Miracle-dz/UniFAR-FTUs huggingface dataset:
Atrial Fibrillation Is Not Associated With Thromboembolism in Left Ventricular Assist Device Patients: A Systematic Review and Meta-Analysis.

Atrial fibrillation (AF) is a well-established risk factor of thromboembolism (TE). Thromboembolism is one of the most common complications in patients supported by continuous-flow left ventricular assisted devices (CF-LVADs). However, the association between AF and TE complications in this population is controversial. We conducted a systematic review and meta-analysis to assess the association between AF and overall TE, stroke, and device thrombosis events in CF-LVAD patients. We performed a comprehensive literature search through September 2017 in the databases of MEDLINE and EMBASE. Included studies were prospective or retrospective cohort studies that compared the risk of developing overall TE, stroke, and device thrombosis events in CF-LVAD patients with AF and those without AF. We calculated pooled relative risk (RR) with 95% confidence intervals (CI) and I statistic using the random-effects model. Eleven studies were included involving 6,351 patients who underwent CF-LVAD implantation. Overall, TE outcome was available in four studies involving 1,106 AF and 3,556 non-AF patients. Stroke outcome was available in seven studies (1,455 AF and 4,037 non-AF patients). Device thrombosis outcome was available in three studies (1,010 AF and 3,327 non-AF patients). There was no association between AF and TE events (RR = 0.95; 95% CI: 0.57-1.59, I = 79%, p = 0.85), stroke (RR = 1.10; 95% CI: 0.74-1.64, I = 73%, p = 0.65), and device thrombosis (RR = 0.97; 95% CI: 0.56-1.67, I = 42%, p = 0.91). AF in CF-LVAD patients was not associated with overall TE, stroke, or device thrombosis events. These findings might be explained by the highly thrombogenic property of CF-LVADs that exceeds the thromboembolic risk driven by AF.
Utrophin haploinsufficiency does not worsen the functional performance, resistance to eccentric contractions and force production of dystrophic mice

The lack of dystrophin in Duchenne muscular dystrophy (DMD) compromises the integrity and function of muscle fibers. Skeletal muscles, except the diaphragm, do not undergo progressive degeneration in adult mdx mice due to compensatory mechanisms, including structural protein upregulation. New mouse models, including utrophin haploinsufficient mdx (mdx/utrn+/-) mice, may better recapitulate DMD. Our goal was to determine whether mdx/utrn+/- worsens the mdx phenotype and to characterize the course of the disease on muscle function and contractility at 1, 2, and 5 months of age, which encompass all stages of development relevant to DMD therapy. The functional performances of mdx/utrn+/- mice showed that they are not more affected than mdx/utrn+/+ mice based on downhill treadmill running parameters and subsequent recovery measured by open-field voluntary activity. WT mice ran the entire distance (450 m) on the treadmill, with an additional 561 m during the 4 h of open-field while mdx/utrn+/+ and mdx/utrn+/- mice completed, respectively, 236 m and 273 m on the treadmill and 341 m and 287 m during the open-field period. In addition, isolated ex vivo contractile properties and repeated eccentric contractions showed that mdx/utrn+/- does not significantly worsen the function of dystrophic EDL muscles, which are mainly composed of fast-twitch fibers that are preferentially affected in DMD. Twitch, absolute tetanic, and specific tetanic forces were very similar in dystrophic EDL muscles from mdx/utrn+/+ and mdx utrn+/- mice at 1, 2, and 5 months of age. Five-month-old mdx/utrn+/+ and mdx/utrn+/- mice lost roughly 50% of their force due to repeated eccentric contractions. Thus, histological, morphological, biochemical functional and contractile observations showed that utrophin haploinsufficiency has a very limited impact on mdx mice.
Surface reconstruction in overlapping range images for generating close-surface 3-D object models

