bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structure of histone deacetylase complex Rpd3S bound to nucleosome
Authors: Wang Haibo , Cui Hengjun 
Published: May 2023

Abstract
Histone modifications are involved in the regulation of chromatin-based processes, including transcription, DNA replication and repair1–5. Among them, histone acetylation and deacetylation are usually correlated with transcription activation and repression, respectively6. The Set2-Rpd3S pathway maintains hypoacetylation states of actively transcribed chromatin regions to suppress cryptic transcription initiation7–9. During this process, the histone deacetylase (HDAC) complex Rpd3S is recruited to nucleosomes which are methylated at lysine 36 residue of histone H3 (H3K36me) by histone methyltransferase Set27,9. However, how the five unique subunits of Rpd3S are assembled and coordinated to recognize nucleosomal substrates and exert its deacetylation function remains unclear. Here, we report the cryo-electron microscopy (cryo-EM) structure of Saccharomyces cerevisiae Rpd3S deacetylase bound to H3K36me3-modified nucleosome at 3.1 Å resolution. It reveals that Sin3 and Rco1 subunits orchestrate the assembly of the complex and mediate its contact with nucleosome at multiple sites. In addition, the chromodomain (CHD) of Eaf3 subunit recognizes the H3K36me3 marker and contacts both nucleosomal and linker DNA. The structure not only unravels the architecture of the Rpd3S complex, but also sheds light on the structural basis of its nucleosome targeting and function.
Multiple Conceptual Model Approach for Assessing Groundwater Resources Sustainability Under Multiple Stresses
Authors: Yidana Sandow Mark , Dzikunoo Elikplim , Adams Richard Mejida , Tetteh Jacob Doku 
Published: May 2023

Abstract

Six transient numerical groundwater flow models have been developed and calibrated based on six equally plausible conceptual models of the hydrogeological framework of Akyem and surrounding areas in southern Ghana, for the purpose of assessing the impacts of multiple stresses on groundwater resources sustainability in the area. The six conceptual models were developed to account for possible uncertainties in model predictions arising from uncertainties in the model structure and differ in terms of the characterization of domain boundaries and vertical stratification. The domain was conceptualized based on available data monitored over a 48-month period, from 41 boreholes in the area. Each of the calibrated numerical models was used to assess the impacts of enhanced groundwater abstraction attending population growth, and natural stresses accruing from climate change in the region. The forecasts of the six models provide a range of possible impacts of the various stresses on the system. This research finds that groundwater resources in the terrain are vulnerable to the impacts of climate change and enhanced abstractions to meet growing domestic water resources requirements. The business-as-usual scenario whereby groundwater abstraction rates continue at the current rate of population growth, with the same levels of groundwater recharge is not sustainable. This is likely to be worsened by reduced groundwater recharge resulting from enhanced anthropogenic activities and climate change. The six versions of the numerical model over the domain, although provide different results, all agree on the impacts of prevailing and future stresses on the system.
Noise cancellation with LMS, NLMS and RLS filtering algorithms to improve the fault detection of an industrial measurement system
Authors: Lampl Tanja , Hadi Abdul , Qadir Abdul , Andeme Juan Manuel Nzamio Mba 
Published: May 2023

Abstract
Adaptive noise cancellation is the process of filtering or estimating a desirable signal from a noise-corrupted observation. This method requires adaptive filters due to the unknown or varying input signal and noise properties. This article provides an overview of the three commonly used adaptive filtering algorithms, namely the Least Mean Square LMS, the Normalized Least Mean Square NLMS and the Recursive Least Square RLS algorithm. These filtering algorithms are investigated to cancel out the noise effect from noise-corrupted data when no reference signal is available. The algorithms are used to elaborate on a specific problem of industrial measurement systems for fault detection, which is corrupted by the thermal noise in sensors. A comparison is shown among these three adaptive algorithms that perform the best desired estimation and noise-canceling effect under the white noise environment. Three comparison criteria are used to evaluate the performance of these algorithms: the error performance, the rate of convergence and the signal-to-noise ratio (SNR). The simulation results demonstrate that the NLMS algorithm shows an excellent noise-cancelling effect in terms of a lower error performance and a higher SNR at the same convergence speed compared to the LMS and RLS algorithm, which improves the fault detection performance of the industrial measurement system.
Stabilisation of β-Catenin-WNT signalling by USP10 in APC-truncated colorectal cancer drives cancer stemness and enables super-competitor signalling
Authors: Reissland Michaela , Hartmann Oliver , Tauch Saskia , Prieto-Garcia Cristian , Schulte Clemens , Solvie Daniel , Loebbert Sinah , Jacomin Anne-Claire , Pesic Marina , Bugter Jeroen , Schülein-Völk Christina , Fuss Carmina , Pahor Nikolett , Ade Carsten , Buck Viktoria , Potente Michael , Li Vivian , Beliu Gerti , Wiegering Armin , Bitman-Lotan Eliya , Grossmann Tom , Rosenfeldt Mathias , Eilers Martin , Maric Hans , Maurice Madelon , Greten Florian , Dikic Ivan , Oryan Amir , Gallant Peter , Diefenbacher Markus 
Published: May 2023

