Miracle-dz/UniFAR-FTUs huggingface dataset:
Matched Comparison of Microcirculation Between Healthy Volunteers and Patients with Sepsis.

OBJECTIVE Sepsis-related systemic inflammation resulted in microcirculatory dysfunction. However, information of normal ranges of microcirculatory parameters in the healthy population remain limited. The primary aim of this study was to investigate the differences in microcirculatory parameters between healthy volunteers and patients with sepsis. METHODS This observational study enrolled 45 healthy female and 45 healthy male volunteers, and divided them equally into three age groups: young (20-39 years), middle-age (40-59 years), and elderly (60-79 years). In addition, we enrolled 32 patients with sepsis. Images of sublingual microcirculation were obtained through sidestream dark field videomicroscopy and were analyzed using automated analysis software. Microcirculation parameters, namely the total small vessel density (TSVD), perfused small vessel density (PSVD), proportion of perfused vessel (PPV), microvascular flow index (MFI) score, and heterogeneity index (HI), were evaluated. One-to-one matching between the septic patients and the healthy volunteers was performed on the basis of demographic data including age and gender. RESULTS The elderly group had lower MFI scores and a higher HI. Age was positively correlated with the HI (r = 0.38, p < 0.001) and negatively correlated with the PPV and MFI scores (r = -0.26, p = 0.014 and r = -0.37, p < 0.001, respectively). TSVD was higher in female participants than in male participants. Body mass index (BMI) was negatively correlated with TSVD. In multivariate regression analysis, the independent factors associated with PPV are age and sex, however age is the only independent factor associated with MFI and HI. In patients with sepsis, TSVD (17.5 [3.3] vs. 21.7 [3.0]; p = 0.002), PSVD (15.0 [3.0] vs. 21.1 [2.5]; p < 0.001), and MFI score (2.0 [1.6-2.5] vs. 2.9 [2.5-3.0]; p < 0.001) were lower in the 28-day nonsurvivors than in the survivors. Matched comparison revealed that the TSVD (20.7 [3.7] vs. 25.2 [2.3]; p < 0.001), PSVD (19.8 [3.0] vs. 24.3 [2.7]; p < 0.001), and MFI score (2.7 [2.4-3.0] vs. 3.0 [2.7-3.0]; p = 0.019) were lower in the patients with sepsis than in the healthy volunteers. CONCLUSIONS Age and sex were correlated with microcirculatory parameters of the healthy volunteers. In the patients with sepsis, microcirculatory dysfunction was more severe in the 28-day nonsurvivor group than in the 28-day survivor group. Furthermore, the patients with sepsis exhibited significantly lower TSVD, PSVD, and MFI scores than the healthy volunteers.
Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model

We present a simple and effective feed-forward neural architecture for discriminating between lexico-semantic relations (synonymy, antonymy, hypernymy, and meronymy). Our Specialization Tensor Model (STM) simultaneously produces multiple different specializations of input distributional word vectors, tailored for predicting lexico-semantic relations for word pairs. STM outperforms more complex state-of-the-art architectures on two benchmark datasets and exhibits stable performance across languages. We also show that, if coupled with a bilingual distributional space, the proposed model can transfer the prediction of lexico-semantic relations to a resource-lean target language without any training data.
Low-frequency radio absorption in Cassiopeia A

Context. Cassiopeia A is one of the best-studied supernova remnants. Its bright radio and X-ray emission is due to shocked ejecta. Cas A is rather unique in that the unshocked ejecta can also be studied: through emission in the infrared, the radio-active decay of Ti-44, and the low-frequency free-free absorption caused by cold ionised gas, which is the topic of this paper. Aims. Free-free absorption processes are affected by the mass, geometry, temperature, and ionisation conditions in the absorbing gas. Observations at the lowest radio frequencies can constrain a combination of these properties. Methods. We used Low Frequency Array (LOFAR) Low Band Antenna observations at 30-77 MHz and Very Large Array (VLA) L-band observations at 1-2 GHz to fit for internal absorption as parametrised by the emission measure. We simultaneously fit multiple UV-matched images with a common resolution of 17 '' (this corresponds to 0.25 pc for a source at the distance of Cas A). The ample frequency coverage allows us separate the relative contributions from the absorbing gas, the unabsorbed front of the shell, and the absorbed back of the shell to the emission spectrum. We explored the effects that a temperature lower than the similar to 100-500 K proposed from infrared observations and a high degree of clumping can have on the derived physical properties of the unshocked material, such as its mass and density. We also compiled integrated radio flux density measurements, fit for the absorption processes that occur in the radio band, and considered their effect on the secular decline of the source. Results. We find a mass in the unshocked ejecta of M = 2.95 +/- 0.48 M-circle dot for an assumed gas temperature of T = 100 K. This estimate is reduced for colder gas temperatures and, most significantly, if the ejecta are clumped. We measure the reverse shock to have a radius of 114 '' +/- 6 '' and be centred at 23:23:26, +58:48:54 (J2000). We also find that a decrease in the amount of mass in the unshocked ejecta (as more and more material meets the reverse shock and heats up) cannot account for the observed low-frequency behaviour of the secular decline rate. Conclusions. To reconcile our low-frequency absorption measurements with models that reproduce much of the observed behaviour in Cas A and predict little mass in the unshocked ejecta, the ejecta need to be very clumped or the temperature in the cold gas needs to be low (similar to 10 K). Both of these options are plausible and can together contribute to the high absorption value that we find.
EmojiIt at SemEval-2018 Task 2: An Effective Attention-Based Recurrent Neural Network Model for Emoji Prediction with Characters Gated Words

