Cancer-associated epithelial cell adhesion molecule (EpCAM; CD326) enables epidermal Langerhans cell motility and migration in vivo

After activation, Langerhans cells (LC), a distinct subpopulation of epidermis-resident dendritic cells, migrate from skin to lymph nodes where they regulate the magnitude and quality of immune responses initiated by epicutaneously applied antigens. Modulation of LC-keratinocyte adhesion is likely to be central to regulation of LC migration. LC express high levels of epithelial cell adhesion molecule (EpCAM; CD326), a cell-surface protein that is characteristic of some epithelia and many carcinomas and that has been implicated in intercellular adhesion and metastasis. To gain insight into EpCAM function in a physiologic context in vivo, we generated conditional knockout mice with EpCAM-deficient LC and characterized them. Epidermis from these mice contained increased numbers of LC with normal levels of MHC and costimulatory molecules and T-cell-stimulatory activity in vitro. Migration of EpCAM-deficient LC from skin explants was inhibited, but chemotaxis of dissociated LC was not. Correspondingly, the ability of contact allergen-stimulated, EpCAM-deficient LC to exit epidermis in vivo was delayed, and strikingly fewer hapten-bearing LC subsequently accumulated in lymph nodes. Attenuated migration of EpCAM-deficient LC resulted in enhanced contact hypersensitivity responses as previously described in LC-deficient mice. Intravital microscopy revealed reduced translocation and dendrite motility in EpCAM-deficient LC in vivo in contact allergen-treated mice. These results conclusively link EpCAM expression to LC motility/migration and LC migration to immune regulation. EpCAM appears to promote LC migration from epidermis by decreasing LC-keratinocyte adhesion and may modulate intercellular adhesion and cell movement within in epithelia during development and carcinogenesis in an analogous fashion

Aggregation of soil and climate input data can underestimate simulated biomass loss and nitrate leaching under climate change

Predicting areas of severe biomass loss and increased N leaching risk under climate change is critical for applying appropriate adaptation measures to support more sustainable agricultural systems. The frequency of annual severe biomass loss for winter wheat and its coincidence with an increase in N leaching in a temperate region in Germany was estimated including the error from using soil and climate input data at coarser spatial scales, using the soil-crop model CoupModel. We ran the model for a reference period (1980-2010) and used climate data predicted by four climate model(s) for the Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5. The annual median biomass estimations showed that for the period 2070-2100, under the RCP8.5 scenario, the entire region would suffer from severe biomass loss almost every year. Annual incidence of severe biomass loss and increased N leaching was predicted to increase from RCP4.5 to the 8.5 scenario. During 2070-2100 for RCP8.5, in more than half of the years an area of 95% of the region was projected to suffer from both severe biomass loss and increased N leaching. The SPEI3 predicted a range of 32 (P3 RCP4.5) to 55% (P3 RCP8.5) of the severe biomass loss episodes simulated in the climate change scenarios. The simulations predicted more severe biomass losses than by the SPEI index which indicates that soil water deficits are important in determining crop losses in future climate scenarios. There was a risk of overestimating the area where "no severe biomass loss + increased N leaching" occurred when using coarser aggregated input data. In contrast, underestimation of situations where "severe biomass loss + increased N leaching" occurred when using coarser aggregated input data. Larger annual differences in biomass estimations compared to the finest resolution of input data occurred when aggregating climate input data rather than soil data. The differences were even larger when aggregating both soil and climate input data. In half of the region, biomass could be erroneously estimated in a single year by more than 40% if using soil and climate coarser input data. The results suggest that a higher spatial resolution of especially climate input data would be needed to predict reliably annual estimates of severe biomass loss and N leaching under climate change scenarios

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images

Winter wheat yield prediction using convolutional neural networks from environmental and phenological data

Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phenology variables in 271 counties across Germany from 1999 to 2019. We proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional convolution operation to capture the time dependencies of environmental variables. We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results. Our findings suggested that nonlinear models such as the proposed CNN, Deep Neural Network (DNN), and XGBoost were more effective in understanding the relationship between the crop yield and input data compared to the linear models. Our proposed CNN model outperformed all other baseline models used for winter wheat yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher correlation coefficient than the best performing baseline across test data). We aggregated soil moisture and meteorological features at the weekly resolution to address the seasonality of the data. We also moved beyond prediction and interpreted the outputs of our proposed CNN model using SHAP and force plots which provided key insights in explaining the yield prediction results (importance of variables by time). We found DUL, wind speed at week ten, and radiation amount at week seven as the most critical features in winter wheat yield prediction

