Miracle-dz/UniFAR-FTUs huggingface dataset:
Hemorheological parameters and aging.

BACKGROUND There is increasing evidence that impaired hemorheological parameters are associated with increased risk of cardio- and cerebrovascular events. The aim of our present study was to examine the relationship of these parameters to the advancing age. METHODS The data of 6236 cardio- and cerebrovascular patients (3774 males, mean age 59.8 +/- 13.2 years and 2462 females, mean age 60.9 +/- 12.8 years) were included into this analysis. Males and females were divided into three groups, A < 45 years of age (young), B 45-65 years (middle-aged), C > 65 years (old). To exclude the effect of risk profile, previous diseases and medication, 623 patients (397 males, mean age 60.2 +/- 12.7 yrs and 226 females, mean age 60.5 +/- 12.4 yrs) were selected from the examined group with matching parameters. Blood was collected after an overnight fasting. Hematocrit, fibrinogen, red blood cell aggregation, plasma and whole blood viscosity were determined. RESULTS All the measured parameters correlated significantly with advancing age in the whole population (p < 0.01), however the values of the correlation coefficients were very low. On the other hand, examining the different age-groups we found that these parameters did not consequently correlate with age, in fact hematocrit, red blood cell aggregation and whole blood viscosity values were negatively correlated with age in old males (p < 0.05). In the selected population these parameters did not correlate with advancing age. CONCLUSIONS In the whole population the correlation of hemorheological parameters and advancing age may be just of statistical, but not clinical significance because of the high number of subjects. In the selected population these parameters did not correlate with advancing age. Our results suggest that these parameters are mostly independent of aging, increased values are not associated with older age but the more frequently occurring diseases.
Econometric Analysis of Production Networks with Dominant Units

This paper builds on the work of Acemoglu et al. (2012) and considers a production network with unobserved common technological factor and establishes general conditions under which the network structure contributes to aggregate fluctuations. It introduces the notions of strongly and weakly dominant units, and shows that at most a finite number of units in the network can be strongly dominant, while the number of weakly dominant units can rise with N (the cross section dimension). This paper further establishes the equivalence between the highest degree of dominance in a network and the inverse of the shape parameter of the power law. A new extremum estimator for the degree of pervasiveness of individual units in the network is proposed, and is shown to be robust to the choice of the underlying distribution. Using Monte Carlo techniques, the proposed estimator is shown to have satisfactory small sample properties. Empirical applications to US input-output tables suggest the presence of production sectors with a high degree of pervasiveness, but their effects are not sufficiently pervasive to be considered as strongly dominant.
An Efficient Insertion Operator in Dynamic Ridesharing Services

Dynamic ridesharing refers to services that arrange one-time shared rides on short notice. It underpins various real-world intelligent transportation applications such as car-pooling, food delivery and last-mile logistics. A core operation in dynamic ridesharing is the “<italic>insertion operator</italic>”. Given a worker and a feasible route which contains a sequence of origin-destination pairs from previous requests, the insertion operator inserts a new origin-destination pair from a newly arrived request into the current route such that certain objective is optimized. Common optimization objectives include minimizing the maximum/sum flow time of all requests and minimizing the total travel time of the worker. Despite its frequent usage, the insertion operator has a time complexity of <inline-formula><tex-math notation="LaTeX">$O(n^3)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>n</mml:mi><mml:mn>3</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="tong-ieq1-3027200.gif"/></alternatives></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="tong-ieq2-3027200.gif"/></alternatives></inline-formula> is the number of all requests assigned to the worker. The cubic running time of insertion fundamentally limits the efficiency of urban-scale dynamic ridesharing based applications. In this paper, we propose a novel partition framework and a dynamic programming based insertion with a time complexity of <inline-formula><tex-math notation="LaTeX">$O(n^2)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="tong-ieq3-3027200.gif"/></alternatives></inline-formula>. We further improve the time efficiency of the insertion operator to <inline-formula><tex-math notation="LaTeX">$O(n)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="tong-ieq4-3027200.gif"/></alternatives></inline-formula> harnessing efficient index structures, such as fenwick tree. Evaluations on two real-world large-scale datasets show that our methods can accelerate insertion by 1.5 to 998.1 times.
Oncodiagnosis Panel : 2002 Adult Central Nervous System Neoplasms 1

