bluuebunny/crossref_metadata_2025_split huggingface dataset:
A deleterious frameshift insertion mutation in the ZNF142 gene leads to intellectual developmental disorder with impaired speech in three affected siblings: Clinical features and literature review
Authors: Mir Atefeh , Song Yongjun , Lee Hane , Montazer‐Zohouri Mostafa , Reisi Marziyeh , Tabatabaiefar Mohammad Amin 
Published: July 2023

AbstractBackgroundZNF142 gene is a protein‐coding gene encoding Zinc Finger Protein 142. ZNF proteins are a vast group of cellular effectors with a wide range of functions such as signal transduction, transcriptional regulation, meiotic recombination, DNA repair, development, and cell migration. Mutations in the ZNF142 gene are related to neurodevelopmental disorder with impaired speech and hyperkinetic movements (NEDISHM). This study on a family with three affected siblings identified a pathogenic frameshift insertion variant. In addition, we conducted a review of the literature on previously reported ZNF142 gene variants and their clinical manifestations.Materials and MethodsThree affected siblings with severe intellectual developmental disabilities and speech impairments, their parents, and other sibs in the family were included. The patients were studied by the whole exome sequencing. Sanger sequencing, co‐segregation analysis, and in silico analysis were carried out to verify candidate variant. The identified variant was interpreted based on the ACMG guideline.ResultsWe identified a frameshift insertion variant in the ZNF142 gene, NM_001379659.1: c.3755dup (NP_001366588.1:p.Arg1253ThrfsTer15), that was related to the clinical features of three patients. The identified variant was found to be pathogenic.ConclusionThe current study findings expand the existing knowledge of the variant on the ZNF142 gene implicated in the neurodevelopmental disorder, intellectual disability, and impaired speech and it presents a detailed clinical feature associated with related conditions. The data have implications for genetic diagnosis and counseling in families with the same disorders.
Three‐dimensional quantitative muscle ultrasound in patients with facioscapulohumeral dystrophy and myotonic dystrophy
Authors: de Jong Leon , Greco Anna , Nikolaev Anton , Weijers Gert , van Engelen Baziel G. M. , de Korte Chris L. , Fütterer Jurgen J. 
Published: July 2023

AbstractIntroduction/AimsUltrasound imaging of muscle tissue conventionally results in two‐dimensional sampling of tissue. For heterogeneously affected muscles, a sampling error using two‐dimensional (2D) ultrasound can therefore be expected. In this study, we aimed to quantify and extend ultrasound imaging findings in neuromuscular disorders by using three‐dimensional quantitative muscle ultrasound (3D QMUS).MethodsPatients with facioscapulohumeral dystrophy (n = 31) and myotonic dystrophy type 1 (n = 16) were included in this study. After physical examination, including Medical Research Council (MRC) scores, the tibialis anterior muscle was scanned with automated ultrasound. QMUS parameters were calculated over 15 cm of the length of the tibialis anterior muscle and were compared with a healthy reference data set.ResultsWith 3D QMUS local deviations from the healthy reference could be detected. Significant Pearson correlations (P < .01) between MRC score and QMUS parameters in male patients (n = 23) included the mean echo intensity (EI) (0.684), the standard deviation of EI (0.737), and the residual attenuation (0.841). In 91% of all patients, mean EI deviated by more than 1 standard deviation from the healthy reference. In general, the proportion of muscle tissue with a Z score >1 was about 50%.DiscussionIn addition to mean EI, multiple QMUS parameters reported in this study are potential biomarkers for pathology. Besides a moderate correlation of mean EI with muscle weakness, two other parameters showed strong correlations: standard deviation of EI and residual attenuation. Local detection of abnormalities makes 3D QMUS a promising method that can be used in research and potentially for clinical evaluation.
Simultaneous enhancement of the strength and the roughness of poly(lactic acid) using palmitoylated cellulose nanocrystal
Authors: Xie Zhongyuan , Guo Mingwei , Chen Zhangyun , Jiang Hua 
Published: July 2023

