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Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. Cell segmentation is crucial in many image analysis pipelines. This analysis compares many tools on a multimodal cell segmentation benchmark. A Transformer-based model performed best in terms of performance and general applicability.

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.

SOMAscan is an aptamer-based proteomics assay capable of measuring 1,305 human protein analytes in serum, plasma, and other biological matrices with high sensitivity and specificity. In this work, we present a comprehensive meta-analysis of performance based on multiple serum and plasma runs using the current 1.3 k assay, as well as the previous 1.1 k version. We discuss normalization procedures and examine different strategies to minimize intra- and interplate nuisance effects. We implement a meta-analysis based on calibrator samples to characterize the coefficient of variation and signal-over-background intensity of each protein analyte. By incorporating coefficient of variation estimates into a theoretical model of statistical variability, we also provide a framework to enable rigorous statistical tests of significance in intervention studies and clinical trials, as well as quality control within and across laboratories. Furthermore, we investigate the stability of healthy subject baselines and determine the set of analytes that exhibit biologically stable baselines after technical variability is factored in. This work is accompanied by an interactive web-based tool, an initiative with the potential to become the cornerstone of a regularly updated, high quality repository with data sharing, reproducibility, and reusability as ultimate goals.

Tandem repeat (TR) variation is associated with gene expression changes and numerous rare monogenic diseases. Although long-read sequencing provides accurate full-length sequences and methylation of TRs, there is still a need for computational methods to profile TRs across the genome. Here we introduce the Tandem Repeat Genotyping Tool (TRGT) and an accompanying TR database. TRGT determines the consensus sequences and methylation levels of specified TRs from PacBio HiFi sequencing data. It also reports reads that support each repeat allele. These reads can be subsequently visualized with a companion TR visualization tool. Assessing 937,122 TRs, TRGT showed a Mendelian concordance of 98.38%, allowing a single repeat unit difference. In six samples with known repeat expansions, TRGT detected all expansions while also identifying methylation signals and mosaicism and providing finer repeat length resolution than existing methods. Additionally, we released a database with allele sequences and methylation levels for 937,122 TRs across 100 genomes. A set of tools maps tandem repeats across complete genomes.

A promising new compound class for treating human malaria is the imidazolopiperazines (IZP) class. IZP compounds KAF156 (Ganaplacide) and GNF179 are effective against Plasmodium symptomatic asexual blood-stage infections, and are able to prevent transmission and block infection in animal models. But despite the identification of resistance mechanisms in P. falciparum , the mode of action of IZPs remains unknown. To investigate, we here combine in vitro evolution and genome analysis in Saccharomyces cerevisiae with molecular, metabolomic, and chemogenomic methods in P. falciparum . Our findings reveal that IZP-resistant S. cerevisiae clones carry mutations in genes involved in Endoplasmic Reticulum (ER)-based lipid homeostasis and autophagy. In Plasmodium , IZPs inhibit protein trafficking, block the establishment of new permeation pathways, and cause ER expansion. Our data highlight a mechanism for blocking parasite development that is distinct from those of standard compounds used to treat malaria, and demonstrate the potential of IZPs for studying ER-dependent protein processing. Imidazolopiperazines (IZPs) are a class of compounds under clinical development for malaria, but their mechanism of action is unclear. Here, the authors show that IZPs inhibit the parasite’s secretory pathway, affecting protein trafficking and export.

