Miracle-dz/UniFAR-FTUs huggingface dataset:
Roseburia intestinalis inhibits oncostatin M and maintains tight junction integrity in a murine model of acute experimental colitis

Abstract Objective: Levels of oncostatin M (OSM) and the composition of gut microbiota predict responses to anti-TNF agents used for IBD therapy. Here, the aim was to investigate the effects of Roseburia intestinalis, a gut microbiota, on OSM and on intestinal barrier in colitis. Methods: In the murine model of 3% dextran sulfate sodium (DSS)-induced colitis, we tested disease activity index (DAI), colon length, histological score and expression of tight junction (TJ) proteins (ZO-1, occludin and claudin-1), OSM, TNF-α and TLR5. In addition, a cellular model was used to examine the role of R. intestinalis during secretion of OSM by lipopolysaccharide (LPS)-induced bone marrow-derived macrophages (BMDMs) isolated from wild-type (WT) and TLR5 knockout (TLR5 KO) mice. Furthermore, we evaluated the impact of OSM on expressions of TJ proteins by Caco-2 cells. Results: R. intestinalis in DSS-induced colitis decreased DAI score (p < .001), colon length shortening (6.46 ± 0.36 cm vs 5.65 ± 0.47 cm, p = .022), histological score (2.667 ± 1.15 vs 5.33 ± 1.14, p = .018) and increased expression of TJ proteins (p < .05). In addition, R. intestinalis reduced expression of OSM (p < .05) and TNF-α (p < .05), while increasing expression of TLR5 (p < .05). Furthermore, R. intestinalis reduced secretion of OSM (p < .05) by LPS-induced BMDMs isolated from WT and TLR5 KO mice. Moreover, OSM downregulated expression of TJ proteins (p < .05) by Caco-2 cells in a concentration-dependent manner. Conclusions: These results indicate that R. intestinalis attenuates inflammation in IBD by decreasing secretion of OSM and by promoting intestinal barrier function. Taken together, the data provide insight into the role of the gut microbiota in patients with IBD who are resistant to anti-TNF therapy.
Heterogeneous 32-Core Fiber with Square-Lattice Layout for High-Density Transmissions

We propose a 32-core fiber with rod/trench structures and achieve average <tex>$A_{\text{eff}}$</tex> of about <tex>$75\ \mu\mathrm{m}^{2}$</tex>, low XT of less than ∓27 dB/100km, and cable cutoff wavelength of less than <tex>$1.53\ \mu\mathrm{m}$</tex>.
Compact Simple Planar Millimeter- Wave Antenna for Unlicensed 57–64 GHz Band Applications

A compact millimeter-wave (mm-wave) microstrip antenna with a simple construction is presented in this paper. A defected ground plane with a rectangular radiating patch fed by a microstrip-line is manipulated to operate in the unlicensed V-band. A circular aperture in the ground plane is utilized to enhance the impedance bandwidth up to 15.12% ranges from 55 to 64 GHz to be suitable for industrial, scientific and medical (ISM) broadband applications. The proposed antenna parameters are analyzed extensively in order to achieve optimal operation in the desired frequency band. The proposed antenna is fabricated and tested. The experimental data of the fabricated prototype in terms of the reflection coefficient, show good agreement with the simulated results. The proposed design shows good radiation characteristics of average gain 6.5 dBi over the entire operating band. These features make the proposed antenna suitable candidate for mm-wave applications.
Deep Convolutional Network for Arabic Sentiment Analysis

Applying deep Convolutional Neural Networks (CNNs) for Sentiment Analysis (SA) has achieved improvements over the state-of-the-art. CNNs are powerful at extracting hierarchical representation of the input by stacking multiple convolutional and pooling layers. Word embedding is the common approach used for text representation in convolutional networks applied for sentiment analysis. Another technique is combining both word level and character level features. Recently, deep architectures based on character level features only showed more enhanced performance. In this paper two deep CNNs are applied for Arabic sentiment analysis using character level features only. A large scale dataset is constructed from available SA datasets in order to train networks. The dataset maintains opinions from different domains expressed in different Arabic forms (Modern Standard, Dialectal). Besides different machine learning algorithms as Logistic Regression, Support Vector Machine and Naïve Bayes have been applied to assess the performance on such a large dataset. Up to the available knowledge this is the first application of character level deep CNNs for Arabic language sentiment analysis. Results show the ability of Deep CNNs models to classify Arabic opinions depending on character representation only and register 7% enhanced accuracy compared to machine learning classifiers.
Saffron (Crocus sativus) is an autotriploid that evolved in Attica (Greece) from wild Crocus cartwrightianus

