bluuebunny/crossref_metadata_2025_split huggingface dataset:
Methods for structural variant detection with long-read sequencing data
Authors: Liu Yichen Henry , Luo Can , Golding Staunton , Ioffe Jacob , Zhou Xin 
Published: November 2022

Abstract
The advent of third-generation sequencing and particularly long-read sequencing, which produces contiguous DNA fragments thousands of bases long, can greatly improve diploid genome assembly and detection of structural variants (SVs), in principle. With long-read whole genome sequencing data becoming increasingly available, designing efficient and reliable algorithms to identify SVs is critical. As a result, a plethora of SV callers have been recently developed to identify SVs. Alignment-based approaches are most popular because they are less computationally demanding and require substantially less sequencing coverage. Alternative approaches assembling the whole genome based on available reads alone (de novo assembly) and comparing assemblies to the reference genome with assembly-based tools to detect SVs, are much more demanding in data coverage and computational resources. However, the lack of comprehensive benchmarking limits our understanding of these two general approaches and hampers further algorithm development. Here we systematically compared 12 read alignment-based SV callers, and 4 assembly-based SV callers, along with 4 upstream aligners and 7 assemblers based on a rigorous and comprehensive benchmarking design. We provide performance results of these tools for different sizes of SVs, insertions vs. deletions, criteria for breakpoint and novel sequence detection, and alignment-based strategy vs. assembly-based strategies under different evaluation scenarios. Based on these results, we offer guidelines and recommendations for different tools and directions for further method development.
Potential Mechanisms of Osthole against Bladder Cancer Based on Network Pharmacology, Molecular Docking, and Experimental Validation
Authors: Jiang Yunzhong , Zhang Mengzhao , Wang Lu , Zhang Lu , Ma Minghai , Jing Minxuan , Li Jianpeng , Song Rundong , Zhang Yuanquan , Yang Zezhong , Zhang Yaodong , Pu Yuanchun , Qu Xiaowei , Fan Jinhai 
Published: November 2022

Abstract
Backgroud: Osthole, a compound extracted from the fruit of Fructus cnidii was traditionally used in treatment for various diseases, such as male impotence, skin related dieases, and foul disease. In addition, it was reported that osthole could also inhibit various cancer. However, few studies had demonstrated that osthole could suppress bladder cancer and its mechanism was unclear. Therefore, we performed a research to explore the potential mechanism for osthole against bladder cancer. Methods: Internet web servers SwissTargetPrediction, PharmMapper, SuperPRED, and TargetNet were used to predict the osthole targets. GeneCards and the OMIM database were used to indicate bladder cancer targets. The intersection of two target gene fragments was used to obtain the key target genes. Protein–protein interaction (PPI) analysis was performed using the Search Tool for the Retrieval of Interacting Genes (STRING) database. Furthermore, we used gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to explore the molecular function of target genes. AutoDock software was then used to perform molecular docking of target genes and osthole. Finally, an in vitro experiment was conducted to validate bladder cancer inhibition by osthole. Results: Our analysis identified 369 intersection genes for osthole, the top ten target genes included MAPK1, AKT1, SRC, HRAS, HASP90AA1, PIK3R1, PTPN11, MAPK14, CREBBP, and RXRA. The GO and KEGG pathway enrichment results revealed that the PI3K-AKT pathway was closely correlated with osthole against bladder cancer. The osthole inhibited the proliferation and migration of bladder cancer cells. Additionally, osthole blocked the bladder cancer epithelial-mesenchymal transition and promoted bladder cancer cell apoptosis by inhibiting the PI3K-AKT and Janus kinase/signal transducer and activator of transcription (JAK/STAT3) pathways. Conclusions: Osthole targets and related pathways have potential in the treatment of bladder cancer. Osthole therapy will likely inhibit proliferation and migration of bladder cancer cells by inhibiting PI3K-AKT and JAK/STAT3 pathways. Subjects: Bioinformatics, Computational Biology, Molecular Biology
Rethinking Feature Aggregation Method in Single Image Super-Resolution
Authors: Chen Xiaozhen , Guo Yaoguang , Zhang Yumei , Fang Haoda 
Published: November 2022

Abstract
With the tremendous development of lightweight convolution neural networks, they have been widely employed in the research of lightweight single image super-resolution. However, these methods focus on the lightweight design of feature extraction blocks and neglect the lightweight aggregation methods that aggregate features from the feature extraction blocks. This paper analyzes the memory consumption of generic aggregation methods and proposes an adaptive local aggregation method. It adopts a set of trainable parameters and channels attention to implement a selective aggregation approach, further enhancing the aggregated network's generalization ability. To further lighten the model and improve the overall performance, we propose a novel re-parameterized branching block structure with spatial and channel diversity. While re-parameterizable structures have been employed in several types of research, they ignore that too many branches add additional training time overhead and are not friendly to devices with strong parallel computing power like GPU. Therefore, we redesign the branch structure by retaining part of the branches from the prior work with a significant performance gain and adding a lightweight branch structure, successfully balancing the expressivity and training time-cost of the block. Comprehensive experiments demonstrate the necessity and superiority of our method over state-of-the-art single image super-resolution methods.
Missing Value Imputation on Gene Expression Data using Bee-based algorithm to Improve Classification Performance
Authors: Chungnoy Kritanat , Tanantong Tanatorn , Songmuamg Pokpong 
Published: November 2022

Abstract
Motivation: Existing missing value imputation methods focused on imputing the data regarding actual values. These mainly assisted towards a completion of datasets as an input for machine learning tasks. Gene expression dataset was one of expensive datasets that usually contains missing values from insufficient resolution and dust contaminants. As gene expression data were often used in machine learning for automated classification, we propose an imputation of missing value towards improvement of classification performance. The proposed method was based on Bee algorithm and K-nearest neighborhood with linear regression. Among the processes, GINI importance score was utilized in selecting values for imputation. The imputed values thus reflected on improving a discriminative power in classification tasks instead of replicating the actual values from the original dataset.
Result: In this study, we evaluated the proposed method in terms of classification performance against frequently used imputation methods including K-nearest neighborhood, PCA, and MICE with 3 cancer-identifying gene expression datasets. The values in the datasets were randomly removed for 1, 2, 3, 4, 5, 10, and 20% to obtain missing value datasets in different missing amount. The results indicated that the proposed method obtained the best classification performance from all datasets comparing to other methods. In comparison to original dataset, the classification model from imputed datasets using the proposed method yielded 15-25% higher classification accuracy. The results showed that feature ranking for classification was noticeably changed as the imputed data played the role to boost a discriminative power in a classification task.
The role of Dodola Community Conservation Area for large mammals conservation, Ethiopia.
Authors: Mekonnen Geremew , Girma Zerihun 
Published: November 2022

Abstract
The role of community conservation areas for large mammals is rarely evaluated. We investigate the species diversity and relative abundance of large mammals in the Dodola Community Conservation Area and compared the results with a large mammals survey 12 years prior. The study area was stratified into three habitat types, and 49 lines transect was laid (27 Dry evergreen Afromontane forests, 20 Sub-afro-alpine habitats, and 2 plantation forests) based on topography, land use, and vegetation cover of the study area. A total of 24 species of large mammals were identified and recorded in the study area. Seasonal variation in abundance of individuals of large mammals was significant (χ2 = 26.66, df = 1, p < 0.05). We conclude that most large mammal population sizes have increased over the last 12 years. Though, the community conservation area is home to diverse species of mammals including some endemic and endangered ones such as mountain nyala and Bale Monkey, human encroachment, agriculture, and overgrazing are prominent in the area, putting huge pressure on flora and fauna. Therefore, we recommend the participatory approach be strengthened to ensure sustainable co-existence between people and wildlife.
Cardiometabolic comorbidities and associated patterns of healthcare utilization and quality of life: results from the Study on Global AGEing and Adult Health (SAGE) Wave 2 in Ghana
Authors: Otieno Peter , Asiki Gershim , Wilunda Calistus , Wami Welcome , Agyemang Charles 
Published: November 2022

