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Bases: astropy.utils.misc.OrderedDescriptor

Wraps individual parameters.

This class represents a model’s parameter (in a somewhat broad sense). It acts as both a descriptor that can be assigned to a class attribute to describe the parameters accepted by an individual model (this is called an “unbound parameter”), or it can act as a proxy for the parameter values on an individual model instance (called a “bound parameter”).

Parameter instances never store the actual value of the parameter directly. Rather, each instance of a model stores its own parameters parameter values in an array. A bound Parameter simply wraps the value in a Parameter proxy which provides some additional information about the parameter such as its constraints. In other words, this is a high-level interface to a model’s adjustable parameter values.

Unbound Parameters are not associated with any specific model instance, and are merely used by model classes to determine the names of their parameters and other information about each parameter such as their default values and default constraints.

See Parameters for more details.

Attributes Summary

Methods Summary

Attributes Documentation

The minimum and maximum values of a parameter as a tuple

Types of constraints a parameter can have. Excludes ‘min’ and ‘max’ which are just aliases for the first and second elements of the ‘bounds’ constraint (which is represented as a 2-tuple).

Parameter default value

Boolean indicating if the parameter is kept fixed during fitting.

A value used as an upper bound when fitting a parameter

A value used as a lower bound when fitting a parameter

Parameter name

This parameter, as a Quantity instance.

The shape of this parameter’s value array.

The size of this parameter’s value array.

Indicates that this parameter is linked to another one.

A callable which provides the relationship of the two parameters.

The unit attached to this parameter, if any.

On unbound parameters (i.e. parameters accessed through the model class, rather than a model instance) this is the required/ default unit for the parameter.

Used as a decorator to set the validator method for a Parameter . The validator method validates any value set for that parameter. It takes two arguments– self , which refers to the Model instance (remember, this is a method defined on a Model ), and the value being set for this parameter. The validator method’s return value is ignored, but it may raise an exception if the value set on the parameter is invalid (typically an InputParameterError should be raised, though this is not currently a requirement).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.


Two complementary and timely advances are described for genetic tagging and reconstruction of subsets of retinal and Drosophila neurons by electron microscopy. Viral vectors are used that required Cre-dependent recombination and express erHRP, APX or APEX2. These were tested in mouse retinas, in which certain retinal ganglion cells expressed Cre-recombinase, or tamoxifen activated Cre, and also in Drosophila that expressed APEX fused to GFP in direction selective tangential cells. Listing the specimen preparation methods that led to the success or failure of APEX and HRP-dependent staining will benefit the field. Likewise, the combined application of rapid low resolution with selected high-resolution analysis will facilitate the time-consuming, but much-needed large volume reconstructions of genetically specified neurons by serial electron microscopy. The paper also describes other aspects of a workflow to reconstruct the labeled cells. These approaches are described as being quicker than other current methods. The entire method has considerable potential and will be of interest to the neuroscience community. Despite this overall favorable assessment of the study, several points should be addressed.

Essential revisions:

1) An important question concerns efficiency. Are all of the genetically specified neurons labeled or just a subset? For example, the Drosophila DB331 driver line should target a bundle of motion-sensitive cells with numerous spatially constrained projections, yet Domineering casual shoes for men Cheap Exclusive Sale 2018 Purchase Cheap Price XBEgrxbj4
displays only a handful of processes. In both Figure 1 (C-D) and Figure 2 , a low-resolution comparison of Cre-dependent fluorophore and DAB staining patterns are needed to address this concern.

2) The paper does a convincing job of showing how 'rapid' low-resolution images and reconstructions can be made of axons and dendrites. Nevertheless, to validate the method's use in circuit analysis, consistent high quality images are needed that show synapses identified by arrows with detailed descriptions in the figure legends and results (a few of the images are of sufficient magnification and quality but synapses should be identifiable ( ENMAYER Crystal Charms White Shoes Woman Supper High Heels Shop For Cheap Price 2018 New Online rryATebgKX
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)). Figure 1—figure – supplement 3f is the only image that actually identifies the synapses with arrows, yet still provides too little description.

