bluuebunny/crossref_metadata_2025 huggingface dataset:
A Near-linear Time ε-Approximation Algorithm for Geometric Bipartite Matching
Authors: Raghvendra Sharath , Agarwal Pankaj K.
Published: June 2020

For point sets
A
,
B
⊂ R

d

, ∣
A
∣ = ∣
B
∣ =
n
, and for a parameter ε > 0, we present a Monte Carlo algorithm that computes, in
O
(
n
poly(log
n
, 1/ε)) time, an ε-approximate perfect matching of
A
and
B
under any
L

p

-norm with high probability; the previously best-known algorithm takes Ω(
n
3/2
) time. We approximate the
L

p

-norm using a distance function, d(⋅, ⋅) based on a randomly shifted quad-tree. The algorithm iteratively generates an approximate minimum-cost augmenting path under d(⋅, ⋅) in time proportional, within a polylogarithmic factor, to the length of the path. We show that the total length of the augmenting paths generated by the algorithm is
O
(
n
/ε)log
n
), implying that the running time of our algorithm is
O
(
n
poly(log
n
, 1/ε)).
Adversarial Attacks on Graph Neural Networks
Authors: Zügner Daniel , Borchert Oliver , Akbarnejad Amir , Günnemann Stephan
Published: June 2020

Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, little is known about their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.g., the web, adversaries are common. Can deep learning models for graphs be easily fooled? In this work, we present a study of adversarial attacks on attributed graphs, specifically focusing on models exploiting ideas of graph convolutions. In addition to attacks at test time, we tackle the more challenging class of poisoning/causative attacks, which focus on the training phase of a machine learning model. We generate adversarial perturbations targeting the
node’s features
and the
graph structure
, thus, taking the dependencies between instances in account. Moreover, we ensure that the perturbations remain
unnoticeable
by preserving important data characteristics. To cope with the underlying discrete domain, we propose an efficient algorithm N
ettack
exploiting incremental computations. Our experimental study shows that accuracy of node classification significantly drops even when performing only few perturbations. Even more, our attacks are transferable: the learned attacks generalize to other state-of-the-art node classification models and unsupervised approaches, and likewise are successful even when only limited knowledge about the graph is given. For the first time, we successfully identify important patterns of adversarial attacks on graph neural networks (GNNs) — a first step towards being able to detect adversarial attacks on GNNs.
An Adaptive Two-Layer Light Field Compression Scheme Using GNN-Based Reconstruction
Authors: Hu Xinjue , Shan Jingming , Liu Yu , Zhang Lin , Shirmohammadi Shervin
Published: June 2020

As a new form of volumetric media, Light Field (LF) can provide users with a true six degrees of freedom immersive experience because LF captures the scene with photo-realism, including aperture-limited changes in viewpoint. But uncompressed LF data is too large for network transmission, which is the reason why LF compression has become an important research topic. One of the more recent approaches for LF compression is to reduce the angular resolution of the input LF during compression and to use LF reconstruction to recover the discarded viewpoints during decompression. Following this approach, we propose a new LF reconstruction algorithm based on Graph Neural Networks; we show that it can achieve higher compression and better quality compared to existing reconstruction methods, although suffering from the same problem as those methods—the inability to deal effectively with high-frequency image components. To solve this problem, we propose an adaptive two-layer compression architecture that separates high-frequency and low-frequency components and compresses each with a different strategy so that the performance can become robust and controllable. Experiments with multiple datasets
1
show that our proposed scheme is capable of providing a decompression quality of above 40 dB, and can significantly improve compression efficiency compared with similar LF reconstruction schemes.
Self-weighted Multi-view Fuzzy Clustering
Authors: Zhu Xiaofeng , Zhang Shichao , Zhu Yonghua , Zheng Wei , Yang Yang
Published: June 2020

Since the data in each view may contain distinct information different from other views as well as has common information for all views in multi-view learning, many multi-view clustering methods have been designed to use these information (including the distinct information for each view and the common information for all views) to improve the clustering performance. However, previous multi-view clustering methods cannot effectively detect these information so that difficultly outputting reliable clustering models. In this article, we propose a fuzzy, sparse, and robust multi-view clustering method to consider all kinds of relations among the data (such as view importance, view stability, and view diversity), which can effectively extract both distinct information and common information as well as balance these two kinds of information. Moreover, we devise an alternating optimization algorithm to solve the resulting objective function as well as prove that our proposed algorithm achieves fast convergence. It is noteworthy that existing multi-view clustering methods only consider a part of the relations, and thus are a special case of our proposed framework. Experimental results on synthetic datasets and real datasets show that our proposed method outperforms the state-of-the-art clustering methods in terms of evaluation metrics of clustering such as clustering accuracy, normalized mutual information, purity, and adjusted rand index.
Discovering Anomalies by Incorporating Feedback from an Expert
Authors: Das Shubhomoy , Wong Weng-Keen , Dietterich Thomas , Fern Alan , Emmott Andrew
Published: June 2020

Unsupervised anomaly detection algorithms search for outliers and then predict that these outliers are the anomalies. When deployed, however, these algorithms are often criticized for high false-positive and high false-negative rates. One main cause of poor performance is that not all outliers are anomalies and not all anomalies are outliers. In this article, we describe the Active Anomaly Discovery (AAD) algorithm, which incorporates feedback from an expert user that labels a queried data instance as an anomaly or nominal point. This feedback is intended to adjust the anomaly detector so that the outliers it discovers are more in tune with the expert user’s semantic understanding of the anomalies.

