bluuebunny/crossref_metadata_2025_split huggingface dataset:
Driving forces of digital transformation in chinese enterprises based on machine learning
Authors: Chen Qi-an , Zhao Xu , Zhang Xinyi , Jiang Zizhe , Wang Yuxuan 
Published: March 2024

AbstractWith advanced science and digital technology, digital transformation has become an important way to promote the sustainable development of enterprises. However, the existing research only focuses on the linear relationship between a single characteristic and digital transformation. In this study, we select the data of Chinese A-share listed companies from 2010 to 2020, innovatively use the machine learning method and explore the differences in the predictive effects of multi-dimensional features on the digital transformation of enterprises based on the Technology-Organization-Environment (TOE) theory, thus identifying the main drivers affecting digital transformation and the fitting models with stronger predictive effect. The study found that: first, by comparing machine learning and traditional linear regression models, it is found that the prediction ability of ensemble earning method is generally higher than that of tradition measurement method. For the sample data selected in this research, XGBoost and LightGBM have strong explanatory ability and high prediction accuracy. Second, compared with the technical driving force and environmental driving force, the organizational driving force has a greater impact. Third, among these characteristics, equity concentration and executives’ knowledge level in organizational dimension have the greatest impact on digital transformation. Therefore, enterprise managers should always pay attention to the decision-making role of equity concentration and executives’ knowledge level. This study further enriches the literature on digital transformation in enterprises, expands the application of machine learning in economics, and provides a theoretical basis for enterprises to enhance digital transformation.
Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence
Authors: Ahmed Fahad , Abbas Sagheer , Athar Atifa , Shahzad Tariq , Khan Wasim Ahmad , Alharbi Meshal , Khan Muhammad Adnan , Ahmed Arfan 
Published: March 2024

AbstractA kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies.
Discovering therapeutic possibilities for polycystic ovary syndrome by targeting XIST and its associated ceRNA network through the analysis of transcriptome data
Authors: Berenji Elahe , Valipour Motlagh Ali , Fathi Marziyeh , Esmaeili Maryam , Izadi Tayebeh , Rezvanian Parsa , Zanjirband Maryam , Safaeinejad Zahra , Nasr-Esfahani Mohammad Hossein 
Published: March 2024

AbstractLong non-coding RNA (lncRNA) regulates many physiological processes by acting as competitive endogenous RNA (ceRNA). The dysregulation of lncRNA X-inactive specific transcript (XIST) has been shown in various human disorders. However, its role in the pathogenesis of polycystic ovary syndrome (PCOS) is yet to be explored. This study aimed to explore the underlying mechanism of XIST in the pathogenesis of PCOS, specifically through dataset functional analysis. GEO PCOS datasets including RNA-seq, microarray, and miRNA-seq in granulosa cells (GCs) and blood, were examined and comprehensively analyzed. Enrichment analysis, ROC curve constructions, lncRNA-miRNA-mRNA interaction network analyses, and qRT-PCR validation were performed followed by a series of drug signature screenings. Our results revealed significant dysregulation in the expression of 1131 mRNAs, 30 miRNAs, and XIST in GCs of PCOS patients compared to healthy individuals. Of the120 XIST-correlated upregulated genes, 25 were enriched in inflammation-related pathways. Additionally, 5 miRNAs were identified as negative regulators of XIST-correlated genes. Accordingly, a ceRNA network containing XIST-miRNAs-mRNAs interactions was constructed. Furthermore, 6 genes, including AQP9, ETS2, PLAU, PLEK, SOCS3, and TNFRSF1B served as both GCs and blood-based biomarkers. By analyzing the number of interactions among XIST, miRNAs, and mRNAs, we pinpointed ETS2 as the pivotal gene within the ceRNA network. Our findings reveal a novel XIST- hsa-miR-146a-5p, hsa-miR-144-3p, and hsa-miR-1271-5p-ETS2 axis that comprehensively elucidates the XIST-associated mechanism underlying PCOS onset. qRT-PCR analysis further confirmed the, overexpression of both XIST and ETS2 . Furthermore, our results demonstrated that XIST and ETS2 were correlated with some assisted reproductive technologies outcomes. Finally, we identified two novel compounds including, methotrexate/folate and threonine using drug–gene interaction databases for PCOS management. These findings provide novel insights into the molecular etiology, diagnosis, and potential therapeutic interventions for PCOS.
Prognostic mutation signature would serve as a potential prognostic predictor in patients with diffuse large B-cell lymphoma
Authors: Cho Shih-Feng , Yeh Tsung-Jang , Wang Hui-Ching , Du Jeng-Shiun , Gau Yuh-Ching , Lin Yu-Yin , Chuang Tzer-Ming , Liu Yi-Chang , Hsiao Hui-Hua , Moi Sin-Hua 
Published: March 2024

