98 research outputs found
Structural studies of cytidine repressor and catabolite activator protein
No full textThe Escherichia Coli Catabolite Activator Protein (CAP) activates DNA transcription at more than a hundred promoters. Cytidine Repressor (CytR), in conjunction with two CAP dimers, acts as a repressor of DNA transcription at the deoP2 promoter. In the first part of this work, we describe a method by which a (CAP)[subscript] 2-CytR-DNA complex can be prepared for structural studies. In the second part of this project, we describe the crystallization of what was initially
intended to be a (CAP)[subscript] 2-DNA complex in order to study the effects of two CAP dimers on transcription at an artificially constructed promoter containing tandem CAP binding sites. Upon structure determination of the crystal, we observed that while there was no DNA present, the protein had bound multiple Co[superscript]2+ and SO2−/4 ligands. We provide an analysis of the crystal structure and present a possible explanation for the absence of DNA in the structure.M.S.Includes bibliographical referencesby Ramya Rangesh Ra
Knot theory addendum
No full textAt Right Angles met up with author Ramya to discuss her article on Knot Theory. Over coffee at Starbucks, Ramya adeptly made sense of a tangled bunch of wool which I had carried with me to try and see if Knot Theory could help me untangle the web
Water mass classification using band ratios
No full textThe Hudson River plume has been the topic of consideration and observation in order to try and understand the physical, chemical and biological behavior of the plume which is a key component of the oceanography of the Mid-Atlantic Bight (MAB) region off the east coast of the United States. One approach towards understanding the chlorophyll production for the plume would be to make use of satellite data to measure the optical ocean color properties of these waters. In this direction classifying the water masses of the Hudson River plume according to these optical properties would be an interesting method of analyzing the satellite data for the purpose of understanding and identifying the physical and biological changes and the correlation between them in this region.
The first step is to design and implement a water mass classification algorithm in the LaTTE (Lagrangian Transport and Transformation Experiment) region of the MAB. It takes about 1-2 weeks for the nutrients from the freshwater from the Hudson Estuary to be dissipated and mixed with the open ocean. This classification algorithm is developed using ocean color data from the Sea viewing WIde Field of view Sensor (SeaWiFS). The algorithm is validated by overlaying ship salinity tracks on the classified water masses to show that salinity values change at the boundaries of the classified regions, due to the mixing and export of freshwater across the shelf.
We analyze global Sea Surface Temperature (SST) data collected over the years 1995-2005 for summer and winter in order to find coastal estuarine ecosystems that may display similar behavior as the Hudson River Estuary. Looking at the seasonal variation in this data, we observe that the regions of MAB and the East Asian coast are found to have strikingly similar seasonal behavior.
This leads into the third and the last step of the process which involves applying the water mass classification algorithm to ocean color data from eastern coastal Asia. It is observed that the algorithm well in the seas of Okhotsk, Japan and East China where it is able to identify plume water and non river water.M.S.Includes bibliographical references (p. 86-92)
Socioeconomic impact of TB on patients registered within RNTCP and their families in the year 2007 in Chennai, India
No full textBackground: Tuberculosis patients are registered in government clinics under Directly Observed Treatment Short-course (DOTS) program in Chennai city catering to 4.34 million population. With the entire country geographically covered under the DOTS program, research into socioeconomic impact of TB on patients and their households is crucial for providing comprehensive patient-friendly TB services and to document the benefits of DOTS. Objective: To assess the social and economic impact of TB on patients registered under DOTS program and their families. Materials and Methods: A cross-sectional study of 300 TB patients was done using a pre-coded semi-quantitative questionnaire between March and June 2007 in all the Tuberculosis Units (TUs) of Chennai city. Results: Social and economic impact