Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The association of race and COVID-19 mortality

BACKGROUND:COVID-19 mortality disproportionately affects the Black population in the United States (US). To explore this association a cohort study was undertaken. METHODS: We assembled a cohort of 505,992 patients receiving ambulatory care at Bronx Montefiore Health System (BMHS) between 1/1/18 and 1/1/20 to evaluate the relative risk of hospitalization and death in two time-periods, the pre-COVID time-period (1/1/20–2/15/20) and COVID time-period (3/1/20–4/15/20). COVID testing, hospitalization and mortality were determined with the Black and Hispanic patient population compared separately to the White population using logistic modeling. Evaluation of the interaction of pre-COVID and COVID time periods and race, with respect to mortality was completed. FINDINGS: A total of 9,286/505,992 (1.8%) patients were hospitalized during either or both pre-COVID or COVID periods. Compared to Whites the relative risk of hospitalization of Black patients did not increase in the COVID period (p for interaction=0.12). In the pre- COVID period, compared to Whites, the odds of death for Blacks and Hispanics adjusted for comorbidity was statistically equivalent. In the COVID period compared to Whites the adjusted odds of death for Blacks was 1.6 (95% CI 1.2–2.0, p = 0.001). There was a significant increase in Black mortality risk from pre-COVID to COVID periods (p for interaction=0.02). Adjustment for relevant clinical and social indices attenuated but did not fully explain the observed difference in Black mortality. INTERPRETATION: The BMHS COVID experience demonstrates that Blacks do have a higher mortality with COVID incompletely explained by age, multiple reported comorbidities and available metrics of sociodemographic disparity

Exploiting submodular value functions for scaling up active perception

In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty about one or more hidden variables. For example, a mobile robot takes sensory actions to efficiently navigate in a new environment. While partially observable Markov decision processes (POMDPs) provide a natural model for such problems, reward functions that directly penalize uncertainty in the agent’s belief can remove the piecewise-linear and convex (PWLC) property of the value function required by most POMDP planners. Furthermore, as the number of sensors available to the agent grows, the computational cost of POMDP planning grows exponentially with it, making POMDP planning infeasible with traditional methods. In this article, we address a twofold challenge of modeling and planning for active perception tasks. We analyze rhoPOMDP and POMDP-IR, two frameworks for modeling active perception tasks, that restore the PWLC property of the value function. We show the mathematical equivalence of these two frameworks by showing that given a rhoPOMDP along with a policy, they can be reduced to a POMDP-IR and an equivalent policy (and vice-versa). We prove that the value function for the given rhoPOMDP (and the given policy) and the reduced POMDP-IR (and the reduced policy) is the same. To efficiently plan for active perception tasks, we identify and exploit the independence properties of POMDP-IR to reduce the computational cost of solving POMDP-IR (and rhoPOMDP). We propose greedy point-based value iteration (PBVI), a new POMDP planning method that uses greedy maximization to greatly improve scalability in the action space of an active perception POMDP. Furthermore, we show that, under certain conditions, including submodularity, the value function computed using greedy PBVI is guaranteed to have bounded error with respect to the optimal value function. We establish the conditions under which the value function of an active perception POMDP is guaranteed to be submodular. Finally, we present a detailed empirical analysis on a dataset collected from a multi-camera tracking system employed in a shopping mall. Our method achieves similar performance to existing methods but at a fraction of the computational cost leading to better scalability for solving active perception tasks.Algorithmic