Explore the vast range of titles available.

Bacteria of the SAR11 clade constitute up to one half of all microbial cells in the oxygen-rich surface ocean. SAR11 bacteria are also abundant in oxygen minimum zones (OMZs), where oxygen falls below detection and anaerobic microbes have vital roles in converting bioavailable nitrogen to N 2 gas. Anaerobic metabolism has not yet been observed in SAR11, and it remains unknown how these bacteria contribute to OMZ biogeochemical cycling. Here, genomic analysis of single cells from the world’s largest OMZ revealed previously uncharacterized SAR11 lineages with adaptations for life without oxygen, including genes for respiratory nitrate reductases (Nar). SAR11 nar genes were experimentally verified to encode proteins catalysing the nitrite-producing first step of denitrification and constituted ~40% of OMZ nar transcripts, with transcription peaking in the anoxic zone of maximum nitrate reduction activity. These results link SAR11 to pathways of ocean nitrogen loss, redefining the ecological niche of Earth’s most abundant organismal group. Bacteria of the SAR11 clade constitute up to one half of all marine microbes and are thought to require oxygen for growth; here, a subgroup of SAR11 bacteria are shown to thrive in ocean oxygen minimum zones and to encode abundant respiratory nitrate reductases. An anoxic niche for SAR11 bacteria SAR11 bacteria, the most abundant type of microbe in the world's oceans, are thought to require oxygen for growth, yet they are also abundant in waters where oxygen levels are low. Frank Stewart and colleagues show here that a subgroup of SAR11 bacteria that thrives in ocean oxygen minimum zones have adapted to the microaerobic/anaerobic conditions there, and they encode abundant respiratory nitrate reductases that perform the first step in denitrification. These results redefine the ecological niche of Earth's most abundant organismal group and suggest that they are substantial contributors to nitrogen loss in oxygen minimum zones.

Federally qualified health centers (FQHCs) are bound by Uniform Data System quality metrics and typically need to layer on additional measures to participate in accountable care. Whereas a colonoscopy requires bowel preparation, transportation to and from examination, and sedation, a FIT can be completed with a single stool sample.2 The USPSTF evaluated FITs as saving 292 life-years per 1000 individuals screened if starting screening at age 50 years compared with colonoscopies at 310 life-years.2 This difference in a perfect world is real; however, real-world data show that half of colonoscopies ordered are never scheduled and that only 40% of patients are made aware of alternatives.3 The USPSTF also concluded that there are 40% more harms associated with colonoscopies, albeit at only 14 per 1000 procedures. [...]Black adults have the highest incidence of and mortality from CRC.6 Safety-net populations, consisting of Medicaid-insured and uninsured patients, have the lowest CRC screening rates compared with privately and commercially insured patients.7 Gupta et al highlighted many multilevel challenges that affect screening, from the level of the individual patient to providers/teams and national policy.8 On the individual level, personal attitudes, such as disgust and stigmas, have been shown to affect completion rates.9,10 From a structural perspective, patient access to CRC screening in general is inextricably linked to SDOH.11 When outcomes, quality, and cost are commingled with actual patient preference and adherence, a test that is \"less perfect\"—but more achievable by patients—could be the best, most efficacious choice. If that test also decreases the procedural burden in the local system of care such that those who need procedures receive more timely care, an additional societal benefit is achieved, as colonoscopy occurring 10 or more months after an abnormal FIT result has been associated with a higher risk of CRC and more advanced-stage disease at the time of diagnosis.12 To improve our performance in CRC screening, we analyzed FIT and colonoscopy data from our electronic health record (EHR) system (Intergy by Greenway), without making any changes in clinical practice other than beginning to systematically collect SDOH and assist patients with accessing resources.