Abstract
Background
Understanding the patterns of comorbidities, defined as the co-occurrence of more than one chronic condition, is important for planning health system capacity and response. In this study, we identified classes of adults with cardiometabolic comorbidities and assessed the association of different comorbidity combinations with healthcare utilization and quality of life (QoL).
Methods
Data were from the World Health Organization (WHO) study on global AGEing and adult health Wave 2 (2015) conducted in Ghana. We analysed the clustering of cardiometabolic diseases including angina, stroke, diabetes, and hypertension with unrelated conditions such as asthma, chronic lung disease, arthritis, cataract and depression. Healthcare utilization was measured as the frequency of outpatient visits and hospitalization in the past 12 months. The QoL index was constructed using the WHOQoL instrument and recorded in tertiles labelled as poor, moderate and high. The clusters of adults with comorbidities were identified using latent class analysis (LCA). We used negative binomial regression and ordinal logistic regression to determine the association of comorbidity combinations with healthcare utilization and QoL.
Results
Data from 1,538 adults aged over 50 years who had used outpatient care in the 12 months preceding the survey were analysed. LCA identified three distinct patterns of comorbidities: minimal cardiometabolic comorbidities (72.3%), hypertension and arthritis (18.9%), and cardiopulmonary diseases, hypertension, angina, chronic lung disease, and asthma (8.9%). Relative to the minimal comorbidity class, hypertension and arthritis comorbidity was associated with a higher frequency of outpatient visits [β = 0.2; 95% CI 0.1 to 0.4] and hospitalization [β = 0.7; 95% CI 0.2 to 1.2]. However, cardiopulmonary comorbidity was associated with a higher frequency of hospitalization [β = 1.1; 95% CI 0.2 to 1.9] and not outpatient visits. Compared to the participants with minimal cardiometabolic comorbidities, the odds of moderate and high quality of life were lower among participants with hypertension and arthritis comorbidity [aOR = 0.5; 95% CI 0.3 to 0.6] and cardiopulmonary comorbidities [aOR = 0.3; 95% CI 0.2 to 0.5].
Conclusion
Our findings show that cardiometabolic comorbidities among older persons in Ghana cluster together in distinct patterns that differ in healthcare utilization. This evidence may be used in healthcare planning to optimize treatment and care.
The Course of Glaucoma in Recovered COVID-19 Patients
Authors: Sevimli Neslihan , Saygın Işılay Özsoy , Saygın Efe 
Published: November 2022

Abstract
Purpose

To observe the course of glaucoma progression after coronavirus disease 2019 (COVID-19) treatment of patients with a previous history of glaucoma.

Design

Multicentric observational case-control study

Materials and Methods

This observational case-control study included total 74 patients with COVID-19 infection who were diagnosed with glaucoma previously. The study focused on the left eye of 37 patients each were treated as inpatient or outpatient. Age, gender, existence of systemic and ocular diseases, symptoms, laboratory results, drugs used for COVID-19 and glaucoma, length of hospital stay, intraocular pressure (IOP), and central corneal thickness (CCT) values were recorded. Peripapillary retinal nerve fiber layer thickness (ppRNFLT), ganglion cell–inner plexiform layer complex thickness (GCIPLT) and vertical cup-disc (C-D) ratio results were compared before (pre-COVID-19) and after (post-COVID-19) COVID-19 treatment in both groups.

Results

A significant increase was observed in IOP values in both inpatient and outpatient groups (p = 0,02, p < 0,01 respectively) after COVID-19 infection. However, mean difference (MD) was higher in inpatient group for IOP levels (-1,76). Inpatient and outpatient groups showed statistically significant decrease in GCIPLT values post-COVID-19 (p < 0,01 and p = 0,02, respectively). In addition, MD value was higher in inpatient group (2,72). A significant decrease was observed in ppRNFLT values in inpatient group after COVID-19 infection (p = 0,03). In both groups, the mean C-D ratio was higher post-COVID-19.

Conclusion

PpRNFLT and GCIPLT values were reduced and IOP and C-D ratio values were increased in glaucoma patients after COVID-19 infection. Infection progression observed to be worse in inpatient group.
Optimizing the performance of the neural network by using a mini dataset processing method
Authors: chen Jingliang , wu chenchen , Li hao 
Published: November 2022

