bluuebunny/crossref_metadata_2025_split huggingface dataset:
Restoring public trust in COVID-19 vaccine among Africans, Caribbean, and Black Canadians Community based participatory research (CBPR)
Authors: Etowa Josephine , Jacques Amoy , Malemo Luc , Ghose Bishwajit , Etowa Egbe , Dabone Charles , Sangwa Sylvia 
Published: July 2024

Abstract

Background
The COVID-19 pandemic had a significant global impact predominantly among Africans, Caribbean and Black (ACB) Canadians. ACB people experienced higher infection and recovery rates, greater health risks, and access to care. Public mistrust, due to anti-Black racism and historical trauma contributed to low vaccine uptake among ACB populations. Our study examined multi-level and intersectional issues impacting vaccine uptake and acceptance in Ottawa. This paper will focus on the themes of agency and community resilience.
Methods
A mixed methods research (MMR) approach guided this study using the socio-ecological model (SEM), intersectionality lens, and community-based participatory research principles to generate the qualitative results. The sample included 49 ACB participants involved in focus group discussions (FGDs). The sample population included service providers (20), social workers (15), doctors (3), and nurses (3) and others (8) who were predominantly Black African (70%), mostly female (80%), age range was 35–44 (33%) with high school diplomas (40%). Data were recorded digitally, transcribed verbatim, analyzed inductively. Data analysis methods were thematic analysis including inductive coding using NVivo software and rigor methods were member-checking and external audits. Ethical approval retrieved from affiliated institutions and measures taken to ensure trustworthiness of data.
Results
Our data showed rebuilding public trust is possible by bridging knowledge gaps on vaccine information, increasing service providers’ cultural competence capacity, and providing governance/leadership opportunities for ACB communities.
Conclusion
Restoring public trust will require dismantling racism by prioritizing ACB community’ needs, proactive and accessible culturally appropriate messages, and opportunities to develop policies to improve health outcomes.
Fundamental of human interaction: Type reciprocity
Authors: Lindenberg Siegwart , Denissen Jaap , Damme Eric van 
Published: July 2024

Abstract

Reciprocity belongs to the fundamentals of human interaction. However, it is mostly identified as act reciprocity (“I scratch your back and you scratch mine”). We argue that there also is another kind of reciprocity - not related to acts but to character types (“I treat you nicely because I believe you are a nice person”). In the literature act and type reciprocity are often intermixed despite their fundamental differences. The former requires much bookkeeping and is prone to downward spirals when errors or mishaps occur. By contrast, type reciprocity reduces bookkeeping and potentially contributes to more stable cooperative and competitive relationships. However, so far it lacked direct investigation. In six experiments with economic games with a total of 679 participants and more than 60.000 choices, we found type reciprocity to be (a) very prominent, b) moderately “strong” (i.e. positive and negative), and (c) highly robust against disturbing influences (except in conflictful relationships).
Triangulation of self-other overlap, shared time, and shared physiology best predicts shared flow in music ensembles
Authors: Gibbs Hannah Jennet , Schiavio Andrea 
Published: July 2024

Abstract

Shared flow can be conceptualised as a collective state of flow that emerges within a group. It has been recently suggested that shared flow involves a spectrum of self-other overlap, joint attention, and social interaction, further facilitated by context and experience. To empirically test this, four gamelan groups - a musical ensemble originating from Indonesia - took part in a study (N=36), whereby aspects of the theorised spectrum were operationalised via (i) a self-report measure of self-other overlap, (ii) a measure of consensus of time distortion, and (iii) physiological synchrony. Using linear mixed-effects models, we tested whether associations between shared flow and these measures are modulated by different performance conditions and musical training. Lastly, we tested whether shared flow could be best predicted by all measures combined. While the relationship between self-other overlap and shared flow was not reliant on condition and expertise, it was for synchrony of skin conductance and consensus of time distortion. Furthermore, we found that models predicting shared flow encompassed combinations of all the above measures. The findings reveal the potential of physiological measures and a novel measure of consensus of time distortion as a supplement to self-reports in understanding the underlying social dynamics of shared flow.
Molecular beam epitaxial step-edge growth of Bi2Te3/multi-stepped Sb2Te3 nanoplate hetero-structures
Authors: Yi Gyu-Chul , Kim Yoonkang , Lee Sangmin , Lee Eunsu , Kim Seongbeom , Suh Wonwoo , Kim Imhwan , Jeon Junyeop , Kim Miyoung 
Published: July 2024

