A scalable, passive sampling method for the quantification of airborne allergens.

Traditionally, airborne concentrations of aeroallergens are sampled in a single location by an active sampler, which requires electricity and regular maintenance. However, aeroallergen concentrations may vary widely over urban and rural environments, requiring a method that is cost-effective and scalable so that many measurements can be made across an air shed. We developed such a method that uses passive sampling and light microscopy for analysis. Inexpensive and easy to operate, passive samplers rely on the gravitational settling of particles onto microscope slides. This determines airborne pollen concentration through: 1) sample collection using a modified Durham sampler, 2) preparation of samples for microscopy and strategic sample imaging, and 3) simplified particle measurements and calculation of pollen concentration following deposition velocity models proposed by Scheppegrell [1] and Wagner and Leith [2]. This method was verified with two sampling campaigns during the ragweed season of 2020 and the tree pollen season of 2021. The concentrations determined with the passive and Burkard sampling methods were found to be well-correlated (r > 0.99, r = 0.87) and precise (%CV = 20 %, 21 %). The validation of passive samplers will enable measurements of aeroallergens over wider spatial scales and help determine where aeroallergen exposure risks are greatest. •An inexpensive and low-cost method was developed to determine airborne pollen counts.•The method was evaluated for its accuracy and reproducibility.•The method can be applied to examine the concentrations and spatial variability of airborne pollen.

TG-Net: Using text prompts for improved skin lesion segmentation.

Automatic skin segmentation is an efficient method for the early diagnosis of skin cancer, which can minimize the missed detection rate and treat early skin cancer in time. However, significant variations in texture, size, shape, the position of lesions, and obscure boundaries in dermoscopy images make it extremely challenging to accurately locate and segment lesions. To address these challenges, we propose a novel framework named TG-Net, which exploits textual diagnostic information to guide the segmentation of dermoscopic images. Specifically, TG-Net adopts a dual-stream encoder-decoder architecture. The dual-stream encoder comprises Res2Net for extracting image features and our proposed text attention (TA) block for extracting textual features. Through hierarchical guidance, textual features are embedded into the process of image feature extraction. Additionally, we devise a multi-level fusion (MLF) module to merge higher-level features and generate a global feature map as guidance for subsequent steps. In the decoding stage of the network, local features and the global feature map are utilized in three multi-scale reverse attention modules (MSRA) to produce the final segmentation results. We conduct extensive experiments on three publicly accessible datasets, namely ISIC 2017, HAM10000, and PH2. Experimental results demonstrate that TG-Net outperforms state-of-the-art methods, validating the reliability of our method. Source code is available at https://github.com/ukeLin/TG-Net.

Moonshot. Long shot. Or sure shot. What needs to happen to realize the full potential of AI in the fertility sector?

Quality healthcare requires two critical components: patients' best interests and best decisions to achieve that goal. The first goal is the lodestar, unchanged and unchanging over time. The second component is a more dynamic and rapidly changing paradigm in healthcare. Clinical decision-making has transitioned from an opinion-based paradigm to an evidence-based and data-driven process. A realization that technology and artificial intelligence can bring value adds a third component to the decision process. And the fertility sector is not exempt. The debate about AI is front and centre in reproductive technologies. Launching the transition from a conventional provider-driven decision paradigm to a software-enhanced system requires a roadmap to enable effective and safe implementation. A key nodal point in the ascending arc of AI in the fertility sector is how and when to bring these innovations into the ART routine to improve workflow, outcomes, and bottom-line performance. The evolution of AI in other segments of clinical care would suggest that caution is needed as widespread adoption is urged from several fronts. But the lure and magnitude for the change that these tech tools hold for fertility care remain deeply engaging. Exploring factors that could enhance thoughtful implementation and progress towards a tipping point (or perhaps not) should be at the forefront of any 'next steps' strategy. The objective of this Opinion is to discuss four critical areas (among many) considered essential to successful uptake of any new technology. These four areas include value proposition, innovative disruption, clinical agency, and responsible computing.

Quantifying superimposed protein flow dynamics in live cells using spatial filtering and spatiotemporal image correlation spectroscopy.

