bluuebunny/crossref_metadata_2025_split huggingface dataset:
Time Evolution and Diurnal Variability of the Parametric Sensitivity of Turbine‐Height Winds in the MYNN‐EDMF Parameterization
Authors: Berg L. K. , Liu Y. , Yang Ben , Qian Y. , Krishnamurthy R. , Sheridan L. , Olson J. 
Published: April 2021

AbstractThe Mellor‐Yamada‐Nakanishi‐Niino (MYNN) parameterization applied in the Weather Research and Forecasting (WRF) model has been augmented to include the Eddy‐Diffusion Mass‐Flux (EDMF) approach to better represent transport by boundary‐layer eddies. This change includes the addition of new parameters associated with convective updrafts and boundary‐layer clouds that lead to new parametric sensitivities in the turbine‐height wind speed compared to simulations using the standard MYNN parameterization. This work builds on efforts focused on WRF's MYNN parameterization by examining the sensitivity of wind speed to parameters in the MYNN‐EDMF parameterization as a function of simulation duration. Summer and winter periods were selected from the second Wind Forecast Improvement Project (WFIP2). Five sets of simulations were completed for each season, with durations ranging from 2 to 6 days. The results show that the sensitivity to the new parameters associated with the EDMF scheme is generally small compared to other parameters in clear conditions, but the sensitivity to the entrainment becomes significant when the updraft fraction is large. The spread in the perturbed parameter ensembles was found to grow quickly over the first 8–19 h in the summer simulations and 17–24 h in the winter simulations with little change after that, regardless of the simulation length. A strong diurnal cycle in the parameter sensitivity was also found associated with the atmospheric stability, as well as an increase in the sensitivity to the entrainment parameter used in the EDMF parameterization that is associated with increasing fractional area covered by plumes.
Monitoring the Greater Agulhas Current With AIS Data Information
Authors: Le Goff Clément , Boussidi Brahim , Mironov Alexei , Guichoux Yann , Zhen Yicun , Tandeo Pierre , Gueguen Simon , Chapron Bertrand 
Published: April 2021

AbstractOver the core region of the Agulhas Current, new estimations of ocean surface velocities are reported using the increasing data set from the Automatic Identification System (AIS), initially designed to monitor vessel traffic. A two‐step strategy is suggested. A first guess is evaluated from the collective behavior of vessels for a given space‐time interval. Individual vessel trajectories are then re‐analyzed and interpolated. Applied during year 2016, these ocean surface current estimates are demonstrated to well determine the intensity of surface currents. The improved spatial resolution helps the decomposition of the optimally interpolated surface current vector field between irrotational ≈80%–90% and divergence‐free components ≈10%–20%, for example, Helmholtz‐Hodge decomposition. Comparisons are performed between in situ drifting‐buoys and represent up to 25% gain in respect of the altimetry gridded current (for the meridional component). Others comparisons with data collected during the Agulhas Current Time‐series experiment, as well as with the mean Doppler‐derived surface currents obtained from satellite synthetic aperture radar measurements also reveal a significant benefit of using the AIS derived estimates. Comparisons with the Sea Surface Temperature from MODIS sensors confirm the occurrence of meandering events for the current path. For the Agulhas Current region, the high density of vessel traffic can provide new means to study and monitor intense upper ocean currents with more detailed resolution and precision.
Genome wide association study identifies four loci for early onset schizophrenia
Authors: Guo Suqin , Liu Jiewei , Li Wenqiang , Yang Yongfeng , Lv Luxian , Xiao Xiao , Li Ming , Guan Fanglin , Luo Xiong-Jian 
Published: April 2021

