A new statistical framework for the quantification of covariate associations with species distributions

Identifying processes that shape species geographical ranges is a prerequisite for understanding environmental change. Currently, species distribution modelling methods do not offer credible statistical tests of the relative influence of climate factors and typically ignore other processes (e.g. biotic interactions and dispersal limitation). We use a hierarchical model fitted with Markov Chain Monte Carlo to combine ecologically plausible niche structures using regression splines to describe unimodal but potentially skewed response terms. We apply spatially explicit error terms that account for (and may help identify) missing variables. Using three example distributions of European bird species, we map model results to show sensitivity to change in each covariate. We show that the overall strength of climatic association differs between species and that each species has considerable spatial variation in both the strength of the climatic association and the sensitivity to climate change. Our methods are widely applicable to many species distribution modelling problems and enable accurate assessment of the statistical importance of biotic and abiotic influences on distributions.

Nature or nurture BMI and blood pressure at 90. Findings from the Belfast Elderly longitudinal free-living aging study Belfast

Hypertension is a key risk factor for stroke, cardiovascular disease and dementia. Although the link between weight, sodium and hypertension is established in younger people, little is known about their inter-relationship in people beyond 80 years of age. Associations between blood pressure, anthropometric indices and sodium were investigated in 495 apparently healthy, community-living participants (age 90, SD 4.8; range 80–106), from the cross-sectional Belfast Elderly Longitudinal Free-living Aging STudy (BELFAST) study. In age-sex-adjusted logistic regression models, blood pressure =140/90 mmHg significantly associated with body mass index (BMI) [odds ratio (OR)?=?1.28/ kg/m2], with weight (OR?=?1.22/kg) approaching significance (P?=?0.07). In further age-sex-adjusted models, blood pressure above the 120/80 mmHg normotensive reference value significantly associated with BMI (OR?=?1.44/kg/m2), weight (OR?=?1.36/kg), skin-fold-thickness (OR?=?1.33/mm) and serum sodium (OR?=?1.37 mmol/l). In BELFAST participants over 80 years old, blood pressure =140/90 mmHg is associated with BMI, in apparently similar ways to younger groups.

FMRFamide-like peptides in root knot nematodes and their potential role in nematode physiology

FMRFamide-like peptides (FLPs) are a diverse group of neuropeptides that are expressed abundantly in nematodes. They exert potent physiological effects on locomotory, feeding and reproductive musculature and also act as neuromodulators. However, little is known about the specific expression patterns and functions of individual peptides. The current study employed rapid amplification of cDNA ends-polymerase chain reaction (RACE-PCR) to characterize flp genes from infective juveniles of the root knot nematodes, Meloidogyne incognita and Meloidogyne minor. The peptides identified from these transcripts are sequelogs of FLPs from the free-living nematode, Caenorhabditis elegans; the genes have therefore been designated as Mi-flp-1, Mi-flp-7, Mi-flp-12, Mm-flp-12 and Mi-flp-14. Mi-flp-1 encodes five FLPs with the common C-terminal moiety, NFLRFamide. Mi-flp-7 encodes two copies of APLDRSALVRFamide and APLDRAAMVRFamide and one copy of APFDRSSMVRFamide. Mi-flp-12 and Mm-flp-12 encode the novel peptide KNNKFEFIRFamide (a longer version of RNKFEFIRFamide found in C. elegans). Mi-flp-14 encodes a single copy of KHEYLRFamide (commonly known as AF2 and regarded as the most abundant nematode FLP), and a single copy of the novel peptide KHEFVRFamide. These FLPs share a high degree of conservation between Meloidogyne species and nematodes from other clades, including those of humans and animals, perhaps suggesting a common neurophysiological role which may be exploited by novel drugs. FLP immunoreactivity was observed for the first time in Meloidogyne, in the circumpharyngeal nerve ring, pharyngeal nerves and ventral nerve cord. Additionally, in situ hybridization revealed Mi-flp-12 expression in an RIR-like neuron and Mi-flp-14 expression in SMB-like neurons, respectively. These localizations imply physiological roles for FLP-12 and FLP-14 peptides, including locomotion and sensory perception.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

Low MAD2 expression levels associate with reduced progression-free survival in patients with high-grade serous epithelial ovarian cancer

