Miracle-dz/UniFAR-FTUs huggingface dataset:
Associations of PM2.5 Constituents and Sources with Hospital Admissions: Analysis of Four Counties in Connecticut and Massachusetts (USA) for Persons ≥ 65 Years of Age

Background: Epidemiological studies have demonstrated associations between short-term exposure to PM2.5 and hospital admissions. The chemical composition of particles varies across locations and time periods. Identifying the most harmful constituents and sources is an important health and regulatory concern. Objectives: We examined pollutant sources for associations with risk of hospital admissions for cardiovascular and respiratory causes. Methods: We obtained PM2.5 filter samples for four counties in Connecticut and Massachusetts and analyzed them for PM2.5 elements. Source apportionment was used to estimate daily PM2.5 contributions from sources (traffic, road dust, oil combustion, and sea salt as well as a regional source representing coal combustion and other sources). Associations between daily PM2.5 constituents and sources and risk of cardiovascular and respiratory hospitalizations for the Medicare population (> 333,000 persons ≥ 65 years of age) were estimated with time-series analyses (August 2000–February 2004). Results: PM2.5 total mass and PM2.5 road dust contribution were associated with cardiovascular hospitalizations, as were the PM2.5 constituents calcium, black carbon, vanadium, and zinc. For respiratory hospitalizations, associations were observed with PM2.5 road dust, and sea salt as well as aluminum, calcium, chlorine, black carbon, nickel, silicon, titanium, and vanadium. Effect estimates were generally robust to adjustment by co-pollutants of other constituents. An interquartile range increase in same-day PM2.5 road dust (1.71 μg/m3) was associated with a 2.11% (95% CI: 1.09, 3.15%) and 3.47% (95% CI: 2.03, 4.94%) increase in cardiovascular and respiratory admissions, respectively. Conclusions: Our results suggest some particle sources and constituents are more harmful than others and that in this Connecticut/Massachusetts region the most harmful particles include black carbon, calcium, and road dust PM2.5. Citation: Bell ML, Ebisu K, Leaderer BP, Gent JF, Lee HJ, Koutrakis P, Wang Y, Dominici F, Peng RD. 2014. Associations of PM2.5 constituents and sources with hospital admissions: analysis of four counties in Connecticut and Massachusetts (USA) for persons ≥ 65 years of age. Environ Health Perspect 122:138–144; http://dx.doi. org/10.1289/ehp.1306656
Real-space cosmic fields from redshift-space distributions: a Green function approach

We present a new method for reconstructing the cosmological density, peculiar velocity and peculiar gravitational potential on large scales from redshift data. We remove the distorting effects of line-of-sight peculiar motions by using the linear theory of gravitational instability, in which the potential is the solution to a linear partial differential equation first derived by Kaiser. We solve this equation by deriving its Green function; the Green functions for the peculiar velocity and the real-space density follow directly. Reconstruction of the cosmic fields is thus reduced to integration over redshift space with the appropriate Green function kernel. Our algorithm has a single input parameter, beta=Omega^0.6/b, where b is the linear bias factor and Omega is the cosmological density parameter. We discuss the virtues of this method for error control, for estimating beta, and for constraining the bias mechanism.
Hierarchical subtopic mining for topic annotation

Some authors of web documents attached keywords as metadata to the documents. These keywords reflected authors' intents for the main topics. To automatically acquire topic-related keywords, this paper proposes a method that mines subtopics as topic-related keywords using simple patterns and the hierarchical structure of candidate strings based on the clusters of relevant documents using web documents for the Japanese and English languages. We created alternative partial-topics from the original topic, and extracted various candidate strings using simple patterns based on each partial-topic and POS tags. We constructed the hierarchical structure of the candidate strings according to the proposed process, and ranked them using this structure and the frequency information for each group of the candidate strings in the area satisfying the diversity requirement of the hierarchical structure. Our method outperformed the baselines, and the results will be useful in various topic annotations.
Clustering Technique in Data Mining for Text Documents

