Miracle-dz/UniFAR-FTUs huggingface dataset:
JMJD5, a Jumonji C (JmjC) Domain-containing Protein, Negatively Regulates Osteoclastogenesis by Facilitating NFATc1 Protein Degradation*

Background: NFATc1 is a necessary and sufficient transcription factor for osteoclastogenesis. Results: JMJD5 negatively regulates NFATc1 protein level through its hydroxylase activity. Conclusion: JMJD5 is a novel osteoclastogenic repressor that induces the degradation of NFATc1 protein. Significance: This study revealed a novel mechanism that regulates NFATc1 activity during osteoclastogenesis. Osteoclastogenesis is a highly regulated process governed by diverse classes of regulators. Among them, nuclear factor of activated T-cells calcineurin-dependent 1 (NFATc1) is the primary osteoclastogenic transcription factor, and its expression is transcriptionally induced during early osteoclastogenesis by receptor activation of nuclear factor κB ligand (RANKL), an osteoclastogenic cytokine. Here, we report the novel enzymatic function of JMJD5, which regulates NFATc1 protein stability. Among the tested Jumonji C (JmjC) domain-containing proteins, decreased mRNA expression levels during osteoclastogenesis were found for JMJD5 in RAW264 cells stimulated by RANKL. To examine the functional role of JMJD5 in osteoclast differentiation, we established stable JMJD5 knockdown cells, and osteoclast formation was assessed. Down-regulated expression of JMJD5 led to accelerated osteoclast formation together with induction of several osteoclast-specific genes such as Ctsk and DC-STAMP, suggesting that JMJD5 is a negative regulator in osteoclast differentiation. Although JMJD5 was recently reported as a histone demethylase for histone H3K36me2, no histone demethylase activity was detected in JMJD5 in vitro or in living cells, even for other methylated histone residues. Instead, JMJD5 co-repressed transcriptional activity by destabilizing NFATc1 protein. Protein hydroxylase activity mediated by the JmjC domain in JMJD5 was required for the observed functions of JMJD5. JMJD5 induced the association of hydroxylated NFATc1 with the E3 ubiquitin ligase Von Hippel-Lindau tumor suppressor (VHL), thereby presumably facilitating proteasomal degradation of NFATc1 via ubiquitination. Taken together, the present study demonstrated that JMJD5 is a post-translational co-repressor for NFATc1 that attenuates osteoclastogenesis.
Prereferral Intervention: A Prescriptive Approach

This investigation assessed effects of three increasingly inclusive versions of the Behavioral Consultation (BC) model on problem behavior of students in mainstream classrooms in an effort to develop an effective and efficient approach to prereferral intervention. Subjects were 43 general educators, their 43 most difficult-to-teach students (without disabilities), and 12 school consultants, representing seven inner-city middle schools. Teachers and students were assigned to three BC groups, representing least (BC 1), more (BC 2), and most (BC 3) inclusive variants of BC, and one control group. Preintervention, postintervention, and follow-up observations of student behavior indicated that more inclusive BC versions exerted stronger effects than the least inclusive variant in reducing problem behavior. Theoretical and practical implications for consultation-related activity are discussed.
Pathwise MIMO channel modeling and estimation

A specular approach to time-varying frequency-selective MIMO channel modeling is proposed, that yields a parsimonious channel representation. Essentially, the vector of channel impulse response coefficients lives in a subspace that is subject to slow fading and hence is time-invariant over a reasonable time interval. The subspace is parameterized by the (deterministic) slow path parameters, that may comprise path delay, angle of departure and angle of arrival. The channel expansion into this propagation signature subspace yields coefficients that are subject to fast fading. They are modeled as independent lowpass autoregressive processes. We present an analysis of the identifiability of specular channels, and an algorithm achieving identification, as well as a performance evaluation over actual channel measurements.
MAIT Recognition of a Stimulatory Bacterial Antigen Bound to MR1

