Miracle-dz/UniFAR-FTUs huggingface dataset:
All-Trans Retinoic Acid Enhances Matrix Metalloproteinase 2 Expression and Secretion in Human Myeloid Leukemia THP-1 Cells

All-trans retinoic acid (ATRA) is an effective drug for the induction therapy of acute promyelocytic leukemia. However, the treatment is associated with adverse events such as retinoic acid syndrome (RAS) in some patients, whose histologic characteristics included organ infiltration by leukemic cells. Matrix metalloproteinase 2 (MMP-2) is often upregulated in tumor cells and plays a role in tumor cell migration and invasion by degrading the extracellular matrix. In this study, we examined the possible modulatory effects of ATRA on MMP-2 expression and secretion in human myeloid leukemia cell line THP-1. The cells were treated with various concentrations of ATRA, and MMP-2 expression and secretion were examined. MMP-2 expression and secretion started to increase with ATRA concentration as low as 0.1 nM and gradually increased thereafter. Agonists of retinoic acid receptor (RAR) or retinoid X receptor (RXR) alone could enhance MMP-2 secretion, and RAR or RXR antagonists alone could reverse ATRA-induced MMP-2 secretion. ATRA increased intracellular calcium ion levels, and a calcium-channel blocker inhibited ATRA-induced MMP-2 secretion. Dexamethasone suppressed ATRA-induced MMP-2 secretion. Our results suggest that ATRA enhances MMP-2 expression and secretion in human myeloid leukemia THP-1 cells in a calcium ion dependent manner through RAR/RXR signaling pathways, and this enhanced expression and secretion may be associated with the possible mechanisms of RAS.
Exhaled Nitric Oxide and Exhaled Breath Temperature as Potential Biomarkers in Patients with Pulmonary Hypertension

Background Pulmonary hypertension (PH) is a progressive fatal disease thus, noninvasive prognostic tools are needed to follow these patients. The aim of our study was to evaluate fractional exhaled nitric oxide (FeNO) and exhaled breath temperature (EBT) values in patients with PH from different causes and to correlate them with respiratory functional data. Methods Twenty-four PH patients underwent spirometry, carbon monoxide diffusion (DLCO) test, transthoracic echocardiography, right-heart catheterization, and FeNO and EBT measurements. Results We studied 3 groups according to the type of PH: 10 patients with pulmonary arterial hypertension (PAH) (group A), 11 patients with PH due to chronic obstructive pulmonary disease (COPD) (group B), and 3 patients with PH associated with left heart disease (group C). Mean FeNO values tend to be higher in group B (15.0 ± 9.3ppb) compared with other groups (respectively, 9.9 ± 5.7 and 8.5 ± 5.2 ppb in groups A and C; p = 0.271) but no statistical significance has been reached. Mean values of alveolar NO concentration (CANO) were higher in groups A and B compared to group C (respectively, 16.9 ± 12.6; 13.9 ± 6.8; and 6.7 ± 2.0 ppb) (p = 0.045). EBT mean values were significantly lower in group C when compared with other groups (group C: 29.0 +- 1.3°C, groups A and B: 30.9 ± 1.3 and 31.2 ± 1.2°C, respectively: p = 0.041). EBT levels were inversely correlated to mean pulmonary artery pressure (PAPm) levels (Spearman coefficient -0.481; p = 0.017). Conclusions eNO, CANO, and EBT have been evaluated in three groups of PH patients. Interestingly EBT reduction was correlated with PAPm increase, whereas FeNO was higher in COPD patients and CANO in PAH and COPD groups. Further studies are needed to clarify EBT, FeNO, and CANO roles as biomarkers in the monitoring of patients with PH.
Tomoelastography Paired With T2* Magnetic Resonance Imaging Detects Lupus Nephritis With Normal Renal Function

