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Background: Wildfire fuels are commonly mapped via manual interpretation of aerial photos. Alternatively, RGB satellite imagery offers data across large spatial extents. A method of individual tree detection and classification is developed with implications to fuel mapping and community wildfire exposure assessments. Methods: Convolutional neural networks are trained using a novel generational training process to detect trees in 0.50 m/px RGB imagery collected in Rocky Mountain and Boreal natural regions in Alberta, Canada by Pleiades-1 and WorldView-2 satellites. The workflow classifies detected trees as 'green-in-winter'/'brown-in-winter', a proxy for coniferous/deciduous, respectively. Key results: A k-fold testing procedure compares algorithm detections to manual tree identification densities reaching an R 2 of 0.82. The generational training process increased achieved R 2 by 0.23. To assess classification accuracy, satellite detections are compared to manual annotations of 2 cm/px drone imagery resulting in average F 1 scores of 0.85 and 0.82 for coniferous and deciduous trees respectively. The use of model outputs in tree density mapping and community-scale wildfire exposure assessments is demonstrated. Conclusion & Implications: The proposed workflow automates fine-scale overstorey tree mapping anywhere seasonal (winter and summer) 0.50 m/px RGB satellite imagery exists. Further development could enable the extraction of additional properties to inform a more complete fuel map. High-resolution satellite imagery is collected around various communities in Alberta, Canada. Machine learning algorithms are used to detect and classify individual trees from collected imagery in an automated fashion. Use of the algorithm in fuel mapping and community wildfire exposure assessments is explored. [ABSTRACT FROM AUTHOR]

Azole antifungals are the main drugs used to treat fungal infections. Amino acid substitutions in the drug target Erg11 (Cyp51) are a common resistance mechanism in pathogenic yeasts. How many and which mutations confer resistance is, however, largely unknown. Here we measure the impact of nearly 4,000 amino acid variants of Candida albicans Erg11 on the susceptibility to six clinical azoles. This was achieved by deep mutational scanning of CaErg11 expressed in Saccharomyces cerevisiae. We find that a large fraction of mutations lead to resistance (33%), most resistance mutations confer cross-resistance (88%) and only a handful of resistance mutations show a significant fitness cost (9%). Our results reveal that resistance to azoles can arise through a large set of mutations and this will probably lead to azole pan-resistance, with little evolutionary compromise. This resource will help inform treatment choices in clinical settings and guide the development of new drugs., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Measuring the 3D shape of semi-transparent surfaces with projector-camera 3D scanners is a difficult task because these surfaces weakly reflect light in a diffuse manner, and transmit a large part of the incident light. The task is even harder in the presence of participating background surfaces. The two methods proposed in this paper use sinusoidal patterns, each with a frequency chosen in the frequency range allowed by the projection optics of the projector-camera system. They differ in the way in which the camera-projector correspondence map is established, as well as in the number of patterns and the processing time required. The first method utilizes the discrete Fourier transform, performed on the intensity signal measured at a camera pixel, to inventory projector columns illuminating directly and indirectly the scene point imaged by that pixel. The second method goes beyond discrete Fourier transform and achieves the same goal by fitting a proposed analytical model to the measured intensity signal. Once the one (camera pixel) to many (projector columns) correspondence is established, a surface continuity constraint is applied to extract the one to one correspondence map linked to the semi-transparent surface. This map is used to determine the 3D point cloud of the surface by triangulation. Experimental results demonstrate the performance (accuracy, reliability) achieved by the proposed methods.

Introduction: Lifelong adherence to a gluten-free diet (GFD) is the primary treatment of celiac disease (CeD), a gluten-driven enteropathy. Concerns have been raised about increased exposure to arsenic from a GFD because rice, which naturally bioaccumulates arsenic, is commonly used as a substitute for gluten-containing grains such as wheat. We hypothesize that arsenic exposure increases in newly diagnosed children with CeD after they adopt a GFD., Methods: This is a single-center prospective longitudinal cohort study of children (age 2-18 years) with elevated celiac serology who underwent a diagnostic endoscopy before initiation of a GFD between January and May 2022. The primary outcome was change in urinary arsenic concentration between endoscopy and after 6 months on a GFD., Results: Of the 67 recruited participants, 50 had a biopsy diagnostic of CeD and were invited to continue the study. Thirty-five participants completed sample collection. Participants were from a middle-class, well-educated population that was predominantly White with presenting symptoms of abdominal pain (51%) and diarrhea (29%). After 6 months on a GFD, there was a significant increase in the median urinary arsenic concentration (3.3 µg/L vs 13.6 µg/L, P = 0.000004). In regression models, family history of CeD and Hispanic ethnicity were associated with having a higher urinary arsenic concentration after 6 months on a GFD., Discussion: Children with newly diagnosed CeD have increased arsenic exposure shortly after transitioning to a GFD. While the arsenic levels were well below acutely toxic concentrations, the clinical impact of chronic exposure to mildly elevated arsenic levels is unknown., (Copyright © 2024 by The American College of Gastroenterology.)