Feature selection is an important method for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the Data warehouse. The semantic clustering and feature selection method is proposed to improve the clustering and feature selection mechanism with semantic relations of the text documents. Also a new text clustering algorithm TCFS, which stands for Text Clustering with Feature Selection is proposed. TCFS can incorporate CHIR, a new supervised feature selection method to identify relevant features (i.e., terms) iteratively, and the clustering becomes a learning process. The proposed system is designed to identify the semantic relations using the ontology. The ontology is used represent the term and concept relationship. The synonym, meronym and hypernym relationships are represented in the ontology. The concept weights are estimated with reference to the ontology. The concept weight is used for the clustering process. Statistical methods are used in the text clustering and feature selection algorithm. The cube size is very high and accuracy is low in the term based text clustering and feature selection method 1 INTRODUCTION The term Data Mining generally refers to a process by which accurate and previously unknown information can be extracted from large volumes of data in a form that can be understood, acted upon, and used for improving decision processes. Data Mining is most often associated with the broader process of Knowledge Discovery in Databases (KDD), " the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data ". By analogy, this system defines Textual Data Mining as the process of acquiring valid, potentially useful and ultimately understandable knowledge from large text collections. How to explore and utilize the huge amount of text documents is a major question in the areas of information retrieval and text mining. Document clustering is one of the most important text mining methods that are developed to help users effectively navigate, summarize, and organize text documents [5]. By organizing a large amount of
Bayesian ANN classifier for ECG arrhythmia diagnostic system: a comparison study

This paper outlines a system for detection of cardiac arrhythmias within ECG signals, based on a Bayesian artificial neural network (ANN) classifier. The Bayesian (or probabilistic) ANN classifier is built by the use of a logistic regression model and the backpropagation algorithm based on a Bayesian framework. Its performance for this task is evaluated by comparison with other classifiers including Naive Bayes, decision trees, logistic regression, and RBF networks. A paired t-test is employed in comparing classifiers to select the optimum model. The system is evaluated using noisy ECG data, to simulate a real-world environment. It is hoped that the system can be further developed and fine-tuned for practical application.
Workplace Toxic Online Disinhibition: Causes and Effects

Although the prevalence of information and communication has enabled collaboration within geographically distributed organizations, the prevalence of toxic online disinhibition within these new technologies also poses novel intrapersonal problems to front line managers and people leaders. This annotated bibliography consists of peer reviewed literature published between 2005 and 2015. Literature has been selected and reviewed with the intent of exploring the causes and repercussions of toxic online disinhibition in the workplace.
Human Motor Cortical Activity Is Selectively Phase-Entrained on Underlying Rhythms

The functional significance of electrical rhythms in the mammalian brain remains uncertain. In the motor cortex, the 12–20 Hz beta rhythm is known to transiently decrease in amplitude during movement, and to be altered in many motor diseases. Here we show that the activity of neuronal populations is phase-coupled with the beta rhythm on rapid timescales, and describe how the strength of this relation changes with movement. To investigate the relationship of the beta rhythm to neuronal dynamics, we measured local cortical activity using arrays of subdural electrocorticographic (ECoG) electrodes in human patients performing simple movement tasks. In addition to rhythmic brain processes, ECoG potentials also reveal a spectrally broadband motif that reflects the aggregate neural population activity beneath each electrode. During movement, the amplitude of this broadband motif follows the dynamics of individual fingers, with somatotopically specific responses for different fingers at different sites on the pre-central gyrus. The 12–20 Hz beta rhythm, in contrast, is widespread as well as spatially coherent within sulcal boundaries and decreases in amplitude across the pre- and post-central gyri in a diffuse manner that is not finger-specific. We find that the amplitude of this broadband motif is entrained on the phase of the beta rhythm, as well as rhythms at other frequencies, in peri-central cortex during fixation. During finger movement, the beta phase-entrainment is diminished or eliminated. We suggest that the beta rhythm may be more than a resting rhythm, and that this entrainment may reflect a suppressive mechanism for actively gating motor function.
Utilizing an ensemble of SVMs with GMM voting-based mechanism in predicting dangerous seismic events in active coal mines

