bluuebunny/crossref_metadata_2025_split huggingface dataset:
Morphological entropy encodes cellular migration strategies on multiple length scales
Authors: Liu Yanping , Jiao Yang , Fan Qihui , Li Xinwei , Liu Zhichao , Qin Dui , Hu Jun , Liu Liyu , Shuai Jianwei , Li Zhangyong 
Published: March 2024

AbstractCell migration is crucial for numerous physiological and pathological processes. A cell adapts its morphology, including the overall and nuclear morphology, in response to various cues in complex microenvironments, such as topotaxis and chemotaxis during migration. Thus, the dynamics of cellular morphology can encode migration strategies, from which diverse migration mechanisms can be inferred. However, deciphering the mechanisms behind cell migration encoded in morphology dynamics remains a challenging problem. Here, we present a powerful universal metric, the Cell Morphological Entropy (CME), developed by combining parametric morphological analysis with Shannon entropy. The utility of CME, which accurately quantifies the complex cellular morphology at multiple length scales through the deviation from a perfectly circular shape, is illustrated using a variety of normal and tumor cell lines in different in vitro microenvironments. Our results show how geometric constraints affect the MDA-MB-231 cell nucleus, the emerging interactions of MCF-10A cells migrating on collagen gel, and the critical transition from proliferation to invasion in tumor spheroids. The analysis demonstrates that the CME-based approach provides an effective and physically interpretable tool to measure morphology in real-time across multiple length scales. It provides deeper insight into cell migration and contributes to the understanding of different behavioral modes and collective cell motility in more complex microenvironments.
GOES-R land surface products at Western Hemisphere eddy covariance tower locations
Authors: Losos Danielle , Hoffman Sophie , Stoy Paul C. 
Published: March 2024

AbstractThe terrestrial carbon cycle varies dynamically on hourly to weekly scales, making it difficult to observe. Geostationary (“weather”) satellites like the Geostationary Environmental Operational Satellite - R Series (GOES-R) deliver near-hemispheric imagery at a ten-minute cadence. The Advanced Baseline Imager (ABI) aboard GOES-R measures visible and near-infrared spectral bands that can be used to estimate land surface properties and carbon dioxide flux. However, GOES-R data are designed for real-time dissemination and are difficult to link with eddy covariance time series of land-atmosphere carbon dioxide exchange. We compiled three-year time series of GOES-R land surface attributes including visible and near-infrared reflectances, land surface temperature (LST), and downwelling shortwave radiation (DSR) at 314 ABI fixed grid pixels containing eddy covariance towers. We demonstrate how to best combine satellite and in-situ datasets and show how ABI attributes useful for ecosystem monitoring vary across space and time. By connecting observation networks that infer rapid changes to the carbon cycle, we can gain a richer understanding of the processes that control it.
A novel optimized neural network model for cyber attack detection using enhanced whale optimization algorithm
Authors: Jyothi Koganti Krishna , Borra Subba Reddy , Srilakshmi Koganti , Balachandran Praveen Kumar , Reddy Ganesh Prasad , Colak Ilhami , Dhanamjayulu C. , Chinthaginjala Ravikumar , Khan Baseem 
Published: March 2024

AbstractCybersecurity is critical in today’s digitally linked and networked society. There is no way to overestimate the importance of cyber security as technology develops and becomes more pervasive in our daily lives. Cybersecurity is essential to people’s protection. One type of cyberattack known as “credential stuffing” involves using previously acquired usernames and passwords by attackers to access user accounts on several websites without authorization. This is feasible as a lot of people use the same passwords and usernames on several different websites. Maintaining the security of online accounts requires defence against credential-stuffing attacks. The problems of credential stuffing attacks, failure detection, and prediction can be handled by the suggested EWOA-ANN model. Here, a novel optimization approach known as Enhanced Whale Optimization Algorithm (EWOA) is put on to train the neural network. The effectiveness of the suggested attack identification model has been demonstrated, and an empirical comparison will be carried out with respect to specific security analysis.
Detecting cognitive traits and occupational proficiency using EEG and statistical inference
Authors: Mikheev Ilya , Steiner Helen , Martynova Olga 
Published: March 2024

