Miracle-dz/UniFAR-FTUs huggingface dataset:
deepMiRGene: Deep Neural Network based Precursor microRNA Prediction

Since microRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation, miRNA identification is one of the most essential problems in computational biology. miRNAs are usually short in length ranging between 20 and 23 base pairs. It is thus often difficult to distinguish miRNA-encoding sequences from other non-coding RNAs and pseudo miRNAs that have a similar length, and most previous studies have recommended using precursor miRNAs instead of mature miRNAs for robust detection. A great number of conventional machine-learning-based classification methods have been proposed, but they often have the serious disadvantage of requiring manual feature engineering, and their performance is limited as well. In this paper, we propose a novel miRNA precursor prediction algorithm, deepMiRGene, based on recurrent neural networks, specifically long short-term memory networks. deepMiRGene automatically learns suitable features from the data themselves without manual feature engineering and constructs a model that can successfully reflect structural characteristics of precursor miRNAs. For the performance evaluation of our approach, we have employed several widely used evaluation metrics on three recent benchmark datasets and verified that deepMiRGene delivered comparable performance among the current state-of-the-art tools.
Exemplar-Based Processing for Speech Recognition: An Overview

Solving real-world classification and recognition problems requires a principled way of modeling the physical phenomena generating the observed data and the uncertainty in it. The uncertainty originates from the fact that many data generation aspects are influenced by nondirectly measurable variables or are too complex to model and hence are treated as random fluctuations. For example, in speech production, uncertainty could arise from vocal tract variations among different people or corruption by noise. The goal of modeling is to establish a generalization from the set of observed data such that accurate inference (classification, decision, recognition) can be made about the data yet to be observed, which we refer to as unseen data.
Predictors of outcome after anterior temporal lobectomy

We assessed the relationship between temporal lobe metabolism measured quantitatively and qualitatively with PET using [18F]-fluorodeoxyglucose (FDG) and postoperative seizure frequency after anterior temporal lobectomy. Forty-three patients with refractory partial epilepsy had anterior temporal lobectomy and preoperative assessment with PET-FDG. Qualitative PET analysis was performed visually by two blinded observers, and quantitative PET analysis was performed using an anatomic template for six control and six temporal lobe subregions, deriving an asymmetry index for each region. Seizure outcome was assessed 1 year after surgery; patients were classified as being seizure-free or as having persistent seizures. Qualitative data were analyzed using Fisher's exact test and the t test, and quantitative data were analyzed using a repeated-measures ANOVA. Thirty-two patients (74%) were seizure-free at follow-up, and 11 had persistent seizures, although most improved. Twenty-nine of 35 patients (83%) with restricted temporal lobe hypometabolism by visual analysis were seizure-free, compared with three of eight patients (37.5%) with normal scans or multilobar hypometabolism. Quantitative analysis revealed that an asymmetry of mesial temporal lobe glucose consumption (uncal region) correlated with improved surgical outcome (p < 0.02). We developed a logistic regression model to predict individual outcome based on the asymmetry in uncal metabolism. Lateral temporal metabolism did not correlate with outcome. We conclude that both visual PET analysis and quantitative PET analysis predict outcome after temporal lobectomy, although quantitative measures offer more precise information.
Study the performance of mobile WiMAX convolutional turbo code

A useful tool in the design of reliable digital communication systems is channel coding. Turbo codes have been shown to yield an outstanding coding gain close to theoretical Shannon limit in the Additive White Gaussian Noise (AWGN) channel. This is due to relatively large coding gains that can be achieved. Over the past decade, turbo codes have been widely considered to be one of the most powerful error control code of a practical importance. This paper introduces the topic of WiMAX turbo coding. First, it gives an overview about the WiMAX Standards, then the WiMAX convolutional turbo code is introduced and its bit error rate performance is evaluated, by simulation, for different transmission conditions.
Dynamic modality weighting for multi-stream hmms inaudio-visual speech recognition

Merging decisions from different modalities is a crucial problem in Audio-Visual Speech Recognition. To solve this, state synchronous multi-stream HMMs have been proposed for their important advantage of incorporating stream reliability in their fusion scheme. This paper focuses on stream weight adaptation based on modality confidence estimators. We assume different and time-varying environment noise, as can be encountered in realistic applications, and, for this, adaptive methods are best suited. Stream reliability is assessed directly through classifier outputs since they are not specific to either noise type or level. The influence of constraining the weights to sum to one is also discussed.
Do cartilage and subchondral bone act together in development and disease