This paper presents our single model to Subtask 1 of SemEval 2018 Task 2: Emoji Prediction in English. In order to predict the emoji that may be contained in a tweet, the basic model we use is an attention-based recurrent neural network which has achieved satisfactory performs in Natural Language processing. Considering the text comes from social media, it contains many discrepant abbreviations and online terms, we also combine word-level and character-level word vector embedding to better handling the words not appear in the vocabulary. Our single model1 achieved 29.50% Macro F-score in test data and ranks 9th among 48 teams.
Angioedema: A Life-threatening Complication of Tissue Plasminogen Activator

Angioedema is a localized, non-pitting, non-dependent, submucosal, and subcutaneous swelling resulting from the extravasation of fluid into the interstitium due to the increased production of plasma kinins and histamine. It can present with urticaria or anaphylaxis and is usually associated with angiotensin-converting enzyme inhibitors (ACEis), complement deficiencies, or the side effects of tissue plasminogen activator (tPA). Orolingual angioedema following tPA for acute ischemic stroke is a transient, self-resolving hemifacial swelling contralateral to neurological deficits that can rarely progress to the airway, compromising it and leading to a life-threatening situation if not managed promptly.
Distance Distribution to Received Words in Reed-Solomon Codes

In this paper, we obtain an asymptotic formula for the number of codewords with a fixed distance to a given received word of degree $k+m$ in the standard Reed-Solomon code $[q, k, q-k+1]_q$. Previously, explicit formulas were known only for the cases $m=0, 1, 2$.
The relationship between health-promoting lifestyle and sleep quality in postmenopausal women

Background: Menopausal women are widely reported to have poor sleep quality and sleep problems. It is not clear whether increases in sleep disturbance are brought about by hormone changes associated with menopause or due to psychosocial and physical problems. Method: This cross-sectional study was conducted on 600 menopausal women aged between 40 and 60 without any known severe illnesses in the city of Qaen, Iran, from April 2015 to May 2016. Data were collected by Health-Promoting Lifestyle Profile II and Pittsburgh Sleep Quality Index. Main outcome measures: This study was conducted to investigate the relationship between health-promoting lifestyle and sleep quality in Iranian postmenopausal women. Data were analyzed using an independent t-test, Mann-Whitney, Chi-square, Spearman and univariate logistic regression. Result: The univariate logistic regression suggested that the physical activity dimension of lifestyle (OR = 1.095, 95% CI: 1.035-1.158, P < 0.006), non-smoking status (OR = 0.549, 95% CI: 0.331-0.912, P < 0.021) and occupation (women who were farmer compared with housewives) (OR = 0.239, 95% CI (0.074-0.775), P < 0.017) were associated with sleep quality. Conclusion: Postmenopausal women in this study were at high risk for poor sleep quality. Poor sleep quality was associated with low levels of physical activity, smoking and being a housewife compared to being a farmer. Therefore, there is an essential need to educate women about health-promoting behaviors including daily physical activity and avoiding smoking which are associated with quality of sleep.
Time-Frequency analysis for nonlinear and non-stationary signals using HHT: A mode mixing separation technique

Time and frequency localizations are of crucial importance in the analysis of nonlinear and non-stationary processes, especially in systems with high level of complexity where detection of information/events, estimation of parameters and classification of signals in classes is necessary to take decisions. The Hilbert Huang Transform (HHT) offers an adaptive approach to analyze no-linear and non-stationary processes. This paper exposes the HHT approach and its new methodologies for improvement of the analysis, such as the masking process. Two examples are given to show the techniques, first a synthetic signal, representing a typical behavior of an electrical signal immersed in a power electronic environment and second a brain signal to extend the acknowledgment to a biological process. Finally a mode mixing separation technique is presented.
Evaluation of grade prediction using model-based collaborative filtering methods