was perceived by 69.0% and 30.3% patients, respectively. About 24.3% suffered from both social and economic impact, while 75% patients suffered from any one form of impact. Social impact was perceived by more female patients as compared to males (80.7% vs. 62%; P < 0.001). More patients with extra-pulmonary disease (44.4%) and patients belonging to joint families (40.7%) perceived economic impact (P < 0.05). Conclusion: After 8 years of DOTS implementation, the present study has shown that with the availability of DOTS, percentage of patients who mortgaged assets or took loans has reduced. Social impact of TB is still perceived by two-thirds of the patients (69%). Elimination or reduction of social stressors with specific, focused, and intense social support services, awareness generation, and counseling to patients and families need to be built into the program
Sentiment analysis of big data with intensity analysis by rule engine, 2015
No full textThe use of social media is an emerging way for the public to express their views on companies and other organizations. The success of these entities can depend on a positive presence on social media, leading to an increasing interest in understanding public opinion expressed there. This thesis presents a method for gathering and storing a large number of social media posts, analyzing the sentiments expressed, and further classifying the specific emotions conveyed. The social media platform Twitter was used as a source of millions of publicly viewable posts. The big data software tools Twitter4j, Apache Hadoop, and Apache Hive were used to gather and store these posts. These were then classified as communicating a positive, negative, or neutral sentiment through the technique of sentiment analysis, performed using the tool Lingpipe. To further identify the particular emotions expressed in the Tweet, a rule engine, specifically the DROOLS software, was used
Study of health franchises in resource limited settings
Get PDFThesis (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 62-64).Billions of dollars are spent to develop drugs for infectious diseases in developing countries. How will these drugs along with clinical services be delivered to the patients who currently do not have access to them? Health franchises have been around since early 1990s, creating networks of shops and clinics that provide specialized care to low income individuals. This thesis attempts to understand the underlying mechanisms of successful health franchises. Two cases are taken into consideration, CFWshops in Kenya and Mi Farmacita Nacional (MFN) in Mexico. Both are pharmaceutical shops with small clinics attached to them. The two cases were examined through a framework derived from successful commercial franchises and franchise theory. The elements that were addressed include operational structure, marketing strategy, product and service offerings, monitoring of businesses, and financial structure. CFWshops and MFN had some stark differences in how they addressed each of these elements. Unlike typical commercial franchises, health franchises aim to provide social benefits to the population. This goal requires franchises to not only create a business strategy to be financially sustainable and take advantage of networks, but also show health improvements in the community. The success of a health franchise is dependent on the health impacts it provides because its mission is not to generate a profit for the stakeholders but rather the value added to the customer by providing access that was not there before.(cont.) The comparative case analysis suggests several key recommendations. Health innovations in resource limited settings should create networks with other public and private health groups to leverage existing knowledge and best practices. This reduces cost and time of learning and allows businesses to utilize existing channels to provide access for drugs and services to individuals who currently are not receiving them.by Ramya Sankar.S.M.in Technology and Polic
PP-205 Private pharmacies in tuberculosis control: a survey to explore possibility of involving pharmacists in DOTS program in Chennai, India
No full textMotivating, your way: Tailoring your fitness journey
