Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergence of Novel Lineage of Foot-and-Mouth Disease Virus Serotype Asia1 BD-18 (G-IX) in Bangladesh

In 2018, a novel circulatory foot-and-mouth disease virus serotype Asia1 BD-18 (G-IX) lineage containing a unique mutation has emerged in Bangladesh. This emergence may be following the evolutionary roadmap of previously reported lineage. Inappropriate vaccination and inefficient outbreak surveillance possibly contributed to the current episode of emergence.
Psychological predictors of adherence to oral hypoglycaemic agents: an application of the ProMAS questionnaire

Abstract Objectives: This study aimed to identify psychological predictors of oral hypoglycaemic agent (OHA) adherence and to compare adherence rates between a novel and well-known instrument, i.e. the Probabilistic Medication Adherence Scale (ProMAS) and Medication Adherence Report Scale (MARS-5). Design and main outcome measures: A longitudinal study design was applied with surveys at baseline and 6-month follow-up. At baseline, OHA adherence using the ProMAS and MARS-5, socio-cognitive determinants and demographics were assessed. At follow-up, the ProMAS was applied as outcome measure, on which socio-cognitive determinants and demographics were regressed using linear regression analysis. Results: The baseline and follow-up sample included 304 and 231 participants, respectively. When applying cut-off points of ≥15 for the ProMAS and ≥23 for the MARS-5, 47.4 and 89.5% adhered to their OHAs. Consistent predictors of better adherence comprised a low education, lower severity perceptions, and higher self-efficacy and intention. After correcting for baseline adherence, a low education and higher self-efficacy remained significant adherence predictors. Conclusions: Compared to the MARS-5, ProMAS data was less skewed, similar to objectively collected data, and yielded insights in a broader spectrum of (non)-adherence behaviours. Results stress the need for adherence improving interventions which particularly should target higher educated patients and patients with low self-efficacy, low intention and high severity perceptions.
Systematic profiling of alternative splicing events and splicing factors in left- and right-sided colon cancer

Left- and right-sided colon cancer (LC and RC) differ substantially in their molecular characteristics and prognoses, and are thus treated using different strategies. We systematically analyzed alternative splicing (AS) events and splicing factors in LC and RC. RNA-seq data were used for genome-wide profiling of AS events that could distinguish LC from RC. The Exon Skip splicing pattern was more common in RC, while the Retained Intron pattern was more common in LC. The AS events that were upregulated in RC were enriched for genes in the axon guidance pathway, while those that were upregulated in LC were enriched for genes in immune-related pathways. Prognostic models based on differentially expressed AS events were built, and a prognostic signature based on these AS events performed well for risk stratification in colon cancer patients. A correlation network of differentially expressed AS events and differentially expressed splicing factors was constructed, and RBM25 was identified as the hub gene in the network. In conclusion, large differences in AS events may contribute to the phenotypic differences between LC and RC. The differentially expressed AS events reported herein could be used as biomarkers and treatment targets for colon cancer.
Stimulation of Piezo1 by mechanical signals promotes bone anabolism

Mechanical loading, such as caused by exercise, stimulates bone formation by osteoblasts and increases bone strength, but the mechanisms are poorly understood. Osteocytes reside in bone matrix, sense changes in mechanical load, and produce signals that alter bone formation by osteoblasts. We report that the ion channel Piezo1 is required for changes in gene expression induced by fluid shear stress in cultured osteocytes and stimulation of Piezo1 by a small molecule agonist is sufficient to replicate the effects of fluid flow on osteocytes. Conditional deletion of Piezo1 in osteoblasts and osteocytes notably reduced bone mass and strength in mice. Conversely, administration of a Piezo1 agonist to adult mice increased bone mass, mimicking the effects of mechanical loading. These results demonstrate that Piezo1 is a mechanosensitive ion channel by which osteoblast lineage cells sense and respond to changes in mechanical load and identify a novel target for anabolic bone therapy.
Charge Storage Mechanisms of Single Layer Graphene in Ionic Liquid.