ObjectivesThe aim of this study was to test multiparametric magnetic resonance imaging including blood oxygen level–dependent (BOLD) imaging by T2* mapping, magnetic resonance elastography (MRE) by tomoelastography, and diffusion-weighted imaging (DWI) for detecting nephropathy in patients with lupus nephritis (LN). MethodsForty-one subjects (25 patients with LN and 16 age- and sex-matched healthy volunteers; LN: mean age, 47.3 ± 14.8 years; 22 female subjects; volunteers: mean age, 43.9 ± 11.6 years; 13 female subjects) were prospectively enrolled. The LN group was further divided into subgroups with normal (LN-nRF, GFR > 90 mL/min per 1.73 m2) and compromised renal function (LN-cRF, GFR < 90 mL/min per 1.73 m2). All subjects were examined by multifrequency MRE, BOLD imaging, and DWI, yielding shear wave speed (SWS; in meter per second), T2* relaxation times (in millisecond), and apparent diffusion coefficient (ADC; in millimeter square per second), respectively. Renal subregional analysis was performed for the medulla (ME), inner cortex (CoI), and outer cortex (CoO). Imaging markers were correlated to clinical parameters such as GFR and protein-to-urine creatinine ratio. Cutoffs and area under the receiver operating curve (AUROC) were computed to test diagnostic performances. ResultsCompared with CoI and CoO, LN-nRF predominantly affects ME tissue (SWS: −7%, P < 0.01; T2*: +9%, P < 0.05; ADC: −5%, P = 0.27). Detection of LN-nRF was better with MRE compared with BOLD imaging and DWI (AUROC = 0.81, 0.76, not significant), whereas pairing MRE with T2* further increased diagnostic power (AUROC = 0.91). Disease progression was associated with reduction of SWS also in CoI (LN-nRF, 3.04 ± 0.38 m/s; LN-cRF, 2.60 ± 0.26 m/s; p = 0.013), allowing distinction of LN-nRF from LN-cRF (AUROC = 0.83). Diffusion-weighted imaging was only sensitive to LN-cRF in ME tissue (ADC, –12%; P < 0.05). ConclusionsLupus nephritis with normal renal function first arises in MRE and BOLD images within ME tissue, progressing to CoI tissue once renal function becomes impaired and diffusion of tissue water changes.
Convolutional Group-Sparse Coding and Source Localization

In this paper, we present a new interpretation of non-negatively constrained convolutional coding problems as blind deconvolution problems with spatially variant point spread function. In this light, we propose an optimization framework that generalizes our previous work on non-negative group sparsity for convolutional models. We then link these concepts to source localization problems that arise in scientific imaging, and provide a visual example on an image derived from data captured by the Hubble telescope.
An Empirical Investigation on Supply Chain Power and Dependence on CSR Performance

Downstream firms must pay more attention to social issues in supply chain, as stakeholders always hold them responsible for activities of their suppliers or sub-suppliers. Although many researchers have studied corporate social responsibility (CSR) and the relationship between CSR and supply chain, there is a dearth of research in the literature on how power asymmetry and joint dependence in Chinese supply chain affect the social responsibility practices of suppliers or customers and further whole supply chain. Based on resources dependence theory, this paper empirically investigated the influence of downstream power adventage (DP A) on suppliers' and whole supply chain's social performance. We also examined if joint dependence (JD) affects the social performance of the whole supply chain. We test the model using data collected from all manufacturing companies that are listed on Shenzhen or Shanghai Stock Exchange in China during 2009–2015. The results show that downstream power advantage (DPA) is positively associated with the social performance of whole supply chain and the usage of power of downstream firms can positively affect the social responsibility behaviors of their suppliers. It is interesting that there is a negative and significant relation-ship between joint dependence (JD) and downstream firms' social performance. At last, some implications of the results are discussed.
Event-Triggered Control for Consensus of Multiagent Systems With Fixed/Switching Topologies

