When meta-research (research on research) makes an observation or points out a problem (such as a flaw in methodology), the project should be repeated later to determine whether the problem remains. For this we need meta-research that is reproducible and updatable, or living meta-research. In this talk, we introduce the concept of living meta-research, examine prequels to this idea, and point towards standards and technologies that could assist researchers in doing living meta-research. We introduce technologies like natural language processing, which can help with automation of meta-research, which in turn will make the research easier to reproduce/update. Further, we showcase our open-source litmining ecosystem, which includes pubget (for downloading full-text journal articles), labelbuddy (for manually extracting information), and pubextract (for automatically extracting information). With these tools, you can simplify the tedious data collection and information extraction steps in meta-research, and then focus on analyzing the text. We will then describe some living meta-research projects to illustrate the use of these tools. For example, we’ll show how we used GPT along with our tools to extract information about study participants. Essentially, this talk will introduce you to the concept of meta-research, some tools for doing meta-research, and some examples. Particularly, we want you to take away the fact that there are many interesting open questions in meta-research, and you can easily learn the tools to answer them. Check out our tools at https://litmining.github.io/

Fly Escape Behaviors: Flexible and Modular We have identified a set of escape maneuvers performed by a fly when confronted by a looming object. These escape responses can be divided into distinct behavioral modules. Some of the modules are very stereotyped, as when the fly rapidly extends its middle legs to jump off the ground. Other modules are more complex and require the fly to combine information about both the location of the threat and its own body posture. In response to an approaching object, a fly chooses some varying subset of these behaviors to perform. We would like to understand the neural process by which a fly chooses when to perform a given escape behavior. Beyond an appealing set of behaviors, this system has two other distinct advantages for probing neural circuitry. First, the fly will perform escape behaviors even when tethered such that its head is fixed and neural activity can be imaged or monitored using electrophysiology. Second, using Drosophila as an experimental animal makes available a rich suite of genetic tools to activate, silence, or image small numbers of cells potentially involved in the behaviors. Neural Circuits for Escape Until recently, visually induced escape responses have been considered a hardwired reflex in Drosophila. White-eyed flies with deficient visual pigment will perform a stereotyped middle-leg jump in response to a light-off stimulus, and this reflexive response is known to be coordinated by the well-studied giant fiber (GF) pathway. The GFs are a pair of electrically connected, large-diameter interneurons that traverse the cervical connective. A single GF spike results in a stereotyped pattern of muscle potentials on both sides of the body that extends the fly's middle pair of legs and starts the flight motor. Recently, we have found that a fly escaping a looming object displays many more behaviors than just leg extension. Most of these behaviors could not possibly be coordinated by the known anatomy of the GF pathway. Response to a looming threat thus appears to involve activation of numerous different neural pathways, which the fly may decide if and when to employ. Our goal is to identify the descending pathways involved in coordinating these escape behaviors as well as the central brain circuits, if any, that govern their activation. Automated Single-Fly Screening We have developed a new kind of high-throughput genetic screen to automatically capture fly escape sequences and quantify individual behaviors. We use this system to perform a high-throughput genetic silencing screen to identify cell types of interest. Automation permits analysis at the level of individual fly movements, while retaining the capacity to screen through thousands of GAL4 promoter lines. Single-fly behavioral analysis is essential to detect more subtle changes in behavior during the silencing screen, and thus to identify more specific components of the contributing circuits than previously possible when screening populations of flies. Our goal is to identify candidate neurons involved in coordination and choice of escape behaviors. Measuring Neural Activity During Behavior We use whole-cell patch-clamp electrophysiology to determine the functional roles of any identified candidate neurons. Flies perform escape behaviors even when their head and thorax are immobilized for physiological recording. This allows us to link a neuron's responses directly to an action.

Controlling biological processes using light has increased the accuracy and speed with which researchers can manipulate many biological processes. Optical control allows for an unprecedented ability to dissect function and holds the potential for enabling novel genetic therapies. However, optogenetic experiments require adequate light sources with spatial, temporal, or intensity control, often a bottleneck for researchers. Here we detail how to build a low-cost and versatile LED illumination system that is easily customizable for different available optogenetic tools. This system is configurable for manual or computer control with adjustable LED intensity. We provide an illustrated step-by-step guide for building the circuit, making it computer-controlled, and constructing the LEDs. To facilitate the assembly of this device, we also discuss some basic soldering techniques and explain the circuitry used to control the LEDs. Using our open-source user interface, users can automate precise timing and pulsing of light on a personal computer (PC) or an inexpensive tablet. This automation makes the system useful for experiments that use LEDs to control genes, signaling pathways, and other cellular activities that span large time scales. For this protocol, no prior expertise in electronics is required to build all the parts needed or to use the illumination system to perform optogenetic experiments.

Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this presentation, I will provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.

Deep learning is an emerging computational approach inspired by the human brain’s neural connectivity that has transformed machine-based image analysis. By using histopathology as a model of an expert-level pattern recognition exercise, we explore the ability for humans to teach machines to learn and mimic image-recognition and decision making. Moreover, these models also allow exploration into the ability for computers to independently learn salient histological patterns and complex ontological relationships that parallel biological and expert knowledge without the need for explicit direction or supervision. Deciphering the overlap between human and unsupervised machine reasoning may aid in eliminating biases and improving automation and accountability for artificial intelligence-assisted vision tasks and decision-making. Aleksandar Ivanov Title: