Miracle-dz/UniFAR-FTUs huggingface dataset:
Dyrk1A Is Dynamically Expressed on Subsets of Motor Neurons and in the Neuromuscular Junction: Possible Role in Down Syndrome

Individuals with Down syndrome (DS) present important motor deficits that derive from altered motor development of infants and young children. DYRK1A, a candidate gene for DS abnormalities has been implicated in motor function due to its expression in motor nuclei in the adult brain, and its overexpression in DS mouse models leads to hyperactivity and altered motor learning. However, its precise role in the adult motor system, or its possible involvement in postnatal locomotor development has not yet been clarified. During the postnatal period we observed time-specific expression of Dyrk1A in discrete subsets of brainstem nuclei and spinal cord motor neurons. Interestingly, we describe for the first time the presence of Dyrk1A in the presynaptic terminal of the neuromuscular junctions and its axonal transport from the facial nucleus, suggesting a function for Dyrk1A in these structures. Relevant to DS, Dyrk1A overexpression in transgenic mice (TgDyrk1A) produces motor developmental alterations possibly contributing to DS motor phenotypes and modifies the numbers of motor cholinergic neurons, suggesting that the kinase may have a role in the development of the brainstem and spinal cord motor system.
A Unifying Order-Theoretic Framework for Superposition Coding: Polymatroidal Structure and Optimality in the Multiple-Access Channel With General Message Sets

Two different random coding techniques, both referred to as superposition coding in the literature, have been widely used to obtain inner bounds for the capacity regions of various communication networks. In one, auxiliary codewords are generated independently, and in the other, they are generated in a dependent manner. Using the multiple-access channel with general message sets as a case study, we place the two techniques under a common, order-theoretic framework. The key attribute of this framework is that it explicitly accounts for the acyclic direction and transitivity of the possible auxiliary codeword dependencies, leading to three significant discoveries. First, with respect to a fixed coding distribution, the set of rates achievable by superposition coding with dependent auxiliary codeword generation forms a polymatroid, thereby generalizing the same previously known result for superposition coding with independent auxiliary codewords. Second, we obtain a large class of superposition coding schemes by intermingling dependent and independent auxiliary codeword generation, and demonstrate that the constituent polyhedral achievable rate regions are also polymatroids in each case. The third discovery is that, in the multiple-access channel with general message sets, each associated superposition coding inner bound attains the capacity region. These results demonstrate a tradeoff between the complexity of dependencies in auxiliary codeword generation and that of the function that maps them into transmitted codewords.
Scalable classification over SQL databases

We identify data-intensive operations that are common to classifiers and develop a middleware that decomposes and schedules these operations efficiently using a backend SQL database. Our approach has the added advantage of not requiring any specialized physical data organization. We demonstrate the scalability characteristics of our enhanced client with experiments on Microsoft SQL Server 7.0 by varying data size, number of attributes and characteristics of decision trees.
MicroRNA-101 Targets MAPK Phosphatase-1 To Regulate the Activation of MAPKs in Macrophages

MAPK phosphatase-1 (MKP-1) is an archetypical member of the dual-specificity phosphatase family that deactivates MAPKs. Induction of MKP-1 has been implicated in attenuating the LPS- or peptidoglycan-induced biosynthesis of proinflammatory cytokines, but the role of noncoding RNA in the expression of the MKP-1 is still poorly understood. In this study, we show that MKP-1 is a direct target of microRNA-101 (miR-101). Transfection of miR-101 attenuates induction of MKP-1 by LPS as well as prolonged activation of p38 and JNK/stress-activated protein kinase, whereas inhibition of miR-101 enhances the expression of MKP-1 and shortens p38 and JNK activation. We also found that expression of miR-101 is induced by multiple TLR ligands, including LPS, peptidoglycan, or polyinosinic-polycytidylic acid, and that inhibition of PI3K/Akt by LY294002 or Akt RNA interference blocks the induction of miR-101 by LPS in RAW264.7 macrophage cells. Moreover, treatment of cells with dexamethasone, a widely used anti-inflammatory agent, markedly inhibits miR-101 expression and enhances the expression of MKP-1 in LPS-stimulated macrophages. Together, these results indicate that miR-101 regulates the innate immune responses of macrophages to LPS through targeting MKP-1.
Neuronal Patterns in the Cavity Wall of Lesions during Gait Cycle in a Rat Model of Brain Lesion Cavities

Oscillatory neural activity was reported to have various physiological roles in information processing of brain functions. It is now established that extracellular activity in the motor cortex encodes aspects of movement, involving planning and motor control. Oscillatory patterns have also been hypothesized to play a role in brain recovery and functional remapping. In this study, we measured neural activity from within the cavity wall of a motor cortex lesion, in a rat model, while the animals performed a skilled walking task. We aim at providing a possible framework of analysis, focused on revealing oscillatory patterns in the cavity wall and their correlation with motor deficits, by using a combination of spectral features, involving power spectra and coherence estimates in the beta and gamma frequency bands.
Analysis of Gene Expression Signatures for Osteogenic 3D Perfusion-Bioreactor Cell Cultures Based on a Multifactorial DoE Approach

