Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Short-Term Exposure to Ambient Fine Particulate Matter with Skin Symptoms in Schoolchildren: A Panel Study in a Rural Area of Western Japan

Numerous studies have unmasked the deleterious effects of particulate matter less than 2.5 μm (PM2.5) on health. However, epidemiologic evidence focusing on the effects of PM2.5 on skin health remains limited. An important aspect of Asian dust (AD) in relationship to health is the amount of PM2.5 contained therein. Several studies have demonstrated that AD can aggravate skin symptoms. The current study aimed to investigate the effects of short-term exposure to PM2.5 and AD particles on skin symptoms in schoolchildren. A total of 339 children recorded daily skin symptom scores during February 2015. Light detection and ranging were used to calculate AD particle size. Generalized estimating equation logistic regression analyses were used to estimate the associations among skin symptoms and the daily levels of PM2.5 and AD particles. Increases in the levels of PM2.5 and AD particles were not related to an increased risk of skin symptom events, with increases of 10.1 μg/m3 in PM2.5 and 0.01 km−1 in AD particles changing odds ratios by 1.03 and 0.99, respectively. These results suggest that short-term exposure to PM2.5 and AD does not impact skin symptoms in schoolchildren.
Blind and Fully Constrained Unmixing of Hyperspectral Images

This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images. Unmixing is performed without the use of any dictionary, and assumes that the number of constituent materials in the scene and their spectral signatures are unknown. The estimated abundances satisfy the desired sum-to-one and nonnegativity constraints. Two models with increasing complexity are developed to achieve this challenging task, depending on how noise interacts with hyperspectral data. The first one leads to a convex optimization problem and is solved with the alternating direction method of multipliers. The second one accounts for signal-dependent noise and is addressed with a reweighted least squares algorithm. Experiments on synthetic and real data demonstrate the effectiveness of our approach.
Mindfulness And Suppression As Emotion Regulation Strategies For Sadness

The primary purpose of this study was to explore how emotion regulation impacts memory, and how depression might moderate these effects. I recruited 53 participants through Appalachian State’s SONA system. Participants consisted of 13 males and 40 females, all undergraduate students at Appalachian State University, ages 18-25. Each participant completed practice sessions in emotional suppression and mindfulness before viewing a series of photographs intended to elicit happiness or sadness emotions. Participants were instructed to use different emotion regulation strategies (mindfulness, suppression, and natural) while viewing each section of images, and rated how similar they felt, emotionally, to the people in the pictures. Next, participants completed questionnaires measuring depression, emotion regulation, and mindfulness before completing a surprise recall test of the images they viewed previously. Although there was no significant effects of either depression or emotion regulation on memory, depression was associated with other variables. Results showed that individuals with higher levels of depression were more likely to identify with the photographs while suppressing their emotions, and that two facets of mindfulness, nonjudging and acting with awareness, were associated with decreased levels of depression.
Clever-1/Stabilin-1 Controls Cancer Growth and Metastasis

Purpose: Immunosuppressive leukocytes and vasculature are important host cell components regulating tumor progression. Clever-1/Stabilin-1, a multifunctional scavenger and adhesion receptor, is constitutively present on a subset of type II macrophages and lymphatic endothelium, but its functional role in cancer is unknown. Experimental Design: Here, we generated full Clever-1 knockout mice and cell-specific ones lacking Clever-1 either on macrophages or endothelium. We also used anti-Clever-1 antibody therapy to treat B16 melanoma and EL-4 lymphoma. Results: Clever-1–deficient mice had smaller primary and metastatic tumors than wild-type (WT) controls. Growth of primary tumors, but not of metastases, was attenuated also in mice lacking Clever-1 selectively in macrophages or in vascular endothelium. Anti-Clever-1 antibody treatment inhibited tumor progression in WT mice. Both genetically and therapeutically induced absence of functional Clever-1 led to diminished numbers of immunosuppressive leukocyte types in tumors. Functionally Clever-1 mediated binding of immunosuppressive leukocytes to the intratumoral blood vessels aberrantly expressing Clever-1, and tumor cell traffic via the lymphatics. The antibody therapy did not aggravate autoimmunity. Conclusion: This work identifies Clever-1 in type II macrophages and in tumor vasculature as a new immunosuppressive molecule in cancer. Our finding that Clever-1 supports binding of tumor-infiltrating lymphocytes to tumor vasculature increases our understanding of leukocyte immigration to tumors. The ability of anti-Clever-1 antibody treatment to attenuate tumor progression in WT mice in vivo is therapeutically relevant. Thus, Clever-1 may be an emerging new target for modulating immune evasion and lymphatic spread in cancer. Clin Cancer Res; 20(24); 6452–64. ©2014 AACR.
Visualizing revisions and building semantic network in Wikipedia

