bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantifying the intra- and inter-species community interaction in a microbiome by dynamic covariance mapping
Authors: Serohijos Adrian , Gencel Melis , Hui Cang , Sahaf Zahra , Gauthier Louis , Matta Chloé Chloé , Gagné-Leroux David , Tsang Derek , Philpott Dana , Ramathan Sheela , Cofino Gisela Marrero , Menendez Alfredo , Bershtein Shimon 
Published: May 2024

Abstract

A microbiome’s composition, stability, and response to perturbations is dictated by the community interaction matrix 1-10 that is commonly assayed by pair-wise species competition. In their natural environment however, microbes concurrently experience multiple species, face conditions that may be difficult to mimic in vitro, and have members that are impractical to isolate. Additionally, due to overlapping of evolutionary and ecological timescales, the community interaction matrix is also influenced by intra-species diversity, but how and to what extent remains poorly understood 11-14. Here, we develop a general approach called Dynamic Covariance Mapping (DCM) to estimate the interaction matrix of multispecies microbiome community in its natural environment from abundance time-series data. Together with intra-species high-resolution lineage tracking via chromosomal barcoding, we quantify the inter- and intra-species community interaction matrix during E. coli colonization of mice gut microbiome with increasing complexity: germ-free, antibiotic-perturbed, and innate microbiota. With DCM, we differentiate three temporal phases of invasion in the susceptible communities: 1) initial loss of community stability as E. coli enters; 2) recolonization of some gut bacteria; and 3) recovery of stability with E. coli clones coexisting with resident bacteria in a quasi-steady state. These phases are influenced by specific interactions between E. coli sub-lineages with other species in the community. These results highlight the transient nature and time-dependence of community interaction networks in microbiomes driven by the persistent coupling of ecological and evolutionary dynamics. Our theoretical and experimental approach can be applied to characterize coupled ecological-evolutionary dynamics of bacterial communities in vitro and in situ.
Neuroendoscopy in Acute and Severe Neurological Diseases: A Promising Approach for Improved Patient Outcomes
Authors: SHAFIQ ZOHAIB , Li Zhiyang , Lei Pan , Song Ping , Zhou Long , Cai Qiang 
Published: May 2024

Abstract

Objective: To investigate the utilization, effectiveness, and safety of neuroendoscopy in the treatment of acute and severe neurological conditions and to evaluate the clinical outcome.
Methods: Between September 2016 to December 2023, a total of 815 patients with acute and severe neurological disease treated by transcranial neuroendoscopic approach were retrospectively reviewed in our department.
Results: Among the 815 acute and severe cases, 474 cases of acute cerebral hemorrhage, 248 cases of hemorrhagic cerebrovascular diseases, 78 cases of craniocerebral injury and 15 cases of acute hydrocephalus. The outcomes of our neuroendoscopic surgery were satisfactory without any severe complications and the procedure was life-saving and showed promising outcomes in terms of patient safety and recovery.
Conclusion: While neuroendoscopy represents a promising approach in the treatment of some acute and severe neurological disease, ongoing research and technological advancements are necessary to optimize its utility and ensure its widespread adoption. This article aims to provide a comprehensive overview of the current state of neuroendoscopy in acute and severe neurological diseases, underscoring its potential to improve patient outcomes and quality of care in neurosurgical practice.
Exploiting ChatGPT for Diagnosing Autism-Associated Language Disorders and Identifying Distinct Features
Authors: Li Xin , Hu Chuanbo , Li Wenqi , Ruan Mindi , Yu Xiangxu , Paul Lynn , Wang Shuo 
Published: May 2024