The use of multifactorial design of experiments (DoE) in tissue engineering bioprocess development will contribute to the robust manufacturing of tissue engineered constructs by linking their quality characteristics to bioprocess operating parameters. In this work, perfusion bioreactors were used for the in vitro culture and osteogenic differentiation of human periosteum-derived cells (hPDCs) seeded on three-dimensional titanium (Ti) alloy scaffolds. A CaP-supplemented medium was used to induce differentiation of the cultured hPDCs. A two-level, three-factor fractional factorial design was employed to evaluate a range of bioreactor operating conditions by changing the levels of the following parameters: flow rate (0.5–2 mL/min), cell culture duration (7–21 days) and cell seeding density (1.5 × 103–3 × 103 cells/cm2). This approach allowed for evaluating the individual impact of the aforementioned process parameters upon a range of genes that are related to the osteogenic lineage, such as collagen type I, alkaline phosphatase, osterix, osteopontin and osteocalcin. Furthermore, by overlaying gene-specific response surfaces, an integrated operating process space was highlighted within which predetermined values of the six genes of interest (i.e., gene signature) could be minimally met over the course of the bioreactor culture time.
Automated Modularization of GUI Test Cases

Test cases that drive an application under test via its graphical user interface (GUI) consist of sequences of steps that perform actions on, or verify the state of, the application user interface. Such tests can be hard to maintain, especially if they are not properly modularized - that is, common steps occur in many test cases, which can make test maintenance cumbersome and expensive. Performing modularization manually can take up considerable human effort. To address this, we present an automated approach for modularizing GUI test cases. Our approach consists of multiple phases. In the first phase, it analyzes individual test cases to partition test steps into candidate subroutines, based on how user-interface elements are accessed in the steps. This phase can analyze the test cases only or also leverage execution traces of the tests, which involves a cost-accuracy tradeoff. In the second phase, the technique compares candidate subroutines across test cases, and refines them to compute the final set of subroutines. In the last phase, it creates callable subroutines, with parameterized data and control flow, and refactors the original tests to call the subroutines with context-specific data and control parameters. Our empirical results, collected using open-source applications, illustrate the effectiveness of the approach.
Constrained Metric Learning by Permutation Inducing Isometries

The choice of metric critically affects the performance of classification and clustering algorithms. Metric learning algorithms attempt to improve performance, by learning a more appropriate metric. Unfortunately, most of the current algorithms learn a distance function which is not invariant to rigid transformations of images. Therefore, the distances between two images and their rigidly transformed pair may differ, leading to inconsistent classification or clustering results. We propose to constrain the learned metric to be invariant to the geometry preserving transformations of images that induce permutations in the feature space. The constraint that these transformations are isometries of the metric ensures consistent results and improves accuracy. Our second contribution is a dimension reduction technique that is consistent with the isometry constraints. Our third contribution is the formulation of the isometry constrained logistic discriminant metric learning (IC-LDML) algorithm, by incorporating the isometry constraints within the objective function of the LDML algorithm. The proposed algorithm is compared with the existing techniques on the publicly available labeled faces in the wild, viewpoint-invariant pedestrian recognition, and Toy Cars data sets. The IC-LDML algorithm has outperformed existing techniques for the tasks of face recognition, person identification, and object classification by a significant margin.
A Parameterized and Annotated Spoken Dialog Corpus of the CMU Let’s Go Bus Information System

Standardized corpora are the foundation for spoken language research. In this work, we introduce an annotated and standardized corpus in the Spoken Dialog Systems (SDS) domain. Data from the Let's Go Bus Information System from the Carnegie Mellon University in Pittsburgh has been formatted, parameterized and annotated with quality, emotion, and task success labels containing 347 dialogs with 9,083 system-user exchanges. A total of 46 parameters have been derived automatically and semi-automatically from Automatic Speech Recognition (ASR), Spoken Language Understanding (SLU) and Dialog Manager (DM) properties. To each spoken user utterance an emotion label from the set garbage, non-angry, slightly angry, very angry has been assigned. In addition, a manual annotation of Interaction Quality (IQ) on the exchange level has been performed with three raters achieving a Kappa value of 0.54. The IQ score expresses the quality of the interaction up to each system-user exchange on a score from 1-5. The presented corpus is intended as a standardized basis for classification and evaluation tasks regarding task success prediction, dialog quality estimation or emotion recognition to foster comparability between different approaches on these fields.
Managing the Data Replicas Efficiently in Time-Zoned Multilayer Chord

