Epigenetic regulation of axon and dendrite growth

Neuroregenerative therapies for central nervous system (CNS) injury, neurodegenerative disease, or stroke require axons of damaged neurons to grow and re-innervate their targets. However, mature mammalian CNS neurons do not regenerate their axons, limiting recovery in these diseases. Although neurons' intrinsic capacity for axon growth may depend in part on the panoply of expressed transcription factors, epigenetic factors such as the accessibility of DNA and organization of chromatin are required for downstream genes to be transcribed. Thus, a potential approach to overcoming regenerative failure focuses on the epigenetic mechanisms regulating regenerative gene expression in the CNS. Here we review molecular mechanisms regulating the epigenetic state of DNA through chromatin modifications, their implications for regulating axon and dendrite growth, and important new directions for this field of study

Measuring Together: A Learning Approach for Inclusive Economies

In 2020, the Surdna Foundation's Inclusive Economies and Learning Grants Operations teams, alongside a dedicated group of grantee partners, completed phase one of a two-part pilot program to co-create a set of metrics and indicators to measure progress toward collective goals. Phase one focused on the metrics identification and collection process. Phase two of the report, Measuring Together: A Learning Approach for Inclusive Economies, examines one year of metrics data reported by grantees to:  Explore grantees' progress toward their self-selected targetsGain understanding about the types of successes and challenges grantees are experiencingIncorporate grantees' feedback on the metrics collection and analysis process as we move beyond the pilot phase and implement this part of our learning systems fully.

Long path problems

We demonstrate the interesting, counter-intuitive result that simple paths to the global optimum can be so long that climbing the path is intractable. This means that a unimodal search space, which consists of a single hill and in which each point in the space is on a simple path to the global optimum, can be difficult for a hillclimber to optimize. Various types of hillclimbing algorithms will make constant progress toward the global optimum on such long path problems. They will continuously improve their best found solutions, and be guaranteed to reach the global optimum. Yet we cannot wait for them to arrive. Early experimental results indicate that a genetic algorithm (GA) with crossover alone outperforms hillclimbers on one such long path problem. This suggests that GAs can climb hills faster than hillclimbers by exploiting building blocks when they are present. Although these problems are artificial, they introduce a new dimension of problem difficulty for evolutionary computation. Path length can be added to the ranks of multimodality, deception/misleadingness, noise, variance, etc., as a measure of fitness landscapes and their amenability to evolutionary optimization