In this paper, the leader-following consensus problem of high-order multiagent systems via event-triggered control is discussed. A novel distributed event-triggered communication protocol based on state estimates of neighboring agents is proposed to solve the consensus problem of the leader-following systems. We first investigate the consensus problem in a fixed topology, and then extend to the switching topologies. State estimates in fixed topology are only updated when the trigger condition is satisfied. However, state estimates in switching topologies are renewed with two cases: 1) the communication topology is switched or 2) the trigger condition is satisfied. Clearly, compared to continuous-time interaction, this protocol can greatly reduce the communication load of multiagent networks. Besides, the event-triggering function is constructed based on the local information and a new event-triggered rule is given. Moreover, “Zeno behavior” can be excluded. Finally, we give two examples to validate the feasibility and efficiency of our approach.
Physicochemical and Microbiological Quality of Dietetic Functional Mixed Cerrado Fruit Jam during Storage

The objective of the research was to evaluate changes of dietetic functional mixed cerrado fruit jam (marolo, sweet passion fruit, and soursop) processed in a vacuum pot and stored for 180 days in BODs at 25°C and 35°C. The parameters evaluated were pH, soluble solids (SS), titratable acidity (TA), total sugars (TS), total carotenoids (TC), total phenolics (TP), vitamin C, antioxidant activity (DPPH), and microbiological analysis. There was a significant effect of storage time on pH, SS, TA, TC, TS, and TP. Vitamin C and DPPH showed an effect for the temperature x storage time interaction. Statistical models are not adjusted for pH and SS, presenting an average of 4.15 and 61%, respectively. Carotenoids decreased up to105 days; total sugars increased up to 105 days. The TP, vitamin C, and DPPH, at the temperatures evaluated, showed a decrease up to 105 days. Yeasts and filamentous fungi were not detected.
Robust Sequential Testing of Multiple Hypotheses in Distributed Sensor Networks

The problem of sequential multiple hypothesis testing in a distributed sensor network is considered and two algorithms are proposed: the Consensus + Innovations Matrix Sequential Probability Ratio Test $(\mathcal{CI}\mathrm{MSPRT}$ for multiple simple hypotheses and the robust Least-Favorable-Density- $\mathcal{CI}\mathrm{MSPRT}$ for hypotheses with uncertainties in the corresponding distributions. Simulations are performed to verify and evaluate the performance of both algorithms under different network conditions and noise contaminations.
Accurate Detection and Localization of Checkerboard Corners for Calibration

The calibration of cameras is a crucial step in machine vision and usually relies on an accurate detection and localization of calibration patterns in images. Therefore, checker-boards are often used, allowing precise subpixel estimation of their corners. However, noise in localization generates a proportional noise in the derived model parameters. Therefore, it is important that the localization has a certain robustness against image noise. This is even more important for deteriorated imaging conditions strongly affecting subpixel detectors. This paper presents a new checkerboard corner detector based on a localized Radon transform implemented by large box ﬁlters making it robust to low contrast, image noise, and blur while maintaining high subpixel accuracy.
Soft-Target Training with Ambiguous Emotional Utterances for DNN-Based Speech Emotion Classification

This paper presents a novel emotion classification method for natural speech. One of the problems in the state-of-the-art method based on Deep Neural Network (DNN) is the paucity of the training data compared to model complexity. To solve this problem, this paper utilizes the ambiguous emotional utterances, utterances that have no dominant target emotion label. While previous work ignored ambiguous emotional utterances for training, the proposed method leverages all annotated labels via soft-target training. In addition, this paper modifies the soft-target training in order to effectively handle both clear and ambiguous emotional utterances. Experiments show that the proposed method yields performance improvements in terms of both weighted and unweighted accuracies.
A Vocabulary for Growth: Topic Modeling of Content Popularity Evolution

In this paper, we present a novel method to predict the long-term popularity of user-generated content (UGC). At first, the method clusters the dynamics of UGC popularity into a vocabulary of growth in popularity (sequence) by using a mixture model. Eventually, the method assigns to each sequence a topic model to describe the dynamics of the sequence in a compact way. We then use this topic model to identify similar patterns of growth in popularity of newly observed UGC. The proposed method has two key features: First, it considers the historical dynamics of the UGC popularity, and second it provides long-term popularity prediction. Results on the real dataset of UGC show that the proposed method is flexible, and able to accurately forecast the complete growth in popularity of a given UGC.
A Hybrid Probabilistic Model for Camera Relocalization

