Miracle-dz/UniFAR-FTUs huggingface dataset:
Associations between maternal overweight/obesity during pregnancy and body composition in young adult offspring

Background Maternal obesity is associated with an increased risk of large-for-gestational-age births and childhood obesity. However, evidence on its potential associations with long-term offspring body composition remains limited. This prospective cohort study examined associations between maternal body mass index (BMI) during pregnancy and body composition in the young adult offspring. Methods Participants were the offspring from a birth cohort in Chiang Mai (Thailand). Maternal BMI was assessed at the first antenatal clinic visit (≤24 weeks of gestation) in 1989–1990. In 2010–2011, we followed up the offspring at approximately 20 years of age, assessing their body composition using whole-body dual-energy X-ray absorptiometry (DXA) scans. Associations between maternal BMI and offspring body composition were explored using unadjusted and adjusted analyses. Results We assessed 391 young adults (55% were females). Higher maternal BMI was associated with increased offspring fat mass and lean mass. In adjusted analyses, offspring of mothers with overweight/obesity exhibited total body fat percentages 1.5 (95% CI 0.1, 2.9; p = 0.032) and 2.3 (95% CI 0.2, 4.5; p = 0.036) percentage points higher than offspring of normal-weight and underweight mothers, respectively. Fat mass index was similarly higher: 0.9 kg/m2 (95% CI 0.3, 1.5 kg/m2; p = 0.002) and 1.4 kg/m2 (95% CI 0.5, 2.3 kg/m2; p = 0.002), respectively. However, no differences in visceral adiposity were detected. Conclusion Higher maternal BMI during pregnancy was associated with increased adiposity in young adult offspring. Our findings suggest that the cross-generational transmission of maternal obesity-related traits is associated with increased offspring adiposity in the long term.
Toward a definition of visual complexity as an implicit measure of cognitive load

The visual complexity of Web pages is much talked about; “complex Web pages are difficult to use,” but often regarded as a subjective decision by the user. This subjective decision is of limited use if we wish to understand the importance of visual complexity, what it means, and how it can be used. We theorize that by understanding a user's visual perception of Web page complexity, we can understand the cognitive effort required for interaction with that page. This is important because by using an easily identifiable measure, such as visual complexity, as an implicit marker of cognitive load, we can design Web pages which are easier to interact with. We have devised an initial empirical experiment, using card sorting and triadic elicitation, to test our theories and assumptions, and have built an initial baseline sequence of 20 Web pages along with a library of qualitative and anecdotal feedback. Using this library, we define visual complexity, ergo perceived interaction complexity, and by taking these pages as “prototypes” and ranking them into a sequence of complexity, we are able to group them into: simple, neutral, and complex. This means we can now work toward a definition of visual complexity as an implicit measure of cognitive load.
A NEW VERSION OF THE DISCRETE-ORDINATES METHOD

A discussion of a modern version of the discrete-ordinates method is given, and in order to demonstrate well some aspects of the method, a very basic transport model is used to solve the elementary critical problem for a bare slab and for a bare cylinder. In addition to numerical results for these basic applications, various extensions of the method made to more challenging problems are noted.
A new algorithm for segmenting warped text-lines in document images

The digitalization of bound documents either using flatbed scanners or digital cameras often yield images with non-straight text-lines due to a geometrical warp. This paper presents a new algorithm for text-line segmentation for documents captured by digital cameras or scanners. The proposed method reached 97.84% correct segmentation, while the best results offered by its predecessors in the literature yields 95.21%.
Large-deviation analysis and applications Of learning tree-structured graphical models

