A Subset of Secreted Proteins in Ascites Can Predict Platinum-Free Interval in Ovarian Cancer

The time between the last cycle of chemotherapy and recurrence, the platinum-free interval (PFI), predicts overall survival in high-grade serous ovarian cancer (HGSOC). To identify secreted proteins associated with a shorter PFI, we utilized machine learning to predict the PFI from ascites composition. Ascites from stage III/IV HGSOC patients treated with neoadjuvant chemotherapy (NACT) or primary debulking surgery (PDS) were screened for secreted proteins and Lasso regression models were built to predict the PFI. Through regularization techniques, the number of analytes used in each model was reduced; to minimize overfitting, we utilized an analysis of model robustness. This resulted in models with 26 analytes and a root-mean-square error (RMSE) of 19 days for the NACT cohort and 16 analytes and an RMSE of 7 days for the PDS cohort. High concentrations of MMP-2 and EMMPRIN correlated with a shorter PFI in the NACT patients, whereas high concentrations of uPA Urokinase and MMP-3 correlated with a shorter PFI in PDS patients. Our results suggest that the analysis of ascites may be useful for outcome prediction and identified factors in the tumor microenvironment that may lead to worse outcomes. Our approach to tuning for model stability, rather than only model accuracy, may be applicable to other biomarker discovery tasks

Applications of Future NASA Decadal Missions for Observing Earths Land and Water Processes

No abstract availabl

Culturable Root Endophyte Communities are Shaped by Both Warming and Plant Host Identity in the Rocky Mountains, USA

Understanding the biogeographic patterns of root-associated fungi and their sensitivity to temperature may improve predictions of future changes in terrestrial biodiversity and associated ecosystem processes, but data are currently limited. Anticipating change will require combining observational data, which predict how climatic factors limit current species distributions, with direct manipulations of climate, which can isolate responses to specific climate variables. Root endophytes are common symbionts of plants, particularly in arctic and alpine environments, yet their responses to climate warming are not resolved. Here, we directly cultured endophytic fungi from roots collected along altitudinal gradients in replicated mountain watersheds and from a 27 y field warming experiment in the Rocky Mountains, USA, to improve understanding of climate impacts on fungal root endophytes. Fungal taxa that were common at high elevations declined most under climate warming, whereas low elevation dominants responded neutrally or increased with experimental warming. Altitudinal gradients in fungal communities were strongly specific to the plant host species. Specifically, Poa species had 25–60% greater fungal isolate abundance and 25–38% greater fungal diversity at high elevations than at low elevation sites. In contrast, Festuca thurberi had 64% lower fungal diversity on roots at high elevation than at low elevation. Our results help to improve understanding of the potential for climate change to alter plant-fungal interactions in mountain ecosystems

A Subset of Secreted Proteins in Ascites Can Predict Platinum-Free Interval in Ovarian Cancer

Simple SummaryIdentifying proteins that correlate with better or worse outcomes may help to identify new treatment approaches for advanced high-grade serous ovarian cancer. Here, we utilize a machine learning technique to correlate the levels of 58 secreted proteins in tumor ascites with the time to disease recurrence after chemotherapy, which is known clinically as the platinum-free interval. We identify several candidate proteins correlated to shorter or longer platinum-free intervals and describe model analysis methods that may be useful for other studies aiming to identify factors impacting patient outcomes. Future validation of these factors in a prospective cohort would confirm their clinical utility, whereas a study of the mechanisms that they impact may identify new therapies. The time between the last cycle of chemotherapy and recurrence, the platinum-free interval (PFI), predicts overall survival in high-grade serous ovarian cancer (HGSOC). To identify secreted proteins associated with a shorter PFI, we utilized machine learning to predict the PFI from ascites composition. Ascites from stage III/IV HGSOC patients treated with neoadjuvant chemotherapy (NACT) or primary debulking surgery (PDS) were screened for secreted proteins and Lasso regression models were built to predict the PFI. Through regularization techniques, the number of analytes used in each model was reduced; to minimize overfitting, we utilized an analysis of model robustness. This resulted in models with 26 analytes and a root-mean-square error (RMSE) of 19 days for the NACT cohort and 16 analytes and an RMSE of 7 days for the PDS cohort. High concentrations of MMP-2 and EMMPRIN correlated with a shorter PFI in the NACT patients, whereas high concentrations of uPA Urokinase and MMP-3 correlated with a shorter PFI in PDS patients. Our results suggest that the analysis of ascites may be useful for outcome prediction and identified factors in the tumor microenvironment that may lead to worse outcomes. Our approach to tuning for model stability, rather than only model accuracy, may be applicable to other biomarker discovery tasks.</p