This paper presents an application of a Gaussian Mixture Model-based voting mechanism for an ensemble of Support Vector Machines (SVMs) to the problem of predicting dangerous seismic events in active coal mines. The author proposes a method of preparing an ensemble of SVMs with different parameters and using the “wisdom of the crowd” for a classification problem. Experiments performed during the research showed an improvement in the quality of the classification after the mixture of Gaussian distributions was applied as votes distribution. The author also proposes a method of data selection for long sequences of measurement arranged chronologically with highly unbalanced occurrence of the positive class in the two-class classification problem. Finally, using the proposed model to solve the problem defined by the organizers of AAIA'16 DM showed an increase in the stability of the ensemble classifier and an improvement in the quality of the classification problem solution.
The Mouse Epididymal Transcriptome: Transcriptional Profiling of Segmental Gene Expression in the Epididymis1

Abstract Maturation of spermatozoa, including the acquisition of motility and the ability to undergo capacitation, occurs during transit through the dynamic environment of the epididymis. The microenvironments created along the length of the epididymal tubule are essential to the molecular modifications of spermatozoa that result in fertile gametes. The secretory and resorptive processes of the epithelial cells that line this tubule generate these microenvironments. In the current study, 10 morphologically distinct segments of the mouse epididymis were identified by microdissection. We hypothesized that the changing environments of the epididymal lumen are established by differential gene expression among these segments. RNA isolated from each of the 10 segments was analyzed by microarray analysis. More than 17 000 genes are expressed in the mouse epididymis, compared with about 12 000 genes identified from whole epididymal samples. Screening a panel of normal mouse tissues identified both epididymal-selective and epididymal-specific transcripts. In addition, this study identified 2168 genes that are up-regulated or down-regulated by greater than 4-fold between at least two different segments. The expression patterns of these genes identify distinct patterns of segmental regulation. Using principal component analysis, we determined that the 10 segments form 6 different transcriptional units. These analyses elucidate the changes in gene expression along the length of the epididymis for 17 000 expressed transcripts and provide a powerful resource for the research community in future studies of the biological factors that mediate epididymal sperm maturation.
Macrophage production of transforming growth factor beta and fibroblast collagen synthesis in chronic pulmonary inflammation

A rat model of bleomycin-induced pulmonary inflammation and fibrosis was used to examine the relationship between collagen synthesis and transforming growth factor beta (TGF-beta) production, and cellular distribution. Total lung TGF-beta was elevated within 2 h of intratracheal bleomycin administration and peaked 7 d later at levels 30-fold higher than controls. This was followed by a gradual decline with lower but persistent levels of production in the late phase of the response between 21 and 28 d later. The peak TGF-beta levels preceded the maximum collagen and noncollagen protein synthesis measured by [3H]proline incorporation into lung fibroblast explants of bleomycin- treated rats. The pattern of immunohistochemical staining localized TGF- beta initially in the cytoplasm of bronchiolar epithelium cells and subepithelial extracellular matrix. The peak of lung TGF-beta levels at 7 d coincided with intense TGF-beta staining of macrophages dispersed in the alveolar interstitium and in organized clusters. Later in the course of the response. TGF-beta was primarily associated with extracellular matrix in regions of increased cellularity and tissue repair, and coincided with the maximum fibroblast collagen synthesis. This temporal and spatial relationship between collagen production and TGF-beta production by macrophages suggests an important if not primary role for TGF-beta in the pathogenesis of the pulmonary fibrosis.
An extended image force model of snakes for medical image segmentation and smoothing