The AAD algorithm is based on a weighted ensemble of anomaly detectors. When it receives a label from the user, it adjusts the weights on each individual ensemble member such that the anomalies rank higher in terms of their anomaly score than the outliers. The AAD approach is designed to operate in an interactive data exploration loop. In each iteration of this loop, our algorithm first selects a data instance to present to the expert as a potential anomaly and then the expert labels the instance as an anomaly or as a nominal data point. When it receives the instance label, the algorithm updates its internal model and the loop continues until a budget of
B
queries is spent. The goal of our approach is to maximize the total number of true anomalies in the
B
instances presented to the expert. We show that the AAD method performs well and in some cases doubles the number of true anomalies found compared to previous methods. In addition we present approximations that make the AAD algorithm much more computationally efficient while maintaining a desirable level of performance.
QoE-Fair DASH Video Streaming Using Server-side Reinforcement Learning
Authors: Altamimi Sa’di , Shirmohammadi Shervin
Published: June 2020

To design an optimal adaptive video streaming method, video service providers need to consider both the efficiency and the fairness of the Quality of Experience (QoE) of their users. In Reference [8], we proposed a server-side QoE-fair rate adaptation method that considers both efficiency and fairness of the QoE. The server uses Reinforcement Learning (RL) to select a bitrate for each client sharing the same bottleneck link to the server in a way that achieves fairness among concurrent DASH clients and imposes that bitrate by dynamically modifying the client’s Media Presentation Description (MPD) file. In this article, we extend that work to minimize the number of actions the server needs to take to keep the system in its equilibrium state. By incorporating a Recurrent Neural Network, specifically an LSTM model, we modify the server’s training algorithm to achieve improvements in both the quality and the quantity of actions the server takes to guide the client. Performance evaluation of the modified algorithm for clients running both homogeneous and heterogeneous adaptation algorithms showed that the number of server actions dropped by 14% and 22%, respectively, while QoE-fairness improved by at least 6% and 10%, respectively.
On Scalability of Association-rule-based Recommendation
Authors: Wu Zhiang , Li Changsheng , Cao Jie , Ge Yong
Published: June 2020

The association-rule-based approach is one of the most common technologies for building recommender systems and it has been extensively adopted for commercial use. A variety of techniques, mainly including eligible rule selection and multiple rules combination, have been developed to create effective recommendation. Unfortunately, little attention has been paid to the scalability concern of rule-based recommendation methods. However, the computational complexity of rule-based methods shall increase drastically with the growth of both online customers and rules, which are usually several millions in typical e-commerce platforms. Moreover, the dynamic change of users’ actions requires rule-based methods make recommendations in nearly real-time, which further highlights the scalability issue of rule-based recommender systems. In this article, we present a distributed framework that can scale different association-rule-based recommendation methods in a unified way. Specifically, based on the summarization of existing rule-based approaches, a generic tree-type structure is defined to store separate kinds of patterns, and an efficient algorithm is designed for mining eligible patterns along with computing recommendation scores. To handle the ever-increasing number of online customers, a distributed framework is proposed, where two load-balanced strategies for partitioning tree are put forward to fit sparse and dense data, respectively. Extensive experiments on five real-life data sets demonstrate that the efficiency of association-rule-based recommender systems can be significantly improved by the proposed framework.
Standing on the Shoulders of Viruses
Authors: Helenius Ari
Published: June 2020

My coworkers and I have used animal viruses and their interaction with host cells to investigate cellular processes difficult to study by other means. This approach has allowed us to branch out in many directions, including membrane protein characterization, endocytosis, secretion, protein folding, quality control, and glycobiology. At the same time, our aim has been to employ cell biological approaches to expand the fundamental understanding of animal viruses and their pathogenic lifestyles. We have studied mechanisms of host cell entry and the uncoating of incoming viruses as well as the synthesis, folding, maturation, and intracellular movement of viral proteins and molecular assemblies. I have had the privilege to work in institutions in four different countries. The early years in Finland (the University of Helsinki) were followed by 6 years in Germany (European Molecular Biology Laboratory), 16 years in the United States (Yale School of Medicine), and 16 years in Switzerland (ETH Zurich).
Double the Fun, Double the Trouble: Paralogs and Homologs Functioning in the Endoplasmic Reticulum
Authors: Fenech Emma J. , Ben-Dor Shifra , Schuldiner Maya
Published: June 2020

