Miracle-dz/UniFAR-FTUs huggingface dataset:
Thiamine status, metabolism and application in dairy cows: a review

Abstract As the co-enzyme of pyruvate dehydrogenase and α-ketoglutarate dehydrogenase, thiamine plays a critical role in carbohydrate metabolism in dairy cows. Apart from feedstuff, microbial thiamine synthesis in the rumen is the main source for dairy cows. However, the amount of ruminal thiamine synthesis, which is influenced by dietary N levels and forage to concentrate ratio, varies greatly. Notably, when dairy cows are overfed high-grain diets, subacute ruminal acidosis (SARA) occurs and results in thiamine deficiency. Thiamine deficiency is characterised by decreased ruminal and blood thiamine concentrations and an increased blood thiamine pyrophosphate effect to >45 %. Thiamine deficiency caused by SARA is mainly related to the increased thiamine requirement during high grain feeding, decreased bacterial thiamine synthesis in the rumen, increased thiamine degradation by thiaminase, and decreased thiamine absorption by transporters. Interestingly, thiamine deficiency can be reversed by exogenous thiamine supplementation in the diet. Besides, thiamine supplementation has beneficial effects in dairy cows, such as increased milk and component production and attenuated SARA by improving rumen fermentation, balancing bacterial community and alleviating inflammatory response in the ruminal epithelium. However, there is no conclusive dietary thiamine recommendation for dairy cows, and the impacts of thiamine supplementation on protozoa, solid-attached bacteria, rumen wall-adherent bacteria and nutrient metabolism in dairy cows are still unclear. This knowledge is critical to understand thiamine status and function in dairy cows. Overall, the present review described the current state of knowledge on thiamine nutrition in dairy cows and the major problems that must be addressed in future research.
Small but large enough: structural properties of armless mitochondrial tRNAs from the nematode Romanomermis culicivorax

Abstract As adapter molecules to convert the nucleic acid information into the amino acid sequence, tRNAs play a central role in protein synthesis. To fulfill this function in a reliable way, tRNAs exhibit highly conserved structural features common in all organisms and in all cellular compartments active in translation. However, in mitochondria of metazoans, certain dramatic deviations from the consensus tRNA structure are described, where some tRNAs lack the D- or T-arm without losing their function. In Enoplea, this miniaturization comes to an extreme, and functional mitochondrial tRNAs can lack both arms, leading to a considerable size reduction. Here, we investigate the secondary and tertiary structure of two such armless tRNAs from Romanomermis culicivorax. Despite their high AU content, the transcripts fold into a single and surprisingly stable hairpin structure, deviating from standard tRNAs. The three-dimensional form is boomerang-like and diverges from the standard L-shape. These results indicate that such unconventional miniaturized tRNAs can still fold into a tRNA-like shape, although their length and secondary structure are very unusual. They highlight the remarkable flexibility of the protein synthesis apparatus and suggest that the translational machinery of Enoplea mitochondria may show compensatory adaptations to accommodate these armless tRNAs for efficient translation.
Methodologic Innovation in Creating Clinical Practice Guidelines: Insights From the 2018 Society of Critical Care Medicine Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption Guideline Effort

Objectives: To describe novel guideline development strategies created and implemented as part of the Society of Critical Care Medicine’s 2018 clinical practice guidelines for pain, agitation (sedation), delirium, immobility (rehabilitation/mobility), and sleep (disruption) in critically ill adults. Design: We involved critical illness survivors from start to finish, used and expanded upon Grading of Recommendations, Assessment, Development and Evaluation methodology for making recommendations, identified evidence gaps, and developed communication strategies to mitigate challenges. Setting/Subjects: Thirty-two experts from five countries, across five topic-specific sections; four methodologists, two medical librarians, four critical illness survivors, and two Society of Critical Care Medicine support staff. Interventions: Unique approaches included the following: 1) critical illness survivor involvement to help ensure patient-centered questions and recommendations; 2) qualitative and semiquantitative approaches for developing descriptive statements; 3) operationalizing a three-step approach to generating final recommendations; and 4) systematic identification of evidence gaps. Measurements and Main Results: Critical illness survivors contributed to prioritizing topics, questions, and outcomes, evidence interpretation, recommendation formulation, and article review to ensure that their values and preferences were considered in the guidelines. Qualitative and semiquantitative approaches supported formulating descriptive statements using comprehensive literature reviews, summaries, and large-group discussion. Experts (including the methodologists and guideline chairs) developed and refined guideline recommendations through monthly topic-specific section conference calls. Recommendations were precirculated to all members, presented to, and vetted by, most members at a live meeting. Final electronic voting provided links to all forest plots, evidence summaries, and “evidence to decision” frameworks. Written comments during voting captured dissenting views and were integrated into evidence to decision frameworks and the guideline article. Evidence gaps, reflecting clinical uncertainty in the literature, were identified during the evidence to decision process, live meeting, and voting and formally incorporated into all written recommendation rationales. Frequent scheduled “check-ins” mitigated communication gaps. Conclusions: Our multifaceted, interdisciplinary approach and novel methodologic strategies can help inform the development of future critical care clinical practice guidelines.
Modulation of TORC2 Signaling by a Conserved Lkb1 Signaling Axis in Budding Yeast