MR1-restricted mucosal-associated invariant T (MAIT) cells represent a subpopulation of αβ T cells with innate-like properties and limited TCR diversity. MAIT cells are of interest because of their reactivity against bacterial and yeast species, suggesting that they play a role in defense against pathogenic microbes. Despite the advances in understanding MAIT cell biology, the molecular and structural basis behind their ability to detect MR1–Ag complexes is unclear. In this study, we present our structural and biochemical characterization of MAIT TCR engagement of MR1 presenting an Escherichia coli–derived stimulatory ligand, rRL-6-CH2OH, previously found in Salmonella typhimurium. We show a clear enhancement of MAIT TCR binding to MR1 due to the presentation of this ligand. Our structure of a MAIT TCR/MR1/rRL-6-CH2OH complex shows an evolutionarily conserved binding orientation, with a clear role for both the CDR3α and CDR3β loops in recognizing the rRL-6-CH2OH stimulatory ligand. We also present two additional xenoreactive MAIT TCR/MR1 complexes that recapitulate the docking orientation documented previously, despite having variation in the CDR2β and CDR3β loop sequences. Our data support a model by which MAIT TCRs engage MR1 in a conserved fashion, with their binding affinities modulated by the nature of the MR1-presented Ag or diversity introduced by alternate Vβ usage or CDR3β sequences.
Automated Brain-Tissue Segmentation by Multi-Feature SVM Classification

We present a method for automated brain-tissue segmentation through voxelwise classification. Our algorithm uses manually labeled training images to train a support vector machine (SVM) classifier, which is then used for the segmentation of target images. The classification incorporates voxel intensities from a T1-weighted scan, an IR scan, and a FLAIR scan; features to encode the voxel position in the image; and Gaussian-scale-space features and Gaussian-derivative features at multiple scales to facilitate a smooth segmentation. An experiment on data from the MRBrainS13 brain-tissue-segmentation challenge showed that our algorithm produces reasonable segmentations in a reasonable amount of time.
Cooperative Neural Fitted Learning for Distributed Energy Management in Microgrids via Wireless Networks

With the proliferation of renewable energy sources and the elevation of environmental concerns, it is expected that microgrids will become one of the major means for residential energy supply. However, the distributed nature of microgrid operation brings new technical challenges to energy management. Endowing wireless communication capability to the distributed generation (DG) units and energy storage (ES) devices in a microgrid is beneficial for their cooperation without a centralized controller. Yet, how to establish distributed energy management without \emph{a priori} statistical information for all the DG units and loads still requires extensive research. In this paper, a reinforcement learning algorithm with cooperative neural fitting iteration is proposed for distributed energy management in microgrids via wireless networks. The reinforcement learning algorithm leverages a distributed actor- critic structure to adopt the continuous states and action spaces of a microgrid. A diffusion strategy is incorporated in the reinforcement learning algorithm to coordinate the actions of DG units and ES devices by exchanging their evaluations and decisions via a wireless network. Simulation results based on realistic renewable power generation and load data are presented to evaluate the performance of the proposed algorithm.
Towards formal specification and verification of a role-based authorization engine using JML

Employing flexible access control mechanisms, formally specifying and correctly implementing relevant security properties, and ensuring that the implementation satisfies its formal specification, are some of the important aspects towards achieving higher-level organization-wide access control that maintains the characteristics of software quality. In the access control arena, the role-based access control (RBAC) has emerged as a powerful model for laying out and developing higher-level organizational rules such as separation of duty, and for simplifying the access management process. One of the important aspects of RBAC is authorization constraints that allow one to express such organizational rules. On the other hand, the Java Modeling Language (JML) has evolved as a flexible formal behavioral interface specification language that can be used as a Design by Contract (DBC) approach for developing software written in Java. In this paper, we adopt JML as a DBC approach to implement a prototype of a role-based authorization engine. We specifically focus on how JML can effectively be used in precisely specifying the functional behavior of the authorization engine, including various constraints such as authorization constraints and integrity constraints. We employ few JML tools towards verifying the correctness of the implementation of the authorization engine against its JML specification.
Mobile wireless sensor network architecture: Applications to mobile sensor deployment