Abstract
Data processing is one of the essential methods to optimize the performance of neural networks. In this paper, we give up the traditional data processing method and propose a method to optimize the deep neural network by processing the mini data set based on the loss. Using this method, each Literation training can obtain a relatively optimal result, and the optimization effects of each time are integrated to optimize the results of each epoch finally At the same time, in order to verify the effectiveness of this data processing method, experiments are carried out on MNIST, HAGRID, and CIFAR-10 datasets to compare the effects of using this method and not using this method under different hyperparameters, and finally, the effectiveness of this data processing method is verified. On this basis, the advantages and disadvantages of this method are summarized. Finally, the future research direction has been prospected.
Repair of aged restorations made in direct resin composite – A Systematic Review
Authors: Furtado Marcieli Dias , Immich Felipe , Rosa Wellington Luiz Oliveira da , Piva Evandro , Silva Adriana Fernandes da 
Published: November 2022

Abstract
Objective: to evaluate the different bonding strategies for repairing aged resin composite restorations.
Materials and Methods: The review was reported according to PRISMA Statement and registered in the Open Science Framework (DOI:10.17605/OSF.IO/WZTGS). Two reviewers performed a literature search across Embase (758), Pubmed (1244), Scopus (4300), The Cochrane library (128), and Web of Science (2538) databases. The risk of bias was assessed according to random sequence generation, blinding of outcome assessment, incomplete outcome data, selective reporting, coefficient of variation, and other bias.
Results: Forty-four in vitro studies with different repair strategies on aged resin composite substrates were included from 1990 up to July 2022 which evaluated superficial treatment using shear, flexural, tensile, and bond strength tests. The most frequent treatments were diamond bur and air abrasion. Meta-analysis showed an overall effect significant to diamond bur (shear test; p = 0.02), air abrasion (shear; p = 0.009; flexural, p = 0.003; tensile; p = 0.004 tests), and to phosphoric acid (tensile test, p < 0.00001).
Conclusions: Within the limitations of this study, strategies for improving bond to aged composites may consider better bond using air abrasion or diamond burs, and surface etching with phosphoric followed by the application of an adhesive system. Clinical Significance: This review presents the best treatments for performing the repair on aged resin composite, as guidance for clinical studies to improve emphasis on these findings, with the aim of creating a protocol that will enable dentists to promote minimally invasive treatments.
Clinical Significance: This review presents the best treatments for performing repair with resin composite, as guidance for clinical studies to improve emphasis on these findings, with the aim of creating a protocol that will enable dentists to promote minimally invasive treatments.
Assessment of a deep-learning system for colorectal cancer diagnosis using histopathology images
Authors: Kar Purna , Rowlands Sareh 
Published: November 2022

Abstract
Colorectal Cancer is the one of the most common forms of cancer hence, an early and accurate detection is crucial. Manual diagnosis is a tedious and time-consuming job which is prone to human errors as it involves visual examinations of pathological images. Therefore, it is imperative to use computer-aided detection (CAD) systems to interpret the medical images for a quicker and more accurate diagnosis. The traditional methods for diagnosis comprise extraction of features based on texture, pattern, illumination etc. from pathological images and then use these features in a Machine Learning model for binary classification i.e., cancerous, or non-cancerous. Deep-learning approaches like the Convolutional neural networks (CNNs) have proved to be very effective in classifying and predicting cancer from pathological images. In this study, we have assessed several CNN-based techniques for cancer diagnosis on digitized histopathology images. We have also compared the results of traditional methods for diagnosis with the deep-learning models. Moreover, we have proposed a new model by borrowing the idea from Xception architecture (Xception+), which outperforms the existing architectures. Furthermore, we have studied the effect of transfer learning technique by using models pre-trained on unrelated histopathology images.
Cytotoxic and Apoptotic Effects of Olive Leaf Extract Chitosan Nanoparticles on Breast Cancer MCF-7 and Lung Cancer A549 Cells
Authors: Özdamar Burcu , Sürmeli Yusuf , Mohamed Gülşah Şanlı 
Published: November 2022