AbstractThe nanocomposite films from poly(lactic acid) (PLA) and palmitoylated cellulose nanocrystals (e‐CNCs) were characterized using different techniques. Raman spectra showed the formation of hydrogen bonding interactions between the PLA and the e‐CNCs. The proper amount (1.0 wt%) of the e‐CNCs led to the formation of greater amounts of smaller PLA spherulites and increased the PLA crystallinity in comparison with the neat PLA sample. The scanning and transmission electron microscopy images showed that 1.0 wt% of the e‐CNCs could uniformly be dispersed in the PLA matrix and that the nanocomposite exhibited some elasticity. The dynamic rheological properties showed the formation of interconnected networks in the PLA/e‐CNCs composite at 1.0 wt% of the e‐CNCs. The analysis of the mechanical performance of the ensuing nanocomposite showed a 74% increase in tensile strength, a 62.1% increase in Young's modulus, and a 68.4% increase in the elongation at the break after the incorporation of 1.0 wt% of the e‐CNCs into the PLA. Therefore, the palmitoylated CNCs were highly promising for the simultaneous enhancement in the strength and the toughness of PLA.Highlights
Reinforcement of poly(lactic acid) (PLA) by the palmitoylated CNCs.
Simultaneous improvement in the strength and roughness of PLA sample.
Entanglement and H‐bond between the palmitoylated CNCs and PLA molecular chain.
More and smaller PLA crystals induced by the palmitoylated CNCs.
A matrix computing method for visualizing switchless controlled 3D hexagonal‐annular rotary braiding process architecture
Authors: Li Jinyu , Zhang Yifan , Zhang Tao , Yuan Lin , Wang Chi , Ren Chengwei , Gong Xiaobo , Guo Bin , Yang He 
Published: July 2023

AbstractNumerous process architectures for fabric molding are investigated because fiber braided structure is closely related to the performance of fiber‐reinforced composites. However, existing methods for calculating process architectures have to deal with the coordinates of each fiber when modeling the three‐dimensional braiding method without individually controlled switches. The amount of data grows geometrically as the number of fibers and braiding steps increases. A methodology that enables simple and efficient computation of a model of the relationship between braiding parameters and fiber structure is urgently needed. This paper proposes a novel algorithm to trace the yarn carrier trajectories through machine simulation with Euler rotation‐matrix operations. To predict the real fabric architecture, a contraction factor is introduced to optimize the yarn trajectory while considering the volume of the yarn. The optimized fabric architecture is explored and the braiding process architecture with different braiding parameters is simulated according to the algorithm. Based on the exploration of the relationship between the braiding parameters and the process structure, a hexagonal‐annular braiding machine was established, which can fabricate fabrics with different cross‐sectional shapes. The simulation results were verified by the braiding experiments.
Three‐dimensional polymer composite flow simulation and associated fiber orientation prediction for large area extrusion deposition additive manufacturing
Authors: Wang Zhaogui , Luo Chenjun , Xie Zhongqi , Fang Zhenyu 
Published: July 2023

AbstractMaterial flow and associated fiber orientation are among the most important features in Large Area extrusion deposition Additive Manufacturing (LAAM) of short fiber‐filled composites. Endeavors have been done in 2D flow models, where significant knowledge is achieved in explaining the material anisotropy of the deposited composite parts. Nevertheless, 2D flow models are limited by a couple of assumptions that lose significant characters of the deposition flow. This study is one of the first few that employ a 3D flow model which focuses on the quasi‐steady state of the polymer composite melt flow within the nozzle and the subsequent 90‐degree turning deposition onto the material substrate. The material flow is assumed as a highly viscous Newtonian flow. The fiber orientation is evaluated using the advanced pARD‐RSC fiber orientation tensor evaluation model, via the one‐way weakly‐coupled flow/orientation analysis formulation. Computed results show the fiber orientation state on the entire cross‐section of a deposited bead, which yields a clear view of how the material anisotropy is affected by the locally varied fiber orientation. The 3D‐flow prediction proves that the transverse directional fiber alignments are in high degree of similarity as compared to the over‐predicted results from the simplified 2D planar flows. In addition, the 3D‐model results exhibit a more favorable agreement with the reported experimental data than that yielded by the conventional 2D models.
IFF: Identifying key residues in intrinsically disordered regions of proteins using machine learning
Authors: Ho Wen‐Lin , Huang Hsuan‐Cheng , Huang Jie‐rong 
Published: July 2023

