bluuebunny/crossref_metadata_2025 huggingface dataset:
Compression for population genetic data through finite-state entropy
Authors: Chen Winfield , Elliott Lloyd T.
Published: September 2021

We improve the efficiency of population genetic file formats and GWAS computation by leveraging the distribution of samples in population-level genetic data. We identify conditional exchangeability of these data, recommending finite state entropy algorithms as an arithmetic code naturally suited for compression of population genetic data. We show between [Formula: see text] and [Formula: see text] speed and size improvements over modern dictionary compression methods that are often used for population genetic data such as Zstd and Zlib in computation and decompression tasks. We provide open source prototype software for multi-phenotype GWAS with finite state entropy compression demonstrating significant space saving and speed comparable to the state-of-the-art.
Quantum non-Gaussianity from an indefnite causal order of Gaussian operations
Authors: Koudia Seid , Gharbi Abdelhakim
Published: September 2021

Quantum non-Gaussian states are considered a useful resource for many tasks in quantum information processing, from quantum metrology and quantum sensing to quantum communication and quantum key distribution. Another useful tool that is gaining attention is the newly constructed quantum switch. Its applications in many tasks in quantum information have been proved to outperform many existing schemes in quantum communication and quantum thermometry. In this contribution, we demonstrate this to be very useful for engineering highly non-Gaussian states from Gaussian operations whose order is controlled by degrees of freedom of a control qubit. The nonconvexity of the set of Gaussian states and the set of Gaussian operations guarantees the emergence of non-Gaussianity after post-selection on the control qubit deterministically, in contrast to existing protocols in the literature. The nonclassicality of the resulting states is discussed accordingly.
Wormhole solutions via embedding approach in ℛ + βℛ2 gravity with matter coupling
Authors: Wang Dan , Mustafa G.
Published: September 2021

In this paper, we examine the embedded wormhole solutions in the modified [Formula: see text] theory of gravity, where [Formula: see text] denotes the trace of the energy–momentum tensor and [Formula: see text] is the Ricci scalar. We derive the embedded class-1 solutions by considering spherically symmetric static spacetime. The shape function is calculated in the framework of embedded class-1 spacetime. It is necessary to mention here that the calculated shape function can be used in other modified theories of gravity. We explore the feasible solutions for the specific model of [Formula: see text] theory of gravity. Energy conditions have been explored using the approach mentioned above. Conclusively, we find that obtained wormhole solutions are acceptable, as the null energy condition is violated in the specific region.
A topological approach for emerging D-branes and its implications for gravity
Authors: Pincak Richard , Pigazzini Alexander , Jafari Saeid , Özel Cenap , DeBenedictis Andrew
Published: September 2021

In this paper, we introduce a new geometric/topological approach to the emerging braneworld scenario in the context of D-branes using partially negative-dimensional product (PNDP) manifolds. The working hypothesis is based on the fact that the orientability of PNDP manifolds can be arbitrary, and starting from this, we propose that gravitational interaction can derive naturally from the non-orientability. According to this hypothesis, we show that topological defects can emerge from non-orientability and they can be identified as gravitational interaction at macroscopic level. In other words, the orientability of fundamental PNDPs can be related to the appearance of curvature at low-energy scales.
A remark on skew factorization and new 𝔽4-linear codes
Authors: Seneviratne Padmapani
Published: September 2021

Nine new [Formula: see text] linear codes with lengths [Formula: see text] and [Formula: see text] that improve previously best known parameters are constructed. By modifying these codes, another 17 new codes are obtained. It is conjectured that the complete set of factors of [Formula: see text] can be derived from the factors of [Formula: see text] for even values of [Formula: see text] in the skew polynomial ring [Formula: see text]. It is further shown that the [Formula: see text] code obtained is linearly complementary dual.
Multiclass recognition of Alzheimer’s and Parkinson’s disease using various machine learning techniques: A study
Authors: Balaji Chetan , Suresh D. S.
Published: September 2021

The aging population is primarily affected by Alzheimer’s disease (AD) that is an incurable neurodegenerative disorder. There is a need for an automated efficient technique to diagnose Alzheimer’s in its early stage. Various techniques are used to diagnose AD. EEG and neuroimaging methodologies are widely used to highlight changes in the electrical activity of the brain signals that are helpful for early diagnosis. Parkinson’s disease (PD) is a major neurological disease that results in an average of 50,000 new clinical diagnoses worldwide every year. The voice features are majorly used as the main means to diagnose PD. The major symptoms of PD are loss of intensity, the monotony of loudness and pitch, reduction in stress, unidentified silences, and dysphonia. Even though various innovative models are proposed by explorers about Alzheimer’s and Parkinson’s classification diseases, still there is a need for efficient learning methodologies and techniques. This paper provides a review on using machine learning (ML) together with several feature extraction techniques that is helpful in the early detection of AD with Parkinson’s. The novelty and objective of this study are that the CAD technique is used to improve the accuracy of early diagnosis of AD. The proposed technique depends on the nonlinear process for data dimension reduction, feature removal, and classification using kernel-based support vector machine (SVM) classifiers. The dimension of the input space is radically diminished with kernel methods. As the learning set is labeled, it creates sense to utilize this information to make a dependable method of dropping the input space dimension. The different techniques of ML are explained under the major approaches viz. SVM, artificial neural network (ANN), deep learning (DL), and ensemble methods. A comprehensive assessment is presented at SVM, ANN, and DL approaches for better detection of Alzheimer’s with PD highlighting future insights.
The Role of Acoustics in the Conservation of the Ivory-Billed Woodpecker (Campephilus principalis)
Authors: Collins Michael D.
Published: September 2021

