bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical and demographic factors modify the association between plasma phosphorylated tau‐181 and cognition
Authors: Bolton Corey J. , Steinbach Marilyn , Khan Omair A. , Liu Dandan , O'Malley Julia , Dumitrescu Logan , Peterson Amalia , Jefferson Angela L. , Hohman Timothy J. , Zetterberg Henrik , Gifford Katherine A. ,   for the Alzheimer's Disease Neuroimaging Initiative
Published: December 2024

AbstractINTRODUCTIONPlasma phosphorylated tau‐181 (p‐tau181) associations with global cognition and memory are clear, but the link between p‐tau181 with other cognitive domains and subjective cognitive decline (SCD) across the clinical spectrum of Alzheimer's disease (AD) and how this association changes based on genetic and demographic factors is poorly understood.METHODSParticipants were drawn from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and included 1185 adults >55 years of age with plasma p‐tau181 and neuropsychological test data. Linear regression models related plasma p‐tau181 to neuropsychological composite and SCD scores with follow‐up models examining plasma p‐tau181 interactions with cognitive diagnosis, apolipoprotein E (APOE) ε4 carrier status, age, and sex on cognitive outcomes.RESULTSHigher plasma p‐tau181 level was associated with worse memory, executive functioning, and language abilities, and greater informant‐reported SCD. Visuospatial abilities and self‐report SCD were not associated with plasma p‐tau181. Associations were generally stronger in mild cognitive impairment (MCI) or dementia, APOE ε4 carriers, women, and younger participants.DISCUSSIONHigher levels of plasma p‐tau181 are associated with worse neuropsychological test performance across multiple cognitive domains; however, these associations vary based on disease stage, genetic risk status, age, and sex.Highlights
Greater plasma p‐tau181 was associated with lower cognition across most domains.
Associations between p‐tau181 and cognition were modified by age and sex.
Level of p‐tau181 was more strongly associated with cognition in people with mild cognitive impairment (MCI) and apolipoprotein E (APOE) ε4.
Characterization of Human CD8αβ Interaction With Classical and Unconventional MHC Molecules
Authors: de Wet Ben , Simmons Robert Alan , Suckling Richard J. , Szoke‐Kovacs Rita , Mansour Salah , Lepore Marco , Cole David K. , Jaworski Jakub , Chapman Alexandra L. , Aleksic Milos 
Published: December 2024

ABSTRACTThe CD8 co‐receptor exists as both an αα homodimer, expressed on subsets of specialized lymphoid cells, and as an αβ heterodimer, which is the canonical co‐receptor on cytotoxic T‐cells, tuning TCR thymic selection and antigen‐reactivity in the periphery. However, the biophysical parameters governing human CD8αβ interactions with classical MHC class I (MHCI) and unconventional MHC‐like molecules have not been determined. Using hetero‐dimerized Fc‐fusions to generate soluble human CD8αβ, we demonstrate similar weak binding affinity to multiple different MHCI alleles compared with CD8αα. We observed that both forms of CD8 bound to certain alleles with stronger affinity than others and found that the affinity of thymically selected TCRs was inversely associated with the affinity of the CD8 co‐receptor for the different alleles. We further demonstrated the binding of CD8αα and CD8αβ to the unconventional MHC‐like molecule, MHCI‐related protein 1, with a similar affinity as for classical MHCI, but no interaction was observed for the other unconventional MHC‐like molecules, CD1a, b, c, or d. In summary, this is the first characterization of human CD8αβ binding to MHCI and MHC‐like molecules that revealed an intriguing relationship between CD8 binding affinity for different MHCI alleles and the selection of TCRs in the thymus.
Cold Plasma Treatment of Quinoa Grains: Changes in Phytic Acid, Saponin, Content, and Antioxidant Capacity
Authors: Arjmand Sanaz , Sadrabad Elham Khalili , Ramroudi Fereshteh , Mollakhalili‐meybodi Neda 
Published: December 2024

