bluuebunny/crossref_metadata_2025 huggingface dataset:
Temporal dynamics of dissolved organic matter (DOM) in mountain lakes: the role of catchment characteristics
Authors: Olson Mark H. , Fischer Janet M. , Hayashi Masaki
Published: July 2021

Climate change is affecting mountain ecosystems by increasing vegetation coverage and altering meteorological conditions. These changes are likely to impact the timing and magnitude of dissolved organic matter (DOM) inputs to lakes from the surrounding catchment. We examined temporal dynamics of DOM using in situ optical sensors that measured DOM fluorescence (fDOM) through the ice-free season in five lakes with differing catchment characteristics. We also measured changes in lake level and compiled daily meteorological data from nearby weather stations. At a seasonal time scale, fDOM dynamics occurred in two phases. fDOM declined in the first phase, which lasted until late July – mid-August, and corresponded to a decline in lake level following spring snowmelt. This decline was more pronounced in lakes with more vegetated catchments. At a shorter time scale, fDOM increased following precipitation events with a 0- to 1-day lag. Rates of fDOM increase per centmetre change in lake level were greater in lakes with vegetated catchments. As climate change increases vegetation coverage, DOM will likely become more dynamic at daily and seasonal time scales and impact water transparency and productivity of mountain lakes.
Effect of nanosized calcium and magnesium particles on absorption in peach tree leaves
Authors: Park Jae-Ryoung , Jang Yoon-Hee , Kyung Chung Il , Kim Kyung-Min
Published: July 2021

Plants require a variety of elements to grow. Of these, calcium (Ca) and magnesium (Mg) play an important role in strengthening the cell wall. Although peaches (Prunus Persica) are highly preferred by consumers, they ripen quickly and become soft over a relatively short period of time after harvesting, making them difficult to transport and store. In addition, the ripening process of peaches proceeds very quickly; cell walls are weakened during maturation, and various pathogens can easily grow, causing rapid decay. Therefore, yield loss occurs during long-term storage or transport. To increase the storage period, a method to delay softening action is required. One potential means to improve firmness is to improve Ca and Mg content as these elements make up and strengthen cell walls. However, Ca and Mgare not readily absorbed by plants. In this study, the size of Ca and Mg particles were reduced to less than 900 nm via grinding and their absorption rates were evaluated in the leaves of peach trees. When plant nutrients with a small particle size by nanotechnology were sprayed on peach trees, the content of Ca and Mg was increased in the petioles, adaxial, abaxial, and leaf side. Therefore, a reduction in the particle size of Ca and Mg increases the absorption rate in peach leaves.
Design and analysis of a tendon-driven snake-arm robot based on a spherical magnets
Authors: Liu Bei , Huang Long , Yin Lairong , Zhang Peng , Yi Kefu
Published: July 2021

A tendon-driven snake-arm robot can achieve a bending motion with multiple degrees of freedom (DOF) with a compact structure, which enables the robot to be widely used in confined environments. However, if a conventional tendon-driven snake-arm robot is subject to a lateral force on the distal end, it will experience passive compliance. In this paper, a 2-DOF rolling joint is proposed, based on the opposite-pole attraction of spherical magnets, which has a relatively simple structure compared with traditional joints. By serially connecting the 2-DOF rolling joints, a novel snake-arm robot is designed utilizing a tendon-driven approach. The kinematic model and workspace of the snake-arm robot are obtained, and the bending motion is validated. Based on the kinematic model, it is theoretically proven that the proposed robot can avoid passive compliance. In addition, this feature is verified through load experiments on the developed prototype.
Quantification of major and trace elements contained in aircraft JET A-1 fuel by in-vacuum PIXE analysis
Authors: Saitoh Katsumi , Fushimi Akihiro , Takegawa Nobuyuki , Sera Koichiro
Published: July 2021

To characterize the chemical composition of aircraft exhaust particles, we developed a treatment method of jet fuel for an elemental analysis by an in-vacuum PIXE system. Eleven elements (Si, S, Cl, K, Ca, Cr, Fe, Ni, Cu, Zn, and Pb) were identified from each sample. The concentrations of S from five JET A-1 fuel samples collected on different days ranged from 30.4 to 440 wt.-ppm. The concentration level of S agreed well with the measurement results obtained by an in-air PIXE analysis, which we have previously performed to determine the major content elements and their concentration levels. Nine elements out of the identified 11 elements (Si, Cl, K, Ca, Cr, Ni, Cu, Zn, and Pb), which were not detected by the in-air PIXE analysis, were detected in all the JET A-1 fuel samples measured. Among these elements, Si, Ni, Cu, Zn, and Pb were found to be the major components. It is suggested that particles emitted from aircraft jet engines, which are generally in the size range smaller than 100 nm (ultrafine particles: UFPs), may contain Si, Ni, Cu, Zn, and Pb. These findings provide useful insights into the source apportionment of UFPs in and around airports.
Inverse problem of left invariant sprays on Lie groups
Authors: Huang Libing , Mo Xiaohuan
Published: July 2021