AbstractEarly onset schizophrenia (EOS, defined as first onset of schizophrenia before age 18) is a rare form of schizophrenia (SCZ). Though genome-wide association studies (GWASs) have identified multiple risk variants for SCZ, most of the cases included in these GWASs were not stratified according to their first age at onset. To date, the genetic architecture of EOS remains largely unknown. To identify the risk variants and to uncover the genetic basis of EOS, we conducted a two-stage GWAS of EOS in populations of Han Chinese ancestry in this study. We first performed a GWAS using 1,256 EOS cases and 2,661 healthy controls (referred as discovery stage). The genetic variants with a P < 1.0 × 10−04 in discovery stage were replicated in an independent sample (903 EOS cases and 3,900 controls). We identified four genome-wide significant risk loci for EOS in the combined samples (2,159 EOS cases and 6,561 controls), including 1p36.22 (rs1801133, Pmeta = 4.03 × 10−15), 1p31.1 (rs1281571, Pmeta = 4.14 × 10−08), 3p21.31 (rs7626288, Pmeta = 1.57 × 10−09), and 9q33.3 (rs592927, Pmeta = 4.01 × 10−11). Polygenic risk scoring (PRS) analysis revealed substantial genetic overlap between EOS and SCZ. These discoveries shed light on the genetic basis of EOS. Further functional characterization of the identified risk variants and genes will help provide potential targets for therapeutics and diagnostics.
Transcriptomics and metabolomics reveal the adaption of Akkermansia muciniphila to high mucin by regulating energy homeostasis
Authors: Liu Xinyue , Zhao Fan , Liu Hui , Xie Yunting , Zhao Di , Li Chunbao 
Published: April 2021

AbstractIn gut, Akkermansia muciniphila (A. muciniphila) probably exerts its probiotic activities by the positive modulation of mucus thickness and gut barrier integrity. However, the potential mechanisms between A. muciniphila and mucin balance have not been fully elucidated. In this study, we cultured the bacterium in a BHI medium containing 0% to 0.5% mucin, and transcriptome and gas chromatography mass spectrometry (GC–MS) analyses were performed. We found that 0.5% (m/v) mucin in a BHI medium induced 1191 microbial genes to be differentially expressed, and 49 metabolites to be changed. The metabolites of sorbose, mannose, 2,7-anhydro-β-sedoheptulose, fructose, phenylalanine, threonine, lysine, ornithine, asparagine, alanine and glutamic acid were decreased by 0.5% mucin, while the metabolites of leucine, valine and N-acetylneuraminic acid were increased. The association analysis between transcriptome and metabolome revealed that A. muciniphila gave strong responses to energy metabolism, amino sugar and nucleotide sugar metabolism, and galactose metabolism pathways to adapt to high mucin in the medium. This finding showed that only when mucin reached a certain concentration in a BHI medium, A. muciniphila could respond to the culture environment significantly at the level of genes and metabolites, and changed its metabolic characteristics by altering the effect on carbohydrates and amino acids.
Machine-learning based prediction of Cushing’s syndrome in dogs attending UK primary-care veterinary practice
Authors: Schofield Imogen , Brodbelt David C. , Kennedy Noel , Niessen Stijn J. M. , Church David B. , Geddes Rebecca F. , O’Neill Dan G. 
Published: April 2021

AbstractCushing’s syndrome is an endocrine disease in dogs that negatively impacts upon the quality-of-life of affected animals. Cushing’s syndrome can be a challenging diagnosis to confirm, therefore new methods to aid diagnosis are warranted. Four machine-learning algorithms were applied to predict a future diagnosis of Cushing's syndrome, using structured clinical data from the VetCompass programme in the UK. Dogs suspected of having Cushing's syndrome were included in the analysis and classified based on their final reported diagnosis within their clinical records. Demographic and clinical features available at the point of first suspicion by the attending veterinarian were included within the models. The machine-learning methods were able to classify the recorded Cushing’s syndrome diagnoses, with good predictive performance. The LASSO penalised regression model indicated the best overall performance when applied to the test set with an AUROC = 0.85 (95% CI 0.80–0.89), sensitivity = 0.71, specificity = 0.82, PPV = 0.75 and NPV = 0.78. The findings of our study indicate that machine-learning methods could predict the future diagnosis of a practicing veterinarian. New approaches using these methods could support clinical decision-making and contribute to improved diagnosis of Cushing’s syndrome in dogs.
Mode of conception in relation to nausea and vomiting of pregnancy: a nested matched cohort study in Sweden
Authors: Bazargani Farnaz , Iliadis S. I. , Elenis E. 
Published: April 2021

