bluuebunny/crossref_metadata_2025_split huggingface dataset:
SARS-CoV-2 Omicron: evasion of potent humoral responses and resistance to clinical immunotherapeutics relative to viral variants of concern
Authors: Aggarwal Anupriya , Stella Alberto , Walker Gregory , Akerman Anouschka , Milogiannakis Vanessa , Brilot Fabienne , Amatayakul-Chantler Supavadee , Roth Nathan , Coppola Germano , Schofield Peter , Jackson Jennifer , Lenthall Helen , Mazigi Ohan , Langley David , Lu Yonghui , Forster Charles , McAllery Samantha , Mathivanan Vennila , Fitcher Christina , Hoppe Alexandra Carey , Munier C. Mee Ling , Jack Hans-Martin , Cromer Deborah , Darley David , Matthews Gail , Christ Daniel , Khoury David , Davenport Miles , Rawlinson William , Kelleher Anthony , Turville Stuart 
Published: January 2022

Abstract
Genetically distinct viral variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been recorded since January 2020. Over this time global vaccine programs have been introduced, contributing to lowered COVID-19 hospitalisation and mortality rates, particularly in the first world. In late 2021, the Omicron (B.1.1.529) virus variant emerged, with significant genetic differences and clinical effects from other variants of concern (VOC). This variant a demonstrated higher number of polymorphisms in the gene encoding the Spike (S) protein, and there has been displacement of the dominant Delta variant. We assessed the impact of Omicron infection on the ability of: serum from vaccinated and/or previously infected individuals; concentrated human IgG from plasma donors, and licensed monoclonal antibody therapies to neutralise the virus in vitro. There was a 17 to 27-fold reduction in neutralisation titres across all donors who had a detectable neutralising antibody titre to the Omicron variant. Concentrated pooled human IgG from convalescent and vaccinated donors had greater breadth of neutralisation, although the potency was still reduced 16-fold. Of all therapeutic antibodies tested, significant neutralisation of the Omicron variant was only observed for Sotrovimab, with other monoclonal antibodies unable to neutralise Omicron in vitro. These results have implications for ongoing therapy of individuals infected with the Omicron variant.
Identification of Sulfavant A as the First Synthetic TREM2 Ligand Discloses a Homeostatic Response of Dendritic Cells After Receptor Engagement
Authors: Gallo Carmela , Manzo Emiliano , Barra Giusi , Fioretto Laura , Ziaco Marcello , Nuzzo Genoveffa , d'Ippolito Giuliana , Ferrera Francesca , Contini Paola , Castiglia Daniela , Angelini Claudia , De Palma Raffaele , Fontana Angelo 
Published: January 2022

Abstract
The immune response arises from a fine balance of cellular and molecular mechanisms that provide for surveillance, tolerance, and elimination of dangers as pathogens. Improving the quality of the immune response remains a major goal in immunotherapy and vaccine development. Sulfavant A (SULF A) is a sulfolipid that has shown promising adjuvant activity in a cancer vaccine model. Here we report that SULF A is the first synthetic small molecule binding to the Triggering Receptor Expressed on Myeloid cells-2 (TREM2). The receptor engagement initiates an unconventional maturation of Dendritic cells (DCs) leading to upregulation of the Major Histocompatibility Complex class II (MHC Class II) and costimulatory molecules (CD83, CD86, DC54) without release of T helper type 1 (Th1) or 2 (Th2) cytokines. According to a TREM2 mechanism, this response is mediated by SYK-NFAT axis and is compromised by blockade and gene silencing of the receptor. Activation by SULF A preserved the DC functions to excite the allogeneic T cell response, and induced interleukin-10 (IL-10) release after lipopolysaccharide (LPS) stimulation. These results well support the adjuvant effect of SULF A and offer novel insights into the role of TREM2 in the differentiation of an unprecedented DC phenotype (homeDCs) that contributes to the maintenance of immune homeostasis without compromising lymphocyte activation and immunogenic response. The biological function of SULF-A may be of interest in various physiological and pathological processes involving the immune system.
Processing of polymer wood composite material from pine cone and the binder of phenol formaldehyde/PVAc/molasses and improvement of its properties
Authors: Şahinöz Melih , ARUNTAŞ Hüseyin Yılmaz , GÜRÜ Metin 
Published: January 2022

