bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-Term Progression-Free Survival of Patients with Metastatic Melanoma or Renal Cell Carcinoma following High-Dose Interleukin-2
Authors: Clark Joseph I , Curti Brendan , Davis Elizabeth J , Kaufman Howard , Amin Asim , Alva Ajjai , Logan Theodore F , Hauke Ralph , Miletello Gerald P , Vaishampayan Ulka , Johnson Douglas B , White Richard L , Wiernik Peter H , Dutcher Janice P
Published: February 2021

High-dose interleukin-2 (HD IL-2) was approved in the 1990s after demonstrating durable complete responses (CRs) in some patients with metastatic melanoma (mM) and metastatic renal cell carcinoma (mRCC). Patients who achieve this level of disease control have also demonstrated improved survival compared with patients who progress, but limited data are available describing the long-term course. The aim of this study was to better characterize long-term survival following successful HD IL-2 treatment in patients with no subsequent systemic therapy. Eleven HD IL-2 treatment centers identified patients with survival ≥5 years after HD IL-2, with no subsequent systemic therapy. Survival was evaluated from the date of IL-2 treatment to June 2017. Treatment courses consisted of 2 1-week cycles of HD IL-2. Patients were treated with HD IL-2 alone, or HD IL-2 followed by local therapy to achieve maximal response. 100 patients are reported: 54 patients with mM and 46 patients with mRCC. Progression-free survival (PFS) after HD IL-2 ranges from 5+ years to 30+ years, with a median follow-up of 10+ years. 27 mRCC and 32 mM are alive ≥10 years after IL-2. Thus, a small subset of patients with mM and mRCC achieve long-term PFS (≥5 years) after treatment with HD IL-2 as their only systemic therapy. The ability of HD IL-2 therapy to induce prolonged PFS should be a major consideration in studies of new immunotherapy combinations for mM and mRCC.
Effect of Intensive Blood Pressure on the Progression of Non-Diabetic Chronic Kidney Disease at Varying Degrees of Proteinuria
Authors: Der Mesropian Paul J , Shaikh Gulvahid , Beers Kelly H , Mehta Swati , Monrroy Prado Mauricio R , Hongalgi Krishnakumar , Mathew Roy O , Feustel Paul J , Salman Loay H , Perna Annalisa , Gosmanova Elvira O
Published: February 2021

The ideal blood pressure (BP) target for renoprotection is uncertain in patients with non-diabetic chronic kidney disease (CKD), especially considering the influence exerted by pre-existing proteinuria. In this pooled analysis of landmark trials, we coalesced individual data from 5001 such subjects randomized to intensive versus standard BP targets. We employed multivariable regression to evaluate the relationship between follow-up systolic blood pressure (SBP) and diastolic blood pressure (DBP) on CKD progression (defined as glomerular filtration rate decline by 50% or end-stage renal disease), focusing on the potential for effect modification by baseline proteinuria or albuminuria. The median follow-up was 3.2 years. We found that SBP rather than DBP was the primary predictor of renal outcomes. The optimal SBP target was 110-129 mm Hg. We observed a strong interaction between SBP and proteinuria such that lower SBP ranges were significantly linked with progressively lower CKD risk in grade A3 albuminuria or ≥0.5-1 g/day proteinuria (relative to SBP 110-119 mm Hg, the adjusted HR for SBP 120-129 mm Hg, 130-139 mm Hg, and 140-149 mm Hg was 1.5, 2.3, and 3.3, respectively; all p<0.05). In grade A2 microalbuminuria or proteinuria near 0.5 g/day, a non-significant but possible connection was seen between tighter BP and decreased CKD (aforementioned HRs all <2; all p>0.05), while in grade A1 albuminuria or proteinuria <0.2 g/day no significant association was apparent (HRs all <1.5; all p>0.1). We conclude that in non-diabetic CKD, stricter BP targets <130 mm Hg may help limit CKD progression as proteinuria rises.
Comparing Supply Chain Risks Ranking in Multi-Attribute Decision-Making Methods Using the Proposed Three-Dimensional Integration Mean Method
Authors: Fakhrzad M. B. , Firozpour Mohammad Reza , Hosseini Nasab Hasan
Published: February 2021

Risks play an important role in supply chain vulnerability, performance maintenance, and competitive advantage. Therefore, appropriate risk assessment and ranking are among the top priorities of managers and experts in the supply chain. Because there are several quantitative and qualitative risks in the supply chain, multi-criteria decision-making (MCDM) and, in particular, multi attribute decision making (MADM) methods are used to rank them. Depending on the type of supply chain, the difference in criteria and information available, it is possible to use existing MCDM methods to rank supply chain risks or introduce new methods. In these methods, options are compared on the basis of different criteria according to mathematical methods. Each of these methods uses their own approaches and assumptions. Therefore, there will be different solutions to the ranking. In this case, one or more methods may be selected as the best method. But it cannot be sure that the superior method is chosen correctly. In this paper, decision making methods are compared with each other for an appropriate method to be chosen. Case examples from the related literature have been compared with the proposed method (three-dimensional integrated mean method). To evaluate the validity of the proposed method, the results were sent to experts in various industries along with the proposed method. For analysis the accuracy of experts’ opinions, One-Sample Kolmogorov-Smirnov, Frequencies and Explore tests have been used in SPSS software with 95% confidence level. The obtained results show high reliability of the proposed method for determining an appropriate decision-making method in supply chain risk assessment. However, the proposed method is used for ranking of supplier selection in this paper, but it can be used for many decision-making procedures.
Real Options in a Duopoly with Jump Diffusion Prices
Authors: Sun Wei , Zhao Yonggan , MacLean Leonard
Published: February 2021

