Miracle-dz/UniFAR-FTUs huggingface dataset:
Paragraph Generation Network with Visual Relationship Detection

Paragraph generation of images is a new concept, aiming to produce multiple sentences to describe a given image. In this paper, we propose a paragraph generation network with introducing visual relationship detection. We first detect regions which may contain important visual objects and then predict their relationships. Paragraphs are produced based on object regions which have valid relationship with others. Compared with previous works which generate sentences based on region features, we explicitly explore and utilize visual relationships in order to improve final captions. The experimental results show that such strategy could improve paragraph generating performance from two aspects: more details about object relations are detected and more accurate sentences are obtained. Furthermore, our model is more robust to region detection fluctuation.
Inferring Personality Traits from User Liked Images via Weakly Supervised Dual Convolutional Network

With the prevalence of social media, users tend to use visual content to convey their preferences in on-line communication. This allows us to infer users' personality traits by their preferred images. Since users tend to have different preferences on image regions, modeling their traits from the holistic image may be problematic. In this paper, we propose an end-to-end weakly supervised dual convolutional network (WSDCN) for personality prediction, which consists of a classification network and a regression network. The classification network has the advantage of capturing class-specific localized image regions while only requiring the image-level class labels. In order to obtain the personality class-specific localized activation map (class activation map), the Big-Five (BF) traits are firstly converted into ten personality class labels for the classification network. Secondly, the Multi-Personality Class Activation Map (MPCAM) is utilized as the localized activation of deep feature maps to generate local deep features, which are then combined with the holistic deep features for the regression network. Finally, the user liked images and the associated personality labels are trained by the end-to-end WSDCN for predicting the BF personality traits. Experimental results on the annotated PsychoFlickr database show that the proposed method is superior to the state-of-the-art approaches, and the BF personality traits can be predicted simultaneously by the trained WSDCN model.
The Effect of Chinese Medicine on Lipid and Glucose Metabolism in Acute Myocardial Infarction Through PPARγ Pathway

Aim: Danqi Pill (DQP), a Chinese medicine frequently prescribed in China, has been approved to improve cardiac function by regulating cardiac energy metabolism in heart failure (HF) after acute myocardial infarction (AMI) patients. The aim of this study was to explore whether the mechanism of DQP is associated to the lipid and glucose metabolism mediated via PPARγ (peroxisome proliferator-activated receptor gamma) pathway both in vivo and in vitro. Materials and Methods: Model of HF after AMI was established with ligation of left anterior descending artery on Sprague-Dawley (SD) rats. Twenty-eight days after treatment, hematoxylin–eosin (HE) staining was applied to visualize cardiomyocyte morphological changes. High performance liquid chromatography (HPLC) was performed to assess the contents of adenosine phosphates in heart. Positron emission tomography and computed tomography (PET-CT) was conducted to evaluate the cardiac glucose metabolism. Expressions of key molecules such as PPARγ, sterol carrier protein 2 (SCP2) and long chain acyl CoA dehydrogenase (ACADL) were measured by Western blotting (WB) and immunohistochemistry (IHC). Oxygen-glucose deprivation-reperfusion (OGD/R)-induced H9C2 injury cardiomyocyte model was adopted for potential mechanism research in vitro. Results: Treatment with DQP rescued hearts from structural and functional damages as well as inflammatory infiltration. Levels of adenosine triphosphate (ATP) and energy charge (EC) in DQP group were also up-regulated compared to model group. Further results demonstrated that critical enzymes both in lipid metabolism and glucose metabolism compromised in model group compared to sham group. Intriguingly, DQP could up-regulate critical enzymes including ACADL and SCP2 in lipid metabolism accompanying with promoting effect on molecules in glycolysis simultaneously. Results on upstreaming signaling pathway demonstrated that DQP could dramatically increase the expressions of PPARγ. In vitro study suggested the efficacy of DQP could be blocked by T0070907, a selective PPARγ inhibitor. Conclusion: DQP has cardioprotective effect in improving cardiac function and energy metabolism through regulating lipid and glucose metabolism. The effects may be mediated by PPARγ pathway.
In vitro antioxidant activities and inhibitory effects of phenolic extract of Senecio biafrae (Oliv and Hiern) against key enzymes linked with type II diabetes mellitus and Alzheimer's disease

