Miracle-dz/UniFAR-FTUs huggingface dataset:
Endogenous peptides profiles of human infantile hemangioma tissue and their clinical significance for treatment

Endogenous peptides play crucial roles in various biological processes. Their effects on the pathogenesis of human infantile hemangioma remains poorly understood. In this study, we construct a comparative peptidomic profiling between human infantile hemangioma tissue and matched normal skin using liquid chromatography‐tandem mass spectrometry. A total of 192 peptides were significantly differentially expressed in human infantile hemangioma tissue, with 182 upregulated, and 10 downregulated peptides between infantile hemangioma tissue and matched normal skin. Performing bioinformatics analysis (Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis), we found that precursor proteins of these differentially expressed peptides correlate with metabolic process, biological regulation, binding, catalytic activity, and pathways in cancer occurring during pathological changes of infantile hemangioma. Furthermore, 89 differentially expressed peptides shared homology with antimicrobial peptides and 13 matched known immunomodulatory peptides based on prediction database. In conclusion, our results reveal significantly altered expression profiles of peptides in human infantile hemangioma tissue. These peptides may participate in the etiology of infantile hemangioma and provide beneficial scheme for clinical treatments.
RVM‐based multi‐class classification of remotely sensed data

The relevance vector machine (RVM), a Bayesian extension of the support vector machine (SVM), has considerable potential for the analysis of remotely sensed data. Here, the RVM is introduced and used to derive a multi‐class classification of land cover with an accuracy of 91.25%, a level comparable to that achieved by a suite of popular image classifiers including the SVM. Critically, however, the output of the RVM includes an estimate of the posterior probability of class membership. This output may be used to illustrate the uncertainty of the class allocations on a per‐case basis and help to identify possible routes to further enhance classification accuracy.
Use of a cocktail probe to assess potential drug interactions with cytochrome P450 after administration of belatacept, a costimulatory immunomodulator

Aim This open‐label study investigated the effect of belatacept on cytokine levels and on the pharmacokinetics of caffeine, losartan, omeprazole, dextromethorphan and midazolam, as CYP probe substrates after oral administration of the Inje cocktail in healthy volunteers. Methods Twenty‐two evaluable subjects received the Inje cocktail on Days 1, 4, 7 and 11 and belatacept infusion on Day 4. Results Since belatacept caused no major alterations to cytokine levels, there were no major effects on CYP‐substrate pharmacokinetics, except for a slight (16–30%) increase in omeprazole exposure, which was probably due to omeprazole‐mediated, time‐dependent CYP inhibition. Belatacept did not cause major alterations in the pharmacokinetics, as measured by the geometric mean ratios and associated 90% confidence interval for area under the plasma concentration ‐time curve from time zero to infinity on Day 7 comparing administration with and without belatacept for caffeine (1.002 [0.914, 1.098]), dextromethorphan (1.031 [0.885, 1.200]), losartan (1.016 [0.938, 1.101)], midazolam (0.968 [0.892, 1.049]) or their respective metabolites. Conclusions Therefore, no dose adjustments of CYP substrates are indicated with belatacept coadministration.
Modeling the Glass Transition Temperature of Polymers via Multipole Moments Using Support Vector Regression

This study introduces support vector regression (SVR) approach to model the relationship between the glass transition temperature (Tg) and multipole moments for polymers. SVR was trained and tested via 60 samples by using two quantum chemical descriptors including the molecular traceless quadrupole moment and the molecular average hexadecapole moment Φ. The prediction performance of SVR was compared with that of reported quantitative structure property relationship (QSPR) model. The results show that the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of training samples and test samples achieved by SVR model, are smaller than those achieved by the QSPR model, respectively. This investigation reveals that SVR-based modeling is a practically useful tool in prediction of the glass transition temperature of polymers.
Forecasting the spatial transmission of influenza in the United States

