bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pre-existing and machine learning-based models for cardiovascular risk prediction
Authors: Cho Sang-Yeong , Kim Sun-Hwa , Kang Si-Hyuck , Lee Kyong Joon , Choi Dongjun , Kang Seungjin , Park Sang Jun , Kim Tackeun , Yoon Chang-Hwan , Youn Tae-Jin , Chae In-Ho 
Published: April 2021

AbstractPredicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre-existing cardiovascular risk prediction models and developed machine learning-based prediction algorithms. This study included 222,998 Korean adults aged 40–79 years, naïve to lipid-lowering therapy, had no history of cardiovascular disease. Pre-existing models showed moderate to good discrimination in predicting future cardiovascular events (C-statistics 0.70–0.80). Pooled cohort equation (PCE) specifically showed C-statistics of 0.738. Among other machine learning models such as logistic regression, treebag, random forest, and adaboost, the neural network model showed the greatest C-statistic (0.751), which was significantly higher than that for PCE. It also showed improved agreement between the predicted risk and observed outcomes (Hosmer–Lemeshow χ2 = 86.1, P < 0.001) than PCE for whites did (Hosmer–Lemeshow χ2 = 171.1, P < 0.001). Similar improvements were observed for Framingham risk score, systematic coronary risk evaluation, and QRISK3. This study demonstrated that machine learning-based algorithms could improve performance in cardiovascular risk prediction over contemporary cardiovascular risk models in statin-naïve healthy Korean adults without cardiovascular disease. The model can be easily adopted for risk assessment and clinical decision making.
Antibody response against selected epitopes in the HIV-1 envelope gp41 ectodomain contributes to reduce viral burden in HIV-1 infected patients
Authors: Marcelino Rute , Gramacho Filipa , Martin Francisco , Brogueira Pedro , Janeiro Nuno , Afonso Claudia , Badura Robert , Valadas Emília , Mansinho Kamal , Caldeira Luís , Taveira Nuno , Marcelino José M. 
Published: April 2021

AbstractThe ectodomain of gp41 is the target of potent binding and neutralizing antibodies (NAbs) and is being explored in new strategies for antibody-based HIV vaccines. Previous studies have suggested that the W164A-3S (3S) and EC26-2A4 (EC26) peptides located in the gp41 ectodomain may be potential HIV vaccine candidates. We assessed 3S- and EC26-specific binding antibody responses and related neutralizing activity in a large panel of chronic HIV-1-infected Portuguese individuals on ART. A similar proportion of participants had antibodies binding to 3S (9.6%) and EC26 (9.9%) peptides but the level of reactivity against 3S was significantly higher compared to EC26, except in the rare patients with double peptide reactivity. The higher antigenicity of 3S was unrelated with disease stage, as assessed by CD4+ T cell counts, but it was directly related with plasma viral load. Most patients that were tested (89.9%, N = 268) showed tier 1 neutralizing activity, the potency being inversely associated with plasma viral load. In the subset of patients that were tested for neutralization of tier 2 isolates, neutralization breadth was inversely correlated with plasma viral load and directly correlated with CD4+ T cell counts. These results are consistent with a role for neutralizing antibodies in controlling viral replication and preventing the decline of CD4+ T lymphocytes. Importantly, in patients with 3S-specific antibodies, neutralizing titers were inversely correlated with viral RNA levels and proviral DNA levels. Moreover, patients with 3S and/or EC26-specific antibodies showed a 1.9-fold higher tier 2 neutralization score than patients without antibodies suggesting that 3S and/or EC26-specific antibodies contribute to neutralization breadth and potency in HIV-1 infected patients. Overall, these results suggest that antibodies targeting the S3 and EC26 epitopes may contribute to reduce viral burden and provide further support for the inclusion of 3S and EC26 epitopes in HIV-1 vaccine candidates.
Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data
Authors: Chaudhary Vaishali , Kumar Shashi 
Published: April 2021