The paper addresses the problem of building close-surface (CS) models of 3-D objects by integrating multi-view range images of the object. For the purpose, we have to use overlapping regions between the images to register them into a common view-independent coordinate system. In this paper, we propose a method of removing redundancy in the registered multi-view data due to the image overlap. It is assumed that each image has been triangulated into a connected mesh after the image acquisition. We firstly remove triangle edges in the overlapping regions, and then reconstruct the regions by forcing both border lines to advance toward each other in an optimal retriangulation procedure.
A 6–18-GHz GaN Reactively Matched Distributed Power Amplifier Using Simplified Bias Network and Reduced Thermal Coupling

Two-stage reactively matched gain cells are proposed to implement a high-gain multioctave distributed power amplifier (DPA). The proposed reactively matched distributed amplifier (RMDA) structure shows high gain and power in a small die size. Detailed analysis is presented to understand the design criteria for interstage matching of reactively matched cells. A shared dc bias network is proposed to simplify the biasing of each section to reduce the DPA die size. The thermal coupling effect of GaN high-power amplifier is minimized by optimizing the chip layout. The theoretical analysis is verified by the simulation and supported by the measured data. Two RMDAs are fabricated with a commercial 0.25- $\mu \text{m}$ GaN HEMT process. The implemented RMDA with the compact transistor layout has been implemented in a small die size of 10.7 mm2 and shows output powers reaching 40.3–43.9 dBm, power added efficiencies (PAEs) of 16–27%, and small-signal gains of 15.3–23.2 dB. The RMDA with the reduced thermal coupling achieves 40.6–43.4 dBm with a peak PAE of 29% in a slightly larger die size of 13.8 mm2. To the best of our knowledge, this is the first demonstration of a GaN DPA using reactively matched gain cells, showing very high gain and efficiency over multioctave bandwidth in a small die size.
Differences in Mode of Action of Cochinchinenin A and B on Tetrodotoxin-Resistant Sodium Channels

Purpose: To explore the mechanism of antagonistic interaction between cochinchinenin A and B in modulating tetrodotoxin-resistant (TTR-X) sodium currents. Methods: The time variation of the effects induced by cochinchinenin A and B on the TTX-R sodium currents in dorsal root ganglion (DRG) neurons of rats were observed using whole-cell patch clamp technique. Based on pharmacological fundamental theory, the modes of action of cochinchinenin A and B on TTX-R channels were distinguished. Results: The scatter diagram which reflected the time variation of inhibition effect on TTX-R sodium currents induced by cochinchinenin A fitted well with occupancy theory equation (goodness of fit test, p > 0.05), while that of cochinchinenin B fitted well with rate theory equation (p > 0.05). The rate constants for combination and dissociation between cochinchinenin A and TTX-R sodium channel were (198.7 ± 39.9) x 10 -3 and (41.1 ± 6.2) x 10 -3 respectively; while corresponding values for combination and association between cochinchinenin B and TTX-R sodium channel were (99.9 ± 16.8) x 10 -3 and (5.3 ± 0.4) x 10 -3 respectively. Conclusion: The main cause of the antagonistic interaction between cochinchinenin A and B may be attributed to the different modes of their action on TTX-R sodium channels.
Relationship between tree growth and physical dimensions of Fagus sylvatica crowns assessed from terrestrial laser scanning

Abstract: Measurements of physical tree crown dimensions were of subjective character in the past, even though they can be considered important for the management of many silvicultural operations, such as timing of thinning operations. In our study we investigated if and how measures of physical crown dimensions of trees differed when quantified conventionally versus based on 3D-terrestrial laser scanning and how they are related to basal area increment. Some 24 randomly selected predominant or dominant beech trees between 90 and 110 yrs of age and of varying height were used as study trees. We hypothesized that tree crown dimensions obtained from scans are more closely related to tree radial growth than those obtained from conventional field measurements. It was found that from a variety of compared crown size characteristics the scan-based tree attributes mean crown radius, maximum area of the crown and crown projection area were most closely related to individual tree growth. We conclude that the horizontal extension of a tree crown in general is to be considered one of the most important drivers of tree growth. We also conclude that terrestrial laser scanning is a powerful tool to reliably measure physical crown dimensions and TLS-based measurements are more reliable than conventional ones.
A Wireless Fiber Photometry System Based on a High-Precision CMOS Biosensor With Embedded Continuous-Time ΣΔ Modulation

