bluuebunny/crossref_metadata_2025_split huggingface dataset:
Benchmarking DNA isolation methods for marine metagenomics
Authors: Demkina Alina , Slonova Darya , Mamontov Viktor , Konovalova Olga , Yurikova Daria , Rogozhin Vladimir , Belova Vera , Korostin Dmitriy , Sutormin Dmitry , Severinov Konstantin , Isaev Artem 
Published: December 2023

AbstractMetagenomics is a powerful tool to study marine microbial communities. However, obtaining high-quality environmental DNA suitable for downstream sequencing applications is a challenging task. The quality and quantity of isolated DNA heavily depend on the choice of purification procedure and the type of sample. Selection of an appropriate DNA isolation method for a new type of material often entails a lengthy trial and error process. Further, each DNA purification approach introduces biases and thus affects the composition of the studied community. To account for these problems and biases, we systematically investigated efficiency of DNA purification from three types of samples (water, sea sediment, and digestive tract of a model invertebrate Magallana gigas) with eight commercially available DNA isolation kits. For each kit-sample combination we measured the quantity of purified DNA, extent of DNA fragmentation, the presence of PCR-inhibiting contaminants, admixture of eukaryotic DNA, alpha-diversity, and reproducibility of the resulting community composition based on 16S rRNA amplicons sequencing. Additionally, we determined a “kitome”, e.g., a set of contaminating taxa inherent for each type of purification kit used. The resulting matrix of evaluated parameters allows one to select the best DNA purification procedure for a given type of sample.
Home-based arm cycling exercise improves trunk control in persons with incomplete spinal cord injury: an observational study
Authors: van Helden Joeri F. L. , Alexander Emma , Cabral Hélio V. , Strutton Paul H. , Martinez-Valdes Eduardo , Falla Deborah , Chowdhury Joy Roy , Chiou Shin-Yi 
Published: December 2023

AbstractArm cycling is used for cardiorespiratory rehabilitation but its therapeutic effects on the neural control of the trunk after spinal cord injury (SCI) remain unclear. We investigated the effects of single session of arm cycling on corticospinal excitability, and the feasibility of home-based arm cycling exercise training on volitional control of the erector spinae (ES) in individuals with incomplete SCI. Using transcranial magnetic stimulation, we assessed motor evoked potentials (MEPs) in the ES before and after 30 min of arm cycling in 15 individuals with SCI and 15 able-bodied controls (Experiment 1). Both groups showed increased ES MEP size after the arm cycling. The participants with SCI subsequently underwent a 6-week home-based arm cycling exercise training (Experiment 2). MEP amplitudes and activity of the ES, and movements of the trunk during reaching, self-initiated rapid shoulder flexion, and predicted external perturbation tasks were measured. After the training, individuals with SCI reached further and improved trajectory of the trunk during the rapid shoulder flexion task, accompanied by increased ES activity and MEP amplitudes. Exercise adherence was excellent. We demonstrate preserved corticospinal drive after a single arm cycling session and the effects of home-based arm cycling exercise training on trunk function in individuals with SCI.
Forest age estimation in northern Arkhangelsk region based on machine learning pipeline on Sentinel-2 and auxiliary data
Authors: Smolina Alina , Illarionova Svetlana , Shadrin Dmitrii , Kedrov Alexander , Burnaev Evgeny 
Published: December 2023

