bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluating AI in medicine: a comparative analysis of expert and ChatGPT responses to colorectal cancer questions
Authors: Peng Wen , feng Yifei , Yao Cui , Zhang Sheng , Zhuo Han , Qiu Tianzhu , Zhang Yi , Tang Junwei , Gu Yanhong , Sun Yueming 
Published: February 2024

AbstractColorectal cancer (CRC) is a global health challenge, and patient education plays a crucial role in its early detection and treatment. Despite progress in AI technology, as exemplified by transformer-like models such as ChatGPT, there remains a lack of in-depth understanding of their efficacy for medical purposes. We aimed to assess the proficiency of ChatGPT in the field of popular science, specifically in answering questions related to CRC diagnosis and treatment, using the book “Colorectal Cancer: Your Questions Answered” as a reference. In general, 131 valid questions from the book were manually input into ChatGPT. Responses were evaluated by clinical physicians in the relevant fields based on comprehensiveness and accuracy of information, and scores were standardized for comparison. Not surprisingly, ChatGPT showed high reproducibility in its responses, with high uniformity in comprehensiveness, accuracy, and final scores. However, the mean scores of ChatGPT’s responses were significantly lower than the benchmarks, indicating it has not reached an expert level of competence in CRC. While it could provide accurate information, it lacked in comprehensiveness. Notably, ChatGPT performed well in domains of radiation therapy, interventional therapy, stoma care, venous care, and pain control, almost rivaling the benchmarks, but fell short in basic information, surgery, and internal medicine domains. While ChatGPT demonstrated promise in specific domains, its general efficiency in providing CRC information falls short of expert standards, indicating the need for further advancements and improvements in AI technology for patient education in healthcare.
Development of stable S-scheme 2D–2D g-C3N4/CdS nanoheterojunction arrays for enhanced visible light photomineralisation of nitrophenol priority water pollutants
Authors: Saad Muhammad , Bahadur Ali , Iqbal Shahid , Mahmood Sajid , Tayyab Muhammad , Alshalwi Matar , Shah Mazloom 
Published: February 2024

AbstractThe investigation focused on creating and studying a new 2D–2D S-scheme CdS/g-C3N4 heterojunction photocatalyst. Various techniques examined its structure, composition, and optical properties. This included XRD, XPS, EDS, SEM, TEM, HRTEM, DRS, and PL. The heterojunction showed a reduced charge recombination rate and more excellent stability, helping to lessen photocorrosion. This was due to photogenerated holes moving more quickly out of the CdS valence band. The interface between g-C3N4 and CdS favored a synergistic charge transfer. A suitable flat band potential measurement supported enhanced reactive oxygen species (ROS) generation in degrading 4-nitrophenol and 2-nitrophenol. This resulted in remarkable degradation efficiency of up to 99% and mineralization of up to 79%. The findings highlighted the practical design of the new 2D–2D S-scheme CdS/g-C3N4 heterojunction photocatalyst and its potential application in various energy and environmental settings, such as pollutant removal, hydrogen production, and CO2 conversion.
Reactive transport modeling of organic carbon degradation in marine methane hydrate systems
Authors: Wei Li , Malinverno Alberto , Colwell Frederick , Goldberg David S. 
Published: February 2024

AbstractNatural methane hydrate has often been observed in sand layers that contain no particulate organic carbon (POC), but are surrounded by organic-rich, fine-grained marine muds. In this paper, we develop a reactive transport model (RTM) of a microbially-mediated set of POC degradation reactions, including hydrolysis of POC driven by extracellular enzymes, fermentation of the resulting high-molecular weight dissolved organic carbon (HMW-DOC), and methanogenesis that consumes low-molecular weight dissolved organic carbon (LMW-DOC). These processes are mediated by two groups of microbes, fermenters and methanogens that are heterogeneously distributed in different lithologies, with the largest numbers of microbes in the large pores of coarse-grained layers. We find that the RTM can reproduce methane hydrate occurrences observed in two different geological environments, at Walker Ridge Site 313-H (Gulf of Mexico) and IODP Site U1325 (Cascadia Margin). We also find that microbes can degrade POC even if they are physically separated, as extracellular enzymes and DOC can diffuse away from where they are produced by microbes. Microbial activity is highest at relatively early times after burial at shallow depths and near lithological boundaries, where concentration gradients transport solutes to intervals that contain the most microbes.
The role of the six factors model of athletic mental energy in mediating athletes’ well-being in competitive sports
Authors: Singh Amisha , Kaur Arora Mandeep , Boruah Bahniman 
Published: February 2024