Abstract
The contribution of deubiquitylating enzymes to β-Catenin stabilisation in intestinal stem cells and colorectal cancer (CRC) is poorly understood. Here, we report the deubiquitylase USP10 as an APC-truncation- specific enhancer of β-Catenin stability, potentiating WNT signalling and cancer stem cells and CRC. Mechanistically, interaction and in vitro binding studies, together with computational modelling, revealed that USP10 binding to β-Catenin is mediated via the unstructured N-terminus of USP10 and requires the absence of full-length APC. Reduction of USP10 induces the expression of differentiation genes and opposes the APC-truncated phenotype in an intestinal hyperplasia model. Notably, loss of USP10 in CRISPR engineered intestinal organoids opposed the super competitor-signalling and reduced tumorigenic properties of APC-mutated CRC. Taken together, our findings reveal USP10s role in CRC cell identity, stemness and tumour growth by stabilising β-Catenin, leading to aberrant WNT signalling, and implicate USP10 as a cancer specific therapeutic vulnerability in Apc truncated CRC.
Physics-Informed Neural Networks for Hurricane Reconstruction and Data Assimilation
Authors: Eusebi Ryan , Vecchi Gabriel , Lai Ching-Yao , Tong Mingjing 
Published: May 2023

Abstract
Tropical cyclones (TCs) are responsible for large-scale loss of life and property, and billions of dollars worth of damage annually. Forecasting is typically accomplished through the use of dynamical models which directly integrate the governing equations of motion. These forecasts, and especially intensity forecasts, rely on accurate initial conditions obtained by assimilating real-time observational data. However, current methods used to generate these initial conditions are computationally expensive and limited, and TC intensity forecasts have much room for improvement. Here, we show that a Physics-Informed Neural Network can provide a promising alternative for data assimilation for TC initial conditions. Using synthetic training data sparsely sampled from hurricanes simulated in a forecast model, a PINN is able to accurately reconstruct full 2- and 3-dimensional wind and pressure fields of a TC. We also demonstrate how a set of sparse, real-time observations, can be used to accurately reconstruct Hurricane Ida. Our results demonstrate how recent advances in computational capabilities and deep learning can be applied to TC data assimilation, offering an accurate and efficient complement to existing data assimilation methods.
Nonlinear Dynamics of FG-GNPRC Multiphase Composite Membrane with Internal Pores and Dielectric Properties
Authors: Ni Zhi , Fan Yucheng , Yang Jinlong , Hang Ziyan , Feng Chuang , Yang Jie 
Published: May 2023

Abstract
In this paper, the nonlinear dynamics of the functionally graded graphene nanoplatelet reinforced composite (FG-GNPRC) dielectric and porous membrane subjected to electro-mechanical loading is investigated. The effective material properties of multiphase composites are determined via a two-step hybrid micromechanical model. Based on the hyperelastic membrane theory, Neo-Hookean constitutive model and the couple dielectric theory, the governing equations are obtained using an energy method considering damping and dielectric properties. Taylor series expansion (TSE) and differential quadrature (DQ) methods are utilized to discretize equations, which are then solved numerically by the incremental harmonic balance (IHB) method combined with arc-length continuation technique. The convergence analysis is carried out and the accuracy of the solution method is verified by comparing with the results of previous studies. The influence of the attributes of the internal pore, GNP, the geometric characteristics of membrane, stretching ratio and the applied electric filed on forced vibration and resonance response of the system are analyzed.
Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review
Authors: Vimbi Viswan , Shaffi Noushath , Mahmud Mufti , Subramanian Karthikeyan , Hajamohideen Faizal 
Published: May 2023

