A structural model of the E. coli PhoB Dimer in the transcription initiation complex

<p>Abstract</p> <p>Background</p> <p>There exist > 78,000 proteins and/or nucleic acids structures that were determined experimentally. Only a small portion of these structures corresponds to those of protein complexes. While homology modeling is able to exploit knowledge-based potentials of side-chain rotomers and backbone motifs to infer structures for new proteins, no such general method exists to extend our understanding of protein interaction motifs to novel protein complexes.</p> <p>Results</p> <p>We use a Motif Binding Geometries (MBG) approach, to infer the structure of a protein complex from the database of complexes of homologous proteins taken from other contexts (such as the helix-turn-helix motif binding double stranded DNA), and demonstrate its utility on one of the more important regulatory complexes in biology, that of the RNA polymerase initiating transcription under conditions of phosphate starvation. The modeled PhoB/RNAP/σ-factor/DNA complex is stereo-chemically reasonable, has sufficient interfacial Solvent Excluded Surface Areas (SESAs) to provide adequate binding strength, is physically meaningful for transcription regulation, and is consistent with a variety of known experimental constraints.</p> <p>Conclusions</p> <p>Based on a straightforward and easy to comprehend concept, "proteins and protein domains that fold similarly could interact similarly", a structural model of the PhoB dimer in the transcription initiation complex has been developed. This approach could be extended to enable structural modeling and prediction of other bio-molecular complexes. Just as models of individual proteins provide insight into molecular recognition, catalytic mechanism, and substrate specificity, models of protein complexes will provide understanding into the combinatorial rules of cellular regulation and signaling.</p

Altered lipoprotein metabolism in chronic inflammatory states: proinflammatory high-density lipoprotein and accelerated atherosclerosis in systemic lupus erythematosus and rheumatoid arthritis

In this review, the authors discuss the formation and structure of high-density lipoproteins (HDLs) and how those particles are altered in inflammatory or stress states to lose their capacity for reverse cholesterol transport and for antioxidant activity. In addition, abnormal HDLs can become proinflammatory (piHDLs) and actually contribute to oxidative damage. The assay by which piHDLs are identified involves studying the ability of test HDLs to prevent oxidation of low-density lipoproteins. Finally, the authors discuss the potential role of piHDLs (found in some 45% of patients with systemic lupus erythematosus and 20% of patients with rheumatoid arthritis) in the accelerated atherosclerosis associated with some chronic rheumatic diseases

Decision Support for Mitigation of Livestock Disease: Rinderpest as a Case Study

A versatile, interactive model to predict geographically resolved epidemic progression after pathogen introduction into a population is presented. Deterministic simulations incorporating a compartmental disease model run rapidly, facilitating the analysis of mitigations such as vaccination and transmission reduction on epidemic spread and progression. We demonstrate the simulation model using rinderpest infection of cattle, a devastating livestock disease. Rinderpest has been extinguished in the wild, but it is still a threat due to stored virus in some laboratories. Comparison of simulations to historical outbreaks provides some validation of the model. Simulations of potential outbreaks demonstrate potential consequences of rinderpest virus release for a variety of possible disease parameters and mitigations. Our results indicate that a rinderpest outbreak could result in severe social and economic consequences

Survival analysis of localized prostate cancer with deep learning.

In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics [Formula: see text] over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved [Formula: see text] 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained [Formula: see text] 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system

Summary results of the 2014-2015 DARPA Chikungunya challenge

BACKGROUND: Emerging pathogens such as Zika, chikungunya, Ebola, and dengue viruses are serious threats to national and global health security. Accurate forecasts of emerging epidemics and their severity are critical to minimizing subsequent mortality, morbidity, and economic loss. The recent introduction of chikungunya and Zika virus to the Americas underscores the need for better methods for disease surveillance and forecasting. METHODS: To explore the suitability of current approaches to forecasting emerging diseases, the Defense Advanced Research Projects Agency (DARPA) launched the 2014–2015 DARPA Chikungunya Challenge to forecast the number of cases and spread of chikungunya disease in the Americas. Challenge participants (n=38 during final evaluation) provided predictions of chikungunya epidemics across the Americas for a six-month period, from September 1, 2014 to February 16, 2015, to be evaluated by comparison with incidence data reported to the Pan American Health Organization (PAHO). This manuscript presents an overview of the challenge and a summary of the approaches used by the winners. RESULTS: Participant submissions were evaluated by a team of non-competing government subject matter experts based on numerical accuracy and methodology. Although this manuscript does not include in-depth analyses of the results, cursory analyses suggest that simpler models appear to outperform more complex approaches that included, for example, demographic information and transportation dynamics, due to the reporting biases, which can be implicitly captured in statistical models. Mosquito-dynamics, population specific information, and dengue-specific information correlated best with prediction accuracy. CONCLUSION: We conclude that with careful consideration and understanding of the relative advantages and disadvantages of particular methods, implementation of an effective prediction system is feasible. However, there is a need to improve the quality of the data in order to more accurately predict the course of epidemics

Design of 280 GHz feedhorn-coupled TES arrays for the balloon-borne polarimeter SPIDER