Abstract The phenolic extract of Senecio biafrae leaves was investigated to determine the in vitro antioxidant, phenolic profiles, and inhibition of key enzymes relevant to type II diabetes mellitus (α‐amylase and α‐glucosidase) and Alzheimer's disease (acetylcholinesterase and butrylcholinesterase). The phenolic extract demonstrated significant scavenging abilities against all in vitro antioxidant parameters assessed. Reversed‐phase HPLC of the extract revealed the presence of gallic acid, chlorogenic, caffeic acid, rutin, quercetin, and kaempferol. The extract also inhibited activities of α‐amylase (IC 50 = 126.90 μg/ml), α‐glucosidase (IC 50 = 139.66 μg/ml), acetylcholinesterase (IC 50 = 347.22 μg/ml), and butrylcholinesterase (IC 50 = 378.79 μg/ml), which may be attributed to the antioxidant potential of the extract and its phenolic composition. Therefore, this study suggests that the leaves of S. biafrae may be useful in the management of diabetes mellitus and Alzheimer's disease.
Unprecedented Usage of Pre-trained CNNs on Beauty Product

How does a pre-trained Convolution Neural Network (CNN) model perform on beauty and personal care items (i.e Perfect-500K) This is the question we attempt to answer in this paper by adopting several well known deep learning models pre-trained on ImageNet, and evaluate their performance using different distance metrics. In the Perfect Corp Challenge, we manage to secure fourth position by using only the pre-trained model.
Suspicious index in Lyme carditis: Systematic review and proposed new risk score

Lyme carditis (LC), an early manifestation of Lyme disease that most commonly presents as high‐degree atrioventricular block (AVB), usually resolves with antibiotic treatment. When LC is not identified as the cause of AVB, a permanent pacemaker may be inappropriately implanted in a reversible cardiac conduction disorder.
White Lane Detection Using Semantic Segmentation

This paper deals with the application of machine learning techniques to the detection of white lanes for autonomous driving assistance using only a single visual camera. When performing white line detection, a method called semantic segmentation using fully convolutional network is used. This method is chosen to flexibly detect the shape of objects, since detection of white lanes cannot be done well with rectangular detection. The convolutional neural network which is characterized by the absence of fully connected layer outputs an image for a given input. FCN-8s are used for fully convolutional network. FCN-8s has an easy-to-understand structure and an advantage of being easy to create. In addition, we also created a dataset manually by extracting white lines from public roads and used it for training and testing the learning algorithm. Our segmentation algorithm is found to accurately detect the white lane markings from the dataset.
Development of a method to maximize the transcutaneous electrical nerve stimulation intensity in women with fibromyalgia

Introduction Transcutaneous electrical nerve stimulation (TENS) is a non-pharmacological intervention clinically used for pain relief. The importance of utilizing the adequate stimulation intensity is well documented; however, clinical methods to achieve the highest possible intensity are not established. Objectives Our primary aim was to determine if exposure to the full range of clinical levels of stimulation, from sensory threshold to noxious, would result in higher final stimulation intensities. A secondary aim explored the association of pain, disease severity, and psychological variables with the ability to achieve higher final stimulation intensity. Methods Women with fibromyalgia (N=143) were recruited for a dual-site randomized controlled trial – Fibromyalgia Activity Study with TENS (FAST). TENS electrodes and stimulation were applied to the lumbar area, and intensity was increased to sensory threshold (ST), then to “strong but comfortable” (SC1), then to “noxious” (N). This was followed by a reduction to the final stimulation intensity of “strong but comfortable” (SC2). We called this the Setting of Intensity of TENS (SIT) test. Results There was a significant increase from SC1 (37.5 mA IQR: 35.6–39.0) to SC2 (39.2 mA IQR: 37.1–45.3) (p<0.0001) with a mean increase of 1.7 mA (95% CI: 1.5, 2.2). Linear regression analysis showed that those with the largest increase between SC1 and N had the largest increase in SC2–SC1. Further, those with older age and higher anxiety were able to achieve greater increases in intensity (SC2–SC1) using the SIT test. Conclusion The SC2–SC1 increase was significantly associated with age and anxiety, with greater mean increases associated with older age and higher anxiety. Thus, although all patients may benefit from this protocol, older women and women with elevated anxiety receive the greatest benefit.
Extractive Video Summarizer with Memory Augmented Neural Networks