Wikipedia, one of the largest online encyclopedias, is competent to Britannica. Articles are subject to day to day changes by authors, and each such change is recorded as a new revision. In this paper, we visualize the article's revisions and build the semantic network between articles. First, we analyze the revisions difference of article and using color to show the revisions change. Second, through the article's classified information, we constructed a semantic network of articles' relationship.
Extrasynaptic α7-Nicotinic Acetylcholine Receptor Expression in Developing Neurons Is Regulated by Inputs, Targets, and Activity

α7-Nicotinic acetylcholine receptors (nAChRs) are widely expressed in the vertebrate nervous system. α7-nAChR functions include postsynaptic transmission, modulating neurotransmitter release, reinforcing nicotine addiction, and a role in neurological disorders, such as schizophrenia and Alzheimer's disease. In chick parasympathetic ciliary ganglion (CG) neurons, α7-nAChRs are excluded from the synapse and localize perisynaptically. Despite their extrasynaptic distribution, the highly Ca2+-permeable α7-nAChRs have important synapse-related Ca2+-dependent signaling functions in the CG. We show here that the synaptic partners regulate α7-nAChR expression during synapse formation in embryonic CG neurons in situ. The absence of inputs and target tissues cause reductions in α7-nAChR mRNA and protein levels that primarily resemble those seen for synaptic α3-nAChRs. However, there is a difference in their regulation. α7-nAChR levels are downregulated by reduced activity, whereas α3-nAChR levels are not. We propose that the activity-dependent regulation of extrasynaptic α7-nAChR levels may be an important mechanism for postsynaptic CG neurons to detect changes in presynaptic activity levels and respond with Ca2+-dependent plasticity changes in gene expression.
Perspectiva histórica y empatía: su interrelación en futuros profesores de Educación Primaria

La investigacion explora la empatia desde la optica de la Psicologia y su vinculacion con la comprension del pasado. Los objetivos son (a) examinar si existe diferencia entre las dos dimensiones de la empatia psicologica (cognitiva y afectiva), (b) analizar la capacidad de perspectiva historica a traves del trabajo desarrollado en una experiencia de contextualizacion historica, y (c) poner ambos resultados en relacion, para concluir si existe un vinculo entre ellos. Los instrumentos empleados, en una muestra de 119 futuros profesores de la Uni- versidad de Valladolid, son el Interpersonal Reactivity Index, cuestionario de Davis (1980) que evalua la empatia desde una perspectiva multidimensional, y una adaptacion del trabajo desarrollado por Foster (1999) sobre la politica de apaciguamiento de Chamberlain. El enfoque metodologico es mixto, analizando datos mediante tecnicas cuantitativas y cualitativas, y considerando como variable in- dependiente el genero. La investigacion revela valores superiores en la dimension cognitiva de la empatia frente a la afectiva. Solo un reducido porcentaje de los participantes proporciona argumentos contextualizados en el pasado.
Linear Kalman Filter Algorithm with Clarke Transformation for Power System Frequency Estimation

