bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling
Authors: Huang Tao , Merwade Venkatesh 
Published: October 2023

AbstractAs all kinds of numerical models are emerging in hydrologic and hydraulic engineering, Bayesian model averaging (BMA) is one of the popular multi‐model methods used to account for various uncertainties in the flood modeling process and generate robust ensemble predictions. The reliability of BMA parameters (weights and variances) determines the accuracy of BMA predictions. However, the uncertainty in BMA parameters with fixed values, which are usually obtained from Expectation‐Maximization (EM) algorithm, has not been adequately investigated in BMA‐related applications over the past few decades. Given the limitations of the default EM algorithm, Metropolis‐Hastings (M‐H) algorithm, which is one of the most widely used algorithms in Markov Chain Monte Carlo (MCMC), is proposed to estimate BMA parameters. Both numerical experiments and one‐dimensional Hydrologic Engineering Center‐River Analysis System models are employed to examine the applicability of M‐H algorithm with multiple independent Markov chains. The performances of EM and M‐H algorithms are compared based on the daily water stage predictions from 10 model members. Results show that BMA weights estimated from both algorithms are comparable, while BMA variances obtained from M‐H algorithm are closer to the given variances in the numerical experiment. Moreover, the normal proposal distribution used in M‐H algorithm can yield narrower distributions for BMA weights than those from the uniform proposal distribution. Overall, MCMC approach with multiple chains can provide more information associated with the uncertainty of BMA parameters and its performance is better than the default EM algorithm in terms of both deterministic and probabilistic evaluation metrics as well as algorithm flexibility.
Paternal methamphetamine exposure induces higher sensitivity to methamphetamine in male offspring through driving ADRB1 on CaMKII-positive neurons in mPFC
Authors: Zheng Yanyan , Liu Dekang , Guo Hao , Chen Wenwen , Liu Zhaoyu , Li Zhaosu , Hu Tao , Zhang Yuanyuan , Li Xiang , Zhao Ziheng , Cai Qinglong , Ge Feifei , Fan Yu , Guan Xiaowei 
Published: October 2023

AbstractPaternal abuse of drugs, such as methamphetamine (METH), elevates the risk of developing addiction in subsequent generations, however, its underlying molecular mechanism remains poorly understood. Male adult mice (F0) were exposed to METH for 30 days, followed by mating with naïve female mice to create the first-generation mice (F1). When growing to adulthood, F1 were subjected to conditioned place preference (CPP) test. Subthreshold dose of METH (sd-METH), insufficient to induce CPP normally, were used in F1. Selective antagonist (betaxolol) for β1-adrenergic receptor (ADRB1) or its knocking-down virus were administrated into mPFC to regulate ADRB1 function and expression on CaMKII-positive neurons. METH-sired male F1 acquired sd-METH-induced CPP, indicating that paternal METH exposure induce higher sensitivity to METH in male F1. Compared with saline (SAL)-sired male F1, CaMKII-positive neuronal activity was normal without sd-METH, but strongly evoked after sd-METH treatment in METH-sired male F1 during adulthood. METH-sired male F1 had higher ADRB1 levels without sd-METH, which was kept at higher levels after sd-METH treatment in mPFC. Either inhibiting ADRB1 function with betaxolol, or knocking-down ADRB1 level on CaMKII-positive neurons (ADRB1CaMKII) with virus transfection efficiently suppressed sd-METH -evoked mPFC activation, and ultimately blocked sd-METH-induced CPP in METH-sired male F1. In the process, the p-ERK1/2 and ΔFosB may be potential subsequent signals of mPFC ADRB1CaMKII. The mPFC ADRB1CaMKII mediates paternal METH exposure-induced higher sensitivity to drug addiction in male offspring, raising a promising pharmacological target for predicting or treating transgenerational addiction.
KCNAB2 overexpression inhibits human non-small-cell lung cancer cell growth in vitro and in vivo
Authors: Cheng Feng , Tang Yu-fei , Cao Yang , Peng Shi-qing , Zhu Xiao-ren , Sun Yue , Wang Shu-Hang , Wang Bin , Lu Yi-min 
Published: October 2023