The design and analysis of complexity-reduced representations for multivariate data is important in many scientific and engineering domains. This thesis explores such representations from two different perspectives: deriving and analyzing performance measures for learning tree-structured graphical models and salient feature subset selection for discrimination. Graphical models have proven to be a flexible class of probabilistic models for approximating high-dimensional data. Learning the structure of such models from data is an important generic task. It is known that if the data are drawn from tree-structured distributions, then the algorithm of Chow and Liu (1968) provides an efficient algorithm for finding the tree that maximizes the likelihood of the data. We leverage this algorithm and the theory of large deviations to derive the error exponent of structure learning for discrete and Gaussian graphical models. We determine the extremal tree structures for learning, that is, the structures that lead to the highest and lowest exponents. We prove that the star minimizes the exponent and the chain maximizes the exponent, which means that among all unlabeled trees, the star and the chain are the worst and best for learning respectively. The analysis is also extended to learning foreststructured graphical models by augmenting the Chow-Liu algorithm with a thresholding procedure. We prove scaling laws on the number of samples and the number variables for structure learning to remain consistent in high-dimensions. The next part of the thesis is concerned with discrimination. We design computationally efficient tree-based algorithms to learn pairs of distributions that are specifically adapted to the task of discrimination and show that they perform well on various datasets vis-`a-vis existing tree-based algorithms. We define the notion of a salient set for discrimination using information-theoretic quantities and derive scaling laws on the number of samples so that the salient set can be recovered asymptotically.
Neonatal C‐reactive protein value in prediction of Outcome of Preterm Premature Rupture of Membranes: Comparison of Singleton and Twin Pregnancies

Aim: The clinical importance of preterm premature rupture of the membranes (PPROM) is its relationship to maternal and neonatal mortality and morbidity, especially in twin pregnancies. The aim of this study was to determine and compare the role of inflammatory factors as predictors of the PPROM outcome between singleton and twin pregnancies.
Capacity of SIMO and MISO Phase-Noise Channels With Common/Separate Oscillators

In multiple antenna systems, phase noise due to instabilities of the radio-frequency (RF) oscillators, acts differently depending on whether the RF circuitries connected to each antenna are driven by separate (independent) local oscillators (SLO) or by a common local oscillator (CLO). In this paper, we investigate the high-SNR capacity of single-input multiple-output (SIMO) and multiple-output single-input (MISO) phase-noise channels for both the CLO and the SLO configurations. Our results show that the first-order term in the high-SNR capacity expansion is the same for all scenarios (SIMO/MISO and SLO/CLO), and equal to 0.5 ln(p), where p stands for the SNR. On the contrary, the second-order term, which we refer to as phase-noise number, turns out to be scenario-dependent. For the SIMO case, the SLO configuration provides a diversity gain, resulting in a larger phase-noise number than for the CLO configuration. For the case of Wiener phase noise, a diversity gain of at least 0.5 ln(M) can be achieved, where M is the number of receive antennas. For the MISO, the CLO configuration yields a higher phase-noise number than the SLO configuration. This is because with the CLO configuration one can obtain a coherent-combining gain through maximum ratio transmission (a.k.a. conjugate beamforming). This gain is unattainable with the SLO configuration.
Symbolic Magnifying Lens Abstraction in Markov Decision Processes

In this paper, we combine abstraction-refinement and symbolic techniques to fight the state-space explosion problem when model checking Markov decision processes (MDPs). The abstract-refinement technique, called "magnifying-lens abstraction" (MLA), partitions the state-space into regions and computes upper and lower bounds for reachability and safety properties on the regions, rather than the states. To compute such bounds, MLA iterates over the regions, analyzing the concrete states of each region in turn - as if one was sliding a magnifying lens across the system to view the states. The algorithm adaptively refines the regions, using smaller regions where more detail is required, until the difference between the bounds is below a specified accuracy. The symbolic technique is based on multi-terminal binary decision diagrams (MTBDDs) which have been used extensively to provide compact encodings of probabilistic models. We introduce a symbolic version of the MLA algorithm, called "symbolic MLA", which combines the power of both practical techniques when verifying MDPs. An implementation of symbolic MLA in the probabilistic model checker PRISM and experimental results to illustrate the advantages of our approach are presented.
A comparison of algorithms for the pairwise alignment of biological networks