Abstract
This paper deals with the processing of polymer wood composite material from pine cone and the binder of phenol formaldehyde/PVAc/molasses and improvement of its properties. The production of pine cone based polymer binding and molasses added composite material, and the development of the non-flammability, insect attack and water resistance properties of this material has been studied in the research. To this end, pine cone, polyvinyl acetate (PVAc), phenol-formaldehyde, molasses, hemp fiber and waste colemanite have been used in the production of composite materials. It is aimed to produce a cheaper composite material less harmful to human health using plant based waste materials. According to the results of the flexural strength test conducted in the laboratory, the most suitable composite material producing parameters were detected as 0.25 filler/binder (f/b) ratio, 35% molasses ratio, 100°C molding pressure temperature, 49 kg/cm2 molding pressure, 240 µm mean particle size, 20 minutes for molding pressure time, 20% PF ratio and 0.5% hemp fiber ratio. It was determined that molasses could be used at a ratio of 35% for producing composite materials and, PF resin and hemp fiber samples provide the necessary water resistance. It was observed that the colemanite waste used in the mixture adds the nonflammability property to the composite material and decreases flexural strength and screw withdrawal strength.
Multi-Frequency Broadband Vibration Mitigation of a Beam Under Tensile Load
Authors: Machado Marcela , Dutkiewicz Maciej 
Published: January 2022

Abstract
The vibration characterization is directly associated with the system’s physical properties, such as mass, damping, and stiffness. For over a century, vibration resonator or dynamic absorber has been used for vibration control and mitigation in many sectors of engineering. A limitation of this device is that it acts as a notch filter, which is only effective over a narrow band of frequencies. Therefore, researchers have designed the call metamaterial, which in this case, targets the improvement of vibration attenuation and induces locally resonant bandgaps. This work investigates the broadband vibration mitigation of a beam under tensile load with periodically attached dynamic absorbers. The study uses the modal analysis approach, a simple formulation that only depends on the resonator target frequency and total mass ratio to investigate single and multiple-frequency bandgap formation. Metamaterial and rainbow metamaterial beam under tensile load are employed to widen the gap. In practical designs, a finite number of resonators is required for the open bandgap, and this ideal number is explored in the paper. Additionally, a tensiled beam (cable) virtual twin is built from a physical system to forecast its broadband vibration mitigation with the metamaterial approach. Numerical investigations are conducted regarding the effects of mass ratio and the ideal mass ratio on the open and on the gap convergence, as well as resonators in single and multiple arrangements inducing multiple gaps.
A Simple Scoring Model Based on Machine Learning Predicts Intravenous Immunoglobulin Resistance in Kawasaki Disease
Authors: Sunaga Yuto , Watanabe Atsushi , Katsumata Nobuyuki , Toda Takako , Yoshizawa Masashi , Kono Yosuke , Hasebe Yohei , Koizumi Keiichi , Hoshiai Minako , Inukai Takeshi 
Published: January 2022

Abstract
In Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions. To establish a simple and accurate scoring model predicting IVIG resistance, we conducted a retrospective cohort study of 996 KD patients that were diagnosed at 11 facilities for 10 years, in which 108 cases (23.5%) were resistant to initial IVIG treatment. We performed machine learning with random forest model using 30 clinical variables at diagnosis in 796 and 200 cases for training and test datasets, respectively. Random forest model accurately predicted IVIG resistance (AUC; 0.75, sensitivity; 0.54, specificity; 0.80). Next, using top five influential features (days of illness at initial therapy, serum levels of C-reactive protein, sodium, total bilirubin, and total cholesterol) in the random forest model, we designed a simple scoring system. In spite of its simplicity, the scoring system predicted IVIG resistance (AUC; 0.73, sensitivity; 0.55, specificity; 0.83) as accurately as the random forest model itself. Moreover, accuracy of our scoring system with five clinical features was almost identical to that of Gunma score with seven clinical features (AUC; 0.73, sensitivity; 0.53, specificity; 0.83), a well-known logistic regression scoring model, and superior to that of two widely used scores (Kurume score; 0.67, 0.46 and 0.76, respectively, and Osaka score; 0.69, 0.33 and 0.84, respectively). Conclusions: Our simple scoring system based on the findings in machine learning, as well as machine learning itself, seems to be useful to accurately predict IVIG resistance in KD patients.
Early Daytime Sleepiness and Nighttime Slow-wave Sleep Disorganization in Progressive Macaque Model of Parkinson’s Disease
Authors: Davin Aurélie , Chabardès Stéphan , Belaid Hayat , Fagret Daniel , Djaileb Loic , Dauvilliers Yves , David Olivier , Torres-Martinez Napoléon , Piallat Brigitte 
Published: January 2022

