Miracle-dz/UniFAR-FTUs huggingface dataset:
Intensity Particle Flow SMC-PHD Filter For Audio Speaker Tracking

Non-zero diffusion particle flow Sequential Monte Carlo probability hypothesis density (NPF-SMC-PHD) filtering has been recently introduced for multi-speaker tracking. However, the NPF does not consider the missing detection which plays a key role in estimation of the number of speakers with their states. To address this limitation, we propose to use intensity particle flow (IPF) in NPFSMC-PHD filter. The proposed method, IPF-SMC-PHD, considers the clutter intensity and detection probability while no data association algorithms are used for the calculation of particle flow. Experiments on the LOCATA (acoustic source Localization and Tracking) dataset with the sequences of task 4 show that our proposed IPF-SMC-PHD filter improves the tracking performance in terms of estimation accuracy as compared to its baseline counterparts.
Data Stream Mining with Swarm Decision Table in Fog Computing Environment

Fog computing, as an expansion of Cloud computing, provides edge intelligence where data mining will be implemented. Compared with big data computation at the Cloud platform, distributed Fog nodes process data generated by Internet of Things (IoT) sensors directly at the edge of network. Fog computing can not only relieve heavy workload at the Cloud server, but also increase the speed of data analytics locally. However, faced with continuous data stream, the Fog node should be capable of real-time data mining with high accuracy and lightweight as well. In this paper, a combination of feature selection methods coupled with swarm intelligence and decision Table classifier called Swarm Decision Table (SDT) are proposed. SDT is designed to find appropriate data mining model in the Fog computing environment. Based on a scenario of chemical gas sensors, a simulation experiment will be carried out to evaluate the performance of different swarm feature selection algorithms with decision Table model. The results revealed that the SDT model with the right feature selection method is suitable for Fog computing node, in terms of speed and accuracy.
Osteogenesis-Related Behavior of MC3T3-E1 Cells on Substrates with Tunable Stiffness

Osteogenic differentiation of cells has considerable clinical significance in bone defect treatment, and cell behavior is linked to extracellular matrix stiffness. This study aimed to determine how matrix stiffness affects cell morphology and subsequently regulates the osteogenic phenotype of osteogenesis precursor cells. Four PDMS substrates were prepared with stiffness corresponding to the elastic modulus ranging from 0.6 MPa to 2.7 MPa by altering the Sylgard 527 and Sylgard 184 concentrations. MC3T3-E1 cells were cultured on the matrices. Cell morphology, vinculin expression, and key osteogenic markers, Col I, OCN, OPN, and calcium nodule, were examined. The activity and expression level of Yes-associated protein (YAP) were evaluated. Results showed that cell spreading exhibited no correlation with the stiffness of matrix designed in this paper, but substratum stiffness did modulate MC3T3-E1 osteogenic differentiation. Col I, OPN, and OCN proteins were significantly increased in cells cultured on soft matrices compared with stiff matrices. Additionally, cells cultured on the 1:3 ratio matrices had more nodules than those on other matrices. Accordingly, cells on substrates with low stiffness showed enhanced expression of the osteogenic markers. Meanwhile, YAP expression was downregulated on soft substrates although the subcellular location was not affected. Our results provide evidence that matrix stiffness (elastic modulus ranging from 0.6 MPa to 2.7 MPa) affects the osteogenic differentiation of MC3T3-E1, but it is not that “the stiffer, the better” as showed in some of the previous studies. The optimal substrate stiffness may exist to promote osteoblast differentiation. Cell differentiation triggered by the changes in substrate stiffness may be independent of the YAP signal. This study has important implications for biomaterial design and stem cell-based tissue engineering.
A Spark-based Incremental Algorithm for Frequent Itemset Mining