In this paper, we discuss inverse problem in spray geometry. We find infinitely many sprays with non-diagonalizable Riemann curvature on a Lie group, these sprays are not induced by Finsler metrics. We also study left invariant sprays with non-vanishing spray vectors on Lie groups. We prove that if such a spray [Formula: see text] on a Lie group [Formula: see text] satisfies that [Formula: see text] is commutative or [Formula: see text] is projective, then [Formula: see text] is not induced by any (not necessary positive definite) left invariant Finsler metric. Finally, we construct an abundance of the left invariant sprays on Lie groups which satisfy the conditions in above result.
Breviscapine: A Review on its Phytochemistry, Pharmacokinetics and Therapeutic Effects
Authors: Wen Luan , He Ting , Yu AXiang , Sun Siqi , Li Xiang , Wei Jing , Song Ruolan , Yan Xin , Li Ruiwen , Ren Xueyang , Wang Yu , Liu Xiaoyun , Dong Ying , Fu Xueyan , She Gaimei
Published: July 2021

Breviscapine is one of the extracts of several flavonoids of Erigeron breviscapus. Scutellarin is the main active component of breviscapine, and the qualitative or quantitative criteria as well. Scutellarin and its analogs share a similar skeleton of the flavonoids. Breviscapine has been widely used in the treatment of cerebral infarction and its sequelae, cerebral thrombus, coronary heart disease (CHD), and angina pectoris. Breviscapine has a broad spectrum of pharmacological activities, such as increasing blood flow, improving microcirculation, dilating blood vessels, decreasing blood viscosity, promoting fibrinolysis, inhibiting platelet aggregation, and thrombosis formation, etc. In addition, breviscapine and its analogs have significant value for drug research and development because of the superiority of those significant bioactivities. Furthermore, an increasing number of pharmacokinetic studies have explored the mechanism of scutellarin and its analogs. To provide a comprehensive understanding of the current research on breviscapine, scutellarin, and the analogs, the structural features, distribution situation, preparation method, content determination method, clinical applications, pharmacological action as well as pharmacokinetics are summarized in the present review.
Phenomenological Tsallis distribution from thermal field theory
Authors: Rahaman Mahfuzur , Bhattacharyya Trambak , Alam Jan-e
Published: July 2021

Classical and quantum Tsallis distributions have been widely used in many branches of natural and social sciences. But, the quantum field theory of the Tsallis distributions is relatively a less explored arena. In this paper, we derive the expression for the thermal two-point functions in the Tsallis statistics with the help of the corresponding statistical mechanical formulations. We show that the quantum Tsallis distributions used in the literature appear in the thermal part of the propagator much in the same way the Boltzmann–Gibbs distributions appear in the conventional thermal field theory. As an application of our findings, we calculate the thermal mass in the [Formula: see text] scalar field theory within the realm of the Tsallis statistics.
Characteristics of GZO-based multilayer transparent conducting films
Authors: Liu Chih-Yi , Lin Chao-Cheng , Lai Chun-Hung , Liu Shih-Kun , Ye Chang-Sin , Tien Wei-Chen , Hsu Meng-Ren
Published: July 2021

Ga-doped ZnO (GZO)/metal/GZO structures were fabricated on glass substrates to be the transparent conducting layers in this study. GZO films and metal films were deposited at room-temperature by a radio-frequency sputter and a thermal evaporator, respectively. The GZO/Ag/GZO (GAG) structures had poor electrical and optical properties due to the formation of Ag islands on the GZO layer. A 1-nm Cu seed layer was deposited on the GZO layer to fabricate the GZO/Ag/Cu/GZO (GACG) structure to improve its electrical and optical properties. The GACG structure had sheet resistance of 9 [Formula: see text], average visible transmittance of 86% and figure of merit of [Formula: see text] [Formula: see text]. In addition, the sheet resistance of the GACG structure kept almost the same after annealing at [Formula: see text]C in atmosphere for more than 5 h, which showed good thermal stability.
Understanding of spatiotemporal congestion patterns: A lattice model with predictive effect and density integral
Authors: Wang Tao , Zhang Sainan , Li Zhen , Li Shubin , Yuan Jing , Zhang Jing
Published: July 2021

To further enhance the adaptability of traffic model in actual traffic flow, this paper puts forward a lattice model with considering both the predictive effect and the continuous density of historical information. The critical stability condition is derived from linear stability analysis, and the phase diagram clearly shows that considering the predictive effect and the continuous historical density information is beneficial to reduce traffic congestion. Then, a mKdV equation is obtained by nonlinear analysis, which enable to depict the development process of blocked flow. Finally, the numerical simulation results are confirmed that the predictive effects and continuous historical density information have the ability to suppress traffic congestion.
Charged gravastars in f(R,T,RμνTμν) gravity
Authors: Yousaf Z. , Bhatti M. Z.
Published: July 2021

We explore the aspects of the electromagnetism on the stability of gravastar in a particular modified theory, i.e. [Formula: see text] where [Formula: see text], [Formula: see text] is the Ricci scalar and [Formula: see text] is the trace of energy–momentum tensor. We assume a spherically symmetric static metric coupled comprising of perfect fluid in the presence of electric charge. The purpose of this paper is to extend the results of [S. Ghosh, F. Rahaman, B. K. Guha and S. Ray, Phys. Lett. B 767 (2017) 380.] to highlight the effects of [Formula: see text] gravity in the formation of charged gravastars. We demonstrated the mathematical formulation, utilizing different equations of state, for the three respective regions (i.e. inner, shell, exterior) of the gravastar. We have matched smoothly the interior de Sitter and the exterior Reissner–Nordström metric at the hypersurface. At the end we extracted few conclusions by working on the physical features of the charged gravastar, mathematically and graphically.
Dynamic efficiency of China’s commodity futures market through the lens of high frequency data
Authors: Chengying He , Ke Huang , Zhang Wen , Qingcheng Huang
Published: July 2021

