bluuebunny/crossref_metadata_2025_split huggingface dataset:
Decision tree based ensemble machine learning model for the prediction of Zika virus T-cell epitopes as potential vaccine candidates
Authors: Bukhari Syed Nisar Hussain , Webber Julian , Mehbodniya Abolfazl 
Published: May 2022

AbstractZika fever is an infectious disease caused by the Zika virus (ZIKV). The disease is claiming millions of lives worldwide, primarily in developing countries. In addition to vector control strategies, the most effective way to prevent the spread of ZIKV infection is vaccination. There is no clinically approved vaccine to combat ZIKV infection and curb its pandemic. An epitope-based peptide vaccine (EBPV) is seen as a powerful alternative to conventional vaccinations because of its low production cost and short production time. Nonetheless, EBPVs have gotten less attention, despite the fact that they have a significant untapped potential for enhancing vaccine safety, immunogenicity, and cross-reactivity. Such a vaccine technology is based on target pathogen’s selected antigenic peptides called T-cell epitopes (TCE), which are synthesized chemically based on their amino acid sequences. The identification of TCEs using wet-lab experimental approach is challenging, expensive, and time-consuming. Therefore in this study, we present computational model for the prediction of ZIKV TCEs. The model proposed is an ensemble of decision trees that utilizes the physicochemical properties of amino acids. In this way a large amount of time and efforts would be saved for quick vaccine development. The peptide sequences dataset for model training was retrieved from Virus Pathogen Database and Analysis Resource (ViPR) database. The sequences dataset consist of experimentally verified T-cell epitopes (TCEs) and non-TCEs. The model demonstrated promising results when evaluated on test dataset. The evaluation metrics namely, accuracy, AUC, sensitivity, specificity, Gini and Mathew’s correlation coefficient (MCC) recorded values of 0.9789, 0.984, 0.981, 0.987, 0.974 and 0.948 respectively. The consistency and reliability of the model was assessed by carrying out the five (05)-fold cross-validation technique, and the mean accuracy of 0.97864 was reported. Finally, model was compared with standard machine learning (ML) algorithms and the proposed model outperformed all of them. The proposed model will aid in predicting novel and immunodominant TCEs of ZIKV. The predicted TCEs may have a high possibility of acting as prospective vaccine targets subjected to in-vivo and in-vitro scientific assessments, thereby saving lives worldwide, preventing future epidemic-scale outbreaks, and lowering the possibility of mutation escape.
Improvement in sensitivity for lateral flow immunoassay of ferritin using novel device design based on gold-enhanced gold nanoparticles
Authors: Preechakasedkit Pattarachaya , Teekayupak Kanyapat , Citterio Daniel , Ruecha Nipapan 
Published: May 2022

AbstractThis work introduces a low-cost adhesive tape combined with a hydroxylamine/polyvinyl alcohol/polyethylene oxide (HA/PVA/PEO) blend film to fabricate novel devices for improving sensitivity of gold nanoparticle (AuNP)-based lateral flow immunoassays (LFIAs) via two platforms: (1) LFIA device with integrated gold enhancement and (2) LFIA device with two independent sample inlets. The detection of ferritin has been used for proof-of-concept. The adhesive tape inserted in the devices assists to separate two solutions independently flowing from two different inlets toward a nitrocellulose membrane. On-device gold enhancement was achieved by the enlargement of AuNPs via the catalytic reaction of KAuCl4 and HA using the HA/PVA/PEO blend film easily prepared via a solution-casting technique, which could delay the flow of HA released from the film for 180s and improve storage stability of the device. Under optimal conditions evaluated by naked eyes, the gold enhancement (LOD = 0.5 ng/mL) and double-sample inlet (LOD = 2 ng/mL) devices exhibited 20-fold and fivefold higher sensitivity respectively than a conventional device, verifying the sensitivity improvement. Furthermore, the proposed device was successfully detected ferritin in human serum samples within 10 min via naked-eye observation, exhibiting rapidity and simplicity of use, and the capability to perform on-site assays.
Analysis of voltage rise phenomena in electrical power network with high concentration of renewable distributed generations
Authors: Akinyemi Ayodeji Stephen , Musasa Kabeya , Davidson Innocent E. 
Published: May 2022

