Search for: All records

Abstract Computational thinking is crucial for STEM researchers and practitioners, as it involves more than just developing skills—it is a way of thinking that enables effective problem-solving. STEM disciplines approach different problems and as such employ computational thinking uniquely, so students cannot rely solely on computer science to develop computational thinking. Less attention has been given to social aspects of computation, such as collaborating and communicating with and about computation even though social aspects are essential to problem solving. We utilized computational literacy as an alternative framework that explicitly includes social elements as a primary pillar. We conducted 15 interviews with STEM researchers to identify and organize the social aspects that play a role in their research. We organized goals by motivation (persuasion and productivity) and representation (visual and non-visual) to contextualize the use of communication in computation. We found that researchers use computation to explain research results, navigate decision making, establish rigor, ensure reproducibility, facilitate lab stability, and promote research efficiency. We used Activity Theory to describe the tools, norms, and communities associated with these goals to offer a more detailed framework for the social pillar of computational literacy within the context of science and engineering. Examples from each discipline within STEM are described. This social computational literacy framework can act as a guide for STEM educators and practitioners alike to use and teach social aspects of computation. 

Editorial in Biochemistry & Molecular Biology Education 

Abstract Unrepaired DNA damage encountered by the cellular replication machinery can stall DNA replication, ultimately leading to cell death. In the DNA damage tolerance pathway translesion synthesis (TLS), replication stalling is alleviated by the recruitment of specialized polymerases to synthesize short stretches of DNA near a lesion. Although TLS promotes cell survival, most TLS polymerases are low-fidelity and must be tightly regulated to avoid harmful mutagenesis. The gram-negative bacterium Escherichia coli has served as the model organism for studies of the molecular mechanisms of bacterial TLS. However, it is poorly understood whether these same mechanisms apply to other bacteria. Here, we use in vivo single-molecule fluorescence microscopy to investigate the TLS polymerase Pol Y1 in the model gram-positive bacterium Bacillus subtilis. We find significant differences in the localization and dynamics of Pol Y1 in comparison to its E. coli homolog, Pol IV. Notably, Pol Y1 is constitutively enriched at or near sites of replication in the absence of DNA damage through interactions with the DnaN clamp; in contrast, Pol IV has been shown to be selectively enriched only upon replication stalling. These results suggest key differences in the roles and mechanisms of regulation of TLS polymerases across different bacterial species. 

Explore three challenges that students faced and how they made progress. 

Here, we report an air-free approach to infiltrate isostructural metal–organic frameworks (MOFs), M-MOF-74 (M = Cu, Mn, Zn, Mg), with conjugated acceptor 7,7,8,8-tetracyanoquinodimethane (TCNQ). The TCNQ@M-MOF-74 compounds exhibit a striking correlation between their bulk conductivities and the open d shell variants (Cu, Mn), arising from TCNQ p-doping of the MOFs. Importantly, conjugation of the guest molecule is a prerequisite for inducing electrical conductivity in these systems. 

Biomedicine today is experiencing a shift towards decentralized data collection, which promises enhanced reproducibility and collaboration across diverse laboratory environments. This inter-laboratory study evaluates the performance of biocytometry, a method utilizing engineered bioparticles for enumerating cells based on their surface antigen patterns. In centralized and aggregated inter-lab studies, biocytometry demonstrated significant statistical power in discriminating numbers of target cells at varying concentrations as low as 1 cell per 100,000 background cells. User skill levels varied from expert to beginner capturing a range of proficiencies. Measurement was performed in a decentralized environment without any instrument cross-calibration or advanced user training outside of a basic instruction manual. The results affirm biocytometry to be a viable solution for immunophenotyping applications demanding sensitivity as well as scalability and reproducibility and paves the way for decentralized analysis of rare cells in heterogeneous samples. 

The lack of routine viral genomic surveillance delayed the initial detection of SARS-CoV-2, allowing the virus to spread unfettered at the outset of the U.S. epidemic. Over subsequent months, poor surveillance enabled variants to emerge unnoticed. Against this backdrop, long-standing social and racial inequities have contributed to a greater burden of cases and deaths among minority groups. To begin to address these problems, we developed a new variant surveillance model geared toward building ‘next generation’ genome sequencing capacity at universities in or near rural areas and engaging the participation of their local communities. The resulting genomic surveillance network has generated more than 1,000 SARS-CoV-2 genomes to date, including the first confirmed case in northeast Louisiana of Omicron, and the first and sixth confirmed cases in Georgia of the emergent BA.2.75 and BQ.1.1 variants, respectively. In agreement with other studies, significantly higher viral gene copy numbers were observed in Delta variant samples compared to those from Omicron BA.1 variant infections, and lower copy numbers were seen in asymptomatic infections relative to symptomatic ones. Collectively, the results and outcomes from our collaborative work demonstrate that establishing genomic surveillance capacity at smaller academic institutions in rural areas and fostering relationships between academic teams and local health clinics represent a robust pathway to improve pandemic readiness. 

Abstract Biomedicine today is experiencing a shift towards decentralized data collection, which promises enhanced reproducibility and collaboration across diverse laboratory environments. This inter-laboratory study evaluates the performance of biocytometry, a method utilizing engineered bioparticles for enumerating cells based on their surface antigen patterns. In a decentralized framework, spanning 78 assays conducted by 30 users across 12 distinct laboratories, biocytometry consistently demonstrated significant statistical power in discriminating numbers of target cells at varying concentrations as low as 1 cell per 100,000 background cells. User skill levels varied from expert to beginner capturing a range of proficiencies. Measurement was performed in a decentralized environment without any instrument cross-calibration or advanced user training outside of a basic instruction manual. The results affirm biocytometry to be a viable solution for immunophenotyping applications demanding sensitivity as well as scalability and reproducibility and paves the way for decentralized analysis of rare cells in heterogeneous samples.