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Statin drugs lower blood cholesterol levels for cardiovascular disease prevention. Women are more likely than men to experience adverse statin effects, particularly new-onset diabetes (NOD) and muscle weakness. Here we find that impaired glucose homeostasis and muscle weakness in statin-treated female mice are associated with reduced levels of the omega-3 fatty acid, docosahexaenoic acid (DHA), impaired redox tone, and reduced mitochondrial respiration. Statin adverse effects are prevented in females by administering fish oil as a source of DHA, by reducing dosage of the X chromosome or the Kdm5c gene, which escapes X chromosome inactivation and is normally expressed at higher levels in females than males. As seen in female mice, we find that women experience more severe reductions than men in DHA levels after statin administration, and that DHA levels are inversely correlated with glucose levels. Furthermore, induced pluripotent stem cells from women who developed NOD exhibit impaired mitochondrial function when treated with statin, whereas cells from men do not. These studies identify X chromosome dosage as a genetic risk factor for statin adverse effects and suggest DHA supplementation as a preventive co-therapy.

Quantifying the temperature sensitivity of methane (CH4) production is crucial for predicting how wetland ecosystems will respond to climate warming. Typically, the temperature sensitivity (often quantified as a Q10 value) is derived from laboratory incubation studies and then used in biogeochemical models. However, studies report wide variation in incubation-inferred Q10 values, with a large portion of this variation remaining unexplained. Here we applied observations in a thawing permafrost peatland (Stordalen Mire) and a well-tested process-rich model (ecosys) to interpret incubation observations and investigate controls on inferred CH4 production temperature sensitivity. We developed a field-storage-incubation modeling approach to mimic the full incubation sequence, including field sampling at a particular time in the growing season, refrigerated storage, and laboratory incubation, followed by model evaluation. We found that CH4 production rates during incubation are regulated by substrate availability and active microbial biomass of key microbial functional groups, which are affected by soil storage duration and temperature. Seasonal variation in substrate availability and active microbial biomass of key microbial functional groups led to strong time-of-sampling impacts on CH4 production. CH4 production is higher with less perturbation post-sampling, i.e. shorter storage duration and lower storage temperature. We found a wide range of inferred Q10 values (1.2-3.5), which we attribute to incubation temperatures, incubation duration, storage duration, and sampling time. We also show that Q10 values of CH4 production are controlled by interacting biological, biochemical, and physical processes, which cause the inferred Q10 values to differ substantially from those of the component processes. Terrestrial ecosystem models that use a constant Q10 value to represent temperature responses may therefore predict biased soil carbon cycling under future climate scenarios.

Managing patients with kidney allografts largely depends on biopsy diagnosis which is based on semiquantitative assessments of rejection features and extent of acute and chronic changes within the renal parenchyma. Current methods lack reproducibility while digital image data-driven computational models enable comprehensive and quantitative assays. In this study we aimed to develop a computational method for automated assessment of histopathology transformations within the tubulointerstitial compartment of the renal cortex. Whole slide images of modified Picrosirius red-stained biopsy slides were used for the training (n = 852) and both internal (n = 172) and external (n = 94) tests datasets. The pipeline utilizes deep learning segmentations of renal tubules, interstitium, and peritubular capillaries from which morphometry features were extracted. Seven indicators were selected for exploring the intrinsic spatial interactions within the tubulointerstitial compartment. A principal component analysis revealed two independent factors which can be interpreted as representing chronic and acute tubulointerstitial injury. A K-means clustering classified biopsies according to potential phenotypes of combined acute and chronic transformations of various degrees. We conclude that multivariate analyses of tubulointerstitial morphometry transformations enable extraction of and quantification of acute and chronic components of injury. The method is developed for renal allograft biopsies; however, the principle can be applied more broadly for kidney pathology assessment.

Kidney complications can occur due to infective endocarditis, one of which is glomerulonephritis. Most often, an immune complex or complement-mediated glomerulonephritis is seen on kidney biopsy. In a minor subset of cases, pauci-immune glomerulonephritis may be present. Most often, such patients will demonstrate the presence of antineutrophil cytoplasmic antibodies (ANCA) on serologic testing. A growing number of cases of ANCA-associated glomerulonephritis due to Bartonella endocarditis have been reported. This type of endocarditis can present diagnostic difficulties given that these patients are often culture negative. Herein, we report a challenging case of ANCA-negative pauci-immune glomerulonephritis showing florid crescents on biopsy that was associated with Bartonella endocarditis.

