bluuebunny/crossref_metadata_2025_split huggingface dataset:
The prevalence and related factors of metabolic syndrome in outpatients with first-episode drug-naive major depression comorbid with anxiety
Authors: Zhong Yinghua , Hu Manji , Wang Qiang , Yang Zhendong , Zhu Na , Wang Fei , Zhang Xiyan , Zhang Chengfang , Min Jie , Wang Hao , Chen Fazhan , Zhao Xudong , Zhang Xiangyang 
Published: February 2021

AbstractMetabolic syndrome (MetS) is associated with depression, but its role in major depressive disorder comorbid with anxiety (AMD) is unclear. This study aimed to investigate the prevalence and clinical correlates of MetS in first-episode drug-naive (FEDN) patients with AMD in a Chinese Han population. In total, 1380 FEDN outpatients with AMD were recruited in this cross-sectional study. The sociodemographic features, clinical characteristics, history of suicide attempts, thyroid-stimulating hormone (TSH) levels, and MetS parameters of each subject were evaluated. All subjects were rated on the Hamilton Depression Rating Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), and the Positive and Negative Syndrome Scale positive symptom subscale. The prevalence of MetS among AMD patients was 8.04%. Compared to the non-MetS group, age, age of onset, TSH level, HAM-A and HAM-D scores, history of attempted suicide, and comorbid psychiatric symptoms were higher in the MetS group. Those in this group were also more likely to be married, and they had a lower educational level. Furthermore, age, psychiatric symptoms, suicide attempts, and higher TSH levels were independently associated with MetS in AMD patients. This study suggests a lower prevalence of MetS in FEDN patients with AMD in a Chinese Han population. Older age, comorbid psychiatric symptoms, history of attempted suicide, and higher TSH levels are related factors for MetS in AMD patients.
Bladder and bowel responses to lumbosacral epidural stimulation in uninjured and transected anesthetized rats
Authors: Hoey Robert F. , Medina-Aguiñaga Daniel , Khalifa Fahmi , Ugiliweneza Beatrice , Zdunowski Sharon , Fell Jason , Naglah Ahmed , El-Baz Ayman S. , Herrity April N. , Harkema Susan J. , Hubscher Charles H. 
Published: February 2021

AbstractSpinal cord epidural stimulation (scES) mapping at L5-S1 was performed to identify parameters for bladder and bowel inhibition and/or contraction. Using spinally intact and chronic transected rats of both sexes in acute urethane-anesthetized terminal preparations, scES was systematically applied using a modified Specify 5–6–5 (Medtronic) electrode during bladder filling/emptying cycles while recording bladder and colorectal pressures and external urethral and anal sphincter electromyography activity. The results indicate frequency-dependent effects on void volume, micturition, bowel peristalsis, and sphincter activity just above visualized movement threshold intensities that differed depending upon neurological intactness, with some sex-dependent differences. Thereafter, a custom-designed miniature 15-electrode array designed for greater selectivity was tested and exhibited the same frequency-dependent urinary effects over a much smaller surface area without any concurrent movements. Thus, select activation of autonomic nervous system circuitries with scES is a promising neuromodulation approach for expedient translation to individuals with SCI and potentially other neurologic disorders.
Computer modeling of whole-cell voltage-clamp analyses to delineate guidelines for good practice of manual and automated patch-clamp
Authors: Montnach Jérôme , Lorenzini Maxime , Lesage Adrien , Simon Isabelle , Nicolas Sébastien , Moreau Eléonore , Marionneau Céline , Baró Isabelle , De Waard Michel , Loussouarn Gildas 
Published: February 2021

AbstractThe patch-clamp technique and more recently the high throughput patch-clamp technique have contributed to major advances in the characterization of ion channels. However, the whole-cell voltage-clamp technique presents certain limits that need to be considered for robust data generation. One major caveat is that increasing current amplitude profoundly impacts the accuracy of the biophysical analyses of macroscopic ion currents under study. Using mathematical kinetic models of a cardiac voltage-gated sodium channel and a cardiac voltage-gated potassium channel, we demonstrated how large current amplitude and series resistance artefacts induce an undetected alteration in the actual membrane potential and affect the characterization of voltage-dependent activation and inactivation processes. We also computed how dose–response curves are hindered by high current amplitudes. This is of high interest since stable cell lines frequently demonstrating high current amplitudes are used for safety pharmacology using the high throughput patch-clamp technique. It is therefore critical to set experimental limits for current amplitude recordings to prevent inaccuracy in the characterization of channel properties or drug activity, such limits being different from one channel type to another. Based on the predictions generated by the kinetic models, we draw simple guidelines for good practice of whole-cell voltage-clamp recordings.
Assessing biophysical and socio-economic impacts of climate change on regional avian biodiversity
Authors: Kapitza Simon , Van Ha Pham , Kompas Tom , Golding Nick , Cadenhead Natasha C. R. , Bal Payal , Wintle Brendan A. 
Published: February 2021

