Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on the Relationship between Tocotrienol and Alzheimer Disease

Alzheimer’s disease (AD) is plaguing the aging population worldwide due to its tremendous health care and socioeconomic burden. Current treatment of AD only offers symptomatic relief to patients. Development of agents targeting specific pathologies of AD is very slow. Tocotrienol, a member of the vitamin E family, can tackle many aspects of AD, such as oxidative stress, mitochondrial dysfunction and abnormal cholesterol synthesis. This review summarizes the current evidence on the role of tocotrienol as a neuroprotective agent. Preclinical studies showed that tocotrienol could reduce oxidative stress by acting as a free-radical scavenger and promoter of mitochondrial function and cellular repair. It also prevented glutamate-induced neurotoxicity in the cells. Human epidemiological studies showed a significant inverse relationship between tocotrienol levels and the occurrence of AD. However, there is no clinical trial to support the claim that tocotrienol can delay or prevent the onset of AD. As a conclusion, tocotrienol has the potential to be developed as an AD-preventing agent but further studies are required to validate its efficacy in humans.
TreeNet: Learning Sentence Representations with Unconstrained Tree Structure

Recursive neural network (RvNN) has been proved to be an effective and promising tool to learn sentence representations by explicitly exploiting the sentence structure. However, most existing work can only exploit simple tree structure, e.g., binary trees, or ignore the order of nodes, which yields suboptimal performance. In this paper, we proposed a novel neural network, namely TreeNet, to capture sentences structurally over the raw unconstrained constituency trees, where the number of child nodes can be arbitrary. In TreeNet, each node is learning from its left sibling and right child in a bottom-up left-to-right order, thus enabling the net to learn over any tree. Furthermore, multiple soft gates and a memory cell are employed in implementing the TreeNet to determine to what extent it should learn, remember and output, which proves to be a simple and efficient mechanism for semantic synthesis. Moreover, TreeNet significantly suppresses convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) with fewer parameters. It improves the classification accuracy by 2%-5% with 42% of the best CNN’s parameters or 94% of standard LSTM’s. Extensive experiments demonstrate TreeNet achieves the state-of-the-art performance on all four typical text classification tasks.
Synergy between Circular Bacteriocin AS-48 and Ethambutol against Mycobacterium tuberculosis

The increasing incidence of multidrug-resistant Mycobacterium tuberculosis strains and the very few drugs available for treatment are promoting the discovery and development of new molecules that could help in the control of this disease. Bacteriocin AS-48 is an antibacterial peptide produced by Enterococcus faecalis and is active against several Gram-positive bacteria. ABSTRACT The increasing incidence of multidrug-resistant Mycobacterium tuberculosis strains and the very few drugs available for treatment are promoting the discovery and development of new molecules that could help in the control of this disease. Bacteriocin AS-48 is an antibacterial peptide produced by Enterococcus faecalis and is active against several Gram-positive bacteria. We have found that AS-48 was active against Mycobacterium tuberculosis, including H37Rv and other reference and clinical strains, and also against some nontuberculous clinical mycobacterial species. The combination of AS-48 with either lysozyme or ethambutol (commonly used in the treatment of drug-susceptible tuberculosis) increased the antituberculosis action of AS-48, showing a synergic interaction. Under these conditions, AS-48 exhibits a MIC close to some MICs of the first-line antituberculosis agents. The inhibitory activity of AS-48 and its synergistic combination with ethambutol were also observed on M. tuberculosis-infected macrophages. Finally, AS-48 did not show any cytotoxicity against THP-1, MHS, and J774.2 macrophage cell lines at concentrations close to its MIC. In summary, bacteriocin AS-48 has interesting antimycobacterial activity in vitro and low cytotoxicity, so further studies in vivo will contribute to its development as a potential additional drug for antituberculosis therapy.
Get The Point of My Utterance! Learning Towards Effective Responses with Multi-Head Attention Mechanism

