Miracle-dz/UniFAR-FTUs huggingface dataset:
Essential oil mixture on rumen fermentation and microbial community – an in vitro study

Objective The objective of this study was to investigate the effects of essential oil mixture (EOM) supplementation on rumen fermentation characteristics and microbial changes in an in vitro. Methods Three experimental treatments were used: control (CON, no additive), EOM 0.1 (supplementation of 1 g EOM/kg of substrate), and EOM 0.2 (supplementation of 2 g EOM/kg of substrate). An in vitro fermentation experiment was carried out using strained rumen fluid for 12 and 24 h incubation periods. At each time point, in vitro dry matter digestibility (IVDMD), neutral detergent fiber digestibility (IVNDFD), pH, ammonia nitrogen (NH3-N), and volatile fatty acid (VFA) concentrations, and relative microbial diversity were estimated. Results After 24 h incubation, treatments involving EOM supplementation led to significantly higher IVDMD (treatments and quadratic effect; p = 0.019 and 0.008) and IVNDFD (linear effect; p = 0.068) than did the CON treatment. The EOM 0.2 supplementation group had the highest NH3-N concentration (treatments; p = 0.032). Both EOM supplementations did not affect total VFA concentration and the proportion of individual VFAs; however, total VFA tended to increase in EOM supplementation groups, after 12 h incubation (linear; p = 0.071). Relative protozoa abundance significantly increased following EOM supplementation (treatments, p<0.001). Selenomonas ruminantium and Ruminococcus albus (treatments; p<0.001 and p = 0.005), abundance was higher in the EOM 0.1 treatment group than in CON. The abundance of Butyrivibrio fibrisolvens, fungi and Ruminococcus flavefaciens (treatments; p< 0.001, p<0.001, and p = 0.005) was higher following EOM 0.2 treatment. Conclusion The addition of newly developed EOM increased IVDMD, IVNDFD, and tended to increase total VFA indicating that it may be used as a feed additive to improve rumen fermentation by modulating rumen microbial communities. Further studies would be required to investigate the detailed metabolic mechanism underlying the effects of EOM supplementation.
Senti-Attend: Image Captioning using Sentiment and Attention

There has been much recent work on image captioning models that describe the factual aspects of an image. Recently, some models have incorporated non-factual aspects into the captions, such as sentiment or style. However, such models typically have difficulty in balancing the semantic aspects of the image and the non-factual dimensions of the caption; in addition, it can be observed that humans may focus on different aspects of an image depending on the chosen sentiment or style of the caption. To address this, we design an attention-based model to better add sentiment to image captions. The model embeds and learns sentiment with respect to image-caption data, and uses both high-level and word-level sentiment information during the learning process. The model outperforms the state-of-the-art work in image captioning with sentiment using standard evaluation metrics. An analysis of generated captions also shows that our model does this by a better selection of the sentiment-bearing adjectives and adjective-noun pairs.
Construction and evaluation of a prototype topological information retrieval system

A prototype topological information retrieval system (TIRS) has been created and subjected to preliminary testing. Two standard test suites, the CACM collection and the MED collection, were used in the test. The queries in each test suite were converted into disjunctive normal form and one document-retrieval pass was performed for each converted query. Precision and recall were measured for each query. The average value for precision was 0.763, while the average value for recall was 0.188. These values correspond to values of 0.529 and 0.114 for a slightly modified Boolean retrieval run as a control case. An analysis of the results indicates that the TIRS prototype performs significantly better than the Boolean control at >or=0.999 level of confidence. <<ETX>>
Unsupervised Image Captioning

