Miracle-dz/UniFAR-FTUs huggingface dataset:
Non‐Coding RNA Analysis Using the Rfam Database

Rfam is a database of non‐coding RNA families in which each family is represented by a multiple sequence alignment, a consensus secondary structure, and a covariance model. Using a combination of manual and literature‐based curation and a custom software pipeline, Rfam converts descriptions of RNA families found in the scientific literature into computational models that can be used to annotate RNAs belonging to those families in any DNA or RNA sequence. Valuable research outputs that are often locked up in figures and supplementary information files are encapsulated in Rfam entries and made accessible through the Rfam Web site. The data produced by Rfam have a broad application, from genome annotation to providing training sets for algorithm development. This article gives an overview of how to search and navigate the Rfam Web site, and how to annotate sequences with RNA families. The Rfam database is freely available at http://rfam.org. © 2018 by John Wiley & Sons, Inc.
Fracture Resistance of Zirconia Restorations with a Modified Framework Design

Objectives: Chipping is one of the concerns related to zirconia crowns. The reasons of chipping have not been completely understood. This in-vitro study aimed to assess the effect of coping design on the fracture resistance of all-ceramic single crowns with zirconia frameworks. Materials and Methods: Two types of zirconia copings were designed (n=12): (1) a standard coping (SC) with a 0.5mm uniform thickness and (2) a modified coping (MC) consisted of a lingual margin of 1mm thickness and 2mm height connected to a proximal strut of 4mm height and a 0.3mm-wide facial collar. After veneer porcelain firing, the crowns were cemented to metal dies. Afterwards, a static vertical load was applied until failure. The modes of failure were determined. Data were calculated and statistically analyzed by independent samples T-test. P<0.05 was considered statistically significant. Results: The mean and standard deviation (SD) of the final fracture resistance equaled to 3519.42±1154.96 N and 3570.01±1224.33 N in SC and MC groups, respectively; the difference was not statistically significant (P=0.9). Also, the mean and SD of the initial fracture resistance equaled to 3345.34±1190.93 N and 3471.52±1228.93 N in SC and MC groups, respectively (P=0.8). Most of the specimens in both groups showed the mixed failure mode. Conclusions: Based on the results, the modified core design may not significantly improve the fracture resistance.
Bitcoin price prediction using ensembles of neural networks

This paper explores the relationship between the features of Bitcoin and the next day change in the price of Bitcoin using an Artificial Neural Network ensemble approach called Genetic Algorithm based Selective Neural Network Ensemble, constructed using Multi-Layered Perceptron as the base model for each of the neural network in the ensemble. To better understand the practicality and its effectiveness in real-world application, the ensemble was used to predict the next day direction of the price of Bitcoin given a set of approximately 200 features of the cryptocurrency over a span of 2 years. Over a span of 50 days, a trading strategy based on the ensemble was compared against a “previous day trend following” trading strategy through back-testing. The former trading strategy generated almost 85% returns, outperforming the “previous day trend following” trading strategy which produced an approximate 38% returns and a trading strategy that follows the single, best MLP model in the ensemble that generated approximately 53% in returns.
A new supervised image classification using data set partition

Random vector functional-link net (RVFL) is a single hidden layer network and extreme learning machine (ELM) is its special case with making weights from the output to the input be zero. RVFL (or ELM) and sparse representation classifier classification approach, called RVFLSRC or ELMSRC one, was an efficient hybrid model to balance between the time and the accuracy of classification. However, the classification time of RVFLSRC is still long. Besides, some images are wrongly classified by sparse representation classifier in RVFLSRC, which degrades the performance of RVFLSRC. In order to improve the recognition rate and reduce the classification time, we propose a new supervised image classification using data set partition (SIC-SP). In SIC-SP, we introduce a formula to estimate the number of images being divided wrongly by RVFLSRC (or ELMSRC), which are used to compute the division threshold. Using that division threshold can reduce the number of images being divided wrongly by RVFLSRC to improve the classification accuracy and reduce the classification time. In addition, we use principal component analysis to reduce the sample dimension, which also shortens the classification time. Experimental results on USPS handwritten digit database and UMIST face one show that the classification time of our SIC-SP is only 1/224 of classification time of SRC, and 1/30 of classification time of ELMSRC (or RVFLSRC). Meanwhile, the classification accuracy of SIC-SP is 97.62% on USPS, and 99.27% on UMIST, higher than those of ELM (or RVFL), SRC, and ELMSRC (or RVFLSRC).
Variational hidden conditional random fields with beta processes