Significance In the last two decades, multiple outbreaks of emerging pathogens have unexpectedly swept the planet. In these public health emergencies, pathogens invade new regions in the span of just a few weeks to months, leaving a critical window of opportunity during which real-time warning could be sounded. As such, accurate prediction of the spatial spread of pathogens could provide invaluable benefits to global public health. Here we develop and validate an operational forecast system that is capable of predicting the spatial transmission of influenza in the United States. In particular, the onset week of local outbreaks can be accurately predicted up to 6 wk in advance at state level. Recurrent outbreaks of seasonal and pandemic influenza create a need for forecasts of the geographic spread of this pathogen. Although it is well established that the spatial progression of infection is largely attributable to human mobility, difficulty obtaining real-time information on human movement has limited its incorporation into existing infectious disease forecasting techniques. In this study, we develop and validate an ensemble forecast system for predicting the spatiotemporal spread of influenza that uses readily accessible human mobility data and a metapopulation model. In retrospective state-level forecasts for 35 US states, the system accurately predicts local influenza outbreak onset,—i.e., spatial spread, defined as the week that local incidence increases above a baseline threshold—up to 6 wk in advance of this event. In addition, the metapopulation prediction system forecasts influenza outbreak onset, peak timing, and peak intensity more accurately than isolated location-specific forecasts. The proposed framework could be applied to emergent respiratory viruses and, with appropriate modifications, other infectious diseases.
The Power of Side-Information in Subgraph Detection

In this paper, we tackle the problem of hidden community detection. We consider belief propagation (BP) applied to the problem of detecting a hidden Erdős-Rényi (ER) graph embedded in a larger and sparser ER graph, in the presence of side-information. We derive two related algorithms based on BP to perform subgraph detection in the presence of two kinds of side-information. The first variant of side-information consists of a set of nodes, called cues, known to be from the subgraph. The second variant of side-information consists of a set of nodes that are cues with a given probability. It was shown in past works that BP without side-information fails to detect the subgraph correctly when a so-called effective signal-to-noise ratio parameter falls below a threshold. In contrast, in the presence of nontrivial side-information, we show that the BP algorithm achieves asymptotically zero error for any value of a suitably defined phase-transition parameter. We validate our results on synthetic datasets and a few real world networks.
Activation of the ERK1/2-MAPK Signaling Pathway by Complement Serum in UV-POS-Pretreated ARPE-19 Cells

Background: Retinal pigment epithelial (RPE) cells undergo functional changes upon complement stimulation, which play a role in the pathogenesis of age-related macular degeneration (AMD). These effects are in part enhanced by pretreating ARPE-19 cells with UV-irradiated photoreceptor outer segments (UV-POS) in vitro. The aim of this study was to investigate the effects of human complement serum (HCS) treatment on p44/42 mitogen-activated protein kinase (extracellular signal-regulated kinase 1/2 [ERK1/2]) activation in ARPE-19 cells pretreated with UV-POS. Methods: UV-POS-pretreated ARPE-19 cells were stimulated with 5% HCS or heat-inactivated HCS (HI-HCS) as a control. Pro tein expression of phosphorylated (activated) ERK1/2, total ERK1/2, Bax, and Bcl-2 was analyzed by Western blotting. Cell culture supernatants were analyzed for IL-6, IL-8, MCP-1, and VEGF by enzyme-linked immunosorbent assay (ELISA). Furthermore, extra- and intracellular reactive oxygen species (ROS) were determined. Results: The amount of phosphorylated ERK1/2 was increased in UV-POS-pretreated ARPE-19 cells, especially in combination with HCS stimulation, compared to non-pretreated ARPE-19 cells incubated with HCS alone or HI-HCS. The same observation was made for Bax and Bcl-2 expression. Furthermore, an increase in extra- and intracellular ROS was detected in UV-POS-pretreated ARPE-19 cells. The ELISA data showed that the production of IL-6, IL-8, and MCP-1 tended to increase in response to HCS in both UV-POS-pretreated and non-pretreated ARPE-19 cells. Conclusions: Our data imply that ERK1/2 activation in ARPE-19 cells may represent a response mechanism to cellular and oxidative stress, associated with apoptosis-regulating factors such as Bax and Bcl-2, which might play a role in AMD, while ERK1/2 seems not to represent the crucial signaling pathway mediating the functional changes in RPE cells in response to complement stimulation.
Fabrication and Improved Performance of Poly-(3-hydroxybutyrate-co-3-hydroxyvalerate) for Packaging By Addition of High Molecular Weight Natural Rubber

