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This study explored the impact of an active project-based, aquaculture constructivist-learning program, as perceived by high school students. The purpose of this case study was to discover if participation in the program influenced students' interest, engagement, and future educational and career aspirations in science, technology, engineering, and mathematics (STEM) when integrating aquaculture in and outside the classroom. Likewise, the study also wanted to explore students' knowledge about aquaculture and skill development after their participation in the program. The study employed a qualitative methods approach to explore students' attitudes and experiences. Qualitative data were collected from post-student focus groups at three different public, rural high schools in Kentucky. Other qualitative data included teacher journal reflections (e.g., personal documents) and public newspaper articles (e.g., public documents). Four emergent themes were found: (1) Students show excitement and enthusiasm in the hands-on, aquaculture program; (2) students show attention to detail in the hands-on, aquaculture tasks, it sticks, and are more responsible; (3) students are collaboratively engaged with their peers; and (4) greater interest and confidence in STEM through practical application. Results demonstrated that the program engaged learners in real-world problem-solving and decision-making situations while working collaboratively in small works. It also appears that students gained an important life skill, responsibility, as well as self-confidence in STEM, after participating in the program.

Z-nucleic acid structures play vital roles in cellular processes and have implications in innate immunity due to their recognition by Zα domains containing proteins (Z-DNA/Z-RNA binding proteins, ZBPs). Although Zα domains have been identified in six proteins, including viral E3L, ORF112, and I73R, as well as, cellular ADAR1, ZBP1, and PKZ, their prevalence across living organisms remains largely unexplored. In this study, we introduce a computational approach to predict Zα domains, leading to the revelation of previously unidentified Zα domain-containing proteins in eukaryotic organisms, including non-metazoan species. Our findings encompass the discovery of new ZBPs in previously unexplored giant viruses, members of the Nucleocytoviricota phylum. Through experimental validation, we confirm the Zα functionality of select proteins, establishing their capability to induce the B-to-Z conversion. Additionally, we identify Zα-like domains within bacterial proteins. While these domains share certain features with Zα domains, they lack the ability to bind to Z-nucleic acids or facilitate the B-to-Z DNA conversion. Our findings significantly expand the ZBP family across a wide spectrum of organisms and raise intriguing questions about the evolutionary origins of Zα-containing proteins. Moreover, our study offers fresh perspectives on the functional significance of Zα domains in virus sensing and innate immunity and opens avenues for exploring hitherto undiscovered functions of ZBPs.

This study aimed to evaluate the fatty acid (FA) profile in milk from commercial farms with varying pasture levels in the diet during spring and fall, and to investigate the physical and chemical properties of butter to assess the impact of FAs on technological and nutritional properties. Milk sampling was conducted biweekly from six farms, categorized into high (HP) and low (LP) pasture treatments based on pasture intake: >60% and p = 0.01), 74–48% more trans-vaccenic acid (TVA, p = 0.01), and 21–15% more branched-chain FAs (BCFAs, p = 0.006) than LP in spring and fall, respectively. In fall, butter from C had lower saturated FAs (SFAs, p = 0.005), higher unsaturated FAs (UFA, p = 0.008), and a lower spreadability index (SI, p = 0.005) than GRZ, resulting in softer butter. In conclusion, HP in both seasons had higher contents of FAs considered healthy for consumers compared to LP. Contrary to expectations, in fall, C showed higher UFAs and lower SFAs in butter, leading to better technological characteristics than GRZ.

We aim to understand the distribution and environmental drivers for the occurrence of the European green toad (Bufotes viridis) in Austria and create breeding habitats for it. Citizen scientists can use a custom smartphone application (AmphiApp) to record data such as the calls of anurans and photographic documentation. The records are validated by experts. To provide breeding habitats for green toads, we gave citizen scientists 300 small plastic ponds (1.20L × 0.9W × 0.4D m) to place on their land (garden, backyard). These citizen scientists will monitor their pond every two weeks for two seasons (March–August 2024 & 2025) for the occurrence of amphibians and their invertebrate prey. During the first two months, most pond owners have been highly motivated and have followed the monitoring scheme, despite the involved procedure, likely due to our active engagement with them (e.g., during the pond delivery by team members, emails, phone calls and messaging within AmphiApp).

