Miracle-dz/UniFAR-FTUs huggingface dataset:
LDL cholesterol is associated with systemic vascular resistance and wave reflection in subjects naive to cardiovascular drugs

Abstract Background and aim: Low density lipoprotein cholesterol (LDL-C) is a primary risk factor for atherosclerosis, but it is also associated with elevated blood pressure (BP) and future development of hypertension. We examined the relationship between LDL-C and haemodynamic variables in normotensive and never-treated hypertensive subjects. Methods: We recruited 615 volunteers (19–72 years) without lipid-lowering and BP-lowering medication. Supine haemodynamics were recorded using continuous radial pulse wave analysis, whole-body impedance cardiography, and single channel electrocardiogram. The haemodynamic relations of LDL-C were examined using linear regression analyses with age, sex, body mass index (BMI) (or height and weight as appropriate), smoking status, alcohol use, and plasma C-reactive protein, sodium, uric acid, high density lipoprotein cholesterol (HDL-C), triglycerides, estimated glomerular filtration rate, and quantitative insulin sensitivity check index as the other included variables. Results: The mean (SD) characteristics of the subjects were: age 45 (12) years, BMI 27 (4) kg/m2, office BP 141/89 (21/13) mmHg, creatinine 74 (14) µmol/l, total cholesterol 5.2 (1.0), LDL-C 3.1 (0.6), triglycerides 1.2 (0.8), and HDL-C 1.6 (0.4) mmol/l. LDL-C was an independent explanatory factor for aortic systolic and diastolic BP, augmentation index, pulse wave velocity (PWV), and systemic vascular resistance index (p < 0.05 for all). When central BP was included in the model for PWV, LDL-C was no longer an explanatory factor for PWV. Conclusions: LDL-C is independently associated with BP via systemic vascular resistance and wave reflection. These results suggest that LDL-C may play a role in the pathogenesis of primary hypertension.
Why so serious? The Role of Gamification on Motivation and Engagement in e-Participation

Since the emergence of e-participation platforms, an increase of public participation has not yet been documented. Recently practitioners and researchers have begun to explore the applicability of gamification in the hope to promote the usage of e-participation services. Gamification has been proven partially successful in several domains, but is only emerging in public participation. This work provides insights on whether game components can promote public participation. We report on the evaluation of a gamified mobile participation application in a field trial that took place in Turku, Finland over a period of five months. We focus on whether specific factors (internal & external) can be used to predict active participation. Our results indicate that citizens are primarily motivated by genuine interest in urban planning. Although gamification had little influence, it did add to some users' motivation. The approach did, however, not succeed in engaging new groups.
Energy-Efficient Fault-Tolerant Mapping and Scheduling on Heterogeneous Multiprocessor Real-Time Systems

Energy saving and system reliability are two crucial issues for designing modern multiprocessor systems. There has been reliability-aware power management with dynamic voltage–frequency scaling (DVFS) schemes in recent studies. However, they are limited to optimization under the impact of DVFS on energy and reliability and have not considered reducing the non-negligible leakage energy consumption. In this paper, we focus on co-management of system reliability and total energy for applications with precedence constrained tasks on heterogeneous multiprocessor real-time systems. We first investigate the impact of energy management techniques on both reliability and energy of the systems using task recovery for fault tolerance and then propose an Energy-efficient Fault-tolerant Scheduling (EFS) scheme integrated with power mode management, which can mitigate the negative impact of DVFS on system reliability. To obtain the optimal energy-efficient reliability-guaranteed scheduling for pre-mapped applications on systems considering various realistic issues, we build mixed integer linear programing formulations with the proposed EFS scheme. To address mapping and scheduling for energy-efficiency and fault-tolerance, we finally develop a framework implemented by a List-based Binary Particle Swarm Optimization algorithm. The extensive comparative evaluations for synthetic and realistic benchmarks show that our approaches outperform several related studies in terms of energy consumption and system reliability.
Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network

