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The integration of computational modelling in science classrooms provides a unique opportunity to promote key 21st century skills including computational thinking (CT) and collaboration. The open-ended, problem-solving nature of the task requires groups to grapple with the combination of two domains (science and computing) as they collaboratively construct computational models. While this approach has produced significant learning gains for students in both science and CT in K-12 settings, the collaborative learning processes students use, including learner regulation, are not well understood. In this paper, we present a systematic analysis framework that combines natural language processing (NLP) of collaborative dialogue, log file analyses of students' model-building actions, and final model scores. This analysis is used to better understand students' regulation of collaborative problem solving (CPS) processes over a series of computational modelling tasks of varying complexity. The results suggest that the computational modelling challenges afford opportunities for students to a) explore resource-intensive processes, such as trial and error, to more systematic processes, such as debugging model errors by leveraging data tools, and b) learn from each other using socially shared regulation (SSR) and productive collaboration. The use of such SSR processes correlated positively with their model-building scores. Our paper aims to advance our understanding of collaborative, computational modelling in K-12 science to better inform classroom applications.

Epithelial-to-mesenchymal transition (EMT) is a dramatic change in cellular physiology during development and metastasis, which requires coordination between cell signaling, adhesion, and membrane protrusions. These processes all involve dynamic changes in the plasma membrane; yet, how membrane lipid content regulates membrane function during EMT remains incompletely understood. By screening for differential expression of lipid-modifying genes over the course of EMT in the avian neural crest, we have identified the ceramide-producing enzyme neutral sphingomyelinase 2 (nSMase2) as a critical regulator of a developmental EMT. nSMase2 expression begins at the onset of EMT, and in vivo knockdown experiments demonstrate that nSMase2 is necessary for neural crest migration. We find that nSMase2 promotes Wnt and BMP signaling and is required to activate the mesenchymal gene expression program. Mechanistically, we show that nSMase2-dependent ceramide production is necessary for and sufficient to up-regulate endocytosis and is required for Wnt co-receptor internalization. Finally, inhibition of endocytosis in the neural crest mimics the loss of migration and Wnt signaling observed following nSMase2 knockdown. Our results support a model in which nSMase2 is expressed at the onset of neural crest EMT to produce ceramide and facilitate receptor-mediated endocytosis of Wnt and BMP signaling complexes, thereby activating promigratory gene expression. These results highlight the critical role of plasma membrane lipid metabolism in regulating transcriptional changes during developmental EMT programs.

Extracellular vesicles (EVs) play important roles in cell-to-cell communications and carry high potential as markers targeted in disease diagnosis, prognosis, and therapeutic development. The main obstacles to EV study are their high heterogeneity; low amounts present in samples; and physical similarity to the abundant, interfering matrix components. Multiple rounds of separation and purification are often needed prior to EV characterization and function assessment. Herein, we report the offline coupling of asymmetrical flow field-flow fractionation (AF4) and capillary electrophoresis (CE) for EV analysis. While AF4 provides gentle and fast EV separation by size, CE resolves EVs from contaminants with similar sizes but different surface charges. Employing Western Blotting, ELISA, and SEM, we confirmed that intact EVs were eluted within a stable time window under the optimal AF4 and CE conditions. We also proved that EVs could be resolved from free proteins and high-density lipoproteins by AF4 and be further separated from the low-density lipoproteins co-eluted in AF4. The effectiveness of the coupled AF4-CE system in EV analysis was demonstrated by monitoring the changes in EV secretion from cells and by direct injection of human serum and detection of serum EVs. We believe that coupling AF4 and CE can provide rapid EV quantification in biological samples with much reduced matrix interference and be valuable for the study of total EVs and EV subpopulations produced by cells or present in clinical samples.

Abstract The relentless pursuit of miniaturization and performance enhancement in electronic devices has led to a fundamental challenge in the field of circuit design and simulation-how to accurately account for the inherent stochastic nature of certain devices. While conventional deterministic models have served as indispensable tools for circuit designers, they fall short when it comes to capturing the subtle yet critical variability exhibited by many electronic components. In this paper, we present an innovative approach that transcends the limitations of traditional modeling techniques by harnessing the power of machine learning, specifically Mixture Density Networks (MDNs), to faithfully represent and simulate the stochastic behavior of electronic devices. We demonstrate our approach to model heater cryotrons, where the model is able to capture the stochastic switching dynamics observed in the experiment. Our model shows 0.82% mean absolute error for switching probability. This paper marks a significant step forward in the quest for accurate and versatile compact models, poised to drive innovation in the realm of electronic circuits.

