Miracle-dz/UniFAR-FTUs huggingface dataset:
Type 2 Diabetes Mellitus May Exacerbate Gray Matter Atrophy in Patients With Early-Onset Mild Cognitive Impairment

Background The precise physiopathological association between the courses of neurodegeneration and cognitive decline in type 2 diabetes mellitus (T2DM) remains unclear. This study sought to comprehensively investigate the distribution characteristics of gray matter atrophy in middle-aged T2DM patients with newly diagnosed mild cognitive impairment (MCI). Methods Four groups, including 28 patients with early-onset MCI, 28 patients with T2DM, 28 T2DM patients with early-onset MCI (T2DM-MCI), and 28 age-, sex-, and education-matched healthy controls underwent three-dimensional high-resolution structural magnetic resonance imaging. Cortical and subcortical gray matter volumes were calculated, and a structural covariance method was used to evaluate the morphological relationships within the default mode network (DMN). Results Overlapped and unique cortical/subcortical gray matter atrophy was found in patients with MCI, T2DM and T2DM-MCI in our study, and patients with T2DM-MCI showed lower volumes in several areas than patients with MCI or T2DM. Volume loss in subcortical areas (including the thalamus, putamen, and hippocampus), but not in cortical areas, was related to cognitive impairment in patients with MCI and T2DM-MCI. No associations between biochemical measurements and volumetric reductions were found. Furthermore, patients with MCI and those with T2DM-MCI showed disrupted structural connectivity within the DMN. Conclusion These findings provide further evidence that T2DM may exacerbate atrophy of specific gray matter regions, which may be primarily associated with MCI. Impairments in gray matter volume related to T2DM or MCI are independent of cardiovascular risk factors, and subcortical atrophy may play a more pivotal role in cognitive impairment than cortical alterations in patients with MCI and T2DM-MCI. The enhanced structural connectivity within the DMN in patients with T2DM-MCI may suggest a compensatory mechanism for the chronic neurodegeneration.
Alteration of Brain Gray Matter Density After 24 h of Sleep Deprivation in Healthy Adults

It has been reported that one night of acute sleep deprivation (SD) could induce brain structural changes at the synaptic and neuronal levels in animal studies, and could lead to white matter microstructure and cortical thickness change in human neuroimaging studies. In this study, we focused on changes of brain gray matter density (GMD) after one night of acute SD, which has not been explored previously. Twenty-three normal young participants completed the experiment. Each participant underwent twice T1-weighted structural image scanning with one at 08:00 after normal sleep [resting wakeful (RW)] and the other at 08:00 after 24 h of SD. Using voxel-based morphometry (VBM) analysis by FSL-VBM software, we compared GMD between RW and SD. In addition, the gray matter volume (GMV) and cortical thickness (CT) were also calculated based on volumetric and surface measures with FreeSurfer software. The psychomotor vigilance test (PVT) and the Karolinska Sleepiness Scale (KSS) were performed and evaluated for correlation analysis with GMD, GMV, and CT of the significant regions. Our results showed that the GMD in the right frontal pole (FP), right superior frontal gyrus (SFG), and right middle frontal gyrus significantly increased and GMV and CT in the right temporal pole (TP) significantly decreased after 24 h of acute SD. SD-induced changes in GMD in the right middle frontal gyrus were positively correlated with the changes of KSS scores (Spearman’s correlation r = 0.625, p = 0.0014, Bonferroni correction with p < 0.05/25). Taken together, our findings suggested that one night of acute SD could induce substantial brain structure changes and the alterations in GMD in the right middle frontal gyrus (MFG) might be implicated in sleepiness after SD.
Long Non-coding RNA FGD5-AS1 Regulates Cancer Cell Proliferation and Chemoresistance in Gastric Cancer Through miR-153-3p/CITED2 Axis

