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The production of three-dimensional (3D) digital meshes of surface and computed tomographic (CT) data has become widespread in morphometric analyses of anthropological and archaeological data. Given that processing methods are not standardized, this leaves questions regarding the comparability of processed and digitally curated 3D datasets. The goal of this study was to identify those processing parameters that result in the most consistent fit between CT-derived meshes and a 3D surface model of the same human mandible. Eight meshes, each using unique thresholding and smoothing parameters, were compared to assess whole-object deviations, deviations along curves, and deviations between specific anatomical features on the surface model when compared with the CT scans using a suite of comparison points. Based on calculated gap distances, the mesh that thresholded at “0” with an applied smoothing technique was found to deviate least from the surface model, although it is not the most biologically accurate. Results have implications for aggregated studies that employ multimodal 3D datasets, and caution is recommended for studies that enlist 3D data from websites and digital repositories, particularly if processing parameters are unknown or derived for studies with different research foci.
Due to lack of data on the epidemiology, cardiac, and neurological complications among Ontario visible minorities (Chinese and South Asians) affected by coronavirus disease (COVID-19), this population-based retrospective study was undertaken to study them systematically.
From January 1, 2020 to September 30, 2020 using the last name algorithm to identify Ontario Chinese and South Asians who were tested positive by PCR for COVID-19, their demographics, cardiac, and neurological complications including hospitalization and emergency visit rates were analyzed compared to the general population.
Chinese (N = 1,186) with COVID-19 were found to be older (mean age 50.7 years) compared to the general population (N = 42,547) (mean age 47.6 years) (p < 0.001), while South Asians (N = 3,459) were younger (age of 42.1 years) (p < 0.001). The 30-day crude rate for cardiac complications among Chinese was 169/10,000 (p = 0.069), while for South Asians, it was 64/10,000 (p = 0.008) and, for the general population, it was 112/10,000. For neurological complications, the 30-day crude rate for Chinese was 160/10,000 (p < 0.001); South Asians was 40/10,000 (p = 0.526), and general population was 48/10,000. The 30-day all-cause mortality rate was significantly higher for Chinese at 8.1% vs 5.0% for the general population (p < 0.001), while it was lower in South Asians at 2.1% (p < 0.001).
Chinese and South Asians in Ontario affected by COVID-19 during the first wave of the pandemic were found to have a significant difference in their demographics, cardiac, and neurological outcomes.
The Subglacial Antarctic Lakes Scientific Access (SALSA) Project accessed Mercer Subglacial Lake using environmentally clean hot-water drilling to examine interactions among ice, water, sediment, rock, microbes and carbon reservoirs within the lake water column and underlying sediments. A ~0.4 m diameter borehole was melted through 1087 m of ice and maintained over ~10 days, allowing observation of ice properties and collection of water and sediment with various tools. Over this period, SALSA collected: 60 L of lake water and 10 L of deep borehole water; microbes >0.2 μm in diameter from in situ filtration of ~100 L of lake water; 10 multicores 0.32–0.49 m long; 1.0 and 1.76 m long gravity cores; three conductivity–temperature–depth profiles of borehole and lake water; five discrete depth current meter measurements in the lake and images of ice, the lake water–ice interface and lake sediments. Temperature and conductivity data showed the hydrodynamic character of water mixing between the borehole and lake after entry. Models simulating melting of the ~6 m thick basal accreted ice layer imply that debris fall-out through the ~15 m water column to the lake sediments from borehole melting had little effect on the stratigraphy of surficial sediment cores.
Ecosystem modeling, a pillar of the systems ecology paradigm (SEP), addresses questions such as, how much carbon and nitrogen are cycled within ecological sites, landscapes, or indeed the earth system? Or how are human activities modifying these flows? Modeling, when coupled with field and laboratory studies, represents the essence of the SEP in that they embody accumulated knowledge and generate hypotheses to test understanding of ecosystem processes and behavior. Initially, ecosystem models were primarily used to improve our understanding about how biophysical aspects of ecosystems operate. However, current ecosystem models are widely used to make accurate predictions about how large-scale phenomena such as climate change and management practices impact ecosystem dynamics and assess potential effects of these changes on economic activity and policy making. In sum, ecosystem models embedded in the SEP remain our best mechanism to integrate diverse types of knowledge regarding how the earth system functions and to make quantitative predictions that can be confronted with observations of reality. Modeling efforts discussed are the Century ecosystem model, DayCent ecosystem model, Grassland Ecosystem Model ELM, food web models, Savanna model, agent-based and coupled systems modeling, and Bayesian modeling.
