bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Eye Cataract Diagnosis and Classification Using Deep Neural Network
Authors: Jayachitra S. , Nitheesh Kanna K. , Pavithra G. , Ranjeetha T. 
Published: June 2021

AbstractEye Cataract is one of the main causes of blindness. It affects mostly people at the age of 60.In India, half of the aged people have cataract or have already treated by a surgery. The cataract identification is highly complicated in an early stage. To achieve this, experts chose the concepts of Deep Learning. In this paper we proposed Dense-Net and U-Net to detect and classify the eye cataract. Further, we took 200 samples of eye image to determine the presence of cataract with its severity. Finally, the comparison of Dense-Net and U-Net are tabulated interms of accuracy, sensitivity, and specificity. Hence, it proven that U- Net gives 10% accurate results than Dense Net.
Medical Image Denoising Using Two-Stage Iterative Down-Up CNN and SURF Features
Authors: Jospine Shiney M.R. , Hepsiba D , Vijay Anand L. D. 
Published: June 2021

Abstract
This paper proposes a Two-Stage Deep Iterative Down-Up Convolutional Neural Network for denoising the medical images, which more often increase and decrease the feature map resolution. The function, Speeded Up Robust Features (SURF) is employed in the proposed system to handle the problem of gradient vanishing. The SURF is a patented local feature detector and descriptor. Experiment is conducted using several different medical noise images like Computed Tomography (CT) and Ultrasound Image and found that the proposed system outperforms the current state-of-the-art consistently in image denoising methodologies.
An Early Prediction of Parkinson’s Disease Using Facial Emotional Recognition
Authors: Anusri U , Dhatchayani G , Princely Angelinal Y , Kamalraj S. 
Published: June 2021

Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder which challenges the population due to its uncertainty in prediction of the disease. It is a progressive disorder of the nervous system marked by tremor, muscular rigidity and slow, imprecise movement, chiefly affecting middle-aged and elderly people. In our proposed work, we utilize the facial emotions of PD patients and normal person to identify their facial emotions like sad, happy, anger, and depression. For this predictive analysis, the datasets are acquired from Parkinson’s Progression Markers Initiative (PPMI) which consists of 188 PD patients and 50 normal people for testing and training process. By utilize this dataset, we applying the CNN architecture of Alex Net, and Vgg 16 to achieve their performance in terms of accuracy, sensitivity, specificity, F1 score and area under curve. Finally, it proven that Vgg 16 gives 10% more accurate results than Alex Net. This research outcome will be very useful in diagnosis of early-stage Parkinson’s disease in healthcare.
A robust Kalman filter time scale algorithm with data anomaly
Authors: Song H. , Dong S. , Qu L. , Wang X. , Guo D. 
Published: June 2021

Abstract
Kalman filtering is utilized in many fields because of its
capability to separate data from white phase noise. In time and
frequency domain, employing Kalman filter is particularly important
because of its use in building time scales. The studied time scale
algorithms have been usually based on an ensemble of clocks without
data anomaly, or the anomaly data is processed in advance to secure
the reliability of the data used in Kalman filter algorithm. This
increases the amount of computation and affects the real-time
performance of the algorithm. In this study a robust Kalman filter
is employed to present a method of time scale calculation. It
extends a previously published Kalman filter algorithm that is
useful for an ensemble of clocks without phase anomalies. In the
algorithm, the inflation factor and the optimal adaptive factor are
applied to the clock ensemble. The introduced algorithm may be
useful for an ensemble of clocks with measurement outliers and phase
jumps. The effectiveness of the proposed method can be verified
through simulation and experimental analysis. The analysis result
shows that the robust Kalman filter algorithm can resist the
influence of measurement outliers and phase jumps on time scale
performance. So, the accuracy and the stability of time scale can be
improved.
First attempt of global-scale assimilation of subdaily scale soil moisture estimates from CYGNSS and SMAP into a land surface model
Authors: Kim Hyunglok , Lakshmi Venkataraman , Kwon Yonghwan , Kumar Sujay V 
Published: June 2021

