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A mixed-methods study was used to determine if an online, mastery-based learning resource was successful in improving content knowledge and retention of critical elements and developmental stages of 16 fundamental movement skills (FMS); a secondary purpose was to determine participant perceptions of their experience with the online resource. Participants (N = 323; motor development (MD) = 94; movement education (ME) = 124; elementary physical education (EPE) = 105) were Teacher Candidates (TCs) in a Physical Education Teacher Education (PETE) Program. Pre- and post-survey data, and pre-, post-, and retention test scores were used to evaluate the program's effectiveness. Paired samples t-tests indicated higher post-test scores for MD ([delta]37.23, p < 0.001, d = 2.32) and ME ([delta]32.12, p < 0.001, d = 1.72). Results from a one-way ANOVA indicated significant improvement and retention of test scores over time, F(3, 331) = 27.761, p < 0.001, [eta-squared] = 0.963. Perceptions of PETE TCs reported positive reactions to the use of the online modules to improve their ability to analyze FMS. Based on these findings, the authors propose that implementing an online, mastery-based resource to analyze FMS may be a positive learning experience for TCs and could lead to long-term content knowledge acquisition of critical elements of FMS.

Estimation of damage in plants is a key issue for crop protection. Currently, experts in the field manually assess the plots. This is a time-consuming task that can be automated thanks to the latest technology in computer vision (CV). The use of image-based systems and recently deep learning-based systems have provided good results in several agricultural applications. These image-based applications outperform expert evaluation in controlled environments, and now they are being progressively included in non-controlled field applications.A novel solution based on deep learning techniques in combination with image processing methods is proposed to tackle the estimate of plant damage in the field. The proposed solution is a two-stage algorithm. In a first stage, the single plants in the plots are detected by an object detection YOLO based model. Then a regression model is applied to estimate the damage of each individual plant. The solution has been developed and validated in oilseed rape plants to estimate the damage caused by flea beetle.The crop detection model achieves a mean precision average of 91% with a mAP@0.50 of 0.99 and a mAP@0.95 of 0.91 for oilseed rape specifically. The regression model to estimate up to 60% of damage degree in single plants achieves a MAE of 7.11, and R2 of 0.46 in comparison with manual evaluations done plant by plant by experts. Models are deployed in a docker, and with a REST API communication protocol they can be inferred directly for images acquired in the field from a mobile device.

Abstract Honey bees are commonly co-exposed to pesticides during crop pollination, including the fungicide captan and neonicotinoid insecticide thiamethoxam. We assessed the impact of exposure to these two pesticides individually and in combination, at a range of field-realistic doses. In laboratory assays, mortality of larvae treated with captan was 80–90% greater than controls, dose-independent, and similar to mortality from the lowest dose of thiamethoxam. There was evidence of synergism (i.e., a non-additive response) from captan-thiamethoxam co-exposure at the highest dose of thiamethoxam, but not at lower doses. In the field, we exposed whole colonies to the lowest doses used in the laboratory. Exposure to captan and thiamethoxam individually and in combination resulted in minimal impacts on population growth or colony mortality, and there was no evidence of synergism or antagonism. These results suggest captan and thiamethoxam are each acutely toxic to immature honey bees, but whole colonies can potentially compensate for detrimental effects, at least at the low doses used in our field trial, or that methodological differences of the field experiment impacted results (e.g., dilution of treatments with natural pollen). If compensation occurred, further work is needed to assess how it occurred, potentially via increased queen egg laying, and whether short-term compensation leads to long-term costs. Further work is also needed for other crop pollinators that lack the social detoxification capabilities of honey bee colonies and may be less resilient to pesticides.