No full textA significant cardiovascular health risk is insufficient physical activity. The World Health Organization recommends 150 minutes of strenuous physical activity every week. Inadequate physical activity increases the risk of chronic diseases and other health conditions like cholesterol and obesity. This thesis researches the role of data monitoring as a persuasion strategy in monitoring a user’s progress in their journey to becoming more physically active and how it can be leveraged to decrease the risk of cardiovascular diseases. Specifically, the focus of the thesis is to determine the effectiveness of expert-generated tailored messages to motivate a user in their physical activity behaviour. We designed the content of the messages by adapting an existing ontology for tailoring motivational messages in the context of physical activity. Messages were then generated by experts through a scenario-based feedback generation process, where the scenarios were tailored to a user’s mood, self-efficacy and progress. The design of these tailored messages was tested against generic messages to determine which type of message was more motivating to the user. An experiment was conducted by recruiting crowd workers who were asked to rate the motivational levels of the two message types with respect to a given scenario. The results of the experiment supported the initial hypothesis that messages tailored to mood, self-efficacy and progress are more motivating than generic messages. Additionally, we have shown a systematic and reproducible way to obtain motivating messages. We have also provided a dataset of motivational messages that can be used during various stages of a user’s physical activity intervention, along with a set of scenarios representing different levels of a user’s state (mood, self-efficacy and progress)
FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures
No full text<h2>General</h2>
<p>For more details and the most up-to-date information please consult our project page: <a href="https://kainmueller-lab.github.io/fisbe" target="_blank" rel="noopener">https://kainmueller-lab.github.io/fisbe</a>.</p>
<h2>Summary</h2>
<ul>
<li>A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains
<ul>
<li>30 completely labeled (segmented) images</li>
<li>71 partly labeled images</li>
<li>altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)</li>
</ul>
</li>
<li>To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects</li>
<li>A set of metrics and a novel ranking score for respective meaningful method benchmarking</li>
<li>An evaluation of three baseline methods in terms of the above metrics and score</li>
</ul>
<h2>Abstract</h2>
<p>Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.</p>
<h2>Dataset documentation:</h2>
<p>We provide a detailed documentation of our dataset, following the <a href="https://arxiv.org/abs/1803.09010" target="_blank" rel="noopener">Datasheet for Datasets</a> questionnaire:</p>
<p><em>>> <a href="https://kainmueller-lab.github.io/fisbe/datasheet" target="_blank" rel="noopener">FISBe Datasheet</a></em></p>
<p>Our dataset originates from the <a href="https://www.janelia.org/project-team/flylight" target="_blank" rel="noopener">FlyLight project</a>, where the authors released a large image collection of nervous systems of ~74,000 flies, <a href="https://gen1mcfo.janelia.org/cgi-bin/gen1mcfo.cgi" target="_blank" rel="noopener">available for download</a> under CC BY 4.0 license.</p>
<h2>Files</h2>
<ul>
<li>fisbe_v1.0_{completely,partly}.zip
<ul>
<li>contains the image and ground truth segmentation data; there is one <em>zarr</em> file per sample, see below for more information on how to access <em>zarr</em> files.</li>
</ul>
</li>
<li>fisbe_v1.0_mips.zip
<ul>
<li>maximum intensity projections of all samples, for convenience.</li>
</ul>
</li>
<li>sample_list_per_split.txt
<ul>
<li>a simple list of all samples and the subset they are in, for convenience.</li>
</ul>
</li>
<li>view_data.py
<ul>
<li>a simple python script to visualize samples, see below for more information on how to use it.</li>
</ul>
</li>
<li>dim_neurons_val_and_test_sets.json
<ul>
<li>a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.</li>
</ul>
</li>
<li>Readme.md
<ul>
<li>general information</li>
</ul>
</li>
</ul>
<h2>How to work with the image files</h2>
<p>Each sample consists of a single 3d MCFO image of neurons of the fruit fly.<br>For each image, we provide a pixel-wise instance segmentation for all separable neurons.<br>Each sample is stored as a separate <em>zarr</em> file (<a href="https://zarr.readthedocs.io" target="_blank" rel="noopener">zarr</a> is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").<br>The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.<br>The segmentation mask for each neuron is stored in a separate channel.<br>The order of dimensions is CZYX.</p>
