Miracle-dz/UniFAR-FTUs huggingface dataset:
Watercress-based gold nanoparticles: biosynthesis, mechanism of formation and study of their biocompatibility in vitro

To avoid the potential adverse effects of chemically-synthesised gold nanoparticles (AuNPs), a new method is developed for their biosynthesis using watercress total extract (WTE) and it is compared it with the citrate-based method. Synthesised AuNPs were characterised using transmission electron microscopy (TEM), particle size analysis and infrared spectroscopy (FTIR), and the mechanism and biocompatibility of WTE-based AuNPs were evaluated by MTT assay. The colour of the WTE-based AuNPs was directly changed from yellow to deep red without any intermediate colour change and spectrophotometric analysis showed significant differences in absorbance as well as peak surface plasmon resonance of synthesised AuNPs as compared with the use of sodium citrate. Formation of AuNPs in the WTE-based method was faster than the method using citrate. TEM pictures showed similar sizes of the AuNPs obtained by both methods, but WTE-based AuNPs were more spherical and the range of sizes provided minor differences with TEM results. FTIR confirmed the involvement of WTE compounds in the structure of AuNPs during biosynthesis and the MTT assay did not show any considerable anti-growth effect of WTE-based AuNPs on target cells. Hence, it seems clear that WTE-AuNPs could be used for various biological-related purposes including drug delivery, diagnosis and treatment of various diseases.
CrimeSTC: A Deep Spatial-Temporal-Categorical Network for Citywide Crime Prediction

Crime is one of the most complex social problems around the world, posing a major threat to human life and property. Predicting crime incidents in advance can be a great help in fighting against crime and has drawn continuous attention from both academic and industrial communities. Although a plethora of methods have been proposed over the past decade, most of the algorithms either perform prediction by leveraging linear or other oversimplified models or fail to fully explore the dynamic patterns in the crime data. In this paper, we propose a novel deep learning based crime prediction framework called CrimeSTC to jointly learn the intricate spatial-temporal-categorical correlations hidden inside the crime and big urban data. Specifically, our framework consists of four parts: dynamic module (handling the data that change every day via local CNN and GRU), static module (handling the data that remain the same over time via fully connected layers), categorical module (capturing the categorical dependency via graph convolutional network) and joint training module (concatenating dynamic and static representations to forecast crime numbers). Extensive experiments on real world datasets validate the effectiveness of our framework.
Hand Gesture Recognition Using IR-UWB Radar with ShuffleNet V2

In recent years, gesture recognition has developed rapidly in non-contact Human-Computer Interaction(HCI). This paper presents an efficient hand gesture recognition system for HCI based on Impulse-Radio Ultra-Wideband (IR-UWB) radar using ShuffleNet V2, which performed well on accuracy, speed and robustness. We convert time-domain radar signals to continuous Range-Doppler Map(RDM) by algorithm1. RDM images are easier to understand than waveform diagrams for Convolutional Neural Networks(CN-N). ShuffleNet V2 is a masterpiece of lightweight CNN and is used to analyze the patterns of different gesture RDM images to classify gestures in this paper. In order to ensure the robustness of the algorithm, we invited 7 participants to construct the gesture data set. The proposed hand gesture recognition system can classify 7 gestures with a promising accuracy of 98.52%.
Networks Ensemble for Multi-modal Cross-ethnicity Face Anti-spoofing

Face anti-spoofing is a crucial real-word detection problem in face recognition systems, which has a rapid development with the blooming of convolution neural network. The recently proposed CASIA-SURF CeFA dataset [1] is the largest multi-modal dataset containing 1607 subjects, 23583 videos and 2D plus 3D attack types, which is first time to consider the impact of cross-ethnicity for face anti-spoofing. In this paper, we adopt a simple yet effective multi-stream architecture which implements the feature extraction of each modality and the aggregation of the multi-modal features. Specifically, in order to alleviate the impact of the cross-ethnicity attacks and unknown spoofs proposed by Protocol 4 of the CASIA-SURF CeFA dataset, we design several networks trained separately based on the multi-stream architecture and then fuse the networks that with good robustness and generalization performance. In additional, we propose a data preprocessing method by enlarging the target area. The experimental results indicate the effectiveness of the proposed method.
An Enhanced HDCA Algorithm for Single-trial EEG Classification

