bluuebunny/crossref_metadata_2025 huggingface dataset:
Robust Tensor Recovery with Fiber Outliers for Traffic Events
Authors: Hu Yue , Work Daniel B.
Published: December 2020

Event detection is gaining increasing attention in smart cities research. Large-scale mobility data serves as an important tool to uncover the dynamics of urban transportation systems, and more often than not the dataset is incomplete. In this article, we develop a method to detect extreme events in large traffic datasets, and to impute missing data during regular conditions. Specifically, we propose a robust tensor recovery problem to recover low-rank tensors under fiber-sparse corruptions with partial observations, and use it to identify events, and impute missing data under typical conditions. Our approach is scalable to large urban areas, taking full advantage of the spatio-temporal correlations in traffic patterns. We develop an efficient algorithm to solve the tensor recovery problem based on the
alternating direction method of multipliers
(ADMM) framework. Compared with existing
l
1
norm regularized tensor decomposition methods, our algorithm can exactly recover the values of uncorrupted fibers of a low-rank tensor and find the positions of corrupted fibers under mild conditions. Numerical experiments illustrate that our algorithm can achieve exact recovery and outlier detection even with missing data rates as high as 40% under 5% gross corruption, depending on the tensor size and the Tucker rank of the low rank tensor. Finally, we apply our method on a real traffic dataset corresponding to downtown Nashville, TN and successfully detect the events like severe car crashes, construction lane closures, and other large events that cause significant traffic disruptions.
Refresh Triggered Computation
Authors: Jafri Syed M. A. H. , Hassan Hasan , Hemani Ahmed , Mutlu Onur
Published: December 2020

To employ a Convolutional Neural Network (CNN) in an energy-constrained embedded system, it is critical for the CNN implementation to be highly energy efficient. Many recent studies propose CNN accelerator architectures with custom computation units that try to improve the energy efficiency and performance of CNNs by minimizing data transfers from DRAM-based main memory. However, in these architectures, DRAM is still responsible for half of the overall energy consumption of the system, on average. A key factor of the high energy consumption of DRAM is the
refresh overhead
, which is estimated to consume 40% of the total DRAM energy.

In this article, we propose a new mechanism,
Refresh Triggered Computation (RTC)
, that exploits the memory access patterns of CNN applications to reduce the number of
refresh operations
. RTC uses two major techniques to mitigate the refresh overhead. First,
Refresh Triggered Transfer (RTT)
is based on our
new
observation that a CNN application accesses a large portion of the DRAM in a predictable and recurring manner. Thus, the read/write accesses of the application inherently refresh the DRAM, and therefore a significant fraction of refresh operations can be skipped. Second,
Partial Array Auto-Refresh (PAAR)
eliminates the refresh operations to DRAM regions that do not store any data.

We propose three RTC designs (min-RTC, mid-RTC, and full-RTC), each of which requires a different level of aggressiveness in terms of customization to the DRAM subsystem. All of our designs have small overhead. Even the most aggressive RTC design (i.e., full-RTC) imposes an area overhead of only 0.18% in a 16
Gb
DRAM chip and can have less overhead for denser chips. Our experimental evaluation on six well-known CNNs shows that RTC reduces average DRAM energy consumption by 24.4% and 61.3% for the least aggressive and the most aggressive RTC implementations, respectively. Besides CNNs, we also evaluate our RTC mechanism on three workloads from other domains. We show that RTC saves 31.9% and 16.9% DRAM energy for
Face Recognition
and
Bayesian Confidence Propagation Neural Network (BCPNN)
, respectively. We believe RTC can be applied to other applications whose memory access patterns remain predictable for a sufficiently long time.
Decentralized Firmware Attestation for In-Vehicle Networks
Authors: Rawat Abhimanyu , Khodari Mohammad , Asplund Mikael , Gurtov Andrei
Published: December 2020

Today’s vehicles are examples of Cyber-Physical Systems (CPS) controlled by a large number of
electronic control units
(ECUs), which manage everything from heating to steering and braking. Due to the increasing complexity and inter-dependency of these units, it has become essential for an ECU to be able to ensure the integrity of the firmware running on other ECU’s to guarantee its own correct operation. Existing solutions for firmware attestation use a centralized approach, which means a single point of failure. In this article, we propose and investigate a decentralized firmware attestation scheme for the automotive domain. The basic idea of this scheme is that each ECU can attest to the state of those ECU’s on which it depends. Two flavors of ECU attestation, i.e., parallel and serial solution, were designed, implemented, and evaluated. The two variants were compared in terms of both detection performance (i.e., the ability to identify unauthorized firmware modifications) and timing performance. Our results show that the proposed scheme is feasible to implement and that the parallel solution showed a significant improvement in timing performance over the serial solution.
Bayesian Optimization for Efficient Accelerator Synthesis
Authors: Mehrabi Atefeh , Manocha Aninda , Lee Benjamin C. , Sorin Daniel J.
Published: December 2020

