NV in OIR. Appropriately timed supplementation of pyruvate may be a novel approach in neovascular retinal diseases.
The political ecology of cocoa agroforestry and implications for equitable land use in rural Ghana
Authors: Kumeh Eric Mensah 
Published: August 2024

AbstractThe relevance of cocoa agroforestry is widely discussed in debates on sustainability transition in cocoa, especially in the context of ending hunger and poverty among cocoa farmers. Whereas this has led to multiple cocoa agroforestry investments by NGOs, governments, and cocoa and chocolate companies in West and Central Africa, a notable gap exists in the literature on how these interventions respond to the needs of cocoa farmers who are typically framed as the primary target of equity in cocoa sustainability discussions. This paper contributes to bridging this gap by analyzing equity in implementing various cocoa agroforestry projects by different actors in Ghana’s Juabeso-Bia Landscape (JBL). I find that the ongoing cocoa agroforestry initiatives may be broadly characterized as renovative or additive in terms of how they (re)shape the various components on cocoa farms. Yet, they are all designed to primarily enhance cocoa productivity even when seasonal food insecurity is one of the most pressing challenges among cocoa farmers in the JBL. The persistent neglect of food in cocoa production risks leaving poverty and hunger in cocoa households unaddressed. Additionally, it increases cocoa households' predisposition to forest conversion, making current forms of cocoa agroforestry an indirect driver of deforestation in the landscape. To transform the current situation, policymakers and scholars must reflexively integrate household food security in designing cocoa agroforestry, prioritizing farmers’ involvement in dynamic agroforestry technologies that contribute directly to local food access over time. This contrasts the current policy and practice of cocoa agroforestry, focused on maintaining a prescribed number of shade trees on new or existing cocoa farms but highly likely to engender significantly greater inequity in the cocoa sector.
A survey: knowledge graph entity alignment research based on graph embedding
Authors: Zhu Beibei , Wang Ruolin , Wang Junyi , Shao Fei , Wang Kerun 
Published: August 2024

AbstractEntity alignment (EA) aims to automatically match entities in different knowledge graphs, which is beneficial to the development of knowledge-driven applications. Representation learning has powerful feature capture capability and it is widely used in the field of natural language processing. Compared with traditional EA methods, EA methods based on representation learning have better performance and efficiency. Hence, we summarize and analyze the representative EA approaches based on representation learning in this paper. We present the problem description and data preprocessing for EA and other related fundamental knowledge. We propose a new EA framework for the latest models, which includes information aggregation module, entity alignment module, and post-alignment module. Based on these three modules, the various technologies are described in detail. In the experimental part, we first explore the effect of EA direction on model performance. Then, we classify the models into different categories in terms of alignment inference strategy, noise filtering strategy, and whether additional information is utilized. To ensure fairness, we perform the comparative analysis of the performance of the models within the categories separately on different datasets. We investigate both unimodal and multimodal EA. Finally, we present future research perspectives based on the shortcomings of existing EA methods.
Selection of marine macroalgae for nutrient biofilter and bioproduct trials in the coastal waters of Queensland, Australia
Authors: Theobald Emma J. , Irving Andrew D. , Capper Angela , Costa Joana F. , Diaz-Pulido Guillermo , Andrews Elizabeth L. , Kelly Jo , Jackson Emma L. 
Published: August 2024

AbstractThe capacity of many macroalgae to rapidly absorb soluble inorganic nutrients and convert them into primary biomass provides opportunities for their use in the bioremediation of nutrient-enriched waters. Marine-based macroalgal cultivation has the potential to target diffuse source nutrient discharges and produce valuable bioproducts such as fertiliser, soil conditioning agents and agricultural feed additives and phycocolloids. Whilst macroalgal diversity offers benefits for improving and maintaining healthy marine ecosystems, it also presents a challenge for determining the best candidate species for cultivation as a nutrient biofilter and, ultimately, a source of bioproducts. We developed and applied a multi-criteria selection model to identify likely candidate macroalgal species for cultivation in Queensland’s coastal waters for biofiltration and bioproduct development, focusing on native species and product development for the agronomical and horticultural sectors. From a species database of 1380 macroalgal species, candidates were initially selected based on their regional abundance, distribution and morphological characteristics considered amenable to in-situ cultivation and harvesting. From a resulting shortlist of 17 species, five systematic literature searches were applied to identify biofilter potential, bioproduct potential and current cultivation status. Of the shortlisted species, Ulva lactuca, Gracilaria edulis and Hypnea cervicornis ranked most highly in the multi-criteria analysis, with several other species showing good potential, but requiring further investment in understanding key biological processes and the development of cultivation protocols.
Novel therapeutic targets: bifidobacterium-mediated urea cycle regulation in colorectal cancer
Authors: Nie Xusheng , Zhang Tingting , Huang Xiumei , Gu Chongqi , Zuo Wei , Fu Li-Juan , Dong Yiping , Liu Hao 
Published: August 2024

Abstract
Background and purpose
Colorectal cancer (CRC) is a widespread malignancy with a complex and not entirely elucidated pathogenesis. This study aims to explore the role of Bifidobacterium in the urea cycle (UC) and its influence on the progression of CRC, a topic not extensively studied previously.

