Miracle-dz/UniFAR-FTUs huggingface dataset:
Intermediate Progenitors Facilitate Intracortical Progression of Thalamocortical Axons and Interneurons through CXCL12 Chemokine Signaling

Glutamatergic principal neurons, GABAergic interneurons and thalamocortical axons (TCAs) are essential elements of the cerebrocortical network. Principal neurons originate locally from radial glia and intermediate progenitors (IPCs), whereas interneurons and TCAs are of extrinsic origin. Little is known how the assembly of these elements is coordinated. C-X-C motif chemokine 12 (CXCL12), which is known to guide axons outside the neural tube and interneurons in the cortex, is expressed in the meninges and IPCs. Using mouse genetics, we dissected the influence of IPC-derived CXCL12 on TCAs and interneurons by showing that Cxcl12 ablation in IPCs, leaving meningeal Cxcl12 intact, attenuates intracortical TCA growth and disrupts tangential interneuron migration in the subventricular zone. In accordance with strong CXCR4 expression in the forming thalamus and TCAs, we identified a CXCR4-dependent growth-promoting effect of CXCL12 on TCAs in thalamus explants. Together, our findings indicate a cell-autonomous role of CXCR4 in promoting TCA growth. We propose that CXCL12 signals from IPCs link cortical neurogenesis to the progression of TCAs and interneurons spatially and temporally. SIGNIFICANCE STATEMENT The cerebral cortex exerts higher brain functions including perceptual and emotional processing. Evolutionary expansion of the mammalian cortex is mediated by intermediate progenitors, transient amplifying cells generating cortical excitatory neurons. During the peak period of cortical neurogenesis, migrating precursors of inhibitory interneurons originating in subcortical areas and thalamic axons invade the cortex. Although defects in the assembly of cortical network elements cause neurological and mental disorders, little is known how neurogenesis, interneuron recruitment, and axonal ingrowth are coordinated. We demonstrate that intermediate progenitors release the chemotactic cytokine CXCL12 to promote intracortical interneuron migration and growth of thalamic axons via the cognate receptor CXCR4. This paracrine signal may ensure thalamocortical connectivity and dispersion of inhibitory neurons in the rapidly growing cortex.
Tension Band Wiring for Proximal Femoral Varus Osteotomy Fixation in Children

AbstractFor proximal varus osteotomy of the femur in children, a stable fixation, is a concern regarding the safety and the protection of the surgery. The methods of fixation are mostly represented by plating systems; however, the tension band wiring is a simple and eligible option for fixation of proximal femur osteotomy. The purpose of this retrospective case series study is to evaluate outcomes and bone remodelation after using the tension band wiring technique to fixate proximal femoral varus osteotomies in children.The study enrolled 20 patients who underwent femoral varus osteotomy to treat Legg–Calvé–Perthes disease and other causes of necrosis of the femoral head. The mean patient age at the time of surgery was 7.4 ± 2.3 years (range, 4.3–13.8 y), and the follow-up period averaged 10 ± 4.3 years (range, 4.7–20.8 y). To assess the effects of the osteotomy at the neck-shaft angle and its evolution over time, radiographs obtained preoperatively and during the follow-up were analyzed. The neck-shaft angle was measured independently by 2 observers, and intraclass correlation coefficients (ICCs) were assessed for reliability.The ICC analysis showed good reliability. There was a significant reduction in the neck-shaft angle (P < 0.001) between the preoperative and the immediate postoperative periods, with an estimated difference of 14.3°. Between the immediate and the early postoperative periods, the estimated difference was 2.1°, which indicated a significant increase in the neck-shaft angle (P = 0.003). Between the early postoperative period and the final evaluation, there was a significant increase (P < 0.001), with an estimated difference of 7.0°. No implant failures or other complications were observed, but 1 case of peri-implant femoral fracture occurred >17 years postsurgery.The tension band wiring technique proved to be a simple and effective method for fixating proximal femoral varus osteotomy in children.The level IV is appropriated for our study (case series, retrospective).
Event-Triggered Pinning Control of Switching Networks

This paper investigates event-triggered pinning control for the synchronization of complex networks of nonlinear dynamical systems. We consider networks described by time-varying weighted graphs and featuring generic linear interaction protocols. Sufficient conditions for the absence of Zeno behavior are derived and exponential convergence of a global normed error function is proven. Static networks are considered as a special case, wherein the existence of a lower bound for interevent times is also proven. Numerical examples demonstrate the effectiveness of the proposed control strategy.
Iterative Improvement plus Random Restart Stochastic Search

