bluuebunny/crossref_metadata_2025_split huggingface dataset:
Discussing Personalized Prognosis in Amyotrophic Lateral Sclerosis: Development of a Communication Guide
Authors: Eenennaam Remko M. van , Kruithof Willeke J. , Es Michael A. van , Reenen Esther T. Kruitwagen-van , Westeneng Henk-Jan , Visser-Meily Johanna M.A. , Berg Leonard H. van den , Beelen Anita 
Published: December 2020

Abstract
Background: Personalized ENCALS survival prediction model reliably estimates the personalized prognosis of patients with amyotrophic lateral sclerosis. Concerns were raised on discussing personalized prognosis without causing anxiety and destroying hope. Tailoring communication to patient readiness and patient needs mediates the impact of prognostic disclosure. We developed a communication guide to support physicians in discussing personalized prognosis tailored to individual needs and preferences of people with ALS and their families.Methods: A multidisciplinary working group of neurologists, rehabilitation physicians, and healthcare researchers A) identified relevant topics for guidance, B) conducted a systematic review on needs of patients regarding prognostic discussion in life-limiting disease, C) drafted recommendations based on evidence and expert opinion, and refined and finalized these recommendations in consensus rounds, based on feedback of an expert advisory panel (patients, family member, ethicist and spiritual counsellor).Results: A) Topics identified for guidance were 1) filling in the ENCALS survival model, and interpreting outcomes and uncertainty, and 2) tailoring discussion to individual needs and preferences of patients (information needs, role and needs of family, severe cognitive impairment or frontotemporal dementia, and non-western patients). B) 17 studies were included in the systematic review. C) Consensus procedures on drafted recommendations focused on selection of outcomes, uncertainty about estimated survival, culturally sensitive communication, and lack of decisional capacity.Recommendations for discussing the prognosis include the following: discuss prognosis based on the prognostic groups and their median survival, or, if more precise information is desired, on the interquartile range of the survival probability. Investigate needs and preferences of the patients and their families for prognostic disclosure, regardless of cultural background. If the patient does not want to know their prognosis, with patient permission discuss the prognosis with their family. If the patient is judged to lack decisional capacity, ask the family if they want to discuss the prognosis. Tailor prognostic disclosure step by step, discuss it in terms of time range, and emphasize uncertainty of individual survival time.Conclusion: This communication guide supports physicians in tailoring discussion of personalized prognosis to the individual needs and preferences of people with ALS and their families.
Screening of the College Students at Clinical High Risk for Psychosis in China: A Multicenter Epidemiological Study
Authors: Wu JiaXin , Long XiangYun , Liu Fei , Qi AnSi , Chen Qi , Guan XiaoFeng , Zhang Qiong , Yao YuHong , Shi JingYu , Xie ShiPing , Yan Wei , Hu MaoRong , Yuan Xin , Tang Jun , Wu SiLiang , Zhang TianHong , Wang JiJun , Lu Zheng 
Published: December 2020

Abstract
Background: To investigate a 3-stage screening procedure and explore the clinical features of subjects at Clinical High Risk (CHR) for psychosis in a representative sample of Chinese college students. Methods: An epidemiological survey of the prevalence of CHR syndrome in Chinese college students that was selected by stratified random sampling from Shanghai, Nanjing and Nanchang cities was done followed a 3-stage procedure. Participants was initially screened with the Prodromal Questionnaire-brief version (PQ-B), and whose distress score of PQ-B exceeded 24 would be invited to a telephone assessment using the subscale for positive symptoms of the Scale of Prodromal Symptoms (SOPS)/Structured Interview for Prodromal Syndromes (SIPS). Lastly, participants who scored 3 or higher in any item of the subscale would be administered with the SIPS interview conducted by trained researchers to confirm the diagnosis of CHR syndrome. Results: 23 063 college students completed the survey during September 2017 to October 2018. 72 students were diagnosed as CHR subjects, and the detection rate in the total sample was 0.3%. The peak age range for the first diagnosis of CHR was 17 to 20 years. 13 and 46 were set as the cut-off points of PQ-B total score and distress score to balance the greatest sensitivity and specificity. Binary logistic regression revealed that 8 items in PQ-B showed significant distinction for detecting CHR subjects. Conclusions: The 3-stage screening method can be utilized in the detection of CHR subjects for psychosis in general population, during which delusional ideas, perceptual abnormalities and suspiciousness deserve great attention.
Respiratory microbiome profiles differ by recent hospitalization and nursing home residence in patients on mechanical ventilation
Authors: Baek Min-gyung , Woo Seong Ji , Kim Nam-Eun , Baek Chaeyun , Won Sungho , Kim Youngmi , Lee Jae Jun , Yi Hana , Hong Ji Young 
Published: December 2020