In this paper, we use the permutation entropy algorithm to derive the static and dynamic permutation entropy of commodity futures, and to evaluate the effectiveness of main products in China’s commodity futures market. The intraday data of six varieties belonging to six categories in China’s commodity futures market are taken as samples. We find the following: (1) The return distribution of the main varieties shows high peaks, fat tails and asymmetry, and follows the biased random walk distribution characteristics; (2) The permutation entropy of all varieties decreases significantly in the same time window, during which the price volatility of major commodity markets rises. And the time window coincides with the impact time of COVID-19 epidemic; (3) By comparing the distribution of permutation entropy of main varieties in different stages of event shock, we found that the mean value of permutation entropy decreases significantly during the process of event shock, and the price fluctuates greatly. Therefore, the significant decrease of permutation entropy is a valuable warning signal for regulators and investors.
ConsciousControlFlow(CCF): Conscious Artificial Intelligence Based on Needs
Authors: Wang Hongzhi , Chen Bozhou , Xu Yueyang , Zhang Kaixin , Zheng Shengwen
Published: July 2021

The major criteria to distinguish conscious Artificial Intelligence (AI) and non-conscious AI is whether the conscious is from the needs. Based on this criteria, we develop ConsciousControlFlow(CCF) to show the need-based conscious AI. The system is based on the computational model with a short-term memory (STM) and long-term memory (LTM) for consciousness and the hierarchy of needs. To generate AI based on real needs of the agent, we developed several LTMs for special functions such as feeling and sensor. Experiments have demonstrated that the agents in the proposed system behave according to the needs, which coincides with the prediction.
Iron and sulfur speciation and cycling in the sediments of marine systems located in arid environments: the northern Red Sea
Authors: Boyko Valeria , Pätzold Jürgen , Kamyshny Alexey
Published: July 2021

The high fluxes of iron minerals associated with aeolian dry deposition may result in an anomalously high reactive iron content and the rapid reoxidation of hydrogen sulfide in sediments. This will prevent the formation of pyrite and result in a ‘cryptic’ sulfur cycle. We studied the cycling of iron and sulfur in deep water (>800 m water depth) sediments of the Red Sea and its northern extension, the Gulf of Aqaba. We found that the reactive iron content in the surface sediments of the Gulf of Aqaba and the Red Sea is high, whereas the amount of sulfur-bound iron is very low and decreases with the water depth. The presence of trace amounts of pyrite and zero-valent sulfur, as well as the isotopic compositions of sulfate and pyrite, which are consistent with sulfate reduction under substrate-limiting conditions, suggest that cryptic sulfur cycling is likely to be a result of the rapid reoxidation of hydrogen sulfide rather than the suppression of microbial sulfate reduction. The low amount of reactive iron and high organic carbon content in the sediments of the Shaban Deep, which are overlain by hypersaline hydrothermal brines, result in a non-cryptic sulfur cycle characterized by the preservation of pyrite in the sediments.

Supplementary material:
Data for sulfur speciation, iron speciation and sulfur isotope composition are available at
https://doi.org/10.6084/m9.figshare.c.5508155

Thematic collection:
This article is part of the Sulfur in the Earth system collection available at:
https://www.lyellcollection.org/cc/sulfur-in-the-earth-system
Physical and chemical depositional processes when volcanoes meet lacustrine environments: the Cretaceous Imjado Volcanics, Jeungdo, southwestern Korea
Authors: Gihm Yong Sik
Published: July 2021

A Cretaceous volcano-sedimentary succession (Imjado Volcanics, Jeungdo, SW Korea) was analysed to understand volcanic influences on physical and chemical depositional processes of a shallow alkaline lake during and after explosive eruptions. The succession is composed of primary and resedimented volcaniclastic deposits interbedded with fine-grained sediments and a bedded chert. The primary volcaniclastic deposits are characterized by two end-members: thick (20 m), welded lapilli tuff and thin (0.9 m), planar stratified tuff deposits. The first member deposits were accumulated by steady pyroclastic density currents that displaced the lake water from the shoreline. The second end-member deposits were accumulated by unsteady pyroclastic density currents that rapidly disintegrated at the shoreline and transformed into turbidity currents. The reverse to normally graded volcaniclastic sandstone is one of the resedimented volcaniclastic deposits accumulated by hyperpycnal flows originating from subaerial discharge. On top of this deposit, a bedded chert is exposed and composed of microcrystalline texture without biogenic remains. The microscopic analysis and stratigraphic relationship suggest that the chert bed was formed by chemical precipitation as a result of changes in the hydrochemistry of lake water by interaction between fresh water (hyperpycnal flows) and the alkaline bottom water of the lake.
Triage of 2D Mammographic Images Using Multi-view Multi-task Convolutional Neural Networks
Authors: Kyono Trent , Gilbert Fiona J. , Schaar Mihaela Van Der
Published: July 2021

