bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implantation of dedifferentiated fat cells ameliorated antineutrophil cytoplasmic antibody glomerulonephritis by immunosuppression and increases in tumor necrosis factor-stimulated gene-6
Authors: Utsunomiya Kei , Maruyama Takashi , Shimizu Satoshi , Matsumoto Taro , Endo Morito , Kobayashi Hiroki , Kano Koichiro , Abe Masanori , Fukuda Noboru 
Published: July 2022

Abstract
Introduction
The implantation of dedifferentiated fat (DFAT) cells has been shown to exert immunosuppressive effects. To develop DFAT cell therapy for antineutrophil cytoplasmic antibody (ANCA) glomerulonephritis, the effects of the implantation of DFAT cells on ANCA glomerulonephritis were investigated in mice.

Methods
PKH26-labeled DFAT cells (105) were infused through the posterior orbital venous plexus to investigate delivery of DFAT cells in ICR mice. DFAT cells (105) were also implanted in SCG mice as a model for ANCA glomerulonephritis. Expression of tumor necrosis factor-stimulated gene-6 (TSG-6) mRNA and protein in kidney was evaluated, and the expression of microRNAs associated with TSG-6 in plasma, lung and kidney was analyzed. Expressions of CD44, prostaglandin (PG) E2, interleukin (IL)-10, IL-1β, tumor necrosis factor (TNF)-α mRNAs, C–C motif chemokine ligand 17 (CCL-17) and monocyte chemoattractant protein (MCP)-1 proteins were measured in kidney from SCG mice implanted with DFAT cells.

Results
After their intravenous infusion, almost all DFAT cells were trapped in the lung and not delivered into the kidney. Implantation of DFAT cells in SCG mice suppressed glomerular crescent formation, decreased urinary protein excretions and increased expression of TSG-6 mRNA, protein and immunostaining in kidney from these mice. Increased expression of microRNA 23b-3p in plasma, kidney and lung; decreased expression of CD44 mRNA; and increased expression of PGE2 and IL-10 mRNAs were also observed in kidney from these mice. Implantation of DFAT cells also decreased the expression of TNF-α and MCP-1 proteins and increased that of CCL-17 protein in kidney from the SCG mice. Survival rates were higher in SCG mice implanted with DFAT cells than in SCG mice without implantation.

Conclusion
Mechanisms underlying the effects of improvement of ANCA glomerulonephritis are associated with immunosuppressive effects by TSG-6 and the transition of M1–M2 macrophages, suggesting that implantation of DFAT cells may become a cell therapy for ANCA glomerulonephritis.
Diversity of epiphytic bacterial communities on male and female Sargassum thunbergii
Authors: Wang Jing , Yang Zhibo , Wang Gaoge , Shang Shuai , Tang Xuexi , Xiao Hui 
Published: July 2022

AbstractThe epiphytic bacteria are the most abundant microorganisms on marine macroalga. However, there are few studies on the distribution of these epiphytic bacteria on male and female Sargassum thunbergii. In this study, the composition and diversity of epiphytic bacterial communities on male and female S. thunbergii were investigated by using the traditional culture-based method and 16S rDNA high-throughput sequencing. The results showed that the dominant bacterial phyla and genera were the same on both male and female S. thunbergii. However, there were significant differences in the relative abundance of epiphytic bacteria at the genus level. Furthermore, male and female S. thunbergii had their own indicative species and specific bacteria. In addition, the predicted functions of the epiphytic bacteria mainly differed in transport and metabolism, environmental adaptation and spore development. This study enriches the baseline knowledge of epiphytic bacteria related to dioecious algae and paves the way for further studies of the relationships between epiphytic microbial communities and the sex of algae.
Evaluation of outcomes in the management of primary sporadic desmoid-type fibromatosis at a specialist soft tissue sarcoma unit
Authors: Khan Misbah , Almond Max , Ford Samuel , Desai Anant 
Published: July 2022

Abstract
Background
Desmoids are rare fibroblastic tumours whose treatment in any individual case presents a persistent challenge. We endeavoured to evaluate various clinicopathological factors contributing to tumour behaviour.