MOTIVATION As biological inquiry produces ever more network data, such as protein-protein interaction networks, gene regulatory networks and metabolic networks, many algorithms have been proposed for the purpose of pairwise network alignment-finding a mapping from the nodes of one network to the nodes of another in such a way that the mapped nodes can be considered to correspond with respect to both their place in the network topology and their biological attributes. This technique is helpful in identifying previously undiscovered homologies between proteins of different species and revealing functionally similar subnetworks. In the past few years, a wealth of different aligners has been published, but few of them have been compared with one another, and no comprehensive review of these algorithms has yet appeared. RESULTS We present the problem of biological network alignment, provide a guide to existing alignment algorithms and comprehensively benchmark existing algorithms on both synthetic and real-world biological data, finding dramatic differences between existing algorithms in the quality of the alignments they produce. Additionally, we find that many of these tools are inconvenient to use in practice, and there remains a need for easy-to-use cross-platform tools for performing network alignment.
Effect of urbanization on stream hydraulics

Urbanization results in major changes to stream morphology and hydrology with the latter often cited as a primary stressor of urban stream ecosystems. These modifications unequivocally alter stream hydraulics, but little is known about such impacts. Hydraulic changes due to urbanization were demonstrated using two‐dimensional hydrodynamic model simulations, comparing urban and non‐urban stream reaches. We investigated three ecologically relevant hydraulic characteristics: bed mobilization, retentive habitat, and floodplain inundation, using hydraulic metrics bed shear stress, shallow slow‐water habitat (SSWH) area, and floodplain inundation area. We hypothesized that urbanization would substantially increase bed mobilization, decrease retentive habitat, and due to increased channel size would decrease floodplain inundation. Relative percent area of bed disturbance was 4 times higher, compared with that of the non‐urban stream at bankfull discharge. SSWH availability rapidly diminished in the urban stream as discharge increased, with SSWH area and patch size 2 times smaller than the non‐urban stream for a frequently occurring flow 0.7 times bankfull discharge. Floodplain inundation decreased in frequency and duration. These results demonstrate changes in hydraulics due to urbanization that may impact on physical habitat in streams. New “water sensitive” approaches to stormwater management could be enhanced by specification of hydraulic regimes capable of supporting healthy stream habitats. We propose that a complete management approach should include the goals of restoration and protection of natural hydraulic processes, particularly those that support ecological and geomorphic functioning of streams.
Correlation between radio luminosity and X-ray timing frequencies in neutron star and black hole X-ray binaries

We report on correlations between radio luminosity and X-ray timing features in X-ray binary systems containing low magnetic field neutron stars and black holes. The sample of neutron star systems consists of 4U 1728-34, 4U 1820-34, Ser X-1, MXB 1730-335, GX 13+1, the millisecond X-ray pulsars SAX J1808.4-3658 and IGR J00291+5934, and these are compared with the black hole system GX 339-4. The analysis has been performed using data from pointed observations of the Rossi X-ray Timing Explorer coordinated with radio observations. In the neutron star systems the radio luminosity LR is correlated with the characteristic frequency of the Lh Lorentzian component detected contemporaneously in the power spectrum, and anticorrelated with its strength. Similarly, in the black hole system GX 339-4 LR is correlated with the frequency of the Ll component in the power spectrum and anticorrelated with its strength. The index of a power-law fit to the correlation is similar in both cases, LR~nu~1.4 and LR~ (rms)-2.3. At lower timing frequencies, the radio luminosity is further found to be correlated with the characteristic (break) frequency of the Lb component of the power spectra in the neutron stars and, marginally, with the equivalent break frequency in GX 339-4. We briefly discuss the coupling between the innermost regions of the accretion disc and the production of the jet and, from the behaviour of the millisecond accreting X-ray pulsars, the possible role of the neutron star magnetic field.
Cardiac-coronary interactions in humans: Mechanistic insights from wave intensity analysis

