bluuebunny/crossref_metadata_2025_split huggingface dataset:
Progresses on cryo-tribology: lubrication mechanisms, detection methods and applications
Authors: Cui Wenyan , Chen Hongzhan , Zhao Jianxun , Ma Quansheng , Xu Qiang , Ma Tianbao 
Published: March 2023

Abstract
Tribology at cryogenic temperatures has attracted much attention since the 1950s with the acceleration of its applications in high-tech equipment such as cryogenic wind tunnels, liquid fuel rockets, space infrared telescopes, superconducting devices, and planetary exploration, which require solid lubrication for moving parts at low temperatures down to 4 K in cryogenic liquid, gaseous, or vacuum environments. Herein, the research progress regarding cryo-tribology is reviewed. The tribological properties and mechanisms of solid lubricants listed as carbon materials, molybdenum disulfide, polymers, and polymer-based composites with decreasing temperature are summarized. The friction coefficient increases with decreasing temperature induced by thermally activated processes. The mechanism of transfer film formation should be considered as a significant way to enhance the tribological properties of solid lubricants. In addition, applications of solid lubrication on moving parts under cryogenic conditions, such as spherical plain bearings and roller bearings, are introduced. The technology for tribological testing of materials and bearings at cryogenic temperatures is summarized, where the environmental control, motion and loading realization, as well as friction and wear measurement together in a low-temperature environment, result in the difficulties and challenges of the low-temperature tribotester. In particular, novel technologies and tribotesters have been developed for tribotests and tribological studies of solid lubricants, spherical plain bearings, and roller bearings, overcoming limitations regarding cooling in vacuum and resolution of friction measurement, among others, and concentrating on in-situ observation of friction interface. These not only promote a deep understanding of friction and wear mechanism at low temperatures, but also provide insights into the performance of moving parts or components in cryogenic applications.
B2-Net: an artificial intelligence powered machine learning framework for the classification of pneumonia in chest x-ray images
Authors: Abubeker K M , Baskar S 
Published: March 2023

Abstract
A chest x-ray radiograph is still the global standard for diagnosing pneumonia and helps distinguish between bacterial and viral pneumonia. Despite several studies, radiologists and physicians still have trouble correctly diagnosing and classifying pneumonia without false negatives. Modern mathematical modeling and artificial intelligence could help to reduce false-negative rates and improve diagnostic accuracy. This research aims to create a novel and efficient multiclass machine learning framework for analyzing and classifying chest x-ray images on a graphics processing unit (GPU). Researchers initially applied a geometric augmentation using a positional transformation function to the original dataset to enhance the sample size and aid future transfer learning. Models with the best accuracy, area under the receiver operating characteristics (AUROC), F1 score, precision, recall, and specificity are chosen from a pool of nine state-of-the-art neural network models. The best-performing models are then retrained using an ensemble technique using depth-wise convolutions, demonstrating significant improvements over the baseline models employed in this research. With a remarkable 97.69% accuracy, 100% recall, and 0.9977 AUROC scores, the proposed Bek-Bas network (B2-Net) model can differentiate between normal, bacterial, and viral pneumonia in chest x-ray images. A superior model is retrained using the chosen dense convolutional network-160, residual network-121, and visual geometry group network-16 ensemble models. The diagnostic accuracy of the x-ray classification unit is enhanced by the newly designed multiclass network, the B2-Net model. The developed GPU-based framework has been examined and tested to the highest clinical standards. After extensive clinical testing, the final B2-Net model is implemented on an NVIDIA Jetson Nano GPU computer. Healthcare facilities have confirmed the B2-Net is the most effective framework for identifying bacterial and viral pneumonia in chest x-rays.
Perspective on investigation of neurodegenerative diseases with neurorobotics approaches
Authors: Tolu Silvia , Strohmer Beck , Zahra Omar 
Published: March 2023