Bacteria of the SAR11 clade constitute up to one half of all microbial cells in the oxygen-rich surface ocean. SAR11 bacteria are also abundant in oxygen minimum zones (OMZs), where oxygen falls below detection and anaerobic microbes have vital roles in converting bioavailable nitrogen to N^sub 2^ gas. Anaerobic metabolism has not yet been observed in SAR11, and it remains unknown how these bacteria contribute to OMZ biogeochemical cycling. Here, genomic analysis of single cells from the world's largest OMZ revealed previously uncharacterized SAR11 lineages with adaptations for life without oxygen, including genes for respiratory nitrate reductases (Nar). SAR11 nar genes were experimentally verified to encode proteins catalysing the nitrite-producing first step of denitrification and constituted ~40% of OMZ nar transcripts, with transcription peaking in the anoxic zone of maximum nitrate reduction activity. These results link SAR11 to pathways of ocean nitrogen loss, redefining the ecological niche of Earth's most abundant organismal group.

Study of life history strategies may help predict the performance of microorganisms in nature by organizing the complexity of microbial communities into groups of organisms with similar strategies. Here, we tested the extent that one common application of life history theory, the copiotroph-oligotroph framework, could predict the relative population growth rate of bacterial taxa in soils from four different ecosystems. We measured the change of in situ relative growth rate to added glucose and ammonium using both 18 O–H 2 O and 13 C quantitative stable isotope probing to test whether bacterial taxa sorted into copiotrophic and oligotrophic groups. We saw considerable overlap in nutrient responses across most bacteria regardless of phyla, with many taxa growing slowly and few taxa that grew quickly. To define plausible life history boundaries based on in situ relative growth rates, we applied Gaussian mixture models to organisms’ joint 18 O– 13 C signatures and found that across experimental replicates, few taxa could consistently be assigned as copiotrophs, despite their potential for fast growth. When life history classifications were assigned based on average relative growth rate at varying taxonomic levels, finer resolutions (e.g., genus level) were significantly more effective in capturing changes in nutrient response than broad taxonomic resolution (e.g., phylum level). Our results demonstrate the difficulty in generalizing bacterial life history strategies to broad lineages, and even to single organisms across a range of soils and experimental conditions. We conclude that there is a continued need for the direct measurement of microbial communities in soil to advance ecologically realistic frameworks.

Soil microorganisms shape global element cycles in life and death. Living soil microorganisms are a major engine of terrestrial biogeochemistry, driving the turnover of soil organic matter — Earth’s largest terrestrial carbon pool and the primary source of plant nutrients. Their metabolic functions are influenced by ecological interactions with other soil microbial populations, soil fauna and plants, and the surrounding soil environment. Remnants of dead microbial cells serve as fuel for these biogeochemical engines because their chemical constituents persist as soil organic matter. This non-living microbial biomass accretes over time in soil, forming one of the largest pools of organic matter on the planet. In this Review, we discuss how the biogeochemical cycling of organic matter depends on both living and dead soil microorganisms, their functional traits, and their interactions with the soil matrix and other organisms. With recent omics advances, many of the traits that frame microbial population dynamics and their ecophysiological adaptations can be deciphered directly from assembled genomes or patterns of gene or protein expression. Thus, it is now possible to leverage a trait-based understanding of microbial life and death within improved biogeochemical models and to better predict ecosystem functioning under new climate regimes.Soil microorganisms shape global element cycles in life and death. In this Review, Sokol and colleagues explore how the biogeochemical cycling of organic matter depends on both living and dead soil microorganisms, their functional traits, and their interactions with the soil matrix and other organisms. They also discuss incorporating microbial life and death into trait-based models that predict soil biogeochemical dynamics.

