bluuebunny/crossref_metadata_2025_split huggingface dataset:
Proportions of IgA antibodies targeting glycosylated epitopes of secreted Escherichia coli mucinase YghJ in initial plasmablast response differ from salivary and intestinally secreted IgA
Authors: Riaz Saman , Steinsland Hans , Andersen Ann Z. , Boysen Anders , Hanevik Kurt 
Published: December 2024

AbstractMucosal infections normally cause an immune response including activation of antigen-specific B cells in regional mucosa-associated lymphoid tissue. After recirculation of plasmablasts, and maturation at mucosal surfaces or bone marrow, plasma cells produce secretory or systemic IgA. It remains uncertain to what extent secretory and systemic IgA share the same target specificities. For vaccine candidate optimization, it is important to know whether IgA targeting of glycosylated epitopes of a protein antigen vary between mucosal and systemic sites. We evaluated glycosylated epitope specificity of systemic and mucosally secreted IgA against YghJ, a potential vaccine candidate antigen secreted by most pathogenic Escherichia coli. IgA from intestinal lavage, saliva, serum, and blood-derived antibody in lymphocyte supernatants (ALS) were collected from 21 volunteers following experimental infection with enterotoxigenic E. coli. Methods for preparing IgA from saliva and ALS were developed, and multiplex bead flow cytometric immunoassays were used to determine levels of IgA targeting natively glycosylated YghJ and estimating what proportion of these antibodies specifically targeted glycosylated epitopes. Following infection, anti-YghJ IgA levels increased substantially for most volunteers across all four specimen types. Target specificity of ALS IgA correlated well with serum IgA, but not with mucosally secreted IgA. Furthermore, glycosylation-specific proportion of salivary IgA was higher than, and did not correlate with, intestinally secreted IgA. These results indicate a new degree of complexity to our understanding of epitope-targeting and tissue specificity of mucosal antibody responses. Our findings also suggest that all features of an intestinal IgA response may not be well reflected in serum, saliva, or ALS, which are commonly used proxy specimens for evaluating intestinal immune responses.
Evolving strategies for addressing CAR T-cell toxicities
Authors: Rankin Alexander W. , Duncan Brynn B. , Allen Cecily , Silbert Sara K. , Shah Nirali N. 
Published: December 2024

AbstractThe field of chimeric antigen receptor (CAR) T-cell therapy has grown from a fully experimental concept to now boasting a multitude of treatments including six FDA-approved products targeting various hematologic malignancies. Yet, along with their efficacy, these therapies come with side effects requiring timely and thoughtful interventions. In this review, we discuss the most common toxicities associated with CAR T-cells to date, highlighting risk factors, prognostication, implications for critical care management, patient experience optimization, and ongoing work in the field of toxicity mitigation. Understanding the current state of the field and standards of practice is critical in order to improve and manage potential toxicities of both current and novel CAR T-cell therapies as they are applied in the clinic.
Can We Ethically Influence Learning Processes by Technology? Effects of Interruptions on Knowledge Assessment
Authors: Jörs Julian Marvin , De Luca Ernesto William 
Published: December 2024

AbstractThe real-time availability of information and the intelligence of information systems have changed the way we deal with information. Current research is primarily concerned with the interplay between internal and external memory, i.e., how much and which forms of cognitively demanding processes we handle internally and when we use external storage media (analog on paper, digital on the computer). This interplay influences how and what we memorize and learn consequently. This study was motivated by the finding that people perform significantly worse in a quiz setting when they obtain content from external sources instead of reading the same content directly in the question system. In our experiments, we wanted to investigate whether interruption by the user interface (ethical appeal or forced time delay) can improve performance in the quiz when users obtain external content. We evaluated the results of 262 valid participants to complete one of three topics at random and then randomly assigned them to one of three possible conditions (ethical appeal, forced time delay, no interruption). The calculated one-way ANOVA shows a statically significant F-value ($$3.25,\,p < 0.05$$