AbstractNausea and vomiting of pregnancy (NVP) is a common condition reported however inconclusively among pregnancies after assisted conception. The study objective was thus to explore whether NVP is associated to mode of conception or other in vitro fertilization (IVF)-related variables. This nested matched cohort study, originating from the BASIC-project, was conducted at the Uppsala University Hospital in Sweden between 2010 and 2016. IVF pregnancies (n = 210) and age and parity-matched women with spontaneous pregnancies (n = 420) comprised the study sample. The study outcome was self-reported NVP at gestational week 17. IVF treatment and pregnancy data were obtained after scrutinization of the medical records. NVP with or without medications was not associated with mode of conception (chi-square test, p = 0.889), even after adjusting for potential confounders. In a subgroup analysis among IVF pregnancies, NVP without medication was more frequently seen in the group who received cleavage stage embryos vs blastocysts (chi-square test, p = 0.019), exhibiting a marginally significant but strongly increased effect even after adjustment [crude RRR 3.82 (95% CI 1.23–11.92) and adjusted RRR 3.42 (95% CI 0.96–12.11)]. No difference in the rate of NVP with or without medication between women that underwent fresh and frozen/thawed embryo transfers as well as IVF or ICSI was observed. Conception through IVF is not associated with NVP. Transfer of a blastocyst may decrease the risk of developing NVP and further, large-scale prospective studies are required to validate this finding.
A calibrated deep learning ensemble for abnormality detection in musculoskeletal radiographs
Authors: He Minliang , Wang Xuming , Zhao Yijun 
Published: April 2021

AbstractMusculoskeletal disorders affect the locomotor system and are the leading contributor to disability worldwide. Patients suffer chronic pain and limitations in mobility, dexterity, and functional ability. Musculoskeletal (bone) X-ray is an essential tool in diagnosing the abnormalities. In recent years, deep learning algorithms have increasingly been applied in musculoskeletal radiology and have produced remarkable results. In our study, we introduce a new calibrated ensemble of deep learners for the task of identifying abnormal musculoskeletal radiographs. Our model leverages the strengths of three baseline deep neural networks (ConvNet, ResNet, and DenseNet), which are typically employed either directly or as the backbone architecture in the existing deep learning-based approaches in this domain. Experimental results based on the public MURA dataset demonstrate that our proposed model outperforms three individual models and a traditional ensemble learner, achieving an overall performance of (AUC: 0.93, Accuracy: 0.87, Precision: 0.93, Recall: 0.81, Cohen’s kappa: 0.74). The model also outperforms expert radiologists in three out of the seven upper extremity anatomical regions with a leading performance of (AUC: 0.97, Accuracy: 0.93, Precision: 0.90, Recall:0.97, Cohen’s kappa: 0.85) in the humerus region. We further apply the class activation map technique to highlight the areas essential to our model’s decision-making process. Given that the best radiologist performance is between 0.73 and 0.78 in Cohen’s kappa statistic, our study provides convincing results supporting the utility of a calibrated ensemble approach for assessing abnormalities in musculoskeletal X-rays.
Levels of physical activity before and after stroke in relation to early cognitive function
Authors: Viktorisson Adam , Andersson Elisabeth M. , Lundström Erik , Sunnerhagen Katharina S. 
Published: April 2021

AbstractRegular physical activity is widely recommended in the primary and secondary prevention of stroke. Physical activity may enhance cognitive performance after stroke, but cognitive impairments could also hinder a person to take part in physical activity. However, a majority of previous studies have not found any association between post-stroke cognitive impairments and a person’s subsequent level of activity. In this explorative, longitudinal study, we describe the intraindividual change in physical activity from before to 6 months after stroke, in relation to early screening of post-stroke cognitive impairments. Participants were recruited at 2 to 15 days after stroke, and screened for cognitive impairments using the Montreal Cognitive Assessment tool. Information on pre-stroke physical activity was retrospectively collected at hospital admittance by physiotherapists. Post-stroke physical activity was evaluated after 6 months. Of 49 participants included, 44 were followed up. The level of physical activity changed in more than half of all participants after stroke. Participants who were physically active 6 months after stroke presented with significantly less cognitive impairments. These results highlight that many stroke survivors experience a change in their physical activity level following stroke, and that unimpaired cognition may be important for a stroke survivors’ ability to be physically active.
Evaluation of mobile real-time polymerase chain reaction tests for the detection of severe acute respiratory syndrome coronavirus 2
Authors: Onyilagha Chukwunonso , Mistry Henna , Marszal Peter , Pinette Mathieu , Kobasa Darwyn , Tailor Nikesh , Berhane Yohannes , Nfon Charles , Pickering Bradley , Mubareka Samira , Bulir David , Chong Sylvia , Kozak Robert , Ambagala Aruna 
Published: April 2021