AbstractIn the realm of high-performance sports, athletes often prioritize success at the expense of their well-being. Consequently, sports psychology researchers are now focusing on creating psychological profiles for athletes that can forecast their performance while safeguarding their overall well-being. A recent development in this field is the concept of athletic mental energy (AME), which has been associated with both sporting success and positive emotions. Therefore, the aim of this study was to explore if AME in athletes can mediate this directly observed relationship between performance and psychological well-being. For stronger predictive validity these relationships were examined across two studies with each involving distinct sets of participants engaged in various sports disciplines, including football, cricket, basketball, archery, and more. The self-report measures of sports performance, athletic mental energy (AME), and psychological well-being (PWB) were administered post-competition on the local, regional, state, national, international, and professional level athletes of age 18 and above. Our study found that both, the affective and cognitive components of AME mediated the athletes’ performance and psychological well–being relationship. Interestingly, the study found no significant gender differences in AME and PWB scores. While family structures didn’t yield significant variations in AME scores, there were some descriptive distinctions in PWB scores across different family structures. Our research offers preliminary evidence suggesting that AME can play a pivotal role in preserving athletes’ psychological well-being following competitive events.
Improving the efficiency of DNA extraction from iron incrustations and oilfield-produced water
Authors: Foysal Md Javed , Salgar-Chaparro Silvia J. 
Published: February 2024

AbstractThe quantity and quality of DNA isolated from environmental samples are crucial for getting robust high-throughput sequencing data commonly used for microbial community analysis. The differences in the nature and physicochemical properties of environmental samples impact DNA yields, and therefore, an optimisation of the protocols is always recommended. For instance, samples collected from corroded areas contain high concentrations of metals, salts, and hydrocarbons that can interfere with several steps of the DNA extraction protocols, thereby reducing yield and quality. In this study, we compared the efficiency of commercially available DNA extraction kits and laboratory-adopted methods for microbial community analysis of iron incrustations and oilfield-produced water samples. Modifications to the kits manufacturers’ protocols were included to maximise the yield and quality. For iron incrustations, the modified protocol for FastDNA Spin Kit for Soil yielded higher DNA and resulted in higher diversity, including the recovery of low-abundant and rare taxa in the samples, compared to DNeasy PowerSoil Pro Kit. The DNA extracted with modified phenol–chloroform methods yielded higher DNA but failed to pass quality control PCR for 16S sequencing with and without purification. The protocols mentioned here can be used to maximise DNA recovery from iron incrustations and oilfield-produced water samples.
Design of a binary programmable transmitarray based on phase change material for beam steering applications in D-band
Authors: Gharbieh Samara , Milbrandt Jorick , Reig Bruno , Mercier Denis , Allain Marjolaine , Clemente Antonio 
Published: February 2024