<p>We recommend to work in a virtual environment, e.g., by using conda:</p>
<p><code>conda create -y -n flylight-env -c conda-forge python=3.9</code><br><code>conda activate flylight-env</code></p>
<h3>How to open <em>zarr</em> files</h3>
<ol>
<li>Install the python zarr package:
<pre><code>pip install zarr</code></pre>
</li>
<li>Opened a zarr file with:<br>
<p><code>import zarr</code><br><code>raw = zarr.open(<path_to_zarr>, mode='r', path="volumes/raw")</code><br><code>seg = zarr.open(<path_to_zarr>, mode='r', path="volumes/gt_instances")</code></p>
<p><code># optional:</code><br><code>import numpy as np</code><br><code>raw_np = np.array(raw)</code></p>
</li>
</ol>
<p>Zarr arrays are read lazily on-demand.<br>Many functions that expect numpy arrays also work with zarr arrays.<br>Optionally, the arrays can also explicitly be converted to numpy arrays.</p>
<h3>How to view <em>zarr</em> image files</h3>
<p>We recommend to use <a href="https://napari.org" target="_blank" rel="noopener">napari</a> to view the image data.</p>
<ol>
<li>Install napari:
<pre><code>pip install "napari[all]"</code></pre>
</li>
<li>Save the following Python script: <br>
<p><code>import zarr, sys, napari</code></p>
<p><code>raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")</code><br><code>gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")</code></p>
<p><code>viewer = napari.Viewer(ndisplay=3)</code><br><code>for idx, gt in enumerate(gts):</code><br><code> viewer.add_labels(</code><br><code> gt, rendering='translucent', blending='additive', name=f'gt_{idx}')</code><br><code>viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')</code><br><code>viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')</code><br><code>viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')</code><br><code>napari.run()</code></p>
</li>
<li>Execute:
<pre><code>python view_data.py <path-to-file>/R9F03-20181030_62_B5.zarr</code></pre>
</li>
</ol>
<h2>Metrics</h2>
<ul>
<li>S: Average of avF1 and C</li>
<li>avF1: Average F1 Score</li>
<li>C: Average ground truth coverage</li>
<li>clDice_TP: Average true positives clDice</li>
<li>FS: Number of false splits</li>
<li>FM: Number of false merges</li>
<li>tp: Relative number of true positives</li>
</ul>
<p>For more information on our selected metrics and formal definitions please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p>
<h2>Baseline</h2>
<p>To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely <a href="https://github.com/Kainmueller-Lab/PatchPerPix" target="_blank" rel="noopener">PatchPerPix (ppp)</a>, <a href="https://github.com/google/ffn" target="_blank" rel="noopener">Flood Filling Networks (FFN)</a> and a non-learnt application-specific <a href="https://www.biorxiv.org/content/10.1101/2020.06.07.138941v1" target="_blank" rel="noopener">color clustering from Duan et al.</a>.<br>For detailed information on the methods and the quantitative results please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p>
<h2>License</h2>
<p>The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0" target="_blank" rel="noopener">Creative Commons Attribution 4.0 International (CC BY 4.0) license</a>.</p>
<h2>Citation</h2>
<p>If you use <em>FISBe</em> in your research, please use the following BibTeX entry: </p>
<pre><code>@misc{mais2024fisbe,
title = {FISBe: A real-world benchmark dataset for instance
segmentation of long-range thin filamentous structures},
author = {Lisa Mais and Peter Hirsch and Claire Managan and Ramya
Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena
Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller},
year = 2024,
eprint = {2404.00130},
archivePrefix ={arXiv},
primaryClass = {cs.CV}
}</code></pre>
<h2>Acknowledgments</h2>
<p>We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuable<br>discussions.<br>P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.<br>This work was co-funded by Helmholtz Imaging.</p>
<h2>Changelog</h2>
<p>There have been no changes to the dataset so far.<br>All future change will be listed <a href="https://kainmueller-lab.github.io/fisbe/changelog" target="_blank" rel="noopener">on the changelog page</a>.</p>
<h2>Contributing</h2>
<p>If you would like to contribute, have encountered any issues or have any suggestions, please <a href="https://github.com/Kainmueller-Lab/fisbe/issues" target="_blank" rel="noopener">open an issue</a> for the FISBe dataset in the accompanying github repository.</p>
<p>All contributions are welcome!</p>
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