The evolution of eukaryotic genomes has been propelled by a series of gene duplication events, leading to an expansion in new functions and pathways. While duplicate genes may retain some functional redundancy, it is clear that to survive selection they cannot simply serve as a backup but rather must acquire distinct functions required for cellular processes to work accurately and efficiently. Understanding these differences and characterizing gene-specific functions is complex. Here we explore different gene pairs and families within the context of the endoplasmic reticulum (ER), the main cellular hub of lipid biosynthesis and the entry site for the secretory pathway. Focusing on each of the ER functions, we highlight specificities of related proteins and the capabilities conferred to cells through their conservation. More generally, these examples suggest why related genes have been maintained by evolutionary forces and provide a conceptual framework to experimentally determine why they have survived selection.
Ribonucleotide Reductases: Structure, Chemistry, and Metabolism Suggest New Therapeutic Targets
Authors: Greene Brandon L. , Kang Gyunghoon , Cui Chang , Bennati Marina , Nocera Daniel G. , Drennan Catherine L. , Stubbe JoAnne
Published: June 2020

Ribonucleotide reductases (RNRs) catalyze the de novo conversion of nucleotides to deoxynucleotides in all organisms, controlling their relative ratios and abundance. In doing so, they play an important role in fidelity of DNA replication and repair. RNRs’ central role in nucleic acid metabolism has resulted in five therapeutics that inhibit human RNRs. In this review, we discuss the structural, dynamic, and mechanistic aspects of RNR activity and regulation, primarily for the human and Escherichia coli class Ia enzymes. The unusual radical-based organic chemistry of nucleotide reduction, the inorganic chemistry of the essential metallo-cofactor biosynthesis/maintenance, the transport of a radical over a long distance, and the dynamics of subunit interactions all present distinct entry points toward RNR inhibition that are relevant for drug discovery. We describe the current mechanistic understanding of small molecules that target different elements of RNR function, including downstream pathways that lead to cell cytotoxicity. We conclude by summarizing novel and emergent RNR targeting motifs for cancer and antibiotic therapeutics.
How Does the Ribosome Fold the Proteome?
Authors: Cassaignau Anaïs M.E. , Cabrita Lisa D. , Christodoulou John
Published: June 2020

Folding of polypeptides begins during their synthesis on ribosomes. This process has evolved as a means for the cell to maintain proteostasis, by mitigating the risk of protein misfolding and aggregation. The capacity to now depict this cellular feat at increasingly higher resolution is providing insight into the mechanistic determinants that promote successful folding. Emerging from these studies is the intimate interplay between protein translation and folding, and within this the ribosome particle is the key player. Its unique structural properties provide a specialized scaffold against which nascent polypeptides can begin to form structure in a highly coordinated, co-translational manner. Here, we examine how, as a macromolecular machine, the ribosome modulates the intrinsic dynamic properties of emerging nascent polypeptide chains and guides them toward their biologically active structures.
Teaching cardiac excitation-contraction coupling using a mathematical computer simulation model of human ventricular myocytes
Authors: Jeon Young Keul , Youm Jae Boum , Ha Kotdaji , Woo JooHan , Yoo Hae Young , Leem Chae Hun , Lee Seung Hee , Kim Sung Joon
Published: June 2020

To understand the excitation-contraction (E-C) coupling of cardiomyocytes, including the electrophysiological mechanism of their characteristically long action potential duration, is one of the major learning goals in medical physiology. However, the integrative interpretation of the responses occurring during the contraction-relaxation cycle is challenging due to the dynamic interaction of underlying factors. Starting in 2017, we adopted the mathematical computer simulation model of human ventricular myocyte (Cardiac E-C_Sim), hypothesizing that this educational technology may facilitate students’ learning of cardiac physiology. Here, we describe the overall process for the educational application of Cardiac E-C_Sim in the human physiology practicum of Seoul National University College of Medicine. We also report the results from questionnaires covering detailed assessment of the practicum class. The analysis of results and feedback opinions enabled us to understand how the students had approached the problem-solving process. As a whole, the students could better accomplish the learning goals using Cardiac E-C_Sim, followed by constructive discussions on the complex and dynamic mechanisms of cardiac E-C coupling. We suggest that the combined approach of lecture-based teaching and computer simulations guided by a manual containing clinical context would be broadly applicable in physiology education.
Augmented reality-based learning for the comprehension of cardiac physiology in undergraduate biomedical students
Authors: Gonzalez Alexis A. , Lizana Pablo A. , Pino Sonia , Miller Brant G. , Merino Cristian
Published: June 2020