Endoscopic image diagnosis assisted by machine learning is useful for reducing misdetection and interobserver variability. Although many results have been reported, few effective methods are available to automatically detect early gastric cancer. Early gastric cancer have poor morphological features, which implies that automatic detection methods can be extremely difficult to construct. In this study, we proposed a convolutional neural network-based automatic detection scheme to assist the diagnosis of early gastric cancer in endoscopic images. We performed transfer learning using two classes (cancer and normal) of image datasets that have detailed texture information on lesions derived from a small number of annotated images. The accuracy of our trained network was 87.6%, and the sensitivity and specificity were well balanced, which is important for future practical use. We also succeeded in presenting a candidate region of early gastric cancer as a heat map of unknown images. The detection accuracy was 82.8%. This means that our proposed scheme may offer substantial assistance to endoscopists in decision making.
SDM-Based Means of Gradient for Eye Center Localization

For eye gaze estimation and eye tracking, localizing eye center is a crucial requirement. This task is challenging work because of the significant variability of eye appearance in illumination, shape, color and viewing angles. In this paper, we improve the performance of means of gradient method in low resolution images, which could locate the eye center more accurately. The proposed method applies Supervised Descent Method (SDM), which has remarkable achievement in the field of face alignment, to improve the traditional means of gradient method in localizing eye center. We extensively evaluate our method on BioID database which is very challenging and realistic for eye center localization. Moreover, we have com-pared our method with existing state of the art methods and the results of the experiment confirm that the proposed method is an attractive alternative for eye center localization.
Logit Pairing Methods Can Fool Gradient-Based Attacks

Recently, Kannan et al. [2018] proposed several logit regularization methods to improve the adversarial robustness of classifiers. We show that the computationally fast methods they propose - Clean Logit Pairing (CLP) and Logit Squeezing (LSQ) - just make the gradient-based optimization problem of crafting adversarial examples harder without providing actual robustness. We find that Adversarial Logit Pairing (ALP) may indeed provide robustness against adversarial examples, especially when combined with adversarial training, and we examine it in a variety of settings. However, the increase in adversarial accuracy is much smaller than previously claimed. Finally, our results suggest that the evaluation against an iterative PGD attack relies heavily on the parameters used and may result in false conclusions regarding robustness of a model.
高热、咽喉肿痛伴颈部淋巴结肿大

