5,636 research outputs found
MTGO-SC, A Tool to Explore Gene Modules in Single-Cell RNA Sequencing Data
No full textThe identification of functional modules in gene interaction networks is a key step in understanding biological processes. Network interpretation is essential for unveiling biological mechanisms, candidate biomarkers, or potential targets for drug discovery/repositioning. Plenty of biological module identification algorithms are available, although none is explicitly designed to perform the task on single-cell RNA sequencing (scRNA-seq) data. Here, we introduce MTGO-SC, an adaptation for scRNA-seq of our biological network module detection algorithm MTGO. MTGO-SC isolates gene functional modules by leveraging on both the network topological structure and the annotations characterizing the nodes (genes). These annotations are provided by an external source, such as databases and literature repositories (e.g., the Gene Ontology, Reactome). Thanks to the depth of single-cell data, it is possible to define one network for each cell cluster (typically, cell type or state) composing each sample, as opposed to traditional bulk RNA-seq, where the emerging gene network is averaged over the whole sample. MTGO-SC provides two complexity levels for interpretation: the gene-gene interaction and the intermodule interaction networks. MTGO-SC is versatile in letting the users define the rules to extract the gene network and integrated with the Seurat scRNA-seq analysis pipeline. MTGO-SC is available at https://github.com/ne1s0n/MTGOsc
SC author and illustrator Kate Salley Palmer signing book
No full textPhotograph of SC author and illustrator Kate Salley Palmer signing boo
Book signing by SC author and illustrator Kate Salley Palmer
No full textPhotograph of Book signing by SC author and illustrator Kate Salley Palme
High-resolution clean-sc
No full textIn this paper a high-resolution extension of CLEAN-SC is proposed: HR-CLEAN-SC. Where CLEAN-SC uses peak sources in “dirty maps” to define so-called source components, HR-CLEAN-SC takes advantage of the fact that source components can likewise be derived from points at some distance from the peak, as long as these “source markers” are on the main lobe of the Point Spread Function (PSF). This is very useful when sources are closely spaced together, such that their PSFs interfere. Then, alternative markers can be sought in which the relative influence by PSFs of other source positions is minimised. For those markers the source components better agree with the actual sources, which allows for better estimation of their locations and strengths. This paper outlines the theory needed to understand this approach and discusses applications to 2D and 3D microphone array simulations with closely spaced sources
SC author and illustrator Kate Salley Palmer talking to event attendees
No full textPhotograph of SC author and illustrator Kate Salley Palmer talking to Rita Lewi
Ca-modified Al–Mg–Sc alloy with high strength at elevated temperatures due to a hierarchical microstructure
No full textAl-Mg alloys are normally prone to lose part of their yield and tensile strength at high temperatures due to insufficient thermal stability of the microstructure. Here, we present a Ca-modified Al–Mg–Sc alloy demonstrating high strength at elevated temperatures. The microstructure contains Al4Ca phases distributed as a network along the grain boundary and Al3(Sc,Zr) nano-particles dispersed within the grains. The microstructure evolution and age-hardening analysis indicate that the combination of an Al4Ca network and Sc-rich nano-particles leads to excellent thermal stability even upon aging at 300 °C. The tensile strength of the alloy for temperatures up to 250 °C is significantly improved by an aging treatment and is comparable with the commercial heat-resistant aluminum alloys, i.e., A356 and A319. At a high temperature of 300 °C, the tensile strength is superior to the above-mentioned commercial alloys, even more so when expressed as the specific strength due to the low density of Ca-modified Al–Mg–Sc alloy. The excellent high-temperature strength results from a synergistic effect of solid solution strengthening, grain boundary strengthening and nanoparticle order strengthening.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Novel Aerospace Material
SC-Square: Overview to 2021.
No full textThis extended abstract was written to accompany an invited talk at the 2021 SC-Square Workshop, where the author was asked to give an overview of SC-Square progress to date. The author first reminds the reader of the definition of SC-Square, then briefly outlines some of the history, before picking out some (personal) scientific highlights
SC-Square: Overview to 2021.
No full textThis extended abstract was written to accompany an invited talk at the 2021 SC-Square Workshop, where the author was asked to give an overview of SC-Square progress to date. The author first reminds the reader of the definition of SC-Square, then briefly outlines some of the history, before picking out some (personal) scientific highlights
Scaling betweenness centrality using communication-efficient sparse matrix multiplication
No full textBetweenness centrality (BC) is a crucial graph problem that measures the significance of a vertex by the number of shortest paths
leading through it. We propose Maximal Frontier Betweenness Centrality (MFBC): a succinct BC algorithm based on novel sparse matrix multiplication routines that performs a factor of p 1/3 less communication on p processors than the best known alternatives, for
graphs withn vertices and average degree k = n/p 2/3. We formulate, implement, and prove the correctness of MFBC for weighted graphs by leveraging monoids instead of semirings, which enables a surprisingly succinct formulation. MFBC scales well for both extremely sparse and relatively dense graphs. It automatically searches a space of distributed data decompositions and sparse matrix multiplication algorithms for the most advantageous configuration. The MFBC implementation outperforms the well-known CombBLAS library by up to 8x and shows more robust performance. Our design methodology is readily extensible to other graph problems
Development, Implementation and Experimental Assessment of Path-Following Controllers on a 1:5 Scale Vehicle Testbed
No full textThe development of control strategies for autonomous vehicles requires a reliable and cost-effective validation approach. In this context, testbeds enabling repeatable experiments under controlled conditions are gaining relevance. Scaled vehicles have proven to be a valuable alternative to full-scale or simulation-based testing, enabling experimental validation while reducing costs and risks. This work presents a 1:5 scale modular vehicle platform, derived from a commercial Radio-Controlled (RC) vehicle and adapted as experimental testbed for control strategy validation and vehicle dynamics studies. The vehicle features an electric powertrain, operated through a Speedgoat Baseline Real-Time Target Machine (SBRTM). The hardware architecture includes a high-performance Inertial Measurement Unit (IMU) with embedded Global Navigation Satellite System (GNSS). An Extended Kalman Filter (EKF) is implemented to enhance positioning accuracy by fusing inertial and GNSS data, providing reliable estimates of the vehicle position, velocity, and orientation. Two path-following algorithms, i.e., Stanley Controller (SC) and the Linear Quadratic Regulator (LQR), are designed and integrated. Outdoor experimental tests enable the evaluation of tracking accuracy and robustness. The results demonstrate that the proposed scaled testbed constitutes a reliable and flexible platform for benchmarking autonomous vehicle controllers and enabling experimental testing
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