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The use of roller blind as a surrogate for a switchable glazing in a dynamic building environmental simulation is investigated. Seven different control algorithms were applied to simulations of both operations of the blind and of the switchable glazing. The configurations compared were an electrochromic glazing and a roller blind, the controllers used were rule-based, proportional-integral-derivative (PID), anti-windup PID (aPID) and a model predictive controller (MPC). Particular case studies were examined in the weather conditions of Dublin, Ireland to make comparisons of simulated energy savings and occupancy daylight comfort from the use of electrochromic glazing or a roller blind with those for a double-glazed window. The results suggest that previous studies that simulated electrochromic window as an integrated roller blind in a heating-dominated climate would have overestimated building energy loads, and depending on the controller used, overestimated occupancy daylight comfort.

BACKGROUND: There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer. Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for pancreatic cancer. METHODS: Data from genome-wide association studies (GWAS) within the Pancreatic Cancer Cohort Consortium (PanScan; cases n = 5,090, controls n = 8,733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n = 4,163, controls n = 3,792) were analyzed. We used data on 68 genetic variants with four different MR methods [inverse variance weighting (IVW), MR-Egger, simple median, and penalized weighted median] separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and pancreatic cancer risk, using logistic regression to calculate ORs and 95% confidence intervals (CI), adjusting for PC risk factors, including obesity and diabetes. RESULTS: No association was found between genetically predicted NAFLD and pancreatic cancer risk in the PanScan or PanC4 samples [e.g., PanScan, IVW OR, 1.04; 95% confidence interval (CI), 0.88-1.22; MR-Egger OR, 0.89; 95% CI, 0.65-1.21; PanC4, IVW OR, 1.07; 95% CI, 0.90-1.27; MR-Egger OR, 0.93; 95% CI, 0.67-1.28]. None of the four MR methods indicated an association between genetically predicted NAFLD and pancreatic cancer risk in either sample. CONCLUSIONS: Genetic predisposition to NAFLD is not associated with pancreatic cancer risk. IMPACT: Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and pancreatic cancer might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and pancreatic cancer.

The crowd wisdom effect is a well-established phenomenon that is widely employed for predicting and estimating variables across various domains. Previous research has focused on enhancing the wisdom of crowds by improving individual estimates while maintaining some of the initial opinion diversity. However, it is theoretically possible to increase collective accuracy by largely increasing the diversity in a crowd. In this study, we propose a method that leverages the anchoring effect to extremize individual judgments and thus increase the diversity of opinions in a crowd. This is achieved by dividing the crowd into two groups, anchoring each group to either a low or high value, and aggregating all estimates. We use a mathematical model of anchoring to determine when this strategy is expected to outperform the crowd wisdom effect. Results from three experiments provide converging evidence that the proposed approach outperforms traditional methods in estimating and forecasting unknown quantities.

This article details a correction to: Röseler, L., Weber, L., Helgerth, K., Stich, E., Günther, M., Tegethoff, P., … Schütz, A. (2022). The Open Anchoring Quest Dataset: Anchored Estimates from 96 Studies on Anchoring Effects. Journal of Open Psychology Data, 10(1), 16. DOI: https://doi.org/10.5334/jopd.67

The first years of teaching are often described as 'emotionally challenging' by English as a foreign language (EFL) teachers in secondary schools in France because their professional identity and skills are still under construction. How, then, can novice teachers (NTs) be supported during this process? The present study focuses on 14 NTs (pre-service, Years 1 and 2 in-service teachers) who volunteered to be filmed in class and to take part in 'self-confrontation interviews.' These followed the iterative EmoDERE model where the NTs reviewed their video-recorded classroom practice, focusing on motion(s) felt during action, Description of action, Evaluation of action, Reflective search for alternatives, and Emotion(s) felt at the end of the interview. This article analyses how this model helped participants reflect on their practice through the prism of emotion (Plutchik 1980). It shows how reflexivity led them to reverse the valence of initial emotions, encouraged them to continue new cycles of reflection and allowed them to develop their growth competence, thus building their sense of self-efficacy. Limitations to this approach were seen in pre-service teachers' limited emotional granularity. The paper concludes with practical ideas for helping NTs build their emotional awareness and develop their professional reflexivity.