We describe 280 GHz bolometric detector arrays that instrument the balloon-borne polarimeter SPIDER. A primary science goal of SPIDER is to measure the large-scale B-mode polarization of the cosmic microwave background in search of the cosmic-inflation, gravitational-wave signature. 280 GHz channels aid this science goal by constraining the level of B-mode contamination from galactic dust emission. We present the focal plane unit design, which consists of a 16$\times$16 array of conical, corrugated feedhorns coupled to a monolithic detector array fabricated on a 150 mm diameter silicon wafer. Detector arrays are capable of polarimetric sensing via waveguide probe-coupling to a multiplexed array of transition-edge-sensor (TES) bolometers. The SPIDER receiver has three focal plane units at 280 GHz, which in total contains 765 spatial pixels and 1,530 polarization sensitive bolometers. By fabrication and measurement of single feedhorns, we demonstrate 14.7$^{\circ}$ FHWM Gaussian-shaped beams with $<$1% ellipticity in a 30% fractional bandwidth centered at 280 GHz. We present electromagnetic simulations of the detection circuit, which show 94% band-averaged, single-polarization coupling efficiency, 3% reflection and 3% radiative loss. Lastly, we demonstrate a low thermal conductance bolometer, which is well-described by a simple TES model and exhibits an electrical noise equivalent power (NEP) = 2.6 $\times$ 10$^{-17}$ W/$\sqrt{\mathrm{Hz}}$, consistent with the phonon noise prediction.Comment: Proceedings of SPIE Astronomical Telescopes + Instrumentation 201

Detection of the Power Spectrum of Cosmic Microwave Background Lensing by the Atacama Cosmology Telescope

We report the first detection of the gravitational lensing of the cosmic microwave background through a measurement of the four-point correlation function in the temperature maps made by the Atacama Cosmology Telescope. We verify our detection by calculating the levels of potential contaminants and performing a number of null tests. The resulting convergence power spectrum at 2-degree angular scales measures the amplitude of matter density fluctuations on comoving length scales of around 100 Mpc at redshifts around 0.5 to 3. The measured amplitude of the signal agrees with Lambda Cold Dark Matter cosmology predictions. Since the amplitude of the convergence power spectrum scales as the square of the amplitude of the density fluctuations, the 4-sigma detection of the lensing signal measures the amplitude of density fluctuations to 12%.Comment: 4 pages, 4 figures, replaced title and author list with version accepted by Physical Review Letters. Likelihood code can be downloaded from http://bccp.lbl.gov/~sudeep/ACTLensLike.htm

The Politics of Pleasure: Promenading on the Corniche and Beachgoing

Can the pleasures of young Palestinian women from refugee camps in promenading on the Beirut seaside Corniche on a warm summer evening be political? Or days spent at women-only beaches? If so, how do we understand such pleasure as everyday practices, as a politics of the present moment, or conversely (or simultaneously) as mechanisms of being co-opted into a broader apparatus of consumerist ideology and capitalist complacency? Drawing on ethnographic research over 2 years I argue that these moments of pleasure are caesuras in the massive apparatus of power – welded from strands of work, neoliberal practice, nationalist certitudes and political exclusion – which binds these women. These acts of pleasure cannot easily be categorised as ‘resistance’ but I argue that they should not facilely be considered reinforcements of hegemonic control either. They are momentary and ephemeral recognitions of ordinary life lived in hard times, attempts at clawing back an instant of joy from the drudgery of the everyday, and a surrender to the enjoyment of conviviality in public and urban spaces. If they are at all political, they are so because such conviviality is ever harder to sustain in the calamity of hopelessness that characterises so much politics today

CD or not CD, that is the question - a digital interobserver agreement study in coeliac disease

OBJECTIVE: Coeliac disease (CD) diagnosis generally depends on histological examination of duodenal biopsies. We present the first study analysing the concordance in examination of duodenal biopsies using digitised whole-slide images (WSIs). We further investigate whether the inclusion of IgA tTG and haemoglobin (Hb) data improves the inter-observer agreement of diagnosis.DESIGN: We undertook a large study of the concordance in histological examination of duodenal biopsies using digitised WSIs in an entirely virtual reporting setting. Our study was organised in two phases: in phase one, 13 pathologists independently classified 100 duodenal biopsies (40 normal; 40 CD; 20 indeterminate enteropathy) in the absence of any clinical or laboratory data. In phase two, the same pathologists examined the (re-anonymised) WSIs with the inclusion of IgA tTG and Hb data.RESULTS: We found the mean probability of two observers agreeing in the absence of additional data to be 0.73 (±0.08) with a corresponding Cohen's kappa of 0.59 (±0.11). We further showed that the inclusion of additional data increased the concordance to 0.80 (±0.06) with a Cohen's kappa coefficient of 0.67 (±0.09).CONCLUSION: We showed that the addition of serological data significantly improves the quality of CD diagnosis. However, the limited inter-observer agreement in CD diagnosis using digitised WSIs, even after the inclusion of IgA tTG and Hb data, indicates the important of interpreting duodenal biopsy in the appropriate clinical context. It further highlights the unmet need for an objective means of reproducible duodenal biopsy diagnosis, such as the automated analysis of WSIs using AI.<br/