bluuebunny/crossref_metadata_2025_split huggingface dataset:
“Assessing Predictability of Environmental Time Series With Statistical and Machine Learning Models”
Authors: Newlands Nathaniel K. , Lyubchich Vyacheslav 
Published: February 2025

ABSTRACTThe relative merits of machine learning and statistical methods are discussed recently by Bonas et al. 2004, who raise important open questions for the statistical community regarding the value‐added benefits of data science and the future role of environmental statistics. Specifically, they identify three major knowledge gaps where statistics is seen as crucial to strengthening inference in machine learning (ML): to provide an ML model‐based framework amenable to explainability, to determine the best approach for quantifying uncertainty in relation to complex environmental dynamics, and to comprehensively identify ML's value‐added benefits. We continue this discussion by exploring these general questions and sharing our perspective and insights from our modeling of marine and terrestrial ecosystem dynamics. We propose several lines of inquiry where environmental statisticians and data scientists could collaboratively advance predictive analytics.
Hypermotor temporal lobe epilepsy revisited‐expanding the spectrum
Authors: Rudrabhatla Pavan Kumar , Nandana Jayakumari , Vilanilam George C. , Thomas Bejoy , Kesavadas Chandrasekharan , Radhakrishnan Ashalatha , Menon Ramshekhar N. 
Published: February 2025

AbstractObjectiveTypical semiology with associated 5As (Antecedent, Aura, Arrest, Automatisms, and Amnesia) is the prototypical seizure manifestation of temporal lobe epilepsy (TLE) in adults. However, patients with TLE can present with seizure phenomenology referred to as “hypermotor TLE” (HMS‐TLE) which can mimic extratemporal focal epilepsy. We studied the clinico‐electrographic, imaging profile, and surgical outcomes of drug‐resistant HMS‐TLE patients.MethodsWe analyzed the ictal signs and electroencephalography data of HMS‐TLE patients who became seizure free following epilepsy surgery with a minimum follow‐up of 1 year and compared them with patients with typical TLE (1:2 ratio). Hypermotor seizures were identified based on the presence of stereotyped ictal complex movements at seizure onset including body rocking, pelvic thrusting, and bimanual and bipedal movements with/without affective components.ResultsOut of 684 patients with drug‐resistant TLE who underwent surgery during 2010–2020, 16 patients (2.34%) met criteria for HMS‐TLE and were compared with 32 patients with typical TLE. Predominant “hypermotor” components included motor agitation, bicycling/kicking movements, body gyration followed by violent automatisms, and pelvic thrusting. Age at epilepsy onset, age at video telemetry recording, age at surgery, duration of epilepsy at surgery, gender distribution, and presence of aura were comparable between the two groups. History of febrile seizures (31.3% vs. 65.6%; p‐value = .024), presence of hippocampal sclerosis on MRI, and histopathology (56.3% vs. 90.6%; p‐value = .006 and 50% vs. 96.9%; p‐value = .002, respectively) were lower, and temporal neocortical abnormalities on MRI (87.5% vs. 59.4%, p‐value = .048), dysplasia on histopathological evaluation (25% vs. 0%, p‐value = .003) were higher in HMS‐TLE. HMS‐TLE group had lower number of patients with focal‐onset ictal patterns (37.5% vs. 90.6%, p‐value = .0001).SignificanceHypermotor seizures are rare in TLE, however it does not preclude non‐invasive selection for surgery. Hence, physicians should be aware of this subtype of drug‐resistant TLE as early intervention can have good surgical outcomes, akin to typical TLE.
Integrating Deep Learning Into an Energy Framework for Rapid Regional Damage Assessment and Fragility Analysis Under Mainshock‐Aftershock Sequences
Authors: Tao Yuxuan , Xu Zhao‐Dong , Wei Yaxin , Liu Xin‐Yu , Dong Yao‐Rong , Dai Jun 
Published: February 2025

