Miracle-dz/UniFAR-FTUs huggingface dataset:
The Sodium Channel β3-Subunit Induces Multiphasic Gating in NaV1.3 and Affects Fast Inactivation via Distinct Intracellular Regions

Electrical excitability in neurons depends on the activity of membrane-bound voltage gated sodium channels (Nav) that are assembled from an ion conducting α-subunit and often auxiliary β-subunits. The α-subunit isoform Nav1.3 occurs in peripheral neurons together with the Nav β3-subunit, both of which are coordinately up-regulated in rat dorsal root ganglion neurons after nerve injury. Here we examine the effect of the β3-subunit on the gating behavior of Nav1.3 using whole cell patch clamp electrophysiology in HEK-293 cells. We show that β3 depolarizes the voltage sensitivity of Nav1.3 activation and inactivation and induces biphasic components of the inactivation curve. We detect both a fast and a novel slower component of inactivation, and we show that the β3-subunit increases the fraction of channels inactivating by the slower component. Using CD and NMR spectroscopy, we report the first structural analysis of the intracellular domain of any Nav β-subunit. We infer the presence of a region within the β3-subunit intracellular domain that has a propensity to form a short amphipathic α-helix followed by a structurally disordered sequence, and we demonstrate a role for both of these regions in the selective stabilization of fast inactivation. The complex gating behavior induced by β3 may contribute to the known hyperexcitability of peripheral neurons under those physiological conditions where expression of β3 and Nav1.3 are both enhanced.
Up-regulation of CDK9 kinase activity and Mcl-1 stability contributes to the acquired resistance to cyclin-dependent kinase inhibitors in leukemia

Flavopiridol is a small molecule inhibitor of cyclin-dependent kinases (CDK) known to impair global transcription via inactivation of positive transcription elongation factor b. It has been demonstrated to have significant activity predominantly in chronic lymphocytic leukemia and acute myeloid leukemia in phase I/II clinical trials while other similar CDK inhibitors are vigorously being pursued in pre-clinical and clinical studies. Although flavopiridol is a potent therapeutic agent against blood diseases, some patients still have primary or acquired resistance throughout their clinical course. Considering the limited knowledge of resistance mechanisms of flavopiridol, we investigated the potential mechanisms of resistance to flavopiridol in a cell line system, which gradually acquired resistance to flavopiridol in vitro, and then confirmed the mechanism in patient samples. Herein, we present that this resistant cell line developed resistance through up-regulation of phosphorylation of RNA polymerase II C-terminal domain, activation of CDK9 kinase activity, and prolonged Mcl-1 stability to counter flavopiridol's drug actions. Further analyses suggest MAPK/ERK activation-mediated Mcl-1 stabilization contributes to the resistance and knockdown of Mcl-1 in part restores sensitivity to flavopiridol-induced cytotoxicity. Altogether, these findings demonstrate that CDK9 is the most relevant target of flavopiridol and provide avenues to improve the therapeutic strategies in blood malignancies.
A hierarchical multilayer QoS routing system with dynamic SLA management

This paper proposes a hierarchical multilayer QoS routing system with dynamic SLA management for large-scale IP networks. Previously, the promising approach to provide QoS in large-scale IP networks using a mixture of DiffServ-based QoS management and MPLS-based traffic engineering has been actively discussed. However, the introduction of QoS exacerbates the already existing scalability problems of the standard IP routing protocols. In order to address this issue, we propose a new scalable routing framework based on hierarchical QoS-aware path computation. We augment the existing OSPF and CR-LDP protocols to support hierarchical QoS routing, QoS aggregation, and QoS reservation in our MPLS-DiffServ-based hierarchical routing network. In order to provide additional flexibility and cost-efficiency, we augment the network with a policy server which is capable of dynamically handling SLAs between the networks and providing load balancing management within the network. We implement a prototype of the proposed framework and study its performance with a virtual network simulator and specially designed QoS routing algorithm simulator. In our simulations, we evaluate both the implementation complexity and algorithms performance; the results demonstrate the efficiency of the framework and its advantages over the existing proposals.
Parallelizable Bayesian tomography algorithms with rapid, guaranteed convergence

Bayesian tomographic reconstruction algorithms generally require the efficient optimization of a functional of many variables. In this setting, as well as in many other optimization tasks, functional substitution (FS) has been widely applied to simplify each step of the iterative process. The function to be minimized is replaced locally by an approximation having a more easily manipulated form, e.g., quadratic, but which maintains sufficient similarity to descend the true functional while computing only the substitute. We provide two new applications of FS methods in iterative coordinate descent for Bayesian tomography. The first is a modification of our coordinate descent algorithm with one-dimensional (1-D) Newton-Raphson approximations to an alternative quadratic which allows convergence to be proven easily. In simulations, we find essentially no difference in convergence speed between the two techniques. We also present a new algorithm which exploits the FS method to allow parallel updates of arbitrary sets of pixels using computations similar to iterative coordinate descent. The theoretical potential speed up of parallel implementations is nearly linear with the number of processors if communication costs are neglected.
Contributions of infant vagal regulation at 1 month to subsequent joint attention abilities