Association rule mining plays an important role in many areas, including market basket analysis, intrusion detection, bioinformatics and so on. As an efficient approach of finding frequent itemset among large datasets, several parallel Apriori-based algorithms are widely used in association rule mining. Moreover, datasets are always changed in many real-world applications. For example, datasets of the purchased products from an e-commercial website is growing all the time. However, the existing parallel Apriori-based algorithms cannot update the frequent itemset efficiently for these large and evolving datasets. So we propose an incremental parallel Apriori-based algorithm in this paper. As the datasets increase, our algorithm updates the frequent itemset based on frequent itemset in previous, instead of re-computing the whole datasets from scratch. We implement the proposed algorithm on Spark and evaluate its performance via groups of experiments on some real-world datasets. It is demonstrated by the experimental results that the proposed algorithm improves the performance of mining frequent itemset on the large and evolving data sets significantly.
Improve Neural Machine Translation by Syntax Tree

Most of the neural machine translation methods are devoted to using syntactic information at one end of the Encoder-Decoder framework. They didn't use syntactic information at both ends, so that the syntactic information cannot be fully utilized to improve the translation effect. There are still errors due to insufficient grammar information. In order to solve this problem and make full use of syntactic information, we proposed a new tree-to-tree neural machine translation model. Syntax tree is added to the encoder as priori knowledge, and the bidirectional tree is used to obtain the information of the syntax tree, thereby generating a high-quality representation. At the same time, syntax structure is also added to the decoder to guide sentence generation. The experiment was carried out on Chinese-English language pairs, which proved to improve the effect of the neural machine translation.
Clinical Characteristics of Type 2 Diabetics Who are at High Risk for Obstructive Sleep Apnea

Introduction: Obstructive sleep apnea (OSA) has been associated with insulin resistance and glucose intolerance. Recent reports have indicated that the majority of patients with type 2 diabetes (T2D) are also having OSA. Aim/Objective: The primary goal of the study is to assess the risk of OSA among T2D patients and its impact on T2D related control and complications. Method: A cross-sectional study for the adult patients with T2D who had a routine follow up visit from Jun 2013- Aug 2014 was asked to participate. We excluded patients with existed psychiatric illness and those with history of diagnosed OSA. To screen for OSA we used Berlin Questionnaire. Result: A total of 265 T2D patients were included in the study with a mean age of 57.2+12.5 years and long standing T2D. Around 53.2% were classified as high risk for OSA. Compared to those who are considered low risk for OSA, those who are high risk for OSA have higher BMI (p <0.001), higher mean SBP (p 0.002), less likely to be male (p 0.003), more likely to have hyperlipidemia (p 0.058), more likely have neuropathy (p 0.021), more likely to report sedentary lifestyle (p 0.046), and more likely to report low income (p 0.068). Conclusion: High risk T2D patients for OSA tend to be older, have significantly higher BMI, systolic BP and tend to have significantly higher risk for neuropathy.
S-Allyl Cysteine Alleviates Hydrogen Peroxide Induced Oxidative Injury and Apoptosis through Upregulation of Akt/Nrf-2/HO-1 Signaling Pathway in HepG2 Cells

Hydrogen peroxide (H2O2) mediated oxidative stress leading to hepatocyte apoptosis plays a pivotal role in the pathophysiology of several chronic liver diseases. This study demonstrates that S-allyl cysteine (SAC) renders cytoprotective effects on H2O2 induced oxidative damage and apoptosis in HepG2 cells. Cell viability assay showed that SAC protected HepG2 cells from H2O2 induced cytotoxicity. Further, SAC treatment dose dependently inhibited H2O2 induced apoptosis via decreasing the Bax/Bcl-2 ratio, restoring mitochondrial membrane potential (∆Ψm), inhibiting mitochondrial cytochrome c release, and inhibiting proteolytic cleavage of caspase-3. SAC protected cells from H2O2 induced oxidative damage by inhibiting reactive oxygen species accumulation and lipid peroxidation. The mechanism underlying the antiapoptotic and antioxidative role of SAC is the induction of the heme oxygenase-1 (HO-1) gene in an NF-E2-related factor-2 (Nrf-2) and Akt dependent manner. Specifically SAC was found to induce the phosphorylation of Akt and enhance the nuclear localization of Nrf-2 in cells. Our results were further confirmed by specific HO-1 gene knockdown studies which clearly demonstrated that HO-1 induction indeed played a key role in SAC mediated inhibition of apoptosis and ROS production in HepG2 cells, thus suggesting a hepatoprotective role of SAC in combating oxidative stress mediated liver diseases.
Semantic enhanced deep learning for image classification