AbstractClimate change threatens biodiversity directly by influencing biophysical variables that drive species’ geographic distributions and indirectly through socio-economic changes that influence land use patterns, driven by global consumption, production and climate. To date, no detailed analyses have been produced that assess the relative importance of, or interaction between, these direct and indirect climate change impacts on biodiversity at large scales. Here, we apply a new integrated modelling framework to quantify the relative influence of biophysical and socio-economically mediated impacts on avian species in Vietnam and Australia and we find that socio-economically mediated impacts on suitable ranges are largely outweighed by biophysical impacts. However, by translating economic futures and shocks into spatially explicit predictions of biodiversity change, we now have the power to analyse in a consistent way outcomes for nature and people of any change to policy, regulation, trading conditions or consumption trend at any scale from sub-national to global.
Meta-analysis cum machine learning approaches address the structure and biogeochemical potential of marine copepod associated bacteriobiomes
Authors: Sadaiappan Balamurugan , PrasannaKumar Chinnamani , Nambiar V. Uthara , Subramanian Mahendran , Gauns Manguesh U. 
Published: February 2021

AbstractCopepods are the dominant members of the zooplankton community and the most abundant form of life. It is imperative to obtain insights into the copepod-associated bacteriobiomes (CAB) in order to identify specific bacterial taxa associated within a copepod, and to understand how they vary between different copepods. Analysing the potential genes within the CAB may reveal their intrinsic role in biogeochemical cycles. For this, machine-learning models and PICRUSt2 analysis were deployed to analyse 16S rDNA gene sequences (approximately 16 million reads) of CAB belonging to five different copepod genera viz., Acartia spp., Calanus spp., Centropages sp., Pleuromamma spp., and Temora spp.. Overall, we predict 50 sub-OTUs (s-OTUs) (gradient boosting classifiers) to be important in five copepod genera. Among these, 15 s-OTUs were predicted to be important in Calanus spp. and 20 s-OTUs as important in Pleuromamma spp.. Four bacterial s-OTUs Acinetobacter johnsonii, Phaeobacter, Vibrio shilonii and Piscirickettsiaceae were identified as important s-OTUs in Calanus spp., and the s-OTUs Marinobacter, Alteromonas, Desulfovibrio, Limnobacter, Sphingomonas, Methyloversatilis, Enhydrobacter and Coriobacteriaceae were predicted as important s-OTUs in Pleuromamma spp., for the first time. Our meta-analysis revealed that the CAB of Pleuromamma spp. had a high proportion of potential genes responsible for methanogenesis and nitrogen fixation, whereas the CAB of Temora spp. had a high proportion of potential genes involved in assimilatory sulphate reduction, and cyanocobalamin synthesis. The CAB of Pleuromamma spp. and Temora spp. have potential genes accountable for iron transport.
Comprehensive evaluation of candidate reference genes for quantitative real-time PCR-based analysis in Caucasian clover
Authors: Yin Xiujie , He Taotao , Yi Kun , Zhao Yihang , Hu Yao , Liu Jiaxue , Zhang Xiaomeng , Meng Lingdong , Wang Lina , Liu Haoyue , Li Yonggang , Cui Guowen 
Published: February 2021