AbstractConserved residues in protein homolog sequence alignments are structurally or functionally important. For intrinsically disordered proteins or proteins with intrinsically disordered regions (IDRs), however, alignment often fails because they lack a steric structure to constrain evolution. Although sequences vary, the physicochemical features of IDRs may be preserved in maintaining function. Therefore, a method to retrieve common IDR features may help identify functionally important residues. We applied unsupervised contrastive learning to train a model with self‐attention neuronal networks on human IDR orthologs. Parameters in the model were trained to match sequences in ortholog pairs but not in other IDRs. The trained model successfully identifies previously reported critical residues from experimental studies, especially those with an overall pattern (e.g., multiple aromatic residues or charged blocks) rather than short motifs. This predictive model can be used to identify potentially important residues in other proteins, improving our understanding of their functions. The trained model can be run directly from the Jupyter Notebook in the GitHub repository using Binder (mybinder.org). The only required input is the primary sequence. The training scripts are available on GitHub (https://github.com/allmwh/IFF). The training datasets have been deposited in an Open Science Framework repository (https://osf.io/jk29b).
Monitoring the temporal changes in herbicide‐resistantAmaranthus tuberculatus: a landscape‐scale probability‐based estimation in Iowa
Authors: Hamberg Ryan C. , Yadav Ramawatar , Dixon Philip M , Licht Mark A. , Owen Micheal DK 
Published: July 2023

AbstractBackgroundA landscape‐scale probability‐based sampling of Iowa soybean [Glycine max(L.) Merr.] fields was conducted in 2013 and 2019;Amaranthus tuberculatus[Moq.] J.D. Sauer seed was collected from 97 random geospatial selected fields. The objectives were to evaluate the prevalence and distribution of herbicide‐resistantA. tuberculatus(waterhemp) in soybean fields and evaluate temporal changes over 6 years.Amaranthus tuberculatusseedlings were evaluated for resistance to imazethapyr, atrazine, glyphosate, lactofen and mesotrione at 1× and 4× label rates.ResultsResistance to imazethapyr, glyphosate, lactofen and mesotrione at the 1× rate increased significantly from 2013 to 2019 and was found in 99%, 97%, 16% and 15% of IowaA. tuberculatuspopulations in 2019, respectively. Resistance to atrazine at the 4× rate increased over time; atrazine resistance was found in 68% of populations in 2019. Three‐way multiple herbicide‐resistantA. tuberculatuswas the most frequent and increased significantly to 4× rates from 16% in 2013 to 43% of populations in 2019. AllA. tuberculatuspopulations resistant to HPPD‐inhibitor herbicides also were resistant to atrazine.ConclusionTo the best of our knowledge, this is the first probability‐based study that presented evolution ofA. tuberculatusherbicide resistance over time. The results demonstrated that imazethapyr, atrazine and glyphosate resistance in IowaA. tuberculatuspopulations was frequent whereas resistance to lactofen and mesotrione was less frequent. Most IowaA. tuberculatuspopulations evolved resistance to multiple sites of action over time. The results of our study are widely applicable given the similarities in weed management practices throughout the Midwest United States. © 2023 The Authors.Pest Management Sciencepublished by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Sensitivity analysis of G‐estimators to invalid instrumental variables
Authors: Vancak Valentin , Sjölander Arvid 
Published: July 2023

AbstractInstrumental variables regression is a tool that is commonly used in the analysis of observational data. The instrumental variables are used to make causal inference about the effect of a certain exposure in the presence of unmeasured confounders. A valid instrumental variable is a variable that is associated with the exposure, affects the outcome only through the exposure (exclusion), and is not confounded with the outcome (exogeneity). Unlike the first assumption, the other two are generally untestable and rely on subject‐matter knowledge. Therefore, a sensitivity analysis is desirable to assess the impact of assumptions' violation on the estimated parameters. In this paper, we propose and demonstrate a new method of sensitivity analysis for G‐estimators in causal linear and non‐linear models. We introduce two novel aspects of sensitivity analysis in instrumental variables studies. The first is a single sensitivity parameter that captures violations of exclusion and exogeneity assumptions. The second is an application of the method to non‐linear models. The introduced framework is theoretically justified and is illustrated via a simulation study. Finally, we illustrate the method by application to real‐world data and provide guidelines on conducting sensitivity analysis.
Loganin alleviates myocardial ischemia–reperfusion injury through GLP‐1R/NLRP3‐mediated pyroptosis pathway
Authors: Li Wenfan , Fan Pei , Wang Xiaobo , Tang Haifeng 
Published: July 2023

