bluuebunny/crossref_metadata_2025_split huggingface dataset:
What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study
Authors: Jing Yuhui , Wang Haoming , Chen Xiaojiao , Wang Chengliang 
Published: February 2024

AbstractThe emergence of ChatGPT has sparked new expectations for AI-empowered educational transformation. However, it remains unknown which factors affect its effectiveness in empowering learners to solve programming problems. Therefore, this study employed a quasi-experimental research design and used Python graphing in programming education as an example to investigate the factors influencing the effectiveness of learners in applying ChatGPT to problem-solving. Findings: AI literacy significantly influences learners’ effectiveness in using ChatGPT to solve problems, with AI awareness and AI usage being key factors. The knowledge base of programming language significantly affects learners’ effectiveness in applying ChatGPT to solve programming problems. Learners’ cognitive level of ChatGPT significantly influences their effectiveness in applying ChatGPT to problem-solving, while usage intention does not have a significant impact. However, learners’ intention to use ChatGPT significantly improves after application. Based on these findings, this study proposes that in the process of empowering education with Artificial Intelligence Generated Content (AIGC) products, the focus on learners should shift from cultivating their AI usage to AI literacy, laying the foundation for empowering learning with AIGC products. It is suggested to shift from mastering specific knowledge to graph-based rules as a method for empowering learning with AIGC products. Additionally, the focus should shift from enhancing learners’ intention to use the technology to strengthen their technological awareness, thereby creating practical pathways for empowering learning with AIGC products.
The role of the oviduct environment in embryo survival
Authors: Juengel Jennifer L. , Reader Karen L. , Maclean Paul H. , Quirke Laurel D. , Zellhuber-McMillan Sylvia , Haack Neville A. , Heiser Axel 
Published: February 2024

Context Declining fertility is an issue in multiple mammalian species. As the site of fertilisation and early embryo development, the oviduct plays a critical role in embryo survival, yet there is a paucity of information on how the oviduct regulates this process. Aims We hypothesised that differences in steroid hormone signalling and/or immune function would be observed in a model of poor embryo survival, the peripubertal ewe. Methods We examined expression of steroid hormones in systemic circulation, oviductal expression of oestrogen receptor α and genes important in steroid hormone signalling, and immune function in pregnant and cyclic peripubertal and adult ewes on day 3 after oestrus. Key results Concentrations of progesterone, but not oestradiol, were decreased in the peripubertal ewe compared to the adult ewe. Oestrogen receptor α protein expression was increased in the peripubertal ewe, but pathway analysis of gene expression revealed downregulation of the oestrogen signalling pathway compared to the adult ewe. Differential expression of several genes involved in immune function between the peripubertal and adult ewe was consistent with an unfavourable oviductal environment in the peripubertal ewe lamb. Oestradiol concentration was positively correlated with the expression of multiple genes involved in the regulation of immune function. Conclusions Differences in the immune environment of the oviduct, potentially linked to differential modulation by steroid hormones, may partially underly the poor fertilisation and early embryo survival observed in the peripubertal ewe. Implications A unfavourable oviductal environment may play an important role in limiting reproductive success.
The utility of (political) dogwhistles – a life cycle perspective
Authors: Sayeed Asad , Breitholtz Ellen , Cooper Robin , Lindgren Elina , Rettenegger Gregor , Rönnerstrand Björn 
Published: February 2024

Abstract
The term dogwhistle refers to an expression conveying a message to a subset of an audience which is not perceived by the rest of the group, in addition to a primary meaning directed at the group at large. We follow up on previous work in linguistics and political communication on defining dogwhistles, taking into account how they likely function in real-life political contexts. We consider the utility of dogwhistles in terms of their sensitivity and their specificity, which allows us to consider dogwhistles in terms of an idealized “life cycle”, whose phases we describe in terms of a multi-dimensional utility tradeoff, described in terms of dogwhistle users, the benefit they expect to receive from dogwhistling, and the deniability of controversial dogwhistle meanings. We propose an approach for the longitudinal study of dogwhistles, and describe the first stages of an experiment to characterize dogwhistles in terms of their lexical properties.
Contrasting biomass allocations explain adaptations to cold and drought in the world’s highest-growing angiosperms
Authors: Doležal Jiří , Chondol Thinles , Chlumská Zuzana , Altman Jan , Čapková Kateřina , Dvorský Miroslav , Fibich Pavel , Korznikov Kirill A , Ruka Adam T , Kopecký Martin , Macek Martin , Řeháková Klára 
Published: February 2024

Abstract

Background and Aims
Understanding biomass allocation among plant organs is crucial for comprehending plant growth optimization, survival and responses to the drivers of global change. Yet, the mechanisms governing mass allocation in vascular plants from extreme elevations exposed to cold and drought stresses remain poorly understood.

