Miracle-dz/UniFAR-FTUs huggingface dataset:
The contribution of brain imaging to the understanding of psychopathy

Abstract Psychopathy is a personality type characterized by both callous emotional dysfunction and deviant behavior that affects society in the form of actions that harm others. Historically, researchers have been concerned with seeking data and arguments to support a neurobiological foundation of psychopathy. In the past few years, increasing research has begun to reveal brain alterations putatively underlying the enigmatic psychopathic personality. In this review, we describe the brain anatomical and functional features that characterize psychopathy from a synthesis of available neuroimaging research and discuss how such brain anomalies may account for psychopathic behavior. The results are consistent in showing anatomical alterations involving primarily a ventral system connecting the anterior temporal lobe to anterior and ventral frontal areas, and a dorsal system connecting the medial frontal lobe to the posterior cingulate cortex/precuneus complex and, in turn, to medial structures of the temporal lobe. Functional imaging data indicate that relevant emotional flow breakdown may occur in both these brain systems and suggest specific mechanisms via which emotion is anomalously integrated into cognition in psychopathic individuals during moral challenge. Directions for future research are delineated emphasizing, for instance, the relevance of further establishing the contribution of early life stress to a learned blockage of emotional self-exposure, and the potential role of androgenic hormones in the development of cortical anomalies.
[Chemical Constituents from Infructescence of Platycarya strobilacea].

Objective To study the chemical constituents of the chloroform and acetic ether fractions from infructescence of Platycarya strobilacea. Methods The compounds were isolated and purified by various chromatographic methods. Spectral analysis were taken to identify the structures. Results Eleven compounds were isolated and identified as dotriacontane( 1), ursolic acid( 2),hexacosene( 3),β-sitosterol( 4),daucosterol( 5),2,5,8-trihydroxy-3-methoxy-1,4-naphthalenedione( 6),3,3’-dimethoxyellagic acid( 7),ellagic acid( 8),4’-hydroxyisoflavone-7-O-β-D-galactoside( 9),3,3’-dimethoxyellagic acid-4’-O-β-D-xylopyroside( 10), gallic acid( 11). Conclusion Compounds 1,3 ~ 6,9,10 are isolated from this genus for the first time, compounds 1 ~ 6,9,10 are isolated from this plant for the first time.
A Study on the 4D Sparsity of JPEG Pleno Light Fields Using the Discrete Cosine Transform

In this work we study the 4D sparsity of light fields using as main tool the 4D- Discrete Cosine Transform. We analyze the two JPEG Pleno light field datasets, namely the lenslet-based and the High-Densi ty Camera Array (HDCA) datasets. The results suggest that the lenslets datasets exhibit a high 4D redundancy, with a larger inter-view sparsity than the intra-view one. For the HDCA datasets, there is also 4D redundancy worthy to be exploited, yet in a smaller degree. Unlike the lenslets case, the intra-view redundancy is much larger than the inter-view one. The results and conclusions of this first study for light field imaging may have a strong impact on the current and future design of efficient codecs for this type of emerging data, notably in the context of the JPEG Pleno standard.
Gamma Mixture Models for Outlier Removal

In this paper, we introduce a probabilistic outlier model which is seamlessly integrated into machine learning frameworks (e.g., boosting and deep neural network) to accurately identify outliers in training samples. With two Gamma mixtures, the proposed model can estimate the distribution of inlier and outlier samples respectively and generates their posterior probabilities. The iterative removal method gradually reduces the outliers and boosts the performance of classifier by correcting the bias and variance caused by outliers. Experimental results on INRIA pedestrian dataset and MNIST handwritten-digit dataset demonstrate that the proposed outlier removal method achieves superior detection performance.
The Effect of Educational Program Based on Belief, Attitude, Subjective Norm, and Enabling Factors Model on Changing the Metabolic Indices in Elderly Patients with Type II Diabetes

