Miracle-dz/UniFAR-FTUs huggingface dataset:
LPGNMF: Predicting Long Non-Coding RNA and Protein Interaction Using Graph Regularized Nonnegative Matrix Factorization

Long non-coding RNAs (lncRNA) play crucial roles in a variety of biological processes and complex diseases. Massive studies have indicated that lncRNAs interact with related proteins to exert regulation of cellular biological processes. Because it is time-consuming and expensive to determine lncRNA-protein interaction by experiment, more accurate predictions of interaction by computational methods are imperative. We propose a novel computational approach, predicting lncRNA-protein interaction using graph regularized nonnegative matrix factorization (LPGNMF), to discover unobserved lncRNA-protein association. First, we calculate lncRNA similarity and protein similarity by integrating the lncRNA expression information and gene ontology information. Subsequently, we utilize graph regularized nonnegative matrix factorization framework to predict potential interactions for all lncRNA simultaneously. In the cross validation test, LPGNMF achieves an AUC of 85.2 percent, higher than those of other compared methods. In addition, novel lncRNA-protein interactions detected by LPGNMF are validated by literatures or database. The results indicate that our method is effective to discover potential lncRNA-protein interaction.
Iterative Back-Translation for Neural Machine Translation

We present iterative back-translation, a method for generating increasingly better synthetic parallel data from monolingual data to train neural machine translation systems. Our proposed method is very simple yet effective and highly applicable in practice. We demonstrate improvements in neural machine translation quality in both high and low resourced scenarios, including the best reported BLEU scores for the WMT 2017 German↔English tasks.
A Rice B-Box Protein, OsBBX14, Finely Regulates Anthocyanin Biosynthesis in Rice

Anthocyanins are responsible pigments for giving attractive colors of plant organs and nutraceutical benefits of grains. Anthocyanin biosynthesis is known to be regulated by transcription factors and other regulatory proteins. In rice (Oryza sativa), the R2R3 MYB transcription factor (TF) OsC1 and a bHLH TF, OsB2, were previously reported to control anthocyanin biosynthesis in vegetative tissues and seeds, respectively; however, the regulatory mechanisms of the anthocyanin biosynthesis by TFs remain largely unknown. In this study, we identified OsBBX14, a homolog of Arabidopsis thaliana B-box domain protein 22 (AtBBX22), and investigated its function. The transcript level of OsBBX14 was high in pigmented rice seeds and gradually increased as the seeds matured. The ectopic expression of OsBBX14 in Arabidopsis resulted in a dramatic increase in anthocyanin accumulation in its seedlings. Using a steroid receptor-based inducible activation system, OsBBX14 and OsHY5 were found to directly activate OsC1 or OsB2 in an independent or collaborative manner. Yeast two hybrid revealed that the second B-box domain of OsBBX14 physically interacts with the bZIP domain of OsHY5. These results suggest that the anthocyanin biosynthesis in rice is induced and finely tuned by OsBBX14 in collaboration with OsHY5.
An Efficient CS-Based and Statistically Robust Beam Alignment Scheme for mmWave Systems

Millimeter-Wave (mmWave) communication has come into the spotlight as an enabling approach for next generation wireless networks. Communication at mmWave is however challenging due to the large path loss and limited power. This implies that antenna arrays with large directional gain are required both at the Base Station (BS) and the user sides. Finding the strongest narrow beam pair connecting the BS and the user is referred to as Beam Alignment (BA). In this paper, we propose an efficient BA scheme for multi-user systems via estimating the second order statistics of the channel. In the proposed scheme, the BS probes the channel in the downlink letting each user estimate its own channel, where all the users within the BS coverage are trained simultaneously. We formulate the channel estimation at the user side as a Compressed Sensing (CS) of a non- negative sparse vector and use the recently developed Non- Negative Least Squares (NNLS) technique to solve it efficiently. We evaluate our method via numerical simulations and compare it with other competitive algorithms. It has been verified that the proposed approach incurs less training overhead, exhibits higher efficiency in multi-user scenarios, and is highly robust to fast time- varying channels.
Acute and Longer-Term Outcomes Using Ketamine as a Clinical Treatment at the Yale Psychiatric Hospital.

OBJECTIVE Ketamine has emerged as a rapid-acting antidepressant, though controversy remains whether sufficient data exist to justify its use outside of research protocols. In October 2014, the authors' institution began providing ketamine as an off-label therapy on a case-by-case basis for patients unable to participate in research protocols. Here, the participant experience during 29 months of providing ketamine as a clinical treatment for severe and treatment-resistant mood disorders through February 2017 is described. METHODS Patients were initially treated with a single- or double-infusion protocol (0.5 mg/kg for 40 minutes intravenously) and were later transitioned to a 4-infusion protocol over 2 weeks. RESULTS Fifty-four patients received ketamine, with 518 total infusions performed. A subset of 44 patients with mood disorders initiated the 4-infusion protocol, of whom 45.5% responded and 27.3% remitted by the fourth infusion. A subsample (n = 14) received ketamine on a long-term basis, ranging from 12 to 45 total treatments, over a course of 14 to 126 weeks. No evidence was found of cognitive decline, increased proclivity to delusions, or emergence of symptoms consistent with cystitis in this subsample. CONCLUSIONS In general, ketamine infusions were tolerated well. The response and remission rates in this clinical sample were lower than those observed in some research protocols. The small number of patients who were treated on a maintenance schedule limits the conclusions that can be drawn regarding the long-term safety of ketamine; however, no long-term adverse effects were observed in this sample.
Tectorigenin inhibits RANKL‐induced osteoclastogenesis via suppression of NF‐κB signalling and decreases bone loss in ovariectomized C57BL/6

Metabolism of bone is regulated by the balance between osteoblast‐mediated bone formation and osteoclast‐mediated bone resorption. Activation of osteoclasts could lead to osteoporosis. Thus, inhibiting the activity of osteoclasts becomes an available strategy for the treatment of osteoporosis. Tectorigenin is an extract of Belamcanda chinensis In the present study, the anti‐osteoclastogenesis effects of tectorigenin were investigated in vitro and in vivo. The results showed preventive and therapeutic effects of tectorigenin at concentrations of 0, 10, 40, and 80 μmol/L in the maturation and activation of osteoclasts. A signalling study also indicated that tectorigenin treatment reduces activation of NF‐κB signalling in osteoclastogenesis. Animal experiment demonstrated that tectorigenin treatment (1‐10 mg/kg, abdominal injection every 3 days) significantly inhibits bone loss in ovariectomized C57BL/6. Our data suggest that tectorigenin is a potential pharmacological choice for osteoporosis.
Direct Conversion of Syngas into Methyl Acetate, Ethanol, and Ethylene by Relay Catalysis via the Intermediate Dimethyl Ether.

Selective conversion of syngas (CO/H2 ) into C2+ oxygenates is a highly attractive but challenging target. Herein, we report the direct conversion of syngas into methyl acetate (MA) by relay catalysis. MA can be formed at a lower temperature (ca. 473 K) using Cu-Zn-Al oxide/H-ZSM-5 and zeolite mordenite (H-MOR) catalysts separated by quartz wool (denoted as Cu-Zn-Al/H-ZSM-5|H-MOR) and also at higher temperatures (603-643 K) without significant deactivation using spinel-structured ZnAl2 O4 |H-MOR. The selectivity of MA and acetic acid (AA) reaches 87 % at a CO conversion of 11 % at 643 K. Dimethyl ether (DME) is the key intermediate and the carbonylation of DME results in MA with high selectivity. We found that the relay catalysis using ZnAl2 O4 |H-MOR|ZnAl2 O4 gives ethanol as the major product, while ethylene is formed with a layer-by-layer ZnAl2 O4 |H-MOR|ZnAl2 O4 |H-MOR combination. Close proximity between ZnAl2 O4 and H-MOR increases ethylene selectivity to 65 %.
Pedestrian Retrieval via Part-Based Gradation Regularization in Sensor Networks

