Super-Resolved Enhancement of a Single Image and Its Application in Cardiac MRI

Super-resolved image enhancement is of great importance in medical imaging. Conventional methods often require multiple low resolution (LR) images from different views of the same object or learning from large amount of training datasets to achieve success. However, in real clinical environments, these prerequisites are rarely fulfilled. In this paper, we present a self-learning based method to perform superresolution (SR) from a single LR input. The mappings between the given LR image and its downsampled versions are modeled using support vector regression on features extracted from sparse coded dictionaries, coupled with dual-tree complex wavelet transform based denoising. We demonstrate the efficacy of our method in application of cardiac MRI enhancement. Both quantitative and qualitative results show that our SR method is able to preserve fine textural details that can be corrupted by noise, and therefore can maintain crucial diagnostic information

An extracellular steric seeding mechanism for Eph-ephrin signaling platform assembly

Erythropoetin-producing hepatoma (Eph) receptors are cell-surface protein tyrosine kinases mediating cell-cell communication. Upon activation, they form signaling clusters. We report crystal structures of the full ectodomain of human EphA2 (eEphA2) both alone and in complex with the receptor-binding domain of the ligand ephrinA5 (ephrinA5 RBD). Unliganded eEphA2 forms linear arrays of staggered parallel receptors involving two patches of residues conserved across A-class Ephs. eEphA2-ephrinA5 RBD forms a more elaborate assembly, whose interfaces include the same conserved regions on eEphA2, but rearranged to accommodate ephrinA5 RBD. Cell-surface expression of mutant EphA2s showed that these interfaces are critical for localization at cell-cell contacts and activation-dependent degradation. Our results suggest a 'nucleation' mechanism whereby a limited number of ligand-receptor interactions 'seed' an arrangement of receptors which can propagate into extended signaling arrays

Does publication bias inflate the apparent efficacy of psychological treatment for major depressive disorder? A systematic review and meta-analysis of US national institutes of health-funded trials

Background The efficacy of antidepressant medication has been shown empirically to be overestimated due to publication bias, but this has only been inferred statistically with regard to psychological treatment for depression. We assessed directly the extent of study publication bias in trials examining the efficacy of psychological treatment for depression. Methods and Findings We identified US National Institutes of Health grants awarded to fund randomized clinical trials comparing psychological treatment to control conditions or other treatments in patients diagnosed with major depressive disorder for the period 1972–2008, and we determined whether those grants led to publications. For studies that were not published, data were requested from investigators and included in the meta-analyses. Thirteen (23.6%) of the 55 funded grants that began trials did not result in publications, and two others never started. Among comparisons to control conditions, adding unpublished studies (Hedges’ g = 0.20; CI95% -0.11~0.51; k = 6) to published studies (g = 0.52; 0.37~0.68; k = 20) reduced the psychotherapy effect size point estimate (g = 0.39; 0.08~0.70) by 25%. Moreover, these findings may overestimate the "true" effect of psychological treatment for depression as outcome reporting bias could not be examined quantitatively. Conclusion The efficacy of psychological interventions for depression has been overestimated in the published literature, just as it has been for pharmacotherapy. Both are efficacious but not to the extent that the published literature would suggest. Funding agencies and journals should archive both original protocols and raw data from treatment trials to allow the detection and correction of outcome reporting bias. Clinicians, guidelines developers, and decision makers should be aware that the published literature overestimates the effects of the predominant treatments for depression

Stochastic Multichannel Ranking with Brain Dynamics Preferences.