Methods
This is a retrospective review of 95 primary truncal sporadic fibromatosis managed between 2011 and 2020. We studied progression rate for wait and watch (WW) and recurrence rate for the surgically treated group as adverse events. Relevant event-free survivals and potential risk factors were analysed over a median follow-up of 27 months.

Results
66 patients (69.5%) received watchful surveillance and 28 upfront surgery. 2-year progression-free survival in WW group (88.9%) was higher than RFS in the surgical group (77.1%) p = 0.02. Adverse event rate compared favourably, 28.8% in WW and 28.6% in surgical group. At final follow-up, rate of stable disease for WW was 47%, and the regression rate was 24.2%. On Cox regression analysis, meantime to progress was 14 ± 2.0 months, with larger tumour size as a significant prognostic indicator (p = 0.05). Surgical group's mean time to recurrence was 13.8 ± 2.76 months, with tumour location a significant contributing factor (p = 0.05).

Conclusions
This study confirms to the safety of both treatment approaches. Adverse event rates remained comparable, but event-free survival was longer for the watchful surveillance group.
Association of multidrug resistance behavior of clinical Pseudomonas aeruginosa to pigment coloration
Authors: Kothari Ashish , Kumar Shyam Kishor , Singh Vanya , Kumar Prashant , Kaushal Karanvir , Pandey Atul , Jain Neeraj , Omar Balram Ji 
Published: July 2022

AbstractPseudomonas aeruginosa is an adaptable bacterial pathogen that infects a variety of organs, including the respiratory tract, vascular system, urinary tract, and central nervous system, causing significant morbidity and mortality. As the primary goal of this study, we wanted to determine how pigment color production differed between clinical strains of P. aeruginosa, and whether or not that variation was associated with multidrug resistance or the ability to form biofilms. We screened in total 30.1% of yellow, 39.8% green and 30.1% of no pigment-producing P. aeruginosa strains from a total of 143 various clinical isolates. Yellow pigment-producing strains presented significant resistance to antibiotics groups, including β-lactam (91.5%), aminoglycosides (70.5%), and carbapenems (51.9%) compared to green and non-pigmented strains. Notably, 16.3% of yellow pigment-producing strains were resistant to colistin which is used as a last-resort treatment for multidrug-resistant bacteria, whereas only 2.3% of non-pigmented and 1.8% of green pigmented strains were resistant to colistin. Aside from that, yellow pigment-producing strains were frequent producers of enzymes belonging to the lactamase family, including ESBL (55.6%), MBL (55.6%), and AmpC (50%). Compared to the green groups (7.14%) and non-pigmented groups (28.5%), they had a higher frequency of efflux positive groups (64.2%). Notably, when compared to non-pigmented groups, green pigment-producing strains also displayed antibiotic susceptibility behavior similar to yellow pigment-producing strains. The majority of yellow pigment-producing strains outperformed the green and non-pigmented strains in terms of MIC levels when compared to the other two groups of strains. Despite the fact that previous studies have demonstrated a direct correlation between multidrug resistance behaviors and biofilm production, no such statistically significant association between pigment and biofilm formation was found in our investigation. Our research has demonstrated that the correlation of bacterial pigments on their susceptibility to antimicrobial agents. Yellow pigment-producing P. aeruginosa strains posed a significant problem due to the lack of alternative agents against such transformed strains, which may be associated with the development of multidrug resistance.
Graphical Abstract
Real-world safety of ulinastatin: a post-marketing surveillance of 11,252 patients in China
Authors: Li Jin , Li Meijun , Li Liren , Ma Lin , Cao Ailin , Wen Aiping , Chen Wenge , Li Lingling , Liang Yan , Deng Jianxiong 
Published: July 2022

Abstract
Background
The safety assessment of ulinastatin can guide clinical practice. The present study aimed to investigate the real-world safety of ulinastatin in China.