The packaging industry is searching for alternative materials to attain environmental sustainability. Poly(3-hydroxybutyrate-co-3-hydroxyvalerate (PHBV) is a semicrystalline polymer that meets this sustainability goal since it is bioderived and biodegradable. However, its brittle nature and relatively high water permeation and transmission rates make it unsuitable for packaging applications. In addition, PHBV has poor mechanical, thermal, and rheological properties above 160 °C, limiting its use in cast sheets and thermo-formed packaging applications. To improve these properties, new blends of PHBV with high molecular weight natural rubber at 5, 10, 15, and 25% by weight were fabricated, and physico-chemical properties of the blends were characterized. The rubber in the blends aided in the following: increased thermal stability since the complex viscosities of the blends were improved by one log over pure PHBV at 170 °C, created more uniform melting peaks attesting to improved homogeneity, decreased water permeation to a level similar to that of traditional thermoplastics; increased the elongation at break, and stabilized the Young's modulus. Therefore, these blends can potentially be used in-place of traditional, petroleum-based thermoplastics in cast sheets and thermoforms. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016, 133, 43937.
Explant-cell culture of primary mammary tumors from MMTV-c-Myc transgenic mice

SummaryWe have established an explant-cell culture system for mammary gland tumors from c-myc oncogene-expresing trangenic mice and potentially other transgenic strains. By coating culture dish surfaces with fetal bovine serum and using culture media supplemented with low serum and growth factors, the mammary tumor specimens could be maintained in culture for over 3 mo. Throughout the culture period, the explants produced adundant outgrowths of epithelial cells. As the outgowths of epithelial cells filled the dishes, the explants were serially transferred from one dish to another—a process that could be repeated at least six times, thus providing a continuous supply of primary tumor cells. This culture system provides a useful tool for studying the biology of mouse mammary gland tumors and possibly tumors from other organ sites.
CAMsure

We introduce CAMsure, the first realization of secure Content Addressable Memory (CAM) in the context of approximate search using near-neighbor algorithms. CAMsure provides a lightweight solution for practical secure (approximate) search with a minimal drop in the accuracy of the search results. CAM has traditionally been used as a hardware search engine that explores the entire memory in a single clock cycle. However, there has been little attention to the security of the data stored in CAM. Our approach stores distance-preserving hash embeddings within CAM to ensure data privacy. The hashing method provides data confidentiality while preserving similarity in the sense that a high resemblance in the data domain is translated to a small Hamming distance in the hash domain. Consequently, the objective of near-neighbor search is converted to approximate lookup table search which is compatible with the realizations of emerging content addressable memories. Our methodology delivers on average two orders of magnitude faster response time compared to RAM-based solutions that preserve the privacy of data owners.
More Harm Than Good? How Messages That Interrupt Can Make Us Vulnerable