The Ivory-billed Woodpecker (Campephilus principalis) is an iconic species that has survived in barely detectable numbers for the past 100 years, during which it has been feared extinct only to be rediscovered several times. The most recent rediscovery was announced in an article that was featured on the cover of Science in 2005. The persistence of the Ivory-billed Woodpecker became controversial when ornithologists were unable to obtain a clear photo for documenting this ultra-elusive bird during multi-year searches at sites in Arkansas and Florida, where they had several sightings and were convinced these birds were present. Audio recordings of ‘kent’ calls and double knocks were obtained at both sites, but such recordings are not regarded as conclusive evidence of persistence. A debate on this issue that took place in Science and Nature focused on relatively weak video evidence obtained in Arkansas but excluded three videos obtained in Louisiana and Florida that show flights, field marks, and other behaviors and characteristics that are consistent with the Ivory-billed Woodpecker but no other species of the region. Kent calls were recorded in the 1930s, when other types of vocalizations were observed but not recorded, including a high-pitched alarm call. On two occasions in Louisiana, high-pitched calls were observed coming from the direction of an alarmed Ivory-billed Woodpecker, and several of them were recorded. The spectrograms of the high-pitched calls and all other known and putative vocalizations of the Ivory-billed Woodpecker consist of simultaneously excited harmonics. A harmonic oscillator model has been used to draw a connection between the drumming that is typical of most woodpeckers and the double knocks of the Ivory-billed Woodpecker and other Campephilus woodpeckers. Drumming corresponds to periodic forcing; double knocks correspond to impulsive forcing, and a single thrust of the body is sufficient to produce two impacts of the bill in rapid succession. The audio recordings from Arkansas and Florida were obtained with single microphones. A horizontal array of microphones would make it possible to detect weaker sounds and determine the directions of sources. This approach has the potential to lead to the discovery of a nest, and it might be more effective if the array is placed above the treetops, where sounds might propagate to longer ranges.
Quantum Machine Learning Algorithm for Knowledge Graphs
Authors: Ma Yunpu , Tresp Volker
Published: September 2021

Semantic knowledge graphs are large-scale triple-oriented databases for knowledge representation and reasoning. Implicit knowledge can be inferred by modeling the tensor representations generated from knowledge graphs. However, as the sizes of knowledge graphs continue to grow, classical modeling becomes increasingly computationally resource intensive. This article investigates how to capitalize on quantum resources to accelerate the modeling of knowledge graphs. In particular, we propose the first quantum machine learning algorithm for inference on tensorized data, i.e., on knowledge graphs. Since most tensor problems are NP-hard [18], it is challenging to devise quantum algorithms to support the inference task. We simplify the modeling task by making the plausible assumption that the tensor representation of a knowledge graph can be approximated by its low-rank tensor singular value decomposition, which is verified by our experiments. The proposed sampling-based quantum algorithm achieves speedup with a polylogarithmic runtime in the dimension of knowledge graph tensor.
Distribution-free, Risk-controlling Prediction Sets
Authors: Bates Stephen , Angelopoulos Anastasios , Lei Lihua , Malik Jitendra , Jordan Michael
Published: September 2021

While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making. Deploying learning systems in consequential settings also requires calibrating and communicating the uncertainty of predictions. To convey instance-wise uncertainty for prediction tasks, we show how to generate set-valued predictions from a black-box predictor that controls the expected loss on future test points at a user-specified level. Our approach provides explicit finite-sample guarantees for any dataset by using a holdout set to calibrate the size of the prediction sets. This framework enables simple, distribution-free, rigorous error control for many tasks, and we demonstrate it in five large-scale machine learning problems: (1) classification problems where some mistakes are more costly than others; (2) multi-label classification, where each observation has multiple associated labels; (3) classification problems where the labels have a hierarchical structure; (4) image segmentation, where we wish to predict a set of pixels containing an object of interest; and (5) protein structure prediction. Last, we discuss extensions to uncertainty quantification for ranking, metric learning, and distributionally robust learning.
Brain Mechanisms Underlying the Subjective Experience of Remembering
Authors: Simons Jon S. , Ritchey Maureen , Fernyhough Charles
Published: September 2021