AbstractOil spills are a potential hazard, causing the deaths of millions of aquatic animals and this leaves a calamitous effect on the marine ecosystem. This research focuses on evaluating the potential of polarimetric parameters in discriminating the oil slick from water and also possible thicker/thinner zones within the slick. For this purpose, L-band UAVSAR quad-pol data of the Gulf of Mexico region is exploited. A total number of 19 polarimetric parameters are examined to study their behavior and ability in distinguishing oil slick from water and its own less or more oil accumulated zones. The simulation of compact-pol data from UAVSAR quad-pol data is carried out which has shown good performance in detection and discrimination of oil slick from water. To know the extent of separation between oil and water classes, a statistical separability analysis is carried out. The outcomes of each polarimetric parameter from separability analysis are then quantified with the radial basis function (RBF) supervised Support Vector Machine classifier followed with an accurate estimation of the results. Moreover, a comparison of the achieved and estimated accuracy has shown a significant drop in accuracy values. It has been observed that the highest accuracy is given by LHV compact-pol decomposition and coherency matrix with a classification accuracy of ~ 94.09% and ~ 94.60%, respectively. The proposed methodology has performed well in discriminating the oil slick by utilizing UAVSAR dataset for both quad-pol and compact-pol simulation.
Whole genome-based characterisation of antimicrobial resistance and genetic diversity in Campylobacter jejuni and Campylobacter coli from ruminants
Authors: Ocejo Medelin , Oporto Beatriz , Lavín José Luis , Hurtado Ana 
Published: April 2021

AbstractCampylobacter, a leading cause of gastroenteritis in humans, asymptomatically colonises the intestinal tract of a wide range of animals.Although antimicrobial treatment is restricted to severe cases, the increase of antimicrobial resistance (AMR) is a concern. Considering the significant contribution of ruminants as reservoirs of resistant Campylobacter, Illumina whole-genome sequencing was used to characterise the mechanisms of AMR in Campylobacter jejuni and Campylobacter coli recovered from beef cattle, dairy cattle, and sheep in northern Spain. Genome analysis showed extensive genetic diversity that clearly separated both species. Resistance genotypes were identified by screening assembled sequences with BLASTn and ABRicate, and additional sequence alignments were performed to search for frameshift mutations and gene modifications. A high correlation was observed between phenotypic resistance to a given antimicrobial and the presence of the corresponding known resistance genes. Detailed sequence analysis allowed us to detect the recently described mosaic tet(O/M/O) gene in one C. coli, describe possible new alleles of blaOXA-61-like genes, and decipher the genetic context of aminoglycoside resistance genes, as well as the plasmid/chromosomal location of the different AMR genes and their implication for resistance spread. Updated resistance gene databases and detailed analysis of the matched open reading frames are needed to avoid errors when using WGS-based analysis pipelines for AMR detection in the absence of phenotypic data.
Clinical relevance of a degree of extracapsular extension in a sentinel lymph node in breast cancer patients: a single-centre study
Authors: Nowikiewicz Tomasz , Kurylcio Andrzej , Głowacka-Mrotek Iwona , Szymankiewicz Maria , Nowikiewicz Magdalena , Zegarski Wojciech 
Published: April 2021

AbstractIn some breast cancer (BC) patients, an examination of lymph nodes dissected during sentinel lymph node biopsy (SLNB) demonstrates a presence of metastatic lesions and extracapsular extension (ECE) in a SLN. This study aimed to evaluate clinical relevance of ECE in BC patients. This is a retrospective analysis of 891 patients with cancer metastases to SLN, referred to supplementary axillary lymph node dissection (ALND), hospitalized between Jan 2007 and Dec 2017. Clinical and epidemiological data was evaluated. Long-term treatment outcomes were analysed. In 433 (48.6%) patients, cancer metastases were limited to the SLN (group I), in 61 (6.8%) patients the SLN capsule was exceeded focally (≤ 1 mm—group II). In 397 (44.6%) patients, a more extensive ECE was found (> 1 mm—group III). Metastases to non-sentinel lymph nodes (nSLNs) were diagnosed in 27.0% patients from group I, 44.3% patients from group II and in 49.6% patients from group III. No statistically significant differences were observed in long-term treatment outcomes for compared groups. The presence of ECE is accompanied by a higher stage of metastatic lesions in the lymphatic system. The differences in this respect were statistically significant, when compared to the group of ECE(−) patients. ECE, regardless of its extent, did not impact the long-term treatment results. ECE remains an indication for supplementary ALND and for other equivalent cancer treatment procedures, regardless of ECE size.
Tetraploid Citrumelo 4475 rootstocks improve diploid common clementine tolerance to long-term nutrient deficiency
Authors: Oustric Julie , Herbette Stéphane , Quilichini Yann , Morillon Raphaël , Giannettini Jean , Berti Liliane , Santini Jérémie 
Published: April 2021