AbstractThe increasing penetration levels of renewable distributed generation (RDG) into a power system have proven to bring both positive and negative impacts. The occurrence of under voltage at the far end of a conventional electrical distribution network (DN) may not raise concern anymore with RDGs integration into a power system. However, a penetration of RDGs into power system may cause problems such as voltage rise or over-voltage and reverse power flows at the Point of Common Coupling (PCC) between RDG and DN. This research paper presents the impact of voltage rise effect and reverse power flow constraint in power system with high concentration of RDG. The analysis is conducted on a sample DN, i.e., IEEE 13-bus test system, with RDG penetration by considering the most critical scenario such as low power demand in DN and a peak power injection by RDG. For studying the impact of voltage rise and reverse power flow, a mathematical model of a DN integrating RDG is developed. Furthermore, a controller incorporating an advance control-algorithm is proposed to be installed at PCC between DN and RDG to regulate the voltage rise effects and to mitigate the reverse power flow when operating at a worst critical scenario of minimum load and maximum generation from RDG. The proposed control strategy also mitigates the voltage–current harmonic distortions, improves the power factor, and maintain the voltage stability at PCC. The simulations are carried out using MATLAB/Simulink software. Finally, recommendations are provided for the power producers to counteract the effects of voltage rise at PCC. The study has demonstrated that, voltage at PCC can be sustained with a high concentration of RDG during a worst-case scenario without a reverse power flow and voltage rise beyond grid code limits.
In silico mutational analysis of ACE2 to check the susceptibility of lung cancer patients towards COVID-19
Authors: Khalid Zumama , Khan Abeedha Tu-Allah , Alnajjar Radwan , Santali Eman , Shakoori Abdul Rauf 
Published: May 2022

AbstractBeing the second major cause of death worldwide, lung cancer poses a significant threat to the health of patients. This worsened during the era of pandemic since lung cancer is found to be more prone to SARS-CoV-2 infection. Many recent studies imply a high frequency of COVID-19 infection associated severe outcome. However, molecular studies are still lacking in this respect. Hence the current study is designed to investigate the binding affinities of ACE2 lung cancer mutants with the viral spike protein to find the susceptibility of respective mutants carrying patients in catching the virus. Quite interestingly, our study found lesser binding affinities of all the selected mutants thus implying that these cancer patients might be less affected by the virus than others. These results are opposed to the recent studies’ propositions and open new avenues for more in-depth studies.
Preparation, mechanics and self-sensing performance of sprayed reactive powder concrete
Authors: Zhang Yunlong , Wang Jianxin , Wang Jing , Qian Xuesong 
Published: May 2022

AbstractThe emergence of shotcrete provides a new idea for construction methods, but with the development of society, the traditional shotcrete has been unable to meet the needs of structure. Therefore, concrete with better material properties is needed to replace traditional shotcrete. Reactive powder concrete (RPC) is a well-known ultrahigh strength concrete and widely used. Its material properties are better than shotcrete. However, the sprayable performance of RPC and the properties of this sprayed materials have not been reported. Therefore, to make up for the deficiency of ordinary shotcrete, the material properties of sprayed RPC were studied in depth. Response surface method was used to study the effects of different silica fume content, fly ash content and steel fiber volume content on workability, mechanical properties and crack sensitivity. The sprayed reactive powder concrete (sprayed RPC) was proposed for the first time. All models were reliable through variance analysis. The performance of sprayed RPC was better when the workability was between 140 and 160 mm. When the silica fume/binder ratio was 15%, the fly ash/binder ratio was 13.203%, and the volume content of steel fibers was 2%, the mechanical properties and crack sensitivity of sprayed RPC can reach a satisfactory degree. By studying the workability, mechanical properties and crack sensitivity of sprayed RPC, the optimum mix ratio of sprayed RPC was obtained. Steel fiber sprayed RPC can detect structural damage. Results lay the foundation for popularization and application to practical engineering.
Human-to-dog transmission of SARS-CoV-2, Colombia
Authors: Rivero Ricardo , Garay Evelin , Botero Yesica , Serrano-Coll Héctor , Gastelbondo Bertha , Muñoz Marina , Ballesteros Nathalia , Castañeda Sergio , Patiño Luz Helena , Ramirez Juan David , Calderon Alfonso , Guzmán Camilo , Martinez-Bravo Caty , Aleman Ader , Arrieta Germán , Mattar Salim 
Published: May 2022

AbstractSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of the current COVID-19 pandemic, has evolved to have a wide range of hosts, including non-human primates, wild and domestic animals. The ACE2 protein has a high level of conservation and is the common receptor invertebrate species for a viral infection to occur; this receptor could give rise to anthroponotic events. This article describes the first event of symptomatic transmission in Latin America from a human to a dog by the B.1.625 lineage of SARS-CoV-2. We found 21 shared mutations in the complete genomes of viral sequences from owners and dogs. Further phylogenetic and molecular analysis showed that 100% co-localization of the clade helps to understand human-animal transmission. Prediction of the Spike protein structure of the sequenced virus and docking analyzes showed that the E484K mutation in the receptor-binding domain (RBD) could contribute to the viral affinity of dACE2. Therefore, close contact between SARS-CoV-2-infected humans and pets should be avoided to prevent the emergence of novel mutations of public health importance from anthroponotic events.
MEA-Net: multilayer edge attention network for medical image segmentation
Authors: Liu Huilin , Feng Yue , Xu Hong , Liang Shufen , Liang Huizhu , Li Shengke , Zhu Jiajian , Yang Shuai , Li Fufeng 
Published: May 2022

AbstractMedical image segmentation is a fundamental step in medical analysis and diagnosis. In recent years, deep learning networks have been used for precise segmentation. Numerous improved encoder–decoder structures have been proposed for various segmentation tasks. However, high-level features have gained more research attention than the abundant low-level features in the early stages of segmentation. Consequently, the learning of edge feature maps has been limited, which can lead to ambiguous boundaries of the predicted results. Inspired by the encoder–decoder network and attention mechanism, this study investigates a novel multilayer edge attention network (MEA-Net) to fully utilize the edge information in the encoding stages. MEA-Net comprises three major components: a feature encoder module, a feature decoder module, and an edge module. An edge feature extraction module in the edge module is designed to produce edge feature maps by a sequence of convolution operations so as to integrate the inconsistent edge information from different encoding stages. A multilayer attention guidance module is designed to use each attention feature map to filter edge information and select important and useful features. Through experiments, MEA-Net is evaluated on four medical image datasets, including tongue images, retinal vessel images, lung images, and clinical images. The evaluation values of the Accuracy of four medical image datasets are 0.9957, 0.9736, 0.9942, and 0.9993, respectively. The values of the Dice coefficient are 0.9902, 0.8377, 0.9885, and 0.9704, respectively. Experimental results demonstrate that the network being studied outperforms current state-of-the-art methods in terms of the five commonly used evaluation metrics. The proposed MEA-Net can be used for the early diagnosis of relevant diseases. In addition, clinicians can obtain more accurate clinical information from segmented medical images.
Short-term causal effects of common treatments in ambulatory children and young adults with cerebral palsy: three machine learning estimates
Authors: Schwartz Michael H. , Ries Andrew J. , Georgiadis Andrew G. 
Published: May 2022

AbstractOrthopedic and neurological impairments (e.g., muscle contractures, spasticity) are often treated in children and young adults with cerebral palsy (CP). Due to challenges arising from combinatorics, research funding priorities, and medical practicalities, and despite extensive study, the evidence base is weak. Our goal was to estimate the short-term effectiveness of 13 common orthopedic and neurological treatments at four different levels of outcome in children and young adults diagnosed with CP. The outcome levels considered were body structures, specific gait kinematic deviations, overall gait kinematic deviations, and functional mobility. We used three well-establish causal inference approaches (direct matching, virtual twins, and Bayesian causal forests) and a large clinical gait analysis database to estimate the average treatment effect on the treated (ATT). We then examined the effectiveness across treatments, methods, and outcome levels. The dataset consisted of 2851 limbs from 933 individuals (some individuals underwent multiple treatment episodes). Current treatments have medium effects on body structures, but modest to minimal effects on gait and functional mobility. The median ATT of 13 common treatments in children and young adults with CP, measured as Cohen’s D, bordered on medium at the body structures level (median [IQR] = 0.42 [0.05, 0.60]) and became smaller as we moved along the causal chain through specific kinematic deviations (0.21 [0.01, 0.33]), overall kinematic deviations (0.09 [0.03, 0.19]), and functional mobility (-0.01 [-0.06, 0.13]). Further work is needed to understand the source of heterogeneous treatment effects, which are large in this patient population. Replication or refutation of these findings by other centers will be valuable to establish the generalizability of these results and for benchmarking of best practices.
A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients
Authors: Bove Samantha , Comes Maria Colomba , Lorusso Vito , Cristofaro Cristian , Didonna Vittorio , Gatta Gianluca , Giotta Francesco , La Forgia Daniele , Latorre Agnese , Pastena Maria Irene , Petruzzellis Nicole , Pomarico Domenico , Rinaldi Lucia , Tamborra Pasquale , Zito Alfredo , Fanizzi Annarita , Massafra Raffaella 
Published: May 2022