Estimating grades for courses that are yet to be enrolled by students can help them in making decisions towards timely graduation and achieving better overall results. This paper presents an evaluation of grade prediction for future courses using the model-based collaborative filtering methods: Probabilistic Matrix Factorization and Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo. The prediction model was evaluated in a simulated scenario of an enrollment cycle in a winter and summer semester, based on a real data-set of enrollments and grades over several years at the authors' institution. Several evaluation metrics were used in order to assess the accuracy of predictions and analyze the distribution of the prediction deviation across study programs and grades. Beside the standard approach in predicting the final grade that is to be achieved by a student in a future course, we have also devised a method to estimate if the student will fail the course, so that he will have to re-enroll it at least once. The results showed that the predicted grades were in the range ±1 compared to the actual grades in more than 80% of the records.
A Simple Riemannian Manifold Network for Image Set Classification

In the domain of image-set based classification, a considerable advance has been made by representing original image sets as covariance matrices which typical lie in a Riemannian manifold. Specifically, it is a Symmetric Positive Definite (SPD) manifold. Traditional manifold learning methods inevitably have the property of high computational complexity or weak performance of the feature representation. In order to overcome these limitations, we propose a very simple Riemannian manifold network for image set classification. Inspired by deep learning architectures, we design a fully connected layer to generate more novel, more powerful SPD matrices. However we exploit the rectifying layer to prevent the input SPD matrices from being singular. We also introduce a non-linear learning of the proposed network with an innovative objective function. Furthermore we devise a pooling layer to further reduce the redundancy of the input SPD matrices, and the log-map layer to project the SPD manifold to the Euclidean space. For learning the connection weights between the input layer and the fully connected layer, we use Two-directional two-dimensional Principal Component Analysis ((2D)2PCA) algorithm. The proposed Riemannian manifold network (RieMNet) avoids complex computing and can be built and trained extremely easy and efficient. We have also developed a deep version of RieMNet, named as DRieMNet. The proposed RieMNet and DRieMNet are evaluated on three tasks: video-based face recognition, set-based object categorization, and set-based cell identification. Extensive experimental results show the superiority of our method over the state-of-the-art.
Autophagy-Related Genes mRNA Expression in Patients with Otitis Media with Effusion

Objective: Bacterial infection is a major cause of otitis media with effusion (OME) in patients with dysfunctional Eustachian tubes. Immune reactions to bacterial infection in the middle ear cavity are associated with chronic OME. This study evaluated the expression of autophagyrelated genes mRNAs in effusion fluid. Methods: Effusion fluid samples were obtained at the time of ventilation tube insertion in 97 patients diagnosed with OME. The levels of mRNA encoding autophagy-related genes in effusion fluid were measured by real-time RT-PCR. Levels were compared in patients with and without bacteria in effusion fluid, and relative to fluid characteristics. Results: Autophagy-related genes mRNAs were present in all effusion fluid samples. Levels of autophagy-related genes mRNAs were lower in effusion fluid with than without bacteria, with mTOR and Bcl2 mRNA levels differing significantly (p<0.05 each). However, the levels of expression of autophagy-related genes mRNAs were unrelated to effusion fluid characteristics (p>0.05 each). Conclusion: Autophagy-related genes mRNAs are involved in the pathophysiology of OME. The levels of mTOR and Bcl2 mRNAs were significantly lower in effusion fluid with than without bacteria.
Neural Generative Models for Global Optimization with Gradients

The aim of global optimization is to find the global optimum of arbitrary classes of functions, possibly highly multimodal ones. In this paper we focus on the subproblem of global optimization for differentiable functions and we propose an Evolutionary Search-inspired solution where we model point search distributions via Generative Neural Networks. This approach enables us to model diverse and complex search distributions based on which we can efficiently explore complicated objective landscapes. In our experiments we show the practical superiority of our algorithm versus classical Evolutionary Search and gradient-based solutions on a benchmark set of multimodal functions, and demonstrate how it can be used to accelerate Bayesian Optimization with Gaussian Processes.
Expression of Novel Gene Content Drives Adaptation to Low Iron in the Cyanobacterium Acaryochloris

Abstract Variation in genome content is a potent mechanism of microbial adaptation. The genomes of members of the cyanobacterial genus Acaryochloris vary greatly in gene content as a consequence of the idiosyncratic retention of both recent gene duplicates and plasmid-encoded genes acquired by horizontal transfer. For example, the genome of Acaryochloris strain MBIC11017, which was isolated from an iron-limited environment, is enriched in duplicated and novel genes involved in iron assimilation. Here, we took an integrative approach to characterize the adaptation of Acaryochloris MBIC11017 to low environmental iron availability and the relative contributions of the expression of duplicated versus novel genes. We observed that Acaryochloris MBIC11017 grew faster and to a higher yield in the presence of nanomolar concentrations of iron than did a closely related strain. These differences were associated with both a higher rate of iron assimilation and a greater abundance of iron assimilation transcripts. However, recently duplicated genes contributed little to increased transcript dosage; rather, the maintenance of these duplicates in the MBIC11017 genome is likely due to the sharing of ancestral dosage by expression reduction. Instead, novel, horizontally transferred genes are responsible for the differences in transcript abundance. The study provides insights on the mechanisms of adaptive genome evolution and gene expression in Acaryochloris.
Pattern Reconfigurable Dielectric Resonator Antenna Using Parasitic Feed Elements for LTE Femtocell Base Stations