Accelerator design is expensive due to the effort required to understand an algorithm and optimize the design. Architects have embraced two technologies to reduce costs. High-level synthesis automatically generates hardware from code. Reconfigurable fabrics instantiate accelerators while avoiding fabrication costs for custom circuits. We further reduce design effort with statistical learning. We build an automated framework, called Prospector, that uses Bayesian techniques to optimize synthesis directives, reducing execution latency and resource usage in field-programmable gate arrays. We show in a certain amount of time that designs discovered by Prospector are closer to Pareto-efficient designs compared to prior approaches. Prospector permits new studies for heterogeneous accelerators.
A Distributed Hardware Monitoring System for Runtime Verification on Multi-Tile MPSoCs
Authors: Mettler Marcel , Mueller-Gritschneder Daniel , Schlichtmann Ulf
Published: December 2020

Exhaustive verification techniques do not scale with the complexity of today’s multi-tile Multi-processor Systems-on-chip (MPSoCs). Hence, runtime verification (RV) has emerged as a complementary method, which verifies the correct behavior of applications executed on the MPSoC during runtime.

In this article, we propose a decentralized monitoring architecture for large-scale multi-tile MPSoCs. In order to minimize performance and power overhead for RV, we propose a lightweight and non-intrusive hardware solution. It features a new specialized tracing interconnect that distributes and sorts detected events according to their timestamps. Each tile monitor has a consistent view on a globally sorted trace of events on which the behavior of the target application can be verified using logical and timing requirements. Furthermore, we propose an integer linear programming-based algorithm for the assignment of requirements to monitors to exploit the local resources best. The monitoring architecture is demonstrated for a four-tiled MPSoC with 20 cores implemented on a Virtex-7 field-programmable gate array (FPGA).
A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels
Authors: Braun Lorenz , Nikas Sotirios , Song Chen , Heuveline Vincent , Fröning Holger
Published: December 2020

Characterizing compute kernel execution behavior on GPUs for efficient task scheduling is a non-trivial task. We address this with a simple model enabling portable and fast predictions among different GPUs using only hardware-independent features. This model is built based on random forests using 189 individual compute kernels from benchmarks such as Parboil, Rodinia, Polybench-GPU, and SHOC. Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 8.86–52.0% for time and 1.84–2.94% for power prediction across five different GPUs, while latency for a single prediction varies between 15 and 108 ms.
SPX64
Authors: Singh Abhishek , Dave Shail , Zardoshti Pantea , Brotzman Robert , Zhang Chao , Guo Xiaochen , Shrivastava Aviral , Tan Gang , Spear Michael
Published: December 2020

General-purpose computing systems employ memory hierarchies to provide the appearance of a single large, fast, coherent memory. In special-purpose CPUs, programmers manually manage distinct, non-coherent scratchpad memories. In this article, we combine these mechanisms by adding a virtually addressed, set-associative scratchpad to a general purpose CPU. Our scratchpad exists alongside a traditional cache and is able to avoid many of the programming challenges associated with traditional scratchpads without sacrificing generality (e.g., virtualization). Furthermore, our design delivers increased security and improves performance, especially for workloads with high locality or that interact with nonvolatile memory.
Network Pharmacology Approach to Explore the Potential Mechanisms of Jieduan‐Niwan Formula Treating Acute‐on‐Chronic Liver Failure
Authors: Liang Jiajun , Wu Mengli , Bai Chen , Ma Chongyang , Fang Peng , Hou Weixin , Wei Xiaoyi , Zhang Qiuyun , Du Yuqiong
Published: December 2020

Background. Acute‐on‐chronic liver failure (ACLF) is a clinical syndrome with acute jaundice and coagulation dysfunction caused by various inducements on the basis of chronic liver disease. Western medical treatment is limited. Previous studies have confirmed that Jieduan‐Niwan Formula (JDNW Formula), an empirical prescription for the treatment of ACLF, can inhibit inflammation and resist hepatocyte apoptosis. However, potential targets and mechanisms still need to be explored. Methods. In this study, network pharmacological analysis was performed to investigate the key components and potential mechanisms of JDNW Formula treating ACLF. Firstly, we predicted the potential active ingredients of JDNW Formula and the corresponding potential targets through TCMSP, BATMAN‐TCM platform, and literature supplement. Then, the ACLF targets database was built using OMIM, DisGeNET, and GeneCard database. Based on the matching targets between JDNW Formula and ACLF, the PPI network was constructed for MCODE analysis and common targets were enriched by Metascape. Furthermore, the ACLF rat model was used to verify the potential mechanism of JDNW Formula in treating ACLF. Results. 132 potential bioactive components of JDNW Formula and 168 common targets were obtained in this study. The enrichment analysis shows that the AMPK signaling pathway was associated with the treating effects of JDNW Formula. Quercetin was hypothesized to be the key bioactive ingredient in JDNW Formula and has a good binding affinity to AMPK based on molecular docking verification. JDNW Formula and quercetin were verified to treat ACLF by regulating the AMPK/PGC‐1α signaling pathway as a prediction. Conclusion. The study predicted potential mechanisms of JDNW Formula in the treatment of ACLF, involving downregulation of inflammatory factor expression, antioxidant stress, and inhibition of hepatocyte apoptosis. JDNW Formula may improve mitochondrial quality in ACLF via the AMPK signaling pathway, which serves as a guide for further study.
Clear Cell Chondrosarcoma: Clinical Characteristics and Outcomes in 15 Patients
Authors: Flint James H. , Conley Anthony P. , Rubin M. Laura , Feng Lei , Lin Patrick P. , Moon Bryan , Bird Justin , Satcher Robert L. , Lewis Valerae O.
Published: December 2020