AbstractIn breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated performing the sentinel lymph-node biopsy, that is a time-consuming and expensive intraoperative procedure for the sentinel lymph-node (SLN) status assessment. The aim of this study was to predict the nodal status of 142 clinically negative breast cancer patients by means of both clinical and radiomic features extracted from primary breast tumor ultrasound images acquired at diagnosis. First, different regions of interest (ROIs) were segmented and a radiomic analysis was performed on each ROI. Then, clinical and radiomic features were evaluated separately developing two different machine learning models based on an SVM classifier. Finally, their predictive power was estimated jointly implementing a soft voting technique. The experimental results showed that the model obtained by combining clinical and radiomic features provided the best performances, achieving an AUC value of 88.6%, an accuracy of 82.1%, a sensitivity of 100% and a specificity of 78.2%. The proposed model represents a promising non-invasive procedure for the SLN status prediction in clinically negative patients.
Biomechanical comparison of the femoral neck system and the dynamic hip screw in basicervical femoral neck fractures
Authors: Moon Jun-Ki , Lee Jung Il , Hwang Kyu-Tae , Yang Jae-Hyuk , Park Ye-Soo , Park Ki-Chul 
Published: May 2022

AbstractThe purpose of this study was to compare the fixation stability of proximal fragments and the mechanical characteristics in proximal femur models of basicervical femoral neck fracture fixed by the femoral neck system (FNS) versus the dynamic hip screw. The mean axial stiffness was 234 ± 35 N/mm in the FNS group and 253 ± 42 N/mm in the DHS group, showing no significant difference (p = 0.654). Mean values for x-axis rotation, y-axis rotation, and z-axis rotation after cycle load were 2.2 ± 0.5°, 6.5 ± 1.5°, and 2.5 ± 0.6°, respectively, in the FNS group and 2.5 ± 0.7°, 5.8 ± 2.1°, and 2.2 ± 0.9°, respectively, in the DHS group, showing no significant differences (p = 0.324, p = 0.245, and p = 0.312, respectively). The mean values of cranial and axial migration of screws within the femoral head were 1.5 ± 0.3 and 2.1 ± 0.2 mm, respectively, in the FNS group and 1.2 ± 0.3 and 2.4 ± 0.3 mm, respectively, in the DHS group, showing no significant differences (p = 0.425 and p = 0.625, respectively). The average failure load at vertical load was 1342 ± 201 N in the FNS group and 1450 ± 196 N in the DHS group, showing no significant difference (p = 0.452). FNS fixation might provide biomechanical stability comparable to that of DHS for treating displaced basicervical femoral neck fractures in young adults.
Metabolomic spectra for phenotypic prediction of malting quality in spring barley
Authors: Guo Xiangyu , Jahoor Ahmed , Jensen Just , Sarup Pernille 
Published: May 2022

AbstractWe investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes. Data of five MQ traits was measured on 2667 individual plots of 564 malting spring barley lines from three years and two locations. A total of 24,018 metabolomic features (MFs) were measured on each wort sample. Two statistical models were used, a metabolomic best linear unbiased prediction (MBLUP) and a partial least squares regression (PLSR). Predictive ability within location and across locations were compared using cross-validation methods. For all traits, more than 90% of the total variance in MQ traits could be explained by MFs. The prediction accuracy increased with increasing TP size and stabilized when the TP size reached 1000. The optimal number of components considered in the PLSR models was 20. The accuracy using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation from 0.517 to 0.817. In conclusion, the prediction accuracy of metabolomic prediction of MQ traits using MFs was high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.
Hierarchical network analysis of co-occurring bioentities in literature
Authors: Yang Heejung , Lee Namgil , Park Beomjun , Park Jinyoung , Lee Jiho , Jang Hyeon Seok , Yoo Hojin 
Published: May 2022