Graphene-based carbon materials are promising candidates for electrical double-layer (EDL) capacitors, and there is considerable interest in understanding the structure and properties of graphene/electrolyte interface. Here, electro-chemical impedance spectroscopy (EIS) and electrochemical quartz crystal microbalance (EQCM) are used to character-ize the ion fluxes and adsorption on single layer graphene in neat ionic liquid (EMI-TFSI) electrolyte. It is found that a positively charged ion-species desorption and an ion re-organization dominate the double layer charging during positive and negative polarizations, respectively, leading to the increase in EDL capacitance with applied potential.
Deriving and refining atomic models in crystallography and cryo-EM: the latest Phenix tools to facilitate structure analysis

In structural biology, deriving and refining atomic models into maps obtained from X-ray crystallography or cryo electron microscopy (cryo-EM) is essential for the detailed interpretation of a structure and its functional implications through interactions so that for example hydrogen bonds, drug specificity and associated molecular mechanisms can be analysed. This commentary summarizes the latest features of the Phenix software and also highlights the fact that cryo-EM increasingly contributes to data depositions in the PDB and EMDB.
Mogroside V protects porcine oocytes from in vitro ageing by reducing oxidative stress through SIRT1 upregulation

Postovulatory ageing compromises oocyte quality and subsequent development in various manners. We aimed to assay the protective effects of mogroside V on porcine oocyte quality during in vitro ageing and explore the related causes. We observed that mogroside V can effectively maintain normal oocyte morphology and early embryo development competence after prolonged culture for 24 h. Moreover, mogroside V can markedly reduce reactive oxygen species (ROS) levels, alleviate spindle formation and chromosome alignment abnormalities, improve mitochondrial contents, adenosine triphosphate (ATP) levels and the membrane potential (ΔΨm), and reduce early apoptosis in aged oocytes. We examined the molecular changes and found that SIRT1 expression was decreased in in vitro aged oocytes but was maintained by exposure to mogroside V. However, when SIRT1 was successfully inhibited by the specific inhibitor EX-527, mogroside V could not reduce ROS levels or alleviate abnormal spindle organization and chromosome misalignment. In summary, our results demonstrated that mogroside V can alleviate the deterioration of oocyte quality during in vitro ageing, possibly by reducing oxidative stress through SIRT1 upregulation.
Predictive model for chaplain taxonomy patterns identified through latent class analysis among infants in a pediatric inpatient setting

Abstract This study investigated patterns of spiritual care provided to inpatient infants and their parents, based on a taxonomy developed to describe spiritual care activities provided by chaplains. Data from 821 visits with 433 patients were included in the analyses. We applied a data-driven statistical approach, Latent Class Analysis (LCA), to identify patterns of taxonomy items that may be used for spiritual care. Three distinct patterns were identified and a predictive model was built to link a series of predictors to these patterns. Hospital length of stay and whether a visit is an initial or follow-up within an admission were significantly associated with the identified taxonomy patterns. These findings are helpful in understanding predictors and the nature of spiritual care delivery in an inpatient setting with infants. To our knowledge, this is the first application of LCA in research related to healthcare chaplaincy.
Biotransformation of lignin: Mechanisms, applications and future work

As one of the most abundant polymers in biosphere, lignin has attracted extensive attention as a kind of promising feedstock for biofuel and bio‐based products. However, the utilization of lignin presents various challenges in that its complex composition and structure and high resistance to degradation. Lignin conversion through biological platform harnesses the catalytic power of microorganisms to decompose complex lignin molecules and obtain value‐added products through biosynthesis. Given the heterogeneity of lignin, various microbial metabolic pathways are involved in lignin bioconversion processes, which has been characterized in extensive research work. With different types of lignin substrates (e.g., model compounds, technical lignin, and lignocellulosic biomass), several bacterial and fungal species have been proved to own lignin‐degrading abilities and accumulate microbial products (e.g., lipid and polyhydroxyalkanoates), while the lignin conversion efficiencies are still relatively low. Genetic and metabolic strategies have been developed to enhance lignin biodegradation by reprogramming microbial metabolism, and diverse products, such as vanillin and dicarboxylic acids were also produced from lignin. This article aims at presenting a comprehensive review on lignin bioconversion including lignin degradation mechanisms, metabolic pathways, and applications for the production of value‐added bioproducts. Advanced techniques on genetic and metabolic engineering are also covered in the recent development of biological platforms for lignin utilization. To conclude this article, the existing challenges for efficient lignin bioprocessing are analyzed and possible directions for future work are proposed.
Robotic Manipulator Control by Using Machine Learning Algorithms, A Review