Background. Clear cell chondrosarcoma (CCC) represents less than 6% of all chondrosarcomas, and thus, our understanding of this rare entity is limited. Analyzing clinical characteristics and treatment patterns, thus increasing our knowledge, may improve treatment strategy. We review our institutional experience with 15 patients, including one case with dedifferentiation. Methods. A retrospective review was conducted in CCC patients treated at our institution from 1996 to 2015, with at least 2-year follow-up. Descriptive statistics and Kaplan–Meier survival analyses were performed. Results. Of 19 patients identified, 15 patients had at least 2-year follow-up and were included. The median age at diagnosis was 43 years. 80% were male. The most common presenting signs were pain (12 patients; 80%) and fracture (2 patients; 13.3%). The most common site was proximal femur (8 patients; 53%). All patients had MSTS Stage I disease. Primary treatment included wide resection in 10 patients (67%) and intralesional or marginal resection in 5 patients (33%). Three patients died of disease during the study period, 1 with dedifferentiation of recurrent CCC. The median time to death from disease was 15.3 years (95% CI: (14.2; NA)). The median time to either recurrence or death was 7.73 years for patients who had intralesional/marginal resection and 16.44 years for patients with wide resection (HR (wide vs. intralesional/marginal) = 0.21, 95% CI: (0.04; 1.18),

p
=
0.053

). The median time to recurrence or death was significantly shorter for patients not initially treated at a sarcoma center (

p
=
0.01

). Conclusions. CCC is a rare entity, and our understanding of it is still evolving. We observed a higher recurrence rate for intralesional or marginal resection, and wide resection alone remains the mainstay of treatment. Better outcomes were observed in patients initially treated by trained musculoskeletal oncologists. Due to the propensity of CCC to recur decades after initial resection, lifelong surveillance is recommended.
Danhong Injection Alleviates Cardiac Fibrosis via Preventing the Hypermethylation of Rasal1 and Rassf1 in TAC Mice
Authors: Li Sinai , Li Ping , Liu Weihong , Shang Juju , Qiu Shenglei , Li Xiang , Liu Wei , Shi Haoyue , Zhou Mingxue , Liu Hongxu
Published: December 2020

Background/Aim. Danhong injection (DHI) is a Chinese patent drug used for relieving cardiovascular diseases. Recent studies have suggested that DNA methylation plays a pivotal role in the maintenance of cardiac fibrosis (CF) in cardiovascular diseases. This study was aimed at identifying the effect and the underlying mechanism of DHI on CF, especially the DNA methylation. Methods. A CF murine model was established by thoracic aortic constriction (TAC). A 28-day daily treatment with or without DHI via intraperitoneal injection was carried out immediately following TAC surgery. The changes in cardiac function, pathology, and fibrosis following TAC were measured by echocardiography and immunostaining. We used methyl-seq analysis to assess the DNA methylation changes in whole genes and identified the methylation changes of two Ras signaling-related genes in TAC mice, including Ras protein activator like-1 (Rasal1) and Ras-association domain family 1 (Rassf1). Next, the methylation status and expression levels of Rasal1 and Rassf1 genes were consolidated by bisulfite sequencing, quantitative reverse transcription polymerase chain reaction (RT-qPCR), and Western blotting, respectively. To determine the underlying molecular mechanism, the expressions of DNA methyltransferases (DNMTs), Tet methylcytosine dioxygenase 3 (TET3), fibrosis-related genes, and the activity of Ras/ERK were measured by RT-qPCR and Western blotting. Results. DHI treatment alleviated CF and significantly improved cardiac function on day 28 of TAC. The methyl-seq analysis identified 42,606 differential methylated sites (DMSs), including 19,618 hypermethylated DMSs and 22,988 hypomethylated DMSs between TAC and sham-operated mice. The enrichment analysis of these DMSs suggested that the methylated regulation of Ras signal transduction and focal adhesion-related genes would be involved in the TAC-induced CF development. The results of bisulfite sequencing revealed that the TAC-induced methylation affected the CpG site in both of Rasal1 and Rassf1 genes, and DHI treatment remarkably downregulated the promoter methylation of Rasal1 and Rassf1 in CF hearts. Furthermore, DHI treatment upregulated the expressions of Rasal1 and Rassf1, inhibited the hyperactivity of Ras/ERK, and decreased the expressions of fibrosis-related genes. Notably, we found that DHI treatment markedly downregulated the expression of DNMT3B in CF hearts, while it did not affect the expressions of DNMT1, DNMT3A, and TET3. Conclusion. Aberrant DNA methylation of Rasal1 and Rassf1 genes was involved in the CF development. DHI treatment alleviated CF, prevented the hypermethylation of Rasal1 and Rassf1, and downregulated DNMT3B expression in CF hearts.
Complete Chloroplast Genome Sequencing and Phylogenetic Analysis of Two Dracocephalum Plants
Authors: Yao Junjun , Zhao Fangyu , Xu Yuanjiang , Zhao Kaihui , Quan Hong , Su Yanjie , Hao Peiyu , Liu Jiang , Yu Benxia , Yao Min , Ma Xiaojing , Liao Zhihua , Lan Xiaozhong
Published: December 2020