Abstract
Neurorobotics has emerged from the alliance between neuroscience and robotics. It pursues the investigation of reproducing living organism-like behaviors in robots by means of the embodiment of computational models of the central nervous system. This perspective article discusses the current trend of implementing tools for the pressing challenge of early-diagnosis of neurodegenerative diseases and how neurorobotics approaches can help. Recently, advances in this field have allowed the testing of some neuroscientific hypotheses related to brain diseases, but the lack of biological plausibility of developed brain models and musculoskeletal systems has limited the understanding of the underlying brain mechanisms that lead to deficits in motor and cognitive tasks. Key aspects and methods to enhance the reproducibility of natural behaviors observed in healthy and impaired brains are proposed in this perspective. In the long term, the goal is to move beyond finding therapies and look into how researchers can use neurorobotics to reduce testing on humans as well as find root causes for disease.
Climate change adaptation across the life-course - from pregnancy to adolescence – it’s time to advance the field of climate change and child health
Authors: Helldén Daniel , Ndejjo Rawlance , Sturm Amanda , Persson Ida , Wanyenze Rhoda , Ebi Kristie L , Nilsson Maria , Alfvén Tobias 
Published: March 2023

Abstract
Children are bearing and will continue to bear a high burden from climate change, through direct and indirect pathways. Climate change adaptation interventions are essential to limit the current and projected impacts of climate change on child health. However, the perspective of children and their health with regards to adaptation in research and policy is limited. Ensuring that adaptation interventions are context specific, consider interrelated barriers to implementation and leverage existing structures including multisectoral collaboration is vital. Further, a life-course perspective on child health and well-being could serve as a template for tailoring adaptation interventions to the specific vulnerabilities and needs of specific development periods of the child. A meaningful engagement of children and young persons in the design and implementation of adaptation interventions is essential to increase effectiveness and uptake. With climate change effecting millions of children worldwide, it is high time to put child health at the center of adaptation.
Increased Breast and Colorectal Cancer Risk in Type 2 Diabetes: Awareness Among Adults With and Without Diabetes and Information Provision on Diabetes Websites
Authors: Ashley Laura , Robb Kathryn A , O’Connor Daryl B , Platt Rebecca , Price Mollie , Robinson Olivia , Travis Elizabeth , Lipscombe Lorraine , Ajjan Ramzi , Birch Rebecca 
Published: March 2023

Abstract

Background
People with type 2 diabetes mellitus (T2DM) have a higher risk of developing breast and bowel cancers but are less likely to participate in cancer screening.

Purpose
Two interlinked studies examined public awareness of the fact that T2DM increases breast and bowel cancer risk, and provision of this information on diabetes websites.

Methods
Study-1: phase-1 surveyed awareness of T2DM-increased cancer risk in a nationally-representative British sample aged 50–74 (N = 1,458) and compared respondents with and without T2DM (n = 125 vs. n = 1,305); phase-2 surveyed an additional exclusively T2DM sample (N = 319). Study-2: High-ranking diabetes websites (N = 25) were reviewed to determine the rate of inclusion of cancer risk and cancer screening information in evident sections about diabetes-related health conditions.

Results
A low proportion of respondents were aware that T2DM increases risk of breast (13.7%) and bowel (27.6%) cancers, compared to much higher awareness of other diabetes-related conditions such as sight loss (82.2%) and foot problems (81.8%). Respondents with T2DM were significantly more likely than those without T2DM to be aware of all the surveyed diabetes-related health conditions (e.g., sight loss, OR: 3.14, 95%CI: 1.61–6.15; foot problems, OR: 2.58, 95%CI: 1.38–4.81), except breast (OR: 0.82, 95%CI: 0.46–1.45) and bowel (OR: 0.95, 95%CI: 0.63–1.45) cancer, for which awareness was equally low among people with and without T2DM. Few diabetes websites with a section on diabetes-related health conditions included cancer in this section (n = 4/19), and fewer still included cancer screening among any noted cancer-protective behaviors (n = 2/4).

