Miracle-dz/UniFAR-FTUs huggingface dataset:
On-Beads Digestion in Conjunction with Data-Dependent Mass Spectrometry: A Shortcut to Quantitative and Dynamic Interaction Proteomics

With the advent of the “-omics” era, biological research has shifted from functionally analyzing single proteins to understanding how entire protein networks connect and adapt to environmental cues. Frequently, pathological processes are initiated by a malfunctioning protein network rather than a single protein. It is therefore crucial to investigate the regulation of proteins in the context of a pathway first and signaling network second. In this study, we demonstrate that a quantitative interaction proteomic approach, combining immunoprecipitation, in-solution digestion and label-free quantification mass spectrometry, provides data of high accuracy and depth. This protocol is applicable, both to tagged, exogenous and untagged, endogenous proteins. Furthermore, it is fast, reliable and, due to a label-free quantitation approach, allows the comparison of multiple conditions. We further show that we are able to generate data in a medium throughput fashion and that we can quantify dynamic interaction changes in signaling pathways in response to mitogenic stimuli, making our approach a suitable method to generate data for system biology approaches.
Treating Patients as Persons: A Capabilities Approach to Support Delivery of Person-Centered Care

Health services internationally struggle to ensure health care is “person-centered” (or similar). In part, this is because there are many interpretations of “person-centered care” (and near synonyms), some of which seem unrealistic for some patients or situations and obscure the intrinsic value of patients’ experiences of health care delivery. The general concern behind calls for person-centered care is an ethical one: Patients should be “treated as persons. ” We made novel use of insights from the capabilities approach to characterize person-centered care as care that recognizes and cultivates the capabilities associated with the concept of persons. This characterization unifies key features from previous characterisations and can render person-centered care applicable to diverse patients and situations. By tying person-centered care to intrinsically valuable capability outcomes, it incorporates a requirement for responsiveness to individuals and explains why person-centered care is required independently of any contribution it may make to health gain.
Using Extended Random Set to Find Specific Patterns

With the overwhelming increase in the amount of data on the web and data bases, many text mining techniques have been proposed for mining useful patterns in text documents. Extracting closed sequential patterns using the Pattern Taxonomy Model (PTM) is one of the pruning methods to remove noisy, inconsistent, and redundant patterns. However, PTM model treats each extracted pattern as whole without considering included terms, which could affect the quality of extracted patterns. This paper propose an innovative and effective method that extends the random set to accurately weigh patterns based on their distribution in the documents and their terms distribution in patterns. Then, the proposed approach will find the specific closed sequential patterns (SCSP) based on the new calculated weight. The experimental results on Reuters Corpus Volume 1 (RCV1) data collection and TREC topics show that the proposed method significantly outperforms other state-of-the-art methods in different popular measures.
Optimal Filter Design to Compute the Mean of Cardiovascular Pressure Signals

The mean pressure is a term used to describe the baseline trend of physiological pressure signals that excludes fluctuations due to the cardiac cycle and, in some cases, the respiratory cycle. In many clinical applications and bedside monitoring devices, the mean pressure is estimated with a 3-8 s moving average. We suggest that the mean pressure is best defined in terms of its frequency domain properties. This definition makes it possible to determine solutions that are both optimal and practical. We demonstrate that established methods of optimal finite impulse response (FIR) filter design produce estimates of the mean pressure that are significantly more accurate than the moving average. These filters have no more computational cost, are less sensitive to artifact, have shorter delays, and greater sensitivity to acute events.
Fast and Accurate Registration of Structured Point Clouds with Small Overlaps

To perform registration of structured point clouds with large rotation and small overlaps, this paper presents an algorithm based on the direction angles and the projection information of dense points. This algorithm fully employs the geometric information of structured environment. It consists of two parts: rotation estimation and translation estimation. For rotation estimation, a direction angle is defined for a point cloud and then the rotation matrix is obtained by comparing the difference between the distributions of angles. For translation estimation, the point clouds are projected onto three orthogonal planes and then a correlation operation is performed on the projection images to calculate the translation vector. Experiments have been conducted on several datasets. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art approaches in terms of both accuracy and efficiency.
A Kernel-based predictive model of EV capacity for distributed voltage control and demand response