3.25
,

p
<
0.05

), and the separate t-tests show that an ethical appeal ($$t = 2.29,\,p < 0.05$$

t
=
2.29
,

p
<
0.05

) and an enforced delay ($$t = 2.08,\,p < 0.05$$

t
=
2.08
,

p
<
0.05

) lead to a significantly higher mean of quiz scores across all three topics. The mean values of the quiz scores of the ethical appeal and the forced time delay are not significantly different. The present results should contribute to further studies of proactive learning interfaces.
Metabolomic heterogeneity of ageing with ethnic diversity: a step closer to healthy ageing
Authors: Trivedi Dakshat , Hollywood Katherine A. , Xu Yun , Wu Fredrick C. W. , Trivedi Drupad K. , Goodacre Royston 
Published: December 2024

Abstract

Introduction
Outside of case–control settings, ethnicity specific changes in the human metabolome are understudied especially in community dwelling, ageing men. Characterising serum for age and ethnicity specific features can enable tailored therapeutics research and improve our understanding of the interplay between age, ethnicity, and metabolism in global populations.

Objective
A metabolomics approach was adopted to profile serum metabolomes in middle-aged and elderly men of different ethnicities from the Northwest of England, UK.

Methods
Serum samples from 572 men of White European (WE), South Asian (SA), and African-Caribbean (AC) ethnicities, ranging between 40 and 86 years were analysed. A combination of liquid chromatography (LC) and gas chromatography (GC) coupled to high-resolution mass spectrometry (MS) was used to generate the metabolomic profiles. Partial Least Squares Discriminant Analysis (PLS-DA) based classification models were built and validated using resampling via bootstrap analysis and permutation testing. Features were putatively annotated using public Human Metabolome Database (HMDB) and Golm Metabolite Database (GMD). Variable Importance in Projection (VIP) scores were used to determine features of interest, after which pathway enrichment analysis was performed.

Results
Using profiles from our analysis we classify subjects by their ethnicity with an average correct classification rate (CCR) of 90.53% (LC–MS data) and 85.58% (GC–MS data). Similar classification by age (< 60 vs. ≥ 60 years) returned CCRs of 90.20% (LC–MS) and 71.13% (GC–MS). VIP scores driven feature selection revealed important compounds from putatively annotated lipids (subclasses including fatty acids and carboxylic acids, glycerophospholipids, steroids), organic acids, amino acid derivatives as key contributors to the classifications. Pathway enrichment analysis using these features revealed statistically significant perturbations in energy metabolism (TCA cycle), N-Glycan and unsaturated fatty acid biosynthesis linked pathways amongst others.

Conclusion
We report metabolic differences measured in serum that can be attributed to ethnicity and age in healthy population. These results strongly emphasise the need to consider confounding effects of inherent metabolic variations driven by ethnicity of participants in population-based metabolic profiling studies. Interpretation of energy metabolism, N-Glycan and fatty acid biosynthesis should be carefully decoupled from the underlying differences in ethnicity of participants.
Thermal and non-thermal bioprocessing: a comprehensive review on millet starch properties and digestibility
Authors: Nanje Gowda N. A. , Kambhampati Vivek , Pulivarthi Manoj K. , Chauhan Ruchi , Pandiselvam Ravi , Farahnaky Asgar 
Published: December 2024

Abstract
Millets are known for their unique nutritional composition and functional properties, making them a promising solution to nutritional challenges and food security. This review examines the effects of various processing techniques, both thermal (such as cooking, boiling, roasting, and extrusion) and non-thermal biological (such as fermentation and germination), on the properties of millet starch, its digestibility, and nutritional value. Thermal processing methods cause gelatinization, retrogradation, and structural changes in millets, which in turn affect their digestibility, texture, and nutritional attributes. Non-thermal bioprocessing methods, like fermentation, modify starch composition and increase the availability of bioactive compounds in millets, while germination boosts nutritional content and reduces anti-nutritional factors. This review summarizes recent research explaining the mechanisms through which these processing techniques influence millet starch properties and addresses the importance of optimizing processing parameters such as time, temperature, and moisture levels to achieve desired product characteristics while minimizing nutrient loss. Additionally, the implications of these processing methods for improving the functionality, sensory qualities, and nutritional value of millet-based products are discussed. Overall, this review provides valuable insights into processing strategies to enhance the nutritive value and functionality of millets in diverse food applications.
Status of Potato Cyst Nematode (PCN) Globodera pallida and G. rostochiensis Resistance Breeding in Africa
Authors: Onditi John O. , Whitworth Jonathan L. 
Published: December 2024