Background: Diabetes is the most common disease related to metabolism disorders. Today, diabetes in the elderly is one of the major epidemics of the century. This study aimed to investigate the effect of nutrition and jogging-based Belief, Attitude, Subjective Norm, and Enabling Factors (BASNEF) model in changes in metabolic parameters that performed in elderly patients with type II diabetes in Fasa city. Methods: This study was a randomized clinical trial on 108 elderly patients with type II diabetes (54 patients in the experimental group and 54 in control group) referred to the diabetes center in Fasa City, Fars Province, Iran, in 2016. Data were collected using a valid self-reported questionnaire including demographic variables and BASNEF Model (knowledge, attitudes, subjective norms, and enabling factors), checklist for patient's practices nutrition and jogging based on self-reporting patients, assess the level of fasting blood sugar (FBS), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), and HbAlc patients which was completed by both groups before and 3 months after the intervention. The experimental group received the training in eight sessions; each session lasted for 70 min. Then, the data were entered into the SPSS 22 software (SPSS Inc., IBM, Chicago, IL, USA) and were analyzed using descriptive statistics, Chi-square test, independent t-test, and paired t-test. P <0.05 was considered as statistically significant. Results: The results showed that the mean scores of the BASNEF components (knowledge, attitudes, subjective norms, and enabling factors) in two experimental and control groups after intervention were a significant difference. After training program, nutritional and jogging performance in the experimental group was better than the control group. Furthermore, FBS (P < 0.001), LDL (P < 0.001), HDL (P < 0.001), TG (P < 0.001), and HbAlc (P < 0.001) improved significantly in the experimental group compared to the control group. Conclusions: Plan and implement nutrition and jogging education using the BASNEF model to improve metabolic parameters in diabetic patients are very effective and beneficial. Moreover, in the implementation of these programs, control, monitoring, and follow-up educational are recommended.
Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples

Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey. As a promising solution, sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint database. However, crowdsourced samples may be annotated with erroneous locations, which raises a serious question about whether they are reliable for database construction. In this paper, we propose a cross-domain cluster intersection algorithm to weight each sample reliability. We then select those samples with higher weight to construct radio propagation surfaces by fitting polynomial functions. Furthermore, we employ an entropy-like measure to weight constructed surfaces for quantifying their different subarea consistencies and location discriminations in online positioning. Field measurements and experiments show that the proposed scheme can achieve high localization accuracy by well dealing with the sample annotation error and nonuniform density challenges.
Clozapine attenuates mitochondrial burdens and abnormal behaviors elicited by phencyclidine in mice via inhibition of p47 phox ; Possible involvements of phosphoinositide 3-kinase/Akt signaling

Background: Oxidative stress and mitochondrial dysfunction have been implicated in the pathophysiology of schizophrenia. Aims: We investigated whether antipsychotic clozapine modulates nicotinamide adenine dinucleotide phosphate oxidase and mitochondrial burdens induced by phencyclidine in mice. Methods: We examined the effect of clozapine on nicotinamide adenine dinucleotide phosphate oxidase activation, mitochondrial burdens (i.e. oxidative stress and mitochondrial dysfunction), and activities of enzymatic antioxidant in the prefrontal cortex, and subsequent abnormal behaviors induced by repeated treatment with phencyclidine. p47 phox Knockout mice and LY294002, a phosphoinositide 3-kinase inhibitor, were employed to elucidate the pharmacological mechanism of clozapine. Results: Phencyclidine treatment resulted in an early increase nicotinamide adenine dinucleotide phosphate oxidase activity, membrane translocation of p47 phox , interaction between p-Akt and p47 phox , and mitochondrial burdens in wild-type mice. Although these increases returned to near control level four days post-phencyclidine, mitochondrial superoxide dismutase and glutathione peroxidase activities were decreased at that time. Clozapine, LY294002, or p47 phox knockout significantly ameliorated social withdrawal and recognition memory deficits produced by phencyclidine. Importantly, LY294002 did not significantly alter the effects of clozapine against abnormal behaviors and the interaction between p-Akt and p47 phox induced by phencyclidine. Furthermore, neither LY294002 nor clozapine exhibited any additive effects to the protection afforded by p47 phox knockout against phencyclidine insult. Conclusion: Our results suggest that p47 phox gene mediates phencyclidine-induced mitochondrial burdens and abnormal behaviors, and that the interactive modulation between p47 phox and phosphoinositide 3-kinase/Akt is important for the understanding on the pharmacological mechanism of clozapine.
Slot Filling with Delexicalized Sentence Generation