Since birth, humans develop an ability to regulate their inner states and behaviors, when facing demanding situations, in order to restore calmness and engage with other persons and the surrounding environment. The present study analyzed whether 1-month infant vagal regulation to auditory stimuli was associated with later joint attention abilities-responding to and initiating joint attention-in interaction with their mothers. Twenty-three infants were assessed and measures of respiratory sinus arrhythmia-RSA (baseline and vagal tone change during auditory stimulation) were used as index of vagal regulation. At 12-months, joint attention behaviors were assessed in a 10-min toy-play mother-infant interaction. Correlational analyses showed that lower baseline RSA and larger increases in vagal tone during auditory stimulation were related to more instances of joint attention behaviors at 12 months, especially responding to joint attention. Results suggest that distinct profiles of autonomic functioning may contribute to joint attention skills.
Exploring MRAM Technologies for Energy Efficient Systems-On-Chip

It has become increasingly challenging to respect Moore's well-known law in recent years. Energy efficiency and manufacturing constraints are among the main challenges to current integrated circuits today. The energy efficiency issue is mainly due to the high leakage current from the CMOS transistors that are used to build almost all logic devices. As a result, performance is limited to a few gigahertz due to high power dissipation. A significant proportion of total power is spent on memory systems due to the increasing trend of embedding volatile memory into systems-on-chip devices. New non-volatile memory technologies are one possible way to solve the energy efficiency issue. Among these technologies, magnetic memory is a promising candidate to replace current memories since it combines non-volatility, high density, low latency and low leakage. This paper describes an approach to obtain large, fine-grained exploration of how magnetic memory can be included in the memory hierarchy of processor-based systems by analyzing both performance and energy consumption.
Comparison of the repeatability of GABA‐edited magnetic resonance spectroscopy with and without macromolecule suppression

The inhibitory neurotransmitter γ‐aminobutyric acid (GABA) can be measured in vivo using edited magnetic resonance spectroscopy (MRS), but quantification suffers from contamination by macromolecules (MM). It is possible to suppress this contamination using symmetric editing, but this procedure potentially compromises reliability of the GABA measurement. The aim of this study was to compare the repeatability of GABA‐edited MRS with and without MM suppression.
Multi-modal scene segmentation using scene transition graphs

In this work the problem of automatic decomposition of video into elementary semantic units, known in the literature as scenes, is addressed. Two multi-modal automatic scene segmentation techniques are proposed, both building upon the Scene Transition Graph (STG). In the first of the proposed approaches, speaker diarization results are used for introducing a post-processing step to the STG construction algorithm, with the objective of discarding scene boundaries erroneously identified according to visual-only dissimilarity. In the second approach, speaker diarization and additional audio analysis results are employed and a separate audio-based STG is constructed, in parallel to the original STG based on visual information. The two STGs are subsequently combined. Preliminary results from the application of the proposed techniques to broadcast videos reveal their improved performance over previous approaches.
Selecting instruments for Mendelian randomization in the wake of genome-wide association studies

Mendelian randomization (MR) studies typically assess the pathogenic relevance of environmental exposures or disease biomarkers, using genetic variants that instrument these exposures. The approach is gaining popularity—our systematic review reveals a greater than 10-fold increase in MR studies published between 2004 and 2015. When the MR paradigm was first proposed, few biomarker- or exposure-related genetic variants were known, most having been identified by candidate gene studies. However, genome-wide association studies (GWAS) are now providing a rich source of potential instruments for MR analysis. Many early reviews covering the concept, applications and analytical aspects of the MR technique preceded the surge in GWAS, and thus the question of how best to select instruments for MR studies from the now extensive pool of available variants has received insufficient attention. Here we focus on the most common category of MR studies—those concerning disease biomarkers. We consider how the selection of instruments for MR analysis from GWAS requires consideration of: the assumptions underlying the MR approach; the biology of the biomarker; the genome-wide distribution, frequency and effect size of biomarker-associated variants (the genetic architecture); and the specificity of the genetic associations. Based on this, we develop guidance that may help investigators to plan and readers interpret MR studies.
Neural Network Applications in Sensor Fusion for a Mobile Robot Motion