The multistart or iterative improvement plus random restart (IR) method for global optimization combines local search with random initialization. This method partitions the solution space into basins of attraction to the local optima. The method's convergence rate to a global optimizer as the number of iterations t increases is given as 1 s t in terms of two scalar parameters, acceleration s, and retention . Acceleration is estimated as s > 1= > 1. The expected running time of the algorithm in order to nd a basin containing a global optimizer is estimated as
Effects of Fruit and Vegetable Low Molecular Mass Fractions on Gene Expression in Gingival Cells Challenged with Prevotella intermedia and Actinomyces naeslundii

Low molecular mass (LMM) fractions obtained from extracts of raspberry, red chicory, and Shiitake mushrooms have been shown to be an useful source of specific antibacterial, antiadhesion/coaggregation, and antibiofilm agent(s) that might be used for protection towards caries and gingivitis. In this paper, the effects of such LMM fractions on human gingival KB cells exposed to the periodontal pathogens Prevotella intermedia and Actinomyces naeslundii were evaluated. Expression of cytokeratin 18 (CK18) and β4 integrin (β4INT) genes, that are involved in cell proliferation/differentiation and adhesion, and of the antimicrobial peptide β2 defensin (HβD2) in KB cells was increased upon exposure to either live or heat-killed bacteria. All LMM fractions tested prevented or reduced the induction of gene expression by P. intermedia and A. naeslundii depending on the experimental conditions. Overall, the results suggested that LMM fractions could modulate the effects of bacteria associated with periodontal disease in gingival cells.
Distributed Sensor Fault Diagnosis for a Network of Interconnected Cyberphysical Systems

This paper proposes a distributed methodology for detecting and isolating multiple sensor faults in interconnected cyberphysical systems. The distributed sensor fault detection and isolation process is conducted in the cybersuperstratum, in two levels. The first-level diagnosis is based on the design of monitoring agents, where every agent is dedicated to a corresponding interconnected subsystem. The monitoring agent is designed to isolate multiple sensor faults occurring in the sensor set of the physical part, while it is allowed to exchange information with its neighboring monitoring agents. The second-level diagnosis is realized by applying a global decision logic designed to isolate multiple sensor faults that may propagate in the cybersuperstratum through the exchange of information between monitoring agents. The decision-making process, executed in both levels of diagnosis, relies on a multiple sensor fault combinatorial logic and diagnostic reasoning. The performance of the proposed methodology is analyzed with respect to the sensor fault propagation effects and the distributed sensor fault detectability.
Engagement in Motion: Exploring Short Term Dynamics in Page-Level Social Media Metrics

Using page-level metrics of a randomly selected group of 15,625 among the top 100,000 Face book check-in locations which rank high in terms of customer engagement, we explore if the short-term dynamical information on these metrics could deliver, via a clustering approach, some new insights for marketing decision making. Using a highly-scalable clustering algorithm, statistical methods, and combinatorial optimization met heuristics based on memetic algorithms, we have observed that some pages naturally cluster with others that share the same user-defined category. Our results highlight the need of suggesting other further "meta-categories" that encompass several user-reported categories for pages and that a priori geographical segmentation might be necessary to investigate more relevant patterns that take into account seasonal variability of behaviours and physical proximity.
Analysis of sensor characteristics and error eliminations of a wheeled mobile robot with range sensors

This paper introduces some preprocedures that eliminate nonzero mean errors of odometry result and range sensor data of a wheeled mobile robot. After that, the sensing results from odometry and range sensor are more reliable. In the beginning of this paper, some analyses of sensor characteristics are discussed and three kinds of error are classified. Because of these errors, results of sensing data are unreliable and make inappropriate control decisions. To solve this problem, some “error elimination architectures” are designed to lower the effects of errors or remove errors.
REGIME SWITCHING VECTOR AUTOREGRESSIONS : A BAYESIAN MARKOV CHAIN MONTE CARL 0 APPROACH

Many financial time series processes appear subject to periodic structural changes in their dynamics. Regression relationships are often not robust to outliers nor stable over time, whilst the existence of changes in variance over time is well documented. This paper considers a vector autoregression subject to pseudocyclical structural changes. The parameters of a vector autoregression are modelled as the outcome of an unobserved discrete Markov process with unknown transition probabilities. The unobserved regimes, one for each time point, together with the regime transition probabilities, are to be determined in addition to the vector autoregression parameters within each regime.
Hydrodynamic Problems Involving Large Fluid Distortions

Lagrangian hydrodynamic methods applied to two.dimensional calculations have had striking success in problems involving small fluid distortions. Although the interfluid boundary motions can be calculated well, the methods break down when the fluids (and thus the coordinate system) become strongly distorted. Eulerian methods can survive strong distortions but suffer from false diffusion of boundaries. It is the purpose of this note to describe a method of calculation that, in preliminary tests, has achieved some success in solving large distortion, twodimensional problems involving several fluids.
Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms

Constructive learning algorithms o er an approach to incremental construction of near-minimal arti cial neural networks for pattern classi cation. Examples of such algorithms include Tower, Pyramid, Upstart, and Tiling algorithms which construct multilayer networks of threshold logic units (or, multilayer perceptrons). These algorithms di er in terms of the topology of the networks that they construct which in turn biases the search for a decision boundary that correctly classi es the training set. This paper presents an analysis of such algorithms from a geometrical perspective. This analysis helps in a better characterization of the search bias employed by the di erent algorithms in relation to the geometrical distribution of examples in the training set. Simple experiments with non linearly separable training sets support the results of mathematical analysis of such algorithms. This suggests the possibility of designing more e cient constructive algorithms that dynamically choose among di erent biases to build near-minimal networks for pattern classi cation.
Same domain different discourse style - A case study on Language Resources for data-driven Machine Translation

Data-driven machine translation (MT) approaches became very popular during last years, especially for language pairs for which it is difficult to find specialists to develop transfer rules. Statistical (SMT) or example-based (EBMT) systems can provide reasonable translation quality for assimilation purposes, as long as a large amount of training data is available. Especially SMT systems rely on parallel aligned corpora which have to be statistical relevant for the given language pair. The construction of large domain specific parallel corpora is time- and cost-consuming; the current practice relies on one or two big such corpora per language pair. Recent developed strategies ensure certain portability to other domains through specialized lexicons or small domain specific corpora. In this paper we discuss the influence of different discourse styles on statistical machine translation systems. We investigate how a pure SMT performs when training and test data belong to same domain but the discourse style varies.
Analysis and Design of Weakly Coupled LC Oscillator Arrays Based on Phase-Domain Macromodels

An array of weakly coupled oscillators can generate multiphase signals, i.e., multiple sinusoidal signals with specific phase separations. Multiphase oscillators are attractive solutions in many electronic applications such as the synchronization of multiple processing units in digital electronics and the frequency synthesis in mixed-signal radio frequency circuits. Due to the complexity of multiphase oscillators and the large number of design parameters, novel simulation techniques are highly desired to efficiently handle such large-scale problems. In this paper, an efficient phase-domain simulation technique is proposed to calculate the phase response of inductance capacitance oscillator array. By some practical examples, it is shown how the proposed method can be exploited to identify the array topologies and parameter settings that guarantee stable phase separations. It is also shown how the proposed technique can be used to evaluate phase-noise performance.
Dynamic Trustiness Authentication Framework Based on Software's Behavior Integrity

A dynamic trustiness authentication framework based on the integrity of softwarepsilas behavior is proposed in this paper. The method to extract SIBDS (Software Intended Behaviors Describing Sets) and SBAC (Software Behavior Authentication Code) from the binary executable is introduced. In the framework, when the software begin to run, it should be monitored by SBMC (Software Behavior Monitoring Center), then the real API function invoking sequence will be acquired. The framework uses the software behavior comparison algorithm to verify whether the API invoking sequence gotten from the actual behavior is in accordance with SBAC; thereby the softwarepsilas dynamic trustiness can be detected and guaranteed. The experiment results demonstrate the efficacy of the dynamic trustiness authentication framework.
A model-driven method for gesture-based interface requirements specification

Currently there are several software development suites that include tools for user interface design and implementation (mainly by programming source code). Some of the tools are multi-platform and multi-style; that is, allowing the specification of devices, e.g. computer, notebook, smartphone, and user interaction styles, e.g. based on gestures, voice, mouse and keyboard. Among the styles, gesture-based interaction is neglected, despite the proliferation of gesture-recognizing devices. Given the variety of styles of human-computer interaction currently available, it is necessary to include information on these styles in software requirements specification to obtain a complete specification prior to code generation. In this paper, we propose the design of a model-driven method and tool that allows specifying gesture-based interactions and then generates gesture-based interface requirements specification. We intend to our proposal be interoperable with existing methods and tools. The research method follows design science and we plan to validate our proposals by means of technical action-research.
Block-Wise Two-Dimensional Maximum Margin Criterion for Face Recognition

Maximum margin criterion (MMC) is a well-known method for feature extraction and dimensionality reduction. However, MMC is based on vector data and fails to exploit local characteristics of image data. In this paper, we propose a two-dimensional generalized framework based on a block-wise approach for MMC, to deal with matrix representation data, that is, images. The proposed method, namely, block-wise two-dimensional maximum margin criterion (B2D-MMC), aims to find local subspace projections using unilateral matrix multiplication in each block set, such that in the subspace a block is close to those belonging to the same class but far from those belonging to different classes. B2D-MMC avoids iterations and alternations as in current bilateral projection based two-dimensional feature extraction techniques by seeking a closed form solution of one-side projection matrix for each block set. Theoretical analysis and experiments on benchmark face databases illustrate that the proposed method is effective and efficient.
Media annotations in hyperlinked pedagogical platforms

