Miracle-dz/UniFAR-FTUs huggingface dataset:
RNA Replication and Membrane Modification Require the Same Functions of Alphavirus Nonstructural Proteins

ABSTRACT The alphaviruses induce membrane invaginations known as spherules as their RNA replication sites. Here, we show that inactivation of any function (polymerase, helicase, protease, or membrane association) essential for RNA synthesis also prevents the generation of spherule structures in a Semliki Forest virus trans-replication system. Mutants capable of negative-strand synthesis, including those defective in RNA capping, gave rise to spherules. Recruitment of RNA to membranes in the absence of spherule formation was not detected.
Simultaneous Determination of Modulation Types and Signal-to-Noise Ratios Using Feature-Based Approach

This paper presents a low-complexity technique for simultaneous determination of modulation types and signal-to-noise ratios (SNRs) in wireless communication systems. The proposed approach exploits the extracted features of patterns observed in signals’ asynchronous amplitudes histograms, for the simultaneous determination of these quantities using support vector machine. Features extraction has been performed by a well-known technique called principal component analysis which is used to extract the most significant features before being supplied to the artificial intelligent system. Simulations for three commonly-used modulation types have been conducted under real-world channel conditions. The results conclude that the presented approach can accurately identify the modulation types with 99.83% accuracy despite the existence of real-world channel impairments. Furthermore, the algorithm is capable of SNRs estimation over a broad range of 0–30 dB with average estimation error of 0.79 dB. The proposed paper exploits the simplicity of generating asynchronous amplitudes histograms to enable cost-effective and reduced-complexity implementation in cognitive wireless systems.
From Nodes to Networks: Evolving Recurrent Neural Networks

Gated recurrent networks such as those composed of Long Short-Term Memory (LSTM) nodes have recently been used to improve state of the art in many sequential processing tasks such as speech recognition and machine translation. However, the basic structure of the LSTM node is essentially the same as when it was first conceived 25 years ago. Recently, evolutionary and reinforcement learning mechanisms have been employed to create new variations of this structure. This paper proposes a new method, evolution of a tree-based encoding of the gated memory nodes, and shows that it makes it possible to explore new variations more effectively than other methods. The method discovers nodes with multiple recurrent paths and multiple memory cells, which lead to significant improvement in the standard language modeling benchmark task. The paper also shows how the search process can be speeded up by training an LSTM network to estimate performance of candidate structures, and by encouraging exploration of novel solutions. Thus, evolutionary design of complex neural network structures promises to improve performance of deep learning architectures beyond human ability to do so.
A personal knowledge base integrating user data and activity timeline. (Une base de connaissance personnelle intégrant les données d'un utilisateur et une chronologie de ses activités)

Typical Internet users today have their data scattered over several devices, applications, and services. Managing and controlling one's data is increasingly difficult. In this thesis, we adopt the viewpoint that the user should be given the means to gather and integrate her data, under her full control. In that direction, we designed a system that integrates and enriches the data of a user from multiple heterogeneous sources of personal information into an RDF knowledge base. The system is open-source and implements a novel, extensible framework that facilitates the integration of new data sources and the development of new modules for deriving knowledge. We first show how user activity can be inferred from smartphone sensor data. We introduce a time-based clustering algorithm to extract stay points from location history data. Using data from additional mobile phone sensors, geographic information from OpenStreetMap, and public transportation schedules, we introduce a transportation mode recognition algorithm to derive the different modes and routes taken by the user when traveling. The algorithm derives the itinerary followed by the user by finding the most likely sequence in a linear-chain conditional random field whose feature functions are based on the output of a neural network. We also show how the system can integrate information from the user's email messages, calendars, address books, social network services, and location history into a coherent whole. To do so, it uses entity resolution to find the set of avatars used by each real-world contact and performs spatiotemporal alignment to connect each stay point with the event it corresponds to in the user's calendar. Finally, we show that such a system can also be used for multi-device and multi-system synchronization and allow knowledge to be pushed to the sources. We present extensive experiments.
Influence of Short-Term Orthokeratology to Corneal Tangent Modulus: A Randomized Study