AbstractMyocardial ischemia–reperfusion (I/R) injury is one of main pathological manifestations of cardiovascular outcomes related to NLRP3 inflammasome‐mediated pyroptosis pathway. Loganin is an iridoid glycoside extracted from traditional Chinese medicines, which has multiple activities. However, the roles and mechanism of loganin in myocardial I/R injury remain largely unknown. The models of myocardial I/R injury were established using I/R‐treated rats or OGD/R‐treated H9C2 cardiomyocytes. Myocardial damage was assessed by TTC and hematoxylin–eosin staining. Pyroptosis‐related marker levels were detected by immunohistochemistry, immunofluorescence and western blotting assays. Cell proliferation was examined via EdU assay. Cell apoptosis was investigated by LDH release and flow cytometry. The integrity of cell membrane was analyzed via Dil staining. GLP‐1R and NLRP3 levels were detected by immunofluorescence and western blotting assays. Our results showed that loganin suppressed I/R‐induced myocardial damage in rats by reducing myocardial infarct, injury and pyroptosis. In addition, loganin attenuated OGD/R‐induced cardiomyocyte apoptosis through increasing cell proliferation and reducing LDH release and apoptotic rate. Loganin also mitigated OGD/R‐induced cardiomyocyte pyroptosis by reducing cell membrane damage and levels of cleaved caspase‐1, IL‐1β and IL‐18. Furthermore, loganin repressed GLP‐1R/NLRP3 pathway activation in OGD/R‐treated H9C2 cardiomyocytes by enhancing GLP‐1R expression and decreasing NLRP3 level. GLP‐1R/NLRP3 activation by GLP‐1R inhibition or NLRP3 overexpression reversed the suppressive effects of loganin on OGD/R‐induced cardiomyocyte pyroptosis. These data indicated that loganin prevented OGD/R‐induced proliferation inhibition, apoptosis and pyroptosis in OGD/R‐treated cardiomyocytes by inhibiting GLP‐1R/NLRP3 activity, indicating the therapeutic potential of loganin in myocardial I/R injury.
Theoretical designs of low‐barrier ferroelectricity
Authors: Zhong Ting‐Ting , Gao Yaxin , Ren Yangyang , Wu Menghao 
Published: July 2023

AbstractFerroelectrics with electrically switchable spontaneous polarizations can be used for information storage, where a low switching barrier is favorable to reduce the energy cost and enhance the speed for data writing. Meanwhile their robustness at working temperature should be ensured, which is a challenge for the designs of low‐barrier ferroelectrics. Here we review several types of ferroelectric mechanisms that may render both low switching barriers and room‐temperature robustness, which have been theoretically proposed in previous studies. (1) The prediction of sliding ferroelectricity with ultralow switching barriers has been experimentally confirmed in a series of van der Waals layers, which may enable convenient electrical control of various physical properties in 2D materials, like magnetic, photovoltaic, valleytronic and topological properties. (2) Hydrogen‐bonded ferroelectricity spontaneously formed by head‐to‐tail chains can be switched by proton‐transfer crossing a low barrier, and a mechanism of ultra‐high piezoelectricity utilizing the specific features of hydrogen bonding has been proposed. (3) High‐ionicity ferroelectricity induced by covalent‐like ionic bondings may entail high polarizations and low barriers during switching, which is attributed to the features of long‐range Coulomb interaction, and the long ion‐displacements crossing unitcell may give rise to unconventional ferroelectricity with quantized polarizations even in crystals of non‐ferroelectric point groups. Those low‐barrier ferroelectric mechanisms may bring in both new physics and technological advances, which are to be further explored.This article is categorized under:
Structure and Mechanism > Computational Materials Science
Prince Charles’ A Vision of Britain as Populist Retrotopia
Authors: Rajaniemi Juho , Koponen Olli-Paavo 
Published: July 2023