Today's eLearning websites are heavily loaded with multimedia contents, which are often unstructured, unedited, unsynchronized, and lack inter-links among different multimedia components. Hyperlinking different media modality may provide a solution for quick navigation and easy retrieval of pedagogical content in media rich eLearning platforms. Creating hyperlinks in media rich platforms often requires video and presentation slides annotation. Manually annotating different media is a challenging, laborious, prone to errors, and a time-consuming task. In this paper, an approach combining state-of-the-art SIFT together with wavelet energy transform is proposed for automatic lecture video annotations for the hyper interactive presenter (HIP). New video annotations are implemented automatically providing the needed random access in lecture videos within the HIP platform. A preliminary study of the usefulness of the system is carried out, and the initial results are suggesting that 70% of the students opted for using HIP as their preferred eLearning platform at Gjovik university college (GUC).
Multimodal 3-D tracking and event detection via the particle filter

Determining the occurrence of an event is fundamental to developing systems that can observe and react to them. Often, this determination is based on collecting video and/or audio data and determining the state or location of a tracked object. We use Bayesian inference and the particle filter for tracking moving objects, using both video data obtained from multiple cameras and audio data obtained using arrays of microphones. The algorithms developed are applied to determining events arising in two fields of application. In the first, the behavior of a flying echo locating bat as it approaches a moving prey is studied, and the events of search, approach and capture are detected. In a second application we describe detection of turn-taking in a conversation between possibly moving participants recorded using a smart video conferencing setup.
A Game-Theoretic Approach to Energy-Efficient Resource Allocation in Device-to-Device Underlay Communications

Despite the numerous benefits brought by Device-to-Device (D2D) communications, the introduction of D2D into cellular networks poses many new challenges in the resource allocation design due to the co-channel interference caused by spectrum reuse and limited battery life of User Equipments (UEs). Most of the previous studies mainly focus on how to maximize the Spectral Efficiency (SE) and ignore the energy consumption of UEs. In this paper, we study how to maximize each UE's Energy Efficiency (EE) in an interference-limited environment subject to its specific Quality of Service (QoS) and maximum transmission power constraints. We model the resource allocation problem as a noncooperative game, in which each player is self-interested and wants to maximize its own EE. A distributed interference-aware energy-efficient resource allocation algorithm is proposed by exploiting the properties of the nonlinear fractional programming. We prove that the optimum solution obtained by the proposed algorithm is the Nash equilibrium of the noncooperative game. We also analyze the tradeoff between EE and SE and derive closed-form expressions for EE and SE gaps.
Formación y desarrollo de competencias TIC e intercultural de educadores infantiles para la convivencia escolar

La multiculturalidad es un fenómeno que, con la globalización y el crecimiento y evolución de las Tecnologías de la Información y Comunicación (TIC), trae consigo la necesidad de formar ciudadanos capaces de valorar las diferencias que hay en otros, para así generar un diálogo intercultural genuino y mejorar la convivencia escolar desde la competencia intercultural. En el presente artículo, se analizarán las características de un programa de formación basado en el modelo INCULTIC y su relación con el desarrollo de las competencias TIC e Intercultural de futuros pedagogos en formación inicial. Este estudio, tomó forma de investigación-acción y combinando métodos,instrumentos y técnicas cuantitativas y cualitativas, se logró indagar, diseñar, implementar y evaluar el programa de formación logrando determinar que, luego del proceso formativo basado en el modelo INCULTIC, se generaron cambios significativos en las competencias TIC e Intercultural de la población objeto. En este sentido, es evidente que los participantes manifestaron tener habilidades y destrezas para diseñar y aplicar estrategias pedagógicas culturalmente apropiadas para favorecer el desarrollo de las competencias ciudadanas y comunicativas, con el fin de fomentar la convivencia escolar.
Towards a Balanced Account of Expertise

ABSTRACT The interdisciplinary debate about the nature of expertise often conflates having expertise with either the individual possession of competences or a certain role ascription. In contrast to this, the paper attempts to demonstrate how different dimensions of expertise ascription are inextricably interwoven. As a result, a balanced account of expertise will be proposed that more accurately determines the closer relationship between the expert’s dispositions, their manifestations and the expert’s function. This finally results in an advanced understanding of expertise that views someone as an expert only if she is undefeatedly disposed to fulfill a contextually salient service function adequately at the moment of assessment.