The principal goal of this work was to test the hypothesis that the joint should not be viewed as a composite structure consisting of a number of different and distinct connective tissue layers, but rather as an “organ” that is made up of functionally related tissues that share many characteristics and reaction patterns. A second aim, more practical in character, was to identify pathology-driven changes in this entity of functionally related tissue layers and in synovial fluid that might be classified as early signs of OA. To realise the main goals of this thesis, i.e. to determine whether there is indeed a functional connection between articular cartilage, subchondral and trabecular bone, an extensive comparative biochemical analysis was performed to assess the normal biochemical composition in mature animals without signs of disease (Chapter II). The strong influence of biomechanical loading on local tissue characteristics led to the study of two differently loaded sites. The main question in Chapter III is to what extent the functional adaptation to loading is a generalised phenomenon across all layers in the joint and also whether differences exist in the age at which site differences develop between the organic and inorganic (mineral) components of subchondral or trabecular bone. This area has not been previously explored, but is of great interest in the equine industry given the high incidence of bone-related problems in horses that are strenuously trained at a young age. Chapter IV compares normal tissue specimens with specimens from horses showing signs of (early) OA. Are these changes more evident or more extensive in one layer than in the other? Without the pretension of providing the definitive answer, it could be expected that, if such differences were to exist, they might at last give an indication on the origin of OA. In Chapter V the matter of early OA is pursued further. This chapter can be seen as complimentary to the other four given the role of synovial fluid as a reflection of what happens at tissue level. Chapter VI returns to the tissue level, but uses new and so far unexplored parameters for the detection of pathological processes in the three layers of tissue that make up the load bearing surface of the joint. In this chapter attention is focused on nitrite as a measure for the highly volatile NO, and on nitrotyrosine, a metabolite of NO. The combination of the two may give information on both metabolic and disease status of tissues. In Chapter VII the key word is load and the crucial question regards the way the horse, or more specifically the equine musculoskeletal system, copes with load. As the last chapter of this thesis, it places the main findings of the work in this perspective. The chapter further tries to relate these findings to other recent work in the horse and in other species, as the general concepts of articular (patho)biology have a much wider application than a single species.
A Role for RalGDS and a Novel Ras Effector in the Ras-mediated Inhibition of Skeletal Myogenesis*

Oncogenic Ras inhibits the differentiation of skeletal muscle cells through the activation of multiple downstream signaling pathways, including a Raf-dependent, mitogen-activated or extracellular signal-regulated kinase kinase/mitogen-activated protein kinase (MEK/MAPK)-independent pathway. Here we report that a non-Raf binding Ras effector-loop variant (H-Ras G12V,E37G), which retains interaction with the Ral guanine nucleotide dissociation stimulator (RalGDS), inhibits the conversion of MyoD-expressing C3H10T1/2 mouse fibroblasts to skeletal muscle. We show that H-Ras G12V,E37G, RalGDS, and the membrane-localized RalGDS CAAX protein inhibit the activity of α-actin-Luc, a muscle-specific reporter gene containing a necessary E-box and serum response factor (SRF) binding site, while a RalGDS protein defective for Ras interaction has no effect on α-actin-Luc transcription. H-Ras G12V,E37G does not activate endogenous MAPK, but does increase SRF-dependent transcription. Interestingly, RalGDS, RalGDS CAAX, and RalA G23V inhibit H-Ras G12V,E37G-induced expression of an SRF-regulated reporter gene, demonstrating that signaling through RalGDS does not duplicate the action of H-Ras G12V,E37G in this system. As additional evidence for this, we show that H-Ras G12V,E37G inhibits the expression of troponin I-Luc, an SRF-independent muscle-specific reporter gene, whereas RalGDS and RalGDS CAAX do not. Although our studies show that signaling through RalGDS can interfere with the expression of reporter genes dependent on SRF activity (including α-actin-Luc), our studies also provide strong evidence that an additional signaling molecule(s) activated by H-Ras G12V,E37G is required to achieve the complete inhibition of the myogenic differentiation program.
Properties of a purified pore-forming protein (perforin 1) isolated from H-2-restricted cytotoxic T cell granules

Histocompatibility-restricted cytotoxic T lymphocytes produce circular lesions on target cell membranes. The pore-forming protein (PFP or perforin 1) that forms these membrane lesions has been purified from lymphocytes. At 37 degrees C, in the presence of Ca2+, this protein polymerizes into a supramolecular tubular complex of Mr greater than 10(6) that partially resists dissociation by SDS and reducing agents. It incorporates spontaneously into planar lipid bilayers during polymerization to form nonselective ion channels, showing heterogeneous size distribution, the smallest conductance per unit being identified as 400 pS in 0.1 M NaCl. PFP/P1 that had been assembled in lipid vesicles before incorporation into planar bilayer show much larger single channel conductance, ranging from 1 to 6 nS in 0.1 M NaCl, suggesting that PFP/P1 may assume multiple functional sizes in proportion to its state of polymerization. The reconstituted channels are relatively voltage-insensitive, with most channels persisting in the open state for seconds to minutes. Nucleated cells are rapidly depolarized by this protein. The purified protein lyses a variety of tumor cells. Polymerization and functional channel activity are absolutely Ca2+-dependent. The activity of this protein may play a direct role in T lymphocyte-mediated cytolysis.
Heat Transfer and Effective Thermal Conductivity Analyses in Carbon-Based Foams for Use in Thermal Protection Systems