Abstract
I. Background
Chitosan, which is a biocompatible and nontoxic material, is predominantly used as a polymer having the ability to nanoparticle formation. In this work, we immobilized olive leaf extract (OLE) with chitosan nanoparticles (CNPs), and elaborately characterized the OLE-CNPs. Also, the cytotoxic influence, the cell cycle distribution, and apoptosis stage of OLE and OLE-CNPs were analyzed on lung carcinoma (A549) and breast adenocarcinoma (MCF-7).
II. Methods and Results
OLE-CNPs were characterized by Zetasizer Nano-ZS and FT-IR Spectrometer. The cytotoxic effects of OLE-CNPs were performed by MTT assay, and cell cycle distribution and apoptotic effects of OLE-CNPs were carried out by using flow cytometer. The loading capacity and the size of OLE-loaded nanoparticles were found as 97.5% and about 100 nm, respectively, in the optimum conditions. In addition, OLE-CNPs were characterized by unique FTIR peaks and morphological display compared to the CNPs. In vitro cytotoxic assay indicated that IC50 values of OLE-CNPs were determined as 540 µg/mL for A549 and 810 µg/mL for MCF-7. The treatment of both A549 and MCF-7 with OLE-CNPs caused the highest cell arrest in G0/G1 in a dose-independent manner. OLE-CNPs affected cell cycle distribution different from free OLE treatment in both cancer cells. A549 and MCF-7 cells were predominantly found in the late apoptosis and necrosis phase, respectively, upon treatment of 1000 µM OLE-CNPs.
III. Conclusions
Our results suggest that CNPs enhance bioavailability OLE as nutraceuticals in cancer and OLE-CNPs might be offered as supplements for cancer therapy.
Fractal characterizations of MeV ion fractured CaF2 thin films
Authors: Pandey Ratnesh Kumar , Yadav Ram Pratap , Kumar Tanuj , Kumar Ashish , Pathak Sachin , Awasthi Shikha , Singh Udai B , Pandey Avinash C 
Published: November 2022

Abstract
We present the morphological evolution and fractal characterization of CaF2 thin film surfaces modified by bombardment with 100 MeV Au+8 ions at various fluences. Atomic force microscopy (AFM) combined with line profile and two-dimensional power spectral density (2D-PSD) analysis was utilized to investigate the evolution of surface morphology as a function of fluence. The AFM images were utilized to investigate the relationship between fractal dimension, roughness exponent, lateral correlation length, and ion fluence. The surface erosion owing to sputtering was depicted using Rutherford backscattering spectrometry (RBS). The structural characteristics’ dependency on fluence was explored with the help of glancing angle X-ray diffraction (GAXRD) measurements on virgin and irradiated samples. Tensile stress calculated using peak shift in the glancing angle X-ray diffractogram showed an increase in tensile stress with fluence that caused the surface to crack after the fracture strength of the surface was crossed. 2D-PSD analysis signified the role of sputtering over surface diffusion for the observed surface modifications. Fractal dimensions first increased and then decreased with ion fluence. Lateral correlation length decreased while roughness exponent increased with fluence after the threshold value.
A tissue-hybrid right ventricular pulsatile circulatory simulator with a functional tricuspid regurgitation
Authors: Takada Jumpei , Morimura Hayato , Hamada Kohei , Okamoto Yusei , Mineta Shiho , Tsuboko Yusuke , Hattori Kaoru , Iwasaki Kiyotaka 
Published: November 2022

Abstract
We hypothesize that the development of a pulsatile circulatory simulator with physiological morphology of tricuspid valve (TV) and right ventricle is essential to evaluate the performance after tricuspid valvuloplasty. This study aimed to develop a tissue-hybrid pulsatile circulatory simulator with clinically relevant tricuspid regurgitation (TR) to evaluate the effects of tricuspid annuloplasty. The tissue-silicone hybrid model consisted of a porcine TV sutured to a silicone right ventricular model with enlarged annulus, which can be pulsated while maintaining the relative position between the TV annulus and papillary muscle. The annulus of porcine TV was enlarged by degrading collagen fibers (pre- and post- annular perimeter 115.7 ± 5.5 vs. 133.1 ± 8.2 mm; p = 0.036). Severe TR models were successfully produced (pre- and post- regurgitation 0.64 ± 0.22 vs. 3.1 ± 0.59 L/min p = 0.0002). After the TV annuloplasty, regurgitation was reduced to a clinically mild grade (pre- and post-regurgitation 3.1 ± 0.59 vs 1.2 ± 0.27 L/min p = 0.013). The regurgitant jet in TR models was remarkably reduced after the annuloplasty. The novel tissue-hybrid system was useful to simulate severe TR and quantify the effects of the annuloplasty. The simulator would be useful to evaluate efficacy of emerging medical treatments and optimize them.