Flow or collective movement is a frequently observed phenomenon for many cellular components including the cytoskeletal proteins actin and myosin. To study protein flow in living cells, we and others have previously used spatiotemporal image correlation spectroscopy (STICS) analysis on fluorescence microscopy image time series. Yet, in cells, multiple protein flows often occur simultaneously on different scales resulting in superimposed fluorescence intensity fluctuations that are challenging to separate using STICS. Here, we exploited the characteristic that distinct protein flows often occur at different spatial scales present in the image series to disentangle superimposed protein flow dynamics. We employed a newly developed and an established spatial filtering algorithm to alternatively accentuate or attenuate local image intensity heterogeneity across different spatial scales. Subsequently, we analysed the spatially filtered time series with STICS, allowing the quantification of two distinct superimposed flows within the image time series. As a proof of principle of our analysis approach, we used simulated fluorescence intensity fluctuations as well as time series of nonmuscle myosin II in endothelial cells and actin-based podosomes in dendritic cells and revealed simultaneously occurring contiguous and noncontiguous flow dynamics in each of these systems. Altogether, this work extends the application of STICS for the quantification of multiple protein flow dynamics in complex biological systems including the actomyosin cytoskeleton.

The best of two worlds: reprojecting 2D image annotations onto 3D models.

Imagery has become one of the main data sources for investigating seascape spatial patterns. This is particularly true in deep-sea environments, which are only accessible with underwater vehicles. On the one hand, using collaborative web-based tools and machine learning algorithms, biological and geological features can now be massively annotated on 2D images with the support of experts. On the other hand, geomorphometrics such as slope or rugosity derived from 3D models built with structure from motion (sfm) methodology can then be used to answer spatial distribution questions. However, precise georeferencing of 2D annotations on 3D models has proven challenging for deep-sea images, due to a large mismatch between navigation obtained from underwater vehicles and the reprojected navigation computed in the process of building 3D models. In addition, although 3D models can be directly annotated, the process becomes challenging due to the low resolution of textures and the large size of the models. In this article, we propose a streamlined, open-access processing pipeline to reproject 2D image annotations onto 3D models using ray tracing. Using four underwater image datasets, we assessed the accuracy of annotation reprojection on 3D models and achieved successful georeferencing to centimetric accuracy. The combination of photogrammetric 3D models and accurate 2D annotations would allow the construction of a 3D representation of the landscape and could provide new insights into understanding species microdistribution and biotic interactions.

Fluid-Dynamic Modeling of Flow in Embryonic Tissue Indicates That Lymphatic Valve Location Is Not Consistently Determined by the Local Fluid Shear or Its Gradient.

OBJECTIVE: Intravascular lymphatic valves often occur in proximity to vessel junctions. It is commonly held that disturbed flow at junctions is responsible for accumulation of valve-forming cells (VFCs) at these locations as the initial step in valve creation, and the one which explains the association with these sites. However, evidence in favor is largely limited to cell culture experiments. METHODS: We acquired images of embryonic lymphatic vascular networks from day E16.5, when VFC accumulation has started but the developing valve has not yet altered the local vessel geometry, stained for Prox1, which co-localizes with Foxc2. Using finite-element computational fluid mechanics, we simulated the flow through the networks, under conditions appropriate to this early development stage. Then we correlated the Prox1 distributions with the distributions of simulated fluid shear and shear stress gradient. RESULTS: Across a total of 16 image sets, no consistent correlation was found between Prox1 distribution and the local magnitude of fluid shear, or its positive or negative gradient. CONCLUSIONS: This, the first direct semi-empirical test of the localization hypothesis to interrogate the tissue from in vivo at the critical moment of development, does not support the idea that a feature of the local flow determines valve localization.

Longitudinal associations of retinal vessel morphology with intraocular pressure and blood pressure at follow-up visit-Findings from a Danish eye and vision cohort, Project FOREVER.

