Miracle-dz/UniFAR-FTUs huggingface dataset:
The Pivotal Role of Thymus in Atherosclerosis Mediated by Immune and Inflammatory Response

Atherosclerosis is one kind of chronic inflammatory disease, in which multiple types of immune cells or factors are involved. Data from experimental and clinical studies on atherosclerosis have confirmed the key roles of immune cells and inflammation in such process. The thymus as a key organ in T lymphocyte ontogenesis has an important role in optimizing immune system function throughout the life, and dysfunction of thymus has been proved to be associated with severity of atherosclerosis. Based on previous research, we begin with the hypothesis that low density lipoprotein or cholesterol reduces the expression of the thymus transcription factor Foxn1 via low density lipoprotein receptors on the membrane surface and low density lipoprotein receptor related proteins on the cell surface, which cause the thymus function decline or degradation. The imbalance of T cell subgroups and the decrease of naive T cells due to thymus dysfunction cause the increase or decrease in the secretion of various inflammatory factors, which in turn aggravates or inhibits atherosclerosis progression and cardiovascular events. Hence, thymus may be the pivotal role in coronary heart disease mediated by atherosclerosis and cardiovascular events and it can imply a novel treatment strategy for the clinical management of patients with atherosclerosis in addition to different commercial drugs. Modulation of immune system by inducing thymus function may be a therapeutic approach for the prevention of atherosclerosis. Purpose of this review is to summarize and discuss the recent advances about the impact of thymus function on atherosclerosis by the data from animal or human studies and the potential mechanisms.
A 5-MicroRNA Signature Identified from Serum MicroRNA Profiling Predicts Survival in Patients with Advanced Stage Non-Small Cell Lung Cancer.

Circulating microRNAs (miRNAs) are potential biomarkers for cancer diagnosis, screening, and prognosis. This study aimed to identify serum miRNAs as predictors of survival in patients with advanced non-small cell lung cancer (NSCLC). We profiled serum miRNAs in a pilot set of 4 patients with good survival (>24 months) and 4 patients with poor survival (<6 months). We selected 140 stably detectable miRNAs and 42 miRNAs reported in literature for further analysis. Expression of these 182 miRNAs was measured using high-throughput polymerase chain reaction assay, and their association with 3-year survival in the discovery (n=345) and validation (n=177) cohorts was assessed. Five serum miRNAs (miR-191, miR-28-3p, miR-145, miR-328, and miR-18a) were significantly associated with 3-year overall survival in both cohorts. A combined 5-miRNA risk score was created to assess the cumulative impact of these miRNAs on risk of death. Quartile analysis of the risk score showed significant association with 3-year death risk, with a 4.6-month, 6.8-month, and 9.3-month reduction in median survival time for the second, third, and fourth quartile, respectively. Survival tree analysis also identified distinct risk groups with different 3-year survival durations. Data from The Cancer Genome Atlas revealed all 5 miRNAs were differentially expressed (P<0.0001) in paired tumor and normal tissues. Pathway analysis indicated that target genes of these 5 miRNAs were mainly enriched in inflammatory/immune response pathways and pathways implicated in resistance to chemoradiotherapy and/or targeted therapy. Our results suggested that the 5-miRNA signature could serve as a prognostic predictor in advanced NSCLC patients.
Optimized SCMA Codebook Design by QAM Constellation Segmentation With Maximized MED

An optimized design of a sparse code multiple access (SCMA) codebook for uplink wireless communications is presented by dividing an optimized 16-point round quadrature amplitude modulation (QAM) into several subsets. The main goal of the scheme is to maximize the minimum Euclidean distance and thus reduces the collisions of the information bits on the resources. The final SCMA codebook is obtained with the mapping matrix, which indicates the sub-constellations generating by dividing the mother QAM constellation. Simulation results show that, in a Nakagami fading channel, the optimized SCMA scheme by the proposed design method achieves significantly performance gains approximately 1.0, 1.4, 2.5, 3.5, and 4.0 dB at bit error rate of $10^{-4}$ , respectively, when compared with those of an undivided 16-QAM constellation, a trellis code modulation (TCM) division, an original SCMA codebooks, a low-density signature (LDS), and an irregular LDS (IrLDS) schemes. In addition, at the signal-to-noise ratio ranging from 0 to 10 dB, the constellation constrained capacity of the scheme by the proposed method achieves more gains over those of the original SCMA, TCM, the undivided star-QAM, the LDS, and the IrLDS schemes. Thus, it can be combined with the grant-free random access mechanism to obtain rapid and low-cost access in next-generation wireless packet services and other applications.
Reactive oxygen species induced by icaritin promote DNA strand breaks and apoptosis in human cervical cancer cells