AbstractIn 1989, Charles, Prince of Wales (now King Charles III), published A Vision of Britain: A Personal View of Architecture, a book promoting traditional over modernist architecture. He called for an architecture which could please the more traditional taste of ‘ordinary’ people. Prince Charles’ opinions generated extensive positive feedback by the audience, and his ideas fuelled an architectural debate for several years, forcing many architects to re-evaluate and clarify their views on modernist architecture. In our chapter, we scrutinize three aspects intertwined in A Vision of Britain: firstly, the author’s attack on modernism in architecture; secondly, the populist arguments he uses to legitimize his attack; and thirdly, the retrotopic tendencies of his proposal for a reversion to the architecture of the past. In addition, we take a look at Poundbury, Prince Charles’ retrotopia realized on the outskirts of Dorchester, and consider if and how his nostalgic ideas have been successfully constructed.
Indonesia’s Student and Non-student Protesters in May 1998: Break and Reunification
Authors: Tehusijarana Jonathan Peter 
Published: July 2023

AbstractAs Indonesia moves temporally further away from the events that heralded reformasi in 1998, the memory of the pivotal role played by students in those events has also begun to shift. This chapter examines how the student activists of 1998 justified their actions based on memories of past experiences with the Indonesian people as well as the longer lineage of the role youth and youth activism have played within Indonesian history.
A prognostic nomogram for predicting recurrence-free survival of stage I–III colon cancer based on immune-infiltrating Treg-related genes
Authors: Xu Longwen , Liu Mengjie , Lian Jie , Li Enmeng , Dongmin Chang , Li Xuqi , Wang Wenjuan 
Published: July 2023

Abstract
Purpose
A high postoperative recurrence rate seriously impedes colon cancer (CC) patients from achieving long-term survival. Here, we aimed to develop a Treg-related classifier that can help predict recurrence-free survival (RFS) and therapy benefits of stage I–III colon cancer.

Methods
A Treg-related prognostic classifier was built through a variety of bioinformatic methods, whose performance was assessed by KM survival curves, time-dependent receiver operating characteristic (tROC), and Harrell’s concordance index (C-index). A prognostic nomogram was generated using this classifier and other traditional clinical parameters. Moreover, the predictive values of this classifier for immunotherapy and chemotherapy therapeutic efficacy were tested using multiple immunotherapy sets and R package “pRRophetic".

Results
A nine Treg-related classifier categorized CC patients into high- and low-risk groups with distinct RFS in the multiple datasets (all p < 0.05). The AUC values of 5-year RFS were 0.712, 0.588, 0.669, and 0.662 in the training, 1st, 2nd, and entire validation sets, respectively. Furthermore, this classifier was identified as an independent predictor of RFS. Finally, a nomogram combining this classifier and three clinical variables was generated, the analysis of tROC, C-index, calibration curves, and the comparative analysis with other signatures confirmed its predictive performance. Moreover, KM analysis exhibited an obvious discrepancy in the subgroups, especially in different TNM stages and with adjuvant chemotherapy. We detected the difference between the two risk subsets of immune cell sub-population and the response to immunotherapy and chemotherapy.

Conclusions
We built a robust Treg-related classifier and generated a prognostic nomogram that predicts recurrence-free survival in stage I–III colon cancer that can identify high-risk patients for more personalized and effective therapy.
The benefits and pitfalls of machine learning for biomarker discovery
Authors: Ng Sandra , Masarone Sara , Watson David , Barnes Michael R. 
Published: July 2023

AbstractProspects for the discovery of robust and reproducible biomarkers have improved considerably with the development of sensitive omics platforms that can enable measurement of biological molecules at an unprecedented scale. With technical barriers to success lowering, the challenge is now moving into the analytical domain. Genome-wide discovery presents a problem of scale and multiple testing as standard statistical methods struggle to distinguish signal from noise in increasingly complex biological systems. Machine learning and AI methods are good at finding answers in large datasets, but they have a tendency to overfit solutions. It may be possible to find a local answer or mechanism in a specific patient sample or small group of samples, but this may not generalise to wider patient populations due to the high likelihood of false discovery. The rise of explainable AI offers to improve the opportunity for true discovery by providing explanations for predictions that can be explored mechanistically before proceeding to costly and time-consuming validation studies. This review aims to introduce some of the basic concepts of machine learning and AI for biomarker discovery with a focus on post hoc explanation of predictions. To illustrate this, we consider how explainable AI has already been used successfully, and we explore a case study that applies AI to biomarker discovery in rheumatoid arthritis, demonstrating the accessibility of tools for AI and machine learning. We use this to illustrate and discuss some of the potential challenges and solutions that may enable AI to critically interrogate disease and response mechanisms.