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In 2015, the largest recorded harmful algal bloom (HAB) occurred in the Northeast Pacific, causing nearly 100 million dollars in damages to fisheries and killing many protected marine mammals. Dominated by the toxic diatom Pseudo-nitzschia australis, this bloom produced high levels of the neurotoxin domoic acid (DA). Through molecular and transcriptional characterization of 52 near-weekly phytoplankton net-tow samples collected at a bloom hotspot in Monterey Bay, California, we identified active transcription of known DA biosynthesis (dab) genes from the three identified toxigenic species, including P. australis as the primary origin of toxicity. Elevated expression of silicon transporters (sit1) during the bloom supports the previously hypothesized role of dissolved silica (Si) exhaustion in contributing to bloom physiology and toxicity. We find that coexpression of the dabA and sit1 genes serves as a robust predictor of DA one week in advance, potentially enabling the forecasting of DA-producing HABs. We additionally present evidence that low levels of iron could have colimited the diatom population along with low Si. Iron limitation represents an overlooked driver of both toxin production and ecological success of the low-iron-adapted Pseudo-nitzschia genus during the 2015 bloom, and increasing pervasiveness of iron limitation may fuel the escalating magnitude and frequency of toxic Pseudo-nitzschia blooms globally. Our results advance understanding of bloom physiology underlying toxin production, bloom prediction, and the impact of global change on toxic blooms.

BackgroundCurrent online prognostic prediction models for breast cancer, such as Adjuvant! Online and PREDICT, are based on specific populations. They have been well validated and widely used in the United States and Western Europe; however, several validation attempts in non-European countries have revealed suboptimal predictions. ObjectiveWe aimed to develop an advanced breast cancer prognosis model for disease progression, cancer-specific mortality, and all-cause mortality by integrating tumor, demographic, and treatment characteristics from a large breast cancer cohort in China. MethodsThis study was approved by the Clinical Test and Biomedical Ethics Committee of West China Hospital, Sichuan University on May 17, 2012. Data collection for this project was started in May 2017 and ended in March 2019. Data on 5293 women diagnosed with stage I to III invasive breast cancer between 2000 and 2013 were collected. Disease progression, cancer-specific mortality, all-cause mortality, and the likelihood of disease progression or death within a 5-year period were predicted. Extreme gradient boosting was used to develop the prediction model. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and the model was calibrated and compared with PREDICT. ResultsThe training, test, and validation sets comprised 3276 (499 progressions, 202 breast cancer-specific deaths, and 261 all-cause deaths within 5-year follow-up), 1405 (211 progressions, 94 breast cancer-specific deaths, and 129 all-cause deaths), and 612 (109 progressions, 33 breast cancer-specific deaths, and 37 all-cause deaths) women, respectively. The AUROC values for disease progression, cancer-specific mortality, and all-cause mortality were 0.76, 0.88, and 0.82 for training set; 0.79, 0.80, and 0.83 for the test set; and 0.79, 0.84, and 0.88 for the validation set, respectively. Calibration analysis demonstrated good agreement between predicted and observed events within 5 years. Comparable AUROC and calibration results were confirmed in different age, residence status, and receptor status subgroups. Compared with PREDICT, our model showed similar AUROC and improved calibration values. ConclusionsOur prognostic model exhibits high discrimination and good calibration. It may facilitate prognosis prediction and clinical decision making for patients with breast cancer in China.

BackgroundRisk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. ObjectiveThis study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. MethodsA dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. ResultsThe three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. ConclusionsThe novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.