Energy storage and reactive power supplied by electric vehicles (EV) through vehicle-to-grid (V2G) operation can be coordinated to provide voltage support, thus reducing the need of grid reinforcement and active power curtailment. Optimization and control approaches for V2G-enabled reactive power control should be robust to variations and offer a certain level of optimality by combining real-time control with an hours-ahead scheduling scheme. This paper introduces an optimization and control framework that can be used for charging batteries and managing available storage while using the remaining capacity of the chargers to generate reactive power and cooperatively perform voltage control. Stochastic distributed optimization of reactive power is realized by integrating a robust distributed sub-gradient method with conditional ensemble predictions of V2G capacity. Hence, the proposed solutions can meet system operational requirements for the upcoming hours by enabling instantaneous cooperation among distributed EVs.
The Deterministic Plus Stochastic Model of the Residual Signal and Its Applications

The modeling of speech production often relies on a source-filter approach. Although methods parameterizing the filter have nowadays reached a certain maturity, there is still a lot to be gained for several speech processing applications in finding an appropriate excitation model. This manuscript presents a Deterministic plus Stochastic Model (DSM) of the residual signal. The DSM consists of two contributions acting in two distinct spectral bands delimited by a maximum voiced frequency. Both components are extracted from an analysis performed on a speaker-dependent dataset of pitch-synchronous residual frames. The deterministic part models the low-frequency contents and arises from an orthonormal decomposition of these frames. As for the stochastic component, it is a high-frequency noise modulated both in time and frequency. Some interesting phonetic and computational properties of the DSM are also highlighted. The applicability of the DSM in two fields of speech processing is then studied. First, it is shown that incorporating the DSM vocoder in HMM-based speech synthesis enhances the delivered quality. The proposed approach turns out to significantly outperform the traditional pulse excitation and provides a quality equivalent to STRAIGHT. In a second application, the potential of glottal signatures derived from the proposed DSM is investigated for speaker identification purpose. Interestingly, these signatures are shown to lead to better recognition rates than other glottal-based methods.
Construction of multimedia map database using urban city images

In this paper, we present a multimedia map database system. The system is mainly employed for acquisition, storage and retrieval of the real-world urban images with viewpoint information. Here, the images and the viewpoint information are assumed to be collected from the mobile multimedia terminal users. By using the viewpoint information in the database system, an "Extended Visual Map" is proposed for presenting the images according to the actual viewpoint of any user; also a position information service is implemented to retrieval the map information by building images. To realize these objectives, the features of the regions containing a building in every real-world urban image are extracted and compared automatically by the proposed system.
Angular power spectrum of gamma-ray sources for GLAST: blazars and clusters of galaxies

Blazars, a beamed population of active galactic nuclei, radiate high-energy γ-rays, and thus are a good target for the Gamma Ray Large Area Space Telescope (GLAST). As the blazars trace the large-scale structure of the Universe, one may observe spatial clustering of blazars. We calculate the angular power spectrum of blazars that would be detected by GLAST. We show that we have the best chance of detecting their clustering at large angular scales, θ ≳ 10°, where shot noise is less important, and the dominant contribution to the correlation comes from relatively low redshift, z ≲ 0.1. The GLAST can detect the correlation signal, if the blazars detected by GLAST trace the distribution of low-z quasars observed by optical galaxy surveys, which have the bias of unity. If the bias of blazars is greater than 1.5, GLAST will detect the correlation signal unambiguously. We also find that GLAST may detect spatial clustering of clusters of galaxies in γ-rays. The shape of the angular power spectrum is different for blazars and clusters of galaxies; thus, we can separate these two contributions on the basis of the shape of the power spectrum.
Fusing binary templates for multi-biometric cryptosystems

Biometric cryptosystem has been proven to be one of the promising approaches for template protection. Since most methods in this approach require binary input, to extend it for multiple modalities, binary template fusion is required. This paper addresses the issues of multi-biometrics' performance and security, and proposes a new binary template fusion method which could maximize the fused template discriminability and its entropy by reducing the bits dependency. Three publicly available datasets are used for experiments. Experimental results show that the proposed method outperforms the state of the art methods.
SWIRL: A Scalable Watermark to Detect Correlated Network Flows

Flow watermarks are active traffic analysis techniques that help establish a causal connection between two network flows by content-independent manipulations, e.g., altering packet timings. Watermarks provide a much more scalable approach for flow correlation than passive traffic analysis. Previous designs of scalable watermarks, however, were subject to multi-flow attacks. They also introduced delays too large to be used in most environments. We design SWIRL, a Scalable Watermark that is Invisible and Resilient to packet Losses. SWIRL is the first watermark that is practical to use for large-scale traffic analysis. SWIRL uses a flow-dependent approach to resist multi-flow attacks, marking each flow with a different pattern. SWIRL is robust to packet losses and network jitter, yet it introduces only small delays that are invisible to both benign users and determined adversaries. We analyze the performance of SWIRL both analytically and on the PlanetLab testbed, demonstrating very low error rates. We consider applications of SWIRL to stepping stone detection and linking anonymous communication. We also propose a novel application of watermarks to defend against congestion attacks on Tor.
Forward and Backward Blocking in Human Contingency Judgement