Abstract

Diagnosing language disorders associated with autism is a complex and nuanced challenge, often hindered by the subjective nature and variability of traditional assessment methods. Traditional diagnostic methods not only require intensive human effort but also often result in delayed interventions due to their lack of speed and specificity. In this study, we explored the application of ChatGPT, a state-of-the-art large language model, to overcome these obstacles by enhancing diagnostic accuracy and profiling specific linguistic features indicative of autism. Leveraging ChatGPT's advanced natural language processing capabilities, this research aims to streamline and refine the diagnostic process. Specifically, we compared ChatGPT's performance with that of conventional supervised learning models, including BERT, a model acclaimed for its effectiveness in various natural language processing tasks. We showed that ChatGPT substantially outperformed these models, achieving over 13\% improvement in both accuracy and F1-score in a zero-shot learning configuration. This marked enhancement highlights the model’s potential as a superior tool for neurological diagnostics. Additionally, we identified ten distinct features of autism-associated language disorders that vary significantly across different experimental scenarios. These features, which included echolalia, pronoun reversal, and atypical language usage, were crucial for accurately diagnosing ASD and customizing treatment plans. Together, our findings advocate for adopting sophisticated AI tools like ChatGPT in clinical settings to assess and diagnose developmental disorders. Our approach not only promises greater diagnostic precision but also aligns with the goals of personalized medicine, potentially transforming the evaluation landscape for autism and similar neurological conditions.
Current Trends and Application of Endovascular Thrombectomy in Patients with Acute Ischemic Stroke and Coexisting Cardiac Thrombus
Authors: Ali Zafar , Shatla Islam , Mufarrih Sayyeda Aleena , Khan Abdul Wali , Talluri Rithvik , Asif Talal 
Published: May 2024

Abstract

Cardiac embolism plays a very significant role in acute ischemic strokes (AIS), constituting approximately one-third of cases. The origin of these emboli often stems from intracardiac thrombi in the left atrium or left ventricle. Utilizing the National Readmission Database from 2016 to 2019, we investigate the prevalence of cardiac thrombi in AIS patients and explore their potential correlation with stroke severity, EVT utilization, and mortality rates. Among 1,272,456 AIS patients, 0.58% had concurrent cardiac thrombus, with an increasing trend observed over the study period (P value < 0.001). The cardiac thrombus cohort showed a higher prevalence of comorbidities such as congestive heart failure and atrial fibrillation. After propensity-score matching, groups were well-balanced in terms of baseline characteristics. Patients within the cardiac thrombus cohort experienced a longer hospital stay (median 5 days vs. 3 days), but no significant difference in mortality was noted. Importantly, the cardiac thrombus cohort demonstrated a higher frequency of EVT utilization, suggesting a link to larger vessel occlusions. Despite matching based on atrial fibrillation, the EVT utilization in the cardiac thrombus cohort remained high, highlighting a significant association. While 30-day readmission rates were comparable, cardiac thrombus patients faced a higher risk of gastrointestinal bleeding and hemorrhagic stroke, likely attributed to anticoagulation use. Limitations include potential miscoding in the administrative database and a lack of detailed imaging findings. In conclusion, this study highlights the increased likelihood of EVT in AIS patients with cardiac thrombus, possibly indicative of larger strokes. The observed rise in cardiac thrombus prevalence may stem from improved coding and increased utilization of cardiac MRI.
Relationships among Vitamin D Insufficiency, Health belief, Quality of Life and Metabolic Syndrome in Northeastern Taiwanese Adults: A Cross-sectional Community-based Cohort Study
Authors: Chen Sue-Hsien , Chen Shu-Ching , Lai Yo-Ping , Yeh Kun-Yun 
Published: May 2024

Abstract

Background
The association between vitamin D insufficiency (VDI) and metabolic syndrome (MetS) remains unclear. Little information is available regarding the association between the VDI and health-related quality of life (HRQoL) in the health belief model (HBM). This study aimed to investigate the association between the VDI, HBM, HRQoL, and MetS in community-based Taiwanese adults.
Methods
This cross-sectional study recruited 1015 Taiwanese patients aged ≥ 30 years between August 2019 and December 2021. Sociodemographic data, anthropometric variables, and vitamin D levels, defined as 25 (OH) D concentrations, were acquired from medical records and physical examinations. Data regarding health beliefs and HRQoL were obtained using a questionnaire.
Results
The mean 25(OH)D concentration was 29.59 ± 10.80 ng/mL and the overall prevalence of VDI was MetS was 54.9%. There was no significant difference in the incidence of MetS between the VDI and sufficiency groups. Multivariate analysis revealed that the risk factors associated with VDI development were female sex, age < 65 years, college and above education attainment, high LDL concentration, no alcohol use, and low scores on the emotional role item of HRQoL. There was no association between the VDI and HBM. In people with VDI, age, smoking, education level, blood pressure, perceived barriers, and self-efficacy in the HBM were correlated with HRQoL.
Conclusions
The VDI was not correlated with MetS incidence or HBM. Risk factors including age, female sex, education level, serum LDL concentration, alcohol use, and role-emotional items of HRQoL could be integrated into health initiative programs to reduce the risk of VDI.
Dual-layer Label Perception Algorithm for POI data with long-tailed distribution
Authors: TAN SHUTAO , WANG ZHEN 
Published: May 2024

