Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification

This paper focuses on the problem of lung nodule image classification, which plays a key role in lung cancer early diagnosis. In this work, we propose a novel model for lung nodule image feature representation that incorporates both local and global characters. First, lung nodule images are divided into local patches with Superpixel. Then these patches are transformed into fixed-length local feature vectors using unsupervised deep autoencoder (DAE). The visual vocabulary is constructed based on the local features and bag of visual words (BOVW) is used to describe the global feature representation of lung nodule image. Finally, softmax algorithm is employed for lung nodule type classification, which can assemble the whole training process as an end-to-end mode. Comprehensive evaluations are conducted on the widely used public available ELCAP lung image database. Experimental results with regard to different parameter setting, data augmentation, model sparsity, classifier algorithms, and model ensemble validate the effectiveness of our proposed approach.
Text Emotion Distribution Learning via Multi-Task Convolutional Neural Network

Emotion analysis of on-line user generated textual content is important for natural language processing and social media analytics tasks. Most of previous emotion analysis approaches focus on identifying users’ emotional states from text by classifying emotions into one of the finite categories, e.g., joy, surprise, anger and fear. However, there exists ambiguity characteristic for the emotion analysis, since a single sentence can evoke multiple emotions with different intensities. To address this problem, we introduce emotion distribution learning and propose a multi-task convolutional neural network for text emotion analysis. The end-to-end framework optimizes the distribution prediction and classification tasks simultaneously, which is able to learn robust representations for the distribution dataset with annotations of different voters. While most work adopt the majority voting scheme for the ground truth labeling, we also propose a lexiconbased strategy to generate distributions from a single label, which provides prior information for the emotion classification. Experiments conducted on five public text datasets (i.e., SemEval, Fairy Tales, ISEAR, TEC, CBET) demonstrate that our proposed method performs favorably against the state-of-the-art approaches.
Oxidative Stress Levels in the Brain Are Determined by Post-Mortem Interval and Ante-Mortem Vitamin C State but Not Alzheimer’s Disease Status

The current study highlighted several changes in measures of oxidative stress and antioxidant status that take place in the mouse brain over the course of 24 h post-mortem. Ascorbic acid (vitamin C) and glutathione both decreased significantly in cortex in as little as 2 h and malondialdehyde levels increased. Further change from baseline was observed up to 24 h, including carbonyl and sulfhydryl formation. The greatest changes were observed in brains that began with low ascorbic acid levels (gulo−/− mice) compared to wild-type or 5XFAD mice. Cortical samples from nine Alzheimer’s Disease cases and five controls were also assayed under the same conditions. Post mortem intervals ranged from 6 to 47 h and all samples had low ascorbic acid levels at time of measurement. Malondialdehyde levels were lower in Alzheimer’s Disease cases. Despite a strong positive correlation between ascorbic acid and glutathione levels, no other correlations among oxidative stress measures or post mortem interval were observed. Together the data suggest that molecular changes occurring within the first hours of death may mask differences between patient groups. Care must be taken interpreting studies in human brain tissue where ante-mortem nutrient status is not known to avoid bias or confounding of results.
Exploring Encoder-Decoder Model for Distant Supervised Relation Extraction

In this paper, we present an encoder-decoder model for distant supervised relation extraction. Given an entity pair and its sentence bag as input, in the encoder component, we employ the convolutional neural network to extract the features of the sentences in the sentence bag and merge them into a bag representation. In the decoder component, we utilize the long short-term memory network to model relation dependencies and predict the target relations in a sequential manner. In particular, to enable the sequential prediction of relations, we introduce a measure to quantify the amounts of information the relations take in their sentence bag, and use such information to determine the order of the relations of a sentence bag during model training. Moreover, we incorporate the attention mechanism into our model to dynamically adjust the bag representation to reduce the impact of sentences whose corresponding relations have been predicted. Extensive experiments on a popular dataset show that our model achieves significant improvement over state-of-the-art methods.
Réseaux de neurones récurrents multi-tâches pour l'analyse automatique d'arguments(Argument mining: A multi-task approach using deep neural networks)