Acne vulgaris, a prevalent dermatological disorder, frequently affects individuals' self-perception and general psychosocial functioning. The growing demand for home-based light therapy devices prompted a systematic review to assess the efficacy of visible light in acne treatment. Overall, 35 studies were included, representing 1185 cases of acne vulgaris (mean age: 23.7 years; sex: 63% female). A total of 69% of the included studies were level 2 evidence. Overall, 92% of patients achieved partial remission of their acne lesions using visible light therapy. Among the patients who experienced a partial remission of their acne, 46% experienced a reduction of 0% to 50% in their acne lesions, while 33% and 12% experienced reductions of 51% to 74% and 75% to 99%, respectively. Notably, blue light emerged as the predominant treatment modality in this context, used in around 64% of the cases. A total of 95% of the patients who were treated with blue light experienced a partial clearance of acne lesions; 44% experienced a reduction of 0% to 50% in their acne lesions, while 42% and 9% experienced reductions of 51% to 74% and 75% to 99%, respectively. Overall, the average total lesion count improved by 43% at week 4 compared to the baseline. The average number of treatment sessions was 22 (range 1-112 sessions). On average, treated patients responded within a period of 4 weeks. Commonly reported side effects included skin irritation and erythema. This review highlights the potential of visible light therapy in acne treatment. Head-to-head studies are needed to compare the efficacy of visible light compared to existing therapies for the treatment of acne., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Volumetric additive manufacturing techniques are a promising pathway to ultra-rapid light-based 3D fabrication. Their widespread adoption, however, demands significant improvement in print fidelity. Currently, volumetric additive manufacturing prints suffer from systematic undercuring of fine features, making it impossible to print objects containing a wide range of feature sizes, precluding effective adoption in many applications. Here, we uncover the reason for this limitation: light dose spread in the resin due to chemical diffusion and optical blurring, which becomes significant for features ⪅0.5 mm. We develop a model that quantitatively predicts the variation of print time with feature size and demonstrate a deconvolution method to correct for this error. This enables prints previously beyond the capabilities of volumetric additive manufacturing, such as a complex gyroid structure with variable thickness and a fine-toothed gear. These results position volumetric additive manufacturing as a mature 3D printing method, all but eliminating the gap to industry-standard print fidelity., (© 2023. Crown.)

The coronavirus disease (COVID-19) pandemic shows the rapid pace at which vaccine development can occur which highlights the need for more fast and efficient analytical methodologies to track and characterize candidate vaccines during manufacturing and purification processes. The candidate vaccine in this work comprises plant-derived Norovirus-like particles (NVLPs) which are structures that mimic the virus but lack any infectious genetic material. Presented here is a liquid chromatography-tandem mass spectrometry (LC-MS/MS) methodology for the quantification of viral protein VP1, the main component of the NVLPs in this study. It combines isotope dilution mass spectrometry (IDMS) with multiple reaction monitoring (MRM) to quantify targeted peptides in process intermediates. Multiple MRM transitions (precursor/product ion pairs) for VP1 peptides were tested with varying MS source conditions and collision energies. Final parameter selection for quantification includes three peptides with two MRM transitions each offering maximum detection sensitivity under optimized MS conditions. For quantification, a known concentration of the isotopically labeled version of the peptides to be quantified was added into working standard solutions to serve as an internal standard (IS); calibration curves were generated for concentration of native peptide vs. the peak area ratio of native-to-isotope labeled peptide. VP1 peptides in samples were quantified with labeled versions of the peptides added at the same level as that of the standards. Peptides were quantified with limit of detection (LOD) as low as 1.0 fmol μL -1 and limit of quantitation (LOQ) as low as 2.5 fmol μL -1 . NVLP preparations spiked with known quantities of either native peptides or drug substance (DS) comprising assembled NVLPs produced recoveries indicative of minimal matrix effects. Overall, we report a fast, specific, selective, and sensitive LC-MS/MS strategy to track NVLPs through the purification steps of the DS of a Norovirus candidate vaccine. To the best of our knowledge, this is the first application of an IDMS method to track virus-like particles (VLPs) produced in plants as well as measurements performed with VP1, a Norovirus capsid protein.

Tomographic volumetric additive manufacturing (VAM) is an optical 3D printing technique where an object is formed by photopolymerizing resin via tomographic projections. Currently, these projections are calculated using the Radon transform from computed tomography but it ignores two fundamental properties of real optical projection systems: finite etendue and non-telecentricity. In this work, we introduce 3D ray tracing as a new method of computing projections in tomographic VAM and demonstrate high fidelity printing in non-telecentric and higher etendue systems, leading to a 3x increase in vertical build volume than the standard Radon method. The method introduced here expands the possible tomographic VAM printing configurations, enabling faster, cheaper, and higher fidelity printing.