SRF-deficient astrocytes provide neuroprotection in mouse models of excitotoxicity and neurodegeneration.

Reactive astrogliosis is a common pathological hallmark of CNS injury, infection, and neurodegeneration, where reactive astrocytes can be protective or detrimental to normal brain functions. Currently, the mechanisms regulating neuroprotective astrocytes and the extent of neuroprotection are poorly understood. Here, we report that conditional deletion of serum response factor (SRF) in adult astrocytes causes reactive-like hypertrophic astrocytes throughout the mouse brain. These SrfGFAP-ERCKO astrocytes do not affect neuron survival, synapse numbers, synaptic plasticity or learning and memory. However, the brains of Srf knockout mice exhibited neuroprotection against kainic-acid induced excitotoxic cell death. Relevant to human neurodegenerative diseases, SrfGFAP-ERCKO astrocytes abrogate nigral dopaminergic neuron death and reduce ß-amyloid plaques in mouse models of Parkinson's and Alzheimer's disease, respectively. Taken together, these findings establish SRF as a key molecular switch for the generation of reactive astrocytes with neuroprotective functions that attenuate neuronal injury in the setting of neurodegenerative diseases.

DBscorer: An Open-Source Software for Automated Accurate Analysis of Rodent Behavior in Forced Swim Test and Tail Suspension Test.

Forced swim test (FST) and tail suspension test (TST) are commonly used behavioral tests for screening antidepressant drugs with a high predictive validity. These tests have also proved useful to assess the non-motor symptoms in the animal models of movement disorders such as Parkinson's disease and Huntington's disease. Manual analysis of FST and TST is a time-consuming exercise and has large observer-to-observer variability. Automation of behavioral analysis alleviates these concerns, but there are no easy-to-use open-source tools for such analysis. Here, we describe the development of Depression Behavior Scorer (DBscorer), an open-source program installable on Windows, with an intuitive graphical user interface (GUI), that helps in accurate quantification of immobility behavior in FST and TST from video analysis. Several calibration options allow customization of various parameters to suit the experimental requirements. Apart from the readout of time spent immobile, DBscorer also provides additional data and graphics of immobility/mobility states across time revealing the evolution of behavioral despair over the duration of the test and allows the analysis of additional parameters. Such comprehensive analysis allows a more nuanced understanding of the expression of behavioral despair in FST and TST. We believe that DBscorer would make analysis of behavior in FST and TST unbiased, automated and rapid, and hence prove to be helpful to the wider neuroscience community.

Subfield-specific effects of chronic mild unpredictable stress on hippocampal astrocytes.

Major depressive disorder (MDD) is a debilitating neuropsychiatric illness affecting over 20% of the population worldwide. Despite its prevalence, our understanding of its pathophysiology is severely limited, thus hampering the development of novel therapeutic strategies. Recent advances have clearly established astrocytes as major players in the pathophysiology, and plausibly pathogenesis, of major depression. In particular, astrocyte density in the hippocampus is severely diminished in MDD patients and correlates strongly with the disease outcome. Moreover, astrocyte densities from different subfields of the hippocampus show varying trends in terms of their correlation to the disease outcome. Given the central role that hippocampus plays in the pathophysiology of depression and in the action of antidepressant drugs, changes in hippocampal astrocyte density and physiology may have a significant effect on behavioral symptoms of MDD. In this study, we used chronic mild unpredictable stress (CMUS) in mice, which induces a depressive-like state, and examined its effects on astrocytes from different subfields of the hippocampus. We used SOX9 and S100ß immunostaining to estimate the number of astrocytes per square millimeter from various hippocampal subfields. Furthermore, using confocal images of fluorescently labeled glial fibrillary acidic protein (GFAP)-immunopositive hippocampal astrocytes, we quantified various morphology-related parameters and performed Sholl analysis. We found that CMUS exerts differential effects on astrocyte cell numbers, ramification, cell radius, surface area, and process width of hippocampal astrocytes from different hippocampal subfields. Taken together, our study reveals that chronic stress does not uniformly affect all hippocampal astrocytes; but exerts its effects differentially on different astrocytic subpopulations within the hippocampus.

Amplifying Domain Expertise in Clinical Data Pipelines.

Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts' interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study.

