bluuebunny/crossref_metadata_2025_split huggingface dataset:
Forecasting the evolution of fast-changing transportation networks using machine learning
Authors: Lei Weihua , Alves Luiz G. A. , Amaral Luís A. Nunes 
Published: July 2022

AbstractTransportation networks play a critical role in human mobility and the exchange of goods, but they are also the primary vehicles for the worldwide spread of infections, and account for a significant fraction of CO2 emissions. We investigate the edge removal dynamics of two mature but fast-changing transportation networks: the Brazilian domestic bus transportation network and the U.S. domestic air transportation network. We use machine learning approaches to predict edge removal on a monthly time scale and find that models trained on data for a given month predict edge removals for the same month with high accuracy. For the air transportation network, we also find that models trained for a given month are still accurate for other months even in the presence of external shocks. We take advantage of this approach to forecast the impact of a hypothetical dramatic reduction in the scale of the U.S. air transportation network as a result of policies to reduce CO2 emissions. Our forecasting approach could be helpful in building scenarios for planning future infrastructure.
A multi-omic dissection of super-enhancer driven oncogenic gene expression programs in ovarian cancer
Authors: Kelly Michael R. , Wisniewska Kamila , Regner Matthew J. , Lewis Michael W. , Perreault Andrea A. , Davis Eric S. , Phanstiel Douglas H. , Parker Joel S. , Franco Hector L. 
Published: July 2022

AbstractThe human genome contains regulatory elements, such as enhancers, that are often rewired by cancer cells for the activation of genes that promote tumorigenesis and resistance to therapy. This is especially true for cancers that have little or no known driver mutations within protein coding genes, such as ovarian cancer. Herein, we utilize an integrated set of genomic and epigenomic datasets to identify clinically relevant super-enhancers that are preferentially amplified in ovarian cancer patients. We systematically probe the top 86 super-enhancers, using CRISPR-interference and CRISPR-deletion assays coupled to RNA-sequencing, to nominate two salient super-enhancers that drive proliferation and migration of cancer cells. Utilizing Hi-C, we construct chromatin interaction maps that enable the annotation of direct target genes for these super-enhancers and confirm their activity specifically within the cancer cell compartment of human tumors using single-cell genomics data. Together, our multi-omic approach examines a number of fundamental questions about how regulatory information encoded into super-enhancers drives gene expression networks that underlie the biology of ovarian cancer.
Structure of DNMT3B homo-oligomer reveals vulnerability to impairment by ICF mutations
Authors: Gao Linfeng , Guo Yiran , Biswal Mahamaya , Lu Jiuwei , Yin Jiekai , Fang Jian , Chen Xinyi , Shao Zengyu , Huang Mengjiang , Wang Yinsheng , Wang Gang Greg , Song Jikui 
Published: July 2022

AbstractDNA methyltransferase DNMT3B plays an essential role in establishment of DNA methylation during embryogenesis. Mutations of DNMT3B are associated with human diseases, notably the immunodeficiency, centromeric instability and facial anomalies (ICF) syndrome. How ICF mutations affect DNMT3B activity is not fully understood. Here we report the homo-oligomeric structure of DNMT3B methyltransferase domain, providing insight into DNMT3B-mediated DNA methylation in embryonic stem cells where the functional regulator DNMT3L is dispensable. The interplay between one of the oligomer interfaces (FF interface) and the catalytic loop renders DNMT3B homo-oligomer a conformation and activity distinct from the DNMT3B-DNMT3L heterotetramer, and a greater vulnerability to certain ICF mutations. Biochemical and cellular analyses further reveal that the ICF mutations of FF interface impair the DNA binding and heterochromatin targeting of DNMT3B, leading to reduced DNA methylation in cells. Together, this study provides a mechanistic understanding of DNMT3B-mediated DNA methylation and its dysregulation in disease.
Ultrastrong and multifunctional aerogels with hyperconnective network of composite polymeric nanofibers
Authors: He Huimin , Wei Xi , Yang Bin , Liu Hongzhen , Sun Mingze , Li Yanran , Yan Aixin , Tang Chuyang Y. , Lin Yuan , Xu Lizhi 
Published: July 2022

