Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic influences on childhood hyperactivity: contrast effects imply parental rating bias, not sibling interaction

Background. Previous twin studies of hyperactivity have supported a ‘contrast effect’, whereby the more hyperactive the rating of one twin, the less the rating of the other. It has not been clear whether contrast effects occur in the twins' behaviour or in the ratings made of their behaviour but the implications for hyperactivity are different under the two models. Method. We use hyperactivity ratings from mothers and teachers for 1644 twin pairs in the Virginia Twin Study of Adolescent Behavioral Development (VTSABD) to explore the origin of contrast effects, making use of independent teacher reports in a proportion of twins. Models were fitted separately for the two informants and jointly to ratings combined through a latent variable, using structural equation modelling. Results. Models for maternal data confirm the contrast effect previously reported. Teacher ratings show a different form of bias, with both twin confusion and correlated errors representing alternative but not mutually exclusive explanations of the data. Latent variable modelling of the joint responses allowed comparison of a model in which the contrast effect was placed on maternal ratings, representing bias, versus one in which the contrast occurred in the underlying ‘true’ phenotype. The fit of the former model was significantly better. Conclusions. Support is provided for the notion of contrast effects as a form of rater bias in maternal hyperactivity ratings. Different bias in teacher reports highlight that no one report can be considered a gold standard. The extent to which such biases may distort information for other data sources such as sib-pair studies of concordance/discordance is discussed.
The privilege of primacy: Experimental data and cognitive explanations

Many things are arranged sequentially: the order in which children are born into a family; the order in which words occur in a sentence; and the order in which utterances occur in a discourse. Sequential order requires that some things come first. Items, events, or stimuli that occur in initial posi­ tion often gain a unique psychological status. Indeed, some of the earliest experiments in contemporary American psychology document the psychological privilege of primacy. For instance, the qualities of a person that we learn about first, figure most prominently in the impression we form of that person (Asch 1946). Consider the traits listed in ( 1) versus (2) below.
A Novel Strategy for Detection and Enumeration of Circulating Rare Cell Populations in Metastatic Cancer Patients Using Automated Microfluidic Filtration and Multiplex Immunoassay

Size selection via filtration offers an antigen-independent approach for the enrichment of rare cell populations in blood of cancer patients. We evaluated the performance of a novel approach for multiplex rare cell detection in blood samples from metastatic breast (n = 19) and lung cancer patients (n = 21), and healthy controls (n = 30) using an automated microfluidic filtration and multiplex immunoassay strategy. Captured cells were enumerated after sequential staining for specific markers to identify circulating tumor cells (CTCs), circulating mesenchymal cells (CMCs), putative circulating stem cells (CSCs), and circulating endothelial cells (CECs). Preclinical validation experiments using cancer cells spiked into healthy blood demonstrated high recovery rate (mean = 85%) and reproducibility of the assay. In clinical studies, CTCs and CMCs were detected in 35% and 58% of cancer patients, respectively, and were largely absent from healthy controls (3%, p = 0.001). Mean levels of CTCs were significantly higher in breast than in lung cancer patients (p = 0.03). Fifty-three percent (53%) of cancer patients harbored putative CSCs, while none were detectable in healthy controls (p<0.0001). In contrast, CECs were observed in both cancer and control groups. Direct comparison of CellSearch® vs. our microfluidic filter method revealed moderate correlation (R2 = 0.46, kappa = 0.47). Serial blood analysis in breast cancer patients demonstrated the feasibility of monitoring circulating rare cell populations over time. Simultaneous assessment of CTCs, CMCs, CSCs and CECs may provide new tools to study mechanisms of disease progression and treatment response/resistance.
Reconfigurable Architecture for Network Flow Analysis

This paper describes a reconfigurable architecture based on field-programmable gate-array (FPGA) technology for monitoring and analyzing network traffic at increasingly high network data rates. Our approach maps the performance-critical tasks of packet classification and flow monitoring into reconfigurable hardware, such that multiple flows can be processed in parallel. We explore the scalability of our system, showing that it can support flows at multi-gigabit rate; this is faster than most software-based solutions where acceptable data rates are typically no more than 100 million bits per second.
System-Level Channel Modeling Based on Active Measurements from a Public 3G/UMTS Network

In this contribution, two measurement campaigns are reported which were conducted in urban areas of Shanghai for characterizing the radio channels in an operating Universal Mobile Telecommunications System (UMTS). The signals on the common pilot channels transmitted by multiple NodeBs (NBs) are used to calculate the channel impulse responses (CIRs). A Space-Alternating Generalized Expectation-maximization (SAGE) algorithm is applied to extracting the delays, Doppler frequencies and complex attenuations of multiple specular path components from the CIRs. Based on the delay spreads calculated, the channels are categorized into three types, i.e. the single-path, two-path and multi-path. Based on the measurements at more than 1400 locations for 74 NBs, the statistics of system-level channel characteristics, such as the percentage of channels per NB belonging to different categories and the length of the distance within which the same channel category is observed are extracted.
Predominant Infiltration of Monocytes in Chronic Graft-Versus-Host Disease