A driver's cognitive state of mental fatigue significantly affects his or her driving performance and more important, public safety. Previous studies have leveraged reaction time (RT) as the metric for mental fatigue and aim at estimating the exact value of RT using electroencephalogram (EEG) signals within a regression model. However, due to the easily corrupted and also nonsmooth properties of RTs during data collection, methods focusing on predicting the exact value of a noisy measurement, RT generally suffer from poor generalization performance. Considering that human RT is the reflection of brain dynamics preference (BDP) rather than a single regression output of EEG signals, we propose a novel channel-reliability-aware ranking (CArank) model for the multichannel ranking problem. CArank learns from BDPs using EEG data robustly and aims at preserving the ordering corresponding to RTs. In particular, we introduce a transition matrix to characterize the reliability of each channel used in the EEG data, which helps in learning with BDPs only from informative EEG channels. To handle large-scale EEG signals, we propose a stochastic-generalized expectation maximum (SGEM) algorithm to update CArank in an online fashion. Comprehensive empirical analysis on EEG signals from 40 participants shows that our CArank achieves substantial improvements in reliability while simultaneously detecting noisy or less informative EEG channels

Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation

Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation

Conformational changes during human P2X7 receptor activation examined by structural modelling and cysteine-based cross-linking studies

The P2X7 receptor (P2X7R) is important in mediating a range of physiological functions and pathologies associated with tissue damage and inflammation and represents an attractive therapeutic target. However, in terms of their structure-function relationships, the mammalian P2X7Rs remain poorly characterised compared to some of their other P2XR counterparts. In this study, combining cysteine-based cross-linking and whole-cell patch-clamp recording, we examined six pairs of residues (A44/I331, D48/I331, I58/F311, S60/L320, I75/P177 and K81/V304) located in different parts of the extracellular and transmembrane domains of the human P2X7R. These residues are predicted to undergo substantial movement during the transition of the receptor ion channel from the closed to the open state, predictions which are made based on structural homology models generated from the crystal structures of the zebrafish P2X4R. Our results provide evidence that among the six pairs of cysteine mutants, D48C/I133C and K81C/V304C formed disulphide bonds that impaired the channel gating to support the notion that such conformational changes, particularly those in the outer ends of the transmembrane domains, are critical for human P2X7R activation

Analysis of the Endocrine Responses to Anti-Diabetes Drugs: An Issue of Elevated Plasma Renin Concentration in Sodium-Glucose Co-Transporter 2 Inhibitor

Cheng-Wei Lin,1– 3 Shih-Yuan Hung,1– 3 I-Wen Chen1,2 1Division of Endocrinology and Metabolism, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan; 2College of Medicine, Chang Gung University, Taoyuan City, Taiwan; 3School of Medicine, National Tsing Hua University, Hsinchu City, TaiwanCorrespondence: Cheng-Wei Lin, Division of Endocrinology and Metabolism, Chang Gung Memorial Hospital at Linkou, 5, Fusing St., Guishan Dist, Taoyuan City, 333, Taiwan, Tel +886-3-328-1200 #8821, Fax +886-3-328-8257, Email [email protected]: Glucose metabolism is associated with several endocrine disorders. Anti-diabetes drugs are crucial in controlling diabetes and its complications; nevertheless, few studies have been carried out involving endocrine function. This study aimed to investigate the association between anti-diabetes drugs and endocrine parameters.Patients and Methods: We performed a study of 180 consecutive patients with type 2 diabetes who attended a medical center. Laboratory measurements of metabolic values and endocrine parameters were assessed after a stable treatment regimen of more than 12 weeks. The differences in various endocrine parameters were compared between subjects with or without certain anti-diabetes drugs, with the administrated anti-diabetes drugs being analyzed to find independent risks associated with elevated endocrine parameters.Results: After maintaining stable treatment, acceptable glycemic control was noted with an average HbA1c of 7.55% in females and 7.43% in males. Participants taking sulfonylurea (55.8 vs 26.34 ng/L, P=0.043), dipeptidyl peptidase-4 inhibitor (DPP4i) (47.14 vs 32.26 ng/L, P=0.096), or sodium-glucose co-transporter 2 inhibitor (SGLT2i) (64.58 vs 28.11 ng/L, P=0.117) had higher plasma renin concentrations compared to those without this drug but the aldosterone levels did not differ, as well as for other adrenal tests and thyroid function. Under linear regression modeling, SGLT2i was found to be independently associated with a risk of high renin level (beta coefficient: 30.186, 95% confidence interval: 1.71─58.662, P=0.038), whereas sulfonylurea only had borderline associations (B: 21.143, 95% CI: − 2.729─45.014, P=0.082). Additionally, renin-angiotensin-aldosterone system (RAAS) blockade (B: 36.728, 95% CI: 12.16─61.295, P=0.004) or diuretics (B: 47.847, 95% CI: 2.039─93.655, P=0.041) was also independently associated with increased renin levels.Conclusion: SGLT2i was the only class of anti-diabetes drugs independently associated with elevated renin levels, with results similar to RAAS blockade and diuretics. Although SGLT2i appears to protect reno- and cardio-function, the clinical impact of increased renin warrants further precise study for verification.Keywords: type 2 diabetes, renin-angiotensin-aldosterone system, plasma renin concentration, sodium-glucose co-transporter 2 inhibitor, endocrin