The advances in mobile robotics allow us today to add the mobility concept into many different classes of Wireless Sensor Networks. The deployment of mobile sensors is possible and useful in many application scenarios, ranging from the environmental monitoring and public safety applications, to the industry, healthcare and military applications. Two topics are elaborated in this thesis: networked robot middleware design and a set of approaches for mobile robot deployment in the context of wireless sensor networks. The middleware proposed and described in this thesis allows the user to easily implement different types of deployment algorithms for mobile robots. In the second part of the thesis, three main problems are presented and analyzed. The first is the problem of improving the quality of service with the use of mobile robotic networks, which is solved with the use of deployment algorithm that improves the performance of a multimedia communication by using an intrusive methods to gather the necessary transmission quality evaluation metrics. Second problem is the coverage of the point of interest with mobile robots, where the fixed base station is placed inside the field of interest, while the available mobile robots cover the point of interest and relay the information about it towards the base station in a multi-hop manner. The third problem is the point of interest discovery and coverage with the use of mobile robots which follow concentric circular paths to explore and cover the field of interest, and, by adjusting the movement velocity, they satisfy the constraints on point of interest coverage and connectivity with the base station.
MCA-NMF: Multimodal Concept Acquisition with Non-Negative Matrix Factorization

In this paper we introduce MCA-NMF, a computational model of the acquisition of multimodal concepts by an agent grounded in its environment. More precisely our model finds patterns in multimodal sensor input that characterize associations across modalities (speech utterances, images and motion). We propose this computational model as an answer to the question of how some class of concepts can be learnt. In addition, the model provides a way of defining such a class of plausibly learnable concepts. We detail why the multimodal nature of perception is essential to reduce the ambiguity of learnt concepts as well as to communicate about them through speech. We then present a set of experiments that demonstrate the learning of such concepts from real non-symbolic data consisting of speech sounds, images, and motions. Finally we consider structure in perceptual signals and demonstrate that a detailed knowledge of this structure, named compositional understanding can emerge from, instead of being a prerequisite of, global understanding. An open-source implementation of the MCA-NMF learner as well as scripts and associated experimental data to reproduce the experiments are publicly available.
Class Modularization Using Indirect Relationships

Cohesion significantly impacts on posterior development and maintenance. A well-designed module should be coherent with a single responsibility and less propagative for changes to other modules. In object-oriented development, classes are often modularized based on their relationships to localize change impact. There has been much work on grouping classes using direct class interactions and similarity of lexical information of classes. However, there has been little attention to indirect class relationships which are significant in defining functional semantics in object-oriented systems. In this paper, we present an approach for grouping classes using both direct and indirect class relationships. In the approach, we define a set of metrics for measuring couplings based on indirect class relationships and use them to group classes with the aim of localizing the impact of changes. We evaluate the approach using three applications - JHotDraw, JMeter, and ArgoUML and compare the results with existing work. The evaluation shows that the proposed approach reduces 17.2% and 22.2% of the number of packages that are affected by changes over the existing works.
Evaluation of a Pseudo‐Elastic Model for the Mullins Effect

Abstract Typically, the stress‐strain response in filled rubbers depends strongly on the maximum loading previously encountered. The phenomenon, known as the Mullins effect, can be idealized for many purposes as an instantaneous and irreversible softening of the stress‐strain curve that occurs whenever the load increases beyond its prior all‐time maximum value. At times when the load is less than a prior maximum, nonlinear elastic behavior prevails. Ogden and Roxburgh proposed an empirical model capable of describing this phenomenon, based on a pseudo‐elastic concept. Their model, with minor adaptations, has recently been implemented in a commercial finite element program. This paper demonstrates the effectiveness of the implemented model for several benchmark cases including uniform hydrostatic loading, simple tension, pure shear, and equibiaxial tension. The paper also compares model predictions with experimental results for a series of experiments conducted with various combinations of axial tension/co.
Automation of soil flux chamber measurements: potentials and pitfalls