We present a hybrid deep learning method for modelling the uncertainty of camera relocalization from a single RGB image. The proposed system leverages the discriminative deep image representation from a convolutional neural networks, and uses Gaussian Process regressors to generate the probability distribution of the six degree of freedom (6DoF) camera pose in an end-to-end fashion. This results in a network that can generate uncertainties over its inferences with no need to sample many times. Furthermore we show that our objective based on KL divergence reduces the dependence on the choice of hyperparameters. The results show that compared to the state-of-the-art Bayesian camera relocalization method, our model produces comparable localization uncertainty and improves the system efﬁciency signiﬁcantly, without loss of accuracy.
Pornographic Image Recognition via Weighted Multiple Instance Learning

In the era of Internet, recognizing pornographic images is of great significance for protecting children’s physical and mental health. However, this task is very challenging as the key pornographic contents (e.g., breast and private part) in an image often lie in local regions of small size. In this paper, we model each image as a bag of regions, and follow a multiple instance learning (MIL) approach to train a generic region-based recognition model. Specifically, we take into account the regions’ degree of pornography, and make three main contributions. First, we show that based on very few annotations of the key pornographic contents in a training image, we can generate a bag of properly sized regions, among which the potential positive regions usually contain useful contexts that can aid recognition. Second, we present a simple quantitative measure of a region’s degree of pornography, which can be used to weigh the importance of different regions in a positive image. Third, we formulate the recognition task as a weighted MIL problem under the convolutional neural network framework, with a bag probability function introduced to combine the importance of different regions. Experiments on our newly collected large scale dataset demonstrate the effectiveness of the proposed method, achieving an accuracy with 97.52% true positive rate at 1% false positive rate, tested on 100K pornographic images and 100K normal images.
Reg-Gan: Semi-Supervised Learning Based on Generative Adversarial Networks for Regression

This research concerns introducing a method to solve the semi-supervised learning problem with generative adversarial networks (GANs) for regression. In contrast to classification, where only a limited number of distinct classes is given, the regression task is defined as predicting continuous labels for a given dataset. This method will be of particular interest for the applications in which a small number of labeled samples is available, and the labels are continuous such as predicting steering angles from the front camera image in the end-to-end task of autonomous driving. Semi-supervised learning is of vital importance for the applications where a small number of labeled samples is available, or labeling samples is difficult or expensive to collect. A case in point is autonomous driving in which obtaining sufficient labeled samples covering all driving conditions is costly. In this context, we can take advantage of semi-supervised learning techniques with groundbreaking generative models, such as generative adversarial networks. However, currently almost all proposed GAN-based semi-supervised techniques in the literature are focused on solving the classification problem. Hence, developing a GAN-based semi-supervised method for the regression task is still an open problem. In this work, two different architectures will be proposed to address this problem. In summary, our introduced method is able to predict continuous labels for a training dataset which has only a limited number of labeled samples. Moreover, the application of this technique for solving the end-to-end task in autonomous driving will be presented. We performed several experiments to evaluate our proposed method, and the results are very promising compared with the state-of-the-art Improved-GAN technique [1].
Characterization of Vitellogenin and Vitellogenin Receptor of Conopomorpha sinensis Bradley and Their Responses to Sublethal Concentrations of Insecticide