Evidence for the Higgs-boson Yukawa coupling to tau leptons with the ATLAS detector

Results of a search for H → τ τ decays are presented, based on the full set of proton-proton collision data recorded by the ATLAS experiment at the LHC during 2011 and 2012. The data correspond to integrated luminosities of 4.5 fb−1 and 20.3 fb−1 at centre-of-mass energies of √s = 7 TeV and √s = 8 TeV respectively. All combinations of leptonic (τ → `νν¯ with ` = e, µ) and hadronic (τ → hadrons ν) tau decays are considered. An excess of events over the expected background from other Standard Model processes is found with an observed (expected) significance of 4.5 (3.4) standard deviations. This excess provides evidence for the direct coupling of the recently discovered Higgs boson to fermions. The measured signal strength, normalised to the Standard Model expectation, of µ = 1.43 +0.43 −0.37 is consistent with the predicted Yukawa coupling strength in the Standard Model

The MCL-1 BH3 helix is an exclusive MCL-1 inhibitor and apoptosis sensitizer

available in PMC 2011 February 3.MCL-1 has emerged as a major oncogenic and chemoresistance factor. A screen of stapled peptide helices identified the MCL-1 BH3 domain as selectively inhibiting MCL-1 among the related anti-apoptotic Bcl-2 family members, providing insights into the molecular determinants of binding specificity and a new approach for sensitizing cancer cells to apoptosis.National Institutes of Health (U.S.) (NIH award 5RO1GM084181)National Institutes of Health (U.S.) (NIH grant 5P01CA92625)National Institutes of Health (U.S.) (Ruth L. Kirschstein National Research Service Award 1F31CA144566)Burroughs Wellcome Fund (Career Award

Methanogens, sulphate and heavy metals: a complex system

Anaerobic digestion (AD) is a well-established technology used for the treatment of wastes and wastewaters with high organic content. During AD organic matter is converted stepwise to methane-containing biogasa renewable energy carrier. Methane production occurs in the last AD step and relies on methanogens, which are rather sensitive to some contaminants commonly found in wastewaters (e.g. heavy metals), or easily outcompeted by other groups of microorganisms (e.g. sulphate reducing bacteria, SRB). This review gives an overview of previous research and pilot-scale studies that shed some light on the effects of sulphate and heavy metals on methanogenesis. Despite the numerous studies on this subject, comparison is not always possible due to differences in the experimental conditions used and parameters explained. An overview of the possible benefits of methanogens and SRB co-habitation is also covered. Small amounts of sulphide produced by SRB can precipitate with metals, neutralising the negative effects of sulphide accumulation and free heavy metals on methanogenesis. Knowledge on how to untangle and balance sulphate reduction and methanogenesis is crucial to take advantage of the potential for the utilisation of biogenic sulphide as a metal detoxification agent with minimal loss in methane production in anaerobic digesters.The research was financially supported by the People Program (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007-2013 under REA agreement 289193