Single-trial electroencephalography (EEG) image classification algorithms mainly depend on P300 component detection. To overcome the P300 latency instability, an enhanced hierarchical discriminant component analysis (eHDCA) algorithm is proposed based on previous hierarchical discriminant component analysis (HDCA) algorithm. In the proposed method, the overlapping time window is introduced to enhance the correlation of adjacent time window to deal with the problem of P300 latency instability. A rapid serial visual presentation (RSVP) paradigm is designed to train a single-trial image classification model. Results indicate that the proposed eHDCA outperforms existing sliding HDCA (sHDCA) with better detection performance and less computational complexity, which will contribute to real-time detection system.
INDUSTRIAL CRYSTALLIZATION AND PRECIPITATION FROM SOLUTIONS: STATE OF THE TECHNIQUE

Crystallization and precipitation from solutions are responsible for 70% of all solid materials produced by the chemical industry. Competing with distillation as a separation and purification technique, their use is widespread. They operate at low temperatures with low energy consumption and yield with high purifications in one single step. Operational conditions largely determine product quality in terms of purity, filterability, flowability and reactivity. Producing a material with the desired quality often requires a sound knowledge of the elementary steps involved in the process: creation of supersaturation, nucleation, crystal growth, aggregation and other secondary processes. Mathematical models coupling these elementary processes to all particles in a crystallizer have been developed to design and optimize crystallizer operation. For precipitation, the spatial distribution of reactants and particles in the reactor is important; thus the tools of computational fluid dynamics are becoming increasingly important. For crystallization of organic chemicals, where incorporation of impurities and crystal shape are critical, molecular modeling has recently appeared as a useful tool. These theoretical developments must be coupled to experimental data specific to each material. Theories and experimental techniques of industrial crystallization and precipitation from solutions are reviewed, and recent developments are highlighted.
P53 Pathway Activate Detection based on Machine Learning: The modified XGBoost-based method of pan-cancer pathway activity detection in the Cancer Genome Atlas

Precise and effective prediction of aberrant pathway activity in tumors will aid in identification for the selection of targeted drugs. Herein we develop an XGBoost-based machine learning method, called XPPA. It detects the extent to which alterations in gene expression effect aberrant p53 pathway activity. This paper integrates RNA-Seq, copy number and mutations data from The Cancer Genome Atlas (TCGA). Compared with matched normal tissues, global P53 aberrant activation was observed across cancer types. We analyzed tens of driver genes that highly associate with aberrant activity. The works across different cancer types demonstrate that our method is effective in predicting the aberrant pathway activity and achieve better performance in AUROC and AUPR as compared to previous method.
Attention Backpropagation: A Backtracking Approach to Visualize the Discriminative Image Regions

In recent years, deep convolutional neural networks (CNNs) have achieved great success in computer vision tasks. However, they are often perceived as "black box" methods for the lack of interpretability. In this work, we propose an approach to alleviate the opaqueness of deep learning models by providing visual explanations to the predictions of the network. Our approach entitled Attention Backpropagation will backpropagate the network's attention to produce an attention map highlighting the important regions in the image for predicting. This is a generalized method that can be used for all the networks without modification of the network architecture or retraining. We apply the proposed approach to provide visual explanations for the whole inference process of VGG-16 model, and analyze the cause for the erroneous predictions. Furthermore, numerical results suggest that our approach has better ability to localize the discriminative image regions, and outperforms previous methods in weakly-supervised localization task and the pointing game test on ILSVRC-15 dataset.
The Role of RNNs for Contextual Representations: A Case Study Using DMN-plus