AbstractThe present study aimed to elucidate the prognostic mutation signature (PMS) associated with long-term survival in a diffuse large B-cell lymphoma (DLBCL) cohort. All data including derivation and validation cohorts were retrospectively retrieved from The Cancer Genome Atlas (TCGA) database and whole-exome sequencing (WES) data. The Lasso Cox regression analysis was used to construct the PMS based on WES data, and the PMS was determined using the area under the receiver operating curve (AUC). The predictive performance of eligible PMS was analyzed by time-dependent receiver operating curve (ROC) analyses. After the initial evaluation, a PMS composed of 94 PFS-related genes was constructed. Notably, this constructed PMS accurately predicted the 12-, 36-, and 60-month PFS, with AUC values of 0.982, 0.983, and 0.987, respectively. A higher level of PMS was closely linked to a significantly worse PFS, regardless of the molecular subtype. Further evaluation by forest plot revealed incorporation of international prognostic index or tumor mutational burden into PMS increased the prediction capability for PFS. The drug-gene interaction and pathway exploration revealed the PFS-related genes were associated with DNA damage, TP53, apoptosis, and immune cell functions. In conclusion, this study utilizing a high throughput genetic approach demonstrated that the PMS could serve as a prognostic predictor in DLBCL patients. Furthermore, the identification of the key signaling pathways for disease progression also provides information for further investigation to gain more insight into novel drug-resistant mechanisms.
Assessing the impact of post-mortem damage and contamination on imputation performance in ancient DNA
Authors: Garrido Marques Antonio , Rubinacci Simone , Malaspinas Anna-Sapfo , Delaneau Olivier , Sousa da Mota Bárbara 
Published: March 2024

AbstractLow-coverage imputation is becoming ever more present in ancient DNA (aDNA) studies. Imputation pipelines commonly used for present-day genomes have been shown to yield accurate results when applied to ancient genomes. However, post-mortem damage (PMD), in the form of C-to-T substitutions at the reads termini, and contamination with DNA from closely related species can potentially affect imputation performance in aDNA. In this study, we evaluated imputation performance (i) when using a genotype caller designed for aDNA, ATLAS, compared to bcftools, and (ii) when contamination is present. We evaluated imputation performance with principal component analyses and by calculating imputation error rates. With a particular focus on differently imputed sites, we found that using ATLAS prior to imputation substantially improved imputed genotypes for a very damaged ancient genome (42% PMD). Trimming the ends of the sequencing reads led to similar improvements in imputation accuracy. For the remaining genomes, ATLAS brought limited gains. Finally, to examine the effect of contamination on imputation, we added various amounts of reads from two present-day genomes to a previously downsampled high-coverage ancient genome. We observed that imputation accuracy drastically decreased for contamination rates above 5%. In conclusion, we recommend (i) accounting for PMD by either trimming sequencing reads or using a genotype caller such as ATLAS before imputing highly damaged genomes and (ii) only imputing genomes containing up to 5% of contamination.
Solubility of α-synuclein species in the L62 mouse model of synucleinopathy
Authors: Schwab Karima , Magbagbeolu Mandy , Theuring Franz , Harrington Charles R. , Wischik Claude M. , Riedel Gernot 
Published: March 2024