3) All of the example images reveal suboptimal ultrastructure inside the labelled neurites, relative to other methods that have been used to label synapses in the literature. The dense reaction product obscures pre- and postsynaptic active zones, as well as presynaptic vesicles. This being the case, it is not clear how synapses will be identified on the specifically labeled cells. This issue must be addressed with images, labels, and clear examples of labeled synapses.

We computed the SNR of extracted cellular traces to quantitatively compare the performances of two approaches. For each cellular trace 𝒚 , we first computed its denoised trace 𝒄 using the selected deconvolution algorithm (here, it is thresholded OASIS); then the SNR of 𝒚 is


For PCA/ICA results, the calcium signal 𝒚 of each IC is the output of its corresponding spatial filter, while for CNMF-E results, it is the trace before applying temporal deconvolution, thatis, 𝒚 ^ i in Summer New Girl Child Bow Zipper Vest Dress Princess Dress Evening Dress Childrens Clothing Pay With Paypal Cheap Online Enjoy For Sale Finishline jgQdNmgT9
. All the data can be freely accessed online ( Zhou et al., 2017 ).

Expression of the genetically encoded calcium indicator GCaMP6f in neurons was achieved using a recombinant adeno-associated virus (AAV) encoding the GCaMP6f protein under transcriptional control of the synapsin promoter (AAV-Syn-GCaMP6f). This viral vector was packaged (Serotype 1) and stored in undiluted aliquots at a working concentration of genomic copies per ml at C until intracranial injection. l of AAV1-Syn-GCaMP6f was injected unilaterally into dorsal striatum ( mm anterior to Bregma, mm lateral to Bregma, mm ventral to the surface of the brain). 1 week post-injection, a mm gradient index of refraction (GRIN) lens was implanted into dorsal striatum m above the center of the viral injection. Three weeks after the implantation, the GRIN lens was reversibly coupled to a miniature one-photon microscope with an integrated nm LED (Inscopix). Using nVistaHD Acquisition software, images were acquired at 30 frames per second with the LED transmitting to mW of light while the mouse was freely moving in an open-field arena. Images were down sampled to Hz and processed into TIFFs using Mosaic software. All experimental manipulations were performed in accordance with protocols approved by the Harvard Standing Committee on Animal Care following guidelines described in the US NIH Guide for the Care and Use of Laboratory Animals.

The parameters used in running CNMF-E were: pixels, pixels, , and components were initialized from the raw data in the first pass before subtracting the background, and then additional components were initialized in a second pass. Highlycorrelated nearby components were merged and false positives were removed using the automated approach described above. In the end, we obtained components.

Cortical neurons were targeted by administering two microinjections of 300 ul of AAV-DJ-CamkIIa-GCaMP6s (titer: 5.3 × 1012, 1:6 dilution, UNC vector core) into the prefrontal cortex (PFC) (coordinates relative to bregma; injection 1: +1.5 mm AP, 0.6 mm ML, −2.4 ml DV; injection 2: +2.15 AP, 0.43 mm ML, −2.4 mm DV) of an adult male wild type (WT) mice. Immediately following the virus injection procedure, a 1 mm diameter GRIN lens implanted 300 um above the injection site (coordinates relative to bregma: +1.87 mm AP, 0.5 mm ML, −2.1 ml DV). After sufficient time had been allowed for the virus to express and the tissue to clear underneath the lens (3 weeks), a baseplate was secured to the skull to interface the implanted GRIN lens with a miniature, integrated microscope (nVista, 473 nm excitation LED, Inscopix) and subsequently permit the visualization of Ca2 +signals from the PFC of a freely behaving mouse. The activity of PFC neurons were recorded at 15 Hz over a 10 min period (nVista HD Acquisition Software, Inscopix) while the test subject freely explored an empty novel chamber. Acquired data was spatially down sampled by a factor of 2, motion corrected, and temporally down sampled to 15 Hz (Mosaic Analysis Software, Inscopix). All procedures were approved by the University of North Carolina Institutional Animal Care and Use Committee (UNC IACUC).