Methodology
We analysed organ mass weights and fractions in 258 Himalayan herbaceous species across diverse habitats (wetland, steppe, alpine), growth forms (annual, perennial taprooted, rhizomatous and cushiony) and climatic gradients (3500–6150 m elevation) to explore whether biomass distribution adhered to fixed allometric or optimal partitioning rules, and how variations in size, phylogeny and ecological preferences influence their strategies for resource allocation.

Key Findings
Following optimal partitioning theory, Himalayan plants distribute more biomass to key organs vital for acquiring and preserving limited resources necessary for their growth and survival. Allocation strategies are mainly influenced by plant growth forms and habitat conditions, notably temperature, water availability and evaporative demands. Alpine plants invest primarily in below-ground stem bases for storage and regeneration, reducing above-ground stems while increasing leaf mass fraction to maximize carbon assimilation in their short growing season. Conversely, arid steppe plants prioritize deep roots over leaves to secure water and minimize transpiration. Wetland plants allocate resources to above-ground stems and below-ground rhizomes, enabling them to resist competition and grazing in fertile environments.

Conclusions
Himalayan plants from extreme elevations optimize their allocation strategies to acquire scarce resources under specific conditions, efficiently investing carbon from supportive to acquisitive and protective functions with increasing cold and drought. Intraspecific variation and shared ancestry have not significantly altered biomass allocation strategies of Himalayan plants. Despite diverse evolutionary histories, plants from similar habitats have developed comparable phenotypic structures to adapt to their specific environments. This study offers new insights into plant adaptations in diverse Himalayan environments and underscores the importance of efficient resource allocation for survival and growth in challenging conditions.
Translation and Adaptation of Cognistat in Hindi Language: For Computerized Evaluation of Cognitive Functioning
Authors: Sagar Rajesh , Talwar Shivangi , Verma Rohit , Tripathi Manjari 
Published: February 2024

Abstract

Objective
Cognitive assessment is a crucial element of the mental health screening process. However, there is a lack of culturally appropriate cognitive screening tools for the Hindi-speaking aging population in India. We aimed to translate and culturally tailor the English version of the computerized Cognistat test for the Hindi-speaking aging population.

Method
The computer-based Cognistat Assessment System was culturally adapted for the North Indian Hindi-speaking population. Participants with mild cognitive impairment (MCI) (n = 30), depression (n = 30), and healthy control (n = 30) groups between the age range of 50 and 83 years were assessed using the translated version of the tool. The group differences were analyzed using analysis of variance, multivariate analysis of variance, Tukey’s post hoc analysis for continuous variables, and Fisher’s exact test for categorical variables. The correlation between the Cognistat domains was analyzed using Pearson’s correlation, and test reliability was assessed using Cronbach’s alpha.

Results
The MCI group had significantly lower scores as compared with the depression and control groups on orientation, language, repetition, and calculation domain tasks. For attention and construction tasks, the MCI group had lower scores than the control group. MCI group scores were significantly lower than the control group on naming domain. The Hindi version of the test was reliable for the Hindi-speaking aging population.

Conclusions
Cognistat Hindi can be used by trained healthcare professionals with Hindi-speaking populations for culturally tailored primary evaluation of cognitive difficulties. Future research should focus on further validation and assessment of its properties.
Executive Function as a Predictor of Pain Perception in Healthy Young Adults
Authors: Garcia Sarah , Foster Elodie , Johnson Peter J , Thomas Brittany , Askew Robert L 
Published: February 2024

Abstract

Objective
Pain’s impact on executive function is understood and specific cognitive abilities may contribute to coping with pain, though past work is confounded by chronic pain populations. This study aims to understand how executive functioning may predict the experience of pain among healthy adults. It was hypothesized that poorer executive functioning would predict more intense pain perception.

Method
A total of 172 young adults were recruited for participation. Three aspects of executive functioning (i.e., impulsivity, cognitive flexibility, working memory) were assessed before randomizing participants to varying types and levels of stimulated pain.

Results
Results supported the hypothesis that poorer performance on tasks of working memory predicts more intense pain perception.