SWETHA DANTHALA , SEERAMSRINIVASA RAO , KASIPRASAD MANNEPALLI , & DHANTALA SHILPA 4 1 Research Scholar, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Vijayawada, Guntur, Andhra Pradesh, India Professor, Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Vijayawada, Guntur, Andhra Pradesh, India Associate Professor, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Vijayawada, Guntur, Andhra Pradesh, India Assistant Professor, Department of Mechanical Engineering, Guntur Engineering College, Guntur, Andhra Pradesh, India ABSTRACT
Status of Genome Mapping and Use in Cotton Improvement

Cotton (Gossypium sp.) is the world’s leading natural fiber crop. The use of molecular markers to enhance plant-breeding efforts is being widely studied. A major area of research is the use of molecular markers to identify and manipulate chromosome segments QTL (quantitative trait loci) controlling quantitative traits. The importance of genetic maps composed of molecular markers well documented for several economically important crops. The objective of this paper is to review current marker technology and genome mapping efforts and use of marker-assisted selection in cotton improvement.
New peptide vaccine for HER2-expressing breast tumors.

A new HER2-derived peptide vaccine called GP2 reduces risk of breast cancer recurrence in patients whose tumors have any degree of HER2 expression— especially for HER2+ tumors. Elizabeth Mittendorf, M.D., Ph.D., associate professor of surgical oncology at the University of Texas M. D. Anderson Cancer Center in Houston, led the phase II randomized trial that made this finding. She presented the study at last September’s American Society of Clinical Oncology Breast Cancer Symposium. The phase II GP2 vaccine trial enrolled patients “with any degree of HER2 expression on IHC,” Mittendorf said, referring to the immunohistochemistry test used to determine a breast tumor’s level of HER2 expression. Tumors with a normal level of HER2 protein are scored 0–1+ and classified HER2–. Tumors with a moderate HER2 level are scored 2+ and sent for more testing to determine treatment. Tumors with a high HER2 level are scored 3+ and considered HER2+. A patient with an HER2+ tumor is a candidate for trastuzumab (Herceptin), which targets HER2. About 15%–20% of breast tumors are HER2+. GP2 is given monthly for 6 months, followed by four cycles of booster shots every 6 months. Because it is a major histocompatibility complex (MHC) class I peptide that stimulates CD8+ T cells, GP2 can work only in patients who carry the allele for human leukocyte antigen (HLA)-A2 or HLA-A3. To enter the study, a patient had to be HLA-A2+ and have no evidence of disease after completing standard breast cancer treatment.
Location-Aware Personalized News Recommendation with Deep Neural Network

Recently attractiveness of cell phones also rapid development of the mobile web increases, clients would now be able see the news feed whenever they need; along these lines, their news partialities are usually identified with users geographical circumstances. Thus, there are numerous researches focus on location aware news recommendation techniques, which recommends the clients news happening closest to them. However, in a real world, clients' news recommendations are not just associated with their locations, yet in addition strongly identified with their own interests. This system propose a hybrid strategy called location aware customized news recommendations with explicit semantic analysis, which prescribes news utilizing both the clients' personal interests and their topographical contexts. In any case, the Wikipedia based theme space in LP-ESA experiences the issues of high dimensionality, sparsity, and redundancy, which incredibly degrade the execution of LP-ESA. For tackling this problem, here proposed a LP-DSA for exploiting recommendation oriented deep neural system for removing dense, abstract, low dimensional and effective feature representations for users, news and locations.
A SAREF Extension for Semantic Interoperability in the Industry and Manufacturing Domain