Abstract The aquatic networks that connect soils with oceans receive each year 5.1 Pg of terrestrial carbon to transport, bury and process. Stagnant sections of aquatic networks often become anoxic. Mineral surfaces attract specific components of organic carbon, which are released under anoxic conditions to the pool of dissolved organic matter (DOM). The impact of the anoxic release on DOM molecular composition and reactivity in inland waters is unknown. Here, we report concurrent release of iron and DOM in anoxic bottom waters of northern lakes, removing DOM from the protection of iron oxides and remobilizing previously buried carbon to the water column. The deprotected DOM appears to be highly reactive, terrestrially derived and molecularly distinct, generating an ambient DOM pool that relieves energetic constraints that are often assumed to limit carbon turnover in anoxic waters. The Fe-to-C stoichiometry during anoxic mobilization differs from that after oxic precipitation, suggesting that up to 21% of buried OM escapes a lake-internal release-precipitation cycle, and can instead be exported downstream. Although anoxic habitats are transient and comprise relatively small volumes of water on the landscape scale, our results show that they may play a major role in structuring the reactivity and molecular composition of DOM transiting through aquatic networks and reaching the oceans.

Abstract Background Insecticide resistance is reducing the efficacy of vector control interventions, consequently threatening efforts to control vector-borne diseases, including malaria. Investigating the prevalence of molecular markers of resistance is a useful tool for monitoring the spread of insecticide resistance in disease vectors. The Bijagós Archipelago (Bijagós) in Guinea-Bissau is a region of stable malaria transmission where insecticide-treated nets are the mainstay for malaria control. However, the prevalence of molecular markers of insecticide resistance in malaria vectors is not well understood. Methods A total of 214 Anopheles mosquitoes were analysed from 13 islands across the Bijagós. These mosquitoes were collected using CDC light traps in November 2019, during the peak malaria transmission season. High-throughput multiplex amplicon sequencing was used to investigate the prevalence of 17 different molecular markers associated with insecticide resistance in four genes: vgsc, rdl, ace1 and gste2. Results Of the 17 screened mutations, four were identified in mosquitoes from the Bijagós: vgsc L995F (12.2%), N1570Y (6.2%) and A1746S (0.7%) and rdl A269G (1.1%). This study is the first to report the L995F knock-down resistance (kdr)-west allele in Anopheles melas on the Archipelago. An additional eight non-synonymous single-nucleotide polymorphisms were identified across the four genes which have not been described previously. The prevalences of the vgsc L995F and N1570Y mutations were higher on Bubaque Island than on the other islands in this study; Bubaque is the most populous island in the archipelago, with the greatest population mobility and connection to continental Guinea-Bissau. Conclusions This study provides the first surveillance data for genetic markers present in malaria vectors from islands across the Bijagós Archipelago. Overall prevalence of insecticide resistance mutations was found to be low. However, the identification of the vgsc L995F and N1570Y mutations associated with pyrethroid resistance warrants further monitoring. This is particularly important as the mainstay of malaria control on the islands is the use of pyrethroid insecticide-treated nets. Graphical Abstract

Abstract In contrast to rodents, the mechanisms underlying human trophectoderm and early placenta specification are understudied due to ethical barriers and the scarcity of embryos. Recent reports have shown that human pluripotent stem cells (PSCs) can differentiate into trophectoderm (TE)-like cells (TELCs) and trophoblast stem cells (TSCs), offering a valuable in vitro model to study early placenta specification. Here, we demonstrate that the VGLL1 (vestigial-like family member 1), which is highly expressed during human and non-human primate TE specification in vivo but is negligibly expressed in mouse, is a critical regulator of cell fate determination and self-renewal in human TELCs and TSCs derived from naïve PSCs. Mechanistically, VGLL1 partners with the transcription factor TEAD4 (TEA domain transcription factor 4) to regulate chromatin accessibility at target gene loci through histone acetylation and acts in cooperation with GATA3 and TFAP2C. Our work is relevant to understand primate early embryogenesis and how it differs from other mammalian species.