PURPOSE: To characterise the retinal vasculometry of a Danish eye and vision cohort and examine associations with systolic blood pressure (BP), diastolic BP, mean arterial BP, and intraocular pressure (IOP). DESIGN: Longitudinal study. METHODS: The retinal vasculature of fundus images from the FOREVER (Finding Ophthalmic Risks and Evaluating the Value of Eye exams and their predictive Reliability) cohort was analysed using a fully automated image analysis program. Longitudinal associations of retinal vessel morphology at follow-up visit with IOP (baseline and follow-up) and BP (follow-up) were examined using multilevel linear regression models adjusting for age, sex and retinal vasculometry at baseline as fixed effects and person as random effect. Width measurements were additionally adjusted for the spherical equivalent. RESULTS: A total of 2089 subjects (62% female) with a mean age of 61 (standard deviation 8) years and a mean follow-up period of 4.1 years (SD 0.6 years) were included. The mean arteriolar diameter was approximately 20% thinner than the mean venular diameter, and venules were about 21%-23% less tortuous than arterioles. BP at follow-up was associated with decreased arteriolar diameter from baseline to follow-up. After adjusting for baseline IOP, IOP at follow-up was associated with increased arteriolar tortuosity above baseline (0.59%, 95% CI 0.08-1.10, p-value 0.024). CONCLUSION: In a Danish eye and vision cohort, variations in BP and alterations in IOP over time were associated with changes in the width and tortuosity of retinal vessels. Our findings contribute novel insights into retinal vascular alterations over time.

Combined effects of herbicides and insecticides reduce biomass of sensitive aquatic invertebrates.

The structure and biomass of aquatic invertebrate communities play a crucial role in the matter dynamics of streams. However, biomass is rarely quantified in ecological assessments of streams, and little is known about the environmental and anthropogenic factors that influence it. In this study, we aimed to identify environmental factors that are associated with invertebrate structure and biomass through a monitoring of 25 streams across Germany. We identified invertebrates, assigned them to taxonomic and trait-based groups, and quantified biomass using image-based analysis. We found that insecticide pressure generally reduced the abundance of insecticide-vulnerable populations (R2 = 0.43 applying SPEARpesticides indicator), but not invertebrate biomass. In contrast, herbicide pressure reduced the biomass of several biomass aggregations. Especially, insecticide-sensitive populations, that were directly (algae feeder, R2 = 0.39) or indirectly (predators, R2 = 0.29) dependent on algae, were affected. This indicated a combined effect of possible food shortage due to herbicides and direct insecticide pressure. Specifically, all streams with increased herbicide pressure showed a reduced overall biomass share of Trichoptera from 43 % to 3 % and those of Ephemeroptera from 20 % to 3 % compared to streams grouped by low herbicide pressure. In contrast, insecticide-insensitive Gastropoda increased from 10 % to 45 %, and non-vulnerable leaf-shredding Crustacea increased from 10 % to 22 %. In summary, our results indicate that at the community level, the direct effects of insecticides and the indirect, food-mediated effects of herbicides exert a combined effect on the biomass of sensitive insect groups, thus disrupting food chains at ecosystem level.

Quantification of membrane geometry and protein sorting on cell membrane protrusions using fluorescence microscopy.

Plasma membranes are flexible and can exhibit numerous shapes below the optical diffraction limit. The shape of cell periphery can either induce or be a product of local protein density changes, encoding numerous cellular functions. However, quantifying membrane curvature and the ensuing sorting of proteins in live cells remains technically demanding. Here, we demonstrate the use of simple widefield fluorescence microscopy to study the geometrical properties (i.e., radius, length, and number) of thin membrane protrusions. Importantly, the quantification of protrusion radius establishes a platform for studying the curvature preferences of membrane proteins.

Smartphone image dataset to distinguish healthy and unhealthy leaves in papaya orchards in Bangladesh.

Papaya, renowned for its nutritional benefits, represents a highly profitable crop. However, it is susceptible to various diseases that can significantly impede fruit productivity and quality. Among these, leaf diseases pose a substantial threat, severely impacting the growth of papaya plants. Consequently, papaya farmers frequently encounter numerous challenges and financial setbacks. To facilitate the easy and efficient identification of papaya leaf diseases, a comprehensive dataset has been assembled. This dataset, comprising approximately 1400 images of diseased, infected, and healthy leaves, aims to enhance the understanding of how these ailments affect papaya plants. The images, meticulously collected from diverse regions and under varying weather conditions, offer detailed insights into the disease patterns specific to papaya leaves. Stringent measures have been taken to ensure the dataset's quality and enhance its utility. The images, captured from multiple angles and boasting high resolution are designed to aid in the development of a highly accurate model. Additionally, RGB mode has been employed to meticulously capture each detail, ensuring a flawless representation of the leaves. The dataset meticulously identifies and categorizes five primary types of leaf diseases: Leaf Curl (inclusive of its initial stage), Papaya Mosaic, Ring Spot, Mites (specifically, those affected by Red Spider Mites), and Mealybug. These diseases are recognized for their detrimental effects on both the leaves and the overall fruit production of the papaya plant. By leveraging this curated dataset, it is possible to train a model for the real-time detection of leaf diseases, significantly aiding in the timely identification of such conditions.