The wound-healing process is a physiological response that begins after a disruption to the integrity of tissues present in the skin. To understand the intricacies involved in this process, many groups have tried to develop different in vitro models; however, the lack of a systemic response has, until this day, been the major barrier to the establishment of these models as the main study platform. Therefore, in vivo models are still the most common system for studying healing responses following different treatments, especially porcine models, which share several morphological similarities to the human skin. In this work, we developed a porcine excisional wound model and used semi-automated software as a strategy to generate quantitative morphometric results of healing responses by specific tissues and compartments. Our aim was to extract the most information from the model while producing reliable, reproducible, and standardized results. In order to achieve this, we established a 7-day treatment using a bacterial cellulose dressing as our standard for all the analyzed wounds. The thickness of the residual dermis under the wound (DUtW) bed was shown to influence the healing outcome, especially for the regeneration of epidermal tissue, including the wound closure rate. The analysis of the DUtW throughout the entire dorsal region of the animals opened up the possibility of establishing a map that will facilitate the experimental design of future works, increasing their standardization and reproducibility and ultimately reducing the number of animals needed. Thus, the developed model, together with the automated morphometric analysis approach used, offers the possibility to generate robust quantitative results with a rapid turnaround time while allowing the study of multiple extra morphometric parameters, creating a more holistic analysis.

BACKGROUND: The risks of breast cancer in African American (AA) women associated with inherited mutations in breast cancer predisposition genes are not well defined. Thus, whether multigene germline hereditary cancer testing panels are applicable to this population is unknown. We assessed associations between mutations in panel-based genes and breast cancer risk in 5054 AA women with breast cancer and 4993 unaffected AA women drawn from 10 epidemiologic studies. METHODS: Germline DNA samples were sequenced for mutations in 23 cancer predisposition genes using a QIAseq multiplex amplicon panel. Prevalence of mutations and odds ratios (ORs) for associations with breast cancer risk were estimated with adjustment for study design, age, and family history of breast cancer. RESULTS: Pathogenic mutations were identified in 10.3% of women with estrogen receptor (ER)-negative breast cancer, 5.2% of women with ER-positive breast cancer, and 2.3% of unaffected women. Mutations in BRCA1, BRCA2, and PALB2 were associated with high risks of breast cancer (OR = 47.55, 95% confidence interval [CI] = 10.43 to >100; OR = 7.25, 95% CI = 4.07 to 14.12; OR = 8.54, 95% CI = 3.67 to 24.95, respectively). RAD51D mutations were associated with high risk of ER-negative disease (OR = 7.82, 95% CI = 1.61 to 57.42). Moderate risks were observed for CHEK2, ATM, ERCC3, and FANCC mutations with ER-positive cancer, and RECQL mutations with all breast cancer. CONCLUSIONS: The study identifies genes that predispose to breast cancer in the AA population, demonstrates the validity of current breast cancer testing panels for use in AA women, and provides a basis for increased referral of AA patients for cancer genetic testing.

Women of African ancestry have lower incidence of epithelial ovarian cancer (EOC) yet worse survival compared to women of European ancestry. We conducted a genome-wide association study in African ancestry women with 755 EOC cases, including 537 high-grade serous ovarian carcinomas (HGSOC) and 1,235 controls. We identified four novel loci with suggestive evidence of association with EOC (p

An association between genetic variants in the vitamin D receptor (VDR) gene and epithelial ovarian cancer (EOC) was previously reported in women of African ancestry (AA). We sought to examine associations between genetic variants in VDR and additional genes from vitamin D biosynthesis and pathway targets (EGFR, UGT1A, UGT2A1/2, UGT2B, CYP3A4/5, CYP2R1, CYP27B1, CYP24A1, CYP11A1, and GC). Genotyping was performed using the custom-designed 533,631 SNP Illumina OncoArray with imputation to the 1,000 Genomes Phase 3 v5 reference set in 755 EOC cases, including 537 high-grade serous (HGSOC), and 1,235 controls. All subjects are of African ancestry (AA). Logistic regression was performed to estimate odds ratios (OR) and 95% confidence intervals (CI). We further evaluated statistical significance of selected SNPs using the Bayesian False Discovery Probability (BFDP). A significant association with EOC was identified in the UGT2A1/2 region for the SNP rs10017134 (per allele OR = 1.4, 95% CI = 1.2-1.7, P = 1.2 × 10-6 , BFDP = 0.02); and an association with HGSOC was identified in the EGFR region for the SNP rs114972508 (per allele OR = 2.3, 95% CI = 1.6-3.4, P = 1.6 × 10-5 , BFDP = 0.29) and in the UGT2A1/2 region again for rs1017134 (per allele OR = 1.4, 95% CI = 1.2-1.7, P = 2.3 × 10-5 , BFDP = 0.23). Genetic variants in the EGFR and UGT2A1/2 may increase susceptibility of EOC in AA women. Future studies to validate these findings are warranted. Alterations in EGFR and UGT2A1/2 could perturb enzyme efficacy, proliferation in ovaries, impact and mark susceptibility to EOC.