In this paper, we propose a novel label distribution approach named part-based gradation regularization (PGR) for pedestrian retrieval in sensor networks. Considering different importance of various body parts, we present a gradual function to assign pedestrian label for each horizontal part. In this way, we can conduct part-based supervised learning using the identification network. The proposed PGR not only learns the discriminative local convolutional neural network-based features, but also considers the significance of assigning pedestrian label for different horizontal parts. Experimental results show that the proposed PGR obtains better performance than other approaches on three pedestrian retrieval databases, i.e., Market-1501, CUHK03, and DukeMTMC-reID databases.
Self-driving car ISEAUTO for research and education

The paper introduces a research and educational project targeted to self-driving cars and urban mobility issues. The project focuses on design and development of the self-driving car in cooperation with a partner company, university researchers, and students. Research and implementation results of simulations, software development and mechanical design are presented, and a potential smart city use case discussed.
GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data

A knowledge base is a large repository of facts that are mainly represented as RDF triples, each of which consists of a subject, a predicate (relationship), and an object. The RDF triple representation offers a simple interface for applications to access the facts. However, this representation is not in a natural language form, which is difficult for humans to understand. We address this problem by proposing a system to translate a set of RDF triples into natural sentences based on an encoder-decoder framework. To preserve as much information from RDF triples as possible, we propose a novel graph-based triple encoder. The proposed encoder encodes not only the elements of the triples but also the relationships both within a triple and between the triples. Experimental results show that the proposed encoder achieves a consistent improvement over the baseline models by up to 17.6%, 6.0%, and 16.4% in three common metrics BLEU, METEOR, and TER, respectively.
Eigenspace-Based Minimum Variance Combined With Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging

In photoacoustic imaging, delay-and-sum (DAS) algorithm is the most commonly used beamformer. However, it leads to a low resolution and high level of sidelobes. Delay-multiply-and-sum (DMAS) was introduced to provide lower sidelobes compared to DAS. In this paper, to improve the resolution and sidelobes of DMAS, a novel beamformer is introduced using the eigenspace-based minimum variance (EIBMV) method combined with DMAS, namely EIBMV-DMAS. It is shown that expanding the DMAS algebra leads to several terms, which can be interpreted as DAS. Using the EIBMV adaptive beamforming instead of the existing DAS (inside the DMAS algebra expansion) is proposed to improve the image quality. EIBMV-DMAS is evaluated numerically and experimentally. It is shown that EIBMV-DMAS outperforms DAS, DMAS, and EIBMV in terms of resolution and sidelobes. In particular, at the depth of 11 <inline-formula> <tex-math notation="LaTeX">$\text{mm}$</tex-math></inline-formula> of the experimental images, EIBMV-DMAS results in about 113 <inline-formula><tex-math notation="LaTeX">$\text{dB}$</tex-math></inline-formula> and 50 <inline-formula><tex-math notation="LaTeX">$\text{dB}$</tex-math></inline-formula> sidelobe reduction, compared to DMAS and EIBMV, respectively. At the depth of 7 <inline-formula><tex-math notation="LaTeX">$\text{mm}$</tex-math> </inline-formula>, for the experimental images, the quantitative results indicate that EIBMV-DMAS leads to improvement in signal-to-noise ratio of about 75% and 34%, compared to DMAS and EIBMV, respectively.
Regression and ANN Models for Electronic Circuit Design

This paper presents a methodology to design and to predict the behaviour of electronic circuits, which combines artificial neural networks and design of experiments. This methodology can be used to model output variables in electronic circuits either with similar features to the circuit configuration that is analysed in this study or with more complex configurations in order to improve the process of electronic circuit design.
Public Acceptance of Fully Automated Driving: Effects of Social Trust and Risk/Benefit Perceptions

Automated driving (AD) is one of the most significant technical advances in the transportation industry. Its safety, economic, and environmental benefits cannot be realized if it is not used. To explain, predict, and increase its acceptance, we need to understand how people perceive and why they accept or reject AD technology. Drawing upon the trust heuristic, we tested a psychological model to explain three acceptance measures of fully AD (FAD): general acceptance, willingness to pay (WTP), and behavioral intention (BI). This heuristic suggests that social trust can directly affect acceptance or indirectly affect acceptance through perceived benefits and risks. Using a survey (N = 441), we found that social trust retained a direct effect as well as an indirect effect on all FAD acceptance measures. The indirect effect of social trust was more prominent in forming general acceptance; the direct effect of social trust was more prominent in explaining WTP and BI. Compared to perceived risk, perceived benefit was a stronger predictor of all FAD acceptance measures and also a stronger mediator of the trust–acceptance relationship. Predictive ability of the proposed model for the three acceptance measures was confirmed. We discuss the implications of our results for theory and practice.
Discrepancies in sleep diary and actigraphy assessments in adults with fibromyalgia: Associations with opioid dose and age

