bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comprehensive Transcriptome Analysis of Hypothalamus Reveals Genes Associated With Disorders of Sex Development in Pigs
Authors: Tan Shuwen , Zhou Yi , Zhao Haiquan , Wu Jinhua , Yu Hui , Yang Yin , Yang Yalan , Zhao Huabin , Li Hua 
Published: July 2020

Abstract
Background
Disorders of sex development (DSD) is a chronic autoimmune disease characterized by systemic inflammation, pathological osteogenesis, and endocrine abnormality. However, its genetic etiology remains mostly unknown. In addition, little research focuses on the regulation mechanism from the view of transcriptomics in the hypothalamic-pituitary-gonadal axis (HPGA). The hypothalamus is the integrated center of the HPGA mediating neural, hormonal, and environmental stimulus to sex development.
Methods
Three XX-DSD (SRY-negative) pig (DSD) and three NF pigs (five months old, 40 kg ± 5 kg) were selected by external genitalia observation and sex determining region Y gene (SRY) detection. The hypothalamus were sampled for RNA isolation, and the mRNA, lncRNA and miRNA expression profiles were analyzed by sequencing.
Results
A total of 1,258 lncRNAs, 1,086 mRNAs, and 61 microRNAs were found to differentially express in XX-DSD pigs compared with normal female pigs. Many genes in hormone biosynthesis and secretion pathway are significantly up-regulated, and the up-regulation of GNRH1, KISS1 and AVP may be the candidate genes leading the abnormal secretion of GnRH. Next, we predicted the lncRNA-miRNA-mRNA co-expression triplets and constructed three competing endogenous RNA (ceRNA) potentially associated with DSD. Functional enrichment suggested TCONS_00340886, TCONS_00000204 and miR-181a were related to GnRH secretion.
Conclusions
Our research revealed the first transcriptomic profile in the hypothalamus of XX-DSD pigs and provided new insight in coding and non-coding RNAs that may be associated with DSD in pigs.
Research on the Secure Medical Big Data Ecosystem Based on Hadoop
Authors: Zhang Xiangfeng , Wang Yanmei 
Published: July 2020

Abstract
This paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. Since traditional healing activities take place in medical institutions, patient users must travel to these institutions to learn about their treatment status. The personalized health information system designed for this purpose enables patient users to understand their treatment and rehabilitation status anytime and anywhere. The above is a consideration from the perspective of the patient user, from the perspective of personal health data. Because traditional medical health data is scattered throughout different independent medical institutions, and these databases are heterogeneous. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. The characteristics of blockchain without a central server make the system without a single point In case of failure, the stability of the system is well maintained. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data is stored and analysed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.
Stereotactic Body Radiotherapy Versus Conventional/Moderate Fractionated Radiation Therapy with Androgen Deprivation Therapy for Unfavorable Risk Prostate Cancer
Authors: Patel Sagar Anil , Switchenko Jeffrey , Fischer-Valuck Ben , Zhang Chao , Rose Brent , Chen Ronald , Jani Ashesh , Royce Trevor J 
Published: July 2020

Abstract
Background:Ultrahypofractionation using stereotactic body radiotherapy (SBRT) is an increasingly utilized technique for men with prostate cancer (PC). The comparative efficacy of SBRT plus androgen deprivation therapy (ADT) compared to fractionated radiotherapy (EBRT) plus ADT in higher-risk prostate cancer is unknown. Methods:Men >40 years old with localized PC treated with external beam radiation and concomitant ADT for curative intent between 2004-2016 were analyzed from the National Cancer Database. Patients who lacked ADT or risk stratification data were excluded. 558 men treated with SBRT versus 40,797 men treated with conventional or moderately hypofractionated EBRT were included. Patients were stratified by unfavorable intermediate (UIR) and high (HR) risk using NCCN criteria. Kaplan Meier and Cox proportional hazards were used to compare overall survival (OS) between RT modality, adjusting for age, race, and comorbidity index.Results:With a median follow up of 74 months, there was no difference in estimated 6-year OS between men treated with SBRT versus EBRT regardless of risk group. On multivariable analysis, there was no difference in risk of death for men treated with SBRT compared to EBRT (UIR: adjusted HR 1.09, 95% CI 0.68-1.74, p=.72; HR: adjusted HR 0.93, 95% CI 0.76-1.14, p=.51). On sensitivity analyses, when confining the cohort to men treated with NCCN-preferred dose fractionations, with no comorbidities, or <65 years old, there remained no survival difference between treatment groups for both UIR and HR.Conclusion: Within study limitations, we found no difference in survival between SBRT+ADT and standard of care EBRT+ADT for UIR or HR PC. These results support recent NCCN guideline updates, which include SBRT as a non-preferred option for higher risk men. Prospective validation would further strengthen the evidence basis behind these recommendations.
Practice Patterns for Postoperative Radiation Therapy in Patients with Metastases to the Long Bones: Survey of the Japanese Radiation Oncology Study Group
Authors: Kubota Hikaru , Nakamura Naoki , Shikama Naoto , Tonari Ayako , Wada Hitoshi , Harada Hideyuki , Nagakura Hisayasu , Heianna Joichi , Ito Kei , Nozaki Miwako , Tago Masao , Fushiki Masato , Uchida Nobue , Araki Norio , Sekii Shuhei , Kosugi Takashi , Takahashi Takeo , Kawamoto Terufumi , Saito Tetsuo , Yamada Kazunari 
Published: July 2020

