bluuebunny/crossref_metadata_2025_split huggingface dataset:
Significant phenotypic variability in a multigenerational family with an NFIA missense mutation: Case series and review of the literature
Authors: Paschell Peyton , Laukaitis Christina 
Published: January 2024

Key Clinical MessageWe report the first multigenerational family with NFIA‐related disorder from a missense variant. This case highlights the condition's phenotypic variability and the need for genetic testing when an initial diagnosis fails to explain all symptoms.
Nanofatigue of supercrystalline nanocomposites
Authors: Yan Cong , Giuntini Diletta 
Published: January 2024

AbstractSupercrystalline nanocomposites (SCNCs) are a new category of hybrid materials consisting of inorganic nanoparticles surface‐functionalized with organic ligands and with periodic nanostructures, featuring multi‐functionality and able to reach exceptional mechanical properties. Although efforts have been made to explore their mechanical behavior, their response to cyclic loading remains to be unveiled. Here, the fatigue behavior of SCNCs with different degrees of organic crosslinking is investigated via nanoindentation. The nanocomposites’ fatigue life is assessed, and it emerges that SCNCs without crosslinking are more efficient in dissipating energy under cyclic loading and thus feature a longer fatigue life. Chipping is identified as the main fatigue failure mechanism, whereas different mechanisms, intrinsic or extrinsic, dominate in the indentation depth propagation, again depending on crosslinking.
Construction and Application of a Pepsin‐Functionalized Covalent Organic Framework with Prominent Chiral Recognition Ability
Authors: Zhang Jiale , Liu Xue , Mu Qixuan , Li Ruijun , Ji Yibing 
Published: January 2024

AbstractThe stable Pepsin@covalent organic framework (Pepsin@COF) were constructed base on matching COF pore diameter to pepsin dimension. It exhibits excellent chiral recognition capabilities (e. e. % up to 62.63 %) and potential for enantioseparation. Furthermore, a positive correlation between the immobilized enzyme activity and chiral recognition was revealed, offering insights for the design of biocatalytic nanosystems in chiral separation.
An algorithm/hardware co‐optimized method to accelerate CNNs with compressed convolutional weights on FPGA
Authors: Shang Jiangwei , Zhang Zhan , Zhang Kun , Li Chuanyou , Qian Lei , Liu Hongwei 
Published: January 2024

SummaryConvolutional neural networks (CNNs) have shown remarkable advantages in a wide range of domains at the expense of huge parameters and computations. Modern CNNs still tend to be more complex and larger to achieve better inference accuracy. However, the complex and large structures of CNNs could slow down the inference speed. Recently, Compressing the convolutional weights to be sparse by pruning the unimportant parameters has been demonstrated as an efficient way to reduce the computations of CNNs. On the other hand, field‐programmable gate arrays (FPGAs) have been a popular hardware platform to accelerate CNN inference. In this paper, we propose an algorithm/hardware co‐optimized method for accelerating CNN inference on FPGAs. For the algorithm, we take advantage of unstructured and structured parameter sparsifying methods to achieve high sparsity and keep the regularity of convolutional weights. Correspondingly, hardware‐friendly index representations of sparse convolutional weights are proposed. For the hardware architecture, we propose row‐wise input‐stationary dataflow, which is tightly coupled with the algorithm. A row‐wise computing engine (RConv Engine) is proposed, which is based on the dataflow. Inside the RConv Engine, the scalar‐vector structure is applied to implement the basic processing elements (PEs). To flexibly calculate the feature map with various sizes, the PEs are organized in a 2D structure with two work modes. The experimental results demonstrate that our co‐optimized method implements high sparsity of convolutional weights, and the computing engine achieves high computation efficiency. Compared with other accelerators, our co‐optimized method implements a 10.9 speedup on FPS at most with the highest sparsity of convolutional weights and negligible accuracy loss.
Investigation of the uptake and catalytic effect of calcium and potassium‐based additives under low‐temperature pyrolysis of polyvinyl chloride
Authors: Loong Glen Khew Mun , Okada Keita , Morishige Naoto , Konakahara Naoya , Yokota Morihisa , Tanoue Ken‐ichiro 
Published: January 2024