This chapter builds on the success achieved in the past years with SAREF, a reference ontology for IoT created in close interaction with the industry during a study requested by the European Commission in 2015. SAREF is published as an ETSI Technical Specification series that also includes dedicated extensions to specific domains. A proof-of-concept solution based on SAREF and implemented on existing commercial products has been demonstrated in 2017. This chapter introduces a specialists task force that was recently requested by ETSI and the European Commission to further extend SAREF to the industry and manufacturing domain
A simplified model of a fueldraulic actuation system with application to load estimation

In this work, a simplified model of the compressor variable stator vane fueldraulic actuation system of a jet engine is presented. The actuation system is a sub-assembly of the engine’s hydro-mechanical unit. A unique characteristic of the actuator is an internal cooling flow which prevents the overheating of fuel. It is shown that the effect of the cooling flow is well represented by a static input nonlinearity. The resulting model is of the Hammerstein structure. It is then shown that the model can be used for the estimation of the actuator’s external load. The results are validated using an accurate real system simulator.
A scaled‐down neural conversational model for chatbots

Deep learning has revolutionized the field of conversation modeling. A lot of the research has been toward making the conversational agent more human‐like. As a result, overall the model size increases. Bigger models require more data and are costly to build and maintain. Often, for some tasks, high‐quality responses are not necessary. In this paper, a model that consumes fewer resources and a way to augment conversation data without increasing the size of the vocabulary is proposed. The proposed model uses a modified version of the GRU instead of the LSTM to encode and decode sequences of text.
Phonetic aware techniques for Speaker Verification

The goal of this thesis is to improve current state-of-the-art techniques in speaker verification (SV), typically based on “identity-vectors” (i-vectors) and deep neural network (DNN), by exploiting diverse (phonetic) information extracted using various techniques such as automatic speech recognition (ASR). Different speakers span different subspaces within a universal acoustic space, usually modelled by “universal background model”. The speaker-specific subspace depends on the speaker’s voice characteristics, but also on the verbalised text of a speaker. In current state-of-the-art SV systems, i-vectors are extracted by applying a factor analysis technique to obtain low dimensional speaker-specific representation. Furthermore, DNN output is also employed in a conventional i-vector framework to model phonetic information embedded in the speech signal. This thesis proposes various techniques to exploit phonetic knowledge of speech to further enrich speaker characteristics. More specifically, the techniques proposed in this thesis are applied to various SV tasks, namely, text-independent and text-dependent SV. For text-independent SV task, several ASR systems are developed and applied to compute phonetic posterior probabilities, subsequently exploited to enhance the speaker-specific information included in i-vectors. These approaches are then extended for text-dependent SV task, exploiting temporal information in a principled way, i.e., by using dynamic time warping applied on speaker informative vectors. Finally, as opposed to training DNN with phonetic information, DNN is trained in an end-toend fashion to directly discriminate speakers. The baseline end-to-end SV approach consists of mapping a variable length speech segment to a fixed dimensional speaker vector by estimating the mean of hidden representations in DNN structure. We improve upon this technique by computing a distance function between two utterances which takes into account common phonetic units. The whole network is optimized by employing a triplet-loss objective function. The proposed approaches are evaluated on commonly used datasets such as NIST SRE 2010 and RSR2015. Significant improvements are observed over the baseline systems on both the text-dependent and textindependent SV tasks by applying phonetic knowledge.
FUSION OF RECIRCULATION NEURAL NETWORKS FOR REAL-TIME NETWORK INTRUSION DETECTION AND RECOGNITION

