First evidence of transovarial transmission of Kyasanur Forest disease virus in Haemaphysalis and Rhipicephalus ticks in the wild.

Background: Kyasanur forest disease virus (KFDV) is a tick-borne flavivirus causing debilitating and potentially fatal disease in people in the Western Ghats region of India. The transmission cycle is complex, involving multiple vector and host species, but there are significant gaps in ecological knowledge. Empirical data on pathogen-vector-host interactions and incrimination have not been updated since the last century, despite significant local changes in land use and the expansion of KFD to new areas.

Accurate machine learning of rate coefficients for state-to-state transitions in molecular collisions.

We present an algorithm that combines quantum scattering calculations with probabilistic machine-learning models to predict quantum dynamics rate coefficients for a large number of state-to-state transitions in molecule-molecule collisions much faster than with direct solutions of the Schrödinger equation. By utilizing the predictive power of Gaussian process regression with kernels, optimized to make accurate predictions outside of the input parameter space, the present strategy reduces the computational cost by about 75%, with an accuracy within 5%. Our method uses temperature dependences of rate coefficients for transitions from the isolated states of initial rotational angular momentum j, determined via explicit calculations, to predict the temperature dependences of rate coefficients for other values of j.

Profile and outcomes of retinal artery occlusion: The underrealized need to expedite presentation.

Purpose: To evaluate the clinical profile of retinal artery occlusion (RAO) and impact of presentation on visual outcomes.

Design: A retrospective analysis of case files of 3070 patients with RAO was performed using electronic medical records.

Methods: Demographic data were analyzed using descriptive statistics.

Best-subset instrumental variable selection method using mixed integer optimization with applications to health-related quality of life and education-wage analyses.

The classical best-subset selection method has been demonstrated to be nondeterministic polynomial-time hard and thus presents computational challenges. This problem can now be solved via advanced mixed integer optimization (MIO) algorithms for linear regression. We extend this methodology to linear instrumental variable (IV) regression and propose the best-subset instrumental variable (BSIV) method incorporating the MIO procedure.

LASSO-type instrumental variable selection methods with an application to Mendelian randomization.

Valid instrumental variables (IVs) must not directly impact the outcome variable and must also be uncorrelated with nonmeasured variables. However, in practice, IVs are likely to be invalid. The existing methods can lead to large bias relative to standard errors in situations with many weak and invalid instruments.