Abstract. Recent technological advances have enabled the wider application of automated chambers for soil greenhouse gas (GHG) flux measurements, several of them commercially available. However, few studies addressed the challenges associated with operating these systems. In this contribution we compared two commercial soil GHG chamber systems – the LI-8100A Automated Soil CO2 Flux System and the greenhouse gas monitoring system AGPS. From April until August 2014, the two systems monitored in parallel soil respiration (SR) fluxes at a recently harvested poplar (Populus) plantation, which provided a bare field situation directly after the harvest as well as a closed canopy later on. For the bare field situation (15 April–30 June 2014), the cumulated average SR obtained from the unfiltered data sets of the LI-8100A and the AGPS were 520 and 433 g CO2 m−2 respectively. For the closed canopy phase (1 July–31 August 2014), which was characterized by a higher soil moisture content, the cumulated average SR estimates were not significantly different with 507 and 501 g CO2 m−2 for the AGPS and the LI-8100A respectively. Flux quality control and filtering did not significantly alter the results obtained by the LI-8100A, whereas the AGPS SR estimates were reduced by at least 20 %. The main reasons for the observed differences in the performance of the two systems were (i) a lower data coverage provided by the AGPS due to technical problems; (ii) incomplete headspace mixing in the AGPS chambers; (iii) lateral soil CO2 diffusion below the collars during AGPS chamber measurements; and (iv) a possible overestimation of nighttime SR fluxes by the LI-8100A. Additionally, increased root growth was observed within the LI-8100A collars but not within the AGPS collars, which might have also contributed to the observed differences. In contrast to the LI-8100A, the AGPS had the gas sample inlets installed inside the collars and not the chambers. This unique design feature enabled for the first time the detection of disturbed chamber measurements during nights with a stratified atmosphere, resulting in unbiased nighttime SR estimates. Thus besides providing high temporal frequency flux data, automated chamber systems offer another possibility to greatly improve our understanding of SR fluxes.
Statistical Convergence Analysis for Optimal Control of DFT-Domain Adaptive Echo Canceler

The frequency-domain adaptive filter (FDAF) is widely used in echo cancellation systems due to its low complexity and fast convergence rate. However, the FDAF algorithm with a fixed step size exhibits a tradeoff among the convergence rate, steady-state misalignment, tracking ability, and robustness to near-end speech interferences. Several variable step-size FDAF algorithms were presented to address this problem. However, the state-of-the-art variable step-size FDAF algorithms did not handle this problem comprehensively. This paper presents a new robust variable step-size control approach to the FDAF algorithm. Based on a statistical analysis of the FDAF algorithm, an optimal step size for each frequency bin is derived by minimizing the mean-square deviation (MSD) between the true weight vector and estimated weight vector at each frame. Calculation of the step size requires the system distance and the observation noise power spectral density (PSD). The system distance is estimated using the deterministic recursive equations of MSD and the noise PSD is computed using the magnitude squared coherence function between the far-end signal and error signal. Moreover, a close link between the proposed FDAF and the frequency-domain Kalman filter is revealed. Specifically, the work presented here can be understood as a means to adaptively monitor and control the underlying acoustic state space of the Kalman filter, including means for fast readaptation of the adaptive filter after abrupt echo path changes. Simulation results demonstrate that the proposed algorithm can achieve fast convergence and low steady-state misalignment. Furthermore, the algorithm is robust to the double-talk interferences, but it does not require an explicit double-talk detector.
The extremal generalised Randi\'c index for a given degree range

O and Shi proved that the Randi\'c index of any graph $G$ with minimum degree at least $\delta$ and maximum degree at most $\Delta$ is at least $\frac{\sqrt{\delta\Delta}}{\delta+\Delta}|G|$, with equality if and only if the graph is $(\delta,\Delta)$-biregular. In this note we give a short proof via a more general statement. We apply the latter to classify the graphs in any given degree range which minimise (or maximise) the generalised Randi\'c index for any exponent, and describe the transitions between different types of behaviour precisely.
Social-Based Cooperative Caching in DTNs: A Contact Duration Aware Approach