AbstractThree-dimensional (3D) microfibrillar network represents an important structural design for various natural tissues and synthetic aerogels. Despite extensive efforts, achieving high mechanical properties for synthetic 3D microfibrillar networks remains challenging. Here, we report ultrastrong polymeric aerogels involving self-assembled 3D networks of aramid nanofiber composites. The interactions between the nanoscale constituents lead to assembled networks with high nodal connectivity and strong crosslinking between fibrils. As revealed by theoretical simulations of 3D networks, these features at fibrillar joints may lead to an enhancement of macroscopic mechanical properties by orders of magnitude even with a constant level of solid content. Indeed, the polymeric aerogels achieved both high specific tensile modulus of ~625.3 MPa cm3 g−1 and fracture energy of ~4700 J m−2, which are advantageous for diverse structural applications. Furthermore, their simple processing techniques allow fabrication into various functional devices, such as wearable electronics, thermal stealth, and filtration membranes. The mechanistic insights and manufacturability provided by these robust microfibrillar aerogels may create further opportunities for materials design and technological innovation.
Targeting loop3 of sclerostin preserves its cardiovascular protective action and promotes bone formation
Authors: Yu Yuanyuan , Wang Luyao , Ni Shuaijian , Li Dijie , Liu Jin , Chu Hang Yin , Zhang Ning , Sun Meiheng , Li Nanxi , Ren Qing , Zhuo Zhenjian , Zhong Chuanxin , Xie Duoli , Li Yongshu , Zhang Zong-Kang , Zhang Huarui , Li Mei , Zhang Zhenlin , Chen Lin , Pan Xiaohua , Xia Weibo , Zhang Shu , Lu Aiping , Zhang Bao-Ting , Zhang Ge 
Published: July 2022

AbstractSclerostin negatively regulates bone formation by antagonizing Wnt signalling. An antibody targeting sclerostin for the treatment of postmenopausal osteoporosis was approved by the U.S. Food and Drug Administration, with a boxed warning for cardiovascular risk. Here we demonstrate that sclerostin participates in protecting cardiovascular system and inhibiting bone formation via different loops. Loop3 deficiency by genetic truncation could maintain sclerostin’s protective effect on the cardiovascular system while attenuating its inhibitory effect on bone formation. We identify an aptamer, named aptscl56, which specifically targets sclerostin loop3 and use a modified aptscl56 version, called Apc001PE, as specific in vivo pharmacologic tool to validate the above effect of loop3. Apc001PE has no effect on aortic aneurysm and atherosclerotic development in ApoE−/− mice and hSOSTki.ApoE−/− mice with angiotensin II infusion. Apc001PE can promote bone formation in hSOSTki mice and ovariectomy-induced osteoporotic rats. In summary, sclerostin loop3 cannot participate in protecting the cardiovascular system, but participates in inhibiting bone formation.
Effect of exchange-correlation functionals on the estimation of migration barriers in battery materials
Authors: Devi Reshma , Singh Baltej , Canepa Pieremanuele , Sai Gautam Gopalakrishnan 
Published: July 2022

AbstractFacile ionic mobility within host frameworks is crucial to the design of high-energy-density batteries with high-power-densities, where the migration barrier (Em) is the governing factor. Here, we assess the accuracy and computational performance of generalized gradient approximation (GGA), the strongly constrained and appropriately normed (SCAN), and their HubbardUcorrections, GGA+Uand SCAN+U, within the density functional theory-nudged elastic band framework, in the prediction of Emas benchmarked against experimental data. Importantly, we observe SCAN to be more accurate than other frameworks, on average, albeit with higher computational costs and convergence difficulties, while GGA is a feasible choice for “quick” and “qualitative” Empredictions. Further, we quantify the sensitivity of Emwith adding uniform background charge and/or the climbing image approximation in solid electrolytes, and the HubbardUcorrection in electrodes. Our findings will improve the quality of Empredictions which will enable identifying better materials for energy storage applications.
A dominant negative ADIPOQ mutation in a diabetic family with renal disease, hypoadiponectinemia, and hyperceramidemia
Authors: Simeone Christopher A. , Wilkerson Joseph L. , Poss Annelise M. , Banks James A. , Varre Joseph V. , Guevara Jose Lazaro , Hernandez Edgar Javier , Gorsi Bushra , Atkinson Donald L. , Turapov Tursun , Frodsham Scott G. , Morales Julio C. Fierro , O’Neil Kristina , Moore Barry , Yandell Mark , Summers Scott A. , Krolewski Andrzej S. , Holland William L. , Pezzolesi Marcus G. 
Published: July 2022