In this article we propose a method performing automatic extraction and analysis of arguments from raw texts, without using handcrafted features. We introduce a multi-task deep learning model stacking several layers of recurrent neural networks. Specifically, we make use of weight parameters trained on simple tasks, such as Part-Of-Speech tagging and chunking, in order to obtain a model able to handle more complex tasks that require a detailed understanding of the text.
Feasibility of a computer application to develop social workers knowledge of integrated healthcare concepts

ABSTRACT Disseminating evidence-based knowledge and strengthening the education program of the healthcare workforce is an essential step toward achieving transformation of the U.S. healthcare delivery system to integrated healthcare. Computer applications may be an effective approach for social workers to learn integrated concepts. A feasibility study using an experimental research design was used to compare instructional approaches among MSW students (N = 15) composed of experimental (N = 7) and control (N = 8) group participants. Students completed a pre-test on integrated healthcare concepts and screening measures along with a post-test on screening measures. While ANCOVA results suggest no significant differences on post-test scores between the two groups, dependent sample t-test revealed a significant increase for both groups. Results suggest training on an integrated healthcare topic using a computer application is feasible. Future research should be conducted to determine if the computer application is comparable to other instructional methods.
A Joint Learning Approach to Intelligent Job Interview Assessment

The job interview is considered as one of the most essential tasks in talent recruitment, which forms a bridge between candidates and employers in fitting the right person for the right job. While substantial efforts have been made on improving the job interview process, it is inevitable to have biased or inconsistent interview assessment due to the subjective nature of the traditional interview process. To this end, in this paper, we propose a novel approach to intelligent job interview assessment by learning the large-scale real-world interview data. Specifically, we develop a latent variable model named Joint Learning Model on Interview Assessment (JLMIA) to jointly model job description, candidate resume and interview assessment. JLMIA can effectively learn the representative perspectives of different job interview processes from the successful job application records in history. Therefore, a variety of applications in job interviews can be enabled, such as person-job fit and interview question recommendation. Extensive experiments conducted on real-world data clearly validate the effectiveness of JLMIA, which can lead to substantially less bias in job interviews and provide a valuable understanding of job interview assessment.
A Local Algorithm for Product Return Prediction in E-Commerce

With the rapid growth of e-tail, the cost to handle returned online orders also increases significantly and has become a major challenge in the e-commerce industry. Accurate prediction of product returns allows e-tailers to prevent problematic transactions in advance. However, the limited existing work for modeling customer online shopping behaviors and predicting their return actions fail to integrate the rich information in the product purchase and return history (e.g., return history, purchase-no-return behavior, and customer/product similarity). Furthermore, the large-scale data sets involved in this problem, typically consisting of millions of customers and tens of thousands of products, also render existing methods inefficient and ineffective at predicting the product returns. To address these problems, in this paper, we propose to use a weighted hybrid graph to represent the rich information in the product purchase and return history, in order to predict product returns. The proposed graph consists of both customer nodes and product nodes, undirected edges reflecting customer return history and customer/product similarity based on their attributes, as well as directed edges discriminating purchase-no-return and no-purchase actions. Based on this representation, we study a random-walk-based local algorithm for predicting product return propensity for each customer, whose computational complexity depends only on the size of the output cluster rather than the entire graph. Such a property makes the proposed local algorithm particularly suitable for processing the large-scale data sets to predict product returns. To test the performance of the proposed techniques, we evaluate the graph model and algorithm on multiple e-commerce data sets, showing improved performance over state-of-the-art methods.
Comparison of the Active Compositions between Raw and Processed Epimedium from Different Species