AbstractPotato (Solanum tuberosum L.) is one of the major crops in Africa with the potential of improving food and nutritional security. Potato cyst nematodes (PCN), Globodera pallida and G. rostochiensis have more recently been reported as a new pest challenging production of the crop in the region. This review analyses development and selection of PCN resistant cultivars in Africa as a PCN control strategy with major focus on sources of host resistance in existing cultivars. A limited number of studies on development and selection of PCN resistant cultivars were found. Despite this, there were multiple PCN resistances particularly among cultivars originally sourced from Europe and grown by farmers in the region. This review proposed identification and utilization of resistant cultivars already adopted by farmers as an immediate strategy for PCN control while waiting for the development of new resistant cultivars as a long-term solution to the problem.
Quantitative Oculomotor and Vestibular Profile in Spinocerebellar Ataxia Type 6 – Systematic Review and Meta-Analysis
Authors: Tarnutzer Alexander A. , Garces Pilar , Antoniades Chrystalina A. 
Published: December 2024

Abstract
Whereas several studies have reported on quantitative oculomotor and vestibular measurements in spinocerebellar ataxia type 6 (SCA6), selecting the most suitable paradigms remains challenging. We aimed to address this knowledge gap through a systematic literature review and providing disease-specific recommendations for a tailored set of eye-movement recordings in SCA6. A literature search (MEDLINE, Embase) was performed focusing on studies reporting on quantitative oculomotor and/or vestibular measurements in SCA6-patients. Oculomotor and vestibular parameters were extracted and correlations with various epidemiologic and clinical parameters were sought. Twenty-two studies were included reporting on 154 patients. Abnormalities observed included reduced pursuit gain (58/69), frequent square-wave jerks (23/40), spontaneous downbeat nystagmus (DBN, 34/55) and triggered nystagmus including positional nystagmus (25/34) and vertical (“perverted”) head-shaking nystagmus (21/34), gaze-evoked nystagmus (48/70) and angular vestibulo-ocular reflex (aVOR)-suppression (21/25), and high-frequency aVOR-deficits (26/33). For horizontal visually-guided saccades (VGS), changes in metrics (36/66) were frequently observed, whereas saccade velocity was usually preserved (39/44) and saccade latency within normal limits. Reduced high-frequency aVOR gains, VGS-latency and metrics correlated with disease severity. Longitudinal data indicated deterioration of individual video-head-impulse testing gains over time. A broad range of oculomotor and vestibular domains are affected in SCA6. Impairments in pursuit, saccade metrics, gaze-holding (gaze-evoked nystagmus, DBN) and high-frequency aVOR were most frequently identified and as such, should be prioritized as disease markers. Quantitative oculomotor testing in SCA6 may facilitate an early diagnosis and prove valuable in monitoring disease progression.
Comparing Environmental Literacy in Stem and Non-Stem Preschools: Evaluating the Impact on Young Children's Understanding and Attitudes
Authors: Ramulumo Moleboheng , Shabalala Nonkanyiso Pamella 
Published: December 2024