AbstractAdiponectin, encoded by ADIPOQ, is an insulin-sensitizing, anti-inflammatory, and renoprotective adipokine that activates receptors with intrinsic ceramidase activity. We identified a family harboring a 10-nucleotide deletion mutation in ADIPOQ that cosegregates with diabetes and end-stage renal disease. This mutation introduces a frameshift in exon 3, resulting in a premature termination codon that disrupts translation of adiponectin’s globular domain. Subjects with the mutation had dramatically reduced circulating adiponectin and increased long-chain ceramides levels. Functional studies suggest that the mutated protein acts as a dominant negative through its interaction with non-mutated adiponectin, decreasing circulating adiponectin levels, and correlating with metabolic disease.
Quantum computational phase transition in combinatorial problems
Authors: Zhang Bingzhi , Sone Akira , Zhuang Quntao 
Published: July 2022

AbstractQuantum Approximate Optimization algorithm (QAOA) aims to search for approximate solutions to discrete optimization problems with near-term quantum computers. As there are no algorithmic guarantee possible for QAOA to outperform classical computers, without a proof that bounded-error quantum polynomial time (BQP) ≠ nondeterministic polynomial time (NP), it is necessary to investigate the empirical advantages of QAOA. We identify a computational phase transition of QAOA when solving hard problems such as SAT—random instances are most difficult to train at a critical problem density. We connect the transition to the controllability and the complexity of QAOA circuits. Moreover, we find that the critical problem density in general deviates from the SAT-UNSAT phase transition, where the hardest instances for classical algorithms lies. Then, we show that the high problem density region, which limits QAOA’s performance in hard optimization problems (reachability deficits), is actually a good place to utilize QAOA: its approximation ratio has a much slower decay with the problem density, compared to classical approximate algorithms. Indeed, it is exactly in this region that quantum advantages of QAOA over classical approximate algorithms can be identified.
Dataset of Speech Production in intracranial Electroencephalography
Authors: Verwoert Maxime , Ottenhoff Maarten C. , Goulis Sophocles , Colon Albert J. , Wagner Louis , Tousseyn Simon , van Dijk Johannes P. , Kubben Pieter L. , Herff Christian 
Published: July 2022

AbstractSpeech production is an intricate process involving a large number of muscles and cognitive processes. The neural processes underlying speech production are not completely understood. As speech is a uniquely human ability, it can not be investigated in animal models. High-fidelity human data can only be obtained in clinical settings and is therefore not easily available to all researchers. Here, we provide a dataset of 10 participants reading out individual words while we measured intracranial EEG from a total of 1103 electrodes. The data, with its high temporal resolution and coverage of a large variety of cortical and sub-cortical brain regions, can help in understanding the speech production process better. Simultaneously, the data can be used to test speech decoding and synthesis approaches from neural data to develop speech Brain-Computer Interfaces and speech neuroprostheses.
The near-global ocean mesoscale eddy atmospheric-oceanic-biological interaction observational dataset
Authors: Dong Changming , Liu Lingxiao , Nencioli Francesco , Bethel Brandon J. , Liu Yu , Xu Guangjun , Ma Jing , Ji Jinlin , Sun Wenjin , Shan Haixia , Lin Xiayan , Zou Bin 
Published: July 2022

AbstractAmongst the variety of oceanic processes running the gamut of spatiotemporal scales, mesoscale eddies are the most common and often have region-specific characteristics. The large kinetic energy inherent to eddies themselves is a strong modulator of the global climate, ocean circulation, productivity, and freshwater transport. This study uses multi-source satellite remote sensing observation data to construct a multi-parameter eddy dataset for the 1993–2019 period, which differs significantly from a few of previous published eddy datasets that include only basic sea surface eddy physical features. Eddies within the dataset have life cycles of greater than four weeks, and their corresponding sea surface chlorophyll, sea surface temperature, and wind fields are provided. Atmospheric and oceanic variables are used to present a comprehensive picture of a given mesoscale eddy’s impact on the local physical, but also biological environment. The dataset would find immense value in research on mesoscale eddies, their impact on the atmosphere, and related biological processes.
Improvisation is a novel tool to study musicality
Authors: Weiss Michael W. , Peretz Isabelle 
Published: July 2022