HOW NURSES IDENTIFY HOSPITALIZED PATIENTS ON THEIR PERSONAL NOTES: FINDINGS FROM ANALYZING 'BRAINS' HEADERS WITH MULTIPLE RATERS.

The overarching objective of this research is to reduce the burden of documentation in electronic health records by registered nurses in hospitals. Registered nurses have consistently reported that e-documentation is a concern with the introduction of electronic health records. As a result, many nurses use handwritten notes in order to avoid using electronic health records to access information about patients. At the top of these notes are patient identifiers. By identifying aspects of good and suboptimal headers, we can begin to form a model of how to effectively support identifying patients during assessments and care activities. The primary finding is that nurses use room number as the primary patient identifier in the hospital setting, not the patient's last name. In addition, the last name, gender, and age are sufficiently important identifiers that they are frequently recorded at the top of handwritten notes. Clearly distinguishable field labels and values are helpful in quickly scanning the identifier for identifying information. A web based annotator was designed as a first step towards machine learning approaches to recognize handwritten or printed data on paper sheets in future research.

ICARUS: Minimizing Human Effort in Iterative Data Completion.

An important step in data preparation involves dealing with incomplete datasets. In some cases, the missing values are unreported because they are characteristics of the domain and are known by practitioners. Due to this nature of the missing values, imputation and inference methods do not work and input from domain experts is required. A common method for experts to fill missing values is through rules. However, for large datasets with thousands of missing data points, it is laborious and time consuming for a user to make sense of the data and formulate effective completion rules. Thus, users need to be shown subsets of the data that will have the most impact in completing missing fields. Further, these subsets should provide the user with enough information to make an update. Choosing subsets that maximize the probability of filling in missing data from a large dataset is computationally expensive. To address these challenges, we present ICARUS, which uses a heuristic algorithm to show the user small subsets of the database in the form of a matrix. This allows the user to iteratively fill in data by applying suggested rules based on their direct edits to the matrix. The suggested rules amplify the users' input to multiple missing fields by using the database schema to infer hierarchies. Simulations show ICARUS has an average improvement of 50% across three datasets over the baseline system. Further, in-person user studies demonstrate that naive users can fill in 68% of missing data within an hour, while manual rule specification spans weeks.

Surpassing Humans and Computers with JellyBean: Crowd-Vision-Hybrid Counting Algorithms.

Counting objects is a fundamental image processisng primitive, and has many scientific, health, surveillance, security, and military applications. Existing supervised computer vision techniques typically require large quantities of labeled training data, and even with that, fail to return accurate results in all but the most stylized settings. Using vanilla crowd-sourcing, on the other hand, can lead to significant errors, especially on images with many objects. In this paper, we present our JellyBean suite of algorithms, that combines the best of crowds and computer vision to count objects in images, and uses judicious decomposition of images to greatly improve accuracy at low cost. Our algorithms have several desirable properties: (i) they are theoretically optimal or near-optimal, in that they ask as few questions as possible to humans (under certain intuitively reasonable assumptions that we justify in our paper experimentally); (ii) they operate under stand-alone or hybrid modes, in that they can either work independent of computer vision algorithms, or work in concert with them, depending on whether the computer vision techniques are available or useful for the given setting; (iii) they perform very well in practice, returning accurate counts on images that no individual worker or computer vision algorithm can count correctly, while not incurring a high cost.

Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together.

Protein interaction data exists in a number of repositories. Each repository has its own data format, molecule identifier and supplementary information. Michigan Molecular Interactions (MiMI) assists scientists searching through this overwhelming amount of protein interaction data. MiMI gathers data from well-known protein interaction databases and deep-merges the information. Utilizing an identity function, molecules that may have different identifiers but represent the same real-world object are merged. Thus, MiMI allows the users to retrieve information from many different databases at once, highlighting complementary and contradictory information. To help scientists judge the usefulness of a piece of data, MiMI tracks the provenance of all data. Finally, a simple yet powerful user interface aids users in their queries, and frees them from the onerous task of knowing the data format or learning a query language. MiMI allows scientists to query all data, whether corroborative or contradictory, and specify which sources to utilize. MiMI is part of the National Center for Integrative Biomedical Informatics (NCIBI) and is publicly available at: http://mimi.ncibi.org.