Introduction An estimated 17,000 new cases of primary central nervous system (CNS) tumors will be diagnosed in the United States in 2002 (1). The number of related deaths is projected to be 13,100. These numbers represent a small proportion of the total number of new cancer cases and cancer deaths; however, because of the diversity and complexity of CNS tumors, a multidisciplinary approach is required for their diagnosis and treatment. Treatment options for the different tumors vary widely from observation to aggressive combinations of surgery, radiation therapy, and chemotherapy. Because of the complexity of as well as the controversy surrounding management issues, adult CNS neoplasms were the featured topic of the Oncodiagnosis Panel at the 2002 scientific assembly of the Radiological Society of North America. Experts on neuro-oncology (T.F.C.), neuro–radiation oncology (G.S.B.), diagnostic neuroradiology (J.T.W.), and neurosurgery (M.W.M.) were presented adult CNS neoplasm cases for discussion. The ensuing article outlines each case as presented in the Oncodiagnosis Panel followed by questions posed to the experts and their commentaries given in response. The CNS neoplasms discussed include oligodendroglioma, multiple brain metastases from non-small cell lung cancer, glioblastoma multiforme, and meningioma of the optic nerve.
Hyperacute Lung Rejection in the Pig-to-Human Model 4: Evidence for Complement and Antibody Independent Mechanisms

Background. We assessed whether the combination of complement regulation and depletion of xenoreactive antibodies improves the outcome of pulmonary xenografts compared with either strategy alone. Methods. Lungs from pigs heterozygous (hDAF±) or homozygous (hDAF+/+) for the human decay accelerating factor transgene (hDAF) or their nontransgenic litter mates (hDAF−/−) were perfused with heparinized whole human blood. In additional groups, xenoreactive natural antibodies (XNA) were depleted by pig lung perfusion (hDAF−/−/AbAbs, hDAF±/AbAbs) before the experiment. This combined approach was augmented by adding soluble complement receptor 1 (sCR1) to the perfusate in one further group (hDAF±/AbAbs/sCR1). Results. HDAF−/− lungs perfused with unmodified human blood were rejected after 32.5 min (interquartile range, IQR 5 to 210). HDAF± lungs survived for 90 min (IQR 10 to 161, P=0.54). Both groups showed a rapid rise in pulmonary vascular resistance (PVR), which is a characteristic feature of hyperacute rejection (HAR). This phenomenon was blunted in the hDAF+/+ group, although survival (48 min, IQR 14 to 111) was not further prolonged. Antibody depletion (AbAbs) led to a significant increase in survival time (hDAF−/−/AbAbs: 315 min, IQR 230 to 427; hDAF±/AbAbs: 375 min, IQR 154 to 575), reduced PVR and less complement production. Addition of sCR1 reduced complement elaboration but did not further improve survival (200 min, IQR 128 to 580) and surprisingly tended to increase PVR. Conclusions. Depletion of xenoreactive antibodies is more effective than membrane-bound complement regulation to blunt hyperacute rejection of pulmonary xenografts, but even the combined approach including soluble-phase complement inhibition is not sufficient to reliably prevent organ failure within hours. It therefore seems likely that other factors independent of antibody and complement contribute to HAR in this model.
Performance-Reliability Modeling for Long-Running Programs

This paper studies the execution of a long-running program in an unreliable system that can eventually fail, and then it is being repaired, but all performed work is lost as the system fails and the computation has to start anew. Practitioners are now interested in a redundancy mechanism which should introduce small overhead and is aimed at reducing the amount of work that is lost upon failure of the system. In connection with such interest, bounded accumulated unproductive time is considered and an expression is obtained for the moment-generating function of this random variable in the case of a successful program completion. Rigorous mathematical models for detailed studies of the accumulated unproductive time under different operating conditions are presented. Conducted studies indicate that the mechanism of checkpointing can significantly reduce such metric as average loss factor (based on unproductive time), but in some cases it introduces an unacceptable overhead depending on the strategy used for its implementation.
Contextual Community Search Over Large Social Networks

Community search on attributed networks has recently attracted great deal of research interest. However, most of existing works require query users to specify some community structure parameters. This may not be always practical as sometimes a user does not have the knowledge and experience to decide the suitable parameters. In this paper, we propose a novel parameter-free contextual community model for attributed community search. The proposed model only requires a query context, i.e., a set of keywords describing the desired matching community context, while the community returned is both structure and attribute cohesive w.r. t. the provided query context. We theoretically show that both our exact and approximate contextual community search algorithms can be executed in worst case polynomial time. The exact algorithm is based on an elegant parametric maximum flow technique and the approximation algorithm that significantly improves the search efficiency is analyzed to have an approximation factor of 1/3. In the experiment, we use six real networks with ground-truth communities to evaluate the effectiveness of our contextual community model. Experimental results demonstrate that the proposed model can find near ground-truth communities. We also test both our exact and approximate algorithms using eight large real networks to demonstrate the high efficiency of the proposed algorithms.
Arrhythmia classification on ECG using Deep Learning