The ability to remember events in vivid, multisensory detail is a significant part of human experience, allowing us to relive previous encounters and providing us with the store of memories that shape our identity. Recent research has sought to understand the subjective experience of remembering, that is, what it feels like to have a memory. Such remembering involves reactivating sensory-perceptual features of an event and the thoughts and feelings we had when the event occurred, integrating them into a conscious first-person experience. It allows us to reflect on the content of our memories and to understand and make judgments about them, such as distinguishing events that actually occurred from those we might have imagined or been told about. In this review, we consider recent evidence from functional neuroimaging in healthy participants and studies of neurological and psychiatric conditions, which is shedding new light on how we subjectively experience remembering.
Sodium bicarbonate ingestion mitigates the heat-induced hyperventilation and reduction in cerebral blood velocity during exercise in the heat
Authors: Katagiri Akira , Kitadai Yasuhiko , Miura Akira , Fukuba Yoshiyuki , Fujii Naoto , Nishiyasu Takeshi , Tsuji Bun
Published: September 2021

Hyperthermia causes hyperventilation and concomitant hypocapnia and cerebral hypoperfusion. The cerebral hypoperfusion may underlie central fatigue. We demonstrate that sodium bicarbonate ingestion reduces heat-induced hyperventilation and attenuates hypocapnia-related cerebral hypoperfusion during prolonged exercise in the heat. In addition, we show that sodium bicarbonate ingestion reduces ratings of perceived exertion during the exercise. This study provides new insight into the development of an effective strategy for preventing central fatigue during exercise in the heat.
Key role of left ventricular untwisting in endurance cyclists at onset of exercise
Authors: Izem Omar , Mourot Laurent , Tordi Nicolas , Grandperrin Antoine , Obert Philippe , Rupp Thomas , Nottin Stéphane
Published: September 2021

Our study assessed for the first time, to our knowledge, the kinetics of left ventricular function during the transition from rest to constant-load exercise in endurance-trained subjects. We observed a faster cardiac response in cyclists characterized by a faster response of cardiac output, left ventricular transmitral filling, basal rotation, and untwisting. This study highlighted the key role of left ventricular twisting mechanics in the more efficient acute cardiac response of endurance-trained athletes at onset of exercise.
Optimization of Vehicle Transportation Route Based on IoT
Authors: Yu Qian , Wang Yuanguo , Jiang Xiaogang , Zhao Bailu , Zhang Xiuling , Wang Xiaobei , Liu Qingqing
Published: September 2021

With the rapid development of logistics industry, optimization of road transport has become a constraint that must be overcome in the development of related industries. In the IoT era, classic car routing solutions could not meet many different needs. The relevant research findings are endless but not suitable to reduce costs in logistics and distribution processes and meet the needs of customers. This paper researches on vehicle path optimization using IoT technology and intelligent algorithms. Firstly, the traditional GA is optimized, and its coding mode, fitness function, selection, crossover, and mutation operators are studied. The crossover probability was set to 0.6, and the mutation probability was set to 0.1; then, according to the improved GA, a vehicle route optimization model was created. Finally, simulations were conducted to optimize vehicle routes for some distribution centers and 15 customer sites, and the model’s validity was tested. Experimental data show that the improved genetic algorithm begins to converge in 100 generations with a running time of 37.265 s. We calculate the time sensitivity of the customer. An algorithmic model is then used to determine distribution plans based on product demand and time sensitivity. In addition, we compare distribution costs and customer satisfaction of algorithmic and randomized plans. The distribution cost and customer satisfaction of the algorithmic and random patterns were 498.09 yuan and 573.13 yuan and 140.45 and 131.35, respectively. This shows that the vehicle routing optimization model using IoT technology and an improved GA can reduce distribution costs and increase customer satisfaction.
Complexed Crystal Structure of Saccharomyces cerevisiae Dihydroorotase with Inhibitor 5-Fluoroorotate Reveals a New Binding Mode
Authors: Guan Hong-Hsiang , Huang Yen-Hua , Lin En-Shyh , Chen Chun-Jung , Huang Cheng-Yang
Published: September 2021

Dihydroorotase (DHOase) possesses a binuclear metal center in which two Zn ions are bridged by a posttranslationally carbamylated lysine. DHOase catalyzes the reversible cyclization of N-carbamoyl aspartate (CA-asp) to dihydroorotate (DHO) in the third step of the pathway for the biosynthesis of pyrimidine nucleotides and is an attractive target for potential anticancer and antimalarial chemotherapy. Crystal structures of ligand-bound DHOase show that the flexible loop extends toward the active site when CA-asp is bound (loop-in mode) or moves away from the active site, facilitating the product DHO release (loop-out mode). DHOase binds the product-like inhibitor 5-fluoroorotate (5-FOA) in a similar mode to DHO. In the present study, we report the crystal structure of DHOase from Saccharomyces cerevisiae (ScDHOase) complexed with 5-FOA at 2.5 Å resolution (PDB entry 7CA0). ScDHOase shares structural similarity with Escherichia coli DHOase (EcDHOase). However, our complexed structure revealed that ScDHOase bound 5-FOA differently from EcDHOase. 5-FOA ligated the Zn atoms in the active site of ScDHOase. In addition, 5-FOA bound to ScDHOase through the loop-in mode. We also characterized the binding of 5-FOA to ScDHOase by using the site-directed mutagenesis and fluorescence quenching method. Based on these lines of molecular evidence, we discussed whether these different binding modes are species- or crystallography-dependent.
A Seismic Demand Model for Bridges Based on IM and EDP Parameter Matching and Bidirectional Optimization
Authors: Wang Shuai , Song Shuai , Wu Gang
Published: September 2021