This paper presents a new application for linear Kalman filter algorithm for power system frequency estimation. The filter uses the digitized samples of the three-phase voltages or current waveform signals. These three phases are transformed into two αβ-phases, using the well-known αβ-transformation matrix (Clarke Transformation). Having obtained the signal of the two new phases, a complex phasor is constructed using the new two-phase voltages. Kalman filter is then applied to extract the frequency and phase angle of the fundamental component of the complex phasor. The proposed algorithm is tested on simulated and actual recorded data at different conditions for the three phase signals; noise free and balanced three phase signals, unbalanced three-phase system and harmonics contaminated three phase signals. It has been shown that the proposed filters with the proposed transformation are succeeded to estimate the signal frequency at off nominal, near nominal and at nominal frequency.
An Approximation Approach for the Deviation Matrix of Continuous-Time Markov Processes with Application to Markov Decision Theory

We present an update formula that allows the expression of the deviation matrix of a continuous-time Markov process with denumerable state space having generator matrix Q* through a continuous-time Markov process with generator matrix Q. We show that under suitable stability conditions the algorithm converges at a geometric rate. By applying the concept to three different examples, namely, the M/M/1 queue with vacations, the M/G/1 queue, and a tandem network, we illustrate the broad applicability of our approach. For a problem in admission control, we apply our approximation algorithm to Markov decision theory for computing the optimal control policy. Numerical examples are presented to highlight the efficiency of the proposed algorithm.
Idealized Modeling of the Atmospheric Boundary Layer Response to SST Forcing in the Western Indian Ocean

The atmospheric response to sea surface temperature (SST) variations forced by oceanic downwelling equatorial Rossby waves is investigated using an idealized convection-resolving model. Downwelling equatorial Rossby waves sharpen SST gradients in the western Indian Ocean. Changes in SST cause the atmosphere to hydrostatically adjust, subsequently modulating the low-level wind field. In an idealized cloud model, surface wind speeds, surface moisture fluxes, and low-level precipitable water maximize near regions of strongest SST gradients, not necessarily in regions of warmest SST. Simulations utilizing the steepened SST gradient representative of periods with oceanic downwelling equatorial Rossby waves show enhanced patterns of surface convergence and precipitation that are linked to a strengthened zonally overturning circulation. During these conditions, convection is highly organized, clustering near the maximum SST gradient and ascending branch of the SST-induced overturning circulation. When the SST gradient is reduced, as occurs during periods of weak or absent oceanic equatorial Rossby waves, convection is much less organized and total rainfall is decreased. This demonstrates the previously observed upscale organization of convection and rainfall associated with oceanic downwelling equatorial Rossby waves in the western Indian Ocean. These results suggest that the enhancement of surface fluxes that results from a steepening of the SST gradient is the leading mechanism by which oceanic equatorial Rossby waves prime the atmospheric boundary layer for rapid convective development.
Messung von Mobilität und Funktion der unteren Extremität in einem ambulanten neurorehabilitativen Setting

Zusammenfassung Hintergrund Erworbene neurologische Erkrankungen mit zerebraler Beteiligung führen häufig zu Einschränkungen der Mobilität und damit auch zu einer erschwerten Teilhabe am gesellschaftlichen Leben. Daher ist die Ermöglichung und Verbesserung von Teilhabe eines der wichtigsten Ziele individuell abgestimmter Rehabilitationsmaßnahmen. Die Wiederherstellung der motorischen Funktionen hängt im Rehabilitationsverlauf nicht zuletzt von der vergangenen Zeit seit dem neurologischen Ereignis ab. Ziel Die vorliegende Untersuchung ging der Frage nach, ob die zu Beginn und am Ende einer komplexen interdisziplinären ambulanten neurologischen Rehabilitation routinemäßig eingesetzten Messinstrumente Veränderungen im Verlauf erfassen können und sich diese bei Personen mit akuten/subakuten und chronischen Geschehen unterscheiden. Methode Retrospektiv wurden die Daten im Zeitraum Januar 2004 bis Dezember 2014 aus dem Neuro Reha Team Pasing, München, ausgewertet. Neben demografischen Daten und der Diagnose wurden die Messergebnisse der Instrumente zur Einschätzung der Funktion der unteren Extremität erhoben. Die Berechnung der intra- und intergruppalen Mittelwertunterschiede in den Messergebnissen erfolgten mittels t-Tests für unabhängige Stichproben und Chi2-Tests. Ergebnisse Die Personen mit neurologischen Geschehen wurden im Mittel 82 ± 36 Tage behandelt. Bei allen eingesetzten Messinstrumenten zeigte sich eine signifikante Veränderung von Ausgangs- zu Endbefund. Bis auf den 10-Meter-Gehtest ergaben sich bei allen Messinstrumenten zwischen der akuten/subakuten und der chronischen Gruppe signifikante Unterschiede im Endbefund zugunsten der akuten/subakuten Gruppe. Schlussfolgerung Der routinemäßige Einsatz von Messinstrumenten in einem neurorehabilitativen Setting erlaubt zuverlässig das schnelle Erfassen von Veränderungen im Therapieverlauf sowohl bei Personen mit akutem/subakutem als auch chronischem neurologischen Geschehen.
Time series anomaly detection using recessive subsequence