AbstractNutrient deficiency alters growth and the production of high-quality nutritious food. In Citrus crops, rootstock technologies have become a key tool for enhancing tolerance to abiotic stress. The use of doubled diploid rootstocks can improve adaptation to lower nutrient inputs. This study investigated leaf structure and ultrastructure and physiological and biochemical parameters of diploid common clementine scions (C) grafted on diploid (2x) and doubled diploid (4x) Carrizo citrange (C/CC2x and C/CC4x) and Citrumelo 4475 (C/CM2x and C/CM4x) rootstocks under optimal fertigation and after 7 months of nutrient deficiency. Rootstock ploidy level had no impact on structure but induced changes in the number and/or size of cells and some cell components of 2x common clementine leaves under optimal nutrition. Rootstock ploidy level did not modify gas exchanges in Carrizo citrange but induced a reduction in the leaf net photosynthetic rate in Citrumelo 4475. By assessing foliar damage, changes in photosynthetic processes and malondialdehyde accumulation, we found that C/CM4x were less affected by nutrient deficiency than the other scion/rootstock combinations. Their greater tolerance to nutrient deficiency was probably due to the better performance of the enzyme-based antioxidant system. Nutrient deficiency had similar impacts on C/CC2x and C/CC4x. Tolerance to nutrient deficiency can therefore be improved by rootstock polyploidy but remains dependent on the rootstock genotype.
A study on Electrical Discharge Machining of Titanium Grade2 with experimental and theoretical analysis
Authors: Papazoglou Emmanouil L. , Karmiris-Obratański Panagiotis , Leszczyńska-Madej Beata , Markopoulos Angelos P. 
Published: April 2021

AbstractTitanium alloys, due to their unique properties, are utilized in numerous modern high-end applications. Electrical Discharge Machining (EDM) is a non-conventional machining process, commonly used in machining of hard-to-cut materials. The current paper, presents an experimental study regarding the machining of Titanium Grade2 with EDM, coupled with the development of a simulation model. The machining performance indexes of Material Removal Rate, Tool Wear Ratio, and Average White Layer Thickness were measured and calculated for different pulse-on currents and pulse-on times. Moreover, the developed model that integrates a heat transfer analysis with deformed geometry, allows to estimate the power distribution between the electrode and the workpiece, as well as the Plasma Flushing Efficiency, giving an insight view of the process. Finally, by employing the Response Surface Methodology, educed regression models that correlate the machining parameters with the corresponding results, while for all the aforementioned indexes, ANOVA was performed.
Global-scale patterns of observed sea surface salinity intensified since the 1870s
Authors: Gould W. John , Cunningham Stuart A. 
Published: April 2021

AbstractSea surface salinity patterns have intensified between the mid-20thcentury and present day, with saline areas becoming saltier and fresher areas fresher. This change has been linked to a human-induced strengthening of the global hydrological cycle as global mean surface temperatures rose. Here we analyse salinity observations from the round-the-world voyages ofHMS ChallengerandSMS Gazellein the 1870s, early in the industrial era, to reconstruct surface salinity changes since that decade. We find that the amplification of the salinity change pattern between the 1870s and the 1950s was at a rate that was 54 ± 10% lower than the post-1950s rate. The acceleration in salinity pattern amplification over almost 150 years implies that the hydrological cycle would have similarly accelerated over this period.
The inner and outer bounds on the capacity of the 3‐user GBC‐SKT with unequal state variances
Authors: Ghabeli Leila 
Published: April 2021