Background In this study, we investigated the molecular mechanisms of human long non-coding RNA (lncRNA) FYVE RhoGEF And PH Domain Containing 5 Antisense RNA 1 (FGD5-AS1) and its downstream epigenetic axis, human microRNA-153-3p (hsa-miR-153-3p)/Cbp/P300-interacting transactivator with Glu/Asp-rich carboxy-terminal domain 2 (CITED2) in human gastric cancer. Methods Gastric cancer cell lines and clinical tumor samples were used to assess FGD5-AS1 expression levels. Lentivirus containing FGD5-AS1 small interfering RNA (sh-FGD5AS1) was applied to knockdown FGD5-AS1 expression. Cancer cells in vitro and in vivo proliferation, and 5-FU chemoresistance were assessed, respectively. Expressions of hsa-miR-153-3p/CITED2 were also assessed in FGD5-AS1-downregulated gastric cancer cells. Hsa-miR-153-3p was knocked down and CITED2 was upregulated to assess their direct functional correlations with FGD5-AS1 in gastric cancer. Results Both gastric cancer cell lines and human tumor samples showed aberrant FGD5-AS1 upregulation. Lentiviral-induced FGD5-AS1 knockdown reduced cancer proliferation, 5-FU chemoresistance in vitro, and tumorigenicity in vivo. Hsa-miR-153-3p/CITED2 axis was confirmed to be downstream of FGD5-AS1 in gastric cancer. Hsa-miR-153-3p inhibition or CITED2 upregulation reversed the tumor-suppressing effects of FGD5-AS1 downregulation on gastric cancer proliferation and 5-FU chemoresistance. Conclusion We demonstrated that FGD5-AS1 can regulate human gastric cancer cell functions, possibly through its downstream epigenetic axis of hsa-miR-153-3p/CITED2.
Properties of Pain Assessment Tools for Use in People Living With Stroke: Systematic Review

Background: Pain is a common problem after stroke and is associated with poor outcomes. There is no consensus on the optimal method of pain assessment in stroke. A review of the properties of tools should allow an evidence based approach to assessment. Objectives: We aimed to systematically review published data on pain assessment tools used in stroke, with particular focus on classical test properties of: validity, reliability, feasibility, responsiveness. Methods: We searched multiple, cross-disciplinary databases for studies evaluating properties of pain assessment tools used in stroke. We assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies tool. We used a modified harvest plot to visually represent psychometric properties across tests. Results: The search yielded 12 relevant articles, describing 10 different tools (n = 1,106 participants). There was substantial heterogeneity and an overall high risk of bias. The most commonly assessed property was validity (eight studies) and responsiveness the least (one study). There were no studies with a neuropathic or headache focus. Included tools were either scales or questionnaires. The most commonly assessed tool was the Faces Pain Scale (FPS) (6 studies). The limited number of papers precluded meaningful meta-analysis at level of pain assessment tool or pain syndrome. Even where common data were available across papers, results were conflicting e.g., two papers described FPS as feasible and two described the scale as having feasibility issues. Conclusion: Robust data on the properties of pain assessment tools for stroke are limited. Our review highlights specific areas where evidence is lacking and could guide further research to identify the best tool(s) for assessing post-stroke pain. Improving feasibility of assessment in stroke survivors should be a future research target. Systematic Review Registration Number: PROSPERO CRD42019160679 Available online at: https://www.crd. york.ac. uk/prospero/display_record. php? ID=CRD42019160679.
Costs, Climate and Contamination: Three Drivers for Citywide Sanitation Investment Decisions

Significant progress is needed, in both large cities and small towns, to meet the ambitious targets set at international and national levels relating to universal access to safely managed sanitation. There has been increased recognition in the urban sanitation sector that in rapidly growing cities, there is unlikely to be a single centralized sanitation solution which can effectively deliver services to all demographics, and that heterogeneous approaches to urban sanitation are required. At the same time, due to competing investment priorities, there is a greater focus on the need for sanitation investments to address multiple objectives. However, calls for more informed sanitation planning and a more dynamic and disaggregated approach to the delivery and management of sanitation services have had limited impacts. This is in part due to the complexity of the drivers for sanitation investment, and the difficulties involved in identifying and addressing these multiple, often conflicting, goals. This paper examines three potential drivers of citywide sanitation decision-making – public health, sustainability and economic performance – via the three proxies of contamination, climate change and costs. It examines the importance of each driver and proxies, how they are considered in investment decisions, the current state of knowledge about them, and priority aspects to be included in decisions. At present, while public health is a common driver for improving sanitation, there are significant gaps in our understanding of fecal contamination spread and exposure, and how to select sanitation solutions which can best address them. Climate change is sometimes seen as a low priority for the sanitation sector given the immediacy and scale of existing challenges and the uncertainty of future climate predictions. However, potential risks are significant, and uninformed decisions may result in greater costs and increased inequalities. Cost data are sparse and unreliable, and it is challenging to build robust cost-effectiveness analyses. Yet these are needed to compare citywide options based on least-cost over their full life cycle. This paper provides insights into how existing evidence on contamination, climate change and costs can inform decisions on sanitation investments and help chart a sustainable way forward for achieving citywide services.
Oxytocin Facilitation of Emotional Empathy Is Associated With Increased Eye Gaze Toward the Faces of Individuals in Emotional Contexts

