Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential Activation of Extracellular Signal-Regulated Protein Kinase in Primary Afferent Neurons Regulates Brain-Derived Neurotrophic Factor Expression after Peripheral Inflammation and Nerve Injury

To investigate the intracellular signal transduction pathways involved in regulating the gene expression of brain-derived neurotrophic factor (BDNF) in primary afferent neurons, we examined the activation of extracellular signal-regulated protein kinase (ERK) in dorsal root ganglion (DRG) neurons after peripheral inflammation and sciatic nerve transection. Peripheral inflammation induced an increase in the phosphorylation of ERK, mainly in tyrosine kinase A-containing small-to-medium-diameter DRG neurons. The treatment of the mitogen-activated protein kinase (MAPK) kinase 1/2 inhibitor U0126 reversed the pain hypersensitivity and the increase in phosphorylated-ERK (p-ERK) and BDNF in DRG neurons induced by complete Freund's adjuvant. On the other hand, axotomy induced the activation of ERK mainly in medium-and large-sized DRG neurons and in satellite glial cells. U0126 suppressed the axotomy-induced autotomy behavior and reversed the increase in p-ERK and BDNF. The intrathecal application of nerve growth factor (NGF) induced an increase in the number of p-ERK-and BDNF-labeled cells, mainly small neurons, and the application of anti-NGF induced an increase in p-ERK and BDNF in some medium-to-large-diameter DRG neurons. The activation of MAPK in the primary afferents may occur in different populations of DRG neurons after peripheral inflammation and axotomy, respectively, through alterations in the target-derived NGF. These changes, including the changes in BDNF expression, might be involved in the pathophysiological changes in primary afferent neurons.
The Productivity of Listed Companies of Computers and Related Equipment Manufacturing Industry

Indicators system of China’s computer manufacturing enterprises of listed companies in Shanghai and Shenzhen can be established to reflect the efficiency of production, which accord to the number of employees, fixed assets and other economic data. Integrated data envelopment analysis (DEA) models are used, combining with evaluation and statistical tests of Synthetic Average method, Borda method, Copeland method and Fuzzy Borda method (SABCB Method). The productivities and returns to scale (RTS) of these computer companies can be figured out. Some results could be obtained through the empirical analysis: firstly, productivities of China’s computer manufacturers are acceptable in general, although there are still great differentiations among them; secondly, due to the lack of outputs, RTS in some enterprises are decreasing; thirdly, it is currently urgent task that most of computer manufacturers continue to refine and deepen specialization transform existing business processes.
School Management Competencies: Perceptions and Self-Efficacy Beliefs of School Principals

In several contexts, reform in the training and nomination of future school principals was based on competency profiles. In Quebec, it was not until 2008 that its Ministry of Education established a profile of competencies specifically for school principals. This study proposed a questionnaire on the perceptions and self-efficacy beliefs of school principals with regard to these competencies and their associated factors. Respondents gave greater weight to the management of education services, followed by human resources, educational environment, and finally, administration. A significant difference was observed between administrative management and the teaching level and school size. The principals whose professional development activities consisted of conventions and seminars also felt a greater sense of personal efficacy on this factor compared to the principals whose professional development was done through mentoring.
Operator Selection using Improved Dynamic Multi-Armed Bandit

Evolutionary algorithms greatly benefit from an optimal application of the different genetic operators during the optimization process: thus, it is not surprising that several research lines in literature deal with the self-adapting of activation probabilities for operators. The current state of the art revolves around the use of the Multi-Armed Bandit (MAB) and Dynamic Multi-Armed bandit (D-MAB) paradigms, that modify the selection mechanism based on the rewards of the different operators. Such methodologies, however, update the probabilities after each operator's application, creating possible issues with positive feedbacks and impairing parallel evaluations, one of the strongest advantages of evolutionary computation in an industrial perspective. Moreover, D-MAB techniques often rely upon measurements of population diversity, that might not be applicable to all real-world scenarios. In this paper, we propose a generalization of the D-MAB approach, paired with a simple mechanism for operator management, that aims at removing several limitations of other D-MAB strategies, allowing for parallel evaluations and self-adaptive parameter tuning. Experimental results show that the approach is particularly effective with frameworks containing many different operators, even when some of them are ill-suited for the problem at hand, or are sporadically failing, as it commonly happens in the real world.
In Defense of Cutscenes