Hidden conditional random fields (HCRFs) are an effective method for sequential classification. It extends the conditional random fields (CRFs) by introducing latent variables to represent the hidden states, which helps to learn the hidden structures in the sequential data. In order to enhance the flexibility of the HCRF, Dirichlet processes (DPs) are employed as priors of the state transition probabilities, which allows the model to have countable infinite hidden states. Besides DPs, Beta processes (BPs) are another kinds of prior models for Bayesian nonparametric modeling, which are more suitable for latent feature models. In this paper, we propose a novel Bayesian nonparametric version of the HCRF referred as BP-HCRF, which takes the advantages of the BPs on modeling hidden states. In the BP-HCRF, BPs are employed as priors for the state indicator variables for each sequence, and the modeled sequences can have different state spaces with infinite hidden states. We develop a variational inference approach for the BP-HCRF using the stick-breaking construction of BPs. We conduct experiments on synthetic dataset to demonstrate the effectiveness of our proposed model.
Socio-ecological determinants of alcohol, tobacco, and drug use behavior of adolescents in Kilifi County at the Kenyan coast

Alcohol, tobacco, and other drug use form a risk factor for health and social problems during adolescence. From a socio-ecological model, perceptions of 85 young people and 10 stakeholders on the types of alcohol, tobacco, and other drugs used and the predisposing and protective factors were explored; among adolescents at the Kenyan Coast in the Kilifi County. We found that the consumption of home-brewed alcohol, tobacco and marijuana smoking, and khat chewing was common and requires multi-component and community-centered intervention. Countering alcohol, tobacco, and other drug use needs enforcement of strong measures to regulate access to alcohol, tobacco, and other drugs for minors; addressing social and cultural norms; strategies for poverty alleviation; and community empowerment.
IRE1B degrades RNAs encoding proteins that interfere with the induction of autophagy by ER stress in Arabidopsis thaliana

ABSTRACT Macroautophagy/autophagy is a conserved process in eukaryotes that contributes to cell survival in response to stress. Previously, we found that endoplasmic reticulum (ER) stress induces autophagy in plants via a pathway dependent upon AT5G24360/IRE1B (INOSITOL REQUIRING 1–1), an ER membrane-anchored factor involved in the splicing of AT1G42990/BZIP60 (basic leucine zipper protein 60) mRNA. IRE1B is a dual protein kinase and ribonuclease, and here we determined the involvement of the protein kinase catalytic domain, nucleotide binding and RNase domains of IRE1B in activating autophagy. We found that the nucleotide binding and RNase activity of IRE1B, but not its protein kinase activity or splicing target BZIP60, are required for ER stress-mediated autophagy. Upon ER stress, the RNase activity of IRE1B engages in regulated IRE1-dependent decay of messenger RNA (RIDD), in which mRNAs of secreted proteins are degraded by IRE1 upon ER stress. Twelve genes most highly targeted by RIDD were tested for their role in inhibiting ER stress-induced autophagy, and 3 of their encoded proteins, AT1G66270/BGLU21 (β-glucosidase 21), AT2G16005/ROSY1/ML (MD2-related lipid recognition protein) and AT5G01870/PR-14 (pathogenesis-related protein 14), were found to inhibit autophagy upon overexpression. From these findings, IRE1B is posited to be a ‘licensing factor’ linking ER stress to autophagy by degrading the RNA transcripts of factors that interfere with the induction of autophagy. Abbreviations: ACT2: actin 2; ATG: autophagy-related; BGLU21: β-glucosidase 21; BIP3: binding protein 3; BZIP: basic leucine zipper; DAPI: 4ʹ, 6-diamidino-2-phenylindole; DTT: dithiothreitol; ER: endoplasmic reticulum; ERN1: endoplasmic reticulum to nucleus signaling 1; IRE1: inositol requiring 1; GFP: green fluorescent protein; MAP3K5/ASK1: mitogen-activated protein kinase kinase kinase 5; MAPK8/JNK1: mitogen-activated protein kinase 8/c-Jun N-terminal kinase 1; MDC: monodansylcadaverine; PR-14: pathogenesis-related protein 14; RIDD: Regulated IRE1-Dependent Decay of Messenger RNA; ROSY1/ML: interactor of synaptotagmin1/MD2-related lipid recognition protein; Tm: tunicamycin; UPR: unfolded protein response; WT: wild-type.
Effects of equal-volume resistance training with different training frequencies in muscle size and strength in trained men