Epithelial ovarian cancer (EOC) has an innate susceptibility to become chemoresistant. Up to 30% of patients do not respond to conventional chemotherapy [paclitaxel (Taxol®) in combination with carboplatin] and, of those who have an initial response, many patients relapse. Therefore, an understanding of the molecular mechanisms that regulate cellular chemotherapeutic responses in EOC cells has the potential to impact significantly on patient outcome. The mitotic arrest deficiency protein 2 (MAD2), is a centrally important mediator of the cellular response to paclitaxel. MAD2 immunohistochemical analysis was performed on 82 high-grade serous EOC samples. A multivariate Cox regression analysis of nuclear MAD2 IHC intensity adjusting for stage, tumour grade and optimum surgical debulking revealed that low MAD2 IHC staining intensity was significantly associated with reduced progression-free survival (PFS) (p = 0.0003), with a hazard ratio of 4.689. The in vitro analyses of five ovarian cancer cell lines demonstrated that cells with low MAD2 expression were less sensitive to paclitaxel. Furthermore, paclitaxel-induced activation of the spindle assembly checkpoint (SAC) and apoptotic cell death was abrogated in cells transfected with MAD2 siRNA. In silico analysis identified a miR-433 binding domain in the MAD2 3' UTR, which was verified in a series of experiments. Firstly, MAD2 protein expression levels were down-regulated in pre-miR-433 transfected A2780 cells. Secondly, pre-miR-433 suppressed the activity of a reporter construct containing the 3'-UTR of MAD2. Thirdly, blocking miR-433 binding to the MAD2 3' UTR protected MAD2 from miR-433 induced protein down-regulation. Importantly, reduced MAD2 protein expression in pre-miR-433-transfected A2780 cells rendered these cells less sensitive to paclitaxel. In conclusion, loss of MAD2 protein expression results in increased resistance to paclitaxel in EOC cells. Measuring MAD2 IHC staining intensity may predict paclitaxel responses in women presenting with high-grade serous EOC.

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions. (C) 2012 Elsevier B.V. All rights reserved.

Regression analysis of spatial data

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Killer immunoglobulin-like Receptors (KIR) haplogroups A and B track with Natural Killer Cells and Cytokine Profile in Aged Subjects: Observations from Octo/Nonagenarians in the Belfast Elderly Longitudinal Free-living Aging STudy (BELFAST)

Background: Natural Killer Cells (NK) play an important role in detection and elimination of virus-infected, damaged or cancer cells. NK cell function is guided by expression of Killer Immunoglobulin-like Receptors (KIRs) and contributed to by the cytokine milieu. KIR molecules are grouped on NK cells into stimulatory and inhibitory KIR haplotypes A and B, through which NKs sense and tolerate HLA self-antigens or up-regulate the NK-cytotoxic response to cells with altered HLA self-antigens, damaged by viruses or tumours. We have previously described increased numbers of NK and NK-related subsets in association with sIL-2R cytokine serum levels in BELFAST octo/nonagenarians. We hypothesised that changes in KIR A and B haplotype gene frequencies could explain the increased cytokine profiles and NK compartments previously described in Belfast Elderly Longitudinal Free-living Aging STudy (BELFAST) octo/nonagenarians, who show evidence of ageing well.

Results: In the BELFAST study, 24% of octo/nonagenarians carried the KIR A haplotype and 76% KIR B haplotype with no differences for KIR A haplogroup frequency between male or female subjects (23% v 24%; p=0.88) or for KIR B haplogroup (77% v 76%; p=0.99). Octo/nonagenarian KIR A haplotype carriers showed increased NK numbers and percentage compared to Group B KIR subjects (p=0.003; p=0.016 respectively). There were no KIR A/ B haplogroup-associated changes for related CD57+CD8 (high or low) subsets. Using logistic regression, KIR B carriers were predicted to have higher IL-12 cytokine levels compared to KIR A carriers by about 3% (OR 1.03, confidence limits CI 0.99–1.09; p=0.027) and 14% higher levels for TGF-ß (active), a cytokine with an anti-inflammatory role, (OR 1.14, confidence limits CI 0.99–1.09; p=0.002).

Conclusion: In this observational study, BELFAST octo/nonagenarians carrying KIR A haplotype showed higher NK cell numbers and percentage compared to KIR B carriers. Conversely, KIR B haplotype carriers, with genes encoding for activating KIRs, showed a tendency for higher serum pro-inflammatory cytokines compared to KIR A carriers. While the findings in this study should be considered exploratory they may serve to stimulate debate about the immune signatures of those who appear to age slowly and who represent a model for good quality survivor-hood.© 2013 Rea et al.; licensee BioMed Central Ltd.