Fluorescence biophotometry measurements require wide dynamic range (DR) and high-sensitivity laboratory apparatus. Indeed, it is often very challenging to accurately resolve the small fluorescence variations in presence of noise and highbackground tissue autofluorescence. There is a great need for smaller detectors combining high linearity, high sensitivity, and high-energy efficiency. This paper presents a new biophotometry sensor merging two individual building blocks, namely a low-noise sensing front-end and a 2nd order continuous-time ΣΔ modulator (CTSDM), into a single module for enabling high-sensitivity and high energy-efficiency photo-sensing. In particular, a differential CMOS photodetector associated with a differential capacitive transimpedance amplifier-based sensing front-end is merged with an incremental 2nd order 1-bit CTSDM to achieve a large DR, low hardware complexity, and high-energy efficiency. The sensor leverages a hardware sharing strategy to simplify the implementation and reduce power consumption. The proposed CMOS biosensor is integrated within a miniature wireless head mountable prototype for enabling biophotometry with a single implantable fiber in the brain of live mice. The proposed biophotometry sensor is implemented in a 0.18-μm CMOS technology, consuming 41 μW from a 1.8-V supply voltage, while achieving a peak dynamic range of 86 dB over a 50-Hz input bandwidth, a sensitivity of 24 mV/nW, and a minimum detectable current of 2.46-pArm s at a 20-kS/s sampling rate.
Antimicrobial surfaces and their potential in reducing the role of the inanimate environment in the incidence of hospital-acquired infections

Environmental surfaces and their role in the epidemiology of hospital-acquired infections (HAIs) have become an area of great scientific interest, particularly in light of the much publicised cases of infections due to methicillin-resistant Staphylococcus aureus (MRSA) and Clostridium difficile in UK hospitals. This feature article sets out to examine the role of surfaces and the inanimate environment in the spread of HAIs, and looks at various antimicrobial techniques being researched to reduce microbial contamination of surfaces. Preventative measures such as coatings which reduce initial microbial adhesion to surfaces will be considered alongside actively antimicrobial measures which inactivate microorganisms already adherent to a surface. The principal focus of this feature article will be given to light-activated antimicrobial surfaces such as the photocatalyst TiO2 and surfaces with embedded photosensitisers. Surfaces which release antimicrobial compounds or metal ions such as silver and copper are also examined, alongside materials which kill microbes upon contact. The widespread research and development of these antimicrobial surfaces is of great importance in maintaining acceptable levels of hygiene in hospitals and will help to fight the spread of HAIs via the contamination of inanimate surfaces in the healthcare environment.
DIVA Vaccines : A Eradication of Infections of Livestock and Poultry

marker vaccines. The term DIVA vaccine was tests were developed by J.T. van Oirschot and colleagues at the Central [1], [2]. against pseudorabies (Aujeszky's deletions in the viral genome es that are produced against the antibodies against gE. In addition, novel gE-negative vaccines were . Following the same principle, DIVA vaccines and companion (BHV-1) infections[2 should also accompany along with the use of DIVA vaccines against exotic viral infections in production animals was exclusively -and-mouth disease in cloven-hoofed animals, classical -N1 antibodies. This was achieved using a baculovirus expressing a 22-23
Range-Dependent Utility

This paper introduces the concept of range-dependent utility. Instead of reference dependence which evaluates outcomes relative to some reference point, we postulate dependence on a given lottery (set of lotteries) outcomes range. In this way the decision maker is a fully rational expected utility maximizer only within a certain range. Range-dependent utility enables experimental results to be explained without recourse to the probability weighting function. Experimental data show that range-dependent utilities can be normalized to obtain decision utility -- a single utility function able to describe decisions involving lotteries denied over different ranges. Both the data analysis as well as theoretical considerations concerning monotonicity indicate that the decision utility should be of S-shape.
Observation Goal Particle Observation Goal Reacher Observation Goal Fetch Pick

