Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical Marijuana Use in a Community Cancer Center.

PURPOSE The primary purpose of this study was to compare the incidence of marijuana use between patients with early- versus advanced-stage cancers. Differences in adverse effects, drug-drug interactions, and drug-disease interactions between those who use marijuana and those who do not were also compared. METHODS Patients age 18 years and older who were receiving chemotherapy were asked to complete an electronic self-reported questionnaire. In addition to questions about patient demographics, current adverse effects, cancer type and stage, comorbidities, performance status, treatment regimen, and general marijuana use, those patients who used marijuana within the last 30 days (current marijuana users) were asked additional questions about the route and frequency of marijuana administration, about reason(s) for use, about possession of a marijuana card, and if they had received any counseling about marijuana. Drug-drug and drug-disease interactions were also analyzed. RESULTS The overall incidence of marijuana use was 18.3% (32 of 175 patients). The incidence of marijuana use in patients with early- versus advanced-stage cancers was 19.6% (11 of 56 patients) versus 17.6% (21 of 119 patients; P = .75). Patients who use marijuana reported more pain, nausea, appetite issues, and anxiety. There were more drug-drug interactions associated with marijuana use, primarily with concurrent CNS depressants. The frequency of drug-disease interactions between those who use marijuana versus those who do not was similar. CONCLUSION Approximately one in five patients with cancer who were receiving chemotherapy were using marijuana, and the frequency was equal in early- and advanced-stage cancer groups. The risks versus benefits should be discussed with all patients who use marijuana.
Pneumonia in patients with systemic lupus erythematosus: Epidemiology, microbiology and outcomes

Background and objective Pneumonia remains the main cause of mortality in patients with systemic lupus erythematosus (SLE). The aim of the study was to establish the clinical characteristics, microbiology and risk factors for poor prognosis in patients with SLE and pneumonia. Methods We reviewed medical records of patients with SLE (American College of Rheumatology criteria) and pneumonia who attended the emergency room in a single tertiary care center (January 2010–March 2015). We collected demographics, treatment and disease activity (SLEDAI-2K) data. Severity scales of pneumonia (CURB-65 (acronym for risk factors measured: confusion, urea nitrogen, respiratory rate, blood pressure, 65 years of age and older) and Pneumonia Severity Index (PSI)) were obtained. A negative composite outcome was defined as need for mechanical ventilation, septic shock or death secondary to pneumonia up to 30 days after discharge. We conducted a univariate and multivariable analysis. Results We studied 158 patients (76% women) with 187 episodes of pneumonia. There were no differences in age, SLE duration, SLE activity, treatment or comorbidities between patients with negative composite outcome vs the other group. In 53 episodes, patients presented with a negative composite outcome. Of these, 46 (24.6%) required intubation, 13 (7%) developed shock and 12 (6.4%) died. The most common bacteria isolated was S. aureus, and we observed a high percentage of nonhabitual microorganisms. Fifteen percent of patients who presented with a negative outcome had low values on CURB-65 and PSI scales. Conclusion Patients with SLE and pneumonia have a high risk of complications and present with a high percentage of nonhabitual microorganisms. Severity scales for pneumonia can misclassify as low risk SLE patients with poor prognosis.
A Real-Time Multi-Task Single Shot Face Detector

Face, fiducial detection, and 3D head pose estimation are important face preprocessing modules for face recognition which are usually performed separately and loosely coupled. In this paper, we propose a unifying framework to simultaneously detect face, fiducial points, and head pose in real-time. In addition, since no single dataset contains all the required and best annotations, we develop a progressive training strategy to overcome the annotation discrepancy across different datasets. Extensive experiments on face detection, fiducial detection, and pose estimation benchmarks demonstrate the proposed approach can achieve comparable performance to a state-of-the-art system [1] but runs 60 times faster. (i.e., 20 frames per second)
Deciphering Resistome and Virulome Diversity in a Porcine Slaughterhouse and Pork Products Through Its Production Chain

