bluuebunny/crossref_metadata_2025 huggingface dataset:
Genome Sequence of an
Okra Leaf Curl Virus
from Egypt
Authors: Magdy Mahmoud , Appiah Andrew S. , Rizk Samah M. , Elhifnawi Hagar T. , Karikari Benjamin , Ahiakpa John K. , El-Dougdoug Noha K.
Published: July 2021

A complete
Okra leaf curl virus
DNA-A was sequenced from okra in Egypt. Here, we report the complete genome sequence of this monopartite virus, comprising 2,764 bp and encoding 6 open reading frames (ORFs) with a GC content of 44.6% and 88.3% similarity to a virus reported earlier from Cameroon.
Complete Genome Sequence of
Alphaproteobacteria
Bacterium Strain SO-S41, Isolated from Forest Soil
Authors: Watanabe Motoyuki , Igarashi Kensuke , Kato Souichiro , Kamagata Yoichi , Kitagawa Wataru
Published: July 2021

The complete genome of hydrogen peroxide-sensitive alphaproteobacterial strain SO-S41 was sequenced. The complete genome contains a single chromosome, is 4,443,179 bp in length, contains a total of 4,632 genes, and has a G+C content of 66.2%.
Supplemental Material: From subduction initiation to hot subduction: Life of a Neoarchean subduction zone from the Dengfeng Greenstone Belt, North China Craton
Authors: Deng Hao , al. et
Published: July 2021

Detailed descriptions of analytical methods, Table S1–S4 and Figure S1 are included.
Future of machine learning in paediatrics
Authors: Clarke Sarah LN , Parmesar Kevon , Saleem Moin A , Ramanan Athimalaipet V
Published: July 2021

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn without being explicitly programmed, through a combination of statistics and computer science. It encompasses a variety of techniques used to analyse and interpret extremely large amounts of data, which can then be applied to create predictive models. Such applications of this technology are now ubiquitous in our day-to-day lives: predictive text, spam filtering, and recommendation systems in social media, streaming video and e-commerce to name a few examples. It is only more recently that ML has started to be implemented against the vast amount of data generated in healthcare. The emerging role of AI in refining healthcare delivery was recently highlighted in the ‘National Health Service Long Term Plan 2019’. In paediatrics, workforce challenges, rising healthcare attendance and increased patient complexity and comorbidity mean that demands on paediatric services are also growing. As healthcare moves into this digital age, this review considers the potential impact ML can have across all aspects of paediatric care from improving workforce efficiency and aiding clinical decision-making to precision medicine and drug development.
Left congenital diaphragmatic hernia and gastroschisis in a term male infant
Authors: McGann Kevin C , Arca Marjorie J , Pulhamus Marsha , Livingston Michael H
Published: July 2021

A term male infant was born to a healthy 24-year-old mother with antenatally diagnosed liver-up, left congenital diaphragmatic hernia (CDH) and gastroschisis. The infant was stabilised in the neonatal intensive care unit and then underwent primary repair of the CDH via left subcostal incision and silo placement for the gastroschisis. Serial silo reductions were started postoperatively and umbilical flap closure for the gastroschisis was performed on day of life 6. The patient was weaned from respiratory support, started on enteral feeds, and discharged home at 1 month of age. He was weaned from supplemental nasogastric feeds by 6 weeks of age and is currently well and thriving at 11 months of age.
Case of acute kidney injury due to bilimbi fruit ingestion
Authors: Wong Koh-Wei , Lansing Meryl Grace
Published: July 2021

Bilimbi fruit is widely eaten in Malaysia. Rarely reported is its potential to cause acute kidney injury (AKI) in patients with prior normal renal function. The possible dangers of its consumption are still relatively unknown among many. This case highlights the importance of taking a thorough dietary history in patients with AKI. We also hope to increase awareness among healthcare professionals on the nephrotoxic and neurotoxic effects of bilimbi fruits.
Temporal trends in the association of social vulnerability and race/ethnicity with county-level COVID-19 incidence and outcomes in the USA: an ecological analysis
Authors: Islam Shabatun J , Nayak Aditi , Hu Yingtian , Mehta Anurag , Dieppa Katherine , Almuwaqqat Zakaria , Ko Yi-An , Patel Shivani A , Goyal Abhinav , Sullivan Samaah , Lewis Tené T , Vaccarino Viola , Morris Alanna A , Quyyumi Arshed A
Published: July 2021

Background

The COVID-19 pandemic adversely affected the socially vulnerable and minority communities in the USA initially, but the temporal trends during the year-long pandemic remain unknown.

Objective

We examined the temporal association of county-level Social Vulnerability Index (SVI), a percentile-based measure of social vulnerability to disasters, its subcomponents and race/ethnic composition with COVID-19 incidence and mortality in the USA in the year starting in March 2020.

Methods

Counties (n=3091) with ≥50 COVID-19 cases by 6 March 2021 were included in the study. Associations between SVI (and its subcomponents) and county-level racial composition with incidence and death per capita were assessed by fitting a negative-binomial mixed-effects model. This model was also used to examine potential time-varying associations between weekly number of cases/deaths and SVI or racial composition. Data were adjusted for percentage of population aged ≥65 years, state-level testing rate, comorbidities using the average Hierarchical Condition Category score, and environmental factors including average fine particulate matter of diameter ≥2.5 μm, temperature and precipitation.