Abstract
BackgroundBecause of the lack of evidence on practice patterns of postoperative radiation therapy (PORT)in patients with metastases to the long bones, it is essential to characterize the current practice patterns and identify factors affecting dose-fractionation to aid future clinical trials. MethodsThe palliative radiation therapy subgroup of the Japanese Radiation Oncology Study Group conducted an internet-based survey to determine the prescription practices and various dose-fractionations used for PORT. Additionally, responders were asked to recommend dose-fractionations in four hypothetical cancer cases, wherein each case represented a patient with impending pathological fractures and one of the four features: limited prognosis, solitary metastasis, radio-resistant primary tumor, or long-term survival. Responders were asked to indicate their preferred irradiation fields and the reasons for choosing long-course radiotherapy over short-course radiotherapy (RT). ResultsEighty-nine radiation oncologists from 67 institutions and 151 RT plans were included. Twenty-two different dose-fractionations were used; the most commonly used and recommended dose-fractionation was 30 Gy in 10 fractions. “Local control” was most the common reason for preferring longer course RT. While fractionated higher-dose regimens were preferred in case of oligometastasis, low dose regimens were preferred in case of limited prognosis; single fraction RT was never preferred. Most respondents recommended involvement of “the entire orthopedic prosthesis.” ConclusionFor PORT of metastases to the long bones, 30 Gy in 10 fractions for the entire orthopedic prosthesis is preferred in current practice. Oligometastasis and a limited prognosis influence the selection of high- and low-dose regimens, respectively. Single fraction RT is never preferred.
Influences of Branch Death on Leaf Nutrient Status and Stoichiometry of Wild Apple Trees (Malus sieversii) During Annual Growth Period in the Western Tianshan Mountains, China
Authors: TAO Ye , NUERHAILATI Maziyirea , ZHANG Jing , YIN Ben-Feng , ZHOU Xiao-Bing , ZHANG Yuan-Ming 
Published: July 2020

Abstract
Background: Malus sieversii (wild apple) is a tertiary relict tree species and a key ancestor of the common cultivated apples today. In recent years, a serious ecological degradation has occurred in the wild apple forests, and many individuals of wild apples have died or have severe dead branches. Whether the death of branches would lead to the change in leaf nutrient stoichiometry of M. sieversii remains unclear. In this study, the N, P, and K contents and the N:P, N:K, and P:K ratios of wild apple trees with three dead branch rates (DBRs; low [LD, < 20%], moderate [MD, 40–60%], and high [HD, > 80%]) during annual growth period, elemental scaling relations, and the possible influencing factors were systematically compared and discussed.Results: Leaf N, P, and K decreased during growth, and N and P did not show significant differences among the different DBRs; however, the HD wild apple trees had the lowest K contents in the photosynthetic and the reproductive organs. Flowers had higher P and K contents than leaves, whereas fruits had low N content. The growth of M. sieversii was always limited by N due to low N:P ratio and N resorption efficiency. The scaling exponents of leaf N–P, N–K, or P–K among the three DBRs did not show any significant difference, and the relationships between stoichiometric ratios and three elements were consistent among three DBRs, revealing an inherent property of M. sieversii. Leaf K content was strongly and negatively influenced by most soil variables. Leaf nutrients were significantly and positively correlated with precipitation and relative humidity, whereas temperature showed the opposite trend.Conclusions: These findings suggest that increasing water input, and improving plant K content and utilization efficiency may be conducive to enhance the resistance and recovery ability of diseased wild apple trees.
Numerical prediction of sporadic E layer occurrence using GAIA
Authors: Shinagawa Hiroyuki , Tao Chihiro , Jin Hidekatsu , Miyoshi Yasunobu , Fujiwara Hitoshi 
Published: July 2020