A scheme of the image force for Snakes, named the eSnake Model, has been introduced based on the electrostatic theory. To improve the ability for segmenting medical images with uneven grayscale intensity, a novel extended eSnake model, eeSnake, is developed to get the desirable image force distribution for Snakes. Breaking through the limitation of the Coulomb law, it can work better with the elasticity and balloon forces of Snakes than eSnake. The pre-calculated data stencil of distance functions is adopted to accelerate the computing speed of the potential field. The analysis points out that the eeSnake model can also function as a smoothing filter. Experiments show that the proposed eeSnake model is satisfactory in both segmentation and noise reduction applications
Economic Growth , Income Inequality , and Poverty Reduction in People ’ s Republic of China

The paper proposes a poverty reduction index demonstrating that the selection of growth policies should be to maximize the sum of income and inequality effects. The empirical results indicate that economic growth in the People’s Republic of China (PRC) between 1985 and 2001 was effective in reducing poverty but rising inequality reduced the effectiveness of poverty reduction. The empirical results also indicate that initial level of inequality is important in determining growth policies for a country whose regions are in different development stages. A development stage with low initial inequality will achieve greater poverty reduction from growth, whereas a development stage with high initial inequality will require a deeper poverty focus. In the current stage of economic development in the PRC, economic growth is still effective in reducing poverty. Infrastructure projects that support economic growth will still lead to poverty reduction, even if the nonpoor might get more direct benefits from these projects. Well-designed infrastructure projects with a poverty focus will be more effective in poverty reduction.
Experimental study on extensional vibration of a piezoelectric/piezomagnetic laminated plate

In this paper, the electromagnetic energy harvesting scheme by using a Terfenol-D/PZT/Terfenol-D composite magnetoelectric (ME) transducer and a circuit is presented. In the experiments, the electrodes on the piezoelectric layer are connected by a circuit whose impedance is denoted by Z, which is the load impedance. we have analyzed and observed the load dependence of the ME coefficient and the basic behaviors of the power density. A giant sharp ME coefficient αE a of 24.5 mv/Oe appears at 3.72×105 rad/s under the load impedance Z of 2000Ω.
N-11C-Methyl-Dopamine PET Imaging of Sympathetic Nerve Injury in a Swine Model of Acute Myocardial Ischemia: A Comparison with 13N-Ammonia PET

Objective. Using a swine model of acute myocardial ischemia, we sought to validate N-11C-methyl-dopamine (11C-MDA) as an agent capable of imaging cardiac sympathetic nerve injury. Methods. Acute myocardial ischemia was surgically generated in Chinese minipigs. ECG and serum enzyme levels were used to detect the presence of myocardial ischemia. Paired 11C-MDA PET and 13N-ammonia PET scans were performed at baseline, 1 day, and 1, 3, and 6 months after surgery to relate cardiac sympathetic nerve injury to blood perfusion. Results. Seven survived the surgical procedure. The ECG-ST segment was depressed, and levels of the serum enzymes increased. Cardiac uptake of tracer was quantified as the defect volume. Both before and immediately after surgery, the images obtained with 11C-MDA and 13N-ammonia were similar. At 1 to 6 months after surgery, however, 11C-MDA postsurgical left ventricular myocardial defect volume was significantly greater compared to 13N-ammonia. Conclusions. In the Chinese minipig model of acute myocardial ischemia, the extent of the myocardial defect as visualized by 11C-MDA is much greater than would be suggested by blood perfusion images, and the recovery from myocardial sympathetic nerve injury is much slower than the restoration of blood perfusion. 11C-MDA PET may provide additional biological information during recovery from ischemic heart disease.
Design and implementation of experimental SPB network simulator on NS-3

Network virtualization makes it possible for Ethernet to distinguish between different customer network traffic in the provider network. Virtualization is also necessary for cloud services in a data center network. In this paper, we designed and implemented on NS-3 (open source simulator) an experimental Shortest Path Bridging (SPB, IEEE 802.1aq) protocol designed for meshed network like data center. Modified version of Floyd-Warshall algorithm is used to compute the route. In order to show simulator's capability, the basic function of SPBM (SPB Mac-in-Mac) is simulated. In addition, All nodes have objects for Intermediate System to Intermediate System (ISIS).
The Complexity of Kleene Algebra with Tests

