Which learning techniques supported by cognitive research do students use at secondary school? Prevalence and associations with students' beliefs and achievement.

Research in cognitive science has highlighted the effectiveness of several learning techniques, and a number of studies have analyzed their prevalence among university students and their relationship with academic achievement. In this study, we surveyed a large, heterogeneous sample of secondary school students to reveal how often they use research-supported techniques in comparison with other frequent techniques, and we analyzed the association between their study strategies and school achievement. We also assessed the associations between study techniques and several students' beliefs and attitudes toward learning (self-efficacy, goal orientation, control beliefs, growth mindset, and examination anxiety). Results showed that, except for distributed practice, only those techniques that are supported by previous research yielded an association with achievement, and they exhibited higher associations with self-efficacy, growth mindset, control beliefs, and learning goal orientation than non-supported techniques.

Nonsense CD247 mutations show dominant-negative features in TCR expression and function.

BACKGROUND: The invariant TCRζ/CD247 homodimer is crucial for TCR/CD3 expression and signaling through its three immunoreceptor tyrosine-based activation motifs (ITAMs). Homozygous null mutations in CD247 lead to immunodeficiency, while carriers exhibit 50% reduced surface CD3. It is unclear whether carriers of other CD247 variants show dominant-negative effects. OBJECTIVE: To analyze and model the potential impact on TCR expression and function of heterozygous nonsense CD247 mutations found in patients with signs of immunodeficiency or autoimmunity. METHODS: Jurkat T cells, either wild-type (WT) or CRISPR/Cas9-edited CD247-deficient (ZKO), were lentivirally transduced with wild-type CD247 or mutations ablating one (Q142X), two (Q101X), or three (Q70X) ITAMs. RESULTS: Three patients from unrelated families were studied. Two heterozygous nonsense CD247 mutations were identified (p.Y152X and p.Q101X), which affected ITAM-3 and ITAM-2+3, respectively. Both mutations were associated with low surface CD3 expression, normal intracellular CD247 levels using a transmembrane-specific antibody but very low intracellular CD247 levels using an ITAM-3-specific one, suggesting the presence of truncated variants in T cells. Transduction of the mutations lacking 1, 2, or 3 ITAMs into ZKO could not restore normal surface CD3 expression (only 60%, 22% and 10%, respectively), whereas in WT they reduced it (to 39%, 19% and 9% of normal levels), and both effects were ITAM number dependent. All six transfectants showed reduced CD69 induction (25-50%), indicating that they were unable to signal downstream properly neither isolated nor associated with wild-type CD247. CONCLUSION: Our results suggest that CD247 variants lacking ITAMs due to nonsense, but not null, mutations are defective for normal TCR assembly and exert a dominant-negative effect on TCR expression and signaling in vitro. This, in turn, may correlate with clinical features in vivo.

Dataset used to develop soft computing models that predict the stiffness modulus of bituminous mixtures.

This data article presents information on the measurement of Indirect Tensile Stiffness Modulus of laboratory and field asphalt mixtures. The asphalt mixes are composed of three distinct binders that were categorised by their penetration grade (40/55-TLA, 60/75-TLA, and 60/70-MB) and aggregates (limestone, sharp sand, and filler). The asphalt mixtures are called dense-graded hot mix asphalt (HMA) and gap-graded stone matrix asphalt (SMA). The variables in the dataset were selected in accordance with the specifications of the dynamic modulus models that are currently in use as well as the needs for the quality control and assurance (QC & QA) assessment of asphalt concrete mixes. The data parameters included are temperature, asphalt content, and binder viscosity, air void content, cumulative percent retained on 19, 12.5, and 4.75 mm sieves, maximum theoretical specific gravity, aggregate passing #200 sieve, effective asphalt content, density, flow, marshal stability, coarse-to-fine particle ratio and the Indirect Tensile Stiffness Modulus (ITSM). Utilising soft computing techniques, models were developed utilising the data thus eliminating the requirement for complex and time-consuming laboratory testing.

Genome-scale and pathway engineering for the sustainable aviation fuel precursor isoprenol production in Pseudomonas putida.