The integrated mechanisms of heart contraction are some of the most complex processes for undergraduate biomedical students to understand. Visual models have the potential to enhance learning environments by providing visual representations of complex mechanisms. Despite their benefits, the use of visual models in undergraduate classrooms is still limited. For this study, we tested the effect of a learning sequence of activities related to the cardiac cycle using an augmented reality (AR) application for smartphones and tablets. We were interested in understanding the ability of students to draw and label figures reflecting cardiac function after experiencing the learning sequence using AR. Undergraduate students of the biomedical sciences (control n = 43, experimental n = 58) were enrolled in the course, and their drawings were evaluated using multiple levels of complexity (1 = basic to 5 = complex) through a pre-/posttest structure that included a learning sequence based on AR in the experimental group and regular lecture-based activities in the control group. The complexity of students’ drawings was evaluated on the anatomical, physiological, and molecular aspects of heart contraction. We used Cohen’s kappa index for interrater reliability when determining the complexity of drawings. Control and experimental groups showed no differences in baseline knowledge (preexamination quiz). The students who experienced the AR activities showed an increase in the complexity of representation levels in posttest results and also showed a significant difference in scores for the final exam in the heart physiology course. Our results indicate that using AR enhances the comprehension of anatomical and physiological concepts of the cardiac cycle for undergraduate biomedical students.
Glucocorticoids and serum- and glucocorticoid-inducible kinase 1 are potent regulators of CFTR in the native intestine: implications for stress-induced diarrhea
Authors: Ahsan Md Kaimul , Figueroa-Hall Leandra , Baratta Vanessa , Garcia-Milian Rolando , Lam TuKiet T. , Hoque Kazi , Salas Pedro J. , Ameen Nadia A.
Published: June 2020

This is the first study to examine the mechanisms of glucocorticoid, serum- and glucocorticoid-inducible kinase 1, and nongenomic kinase signaling of CFTR in the native intestine. We identified unique and druggable intestine-specific factors of the pathway that are targets for treating stress-induced diarrhea.
Quantitative intravital Ca2+ imaging maps single cell behavior to kidney tubular structure
Authors: Martins Joana R. , Haenni Dominik , Bugarski Milica , Figurek Andreja , Hall Andrew M.
Published: June 2020

Ca2+ is an important second messenger that translates extracellular stimuli into intracellular responses. Although there has been significant progress in understanding Ca2+ dynamics in organs such as the brain, the nature of Ca2+ signals in the kidney is still poorly understood. Here, we show that by using a genetically expressed highly sensitive reporter (GCaMP6s), it is possible to perform imaging of Ca2+ signals at high resolution in the mouse kidney in vivo. Moreover, by applying machine learning-based automated analysis using a Ca2+-independent signal, quantitative data can be extracted in an unbiased manner. By projecting the resulting data onto the structure of the kidney, we show that different tubular segments display highly distinct spatiotemporal patterns of Ca2+ signals. Furthermore, we provide evidence that Ca2+ activity in the proximal tubule decreases with increasing distance from the glomerulus. Finally, we demonstrate that substantial changes in intracellular Ca2+ can be detected in proximal tubules in a cisplatin model of acute kidney injury, which can be linked to alterations in cell structure and transport function. In summary, we describe a powerful new tool to investigate how single cell behavior is integrated with whole organ structure and function and how it is altered in disease states relevant to humans.
Sleep fragmentation increases blood pressure and is associated with alterations in the gut microbiome and fecal metabolome in rats
Authors: Maki Katherine A. , Burke Larisa A. , Calik Michael W. , Watanabe-Chailland Miki , Sweeney Dagmar , Romick-Rosendale Lindsey E. , Green Stefan J. , Fink Anne M.
Published: June 2020

The gut microbiota, via the production of metabolites entering the circulation, plays a role in blood pressure regulation. Blood pressure is also affected by the characteristics of sleep. To date, no studies have examined relationships among the gut microbiota/metabolites, blood pressure, and sleep. We hypothesized that fragmented sleep is associated with elevated mean arterial pressure, an altered and dysbiotic gut microbial community, and changes in fecal metabolites. In our model system, rats were randomized to 8 h of sleep fragmentation during the rest phase (light phase) or were undisturbed (controls) for 28 consecutive days. Rats underwent sleep and blood pressure recordings, and fecal samples were analyzed during: baseline ( days −4 to −1), early sleep fragmentation ( days 0–3), midsleep fragmentation ( days 6–13), late sleep fragmentation ( days 20–27), and recovery/rest ( days 28–34). Less sleep per hour during the sleep fragmentation period was associated with increased mean arterial pressure. Analyses of gut microbial communities and metabolites revealed that putative short chain fatty acid-producing bacteria were differentially abundant between control and intervention animals during mid-/late sleep fragmentation and recovery. Midsleep fragmentation was also characterized by lower alpha diversity, lower Firmicutes:Bacteroidetes ratio, and higher Proteobacteria in intervention rats. Elevated putative succinate-producing bacteria and acetate-producing bacteria were associated with lower and higher mean arterial pressure, respectively, and untargeted metabolomics analysis demonstrates that certain fecal metabolites are significantly correlated with blood pressure. These data reveal associations between sleep fragmentation, mean arterial pressure, and the gut microbiome/fecal metabolome and provide insight to links between disrupted sleep and cardiovascular pathology.
Triple Procedure for Dense Cataractous Neovascular Glaucoma Patients
Authors: Moharram Hossam M. , Abd-Elhamid Mehany Elwan Shaaban , Nassar Mahmoud M. , Abdelkader Mohamed F.
Published: June 2020