ABSTRACT Purpose: Influence of orthokeratology on corneal biomechanics is equivocal using Ocular Response Analyzer, ORA. Implementing indentation method, corneal tangent modulus was measured and monitored in short-term orthokeratology. Materials and Methods: Sixteen young subjects with refractive errors between –4D to –5D sphere and astigmatism within –1.50D were recruited. One randomly selected eye wore orthokeratology lens (treatment), and the fellow eye wore conventional rigid gas permeable lens (control). Lenses were worn for 30 and 60 minutes and one night separately with a week of washout period in between. The first two visits were randomly scheduled and before the overnight visit. Eyes were kept closed during all the lens wearing periods. Corneal radius, thickness, and biomechanics (using both ORA and an indentation device) were compared between eyes prior to each visit, and then before and after lens wear. Associations between baseline corneal biomechanics and central cornea from overnight visit were investigated. Results: Corneal parameters were similar in each visit before lens wear. Significant corneal flattening was observed in treatment eyes, and flattening increased with wearing time. Control eyes showed no significant corneal curvature changes. Corneal resistance factor (CRF) reduced by 0.42mmHg (± 0.68mmHg) after 30 minutes of orthokeratology treatment. Corneal hysteresis (CH) reduced by 0.42mmHg (+/- 0.63mmHg) in control eyes from overnight wear. Both eyes showed stable tangent modulus, E, throughout the study. A lower CH (r = 0.51, p = 0.046) and a higher E (r = 0.53, p = 0.037) at baseline was significantly associated with greater corneal flattening along the flattest meridian in treatment eyes. Conclusions: Short‐term orthokeratology had no significant effect on corneal tangent modulus. Changes in CH and CRF could be related to their intrinsic measurement variability. Corneal tangent modulus provided another measure of corneal biomechanics. Long-term study is required to investigate predictive role of corneal biomechanics in orthokeratology.
Upregulation of the Cell-Cycle Regulator RGC-32 in Epstein-Barr Virus-Immortalized Cells

Epstein-Barr virus (EBV) is implicated in the pathogenesis of multiple human tumours of lymphoid and epithelial origin. The virus infects and immortalizes B cells establishing a persistent latent infection characterized by varying patterns of EBV latent gene expression (latency 0, I, II and III). The CDK1 activator, Response Gene to Complement-32 (RGC-32, C13ORF15), is overexpressed in colon, breast and ovarian cancer tissues and we have detected selective high-level RGC-32 protein expression in EBV-immortalized latency III cells. Significantly, we show that overexpression of RGC-32 in B cells is sufficient to disrupt G2 cell-cycle arrest consistent with activation of CDK1, implicating RGC-32 in the EBV transformation process. Surprisingly, RGC-32 mRNA is expressed at high levels in latency I Burkitt's lymphoma (BL) cells and in some EBV-negative BL cell-lines, although RGC-32 protein expression is not detectable. We show that RGC-32 mRNA expression is elevated in latency I cells due to transcriptional activation by high levels of the differentially expressed RUNX1c transcription factor. We found that proteosomal degradation or blocked cytoplasmic export of the RGC-32 message were not responsible for the lack of RGC-32 protein expression in latency I cells. Significantly, analysis of the ribosomal association of the RGC-32 mRNA in latency I and latency III cells revealed that RGC-32 transcripts were associated with multiple ribosomes in both cell-types implicating post-initiation translational repression mechanisms in the block to RGC-32 protein production in latency I cells. In summary, our results are the first to demonstrate RGC-32 protein upregulation in cells transformed by a human tumour virus and to identify post-initiation translational mechanisms as an expression control point for this key cell-cycle regulator.
Driving Scene Perception Network: Real-Time Joint Detection, Depth Estimation and Semantic Segmentation

As the demand for enabling high-level autonomous driving has increased in recent years and visual perception is one of the critical features to enable fully autonomous driving, in this paper, we introduce an efficient approach for simultaneous object detection, depth estimation and pixel-level semantic segmentation using a shared convolutional architecture. The proposed network model, which we named Driving Scene Perception Network (DSPNet), uses multi-level feature maps and multi-task learning to improve the accuracy and efficiency of object detection, depth estimation and image segmentation tasks from a single input image. Hence, the resulting network model uses less than 850 MiB of GPU memory and achieves 14.0 fps on NVIDIA GeForce GTX 1080 with a 1024 512 input image, and both precision and efficiency have been improved over combination of single tasks.
Energy-Efficient Bandwidth Aggregation for Delay-Constrained Video Over Heterogeneous Wireless Networks

The spectrum limitation of single wireless networks prompts the bandwidth aggregation of heterogeneous access medium (e.g., LTE and Wi-Fi) to support high-quality real-time video services. Energy consumption of mobile devices is of vital significance to provide user-satisfied multimedia streaming applications. However, it is challenging to develop an energy-efficient bandwidth aggregation scheme with regard to the stringent delay and quality constraints imposed by wireless video transmission. To address the critical problem, this paper presents an Energy-quaLity aware Bandwidth Aggregation (ELBA) scheme. First, we develop an analytical framework to model the delay-constrained energy-quality tradeoff for multipath video transmission over heterogeneous wireless networks. Second, we propose a bandwidth aggregation framework that integrates energy-minimized rate adaptation, delay-constrained unequal protection, and quality-aware packet distribution. The proposed ELBA scheme is able to effectively leverage the wireless channel diversity and video frame priority for enabling energy-minimized quality-guaranteed streaming to multihomed devices within imposed deadline. We conduct the performance evaluation through both experiments over real wireless networks and extensive emulations in Exata platform. Experimental results demonstrate the performance advantages of ELBA over existing bandwidth aggregation schemes in energy conservation, video quality, and end-to-end delay.
Four‐way multimodal fusion of 7 T imaging data using an mCCA+jICA model in first‐episode schizophrenia