Dracocephalum tanguticum and Dracocephalum moldavica are important herbs from Lamiaceae and have great medicinal value. We used the Illumina sequencing technology to sequence the complete chloroplast genome of D. tanguticum and D. moldavica and then conducted de novo assembly. The two chloroplast genomes have a typical quadripartite structure, with the gene’s lengths of 82,221 bp and 81,450 bp, large single‐copy region’s (LSC) lengths of 82,221 bp and 81,450 bp, and small single‐copy region’s (SSC) lengths of 17,363 bp and 17,066 bp, inverted repeat region’s (IR) lengths of 51,370 bp and 51,352 bp, respectively. The GC content of the two chloroplast genomes was 37.80% and 37.83%, respectively. The chloroplast genomes of the two plants encode 133 and 132 genes, respectively, among which there are 88 and 87 protein‐coding genes, respectively, as well as 37 tRNA genes and 8 rRNA genes. Among them, the rps2 gene is unique to D. tanguticum, which is not found in D. moldavica. Through SSR analysis, we also found 6 mutation hotspot regions, which can be used as molecular markers for taxonomic studies. Phylogenetic analysis showed that Dracocephalum was more closely related to Mentha.
Nonalcoholic Fatty Liver Disease-Related Chronic Kidney Disease
Authors: Cojocariu Camelia , Singeap Ana-Maria , Girleanu Irina , Chiriac Stefan , Muzica Cristina M. , Sfarti Catalin Victor , Cuciureanu Tudor , Huiban Laura , Stanciu Carol , Trifan Anca
Published: December 2020

Nonalcoholic fatty liver disease has become the main concern of hepatologists around the world and the main research topic for identifying effective and safe therapy. Advances in the treatment of chronic viral hepatitis in recent years have opened the way towards reducing mortality in patients with chronic liver disease. This goal has not yet been reached, as the burden of chronic liver disease remains a future major health problem as the incidence of the nonalcoholic fatty liver disease continues to rise. The proportion of patients with liver cirrhosis and those with hepatocellular carcinoma due to nonalcoholic liver disease on the liver transplant waiting list has increased in the last years. The upward trend in the incidence and prevalence of the disease in recent decades raises concern over a possible global epidemic, especially as the disease is still underestimated and underdiagnosed. Chronic kidney disease presented an increase in incidence and prevalence during the last years, and it has been associated not only with increased morbidity and mortality but also with high costs for the health system. During the last decade, several studies have shown the association between nonalcoholic fatty disease and chronic kidney disease, two major worldwide health problems.
Iron Deficiency and Iron Deficiency Anemia Are Common Epidemiological Conditions in Saudi Arabia: Report of the National Epidemiological Survey
Authors: Owaidah Tarek , Al-Numair Nouf , Al-Suliman Ayman , Zolaly Mohammed , Hasanato Rana , Al Zahrani Faisal , Albalawi Mohameed , Bashawri Layla , Siddiqui Khawar , Alalaf Faisal , Almomen Abdulkareem , Sajid Muhammad Raihan
Published: December 2020

Iron deficiency is the most prevalent nutritional deficiency worldwide. According to an estimate by the World Health Organization, up to 27% of the world’s population experience iron deficiency anemia (IDA). Studies conducted in the Middle East, including Saudi Arabia, have suggested that IDA is the most common cause of anemia, especially among females. This study aimed to determine the prevalence of IDA and iron deficiency (ID) among apparently healthy young university students from four regions in Saudi Arabia. Students were asked to complete a simple survey questionnaire; blood samples were then collected and analyzed after obtaining informed consent. A total of 981 students completed the survey, with 11% of the participants reporting symptoms of anemia; 34% of participants were diagnosed with IDA and 6% reported a diagnosis of hemoglobinopathy. Blood analysis confirmed the prevalence of ID and IDA in 28.6% and 10.7% of the participants, respectively; those with ID and IDA were mostly females (88.5% and 94%, resp.). Thalassemia trait and sickle cell trait were detected in 1.3% and 7% of participants, respectively. Our findings from a national survey among young university in Saudi Arabia indicate a high prevalence of ID and IDA.
Water Quality Prediction Using Artificial Intelligence Algorithms
Authors: Aldhyani Theyazn H. H , Al-Yaari Mohammed , Alkahtani Hasan , Maashi Mashael
Published: December 2020

During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM),

K

-nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient (

RNARNET
=
96.17
%

and

RLSTM
=
94.21
%

). This kind of promising research can contribute significantly to water management.
Alanylglutamine Relieved Asthma Symptoms by Regulating Gut Microbiota and the Derived Metabolites in Mice
Authors: Liu Shao-Kun , Ma Li-Bing , Yuan Yu , Ji Xiao-Ying , Sun Wen-Jin , Duan Jia-Xi , Zeng Qing-Ping , Wasti Binaya , Xiao Bing , Zheng Jian-Fei , Chen Ping , Xiang Xu-Dong
Published: December 2020