Background The objective of the present study was to compare the effects of equal-volume resistance training (RT) performed with different training frequencies on muscle size and strength in trained young men. Methods Sixteen men with at least one year of RT experience were divided into two groups, G1 and G2, that trained each muscle group once and twice a week, respectively, for 10 weeks. Elbow flexor muscle thickness (MT) was measured using a B-Mode ultrasound and concentric peak torque of elbow extensors and flexors were assessed by an isokinetic dynamometer. Results ANOVA did not reveal group by time interactions for any variable, indicating no difference between groups for the changes in MT or PT of elbow flexors and extensors. Notwithstanding, MT of elbow flexors increased significantly (3.1%, P < 0.05) only in G1. PT of elbow flexors and extensors did not increase significantly for any group. Discussion The present study suggest that there were no differences in the results promoted by equal-volume resistance training performed once or twice a week on upper body muscle strength in trained men. Only the group performing one session per week significantly increased the MT of their elbow flexors. However, with either once or twice a week training, adaptations appear largely minimal in previously trained males.
Factors Predictive of Quality of Life among Breast Cancer Patients

Background: Due to progress in medical care, the number of survivors from cancer has increased significantly during recent years and this raises the question of the quality of life (QoL), especially of the many women treated for a breast cancer. This paper focuses on correlations of QoL with anxiety and depression on the one hand and with socio demographic, anatomo-clinical and therapeutic parameters on the other. Methods: In this cross-sectional study, seventy patients were enrolled and filled in two auto-questionnaires, both in validated Arabic versions: The SF-36 for assessment of QoL and the Hospital Anxiety and Depression Scale (HAD-S) for evaluation of anxiety and depression. The statistical approaches used to determine predictive factors were bivariate correlations to determine relationships between quantitative variables, and T-tests and one-way Anova to analyze links between qualitative and quantitative variables. Results: The QoL of patients was altered with an SF-36 mean total score of 54.0 ± 22.7, and the alteration affects the different aspects. The mean scores for anxiety and depression in patients were 6.91 ± 4.72 and 6.24 ± 3.88, respectively. The results of this study suggested an association between the QoL and chemotherapy (p= 0.014) and its adverse effects (p=0.01), as well as anxiety (p= 0.0001) and depressive symptoms (p= 0.0001). Socio-demographic factors, the stage of the cancer, and surgery, radiotherapy or hormone therapy did not appear to have significant effects. Conclusion: The management of breast cancer patients needs a collaborative approach between oncologists, gynecologists, psychologist and psychiatrists.
A novel multi-variate analysis method for searching particles in high energy physics