The systems ecology paradigm (SEP) is presented as the right science and analytical approach at the right time for resolving many of the Earth’s natural resource, environmental, and societal challenges. SEP embodies two major parts. One, the systems ecology approach, which is the holistic, systems thinking perspective and methodology developed for the rigorous study of ecosystems, including humans. Two, the use of ecosystem science, the vast body of scientific knowledge, much of which has been assembled using the ecosystem and systems ecology approaches. The fundamental philosophy, evolution, and application of the SEP are defined in this chapter. The organizing principles of the SEP include: many problems are complex and complicated and may have multiple causes; precise definitions of problems and their spatial, temporal, and organizational hierarchical scales are critical; collaborative decision making including scientists, technical and administrative staff members, and essential stakeholders is essential; transparent, honest, and effective communication is required; globalization of collaboration within interdisciplinary networks has been a hallmark of the paradigm; and integration of simulation modeling, field and laboratory studies has proven indispensable for many scientific breakthroughs. A call for integration of transdisciplinary science, policy making, and management is presented.
The evolution of ecosystem science and systems ecology as legitimate branches of science has occurred since the late 1960s. They have flourished because of their essential contributions to understanding and management of natural resources and the environment. Scientific knowledge about the structure and functioning of ecosystems, the services ecosystems provide to people, and the roles people play therein, have become commonplace. Scientists know what challenges face Earth’s environments and they know many of the solutions available to resolve them. But scientific knowledge alone is insufficient to implement change. Knowledge transfer to people who manage our lands, waters, and other natural resources is essential and they must become engaged in implementing solutions to major natural resource and environmental challenges. Adoption of new concepts and technologies is critical. Overcoming the barriers to adoption of best management practices is critically needed. Many of the barriers are created by adherence to dogmatic cultural norms and ideologies by landowners, managers, and policy makers. Behavioral, organizational, learning, and marketing professionals study behavioral change. The systems ecology paradigm must incorporate behavioral, organizational, learning, and marketing professionals as partners in implementing concepts of adoption cycles and community-based social marketing to solve wicked problems.
The systems ecology paradigm (SEP) emerged in the late 1960s at a time when societies throughout the world were beginning to recognize that our environment and natural resources were being threatened by their activities. Management practices in rangelands, forests, agricultural lands, wetlands, and waterways were inadequate to meet the challenges of deteriorating environments, many of which were caused by the practices themselves. Scientists recognized an immediate need was developing a knowledge base about how ecosystems function. That effort took nearly two decades (1980s) and concluded with the acceptance that humans were components of ecosystems, not just controllers and manipulators of lands and waters. While ecosystem science was being developed, management options based on ecosystem science were shifting dramatically toward practices supporting sustainability, resilience, ecosystem services, biodiversity, and local to global interconnections of ecosystems. Emerging from the new knowledge about how ecosystems function and the application of the systems ecology approach was the collaboration of scientists, managers, decision-makers, and stakeholders locally and globally. Today’s concepts of ecosystem management and related ideas, such as sustainable agriculture, ecosystem health and restoration, consequences of and adaptation to climate change, and many other important local to global challenges are a direct result of the SEP.
National and international agencies and organizations have published reports outlining critical natural resource, environmental, and societal challenges facing global inhabitants. These reports include the UN Sustainability Goals, Future Earth, Global Land Project, and the Resilience Alliance. Recognizing many of the topics listed in these reports are broad and aspirational, the authors of this chapter have disaggregated many topics into research and management challenges for which the systems ecology paradigm is well suited. Disaggregation is based on challenges at different spatial hierarchical scales: organisms/populations; ecological sites; landscapes; small regions/watersheds; regions/nations; continents; and the globe. Emphasis is placed on research needs at landscape and larger hierarchical levels. Biophysical knowledge acquired during the past 50 years about organism/population and ecological site levels is available now to better manage ecosystems and natural resources. However, research blending the ecosystem knowledge base with behavioral, learning, organizational, and marketing sciences is vitally needed to affect management practice change at scales where people manage land and waters. The goal is to engage managers, policy makers, thought leaders, and concerned citizens to resolve critical problems and adopt best management practices to meet current and future environmental challenges (e.g., provision of ecosystem services and climate change effects on ecosystem).