The parameters used in running CNMF-E were: pixels, pixels, , and . There were components initialized in the first pass and we obtained components after running the whole CNMF-E pipeline.

The calcium indicator GCaMP6f was expressed in ventral hippocampal-amygdala projecting neurons by injecting a retrograde canine adeno type 2-Cre virus (CAV2-Cre; from Larry Zweifel, University of Washington) into the basal amydala (coordinates relative to bregma: −1.70 AP, 3.00 mm ML, and −4.25 mm DV from brain tissue at site), and a Cre-dependent GCaMP6f adeno associated virus (AAV1-flex-Synapsin-GCaMP6f, UPenn vector core) into ventral CA1 of the hippocampus (coordinates relative to bregma: −3.16 mm AP, 3.50 mm ML, and −3.50 mm DV from brain tissue at site). A 0.5 mm diameter GRIN lens was then implanted over the vCA1 subregion and imaging began 3 weeks after surgery to allow for sufficient viral expression. Mice were then imaged with Inscopix miniaturized microscopes and nVistaHD Acquisition software as described above; images were acquired at 15 frames per second, while mice explored an anxiogenic Elevated Plus Maze arena. Videos were motion corrected and spatially downsampled using Mosaic software. All procedures were performed in accordance with protocols approved by the New York State Psychiatric Institutional Animal Care and Use Committee following guidelines described in the US NIH Guide for the Care and Use of Laboratory Animals.

The parameters used in running CNMF-E were: pixels, pixels, , , and . We first temporally downsampled the data by . Then we applied CNMF-E to the downsampled data. There were components initialized. After updating the background component, the algorithm detected six more neurons from the residual. We merged most of these components and deleted false positives. In the end, there were components left. The intermediate results before and after each manual intervention are shown in Video 10 .

Calcium indicator GCaMP6s was expressed within CaMKII-expressing neurons in the BNST by injecting the recombinant adeno-associated virus AAVdj-CaMKII-GCaMP6s (packaged at UNC Vector Core) into the anterior dorsal portion of BNST (coordinates relative to bregma: 0.10 mm AP, −0.95 mm ML, −4.30 mm DV). A 0.6 mm diameter GRIN lens was implanted above the injection site within the BNST. As described above, images were acquired using a detachable miniature one-photon microscope and nVistaHD Acquisition Software (Inscopix). Images were acquired at 20 frames per second while the animal was freely moving inside a sound-attenuated chamber equipped with a house light and a white noise generator (Med Associates). Unpredictable foot shocks were delivered through metal bars in the floor as an aversive stimulus during a 10 min session. Each unpredictable foot shock was 0.75 mA in intensity and 500 ms in duration on a variable interval (VI-60). As described above, images were motion corrected, downsampled and processed into TIFFs using Mosaic Software. These procedures were conducted in adult C57BL/6J mice (Jackson Laboratories) and in accordance with the Guide for the Care and Use of Laboratory Animals, as adopted by the NIH, and with approval from the Institutional Animal Care and Use Committee of the University of North Carolina at Chapel Hill (UNC).

The parameters used in running CNMF-E were: pixels, pixels, , , and . There were components initialized and we detected more components from the residual after estimating the background. there were components left after running the whole pipeline.

All analyses were performed with custom-written MATLAB code. MATLAB implementations of the CNMF-E algorithm can be freely downloaded from https://github.com/zhoupc/CNMF_E ( Zhou, 2017a ). We also implemented CNMF-E as part of the Python package CaImAn ( Giovannucci et al., 2017b ), a computational analysis toolbox for large-scale calcium imaging and behavioral data ( led flashing light kids led flash shoes for child Cheap Lowest Price For Sale Sale Online Best Store To Get For Sale uQXncZ2
[ Giovannucci et al., 2017a ]).