Abstract The applicability of carbon-based foams as an insulation material in the thermal protection systems (TPSs) of space vehicles is considered using a physical analysis and computer modelling. The heat transfer through the foam is considered through its solid phase and the gas residing in the foam pores via conduction and radiation. As the cellular structure of the foam prevents a large-scale motion of the gas, thermal convection is neglected as a heat transfer mode. The results obtained show that, although the gas-phase conduction and radiation can be ignored at near-room temperatures and at sub-atmospheric pressures, their contributions at high temperatures and at near-atmospheric pressure become very significant. It is also found that one can derive an analytical expression for the effective thermal conductivity (a parameter that combines the contributions of both conduction and radiation) as a function of temperature and pressure. Such an expression is shown to be valid for quite large ranges of temperature, pressure, and insulation thickness and, due to its mathematical simplicity, is very suitable for use in computationally intensive large-scale thermo-mechanical analyses of the entire TPS of a space vehicle.
Distributed Assessment of Network Centrality

We propose a method for the Distributed Assessment of Network CEntrality (DANCE) in complex networks. DANCE attributes to each node a volume-based centrality computed using only localized information, thus not requiring knowledge of the full network topology. We show DANCE is simple, yet efficient, in assessing node centrality in a distributed way. Our proposal also provides a way for locating the most central nodes, again using only the localized information at each node. We also show that the node rankings based on DANCE's centrality and the traditional closeness centrality correlate very well. This is quite useful given the vast potential applicability of closeness centrality, which is however limited by its high computational costs. We experimentally evaluate DANCE against a state-of-the-art proposal to distributively assess network centrality. Results attest that DANCE achieves similar effectiveness in assessing network centrality, but with a significant reduction in the associated costs for practical applicability. In contrast to previous work, this outcome allows DANCE to be applied to large-scale networks.
State-space multitaper time-frequency analysis

Significance Rapid growth in sensor and recording technologies is spurring rapid growth in time series data. Nonstationary and oscillatory structure in time series is commonly analyzed using time-varying spectral methods. These widely used techniques lack a statistical inference framework applicable to the entire time series. We develop a state-space multitaper (SS-MT) framework for time-varying spectral analysis of nonstationary time series. We efficiently implement the SS-MT spectrogram estimation algorithm in the frequency domain as parallel 1D complex Kalman filters. In analyses of human EEGs recorded under general anesthesia, the SS-MT paradigm provides enhanced denoising (>10 dB) and spectral resolution relative to standard multitaper methods, a flexible time-domain decomposition of the time series, and a broadly applicable, empirical Bayes’ framework for statistical inference. Time series are an important data class that includes recordings ranging from radio emissions, seismic activity, global positioning data, and stock prices to EEG measurements, vital signs, and voice recordings. Rapid growth in sensor and recording technologies is increasing the production of time series data and the importance of rapid, accurate analyses. Time series data are commonly analyzed using time-varying spectral methods to characterize their nonstationary and often oscillatory structure. Current methods provide local estimates of data features. However, they do not offer a statistical inference framework that applies to the entire time series. The important advances that we report are state-space multitaper (SS-MT) methods, which provide a statistical inference framework for time-varying spectral analysis of nonstationary time series. We model nonstationary time series as a sequence of second-order stationary Gaussian processes defined on nonoverlapping intervals. We use a frequency-domain random-walk model to relate the spectral representations of the Gaussian processes across intervals. The SS-MT algorithm efficiently computes spectral updates using parallel 1D complex Kalman filters. An expectation–maximization algorithm computes static and dynamic model parameter estimates. We test the framework in time-varying spectral analyses of simulated time series and EEG recordings from patients receiving general anesthesia. Relative to standard multitaper (MT), SS-MT gave enhanced spectral resolution and noise reduction (>10 dB) and allowed statistical comparisons of spectral properties among arbitrary time series segments. SS-MT also extracts time-domain estimates of signal components. The SS-MT paradigm is a broadly applicable, empirical Bayes’ framework for statistical inference that can help ensure accurate, reproducible findings from nonstationary time series analyses.
Efficient decoding with generative score-spaces using the expectation semiring