Deep reinforcement learning (RL) has proven a powerful technique in many sequential decision making domains. However, robotics poses many challenges for RL, most notably training on a physical system can be expensive and dangerous, which has sparked significant interest in learning control policies using a physics simulator. While several recent works have shown promising results in transferring policies trained in simulation to the real world, they often do not fully utilize the advantage of working with a simulator. In this work, we propose the Asymmetric Actor Critic, which learns a vision-based control policy while taking advantage of access to the underlying state to significantly speed up training. Concretely, our algorithm employs an actor-critic training algorithm in which the critic is trained on full states while the actor (or policy) is trained on images. We show that using these asymmetric inputs improves performance on a range of simulated tasks. Finally, we combine this method with domain randomization and show real robot experiments for several tasks like picking, pushing, and moving a block. We achieve this simulation to real-world transfer without training on any real-world data. Videos of these experiments can be found in www.goo. gl/b57WTs.
Domain Adversarial Training for Accented Speech Recognition

In this paper, we propose a domain adversarial training (DAT) algorithm to alleviate the accented speech recognition problem. In order to reduce the mismatch between labeled source domain data (“standard” accent) and unlabeled target domain data (with heavy accents), we augment the learning objective for a Kaldi TDNN network with a domain adversarial training (DAT) objective to encourage the model to learn accent-invariant features. In experiments with three Mandarin accents, we show that DAT yields up to 7.45% relative character error rate reduction when we do not have transcriptions of the accented speech, compared with the baseline trained on standard accent data only. We also find a benefit from DAT when used in combination with training from automatic transcriptions on the accented data. Furthermore, we find that DAT is superior to multi-task learning for accented speech recognition.
A Novel c-VEP BCI Paradigm for Increasing the Number of Stimulus Targets Based on Grouping Modulation With Different Codes

In an existing brain-computer interface (BCI) based on code modulated visual evoked potentials (c-VEP), a method with which to increase the number of targets without increasing code length has not yet been established. In this paper, a novel c-VEP BCI paradigm, namely, grouping modulation with different codes that have good autocorrelation and crosscorrelation properties, is presented to increase the number of targets and information transfer rate (ITR). All stimulus targets are divided into several groups and each group of targets are modulated by a distinct pseudorandom binary code and its circularly shifting codes. Canonical correlation analysis is applied to each group for yielding a spatial filter and templates for all targets in a group are constructed based on spatially filtered signals. Template matching is applied to each group and the attended target is recognized by finding the maximal correlation coefficients of all groups. Based on the paradigm, a BCI with a total of 48 targets divided into three groups was implemented; 12 and 10 subjects participated in an off-line and a simulated online experiments, respectively. Data analysis of the offline experiment showed that the paradigm can massively increase the number of targets from 16 to 48 at the cost of slight compromise in accuracy (95.49% vs. 92.85%). Results of the simulated online experiment suggested that although the averaged accuracy across subjects of all three groups of targets was lower than that of a single group of targets (91.67% vs. 94.9%), the average ITR of the former was substantially higher than that of the later (181 bits/min vs. 135.6 bit/min) due to the large increase of the number of targets. The proposed paradigm significantly improves the performance of the c-VEP BCI, and thereby facilitates its practical applications such as high-speed spelling.
Time-Frequency Convolutional Neural Network for Automatic Sleep Stage Classification Based on Single-Channel EEG