Drivers of inequality in disability-free expectancy at birth and age 85 across space and time in Great Britain

Background: Although mortality and health inequalities at birth have increased both geographically and in socioeconomic terms, little is known about inequalities at age 85, the fastest growing sector of the population in Great Britain (GB).

Aim: To determine whether trends and drivers of inequalities in life expectancy (LE) and disability-free life expectancy (DFLE) at age 85 between 1991 and 2001 are the same as those at birth.

Methods: DFLE at birth and age 85 for 1991 and 2001 by gender were calculated for each local authority in GB using the Sullivan method. Regression modelling was used to identify area characteristics (rurality, deprivation, social class composition, ethnicity, unemployment, retirement migration) that could explain inequalities in LE and DFLE.

Results: Similar to values at birth, LE and DFLE at age 85 both increased between 1991 and 2001 (though DFLE increased less than LE) and gaps across local areas widened (and more for DFLE than LE). The significantly greater increases in LE and DFLE at birth for less-deprived compared with more-deprived areas were still partly present at age 85. Considering all factors, inequalities in DFLE at birth were largely driven by social class composition and unemployment rate, but these associations appear to be less influential at age 85.

Conclusions: Inequalities between areas in LE and DFLE at birth and age 85 have increased over time though factors explaining inequalities at birth (mainly social class and unemployment rates) appear less important for inequalities at age 85.

Efficient Marginal Likelihood Computation for Gaussian Process Regression

In a Bayesian learning setting, the posterior distribution of a predictive model arises from a trade-off between its prior distribution and the conditional likelihood of observed data. Such distribution functions usually rely on additional hyperparameters which need to be tuned in order to achieve optimum predictive performance; this operation can be efficiently performed in an Empirical Bayes fashion by maximizing the posterior marginal likelihood of the observed data. Since the score function of this optimization problem is in general characterized by the presence of local optima, it is necessary to resort to global optimization strategies, which require a large number of function evaluations. Given that the evaluation is usually computationally intensive and badly scaled with respect to the dataset size, the maximum number of observations that can be treated simultaneously is quite limited. In this paper, we consider the case of hyperparameter tuning in Gaussian process regression. A straightforward implementation of the posterior log-likelihood for this model requires O(N^3) operations for every iteration of the optimization procedure, where N is the number of examples in the input dataset. We derive a novel set of identities that allow, after an initial overhead of O(N^3), the evaluation of the score function, as well as the Jacobian and Hessian matrices, in O(N) operations. We prove how the proposed identities, that follow from the eigendecomposition of the kernel matrix, yield a reduction of several orders of magnitude in the computation time for the hyperparameter optimization problem. Notably, the proposed solution provides computational advantages even with respect to state of the art approximations that rely on sparse kernel matrices.

Feature recognition & tool path generation for 5 axis step-nc machining of free form / irregular contoured surfaces

This research paper presents a five step algorithm to generate tool paths for machining Free form / Irregular Contoured Surface(s) (FICS) by adopting STEP-NC (AP-238) format. In the first step, a parametrized CAD model with FICS is created or imported in UG-NX6.0 CAD package. The second step recognizes the features and calculates a Closeness Index (CI) by comparing them with the B-Splines / Bezier surfaces. The third step utilizes the CI and extracts the necessary data to formulate the blending functions for identified features. In the fourth step Z-level 5 axis tool paths are generated by adopting flat and ball end mill cutters. Finally, in the fifth step, tool paths are integrated with STEP-NC format and validated. All these steps are discussed and explained through a validated industrial component.

Integration of cad/ tool path data for 5-axis step-nc machining of free form / irrregular contoured surfaces

This research paper presents the work on feature recognition, tool path data generation and integration with STEP-NC (AP-238 format) for features having Free form / Irregular Contoured Surface(s) (FICS). Initially, the FICS features are modelled / imported in UG CAD package and a closeness index is generated. This is done by comparing the FICS features with basic B-Splines / Bezier curves / surfaces. Then blending functions are caculated by adopting convolution theorem. Based on the blending functions, contour offsett tool paths are generated and simulated for 5 axis milling environment. Finally, the tool path (CL) data is integrated with STEP-NC (AP-238) format. The tool path algorithm and STEP- NC data is tested with various industrial parts through an automated UFUNC plugin.