Algorithmic encoding of protected characteristics in chest X-ray disease detection models

Background It has been rightfully emphasized that the use of AI for clinical decision making could amplify health disparities. An algorithm may encode protected characteristics, and then use this information for making predictions due to undesirable correlations in the (historical) training data. It remains unclear how we can establish whether such information is actually used. Besides the scarcity of data from underserved populations, very little is known about how dataset biases manifest in predictive models and how this may result in disparate performance. This article aims to shed some light on these issues by exploring methodology for subgroup analysis in image-based disease detection models. Methods We utilize two publicly available chest X-ray datasets, CheXpert and MIMIC-CXR, to study performance disparities across race and biological sex in deep learning models. We explore test set resampling, transfer learning, multitask learning, and model inspection to assess the relationship between the encoding of protected characteristics and disease detection performance across subgroups. Findings We confirm subgroup disparities in terms of shifted true and false positive rates which are partially removed after correcting for population and prevalence shifts in the test sets. We find that transfer learning alone is insufficient for establishing whether specific patient information is used for making predictions. The proposed combination of test-set resampling, multitask learning, and model inspection reveals valuable insights about the way protected characteristics are encoded in the feature representations of deep neural networks. Interpretation Subgroup analysis is key for identifying performance disparities of AI models, but statistical differences across subgroups need to be taken into account when analyzing potential biases in disease detection. The proposed methodology provides a comprehensive framework for subgroup analysis enabling further research into the underlying causes of disparities. Funding European Research Council Horizon 2020, UK Research and Innovation

Effective User Experience in Online Technical Communication Courses: Employing Multiple Methods within Organizational Contexts to Assess Usability

In teaching online technical communication courses, shaping an electronic interface requires extensive consideration of the user experience, both for students and for faculty members who design and teach the courses. Technical communication faculty members should provide strong examples of effective user experiences and should be leaders in making the interfaces of online learning management systems as usable as possible. Principles of usability designed for general web sites may or may not apply to learning management systems designed for educational purposes. In order to create effective online technical communication courses, one needs to consider both usability concerns and pedagogical concerns. To assess the usability and pedagogical effectiveness of online courses, faculty members may use indirect means such as heuristic analyses. In addition, they may use direct means such as usability testing, student feedback, and analytic tools. Each approach has advantages as well as limitations. Faculty members will gain the richest information through using multiple approaches. In assessing usability and pedagogical effectiveness, faculty members also need to consider the situational constraints and resources in their unique contexts. Understanding and adapting their approaches to use resources well and to work within constraints will benefit their abilities to enhance their student users' experiences with online courses

Novel one-dimensional structures and solution behaviour of copper(II) bromide and chloride complexes of a new pentapyridyldiamine ligand

Copper(II) bromide and chloride complexes of the new heptadentate ligand 2,6-bis(bis(2-pyridylmethyl)amino)methylpyridine (L) have been prepared. For the bromide complexes, chains of novel, approximately C-2-symmetric, chiral [Cu-2(L)Br-2](2+) 'wedge-shaped' tectons are found. The links between the dicopper tectons and the overall chirality and packing of the chains are dictated by the bromide ion content, not the counter anion. In contrast, the chloride complexes exhibit linked asymmetric [Cu-2(L)Cl-3](+) tectons with distinct N3CuCl2 and N4CuCl2 centres in the solid. The overall structures of the dicopper bromide and chloride units persist in solution irrespective of the halide. The redox chemistry of the various species is also described

Multilocus sequence typing of Scedosporium apiospermum and Pseudallescheria boydii isolates from cystic fibrosis patients