With an aging and growing population, the number of women receiving mammograms is increasing. However, existing techniques for autonomous diagnosis do not surpass a well-trained radiologist. Therefore, to reduce the number of mammograms that require examination by a radiologist, subject to preserving the diagnostic accuracy observed in current clinical practice, we develop Man and Machine Mammography Oracle (MAMMO)—a clinical decision support system capable of determining whether its predicted diagnoses require further radiologist examination. We first introduce a novel multi-view convolutional neural network (CNN) trained using multi-task learning (MTL) to diagnose mammograms and predict the radiological assessments known to be associated with cancer. MTL improves diagnostic performance and triage efficiency while providing an additional layer of model interpretability. Furthermore, we introduce a novel triage network that takes as input the radiological assessment and diagnostic predictions of the multi-view CNN and determines whether the radiologist or CNN will most likely provide the correct diagnosis. Results obtained on a dataset of over 7,000 patients show that MAMMO reduced the number of diagnostic mammograms requiring radiologist reading by 42.8% while improving the overall diagnostic accuracy in comparison to readings done by radiologists alone.
A Fast ECG Diagnosis by Using Non-Uniform Spectral Analysis and the Artificial Neural Network
Authors: Chen Kun-chih (Jimmy) , Chien Po-Chen , Gao Zi-Jie , Wu Chi-Hsun
Published: July 2021

The electrocardiogram (ECG) has been proven as an efficient diagnostic tool to monitor the electrical activity of the heart and has become a widely used clinical approach to diagnose heart diseases. In a practical way, the ECG signal can be decomposed into P, Q, R, S, and T waves. Based on the information of the features in these waves, such as the amplitude and the interval between each wave, many types of heart diseases can be detected by using the neural network (NN)-based ECG analysis approach. However, because of a large amount of computing to preprocess the raw ECG signal, it is time consuming to analyze the ECG signal in the time domain. In addition, the non-linear ECG signal analysis worsens the difficulty to diagnose the ECG signal. To solve the problem, we propose a fast ECG diagnosis approach based on spectral analysis and the artificial neural network. Compared with the conventional time-domain approaches, the proposed approach analyzes the ECG signal only in the frequency domain. However, because most of the noises in the raw ECG signal belong to high-frequency signals, it is necessary to acquire more features in the low-frequency spectrum and fewer features in the high-frequency spectrum. Hence, a non-uniform feature extraction approach is proposed in this article. According to less data preprocessing in the frequency domain than the one in the time domain, the proposed approach not only reduces the total diagnosis latency but also reduces the computing power consumption of the ECG diagnosis. To verify the proposed approach, the well-known MIT-BIH arrhythmia database is involved in this work. The experimental results show that the proposed approach can reduce ECG diagnosis latency by 47% to 52% compared with conventional ECG analysis methods under similar diagnostic accuracy of heart diseases. In addition, because of less data preprocessing, the proposed approach can achieve lower area overhead by 22% to 29% and lower computing power consumption by 29% to 34% compared with the related works, which is proper for applying this approach to portable medical devices.
Crystallization Properties of Al-Sb Alloys for Phase Change Memory Applications
Authors: Ume Rubab , Gong Haibo , Tokranov Vadim , Yakimov Michael , Sadana Devendra , Brew Kevin , Cohen Guy , Lavoie Christian , Schujman Sandra , Beckmann Karsten , Cady Nathaniel , Oktyabrsky Serge
Published: July 2021

Material properties of Al-Sb binary alloy thin films deposited under ultra-high vacuum conditions were studied for multi-level phase change memory applications. Crystallization of this alloy was shown to occur in the temperature range of 180 °C–280 °C, with activation energy >2 eV. X-ray diffraction (XRD) from annealed alloy films indicates the formation of two crystalline phases, (i) an Al-doped A7 antimony phase, and (ii) a stable cubic AlSb phase. In-situ XRD analysis of these films show the AlSb phase crystalizes at a much higher temperature as compared to the A7 phase after annealing of the film to 650 °C. Mushroom cell structures formed with Al-Sb alloys on 120 nm TiN heater show a phase change material resistance switching behavior with reset/set resistance ratio >1000 under pulse measurements. TEM and in situ synchrotron XRD studies indicate fine nucleation grain sizes of ∼8–10 nm, and low elemental redistribution that is useful for improving reliability of the devices. These results indicate that Te-free Al-Sb binary alloys are possible candidates for analog PCM applications.
Neutrophil elastase-regulated macrophage sheddome/secretome and phagocytic failure
Authors: Ma Jonathan , Kummarapurugu Apparao B. , Hawkridge Adam , Ghosh Shobha , Zheng Shuo , Voynow Judith A.
Published: July 2021