We aimed to better understand resistome and virulome patterns on animal and process-area surfaces through a pig slaughterhouse to track possible contamination within the food production chain. Culture-dependent methods revealed high levels of microbial contamination, corresponding to mesophilic and pathogenic bacteria on both the animal and process-area surfaces mainly in the anesthesia (AA and AS) zone followed by “scorching and whip” (FA and FS) zone and also in the end products. To evaluate the potential risk of antibiotic resistance and virulence determinants, shotgun metagenomic DNA-sequencing of isolates from selected areas/products uncovered a high diversity and richness of antibiotic resistance genes (ARGs): 55–62 genes in the anesthesia area (AA and AS) and 35–40 in “animal-arrival zone” (MA and MS). The “scorching and whip” (FA and FS) area, however, exhibited lowered abundance of ARGs (1–6), indicating that the scalding and depilating process (an intermediate zone between “anesthesia” and “scorching and whip”) significantly decreased bacterial load by 1–3 log10 but also diminished the resistome. The high prevalence of antibiotic-inactivating enzyme genes in the “animal-arrival zone” (60–65%) and “anesthesia” area (56%) were mainly represented by those for aminoglycoside (46–51%) and lincosamide (14–19%) resistance, which did not reflect selective pressures by antibiotics most commonly used in pig therapy—tetracyclines and beta-lactams. Contrary to ARGs, greater number of virulence resistance genes were detected after evisceration in some products such as kidney, which reflected the poor hygienic practices. More than 19 general virulence features—mainly adherence, secretion system, chemotaxis and motility, invasion and motility were detected in some products. However, immune evasion determinants were detected in almost all samples analyzed from the beginning of the process, with highest amounts found from the anesthesia area. We conclude that there are two main sources of contamination in a pig slaughterhouse: the microorganisms carried on the animals’ hide, and those from the evisceration step. As such, focussing control measures, e.g., enhanced disinfection procedures, on these contamination-source areas may reduce risks to food safety and consumer health, since the antibiotic and virulence determinants may spread to end products and the environment; further, ARG and virulence traits can exacerbate pathogen treatments.
Infrared and Visible Image Registration Using Transformer Adversarial Network

In this paper we address the task of infrared and visible image registration in complex scenes. Due to the difference of infrared and visible images, it is neither easy to reliably find features nor suitable for directly training in deep learning architecture. Thus, we propose a two-stage adversarial network, which first conducts a multi-spectral image transfer to obtain a mapped image. And then the proposed network incorporate a transformer module into the conditional adversarial network architecture to get the refined warped image. Our method can back propagate the multi-spectral registration loss and achieve end-to-end training. Experiments on our multi -spectral dataset demonstrate that this approach is effective and robust, which outperforms other state-of-the-art methods.
DHA but not AA Enhances Cisplatin Cytotoxicity in Ovarian Cancer Cells

Abstract Clinical and epidemiological studies show that docosahexaenoic acid (DHA) and arachidonic acid (AA) exert multiple effects on ovarian cancer. While DHA seems to inhibit growth and prevent carcinogenic processes, stimulation of leukotriene B4 receptors BLT1 and BLT2 by several eicosanoids derived from AA plays an important role in mediating cisplatin resistance in ovarian cancer cells. We examined whether DHA and AA exerted antiproliferative effect on epithelial ovarian cancer cells and whether these polyunsaturated fatty acids could alter their susceptibility to cisplatin. Using SKOV3 and OVCAR3 cell lines, we found that DHA but not AA suppressed the cells viability, proliferation, enhanced cell death, and induced activation of caspase-3/7 in the concentration- and time-dependent manner. The OVCAR3 cells were less susceptible to cisplatin than SKOV3 cells. DHA but not AA significantly potentiated cisplatin cytotoxicity in SKOV3 and OVCAR3 cells. We did not observe any significant influence of AA on the above mentioned processes in both cell lines. Similar effect can occur in ovarian cancer patients treated with cisplatin and supplemented with DHA.
Learning Structured Dictionaries for Exemplar-based Voice Conversion