Sleep diary and actigraphy assessments of insomnia symptoms in patients with fibromyalgia (FM) are often discrepant. We examined whether opioid dose and age interact in predicting magnitude or direction of discrepancies. Participants (N = 199, M = 51.5 years, SD = 11.7) with FM and insomnia completed 14 days of diaries and actigraphy. Multiple regressions determined whether average opioid dose and its interaction with age predicted magnitude or direction of diary/actigraphy discrepancies in sleep onset latency (SOL), wake after sleep onset (WASO) and sleep efficiency (SE), controlling for sex, use of sleep medication, evening pain and total sleep time. Higher opioid dose predicted greater magnitude of discrepancy in SOL and SE. Opioid dose interacted with age to predict direction but not magnitude of discrepancy in SOL and SE. Specifically, higher opioid use was associated with better subjective (shorter SOL, higher SE) than objective reports of sleep among younger adults, and longer subjective than objectively measured SOL among older adults. Opioid dose did not predict magnitude or direction of WASO discrepancies. In FM, a higher opioid dose increases diary/actigraphy SOL and SE discrepancies, and direction of discrepancies may depend on age. We speculate that increased opioid use combined with age‐related factors, such as slow wave sleep disruption, increased awakenings and/or cognitive decline, may impact perceived sleep.
Nonalcoholic Fatty Liver Disease Cirrhosis: A Review of Its Epidemiology, Risk Factors, Clinical Presentation, Diagnosis, Management, and Prognosis

Cirrhosis is the common end stage of a number of chronic liver conditions and a significant cause of morbidity and mortality. With the growing epidemic of obesity and metabolic syndrome, nonalcoholic fatty liver disease (NAFLD) has become the most common cause of chronic liver disease worldwide and will become one of the leading causes of cirrhosis. Increased awareness and understanding of NAFLD cirrhosis are essential. To date, there has been no published systematic review on NAFLD cirrhosis. Thus, this article reviews recent studies on the epidemiology, risk factors, clinical presentation, diagnosis, management, and prognosis of NAFLD cirrhosis.
Exploring Content Clustering for User Association in Small Cell Networks

User association has redrawn much attention lately, due to the introduction of content caching in small base stations (SBSs). To reduce traffic burden on backhaul links, users are associated with different SBSs when requesting different contents. However, user-perceived delay increases if the serving SBSs that have the desired contents are overloaded. Moreover, the user association problem becomes complex due to the vast number of contents. In this paper, to reduce user-perceived delay as well as backhaul loads, we propose a cluster-level user association scheme where content clustering is leveraged to simplify user association and reduce its complexity. Particularly, similar contents are clustered together according to the content preferences of users and cached contents in SBSs. Thus, the dimensionality of the user problem becomes smaller. On this basis, we propose a distributed cluster-level user association scheme, where each user selects SBSs based on their traffic loads and cached contents. Simulation results show that our scheme based on clustered contents outperforms the traditional schemes.
Social interactions in online eating disorder communities: A network perspective

Online health communities facilitate communication among people with health problems. Most prior studies focus on examining characteristics of these communities in sharing content, while limited work has explored social interactions between communities with different stances on a health problem. Here, we analyse a large communication network of individuals affected by eating disorders on Twitter and explore how communities of individuals with different stances on the disease interact online. Based on a large set of tweets posted by individuals who self-identify with eating disorders online, we establish the existence of two communities: a large community reinforcing disordered eating behaviours and a second, smaller community supporting efforts to recover from the disease. We find that individuals tend to mainly interact with others within the same community, with limited interactions across communities and inter-community interactions characterized by more negative emotions than intra-community interactions. Moreover, by studying the associations between individuals’ behavioural characteristics and interpersonal connections in the communication network, we present the first large-scale investigation of social norms in online health communities, particularly on how a community approves of individuals’ behaviours. Our findings shed new light on how people form online health communities and can have broad clinical implications on disease prevention and online intervention.
Combining deep reinforcement learning with prior knowledge and reasoning

