With a little help from my friends: importance of protist-protist interactions in structuring marine protistan communities in the San Pedro Channel.

Unlabelled: Marine protists form complex communities that are shaped by environmental and biological ecosystem properties, as well as ecological interactions between organisms. While all of these factors play a role in shaping protistan communities, the specific ways in which these properties and interactions influence protistan communities remain poorly understood. Fourteen years and 9 months of eukaryotic amplicon (18S-V4 rRNA gene) data collected monthly at the San Pedro Ocean Time-series (SPOT) station were used to evaluate the impacts that environmental and biological factors, and protist-protist interactions had on protistan community composition.

Multi-institutional development and testing of attention-enhanced deep learning segmentation of thyroid nodules on ultrasound.

Purpose: Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS classification is a key step in predicting nodule pathology. Determining thyroid nodule contours is necessary for the calculation of TIRADS scores and can also be used in the development of machine learning nodule diagnosis systems. This paper presents the development, validation, and multi-institutional independent testing of a machine learning system for the automatic segmentation of thyroid nodules on ultrasound.

Slower Pace of Epigenetic Aging and Lower Inflammatory Indicators in Females Following a Nutrient-Dense, Plant-Rich Diet Than Those in Females Following the Standard American Diet.

Background: Plant-based diets are associated with lower inflammatory biomarkers and reduced risk of age-related chronic diseases. Epigenetic biomarkers of aging are DNA methylation-based tools that estimate biological age and rate of aging, providing insights into age-related health risks. Healthy diet and lifestyle indicators correlate with slower epigenetic aging.

Phylogenetic proximity drives temporal succession of marine giant viruses in a five-year metagenomic time-series.

Nucleocytoplasmic Large DNA Viruses (NCLDVs, also called giant viruses) are widespread in marine systems and infect a broad range of microbial eukaryotes (protists). Recent biogeographic work has provided global snapshots of NCLDV diversity and community composition across the world's oceans, yet little information exists about the guiding 'rules' underpinning their community dynamics over time. We leveraged a five-year monthly metagenomic time-series to quantify the community composition of NCLDVs off the coast of Southern California and characterize these populations' temporal dynamics.

Microbial community composition predicts bacterial production across ocean ecosystems.

Microbial ecological functions are an emergent property of community composition. For some ecological functions, this link is strong enough that community composition can be used to estimate the quantity of an ecological function. Here, we apply random forest regression models to compare the predictive performance of community composition and environmental data for bacterial production (BP).

Deployment expectations of multi-gigatonne scale carbon removal could have adverse impacts on Asia's energy-water-land nexus.

Existing studies indicate that future global carbon dioxide (CO) removal (CDR) efforts could largely be concentrated in Asia. However, there is limited understanding of how individual Asian countries and regions will respond to varying and uncertain scales of future CDR concerning their energy-land-water system. We address this gap by modeling various levels of CDR-reliant pathways under climate change ambitions in Asia.

The Science, Engineering, and Validation of Marine Carbon Dioxide Removal and Storage.

Scenarios to stabilize global climate and meet international climate agreements require rapid reductions in human carbon dioxide (CO) emissions, often augmented by substantial carbon dioxide removal (CDR) from the atmosphere. While some ocean-based removal techniques show potential promise as part of a broader CDR and decarbonization portfolio, no marine approach is ready yet for deployment at scale because of gaps in both scientific and engineering knowledge. Marine CDR spans a wide range of biotic and abiotic methods, with both common and technique-specific limitations.

Integrated assessment modeling of a zero-emissions global transportation sector.

Currently responsible for over one fifth of carbon emissions worldwide, the transportation sector will need to undergo a substantial technological transition to ensure compatibility with global climate goals. Few studies have modeled strategies to achieve zero emissions across all transportation modes, including aviation and shipping, alongside an integrated analysis of feedbacks on other sectors and environmental systems. Here, we use a global integrated assessment model to evaluate deep decarbonization scenarios for the transportation sector consistent with maintaining end-of-century warming below 1.

Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal.

Purpose: Current clinical assessment qualitatively describes background parenchymal enhancement (BPE) as minimal, mild, moderate, or marked based on the visually perceived volume and intensity of enhancement in normal fibroglandular breast tissue in dynamic contrast-enhanced (DCE)-MRI. Tumor enhancement may be included within the visual assessment of BPE, thus inflating BPE estimation due to angiogenesis within the tumor. Using a dataset of 426 MRIs, we developed an automated method to segment breasts, electronically remove lesions, and calculate scores to estimate BPE levels.

DeepMicroClass sorts metagenomic contigs into prokaryotes, eukaryotes and viruses.

Sequence classification facilitates a fundamental understanding of the structure of microbial communities. Binary metagenomic sequence classifiers are insufficient because environmental metagenomes are typically derived from multiple sequence sources. Here we introduce a deep-learning based sequence classifier, DeepMicroClass, that classifies metagenomic contigs into five sequence classes, i.

Modeling direct air carbon capture and storage in a 1.5 °C climate future using historical analogs.