Conclusions
There is low public awareness that T2DM increases the risk of developing breast and bowel cancers, even among people with T2DM, which may be partly due to limited information provision regarding T2DM-increased cancer risk from diabetes care providers and organizations.
Invited Commentary: Aligning Methodological Research on Pregnancy Weight Gain With the Questions That Matter Most for Public Health Guidelines
Authors: Hutcheon Jennifer A , Platt Robert W 
Published: March 2023

Abstract
The inherent correlation between the total amount of weight gained in pregnancy and the duration of pregnancy creates major methodological challenges in the study of pregnancy weight gain. In this issue (Am J Epidemiol. 2022;191(10):1687–1699), Richards et al. examine the extent to which different measures of pregnancy weight gain (including covariate adjustment for gestational age and standardizing weight gain for gestational duration using a pregnancy weight gain chart) are able to disentangle the effects of low weight gain on perinatal health from the role of younger gestational age at delivery for 3 outcomes: small-for-gestational-age birth, cesarean delivery, and low birth weight. While methodological research to understand how to best disentangle the effects of gestational weight gain from pregnancy duration is valuable, we argue that the practical utility of this type of research would be increased by aligning the specific research questions more closely with health outcomes on which evidence is most needed—those not considered in current weight gain guidelines due to lack of high-quality evidence (such as pre-eclampsia and stillbirth). Further, evaluations of weight gain charts should separate out the potential for bias introduced by the use of a normative chart per se from the use of a chart unsuitable for the study population.
Invited Commentary: Concealed Carrying of Firearms, Public Policy, and Opportunities for Mitigating Harm
Authors: Matthay Ellicott C , Kagawa Rose M C 
Published: March 2023

Abstract
In the last 30 years, 25 US states have relaxed laws regulating the concealed carrying of firearms (concealed-carry weapons (CCW) laws). These changes may have substantial impacts on violent crime. In a recent study, Doucette et al. (Am J Epidemiol. 2023;192(3):342–355) used a synthetic control approach to assess the effects of shifting from more restrictive “may/no-issue” CCW laws to less restrictive “shall-issue” CCW laws on homicides, aggravated assaults, and robberies involving a gun or committed by other means. The study adds to the evidence that more permissive CCW laws have probably increased rates of firearm assault in states adopting these laws. Importantly, this study is the first to identify that specific provisions of shall-issue CCW laws—including denying permits to persons with violent misdemeanor convictions, a history of dangerous behavior, or “questionable character” and live-fire training requirements—may help mitigate harms associated with shall-issue CCW laws. These findings are timely and salient given the recent Supreme Court ruling striking down a defining element of may-issue laws. This thorough study offers actionable results and provides a methodological model for state firearm policy evaluations. Its limitations reflect the needs of the field more broadly: greater focus on racial/ethnic equity and within-state variation, plus strengthening the data infrastructure on firearm violence and crime.
Evaluation of Phosphatidylethanol Elimination in Alcohol Use Disorder Patients Undergoing Withdrawal Treatment
Authors: Stöth Frederike , Weinmann Wolfgang , Soravia Leila Maria , Pfeifer Philippe 
Published: March 2023

Abstract

Aims
Phosphatidylethanol (PEth) is used to monitor alcohol consumption in alcohol use disorder (AUD). In this study, we aim to evaluate the elimination time of PEth with regard to the clinically established 200 and 20 ng/ml cutoffs for PEth 16:0/18:1.

Methods
Data from 49 patients undergoing treatment for AUD were evaluated. PEth concentrations were measured at the beginning and repeatedly during the treatment period of up to 12 weeks to monitor the elimination of PEth. We evaluated the time in weeks until the cutoff concentrations of &lt;200 and &lt;20 ng/ml were achieved. The correlation between the initial PEth concentration and the number of days until the PEth concentration had dropped below 200 and 20 ng/ml was assessed by calculating Pearson’s correlation coefficients.

Results
The initial PEth concentrations ranged from &lt;20 to &gt;2500 ng/ml. In 31 patients, the time until the cutoff values were reached could be documented. Even after 6 weeks of abstinence, PEth concentrations above the cutoff of 200 ng/ml could still be detected in two patients. A strong significant positive correlation was found between the initial PEth concentration and the time required to drop below the two cutoffs.