Recurrent neural networks (RNNs) have been used prevalently to capture long-term dependencies of sequential inputs. In particular, for question answering systems, variants of RNNs, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), allow the positional, ordering or contextual information to be encoded into latent contextual representations. While applying RNNs for encoding this information is intuitively reasonable, no specific research has been conducted to investigate how effective is their use in such systems when the sequence of sentences is unimportant. In this paper we conduct a case study on the effectiveness of using RNNs to generate context representations using the DMN+ network. Our results based on a three-fact task in the bAbI dataset show that sequences of facts in the training dataset influence the predictive performance of the trained system. We propose two methods to resolve this problem, one is data augmentation and the other is the optimization of the DMN+ structure by replacing the GRU in the episodic memory module with a non-recurrent operation. The experimental results demonstrate that our proposed solutions can resolve the problem effectively.
Identifying Chinese Herbal Medicine by Image with Three Deep CNNs

Chinese herbal medicine (CHM) plays a key role in restoring the balance of the body for sick people and maintaining health for common people. Automated identification of CHM using images is a challenging task owing to the fine-grained variability in the appearance of CHM. This paper presented an application study of transfer learning using deep convolutional neural networks (CNNs) for image classification to identify CHM. 72,026 images of 7 categories of easily confusing CHM were collected. Fine-tuning the pre-training model of AlexNet, VGGNet, and ResNet based on transfer learning. The verification results of the three models with 10,806 images show, that the recognition accuracy of the ResNet model is the highest at 98.17%. Compared with other previous CHM image recognition methods, this model has reached a comparable level of CHM experience identification experts. Also, the key factors influencing the fine-tuning of pre-training models are evaluated. We describe a general artificial intelligence (AI) platform for the identification of CHM, and this system can be extended to other varieties of CHM.
Dynamic Portfolio Management Based on Pair Trading and Deep Reinforcement Learning

Existing portfolio management methods have made great progress in diversifying non-systematic risks, but they have ignored systemic risks. In response to this issue, we proposed a dynamic, market-neutral, risk-diversified portfolio management model by combining the ideas of pair trading strategy, deep reinforcement learning with traditional portfolio management model. We conduct an experiment on the Chinese A-share market by selecting 32 pairs of stocks. The experiment results showed that the proposed pair-based deep portfolio model has superiority for dynamic portfolio management problem in trade-off investment returns and risks.
Adaptive Brain-Machine Interface of Brain-Controlled Vehicles Using Semi-MIM and TSVM

Brain-machine interfaces (BMIs) have been developed for healthy individuals to control external devices. However, like all the existing BMIs, a time-consuming training process is required. To address this problem, a semi-supervised decoding framework is proposed to develop an adaptive BMI. The adaptive BMI is firstly initialized using a small labeled training set, and then increasingly adjusts itself by updating with newly collected unlabeled electroencephalogram (EEG) samples. The semi-supervised decoding framework starts with a semi-supervised mutual information maximization (semi-MIM) method to select optimal features and then uses the transductive support vector machine (TSVM) for classification. Experimental results show that the proposed semi-supervised framework performs better than other semi-supervised approaches and enables the adaptive BMI to catch up with the performance of the supervised learning-based BMI. Since the adaptive BMI uses a smaller training set, it can significantly reduce the training effort.
Security Method for Internet of Things Using Machine Learning Against Cyber Attacks

Abstract. Internet of things is a huge network of large number of devices with sensors. This group of devices is estimated to grow over 25 billion in 2020 as its growth since its evolution has been truly more than just rapid growth. With such a growth in network expansion and increase in number of connected devices, security always have been an issue to be improved and one of the weak areas in IoT environment. While applying security in IoT environment the main characteristics of IoT being heterogenetic and the number of IoT devices are the challenges that need to be dealt with some efficient and feasible approach. Therefore, to address this problem, we propose a method of securing IoT system using basic principles of machine learning. The system will analyze the data and detect the malicious packets received from the edge devices. The experimental study showed improved results with the dummy data sets in an IoT environment.
Separation and Quantitation of 2,3-Butanediol Isomers ((−), (+), and meso) by a Combined Use of Enzyme and Gas Chromatography