Purpose. One of the most difficult refractory glaucomas is the neovascular type (NVG), and its association with dense cataract adds to this difficulty. This study aimed to provide results of the triple surgical procedure for such conditions. Methods. 12 eyes of 12 patients with NVG and dense cataract were included in this case series study. The mean age of patients was 57.25 ± 5.9 years. The mean preoperative intraocular pressure (IOP) was 47.25 ± 4.04 mmHg with maximum antiglaucoma therapy. The mean best corrected distant visual acuity (BCDVA) in LogMAR was 2.13 ± 0.38. All patients received intravitreal injection of 1.25 mg (0.05 ml) bevacizumab followed by phacoemulsification, pars plana vitrectomy (PPV) including panretinal photocoagulation (PRP), and trabeculectomy with mitomycin C (MMC). Mean IOP and BCDVA changes were the main outcome results of this study. Results. The follow-up period was 2 years. The mean BCDVA was improved to 1.22 ± 0.35, 1.13 ± 0.34, 1.12 ± 0.37, 1.06 ± 0.38, and 1.01 ± 0.37 at 1, 3, 6, 12, and 24 months, respectively, after this procedure. This improvement was statistically significant when compared with preoperative BCDVA (P<0.0001). The mean postoperative IOP was dropped to 20.08 ± 4.1, 17.08 ± 2.1, 17.17 ± 5, 15.75 ± 4.7, and 16.17 ± 6.1 mmHg, respectively. At the last follow-up, the mean IOP was statistically significantly lower than preoperative IOP (P<0.0001) at the previously mentioned time points. The success rate was complete in 90.9% of eyes and qualified in 100% of eyes. Iris and angle neovascularization had regressed significantly in all patients, and no serious complications occurred during the follow-up period. Conclusions. This triple surgery can safely improve patients with NVG and dense cataract regarding BCDVA and IOP control. This trial is registered with NCT04143620.
Antidiabetic Potency, Antioxidant Effects, and Mode of Actions ofCitrus reticulataFruit Peel Hydroethanolic Extract, Hesperidin, and Quercetin in Nicotinamide/Streptozotocin-Induced Wistar Diabetic Rats
Authors: Ali Alaa M. , Gabbar Mohamed Abdel , Abdel-Twab Sanaa M. , Fahmy Eman M. , Ebaid Hossam , Alhazza Ibrahim M. , Ahmed Osama M.
Published: June 2020

This study is aimed at assessing the antihyperglycemic, antihyperlipidemic, and antioxidant effects ofCitrus reticulata(C. reticulata) fruit peel hydroethanolic extract and two flavonoids, hesperidin and quercetin, in nicotinamide (NA)/streptozotocin- (STZ-) induced type 2 diabetic rats. In addition, GC-MS and HPLC-MS analyses of the extract were performed and the results indicated the presence of multiple flavonoids including hesperidin, quercetin, naringin, and polymethoxylated flavones (nobiletin and tangeretin). To achieve the aim of the study, diabetic rats with NA/STZ-induced T2DM were orally treated withC. reticulatafruit peel hydroethanolic extract, hesperidin, and quercetin at a dose of 100 mg/kg b.w./day for four weeks. The treatments withC. reticulatafruit peel extract, hesperidin, and quercetin significantly ameliorated the impaired oral glucose tolerance; the elevated serum fructosamine level; the diminished serum insulin and C-peptide levels; the altered HOMA-IR, HOMA-IS, and HOMA-βcell function; the decreased liver glycogen content; the increased liver glucose-6-phosphatase and glycogen phosphorylase activities; the deleteriously affected serum lipid profile; the elevated serum AST and ALT activities; and the raised serum creatinine and urea levels in the diabetic rats. The treatments also produced remarkable improvement in the antioxidant defense system manifested by a decrease in the elevated liver lipid peroxidation and an increase in the lowered glutathione content and GPx, GST, and SOD activities. Furthermore, the three treatments enhanced the mRNA expression of GLUT-4 and the insulin receptorβ-subunit, but only quercetin produced a significant increase in the expression of adiponectin in adipose tissue of diabetic rats. In conclusion,C. reticulatafruit peel hydroethanolic extract, hesperidin, and quercetin have potent antidiabetic effects which may be mediated through their insulinotropic effects and insulin-sensitizing actions. In addition, the alleviation of the antioxidant defense system by the extract, hesperidin, and naringin may have an important action to enhance the antidiabetic actions and to improve liver and kidney functions in NA/STZ-induced diabetic rats.
Association between Admission Hyperglycemia and Culprit Lesion Characteristics in Nondiabetic Patients with Acute Myocardial Infarction: An Intravascular Optical Coherence Tomography Study
Authors: Zhou Jinying , Sheng Zhaoxue , Liu Chen , Zhou Peng , Li Jiannan , Chen Runzhen , Song Li , Zhao Hanjun , Yan Hongbing
Published: June 2020