Results

Higher SVI, indicative of greater social vulnerability, was independently associated with higher COVID-19 incidence (adjusted incidence rate ratio per 10 percentile increase: 1.02, 95% CI 1.02 to 1.03, p<0.001) and death per capita (1.04, 95% CI 1.04 to 1.05, p<0.001). SVI became an independent predictor of incidence starting from March 2020, but this association became weak or insignificant by the winter, a period that coincided with a sharp increase in infection rates and mortality, and when counties with higher proportion of white residents were disproportionately represented (‘third wave’). By spring of 2021, SVI was again a predictor of COVID-19 outcomes. Counties with greater proportion of black residents also observed similar temporal trends in COVID-19-related adverse outcomes. Counties with greater proportion of Hispanic residents had worse outcomes throughout the duration of the analysis.

Conclusion

Except for the winter ‘third wave’, when majority of the white communities had the highest incidence of cases, counties with greater social vulnerability and proportionately higher minority populations experienced worse COVID-19 outcomes.
Risk factors for severe outcomes in people with diabetes hospitalised for COVID-19: a cross-sectional database study
Authors: Ortega Emilio , Corcoy Rosa , Gratacòs Mònica , Cos Claramunt Francesc Xavier , Mata-Cases Manel , Puig-Treserra Ramon , Real Jordi , Vlacho Bogdan , Castelblanco Esmeralda , Domingo Pere , Khunti Kamlesh , Franch-Nadal Josep , Mauricio Didac
Published: July 2021

Aim

This study’s objective was to assess the risk of severe in-hospital complications of patients admitted for COVID-19 and diabetes mellitus (DM).

Design

This was a cross-sectional study.

Settings

We used pseudonymised medical record data provided by six general hospitals from the HM Hospitales group in Spain.

Outcome measures

Multiple logistic regression analyses were used to identify variables associated with mortality and the composite of mortality or invasive mechanical ventilation (IMV) in the overall population, and stratified for the presence or absence of DM. Spline analysis was conducted on the entire population to investigate the relationship between glucose levels at admission and outcomes.

Results

Overall, 1621 individuals without DM and 448 with DM were identified in the database. Patients with DM were on average 5.1 years older than those without. The overall in-hospital mortality was 18.6% (N=301), and was higher among patients with DM than those without (26.3% vs 11.3%; p<0.001). DM was independently associated with death, and death or IMV (OR=2.33, 95% CI: 1.7 to 3.1 and OR=2.11, 95% CI: 1.6 to 2.8, respectively; p<0.001). In subjects with DM, the only variables independently associated with both outcomes were age >65 years, male sex and pre-existing chronic kidney disease. We observed a non-linear relationship between blood glucose levels at admission and risk of in-hospital mortality and death or IMV. The highest probability for each outcome (around 50%) was at random glucose of around 550 mg/dL (30.6 mmol/L), and the risks flattened above this value.

Conclusion

The results confirm the high burden associated with DM in patients hospitalised with COVID-19 infection, particularly among men, the elderly and those with impaired kidney function. Moreover, hyperglycaemia on admission was strongly associated with poor outcomes, suggesting that personalised optimisation could help to improve outcome during the hospital stay.
Uterine fibroids and incidence of depression, anxiety and self-directed violence: a cohort study
Authors: Chiuve Stephanie E , Huisingh Carrie , Petruski-Ivleva Natalia , Owens Charlotte , Kuohung Wendy , Wise Lauren A
Published: July 2021

Background

Depression and anxiety are prevalent among women with uterine fibroids (UF). The rate of mental health diagnoses in women with UF has not been studied.

Methods

Women aged 18–50 years with diagnosed UF were identified in the Optum Clinformatics commercial insurance claims database (OptumInsight, Eden Prairie, Minnesota) from 1 May 2000 to 31 March 2020 (n=313 754) and were matched 1:2 on age and calendar time to women without (n=627 539). Cox proportional hazards models estimated HRs and 95% CIs between UF and diagnosed depression, anxiety and self-directed violence, adjusting for demographics and comorbidities. Among women with diagnosed UF, the association between hysterectomy and mental health outcomes was estimated.

Results

After adjusting for confounders, women with diagnosed UF had a higher rate of depression (HR: 1.12; 95% CI 1.10 to 1.13), anxiety (HR: 1.12; 95% CI 1.10 to 1.13) and self-directed violence (HR: 1.46; 95% CI 1.29 to 1.64) than women without. Among women with pain symptoms and heavy menstrual bleeding, the HR comparing women with diagnosed UF to women without was 1.21 (95% CI 1.18 to 1.25) for depression, 1.18 (95% CI 1.15 to 1.21) for anxiety and 1.68 (95% CI 1.35 to 2.09) for self-directed violence. Among women with diagnosed UF, the HR comparing women who underwent a hysterectomy to women who did not was 1.22 (95% CI 1.17 to 1.27) for depression, 1.13 (95% CI 1.09 to 1.17) for anxiety and 1.86 (95% CI 1.39 to 2.49) for self-directed violence.