Deep neural networks have achieved great successes on the image captioning task. However, most of the existing models depend heavily on paired image-sentence datasets, which are very expensive to acquire. In this paper, we make the first attempt to train an image captioning model in an unsupervised manner. Instead of relying on manually labeled image-sentence pairs, our proposed model merely requires an image set, a sentence corpus, and an existing visual concept detector. The sentence corpus is used to teach the captioning model how to generate plausible sentences. Meanwhile, the knowledge in the visual concept detector is distilled into the captioning model to guide the model to recognize the visual concepts in an image. In order to further encourage the generated captions to be semantically consistent with the image, the image and caption are projected into a common latent space so that they can reconstruct each other. Given that the existing sentence corpora are mainly designed for linguistic research and are thus with little reference to image contents, we crawl a large-scale image description corpus of two million natural sentences to facilitate the unsupervised image captioning scenario. Experimental results show that our proposed model is able to produce quite promising results without any caption annotations.
Model Learning for Look-ahead Exploration in Continuous Control

We propose an exploration method that incorporates lookahead search over basic learnt skills and their dynamics, and use it for reinforcement learning (RL) of manipulation policies. Our skills are multi-goal policies learned in isolation in simpler environments using existing multigoal RL formulations, analogous to options or macroactions. Coarse skill dynamics, i.e., the state transition caused by a (complete) skill execution, are learnt and are unrolled forward during lookahead search. Policy search benefits from temporal abstraction during exploration, though itself operates over low-level primitive actions, and thus the resulting policies does not suffer from suboptimality and inflexibility caused by coarse skill chaining. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parametrized skills as building blocks of the policy itself, as opposed to guiding exploration. We show that the proposed exploration strategy results in effective learning of complex manipulation policies faster than current state-of-the-art RL methods, and converges to better policies than methods that use options or parameterized skills as building blocks of the policy itself, as opposed to guiding exploration.
Sleep quality associated with mood in elite athletes

ABSTRACT Objectives: This study aimed to analyze the association between sleep quality and mood in elite athletes of different competitive levels. Methods: Participants were 1041 elite athletes (aged 20.82 ± 6.62 years), with 671 men (64.5%/21.52 ± 6.90 years) and 370 women (35.5%/19.55 ± 5.89 years) from 10 individual sports and 6 team sports. Participants self-reported sleep quality on a Likert-type scale and mood was measured with the Brunel Mood Scale (BRUMS). The data were analyzed using the Kruskal–Wallis test, and binary logistic regression. Results: Results revealed that athletes who compete internationally are 84% more likely to have poor sleep quality than athletes who compete at a regional level. International athletes with good sleep quality showed greater vigor. National athletes with poor sleep quality showed more confusion, depression, and fatigue. Thus, mood and competitive level are factors associated with sleep quality. Confusion, fatigue, and tension impair sleep, and vigor reduces the likelihood of poor sleep. Conclusions: Sleep should be monitored, especially in international level athletes, in order to prevent sleep disorders during competitions. Coaches and athletes should use techniques and strategies for appropriate management of sleep and mood, to maintain the athletes in optimal condition before important competitions.
Prevalence of depressive symptoms among medical students: overview of systematic reviews

The prevalence of depressive symptoms among medical students has been found to be higher than among other students because of their longer studying time, higher workload and larger financial burden. Despite the availability of reviews examining this, some have focused solely on one country, whereas others used databases containing papers of solely one language; therefore, the results from reviews might not be comprehensive. Against this background, this overview aims to synthesise the results from all the published systematic reviews of depression among medical students, in order to provide a more accurate result.
A Thioredoxin Domain-Containing Protein Interacts with Pepino mosaic virus Triple Gene Block Protein 1