Three experiments investigated whether a process akin to Kamin's (1969) blocking effect would occur with human contingency judgements in the context of a video game. Subjects were presented with sets of trials on each of which they could perform a particular action and observe whether the action produced a particular outcome in a situation in which there was an alternative potential cause of the outcome. In Experiment 1 it was found that prior observation of the relationship between the alternative cause and the outcome did indeed block or reduce learning about the subsequent action-outcome relationship. However, exposure to the relationship between the alternative cause and the outcome after observing the association between the action and the outcome also reduced judgements of the action–outcome contingency (backward blocking), a finding at variance with conditioning theory. In Experiment 2 it was found that, just as is the case with forward blocking, the degree of backward blocking depended on how good a predictor of the outcome the alternative cause was. Finally, in Experiment 3 it was shown that the backward blocking effect was not the result of greater forgetting about the action–outcome relationship in the experimental than in the control condition. These results cast doubt upon the applicability of contemporary theories of conditioning to human contingency judgement.
Predictors of leadership effectiveness for Chinese managers

Purpose – This study sets out to investigate the leadership roles, the predictors of leadership effectiveness for Chinese managers, and the relative effects of gender and rater differences on perceived leadership roles. The study is important as it seeks to investigate whether a leadership model (the Integrated Competing Values Framework) developed for Western cultures explains the leadership behaviours of Chinese managers.Design/methodology/approach – Using a 360° feedback method, the data for the study were collected from 49 middle managers and 142 of their significant others (boss, peers and staff). The data were submitted to a repeated measures ANOVA, with role displayed and position as the within‐subjects factors and gender as the between‐subjects factor, to determine whether there were any significant main or interaction effects. A standard multiple regression was performed, between the effectiveness as the outcome and the leadership roles. This was followed by a forward regression analysis.Findings.
Low Energy Reporting Related to Lifestyle, Clinical, and Psychosocial Factors in a Randomly Selected Population Sample of Greek Adults: The ATTICA Study

Objective: Aim of the present study was to identify potential dietary, lifestyle, psychosocial and clinical correlates of underreporting in a population-based sample. Methods: Following a random multistage sampling, 1514 men (46±13 years old) and 1528 women (45±13 years old) from the Attica area, in Greece, participated in this study. All participants underwent a standard assessment procedure that included clinical, psychosocial and lifestyle parameters. Food consumption was assessed through a validated semi-quantitative food frequency questionnaire. The ratio of energy intake to estimated basal metabolic rate (EI/BMR) and the Goldberg cut-off points were used for the classification of subjects as low energy reporters (LERs) and non-LERs. Results: LERs represented 12.2% of the sample. This percentage was higher in obese subjects compared to overweight or normal weight (20.6 % vs. 9.9 % vs. 10.6 %, p = 0.05), as well as in women compared to men (14.6% vs. 9.9%, p<0.001). Data analysis was stratified by gender, since a significant interaction was observed between gender and LER group on several dietary parameters. Female LERs had higher Med Diet Score compared to non-LERs (30.6 ± 8.2, 95%CI 30.2–31.04 vs. 26.9 ± 6.3, 95%CI 26.05–27.7, p<0.001). Multiple regression analysis revealed that lower EI/BMR values were associated with younger age (p<0.001), higher BMI (p<0.001), presence of diabetes mellitus (p=0.012) and lower depression score (p=0.056) in women, whereas with younger age (p<0.001), higher BMI (p<0.001), higher education level (p=0.046) and higher anxiety score (p=0.08) in men. Conclusion: Several psychosocial and clinical characteristics operate in low energy reporting in both genders. Nutrition-related professionals should be aware of these gender-specific trends in dietary assessment procedures.
Monte-Carlo-based perturbation and beam quality correction factors for thimble ionization chambers in high-energy photon beams