Abstract

We report the synthesis of multiple Bi2Te3 shells on multi-stepped Sb2Te3 nanoplates using molecular beam epitaxial (MBE) step-edge growth. For the growth of Bi2Te3/Sb2Te3 hetero-structures, multi-stepped Sb2Te3 nanoplates with stair-like morphology following layer-by-layer (LBL) growth mode were obtained by optimizing the growth temperature, and the growth of Bi2Te3 on the step-edges of the Sb2Te3 nanoplates was followed. Width of Bi2Te3 on the Sb2Te3 nanoplates was controlled by changing the growth time. Structural properties of the hetero-structures were investigated using aberration-corrected (Cs-corrected) high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), revealing the interface between Sb2Te3 and Bi2Te3. In-plane epitaxial relation at the interface was confirmed using fast Fourier transforms (FFTs). Compositional analysis of Bi2Te3 and Sb2Te3 was verified through energy-dispersive X-ray spectroscopy. Furthermore, we performed density functional theory (DFT) calculations to confirm the preferential growth of Bi2Te3 on the step-edges of Sb2Te3. By forming multi-stepped core structure, it would be feasible to create various integrated hetero-structures.
Evaluation of a binary amphiphile/solvent mixture for the formulation of an ecological detergent as an alternative for removing fat from natural fiber surfaces
Authors: Huillca-Mamani Jenifer , Pacheco-Meza Carla Alessandra , Gutiérrez-Oppe Evelyn Edith , Salas-Chávez Sergio André , Filho Pedro de Alcântara Pessôa 
Published: July 2024

Abstract

The current research focused on the use of a binary amphiphile/solvent mixture consisting of a natural surfactant (alkylpolyglucoside) and the green solvent ethyl lactate (EL) as an active compound for the formulation of a new ecological detergent, to improve the conditions of the washing process as temperature, pH, lipase integration, type of surfactant and detergent dose, to remove fat and dirt from natural fiber surfaces effectively. The best conditions for residual fat percentage <0.37% were achieved at 25 °C, pH 9, and using 0.3% detergent doses without lipase. The study also found that pH and the interaction between pH and surfactant type significantly influenced the percentage of residual fat. All the estimated results mean that the detergent obtained has excellent cleaning properties because there is a strong interaction between the alkylpolyglucoside/ethyl lactate binary mixture, which has a positive effect on reducing the percentage of residual fat and dirt and suggests its use potential in the formulation of green detergents for washing natural and synthetic textile fibers as a replacement for detergents based on ethoxylated fatty alcohols.
Real-Time Identification of Rock Failure Stages Using Deep Learning: A Case Study of Acoustic Emission Analysis in Rock Engineering
Authors: Pu Yuanyuan , Chen Jie , Apel Derek B. , Shang Xueyi 
Published: July 2024

Abstract

The accurate and timely determination of rock failure processes is crucial for various rock engineering applications, especially for preventing dynamic disasters such as rock bursts and roof failures. The primary aim of this study was to determine the current rock failure stage using a single acoustic emission (AE) event signal recorded during the failure process. To achieve this, we proposed a deep learning model that employs advanced convolutional modules and a soft-threshold technique to extract the full waveform features of AE events from four different stages of rock failure in a laboratory uniaxial compressive strength (UCS) test. Once fully trained, our model can instantaneously determine the current rock failure stage from a raw waveform of a single recorded AE event. Subsequently, the trained model was applied to on-site microseismic data analysis at a coal mine working face. Compared to traditional methods of microseismic data analysis that consider large-energy events, our model can identify the rock failure stage at the time of a specific microseismic event. Furthermore, by analyzing microseismic events triggered by post-peak rock fracturing, we identified potential hazard areas for rock bursts in the working face, and the results closely matched the site's burst prevention logs. This study successfully developed a real-time method for determining rock failure stages using deep learning, which can be effectively applied to microseismic data analysis in engineering sites to provide more precise early warnings of rock dynamic disasters.
Decoding the Glioma Microenvironment: Single-Cell RNA Sequencing Reveals the Impact of Cell-to-Cell Communication on Tumor Progression and Immunotherapy Response
Authors: Li Xia , Chen Shenbo , Ding Ming , Ding Hui , Yang Kun 
Published: July 2024