Recent improvements in deep reinforcement learning have allowed to solve problems in many 2D domains such as Atari games. However, in complex 3D environments, numerous learning episodes are required which may be too time consuming or even impossible especially in real-world scenarios. We present a new architecture to combine external knowledge and deep reinforcement learning using only visual input. A key concept of our system is augmenting image input by adding environment feature information and combining two sources of decision. We evaluate the performances of our method in 3D partially-observable environments from the Microsoft Malmo platform. Experimental evaluation exhibits higher performance and faster learning compared to a single reinforcement learning model.
Minimally Invasive Corneal Neurotization With Acellular Nerve Allograft: Surgical Technique and Clinical Outcomes.

PURPOSE To describe a minimally invasive surgical technique and its clinical outcomes with the use of acellular nerve allograft to re-establish corneal sensibility in patients with neurotrophic keratopathy. METHODS Acellular nerve allograft was coapted to an intact supraorbital, supratrochlear, or infraorbital nerve and transferred to the affected eye. Donor nerve pedicles were isolated through a transpalpebral or transconjunctival approach. Retrospective evaluation of preoperative and postoperative corneal sensibility, ocular surface, and best-corrected visual acuity was performed in all patients. Mean follow-up period was 6 months (range: 3-10 months). RESULTS Corneal neurotization with acellular nerve allograft was successfully performed in 7 patients with restoration of corneal sensibility and corneal epithelial integrity. In vivo confocal microscopy demonstrated increased nerve density in corneal stroma at 4 months after surgery. CONCLUSIONS The use of acellular nerve allograft allows for a minimally invasive approach to successful corneal neurotization.
Wavelength Dependence of Ultrahigh-Resolution Optical Coherence Tomography Using Supercontinuum for Biomedical Imaging

Optical coherence tomography (OCT) is a noninvasive cross-sectional imaging technique with micrometer resolution. The theoretical axial resolution is determined by the center wavelength and bandwidth of the light source, and the wider the bandwidth, the higher the axial resolution. The characteristics of OCT imaging depend on the optical wavelength used. In this paper, we investigated the wavelength dependence of ultrahigh-resolution (UHR) OCT using a supercontinuum for biomedical imaging. Wideband, high-power, low-noise supercontinua (SC) were generated at <italic> λ</italic> = 0.8, 1.1, 1.3, and 1.7 <italic>μ</italic>m based on ultrashort pulses and nonlinear fibers. The wavelength dependence of OCT imaging was examined quantitatively using biological phantoms. Ultrahigh-resolution imaging of a rat lung was demonstrated with <italic>λ</italic> = 0.8–1.0 <italic>μ</italic>m UHR-OCT. The variation of alveolar volume was estimated using three-dimensional image analysis. Finally, UHR-spectral domain-OCT and optical coherence microscopy at 1.7 <italic>μ</italic>m were developed, and high-resolution and high-penetration imaging of turbid tissue, especially mouse brain, was demonstrated.
Chronic Poisoning against Machine Learning Based IDSs Using Edge Pattern Detection

In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). Poisoning attack, which is one of the most recognized security threats towards machine learning- based IDSs, injects some adversarial samples into the training phase, inducing data drifting of training data and a significant performance decrease of target IDSs over testing data. In this paper, we adopt the Edge Pattern Detection (EPD) algorithm to design a novel poisoning method that attack against several machine learning algorithms used in IDSs. Specifically, we propose a boundary pattern detection algorithm to efficiently generate the points that are near to abnormal data but considered to be normal ones by current classifiers. Then, we introduce a Batch-EPD Boundary Pattern (BEBP) detection algorithm to overcome the limitation of the number of edge pattern points generated by EPD and to obtain more useful adversarial samples. Based on BEBP, we further present a moderate but effective poisoning method called chronic poisoning attack. Extensive experiments on synthetic and three real network data sets demonstrate the performance of the proposed poisoning method against several well-known machine learning algorithms and a practical intrusion detection method named FMIFS-LSSVM-IDS.