Gold Standard for Green Spiders: Proposals for Excellence in Sustainability at the University of Richmond

The University of Richmond should commit to achieving the Gold Standard rating by the AASHE Campus Sustainability Index by 2024. Gold Standard for Green Spiders includes eight proposals that taken together, will move UR up from our current silver rating to gold. This volume also includes short summaries of the sustainability programs of eight of the top ten most sustainable liberal arts colleges in the nation. The eight student authors of these chapters completed their works as part of the Environmental Studies Senior Seminar course, the capstone course for Environmental Studies majors

Menstruation, Myopia, and Marginalization: Advancing Menstrual Policies to “Keep Girls in School” at the Risk of Exacerbating Inequalities

As countries across the world adopt policies addressing menstruation, it is imperative to identify who benefits from such policies and to understand the dynamics of inclusion and exclusion. We examine such policies through the lens of human rights, as a framework that demands addressing marginalization, ensuring substantive equality, and guaranteeing inclusive participation to ensure that the menstrual needs of everyone, everywhere are met. Our review is focused on four countries (India, Kenya, Senegal, and the United States) and is based on data from 34 policy documents and interviews with 85 participants. We show that girls, particularly school-going girls, are the main target group of policies. Due to this myopic view of menstrual needs, policies risk leaving the needs of adult menstruators, including those experiencing (peri)menopause, unaddressed. Moreover, the intersection between menstrual status and markers of identity such as disability and gender identity produces further policy gaps. These gaps can be attributed to the exclusion of marginalized menstruators from decision-making processes by creating barriers and failing to ensure meaningful inclusive participation. To address inequalities, policy makers need to make a concerted effort to understand and accommodate the needs of menstruators in all their diversity

Third Annual Families and Neighborhoods Network Update

Welcome to the Third Annual Families and Neighborhoods Network Update. In this edition, which focuses on Family Development, you'll find plenty of relevant, interesting news, as well as diversity and spirituality in the context of family development.As always, we strive to provide information and resources regarding human service efforts that support families and neighborhoods. The selection of information and articles for this issue of the Network Update was based on issues raised by the seven Comprehensive Community-BasedModels (CCBMs).Among the highlights of this issue is an article by Dr. Susan Stern and Cassandra Clay, professors at Boston University School of Social Work. In their article, titled "Supporting Children and Families in a Caring Community," they challenge our thinking about family development, while guiding practitioners, policymakers, fund providers, and grassroots community-based organizations into the next century.Also in this issue, you'll find two annotated bibliographies that explore community-based, family centered strategies for integrating education and human services. These bibliographies also present practical ways to design policies that reflect the importance of the family in the development of children and society. As an additional resource, you'll also find a directory of federally-funded resource centers and clearinghouses that compile information on child andfamily welfare, health, and education issues.This issue of the Network Update also offers a personal glimpse of the seven W.K. Kellogg Foundation-funded family development sites. Each of the sites was asked to share its definition of family development, and to specify how that definition translates into services or opportunities for families. Staff members at the seven sites also were asked to discuss their philosophies about family development and how that philosophy differs from a mainstream view. Their thought provoking answers are just a few pages away

Long-Term Associations between Cholinesterase Inhibitors and Memantine Use and Health Outcomes among Patients with Alzheimer's Disease