The technique of cutscenes, as typically found in story-based action games, is placed within a wider discursive problematic, focusing on the role of pre-written narratives in general. Within a theoretical framework raised by Espen Aarseth, Markku Eskelinen and Marie-Laure Ryan, I discuss the relations between the ergodic and the representational, and between play and narration. I argue that any game event is also a representational event, a part of a typical and familiar symbolic action, in which cutscenes often play a crucial part. Through cutscenes, the ergodic effort acquires typical meanings from the generic worlds of popular culture.
Modular on-line function approximation for scaling up reinforcement learning

Reinforcement l e a r n i n g i s a p o werful learning paradigm for autonomous agents which i n teract with unknown environments with the objective of maximizing cumulative p a yoo. Recent research has addressed issues concerning the scaling up of reinforcement learning methods in order to solve problems with large state spaces, composite tasks and tasks involving non-Markovian situations. In this dissertation, I extend existing ways of scaling up reinforcement learning methods and propose several new approaches. An array of Cerebellar Model Articulation Controller (CMAC) networks is used as fast function approximators so that the evaluation function and policy can be learnt on-line as the agent i n teracts with the environment. Learning systems which combine reinforcement learning techniques with CMAC networks are developed to solve problems with large state and action spaces. Actions can be either discrete or real-valued. The problem of generating a sequence of torque or position change commands in order to drive a simulated multi-linked manipulator towards desired arm conngurations is examined. A hierarchical and modular function approximation architecture using CMAC n e t works is then developed, following the Hierarchical Mixtures of Experts framework. The non-linear function approximation ability o f C M A C n e t works enables non-linear functions to be modelled in expert and gating networks, while permitting fast linear learning rules to be used. An on-line gradient ascent learning procedure derived from the Expectation Maximization algorithm is proposed, enabling faster learning to be achieved. The new architecture can be used to enable reinforcement learning agents to acquire context-dependent evaluation functions and policies. This is demonstrated in an implementation of the Compositional Q-Learning framework in which composite tasks consisting of several elemental tasks are decomposed using reinforcement learning. The framework is extended to the case where rewards can be received in non-terminal states of elemental tasks, and tòvector of actions' situations where the agent produces several coordinated actions in order to achieve a goal. The resulting system is employed to enable the simulated multi-linked manipulator to position its end-eeector at several positions in the workspace sequentially. Finally, the beneets of using prior knowledge in order to extend the capabilities of reinforcement learning agents are examined. A classiier system-based Q-learning scheme is developed to enable agents to reason using condition-action rules. The utility o f t h i s s c heme is illustrated in a …
GPU Based Affine Linear Image Registration using Normalized Gradient Fields

We present a CUDA implementation of a complete registration algorithm, which is capable of aligning two multimodal images, using affine linear transformations and normalized gradient fields. Through the extensive use of different memory types, well handled thread management and efficient hardware interpolation we gained fast executing code. Contrary to the common technique of reducing kernel calls, we significantly increased performance by rearranging a single kernel into multiple smaller ones. Our GPU implementation achieved a speedup of up to 11 compared to parallelized CPU code. Matching two 512 × 512 pixel images is performed in 37 milliseconds, thus making state-of-the-art multimodal image registration available in real time scenarios.
An IoT-oriented data storage framework in smart city applications