Conclusion
A waiting period of more than 6 weeks after declared abstinence should be granted for individuals with AUD before using only one single PEth concentration to assess the consumption behavior. However, we recommend to always use at least two PEth concentrations for the evaluation of alcohol-drinking behaviors in AUD patients.
NeuroPred-PLM: an interpretable and robust model for neuropeptide prediction by protein language model
Authors: Wang Lei , Huang Chen , Wang Mingxia , Xue Zhidong , Wang Yan 
Published: March 2023

AbstractNeuropeptides are a diverse and complex class of signaling molecules that regulate a variety of biological processes. Neuropeptides provide many opportunities for the discovery of new drugs and targets for the treatment of a wide range of diseases, and thus, computational tools for the rapid and accurate large-scale identification of neuropeptides are of great significance for peptide research and drug development. Although several machine learning-based prediction tools have been developed, there is room for improvement in the performance and interpretability of the proposed methods. In this work, we developed an interpretable and robust neuropeptide prediction model, named NeuroPred-PLM. First, we employed a language model (ESM) of proteins to obtain semantic representations of neuropeptides, which could reduce the complexity of feature engineering. Next, we adopted a multi-scale convolutional neural network to enhance the local feature representation of neuropeptide embeddings. To make the model interpretable, we proposed a global multi-head attention network that could be used to capture the position-wise contribution to neuropeptide prediction via the attention scores. In addition, NeuroPred-PLM was developed based on our newly constructed NeuroPep 2.0 database. Benchmarks based on the independent test set show that NeuroPred-PLM achieves superior predictive performance compared with other state-of-the-art predictors. For the convenience of researchers, we provide an easy-to-install PyPi package (https://pypi.org/project/NeuroPredPLM/) and a web server (https://huggingface.co/spaces/isyslab/NeuroPred-PLM).
MCANet: shared-weight-based MultiheadCrossAttention network for drug–target interaction prediction
Authors: Bian Jilong , Zhang Xi , Zhang Xiying , Xu Dali , Wang Guohua 
Published: March 2023

AbstractAccurate and effective drug–target interaction (DTI) prediction can greatly shorten the drug development lifecycle and reduce the cost of drug development. In the deep-learning-based paradigm for predicting DTI, robust drug and protein feature representations and their interaction features play a key role in improving the accuracy of DTI prediction. Additionally, the class imbalance problem and the overfitting problem in the drug–target dataset can also affect the prediction accuracy, and reducing the consumption of computational resources and speeding up the training process are also critical considerations. In this paper, we propose shared-weight-based MultiheadCrossAttention, a precise and concise attention mechanism that can establish the association between target and drug, making our models more accurate and faster. Then, we use the cross-attention mechanism to construct two models: MCANet and MCANet-B. In MCANet, the cross-attention mechanism is used to extract the interaction features between drugs and proteins for improving the feature representation ability of drugs and proteins, and the PolyLoss loss function is applied to alleviate the overfitting problem and the class imbalance problem in the drug–target dataset. In MCANet-B, the robustness of the model is improved by combining multiple MCANet models and prediction accuracy further increases. We train and evaluate our proposed methods on six public drug–target datasets and achieve state-of-the-art results. In comparison with other baselines, MCANet saves considerable computational resources while maintaining accuracy in the leading position; however, MCANet-B greatly improves prediction accuracy by combining multiple models while maintaining a balance between computational resource consumption and prediction accuracy.
RNAget: an API to securely retrieve RNA quantifications
Authors: Upchurch Sean , Palumbo Emilio , Adams Jeremy , Bujold David , Bourque Guillaume , Nedzel Jared , Graham Keenan , Kagda Meenakshi S , Assis Pedro , Hitz Benjamin , Righi Emilio , Guigó Roderic , Wold Barbara J ,   GA4GH RNA-Seq Task Team, Adams Jeremy , Brazma Alvis , Bujold David , Burchard Julia , Capka Joe , Cherry Michael , Clarke Laura , Craft Brian , Dermitzakis Manolis , Diekhans Mark , Dursi John , Fitzsimons Michael Sean , Flaming Zac , Garrido Romina , Gil Alfred , Godden Paul , Green Matt , Guigo Roderic , Guttman Mitch , Haas Brian , Haeussler Max , Hitz Benjamin , Li Bo , Linnarsson Sten , Lipski Adam , Liu David , Longerich Simonne , Lougheed David , Manning Jonathan , Marioni John , Meyer Christopher , Montgomery Stephen , Morrow Alyssa , Munoz-Power Fuentes Alfonso , Nedzel Jared , Nguyen David , Osborn Kevin , Ouellette Francis , Palumbo Emilio , Papatheodorou Irene , Pervouchine Dmitri , Ramani Arun , Rambla Jordi , Sadjad Bashir , Steinberg David , Talkar Jeremiah , Tickle Timothy , Tzeng Kathy , Upchurch Sean , Vaisipour Saman , Watford Sean , Wold Barbara , Zhang Zhenyu , Zhu Jing 
Published: March 2023