Time series arise frequently in many sciences and engineering application, including finance, digital audio, motion capture, network security, and transportation. In this work, we propose a technique for discovering anomalies in time series that takes advantages of the Symbolic Aggregate approXimation (SAX) technique and inspiration from a motif discovery algorithm. We use SAX to reduce the dimension of the time series and apply the idea of motif discovery to detect anomalies. We consider recessive sequences instead of frequent sequences similar to motif finding. We evaluate the algorithm on several real-world data from different areas, such as the car speed data, the motion capture data, and the weather data. Experiments demonstrate the effectiveness of the proposed algorithm to discover anomalies in real-world time series.
Hybrid FDTD modeling of a two-level atomic system

The new concept of coupling rate equations, describing the evolution of a two-level atomic system, with rigorous electromagnetic simulations undertaken with a finite-difference time-domain method is presented. The proposed procedure allows updating electromagnetic fields and population densities with different time-scales, which leads to the significant reduction of computational effort of the analysis of absorbing materials.
Texture analysis and classification of polarimetric SAR images using histogram measures

In polarized synthetic aperture radar (SAR) image classification, texture produces very significant information about land cover. In order to perform the classification in a much better way, a detailed study of histogram measures has been performed. Good accuracies show that histogram measures are used which are best suited for several applications like land use monitoring, surface topology, crustal change, agricultural monitoring, map updating of urban areas, medical field and military system. Histogram measures improve the classification accuracy desirably. PALSAR data, that is unclassified radar images of different polarizations, of Solani River, Roorkee, India and its neighboring regions, is used for classifying the image based on texture features. The textures of images are found on the basis of histogram measures (mean, variance, standard deviation, correlation and skewness). As observed, the area can be characterized into a well-planned dispersed urban area with water bodies and vegetation. The accuracies vary for different classes. For efficient land cover classification, histogram measures have to be chosen suitably. Therefore, the role of various textural histogram measures is analyzed for their discriminative ability for SAR image classification into various land cover types like urban, vegetation and water bodies.
Vascular hydraulics associated with idiopathic and secondary intracranial hypertension.

BACKGROUND AND PURPOSE The radiologic diagnosis of idiopathic intracranial hypertension (IIH) is one of exclusion, with no reproducible positive features described in the imaging literature. Because MR venography is prone to flow artifacts, diagnosis of secondary intracranial hypertension (SIH) can also be problematic. Vascular hydraulics can be useful for diagnosis of these conditions when measured by invasive or sonographic means. The purpose of this study was to measure vascular flow and pulsatility characteristics with a noninvasive MR imaging method. METHODS Twelve patients with clinical and lumbar puncture findings of IIH or SIH and 12 control subjects were examined with MR venography and MR flow quantification studies of the cerebral arteries and veins. Total cerebral, superior sagittal sinus (SSS), and straight sinus blood flows were measured. Pulsatility indices from the arterial and venous flow for all patients were compared using the Student t test. RESULTS MR venography confirmed that seven of the 12 patients had venous outflow obstruction, and thus, SIH. The remaining five patients had IIH. All patients showed reduced sinus pulsatility compared with that of the control group; reductions of 42% in the SSS and 32% in the straight sinus were noted (P =.0001 and.005, respectively). In the IIH group, total blood flow was 46% higher than that in the control group (P =.0002), and SSS flow was normal. In the SIH group, total blood flow was normal; however, SSS flow was reduced by 25% (P =.003). CONCLUSION Reduced venous sinus pulsatility is a marker of intracranial hypertension secondary to raised venous sinus pressure. When suspicion of IIH or SIH exists and the MR venogram is difficult to interpret, raised total blood flow indicates IIH, whereas reduced SSS flow indicates SIH.
ELMRES for Solving Ill-Conditioned Linear Equations