Incorporating phonetic information has been shown to improve the performance of exemplar-based voice conversion. A standard approach is to build a phonetically structured dictionary, where exemplars are categorized into sub-dictionaries according to their phoneme labels. However, acquiring phoneme labels can be expensive, and the phoneme labels can have inaccuracies. The latter problem becomes more salient when the speakers are non-native speakers. This paper presents an iterative dictionary-learning algorithm that avoids the need for phoneme labels, and instead learns the structured dictionaries in an unsupervised fashion. At each iteration, two steps are alternatively performed: cluster update and dictionary update. In the cluster update step, each training frame is assigned to a cluster whose sub-dictionary represents it with the lowest residual. In the dictionary update step, the sub-dictionary for a cluster is updated using all the speech frames in the cluster. We evaluate the proposed algorithm through objective and subjective experiments on a new corpus of non-native English speech. Compared to previous studies, the proposed algorithm improves the acoustic quality of voice-converted speech while retaining the target speaker’s identity.
Management of Febrile Neutropenia: A Description of Clinical and Microbiological Findings by Focusing on Risk Factors and Pitfalls

Objective: Febrile neutropenia (FN) is one of the most serious clinical problems in patients with hematologic malignancies and patients receiving chemotherapy. The present study was implemented to determine precisely how FN is managed in most referral hospitals in Isfahan (Iran) and what are the characteristics of FN patients as well as risk factors associated with FN development. Methods: This study was a cross-sectional study performed over a period of 6 months on patients hospitalized in the Hematology-Oncology Center of Omid Hospital, Isfahan, Iran. The information was collected by filling the designed data abstraction form. Findings: A total of 115 oncology patients were admitted with or encountered to FN. This equates to a cumulative incidence of 1.26% of FN cases per 1000 oncology admissions. The average age was 49.5 ± 18.02 years (range 18–85 years), with 42.6% of patients being female. The most frequently prescribed antibiotic agents were meropenem (91.3%) and vancomycin (47.82%) alone or in combination. Empiric antifungal agents initiated in 20.86% of cases, and we could not find any patients who needed to receive antiviral treatment. From all positive cultures, Gram-positive microorganisms were the most found pathogen. Among them, female sex (42.6%) and lymphopenia (26.5%) were the most noted predictors. Neutropenia (81.7%) was the most reported risk factors for serious complications. Conclusion: Although our center is university-affiliated, there are still several points, and pitfalls must be considered and revised in the management of FN patients. Obtaining and assessing the samples microbiologically and antibiotic therapy accordingly were the most troublesome complications.
Structured Word Embedding for Low Memory Neural Network Language Model

Neural network language model (NN LM), such as long short term memory (LSTM) LM, has been increasingly popular due to its promising performance. However, the model size of an uncompressed NN LM is still too large to be used in embedded or portable devices. The dominant part of memory consumption of NN LM is the word embedding matrix. Directly compressing the word embedding matrix usually leads to performance degradation. In this paper, a product quantization based structured embedding approach is proposed to signiﬁcantly reduce memory consumption of word embeddings without hurting LM performance. Here, each word embedding vector is cut into partial embedding vectors which are then quantized separately. Word embedding matrix can then be represented by an index vector and a code-book tensor of the quantized partial embedding vectors. Experiments show that the proposed approach can achieve 10 to 20 times embedding parameter reduction rate with negligible performance loss.
Numerical investigation of the blood flow through the middle cerebral artery

Introduction: The middle cerebral artery (MCA) is one of the three major paired arteries that supply the blood to the cerebrum. In the present study, the three-dimensional (3D) blood flow in the left MCA was numerically simulated by using the medical imaging. Methods: The arterial geometry was obtained by applying the CT angiography of the MCA of a 75-year-old man. The blood flow was assumed to be laminar and unsteady. Numerical simulations were done by commercial software package COMSOL Multiphysics 5.2. In this software, the Galerkin’s finite element method was applied to solve the governing equations. Results: It was found that the results obtained for the Newtonian and non-Newtonian models of blood do not differ from each other significantly. Thus, the Newtonian model for blood flow in the MCA is acceptable. Also, the most susceptible region of the MCA for Atherosclerosis was detected. Conclusion: It can be concluded that the application of the Newtonian model for the blood flowing in the MCA is acceptable. Also, atherosclerosis has the potential to occur at the middle of a branch of the MCA which has the highest geometrical curvature.
Multisite Randomized Clinical Trial Evaluating an Online Self-Management Program for Adolescents With Juvenile Idiopathic Arthritis