Conopomorpha sinensis Bradley is the dominant borer pest of Litchi chinesis and Euphoria longan. Current management of C. sinensis relies upon insecticide application to adult moths. In addition to the direct mortality induced by insecticides, a sublethal dose of insecticides also affects growth, survival, and reproduction in the exposed insects. Vitellogenin (Vg) and vitellogenin receptor (VgR) are normally identified as essential reproduction-related proteins in insects. In this study, we characterized these two genes from C. sinensis, and investigated their differential responses to sublethal concentrations of insecticide. Cloned CsVg and CsVgR consist of 5391 and 5424-bp open reading frames, which encode proteins of 1796 and 1807 amino acid residues, respectively. The CsVg protein contains the typical vitellogenin, DUF1943 and VWFD domains as other reported lepidopteran Vgs. The CsVgR was characterized as a typical low density lipoprotein receptor with two highly conserved LBD and EGF precursor domains, one hydrophobic transmembrane domain, one cytoplasmic domain, and 13 putative N-glycosylation sites. We next assessed the sublethal effect of four major insecticides on egg-laying in C. sinensis. The toxicity against C. sinensis varied among the insecticides tested, with LC50 values ranging from 0.23 ppm for chlorpyrifos to 20.00 ppm for β-cypermethrin, among which emamectin benzoate (EB) showed a significant negative impact on egg-laying, survival rate, ovarian development, and mating rate of C. sinensis at LC30 doses. Further investigation showed that the transcriptional level of CsVg and CsVgR were impaired in different way at 24, 48, and 72 h after EB exposure, and this result was in agreement with the diminished egg-laying of C. sinensis in the sublethal concentration EB-treated group. A repressed transcription level of CsVgR was observed at 48 h after treatment, suggesting that EB elicits a delayed response in the abundance of CsVgR. These results established different roles of CsVg and CsVgR in response to the sublethal effect of insecticides. CsVg might be a better parameter than CsVgR for assessing the effect of sublethal insecticides on reproduction in C. sinensis.
Framework of Knowledge-Based System for United Nations Peacekeeping Operations Using Data Mining Technique

This paper presents the framework of “Knowledge-Based System” to assist the Malaysian United Nations Military Observers (UNMO) in conducting the United Nations (UN) peacekeeping operations at the deployment country. Further, a suitable data mining technique (e.g. clustering or association rule mining) will be applied to extract interesting and beneficial knowledge including hidden patterns from various sources related to the peacekeeping mission that is required by UNMO. The proposed system will be integrated into the rugged tablet or any pervasive equipment that is used for the peacekeeping operations. Here, the knowledge-based system can be used to assist the UNMO in decision-making when facing all possible scenarios while conducting the peacekeeping operations.
Light-Weight RefineNet for Real-Time Semantic Segmentation

We consider an important task of effective and efficient semantic image segmentation. In particular, we adapt a powerful semantic segmentation architecture, called RefineNet, into the more compact one, suitable even for tasks requiring real-time performance on high-resolution inputs. To this end, we identify computationally expensive blocks in the original setup, and propose two modifications aimed to decrease the number of parameters and floating point operations. By doing that, we achieve more than twofold model reduction, while keeping the performance levels almost intact. Our fastest model undergoes a significant speed-up boost from 20 FPS to 55 FPS on a generic GPU card on 512x512 inputs with solid 81.1% mean iou performance on the test set of PASCAL VOC, while our slowest model with 32 FPS (from original 17 FPS) shows 82.7% mean iou on the same dataset. Alternatively, we showcase that our approach is easily mixable with light-weight classification networks: we attain 79.2% mean iou on PASCAL VOC using a model that contains only 3.3M parameters and performs only 9.3B floating point operations.
RADM:Real-Time Anomaly Detection in Multivariate Time Series Based on Bayesian Network

Aiming at the anomaly detection in multivariate time series(MTS), we propose a real-time anomaly detection algorithm in MTS based on Hierarchical Temporal Memory(HTM) and Bayesian Network(BN), called RADM. First of all, we use HTM model to evaluate the real-time anomalies of each univariate time series(UTS) in MTS. Secondly, a model of anomalous state detection in MTS based on Naive Bayesian is designed to analyze the validity of the above MTS. Lastly, considering the real-time monitoring cases of the system states of terminal nodes in Cloud Platform, we utilize ternary time series of CPU utilization, Network speed and Memory occupancy ratio as data samples, and through the experimental simulation, we verify that RADM proposed in this paper can take advantage of the specific relevance in MTS and make a more effective judgment on the system anomalies.