Response of a Lake Michigan coastal lake to anthropogenic catchment disturbance

A paleolimnological investigation of post-European sediments in a Lake Michigan coastal lake was used to examine the response of Lower Herring Lake to anthropogenic impacts and its role as a processor of watershed inputs. We also compare the timing of this response with that of Lake Michigan to examine the role of marginal lakes as ‘early warning’ indicators of potential changes in the larger connected system and their role in buffering Lake Michigan against anthropogenic changes through biotic interactions and material trapping. Sediment geochemistry, siliceous microfossils and nutrient-related morphological changes in diatoms, identified three major trophic periods in the recent history of the lake. During deforestation and early settlement (pre-1845–1920), lake response to catchment disturbances results in localized increases in diatom abundances with minor changes in existing communities. In this early phase of disturbance, Lower Herring Lake acts as a sediment sink and a biological processor of nutrient inputs. During low-lake levels of the 1930s, the lake goes through a transitional period characterized by increased primary productivity and a major shift in diatom communities. Post-World War II (late 1940s–1989) anthropogenic disturbances push Lower Herring Lake to a new state and a permanent change in diatom community structure dominated by Cyclotella comensis . The dominance of planktonic summer diatom species associated with the deep chlorophyll maximum (DCM) is attributed to epilimnetic nutrient depletion. Declining Si:P ratios are inferred from increased sediment storage of biogenic silica and morphological changes in the silica content of Aulacoseira ambigua and Stephanodiscus niagarae . Beginning in the late 1940s, Lower Herring Lake functions as a biogeochemical processor of catchment inputs and a carbon, nutrient and silica sink. Microfossil response to increased nutrients and increased storage of biogenic silica in Lower Herring Lake and other regional embayments occur approximately 20–25 years earlier than in a nearby Lake Michigan site. Results from this study provide evidence for the role of marginal lakes and bays as nutrient buffering systems, delaying the impact of anthropogenic activities on the larger Lake Michigan system.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43091/1/10933_2004_Article_1688.pd

Machine learning in neurosurgery: a global survey

Background: Recent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including neurosurgery. Our goal was to conduct a comprehensive survey of neurosurgeons to assess the acceptance of and attitudes toward ML in neurosurgical practice and to identify factors associated with its use. Methods: The online survey consisted of nine or ten mandatory questions and was distributed in February and March 2019 through the European Association of Neurosurgical Societies (EANS) and the Congress of Neurosurgeons (CNS). Results: Out of 7280 neurosurgeons who received the survey, we received 362 responses, with a response rate of 5%, mainly in Europe and North America. In total, 103 neurosurgeons (28.5%) reported using ML in their clinical practice, and 31.1% in research. Adoption rates of ML were relatively evenly distributed, with 25.6% for North America, 30.9% for Europe, 33.3% for Latin America and the Middle East, 44.4% for Asia and Pacific and 100% for Africa with only two responses. No predictors of clinical ML use were identified, although academic settings and subspecialties neuro-oncology, functional, trauma and epilepsy predicted use of ML in research. The most common applications were for predicting outcomes and complications, as well as interpretation of imaging. Conclusions: This report provides a global overview of the neurosurgical applications of ML. A relevant proportion of the surveyed neurosurgeons reported clinical experience with ML algorithms. Future studies should aim to clarify the role and potential benefits of ML in neurosurgery and to reconcile these potential advantages with bioethical considerations