Abstract
A sporadic E layer has significant influence on radio communications and broadcasting, and predicting the occurrence of sporadic E layers is one of the most important issues in space weather forecast. While sporadic E layer occurrence and the magnitude of the critical sporadic E frequency (foEs) have clear seasonal variations, significant day-to-day variations as well as regional and temporal variations also occur. Because of the highly complex behavior of sporadic E layers, the prediction of sporadic E layer occurrence has been one of the most difficult issues in space weather forecast. To explore the possibility of numerically predicting sporadic E layer occurrence, we have examined various parameters related to sporadic E layer formation using the whole atmosphere–ionosphere coupled model GAIA. By comparing the parameters in GAIA with observed foEs data obtained by ionosonde observations in Japan, we found that variations in the vertical ion convergence at 120 km altitude agree fairly well with variations in foEs. This result suggests that sporadic E layer occurrence can be numerically predicted using the parameters derived from GAIA. We have recently developed a real-time GAIA simulation system that can predict atmosphere–ionosphere conditions for a few days ahead. We present an experimental prediction scheme and a preliminary result for predicting sporadic E layer occurrence.
Artificial Intelligence and Machine Learning in Orthopedic Surgery: A Systematic Review Protocol
Authors: Maffulli Nicola , Rodriguez Hugo C. , Stone Ian W. , Nam Andrew , Song Albert , Gupta Manu , Alvarado Rebecca , Ramon David , Gupta Ashim 
Published: July 2020

Abstract
Background:Artificial Intelligence (AI) and Machine Learning (ML)is interwoven into our everyday lives and has grown enormously in some major fields in medicine including cardiology and radiology. While these specialties have quickly embraced AI and ML, orthopedic surgery has been slower to do so. Fortunately, there has been a recent surge in new research emphasizing the need for a systematic review. The primary objective of this review is to provide an update on the advances of AI and ML in the field of orthopedic surgery. The secondary objectives of this review are to evaluate the applications of AI and ML in providing a clinical diagnosis and predicting post-operative outcomes and complications in orthopedic surgery.Methods:A systematic search will be conducted in PubMed, Science Direct, and Google Scholar databases for articles written in English, Italian, French, Spanish and Portuguese language articles published up to July 2020. References will be screened and assessed for eligibility by at least two independent reviewers as per PRISMA guidelines. Studies must apply to orthopedic interventions, acute and chronic orthopedic musculoskeletal injuries to be considered eligible. Studies will be excluded if they are animal studies, do not relate to orthopedic interventions, or if no clinical data were used. Gold standard processes and practices to obtain a clinical diagnosis and predict post-operative outcomes shall be compared with and without the use of ML algorithms. Any case reports and other primary studies assessing the prediction rate of post-operative outcomes or the ability to identify a diagnosis in orthopedic surgery will be included. Systematic reviews or literature reviews will be examined to identify further studies for inclusion, and results of meta-analyses will not be included in the analysis.Discussion:Our findings will evaluate the advances of AI and ML in the field of orthopedic surgery. We expect to find a large quantity of uncontrolled studies, and a smaller subset of papers describing actual applications and outcomes for clinical care. Cohort studies and large randomized control trial will likely be needed.Trial registration: The Protocol will be registered on PROSPERO international prospective register of systematic reviews prior to commencement.
Total Metals in University of Lagos Wetlands and Sediments and Their Spatial Distribution
Authors: Sowunmi Kehinde , Titilayo Soyebo , Sowunmi Lukman 
Published: July 2020

Abstract
Wetlands are carriers of metals and organic pollutants from natural and anthropogenic sources. Studies have shown that sediments are sinks for these pollutants as they carry more than 99% of the total amount of metals which are released as soluble metals into the Lagoon. The University of Lagos, located at Akoka North eastern part of Yaba in Lagos Mainland runs along a highly industrialized and residential area. This paper reports the total metal concentrations in wetland and sediment and their seasonal variations. Wetland and sediment samples were collected for two seasons in 8 sampling sites. Results showed wetland data having lower concentrations of metals as compared to sediments. Wet season had higher concentrations of metals as compared to other season. Total concentrations in wetlands were Cd (1.0-6.0), Cu (1.0-11.0), Cr (0.3-82.7), Zn (2.7-65.0) and Pb (0.3-16.0) all in μg L-1 respectively. Statistical analysis showed that there was a significant difference between the wet and dry seasons for sediments. Cd was the only metal at acceptable levels while Cr, Cu, Pb and Zn concentrations all exceeded levels deemed acceptable by the SABS and the EPA. Therefore, the wetland is in poor health.
Validation of the French Version of the Fear of COVID-19 Scale and its Associations with Depression, Anxiety and Differential Emotions
Authors: Mailliez Mélody , Griffiths Mark D. , Carre Arnaud 
Published: July 2020