One of the most robust effects of intranasal oxytocin treatment is its enhancement of emotional empathy responses across cultures to individuals displaying emotions in realistic contexts in the Multifaceted Empathy Task (MET). However, it is not established if this effect of oxytocin on emotional empathy is due to altered visual attention toward different components of the stimulus pictures or an enhanced empathic response. In the current randomized placebo-controlled within-subject experiment on 40 healthy male individuals, we both attempted a further replication of emotional empathy enhancement by intranasal oxytocin (24 IU) and used eye-tracking measures to determine if this was associated by altered visual attention toward different components of the picture stimuli (background context, human face, and body posture). Results replicated previous findings of enhanced emotional empathy in response to both negative and positive stimuli and that this was associated with an increased proportion of time viewing the faces of humans in the pictures and a corresponding decrease in that toward the rest of the body and/or background context. Overall, our findings suggest that enhanced emotional empathy following oxytocin administration is due to increased attention to the faces of others displaying emotions and away from other contextual and social cues. Clinical Trial Registration: www.ClinicalTrials. gov Oxytocin Modulates Eye Gaze Behavior During Social Processing; registration ID: NCT03293511; URL: https://clinicaltrials.gov/ct2/show/NCT03293511.
Autism and Epilepsy in Patients With Tuberous Sclerosis Complex

Introduction: Individuals with Tuberous Sclerosis Complex (TSC) are at increased risk of developing both epilepsy and autism spectrum disorder (ASD), but the relationship between these conditions is little understood. We reviewed published reports to elucidate the relationship between ASD, epilepsy, and TSC, and to define the genetic and neurological risk factors. Methods: Articles (January 2004–May 2019) were identified via PubMed, EMBASE, and CENTRAL databases. Article inclusion required report on individuals with TSC-associated ASD and epilepsy with prevalence, odds ratio, or rate report on the comorbidity of ASD in epileptic patients due to TSC. Results: A total of 841 abstracts were identified in the original search. Thirty-six articles were included, which identified study populations, ASD measures used, and study confounders as bias factors. This review included 2,666 TSC patients, with a mean age of 15.9 years (range 1.94–30.3 years). The percentage of TSC patients with epilepsy and autism was 33.7%. Patients with TSC and autism showed more frequent seizures and earlier epilepsy onset than TSC patients without autism. ASD and intractable epilepsy were both predicted by a higher number of areas with dysplastic features revealed in brain MR scans. ASD, the onset of seizures in children <2 years of age, and >3 tubers have all been associated with an increased risk of refractory epilepsy in TSC patients. However, the direction of the relationship is not clear because a history of epilepsy, or infantile spasms in patients with TSC is also associated with an increased likelihood of ASD. Overall, 73.2% of patients carried TSC2 genetic variant and, among patients with TSC and autism, the percentage of TSC2 individuals was 85.6%. Conclusions: The complex interrelationship between TSC, autism, and epilepsy, coupled with limited knowledge on the neurobiological basis for the interrelationship, limits overall understanding and opportunities for management. The results of this review highlight the need for early identification and management to optimize favorable outcomes in the most vulnerable individuals with TSC. Regardless of whether studies are considered individually or collectively, interpretation is made difficult due to the differences between the studies, most notably between methods and diagnostic criteria used to assess intellectual ability.
Voiceprint Mimicry Attack Towards Speaker Verification System in Smart Home

The advancement of voice controllable systems (VC-Ses) has dramatically affected our daily lifestyle and catalyzed the smart home’s deployment. Currently, most VCSes exploit automatic speaker verification (ASV) to prevent various voice attacks (e.g., replay attack). In this study, we present VMask, a novel and practical voiceprint mimicry attack that could fool ASV in smart home and inject the malicious voice command disguised as a legitimate user. The key observation behind VMask is that the deep learning models utilized by ASV are vulnerable to the subtle perturbations in the voice input space. To generate these subtle perturbations, VMask leverages the idea of adversarial examples. Then by adding the subtle perturbations to the recordings from an arbitrary speaker, VMask can mislead the ASV into classifying the crafted speech samples, which mirror the former speaker for human, as the targeted victim. Moreover, psychoacoustic masking is employed to manipulate the adversarial perturbations under human perception threshold, thus making victim unaware of ongoing attacks. We validate the effectiveness of VMask by performing comprehensive experiments on both grey box (VGGVox) and black box (Microsoft Azure Speaker Verification) ASVs. Additionally, a real-world case study on Apple HomeKit proves the VMask’s practicability on smart home platforms.
Online Network Flow Optimization for Multi-Grade Service Chains

