HLA Class I Binding of HBZ Determines Outcome in HTLV-1 Infection

CD8(+) T cells can exert both protective and harmful effects on the virus-infected host. However, there is no systematic method to identify the attributes of a protective CD8(+) T cell response. Here, we combine theory and experiment to identify and quantify the contribution of all HLA class I alleles to host protection against infection with a given pathogen. In 432 HTLV-1-infected individuals we show that individuals with HLA class I alleles that strongly bind the HTLV-1 protein HBZ had a lower proviral load and were more likely to be asymptomatic. We also show that in general, across all HTLV-1 proteins, CD8(+) T cell effectiveness is strongly determined by protein specificity and produce a ranked list of the proteins targeted by the most effective CD8(+) T cell response through to the least effective CD8(+) T cell response. We conclude that CD8(+) T cells play an important role in the control of HTLV-1 and that CD8(+) cells specific to HBZ, not the immunodominant protein Tax, are the most effective. We suggest that HBZ plays a central role in HTLV-1 persistence. This approach is applicable to all pathogens, even where data are sparse, to identify simultaneously the HLA Class I alleles and the epitopes responsible for a protective CD8(+) T cell response

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation

Experimental Evidence on the Fairness of Medical Data Sales

Data-driven AI is essential to the future of medicine. Yet, medical institutions routinely face resistance from the public and even from their own employees when conducting medical transfers that are uncontroversially legal. This state of affairs suggests underlying dissonance between the legal status of and normative judgments about such transactions. Here, the judged fairness of medical data transfers is studied in a participant pool of lay people. The judged fairness of a proposed data transfer depends on the buyer’s anticipated use for the data as well as the medical institutions’ reason for selling the data. Data sales that would produce material gains for the medical institution holding the data do not achieve as high a fairness rating as those that serve a sacred purpose, even though the material gains could benefit the healthcare data subjects. Finally, lay people weigh the importance of medical data privacy so heavily that refusing to sell medical data to fund a child’s life-saving surgery is judged equally fair to selling the data to fund the operation. These findings suggest that controllers of healthcare datasets should consider normative values important to data subjects rather than solely looking to maximize material gains on their behalf when they seek to identify widely acceptable data transfer practices

Measuring Lay Reactions to Personal Data Markets

The recording, aggregation, and exchange of personal data is necessary to the development of socially-relevant machine learning applications. However, anecdotal and survey evidence show that ordinary people feel discontent and even anger regarding data collection practices that are currently typical and legal. This suggests that personal data markets in their current form do not adhere to the norms applied by ordinary people. The present study experimentally probes whether market transactions in a typical online scenario are accepted when evaluated by lay people. The results show that a high percentage of study participants refused to participate in a data pricing exercise, even in a commercial context where market rules would typically be expected to apply. For those participants who did price the data, the median price was an order of magnitude higher than the market price. These results call into question the notice and consent market paradigm that is used by technology firms and government regulators when evaluating data flows. The results also point to a conceptual mismatch between cultural and legal expectations regarding the use of personal data

The Too Accurate Algorithm

Much research on the law and policy concerns related to increasing use of algorithms has focused on ways to detect or prevent algorithmic misbehavior or mistakes. However, there are also problems that result when algorithms perform too well rather than too poorly. This Article makes the case that significant individual harms and social welfare losses alike can and already have occurred when algorithms are too performant. Thus, this Article presents an original contribution to the scholarly discussion of the algorithmic society through an exploration of concerns that arise when algorithms become too performant and an initial showing that the too accurate algorithm is likely a real and present danger to human well being. This Article proceeds in five Parts. Part I provides a motivating example, in which the many controversies surrounding content recommender systems and, likewise, proposed solutions to perceived threats from these systems are interpreted through the lens of overly high algorithmic performance. Part II surveys recent legislative proposals aimed at taming the perceived ills of the algorithmic society and identifies a persistent assumption that better algorithmic performance is an unmitigated good. Part III disproves this assumption of unmitigated benefits from algorithmic accuracy, as evaluated according to widely shared intuitions premised on deontological ethics, and Part IV offers a multidisciplinary account of problems supporting the notion of the too accurate algorithm, as seen from the perspective of results from psychology, economics, and computer science. Part V offers a causal taxonomy of harms that can result from the too accurate algorithm. The recognition of algorithmic performance as a factor that can produce harm is an essential step towards better design of and regulation for algorithmically-driven technologies. Technology designers, regulators, and legislators alike should account for accuracy harms in ongoing reform efforts to protect consumers and other vulnerable decision subjects in our increasingly algorithmic society

Private Sector Regulation Increases Contact Tracing App Uptake: A Lesson for Vaccines and Digital Health Passports

COVID-19 contact tracing apps have not achieved targeted levels of uptake in the U.S.. We hypothesized that the promotion of contact tracing apps by private sector actors such as employers and retailers could increase uptake relative to current policies. We find empirical support for this hypothesis in an online experiment run on a representative sample of US adults. The increase in uptake expected from private sector policies is correlated with differential levels of confidence in the private and public sectors. What’s more, private sector regulation enjoys more support than governmental app mandates. The novel option of using the private sector to promote contact tracing apps could offer important lessons for vaccination programs and vaccination passports

Can a Computer Invade Your Privacy? An Experimental Matchup of Algorithmic and Human Surveillance

The status of computers as surveillers has become increasingly important with the proliferation of computational surveillance employed in both governmental and commercial use cases. Anecdotal evidence and received wisdom alike suggest that algorithmic surveillance is not considered to be as invasive of privacy as human surveillance. Yet, despite a widely held privacy intuition in favor of algorithms, legal treatments of the divide between human and algorithmic observation have demonstrated a mixed set of outcomes. Likewise, the extent of information collected by surveillance is assumed to be relevant to acceptance of surveillance. Domestic surveillance programs are routinely defended on the basis that they merely collect metadata. Yet (again), despite appeals to intuition, the legal treatment of this distinction is far from uniform. These observations of appeals to intuition combined with inconsistent legal outcomes suggests a helpful role for experimental evidence. This paper presents an experimental vignette study of judgments about surveillance. Consistent with intuitions about humans versus computers, algorithmic surveillance is judged to be more privacy preserving than is human surveillance. However, this perceived privacy is not a dispositive factor for determining support for a surveillance program or personal preference of surveillance technology. Rather, accuracy - a factor rarely discussed in defenses of domestic surveillance - is more influential than privacy in lay assessments of a domestic surveillance program. These results provide behavioral information regarding the extent to which policy justifications for domestic surveillance match up with the judgments of ordinary people. Specifically, there is key missing information in ongoing debates about appropriate levels of government surveillance, particularly in the case of algorithmically-driven surveillance programs. Ordinary people care deeply about the accuracy of such programs. If state surveillance is to be justified on the basis of well-informed democratic support, policymakers should provide this information to the electorate