Background. Hyperglycemia is frequently observed in acute myocardial infarction (AMI). Diabetes mellitus (DM) patients and non-DM patients have different culprit lesion phenotypes and few data are available on non-DM patients with admission hyperglycemia. Therefore, we aimed to investigate the association between admission hyperglycemia and culprit lesion characteristics using optical coherence tomography (OCT) in AMI patients. Methods and Results. We consecutively enrolled 434 patients with AMI, and 277 patients were included in analysis: 65.7% (n=182) non-DM patients and 34.3% (n=95) DM patients. We measured acute blood glucose (ABG) and hemoglobin A1c to calculate the acute-to-chronic glycemic ratio (A/C). Then, we grouped non-DM patients into tertiles of A/C. OCT-based culprit lesion characteristics were compared across A/C tertiles in non-DM patients and between DM and non-DM patients. Non-DM patients had fewer lipid-rich plaques (52.7% versus 68.4%, p=0.012) and thin-cap fibroatheroma (TCFA) (19.8% versus 34.7%, p=0.006) than DM patients but similar prevalence of plaque rupture (47.3% versus 56.8%, p=0.130). Non-DM patients with the highest A/C tertile had the highest prevalence of plaque rupture (pfor trend=0.002), lipid-rich plaque (pfor trend=0.001), and TCFA (pfor trend=0.003). A/C>1.22 but not ABG>140 mg/dl predicted a high prevalence of plaque rupture, lipid-rich plaque, and TCFA in non-DM patients. Conclusions. In AMI patients without DM, admission hyperglycemia is associated with vulnerable culprit lesion characteristics, and A/C is a better predictor for vulnerable culprit plaque characteristics than ABG. These results call for a tailored evaluation and management of glucose metabolism in nondiabetic AMI patients. This trial is registered with NCT03593928.
EHL Analysis of Spiral Bevel Gear Pairs considering the Contact Point Migration due to Deformation under Load
Authors: Sun Xiaoyu , Liu Yanping , Zhao Yongqiang , Liu Ming
Published: June 2020

The actual contact point of a spiral bevel gear pair deviates from the theoretical contact point due to the gear deformation caused by the load. However, changes in meshing characteristics due to the migration of contact points are often ignored in previous studies on the elastohydrodynamic lubrication (EHL) analysis of spiral bevel gears. The purpose of this article is to analyze the impact of contact point migration on the results of EHL analysis. Loaded tooth contact analysis (LTCA) based on the finite element method is applied to determine the loaded contact point of the meshing tooth pair. Then, the osculating paraboloids at this point are extracted from the gear tooth surface geometry. The geometric and kinematic parameters for EHL simulation are determined according to the differential geometry theory. Numerical solutions to the Newtonian isothermal EHL of a spiral bevel gear pair at the migrated and theoretical contact points are compared to quantify the error involved in neglecting the contact point adjustment. The results show that under heavy-loaded conditions, the actual contact point of the deformed gear pair at a given pinion (gear) roll angle is different from the theoretical contact point considerably, and so do the meshing parameters. EHL analysis of spiral bevel gears under significant load using theoretical meshing parameters will result in obvious errors, especially in the prediction of film thickness.
A Biological Immune Mechanism-Based Quantum PSO Algorithm and Its Application in Back Analysis for Seepage Parameters
Authors: Tan Jiacheng , Xu Liqun , Zhang Kailai , Yang Chao
Published: June 2020

Back analysis for seepage parameters is a classic issue in hydraulic engineering seepage calculations. Considering the characteristics of inversion problems, including high dimensionality, numerous local optimal values, poor convergence performance, and excessive calculation time, a biological immune mechanism-based quantum particle swarm optimization (IQPSO) algorithm was proposed to solve the inversion problem. By introducing a concentration regulation strategy to improve the population diversity and a vaccination strategy to accelerate the convergence rate, the modified algorithm overcame the shortcomings of traditional PSO which can easily fall into a local optimum. Furthermore, a simple multicore parallel computation strategy was applied to reduce computation time. The effectiveness and practicability of IQPSO were evaluated by numerical experiments. In this paper, taking one concrete face rock-fill dam (CFRD) as a case, a back analysis for seepage parameters was accomplished by utilizing the proposed optimization algorithm and the steady seepage field of the dam was analysed by the finite element method (FEM). Compared with immune PSO and quantum PSO, the proposed algorithm had better global search ability, convergence performance, and calculation rate. The optimized back analysis could obtain the permeability coefficient of CFRD with high accuracy.
Relationship between the Molecular Coil Dimension and the Energy Storage Modulus of Polymer Solution Configured with Oilfield-Produced Sewage
Authors: Wang Peng , Ma Wenguo , Zhang Yunbao , Yan Qiuyan
Published: June 2020