Most of the krylov subspace methods use some projection techniques to solve linear system of equations. ELMRES is a krylov subspace method which uses an oblique projection technique. This method transfers the original linear system into an upper least square problem by using the Hessenberg decomposition algorithm and update the current approximation iterate by the solution of this least square. Here the algorithm of ELMRES is described and it is applied to solve some popular ill-posed linear system of equations by using Tikhonov regularization technique.
Improving classification accuracy of SVM ensemble using random training set for BCI P300-speller

P300-speller is a communication system based on brain-computer interface (BCI) which allows users to input characters by focused attention. Support vector machine (SVM) ensemble has been successfully applied to classify in the P300-speller. However, large scale of training data was needed for the sub-classifiers to ensure the performance of classification. Subjects gradually got fatigue during the period of training data collection, which resulted in an obvious difference of EEG data from early to later and affected the performance of classification. In present study, we adopted a random selection strategy to construct the training set for SVM sub-classifiers in a BCI P300-speller with familiar face paradigm. Our results showed that the random selection strategy improved the classification accuracy of SVM ensemble in BCI P300-speller.
On C*-algebras generated by pairs of q-commuting isometries This material is based upon work support

We consider the C*-algebras and generated, respectively, by isometries s1, s2 satisfying the relation s*1s2 = qs2s*1 with |q| < 1 (the deformed Cuntz relation), and by isometries s1, s2 satisfying the relation s2s1 = qs1s2 with |q| = 1. We show that is isomorphic to the Cuntz–Toeplitz C*-algebra for any |q| < 1. We further prove that if and only if either q1 = q2 or . In the second part of our paper, we discuss the complexity of the representation theory of . We show that is *-wild for any q in the circle |q| = 1, and hence that is not nuclear for any q in the circle.
Joint Resource Allocation Scheme for Device-To-Device Communication under a Cellular Network

Abstract Device-To-Device (D2D) communication is a new technology that allows the mobile terminals to directly communicate with each other by sharing the resources of the cellular network under the control of a cellular system. D2D communication was introduced into the cellular network in order to improve the efficiency of resource utilization and increase the system throughput. However, there exist interferences between D2D links and cellular links due to the resource reuse. In order to efficiently limit the cases of interference, a joint resource allocation scheme for D2D communication under a cellular network is proposed. The scheme, based on the user’s Quality of Service (QoS), defines a new utility function to balance the increasing system throughput and the interference in the cellular users D2D communication. The optimal D2D transmitting power is obtained through optimizing the utility function by using the convex optimization theory. The simulation shows that the scheme can fulfill the user’s QoS requirements efficiently, improve the system throughput and the reliability of the resources as well.
Challenging statistical classification for operational usage: the ADSL case

Accurate identification of network traffic according to application type is a key issue for most companies, including ISPs. For example, some companies might want to ban p2p traffic from their network while some ISPs might want to offer additional services based on the application. To classify applications on the fly, most companies rely on deep packet inspection (DPI) solutions. While DPI tools can be accurate, they require constant updates of their signatures database. Recently, several statistical traffic classification methods have been proposed. In this paper, we investigate the use of these methods for an ADSL provider managing many Points of Presence (PoPs). We demonstrate that statistical methods can offer performance similar to the ones of DPI tools when the classifier is trained for a specific site. It can also complement existing DPI techniques to mine traffic that the DPI solution failed to identify. However, we also demonstrate that, even if a statistical classifier is very accurate on one site, the resulting model cannot be applied directly to other locations. We show that this problem stems from the statistical classifier learning site specific information.