The accuracy of seismic demand models in seismic vulnerability analysis of structures or components mainly depends on the seismic intensity measures (IMs) and engineering demand parameters (EDPs). This paper proposes a novel method to obtain the optimal seismic demand model for the seismic vulnerability analysis of bridges. The method obtains the IM‐EDP combination by matching all IMs and EDPs within a wide range one by one, considering the contribution of multiple IM parameters to the seismic response of the structure and avoiding the blindness of EDP selection. The IM is determined by calculating Pearson correlation coefficient and partial correlation coefficient, controlling the correlation between EDP and IM (or IMs) to a minimum to reduce the multicollinearity within the vector IMs and avoid ill‐conditioned models. The optimal seismic demand model is obtained by inspecting the scatter plot and residual plot of suboptimal seismic demand models determined from all combinations by guaranteeing efficiency and sufficiency. The efficiency of seismic demand models is guaranteed by controlling the root mean square error (RMSE) and the coefficient of determination (R2). The sufficiency of models is guaranteed by controlling the slope of fitted line. A continuous rigid frame bridge with double thin‐walled piers is used as a case study and a dynamic time‐history analysis is performed to obtain the seismic vulnerability of bridge with the proposed method. The results show that the proposed method is feasible and ideally suited for optimizing seismic demand model.
Analysis of Carbapenemase-Resistant Genotypes of Highly Virulent Klebsiella pneumoniae and Clinical Infection Characteristics of Different MLST Types
Authors: Liu Shuli , Wang Xiaobo , Ge Jingjing , Wu XiangBing , Zhao Qiu , Li Yue Man , Wang Renshu
Published: September 2021

Carbapenemase-resistant Klebsiella pneumoniae (CR-KP) has become one of the nosocomial infections that seriously threaten the lives of patients, greatly increasing the burden on patients. In order to explore the resistance mechanism of clinically isolated CR-KP to carbapenems and perform multilocus sequence typing (MLST), to study the clinical characteristics of patients with different ST types of infection, we collected 74 CR-KP strains clinically isolated from the main 6 hospitals in Zhejiang province from January 2018 to July 2020. The sensitivity of the tested strains to 23 antibacterial drugs was determined by the microbroth dilution method, and PCR was applied. Gene amplification technology and DNA sequencing methods were used to detect the carbapenemase gene of the tested strains. Through the MLST of the tested strains, the clonal correlation and molecular epidemiological characteristics of the tested strains were explored, and the characteristics of CR-KP resistance, resistance mechanisms, and clinical characteristics of bacterial infections under different MLST types were analyzed at the same time. The results showed that 74 carbapenem-resistant Klebsiella pneumoniae strains showed high resistance to 21 commonly used antibacterial drugs, and all carbapenemase phenotypic screening tests were positive. MLST typing showed that 74 CR-KP strains had 17 ST typings, and ST11 was the dominant type (54.05%). The study also found that these ST11 strains are more likely to be resistant to carbapenem antibiotics. Most of them produce KPC carbapenemase, and a few are IMP, VIM, and NDM. Univariate analysis suggested that the proportion of patients in the ST11 group receiving treatment in ICU, the use rate of mechanical ventilation, and the proportion of drainage tube indwelling were higher than those in the non-ST11 group, and the survival rate of the ST11 group was lower than that of the non-ST11 group. Clinical data suggested that the same hospital was dominated by the same clonal epidemic in the same period. In view of the analysis of clinical data suggesting that patients who have received ICU treatment, mechanical ventilation, and drainage tube indwelling are prone to the risk of CR-KP strain (especially ST11) infection and low survival rate, such patients should arouse extensive clinical attention.
Design of Automatic Course Arrangement System for Electronic Engineering Teaching Based on Monte Carlo Genetic Algorithm
Authors: Shuai Chun-jiang
Published: September 2021

In order to overcome the problems of convergence and low satisfaction in the traditional course scheduling system, a new Electronic Engineering Teaching Automatic Course Scheduling System based on the Monte Carlo genetic algorithm is proposed in this paper. The overall structure and hardware structure of the course scheduling system are designed. The hardware includes system management, course scheduling information input, course scheduling management, and course schedule query. In the software part, the Monte Carlo genetic algorithm is used to optimize the course scheduling optimization process, and a course scheduling scheme more in line with the needs of students and teachers is obtained. The experimental results show that the Monte Carlo genetic algorithm has higher convergence and higher user satisfaction compared with the traditional genetic algorithm. Therefore, it shows that the performance of the course scheduling system has been effectively improved.
HLA-DRB1
∗
16:01 and HLA-DQB1
∗
05:02 Alleles Influence the Susceptibility and Progression of Cutaneous Malignant Melanoma
Authors: Wang Xu , Almazan Francisco , Montoyo-Pujol Yoel Genaro , Martin-Casares Antonia , Martin Aurelio , Cabrera Teresa , López-Nevot Miguel Angel
Published: September 2021

