Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomic Dysreflexia Causes Chronic Immune Suppression after Spinal Cord Injury

Autonomic dysreflexia (AD), a potentially dangerous complication of high-level spinal cord injury (SCI) characterized by exaggerated activation of spinal autonomic (sympathetic) reflexes, can cause pulmonary embolism, stroke, and, in severe cases, death. People with high-level SCI also are immune compromised, rendering them more susceptible to infectious morbidity and mortality. The mechanisms underlying postinjury immune suppression are not known. Data presented herein indicate that AD causes immune suppression. Using in vivo telemetry, we show that AD develops spontaneously in SCI mice with the frequency of dysreflexic episodes increasing as a function of time postinjury. As the frequency of AD increases, there is a corresponding increase in splenic leucopenia and immune suppression. Experimental activation of spinal sympathetic reflexes in SCI mice (e.g., via colorectal distension) elicits AD and exacerbates immune suppression via a mechanism that involves aberrant accumulation of norepinephrine and glucocorticoids. Reversal of postinjury immune suppression in SCI mice can be achieved by pharmacological inhibition of receptors for norepinephrine and glucocorticoids during the onset and progression of AD. In a human subject with C5 SCI, stimulating the micturition reflex caused AD with exaggerated catecholamine release and impaired immune function, thus confirming the relevance of the mouse data. These data implicate AD as a cause of secondary immune deficiency after SCI and reveal novel therapeutic targets for overcoming infectious complications that arise due to deficits in immune function.
Identification of Patterns and Document Ranking of Internet Texts : A Frequency-based Approach

This paper presents an automatic method to process Portuguese internet data using the Text Engineering Software Laboratory, Tesla. The experiments presented here were carried out in two stages. On the first step, frequency information was used to identify salient lexical and orthographical features in internet language in comparison to a standard journalistic corpus. The results obtained in the first step were later used to rank texts according to the their features from standard to non-standard language. Results are presented and dis-
Computational Study of Fundamental Frequency of Standard Yorùbá monosyllabic Utterances

This paper presents the development and evaluation of a computational model for the fundamental frequency (F0) of Standard Yorùbá (SY) monosyllabic utterances. This was done with a view to approximating the F0 curves on SY syllables in the context of speech applications and other speech technologies. A list of 39 SY syllables which are the most frequently occurring syllables in Yorùbá newspapers and textbooks was compiled and the speech sound corresponding to the selected SY syllables were recorded for 5 adult native male speakers of SY. The F0 of the speech data were extracted using Praat speech processing package. Thereafter, Least Square Method (using polynomial degree of 1 to 7) was used to design the computational model for the F0 patterns extracted. The computational model was evaluated using the quantitative and the qualitative techniques. The results showed that the 7 degree polynomial had the lowest RMSE value for monosyllabic speech data. The results of the model showed that polynomials with degree 3 gave a good approximation for monosyllabic. The study thereafter established the computational and perceptual correlations between the F0 curves and the three Yorùbá tones (High, Mid and Low) for SY monosyllabic utterances. The modeling of F0 contour for Yorùbá tones for bisyllabic utterances and continuous speech are the areas of further research works, in which the principle of this work could be extended.
Multiclass Informative Instance Transfer Learning Framework for Motor Imagery-Based Brain-Computer Interface

A widely discussed paradigm for brain-computer interface (BCI) is the motor imagery task using noninvasive electroencephalography (EEG) modality. It often requires long training session for collecting a large amount of EEG data which makes user exhausted. One of the approaches to shorten this session is utilizing the instances from past users to train the learner for the novel user. In this work, direct transferring from past users is investigated and applied to multiclass motor imagery BCI. Then, active learning (AL) driven informative instance transfer learning has been attempted for multiclass BCI. Informative instance transfer shows better performance than direct instance transfer which reaches the benchmark using a reduced amount of training data (49% less) in cases of 6 out of 9 subjects. However, none of these methods has superior performance for all subjects in general. To get a generic transfer learning framework for BCI, an optimal ensemble of informative and direct transfer methods is designed and applied. The optimized ensemble outperforms both direct and informative transfer method for all subjects except one in BCI competition IV multiclass motor imagery dataset. It achieves the benchmark performance for 8 out of 9 subjects using average 75% less training data. Thus, the requirement of large training data for the new user is reduced to a significant amount.
Real Time Ambient Air Quality Status during Diwali Festival in Central, India