Crocus sativus is the source of saffron, which is made from dried stigmas of the plant. It is a male-sterile triploid that ever since its origin has been propagated vegetatively. The mode of evolution and area of origin of saffron are matters of long-lasting debates. Here we analyzed chloroplast genomes, genotyping-by-sequencing (GBS) data, nuclear single-copy genes, and genome sizes to solve these controversial issues. We could place 99.3% of saffron GBS alleles in Crocus cartwrightianus, a species occurring in southern mainland Greece and on Aegean islands, identifying it as the sole progenitor of saffron. Phylogenetic and population assignment analyses together with chloroplast polymorphisms indicated the wild C. cartwrightianus population south of Athens as most similar to C. sativus. We conclude that the crop is an autotriploid that evolved in Attica by combining two different genotypes of C. cartwrightianus. Vegetative propagation prevented afterwards segregation of the favorable traits of saffron.
CEAM: A Novel Approach Using Cycle Embeddings with Attention Mechanism for Stock Price Prediction

This paper presents a novel deep learning approach for the stock price prediction using a cycle embeddings with attention mechanism (CEAM) applying on Dual-Stage Attention-Based RNN (DA-RNN) model. The cycle characteristic is an important factor in time series prediction problem since it affects the trend of stock price. Thus, an effective cycle information can improve the prediction performance of stock price. In past years, many researches use the cycle feature with other features together as equally important, which might dilute the weight of cycle information since the cycle information should be paid more attention when making prediction on periodic data. As the result, we use CEAM making prediction with cycle information hidden in periodic data. The deep learning-based method has been developed in many fields and is a powerful prediction system. In addition, many researches use the embeddings feature and the attention mechanism to improve the prediction performance. In this paper, we propose a novel approach to capture the cycle information and use it to predict stock prices in U.S. stock market. The cycle information can be formed as a distributed vector as embeddings, called cycle embeddings. The CEAM approach use cycle embeddings to pay attention on periodically historical time series data by learning the cycle semantic relations between cycle characteristics and historical stock prices to optimize the prediction model. Therefore, the CEAM approach can improve the prediction performance for stock price. The experiments in this paper show that our proposed CEAM approach outperforms the another model which combines cycle feature with other features together as equally important.
Road Congestion based Traffic Management System with Dynamic Time Quantum

Existing traffic signaling systems follow a fair round robin-based scheduling where each lane gets an equal time quantum in the active(green) state. The paper proposes an alternative system where the allotted time for each lane is dynamically adjusted on the basis of traffic inflow. While this paper is similar to other such solutions with varied time quantum in terms of reliance on traffic density, the method of evaluating the traffic density is unique. A pixel density-based approach is proposed which provides a reliable yet cost-effective solution designed for cities of developing and under-developed nations.
A Markov-Based Prediction Algorithm for User Mobility at Heterogeneous Cloud Radio Access Network

To provide high-quality services to the explosion of users in cellular networks, forecasts of users' next location have been studied. But it is difficult to predict users' next location in urban areas because of mobility and dynamic road and traffic condition. To address the problems, it is used that users tend to move based on social relations. If the user's next location is predictable, the traffic changes will be also predictable. It can be applied to various studies, such as base station switching and handover forecasts that can solve energy consumption and service delay problems. In this study, we propose a Markov-based prediction algorithm to forecast the next location of users in H-CRAN (heterogeneous cloud radio access network). We use RRH(radio remote head) trajectory that consists of histories stored serving RRH for each user. Simulation results and discussions demonstrate the prediction accuracy compared with those of autoregressive models.
Transient and Crosstalk Analysis of Doped and Dielectric Inserted MLGNR Interconnects