Abstract
Soil moisture performs a key function in the hydrologic process and understanding the global-scale water cycle. However, estimations of soil moisture taken from current sun-synchronous orbit satellites are limited in that they are neither spatially nor temporally continuous. This limitation creates discontinuous soil moisture observation from space and hampers our understanding of the fundamental processes that control the surface hydrologic cycle across both time and space domains. Here, we propose to use frequent soil moisture observations from NASA’s constellation of eight micro-satellites called the Cyclone Global Navigation Satellite System (CYGNSS) together with the Soil Moisture Active Passive (SMAP) to assimilate subdaily scale soil moisture into a land surface model (LSM). Our results, which are based on triple collocation analysis (TCA), show how current scientific advances in satellite systems can fill previous gaps in soil moisture observations in subdaily scale by past observations, and eventually adds value to improvements in global scale soil moisture estimates in LSMs. Overall, TCA-based fractional mean square errors of LSM soil moisture are improved by 61.3% with the synergetic assimilation of CYGNSS data with SMAP soil moisture observations. However, assimilating satellite-based soil moisture over dense vegetation areas can degrade the performance of LSMs as these areas propagate erroneous soil moisture information to LSMs. To our knowledge, this study is the first global assimilation of GNSS-based soil moisture observations in LSMs.
Air quality assessment in three East African cities using calibrated low-cost sensors with a focus on road-based hotspots
Authors: Singh Ajit , Ng’ang’a David , Gatari Michael J , Kidane Abel W , Alemu Zinabu A , Derrick Ndawula , Webster Mbujje J , Bartington Suzanne E , Thomas G Neil , Avis William , Pope Francis D 
Published: June 2021

Abstract
Poor air quality is a development challenge. Urbanization and industrial development along with increased populations have brought clear socio-economic benefits to Low-and Middle-Income Countries (LMICs) but can also bring disadvantages such as decreasing air quality. A lack of reliable air quality data in East African cities makes it difficult to understand air pollution exposure and to predict future air quality trends. This work documents urban air quality and air pollution exposure in the capital cities of Kampala (Uganda), Addis Ababa (Ethiopia) and Nairobi (Kenya). We build a situational awareness of air pollution through repeated static and dynamic mobile monitoring in a range of urban locations, including urban background, roadside (pavement and building), rural background, and bus station sites, alongside vehicle-based measurements including buses and motorcycle taxis. Data suggest that the measured particulate matter mass concentrations (PM2.5, PM10) in all studied cities was at high concentrations, and often hazardous to human health, as defined by WHO air quality guidelines. Overall, the poorest air quality was observed in Kampala, where mean daily PM2.5 and PM10 concentrations were significantly above the WHO limits at urban background locations by 122% and 69% and at roadside locations by 193% and 215%, respectively. Traffic is clearly a major contributor to East African urban air pollution; monitoring in Kampala and Addis Ababa, on motorcycle taxis, in buses and at bus stations indicated that drivers and commuters were exposed to poor air quality throughout their commute. Road-related air pollution can also impact indoor locations near roads. Using one exemplar building located within Nairobi’s Central Business District, it is shown that measured outdoor PM concentrations significantly correlate with the indoor air quality (r = 0.84). This link between roadside emissions and indoor air pollution within buildings located close to the road should be explored more fully. This study, through a series of case studies, provides clear evidence that roads and traffic need to be a focus for mitigation strategies to reduce air pollution exposure in East African cities.
Rapid shrub expansion in a subarctic mountain basin revealed by repeat airborne LiDAR
Authors: Leipe Sean C , Carey Sean K 
Published: June 2021