ABSTRACTThis study introduces an energy‐based framework for rapidly assessing damage and fragility in regional buildings under mainshock‐aftershock sequences. First, the ultimate energy of the structure is derived from the relationship between two time‐varying parameters: effective intrinsic energy and input energy. An energy‐based damage index is then defined, with uncertainties of the structure and earthquake quantified through Latin hypercube sampling. Subsequently, a Gaussian Process model, enhanced with K‐Means clustering and Bayesian optimization, is employed to predict the structural ultimate energy. A Convolutional Neural Network‐Long Short‐Term Memory Network with an Attention mechanism model with a weighted loss function is developed to capture the structural energy time‐history responses, integrating correlation analysis and hyperparameter optimization. A comparative analysis is performed with previous machine learning models. The framework's effectiveness is validated through a comparative study with the inter‐story drift ratio (IDR) index. Finally, the framework is applied to Zeytinburnu in Istanbul, Turkey. The results indicate that reducing the dimensionality of the database through correlation analysis effectively decreases data dimensions while maintaining accuracy. In rapid damage assessment tasks, energy is a superior damage indicator compared to IDR, as it correlates positively with critical parameters such as building height and peak ground acceleration (PGA). It enables a tenfold reduction in response data, enhancing training efficiency by 7.4 times. PGAas/PGAms of 1.0 is recommended for analyzing mainshock‐aftershock effects, providing a more comprehensive perspective on structural performance and ensuring a conservative estimate of regional structural fragility.
An Optimized Dual Generative Hyperbolic Graph Adversarial Network With Multi‐Factor Random Permutation Pseudo Algorithm Based Encryption for Secured Industrial Healthcare Data Transferring
Authors: Muthulakshmi R. Mahesh , Anithaashri T. P 
Published: February 2025

ABSTRACTThe industrial healthcare system suffers from severe security threats while sharing sensitive medical information. Inefficiency in processing data and misclassification of data are some of the problems faced by the system. This paper introduces a new framework, Deep Greylag‐GHGAN, to address these problems. The proposed framework consists of a Dual Generative Hyperbolic Attention Graph Adversarial Network (DG‐HGAN) with a Multi‐Factor Random Permutation Pseudo Algorithm‐based encryption system to monitor the health and ensure secure and efficient data transfer. The healthcare data undergo authentication based on a Multi‐Factor Role‐Based Access Control (MFRBAC). Then encryption with Improved Secure Encryption with Energy Optimization using Random Permutation Pseudo Algorithm (ISEEO‐RPPA) is implemented to secure the data transfer. To clean it, pre‐processing by Grid Constrained Data Cleansing Methods improves the data quality concerning noise reduction and normalization data besides redundancy removals. Classification is conducted with optimized DG‐HGAN through an application of Greylag Goose Optimization (GGO) to achieve high‐accuracy results with efficiency in execution. Experimental results show that there is a 93% improvement in security and a 99.9% accuracy in data classification compared to the existing methodologies. This comprehensive approach ensures the secure handling of sensitive medical data while maintaining processing efficiency and accuracy, making it a promising solution for real‐world industrial healthcare applications.
Lattice Homomorphic Assisted Privacy Preserving Electronic Health Records Data Transmission in Internet of Medical Things Using Blockchain
Authors: Vijay Anand R. , Alagiri I. , Jayalakshmi P. , Brahmam Madala Guru , Abdullah Azween Bin 
Published: February 2025

ABSTRACTThe advancement of healthcare technology, along with the integration of the Internet of Medical Things (IoMT), has improved the efficacy of patient treatment. However, IoMT devices continue to encounter significant security challenges, such as data tampering and unauthorized modifications during data sharing, which threaten the privacy and integrity of medical records. Although advancements in blockchain and cryptography technologies, current methodologies inadequately address these difficulties. To address these security gaps, this paper introduces a novel security framework that leverages lightweight cryptography and blockchain technology for safeguarding Electronic Health Records (EHRs). The proposed approach employs an Improved Merkel Tree (IMM) hashing technique for data integrity, an Amended Elliptic Scheme (AES) for secure key generation, and Lattice Homomorphic Re‐encryption (LHoRe) for data encryption. Key optimization is performed using the Opposition‐Based Coati Optimization technique, while encrypted data is stored in the Inter Planetary File System (IPFS). Performance evaluation shows that the proposed framework achieves a high average trust value of 98.75% and reduces data retrieval latency to 2.24%, offering a more secure and efficient solution for IoMT data sharing compared to existing methods.
Jaw‐Muscle Structure and Function in Primates: Insights Into Muscle Performance and Feeding‐System Behaviors
Authors: Taylor Andrea B. , Holmes Megan A. , Laird Myra F. , Terhune Claire E. 
Published: February 2025