State-of-the-art speech recognisers are usually based on hidden Markov models (HMMs). They model a hidden symbol sequence with a Markov process, with the observations independent given that sequence. These assumptions yield efficient algorithms, but limit the power of the model. An alternative model that allows a wide range of features, including word- and phone-level features, is a log-linear model. To handle, for example, word-level variable-length features, the original feature vectors must be segmented into words. Thus, decoding must find the optimal combination of segmentation of the utterance into words and word sequence. Features must therefore be extracted for each possible segment of audio. For many types of features, this becomes slow. In this paper, long-span features are derived from the likelihoods of word HMMs. Derivatives of the log-likelihoods, which break the Markov assumption, are appended. Previously, decoding with this model took cubic time in the length of the sequence, and longer for higher-order derivatives. This paper shows how to decode in quadratic time.
Dataflow Virtual Machine Profiling

In the Dataflow model instructions are executed as soon as their input operands are ready, allowing the natural exploitation of instruction level parallelism (ILP), which makes it extremely useful for increasing applications' performance on multicore machines. However, the lack of accurate information on the parallel code can make it more difficult for programmers to perform code analysis and optimization. Thus, the aim of this work is to propose and implement a profiling mechanism for Dataflow runtime environments. To validate the profiling tool implemented, an analysis of the overhead is presented and, also, how the data generated can be used to optimize the code.
Nuclear Export Is Evolutionarily Conserved in CVC Paired-Like Homeobox Proteins and Influences Protein Stability, Transcriptional Activation, and Extracellular Secretion

ABSTRACT Homeodomain transcription factors control a variety of essential cell fate decisions during development. To understand the developmental regulation by these transcription factors, we describe here the molecular analysis of paired-like CVC homeodomain protein (PLC-HDP) trafficking. Complementary experimental approaches demonstrated that PLC-HDP family members are exported by the Crm1 pathway and contain an evolutionary conserved leucine-rich nuclear export signal. Importantly, inactivation of the nuclear export signal enhanced protein stability, resulting in increased transactivation of transfected reporters and decreased extracellular secretion. In addition, PLC-HDPs harbor a conserved active nuclear import signal that could also function as a protein transduction domain. In our study, we characterized PLC-HDPs as mobile nucleocytoplasmic shuttle proteins with the potential for unconventional secretion and intercellular transfer. Nucleocytoplasmic transport may thus represent a conserved control mechanism to fine-tune the transcriptional activity of PLC-HDPs prerequisite for regulating and maintaining the complex expression pattern during development.
Numerical characterization for optimal designed waveform to multicarrier systems in 5G

High mobility of terminals constitutes a hot topic that is commonly envisaged for the next Fifth Generation (5G) of mobile communication systems. The wireless propagation channel is a time-frequency variant. This aspect can dramatically damage the waveforms orthogonality that is induced in the Orthogonal frequency division multiplexing (OFDM) signal. Consequently, this results in oppressive Inter-Carrier Interference (ICI) and Inter-Symbol Interference (ISI), which leads to performance degradation in OFDM systems. To efficiently overcome these drawbacks, we developed in [1] an adequate algorithm that maximizes the received Signal to Interference plus Noise Ratio (SINR) by optimizing systematically the OFDM waveforms at the Transmitter (TX) and Receiver (RX) sides. In this paper, we go further by investigating the performance evaluation of this algorithm. We start by testing its robustness against time and frequency synchronization errors. Then, as this algorithm banks on an iterative approach to find the optimal waveforms, we study the impact of the waveform initialization on its convergence. The obtained simulation results confirm the efficiency of this algorithm and its robustness compared to the conventional OFDM schemes, which makes it an appropriate good candidate for 5G systems.
Abstract Related Work

One of the main purposes of having a humanoid robot is the biped walk. This feature is undoubtedly essential for this type of robot. Being able to move in order to explore, interact and understand the environment is critical in many situations. An other key feature is the stability of the walk process: the robot has to be able to walk on different ground type (carpet, wood.) and has to be able to walk on small obstacles automatically without falling down. The " Stable and Omnidirectional Walk " feature addresses this issue by offering a high level interface to control the NAO's walk and by implementing an automatic stabilization of this process. Humanoid robot research is an active field. Over the past decade, several anthropomorphic robots have been constructed. Some of them became well known even for non-specialists : Honda's robot Asimo, Sony's biped QRIO and Kawada's humanoid HRP-2. For each of these robots, there is much scientific literature describing their walk engines [1][2][3], even on unknown and rough ground [4]. All these scientific contributions give a good description of the algorithm used to create different walk gaits. Principles The walking gait presented in this paper is mainly based on the work of S. KAJITA et al. [5]. These authors introduce the notion of the preview controller and the Three-Dimensional Linear Inverted Pendulum mode (3D-LIPM) to generate the trajectory for a biped walk. FIGURE 1 gives an outline of this approach, each boxes is detailed in this section.