System-generated alerts are ubiquitous in personal computing and, with the proliferation of mobile devices, daily activity. While these interruptions provide timely information, research shows they come at a high cost in terms of increased stress and decreased productivity. This is due to dual-task interference (DTI), a cognitive limitation in which even simple tasks cannot be simultaneously performed without significant performance loss. Although previous research has examined how DTI impacts the performance of a primary task (the task that was interrupted), no research has examined the effect of DTI on the interrupting task. This is an important gap because in many contexts, failing to heed an alert—the interruption itself—can introduce critical vulnerabilities.Using security messages as our context, we address this gap by using functional magnetic resonance imaging (fMRI) to explore how (1) DTI occurs in the brain in response to interruptive alerts, (2) DTI influences message security disregard, and (3) the effects of DTI can be mitigated by finessing the timing of the interruption. We show that neural activation is substantially reduced under a condition of high DTI, and the degree of reduction in turn significantly predicts security message disregard. Interestingly, we show that when a message immediately follows a primary task, neural activity in the medial temporal lobe is comparable to when attending to the message is the only task.Further, we apply these findings in an online behavioral experiment in the context of a web-browser warning. We demonstrate a practical way to mitigate the DTI effect by presenting the warning at low-DTI times, and show how mouse cursor tracking and psychometric measures can be used to validate low-DTI times in other contexts.Our findings suggest that although alerts are pervasive in personal computing, they should be bounded in their presentation. The timing of interruptions strongly influences the occurrence of DTI in the brain, which in turn substantially impacts alert disregard. This paper provides a theoretically grounded, cost-effective approach to reduce the effects of DTI for a wide variety of interruptive messages that are important but do not require immediate attention.
Model averaged double robust estimation

Researchers estimating causal effects are increasingly challenged with decisions on how to best control for a potentially high‐dimensional set of confounders. Typically, a single propensity score model is chosen and used to adjust for confounding, while the uncertainty surrounding which covariates to include into the propensity score model is often ignored, and failure to include even one important confounder will results in bias. We propose a practical and generalizable approach that overcomes the limitations described above through the use of model averaging. We develop and evaluate this approach in the context of double robust estimation. More specifically, we introduce the model averaged double robust (MA‐DR) estimators, which account for model uncertainty in both the propensity score and outcome model through the use of model averaging. The MA‐DR estimators are defined as weighted averages of double robust estimators, where each double robust estimator corresponds to a specific choice of the outcome model and the propensity score model. The MA‐DR estimators extend the desirable double robustness property by achieving consistency under the much weaker assumption that either the true propensity score model or the true outcome model be within a specified, possibly large, class of models. Using simulation studies, we also assessed small sample properties, and found that MA‐DR estimators can reduce mean squared error substantially, particularly when the set of potential confounders is large relative to the sample size. We apply the methodology to estimate the average causal effect of temozolomide plus radiotherapy versus radiotherapy alone on one‐year survival in a cohort of 1887 Medicare enrollees who were diagnosed with glioblastoma between June 2005 and December 2009.
Combining biomarkers linearly and nonlinearly for classification using the area under the ROC curve

In biomedical studies, it is often of interest to classify/predict a subject's disease status based on a variety of biomarker measurements. A commonly used classification criterion is based on area under the receiver operating characteristic curve (AUC). Many methods have been proposed to optimize approximated empirical AUC criteria, but there are two limitations to the existing methods. First, most methods are only designed to find the best linear combination of biomarkers, which may not perform well when there is strong nonlinearity in the data. Second, many existing linear combination methods use gradient‐based algorithms to find the best marker combination, which often result in suboptimal local solutions. In this paper, we address these two problems by proposing a new kernel‐based AUC optimization method called ramp AUC (RAUC). This method approximates the empirical AUC loss function with a ramp function and finds the best combination by a difference of convex functions algorithm. We show that as a linear combination method, RAUC leads to a consistent and asymptotically normal estimator of the linear marker combination when the data are generated from a semiparametric generalized linear model, just as the smoothed AUC method. Through simulation studies and real data examples, we demonstrate that RAUC outperforms smooth AUC in finding the best linear marker combinations, and can successfully capture nonlinear pattern in the data to achieve better classification performance. We illustrate our method with a dataset from a recent HIV vaccine trial. Copyright © 2016 John Wiley & Sons, Ltd.
Lovastatin upregulates microRNA-29b to reduce oxidative stress in rats with multiple cardiovascular risk factors