Benchmarking PathCLIP for Pathology Image Analysis.

Accurate image classification and retrieval are of importance for clinical diagnosis and treatment decision-making. The recent contrastive language-image pre-training (CLIP) model has shown remarkable proficiency in understanding natural images. Drawing inspiration from CLIP, pathology-dedicated CLIP (PathCLIP) has been developed, utilizing over 200,000 image and text pairs in training. While the performance the PathCLIP is impressive, its robustness under a wide range of image corruptions remains unknown. Therefore, we conduct an extensive evaluation to analyze the performance of PathCLIP on various corrupted images from the datasets of osteosarcoma and WSSS4LUAD. In our experiments, we introduce eleven corruption types including brightness, contrast, defocus, resolution, saturation, hue, markup, deformation, incompleteness, rotation, and flipping at various settings. Through experiments, we find that PathCLIP surpasses OpenAI-CLIP and the pathology language-image pre-training (PLIP) model in zero-shot classification. It is relatively robust to image corruptions including contrast, saturation, incompleteness, and orientation factors. Among the eleven corruptions, hue, markup, deformation, defocus, and resolution can cause relatively severe performance fluctuation of the PathCLIP. This indicates that ensuring the quality of images is crucial before conducting a clinical test. Additionally, we assess the robustness of PathCLIP in the task of image-to-image retrieval, revealing that PathCLIP performs less effectively than PLIP on osteosarcoma but performs better on WSSS4LUAD under diverse corruptions. Overall, PathCLIP presents impressive zero-shot classification and retrieval performance for pathology images, but appropriate care needs to be taken when using it.

Analysis of the spatial and morphological characteristics of oligodendrocytes from images of in vitro culture.

Oligodendrocytes (OLs) are glial cells responsible for the formation of myelin sheaths in the central nervous system. The characteristic features of the oligodendrocyte lineage, ranging from proliferative and migratory oligodendrocyte progenitor cells (OPC) to myelinating mature OLs, can be observed in vitro cultures of OL lineage cells. Here, we introduce a method for analyzing the spatial distribution of OPCs, which reflects their capacity for proliferation and migration, and the morphological complexity of mature OLs, which reflects their capacity for myelin formation, from immunostaining images of in vitro OL cultures. Through the methods described, we have demonstrated the tendency for OPCs to cluster in an environment with epidermal growth factor (EGF), and the differing morphological complexity of mature OLs according to culture medium and duration of differentiation.•The proliferative and migratory characteristics of OPCs can be evaluated by analyzing their spatial distribution.•The myelin-forming capacity of mature OLs can be measured by analyzing their morphological complexity.•Image-based analyses may be a substitute for more convoluted experiments to assess OL function.

Multi-scale structural characterization of ceramic-based photonic glasses for structural colors.

Structural colors arise from selective light interaction with (nano)structures, which give them advantages over pigmented colors such as resistance to fading and possibility to be fabricated out of traditional low-cost and non-toxic materials. Since the color arises from the photonic (nano)structures, different structural features can impact their photonic response and thus, their color. Therefore, the detailed characterization of their structural features is crucial for further improvement of structural colors. In this work, we present a detailed multi-scale structural characterization of ceramic-based photonic glasses by using a combination of high-resolution ptychographic X-ray computed tomography and small angle X-ray scattering. Our results uncover the structure-processing-properties' relationships of such nanoparticles-based photonic glasses and point out to the need of a review of the structural features used in simulation models concomitantly with the need for further investigations by experimentalists, where we point out exactly which structural features need to be improved.

Multiparameter quantitative analyses of diagnostic cells in brain tissues from tuberous sclerosis complex.