AbstractTree age is one of the key characteristics of a forest, along with tree species and height. It affects management decisions of forest owners and allows researchers to analyze environmental characteristics in support of sustainable development. Although forest age is of primary significance, it can be unknown for remote areas and large territories. Currently, remote sensing (RS) data supports rapid information gathering for wide territories. To automate RS data processing and estimate forest characteristics, machine learning (ML) approaches are applied. Although there are different data sources that can be used as features in ML models, there is no unified strategy on how to prepare a dataset and define a training task to estimate forest age. Therefore, in this work, we aim to conduct a comprehensive study on forest age estimation using remote sensing observations of the Sentinel-2 satellite and two ML-based approaches for forestry inventory data, namely stand-based and pixel-based. We chose the CatBoost algorithm to assess these two approaches. To establish the robustness of the pipeline, an in-depth analysis is conducted, embracing diverse scenarios incorporating dominant species information, tree height, Digital Elevation Model (DEM), and vegetation indices. We performed experiments on forests in the northern Arkhangelsk region and obtained the best Mean Absolute Error (MAE) result of 7 years in the case of the stand-based approach and 6 years in the case of the pixel-based approach. These results are achieved for all available input data such as spectral satellites bands, vegetation indices, and auxiliary forest characteristics (dominant species and height). However, when only spectral bands are used, the MAE metric is the same both for per-pixel and per-stand approaches and equals 11 years. It was also shown that, despite high correlation between forest age and height, only height can not be used for accurate age estimation: the MAE increases to 18 and 26 years for per-pixel and per-stand approaches, respectively. The conducted study might be useful for further investigation of forest ecosystems through remote sensing observations.
Predicting acute brain lesions on magnetic resonance imaging in acute carbon monoxide poisoning: a multicenter prospective observational study
Authors: Yoo Kyung Hun , Kang Hyunggoo , Oh Jaehoon , Lim Tae Ho , Cho Yongil , Lee Juncheol , Lee Sang Hwan , Jung Seungkyo , Kim Won Young , Sohn Chang Hwan , Ko Byuk Sung 
Published: December 2023

AbstractAn acute brain lesion (ABL) identified by brain magnetic resonance imaging (MRI) after acute carbon monoxide (CO) poisoning is a strong prognostic factor for the development of delayed neuropsychiatric syndrome (DNS). This study aimed to identify predictors of ABLs on MRI in patients with acute CO poisoning. This was a multicenter prospective registry-based observational study conducted at two tertiary hospitals. A total of 1,034 patients were included. Multivariable logistic regression analysis showed that loss of consciousness (LOC) (adjusted odds ratio [aOR] 2.68, 95% Confidence Interval [CI]: 1.49–5.06), Glasgow Coma Scale (GCS) score < 9 (aOR 2.41, 95% CI: 1.49–3.91), troponin-I (TnI) (aOR 1.22, 95% CI: 1.08–1.41), CO exposure duration (aOR 1.09, 95% CI: 1.05–1.13), and white blood cell (WBC) (aOR 1.05, 95% CI: 1.01–1.09) were independent predictors of ABLs on MRI. LOC, GCS score, TnI, CO exposure duration, and WBC count can be useful predictors of ABLs on MRI in patients with acute CO poisoning, helping clinicians decide the need for an MRI scan or transfer the patient to an appropriate institution for MRI or hyperbaric oxygen therapy.
Effect of coherent breathing on mental health and wellbeing: a randomised placebo-controlled trial
Authors: Fincham Guy W. , Strauss Clara , Cavanagh Kate 
Published: December 2023

AbstractBreathwork may offer simple tools for stress resilience. We conducted the largest parallel randomised-controlled trial on breathwork to date (NCT05676658) wherein 400 participants on the research platform Prolific were randomised, in blocks of 2 via remote software, to coherent breathing at ~ 5.5 breaths/min or a matched attention-placebo at 12 breaths/min, for ~ 10 min/day over 4 weeks. Participants were blinded to their allocated interventions, both of which were paced with equal inhalation:exhalation ratios. There were no differences on credibility and expectancy of benefit between conditions. At the primary timepoint post-intervention for the primary outcome subjective stress, there was no significant group by time interaction (F(1,377) = 0.089, p = 0.765, ηp2 < 0.001) nor main effect of group (F = 0.002, p = 0.961, ηp2 < 0.001), however there was a significant main effect of time (F = 72.1, p < 0.001, ηp2 = 0.161). Similar results were found at 1-month follow-up for stress and for secondary outcomes of anxiety, depression and wellbeing. There were overall improvements on these mental health and wellbeing outcomes from baseline to post-intervention and follow-up across both groups, yet the magnitude of this improvement was not different between arms. Accordingly, we found no measurable effect of coherent breathing over and above a well-designed breathwork placebo at improving mental health and wellbeing. Methodological considerations and recommendations for robust future research are discussed. Funder: Sasakawa Young Leaders Fellowship Fund, Tokyo, Japan.
Daily breath-based mindfulness exercises in a randomized controlled trial improve primary school children’s performance in arithmetic
Authors: Voltmer Katharina , Hondrich Finja , von Salisch Maria 
Published: December 2023