Objective. Allergic asthma is a chronic inflammatory disease, which seriously affects the life quality of patients, especially children. Alanylglutamine is a nutritional supplement with potential protective and anti-inflammatory effects, but its function in allergic asthma remains elusive. In this study, we focused on the investigations of the roles and functional mechanism of Alanylglutamine in asthma. Methods. Ovalbumin (OVA) induction was utilized to establish a mouse asthma model. 16S rDNA sequencing was performed to compare the diversity of intestinal microorganisms under different treatments. Gas chromatography was utilized to screen the intestinal microbe-short-chain fatty acids in the stool. The lung tissue was extracted to determine signaling pathways, including AMPK, NF-κB, mTOR, STAT3, IKKβ, TGF-β, and IL-1β through Western blot or RT-qPCR. Results. It was observed that Alanylglutamine reduced the cytokine in OVA-induced allergic asthma mice. H&E staining showed obvious pneumonia symptoms in the asthma group, while Alanylglutamine alleviated the inflammatory infiltration. Alanylglutamine reversed gut microbiota compositions in OVA-induced allergic asthma mice and enhanced the butyric acid level. The protective role of Alanylglutamine may be associated with the gut microbiota-butyric acid-GPR43 pathway in asthma mice. In contrast to the OVA group, Alanylglutamine activated the protein expression of P-AMPK/AMPK and inhibited the protein expression of P-mTOR/mTOR, P-P65/P65, P-STAT3/STAT3, P-IKKβ/IKKβ, TGF-β, and IL-1β, with similar effects from butyric acid. Conclusion. The results indicated that Alanylglutamine might be beneficial for asthma, and its effect was achieved through the regulation on microbiota and the derived metabolites. The therapeutic effects might be associated with AMPK, NF-κB, mTOR, and STAT3 signaling pathways. These findings will help identify effective therapeutic direction to alleviate allergic inflammation of the lungs and airways.
Prognostic Correlation of an Autophagy-Related Gene Signature in Patients with Head and Neck Squamous Cell Carcinoma
Authors: Yang Cai , Mei Hongxiang , Peng Liang , Jiang Fulin , Xie Bingjie , Li Juan
Published: December 2020

Considerable evidence indicates that autophagy plays a vital role in the biological processes of various cancers. The aim of this study is to evaluate the prognostic value of autophagy-related genes in patients with head and neck squamous cell carcinoma (HNSCC). Transcriptome expression profiles and clinical data acquired from The Cancer Genome Atlas (TCGA) database were analyzed by Cox proportional hazards model and Kaplan–Meier survival analysis to screen autophagy-related prognostic genes that were significantly correlated with HNSCC patients’ overall survival. Functional enrichment analyses were performed to explore biological functions of differentially expressed autophagy-related genes (ARGs) identified in HNSCC patients. Six ARGs (EGFR, HSPB8, PRKN, CDKN2A, FADD, and ITGA3) identified with significantly prognostic values for HNSCC were used to construct a risk signature that could stratify patients into the high-risk and low-risk groups. This signature demonstrated great value in predicting prognosis for HNSCC patients and was indicated as an independent prognostic factor in terms of clinicopathological characteristics (sex, age, clinical stage, histological grade, anatomic subdivision, alcohol history, smoking status, HPV status, and mutational status of the samples). The prognostic signature was also validated by data from the Gene Expression Omnibus (GEO) database and International Cancer Genome Consortium (ICGC). In conclusion, this study provides a novel autophagy-related gene signature for predicting prognosis of HNSCC patients and gives molecular insights of autophagy in HNSCC.
Therapeutic Role of Protein Tyrosine Phosphatase 1B in Parkinson’s Disease via Antineuroinflammation and Neuroprotection In Vitro and In Vivo
Authors: Feng Chien-Wei , Chen Nan-Fu , Chan Te-Fu , Chen Wu-Fu
Published: December 2020

Parkinson’s disease (PD) is one of the most widespread neurodegenerative diseases. However, the currently available treatments could only relieve symptoms. Novel therapeutic targets are urgently needed. Several previous studies mentioned that protein tyrosine phosphatase 1B (PTP1B) acted as a negative regulator of the insulin signal pathway and played a significant role in the inflammation process. However, few studies have investigated the role of PTP1B in the central nervous system. Our study showed that suramin, an inhibitor of PTP1B, could improve neuronal damage. It could significantly attenuate the interferon-gamma-induced upregulation of proinflammatory cytokines, including inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB). It enhanced M2 type microglia markers, such as arginase-1 and Ym-1 in BV2 murine microglial cells. PTP1B inhibition also reversed 6-hydroxydopamine- (6-OHDA-) induced downregulation of phospho-cAMP response element-binding protein (p-CREB) and brain-derived neurotrophic factor (BDNF) in SH-SY5Y cells. Besides, we knocked down and overexpressed PTP1B in the SH-SY5Y cells to confirm its role in neuroprotection. We also verified the effect of suramin in the zebrafish PD model. Treatment with suramin could significantly reverse 6-OHDA-induced locomotor deficits and improved tyrosine hydroxylase (TH) via attenuating endoplasmic reticulum (ER) stress biomarkers. These results support that PTP1B could potentially regulate PD via antineuroinflammation and antiapoptotic pathways.
Functional Correlates of Action Observation of Gait in Patients with Parkinson’s Disease
Authors: Bommarito Giulia , Putzolu Martina , Avanzino Laura , Cosentino Carola , Botta Alessandro , Marchese Roberta , Inglese Matilde , Pelosin Elisa
Published: December 2020