Detection of Cocaine Use with Wireless Electrocardiogram Sensors

In recent years, the ability to continuously monitor activities, health, and lifestyles of individuals using sensor technologies has reached unprecedented levels. Such ubiquitous physiological sensing has the potential to profoundly improve our understanding of human behavior, leading to more targeted treatments for a variety of disorders. The long terms goal of this work is development of novel computational tools to support the study of addiction in the context of cocaine use. The current paper takes the first step in this important direction by posing a simple, but crucial question: Can cocaine use be reliably detected using wearable on-body sensors and current machine learning algorithms? We select wireless ECG as the most promising sensing modality for cocaine use detection. The main contributions in this paper include the presentation of a novel clinical study of cocaine use in which a unique set of wireless ECG data were collected, the description of a computational pipeline for inferring morphological features from noisy wireless ECG waveforms, and the evaluation of cocaine use detection algorithms based on data-driven and knowledge-based feature representations. Our results show that cocaine use can be detected with AUC levels above 0.9 in both the within-subjects and between-subjects cases at the 32mg/70kg dosage level

Performance of Three Sorghum Cultivars under Excessive Rainfall and Waterlogged Conditions in the Sudano-Sahelian Zone of West Africa: A Case Study at the Climate-Smart Village of Cinzana in Mali

Recent climate analyses show trends for increasing precipitation variability with increasing precipitation sums in Mali. The increasing occurrence of temporary intra-seasonal droughts and waterlogging longer than a week demands climate-smart solutions. Research has focused on water deficits since the 1980s. However, besides droughts, waterlogging can restrict productivity of sensitive cash and staple crops as cotton and corn. The year 2019 offered the historically unique opportunity to monitor waterlogging effects with 1088 mm precipitation in the rural commune Cinzanawith an isohyet of 681 mm. Impacts of two extreme downpours on three sorghum cultivars were monitored in a farmers-field experiment with three replications. All sorghum cultivars performed well in 2019 with significantly higher grain and above ground biomass yields than in the reference year 2007, with well distributed rainfall in Cinzana. “Jakumbè” (CSM63E) produced significantly higher grain yields than the hybrid cultivar “PR3009B” bred for high harvest index. The local cultivar “Gnofing” selected by local farmers produced significantly higher above ground biomass. All cultivars tolerated without severe stress symptoms 20 days waterlogging and 72 h inundation. Further waterlogging resilience research of other crops and other sorghum cultivars is needed to strengthen food security in Mali with expected increasing precipitation variation in the future

epsilon'/epsilon at the NLO: 10 Years Later

During the last four years several parameters relevant for the analysis of the CP-violating ratio epsilon'/epsilon improved and/or changed significantly. In particular, the experimental value of epsilon'/epsilon and the strange quark mass decreased, the uncertainty in the CKM factor has been reduced, and for a value of the hadronic matrix element of the dominant electroweak penguin operator Q_8, some consensus has been reached among several theory groups. In view of this situation, ten years after the first analyses of epsilon'/epsilon at the next-to-leading order, we reconsider the analysis of epsilon'/epsilon within the SM and investigate what can be said about the hadronic Q_6 matrix element of the dominant QCD penguin operator on the basis of the present experimental value of epsilon'/epsilon and todays values of all other parameters. Employing a conservative range for the reduced electroweak penguin matrix element R_8=1.0+-0.2 from lattice QCD, and present values for all other input parameters, on the basis of the current world average for epsilon'/epsilon, we obtain the reduced hadronic matrix element of the dominant QCD penguin operator R_6=1.23+-0.16 implying _0^NDR(m_c) ~ -0.8 _2^NDR(m_c). We compare these results with those obtained in large-N_c approaches in which generally R_6 ~ R_8 and _0^NDR(m_c) is chirally suppressed relatively to _2^NDR(m_c). We present the correlation between R_6 and R_8 that is implied by the data on epsilon'/epsilon provided new physics contributions to epsilon'/epsilon can be neglected.Comment: 18 pages, 1 eps figure, version to appear in JHE

An upper bound on the Kaon B-parameter and Re(epsilon_K)

New precise data in B physics and theoretical developments in K physics lead us to reconsider the weak K^0-\bar{K}^0 transition from a large-N_c viewpoint, N_c being the number of colors. In this framework, we infer an upper limit on \hat{B}_K and the Kaon indirect CP violation.Comment: 11 pages, 4 figures. V2 : Minor corrections, final version accepted for publication in JHE