AbstractManaging plastic waste containing polyvinyl chloride is difficult due to the release of hazardous substances such as hydrogen chloride. Alkaline metal has been proven to minimize the release of hydrogen chloride during thermal degradation of polyvinyl chloride. However, most past studies conducted heating experiments under a high temperature which promotes the production of aromatic hydrocarbons. In this study, low‐temperature pyrolysis was utilized which focuses on the dehydrochlorination stage while minimizing further degradation of hydrocarbons. The effectiveness of calcium oxide, calcium carbonate and potassium carbonate in minimizing the release of hydrogen chloride was also evaluated. Among those three additives, calcium oxide and potassium carbonate have shown some potential in uptaking hydrogen chloride. Especially when the molar ratio of additive to PVC is 1:1.6, calcium oxide reduced the generation of hydrogen chloride to as low as 20%. Potassium carbonate also exhibits an uptake effect but has a slight catalytic effect when the molar ratio is less than 1:1. On the other hand, calcium carbonate not only shows little to no uptake effect, but also promotes the thermal degradation of hydrocarbon.
Bibliometric and visualized analysis of resveratrol in anticancer investigations
Authors: Fu Qiang , Lu Zhongqi , Chang Ying , Jin Tiefeng , Zhang Meihua 
Published: January 2024

AbstractA growing number of publications have shown that resveratrol has anticancer effects and has become a hotspot in cancer research. The purpose of this study is to analyze the academic results and research trends in resveratrol within the field of anticancer and to predict the future trends in this field. We conducted a literature search for resveratrol in anticancer research from 2003 to 2022 using the Science Citation Index Expanded of the Web of Science Core Collection. The visualization software was used to perform the bibliometric analysis. A total of 1463 publications from 2003 to 2022 were retrieved. China had the highest number of publications. Taipei Medical University became the research institution with the largest number of publications worldwide. The journals with the highest output and co‐citation frequency were Molecules and Cancer Research. Levenson, Anait S and Jaeger, Walter published the largest number of papers. Jang, MS was the most co‐cited author. Timeline View shows trends and relationship between research topics over time and suggests that the emerging frontier of resveratrol in anticancer may be “resveratrol induces apoptosis.” As more and more evidence shows the important role of resveratrol in anticancer, further research on its mechanisms and target discovery may become a major direction for future research. The bibliometric analysis findings of this study will significantly contribute to scholars' comprehensive understanding of the anticancer effects and mechanisms of action of resveratrol, aiding in delineating research hotspots and frontier directions within this field, thereby providing guidance for future investigations.
Experimental study on the outburst potential energy caused by added water under stepwise depressurization
Authors: Li Xinjian , Chen Xiangjun , Wang Lin , Shi Haoyang , Yu Tongyong 
Published: January 2024

AbstractThe research on the mechanism of coal and gas outburst is still in the hypothesis stage, and exploration of the outburst mechanism fro m an energy perspective often focuses on the calculation of coal rock elastic energy and gas expansion energy. There are some studies on elastic energy and gas expansion energy of coal rock caused by added water during outburst, although hydr aulic measures not only improve the permeability of coal seam, but also increase the water content. For calculating the gas expansion energy, the atmospheric gas desorption characteristic is generally utilized, while the gas desorption is completed on the condition of dropping pressure in outburst, and the expansion energy research, based on that law, inevitably brings about errors, thus affecting the objectivity of the potential research. In this study, uniaxial cyclic loading experiments were carried out on briquette coal samples with water content of 0%, 1%, 2% and 4%, whose elastic energy density was analyzed, in addition to examining how the added water affected the mechanical properties and the elastic energy of coal. The pressure drop gradient of the experiment is set 2.5 –2 MPa, 1.5 –1 MPa, 0.5 MPa‐0 Pa. By stepwise depressurization desorption of coal samples after water injection, the gas expansion energy in different moisture is measured in each pressure drop stage, and the influence of moisture on gas expansion energy is quantitatively explored. Research has shown that the higher the water content, the lower the elastic energy density, while the higher the stress, the greater the elastic energy of coal. The gas expansion energy grows linearly with the increase of adsorption equilibrium pressure and diminishes in negative exponential law with the increasing moisture. Under the experimental conditions, the expansion energy decreases by 7%–9% and the elastic energy by 9.7% on average for every 1% increase in added water, and the influence gradually weakens when the moisture exceeds the critical value. This study innovatively simulates the pressure swing desorption when a coal and gas outburst occurs in the laboratory, confirms the critical moisture that affects the outburst potential, and is a useful exploration in the coal and gas outburst mechanism. Significantly the research results can guide the engineering practice when using hydraulic measures to prevent and control outburst disasters. © 2023 Society of Chemical Industry and John Wiley & Sons, Ltd.
Cerebrovascular reactivity increases across development in multiple networks as revealed by a breath‐holding task: A longitudinal fMRI study
Authors: Chen Donna Y. , Di Xin , Biswal Bharat 
Published: January 2024