The scripts for generating all figures and the experimental data in this paper can be accessed from https://github.com/zhoupc/eLife_submission ( Zhou, 2017b ).

Automatic neuron detection in calcium imaging data using convolutional networks
Robust regression via hard thresholding
Cellular resolution calcium imaging and optogenetic excitation reveal a role for IL to NAc projection neurons in encoding of spatial information during cocaine-seeking
Society for Neuroscience.
Function of Basal Ganglia Circuitry in Motivation
Society for Neuroscience.
CaImAn: an open source toolbox for large scale calcium imaging data analysis on standalone machines
Subcortical projection-specific control of innate anxiety and learned fear by the ventral hippocampus
Society for Neuroscience.
In vivo calcium imaging of hippocampal neuronal network activity associated with memory behavior deficits in the Alzheimer’s disease mouse model
Society for Neuroscience.
Neural sequences underlying the rapid learning of new syllables in juvenile zebra finches
Society for Neuroscience.
In vivo calcium imaging to assess the role of prelimbic cortex neuronal ensembles in encoding reinstatement of palatable food-seeking in rats
Society for Neuroscience.
Detecting action potentials in neuronal populations with calcium imaging
Society for Neuroscience.
Extracting regions of interest from biological images with convolutional sparse block coding
2013 Asilomar Conference on Signals, Systems and Computers
349–353, Bayesian spike inference from calcium imaging data, 2013 Asilomar Conference on Signals, Systems and Computers, 10.1109/ACSSC.2013.6810293.
Nociceptin neurons in the bed nucleus of the stria terminalis regulate anxiety
Society for Neuroscience.
Action planning and action observation in rodent parietal cortex
Society for Neuroscience.
Encoding the relationship between anxiety-related behaviors and nociceptin neurons of the bed nucleus of the stria terminalis
Society for Neuroscience.
Data from: efficient and accurate extraction of in vivo calcium signals from microendoscopic video data
Dryad Digital Repository, 10.5061/dryad.kr17k.
David C Van Essen
Reviewing Editor; Washington University in St. Louis, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article " Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data" for consideration by eLife . Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by David Van Essen as the Senior Editor and Reviewing Editor. One of the reviewers was Dr Timothy Holy (Reviewer 2); the other two have opted to remain anonymous.

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Visual Studio Team Services (VSTS) and Team Foundation Server (TFS) provide a highly configurable and manageable pipeline for releases to multiple environments such as development, staging, QA, and production environments; including requiring approvals at specific stages.

In this tutorial, you learn about:


You'll need:

A release definition that contains at least one environment. If you don't already have one, you can create it by working through any of the following quickstarts and tutorials:

Two separate targets where you will deploy the app. These could be virtual machines, web servers, on-premises physical deployment groups, or other types of deployment target. In this example, we are using Azure App Services website instances. If you decide to do the same, you will have to choose names that are unique, but it's a good idea to include "QA" in the name of one, and "Production" in the name of the other so that you can easily identify them. Use the Azure portal to create a new web app.

In this section, you will check that the triggers you need for continuous deployment are configured in your release definition.

In the Build Release hub, open the Releases tab. Select your release definition and, in the right pane, choose Edit .

Build Release Releases

Choose the Continuous deployment trigger icon in the Artifacts section to open the trigger panel. Make sure this is enabled so that a new release is created after every new successful build is completed.

Continuous deployment trigger Artifacts

For more information, see Release triggers in the Release Management documentation.

Choose the Pre-deployment conditions icon in the Environments section to open the conditions panel. Make sure that the trigger for deployment to this environment is set to Release . This means that a deployment will be initiated automatically when a new release is created from this release definition.

Pre-deployment conditions Environments Release

Notice that you can also define artifact filters that determine a condition for the release to proceed, and set up a schedule for deployments. You can use features to, for example, specify a branch from which the build artifacts must have been created, or a specific time of day when you know the app will not be heavily used. For more information, see Environment triggers in the Release Management documentation.



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