Abstract
As a consequence of increasing temperatures, a rapid increase in shrub vegetation has occurred throughout the circumpolar North and is expected to continue. Rates of shrub expansion are highly variable, both at the regional scale and within local study areas. This study uses repeat airborne LiDAR and field surveys to measure changes in shrub vegetation cover along with landscape-scale variations in a well-studied subarctic headwater catchment in Yukon Territory, Canada. Airborne LiDAR surveys were conducted in August 2007 and 2018, whereas vegetation surveys were conducted in summer 2019. Machine learning classification algorithms were used to predict shrub presence/absence in 2018 based on rasterized LiDAR metrics, with the best-performing model applied to the 2007 LiDAR to create binary shrub cover layers to compare between survey years. Results show a 63.3% total increase in detectable shrub cover >= 0.45 m in height between 2007 and 2018, with an average yearly expansion of 5.8%. These changes were compared across terrain derivatives to quantify the influence of topography on shrub expansion. Terrain comparisons show that shrubs are located in and are preferentially expanding into lower and flatter areas near stream networks, at lower slope positions and with a higher potential for topographic wetness. Overall, the findings from this research reinforce the documented increase in pan-Arctic shrub vegetation in recent years, quantify the variation in shrub expansion over terrain derivatives at the landscape scale, and demonstrate the feasibility of using LiDAR to compare changes in shrub properties over time.
Two Sources of Miscommunication in Oncology Consultations: An Observational Study Using Conversation Analysis
Authors: Pino Marco , Fatigante Marilena , Alby Francesca , Zucchermaglio Cristina 
Published: June 2021

AbstractThis article investigates miscommunication in sequences of talk where an oncologist asks about patients’ co-morbidities, that is, their other illnesses beyond cancer. Using conversation analysis, we examine ways in which the participants identify and manage two sources of miscommunication: a divergence in the doctor’s and the patient’s understandings about the scope of the question; and a divergence in the doctor’s and the patient’s (and sometimes their companion’s) understandings about the matters that the question targets. Our findings have implications for practice, highlighting ways in which clinicians and patients can manage these sources of miscommunication. These include practices to retrospectively or pre-emptively manage ambiguities and to check the accuracy of patients’ answers. Additionally, our study addresses some theoretical and methodological problems in the study of miscommunication. Our data consist of 25 video-recorded first consultations in an Italian hospital; the participants speak Italian.
Test–Retest Reliability of Remote ImPACT Administration
Authors: Netzel Lauren , Moran Ryan , Hopfe Dustin , Salvatore Anthony P , Brown Warren , Murray Nicholas G 
Published: June 2021

AbstractObjectiveTo evaluate the performance and test–retest reliability obtained when administering a computerized baseline neurocognitive exam to NCAA Division I student-athletes in a controlled laboratory setting versus an uncontrolled remote location.MethodA sample of 129 (female = 100) Division I student-athletes completed Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) pre-season assessments for two distinct and respective sports seasons in a controlled laboratory environment and an uncontrolled remote environment. Depending on the environment, participants were given verbal (controlled) or written (uncontrolled) guidelines for taking the test.ResultsMultivariate repeated-measures ANOVA’s determined that there were no within-subject differences between testing environments on ImPACT composite scores and cognitive efficiency index (CEI). The Chi-square test did not find any significant differences in impulse control or the number of invalid test scores, as determined by ImPACT, between environments. Intraclass correlations found the ImPACT subtest scores to range in test–retest reliability across testing environments, demonstrating moderate (verbal memory composite, r = 0.46; visual memory composite, r = 0.64; reaction time, r = 0.61; impulse control, r = 0.52; and CEI, r = 0.61) and good (visual motor composite, r = 0.77) test–retest reliability.ConclusionsResults indicate that ImPACT is reliable between controlled and uncontrolled testing environments. This further suggests that ImPACT can be administered in a remote environment, pending specific adherence to testing instructions, or in the event of social distancing or isolation policies.
A systematic view of computational methods for identifying driver genes based on somatic mutation data
Authors: Kan Yingxin , Jiang Limin , Tang Jijun , Guo Yan , Guo Fei 
Published: June 2021

