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Summary: Seizures originate from an excess of excitatory over inhibitory neural activity in confined regions of the brain, and spread only when they overcome strong inhibitory activity in surrounding regions.

Source: Weill Cornell University.

New evidence from a zebrafish model of epilepsy may help resolve a debate into how seizures originate, according to Weill Cornell Medicine and NewYork-Presbyterian investigators. The findings may also be useful in the discovery and development of future epilepsy drugs.

Analyzing sodium levels in breast cancer tumors can give an accurate indication of how aggressive a cancer is and whether chemotherapy treatments are taking effect, new research has shown.

In a study, by the universities of York and Cambridge and funded by charities Cancer Research UK and Breast Cancer Now, researchers developed a technique using sodium magnetic resonance imaging (MRI) to detect salt levels in breast cancer tumors in mice.

Using this technique, the researchers looked at breast cancer tumors and discovered that salt (sodium) was being accumulated inside cancer cells and that more active tumors accumulate more sodium.

As the pandemic rages on, technology companies have two choices: adapt or fail.

Startups have tried making fuel with engineered bacteria before. Can Cemvita Factory succeed?

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One company in California has found a new delicious way to use the technology.

Next, we aimed to determine whether the model type, i.e., a linear regression vs. a neural network, would significantly impact the performance. We, therefore, compared the aforementioned linear models with the neural network AltumAge using the same set of features. AltumAge outperformed the respective linear model with Horvath’s 353 CpG sites (MAE = 2.425 vs. 3.011, MSE = 32.732 vs. 46.867) and ElasticNet-selected 903 CpG sites (MAE = 2.302 vs. 2.621, MSE = 30.455 vs. 39.198). This result shows that AltumAge outperforms linear models given the same training data and set of features.

Lastly, to compare the effect of the different sets of CpG sites, we trained AltumAge with all 20,318 CpG sites available and compared the results from the smaller sets of CpG sites obtained above. There is a gradual improvement in performance for AltumAge by expanding the feature set from Horvath’s 353 sites (MAE = 2.425, MSE = 32.732) to 903 ElasticNet-selected CpG sites (MAE = 2.302, MSE = 30.455) to all 20,318 CpG sites (MAE = 2.153, MSE = 29.486). This result suggests that the expanded feature set helps improve the performance, likely because relevant information in the epigenome is not entirely captured by the CpG sites selected by an ElasticNet model.

Overall, these results indicate that even though more data samples lower the prediction error, AltumAge’s performance improvement is greater than the increased data effect. Indeed, the lower error of AltumAge when compared to the ElaticNet is robust to other data splits (Alpaydin’s Combined 5x2cv F test p-value = 9.71e−5).

New strain survives powdery mildew, a costly disease, without side effects.

Discovering a system’s causal relationships and structure is a crucial yet challenging problem in scientific disciplines ranging from medicine and biology to economics. While researchers typically adopt the graphical formalism of causal Bayesian networks (CBNs) to induce a graph structure that best describes these relationships, such unsupervised score-based approaches can quickly lead to prohibitively heavy computation burdens.

A research team from DeepMind, Mila – University of Montreal and Google Brain challenges the conventional causal induction approach in their new paper Learning to Induce Causal Structure, proposing a neural network architecture that learns the graph structure of observational and/or interventional data via supervised training on synthetic graphs. The team’s proposed Causal Structure Induction via Attention (CSIvA) method effectively makes causal induction a black-box problem and generalizes favourably to new synthetic and naturalistic graphs.

The team summarizes their main contributions as:

Aulos Biosciences is now recruiting cancer patients in Australian medical centers for a trial of the world’s first antibody drug designed by a computer.

The computationally designed antibody, known as AU-007, was planned by the artificial intelligence platform of Israeli biotech company Biolojic Design from Rehovot, in a way that would target a protein in the human body known as interleukin-2 (IL-2).

The goal is for the IL-2 pathway to activate the body’s immune system and attack the tumors.