Replica is better than erasure coding when managing the redundancy of a storage system under high churn. It needs extra network communication to keep the target redundancy. By classifying the hosts into different categories of availability and placing the data replicas carefully may reduce the maintenance overhead. Considering a globe scale P2P system, the time-zoned multilayer Chord divides the hosts into 24 categories according to the time zone where they located. All the hosts are belongs to a Main Chord which consisted of all the hosts and one Local Chord which consisted of the hosts located in the same time zone. Because of the daily behavior of the people in same time zone are nearly similar, which means their computers are more likely to be available in the daytime and less likely to be available in the nighttime. So, for a certain time, there is nearly half of the hosts get higher availability than the other hosts. As the hosts in the same time zone are considered, their availability evaluated by the system is shifting periodically in a 24 hours cycle. By give a carefully placement of the data replicas on this two layered Chord, the less communication is needed to maintain the same target availability. The experiments show that dividing hosts by location of time zone might reduce the overhead of redundancy management.
Critical random hypergraphs: The emergence of a giant set of identifiable vertices

We consider a model for random hypergraphs with idemifiability, an analogue of connectedness. This model has a phase transition in the proportion of identifiable vertices when the underlying random graph becomes critical. The phase transition takes various forms, depending on the values of the parameters controlling the different types of hyperedges. It may be continuous as in a random graph. (In fact, when there are no higher-order edges, it is exactly the emergence of the giant component.) In this case, there is a sequence of possible sizes of components (including but not restricted to N 2/3 ). Alternatively, the phase transition may be discontinuous. We are particularly interested in the nature of the discontinuous phase transition and are able to exhibit precise asymptotics. Our method extends a result of Aldous [Ann. Probab, 25 (1997) 812-854] on component sizes in a random graph.
Stopping Rules for Bag-of-Words Image Search and Its Application in Appearance-Based Localization

We propose a technique to improve the search efficiency of the bag-of-words (BoW) method for image retrieval. We introduce a notion of difficulty for the image matching problems and propose methods that reduce the amount of computations required for the feature vector-quantization task in BoW by exploiting the fact that easier queries need less computational resources. Measuring the difficulty of a query and stopping the search accordingly is formulated as a stopping problem. We introduce stopping rules that terminate the image search depending on the difficulty of each query, thereby significantly reducing the computational cost. Our experimental results show the effectiveness of our approach when it is applied to appearance-based localization problem.
Effect Of Dust Particles On Thermal Convection In A Ferromagnetic Fluid

This paper deals with the theoretical investigation of the effect of dust particles on the thermal convection in a ferromagnetic fluid subjected to a transverse uniform magnetic field. For a flat ferromagnetic fluid layer contained between two free boundaries, the exact solution is obtained, using a linear stability analysis. For the case of stationary convection, dust particles and non-buoyancy magnetization have always a destabilizing effect. The critical wavenumber and critical magnetic thermal Rayleigh number for the onset of instability are also determined numerically for sufficiently large values of the buoyancy magnetization parameter M1. The results are depicted graphically. It is observed that the critical magnetic thermal Rayleigh number is reduced because the heat capacity of the clean fluid is supplemented by that of the dust particles. The principle of exchange of stabilities is found to hold true for the ferromagnetic fluid heated from below in the absence of dust particles. The oscillatory modes are introduced by the dust particles. A sufficient condition for the non-existence of overstability is also obtained.
Recursive Gabor filtering

We present a recursive algorithm for the Gabor filter that achieves, to within a multiplicative constant, the fastest possible implementation. For a signal consisting of N samples, our implementation requires O(N) multiply-and-add operations. Further, the complexity is and coefficients of the recursive equation have a simple independent of the values of /spl sigma/ and /spl omega/ in the Gabor kernel closed-form solution given /spl sigma/ and /spl omega/. Our implementation admits not only a forward Gabor transform from t/spl rarr//spl omega/ but an inverse transform from /spl omega//spl rarr/t that is also O(N) complexity.
Hybrid Long-Range Collision Avoidance for Crowd Simulation

Local collision avoidance algorithms in crowd simulation often ignore agents beyond a neighborhood of a certain size. This cutoff can result in sharp changes in trajectory when large groups of agents enter or exit these neighborhoods. In this work, we exploit the insight that exact collision avoidance is not necessary between agents at such large distances, and propose a novel algorithm for extending existing collision avoidance algorithms to perform approximate, long-range collision avoidance. Our formulation performs long-range collision avoidance for distant agent groups to efficiently compute trajectories that are smoother than those obtained with state-of-the-art techniques and at faster rates. Comparison to real-world data demonstrates that crowds simulated with our algorithm exhibit an improved speed sensitivity to density similar to human crowds. Another issue often sidestepped in existing work is that discrete and continuum collision avoidance algorithms have different regions of applicability. For example, low-density crowds cannot be modeled as a continuum, while high-density crowds can be expensive to model using discrete methods. We formulate a hybrid technique for crowd simulation which can accurately and efficiently simulate crowds at any density with seamless transitions between continuum and discrete representations. Our approach blends results from continuum and discrete algorithms, based on local density and velocity variance. In addition to being robust across a variety of group scenarios, it is also highly efficient, running at interactive rates for thousands of agents on portable systems.
Mapping Selective Logging in Mixed Deciduous Forest: A Comparison of Machine Learning Algorithms