In India, festivals are celebrated with lot of enthusiasm and Diwali is the major festival of light. In this festival, houses are illuminated by lights and sky is illuminated by fireworks. These fireworks though create lot of amusement but also pollute the atmosphere in terms of air pollution. The continuous air pollution monitoring was undertaken during Diwali festival (2014) at residential site NEERI, Nagpur. Air quality parameters were compared with CPCB standard. On Diwali day, PM10 and PM2.5 concentration achieve its highest value of 900 μg/m3 and 950 respectively μg/m3. This high concentration is maintained in atmosphere for two days of this festival in atmosphere which is approximately 8 - 9 times more than that regulatory standard. These particles carry all the components of the cracker including heavy metals, alkali metals, alkaline earth and change the atmosphere with positive and negative ions apart from impaction of sulfur and other acid gases to the atmosphere.
A quantitative study of source imaging in random waveguides

We present a quantitative study of coherent array imaging of remote sources in randomly perturbed waveguides with bounded cross-section. We study how long range cumulative scattering by perturbations of the boundary and the medium impedes the imaging process. We show that boundary scattering effects can be mitigated with filters that enhance the coherent part of the data. The filters are obtained by optimizing a measure of quality of the image. The point is that there is an optimal trade-off between the robustness and resolution of images in such waveguides, which can be found adaptively, as the data are processed to form the image. Long range scattering by perturbations of the medium is harder to mitigate than scattering by randomly perturbed boundaries. Coherent imaging methods do not work and more complex incoherent methods, based on transport models of energy, should be used instead. Such methods are nor useful, nor needed in waveguides with perturbed boundaries. We explain all these facts using rigorous asymptotic stochastic analysis of the wave field in randomly perturbed waveguides. We also analyze the adaptive coherent imaging method and obtain a quantitative agreement with the results of numerical simulations.
Utilization of the Polarized Infrared Light for Prostate Cancer Visualization in Isolated Prostates

The current imaging methods for the prostate cancer diagnosis are complicated and partially invasive. Therefore, a key challenge for prostate cancer detection is to use a simple and non-invasive method. Imaging plays a crucial role in the non-invasive identification, localization and grading of prostate carcinoma. In this paper, we demonstrate the possibility using of polarized a near infrared (NIR) light for the in vitro prostate cancer detection and imaging. Because the intensity of NIR light passing through the cancerous outgrowth is lower than the intensity of NIR light passing through the noncancerous one, the cancerous formations are differentiated as the dark areas in the relatively white background. Specially developed software analyses and processes a distribution of intensities of the grayscale images, measures the ratios of their strength, and determines the rate of prostate malignancy. Obtained results may hold promise to make an important contribution to the diagnosis of prostate cancer in the early stage of its development.
Hand-held thoracic sonography for detecting post-traumatic pneumothoraces: the Extended Focused Assessment with Sonography for Trauma (EFAST).

