bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future
Authors: Duverdier Ariane , Custovic Adnan , Tanaka Reiko J. 
Published: June 2022

AbstractBackgroundThe past decade has seen a substantial rise in the employment of modern data‐driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data‐driven AD research, and identify areas in the field that would benefit from the application of these methods.MethodsWe retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning‐ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature.ResultsFive key themes of data‐driven research on AD and eczema were identified: (1) allergic co‐morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life and treatment response, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co‐morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were “eczema” papers.ConclusionsResearch areas that could benefit from the application of data‐driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalised severity management and prognosis. We highlight BS as a new and promising approach in AD and eczema research.
Cellulose‐Based Electrolytes for Advanced Lithium‐Ion Batteries: Recent Advances and Future Perspectives
Authors: Zhang Lei , Gao Haiqi , Jin Gan , Liu Shuo , Wu Jiawei , Wu Hao , Yang Yuqing , Wang Qian , Wang Shi 
Published: June 2022

AbstractIn the coming decades, it is urgent to find a way to power the future while keeping a clean environment and strong economic growth. Thus, it is expected to transition from using fossil fuels to renewable energy. Materials based on cellulose are abundant, low cost, sustainable, possess a high surface area, good thermal stability, biocompatibility, mechanical flexibility, and inherit unique layered fiber structure from cellulose, which can act as an ideal electrolyte matrix for sustainable and high energy density lithium‐ion batteries (LIBs). In this review, we focus on energy storage application of different forms of cellulose‐based electrolytes in LIBs. Specifically, we summarize the recent advances of cellulose‐based gel and solid‐state electrolytes, focusing on the gel electrolytes based on different types of plasticizers (cellulose‐based electrolytes for flexible LIBs also are introduced). Finally, several perspectives related to the cellulose‐based‐electrolytes for LIBs are proposed based on recent progress and our own evaluation.
Fast and accurate variable batch size convolution neural network training on large scale distributed systems
Authors: Hu Zhongzhe , Xiao Junmin , Sun Ninghui , Tan Guangming 
Published: June 2022

AbstractLarge‐scale distributed convolution neural network (CNN) training brings two performance challenges: model performance and system performance. Large batch size usually leads to model test accuracy loss, which counteracts the benefits of parallel SGD. The existing solutions require massive hyperparameter hand‐tuning. To overcome this difficult, we analyze the training process and find that earlier training stages are more sensitive to batch size. Accordingly, we assert that different stages should use different batch size, and propose a variable batch size strategy. In order to remain high test accuracy under larger batch size cases, we design an auto‐tuning engine for automatic parameter tuning in the proposed variable batch size strategy. Furthermore, we develop a dataflow implementation approach to achieve the high‐throughput CNN training on supercomputer system. Our approach has achieved high generalization performance on SOAT CNN networks. For the ShuffleNet, ResNet‐50, and ResNet‐101 training with ImageNet‐1K dataset, we scale the batch size to 120 K without accuracy loss and to 128 K with only a slight loss. And the dataflow implementation approach achieves 93.5% scaling efficiency on 1024 GPUs compared with the state‐of‐the‐art.
Automated Bonding Analysis with Crystal Orbital Hamilton Populations
Authors: George Janine , Petretto Guido , Naik Aakash , Esters Marco , Jackson Adam J. , Nelson Ryky , Dronskowski Richard , Rignanese Gian‐Marco , Hautier Geoffroy 
Published: June 2022

AbstractUnderstanding crystalline structures based on their chemical bonding is growing in importance. In this context, chemical bonding can be studied with the Crystal Orbital Hamilton Population (COHP), allowing for quantifying interatomic bond strength. Here we present a new set of tools to automate the calculation of COHP and analyze the results. We use the program packagesVASPandLOBSTER, and the Python packagesatomateandpymatgen. The analysis produced by our tools includes plots, a textual description, and key data in a machine‐readable format. To illustrate those capabilities, we have selected simple test compounds (NaCl, GaN), the oxynitrides BaTaO2N, CaTaO2N, and SrTaO2N, and the thermoelectric material Yb14Mn1Sb11. We show correlations between bond strengths and stabilities in the oxynitrides and the influence of the Mn−Sb bonds on the magnetism in Yb14Mn1Sb11. Our contribution enables high‐throughput bonding analysis and will facilitate the use of bonding information for machine learning studies.
A Comprehensive and Advanced Mouse Model of Post‐Traumatic Epilepsy with Robust Spontaneous Recurrent Seizures
Authors: Reddy Doodipala Samba , Golub Victoria M. , Ramakrishnan Sreevidhya , Abeygunaratne Hasara , Dowell Samantha , Wu Xin 
Published: June 2022