Smart City applications are addressed to city authorities, infrastructure and utilities managers, and city stakeholders wishing to use smart city solutions in order to increase the competitiveness, cohesion, and sustainability of the city. Facing with these massive, distributed, heterogeneous and complex state data, conventional data storage and management will encounter great difficulties. Conventional infrastructure uses centralized approach, expensive large-scale server, disk array storage hardware and relational database management system; which leads to poor system scalability, higher costs, and essentially, is difficult to adapt to the requirement of higher reliability on real-time state data of the smart city applications. Accurate, fast, open, shared information system is the basis for the smart city applications. In this paper, we proposed a scalable distributed cloud data storage and management platform which provides reliable data storage and management of the vast amount of information in the smart city.
Local POD Plus Galerkin Projection in the Unsteady Lid-Driven Cavity Problem

A local proper orthogonal decomposition (POD) plus Galerkin projection method is applied to the unsteady lid-driven cavity problem, namely the incompressible fluid flow in a two-dimensional box whose upper wall is moved back and forth at moderately large values of the Reynolds number. Such a method was recently developed for one-dimensional parabolic problems. Its extension to fluid dynamics problems is nontrivial (especially if rough CFD codes are used) and consists of using a computational fluid dynamics (CFD) code and a Galerkin system (GS) in a sequence of interspersed intervals $I_{CFD}$ and $I_{GS}$, respectively. The POD manifold is calculated retaining the most energetic POD modes resulting from the snapshots computed in the $I_{CFD}$ intervals; in fact, the POD manifold is completely calculated in the first $I_{CFD}$ interval but only updated in subsequent $I_{CFD}$ intervals. Intended to mimic industrial solvers, the CFD code contains unphysical terms that are introduced for purely numerical reasons to accelerate runs. The use of such a CFD code poses the question of which equations should be Galerkin projected onto the POD manifold, the exact governing equations or the CFD numerical scheme. Also, Galerkin projection can be made using either the standard $L_2$ inner product or a nonstandard one, based on a limited number of mesh points. After addressing these issues, a method is constructed that is able to accelerate the CFD code by a factor of the order of 5-15, depending on the Reynolds number and the nature (steady, periodic, or quasi-periodic) of the forcing velocity.
Early and Late Acute Kidney Injury in Severely Burned Patients

Background This study evaluated factors influencing early and late occurrence of AKI in severely burned patients and assessed the relationship between time of occurrence of AKI and mortality of AKI patients. Material/Methods Renal function was evaluated at 3 time points: at admission, at the critical point or middle point of hospitalization, and at the endpoint for which death or a discharge from the center was considered. AKI criteria were: decrease in GFR of less than 60 ml/min at admission, decrease in GFR of more than 75% compared to baseline, and decrease in the daily diuresis of less than 500 ml/24 h. Results At admission, 15.1% of the patients had eGFR <60 ml/min. AKI occurred in 38.5% of cases. The occurrence of AKI was associated with: elderly age (p<0.001), female sex (p=0.017), overweight and obesity (p=0.055); extent and depth of burns, respiratory failure, low protein concentration (for all p<0.001), low blood pressure (p=0.014), and high WBC (p=0.010). Early AKI was detected in 28% of patients. Mortality was 100% with the initial GFR ≥60, 100% with the initial GFR <60 and early deterioration of renal function, 80% with the initial GFR <60 and late worsening, and 60% with the initial GFR <60 and no worsening. Late AKI was observed in 10% of patients and mortality in this group was 79.2%. Mortality in the entire group with AKI was 88.0% versus 24.5%. Conclusions The frequent occurrence of AKI, especially early, worsens the prognosis for survival. Assessment of renal function should be included in the prognostic scales for burned patients.
Effect of Alcohol on Diffuse Axonal Injury in Rat Brainstem: Diffusion Tensor Imaging and Aquaporin-4 Expression Study