Abstract
The COVID-19 crisis represents a global health concern, including mental health. Regarding the necessity to assess anxiety related to COVID-19, a scale was developed (Ahorsu et al., 2020). The goal of the present study was to validate of the Fear of COVID-19 Scale (FCV-19S) in French (official language in 29 countries), and investigate its relationship with indicators of affective disturbances. The study was performed on 316 participants. The FCV-19S underwent a double back translation procedure. Its psychometric properties were evaluated.The French FCV-19S demonstrated a stable unidimensional structure with robust psychometric properties (strong internal consistency, good convergent and divergent validity, and good test-retest validity). With its robust psychometric properties in assessing the unidimensional construct of the fear of COVID-19, this scale will help to provide a better understanding of the relationship between emotions and psychological or psychiatric disorders during the pandemics in French-speaking countries.
Machine learning based prognostic model for predicting infection susceptibility of COVID-19 using health care data
Authors: Srivat R , Indi Prithviraj N , Agrahari Swapnil , Menon Siddharth , Ashok S. Denis 
Published: July 2020

Abstract
From public health perspectives of COVID-19 pandemic, accurate estimates of infection severity of individuals are extremely valuable for the informed decision making and targeted response to an emerging pandemic. This paper presents machine learning based prognostic model for providing early warning to the individuals for COVID-19 infection using the health care data set. In the present work, a prognostic model using Random Forest classifier and support vector regression is developed for predicting the susceptibility of COVID-19 infection and it is applied on an open health care data set containing 27 field values. The typical fields of the health care data set include basic personal details such as age, gender, number of children in the household, marital status along with medical data like Coma score, Pulmonary score, Blood Glucose level, HDL cholesterol etc. An effective preprocessing method is carried out for handling the numerical, categorical values (non-numerical), missing data in the health care data set. Principal component analysis is applied for dimensionality reduction of the health care data set. From the classification results, it is noted that the random forest classifier provides a higher accuracy as compared to Support vector regression for the given health data set. Proposed machine learning approach can help the individuals to take additional precautions for protecting against COVID-19 infection. Based on the results of the proposed method, clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread. Methods In the present work, Random Forest classifier and support vector regression techniques are applied to a medical health care dataset containing 27 variables for predicting the susceptibility score of an individual towards COVID-19 infection and the accuracy of prediction is compared. An effective preprocessing is carried for handling the missing data in the health care data set. Principal Component Analysis is carried out on the data set for dimensionality reduction of the feature vectors. Results From the classification results, it is noted that the Random Forest classifier provides an accuracy of 90%, sensitivity of 94% and specificity of 81% for the given medical data set.Conclusion Proposed machine learning approach can help the individuals to take additional precautions for protecting people from the COVID-19 infection, clinicians and government officials can focus on the highly susceptible people for limiting the pandemic spread.
A Comprehensive Investigation of Metagenome Assembly by Linked-Read Sequencing
Authors: Zhang Lu , Fang Xiaodong , Liao Herui , Zhang Zhenmiao , Zhou Xin , Han Lijuan , Chen Yang , Qiu Qinwei , Li ShuaiCheng 
Published: July 2020

Abstract
Background: The human microbiota are complex systems with important roles in our physiological activities and diseases. Sequencing the microbial genomes in the microbiota can help in our interpretation of their activities. The vast majority of the microbes in the microbiota cannot be isolated for individual sequencing. Current metagenomics practices use short-read sequencing to simultaneously sequence a mixture of microbial genomes. However, these results are in ambiguity during genome assembly, leading to unsatisfactory microbial genome completeness and contig continuity. Linked-Read sequencing is able to remove some of these ambiguities by attaching the same barcode to the reads from a long DNA fragment (10kb-100kb), thus improving metagenome assembly. However, it is not clear how the choices for several parameters in the use of Linked-Read sequencing affect the assembly quality.Results: We first examined the effects of read depth (C) on metagenome assembly from linked-reads in simulated data and a mock community. The results showed that C positively correlated with the length of assembled sequences but had little effect on their qualities. The latter observation was corroborated by tests using real data from the human gut microbiome, where C demonstrated minor impact on the sequence quality as well as on the proportion of bins annotated as draft genomes. On the other hand, metagenome assembly quality was susceptible to read depth per fragment (CR), and DNA fragment physical depth (CF). For the same C, deeper CR resulted in more draft genomes while deeper CF improved the quality of the draft genomes. We also found that average fragment length (μFL), had marginal effect on assemblies, while fragments per partition (NF/P) impacted the off-target reads involved in local assembly, namely, lower NF/P values would lead to better assemblies by reducing the ambiguities of the off-target reads. In general, the use of linked-reads improved the assembly for contig N50 when compared to Illumina short-reads, but not when compared to PacBio CCS (circular consensus sequencing) long-reads.Conclusions: We investigated the influence of Linked-Read sequencing parameters on metagenome assembly comprehensively. While the quality of genome assembly from linked-reads cannot rival that from PacBio CCS long-reads, the case for using Linked-Read sequencing remains persuasive due to its low cost and high base-quality. Our study revealed that the probable best practice in using linked-reads for metagenome assembly was to merge the linked-reads from multiple libraries, where each had sufficient CR but a smaller amount of input DNA.