Miracle-dz/UniFAR-FTUs huggingface dataset:
Inorganic Reactive Sulfur-Nitrogen Species: Intricate Release Mechanisms or Cacophony in Yellow, Blue and Red?

Since the heydays of Reactive Sulfur Species (RSS) research during the first decade of the Millennium, numerous sulfur species involved in cellular regulation and signalling have been discovered. Yet despite the general predominance of organic species in organisms, recent years have also seen the emergence of inorganic reactive sulfur species, ranging from inorganic polysulfides (HSx−/Sx2−) to thionitrous acid (HSNO) and nitrosopersulfide (SSNO−). These inorganic species engage in a complex interplay of reactions in vitro and possibly also in vivo. Employing a combination of spectrophotometry and sulfide assays, we have investigated the role of polysulfanes from garlic during the release of nitric oxide (•NO) from S-nitrosoglutathione (GSNO) in the absence and presence of thiol reducing agents. Our studies reveal a distinct enhancement of GSNO decomposition by compounds such as diallyltrisulfane, which is most pronounced in the presence of cysteine and glutathione and presumably proceeds via the initial release of an inorganic mono- or polysulfides, i.e., hydrogen sulfide (H2S) or HSx−, from the organic polysulfane. Albeit being of a preliminary nature, our spectrophotometric data also reveals a complicated underlying mechanism which appears to involve transient species such as SSNO−. Eventually, more in depth studies are required to further explore the underlying chemistry and wider biological and nutritional implications of this interplay between edible garlic compounds, reductive activation, inorganic polysulfides and their interplay with •NO storage and release.
SUMO-Specific Protease 2 Is Essential for Modulating p53-Mdm2 in Development of Trophoblast Stem Cell Niches and Lineages

SUMO-specific protease 2 (SENP2) modifies proteins by removing SUMO from its substrates. Although SUMO-specific proteases are known to reverse sumoylation in many defined systems, their importance in mammalian development and pathogenesis remains largely elusive. Here we report that SENP2 is highly expressed in trophoblast cells that are required for placentation. Targeted disruption of SENP2 in mice reveals its essential role in development of all three trophoblast layers. The mutation causes a deficiency in cell cycle progression. SENP2 has a specific role in the G–S transition, which is required for mitotic and endoreduplication cell cycles in trophoblast proliferation and differentiation, respectively. SENP2 ablation disturbs the p53–Mdm2 pathway, affecting the expansion of trophoblast progenitors and their maturation. Reintroducing SENP2 into the mutants can reduce the sumoylation of Mdm2, diminish the p53 level and promote trophoblast development. Furthermore, downregulation of p53 alleviates the SENP2-null phenotypes and stimulation of p53 causes abnormalities in trophoblast proliferation and differentiation, resembling those of the SENP2 mutants. Our data reveal a key genetic pathway, SENP2–Mdm2–p53, underlying trophoblast lineage development, suggesting its pivotal role in cell cycle progression of mitosis and endoreduplication.
Fluence rate-dependent intratumor heterogeneity in physiologic and cytotoxic responses to Photofrin photodynamic therapy

Photodynamic therapy (PDT) can lead to the creation of heterogeneous, response-limiting hypoxia during illumination, which may be controlled in part through illumination fluence rate. In the present report we consider (1) regional differences in hypoxia, vascular response, and cell kill as a function of tumor depth and (2) the role of fluence rate as a mediator of depth-dependent regional intratumor heterogeneity. Intradermal RIF murine tumors were treated with Photofrin PDT using surface illumination at an irradiance of 75 or 38 mW cm^-2. Regional heterogeneity in tumor response was examined through comparison of effects in the surface vs. base of tumors, i.e. along a plane parallel to the skin surface and perpendicular to the incident illumination. 75 mW cm^-2 PDT created significantly greater hypoxia in tumor bases relative to their surfaces. Increased hypoxia in the tumor base could not be attributed to regional differences in Photofrin concentration nor effects of fluence rate distribution on photochemical oxygen consumption, but significant depth-dependent heterogeneity in vascular responses and cytotoxic response were detected. At a lower fluence rate of 38 mW cm^-2, no detectable regional differences in hypoxia or cytotoxic responses were apparent, and heterogeneity in vascular response was significantly less than that during 75 mW cm^-2 PDT. This research suggests that the benefits of low-fluence-rate PDT are mediated in part by a reduction in intratumor heterogeneity in hypoxic, vascular and cytotoxic responses.
Meta-analysis of the association between SCARB1 polymorphism and fasting blood lipid levels