Abstract
Background: Healthcare-associated pneumonia (HCAP) is a heterogeneous disease. We redefined nursing-home- and hospital-associated infections (NHAI) group by revising existing HCAP risk factors. The NHAI group comprised nursing home residents with a poor functional status, or recent (past 90 days) hospitalization or recent (past 180 days) antibiotic therapy. Our aim was to determine whether respiratory microbiota profiles are related to newly defined NHAI group in critically ill patients on mechanical ventilation.Methods: The 180 endotracheal aspirates (ETAs) from 60 mechanically ventilated ICU patients (NHAI group, n = 24; non-NHAI group, n = 36) were prospectively collected on days 1, 3 and 7 in a university hospital. The bacterial community profiles of the ETAs were explored by 16S rRNA gene sequencing. A phylogenetic-tree-based microbiome association test (TMAT), generalized linear mixed models (GLMMs), the Wilcoxon test and the reference frame method were used to analyze the association between microbiome abundance and disease phenotype.Results: The relative abundance of the genus Corynebacterium was significantly higher in the pneumonia than in the non-pneumonia group. The microbiome analysis revealed significantly lower α-diversity in the NHAI group than in the non-NHAI group. In the analysis of β-diversity, the structure of the microbiome also differed significantly between the two groups (weighted UniFrac distance, Adonis, P <0.001). The abundance of Corynebacterium was significantly higher, and the relative abundances of Granulicatella, Staphylococcus, Streptococcus and Veillonella were significantly lower, in the NHAI group than in the non-NHAI group.Conclusions: The microbiota signature of the ETAs distinguished between patients with and without risk factors for NHAI. The lung microbiome may serve as a therapeutic target for NHAI group.
COVID-19 detection in Chest X-ray Images using Deep Learning
Authors: Erdem Ebru , Aydın Tolga 
Published: December 2020

Abstract
COVID-19 is an important threat worldwide. This disease is caused by the novel SARS-CoV-2. CXR and CT images reveal specific information about the disease. However, when interpreting these images, experiencing an overlap with other lung infections complicates the detection of the disease. Due to this situation, the need for computer-aided systems is increasing day by day. In this study, solutions were developed with proposed models based on deep neural networks (DNN). All the analyses were performed on a publicly available CXR dataset. This study offers a comparison of the deep learning models (SqueezeNet, Inception-V3, VGG16, MobileNet, Xception, VGG19+MobileNet (Concatenated)) that results in the detection and classification of a disease. Empirical evaluation demonstrates that the Inception-V3 model gives 90% accuracy with 100% precision for the COVID-19 infection. This model has been provided with better results compared to other models. In addition to the studies in the literature, it has been observed that the proposed pre-trained-based concatenated model gives very similar successful results to the other models.
KVarPredDB: A database for predicting pathogenicity of missense sequence variants of keratin genes associated with genodermatoses
Authors: Ying Yuyi , Lu Lu , Banerjee Santasree , Xu Lizhen , Zhao Qiang , Wu Hao , Li Ruiqi , Xu Xiao , Yu Hua , Neculai Dante , Xi Yongmei , Yang Fan , Qin Jiale , Li Chen 
Published: December 2020

Abstract
Background: Germline variants of ten keratin genes (K1, K2, K5, K6A, K6B, K9, K10, K14, K16 and K17) have been reported for causing different types of genodermatoses with an autosomal dominant mode of inheritance. Among all the variants of these ten keratin genes, most of them are missense variants. Unlike pathogenic and likely pathogenic variants, understanding the clinical importance of novel missense variants or variants of uncertain significance (VUS) is the biggest challenge for clinicians or medical geneticists. Functional characterization is the only way to understand the clinical association of novel missense variants or VUS but it is time-consuming, costly and depends on the availability of patient’s samples. Existing databases report the pathogenic variants of the keratin genes, but never emphasize the systematic effects of these variants on keratin protein structure and genotype-phenotype correlation. Results: To address this need, we developed a comprehensive database KVarPredDB, which contains information of all ten keratin genes associated with genodermatoses. We integrated and curated 400 reported pathogenic missense variants as well as 4629 missense VUS. KVarPredDB predicts the pathogenicity of novel missense variants as well as to understand the severity of disease phenotype, based on four criteria; firstly, difference in physico-chemical properties between the wild-type and substituted amino acids, secondly, the loss of inter/intra-chain interactions, thirdly, evolutionary conservation of the wild type amino acids and lastly, the effect of the substituted amino acids in the heptad repeat. Molecular docking simulations based on resolved crystal structures were adopted to predict stability changes and get the binding energy to compare the wild type protein with the mutated one. We use this basic information to determine the structural and functional impact of novel missense variants on the keratin coiled-coil heterodimer. KVarPredDB was built under the integrative web application development framework SSM (SpringBoot, Spring MVC, MyBatis) and implemented in Java, Bootstrap, React-mutation-mapper, MySQL, Tomcat. The website can be accessed through http://bioinfo.zju.edu.cn/KVarPredDB. The genomic variants and analysis results are freely available under the Creative Commons license.Conclusions: KVarPredDB provides an intuitive and user-friendly interface with computational analytical investigation for each missense variant of the keratin genes associated with genodermatoses.
Redox-Sensitive Polymeric Micelles with Aggregation-Induced Emission for Bioimaging and Delivery of Anticancer Drugs
Authors: Sun Changzhen , Lu Ji , Wang Jun , Hao Ping , Li Chunhong , Qi Lu , Yang Lin , He Bin , Zhong Zhirong , Hao Na 
Published: December 2020

