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Schools are increasingly offering opportunities for students to take classes in computer and information sciences, but the numbers and diversity of students who enroll and persist are not always representative of a school's student population. To meet these goals, students' needs and interests must be addressed. This paper will describe what matters for students in high school and community college computing classes. The data include interviews with 30 students (73% Latinx), surveys from 58 students (77% Latinx) and interviews with three counselors (2 college, 1 high school). The findings show that students will engage and persist in computing pathways when they: a) are project-based where those projects are hands-on and allow them to see the results of their work, b) create positive social connections and a sense of belonging, and c) create opportunities for learners to be active agents in their learning. Students will also enroll in computing classes to fulfill requirements for graduation or for a different major, but they are less likely to persist if they don't see the results of their work or have support and encouragement from teachers or counselors. The factors that are most important vary for high school and college students, and counselors are more likely to describe extrinsic sources of motivation. The findings are interpreted using self-determination theory, which provides a framework for understanding how students' sense of autonomy, competence, and connection influence their motivation to engage and persist in computing. 

As open-access institutions serving diverse student populations, community colleges are perfect settings for broadening participation in computing efforts in higher education. The very nature of open access, however, places students with a wide variety of previous experience in the same introductory computer science classroom, intimidating the students with little programming exposure, many of whom are traditionally underrepresented in computer science. This paper reports on a pre-semester, intensive program designed to increase the computer science confidence and motivation of students with no previous programming exposure implemented at a community college in California. Framed using social cognitive career theory, results from the accompanying research project indicate preliminary success; we found the program to be well received by the majority of students, who had increased self-efficacy and interest in computer science. Implications for practice and research are discussed. 

The resolution of fluorescence microscopy images is limited by the physical properties of light. In the last decade, numerous super-resolution microscopy (SRM) approaches have been proposed to deal with such hindrance. Here we present Mean-Shift Super Resolution (MSSR), a new SRM algorithm based on the Mean Shift theory, which extends spatial resolution of single fluorescence images beyond the diffraction limit of light. MSSR works on low and high fluorophore densities, is not limited by the architecture of the optical setup and is applicable to single images as well as temporal series. The theoretical limit of spatial resolution, based on optimized real-world imaging conditions and analysis of temporal image stacks, has been measured to be 40 nm. Furthermore, MSSR has denoising capabilities that outperform other SRM approaches. Along with its wide accessibility, MSSR is a powerful, flexible, and generic tool for multidimensional and live cell imaging applications.

 

Genomes store information at scales beyond the linear nucleotide sequence, which impacts genome function at the level of an individual, while influences on populations and long-term genome function remains unclear. Here, we addressed how physical and chemical DNA characteristics influence genome evolution in the plant pathogenic fungus Verticillium dahliae . We identified incomplete DNA methylation of repetitive elements, associated with specific genomic compartments originally defined as Lineage-Specific (LS) regions that contain genes involved in host adaptation. Further chromatin characterization revealed associations with features such as H3 Lys-27 methylated histones (H3K27me3) and accessible DNA. Machine learning trained on chromatin data identified twice as much LS DNA as previously recognized, which was validated through orthogonal analysis, and we propose to refer to this DNA as adaptive genomic regions. Our results provide evidence that specific chromatin profiles define adaptive genomic regions, and highlight how different epigenetic factors contribute to the organization of these regions. 

Abstract This is the first in a series of papers dedicated to the study of Poisson manifolds of compact types (PMCTs). This notion encompassesseveral classes of Poisson manifolds defined via properties of their symplectic integrations. In this first paper we establish some fundamentalproperties and constructions of PMCTs. For instance, we show that their Poisson cohomology behaves very much like thede Rham cohomology of a compact manifold (Hodge decomposition, non-degenerate Poincaré duality pairing, etc.)and that the Moser trick can be adapted to PMCTs. More important, we find unexpected connections between PMCTs and symplectic topology: PMCTsare related with the theory of Lagrangian fibrations and we exhibit a construction of a non-trivialPMCT related to a classical question on the topology of the orbits of a free symplectic circle action.In subsequent papers, we will establish deep connections between PMCTs and integral affine geometry,Hamiltonian G -spaces, foliation theory, orbifolds, Lie theory and symplectic gerbes.