In the present work, performance of normal multilayer graphene nanoribbon (NMLGNR), doped MLGNR (DMLGNR) and dielectric inserted MLGNR (DiMLGNR) are analysed using coupled line interconnect system. The transient and crosstalk analysis of NMLGNR, DMLGNR and DiMLGNR interconnects are presented. By performing transient analysis, the average propagation delay for DiMLGNR exhibits the best performance amongst the three types of interconnect-systems under consideration. The crosstalk delay of DiMLGNR interconnect is 62.7% and 25.7% lesser compared to that of NMLGNR and DMLGNR interconnects respectively.
Disaggregate Hotel Evaluation by Using Diverse Aspects from User Reviews

Experienced opinions about products and services can guide a potential user for a better purchase decision. Fine-grained aspect level opinions embedded within reviews must be explored to discover experienced users' latent opinion about the aspects (i.e. features of products like cost, value for money, etc.) and their relative importance. In this paper, we present an unsupervised approach for discovering coherent hotel aspects based on the user attention. This model effectively integrates techniques like topic modeling and word embeddings along with the frequent noun-adjective co-occurrence statistics to automatically discover coherent hotel aspects. Further supervised methods are used to understand the user's relative emphasis on the aspects and finally rank the hotels. This method does not assume any predefined seed words and discovers coherent level aspects by directly using user attention and word co-occurrence statistics in addition to topic modeling and word embeddings. The performance evaluation of this method was done by collecting various hotel reviews from multiple travel websites. Results show that the proposed methods improved the baseline performance up to 90%. Hence, the results thus obtained are very promising and indicate that the system is simple, scalable and most of all accurate in ranking hotels based on the latent aspects expressed in the user reviews.
Comparison of ELM and ANN on EMG Signals Obtained for Control of Robotic-Hand

The intelligent robotics industry is evolving and humanoid robots can be made, as well as being able to perform the physical functions of people. Robotic hands are vital for many people at this point. In this study, it was aimed to classify the Electromyography (EMG) signals received from the human arm in the correct direction and then to perform robotic hand application. This is very important in understanding and classifying the geometric structure of the object held in robotic hand applications. The classification time and accuracy ratio between ANN and ELM used for this classification were investigated. For this, 11 features were extracted and classifications were tried by using Extreme Learning Machine (ELM) and Artificial Neural Networks (ANN). The obtained successful classification results were compared with each other and applied to a robotic-hand.
Spatiotemporal Modeling of the Key Migratory Events During the Initiation of Adaptive Immunity

Initiation of adaptive immunity involves distinct migratory cell populations coming together in a highly dynamic and spatially organized process. However, we lack a detailed spatiotemporal map of these events due to our inability to track the fate of cells between anatomically distinct locations or functionally identify cell populations as migratory. We used photo-convertible transgenic mice (Kaede) to spatiotemporally track the fate and composition of the cell populations that leave the site of priming and enter the draining lymph node to initiate immunity. We show that following skin priming, the lymph node migratory population is principally composed of cells recruited to the site of priming, with a minor contribution from tissue resident cells. In combination with the YAe/Eα system, we also show that the majority of cells presenting antigen are CD103+CD11b+ dendritic cells that were recruited to the site of priming during the inflammatory response. This population has previously only been described in relation to mucosal tissues. Comprehensive phenotypic profiling of the cells migrating from the skin to the draining lymph node by mass cytometry revealed that in addition to dendritic cells, the migratory population also included CD4+ and CD8+ T cells, B cells, and neutrophils. Taking our complex spatiotemporal data set, we then generated a model of cell migration that quantifies and describes the dynamics of arrival, departure, and residence times of cells at the site of priming and in the draining lymph node throughout the time-course of the initiation of adaptive immunity. In addition, we have identified the mean migration time of migratory dendritic cells as they travel from the site of priming to the draining lymph node. These findings represent an unprecedented, detailed and quantitative map of cell dynamics and phenotypes during immunization, identifying where, when and which cells to target for immunomodulation in autoimmunity and vaccination strategies.
On Practical Considerations for Designing Indoor Visible Light Communication Systems

In this paper, we review on our recent proposed schemes based on multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) techniques so as to raise concerns about the practicability and applicability of futuristic indoor visible light communication (VLC) systems. These works include a precoding method for indoor MIMO-VLC system under the multi-user application scenario, the methods to weaken the correlation between receivers, a time domain reshuffling method for incoherent optical OFDM, and the methods combining OFDM transmission and dimming control pulses. These conceptual designs of VLC systems over indoor lighting infrastructure are feasible in practical VLC applications.
Patent Quality Classification System Using the Feature Extractor of Deep Recurrent Neural Network