ABSTRACTThe impact of atmospheric cold plasma (ACP) treatment (at 50 and 60 kV for 5 and 10 min) on nutritional (total phenolic and flavonoids contents, antioxidant capacity, and TBARs) and antinutritional (saponin and phytic acid) characteristics of quinoa grains has been investigated at this study. Results indicated that ACP treatment is significantly effective to reduce the antinutritional compounds compared with the control sample (p ≤ 0.05), among which S4 (i.e., treated at 60 kV for 10 min) and S2 (i.e., treated at 50 kV for 10 min) samples showed the highest decrease in saponin and phytic acid content, respectively. Also, total phenolic content and antioxidant capacity (DPPH and FRAP) of ACP‐treated samples have decreased compared with the control sample. The flavonoid content of ACP‐treated samples has been increased compared with the control sample (p ≤ 0.05). In general, the S4 (at 60 kV for 10 min) samples had the highest amount of flavonoid and phenolic content compared with the other samples. A significant reduction in TBAR values has been observed by ACP treatment with the maximum reduction at S4 (i.e., treated at 60 kV for 10 min) samples. Results indicated that ACP treatment at 60 KV for 10 min is effective to reduce the antinutritional compounds and maintain the antioxidant compounds of quinoa grains as well. Considering the necessity of keeping the nutritional characteristics of grains through processing, it needs to be monitored and optimized the condition in a way that nutritional characteristics are preserved.
Assessment of ComBat Harmonization Performance on Structural Magnetic Resonance Imaging Measurements
Authors: Tassi Emma , Bianchi Anna Maria , Calesella Federico , Vai Benedetta , Bellani Marcella , Nenadić Igor , Piras Fabrizio , Benedetti Francesco , Brambilla Paolo , Maggioni Eleonora 
Published: December 2024

ABSTRACTData aggregation across multiple research centers is gaining importance in the context of MRI research, driving diverse high‐dimensional datasets to form large‐scale heterogeneous sample, increasing statistical power and relevance of machine learning and deep learning algorithm. Site‐related effects have been demonstrated to introduce bias in MRI features and confound subsequent analyses. Although Combating Batch (ComBat) technique has been recently reported to successfully harmonize multi‐scale neuroimaging features, its performance assessments are still limited and largely based on qualitative visualizations and statistical analyses. In this study, we stand out by using a robust cross‐validation approach to assess ComBat performances applied on volume‐ and surface‐based measures acquired across three sites. A machine learning approach based on Multi‐Class Gaussian Process Classifier was applied to predict imaging site based on raw and harmonized brain features, providing quantitative insights into ComBat effectiveness, and verifying the association between biological covariates and harmonized brain features. Our findings showed differences in terms of ComBat performances across measures of regional brain morphology, demonstrating tissue specific site effect modeling. ComBat adjustment of site effects also varied across regional level of each specific volume‐based and surface‐based measures. ComBat effectively eliminates unwanted data site‐related variability, by maintaining or even enhancing data association with biological factors. Of note, ComBat has demonstrated flexibility and robustness of application on unseen independent gray matter volume data from the same sites.
Predicting Subject Traits From Brain Spectral Signatures: An Application to Brain Ageing
Authors: Jarne Cecilia , Griffin Ben , Vidaurre Diego 
Published: December 2024

ABSTRACTThe prediction of subject traits using brain data is an important goal in neuroscience, with relevant applications in clinical research, as well as in the study of differential psychology and cognition. While previous prediction work has predominantly been done on neuroimaging data, our focus is on electroencephalography (EEG), a relatively inexpensive, widely available and non‐invasive data modality. However, EEG data is complex and needs some form of feature extraction for subsequent prediction. This process is sometimes done manually, risking biases and suboptimal decisions. Here we investigate the use of data‐driven Kernel methods for prediction from single channels using the EEG spectrogram, which reflects macro‐scale neural oscillations in the brain. Specifically, we introduce the idea of reinterpreting the spectrogram of each channel as a probability distribution, so that we can leverage advanced machine learning techniques that can handle probability distributions with mathematical rigour and without the need for manual feature extraction. We explore how the resulting technique, Kernel mean embedding regression, compares to a standard application of Kernel ridge regression as well as to a non‐Kernelised approach. Overall, we found that the Kernel methods exhibit improved performance thanks to their capacity to handle nonlinearities in the relation between the EEG spectrogram and the trait of interest. We leveraged this method to predict biological age in a multinational EEG data set, HarMNqEEG, showing the method's capacity to generalise across experiments and acquisition setups.
Flexible and Adaptive Behavioral Strategies: A Personal Journey
Authors: Mizumori Sheri J. Y. 
Published: December 2024