Abstract The dissolution of amorphous and crystalline oligoclase, a Na-feldspar with traces of Ca and K, was investigated at 90 °C and acidic pH of 1.5 and 3 to unravel the impact of long- and short-range orders on silicate dissolution mechanisms. Experiments were conducted in solutions spiked with 29SiO2(aq) and saturated with respect to SiO2(am). Through morphology, structural, and composition characterizations, we showed that on the amorphous samples (glass samples), the altered layer was mostly formed by leaching, while a combination of both interfacial precipitation and leaching explains the layers formed on the crystalline sample. As expected, the altered layer was thicker at the most acidic pH and it became passivating on crystalline sample at pH 3. The alteration was faster on amorphous oligoclase than on its crystalline equivalent due to the more open structure of the glass. The preferential release of Al was suggested to play a key role, by weakening the silicate network of both substrates. Finally, in this study, a large overestimation of the global alteration of the materials was noticed based on the solution analyses. Discrepancies with solid analyses were attributed to an underestimation (≥2-fold factor) of the total reactive surface area.

Label discrimination is challenging in fluorescence microscopy due to broad spectra and narrow lifetime distribution. Here, the authors introduce extra kinetic dimensions by illuminating reversibly photoswitchable fluorophores with different intensities, and discriminate 20 spectrally similar fluorophores.

Abstract Acetic acid bacteria (AAB) can selectively oxidize diols into their corresponding hydroxyacids. Notably, they can convert 1,3-propanediol (1,3-PDO) into 3-hydroxypropionic acid (3-HP), which is a promising building-block. Until now, 3-HP production with AAB is carried out in batch and using resting cells at high cell densities (up to 10 g L−1 of cell dry weight). This approach is likely limited by detrimental accumulation of the intermediate 3-hydroxypropanal (3-HPA). Herein, we investigate an alternative implementation that allows highly efficient 3-HP production with lower cell densities of growing cells and that prevents 3-HPA accumulation. First, growth and 3-HP production of Acetobacter sp. CIP 58.66 were characterized with 1,3-PDO or glycerol as growth substrate. The strain was then implemented in a bioreactor, during a sequential process where it was first cultivated on glycerol, then the precursor 1,3-PDO was continuously supplied at a varying rate, easily controlled by the pH control. Different pH set points were tested (5.0, 4.5, and 4.0). This approach used the natural resistance of acetic acid bacteria to acidic conditions. Surprisingly, when pH was controlled at 5.0, the performances achieved in terms of titer (69.76 g3-HP L−1), mean productivity (2.80 g3-HP L−1 h−1), and molar yield (1.02 mol3-HP mol−1 1,3-PDO) were comparable to results obtained with genetically improved strains at neutral pH. The present results were obtained with comparatively lower cell densities (from 0.88 to 2.08 g L−1) than previously reported. This feeding strategy could be well-suited for future scale-up, since lower cell densities imply lower process costs and energy needs.

Embodied theories of semantic cognition predict that brain regions involved in motion perception are engaged when people comprehend motion concepts expressed in language. Left lateral occipitotemporal cortex (LOTC) is implicated in both motion perception and motion concept processing but prior studies have produced mixed findings on which parts of this region are engaged by motion language. We scanned participants performing semantic judgements about sentences describing motion events and static events. We performed univariate analyses, multivariate pattern analyses (MVPA) and psychophysiological interaction (PPI) analyses to investigate the effect of motion on activity and connectivity in different parts of LOTC. In multivariate analyses that decoded whether a sentence described motion or not, the middle and posterior parts of LOTC showed above-chance level performance, with performance exceeding that of other brain regions. Univariate ROI analyses found the middle part of LOTC was more active for motion events than static ones. Finally, PPI analyses found that when processing motion events, the middle and posterior parts of LOTC (overlapping with motion perception regions), increased their connectivity with cognitive control regions. Taken together, these results indicate that the more posterior parts of LOTC, including motion perception cortex, respond differently to motion vs. static events. These findings are consistent with embodiment accounts of semantic processing, and suggest that understanding verbal descriptions of motion engages areas of the occipitotemporal cortex involved in perceiving motion.