AbstractTraumatic brain injury (TBI) is a leading cause of epilepsy in military persons and civilians. Spontaneous recurrent seizures (SRSs) occur in the months or years following the injury, which is commonly referred to as post‐traumatic epilepsy (PTE). Currently, there is no effective treatment or cure for PTE; therefore, there is a critical need to develop animal models to help further understand and assess mechanisms and interventions related to TBI‐induced epilepsy. Despite many attempts to induce PTE in animals, success has been limited due to a lack of consistent SRSs after TBI. We present a comprehensive protocol to induce PTE after contusion brain injury in mice, which exhibit robust SRSs along with neurodegeneration and neuroinflammation. This article provides a complete set of protocols for injury, outcomes, troubleshooting, and data analysis. Our broad profiling of a TBI mouse reveals features of progressive, long‐lasting epileptic activity, hippocampal sclerosis, and comorbid mood and memory deficits. Overall, the PTE mouse shows striking consistency in recapitulating major hallmark features of human PTE. This mouse model will be helpful in assessing mechanisms of and interventions for TBI‐induced epileptogenesis, epilepsy, and neuropsychiatric dysfunction. © 2022 Wiley Periodicals LLC.Basic Protocol 1: Inducing controlled cortical impact injuriesSupport Protocol: Creating the custom domed campBasic Protocol 2: Recording long‐term video‐EEG signalsBasic Protocol 3: Analyzing video‐EEG recordings
A low‐power wideband LNA exploiting current‐reuse and noise cancelation techniques
Authors: Khanehbeygi Mehran , Jafarnejad Roya , Koozehkanani Ziaddin Daie , Sobhi Jafar 
Published: June 2022

AbstractThis paper presents an inductorless low‐power wideband Low Noise Amplifier (LNA) in TSMC 0.18 μm CMOS technology. The proposed LNA is based on the Common‐Gate (CG) topology, which consists of three consecutive stages: CG, Common‐Source (CS), and Collector stage. The CG and CS configurations are responsible for providing the wideband input impedance matching and the majority of the gain, respectively. The overall performance of the LNA has been improved by applying noise cancelation, capacitive‐peaking, and current reuse techniques. Unlike the conventional structure, the CG and CS sections' noise is canceled by utilizing a novel noise‐canceling mechanism, simultaneously. Furthermore, with the capacitive‐peaking technique, the voltage gain drop across hi gher frequencies is prevented, and thus, the bandwidth of the structure is improved. The trade‐off between power consumption and input impedance has been dramatically resolved by employing the current reuse technique in the CG stage. Post‐layout simulation results of the presented noise‐canceling LNA demonstrate a Noise Figure (NF) of 2.19–2.58 dB and a maximum voltage gain of 19.84 dB with a −3 dB bandwidth of 0.2–2.85 GHz. The simulated input/output matching (i.e., S11/S22) is better than −14.73/−19.43 dB in the entire bandwidth. Additionally, Input Third Intercept Point (IIP3) of −4.56 dBm is achieved. The results are gained by consuming only 2.87 mW under a supply voltage of 1.2 V and occupying a 0.0429 mm2 of die area.
Understanding the tumor microenvironment in head and neck squamous cell carcinoma
Authors: Rad Habib Sadeghi , Shiravand Yavar , Radfar Payar , Ladwa Rahul , Perry Chris , Han Xiaoyuan , Warkiani Majid Ebrahimi , Adams Mark N , Hughes Brett GM , O'Byrne Ken , Kulasinghe Arutha 
Published: June 2022