AbstractMachine learning (ML) is widely used in classification tasks aimed at detecting various cognitive states or neurological diseases using noninvasive electroencephalogram (EEG) time series. However, successfully detecting specific cognitive skills in a healthy population, independent of subject, remains challenging. This study compared the subject-independent classification performance of three different pipelines: supervised and Riemann projections with logistic regression and handcrafted power spectral features with light gradient boosting machine (LightGBM). 128-channel EEGs were recorded from 26 healthy volunteers while they solved arithmetic, logical, and verbal tasks. The participants were divided into two groups based on their higher education and occupation: specialists in mathematics and humanities. The balanced accuracy of the education type was significantly above chance for all pipelines: 0.84–0.89, 0.85–0.88, and 0.86–0.88 for each type of task, respectively. All three pipelines allowed us to distinguish mathematical proficiency based on learning experience with different trade-offs between performance and explainability. Our results suggest that ML approaches could also be effective for recognizing individual cognitive traits using EEG.
Reservoir rock typing assessment in a coal-tight sand based heterogeneous geological formation through advanced AI methods
Authors: Ashraf Umar , Shi Wanzhong , Zhang Hucai , Anees Aqsa , Jiang Ren , Ali Muhammad , Mangi Hassan Nasir , Zhang Xiaonan 
Published: March 2024

AbstractGeoscientists now identify coal layers using conventional well logs. Coal layer identification is the main technical difficulty in coalbed methane exploration and development. This research uses advanced quantile–quantile plot, self-organizing maps (SOM), k-means clustering, t-distributed stochastic neighbor embedding (t-SNE) and qualitative log curve assessment through three wells (X4, X5, X6) in complex geological formation to distinguish coal from tight sand and shale. Also, we identify the reservoir rock typing (RRT), gas-bearing and non-gas bearing potential zones. Results showed gamma-ray and resistivity logs are not reliable tools for coal identification. Further, coal layers highlighted high acoustic (AC) and neutron porosity (CNL), low density (DEN), low photoelectric, and low porosity values as compared to tight sand and shale. While, tight sand highlighted 5–10% porosity values. The SOM and clustering assessment provided the evidence of good-quality RRT for tight sand facies, whereas other clusters related to shale and coal showed poor-quality RRT. A t-SNE algorithm accurately distinguished coal and was used to make CNL and DEN plot that showed the presence of low-rank bituminous coal rank in study area. The presented strategy through conventional logs shall provide help to comprehend coal-tight sand lithofacies units for future mining.
Decision-making processes in perceptual learning depend on effectors
Authors: Ivanov Vladyslav , Manenti Giorgio L. , Plewe Sandrin S. , Kagan Igor , Schwiedrzik Caspar M. 
Published: March 2024

AbstractVisual perceptual learning is traditionally thought to arise in visual cortex. However, typical perceptual learning tasks also involve systematic mapping of visual information onto motor actions. Because the motor system contains both effector-specific and effector-unspecific representations, the question arises whether visual perceptual learning is effector-specific itself, or not. Here, we study this question in an orientation discrimination task. Subjects learn to indicate their choices either with joystick movements or with manual reaches. After training, we challenge them to perform the same task with eye movements. We dissect the decision-making process using the drift diffusion model. We find that learning effects on the rate of evidence accumulation depend on effectors, albeit not fully. This suggests that during perceptual learning, visual information is mapped onto effector-specific integrators. Overlap of the populations of neurons encoding motor plans for these effectors may explain partial generalization. Taken together, visual perceptual learning is not limited to visual cortex, but also affects sensorimotor mapping at the interface of visual processing and decision making.
The model student: GPT-4 performance on graduate biomedical science exams
Authors: Stribling Daniel , Xia Yuxing , Amer Maha K. , Graim Kiley S. , Mulligan Connie J. , Renne Rolf 
Published: March 2024