Pepino mosaic virus (PepMV) is a mechanically-transmitted tomato pathogen of importance worldwide. Interactions between the PepMV coat protein and triple gene block protein (TGBp1) with the host heat shock cognate protein 70 and catalase 1 (CAT1), respectively, have been previously reported by our lab. In this study, a novel tomato interactor (SlTXND9) was shown to bind the PepMV TGBp1 in yeast-two-hybrid screening, in vitro pull-down and bimolecular fluorescent complementation (BiFC) assays. SlTXND9 possesses part of the conserved thioredoxin (TRX) active site sequence (W__PC vs. WCXPC), and TXND9 orthologues cluster within the TRX phylogenetic superfamily closest to phosducin-like protein-3. In PepMV-infected and healthy Nicotiana benthamiana plants, NbTXND9 mRNA levels were comparable, and expression levels remained stable in both local and systemic leaves for 10 days post inoculation (dpi), as was also the case for catalase 1 (CAT1). To localize the TXND9 in plant cells, a polyclonal antiserum was produced. Purified α-SlTXND9 immunoglobulin (IgG) consistently detected a set of three protein bands in the range of 27–35 kDa, in the 1000 and 30,000 g pellets, and the soluble fraction of extracts of healthy and PepMV-infected N. benthamiana leaves, but not in the cell wall. These bands likely consist of the homologous protein NbTXND9 and its post-translationally modified derivatives. On electron microscopy, immuno-gold labelling of ultrathin sections of PepMV-infected N. benthamiana leaves using α-SlTXND9 IgG revealed particle accumulation close to plasmodesmata, suggesting a role in virus movement. Taken together, this study highlights a novel tomato-PepMV protein interaction and provides data on its localization in planta. Currently, studies focusing on the biological function of this interaction during PepMV infection are in progress.
Penalized likelihood and multiple testing

The classical multiple testing model remains an important practical area of statistics with new approaches still being developed. In this paper we develop a new multiple testing procedure inspired by a method sometimes used in a problem with a different focus. Namely, the inference after model selection problem. We note that solutions to that problem are often accomplished by making use of a penalized likelihood function. A classic example is the Bayesian information criterion (BIC) method. In this paper we construct a generalized BIC method and evaluate its properties as a multiple testing procedure. The procedure is applicable to a wide variety of statistical models including regression, contrasts, treatment versus control, change point, and others. Numerical work indicates that, in particular, for sparse models the new generalized BIC would be preferred over existing multiple testing procedures.
The Peak-SNR Performances of Voltage-Mode versus Time-Mode Circuits

Representing signals in the time domain, as pulses of variable time duration, is a promising solution for analog signal processing in CMOS technologies with low supply voltages. This brief aims at determining the peak signal-to-noise ratio of a pMOS–nMOS transistor stack used in both voltage-mode and time-mode circuits. A detailed noise analysis which includes both thermal and flicker noise contributions is performed in both domains. The analysis is applied to different CMOS technology nodes and compared to Spectre transient noise analysis tools. A silicon prototype was fabricated in the IBM 130-nm CMOS technology. Measurements confirm the accuracy of the proposed analysis.
GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation

Medical imaging is a domain which suffers from a paucity of manually annotated data for the training of learning algorithms. Manually delineating pathological regions at a pixel level is a time consuming process, especially in 3D images, and often requires the time of a trained expert. As a result, supervised machine learning solutions must make do with small amounts of labelled data, despite there often being additional unlabelled data available. Whilst of less value than labelled images, these unlabelled images can contain potentially useful information. In this paper we propose combining both labelled and unlabelled data within a GAN framework, before using the resulting network to produce images for use when training a segmentation network. We explore the task of deep grey matter multi-class segmentation in an AD dataset and show that the proposed method leads to a significant improvement in segmentation results, particularly in cases where the amount of labelled data is restricted. We show that this improvement is largely driven by a greater ability to segment the structures known to be the most affected by AD, thereby demonstrating the benefits of exposing the system to more examples of pathological anatomical variation. We also show how a shift in domain of the training data from young and healthy towards older and more pathological examples leads to better segmentations of the latter cases, and that this leads to a significant improvement in the ability for the computed segmentations to stratify cases of AD.
Visible-light optical coherence tomography-based multimodal system for quantitative fundus autofluorescence imaging