AbstractHumans spontaneously invent songs from an early age. Here, we exploit this natural inclination to probe implicit musical knowledge in 33 untrained and poor singers (amusia). Each sang 28 long improvisations as a response to a verbal prompt or a continuation of a melodic stem. To assess the extent to which each improvisation reflects tonality, which has been proposed to be a core organizational principle of musicality and which is present within most music traditions, we developed a new algorithm that compares a sung excerpt to a probability density function representing the tonal hierarchy of Western music. The results show signatures of tonality in both nonmusicians and individuals with congenital amusia, who have notorious difficulty performing musical tasks that require explicit responses and memory. The findings are a proof of concept that improvisation can serve as a novel, even enjoyable method for systematically measuring hidden aspects of musicality across the spectrum of musical ability.
Short-term forecasting of Mmax during hydraulic fracturing
Authors: Li Ziyan , Eaton David , Davidsen Jörn 
Published: July 2022

AbstractPrevious studies of injection-induced earthquake sequences have shown that the maximum magnitude (Mmax) of injection-induced seismicity increases with the net injected volume (V); however, different proposed seismic-hazard paradigms predict significantly different values of Mmax. Using injection and seismicity data from two project areas in northeastern British Columbia, Canada, where hydraulic fracturing induced seismicity was observed, we test the predictive power and robustness of three existing and one novel method to estimate Mmax. Due to their vastly different values of seismogenic index (Σ), these two project areas represent end-member cases of seismogenic response. Our novel method progressively adjusts the Mmax forecast under the assumption that each recorded event embodies an incremental release of fluid-induced stress. The results indicate that our method typically provides the lowest upper bound of the tested methods and it is less sensitive to site-specific calibration parameters such as Σ. This makes the novel method appealing for operational earthquake forecasting schemes as a real-time mitigation strategy to manage the risks of induced seismicity.
Wear resistance of an additively manufactured high-carbon martensitic stainless steel
Authors: Iakovakis Eleftherios , Avcu Egemen , Roy Matthew J. , Gee Mark , Matthews Allan 
Published: July 2022

AbstractThe dry sliding wear behaviour of a high carbon martensitic stainless steel (HCMSS) consisting of ~ 22.5 vol% of chromium (Cr)- and vanadium (V)-rich carbides processed by electron beam melting (EBM) has been captured. The microstructure consisted of martensite and retained austenite phases with a homogeneous distribution of sub-micron-sized V-rich and micron-sized Cr-rich carbides, leading to relatively high hardness. The CoF decreased ~ 14.1% with increasing load in the steady-state, due to the material transferred from the wear track over the counterbody. The wear rate of the HCMSS compared to martensitic tool steel processed in the same manner, and it was nearly identical under low applied load. The dominant wear mechanism was removal of the steel matrix through abrasion, followed by the oxidation of the wear track, while three-body abrasive wear occurred with increasing load. A plastically deformed zone beneath the wear track was revealed through cross-sectional hardness mapping. Specific phenomena occurred with increasingly aggressive wear conditions were described with carbide cracking, pull-out of V-rich carbides and matrix cracking. This study revealed the wear performance of the additively manufactured HCMSS, which could pave the way for producing components for wear-related applications ranging from shafts to plastic injection moulds via EBM.
An innovative dual recognition aptasensor for specific detection of Staphylococcus aureus based on Au/Fe3O4 binary hybrid
Authors: El-Wekil Mohamed M. , Halby Hamada Mohamed , Darweesh Mahmoud , Ali Mohamed E. , Ali Ramadan 
Published: July 2022

AbstractPathogenic bacteria cause disease outbreaks and threaten human health, prompting the research on advanced detection assays. Herein, we developed a selective molecular imprinted aptasensor for sensitive and prompt quantitation of Staphylococcus aureus (S. aureus) bacteria. The aptasensor was constructed by immobilization of aptamer on gold nanoparticles modified magnetic nanoparticles (apt-AuNPs@ Fe3O4). A functional monomer (o-phenylenediamine, o-phen) was electro-polymerized on the surface of the as-synthesized nanocomposite in the presence of a template (S. aureus). After removing S. aureus, the formed imprinted sites were available to extract pathogenic bacteria from complicated matrices. The surface morphology of the as-fabricated nanocomposites was characterized using different spectroscopic and electrochemical methods. Moreover, we thoroughly evaluated factors affecting the synthesis and determination procedures. The molecular imprinted aptasensor exhibited a wide linear range of 101–107 CFU mL−1 with a Limit of Detection, LOD (signal to noise = 3) of 1 CFU mL−1. The aptasensor detected S. aureus in milk, conduit water, and apple juice samples with good recoveries % and satisfactory relative standard deviations (RSDs %) values.
The role of demographic and academic features in a student performance prediction
Authors: Bilal Muhammad , Omar Muhammad , Anwar Waheed , Bokhari Rahat H. , Choi Gyu Sang 
Published: July 2022