Increased production of reactive oxygen species (ROS) is a distinct feature of various types of cancer. ROS drive tumor progression and render cancer cells vulnerable to additional oxidative insult. The various natural herb compounds have been shown to induce additional production of ROS in cancer cells, although the physiological implications of ROS under these conditions are not fully determined. In the present study, icaritin, a natural compound derived from the medicinal plants Epimedium, was demonstrated to potently suppresses the proliferation of human HeLa and SiHa cervical cancer cells, without similar affects on non-cancerous CCD-1095Sk fibroblasts and 293 cells, as measured by MTT and colony formation assays. Icaritin treatment caused a rapid increase in ROS in HeLa and SiHa cells, which was followed by a prominent increase in the number of DNA strand breaks. Consequently, the levels of the pro-apoptotic protein Bax and activated caspase 3 and 9 enzymes were increased, while the levels of the anti-apoptotic proteins Bcl-2 and XIAP were downregulated. These protein expression changes were accompanied by marked induction of apoptosis in icaritin-treated cancer cells. The results suggested that the icaritin-induced ROS overload promoted cancer cell death via induction of extensive oxidative DNA damage, which subsequently resulted in large numbers of DNA strand breaks and the activation of the intrinsic apoptotic pathway.
LinkNet: Relational Embedding for Scene Graph

Objects and their relationships are critical contents for image understanding. A scene graph provides a structured description that captures these properties of an image. However, reasoning about the relationships between objects is very challenging and only a few recent works have attempted to solve the problem of generating a scene graph from an image. In this paper, we present a method that improves scene graph generation by explicitly modeling inter-dependency among the entire object instances. We design a simple and effective relational embedding module that enables our model to jointly represent connections among all related objects, rather than focus on an object in isolation. Our method significantly benefits the main part of the scene graph generation task: relationship classification. Using it on top of a basic Faster R-CNN, our model achieves state-of-the-art results on the Visual Genome benchmark. We further push the performance by introducing global context encoding module and geometrical layout encoding module. We validate our final model, LinkNet, through extensive ablation studies, demonstrating its efficacy in scene graph generation.
Lateral Road-mark Reconstruction Using Neural Network for Safe Autonomous Driving in Snow-wet Environments

This paper highlights the importance of utilizing artificial intelligence to safely localize autonomous vehicles in hard weather and environmental conditions. An edge profile is created to encode the lateral road-marks as a series of peaks. A neural network is utilized to explore different relationships between the peaks among a set of map edge profiles. Based on the generalization capability of the trained network, the LIDAR profiles are reconstructed during the autonomous driving to regularly reform the peaks in terms of distribution and amplitude patterns. A reconstructed profile is then matched to the corresponding map profile to estimate the lateral vehicle position accurately. The proposed framework has been tested in very hard situations of snow and grass accumulation at the roadside, snow lines inside lanes and, on wet and old road surfaces. The experimental results have verified the reliability and safety of reconstructing LIDAR profiles to achieve precise lateral localization against the changes of road, weather and environmental conditions.
Reward-estimation variance elimination in sequential decision processes