Conclusions

Rates of depression, anxiety and self-directed violence were higher among women with diagnosed UF, particularly among those who experienced pain symptoms or who underwent hysterectomy.
Verbenone and green-leaf volatiles reduce whitebark pine mortality in a northern range-expanding mountain pine beetle outbreak
Authors: Cardinal Etienne , Shepherd Brenda , Krakowski Jodie , Schwarz Carl James , Stirrett-Wood John
Published: July 2021

This is the first study testing the effectiveness of semiochemical treatments to protect individual trees from a range-expanding mountain pine beetle (MPB; Dendroctonus ponderosae Hopkins, 1902) attack into newly exposed host populations of endangered whitebark pine (Pinus albicaulis Engelm.). We investigated the effectiveness of a combination of verbenone and green-leaf volatiles (GLV) to protect rare and valuable disease-resistant trees during a MPB epidemic from 2015 to 2018 in Jasper National Park, Canada. Treatments reduced the proportion of trees attacked by MPB for all diameter classes, across all stands, from 46% to 60%. We also evaluated the effect of the exotic disease white pine blister rust (caused by the fungus Cronartium ribicola J.C. Fisch.) — the species’ other main regional threat. MPB were less likely to attack large rust-infected trees than healthy trees, emphasizing the value of the semiochemical treatment. Protecting large cone-bearing disease-resistant whitebark pine trees is fundamental to whitebark pine recovery. Maintaining reproductive trees on the landscape increases the frequency and diversity of rust-resistant genotypes more effectively than just planting seedlings to replace MPB-killed trees, because this slow-growing species takes over 80 years to reproduce. Our study confirmed that protecting large rust-resistant trees with verbenone and GLV is a proactive and effective treatment against MPB for whitebark pine in naïve populations.
Testing hypotheses of habitat use and temporal activity in relation to body plan in a Mediterranean lizard community
Authors: Rugiero Lorenzo , Capula Massimo , Dendi Daniele , Petrozzi Fabio , Fa Julia E. , Funk Stephan M. , Burke Russell L. , Luiselli Luca
Published: July 2021

A body plan (bauplan) is a suite of morphological characters shared by phylogenetically related animals at some point during their development. Despite its value, the bauplan concept is still rarely employed to characterize functional groups in community ecology. Here, we examine habitat use and spatio-temporal activity correlates of an entire seven-species community of lizards with different bauplans. The study was carried out in three locations in central Italy, encompassing a complex landscape with a patchy mosaic of a wide variety of habitats and microclimates. We tested four hypotheses regarding niche breadth, habitat use, and activity patterns. The first hypothesis, niche complementarity, in which species with similar body shapes should non-randomly partition available habitats, was not supported. By contrast, the hypotheses that larger bodied species should have a wider niche breadth, that slower species should inhabit habitat types of higher cover, and species inhabiting open sunny habitats should exhibit more seasonally variable activity patterns, were all supported by the data. Sympatric lizard communities in our study area were clearly organized by autecological constraints and eco-physiological attributes.
The role of science advice in recovery potential assessments in freshwater fish listing decisions under the Canadian Species at Risk Act
Authors: Montgomery Fielding A. , Stratton Gadfly , Bzonek Paul A. , Campbell Sara E. , Castañeda Rowshyra A. , Chenery Emily S. , Gallage Kavishka , Heer Tej , Kindree Meagan M. , Mandrak Nicholas E.
Published: July 2021

Fishes assessed as Threatened or Endangered by the Committee on the Status of Endangered Wildlife in Canada are disproportionately less likely to be listed under the federal Species at Risk Act (SARA) compared to other taxa. We examined the extent to which the amount and type of science advice in a Recovery Potential Assessment (RPA) contributes to SARA-listing decisions for 34 wildlife species of freshwater fishes in Canada. We used a generalized linear mixed model to describe SARA listing status as a function of RPA completeness. Principal coordinates analyses were conducted to assess similarity in answers to RPA questions among listed and nonlisted species. The amount and type of science advice within an RPA were weakly related to SARA status. RPA completeness accounted for only 7.4% of model variation when family was included as a random effect, likely because nine species not listed under SARA (64%) belong to the sturgeon family. Our results suggest that, while potentially useful for informing recovery strategies, RPAs do not appear to be driving listing status for freshwater fishes in Canada. Factors beyond scientific advice likely contribute to nonlisted species and delays in listing decisions.
Near threshold heavy vector meson photoproduction at LHC and EicC
Authors: Xie Ya-Ping , Gonçalves V. P.
Published: July 2021

The exclusive $$J/\Psi $$

J
/
Ψ

and $$\Upsilon $$
Υ
photoproduction in fixed - target collisions at the LHC and ep(A) collisions at the Electron ion collider in China (EicC) is investigated considering different models for the treatment of the vector meson production at low energies, close to the threshold. Results for the total cross sections and associated distributions are presented. We predict a large number of $$J/\Psi $$

J
/
Ψ

events at the LHC in the rapidity range covered by the LHCb detector. For the EicC, our predictions point out that a detailed analysis of the near threshold $$J/\Psi $$

J
/
Ψ

and $$\Upsilon $$
Υ
photoproduction is feasible. Moreover, our results indicate that the modeling of the near threshold vector meson production can be constrained by future experimental analyzes at the LHC and EicC.
Analysis of correlation and ionization from pair distributions in many-electron systems
Authors: López-Rosa S. , Angulo J. C. , Martín A. L. , Antolín J.
Published: July 2021