Methods
This multicenter study retrospectively analyzed the post-marketing surveillance data of consecutive patients treated with ulinastatin between August 2014 and June 2017 in the general wards and the intensive care units (ICU) of nine hospitals in China. Adverse drug reactions/adverse drug events (ADRs/ADEs) were collected and evaluated in a post-marketing database.

Results
A total of 11,252 consecutive patients were included in the study: 7009 ICU patients and 4243 general ward patients. Eleven patients with ADRs/ADEs were observed, including nine ICU patients and two general ward patients. The clinical manifestations were liver dysfunction (n = 5 ICU cases, n = 1 general case), thrombocytopenia (n = 2 ICU cases, n = 1 general case), leukopenia (n = 1 ICU case), and rash (n = 1 ICU case). During the study period, the drug ADR/ADE rate of ulinastatin injection was 0.98‰ (11/11,252 × 1000‰). Among the 11,252 valid patients, only 327 received ulinastatin in accordance with the drug specifications. After excluding unreasonable drug use, the calculated ADR rate was 3.06‰ (1/327 × 1000‰) (95% confidence interval: 0.0‰-17.1‰). In ICU and general ward patients, the use of other drugs combined with ulinastatin was associated with the occurrence of ADRs/ADEs (100% with ADRs/ADEs vs. 0% in controls, P < 0.001).

Conclusions
The incidence of ADRs/ADEs of ulinastatin is < 5‰. The ADRs/ADEs involved limited organs, mainly the skin, gastrointestinal tract, and blood. In most cases, the ADRs/ADEs gradually alleviated or recovered after drug withdrawal. The inappropriate/off-label use of ulinastatin should be the focus of surveillance.
An efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis
Authors: Li Qing , Pang Guansong , Shang Mingsheng 
Published: July 2022

AbstractA high-dimensional and incomplete (HDI) matrix is a typical representation of big data. However, advanced HDI data analysis models tend to have many extra parameters. Manual tuning of these parameters, generally adopting the empirical knowledge, unavoidably leads to additional overhead. Although variable adaptive mechanisms have been proposed, they cannot balance the exploration and exploitation with early convergence. Moreover, learning such multi-parameters brings high computational time, thereby suffering gross accuracy especially when solving a bilinear problem like conducting the commonly used latent factor analysis (LFA) on an HDI matrix. Herein, an efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis (ADMA) is proposed to address these problems. First, a periodic equilibrium mechanism is employed using the physical mechanism annealing, which is embedded in the mutation operation of differential evolution (DE). Then, to further improve its efficiency, we adopt a probabilistic evaluation mechanism consistent with the crossover probability of DE. Experimental results of both adaptive and non-adaptive state-of-the-art methods on industrial HDI datasets illustrate that ADMA achieves a desirable global optimum with reasonable overhead and prevails competing methods in terms of predicting the missing data in HDI matrices.
Machine learning approach for predicting production delays: a quarry company case study
Authors: Kannan Rathimala , Abdul Halim Haq’ul Aqif , Ramakrishnan Kannan , Ismail Shahrinaz , Wijaya Dedy Rahman 
Published: July 2022

AbstractPredictive maintenance employing machine learning techniques and big data analytics is a benefit to the industrial business in the Industry 4.0 era. Companies, on the other hand, have difficulties as they move from reactive to predictive manufacturing processes. The purpose of this paper is to demonstrate how data analytics and machine learning approaches may be utilized to predict production delays in a quarry firm as a case study. The dataset contains production records for six months, with a total of 20 columns for each production record for two machines. Cross Industry Standard Process for Data Mining approach is followed to build the machine learning models. Five predictive models were created using machine learning algorithms such as Decision Tree, Neural Network, Random Forest, Nave Bayes and Logistic Regression. The results show that Multilayer Perceptron Neural Network and Logistic Regression outperform other techniques and accurately predicts production delays with a F-measure score of 0.973. The quarry company's improved decision-making reducing potential production line delays demonstrates the value of this study.
Measuring science self-efficacy with a focus on the perceived competence dimension: using mixed methods to develop an instrument and explore changes through cross-sectional and longitudinal analyses in high school
Authors: Hu Xinyang , Jiang Yanxia , Bi Hualin 
Published: July 2022