Abstract
Background: Nano-drug delivery systems show considerable promise for effective cancer therapy. Polymeric micelles have attracted extensive attention as practical nanocarriers for target drug delivery and controlled drug delivery system, however, the distribution of micelles and the release of the drug are difficult to trace in cancer cells. Therefore, the construction of a redox-sensitive multifunctional drug delivery system for intelligent release of anticancer drugs and simultaneous diagnostic imaging and therapy remains an attractive research subject.Results: To construct a smart drug delivery system for simultaneous imaging and cancer chemotherapy, mPEG-ss-Tripp was prepared and self-assembled into redox-sensitive polymeric micelles with a diameter of 105 nm that were easily detected within cells using confocal laser scanning microscopy based on aggregation-induced emission. Doxorubicin-loaded micelles rapidly released the drug intracellularly when GSH reduced the disulfide bond. The drug-loaded micelles inhibited tumor xenografts in mice, while this efficacy was lower without the GSH-responsive disulfide bridge. These results establish an innovative multi-functional polymeric micelle for intracellular imaging and redox-triggered drug deliver to cancer cells.Conclusions: A novel redox-sensitive drug delivery system with AIE property was constructed for simultaneous cellular imaging and intelligent drug delivery and release. This smart drug delivery system opens up new possibilities for multifunctional drug delivery systems.
Synthetic Wavelength Holography: Snapshot Non-Line-of-Sight Imaging with High-Resolution and Wide Field of View
Authors: Willomitzer Florian , Rangarajan Prasanna , Li Fengqiang , Balaji Muralidhar , Christensen Marc , Cossairt Oliver 
Published: December 2020

Abstract
The presence of a scattering medium in the imaging path between an object and an observer is known to severely limit the visual acuity of the imaging system. We present an approach to circumvent the deleterious effects of scattering, by exploiting spectral correlations in scattered wavefronts. Our Synthetic Wavelength Holography (SWH) method is able to recover a holographic representation of hidden targets with high resolution over a wide field of view. The complete object field is recorded in a snapshot-fashion, by monitoring the scattered light return in a small probe area. This unique combination of attributes opens up a plethora of new Non-Line-of-Sight imaging applications ranging from medical imaging and forensics, to early-warning navigation systems and reconnaissance. Adapting the findings of this work to other wave phenomena will help unlock a wider gamut of applications beyond those envisioned in this paper.
Mathematical Model for COVID-19 Management in Crowded Settlements and High-Activity Areas
Authors: Ssematimba Amos , Nakakawa Juliet , Ssebuliba Joseph , Mugisha Joseph Y. T 
Published: December 2020

Abstract
This paper develops and analyses a population density-dependent mathematical model to study the transmission dynamics of COVID-19 in crowded settlements such as refugee camps, schools, markets and churches. The model quantifies the potential impact of physical/social distancing and population density on the disease burden. Results reveal that with no fatalities and no infected entrants, the reproduction numbers associated with asymptomatic and symptomatic cases are inversely proportional to; the habitat area size, and the efforts employed in tracing and hospitalising these cases. The critical habitat area below which the disease dies out is directly proportion to the time taken to identify and hospitalise infected individuals. Results also show that disease persistence in the community is guaranteed even with minimal admission of infected individuals. Our results further show that as the level of compliance to standard operating procedures (SOPs) increases, then the disease prevalence peaks are greatly reduced and delayed. Therefore, proper adherence to SOPs such as use of masks, physical distancing measures and effective contact tracing should be highly enforced in crowded settings if COVID-19 is to be mitigated.
Comparative assessment of the inline and branching design strategies based on the compound technique
Authors: Triki Ali 
Published: December 2020