Sustainable aviation fuel (SAF) will significantly impact global warming in the aviation sector, and important SAF targets are emerging. Isoprenol is a precursor for a promising SAF compound DMCO (1,4-dimethylcyclooctane) and has been produced in several engineered microorganisms. Recently, Pseudomonas putida has gained interest as a future host for isoprenol bioproduction as it can utilize carbon sources from inexpensive plant biomass. Here, we engineer metabolically versatile host P. putida for isoprenol production. We employ two computational modeling approaches (Bilevel optimization and Constrained Minimal Cut Sets) to predict gene knockout targets and optimize the "IPP-bypass" pathway in P. putida to maximize isoprenol production. Altogether, the highest isoprenol production titer from P. putida was achieved at 3.5 g/L under fed-batch conditions. This combination of computational modeling and strain engineering on P. putida for an advanced biofuels production has vital significance in enabling a bioproduction process that can use renewable carbon streams.

Data exploration on the factors associated with cost overrun on social housing projects in Trinidad and Tobago.

This data article explores the factors that contribute to cost overrun on public sector projects within Trinidad and Tobago. The data was obtained through literature research, and structured questionnaires, designed using open-ended questions and the Likert scale. The responses were gathered from project actors and decision-makers within the public and private construction industry, mainly, project managers, contractors, engineers, architects, and consultants. The dataset was analysed using frequency, simple percentage, mean, risk impact, and fuzzy logic via the fuzzy synthetic evaluation method (FSE). The significance of the analysed data is to determine the critical root causes of cost overrun which affect public sector infrastructure development projects (PSIDPs), from being completed on time and within budget. The dataset is most useful to project and construction management professionals and academia, to provide additional insight into the understanding of the leading factors associated with cost overrun and the critical group in which they occur (political factors). Such understanding can encourage greater decisions under uncertainty and complexity, thus accounting for and reducing cost overrun on public sector projects.

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale.

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in "non-gaussian" situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism.

Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2's flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.

Multilevel diffractive lens in the MWIR with extended depth-of-focus and wide field-of-view.

Optics in the mid-wave-infra-red (MWIR) band are generally heavy, thick and expensive. Here, we demonstrate multi-level diffractive lenses; one designed using inverse design and another using the conventional propagation phase (the Fresnel zone plate or FZP) with diameter = 25 mm and focal length = 25 mm operating at λ=4µm. We fabricated the lenses by optical lithography and compared their performance. We show that the inverse-designed MDL achieves larger depth-of-focus and better off-axis performance when compared to the FZP at the expense of larger spot size and reduced focusing efficiency. Both lenses are flat with thickness ≤0.5 mm and weigh ≤3.63 g, which are far smaller than their conventional refractive counterparts.

Perspectives for self-driving labs in synthetic biology.

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed toward solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.

ClusterCAD 2.0: an updated computational platform for chimeric type I polyketide synthase and nonribosomal peptide synthetase design.

Megasynthase enzymes such as type I modular polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs) play a central role in microbial chemical warfare because they can evolve rapidly by shuffling parts (catalytic domains) to produce novel chemicals. If we can understand the design rules to reshuffle these parts, PKSs and NRPSs will provide a systematic and modular way to synthesize millions of molecules including pharmaceuticals, biomaterials, and biofuels. However, PKS and NRPS engineering remains difficult due to a limited understanding of the determinants of PKS and NRPS fold and function. We developed ClusterCAD to streamline and simplify the process of designing and testing engineered PKS variants. Here, we present the highly improved ClusterCAD 2.0 release, available at https://clustercad.jbei.org. ClusterCAD 2.0 boasts support for PKS-NRPS hybrid and NRPS clusters in addition to PKS clusters; a vastly enlarged database of curated PKS, PKS-NRPS hybrid, and NRPS clusters; a diverse set of chemical 'starters' and loading modules; the new Domain Architecture Cluster Search Tool; and an offline Jupyter Notebook workspace, among other improvements. Together these features massively expand the chemical space that can be accessed by enzymes engineered with ClusterCAD.

Reduced neutralization against Delta, Gamma, Mu, and Omicron BA.1 variants of SARS-CoV-2 from previous non-Omicron infection.