An initial cuboid shape dielectric resonator antenna is made from a ceramic compound with a high dielectric constant of 30. A feeding mechanism that consists of a coaxial probe in the middle of the antenna is installed underneath, Parasitic feed elements are used to guide the flow of the surface current into the desired direction. Each parasitic feed possesses a switch to control the surface current flow and obtain a reconfigurable pattern. The impedance bandwidth of the proposed antenna is 320 MHz from 2.45–2.72 GHz with a total efficiency exceeding 96%, which is suitable for long-term evolution (LTE) of bands 7 (2,500–2,570 MHz, 2,620–2,690 MHz) and 38 (2.57–2.62 GHz) femtocell base stations. The proposed antenna is fabricated, and results indicate satisfactory agreement between simulated and fabricated antennas. 
Model-based analysis of chest radiographs

Chest radiographs represent a difficult class of images concerning automatic analysis with image processing methods. In our former work we presented a model based method to detect the rib borders and implemented a compensation algorithm of the rib structures. Recently we developed an improved method for rib border detection and algorithms to find the objects like chest border, vertebral spine, heart and intravascular catheter within a model driven approach. The determined borders of these objects allow further analysis and image enhancement for diagnose assistance.
Automated extraction of bronchus from 3D CT images of lung based on genetic algorithm and 3D region growing

In this paper, we propose a method to automate the segmentation of airway tree structures in lung from a stack of gray-scale computed tomography (CT) images. A three- dimensional seeded region growing is performed on images without any preprocessing operation to obtain the segmented bronchus area. We first apply genetic algorithm (GA) to retrieve the seed point and it is based on the geometric features (shape, location and size) of the airway tree. By the feature of the size of the lung and airway tree, an optimal threshold value is obtained. The final extracted bronchus area with the optimal threshold value is reconstructed and visualized by 3D texture mapping method.
User throughput-based quality of experience evaluation for in flight video streaming

Satisfaction of end users is one of the most important factors for every service provider. Given that a lot of factors along with the network conditions affect the user's experience, the network quality given by the Quality of Service (QoS) parameters alone cannot provide an accurate measure of the users Quality of Experience (QoE). Today, most of the airlines have started providing in-flight wi-fi services, which allow the passengers to use various Internet services while on-board the flight, such as send and receive e-mails, stream video from diverse online service providers, and so forth. Statistics show that more than 50% of the passengers use the provided wi-fi service to stream video, therefore, their perception of the video service will be determining for the service provider's performance. One easy way to evaluate the perceived video streaming (i.e. QoE) is by estimating the frequency of stalls. In our paper, we propose an evaluation method based on the user throughput values to determine the number of stalls the user may have experienced during an adaptive video streaming service. Our algorithm is not dependent on any user device property and requires only one input, namely, the throughput values delivered to the user device, which can be collected at either the router or if available, at the traffic shaper.
Passive Optical Sensing of the Near-Surface Wind-Driven Current Profile

AbstractEstimation of near-surface current is essential to the estimation of upper-ocean material transport. Wind forcing and wave motions are dominant in the near-surface layer [within O(0.01) m of the surface], where the highly sheared flows can differ greatly from those at depth. This study presents a new method for remotely measuring the directional wind and wave drift current profile near to the surface (between 0.01 and 0.001 m for the laboratory and between 0.1 and 0.001 m for the field). This work follows the spectral analysis of high spatial (0.002 m) and temporal resolution (60 Hz) wave slope images, allowing for the evaluation of near-surface current characteristics without having to rely on instruments that may disturb the flow. Observations gathered in the 15 m × 1 m × 1 m wind-wave flume at the University of Miami’s Surge-Structure-Atmosphere Interaction (SUSTAIN) facility show that currents retrieved via this method agree well with the drift velocity of camera-tracked dye. Application of th.
A physical current model for junction-modulated tunneling field-effect transistor with steep switching behavior

Compared with conventional tunneling field-effect transistor (TFET), the new junction-modulated TFET (JTFET) can reliably and effectively achieve much steeper switching behavior and higher ON current by only changing the gate layout configuration. To facilitate the JTFET circuit simulation, in this work, we establish a physical tunneling current model of JTFET based on the analytical surface potential modeling. The modeled results with different device parameters are in good agreement with the simulated results by Sentaurus TCAD tools, showing the validity of proposed model.