Nutrient availability, growth rate, and cell size are closely linked. For example, in budding yeast, the rate of cell growth is proportional to nutrient availability, cell size is proportional to growth rate, and growth rate is proportional to cell size. Thus, cells grow slowly in poor nutrients and are nearly half the size of cells growing in rich nutrients. Moreover, large cells grow faster than small cells. A signaling network that surrounds TOR kinase complex 2 (TORC2) plays an important role in enforcing these proportional relationships. Cells that lack components of the TORC2 network fail to modulate their growth rate or size in response to changes in nutrient availability. Here, we show that budding yeast homologs of the Lkb1 tumor suppressor kinase are required for normal modulation of TORC2 signaling in response to changes in carbon source. Lkb1 kinases activate Snf1/AMPK to initiate transcription of genes required for utilization of poor carbon sources. However, Lkb1 influences TORC2 signaling via a novel pathway that is independent of Snf1/AMPK. Of the three Lkb1 homologs in budding yeast, Elm1 plays the most important role in modulating TORC2. Elm1 activates a pair of related kinases called Gin4 and Hsl1. Previous work found that loss of Gin4 and Hsl1 causes cells to undergo unrestrained growth during a prolonged mitotic arrest, which suggests that they play a role in linking cell cycle progression to cell growth. We found that Gin4 and Hsl1 also control the TORC2 network. In addition, Gin4 and Hsl1 are themselves influenced by signals from the TORC2 network, consistent with previous work showing that the TORC2 network constitutes a feedback loop. Together, the data suggest a model in which the TORC2 network sets growth rate in response to carbon source, while also relaying signals via Gin4 and Hsl1 that set the critical amount of growth required for cell cycle progression. This kind of close linkage between control of cell growth and size would suggest a simple mechanistic explanation for the proportional relationship between cell size and growth rate.
Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations

The unbiasedness of online product ratings, an important property to ensure that users' ratings indeed reflect their true evaluations to products, is vital both in shaping consumer purchase decisions and providing reliable recommendations. Recent experimental studies showed that distortions from historical ratings would ruin the unbiasedness of subsequent ratings. How to "discover" the distortions from historical ratings in each single rating (or at the micro-level), and perform the "debiasing operations" in real rating systems are the main objectives of this work. Using 42 million real customer ratings, we first show that users either "assimilate" or "contrast" to historical ratings under different scenarios: users conform to historical ratings if historical ratings are not far from the product quality (assimilation), while users deviate from historical ratings if historical ratings are significantly different from the product quality (contrast). This phenomenon can be explained by the well-known psychological argument: the "Assimilate-Contrast" theory. However, none of the existing works on modeling historical ratings' influence have taken this into account, and this motivates us to propose the Historical Influence Aware Latent Factor Model (HIALF), the first model for real rating systems to capture and mitigate historical distortions in each single rating. HIALF also allows us to study the influence patterns of historical ratings from a modeling perspective, and it perfectly matches the assimilation and contrast effects we previously observed. Also, HIALF achieves significant improvements in predicting subsequent ratings, and accurately predicts the relationships revealed in previous empirical measurements on real ratings. Finally, we show that HIALF can contribute to better recommendations by decoupling users' real preference from distorted ratings, and reveal the intrinsic product quality for wiser consumer purchase decisions.
Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection

In this paper, we investigate the research problem of unsupervised multi-view feature selection. Conventional solutions first simply combine multiple pre-constructed view-specific similarity structures into a collaborative similarity structure, and then perform the subsequent feature selection. These two processes are separate and independent. The collaborative similarity structure remains fixed during feature selection. Further, the simple undirected view combination may adversely reduce the reliability of the ultimate similarity structure for feature selection, as the view-specific similarity structures generally involve noises and outlying entries. To alleviate these problems, we propose an adaptive collaborative similarity learning (ACSL) for multi-view feature selection. We propose to dynamically learn the collaborative similarity structure, and further integrate it with the ultimate feature selection into a unified framework. Moreover, a reasonable rank constraint is devised to adaptively learn an ideal collaborative similarity structure with proper similarity combination weights and desirable neighbor assignment, both of which could positively facilitate the feature selection. An effective solution guaranteed with the proved convergence is derived to iteratively tackle the formulated optimization problem. Experiments demonstrate the superiority of the proposed approach.
Multi-GPU Development of a Neural Networks Based Reconstructor for Adaptive Optics

Aberrations introduced by the atmospheric turbulence in large telescopes are compensated using adaptive optics systems, where the use of deformable mirrors and multiple sensors relies on complex control systems. Recently, the development of larger scales of telescopes as the E-ELT or TMT has created a computational challenge due to the increasing complexity of the new adaptive optics systems. The Complex Atmospheric Reconstructor based on Machine Learning (CARMEN) is an algorithm based on artificial neural networks, designed to compensate the atmospheric turbulence. During recent years, the use of GPUs has been proved to be a great solution to speed up the learning process of neural networks, and different frameworks have been created to ease their development. The implementation of CARMEN in different Multi-GPU frameworks is presented in this paper, along with its development in a language originally developed for GPU, like CUDA. This implementation offers the best response for all the presented cases, although its advantage of using more than one GPU occurs only in large networks.
Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition

The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.
Multi-modal Sentence Summarization with Modality Attention and Image Filtering

In this paper, we introduce a multi-modal sentence summarization task that produces a short summary from a pair of sentence and image. This task is more challenging than sentence summarization. It not only needs to effectively incorporate visual features into standard text summarization framework, but also requires to avoid noise of image. To this end, we propose a modality-based attention mechanism to pay different attention to image patches and text units, and we design image filters to selectively use visual information to enhance the semantics of the input sentence. We construct a multimodal sentence summarization dataset and extensive experiments on this dataset demonstrate that our models significantly outperform conventional models which only employ text as input. Further analyses suggest that sentence summarization task can benefit from visually grounded representations from a variety of aspects.
Kmer-db: instant evolutionary distance estimation

Summary Kmer-db is a new tool for estimating evolutionary relationship on the basis of k-mers extracted from genomes or sequencing reads. Thanks to an efficient data structure and parallel implementation, our software estimates distances between 40,715 pathogens in less than 4 minutes (on a modern workstation), 44 times faster than Mash, its main competitor. Availability and Implementation https://github.com/refresh-bio/kmer-db Contact sebastian.deorowicz@polsl.pl Supplementary information Supplementary data are available at publisher’s Web site
FISH-MML: Fisher-HSIC Multi-View Metric Learning

This work presents a simple yet effective model for multi-view metric learning, which aims to improve the classification of data with multiple views, e.g., multiple modalities or multiple types of features. The intrinsic correlation, different views describing same set of instances, makes it possible and necessary to jointly learn multiple metrics of different views, accordingly, we propose a multi-view metric learning method based on Fisher discriminant analysis (FDA) and Hilbert-Schmidt Independence Criteria (HSIC), termed as Fisher-HSIC Multi-View Metric Learning (FISH-MML). In our approach, the class separability is enforced in the spirit of FDA within each single view, while the consistence among different views is enhanced based on HSIC. Accordingly, both intra-view class separability and inter-view correlation are well addressed in a unified framework. The learned metrics can improve multi-view classification, and experimental results on real-world datasets demonstrate the effectiveness of the proposed method.
The Production of Human β-Glucocerebrosidase in Nicotiana benthamiana Root Culture