ABSTRACTThe jaw‐adductor muscles drive the movements and forces associated with primate feeding behaviors such as biting and chewing as well as social signaling behaviors such as wide‐mouth canine display. The past several decades have seen a rise in research aimed at the anatomy and physiology of primate chewing muscles to better understand the functional and evolutionary significance of the primate masticatory apparatus. This review summarizes variation in jaw‐adductor fiber types and muscle architecture in primates, focusing on physiological, architectural, and behavioral performance variables such as specific tension, fatigue resistance, muscle and bite force, and muscle stretch and gape. Paranthropus and Australopithecus are used as one paleontological example to showcase the importance of these data for addressing paleobiological questions. The high degree of morphological variation related to sex, age, muscle, and species suggests future research should bracket ranges of performance variables rather than focus on single estimates of performance.
Continuous Adjoint to Proudman's Formula for Aeroacoustic Shape Optimization
Authors: Farhikhteh M. Erfan , Papoutsis Kiachagias E. M. , Giannakoglou K. C. 
Published: February 2025

ABSTRACTThis paper presents an approach for aeroacoustic optimization through the reduction of acoustic sources, based on the integration of Proudman's formula into a continuous adjoint framework coupled with the Reynolds‐averaged Navier–Stokes equations, for the first‐time. The development includes the adjoint to the turbulence model. Here, Proudman's formula is used to compute acoustic emissions of turbulent flows around aerodynamic bodies using the turbulent kinetic energy and specific rate of dissipation. Broadband noise generation through Proudman's formula is initially validated for a case including the flow around an isolated airfoil. Subsequently, the sensitivity derivatives of an objective function quantifying acoustic sources are verified against finite differences, with optimizations of two isolated airfoils and the MEXICO wind turbine following. Optimizations are conducted by extending the tool in OpenFOAM, developed and made publicly available by the group. During the optimization, constraints on the lift force, the drag force, the pitching moment coefficient, the torque, the trailing edge thickness, and airfoil volume are imposed, depending on the case. The geometries and grids are parameterized using PARSEC and morphing boxes based on volumetric B‐Splines. The optimizations result in shapes with reduced acoustic sources while preserving aerodynamic efficiency, highlighting the effectiveness of the proposed method and programmed software.
Anti‐Obesity Effects of Leuconostoc mesenteroides 4‐Fermented Lemon Peel Filtrate on HFD‐Induced Obese Mice via NFκB/ PPAR‐γ Pathway
Authors: Zhou Xianrong , Fan Yang , Liu Jia , Yi Ruokun , He Yongpeng , Zhao Xin , Chen Lujun 
Published: February 2025

ABSTRACTObesity is a major health problem associated with Type 2 diabetes, non‐alcohol fatty liver disease (NAFLD), and atherosclerosis. Functional lactic acid bacteria‐fermented products have been reported to have potential anti‐obese effect. The present results revealed that Leuconostoc mesenteroides 4 (LMSS4)‐fermented lemon peel filtrate slowed down the increase of body weight, and decreased liver and epididymal fat indices; it also decreased serum levels of TC (total cholesterol), TG (triglyceride), LDL‐C (low‐density lipoprotein cholesterol), ALT (alanine transaminase), AST (aspartate transaminase), and AKP (alkaline phosphatase), TNF‐α (tumor necrosis factor‐α), IFN‐γ (interferon gamma), IL‐1β (interleukin‐1β), IL‐6 (interleukin‐6), and IL‐10 (interleukin‐10), increased the levels of HDL‐C (high‐density lipoprotein cholesterol), IL‐4 (interleukin‐4), and IL‐10 (interleukin‐10). Furthermore, the mRNA expression of NFκB‐p65 (nuclear factor‐κB p65), PPAR‐γ (eroxisome proliferator‐activated receptor γ), TNF‐α, IL‐1β, leptin, SREBP‐1c (sterol regulatory element binding protein‐1c), FAS (fatty acid synthase), and CEBP/α (CCAAT/enhancer binding protein α) were down‐regulated, while the expression of IL‐4, IκB‐α (inhibitory subunit of NF Kappa B alpha), and IL‐10 were upregulated after the mice were treated with LMSS4‐fermented lemon filtrate; the filtrate also downregulated the protein expression of NFκB‐p65 and PPAR‐γ but increased the expression of IκB‐α. The HPLC results found that rutin and hesperidin were the predominant constituents in both the unfermented and LMSS4‐fermented lemon filtrates among the 15 constituents analyzed. In addition, chlorogenic acid, umbelliferone, byakangelicin, and oxypeucedanin hydrate were increased in the fermented lemon filtrate, in which chlorogenic acid showed the highest increase rate (83.51%). In conclusion, the anti‐obesity effect of LMSS 4‐fermented lemon peel filtrate was mediated via the regulation of the NFκB/PPAR‐γ signaling pathway. These results could provide an experimental basis for developing new functional lemon beverages for obesity intervention.
Postoperative Management Following Endoscopic Skull Base Reconstruction: A Multidisciplinary Cross‐Sectional Survey
Authors: Goshtasbi Khodayar , Abiri Arash , Talati Vidit , Patel Jagatkumar A. , Nguyen Theodore V. , Pang Jonathan C. , Craig John R. , Papagiannopoulos Peter , Phillips Katie M. , Tajudeen Bobby A. , Adappa Nithin D. , Palmer James N. , Sedaghat Ahmad R. , Wang Eric W. , Su Shirley Y. , Kuan Edward C. 
Published: February 2025