Epimedium herb is one of the most vital traditional Chinese medicines (TCMs), which is used for “nourishing the kidney and reinforcing the Yang”. In the guidance of TCM theory, Epimedium herb is usually processed with lamb oil to increase its efficacy. The contents of active ingredients in different Epimedium are significantly varied, which may derive from their different species, regions and processing methods. In this research, 13 batches of raw Epimedium collected from 6 provinces were identified. After optimization of the processing method of Epimedium, a liquid chromatography–mass spectrometry (LC–MS/MS) method for simultaneous determination of 16 compounds was established to evaluate the quality of raw and processed. Then the multivariate statistical technique was applied to compare different batches of Epimedium based on the LC–MS/MS data. As a conclusion, the herbs collected from 6 areas were ascribed to 5 species by microscopic and appearance features. Meanwhile, all of the raw and processed samples were classified by partial least squares discriminant analysis (PLS-DA) based on the 16 analyzed compounds. The comparison results indicate that processing and species both have important influences on Epimedium compositions contents.
Representing Urban Functions through Zone Embedding with Human Mobility Patterns

Urban functions refer to the purposes of land use in cities where each zone plays a distinct role and cooperates with each other to serve people’s various life needs. Understanding zone functions helps to solve a variety of urban related problems, such as increasing traffic capacity and enhancing location-based service. Therefore, it is beneficial to investigate how to learn the representations of city zones in terms of urban functions, for better supporting urban analytic applications. To this end, in this paper, we propose a framework to learn the vector representation (embedding) of city zones by exploiting large-scale taxi trajectories. Specifically, we extract human mobility patterns from taxi trajectories, and use the co-occurrence of origin-destination zones to learn zone embeddings. To utilize the spatio-temporal characteristics of human mobility patterns, we incorporate mobility direction, departure/arrival time, destination attraction, and travel distance into the modeling of zone embeddings. We conduct extensive experiments with real-world urban datasets of New York City. Experimental results demonstrate the effectiveness of the proposed embedding model to represent urban functions of zones with human mobility data.
Metadata-dependent Infinite Poisson Factorization for Efficiently Modelling Sparse and Large Matrices in Recommendation

Matrix Factorization (MF) is widely used in Recommender Systems (RSs) for estimating missing ratings in the rating matrix. MF faces major challenges of handling very sparse and large data. Poisson Factorization (PF) as an MF variant addresses these challenges with high efficiency by only computing on those non-missing elements. However, ignoring the missing elements in computation makes PF weak or incapable for dealing with columns or rows with very few observations (corresponding to sparse items or users). In this work, Metadata-dependent Poisson Factorization (MPF) is invented to address the user/item sparsity by integrating user/item metadata into PF. MPF adds the metadata-based observed entries to the factorized PF matrices. In addition, similar to MF, choosing the suitable number of latent components for PF is very expensive on very large datasets. Accordingly, we further extend MPF to Metadata-dependent Infinite Poisson Factorization (MIPF) that integrates Bayesian Nonparametric (BNP) technique to automatically tune the number of latent components. Our empirical results show that, by integrating metadata, MPF/MIPF significantly outperform the state-of-the-art PF models for sparse and large datasets. MIPF also effectively estimates the number of latent components.
Trimethylamine-N-oxide (TMAO) increased aquaporin-2 expression in spontaneously hypertensive rats