Single-cell protein analysis has emerged as a powerful tool for understanding cellular heterogeneity and deciphering the complex mechanisms governing cellular function and fate. This review provides a comprehensive examination of the latest methodologies, including sophisticated cell isolation techniques (Fluorescence-Activated Cell Sorting (FACS), Magnetic-Activated Cell Sorting (MACS), Laser Capture Microdissection (LCM), manual cell picking, and microfluidics) and advanced approaches for protein profiling and protein-protein interaction analysis. The unique strengths, limitations, and opportunities of each method are discussed, along with their contributions to unraveling gene regulatory networks, cellular states, and disease mechanisms. The importance of data analysis and computational methods in extracting meaningful biological insights from the complex data generated by these technologies is also highlighted. By discussing recent progress, technological innovations, and potential future directions, this review emphasizes the critical role of single-cell protein analysis in advancing life science research and its promising applications in precision medicine, biomarker discovery, and targeted therapeutics. Deciphering cellular complexity at the single-cell level holds immense potential for transforming our understanding of biological processes and ultimately improving human health.

Cellular metabolism is tightly regulated by growth factor signaling, which promotes metabolic rewiring to support growth and proliferation. While growth factor-induced transcriptional and post-translational modes of metabolic regulation have been well defined, whether post-transcriptional mechanisms impacting mRNA stability regulate this process is less clear. Here, we present the ZFP36/L1/L2 family of RNA-binding proteins and mRNA decay factors as key drivers of metabolic regulation downstream of acute growth factor signaling. We quantitatively catalog metabolic enzyme and nutrient transporter mRNAs directly bound by ZFP36 following growth factor stimulation-many of which encode rate-limiting steps in metabolic pathways. Further, we show that ZFP36 directly promotes the mRNA decay of Enolase 2 (Eno2), altering Eno2 protein expression and enzymatic activity, and provide evidence of a ZFP36/Eno2 axis during VEGF-stimulated developmental retinal angiogenesis. Thus, ZFP36-mediated mRNA decay serves as an important mode of metabolic regulation downstream of growth factor signaling within dynamic cell and tissue states.

Abstract Skeletal muscle dysfunction in critical illnesses leaves survivors weak and functionally impaired. Macrophages infiltrate muscles; however, their functional role is unclear. We aim to examine muscle leukocyte composition and the effect of macrophages on muscle mass and function in the murine acute lung injury (ALI)‐associated skeletal muscle wasting model. We performed flow cytometry of hindlimb muscle to identify myeloid cells pre‐injury and time points up to 29 days after intratracheal lipopolysaccharide ALI. We evaluated muscle force and morphometrics after systemic and intramuscular clodronate‐induced macrophage depletions between peak lung injury and recovery (day 5–6) versus vehicle control. Our results show muscle leukocytes changed over ALI course with day 3 neutrophil infiltration (130.5 ± 95.6cells/mg control to 236.3 ± 70.6cells/mg day 3) and increased day 10 monocyte abundance (5.0 ± 3.4%CD45+CD11b+ day 3 to 14.0 ± 2.6%CD45+CD11b+ day 10, p = 0.005). Although macrophage count did not significantly change, pro‐inflammatory (27.0 ± 7.2% day 3 to 7.2 ± 3.8% day 10, p = 0.02) and anti‐inflammatory (30.5 ± 11.1% day 3 to 52.7 ± 9.7% day 10, p = 0.09) surface marker expression changed over the course of ALI. Macrophage depletion following peak lung injury increased muscle mass and force generation. These data suggest muscle macrophages beyond peak lung injury limit or delay muscle recovery. Targeting macrophages could augment muscle recovery following lung injury.

The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of power. The human brain's processing efficiency, progressive learning, and plasticity are unmatched by any computer system. Recent advances in stem cell technology have elevated the field of cell culture to higher levels of complexity, such as the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional monolayer cell systems. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. With the latest strides in stem cell technology, bioengineering, and machine learning, we can explore the ability of brain organoids to compute, and store given information (input), execute a task (output), and study how this affects the structural and functional connections in the organoids themselves. Furthermore, understanding how learning generates and changes patterns of connectivity in organoids can shed light on the early stages of cognition in the human brain. Investigating and understanding these concepts is an enormous, multidisciplinary endeavor that necessitates the engagement of both the scientific community and the public. Thus, on Feb 22-24 of 2022, the Johns Hopkins University held the first Organoid Intelligence Workshop to form an OI Community and to lay out the groundwork for the establishment of OI as a new scientific discipline. The potential of OI to revolutionize computing, neurological research, and drug development was discussed, along with a vision and roadmap for its development over the coming decade.