Abstract

Background
Glioma is the most common primary tumor of the central nervous system, characterized by high heterogeneity that poses significant challenges to therapeutic strategies and prognostic assessment. This study investigates the cell-cell communication between malignant glioma cells and macrophages/monocytes and its impact on tumor progression and treatment response through in-depth single-cell RNA sequencing analysis.
Methods
We integrated RNA-seq data from the TCGA and CGGA databases and conducted a comprehensive analysis of glioma samples using single-cell RNA sequencing, functional enrichment analysis, developmental trajectory analysis, cell-cell communication analysis, and gene regulatory network analysis. Additionally, we constructed a prognostic model based on risk scores and evaluated the predictive performance of the model through analyses of immune cell infiltration and immune treatment response.
Results
We successfully identified 14 glioma cellular subpopulations and 7 primary cell types, as well as 4 subtypes of macrophages/monocytes. Developmental trajectory analysis revealed the origins and heterogeneity of malignant cells and macrophages/monocytes. Cellular communication analysis found that macrophages and monocytes interact with malignant cells through multiple pathways, including MIF (Macrophage Migration Inhibitory Factor) and SPP1 (Secreted Phosphoprotein 1), engaging in several key ligand-receptor pairs that influence tumor behavior. Subgroup stratification based on cellular communication characteristics showed a significant association with overall survival (OS). Immune cell infiltration analysis indicated differences in the abundance of immune cells among various subgroups, which may correlate with responses to immunotherapy. A predictive model composed of 29 prognostic genes demonstrated excellent accuracy and robustness across multiple independent cohorts.
Conclusion
Our study reveals the complex heterogeneity of the glioma microenvironment and strengthens the understanding of the diversity and characteristics of glioma cell subpopulations, preliminarily establishing a prognostic model related to prognosis. These findings provide a basis for the development of therapeutic strategies and prognostic models targeting the glioma microenvironment.
Alterations in cerebral perfusion and corresponding brain functional networks in systemic lupus erythematosus with cognitive impairment
Authors: Liu Huiyang , Liu Hu , Tian Bailing , Yang Pingting , Fan Guoguang 
Published: July 2024

Abstract

Cognitive impairment (CI) frequently occurs in patients with systemic lupus erythematosus (SLE) and may result from neuroinflammation processes and neurovascular changes in the brain. The cerebral hemodynamics underlying SLE with CI (SLE-CI) remain unclear. 97 patients with SLE and 51 heathy controls (HCs) matched for age and gender underwent magnetic resonance imaging (MRI). The CI status of patients was measured using the Montreal Cognitive Assessment (MoCA). 3D T1-weighted arterial spin labeling (ASL) and resting-state functional (rs-fMRI) sequences were obtained. Seed-based FC was calculated using the CBF results. Compared with patients with SLE-NC, patients with SLE-CI had higher CBF in the left hippocampus, thalamus, and cerebellum crus II and lower CBF in the left frontal lobe. Left hippocampus gray matter (GM) atrophy was detected in patients with SLE-CI but not in patients with SLE-NC. Secondary analyses revealed that compared with patients with SLE-NC, patients with SLE-CI had increased FC of the left insula gyrus when the left cerebellum crus II was set as the seed region and decreased FC in the homolateral parahippocampus when the left hippocampus was set as the seed region. These changes in brain structure, function, and network may be biomarkers of cognitive impairment in patients with SLE-CI.
The nasopharyngeal microbiome in South African children with lower respiratory tract infection: a nested case-control study of the Drakenstein Child Health Study
Authors: Claassen-Weitz Shantelle , Xia Yao , Workman Lesley , Hannan Luke , Gardner-Lubbe Sugnet , Mwaikono Kilaza S , Mounaud Stephanie Harris , Nierman William C. , Africa Samantha , Patel Fadheela , Dube Felix Sizwe , Allen Veronica , Edries Lemese Ah Tow , Zar Heather J. , Nicol Mark Patrick 
Published: July 2024