ABSTRACTBackgroundThere is limited consensus on management protocols and practice patterns following endoscopic skull base surgery (ESBS).MethodsAn online‐based survey focusing on ESBS practice patterns was anonymously distributed to the American Rhinologic Society, North American Skull Base Society, and American Head and Neck Society Skull Base Section membership.ResultsA total of 130 surgeons (81.5% in academic positions) completed the survey. Regarding reconstructive materials, 36.9% always used autologous as opposed to synthetic materials, with variation in specific materials used. Lumbar drain was never used by 22.3% of respondents, while high BMI or suspected intracranial hypertension (43.1%) and high‐flow leak or large dural defects (50.0%) were indications for lumbar drain usage. There was significant variation in types of nasal packing, type, and duration of postoperative activity restrictions, antibiotic use, and debridement protocols.ConclusionPostoperative management following endoscopic skull base reconstruction is highly complex, with a wide variety of practice patterns.Level of EvidenceN/A.
Baseline Exposure to Antipsychotic Medication in Young People at Clinical High Risk for Psychosis: A 2‐Year Italian Follow‐Up Study
Authors: Pelizza Lorenzo , Di Lisi Alessandro , Leuci Emanuela , Quattrone Emanuela , Palmisano Derna , Pupo Simona , Pellegrini Pietro , Menchetti Marco 
Published: February 2025

ABSTRACTObjectiveDespite various models examining baseline factors, predicting outcomes in individuals at clinical high risk for psychosis (CHR‐P) remains challenging. Specifically, neglecting factors like ongoing antipsychotic (AP) medications introduce bias and reduce method precision. The main aim of this research was to determine if the presence of AP prescription at baseline identifies a CHR‐P subgroup with worse prognostic outcomes over a 2‐year period.MethodsA group of 180 FEP individuals (92 CHR‐P/AP+, 88 CHR‐P/AP−) were evaluated at baseline and after 24 months using the PANSS and GAF scales. Individuals with baseline AP prescription were included in the CHR‐P/AP+ subgroup; those not taking APs were grouped as CHR‐P/AP−. Univariate Cox regression analysis and mixed‐design ANOVA were performed.ResultsAfter 2 years, CHR‐P/AP+ had a higher rate of new hospitalization but lower rate of service disengagement. No significant inter‐group difference in psychosis transition rate was found. A “time‐×‐group” interaction effect on longitudinal improvement in PANSS total score was observed in CHR‐P/AP+ subjects.ConclusionsIt is advisable to conduct a more extensive outcome evaluation beyond the psychometric criteria for CHR‐P and the mere consideration of psychosis transition. Such an approach would facilitate the identification of specific CHR‐P subgroups with divergent prognoses and different AP response.
MicrobiomeStatPlots: Microbiome statistics plotting gallery for meta‐omics and bioinformatics
Authors: Bai Defeng , Ma Chuang , Xun Jiani , Luo Hao , Yang Haifei , Lyu Hujie , Zhu Zhihao , Gai Anran , Yousuf Salsabeel , Peng Kai , Xu Shanshan , Gao Yunyun , Wang Yao , Liu Yong‐Xin 
Published: February 2025