AbstractEducational Data Mining is widely used for predicting student's performance. It’s a challenging task because a plethora of features related to demographics, personality traits, socio-economic, and environmental may affect students' performance. Such varying features may depend on the level of study, program offered, nature of subject, and geographical location. This study attempted to predict the final semester’s results of students studying Doctor of Veterinary Medicine (DVM) based on their pre-admission academic achievements, demographics, and first semester performance. The imbalanced data led to non-generic prediction models, so it was addressed through synthetic minority oversampling technique. Among five prediction models, the Support Vector Machine led the best with 92% accuracy. The decision tree model identified key features affecting students’ performance. The analysis led to the conclusion that marks obtained in Biology, Islamiat, and Urdu at Matric and English at Intermediate level affected the students’ performance in their final semester. The findings provide useful information to predict students’ performance and guidelines for academic institutes’ management regarding improving students’ achievement. It is speculated that adoption of digital transformation may help reduce difficulty faced in data collection and analysis.
Process optimization and technoeconomic environmental assessment of biofuel produced by solar powered microwave pyrolysis
Authors: Fodah Ahmed Elsayed Mahmoud , Abdelwahab Taha Abdelfattah Mohammed 
Published: July 2022

AbstractMicrowave pyrolysis of corn stover has been optimized by Response surface methodology under different microwave power (500, 700, and 900 W) and three ratios of activated carbon additive (10, 15, and 20%) for obtaining maximum bio-oil yield followed by biochar. The optimal result has been evaluated and the environmental and techno-economic impacts of using solar-powered microwave heating have been tested. The optimal pyrolysis condition found to be 700 W microwave power and 10% of activated carbon. The yields of both bio-oil and biochar were about 74 wt% under optimal condition. The higher heat values of 26 MJ/kg and 16 MJ/kg were respectively achieved for biochar and bio-oil. The major components of bio-oil were hydrocarbons (36%) and phenols (28%) with low oxygen-containing compounds (2%) and acids (2%). Using the solar-powered system, 20,549 tonnes of CO2 can be mitigated over the lifetime of the set-up, resulting in USD 51,373 in carbon credit earnings, compared to 16,875 tonnes of CO2 mitigation and USD 42,167 in carbon credit earnings from a grid electricity system. The payback periods for solar-powered and grid-connected electrical systems are estimated to be 1.6 and 0.5 years, respectively, based on biochar and bio-oil income of USD 39,700 and USD 45,400.
End-to-end deep learning framework for printed circuit board manufacturing defect classification
Authors: Bhattacharya Abhiroop , Cloutier Sylvain G. 
Published: July 2022

AbstractWe report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturing defects present on Printed Circuit Board (PCBs). We describe the complete model architecture and compare with the current state-of-the-art using the same PCB defect dataset. These benchmark methods include the Faster Region Based Convolutional Neural Network (FRCNN) with ResNet50, RetinaNet, and You-Only-Look-Once (YOLO) for defect detection and identification. Results show that our method achieves a 98.1% mean average precision(mAP[IoU = 0.5]) on the test samples using low-resolution images. This is 3.2% better than the state-of-the-art using low-resolution images (YOLO V5m) and 1.4% better than the state-of-the-art using high-resolution images (FRCNN-ResNet FPN). While achieving better accuracies, our model also requires roughly 3× fewer model parameters (7.02M) compared with the state-of-the-art FRCNN-ResNet FPN (23.59M) and YOLO V5m (20.08M). In most cases, the major bottleneck of the PCB manufacturing chain is quality control, reliability testing and manual rework of defective PCBs. Based on the initial results, we firmly believe that implementing this model on a PCB manufacturing line could significantly increase the production yield and throughput, while dramatically reducing manufacturing costs.