In order to search channel of Higgs bosons in high energy physics (HEP), in this paper we propose Deep Extreme Feature Extraction (DEFE), a new ensemble multi-variate Analysis method (MVA). DEFE is a deep ensemble learning scheme that trains a strongly diverse set of neural feature learners without explicitly encouraging diversity and penalizing correlations, which is achieved by adopting an implicit neural controller that directly controls and distributes gradient flows from higher level deep prediction network. Based on the construction and approximation of the extreme selection region presented in this paper, the DEFE model is able to be trained efficiently, and extract discriminative features from multiple angles and dimensions, hence the improvement of the selection region of searching new particles in HEP can be achieved. With the application in this model, the selection regions full of signal process can be obtained through the training of a miniature collision events set. In comparison of the classic deep neural network (DNN), DEFE shows a state-of-the-art performance: the error rate has decreased by about 37%, the accuracy has broken through 90% for the first time along with the discovery significance which has reached a standard deviation of 6.0. Experiment shows that DEFE is able to train an ensemble of discriminative feature learners that boosts the over-performance of final prediction. Furthermore, among high-level features, there are still some important patterns that are unidentified by DNN and are independent from low-level features, while DEFE can identify these significant patterns more effectively and efficiently.
The role of the JAK/STAT signal pathway in rheumatoid arthritis

Proinflammatory cytokine activation of the Janus kinase/signal transducers and activators of transcription (JAK/STAT) signal transduction pathway is a critical event in the pathogenesis and progression of rheumatoid arthritis. Under normal conditions, JAK/STAT signaling reflects the influence of negative regulators of JAK/STAT, exemplified by the suppressor of cytokine signaling and protein inhibitor of activated STAT. However, in rheumatoid arthritis (RA) both of these regulators are dysfunctional. Thus, continuous activation of JAK/STAT signaling in RA synovial joints results in the elevated level of matrix metalloproteinase gene expression, increased frequency of apoptotic chondrocytes and most prominently ‘apoptosis resistance’ in the inflamed synovial tissue. Tofacitinib, a JAK small molecule inhibitor, with selectivity for JAK2/JAK3 was approved by the United States Food and Drug Administration (US FDA) for the therapy of RA. Importantly, tofacitinib has demonstrated significant clinical efficacy for RA in the post-US FDA-approval surveillance period. Of note, the success of tofacitinib has spurred the development of JAK1, JAK2 and other JAK3-selective small molecule inhibitors, some of which have also entered the clinical setting, whereas other JAK inhibitors are currently being evaluated in RA clinical trials.
Range-Only-Based Three-Dimensional Circumnavigation of Multiple Moving Targets by a Nonholonomic Mobile Robot

A nonholonomic constant speed underactuated robot with a bounded control range travels in three dimensions. A group of targets unpredictably moves in all three dimensions. The robot measures only the distances to the targets and also has access to a certain spatial direction and its own coordinate (termed “altitude”) along it. We present a new navigation law that drives the robot to the locus of points at a prespecified root-mean-square distance from the targets and ensures dense sweep coverage of this locus within a given range of “altitudes. ” This law is rigorously justified by a nonlocal convergence result supported by recommendations on controller tuning; its applicability and performance are confirmed by extensive computer simulations.
Treatment choices for depression: Young people’s response to a traditional e-health versus a Health 2.0 website

Objective This exploratory experimental study compared young people’s credibility appraisals and behavioural intentions following exposure to depression treatment information on a Health 2.0 website versus a traditional website. The traditional website listed evidence-based treatment recommendations for depression as judged by field experts. The Health 2.0 website contained information about how helpful each treatment was, as aggregated from feedback from young people with lived experience of depression. Method Participants (n = 279) were provided with a vignette asking them to imagine that they had just received a diagnosis of depression and they had gone online to find information to guide their treatment choices. They were randomly allocated to view either the traditional or the Health 2.0 website, and were asked to rate the credibility of the depression treatment information provided. They were also asked to indicate the extent to which they would be likely to act on the advice of the website. Results Participants in the traditional website condition rated their website as significantly more influential than did participants presented with the Health 2.0 website. This difference in treatment influence was fully accounted for the participants’ perception of credibility of the information provided by the websites. Conclusion The traditional website was rated as significantly more credible and influential than the Health 2.0 website. Treatment decisions appeared to be based on the extent to which online information appears credible. In conclusion, health-related content was perceived by users as more credible when endorsed by experts than by other users, and perceived message credibility appears to be a powerful determinant of behavioural intentions within the e-health setting.
Recognization of rice damage area on UAV ortho-images