AbstractThe coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), calls for prompt and accurate diagnosis and rapid turnaround time for test results to limit transmission. Here, we evaluated two independent molecular assays, the Biomeme SARS-CoV-2 test, and the Precision Biomonitoring TripleLock SARS-CoV-2 test on a field-deployable point-of-care real-time PCR instrument, Franklin three9, in combination with Biomeme M1 Sample Prep Cartridge Kit for RNA 2.0 (M1) manual extraction system for rapid, specific, and sensitive detection of SARS-COV-2 in cell culture, human, and animal clinical samples. The Biomeme SARS-CoV-2 assay, which simultaneously detects two viral targets, the orf1ab and S genes, and the Precision Biomonitoring TripleLock SARS-CoV-2 assay that targets the 5′ untranslated region (5′ UTR) and the envelope (E) gene of SARS-CoV-2 were highly sensitive and detected as low as 15 SARS-CoV-2 genome copies per reaction. In addition, the two assays were specific and showed no cross-reactivity with Middle Eastern respiratory syndrome coronavirus (MERS-CoV), infectious bronchitis virus (IBV), porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis (TGE) virus, and other common human respiratory viruses and bacterial pathogens. Also, both assays were highly reproducible across different operators and instruments. When used to test animal samples, both assays equally detected SARS-CoV-2 genetic materials in the swabs from SARS-CoV-2-infected hamsters. The M1 lysis buffer completely inactivated SARS-CoV-2 within 10 min at room temperature enabling safe handling of clinical samples. Collectively, these results show that the Biomeme and Precision Biomonitoring TripleLock SARS-CoV-2 mobile testing platforms could reliably and promptly detect SARS-CoV-2 in both human and animal clinical samples in approximately an hour and can be used in remote areas or health care settings not traditionally serviced by a microbiology laboratory.
Arginyltransferase (Ate1) regulates the RGS7 protein level and the sensitivity of light-evoked ON-bipolar responses
Authors: Fina Marie E. , Wang Junling , Nikonov Sergei S. , Sterling Stephanie , Vardi Noga , Kashina Anna , Dong Dawei W. 
Published: April 2021

AbstractRegulator of G-protein signaling 7 (RGS7) is predominately present in the nervous system and is essential for neuronal signaling involving G-proteins. Prior studies in cultured cells showed that RGS7 is regulated via proteasomal degradation, however no protein is known to facilitate proteasomal degradation of RGS7 and it has not been shown whether this regulation affects G-protein signaling in neurons. Here we used a knockout mouse model with conditional deletion of arginyltransferase (Ate1) in the nervous system and found that in retinal ON bipolar cells, where RGS7 modulates a G-protein to signal light increments, deletion of Ate1 raised the level of RGS7. Electroretinographs revealed that lack of Ate1 leads to increased light-evoked response sensitivities of ON-bipolar cells, as well as their downstream neurons. In cultured mouse embryonic fibroblasts (MEF), RGS7 was rapidly degraded via proteasome pathway and this degradation was abolished in Ate1 knockout MEF. Our results indicate that Ate1 regulates RGS7 protein level by facilitating proteasomal degradation of RGS7 and thus affects G-protein signaling in neurons.
Development of an objective index, neural activity score (NAS), reveals neural network ontogeny and treatment effects on microelectrode arrays
Authors: Passaro Austin P. , Aydin Onur , Saif M. Taher A. , Stice Steven L. 
Published: April 2021