Abstract
Context: Artificial Intelligence (AI) in the medical domain has achieved remarkable results on various metrics primarily due to recent advancements in computational capabilities. However, AI models used for Computer Aided Diagnosis (CAD) have limited acceptance and trust due to their typical {Blackbox} nature. Consequently, explainable AI (XAI) is needed to justify the trustworthiness of the machine's predictions.
Approach: In this work, we undertake a systematic review based on PRISMA guidelines on research articles investigating Alzheimer's Disease (AD) prediction with XAI for the past decade. The review was driven by carefully formulated research questions (RQ) which helped categorize AI models into different XAI methods(Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, etc) and frameworks (LIME, SHAP, GradCAM, LRP, etc.).
Summary: This categorisation provides broad coverage of the interpretation spectrum from intrinsic (model-specific, ante-hoc models) to complex patterns (model-agnostic, post-hoc models) and by taking local explanations to a global scope. Additionally, the study categorizes merit in terms of different forms of interpretation to provide in-depth insight into the factors that support the clinical diagnosis of AD. Finally, limitations and open research challenges are outlined, and possible prospects are presented.
The characteristics, distribution and geological significance of alkaline earth elements in Ordovician limestone groundwater in Huaibei coalfield
Authors: Chen Song , Dong Hui 
Published: May 2023

Abstract
In order to understand the characteristics and distribution of alkaline earth elements of groundwater in Ordovician limestone aquifer in Huaibei coalfield, 22 groundwater samples were collected, and concentrations of major ions and alkaline earth metals (Beryllium, Barium and Strontium) were determined, the results showed: all the groundwater samples are alkaline in nature, and the pH value varied between 7.10 and 10.80. The contents of total dissolved solids (TDS) are varied from 119mg/l to 2443 mg/l. The concentrations of Be, Ba and Sr are ranging from 0.0001μg/l to 0.0716μg/l, 1.38μg/l to 54.25μg/l and 12.66μg/l to 2741.7μg/l, respectively. The major ions are mainly controlled by carbonate dissolution, without the ion exchange reaction. The Be content is mainly influenced by the pH value, while the Ba content is controlled by the concentrations of Mg. The Sr in groundwater have the same source with element Ca and Mg, thus, the contour diagram of Sr in groundwater could be used to identify the groundwater runoff conditions. The Taoyuan coal mine and Shitai coal mine are the discharge areas of groundwater in the south and north in Huaibei coalfield, for the highest and second concentrations of Sr. And the highest and second Sr/Ca ratios are 138.1 and 115.47 in Liudian coal mine and Tongting coal mine, respectively, what suggest the groundwater residence time in the two coal mines is long.
Construction of Predictive Model of Interstitial Fibrosis and Tubular Atrophy (IFTA) After Kidney Transplantation with Machine Learning Algorithm
Authors: Yin Yu , Chen Congcong , Zhang Dong , Han Qianguang , Wang Zijie , Huang Zhengkai , Chen Hao , Sun Li , Fei Shuang , Tao Jun , Han Zhijian , Tan Ruoyun , Gu Min , Ju Xiaobing 
Published: May 2023