The advent of high-dimensional imaging offers new opportunities to molecularly characterize diagnostic cells in disorders that have previously relied on histopathological definitions. One example case is found in tuberous sclerosis complex (TSC), a developmental disorder characterized by systemic growth of benign tumors. Within resected brain tissues from patients with TSC, detection of abnormally enlarged balloon cells (BCs) is pathognomonic for this disorder. Though BCs can be identified by an expert neuropathologist, little is known about the specificity and broad applicability of protein markers for these cells, complicating classification of proposed BCs identified in experimental models of this disorder. Here, we report the development of a customized machine learning pipeline (BAlloon IDENtifier; BAIDEN) that was trained to prospectively identify BCs in tissue sections using a histological stain compatible with high-dimensional cytometry. This approach was coupled to a custom 36-antibody panel and imaging mass cytometry (IMC) to explore the expression of multiple previously proposed BC marker proteins and develop a descriptor of BC features conserved across multiple tissue samples from patients with TSC. Here, we present a modular workflow encompassing BAIDEN, a custom antibody panel, a control sample microarray, and analysis pipelines-both open-source and in-house-and apply this workflow to understand the abundance, structure, and signaling activity of BCs as an example case of how high-dimensional imaging can be applied within human tissues.

Quantifying spatial CXCL9 distribution with image analysis predicts improved prognosis of triple-negative breast cancer.

Background: The C-X-C motif chemokine ligand 9 (CXCL9) plays a pivotal role in tumor immunity by recruiting and activating immune cells. However, the relationship between CXCL9 expression and prognosis in triple-negative breast cancer (TNBC) is unclear. Methods: We investigated CXCL9 mRNA expression, clinicopathological features, and prognosis in TNBC patients. We also used computational image analysis to quantify and assess the distribution of CXCL9 protein in the tumor core (TC) and invasive margin (IM). Results: CXCL9 mRNA expression was significantly higher in TNBC tumors compared to normal tissue (p < 0.001) and was associated with smaller tumors (p = 0.022) and earlier stages (p = 0.033). High CXCL9 mRNA expression was correlated with improved overall survival (OS) in three independent cohorts (all p < 0.05). In a separate analysis, low CXCL9 protein expression was associated with increased lymph node metastasis (p = 0.018 and p = 0.036). High CXCL9 protein expression in the TC, IM, or both was associated with prolonged OS (all p < 0.001). Conclusion: High CXCL9 expression, at both the mRNA and protein levels, is associated with improved prognosis in TNBC patients. CXCL9 expression in the TC and/or IM may be an independent prognostic factor.

An end-to-end approach for single-cell infrared absorption spectroscopy of bacterial inclusion bodies: from AFM-IR measurement to data interpretation of large sample sets.

BACKGROUND: Inclusion bodies (IBs) are well-known subcellular structures in bacteria where protein aggregates are collected. Various methods have probed their structure, but single-cell spectroscopy remains challenging. Atomic Force Microscopy-based Infrared Spectroscopy (AFM-IR) is a novel technology with high potential for the characterisation of biomaterials such as IBs. RESULTS: We present a detailed investigation using AFM-IR, revealing the substructure of IBs and their variation at the single-cell level, including a rigorous optimisation of data collection parameters and addressing issues such as laser power, pulse frequency, and sample drift. An analysis pipeline was developed tailored to AFM-IR image data, allowing high-throughput, label-free imaging of more than 3500 IBs in 12,000 bacterial cells. We examined IBs generated in Escherichia coli under different stress conditions. Dimensionality reduction analysis of the resulting spectra suggested distinct clustering of stress conditions, aligning with the nature and severity of the applied stresses. Correlation analyses revealed intricate relationships between the physical and morphological properties of IBs. CONCLUSIONS: Our study highlights the power and limitations of AFM-IR, revealing structural heterogeneity within and between IBs. We show that it is possible to perform quantitative analyses of AFM-IR maps over a large collection of different samples and determine how to control for various technical artefacts.

Quantification of early biofilm growth in microtiter plates through a novel image analysis software.