AbstractMicroelectrode arrays (MEAs) are valuable tools for electrophysiological analysis, providing assessment of neural network health and development. Analysis can be complex, however, requiring intensive processing of large data sets consisting of many activity parameters, leading to information loss as studies subjectively report relatively few metrics in the interest of simplicity. In screening assays, many groups report simple overall activity (i.e. firing rate) but omit network connectivity changes (e.g. burst characteristics and synchrony) that may not be evident from basic parameters. Our goal was to develop an objective process to capture most of the valuable information gained from MEAs in neural development and toxicity studies. We implemented principal component analysis (PCA) to reduce the high dimensionality of MEA data. Upon analysis, we found the first principal component was strongly correlated to time, representing neural culture development; therefore, factor loadings were used to create a single index score—named neural activity score (NAS)—reflecting neural maturation. For validation, we applied NAS to studies analyzing various treatments. In all cases, NAS accurately recapitulated expected results, suggesting viability of NAS to measure network health and development. This approach may be adopted by other researchers using MEAs to analyze complicated treatment effects and multicellular interactions.
Total flow intensity, active flow intensity and volume related flow intensity as new quantitative metrics in optical coherence tomography angiography
Authors: Arrigo Alessandro , Perra Cristian , Aragona Emanuela , Giusto Daniele , Bandello Francesco , Battaglia Parodi Maurizio 
Published: April 2021

AbstractOptical coherence tomography (OCT) angiography (OCTA) is a non-invasive tool for the in-vivo study of the intraretinal vascular network. It is based on the analysis of motion particles within the retina to reconstruct the paths followed by the erythrocytes, i.e. retinal capillaries. To date, qualitative and quantitative information are based on the morphological features disclosed by retinal capillaries. In the present study, we proposed new quantitative functional metrics, named Total Flow Intensity (TFI), Active Flow Intensity (AFI), and Volume-related Flow Intensity (VFI), based on the processing of the blood flow signal detected by OCTA. We studied these metrics in a cohort of healthy subjects, and we assessed their clinical utility by including a cohort of age-matched patients affected by Stargardt disease. Moreover, we compared TFI, AFI, and VFI to the widely used vessel density (VD) parameter. TFI, AFI, and VFI were able to describe in detail the different properties of the retinal vascular compartment. In particular, TFI was intended as the overall amount of volumetric retinal blood flow. AFI represented a selective measure of voxels disclosing blood flow signal. VFI was developed to put in relationship the volumetric blood flow information with the not vascularized retinal volume. In conclusion, TFI, AFI, and VFI were proposed as feasible functional OCTA biomarkers based on the analysis of retinal blood flow signal.
The role of HER2 and HER3 in HER2-amplified cancers beyond breast cancers
Authors: Majumder Avisek , Sandhu Manbir , Banerji Debarko , Steri Veronica , Olshen Adam , Moasser Mark M. 
Published: April 2021

AbstractHER2 and HER3 play key driving functions in the pathophysiology of HER2-amplified breast cancers, but this function is less well characterized in other cancers driven by HER2 amplification. This study aimed to explore the role of HER2 and HER3 signaling in other types of HER2-amplified cancer. The expression and signaling activity of HER2, HER3, and downstream pathway proteins were studied in cell panels representing HER2-amplified cancers of the breast, bladder, colon and rectal, stomach, esophagus, lung, tongue, and endometrium along with controls lacking HER2 amplification. We report that HER2-amplified cancers are addicted to HER2 across different cancer types and the depth of addiction is best linked with the expression level of HER2, but not with HER3 expression. We report that the expression and constitutive phosphorylation of HER3 are ubiquitous in HER2-amplified breast cancer cell lines, but much more variable in HER2-amplified cancer cells from other tissues. We observed the lapatinib-induced compensatory upregulation of HER3 signaling in many types of HER2-amplified cancers, although with much variability. We find that HER3 expression is essential for in vivo tumorigenic growth in some HER2-amplified tumors but not others. Importantly HER3 expression level does not correlate well with its functional importance. More biomarkers will be needed to guide the optimal use of HER3 inhibitors in HER2-amplified cancers from non-breast origin. Unlike oncogenes activated through mutational events, the activation of HER2 through overexpression represents a gradient of activities and depth of addiction and the response to inhibitors follows a similar gradient.