AbstractThe importance of environmental literacy in early childhood education lies in its inclusion of the requisite knowledge, skills and attitudes for understanding the environment and making well-informed decisions. In preschool settings, promoting environmental literacy helps children develop foundational knowledge about natural systems, cultivates curiosity and encourages responsible environmental behaviour. This study investigates the incorporation of environmental literacy in preschools in Bloemfontein, South Africa, differentiating between STEM-focused and non-STEM settings. It also assesses the influence of this integration on the understanding and attitudes of young children towards the environment. Qualitative data was collected through semi-structured interviews and classroom observations, including pilot research to enhance the approach and triangulation to guarantee the accuracy of the data. The findings indicates that children attending preschools with a STEM concentration demonstrate higher levels of environmental literacy compared to their counterparts in non-STEM environments. This research highlights the need of including environmental education into all academic programs, employing hands-on, inquiry-based methods and ensuring equal opportunities for environmental learning for everyone. Optimising environmental education, namely by integrating STEM subjects, is of significance for preparing children to properly address future environmental issues.
Solution rheology of poly(ionic liquid)s: current understanding and open questions
Authors: Matsumoto Atsushi 
Published: December 2024

AbstractPoly(ionic liquid)s are ion-containing polymers possessing ionic liquid structures on their repeating units. Owing to the unique physicochemical properties of ionic liquids, many existing studies have found that the properties of poly(ionic liquid)s are distinct from those of conventional ion-containing polymers, such as poly(sodium styrene sulfonate). A lot of scientific efforts have been made to understand the relationship between the chemical structure and the material properties of poly(ionic liquid)s, and several good review papers are available in the literature. The aim of this short review is to summarize key results on the viscoelastic properties of poly(ionic liquid)s in solution. We discuss in detail the counterion condensation and the charge screening in poly(ionic liquid) solutions.
Graphical Abstract
Practical stability in terms of two measures of sheaf solutions of impulsive set control differential equations
Authors: Wang Peiguang , Li Fangran , Bao Junyan 
Published: December 2024

Abstract
We define the sheaf solutions of impulsive set control differential equations in this study. Then, for the initial value problem of such equations, we investigate the (uniformly) practical stability and strict (uniformly) practical stability in terms of two measures of the sheaf solutions by using Lyapunov functions method.
Automatic evaluation of planners in dynamic scenes during human–robot interaction
Authors: Hernández Óscar G. , Morell Vicente , Úbeda Andrés , Jara Carlos A. 
Published: December 2024

AbstractThe evaluation of planners is essential to guarantee the efficiency and performance of trajectories executed by collaborative robots in human–robot interaction (HRI) contexts. The main objective of this research is to develop a method that allows obtaining an automated assessment of the performance of planners widely used in robotic environments. In addition to analyzing specific metrics such as planning time, path length, smoothness, and clearance, this paper introduces novel contributions, including a unique method for dynamic scene evaluation and a new comprehensive and global performance index, named Total Performance Index (TPIx), which encompasses all the aforementioned metrics. The system proposed is composed of a collaborative robot and an RGB-D sensor to monitor the environment using 3D data to perform safe trajectories during multiple tasks. In this quantitative evaluation, two single-query planners (RRT, KPIECE) and two multiple-query planners (SPARS, PRM) were analyzed. The results obtained demonstrate the feasibility and effectiveness of the proposed method to automatically evaluate the planners used by collaborative robots. Therefore, this study contributes to the advancement of collaborative robotics by introducing updated methods for planner evaluation and aims to facilitate the development of more efficient and adaptive systems in light of recent advancements in robotic technologies.
Machine learning-based prediction of pharmacokinetic parameters for individualized drug dosage optimization
Authors: Mtshali Sinenhlanhla , Jacobs Byron A. 
Published: December 2024

AbstractThis study presents a data-driven approach to predict pharmacokinetic parameters and generate concentration–time curves for a two-compartment model. The method employs inverse modelling using optimization algorithms to estimate patient-specific parameters from observed data. Machine learning techniques are then applied to solve the forward problem, enabling the prediction of concentration–time profiles for various dose levels. The study incorporates patient background characteristics to improve predictive performance, aiming to enable individualized drug dosing. Results demonstrate accurate parameter prediction and close matching of generated curves to observed data across six dose levels. This approach offers a novel framework for personalizing pharmacokinetic profiles and improving drug dosing strategies and therapeutic outcomes in clinical practice.
Automated cybersecurity compliance and threat response using AI, blockchain and smart contracts
Authors: Alevizos Lampis 
Published: December 2024