This paper analyzes irreversible investments in technology under asymmetric duopoly. Asset prices are defined by a diffusion with Poisson jumps. Assuming negative externalityfor profit flows, we develop a real options and game theoretic valuation model to evaluate the optimal investment strategies under interaction. Three types of equilibrium, i.e., simultaneous equilibrium, preemptive equilibrium, and sequential equilibrium, are attainable depending on the firms’ competitive advantageand first-mover advantage. The role of a firm, as “leader”, “follower”, or “simultaneous entrant”, is analyzed both exogenously and endogenously. We find that preemptive competition lowers both firms’ profits from the investments in the technology. Numerical examples illustrate the key results.
Central Feature Learning for Unsupervised Person Re-identification
Authors: Wang Binquan , Asim Muhammad , Ma Guoqi , Zhu Ming
Published: February 2021

The Exemplar Memory (EM) design has shown its effectiveness in facilitating the unsupervised person re-identification (RE-ID). However, there are obvious defects in the update strategies with most existing results, such as the inability to eliminate static errors and ensure convergence stability of learning. To address these issues, in this paper, we propose a novel center feature learning scheme to improve the update strategies of the traditional EM design for unsupervised RE-ID problems. First, the EM module is regarded as a center feature of a cluster of images, then the goal is transformed into pulling the similar images close to while pushing the dissimilar images away from the center feature space. Second, in order to provide effective guidelines on reducing static errors, we propose an error-memory module to improve the central feature learning performances. In addition, an error-prediction module is designed as well to ensure the stability of convergence. Besides, a camera-invariance learning strategy is also introduced to further improve the proposed algorithm. Finally, extensive comparative experiments are conducted on Market-1501 and DukeMTMC-reID datasets to demonstrate the effectiveness and improvements of the proposed method over existing results. The code of this work is available at https://github.com/binquanwang/CFL_master.
Supervised Anomaly Detection with Highly Imbalanced Datasets Using Capsule Networks
Authors: Piciarelli Claudio , Mishra Pankaj , Foresti Gian Luca
Published: February 2021

Detecting anomalous patterns in data is a relevant task in many practical applications, such as defective items detection in industrial inspection systems, cancer identification in medical images, or attacker detection in network intrusion detection systems. This paper focuses on detection of anomalous images, this is images that visually deviate from a reference set of regular data. While anomaly detection has been widely studied in the context of classical machine learning, the application of modern deep learning techniques in this field is still limited. We here propose a capsule-based network for anomaly detection in an extremely imbalanced fully supervised context: we assume that anomaly samples are available, but their amount is limited if compared to regular data. By using a variant of the standard CapsNet architecture, we achieved state-of-the-art results on the MNIST, F-MNIST and K-MNIST datasets.
An Improved Artificial Neural Network Model for Flights Delay Prediction
Authors: Shi Tongyu , Lai Jinghan , Gu Runping , Wei Zhiqiang
Published: February 2021

With the limitation of air traffic and the rapid increase in the number of flights, flight delay is becoming more frequent. Flight delay leads to financial and time losses for passengers and increases operating costs for airlines. Therefore, the establishment of an accurate prediction model for flight delay becomes vital to build an efficient airline transportation system. The air transportation system has a huge amount of data and complex operation modes, which is suitable for analysis by using machine learning methods. This paper discusses the factors that may affect the flight delay, and presents a new flight delay prediction model. The five warning levels are defined based on flight delay database by using K-means clustering algorithm. After extracting the key factors related to flight operation by the grey relational analysis (GRA) algorithm, an improved machine learning algorithm called GRA — Genetic algorithm (GA) — back propagation neural network, GRA-GA-BP, is introduced, which is optimized by GA. The calculation results show that, compared with models before optimization and other two algorithms in previous papers, the proposed prediction model based on GRA-GA-BP algorithm shows a higher prediction accuracy and more stability. In terms of operation efficiency and memory consumption, it also has good performance. The analysis presented in this paper indicates that this model can provide effective early warnings for flight delay, and can help airlines to intervene in flights with abnormal trend in advance.
Optimization of Urban Bus Stops Setting Based on Data Mining
Authors: Duan Ganglong , Ma Xin , Wang Jianren , Wang Zhishi , Wang Yan
Published: February 2021