Intrusion detection system is one of the essential security tools of modern information systems. Continuous development of new types of attacks re quires the development of intelligent approaches for intrusion detection capable to detect newest attacks. We present recirculation neural network based approach which lets to detect previously unseen attack types in real-time mode and to further correct recognition of this types. In this paper we use recirculation neural networks as an anomaly detector as well as a misuse detector, ensemble of anomaly and misuse detectors, fusion of several detectors for correct detection and recognition of attack types. The experiments held on both KDD’99 data and real network traffic data show promising results.
Pencarian Citra Digital Berbasiskan Konten dengan Ekstraksi Fitur HSV, ACD, dan GLCM

Pencarian citra berbasiskan konten adalah teknik untuk mencari citra yang piksel-pikselnya memiliki karakteristik sama atau mendekati dengan sebuah sekumpulan citra. Penelitian ini menggunakan warna dan tekstur sebagai ekstraksi fitur dari sebuah citra. Untuk warna digunakan dua bentuk pendekatan, yaitu HSV ( Hue , Saturation , Value ) dan ACD (Average Color Dominance). Sedangkan tekstur menggunakan metode Gray Level Co-occurrence Matrices . Kemudian untuk proses pencocokan antara citra query dengan citra target yang ada pada sekumpulan citra dilakukan perhitungan jarak ( Euclidean Distance ) dari citra query dengan citra target pada basis data citra. Dari hasil pengujian didapatkan bahwa pencarian citra melalui ekstraksi warna (HSV, ACD) dan tekstur (GLCM) menghasilkan hasil pencarian yang lebih akurat dibanding dengan hanya ekstraksi warna saja atau ekstraksi tekstur saja. Selain itu, citra yang mengandung Point of Interest (POI) tunggal jelas lebih baik dalam pencarian dibandingkan dengan citra yang memiliki banyak POI. Content-based image retrieval is a technique to search for a set of images based on its pixels that have the same characteristics with query image. This research used color and texture as feature extraction of an image. For color, we use two approaches, namely the HSV (Hue, Saturatuin, Value) and ACD (Average Color Dominance), while for the texture, we use the Gray Level Coocurenes Matrices method. The matching process between the query image with the target image is done by calculating the distance by using Euclidean Distance. The experimental results showed that the extraction of image retrieval through color (HSV, ACD) and texture (GLCM) produces more accurate results compared with extraction using color or texture only. In addition, images that have a single and clear Point of Interest (POI) have better result than the image that has a lot of POI.
Computational Analysis of the Effect of fbn1 Gene Mutations in the Marfan Syndrome

Fibrillin is a large glycoprotein synthesized in the tissues involved in Marfan syndrome, and known to be involved in tissue elasticity. The syndrome is corresponded to fbn1 gene and is characterized by cardiovascular, ocular, and skeletal abnormalities. N-terminus of fibrillin 1 binds to microfibril-associated glycoprotein 1 (MAGP-1) in a calcium-dependent manner. In this study, the amino acid sequence of fibrillin protein of a patient with Marfan syndrome (accession No. XM- 034890) has been compared to the amino acid sequence of normal fibrillin (accession No. P-35555). In this patient, mutations causing a Gly (267) to Thr and Tyr (532) to Cys amino acids changes have been occurred. Method of Garnier was used to predict the secondary structure of the proteins and probable N-glycosylation sites were searched. Results of these analyses show no significant structural difference between the mutant and normal fibrillin proteins. Although in some cases characterization of the binding requirements has shown that a folded, secondary structure of fibrillin was necessary for binding, our results are in agreement with those findings that at least in some cases, fibrillin gene defects are not sole determinants of Marfan phenotype.
Immunization of adult hens against infectious bursal disease virus.

The recent interest in the immunosuppressive effect of early infection with infectious bursal disease virus (IBDV) in chicks without maternal antibody has raised questions of how susceptible breeding flocks can be immunized. Uniform infection and immunization of adults is desirable if chicks are to be protected by passive transfer of maternal antibody from the dam through the egg. Two previous attempts (1) to infect adult birds by ocular administration of virus gave negative results in one case and positive results in the other.