AbstractThis paper introduces the design of a reconfigurable transmitarray operating within the D-band frequency range (110–170 GHz). The transmitarray unit cell is composed of three metal layers and two quartz dielectric substrates. It achieves a 1-bit phase shift resolution through the alternating states of two innovative switches integrated into the active transmitting patch of the unit cell. To address the challenge of miniaturization in the D-band, compact switches compatible with the proposed unit cell dimensions are introduced. These switches are constructed using phase change materials (PCM) that change between amorphous and crystalline states when exposed to heat. The paper includes a full-wave simulation of the unit cell, demonstrating an insertion loss below 1.5 dB across a wide frequency band of 27%. Additionally, a 10 $$\times $$
×
10 elements transmitarray is synthesized using a numerical tool and its theoretical results are compared to full-wave electromagnetic simulations for validation purposes. The results indicate that by incorporating the proposed switches into the unit cell, the transmitarray achieves promising reconfiguration capabilities within the D-band. Moreover, the paper presents the architecture of a command line designed to bias the PCM switches. Notably, this command line represents a novel approach, as it enables individual biasing of each PCM switch using direct current (DC). The influence of these command lines on the transmitarray’s performance is thoroughly investigated. Although there is a compromise in the 1-dB gain bandwidth, the overall behavior of the transmitarray remains encouraging.
A large therian mammal from the Late Cretaceous of South America
Authors: Chimento Nicolás R. , Agnolín Federico L. , García-Marsà Jordi , Manabe Makoto , Tsuihiji Takanobu , Novas Fernando E. 
Published: February 2024

AbstractTheria represent an extant clade that comprises placental and marsupial mammals. Here we report on the discovery of a new Late Cretaceous mammal from southern Patagonia, Patagomaia chainko gen. et sp. nov., represented by hindlimb and pelvic elements with unambiguous therian features. We estimate Patagomaia chainko attained a body mass of 14 kg, which is considerably greater than the 5 kg maximum body mass of coeval Laurasian therians. This new discovery demonstrates that Gondwanan therian mammals acquired large body size by the Late Cretaceous, preceding their Laurasian relatives, which remained small-bodied until the beginning of the Cenozoic. Patagomaia supports the view that the Southern Hemisphere was a cradle for the evolution of modern mammalian clades, alongside non-therian extinct groups such as meridiolestidans, gondwanatherians and monotremes.
Unsupervised machine learning combined with 4D scanning transmission electron microscopy for bimodal nanostructural analysis
Authors: Kimoto Koji , Kikkawa Jun , Harano Koji , Cretu Ovidiu , Shibazaki Yuki , Uesugi Fumihiko 
Published: February 2024

AbstractUnsupervised machine learning techniques have been combined with scanning transmission electron microscopy (STEM) to enable comprehensive crystal structure analysis with nanometer spatial resolution. In this study, we investigated large-scale data obtained by four-dimensional (4D) STEM using dimensionality reduction techniques such as non-negative matrix factorization (NMF) and hierarchical clustering with various optimization methods. We developed software scripts incorporating knowledge of electron diffraction and STEM imaging for data preprocessing, NMF, and hierarchical clustering. Hierarchical clustering was performed using cross-correlation instead of conventional Euclidean distances, resulting in rotation-corrected diffractions and shift-corrected maps of major components. An experimental analysis was conducted on a high-pressure-annealed metallic glass, Zr-Cu-Al, revealing an amorphous matrix and crystalline precipitates with an average diameter of approximately 7 nm, which were challenging to detect using conventional STEM techniques. Combining 4D-STEM and optimized unsupervised machine learning enables comprehensive bimodal (i.e., spatial and reciprocal) analyses of material nanostructures.
Early transcriptomic responses of rice leaves to herbivory by Spodoptera frugiperda
Authors: Leclerc Laëtitia , Nguyen Trang Hieu , Duval Pénélope , Mariotti Victoria , Petitot Anne-Sophie , Orjuela Julie , Ogier Jean-Claude , Gaudriault Sophie , Champion Antony , Nègre Nicolas 
Published: February 2024

AbstractDuring herbivory, chewing insects deposit complex oral secretions (OS) onto the plant wound. Understanding how plants respond to the different cues of herbivory remains an active area of research. In this study, we used an herbivory-mimick experiment to investigate the early transcriptional response of rice plants leaves to wounding, OS, and OS microbiota from Spodoptera frugiperda larvae. Wounding induced a massive early response associated to hormones such as jasmonates. This response switched drastically upon OS treatment indicating the activation of OS specific pathways. When comparing native and dysbiotic OS treatments, we observed few gene regulation. This suggests that in addition to wounding the early response in rice is mainly driven by the insect compounds of the OS rather than microbial. However, microbiota affected genes encoding key phytohormone synthesis enzymes, suggesting an additional modulation of plant response by OS microbiota.
Prediction of the axial compression capacity of stub CFST columns using machine learning techniques
Authors: Megahed Khaled , Mahmoud Nabil Said , Abd-Rabou Saad Elden Mostafa 
Published: February 2024

AbstractConcrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing uncertainty for engineering designers. Moreover, conventional regression analysis fails to accurately predict the intricate relationship between column properties and compressive strength. To address these issues, this study proposes the use of two machine learning (ML) models—Gaussian process regression (GPR) and symbolic regression (SR). These models accept a variety of input variables, encompassing geometric and material properties of stub CFST columns, to estimate their strength. An experimental database of 1316 specimens was compiled from various research papers, including circular, rectangular, and double-skin stub CFST columns. In addition, a dimensionless output variable, referred to as the strength index, is introduced to enhance model performance. To validate the efficiency of the introduced models, predictions from these models are compared with those from two established standard codes and various ML algorithms, including support vector regression optimized with particle swarm optimization (PSVR), artificial neural networks, XGBoost (XGB), CatBoost (CATB), Random Forest, and LightGBM models. Through performance metrics, the CATB, GPR, PSVR and XGB models emerge as the most accurate and reliable models from the evaluation results. In addition, simple and practical design equations for the different types of CFST columns have been proposed based on the SR model. The developed ML models and proposed equations can predict the compressive strength of stub CFST columns with reliable and accurate results, making them valuable tools for structural engineering. Furthermore, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The results of the feature analysis reveal that section slenderness ratio and concrete strength parameters negatively impact the compressive strength index.
Clinico-radiological features of intracranial atherosclerosis-related large vessel occlusion prior to endovascular treatment
Authors: Rodrigo-Gisbert Marc , García-Tornel Alvaro , Requena Manuel , Vielba-Gómez Isabel , Bashir Saima , Rubiera Marta , De Dios Lascuevas Marta , Olivé-Gadea Marta , Piñana Carlos , Rizzo Federica , Muchada Marian , Rodriguez-Villatoro Noelia , Rodríguez-Luna David , Juega Jesus , Pagola Jorge , Hernández David , Molina Carlos A. , Terceño Mikel , Tomasello Alejandro , Ribo Marc 
Published: February 2024

AbstractThe identification of large vessel occlusion with underlying intracranial atherosclerotic disease (ICAS-LVO) before endovascular treatment (EVT) continues to be a challenge. We aimed to analyze baseline clinical-radiological features associated with ICAS-LVO that could lead to a prompt identification. We performed a retrospective cross-sectional study of consecutive patients with stroke treated with EVT from January 2020 to April 2022. We included anterior LVO involving intracranial internal carotid artery and middle cerebral artery. We analyzed baseline clinical and radiological variables associated with ICAS-LVO and evaluated the diagnostic value of a multivariate logistic regression model to identify ICAS-LVO before EVT. ICAS-LVO was defined as presence of angiographic residual stenosis or a trend to re-occlusion during EVT procedure. A total of 338 patients were included in the study. Of them, 28 patients (8.3%) presented with ICAS-LVO. After adjusting for confounders, absence of atrial fibrillation (OR 9.33, 95% CI 1.11–78.42; p = 0.040), lower hypoperfusion intensity ratio (HIR [Tmax > 10 s/Tmax > 6 s ratio], (OR 0.69, 95% CI 0.50–0.95; p = 0.025), symptomatic intracranial artery calcification (IAC, OR .15, 95% CI 1.64–26.42, p = 0.006), a more proximal occlusion (ICA, MCA-M1: OR 4.00, 95% CI 1.23–13.03; p = 0.021), and smoking (OR 2.91, 95% CI 1.08–7.90; p = 0.035) were associated with ICAS-LVO. The clinico-radiological model showed an overall well capability to identify ICAS-LVO (AUC = 0.88, 95% CI 0.83–0.94; p < 0.001). In conclusion, a combination of clinical and radiological features available before EVT can help to identify an ICAS-LVO. This approach could be useful to perform a rapid assessment of underlying etiology and suggest specific pathophysiology-based measures. Prospective studies are needed to validate these findings in other populations.
Automated resolution of the spiral torsion spring inverse design problem
Authors: Silva Alejandro , López-Navarrete Gonzalo , García-Martos Carlos , Muñoz-Guijosa Juan Manuel 
Published: February 2024