Abstract Pediatric central nervous system tumors remain challenging to diagnose. Imaging approaches do not provide sufficient detail to discriminate between different tumor types, while the histopathological examination of tumor tissue shows high inter-observer variability. Recent studies have demonstrated the accurate classification of central nervous system tumors based on the DNA methylation profile of a tumor biopsy. However, a brain biopsy holds significant risk of bleeding and damaging the surrounding tissues. Liquid biopsy approaches analyzing circulating tumor DNA show high potential as an alternative and less invasive tool to study the DNA methylation pattern of tumors. Here, we explore the potential of classifying pediatric brain tumors based on methylation profiling of the circulating cell-free DNA (cfDNA) in cerebrospinal fluid (CSF). For this proof-of-concept study, we collected cerebrospinal fluid samples from 19 pediatric brain cancer patients via a ventricular drain placed for reasons of increased intracranial pressure. Analyses on the cfDNA showed high variability of cfDNA quantities across patients ranging from levels below the limit of quantification to 40 ng cfDNA per milliliter of CSF. Classification based on methylation profiling of cfDNA from CSF was correct for 7 out of 20 samples in our cohort. Accurate results were mostly observed in samples of high quality, more specifically those with limited high molecular weight DNA contamination. Interestingly, we show that centrifugation of the CSF prior to processing increases the fraction of fragmented cfDNA to high molecular weight DNA. In addition, classification was mostly correct for samples with high tumoral cfDNA fraction as estimated by computational deconvolution (> 40%). In summary, analysis of cfDNA in the CSF shows potential as a tool for diagnosing pediatric nervous system tumors especially in patients with high levels of tumoral cfDNA in the CSF. Further optimization of the collection procedure, experimental workflow and bioinformatic approach is required to also allow classification for patients with low tumoral fractions in the CSF.

Abstract Despite limited supporting evidence, the practice of thickening breast milk or infant formula with commercially available thickening agents is prevalent. This study explored the viscosity‐enhancing impact of carob bean gum (CBG) and sodium carboxymethylcellulose (NaCMC) when added to infant formula at various concentrations and for different thickening durations. The findings indicate that thickening leads to an exponential increase in milk viscosity, from 25% of the recommended dosage onward. This suggests that minor adjustments in dosage can significantly impact formula thickness, underscoring the importance of accurately dosing and preparing infant milk. The considerable variability in viscosity also emphasizes the need for thoughtful selection of teat size, considering the energy expenditure of the sucking infant. When using 50% of the recommended CBG dose or 25% of NaCMC, the resulting viscosity matches that of a commercially available casein‐based formula containing CBG for anti‐regurgitation. In the case of CBG, a viscosity plateau is only reached after 30 min. Therefore, educating parents on the correct handling and preparation steps for CBG‐thickened infant milk is crucial, including a 30‐min waiting period to achieve the intended thickening effect.

Abstract Bioresorbable electrochemical sensors remain mostly unexplored despite their ability to provide continuous in situ measurements of critical biomarkers. The primary challenge arises from the direct exposure of the electrodes’ thin metal films to biofluids, which poses difficulties in ensuring both proper operational lifetimes and sensing performance. Molybdenum (Mo) presents itself as a promising biometal due to its uniquely gradual dissolution in biofluids, facilitated by the formation of a slower‐dissolving MoOx surface layer. Consequently, carefully engineered MoOx films can endow transient electrochemical sensors with unparalleled stability during extended operational lifetimes. Herein an unprecedented sensor architecture achieved via the unique pairing of sputtered Mo and MoOx thin films, probed as a pH and dissolved oxygen sensor is reported. Compared to a bare Mo electrode, a bilayer Mo+MoOx electrode subjected to post‐deposition annealing (400 °C, 60 min, N2 environment) displayed a largely improved stability (>24 h) in solution and demonstrated predictable functionality during ongoing film dissolution at 37 °C. Collectively, this work establishes a pioneering strategy for the fabrication of reliable and clinically relevant implantable electrochemical sensors.