AbstractThe forage species Caucasian clover (Trifolium ambiguum M. Bieb.), a groundcover plant, is resistant to both cold and drought. However, reference genes for qRT-PCR-based analysis of Caucasian clover are lacking. In this study, 12 reference genes were selected on the basis of transcriptomic data. These genes were used to determine the most stably expressed genes in various organs of Caucasian clover under cold, salt and drought stress for qRT-PCR-based analysis. Reference gene stability was analyzed by geNorm, NormFinder, BestKeeper, the ∆Ct method and RefFinder. Under salt stress, RCD1 and PPIL3 were the most stable reference genes in the leaves, and NLI1 and RCD1 were the most stable references genes in the roots. Under low-temperature stress, APA and EFTu-GTP were the most stable reference genes in the leaves, and the RCD1 and NLI2 genes were highly stable in the roots. Under 10% PEG-6000 stress, NLI1 and NLI2 were highly stable in the leaves, and RCD1 and PPIL3 were the most stable in the roots. Overall, RCD1 and NLI2 were the most stable reference genes in organs under normal conditions and across all samples. The most and least stable reference genes were validated by assessing their appropriateness for normalization via WRKY genes.
MEG signatures of long-term effects of agreement and disagreement with the majority
Authors: Gorin A. , Klucharev V. , Ossadtchi A. , Zubarev I. , Moiseeva V. , Shestakova A. 
Published: February 2021

AbstractPeople often change their beliefs by succumbing to an opinion of others. Such changes are often referred to as effects of social influence. While some previous studies have focused on the reinforcement learning mechanisms of social influence or on its internalization, others have reported evidence of changes in sensory processing evoked by social influence of peer groups. In this study, we used magnetoencephalographic (MEG) source imaging to further investigate the long-term effects of agreement and disagreement with the peer group. The study was composed of two sessions. During the first session, participants rated the trustworthiness of faces and subsequently learned group rating of each face. In the first session, a neural marker of an immediate mismatch between individual and group opinions was found in the posterior cingulate cortex, an area involved in conflict-monitoring and reinforcement learning. To identify the neural correlates of the long-lasting effect of the group opinion, we analysed MEG activity while participants rated faces during the second session. We found MEG traces of past disagreement or agreement with the peers at the parietal cortices 230 ms after the face onset. The neural activity of the superior parietal lobule, intraparietal sulcus, and precuneus was significantly stronger when the participant’s rating had previously differed from the ratings of the peers. The early MEG correlates of disagreement with the majority were followed by activity in the orbitofrontal cortex 320 ms after the face onset. Altogether, the results reveal the temporal dynamics of the neural mechanism of long-term effects of disagreement with the peer group: early signatures of modified face processing were followed by later markers of long-term social influence on the valuation process at the ventromedial prefrontal cortex.
IE-Map: a novel in-vivo atlas and template of the human inner ear
Authors: Ahmadi Seyed-Ahmad , Raiser Theresa Marie , Rühl Ria Maxine , Flanagin Virginia Lee , zu Eulenburg Peter 
Published: February 2021

AbstractBrain atlases and templates are core tools in scientific research with increasing importance also in clinical applications. Advances in neuroimaging now allowed us to expand the atlas domain to the vestibular and auditory organ, the inner ear. In this study, we present IE-Map, an in-vivo template and atlas of the human labyrinth derived from multi-modal high-resolution magnetic resonance imaging (MRI) data, in a fully non-invasive manner without any contrast agent or radiation. We reconstructed a common template from 126 inner ears (63 normal subjects) and annotated it with 94 established landmarks and semi-automatic segmentations of all relevant macroscopic vestibular and auditory substructures. We validated the atlas by comparing MRI templates to a novel CT/micro-CT atlas, which we reconstructed from 21 publicly available post-mortem images of the bony labyrinth. Templates in MRI and micro-CT have a high overlap, and several key anatomical measures of the bony labyrinth in IE-Map are in line with micro-CT literature of the inner ear. A quantitative substructural analysis based on the new template, revealed a correlation of labyrinth parameters with total intracranial volume. No effects of gender or laterality were found. We provide the validated templates, atlas segmentations, surface meshes and landmark annotations as open-access material, to provide neuroscience researchers and clinicians in neurology, neurosurgery, and otorhinolaryngology with a widely applicable tool for computational neuro-otology.
Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system
Authors: Eon Rehman S. , Bachmann Charles M. 
Published: February 2021

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.
Non-linearity of end-systolic pressure–volume relation in afterload increases is caused by an overlay of shortening deactivation and the Frank–Starling mechanism
Authors: Habigt Moriz A. , Krieger Michelle , Gesenhues Jonas , Ketelhut Maike , Mechelinck Mare , Hein Marc 
Published: February 2021