Background. The influence of HLA class I and II loci on the susceptibility to melanoma remains an area of intense debate. This study aimed to examine whether the HLA system was related to melanoma susceptibility and prognosis in a southern Spanish population. Methods. In this study, HLA class I and class II genotyping were performed using polymerase chain reaction sequence-specific oligonucleotides (PCR-SSO) in 237 Spanish melanoma patients and 636 ethnically matched controls. Data were analyzed according to the clinical characteristics of the defined subgroups. Results. Compared to the control group, DRB1

∗

16:01 (4% vs. 1.3%,

p
=
0.001

, Pc = 0.035, OR = 3.28) and DQB1

∗

05:02 (4.9% vs. 2%,

p
=
0.001

, Pc = 0.017, OR = 2.54) were positivity associated with the susceptibility to melanoma. Both DRB1

∗

16:01 (5.4% vs. 1.3%,

p
=
0.001

, Pc = 0.035, OR = 4.46) and DQB1

∗

05:02 (6.5% vs. 2%,

p
=
0.001

, Pc = 0.017, OR = 3.44) also showed a positive correlation with Breslow thickness >1.5 mm, most notably at an early age of diagnosis (≤58 years), DRB1

∗

16:01 (4.2% vs. 1.3%,

p
=
0.001

, Pc = 0.035, OR = 3.41) and DQB1

∗

05:02 (5.4% vs. 2%,

p
=
0.002

, Pc = 0.034, OR = 2.86). Conclusion. These findings established HLA-DRB1

∗

16:01 and HLA-DQB1

∗

05:02 loci as melanoma risk factors in the southern Spanish population.
[Retracted] Regulation Mechanism of Spatial Capacity of Tourist Resources in Scenic Spots Based on Internet of Things Technology
Authors: Xie Xiaona , Zhang Wenliang
Published: September 2021

At present, it is urgent to solve the problem of scenic area tourism environmental capacity. This paper analyzes the causes of congestion and overload in scenic spots and then points out that the methods to solve the problem are capacity restriction and flow control. This paper first calculates the spatial capacity of various tourism resources in a scenic spot by using mathematical model and then studies the intelligent spatial management model of tourist flow in the scenic spot based on the Internet of Things technology. Based on the Internet of Things technology, the spatial capacity control platform of tourist resources in scenic spots built in this paper can realize the structure analysis of peak forest of tourist flow in scenic spots and the fractal and fractal dimension analysis of tourist flow and determine the spatial and temporal threshold of tourist flow control in tourist destinations. Through capacity restriction and flow regulation, tourists can be scientifically channelled and distributed, which can greatly reduce congestion and overload, enhance tourist experience, and improve tourist satisfaction, so as to promote the sustainable development of the scenic spot.
Image Splicing-Based Forgery Detection Using Discrete Wavelet Transform and Edge Weighted Local Binary Patterns
Authors: Siddiqi Muhammad Hameed , Asghar Khurshed , Draz Umar , Ali Amjad , Alruwaili Madallah , Alhwaiti Yousef , Alanazi Saad , Kamruzzaman M. M.
Published: September 2021

With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.
Evaluating Investors’ Recognition Abilities for Risk and Profit in Online Loan Markets Using Nonlinear Models and Financial Big Data
Authors: He Qizhi , Xia Pingfan , Li Bo , Liu Jia-Bao
Published: September 2021

Financial big data are obtained by web crawler, and investors’ recognition abilities for risk and profit in online loan markets are researched using heteroskedastic Probit models. The conclusions are obtained as follows: First, the preference for the item is reflected directly in the time and indirectly in the number of participants for being full, and the larger the preference, the shorter the time and the fewer the participants. Second, investors can discriminate the default risk not reflected by the interest rate, and the bigger the default risk, the longer the time and the more participants being full. Third, investors can discriminate the pure return rate deducted from the maturity term and credit risk, and the higher the return, the shorter the time and the fewer the participants being full. Fourth, default risks are reflected well by online loan platform interest rates, and inventors do not choose the item blindly according to the interest rate but consider comprehensively the profit and the risk. In the future, interest rate liberalization should be deepened, the choosing function of interest rates should be played better, and the information disclosure, investor education, and investor effective usage of other information should be strengthened.
The Prognostic Value of Serum Uric Acid in Hospitalized Patients with Acute Cerebral Infarction
Authors: Liu Bangjian , Pan Yongchao , Cao Li , Yang Jiajun
Published: September 2021

Background. Previous studies reported that the level of serum uric acid (SUA) was an important risk factor for acute cerebral infarction (ACI). However, the prognostic value of SUA levels in hospitalized patients with ACI has not been fully elucidated. The aim of this study was to investigate whether the SUA level on admission was associated with subsequent mortality in hospitalized patients with ACI. Methods. The clinical data of ACI patients obtained from December 2017 to December 2019 were retrospectively reviewed.