Extracting training data semantics mainly depends on manual annotation samples prepared in advance, and semantic mapping largely relies on the prior conditions in image classification. A majority of the data samples in reality are unlabeled data, which necessitates a more efficient way to extract image semantics in classification. Deep learning, as an advanced machine learning technique, has recently received significant attention from both the academia and industry. Deep learning has the potential to extract semantic features from images in an automated way to fill up the semantic gap. This paper presents 2 novel deep learning models (ie, stacked denoising auto‐encoder and convolution deep Boltzmann machine). A stacked denoising auto‐encoder builds on a stacked auto‐encoder but uses a denoising auto‐encoder to improve the learning quality. Convolution deep Boltzmann machine combines the convolutional neural network model with the deep Boltzmann machine. These 2 models are evaluated on CIFAR‐10 and STL‐10 datasets, respectively. Experimental results show that the proposed 2 models outperform the original models in terms of both precision and recall.
A Game-Theoretic Learning Framework for Multi-Agent Intelligent Wireless Networks

In this article, we introduce a game-theoretic learning framework for the multi-agent wireless network. By combining learning in artificial intelligence (AI) with game theory, several promising properties emerge such as obtaining high payoff in the unknown and dynamic environment, coordinating the actions of agents and making the adversarial decisions with the existence of malicious users. Unfortunately, there is no free lunch. To begin with, we discuss the connections between learning in AI and game theory mainly in three levels, i.e., pattern recognition, prediction and decision making. Then, we discuss the challenges and requirements of the combination for the intelligent wireless network, such as constrained capabilities of agents, incomplete information obtained from the environment and the distributed, dynamically scalable and heterogeneous characteristics of wireless network. To cope with these, we propose a game-theoretic learning framework for the wireless network, including the internal coordination (resource optimization) and external adversarial decision-making (anti-jamming). Based on the framework, we introduce several attractive game-theoretic learning methods combining with the typical applications that we have proposed. What's more, we developed a real-life testbed for the multi-agent anti-jamming problem based on the game-theoretic learning framework. The experiment results verify the effectiveness of the proposed game-theoretic learning method.
On the stability of nucleic acid feedback controllers

Recent work has shown how chemical reaction network theory may be used to design dynamical systems that can be implemented biologically in nucleic acid-based chemistry. While this has allowed the construction of advanced open-loop circuitry based on cascaded DNA strand displacement (DSD) reactions, little progress has so far been made in developing the requisite theoretical machinery to inform the systematic design of feedback controllers in this context. Here, we develop a number of foundational theoretical results on the equilibria, stability, and dynamics of nucleic acid controllers. In particular, we show that the implementation of feedback controllers using DSD reactions introduces additional nonlinear dynamics, even in the case of purely linear designs, e.g. PI controllers. By decomposing the effects of these non-observable nonlinear dynamics, we show that, in general, the stability of the linear system design does not necessarily imply the stability of the underlying chemical network, which can be lost under experimental variability when feedback interconnections are introduced. We provide an in-depth theoretical analysis of an example illustrating this phenomenon, whereby the linear design does not capture the instability of the full nonlinear system implemented as a DSD reaction network, and we further confirm these results using VisualDSD, a bespoke software tool for simulating nucleic acid-based circuits. Our analysis highlight the many interesting and unique characteristics of this important new class of feedback control systems.
Detecting the QTL-Allele System of Seed Oil Traits Using Multi-Locus Genome-Wide Association Analysis for Population Characterization and Optimal Cross Prediction in Soybean