Abstract
Background
In many countries and regions, such as the United States, Europe and China, a trend has emerged in which students’ enthusiasm for STEM is declining. This decline may be related to students’ lack of science self-efficacy. An accurate examination of students’ science self-efficacy can provide a research foundation for how to cultivate it. This paper used mixed methods to develop a valid science self-efficacy scale for high school students, focusing on the perceived competence dimension. A cross-sectional analysis exploring and interpreting differences across grades and genders in science self-efficacy among high school students was conducted. Subsequently, a 1-year longitudinal study was conducted on the development of science self-efficacy in China.

Results
This study developed a 24-item science self-efficacy instrument based on the Rasch model, and the validity of the instrument was assessed through multiple aspects, including face, content, construct, and predictive validity. This instrument was used to divide students' science self-efficacy into four different levels. A cross-sectional study examining 1564 high school students in 10th–12th grades revealed that students’ science self-efficacy exhibited a complex process of decreasing and then increasing by grade. Most girls’ science self-efficacy was higher than that of boys for Levels 1 and 4, while for the intermediate levels, i.e., Levels 2 and 3, most boys had higher science self-efficacy than girls. The quantitative and qualitative results of the longitudinal study through a 1-year follow-up of 233 high school students indicated that students’ science self-efficacy significantly improved. We revealed inconsistencies between cross-sectional and longitudinal studies of the change in science self-efficacy from 10 to 11th grade.

Conclusions
This study makes many contributions. First, we developed a science self-efficacy measurement instrument for high school students with high reliability and validity based on the Rasch model and characterized four different levels of student science self-efficacy. Second, the gender differences in science self-efficacy and the complex changes among grades were explained from the perspective of science self-efficacy level. Finally, students’ science self-efficacy significantly improved in the longitudinal study, which was explained by self-efficacy theory and the Chinese core competency-oriented science curriculum.
Development of the Cooperative Adoption Factors Instrument to measure factors associated with instructional practice in the context of institutional change
Authors: McAlpin Jacob D. , Ziker John P. , Skvoretz John , Couch Brian A. , Earl Brittnee , Feola Stephanie , Lane A. Kelly , Mertens Karl , Prevost Luanna B. , Shadle Susan E. , Stains Marilyne , Lewis Jennifer E. 
Published: July 2022

Abstract
Background
Many institutional and departmentally focused change efforts have sought to improve teaching in STEM through the promotion of evidence-based instructional practices (EBIPs). Even with these efforts, EBIPs have not become the predominant mode of teaching in many STEM departments. To better understand institutional change efforts and the barriers to EBIP implementation, we developed the Cooperative Adoption Factors Instrument (CAFI) to probe faculty member characteristics beyond demographic attributes at the individual level. The CAFI probes multiple constructs related to institutional change including perceptions of the degree of mutual advantage of taking an action (strategic complements), trust and interconnectedness among colleagues (interdependence), and institutional attitudes toward teaching (climate).

Results
From data collected across five STEM fields at three large public research universities, we show that the CAFI has evidence of internal structure validity based on exploratory and confirmatory factor analysis. The scales have low correlations with each other and show significant variation among our sampled universities as demonstrated by ANOVA. We further demonstrate a relationship between the strategic complements and climate factors with EBIP adoption through use of a regression analysis. In addition to these factors, we also find that indegree, a measure of opinion leadership, correlates with EBIP adoption.