The aim of this study is to assess the effects of alcohol on traumatic brain injury by using diffusion tensor imaging (DTI) and evaluate aquaporin-4(AQP4) expression changes in rat brainstems following acute alcohol intoxication with diffuse axonal injury (DAI). We further investigated the correlation between the AQP4 expression and DTI in the brain edema. Eighty-five rats were imaged before and after injury at various stages. DTI was used to measure brainstem apparent diffusion coefficient (ADC) and fractional anisotropy (FA), with immunostaining being used to determine AQP4 expression. After acute alcoholism with DAI, ADC values of the brainstem first decreased within 6 h and then elevated. FA values began to decline by 1 h, reaching a minimum at 24 h after trauma. There was a negative correlation between ADC values and brainstem AQP4 expression at 6 h and positive correlation at 6 h to 24 h. Changes of ADC and FA values in DAI with acute alcoholism indicate the effects of ethanol on brain edema and the severity of axonal injury. The correlations between ADC values and the brainstem AQP4 expression at different time points suggest that AQP4 expression follows an adaptative profile to the severity of brain edema.
JAMM: A Metalloprotease-Like Zinc Site in the Proteasome and Signalosome

The JAMM (JAB1/MPN/Mov34 metalloenzyme) motif in Rpn11 and Csn5 underlies isopeptidase activities intrinsic to the proteasome and signalosome, respectively. We show here that the archaebacterial protein AfJAMM possesses the key features of a zinc metalloprotease, yet with a distinct fold. The histidine and aspartic acid of the conserved EXnHS/THX7SXXD motif coordinate a zinc, whereas the glutamic acid hydrogen-bonds an aqua ligand. By analogy to the active site of thermolysin, we predict that the glutamic acid serves as an acid-base catalyst and the second serine stabilizes a tetrahedral intermediate. Mutagenesis of Csn5 confirms these residues are required for Nedd8 isopeptidase activity. The active site-like architecture specified by the JAMM motif motivates structure-based approaches to the study of JAMM domain proteins and the development of therapeutic proteasome and signalosome inhibitors.
Optimization of a Multi-Step Procedure for Isolation of Chicken Bone Collagen

Chicken bone is not adequately utilized despite its high nutritional value and protein content. Although not a common raw material, chicken bone can be used in many different ways besides manufacturing of collagen products. In this study, a multi-step procedure was optimized to isolate chicken bone collagen for higher yield and quality for manufacture of collagen products. The chemical composition of chicken bone was 2.9% nitrogen corresponding to about 15.6% protein, 9.5% fat, 14.7% mineral and 57.5% moisture. The lowest amount of protein loss was aimed along with the separation of the highest amount of visible impurities, non-collagen proteins, minerals and fats. Treatments under optimum conditions removed 57.1% of fats and 87.5% of minerals with respect to their initial concentrations. Meanwhile, 18.6% of protein and 14.9% of hydroxyproline were lost, suggesting that a selective separation of non-collagen components and isolation of collagen were achieved. A significant part of impurities were selectively removed and over 80% of the original collagen was preserved during the treatments.
A fine-structure image/video quality measure using local statistics

An objective no-reference measure is presented to assess line-structure image/video quality. It was designed to measure image/video quality for video surveillance applications, especially for background modeling and foreground object detection. The proposed measure using local statistics reflects image degradation well in terms of noise and blur. The experimental results on a background subtraction algorithm validate the usefulness of the proposed measure, by showing its correlation with the algorithm's performance.
False Error Study of On-line Soft Error Detection Mechanisms

With technology scaling, vulnerability to soft errors in random logic is increasing. There is a need for on-line error detection and protection for logic gates even at sea level. The error checker is the key element for an on-line detection mechanism. We compare three different checkers for error detection from the point of view of area, power and false error detection rates. We find that the double sampling checker (used in Razor), is the simplest and most area and power efficient, but suffers from very high false detection rates of 1.15 times the actual error rates. We also find that the alternate approaches of triple sampling and integrate and sample method (I&S) can be designed to have zero false detection rates, but at an increased area, power and implementation complexity. The triple sampling method has about 1.74 times the area and twice the power as compared to the Double Sampling method and also needs a complex clock generation scheme. The I&S method needs about 16% more power with 0.58 times the area as double sampling, but comes with more stringent implementation constraints as it requires detection of small voltage swings.
RF integrated feedback circuits using on-chip magnetics