ABSTRACT Recent evidence suggests that elevated plasma levels of Trimethylamine-N-oxide (TMAO) can prolong the duration of elevated blood pressure in rats. The purpose of this study was to investigate the plasma TMAO level in Spontaneously Hypertensive Rats (SHR) and to explore the possible relationship between TMAO and aquaporin-2 (AQP-2) in the formation of hypertension. Twelve-week-old, male Spontaneously Hypertensive rats (SHR, n = 40) and Wistar-Kyoto rats (WKY, n = 40) were accordingly grouped into SHR group and WKY group. Each group was divided randomly into four subgroups: Untreated group, TMAO group, TMAO+Tolvaptan (TMAO+TVP) group, and TVP group, respectively. Systolic blood pressure (SBP), plasma TMAO, plasma osmolality (POsm), plasma vasopressin (PAVP), and plasma AQP-2 (PAQP-2) concentration were measured, and the expression of AQP-2 in kidney medulla was detected by RT-PCR and Western blot. At 14 weeks, rats in SHR TMAO group were shown the increased plasma TMAO, POsm, PAVP, and PAQP-2 levels, while those rats in SHR TMAO+TVP group were shown the decreased plasma TMAO, POsm, and PAQP-2 levels, but an even higher PAVP (due to the blockage of TVP to V2 receptor). These findings indicate that an increase of plasma TMAO levels in SHR leads to a higher plasma osmotic pressure, triggers the regulation of the TMAO-AVP-AQP-2 axis in SHR, elicits the greater water reabsorption, and eventually leads to hypertension.
Underwater endoscopic mucosal resection for anal canal neoplasia in a patient with human immunodeficiency virus infection

Human immunodeficiency virus (HIV)-positive patients are at high risk of anal cancer because the majority are concurrently infected with human papillomavirus (HPV) in the anus [1]. As high-grade anal intraepithelial neoplasia (HAIN), precursor of cancer, is caused by HPV infection, early detection and treatment of HAIN is recommended [2]. Despite an early diagnosis of HAIN, conventional endoscopic treatment is often difficult, because of the narrow lumen, poor operability and susceptibility to bleeding during the procedure. Furthermore, when a large bleb arises in the anal canal as a result of submucosal injection, it sometimes makes endoscopic resection more difficult and leads to incomplete resection. Theoretically, the novel technique of underwater endoscopic mucosal resection (UEMR), which does not require submucosal injection, can overcome these technical difficulties [3]. Herein we report how we applied UEMR for an anal canal lesion. An HIV-positive man in his 60s underwent screening colonoscopy, during which a 10-mm elevated lesion was detected in the anal canal (Fig. 1A). As biopsies showed HAIN, we performed UEMR. After air deflation in the rectum and anus, the lumen was filled with physiological saline and the lesion was adequately visualized. The lesion was easily captured by a polypectomy snare (15 mm, Captivator II; Boston Scientific, USA), and removed en bloc with an electric current (Fig. 1B). The submucosal vessels remained in the wound without bleeding (Fig. 1C) and were coagulated safely (Fig. 1D). Histological examination revealed HAIN with positive staining for P16, a marker of HPV infection (Fig. 2A). There was no recurrence at follow up 4 months later (Fig. 2B). UEMR was effective in treating this anal canal lesion.
Search Progress and Potentially Expanded States in Greedy Best-First Search

A classical result in optimal search shows that A* with an admissible and consistent heuristic expands every state whose f-value is below the optimal solution cost and no state whose f-value is above the optimal solution cost. For satisficing search algorithms, a similarly clear understanding is currently lacking. We examine the search behavior of greedy best-first search (GBFS) in order to make progress towards such an understanding. We introduce the concept of high-water mark benches, which separate the search space into areas that are searched by a GBFS algorithm in sequence. High-water mark benches allow us to exactly determine the set of states that are expanded by at least one GBFS tie-breaking strategy and give us a clearer understanding of search progress.
Protein Arginine Methyltransferase PRMT1 Is Essential for Palatogenesis