Background: Scedosporium and Pseudallescheria species are the second most common lung-colonising fungi in cystic fibrosis (CF) patients. For epidemiological reasons it is important to trace sources of infection, routes of transmission and to determine whether these fungi are transient or permanent colonisers of the respiratory tract. Molecular typing methods like multilocus sequence typing (MLST) help provide this data. Methods: Clinical isolates of the P. boydii complex (including S. apiospermum and P. boydii) from CF patients in different regions of Germany were studied using MLST. Five gene loci, ACT, CAL, RPB2, BT2 and SOD2, were analysed. Results: The S. apiospermum isolates from 34 patients were assigned to 32 sequence types (STs), and the P. boydii isolates from 14 patients to 8 STs. The results revealed that patients can be colonised by individual strains for years. Conclusions: The MLST scheme developed for S. apiospermum and P. boydii is a highly effective tool for epidemiologic studies worldwide. The MLST data are accessible at http://mlst.mycologylab.org/

Developmental MRI markers cosegregate juvenile patients with myoclonic epilepsy and their healthy siblings

OBJECTIVE: MRI studies of genetic generalized epilepsies have mainly described group-level changes between patients and healthy controls. To determine the endophenotypic potential of structural MRI in juvenile myoclonic epilepsy (JME), we examined MRI-based cortical morphologic markers in patients and their healthy siblings. METHODS: In this prospective, cross-sectional study, we obtained 3T MRI in patients with JME, siblings, and controls. We mapped sulco-gyral complexity and surface area, morphologic markers of brain development, and cortical thickness. Furthermore, we calculated mean geodesic distance, a surrogate marker of cortico-cortical connectivity. RESULTS: Compared to controls, patients and siblings showed increased folding complexity and surface area in prefrontal and cingulate cortices. In these regions, they also displayed abnormally increased geodesic distance, suggesting network isolation and decreased efficiency, with strongest effects for limbic, fronto-parietal, and dorsal-attention networks. In areas of findings overlap, we observed strong patient-sibling correlations. Conversely, neocortical thinning was present in patients only and related to disease duration. Patients showed subtle impairment in mental flexibility, a frontal lobe function test, as well as deficits in naming and design learning. Siblings' performance fell between patients and controls. CONCLUSION: MRI markers of brain development and connectivity are likely heritable and may thus serve as endophenotypes. The topography of morphologic anomalies and their abnormal structural network integration likely explains cognitive impairments in patients with JME and their siblings. By contrast, cortical atrophy likely represents a marker of disease

BrainStat: A toolbox for brain-wide statistics and multimodal feature associations

Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation

The acquisition of Sign Language: The impact of phonetic complexity on phonology

Research into the effect of phonetic complexity on phonological acquisition has a long history in spoken languages. This paper considers the effect of phonetics on phonological development in a signed language. We report on an experiment in which nonword-repetition methodology was adapted so as to examine in a systematic way how phonetic complexity in two phonological parameters of signed languages — handshape and movement — affects the perception and articulation of signs. Ninety-one Deaf children aged 3–11 acquiring British Sign Language (BSL) and 46 hearing nonsigners aged 6–11 repeated a set of 40 nonsense signs. For Deaf children, repetition accuracy improved with age, correlated with wider BSL abilities, and was lowest for signs that were phonetically complex. Repetition accuracy was correlated with fine motor skills for the youngest children. Despite their lower repetition accuracy, the hearing group were similarly affected by phonetic complexity, suggesting that common visual and motoric factors are at play when processing linguistic information in the visuo-gestural modality

Genetic and phylogenetic uncoupling of structure and function in human transmodal cortex

Brain structure scaffolds intrinsic function, supporting cognition and ultimately behavioral flexibility. However, it remains unclear how a static, genetically controlled architecture supports flexible cognition and behavior. Here, we synthesize genetic, phylogenetic and cognitive analyses to understand how the macroscale organization of structure-function coupling across the cortex can inform its role in cognition. In humans, structure-function coupling was highest in regions of unimodal cortex and lowest in transmodal cortex, a pattern that was mirrored by a reduced alignment with heritable connectivity profiles. Structure-function uncoupling in macaques had a similar spatial distribution, but we observed an increased coupling between structure and function in association cortices relative to humans. Meta-analysis suggested regions with the least genetic control (low heritable correspondence and different across primates) are linked to social-cognition and autobiographical memory. Our findings suggest that genetic and evolutionary uncoupling of structure and function in different transmodal systems may support the emergence of complex forms of cognition