BACKGROUND: Pathology reports contain key information about the patients diagnosis as well as important gross and microscopic findings. These information-rich clinical reports offer an invaluable resource for clinical studies, but data extraction and analysis from such unstructured texts is often manual and tedious. While neural information retrieval systems (typically implemented as deep learning methods for natural language processing) are automatic and flexible, they typically require a large domain-specific text corpus for training, making them infeasible for many medical subdomains. Thus, an automated data extraction method for pathology reports that does not require a large training corpus would be of significant value and utility. OBJECTIVE: To develop a language model-based neural information retrieval system that can be trained on small datasets and validate it by training it on renal transplant-pathology reports to extract relevant information for two predefined questions: (1) What kind of rejection does the patient show?; (2) What is the grade of interstitial fibrosis and tubular atrophy (IFTA)? METHODS: Kidney BERT was developed by pre-training Clinical BERT on 3.4K renal transplant pathology reports and 1.5M words. Then, exKidneyBERT was developed by extending Clinical BERTs tokenizer with six technical keywords and repeating the pre-training procedure. This extended the models vocabulary. All three models were fine-tuned with information retrieval heads. RESULTS: The model with extended vocabulary, exKidneyBERT, outperformed Clinical BERT and Kidney BERT in both questions. For rejection, exKidneyBERT achieved an 83.3% overlap ratio for antibody-mediated rejection (ABMR) and 79.2% for T-cell mediated rejection (TCMR). For IFTA, exKidneyBERT had a 95.8% exact match rate. CONCLUSION: ExKidneyBERT is a high-performing model for extracting information from renal pathology reports. Additional pre-training of BERT language models on specialized small domains does not necessarily improve performance. Extending the BERT tokenizers vocabulary library is essential for specialized domains to improve performance, especially when pre-training on small corpora.

KEY POINTS: The authors leverage the unique benefits of panoptic segmentation to perform the largest ever quantitation of reference kidney morphometry. Kidney features vary with age and sex; and glomeruli size may intricately link to creatinine, defying prior notions. BACKGROUND: Reference histomorphometric data of healthy human kidneys are largely lacking because of laborious quantitation requirements. Correlating histomorphometric features with clinical parameters through machine learning approaches can provide valuable information about natural population variance. To this end, we leveraged deep learning (DL), computational image analysis, and feature analysis to associate the relationship of histomorphometry with patient age, sex, serum creatinine (SCr), and eGFR in a multinational set of reference kidney tissue sections. METHODS: A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in the digitized images of 79 periodic acid–Schiff-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were quantified from the segmented classes. Regression analysis aided in determining the association of histomorphometric parameters with age, sex, SCr, and eGFR. RESULTS: Our DL model achieved high segmentation performance for all test compartments. The size and density of glomeruli, tubules, and arteries/arterioles varied significantly among healthy humans, with potentially large differences between geographically diverse patients. Glomerular size was significantly correlated with SCr and eGFR. Slight, albeit significant, differences in renal vasculature were observed between sexes. Glomerulosclerosis percentage increased, and cortical density of arteries/arterioles decreased, as a function of increasing age. CONCLUSIONS: Using DL, we automated precise measurements of kidney histomorphometric features. In the reference kidney tissue, several histomorphometric features demonstrated significant correlation to patient demographics, SCr, and eGFR. DL tools can increase the efficiency and rigor of histomorphometric analysis.

Abstract Chest X-rays (CXRs) are primarily used to detect lung lesions. While the abdominal portion of CXRs can sometimes reveal critical conditions, research in this area is limited. To address this, we introduce a two-stage architecture that separates the abdominal region from the CXR and detects abdominal lesions using a specialized dataset. We compared the performance of our method on whole CXRs versus isolated abdominal regions. First, we created masks for the right upper quadrant (RUQ), left upper quadrant (LUQ), and upper abdomen (ABD) regions and trained corresponding segmentation models for each area. For detecting abdominal lesions, we curated a dataset of 5,996 images, categorized into 19 classes based on anatomical locations, air patterns, and levels of stomach or bowel dilation. The detection process was initially conducted on the original images, followed by the three regional areas, RUQ, LUQ, and ABD, extracted by the segmentation models. The results showed that the detection model trained on the entire ABD region achieved the highest accuracy, followed closely by the RUQ and LUQ models. In contrast, the CXR model had the lowest accuracy. This study highlights that the two-stage architecture effectively manages distinct segmentation and detection tasks in CXRs, offering a promising avenue for more accurate diagnoses. It also suggests that an optimal ratio between the sizes of the target lesions and the input images may exist.