Although agricultural intensification has generally increased crop yields, it also resulted in a range of environmental issues. These include increased erosion rates and declined soil organic carbon (SOC) stocks. In order to improve our understanding on how erosion impacts the overall SOC storage capacity of croplands, this study analyses the 3D distribution of SOC as a function of water and tillage erosion in a conventionally ploughed field in the Belgian silt loam region. We present a novel methodological framework to integrate the output of an advanced erosion model as a co-variate within a Digital Soil Mapping (DSM) approach with the objective to create detailed SOC maps. More precisely, we combined (i) the Water and Tillage Erosion Model and Sediment Delivery Model (WaTEM/SEDEM), simulating spatial patterns of soil erosion and sediment deposition due to water and tillage erosions, with (ii) a SOC sampling campaign, resulting in a SOC spatial distribution model that considers both types of erosion. The results show that, as compared to plateaus, SOC stocks are nearly half as large along eroding slopes (i.e. convex slope positions affected by tillage erosion and steep slopes affected by water erosion). Yet, they are up to twice as large in areas characterized by sediment deposition (i.e. concave positions due to tillage erosion and foot slope positions due to water erosion). Our results further show that tillage erosion has a significant influence on the SOC stocks in the top 0.7 m and in particular on the top 0.4 m. The influence of water erosion is less strong but mostly significant along the entire depth profile. Overall, this work demonstrates the relevance of considering different erosion processes when aiming to predict spatial patterns of SOC. Considering the top 1 m and a WaTEM/SEDEM application at a resolution of 10 m our 3D SOC modelling approach obtained a coefficient of determination (R2) of 0.62, a relative root mean square error (RRMSE) of 30.5 % and a relative mean absolute error (RMAE) of 26.0 %. While future work may likely lead to further improvements e.g. a more detailed SOC sampling network along the foot slopes and thalwegs in order to obtain a more spatially detailed prediction of the area characterized by depositions due to water erosion, we demonstrate the great potential of existing erosion and deposition models in doing so.

BackgroundGenome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer.MethodsAnalyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs.ResultsAssuming a 1 × 10-5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability

BackgroundLow-frequency variants play an important role in breast cancer (BC) susceptibility. Gene-based methods can increase power by combining multiple variants in the same gene and help identify target genes.MethodsWe evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes' coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry.ResultsIn European ancestry samples, 14 genes were significantly associated (q

Abstract Background Lack of Cytomegalovirus (CMV) knowledge among healthcare professionals has been proven to be the main threat to pregnant women's awareness, preventing them from reducing the risk of infection. The aims of this study were to assess the knowledge and practices of French-speaking Swiss perinatal professionals in terms of CMV prevention, as well as the sociodemographic-professional factors that influence them. Methods This observational study used a cross-sectional design to collect data-via an anonymous electronic questionnaire in French distributed to gynecologists-obstetricians, general practitioners and midwives via various channels: e-mails and social networks of partner centers, professional associations, and conferences. The 41-item questionnaire collected data on sociodemographic and professional characteristics, general CMV knowledge, national recommendation knowledge and prevention practices. Univariable and multivariable analyses were performed. Results A total of 110 gynecologist-obstetricians, 5 general practitioners and 226 midwives participated in the study. While more than 80% of practitioners were familiar with protective hygiene measures, significant gaps were highlighted concerning the transmission routes, as well as the signs of short- and long-term congenital CMV infection. Regarding practice, 63.3% of participants provided information on CMV to their patients, mainly during the first antenatal visit. Among those who did not, lack of knowledge and forgetfulness were the two main reasons cited. Concerning systematic screening, 45.7% of participants offered it to their patients, and 37.3% only offered it to “at risk” groups. The existence of national guidelines on CMV was known by 62.0% of participants. Multivariable analysis revealed that working as a gynecologist-obstetrician was independently associated with higher score of preventive practices, while performing ultrasound or preconception consultations was independently associated with a higher score of general CMV knowledge, and working in a university hospital was independently associated with a higher score of Swiss recommendation knowledge. A level of training higher than the basic medical or midwifery diploma and participation in fetal medicine symposia both promote a higher score of CMV knowledge and prevention practices in line with current recommendations. Conclusion This study confirms the significant gaps in CMV knowledge among French-speaking Swiss caregivers along with the heterogeneity of their prevention practices. To raise awareness among pregnant women and reduce the burden of congenital CMV infections, improving professional knowledge through access to specific training and standardizing practices should be a national priority.