Jensen–Shannon divergence is used to quantify the discrepancy between the Hartree–Fock pair density and the product of its marginals for differentN-electron systems, enclosing neutral atoms (with nuclear charge$$Z=N$$Z=N) and singly-charged ions ($$N = Z \pm 1$$N=Z±1). This divergence measure is applied to determine the interelectronic correlation in atomic systems. A thorough study was carried out, by considering (i) both position and momentum conjugated spaces, and (ii) systems with a nuclear charge as far as$$Z = 103$$Z=103. The correlation among electrons was measured by comparing, for an arbitrary system, the double-variable electron-pair density with the product of the respective one-particle densities. A detailed analysis throughout the Periodic Table highlights the relevance not only of weightiness for the systems considered, but also of their shell structure. Besides, comparative computations between two-electron densities of different atomic systems (neutrals, cations, anions) quantify their dissimilarities, patently governed by shell-filling patterns throughout the Periodic Table.
Motzkin algebras and the An tensor categories of bimodules
Authors: Jones Vaughan F. R. , Yang Jun
Published: July 2021

We discuss the structure of the Motzkin algebra [Formula: see text] by introducing a sequence of idempotents and the basic construction. We show that [Formula: see text] admits a factor trace if and only if [Formula: see text] and the higher commutants of these factors depend on [Formula: see text]. Then a family of irreducible bimodules over these factors is constructed. A tensor category with [Formula: see text] fusion rule is obtained from these bimodules.
Light from Reissner–Nordstrom–de Sitter black holes
Authors: Cotăescu Ion I.
Published: July 2021

We derive for the first time the form of the spiral null geodesics around the photon sphere of the Reissner–Nordstrom black hole in the de Sitter expanding universe. Moreover, we obtain the principal parameter we need for deriving, according to our method [I. I. Cotăescu, Eur. Phys. J. C 81, 32 (2021)], the black hole shadow and the related redshift as measured by a remote observer situated in the asymptotic zone. We obtain thus a criterion of detecting charged black holes without peculiar velocities when one knows the mass, redshift and the black hole shadow.
Functional Brain Connectivity Hyper-Network Embedded with Structural Information for Epilepsy Diagnosis
Authors: Zhang Geng , Zhu Qi , Yang Jing , Xu Ruting , Zhang Zhiqiang , Zhang Daoqiang
Published: July 2021

Automatic diagnosis of brain diseases based on brain connectivity network (BCN) classification is one of the hot research fields in medical image analysis. The functional brain network reflects the brain functional activities and structural brain network reflects the neural connections of the main brain regions. It is of great significance to explore and explain the inner mechanism of the brain and to understand and treat brain diseases. In this paper, based on the graph structure characteristics of brain network, the fusion model of functional brain network and structural brain network is designed to classify the diagnosis of brain mental diseases. Specifically, the main work of this paper is to use the Laplacian graph embed the information of diffusion tensor imaging, which contains the characteristics of structural brain networks, into the functional brain network with hyper-order functional connectivity information built based on functional magnetic resonance data using the sparse representation method, to obtain brain network with both functional and structural characteristics. Projection of the brain network and the two original modes data to the kernel space respectively and then classified by the multi-task learning method. Experiments on the epilepsy dataset show that our method has better performance than several state-of-the-art methods. In addition, brain regions and connections that are highly correlated with disease revealed by our method are discussed.
医疗、慈善与战前槟城华人社会
Authors: 李 恒俊
Published: July 2021

1786年槟城开埠以后，健康问题日益成为华人社会的重要议题。1883年，闽粤两地绅商创办南华医院，成为槟城华人社会第一个跨帮权，以施医赠药、救治贫病为宗旨的慈善机构。南华医院建院以后，逐步形成了完善的组织结构、严格的资金制度，吸纳优秀医师，并积极发展多重医疗和社会慈善服务功能。南华医院的出现是19世纪末海外华人社会变迁的产物，而作为海外华人医院慈善网络的重要节点，它的存在也成为海外华人沟通原乡、造福乡梓的关键纽带。
新加坡中华商务总会成立背景的再考察
Authors: 朱 庆
Published: July 2021

新加坡中华商务总会是新加坡最重要的华人组织之一，其成立亦是新加坡华人社会的重大事件。已有研究过于强调清政府倡导设立海外华商会政策的重要影响，进而认为新加坡中华商务总会是清政府实行海外政治控制的半官方机构，是清廷进行募捐活动和发展中国实业的中介机构。本文指出新加坡中华商务总会的筹备与成立受到多重因素的影响，并不简单是清政府政策倡导的结果。新加坡华人会馆、公所、行会的建立，增强了华人之间的组织程度，尤其是跨帮群和跨行业组织的发展，为华人商会的成立奠定了基础。海峡殖民地西商会和槟榔屿、雪兰莪华人商务局的成立，为新加坡华人创设商会起到刺激和示范作用。殖民政府的新社团条例，为商会成立提供了制度保障。抵货运动唤起海外华人的文化民族主义，亦为商会的成立提供助力。过分重视清政府的倡设之功，使研究者常常将新加坡华商会视为中国政府在海外的半官方机构，进而影响到对新加坡中华商会性质、组织和功能等一系列问题的探讨。全面、客观地考察其成立背景，是深入探讨新加坡中华商会的前提与基础。新加坡中华商务总会的成立是新加坡华人自身的利益需求，在其筹建过程中非常重视向海峡殖民政府注册及获得合法地位。新加坡中华商务总会向清政府请求颁发关防的主要目的也是为了维护新加坡华商利益，方便与中国国内经济往来和信息交流。团结华商、联络商情、调和纠葛和沟通政府才是新加坡中华商务总会成立的根本目的与宗旨。
Why are Female Professors Willing to Have a Second Child in China? A Case Study of Chengdu
Authors: Hu Jieren , Mei Shuqin
Published: July 2021

