Discrimination-aware data transformations

A deep use of people-related data in automated decision processes might lead to an amplification of inequities already implicit in real world data. Nowadays, the development of technological solutions satisfying nondiscriminatory requirements is therefore one of the main challenges for the data management and data analytics communities. Nondiscrimination can be characterized in terms of different properties, like fairness, diversity, and coverage. Such properties should be achieved through a holistic approach, incrementally enforcing nondiscrimination constraints along all the stages of the data processing life-cycle, through individually independent choices rather than as a constraint on the final result. In this respect, the design of discrimination-aware solutions for the initial phases of the data processing pipeline (like data preparation), is extremely relevant: the sooner you spot the problem fewer problems you will get in the last analytical steps of the chain. In this PhD thesis, we are interested in nondiscrimination constraints defined in terms of coverage. Coverage aims at guaranteeing that the input dataset includes enough examples for each (protected) category of interest, thus increasing diversity to limit the introduction of bias during the next analytical steps. While coverage constraints have been mainly used for repairing raw datasets, we investigate their effects on data transformations, during data preparation, through query execution. To this aim, we propose coverage-based queries, as a means to achieve coverage constraint satisfaction on the result of data transformations defined in terms of selection-based queries, and specific algorithms for their processing. The proposed solutions rely on query rewriting, a key approach for enforcing specific constraints while guaranteeing transparency and avoiding disparate treatment discrimination. As far as we know and according to recent surveys in this domain, no other solutions addressing coverage-based rewriting during data transformations have been proposed so far. To guarantee a good compromise between efficiency and accuracy, both precise and approximate algorithms for coverage-based query processing are proposed. The results of an extensive experimental evaluation, carried out on both synthetic and real datasets, shows the effectiveness and the efficiency of the proposed approaches. Coverage-based queries can be easily integrated in relational machine learning data processing environments; to show their applicability, we integrate some of the designed algorithms in a machine learning data processing Python toolkit

Use of a biochar-based formulation for coating corn seeds

The series of experiments summarized here were conducted with the objective to evaluate the benefits of using biochar for coating corn seeds. Seeds coated with a slurry containing bio-based ingredients and biochar were tested for germination and vigor, and for their potential to being infected by the fungus Aspergillus flavus, using a novel single seed incubator specifically designed for these purposes. Biochar-treated seeds were also planted for two years in experimental fields in the Mississippi Delta to evaluate their effect on corn yield and aflatoxin contamination of kernels. Experiments were conducted with two types of commercial biochar; one was obtained from hardwood residues and the other from coconut shells. Application of both types of biochar for coating the seeds did not affect seed germination and vigor. However, treated seeds showed increased wettability and a more rapid water uptake. This resulted in a 8.5% shortening of germination time. Microbiological analysis using plate culturing and qPCR methods showed that biochar was not conducive to the growth of A. flavus. This was also confirmed by analyzing soil samples that were collected from experimental fields located in the Mississippi Delta. Most importantly, although aflatoxin contamination was different in the two experimental years, aflatoxin contamination of corn kernels was not affected by biochar-based formulations

Migraine and aura triggered by normobaric hypoxia

Background For future experimental studies or the development of targeted pharmaceutical agents, a deeper insight into the pathophysiology of migraine is of utmost interest. Reliable methods to trigger migraine attacks including aura are desirable to study this complex disease in vivo. Methods To investigate hypoxia as a trigger for migraine and aura, we exposed volunteers diagnosed with migraine, with (n = 16) and without aura (n = 14), to hypoxia utilizing a hypoxic chamber adjusted to a FiO2 of 12.6%. The occurrence of headache, migraine, aura, and accompanying symptoms were registered and vital signs were collected for 6 hours under hypoxia and 2 hours of follow-up. A binary logistic regression analysis examined the probability of triggering headaches, migraines, aura, photo- and phonophobia. Findings Of 30 participants, 24 (80.0%) developed headaches and 19 (63.3%) migraine, five (16.7%) reported aura. Two patients that developed aura never experienced aura symptoms before in their life. The increase of mean heart frequency was higher in patients developing headaches or migraine. Mean SpO2 during hypoxia was 83.39%. Conclusion Hypoxia was able to trigger migraine attacks and aura independently of any pharmacological agent