AbstractThe “Gaussian BroadCast channel (GBC) with States non‐causally Known at the Transmitter” (GBC‐SKT) introduces Gaussian broadcast channel with additive i.i.d. Gaussian states non‐causally known at the transmitter. In this model additive interferences on links are considered as states which are only non‐causally known to the transmitter. Such cases are encountered in, e.g., wireless multiple‐antenna multicasting scenario. Previously the approximate capacity of the K‐user GBC‐SKT has been studied where all states but one are assumed to have the same variance. In this work, the capacity of the 3‐user GBC‐SKT channel communicating only common messages is studied. All three states are assumed to have different variances. The key point is how to simultaneously pre‐code the common message against three states. Two transmission strategies are proposed by combination of power‐sharing and time‐sharing methods and two inner bounds are derived. From analytical and simulation results it is seen that one inner bound outperforms the other inner bound. Moreover, the outer bound on the capacity of this channel is driven and the conditions on the state variances for which, the capacity of the 3‐user GBC‐SKT channel is derived to within 1 bpcu, are extracted.
Learning view‐invariant features using stacked autoencoder for skeleton‐based gait recognition
Authors: Hasan Md Mahedi , Mustafa Hossen Asiful 
Published: April 2021

AbstractHuman gait recognition in a multicamera environment is a challenging task in biometrics because of the presence of the large pose and variations in illumination among different views. In this work, to address the problem of variations in view, we present a novel stacked autoencoder for learning discriminant view‐invariant gait representations. Our autoencoder can efficiently and progressively translate skeleton joint coordinates from any arbitrary view to a common canonical view without requiring the prior estimation of the view angle or covariate type and without losing temporal information. Then, we construct a discriminative gait feature vector by fusing the encoded features with two other spatiotemporal gait features to feed into the main recurrent neural network. Experimental evaluations of the challenging CASIA A and CASIA B gait datasets demonstrate that the proposed approach outperformed other state‐of‐the‐art methods on single‐view gait recognition. In particular, the proposed method achieved 46.31% and 33.86% average correct class recognition on probe set ProbeBG and ProbeCL, respectively, of the CASIA B dataset while considering the view variation; this is 0.3% and 30.68% higher than previous best‐performing methods. Furthermore, in cross‐view recognition, our method shows better results over other state‐of‐the‐art methods when the view‐angle variation is large than 36°.
Modelling power amplifier impairments in mmWave phased‐array systems
Authors: Maletic Nebojsa , Grass Eckhard 
Published: April 2021

AbstractThis letter presents an analytical framework to study the distortion effect of power amplifiers (PA) in a single‐stream millimetre wave (mmWave) system with a radio‐frequency beamforming architecture. A third‐order nonlinearity PA model is used. The impact of PA nonlinearity on the transmitter performance is evaluated by means of its normalised mean square error. Also, the effect of PA compression on the system capacity in a line‐of‐sight channel is analysed. All analytical results are supported with Monte‐Carlo simulations.
Virtual current vector‐based method for inverter open‐switch and open‐phase fault diagnosis in multiphase permanent magnet synchronous motor drives
Authors: Trabelsi Mohamed , Semail Eric 
Published: April 2021

AbstractThis paper presents a diagnostic method of inverter open‐switch and open‐phase faults in multiphase permanent magnet synchronous motor (PMSM) drives. First defined are adequate variables called virtual current vectors. The projection of the zero‐sequence current component on these variables was used to define two simple fault indices. High sensitivity to fault is thus induced but with a good robustness to transient states and variation of machine parameters. The mathematical development of the proposed method is provided and supported by experimental tests conducted on two prototypes of multiphase machines in the laboratory: sinusoidal and bi‐harmonic PMSMs. The experimental results confirm the effectiveness and the robustness of the proposed method and its capability to detect the single and multiple open‐switches and open‐phase faults in the electric drive.
Pedestrian re‐identification based on attribute mining and reasoning
Authors: Li Chao , Yang Xiaoyu , Yin Kangning , Chang Yifan , Wang Zhiguo , Yin Guangqiang 
Published: April 2021