AbstractThe accumulation of α-synuclein (α-Syn) into Lewy bodies is a hallmark of synucleinopathies, a group of neurological disorders that include Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). Small oligomers as well as larger fibrils of α-Syn have been suggested to induce cell toxicity leading to a degenerative loss of neurones. A richer understanding of α-Syn aggregation in disease, however, requires the identification of the different α-Syn species and the characterisation of their biochemical properties. We here aimed at a more in-depth characterisation of the α-Syn transgenic mice, Line 62 (L62), and examined the deposition pattern and solubility of human and murine α-Syn in these mice using immunohistochemical and biochemical methods. Application of multiple antibodies confirmed mAb syn204 as the most discriminatory antibody for human α-Syn in L62. Syn204 revealed an intense and widespread immunohistochemical α-Syn labelling in parietal cortex and hippocampus, and to a lower level in basal forebrain and hindbrain regions. The labelled α-Syn represented somatic inclusions as well as processes and synaptic endings. Biochemical analysis revealed a Triton-resistant human α-Syn pool of large oligomers, a second pool of small oligomers that was not resistant to solubilization with urea/Triton. A third SDS-soluble pool of intermediate sized aggregates containing a mixture of both, human and mouse α-Syn was also present. These data suggest that several pools of α-Syn can exist in neurones, most likely in different cellular compartments. Information about these different pools is important for the development of novel disease modifying therapies aimed at α-Syn.
Imeglimin mitigates the accumulation of dysfunctional mitochondria to restore insulin secretion and suppress apoptosis of pancreatic β-cells from db/db mice
Authors: Aoyagi Kyota , Nishiwaki Chiyono , Nakamichi Yoko , Yamashita Shun-ichi , Kanki Tomotake , Ohara-Imaizumi Mica 
Published: March 2024

AbstractMitochondrial dysfunction in pancreatic β-cells leads to impaired glucose-stimulated insulin secretion (GSIS) and type 2 diabetes (T2D), highlighting the importance of autophagic elimination of dysfunctional mitochondria (mitophagy) in mitochondrial quality control (mQC). Imeglimin, a new oral anti-diabetic drug that improves hyperglycemia and GSIS, may enhance mitochondrial activity. However, chronic imeglimin treatment’s effects on mQC in diabetic β-cells are unknown. Here, we compared imeglimin, structurally similar anti-diabetic drug metformin, and insulin for their effects on clearance of dysfunctional mitochondria through mitophagy in pancreatic β-cells from diabetic model db/db mice and mitophagy reporter (CMMR) mice. Pancreatic islets from db/db mice showed aberrant accumulation of dysfunctional mitochondria and excessive production of reactive oxygen species (ROS) along with markedly elevated mitophagy, suggesting that the generation of dysfunctional mitochondria overwhelmed the mitophagic capacity in db/db β-cells. Treatment with imeglimin or insulin, but not metformin, reduced ROS production and the numbers of dysfunctional mitochondria, and normalized mitophagic activity in db/db β-cells. Concomitantly, imeglimin and insulin, but not metformin, restored the secreted insulin level and reduced β-cell apoptosis in db/db mice. In conclusion, imeglimin mitigated accumulation of dysfunctional mitochondria through mitophagy in diabetic mice, and may contribute to preserving β-cell function and effective glycemic control in T2D.
Immediate angiographic control after intra-arterial nimodipine administration underestimates the vasodilatory effect
Authors: Zaeske Charlotte , Zopfs David , Laukamp Kai , Lennartz Simon , Kottlors Jonathan , Goertz Lukas , Stetefeld Henning , Hof Marion , Abdullayev Nuran , Kabbasch Christoph , Schlamann Marc , Schönfeld Michael 
Published: March 2024

AbstractIntra-arterial nimodipine administration is a widely used rescue therapy for cerebral vasospasm. Although it is known that its effect sets in with delay, there is little evidence in current literature. Our aim was to prove that the maximal vasodilatory effect is underestimated in direct angiographic controls. We reviewed all cases of intra-arterial nimodipine treatment for subarachnoid hemorrhage-related cerebral vasospasm between January 2021 and December 2022. Inclusion criteria were availability of digital subtraction angiography runs before and after nimodipine administration and a delayed run for the most affected vessel at the end of the procedure to decide on further escalation of therapy. We evaluated nimodipine dose, timing of administration and vessel diameters. Delayed runs were performed in 32 cases (19 patients) with a mean delay of 37.6 (± 16.6) min after nimodipine administration and a mean total nimodipine dose of 4.7 (± 1.2) mg. Vessel dilation was more pronounced in delayed vs. immediate controls, with greater changes in spastic vessel segments (n = 31: 113.5 (± 78.5%) vs. 32.2% (± 27.9%), p < 0.0001) vs. non-spastic vessel segments (n = 32: 23.1% (± 13.5%) vs. 13.3% (± 10.7%), p < 0.0001). In conclusion intra-arterially administered nimodipine seems to exert a delayed vasodilatory effect, which should be considered before escalation of therapy.
Identifying microRNAs associated with tumor immunotherapy response using an interpretable machine learning model
Authors: Nam Dong-Yeon , Rhee Je-Keun 
Published: March 2024