We study the problem of in-network execution of data analytic services using multi-grade VNF chains. The nodes host VNFs offering different and possibly time-varying gains for each stage of the chain, and our goal is to maximize the analytics performance while minimizing the data transfer and processing costs. The VNFs’ performance is revealed only after their execution, since it is data-dependent or controlled by third-parties, while the service requests and network costs might also vary with time. We devise an operation algorithm that learns, on the fly, the optimal routing policy and the composition and length of each chain. Our algorithm combines a lightweight sampling technique and a Lagrange-based primal-dual iteration, allowing it to be scalable and attain provable optimality guarantees. We demonstrate the performance of the proposed algorithm using a video analytics service, and explore how it is affected by different system parameters. Our model and optimization framework is readily extensible to different types of networks and services.
Rldish: Edge-Assisted QoE Optimization of HTTP Live Streaming with Reinforcement Learning

Recent years have seen a rapidly increasing traffic demand for HTTP-based high-quality live video streaming. The surging traffic demand, as well as the real-time property of live videos, make it challenging for content delivery networks (CDNs) to guarantee the Quality-of-Experiences (QoE) of viewers. The initial video segment (IVS) of live streaming plays an important role in the QoE of live viewers, particularly when users require fast join time and smooth view experience. State-of-the-art research on this regard estimates network throughput for each viewer and thus may incur a large overhead that offsets the benefit. To tackle the problem, we propose Rldish, a scheme deployed at the edge CDN server, to dynamically select a suitable IVS for new live viewers based on Reinforcement Learning (RL). Rldish is transparent to both the client and the streaming server. It collects the real-time QoE observations from the edge without any client-side assistance, then uses these QoE observations as real-time rewards in RL. We deploy Rldish as a virtualized network function (VNF) in a real HTTP cache server, and evaluate its performance using streaming servers distributed over the world. Our experiments show that Rldish improves the state- of-the-art IVS selection scheme w.r. t. the average QoE of live viewers by up to 22%.
A New Approach for Extracting and Characterizing Fetal Electrocardiogram

Received: 21 February 2020 Accepted: 15 May 2020 This paper presents a new approach for extracting and characterizing the fetal electrocardiogram from a mixture of maternal and fetal electrocardiograms, which is of very low amplitude and therefore its medical characterization would be very difficult and unreliable. This method is based on time-scale analysis by using Continuous Wavelet Transform and the Scalogram. Previous work in this field has only investigated on time and time-frequency methods using the Short Fourier Transform, which does not give convincing and accurate results for biomedical signals that require high precision because any part of the extracted signal may indicate a dangerous pathology. The effectiveness of this approach lies in the fact that the time-scale analysis or scalogram of fetal-maternal electrocardiogram mixture has several energetic zones corresponding either to the electrical activity of the heart of the fetus or of her mother, which it facilitates considerably the use of these diagrams in order to separate maternal and fetal electrocardiograms. Compared to other more recent, the results found by simulations are very interesting and the extracted signal corresponds approximately to the source. As a consequence, we can characterize and extract all useful medical parameters. More importantly, our approach can be implemented on real time life by using embedded system such as Raspberry and Digital Signal Processor.
Machine Learning Clustering Techniques for Selective Mitigation of Critical Design Features

Selective mitigation or selective hardening is an effective technique to obtain a good trade-off between the improvements in the overall reliability of a circuit and the hardware overhead induced by the hardening techniques. Selective mitigation relies on preferentially protecting circuit instances according to their susceptibility and criticality. However, ranking circuit parts in terms of vulnerability usually requires computationally intensive fault-injection simulation campaigns. This paper presents a new methodology which uses machine learning clustering techniques to group flip-flops with similar expected contributions to the overall functional failure rate, based on the analysis of a compact set of features combining attributes from static elements and dynamic elements. Fault simulation campaigns can then be executed on a per-group basis, significantly reducing the time and cost of the evaluation. The effectiveness of grouping similar sensitive flip-flops by machine learning clustering algorithms is evaluated on a practical example.Different clustering algorithms are applied and the results are compared to an ideal selective mitigation obtained by exhaustive fault-injection simulation.
Identification and Classification of Surface Cracks on Concrete Members Based on Image Processing

Received: 20 December 2019 Accepted: 3 April 2020 The crack identification and classification are essential to the discovery and maintenance of early defects in concrete members. This paper designs a novel method that supports accurate identification and classification of surface cracks on concrete members. Specifically, each sample image was preprocessed through cropping and neighborhood operation, subject to gray adaptive thresholding, and further processed based on morphological gradient and local gray value dissimilarity. Next, the Gauss-Laplace algorithm was employed to extract the contours of the surface cracks, and each image was matched with the normalized crack templates one by one. Finally, a densely connected neural network of transfer learning was introduced to rapidly classify the surface cracks on concrete members. The effectiveness and accuracy of our method were fully demonstrated through experiments. The research findings provide a reference for surface crack identification and classification in other fields.