Abstract Background 68Ga-PSMA-11 positron emission tomography enables the detection of primary, recurrent, and metastatic prostate cancer. Regional radiopharmaceutical uptake is generally evaluated in static images and quantified as standard uptake values (SUVs) for clinical decision-making. However, analysis of dynamic images characterizing both tracer uptake and pharmacokinetics may offer added insights into the underlying tissue pathophysiology. This study was undertaken to evaluate the suitability of various kinetic models for 68Ga-PSMA-11 PET analysis. Twenty-three lesions in 18 patients were included in a retrospective kinetic evaluation of 55-min dynamic 68Ga-PSMA-11 pre-prostatectomy PET scans from patients with biopsy-demonstrated intermediate- to high-risk prostate cancer. Three kinetic models—a reversible one-tissue compartment model, an irreversible two-tissue compartment model, and a reversible two-tissue compartment model, were evaluated for their goodness of fit to lesion and normal reference prostate time-activity curves. Kinetic parameters obtained through graphical analysis and tracer kinetic modeling techniques were compared for reference prostate tissue and lesion regions of interest. Results Supported by goodness of fit and information loss criteria, the irreversible two-tissue compartment model optimally fit the time-activity curves. Lesions exhibited significant differences in kinetic rate constants (K 1, k 2, k 3, K i) and semiquantitative measures (SUV and %ID/kg) when compared with reference prostatic tissue. The two-tissue irreversible tracer kinetic model was consistently appropriate across prostatic zones. Conclusions An irreversible tracer kinetic model is appropriate for dynamic analysis of 68Ga-PSMA-11 PET images. Kinetic parameters estimated by Patlak graphical analysis or full compartmental analysis can distinguish tumor from normal prostate tissue.

The extracellular RNA communication consortium (ERCC) is an NIH-funded program aiming to promote the development of new technologies, resources, and knowledge about exRNAs and their carriers. After Phase 1 (2013-2018), Phase 2 of the program (ERCC2, 2019-2023) aims to fill critical gaps in knowledge and technology to enable rigorous and reproducible methods for separation and characterization of both bulk populations of exRNA carriers and single EVs. ERCC2 investigators are also developing new bioinformatic pipelines to promote data integration through the exRNA atlas database. ERCC2 has established several Working Groups (Resource Sharing, Reagent Development, Data Analysis and Coordination, Technology Development, nomenclature, and Scientific Outreach) to promote collaboration between ERCC2 members and the broader scientific community. We expect that ERCC2's current and future achievements will significantly improve our understanding of exRNA biology and the development of accurate and efficient exRNA-based diagnostic, prognostic, and theranostic biomarker assays.

Background: Significant advances in the treatment of Cystic Fibrosis (CF) have extended life expectancy, increasing the number of CF caregivers who deliver demanding daily treatment regimens and manage uncertainty. With the impact these changes have on CF caregivers, it is critical to understand CF caregiver quality of life (QoL). Purpose: To examine how QoL is measured in CF caregiver research and identify which QoL domains from the World Health Organization (WHO) framework are represented in CF caregiver QoL outcomes. Methods: Literature published from 2003 to the present was searched to identify all relevant articles in online academic databases related to measurement of CF caregiver QoL. Results: Findings suggest there is inconsistency within QoL measurement with CF caregivers and no existing scale accounts for all aspects of CF caregiver QoL from the WHO's framework. Discussion: The disconnect between how CF caregiver QoL is measured and how it is defined presents a challenge in utilizing information gleaned to best provide targeted and relevant services and resources to CF caregivers. Translation to Health Education Practice: A better understanding of caregiver QoL across all WHO domains will allow development of targeted programs to adequately address challenges for CF caregivers.

Abstract Iron stable isotopes (δ56Fe) are a useful tool for studying Earth processes, many of which involve redox transformations between Fe(III) and Fe(II). Here, we present two related experimental efforts, a study of the kinetic isotope effects (KIEs) associated with the reduction of Fe(III)‐ethylenediaminetetraacetic acid (EDTA) to Fe(II), and measurements of the biological fractionation of Fe isotopes by phytoplankton in culture. Reductants tested were ascorbate, hydroxylamine, Mn(II), dithionite, and photoreduction at pH between 5 and 9 and temperatures from 0 to 100°C. Isotope fractionations were very large, and included both normal and inverse KIEs, ranging from −4‰ to +5‰. Experiments were reproducible, yielding similar results for triplicate experiments run concurrently and for experiments run weeks apart. However, fractionations were not predictable, without a clear relationship to reaction rate, temperature, pH, or the reductant used. Acquisition of Fe by eukaryotic phytoplankton also often involves the reduction of Fe(III) to Fe(II). Several species of diatoms and a coccolithophore were tested for Fe isotope fractionation in culture using EDTA, NTA, and DFB as Fe(III) chelating ligands, yielding fractionations from −1.3‰ to +0.6‰. Biological isotope effects were also unpredictable, showing no clear relationship to species, growth rate, or Fe concentration. Variability in Fe isotope fractionation observed in culture may be explained in part by the sensitivity of KIEs. This work has implications for the industrial purification of isotopes, interpretation of natural δ56Fe, and the use of Fe isotopes as a tracer Fe source and biological processes in the ocean and other natural systems.

Background and Aim Recently, there has been a lot of interest surrounding the term gestalt language processor (GLP) which is associated with Natural Language Acquisition (NLA): a protocol intended to support the language development of autistic people. In NLA, delayed echolalia is presumed raw source material that GLPs use to acquire language in a stage-like progression from delayed echolalia to spontaneous speech. The aim of this article is to evaluate NLA in light of relevant literatures to allow scrutiny of NLA claims. Main contributions First, we review the notion of gestalt language and situate it in the broader literature on language styles to update understanding of its significance. We then review the links from gestalt language processing to autism and identify definitional and conceptual problems and clarify the construct ‘episodic memory’. We discuss the ‘raw material view of delayed echolalia’ and identify theoretical and empirical shortcomings. Finally, we review Blanc’s language stages and their accompanying assessment and language support recommendations and challenge their validity. Conclusions & Implications The term ‘gestalt language processor’ is definitionally and conceptually troubled, the assertion that autistic people are GLPs is misleading and unhelpful, and evidence is lacking that GLP represents a legitimate clinical entity. The theoretical basis of NLA lacks empirical support. NLA stages are implausible and their accompanying assessment and support recommendations lack justification. We recommend the use of alternate, individualized, theoretically-sound, evidence-based, neurodiversity-affirming supports that are sensitive and responsive to the heterogeneity that defines autism.

Abstract A substantial fraction of nitrogen (N) fertilizer applied in agricultural systems is not incorporated into crops and moves below the rooting zone as nitrate (NO3−). Understanding mechanisms for soil N retention below the rooting zone and leaching to groundwater is essential for our ability to track the fate of added N. We used dual stable isotopes of nitrate (δ15N–NO3− and δ18O–NO3−) and water (δ18O–H2O and δ2H–H2O) to understand the mechanisms driving nitrate leaching at three depths (0.8, 1.5, and 3.0 m) of an irrigated corn field sampled every 2 weeks from 2016 to 2020 in the southern Willamette Valley, Oregon, USA. Distinct periods of high nitrate concentrations with lower δ15N–NO3− values indicated that a portion of that nitrate was from recent fertilizer applications. We used a mixing model to quantify nitrate fluxes associated with recently added fertilizer N versus older, legacy soil N during these “fertilizer signal periods.” Nitrate leached below 3.0 m in these periods made up a larger proportion of the total N leached at that depth (∼52%) versus the two shallower depths (∼13%–16%), indicating preferential movement of recently applied fertilizer N through the deep soil into groundwater. Further, N associated with recent fertilizer additions leached more easily when compared to remobilized legacy N. A high volume of fall and winter precipitation may push residual fertilizer N to depth, potentially posing a larger threat to groundwater than legacy N. Optimizing fertilizer N additions could minimize fertilizer losses and reduce nitrate leaching to groundwater.

Strategies are an important component of self-regulated learning frameworks. However, the characterization of strategies in these frameworks is often incomplete: (1) they lack an operational definition of strategies; (2) there is limited understanding of how students develop and apply strategies; and (3) there is a dearth of systematic and generalizable approaches to measure and evaluate strategies when students' work in open-ended learning environments (OELEs). This paper develops systematic methods for detecting, interpreting, and analyzing students' use of strategies in OELEs, and demonstrates how students' strategies evolve across tasks. We apply this framework in the context of tasks that students perform as they learn science topics by building conceptual and computational models in an OELE. Data from a classroom study, where sixth-grade students (N = 52) worked on science model-building activities in our Computational Thinking using Simulation and Modeling (CTSiM) environment demonstrates how we interpret students' strategy use, and how strategy use relates to their learning performance. We also demonstrate how students' strategies evolve as they work on multiple model-building tasks. The results demonstrate the effectiveness of our strategy framework in analyzing students' behaviors and performance in CTSiM.