Abstract
To address the challenges of internal security policy compliance and dynamic threat response in organizations, we present a novel framework that integrates artificial intelligence (AI), blockchain, and smart contracts. We propose a system that automates the enforcement of security policies, reducing manual effort and potential human error. Utilizing AI, we can analyse cyber threat intelligence rapidly, identify non-compliances and automatically adjust cyber defence mechanisms. Blockchain technology provides an immutable ledger for transparent logging of compliance actions, while smart contracts ensure uniform application of security measures. The framework’s effectiveness is demonstrated through simulations, showing improvements in compliance enforcement rates and response times compared to traditional methods. Ultimately, our approach provides for a scalable solution for managing complex security policies, reducing costs and enhancing the efficiency while achieving compliance. Finally, we discuss practical implications and propose future research directions to further refine the system and address implementation challenges.
Representation alignment contrastive regularisation for multi‐object tracking
Authors: Chen Shujie , Liu Zhonglin , Dong Jianfeng , Wang Xun , Zhou Di 
Published: December 2024

AbstractAchieving high‐performance in multi‐object tracking algorithms heavily relies on modelling spatial‐temporal relationships during the data association stage. Mainstream approaches encompass rule‐based and deep learning‐based methods for spatial‐temporal relationship modelling. While the former relies on physical motion laws, offering wider applicability but yielding suboptimal results for complex object movements, the latter, though achieving high‐performance, lacks interpretability and involves complex module designs. This work aims to simplify deep learning‐based spatial‐temporal relationship models and introduce interpretability into features for data association. Specifically, a lightweight single‐layer transformer encoder is utilised to model spatial‐temporal relationships. To make features more interpretative, two contrastive regularisation losses based on representation alignment are proposed, derived from spatial‐temporal consistency rules. By applying weighted summation to affinity matrices, the aligned features can seamlessly integrate into the data association stage of the original tracking workflow. Experimental results showcase that our model enhances the majority of existing tracking networks' performance without excessive complexity, with minimal increase in training overhead and nearly negligible computational and storage costs.
A V2G power response model based on the prediction, evaluation, and correction of charging stations real‐time frequency regulation capability
Authors: Ding Leyan , Yang Jun , Ke Song , Shi Xingye , Fan Peixiao , Wang Hongli 
Published: December 2024

AbstractWith the charging stations (CSs) construction and the vehicle‐to‐grid (V2G) development, electric vehicles (EVs) have become an important load‐side controllable resource. Therefore, a V2G power response model based on the prediction, evaluation, and correction of CSs real‐time frequency regulation (FR) capability is proposed in this paper. Firstly, a hierarchical control framework for large‐scale EVs aggregated to participate in power grid dispatching/FR service is proposed. Secondly, an extreme gradient boosting (XGBoost)‐convolutional neural network (CNN)‐bidirectional long‐term and short‐term memory (BiLSTM)‐attention prediction model for CSs FR capability is proposed, which combines the advantages of CNN and BiLSTM to strengthen the mining of multi‐dimensional features. Meanwhile, a rolling evaluation‐correction model for CSs FR capability based on the EV CC–CV charging process is proposed, which improves the evaluation fineness and aggregation fitness. Furthermore, a V2G power response model considering the EV battery loss is established. Finally, the simulation results show that compared with LSTM, XGBoost‐CNN‐BiLSTM, support vector machine, and other prediction models, the proposed XGBoost‐CNN‐BiLSTM‐attention CSs FR capability prediction model with improvement has a better prediction accuracy. In addition, the V2G power response model can achieve the coordination between the EV users’ charging demands and FR tasks.