Gaucher disease is caused by a deficiency of the enzyme glucocerebrosidase (GCase). Currently, enzyme-replacement therapy using recombinant GCase produced in mammalian cells is considered the most effective treatment. Plants are an attractive alternative host for recombinant protein production due to the low cost of large-scale production and lack of risk of contamination by human pathogens. Compared to whole plants, root cultures can grow faster. Therefore, this study aimed to produce recombinant GCase in a Nicotiana benthamiana root culture. Root culture of a GCase-producing transgenic plant was induced by indole-3-acetic acid at the concentration of 1 mg/L. Recombinant GCase was successfully produced in roots as a functional protein with an enzyme activity equal to 81.40 ± 17.99 units/mg total protein. Crude proteins were extracted from the roots. Recombinant GCase could be purified by concanavalin A and phenyl 650C chromatography. The productivity of GCase was approximately 1 µg/g of the root. A N-glycan analysis of purified GCase was performed using nano LC/MS. The Man3XylFucGlcNAc2 structure was predominant in purified GCase with two plant-specific glycan residues. This study presents evidence for a new, safe and efficient system of recombinant GCase production that might be applied to other recombinant proteins.
Video Captioning with Tube Features

Visual feature plays an important role in the video captioning task. Considering that the video content is mainly composed of the activities of salient objects, it has restricted the caption quality of current approaches which just focus on global frame features while paying less attention to the salient objects. To tackle this problem, in this paper, we design an object-aware feature for video captioning, denoted as tube feature. Firstly, Faster-RCNN is employed to extract object regions in frames, and a tube generation method is developed to connect the regions from different frames but belonging to the same object. After that, an encoder-decoder architecture is constructed for video caption generation. Specifically, the encoder is a bi-directional LSTM, which is utilized to capture the dynamic information of each tube. The decoder is a single LSTM extended with an attention model, which enables our approach to adaptively attend to the most correlated tubes when generating the caption. We evaluate our approach on two benchmark datasets: MSVD and Charades. The experimental results have demonstrated the effectiveness of tube feature in the video captioning task.
Smarter Response with Proactive Suggestion: A New Generative Neural Conversation Paradigm

Conversational systems are becoming more and more promising by playing an important role in human-computer communications. A conversational system is supposed to be intelligent to enable human-like interactions. The long-term goal of smart human-computer conversations is challenging and heavily driven by data. Thanks to the prosperity of Web 2.0, a large volume of conversational data become available to establish human-computer conversational systems. Given a human issued message, namely a query, a traditional conversational system would provide a response after proper training of how to respond like humans. In this paper, we propose a new paradigm for neural generative conversations: smarter response with a suggestion is provided given the query. We assume that the new conversation mode which proactively introduces contents as next utterances, keeping user actively engaged. To address the task, we propose a novel integrated model to handle both the response generation and the suggestion generation. From the experimental results, we verify the effectiveness of the new neural generative conversation paradigm.
Do not Lose the Details: Reinforced Representation Learning for High Performance Visual Tracking

This work presents a novel end-to-end trainable CNN model for high performance visual object tracking. It learns both low-level fine-grained representations and a high-level semantic embedding space in a mutual reinforced way, and a multi-task learning strategy is proposed to perform the correlation analysis on representations from both levels. In particular, a fully convolutional encoder-decoder network is designed to reconstruct the original visual features from the semantic projections to preserve all the geometric information. Moreover, the correlation filter layer working on the fine-grained representations leverages a global context constraint for accurate object appearance modeling. The correlation filter in this layer is updated online efficiently without network fine-tuning. Therefore, the proposed tracker benefits from two complementary effects: the adaptability of the fine-grained correlation analysis and the generalization capability of the semantic embedding. Extensive experimental evaluations on four popular benchmarks demonstrate its state-of-the-art performance.
Identification and Functional Analysis of the psaD Promoter of Chlorella vulgaris Using Heterologous Model Strains

Chlorella has great potential as a bio-factory for production of value-added compounds. To produce the desired chemicals more efficiently in Chlorella, genetic tools for modification of Chlorella need to be developed, especially an endogenous promoter. In this study, the promoter of photosystem I protein D (psaD) from Chlorella vulgaris UTEX395 was identified. Computational analysis revealed the presence of several putative cis-acting elements, including a potential core element, and light-responsive or stress-responsive elements. Gene expression analysis in heterologous expression system in Chlamydomonas reinhardtii and Nicotiana benthamiana showed that CvpsaD promoter can be used to drive the expression of genes. Functional analysis of this promoter suggested that the initiator element (Inr) is important for its function (i.e., TATA-less promoter) and that an additional factor (e.g., downstream of the transcriptional start site) might be needed for light response. We have shown that the CvpsaD promoter is functional, but not sufficiently strong, both in microalgae and higher plant.