患者,男,18岁,学生。 因“间歇性发热、咽喉肿痛伴颈 部淋巴结肿大20 d”于 2015年11月23日入住我科。 患者曾 于2015年11月5日因“咽痛、颈部淋巴结肿痛伴发热3 d”就 诊于外院。 外院门诊查血常规示:WBC 24.84×10/L,C反应 蛋白 11.49 mg/L,镜检见异形淋巴细胞 26%,遂收入血液科 治疗。 住院期间查体颈部双侧多枚淋巴结肿大(大小2 cm× 3 cm),伴触痛,扁桃体III度肿大,伴大量脓性分泌物,脾脏肋 缘下 2 cm。 11月 6日检查,血常规:WBC 18.44×10/L,HGB 127 g/L,淋巴细胞比例 0.68;生化常规:ALT 171 U/L,AST 132 U/L,ALP 138 U/L,LDH 730 U/L;抗巨细胞病毒抗体 IgG>250.0 U/ml(阳性),抗巨细胞病毒抗体 IgM 1.27 U/ml (阳性);浅表淋巴结超声检查:颈部两侧、两侧腋下淋巴结肿 大,两侧腹股沟淋巴结可见;腹部超声检查:脾肿大(厚 51 mm,长157 mm),余未见明显异常;胸部CT:右肺下叶小 结节,直径约5 mm,边界清晰;腹部CT:脾脏增大,余未见明 显异常;PET-CT示:双侧鼻咽部、咽顶后壁及双侧腭扁桃体 饱满(FDG 摄取增高,平均 SUV=13.8,最大 SUV=20),双 侧腋下、颈部、纵隔、肝门及门静脉间、腹膜后、双侧盆壁及双 侧腹股沟区见多发肿大淋巴结,以颈部双侧淋巴结肿大明 显,FDG不同程度摄取增高,平均SUV=3.0,最大SUV=8.8; 外周血流式细胞术免疫分型:CD3 92.1%,CD3+CD4-CD865.8%,CD3 + CD4 + 6.2%,CD3 + CD8 + 19.2%,CD7 + 63.1%, CD2 95%,CD5 89.2%,NK 及 CD19、CD20 阴性,考虑 T 细 胞淋巴瘤可能;骨髓象:增生明显活跃,粒红比2. 1∶1,淋巴细 胞比例偏高,可见10. 5%的异常细胞,体积偏大,胞质多呈淡 蓝色,核染色质粗,有时可见隐匿核仁,似淋巴瘤细胞,考虑 淋巴瘤骨髓浸润可能;骨髓流式细胞术分型:CD3 89%, CD3 CD4CD881.6%,HLA-DR 24.1%,高度怀疑γ/δT细胞 性淋巴瘤。 故外院考虑为T细胞淋巴瘤,先后予以哌拉西林 他唑巴坦、甲磺酸帕珠沙星、更昔洛韦、帕尼培南/倍他米隆 抗感染,异甘草酸镁保肝,奥美拉唑护胃,基肽调节免疫等对 症支持治疗,症状未见好转。 11月 16日再次行骨髓穿刺及活检,送上海金域医学检 验所有限公司(简称金域公司)检测,骨髓流式细胞术检测结 果示CD34细胞占有核细胞总数约0. 5%,免疫表型未见明显 异常,淋巴细胞比例相对增多(40.1%),以 T 淋巴细胞为主 (94.5%),CD4/CD8比例倒置,少部分CD5和CD7缺失表达 (T细胞占有核细胞总数约37.9%,其中约11.3%免疫表型为 CD3CD8 弱+CD4-)。 骨髓活检:增生活跃,小淋巴细胞数量 增多,呈散在、小簇状,粒系、红系以偏成熟阶段细胞为主,未 见幼稚细胞增多,巨核细胞数量在正常范围,以分叶核巨细 胞为主,散在分布,未见纤维化。 免疫组化:PAX-5、CD20反 应性B细胞偶见(+),CD235a内对照(+),有核红细胞散在、 小簇少(+),CD3、CD2、CD5、CD7 T细胞散在、簇状多(+), TIA-1T细胞散在/簇状弱(+),考虑T细胞淋巴瘤。 由于持续 高热,需要化疗,患者转入我院治疗。 入院查体:体温 39.5 °C,心率 85次/min,呼吸 23次/min, 血压 110/80 mmHg(1 mmHg=0.133 kPa),神志清楚,发育正 常,全身皮肤无黄染、皮疹及出血点,无肝掌。 颈部双侧多枚 淋巴结肿大,最大 2.5 cm×3.0 cm,伴触痛,扁桃体III度肿 大。 胸廓对称无畸形,胸骨无压痛;双肺呼吸音清晰,未闻及 干、湿性啰音。 心率85次/min,律齐;腹平坦,右下腹可见一 长约15 cm手术疤痕,腹壁软,全腹无压痛,无肌紧张及反跳 痛,肝肋下未触及,脾脏肋缘下 3 cm,质地中等,无明显触 痛。 肝肾脏无叩击痛,肠鸣音4次/min。 肛门及外生殖器未 检,脊柱、四肢无畸形,关节无红肿,无杵状指(趾),双下肢无 水肿。 四肢肌力、肌张力未见异常,生理反射正常,病理反射 未引出。 患者自发病以来,精神和胃纳可,无盗汗,二便如常,否 认近期体重明显改变。 阑尾切除术史已 5年,余既往史、个 人史、家族史无特殊。
Proarrhythmic Remodeling of Calcium Homeostasis in Cardiac Disease; Implications for Diabetes and Obesity