AbstractFunctional magnetic resonance imaging (fMRI) has been widely used to understand the neurodevelopmental changes that occur in cognition and behavior across childhood. The blood‐oxygen‐level‐dependent (BOLD) signal obtained from fMRI is understood to be comprised of both neuronal and vascular information. However, it is unclear whether the vascular response is altered across age in studies investigating development in children. Since the breath‐hold (BH) task is commonly used to understand cerebrovascular reactivity (CVR) in fMRI studies, it can be used to account for developmental differences in vascular response. This study examines how the cerebrovascular response changes over age in a longitudinal children's BH data set from the Nathan Kline Institute (NKI) Rockland Sample (aged 6–18 years old at enrollment). A general linear model approach was applied to derive CVR from BH data. To model both the longitudinal and cross‐sectional effects of age on BH response, we used mixed‐effects modeling with the following terms: linear, quadratic, logarithmic, and quadratic‐logarithmic, to find the best‐fitting model. We observed increased BH BOLD signals in multiple networks across age, in which linear and logarithmic mixed‐effects models provided the best fit with the lowest Akaike information criterion scores. This shows that the cerebrovascular response increases across development in a brain network‐specific manner. Therefore, fMRI studies investigating the developmental period should account for cerebrovascular changes that occur with age.
From mechanism to application: Decrypting light‐regulated denitrifying microbiome through geometric deep learning
Authors: Liao Yang , Zhao Jing , Bian Jiyong , Zhang Ziwei , Xu Siqi , Qin Yijian , Miao Shiyu , Li Rui , Liu Ruiping , Zhang Meng , Zhu Wenwu , Liu Huijuan , Qu Jiuhui 
Published: January 2024

AbstractRegulation on denitrifying microbiomes is crucial for sustainable industrial biotechnology and ecological nitrogen cycling. The holistic genetic profiles of microbiomes can be provided by meta‐omics. However, precise decryption and further applications of highly complex microbiomes and corresponding meta‐omics data sets remain great challenges. Here, we combined optogenetics and geometric deep learning to form a discover–model–learn–advance (DMLA) cycle for denitrification microbiome encryption and regulation. Graph neural networks (GNNs) exhibited superior performance in integrating biological knowledge and identifying coexpression gene panels, which could be utilized to predict unknown phenotypes, elucidate molecular biology mechanisms, and advance biotechnologies. Through the DMLA cycle, we discovered the wavelength‐divergent secretion system and nitrate‐superoxide coregulation, realizing increasing extracellular protein production by 83.8% and facilitating nitrate removal with 99.9% enhancement. Our study showcased the potential of GNNs‐empowered optogenetic approaches for regulating denitrification and accelerating the mechanistic discovery of microbiomes for in‐depth research and versatile applications.
Construction and validation of molecular subtype and signature of immune cell‐related telomeric genes and prediction of prognosis and immunotherapy efficacy in ovarian cancer patients
Authors: Ling Lele , Li Bingrong , Wu Huijing , Zhang Kaiyong , Li Siwen , Ke Boliang , Zhu Zhengyang , Liu Te , Liu Peng , Zhang Bimeng 
Published: January 2024