Polymer viscoelastic solution is the non-Newtonian fluid and widely used in oil production. In the process of seepage, the mechanism of the polymer solution with different molecular coil dimensions (Dh) flooding on remaining oil is unknown. By using the dynamic light scattering instrument, the molecular coil dimension of the polymer solution is tested. By using the HAAKE rheometer, the creep recovery test data of the polymer solution under the same creep time condition are obtained. The effects of polymer solutions with different Dh on residual oil are observed, by using the visible pore model. The results show that the higher the molecular weight (Mw) of the polymer, the larger the size of the molecular coil dimension. The elasticity characteristics of the polymer solution are sensitive to the molecular coil dimension. As Dh of polymer molecules becomes larger, the contribution of the elastic portion to the viscosity of the polymer solution increases. The higher the Mw of polymer is, the longer the molecular chain is and the size of Dh is larger. On the condition of the polymer solution with different Mw with 2.5 g/L, when Dh is between 320.0 nm and 327.8 nm, the ratio of the elastic part in the apparent viscosity exceeds the proportion of the viscous part, and the polymer solution composition after these data can be used as a comparative study of elasticity for residual oil use. In the visible pore model, the pore-throat ratio is 3.5, the ER of water flooding is 54.26%, the ER of the polymer solution with Dh = 159.7 nm is 75.28%, and the increase of ER is 21.02% than that of water flooding. With the increase of Dh to 327.8 nm, the final ER of the experimental polymer solution is 97.82%, and the increase of ER of the polymer solution than that of water flooding is 43.56%. However, in the model with a pore-throat ratio of 7.0 and the same polymer solution with Dh = 327.8 nm, the increase of ER of the polymer solution is only 10.44% higher than that of water flooding. The effect of the polymer solution with the same Dh is deteriorated with the increase of the pore-throat ratio.
A Two-Class Stochastic Network Equilibrium Model under Adverse Weather Conditions
Authors: Jiang Chenming , Lu Linjun , He Junliang , Tan Caimao
Published: June 2020

Adverse weather condition is one of the inducements that lead to supply uncertainty of an urban transportation system, while travelers’ multiple route choice criteria are the nonignorable reason resulting in demand uncertainty. This paper proposes a novel stochastic traffic network equilibrium model considering impacts of adverse weather conditions on roadway capacity and route choice criteria of two-class mixed roadway travellers on demand modes, in which the two-class route choice criteria root in travelers’ different network information levels (NILs). The actual route travel time (ARTT) and perceived route travel time (PRTT) are considered as the route choice criteria of travelers with perfect information (TPI) and travelers with bounded information (TBI) under adverse weather conditions, respectively. We then formulate the user equilibrium (UE) traffic assignment model in a variational inequality problem and propose a solution algorithm. Numerical examples including a small triangle network and the Sioux Falls network are presented to testify the validity of the model and to clarify the inner mechanism of the two-class UE model under adverse weather conditions. Managerial implications and applications are also proposed based on our findings to improve the operation efficiency of urban roadway network under adverse weather conditions.
A Model with Leaf Area Index and Trunk Diameter for LoRaWAN Radio Propagation in Eastern China Mixed Forest
Authors: Wu Yin , Guo Genwei , Tian Guiyun , Liu Wenbo
Published: June 2020

Internet of Things (IoT) is a very promising technology in forest engineering, especially for the environment and plant growth monitoring. LoRa Wide Area Network (LoRaWAN) is a prevailing choice for the Forestry IoT owing to its low-power and long-range ability. Real-world deployment and network optimization require accurate path-loss modeling, so the LoRaWAN radio channel in the forest is needed to be intensively studied. However, most of the subsistent propagation models do not involve specific forestry environmental parameters. In this paper, two parameters related with the trees are considered: the leaf area index and the tree trunk diameter. Due to the time-changing characteristics of these two items (from spring to winter), an empirical model has been developed through extensive measurement campaigns: Firstly, the channel measurement platform is designed based on a real scene of mixed forest. Secondly, the fading characteristics of the channel transmission for LoRa nodes are tested, and the corresponding model is presented and evaluated. Lastly, an energy harvesting LoRaWAN is deployed and operated in a sampled forest region of Eastern China for environment monitoring based on our propagation model. The results show that 433 MHz LoRa path loss in the mingled forest could be precisely predicted by our proposed model. Moreover, network coverage and energy consumption optimization of the LoRa nodes could be performed, which enables the perpetual development of reliable forestry evolution monitoring system.
Chemical Characterization of Specific Micropollutants from Textile Industry Effluents in Fez City, Morocco
Authors: Talouizte Hakima , Merzouki Mohammed , Benlemlih Mohammed , Bendriss Amraoui Mohammed
Published: June 2020

Textile industry is one of the most polluting industries in the world. It has a high importance in terms of its environment impact, since it consumes a considerably large amount of water and produces highly polluted discharge water. In this work, characterization of toxic organic compounds is proposed. Based on gas chromatography coupled to mass spectrometry (GC/MS) screening analysis, organic micropollutant diversity of textile effluents from a local textile processing factory was investigated. In the present work, physicochemical characterization of the studied textile effluents showed considerably high values of principal pollution parameters above the prescribed discharge water limits. Heavy metals like zinc (Zn), copper (Cu), iron (Fe), nickel (Ni), cadmium (Cd), chromium (Cr), and lead (Pb) were found to be present within the permissible limits. The results of GC/MS revealed the presence of various organic compounds belonging to a wide range of chemical classes. Main groups of chemical compounds detected in these effluents were aromatic carboxylic acids, alkanes, aromatic amines, phthalates, aliphatic carboxylic acids, and linear aliphatic alcohols. The results of this study allowed significant contributions to the chemical characterization of textile industry contaminants and identification of indicators that can be considered an important tool for assessment of the potential impact of textile activities to the contamination of aquatic environment and health hazard.
Understanding Human Cerebral Malaria through a Blood Transcriptomic Signature: Evidences for Erythrocyte Alteration, Immune/Inflammatory Dysregulation, and Brain Dysfunction
Authors: Cabantous Sandrine , Poudiougou Belco , Bergon Aurélie , Barry Abdoulaye , Oumar Aboubacar A. , Traore Abdoulaye M. , Chevillard Christophe , Doumbo Ogobara , Dessein Alain , Marquet Sandrine
Published: June 2020