AbstractMany mechanical applications take advantage of spiral torsion springs due to their robustness, compactness, and simplicity. Brand-new manufacturing methods allow to create spiral springs with unconventional geometries and materials that suit a wider range of uses demanding either linearity or nonlinearity. Designing a spiral torsion spring with a nonlinear desired torque curve may be a great challenge, due to their many degrees-of-freedom (length, width, thickness, arbor, and barrel diameters, etc.) and the complexity of the geometrical and mechanical requirements to ensure their manufacturability, system compatibility, operation safety and reliability; and the solution is never unique. This manuscript proposes and validates an innovative methodology for the resolution of this inverse design problem based on the application of a nonlinear restrained global optimization algorithm. This algorithm is adjusted to converge, out of the infinity of designs that match the desired torque curve and hold all the functional and manufacturing constraints, to a design solution that minimizes strip mass. The methodology is built on a formulation for the calculation of the torque curve of a generalized spiral spring, with or without coiling and with any along-the-length cross-section, already published by the authors.
Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases
Authors: Yuan Xiao , Su Jieqiong , Wang Jing , Dai Bing , Sun Yanfang , Zhang Keke , Li Yinghua , Chuan Jun , Tang Chunyan , Yu Yan , Gong Qiang 
Published: February 2024

AbstractPhenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and singleton mode. With a large-scale family dataset from Deciphering Developmental Disorders (DDD) project (N = 305) and an in-house trio cohort (N = 152), the four optimal performers in our prior study including Exomiser, PhenIX, AMELIE, and LIRCIAL were further assessed through parameter optimization and/or the utilization of trio mode. The in-depth assessment revealed high diagnostic yields of the four prioritizers with refined preferences, each alone or together: (1) 83.3–91.8% of the causal genes were presented among the first ten candidates in the final ranking lists of the four tools; (2) Over 97.7% of the causal genes were successfully captured within the top 50 by either of the four software. Exomiser did best in directly hitting the target (ranking the causal gene at the very top) while LIRICAL displayed a predominant overall detection capability. Besides, cases affected by low-penetrance and high-frequency pathogenic variants were found misjudged during the automated prioritization process. The discovery of the limitations shed light on the specific directions of future enhancement for causal-gene ranking tools.
The effect of data resampling methods in radiomics
Authors: Demircioğlu Aydin 
Published: February 2024

AbstractRadiomic datasets can be class-imbalanced, for instance, when the prevalence of diseases varies notably, meaning that the number of positive samples is much smaller than that of negative samples. In these cases, the majority class may dominate the model's training and thus negatively affect the model's predictive performance, leading to bias. Therefore, resampling methods are often utilized to class-balance the data. However, several resampling methods exist, and neither their relative predictive performance nor their impact on feature selection has been systematically analyzed. In this study, we aimed to measure the impact of nine resampling methods on radiomic models utilizing a set of fifteen publicly available datasets regarding their predictive performance. Furthermore, we evaluated the agreement and similarity of the set of selected features. Our results show that applying resampling methods did not improve the predictive performance on average. On specific datasets, slight improvements in predictive performance (+ 0.015 in AUC) could be seen. A considerable disagreement on the set of selected features was seen (only 28.7% of features agreed), which strongly impedes feature interpretability. However, selected features are similar when considering their correlation (82.9% of features correlated on average).