A rapid growth in the incidence of diabetes and obesity has transpired to a major heath issue and economic burden in the postindustrial world, with more than 29 million patients affected in the United States alone. Cardiovascular defects have been established as the leading cause of mortality and morbidity of diabetic patients. Over the last decade, significant progress has been made in delineating mechanisms responsible for the diminished cardiac contractile function and enhanced propensity for malignant cardiac arrhythmias characteristic of diabetic disease. Rhythmic cardiac contractility relies upon the precise interplay between several cellular Ca2+ transport protein complexes including plasmalemmal L-type Ca2+ channels (LTCC), Na+-Ca2+ exchanger (NCX1), Sarco/endoplasmic Reticulum (SR) Ca2+-ATPase (SERCa2a) and ryanodine receptors (RyR2s), the SR Ca2+ release channels. Here we provide an overview of changes in Ca2+ homeostasis in diabetic ventricular myocytes and discuss the therapeutic potential of targeting Ca2+ handling proteins in the prevention of diabetes-associated cardiomyopathy and arrhythmogenesis.
Translating N‐Glycan Analytical Applications into Clinical Strategies for Ovarian Cancer

Protein glycosylation, particularly N‐linked glycosylation, is a complex posttranslational modification (PTM), which plays an important role in protein folding and conformation, regulating protein stability and activity, cell–cell interaction, and cell signaling pathways. This review focuses on analytical techniques, primarily MS‐based techniques, to qualitatively and quantitatively assess N‐glycosylation while successfully characterizing compositional, structural, and linkage features with high specificity and sensitivity. The analytical techniques explored in this review include LC–ESI–MS/MS and MALDI time‐of‐flight MS (MALDI‐TOF‐MS), which have been used to analyze clinical samples, such as serum, plasma, ascites, and tissue. Targeting the aberrant N‐glycosylation patterns observed in MALDI–MS imaging (MSI) offers a platform to visualize N‐glycans in tissue‐specific regions. The studies on the intra‐patient (i.e., a comparison of tissue‐specific regions from the same patient) and inter‐patient (i.e., a comparison of tissue‐specific regions between different patients) variation of early‐ and late‐stage ovarian cancer (OC) patients identify specific N‐glycan differences that improve understanding of the tumor microenvironment and potentially improve therapeutic strategies for the clinic.
Optimization of Schizophrenia Diagnosis Prediction using Machine Learning Techniques

The objective of this paper is to automatically diagnose the mental state disorder named schizophrenia by using multimodal features which are extracted from Magnetic Resonance Imaging (MRI) brain scans. The aim is to achieve highest possible classification (binary) accuracy to achieve best possible prediction of the schizophrenia diagnosis. The importance of feature selection in combination with fine-tuning the parameters of Machine Learning classifiers to solve this problem is explained. Various supervised Machine Learning classifiers were employed and compared with themselves and then with existing systems. The proposed solution achieved AUC score of 0.9473 and an accuracy of 0.9412 as opposed to till date best existing system’s AUC score of 0.928.
HIFU Drive System Miniaturization Using Harmonic Reduced Pulsewidth Modulation

Switched excitation has the potential to improve on the cost, efficiency, and size of the linear amplifier circuitry currently used in high-intensity focused ultrasound (HIFU) systems. Existing switching schemes are impaired by high harmonic distortion or lack array apodisation capability, so require adjustable supplies and/or large power filters to be useful. A multilevel pulsewidth modulation (PWM) topology could address both of these issues but the switching-speed limitations of transistors mean that there are a limited number of pulses available in each waveform cycle. In this study, harmonic reduction PWM (HRPWM) is proposed as an algorithmic solution to the design of switched waveforms. Its appropriateness for HIFU was assessed by design of a high power five-level unfiltered amplifier and subsequent thermal-only lesioning of ex vivo chicken breast. Three switched waveforms of different electrical powers (16, 26, 35 W) were generated using the HRPWM algorithm. Lesion sizes were measured and compared with those made at the same electrical power using a linear amplifier and bi-level excitation. HRPWM produced symmetric, thermal-only lesions that were the same size as their linear amplifier equivalents ( $p > 0.05$ ). At 16 W, bi-level excitation produced smaller lesions but at higher power levels large transients in the acoustic waveform nucleated undesired cavitation. These results demonstrate that HRPWM can minimize HIFU drive circuity size without the need for filters to remove harmonics or adjustable power supplies to achieve array apodisation.
Low-complexity Recurrent Neural Network-based Polar Decoder with Weight Quantization Mechanism

