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Bacterial biofilms are often defined as communities of surface-attached bacteria and are typically depicted with a classic mushroom-shaped structure characteristic of Pseudomonas aeruginosa. However, it has become evident that this is not how all biofilms develop, especially in vivo, in clinical and industrial settings, and in the environment, where biofilms often are observed as non-surface-attached aggregates. In this Review, we describe the origin of the current five-step biofilm development model and why it fails to capture many aspects of bacterial biofilm physiology. We aim to present a simplistic developmental model for biofilm formation that is flexible enough to include all the diverse scenarios and microenvironments where biofilms are formed. With this new expanded, inclusive model, we hereby introduce a common platform for developing an understanding of biofilms and anti-biofilm strategies that can be tailored to the microenvironment under investigation.In this Review, Bjarnsholt and colleagues propose a revised conceptual model of the biofilm life cycle that encompasses the three major steps of biofilm formation — aggregation, growth and disaggregation — independently of surfaces, and initiation from single-cell planktonic bacteria, and thus represents a broader range of biofilm systems.

Population-level health outcomes and measures of well-being are often described relative to broad racial/ethnic categories such as White or Caucasian; Black or African American; Latino or Hispanic; Asian American; Native Hawaiian and Pacific Islander; or American Indian and Alaska Native. However, the aggregation of data into these groups masks critical within-group differences and disparities, limiting the health and social services fields’abilities to target their resources where most needed. While researchers and policymakers have recognized the importance of disaggregating racial/ethnic data—and many organizations have advocated for it over the years—progress has been slow and disparate. The ongoing lack of racial/ethnic data disaggregation perpetuates existing inequities in access to much-needed resources that can ensure health and well-being. In its efforts to help build a Culture of Health and promote health equity, the Robert Wood Johnson Foundation has supported activities aimed to advance the meaningful disaggregation of racial/ethnic data—at the collection, analysis, and reporting phases. This special issue presents further evidence for the importance of disaggregation, the technical and policy challenges to creating change in practice, and the implications of improving the use of race and ethnicity data to identify and address gaps in health.

Cancer statistics for adolescents and young adults (AYAs) (aged 15‐39 years) are often presented in aggregate, masking important heterogeneity. The authors analyzed population‐based cancer incidence and mortality for AYAs in the United States by age group (ages 15‐19, 20‐29, and 30‐39 years), sex, and race/ethnicity. In 2020, there will be approximately 89,500 new cancer cases and 9270 cancer deaths in AYAs. Overall cancer incidence increased in all AYA age groups during the most recent decade (2007‐2016), largely driven by thyroid cancer, which rose by approximately 3% annually among those aged 20 to 39 years and 4% among those aged 15 to 19 years. Incidence also increased in most age groups for several cancers linked to obesity, including kidney (3% annually across all age groups), uterine corpus (3% in the group aged 20‐39 years), and colorectum (0.9%‐1.5% in the group aged 20‐39 years). Rates declined dramatically for melanoma in the group aged 15 to 29 years (4%‐6% annually) but remained stable among those aged 30 to 39 years. Overall cancer mortality declined during 2008 through 2017 by 1% annually across age and sex groups, except for women aged 30 to 39 years, among whom rates were stable because of a flattening of declines in female breast cancer. Rates increased for cancers of the colorectum and uterine corpus in the group aged 30 to 39 years, mirroring incidence trends. Five‐year relative survival in AYAs is similar across age groups for all cancers combined (range, 83%‐86%) but varies widely for some cancers, such as acute lymphocytic leukemia (74% in the group aged 15‐19 years vs 51% in the group aged 30‐39 years) and brain tumors (77% vs 66%), reflecting differences in histologic subtype distribution and treatment. Progress in reducing cancer morbidity and mortality among AYAs could be addressed through more equitable access to health care, increasing clinical trial enrollment, expanding research, and greater alertness among clinicians and patients for early symptoms and signs of cancer. Further progress could be accelerated with increased disaggregation by age in research on surveillance, etiology, basic biology, and survivorship.