The understanding of the host immune response to SARS-CoV-2 variants of concern is critical for improving diagnostics, therapy development, and vaccines. Here, we analyzed the level of neutralizing antibodies against SARS-CoV-2 D614G, Delta, Gamma, Mu, and Omicron variants in D614G infected healthcare workers during a follow-up up to 6 months after recovery. We followed up 76 patients: 60.5% were women and 39.5% men. The 96.1% and 3.9% were symptomatic and asymptomatic, respectively. The most frequent symptoms were headache, myalgia, and cough. The 65.8%, 65.8%, and 92.1% of the infected individuals were positive for neutralizing antibodies against D614G variant at 2, 4, and 6 months of follow-up, respectively. The 26.3%, 48.7% and 65.8% of patients neutralized Delta variant, 19.7%, 32.9% and 52.6% of patients neutralized Gamma, 7.9%, 19.7% and 44.7% of patients neutralized Mu, and 4.0%, 9.2% and 15.8% of patients neutralized Omicron. Low neutralization against Gamma and Mu variants was observed during the follow-up, and very low against the Omicron variant was detected during the same period. The median of neutralizing antibody titers against D614G and Delta variants increased significantly during the follow-up. An association was observed between the levels of neutralizing antibodies against D614G and Delta variants and the severity of the disease. Our results suggest an immune escape from neutralizing antibodies with the Omicron variant because of the many mutations localized in the S protein.

Tenacious educational neuromyths: Prevalence among teachers and an intervention.

BACKGROUND: Several studies have revealed a common high prevalence of educational neuromyths among teachers from different countries. However, only one intervention aimed at reducing these beliefs among in-service teachers has been reported to date, and it was conducted in a non-naturalistic setting. PROCEDURE: In the present study, we administered a survey to measure the prevalence of common neuromyths in a large sample (n = 807) of primary and secondary teachers from 203 schools across Catalonia (Spain), and then we evaluated the impact that a 15-hour online course on neuroscience had on a sample of them as compared to a control group. MAIN FINDINGS: Results showed an initial distribution of neuromyth beliefs similar to those of previous studies and a large effect of the intervention on reducing their prevalence shortly after the training and in the long term. CONCLUSIONS: These findings provide evidence that an intervention addressed to in-service teachers that is low-cost and easy to implement can cast corrective effects that persist over time in neuromyth beliefs.

Not all autoantibodies are clinically relevant. Classic and novel autoantibodies in Sjögren's syndrome: A critical review.

Sjögren's syndrome (SjS) is a heterogeneous systemic disease. The abnormal responses to La/SSB and Ro/SSA of both B-cells and T-cells are implicated as well as others, in the destruction of the epithelium of the exocrine glands, whose tissue characteristically shows a peri-epithelial lymphocytic infiltration that can vary from sicca syndrome to systemic disease and lymphoma. Despite the appearance of new autoantibodies, anti-Ro/SSA is still the only autoantibody included in the American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) classification criteria and is used extensively as a traditional biomarker in clinical practice. The study and findings of new autoantibodies in SjS has risen in the previous decade, with a central role given to diagnosis and elucidating new aspects of SjS physiopathology, while raising the opportunity to establish clinical phenotypes with the goal of predicting long-term complications. In this paper, we critically review the classic and the novel autoantibodies in SjS, analyzing the methods employed for detection, the pathogenic role and the wide spectrum of clinical phenotypes.

MACAW: An Accessible Tool for Molecular Embedding and Inverse Molecular Design.

The growing capabilities of synthetic biology and organic chemistry demand tools to guide syntheses toward useful molecules. Here, we present Molecular AutoenCoding Auto-Workaround (MACAW), a tool that uses a novel approach to generate molecules predicted to meet a desired property specification (e.g., a binding affinity of 50 nM or an octane number of 90). MACAW describes molecules by embedding them into a smooth multidimensional numerical space, avoiding uninformative dimensions that previous methods often introduce. The coordinates in this embedding provide a natural choice of features for accurately predicting molecular properties, which we demonstrate with examples for cetane and octane numbers, flash points, and histamine H1 receptor binding affinity. The approach is computationally efficient and well-suited to the small- and medium-size datasets commonly used in biosciences. We showcase the utility of MACAW for virtual screening by identifying molecules with high predicted binding affinity to the histamine H1 receptor and limited affinity to the muscarinic M2 receptor, which are targets of medicinal relevance. Combining these predictive capabilities with a novel generative algorithm for molecules allows us to recommend molecules with a desired property value (i.e., inverse molecular design). We demonstrate this capability by recommending molecules with predicted octane numbers of 40, 80, and 120, which is an important characteristic of biofuels. Thus, MACAW augments classical retrosynthesis tools by providing recommendations for molecules on specification.

