The forced swim test is a rodent behavioral test used for evaluation of antidepressant drugs, antidepressant efficacy of new compounds, and experimental manipulations that are aimed at rendering or preventing depressive-like states. Mice are placed in an inescapable transparent tank that is filled with water and their escape related mobility behavior is measured. The forced swim test is straightforward to conduct reliably and it requires minimal specialized equipment. Successful implementation of the forced swim test requires adherence to certain procedural details and minimization of unwarranted stress to the mice. In the protocol description and the accompanying video, we explain how to conduct the mouse version of this test with emphasis on potential pitfalls that may be detrimental to interpretation of results and how to avoid them. Additionally, we explain how the behaviors manifested in the test are assessed. Video LinkThe video component of this article can be found at http://www.jove.com/video/3638/ Protocol 1. Materials and Method The water tanksThe cylindrical tanks (30 cm height x 20 cm diameters) required for the mouse forced swim test (FST) in our laboratory are constructed of transparent Plexiglas, as this material is able to withstand the frequent movement of the tanks and accidents better than glass. The water level is 15 cm from the bottom and should be marked on the tank to ensure that the volume of water is consistent across mice. The number of tanks should ideally be at least twice as many as the number of mice being tested at a time, so that the second water tank set can be filled while the first set is in use. The dimensions of the tanks should be selected in a way that the mice will not be able to touch the bottom of the tank, either with their feet or their tails, during the swimming test. The height of the tank should be high enough to prevent the mice from escaping from the tank. Please note that the diameter of tank and the depth of water are important parameters that can be adjusted to change the behavior of mice (for a detailed analysis of these issues see 1-3 ). ThermometerA water resistant infrared thermometer is preferable, since rapid measurement of temperature reduces the amount of time required to conduct the test. However, a glass mercury thermometer will also be sufficient for this task.

The tail-suspension test is a mouse behavioral test useful in the screening of potential antidepressant drugs, and assessing of other manipulations that are expected to affect depression related behaviors. Mice are suspended by their tails with tape, in such a position that it cannot escape or hold on to nearby surfaces. During this test, typically six minutes in duration, the resulting escape oriented behaviors are quantified. The tail-suspension test is a valuable tool in drug discovery for high-throughput screening of prospective antidepressant compounds. Here, we describe the details required for implementation of this test with additional emphasis on potential problems that may occur and how to avoid them. We also offer a solution to the tail climbing behavior, a common problem that renders this test useless in some mouse strains, such as the widely used C57BL/6. Specifically, we prevent tail climbing behaviors by passing mouse tails through a small plastic cylinder prior to suspension. Finally, we detail how to manually score the behaviors that are manifested in this test.

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One of the most consistent genetic findings to have emerged from bipolar disorder genome wide association studies (GWAS) is with CACNA1C, a gene that codes for the α1C subunit of the Cav1.2 voltage-dependent L-type calcium channel (LTCC). Genetic variation in CACNA1C have also been associated with depression, schizophrenia, autism spectrum disorders, as well as changes in brain function and structure in control subjects who have no diagnosable psychiatric illness. These data are consistent with a continuum of shared neurobiological vulnerability between diverse—Diagnostic and Statistical Manual (DSM) defined—neuropsychiatric diseases. While involved in numerous cellular functions, Cav1.2 is most frequently implicated in coupling of cell membrane depolarization to transient increase of the membrane permeability for calcium, leading to activation and, potentially, changes in intracellular signaling pathway activity, gene transcription, and synaptic plasticity. Cav1.2 is involved in the proper function of numerous neurological circuits including those involving the hippocampus, amygdala, and mesolimbic reward system, which are strongly implicated in psychiatric disease pathophysiology. A number of behavioral effects of LTCC inhibitors have been described including antidepressant-like behavioral actions in rodent models. Clinical studies suggest possible treatment effects in a subset of patients with mood disorders. We review the genetic structure and variation of CACNA1C, discussing relevant human genetic and clinical findings, as well as the biological actions of Cav1.2 that are most relevant to psychiatric illness.

Background-Recent genome-wide association studies have associated polymorphisms in the gene CACNA1C, which codes for Ca v 1.2, with a bipolar disorder and depression diagnosis.

Summary: CellProfiler Analyst allows the exploration and visualization of image-based data, together with the classification of complex biological phenotypes, via an interactive user interface designed for biologists and data scientists. CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery). Availability and Implementation: CellProfiler Analyst 2.0 is free and open source, available at http://www.cellprofiler.org and from GitHub (https://github.com/CellProfiler/CellProfiler-Analyst) under the BSD license. It is available as a packaged application for Mac OS X and Microsoft Windows and can be compiled for Linux. We implemented an automatic build process that supports nightly updates and regular release cycles for the software. Contact: anne@broadinstitute.org Supplementary information: Supplementary data are available at Bioinformatics online.

Given a data set D containing millions of data points and a data consumer who is willing to pay for $X to train a machine learning (ML) model over D, how should we distribute this $X to each data point to reflect its "value"? In this paper, we define the "relative value of data" via the Shapley value, as it uniquely possesses properties with appealing real-world interpretations, such as fairness, rationality and decentralizability. For general, bounded utility functions, the Shapley value is known to be challenging to compute: to get Shapley values for all N data points, it requires O(2 N ) model evaluations for exact computation and O(N log N) for ( , δ)-approximation.In this paper, we focus on one popular family of ML models relying on K-nearest neighbors (KNN). The most surprising result is that for unweighted KNN classifiers and regressors, the Shapley value of all N data points can be computed, exactly, in O(N log N) time -an exponential improvement on computational complexity! Moreover, for ( , δ)-approximation, we are able to develop an algorithm based on Locality Sensitive Hashing (LSH) with only sublinear complexity O(N h( ,K) log N) when is not too small and K is not too large. We empirically evaluate our algorithms on up to 10 million data points and even our exact algorithm is up to three orders of magnitude faster than the baseline approximation algorithm. The LSH-based approximation algorithm can accelerate the value calculation process even further.We then extend our algorithms to other scenarios such as (1) weighed KNN classifiers, (2) different data points are clustered by different data curators, and (3) there are data analysts providing computation who also requires proper valuation. Some of these extensions, although also being improved exponentially, are less practical for exact computation (e.g., O(N K ) complexity for weighted KNN). We thus propose a Monte Carlo approximation algorithm, which is O(N(log N) 2 /(log K) 2 ) times more efficient than the baseline approximation algorithm.

HighlightsImaging flow cytometry enables potentially powerful, multiplexed single-cell analysis.Data analysis techniques for imaging flow cytometry are largely manual and subjective.Our machine learning workflow identifies phenotypes in imaging flow cytometry.The workflow uses open-source software and does not require computational expertise.