Polar codes have drawn much attention and been adopted in 5G New Radio (NR) due to their capacity-achieving performance. Recently, as the emerging deep learning (DL) technique has breakthrough achievements in many fields, neural network decoder was proposed to obtain faster convergence and better performance than belief propagation (BP) decoding. However, neural networks are memory-intensive and hinder the deployment of DL in communication systems. In this work, a low-complexity recurrent neural network (RNN) polar decoder with codebook-based weight quantization is proposed. Our test results show that we can effectively reduce the memory overhead by 98% and alleviate computational complexity with slight performance loss.
An Adaptive Power Level Control Algorithm for DSRC Congestion Control

Vehicular industries and researchers have invested efforts to reduce avoidable accidents through the means of Vehicle to Vehicle (V2V) wireless communication using Vehicular Ad Hoc Networks (VANETs). Up-to-date information on the location, speed and other important parameters for each vehicle is shared with neighboring vehicles through the periodic exchange of Basic Safety Messages (BSMs). With a high vehicular density, network congestion can quickly arise in the 5.9GHz spectrum, rendering the system as unreliable because safety messages are not delivered and received on time. To alleviate this problem, there has been considerable research, in recent years, on distributed congestion control algorithms for VANETs. These approaches are generally based on rate control or power control of the transmitted packets. In this paper, we propose a novel, adaptive power control algorithm to reduce packet congestion in VANETs. Instead of requiring all vehicles to use the same power level, our approach allows each vehicle to dynamically adjust the transmit power of BSM packets, depending on its current speed. The goal is to prioritize which other vehicles will receive BSM packets from a given vehicle. Our simulation results demonstrate the advantages of the proposed algorithm regarding commonly used metrics such as packet loss, Beacon Error Rates(BER), Channel Busy Time(CBT) and Inter-Packet Delay(IPD).
Poster: Semantic Clustering in Credible Human Sensed Event Detection

Twitter is one of the most popular social media platforms, and a widely used data channel for the propagation of information. Since too many open end users access and use the powerful channel for information propagation, it is becoming increasingly difficult to separate reliable information from the overwhelming pool of information. With the advent of social media generated "fake news" and with their growing influence on the society, the issue of detecting authentic information gains utmost importance. The purpose of our work is to measure the reliability or correctness of the information that is being propagated using Twitter. However, to measure the reliability it is important to preprocess the tweets and to find the similar events. For doing this, an effective clustering method is required which will measure the similarity between the tweets using both semantic and syntactic similarity. We also propose an efficient way to compute the credibility of the sources and how information propagates around the network.
HmtVar: a new resource for human mitochondrial variations and pathogenicity data

Abstract Interest in human mitochondrial genetic data is constantly increasing among both clinicians and researchers, due to the involvement of mitochondrial DNA (mtDNA) in a number of physiological and pathological processes. Thanks to new sequencing technologies and modern databases, the large amount of information on mtDNA variability may be exploited to gain insights into the relationship between mtDNA variants, phenotypes and diseases. To facilitate this process, we have developed the HmtVar resource, a variant-focused database that allows the exploration of a dataset of over 40 000 human mitochondrial variants. Mitochondrial variation data, initially gathered from the HmtDB platform, are integrated with in-house pathogenicity assessments based on various evaluation criteria and with a set of additional annotations from third-party resources. The result is a comprehensive collection of information of crucial importance for human mitochondrial variation studies and investigation of common and rare diseases in which the mitochondrion may be involved. HmtVar is accessible at https://www.hmtvar. uniba.it and data may be retrieved using either a web interface through the Query page or a state-of-the-art API for programmatic access.
Evidence-based post-exercise recovery strategies in rugby: a narrative review