Background. Action observation (AO) relies on the mirror neuron system (MNS) and has been proposed as a rehabilitation tool in Parkinson’s disease (PD), in particular for gait disorder such as freezing of gait (FOG). In this study, we aimed to explore the brain functional correlates of the observation of human gait in PD patients with (FOG+) and without (FOG-) FOG and to investigate a possible relationship between AO-induced brain activation and gait performance. Methods. Fifty-four participants were enrolled in the study (15 PD FOG+; 18 PD FOG-; 21 healthy subjects (HS)) which consisted of two tasks in two separate days: (i) gait assessment and (ii) task-fMRI during AO of gait. Differences between patients with PD (FOG+ and FOG-) and HS were assessed at the level of behavioral and functional analysis. Results. Gait parameters, including gait velocity, stride length, and their coefficients of variability (CV), were different in PD patients compared to HS, whereas gait performance was similar between FOG+ and FOG-. The PD group, compared to HS, presented reduced functional activation in the frontal, cingulum, and parietooccipital regions. Reduced activity was more pronounced in the FOG+ group, compared to both HS and FOG- groups. Gait variability positively correlated with precuneus neural activity in the FOG+ group. Discussion. Patients with PD present a reduced functional activity during AO of gait, especially if FOG+. A baseline knowledge of the neural correlates of AO of gait in the clinical routine “on” status would help for the design of future AO rehabilitative interventions.
Tumor-Infiltrating Immune Cells and PD-L1 as Prognostic Biomarkers in Primary Esophageal Small Cell Carcinoma
Authors: Wu Xiao , Ke Xiurong , Ni Yangpeng , Kuang Liping , Zhang Fan , Lin Yusheng , Lin Wan , Xiong Xiao , Huang Haihua , Lin Xianjie , Zhang Hao
Published: December 2020

Primary esophageal small cell carcinoma (PESCC) is a weakly prevalent but lethal malignancy with early metastasis and a poor prognosis. Currently, neither effective prognostic indicators nor curative therapies are available for PESCC. Immunotherapy has now evolved into one of the most promising therapies for cancer patients. Tumor-infiltrating immune cells which are integral to the tumor immune microenvironment (TIME) are recognized as highly important for prognosis prediction, while the responsiveness to immune checkpoint blockade may be subject to the features of TIME. In this study, we aim to identify the TIME and provide indication for the applicability of immune checkpoint therapy in PESCC. We found that PD-L1 expression was detected in 33.33% (27/81) of all the patients, mostly exhibiting a stroma-only pattern and that it was positively associated with tumor-infiltrating immune cells (CD4+, CD8+, and CD163+). In 74.07% of PD-L1-positive specimens, PD-L1+CD163+ cells were colocalized more with CD4+ than CD8+ T cells. 83.95% (68/81) of all the specimens were infiltrated with more CD4+ than CD8+ T cells. Further analysis showed FoxP3+ Tregs constituted 13-27% of the total CD4+ T cell population. The Kaplan-–Meier analysis indicated several factors that contribute to poor survival, including negative PD-L1 expression, rich CD4 expression, rich FoxP3 expression, a low CD8/CD4 ratio, and a high FoxP3/CD8 ratio. A nomogram model was constructed and showed good performance for survival prediction. These results highlight that a suppressive TIME contributes to poor survival of patients with PESCC. TIME analyses might be a promising approach to evaluate the possibility and effect of immune checkpoint-based immunotherapeutics in PESCC patients.
An Integrative Analysis Reveals the Potential Mechanism between Herbal Medicine Yinchen and Immunoregulation in Hepatocellular Carcinoma
Authors: Mo Zhuomao , Cao Zhirui , Yu Ling , Wang Yongdan , Li Pan , Lin Youwu , Zhang Shijun
Published: December 2020

Aims. Abundant evidences in traditional Chinese medicine (TCM) supported the therapeutic value of herbal medicine Yinchen in hepatocellular carcinoma (HCC), but the underlying mechanism remains to be investigated. Main Methods. The intersection of immune gene set, module genes, HCC‐associated genes, and target genes of Yinchen was employed for further analyses. The module genes were identified by weighted gene coexpression network analysis, and the other three gene sets were obtained from public databases. Subsequently, we further explored the clinical value and immunoregulation of the hub gene of intersection. The relevant pathways related to hub gene expression were investigated by gene set enrichment analysis. Finally, the interaction of active compounds and target genes was validated by molecular docking. Key Findings. Thirteen active compounds and 90 target genes of Yinchen were included. After constructing the network among Yinchen, target genes, and HCC, BIRC5 was identified as the hub gene. Significant difference was found between the high‐expressed group and the low‐expressed group in survival and stage. Different immune subtypes also presented significant difference in BIRC5 expression. Moreover, NK cell and T cell (CD4+ effector memory and CD4+ memory resting) were negatively correlated with BIRC5 expression, while CTLA4 and LAG3 were positively correlated. The results of molecular docking further validated a good binding activity of quercetin‐BIRC5 interaction. Significance. In summary, our research identified for the first time a novel underlying association among herbal medicine Yinchen, BIRC5, immunotherapy, and HCC. We speculated that Yinchen may target the immune checkpoints (CTLA4 and LAG3) and activate the immune cells by suppressing BIRC5.
Aquifer Vulnerability: Its Protection and Management—A Case Study in Pangkalpinang City, Indonesia
Authors: Nugraha Gumilar Utamas , Gaol Karit Lumban , Hartanto Priyo , Bakti Hendra
Published: December 2020

