Removing the influence of a group variable in high-dimensional predictive modelling

In many application areas, predictive models are used to support or make important decisions. There is increasing awareness that these models may contain spurious or otherwise undesirable correlations. Such correlations may arise from a variety of sources, including batch effects, systematic measurement errors, or sampling bias. Without explicit adjustment, machine learning algorithms trained using these data can produce poor out-of-sample predictions which propagate these undesirable correlations. We propose a method to pre-process the training data, producing an adjusted dataset that is statistically independent of the nuisance variables with minimum information loss. We develop a conceptually simple approach for creating an adjusted dataset in high-dimensional settings based on a constrained form of matrix decomposition. The resulting dataset can then be used in any predictive algorithm with the guarantee that predictions will be statistically independent of the group variable. We develop a scalable algorithm for implementing the method, along with theory support in the form of independence guarantees and optimality. The method is illustrated on some simulation examples and applied to two case studies: removing machine-specific correlations from brain scan data, and removing race and ethnicity information from a dataset used to predict recidivism. That the motivation for removing undesirable correlations is quite different in the two applications illustrates the broad applicability of our approach.Comment: Update. 18 pages, 3 figure

Co-digestion of macroalgae for biogas production: an LCA-based environmental evaluation

Algae represent a favourable and potentially sustainable source of biomass for bioenergy-based industrial pathways in the future. The study, performed on a real pilot plant implemented in Augusta (Italy) within the frame of the BioWALK4Biofuels project, aims to figure out whether seaweed (macroalgae) cultivated in near-shore open ponds could be considered a beneficial aspect as a source of biomass for biogas production within the co-digestion with local agricultural biological waste. The LCA results confirm that the analysed A and B scenarios (namely the algae-based co-digestion scenario and agricultural mix feedstock scenario) present an environmental performance more favourable than that achieved with conventional non-renewable-based technologies (specifically natural gas - Scenario C). Results show that the use of seaweed (Scenario A) represent a feasible solution in order to replace classical biomass used for biofuel production from a land-based feedstock. The improvement of the environmental performances is quantifiable on 10% respect to Scenario B, and 38 times higher than Scenario

Adrenal cortex development and related disorders leading to adrenal insufficiency.

The adult human adrenal cortex produces steroid hormones that are crucial for life, supporting immune response, glucose homeostasis, salt balance and sexual maturation. It consists of three histologically distinct and functionally specialized zones. The fetal adrenal forms from mesodermal material and produces predominantly adrenal C19 steroids from its fetal zone, which involutes after birth. Transition to the adult cortex occurs immediately after birth for the formation of the zona glomerulosa and fasciculata for aldosterone and cortisol production and continues through infancy until the zona reticularis for adrenal androgen production is formed with adrenarche. The development of this indispensable organ is complex and not fully understood. This article gives an overview of recent knowledge gained of adrenal biology from two perspectives: one, from basic science studying adrenal development, zonation and homeostasis; and two, from adrenal disorders identified in persons manifesting with various isolated or syndromic forms of primary adrenal insufficiency