This paper presents a detailed investigation into the calculation of perturbation and beam quality correction factors for ionization chambers in high-energy photon beams with the use of Monte Carlo simulations. For a model of the NE2571 Farmer-type chamber, all separate perturbation factors as found in the current dosimetry protocols were calculated in a fixed order and compared to the currently available data. Furthermore, the NE2571 Farmer-type and a model of the PTW31010 thimble chamber were used to calculate the beam quality correction factor kQ. The calculations of kQ showed good agreement with the published values in the current dosimetry protocols AAPM TG-51 and IAEA TRS-398 and a large set of published measurements. Still, some of the single calculated perturbation factors deviate from the commonly used ones; especially prepl deviates more than 0.5%. The influence of various sources of uncertainties in the simulations is investigated for the NE2571 model. The influence of constructive details of the chamber stem shows a negligible dependence on calculated values. A comparison between a full linear accelerator source and a simple collimated point source with linear accelerator photon spectra yields comparable results. As expected, the calculation of the overall beam quality correction factor is sensitive to the mean ionization energy of graphite used. The measurement setup (source–surface distance versus source–axis distance) had no influence on the calculated values.
Overwriting and rebinding: Why feature-switch detection tasks underestimate the binding capacity of visual working memory

In these two experiments, we explored the ability to store bound representations of colour and location information in visual working memory using three different tasks. In the location-cue task, we probed how well colour information could be recalled when observers are given a location cue. In the feature-cue task, we probed how well location information could be recalled when observers are given a colour cue. Finally, in the feature-switch detection task, we tested how well observers could detect a recombination of features (e.g., switching the locations of the red and green items). We hypothesized that these tasks might reveal differences in binding capacity limits between switching and nonswitching tests of visual working memory. We also hoped the tasks could provide an explanation for those differences in terms of the component processes of working memory—do failures occur in the encoding, maintenance, or retrieval stages of the task? Experiment 1 showed that performance in the two cued-recall tasks was equally high, and was significantly better than performance in the feature-switch detection task. Thus, the feature-switch detection task underestimates the number of colour–location bindings that can be remembered, but is a useful task for examining the fragile nature of feature binding in working memory. Experiment 2 explored why feature-switch detection underestimates the binding capacity of visual working memory by examining whether the feature switch errors occur at the level of encoding, maintaining, or retrieving binding information from visual working memory. The results suggest that feature switch errors reflect failures to maintain bound objects in working memory, perhaps due to the automatic rewriting and rebinding of information in the face of new perceptual input.
Foreground segmentation based on multi-resolution and matting

We propose a foreground segmentation algorithm that does foreground extraction under different scales and refines the result by matting. First, the input image is filtered and resampled to 5 different resolutions. Then each of them is segmented by adaptive figure-ground classification and the best segmentation is automatically selected by an evaluation score that maximizes the difference between foreground and background. This segmentation is upsampled to the original size, and a corresponding trimap is built. Closed-form matting is employed to label the boundary region, and the result is refined by a final figure-ground classification. Experiments show the success of our method in treating challenging images with cluttered background and adapting to loose initial bounding-box.
Molecular sieving and sensing with gold nanotube membranes.

We have developed a new class of synthetic membranes that consist of a porous polymeric support that contains an ensemble of gold nanotubes that span the thickness of the support membrane. The support is a commercially available microporous polycarbonate filter with cylindrical nanoscopic pores. The gold nanotubes are prepared via electroless deposition of Au onto the pore walls; i.e., the pores act as templates for the nanotubes. We have shown that by controlling the Au deposition time, Au nanotubes that have effective inside diameters of molecular dimensions (<1 nm) can be prepared. These nanotube membranes can be used to cleanly separate small molecules on the basis of molecular size. Furthermore, use of these membranes as a novel electrochemical sensor is also discussed. This new sensing scheme involves applying a constant potential across the Au nanotube membrane and measuring the drop in the transmembrane current upon the addition of the analyte. This paper reviews our recent progress on size-based transport selectivity and sensor applications in this new class of membranes.
Application of Logistic Regression to Case-Control Association Studies Involving Two Causative Loci

Models in which two susceptibility loci jointly influence the risk of developing disease can be explored using logistic regression analysis. Comparison of likelihoods of models incorporating different sets of disease model parameters allows inferences to be drawn regarding the nature of the joint effect of the loci.We have simulated case-control samples generated assuming different two-locus models and then analysed them using logistic regression. We show that this method is practicable and that, for the models we have used, it can be expected to allow useful inferences to be drawn from sample sizes consisting of hundreds of subjects. Interactions between loci can be explored, but interactive effects do not exactly correspond with classical definitions of epistasis. We have particularly examined the issue of the extent to which it is helpful to utilise information from a previously identified locus when investigating a second, unknown locus. We show that for some models conditional analysis can have substantially greater power while for others unconditional analysis can be more powerful. Hence we conclude that in general both conditional and unconditional analyses should be performed when searching for additional loci.