In this paper, an intellectual based electrocardiogram (ECG) signal classification approach utilizing Deep Learning(DL) is being developed. ECG plays important role in diagnosing various Cardiac ailments. The ECG signal with irregular rhythm is known as Arrhythmia such as Atrial Fibrillation, Ventricular Tachycardia, Ventricular Fibrillation, and so on. The main aspire of this task is to screen and distinguish the patient with various cardio vascular arrhythmia. This examination encourages us to recognize diverse kinds of arrhythmia utilizing Deep Learning algorithm. Here we use Convolutional Neural Network(CNN) a DL algorithm which is efficient in classifying signals. Utilizing CNN, features are learned Automatically from the time domain ECG signals which are acquired from MIT-BIH Database from Physiobank.com. The feature adapted specifically replaces manually extracted features and this analysis will help the Cardiologists in screening the patient with Cardiac illness effectively. The CNN is trained, tested using ECG Dataset obtained from MIT-BIH Database and from the signal 7 of arrhythmia were classified. The proposed system is compared for Various Activation function by varying the number of epochs. From the result obtained we came to know that ELU activation function gives better result with an accuracy of 93.6% and with a loss of 0.2.
Test Structures for Characterising the Silver Chlorination Process During Integrated Ag/AgCl Reference Electrode Fabrication

Robust and repeatable processes are required to fabricate reference electrodes for micro-scale integrated electrochemical sensors. One method for this is to produce a “silver/silver chloride” (Ag/Agel) electrode through chemical chlorination of a thin film silver layer. This paper presents test structures, which can electrically characterise the process to aid process development and in-line control of the chlorination process.
Differential effects of climate and species interactions on range limits at a hybrid zone: potential direct and indirect impacts of climate change

Abstract The relative contributions of climate versus interspecific interactions in shaping species distributions have important implications for closely related species at contact zones. When hybridization occurs within a contact zone, these factors regulate hybrid zone location and movement. While a hybrid zone's position may depend on both climate and interactions between the hybridizing species, little is known about how these factors interact to affect hybrid zone dynamics. Here, we utilize SDM (species distribution modeling) both to characterize the factors affecting the current location of a moving North American avian hybrid zone and to predict potential direct and indirect effects of climate change on future distributions. We focus on two passerine species that hybridize where their ranges meet, the Black‐capped (Poecile atricapillus) and Carolina (P. carolinensis) chickadee. Our contemporary climate models predict the occurrence of climatically suitable habitat extending beyond the hybrid zone for P. atricapillus only, suggesting that interspecific interactions primarily regulate this range boundary in P. atricapillus, while climatic factors regulate P. carolinensis. Year 2050 climate models predict a drastic northward shift in suitable habitat for P. carolinensis. Because of the greater importance of interspecific interactions for regulating the southern range limit of P. atricapillus, these climate‐mediated shifts in the distribution of P. carolinensis may indirectly lead to a range retraction in P. atricapillus. Together, our results highlight the ways climate change can both directly and indirectly affect species distributions and hybrid zone location. In addition, our study lends support to the longstanding hypothesis that abiotic factors regulate species' poleward range limits, while biotic factors shape equatorial range limits.
Racial bias in the assessment of cognitive functioning of older adults

This study was undertaken to determine if the difference in assessed cognition between Black/African-American and White older adults was due differential item functioning (DIF) and/or differences in the effect of background variables. Participants were 15,257 adults aged 50 and older surveyed in the Study of Asset and Health Dynamics of the Oldest Old (AHEAD) and Health and Retirement Study (HRS). The cognitive measure was a modified telephone interview for cognitive status. The analytic strategy was a multiple group structural equation model grounded in item response theory. Results suggest that most (89%) of the group difference could be attributed to measurement or structural differences, the remainder being not significantly different from zero ( p =0.193). Most items displayed racial DIF, accounting for most of the group difference. After controlling for DIF, the group difference that remained could be attributed to heterogeneity in the effect of background variables. For example, low education was more deleterious for Black/African-Americans, and high income conferred an advantage only for Whites. These findings underscore the importance of efforts to generate culture-fair measurement devices. However, culture-fair assessments may attenuate, but not eliminate, group differences in assessed cognition due to the incommensurate action of background variables
Workload-Driven Fragment Allocation for Partially Replicated Databases Using Linear Programming