We introduce a novel approach that jointly learns slot ﬁlling and delexicalized sentence generation. There have been recent attempts to tackle slot ﬁlling as a type of sequence labeling problem, with encoder-decoder attention framework. We further improve the framework by training the model to generate delexicalized sentences, in which words according to slot values are replaced with slot labels. Slot ﬁlling with delexicalization shows better results compared to models having a single learning objective of ﬁlling slots. The proposed method achieves state-of-the-art slot ﬁlling performance on ATIS dataset. We experiment different variants of our model and ﬁnd that delexi-calization encourages generalization by sharing weights among the words with same labels and helps the model to further leverage certain linguistic features.
Intra-Retinal Layer Segmentation of Optical Coherence Tomography Using 3D Fully Convolutional Networks

Optical coherence tomography (OCT) is a powerful method for imaging the retinal layers. In this paper, we develop a novel 3D fully convolutional deep architecture for automated segmentation of retinal layers in OCT scans. This model extracts features from both the spatial and the inter-frame dimensions by performing 3D convolutions, thereby capturing the information encoded in multiple adjacent frames. The proposed network is based on an encoder-decoder framework in which the convolution layers are interlaced with pooling layers in the encoder and with unpooling layers in the decoder, respectively. Consequently, a hierarchy of shrinking 3D feature maps are learned in the encoder and enlarged to the size of original input image for semantic segmentation in the decoder. The framework is validated on thirteen 3D OCTs captured by the Topcon 3D OCT with comparisons against two state-of-the-art segmentation methods including one recent 2D deep learning based approach to substantiate its effectiveness.
On the Application and Compression of Deep Time Delay Neural Network for Embedded Statistical Parametric Speech Synthesis

Acoustic models based on long short-term memory (LSTM) recurrent neural networks (RNNs) were applied to statistical parametric speech synthesis (SPSS) and shown significant improvements. However, the model complexity and inference time cost of RNNs are much higher than feed-forward neural networks (FNN) due to the sequential nature of the learning algorithm, thus limiting its usage in many runtime applications. In this paper, we explore a novel application of deep time delay neural network (TDNN) for embedded SPSS, which requires low disk footprint, memory and latency. The TDNN could model long short-term temporal dependencies with inference cost comparable to standard FNN. Temporal subsampling enabled by TDNN could reduce computational complexity. Then we compress deep TDNN using singular value decomposition (SVD) to further reduce model complexity, which are motivated by the goal of building embedded SPSS systems which can be run efficiently on mobile devices. Both objective and subjective experimental results show that, the proposed deep TDNN with SVD compression could generate synthesized speech with better speech quality than FNN and comparable speech quality to LSTM, while drastically reduce model complexity and speech parameter generation time.
Removal of Microcystin-LR by a Novel Native Effective Bacterial Community Designated as YFMCD4 Isolated from Lake Taihu