AbstractThe GPT-4 large language model (LLM) and ChatGPT chatbot have emerged as accessible and capable tools for generating English-language text in a variety of formats. GPT-4 has previously performed well when applied to questions from multiple standardized examinations. However, further evaluation of trustworthiness and accuracy of GPT-4 responses across various knowledge domains is essential before its use as a reference resource. Here, we assess GPT-4 performance on nine graduate-level examinations in the biomedical sciences (seven blinded), finding that GPT-4 scores exceed the student average in seven of nine cases and exceed all student scores for four exams. GPT-4 performed very well on fill-in-the-blank, short-answer, and essay questions, and correctly answered several questions on figures sourced from published manuscripts. Conversely, GPT-4 performed poorly on questions with figures containing simulated data and those requiring a hand-drawn answer. Two GPT-4 answer-sets were flagged as plagiarism based on answer similarity and some model responses included detailed hallucinations. In addition to assessing GPT-4 performance, we discuss patterns and limitations in GPT-4 capabilities with the goal of informing design of future academic examinations in the chatbot era.
Use of improved memory type control charts for monitoring cancer patients recovery time censored data
Authors: Raza Syed Muhammad Muslim , Sial Maqbool Hussain , Hassan Najam ul , Mekiso Getachew Tekle , Tashkandy Yusra A. , Bakr M. E. , Kumar Anoop 
Published: March 2024

AbstractControl charts are a statistical approach for monitoring cancer data that can assist discover patterns, trends, and unusual deviations in cancer-related data across time. To detect deviations from predicted patterns, control charts are extensively used in quality control and process management. Control charts may be used to track numerous parameters in cancer data, such as incidence rates, death rates, survival time, recovery time, and other related indicators. In this study, CDEC chart is proposed to monitor the cancer patients recovery time censored data. This paper presents a composite dual exponentially weighted moving average Cumulative sum (CDEC) control chart for monitoring cancer patients recovery time censored data. This approach seeks to detect changes in the mean recovery time of cancer patients which usually follows Weibull lifetimes. The results are calculated using type I censored data under known and estimated parameter conditions. We combine the conditional expected value (CEV) and conditional median (CM) approaches, which are extensively used in statistical analysis to determine the central tendency of a dataset, to create an efficient control chart. The suggested chart's performance is assessed using the average run length (ARL), which evaluates how efficiently the chart can detect a change in the process mean. The CDEC chart is compared to existing control charts. A simulation study and a real-world data set related to cancer patients recovery time censored data is used for results illustration. The proposed CDEC control chart is developed for the data monitoring when complete information about the patients are not available. So, instead of doping the patients information we can used the proposed chart to monitor the patients information even if it is censored. The authors conclude that the suggested CDEC chart is more efficient than competitor control charts for monitoring cancer patients recovery time censored data. Overall, this study introduces an efficient new approach for cancer patients recovery time censored data, which might have significant effect on quality control and process improvement across a wide range of healthcare and medical studies.
Anger and disgust shape judgments of social sanctions across cultures, especially in high individual autonomy societies
Authors: Andersson Per A. , Vartanova Irina , Västfjäll Daniel , Tinghög Gustav , Strimling Pontus , Wu Junhui , Hazin Isabela , Akotia Charity S. , Aldashev Alisher , Andrighetto Giulia , Anum Adote , Arikan Gizem , Bagherian Fatemeh , Barrera Davide , Basnight-Brown Dana , Batkeyev Birzhan , Berezina Elizaveta , Björnstjerna Marie , Boski Paweł , Bovina Inna , Huyen Bui Thi Thu , Čekrlija Đorđe , Choi Hoon-Seok , Contreras-Ibáñez Carlos C. , Costa-Lopes Rui , de Barra Mícheál , de Zoysa Piyanjali , Dorrough Angela R. , Dvoryanchikov Nikolay , Engelmann Jan B. , Euh Hyun , Fang Xia , Fiedler Susann , Foster-Gimbel Olivia A. , Fülöp Márta , Gardarsdottir Ragna B. , Gill C. M. Hew D. , Glöckner Andreas , Graf Sylvie , Grigoryan Ani , Gritskov Vladimir , Growiec Katarzyna , Halama Peter , Hartanto Andree , Hopthrow Tim , Hřebíčková Martina , Iliško Dzintra , Imada Hirotaka , Kapoor Hansika , Kawakami Kerry , Khachatryan Narine , Kharchenko Natalia , Kiyonari Toko , Kohút Michal , Leslie Lisa M. , Li Yang , Li Norman P. , Li Zhuo , Liik Kadi , Maitner Angela T. , Manhique Bernardo , Manley Harry , Medhioub Imed , Mentser Sari , Nejat Pegah , Nipassa Orlando , Nussinson Ravit , Onyedire Nneoma G. , Onyishi Ike E. , Panagiotopoulou Penny , Perez-Floriano Lorena R. , Persson Minna , Pirttilä-Backman Anna-Maija , Pogosyan Marianna , Raver Jana , Rodrigues Ricardo Borges , Romanò Sara , Romero Pedro P. , Sakki Inari , San Martin Alvaro , Sherbaji Sara , Shimizu Hiroshi , Simpson Brent , Szabo Erna , Takemura Kosuke , Teixeira Maria Luisa Mendes , Thanomkul Napoj , Tiliouine Habib , Travaglino Giovanni A. , Tsirbas Yannis , Widodo Sita , Zein Rizqy , Zirganou-Kazolea Lina , Eriksson Kimmo 
Published: March 2024