Process-based land surface models are important tools for estimating global wetland methane (CH4) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site-level patterns of freshwater wetland CH4 fluxes (FCH4) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model-observation disagreements are mainly at multi-day time scales (32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales

Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH4 y−1) and overlaps with top-down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).

Background: Real-time surveillance of COVID-19 in large-scale community outbreaks presents challenges. Simple counts of the daily confirmed cases can be misleading due to constraints from bottlenecks in access to care or laboratory testing. This study aimed to investigate the role of the SARS-CoV-2 antigen rapid diagnostic test (Ag-RDT) in addressing these challenges for real-time COVID-19 surveillance. Methods: This study included the results of 86,933 SARS-CoV-2 Ag-RDT and real-time reverse transcription polymerase chain reaction (RT-PCR) tests. These were conducted at four community testing stations within the Taipei metropolitan area during a community COVID-19 outbreak spanning from May 17, 2021, to August 9, 2021. We examined the correlation between the positive rates of Ag-RDT tests and the epidemic curve of laboratory-confirmed COVID-19 cases by onset date to examine its role in real-time surveillance. Results: During the 85-day study period, the trend of Ag-RDT test positive rates paralleled that of the epidemic curve. The correlation between the Ag-RDT positive rate and the number of cases (Pearson correlation coefficient: 0.968) is comparable to that of the RT-PCR positive rate (Pearson correlation coefficient: 0.964). The Ag-RDT positive rate exhibited a more advanced leading trend, with Ag-RDT leading by 3 days in comparison to the 2-day lead for RT-PCR. Conclusion: The positive rate of SARS-CoV-2 Ag-RDT tests at community testing stations serves as a good surrogate for assessing virus activity within the community and a useful tool for real-time COVID-19 surveillance. It is a robust indicator of the outbreak trend and near-term numbers of cases. This finding may facilitate the management of subsequent outbreaks of emerging infectious diseases.

In order to enable a multi-energy coupling integrated energy system (IES) to meet the needs of load diversity in low-carbon economic operation, a bi-level optimal configuration method for low-carbon economic operation of multi-energy coupling IES in different charging modes of electric vehicles (EVs) is proposed. First, an IES including cold-thermal-electric-gas coupling is established. Then, in the day-to-day operation stage, factors such as hierarchical carbon trading mechanism and different charging modes of EVs are considered to achieve the lowest daily scheduling cost. In the configuration planning stage, based on the daily operation cost, the equipment capacity is configured with the lowest equipment investment cost and annual operation cost. Finally, Cplex is used to solve the above two-stage objective functions and obtain the optimal configuration scheme and scheduling results through mutual iteration. The results show that the charging method considering the remaining charge of EVs and carbon trading mechanism can significantly reduce carbon emissions and operating costs of the system. The proposed configuration approach can well realize low-carbon economic operation of the multi-energy coupling IES.

The 2023 China Health Report on Spine Degeneration noted a significant increase in lumbar surgery among patients under 35 years old in recent years, indicating a trend towards younger onset of cervical and lumbar diseases. Lumbar intervertebral disc herniation has become a major concern, making the study of disc degeneration pathogenesis and treatment methods clinically significant. At present, human intervertebral disc diseases are primarily diagnosed through imaging due to the challenges of obtaining tissue samples from the spine. Therefore, experimental animals have emerged as alternative research subjects because they are cost-effective, have short experimental cycles, and are easily accessible. Given the structural and physiological differences between human and other animal intervertebral discs, comparing their anatomy and histological characteristics forms the foundation of research into human disc degeneration. The purpose of this paper is to collect and review relevant studies on anatomical and histological structures of intervertebral discs in different animals and conduct a comparative analysis from four aspects, namely, intervertebral disc height, lumbar disc geometry, lumbar disc cartilaginous endplate characteristics, and extracellular matrix components. The results show that humans, kangaroos, sheep, pigs, and rats exhibit similar relative heights between the sixth and seventh cervical vertebrae. Mice possess lumbar disc geometries most akin to humans. Compared to other animals, humans have the thickest cartilaginous endplates and the lowest cell densities. The collagen within the fibrous annulus differs most notably in pigs compared to humans, while water content in the nucleus pulposus is consistent across pigs, sheep, rabbits, rats, and humans. Additionally, this paper describes the commonalities and discrepancies in disc degeneration manifestations between humans and animals, and summarizes modeling methods for disc degeneration in different experimental animals. Ultimately, the aims of this paper is to provide fundamental data for selecting suitable experimental animal models for the study of intervertebral disc degeneration.