Conclusions
The CAFI uses the CACAO model of change to link the intended outcome of EBIP adoption with perception of EBIPs as mutually reinforcing (strategic complements), perception of faculty having their fates intertwined (interdependence), and perception of institutional readiness for change (climate). Our work has established that the CAFI is sensitive enough to pick up on differences between three relatively similar institutions and captures significant relationships with EBIP adoption. Our results suggest that the CAFI is likely to be a suitable tool to probe institutional change efforts, both for change agents who wish to characterize the local conditions on their respective campuses to support effective planning for a change initiative and for researchers who seek to follow the progression of a change initiative. While these initial findings are very promising, we also recommend that CAFI be administered in different types of institutions to examine the degree to which the observed relationships hold true across contexts.
MRI-based radiomics value for predicting the survival of patients with locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy
Authors: Zhang Xiaomiao , Zhao Jingwei , Zhang Qi , Wang Sicong , Zhang Jieying , An Jusheng , Xie Lizhi , Yu Xiaoduo , Zhao Xinming 
Published: July 2022

Abstract
Background
To investigate the magnetic resonance imaging (MRI)-based radiomics value in predicting the survival of patients with locally advanced cervical squamous cell cancer (LACSC) treated with concurrent chemoradiotherapy (CCRT).

Methods
A total of 185 patients (training group: n = 128; testing group: n = 57) with LACSC treated with CCRT between January 2014 and December 2018 were retrospectively enrolled in this study. A total of 400 radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient map, arterial- and delayed-phase contrast-enhanced MRI. Univariate Cox regression and least absolute shrinkage and selection operator Cox regression was applied to select radiomics features and clinical characteristics that could independently predict progression-free survival (PFS) and overall survival (OS). The predictive capability of the prediction model was evaluated using Harrell’s C-index. Nomograms and calibration curves were then generated. Survival curves were generated using the Kaplan-Meier method, and the log-rank test was used for comparison.

Results
The radiomics score achieved significantly better predictive performance for the estimation of PFS (C-index, 0.764 for training and 0.762 for testing) and OS (C-index, 0.793 for training and 0.750 for testing), compared with the 2018 FIGO staging system (C-index for PFS, 0.657 for training and 0.677 for testing; C-index for OS, 0.665 for training and 0.633 for testing) and clinical-predicting model (C-index for PFS, 0.731 for training and 0.725 for testing; C-index for OS, 0.708 for training and 0.693 for testing) (P < 0.05). The combined model constructed with T stage, lymph node metastasis position, and radiomics score achieved the best performance for the estimation of PFS (C-index, 0.792 for training and 0.809 for testing) and OS (C-index, 0.822 for training and 0.785 for testing), which were significantly higher than those of the radiomics score (P < 0.05).

Conclusions
The MRI-based radiomics score could provide effective information in predicting the PFS and OS in patients with LACSC treated with CCRT. The combined model (including MRI-based radiomics score and clinical characteristics) showed the best prediction performance.
Ecosystem-specific microbiota and microbiome databases in the era of big data
Authors: Lobanov Victor , Gobet Angélique , Joyce Alyssa 
Published: July 2022

AbstractThe rapid development of sequencing methods over the past decades has accelerated both the potential scope and depth of microbiota and microbiome studies. Recent developments in the field have been marked by an expansion away from purely categorical studies towards a greater investigation of community functionality. As in-depth genomic and environmental coverage is often distributed unequally across major taxa and ecosystems, it can be difficult to identify or substantiate relationships within microbial communities. Generic databases containing datasets from diverse ecosystems have opened a new era of data accessibility despite costs in terms of data quality and heterogeneity. This challenge is readily embodied in the integration of meta-omics data alongside habitat-specific standards which help contextualise datasets both in terms of sample processing and background within the ecosystem. A special case of large genomic repositories, ecosystem-specific databases (ES-DB’s), have emerged to consolidate and better standardise sample processing and analysis protocols around individual ecosystems under study, allowing independent studies to produce comparable datasets. Here, we provide a comprehensive review of this emerging tool for microbial community analysis in relation to current trends in the field. We focus on the factors leading to the formation of ES-DB’s, their comparison to traditional microbial databases, the potential for ES-DB integration with meta-omics platforms, as well as inherent limitations in the applicability of ES-DB’s.