Pathogenesis of chronic graft-versus-host disease (cGVHD) is largely unknown. It is important to determine the responsible cell types and the factors that play roles to recruit these cells into sites of disease. We examined whether monocytes and chemokine fractalkine/receptor CX3CR1 axis might be involved. We found that the absolute number of CX3CR1+ monocytes in the blood was significantly decreased in patients with severe cGVHD. Immunohistochemical staining revealed the extensive infiltration of CD14+ cells as well as strong expression of fractalkine in the cGVHD skin. The number of infiltrated CD14+ cells on the margin of fractalkine+ epidermis was larger in cGVHD skin compared to that of acute graft-versus-host disease, whereas no difference was observed in CD3+ T cells. These results suggest that CX3CR1+ monocytes may be recruited from the circulation to the fractalkine+ epidermis in cGVHD, and highlight these cells and this chemokine/receptor axis as additional targets for cGVHD therapy.
Engineering searchable encryption of mobile cloud networks: when QoE meets QoP

Mobile cloud computing can effectively address the resource limitations of mobile devices, and is therefore essential to enable extensive resource consuming mobile computing and communication applications. Of all the mobile cloud computing applications, data outsourcing, such as iCloud, is fundamental, which outsources a mobile user's data to external cloud servers and accordingly provides a scalable and “always on” approach for public data access. With the security and privacy issues related to outsourced data becoming a rising concern, encryption on outsourced data is often necessary. Although encryption increases the quality of protection (QoP) of data outsourcing, it significantly reduces data usability and thus harms the mobile user's quality of experience (QoE). How to strike a balance between QoP and QoE is therefore an important yet challenging task. In this article we focus on the fundamental problem of QoP and QoE provisioning in searchable encryption of data outsourcing. We develop a fine-grained data search scheme and discuss its implementation on encrypted mobile cloud data, which is an effective balance between QoE and QoP in mobile cloud data outsourcing.
Sharing places: testing psychological effects of location cueing frequency and explicit vs. inferred closeness

Recent ethnomethodological work in context and location awareness has indicated that location cues hold many socially meaningful cues for interaction. Location-aware technologies are therefore expected to bring about a shift in social life. Yet little is known about underlying psychological effects and the role of specific design decisions. The present research aims to experimentally test some of these effects. In a laboratory experiment participants' location in a virtual game world was shared. The effects of location Cueing frequency and Cueing mode (explicit or inferred closeness) on affinity, social presence, awareness, and game experience were explored. Higher cue frequencies resulted in higher perceived challenge and flow experienced in the game. The data also showed trends of heightened awareness and more behavioural engagement. A trend towards more psychological involvement was found when cues explicitly communicated that players shared a location in the game.
A longitudinal analysis of start position and the outcome of World Cup cross-country mountain bike racing

Abstract For any athlete competing at the highest level it is vital to understand the components that lead to successful performance. World cup cross-country mountain biking is a complex sport involving large numbers of athletes (100–200) competing for positional advantage over varied off-road terrain. The start has been deemed a major part of performance outcome in such races. The purpose of the present study was to establish the relationship between start and finish position in cross-country mountain bike World Cup events over a 10 year (1997–2007) period and to make comparisons with a model manipulating start position based on predicted athletic capabilities. Data collection and comparisons included results from World Cup events from 1997 to 2007 (males and females), and modelled race data based on potential performance capabilities over the same period. Analyses involved the association of annual plus pooled start and finish position (Kendall's tau) along with banded mean, standard deviation for number of changes in position, while non-constrained linear regression enabled comparison between seasons. Actual race data showed significant positive correlations between starting position and finishing position (P < 0.01) in all cases but less than the model. A mean 57.4% (s = 5.6) of males changed < 15 positions, while 62.9% (s = 9.1) of females changed < 10 positions compared with modelled data (83.6%, s = 0.8 and 91.6%, s = 1.5 for males and females respectively). Individual season comparisons show general patterns to be identical (P > 0.05) for both males and females. In conclusion, finishing position is highly dependent on start position and strategies need to be devised for competing athletes to progress in the sport.
Extracting circuit models for large RC interconnections that are accurate up to a predefined signal frequency