Patients with cystic fibrosis (CF) have defective macrophage phagocytosis and efferocytosis. Several reports demonstrate that neutrophil elastase (NE), a major inflammatory protease in the CF airway, impairs macrophage phagocytic function. To date, NE-impaired macrophage phagocytic function has been attributed to cleavage of cell surface receptors or opsonins. We applied an unbiased proteomic approach to identify other potential macrophage targets of NE protease activity that may regulate phagocytic function. Using the murine macrophage cell line, RAW 264.7, human blood monocyte-derived macrophages, and primary alveolar macrophages from Cftr-null and wild-type littermate mice, we demonstrated that NE exposure blocked phagocytosis of Escherichia coli bio-particles. We performed liquid chromatography-tandem mass spectroscopy (LC-MS/MS) proteomic analysis of the conditioned media from RAW264.7 treated either with active NE or inactive (boiled) NE as a control. Out of 840 proteins identified in the conditioned media, active NE upregulated 142 proteins and downregulated 211 proteins. NE released not only cell surface proteins into the media but also cytoskeletal, mitochondrial, cytosolic, and nuclear proteins that were detected in the conditioned media. At least 32 proteins were associated with the process of phagocytosis including 11 phagocytic receptors [including lipoprotein receptor-related protein 1 (LRP1)], 7 proteins associated with phagocytic cup formation, and 14 proteins involved in phagocytic maturation (including calpain-2) and phagolysosome formation. NE had a broad effect on the proteome required for regulation of all stages of phagocytosis and phagolysosome formation. Furthermore, the NE sheddome/secretome included proteins from other macrophage cellular domains, suggesting that NE may globally regulate macrophage structure and function.
Mechanisms modulating spinal excitability after nerve stimulation inducing extra torque
Authors: Vitry Florian , Papaiordanidou Maria , Martin Alain
Published: July 2021

Using different electrophysiological techniques, results show that the downregulation of spinal excitability observed after the 20-Hz train could be ascribed to homosynaptic postactivation depression of the Ia afferents terminals, whereas changes in intrinsic motoneuron properties could explain the increased spinal excitability observed after the 100-Hz train. A novel methodology for assessing soleus D1 presynaptic inhibition and heteronymous Ia facilitation, accounting for eventual modulations of test reflex amplitude throughout the session, was developed.
Quantifying upper limb motor impairment in chronic stroke: a physiological profiling approach
Authors: Ingram Lewis A. , Butler Annie A. , Brodie Matthew A. , Lord Stephen R. , Gandevia Simon C.
Published: July 2021

Upper limb motor impairment is a common manifestation after stroke, compromising independence in fundamental daily activities involving the ability to reach, grasp, and manipulate objects. The upper limb Physiological Profile Assessment (PPA) offers a means of quantifying performance of the individual sensorimotor domains that are essential for upper limb function. Establishing individual performance profiles based on age- and sex-based normative scores may facilitate individualized treatment decisions by identifying the stroke patient’s specific strengths and limitations.
The impact of hypoxia on blood-brain, blood-CSF, and CSF-brain barriers
Authors: Dunn Jeff F. , Isaacs Albert M.
Published: July 2021

The blood-brain barrier (BBB), blood-cerebrospinal fluid (CSF) barrier (BCSFB), and CSF-brain barriers (CSFBB) are highly regulated barriers in the central nervous system comprising complex multicellular structures that separate nerves and glia from blood and CSF, respectively. Barrier damage has been implicated in the pathophysiology of diverse hypoxia-related neurological conditions, including stroke, multiple sclerosis, hydrocephalus, and high-altitude cerebral edema. Much is known about the damage to the BBB in response to hypoxia, but much less is known about the BCSFB and CSFBB. Yet, it is known that these other barriers are implicated in damage after hypoxia or inflammation. In the 1950s, it was shown that the rate of radionucleated human serum albumin passage from plasma to CSF was five times higher during hypoxic than normoxic conditions in dogs, due to BCSFB disruption. Severe hypoxia due to administration of the bacterial toxin lipopolysaccharide is associated with disruption of the CSFBB. This review discusses the anatomy of the BBB, BCSFB, and CSFBB and the impact of hypoxia and associated inflammation on the regulation of those barriers.
Satellite cell and myonuclear accretion is related to training-induced skeletal muscle fiber hypertrophy in young males and females
Authors: Abou Sawan Sidney , Hodson Nathan , Babits Paul , Malowany Julia M. , Kumbhare Dinesh , Moore Daniel R.
Published: July 2021

We demonstrate that training-induced increase in SC content in type II fibers and myonuclear content in type I and II fibers is similar between males and females. Furthermore, these changes are related to the extent of muscle fiber hypertrophy. Thus, SC and myonuclear accretion appear to contribute to muscle hypertrophy irrespective of sex, highlighting the importance of these muscle stem cells in human skeletal muscle growth.
Postprandial superior mesenteric artery blood flow is related to changes in peripheral pulse wave harmonics and heart rate: implications for wearable technology?
Authors: Abdullah Eamon , Lee Jordan B. , Notay Karambir , Millar Philip J.
Published: July 2021

We studied the superior mesenteric artery (SMA) blood flow responses to three meals of varying caloric value and examined whether these responses could be modeled using changes in heart rate or the peripheral pulse wave harmonic spectrum. We found that both inputs could explain 66–69% of the SMA blood flow variance over 2 h of postprandial recovery. Noninvasive methods may be able to predict SMA blood flow and thus used to measure caloric intake.
Best Prediction Method for Progressive Type-II Censored Samples under New Pareto Model with Applications
Authors: Haj Ahmad Hanan
Published: July 2021

