Bringing forecasting into the future: Using Google to predict visitation in U.S. national parks.

In recent years, visitation to U.S. National Parks has been increasing, with the majority of this increase occurring in a subset of parks. As a result, managers in these parks must respond quickly to increasing visitor-related challenges. Improved visitation forecasting would allow managers to more proactively plan for such increases. In this study, we leverage internet search data that is freely available through Google Trends to create a forecasting model. We compare this Google Trends model to a traditional autoregressive forecasting model. Overall, our Google Trends model accurately predicted 97% of the total visitation variation to all parks one year in advance from 2013 to 2017 and outperformed the autoregressive model by all metrics. While our Google Trends model performs better overall, this was not the case for each park unit individually; the accuracy of this model varied significantly from park to park. We hypothesized that park attributes related to trip planning would correlate with the accuracy of our Google Trends model, but none of the variables tested produced overly compelling results. Future research can continue exploring the utility of Google Trends to forecast visitor use in protected areas, or use methods demonstrated in this paper to explore alternative data sources to improve visitation forecasting in U.S. National Parks.

Dermal infiltrates of cutaneous T-cell lymphomas with epidermotropism but not other cutaneous lymphomas are abundant with langerin⁺ dendritic cells.

BACKGROUND: The Langerhans cell (LC) hypothesis suggests that cutaneous T-cell lymphomas (CTCL) are diseases of chronic T-cell stimulation by LC-mediated antigen presentation. OBJECTIVE: To investigate a broad panel of CTCL and cutaneous B-cell lymphomas (CBCL) for the spatial association of langerin(+) dendritic cells (DC) with T and B cells in the skin, respectively. METHODS: Fifty-five specimens of CTCL and 10 of CBCL were double-stained with monoclonal antibodies against langerin and CD3 or CD20, respectively, and evaluated by confocal laser scan microscopy. RESULTS: Dermal infiltrates in mycosis fungoides (n = 38), primary cutaneous CD4+ small/medium-sized pleomorphic T-cell lymphoma (n = 3) and primary cutaneous peripheral T-cell lymphoma, unspecified (n = 3) were characterized by a high frequency of dermal langerin(+) DCs. These cells were exclusively present in the malignant infiltrates. No direct co-localization of CD3 and langerin could be resolved. Dermal langerin(+) cells were detected only in one of six primary cutaneous anaplastic large cell lymphomas (C-ALCL), characterized by epidermotropism. In other C-ALCL cases (five of six), in lymphomatoid papulosis (n = 3), subcutaneous panniculitis-like T-cell lymphoma (n = 2), and all variants of CBCL no dermal langerin(+) DCs could be found. CONCLUSIONS: Langerin(+) DCs are abundant in the dermal infiltrates of T-cell lymphomas with specific involvement of the epidermis. This might indicate that immature LC and neoplastic T cells interact and gives rise to further studies to characterize the phenotype of the langerin(+) cell population described here and its role in the pathology of CTCL.