AbstractNon-small-cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases. NSCLC patients often have poor prognosis demanding urgent identification of novel biomarkers and potential therapeutic targets. KCNAB2 (regulatory beta subunit2 of voltage-gated potassium channel), encoding aldosterone reductase, plays a pivotal role in regulating potassium channel activity. In this research, we tested the expression of KCNAB2 as well as its potential functions in human NSCLC. Bioinformatics analysis shows that expression ofKCNAB2mRNA is significantly downregulated in human NSCLC, correlating with poor overall survival. In addition, decreased KCNAB2 expression was detected in different NSCLC cell lines and local human NSCLC tissues. Exogenous overexpression of KCNAB2 potently suppressed growth, proliferation and motility of established human NSCLC cells and promoted NSCLC cells apoptosis. In contrast, CRISPR/Cas9-induced KCNAB2 knockout further promoted the malignant biological behaviors of NSCLC cells. Protein chip analysis in the KCNAB2-overexpressed NSCLC cells revealed that KCNAB2 plays a possible role in AKT-mTOR cascade activation. Indeed, AKT-mTOR signaling activation was potently inhibited following KCNAB2 overexpression in NSCLC cells. It was however augmented by KCNAB2 knockout. In vivo, the growth of subcutaneous KCNAB2-overexpressed A549 xenografts was significantly inhibited. Collectively, KCNAB2 could be a novel effective gene for prognosis prediction of NSCLC. Targeting KCNAB2 may lead to the development of advanced therapies.
Accurate prediction of protein folding mechanisms by simple structure-based statistical mechanical models
Authors: Ooka Koji , Arai Munehito 
Published: October 2023

AbstractRecent breakthroughs in highly accurate protein structure prediction using deep neural networks have made considerable progress in solving the structure prediction component of the ‘protein folding problem’. However, predicting detailed mechanisms of how proteins fold into specific native structures remains challenging, especially for multidomain proteins constituting most of the proteomes. Here, we develop a simple structure-based statistical mechanical model that introduces nonlocal interactions driving the folding of multidomain proteins. Our model successfully predicts protein folding processes consistent with experiments, without the limitations of protein size and shape. Furthermore, slight modifications of the model allow prediction of disulfide-oxidative and disulfide-intact protein folding. These predictions depict details of the folding processes beyond reproducing experimental results and provide a rationale for the folding mechanisms. Thus, our physics-based models enable accurate prediction of protein folding mechanisms with low computational complexity, paving the way for solving the folding process component of the ‘protein folding problem’.
Strain topological metamaterials and revealing hidden topology in higher-order coordinates
Authors: Allein Florian , Anastasiadis Adamantios , Chaunsali Rajesh , Frankel Ian , Boechler Nicholas , Diakonos Fotios K. , Theocharis Georgios 
Published: October 2023

AbstractTopological physics has revolutionized materials science, introducing topological phases of matter in diverse settings ranging from quantum to photonic and phononic systems. Herein, we present a family of topological systems, which we term “strain topological metamaterials”, whose topological properties are hidden and unveiled only under higher-order (strain) coordinate transformations. We firstly show that the canonical mass dimer, a model that can describe various settings such as electrical circuits and optics, among others, belongs to this family where strain coordinates reveal a topological nontriviality for the edge states at free boundaries. Subsequently, we introduce a mechanical analog of the Majorana-supporting Kitaev chain, which supports topological edge states for both fixed and free boundaries within the proposed framework. Thus, our findings not only extend the way topological edge states are identified, but also promote the fabrication of novel topological metamaterials in various fields, with more complex, tailored boundaries.
Anomalous water molecular gating from atomic-scale graphene capillaries for precise and ultrafast molecular sieving
Authors: Zhang Qian , Gao Bo , Zhang Ling , Liu Xiaopeng , Cui Jixiang , Cao Yijun , Zeng Hongbo , Xu Qun , Cui Xinwei , Jiang Lei 
Published: October 2023