The pancreatic tumor microenvironment drives deregulated nutrient availability. Accordingly, pancreatic cancer cells require metabolic adaptations to survive and proliferate. Pancreatic cancer subtypes have been characterized by transcriptional and functional differences, with subtypes reported to exist within the same tumor. However, it remains unclear if this diversity extends to metabolic programming. Here, using metabolomic profiling and functional interrogation of metabolic dependencies, we identify two distinct metabolic subclasses among neoplastic populations within individual human and mouse tumors. Furthermore, these populations are poised for metabolic cross-talk, and in examining this, we find an unexpected role for asparagine supporting proliferation during limited respiration. Constitutive GCN2 activation permits ATF4 signaling in one subtype, driving excess asparagine production. Asparagine release provides resistance during impaired respiration, enabling symbiosis. Functionally, availability of exogenous asparagine during limited respiration indirectly supports maintenance of aspartate pools, a rate-limiting biosynthetic precursor. Conversely, depletion of extracellular asparagine with PEG-asparaginase sensitizes tumors to mitochondrial targeting with phenformin.

In order to identify the potential benefits and challenges of implementing student portfolios as quality mathematics assessment, a pilot study was conducted with teachers in various secondary school settings. The multi-case study consisted of five teacher participants from geographically and demographically differing contexts, four in the USA and one in Canada. They were interviewed and surveyed, exploring their motivations for utilizing a portfolio system of mathematics assessment for the 2020-2021 school year, the design of their portfolio system, and resultant impacts on student dispositions around mathematics. Each participating teacher implemented different structures of portfolio assessment, including the types of items included and how the items were assessed. Results showed that compared to traditional multiple-choice tests, teachers felt portfolios were a better reflection of their students' "extra-mathematical" skills, such as problem-solving and persistence. Teacher surveys and interviews also demonstrated confidence that the use of portfolios as assessment was superior to more traditional measures in terms of adhering to good pedagogical practices. Through questionnaire data and interviews, teachers reported portfolios were particularly beneficial for students for whom English is not their primary language, as well as students with special needs. The paper lays the groundwork for additional research studies in the area of student mathematics portfolios based on the provided framework.

ObjectivesTo measure the rate of benzodiazepine receptor agonist (BZA) dependence in older veterans with insomnia symptoms chronically using BZAs and to assess for associations between high posttraumatic stress disorder (PTSD) risk and BZA dependence.MethodsA cross-sectional study was conducted among veterans aged 55 years and older with insomnia symptoms (current or historical) and chronic use of BZAs (≥3 months). Measurements included the Primary Care-PTSD screen (score >2 indicates high PTSD risk) and Benzodiazepine Dependence Questionnaire. Logistic regression was used to test for associations between PTSD risk and BZA dependence.ResultsA high PTSD risk was observed in 40% of the participants (N = 33). One-fifth (21.7%, N = 18) of participants met the criteria for benzodiazepine dependence (score ≥23 on Benzodiazepine Dependence Questionnaire). Veterans with high PTSD risk were significantly more likely to have BZA dependence (odds ratio 10.09, 95% CI [2.39, 42.54], p = .002).ConclusionsIn older veterans with insomnia symptoms and chronic use of BZAs, high PTSD risk is associated with elevated risk for BZA dependence, which may make discontinuation of these medications difficult.Clinical implicationsClinicians should consider the strong association between PTSD symptoms and benzodiazepine dependence when developing plans to taper a BZA in veterans with these symptoms.

Machine learning approaches for pattern recognition are increasingly popular. However, the underlying algorithms are often not open source, may require substantial data for model training, and are not geared toward specific tasks. We used open-source software to build a green toad breeding call detection algorithm that will aid in field data analysis. We provide instructions on how to reproduce our approach for other animal sounds and research questions. Our approach using 34 green toad call sequences and 166 audio files without green toad sounds had an accuracy of 0.99 when split into training (70%) and testing (30%) datasets. The final algorithm was applied to amphibian sounds newly collected by citizen scientists. Our function used three categories: “Green toad(s) detected”, “No green toad(s) detected”, and “Double check”. Ninety percent of files containing green toad calls were classified as “Green toad(s) detected”, and the remaining 10% as “Double check”. Eighty-nine percent of files not containing green toad calls were classified as “No green toad(s) detected”, and the remaining 11% as “Double check”. Hence, none of the files were classified in the wrong category. We conclude that it is feasible for researchers to build their own efficient pattern recognition algorithm.