Increased anthropogenic activity in urban areas has exacerbated the vulnerability of groundwater resources. The AVI, GOD, SINTACS, and DRASTIC methods were used to analyze groundwater vulnerability in Pangkalpinang City. Schlumberger vertical electrical sounding was used to determine the lithology and aquifer configuration in the study area. There are three vulnerability index areas in the city of Pangkalpinang. Low levels of aquifer vulnerability were generally found in the southeastern and northwestern parts of the study area, whereas high levels of aquifer vulnerability were discovered in the northern and southern parts of the study area. Areas with low aquifer vulnerability levels generally have low hydraulic conductivity values on the protective layer. In these areas, groundwater extraction is possible with a reasonable extraction pattern. Industrial areas can also be built by considering environmental aspects. In an area with high-level aquifer vulnerability, groundwater pollution must be considerably managed. The areas should not be designated for industrial areas and excess groundwater extraction.
Assessing Distance Learning in Higher Education during the COVID-19 Pandemic
Authors: Elfirdoussi Selwa , Lachgar Mohamed , Kabaili Hind , Rochdi Abdelali , Goujdami Driss , El Firdoussi Larbi
Published: December 2020

This qualitative study is an investigation and assessment of distance learning in Morocco during the COVID-19 pandemic. This research surveyed 3037 students and 231 professors enrolled in different stages of higher education programs. It aims to investigate the limitations of e-learning platforms and how these activities take place at public and private Moroccan universities during the coronavirus confinement. For this purpose, two structured questionnaires were constructed by researchers from different specialties, and the type of data was based on the responses of students and professors from 15 universities. In this paper, we have used three methods: descriptive analysis, regression analysis, and qualitative response analysis. As a data analytics tool, Microsoft Power BI was used to analyze data, visualize it, and draw insights. In this study, both professors and students stated that online learning is not more interesting than ordinary learning and professors need to provide at least 50% of their teaching in face-to-face mode. Recommendations at teaching and technical levels, such as the need for technical support and training in the use of these tools, were provided to enhance and promote distance education in Morocco. The contribution of this paper comes as a result of data analysis obtained from a survey conducted in some famous Moroccan universities.
Multicriteria Model Based on FITradeoff Method for Prioritizing Sections of Brazilian Roads by Criticality
Authors: Martins Mateus A. , Garcez Thalles V. , de Gusmão Ana Paula H. , Silva Lucimário G. O. , de Almeida Jônatas A.
Published: December 2020

Three of the most important objectives of the Federal Road Police (Patrol) when managing the traffic on federal roads in Brazil are to ensure that there is an efficient movement of traffic under stable conditions and minimal traffic congestion problems that the federal highways are safe and that accidents do not occur. Therefore, multiple matters are relevant for road safety and user security, such as prompt maintenance of these highways, regular monitoring of their state of conservation and their characteristics; controlling the traffic; and preventing and combatting criminal activities on the highways. However, considering the vast network and different conditions of roads, the different types of traffic accidents and their consequences, and different levels of violence on federal roads, it is essential that these roads undergo regular maintenance and regular inspections, are constantly patrolled, and are subject to continuous improvements. Therefore, defining the prioritization and criticality of roads takes on the characteristics of a multicriteria decision, given the multidimensional aspects of the risks inherent in them. Thus, this paper presents a multicriteria decision model for prioritizing road sections, based on their criticality and the risks that users face. The model was applied using the FITradeoff method, due to its flexibility and due to it requiring less cognitive effort from the decision-maker with regard to providing information regarding his/her preferences. A case study was undertaken on a set of the federal roads of the state of Pernambuco (Brazil), covering 22 different sections with different characteristics. As a result, it was possible to rank and identify the most critical sections of a highway. The use of FITradeoff gave support to decision-making on ordering the sections and also let a general analysis of the data be undertaken.
Proof of Concept for an “eyePhone” App to Measure Video Head Impulses
Authors: Parker T. Maxwell , Farrell Nathan , Otero-Millan Jorge , Kheradmand Amir , McClenney Ayodele , Newman-Toker David E.
Published: December 2020