Online videos have been growing explosively in recent years. How to help human users efficiently browse videos becomes more and more important. Video summarization can automatically shorten a video through extracting key-shots from the raw video, which is helpful for digesting video data. State-of-the-art supervised video summarization algorithms directly learn from manually-created summaries to mimic the key-frame/key-shot selection criterion of humans. Humans usually create a summary after viewing and understanding the whole video, and the global attention mechanism capturing information from all video frames plays a key role in the summarization process. However, previous supervised approaches ignored the temporal relations or simply modeled local inter-dependency across frames. Motivated by this observation, we proposed a memory augmented extractive video summarizer, which utilizes an external memory to record visual information of the whole video with high capacity. With the external memory, the video summarizer simply predicts the importance score of a video shot based on the global understanding of the video frames. The proposed method outperforms previous state-of-the-art algorithms on the public SumMe and TVSum datasets. More importantly, we demonstrate that the global attention modeling has two advantages: good transferring ability across datasets and high robustness to noisy videos.
Translational Research Opportunities Regarding Homologous Recombination in Ovarian Cancer

Homologous recombination (HR) is a DNA repair pathway that is deficient in 50% of high-grade serous ovarian carcinomas (HGSOC). Deficient HR (DHR) constitutes a therapeutic opportunity for these patients, thanks to poly (ADP-ribose) polymerases (PARP) inhibitors (PARPi; olaparib, niraparib, and rucaparib are already commercialized). Although initially, PARPi were developed for patients with BRCA1/2 mutations, robust clinical data have shown their benefit in a broader population without DHR. This breakthrough in daily practice has raised several questions that necessitate further research: How can populations that will most benefit from PARPi be selected? At which stage of ovarian cancer should PARPi be used? Which strategies are reasonable to overcome PARPi resistance? In this paper, we present a summary of the literature and discuss the present clinical research involving PARPi (after reviewing ClinicalTrials.gov) from a translational perspective. Research into the functional biomarkers of DHR and clinical trials testing PARPi benefits as first-line setting or rechallenge are currently ongoing. Additionally, in the clinical setting, only secondary restoring mutations of BRCA1/2 have been identified as events inducing resistance to PARPi. The clinical frequency of this and other mechanisms that have been described in preclinics is unknown. It is of great importance to study mechanisms of resistance to PARPi to guide the clinical development of drug combinations.
Big Data Management and Analytics for Disability Datasets

The disability datasets is the datasets which contains the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can have a better understanding of how to make working plans and policies, so that they support the populations in a better way. In this paper, we proposed a big data management and mining approach for disability datasets. The contributions of this paper are follows: 1) our proposed approach can improve the quality of disability data by estimating miss attribute values and detecting anomaly and low-quality data instances. 2) Our proposed approach can explore useful patterns which reflect the correlation, association and interactional between the disability data attributes. Experiments are conducted at the end to evaluate the performance of our approach.
StripNet: Towards Topology Consistent Strip Structure Segmentation

In this work, we propose to study a special semantic segmentation problem where the targets are long and continuous strip patterns. Strip patterns widely exist in medical images and natural photos, such as retinal layers in OCT images and lanes on the roads, and segmentation of them has practical significance. Traditional pixel-level segmentation methods largely ignore the structure prior of strip patterns and thus easily suffer from the topological inconformity problem, such as holes and isolated islands in segmentation results. To tackle this problem, we design a novel deep framework, StripNet, that leverages the strong end-to-end learning ability of CNNs to predict the structured outputs as a sequence of boundary locations of the target strips. Specifically, StripNet decomposes the original segmentation problem into more easily solved local boundary-regression problems, and takes account of the topological constraints on the predicted boundaries. Moreover, our framework adopts a coarse-to-fine strategy and uses carefully designed heatmaps for training the boundary localization network. We examine StripNet on two challenging strip pattern segmentation tasks, retinal layer segmentation and lane detection. Extensive experiments demonstrate that StripNet achieves excellent results and outperforms state-of-the-art methods in both tasks.
Preoperative screening for bleeding disorders: A comprehensive laboratory assessment of clinical practice