AbstractThe linearity and load insensitivity of the end-systolic pressure–volume-relationship (ESPVR), a parameter that describes the ventricular contractile state, are controversial. We hypothesize that linearity is influenced by a variable overlay of the intrinsic mechanism of autoregulation to afterload (shortening deactivation) and preload (Frank-Starling mechanism). To study the effect of different short-term loading alterations on the shape of the ESPVR, experiments on twenty-four healthy pigs were executed. Preload reductions, afterload increases and preload reductions while the afterload level was increased were performed. The ESPVR was described either by a linear or a bilinear regression through the end-systolic pressure volume (ES-PV) points. Increases in afterload caused a biphasic course of the ES-PV points, which led to a better fit of the bilinear ESPVRs (r20.929 linear ESPVR vs. r20.96 and 0.943 bilinear ESPVR). ES-PV points of a preload reduction on a normal and augmented afterload level could be well described by a linear regression (r20.974 linear ESPVR vs. r20.976 and 0.975 bilinear ESPVR). The intercept of the second ESPVR (V0) but not the slope demonstrated a significant linear correlation with the reached afterload level (effective arterial elastance Ea). Thus, the early response to load could be described by the fixed slope of the ESPVR and variable V0, which was determined by the actual afterload. The ESPVR is only apparently nonlinear, as its course over several heartbeats was affected by an overlay of SDA and FSM. These findings could be easily transferred to cardiovascular simulation models to improve their accuracy.
A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications
Authors: Masoudi-Sobhanzadeh Yosef , Motieghader Habib , Omidi Yadollah , Masoudi-Nejad Ali 
Published: February 2021

AbstractGene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/gene selection algorithms and methods have been introduced, they may suffer from problems such as parameter tuning or low level of performance. To tackle such limitations, in this study, a universal wrapper approach is introduced based on our introduced optimization algorithm and the genetic algorithm (GA). In the proposed approach, candidate solutions have variable lengths, and a support vector machine scores them. To show the usefulness of the method, thirteen classification and regression-based datasets with different properties were chosen from various biological scopes, including drug discovery, cancer diagnostics, clinical applications, etc. Our findings confirmed that the proposed method outperforms most of the other currently used approaches and can also free the users from difficulties related to the tuning of various parameters. As a result, users may optimize their biological applications such as obtaining a biomarker diagnostic kit with the minimum number of genes and maximum separability power.
Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation
Authors: Tang Zhou , Parajuli Atit , Chen Chunpeng James , Hu Yang , Revolinski Samuel , Medina Cesar Augusto , Lin Sen , Zhang Zhiwu , Yu Long-Xi 
Published: February 2021

AbstractAlfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.
Metal doped layered MgB2 nanoparticles as novel electrocatalysts for water splitting
Authors: Sadeghi Ebrahim , Peighambardoust Naeimeh Sadat , Khatamian Masoumeh , Unal Ugur , Aydemir Umut 
Published: February 2021

AbstractGrowing environmental problems along with the galloping rate of population growth have raised an unprecedented challenge to look for an ever-lasting alternative source of energy for fossil fuels. The eternal quest for sustainable energy production strategies has culminated in the electrocatalytic water splitting process integrated with renewable energy resources. The successful accomplishment of this process is thoroughly subject to competent, earth-abundant, and low-cost electrocatalysts to drive the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER), preferably, in the same electrolyte. The present contribution has been dedicated to studying the synthesis, characterization, and electrochemical properties of newfangled electrocatalysts with the formal composition of Mg1−xTMxB2 (x = 0.025, 0.05, and 0.1; TM (transition metal) = Fe and Co) primarily in HER as well as OER under 1 M KOH medium. The electrochemical tests revealed that among all the metal-doped MgB2 catalysts, Mg0.95Co0.05B2 has the best HER performance showing an overpotential of 470 mV at − 10 mA cm−2 and a Tafel slope of 80 mV dec−1 on account of its high purity and fast electron transport. Further investigation shed some light on the fact that Fe concentration and overpotential for HER have adverse relation meaning that the highest amount of Fe doping (x = 0.1) displayed the lowest overpotential. This contribution introduces not only highly competent electrocatalysts composed of low-cost precursors for the water-splitting process but also a facile scalable method for the assembly of highly porous electrodes paving the way for further stunning developments in the field.