We describe the latest version of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). The archive contains downscaled historical and future projections for 1950–2100 based on output from Phase 6 of the Climate Model Intercomparison Project (CMIP6). The downscaled products were produced using a daily variant of the monthly bias correction/spatial disaggregation (BCSD) method and are at 1/4-degree horizontal resolution. Currently, eight variables from five CMIP6 experiments (historical, SSP126, SSP245, SSP370, and SSP585) are provided as procurable from thirty-five global climate models. Measurement(s) temperature of air • volume of hydrological precipitation • humidity • stellar radiation • atmospheric wind speed Technology Type(s) statistical downscaling Sample Characteristic - Environment atmosphere

Non-Intrusive Load Monitoring (NILM) technique or energy disaggregation is a technique used to detect the appliance’s states and estimate their individual energy consumption, given the aggregated data through the main smart meter. Indeed, energy efficiency is the main goal of the NILM techniques, which can be achieved by providing energy disaggregation feedback to the consumers. Unlike single models where training must be performed for each appliance, this work proposes multi-target disaggregation which is more appropriate due to the drastic reduction of resources when training is performed for all target appliances simultaneously. For this purpose, new hybrid models are proposed by combining well-known deep learning models: Convolutional Neural Network (CNN), Denoising Autoencoder (DAE), Recurrent Neural Network (RNN), and Long Short-Term Memory network (LSTM). An implementation and detailed comparative study is then suggested between the proposed hybrid deep learning models and conventional single models in terms of various performance metrics on the UK-Domestic Appliance-Level Electricity (UKDALE) benchmarking database. The experimental results show that the proposed hybrid models provide the best disaggregation performances for multi-target disaggregation compared to single models. Specifically, the CNN-LSTM and the DAE-LSTM are the best hybrid models with the highest overall F1-score of 78.90% and 72.94% respectively.

Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells.CIBERSORTx, a suite of computational tools, enables inference of cell type abundance and cell-type-specific gene expression profiles from bulk RNA profiles.

Numerous studies have focused on the need to expand production of ‘blue foods’, defined as aquatic foods captured or cultivated in marine and freshwater systems, to meet rising population- and income-driven demand. Here we analyze the roles of economic, demographic, and geographic factors and preferences in shaping blue food demand, using secondary data from FAO and The World Bank, parameters from published models, and case studies at national to sub-national scales. Our results show a weak cross-sectional relationship between per capita income and consumption globally when using an aggregate fish metric. Disaggregation by fish species group reveals distinct geographic patterns; for example, high consumption of freshwater fish in China and pelagic fish in Ghana and Peru where these fish are widely available, affordable, and traditionally eaten. We project a near doubling of global fish demand by mid-century assuming continued growth in aquaculture production and constant real prices for fish. Our study concludes that nutritional and environmental consequences of rising demand will depend on substitution among fish groups and other animal source foods in national diets. Global demand for “blue food” is growing. In this quantitative synthesis, the authors analyse global seafood demand and project trends to 2050, finding considerable regional variation in the relationship between wealth and consumption.

The first step to obtain a cellular suspension from tissues is the disaggregation procedure. The cell suspension method has to provide a representative sample of the different cellular subpopulations and to maximize the number of viable functional cells. Here, we analyzed specific cell functions in cell suspensions from several rat tissues obtained by two different methods, automated–mechanical and enzymatic disaggregation. Flow cytometric, confocal, and ultrastructural (TEM) analyses were applied to the spleen, testis, liver and other tissues. Samples were treated by an enzymatic trypsin solution or processed by the Medimachine II (MMII). The automated–mechanical and enzymatic disaggregation procedures have shown to work similarly in some tissues, which displayed comparable amounts of apoptotic/necrotic cells. However, cells obtained by the enzyme-free Medimachine II protocols show a better preservation lysosome and mitochondria labeling, whereas the enzymatic gentle dissociation appears to constantly induce a lower amount of intracellular ROS; nevertheless, lightly increased ROS can be recognized as a complimentary signal to promote cell survival. Therefore, MMII represents a simple, fast, and standardized method for tissue processing, which allows to minimize bias arising from the operator’s ability. Our study points out technical issues to be adopted for specific organs and tissues to obtain functional cells.

Household air pollution generated from the use of polluting cooking fuels and technologies is a major source of disease and environmental degradation in low- and middle-income countries. Using a novel modelling approach, we provide detailed global, regional and country estimates of the percentages and populations mainly using 6 fuel categories (electricity, gaseous fuels, kerosene, biomass, charcoal, coal) and overall polluting/clean fuel use – from 1990-2020 and with urban/rural disaggregation. Here we show that 53% of the global population mainly used polluting cooking fuels in 1990, dropping to 36% in 2020. In urban areas, gaseous fuels currently dominate, with a growing reliance on electricity; in rural populations, high levels of biomass use persist alongside increasing use of gaseous fuels. Future projections of observed trends suggest 31% will still mainly use polluting fuels in 2030, including over 1 billion people in Sub-Saharan African by 2025. Household air pollution derived from cooking fuels is a major source of health and environmental problems. Here, the authors provide detailed global, regional and country estimates of cooking fuel usage from 1990 to 2030 and project that 31% of people will still be mainly using polluting fuels in 2030.