AbstractWhen someone violates a social norm, others may think that some sanction would be appropriate. We examine how the experience of emotions like anger and disgust relate to the judged appropriateness of sanctions, in a pre-registered analysis of data from a large-scale study in 56 societies. Across the world, we find that individuals who experience anger and disgust over a norm violation are more likely to endorse confrontation, ostracism and, to a smaller extent, gossip. Moreover, we find that the experience of anger is consistently the strongest predictor of judgments of confrontation, compared to other emotions. Although the link between state-based emotions and judgments may seem universal, its strength varies across countries. Aligned with theoretical predictions, this link is stronger in societies, and among individuals, that place higher value on individual autonomy. Thus, autonomy values may increase the role that emotions play in guiding judgments of social sanctions.
Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study
Authors: Huang Jiahao , Ferreira Pedro F. , Wang Lichao , Wu Yinzhe , Aviles-Rivero Angelica I. , Schönlieb Carola-Bibiane , Scott Andrew D. , Khalique Zohya , Dwornik Maria , Rajakulasingam Ramyah , De Silva Ranil , Pennell Dudley J. , Nielles-Vallespin Sonia , Yang Guang 
Published: March 2024

AbstractIn vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights into cardiac function and enabling the development of innovative therapeutic strategies. However, the integration of cDTI into routine clinical practice poses challenging due to the technical obstacles involved in the acquisition, such as low signal-to-noise ratio and prolonged scanning times. In this study, we investigated and implemented three different types of deep learning-based MRI reconstruction models for cDTI reconstruction. We evaluated the performance of these models based on the reconstruction quality assessment, the diffusion tensor parameter assessment as well as the computational cost assessment. Our results indicate that the models discussed in this study can be applied for clinical use at an acceleration factor (AF) of $$\times 2$$

×
2

and $$\times 4$$

×
4

, with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores. There is no statistical difference from the reference for all diffusion tensor parameters at AF $$\times 2$$

×
2

or most DT parameters at AF $$\times 4$$

×
4

, and the quality of most diffusion tensor parameter maps is visually acceptable. SwinMR is recommended as the optimal approach for reconstruction at AF $$\times 2$$

×
2

and AF $$\times 4$$

×
4

. However, we believe that the models discussed in this study are not yet ready for clinical use at a higher AF. At AF $$\times 8$$

×
8

, the performance of all models discussed remains limited, with only half of the diffusion tensor parameters being recovered to a level with no statistical difference from the reference. Some diffusion tensor parameter maps even provide wrong and misleading information.
Motivated reasoning about climate change and the influence of Numeracy, Need for Cognition, and the Dark Factor of Personality
Authors: Hutmacher Fabian , Reichardt Regina , Appel Markus 
Published: March 2024

AbstractHuman information processing is not always rational but influenced by prior attitudes, a phenomenon commonly known as motivated reasoning. We conducted two studies (N1 = 556, N2 = 1198; UK samples) investigating motivated reasoning in the context of climate change with a focus on individual differences as potential moderating factors. While previous research investigated motivated reasoning regarding the debate whether climate change is anthropogenic, we focused on current discourses about the effectiveness of different countermeasures. To this end, participants evaluated fictitious scientific data on the effectiveness of regulations to reduce CO2 emissions. In both studies, participants exhibited motivated reasoning as indicated by the observation that prior attitudes about CO2 reduction policies predicted evaluation of the scientific data. The degree of motivated reasoning was not related to individual difference variables, namely the ability to understand and reason with numbers (Numeracy), the willingness to show this ability (Need for Cognition), and the tendency to maximize one’s individual utility (Dark Factor of Personality). However, numeracy was associated with a less biased interpretation of the presented information. Our research demonstrates that motivated reasoning is a general phenomenon, and points to numerical training as one way to improve reasoning.
High inflammatory cytokines gene expression can be detected in workers with prolonged exposure to silver and silica nanoparticles in industries
Authors: Babaei Vahid , Ashtarinezhad Azadeh , Torshabi Maryam , Teimourian Shahram , Shahmirzaie Morteza , Abolghasemi Jamileh , Zeraatgar Gohardani Hamidreza , Vernousfaderani Eisa Kaveh , Shirazi Farshad H. 
Published: March 2024