The unreasonable setting of urban bus stops is a common problem in real life, which seriously affects people’s happiness, sense of belonging and brand in the city. However, the existing related research on the above problems generally has the defects of high technical complexity and high cost. Therefore, we aim to propose a way to optimize the setting of urban public transportation stations and reduce the technical complexity and high cost of existing public transportation station optimization by using artificial intelligence algorithms. First, we extract and integrate bus GPS data and bus card swipe data in the business system and perform exploratory analysis on the pre-processed data. Second, the original k-NN algorithm is improved, and an ik-NN algorithm is proposed to determine the cardholder’s boarding point. Then, we divide the upstream and downstream lines to calculate the total number of upstream and downstream passengers. Third, we propose an algorithm for calculating the number of passengers getting off at bus stations and calculating the number of passengers getting on and off at each bus station. Finally, according to the number of passengers getting on and off at each bus station, the OD matrix is constructed, the residents’ travel rules are analyzed, and optimization suggestions for the setting of urban bus stations are proposed. This paper selects the public transit GPS data set and swipe card data set of Shenzhen, China for experiments. The experimental results show that: (1) Compared with K-means, the ik-NN algorithm we proposed can effectively determine the actual car station of each cardholder, and the algorithm is less sensitive to feature dimensions. At the same time, the ik-NN algorithm has a high operating efficiency and is less affected by the “[Formula: see text]” value. (2) The calculation algorithm for the number of passengers getting off at bus stations can effectively use the existing data of the business system to determine the number of passengers getting off at each bus station. Therefore, the calculation times of this algorithm are low, and the accuracy is high. (3) The optimization suggestions for bus stations based on the OD matrix analysis of residents’ travel rules meet the needs of urban development and have certain reference value.
Distributed Resource Allocation for Green HetNets with Renewable Energy Resources
Authors: Zhang Bin , Liu Yi
Published: February 2021

In fifth generation (5G) systems, green heterogeneous network (HetNet) is capable of achieving energy efficiency by densely deploying renewable-powered small cells. However, the small cells may suffer performance degradation due to the limited backhaul from macro base station (BS) and renewable intermittency. In this paper, we introduce a distributed HetNet architecture in which the renewable-powered small cell BSs collaboratively exchange information and allocate the spectrum and power resources by themselves. Considering the uncertainty of the available spectrum, renewable energy supply and traffic loads, a stochastic optimization problem is formulated to maximize the energy efficiency for distributed small cell BSs. A distributed resource allocation algorithm is proposed to obtain the optimal spectrum and power allocating strategies for each small cell. Finally, the numerical results demonstrate the effectiveness of the proposed algorithm.
Full-Scale Bounds Estimation for the Nonlinear Transient Heat Transfer Problems with Interval Uncertainties
Authors: Peng Ruifei , Yang Haitian , Xue Yanni
Published: February 2021

A package solution is presented for the full-scale bounds estimation of temperature in the nonlinear transient heat transfer problems with small or large uncertainties. When the interval scale is relatively small, an efficient Taylor series expansion-based bounds estimation of temperature is stressed on the acquirement of first and second-order derivatives of temperature with high fidelity. When the interval scale is relatively large, an optimization-based approach in conjunction with a dimension-adaptive sparse grid (DSG) surrogate is developed for the bounds estimation of temperature, and the heavy computational burden of repeated deterministic solutions of nonlinear transient heat transfer problems can be efficiently alleviated by the DSG surrogate. A temporally piecewise adaptive algorithm with high fidelity is employed to gain the deterministic solution of temperature, and is further developed for recursive adaptive computing of the first and second-order derivatives of temperature. Therefore, the implementation of Taylor series expansion and the construction of DSG surrogate are underpinned by a reliable numerical platform. The parallelization is utilized for the construction of DSG surrogate for further acceleration. The accuracy and efficiency of the proposed approaches are demonstrated by two numerical examples.
Unitarity in quantum cosmology: Symmetries protected and violated
Authors: Pandey Sachin , Banerjee Narayan
Published: February 2021

This paper deals with the violation or retention of symmetries associated with the self-adjoint extension of the Hamiltonian for homogeneous but anisotropic Bianchi I cosmological model. This extension is required to make sure the quantum evolution is unitary. It is found that the scale invariance is lost, but the Noether symmetries are preserved.
A Flexible Sensor Based on 3D Gold@Carbonaceous Nanohybrid with Defect Sites of Conductivity for the Wearable Sensing at Low Stress
Authors: Chen Dongzhen , Chen Tongshan , Li Yang , Li Shengxu , Zhang Liang , Ren Yanwei , Wang Yaowu , Liang Junhao , Fu Tao , Zhang Meng , He Xinhai
Published: February 2021

The detection of subtle strain on the human body by flexible sensors suffers from the inferior sensitivity. In general, poor sensitivity derives from the intact structural integrity of building blocks of electric material. Inspired by the rolling friction in daily life, a three-dimensional (3D) nanohybrid of gold@carbonaceous nanoballs (Au@CBs) is developed. Compared with the pure Au nanostructure, the CBs increase the defect sites of conductivity and deformation sensitivity between Au nanostructures. The Flexible Stress Sensor (FSS) based on the 3D nanohybrid of Au@CBs is fabricated, and due to the enough displacement variation of 3D nanohybrid of Au@CBs, the change of resistance could be triggered by the tiny stress. The as-prepared sensor has high sensitivity at wide working range ([Formula: see text][Formula: see text]KPa[Formula: see text], 0.1–10[Formula: see text]KPa and [Formula: see text][Formula: see text]KPa[Formula: see text], 10–100[Formula: see text]KPa) and good cyclic stability (5000 cycles). Finally, the FSS based on the 3D nanohybrid of Au@CBs is employed for the detection of subtle vibration and movement of the human body, such as pulse and joint bending, which exerts great potential as wearable electronics.
Analysis of thermal injuries using classical Fourier and DPL models for multi-layer of skin under different boundary conditions
Authors: Chaudhary Rajneesh Kumar , Kumar Dinesh , Rai Kabindra Nath , Singh Jitendra
Published: February 2021

In this paper, the temperature distribution in the multi-layer of the skin is studied when the skin surface is subjected to most generalized boundary condition. Our skin model consists of three layers known as the epidermis, dermis, and subcutaneous layers. All layers of skin are assumed to be connected with point of interface condition and taking the barrier in between each of the two layers by symmetric flux condition and analyzing each layer separately. The classical Fourier and non-Fourier (DPL) models are extended to analyze the behavior of heat transfer in the multi-layer of the skin. The Laplace transform technique is used to derive analytical solutions for the multi-layer of skin models. The effects of the variability of different parameters such as relaxation time, layer thickness, and different types of boundary conditions on the behavior of temperature distribution in the multi-layer of skin are analyzed and discussed in detail. All the effects are shown graphically. It has been observed that during temperature distribution in the multi-layer of skin, the measurement of skin damage is less on the DPL model ([Formula: see text]) in comparison to the classical Fourier model.
Dynamic resource allocation for service in mobile cloud computing with Markov modulated arrivals
Authors: Mohammed Munatel , Haqiq Abdelkrim
Published: February 2021

Mobile Cloud Computing (MCC) is a modern architecture that brings together cloud computing, mobile computing and wireless networks to assist mobile devices in storage, computing and communication. A cloud environment is developed to support a wide range of users that request the cloud resources in a dynamic environment with possible constraints. Burstiness in users service requests causes drastic and unpredictable increases in the resource requests that have a crucial impact on policies of resource allocation. How can the cloud system efficiently handle such burstiness when the cloud resources are limited? This problem becomes a hot issue in the MCC research area. In this paper, we develop a system model for the resource allocation based on the Semi-Markovian Decision Process (SMDP), able of dynamically assigning the mobile service requests to a set of cloud resources, to optimize the usage of cloud resources and maximize the total long-term expected system reward when the arrival process is a finite-state Markov-Modulated Poisson Process (MMPP). Numerical results show that our proposed model performs much better than the Greedy algorithm in terms of achieving higher system rewards and lower service requests blocking probabilities, especially when the burstiness degree is high, and the cloud resources are limited.
Significant Applications of Big Data in Industry 4.0
Authors: Javaid Mohd , Haleem Abid , Singh Ravi Pratap , Suman Rajiv
Published: February 2021

Industry 4.0 is being implemented with the help of advanced technologies. Big data, Artificial Intelligence (AI), Robotics, Internet of Things (IoT), Cloud computing, and 3D printing are the major technologies used to adopt Industry 4.0 successfully. Here, the study’s need is to discuss the major potential of big data for Industry 4.0. These technologies’ primary purpose is to collect the right data to solve the relevant issue during manufacturing and other required services. This technology plays a significant role in creating advancements in this fourth industrial revolution. Conclusively, big data applications are useful for in-process management and productivity improvement in the automation sector. Complex systems of drivers and intelligent sensors can be easily optimized based on information collected using this technology. Big data is the key to gain a competitive leap by reconnoitring the fundamental issues like deviations during the process, quality discriminations, and energy efficiency squander in a manufacturing process. The study discusses the significant applications of big data in Industry 4.0. For a proper surveillance system, industries need to have an immensely technical or personalized way, making big data a valuable source for predicting analysis and operation management based on market insight statistics or information. In upcoming days, big data will provide further advancement in Industry 4.0 and is supposed to play an efficient role in its successful adoption.
Aspect-Aware Response Generation for Multimodal Dialogue System
Authors: Firdaus Mauajama , Thakur Nidhi , Ekbal Asif
Published: February 2021

Multimodality in dialogue systems has opened up new frontiers for the creation of robust conversational agents. Any multimodal system aims at bridging the gap between language and vision by leveraging diverse and often complementary information from image, audio, and video, as well as text. For every task-oriented dialog system, different aspects of the product or service are crucial for satisfying the user’s demands. Based upon the aspect, the user decides upon selecting the product or service. The ability to generate responses with the specified aspects in a goal-oriented dialogue setup facilitates user satisfaction by fulfilling the user’s goals. Therefore, in our current work, we propose the task of aspect controlled response generation in a multimodal task-oriented dialog system. We employ a multimodal hierarchical memory network for generating responses that utilize information from both text and images. As there was no readily available data for building such multimodal systems, we create a Multi-Domain Multi-Modal Dialog (MDMMD++) dataset. The dataset comprises the conversations having both text and images belonging to the four different domains, such as hotels, restaurants, electronics, and furniture. Quantitative and qualitative analysis on the newly created MDMMD++ dataset shows that the proposed methodology outperforms the baseline models for the proposed task of aspect controlled response generation.
Feature Grouping–based Trajectory Outlier Detection over Distributed Streams
Authors: Mao Jiali , Liu Jiaye , Jin Cheqing , Zhou Aoying
Published: February 2021

Owing to a wide variety of deployment of
GPS
-enabled devices, tremendous amounts of trajectories have been generated in distributed stream manner. It opens up new opportunities to track and analyze the moving behaviors of the entities. In this work, we focus on the issue of outlier detection over distributed trajectory streams, where the outliers refer to a few entities whose motion behaviors are significantly different from their local neighbors. In view of skewed distribution property and evolving nature of trajectory data, and on-the-fly detection requirement over distributed streams, we first design a high-efficiency outlier detection solution. It consists of identifying abnormal trajectory fragment and exceptional fragment cluster at the remote sites and then detecting abnormal evolving object at the coordinator site. Further, given that outlier detection accuracy would be damaged due to using inappropriate proximity thresholds or a few trajectory data not having sufficient neighbors at the remote sites, we extract proximity thresholds of different regions and spatial context relationship of each region from historical data to improve the precision. Built upon this is an improved version consisting of off-line modeling phase and on-line detection phase. During the on-line phase, the proximity thresholds that are derived from historical trajectories during the off-line phase are leveraged to assist in detecting abnormal trajectory fragments and exceptional fragment clusters at the remote sites. Additionally, at the coordinator site, the detection results of some remote sites can be refined by incorporating those of other remote sites with neighborhood relationship. Extensive experimental results on real data demonstrate that our proposed methods own high detection validity, less communication cost and linear scalability for online identifying outliers over distributed trajectory streams.
Characterizing cardiorespiratory interaction in preterm infants across sleep states using visibility graph analysis
Authors: Zhang Dandan , Long Xi , Xu Lin , Werth Jan , Wijshoff Ralph , Aarts Ronald M. , Andriessen Peter
Published: February 2021

The interaction between cardiac and respiratory activities is investigated in preterm infant sleep using an advanced nonlinear method (visibility graph) and some important characteristics are shown to be significantly different across sleep states, which has not been studied before.
Physiological and neurochemical adaptations following abrupt termination of chronic hypercapnia in goats
Authors: Buchholz Kirstyn J. , Burgraff Nicholas J. , Neumueller Suzanne E. , Hodges Matthew Robert , Pan Lawrence G. , Forster Hubert V.
Published: February 2021

In healthy adult goats, steady-state ventilation and most physiological measures return to control within 24 h after termination of chronic hypercapnia (CH). However, the acute [H+] chemoreflex is increased, and measured ventilation exceeds predicted ventilation. At 24 h of recovery, excitatory neuromodulators are above control, but other measured markers of neuroplasticity are unchanged from control. Our data suggest that CH elicits persistent physiological and neurochemical changes for up to 24 h after termination of CH.
Dynamic Characteristics of Microring Driven by the Symmetrically Distributed Electrostatic Force
Authors: Meng Qingheng , Zhang Yuanlin , Wei Jin , Hu Yuh-Chung , Shi Yan , Yu Tao
Published: February 2021

This paper aims at investigating the dynamic characteristics of a microring driven by dual arch electrodes because they are basic elements of microelectrostatic motors. The dual arch electrodes surround the periphery of the microring and are arranged symmetrically to the center of the ring. The electrodes are fixed while the microring is flexible. The electrostatic force will deform the microring, while the deflection of the microring changes the gap between the microring and the electrodes, thereby changing the electrostatic force. Therefore, this is an electromechanical coupling effect. The nonlinear partial‐differential equation that governs the motion of the microring is derived based on thin shell theory. Then, based on the assumption of small deflection, the nonlinear governing equation is linearized by truncating the higher‐order terms of the Taylor series expansion of the nonlinear electrostatic force. After that, the linearized governing equation is discretized into a set of ordinary differential equations using Galerkin method in which the mode shape functions of the ring are adopted. The influences of the structural damping of the microring and the span of the arch electrodes on the forced response and dynamical stabilities of the microring are investigated. The results show that the damping ratio has a great influence on the system instability during high‐frequency excitation. The unstable region of the system can increase with the increase of the electrode span; the response amplitude can also be increased within a certain range.
Downregulated mRNA Expression of ZNF385B Is an Independent Predictor of Breast Cancer
Authors: Yan Ning , Liu Cong , Tian Fang , Wang Ling , Wang Yimin , Yang Zhaoying , Jiao Yan , He Miao
Published: February 2021

Background. ZNF385B, a zinc finger protein, has been known as a potential biomarker in some neurological and hematological studies recently. Although numerous studies have demonstrated the potential function of zinc finger proteins in tumor progression, the effects of ZNF385B in breast cancer (BC) are less studied. Methods. The Oncomine database and “ESurv” tool were used to explore the differential expression of ZNF385B in pan-cancer. Furthermore, data of patients with BC were downloaded from The Cancer Genome Atlas (TCGA). The receiver operating characteristic (ROC) curve of ZNF385B expression was established to explore the diagnostic value of ZNF385B and to obtain the cut-off value of high or low ZNF385B expression in BC. The chi-square test as well as Fisher exact test was used for identification of the relationships between clinical features and ZNF385B expression. Furthermore, the effects of ZNF385B on BC patients’ survival were evaluated by the Kaplan-Meier and Cox regression. Data from the Gene Expression Omnibus (GEO) database were employed to validate the results of TCGA. Protein expression of ZNF385B in BC patient specimens was detected by immunohistochemistry (IHC) staining. Results. ZNF385B expression was downregulated in most types of cancer including BC. Low ZNF385B expression was related with survival status, overall survival (OS), and recurrence of BC. ZNF385B had modest diagnostic value, which is indicated by the area under the ROC curve (

AUC
=
0.671

). Patients with lower ZNF385B expression had shorter OS and RFS (relapse-free survival). It had been demonstrated that low ZNF385B expression represented independent prognostic value for OS and RFS by multivariate survival analysis. The similar results were verified by datasets from the GEO database as well. The protein expression of ZNF385B was decreased in patients’ samples compared with adjacent tissues by IHC. Conclusions. Low ZNF385B expression was an independent predictor for worse prognosis of BC patients.
Selection and Validation of Reference Genes for RT‐qPCR Analysis in Spinacia oleracea under Abiotic Stress
Authors: Xie Hao , Li Bo , Chang Yu , Hou Xiaoyan , Zhang Yue , Guo Siyi , Miao Yuchen , Wang Quanhua , Chen Sixue , Su Yinghua , Li Ying , Dai Shaojun
Published: February 2021

Reverse transcription quantitative real‐time polymerase chain reaction (RT‐qPCR) is an accurate and convenient method for mRNA quantification. Selection of optimal reference gene(s) is an important step in RT‐qPCR experiments. However, the stability of housekeeping genes in spinach (Spinacia oleracea) under various abiotic stresses is unclear. Evaluating the stability of candidate genes and determining the optimal gene(s) for normalization of gene expression in spinach are necessary to investigate the gene expression patterns during development and stress response. In this study, ten housekeeping genes, 18S ribosomal RNA (18S rRNA), actin, ADP ribosylation factor (ARF), cytochrome c oxidase subunit 5C (COX), cyclophilin (CYP), elongation factor 1-alpha (EF1α), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), histone H3 (H3), 50S ribosomal protein L2 (RPL2), and tubulin alpha chain (TUBα) from spinach, were selected as candidates in roots, stems, leaves, flowers, and seedlings in response to high temperature, CdCl2, NaCl, NaHCO3, and Na2CO3 stresses. The expression of these genes was quantified by RT‐qPCR and evaluated by NormFinder, BestKeeper, and geNorm. 18S rRNA, actin, ARF, COX, CYP, EF1α, GAPDH, H3, and RPL2 were detected as optimal reference genes for gene expression analysis of different organs and stress responses. The results were further confirmed by the expression pattern normalized with different reference genes of two heat‐responsive genes. Here, we optimized the detection method of the gene expression pattern in spinach. Our results provide the optimal candidate reference genes which were crucial for RT‐qPCR analysis.
Effects of Subsurface Drip Irrigation on Water Consumption and Yields of Alfalfa under Different Water and Fertilizer Conditions
Authors: Cao Xuesong , Feng Yayang , Li Heping , Zheng Hexiang , Wang Jun , Tong Changfu
Published: February 2021

A field experiment was conducted for the purpose of examining the effects of different combinations of water and fertilizer applications on the water consumption and yields of alfalfa under subsurface drip irrigation (SDI). The results showed that the jointing and branching stages were the key stages for alfalfa water requirement. The water consumption had varied greatly (from 130 to 170 mm) during the growth period of each alfalfa crop. The water consumption during the whole growth period was approximately 500 mm, and the maximum water consumption intensity was 3.64 mm·d-1. The overall changes in water consumption and yields during the growth period of the alfalfa displayed trends of first increasing and then decreasing. The sensitivities of the yields to water changes were much higher than that of fertilizer. The water use efficiency (WUE) of the alfalfa was determined to range from 1.68 to 3.20 kg·m-3, and the rate of growth had ranged from 4.85% to 51.77%. The WUE and rate of growth of the alfalfa indicated the following trend: second crop > third crop > first crop. The results of frequency analysis based on the water‐nitrogen‐yield regression equation are the following: irrigation amounts of 142~165 mm and nitrogen application of 61~80 kg·hm-2 have a 95% probability of obtaining a hay yield of alfalfa of more than 11903 kg·hm-2. These results suggest that SDI is a promising irrigation method, which can increase the WUE and hay yield of alfalfa under the condition of SDI within an appropriate amount of water and nitrogen fertilizer, and too low or too high water and nitrogen fertilizer will adversely affect the WUE and hay yield of alfalfa.
[Retracted] Construction of a Complex System Based on Big Data for the Intelligent Service System of Youth Physical Health
Authors: Li Xinwen , Song Chao , Rochester Christine A. , Yan Chaobing
Published: February 2021

The progress of social economy has created a better environment for the healthy development of young people, but the heavy schoolwork and life pressure have caused many students to ignore the scientific management of physical health. At this stage, people need a scientific physical health service system to help students understand their own health data, propose targeted exercise methods and health knowledge, and actively encourage and guide students to participate in physical exercise. The purpose of this article is to cultivate students’ good self‐exercise awareness and improve their physical fitness and health. To this end, this article has designed a smart health service system for young people. This article introduces the various service functions in the health management service system and explains in detail the entry, induction, and analysis of student physical health data in the system. The essence of the health intelligent service system is to provide students with targeted healthy exercise strategies through data analysis. This paper studies the health intervention plan of the health intelligent service system. From the experimental data, the improved particle swarm algorithm in this paper increases the effectiveness of the system in adolescent health data mining from 80.5% to 92.19%, which undoubtedly optimizes the system. It helps a lot.
Epidemiological Survey of Toxoplasma gondii and Associated Risk Factors in Ruminant Species of the Khyber Pakhtunkhwa Province of Pakistan
Authors: Ali Abid , Omer Talha , Ullah Asad , Haleem Abdul , Naseem Maryam , Ullah Mujeeb , Seemab  , Shamim Fahad , Tehreem Amna , Bilal Muhammad , Khan Muhammad Numan
Published: February 2021

Toxoplasma infection is one of the most common human parasitic diseases. During 2018-2020, in the rural areas of three districts of Pakistan, we surveyed a total of 451 animals, belonging to different asymptomatic ruminant species, to determine the prevalence of Toxoplasma gondii antibodies. We used ELISA assay as well as recorded some associated risk factors contributing to its transmission. IgM antibodies were detected in 17% and IgG in 13.4% of ruminant samples with the highest percentage, 10% for IgM and 8.6% for IgG in sheep. A strong significant association was found between antibodies and different species (IgM,

χ

2

=
29

.280,

P
=
.000

, and IgG,

χ

2

=
22.580

,

P
=
.000

), respectively. Infection with T. gondii seems mainly associated with different geographic features and the presence of cats in the environment, low hygiene water systems and livestock that are mostly dependent on outdoor drinking and grazing. There was no significant association between IgM and age grouping (

χ

2

=
6.660

,

P
=
0.840

nor for IgG (

χ

2

=
8.136

,

P
=
0.43

). The results of this study may be considered the starting point to promote the awareness about parasitic infections in ruminants in Pakistan in order to prevent this infection from further spreading.
Moisture Effect on Characteristics of Slowly Digestible Potato Starch Prepared under Electron Beam Irradiation
Authors: Nguyen Vinh Tien , Nguyen Thi Loc , Trinh Khanh Son
Published: February 2021

This study evaluates the effect of initial moisture contents (11.74–29.84%) on physicochemical changes, in vitro and in vivo digestibilities of potato starch irradiated with electron beam (EB). After a constant dose of EB irradiation, intrinsic viscosity and average molecular weight of potato starch decreased for all investigated moisture contents. When the moisture of starch was lower than 18%, the depolymerization predominated, hence increasing the amylose content. At higher moisture, water can strongly absorb EB and produce highly active species that induced the crosslinking of amylose molecules and the disruption of large crystals into smaller defective crystals. As a result, we found a maximum in amylose content at 14.84% moisture and a minimum in the degree of crystallinity at 17.5% moisture. Thermal stabilities between the irradiated samples were not significantly different. In vitro digestibility results showed that higher moistures during EB treatment induced structural changes that led to the conversion of resistant starch (RS) fraction into slowly digestible starch (SDS). Moreover, an in vivo digestive model in mice showed that EB-treated starch was able to maintain blood glucose at a stable level for a long time. This study showed a potential for SDS production from potato starch using EB irradiation technology, even in large scale.
Design and Performance of Solar PV Integrated Domestic Vapor Absorption Refrigeration System
Authors: Selvaraj Divya Arputham , Victor Kirubakaran
Published: February 2021

The arrival of new technologies has increased the energy demand day by day and does not seem to slow down at any time soon. High energy demand is adding risk on energy depletion and cause of various environmental issues. Air conditioner, chiller, and refrigerator occupy a considerable amount of the world’s total energy usage and have also proved to be a massive contributor to various environmental impacts. This technology might sound like a luxury on the surface, but they are in high demand to achieve food security. They can also help lifesaving vaccines to reach even the isolated parts of the world. Even though solar thermal refrigeration is a popular field, this paper solely concentrates on PV integrated refrigeration. In this paper, a renewable integration technology where a solar photovoltaic system is used to supply the electrical energy required to drive an absorption cycle is studied and compared with the commercial AC absorption refrigeration system. The Coefficient of Performance (COP) of the AC and DC system was 0.18 and 0.14. The simple payback of the system is 10.2 years.
Prediction of Vibration Velocity of Bench Blasting Reflecting Negative Elevation Effect
Authors: Zhang Xiaojun , Shi Jianjun
Published: February 2021

In the field of mine blasting practice, accurate prediction of blasting vibration is considered as a critical task. In accordance with the theory of elastic wave, the reflection of blasting vibration wave at the interface of negative elevation is analyzed in this paper. The negative elevation effect exerted by blasting vibration is interpreted considering the mechanism, and the formula of the blasting vibration prediction step (negative)