Fundus autofluorescence (FAF) imaging is commonly used in ophthalmic clinics for diagnosis and monitoring of retinal diseases. Lipofuscin in the retinal pigment epithelium (RPE), with A2E as its most abundant component and a visual cycle by-product, is the major fluorophore of FAF. Lipofuscin accumulates with age and is implicated in degenerative retinal diseases. The amount of lipofuscin in RPE can be assessed by quantitative measurement of FAF. However, the currently available FAF imaging technologies are not capable of quantifying the absolute intensity of FAF, which is essential for comparing images from different individuals, and from the same individual over time. One major technical difficulty is to compensate the signal attenuation by ocular media anterior to the RPE (pre-RPE media). FAF intensity is also influenced by fluctuations in imaging conditions such as illumination power and detector sensitivity, all of which need to be compensated. In this review, we present the concept and research progress of using visible-light optical coherence tomography-based simultaneous multimodal retinal imaging to compensate signal attenuation by pre-RPE media and the influence of parameters of the acquisition system for accurate measurement of FAF intensities. Impact statement Quantitative fundus autofluorescence imaging with simultaneous visible-light optical coherence tomography-based multimodal technology has potential significant impact on the diagnosis and monitoring the progression of retinal diseases.
Age-Specific Reference Ranges for Serum Prostate-Specific Antigen in Korean Men

From the Department of Urology & Urological Science Institute, Yonsei University College of Medicine, Seoul, Department of Urology, Ilsan Hospital, National Health Insurance Corporation, Ilsan, Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Department of Urology, College of Medicine, Keimyung University, Daegu, Department of Urology, Inha University, Incheon, Department of Urology, Ajou University, Suwon, Department of Urology, Ulsan University, Ulsan, Department of Urology, Inje University, Busan, Department of Urology, Hallym University, Chuncheon, Korea
Automated Threat Report Classification over Multi-Source Data

With an increase in targeted attacks such as advanced persistent threats (APTs), enterprise system defenders require comprehensive frameworks that allow them to collaborate and evaluate their defense systems against such attacks. MITRE has developed a framework which includes a database of different kill-chains, tactics, techniques, and procedures that attackers employ to perform these attacks. In this work, we leverage natural language processing techniques to extract attacker actions from threat report documents generated by different organizations and automatically classify them into standardized tactics and techniques, while providing relevant mitigation advisories for each attack. A naïve method to achieve this is by training a machine learning model to predict labels that associate the reports with relevant categories. In practice, however, sufficient labeled data for model training is not always readily available, so that training and test data come from different sources, resulting in bias. A naïve model would typically underperform in such a situation. We address this major challenge by incorporating an importance weighting scheme called bias correction that efficiently utilizes available labeled data, given threat reports, whose categories are to be automatically predicted. We empirically evaluated our approach on 18,257 real-world threat reports generated between year 2000 and 2018 from various computer security organizations to demonstrate its superiority by comparing its performance with an existing approach.
The relationship between smoking and dysmenorrhea: A meta-analysis

ABSTRACT Many studies have investigated the potential association between smoking and dysmenorrhea. However, results from such studies have been inconsistent. In this study, we assessed the association between smoking and dysmenorrhea by meta-analysis. We performed a systematic search of the international databases, including PubMed, Scopus, Web of Science, EBSCO and Google Scholar by the MeSH heading and/or additional terms to obtain relevant studies published from 1990 until 2017. I2 statistics were used to assess heterogeneity. Pooled effects size was obtained using a random effects model. Subgroup analyses were also conducted. Data were analyzed through Stata software version 12 (Stata Corp, College Station, TX, USA). A total of fourteen studies were included in meta-analysis. A significant positive association was observed between current smoking and dysmenorrhea in both the unadjusted (odds ratio [OR] = 1.60; 95 percent confidence interval [CI]: 1.35, 1.85) and adjusted models (AOR = 1.44; 95 percent CI: 1.18, 1.69). Also, the association between current smoking and primary dysmenorrhea was significant only in the unadjusted model (OR = 1.53; 95 percent CI: 1.21, 1.85). The pooled effects size showed a significant association between smoking and dysmenorrhea in the fourteen eligible studies. This provides a new approach for prevention from dysmenorrhea in females for policymakers.
A Data-Driven Verilog-A ReRAM Model