Intracoronary pressure and flow-velocity signals reflect the interaction between cardiac mechanics and coronary hemodynamics. In this thesis we explored the origin of the time-varying characteristics of these signals in relation to cardiac mechanics in particular in disease, as result of treatment and interventions. Coronary hemodynamic signals were obtained in patients with coronary or aortic valve diseases with a dual-sensor guidewire equipped with both a pressure and a flow-velocity sensor. Based on these signals, wave intensity analysis was performed allowing separating, in terms of waves travelling on the vessel wall, the components responsible for the measured intracoronary signals: forward waves coming from the aorta and backward waves coming from the microcirculation. The magnitude of each wave is the consequence of the force by which it is generated and the parameters that cause its attenuation. All waves are generated by cardiac contraction and relaxation so they are strongly related to parameters of cardiac mechanics such as LVdP/dt. The forward waves are strongly related to aortic pressure waveform while the energy of the coronary backward waves is modulated by the intramyocardial volume. Intracoronary pressure and flow-velocity waveforms are the results of a complicated interaction of different factors. Characterizing these signals in terms of wave intensity analysis allows identifying the separate parameters responsible for the waveforms during the cardiac cycle. This technique therefore not only provides an important tool to understand the physiology behind the intracoronary signals but, in case of disease, could also open new possibilities for improvements in clinical diagnosis and treatment.
Serial Analysis of the Plasma Concentration of SARS Coronavirus RNA in Pediatric Patients with Severe Acute Respiratory Syndrome

The recent identification of a novel coronavirus, SARS coronavirus (SARS-CoV), as an etiologic agent for severe acute respiratory syndrome (SARS) has prompted many groups to develop rapid and accurate molecular assays for the detection of this virus (1)(2)(3)(4). To date, most of the assays have focused predominantly on samples taken from nasopharyngeal aspirates, urine, and stools (5)(6). Although detection of SARS-CoV RNA in the plasma of SARS patients has been reported (1), the relatively low sensitivity of the ultracentrifugation-based approach for detecting SARS-CoV RNA in plasma has made this assay impractical. Recently we showed that a one-step real-time quantitative reverse transcription-PCR (RT-PCR) assay for the polymerase region of the SARS-CoV genome could detect viral RNA in 75–78% of nonultracentrifuged serum samples from adult SARS patients during the early stage of disease and that the serum SARS-CoV concentrations on admission were of prognostic significance (7). This finding demonstrates that plasma/serum SARS-CoV quantification may potentially be useful for the early diagnosis of SARS. Although most existing reports have focused on adult SARS patients, recent reports revealed that the clinical course was less severe in pediatric SARS patients than in adult SARS patients (8)(9). On the whole, the outcomes of pediatric SARS patients have been favorable. In this study, we investigated whether SARS-CoV RNA can be detected in the plasma samples of pediatric patients during different stages of SARS and studied the serial variation in viral loads. We quantified SARS-CoV RNA by real-time RT-PCR in the plasma of eight pediatric patients admitted to the New Territories East Cluster of Hospital Authority Hospitals in Hong Kong and who satisfied the WHO surveillance case definition for SARS (9). These patients were recruited between March 13, 2003, and May 17, 2003. Informed consent was obtained from …
On the importance of the few most massive stars: The ionizing cluster of NGC 588