AbstractThe high‐level semantic information extracted from the pedestrian attribute feature is an important element for pedestrian recognition. Pedestrian attribute recognition plays an important role in both intelligent video surveillance and pedestrian re‐identification promoting the convenience of searching and performance of model. This paper tries finding a practical method to improve the performance of the pedestrian re‐identification by combining pedestrian attributes and identities. The multi‐task learning method combines pedestrian recognition and attribute information in a direct way that considers the correlation between pedestrian attributes and identities but ignores the principle and degree of such correlation. To solve this problem, a new pedestrian recognition framework based on attribute mining and reasoning is proposed in this paper. To enhance the expression ability of attribute features, it designs spatial channel attention module (SCAM) based on attention mechanism to extract features from every attribute. SCAM can not only locate the attributes on the feature map, but also effectively mine channel features with a higher degree of association with attributes. In addition, both spatial attention model and channel attention model are integrated by multiple groups of parallel branches, which further improve the network performance. Finally, using the semantic reasoning and information transmission function of graph convolutional network, the relationship between attribute features and pedestrian features can be mined. Besides, pedestrian features with stronger expression ability can also be obtained. Experiment work is conducted in two databases, DukeMTMC‐reID and Market‐1501, which are commonly used in pedestrian recognition tasks. On the Market‐1501 dataset, the final effect of the algorithm model CMC‐1 can reach 94.74%, and mAP can reach 87.02%; on the DukeMTMC‐reID dataset, CMC‐1 can reach 87.03%, and mAP can reach 77.11%. The results show that our method is at the top of the existing pedestrian recognition methods.
Practical Investigation of a MIMO radar system capabilities for small drones detection
Authors: Yang Fawei , Xu Feng , Fioranelli Francesco , Le Kernec Julien , Chang Shaoqiang , Long Teng 
Published: April 2021

AbstractThe latest progress of the multiple‐input multiple‐output (MIMO) radar system developed for small drones detection at Beijing Institute of Technology is presented herein. A low‐cost S‐band MIMO scanning radar system is designed for the detection of small drones. A practical design optimisation and implementation of a sparse array covering the whole airspace, and a target‐based MIMO radar array calibration method to improve the signal‐to‐noise ratio of the target are proposed. An experiment in the microwave chamber was conducted to verify the designed sparse‐array beam‐pattern, and measurement campaigns for small drones were performed to test the system performance where the radar achieved detection and tracking of a small DJI‐Phantom 4 drone at a range of up to 5 km. Target features, including micro‐Doppler and radar cross‐section signatures were extracted to assist target classification. The experimental results indicate that the system has promising capability in small drones detection and tracking, despite the challenges of low dwell time on targets due to the scanning nature of the system.
Pathway‐based protein–protein association network to explore mechanism of α‐glucosidase inhibitors from Scutellaria baicalensis Georgi against type 2 diabetes
Authors: Wang Le , Diwu Wenbo , Tan Nana , Wang Huan , Hu Jingbo , Xu Bailu , Wang Xiaoling 
Published: April 2021

AbstractNatural products have been widely used in the treatment of type 2 diabetes (T2D). However, their mechanisms are often obscured due to multi‐components and multi‐targets. The authors constructed a pathway‐based protein–protein association (PPA) network for target proteins of 13 α‐glucosidase inhibitors (AGIs) identified from Scutellaria baicalensis Georgi (SBG), designed to explore the underlying mechanisms. This network contained 118 nodes and 1167 connections. An uneven degree distribution and small‐world property were observed, characterised by high clustering coefficient and short average path length. The PPA network had an inherent hierarchy as C(k)∼k−0.71. It also exhibited potential weak disassortative mixing pattern, coupled with a decreased function Knn (k) and negative value of assortativity coefficient. These properties indicated that a few nodes were crucial to the network. PGH2, GNAS, MAPK1, MAPK3, PRKCA, and MAOA were then identified as key targets with the highest degree values and centrality indices. Additionally, a core subnetwork showed that chrysin, 5,8,2′‐trihydroxy‐7‐methoxyflavone, and wogonin were the main active constituents of these AGIs, and that the serotonergic synapse pathway was the critical pathway for SBG against T2D. The application of a pathway‐based protein–protein association network provides a novel strategy to explore the mechanisms of natural products on complex diseases.