Objective: Differentiating benign from dangerous causes of dizziness or vertigo presents a major diagnostic challenge for many clinicians. Bedside presentations of peripheral vestibular disorders and posterior fossa strokes are often indistinguishable other than by a few subtle vestibular eye movements. The most challenging of these to interpret is the head impulse test (HIT) of vestibulo-ocular reflex (VOR) function. There have been major advances in portable video-oculography (VOG) quantification of the video HIT (vHIT), but these specialized devices are not routinely available in most clinical settings. As a first step towards smartphone-based diagnosis of strokes in patients presenting vestibular symptoms, we sought proof of concept that we could use a smartphone application (“app”) to accurately record the vHIT. Methods: This was a cross-sectional agreement study comparing a novel index test (smartphone-based vHIT app) to an accepted reference standard test (VOG-based vHIT) for measuring VOR function. We recorded passive (examiner-performed) vHIT sequentially with both methods in a convenience sample of patients visiting an otoneurology clinic. We quantitatively correlated VOR gains (ratio of eye to head movements during the HIT) from each side/ear and experts qualitatively assessed the physiologic traces by the two methods. Results: We recruited 11 patients; 1 patient’s vHIT could not be reliably quantified with either device. The novel and reference test VOR gain measurements for each ear (n = 20) were highly correlated (Pearson’s r = 0.9, p = 0.0000001) and, qualitatively, clinically equivalent. Conclusions: This preliminary study provides proof of concept that an “eyePhone” app could be used to measure vHIT and eventually developed to diagnose vestibular strokes by smartphone.
Utilization of Machine Learning-Based Computer Vision and Voice Analysis to Derive Digital Biomarkers of Cognitive Functioning in Trauma Survivors
Authors: Schultebraucks Katharina , Yadav Vijay , Galatzer-Levy Isaac R.
Published: December 2020

Background: Alterations in multiple domains of cognition have been observed in individuals who have experienced a traumatic stressor. These domains may provide important insights in identifying underlying neurobiological dysfunction driving an individual’s clinical response to trauma. However, such assessments are burdensome, costly, and time-consuming. To overcome barriers, efforts have emerged to measure multiple domains of cognitive functioning through the application of machine learning (ML) models to passive data sources. Methods: We utilized automated computer vision and voice analysis methods to extract facial, movement, and speech characteristics from semi-structured clinical interviews in 81 trauma survivors who additionally completed a cognitive assessment battery. A ML-based regression framework was used to identify variance in visual and auditory measures that relate to multiple cognitive domains. Results: Models derived from visual and auditory measures collectively accounted for a large variance in multiple domains of cognitive functioning, including motor coordination (R2 = 0.52), processing speed (R2 = 0.42), emotional bias (R2 = 0.52), sustained attention (R2 = 0.51), controlled attention (R2 = 0.44), cognitive flexibility (R2 = 0.43), cognitive inhibition (R2 = 0.64), and executive functioning (R2 = 0.63), consistent with the high test-retest reliability of traditional cognitive assessments. Face, voice, speech content, and movement have all significantly contributed to explaining the variance in predicting functioning in all cognitive domains. Conclusions: The results demonstrate the feasibility of automated measurement of reliable proxies of cognitive functioning through low-burden passive patient evaluations. This makes it easier to monitor cognitive functions and to intervene earlier and at a lower threshold without requiring a time-consuming neurocognitive assessment by, for instance, a licensed psychologist with specialized training in neuropsychology.
Prevalence of Diabetes and Associated Health Risk Factors among Adults in Dubai, United Arab Emirates: Results from Dubai Household Survey 2019
Authors: Al Awadi Fatheya , Hassanein Mohamed , Hussain Hamid Y. , Mohammed Heba , Ibrahim Gamal , Khater Ahmed , Suliman Eldaw
Published: December 2020

Background: The health and social burdens of diabetes mellitus (DM) are steadily increasing worldwide, reflecting the impact of urbanization, industrial transitions, and shifting to nonhealthy, sedentary life patterns’, as well as the high sugar, low-fiber food consumptions. All these factors have contributed to the global increase in the prevalence of DM and metabolic disorders. Objectives: The objective is to study the prevalence of DM among adult cohorts in Dubai and the extent of behavioral health risk factors associations. Methodology: A cross-sectional household health survey with multistage, stratified cluster random sample of 9,630 participants including 2,496 households was carried out in Dubai 2019. About 5,371 non-United Arab Emirates (UAE) national, 2,245 UAE-nationals, of different age-groups, gender, education, nationality, smoking, and marital status were included in the survey. The survey questionnaire was adapted from the one used in the World Bank’s Living Standards Measurement Surveys (LSMSs) and the WHO’s World Health Surveys (WHSs). Self-reported diabetes was considered as well as newly diagnosed diabetes based on HbA1C results. About 75 well-trained surveyors, 25 well-trained nurses, and other laboratory technicians conducted the survey. SPSS 21 and Stata 12 software was used for data management. Results: The data indicate that the prevalence of diabetes among Dubai Emiratis is much higher than Dubai expats (19.3 vs. 12.4% p = 0.000). The association between diabetes and age was evident regardless of nationality. Rates of diabetes were higher in males (15.4%) compared to females (11.8%) (p < 0.001). About 11.2% of those considered overweight have DM, while the prevalence is 21.5% in obese people (p = 0.000). Physical inactivity, smoking, and hypertension (HTN) were strongly associated with higher rates of DM. Regardless of nationality, lower levels of education were statistically significantly associated with the prevalence of DM (p = 0.000), while single marital status was associated with the lowest rate of DM. Conclusions: Dubai household health survey 2019 indicated that the prevalence of DM among Dubai adults was still high as an overall estimation. Higher rates of diabetes were significantly associated with Emirati nationality, older age-groups, male gender, physical inactivity, high BMI, HTN, smoking, marital status of divorced, separated or divorced as well as low educational level groups.