Abstract
Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of CKD and one of the causes of long-term renal loss in transplanted kidneys. The purpose of our study is to screen IFTA-related genes with higher importance scores through Random Forest (RF) and further construct IFTA diagnostic model through Artificial Neural Networks (ANNs).
Methods: We screened all 162 “kidney transplant” related cohorts in the GEO database and obtained 5 data sets (training sets: GSE98320 validation sets: GSE22459, GSE53605 and GSE76882 survival sets: GSE21374). Differentially expressed genes (DEGs) analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Random Forest (RF), Artificial Neural Network (ANN), Unsupervised Clustering analysis, CIBERSORT analysis were used to analyze the data.
Results: A total of 108 common DEGs were identified by taking the intersection of the DEGs of our training sets and validation sets. A total of 15 top IFTA-specific DEGs were screened through the RF, then was used to build ANNs models. The model has good performance in both the training sets [GSE98320 (AUC = 0.9560)] and the validation sets [GSE22459 (AUC = 0.720), GSE53605 (AUC =0.938), GSE76882 (AUC = 0.781)], indicating that we have avoided overfitting while improving the accuracy. Furthermore, samples of survival sets are divided into two clusters using consensus clustering algorithm basing on the expression of 15 top IFTA-specific DEGs. We found significant differences between the two subgroups by survival analysis, and further enrichment analysis and immune cell infiltration analysis were conducted to further explore the causes of survival differences.
Conclusion: we identified key biomarkers of IFTA and developed a new IFTA classification model, basing on the combination of RF and ANNs.
Machine learning assisted calibration of stochastic agent-based models for pandemic outbreak analysis
Authors: Panovska-Griffiths Jasmina , Bayley Thomas , Ward Tony , Das Akashaditya , Imeneo Luca , Kerr Cliff , Maskell Simon 
Published: May 2023

Abstract
Mathematical modelling with agent-based models (ABMs) has gained popularity during the COVID-19 pandemic, but their complexity makes efficient and robust calibration to data challenging. We propose an improved method for calibrating ABMs that combines a machine-learning step with Approximate Bayesian Computation (ML-ABC). We showcase its application to Covasim - a stochastic ABM that has been timely and responsively used to model the English COVID-19 epidemic and inform policy at important junctions. We illustrate the advantage of ML-ABC application in calibrating Covasim during the first and the second COVID-19 epidemic waves of 2020 and early 2021, demonstrating that the use of an ML screening step allows us to derive faster and more efficient estimates of the posterior distribution of the Covasim optimal parameters without compromising on accuracy. This is important for generating timely responsive modelling results during an emerging epidemic.
Clinical effect of Nice knot-assisted minimally invasive titanium elastic nail fixation in the treatment of Robinson 2B midshaft clavicular fracture
Authors: Zhang Qingqing , Kang Yongqiang , Ma Yunhong , Zhou Ming , Jia Xueyuan , Lin Fang , Wu Yongwei , Rui Yongjun 
Published: May 2023

Abstract
Background The treatment of completely displaced midshaft clavicle fractures is still controversial, especially Robinson 2B fractures. Titanium elastic nail (TEN) fixation is a good option for simple fractures, but there are no reports on its use in complex fractures. The aim of this study was to present a surgical method using the Nice knot-assisted TEN fixation to treat Robinson 2B midshaft clavicular fractures.Methods A retrospective analysis of 29 patients who underwent fixation with TEN and had a 1-year postoperative follow-up between 2016 and 2020 was performed. The fractures were classified as Robinson type 2B1 in 17 cases and type 2B2 in 12 cases. Length of the incision, postoperative shoulder function, Disability of Arm Shoulder and Hand (DASH) score, complications, and second surgical incision length were recorded.Results The length of the incision was 2–6 cm (mean, 3.7 cm). All incisions healed by first intention, and no infection or nerve injury occurred. The Constant score was 92–100 (mean, 96) and the DASH score was 0-6.2 (mean, 2.64). ESIN bending and hypertrophic nonunion occurred in one case (3.4%) and implant irritation occurred in four (13.8%) after operation. Internal fixators were removed at 12–26 months (mean, 14.6 months) after operation, and the length of the second incision was 1-2.5 cm (mean, 1.3 cm).Conclusions Intramedullary clavicle fixation with TEN is an ideal surgical technique. Nice knot-assisted fixation effectively stabilizes multifragmentary fractures, thereby achieving fracture healing. Surgeons should consider this technique in cases of Robinson 2B midshaft clavicular fractures.Trial registration: Retrospectively registered. This study was approved by the Ethics Committee of Wuxi Ninth People's Hospital (LW20220021).
Multi-group Analysis of Compositions of Microbiomes with Covariate Adjustments and Repeated Measures
Authors: Peddada Shyamal , Lin Huang 
Published: May 2023