AbstractHead and neck squamous cell carcinoma (HNSCC) represents a heterogeneous group of tumors. While significant progress has been made using multimodal treatment, the 5‐year survival remains at 50%. Developing effective therapies, such as immunotherapy, will likely lead to better treatment of primary and metastatic disease. However, not all HNSCC tumors respond to immune checkpoint blockade therapy. Understanding the complex cellular composition and interactions of the tumor microenvironment is likely to lead to new knowledge for effective therapies and treatment resistance. In this review, we discuss HNSCC characteristics, predictive biomarkers, factors influencing immunotherapy response, with a focus on the tumor microenvironment.
Evolution of genetic mechanisms regulating cortical neurogenesis
Authors: Espinós Alexandre , Fernández‐Ortuño Eduardo , Negri Enrico , Borrell Víctor 
Published: June 2022

AbstractThe size of the cerebral cortex increases dramatically across amniotes, from reptiles to great apes. This is primarily due to different numbers of neurons and glial cells produced during embryonic development. The evolutionary expansion of cortical neurogenesis was linked to changes in neural stem and progenitor cells, which acquired increased capacity of self‐amplification and neuron production. Evolution works via changes in the genome, and recent studies have identified a small number of new genes that emerged in the recent human and primate lineages, promoting cortical progenitor proliferation and increased neurogenesis. However, most of the mammalian genome corresponds to noncoding DNA that contains gene‐regulatory elements, and recent evidence precisely points at changes in expression levels of conserved genes as key in the evolution of cortical neurogenesis. Here, we provide an overview of basic cellular mechanisms involved in cortical neurogenesis across amniotes, and discuss recent progress on genetic mechanisms that may have changed during evolution, including gene expression regulation, leading to the expansion of the cerebral cortex.
Environmental and spatial processes shaping Quercus‐dominated forest communities in the Neotropics
Authors: Figueroa‐Rangel Blanca Lorena , Olvera‐Vargas Miguel 
Published: June 2022

AbstractQuercus‐dominated forests in the Neotropics compose one of the broadest distributed ecosystems of mountainous zones whose species distribution has been explained by climate change adaptation over an abrupt physiography. This study aims to comprehend the contribution of environmental and spatial processes influential in species composition of Quercus‐dominated forests. A database of 86 plots, randomly located in a highly diverse spot of the Mexican Neotropics, was examined; it involved vegetation, environment, and spatial data. Local spatial variables were derived from distance‐based Moran's eigenvector maps, while regional spatial variables were obtained from geographical coordinates (latitude/longitude). Redundancy analysis and variance partitioning were run to reveal the relative importance of environmental and spatial processes explaining plant species composition of Quercus‐dominated forests and ascertain the environmental variables controlling the species composition at the different spatial scales (local and regional). Results showed that both local and regional spatial variables significantly control species composition in Quercus‐dominated forests, with the regional scale accounting for a higher explained variance than the local scale. At the regional scale, elevation, aspect, litter, maturity, and Mg were significant in explaining species community composition; elevation variance was twofold higher than variance for aspect and litter. At the local scale, elevation accounted for the highest variation followed by maturity, P, N, and Mg. The analysis also tested the variation of species community composition over environmental gradients mainly determined by elevation. This study indicated the importance of elevation at both local and regional spatial scales as an important driver in Quercus‐dominated forests distribution, probably related to Quercus species adaptation to temperature and precipitation. Finally, evidence from this research emphasizes the significance and necessity of a multiscale analysis to discern the spatial structured environment responsible for the high species diversity and broad distribution of forests dominated by Quercus genus in the Neotropics.
A 16th‐century biodiversity and crop inventory
Authors: Viana Duarte S. , Blanco‐Garrido Francisco , Delibes Miguel , Clavero Miguel 
Published: June 2022