Abstract
Parkinsonian patients often experience wake/sleep behavior disturbances, which can appear at an early stage of the disease in a way that is still not fully described. We aimed here at reproducing and characterizing these clinical signs in a progressive non-human primate model of the Parkinson’s disease to better understand the underlying physiopathology and to identify biomarkers of the disease. Three adult non-human primates (macaca fascicularis) were equipped with a polysomnographic telemetry system allowing the characterization of the wake/sleep behavior by long-term neurophysiological recordings and a modified multiple sleep latency test. Experiments were first performed in healthy animals and then during the progressive induction of a parkinsonian syndrome by chronic intramuscular injections of low doses of MPTP. We observed a significant early onset of wake/sleep behavior disturbances, before any motor symptoms, resulting in (i) a disorganization of nighttime sleep with more deep sleep and (ii) a disorganization of daytime naps with an excessive daytime sleepiness characterized by longer duration of naps, which occurred faster. These observations persisted and worsened in stable symptomatic state. In that latter state, we observed persistent excessive daytime sleepiness and more disorganized nighttime sleep architecture and continuity. Interpolating to the human condition, the present study suggests that nighttime and daytime sleep disorders may appear in early stage of the disease. They could thus be used as biomarkers of the disease for early stratification of patients who are at risk of developing Parkinson’s disease.
Yield Estimation of High-Density Cotton Fields Using Low-Altitude UAV Imaging and Deep Learning
Authors: Li Fei , Bai Jingya , Zhang Mengyun , Zhang Ruoyu 
Published: January 2022

Abstract
Background: Different from other parts of the world, China has its own cotton planting pattern. Cotton are densely planted in wide-narrow rows to increase yield in Xinjiang, China, causing the difficulty in the accurate evaluation of cotton yields using remote sensing in such field with branches occluded and overlapped. Results: In this study, low-altitude unmanned aerial vehicle (UAV) imaging and deep convolutional neural networks (DCNN) were used to estimate the yields of densely planted cotton. Images of cotton field were acquired by an UAV at the height of 5 m. Cotton bolls were manually harvested and weighted afterwards. Then, a modified DCNN model was developed by applying encoder-decoder recombination and dilated convolution for pixel-wise cotton boll segmentation termed CD-SegNet. Linear regression analysis was employed to build up the relationship between cotton boll pixels ratio and cotton yield. Yield estimations of four cotton fields were verified after machine harvest and weighting. The results showed that CD-SegNet outperformed the other tested models including SegNet, support vector machine (SVM), and random forest (RF). The average error of the estimated yield of the cotton fields was 6.2%. Conclusions: Overall, the yield estimation of densely planted cotton based on lowaltitude UAV imaging is feasible. This study provides a methodological reference for cotton yield estimation in China.
Four New Members of The Family Cytophagaceae: Cryseosolum Histidinii gen. nov., sp. nov., Cryseosolum Indiensis gen. nov., sp. nov., Reichenbachia Cretensis, gen. nov., sp. nov., and Reichenbachia Soli, gen. nov., sp. nov. Isolated From Diverse Habitat
Authors: Octaviana Senlie , Lorenczyk Stefan , Ackert Frederike , Wink Joachim 
Published: January 2022

Abstract
Four strains isolated, PWU4T, PWU20T, PWU5T and PWU37T were from both of soil in Germany, India and a faces sheep collected in Crete Island, respectively. Cells were Gram-negative, strictly aerobic, rod shaped, grew optimally between 28oC and 34oC, pH between 7.0 and 8.0 without the addition of NaCl. Catalase and oxidase-negative and grew on most mono- and disaccharides, a few polysaccharides and organic acid. The predominant menaquinone was MK-7. Major fatty acid was c16:1 ω7c (PWU4T and PWU20T) and c16:1 ω5c (PWU5T and PWU37T). The DNA G+C content of them were 50.2 mol %; 51.6 mol %; 39.8 mol % and 53.8 mol %, respectively. The 16S rRNA gene sequence analysis revealed that the closest relatives of them are less than 93.8% compared to Ohtaekwangia koreensis 3B-2T and Ohtaekwangia kribbensis 10AOT. It classified in two groups, where PWU4T, PWU20T shared 93.0% and PWU5T, PWU37T shared 97.5% sequence similarity. However, in both groups represent different species on the low average nucleotide identity (ANI) of their genomes, 69.7% and 83.8%, respectively. We proposed that the four strains represent four novel species of two new genera in the family Cytophagaceae. The type species of the novel genus Cryseosolum are Cryseosolum histdinii gen. nov., sp. nov. strain PWU4T (=DSM 111594T=NCCB 100798T), Cryseosolum indiensis sp. nov. strain PWU20T (=DSM 111597T=NCCB 100800T). The type species of the novel genus Reichenbachia are Reichenbachia cretensis gen. nov., sp. nov. strain PWU5T (=DSM 111596T=NCCB 100799T), Reichenbachia soli sp. nov. strain PWU37T (=DSM 111595T=NCCB 100801T).
A Visualization Analysis of Hotspots And Frontiers of Cardiovascular Diseases With Frailty Based On CiteSpace
Authors: Bao Xuping , Chung Loretta Yuet Foon , Wen Yujie , Sun Qiyu , Wang Yi , Du Yifei 
Published: January 2022