AbstractOccupational health must be strictly considered in industries particularly in nanoparticle factories where workers were exposed to different types of chemicals. We measured the serum levels of inflammatory cytokines in workers who developed skin lesions after exposure to silver and silica nanoparticles. Using a questionnaire in this cross-sectional study, we identified 110 workers in nanoparticle industries who were exposed to silver and silica nanoparticles. We also included 40 healthy subjects as controls from the administrative department of the same factories who were not exposed to nanoparticles. Peripheral blood samples used to measure the mRNA levels of inflammatory cytokines by qRT-PCR. In comparison with the control group, the workers who developed skin lesions had significantly higher levels of interleukin IL4, IL6, IL8, and TNF-α, particularly after two or three decades of exposure to silver and silica nanoparticles. Participants who were exposed to silver had higher levels of IL6 and IL8 compared with those who were exposed to silica. Necessary measures must be considered to protect workers in nanoparticle industries against the potential toxic effects of these compounds. Our network pharmacology study suggests corresponding biochemical pathways for these disorders.
Evaluation of the mosquitocidal efficacy of fluralaner, a potential candidate for drug based vector control
Authors: Shah Harish Kumar , Srinivasan Vaishnavi , Venkatesan Shakila , Balakrishnan Vijayakumar , Candasamy Sadanandane , Mathew Nisha , Kumar Ashwani , Kuttiatt Vijesh Sreedhar 
Published: March 2024

AbstractVector control is a key intervention against mosquito borne diseases. However, conventional methods have several limitations and alternate strategies are in urgent need. Vector control with endectocides such as ivermectin is emerging as a novel strategy. The short half-life of ivermectin is a limiting factor for its application as a mass therapy tool for vector control. Isoxazoline compounds like fluralaner, a class of veterinary acaricides with long half-life hold promise as an alternative. However, information about their mosquitocidal effect is limited. We explored the efficacy of fluralaner against laboratory reared vector mosquitoes—Aedes aegypti, Anopheles stephensi, and, Culex quinquefasciatus. 24 h post-blood feeding, fluralaner showed a significant mosquitocidal effect with LC50 values in the range of 24.04–49.82 ng/mL for the three different mosquito species tested. Effects on life history characteristics (fecundity, egg hatch success, etc.) were also observed and significant effects were noted at drug concentrations of 20, 25 and 45 ng/mL for Ae. aegypti, An. stephensi, and, Cx. quinquefasciatus respectively. At higher drug concentration of 250 ng/mL, significant mortality was observed within 1–2 h of post blood feeding. Potent mosquitocidal effect coupled with its long half-life makes fluralaner an excellent candidate for drug based vector control strategies.
A chemo-mechanical model for describing sorption hysteresis in a glassy polyurethane
Authors: Foley Brandon L. , Matt Sarah M. , Castonguay Stephen T. , Sun Yunwei , Roy Pratanu , Glascoe Elizabeth A. , Sharma Hom N. 
Published: March 2024

AbstractHysteretic sorption and desorption of water is observed from 0 to 95% relative humidity and 298–333 K on a glassy polyurethane foam. It is postulated that sorption-induced swelling of the glassy polyurethane increases the concentration of accessible hydrogen-bonding adsorption sites for water. The accessibility of sites is kinetically controlled due to the restricted thermal motions of chains in the glassy polymer, causing a difference in accessible site concentrations during sorption and desorption. This discrepancy leads to hysteresis in the sorbed concentrations of water. A coupled chemo-mechanical model relating volumetric strain, adsorption site concentration, and sorbed water concentration is employed to describe water sorption hysteresis in the glassy polyurethane. This model not only describes the final mass uptake for each relative humidity step, but also captures the dynamics of water uptake, which exhibit diffusion and relaxation rate-controlled regimes.