Experimental approach
Utilizing both bioinformatics and experimental methodologies, this research involved analyzing bacterial abundance in CRC patients in comparison to healthy individuals. The study particularly focused on the abundance of BA. Additionally, transcriptomic data analysis and cellular experiments were conducted to investigate the impact of Bifidobacterium on ammonia metabolism and mitochondrial function, specifically examining its regulation of the key UC gene, ALB.

Key results
The analysis revealed a significant decrease in Bifidobacterium abundance in CRC patients. Furthermore, Bifidobacterium was found to suppress ammonia metabolism and induce mitochondrial dysfunction through the regulation of the ALB gene, which is essential in the context of UC. These impacts contributed to the suppression of CRC cell proliferation, a finding corroborated by animal experimental results.

Conclusions and implications
This study elucidates the molecular mechanism by which Bifidobacterium impacts CRC progression, highlighting its role in regulating key metabolic pathways. These findings provide potential targets for novel therapeutic strategies in CRC treatment, emphasizing the importance of microbiota in cancer progression.
The Role of Morality in Corruption, Theft, and Violence in a Ukrainian Context
Authors: Trivedi-Bateman Neema , Markovska Anna , Serdiuk Oleksii , Bogdanov Roman 
Published: August 2024

AbstractMany anti-crime efforts are found to be costly and unsuccessful in reducing crime, and new fresh approaches are needed. Morality has long been neglected in our understanding of crime prevention. This paper uses a theoretical and empirical approach to explore the relationship between individual morality, associated moral emotions, and crime in a sample of 11,671 respondents in Ukraine. To date, few studies have explored morality using large samples in a Ukrainian context. Existing morality studies have been largely limited to nonadult and Global North samples. In the current study, existing standardised scales are applied for the first time in a new context. Morality is measured with how right or wrong respondents feel that various acts of delinquent and criminal behaviour are, and logistic regression is used to analyse its association with self-reported corruption, theft, and violence. Findings show that weak individual morality is associated with higher corruption, theft, and violence. This paper empirically evidences this association, already evidenced in The Global North, in an Eastern-European context. The implications of this are potentially wide-reaching and can inform crime prevention measures in Ukraine and beyond. Individual morality, which develops from early childhood and throughout adolescence, is associated with crime involvement in numerous studies and samples. To reduce crime, suggestions are made to deliver and evaluate morality intervention programmes (such as The Compass Project) in schools (to youth) and in the workplace (to influential employees of organisations and institutions). This is likely to contribute to a societal shift in countries like Ukraine which are presented with politically historic complexities in relation to morally acceptable actions.
The perfect technological storm: artificial intelligence and moral complacency
Authors: Kaas Marten H. L. 
Published: August 2024

AbstractArtificially intelligent machines are different in kind from all previous machines and tools. While many are used for relatively benign purposes, the types of artificially intelligent machines that we should care about, the ones that are worth focusing on, are the machines that purport to replace humans entirely and thereby engage in what Brian Cantwell Smith calls “judgment.” As impressive as artificially intelligent machines are, their abilities are still derived from humans and as such lack the sort of normative commitments that humans have. So while artificially intelligent machines possess a great capacity for “reckoning,” to use Smith’s terminology, i.e., a calculative prowess of extraordinary utility and importance, they still lack the kind of considered human judgment that accompanies the ethical commitment and responsible action we humans must ultimately aspire toward. But there is a perfect technological storm brewing. Artificially intelligent machines are analogous to a perfect storm in that such machines involve the convergence of a number of factors that threaten our ability to behave ethically and maintain meaningful human control over the outcomes of processes involving artificial intelligence. I argue that the storm in the context of artificially intelligent machines makes us vulnerable to moral complacency. That is, this perfect technological storm is capable of lulling people into a state in which they abdicate responsibility for decision-making and behaviour precipitated by the use of artificially intelligent machines, a state that I am calling “moral complacency.” I focus on three salient problems that converge to make us especially vulnerable to becoming morally complacent and losing meaningful human control. The first problem is that of transparency/opacity. The second problem is that of overtrust in machines, often referred to as the automation bias. The third problem is that of ascribing responsibility. I examine each of these problems and how together they threaten to render us morally complacent.
Effect of Thermal Radiation on Fractional MHD Casson Flow with the Help of Fractional Operator
Authors: Abbas Shajar , Parveen Iram , Nisa Zaib Un , Amjad Muhammad , Metwally Ahmed Sayed M. , Nazar Mudassar , Jan Ahmed Zubair 
Published: August 2024