To fully utilize the information from sensors, this paper proposes a technique for autonomous robot safe moving in a partially structured environment. We used fuzzy logic and neural networks to solving the motion planning problem in mobile robot. The method of path construction is based on two neural networks. The first T-S fuzzy neural network is used to fuse the ultrasonic sensor information and to determine the free space. The outputs of the first network and other location information are as inputs of the second multilayer perceptron neural network. The second network is used to finds a safe direction for the next robot section while avoiding the obstacles. The mobile robot could recognize obstacles and environment types online, and decide its action by using the information fusion method put forward. The simulation proved that we realized mobile robot’s movement without collision in an obstacles environment. The method above mentioned is also helpful to solve the motion of mobile robot in complex environment comprising mobile obstacles.
A New Framework for SAR Multitemporal Data RGB Representation: Rationale and Products

This paper presents the multitemporal adaptive processing (MAP3) framework for the treatment of multitemporal synthetic aperture radar (SAR) images. The framework is organized in three major activities dealing with calibration, adaptability, and representation. The processing chain has been designed looking at the simplicity, i.e., the minimization of the operations needed to obtain the products, and at the algorithms' availability in the literature. Innovation has been provided in the cross-calibration step, which is solved introducing the variable amplitude levels equalization (VALE) method, through which it is possible to establish a common metrics for the measurement of the amplitude levels exhibited by the images of the series. Representation issues are discussed with an application-based approach, supported by examples with regard to semiarid and temperate regions in which amplitude maps and interferometric coherence are combined in an original way.
Nuclear mRNA Export Requires Complex Formation between Mex67p and Mtr2p at the Nuclear Pores

ABSTRACT We have identified between Mex67p and Mtr2p a complex which is essential for mRNA export. This complex, either isolated from yeast or assembled in Escherichia coli, can bind in vitro to RNA through Mex67p. In vivo, Mex67p requires Mtr2p for association with the nuclear pores, which can be abolished by mutating eitherMEX67 or MTR2. In all cases, detachment of Mex67p from the pores into the cytoplasm correlates with a strong inhibition of mRNA export. At the nuclear pores, Nup85p represents one of the targets with which the Mex67p-Mtr2p complex interacts. Thus, Mex67p and Mtr2p constitute a novel mRNA export complex which can bind to RNA via Mex67p and which interacts with nuclear pores via Mtr2p.
Learning to Predict Humanoid Fall

Falls are undesirable in humanoid robots, but also inevitable, especially as robots get deployed in physically interactive human environments. We consider the problem of fall prediction: to predict if the balance controller of a robot can prevent a fall from the robot's current state. A trigger from the fall predictor is used to switch the robot from a balance maintenance mode to a fall control mode. It is desirable for the fall predictor to signal imminent falls with sufficient lead time before the actual fall, while minimizing false alarms. Analytical techniques and intuitive rules fail to satisfy these competing objectives on a large robot that is subjected to strong disturbances and exhibits complex dynamics. We contribute a novel approach to engineer fall data such that existing supervised learning methods can be exploited to achieve reliable prediction. Our method provides parameters to control the tradeoff between the false positive rate and the lead time. Several combinations of parameters yield solutions that improve both the false positive rate and the lead time of hand-coded solutions. Learned solutions are decision lists with typical depths of 5–10, in a 16-dimensional feature space. Experiments are carried out in simulation on an ASIMO-like robot.
A remote anonymous attestation protocol in trusted computing

Remote attestation is an important attribute in trusted computing. One of the purpose of remote attestation is to attest the remote platform is trusty but not revealing the actual identity of the platform. Direct anonymous attestation (DAA) is a kind of scheme which is adopted by Trusted Computing Group in the specification 1.2 to hide the privacy of the platform. But DAA involves various of zero-knowledge proofs and is not efficient to implement. To guarantee the trustworthiness and privacy, we propose a remote anonymous attestation protocol based on ring signature in this paper. We also show that our protocol is secure under the RSA assumption in random oracle model. Furthermore, the attestation protocol does not need the third party and extra zero-knowledge proof, which makes it very efficient in realization.
The HER‐2/neu Oncogene in Breast Cancer: Prognostic Factor, Predictive Factor, and Target for Therapy