Attention mechanism has become a popular and widely used component in sequence-to-sequence models. However, previous research on neural generative dialogue systems always generates universal responses, and the attention distribution learned by the model always attends to the same semantic aspect. To solve this problem, in this paper, we propose a novel Multi-Head Attention Mechanism (MHAM) for generative dialog systems, which aims at capturing multiple semantic aspects from the user utterance. Further, a regularizer is formulated to force different attention heads to concentrate on certain aspects. The proposed mechanism leads to more informative, diverse, and relevant response generated. Experimental results show that our proposed model outperforms several strong baselines.
An ETHYLENE RESPONSE FACTOR-MYB Transcription Complex Regulates Furaneol Biosynthesis by Activating QUINONE OXIDOREDUCTASE Expression in Strawberry1

Fragaria × ananassa quinone oxidoreductase, which catalyzes the final step in furaneol biosynthesis in strawberry, is transcriptionally up-regulated by a complex consisting of an ethylene response factor and MYB during ripening. 4-Hydroxy-2,5-dimethyl-3(2H)-furanone is a major contributor to the aroma of strawberry (Fragaria × ananassa) fruit, and the last step in its biosynthesis is catalyzed by strawberry quinone oxidoreductase (FaQR). Here, an ethylene response factor (FaERF#9) was characterized as a positive regulator of the FaQR promoter. Linear regression analysis indicated that FaERF#9 transcript levels were correlated significantly with both FaQR transcripts and furanone content in different strawberry cultivars. Transient overexpression of FaERF#9 in strawberry fruit significantly increased FaQR expression and furaneol production. Yeast one-hybrid assays, however, indicated that FaERF#9 by itself did not bind to the FaQR promoter. An MYB transcription factor (FaMYB98) identified in yeast one-hybrid screening of the strawberry cDNA library was capable of both binding to the promoter and activating the transcription of FaQR by ∼5.6-fold. Yeast two-hybrid assay and bimolecular fluorescence complementation confirmed a direct protein-protein interaction between FaERF#9 and FaMYB98, and in combination, they activated the FaQR promoter 14-fold in transactivation assays. These results indicate that an ERF-MYB complex containing FaERF#9 and FaMYB98 activates the FaQR promoter and up-regulates 4-hydroxy-2,5-dimethyl-3(2H)-furanone biosynthesis in strawberry.
From Reality to Perception: Genre-Based Neural Image Style Transfer

We introduce a novel thought for integrating artists’ perceptions on the real world into neural image style transfer process. Conventional approaches commonly migrate color or texture patterns from style image to content image, but the underlying design aspect of the artist always get overlooked. We want to address the in-depth genre style, that how artists perceive the real world and express their perceptions in the artwork. We collect a set of Van Gogh’s paintings and cubist artworks, and their semantically corresponding real world photos. We present a novel genre style transfer framework modeled after the mechanism of actual artwork production. The target style representation is reconstructed based on the semantic correspondence between real world photo and painting, which enable the perception guidance in style transfer. The experimental results demonstrate that our method can capture the overall style of a genre or an artist. We hope that this work provides new insight for including artists’ perceptions into neural style transfer process, and helps people to understand the underlying characters of the artist or the genre.
Joint Generative Moment-Matching Network for Learning Structural Latent Code

Generative Moment-Matching Network (GMMN) is a deep generative model, which employs maximum mean discrepancy as the objective to learn model parameters. However, this model can only generate samples, failing to infer the latent code from samples for downstream tasks. In this paper, we propose a novel Joint Generative Moment-Matching Network (JGMMN), which learns the structural latent code for unsupervised inference. Specifically, JGMMN has a generation network for the generation task and an inference network for the inference task. We first reformulate this model as the two joint distributions matching problem. To solve this problem, we propose to use the Joint Maximum Mean Discrepancy (JMMD) as the objective to learn these two networks simultaneously. Furthermore, to enforce the consistency between the sample distribution and the inferred latent code distribution, we propose a novel multi-modal regularization to enforce this consistency. At last, extensive experiments on both synthetic and real-world datasets have verified the effectiveness and correctness of our proposed JGMMN.
Compiling Model Representations for Querying Large ABoxes in Expressive DLs