Scalable and automated CRISPR-based strain engineering using droplet microfluidics.

We present a droplet-based microfluidic system that enables CRISPR-based gene editing and high-throughput screening on a chip. The microfluidic device contains a 10 × 10 element array, and each element contains sets of electrodes for two electric field-actuated operations: electrowetting for merging droplets to mix reagents and electroporation for transformation. This device can perform up to 100 genetic modification reactions in parallel, providing a scalable platform for generating the large number of engineered strains required for the combinatorial optimization of genetic pathways and predictable bioengineering. We demonstrate the system's capabilities through the CRISPR-based engineering of two test cases: (1) disruption of the function of the enzyme galactokinase (galK) in E. coli and (2) targeted engineering of the glutamine synthetase gene (glnA) and the blue-pigment synthetase gene (bpsA) to improve indigoidine production in E. coli.

Temperature and Gate-Length Dependence of Subthreshold RF Detection in GaN HEMTs.

The responsivity of AlGaN/GaN high-electron mobility transistors (HEMTs) when operating as zero-bias RF detectors in the subthreshold regime exhibits different behaviors depending on the operating temperature and gate length of the transistors. We have characterized in temperature (8-400 K) the detection performance of HEMTs with different gate lengths (75-250 nm). The detection results at 1 GHz can be reproduced by a quasi-static model, which allows us to interpret them by inspection of the output ID - VDS curves of the transistors. We explain the different behaviors observed in terms of the presence or absence of a shift in the zero-current operating point originating from the existence of the gate-leakage current jointly with temperature effects related to the ionization of bulk traps.

Neutralization of alpha, gamma, and D614G SARS-CoV-2 variants by CoronaVac vaccine-induced antibodies.

Vaccination generates a neutralizing immune response against SARS-CoV-2. The genomic surveillance is showing the emergence of variants with mutations in spike, the main target of neutralizing antibodies. To understand the impact of these variants, we report the neutralization potency against alpha, gamma, and D614G SARS-CoV-2 variants in 44 individuals that received two doses of CoronaVac vaccine, an inactivated SARS-CoV-2 vaccine. Plasma samples collected at 60 days after the second dose of CoronaVac were analyzed by the reduction of cytopathic effect in Vero E6 cells with the three infectious variants of SARS-CoV-2. Plasma showed lower neutralization with alpha (geometric mean titer [GMT] = 18.5) and gamma (GMT = 10.0) variants than with D614G (GMT = 75.1) variant. Efficient neutralization against the alpha and gamma variants was not detected in 31.8% and 59.1% of plasma, respectively. These findings suggest the alpha and gamma variants could escape from neutralization by antibodies elicited by vaccination. Robust genomic and biological surveillance of viral variants could help to develop effective strategies for the control of SARS-CoV-2.

Microbial production of advanced biofuels.

Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs.

Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering.

Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophisticated bioengineering to produce fuels, specialty and commodity chemicals, materials, and other renewable bioproducts. However, despite new tools and exponentially increasing data volumes, synthetic biology cannot yet fulfill its true potential due to our inability to predict the behavior of biological systems. Here, we showcase a set of computational tools that, combined, provide the ability to store, visualize, and leverage multiomics data to predict the outcome of bioengineering efforts. We show how to upload, visualize, and output multiomics data, as well as strain information, into online repositories for several isoprenol-producing strain designs. We then use these data to train machine learning algorithms that recommend new strain designs that are correctly predicted to improve isoprenol production by 23%. This demonstration is done by using synthetic data, as provided by a novel library, that can produce credible multiomics data for testing algorithms and computational tools. In short, this paper provides a step-by-step tutorial to leverage these computational tools to improve production in bioengineered strains.