ABSTRACTThe ground‐breaking research of patient H.M. brought to light the importance of the hippocampus for our memories of everyday and special events. Three quarters of a century of intense neurobiological and neuropsychological research would follow as scientists sought to understand why the hippocampus is such an important memory structure in the brain. Navigating a career during this time required adaptive research strategies as new evidence emerged. Although exciting progress has been made, complex challenges remain.
Distinct Social Factors Are Linked to Epistemic Curiosity and Digital Information‐Seeking Among Adolescents: Generalizability Across 41 Countries
Authors: Ali Farhan 
Published: December 2024

ABSTRACTIntroductionCuriosity, the intrinsic motivation to sense, know, and experience the unknown, plays important roles in adolescent achievement and well‐being. Theoretical considerations and empirical research suggest the contribution of social relationships in fostering curiosity. However, curiosity is expressed in different forms and contexts. Here, we investigated the social predictors of general epistemic curiosity, and of different forms of digital information‐seeking in adolescents.MethodsNationally representative cross‐sectional data from Programme for International Student Assessment 2022 were used (N = 327,778 from 41 countries, 15.8 years, 49.6% female). Multiple regression was implemented using four different types of social relationships—teacher relationship, school belonging, bullying victimization, family relationship—as predictors of three forms of curiosity and information‐seeking—general epistemic curiosity, and digital information‐seeking for formal learning and for informal learning purposes.Results and ConclusionTeacher–student relationship was linked to general epistemic curiosity but less so, or not at all, to digital information‐seeking. Instead, family relationship and bullying victimization were more important drivers of digital information‐seeking for formal and informal learning purposes respectively. These distinctions were largely generalizable across 41 countries examined. The findings paint a complex picture of how figures in different adolescent social spheres matter for different forms of epistemic curiosity and information‐seeking, with practical and theoretical implications.
Workshop on the Latest Advances in Biomedical Applications of Octacalcium Phosphate
Authors: Döbelin Nicola , Suzuki Osamu , Drouet Christophe , Ločs Jānis , Insley Gerard , Procter Philip 
Published: December 2024

ABSTRACTThe first workshop on the “latest advances in biomedical applications of octacalcium phosphate (OCP)” was organized as a satellite symposium to the Bioceramics33 conference in Solothurn, Switzerland, in October 2023. The event brought together leading researchers and industry representatives to present and discuss their latest groundbreaking research aimed at developing and commercializing advanced OCP‐based biomaterials for bone regeneration. The topics presented by the six invited speakers ranged from a fundamental understanding of the OCP crystal chemistry to advanced processing and characterization methods, functionalization, biomineralization, and commercialization. With this summary report, we are laying the foundation for a continuation of a series of workshops on the subject of OCP biomaterials in order to promote the exchange between researchers and industry representatives and to drive forward the development and commercialization of new improved synthetic bone substitute materials.
Classifying Alzheimer's Disease Using a Finite Basis Physics Neural Network
Authors: Dhavamani Logeshwari , Joshi Sagar Vasantrao , Kothapalli Pavan Kumar Varma , Elangovan Muniyandy , Putchanuthala Ramesh Babu , Senthamil Selvan Ramasamy 
Published: December 2024