Essentials Patients with bleeding disorders are at risk of operative bleeding, but screening for these disorders is challenging. Patients with and without bleeding symptoms on a guideline‐based screening questionnaire were included and hemostatically phenotyped. The questionnaire could not differentiate between patients with and without hemostatic abnormalities. The discriminative power of the PT, aPTT, TT, Euglobulin lysis time, PFA, and the ISTH‐BAT was also limited.
Cyber-Attack Classification in Smart Grid via Deep Neural Network

Smart grid1 is a modern power transmission network. With its development, the computing, communication and physical processes is getting more and more connected. However, an adversary can destroy power production by attacking the power secondary equipment. Accurate and fast response to cyber-attacks is a prerequisite for stable grid operation. Therefore, it is critical to identify and classify attacks in the smart grid. In this paper, we propose a novel approach that utilizes machine learning algorithms to help classify cyber-attacks. We built a deep neural network (DNN) model and select the global optimal parameters to achieve high generalization performance. The evaluation result demonstrates that the proposed method can effectively identify cyber-attacks in smart grid with an accuracy as high as 96%.
Whole-body vibration training – better care for COPD patients: a systematic review and meta-analysis

Purpose Whole-body vibrating training (WBVT) is a modality aiming to improve neuromuscular performance of patients with COPD. However, a consensus on the effects of WBVT has not been reached. We aimed to clarify the effects of WBVT on functional exercise capacity, pulmonary function, and quality of life in COPD patients. Patients and methods PubMed, Web of Science, and EMBASE were searched through April 5, 2018. We calculated the pooled weight mean difference (WMD) using a random-effects model. Quality assessment and publication bias analyses were also performed. Results We included eight randomized control trials involving 365 patients. Compared with control group, WBVT increased 6-minute walking distance (6-MWD) (WMD: 62.14 m; 95% CI: 48.12–76.16; P<0.001), the change of 6-MWD (Δ6-MWD) (WMD: 42.33 m; 95% CI: 15.21–69.45; P=0.002), the change of the time to finish five repeated sit-to-stand tests (WMD: −2.07 seconds; 95% CI: −4.00 to −0.05; P=0.04), and decreased the change of St George’s Respiratory Questionnaire score (WMD: −6.65 points; 95% CI: −10.52 to −2.78; P<0.001). However, no significant difference was found between the two groups regarding forced expired volume in 1 second (FEV1) (% predicated), change of FEV1 (% predicated), sit-to-stand test, 6-MWD (% predicated), change of 6-MWD (% predicated), St George’s Respiratory Questionnaire score, COPD Assessment Test score, and change of COPD Assessment Test score. Conclusion WBVT has beneficial effects on functional exercise capacity for COPD patients.
Growing Progress in the Evolving Science, Business, and Policy of Sustainable Nutrition

ABSTRACT A session was convened at the ASN Nutrition 2018 annual meeting to discuss the scientific evidence on what makes individual foods and dietary patterns both sustainable and nutritious, and the role of various stakeholders in the actions needed to implement food systems that deliver “sustainable nutrition. ” This commentary is a structured synthesis of the primary themes of the session, and concludes with a set of implications and research recommendations. Consumers are becoming increasingly aware of the environmental implications of what they eat, and there is growing momentum toward changes in the food system. However, ecological challenges persist, and although the literature is evolving, methodologic improvements are needed in the scientific approaches to address dietary patterns that meet nutrition needs in more holistically sustainable ways. The session concluded with encouraging evidence that consumers, businesses, scientists, and policy-makers are collectively “rising to the occasion,” with cross-sectoral partnerships to address these issues.
A Novel Wide-Baseline Stereo Matching Algorithm Combining MSER and DAISY