AbstractMindfulness-based interventions (MBIs) have been shown to improve children’s academic achievements. Because MBIs include different exercises (possibly with differential effects), the teacher-led Breathing Break Intervention (BBI) was developed which focuses exclusively on breathing exercises and body awareness. The short daily breathing practices of BBI were evaluated in terms of their effects on children’s performance in mathematics. In a randomized controlled trial, N = 140 third and fourth graders (49% female) either received BBI (IG, n = 81) or participated in an active control group (ACG, n = 59). Students took a standardized arithmetic test and teachers rated their mathematics performance before (T1) and after (T2) the nine weeks of BBI, and in a follow-up five months later (T3). A mixed multilevel model with a quadratic term of time indicated a significant interaction effect between group and time on the arithmetic test after controlling for working memory updating and parental educational attainment. IG children did not show a steeper linear increase but differed significantly from ACG children in their trajectory of arithmetic performance. At T3, IG children outperformed ACG children. A multilevel ordinal logistic regression of teachers’ ratings of students’ mathematics performance revealed no significant differences between IG and ACG. Results suggest that daily breathing exercises in primary school classrooms contribute to enhancing children’s performance in arithmetic.Preregistration: The study was preregistered at aspredicted.org (#44925).
Harnessing ChatGPT and GPT-4 for evaluating the rheumatology questions of the Spanish access exam to specialized medical training
Authors: Madrid-García Alfredo , Rosales-Rosado Zulema , Freites-Nuñez Dalifer , Pérez-Sancristóbal Inés , Pato-Cour Esperanza , Plasencia-Rodríguez Chamaida , Cabeza-Osorio Luis , Abasolo-Alcázar Lydia , León-Mateos Leticia , Fernández-Gutiérrez Benjamín , Rodríguez-Rodríguez Luis 
Published: December 2023

AbstractThe emergence of large language models (LLM) with remarkable performance such as ChatGPT and GPT-4, has led to an unprecedented uptake in the population. One of their most promising and studied applications concerns education due to their ability to understand and generate human-like text, creating a multitude of opportunities for enhancing educational practices and outcomes. The objective of this study is twofold: to assess the accuracy of ChatGPT/GPT-4 in answering rheumatology questions from the access exam to specialized medical training in Spain (MIR), and to evaluate the medical reasoning followed by these LLM to answer those questions. A dataset, RheumaMIR, of 145 rheumatology-related questions, extracted from the exams held between 2010 and 2023, was created for that purpose, used as a prompt for the LLM, and was publicly distributed. Six rheumatologists with clinical and teaching experience evaluated the clinical reasoning of the chatbots using a 5-point Likert scale and their degree of agreement was analyzed. The association between variables that could influence the models’ accuracy (i.e., year of the exam question, disease addressed, type of question and genre) was studied. ChatGPT demonstrated a high level of performance in both accuracy, 66.43%, and clinical reasoning, median (Q1–Q3), 4.5 (2.33–4.67). However, GPT-4 showed better performance with an accuracy score of 93.71% and a median clinical reasoning value of 4.67 (4.5–4.83). These findings suggest that LLM may serve as valuable tools in rheumatology education, aiding in exam preparation and supplementing traditional teaching methods.
Human cell-expressed tag-free rhMFG-E8 as an effective radiation mitigator
Authors: Chaung Wayne , Ma Gaifeng , Jacob Asha , Brenner Max , Wang Ping 
Published: December 2023

