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Small (s)RNAs play crucial roles in the regulation of gene expression and genome stability across eukaryotes where they direct epigenetic modifications, post-transcriptional gene silencing, and defense against both endogenous and exogenous viruses. It is known that Chlamydomonas reinhardtii , a well-studied unicellular green algae species, possesses sRNA-based mechanisms that are distinct from those of land plants. However, definition of sRNA loci and further systematic classification is not yet available for this or any other algae. Here, using data-driven machine learning approaches including Multiple Correspondence Analysis (MCA) and clustering, we have generated a comprehensively annotated and classified sRNA locus map for C. reinhardtii. This map shows some common characteristics with higher plants and animals, but it also reveals distinct features. These results are consistent with the idea that there was diversification in sRNA mechanisms after the evolutionary divergence of algae from higher plant lineages.

Charles Swanton and colleagues used multiregion exome sequencing to study the evolutionary histories of ten clear cell renal cell carcinomas. They observed marked intratumoral heterogeneity in all cases, with extensive evidence of parallel evolution of tumor subclones and only a small number of truncal driver events. Clear cell renal carcinomas (ccRCCs) can display intratumor heterogeneity (ITH). We applied multiregion exome sequencing (M-seq) to resolve the genetic architecture and evolutionary histories of ten ccRCCs. Ultra-deep sequencing identified ITH in all cases. We found that 73–75% of identified ccRCC driver aberrations were subclonal, confounding estimates of driver mutation prevalence. ITH increased with the number of biopsies analyzed, without evidence of saturation in most tumors. Chromosome 3p loss and VHL aberrations were the only ubiquitous events. The proportion of C>T transitions at CpG sites increased during tumor progression. M-seq permits the temporal resolution of ccRCC evolution and refines mutational signatures occurring during tumor development.

The rise of generative artificial intelligence has prompted claims that large language models (LLMs) can substitute for human participants, particularly in moral judgment tasks where correlations between ChatGPT and humans approach r  = 1.00. In response, we conducted a pre-registered study where two LLMs (text-davinci-003 and GPT-4o) predicted human moral judgments of 60 scenarios prior to a large human sample ( N  = 940) rating them. Despite strong correlations, difference scores revealed substantial, systematic errors: Compared to humans, LLMs provided more extreme morality ratings of moral and neutral scenarios and more extreme immorality ratings of immoral ones. Moreover, ChatGPT differed significantly and with moderate to large effect sizes from human averages on ~ 87% of scenarios. Further, LLM ratings clustered around a restricted number of values, failing to reflect human variability. Re-examination of earlier published data also reflected this clumping. We conclude that broader evaluation criteria are needed for comparing LLM predictions and human responses in moral reasoning tasks.

Genetic analysis was applied to different regions of renal-cell cancers. The lesions noted in the tumor were not found in every sample, and regions of the tumor had different gene-expression patterns. This suggests that extrapolation from results of a single biopsy may be problematic. Large-scale sequencing analyses of solid cancers have identified extensive heterogeneity between individual tumors. 1 – 6 Genetic intratumor heterogeneity has also been shown 7 – 15 and can contribute to treatment failure and drug resistance. Intratumor heterogeneity may have important consequences for personalized-medicine approaches that commonly rely on single tumor-biopsy samples to portray tumor mutational landscapes. Studies comparing mutational profiles of primary tumors and associated metastatic lesions 16 , 17 or local recurrences 18 have provided evidence of intratumor heterogeneity at nucleotide resolution. Intratumor heterogeneity within primary tumors and associated metastatic sites has not been systematically characterized by next-generation sequencing. We applied exome sequencing, chromosome aberration analysis, . . .

The present study investigated how task demand (cognitive load and interactivity) and avatar sexualization in a video game influenced rape myth acceptance (RMA), hostile sexism, and self-objectification. In a between-subjects design, 300 U.S. college students either played or watched someone else play a videogame as either a sexualized or non-sexualized female avatar under high (memorize 7 symbols) or low (memorize 2 symbols) cognitive load. Hypotheses were derived from the limited capacity model of motivated mediated message processing (LC4MP) and perspectives on stereotype processing. Results contradicted hypotheses that greater task demands and sexualization would produce greater RMA, hostile sexism, and self-objectification. Instead, we found that sexualization did not affect these variables. Greater cognitive load reduced rape myth acceptance and hostile sexism for those in the sexualized avatar condition, but it did not affect self-objectification. We discuss these results with respect to the LC4MP and suggest that the processing of stereotype-inconsistent information might be the underlying cause of these unexpected findings. These results provide tentative evidence that cognitively demanding video game environments may prompt players to focus on stereotype-inconsistent, rather than stereotype-consistent, social information.

Tackling the pressing sustainability needs of society will require the development and application of new technologies. Biotechnology, emboldened by recent advances in synthetic biology, offers to generate sustainable biologically-based routes to chemicals and materials as alternatives to fossil-derived incumbents. Yet, the sustainability potential of biotechnology is not without trade-offs. Here, we probe this capacity for sustainability for the case of bio-based nylon using both deliberative and analytical approaches within a framework of Constructive Sustainability Assessment . We highlight the potential for life cycle CO 2 and N 2 O savings with bio-based processes, but report mixed results in other environmental and social impact categories. Importantly, we demonstrate how this knowledge can be generated collaboratively and constructively within companies at an early stage to anticipate consequences and to inform the modification of designs and applications. Application of the approach demonstrated here provides an avenue for technological actors to better understand and become responsive to the sustainability implications of their products, systems and actions.