AbstractDocumenting and understanding long‐term biodiversity change is limited by the availability of historical data, particularly from periods preceding major anthropogenic changes associated with the Industrial Revolution. We mined the information of a standardized historical survey developed in 628 localities across Spain between 1574 and 1582 (Relaciones Topográficas de Felipe II) with the goal of producing a general characterization of Spanish settlements, including, though not limited to, natural resources. From this survey, we were able to gather 7309 records for 75 wild plant taxa, 89 wild animal taxa, and 60 crop and domestic animal taxa. These data can be used to reconstruct historical land use and habitat cover, and model historical distributions of many species, including emblematic ones such as wolf and bear, which can establish reference distributions to assess range and niche expansion, contraction, and shifts. Data are provided under a Creative Commons Attribution Non Commercial (CC‐BY‐NC) 4.0 License. Please cite this data paper when the data are used in publications. The authors are open to collaborate in projects based on this dataset.
Multimodal piezoelectric wind energy harvester for aerospace applications
Authors: Sheeraz Muhammad Abdullah , Malik Muhammad Sohail , Rahman Khalid , Elahi Hassan , Khurram Muhammad , Eugeni Marco , Gaudenzi Paolo 
Published: June 2022

SummaryPiezoelectric transduction has been an emerging concept, especially in the aerospace industry for sensor networking and onboard generation of reliable electrical energy. A significant gap has been noticed in the generalized and multimodal analytics of piezoelectric sensors for wind energy harvesting. Therefore, herein, a simplified yet effective transfer‐function ‐based, experimentally validated analytical model of the piezoelectric sensor is presented. MATLAB is utilized to analyze the proposed model for two different categories of piezoelectric sensors (ie, PIC‐255, and PZT‐5A) under the various conditions of wind speed, piezoelectric configuration, and piezoelectric geometrical features. The developed model has also shown the promising features of flutter energy harvesting, and limit cycle oscillations (LCOs) that play an imperative role in aerospace applications such as energy harvesting from unmanned aerial vehicles (UAVs). Results emphasize that under the same parametric, and geometric conditions, the PZT‐5A harvester reaches quickly to critical flutter mode as compared to PIC‐255. Subsequently, the PZT‐5A harvester is capable of generating 7.105 mW during the particular region of the limit cycle oscillations (LCOs). Based on the outcomes of multimodal analytics, the proposed model is best suited to both conventional wind energy harvesting (Up to nominal range) and flutter harvesting (Critical limit), making it suitable for aerospace applications. It is also recommended to utilize the model for piezoelectric vibrational energy scavengers by incorporating the additional parameters of the structure in the developed transfer function of the piezoelectric sensor.
Temperature effect and kinetics, LiZr2(PO4)3 and Li1.2Al0.2Zr1.8(PO4)3 and electrochemical properties for rechargeable ion batteries
Authors: Akkinepally Bhargav , Reddy I. Neelakanta , Manjunath V. , Reddy M. V. , Mishra Yogendra Kumar , Ko Tae Jo , Zaghib Karim , Shim Jaesool 
Published: June 2022

SummaryA NASICON‐type LiZr2(PO4)3 (LZP) and Li1.2Al0.2Zr1.8(PO4)3 (LAZP) anode materials are synthesized via a facile method, materials were characterized by a variety of structural techniques, investigated the electrochemical performance for rechargeable battery applications. A highly stable rhombohedral phase of LAZP is noticed at room temperature, with a discharge capacity of 909 mAh.g−1, which is 1.6 times higher than that of LZP. Further, the highly stable charge storage performance is observed up to 3000 cycles with a Coulombic efficiency of 99%. For the first time, we investigated the in‐situ temperature effect on the discharge capacity of the assembled anode material, found a strong effect on the discharge capacity, and achieved ~120 and 149 mAh.g−1 at 65°C for LZP and LAZP anodes. Furthermore, the real‐time charge holding effect of LAZP cell is performed with the computer, the cell worked well in the CMOS battery slot without a CMOS battery error during CPU turn‐on condition for 1536 h. In brief, an optimized electrode material of LAZP showed greater stability and capacity with high efficiency compared with LZP even at elevated temperatures. Hence, LAZP may be utilized to fabricate a coin cell for real time applications.
Highly effective bi‐functional electrochemical activity of Ag2O‐PrO2/γ‐Al2O3 electrocatalysts towards OER and ORR
Authors: Khan Mariam , Janjua Naveed Kausar , Ahmad Awais , Luque Rafael , Al‐Kahtani Abdullah A. , Tighezza Ammar Mohamed 
Published: June 2022