Policy gradient methods are very attractive in reinforcement learning due to their model-free nature and convergence guarantees. These methods, however, suffer from high variance in gradient estimation, resulting in poor sample efficiency. To mitigate this issue, a number of variance-reduction approaches have been proposed. Unfortunately, in the challenging problems with delayed rewards, these approaches either bring a relatively modest improvement or do reduce variance at expense of introducing a bias and undermining convergence. The unbiased methods of gradient estimation, in general, only partially reduce variance, without eliminating it completely even in the limit of exact knowledge of the value functions and problem dynamics, as one might have wished. In this work we propose an unbiased method that does completely eliminate variance under some, commonly encountered, conditions. Of practical interest is the limit of deterministic dynamics and small policy stochasticity. In the case of a quadratic value function, as in linear quadratic Gaussian models, the policy randomness need not be small. We use such a model to analyze performance of the proposed variance-elimination approach and compare it with standard variance-reduction methods. The core idea behind the approach is to use control variates at all future times down the trajectory. We present both a model-based and model-free formulations.
Parking Detection Method Based on Finite-State Machine and Collaborative Decision-Making

A high-accurate parking detection method for wireless magnetic sensor networks is proposed in this paper, which is based on the combination of finite-state machine and collaborative decision-making. The magnetic disturbance induced by a vehicle can be sensed and processed to obtain the availability of a parking space by a magnetic sensor. However, the main challenge lies in the difficulty of eliminating the interferences from adjacent vehicles that decrease the accuracy. The vehicles include moving vehicles on adjacent roads and parking vehicles in adjacent parking spaces. For simplicity and low-energy consumption, a multi-interim finite-state machine (MiFSM) is proposed to deal with the interferences from moving vehicles. Our method contains preliminary detection and final detection. Using MiFSM, most of the parking vehicles can be correctly detected in the preliminary detection. However, the adjacent parking vehicles may cause more complicated interferences. It is hard to distinguish these interferences from the disturbances induced by “weak-magnetic” vehicles above on the detecting sensor. The “weak-magnetic” vehicles cause small magnetic disturbance because of their high chassis or short car-body. Therefore, by using the collaborative information of adjacent sensors, a Dempster–Shafer evidence theory-based collaborative decision-making is developed to cope with these complicated interferences in the final detection. The experimental results show that our work has a significant improvement in detection accuracy, as about 99.8% for vehicle arrival and 99.9% for vehicle departure. The proposed method can also be extended for moving vehicle detection, speed estimation, and vehicle classification in the applications of intelligent traffic system.
Study on the Comparison of the Difference of Reaching Between the Real and the Virtual Environment Using HMD

The purposes of this research are to investigate a difference of human cognitive behavior (in other word, ecological cognition) in VR using HMD and the real environment and to examine a method to compensate the difference. Therefore, this research conducts a comparative experiment using a cognitive behavior called “Reaching”. First, a subject estimates the longest distance to a cylinder placed in front of the subject at which the subject can reach the cylinder. After that, the subject grabs the cylinder and places it as far as possible. The result is investigated in the real and the virtual environment. In the real, it has been found that the longest distance to an object for a person to decide to be able to reach for and grab the object is about 1.1 times as long as the longest distance where a person actually can reach for grab the object. On the other hand, despite the same scale of the real and the VR environment, this research showed the value in the virtual environment is not the same as one in the real. In addition, the result of measuring the trajectory of grasping the cylinder also showed that there is a difference between the real and the VR environment. As the result, it was found that there is an essential cognition difference between the real and the VR environment, and it was shown that the amount of environmental information and experience of the virtual environment may influence the ecological cognition in the virtual environment. In addition, as a method to complement the difference, it was suggested that it is necessary to give sufficient information to cognitive behavior.
생애주기별 우울진입에 영향을 미치는 요인에 관한 연구 - 성년, 중년, 노년층의 3세대 비교를 중심으로 -