OBJECTIVE To determine the efficacy in improving pain and health-related quality of life (HRQOL) of an online self-management program for adolescents with juvenile idiopathic arthritis (JIA). METHODS Youth ages 12-18 years with JIA were recruited from 10 rheumatology clinics across the United States and randomized to complete an online self-management program (n = 144) or an online disease education program (n = 145). Participants in the self-management group worked through multimedia-based modules comprising psychoeducation, training in cognitive-behavioral coping skills and stress management, and other self-management topics over a 12-week period. Participants in the control group viewed a series of preselected quality educational websites about JIA over the same interval. Online content for both groups was made available in English and Spanish to facilitate inclusion of Hispanic participants. Blinded assessment of main outcomes (pain intensity, pain interference, and HRQOL) and process outcomes (disease knowledge, self-efficacy, pain coping, and emotional adjustment) occurred at baseline, posttreatment, and at 6- and 12-month postrandomization follow-up visits. RESULTS Participants on average demonstrated significant improvements over the study period in the main outcomes, with no significant group differences in the degree of improvement. Effect sizes for these improvements were small. The amount of improvement in self-efficacy, emotional avoidance coping, disease knowledge, and emotional functioning in part predicted improvement in pain and HRQOL outcomes. CONCLUSIONS Primarily self-directed online self-management training and online disease education comparably and modestly improve pain and HRQOL in youth with JIA.
Classification of Ceramic Shards Based on Convolutional Neural Network

ARCADIA project aims to enhance the archaeological heritage of ceramic shards extracted in Saran (France). Dating from the High Middle Ages, these shards have been engraved by repeated patterns using a carved wooden wheel. The study of these shards allows the archeologists to better understand the diffusion of ceramic productions. In this paper, we propose to exploit Convolutional Neural Network (CNN) models to classify automatically these ceramic shards. The ultimate goal is to form clusters of shards to derive a map that represents the movements of potters. For that, several models have been tested and compared to some well-known handcrafted-methods. The Fully Connected part of the best model was modified to see its impact in terms of classification. A dataset composed of 888 binary images of ceramic shards was used. The results obtained outperform the state-of-the-art methods and show the relevance of the proposed approach.
Unsupervised Temporal Feature Learning Based on Sparse Coding Embedded BoAW for Acoustic Event Recognition

The performance of an Acoustic Event Recognition (AER) system highly depends on the statistical information and the temporal dynamics in the audio signals. Although the traditional Bag of Audio Words (BoAW) and the Gaussian Mixture Models (GMM) approaches can obtain more statistics information by aggregating multiple frame-level descriptors of an audio segment compared with the frame-level feature learning methods, its temporal information is unreserved. Recently, more and more Deep Neural Networks (DNN) based AER methods have been proposed to effectively capture the temporal information in audio signals, and achieved better performance, however, these methods usually required the manually annotated labels and fixed-length input during feature learning process. In this paper, we proposed a novel unsupervised temporal feature learning method, which can effectively capture the temporal dynamics for an entire audio signal with arbitrary duration by building direct connections between the BoAW histograms sequence and its time indexes using a non-linear Support Vector Regression (SVR) model. Furthermore, to make the feature representation have a better signal reconstruction ability, we embedded the sparse coding approach in the conventional BoAW framework. Compared with the BoAW and Convolutional Neural Network (CNN) baselines, experimental results showed our method brings improvements of 9.7% and 4.1% respectively.
Acoustic Modeling with DFSMN-CTC and Joint CTC-CE Learning