Transistors with gain-bandwidth products exceeding 100 GHz are enabling new opportunities for reactive feedback circuits in RFIC applications. The paper outlines the performance advantages of circuits employing on-chip magnetic feedback components. The state-of-the-art in on-chip magnetics for production silicon technologies is reviewed, and RF front-end examples are then presented that demonstrate both wide dynamic range and 1V operation.
Modeling and removing depth variant blur in 3D fluorescence microscopy

Like many other imaging techniques, 3D fluorescence microscopy suffers from degradations that are basically varying with the depth of the point source. This is due to the light refraction phenomenon. In this article, we focus on modeling and removing depth variant blur in such a system. In particular, we study some of the existing space-variant blur approximations and consider an efficient approximation where the space variant blur function is a linear combination of a set of space-invariant ones. We then focus on restoring space-variant blurred images using such a model. For that, we fit a domain decomposition-based minimization approach to the deconvolution problem with a space variant blur model. We thus obtain a fast restoration algorithm where the image estimation is performed in a parallel way on different sub-images.
THE ADOPTION OF E-LEARNING: AN INSTITUTIONAL THEORY PERSPECTIVE

Several models have been proposed in the literature to understand e-learning acceptance in which social environmental factors are not primarily addressed. This paper aims to improve understanding of what social forces influence employee’s attitude and intention of e-learning adoption within an organizational context. Drawing upon the institutional theory, this study proposes a model to examine three social environmental factors of coercive, normative and mimetic pressures within the e-learning context. An empirical study involving 172 subjects and the partial least square method was conducted to test this model. The results indicate that normative and mimetic pressures significantly influence the attitude and intention of adopting e-learning, while coercive pressures appear not to. Attitude plays a mediating role between both normative and mimetic institutional pressures and e-learning adoption. For organizations, the results suggest that training managers may need to build an e-learning community to create normative expectations and provide success stories of high profiles employee’s e-learning experience to promote the adoption of their e-learning. The paper contributes to a deeper understanding of the social factors that promote the use of e-learning in on-job training.
Molecular Mechanism of Hepatitis C Virus Replicon Variants with Reduced Susceptibility to a Benzofuran Inhibitor, HCV-796

ABSTRACT HCV-796 selectively inhibits hepatitis C virus (HCV) NS5B RNA-dependent RNA polymerase. In hepatoma cells containing a genotype 1b HCV replicon, HCV-796 reduced HCV RNA levels by 3 to 4 log10 HCV copies/μg total RNA (the concentration of the compound that inhibited 50% of the HCV RNA level was 9 nM). Cells bearing replicon variants with reduced susceptibility to HCV-796 were generated in the presence of HCV-796, followed by G418 selection. Sequence analysis of the NS5B gene derived from the replicon variants revealed several amino acid changes within 5 Å of the drug-binding pocket. Specifically, mutations were observed at Leu314, Cys316, Ile363, Ser365, and Met414 of NS5B, which directly interact with HCV-796. The impacts of the amino acid substitutions on viral fitness and drug susceptibility were examined in recombinant replicons and NS5B enzymes with the single-amino-acid mutations. The replicon variants were 10- to 1,000-fold less efficient in forming colonies in cells than the wild-type replicon; the S365L variant failed to establish a stable cell line. Other variants (L314F, I363V, and M414V) had four- to ninefold-lower steady-state HCV RNA levels. Reduced binding affinity with HCV-796 was demonstrated in an enzyme harboring the C316Y mutation. The effects of these resistance mutations were structurally rationalized using X-ray crystallography data. While different levels of resistance to HCV-796 were observed in the replicon and enzyme variants, these variants retained their susceptibilities to pegylated interferon, ribavirin, and other HCV-specific inhibitors. The combined virological, biochemical, biophysical, and structural approaches revealed the mechanism of resistance in the variants selected by the potent polymerase inhibitor HCV-796.