Abstract

Authentic POI datasets often exhibit a long-tailed distribution, wherein instances vary significantly across labels, rendering conventional training methods ill-equipped to handle such real-world scenarios. This paper introduces a Dual-layer Label Perception Algorithm for POI Data, which pioneers the utilization of prevalent two-layer data labels to formulate a label feature cloud, subsequently enhancing classification accuracy by broadening and refining the distribution of label features. To assess the algorithm's efficacy, extensive experimentation is conducted on genuine urban POI datasets, resulting in a notable 5% enhancement in the comprehensive F1-Score, reaching 79.41%. The experimental findings affirm the algorithm model's efficacy in enhancing its detection performance across various advanced text classification algorithms.
Multi-objective optimization design of low-power-driven, large-flux, and fast-response three-stage valve
Authors: Ma Aixiang , Xiao Heruizhi , Hao Yue , Yan Xihao , Zhao Sihai 
Published: May 2024

Abstract

Solenoid valves in coal mining equipment are driven by intrinsically safe solenoids. The low power consumption of these solenoids limits the response time of the solenoid valves. Additionally, the low viscosity and high susceptibility to dust contamination of the emulsion fluid often leads to leakage and sticking of hydraulic valves. To address these issues, this study proposes a low-power-driven, large-flux, fast-response three-stage valve structure with an internal displacement feedback device. The driving power of this structure is less than 1.2 W. The key parameters of the valve were optimized using a novel multi-objective optimization algorithm. A prototype was manufactured based on the obtained parameters and subjected to simulation and experimental verification. The results demonstrate that the valve has an opening time of 21ms, a closing time of 12ms, and a maximum flow rate of approximately 225L/min. By utilizing this valve for hydraulic cylinder control, a positioning accuracy of ±0.15mm was achieved.
The L-shaped Relationship Between Composite Dietary Antioxidant Index and Cognitive Impairment in the American Elderly: A Cross-Sectional Study (NHANES 2011-2014)
Authors: Yang Hang , Wang Xiaoying , Zhou Ye , Zhang Shenyingjie , Gao Zhenzhen 
Published: May 2024

Abstract

Background
Oxidative stress is involved in the development and progression of cognitive impairment. However, the association between composite dietary antioxidant index (CDAI) and cognitive impairment remains unknown.
Objective
This cross-sectional study investigated the non-linear relationship between CDAI and cognitive impairment among the American elderly.
Methods
The public data was available from the United States National Health and Nutrition Examination Survey from 2011 to 2014. Participants aged ≥ 60 years were eligible for cognitive function, including word learning and recall modules from the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), the animal fluency test (AFT), and the digit symbol substitution test (DSST). A composite cognition score was created to evaluate global cognition. The univariate and multivariate logistic regression analysis, restricted cubic spline, stratified and sensitivity analysis were conducted.
Results
CDAI was negatively associated with cognitive impairment. For each standard deviation increase in CDAI, the risk of cognitive impairment decreased by 6% for DSST (OR = 0.94, 95% CI: 0.9, 0.97), 7% for AFT (OR = 0.93, 95% CI: 0.9, 0.96), 4% for CERAD (OR = 0.96, 95% CI: 0.93, 0.99), and 7% for global cognition (OR = 0.93, 95% CI: 0.9, 0.96) after adjusting for multiple potential confounders. This significant negative relationship remained consistent when comparing individuals in the highest CDAI tertile with those in the lowest CDAI tertile. Furthermore, a non-linear relationship was observed between CDAI and cognitive impairment on AFT (P for non-linearity = 0.009) and global cognition (P for non-linearity = 0.006).These negative correlations between CDAI and cognitive impairment were observed across the stratified age, gender, poverty-to-income ratio, body mass index, hypertension, and diabetes. However, the interaction test revealed significance for education on DSST (P for interaction = 0.04). Moreover, vitamin E, zinc, selenium, and carotenoids were independently associated with cognitive impairment in this study. The sensitivity analysis for participants with complete covariates yielded a similar finding.
Conclusion
These findings suggested a negative and L-shaped association between the CDAI and the risk of cognitive impairment among the American elderly. The results have significant implications for public health initiatives to prevent and limit the progression of cognitive impairment through dietary interventions.
CRP and IHF act as host regulators in Royal Jelly's antibacterial activity
Authors: Xia Zhenyu , Li Yunchang , Liu Jinhao , Chen Yanping , Liu Chenguang , Hao Yue 
Published: May 2024