PURPOSE: The OlympiA randomized phase III trial compared 1 year of olaparib (OL) or placebo (PL) as adjuvant therapy in patients with germline BRCA1/2, high-risk human epidermal growth factor receptor 2-negative early breast cancer after completing (neo)adjuvant chemotherapy ([N]ACT), surgery, and radiotherapy. The patient-reported outcome primary hypothesis was that OL-treated patients may experience greater fatigue during treatment. METHODS: Data were collected before random assignment, and at 6, 12, 18, and 24 months. The primary end point was fatigue, measured with the Functional Assessment of Chronic Illness Therapy-Fatigue scale. Secondary end points, assessed with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, Core 30 item, included nausea and vomiting (NV), diarrhea, and multiple functional domains. Scores were compared between treatment groups using mixed model for repeated measures. Two-sided P values

Infection, autoimmunity, and cancer are principal human health challenges of the 21st century. Often regarded as distinct ends of the immunological spectrum, recent studies hint at potential overlap between these diseases. For example, inflammation can be pathogenic in infection and autoimmunity. T resident memory (TRM) cells can be beneficial in infection and cancer. However, these findings are limited by size and scope; exact immunological factors shared across diseases remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune/post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation and increased humoral immunity and that they resemble TRM cells. Our results suggest NKG2A+ biases as a cross-disease factor of protection, supporting suggestions of immunological overlap between infection, autoimmunity, and cancer.

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1-42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer's Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1-42 and higher pTau181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures, but not cortical thickness measures, were more widely associated with Aβ1-42 than pTau181 and better distinguished Aβ+ from Aβ- participants than pTau+ from pTau- participants. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI metrics sensitive to early AD pathogenesis and microstructural damage may be better measures of subtle neurodegeneration in comparison to standard cortical thickness and help to elucidate mechanisms underlying cognitive decline.

Flexible graphite film (FGF), as a traditional interface heat dissipation material, has high anisotropy. It is a challenge to enhance both in-plane and through-plane thermal conductivity of FGF. For this reason, the effects of oxygen content, layer spacing, density and particle size on the in-plane and through-plane thermal conductivity of FGF were studied by both molecular simulation and experimental investigation. The simulation results indicate that the ways to improve the thermal conductivity of FGF include reducing oxygen content and layer spacing, increasing the density and matching the size of graphite sheets. The FGF prepared from room temperature exfoliated graphite (RTFGF) has a wide range of adjustable density (1.3–2.0 g/cm3) and thickness (50–400 μm). The thermal conductivity of the RTFGF is significantly improved after heat treatment owing to reduced oxygen content and layer spacing, which is consistent with the simulation results. Moreover, RTFGF with both high in-plane (518 W⋅m−1⋅K−1) and through-plane (7.2 W⋅m−1⋅K−1) thermal conductivity can be obtained by particle size matching of graphite.

The technological requirements of low-power and high-fidelity color displays have been instrumental in driving research into advanced coloration technologies. At the forefront of these developments is the implementation of dye-free approaches, which overcome previous constraints related to color resolution and fading. Resonant dielectric nanostructures have emerged as a promising paradigm, showing great potential for high efficiency, high color saturation, wide gamut palette, and image reproduction. However, they still face limitations related to color accuracy, purity, and simultaneous brightness tunability. Here, we demonstrate an all-dielectric metasurface empowered by photonic bound states in the continuum (BICs), which supports sharp resonances throughout the visible spectral range, ideally suited for producing a wide range of structural colors. The metasurface design consists of TiO2 ellipses with carefully controlled sizes and geometrical asymmetry, allowing versatile and on-demand variation of the brightness and hue of the output colors, respectively.

Infection, autoimmunity, and cancer are the principal human health challenges of the 21st century and major contributors to human death and disease. Often regarded as distinct ends of the immunological spectrum, recent studies have hinted there may be more overlap between these diseases than appears. For example, pathogenic inflammation has been demonstrated as conserved between infection and autoimmune settings. T resident memory (TRM) cells have been highlighted as beneficial for infection and cancer. However, these findings are limited by patient number and disease scope; exact immunological factors shared across disease remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune and post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation, increased humoral immunity, and resemble TRM cells. Our results suggest that an NKG2A+ bias is a pan-disease immunological factor of protection and thus supports recent suggestions that there is immunological overlap between infection, autoimmunity, and cancer. Our findings underscore the promotion of an NKG2A+ biased response as a putative therapeutic strategy.