Acquisition of multimodal brain imaging data for the same subject has become more common leading to a growing interest in determining the intermodal relationships between imaging modalities to further elucidate the pathophysiology of schizophrenia. Multimodal data have previously been individually analyzed and subsequently integrated; however, these analysis techniques lack the ability to examine true modality inter‐relationships. The utilization of a multiset canonical correlation and joint independent component analysis (mCCA + jICA) model for data fusion allows shared or distinct abnormalities between modalities to be examined. In this study, first‐episode schizophrenia patients (nSZ=19) and matched controls (nHC=21) completed a resting‐state functional magnetic resonance imaging (fMRI) scan at 7 T. Grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and amplitude of low frequency fluctuation (ALFF) maps were used as features in a mCCA + jICA model. Results of the mCCA + jICA model indicated three joint group‐discriminating components (GM‐CSF, WM‐ALFF, GM‐ALFF) and two modality‐unique group‐discriminating components (GM, WM). The joint component findings are highlighted by GM basal ganglia, somatosensory, parietal lobe, and thalamus abnormalities associated with ventricular CSF volume; WM occipital and frontal lobe abnormalities associated with temporal lobe function; and GM frontal, temporal, parietal, and occipital lobe abnormalities associated with caudate function. These results support and extend major findings throughout the literature using independent single modality analyses. The multimodal fusion of 7 T data in this study provides a more comprehensive illustration of the relationships between underlying neuronal abnormalities associated with schizophrenia than examination of imaging data independently.
The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks

The importance of robotic assistive devices grows in our work and everyday life. Cooperative scenarios involving both robots and humans require safe human-robot interaction. One important aspect here is the management of robot errors, including fast and accurate online robot-error detection and correction. Analysis of brain signals from a human interacting with a robot may help identifying robot errors, but accuracies of such analyses have still substantial space for improvement. In this paper we evaluate whether a novel framework based on deep convolutional neural networks (deep ConvNets) could improve the accuracy of decoding robot errors from the EEG of a human observer, both during an object grasping and a pouring task. We show that deep ConvNets reached significantly higher accuracies than both regularized Linear Discriminant Analysis (rLDA) and filter bank common spatial patterns (FB-CSP) combined with rLDA, both widely used EEG classifiers. Deep ConvNets reached mean accuracies of 75% ± 9 %, rLDA 65% ± 10% and FB-CSP + rLDA 63% ± 6% for decoding of erroneous vs. correct trials. Visualization of the time-domain EEG features learned by the ConvNets to decode errors revealed spatiotemporal patterns that reflected differences between the two experimental paradigms. Across subjects, ConvNet decoding accuracies were significantly correlated with those obtained with rLDA, but not CSP, indicating that in the present context ConvNets behaved more “rLDA-like” (but consistently better), while in a previous decoding study with another task but the same ConvNet architecture, it was found to behave more “CSP-like”. Our findings thus provide further support for the assumption that deep ConvNets are a versatile addition to the existing toolbox of EEG decoding techniques, and we discuss steps how ConvNet EEG decoding performance could be further optimized.
Blood pressure responses of defensive hostile women when facing a real stress task

Objective: This study investigated the importance of the combination of the hostility and defensiveness variables as psychosocial factors that predict the risk of cardiovascular dysfunction. We examined the impact of a stressful situation on blood pressure responses, using a continuous psychophysiological assessment approach. Design: We measured the evolution of these responses over three experimental phases (adaptation, task and recovery), also considering a minute-by-minute analysis within each phase. Main Outcome Measures: We used the Cook-Medley Hostility Scale and the Social Desirability Questionnaire to form four groups (high hostility and high defensiveness, high hostility and low defensiveness, low hostility and high defensiveness, and low hostility and low defensiveness). Results: We expected the group of hostile defensive women to record higher activation (task phase: an academic exam) and slower habituation (recovery phase) compared to the other groups. The results confirmed our hypothesis, as the profile of the hostile defensive individuals was one of constant or sensitization during the task phase, while during the recovery phase those individuals underwent a slow recovery. Conclusion: Therefore, it can be indicated that the low hostility and low defensive group is the most adaptive, as recorded very little activation in response, and rapid recovery.
The Anatomy of the Medial Patellofemoral Ligament.