χ

2

and Kaplan–Meier methods were used to compare the clinical differences and overall survival between patients with or without hyperuricemia, respectively. Univariate and multivariate analyses were used to identify independent prognoses. Results. In the total population, the in-hospital mortality of the hyperuricemia group was significantly higher than that of the normal uric acid group (

P
=
0.006

). In the abnormal renal function group, the in-hospital mortality among the hyperuricemia group was significantly higher than the normal uric acid group (

P
=
0.002

). However, there was no statistical difference of in-hospital mortality between the two groups in the normal renal function group (

P
=
0.321

). Univariate and multivariate analyses showed that a previous history of diabetes (

P
=
0.018

), hyperuricemia (

P
=
0.001

), and National Institutes of Health Stroke Scale (NIHSS) score on admission (

P
≤
0.001

) were independent factors for all samples. The hyperuricemia (

P
=
0.003

) on admission were independent factors for patients with abnormal renal function. Conclusions. In ACI patients with abnormal renal function, hyperuricemia may be associated with higher in-hospital mortality than patients with normal uric acid, and hyperuricemia may be an independent associated factor for in-hospital death in the subgroup patients.
Construction and Simulation of a Multiattribute Training Data Mining Model for Basketball Players Based on Big Data
Authors: Li Yunbin , Ge Jinyan , Hao Wei
Published: September 2021

This paper provides an in‐depth analysis and research on the construction and simulation of a big data model for multiattribute training of basketball players. To get a more accurate and three‐dimensional information, the training can use a multitraining target robot, i.e., to detect feedback on multiple indicators at the same time and correct the player’s errors in time; the other is an auxiliary robot, which can actively correct technical movements and train the player to form muscle memory, compared with general training. The analysis results show that by either constructing a human model or designing an active assistive robot, the player’s technical movements can be regulated accordingly, protecting the player’s body laterally and improving the player’s ability. An assisted training system with an accurate model of physiological indicators is constructed based on the data of the player throughout the season. The Warriors, who have applied this system, not only have the best record in recent years but also have the lowest injury rate in the league, indicating that this method has indeed reduced the injury rate of players.
Tear Levels of Inflammatory Cytokines in Keratoconus: A Meta‐Analysis of Case‐Control and Cross‐Sectional Studies
Authors: Zhang Huan , Cao Xiupeng , Liu Ying , Wang Peihong , Li Xuan
Published: September 2021

Purpose. To assess the tear levels of inflammatory cytokines in patients with keratoconus (KC). Design. Systemic review and meta‐analysis. Methods. The following electronic databases and search engine were searched: PubMed, EMBASE, Web of Science, and Google Scholar. A systematic search of all relevant studies published through January 2021 was conducted, and the standardized mean difference (SMD) and 95% confidence interval (CI) of cytokine levels were calculated to estimate the pooled effects. Sensitivity analysis, subgroup analysis, and metaregression were applied to explore the sources of heterogeneity. Results. A total of 7 studies with 374 participants (374 eyes) from clinical studies were included. The tear levels of interleukin‐1 beta (IL‐1β), interleukin‐6 (IL‐6), and tumor necrosis factor alpha (TNF‐α) were significantly increased in KC compared with normal controls. The SMD of IL‐1β was 1.93 (95% CI 0.22 to 3.65, P = 0.03). The SMD of IL‐6 was 1.22 (95% CI 0.59 to 1.84, P < 0.001). The SMD of TNF‐α was 1.75 (95% CI 0.66 to 2.83, P = 0.002). There was no significant difference between the two groups on interleukin‐4 (IL‐4) and interleukin‐10 (IL‐10). The SMD for IL‐4 was 2.36 (95% CI ‐0.28 to 5.00, P = 0.08) and for IL‐10 was 0.30 (95% CI ‐1.29 to 1.89, P = 0.71). Meta‐regression analysis indicated that the heterogeneity maybe significantly correlated with the method of detection, the different ages, and the source of population. Conclusions. Our meta‐analysis demonstrated that proinflammatory cytokines IL‐1β, IL‐6, and TNF‐α were increased, indicating that cytokine profile changed in KC tears and inflammation may play an important role in the pathogenesis and development of KC.
Estrogenic and Antioxidant Activities of Pterocarpus soyauxii (Fabaceae) Heartwood Aqueous Extract in Bilateral Oophorectomized Wistar Rat
Authors: Mengue Ngadena Yolande Sandrine , Owona Pascal Emmanuel , Noubom Michel , Mbock Michel Arnaud , MbolangNguegan Lohik , Chantal Ngoungouré Madeleine , Fifen Rodrigue Ngapout , Bidingha A Goufani Ronald , Kahou Tadah Rivaldo Bernes , Claude Bilanda Danielle , Kamtchouing Pierre , Dzeufiet Djomeni Paul Désiré
Published: September 2021