Answering ontology mediated queries (OMQs) has received much attention in the last decade, but the big gap between practicable algorithms for lightweight ontologies, that are supported by implemented reasoners, and purely theoretical algorithms for expressive ontologies that are not amenable to implementation, has only increased. Towards narrowing the gap, we propose an algorithm to compile a representation of sets of models for ALCHI ontologies, which is sufficient for answering any monotone OMQ. Rather than reasoning for specific ABoxes, or being fully data-independent, we use generic descriptions of families of ABoxes, given by what we call profiles. Our model compilation algorithm runs on TBoxes and sets of profiles, and supports the incremental addition of new profiles. To illustrate the potential of our approach for OMQ answering, we implement a rewriting into an extension of Datalog for OMQs comprising reachability queries, and provide some promising evaluation results.
Gold Nanoparticles in Diagnostics and Therapeutics for Human Cancer

The application of nanotechnology for the treatment of cancer is mostly based on early tumor detection and diagnosis by nanodevices capable of selective targeting and delivery of chemotherapeutic drugs to the specific tumor site. Due to the remarkable properties of gold nanoparticles, they have long been considered as a potential tool for diagnosis of various cancers and for drug delivery applications. These properties include high surface area to volume ratio, surface plasmon resonance, surface chemistry and multi-functionalization, facile synthesis, and stable nature. Moreover, the non-toxic and non-immunogenic nature of gold nanoparticles and the high permeability and retention effect provide additional benefits by enabling easy penetration and accumulation of drugs at the tumor sites. Various innovative approaches with gold nanoparticles are under development. In this review, we provide an overview of recent progress made in the application of gold nanoparticles in the treatment of cancer by tumor detection, drug delivery, imaging, photothermal and photodynamic therapy and their current limitations in terms of bioavailability and the fate of the nanoparticles.
Expression of Genes Involved in Stress, Toxicity, Inflammation, and Autoimmunity in Relation to Cadmium, Mercury, and Lead in Human Blood: A Pilot Study

There is growing evidence of immunotoxicity related to exposure to toxic trace metals, and an examination of gene expression patterns in peripheral blood samples may provide insights into the potential development of these outcomes. This pilot study aimed to correlate the blood levels of three heavy metals (mercury, cadmium, and lead) with differences in gene expression in 24 participants from the Long Island Study of Seafood Consumption. We measured the peripheral blood mRNA expression of 98 genes that are implicated in stress, toxicity, inflammation, and autoimmunity. We fit multiple linear regression models with multiple testing correction to correlate exposure biomarkers with mRNA abundance. The mean blood Hg in this cohort was 16.1 µg/L, which was nearly three times the Environmental Protection Agency (EPA) reference dose (5.8 µg/L). The levels of the other metals were consistent with those in the general population: the mean Pb was 26.8 µg/L, and the mean Cd was 0.43 µg/L. The expression of three genes was associated with mercury, four were associated with cadmium, and five were associated with lead, although none were significant after multiple testing correction. Little evidence was found to associate metal exposure with mRNA abundance for the tested genes that were associated with stress, toxicity, inflammation, or autoimmunity. Future work should provide a more complete picture of physiological reactions to heavy metal exposure.
Polyketides and Alkaloids from the Marine-Derived Fungus Dichotomomyces cejpii F31-1 and the Antiviral Activity of Scequinadoline A against Dengue Virus