Abstract
The inline or branching water hammer control strategies, which are based on the insertion of compound plastic short-penstock or inline section at the transient-induced region of main pipes, illustrated a promising ability to upgrade steel pipe-based hydraulic systems concerning the extension of admissible pressure level. In this respect, prior results suggested that the specific layout utilizing an (HDPE–LDPE) compound short-penstock (where the (HDPE) sub-short-penstock is attached to the main steel pipe and the (LDPE) sub-short-penstock corresponds to the short-penstock dead-end side) provided significant attenuation of pressure magnitude. Concurrently, recent studies concluded that the (HDPE–LDPE) compound short-section-based inline strategy provided substantial attenuation of pressure magnitude. However, these strategies illustrated a drawback relying on the expansion of the period of pressure wave oscillations. Accordingly, this study assessed and compared the capacities of the compound technique concerning the trade-off between the magnitude-attenuation and the period-expansion of pressure wave oscillations. The findings of these analyses showed that the (HDPE–LDPE) compound short-penstock particular setup of the branching strategy allowed the best trade-off between the attenuation of magnitude and the period expansion of pressure wave oscillations. Furthermore, results showed the competitiveness of the latter upgrading strategy as compared to the (HDPE) or (LDPE) main pipe-based renewed hydraulic systems.
Daily water quality evaluation of reservoir and cyanobacteria pollution index calculation
Authors: Zhang Wen , Liu Zhiwei 
Published: December 2020

Abstract
Water quality evaluation is the most direct and quantitative description of a reservoir water environment. Due to the lack of biological factor evaluation in water quality evaluation at present, the conventional pollution index evaluation system was used to evaluate water quality and the correlation between the evaluation indexes and the concentration of cyanobacteria was studied. After determining the correlation, a novel calculation method using the cyanobacteria pollution index was determined by principal component analysis (PCA). The results showed that the eutrophication index and nitrogen phosphorus index of the water body were high. Biological pollution in reservoirs cannot be ignored. The correlation between eutrophication index and cyanobacteria concentration was weak (Pearson correlation = 0.242). For the reservoir, it was necessary to establish a special cyanobacteria pollution index. Five variables were used for the cyanobacteria pollution index calculation by the PCA method. The total variance of the two main components was 77.107%, which reflected most of the data information. In the reservoirs of other areas, similar parameters can also be selected to calculate the cyanobacteria pollution index. This research provides a reference for the biological factor evaluation of similar reservoirs in the world.
Halloysite nanotube blended nanocomposite ultrafiltration membranes for reactive dye removal
Authors: Keskin Başak , Ağtaş Meltem , Ormancı-Acar Türkan , Türken Türker , İmer Derya Y. , Ünal Serkan , Menceloğlu Yusuf Z. , Uçar-Demir Tuğba , Koyuncu İsmail 
Published: December 2020

Abstract
In this paper, ultrafiltration (UF) flat sheet membranes were manufactured by introducing two diverse halloysite nanotubes (HNT) size (5 μm and 63 μm) and five different (0, 0.63, 1.88, 3.13, 6.30 wt %) ratios by wet phase inversion. Some characterization methods which are contact angle, zeta potential, viscosity, scanning electron microscopy (SEM) and Young's modulus measurements were used for ultrafiltration membranes. Synthetic dye waters which were Setazol Red and Reactive Orange were used for filtration performance tests. These dye solutions were filtered in three different pH conditions and three different temperature conditions for pH and temperature resistance to understand how flux and removal efficiency change. The best water permeability results were obtained as 190.5 LMH and 192 LMH, for halloysite nanotubes (HNT) sizes of 5 μm and 63 μm respectively. The best water and dye performance of UF membrane contains 1.88% w/w ratio of HNT, which showed increased water flux and dye flux of membranes according to different HNT concentrations including ultrafiltration membranes.
DeepSSPred: A Deep Learning Based Sulfenylation Site Predictor Via a Novel nSegmented Optimize Federated Feature Encoder
Authors: Khan Zaheer Ullah , Pi Dechang 
Published: December 2020

Background:S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient feature schemes, severe imbalance issues, and lack of an intelligent learning engine.Objective:In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and non-sulfenylation sites.Methods:In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via nSegmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2D-Convolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication.Results:Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method. These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies.Conclusion:In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs in general.