Cleft palate is among the most common birth defects. Currently, only 30% of cases have identified genetic causes, whereas the etiology of the majority remains to be discovered. We identified a new regulator of palate development, protein arginine methyltransferase 1 (PRMT1), and demonstrated that disruption of PRMT1 function in neural crest cells caused complete cleft palate and craniofacial malformations. PRMT1 is the most highly expressed of the protein arginine methyltransferases, enzymes responsible for methylation of arginine motifs on histone and nonhistone proteins. PRMT1 regulates signal transduction and transcriptional activity that affect multiple signal pathways crucial in craniofacial development, such as the BMP, TGFβ, and WNT pathways. We demonstrated that Wnt1-Cre;Prmt1 fl/fl mice displayed a decrease in palatal mesenchymal cell proliferation and failure of palatal shelves to reach the midline. Further analysis in signal pathways revealed that loss of Prmt1 in mutant mice decreased BMP signaling activation and reduced the deposition of H4R3me2a mark. Collectively, our study demonstrates that Prmt1 is crucial in palate development. Our study may facilitate the development of a better strategy to interrupt the formation of cleft palate through manipulation of PRMT1 activity.
In Vivo Pharmacodynamic Characterization of a Novel Odilorhabdin Antibiotic, NOSO-502, against Escherichia coli and Klebsiella pneumoniae in a Murine Thigh Infection Model

NOSO-502 is a novel odilorhabdin antibiotic with potent activity against Enterobacteriaceae. The goal of these studies was to determine which pharmacokinetic/pharmacodynamic (PK/PD) indices and magnitude best correlated with efficacy in the murine thigh infection model. ABSTRACT NOSO-502 is a novel odilorhabdin antibiotic with potent activity against Enterobacteriaceae. The goal of these studies was to determine which pharmacokinetic/pharmacodynamic (PK/PD) indices and magnitude best correlated with efficacy in the murine thigh infection model. Six Escherichia coli and 6 Klebsiella pneumoniae isolates were utilized. MICs were determined using CLSI methods and ranged from 1 to 4 mg/liter. A neutropenic murine thigh infection model was utilized for all treatment studies. Single-dose plasma pharmacokinetics were determined in mice after subcutaneous administration of 7.81, 31.25, 125, and 500 mg/kg of body weight. Pharmacokinetic studies exhibited peak concentration (Cmax) values of 1.49 to 84.6 mg/liter, area under the concentration-time curve from 0 h to infinity (AUC0–∞) values of 1.94 to 352 mg · h/liter, and beta elimination half-lives of 0.41 to 1.1 h. Dose fractionation studies were performed using total drug doses of 7.81 mg/kg to 2,000 mg/kg fractionated into regimens of every 3 h (q3h), q6h, q12h, or q24h. Nonlinear regression analysis demonstrated that AUC/MIC was the PK/PD parameter that best correlated with efficacy (R2, 0.86). In subsequent studies, we used the neutropenic murine thigh infection model to determine the magnitude of NOSO-502 AUC/MIC needed for the efficacy against a diverse group of Enterobacteriaceae. Mice were treated with 4-fold-increasing doses (range, 3.91 to 1,000 mg/kg) of NOSO-502 every 6 h. The mean 24-h free-drug AUC/MIC (fAUC)/MIC) magnitudes associated with net stasis and 1-log kill endpoint for K. pneumoniae were 4.22 and 17.7, respectively. The mean fAUC/MIC magnitude associated with net stasis endpoint for E. coli was 10.4. NOSO-502 represents a promising novel, first-in-class odilorhabdin antibiotic with in vivo potency against Enterobacteriaceae.
Feature Enhancement in Attention for Visual Question Answering

Attention mechanism has been an indispensable part of Visual Question Answering (VQA) models, due to the importance of its selective ability on image regions and/or question words. However, attention mechanism in almost all the VQA models takes as input the image visual and question textual features, which stem from different sources and between which there exists essential semantic gap. In order to further improve the accuracy of correlation between region and question in attention, we focus on region representation and propose the idea of feature enhancement, which includes three aspects. (1) We propose to leverage region semantic representation which is more consistent with the question representation. (2) We enrich the region representation using features from multiple hierarchies and (3) we refine the semantic representation for richer information. With these three incremental feature enhancement mechanisms, we improve the region representation and achieve better attentive effect and VQA performance. We conduct extensive experiments on the largest VQA v2.0 benchmark dataset and achieve competitive results without additional training data, and prove the effectiveness of our proposed feature-enhanced attention by visual demonstrations.