Abstract

Royal Jelly (RJ) is a natural substance produced by honeybees, serving not only as nutrition for bee brood and queens but also as a functional food due to its health-promoting properties. Despite its well-known broad-spectrum antibacterial activity, the precise molecular mechanism underlying its antibacterial action has remained elusive. In this study, we investigated the impact of RJ on the bacteria model MG1655 at its half maximal inhibitory concentration, employing LC-MS/MS to analyze proteomic changes. The differentially expressed proteins were found to primarily contribute to suppressing gene expression processes, specifically transcription and translation, disrupting nutrition and energy metabolism, and inducing oxidative stress. Notably, RJ treatment led to a marked inhibition of superoxide dismutase and catalase activities, resulting in heightened oxidative damage and lipid peroxidation. Furthermore, through a protein-protein interaction network analysis using the STRING database, we identified identified CRP and IHF as crucial host regulators responsive to RJ. These regulators were found to play a pivotal role in suppressing essential hub genes associated with energy production and antioxidant capabilities. Our findings significantly contribute to the understanding of RJ's antibacterial mechanism, highlighting its potential as a natural alternative to conventional antibiotics.
Construction of a new prognosis prediction model and immune infiltration analysis of bladder urothelial cancer based on disulfidptosis-related immune genes
Authors: Cen Kenan , Zhou Jingyao , Lv Guangjia , Zhu Hengyue , Guo Yangyang , Zhang Hewei 
Published: May 2024

Abstract

Introduction:
The intricate nature and varied forms of bladder urothelial carcinoma (BLCA) highlight the need for new signals to define tumor prognosis. Disulfidptosis, a novel cell death form, is closely linked to BLCA progression, prognosis, and treatment outcomes. Our current goal is to develop a novel disulfidptosis-related immune prognostic model to enhance BLCA treatment strategies.
Methods
RNA-seq data from TCGA included 419 patients, with clinical details and prognostic data (19 normal, 400 tumor samples). Weighted gene co-expression network analysis (WGCNA) identified disulfidptosis-related immune genes. Univariate, multivariate Cox, and LASSO regression established a disulfidptosis-related immune risk score. A nomogram combining risk score and clinical features predicted prognosis. Model performance was validated through curve analysis and independent prediction. Immune checkpoints, cell infiltration, and tumor mutation load were assessed. Differential gene enrichment analysis was conducted. Prognostic genes were validated via in vitro experiments.
Results
Eight immune genes related to disulfidptosis were identified and verified in BLCA prognosis. A prognostic model outperformed previous ones in predicting overall survival (OS) for high- and low-risk groups. Patients with low risk-scores had higher OS rates and mutation load expression compared to high risk-score patients. CD4 memory T cells, CD8 T cells, M1 macrophages, and resting NK cells were higher in the low-risk group. ICIS treatment may be more effective for the low risk-score group. High risk-score group exhibited stronger correlation with cancer malignant pathways. Knocking out TNFRSF12A inhibits BLAC cell proliferation and invasion, while overexpressing it has the opposite effect.
Conclusions
We constructed a novel risk score model combining disulfidptosis and immune genes with good prognostic prediction performance. We discovered and verified that the TNFRSF12A gene is an oncogene in BLAC, which may help provide personalized guidance for individualized treatment and immunotherapy selection for BLCA patients to a certain extent.
Using Hybrid Mountain Gazelle Optimization and Particle Swarm Optimization Algorithms to Improve Clustering
Authors: Mosavi Esra , Fazeli Seyed Abolfazl Shahzadeh , Abbasi Elham , Kaveh-yazdy Fatemeh 
Published: May 2024