The following research uses Yuanli Township, Miaoli County as an example. The Multi-spectral UAV aerial images used have high spectral, spatial and temporal definition, which provides High-resolution DEM, which can be applied through height bias to recognize the rice damage Range and type. Furthermore, due to the chlorophyll in the crops having higher NIR reflection, when applied with NDVI, the crop growth range and stadium can be clearly explained more easily. The image processing technique is done by Pix4D and e-GPS measure Ground Control Points to produce a RGB Orthomosaic and NIR Orthomosaic, then using Arc gis to produce interpretation attribute data which is then derived through MATLAB input range values. The result of rice damage will be interpretated by using NDVI images as a major factor and human judgement as a supporting factor.
Energy-efficient power control in interference-limited spectrum sharing networks based on multi-objective optimization

Spectrum sharing networks deployment is considered a promising solution for meeting the exponential increase in demand of data rate in wireless network. However, excessive communication energy consumption and interference is the critical issue, particularly for spectrum sharing networks. In this paper, we propose an energy-efficient power control scheme for downlink transmission of interference-limited spectrum sharing networks, which is formulated as a multi-objective optimization problem based on pricing function. The pricing function is proposed for controlling the inter-tier interference level. The multi-objective optimization problem, considering the mix-min fairness of smallcells and the inter-tier interference constraint to improve the energy efficiency, is introduced in detail. We deal with this problem by using nondominated sorting genetic algorithm version II (NSGA-II). Simulation results show that the proposed energy-efficient power control scheme achieves remarkably excellent performance, comparable with the existing scheme.
Single-Phase Z-Source\/Quasi-Z-Source Inverters and Converters: An Overview of Double-Line-Frequency Power-Decoupling Methods and Perspectives

In this article, we investigate power-decoupling methods used in single-phase Z-source/quasi-Z-source inverters (ZSIs/qZSIs) to handle inherent double-linefrequency (2&omega;) power by comparing their implementation approaches and performances. Passive methods can be used to suppress 2&omega; ripples within the range of engineering tolerance by designing impedance networks, modifying modulation methods, or using closed-loop damping controls. Active power-decoupling methods eliminate 2&omega; ripples from the dc side by employing an active filter that buffers the 2&omega; ripple power. After we address the pros and cons of all methods, we apply a typical active method to a single-phase qZSI and a single-phase qZS matrix converter to demonstrate their high performance, promising perspectives, and potential applications.
Likely to be liked? A study of Facebook images

This paper investigates how aesthetic properties of images posted on Facebook are related to the number of Likes they attract. Aesthetic properties are conceptualized in terms of technical perspectives such as brightness, colorfulness and clarity as well as non-technical perspectives such as artistry, liveliness and ingenuity. The images collected for this paper were coded in terms of the aesthetic properties by two coders. Data analysis was done using Tobit hierarchical regression. Brightness, clarity, liveliness and ingenuity of images turned out to be positively associated with the number of Likes. The significance of the findings is discussed.
Word Spotting and Recognition Using Deep Embedding

Deep convolutional features for word images and textual embedding schemes have shown great success in word spotting. In this work, we follow these motivations to propose an End2End embedding framework which jointly learns both the text and image embeddings using state of the art deep convolutional architectures. The three major contributions of this work are: (i) an End2End embedding scheme to learn a common representation for word images and its labels, (ii) building a state of art word image descriptor and demonstrating its utility as off-the-shelf features for word spotting, and (iii) use of synthetic data as a complementary modality to further enhance word spotting and recognition. On the challenging IAM handwritten dataset, we report a mAP of 0.9509 for query-by-string based retrieval task. Under lexicon based word recognition, our proposed method report a 2.66 and 5.10 CER and WER respectively.