This paper describes two prediction methods for predicting the non-observed (censored) units under progressive Type-II censored samples. The lifetimes under consideration are following a new two-parameter Pareto distribution. Furthermore, point and interval estimation of the unknown parameters of the new Pareto model is obtained. Maximum likelihood and Bayesian estimation methods are considered for that purpose. Since Bayes estimators cannot be expressed explicitly, Gibbs and the Markov Chain Monte Carlo techniques are utilized for Bayesian calculation. We use the posterior predictive density of the non-observed units to construct predictive intervals. A simulation study is performed to evaluate the performance of the estimators via mean square errors and biases and to obtain the best prediction method for the censored observation under progressive Type-II censoring scheme for different sample sizes and different censoring schemes.
Low Levels of MicroRNA-10a in Cardiovascular Endothelium and Blood Serum Are Related to Human Atherosclerotic Disease
Authors: Kuo Jong-Tar , Tsai Hsiao-En , Lin Ching-Ting , Lee Chih-I , Lee Pei-Ling , Ruan Yu-Rong , Chiu Jeng-Jiann , Lee Ding-Yu
Published: July 2021

Background. MicroRNA-10a (miR-10a) inhibits transcriptional factor GATA6 to repress inflammatory GATA6/VCAM-1 signaling, which is regulated by blood flow to affect endothelial function/dysfunction. This study aimed to identify the expression patterns of miR-10a/GATA6/VCAM-1 in vivo and study their implications in the pathophysiology of human coronary artery disease (CAD), i.e., atherosclerosis. Methods. Human atherosclerotic coronary arteries and nondiseased arteries were used to detect the expressions of miR-10a/GATA6/VCAM-1 in pathogenic vs. normal conditions. In addition, sera from CAD patients and healthy subjects were collected to detect the level of circulating miR-10a. Results. The comparison of human atherosclerotic coronary arteries with nondiseased arteries demonstrated that lower levels of endothelial miR-10a are related to human atherogenesis. Moreover, GATA6/VCAM-1 (a downstream target of miR-10a) was highly expressed in the endothelium, accompanied by the reduced levels of miR-10a during the development of human atherosclerosis. In addition, CAD patients had a significantly lower concentration of miR-10a in their serum compared to healthy subjects. Conclusions. Our findings suggest that low miR-10a and high GATA6/VCAM-1 in the cardiovascular endothelium correlates to the development of human atherosclerotic lesions, suggesting that miR-10a signaling has the potential to be developed as a biomarker for human atherosclerosis.
A Deep Pedestrian Tracking SSD-Based Model in the Sudden Emergency or Violent Environment
Authors: Li Zhihong , Dong Yang , Wen Yanjie , Xu Han , Wu Jiahao
Published: July 2021

Public security monitoring is a hot issue that the government and citizens pay close attention to. Multiobject tracking plays an important role in solving many problems for public security. Under crowded scenarios and emergency places, it is a challenging problem to predict and warn owing to the complexity of crowd intersection. There are still many deficiencies in the research of multiobject trajectory prediction, which mostly employ object detection and data association. Compared with the tremendous progress in object detection, data association still relied on hand-crafted constraints such as group, motion, and spatial proximity. Emergencies usually have the characteristics of mutation, target diversification, low illumination, or resolution, which makes multitarget tracking more difficult. In this paper, we harness the advance of the deep learning framework for data association in object tracking by jointly modeling pedestrian features. The proposed deep pedestrian tracking SSD-based model can pair and link pedestrian features in any two frames. The model was trained with open dataset, and the results, accuracy, and speed of the model were compared between normal and emergency or violent environment. The experimental results show that the tracking accuracy of mAP is higher than 95% both in normal and abnormal data sets and higher than that of the traditional detection algorithm. The detection speed of the normal data set is slightly higher than that of the abnormal data set. In general, the model has good tracking results and credibility for multitarget tracking in emergency environment. The research provides technical support for safety assurance and behavior monitoring in emergency environment.
[Retracted] Using Big Data‐Based Neural Network Parallel Optimization Algorithm in Sports Fatigue Warning
Authors: Sun Yudong , He Yahui
Published: July 2021

In high‐paced and efficient life and work, fatigue is one of the important factors that cause accidents such as traffic and medical accidents. This study designs a feature map‐based pruning strategy (PFM), which effectively reduces redundant parameters and reduces the time and space complexity of parallelized deep convolutional neural network (DCNN) training; a correction is proposed in the Map stage. The secant conjugate gradient method (CGMSE) realizes the fast convergence of the conjugate gradient method and improves the convergence speed of the network; in the Reduce stage, a load balancing strategy to control the load rate (LBRLA) is proposed to achieve fast and uniform data grouping to ensure the parallelization performance of the parallel system. Finally, the related fatigue algorithm’s research and simulation based on the human eye are carried out on the PC. The human face and eye area are detected from the video image collected using the USB camera, and the frame difference method and the position information of the human eye on the face are used. To track the human eye area, extract the relevant human eye fatigue characteristics, combine the blink frequency, closed eye duration, PERCLOS, and other human eye fatigue determination mechanisms to determine the fatigue state, and test and verify the designed platform and algorithm through experiments. This system is designed to enable people who doze off, such as drivers, to discover their state in time through the system and reduce the possibility of accidents due to fatigue.
Rehardening of Eroded Enamel with CPP‐ACFP Paste and CO2 Laser Treatment
Authors: Kasraei Shahin , Kasraei Parmis , Valizadeh Sara , Azarsina Mohadeseh
Published: July 2021