본 연구의 목적은 우울진입에 영향을 미치는 요인이 세대별로 어떤 차이가 있는지를 살펴보고 생애 주기별 우울 예방의 실천적 전략을 제시하는 것이다. 이를 위해 한국복지패널의 3~10차 조사 자료를 결합하여, 성년층과 중년층, 노년층의 우울진입 영향요인을 이산시간위험모델을 통해 분석하였다. 분석 결과, 3세대 공통적으로 여자가 남자보다, 자아존중감이 낮을수록, 수입 만족도와 가족관계 만족도가 낮을수록 우울진입 가능성이 높았다. 또한 세대별로 차이를 나타낸 변수는 연령과 교육수준, 건강상태, 만성질환유무, 취업여부, 권역별지역, 여가생활 만족도였다. 성년(20~39세)의 경우 나이가 적을수록, 만성질환이 있는 경우에, 미취업자가 취업자보다 우울진입 가능성이 높다. 반면 중년층(40~64세)의 경 우 나이가 많을수록, 건강상태가 좋지 않은 경우에, 만성질환이 없는 경우에, 여가생활 만족도가 낮은 경우에 우울진입 가능성이 높다. 마지막으로 노년층(65세 이상)은 교육수준이 높을수록, 건강상태가 좋지 않은 경우에, 도농 복합군이 서울보다, 여가생활 만족도가 낮을수록 우울진입 가능성이 높다. 이 러한 결과를 바탕으로 우울진입을 예방하기 위해 세대별로 드러난 특성이 반영된 지원방안을 마련할 필요가 있음을 제시하였다.
Socio-demographic patterning of physical activity and sedentary behaviour in Chile: Crosssectional results from the National Health Survey 2009-2010

BACKGROUND: Surveillance of physical activity (PA) is essential for the development of health promotion initiatives. The aim of the present study was to examine the prevalence of PA and sedentary behaviour with respect to socio-demographic factors in Chile. METHODS: A representative sample of 5434 adults aged ≥15 years (59% women) who participated in the Chilean National Health Survey (2009-2010) were included. Socio-demographic data (age, sex, environment, education level, income level and smoking status) were collected for all participants. PA levels were assessed using the Global Physical Activity Questionnaire. RESULTS: 19.8% [95% CI: 18.1-21.6] of the Chilean population did not meet PA recommendations (≥600 MET min week(-1)). The prevalence of physical inactivity was higher in participants aged ≥65 years, compared with the youngest age groups and was higher in women than in men. However, it was lower for participants with high, compared with low, education or income levels. The overall prevalence of sedentary risk behaviour (spending >4 h sitting per day) was 35.9% [95% CI: 33.738. 2]. CONCLUSION: Physical inactivity correlates strongly with socio-demographic factors such as age, gender and educational level. Results identify social and economic groups to which future public health interventions should be aimed to increase PA in the Chilean population.
Early readmission and mortality in acute exacerbation of chronic obstructive pulmonary disease with community-acquired pneumonia

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are important causes of hospital admission and mortality. Pneumonia is a major contributor to hospitalization for AECOPD and has a close relationship with poor outcomes. We performed a prospective cohort study to evaluate the prognosis of AECOPD patients with or without community-acquired pneumonia (CAP) who hospitalized from January 2012 to December 2015. We investigated mortality and readmission rates within 6 months after the first admission between two groups and analyzed the difference of survival rate according to readmission duration (≤30 vs. >30 days) or intensive care unit (ICU) treatment. Total 308 AECOPD patients (134 with CAP and 174 without CAP) were enrolled. The mean age was 72.3 ± 9.5 years old, and 235 patients (76.3%) were male. The 180-day mortality was higher in AECOPD with CAP than without CAP (24.6% vs. 13.2%; hazard ratio (HR): 1.982; 95% CI: 1.164–3.375; p = 0.012). However, readmission rate showed no significant difference between two groups (51.5% vs. 46.6%; HR: 1.172; 95% CI: 0.850–1.616; p = 0.333). It showed a significantly lower survival rate in AECOPD with CAP rather than without CAP when were readmitted within 30 days (HR: 1.738; 95% CI:1.063–3.017; p = 0.031). According to ICU treatment, survival rate was not significantly different between two groups. Multivariate analysis revealed the readmission within 30 days (p < 0.001), serum hemoglobin concentration (p = 0.010), and albumin level (p = 0.049) were significantly associated with 180-day mortality of AECOPD with CAP. AECOPD with CAP showed lower survival rate than AECOPD without CAP during 6 months. Early readmission within 30 days was significantly associated with an increased risk of mortality.
Modified quadruple therapy versus bismuth-containing quadruple therapy in first-line treatment of Helicobacter pylori infection in Korea; rationale and design of an open-label, multicenter, randomized controlled trial

