A Multidisciplinary Investigation of a Polycythemia Vera Cancer Cluster of Unknown Origin

Cancer cluster investigations rarely receive significant public health resource allocations due to numerous inherent challenges and the limited success of past efforts. In 2008, a cluster of polycythemia vera, a rare blood cancer with unknown etiology, was identified in northeast Pennsylvania. A multidisciplinary group of federal and state agencies, academic institutions, and local healthcare providers subsequently developed a multifaceted research portfolio designed to better understand the cause of the cluster. This research agenda represents a unique and important opportunity to demonstrate that cancer cluster investigations can produce desirable public health and scientific outcomes when necessary resources are available

Creating Patient-Specific Neural Cells for the In Vitro Study of Brain Disorders

Summary As a group, we met to discuss the current challenges for creating meaningful patient-specific in vitro models to study brain disorders. Although the convergence of findings between laboratories and patient cohorts provided us confidence and optimism that hiPSC-based platforms will inform future drug discovery efforts, a number of critical technical challenges remain. This opinion piece outlines our collective views on the current state of hiPSC-based disease modeling and discusses what we see to be the critical objectives that must be addressed collectively as a field

Creating Patient-Specific Neural Cells for the In Vitro Study of Brain Disorders.

As a group, we met to discuss the current challenges for creating meaningful patient-specific in vitro models to study brain disorders. Although the convergence of findings between laboratories and patient cohorts provided us confidence and optimism that hiPSC-based platforms will inform future drug discovery efforts, a number of critical technical challenges remain. This opinion piece outlines our collective views on the current state of hiPSC-based disease modeling and discusses what we see to be the critical objectives that must be addressed collectively as a field

Creating Patient-Specific Neural Cells for the In Vitro Study of Brain Disorders

As a group, we met to discuss the current challenges for creating meaningful patient-specific in vitro models to study brain disorders. Although the convergence of findings between laboratories and patient cohorts provided us confidence and optimism that hiPSC-based platforms will inform future drug discovery efforts, a number of critical technical challenges remain. This opinion piece outlines our collective views on the current state of hiPSC-based disease modeling and discusses what we see to be the critical objectives that must be addressed collectively as a field.status: publishe

A Multidisciplinary Investigation of a Polycythemia Vera Cancer Cluster of Unknown Origin

Cancer cluster investigations rarely receive significant public health resource allocations due to numerous inherent challenges and the limited success of past efforts. In 2008, a cluster of polycythemia vera, a rare blood cancer with unknown etiology, was identified in northeast Pennsylvania. A multidisciplinary group of federal and state agencies, academic institutions, and local healthcare providers subsequently developed a multifaceted research portfolio designed to better understand the cause of the cluster. This research agenda represents a unique and important opportunity to demonstrate that cancer cluster investigations can produce desirable public health and scientific outcomes when necessary resources are available

A quixotic view of spatial bias in modelling the distribution of species and their diversity

Ecological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias; this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them. Our major goal is not to propose methods to remove spatial bias from the modelling procedure, which would be impossible without proper knowledge of all the processes generating it, but rather to propose alternatives to explore and handle it. In particular, we propose and describe three main strategies that may provide a fair account of spatial bias, namely: (i) how to represent spatial bias; (ii) how to simulate null models based on virtual species for testing biogeographical and species distribution hypotheses; and (iii) how to make use of spatial bias - in particular related to sampling effort - as a leverage instead of a hindrance in species distribution modelling. We link these strategies with good practice in accounting for spatial bias in species distribution modelling