Background. Diet and lifestyle can destroy tooth structure due to the dissolution of enamel by acidic beverages. The present study evaluated the effect of CO2 laser irradiation and CPP‐ACFP (casein phosphopeptide and amorphous calcium phosphate with fluoride) paste on the remineralization of enamel eroded by carbonated soft drinks. Methods. In the present in vitro study, 46 human sound premolar teeth were sectioned mesiodistally to achieve 84 samples. Fourteen samples were assigned to the positive control group (G1), and the remaining samples were immersed in 500 mL of cola drink for 2 minutes, followed by rinsing with distilled water for 10 seconds. This procedure was carried out three times to create erosive lesions. Then, the 60 eroded samples were randomly assigned to five groups of G2 to G6 in terms of the treatment as follows: negative control (G2), CO2 laser irradiation (G3), CPP‐ACFP paste (G4), CO2 laser irradiation followed by CPP‐ACFP paste application (G5), and CPP‐ACFP paste application followed by CO2 laser irradiation (G6). The mean surface microhardness of the enamel surface was evaluated and determined at three points for each sample. Data were analyzed with one‐way ANOVA and Tukey HSD tests (α = 0.05). Results. The highest and the lowest hardness values were recorded in the G1 (314 ± 12 kg/mm2) and G2 (213.7 ± 12 kg/mm2) groups, respectively. ANOVA revealed significant differences between the study groups (P < 0.001). Two‐by‐two comparisons showed significant differences between the G2 group and the other groups, indicating the efficacy of all the treatment modalities in tooth remineralization and rehardening procedures (P < 0.05). Only in group G6, the enamel microhardness was not significantly different from the G1 positive control group (P > 0.05). Conclusion. Considering the parameters used in the present study, CO2 laser irradiation or CPP‐ACFP paste application alone increased eroded enamel’s surface hardness; however, their sequential application was more effective in rehardening the eroded enamel’s surface to near‐normal levels.
Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer’s Disease or Mild Cognitive Impairment
Authors: Li Lanlan , Yang Yeying , Zhang Qi , Wang Jiao , Jiang Jiehui , Neuroimaging Initiative Alzheimer’s Disease
Published: July 2021

Objectives. Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis. Methods. In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used. Results. We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively,

98.78
±
1.50
%

,

98.39
%
±
2.50
%

, and

99.44
%
±
1.11
%

using the DLG model, while

71.38
%
±
0.63
%

,

63.13
%
±
2.87
%

, and

85.59
%
±
6.66
%

using traditional GWAS. Similar results were obtained from the other two intergroup classifications. Conclusion. The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.
Spectral Sufficient Conditions on Pancyclic Graphs
Authors: Yu Guidong , Yu Tao , Xia Xiangwei , Xu Huan
Published: July 2021

A pancyclic graph of order n is a graph with cycles of all possible lengths from 3 to n. In fact, it is NP‐complete that deciding whether a graph is pancyclic. Because the spectrum of graphs is convenient to be calculated, in this study, we try to use the spectral theory of graphs to study this problem and give some sufficient conditions for a graph to be pancyclic in terms of the spectral radius and the signless Laplacian spectral radius of the graph.
Fuzzy C-Means Clustering Algorithm-Based Magnetic Resonance Imaging Image Segmentation for Analyzing the Effect of Edaravone on the Vascular Endothelial Function in Patients with Acute Cerebral Infarction
Authors: Yin Jie , Chang Hong , Wang Dongmei , Li Haifei , Yin Aibing
Published: July 2021

This paper aimed to discuss the denoising ability of magnetic resonance imaging (MRI) images based on fuzzy C-means clustering (FCM) algorithm and the influence of Butylphthalide combined with Edaravone treatment on nerve function and vascular endothelial function in patients with acute cerebral infarction (ACI). Based on FCM algorithm, Markov Random Field (MRF) model algorithm was introduced to obtain a novel algorithm (NFCM), which was compared with FCM and MRF algorithm in terms of misclassification rate (MCR) and difference of Kappa index (KI). 90 patients with ACI diagnosed in hospital from December 2018 to December 2019 were selected as subjects, who were divided into combined treatment group (conventional treatment + Edaravone + Butylphthalide) and Edaravone group (conventional treatment + Edaravone) randomly, each consisting of 45 cases. The National Institutes of Health Stroke Scale (NIHSS) score and endothelial function index level such as plasma nitric oxide (NO), human endothelin-1 (ET-1), and vascular endothelial cell growth factor (VEGF) were compared before and after treatment between the two groups. The results showed that the MCR of NFCM was evidently inferior to FCM and MRF, and the KI was notably higher relative to the other two algorithms. After treatment, the NIHSS score of the combined treatment group was (9.09 ± 1.86) points and that of Edaravone group was (14.97 ± 3.44) points, with evident difference between the two groups (