AbstractHuman milk fat globule epidermal growth factor-factor VIII (MFG-E8) functions as a bridging molecule to promote the removal of dying cells by professional phagocytes. E. coli-expressed histidine-tagged recombinant human MFG-E8 (rhMFG-E8) is protective in various disease conditions. However, due to improper recombinant protein glycosylation, misfolding and the possibility of antigenicity, E. coli-expressed histidine-tagged rhMFG-E8 is unsuitable for human therapy. Therefore, we hypothesize that human cell-expressed, tag-free rhMFG-E8 will have suitable structural and functional properties to be developed as a safe and effective novel biologic to treat inflammatory diseases including radiation injury. We produced a new tag-free rhMFG-E8 protein by cloning the human MFG-E8 full-length coding sequence without any fusion tag into a mammalian vector and expressed it in HEK293-derived cells. The construct includes the leader sequence of cystatin S to maximize secretion of rhMFG-E8 into the culture medium. After purification and confirmation of the protein identity, we first evaluated its biological activity in vitro. We then determined its efficacy in vivo utilizing an experimental rodent model of radiation injury, i.e., partial body irradiation (PBI). HEK293 cell supernatant containing tag-free rhMFG-E8 protein was concentrated, purified, and rhMFG-E8 was verified by SDS-PAGE with the standard human MFG-E8 loaded as control and, mass spectrometry followed by analysis using MASCOT for peptide mass fingerprint. The biological activity of human cell-expressed tag-free rhMFG-E8 was superior to that of E. coli-expressed His-tagged rhMFG-E8. Toxicity, stability, and pharmacokinetic studies indicate that tag-free rhMFG-E8 is safe, highly stable after lyophilization and long-term storage, and with a terminal elimination half-life in circulation of at least 1.45 h. In the 15 Gy PBI model, a dose-dependent improvement of the 30-day survival rate was observed after tag-free rhMFG-E8 treatment with a 30-day survival of 89%, which was significantly higher than the 25% survival in the vehicle group. The dose modification factor (DMF) of tag-free rhMFG-E8 calculated using probit analysis was 1.058. Tag-free rhMFG-E8 also attenuated gastrointestinal damage after PBI suggesting it as a potential therapeutic candidate for a medical countermeasure for radiation injury. Our new human cell-expressed tag-free rhMFG-E8 has proper structural and functional properties to be further developed as a safe and effective therapy to treat victims of severe acute radiation injury.
A systematic review reveals that African children of 15–17 years demonstrate low hepatitis B vaccine seroprotection rates
Authors: Muwanda Fahad , Sendagire Hakim , Mboowa Gerald , Kateete David Patrick , Achan Beatrice , Mupere Ezekiel , Kafeero Hussein Mukasa , Bagaya Bernard Ssentalo 
Published: December 2023

AbstractChildhood HBV immunization remains globally fundamental to the elimination of hepatitis B virus (HBV). However, monitoring proportions of HBV vaccine seroprotection and their determinants among African Pediatric recipients is crucial. This study sought to verify extent of immune protection accorded by the HBV vaccine in African children of up to 17 years of age by pooling the prevalence of seroprotection reported by primary studies conducted in the Northern, Western, and Southern African regions. We included 19 eligible articles out of the 197 initially downloaded, published from 1999 to 2021 from African Journals Online (AJOL), EMBASE, Scopus, and PubMed. The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO), University of York Centre for Reviews and Dissemination, under the registration number CRD42022361277. Significantly higher (p < 0.0001) proportion of HBV vaccine seroprotection (69.07%) was found among children under 15 years of age than children 15–17 years (32.368%), 95% CI [34.2454–39.0847%]. Whereas successful integration of the HBV vaccine on the extended programs on immunizations (EPI) has been a major achievement in the reduction of HBV infection in Africa, markedly reduced HBV vaccine seroprotection is persistently demonstrated among adolescent children 15–17 years of age. Future studies are required to clarify the need for booster dose vaccination in most at risk populations and age groups.
Composition-dependent photoluminescence in nanocrystalline La2Hf2−xZrxO7:Eu phosphor: role of chemical twin Zr/Hf environments around a luminescent center
Authors: Gupta Santosh K. , Nigam Sandeep , Mao Yuanbing 
Published: December 2023

Work reports drastically fluctuating result of doping Eu3+ in La2Hf2−xZrxO7 matrix on compositional variation, which is counter to anticipation-based chemical brotherhood of Zr & Hf. Zr-enriched samples showed enhanced photoluminescence quantum yield (PLQY) and thermal stability.
Selective S-arylation of thiols with o-OTf-substituted diaryliodonium salts toward diarylsulfides
Authors: Zhang Yuxuan , Wang Yu , Wang Limin , Han Jianwei 
Published: December 2023

We present a selective sulfenylation of ortho-OTf-substituted diaryliodonium salts with thiols. Diarylsulfides bearing vicinal OTf groups were synthesized under mild conditions, offering various versatile synthons for further transformations.
Potential promising anticancer applications of β-glucans: a review
Authors: Noorbakhsh Varnosfaderani Seyed Mostafa , Ebrahimzadeh Farnoosh , Akbari Oryani Mahsa , Khalili Saeed , Almasi Faezeh , Mosaddeghi Heris Reza , Payandeh Zahra , Li Chen , Nabi Afjadi Mohsen , Alagheband Bahrami Armina 
Published: December 2023