The HER‐2/neu oncogene encodes a transmembrane tyrosine kinase receptor with extensive homology to the epidermal growth factor receptor. HER‐2/neu has been widely studied in breast cancer. In this review, the association of HER‐2/neu gene and protein abnormalities studied by Southern and slot blotting, immunohistochemistry, enzyme immunoassays, and fluorescence in situ hybridization with prognosis in breast cancer is studied in depth by review of a series of 47 published studies encompassing more than 15,000 patients. The relative advantages of gene amplification assays and frozen/fresh tissue immunohistochemistry over paraffin section immunohistochemistry are discussed. The significance of HER‐2/neu overexpression in ductal carcinoma in situ and the HER‐2/neu status in uncommon female breast conditions and male breast cancer are also considered. The potential value of HER‐2/neu status for the prediction of response to therapy in breast cancer is presented in the light of a series of recently published studies showing a range of impact on the outcome of patients treated with hormonal, cytotoxic, and radiation therapies. The evidence that HER‐2/neu gene and protein abnormalities in breast cancer predict resistance to tamoxifen therapy and relative sensitivity to chemotherapy regimens including adriamycin is presented. The review will also evaluate the status of serum‐based testing for circulating the HER‐2/neu receptor protein and its ability to predict disease outcome and therapy response. In the final section, the review will briefly present preliminary data concerning the use of antibody‐based therapies directed against the HER‐2/neu protein and their potential to become a new modality for breast cancer treatment. The recently presented phase III clinical trial evidence that systemic administration of anti‐HER2 antibodies (Herceptin®), alone and in combination with cytotoxic chemotherapy in patients with HER‐2/neu overexpressing primary tumors, can increase the time to recurrence and overall response rates in metastatic breast cancer is reviewed.
Fast Parallel Training of Neural Language Models

Training neural language models (NLMs) is very time consuming and we need parallelization for system speedup. However, standard training methods have poor scalability across multiple devices (e.g., GPUs) due to the huge time cost required to transmit data for gradient sharing in the backpropagation process. In this paper we present a sampling-based approach to reducing data transmission for better scaling of NLMs. As a “bonus”, the resulting model also improves the training speed on a single device. Our approach yields significant speed improvements on a recurrent neural network-based language model. On four NVIDIA GTX1080 GPUs, it achieves a speedup of 2.1+ times over the standard asynchronous stochastic gradient descent baseline, yet with no increase in perplexity. This is even 4.2 times faster than the naive single GPU counterpart.
Structure Motif Discovery and Mining the PDB

MOTIVATION Many of the most interesting functional and evolutionary relationships among proteins are so ancient that they cannot be reliably detected through sequence analysis and are apparent only through a comparison of the tertiary structures. The conserved features can often be described as structural motifs consisting of a few single residues or Secondary Structure (SS) elements. Confidence in such motifs is greatly boosted when they are found in more than a pair of proteins. RESULTS We describe an algorithm for the automatic discovery of recurring patterns in protein structures. The patterns consist of individual residues having a defined order along the protein's backbone that come close together in the structure and whose spatial conformations are similar. The residues in a pattern need not be close in the protein's sequence. The work described in this paper builds on an earlier reported algorithm for motif discovery. This paper describes a significant improvement of the algorithm which makes it very efficient. The improved efficiency allows us to use it for doing unsupervised learning of patterns occurring in small subsets in a large set of structures, a non-redundant subset of the Protein Data Bank (PDB) database of all known protein structures.
Probabilistic Estimates for Mixed Model Validation Problems With ${\cal H}_{\infty}$  Type Uncertainties

A mixed deterministic/probabilistic model validation problem is investigated in this technical note, which consists in an additive uncertain model with model uncertainty characterized by the H∞ norm. The data available for validation are time-domain experimental data corrupted by a random noise sequence. Our aim is to compute the probability for such an uncertain model to be validated by the data, and our main results are bounds on this probability that are computable based on the distribution of Chi-square random variables when the noise is a Gaussian variable, and solvable as an LMI problem when only statistical information such as the expectation and covariance of the noise are known.
Challenges facing agricultural extension in the current institutional context: the case of Hai District, Kilimanjaro Region

The aim of the study was to understand contemporary challenges facing extension services at the District level. The study identified level of participation of agriculture stakeholders in the process of extension services delivery, accountability process in the delivery of extension services, resource capacity of the district to deliver services and farmer views on performance of extension service in Hai District. The researcher used qualitative and quantitative methods to study a decentralized public extension organization. Semistructured interviews supported with document reviews and observations were used for the data collection. District extension system has identified challenges which include low level of participation of stakeholders in the design and implementation of policy, deficit and delays of extension funds, shortage of human resources, low sense of accountability and as a result farmers’ opinions have shown unsatisfactory performance of extension. District Government has to focus on the above challenges so as to improve the performance of extension. Increased involvement of private extension providers, diversification of funding sources and timely supply of funds to the district, deployment of Agricultural Officers and Livestock Officers to the village level, and strengthening accountability system will overcome the challenges.
A Flexible Multi-Rate Allocation Scheme for Balanced Multiple Description Coding Applications

When transmitting multimedia information over non-prioritized networks subject to packet losses, multiple description coding with an arbitrary number of descriptions is an effective choice in order to minimize the end-to-end distortion. Such descriptions should be generated so that the quality obtained decoding a subset of them depends only on their number and not on the particular received subset. In this paper, we propose an encoding procedure to generate an arbitrary number of balanced descriptions using a multi-rate allocation scheme which exploits the R-D characteristic of the data