AbstractBiomedical databases grow by more than a thousand new publications every day. The large volume of biomedical literature that is being published at an unprecedented rate hinders the discovery of relevant knowledge from keywords of interest to gather new insights and form hypotheses. A text-mining tool, PubTator, helps to automatically annotate bioentities, such as species, chemicals, genes, and diseases, from PubMed abstracts and full-text articles. However, the manual re-organization and analysis of bioentities is a non-trivial and highly time-consuming task. ChexMix was designed to extract the unique identifiers of bioentities from query results. Herein, ChexMix was used to construct a taxonomic tree with allied species among Korean native plants and to extract the medical subject headings unique identifier of the bioentities, which co-occurred with the keywords in the same literature. ChexMix discovered the allied species related to a keyword of interest and experimentally proved its usefulness for multi-species analysis.
Modification of the vacuum-steam thawing method of meat by using the initial stage of sublimation dehydration
Authors: Kopeć Adam , Mierzejewska Sylwia , Bać Aldona , Diakun Jarosław , Piepiórka-Stepuk Joanna 
Published: May 2022

AbstractVacuum-steam thawing is one of the methods used for defrosting food, realized in the atmosphere of water vapour under the conditions of reduced pressure. The water vapour formed in vacuum with the temperature of 20 °C fills the defrosting chamber and condenses on the surface of the defrosted product. The condensated steam has the role of thermal energy carried enabling product thawing. The study presents a modification of this method, introducing an additional stage of sublimation-dehydration vacuum steam thawing (SRVST). The study was carried out for different variants of initial sublimation degree (in the range from 0 to 15%) of a slice of pork loin (m. longissimus lumborum) assessing the final effect of the process of vacuum-steam thawing. Thawing kinetics was determined with the SRVST method, degree of sample defrosting and level of their rehydration. Based on the results it was demonstrated that the use of 12% sublimation dehydration of a meat sample enables its complete defrosting (reaching the temperature not exceeding the cryoscopic temperature).
Neutrophil gelatinase-associated lipocalin as a prognostic biomarker of severe acute respiratory distress syndrome
Authors: Son Eunjeong , Cho Woo Hyun , Jang Jin Ho , Kim Taehwa , Jeon Doosoo , Kim Yun Seong , Yeo Hye Ju 
Published: May 2022

AbstractNeutrophil gelatinase-associated lipocalin (NGAL) is produced in the bronchial and alveolar cells of inflamed lungs and is regarded as a potential prognostic biomarker in various respiratory diseases. However, there are no studies on patients with acute respiratory distress syndrome (ARDS). NGAL levels in serum and bronchoalveolar lavage (BAL) were measured at baseline and on day 7 in 110 patients with ARDS. Baseline NGAL levels were significantly higher in ARDS patients than in healthy controls (serum 25 [14.5–41] vs. 214 [114.5–250.3] ng/mL; BAL 90 [65–115] vs. 211 [124–244] ng/mL). In ARDS, baseline NGAL levels in serum and BAL were significantly higher in non-survivors than in survivors (p < 0.001 and p = 0.021, respectively). Baseline NGAL levels showed a fair predictive power for intensive care unit (ICU) mortality (serum area under the curve (AUC) 0.747, p < 0.001; BAL AUC 0.768, p < 0.001). In a multivariate Cox regression analysis, the baseline serum NGAL level (> 240 ng/mL) was significantly associated with ICU mortality (hazard ratio [HR] 5.39, 95% confidence interval [CI] 2.67–10.85, p < 0.001). In particular, day 7 NGAL was significantly correlated with day 7 driving pressure (serum r = 0.388, BAL r = 0.702), and 28 ventilator-free days (serum r = − 0.298, BAL r = − 0.297). Baseline NGAL has good prognostic value for ICU mortality in patients with ARDS. NGAL can be a biomarker for ventilator requirement, as it may be indicative of potential alveolar epithelial injury.