Abstract
Cardiovascular diseases(CVD) and frailty are common health problems among the elderly. This research aims to investigate the hotspots and frontiers of the field of cardiovascular diseases with frailty. Data of publications between 2000 and 2021 was collected from the Web of Science(WoS) and CiteSpace was used for analysing the hotspots and frontiers of cardiovascular diseases with frailty research from high-impact countries, institutions, authors, cited-references, cited-journals, high-frequency keywords and burst keywords. The results showed that the United States, England and Canada were the leading countries in research of cardiovascular diseases with frailty. Other countries and regions lagged behind these developed countries. There is a need to establish cooperation between developed countries and developing countries. Research hotspots focused on frailty in the elderly with CVD, exercise intervention, assessment for CVD patients with frailty, quality of life and common diseases related to CVD with frailty. The frontier fields include care and intervention of CVD patients with frailty, social frailty and validation of CVD with frailty.
Triglyceride to HDL-C Ratio Is Associated with Plasma D-Dimer Levels in Different Types of Pancreatitis: A Retrospective Single-Centered Study in China
Authors: Jia Xiaoqing , Zhang Xiaoting , Sun Dalong , Li Rong , Yang Na , Luo Zheng 
Published: January 2022

Abstract
BackgroundThis study aims to evaluate the relationship between D-dimer and dyslipidemia, especially triglyceride to HDL-C ratio (TG/HDL-C) in different types of pancreatitis. We analyzed the D-dimer and dyslipidemia levels in acute pancreatitis (AP), recurrent acute pancreatitis (RAP) and chronic pancreatitis (CP). Material and MethodsA single-centered retrospective study was conducted on 1013 patients diagnosed with AP, RAP or CP. Only patients hospitalized within 24 h of onset were included, and 204 patients were enrolled in pancreatitis groups. 68 normal persons without pancreatitis, malignant diseases, pregnancy, or organ failure, who had health check-ups, were enrolled in the control group. Blood samples were taken within 24h of admission. The epidemiology and etiology were analyzed. D-dimer and dyslipidemia levels were compared between different types of pancreatitis. Furthermore, the area under the receiver-operating characteristic curve (AUC) was used to estimate the validity of the predictor and to define optimal cut-off points for prediction.ResultsWe found that D-dimer and TG/HDL-C ratio could distinguish mild AP (MAP) and non-MAP in AP and RAP patients. The D-dimer level was related to TG/HDL-C ratio and severity of pancreatitis, with the coefficient correlation of 0.379 and 0.427(p<0.01), respectively. TG/HDL-C was related to D-dimer in different types of pancreatitis. Multivariate analysis was conducted in the parameters at admission like alcohol abuse, dyslipidemia and coagulation disturbance in distinguishing AP and RAP groups from the control group, and the parameter like diabetes in RAP and CP groups significantly increased compared with that of the control group. ConclusionsThe value of D-dimer level and TG/HDL-C ratio in predicting the severity of AP and RAP was confirmed but there was no significant difference between CP group and the control group. The D-dimer level was related to dyslipidemia and TG/HDL-C ratio.
A Long-Term, Portable ECG Patch Monitor Based on Flexible Dry Electrode
Authors: Chu Hao , Yang Chenxi , Xing Yantao , Li Jianqing , Liu Chengyu 
Published: January 2022