AbstractSummaryLarge-scale sharing of genomic quantification data requires standardized access interfaces. In this Global Alliance for Genomics and Health project, we developed RNAget, an API for secure access to genomic quantification data in matrix form. RNAget provides for slicing matrices to extract desired subsets of data and is applicable to all expression matrix-format data, including RNA sequencing and microarrays. Further, it generalizes to quantification matrices of other sequence-based genomics such as ATAC-seq and ChIP-seq.Availability and implementationhttps://ga4gh-rnaseq.github.io/schema/docs/index.html.
Signalling on islands: the case of Lilford’s wall lizard (Podarcis lilfordi gigliolii) from Dragonera
Authors: de La Cruz Ferran , Pérez i de Lanuza Guillem , Font Enrique 
Published: March 2023

AbstractStudies of the effects of insularity on animal signals are scarce, particularly in lizards. Here, we use Lilford’s wall lizard from Dragonera (Podarcis lilfordi gigliolii) to ask how island conditions have affected its repertoire of social signals, focusing on two visual signals shared by many Podarcis species: ultraviolet (UV)–blue-reflecting ventrolateral colour patches and visual displays. We examined whether the number or spectral characteristics of the UV–blue patches are associated with traits related to individual quality. We also used visual models to assess visual conspicuousness and to measure sexual dichromatism. We did not observe foot shakes or any other visual displays usually found in continental Podarcis. We found that none of the UV–blue patch variables covaried with morphometric variables indicative of fighting ability or body condition in males, suggesting that this coloration does not signal individual quality. We also found very little sexual dichromatism. In particular, the UV–blue patches of females seem over-expressed and more similar to those of males than those of continental Podarcis. Ancestral state reconstruction reveals that the lack of sexual dimorphism in the UV–blue patches is a derived condition for P. lilfordi gigliolii and other Podarcis living on small islands. Our results thus show a pattern of reduced social signalling in P. lilfordi gigliolii relative to mainland Podarcis, with some signals being lost or under-expressed (visual displays) and others losing their signalling function (UV–blue patches). We hypothesize that these changes are attributable to the high population density of P. lilfordi gigliolii, which discourages territorial behaviour and promotes extreme social tolerance, making most social signals unnecessary. More work will be needed to determine whether this is a common pattern in lizards inhabiting small and densely populated islands.
Early differentiation and gene expression characteristics of trophoblast lineages
Authors: Qin Jiaying , Li Wanqiong , Lv Bo , Xue Zhigang , Xue Jinfeng 
Published: March 2023

AbstractWith the development of the embryo, the totipotent blastomere undergoes the first lineage decision to the inner cell mass (ICM) and the trophectoderm (TE). The ICM forms the fetus while the TE forms the placenta, which is one of the unique organs in mammals serving as the interface between maternal and fetal bloodstreams. Proper trophoblast lineage differentiation is crucial for correct placental and fetal development, including the TE progenitor self-renewal and its differentiation toward mononuclear cytotrophoblast, which later either develops into invasive extravillous trophoblast, remodeling the uterine vascular, or fuses into multinuclear syncytiotrophoblast, secreting pregnancy-sustaining hormone. Aberrant differentiation and gene expression of trophoblast lineage is associated with severe pregnancy disorders and fetal growth restriction. This review focuses on the early differentiation and key regulatory factors of trophoblast lineage, which have been poorly elucidated. Meanwhile, the recent development of trophoblast stem cells, trophectoderm stem cells, and blastoids derived from pluripotent stem cells bring the accessible model to investigate the profound mystery of embryo implantation and placentation and were also summarized.