Recently, the connectionist temporal classiﬁcation (CTC) based acoustic models have achieved comparable or even better performance, with much higher decoding efﬁciency, than the conventional hybrid systems in LVCSR tasks. For CTC-based models, it usually uses the LSTM-type networks as acoustic models. However, LSTMs are computationally expensive and sometimes difﬁcult to train with CTC criterion. In this paper, inspired by the recent DFSMN works, we propose to replace the LSTMs with DFSMN in CTC-based acoustic modeling and explore how this type of non-recurrent models behave when trained with CTC loss. We have evaluated the performance of DFSMN-CTC using both context-independent (CI) and context-dependent (CD) phones as target labels in many LVCSR tasks with various amount of training data. Experimental results shown that DFSMN-CTC acoustic models using either CI-Phones or CD-Phones can signiﬁcantly outperform the conventional hybrid models that trained with CD-Phones and cross-entropy (CE) criterion. Moreover, a novel joint CTC and CE training method is proposed, which enables to improve the stability of CTC training and performance. In a 20000 hours Mandarin recognition task, joint CTC-CE trained DFSMN can achieve a 11.0% and 30.1% relative performance improvement compared to DFSMN-CE models in a normal and fast speed test set respectively.
Altered levels of immune-regulatory microRNAs in plasma samples of patients with lupus nephritis

Introduction: Lupus nephritis (LN) is a major cause of mortality and morbidity in the patients with lupus, a chronic autoimmune disease. The role of genetic and epigenetic factors is emphasized in the pathogenesis of LN. The aim of the present study was to evaluate the levels of immune-regulatory microRNAs (e.g., miR-31, miR-125a, miR-142-3p, miR-146a, and miR-155) in plasma samples of patients with LN. Methods: In this study, 26 patients with LN and 26 healthy individuals were included. The plasma levels of the microRNAs were evaluated by a quantitative real-time PCR. Moreover, the correlation of circulating plasma microRNAs with disease activity and pathological findings along with their ability to distinguish patients with LN were assessed. Results: Plasma levels of miR-125a (P = 0.048), miR-146a (P = 0.005), and miR-155 (P< 0.001) were significantly higher in comparison between the cases and controls. The plasma level of miR-146a significantly correlated with the level of anti-double strand-DNA antibody and proteinuria. Moreover, there was a significant correlation between miR-142-3p levels and disease chronicity and activity index (P <0.05). The multivariate ROC curve analysis indicated the plasma circulating miR-125a, miR-142-3p, miR-146, and miR-155 together could discriminate most of the patients with LN from controls with area an under curve (AUC) of 0.89 [95% CI, 0.80-0.98, P<0.001], 88% sensitivity, and 78% specificity. Conclusion: Based on the findings of the present study, the studied microRNAs may be involved in the pathogenesis and development of LN and have the potential to be used as diagnostic and therapeutic markers in LN.
Block-Based Motion Estimation Speedup for Dynamic Voxelized Point Clouds

Motion estimation is a key component in dynamic point cloud analysis and compression. We present a method for reducing motion estimation computation when processing block-based partitions of temporally adjacent point clouds. We propose the use of an occupancy map containing information regarding size or other higher-order local statistics of the partitions. By consulting the map, the estimator may significantly reduce its search space, avoiding expensive block-matching evaluations. To form the maps we use 3D moment descriptors efficiently computed with one-pass update formulas and stored as scalar-values for multiple, subsequent references. Results show that a speedup of 2 produces a maximum distortion dropoff of less than 2% for the adopted PSNR-based metrics, relative to distortion of predictions attained from full search. Speedups of 5 and 10 are achievable with small average distortion dropoffs, less than 3% and 5%, respectively, for the tested data set.
Frame Interpolation via Refined Deep Voxel Flow

Traditional frame interpolation methods first estimate motion between two consecutive frames and then synthesize intermediate frames. This problem is challenging because of complex motion and video scenes. In this paper, we present an end-to-end deep network for frame interpolation problem. Based on a video synthesis method deep voxel flow (DVF), refinement modules are designed to increase the accuracy of voxel flow, which we call Refined DVF (RDVF). A deeper architecture with more convolution and deconvolution layers is also utilized to help extract motion. Our results greatly improve the performance of original DVF and compare favorably to state-of-the-art methods both quantitatively and qualitatively.