In our continuous chemical investigation on the marine-derived fungus Dichotomomyces cejpii F31-1, two new polyketides dichocetides B-C (1, 2), two new alkaloids dichotomocejs E-F (3, 4), and three known fumiquinozalines: scequinadoline A (5), quinadoline A (6), and scequinadoline E (7) were discovered from the culture broth and the mycelium in the culture medium, by the addition of l-tryptophan and l-phenylalanine. Their chemical structures were established by one dimensional (1D), two dimensional (2D) nuclear magnetic resonance (NMR) and high resolution mass spectrometry (HR-MS) data. Among them, scequinadoline A (5) exhibited significant inhibitory activity against dengue virus serotype 2 production by standard plaque assay, equivalent to the positive control andrographlide. Scequinadoline A (5) possesses the potential for further development as a dengue virus inhibitor.
Learning with Sparse and Biased Feedback for Personal Search

Personal search, including email, on-device, and personal media search, has recently attracted a considerable attention from the information retrieval community. In this paper, we provide an overview of challenges and opportunities of learning with implicit user feedback (e.g., click data) in personal search. Implicit user feedback provides a convenient source of supervision for ranking models in personal search. This feedback, however, has two major drawbacks: it is highly sparse and biased due to the personal nature of queries and documents. We demonstrate how these drawbacks can be overcome, and empirically demonstrate the benefits of learning with implicit feedback in the context of a large-scale email search engine.
Smartphone-based Acoustic Indoor Space Mapping

Constructing a map of indoor space has many important applications, such as indoor navigation, VR/AR, construction, safety, facility management, and network condition prediction. Existing indoor space mapping requires special hardware (e.g., indoor LiDAR equipment) and well-trained operators. In this paper, we develop a smartphone-based indoor space mapping system that lets a regular user quickly map an indoor space by simply walking around while holding a phone in his/her hand. Our system accurately measures the distance to nearby reflectors, estimates the user's trajectory, and pairs different reflectors the user encounters during the walk to automatically construct the contour. Using extensive evaluation, we show our contour construction is accurate: the median errors are 1.5 cm for a single wall and 6 cm for multiple walls (due to longer trajectory and the higher number of walls). We show that our system provides a median error of 30 cm and a 90-percentile error of 1 m, which is significantly better than the state-of-the-art smartphone acoustic mapping system BatMapper [64], whose corresponding errors are 60 cm and 2.5 m respectively, even after multiple walks. We further show that the constructed indoor contour can be used to predict wireless received signal strength (RSS).
Multi-Task Clustering with Model Relation Learning

Multi-task clustering improves the clustering performance of each task by transferring knowledge among the related tasks. An important aspect of multi-task clustering is to assess the task relatedness. However, to our knowledge, only two previous works have assessed the task relatedness, but they both have limitations. In this paper, we propose a multi-task clustering with model relation learning (MTCMRL) method, which automatically learns the model parameter relatedness between each pair of tasks. The objective function of MTCMRL consists of two parts: (1) within-task clustering: clustering each task by introducing linear regression model into symmetric nonnegative matrix factorization; (2) cross-task relatedness learning: updating the parameter of the linear regression model in each task by learning the model parameter relatedness between the clusters in each pair of tasks. We present an effective alternating algorithm to solve the non-convex optimization problem. Experimental results show the superiority of the proposed method over traditional single-task clustering methods and existing multi-task clustering methods.
Molecular epidemiology of Cryptosporidium spp. in dairy cattle in Guangdong Province, South China

Abstract To determine the prevalence of Cryptosporidium in dairy cattle in Guangdong Province, South China, 1440 fecal samples were collected from 10 farms and screened for Cryptosporidium with PCR. The overall prevalence of Cryptosporidium was 4.38% (63/1440), and the infection rates in preweaned calves, postweaned calves, heifers and adults were 6.4% (19/297), 6.19% (33/533), 1.48% (4/271) and 2.06% (7/339), respectively. Three Cryptosporidium species, Cryptosporidium andersoni (n = 33), Cryptosporidium bovis (n = 22) and Cryptosporidium ryanae (n = 8) were detected by DNA sequence analysis of the 63 positive samples, and C. andersoni was identified as the most common species on the dairy cattle farms. In preweaned calves, C. bovis was the most prevalent species (9/19, 47.4%). In contrast, C. andersoni was the predominant species (19/33, 57.6%) in postweaned calves and the only species found in heifers and adults. The zoonotic species Cryptosporidium parvum was not detected in this study. Twenty-four C. andersoni isolates were successfully classified into three multilocus sequence typing (MLST) subtypes. MLST subtype A4,A4,A4,A1 was the predominant subtype, and MLST subtype A2,A5,A2,A1, previously found in sheep, was detected in cattle for the first time. A linkage disequilibrium analysis showed that the C. andersoni isolates had a clonal genetic population structure. However, further molecular studies are required to better understand the epidemiology of Cryptosporidium in Guangdong.
Convergence Analysis of Gradient Descent for Eigenvector Computation

