Search for: All records

The Lim1 transcription factor is required in Drosophila for patterning the eye-antennal disk. At the adult stage, Lim1 is strongly expressed in Johnston’s Organ (JO) neurons, the antennal auditory organ. Using RNAi-mediated knockdown of Lim1 using a strong neuronal driver, we find a significant reduction in electrophysiological responses to auditory stimuli, recorded from the antennal nerve. This reduction can be accounted for by Lim1 knockdown in the auditory subset of JO neurons, with no effect of knockdown in JO neuron subsets associated with wind or gravity detection. Conversely, Lim1 knockdown in JO sense organ precursors had no effect on hearing. Mosaic animals with antennal clones of the Lim1^E9 null mutation showed morphological defects in the antenna, and significant auditory electrophysiological defects. Our results are consistent with two distinct functions for Lim1 in the antenna, including an early patterning function in the eye-antennal disk, and a later neural differentiation function in the JO neurons. 

Despite the significant advancements in the field of Natural Language Processing (NLP), Large Language Models (LLMs) have shown limitations in performing complex tasks that require arithmetic, commonsense, and symbolic reasoning. Reasoning frameworks like ReAct, Chain-of-thought (CoT), Tree-of-thoughts (ToT), etc. have shown success but with limitations in solving long-form complex tasks. To address this, we propose a knowledge-sharing and collaborative multi-agent assisted framework on LLMs that leverages the capabilities of existing reasoning frameworks and the collaborative skills of multi-agent systems (MASs). The objectives of the proposed framework are to overcome the limitations of LLMs, enhance their reasoning capabilities, and improve their performance in complex tasks. It involves generating natural language rationales and in-context few-shot learning via prompting, and integrates the reasoning techniques with efficient knowledge-sharing and communication driven agent networks. The potential benefits of the proposed framework include saving time and money, improved efficiency for computationally intensive reasoning, and the ability to incorporate multiple collaboration strategies for dynamically changing environments. 

Abstract Homophily, the tendency for individuals to preferentially interact with others similar to themselves is typically documented via self-report and, for children, adult report. Few studies have investigated homophily directly using objective measures of social movement. We quantified homophily in children with developmental disabilities (DD) and typical development (TD) using objective measures of position/orientation in preschool inclusion classrooms, designed to promote interaction between these groups of children. Objective measurements were collected using ultra-wideband radio-frequency tracking to determine social approach and social contact, measures of social movement and interaction. Observations of 77 preschoolers (47 with DD, and 30 TD) were conducted in eight inclusion classrooms on a total of 26 days. We compared DD and TD groups with respect to how children approached and shared time in social contact with peers using mixed-effects models. Children in concordant dyads (DD-DD and TD-TD) both moved toward each other at higher velocities and spent greater time in social contact than discordant dyads (DD-TD), evidencing homophily. DD-DD dyads spent less time in social contact than TD-TD dyads but were comparable to TD-TD dyads in their social approach velocities. Children’s preference for similar peers appears to be a pervasive feature of their naturalistic interactions. 

The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens’ lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus’s rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed. 

The Owens Valley Radio Observatory Long Wavelength Array is a low radio frequency all-sky imaging radio interferometer. The full 352-element array will generate more than 2 TB of visibility data per hour of observation. One of the array’s primary science cases, the search for variable radio emission from exoplanets and for transients, require fast and high dynamic range interferometric imaging. Here we detail the design and implementation of a two-pipeline infrastructure that minimizes development cost: an offline pipeline that facilitates experimentation with existing pack-ages, and a real-time pipeline that minimizes overhead.