Abstract Glutamine synthetase (GS) is vital in maintaining ammonia and glutamate (Glu) homeostasis in living organisms. However, the natural enzyme relies on adenosine triphosphate (ATP) to activate Glu, resulting in impaired GS function during ATP-deficient neurotoxic events. To date, no reports demonstrate using artificial nanostructures to mimic GS function. In this study, we synthesize aggregation-induced emission active polyP-Mn nanosheets (STPE-PMNSs) based on end-labeled polyphosphate (polyP), exhibiting remarkable GS-like activity independent of ATP presence. Further investigation reveals polyP in STPE-PMNSs serves as phosphate source to activate Glu at low ATP levels. This self-feeding mechanism offers a significant advantage in regulating Glu homeostasis at reduced ATP levels in nerve cells during excitotoxic conditions. STPE-PMNSs can effectively promote the conversion of Glu to glutamine (Gln) in excitatory neurotoxic human neuroblastoma cells (SH-SY5Y) and alleviate Glu-induced neurotoxicity. Additionally, the fluorescence signal of nanosheets enables precise monitoring of the subcellular distribution of STPE-PMNSs. More importantly, the intracellular fluorescence signal is enhanced in a conversion-responsive manner, allowing real-time tracking of reaction progression. This study presents a self-sustaining strategy to address GS functional impairment caused by ATP deficiency in nerve cells during neurotoxic events. Furthermore, it offers a fresh perspective on the potential biological applications of polyP-based nanostructures.

Abstract Managing patients with kidney allografts largely depends on biopsy diagnosis which is based on semiquantitative assessments of rejection features and extent of acute and chronic changes within the renal parenchyma. Current methods lack reproducibility while digital image data-driven computational models enable comprehensive and quantitative assays. In this study we aimed to develop a computational method for automated assessment of histopathology transformations within the tubulointerstitial compartment of the renal cortex. Whole slide images of modified Picrosirius red-stained biopsy slides were used for the training (n = 852) and both internal (n = 172) and external (n = 94) tests datasets. The pipeline utilizes deep learning segmentations of renal tubules, interstitium, and peritubular capillaries from which morphometry features were extracted. Seven indicators were selected for exploring the intrinsic spatial interactions within the tubulointerstitial compartment. A principal component analysis revealed two independent factors which can be interpreted as representing chronic and acute tubulointerstitial injury. A K-means clustering classified biopsies according to potential phenotypes of combined acute and chronic transformations of various degrees. We conclude that multivariate analyses of tubulointerstitial morphometry transformations enable extraction of and quantification of acute and chronic components of injury. The method is developed for renal allograft biopsies; however, the principle can be applied more broadly for kidney pathology assessment.

Reference histomorphometric data of healthy human kidneys are lacking due to laborious quantitation requirements. We leveraged deep learning to investigate the relationship of histomorphometry with patient age, sex, and serum creatinine in a multinational set of reference kidney tissue sections. A panoptic segmentation neural network was developed and used to segment viable and sclerotic glomeruli, cortical and medullary interstitia, tubules, and arteries/arterioles in digitized images of 79 periodic acid-Schiff (PAS)-stained human nephrectomy sections showing minimal pathologic changes. Simple morphometrics (e.g., area, radius, density) were measured from the segmented classes. Regression analysis was used to determine the relationship of histomorphometric parameters with age, sex, and serum creatinine. The model achieved high segmentation performance for all test compartments. We found that the size and density of nephrons, arteries/arterioles, and the baseline level of interstitium vary significantly among healthy humans, with potentially large differences between subjects from different geographic locations. Nephron size in any region of the kidney was significantly dependent on patient creatinine. Slight differences in renal vasculature and interstitium were observed between sexes. Finally, glomerulosclerosis percentage increased and cortical density of arteries/arterioles decreased as a function of age. We show that precise measurements of kidney histomorphometric parameters can be automated. Even in reference kidney tissue sections with minimal pathologic changes, several histomorphometric parameters demonstrated significant correlation to patient demographics and serum creatinine. These robust tools support the feasibility of deep learning to increase efficiency and rigor in histomorphometric analysis and pave the way for future large-scale studies.