AbstractThe pressing crisis of clean water shortage requires membranes to possess effective ion sieving as well as fast water flux. However, effective ion sieving demands reduction of pore size, which inevitably hinders water flux in hydrophilic membranes, posing a major challenge for efficient water/ion separation. Herein, we introduce anomalous water molecular gating based on nanofiltration membranes full of graphene capillaries at 6 Å, which were fabricated from spontaneous π-π restacking of island-on-nanosheet graphitic microstructures. We found that the membrane can provide effective ion sieving by suppressing osmosis-driven ion diffusion to negligible levels (~10–4 mol m–2 h–1); unexpectedly, ultrafast bulk flow of water (45.4 L m–2 h–1) was still functional with ease, as gated on/off by adjusting hydrostatic pressures within only 10–2 bar. We attribute this seemingly incompatible observation to graphene nanoconfinement effect, where crystal-like water confined within the capillaries hinders diffusion under osmosis but facilitates high-speed, diffusion-free water transport in the way analogous to Newton’s cradle-like Grotthus conduction. This strategy establishes a type of liquid-solid-liquid, phase-changing molecular transport for precise and ultrafast molecular sieving.
Global tree growth resilience to cold extremes following the Tambora volcanic eruption
Authors: Gao Shan , Camarero J. Julio , Babst Flurin , Liang Eryuan 
Published: October 2023

AbstractAlthough the global climate is warming, external forcing driven by explosive volcanic eruptions may still cause abrupt cooling. The 1809 and 1815 Tambora eruptions caused lasting cold extremes worldwide, providing a unique lens that allows us to investigate the magnitude of global forest resilience to and recovery from volcanic cooling. Here, we show that growth resilience inferred from tree-ring data was severely impacted by cooling in high latitudes and elevations: the average tree growth decreased substantially (up to 31.8%), especially in larch forests, and regional-scale probabilities of severe growth reduction (below −2σ) increased up to 1390%. The influence of the eruptions extended longer (beyond the year 1824) in mid- than in high-latitudes, presumably due to the combined impacts of cold and drought stress. As Tambora-size eruptions statistically occur every 200–400 years, assessing their influences on ecosystems can help humankind mitigate adverse impacts on natural resources through improved management, especially in high latitude and elevation regions.
Machine learning-enabled constrained multi-objective design of architected materials
Authors: Peng Bo , Wei Ye , Qin Yu , Dai Jiabao , Li Yue , Liu Aobo , Tian Yun , Han Liuliu , Zheng Yufeng , Wen Peng 
Published: October 2023

AbstractArchitected materials that consist of multiple subelements arranged in particular orders can demonstrate a much broader range of properties than their constituent materials. However, the rational design of these materials generally relies on experts’ prior knowledge and requires painstaking effort. Here, we present a data-efficient method for the high-dimensional multi-property optimization of 3D-printed architected materials utilizing a machine learning (ML) cycle consisting of the finite element method (FEM) and 3D neural networks. Specifically, we apply our method to orthopedic implant design. Compared to uniform designs, our experience-free method designs microscale heterogeneous architectures with a biocompatible elastic modulus and higher strength. Furthermore, inspired by the knowledge learned from the neural networks, we develop machine-human synergy, adapting the ML-designed architecture to fix a macroscale, irregularly shaped animal bone defect. Such adaptation exhibits 20% higher experimental load-bearing capacity than the uniform design. Thus, our method provides a data-efficient paradigm for the fast and intelligent design of architected materials with tailored mechanical, physical, and chemical properties.
Repair of osteochondral defects: efficacy of a tissue-engineered hybrid implant containing both human MSC and human iPSC-cartilaginous particles
Authors: Nakagawa Shinichi , Ando Wataru , Shimomura Kazunori , Hart David A. , Hanai Hiroto , Jacob George , Chijimatsu Ryota , Yarimitu Seido , Fujie Hiromichi , Okada Seiji , Tsumaki Noriyuki , Nakamura Norimasa 
Published: October 2023