Abstract

Background
Lower respiratory tract infection (LRTI) is a leading cause of infant morbidity and mortality globally. LRTI may be caused by viral or bacterial infections, individually or in combination. We investigated associations between LRTI and infant nasopharyngeal (NP) viruses and bacteria in a South African birth cohort.
Methods
In a case-control study of infants enrolled in the Drakenstein Child Health Study (DCHS), LRTI cases were identified prospectively and age-matched with controls from the cohort. NP swabs were tested using quantitative real-time polymerase chain reaction (qPCR) and 16S rRNA gene amplicon sequencing. We calculated adjusted Conditional Odds Ratios (aORs) for qPCR targets and used mixed effects models to identify differentially abundant taxa between LRTI cases and controls and explore viral-bacterial interactions.
Results
Respiratory Syncytial Virus (RSV) [aOR: 5.69, 95% CI: 3.03–10.69], human rhinovirus (HRV) [1.47, 1.03–2.09], parainfluenza virus [3.46, 1.64–7.26], adenovirus [1.99, 1.08–3.68], enterovirus [2.32, 1.20–4.46], Haemophilus influenzae [1.72, 1.25–2.37], Klebsiella pneumoniae [2.66, 1.59–4.46], or high-density (> 6.9 log10 copies/mL) Streptococcus pneumoniae [1.53, 1.01–2.32] were associated with LRTI. Using 16S sequencing, LRTI was associated with increased relative abundance of Haemophilus (q = 0.0003) and decreased relative abundance of Dolosigranulum (q = 0.001), Corynebacterium (q = 0.091) and Neisseria (q = 0.004). In samples positive for RSV, Staphylococcus and Alloprevotella were present at lower relative abundance in cases than controls. In samples positive for parainfluenza virus or HRV, Haemophilus was present at higher relative abundance in cases.
Conclusions
The associations between bacterial taxa and LRTI are strikingly similar to those identified in high-income countries, suggesting a conserved phenotype. RSV was the major virus associated with LRTI. H. influenzae appears to be the major bacterial driver of LRTI, acting synergistically with viruses. The Gram-positive bacteria Dolosigranulum and Corynebacteria may protect against LRTI, while Staphylococcus was associated with reduced risk of RSV-related LRTI.
Funding
National Institutes of Health of the USA, Bill and Melinda Gates Foundation, National Research Foundation South Africa, South African Medical Research Council, L’Oréal-UNESCO For Women in Science South Africa, Australian National Health and Medical Research Council.
Research paper classification model based on deep learning network and text fine-grained features
Authors: Gao Li , Li Hongjun , Chen Qingkui , Lan Yu 
Published: July 2024

Abstract

Traditional document classification methods in scientific research often overlook the semantic order of words, resulting in accuracy challenges. Although deep learning has addressed this by considering word order, it faces issues with overfitting in complex tasks. This paper aims to enhance document classification accuracy by utilizing features from article keywords and abstracts. The objective is to improve feature representation through weighted keyword extraction and refined abstract processing, followed by training a hierarchical deep learning model for superior performance in complex text multi-classification. Proposed method involves several key steps: 1) Feature Representation: Extracting keywords from article titles and abstracts with enhanced representation using TF-IDF weighting to handle overlapping keywords. 2) Abstract Refinement: Employing POS tagging to refine lengthy abstracts into concise and informative segments. 3) Hierarchical Deep Learning Model: Combining TextCNN and BiLSTM architectures to capture fine-grained features and semantic contexts effectively. 4) Training Strategy: Training the hierarchical model to classify scientific documents based on refined features from keywords and abstracts. The innovative approach, FFDLText, which combines TextCNN and BiLSTM models, achieves higher accuracy and training efficiency compared to baseline models on the WOS dataset. This research contributes a novel method for complex text multi-classification tasks. The paper introduces FFDLText, a novel approach to scientific document classification using fine-grained feature extraction and hierarchical deep learning. By refining keyword representation and abstract content with TF-IDF weighting and POS tagging, and leveraging TextCNN and BiLSTM models, this method significantly enhances accuracy and efficiency in scientific research document classification.
Can Rewards Enhance Creativity? Exploring the Effects of Real and Hypothetical Rewards on Creative Problem Solving and Neural Mechanisms
Authors: Cui Can , Yuan Yuan , Jiang Yingjie 
Published: July 2024