Abstract

Clustering plays a crucial role in data mining and machine learning, with the primary objective being the identification of cohesive and distinct data groups, enabling the extraction of valuable information. However, clustering algorithms often encounter the challenge of getting trapped in local optima, hindering their ability to achieve optimal results. To address this issue, researchers have turned to using meta-heuristic algorithms. This article proposes an enhanced approach for clustering by combining the particle swarm optimization algorithm and the mountain gazelle algorithm. The utilization of this combined algorithm has shown superior performance compared to relying solely on the particle swarm algorithm. By using the strengths of both algorithms, our method overcomes the limitations posed by local optima, leading to more accurate and robust clustering results. The proposed algorithm utilizes the minimum fitness measure to locate the optimal centroid, which is determined based on three constraints: intra-cluster distance, inter-cluster distance, and cluster density. The data is then clustered using the optimal centroid corresponding to the minimal value of the fitness. The performance of our proposed approach has been evaluated on real-world datasets such as Iris, Wine, and Vowel. Our method and PSO and MGO algorithms have been compared on these datasets. The results of the experiments indicate that our proposed method outperforms the PSO and MGO algorithms in terms of clustering quality and convergence speed.
Targeting Metabotropic Glutamate Receptor 7 Promotes Neurogenesis
Authors: Zhao Jian , Wang Ying , Fang Jing , Hu Ganlu , Tang Kailin , Zeng Xianglu , Wang Changzheng , Yu Yong-Chun , Cao Zhiwei , Zeng Tao , Fan Guoping 
Published: May 2024

Abstract

Human neural progenitor cells (hNPCs) have been reported to exist in various regions of the central nervous system (CNS) throughout the lifespan and to maintain homeostasis of the CNS. The endogenous NPC pools as well as their function decline during aging, neurodegeneration, or brain damage; therefore, supplementation with hNPCs or enhancement of the potency of endogenous NPCs could lead to potential treatments for these diseases. Here, we showed that 2-phospho-L-ascorbic acid (Asc-2P) promoted hNPC proliferation and differentiation by maintaining mitochondrial glycolysis under growth factor-lacking conditions. Upregulation of metabotropic glutamate receptor 7 (mGluR7) expression by Asc-2P treatment and retention of mGluR7 on the plasma membrane by the specific group III mGluR antagonist alpha-methylserine-O-phosphate (MSOP) promoted the formation of fibroblast growth factor receptor 1 (FGFR1) and mGluR7 complex on the cytomembrane, thereby enhanced the phosphorylation and activation of cyclic adenosine monophosphate (cAMP) response element-binding protein (CREB), maintained mitochondrial glycolysis, thus enhanced hNPC proliferation and differentiation in vitro and in vivo. Furthermore, Asc-2P treatment alleviated pathogenesis and related cognitive decline in an Alzheimer’s disease mouse model. Taken together, our findings indicated that modulation of mGluR7 by Asc-2P and/or its antagonists could be promising pharmaceutical and therapeutic options against neurodegeneration and related diseases.
The mortar performance research with the addition of silica fume, glass powder, Amazon chestnut, and biopolymer additive
Authors: COSTA Mayara Carelli Paula , BARBOSA Maria Teresa Gomes , SÁ Renato Ribeiro Corrêa , SOUSA Clarissa Dias , SOUZA Nelson Luis Gonçalves Dias , RIVELLI Laura Infante 
Published: May 2024

Abstract

Waste reuse in buildings has proven to be an interesting alternative and more and more research is making it possible to it from many industrial sectors as raw material for making products, reducing the disposal and exploitation of natural resources. Associated with the above, the lime-based mortars in the restoration of historic buildings are since the material provides “breathability” to the substrate, facilitating the exchange of vapors due to its porosity, preventing condensation inside the masonry in addition to the low thermal conductivity, also the ability to accommodate the movements that the building may undergo over time. In this context, the objective of this work is to evaluate the physical, mechanical, chemical properties, and durability of mortars made with binder: sand: biopolymer, in the proportion 1: 3: 0.06 (by weight) where waste was added: silica fume and/or glass powder and/or crushed Amazon chestnuts. To this end, Portland cement or hydrated lime was used as a binder to provide a more in-depth analysis, also allowing the influence of this to be assessed. Mechanical tests (compressive strength and diametral compression strength), durability (immersion absorption, capillarity absorption), and ultrasonic pulse were carried out at 28 days of age. Finally, the benefits of inserting waste in the performance of the mortar were verified, mainly from Amazon chestnuts, which provided significant gains in tensile strength.