Microcystin-LR (MC-LR) is the most toxic and frequently detected monocyclic heptapeptide hepatotoxin produced by cyanobacteria, which poses a great threat to the natural ecosystem and public health. It is very important to seek environment-friendly and cost-efficient methods to remove MC-LR in water. In this study, the MC-degrading capacities of a novel indigenous bacterial community designated as YFMCD4 and the influence of environmental factors including various temperatures, MC concentrations and pH on the MC-degrading activities were investigated utilizing high-performance liquid chromatography (HPLC). In addition, the MC-degrading mechanism of YFMCD4 was also studied using HPLC coupled with a mass spectrometry equipped with electrospray ionization interface (HPLC-ESI-MS). The data showed MC-LR was completely removed at the maximum rate of 0.5 µg/(mL·h) under the optimal condition by YFMCD4. Two pure bacterial strains Alcaligenes faecalis and Stenotrophomonas acidaminiohila were isolated from YFMCD4 degraded MC-LR at a slower rate. The MC-degrading rates of YFMCD4 were significantly affected by different temperatures, pH and MC-LR concentrations. Two intermediates of a tetrapeptide and Adda appeared in the degradation process. These results illustrate that the novel YFMCD4 is one of the highest effective MC-degrading bacterial community, which can completely remove MC-LR and possesses a significant potential to treat water bodies contaminated by MC-LR.
Multi-Attribute Driven Vehicle Re-Identification with Spatial-Temporal Re-Ranking

Vehicle re-identification (re-id) is a promising topic, which focuses on retrieving the same vehicles across different cameras. It is challenging due to the variations of illumination and camera viewpoints. To solve these problems, we present a multi -attribute driven vehicle re-id approach to learn discriminative representations. The proposed approach consists of a multi-branch architecture and a re-ranking strategy. The multi-branch architecture extracts color, model, and appearance features, which explicitly leverages the vehicle attribute cues to enhance the generalization ability, especially for the different vehicles with similar appearance and the same vehicles with different orientations. The re-ranking strategy introduces the spatial-temporal relationship among vehicles from multiple cameras to construct the similar appearance sets and utilizes Jaccard distance between these similar appearance sets to re-rank. Extensive experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art re-id methods on the popular VeRi-776 dataset and VehiclelD dataset.
Three Dimensional Energy Parametrized Generative Adversarial Networks for Electromagnetic Shower Simulation

High Energy Physics (HEP) simulations are traditionally based on the Monte Carlo approach and generally rely on time consuming calculations. The present work investigates the use of Generative Adversarial Networks (GANs) as a fast alternative. Our approach treats the energy deposited by a particle inside a calorimeter detector as a three-dimensional image. True three-dimensional convolutions can be employed to capture the spatio-temporal correlation of shower energy depositions. Three-dimensional images are generated, conditioned on the energy of the incoming particle and validated against Monte Carlo simulation. The results show an agreement to full Mote Carlo simulations well within 10% thus proving that GAN can be used as a fast alternative for simulation of HEP detector response.
Effect of low-level laser therapy on wound healing and patients' response after scalpel gingivectomy: A randomized clinical split-mouth study

Background: Low-level laser therapy (LLLT) is based on the principle of biostimulation or biomodulation effect. LLLT after gingivectomy has resulted in better wound healing because of its action on collagen synthesis, angiogenesis, and growth factor release. Aim: The aim of this split-mouth controlled clinical trial was to assess the effect of LLLT, using diode laser (InGaAsP), on wound healing and patients' response after scalpel gingivectomy. Materials and Methods: Forty patients with gingival enlargement in the maxillary and mandibular anterior region (bilaterally symmetrical) were included in the study. After gingivectomy, a diode laser (InGaAsP) was randomly applied to one side of the surgical area on the 1st, 3rd, and 7th day postoperatively. The surgical areas were disclosed by a solution (Alpha Plac®) to visualize the areas where the epithelium was absent. Comparison of the surface areas on the LLLT-applied sites and controls was made clinically by visualizing the stained area by two examiners. Results: LLLT-applied sites had significantly lower stained areas signifying improved wound healing compared with the controls on the postoperative 7th and 30th day. Conclusion: Within the limitations of this study, the results indicated that LLLT might improve wound healing after gingivectomy.
Deep Learning for Vehicle Detection in Aerial Images

The detection of vehicles in aerial images is widely applied in many domains. In this paper, we propose a novel double focal loss convolutional neural network framework (DFL-CNN). In the proposed framework, the skip connection is used in the CNN structure to enhance the feature learning. Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposed network and the final classifier. We further introduce the first large-scale vehicle detection dataset ITCVD with ground truth annotations for all the vehicles in the scene. The experimental results show that our DFL-CNN outperforms the baselines on vehicle detection.
Objectness-Aware Tracking via Double-Layer Model