The translation of emerging application concepts that exploit resistive random access memory (ReRAM) into large-scale practical systems requires realistic yet computationally efficient device models. Here, we present a ReRAM model, where device current–voltage characteristics and resistive switching rate are expressed as a function of: 1) bias voltage and 2) initial resistive state (RS). The model versatility is validated on detailed characterization data, for both filamentary valence change memory and nonfilamentary ReRAM technologies, where device resistance is swept across its operating range using multiple input voltage levels. Furthermore, the proposed model embodies a window function which features a simple mathematical form analytically describing RS response under constant bias voltage as extracted from physical device response data. Its Verilog-A implementation captures the ReRAM memory effect without requiring integration of the model state variable, making it suitable for fast and/or large-scale simulations and overall interoperable with current design tools.
An OpenCL-based SIFT Accelerator for Image Features Extraction on FPGA in Mobile Edge Computing Environment

Mobile Edge Computing (MEC) has become a promising technology for future cloud computing. MEC enables low-latency service by extending the computation capability to the edge of the network; therefore, hardware accelerators are an essential part of MEC server. As graphics processing unit (GPU) is high-energy consumption, field programmable gate array (FPGA) can be an alternative to accelerate services in power-limited environment as MEC. In this paper, we present an OpenCL-based SIFT accelerator for image features extraction on FPGA that can be deployed as a service on MEC environment. The experimental result on an image with a size of 1024 × 1024 shows that our accelerator speeds-up the bottleneck of the SIFT algorithm up to 13.7 times compared to software version and the energy efficiency is 1.38 times better than the GPU accelerator on an high-end NVIDIA GPU.
Hyperactivation of ERK by multiple mechanisms is toxic to RTK-RAS mutation-driven lung adenocarcinoma cells

Synthetic lethality results when mutant KRAS and EGFR proteins are co-expressed in human lung adenocarcinoma (LUAD) cells, revealing the biological basis for mutual exclusivity of KRAS and EGFR mutations. We have now defined the biochemical events responsible for the toxic effects by combining pharmacological and genetic approaches and to show that signaling through extracellular signal-regulated kinases (ERK1/2) mediates the toxicity. These findings imply that tumors with mutant oncogenes in the RAS pathway must restrain the activity of ERK1/2 to avoid toxicities and enable tumor growth. A dual specificity phosphatase, DUSP6, that negatively regulates phosphorylation of (P)-ERK is up-regulated in EGFR- or KRAS-mutant LUAD, potentially protecting cells with mutations in the RAS signaling pathway, a proposal supported by experiments with DUSP6-specific siRNA and an inhibitory drug. Targeting DUSP6 or other negative regulators might offer a treatment strategy for certain cancers by inducing the toxic effects of RAS-mediated signaling.
Comparison of different molecular and serological assays

| The authors present an update of the molecular and serological techniques used in veterinary parasitology. The application of molecular and serological techniques in veterinary parasitology is increasing worldwide. Several new molecular techniques have been made available for sensitive and specific diagnosis of parasitic diseases. These tests may be either nucleic acid or protein based assays. In nucleic acid based assays, PCR and its variants such as nested PCR, multiplex PCR are the most frequently used tools. The parasitic nucleic acid target sequences are DNA and ribosomal RNA. The DNA sequences provide high specificity for the identification of parasite in biological samples. Real-Time PCR is also increasingly in use since its cost is decreasing. Some other tests such as RAPD, AFLP and RFLP are also used for several parasitic disease diagnoses. Moreover now, complete genome sequencing of many of parasites has been achieved. The complete genome data also provides suitable gene sequence which, when cloned and expressed provides antigens for diagnosis and vaccine preparation. Similarly, protein based serological assays such as ELISA, dot-ELISA, Fluorescence antibody tests have also been used for parasitic diagnosis. However, the use of molecular tools in veterinary parasitology remains restricted to research rather than use in field as diagnostic tool.