BACKGROUND Thoracic ultrasound (EFAST) has shown promise in inferring the presence of post-traumatic pneumothoraces (PTXs) and may have a particular value in identifying occult pneumothoraces (OPTXs) missed by the AP supine chest radiograph (CXR). However, the diagnostic utility of hand-held US has not been previously evaluated in this role. METHODS Thoracic US examinations were performed during the initial resuscitation of injured patients at a provincial trauma referral center. A high frequency linear transducer and a 2.4 kg US attached to a video-recorder were used. Real-time EFAST examinations for PTXs were blindly compared with the subsequent results of CXRs, a composite standard (CXR, chest and abdominal CT scans, clinical course, and invasive interventions), and a CT gold standard (CT only). Charts were reviewed for in-hospital outcomes and follow-up. RESULTS There were 225 eligible patients (207 blunt, 18 penetrating); 17 were excluded from the US examination because of battery failure or a lost probe. Sixty-five (65) PTXs were detected in 52 patients (22% of patients), 41 (63%) being occult to CXR in 33 patients (14.2% whole population, 24.6% of those with a CT). The US and CXR agreed in 186 (89.4%) of patients, EFAST was better in 16 (7.7%), and CXR better in 6 (2.9%). Compared with the composite standard, the sensitivity of EFAST was 58.9% with a likelihood ratio of a positive test (LR+) of 69.7 and a specificity of 99.1%. Comparing EFAST directly to CXR, by looking at each of 266 lung fields with the benefit of the CT gold standard, the EFAST showed higher sensitivity over CXR (48.8% versus 20.9%). Both exams had a very high specificity (99.6% and 98.7%), and very predictive LR+ (46.7 and 36.3). CONCLUSION EFAST has comparable specificity to CXR but is more sensitive for the detection of OPTXs after trauma. Positive EFAST findings should be addressed either clinically or with CT depending on hemodynamic stability. CT should be used if detection of all PTXs is desired.
Mining implicit design templates for actionable code reuse

In this paper, we propose an approach to detecting project-specific recurring designs in code base and abstracting them into design templates as reuse opportunities. The mined templates allow programmers to make further customization for generating new code. The generated code involves the code skeleton of recurring design as well as the semi-implemented code bodies annotated with comments to remind programmers of necessary modification. We implemented our approach as an Eclipse plugin called MICoDe. We evaluated our approach with a reuse simulation experiment and a user study involving 16 participants. The results of our simulation experiment on 10 open source Java projects show that, to create a new similar feature with a design template, (1) on average 69% of the elements in the template can be reused and (2) on average 60% code of the new feature can be adopted from the template. Our user study further shows that, compared to the participants adopting the copy-paste-modify strategy, the ones using MICoDe are more effective to understand a big design picture and more efficient to accomplish the code reuse task.
Dietary assimilation of cadmium associated with bacterial exopolymer sediment coatings by the estuarine amphipod Leptocheirus plumulosus : effects of Cd concentration and salinity

Bacterial extracellular substances (also known as exopolysaccharides, or EPS) may serve as vectors for trophic transfer of metals in benthic systems because these ubiquitous sediment coatings can sorb high concentrations of toxic metals, and because many benthic invertebrates assimilate EPS sediment coatings upon ingestion. We conducted 3 sets of experiments to determine the assimilative bioavailability of EPS-associated Cd to the benthic amphipod Leptocheirus plumulosus as a function of Cd concentration and salinity. Bioavailability was measured as L. plumulosus Cd assimilation efficiency (AE) from EPS-coated silica (EPS-Si) and from uncoated silica (NC-Si) using modified pulse-chase methods with the gamma-emitting radioisotope 109 Cd. Cd AE was significantly greater from NC-Si than from EPS-Si at 7.5‰, but not at 2.5 or 25‰. Overall, Cd AE from EPS-Si was between 15.1 and 21.5%. Because EPS-Si sorbed more Cd than NC-Si, EPS coatings magnified the amount of Cd amphipods accumulated at each salinity by up to a factor of 10. Salinity did not directly affect Cd AE from EPS-Si, but because Cd-EPS partitioning increased with decreasing salinity, amphipods accumulated more Cd from EPS at the lowest Cd-EPS incubation salinity (2.5‰) than at higher salinities (7.5 and 25‰). Finally, Cd concentration in EPS exhibited an inverse relationship with Cd AE at 2.5 ‰, but not at 25 ‰. Specifically, Cd AE was 12 times greater at 1 compared with 10 μg Cd μg -1 EPS. Together, these results show that estuarine benthos can accumulate Cd from EPS sediment coatings, but that the degree to which this phenomenon occurs is dependent upon seawater salinity and Cd concentration in EPS.
Effects of SiC amount on phase compositions and properties of Ti_3SiC_2-based composites