Phytoestrogens are used to ease postmenopausal symptoms, a property probably due to estrogenic and antioxidant effects. Pterocarpus soyauxii (P. soyauxii) is empirically used in Cameroon to treat among others primary and secondary amenorrhea. The aim of this study is to evaluate estrogenic and antioxidant activities of P. soyauxii heartwood aqueous extract in bilateral oophorectomized Wistar rats. Firstly, a characterization of the extract was carried out. For that, flavonoids, phenols, and tannins levels in P. soyauxii extract were evaluated by colorimetric assays and UHPLC-MS analysis was realized. In vitro antioxidant analysis of P. soyauxii was conducted using DPPH, ABTS, and FRAP assays. Secondly, 2 sets of pharmacologic tests were carried out. The results revealed that P. soyauxii aqueous extract contains, respectively, 229.42 ± 3.62 mg EAG/g, 63.42 ± 2.16 mg EQ/g, and 27.88 ± 0.23 mg ETA/g of polyphenols, flavonoids, and tannins. UHPLC-MS enabled identifying seven components including mono(2-ethylhexyl) phthalate, cembrene, 3′,5′-dimethoxy-4-stilbenol, and linoleic acid. DPPH, ABTS, and FRAP assays revealed that P. soyauxii extract possessed a high antioxidant activity with IC50 value of 730.20 µg/mL, 892.90 µg/mL, and 765.75 mEAG/g of extract, respectively. In the uterotrophic assay, P. soyauxii extract induced significant increase of fresh uterine weight, uterine and vaginal epithelial size, and mammary glands differentiation compared to Ovx control. In the postmenopausal model, compared to the sham control, vagina and uterine dystrophies were observed in Ovx rats treated with distilled water. P. soyauxii aqueous extract expressed estrogenic-like effects on vagina and did not affect uterine epithelial height compared with vehicle groups. On the back of these vaginotrophic effects, the extract displayed antiatherogenic properties by reducing (p < 0.001) AI and LDL cholesterol level as compared to Ovx control group. The extract at 200 mg/kg significantly prevented the increase of MDA (p < 0.01) level and decreased nitrites (p < 0.001) and GSH (p < 0.01) levels compared to Ovx rats. These beneficial effects are related at least in part to the presence of compound such as mono(2-ethylhexyl) phthalate, 3′,5′-dimethoxy-4-stilbenol, and linoleic acid. Overall, P. soyauxii aqueous extract exhibits estrogenic and antioxidant effects which can inhibit postmenopausal symptoms by providing vaginal stratification, improving lipid profile and insulin sensitivity, and reducing oxidative stress without side effects on the endometrium and mammary gland in 84-day Ovx rats.
Network Pharmacology-Based Analysis on the Curative Effect of Kunxian Capsules against Rheumatoid Arthritis
Authors: Yan Hong-Xia , Xu Chun-Fang , Yang Hong , Wen Xiao-Ya , Wang Zhi-Peng , Chen Yan-Hong , Liu Jing-Xue , Chen Wan-Sheng , Gao Shou-Hong , Tao Xia
Published: September 2021

Kunxian capsules (KCs), a Chinese patent medicine, have been clinically proven to be effective in the treatment of rheumatoid arthritis (RA). However, the chemical profile of KC remains to be characterized, and the mechanism underlying the protective effect against RA is yet to be elucidated. Here, a network pharmacology-based approach was adopted, integrated with the chemical profiling of KC by UHPLC-Q-TOF/MS. As a result, a total of 67 compounds have been identified from KC extract, among which 43 were authenticated by comparison to the mass spectrum of standard chemicals. ADME behaviors of the chemical constituents of KC were predicted, resulting in 35 putative active ingredients. Through target prediction of both active ingredients of KC and RA and PPI analysis, core targets were screened out, followed by biological process and related pathway enrichment. Then, a TCM-herb-ingredient-target-pathway network was constructed and a multicomponent, multitarget, and multipathway synergistic mechanism was proposed, providing an information basis for further investigation. The active pharmaceutical ingredients included mainly terpenoids (such as triptolide and celastrol), sesquiterpene pyridines (such as wilforgine and wilforine), and flavonoids (such as icariin, epimedin A, B, and C, and 2″-O-rhamnosylicariside II).
Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning
Authors: Wang Zhongxiao
Published: September 2021

With the rapid development of deep learning, computer vision has also become a rapidly developing field in the field of artificial intelligence. Combining the physical training of deep learning will bring good practical value. Physical training has different effects on people’s body shape, physical function, and physical quality. It is mainly reflected in the changes of relevant physical indicators after physical training. Therefore, the purpose of this article is to study the method of evaluating the impact of sports training on physical indicators based on deep learning. This paper mainly uses the convolutional neural network in deep learning to design sports training, then constructs the evaluation system of physical index impact, and finally uses the deep learning algorithm to evaluate the impact of physical index. The experimental results show that the accuracy of the algorithm proposed in this paper is significantly higher than that of the other three algorithms. Firstly, in the angular motion, the accuracy of the mean algorithm is 0.4, the accuracy of the variance algorithm is 0.2, the accuracy of the RFE algorithm is 0.4, and the accuracy of the DLA algorithm is 0.6. Similarly, in foot racing and skill sports, the accuracy of the algorithm proposed in this paper is significantly higher than that of other algorithms. Therefore, the method proposed in this paper is more effective in the evaluation of the impact of physical training on physical indicators.
Deep Learning‐Based Service Scheduling Mechanism for GreenRSUs in the IoVs
Authors: Li Jitong , Wang Chao , Seo Daehee , Cheng Xiaoman , He Yunhua , Sun Limin , Xiao Ke
Published: September 2021