Kleene algebras with tests provide a natural framework for equational specification and verification. Kleene algebras with tests and related systems have been used successfully in basic safety analysis, source-to-source program transformation, and concurrency control. The equational theory of Kleene algebras with tests has been shown to be decidable in at most exponential time by an efficient reduction to Propositional Dynamic Logic. In this paper we prove that the theory is PSPACE-complete.
Oblivious Network RAM

Oblivious RAM (ORAM) is a cryptographic primitive that allows a trusted CPU to securely access untrusted memory, such that the access patterns reveal nothing about sensitive data. ORAM is known to have broad applications in secure processor design and secure multi-party computation for big data. Unfortunately, due to a well-known logarithmic lower bound by Goldreich and Ostrovsky (Journal of the ACM, ’96), ORAM is bound to incur a moderate cost in practice. In particular, with the latest developments in ORAM constructions, we are quickly approaching this limit, and the room for performance improvement is small. In this paper, we consider new models of computation in which the cost of obliviousness can be fundamentally reduced in comparison with the standard ORAM model. We propose the Oblivious Network RAM model of computation, where a CPU communicates with multiple memory banks, such that the adversary observes only which bank the CPU is communicating with, but not the address offset within each memory bank. In other words, obliviousness within each bank comes for free—either because the architecture prevents a malicious party from observing the address accessed within a bank, or because another solution is employed to obfuscate memory accesses within each bank—and hence we only need to obfuscate the communication patterns between the CPU and the memory banks. We present several new constructions for obliviously simulating general or parallel programs in the Network RAM model. We describe applications of the Network RAM model in secure processor design and in distributed storage applications with a network adversary.
Delayed myocardial ischemia induced by anger.

The objectives of this study were to develop a reproducible behavioral model that simulates the anger state and to characterize its influence on myocardial blood flow in both the normal and compromised coronary circulations. Fourteen mongrel dogs of both sexes were studied. The animals were instrumented for the recording of electrocardiogram, arterial blood pressure, and left circumflex coronary arterial blood flow. A critical level of coronary stenosis was achieved with an adjustable occluder placed just distal to the flow probe. Anger was induced in the instrumented animals by having another dog challenge their access to food. In the absence of coronary artery stenosis, provocation of anger increased heart rate from 107 +/- 6 to 215 +/- 15 beats/min, arterial blood pressure from 95 +/- 4 to 142 +/- 5 mm Hg, and coronary blood flow from 31 +/- 5 to 72 +/- 9 ml/min. These variables returned to the preanger levels within 2 to 4 min. Induction of anger was repeated after a critical stenosis was applied to the left circumflex coronary artery. Anger increased heart rate from 112 +/- 6 to 210 +/- 16 beats/min, arterial blood pressure from 99 +/- 3 to 142 +/- 6 mm Hg, and coronary arterial flow from 23 +/- 5 to 35 +/- 7 ml/min. Within 2 to 4 min after the bout of anger, all dogs exhibited significant reductions in coronary arterial flow (35% of baseline; p less than .001), increases in coronary vascular resistance (557% of baseline; p less than .002), and ischemic ST segment changes in leads II, III, and aVF. (ABSTRACT TRUNCATED AT 250 WORDS)
An adaptive Gaussian sum algorithm for radar tracking

In this paper, we propose a new radar tracking algorithm based on the Gaussian sum filter. We have developed a new systematic and efficient way to approximate a non-Gaussian and measurement-dependent function by a weighted sum of Gaussian density functions. We have derived the formula for updating the weights involved in the bank of Kalman-type filters and also suggested a way to alleviate the growing memory problem inherent in the Gaussian sum filter. Our method is compared with the extended Kalman filter (EKF) and the converted measurement Kalman filter (CMKF) and it is shown to be more accurate in term of position and velocity errors.