Soybean is one of the world's major vegetative oil sources, while oleic acid and linolenic acid content are the major quality traits of soybean oil. The restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS), characterized with error and false-positive control, has provided a potential approach for a relatively thorough detection of whole-genome QTL-alleles. The Chinese soybean landrace population (CSLRP) composed of 366 accessions was tested under four environments to identify the QTL-allele constitution of seed oil, oleic acid and linolenic acid content (SOC, OAC, and LAC). Using RTM-GWAS with 29,119 SNPLDBs (SNP linkage disequilibrium blocks) as genomic markers, 50, 98, and 50 QTLs with 136, 283, and 154 alleles (2–9 per locus) were detected, with their contribution 82.52, 90.31, and 83.86% to phenotypic variance, corresponding to their heritability 91.29, 90.97, and 90.24% for SOC, OAC, and LAC, respectively. The RTM-GWAS was shown to be more powerful and efficient than previous single-locus model GWAS procedures. For each trait, the detected QTL-alleles were organized into a QTL-allele matrix as the population genetic constitution. From which the genetic differentiation among 6 eco-populations was characterized as significant allele frequency differentiation on 28, 56, and 30 loci for the three traits, respectively. The QTL-allele matrices were also used for genomic selection for optimal crosses, which predicted transgressive potential up to 24.76, 40.30, and 2.37% for the respective traits, respectively. From the detected major QTLs, 38, 27, and 25 candidate genes were annotated for the respective traits, and two common QTL covering eight genes were identified for further study.
OsDIRP1, a Putative RING E3 Ligase, Plays an Opposite Role in Drought and Cold Stress Responses as a Negative and Positive Factor, Respectively, in Rice (Oryza sativa L.)

As higher plants are sessile organisms, they are unable to move to more favorable places; thus, they have developed the ability to survive under potentially detrimental conditions. Ubiquitination is a crucial post-translational protein modification and participates in abiotic stress responses in higher plants. In this study, we identified and characterized OsDIRP1 (Oryza sativa Drought-Induced RING Protein 1), a nuclear-localized putative RING E3 ubiquitin (Ub) ligase in rice (Oryza sativa L.). OsDIRP1 expression was induced by drought, high salinity, and abscisic acid (ABA) treatment, but not by low temperature (4°C) stress, suggesting that OsDIRP1 is differentially regulated by different abiotic stresses. To investigate its possible role in abiotic stress responses, OsDIRP1-overexpressing transgenic rice plants (Ubi:OsDIRP1-sGFP) were generated, and their phenotypes were analyzed. The T4 Ubi:OsDIRP1-sGFP lines showed decreased tolerance to drought and salt stress as compared to wild-type rice plants. Moreover, Ubi:OsDIRP1-sGFP progeny were less sensitive to ABA than the wild-type during both germination and post-germination growth. In contrast, Ubi:OsDIRP1-sGFP plants exhibited markedly higher tolerance to prolonged cold (4°C) treatment. These results suggest that OsDIRP1 acts as a negative regulator during drought and salt stress, whereas it functions as a positive factor during the cold stress response in rice.
Parallelizing Hartley transform with Hadoop for fast detection of glass defects

Glass defect detection methods based on grating projection can effectively detect various glass defects. The Fourier transform in general can be used as an online processing method for detecting glass defects based on fringe images. Processing fringe images with Fourier transform needs a large amount of computation as Fourier transform is a complex computation method. In order to reduce the amount of computation, an improved fringe image processing method based on the Hartley transform is proposed in this paper. To further speed up the computation process, the Hartley transform is parallelized with Hadoop, which is a major computing technology in support of data intensive applications. Experimental results show that the parallel Hartley transform significantly reduces computation complexity in detection of glass defects.
Survival Mechanisms and Influence Factors of Circulating Tumor Cells

Circulating tumor cells (CTCs) are cancer cells shed from either the primary tumor or its metastases that circulate in the peripheral blood. The CTCs are regarded as the source of tumor recurrence and metastasis and speculated as the indicators of residual tumors, thereby indicating a poor prognosis. Although CTCs play a vital role in tumor metastasis and recurrence, little is known about the underlying survival mechanisms in the blood circulation. The accumulating evidence has revealed that CTCs might survive in the peripheral blood by overcoming the mechanical damage due to shear stress, resistance to anoikis, evasion of immune destruction, and resistance to chemotherapy. The present review addresses the putative survival mechanisms underlying the formation and migration of CTCs according to their biological characteristics and blood microenvironment. In addition, the relationship between CTCs and microenvironment is illustrated, and the influencing factors related to the interactions of CTCs with various components in the peripheral blood are reviewed with respect to the platelets, immune cells, cytokines, and circulating tumor microemboli (CTM). Furthermore, the recent advances in the new treatment strategies targeting the survival mechanisms of CTCs are also discussed.
A Linguistic Approach to Short Query and Answer Systems