OBJECTIVES: To examine in an observational study (1) relationships between cholinesterase inhibitors (ChEI) and memantine use, and functional and cognitive end points and mortality in patients with Alzheimer's disease (AD); (2) relationships between other patient characteristics and these clinical end points; and (3) whether effects of the predictors change across time. METHODS: The authors conducted a multicenter, natural history study that included three university-based AD centers in the United States. A total of 201 patients diagnosed with probable AD with modified Mini-Mental State Examination (MMSE) scores >/= 30 at study entry were monitored annually for 6 years. Discrete-time hazard analyses were used to examine relationships between ChEI and memantine use during the previous 6 months reported at each assessment, and time to cognitive (MMSE score /= 10) end points and mortality. Analyses controlled for clinical characteristics, including baseline cognition, function, and comorbid conditions, and presence of extrapyramidal signs and psychiatric symptoms at each assessment interval. Demographic characteristics included baseline age, sex, education, and living arrangement at each assessment interval. RESULTS: ChEI use was associated with delayed time in reaching the functional end point and death. Memantine use was associated with delayed time to death. Different patient characteristics were associated with different clinical end points. CONCLUSIONS: Results suggest long-term beneficial effects of ChEI and memantine use on patient outcomes. As for all observational cohort studies, observed relationships should not be interpreted as causal effects

Movements of marine fish and decapod crustaceans: Process, theory and application

Many marine species have a multi-phase ontogeny, with each phase usually associated with a spatially and temporally discrete set of movements. For many fish and decapod crustaceans that live inshore, a tri-phasic life cycle is widespread, involving: (1) the movement of planktonic eggs and larvae to nursery areas; (2) a range of routine shelter and foraging movements that maintain a home range; and (3) spawning migrations away from the home range to close the life cycle. Additional complexity is found in migrations that are not for the purpose of spawning and movements that result in a relocation of the home range of an individual that cannot be defined as an ontogenetic shift. Tracking and tagging studies confirm that life cycle movements occur across a wide range of spatial and temporal scales. This dynamic multi-scale complexity presents a significant problem in selecting appropriate scales for studying highly mobile marine animals. We address this problem by first comprehensively reviewing the movement patterns of fish and decapod crustaceans that use inshore areas and present a synthesis of life cycle strategies, together with five categories of movement. We then examine the scale-related limitations of traditional approaches to studies of animal-environment relationships. We demonstrate that studies of marine animals have rarely been undertaken at scales appropriate to the way animals use their environment and argue that future studies must incorporate animal movement into the design of sampling strategies. A major limitation of many studies is that they have focused on: (1) a single scale for animals that respond to their environment at multiple scales or (2) a single habitat type for animals that use multiple habitat types. We develop a hierarchical conceptual framework that deals with the problem of scale and environmental heterogeneity and we offer a new definition of 'habitat' from an organism-based perspective. To demonstrate that the conceptual framework can be applied, we explore the range of tools that are currently available for both measuring animal movement patterns and for mapping and quantifying marine environments at multiple scales. The application of a hierarchical approach, together with the coordinated integration of spatial technologies offers an unprecedented opportunity for researchers to tackle a range of animal-environment questions for highly mobile marine animals. Without scale-explicit information on animal movements many marine conservation and resource management strategies are less likely to achieve their primary objectives

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states

Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer’s disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, ‘shape connections’ between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus

Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1–3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature

The impact of PICALM genetic variations on reserve capacity of posterior cingulate in AD continuum

Phosphatidylinositolbinding clathrin assembly protein (PICALM) gene is one novel genetic player associated with late-onset Alzheimer’s disease (LOAD), based on recent genome wide association studies (GWAS). However, how it affects AD occurrence is still unknown. Brain reserve hypothesis highlights the tolerant capacities of brain as a passive means to fight against neurodegenerations. Here, we took the baseline volume and/or thickness of LOAD-associated brain regions as proxies of brain reserve capacities and investigated whether PICALM genetic variations can influence the baseline reserve capacities and the longitudinal atrophy rate of these specific regions using data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In mixed population, we found that brain region significantly affected by PICALM genetic variations was majorly restricted to posterior cingulate. In sub-population analysis, we found that one PICALM variation (C allele of rs642949) was associated with larger baseline thickness of posterior cingulate in health. We found seven variations in health and two variations (rs543293 and rs592297) in individuals with mild cognitive impairment were associated with slower atrophy rate of posterior cingulate. Our study provided preliminary evidences supporting that PICALM variations render protections by facilitating reserve capacities of posterior cingulate in non-demented elderly

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease