bluuebunny/crossref_metadata_2025 huggingface dataset:
Cardiovascular risk factors and autonomic indices in relation to fatal and non-fatal coronary events
Authors: Zambach Christian , Fedorowski Artur , Borné Yan , Johnson Linda S B , Gerward Sofia , Hamrefors Viktor , Engström Gunnar
Published: April 2021

Objective

Mortality caused by coronary artery disease has markedly decreased in recent years. However, a substantial proportion of patients suffering a coronary event (CE) die within the first day, most of them out of hospital. We aimed to investigate how established cardiovascular (CV) risk factors and CV autonomic indices associate with fatal versus non-fatal CEs in the population.

Methods

33 057 individuals (mean age; 45.6 years; 10 773 women) free of coronary artery disease at baseline were included. Baseline examination, including assessment of traditional CV risk factors and autonomic indices such as heart rate and orthostatic reaction, was performed during 1974–1992, after which the subjects were monitored for incident CV disease. The Lunn-McNeil competing risks approach with a prespecified multivariable model was used to assess differences in risks for fatal and non-fatal CEs in relation to baseline CV risk factors.

Results

During follow-up period of 29.7 years, 5494 subjects (6.10/1000 person-years) had first CE; 1554 of these were fatal. Age, male gender, smoking, body mass index (BMI), blood pressure, pulse pressure and resting heart rate had stronger relationships with fatal CE than with non-fatal events. The effects of diabetes, serum cholesterol, antihypertensive treatment and orthostatic blood pressure responses were similar for fatal and non-fatal CE.

Conclusions

Several cardiovascular risk factors, such as smoking, high BMI, blood pressure and high resting heart rate, were preferentially associated with fatal compared with non-fatal CEs. These observations may require special attention in the overall efforts to further reduce coronary artery disease mortality.
Evaluation of latest viscoelastic coagulation assays in the transcatheter aortic valve implantation setting
Authors: Fanning Jonathon , Roberts Shaun , Merza Megan , Anstey Chris , Poon Karl , Incani Alexander , Natani Sarvesh , Fraser John
Published: April 2021

Background

Point of care viscoelastic measures with thromboelastography (TEG; Haemonetics Corporation, Switzerland) and thromboelastometry (ROTEM, Tem Innovations GmbH, Germany) now supersede laboratory assays in the perioperative assessment and management of coagulation. To the best of our knowledge, this sophisticated coagulation assessment has not been performed to characterise thrombotic changes in the transcatheter aortic valve implantation (TAVI) setting, nor have the two latest iteration cartridge-based systems been directly compared in the elective perioperative period.

Methods

Patients undergoing TAVI were prospectively recruited. Samples (n=44) were obtained at four timepoints (postinduction of anaesthesia, postheparin (100 IU/kg), postprotamine (1 mg/100 IU heparin) and 6 hours postoperatively). Each sample was concurrently assessed with standard laboratory tests (prothrombin time/international normalised ratio, activated partial thromboplastin time, thrombin clotting time, platelet count and direct fibrinogen, ROTEMSigma and TEG6s).

Results

Clot strength showed a statistically significant increase postheparin/TAVI deployment. When considering the subgroup of samples taken following the administration heparin, the heparinase channel of the TEG6s did not yield clotting strength results in 55% of samples and clotting time exceeded the upper limit of normal in 70% of samples. It was retrospectively recognised that the arachidonic acid channel of the TEG6s Platelet Mapping Cartridge had been decommissioned prohibiting assessment of aspirin effect.

Conclusions

This study demonstrated a small intraprocedural prothrombotic change of uncertain clinical importance during the transcatheter aortic valve procedure. Further comparison with percutaneous coronary intervention and aortic valve replacement cohorts are needed to assess the merits of current antithrombotic guidelines, which are extrapolated from the PCI setting. The heparin effect was more consistently quantified by ROTEM.
Indigenous Knowledge of bearded seal (Erignathus barbatus), ringed seal (Pusa hispida), and spotted seal (Phoca largha) behaviour and habitat use near Utqiaġvik, Alaska, USA
Authors: Gryba R. , Huntington H.P. , Von Duyke A.L. , Adams B. , Frantz B. , Gatten J. , Harcharek Q. , Olemaun H. , Sarren R. , Skin J. , Henry G. , Auger-Méthé M.
Published: April 2021

Indigenous peoples possess information of animals’ habitat use and behaviour; information essential for management and conservation of species affected by climate change. Accessibility of species that are important to Indigenous hunters may also change with environmental conditions. We documented Indigenous Knowledge of bearded (ugruk in Iñupiaq), ringed (natchiq), and spotted seals (qasiġiaq) in Utqiaġvik, Alaska, USA, using semi-directed interviews with Iñupiaq hunters. This study originated from discussions with an agency of the regional municipal government to serve co-management efforts and understand habitat use of species subjected to climate change. Results indicated that ringed seals are associated with higher ice concentrations in winter than bearded seals and changes in sea ice retreat in spring may have greater impact on ringed seal habitat use because they are more likely to haul out on ice in spring. Additionally, all three species have foraging hotspots, used over several days by multiple individuals. Bearded seals, and to a lesser extent spotted and ringed seals, will use currents to forage. Results also revealed the use of inland water bodies and terrestrial habitat, which may become more important for bearded and ringed seals with changing ice concentrations and should be considered in management and conservation of these species.
Evaluation and modeling of factors influencing the depth of mixing layer in which soil solute releasing from soil to surface runoff
Authors: Zhou Beibei , Chen Xiaopeng , Su Lijun , Li Hujun , Wang Quanjiu , Tao Wanghai
Published: April 2021

The depth of mixing layer is one of the important parameters which cannot be assigned a constant value affected by many factors in the slope runoff. The objective of this study was to investigate the effect of slope length and underground biomass on slope runoff, solute transport processes, as well as mixing layer depth. In this study, the experimental plots with the four slope lengths (5, 10, 15, and 20 m) and a width of 2 m were built on the slope with the gradient of 20°. In addition, the plots with the millet or wheat planting were built on the slope. The change of runoff and solute transport was analyzed through simulated rainfall experiments and then to estimate mixing layer depth. The results showed that the runoff rate decreased and more runoff seeped into the slope soil with increasing slope length. Increasing underground biomass also promoted greater rainfall infiltration into the soil. The increase in slope length increased the concentration of solute in runoff, but more underground biomass reduced the nutrients transported with runoff. The effective mixing depth increased with an increase in slope length, but effective mixing depth decreased with increased underground biomass. The modified expression of the equivalent mixing model under different slope lengths and underground biomass could accurately describe the solute transfer process in runoff when compared with complete mixing model and incomplete mixing model based on exponential functions. This research provided a reference for improving the application of mixing layer models in the slope management.
Magnetic dominance of axion electrodynamics: photon capture effect and anisotropy of Coulomb potential
Authors: Villalba-Chávez S. , Shabad A. E. , Müller C.
Published: April 2021

For magnetic fields larger than the characteristic scale linked to axion-electrodynamics, quantum vacuum fluctuations due to axion-like fields can dominate over those associated with the electron-positron fields. This conjecture is explored by investigating both the axion-modified photon capture by a strong magnetic field and the Coulomb potential of a static pointlike charge. We show that in magnetic fields characteristic of neutron stars$$\sim 10^{13}$$∼1013–$$10^{15}\;\mathrm{G}$$1015G, the capture of gamma photons prior to the production of a pair can prevent the existence of an electron-positron plasma, essential for explaining the pulsar radiation mechanism. This incompatibility is used to limit the axion parameter space. Our bounds improve existing outcomes in the region of mass$$m\sim 10^{-10}$$m∼10-10–$$10^{-5}\;{\mathrm{eV}}$$10-5eV. The effect of capture, known in QED as relating to gamma-quanta, is extended in axion electrodynamics to include X-ray photons with the result that a specially polarized part of the heat radiation from the surface is canalized along the magnetic field. Besides, we find that in the regime in which the dominance takes place, the running QED coupling depends on the field strength and the modified Coulomb potential is of Yukawa-type in the direction perpendicular to the magnetic field at distances much smaller than the axion Compton wavelength, while along the field it follows approximately the Coulomb law at any length scale. Despite the Coulomb singularity manifested in the latter case, we argue that the ground-state energy of a non-relativistic hydrogen atom placed in a strong magnetic field turns out to be bounded due to the nonrenormalizable feature of axion-electrodynamics.
V–E Algorithm: A New Vital Vertex Identifying Algorithm Based on Vertex–Edge Interaction
Authors: Wang Haoyu , Qi Xingqin
Published: April 2021

Finding vital vertices is an important issue in complex network analysis, which has wide applications in disease control, information diffusion, etc. This topic has attracted increasing attention from various disciplines. In this paper, we propose a new algorithm called Vertex–Edge algorithm to find vital vertices. This algorithm takes both the incident edges and also its neighbors into consideration when evaluating a vertex’s importance, and the importance of vertices and edges construct a mutually updated iterative framework. We also give convergence conditions for the iterative framework. Besides, we verify the stability, effectiveness, accuracy, and superiority of this new Vertex–Edge algorithm by applying it on lots of networks (unweighted or weighted) and comparing the results with other 10 more existing methods. This new method is expected to have promising applications in the future.
Accuracy Improvement of Solar Power Estimation Using Real-Time Degradation Computation of PV Panels
Authors: Bhola Parveen , Bhardwaj Saurabh
Published: April 2021

Many applications including power trading and planning require the accurate estimation of solar power in real time. As the power output of the solar panels degrades over the time period, so its real-time estimation is tough without the degradation parameter. In the proposed method, the effect of degradation in terms of performance ratio is incorporated along with other meteorological parameters. The degradation is calculated in real time using the clustering-based technique without physical inspection on site. Initially, the power is estimated using Support Vector Regression (SVR) model with the meteorological parameters. The estimation is further fine-tuned in sync with the degradation rate. The model is validated on the real data (Meteorological parameters and Solar power) procured from the solar plant. After refinement, the estimation results show significant improvement in terms of statistical measures. Now, the estimation accuracy in terms of coefficient of determination R2 is 92% and the error metrics normalized root mean square error (NMRSE), mean absolute percentage error (MAPE), root mean square error (RMSE) are 7.13, 5.92 and 14.54, respectively.
FINE-GRAINED AND MULTI-SCALE MOTIF FEATURES FOR CROSS-SUBJECT MENTAL WORKLOAD ASSESSMENT USING BI-LSTM
Authors: SHAO SHILIANG , WANG TING , SONG CHUNHE , SU YUN , WANG YONGLIANG , YAO CHEN
Published: April 2021

Mental workload (MW) assessment is crucial for understanding human mental state. Cross-subject MW analysis based on electroencephalogram (EEG) signals is an important way. In this paper, a fine-grained and multi-scale motif (FGMSM) features extraction method is proposed, and the proposed features together with original EEG data are used as the input of bidirectional long short-term memory (Bi-LSTM) to evaluate the cross-subject mental workload. First, the EEG signal of each channel is decomposed based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm. Second, for the motif structure consisting of three nodes, multi-scale detection is carried out in each intrinsic mode function, and the proportion of each motif structure is extracted as the newly extracted features. Then, the statistical differences of the extracted features between different MW levels are analyzed by using the [Formula: see text]-test, and the features with statistical differences are selected for the cross-subject MW assessment. Finally, based on the public dataset with 26 subjects, Bi-LSTM and a variety of machine learning algorithms are used to classify the levels of cross-subject MW. The results show that the Bi-LSTM classification method with the original EEG data and the proposed features show the most positive results. Therefore, the FGMSM features proposed in this paper with Bi-LSTM provide a new technique for the assessment of cross-subject MW based on EEG signals.
Improved composite quantile regression and variable selection with nonignorable dropouts
Authors: Ma Wei , Wang Lei
Published: April 2021

With nonignorable dropouts and outliers, we propose robust statistical inference and variable selection methods for linear quantile regression models based on composite quantile regression and empirical likelihood (EL) that accommodate both the within-subject correlations and nonignorable dropouts. The purpose of our study is threefold. First, we apply the generalized method of moments to estimate the parameters in the nonignorable dropout propensity based on an instrument. Subsequently, the inverse probability weighting and kernel smoothing approaches are applied to obtain the smoothed and bias-corrected generalized estimating equations. Second, we borrow the idea of quadratic inference function to construct the improved EL procedure for nonignorable dropouts. The asymptotic properties of the proposed estimators and their confidence regions are derived. Third, the penalized EL method and algorithm for variable selection are investigated. The finite-sample performance of the proposed estimators is studied through simulation, and an application to HIV-CD4 data set is also presented.
Parameterized Approximation Algorithms for Bidirected Steiner Network Problems
Authors: Chitnis Rajesh , Feldmann Andreas Emil , Manurangsi Pasin
Published: April 2021

The D
irected
S
teiner
N
etwork
(DSN) problem takes as input a directed graph
G
=(
V
,
E
) with non-negative edge-weights and a set
D
⊆
V
×
V
of
k
demand pairs. The aim is to compute the cheapest network
N⊆ G
for which there is an
s\rightarrow t
path for each (
s
,
t
)∈ D. It is known that this problem is notoriously hard, as there is no
k

1/4−
o
(1)

-approximation algorithm under Gap-ETH, even when parametrizing the runtime by
k
[Dinur & Manurangsi, ITCS 2018]. In light of this, we systematically study several special cases of DSN and determine their parameterized approximability for the parameter
k
.

For the
bi
-DSNP
lanar
problem, the aim is to compute a solution
N⊆ G
whose cost is at most that of an optimum planar solution in a bidirected graph
G
, i.e., for every edge
uv
of
G
the reverse edge
vu
exists and has the same weight. This problem is a generalization of several well-studied special cases. Our main result is that this problem admits a parameterized approximation scheme (PAS) for 
k
. We also prove that our result is tight in the sense that (a) the runtime of our PAS cannot be significantly improved, and (b) no PAS exists for any generalization of bi-DSNP
lanar
, under standard complexity assumptions. The techniques we use also imply a polynomial-sized approximate kernelization scheme (PSAKS). Additionally, we study several generalizations of
bi
-DSNP
lanar
and obtain upper and lower bounds on obtainable runtimes parameterized by 
k
.

One important special case of DSN is the S
trongly
C
onnected
S
teiner
S
ubgraph
(SCSS) problem, for which the solution network
N⊆ G
needs to strongly connect a given set of
k
terminals. It has been observed before that for SCSS a parameterized 2-approximation exists for parameter 
k
[Chitnis et al., IPEC 2013]. We give a tight inapproximability result by showing that for
k
no parameterized (2 − ε)-approximation algorithm exists under Gap-ETH. Additionally, we show that when restricting the input of SCSS to bidirected graphs, the problem remains NP-hard but becomes FPT for
k
.
Where There Is Heat, There Is a Way: Thermal to Electric Power Conversion Using Thermoelectric Microconverters
Authors: Ryan M. A. , Fleurial J.-P.
Published: April 2021

Spacecraft for missions to explore the outer planets of our solar system, that is, planets beyond the orbit of Mars, require a source of electric power which is not dependent on external sources such as solar energy. These spacecraft typically rely on a radioisotope system to provide heat which is converted directly to electric power.
Molecule-based Magnets: Magnets for the 21st Century
Authors: Miller Joel S.
Published: April 2021

The use and control of magnetism has enabled the ubiquitous availability of low-cost electricity and electric motors (large and small), as well as the development of telecommunications devices (telephones (terrestrial, cell, satellite), radios, televisions, etc.), and also information storage for computers, among a myriad of other equally important common uses.
Ketogenic Diet Suppressed T‐Regulatory Cells and Promoted Cardiac Fibrosis via Reducing Mitochondria‐Associated Membranes and Inhibiting Mitochondrial Function
Authors: Tao Jun , Chen Hao , Wang Ya-Jing , Qiu Jun-Xiong , Meng Qing-Qi , Zou Rong-Jun , Li Ling , Huang Jun-Gang , Zhao Zong-Kai , Huang Yu-Li , Zhang Hai-Feng , Zheng Jun-Meng
Published: April 2021

Ketogenic diet (KD) is popular in diabetic patients but its cardiac safety and efficiency on the heart are unknown. The aim of the present study is to determine the effects and the underlined mechanisms of KD on cardiac function in diabetic cardiomyopathy (DCM). We used db/db mice to model DCM, and different diets (regular or KD) were used. Cardiac function and interstitial fibrosis were determined. T‐regulatory cell (Treg) number and functions were evaluated. The effects of ketone body (KB) on fatty acid (FA) and glucose metabolism, mitochondria‐associated endoplasmic reticulum membranes (MAMs), and mitochondrial respiration were assessed. The mechanisms via which KB regulated MAMs and Tregs were addressed. KD improved metabolic indices in db/db mice. However, KD impaired cardiac diastolic function and exacerbated ventricular fibrosis. Proportions of circulatory CD4+CD25+Foxp3+ cells in whole blood cells and serum levels of IL‐4 and IL‐10 were reduced in mice fed with KD. KB suppressed the differentiation to Tregs from naive CD4+ T cells. Cultured medium from KB‐treated Tregs synergically activated cardiac fibroblasts. Meanwhile, KB inhibited Treg proliferation and productions of IL‐4 and IL‐10. Treg MAMs, mitochondrial respiration and respiratory complexes, and FA synthesis and oxidation were all suppressed by KB while glycolytic levels were increased. L‐carnitine reversed Treg proliferation and function inhibited by KB. Proportions of ST2L+ cells in Tregs were reduced by KB, as well as the production of ST2L ligand, IL‐33. Reinforcement expressions of ST2L in Tregs counteracted the reductions in MAMs, mitochondrial respiration, and Treg proliferations and productions of Treg cytokines IL‐4 and IL‐10. Therefore, despite the improvement of metabolic indices, KD impaired Treg expansion and function and promoted cardiac fibroblast activation and interstitial fibrosis. This could be mainly mediated by the suppression of MAMs and fatty acid metabolism inhibition via blunting IL‐33/ST2L signaling.
Arabic Fake News Detection: Comparative Study of Neural Networks and Transformer‐Based Approaches
Authors: Al-Yahya Maha , Al-Khalifa Hend , Al-Baity Heyam , AlSaeed Duaa , Essam Amr
Published: April 2021

Fake news detection (FND) involves predicting the likelihood that a particular news article (news report, editorial, expose, etc.) is intentionally deceptive. Arabic FND started to receive more attention in the last decade, and many detection approaches demonstrated some ability to detect fake news on multiple datasets. However, most existing approaches do not consider recent advances in natural language processing, i.e., the use of neural networks and transformers. This paper presents a comprehensive comparative study of neural network and transformer‐based language models used for Arabic FND. We examine the use of neural networks and transformer‐based language models for Arabic FND and show their performance compared to each other. We also conduct an extensive analysis of the possible reasons for the difference in performance results obtained by different approaches. The results demonstrate that transformer‐based models outperform the neural network‐based solutions, which led to an increase in the F1 score from 0.83 (best neural network‐based model, GRU) to 0.95 (best transformer‐based model, QARiB), and it boosted the accuracy by 16% compared to the best in neural network‐based solutions. Finally, we highlight the main gaps in Arabic FND research and suggest future research directions.
Cosmic Consequences of Kaniadakis and Generalized Tsallis Holographic Dark Energy Models in the Fractal Universe
Authors: Jawad Abdul , Sultan Abdul Malik
Published: April 2021

We investigate the recently proposed holographic dark energy models with the apparent horizon as the IR cutoff by assuming Kaniadakis and generalized Tsallis entropies in the fractal universe. The implications of these models are discussed for both the interacting (

Γ
=
3
H

b

2

ρ

m

) and noninteracting (

b

2

=
0

) cases through different cosmological parameters. Accelerated expansion of the universe is justified for both models through deceleration parameter

q

. In this way, the equation of state parameter

ω

d

describes the phantom and quintessence phases of the universe. However, the coincidence parameter

r
~

=

Ω

m

/

Ω

d

shows the dark energy- and dark matter-dominated eras for different values of parameters. It is also mentioned here that the squared speed of sound gives the stability of the model except for the interacting case of the generalized Tsallis holographic dark energy model. It is mentioned here that the current dark energy models at the apparent horizon give consistent results with recent observations.
Research on Fast Algorithm of Radio Wave Propagation in Low-Lossy Obstacles Environment
Authors: Li An-qi , Yin Cheng-you , Zhang Qian-qian , Gan Yong-ji
Published: April 2021

To predict the propagation of radio waves in the environment of dielectric ground and dielectric obstacles, a new two-way parabolic equation (2W-PE) method based on the domain decomposition principle and surface impedance boundary conditions (SIBC) is proposed. First, we decompose the obstacle area into different subdomains and derive the SIBC in each subdomain in detail; then, the discrete hybrid Fourier transform (DMFT) in the upper subdomain and finite difference (FD) algorithm in the lower subdomain is used to solve 2W-PE combined with SIBC, respectively. After that, we explain the algorithm steps in the process of calculating the total field, compared with the traditional 2W-PE, and then finally introduce the method of moments (MoM) combined with the enhanced discrete complex image (E-DCIM) method for accuracy verification of the new 2W-PE algorithm. The simulation results show that no matter how the obstacle medium parameters change, the results of 2W-PE method proposed in this paper and MoM are always in good agreement, which proves the accuracy of 2W-PE and its superiority in speed. Therefore, this paper provides a reliable and efficient method for solving the problem of radio wave propagation in the presence of obstacles, especially in the case of low-lossy obstacles.
SGPNet: A Three‐Dimensional Multitask Residual Framework for Segmentation and IDH Genotype Prediction of Gliomas
Authors: Wang Yao , Wang Yan , Guo Chunjie , Zhang Shuangquan , Yang Lili
Published: April 2021

Glioma is the main type of malignant brain tumor in adults, and the status of isocitrate dehydrogenase (IDH) mutation highly affects the diagnosis, treatment, and prognosis of gliomas. Radiographic medical imaging provides a noninvasive platform for sampling both inter and intralesion heterogeneity of gliomas, and previous research has shown that the IDH genotype can be predicted from the fusion of multimodality radiology images. The features of medical images and IDH genotype are vital for medical treatment; however, it still lacks a multitask framework for the segmentation of the lesion areas of gliomas and the prediction of IDH genotype. In this paper, we propose a novel three‐dimensional (3D) multitask deep learning model for segmentation and genotype prediction (SGPNet). The residual units are also introduced into the SGPNet that allows the output blocks to extract hierarchical features for different tasks and facilitate the information propagation. Our model reduces 26.6% classification error rates comparing with previous models on the datasets of Multimodal Brain Tumor Segmentation Challenge (BRATS) 2020 and The Cancer Genome Atlas (TCGA) gliomas’ databases. Furthermore, we first practically investigate the influence of lesion areas on the performance of IDH genotype prediction by setting different groups of learning targets. The experimental results indicate that the information of lesion areas is more important for the IDH genotype prediction. Our framework is effective and generalizable, which can serve as a highly automated tool to be applied in clinical decision making.
An Immune Checkpoint-Related Gene Signature for Predicting Survival of Pediatric Acute Myeloid Leukemia
Authors: Jiang Feng , Wang Xin-Yu , Wang Ming-Yan , Mao Yan , Miao Xiao-Lin , Wu Chu-Yan , Zhou Guo-Ping
Published: April 2021

Objective. The aim of this research was to create a new genetic signature of immune checkpoint-associated genes as a prognostic method for pediatric acute myeloid leukemia (AML). Methods. Transcriptome profiles and clinical follow-up details were obtained in Therapeutically Applicable Research to Generate Effective Treatments (TARGET), a database of pediatric tumors. Secondary data was collected from the Gene Expression Omnibus (GEO) to test the observations. In univariate Cox regression and multivariate Cox regression studies, the expression of immune checkpoint-related genes was studied. A three-mRNA signature was developed for predicting pediatric AML patient survival. Furthermore, the GEO cohort was used to confirm the reliability. A bioinformatics method was utilized to identify the diagnostic and prognostic value. Results. A three-gene (STAT1, BATF, EML4) signature was developed to identify patients into two danger categories depending on their OS. A multivariate regression study showed that the immune checkpoint-related signature (STAT1, BATF, EML4) was an independent indicator of pediatric AML. By immune cell subtypes analyses, the signature was correlated with multiple subtypes of immune cells. Conclusion. In summary, our three-gene signature can be a useful tool to predict the OS in AML patients.
Deep Transfer Learning Based Multiway Feature Pyramid Network for Object Detection in Images
Authors: Kaur Parvinder , Khehra Baljit Singh , Pharwaha Amar Partap Singh
Published: April 2021

Object detection is being widely used in many fields, and therefore, the demand for more accurate and fast methods for object detection is also increasing. In this paper, we propose a method for object detection in digital images that is more accurate and faster. The proposed model is based on Single-Stage Multibox Detector (SSD) architecture. This method creates many anchor boxes of various aspect ratios based on the backbone network and multiscale feature network and calculates the classes and balances of the anchor boxes to detect objects at various scales. Instead of the VGG16-based deep transfer learning model in SSD, we have used a more efficient base network, i.e., EfficientNet. Detection of objects of different sizes is still an inspiring task. We have used Multiway Feature Pyramid Network (MFPN) to solve this problem. The input to the base network is given to MFPN, and then, the fused features are given to bounding box prediction and class prediction networks. Softer-NMS is applied instead of NMS in SSD to reduce the number of bounding boxes gently. The proposed method is validated on MSCOCO 2017, PASCAL VOC 2007, and PASCAL VOC 2012 datasets and compared to existing state-of-the-art techniques. Our method shows better detection quality in terms of mean Average Precision (mAP).
High‐Efficiency Vibration Isolation for a Three‐Phase Power Transformer by a Quasi‐Zero‐Stiffness Isolator
Authors: Cao Hao , Chang Yaopeng , Zhou Jiaxi , Zhao Xuhui , Lu Ling , Chen Fei , Wu Xiaowen
Published: April 2021

The vibrations generated by a three‐phase power transformer reduce the comfort of residents and the service life of surrounding equipment. To resolve this tough issue, a quasi‐zero‐stiffness (QZS) isolator for the transformer is proposed. This paper is devoted to developing a QZS isolator in a simple way for engineering practices. The vertical springs are used to support the heavy weight of the transformer, while the oblique springs are employed to fulfill negative stiffness to neutralize the positive stiffness of the vertical spring. Hence, a combination of the vertical and oblique spring can yield high static but low dynamic stiffness, and the vibration isolation efficiency can be improved substantially. The dynamic analysis for the QZS vibration isolation system is conducted by the harmonic balance method, and the vibration isolation performance is estimated. Finally, the prototype of the QZS isolator is manufactured, and then the vibration isolation performance is tested comparing with the linear isolator under real power loading conditions. The experimental results show that the QZS isolator prominently outperforms the existing linear isolator. This is the first time to devise a QZS isolator for three‐phase power transformers with heavy payloads in engineering practices.
Sequential Test for a Mixture of Finite Exponential Distribution
Authors: Al-Moisheer A.S.
Published: April 2021

Testing the number of components in a finite mixture is considered one of the challenging problems. In this paper, exponential finite mixtures are used to determine the number of components in a finite mixture. A sequential testing procedure is adopted based on the likelihood ratio test (LRT) statistic. The distribution of the test statistic under the null hypothesis is obtained using a resampling technique based on B bootstrap samples. The quantiles of the distribution of the test statistic are evaluated from the B bootstrap samples. The performance of the test is examined through the empirical power and application on two real datasets. The proposed procedure is not only used for testing the number of components but also for estimating the optimal number of components in a finite exponential mixture distribution. The innovation of this paper is the sequential test, which tests the more general hypothesis of a finite exponential mixture of

k

components versus a mixture of

k
+
1

components. The special case of testing an exponential mixture of one component versus two components is the one commonly used in the literature.
Study on the Axial Compression Postbuckling Similitude Model of the Stiffened Cylindrical Shell with Dimple Imperfections
Authors: Ding Jiawei , Xing Xinkui , Yu Wei , Zhu Wanxu
Published: April 2021

The body of the new‐type dry gas holder is a large stiffened cylindrical shell. Limited by the test site and economic conditions, the buckling characteristics of such holders are generally studied through scale model experiments. Taking the longitudinal‐ring rectangular stiffened cylindrical shell as the research object, the generalized similitude condition and scaling principle formula of the structure are derived innovatively based on Donnell’s assumption and the energy method. By means of displacement loading and node coordinates updating, dimple imperfections are introduced into the ideal structure of the stiffened cylindrical shell, and then, the complete similitude and partial similitude analysis of axial compression nonlinear buckling for imperfect structures are carried out. The analysis results show that the complete similitude analysis of stiffened cylindrical shell axial‐compression nonlinear buckling can be realized accurately; the partial similitude model for stiffened cylindrical shell axial‐compression nonlinear buckling can better predict the buckling characteristics of its prototype structures, and the closer the Poisson’s ratio between the model and the prototype materials is, the more accurate the prediction results are. Meanwhile, the generalized similitude condition and scaling principle formula derived based on the energy method can provide useful reference for the model design and experimental verification of the axial compression buckling of the stiffened cylindrical shell with local geometric imperfections.
Uniformly Most Reliable Three-Terminal Graph of Dense Graphs
Authors: Xie Sun , Zhao Haixing , Yin Jun
Published: April 2021

A graph

G

with

k

specified target vertices in vertex set is a

k

-terminal graph. The

k

-terminal reliability is the connection probability of the fixed

k

target vertices in a

k

-terminal graph when every edge of this graph survives independently with probability

p

. For the class of two-terminal graphs with a large number of edges, Betrand, Goff, Graves, and Sun constructed a locally most reliable two-terminal graph for

p

close to 1 and illustrated by a counterexample that this locally most reliable graph is not the uniformly most reliable two-terminal graph. At the same time, they also determined that there is a uniformly most reliable two-terminal graph in the class obtained by deleting an edge from the complete graph with two target vertices. This article focuses on the uniformly most reliable three-terminal graph of dense graphs with

n

vertices and

m

edges. First, we give the locally most reliable three-terminal graphs of

n

and

m

in certain ranges for

p

close to 0 and 1. Then, it is proved that there is no uniformly most reliable three-terminal graph with specific

n

and

m

, where

n
≥
7

and

n

2

−
⌊

n
−
3

/
2
⌋
≤
m
≤

n

2

−
2

. Finally, some uniformly most reliable graphs are given for

n

vertices and

m

edges, where

4
≤
n
≤
6

and

m
=

n

2

−
2

or

n
≥
5

and

m
=

n

2

−
1

.
Harnessing of Social Capital as a Determinant for Climate Change Adaptation in Mazungunye Communal Lands in Bikita, Zimbabwe
Authors: Nyahunda Louis , Tirivangasi Happy Mathew
Published: April 2021

The livelihoods of rural people have been plagued by the precarious impacts of climate change–related disasters manifesting through floods, heat waves, droughts, cyclones, and erratic temperatures. However, they have not remained passive victims to these impacts. In light of this, rural people are on record of employing a plethora of adaptation strategies to cushion their livelihoods from climate change impacts. In this vew, the role of social capital as a determinant of climate change adaptation is underexplored. Little attention has been paid to how social capital fostered through trust and cooperation amongst rural households and communities is essential for climate change adaptation. This study explored how people in Mazungunye communal lands are embracing social capital to adapt to climate change impacts. The researchers adopted a qualitative research approach guided by the descriptive research design. The population of the study was gathered through simple random and purposive sampling techniques. Accordingly, the population sample consisted of 25 research participants drawn from members of the community following the simple random and purposive sampling techniques. In-depth individual interviews and focus group discussions were used to collect data. Data were analysed through the Thematic Content Analysis. This study established that different forms of social capital are being embraced by the community members to withstand the effects of climate change. These include village savings clubs (fushai), chief’s granary (Zunde raMambo), collective field work (nhimbe), and destocking of livestock (kuronzera) strategies. These strategies illustrate community reliance on indigenous knowledge adaptation strategies as a community response to impacts of climate change on their livelihoods.
Blow-Up for a Stochastic Viscoelastic Lamé Equation with Logarithmic Nonlinearity
Authors: Benramdane Amina , Mezouar Nadia , Alqawba Mohammed Sulaiman , Boulaaras Salah Mahmoud , Cherif Bahri Belkacem
Published: April 2021

In this paper, we consider an initial boundary value problem of stochastic viscoelastic wave equation with nonlinear damping and logarithmic nonlinear source terms. We proved a blow-up result for the solution with decreasing kernel.
Analysis of Influencing Factors of Social Mental Health Based on Big Data
Authors: Zhao Yixuan , Tang Qinghua
Published: April 2021

Big data is a large-scale rapidly growing database of information. Big data has a huge data size and complexity that cannot be easily stored or processed by conventional data processing tools. Big data research methods have been widely used in many disciplines as research methods based on massively big data analysis have aroused great interest in scientific methodology. In this paper, we proposed a deep computational model to analyze the factors that affect social and mental health. The proposed model utilizes a large number of microblog manual annotation datasets. This huge amount of dataset is divided into six main factors that affect social and mental health, that is, economic market correlation, the political democracy, the management law, the cultural trend, the expansion of the information level, and the fast correlation of the rhythm of life. The proposed model compares the review data of different influencing factors to get the correlation degree between social mental health and these factors.
Global Phosphoproteomics Reveal CDK Suppression as a Vulnerability to KRas Addiction in Pancreatic Cancer
Authors: Kazi Aslamuzzaman , Chen Liwei , Xiang Shengyan , Vangipurapu Rajanikanth , Yang Hua , Beato Francisca , Fang Bin , Williams Terence M. , Husain Kazim , Underwood Patrick , Fleming Jason B. , Malafa Mokenge , Welsh Eric A. , Koomen John , Trevino José , Sebti Saïd M.
Published: April 2021

Purpose:

Among human cancers that harbor mutant (mt) KRas, some, but not all, are dependent on mt KRas. However, little is known about what drives KRas dependency.

Experimental Design:

Global phosphoproteomics, screening of a chemical library of FDA drugs, and genome-wide CRISPR/Cas9 viability database analysis were used to identify vulnerabilities of KRas dependency.

Results:

Global phosphoproteomics revealed that KRas dependency is driven by a cyclin-dependent kinase (CDK) network. CRISPR/Cas9 viability database analysis revealed that, in mt KRas-driven pancreatic cancer cells, knocking out the cell-cycle regulators CDK1 or CDK2 or the transcriptional regulators CDK7 or CDK9 was as effective as knocking out KRas. Furthermore, screening of a library of FDA drugs identified AT7519, a CDK1, 2, 7, and 9 inhibitor, as a potent inducer of apoptosis in mt KRas-dependent, but not in mt KRas-independent, human cancer cells. In vivo AT7519 inhibited the phosphorylation of CDK1, 2, 7, and 9 substrates and suppressed growth of xenografts from 5 patients with pancreatic cancer. AT7519 also abrogated mt KRas and mt p53 primary and metastatic pancreatic cancer in three-dimensional (3D) organoids from 2 patients, 3D cocultures from 8 patients, and mouse 3D organoids from pancreatic intraepithelial neoplasia, primary, and metastatic tumors.

Conclusions:

A link between CDK hyperactivation and mt KRas dependency was uncovered and pharmacologically exploited to abrogate mt KRas-driven pancreatic cancer in highly relevant models, warranting clinical investigations of AT7519 in patients with pancreatic cancer.
Durvalumab Combined with Immunomodulatory Drugs (IMiD) Overcomes Suppression of Antitumor Responses due to IMiD-induced PD-L1 Upregulation on Myeloma Cells
Authors: Ishibashi Mariko , Yamamoto Junichi , Ito Takumi , Handa Hiroshi , Sunakawa-Kii Mika , Inokuchi Koiti , Morita Rimpei , Tamura Hideto
Published: April 2021

We previously showed that the interaction of programmed death-ligand 1 (PD-L1) on multiple myeloma (MM) cells with PD-1 not only inhibits tumor-specific cytotoxic T-lymphocyte activity via the PD-1 signaling pathway but also induces drug resistance via PD-L1–mediated reverse signals. We here examined the regulation of PD-L1 expression by immunomodulatory drugs (IMiDs) and antimyeloma effects of the anti–PD-L1 antibody durvalumab in combination with IMiDs. IMiDs induced PD-L1 expression on IMiD-insensitive MM cells and plasma cells from patients newly diagnosed with MM. Gene-expression profiling analysis demonstrated that not only PD-L1, but also a proliferation-inducing ligand (APRIL), was enhanced by IMiDs. PD-L1 induction by IMiDs was suppressed by using the APRIL inhibitor recombinant B-cell maturation antigen (BCMA)-Ig, the antibody against BCMA, or an MEK/ERK inhibitor in in vitro and in vivo assays. In addition, its induction was abrogated in cereblon (CRBN)-knockdown MM cells, whereas PD-L1 expression was increased and strongly induced by IMiDs in Ikaros-knockdown cells. These results demonstrated that PD-L1 upregulation by IMiDs on IMiD-insensitive MM cells was induced by (i) the BCMA–APRIL pathway via IMiD-mediated induction of APRIL and (ii) Ikaros degradation mediated by CRBN, which plays a role in inhibiting PD-L1 expression. Furthermore, T-cell inhibition induced by PD-L1–upregulated cells was effectively recovered after combination treatment with durvalumab and IMiDs. PD-L1 upregulation by IMiDs on MM cells might promote aggressive myeloma behaviors and immune escape in the bone marrow microenvironment.
Cross-Species Genomics Reveals Oncogenic Dependencies in ZFTA/C11orf95 Fusion–Positive Supratentorial Ependymomas
Authors: Zheng Tuyu , Ghasemi David R. , Okonechnikov Konstantin , Korshunov Andrey , Sill Martin , Maass Kendra K. , Benites Goncalves da Silva Patricia , Ryzhova Marina , Gojo Johannes , Stichel Damian , Arabzade Amir , Kupp Robert , Benzel Julia , Taya Shinichiro , Adachi Toma , Shiraishi Ryo , Gerber Nicolas U. , Sturm Dominik , Ecker Jonas , Sievers Philipp , Selt Florian , Chapman Rebecca , Haberler Christine , Figarella-Branger Dominique , Reifenberger Guido , Fleischhack Gudrun , Rutkowski Stefan , Donson Andrew M. , Ramaswamy Vijay , Capper David , Ellison David W. , Herold-Mende Christel C. , Schüller Ulrich , Brandner Sebastian , Driever Pablo Hernáiz , Kros Johan M. , Snuderl Matija , Milde Till , Grundy Richard G. , Hoshino Mikio , Mack Stephen C. , Gilbertson Richard J. , Jones David T.W. , Kool Marcel , von Deimling Andreas , Pfister Stefan M. , Sahm Felix , Kawauchi Daisuke , Pajtler Kristian W.
Published: April 2021

Molecular groups of supratentorial ependymomas comprise tumors with ZFTA–RELA or YAP1-involving fusions and fusion-negative subependymoma. However, occasionally supratentorial ependymomas cannot be readily assigned to any of these groups due to lack of detection of a typical fusion and/or ambiguous DNA methylation–based classification. An unbiased approach with a cohort of unprecedented size revealed distinct methylation clusters composed of tumors with ependymal but also various other histologic features containing alternative translocations that shared ZFTA as a partner gene. Somatic overexpression of ZFTA-associated fusion genes in the developing cerebral cortex is capable of inducing tumor formation in vivo, and cross-species comparative analyses identified GLI2 as a key downstream regulator of tumorigenesis in all tumors. Targeting GLI2 with arsenic trioxide caused extended survival of tumor-bearing animals, indicating a potential therapeutic vulnerability in ZFTA fusion–positive tumors.

Significance:

ZFTA–RELA fusions are a hallmark feature of supratentorial ependymoma. We find that ZFTA acts as a partner for alternative transcriptional activators in oncogenic fusions of supratentorial tumors with various histologic characteristics. Establishing representative mouse models, we identify potential therapeutic targets shared by ZFTA fusion–positive tumors, such as GLI2.

This article is highlighted in the In This Issue feature, p. 2113
Diagnostic Challenges of Medullary Thyroid Carcinoma
Authors: Leimbach Robert D. , Hoang Thanh D. , Shakir Mohamed K.M.
Published: April 2021

Background: Medullary thyroid carcinoma (MTC) comprises 1–2% of all thyroid cancers, yet 15% of all thyroid cancer-related deaths. While up to 20% of cases may be predicted due to autosomal dominant germline mutations, 80% of cases are sporadic. However, due to non-specific presenting symptoms and diagnostic imaging, prompt diagnosis and treatment has remained elusive. This article will further investigate the limitations of MTC diagnosis and look into future areas for diagnostic improvement. Methods: Relevant articles were identified using a systematic PubMed and Google Scholar search. Results: Prophylactic total thyroidectomy for the 20% of MTC cases that are present in autosomal dominant disorder provides definitive treatment. Serum calcitonin (Ctn) screening has several technical limitations due to population variability and laboratory assay interference, but advances in laboratory technology and combined use with fine needle aspiration increase its sensitivity. Other serum assays such as carcinoembryonic antigen and procalcitonin have limited applicability. Thyroid ultrasound remains the gold standard for the initial diagnostic planning, with limited application for CT, MRI, and PET imaging. Conclusion: With complete surgical resection the only definitive treatment, early MTC diagnosis has presented an elusive challenge, mainly due to its relative rarity and difficulty in finding an economic screening strategy. Careful family history combined with fine needle aspiration with serum Ctn analysis can improve diagnostic sensitivity and specificity to greater than 95%.
COVID-19: Risiko für Patienten mit chronisch entzündlichen Erkrankungen?
Authors: Makrantonaki Evgenia
Published: April 2021

COVID-19, caused by the SARS-CoV-2 virus, has become pandemic. With sharply rising infection rates, patient groups characterized by an enhanced infection risk will be challenged by the virus. In this context, patients with chronic immune-mediated inflammatory diseases are of particular interest, as these diseases are characterized by an intrinsic immune dysfunction leading to inflammation that may enhance risk for severe infection.