Given the significant impact of biofilms on human health and material corrosion, research in this field urgently needs more accessible techniques to facilitate the testing of new control agents and general understanding of biofilm biology. Microtiter plates offer a convenient format for standardized evaluations, including high-throughput assays of alternative treatments and molecular modulators. This study introduces a novel Biofilm Analysis Software (BAS) for quantifying biofilms from microtiter plate images. We focused on early biofilm growth stages and compared BAS quantification to common techniques: direct turbidity measurement, intrinsic fluorescence detection linked to pyoverdine production, and standard crystal violet staining which enables image analysis and optical density measurement. We also assessed their sensitivity for detecting subtle growth effects caused by cyclic AMP and gentamicin. Our results show that BAS image analysis is at least as sensitive as the standard method of spectrophotometrically quantifying the crystal violet retained by biofilms. Furthermore, we demonstrated that bacteria adhered after short incubations (from 10 min to 4 h), isolated from planktonic populations by a simple rinse, can be monitored until their growth is detectable by intrinsic fluorescence, BAS analysis, or resolubilized crystal violet. These procedures are widely accessible for many laboratories, including those with limited resources, as they do not require a spectrophotometer or other specialized equipment.

Artificial Intelligence in Head and Neck Surgery.

This article explores artificial intelligence's (AI's) role in otolaryngology for head and neck cancer diagnosis and management. It highlights AI's potential in pattern recognition for early cancer detection, prognostication, and treatment planning, primarily through image analysis using clinical, endoscopic, and histopathologic images. Radiomics is also discussed at length, as well as the many ways that radiologic image analysis can be utilized, including for diagnosis, lymph node metastasis prediction, and evaluation of treatment response. The study highlights AI's promise and limitations, underlining the need for clinician-data scientist collaboration to enhance head and neck cancer care.

Upscaling net ecosystem CO2 exchanges in croplands: The application of integrating object-based image analysis and machine learning approaches.

Accurately estimating the net ecosystem exchange of CO2 (NEE) in cropland ecosystems is essential for understanding the impacts of agricultural practices and climate conditions. However, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling models. Here, we applied an integrated approach that combined object-based image analysis (OBIA) techniques with advanced machine learning (ML) approaches to upscale regional cropland NEE. We conducted a thorough evaluation of the upscaling approach across four distinct cropland areas characterized by diverse climate conditions. Our study confirmed that OBIA techniques can efficiently segment cropland objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorithm, among the three methods used for ML model integration, demonstrated exceptional performance in predicting NEE, with an R2 value exceeding 0.80 across all study areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorest-performing ML model and a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing cropland NEE patterns compared to pixel-based products. Additionally, we utilized the SHapley Additive exPlanations (SHAP) to assess driver importance, revealing that phenology and radiation had the greatest influence on prediction accuracy, followed by temperature and soil moisture. This study highlights the potential of integrating OBIA techniques with machine learning approaches for upscaling regional cropland NEE, while concurrently reducing estimation uncertainties.

GLGFormer: Global Local Guidance Network for Mucosal Lesion Segmentation in Gastrointestinal Endoscopy Images.

Automatic mucosal lesion segmentation is a critical component in computer-aided clinical support systems for endoscopic image analysis. Image segmentation networks currently rely mainly on convolutional neural networks (CNNs) and Transformers, which have demonstrated strong performance in various applications. However, they cannot cope with blurred lesion boundaries and lesions of different scales in gastrointestinal endoscopy images. To address these challenges, we propose a new Transformer-based network, named GLGFormer, for the task of mucosal lesion segmentation. Specifically, we design the global guidance module to guide single-scale features patch-wise, enabling them to incorporate global information from the global map without information loss. Furthermore, a partial decoder is employed to fuse these enhanced single-scale features, achieving single-scale to multi-scale enhancement. Additionally, the local guidance module is designed to refocus attention on the neighboring patch, thus enhancing local features and refining lesion boundary segmentation. We conduct experiments on a private atrophic gastritis segmentation dataset and four public gastrointestinal polyp segmentation datasets. Compared to the current lesion segmentation networks, our proposed GLGFormer demonstrates outstanding learning and generalization capabilities. On the public dataset ClinicDB, GLGFormer achieved a mean intersection over union (mIoU) of 91.0% and a mean dice coefficient (mDice) of 95.0%. On the private dataset Gastritis-Seg, GLGFormer achieved an mIoU of 90.6% and an mDice of 94.6%.