PurposeFactors such as prone body position, hydrostatic pressure, and intermittent breath-holding subject aquatic athletes to unique physical and environmental stressors during swimming exercise. The relationship between exposure to aquatic exercise and both arterial stiffness and wave reflection properties is not well-understood. This study assessed central artery stiffness and wave reflection properties in elite pool-swimmers (SW), long-distance open-water swimmers (OW), and water polo players (WP) to examine the relationship between these variables and aquatic exercise.MethodsAthletes competing in SW, OW and WP events at the FINA World Championships were recruited. Carotid-femoral pulse wave velocity, and pulse wave analysis were used to quantify arterial stiffness, and central wave reflection properties.ResultsAthletes undertook differing amounts of weekly swimming distance in training according to their discipline (SW: 40.2 ± 21.1 km, OW: 59.7 ± 28.4 km, WP: 11.4 ± 6.3 km; all p < 0.05). Pulse wave velocity (Males [SW: 6.0 ± 0.6 m/s, OW: 6.5 ± 0.8 m/s, WP: 6.7 ± 0.9 m/s], Females [SW: 5.4 ± 0.6 m/s, OW: 5.3 ± 0.5 m/s, WP: 5.2 ± 0.8 m/s; p = 0.4]) was similar across disciplines for females but was greater in male WP compared to male SW (p = 0.005). Augmentation index (Males [SW: − 3.4 ± 11%, OW: − 9.6 ± 6.4%, WP: 1.7 ± 10.9%], Females [SW: 3.5 ± 13.5%, OW: − 13.2 ± 10.7%, WP: − 2.8 ± 10.7%]) was lower in male OW compared to WP (p = 0.03), and higher in female SW compared to OW (p = 0.002). Augmentation index normalized to a heart rate of 75 bpm was inversely related to weekly swim distance in training (r = − 0.27, p = 0.004).ConclusionsThis study provides evidence that the central vasculature of elite aquatic athletes differs by discipline, and this is associated with training load.

To help with preparedness efforts of Canadian public health and safety systems for adaptation to climate change, the humidity index (humidex) and three threshold‐based humidex indices (annual number of days with humidex greater than 30, 35 and 40) were computed for a multi‐model ensemble of climate change projections, over Canada. The ensemble consists of one run from each 19 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and offers historical simulations starting in 1950 and future projections out to 2100 following Shared Socioeconomic Pathways (SSPs): SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5. Each ensemble member was bias‐adjusted and statistically downscaled using the Multivariate bias correction—N‐dimensional probability density function transform (MBCn) with hourly data from ERA5‐Land as the target dataset and following a method proposed by Diaconescu et al. (2023; International Journal of Climatology, 43, 837) to calculate humidex from daily climate model outputs. This paper details the steps for data production including evaluation of the target historical gridded data and selection of downscaling method and presents some of the resulting humidex projections at the end of the century. To help with preparedness efforts of Canadian public health and safety systems for adaptation to climate change, humidex and three threshold‐based humidex indices were computed for a multi‐model ensemble of climate change projections, over Canada. The ensemble consists of one run from each 19 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and offers historical simulations starting in 1950 and future projections out to 2100 following three greenhouse gas emission scenarios: SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5.

Climate driven trends in ocean temperature and primary productivity are projected to differ greatly across the globe, likely triggering variable levels of concern for marine biota and ecosystems. Quantifying these changes, and the complex ways in which resource-dependent communities will need to respond, is inherently difficult. Existing uncertainty about the structure, function and responses of marine ecosystems, means that an ensemble model approach is the most robust means of considering potential ecosystem responses to climate change. In this study, climate-ecological projections of 14 marine ecosystem models for regions around Australian were assessed. The models included Ecopath with Ecosim, Atlantis, intermediate complexity, species distribution, and size spectrum models and were all forced by high-resolution ocean forecasting models. Model results found that each Australian region and fishery will face its own challenges in terms of ecosystem shifts and fisheries management responses over the next 40 years. Across assessment regions, demersal systems appear to be more strongly affected by climate change than pelagic systems, with invertebrate species in shallow waters likely to respond first and to a larger degree. With the assistance of qualitative confidence evaluations, the ensemble approach was useful for identifying the likely state of concern for each functional group and thus management priorities. Simulations that considered trophic interactions and feedbacks result in much more realistic responses to climate change, with implications for future assessments and adaption planning. Study results show that fisheries and their management will need to foster pro-active and flexible adaptation options to make the most of coming opportunities and to minimize risks or negative outcomes.