Greater Boston Interfaith Organization

Abstract Learning and memory, central to cognitive function and critical targets for drug discovery in neurological disorders, fundamentally rely on electrophysiological mechanisms such as short- term potentiation (STP) and long-term potentiation (LTP). We developed an embedded electrode array (EEA) platform supporting electrophysiological integration of paired CNS-3D human brain organoids derived from induced pluripotent stem cells (iPSCs), interconnected via guided axonal growth. Using open-loop stimulation protocols, we demonstrated robust, pharmacologically tunable STP and LTP in trained versus untrained organoid networks, confirmed by pharmaceutical modulation with elevated brain-derived neurotrophic factor (BDNF) enhancing LTP, and NMDA receptor blockade (DL-AP5) preventing its induction. Furthermore, a closed-loop "maze-game" paradigm employing reinforcement learning principles elicited adaptive behavioral responses, demonstrating functional learning capabilities dependent on exogenous BDNF. This integrated platform provides a powerful tool for modeling human synaptic plasticity and cognitive processes in vitro, significantly advancing opportunities for therapeutic discovery in cognitive disorders and contributing foundational insights to the emerging field of organoid intelligence.

NEW FEATURES Gains new functions occ_download_datasets and occ_download_dataset_activity to list datasets for a download, and list the downloads activity of a dataset (#275) (#276) Gains a new vignette covering working with GBIF downloads in rgbif (#262) MINOR IMPROVEMENTS Guidance added to docs for downloads functions on length of the request body (#263) Changed authentication details (user name, password, email) for downloads to allow any of the options: pass in as arguments, store as R options, store as environment variables (#187) gbif_citation() function gains an S3 method for passing the output of occ_download_meta() to it. In addition, for downloads gbif_citation() now returns a citation for the entire download (including) its DOI, in addition to citations for each dataset (#274) thanks @dnoesgaard BUG FIXES Fix documentation bug in occ_count() : georeferenced had a misleading description of what the value FALSE did (#265) Fixed bug in gbifmap() - was failing in some cases - better error handlingn now (#271) thanks @TomaszSuchan Fixed occ_download_cancel_staged() : it wasn't passing on authentication parameters correctly (#280)

Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Due to the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions. In this work, we investigate the potential of modern, large-scale machine learning implementations for taxonomic affectation of next-generation sequencing reads based on their k-mers profile. We show that machine learning-based compositional approaches benefit from increasing the number of fragments sampled from reference genome to tune their parameters, up to a coverage of about 10, and from increasing the k-mer size to about 12. Tuning these models involves training a machine learning model on about 10 8 samples in 10 7 dimensions, which is out of reach of standard soft-wares but can be done efficiently with modern implementations for large-scale machine learning. The resulting models are competitive in terms of accuracy with well-established alignment tools for problems involving a small to moderate number of candidate species, and for reasonable amounts of sequencing errors. We show, however, that compositional approaches are still limited in their ability to deal with problems involving a greater number of species, and more sensitive to sequencing errors. We finally confirm that compositional approach achieve faster prediction times, with a gain of 3 to 15 times with respect to the BWA-MEM short read mapper, depending on the number of candidate species and the level of sequencing noise.

Random forests are a learning algorithm proposed by Breiman [Mach. Learn. 45 (2001) 5--32] that combines several randomized decision trees and aggregates their predictions by averaging. Despite its wide usage and outstanding practical performance, little is known about the mathematical properties of the procedure. This disparity between theory and practice originates in the difficulty to simultaneously analyze both the randomization process and the highly data-dependent tree structure. In the present paper, we take a step forward in forest exploration by proving a consistency result for Breiman's [Mach. Learn. 45 (2001) 5--32] original algorithm in the context of additive regression models. Our analysis also sheds an interesting light on how random forests can nicely adapt to sparsity. 1. Introduction. Random forests are an ensemble learning method for classification and regression that constructs a number of randomized decision trees during the training phase and predicts by averaging the results. Since its publication in the seminal paper of Breiman (2001), the procedure has become a major data analysis tool, that performs well in practice in comparison with many standard methods. What has greatly contributed to the popularity of forests is the fact that they can be applied to a wide range of prediction problems and have few parameters to tune. Aside from being simple to use, the method is generally recognized for its accuracy and its ability to deal with small sample sizes, high-dimensional feature spaces and complex data structures. The random forest methodology has been successfully involved in many practical problems, including air quality prediction (winning code of the EMC data science global hackathon in 2012, see http://www.kaggle.com/c/dsg-hackathon), chemoinformatics [Svetnik et al. (2003)], ecology [Prasad, Iverson and Liaw (2006), Cutler et al. (2007)], 3D

d u stry, K in g ' M o n g k u t In stitu te o f T ech n o lo g y L a

We review a recent trend in computational systems biology which aims at using pattern recognition algorithms to infer the structure of large-scale biological networks from heterogeneous genomic data. We present several strategies that have been proposed and that lead to different pattern recognition problems and algorithms. The strenght of these approaches is illustrated on the reconstruction of metabolic, protein-protein and regulatory networks of model organisms. In all cases, state-of-the-art performance is reported.