In replication schemes, replica nodes can process read-only queries on snapshots of the master node without violating transactional consistency. By analyzing the workload, we can identify query access patterns and replicate data depending to its access frequency. In this paper, we define a linear programming (LP) model to calculate the set of partial replicas with the lowest overall memory capacity while evenly balancing the query load. Furthermore, we propose a scalable decomposition heuristic to calculate solutions for larger problem sizes. While guaranteeing the same performance as state-of-the-art heuristics, our decomposition approach calculates allocations with up to 23% lower memory footprint for the TPC-H benchmark.
Efficient and Incremental Clustering Algorithms on Star-Schema Heterogeneous Graphs

Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarity. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. To improve the efficiency of the clustering, we develop two incremental approaches that aim to enable efficient PPR score computation when edge weights are updated. To boost the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insight into the effectiveness and efficiency of our proposals, compared with existing methods.
Monocyte chemoattractant protein 1 released from glycosaminoglycans mediates its profibrotic effects in systemic sclerosis via the release of interleukin-4 from T cells.

OBJECTIVE Monocyte chemoattractant protein 1 (MCP-1; CCL2) has been implicated in the pathogenesis of fibrotic diseases and is up-regulated in patients with systemic sclerosis (SSc). The aim of the present study was to examine the mechanisms by which MCP-1 mediates its profibrotic effects in the setting of SSc. METHODS The expression of receptors for MCP-1 on dermal fibroblasts was analyzed by real-time polymerase chain reaction and fluorescence-activated cell sorting. The ability of extracellular matrix proteins to bind and release MCP-1 was quantified by enzyme-linked immunosorbent assay. Th0 cells were isolated using a magnetic-activated cell sorting system and were stimulated twice in the presence of MCP-1. The synthesis of collagen was measured using the Sircol collagen assay kit. RESULTS The glycosaminoglycan chondroitin sulfate, but not fibronectin or collagens, bound and released MCP-1 in a time-dependent manner. MCP-1 that was released from chondroitin sulfate induced the differentiation of interleukin-4 (IL-4)-producing T cells in a dose-dependent manner. In turn, dermal fibroblasts from patients with SSc expressed IL-4 receptor, and stimulation with IL-4 significantly increased the production of collagen in dermal fibroblasts. In contrast, CCR2a and CCR2b, as well as D6 and US28 (other potential receptors of MCP-1), were not detectable in SSc and normal fibroblasts, and their expression was not induced by platelet-derived growth factor, IL-1beta, or IL-4. In addition, MCP-1 had no direct effects on collagen production by fibroblasts. CONCLUSION MCP-1 has no direct effects on dermal fibroblasts but contributes to fibrosis in patients with SSc by inducing the differentiation of IL-4-producing T cells. Because MCP-1 has both proinflammatory and profibrotic effects, pharmacologic targeting of MCP-1 could be a promising therapeutic approach in SSc.
Query-by-Example Speech Search Using Recurrent Neural Acoustic Word Embeddings With Temporal Context

Acoustic word embeddings (AWEs) have been popular in low-resource query-by-example speech search. They are using vector distances to find the spoken query in search content, which has much lower computation than the conventional dynamic time warping (DTW)-based approaches. The AWE networks are usually trained using variable-length isolated spoken words, while they are applied to fixed-length speech segments obtained by shifting an analysis window on speech content. There is an obvious mismatch between the learning of AWEs and its application on search content. To mitigate such mismatch, we propose to include temporal context information on spoken word pairs to learn recurrent neural AWEs. More specifically, the spoken word pairs are represented by multi-lingual bottleneck features (BNFs) and padded with the neighboring frames of the target spoken words to form fixed-length speech segment pairs. A deep bidirectional long short-term memory (BLSTM) network is then trained with a triplet loss using the speech segment pairs. Recurrent neural AWEs are obtained by concatenating the BLSTM backward and forward outputs. During QbE speech search stage, both spoken query and search content are converted into recurrent neural AWEs. Cosine distances are then measured between them to find the spoken query. The experiments show that using temporal context is essential to alleviate the mismatch. The proposed recurrent neural AWEs trained with temporal context outperform the previous state-of-art features with 12.5% relative mean average precision (MAP) improvement on QbE speech search.
How Noise Trading Affects Markets: An Experimental Analysis

We use a laboratory market to investigate the behavior of traders who lack informational advantages and have no exogenous reason to trade. We find that these uninformed traders behave largely as irrational contrarian "noise traders," trading against recent price movements to their own detriment. The uninformed traders provide some benefits to the market: increasing market volume and depth, while reducing bid-ask spreads and the temporary price impact of trades. However, their noise trading also diminishes the ability of market prices to adjust to new information. A securities transaction tax reduces uninformed trader activity, but it reduces informed trader activity by approximately the same amount; as a result, the tax does not alter the impact of noise trading on the informational efficiency of the market. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org. , Oxford University Press.