Abstract

Reward cues have long been considered to enhance creative performance; however, little is known about whether rewards can affect creative problem solving by manipulating states of flexibility and persistence. This study sought to elucidate the differential impacts of real versus hypothetical rewards on the creative process utilizing the Chinese compound remote association task. Behavioral analysis revealed a significantly enhanced solution rate and response times in scenarios involving real rewards, in contrast to those observed with hypothetical rewards. Furthermore, participants exhibited a greater ability to solve CRA items under low rewards than under high rewards. Electrophysiological findings indicated that hypothetical rewards led to more positive P200-600 amplitudes, in stark contrast to the amplitudes observed in the context of real rewards. These findings indicate a positive impact of real rewards on creative remote associations and contribute new insights into the relationship between rewards and creative problem solving, highlighting the crucial role of the level of control in the formation of creativity.
First Hip Hemiarthroplasty in Göttingen Minipigs; Surgical and Post-mortem Protocol
Authors: Hartmann Katrine Top , Odgaard Anders , Knudsen Ulrik Kragegaard , Aalbaek Bent , Kvich Lasse , Birch Julie Melsted , Petersen Andreas , Bjarnsholt Thomas , Jensen Henrik Elvang , Jensen Louise Kruse 
Published: July 2024

Abstract

Background Prosthetic joint infections (PJI) are recalcitrant, hard-to-treat infections and severe complications of joint arthroplasty. Therefore, there is a need to develop new effective treatment strategies, and animal models of high clinical relevance are needed. This study aimed to develop a detailed surgical protocol for inserting a hip hemiarthroplasty in Göttingen minipig and a thorough post-mortem sampling protocol to pave the way for creating a minipig PJI model.
Methods Three adult female Göttingen minipigs underwent surgery with insertion of a hip hemiarthroplasty, using the anterior approach to the hip joint. After surgery the minipigs were followed closely with daily clinical evaluation and gait scoring. Comprehensive post-mortem analyses were performed with evaluation of macroscopic lesions, microbiology, synovial fluid analysis and histology.
Results The study resulted in the first Göttingen minipig with hip hemiarthroplasty and identified several points of awareness when inserting a hip prosthesis in minipigs, especially the high risk of joint dislocation. A spontaneous PJI occurred in one of the minipigs, revealing an impaired ability of the immune cells to reach the bacteria at the bone-prosthesis interface.
Conclusion The present study provides a detailed description of surgical technique and post-mortem sampling and validates the suitability of the hip hemiarthroplasty minipig model for future experimental modeling of PJI.
A Comparative Study of Lower Limb Alignment between MAKO Robot-Assisted and Conventional Unicompartmental Knee Arthroplasty
Authors: Xu Kaige , Zhang Zian , Xu Hao , Wang Wenzhe , Yu Chaoqun , Zhang Haining 
Published: July 2024

Abstract

Backgrounds:
This retrospective study aims to comparatively analyze the lower limb alignment correction capabilities of MAKO robot-assisted surgery and conventional surgery.
Methods
Clinical data of 70 patients who underwent UKA treatment in the Department of Orthopedics, Qingdao University Affiliated Hospital, from January 2022 to November 2023 were reviewed. Among them, 48 patients who underwent MAKO robot-assisted UKA were included in the MAKO robot group, and 22 patients who underwent conventional UKA were included in the conventional group. The preoperative and postoperative lower limb alignment hip-knee-ankle (HKA) angles of the two groups were recorded and subjected to inter-group and intra-group analysis.
Results
Both groups of patients successfully completed the surgeries. The postoperative HKA angle in the MAKO robot group averaged 176.03°±3.08°, showing an average correction of 5.13°±2.75° compared to the preoperative angle (170.09°±3.74°), with a statistically significant difference (t = 12.90, p < 0.05). In the conventional group, the postoperative HKA angle averaged 175.47°±2.40°, demonstrating an average correction of 5.47°±2.21° compared to the preoperative angle (170.00°±2.95°), also with a statistically significant difference (t = 11.62, p < 0.05). However, the comparison of postoperative HKA angles and correction degrees between the two groups showed no statistically significant difference (t = 0.76, p > 0.05; t = 0.51, p > 0.05).
Conclusion
Both MAKO robot-assisted UKA and conventional UKA effectively corrected the lower limb alignment of patients postoperatively. However, compared to conventional surgery, MAKO robot-assisted UKA did not demonstrate a significant advantage in lower limb alignment correction ability.