AbstractPredicting clinical responses to tumor immunotherapy is essential to reduce side effects and the potential for sustained clinical responses. Nevertheless, preselecting patients who are likely to respond to such treatments remains highly challenging. Here, we explored the potential of microRNAs (miRNAs) as predictors of immune checkpoint blockade responses using a machine learning approach. First, we constructed random forest models to predict the response to tumor ICB therapy using miRNA expression profiles across 19 cancer types. The contribution of individual miRNAs to each prediction process was determined by employing SHapley Additive exPlanations (SHAP) for model interpretation. Remarkably, the predictive performance achieved by using a small number of miRNAs with high feature importance was similar to that achieved by using the entire miRNA set. Additionally, the genes targeted by these miRNAs were closely associated with tumor- and immune-related pathways. In conclusion, this study demonstrates the potential of miRNA expression data for assessing tumor immunotherapy responses. Furthermore, we confirmed the potential of informative miRNAs as biomarkers for the prediction of immunotherapy response, which will advance our understanding of tumor immunotherapy mechanisms.
The temperature effect on perceived income
Authors: Sun Ang , Xiang Wang , Jiang Xu 
Published: March 2024

AbstractExtensive research has focused on the impact of weather on working capacity and income. However, in regions where income data largely relies on surveys, a pivotal yet underexplored question is whether weather not only influence real income but also introduce biases into survey-collected income data. We analyze longitudinal data from the China Health and Nutrition Survey and corresponding weather records from the Global Surface Summary of the Day, and uncover a negative correlation between survey-day temperature and self-reported annual income from the previous year. With a series of robustness checks, we confirm that the effect is primarily driven by behavioral factors rather than actual income changes. And threshold regression analyses show that the impact of temperature is more pronounced on hot days and relatively subdued or even reversed on cooler days. Further analyses indicate that mood, rather than cognitive capacity, plays a central role in causing the observed downward bias.
Enhanced triglyceride adsorption by steam-activated bamboo charcoal based on molecular dynamics investigations
Authors: Zhu Hegang , Zhang Sheng , Zheng Honghui , Wang Guifeng 
Published: March 2024

AbstractIn this study, ordinary bamboo charcoal was activated at 750 °C with a steam flow rate of 6.25 L/min for 1.5 h. The effects of triglyceride adsorption by activated bamboo charcoal were investigated using an orthogonal design, and the adsorption mechanism was explored through molecular dynamics. Experimental results revealed that the adsorption capacity of activated bamboo charcoal for triglycerides reached 27.0%. The activated bamboo charcoal exhibited a specific surface area of 560.0 m2/g. The average pore diameter of activated bamboo charcoal was 1.6 nm, whereas that of ordinary bamboo charcoal was 7.2 nm. Molecular dynamics simulations revealed an interaction energy of − 145.12 kcal/mol between the molecular layers of activated bamboo charcoal and the triglyceride molecules, as well as an interaction energy of − 132.73 kcal/mol between the molecular layers of ordinary bamboo charcoal and the triglyceride molecules. The quantity of triglyceride molecules adsorbed by activated bamboo charcoal per gram was estimated to be 1.77 × 1021 while ordinary bamboo charcoal could adsorb merely 1.56 × 1019 triglyceride molecules per gram. This stark contrast in adsorption capacity underscores the superior performance of activated bamboo charcoal than its counterpart.
Complexation and immobilization of arsenic in maize using green synthesized silicon nanoparticles (SiNPs)
Authors: David Oyinade A. , Labulo Ayomide H. , Hassan Ibrahim , Olawuni Idowu , Oseghale Charles O. , Terna Augustine D. , Ajayi Olamilekan O. , Ayegbusi Samuel A. , Owolabi Michael O. 
Published: March 2024