AbstractAbnormal changes of driver genes are serious for human health and biomedical research. Identifying driver genes, exactly from enormous genes with mutations, promotes accurate diagnosis and treatment of cancer. A lot of works about uncovering driver genes have been developed over the past decades. By analyzing previous works, we find that computational methods are more efficient than traditional biological experiments when distinguishing driver genes from massive data. In this study, we summarize eight common computational algorithms only using somatic mutation data. We first group these methods into three categories according to mutation features they apply. Then, we conclude a general process of nominating candidate cancer driver genes. Finally, we evaluate three representative methods on 10 kinds of cancer derived from The Cancer Genome Atlas Program and five Chinese projects from the International Cancer Genome Consortium. In addition, we compare results of methods with various parameters. Evaluation is performed from four perspectives, including CGC, OG/TSG, Q-value and QQQuantile–Quantileplot. To sum up, we present algorithms using somatic mutation data in order to offer a systematic view of various mutation features and lay the foundation of methods based on integration of mutation information and other types of data.
Comprehensive analysis of partial methylation domains in colorectal cancer based on single-cell methylation profiles
Authors: Huang Yan , Wang Pingping , Zhou Wenyang , Luo Meng , Xu Zhaochun , Cheng Rui , Xu Chang , Jin Xiyun , Li Yiqun , Jiang Qinghua 
Published: June 2021

Abstract
Epigenetic aberrations have played a significant role in affecting the pathophysiological state of colorectal cancer, and global DNA hypomethylation mainly occurs in partial methylation domains (PMDs). However, the distribution of PMDs in individual cells and the heterogeneity between cells are still unclear. In this study, the DNA methylation profiles of colorectal cancer detected by WGBS and scBS-seq were used to depict PMDs in individual cells for the first time. We found that more than half of the entire genome is covered by PMDs. Three subclasses of PMDS have distinct characteristics, and Gain-PMDs cover a higher proportion of protein coding genes. Gain-PMDs have extensive epigenetic heterogeneity between different cells of the same tumor, and the DNA methylation in cells is affected by the tumor microenvironment. In addition, abnormally elevated promoter methylation in Gain-PMDs may further promote the growth, proliferation and metastasis of tumor cells through silent transcription. The PMDs detected in this study have the potential as epigenetic biomarkers and provide a new insight for colorectal cancer research based on single-cell methylation data.
Purchasing Sex in Sweden—A Risky Business
Authors: Grönvall Ylva 
Published: June 2021

Abstract
This article is based on a study exploring how men who purchase sex in Sweden experience and navigate risk. The focus is on individual assessments of risk in relation to experiences of crime, exploitation and stigma. Based on interviews with 30 Swedish men who purchase sex, an inductive thematic analysis has been applied. The participating men had different strategies to avoid crime and exploitation and they navigated in relation to stigma when purchasing sex. Their risk assessments and conceptualizations of purchasing sex were based on own experiences and on the normative discourse on commercial sex in Sweden. Their fields of action were structured through discipline, governing and responsibilization, and they tried to distance themselves from being defined as ‘sex buyers’ and to purchase sex in ‘good’ ways.
Use of hair products in relation to ovarian cancer risk
Authors: White Alexandra J , Sandler Dale P , Gaston Symielle A , Jackson Chandra L , O’Brien Katie M 
Published: June 2021