The phase compositions and properties of Ti3SiC2-based composites with SiC addition of 5%-30% in mass fraction fabricated by in-situ reaction and hot pressing sintering were studied. SiC addition effectively prevented TiC synthesis but facilitated SiC synthesis. The Ti3SiC2/Ti C-SiC composite had better oxidation resistance when SiC added quantity reached 20% but poorer oxidation resistance with SiC addition under 15% than Ti3SiC2/TiC composite at higher temperatures. There were more than half of the original SiC and a few Ti3SiC2 remaining in Ti3SiC2/Ti C-SiC with 20% SiC addition, but all constituents in Ti3Si2/TiC composite were oxidized after 12 h in air at 1500 °C. The oxidation scale thickness of TS30, 1505.78 μm, was near a half of that of T,2715 μm, at 1500 °C for 20 h. Ti3SiC2/Ti C composite had a flexural strength of 474 MPa, which was surpassed by Ti3SiC2/TiC-SiC composites when SiC added amount reached 15%. The strength reached the peak of 518 MPa at 20% SiC added amount.
Cognitive behavioral therapies for informal caregivers of patients with cancer and cancer survivors: a systematic review and meta‐analysis

Informal caregivers (ICs) of patients with cancer and cancer survivors report a number of psychological and physical complaints because of the burden associated with providing care. Given the documented effect of Cognitive Behavioral Therapy (CBT) on ICs' common psychological complaints, such as anxiety and depression, the objective was to conduct a meta‐analysis on the effect of CBTs for adult ICs.
MST kinases in innate immune signaling

The mammalian STE20-like (MST) protein kinases are composed of MST1, MST2, MST3, MST4 and YSK1. They play crucial roles in cell growth, migration, polarity and apoptosis. Dysfunction of these kinases often leads to diseases. MST kinases are extensively involved in development and function of immune system. Here, we review recent progresses on the regulatory function of MST kinases in innate immune signaling.
Densely connected convolutional networks and signal quality analysis to detect atrial fibrillation using short single-lead ECG recordings

The development of new technology such as wearables that record high-quality single channel ECG, provides an opportunity for ECG screening in a larger population, especially for atrial fibrillation screening. The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR), atrial fibrillation (AF), other rhythms (O), and noise from a single channel short ECG segment (9–60 seconds). For this purpose, signal quality index (SQI) along with dense convolutional neural networks was used. Two convolutional neural network (CNN) models (main model that accepts 15 seconds ECG and secondary model that processes 9 seconds shorter ECG) were trained using the training data set. If the recording is determined to be of low quality by SQI, it is immediately classified as noisy. Otherwise, it is transformed to a time-frequency representation and classified with the CNN as NSR, AF, O, or noise. At the final step, a feature-based post-processing algorithm classifies the rhythm as either NSR or O in case the CNN model's discrimination between the two is indeterminate. The best result achieved at the official phase of the PhysioNet/CinC challenge on the blind test set was 0.80 (F1 for NSR, AF, and O were 0.90, 0.80, and 0.70, respectively).
Simultaneous localization and mapping of a mobile robot with surveillance and flying cameras

Localization and exploration of robotic systems has been a highly active research area in recent years. In order to explore unknown environments with better precision, a robotic system employing surveillance and flying cameras is proposed in this paper. Specifically, a mobile robot equipped with an onboard monocular camera performs visual simultaneous localization and mapping with extended Kalman filtering (EKF-VSLAM) and moves from indoor to outdoor environments. To correct the estimation errors of the system during the task, a least squares-based correction approach is proposed. The proposed robotic system employs surveillance cameras in indoor environment and a flying camera in outdoor environment to correct estimation error from the onboard monocular camera. The proposed correction approach, integrated with the conventional EKF-VSLAM, has been validated by experiments demonstrating better precision.
Transgenic crops with an improved resistance to biotic stresses. A review