AbstractArsenic (As) is a heavy metal that is toxic to both plants and animals. Silicon nanoparticles (SiNPs) can alleviate the detrimental effects of heavy metals on plants, but the underlying mechanisms remain unclear. The study aims to synthesize SiNPs and reveal how they promote plant health in Arsenic-polluted soil. 0 and 100% v/v SiNPs were applied to soil, and Arsenic 0 and 3.2 g/ml were applied twice. Maize growth was monitored until maturity. Small, irregular, spherical, smooth, and non-agglomerated SiNPs with a peak absorbance of 400 nm were synthesized from Pycreus polystachyos. The SiNPs (100%) assisted in the development of a deep, prolific root structure that aided hydraulic conductance and gave mechanical support to the maize plant under As stress. Thus, there was a 40–50% increase in growth, tripled yield weights, and accelerated flowering, fruiting, and senescence. SiNPs caused immobilization (As(III)=SiNPs) of As in the soil and induced root exudates Phytochelatins (PCs) (desGly-PC2 and Oxidized Glutathione) which may lead to formation of SiNPs=As(III)–PCs complexes and sequestration of As in the plant biomass. Moreover, SiNPs may alleviate Arsenic stress by serving as co-enzymes that activate the antioxidant-defensive mechanisms of the shoot and root. Thus, above 70%, most reactive ROS (OH) were scavenged, which was evident in the reduced MDA content that strengthened the plasma membrane to support selective ion absorption of SiNPs in place of Arsenic. We conclude that SiNPs can alleviate As stress through sequestration with PCs, improve root hydraulic conductance, antioxidant activity, and membrane stability in maize plants, and could be a potential tool to promote heavy metal stress resilience in the field.
Risk factors of failure results after double-bundle reconstruction with autogenous hamstring grafts for isolated posterior cruciate ligament rupture cases
Authors: Morita Yudai , Tajima Takuya , Yamaguchi Nami , Yokoe Takuji , Nagasawa Makoto , Ota Tomomi , Ouchi Kouki , Chosa Etsuo 
Published: March 2024

AbstractPosterior tibial translation (PTT) after double-bundle posterior cruciate ligament (PCL) reconstruction has sometimes occurred. Purpose of this study is to identify the risk factors for postoperative PTT after double-bundle PCL reconstruction with a hamstring autograft. Comparing the results of bilateral gravity sag view (GSV) at 12 months after surgery, over 5-mm PTT was defined as ‘failure’ in this study. Of 26 isolated PCL reconstruction cases, over 5-mm PTT was seen in 7 cases (group F: 9.57 ± 1.28 mm), and 19 cases had less than 5 mm (group G: 2.84 ± 1.29 mm). Age, sex, body mass index (BMI), preoperative GSV, posterior slope angle of the tibia, anterolateral bundle (ALB) and posteromedial bundle (PMB) graft diameters, and tibial tunnel diameter were evaluated. The two groups were compared with the 2 × 2 chi-squared test, the Mann Whitney U-test, and Spearman’s rank correlation coefficient. Multivariate logistic regression analysis was also performed to determine the risk factor. Statistical significance was indicated as p < 0.01 for correlation with postoperative PTT, and as p < 0.05 for all other comparisons. Mean age (group G 31.8 ± 12.5 vs group F 34.9 ± 15.9 years), sex (male/female: 15/4 vs 3/4), BMI (25.6 ± 4.6 vs 24.9 ± 3.9 kg/m2), preoperative GSV (11.3 ± 2.2 vs 11.6 ± 2.9 mm), PMB diameter (5.37 ± 0.33 vs 5.36 ± 0.48 mm), and tibial tunnel diameter (9.32 ± 0.58 vs 9.29 ± 0.49 mm) showed no significant differences. ALB diameter was significantly greater in group G (7.0 ± 0.5 mm) than in group F (6.5 ± 0.29 mm; p = 0.022). There was also a significant difference in posterior tibial slope angle (group G 9.19 ± 1.94 vs group F 6.54 ± 1.45, p = 0.004). On Spearman rank correlation coefficient analysis, ALB diameter GSV (correlation coefficient: − 0.561, p = 0.003) and posterior tibial slope angle (correlation coefficient: − 0.533, p = 0.005) showed a significant correlation with postoperative PTT. Multivariate logistic regression analysis showed that ALB diameter (OR 19.028; 95% CI 1.082–334.6; p = 0.044) and posterior slope angle of tibia (OR 3.081; 95% CI 1.109–8.556; p = 0.031) were independently associated with postoperative PTT, respectively. In double-bundle PCL reconstruction with hamstring, smaller ALB graft diameter and lower (flatted) tibial slope angle were considered risk factors for postoperative PTT.