AbstractBackgroundOvarian cancer (OVC) has emerged as a fatal gynecological malignancy as a result of a lack of reliable methods for early detection, limited biomarkers and few treatment options. Immune cell‐related telomeric genes (ICRTGs) show promise as potential biomarkers.MethodsICRTGs were discovered using weighted gene co‐expression network analysis (WGCNA). ICRTGs were screened for significant prognosis using one‐way Cox regression analysis. Subsequently, molecular subtypes of prognosis‐relevant ICRTGs were constructed and validated for OVC, and the immune microenvironment's landscape across subtypes was compared. OVC prognostic models were built and validated using prognosis‐relevant ICRTGs. Additionally, chemotherapy susceptibility drugs for OVC patients in the low‐ and high‐risk groups of ICRTGs were screened using genomics of drug susceptibility to cancer (GDSC). Finally, the immunotherapy response in the low‐ and high‐risk groups was detected using the data from GSE78220. We conducted an immune index correlation analysis of ICRTGs with significant prognoses. The MAP3K4 gene, for which the prognostic correlation coefficient is the highest, was validated using tissue microarrays for a prognostic‐immune index correlation.ResultsWGCNA analysis constructed a gene set of ICRTGs and screened 22 genes with prognostic significance. Unsupervised clustering analysis revealed the best molecular typing for two subtypes. The Gene Set Variation Analysis algorithm was used to calculate telomere scores and validate the molecular subtyping. A prognostic model was constructed using 17 ICRTGs. In the The Cancer Genome Atlas‐OVC training set and the Gene Expression Omnibus validation set (GSE30161), the risk score model's predicted risk groups and the actual prognosis were shown to be significantly correlated. GDSC screened Axitinib, Bexarotene, Embelin and the GSE78220 datasets and demonstrated that ICRTGs effectively distinguished the group that responds to immunotherapy from the non‐responsive group. Additionally, tissue microarray validation results revealed that MAP3K4 significantly predicted patient prognosis. Furthermore, MAP3K4 exhibited a positive association with PD‐L1 and a negative relationship with the M1 macrophage markers CD86 and INOS.ConclusionsICRTGs may be reliable biomarkers for the molecular typing of patients with OVC, enabling the prediction of prognosis and immunotherapy efficacy.
Autophagy‐related risk signature based on CDNK2A to facilitate survival prediction of patients with endometrial cancer
Authors: Yue Chaomin , Lin Baohua , Sun Xiang , Xu Xindi , Zhou Chufan , Fan Jiaying 
Published: January 2024

AbstractBackgroundAutophagy plays an important role in immunity and inflammation. The present study aimed to explore the prognostic significance of autophagy‐related genes (ARGs) in endometrial cancer (EC) using bioinformatics.MethodsThe list of ARGs was obtained from the Human Autophagy Database. The differentially expressed ARGs (DEARGs) between the EC and normal endometrial tissue samples were screened from The Cancer Genome Atlas database. Cox regression analysis was performed on the DEARGs to screen the prognostic ARGs and construct risk signatures for overall survival (OS) and progression‐free survival (PFS). The hub ARGs were identified from a protein–protein interaction network, and CDKN2A was obtained from the intersection of prognostic ARGs and hub ARGs. The association of CDKN2A expression with clinical characteristics and immune infiltration were analyzed. Finally, the role of CDKN2A in autophagy was confirmed in EC cell lines.ResultsCDKN2A, PTK6 and DLC1 were used to establish risk signatures for predicting the survival of EC patients. Receiver operating characteristic curve analysis indicated that the risk signatures can accurately predict both OS and PFS. CDKN2A was the only hub prognostic ARG, and showed significant association with the age, survival status, grade, histological type, body mass index and FIGO (i.e. International Federation of Gynecology and Obstetrics) stage (p < 0.05). Furthermore, CDKN2A expression was also correlated with the infiltration of immune cells, indicating that CDKN2A might play a critical role in regulating the immune microenvironment and immune responses in EC. In addition, silencing of CDKN2A gene promoted autophagy in the HEC‐1A cell line and upregulated the expression levels of autophagy‐related proteins.ConclusionsCDKN2A is a prognostic factor and therapeutic target in EC, and is likely associated with the tumor immune landscape and autophagy.
Exploration of the prognostic effect of costimulatory genes in bladder cancer
Authors: Su Hao , Liu Ziqi , Zhang Chaoyue , Deng Zebin , Su Xiaozhe , Wang Yinhuai , Liu Wentao 
Published: January 2024