In the presence of adequate amounts of NADand hydrazine and in an alkaline condition, the equilibrium of the reaction is much in favour of acetoin formation. Weattempted to apply this reaction to the analysis of butanediol isomers. Theorganism was cultivated by the sameprocedures as those previously mentioned under aerobic conditions.40 Preparation of cell-free extract was done in the same way as previously mentioned.40 Phosphate buffer (0.1 m, pH 8.0) containing 2% protamine sulfate was added dropwise with continuous stirring to the cell-free extract to achieve a final ratio of 0.05mg of protamine sulfate per mg of protein. After stirring for 20min, the precipitate was
Breakfast skipping alone and in interaction with inflammatory based quality of diet increases the risk of higher scores of psychological problems profile in a large sample of Iranian adults

Abstract The authors investigate the association of breakfast skipping and its interaction with a dietary inflammatory index (DII) with the severity of psychological disorders. A total of 2876 Iranian general adults were enrolled in this cross-sectional study. Psychological problems profile score was calculated using the regression method in the framework of factor analysis based on depression, anxiety and psychological distress. The higher scores indicate more severity of mental problem. The frequency of breakfast eating in a week was assessed. Dietary intakes were assessed using a food-frequency questionnaire and twenty-seven items were included in the calculation of DII. In the crude model, individuals who ate breakfast seldom had the highest odds for having worse psychological problems profile (OR 3⋅59; 95 % CI 2⋅52, 5⋅11). Adjustment for various confounders did not change the associations (OR 3⋅35; 95 % CI 2⋅11, 5⋅32). In the adjusted multinomial logistic regression model, participants with high DII (>median) who skipped breakfast had highest risk of being in the higher tertiles of psychological problems profile compared with those who had low DII (<median) and ate breakfast (OR 6⋅67; 95 % CI 3⋅45, 12⋅90). Similar results were observed in women and men regarding the impact of breakfast skipping alone and interaction with DII on scores of psychological problems profile. Breakfast skipping is associated with higher risk of psychological problems. Similar findings were obtained in the stratified analysis by sex. Our findings confirmed that the DII and breakfast skipping are associated with mental health, interactionally. Further longitudinal studies are needed to confirm the true link between breakfast skipping and psychological problems.
State anxiety influences P300 and P600 event-related potentials over parietal regions in the hollow-mask illusion experiment

Abstract The hollow-mask illusion is an optical illusion where a concave face is perceived as convex. It has been demonstrated that individuals with schizophrenia and anxiety are less susceptible to the illusion than controls. Previous research has shown that the P300 and P600 event-related potentials (ERPs) are affected in individuals with schizophrenia. Here, we examined whether individual differences in neuroticism and anxiety scores, traits that have been suggested to be risk factors for schizophrenia and anxiety disorders, affect ERPs of healthy participants while they view concave faces. Our results confirm that the participants were susceptible to the illusion, misperceiving concave faces as convex. We additionally demonstrate significant interactions of the concave condition with state anxiety in central and parietal electrodes for P300 and parietal areas for P600, but not with neuroticism and trait anxiety. The state anxiety interactions were driven by low-state anxiety participants showing lower amplitudes for concave faces compared to convex. The P300 and P600 amplitudes were smaller when a concave face activated a convex face memory representation, since the stimulus did not match the active representation. The opposite pattern was evident in high-state anxiety participants in regard to state anxiety interaction and the hollow-mask illusion, demonstrating larger P300 and P600 amplitudes to concave faces suggesting impaired late information processing in this group. This could be explained by impaired allocation of attentional resources in high-state anxiety leading to hyperarousal to concave faces that are unexpected mismatches to standard memory representations, as opposed to expected convex faces.
A model for implementing diagnostic instruction within Doctor of Pharmacy degree programs