Abstract
PurposeLong-term electrocardiogram (ECG) monitoring is an essential approach for the early diagnosis of cardiovascular diseases. Flexible dry electrodes that contains electrolyte without water could be a potential substitution of wet electrodes for long-term ECG monitoring. Therefore, this paper developes a long-term, portable ECG patch based on flexible dry electrodes, namely SEUECG-100.MethodA device consists of analog-front-end acquisition, data acquisition, and storage modules is developed and tested. An impedance test was conducted to compare the skin-electrode impedance of the flexible dry electrode and the Ag/AgCl wet electrode. The ECG signals were simutanously collected from the same subject using the SEUECG-100 and Shimmer device , which were then compared and analyzed from the perspective of ECG morphology, RR interval, and signal quality indices (SQI).ResultsThe experimental results reveal that the flexible dry electrode has the characteristics of low skin-electrode impedance. SEUECG-100 could collect high-quality ECG signals. The ECG signals collected by the two devices have a high RR interval correlation (r=0.999). SQI results show that SEUECG-100 is better than the Shimmer device in overcoming baseline drift. Long-term ECG acquisition and storage experiments show that SEUECG-100 could collect ECG signals with good stability and high reliability.ConclusionThe implementation of the proposed system design with dry electrodes could can effectively record long-term ECG monitoring with high quality in comparison to systems with wet electrodes from both impedance characteristics and signal morphology aspects.
Explainable AI for CNN-based Prostate Tumor Segmentation in Multi-parametric MRI Correlated to Whole Mount Histopathology
Authors: Gunashekar Deepa Darshini , Bielak Lars , Hägele Leonard , Berlin Arnie , Oerther Benedict , Benndorf Matthias , Grosu Anca , Zamboglou Constantinos , Bock Michael 
Published: January 2022

Abstract
Automatic prostate tumor segmentation is often unable to identify the lesion even if in multi-parametric MRI data is used as input, and the segmentation output is difficult to verify due to the lack of clinically established ground truth images. In this work we use an explainable deep learning model to interpret the predictions of a convolutional neural network (CNN) for prostate tumor segmentation. The CNN uses a U-Net architecture which was trained on multi-parametric MRI data from 122 patients to automatically segment the prostate gland and prostate tumor lesions. In addition, co-registered ground truth data from whole mount histopathology images were available in 15 patients that were used as a test set during CNN testing. To be able to interpret the segmentation results of the CNN, heat maps were generated using the Gradient Weighted Class Activation Map (Grad-CAM) method. With the CNN a mean Dice Sorensen Coefficient for the prostate gland and the tumor lesions of 0.62 and 0.31 with the radiologist drawn ground truth and 0.32 with wholemount histology ground truth for tumor lesions could be achieved. Dice Sorensen Coefficient between CNN predictions and manual segmentations from MRI and histology data were not significantly different. In the prostate the Grad-CAM heat maps could differentiate between tumor and healthy prostate tissue, which indicates that the image information in the tumor was essential for the CNN segmentation.
Incidentally Discovered Papillary Thyroid Microcarcinoma in Patients Undergoing Thyroid Surgery for Benign Disease
Authors: de Carlos Joaquin , Ernaga Ander , Irigaray Ana , Pineda Jose Javier , Echegoyen Ana , Salvador Pilar , Anda Emma 
Published: January 2022

Abstract
IntroductionIncidence of thyroid carcinoma (TC) has grown significantly over the last few decades worldwide, partly due to the increase detection of small thyroid microcarcinoma (TMc). TMc are tumors with a maximal diameter ≤ 1 cm, identified during histopathology examination following a thyroidectomy performed for reasons not pertaining to malignancy. The aim of this study is to investigate the prevalence of papillary thyroid microcarcinoma (PTMc) according to the nature of benign pathology that submit patients to thyroid surgery and its trend evolution.MethodsRetrospective cohort analysis of 1815 patients who underwent total thyroidectomy for non-malignant disease from 2005 to 2020. ResultsThe mean age of subjects was 53.5 years, with a higher proportion of women (1481, 82.1%). A total of 167 PTMc (9.3%) were incidentally discovered. Multivariate logistic regression analysis shows no differences in prevalence according to sex or age in patients with PTMc compared to those with final benign histology. Multinodular goiter increases the risk of PTMc with an odds ratio of 2.2 (p=0.001) compared to Hashimoto's thyroiditis and Graves´ disease (GD). There is a statistically significant increase in the incidence of PTMc in the group operated between 2017-2020 vs. 2005-2008 (p=0.005)ConclusionOverall prevalence of PTMc in patients who underwent thyroid surgery for benign disease was 9.3%. Thyroid nodular hyperplasia was the most frequent benign pathology associated to this occult cancer as compared to Hashimoto or GD. Gender and age were not correlated with prevalence of TMc. Over the years, surgical findings of PTMc have grown, particularly in the 2017-2020 period.