Conclusions
Findings are counter to past work that has found inhibition may be important for coping, and future research is needed to understand the impact of specific cognitive abilities as well as how this may differ for chronic pain.
Polyurethane Implants in Revisional Breast Augmentation: A Prospective 5-Year Study
Authors: Hamdi Moustapha , Kapila Ayush K , Peters Ellen , Ramaut Lisa , Waked Karl , Giunta Gabriele , De Baerdemaeker Randy , Zeltzer Assaf 
Published: February 2024

Abstract

Background
Revisional surgery for aesthetic breast augmentation remains a challenging procedure. Polyurethane (PU) implants have been found to avoid capsular contracture recurrence as well as to prevent implant displacement by bio-integrating with the pocket.

Objectives
Our study aimed to assess the use of PU implants in breast revisional surgery and to provide an algorithm.

Methods
Over a 5-year period, a prospective study was conducted involving consecutive patients undergoing implant revision. Patient demographics, previous breast procedures, and specific surgical details were documented. Postoperative outcomes were followed up.

Results
Out of 92 patients (184 breasts), 78 (156 breasts) were included in the analysis. The average age was 47.5, with a BMI of 22.3 and a mean follow-up of 5 years. A majority (63%) represented secondary revisional cases, while 37% were tertiary cases. Implant size averaged 296 cc, with 53% placed in retropectoral position and 47% prepectoral. Significantly more implants in secondary cases were changed from prepectoral to retropectoral (P = .005), and in tertiary changed from retropectoral to prepectoral (P = .002). Complete capsulectomy was performed in 61.5% and partial in 25.6%. Additional lipofilling was performed in 32%, and concurrent mastopexy in 40%. Revisional surgery in our series had a 1.9% acute complication rate, 4.5% longer-term reoperation rate for corrections, 0.6% implant exchange rate, and no recurrent capsular contracture.

Conclusions
This is the first study to provide data on outcomes of revisional breast augmentation surgery with PU implants. It shows that polyurethane implants offer consistent stability and have low rates of recurrent capsular contracture in revisional surgery.

Level of Evidence: 3
MetaQuad: shared informative variants discovery in metagenomic samples
Authors: Xu Sheng , Morgan Daniel C , Qian Gordon , Huang Yuanhua , Ho Joshua W K 
Published: February 2024

Abstract

Motivation
Strain-level analysis of metagenomic data has garnered significant interest in recent years. Microbial single nucleotide polymorphisms (SNPs) are genomic variants that can reflect strain-level differences within a microbial species. The diversity and emergence of SNPs in microbial genomes may reveal evolutionary history and environmental adaptation in microbial populations. However, efficient discovery of shared polymorphic variants in a large collection metagenomic samples remains a computational challenge.

Results
MetaQuad utilizes a density-based clustering technique to effectively distinguish between shared variants and non-polymorphic sites using shotgun metagenomic data. Empirical comparisons with other state-of-the-art methods show that MetaQuad significantly reduces the number of false positive SNPs without greatly affecting the true positive rate. We used MetaQuad to identify antibiotic-associated variants in patients who underwent Helicobacter pylori eradication therapy. MetaQuad detected 7591 variants across 529 antibiotic resistance genes. The nucleotide diversity of some genes is increased 6 weeks after antibiotic treatment, potentially indicating the role of these genes in specific antibiotic treatments.

Availability and implementation
MetaQuad is an open-source Python package available via https://github.com/holab-hku/MetaQuad.
RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification
Authors: Xue Jun , Wang Bingyi , Ji Hongchao , Li WeiHua 
Published: February 2024

Abstract

Motivation
Liquid chromatography retention times prediction can assist in metabolite identification, which is a critical task and challenge in nontargeted metabolomics. However, different chromatographic conditions may result in different retention times for the same metabolite. Current retention time prediction methods lack sufficient scalability to transfer from one specific chromatographic method to another.

Results
Therefore, we present RT-Transformer, a novel deep neural network model coupled with graph attention network and 1D-Transformer, which can predict retention times under any chromatographic methods. First, we obtain a pre-trained model by training RT-Transformer on the large small molecule retention time dataset containing 80 038 molecules, and then transfer the resulting model to different chromatographic methods based on transfer learning. When tested on the small molecule retention time dataset, as other authors did, the average absolute error reached 27.30 after removing not retained molecules. Still, it reached 33.41 when no samples were removed. The pre-trained RT-Transformer was further transferred to 5 datasets corresponding to different chromatographic conditions and fine-tuned. According to the experimental results, RT-Transformer achieves competitive performance compared to state-of-the-art methods. In addition, RT-Transformer was applied to 41 external molecular retention time datasets. Extensive evaluations indicate that RT-Transformer has excellent scalability in predicting retention times for liquid chromatography and improves the accuracy of metabolite identification.