This paper presents a technique that transforms large and complex RC networks into much smaller physically realizable RC networks. These models reflect the transmission behavior of the initial network accurately for frequencies up to a user-defined maximal signal frequency. This technique has been incorporated in a layout-to-circuit extractor, using a scan-line approach. The method guarantees numerical stability and performs excellently in modeling RC interconnects.
Metropolitan Social Environments and Pre-HAART/HAART Era Changes in Mortality Rates (per 10,000 Adult Residents) among Injection Drug Users Living with AIDS

Background Among the largest US metropolitan areas, trends in mortality rates for injection drug users (IDUs) with AIDS vary substantially. Ecosocial, risk environment and dialectical theories suggest many metropolitan areas characteristics that might drive this variation. We assess metropolitan area characteristics associated with decline in mortality rates among IDUs living with AIDS (per 10,000 adult MSA residents) after highly active antiretroviral therapy (HAART) was developed. Methods This is an ecological cohort study of 86 large US metropolitan areas from 1993–2006. The proportional rate of decline in mortality among IDUs diagnosed with AIDS (as a proportion of adult residents) from 1993–1995 to 2004–2006 was the outcome of interest. This rate of decline was modeled as a function of MSA-level variables suggested by ecosocial, risk environment and dialectical theories. In multiple regression analyses, we used 1993–1995 mortality rates to (partially) control for pre-HAART epidemic history and study how other independent variables affected the outcomes. Results In multivariable models, pre-HAART to HAART era increases in ‘hard drug’ arrest rates and higher pre-HAART income inequality were associated with lower relative declines in mortality rates. Pre-HAART per capita health expenditure and drug abuse treatment rates, and pre- to HAART-era increases in HIV counseling and testing rates, were weakly associated with greater decline in AIDS mortality. Conclusions Mortality among IDUs living with AIDS might be decreased by reducing metropolitan income inequality, increasing public health expenditures, and perhaps increasing drug abuse treatment and HIV testing services. Given prior evidence that drug-related arrest rates are associated with higher HIV prevalence rates among IDUs and do not seem to decrease IDU population prevalence, changes in laws and policing practices to reduce such arrests while still protecting public order should be considered.
Prognostic Association of Depression Following Myocardial Infarction With Mortality and Cardiovascular Events: A Meta-analysis

Objective: To assess the association of depression following myocardial infarction (MI) and cardiovascular prognosis. Methods: The authors performed a meta-analysis of references derived from MEDLINE, EMBASE, and PSYCINFO (1975–2003) combined with crossreferencing without language restrictions. The authors selected prospective studies that determined the association of depression with the cardiovascular outcome of MI patients, defined as mortality and cardiovascular events within 2 years from index MI. Depression had to be assessed within 3 months after MI using established psychiatric instruments. A quality assessment was performed. Results: Twenty-two papers met the selection criteria. These studies described follow up (on average, 13.7 months) of 6367 MI patients (16 cohorts). Post-MI depression was significantly associated with all-cause mortality (odds ratio [OR], fixed 2.38; 95% confidence interval [CI], 1.76–3.22; p <.00001) and cardiac mortality (OR fixed, 2.59; 95% CI, 1.77–3.77; p <.00001). Depressive MI patients were also at risk for new cardiovascular events (OR random, 1.95; 95% CI, 1.33–2.85; p = .0006). Secondary analyses showed no significant effects of follow-up duration (0–6 months or longer) or assessment of depression (self-report questionnaire vs. interview). However, the year of data collection (before or after 1992) tended to influence the effect of depression on mortality (p = .08), with stronger associations found in the earlier studies (OR, 3.22; 95% CI, 2.14–4.86) compared with the later studies (OR, 2.01; 95% CI, 1.45–2.78). Conclusions: Post-MI depression is associated with a 2- to 2.5-fold increased risk of impaired cardiovascular outcome. The association of depression with cardiac mortality or all-cause mortality was more pronounced in the older studies (OR, 3.22 before 1992) than in the more recent studies (OR, 2.01 after 1992). CI = confidence interval; CA = cardiac arrest; CABG = coronary artery bypass graft; CAD = coronary artery disease; DIS = modified version of the National Institute of Mental Health Diagnostic Interview Schedule; DM = diabetes mellitus; DSM = Diagnostic and Statistical Manual of Mental Disorders; DISH = Depression Interview and Structured Hamilton; ENRICHD = Enhancing Recovery in Coronary Heart Disease Patients Randomized Trial; FU = follow up; HADS = hospital anxiety and depression scale; IHD = ischemic heart disease; KSb-S = Klinische Selbstbeurteilungsskalen aus dem Münchner psychiatrische Informations-System; LVEF = left ventricular ejection fraction; MADRS = Montgomery Asberg Depression Rating Scale; MI = myocardial infarction; MIND-IT = Myocardial INfarction and Depression–Intervention Trial; NA = not available; OR = odds ratio; PVC = premature ventricular contraction; SCID = Structured Clinical Interview for DSM; SCL-90 = 90-item Symptom Check List; SSRI = selective serotonin re-uptake inhibitor.
Exploiting Determinism to Scale Relational Inference