Adults with Down syndrome (DS) are predisposed to Alzheimer's disease (AD) and reveal early amyloid beta (Aβ) pathology in the brain. Positron emission tomography (PET) provides an in vivo measure of Aβ throughout the AD continuum. Due to the high prevalence of AD in DS, there is need for longitudinal imaging studies of Aβ to better characterize the natural history of Aβ accumulation, which will aid in the staging of this population for clinical trials aimed at AD treatment and prevention. Adults with DS (N = 79; Mean age (SD) = 42.7 (7.28) years) underwent longitudinal [C-11]Pittsburgh compound B (PiB) PET. Global Aβ burden was quantified using the amyloid load metric (AβL). Modeled PiB images were generated from the longitudinal AβL data to visualize which regions are most susceptible to Aβ accumulation in DS. AβL change was evaluated across Aβ(-), Aβ-converter, and Aβ(+) groups to assess longitudinal Aβ trajectories during different stages of AD-pathology progression. AβL change values were used to identify Aβ-accumulators within the Aβ(-) group prior to reaching the Aβ(+) threshold (previously reported as 20 AβL) which would have resulted in an Aβ-converter classification. With knowledge of trajectories of Aβ(-) accumulators, a new cutoff of Aβ(+) was derived to better identify subthreshold Aβ accumulation in DS. Estimated sample sizes necessary to detect a 25% reduction in annual Aβ change with 80% power (alpha 0.01) were determined for different groups of Aβ-status. Modeled PiB images revealed the striatum, parietal cortex and precuneus as the regions with earliest detected Aβ accumulation in DS. The Aβ(-) group had a mean AβL change of 0.38 (0.58) AβL/year, while the Aβ-converter and Aβ(+) groups had change of 2.26 (0.66) and 3.16 (1.34) AβL/year, respectively. Within the Aβ(-) group, Aβ-accumulators showed no significant difference in AβL change values when compared to Aβ-converter and Aβ(+) groups. An Aβ(+) cutoff for subthreshold Aβ accumulation was derived as 13.3 AβL. The estimated sample size necessary to detect a 25% reduction in Aβ was 79 for Aβ(-) accumulators and 59 for the Aβ-converter/Aβ(+) group in DS. Longitudinal AβL changes were capable of distinguishing Aβ accumulators from non-accumulators in DS. Longitudinal imaging allowed for identification of subthreshold Aβ accumulation in DS during the earliest stages of AD-pathology progression. Detection of active Aβ deposition evidenced by subthreshold accumulation with longitudinal imaging can identify DS individuals at risk for AD development at an earlier stage.

Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images from the UK Biobank. We then conducted genome-wide association studies in 39,688 individuals, identifying 82 loci associated with ascending and 47 with descending thoracic aortic diameter, of which 14 loci overlapped. Transcriptome-wide analyses, rare-variant burden tests and human aortic single nucleus RNA sequencing prioritized genes including SVIL , which was strongly associated with descending aortic diameter. A polygenic score for ascending aortic diameter was associated with thoracic aortic aneurysm in 385,621 UK Biobank participants (hazard ratio = 1.43 per s.d., confidence interval 1.32–1.54, P = 3.3 × 10 −20 ). Our results illustrate the potential for rapidly defining quantitative traits with deep learning, an approach that can be broadly applied to biomedical images. Genome-wide association analyses identify variants associated with thoracic aortic diameter. A polygenic score for ascending aortic diameter was associated with a diagnosis of thoracic aortic aneurysm in independent samples.

INTRODUCTION Almost all individuals with Down syndrome (DS) will develop neuropathological features of Alzheimer's disease (AD). Understanding AD biomarker trajectories is necessary for DS‐specific clinical interventions and interpretation of drug‐related changes in the disease trajectory. METHODS A total of 177 adults with DS from the Alzheimer's Biomarker Consortium‐Down Syndrome (ABC‐DS) underwent positron emission tomography (PET) and MR imaging. Amyloid‐beta (Aβ) trajectories were modeled to provide individual‐level estimates of Aβ‐positive (A+) chronicity, which were compared against longitudinal tau change. RESULTS Elevated tau was observed in all NFT regions following A+ and longitudinal tau increased with respect to A+ chronicity. Tau increases in NFT regions I‐III was observed 0–2.5 years following A+. Nearly all A+ individuals had tau increases in the medial temporal lobe. DISCUSSION These findings highlight the rapid accumulation of amyloid and early onset of tau relative to amyloid in DS and provide a strategy for temporally characterizing AD neuropathology progression that is specific to the DS population and independent of chronological age. Highlights Longitudinal amyloid trajectories reveal rapid Aβ accumulation in Down syndrome NFT stage tau was strongly associated with A+ chronicity Early longitudinal tau increases were observed 2.5–5 years after reaching A+

To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.