Studies have shown that the scavenger receptor class B type 1 (SCARB1) rs5888 polymorphism impacts fasting blood lipid levels differently in men and women. A meta-analysis and statistical tests was therefore performed to determine the relationship between the rs5888 polymorphism and lipid levels in men and women. Twelve studies with 12,147 subjects were selected for this study. In a dominant model, the CT+TT genotype group had lower triglyceride levels than the CC group in men (standardized mean difference (SMD): −0.11; 95% confidence interval (CI): −0.21 to −0.02; P = 0.016; I2 = 51.5%). No statistical differences were detected in women. Subgroup analysis of different racial groups revealed significant correlation between the SCARB1 rs5888 polymorphism and higher high-density lipoprotein cholesterol (HDL-C) levels (SMD: 0.15; 95% CI: 0.08 to 0.21; P ≤ 0.001; I2 = 0%) and lower triglyceride levels (SMD: −0.16; 95% CI: −0.26 to −0.04; P = 0.013; I2 = 60.6%) in non-Asian men. No evidence of heterogeneity was observed when eliminating outlier studies, and no publication bias was detected. This meta-analysis suggests the SCARB1 rs5888 polymorphism is associated with higher HDL-C and lower triglyceride levels in non-Asian men.
Metformin reduces glucose intolerance caused by rapamycin treatment in genetically heterogeneous female mice

The use of rapamycin to extend lifespan and delay age-related disease in mice is well-established despite its potential to impair glucose metabolism which is driven partially due to increased hepatic gluconeogenesis. We tested whether a combination therapeutic approach using rapamycin and metformin could diminish some of the known metabolic defects caused by rapamycin treatment in mice. In genetically heterogeneous HET3 mice, we found that chronic administration of encapsulated rapamycin by diet caused a measurable defect in glucose metabolism in both male and female mice as early as 1 month after treatment. In female mice, this defect was alleviated over time by simultaneous treatment with metformin, also by diet, such that females treated with both drugs where indistinguishable from control mice during glucose tolerance tests. While rapamycin-mediated glucose intolerance was unaffected by metformin in males, we found metformin prevented rapamycin-mediated reduction in insulin and leptin concentrations following 9 months of co-treatment. Recently, the Interventions Testing Program showed that mice treated with metformin and rapamycin live at least as long as those treated with rapamycin alone. Together, our data provide compelling evidence that the pro-longevity effects of rapamycin can be uncoupled from its detrimental effects on metabolism through combined therapeutic approaches.
Dabigatran in real-world atrial fibrillation

Summary In the RE-LY clinical trial, dabigatran presented a better effectiveness/ safety profile when compared to warfarin. However, clinical trials are not very representative of the real-world setting. We aimed to assess the performance of dabigatran in real-world patients with atrial fibrillation (AF) by means of a systematic review and meta-analysis of observational comparison studies with vitamin K antagonists (VKA). We searched PubMed, Embase and Scopus databases until November 2015 and selected studies according to the following criteria: observational study performed with nonvalvular AF patients; reporting adjusted hazard ratios (HR) of clinical events in a follow-up period; for dabigatran 75 mg, 110 mg or 150 mg versus VKA. Twenty studies were selected which included 711,298 patients, 210,279 of which were treated with dabigatran and the remaining 501,019 with VKA. Ischaemic stroke incidence was of 1.65 /100 patient-years for dabigatran and 2.85/100 patient-years for VKA (HR 0.86, 95 % confidence interval of 0.74–0.99). Major bleeding rate was 3.93/100 patient-years for dabigatran and 5.61/100 patient-years for VKA (0.79, 0.69–0.89). Risk of mortality (0.73, 0.61–0.87) and intracranial bleeding (0.45, 0.38–0.52) were significantly lower in patients treated with dabigatran when compared to patients on VKA. Risk of gastrointestinal (GI) bleeding was significantly higher in patients treated with dabigatran (1.13, 1.00–1.28). No significant difference was observed in risk of myocardial infarction (0.99, 0.89–1.11). In this combined analysis of real-world observational comparison studies with VKA, dabigatran was associated with a lower risk of ischaemic stroke, major bleeding, intracranial bleeding and mortality, higher risk of GI bleeding and a similar risk of myocardial infarction. Supplementary Material to this article is available online at www.thrombosis-online.com.
Supporting Localized Interactions among Heterogeneous Smart Things with ThingWare