RECOVER: An Automated Cloud-Based Decision Support System for Post-fire Rehabilitation Planning

RECOVER is a site-specific decision support system that automatically brings together in a single analysis environment the information necessary for post-fire rehabilitation decision-making. After a major wildfire, law requires that the federal land management agencies certify a comprehensive plan for public safety, burned area stabilization, resource protection, and site recovery. These burned area emergency response (BAER) plans are a crucial part of our national response to wildfire disasters and depend heavily on data acquired from a variety of sources. Final plans are due within 21 days of control of a major wildfire and become the guiding document for managing the activities and budgets for all subsequent remediation efforts. There are few instances in the federal government where plans of such wide-ranging scope and importance are assembled on such short notice and translated into action more quickly. RECOVER has been designed in close collaboration with our agency partners and directly addresses their high-priority decision-making requirements. In response to a fire detection event, RECOVER uses the rapid resource allocation capabilities of cloud computing to automatically collect Earth observational data, derived decision products, and historic biophysical data so that when the fire is contained, BAER teams will have a complete and ready-to-use RECOVER dataset and GIS analysis environment customized for the target wildfire. Initial studies suggest that RECOVER can transform this information-intensive process by reducing from days to a matter of minutes the time required to assemble and deliver crucial wildfire-related data

A Machine Learning Approach for Identifying Novel Cell Type–Specific Transcriptional Regulators of Myogenesis

Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA–based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression patterns

Using satellite remote sensing and household survey data to assess human health and nutrition response to environmental change

Climate change and degradation of ecosystem services functioning may threaten the ability of current agricultural systems to keep up with demand for adequate and inexpensive food and for clean water, waste disposal and other broader ecosystem services. Human health is likely to be affected by changes occurring across multiple geographic and time scales. Impacts range from increasing transmissibility and the range of vectorborne diseases, such as malaria and yellow fever, to undermining nutrition through deleterious impacts on food production and concomitant increases in food prices. This paper uses case studies to describe methods that make use of satellite remote sensing and Demographic and Health Survey data to better understand individual-level human health and nutrition outcomes. By bringing these diverse datasets together, the connection between environmental change and human health outcomes can be described through new research and analysis

A communal catalogue reveals Earth's multiscale microbial diversity

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity.Peer reviewe

A communal catalogue reveals Earth’s multiscale microbial diversity

Our growing awareness of the microbial world’s importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth’s microbial diversity

Harvesting Memory, Preserving Home: A Cookbook of the Painted Turtle Farm/Cosechando Memoria, Preservando el Hogar: Un Libro de Cocina de la Granja de la Tortuga Pintada

About this Project In the fall of 2018, 14 of the families and 32 students from two first-year seminars, Crossing Borders: Immigration, Identity, and Development and Immigrant Stories, worked together to create this cookbook. Families submitted their favorite dishes and then invited students to their homes to demonstrate the preparation. As they cooked and ate together, students recorded the steps to make the recipe and listened as connections between food, memory, family, migration, traditions, and religion emerged. Harvesting Memory, Preserving Home: A Cookbook of the Painted Turtle Farm is the product of this undertaking. In it, we offer the collection of recipes as well as a short story from each family, bring meaning to the food we eat, the places life brings us, and the memories we share. Sobre este Projecto En el otoño de 2018, 12 de las familias y 32 estudiantes de dos clases, Cruzando Fronteras: Immigración, Identidad, y Desarrollo y Las Historias Immigrantes trabajaron juntos para cear este libro de cocina. Las familias escogieron platos con una importancia o una memoria especial para preparar en sus casas con los estudiantes. Durante el proceso de concinar y comer juntos, los estudiantes anotaron los pasos para preparar la receta. Ellos escucharon las conexiones entre la comida, la memoria, la familia, la migración, las tradiciones, y la religión. Cosechando Memoria, Preservando el Hogar: Un Libro de Cocina de la Granja de la Tortuga Pintada es el fruto de este proyecto. Ofrecemos la collección de recetas además de una historia breve de cada familia, para dar significado a los alimentos que comemos, los lugares donde la vida nos trae, y las memorias que nos compartimos