Data access is an important issue in Delay Tolerant Networks (DTNs), and a common technique to improve the performance of data access is cooperative caching. However, due to the unpredictable node mobility in DTNs, traditional caching schemes cannot be directly applied. In this paper, we propose DAC, a novel caching protocol adaptive to the challenging environment of DTNs. Specifically, we exploit the social community structure to combat the unstable network topology in DTNs. We propose a new centrality metric to evaluate the caching capability of each node within a community, and solutions based on this metric are proposed to determine where to cache. More importantly, we consider the impact of the contact duration limitation on cooperative caching, which has been ignored by the existing works. We prove that the marginal caching benefit that a node can provide diminishes when more data is cached. We derive an adaptive caching bound for each mobile node according to its specific contact patterns with others, to limit the amount of data it caches. In this way, both the storage space and the contact opportunities are better utilized. To mitigate the coupon collector's problem, network coding techniques are used to further improve the caching efficiency. Extensive trace-driven simulations show that our cooperative caching protocol can significantly improve the performance of data access in DTNs.
Fast Nonparametric Machine Learning Algorithms for High-dimensional Massive Data and Applications | a Thesis Proposal

A hermetic motor protector (10) has a non-current carrying snap-acting thermostatic disc (26) freely disposed on a disc seat (22a) having a tab (22c) projecting through a centrally located aperture (26a) in the disc. An outer peripheral portion of the disc is received under a leg (22e) of the bracket limiting upward motion of the disc at that location and causing the disc to act as a lever pivoting about the disc seat (22a) as a fulcrum so that the outer peripheral portion of the disc diametrically opposite to leg (22e) engages a motion transfer bump (28d) formed on a movable contact arm cantilever mounted on leg (22e) when the disc snaps from its normally contacts engaged configuration to its opposite dished contacts open configuration when the disc is heated to its actuation temperature by a heater element (24) along with heat conducted from a motor with which the protector is used.
Error control aspects of high speed networks

Error control problems arising in the broadband ISDN environment are analyzed. The first problem relates to end-to-end integrity. The second problem is related to synchronization. The third problem is the performance of the cell-based cyclic redundancy check (CRC) in the presence of a self-synchronous scrambler to avoid attacks by malicious users. However, the scrambler creates error multiplication whereby a single error on the physical link is translated into two errors in the cell exactly 43 bits apart. In this context, it is proved that the cell-based CRC is capable of correcting correlated double errors produced by the self-asynchronous scrambler. The last problem studied is parallel implementation of CRC coders and decoders. <<ETX>>
On the Performance of Full-Duplex Multi-Relay Channels With DF Relays

In this paper, we consider a two-hop full-duplex multi-relay channel (FD-MRC) for exploiting the high spectral efficiency of FD systems and the diversity gains of multi-relay systems. Employing a decode-and-forward (DF) relaying protocol, the single best relay is selected from those relays that have successfully decoded the source message, which then helps forward the source message to the destination. Meanwhile, a new message is broadcast by the source, and all the <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> relays, including the selected best one, attempt to decode this new message. In view of the effect of both inter-relay interference and self-interference, a <italic>one-step</italic> correlation exists between the relay decoding results in successive time slots. Then, a Markov chain-based analytical model is utilized for analyzing the exact outage probability of FD-MRC. Furthermore, a <italic>close-to-full</italic> diversity order of <inline-formula><tex-math notation="LaTeX">$(N-1+\mu)$</tex-math> </inline-formula> is analytically proved to be attainable for FD-MRC, where <inline-formula><tex-math notation="LaTeX"> $\mu$</tex-math></inline-formula> is a factor characterizing the efficiency of SI mitigation.