Glyphosate-based herbicides (GBHs) are massively used in agriculture. However, few studies have investigated the effects of glyphosate-based herbicides on avian species although they are largely exposed via their food. Here, we investigated the potential reversibility of the effects of chronic dietary exposure to glyphosate-based herbicides in broiler hens. For 42 days, we exposed 32-week-old hens to glyphosate-based herbicides via their food (47 mg/kg/day glyphosate equivalent, glyphosate-based herbicides, n = 75) corresponding to half glyphosate’s no-observed-adverse-effect-level in birds. We compared their performance to that of 75 control animals (CT). Both groups (glyphosate-based herbicides and control animals) were then fed for 28 additional days without glyphosate-based herbicides exposure (Ex-glyphosate-based herbicides and Ex-control animals). Glyphosate-based herbicides temporarily increased the plasma glyphosate and AMPA (aminomethylphosphonic acid) concentrations. Glyphosate and aminomethylphosphonic acid mostly accumulated in the liver and to a lesser extent in the leg muscle and abdominal adipose tissue. Glyphosate-based herbicides also temporarily increased the gizzard weight and plasma oxidative stress monitored by TBARS (thiobarbituric acid reactive substances). Glyphosate-based herbicides temporarily decreased the cecal concentrations of propionate, isobutyrate and propionate but acetate and valerate were durably reduced. The cecal microbiome was also durably affected since glyphosate-based herbicides inhibited Barnesiella and favored Alloprevotella. Body weight, fattening, food intake and feeding behavior as well as plasma lipid and uric acid were unaffected by glyphosate-based herbicides. Taken together, our results show possible disturbances of the cecal microbiota associated with plasma oxidative stress and accumulation of glyphosate in metabolic tissues in response to dietary glyphosate-based herbicides exposure in broiler hens. Luckily, glyphosate-based herbicides at this concentration does not hamper growth and most of the effects on the phenotypes are reversible.

The United Nations (UN) considers universities to be key actors in the pursuit of the Sustainable Development Goals (SDGs). Yet, efforts to evaluate the embeddedness of the SDGs in university curricula tend to rely on manual analyses of curriculum documents for keywords contained in sustainability lexica, with little consideration for the diverse contexts of such keywords. The efficacy of these efforts, relying on expert co-elicitation in both subject-matter contexts and sustainability, suffers from drawbacks associated with keyword searches, such as limited coverage of key concepts, difficulty in extracting intended meaning and potential for greenwashing through “keyword stuffing.” This paper presents a computational technique, derived from natural language processing (NLP), which develops a sustainability lexicon of root keywords (RKs) of relative importance by adapting the Term Frequency–Inverse Document Frequency (TF-IDF) method to a corpus of sustainability documents. Identifying these RKs in module/course descriptors offers a basis for evaluating the embeddedness of sustainability in 5,773 modules in a university's curricula using classification criteria provided by the Association for the Enhancement of Sustainability in Higher Education's (AASHE). Applying this technique, our analysis of these descriptors found 286 modules (5%) to be “sustainability focused” and a further 769 modules (13%) to be “sustainability inclusive,” which appear to address SDGs 1, 17, 3, 7, and 15. Whilst this technique does not exploit machine learning methods applied to large amounts of trained data, it is, nevertheless, systemic and evolutive. It, therefore, offers an appropriate trade-off, which faculty with limited analytics skills can apply. By supplementing existing approaches to evaluating sustainability in the curriculum, the developed technique offers a contribution to benchmarking curricular alignment to the SDGs, facilitating faculty to pursue meaningful curricular enhancement, whilst complying with sustainability reporting requirements. The technique is useful for first-pass analyses of any university curriculum portfolio. Further testing and validation offer an avenue for future design-science research.

The competition between cancer cells and immune system cells in inhomogeneous conditions is described at cell scale within the framework of the thermostatted kinetic theory. Cell learning is reproduced by increased cell activity during favorable interactions. The cell activity fluctuations are controlled by a thermostat. The direction of cell velocity is changed according to stochastic rules mimicking a dense fluid. We develop a kinetic Monte Carlo algorithm inspired from the direct simulation Monte Carlo (DSMC) method initially used for dilute gases. The simulations generate stochastic trajectories sampling the kinetic equations for the distributions of the different cell types. The evolution of an initially localized tumor is analyzed. Qualitatively different behaviors are observed as the field regulating activity fluctuations decreases. For high field values, i.e. efficient thermalization, cancer is controlled. For small field values, cancer rapidly and monotonously escapes from immunosurveillance. For the critical field value separating these two domains, the 3E's of immunotherapy are reproduced, with an apparent initial elimination of cancer, a long quasi-equilibrium period followed by large fluctuations, and the final escape of cancer, even for a favored production of immune system cells. For field values slightly smaller than the critical value, more regular oscillations of the number of immune system cells are spontaneously observed in agreement with clinical observations. The antagonistic effects that the stimulation of the immune system may have on oncogenesis are reproduced in the model by activity-weighted rate constants for the autocatalytic productions of immune system cells and cancer cells. Local favorable conditions for the launching of the oscillations are met in the fluctuating inhomogeneous system, able to generate a small cluster of immune system cells with larger activities than those of the surrounding cancer cells.