Joint spatiotemporal models to predict seabird densities at sea

Introduction: Seabirds are abundant, conspicuous members of marine ecosystems worldwide. Synthesis of distribution data compiled over time is required to address regional management issues and understand ecosystem change. Major challenges when estimating seabird densities at sea arise from variability in dispersion of the birds, sampling effort over time and space, and differences in bird detection rates associated with survey vessel type. Methods: Using a novel approach for modeling seabirds at sea, we applied joint dynamic species distribution models (JDSDM) with a vector-autoregressive spatiotemporal framework to survey data collected over nearly five decades and archived in the North Pacific Pelagic Seabird Database. We produced monthly gridded density predictions and abundance estimates for 8 species groups (77% of all birds observed) within Cook Inlet, Alaska. JDSDMs included habitat covariates to inform density predictions in unsampled areas and accounted for changes in observed densities due to differing survey methods and decadal-scale variation in ocean conditions. Results: The best fit model provided a high level of explanatory power (86% of deviance explained). Abundance estimates were reasonably precise, and consistent with limited historical studies. Modeled densities identified seasonal variability in abundance with peak numbers of all species groups in July or August. Seabirds were largely absent from the study region in either fall (e.g., murrelets) or spring (e.g., puffins) months, or both periods (shearwaters). Discussion: Our results indicated that pelagic shearwaters (Ardenna spp.) and tufted puffin (Fratercula cirrhata) have declined over the past four decades and these taxa warrant further investigation into underlying mechanisms explaining these trends. JDSDMs provide a useful tool to estimate seabird distribution and seasonal trends that will facilitate risk assessments and planning in areas affected by human activities such as oil and gas development, shipping, and offshore wind and renewable energy

Cost-Effectiveness Analysis of Efforts to Reduce Risk of Type 2 Diabetes and Cardiovascular Disease in Southwestern Pennsylvania, 2005-2007

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2938403/pdf/PCD75A109.pd

Profiles and Majority Voting-Based Ensemble Method for Protein Secondary Structure Prediction

Machine learning techniques have been widely applied to solve the problem of predicting protein secondary structure from the amino acid sequence. They have gained substantial success in this research area. Many methods have been used including k-Nearest Neighbors (k-NNs), Hidden Markov Models (HMMs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), which have attracted attention recently. Today, the main goal remains to improve the prediction quality of the secondary structure elements. The prediction accuracy has been continuously improved over the years, especially by using hybrid or ensemble methods and incorporating evolutionary information in the form of profiles extracted from alignments of multiple homologous sequences. In this paper, we investigate how best to combine k-NNs, ANNs and Multi-class SVMs (M-SVMs) to improve secondary structure prediction of globular proteins. An ensemble method which combines the outputs of two feed-forward ANNs, k-NN and three M-SVM classifiers has been applied. Ensemble members are combined using two variants of majority voting rule. An heuristic based filter has also been applied to refine the prediction. To investigate how much improvement the general ensemble method can give rather than the individual classifiers that make up the ensemble, we have experimented with the proposed system on the two widely used benchmark datasets RS126 and CB513 using cross-validation tests by including PSI-BLAST position-specific scoring matrix (PSSM) profiles as inputs. The experimental results reveal that the proposed system yields significant performance gains when compared with the best individual classifier

The Phosphatidylinositol 3,4,5-trisphosphate (PI(3,4,5)P 3 ) Binder Rasa3 Regulates Phosphoinositide 3-kinase (PI3K)-dependent Integrin α IIb β 3 Outside-in Signaling

The class I PI3K family of lipid kinases plays an important role in integrin αIIbβ3 function, thereby supporting thrombus growth and consolidation. Here, we identify Ras/Rap1GAP Rasa3 (GAP1IP4BP) as a major phosphatidylinositol 3,4,5-trisphosphate-binding protein in human platelets and a key regulator of integrin αIIbβ3 outside-in signaling. We demonstrate that cytosolic Rasa3 translocates to the plasma membrane in a PI3K-dependent manner upon activation of human platelets. Expression of wild-type Rasa3 in integrin αIIbβ3-expressing CHO cells blocked Rap1 activity and integrin αIIbβ3-mediated spreading on fibrinogen. In contrast, Rap1GAP-deficient (P489V) and Ras/Rap1GAP-deficient (R371Q) Rasa3 had no effect. We furthermore show that two Rasa3 mutants (H794L and G125V), which are expressed in different mouse models of thrombocytopenia, lack both Ras and Rap1GAP activity and do not affect integrin αIIbβ3-mediated spreading of CHO cells on fibrinogen. Platelets from thrombocytopenic mice expressing GAP-deficient Rasa3 (H794L) show increased spreading on fibrinogen, which in contrast to wild-type platelets is insensitive to PI3K inhibitors. Together, these results support an important role for Rasa3 in PI3K-dependent integrin αIIbβ3-mediated outside-in signaling and cell spreading