This study explores female professors’ willingness to have a second child in Chengdu. Based on a qualitative study, this article focuses on those who have had at least one child and examine their fertility intentions, childbirth behavior and influencing factors of second-child fertility according to their occupational characteristics and academic achievements. In-depth interviews with 24 female professors in Chengdu were conducted in 2018–2020. It finds that their reproductive choices are the cross-effect result of state policy, external support and personal condition. Their educational experience also plays an important role in deciding their choice of having two children. This research aims to shed light on Chinese women’s reproductive intention and fertility behavior and tries to offer policy suggestions under the two-child policy in China.
Big Data Processing on Volunteer Computing
Authors: Lv Zhihan , Chen Dongliang , Singh Amit Kumar
Published: July 2021

In order to calculate the node big data contained in complex networks and realize the efficient calculation of complex networks, based on voluntary computing, taking ICE middleware as the communication medium, the loose coupling distributed framework DCBV based on voluntary computing is proposed. Then, the Master, Worker, and MiddleWare layers in the framework, and the development structure of a DCBV framework are designed. The task allocation and recovery strategy, message passing and communication mode, and fault tolerance processing are discussed. Finally, to calculate and verify parameters such as the average shortest path of the framework and shorten calculation time, an improved accurate shortest path algorithm, the N-SPFA algorithm, is proposed. Under different datasets, the node calculation and performance of the N-SPFA algorithm are explored. The algorithm is compared with four approximate shortest-path algorithms: Combined Link and Attribute (CLA), Lexicographic Breadth First Search (LBFS), Approximate algorithm of shortest path length based on center distance of area division (CDZ), and Hub Vertex of area and Core Expressway (HEA-CE). The results show that when the number of CPU threads is 4, the computation time of the DCBV framework is the shortest (514.63 ms). As the number of CPU cores increases, the overall computation time of the framework decreases gradually. For every 2 additional CPU cores, the number of tasks increases by 1. When the number of Worker nodes is 8 and the number of nodes is 1, the computation time of the framework is the shortest (210,979 ms), and the IO statistics data increase with the increase of Worker nodes. When the datasets are Undirected01 and Undirected02, the computation time of the N-SPFA algorithm is the shortest, which is 4520 ms and 7324 ms, respectively. However, the calculation time in the ca-condmat_undirected dataset is 175,292 ms, and the performance is slightly worse. Overall, however, the performance of the N-SPFA and SPFA algorithms is good. Therefore, the two algorithms are combined. For networks with less complexity, the computational scale coefficient of the SPFA algorithm can be set to 0.06, and for general networks, 0.2. When compared with other algorithms in different datasets, the pretreatment time, average query time, and overall query time of N-SPFA algorithm are the shortest, being 49.67 ms, 5.12 ms, and 94,720 ms, respectively. The accuracy (1.0087) and error rate (0.024) are also the best. In conclusion, voluntary computing can be applied to the processing of big data, which has a good reference significance for the distributed analysis of large-scale complex networks.
Single-shot Semantic Matching Network for Moment Localization in Videos
Authors: Liu Xinfang , Nie Xiushan , Teng Junya , Lian Li , Yin Yilong
Published: July 2021

Moment localization in videos using natural language refers to finding the most relevant segment from videos given a natural language query. Most of the existing methods require video segment candidates for further matching with the query, which leads to extra computational costs, and they may also not locate the relevant moments under any length evaluated. To address these issues, we present a lightweight single-shot semantic matching network (SSMN) to avoid the complex computations required to match the query and the segment candidates, and the proposed SSMN can locate moments of any length theoretically. Using the proposed SSMN, video features are first uniformly sampled to a fixed number, while the query sentence features are generated and enhanced by GloVe, long-term short memory (LSTM), and soft-attention modules. Subsequently, the video features and sentence features are fed to an enhanced cross-modal attention model to mine the semantic relationships between vision and language. Finally, a score predictor and a location predictor are designed to locate the start and stop indexes of the query moment. We evaluate the proposed method on two benchmark datasets and the experimental results demonstrate that SSMN outperforms state-of-the-art methods in both precision and efficiency.
A Real-Time Effective Fusion-Based Image Defogging Architecture on FPGA
Authors: Du Gaoming , Wu Jiting , Cao Hongfang , Xing Kun , Li Zhenmin , Zhang Duoli , Wang Xiaolei
Published: July 2021

Foggy weather reduces the visibility of photographed objects, causing image distortion and decreasing overall image quality. Many approaches (e.g., image restoration, image enhancement, and fusion-based methods) have been proposed to work out the problem. However, most of these defogging algorithms are facing challenges such as algorithm complexity or real-time processing requirements. To simplify the defogging process, we propose a fusional defogging algorithm on the linear transmission of gray single-channel. This method combines gray single-channel linear transform with high-boost filtering according to different proportions. To enhance the visibility of the defogging image more effectively, we convert the RGB channel into a gray-scale single channel without decreasing the defogging results. After gray-scale fusion, the data in the gray-scale domain should be linearly transmitted. With the increasing real-time requirements for clear images, we also propose an efficient real-time FPGA defogging architecture. The architecture optimizes the data path of the guided filtering to speed up the defogging speed and save area and resources. Because the pixel reading order of mean and square value calculations are identical, the shift register in the box filter after the average and the computation of the square values is separated from the box filter and put on the input terminal for sharing, saving the storage area. What’s more, using LUTs instead of the multiplier can decrease the time delays of the square value calculation module and increase efficiency. Experimental results show that the linear transmission can save 66.7% of the total time. The architecture we proposed can defog efficiently and accurately, meeting the real-time defogging requirements on 1920 × 1080 image size.
Chinese Emotional Dialogue Response Generation via Reinforcement Learning
Authors: Lan Rushi , Wang Jing , Huang Wenming , Deng Zhenrong , Sun Xiyan , Chen Zhuo , Luo Xiaonan
Published: July 2021