V
=
K

Q

1
/
3

/
R

α

⋅

1
/
H

β

is derived, reflecting the negative elevation effect. Besides, the formula accuracy is verified by the measured vibration velocity of the mass in the Jinou coal mine. The step (negative) formula acts as a more feasible candidate for the prediction of step blasting vibration.
Pretreatment Prediction of Relapse Risk in Patients with Osteosarcoma Using Radiomics Nomogram Based on CT: A Retrospective Multicenter Study
Authors: Liu Jin , Lian Tao , Chen Haimei , Wang Xiaohong , Quan Xianyue , Deng Yu , Yao Juan , Lu Ming , Ye Qiang , Feng Qianjin , Zhao Yinghua
Published: February 2021

Objective. To develop and externally validate a CT‐based radiomics nomogram for pretreatment prediction of relapse in osteosarcoma patients within one year. Materials and Methods. In this multicenter retrospective study, a total of 80 patients (training cohort: 63 patients from three hospitals; validation cohort: 17 patients from three other hospitals) with osteosarcoma, undergoing pretreatment CT between August 2010 and December 2018, were identified from multicenter databases. Radiomics features were extracted and selected from tumor regions on CT image, and then, the radiomics signature was constructed. The radiomics nomogram that incorporated the radiomics signature and clinical‐based risk factors was developed to predict relapse risk with a multivariate Cox regression model using the training cohort and validated using the external validation cohort. The performance of the nomogram was assessed concerning discrimination, calibration, reclassification, and clinical usefulness. Results. Kaplan‐Meier curves based on the radiomics signature showed a significant difference between the high‐risk and the low‐risk groups in both training and validation cohorts (P < 0.001 and P = 0.015, respectively). The radiomics nomogram achieved good discriminant results in the training cohort (C‐index: 0.779) and the validation cohort (C‐index: 0.710) as well as good calibration. Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinical‐based nomogram (P < 0.001). Conclusions. This multicenter study demonstrates that a radiomics nomogram incorporated the radiomics signature and clinical‐based risk factors can increase the predictive value of the osteosarcoma relapse risk, which supports the clinical application in different institutions.
Obesity and Overweight Are Independently Associated with Greater Survival in Critically Ill Diabetic Patients: A Retrospective Cohort Study
Authors: Huang Wentao , Chen Yongsong , Yin Guoshu , Wang Nasui , Wei Chiju , Xu Wencan
Published: February 2021

Background. The relationship between obesity and the outcomes of critically ill diabetic patients is not completely clear. We aimed to assess the effects of obesity and overweight on the outcomes among diabetic patients in the intensive care unit (ICU). Methods. Critically ill diabetic patients in the ICU were classified into three groups according to their body mass index. The primary outcomes were 30-day and 90-day mortality. ICU and hospital length of stay (LOS) and incidence and duration of mechanical ventilation were also assessed. Cox regression models were developed to evaluate the relationship between obesity and overweight and mortality. Results. A total of 6108 eligible patients were included. The 30-day and 90-day mortality in the normal weight group were approximately 1.8 times and 1.5 times higher than in the obesity group and overweight group, respectively (P<0.001, respectively). Meanwhile, the ICU (median (IQ): 2.9 (1.7, 5.3) vs. 2.7 (1.6, 4.8) vs. 2.8 (1.8, 5.0)) and hospital (median (IQ): 8.3 (5.4, 14.0) vs. 7.9 (5.1, 13.0) vs. 8.3 (5.3, 13.6)) LOS in the obesity group and overweight group were not longer than in the normal weight group. Compared with normal weight patients, obese patients had significantly higher incidence of mechanical ventilation (58.8% vs. 64.7%,P<0.001) but no longer ventilation duration (median (IQ): 19.3 (7.0, 73.1) vs. 19.0 (6.0, 93.7),P=1). Multivariate Cox regression showed that obese and overweight patients had lower 30-day (HR (95% CI): 0.62 (0.51, 0.75); 0.76 (0.62, 0.92), respectively) and 90-day (HR (95% CI): 0.60 (0.51, 0.70); 0.79 (0.67, 0.93), respectively) mortality risks than normal weight patients. Conclusions. Obesity and overweight were independently associated with greater survival in critically ill diabetic patients, without increasing the ICU and hospital LOS. Large multicenter prospective studies are needed to confirm our findings and the underlying mechanisms warrant further investigation.
Connectivity and Wiener Index of Fuzzy Incidence Graphs
Authors: Fang Juanyan , Nazeer Irfan , Rashid Tabasam , Liu Jia-Bao
Published: February 2021

Connectivity is a key theory in fuzzy incidence graphs

FIGs

. In this paper, we introduced connectivity index

CI

, average connectivity index

ACI

, and Wiener index

WI

of

FIGs

. Three types of nodes including fuzzy incidence connectivity enhancing node

FICEN

, fuzzy incidence connectivity reducing node

FICRN

, and fuzzy incidence connectivity neutral node

FICNN

are also discussed in this paper. A correspondence between

WI

and

CI

of a

FIG

is also computed.