The development of ubiquitous computing has witnessed the invention of various smart things (or smart objects), which normally refer to conventional devices equipped with sensing, computing, and communication capabilities. Programming with smart things faces many challenges because they are mobile, dynamic, and heterogeneous. This paper proposes ThingAPI as a uniform programming abstraction to support efficient programming with heterogeneous smart things. ThingAPI tries to accomplish two objectives: making local smart things interactive, and making heterogeneous smart things easily programmable. ThingAPI allows applications on smart things to interact with a wide range of things and flexibly extend their functionalities. We have built a lightweight middleware prototype, ThingWare, to implement ThingAPI over several platforms. With ThingWare, we can easily program household and office appliances such as TV sets, air-conditioners, projectors, etc. To demonstrate the feasibility and efficiency of our approach, we construct a representative application and present detailed analysis of various performance results.
On-Chip Living-Cell Microarrays for Network Biology

The recently developed field of systems biology creates a new framework for understanding the molecular basis of physiological or pathophysiological states of cells. Screening modalities that can be used on single cells are needed to study cellular systems biology. The recent development of cellular microarrays has provided a method for the complex molecular analysis of living, single cells (Chen & Davies, 2006). Unlike other highthroughput systems, such as gene expression profiling microarrays or protein microarrays, cellular microarrays use a printed pattern of geographically distinct spots to probe living cells, rather than cell lysates, or other non-viable samples. Among the most powerful tools to assay gene function on a genome-wide scale in the physiological context of intact living cells are fluorescence microscopy and related imaging techniques (Pepperkok & Ellenberg, 2006). To enable these techniques to be applied to functional genomics experiments, fluorescence microscopy is making the transition to a quantitative and high-throughput technology. The combination of time-lapse microscopy, quantitative image analysis and fluorescent protein reporters has enabled observation of multiple cellular components over time in individual cells (Locke & Elowitz, 2009). In conjunction with mathematical modelling, these techniques are now providing powerful insights into genetic and proteomic behaviour in diverse microbial systems. Recently, a quantitative system-wide analysis of mRNA and protein expression in individual cells with single-molecule sensitivity using a yellow fluorescent protein fusion library for E. coli has been realised (Taniguchi et al., 2010).
Current Advancements of the Smartphone - Based Point - Of - Carediagnosis

Today, mobile phone technology with high-speed internet has been well-developed and been considered as the candidate technology for the next-generation handheld point-of-care (POC) devices with the function of transferrable diagnostic data. In 2015, it was estimated that there were 1.5 billion mobile phone users in developed countries, and 5.6 billion users in developing countries [1]. The high mobile penetration is extremely beneficial to the feasibility of smartphone-based POC diagnosis every where, especially in less developed countries, rural areas, and remote areas. These places lack diagnostic facilities and welltrained personnel. To improve healthcare systems in these under developed areas, rapid on-site diagnosis with data transmitting capability is in desperate need to immediately make medical decisions for patients. Recently, the concept of mobile health (mHealth) is active in many fields ranging from medical diagnostics to environmental detection. The camera of the smartphone stands out as the primary feature as a detection sensor. When the camera is integrated with the optical components, it can be used to sense and measure the changes in both color and optical intensity. Although human eyes can identify the difference in colors and brightness, we cannot quantify what we see into exact values. With the addition of optical components, such as diffraction gratings, micro lens, etc., the smartphone can be used as a visible light spectrum reader [2] or a microscopy [3]. Furthermore, the diagnostic results by smartphone-based devices can be uploaded through internet for doctors to make the treatment in time.
Quiet ego, self-regulatory skills, and perceived stress in college students

Abstract Objective: Examine the unique contributions of self-control and grit subscales (perseverance, interest consistency) as potential mediators of the relationship between quiet ego characteristics and less perceived stress in college students. Participants: Data from 1117 college students were collected between October, 2015 and May, 2016. Methods: The sample was split randomly into exploratory and confirmatory samples. Multiple mediator models were tested with PROCESS module (SPSS v. 24) in both samples. Results: Hypotheses were largely confirmed with self-control fully mediating the link between quiet ego and perceived stress in both samples. Conclusions: Although many self-regulatory constructs may argue for their positive impact on college student outcomes, interventions that strengthen self-control, and not grit, may be most promising to reduce perceived stress. Further, interventions to strengthen quiet ego characteristics may be beneficial for strengthening self-control in college students.
Temporal-Coherency-Aware Human Pose Estimation in Video via Pre-trained Res-net and Flow-CNN

Human pose estimation in image or video is a basic issue in computer vision. The critical element of video compared with still image is temporal coherency information. And on the premise of these constraints, the human pose in the adjacent frames of video will share a similar appearances and similar locations. This paper presents a new temporal-coherency-aware human pose estimation method for video. In our method, we maintain the temporal coherency information by propagating the joint data between adjacent frames and use a novel approach based on a convolutional neural network in optimization stage. We directly get the initial pose data from the key frames, and then a temporal-coherency-aware convolution network based method is designed to generate the optimal pose estimation in video. Our two stage processing method is efficient with low error rates which can effectively handle some tough cases.
Unified approach for underdetermined BSS, VAD, dereverberation and DOA estimation with multichannel factorial HMM