In an open-domain dialogue system, recognition and expression of emotions are the key factors for success. Most of the existing research related to Chinese dialogue systems aims at improving the quality of content but ignores the expression of human emotions. In this article, we propose a Chinese emotional dialogue response generation algorithm based on reinforcement learning that can generate responses not only according to content but also according to emotion. In the proposed method, a multi-emotion classification model is first used to add emotion labels to the corpus of post-response pairs. Then, with the help of reinforcement learning, the reward function is constructed based on two aspects, namely, emotion and content. Among the generated candidates, the system selects the one with long-term success as the best reply. At the same time, to avoid safe responses and diversify dialogue, a diversity beam search algorithm is applied in the decoding process. The comparative experiments demonstrate that the proposed model achieves satisfactory results according to both automatic and human evaluations.
Invertible Grayscale with Sparsity Enforcing Priors
Authors: Du Yong , Xu Yangyang , Ye Taizhong , Wen Qiang , Xiao Chufeng , Dong Junyu , Han Guoqiang , He Shengfeng
Published: July 2021

Color dimensionality reduction is believed as a non-invertible process, as re-colorization results in perceptually noticeable and unrecoverable distortion. In this article, we propose to convert a color image into a grayscale image that can fully recover its original colors, and more importantly, the encoded information is discriminative and sparse, which saves storage capacity. Particularly, we design an invertible deep neural network for color encoding and decoding purposes. This network learns to generate a residual image that encodes color information, and it is then combined with a base grayscale image for color recovering. In this way, the non-differentiable compression process (e.g., JPEG) of the base grayscale image can be integrated into the network in an end-to-end manner. To further reduce the size of the residual image, we present a specific layer to enhance Sparsity Enforcing Priors (SEP), thus leading to negligible storage space. The proposed method allows color embedding on a sparse residual image while keeping a high, 35dB PSNR on average. Extensive experiments demonstrate that the proposed method outperforms state-of-the-arts in terms of image quality and tolerability to compression.
Overview of ASSETS 2020
Authors: Nicolau Hugo , Moffatt Karyn , Guerreiro Tiago
Published: July 2021

In October 2020 was the 22nd edition of the ACM SIGACCESS Conference on Computers and Accessibility(ASSETS 2020), which took place online. The ASSETS conference is the premier computing research conferenceexploring the design, evaluation, and use of computing and information technologies to benefi t people withdisabilities and older adults. This year, the ASSETS conference continued its tradition of presenting innovativeresearch on mainstream and specialized assistive technologies, accessible computing, and assistive applicationsof computer, network, and information technologies. We set a new attendance record with 395 attendees from29 countries from all continents across the globe. Our organization and program committees were open tonominations from the community. We had 50 people attending the conference with the support of theSIGACCESS Diversity and Inclusion Scholarships.
RNA‐seq‐Based Screening in Coal Dust‐Treated Cells Identified PHLDB2 as a Novel Lung Cancer‐Related Molecular Marker
Authors: Ge Deyong , Shao Yuhan , Wang Mengjie , Tao Huihui , Mu Min , Tao Xinrong
Published: July 2021

Lung cancer is one of the most serious leading cancers with high incidence globally. Identifying molecular markers is key for disease diagnosis and treatment. Coal dust might be important triggering factors in disease development. Here, we first performed RNA‐seq‐based screening in coal dust treated and nontreated RAW264.7 cell lines. PHLDB2 was found to be the top differentially expressed gene. By retrieving TCGA lung cancer dataset, we observed that PHLDB2 showed upregulations in males and smoker patients. Patients with lower PHLDB2 expression survived longer than those with higher expressions. Furthermore, PHLDB2 was negatively correlated with EMT makers, and a total of 2.74% mutation rate were observed in 1,059 patients. This finding highlights the critical role of PHLDB2 in lung cancer development. PHLDB2 might be a molecular maker for disease diagnosis or treatment.
The Analysis of the Incidence Rate of the COVID-19 Pandemic Based on Segmented Regression for Kuwait and Saudi Arabia
Authors: Shaukat Muhammad Haroon , Alotaibi Naif , Hussain Ijaz , Shrahili Mansour
Published: July 2021

Since the initial detection of the novel coronavirus in Wuhan, China, at the end of 2019, the virus has spread rapidly worldwide and has become a global health threat. Due to rising infections, it has a significant impact on society as well as the economy. Although vaccines and treatment are available now, there is a need to control the pandemic's spread by appropriate strategies and policies. This study evaluates incidence such as positive rates and mortality rates through breakpoints, which were not undertaken in Kuwait and Saudi Arabia. In this regard, we have two-fold objectives: (1) to forecast the cumulative confirmed cases and death cases and (2) to compute the incidence rate within two consecutive days and evaluate the forecasted periods. The autoregressive integrated moving average model is used to forecast the cumulative confirmed cases and death cases for two months. The segmented regression model is used to split the pandemic time series into six periods and compute the incidence rate. Our results show that cumulative confirmed cases will reach 335733 in Kuwait and 445805 in Saudi Arabia by the beginning of June 2021. The cumulative death cases will reach 1830 and 7283 in Kuwait and Saudi Arabia, respectively. However, the positive rate will increase during the forecasted period in both countries, while the death rate will decrease in Kuwait and increase in Saudi Arabia. The study results can help public health organizations and decision-makers to control the spread of infectious diseases.
Effect of Coverage on Adsorptions of CO on Ni(110) Surface
Authors: Yan Sun
Published: July 2021