AbstractBoth mesenchymal stromal cells (MSC) and induced pluripotent stem cells (iPSC) offer the potential for repair of damaged connective tissues. The use of hybrid implants containing both human MSC and iPSC was investigated to assess their combined potential to yield enhanced repair of osteochondral defects. Human iPSC-CP wrapped with tissue engineered constructs (TEC) containing human MSC attained secure defect filling with good integration to adjacent tissue in a rat osteochondral injury model. The presence of living MSC in the hybrid implants was required for effective biphasic osteochondral repair. Thus, the TEC component of such hybrid implants serves several critical functions including, adhesion to the defect site via the matrix and facilitation of the repair via live MSC, as well as enhanced angiogenesis and neovascularization. Based on these encouraging studies, such hybrid implants may offer an effective future intervention for repair of complex osteochondral defects.
Tethered Balloon-Borne Turbulence Measurements in Winter and Spring during the MOSAiC Expedition
Authors: Akansu Elisa F. , Siebert Holger , Dahlke Sandro , Graeser Jürgen , Jaiser Ralf , Sommerfeld Anja 
Published: October 2023

AbstractDuring the Multidisciplinary Drifting Observatory for the Study of Arctic Climate expedition, a tethered balloon system was operated with a turbulence probe attached to study the lower troposphere in the high Arctic. Overall, measurements were conducted on 34 days between December 2019 and May 2020, resulting in 47 quality-assured sampling records consisting of vertical profiles and constant-altitude measurements. The continuous profiles extend from the surface, i.e., the sea ice floe, to a height of several hundred meters typically. The high-resolution wind velocity measurements using a hot-wire anemometer and temperature measurements using a thermocouple provide a comprehensive basis for examining the dynamical processes and thermodynamic stratification in the Arctic atmospheric boundary layer under cloudless and cloudy conditions. This paper provides a detailed technical description of the turbulence payload, including calibration and quality assurance, and a general overview of the data. A particular focus of this work is the estimation of local energy dissipation rates. The data are freely available from the World Data Center PANGAEA.
Automatic biometry of fetal brain MRIs using deep and machine learning techniques
Authors: She Jiayan , Huang Haiying , Ye Zhijun , Huang Wei , Sun Yan , Liu Chuan , Yang Weilin , Wang Jiaxi , Ye Pengfei , Zhang Lei , Ning Gang 
Published: October 2023

AbstractLinear biometric measurements on magnetic resonance images are important for the assessment of fetal brain development, which is expert knowledge dependent and laborious. This study aims to construct a segmentation-based method for automatic two-dimensional biometric measurements of fetal brain on magnetic resonance images that provides a fast and accurate measurement of fetal brain. A total of 268 volumes (5360 images) magnetic resonance images of normal fetuses were included. The automatic method involves two steps. First, the fetal brain was segmented into four parts with a deep segmentation network: cerebrum, cerebellum, and left and right lateral ventricles. Second, the measurement plane was determined, and the corresponding biometric parameters were calculated according to clinical guidelines, including cerebral biparietal diameter (CBPD), transverse cerebellar diameter (TCD), left and right atrial diameter (LAD/RAD). Pearson correlation coefficient and Bland–Altman plots were used to assess the correlation and agreement between computer-predicted values and manual measurements. Mean differences were used to evaluate the errors quantitatively. Analysis of fetal cerebral growth based on the automatic measurements was also displayed. The experiment results show that correlation coefficients for CBPD, TCD, LAD and RAD were as follows: 0.977, 0.990, 0.817, 0.719, mean differences were − 2.405 mm, − 0.008 mm, − 0.33 mm, − 0.213 mm, respectively. The correlation between the errors and gestational age was not statistically significant (p values were 0.2595, 0.0510, 0.1995, and 0.0609, respectively). The proposed automatic method for linear measurements on fetal brain MRI achieves excellent performance, which is expected to be applied in clinical practice and be helpful for prenatal diagnosis and clinical work efficiency improvement.
Distinct neural representations during a brain–machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum
Authors: Zippi Ellen L. , Shvartsman Gabrielle F. , Vendrell-Llopis Nuria , Wallis Joni D. , Carmena Jose M. 
Published: October 2023

AbstractAlthough brain–machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodents has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguishes control types at the go cue and target acquisition, respectively, while M1 best predicts target-direction at both task events. We also find effective connectivity from DLPFC → M1 throughout both control types and Cd → M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.
Quantization of events in the event-universe and the emergence of quantum mechanics
Authors: Shor Oded , Benninger Felix , Khrennikov Andrei 
Published: October 2023