This study assesses the performance of five Machine Learning Algorithms (MLAs) in a chronically modified mixed deciduous forest in Massachusetts (USA) in terms of their ability to detect selective timber logging and to cope with deficient reference datasets. Multitemporal Landsat Enhanced Thematic Mapperplus (ETM+) imagery is used to assess the performance of three Artificial Neural Networks ‐ Multi-Layer Perceptron, ARTMAP, Self-Organizing Map, and two Classification Tree splitting algorithms: gini and entropy rules. MLA performance evaluations are based on susceptibility to reduced training set size, noise, and variations in the training set, as well as the operability/transparency of the classification process. Classification trees produced the most accurate selective logging maps (gini and entropy rule decision tree mean overall map accuracy � 94 percent and mean per-class kappa of 0.59 and 0.60, respectively). Classification trees are shown to be more robust and accurate when faced with deficient training data, regardless of splitting rule. Of the neural network algorithms, self-organizing maps were least sensitive to the introduction of noise and variations in training data. Given their robust classification capabilities and transparency of the classselection process, classification trees are preferable algorithms for mapping selective logging and have potential in other forest monitoring applications.
Core vs. uncore: The heart of darkness

Even though Moore's Law continues to provide increasing transistor counts, the rise of the utilization wall limits the number of transistors that can be powered on and results in a large region of dark silicon. Prior studies have proposed energy-efficient core designs to address the “dark silico” problem. Nevertheless, the research for addressing dark silicon challenges in uncore components, such as shared cache, on-chip interconnect, etc, that contribute significant on-chip power consumption is largely unexplored. In this paper, we first illustrate that the power consumption of uncore components cannot be ignored to meet the chip's power constraint. We then introduce techniques to design energy-efficient uncore components, including shared cache and on-chip interconnect. The design challenges and opportunities to exploit 3D techniques and non-volatile memory (NVM) in dark-silicon-aware architecture are also discussed.
Towards a Model about Quality of Software Requirements Specification in Agile Projects

The quality of Software Requirements Specifications (SRS) is important to gain a competitive advantage in the software industry. Although the use of agile methods has grown in recent years, empirical studies conducted in industry identified several problems related to SRS in the context of agile projects. The goal of this study is to investigate the phenomenon of the quality of SRS in Agile Software Development (ASD) and build an explanatory model about it. We use a mixed method research strategy for creating a rich description of the factors that affect the quality of SRS in ASD. First, we conducted a systematic mapping study, then we performed a cross-case analysis of two software organizations: one small private organization and a large public organization. We built an initial model from this analysis. The findings were that SRS in agile projects should be directed to the development team, so it should be close to what will be implemented. The fragmentation of the description of the requirements in various artifacts and excessive detail were considered factors that compromise the quality of the SRS.
A role for juvenile hormone in the prepupal development of Drosophila melanogaster

To elucidate the role of juvenile hormone (JH) in metamorphosis of Drosophila melanogaster, the corpora allata cells, which produce JH, were killed using the cell death gene grim. These allatectomized (CAX) larvae were smaller at pupariation and died at head eversion. They showed premature ecdysone receptor B1 (EcR-B1) in the photoreceptors and in the optic lobe, downregulation of proliferation in the optic lobe, and separation of R7 from R8 in the medulla during the prepupal period. All of these effects of allatectomy were reversed by feeding third instar larvae on a diet containing the JH mimic (JHM) pyriproxifen or by application of JH III or JHM at the onset of wandering. Eye and optic lobe development in the Methoprene-tolerant (Met)-null mutant mimicked that of CAX prepupae, but the mutant formed viable adults, which had marked abnormalities in the organization of their optic lobe neuropils. Feeding Met27 larvae on the JHM diet did not rescue the premature EcR-B1 expression or the downregulation of proliferation but did partially rescue the premature separation of R7, suggesting that other pathways besides Met might be involved in mediating the response to JH. Selective expression of Met RNAi in the photoreceptors caused their premature expression of EcR-B1 and the separation of R7 and R8, but driving Met RNAi in lamina neurons led only to the precocious appearance of EcR-B1 in the lamina. Thus, the lack of JH and its receptor Met causes a heterochronic shift in the development of the visual system that is likely to result from some cells ‘misinterpreting’ the ecdysteroid peaks that drive metamorphosis.