Neurons in the primate dorsal striatum signal the uncertainty of object–reward associations

To learn, obtain reward and survive, humans and other animals must monitor, approach and act on objects that are associated with variable or unknown rewards. However, the neuronal mechanisms that mediate behaviours aimed at uncertain objects are poorly understood. Here we demonstrate that a set of neurons in an internal-capsule bordering regions of the primate dorsal striatum, within the putamen and caudate nucleus, signal the uncertainty of object–reward associations. Their uncertainty responses depend on the presence of objects associated with reward uncertainty and evolve rapidly as monkeys learn novel object–reward associations. Therefore, beyond its established role in mediating actions aimed at known or certain rewards, the dorsal striatum also participates in behaviours aimed at reward-uncertain objects

A neural mechanism for conserved value computations integrating information and rewards

Behavioral and economic theory dictate that we decide between options based on their values. However, humans and animals eagerly seek information about uncertain future rewards, even when this does not provide any objective value. This implies that decisions are made by endowing information with subjective value and integrating it with the value of extrinsic rewards, but the mechanism is unknown. Here, we show that human and monkey value judgements obey strikingly conserved computational principles during multi-attribute decisions trading off information and extrinsic reward. We then identify a neural substrate in a highly conserved ancient structure, the lateral habenula (LHb). LHb neurons signal subjective value, integrating information\u27s value with extrinsic rewards, and the LHb predicts and causally influences ongoing decisions. Neurons in key input areas to the LHb largely signal components of these computations, not integrated value signals. Thus, our data uncover neural mechanisms of conserved computations underlying decisions to seek information about the future

Machine Learning in Drug Discovery and Development Part 1: A Primer

Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.Laboratorio de Investigación y Desarrollo de Bioactivo

Plasmodium vivax malaria serological exposure markers: Assessing the degree and implications of cross-reactivity with P. knowlesi.

Serological markers are a promising tool for surveillance and targeted interventions for Plasmodium vivax malaria. P. vivax is closely related to the zoonotic parasite P. knowlesi, which also infects humans. P. vivax and P. knowlesi are co-endemic across much of South East Asia, making it important to design serological markers that minimize cross-reactivity in this region. To determine the degree of IgG cross-reactivity against a panel of P. vivax serological markers, we assayed samples from human patients with P. knowlesi malaria. IgG antibody reactivity is high against P. vivax proteins with high sequence identity with their P. knowlesi ortholog. IgG reactivity peaks at 7 days post-P. knowlesi infection and is short-lived, with minimal responses 1 year post-infection. We designed a panel of eight P. vivax proteins with low levels of cross-reactivity with P. knowlesi. This panel can accurately classify recent P. vivax infections while reducing misclassification of recent P. knowlesi infections

Searching for VHE gamma-ray emission associated with IceCube neutrino alerts using FACT, H.E.S.S., MAGIC, and VERITAS

The realtime follow-up of neutrino events is a promising approach to search for astrophysical neutrino sources. It has so far provided compelling evidence for a neutrino point source: the flaring gamma-ray blazar TXS 0506+056 observed in coincidence with the high-energy neutrino IceCube-170922A detected by IceCube. The detection of very-high-energy gamma rays (VHE, E>100GeV E > 100 G e V ) from this source helped establish the coincidence and constrained the modeling of the blazar emission at the time of the IceCube event. The four major imaging atmospheric Cherenkov telescope arrays (IACTs) - FACT, H.E.S.S., MAGIC, and VERITAS - operate an active follow-up program of target-of-opportunity observations of neutrino alerts sent by IceCube. This program has two main components. One are the observations of known gamma-ray sources around which a cluster of candidate neutrino events has been identified by IceCube (Gamma-ray Follow-Up, GFU). Second one is the follow-up of single high-energy neutrino candidate events of potential astrophysical origin such as IceCube-170922A. GFU has been recently upgraded by IceCube in collaboration with the IACT groups. We present here recent results from the IACT follow-up programs of IceCube neutrino alerts and a description of the upgraded IceCube GFU system