Image1 matching is a fundamental problem in Computer Vision. In the context of feature-based matching, SIFT (Scale-Invariant Feature Transform) and its variants have long excelled in a wide array of applications. However, for wide-baseline images, it is more challenging due to large perspective distortions. In this paper, we introduce a novel affine invariant feature algorithm combining MSER (Maximum Stable Extreme Region) and DAISY descriptor for wide-baseline scenarios. Firstly, MSER algorithm is used to detect maximum stable extreme regions. Then the MSER region is fit to an elliptical shape. The ellipse area is normalized of direction and shape using the dominant orientation and the length of long and short axes of ellipse, respectively. And square feature region is resampled to a specified radius size. Finally, the DAISY descriptor is used to establish descriptor. The algorithm is validated by using benchmark images and real-world images. We demonstrate that our method has better reliability and is superior to SIFT and MSER+SIFT algorithm in the case of wide-baseline.
Role of βarrestin1 in AT1R‐mediated mitogen‐activated protein kinase activation in Wistar and SHR brainstem astrocytes

βarrestin (βarr)‐1 and ‐2 are ubiquitously (outside the retina) expressed G‐protein‐coupled receptor adapter proteins. They uncouple G‐protein‐coupled receptors from G proteins, internalize the receptor, and subsequently initiate their own wave of signaling independently of G proteins. Angiotensin (Ang) II type 1 receptor (AT1R) is a well‐established example of a receptor signaling through βarrs. Despite the pivotal role of brain AT1Rs in the regulation of blood pressure, the involvement of βarr‐dependent signaling, mediated by AT1Rs is not well studied. Particularly, in brain astrocytes very little is known about the effects of βarrs in AT1R signaling. Herein, we utilized a combination of pharmacological and gene manipulation approaches to investigate the role of βarrs in AT1R‐mediated signaling in isolated brainstem astrocytes from spontaneously hypertensive rats (SHRs) and Wistar rats. We observed that βarr1 is the predominant arrestin isoform at the protein level in these cells. Its expression was down‐regulated in SHR astrocytes compared to Wistar rat astrocytes. Ang II, contrary to observations in SHR astrocytes where it had no effect, up‐regulates βarr1 protein in Wistar rat astrocytes. We observed differential involvement of βarr1 in MAPK activation in brainstem astrocytes of SHR versus Wistar rats. The βarr‐biased agonist peptide [Sar1, Il4, Il8] Ang‐II (SII), induced AT1R‐mediated ERK and p38 activation in Wistar rat astrocytes. SII had no effect on ERK and p38 activation in SHRs brainstem astrocytes. Our results indicate, reduced involvement of βarr1 in dampening Ang II‐induced MAPKs activation and diminished βarr1‐mediated ERK and p38 activation in SHR brainstem astrocytes. These findings might be mechanistically related to the development of the brain renin–angiotensin–aldosterone system hyperactivity, which leads to pathogenesis of the hypertensive state of the SHR model.
CLS: A Cross-user Learning based System for Improving QoE in 360-degree Video Adaptive Streaming

Viewport adaptive streaming is emerging as a promising way to deliver high quality 360-degree video. It is still a critical issue to predict user's viewpoint and deliver partial video within the viewport. Current widely-used motion-based or content-saliency methods have low precision, especially for long-term prediction. In this paper, benefiting from data-driven learning, we propose a Cross-user Learning based System (CLS) to improve the precision of viewport prediction. Since users have similar region-of-interest (ROI) when watching a same video, it is possible to exploit cross-users' ROI behavior to predict viewport. We use a machine learning algorithm to group users according to historical fixations, and predict the viewing probability by the class. Additionally, we present a QoE-driven rate allocation to minimize the expected streaming distortion under bandwidth constraint, and give a Multiple-Choice Knapsack solution. Experiments demonstrate that CLS provides 2dB quality improvement than full-image streaming and 1.5 dB quality improvement than linear regression (LR) method. On average, the precision of viewpoint prediction improve 15% compared with LR.