The adsorptions of CO on Ni(110) surface at different coverages have been investigated by first-principle calculations. The results show that CO will be bonded at short bridge site preferentially. And CO preferentially absorbs upright at short bridge sites and top sites at 1/6 monolayer coverage. With increasing coverage to 0.5 ML, the enhanced steric repulsion leads to the slope of CO. For 1/6 and 1/2 monolayer coverage, CO is mainly bonded at short bridge site and atop site simultaneously at a certain ratio. When CO coverage is 1 monolayer, CO absorbs at short bridge site and forms p2mg configuration. As the coverage of CO is increased to finally form the p2mg structure, there is a continuous frequency shift up to the value 1962.27 cm-1. The vibration frequencies we calculated are consistent with relative experimental results. DOS of CO molecules and Ni atoms are discussed too.
Chaotic Dynamics and Chaos Control in a Fractional-Order Satellite Model and Its Time-Delay Counterpart
Authors: Sayed Ahmed M. , Matouk A. E. , Kumar Sanjay , Ali Vakkar , Bachioua Lahcene
Published: July 2021

Fractional analysis provides useful tools to describe natural phenomena, and therefore, it is more convenient to describe models of satellites. This work illustrates rich chaotic behaviors that exist in a fractional-order model for satellite with and without time-delay. The proof for existence and uniqueness of the satellite model’s solution with and without time-delay is shown. Chaos control is achieved in this system via a simple linear feedback control criterion. Chaotic attractors and chaos control are also found in a time-delay version of the proposed fractional-order satellite system. Various tools based on numerical simulations such as 2D and 3D attractors and bifurcation diagrams are used to illustrate the variety of rich chaotic dynamics in the satellite models.
Effect of Interventional Therapy on Iliac Venous Compression Syndrome Evaluated and Diagnosed by Artificial Intelligence Algorithm-Based Ultrasound Images
Authors: Bai Ye , Bo Fei , Ma Wencan , Xu Hongwei , Liu Dawei
Published: July 2021

In order to explore the efficacy of using artificial intelligence (AI) algorithm-based ultrasound images to diagnose iliac vein compression syndrome (IVCS) and assist clinicians in the diagnosis of diseases, the characteristics of vein imaging in patients with IVCS were summarized. After ultrasound image acquisition, the image data were preprocessed to construct a deep learning model to realize the position detection of venous compression and the recognition of benign and malignant lesions. In addition, a dataset was built for model evaluation. The data came from patients with thrombotic chronic venous disease (CVD) and deep vein thrombosis (DVT) in hospital. The image feature group of IVCS extracted by cavity convolution was the artificial intelligence algorithm imaging group, and the ultrasound images were directly taken as the control group without processing. Digital subtraction angiography (DSA) was performed to check the patient’s veins one week in advance. Then, the patients were rolled into the AI algorithm imaging group and control group, and the correlation between May–Thurner syndrome (MTS) and AI algorithm imaging was analyzed based on DSA and ultrasound results. Satisfaction of intestinal venous stenosis (or occlusion) or formation of collateral circulation was used as a diagnostic index for MTS. Ultrasound showed that the AI algorithm imaging group had a higher percentage of good treatment effects than that of the control group. The call-up rate of the DMRF-convolutional neural network (CNN), precision, and accuracy were all superior to those of the control group. In addition, the degree of venous swelling of patients in the artificial intelligence algorithm imaging group was weak, the degree of pain relief was high after treatment, and the difference between the artificial intelligence algorithm imaging group and control group was statistically considerable (

p
<
0.005

). Through grouped experiments, it was found that the construction of the AI imaging model was effective for the detection and recognition of lower extremity vein lesions in ultrasound images. To sum up, the ultrasound image evaluation and analysis using AI algorithm during MTS treatment was accurate and efficient, which laid a good foundation for future research, diagnosis, and treatment.
Information Security of New Media Art Platform of Distributed System Based on Blockchain Technology
Authors: Zhao Bozuo , Kong Rui , Miao Wei
Published: July 2021

New media art is different from ready-made art, installation art, body art, and land art. New media art is a new art subject category with “optical” media and electronic media as the basic language. And, because of the continuous development of computer networks, distributed database management systems are becoming more and more popular. This article aims to study the information security control problem in the distributed new media system. Through comprehensive access control, reliable support, and many-to-many random mutual encryption, it solves the security requirements of supporting mobile computing in a distributed network environment, communication, and hierarchical grouping. Group key management and other issues have studied some key security technologies for building secure distributed information systems. This article proposes covering the behavior-based access control model ABAC (operation-based access control model), the architecture of the new Trusted Platform Module, the architecture of multithreaded crypto chips, and their service methods, as well as distributed information systems and key management solutions. Approximately obeying the chi-square distribution with 2 degrees of freedom, when the significance level is taken as 5%, the chi-square value for the skin is 5.99. Different initial values are selected for the chaotic sequence, 200 groups of chaotic sequences with a length of 2000 are selected for sequence inspection, and the pass rate is 97.5%. It can be seen that the autocorrelation and cross-correlation characteristics of the improved spatiotemporal chaotic sequence are still relatively ideal, so the usability of the platform is relatively high.
Improvement and Nocturnal Extension of the Efficiency of a Solar Still
Authors: Mandi Benaissa , Menni Younes , Maouedj Rachid , Lorenzini Giulio , Ahmadi Mohammad Hossein , Emani Sampath
Published: July 2021