AbstractBackgroundA prognostic model of bladder cancer was constructed based on costimulatory molecules, and its stability and accuracy were verified in different datasets.MethodThe expression profile of bladder cancer RNA and the corresponding clinical data in The Cancer Genome Atlas (TCGA) database were analyzed employing computational biology, and a prognostic model was constructed for costimulating molecule‐related genes. The model was applied in GSE160693, GSE176307, Xiangya_Cohort, GSE13507, GSE19423, GSE31684, GSE32894, GSE48075, GSE69795 and GSE70691 in TCGA dataset and Gene Expression Omnibus database. The role of costimulating molecules in bladder cancer tumor subtypes was also explored. By consistent cluster analysis, bladder cancer in the TCGA dataset was categorized into two subtypes: C1 and C2. The C1 subtype exhibited a poor prognosis, high levels of immune cell infiltration and significant enrichment of natural killer cells, T cells and dendritic cells in the C1 subtype. In addition, the ImmuneScore calculated by the ESTIMATE algorithm differed greatly between the two subtypes, and the ImmuneScore of the C1 subtype was greater than the C2 subtype in a significant manner.ResultsThis study also assessed the relationship between costimulating molecules and immunotherapy response. The high‐risk group responded poorly to immunotherapy, with significant differences in the amount of most immune cells between the two groups. Further, three indices of the ESTIMATE algorithm and 22 immune cells of the CIBERSORT algorithm were significantly correlated with risk values. These findings suggest the potential value of costimulating molecules in predicting immunotherapy response.ConclusionA costimulatory molecule‐based prognostic model for bladder cancer was established and validated across multiple datasets. This model introduces a novel mode for tailoring treatments to each individual with bladder cancer, and offers valuable insights for informed clinical choices. Simultaneously, this research also delved into the significance of costimulating molecules within distinct bladder cancer subtypes, shedding novel insights into improving immunotherapy strategies for the treatment of bladder cancer.
MiR‐431 promotes cardiomyocyte proliferation by targeting FBXO32 expression
Authors: Li Mengsha , Zhang Chenrui , Tan Lirong , Liu Tingyan , Zhu Tingting , Wei Xuejiao , Liu Jiacai , Si Xiaoyun , Li Bing 
Published: January 2024

AbstractBackgroundThe induction of cardiomyocyte (CM) proliferation is a promising approach for cardiac regeneration following myocardial injury. MicroRNAs (miRNAs) have been reported to regulate CM proliferation. In particular, miR‐431 expression decreases during cardiac development, according to Gene Expression Omnibus (GEO) microarray data. However, whether miR‐431 regulates CM proliferation has not been thoroughly investigated.MethodsWe used integrated bioinformatics analysis of GEO datasets to identify the most significantly differentially expressed miRNAs. Real‐time quantitative PCR and fluorescence in situ hybridization were performed to determine the miRNA expression patterns in hearts. Gain‐ and loss‐of‐function assays were conducted to detect the role of miRNA in CM proliferation. Additionally, we detected whether miR‐431 affected CM proliferation in a myocardial infarction model. The TargetScan, miRDB and miRWalk online databases were used to predict the potential target genes of miRNAs. Luciferase reporter assays were used to study miRNA interactions with the targeting mRNA.ResultsFirst, we found a significant reduction in miR‐431 levels during cardiac development. Then, by overexpression and inhibition of miR‐431, we demonstrated that miR‐431 promotes CM proliferation in vitro and in vivo, as determined by immunofluorescence assays of 5‐ethynyl‐2'‐deoxyuridine (EdU), pH3, Aurora B and CM count, whereas miR‐431 inhibition suppresses CM proliferation. Then, we found that miR‐431 improved cardiac function post‐myocardial infarction. In addition, we identified FBXO32 as a direct target gene of miR‐431, with FBXO32 mRNA and protein expression being suppressed by miR‐431. FBXO32 inhibited CM proliferation. Overexpression of FBXO32 blocks the enhanced effect of miR‐431 on CM proliferation, suggesting that FBXO32 is a functional target of miR‐431 during CM proliferation.ConclusionIn summary, miR‐431 promotes CM proliferation by targeting FBXO32, providing a potential molecular target for preventing myocardial injury.