Enteroaggregative Escherichia coli (EAEC) are defined by their stacked-brick adherence pattern to human epithelial cells. There is no all-encompassing genetic marker for EAEC. The category is commonly implicated in diarrhea but research is hampered by perplexing heterogeneity. To identify key EAEC lineages, we applied multilocus sequence typing to 126 E. coli isolates from a Nigerian case-control study that showed aggregative adherence in the HEp-2 adherence assay, and 24 other EAEC strains from diverse locations. EAEC largely belonged to the A, B1 and D phylogenetic groups and only 7 (4.6%) isolates were in the B2 cluster. As many as 96 sequence types (STs) were identified but 60 (40%) of the EAEC strains belong to or are double locus variants of STs 10, 31, and 394. The remainder did not belong to predominant complexes. The most common ST complex, with predicted ancestor ST10, included 32 (21.3%) of the isolates. Significant age-related distribution suggests that weaned children in Nigeria are at risk for diarrhea from of ST10-complex EAEC. Phylogenetic group D EAEC strains, predominantly from ST31- and ST394 complexes, represented 38 (25.3%) of all isolates, include genome-sequenced strain 042, and possessed conserved chromosomal loci. We have developed a molecular phylogenetic framework, which demonstrates that although grouped by a shared phenotype, the category of 'EAEC' encompasses multiple pathogenic lineages. Principal among isolates from Nigeria were ST10-complex EAEC that were associated with diarrhea in children over one year and ECOR D strains that share horizontally acquired loci.

Engineering biology--which combines biology, engineering, and information technology to produce biobased materials and products--promises to create sustainable biomanufacturing around the globe. Already, engineering biology has enabled the scale-up of virus testing and aided rapid vaccine development during the COVID-19 pandemic. Building on research in and tools developed by synthetic biology, engineering biology now aims to translate its approaches of biological design, building, and testing to a wide range of applications, processes, and products. The ambition is to not only transform products that are already use, but also create new ones, making use of nature's intrinsic diversity. Unlike today's petroleum and petrochemical refineries, which are concentrated in a few locations, the technologies of a new bioeconomy could be developed and deployed in a targeted manner, making use of local renewable and sustainably sourced feedstocks.

It's 2032 and biology, applied to some of humanity's biggest problems, has transformed economies and societies around the world. In the decade since the COVID-19 pandemic, global supply chains for chemicals, materials, food, medicines, and energy have become shorter and more resilient--enabled in no small part by the growth of biomanufacturing through thousands of small biorefneries around the world. Applied engineering biology has solved many entrenched problems while also bringing fulfilling jobs home to local communities. Because goods and energy are increasingly derived from biobased and renewable sources, petroleum use is sharply declining. Renewable resources--crops and algae, as well as waste materials and recycled gases--are sustainably managed to preserve biodiversity and minimize carbon emissions and pollution. This bottom-up bioeconomy has also enabled distributed governance systems that empower communities to tailor new approaches to their particular situations, creating a break with the industrial patterns and practices of the past.

Accessible instructional materials are materials which can be used by students with disabilities as easily, effectively, and thoroughly as students without disabilities. Unfortunately, policy evidence suggests the California Community Colleges are not ensuring instructional materials are accessible. Surprisingly, while accessibility is often discussed as an equity issue, little evidence links accessible instructional materials to disabled student success. Additionally, there is no existing instrument which measures disabled students’ perceptions of accessibility. The purpose of this quantitative pilot study was to (1) develop an instrument to measure disabled students’ perceptions of the accessibility of their instructional materials, and (2) determine whether there is a relationship between perceived accessibility and perceived learning. The influence of disability type, use of accommodations, and attitudes towards requesting accommodations on perceived learning were also investigated. In this study, items for the Perceived Accessibility of Instructional Materials (PAIM) instrument were developed based on the Web Content Accessibility Guidelines (WCAG) principles of perceivable, operable, understandable, and robust materials. College students with disabilities (n = 116) completed a survey instrument containing the 33-item PAIM instrument, six items from the CAP Perceived Learning Scale, and additional items related to disability type, use of disability accommodations, attitudes towards requesting accommodations, and demographic information. Results of factor and reliability analyses indicated the four scales of the PAIM instrument are separately valid and reliable indicators of perceived accessibility. Additionally, a regression model indicated perceived accessibility was a significant predictor of perceived learning. Disability type was not a significant predictor of perceived learning and use of accommodations and attitudes towards accommodations did not significantly moderate the relationship between perceived accessibility and learning. The results suggest the initial PAIM instrument is a promising starting point for further scale development work to measure perceived accessibility. Importantly, this study also provides the first known quantitative evidence of a relationship between accessibility and disabled student learning. Recommendations include the use of the PAIM instrument by practitioners in tandem with an expanded focus on institutional accessibility leadership, faculty professional development, and policy resources.