AbstractThe rapid growth of microbiome research has generated an unprecedented amount of multi‐omics data, presenting challenges in data analysis and visualization. To address these issues, we present MicrobiomeStatPlots, a comprehensive platform offering streamlined, reproducible tools for microbiome data analysis and visualization. This platform integrates essential bioinformatics workflows with multi‐omics pipelines and provides 82 distinct visualization cases for interpreting microbiome datasets. By incorporating basic tutorials and advanced R‐based visualization strategies, MicrobiomeStatPlots enhances accessibility and usability for researchers. Users can customize plots, contribute to the platform's expansion, and access a wealth of bioinformatics knowledge freely on GitHub (https://github.com/YongxinLiu/MicrobiomeStatPlot). Future plans include extending support for metabolomics, viromics, and metatranscriptomics, along with seamless integration of visualization tools into omics workflows. MicrobiomeStatPlots bridges gaps in microbiome data analysis and visualization, paving the way for more efficient, impactful microbiome research.
Optimization of Navigation Method for GPS Mass Transit Network Data Based on Support Vector Machine
Authors: Zhao Jing , Li Rong 
Published: February 2025

ABSTRACTMobile robot autonomous navigation is a hot topic in the field of robotics or unmanned vehicles. Support vector machine (SVM) is a new algorithm based on statistical learning theory, which has gradually become a new research hotspot in the field of data mining after artificial neural network. In this article, we focus on the SVM to optimize the data navigation method of global positioning system (GPS) mass transit network. First, the vehicle navigation system and road network data model are introduced theoretically. Then, the navigation scheme of GPS mass transit network data is designed and the navigation model is established. Finally, this article uses the knowledge of support vector machine to optimize the data navigation method of traffic network and analyze the data. The experimental results show that the proposed method is based on the SVM, which has a good experimental effect.
A call to action for ambulatory care pharmacists for the treatment of depression and anxiety in primary care: An opinion paper of the ambulatory care and central nervous system practice research networks of the American College of Clinical Pharmacy
Authors: Silvia Richard J. , Mohammad Insaf , English Clayton , Bradley Bridget A. , Ottney Anne , Crismon M. Lynn 
Published: February 2025

AbstractThe need for mental health care has increased in recent years, especially due to the coronavirus disease 2019 pandemic. Simultaneously, the supply of mental health clinicians has declined alongside decreases in primary care clinicians. As management of depression and anxiety has increasingly moved to primary care, ambulatory care pharmacists must be equipped with the knowledge and skills needed to care for patients with these illnesses. The objective of this collaborative Opinion Paper of the Ambulatory Care and Central Nervous System Practice and Research Networks is to provide practical guidance on the role and educational needs of ambulatory care pharmacists in the management of depression and anxiety within primary care settings and to serve as a call for action. The authors performed a narrative review of the PubMed database from January 1, 1990 to November 10, 2024 using search terms pertaining to primary care pharmacists and mental health care. The reference lists of these articles were then reviewed for additional references. Ambulatory care pharmacists can help meet the increased need for mental health care in primary care. Their knowledge of evidence‐based medicine, drug interactions, adverse effects, and other areas of pharmaceutical care, coupled with their training in interprofessional team‐based care, make them an ideal addition to the primary care team to provide care for patients with depression and anxiety. A variety of educational programs are available for ambulatory care pharmacists to augment their mental health care knowledge and skills. Similarly, the training of pharmacists at all levels, from student to resident, should incorporate mental health experiences to prepare future pharmacists to address the mental health care needs of their patients. Ambulatory care pharmacists should leverage the skills and training they possess to fill the gap in care for patients living with depression and anxiety in primary care settings.
In Vivo Evaluation of Thermally Drawn Biodegradable Optical Fibers as Brain Implants
Authors: Abdollahian Parinaz , Sui Kunyang , Li Guanghui , Wang Jiachen , Zhang Cuiling , Wang Yazhou , Berg Rune W. , Meneghetti Marcello , Markos Christos 
Published: February 2025

ABSTRACTOptical fiber technology plays a critical role in modern neuroscience towards understanding the complex neuronal dynamics within the nervous system. In this study, we manufactured and characterized amorphous thermally drawn poly D, L‐lactic acid (PDLLA) biodegradable optical fibers in different diameters. These optical fibers were then implanted into the lateral posterior region of the mouse brain for four months, allowing us to assess their degradation characteristics. The gradual dissolution of the implanted PDLLA optical fibers in the brain was confirmed by optical, photoacoustic, and scanning electron microscopy (SEM), light propagation characteristics, and molecular weight measurements. The results indicate that the degradation rate of the biodegradable optical fiber was mainly pronounced during the first week. After four months, degradation led to the formation of micropores on the surface of the implanted fiber within the gray matter region of the brain. We believe that the PDLLA biodegradable optical fiber developed in this study constitutes a promising candidate for further functionalization and development of next‐generation biocompatible, soft, and biodegradable bi‐directional neural interfaces.