Dynamic developmental changes in axon arbor morphology may directly reflect the formation, stabilization and elimination of synapses. We used dual-color imaging to study, in the live, developing animal, the relationship between axon arborization and synapse formation at the single cell level, and to examine the participation of brain-derived neurotrophic factor (BDNF) in synaptogenesis. Green fluorescent protein (GFP)-tagged synaptobrevin II served as a marker to visualize synaptic sites in individual fluorescently labeled Xenopus optic axons. Time-lapse confocal microscopy revealed that although most synapses remain stable, synapses are also formed and eliminated as axons branch and increase their complexity. Most new branches originated at GFP-labeled synaptic sites. Increasing BDNF levels significantly increased both axon arborization and synapse number, with BDNF increasing synapse number per axon terminal. The ability to visualize central synapses in real time provides insights about the dynamic mechanisms underlying synaptogenesis, and reveals BDNF as a modulator of synaptogenesis in vivo.

HER2-positive breast cancer accounts for 20–30% of all breast cancers and has the second-poorest prognosis among breast cancer subtypes. The approval of trastuzumab in 1998 has significantly improved patients’ outcomes and paved the way for the beginning of advent of targeted approaches in breast cancer treatment. However, primary or acquired resistance to trastuzumab has been increasingly recognized as a major obstacle in the clinical management of this disease. In addition, in clinical practice, there are currently no conclusive biomarkers for patient response to trastuzumab. Therefore, understanding the molecular mechanism of trastuzumab and the development of resistance to this drug are of interest. Such understanding will provide the guidance critically needed for the design of better combination therapy and will allow the appropriate selection of patients who are responsive to trastuzumab-based strategies. In line with that, our review highlights the well-accepted mechanisms of action and resistance to the therapy and discusses the progress that has been made toward successfully overcoming this resistance.

We propose TandA, an effective technique for fine-tuning pre-trained Transformer models for natural language tasks. Specifically, we first transfer a pre-trained model into a model for a general task by fine-tuning it with a large and high-quality dataset. We then perform a second fine-tuning step to adapt the transferred model to the target domain. We demonstrate the benefits of our approach for answer sentence selection, which is a well-known inference task in Question Answering. We built a large scale dataset to enable the transfer step, exploiting the Natural Questions dataset. Our approach establishes the state of the art on two well-known benchmarks, WikiQA and TREC-QA, achieving the impressive MAP scores of 92% and 94.3%, respectively, which largely outperform the the highest scores of 83.4% and 87.5% of previous work. We empirically show that TandA generates more stable and robust models reducing the effort required for selecting optimal hyper-parameters. Additionally, we show that the transfer step of TandA makes the adaptation step more robust to noise. This enables a more effective use of noisy datasets for fine-tuning. Finally, we also confirm the positive impact of TandA in an industrial setting, using domain specific datasets subject to different types of noise.

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Motivation: Understanding the role of genetics in diseases is one of the most important aims of the biological sciences. The completion of the Human Genome Project has led to a rapid increase in the number of publications in this area. However, the coverage of curated databases that provide information manually extracted from the literature is limited. Another challenge is that determining disease-related genes requires laborious experiments. Therefore, predicting good candidate genes before experimental analysis will save time and effort. We introduce an automatic approach based on text mining and network analysis to predict gene-disease associations. We collected an initial set of known disease-related genes and built an interaction network by automatic literature mining based on dependency parsing and support vector machines. Our hypothesis is that the central genes in this disease-specific network are likely to be related to the disease. We used the degree, eigenvector, betweenness and closeness centrality metrics to rank the genes in the network.Results: The proposed approach can be used to extract known and to infer unknown gene-disease associations. We evaluated the approach for prostate cancer. Eigenvector and degree centrality achieved high accuracy. A total of 95% of the top 20 genes ranked by these methods are confirmed to be related to prostate cancer. On the other hand, betweenness and closeness centrality predicted more genes whose relation to the disease is currently unknown and are candidates for experimental study.Availability: A web-based system for browsing the disease-specific gene-interaction networks is available at: http://gin.ncibi.orgContact: radev@umich.edu