The Topography of Visually Guided Grasping in the Premotor Cortex: A Dense-Transcranial Magnetic Stimulation (TMS) Mapping Study.

Visuomotor transformations at the cortical level occur along a network where posterior parietal regions are connected to homologous premotor regions. Grasping-related activity is represented in a diffuse, ventral and dorsal system in the posterior parietal regions, but no systematic causal description of a premotor counterpart of a similar diffuse grasping representation is available. To fill this gap, we measured the kinematics of right finger movements in 17 male and female human participants during grasping of three objects of different sizes. Single-pulse transcranial magnetic stimulation was applied 100 ms after visual presentation of the object over a regular grid of 8 spots covering the left premotor cortex (PMC) and 2 Sham stimulations. Maximum finger aperture during reach was used as the feature to classify object size in different types of classifiers. Classification accuracy was taken as a measure of the efficiency of visuomotor transformations for grasping. Results showed that transcranial magnetic stimulation reduced classification accuracy compared with Sham stimulation when it was applied to 2 spots in the ventral PMC and 1 spot in the medial PMC, corresponding approximately to the ventral PMC and the dorsal portion of the supplementary motor area. Our results indicate a multifocal representation of object geometry for grasping in the PMC that matches the known multifocal parietal maps of grasping representations. Additionally, we confirm that, by applying a uniform spatial sampling procedure, transcranial magnetic stimulation can produce cortical functional maps independent of a priori spatial assumptions.SIGNIFICANCE STATEMENT Visually guided actions activate a large frontoparietal network. Here, we used a dense grid of transcranial magnetic stimulation spots covering the whole premotor cortex (PMC), to identify with accurate spatial mapping the functional specialization of the human PMC during grasping movement. Results corroborate previous findings about the role of the ventral PMC in preshaping the fingers according to the size of the target. Crucially, we found that the medial part of PMC, putatively covering the supplementary motor area, plays a direct role in object grasping. In concert with findings in nonhuman primates, these results indicate a multifocal representation of object geometry for grasping in the PMC and expand our understanding of how our brain integrates visual and motor information to perform visually guided actions.

Methods for Pangenomic Core Detection.

Computational pangenomics deals with the joint analysis of all genomic sequences of a species. It has already been successfully applied to various tasks in many research areas. Further advances in DNA sequencing technologies constantly let more and more genomic sequences become available for many species, leading to an increasing attractiveness of pangenomic studies. At the same time, larger datasets also pose new challenges for data structures and algorithms that are needed to handle the data. Efficient methods oftentimes make use of the concept of k-mers.Core detection is a common way of analyzing a pangenome. The pangenome's core is defined as the subset of genomic information shared among all individual members. Classically, it is not only determined on the abstract level of genes but can also be described on the sequence level.In this chapter, we provide an overview of k-mer-based methods in the context of pangenomics studies. We first revisit existing software solutions for k-mer counting and k-mer set representation. Afterward, we describe the usage of two k-mer-based approaches, Pangrowth and Corer, for pangenomic core detection.

Panacus: fast and exact pangenome growth and core size estimation.

Motivation: Using a single linear reference genome poses a limitation to exploring the full genomic diversity of a species. The release of a draft human pangenome underscores the increasing relevance of pangenomics to overcome these limitations. Pangenomes are commonly represented as graphs, which can represent billions of base pairs of sequence. Presently, there is a lack of scalable software able to perform key tasks on pangenomes, such as quantifying universally shared sequence across genomes (the core genome) and measuring the extent of genomic variability as a function of sample size (pangenome growth). Results: We introduce Panacus (pangenome-abacus), a tool designed to rapidly perform these tasks and visualize the results in interactive plots. Panacus can process GFA files, the accepted standard for pangenome graphs, and is able to analyze a human pangenome graph with 110 million nodes in less than one hour. Availability: Panacus is implemented in Rust and is published as Open Source software under the MIT license. The source code and documentation are available at https://github.com/marschall-lab/panacus. Panacus can be installed via Bioconda at https://bioconda.github.io/recipes/panacus/README.html.

Exon-Intron Differential Analysis Reveals the Role of Competing Endogenous RNAs in Post-Transcriptional Regulation of Translation.

Stressful conditions induce the cell to save energy and activate a rescue program modulated by mammalian target of rapamycin (mTOR). Along with transcriptional and translational regulation, the cell relies also on post-transcriptional modulation to quickly adapt the translation of essential proteins. MicroRNAs play an important role in the regulation of protein translation, and their availability is tightly regulated by RNA competing mechanisms often mediated by long noncoding RNAs (lncRNAs). In our paper, we simulated the response to growth adverse condition by bimiralisib, a dual PI3K/mTOR inhibitor, in diffuse large B cell lymphoma cell lines, and we studied post-transcriptional regulation by the differential analysis of exonic and intronic RNA expression. In particular, we observed the upregulation of a lncRNA, lncTNK2-2:1, which correlated with the stabilization of transcripts involved in the regulation of translation and DNA damage after bimiralisib treatment. We identified miR-21-3p as miRNA likely sponged by lncTNK2-2:1, with consequent stabilization of the mRNA of p53, which is a master regulator of cell growth in response to DNA damage.