One key challenge in statistical relational learning (SRL) is scalable inference. Unfortunately, most real-world problems in SRL have expressive models that translate into large grounded networks, representing a bottleneck for any inference method and weakening its scalability. In this paper we introduce Preference Relaxation (PR), a two-stage strategy that uses the determinism present in the underlying model to improve the scalability of relational inference. The basic idea of PR is that if the underlying model involves mandatory (i.e. hard) constraints as well as preferences (i.e. soft constraints) then it is potentially wasteful to allocate memory for all constraints in advance when performing inference. To avoid this, PR starts by relaxing preferences and performing inference with hard constraints only. It then removes variables that violate hard constraints, thereby avoiding irrelevant computations involving preferences. In addition it uses the removed variables to enlarge the evidence database. This reduces the effective size of the grounded network. Our approach is general and can be applied to various inference methods in relational domains. Experiments on real-world applications show how PR substantially scales relational inference with a minor impact on accuracy.
Complement factor H and the hemolytic uremic syndrome

Immune recognition is coupled to powerful proinflammatory effector pathways that must be tightly regulated. The ancient alternative pathway of complement activation is one such proinflammatory pathway. Genetic susceptibility factors have been identified in both regulators and activating components of the alternative pathway that are associated with thrombotic microangiopathies, glomerulonephritides, and chronic conditions featuring debris deposition. These observations indicate that excessive alternative pathway activation promotes thrombosis in the microvasculature and tissue damage during debris accumulation. Intriguingly, distinct genetic changes in factor H (FH), a key regulator of the alternative pathway, are associated with hemolytic uremic syndrome (HUS), membranoproliferative glomerulonephritis (dense deposit disease), or age-related macular degeneration (AMD). A mouse model of HUS designed to mirror human mutations in FH has now been developed, providing new understanding of the molecular pathogenesis of complement-related endothelial disorders.
Moving Mitochondria: Establishing Distribution of an Essential Organelle

Mitochondria form a dynamic network responsible for energy production, calcium homeostasis and cell signaling. Appropriate distribution of the mitochondrial network contributes to organelle function and is essential for cell survival. Highly polarized cells, including neurons and budding yeast, are particularly sensitive to defects in mitochondrial movement and have emerged as model systems for studying mechanisms that regulate organelle distribution. Mitochondria in multicellular eukaryotes move along microtubule tracks. Actin, the primary cytoskeletal component used for transport in yeast, has more subtle functions in other organisms. Kinesin, dynein and myosin isoforms drive motor‐based movement along cytoskeletal tracks. Milton and syntabulin have recently been identified as potential organelle‐specific adaptor molecules for microtubule‐based motors. Miro, a conserved GTPase, may function with Milton to regulate transport. In yeast, Mmr1p and Ypt11p, a Rab GTPase, are implicated in myosin V‐based mitochondrial movement. These potential adaptors could regulate motor activity and therefore determine individual organelle movements. Anchoring of stationary mitochondria also contributes to organelle retention at specific sites in the cell. Together, movement and anchoring ultimately determine mitochondrial distribution throughout the cell.
An Enhanced Probabilistic LDA for Multi-Class Brain Computer Interface

Background There is a growing interest in the study of signal processing and machine learning methods, which may make the brain computer interface (BCI) a new communication channel. A variety of classification methods have been utilized to convert the brain information into control commands. However, most of the methods only produce uncalibrated values and uncertain results. Methodology/Principal Findings In this study, we presented a probabilistic method “enhanced BLDA” (EBLDA) for multi-class motor imagery BCI, which utilized Bayesian linear discriminant analysis (BLDA) with probabilistic output to improve the classification performance. EBLDA builds a new classifier that enlarges training dataset by adding test samples with high probability. EBLDA is based on the hypothesis that unlabeled samples with high probability provide valuable information to enhance learning process and generate a classifier with refined decision boundaries. To investigate the performance of EBLDA, we first used carefully designed simulated datasets to study how EBLDA works. Then, we adopted a real BCI dataset for further evaluation. The current study shows that: 1) Probabilistic information can improve the performance of BCI for subjects with high kappa coefficient; 2) With supplementary training samples from the test samples of high probability, EBLDA is significantly better than BLDA in classification, especially for small training datasets, in which EBLDA can obtain a refined decision boundary by a shift of BLDA decision boundary with the support of the information from test samples. Conclusions/Significance The proposed EBLDA could potentially reduce training effort. Therefore, it is valuable for us to realize an effective online BCI system, especially for multi-class BCI systems.