Recurrent patellar dislocation is observed in many patients treated nonoperatively following primary dislocation. Injury to the medial patellofemoral ligament (MPFL) is reported in the majority of patients following dislocation. There is an increased interest in repair or reconstruction of the MPFL for patients experiencing recurrent instability. The femoral attachment of the MPFL is critical in determining graft behavior following reconstruction. The femoral attachment can be determined by referencing local anatomy, fluoroscopic imaging or on the basis of desired graft-length changes. This article reviews the anatomy of the MPFL, with a focus on its femoral insertion site as it pertains to anatomic, isometric, and anisometric reconstruction. [Orthopedics. 2017; 40(4):e583-e588. ].
Restricting dietary sodium reduces plasma sodium response to exercise in the heat

Exercise‐associated hyponatremia can be life‐threatening. Excessive hypotonic fluid ingestion is the primary etiological factor but does not explain all variability. Possible effects of chronic sodium intake are unknown. The aim of this study was to determine whether dietary sodium affects plasma sodium concentration [Na+] during exercise in the heat, when water intake nearly matches mass loss. Endurance‐trained men (n = 9) participated in this crossover experiment. Each followed a low‐sodium (lowNa) or high‐sodium (highNa) diet for 9 days with 24‐h fluid intakes and urine outputs measured before experimental trials (day 10). The trials were ≥2 week apart. Trials comprised 3 h (or if not possible to complete, to exhaustion) cycling (55% VO2max; 34 °C, 65% RH) with water intake approximating mass loss. Plasma [Na+], hematocrit, sweat and urine [Na+], heart rate, core temperature, and subjective perceptions were monitored. Urine [Na+] was lower on lowNa 24 h prior to (31 ± 24, 76 ± 30 mmol/L, P = 0.027) and during trials (10 ± 10, 52 ± 32 mmol/L, P = 0.004). Body mass was lower on lowNa (79.6 ± 8.5, 80.5 ± 8.9, P = 0.03). Plasma [Na+] was lower on lowNa before (137 ± 2, 140 ± 3, P = 0.007) and throughout exercise (P = 0.001). Sweat [Na+] was unaffected by diet (54.5 ± 40, 54.5 ± 23 mmol/L, P = 0.99). Heart rate and core temperature were higher on lowNa (P ≤ 0.001). Despite decreased urinary sodium losses, plasma sodium was lower on lowNa, with decreased mass indicating (extracellular) water may have been less, explaining greater heart rate and core temperature. General population health recommendations to lower salt intake may not be appropriate for endurance athletes, particularly those training in the heat.
IKZF1 alterations and expression of CRLF2 predict prognosis in adult Chinese patients with B-cell precursor acute lymphoblastic leukemia

Abstract Acute Lymphoblastic Leukemia (ALL) is a common hematological malignancy in children, with a prognosis much worse in adults. The molecular characterization of ALL and its correlated prognostic significance are largely unknown. In this study, we analyzed the frequency of IKZF1 deletions, IK6 isoform, and CRLF2 overexpression in 118 Chinese adult B-cell precursor ALL (B-ALL) patients to explore their associations with clinical prognosis. Our data showed that IKZF1 deletions and IK6 isoform were highly detected in adult patients, and both of them were related with worse prognosis in Ph− B-ALL, HR group of Ph− B-ALL, and/or B-ALL patients. Though the frequency of CRLF2 overexpression was similar to children, it had an independent prognostic significance for standard-risk and Ph− adult patients. Our study provided insights into the prognostic significance of certain genetic features in B-ALL patients. Further therapeutic strategies targeting these abnormalities potentially improving the prognosis of B-ALL are warranted.
Histological evaluation of flexible neural implants; flexibility limit for reducing the tissue response?

Objective. Flexible neural probes are hypothesized to reduce the chronic foreign body response (FBR) mainly by reducing the strain–stress caused by an interplay between the tethered probe and the brain’s micromotion. However, a large discrepancy of Young’s modulus still exists (3–6 orders of magnitude) between the flexible probes and the brain tissue. This raises the question of whether we need to bridge this gap; would increasing the probe flexibility proportionally reduce the FBR? Approach. Using novel off-stoichiometry thiol-enes-epoxy (OSTE+) polymer probes developed in our previous work, we quantitatively evaluated the FBR to four types of probes with different softness: silicon (~150 GPa), polyimide (1.5 GPa), OSTE+Hard (300 MPa), and OSTE+Soft (6 MPa). Main results. We observed a significant reduction in the fluorescence intensity of biomarkers for activated microglia/macrophages and blood-brain barrier (BBB) leakiness around the three soft polymer probes compared to the silicon probe, both at 4 weeks and 8 weeks post-implantation. However, we did not observe any consistent differences in the biomarkers among the polymer probes. Significance. The results suggest that the mechanical compliance of neural probes can mediate the degree of FBR, but its impact diminishes after a hypothetical threshold level. This infers that resolving the mechanical mismatch alone has a limited effect on improving the lifetime of neural implants.