This paper proposes a novel method for simultaneously solving the problems of underdetermined blind source separation (BSS), source activity detection, dereverberation and direction-of-arrival (DOA) estimation by introducing an extension of the "multichannel factorial hidden Markov model (MFH-MM). " The MFHMM is an extension of the multichannel non-negative matrix factorization (NMF) modeL in which the basis spectra are allowed to vary over time according to the transitions of the hidden states. This model has allowed us to perform source separation, source activity detection and dereverberation in a unified manner. In our previous model, the spatial covariance of each source has been treated as a model parameter. This has led the entire generative model to have an unnecessarily high degree of freedom, and thus the parameter inference has been prone to getting trapped into undesired local optima. To reasonably restrict the solution space of the spatial covariance matrix of each source, we propose to describe it as a weighted sum of the fixed spatial covariance matrix corresponding to the discrete set of DOAs. Through the parameter inference, the proposed model allows us to simultaneously solve the problems of underdetermined BSS, source activity detection, dereverberation and DOA estimation. Experimental results revealed that the proposed method was superior to a previous method in terms of the signal-to-distortion ratios of separated signals.
On the Effects of Filterbank Design and Energy Computation on Robust Speech Recognition

In this paper, we examine how energy computation and filterbank design contribute to the overall front-end robustness, especially when the investigated features are applied to noisy speech signals, in mismatched training-testing conditions. In prior work (“Auditory Teager energy cepstrum coefficients for robust speech recognition,” D. Dimitriadis, P. Maragos, and A. Potamianos, in Proc. Eurospeech'05, Sep. 2005), a novel feature set called “Teager energy cepstrum coefficients” (TECCs) has been proposed, employing a dense, smooth filterbank and alternative energy computation schemes. TECCs were shown to be more robust to noise and exhibit improved performance compared to the widely used Mel frequency cepstral coefficients (MFCCs). In this paper, we attempt to interpret these results using a combined theoretical and experimental analysis framework. Specifically, we investigate in detail the connection between the filterbank design, i.e., the filter shape and bandwidth, the energy estimation scheme and the automatic speech recognition (ASR) performance under a variety of additive and/or convolutional noise conditions. For this purpose: 1) the performance of filterbanks using triangular, Gabor, and Gammatone filters with various bandwidths and filter positions are examined under different noisy speech recognition tasks, and 2) the squared amplitude and Teager-Kaiser energy operators are compared as two alternative approaches of computing the signal energy. Our end-goal is to understand how to select the most efficient filterbank and energy computation scheme that are maximally robust under both clean and noisy recording conditions. Theoretical and experimental results show that: 1) the filter bandwidth is one of the most important factors affecting speech recognition performance in noise, while the shape of the filter is of secondary importance, and 2) the Teager-Kaiser operator outperforms (on the average and for most noise types) the squared amplitude energy computation scheme for speech recognition in noisy conditions, especially, for large filter bandwidths. Experimental results show that selecting the appropriate filterbank and energy computation scheme can lead to significant error rate reduction over both MFCC and perceptual linear predicion (PLP) features for a variety of speech recognition tasks. A relative error rate reduction of up to ~ 30% for MFCCs and ~ 39% for PLPs is shown for the Aurora-3 Spanish Task.
SEQUENTIAL KERNEL DENSITY APPROXIMATION THROUGH MODE PROPAGATION: APPLICATIONS TO BACKGROUND MODELING

Density-based modeling of visual features is very common in computer vision, either by using non-parametric techniques or through representing the underlying density function as a weighted sum of Gaussians. A number of real-time tasks such as background modeling or modeling the appearance of a moving target require sequential density estimation, where new data is incorporated in the model as it becomes available. Nevertheless, current methods for updating the density function either lack flexibility, by fixing the number of Gaussians in the mixture, or require large memory amounts, by maintaining a non-parametric representation of the density. This paper presents an efficient method for recursive density approximation that relies on the propagation of the density modes. At each time step, the modes of the density are re-estimated and a Gaussian component is assigned to each mode. The covariance of each component is derived from the Hessian matrix estimated at the mode location. To detect the modes we employ the variable-bandwidth mean shift. While the proposed density representation is memory efficient (which is typical for mixture densities), it inherits the flexibility of non-parametric methods, by allowing the number of modes to adapt in time. We show that the same mode propagation principle applies for subspaces derived from eigen analysis. Extensive experimental background modeling results demonstrate the performance of the method.
On a Robust MaxEnt Process Regression Model with Sample-Selection

In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt) process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR) model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR) model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.