This research is focused on using the deep learning technique to improve patent quality classification performance. The patent quality detection is a major issue in the patent analysis field, and it can provide high information of decision making for business and industry. Patent quality can be measured by the predicting system for innovation protection, market value, technical ability, leading position and so on. Therefore, in this research, we propose a powerful feature extraction approach to extract hidden features from the raw text of a patent document using deep recurrent neural network (DRNN) model. The texts such as title and abstract are providing the rich information about a patent status and potential in future. Our proposed patent quality classification system is based on a gradient-based neural network with four functions including word embeddings, DRNN, fully connected neural network, and cross entropy loss computation. In the experimental results, our proposed patent quality classifier has obtained the best classification performance, compared with other classification models.
InP-Based Monolithic Tunable Narrow Linewidth Optical Radio Frequency Signal Generator with Direct Modulation

A monolithic tunable optical radio frequency signal generator consisting of one semiconductor ring laser and two DFB lasers with direct electrical modulation by coplanar waveguide is designed. The key obstacles in the fabrication process are investigated. First-order ridge waveguide DFB lasers with a grating aspect ratio larger than 15 are fabricated.
A Four-element Compact Wideband MIMO Antenna for 5G Applications

We present a wideband four-element MIMO antenna having the overall dimension of $25\times 30\times 0.8\mathrm{mm}3$ for fifth generation (5G) applications. The single antenna consist of a rectangular patch element with an inverted F-shaped slot, a chicken neck shaped feedline and a slotted ground plate. Our proposed MIMO system is able to achieve a wide bandwidth of 13.68 GHz that cover three 5G bands (28/37/39 GHz) at $\mathrm{S}_{11}$ smaller than −15 dB. The antenna possesses better isolation of less than −20 dB for the whole interested band. The envelope correlation coefficient is below 0.002 for the whole bandwidth. The antenna has high gain of more than 5.28 dBi and its radiation pattern is omnidirectional. These results make the proposed antenna suitable for forthcoming 5G MIMO wireless applications.
The Expressions of Dickkopf-Related Protein 1 and Frizzled-Related Protein Are Negatively Correlated to Local Inflammation and Osteoarthritis Severity

Objective To investigate the presence of WNT antagonists Dickkopf-related protein 1 (DKK1), Frizzled-related protein (FRZB) and BMP antagonist Gremlin 1 (GREM1) in synovial fluid (SF) and serum, respectively, from end-stage knee osteoarthritis (OA) patients, and correlate their expression with other markers of OA. Design In a cross-sectional study, SF and serum were collected from OA patients (n = 132). The concentrations of DKK1, FRZB and GREM1 in SF and serum were determined using immunoassays. Correlation measurements were performed between groups and previously assessed disease markers, such as synovium nitric oxide (NO), inerleukin-1β (IL1β), tumor necrosis factor–α (TNFα), and prostaglandin E2 (PGE2). Results The OA patients with the celecoxib treatment till surgery have higher median SF FRZB values compared with the control (no treatment); the celecoxib 3-days before surgery stopped treatment group has higher median serum FRZB values than the control and the naproxen treatment group. The combinational analysis of SF DKK1 and SF FRZB negatively correlated with macroscopic cartilage scores and histological synovium scores in OA patients. The expression of DKK1 and FRZB in SF showed the same expression trend as their expression in serum. Furthermore, the SF concentration of DKK1 was positively correlated with FRZB in both SF and serum. In contrast, it was negatively correlated with synovium NO and IL1β. SF FRZB was negatively correlated with synovium NO, IL1β, cartilage PGE2, and age. Conclusions Our findings suggest DKK1 and FRZB were negatively correlated with OA severity and multiple pro-inflammatory cytokines. Our data indicate that DKK1 and FRZB can be joint disease–specific biomarkers.
ADAR1 promotes robust hypoxia signaling via distinct regulation of multiple HIF‐1α‐inhibiting factors