ABSTRACTThe disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD), resulting in cognitive decline and functional disability. The challenges of dataset quality, interpretability, ethical integration, population variety, and picture standardization must be addressed using deep learning for the functional magnetic resonance imaging (MRI) classification of AD in order to guarantee a trustworthy and practical therapeutic application. In this manuscript Classifying AD using a finite basis physics neural network (CAD‐FBPINN) is proposed. Initially, images are collected from AD Neuroimaging Initiative (ADNI) dataset. The images are fed to Pre‐processing segment. During the preprocessing phase the reverse lognormal Kalman filter (RLKF) is used to enhance the input images. Then the preprocessed images are given to the feature extraction process. Feature extraction is done by Newton‐time‐extracting wavelet transform (NTEWT), which is used to extract the statistical features such as the mean, kurtosis, and skewness. Finally the features extracted are given to FBPINNs for Classifying AD such as early mild cognitive impairment (EMCI), AD, mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), normal control (NC), and subjective memory complaints (SMCs). In General, FBPINN does not express adapting optimization strategies to determine optimal factors to ensure correct AD classification. Hence, sea‐horse optimization algorithm (SHOA) to optimize FBPINN, which accurately classifies AD. The proposed technique implemented in python and efficacy of the CAD‐FBPINN technique is assessed with support of numerous performances like accuracy, precision, Recall, F1‐score, specificity and negative predictive value (NPV) is analyzed. Proposed CAD‐FBPINN method attain 30.53%, 23.34%, and 32.64% higher accuracy; 20.53%, 25.34%, and 29.64% higher precision; 20.53%, 25.34%, and 29.64% higher NP values analyzed with the existing for Classifying AD Stages through Brain Modifications using FBPINNs Optimized with sea‐horse optimizer. Then, the effectiveness of the CAD‐FBPINN technique is compared to other methods that are currently in use, such as AD diagnosis and classification using a convolution neural network algorithm (DC‐AD‐AlexNet), Predicting diagnosis 4 years before Alzheimer's disease incident (PDP‐ADI‐GCNN), and Using the DC‐AD‐AlexNet convolution neural network algorithm, diagnose and classify AD.
Comparative study of paleoflood slackwater deposits in the Yarlung Zangbo and Hanjiang Rivers, China
Authors: Wu Shuaihu , Fu Xudong , Pang Jiangli , Huang Chunchang 
Published: December 2024

ABSTRACTPaleoflood slackwater deposits have recently been investigated in small to moderately sized rivers. However, our understanding of the characteristics of paleoflood slackwater deposits in large rivers remains limited, which poses a notable obstacle to accurately predicting the magnitude, frequency and force of extraordinary floods. To address this research gap, this study compares the characteristics of paleoflood slackwater deposits in the middle Yarlung Zangbo River (YZR) and the upper Hanjiang River in China. Paleoflood slackwater deposits and aeolian deposits (e.g. loess, paleosol S0 and aeolian sand) were investigated and sampled from these locations. For the paleoflood slackwater deposits and aeolian deposits, sedimentology (e.g. grain size distribution, magnetic susceptibility and geochemical elements) was analyzed in the laboratory. The macroscopic features of the paleoflood slackwater deposits in the middle YZR valley are very similar to those of the upper Hanjiang River valley. The paleoflood slackwater deposits were dominated by sand and silt in the middle YZR and by silt and sand in the upper Hanjiang River; this implied that the paleoflood slackwater deposits were suspended sediments of floodwater from different source regions. Compared with the paleoflood slackwater deposits in the upper Hanjiang River and other large rivers in China (e.g. the Yellow and Yangtze Rivers), the paleoflood slackwater deposits contained more coarse particles in the middle YZR; this result may be closely related to the particular hydrogeologic and geomorphic conditions (e.g. rich sandy sediment, high riverbed slope and narrow valley) in the southeastern Tibetan Plateau. Compared with the paleoflood slackwater deposits in the upper Hanjiang River, the paleoflood slackwater deposits presented considerably worse sorting in the middle YZR; this may be closely related to the short transport distance in the middle YZR. The magnetic susceptibility values of the paleoflood slackwater deposits were relatively higher in the middle YZR and the upper Hanjiang River, suggesting that these slackwater deposits with minimal pedogenesis may contain a relatively high ferromagnetic mineral content because of the formation process of slackwater deposits. The sediment provenance of the paleoflood slackwater deposits may be closely related to that of the loess in the middle YZR valley, which implies that the sediment provenance of deposits of different genetic types may be greatly affected by the particular hydrologic–climatic and geomorphic conditions in the southeastern Tibetan Plateau.
Effect of Ocimum basiicum L. essential oil microemulsion on the quality of snakehead (Channa argus) under different dipping conditions during cold storage
Authors: Li Yanhan , Xie Yao , Mei Jun , Xie Jing 
Published: December 2024