AbstractThis study examines the effects of Newtonian heating along with heat generation, and thermal radiation on magnetohydrodynamic Casson fluid over a vertical plate. At the boundary, the Newtonian heating phenomena has been employed. The problem is split into two sections for this reason: momentum equation and energy equations. To transform the equations of the given model into dimensionless equations, some particular dimensionless parameters are defined. In this article, generalized Fourier’s law and the recently proposed Caputo Fabrizio fractional operator are applied. The corresponding results of non-dimensional velocity and heat equations can be identified through the application of Laplace transform. Moreover, Tzou’s algorithm as well as Stehfest’s algorithm is implemented to recognize the inverted Laplace transform of heat and momentum equations. Finally, a graphical sketch is created using Mathcad 15 software to demonstrate the results of numerous physical characteristics. It has been reported that the heat and velocity drop with rising Prandtl number values, whereas the fluid’s velocity has been seen to rise with increasing Grashof number values. Additionally, current research has shown that flow velocity and temperature increase with rising values of a fractional parameter.
Re-Evaluating the Appropriateness of the “Don’t Know” Response Option: Guessing Rate as a Source of Systematic Error on Autism Knowledge Assessments
Authors: McMahon Camilla M. , McClain Maryellen Brunson , Haverkamp Cassity R. , Harris Bryn 
Published: August 2024

AbstractSeveral autism knowledge assessments include “don’t know” as a response option. The inclusion of this response option may lead to systematic error, such that participants’ guessing rate affects the measurement of their autism knowledge. This study examines both predictors of guessing rate for autism knowledge and predictors of autism knowledge, including guessing rate. School-based professionals (n = 396) completed the Autism Spectrum Knowledge Scale Professional Version-Revised (ASKSP-R; McClain et al, Journal of Autism and Developmental Disorders 50(3):998–1006, 2020). and the Autism Stigma and Knowledge Questionnaire (ASK-Q; Harrison et al, Journal of Autism and Developmental Disorders 47(10):3281–3295, 2017). Both assessments include “don’t know” as a response option. Guessing rate was the strongest predictor of autism knowledge across both the ASKSP-R and the ASK-Q assessments. For the ASKSP-R, participants who were school psychologists, practicing for more years, had more autism-related clinical experiences, and who personally knew an autistic person had a higher guessing rate. School psychologists and participants who worked with more autistic students scored higher in autism knowledge. For the ASK-Q, participants with greater self-perceived autism knowledge had a higher guessing rate. Participants with a doctorate degree, who personally knew an autistic person, and who worked with more autistic students scored higher in autism knowledge. Guessing rate can be a source of systematic error on autism knowledge assessments. Potential solutions to correct for guessing rate are examined and recommended for future use.
Managing product-inherent constraints with artificial intelligence: production control for time constraints in semiconductor manufacturing
Authors: May Marvin Carl , Oberst Jan , Lanza Gisela 
Published: August 2024

AbstractContinuous product individualization and customization led to the advent of lot size one in production and ultimately to product-inherent uniqueness. As complexities in individualization and processes grow, production systems need to adapt to unique, product-inherent constraints by advancing production control beyond predictive, rigid schedules. While complex processes, production systems and production constraints are not a novelty per se, modern production control approaches fall short of simultaneously regarding the flexibility of complex job shops and product unique constraints imposed on production control. To close this gap, this paper develops a novel, data driven, artificial intelligence based production control approach for complex job shops. For this purpose, product-inherent constraints are resolved by restricting the solution space of the production control according to a prediction based decision model. The approach validation is performed in a real semiconductor fab as a job shop that includes transitional time constraints as product-inherent constraints. Not violating these time constraints is essential to avoid scrap and similarly increase quality-based yield. To that end, transition times are forecasted and the adherence to these product-inherent constraints is evaluated based on one-sided prediction intervals and point estimators. The inclusion of product-inherent constraints leads to significant adherence improvements in the production system as indicated in the real-world semiconductor manufacturing case study and, hence, contributes a novel, data driven approach for production control. As a conclusion, the ability to avoid a large majority of violations of time constraints shows the approaches effectiveness and the future requirement to more accurately integrate such product-inherent constraints into production control.