Adenosine deaminase acting on RNA (ADAR)‐catalyzed adenosine‐to‐inosine RNA editing is potentially dysregulated in neoplastic progression. However, how this transcriptome recoding process is functionally correlated with tumorigenesis remains largely elusive. Our analyses of RNA editome datasets identify hypoxia‐related genes as A‐to‐I editing targets. In particular, two negative regulators of HIF‐1A—the natural antisense transcript HIF1A‐AS2 and the ubiquitin ligase scaffold LIMD1—are directly but differentially modulated by ADAR1. We show that HIF1A‐AS2 antagonizes the expression of HIF‐1A in the immediate‐early phase of hypoxic challenge, likely through a convergent transcription competition in cis. ADAR1 in turn suppresses transcriptional progression of the antisense gene. In contrast, ADAR1 affects LIMD1 expression post‐transcriptionally, by interfering with the cytoplasmic translocation of LIMD1 mRNA and thus protein translation. This multi‐tier regulation coordinated by ADAR1 promotes robust and timely accumulation of HIF‐1α upon oxygen depletion and reinforces target gene induction and downstream angiogenesis. Our results pinpoint ADAR1‐HIF‐1α axis as a hitherto unrecognized key regulator in hypoxia.
Perbezaan Faktor Demografi dalam Efikasi Kendiri Guru dan Amalan Pengajaran dan Pembelajaran Abad ke 21: Differences in Demographic Factors in Teacher Self-Efficacy and 21st Century Teaching and Learning Practices

Kajian ini bertujuan untuk meninjau perbezaan faktor demografi (jantina, umur, kelayakan ikhtisas dan pengalaman mengajar) dalam efikasi kendiri guru dan amalan pengajaran dan pembelajaran abad ke-21 dalam kalangan guru-guru sekolah menengah di enam (6) buah sekolah menengah daerah Penampang di negeri Sabah. Seramai 329 guru-guru sekolah menengah telah dipilih menggunakan kaedah pensampelan rawak berstrata berkadar. Kajian ini berbentuk kajian kuantitatif bersifat tinjauan menggunakan sepenuhnya borang soal selidik untuk mengumpul data. Instrumen yang telah digunakan untuk memungut data terdiri daripada soal selidik Teachers’ Sense Of Efficacy (TSES) dan Amalan Pengajaran dan Pembelajaran Abad Ke-21. Usaha penerokaan literatur mendapati faktor demografi yang telah dikenal pasti releven dalam konteks kajian ini ialah umur, jantina, pengalaman mengajar dan kelulusan ikhtisas. Pakej analisis statistik ‘Statistical Package for Social Science’ (SPSS) for Windows version 25.0 telah digunakan untuk menganalisis data kuantitatif yang dikumpul. Analisis Ujian-t dan ANOVA mendapati tidak terdapat perbezaan yang signifikan amalan pengajaran dan pembelajaran abad ke-21 berdasarkan faktor demografi guru seperti jantina, umur, pengalaman mengajar dan kelayakan ikhtisas. Demikian juga efikasi kendiri guru, turut didapati tidak berbeza secara signifikan berdasarkan faktor jantina, umur dan kelayakan ikhtisas. Namun, efikasi kendiri guru didapati berbeza secara signifikan berdasarkan faktor pengalaman mengajar. This study aimed to examine differences in demographic factors (gender, age, academic achievement and teaching experience) in teacher self-efficacy and 21st-century teaching and learning practices among secondary school teachers in six (6) Penampang district secondary schools. Sabah state. A total of 329 secondary school teachers were selected using the standardized random sampling method. This study is a quantitative study in the form of a survey using the full questionnaire to collect data. The instruments used to collect data consist of the Teachers' Sense Of Efficacy (TSES) questionnaire and the 21st Century Teaching and Learning Practice. The literature exploration efforts found that demographic factors that were identified as relevant in the context of this study were age, gender, teaching experience and academic approval. The Statistical Package for Social Science (SPSS) for Windows version 25.0 statistical analysis package was used to analyze the quantitative data collected. T-test and ANOVA found that there were no significant differences in 21st-century teaching and learning practices based on teacher demographic factors such as gender, age, teaching experience and academic achievement. Similarly, teacher self-efficacy was found to be statistically insignificant in terms of gender, age, and academic credibility. However, teacher self-efficacy was found to be significantly different in terms of teaching experience factors.