We present the results of a double analysis of the ionizing cluster in NGC 588, a giant H region (GHR) in the outskirts of the nearby galaxy M 33. For this purpose, we obtained ground based long-slit spectroscopy and combined it with archival ground based and space borne imaging and spectroscopy, in the wavelength range 1100−9800 A. A first modeling of the cluster was performed using integrated properties, such as the spectral energy distribution (SED), broad band colors, nebular emission Hβ equivalent width, the main ultraviolet resonance lines, and the presence of Wolf-Rayet star features. By applying standard assumptions about the initial mass function (IMF), we were unable to fit satisfactorily these observational data. This contradictory result led us to carry out a second modeling, based on a resolved photometric analysis of individual stars in Hubble Space Telescope (HST) images, by means of finding the best fit isochrone in color−magnitude diagrams (CMD), and assigning a theoretical SED to each individual star. The overall SED of the cluster, obtained by integrating the individual stellar SEDs, is found to fit better the observed SED than the best solution found through the integrated first analysis, but at a significantly later stage of evolution of the cluster of 4.2 Myr, as obtained from the best fit to the CMD. A comparative analysis of both methods traces the different results to the effects of statistical fluctuations in the upper end of the IMF, which are significant in NGC 588, with a computed cluster mass of 5600 M� , as predicted by Cervino et al. (2002, A&A, 381, 51). We discuss the results in terms of the strong influence of the few most massive stars, six in the case of NGC 588, that dominate the overall SED and, in particular, the ionizing far ultraviolet range beyond the Lyman limit.
An empirical study of regression test selection techniques

Regression testing is the process of validating modified software to detect whether new errors have been introduced into previously tested code and to provide confidence that modifications are correct. Since regression testing is an expensive process, researchers have proposed regression test selection techniques as a way to reduce some of this expense. These techniques attempt to reduce costs by selecting and running only a subset of the test cases in a program's existing test suite. Although there have been some analytical and empirical evaluations of individual techniques, to our knowledge only one comparative study, focusing on one aspect of two of these techniques, has been reported in the literature. We conducted an experiment to examine the relative costs and benefits of several regression test selection techniques. The experiment examined five techniques for reusing test cases, focusing on their relative ablilities to reduce regression testing effort and uncover faults in modified programs. Our results highlight several differences between the techiques, and expose essential trade-offs that should be considered when choosing a technique for practical application.
Reorganization of the brain and heart rhythm during autogenic meditation

The underlying changes in heart coherence that are associated with reported EEG changes in response to meditation have been explored. We measured EEG and heart rate variability (HRV) before and during autogenic meditation. Fourteen subjects participated in the study. Heart coherence scores were significantly increased during meditation compared to the baseline. We found near significant decrease in high beta absolute power, increase in alpha relative power and significant increases in lower (alpha) and higher (above beta) band coherence during 3~min epochs of heart coherent meditation compared to 3~min epochs of heart non-coherence at baseline. The coherence and relative power increase in alpha band and absolute power decrease in high beta band could reflect relaxation state during the heart coherent meditation. The coherence increase in the higher (above beta) band could reflect cortico-cortical local integration and thereby affect cognitive reorganization, simultaneously with relaxation. Further research is still needed for a confirmation of heart coherence as a simple window for the meditative state.
Mutual Kernel Matrix Completion

With the huge influx of various data nowadays, extracting knowledge from them has become an interesting but tedious task among data scientists, particularly when the data come in heterogeneous form and have missing information. Many data completion techniques had been introduced, especially in the advent of kernel methods. However, among the many data completion techniques available in the literature, studies about mutually completing several incomplete kernel matrices have not been given much attention yet. In this paper, we present a new method, called Mutual Kernel Matrix Completion (MKMC) algorithm, that tackles this problem of mutually inferring the missing entries of multiple kernel matrices by combining the notions of data fusion and kernel matrix completion, applied on biological data sets to be used for classification task. We first introduced an objective function that will be minimized by exploiting the EM algorithm, which in turn results to an estimate of the missing entries of the kernel matrices involved. The completed kernel matrices are then combined to produce a model matrix that can be used to further improve the obtained estimates. An interesting result of our study is that the E-step and the M-step are given in closed form, which makes our algorithm efficient in terms of time and memory. After completion, the (completed) kernel matrices are then used to train an SVM classifier to test how well the relationships among the entries are preserved. Our empirical results show that the proposed algorithm bested the traditional completion techniques in preserving the relationships among the data points, and in accurately recovering the missing kernel matrix entries. By far, MKMC offers a promising solution to the problem of mutual estimation of a number of relevant incomplete kernel matrices.