SummaryThe oxygen reduction and evolution reaction (ORR/OER), are the keystones for many energy generating and storage devices, have influence with many electrocatalytic reactions. The sluggish kinetics of these reaction is a challenging situation on the surface of electrodes which imposes large over‐potential and affects the energy output of fuel cells. Although the platinum‐based electrode materials have excellent activity toward ORR/OER but their high cost limits commercialization of fuel cells. To deal with this problem, platinum‐free electrode materials has aroused the considerations of scientists. The present work is proposed to study the oxygen reduction reaction and water oxidation on the surface of xAg2O‐yPrO2/γ‐Al2O3/Glassy‐Carbon. The ORR was studied electrochemically via cyclic voltammetry on the surface of these nano‐catalysts while the OER was investigated via cyclic voltammetry and linear sweep voltammetry in alkaline media. The mechanism of ORR on catalysts surface was enquired and the results indicate that ORR followed two electrons pathway in case of 10PrO2/γ‐Al2O3/Glassy‐Carbon while four electrons reduction pathway is identified on surface of others catalysts which divulged that addition of silver with praseodymium shifted the mechanism from 2e− to 4e− pathway. The electrocatalysts featured the stability during multiple scans and no corrosion of electrode in OER/ORR is observed. Different kinetic parameters for both reactions are determined which affirmed that both ORR and OER on the active surface of catalysts is irreversible and diffusion controlled. 4Ag2O‐6PrO2/γ‐Al2O3 is indicated to be proficient electro‐catalyst among other members of the series, having greater value of mass transport and diffusion coefficients. The bi‐functionality of all electrocatalysts are verified by their electrochemical responses in alkaline medium. The catalytic reduction of oxygen and oxidation of water by these electro‐catalysts imitates their potential as electrode material in batteries and unitized regenerative fuel cells (URFC) technology for energy production as substitute of platinum electrode.
Neurocognitive decline as a major predictor of nonadherence to antiretroviral therapy among adults living with HIV in Dodoma region, central Tanzania
Authors: Nyundo Azan A. 
Published: June 2022

AbstractBackgroundThe survival of people living with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome largely depends on good adherence to antiretroviral medications. Neuropsychiatric conditions such as major depressive disorders (MDDs) and neurocognitive disorders, in particular, are common in the HIV population and attributed to suboptimal adherence to antiretroviral treatment and overall poor clinical outcomes. This study aimed to determine the association between neurocognitive disorders and nonadherence to antiretroviral therapy (ART) in the Dodoma region's adult population living with HIV.MethodsThe study was conducted in Dodoma Regional Referral Hospital using a cross‐sectional design to assess 397 participants through a systematic sampling approach. Montreal Cognitive Assessment was used to determine neurocognitive function, while the Simplified Medical Adherence Questionnaire was used to assess nonadherence to ART. Logistic regression analysis was computed to determine the association between cognitive decline and nonadherence to ART while controlling for sociodemographic and clinical confounders.ResultsOut of the 397 recruited participants, 266 (67.00%) and 41 (10.33%) met the criteria for neurocognitive decline and nonadherence to ART. Participants with cognitive impairment had a significantly poorer adherence rate than those without, even after controlling for confounders adjusted odds ratio (aOR): 2.183 (95% confidence interval [CI]: 1.031, 4.630, p = 0.0413). MDD was the only additional factor that remained significantly associated with ART nonadherence (aOR: 4.332, 95% CI: 1.634, 11.485, p = 0.0032).ConclusionNeurocognitive disorders are strong predictor of suboptimal adherence to ART; a comorbid neuropsychiatric condition such as MDD may further compromise the ART adherence rate leading to poor HIV care and poor clinical outcome. Further research with systematic and more robust studies in the field will provide a baseline to design and integrate appropriate care models to maximize ART adherence in HIV care. Integration of psychiatric services in HIV care can benefit the overall patient outcome.