AbstractBackgroundOcimum basiicum L. essential oil (OBEO) is an herbal essential oil that inhibits microbial activity during food preservation. However, preservation of aquatic products is susceptible to environmental factors. The efficacy of different impregnation techniques was evaluated. In this study, we investigated the application of OBEO in the cold storage process of snakehead fish fillets.ResultsIn terms of microbiological and ammonia content, the best freshness of snakehead fillets was observed under normal impregnation. On the 18th day, the essential oil impregnated group without ultrasonic and vacuum treatment showed a reduction in trimethylamine content by 5.14 mg/100 g, total volatile base nitrogen by 16.32 mg N/100 g, K value by 50.82%, and H2S content by 134.73 mmol/g, as compared to the control group. Similarly, the indexes such as the pH value, free amino acid (FAA) value and ammonia content reflected the bacteriostatic properties of OBEO and the degree of spoilage of snakeheads. In a word, the results showed that although the homogeneity of the emulsion was improved after ultrasonic treatment, it tends to destabilize the snakehead fillets.ConclusionTherefore, this study demonstrated that snakehead fillets exhibited better preservation under normal or vacuum conditions in combination with OBEO impregnation.
Understanding the Supramolecular Chemistry of Lutein with Gelatin, Octenyl Succinic Anhydride‐Modified Starch Polymers in Simulated Gastro‐Intestinal Buffers: A Molecular Dynamics Study
Authors: Das Rontu , Kundu Debashis 
Published: December 2024

AbstractLutein, a carotenoid with significant health benefits, faces challenges in delivery due to its poor solubility and low bioavailability in the human body. Experimental methods are used to deliver lutein but it has limitations in providing detailed molecular‐level insights into lutein interactions within delivery systems. Molecular dynamics (MD) simulations offer a powerful tool to overcome this knowledge gap by providing an atomic‐level understanding of lutein behavior in various physiological conditions. This study employs MD simulations to investigate the binding of lutein in a hydrogel composed of gelatin and octenyl succinic anhydride (OSA)‐modified starch across different gastro‐intestinal buffers. The analysis of the simulation trajectory reveals that while both gelatin and OSA‐starch form hydrogel networks, OSA‐starch maintains better structural integrity in all buffers. Autodock scores indicate a higher binding affinity of lutein for OSA‐starch compared to gelatin, which is further validated by hydrogen bonding and radial distribution function analyses. The mean square displacement results demonstrate the subdiffusive behavior of lutein. These results provide valuable insights into the interactions of lutein with hydrogel components and solvents, contributing to the rational design of improved lutein delivery systems. Future research can focus on optimizing the hydrogel composition to enhance lutein stability and bioavailability in various physiological environments.
The Relationship Between BRI and Depressive Symptoms in Chinese Older Adults: A CLHLS‐Based Study
Authors: Wang Yan , Yang Yuting , Wang Wanning , Chen Qiao , Jia Wangping , Li Ling 
Published: December 2024

ABSTRACTBackgroundThere is a lack of research examining the association between obesity and depressive symptoms in relation to mental health. This study aimed to examine the correlation between Body Roundness Index (BRI) and depressive symptoms in elderly Chinese individuals.MethodsThe study sample consisted of 11,842 individuals aged 65 years or older from the 2018 Chinese Longitudinal Health Longevity Survey (CLHLS) database. A multivariate logistic regression analysis was used to investigate how BRI affects the likelihood of experiencing depressive symptoms, with restricted cubic spline (RCS) curves illustrating this impact. BRI values were calculated using a predefined formula for each participant, and depressive status was assessed using the Center for Epidemiologic Studies Depression Scale (CES‐D‐10).ResultsThe mean age of the participants was 83.1 ± 10.9 years. A non‐linear relationship was identified between the BRI score and the risk of depressive symptoms. The analysis showed that for BRI scores below 5.17, there was a significant 9% increase in the risk of depressive symptoms for every 1‐point decrease in BRI score. Conversely, when the BRI was 5.17 or higher, a decrease in the BRI score did not lead to a significant increase in the risk of depressive symptoms.ConclusionThe study demonstrated a significant association between BRI and depressive symptoms in elderly Chinese individuals. Furthermore, it was noted that older adults classified as overweight and mildly obese had a lower likelihood of experiencing depressive symptoms and demonstrated improved mental health.