Aims Proteasome-linked oxidative stress is believed to cause endothelial dysfunction, an early event in cardiovascular diseases (CVD). Statin, as HMG-CoA reductase inhibitor, prevents endothelial dysfunction in CVD. However, the molecular mechanism of statin-mediated normalization of endothelial function is not completely elucidated. Methods and Results Lovastatin time/dose-dependently increased miR-29b expression and decreased proteasome activity in cultured human umbilical vein endothelial cells (HUVECs). Anti-miR-29b or overexpression of PA200 abolished lovastatin-induced inhibition of proteasome activity in HUVECs. In contrast, pre-miR-29b or PA200 siRNA mimics these effects of lovastatin on proteasome activity. Lovastatin inhibited oxidative stress induced by multiple oxidants including ox-LDL, H2O2, TNFa, homocysteine thiolactone (HTL), and high glucose (HG), which were reversed by inhibition of miR-29b in HUVECs. Ex vivo analysis indicated that lovastatin normalized the acetylcholine-induced endothelium-dependent relaxation and the redox status in isolated rat aortic arteries exposure to multiple cardiovascular risk factors. In vivo analysis revealed that administration of lovastatin remarkably suppressed oxidative stress and prevented endothelial dysfunction in rats with hyperglycemia, dyslipidemia, and hyperhomocysteinemia, as well as increased miR-29b expressions, reduced PA200 protein levels, and suppression of proteasome activity in aortic tissues. Conclusion Upregulation of miR-29b expression is a common mechanism contributing to endothelial dysfunction induced by multiple cardiovascular risk factors through PA200-dependent proteasome-mediated oxidative stress, which is prevented by lovastatin.
SPECIAL ARTICLE The Effects of Geometry, Elasticity, and External Pressures on the Diastolic Pressure-Volume and Stiffness-Stress Relations

SUMMARY The concept of an incremental elastic modulus is applied in the quantification of passive elastic stiffness-stress relations of intact heart muscle, and a transmural pressure-volume relation for the left ventricle is subsequently derived in terms of geometry, muscle elasticity, and external pressures to assess their importance. Physiological and clinical applications of this method indicate that: (1) stiffness-stress relations obtained on the basis of pressure-volume data from dog hearts are not significantly different from those obtained from muscle strips excised from these same hearts; (2) shape and the presence of right, ventricular, pericardial, or pleural pressures are of secondary importance in an assessment of passive elastic stiffness; and (3) dramatic shifts in the left ventricular intracavity pressure-volume relations following drug interventions are primarily due to the presence of substantial pericardial pressures; however, the transmural pressure-volume relations are not markedly altered, implying no alteration in the intrinsic ventricular compliance. Ore Res 44: 601-611, 1979 THE IMPORTANCE of the assessment of the
A Bayesian probit model with spatially varying coefficients for brain decoding using fMRI data

Recent advances in human neuroimaging have shown that it is possible to accurately decode how the brain perceives information based only on non‐invasive functional magnetic resonance imaging measurements of brain activity. Two commonly used statistical approaches, namely, univariate analysis and multivariate pattern analysis often lead to distinct patterns of selected voxels. One current debate in brain decoding concerns whether the brain's representation of sound categories is localized or distributed. We hypothesize that the distributed pattern of voxels selected by most multivariate pattern analysis models can be an artifact due to the spatial correlation among voxels. Here, we propose a Bayesian spatially varying coefficient model, where the spatial correlation is modeled through the variance‐covariance matrix of the model coefficients. Combined with a proposed region selection strategy, we demonstrate that our approach is effective in identifying the truly localized patterns of the voxels while maintaining robustness to discover truly distributed pattern. In addition, we show that localized or clustered patterns can be artificially identified as distributed if without proper usage of the spatial correlation information in fMRI data. Copyright © 2016 John Wiley & Sons, Ltd.
Frequency Calibration for the Scattering Matrix of the Simultaneous Polarimetric Radar

In order to obtain precise measurements of the target scattering matrix, the simultaneous fully polarimetric radar imposes strict requirements on the frequency stability in terms of accurate sampling rate and intermediate frequency (IF). In this letter, the effect of frequency stability on the fully polarimetric radar with a pair of orthogonal linear frequency modulated waveforms is analyzed. Furthermore, a calibration method is proposed to calibrate the IF and the sampling rate. The calibration results demonstrate and verify the validity of the proposed method.