Using Google Searches to Track Infectious Diseases

New research shows Google searches can help health officials track infectious diseases. A team from Harvard University has quickly and accurately tracked dengue fever in some undeveloped nations by using a mathematical model that combines clinical government data and Google searches. The research was published in the journal “PLOS Computational Biology” last week.
Dengue fever is a mosquito-borne disease that has flu-like symptoms such as nausea, muscle aches, and headaches. It is estimated that there are 390 million dengue infections per year. More than 2.5 billion people (30 % of the world’s population) are at risk of this infection.
Dengue mostly occurs in less developed countries. Therefore, tracking this disease is difficult because these countries do not have reliable and effective surveillance systems for it. In these regions, governments rely on hospital reports which are often delayed and have many regions.
The new research built on a previous method to track the flu in the U.S in 2015. The researchers believe that the new study restores hope that online searches can help track infectious diseases after earlier efforts such as Google Dengue Trends and Google Flu Trends returned mixed findings and were discontinued.
The researchers modified their mathematical modeling tool and tracked dengue activity in Taiwan, Singapore, Thailand, Brazil, and Mexico. Google’s “Trends” tool was used to track searches made by individuals in these countries. The researchers found their method produced more precise estimations than other techniques used in other regions.
References
http://money.cnn.com/2017/07/20/technology/tracking-diseases-google/index.html?sr=twCNN072017tracking-diseases-google0439PMStory
http://www.nbc-2.com/story/35933593/how-google-searches-are-being-used-to-track-infectious-diseases

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What is Computational Pharmacology?

Computational methods based on mathematics and statistics are permeating all areas of pharmacology. Theoretical and computational methods are revolutionizing drug discovery and pharmacology. In the drug discovery, predicting, modeling, and simulating therapeutic agents and their relations with target molecules is a great step.
From a computational biology perspective, computational pharmacology is the study of genomic data effects to find relations between diseases and specific genotypes and then screening drug data. The pharma industries need a shift in methods to analyze drug data. In the past, pharmacologists have been using Microsoft Excel to compare genomic data and chemical associated with drug effectiveness. However, the industry is now at what is known as the Excel barricade, arising from the limited cells accessible on a spreadsheet. As a result, scientists have developed computational techniques to analyze massive data sets.
Analysts predict that if medications fail due to intellectual property, computational biology will be needed to replace existing drugs on the market. Currently, many pharmaceutical companies need more competent analysts of the massive data sets who can help in the production of new drugs. Therefore, students in computational biology should be encouraged to pursue careers in computational pharmacology.
References
https://www.csb.pitt.edu/pharmacology-drug-design/
https://www.med.unc.edu/pharm/research/computer-vision-initiative-1

Network of the Brain May Allow Researchers to Understand Neurological Disorders Better

According to a recent study by Weill Cornell Medicine and the University of California, San Francisco, a deeper understanding of neurons connectivity network of the brain could enable scientists to predict spatial patterns of the brain and identify processes that relate to neurological disorders.
In their study that was published in PLoS Computational Biology on 22th June, the researchers used mathematics and Diffusion-Tensor MRI ( a form of magnetic resonance imaging) to better understand how the connections between the brain’s network of fibers and its deep white matter are affected by neurological disorders. They discovered sub-networks that make the connectome, eigenmodes, whose role is to communicate information from one part of the brain to another.
According to senior author Ashish Raj, an associate professor of neuroscience and computer science in radiology in the Feil Family Brain and Mind Research Institute, once an individual understands the eigenmodes, he can start understanding the processes that occur in the brain and the patterns of activity to expect.
Raj described the connectome of the brain as series of tubes through which information runs. The connectome’s components parts (eigenmodes) are “similar to guitar’s vibrations, “he said. The researchers carried out MRIs on ten healthy participants and discovered that eigenmodes are pervasive with high overlap between participants and between scans of the similar participants carried out on different days.
References
https://phys.org/news/2017-04-analysis-brain-network-unique-insight.html
http://news.cornell.edu/stories/2017/07/brains-network-may-provide-insights-neurological-disorders

Computational Neuroscience

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Also known as theoretical neuroscience, computational neuroscience studies functions of the brain in terms of the information processing features of the structures that are part of the nervous system. Known as an interdisciplinary of computational science, it links the various fields of neuroscience, psychology, cognitive neuroscience with physics, mathematics, computer science and engineering.

Computational neuroscience is not only different from psychological connectionism but also learning theories of disciplines such as computational learning, neural networks, and machine learning. This is because it focuses mainly on descriptions of biologically and functional realistic neurons and their dynamics and physiology.

Computational neuroscience uses theoretical tools to predict, interpret, or explain experimental data and the mechanisms that underlie it. In neuroscience, some examples of the use of quantitative methods include:

Advanced data analysis
Creating tools that help to get more information and understanding from neural data on many scales.

Dynamical and statistical modeling
Use of data to develop models whose functions is to look at the role that is played by specific neural mechanisms.

Theory
Create novel models that bring together or explain disparate observations.
Identifying minimal dynamics or structure that explain an important neural phenomenon.
Identifying algorithmic/ computational motifs and principles that explain and optimize neural function.

References
http://www.cnsorg.org/computational-neuroscience
http://compneuro.washington.edu/about/what-is-compneuro/