In theory, comprehensive two-dimensional liquid chromatography (2D-LC) allows for significant enhanced peak capacity compared to one-dimensional high performance LC (1D-HPLC). However, reaching such a separation performance while also obtaining robust, easily implementable, and sensitive methods proves challenging. Because it can hinder the broader use of 2D-LC, there is a need for developing easier, more trouble-free approaches that feature the benefits of LCxLC while not compromising with what can be done with 1D-HPLC. Commercial 2D-LC interfaces are based on two-position multiport valves composed of two loops, which are alternatingly used either to collect the effluent from the first dimension ([.sup.1]D), or to inject their content to the second dimension ([.sup.2]D). This design implies that if large sampling volumes transferred to the second column need to be avoided, a comparatively (much) higher flow rate and broader column i.d. is required for the second column. However, doing so can lead to a loss in sensitivity because of dilution and impractical analytical chromatography as a result of the high flow rates involved. In most LCxLC column combinations, this problem is exacerbated thanks to the high eluotropic strength of transferred loop volumes. However, when the elution strength of the transferred solvent is very small, refocusing the analytes can be obtained, which allows the user to overcome such issues. For example, this is the case when a purely aqueous separation mode is combined with reversed-phase LC (RPLC) in the [.sup.1]D and [.sup.2]D, respectively. Temperature responsive LC (TRLC), which is an emerging LC mode requiring only water as the mobile phase and whereby retention is controlled via temperature only, is promising in this context. In this second installment about TRLC, we illustrate the unique benefits of the combination of this separation mode with RPLC in comprehensive 2D-LC . The potential of the approach is shown through the analysis of representative standard mixtures, active pharmaceutical ingredients, synthetic impurities and phenolics in natural products., Temperature-responsive liquid chromatography (TRLC) is a separation mode in LC that allows for reversedphase (RP)-type separations under purely aqueous conditions. As a result, retention is controlled through column temperature (gradients), [...]

BackgroundSurgical site infections (SSI) are a significant concern following traumatic brain injury (TBI) surgery and often stem from the skin's microbiota near the surgical site, allowing bacteria to penetrate deeper layers and potentially causing severe infections in the cranial cavity. This study investigated the relationship between scalp skin microbiota composition and the risk of SSI after TBI surgery in sub-Saharan Africa (SSA).MethodsThis was a prospective cohort study, enrolling patients scheduled for TBI surgery. Sterile skin swabs were taken from the surrounding normal skin of the head and stored for analysis at -80°Celcius. Patients were monitored postoperatively for up to three months to detect any occurrences of SSI. 16S rRNA sequencing was used to analyze the skin microbiota composition, identifying different taxonomic microorganisms at the genus level. The analysis compared two groups: those who developed SSI and those who did not.ResultsA total of 57 patients were included, mostly male (89.5%) with a mean age of 26.5 years, predominantly from urban areas in Uganda and victims of assault. Graphical visualization and metagenomic metrics analysis revealed differences in composition, richness, and evenness of skin microbiota within samples (α) or within the community (β), and showed specific taxa (phylum and genera) associated with either the group of SSI or the No SSI.ConclusionsMetagenomic sequencing analysis uncovered several baseline findings and trends regarding the skin microbiome's relationship with SSI risk. There is an association between scalp microbiota composition (abundancy and diversity) and SSI occurrence following TBI surgery in SSA. We hypothesize under reserve that the scalp microbiota dysbiosis could potentially be an independent predictor of the occurrence of SSI; we advocate for further studies with larger cohorts.