Availability and implementation
The source code for the model is available at https://github.com/01dadada/RT-Transformer. The web server is available at https://huggingface.co/spaces/Xue-Jun/RT-Transformer.
CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution
Authors: Wu Chiung-Ting , Du Dongping , Chen Lulu , Dai Rujia , Liu Chunyu , Yu Guoqiang , Bhardwaj Saurabh , Parker Sarah J , Zhang Zhen , Clarke Robert , Herrington David M , Wang Yue 
Published: February 2024

Abstract

Motivation
Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments.

Results
We previously developed a fully unsupervised deconvolution method—Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable.

Availability and implementation
The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user’s guide and a vignette are provided.
CryoTransformer: a transformer model for picking protein particles from cryo-EM micrographs
Authors: Dhakal Ashwin , Gyawali Rajan , Wang Liguo , Cheng Jianlin 
Published: February 2024

Abstract

Motivation
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise ratio.

Results
To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labeled cryo-EM protein particle dataset—CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score, and is poised to facilitate the automation of the cryo-EM protein particle picking.

Availability and implementation
The source code and data for CryoTransformer are openly available at: https://github.com/jianlin-cheng/CryoTransformer.
In vitro culture alters cell lineage composition and cellular metabolism of bovine blastocyst
Authors: Ming Hao , Zhang Mingxiang , Rajput Sandeep , Logsdon Deirdre , Zhu Linkai , Schoolcraft William B , Krisher Rebecca L , Jiang Zongliang , Yuan Ye 
Published: February 2024

Abstract
Profiling bovine blastocyst transcriptome at the single-cell level has enabled us to reveal the first cell lineage segregation, during which the inner cell mass (ICM), trophectoderm (TE), and an undefined population of transitional cells were identified. By comparing the transcriptome of blastocysts derived in vivo (IVV), in vitro from a conventional culture medium (IVC), and in vitro from an optimized reduced nutrient culture medium (IVR), we found a delay of the cell fate commitment to ICM in the IVC and IVR embryos. Developmental potential differences between IVV, IVC, and IVR embryos were mainly contributed by ICM and transitional cells. Pathway analysis of these non-TE cells between groups revealed highly active metabolic and biosynthetic processes, reduced cellular signaling, and reduced transmembrane transport activities in IVC embryos that may lead to reduced developmental potential. IVR embryos had lower activities in metabolic and biosynthetic processes but increased cellular signaling and transmembrane transport, suggesting these cellular mechanisms may contribute to improved blastocyst development compared to IVC embryos. However, the IVR embryos had compromised development compared to IVV embryos with notably over-active transmembrane transport activities that impaired ion homeostasis.
Let us settle the controversy—gelfoam is a safe intravascular embolic agent
Authors: Clements Warren , Lukies Matthew , Zia Adil , Fitzgerald Mark , Kavnoudias Helen 
Published: February 2024

Abstract

Objectives
A 2021 safety alert restricted endovascular gelfoam use in Australia and resulted in an embargo on gelfoam sales to Interventional Radiology departments. This study aimed to show that gelfoam is safe in a population of trauma patients with pelvic injury, and discuss the basis of the recent controversies.

Methods
Retrospective cohort study was conducted between 1 January 2010 and 21 May 2021 for the patients who underwent gelfoam embolization for pelvic arterial haemorrhage. Primary outcome was the rate of adverse events related to intravascular gelfoam administration.

Results
Inclusion criteria met in 50 patients, comprising 58% males median age 59.9 years, and median injury severity score 31. There were 0 complications related to gelfoam use and 100% technical success. Thirty-five patients (70%) received a non-targeted embolization approach. All-cause mortality was observed in 5 patients (10%), unrelated to gelfoam.

Conclusions
Gelfoam is a safe and effective embolic agent in pelvic trauma. Patients are in urgent need of universal on-label registration of endovascular gelfoam products, as it is life-saving in major haemorrhage after trauma.

Advances in knowledge
Endovascular gelfoam is mandatory for a high-quality trauma service, and this study shows that it is safe to use intentionally in the endovascular space. Companies should work with interventional radiologists, sharing and collaborating to ensure positive outcomes for patients.