Des cultures transgeniques pour une meilleure resistance aux stress biotiques (synthese bibliographique)Introduction. Les insectes ravageurs, les maladies et les mauvaises herbes constituent des stress biotiques qui sont des facteurs limitatifs importants pour le rendement des cultures et de la production. Les contraintes liees aux methodes classiques de selection rendent indispensable le recours a des methodes d’amelioration alternatives conferant aux nouvelles varietes une plus grande resistance aux stress biotiques. Les progres des techniques moleculaires ont conduit a la mise au point de procedes de transformation genetique d'une grande variete de plantes. L’approche via l'ingenierie genetique a demontre sa capacite a fournir d'enormes possibilites de selection de genes de resistance provenant d’origines diverses pour assurer une meilleure resistance aux differents stress biotiques. Litterature. Dans cet article, nous nous concentrons sur les strategies adoptees pour developper des fonctions insecticides, antifongiques, antibacteriennes, antivirales et de detoxification des herbicides dans de nouvelles varietes vegetales. Conclusions. Malgre les preoccupations au sujet de la commercialisation de produits issus de cultures genetiquement modifiees resistantes aux stress biotiques, on constate que la zone de culture de ces lignees est en plein essor. Compte tenu de cette tendance, il faut non seulement s'attendre a un accroissement continu en termes de production et de commercialisation de cultures genetiquement modifiees resistantes aux stress biotiques, mais aussi a ce que cela s’etende aux cultures resistantes aux stress abiotiques (e.g. secheresse, salinite, etc.) qui sont encore a venir.
Effects of Captivity and Body Condition on Plasma Corticosterone, Locomotor Behavior, and Plasma Metabolites in Curve-Billed Thrashers

The acute stress response involves the secretion of catabolic glucocorticoids, such as corticosterone (CORT) in birds, that mobilize intrinsic energy stores primarily through a gluconeogenic pathway involving fat breakdown, thus linking body condition and stress. We measured changes in CORT and gluconeogenic metabolites (triglycerides, free glycerols, glucose) during handling stress in curve-billed thrashers Toxostoma curvirostre from two habitats (urban vs. desert) that may differ in food abundance in the wild, in captivity, and in response to both food restriction and subsequent recovery. Urban thrashers were heavier and secreted more CORT than desert birds in the field, but differences did not persist in captivity. Decreased access to food resulted in decreased body mass and a diminished ability to elevate plasma CORT in response to handling stress. However, the opposite effect was observed as these birds recovered from food restriction. Plasma levels of glucose and triglycerides did not change with stress. Food restriction also increased locomotor activity, which likely further exacerbated energy loss. These observations suggest that body condition and stress differences between urban and desert birds may be related to differences in their relative energetic states, possibly due to food availability. Body condition may affect the extent to which an individual can elevate CORT and use free glycerol as energy during acute stress.
Smooth, identifiable supermodels of discrete DAG models with latent variables

We provide a parameterization of the discrete nested Markov model, which is a supermodel that approximates DAG models (Bayesian network models) with latent variables. Such models are widely used in causal inference and machine learning. We explicitly evaluate their dimension, show that they are curved exponential families of distributions, and fit them to data. The parameterization avoids the irregularities and unidentifiability of latent variable models. The parameters used are all fully identifiable and causally-interpretable quantities.
Transductive Classification via Dual Regularization Conference

Semi-supervised learning has witnessed increasing interest in the past decade. One common assumption behind semi-supervised learning is that the data labels should be sufficiently smooth with respect to the intrinsic data manifold. Recent research has shown that the features also lie on a manifold. Moreover, there is a duality between data points and features, that is, data points can be classified based on their distribution on features, while features can be classified based on their distribution on the data points. However, existing semi-supervised learning methods neglect these points. Based on the above observations, in this paper, we present a dual regularization, which consists of two graph regularizers and a co-clustering type regularizer. In detail, the two graph regularizers consider the geometric structure of the data points and the features respectively, while the co-clustering type regularizer takes into account the duality between data points and features. Furthermore, we propose a novel transductive classification framework based on dual regularization, which can be solved by alternating minimization algorithm and its convergence is theoretically guaranteed. Experiments on benchmark semi-supervised learning data sets demonstrate that the proposed methods outperform many state of the art transductive classification methods.