Limiting the rise in global temperature to 1.5 °C will rely, in part, on technologies to remove CO from the atmosphere. However, many carbon dioxide removal (CDR) technologies are in the early stages of development, and there is limited data to inform predictions of their future adoption.

Integrated Assessment of the Leading Paths to Mitigate CO Emissions from the Organic Chemical and Plastics Industry.

The chemical industry is a major and growing source of CO emissions. Here, we extend the principal U.S.

U-Net breast lesion segmentations for breast dynamic contrast-enhanced magnetic resonance imaging.

Purpose: Given the dependence of radiomic-based computer-aided diagnosis artificial intelligence on accurate lesion segmentation, we assessed the performances of 2D and 3D U-Nets in breast lesion segmentation on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) relative to fuzzy c-means (FCM) and radiologist segmentations.

Approach: Using 994 unique breast lesions imaged with DCE-MRI, three segmentation algorithms (FCM clustering, 2D and 3D U-Net convolutional neural networks) were investigated. Center slice segmentations produced by FCM, 2D U-Net, and 3D U-Net were evaluated using radiologist segmentations as truth, and volumetric segmentations produced by 2D U-Net slices and 3D U-Net were compared using FCM as a surrogate reference standard.

Supply and Demand Drivers of Global Hydrogen Deployment in the Transition toward a Decarbonized Energy System.

The role of hydrogen in energy system decarbonization is being actively examined by the research and policy communities. We evaluate the potential "hydrogen economy" in global climate change mitigation scenarios using the Global Change Analysis Model (GCAM). We consider major hydrogen production methods in conjunction with delivery options to understand how hydrogen infrastructure affects its deployment.

Ratcheting of climate pledges needed to limit peak global warming.

The new and updated emission reduction pledges submitted by countries ahead of COP26 represent a meaningful strengthening of global ambition compared to the 2015 Paris pledges. Yet, limiting global warming below 1.5°C this century will require countries to ratchet ambition for 2030 and beyond.

Predicting intensive care need for COVID-19 patients using deep learning on chest radiography.

Purpose: Image-based prediction of coronavirus disease 2019 (COVID-19) severity and resource needs can be an important means to address the COVID-19 pandemic. In this study, we propose an artificial intelligence/machine learning (AI/ML) COVID-19 prognosis method to predict patients' needs for intensive care by analyzing chest X-ray radiography (CXR) images using deep learning.

Approach: The dataset consisted of 8357 CXR exams from 5046 COVID-19-positive patients as confirmed by reverse transcription polymerase chain reaction (RT-PCR) tests for the SARS-CoV-2 virus with a training/validation/test split of 64%/16%/20% on a by patient level.

Symbiotic UCYN-A strains co-occurred with El Niño, relaxed upwelling, and varied eukaryotes over 10 years off Southern California.

Biological nitrogen fixation, the conversion of N gas into a bioavailable form, is vital to sustaining marine primary production. Studies have shifted beyond traditionally studied tropical diazotrophs. Candidatus Atelocyanobacterium thalassa (or UCYN-A) has emerged as a focal point due to its streamlined metabolism, intimate partnership with a haptophyte host, and broad distribution.

Medication Deprescribing Among Patients With Type 2 Diabetes: A Qualitative Case Series of Lifestyle Medicine Practitioner Protocols.

This study is a qualitative case series of lifestyle medicine practitioners' protocols for medication de-escalation in the context of reduced need for glucose-lowering medications due to lifestyle modifications. Increasing numbers of lifestyle medicine practitioners report achieving reductions in medications among patients with type 2 diabetes, and in some cases remission, but limited data exist on the clinical decision-making process used to determine when and how medications are deprescribed. Practitioners interviewed here provide accounts of their deprescribing protocols.

Diverse Marine T4-like Cyanophage Communities Are Primarily Comprised of Low-Abundance Species Including Species with Distinct Seasonal, Persistent, Occasional, or Sporadic Dynamics.

Cyanophages exert important top-down controls on their cyanobacteria hosts; however, concurrent analysis of both phage and host populations is needed to better assess phage-host interaction models. We analyzed picocyanobacteria and and T4-like cyanophage communities in Pacific Ocean surface waters using five years of monthly viral and cellular fraction metagenomes. Cyanophage communities contained thousands of mostly low-abundance (<2% relative abundance) species with varying temporal dynamics, categorized as seasonally recurring or non-seasonal and occurring persistently, occasionally, or sporadically (detected in ≥85%, 15-85%, or <15% of samples, respectively).

ViralCC retrieves complete viral genomes and virus-host pairs from metagenomic Hi-C data.

The introduction of high-throughput chromosome conformation capture (Hi-C) into metagenomics enables reconstructing high-quality metagenome-assembled genomes (MAGs) from microbial communities. Despite recent advances in recovering eukaryotic, bacterial, and archaeal genomes using Hi-C contact maps, few of Hi-C-based methods are designed to retrieve viral genomes. Here we introduce ViralCC, a publicly available tool to recover complete viral genomes and detect virus-host pairs using Hi-C data.