In this paper, we propose a new automatic sleep stage classification method based on convolutional neural networks (CNN) using single-channel electroencephalogram (EEG). The sleep stages usually consist of five stages: awake, rapid eye movement (REM), and three non-rapid eye movement stages (N1/N2/SWS). In this method, we introduced two novel ways to convert EEG time series to meaningful matrices which CNN can handle with: the dynamical time-frequency spectrum based on Hilbert-Huang transform and the temporal feature matrix, capturing the characteristics of different sleep stages. Thirtynine whole-night sleep EEG signals from Physionet database were used to evaluate the performance of our proposed method. The results show that our proposed method give good classifications for most sleep stages, especially for awake and the SWS stages. Moreover, we obtain an average accuracy of 84.5%, which outperforms other four existing methods. Different from traditional methods based on artificial feature extracting, this method are simple and more applicable to various time series.
A priori, a posteriori, and back again

Cryptogenic stroke (CS) is common, as is patent foramen ovale (PFO). By chance, they overlap frequently. However, if the relationship was just chance, then PFO prevalence in patients with CS would match the general population (approximately 25%). But PFOs are overrepresented in the CS population (approximately 50%). Paradoxical embolism explains this association—the paradox being a venous source thrombus appearing in an artery (via right-to-left shunt). Because of the high background prevalence, a discovered PFO only has a 50:50 chance of actually causing the stroke in an individual patient.1 The physician asks, “Is the PFO relevant? If I can't know, then why should I look? ” Ergo, diagnostic nihilism.
Dynamic Reorganization of Functional Connectivity Reveals Abnormal Temporal Efficiency in Schizophrenia

Emerging evidence suggests that schizophrenia is associated with brain dysconnectivity. Nonetheless, the implicit assumption of stationary functional connectivity (FC) adopted in most previous resting-state functional magnetic resonance imaging (fMRI) studies raises an open question of schizophrenia-related aberrations in dynamic properties of resting-state FC. This study introduces an empirical method to examine the dynamic functional dysconnectivity in patients with schizophrenia. Temporal brain networks were estimated from resting-state fMRI of 2 independent datasets (patients/controls = 18/19 and 53/57 for self-recorded dataset and a publicly available replication dataset, respectively) by the correlation of sliding time-windowed time courses among regions of a predefined atlas. Through the newly introduced temporal efficiency approach and temporal random network models, we examined, for the first time, the 3D spatiotemporal architecture of the temporal brain network. We found that although prominent temporal small-world properties were revealed in both groups, temporal brain networks of patients with schizophrenia in both datasets showed a significantly higher temporal global efficiency, which cannot be simply attributable to head motion and sampling error. Specifically, we found localized changes of temporal nodal properties in the left frontal, right medial parietal, and subcortical areas that were associated with clinical features of schizophrenia. Our findings demonstrate that altered dynamic FC may underlie abnormal brain function and clinical symptoms observed in schizophrenia. Moreover, we provide new evidence to extend the dysconnectivity hypothesis in schizophrenia from static to dynamic brain network and highlight the potential of aberrant brain dynamic FC in unraveling the pathophysiologic mechanisms of the disease.
Aerosol Synthesis of N and N-S Doped and Crumpled Graphene Nanostructures

Chemically modified graphene–based materials (CMG) are currently attracting a vast interest in their application in different fields. In particular, heteroatom-doped graphenes have revealed great potentialities in the field of electrocatalysis as substitutes of fuel cell noble metal–based catalysts. In this work, we investigate an innovative process for doping graphene nanostructures. We optimize a novel synthetic route based on aerosol preparation, which allows the simultaneous doping, crumpling, and reduction of graphene oxide (GO). Starting from aqueous solutions containing GO and the dopant precursors, we synthesize N- and N,S-dual-doped 3D graphene nanostructures (N-cGO and N,S-cGO). In the aerosol process, every aerosol droplet can be considered as a microreactor where dopant precursors undergo thermal decomposition and react with the GO flakes. Simultaneously, thanks to the relatively high temperature, GO undergoes crumpling and partial reduction. Using a combination of spectroscopic and microscopic characterization techniques, we investigate the morphology of the obtained materials and the chemical nature of the dopants within the crumpled graphene sheets. This study highlights the versatility of the aerosol process for the design of new CMG materials with tailored electrocatalytic properties.