Background. Cerebral malaria (CM), a reversible encephalopathy affecting young children, is a medical emergency requiring rapid clinical assessment and treatment. However, understanding of the genes/proteins and the biological pathways involved in the disease outcome is still limited. Methods. We have performed a whole transcriptomic analysis of blood samples from Malian children with CM or uncomplicated malaria (UM). Hierarchical clustering and pathway, network, and upstream regulator analyses were performed to explore differentially expressed genes (DEGs). We validated gene expression for 8 genes using real-time quantitative PCR (RT-qPCR). Plasma levels were measured for IP-10/CXCL10 and IL-18. Results. A blood RNA signature including 538 DEGs (∣FC∣≥2.0, adjusted P value ≤ 0.01) allowed to discriminate between CM and UM. Ingenuity Pathway Analysis (IPA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed novel genes and biological pathways related to immune/inflammatory responses, erythrocyte alteration, and neurodegenerative disorders. Gene expressions of CXCL10, IL12RB2, IL18BP, IL2RA, AXIN2, and NET were significantly lower in CM whereas ARG1 and SLC6A9 were higher in CM compared to UM. Plasma protein levels of IP-10/CXCL10 were significantly lower in CM than in UM while levels of IL-18 were higher. Interestingly, among children with CM, those who died from a complication of malaria tended to have higher concentrations of IP-10/CXCL10 and IFN-γ than those who recovered. Conclusions. This study identified some new factors and mechanisms that play crucial roles in CM and characterized their respective biological pathways as well as some upstream regulators.
An Oral Small Molecule VEGFR2 Inhibitor, Apatinib, in Patients with Recurrent or Refractory Cervical Cancer: A Real World Study
Authors: Li Ning , Wang Ziyi , Yuan Guangwen , Sun Yangchun , Zhang Rong , Li Xiaoguang , Li Nan , Wang Jing , Wu Lingying
Published: June 2020

To evaluate the efficacy and safety of apatinib, an oral antiangiogenic drug, in patients with recurrent or refractory cervical cancer as salvage treatment, we retrospectively analyzed the medical records of recurrent or refractory cervical cancer patients admitted to the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Hunan Cancer Hospital, from October 1, 2016, to December 31, 2017. Patients who progressed within 6 months after the last treatment were given apatinib orally at a dose of 250 mg daily until disease progression or unacceptable toxicity. Twenty-nine patients were enrolled in our retrospective study. Up to February 1, 2019, the median follow-up time was 18 months. The median progression-free survival was 128 days (95% confidence interval (CI): 20–540 days), and the median overall survival was 9 months (95% CI: 4–23 months). The longest period of apatinib administration was 540 days. No complete response was observed, 5 (17.2%) patients achieved partial response, and 11 (37.9%) achieved stable disease. The objective response rate and disease control rate were 17.2% and 55.1%, respectively. The most common adverse events were hypertension (G1, 65.5%, 19/29), mucositis (G1, 55.2%, 16/29), hand-foot syndrome (G1-2, 44.8%, 13/29), and proteinuria (G1-2, 20.7%, 6/29). Grade 3 proteinuria occurred in only one patient (3.4%, 1/29). Apatinib single-agent use might be an effective and tolerable choice as salvage therapy for patients with recurrent or refractory cervical cancer.
Numerical Exploratory Analysis of Dynamics and Control of an Atomic Force Microscopy in Tapping Mode with Fractional Order
Authors: Ribeiro Mauricio A. , Balthazar Jose M. , Lenz Wagner B. , Rocha Rodrigo T. , Tusset Angelo M.
Published: June 2020

In this paper, we investigate the mechanism of atomic force microscopy in tapping mode (AFM-TM) under the Casimir and van der Waals (VdW) forces. The dynamic behavior of the system is analyzed through a nonlinear dimensionless mathematical model. Numerical tools as Poincaré maps, Lyapunov exponents, and bifurcation diagrams are accounted for the analysis of the system. With that, the regions in which the system presents chaotic and periodic behaviors are obtained and investigated. Moreover, the fractional calculus is introduced into the mathematical model, employing the Riemann-Liouville kernel discretization in the viscoelastic term of the system. The 0-1 test is implemented to analyze the new dynamics of the system, allowing the identification of the chaotic and periodic regimes of the AFM system. The dynamic results of the conventional (integer derivative) and fractional models reveal the need for the application of control techniques such as Optimum Linear Feedback Control (OLFC), State-Dependent Riccati Equations (SDRE) by using feedback control, and the Time-Delayed Feedback Control. The results of the control techniques are efficient with and without the fractional-order derivative.