AbstractQuantum mechanics (QM) is derived based on a universe composed solely of events, for example, outcomes of observables. Such an event universe is represented by a dendrogram (a finite tree) and in the limit of infinitely many events by the p-adic tree. The trees are endowed with an ultrametric expressing hierarchical relationships between events. All events are coupled through the tree structure. Such a holistic picture of event-processes was formalized within the Dendrographic Hologram Theory (DHT). The present paper is devoted to the emergence of QM from DHT. We used the generalization of the QM-emergence scheme developed by Smolin. Following this scheme, we did not quantize events but rather the differences between them and through analytic derivation arrived at Bohmian mechanics. We remark that, although Bohmian mechanics is not the main stream approach to quantum physics, it describes adequately all quantum experiments. Previously, we were able to embed the basic elements of general relativity (GR) into DHT, and now after Smolin-like quantization of DHT, we can take a step toward quantization of GR. Finally, we remark that DHT is nonlocal in the treelike geometry, but this nonlocality refers to relational nonlocality in the space of events and not Einstein’s spatial nonlocality. By shifting from spatial nonlocality to relational we make Bohmian mechanics less exotic.
The complete chloroplast genome of Cicer reticulatum and comparative analysis against relative Cicer species
Authors: Mehmetoğlu Ezgi , Kaymaz Yasin , Ateş Duygu , Kahraman Abdullah , Tanyolaç Muhammed Bahattin 
Published: October 2023

AbstractThe chloroplast (cp) genome is an adequate genomic resource to investigate evolutionary relationships among plant species and it carries marker genes available for species identification. The Cicer reticulatum is one of perennial species as the progenitor of cultivated chickpeas. Although a large part of the land plants has a quadruple chloroplast genome organization, the cp genome of C. reticulatum consists of one LSC (Large Single Copy Region), one SSC (Small Single Copy Region), and one IR (Inverted Repeat) region, which indicates that it has an untypical and unique structure. This type of chloroplast genome belongs to the IR-lacking clade. Chloroplast DNA (cpDNA) was extracted from fresh leaves using a high salt-based protocol and sequencing was performed using DNA Nanoball Sequencing technology. The comparative analysis employed between the species to examine genomic differences and gene homology. The study also included codon usage frequency analysis, hotspot divergence analysis, and phylogenetic analysis using various bioinformatics tools. The cp genome of C. reticulatum was found 125,794 bp in length, with an overall GC content of 33.9%. With a total of 79 protein-coding genes, 34 tRNA genes, and 4 rRNA genes. Comparative genomic analysis revealed 99.93% similarity between C. reticulatum and C. arietinum. Phylogenetic analysis further indicated that the closest evolutionary relative to C. arietinum was C. reticulatum, whereas the previously sequenced wild Cicer species displayed slight distinctions across their entire coding regions. Several genomic regions, such as clpP and ycf1, were found to exhibit high nucleotide diversity, suggesting their potential utility as markers for investigating the evolutionary relationships within the Cicer genus. The first complete cp genome sequence of C. reticulatum will provide novel insights for future genetic research on Cicer crops.
Multiband antenna design with enhanced radiations using characteristic mode analysis
Authors: Fazal Dilaawaiz , Umar Khan Qasim , Hong Ic-Pyo 
Published: October 2023

AbstractIn this research, a multiband patch antenna design based on the superposition of multiple modes at the same resonant frequency is presented. The concept of the contribution of lower order modes (LOMs) with the higher order dominant modes (HODMs) is investigated using characteristic mode analysis (CMA). In order to provide similar broadside radiations in three bands, the radiating capability of the LOMs is enhanced in the resonant frequencies of HODMs. These HODMs when excited alone provide null in the broadside radiations of the antenna. Using a single feed, enhancement in the broadside radiations of the antenna is achieved with the superposition of multiple modes at the same resonant frequency. Based on the proposed concept two antennas have been designed and fabricated. The antenna provides stable and enhanced radiations. simulated and measured results are in good agreement. These antennas find application in many applications including communication systems, base station antennas, 5G communications, satellite communication etc.