Various studies have been made to improve the efficiency of the solar still. These studies had devoted to the combination of solar collectors with solar still. This article proposes the use of all forms of solar thermal or photovoltaic energy. In addition, photovoltaic electric storage systems convert them to thermal energy that increases the temperature of a greenhouse solar still. We investigated the possibility of improving the productivity of a greenhouse still and prolong solar distillation overnight. The proposed system is the incorporation of thermal energy produced by a parabolic-cylindrical concentrator, a greenhouse still, and photovoltaic solar energy by panels. The production at 14 pm reaches 110 L/m2 thanks to the various thermal sources made up of the hybrid still. It has better productivity than other distillers. The distillation is extended in the evening thanks to a storage system using electric batteries. The production at 18 pm to 18 L/m2 is reduced at 24 pm to 5 L/m2 in the dark. The accumulated temperature decreases the negative influence of the physical parameters on the production which exceeds 100 L/m2 per day. In the evening, the production is reached 16 L/m2 at 22 pm, which is an advantage compared to other distillers.
Optic Disc and Optic Cup Segmentation for Glaucoma Detection from Blur Retinal Images Using Improved Mask-RCNN
Authors: Nazir Tahira , Irtaza Aun , Starovoitov Valery
Published: July 2021

Glaucoma is a fatal eye disease that harms the optic disc (OD) and optic cup (OC) and results into blindness in progressed phases. Because of slow progress, the disease exhibits a small number of symptoms in the initial stages, therefore causing the disease identification to be a complicated task. So, a fully automatic framework is mandatory, which can support the screening process and increase the chances of disease detection in the early stages. In this paper, we deal with the localization and segmentation of the OD and OC for glaucoma detection from blur retinal images. We have presented a novel method that is Densenet-77-based Mask-RCNN to overcome the challenges of the glaucoma detection. Initially, we have performed the data augmentation step together with adding blurriness in samples to increase the diversity of data. Then, we have generated the annotations from ground-truth (GT) images. After that, the Densenet-77 framework is employed at the feature extraction level of Mask-RCNN to compute the deep key points. Finally, the calculated features are used to localize and segment the OD and OC by the custom Mask-RCNN model. For performance evaluation, we have used the ORIGA dataset that is publicly available. Furthermore, we have performed cross-dataset validation on the HRF database to show the robustness of the presented framework. The presented framework has achieved an average precision, recall, F-measure, and IOU as 0.965, 0.963, 0.97, and 0.972, respectively. The proposed method achieved remarkable performance in terms of both efficiency and effectiveness as compared to the latest techniques under the presence of blurring, noise, and light variations.
Kidney Autotransplantation for Treatment of Ureteric Obstruction: A Case Report and Brief Review of the Literature
Authors: Joshi Parth , Lin Joanne , Sura Tej , Limbu Posan S. , Melkonian Vatche , Bastani Bahar , Varma Chintalapati
Published: July 2021

Autologous kidney transplantation is a relatively rare procedure that has been used as an alternative treatment for a variety of complex genitourinary problems, in particular for the treatment of complex proximal ureteral strictures. In this case report, a 47-year-old male, who had undergone a living donor nephrectomy 14 years earlier, presented with episodes of acute kidney injury on chronic kidney disease. He was found to have a complex proximal ureter stricture of his solitary right kidney. He underwent nephrectomy with subsequent autotransplantation of the kidney into the right iliac fossa. His renal function improved significantly after surgery. Renal autotransplantation may be considered for the management of proximal ureteral obstruction when alternative options are contraindicated.
CT Image Analysis and Clinical Diagnosis of New Coronary Pneumonia Based on Improved Convolutional Neural Network
Authors: Deng Wu , Yang Bo , Liu Wei , Song Weiwei , Gao Yuan , Xu Jia
Published: July 2021

In this paper, based on the improved convolutional neural network, in-depth analysis of the CT image of the new coronary pneumonia, using the U-Net series of deep neural networks to semantically segment the CT image of the new coronary pneumonia, to obtain the new coronary pneumonia area as the foreground and the remaining areas as the background of the binary image, provides a basis for subsequent image diagnosis. Secondly, the target-detection framework Faster RCNN extracts features from the CT image of the new coronary pneumonia tumor, obtains a higher-level abstract representation of the data, determines the lesion location of the new coronary pneumonia tumor, and gives its bounding box in the image. By generating an adversarial network to diagnose the lesion area of the CT image of the new coronary pneumonia tumor, obtaining a complete image of the new coronary pneumonia, achieving the effect of the CT image diagnosis of the new coronary pneumonia tumor, and three-dimensionally reconstructing the complete new coronary pneumonia model, filling the current the gap in this aspect, provide a basis to produce new coronary pneumonia prosthesis and improve the accuracy of diagnosis.