Health care needs in the United States (US) are expanding along with projected primary care physician shortages by the year 2030. Pharmacists' training and broad accessibility make them well positioned to provide primary care for basic medical conditions. As the profession advocates for greater patient care roles, a critical consideration in pharmacist training is a broader introduction to diagnosis; thus, instructional models will need to be developed for US pharmacy curricula.
Skin Manifestations in COVID-19 Patients: Are They Indicators for Disease Severity? A Systematic Review

Introduction: Until now, there are several reports on cutaneous manifestations in COVID-19 patients. However, the link between skin manifestations and the severity of the disease remains debatable. We conducted a systematic review to evaluate the temporal relationship between different types of skin lesions and the severity of COVID-19. Methods: A systematic search was conducted for relevant studies published between January and July 2020 using Pubmed/Medline, Embase, and Web of knowledge. The following keywords were used: “SARS-CoV-2” or “COVID-19” or “new coronavirus” or “Wuhan Coronavirus” or “coronavirus disease 2019” and “skin disease” or “skin manifestation” or “cutaneous manifestation. ” Results: Out of 381 articles, 47 meet the inclusion criteria and a total of 1,847 patients with confirmed COVID-19 were examined. The overall frequency of cutaneous manifestations in COVID-19 patients was 5.95%. The maculopapular rash was the main reported skin involvement (37.3%) commonly occurred in middle-aged females with intermediate severity of the disease. Forty-eight percentage of the patients had a mild, 32% a moderate, and 20% a severe COVID-19 disease. The mild disease was mainly correlated with chilblain-like and urticaria-like lesions and patients with vascular lesions experienced a more severe disease. Seventy-two percentage of patients with chilblain-like lesions improved without any medication. The overall mortality rate was 4.5%. Patients with vascular lesions had the highest mortality rate (18.2%) and patients with urticaria-like lesions had the lowest mortality rate (2.2%). Conclusion: The mere occurrence of skin manifestations in COVID-19 patients is not an indicator for the disease severity, and it highly depends on the type of skin lesions. Chilblain-like and vascular lesions are the ends of a spectrum in which from chilblain-like to vascular lesions, the severity of the disease increases, and the patient's prognosis worsens. Those with vascular lesions should also be considered as high-priority patients for further medical care.
PDIA5 is Correlated With Immune Infiltration and Predicts Poor Prognosis in Gliomas

Gliomas are the most common and lethal primary malignant tumor of the brain. Routine treatment including surgical resection, chemotherapy, and radiotherapy produced limited therapeutic effect, while immunotherapy targeting the glioma microenvironment has offered a novel therapeutic option. PDIA5 protein is the member of PDI family, which is highly expressed in glioma and participates in glioma progression. Based on large-scale bioinformatics analysis, we discovered that PDIA5 expression level is upregulated in aggressive gliomas, with high PDIA5 expression predicting poor clinical outcomes. We also observed positive correlation between PDIA5 and immune infiltrating cells, immune related pathways, inflammatory activities, and other immune checkpoint members. Patients with high PDIA5 high-expression benefited from immunotherapies. Additionally, immunohistochemistry revealed that PDIA5 and macrophage biomarker CD68 were upregulated in high-grade gliomas, and patients with low PDIA5 level experienced favorable outcomes among 33 glioma patients. Single cell RNA sequencing exhibited that PDIA5 was in high level presenting in neoplastic cells and macrophages. Cell transfection and co-culture of glioma cells and macrophages revealed that PDIA5 in tumor cells mediated macrophages exhausting. Altogether, our findings indicate that PDIA5 overexpression is associated with immune infiltration in gliomas, and may be a promising therapeutic target for glioma immunotherapy.