Abstract
β-Glucans are valuable functional polysaccharides distributed in nature, especially in the cell walls of fungi, yeasts, bacteria, and cereals. The unique features of β-glucans, such as water solubility, viscosity, molecular weight, and so on, have rendered them to be broadly applied in various food systems as well as in medicine to improve human health. Moreover, inhibition of cancer development could be achieved by an increase in immune system activity via β-glucans. β-glucans, which are part of a class of naturally occurring substances known as biological response modifiers (BRMs), have also shown evidence of being anti-tumorogenic, anti-cytotoxic, and anti-mutagenic. These properties make them attractive candidates for use as pharmaceutical health promoters. Along these lines, they could activate particular proteins or receptors, like lactosylceramide (LacCer), Dickin-1, complement receptor 3 (CR3), scavenge receptors (SR), and the toll-like receptor (TLR). This would cause the release of cytokines, which would then activate other antitumor immune cells, like macrophages stimulating neutrophils and monocytes. These cells are biased toward pro-inflammatory cytokine synthesis and phagocytosis enhancing the elicited immunological responses. So, to consider the importance of β-glucans, the present review introduces the structure characteristics, biological activity, and antitumor functions of fungal β-glucans, as well as their application.
MGAF‐net: Gaussian saliency features guided infrared small target detection network
Authors: Ren Xiangyang , Wu Yan , Gao Jianbo , Yang Zhen 
Published: December 2023

AbstractRobust infrared dim small target detection under sparse features is a difficult problem in the field of small target detection. Therefore, in order to solve this problem, this paper proposes an infrared dim small target detection network based on multi‐scale Gaussian significance and attention feature fusion (MGAF)‐net. In MGAF‐net, MGAF module is proposed to extract multi‐scale Gaussian saliency features of small targets. At the same time, in order to better fuse the multi‐scale Gaussian saliency features of the target, the attention mechanism is introduced in the MGAF module to enrich the feature expression of the target. The experimental results show that the proposed detection method has higher detection accuracy and higher detection speed than the existing advanced infrared dim small target detection methods.
Dynamic surface adaptive RBF neural network control for a class of non‐linear uncertain systems
Authors: Zhang Dong , Wang Hongfei , Shan Shaolong , Wang Chunbo 
Published: December 2023

AbstractAn adaptive neural network dynamic surface control method for strict feedback non‐linear systems with model uncertainty is proposed in this paper. The uncertain parts are approached by RBF neural network; meanwhile, the virtual controller is designed to make system stable according to dynamic surface control. The method of Lyapunov is used to prove the stability and convergence of the system. The simulation results prove the feasibility of this controller and prove that the controller has an advantage to approach the uncertain non‐linear system and make system have well convergence and traceability.
An evolutionary computation classification method for high‐dimensional mixed missing variables data
Authors: Li Mengmeng , Liu Yi , Zheng Qibin , Li Gengsong , Qin Wei 
Published: December 2023

AbstractData missing is a prevalent issue in various real‐world systems. It may deteriorate the performance of classification algorithms running on these platforms. Numerous effective imputation methods exist to address this problem. However, traditional data imputation approaches mainly focus on low‐dimensional missing data, and in addition, they do not make use of the randomness of the missing values and the information of labels simultaneously. To solve these problems, the authors propose a novel data imputation algorithm, named Particle Swarm Optimization for High‐dimensional mixed Missing variables data (PSOHM). PSOHM introduces a feature filtering algorithm for feature selection on missing data, followed by a feature discrimination method to categorize chosen features. PSOHM then employs particle swarm optimization to optimize imputation functions for both continuous and discrete features. Continuous features are modelled as Gaussian distributions, with the mean and standard deviation encoded into particles. Additionally, the probabilities of values for discrete features are also encoded. Moreover, accuracy serves as the optimization objective, utilizing both the randomness of missing values and the label information to improve the algorithm's performance. Six typical algorithms are employed to make a comparison. The results demonstrate that the authors’ method is superior to the compared approaches on the six different kinds of classical datasets.