Background: Clarithromycin-containing triple regimen for eradication of Helicobacter pylori is no longer acceptable in Korea due to high clarithromycin resistance. Concomitant therapy or bismuth-containing quadruple therapy is recommended as an alternative regimen. A recent study in Korea has shown that modified quadruple therapy has comparable efficacy and safety to concomitant therapy as a first-line regimen. However, there has been no comparative study of modified quadruple therapy with bismuth-containing quadruple therapy. The aim of this study is to compare the efficacy and safety of modified quadruple therapy with those of bismuth-containing quadruple therapy as a first-line regimen and to present the phenotypic and genotypic antibiotic resistance profile of H pylori. Methods: This study is an open-label, multicenter, randomized controlled trial. We are recruiting subjects endoscopically diagnosed with H pylori infection from 2 hospitals in Korea. Subjects will be randomly allocated either to modified quadruple therapy (proton-pump inhibitor bid, amoxicillin 1 g bid, metronidazole 500 mg tid, bismuth subcitrate 300 mg qid daily) or bismuth-containing quadruple therapy (proton-pump inhibitor bid, tetracycline 500 mg qid, metronidazole 500 mg tid, bismuth subcitrate 300 mg qid daily) for 14 days. The rate of eradication success and adverse events will be checked at least 4 weeks after the treatment. Antibiotic resistance will be established using both a bacterial culture with agar dilutions and DNA sequencing of the clarithromycin resistance point mutations in the 23S rRNA gene of H pylori. Conclusion: The results of this study will provide solid evidence for determining the optimal treatment regimen for first-line H pylori eradication in Korea.
Chlamydomonas WDR92 in association with R2TP-like complex and multiple DNAAFs to regulate ciliary dynein preassembly

Abstract The motility of cilia or eukaryotic flagella is powered by the axonemal dyneins, which are preassembled in the cytoplasm by proteins termed dynein arm assembly factors (DNAAFs) before being transported to and assembled on the ciliary axoneme. Here, we characterize the function of WDR92 in Chlamydomonas. Loss of WDR92, a cytoplasmic protein, in a mutant wdr92 generated by DNA insertional mutagenesis resulted in aflagellate cells or cells with stumpy or short flagella, disappearance of axonemal dynein arms, and diminishment of dynein arm heavy chains in the cytoplasm, suggesting that WDR92 is a DNAAF. Immunoprecipitation of WDR92 followed by mass spectrometry identified inner dynein arm heavy chains and multiple DNAAFs including RuvBL1, RPAP3, MOT48, ODA7, and DYX1C. The PIH1 domain-containing protein MOT48 formed a R2TP-like complex with RuvBL1/2 and RPAP3, while PF13, another PIH1 domain-containing protein with function in dynein preassembly, did not. Interestingly, the third PIH1 domain-containing protein TWI1 was not related to flagellar motility. WDR92 physically interacted with the R2TP-like complex and the other identified DNNAFs. Our data suggest that WDR92 functions in association with the HSP90 co-chaperone R2TP-like complex as well as linking other DNAAFs in dynein preassembly.
Asynchronous Stochastic Composition Optimization with Variance Reduction

Composition optimization has drawn a lot of attention in a wide variety of machine learning domains from risk management to reinforcement learning. Existing methods solving the composition optimization problem often work in a sequential and single-machine manner, which limits their applications in large-scale problems. To address this issue, this paper proposes two asynchronous parallel variance reduced stochastic compositional gradient (AsyVRSC) algorithms that are suitable to handle large-scale data sets. The two algorithms are AsyVRSC-Shared for the shared-memory architecture and AsyVRSC-Distributed for the master-worker architecture. The embedded variance reduction techniques enable the algorithms to achieve linear convergence rates. Furthermore, AsyVRSC-Shared and AsyVRSC-Distributed enjoy provable linear speedup, when the time delays are bounded by the data dimensionality or the sparsity ratio of the partial gradients, respectively. Extensive experiments are conducted to verify the effectiveness of the proposed algorithms.