Green roadside units (RSUs), also called renewable energy‐powered RSUs, are utilized recently rather than the traditional electric‐powered RSUs with high power consumption and the large infrastructure deployment cost in the Internet of vehicles (IoVs). However, the power of the green RSUs is limited and unstable, which is affected by the battery size and charging environment. Therefore, a big challenge to deploy green RSUs in the IoVs is to schedule their service process properly, in order to extend the service efficiency of RSUs. In this paper, a deep learning‐based communication scheduling mechanism is proposed regarding the service scheduling problem. In particular, a three‐part scheduling algorithm consisting of RSU clustering, deep learning‐based traffic prediction, and a vehicle access scheduling algorithm is presented to maximize the service number of vehicles and minimize the energy cost. An extensive simulation is done, and the simulation results indicate that our algorithm can serve more vehicles with less energy consumption compared with other scheduling mechanisms under different scenarios.
Association of Fecal Microbiota with Irritable Bowel Syndrome-Diarrhea and Effect of Traditional Chinese Medicine for Its Management
Authors: Yang Fang , Wu Jiaqi , Ye Ning-Yuan , Miu Jing , Yan Jing , Liu Li-Na , Ye Bai
Published: September 2021

Changes in intestinal microbiota have been linked to the development of diarrhea predominant irritable bowel syndrome (IBS-D). In order to better elucidate the relationship between intestinal microbiota changes and IBS-D, we compared fecal microbiota of IBS-D rats and healthy control using pyrosequencing of bacterial 16S rRNA gene targeted. Furthermore, we explored the effects of different traditional Chinese medicine (TCM) on intestinal microbiota of IBS-D in dose-dependent manner. Our results showed that there was no significant difference in fecal microbial community diversity among the healthy control group, IBS-D rats and IBS-D rats treated with traditional Chinese medicine, but the fecal microbial composition at different taxonomic levels have changed among these groups. Interestingly, the weight of IBS-D rats treated with moderate doses (13.4 g/kg) of TCM increased significantly, and the diarrhea-related symptoms improved significantly, which may be related to the enrichment in Deferribacteres, Proteobacteria, Tenericutes, Lachnospiraceae, and Ruminococcaceae and the reduction in Lactobacillus in fecal samples.
Experimental Study and Numerical Simulation on Mechanical Properties of the Bottom Plate in the Assembled Composite Slab with Additional Steel Trusses
Authors: Yang Xiuying , Wang Yuzhuo , Liu Yongxin , Wei Zhenyu
Published: September 2021

The composite slab with steel trusses is composed of precast bottom plate and cast‐in‐place concrete. In engineering applications, cracks often appear in the bottom plate before casting the upper concrete, which even leads to the failure of the composite slab. To improve the crack resistance of the slab, a composite slab with additional steel trusses is proposed; that is, on the basis of the original longitudinal steel trusses, the transverse steel trusses are added. Static test and numerical analysis were carried out on the bottom plate of the new type of composite slab with the additional transverse steel trusses. The experimental and analytical results show that the load level of the plate with additional steel trusses can be increased by 33% under the normal service limit state; the deflection of the plate is significantly reduced and the crack development is effectively controlled, which illustrates that the new type of composite slab can improve the bearing capacity, increase the bending stiffness, and enhance the crack resistance effectively.
[Retracted] Optimization of Machine Online Translation System Based on Deep Convolution Neural Network Algorithm
Authors: Zhao Juan
Published: September 2021

In order to effectively optimize the machine online translation system and improve its translation efficiency and translation quality, this study uses the deep separable convolution neural network algorithm to construct a machine online translation model and evaluates the quality on the basis of pseudo data learning. In order to verify the performance of the model, the regression performance experiment of the model, the method performance experiment of generating pseudo data for specific tasks, the sorting task performance experiment of the model, and the machine translation quality comparison experiment are designed. RMSE and MAE were used to evaluate the regression task performance of the model. Spearman rank correlation coefficient and delta AVG value were used to evaluate the sorting task performance of the model. The experimental results show that the MAE and RMSE values of the model are decreased by 2.28% and 1.39%, respectively, compared with the baseline system under the same experimental conditions, and the Spearman and delta AVG values are increased by 132% and 100.7%, respectively, compared with the baseline system. The method of generating pseudo data for specific tasks needs less data and can make the translation system reach a better level faster. When the number of instances is more than 10, the quality score of the model output is higher than that of Google translation whose similarity is more than 0.8.