Conversational agents for natural language interaction, in general, face some challenges in providing content to support the responses requested by users. Usually of the content of the responses is manually defined, which is not an adequate solution. This work presents an approach to enabling these systems to use automatically the concepts and relationships stored in Knowledge Bases. The main contribution of our approach is the generalization of the system, which can answer questions related to multiple domains. This is accomplished based on linguistic information as support to the activities of natural language understanding. The results obtained in the classification of question types and identification of nominal complements are presented and indicate promising results.
Long Noncoding RNA FAM201A Mediates the Radiosensitivity of Esophageal Squamous Cell Cancer by Regulating ATM and mTOR Expression via miR-101

Background: The aim of the present study was to identify the potential long non-coding (lnc.)-RNA and its associated molecular mechanisms involved in the regulation of the radiosensitivity of esophageal squamous cell cancer (ESCC) in order to assess whether it could be a biomarker for the prediction of the response to radiotherapy and prognosis in patients with ESCC. Methods: Microarrays and bioinformatics analysis were utilized to screen the potential lncRNAs associated with radiosensitivity in radiosensitive (n = 3) and radioresistant (n = 3) ESCC tumor tissues. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed in 35 ESCC tumor tissues (20 radiosensitive and 15 radioresistant tissues, respectively) to validate the lncRNA that contributed the most to the radiosensitivity of ESCC (named the candidate lncRNA). MTT, flow cytometry, and western blot assays were conducted to assess the effect of the candidate lncRNA on radiosensitivity in vitro in ECA109/ECA109R ESCC cells. A mouse xenograft model was established to confirm the function of the candidate lncRNA in the radiosensitivity of ESCC in vivo. The putative downstream target genes regulated by the candidate lncRNA were predicted using Starbase 2.0 software and the TargetScan database. The interactions between the candidate lncRNA and the putative downstream target genes were examined by Luciferase reporter assay, and were confirmed by PCR. Results: A total of 113 aberrantly expressed lncRNAs were identified by microarray analysis, of which family with sequence similarity 201-member A (FAM201A) was identified as the lncRNA that contributed the most to the radiosensitivity of ESCC. FAM201A was upregulated in radioresistant ESCC tumor tissues and had a poorer short-term response to radiotherapy resulting in inferior overall survival. FAM201A knockdown enhanced the radiosensitivity of ECA109/ECA109R cells by upregulating ataxia telangiectasia mutated (ATM) and mammalian target of rapamycin (mTOR) expression via the negative regulation of miR-101 expression. The mouse xenograft model demonstrated that FAM201A knockdown improved the radiosensitivity of ESCC. Conclusion: The lncRNA FAM201A, which mediated the radiosensitivity of ESCC by regulating ATM and mTOR expression via miR-101 in the present study, may be a potential biomarker for predicting radiosensitivity and patient prognosis, and may be a therapeutic target for enhancing cancer radiosensitivity in ESCC.
JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs

The rapid evolution of deep neural networks is demanding deep learning (DL) frameworks not only to satisfy the requirement of quickly executing large computations, but also to support straightforward programming models for quickly implementing and experimenting with complex network structures. However, existing frameworks fail to excel in both departments simultaneously, leading to diverged efforts for optimizing performance and improving usability. This paper presents JANUS, a system that combines the advantages from both sides by transparently converting an imperative DL program written in Python, the de-facto scripting language for DL, into an efficiently executable symbolic dataflow graph. JANUS can convert various dynamic features of Python, including dynamic control flow, dynamic types, and impure functions, into elements of a symbolic dataflow graph. Experiments demonstrate that JANUS can achieve fast DL training by exploiting the techniques imposed by symbolic graph-based DL frameworks, while maintaining the simple and flexible programmability of imperative DL frameworks at the same time.