We are far from having a complete mechanistic understanding of the brain computations involved in language processing and of the role that syntax plays in those computations. Most language studies do not computationally model syntactic processing, and most studies that do model syntactic processing use effort-based metrics. These metrics capture the effort needed to process the syntactic information given by every word. They can reveal where in the brain syntactic processing occurs, but not what features of syntax are processed by different brain areas. In this paper, we move beyond effort-based metrics and propose explicit features capturing the syntactic structure that is incrementally built while a sentence is read one word at a time. Using these features and functional Magnetic Resonance Imaging (fMRI) recordings of participants reading a natural text, we study the brain representation of syntax. We find that our syntactic structure-based features are better than effort-based metrics at predicting brain activity in various parts of the language system. Our results suggest that the brain represents complex syntactic information such as phrase and clause structures. We see that regions well-predicted by syntactic features are distributed in the language system and are not distinguishable from those that process semantics. Our results call for a shift in the approach used for studying syntactic processing.

Advances in gene delivery technologies are enabling rapid progress in molecular medicine, but require precise expression of genetic cargo in desired cell types, which is predominantly achieved via a regulatory DNA sequence called a promoter; however, only a handful of cell type-specific promoters are known. Efficiently designing compact promoter sequences with a high density of regulatory information by leveraging machine learning models would therefore be broadly impactful for fundamental research and direct therapeutic applications. However, models of expression from such compact promoter sequences are lacking, despite the recent success of deep learning in modelling expression from endogenous regulatory sequences. Despite the lack of large datasets measuring promoter-driven expression in many cell types, data from a few well-studied cell types or from endogenous gene expression may provide relevant information for transfer learning, which has not yet been explored in this setting. Here, we evaluate a variety of pretraining tasks and transfer strategies for modelling cell type-specific expression from compact promoters and demonstrate the effectiveness of pretraining on existing promoter-driven expression datasets from other cell types. Our approach is broadly applicable for modelling promoter-driven expression in any data-limited cell type of interest, and will enable the use of model-based optimization techniques for promoter design for gene delivery applications. Our code and data are available at https://github.com/anikethjr/promoter_models.

With the growth of the internet, the number of fake-news online has been proliferating every year. The consequences of such phenomena are manifold, ranging from lousy decision-making process to bullying and violence episodes. Therefore, fact-checking algorithms became a valuable asset.

Among different characteristics of knowledge bases, data quality is one of the most relevant to maximize the benefits of the provided information. Knowledge base quality assessment poses a number of big data challenges such as high volume, variety, velocity, and veracity. In this article, we focus on answering questions related to the assessment of the veracity of facts through Deep Fact Validation (DeFacto), a triple validation framework designed to assess facts in RDF knowledge bases. Despite current developments in the research area, the underlying framework faces many challenges. This article pinpoints and discusses these issues and conducts a thorough analysis of its pipeline, aiming at reducing the error propagation through its components. Furthermore, we discuss recent developments related to this fact validation as well as describing advantages and drawbacks of state-of-the-art models. As a result of this exploratory analysis, we give insights and directions toward a better architecture to tackle the complex task of fact-checking in knowledge bases.

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