Urgent referral for suspected CNS cancer: which clinical features are associated with a positive predictive value of 3 % or more?

Background Urgent referral for suspected central nervous system (CNS) cancer is recommended, but little analysis of the referral criteria diagnostic performance has been conducted. New 2015 NICE guidance recommends direct brain imaging for patients with symptoms with positive predictive values (PPV) of 3 %, but further guidance is needed. Methods A 12-month retrospective evaluation of 393 patients referred under previous 2005 NICE 2-week rule criteria was conducted. Analysis was based on the three groups of symptoms forming the referral criteria, (1) CNS symptoms, (2) recent onset headaches, (3) rapidly progressive subacute focal deficit/cognitive/behavioural/personality change. Comparison was made with neuroimaging findings. Results Twelve (3.1 %) of 383 patients who attended clinic had CNS cancer suggesting the combination of clinical judgement and application of 2005 criteria matched the 2015 guideline’s PPV threshold. PPVs for the three groups of symptoms were (1) 4.1 % (95 % CIs 2.0 to 7.4 %), (2) 1.2 % (0.1 to 4.3 %) and (3) 3.7 % (0.1 to 19.0 %). Sensitivities were (1) 83.3 % (95 % CIs 51.6 to 97.9 %), (2) 16.7 % (2.1 to 48.4 %), and (3) 8.3 % (0.2 to 38.5 %); specificities were (1) 37.2 % (32.3 to 42.3 %), (2) 55.5 % (50.3 to 60.7 %) and (3) 93.0 % (89.9 to 95.4 %). Of 288 patients who underwent neuroimaging, 59 (20.5 %) had incidental findings, most commonly cerebrovascular disease. Conclusions The 2015 guidance is less prescriptive than previous criteria making clinical judgement more important. CNS symptoms had greatest sensitivity, while PPVs for CNS symptoms and rapidly progressive subacute deficit/cognitive/behavioural/personality change were closest to 3 %. Recent onset headaches had the lowest sensitivity and PPV

Geniculo-Cortical Projection Diversity Revealed within the Mouse Visual Thalamus

This is the final version of the article. It was first available from PLOS via http://dx.doi.org/10.1371/journal.pone.0144846All dLGN cell co-ordinates, V1 injection sites, dLGN boundary coordinates, experimental protocols and analysis scripts are available for download from figshare at https://figshare.com/s/36c6d937b1844eec80a1.The mouse dorsal lateral geniculate nucleus (dLGN) is an intermediary between retina and primary visual cortex (V1). Recent investigations are beginning to reveal regional complexity in mouse dLGN. Using local injections of retrograde tracers into V1 of adult and neonatal mice, we examined the developing organisation of geniculate projection columns: the population of dLGN-V1 projection neurons that converge in cortex. Serial sectioning of the dLGN enabled the distribution of labelled projection neurons to be reconstructed and collated within a common standardised space. This enabled us to determine: the organisation of cells within the dLGN-V1 projection columns; their internal organisation (topology); and their order relative to V1 (topography). Here, we report parameters of projection columns that are highly variable in young animals and refined in the adult, exhibiting profiles consistent with shell and core zones of the dLGN. Additionally, such profiles are disrupted in adult animals with reduced correlated spontaneous activity during development. Assessing the variability between groups with partial least squares regression suggests that 4?6 cryptic lamina may exist along the length of the projection column. Our findings further spotlight the diversity of the mouse dLGN?an increasingly important model system for understanding the pre-cortical organisation and processing of visual information. Furthermore, our approach of using standardised spaces and pooling information across many animals will enhance future functional studies of the dLGN.Funding was provided by a Wellcome Trust grant jointly awarded to IDT and SJE (083205, www.wellcome.ac.uk), and by MRC PhD Studentships awarded to MNL and ACH (http://www.mrc.ac.uk/)