Objectives: The link between aging and pulmonary function decline is well-established, but the underlying mechanisms have yet to be fully revealed. Serum follistatin, a myokine implicated in muscle degeneration, may play a role in age-related pulmonary changes. This study aims to investigate the relationship between serum follistatin levels and pulmonary function decline in community-dwelling older adults, and evaluate their combined association with all-cause mortality. Research design and methods: This longitudinal cohort study utilized data from 751 participants aged ≥50 years in the I-Lan Longitudinal Aging Study between 2018−2019. Serum follistatin levels, spirometry results, demographic and clinical data were retrieved. Participants were stratified based on their follistatin levels. Survival curves and group comparisons based on follistatin levels and decline in peak expiratory flow (PEF) using Kaplan-Meier analysis and log-rank tests. Multivariate Cox proportional hazards models were further used to identify independent predictors of all-cause mortality during the 52-month follow-up. Results: Elevated follistatin levels significantly correlated with worse pulmonary function, particularly decreased PEF (p = 0.030). Kaplan–Meier analysis revealed the combination of elevated follistatin levels and decreased PEF was associated with increased risk of all-cause mortality (Log-rank p = 0.023). Cox proportional hazards models further identified that concurrent presence of higher follistatin levels and decreased PEF predicted higher risk of all-cause mortality (adjusted HR 3.58, 95% CI: 1.22–10.53, p = 0.020). Conclusion: Higher serum follistatin levels correlate with decreased pulmonary function, specifically PEF decline, in community-dwelling older adults. Furthermore, the coexistence of elevated follistatin levels and decreased PEF was associated with risk of all-cause mortality. Follistatin may serve as a biomarker for pulmonary aging and related adverse outcomes.

Plant stomatal conductance regulates photosynthesis and transpiration. This physiological link affects ecosystem responses to microclimate and harmonizes carbon, energy, and water exchanges between the biosphere and atmosphere. The relationship between water losses via transpiration and carbon gains via photosynthesis can be quantified by plant water-use efficiency (WUE). While leaf- and ecosystem-scale observations both suggest rising WUE in recent decades, WUE trends inferred from the ecosystem scale are much larger than those inferred from the leaf scale or implied by theory. The unexpectedly large ecosystem-scale WUE trends complicate interpretation of ecophysiological responses to changing environmental conditions. Here, we analyze ecosystem-scale WUE inferred from 40 FLUXNET sites, each with at least 10 years of measurements. Our results demonstrate that observed ecosystem-scale WUE trends are more sensitive to hourly weather conditions than longer-term changes in atmospheric carbon dioxide or vapor pressure deficit. Our analysis shows that Earth System Models participating in CMIP6 did not capture the observed WUE sensitivity to inter-site variability and microclimatic conditions. Collectively, our findings suggest that ecosystem-scale WUE trends reflect water-carbon interactions across multiple temporal scales, and disentangling factors contributing to emergent ecosystem responses is needed to infer ecophysiological relationships and model structures from observations.

Objectives To evaluate whether nephrotic syndrome (NS) and further corticosteroid (CS) use increase the risk of osteoporosis in Asian population during the period January 2000–December 2010.Design Nationwide population-based retrospective cohort study.Setting All healthcare facilities in Taiwan.Participants A total of 28 772 individuals were enrolled.Interventions 26 614 individuals with newly diagnosed NS between 2000 and 2010 were identified and included in out study. 26 614 individuals with no NS diagnosis prior to the index date were age matched as controls. Diagnosis of osteoporosis prior to the diagnosis of NS or the same index date was identified, age, sex and NS-associated comorbidities were adjusted.Primary outcome measure To identify risk differences in developing osteoporosis among patients with a medical history of NS.Results After adjusting for covariates, osteoporosis risk was found to be 3.279 times greater in the NS cohort than in the non-NS cohort, when measured over 11 years after NS diagnosis. Stratification revealed that age older than 18 years, congestive heart failure, hyperlipidaemia, chronic kidney disease, liver cirrhosis and NS-related disease including diabetes mellitus, hepatitis B infection, hepatitis C infection, lymphoma and hypothyroidism, increased the risk of osteoporosis in the NS cohort, compared with the non-NS cohort. Additionally, osteoporosis risk was significantly higher in NS patients with CS use (adjusted HR (aHR)=3.397). The risk of osteoporosis in NS patients was positively associated with risk of hip and vertebral fracture (aHR=2.130 and 2.268, respectively). A significant association exists between NS and subsequent risk for osteoporosis.Conclusion NS patients, particularly those treated with CS, should be evaluated for subsequent risk of osteoporosis.

Ocean currents play a role in both facilitating and impeding connectivity of marine organisms’ populations, serving as key drivers and potential barriers. The emergence of Taiwan around five million years ago (Mya) impacted the Kuroshio Current which later shaped the distribution of marine organisms. In this study, we examined how the Kuroshio Current influenced the distribution and population divergence of the marine red alga Dichotomaria elegans (Galaxauraceae, Nemaliales). Through phylogenetic analysis, species delimitation, and morphological comparison of Taiwanese species, we determined its phylogenetic position relative to other species within the D. marginata complex of the genus Dichotomaria. We uncovered the hidden cryptic diversity within the D. marginata complex in Taiwan and proposed the new combinations D. elegans and D. latifolia based on Galaxaura elegans and G. latifolia. Genetic analysis based on three loci (mitochondrial cox2-3 and cox1, and plastid rbcL) from 32 locations across the Philippines, Taiwan, and Japan revealed three allopatric lineages within D. elegans. The first lineage is restricted to the northern Philippines and southern Taiwan, while the second has a broader latitudinal range along the Kuroshio Current. The third lineage is found in southeast Luzon, Philippines, close to the bifurcation of the North Equatorial Current, near the start of the Kuroshio Current. The divergence of the first two lineages during the mid-Pliocene (~3.7 Mya) coincided with the emergence of Taiwan, with the current dynamics accentuating their separation. The Kuroshio Current exhibited a dual impact on the biogeography of D. elegans: promoting the dispersal of the second lineage towards higher latitudes and preventing the first lineage from moving northward. Principal component analysis showed that habitat conditions and morphologies differed between the two D. elegans lineages. This study provides insights into the cryptic diversity of Dichotomaria in Taiwan, as well as the genetic divergence, biogeographical patterns, and ecological associations of D. elegans, highlighting the interplay of island formation and currents shaping the diversification of seaweeds in the Northwest Pacific Ocean.

Permafrost thaw is a major potential feedback source to climate change as it can drive the increased release of greenhouse gases carbon dioxide (CO2 ) and methane (CH4 ). This carbon release from the decomposition of thawing soil organic material can be mitigated by increased net primary productivity (NPP) caused by warming, increasing atmospheric CO2 , and plant community transition. However, the net effect on C storage also depends on how these plant community changes alter plant litter quantity, quality, and decomposition rates. Predicting decomposition rates based on litter quality remains challenging, but a promising new way forward is to incorporate measures of the energetic favorability to soil microbes of plant biomass decomposition. We asked how the variation in one such measure, the nominal oxidation state of carbon (NOSC), interacts with changing quantities of plant material inputs to influence the net C balance of a thawing permafrost peatland. We found: (1) Plant productivity (NPP) increased post-thaw, but instead of contributing to increased standing biomass, it increased plant biomass turnover via increased litter inputs to soil; (2) Plant litter thermodynamic favorability (NOSC) and decomposition rate both increased post-thaw, despite limited changes in bulk C:N ratios; (3) these increases caused the higher NPP to cycle more rapidly through both plants and soil, contributing to higher CO2 and CH4  fluxes from decomposition. Thus, the increased C-storage expected from higher productivity was limited and the high global warming potential of CH4 contributed a net positive warming effect. Although post-thaw peatlands are currently C sinks due to high NPP offsetting high CO2 release, this status is very sensitive to the plant community's litter input rate and quality. Integration of novel bioavailability metrics based on litter chemistry, including NOSC, into studies of ecosystem dynamics, is needed to improve the understanding of controls on arctic C stocks under continued ecosystem transition.

The largest bottleneck to the development of convolutional neural network (CNN) models in the computational pathology domain is the collection and curation of diverse training datasets. Training CNNs requires large cohorts of image data, and model generalizability is dependent on training data heterogeneity. Including data from multiple centers enhances the generalizability of CNN-based models, but this is hindered by the logistical challenges of sharing medical data. In this paper, we explore the feasibility of training our recently developed cloud-based segmentation tool (Histo-Cloud) using federated learning. Using a dataset of renal tissue biopsies we show that federated training to segment interstitial fibrosis and tubular atrophy (IFTA) using datasets from three institutions is not found to be different from a training by pooling the data on one server when tested on a fourth (holdout) institution's data. Further, training a model to segment glomeruli for a federated dataset (split by staining) demonstrates similar performance.

BackgroundImage-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces.MethodsWe have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis.ResultsBy segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models.ConclusionsHisto-Cloud is open source, accessible over the internet, and adaptable for segmentation of any histological structure regardless of stain.

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan-Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.