Breast cancer outcomes following predictive BRCA testing in Northern Ireland

Objectives: Since 1995, BRCA testing has identified 445 women in Northern Ireland who carry a pathogenic BRCA1/2 mutation, without breast cancer (bca) at testing. This study examined outcomes with reference to management, bca risk, and incidence following positive predictive testing. Methods: Patients were identified from the regional genetics database. Electronic clinical records were used to obtain management and outcome details. Median follow-up was to bca diagnosis, risk-reducing mastectomy (rrm), death, or last follow-up. Results: 169 women had a BRCA1 mutation, and 276 BRCA2. ■ BRCA1 cohort: Median follow-up post-testing was 3 years. 56 Women (33%) had rrm, and 12 are awaiting rrm (total 68, 40%) at a median age of 36 years. 12 Women (7%) developed bca, at a median of 2 years following testing. 4 Women were diagnosed with bcas incidentally at rrm. 7 Patients had bilateral mastectomies following a cancer diagnosis. 1 Woman developed bca following rrm (1.7%). Three deaths were reported: 1 breast cancer (1.7%), 1 ovarian cancer (1.7%), and 1 with no recorded breast/ovarian cancer diagnosis. ■ BRCA2 cohort: Median follow-up post-testing was 6 years. rrm was carried out in 75 women (27%), with 20 awaiting rrm (total 95, 35%); median age: 39 years. 16 Women developed bca (5.8%), at a median of 5 years from testing. 6 Women were diagnosed with cancer incidentally at rrm; 9 women had bilateral mastectomy following diagnosis, and 1 developed bca following rrm (1.3%). Five deaths were reported: 1 bca, 1 ovarian cancer, and 3 with no recorded breast/ovarian cancer diagnosis. Conclusions: The uptake of rrm following predictive BRCA testing in Northern Ireland is comparable with that reported elsewhere. The incidence of bca following rrm is low (<2%) in our cohort, with low breast and ovarian cancer–specific mortality following positive predictive testing.

Modelling the influence of biotic factors on species distribution patterns

Biotic interactions can have large effects on species distributions yet their role in shaping species ranges is seldom explored due to historical difficulties in incorporating biotic factors into models without a priori knowledge on interspecific interactions. Improved SDMs, which account for biotic factors and do not require a priori knowledge on species interactions, are needed to fully understand species distributions. Here, we model the influence of abiotic and biotic factors on species distribution patterns and explore the robustness of distributions under future climate change. We fit hierarchical spatial models using Integrated Nested Laplace Approximation (INLA) for lagomorph species throughout Europe and test the predictive ability of models containing only abiotic factors against models containing abiotic and biotic factors. We account for residual spatial autocorrelation using a conditional autoregressive (CAR) model. Model outputs are used to estimate areas in which abiotic and biotic factors determine species’ ranges. INLA models containing both abiotic and biotic factors had substantially better predictive ability than models containing abiotic factors only, for all but one of the four species. In models containing abiotic and biotic factors, both appeared equally important as determinants of lagomorph ranges, but the influences were spatially heterogeneous. Parts of widespread lagomorph ranges highly influenced by biotic factors will be less robust to future changes in climate, whereas parts of more localised species ranges highly influenced by the environment may be less robust to future climate. SDMs that do not explicitly include biotic factors are potentially misleading and omit a very important source of variation. For the field of species distribution modelling to advance, biotic factors must be taken into account in order to improve the reliability of predicting species distribution patterns both presently and under future climate change.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.