We present a novel, simple and systematic convergence analysis of gradient descent for eigenvector computation. As a popular, practical, and provable approach to numerous machine learning problems, gradient descent has found successful applications to eigenvector computation as well. However, surprisingly, it lacks a thorough theoretical analysis for the underlying geodesically non-convex problem. In this work, the convergence of the gradient descent solver for the leading eigenvector computation is shown to be at a global rate O(min{ (lambda_1/Delta_p)^2 log(1/epsilon), 1/epsilon }), where Delta_p=lambda_p-lambda_p+1>0 represents the generalized positive eigengap and always exists without loss of generality with lambda_i being the i-th largest eigenvalue of the given real symmetric matrix and p being the multiplicity of lambda_1. The rate is linear at (lambda_1/Delta_p)^2 log(1/epsilon) if (lambda_1/Delta_p)^2=O(1), otherwise sub-linear at O(1/epsilon). We also show that the convergence only logarithmically instead of quadratically depends on the initial iterate. Particularly, this is the first time the linear convergence for the case that the conventionally considered eigengap Delta_1= lambda_1 - lambda_2=0 but the generalized eigengap Delta_p satisfies (lambda_1/Delta_p)^2=O(1), as well as the logarithmic dependence on the initial iterate are established for the gradient descent solver. We are also the first to leverage for analysis the log principal angle between the iterate and the space of globally optimal solutions. Theoretical properties are verified in experiments.
Robust Feature Selection on Incomplete Data

Feature selection is an indispensable preprocessing procedure for high-dimensional data analysis,but previous feature selection methods usually ignore sample diversity (i.e., every sample has individual contribution for the model construction) andhave limited ability to deal with incomplete datasets where a part of training samples have unobserved data. To address these issues, in this paper, we firstly propose a robust feature selectionframework to relieve the influence of outliers, andthen introduce an indicator matrix to avoid unobserved data to take participation in numerical computation of feature selection so that both our proposed feature selection framework and exiting feature selection frameworks are available to conductfeature selection on incomplete data sets. We further propose a new optimization algorithm to optimize the resulting objective function as well asprove our algorithm to converge fast. Experimental results on both real and artificial incompletedata sets demonstrated that our proposed methodoutperformed the feature selection methods undercomparison in terms of clustering performance.
Towards Reading Comprehension for Long Documents

Machine reading comprehension has gained attention from both industry and academia. It is a very challenging task that involves various domains such as language comprehension, knowledge inference, summarization, etc. Previous studies mainly focus on reading comprehension on short paragraphs, and these approaches fail to perform well on the documents. In this paper, we propose a hierarchical match attention model to instruct the machine to extract answers from a specific short span of passages for the long document reading comprehension (LDRC) task. The model takes advantages from hierarchical-LSTM to learn the paragraph-level representation, and implements the match mechanism (i.e., quantifying the relationship between two contexts) to find the most appropriate paragraph that includes the hint of answers. Then the task can be decoupled into reading comprehension task for short paragraph, such that the answer can be produced. Experiments on the modified SQuAD dataset show that our proposed model outperforms existing reading comprehension models by at least 20% regarding exact match (EM), F1 and the proportion of identified paragraphs which are exactly the short paragraphs where the original answers locate.