Abstract
Microbiome differential abundance analysis methods for a pair of groups are well established in the literature. However, many
microbiome studies involve multiple groups, sometimes even ordered groups, such as stages of a disease, and require different types of comparisons. Standard pairwise comparisons are not only inefficient in terms of power and false discovery rates, but they may not address the scientific question of interest. In this paper, we propose a general framework for performing a wide range of multi-group analyses with covariate adjustments and repeated measures. We demonstrate the effectiveness of our methodology through two real data sets. The first example explores the effects of aridity on the soil microbiome, and the second example investigates the effects of surgical interventions on the microbiome of IBD patients.
Optogenetic dissection of RET signaling reveals robust activation of ERK and enhanced filopodia-like protrusions of regenerating axons
Authors: Hyeon Bobae , Lee Heeyoung , Kim Nury , Heo Won Do 
Published: May 2023

Abstract

RET (REarranged during Transfection) is a receptor tyrosine kinase that transduces various external stimuli into biological functions, such as survival and differentiation, in neurons. In the current study, we developed an optogenetic tool for modulating RET signaling, termed optoRET, combining the cytosolic region of human RET with a blue-light–inducible homo-oligomerizing protein. By varying the duration of photoactivation, we were able to dynamically modulate RET signaling. Activation of optoRET recruited Grb2 (growth factor receptor-bound protein 2) and stimulated AKT and ERK (extracellular signal-regulated kinase) in cultured neurons, evoking robust and efficient ERK activation. By locally activating the distal part of the neuron, we were able to retrogradely transduce the AKT and ERK signal to the soma and trigger formation of filopodia-like F-actin structures at stimulated regions through Cdc42 (cell division control 42) activation. Importantly, we successfully modulated RET signaling in dopaminergic neurons of the substantia nigra in the mouse brain. Collectively, our findings suggest that optoRET has potential for treating neurological disorders such as Parkinson’s disease by promoting the ramification of auxiliary fibers on axon terminals.
Assessment of the Microbiome Diversity in Lakes of the West Kazakhstan Region
Authors: Khabibullovich Sergaliyev Nurlan , Galikhanovich Kakishev Murat 
Published: May 2023

Abstract
Background Metagenomic analysis was used in this investigation to present the results of the microbiome studies on Aidyn, Brusyanoe, and Rybny Sakryl lakes in the West Kazakhstan region. The technological basis of the research was high-performance Illumina next-generation sequencingResults The sequencing of 36 amplicon libraries yielded at least 20,000 sequences per library, which were subjected to primary taxonomic analysis; 4000 taxons were identified to belong to approximately 400 genera. Results showed that unclassified bacteria, Proteobacteria, Bacteroidota, and Actinobacteriota were dominant. According to the Shannon metric, the samples from Brusyanoe lake had the richest and the most even communities. Subsequently, samples from the Aidyn and Rybny Sakryl lakes, which also had the most even communities according to the Simpson metric. The study also revealed that the taxonomic structure of microbiomes from different lakes had several common features. All samples were characterized by the presence of the following groups of bacteria: Verrucomicrobiae, Gammaproteobacteria and Alphaproteobacteria, Cyanobacteria, Bacteroidia, Actinobacteria, and Acidimicrobiia. Furthermore, comparative analysis of the taxonomic structure of communities showed differences in their structure at the class level as well. Beta-diversity indicated that the taxonomic composition of the communities varied and depended on the sampling location. Weighted Uni Fracmetric, which reflects the similarity in the qualitative composition of the communities, suggested an even taxonomic composition only for the Rybny Sakryllake microbiome. Microbiome samples from Aidyn and Brusyanoe lakes were characterized by the presence of Candidatusaquilinia (2.6–7.5%) and a relatively high content of Algoriphagus (2–8%). Common features between the Aidyn and Rybny Sakryl lake samples included a relatively abundant Methylophilaceae (1.3–3.8%).Conclusion The conducted studies revealed differences in microbiome composition in Aydi, Brusyanoe and Rybny Sakryl lakes.Data analyses revealed that the results of the metagenomic study of the microbiome of inland water bodies can be used as markers to elucidate the ecological state of the lakes examined in the present study. Furthermore, microbial communities adapted to water with significantly high salt contents are predominantly found in the Aidyn lake. However, microbial communities existing in freshwaters with low salt content are typically foundin the Rybny Sakryl and Brusyanoe lakes.