Abstract
We evaluated whether hair products, which may contain carcinogens and endocrine disruptors that can be absorbed into the bloodstream, are related to ovarian cancer incidence in a prospective cohort. After excluding women with a history of ovarian cancer or bilateral oophorectomy, 40 559 Sister Study participants ages 35–74 at enrollment (2003–2009) were included. Participants completed questionnaires on hair product use, including hair dyes, straighteners/relaxers and permanents/body waves, in the past 12 months. Cox regression was used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between hair products and incident ovarian cancer. We assessed associations stratified by tumor type (serous, non-serous). Over a mean follow-up of 10 years, 241 women were diagnosed with ovarian cancer. Ever use of any of the examined hair products during the past year was not associated with ovarian cancer risk. However, frequent use (&gt;4 times/year) of straighteners/relaxers or pressing products in the past year was associated with an increased risk of ovarian cancer (HR = 2.19, 95% CI: 1.12–4.27). Ever use of permanent hair dye was positively associated with non-serous (HR = 1.94, 95% CI 1.12–3.37), but inversely associated with serous (HR = 0.65, 95% CI: 0.43–0.99) tumors (p-for-heterogeneity = 0.002). Our novel findings suggest that frequent use of hair straighteners/relaxers or pressing products, which are primarily used by African American/Black women, and possibly permanent hair dye, may be associated with the occurrence of ovarian cancers.
Disrupting the Interaction of nNOS with CAPON Prevents the Reinstatement of Morphine Conditioned Place Preference
Authors: Kou Xiaolin , Xian Jiayun , Huang Zhenquan , Tao Yan , Lin Yuhui , Qin Cheng , Wu Haiyin , Chang Lei , Luo Chunxia , Zhu Dongya 
Published: June 2021

Abstract
Drug abuse is a dramatic challenge for the whole society because of high relapse rate. Environmental cues are crucial for the preference memory of drug abuse. Extinction therapy has been developed to inhibit the motivational effect of drug cues to prevent the reinstatement of morphine abuse. However, extinction therapy alone only forms a new kind of unstable inhibitory memory. We found that morphine conditioned place preference (CPP) extinction training increased the association of nitric oxide synthase (nNOS) with its carboxy-terminal PDZ ligand (CAPON) in the dorsal hippocampus (dHPC) significantly and blocking the morphine-induced nNOS–CAPON association using Tat-CAPON-12C during and after extinction training reversed morphine-induced hippocampal neuroplasticity defect and prevented the reinstatement and spontaneous recovery of morphine CPP. Moreover, in the hippocampal selective ERK2 knock-out or nNOS knockout mice, the effect of Tat-CAPON-12C on the reinstatement of morphine CPP and hippocampal neuroplasticity disappeared, suggesting ERK2 is necessary for the effects of Tat-CAPON-12C. Together, our findings suggest that nNOS–CAPON interaction in the dHPC may affect the consolidation of morphine CPP extinction and dissociating nNOS–CAPON prevents the reinstatement and spontaneous recovery of morphine CPP, possibly through ERK2-mediated neuroplasticity and extinction memory consolidation, offering a new target to prevent the reinstatement of drug abuse.
Neural Mechanism Underlying the Sleep Deprivation-Induced Abnormal Bistable Perception
Authors: Zhou Liqin , Tang Zhonghua , Zuo Zhentao , Zhou Ke 
Published: June 2021

Abstract
Quality sleep is vital for physical and mental health. No matter whether sleep problems are a consequence of or contributory factor to mental disorders, people with psychosis often suffer from severe sleep disturbances. Previous research has shown that acute sleep deprivation (SD) can cause transient brain dysfunction and lead to various cognitive impairments in healthy individuals. However, the relationship between sleep disturbance and bistable perception remains unclear. Here, we investigated whether the bistable perception could be affected by SD and elucidated the functional brain changes accompanying SD effects on bistable perception using functional magnetic resonance imaging. We found that the 28-h SD resulted in slower perceptual transitions in healthy individuals. The reduced perceptual transition was accompanied by the decreased activations in rivalry-related frontoparietal areas, including the right superior parietal lobule, right frontal eye field, and right temporoparietal junction. We speculated that SD might disrupt the normal function of these regions crucial for bistable perception, which mediated the slower rivalry-related perceptual transitions in behavior. Our findings revealed the neural changes underlying the abnormal bistable perception following the SD. It also suggested that SD might offer a new window to understand the neural mechanisms underlying the bistable perception.