The prediction drifts to the non-object backgrounds is a critical issue in conversional correlation filter (CF) based trackers. The key insight of this paper is to propose a double-layer model to address this problem. Specifically, the first layer is a CF tracker, which is employed to predict a rough position of the target, and the objectness layer, which is regarded as the second layer, is utilized to reveal the object characteristics of the predicted target. The novel objectness layer firstly constructs a set of target-related object proposals, which satisfy both the spatial and temporal constraints. And then an objectness classifier is learned upon the proposal set to best separate the target from the noise background proposals. The convincing experimental results on the challenging OTB100 and TC128 dataset demonstrate the effectiveness of the presented approach.
Presleep Casein Protein Ingestion: Acceleration of Functional Recovery in Professional Soccer Players.

PURPOSE To examine whether consuming casein protein (CP) before sleep would enhance recovery after a nighttime soccer match in professional players. METHODS In a randomized, crossover design, 10 professional soccer players from the reserve squad of a team in the highest tier of English soccer consumed 40 g of CP or 40 g of carbohydrates (CON) 30 min presleep after a soccer match (kick off: 7 PM). To assess recovery, countermovement-jump height, reactive strength index, muscle soreness, and the adapted Brief Assessment of Mood (BAM+) Questionnaire were measured before and 12, 36, and 60 h after each match. Dietary intake across the testing period was also recorded. RESULTS There were unclear differences in external load in the matches and dietary intake between CON and CP. Casein protein had a most likely and likely beneficial effect on countermovement-jump recovery at 12 and 36 h postmatch (CP -1.6; ±1.2% vs CON -6.6; ±1.7%; -4.1; ±2.3% vs -0.4; ±1.1%, respectively). Reactive strength index recovery was most likely enhanced with CP at 12 and 36 h postmatch, and muscle soreness, as measured with a visual analog scale (in millimeters), was most likely greater in CON versus CP at 12 h postmatch (72; ±17 vs 42; ±20 mm). BAM+ was possibly lower in CON at 36 h postmatch but unaffected at other time points. CONCLUSIONS Presleep CP accelerates functional recovery in professional soccer players and, therefore, provides a practical means of attenuating performance deficits in the days after a match.
Multi-View Fusion Through Cross-Modal Retrieval

Cross-modal retrieval, which takes text queries to retrieve relevant images or vice versa, has drawn much attention in recent years. This topic exhibits dual-heterogeneity: heterogeneity of different modalities and heterogeneous features obtained from multiple views. To address this issue, we propose an effective multi-view fusion method for cross-modal retrieval based on tensor modeling (CMTM) for cross-modal retrieval from the full-order feature interactions within the multimodal data. In order to facilitate integration of heterogeneous features from multiple views, we adopt the tensor structure to model the full-order interactions among the multi-view features effectively. Besides, a tensor factorization is applied to derive model parameters. Extensive experiments demonstrate the effectiveness of CMTM on cross-modal retrieval.
Adjustment disorder diagnosis: Improving clinical utility

Abstract Objectives: Adjustment disorder (AD) is a frequent diagnosis in clinical practice worldwide. After long neglect in mental health research, the new DSM definition and in particular the ICD-11 model of AD is about to create a fresh impulse for research on AD and for refined clinical use of the diagnosis. Methods: This paper outlines the clinical features of AD according to the ICD-10, ICD-11 and DSM-5 definitions, and provides case vignettes of patients with AD with clinical presentations of dominating anxiety, depressed mood or mixed symptom presentations. The available clinical assessments and diagnostic tools are described in detail, together with findings on their psychometric properties. Results: The current AD definitions are consistent with a new nosological grouping of AD with posttraumatic stress disorder in the chapter on trauma- and stressor-related disorders, or stress response syndromes. Conclusions: This nosological specification opens new avenues for neurobiological and psychological research on AD and for developing novel therapies.