P
<
0.05

). After treatment, the NO of the combined treatment was (54.63 ± 4.85), and that of Edaravone group was (41.54 ± 5.27), which was considerably different (

P
<
0.01

), and the VEGF and ET-1 of combined treatment group were greatly inferior to Edaravone group (

P
<
0.01

). It was revealed that the novel algorithm based on FCM can obtain more favorable quality and segmentation accuracy of MRI images. Moreover, Butylphthalide combined with Edaravone treatment can effectively improve nerve function, vascular endothelial function, and short-term prognosis in ACI, which was safe and worthy of clinical adoption.
Integrative Analysis of Differently Expressed Genes Reveals a 17‐Gene Prognosis Signature for Endometrial Carcinoma
Authors: Wang Anna , Guo Hongyan , Long Zaiqiu
Published: July 2021

Endometrial carcinoma (EC) is the fifth widely occurring malignant neoplasm among women all over the world. However, there is still lacking efficacy indicators for EC’s prognosis. Here, we analyzed two databases including an RNA‐sequencing‐based TCGA dataset and a microarray‐based GSE106191. After normalizing the raw data, we identified 114 common genes with upregulation and 308 common genes with downregulation in both the TCGA and GSE106191 databases. Bioinformatics analysis showed that the differently expressed genes in EC were related to the IL17 signaling pathway, PI3K‐Akt signaling pathway, and cGMP‐PKG signaling pathway. Furthermore, we performed the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and generated a signature featuring 17 prognosis‐related genes (MAL2, ANKRD22, METTL7B, IL32, ERFE, OAS1, TRPC1, SRPX, RAPGEF4, PSD3, SIMC1, TRPC6, WFS1, PGR, PAMR1, KCNK6, and FAM189A2) and found that it could predict OS in EC patients. The further analysis showed that OAS1, MAL2, ANKRD22, METTL7B, and IL32 were significantly upregulated in EC samples after comparison with normal samples. However, TRPC1, SRPX, RAPGEF4, PSD3, SIMC1, TRPC6, WFS1, PGR, PAMR1, KCNK6, and FAM189A2 were significantly downregulated in EC samples in comparison with normal samples. And correlation analysis showed that our results showed that the expressions of 17 prognosis‐related hub genes were significantly correlated based on Pearson correlation. We here offer a newly genetic biomarker for the prediction of EC patients’ prognosis.
Adaptive Trajectory Tracking Control for Rotorcraft Using Incremental Backstepping Sliding Mode Control Strategy
Authors: Cho Moon Gyeang , Jung Useok , An Jun-Young , Choi Yoo-Seung , Kim Chang-Joo
Published: July 2021

This paper investigates the adaptive incremental backstepping sliding mode control for the rotorcraft trajectory-tracking control problem to enhance the robustness to the matched uncertainty in the model. First, the incremental dynamics is used for the control design to exclude the adverse effect of the mismatched model uncertainties on the trajectory-tracking performance. Secondly, the sliding-mode control strategy is adopted in the second design stage of the backstepping controller, and the effect of switching gains on the controller robustness is thoroughly studied using the rotorcraft model with different levels of the matched uncertainties. To clarify the robustness enhancement using the adaptive selection of switching gains, this paper chooses three different control structures consisting of the traditional backstepping control and two backstepping sliding mode controls with the fixed or adaptively adjusted switching gains. These control designs are applied to the trajectory-tracking control for the helical-turn maneuver of the Bo-105 helicopter to compare their relative robustness to the matched uncertainties. The results prove that adaptive incremental backstepping sliding mode control shows much higher robustness than other two designs, and the controller even with the fixed switching gains can be used to improve the robustness of the pure backstepping control design. Therefore, the present adaptive incremental backstepping sliding mode control is effectively applicable with the rotorcraft model which typically contains many different sources of both matched and mismatched uncertainties.
Deep Learning on MRI Images for Diagnosis of Lung Cancer Spinal Bone Metastasis
Authors: Fan Xiaojie , Zhang Xiaoyu , Zhang Zibo , Jiang Yifang
Published: July 2021

This paper aimed to explore the adoption of deep learning algorithms in lung cancer spinal bone metastasis diagnosis. Comprehensive analysis was carried out with the aid of AdaBoost algorithm and Chan-Vese (CV) algorithm. 87 patients with lung cancer spinal bone metastasis were taken as research subjects, and comprehensive evaluation was made in terms of preliminary classification of images, segmentation results, Dice index, and Jaccard coefficient. After the case of misjudgment on whether there was hot spot was excluded, the initial classification accuracy of the AdaBoost algorithm can reach 96.55%. True positive rate (TPR) was 2.3%, and false negative rate (FNR) was 1.15%. 45 MRI images with hot spots were utilized as test set to detect the segmentation accuracy of CV, maximum between-cluster variance method (OTSU), and region growing algorithm. The results showed that the Dice index and Jaccard coefficient of the CV algorithm were 0.8591 and 0.8002, respectively, which were considerably superior to OTSU (0.6125 and 0.5541) and region growing algorithm (0.7293 and 0.6598). In summary, the AdaBoost algorithm was adopted for image preliminary classification, and CV algorithm for image segmentation was ideal for the diagnosis of lung cancer spinal bone metastasis and it was worthy of clinical promotion.