ABSTRACT In the sport of rugby, athletes need a multitude of sport-specific skills along with endurance, power, and speed to optimize performance. Further, it is not unusual for athletes to play several competitive matches with insufficient recovery time. Rugby requires repeated bouts of high-intensity actions intermixed with brief periods of low-to-moderate active recovery or passive rest. Specifically, a match is characterized by repeated explosive activities, such as jumps, shuffles, and rapid changes of direction. To facilitate adequate recovery, it is necessary to understand the type of fatigue induced and, if possible, its underlying mechanisms. Common approaches to recovery may include nutritional strategies as well as active (active recovery) and passive recovery (water immersions, stretching, and massage) methods. However, limited research exists to support the effectiveness of each strategy as it related to recovery from the sport of rugby. Therefore, the main aim of the current brief review is to present the relevant literature that pertains to recovery strategies in rugby.
Force feasible set prediction with artificial neural network and musculoskeletal model

Abstract Developing tools to predict the force capabilities of the human limbs through the Force Feasible Set (FFS) may be of great interest for robotic assisted rehabilitation and digital human modelling for ergonomics. Indeed, it could help to refine rehabilitation programs for active participation during exercise therapy and to prevent musculoskeletal disorders. In this framework, the purpose of this study is to use artificial neural networks (ANN) to predict the FFS of the upper-limb based on joint centre Cartesian positions and anthropometric data. Seventeen right upper-limb musculoskeletal models based on individual anthropometric data are created. For each musculoskeletal model, the FFS is computed for 8428 different postures. For any combination of force direction and joint positions, ANNs can predict the FFS with high values of coefficient of determination (R2 > 0.89) between the true and predicted data.
EEG Data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN

Due to the lack of electroencephalography (EEG) data, it is hard to build an emotion recognition model with high accuracy from EEG signals using machine learning approach. Inspired by generative adversarial networks (GANs), we introduce a Conditional Wasserstein GAN (CWGAN) framework for EEG data augmentation to enhance EEG-based emotion recognition. A Wasserstein GAN with gradient penalty is adopted to generate realistic-like EEG data in differential entropy (DE) form. Three indicators are used to judge the qualities of the generated high-dimensional EEG data, and only high quality data are appended to supplement the data manifold, which leads to better classification of different emotions. We evaluate the proposed CWGAN framework on two public EEG datasets for emotion recognition, namely SEED and DEAP. The experimental results demonstrate that using the EEG data generated by CWGAN significantly improves the accuracies of emotion recognition models.
Planar Transitive Graphs

We prove that the first homology group of every planar locally finite transitive graph $G$ is finitely generated as an $\Aut(G)$-module and we prove a similar result for the fundamental group of locally finite planar Cayley graphs. Corollaries of these results include Droms's theorem that planar groups are finitely presented and Dunwoody's theorem that planar locally finite transitive graphs are accessible.
Porting the Pip Proto-Kernel's Model to Multi-core Environments

In order to bring integrity and confidentiality through memory isolation in embedded devices, we developed Pip, a proto-kernel ensuring only memory isolation and control flow in the Internet of Things's computing environments. Its API is verified, ensuring the correctness of the memory isolation properties. Nevertheless, today, like Cloud Computing environments, IoT environments are becoming more and more multi-core and parallelized. Those environments introduce many new issues of consistency and concurrency, and require specific software architectures. In this paper, we discuss those new aspects, and the evolution of Pip's model into a multi-core model designed for those environments.