Analyze informant-based questionnaire for the early diagnosis of senile dementia using deep learning

OBJECTIVE: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire. METHODS: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score. RESULTS: Compared with the seven conventional machine learning algorithms, the DNN showed higher stability and achieved the best accuracy with 0.88, which also showed good results for identifying normal (F1-score = 0.88), mild cognitive impairment (MCI) (F1-score = 0.87), very mild dementia (VMD) (F1-score = 0.77) and Severe dementia (F1-score = 0.94). CONCLUSION: The deep neural network (DNN) classification model can effectively help doctors accurately screen patients who have normal cognitive function, mild cognitive impairment (MCI), very mild dementia (VMD), mild dementia (Mild), moderate dementia (Moderate), and severe dementia (Severe)

A Knowledge Brokering Framework for Integrated Landscape Management

Sustainable land management is at the heart of some of the most intractable challenges facing humanity in the 21st century. It is critical for tackling biodiversity loss, land degradation, climate change and the decline of ecosystem services. It underpins food production, livelihoods, dietary health, social equity, climate change adaptation, and many other outcomes. However, interdependencies, trade-offs, time lags, and non-linear responses make it difficult to predict the combined effects of land management decisions. Policy decisions also have to be made in the context of conflicting interests, values and power dynamics of those living on the land and those affected by the consequences of land use decisions. This makes designing and coordinating effective land management policies and programmes highly challenging. The difficulty is exacerbated by the scarcity of reliable data on the impacts of land management on the environment and livelihoods. This poses a challenge for policymakers and practitioners in governments, development banks, non-governmental organisations, and other institutions. It also sets demands for researchers, who are under ever increasing pressure from funders to demonstrate uptake and impact of their work. Relatively few research methods exist that can address such questions in a holistic way. Decision makers and researchers need to work together to help untangle, contextualise and interpret fragmented evidence through systems approaches to make decisions in spite of uncertainty. Individuals and institutions acting as knowledge brokers can support these interactions by facilitating the co-creation and use of scientific and other knowledge. Given the patchy nature of data and evidence, particularly in developing countries, it is important to draw on the full range of available models, tools and evidence. In this paper we review the use of evidence to inform multiple-objective integrated landscape management policies and programmes, focusing on how to simultaneously achieve different sustainable development objectives in diverse landscapes. We set out key success factors for evidence-based decision-making, which are summarised into 10 key principles for integrated landscape management knowledge brokering in integrated landscape management and 12 key skills for knowledge brokers. We finally propose a decision-support framework to organise evidence that can be used to tackle different types of land management policy decision

KAPAO: a MEMS-based natural guide star adaptive optics system

We describe KAPAO, our project to develop and deploy a low-cost, remote-access, natural guide star adaptive optics (AO) system for the Pomona College Table Mountain Observatory (TMO) 1-meter telescope. We use a commercially available 140-actuator BMC MEMS deformable mirror and a version of the Robo-AO control software developed by Caltech and IUCAA. We have structured our development around the rapid building and testing of a prototype system, KAPAO-Alpha, while simultaneously designing our more capable final system, KAPAO-Prime. The main differences between these systems are the prototype's reliance on off-the-shelf optics and a single visible-light science camera versus the final design's improved throughput and capabilities due to the use of custom optics and dual-band, visible and near-infrared imaging. In this paper, we present the instrument design and on-sky closed-loop testing of KAPAO-Alpha as well as our plans for KAPAO-Prime. The primarily undergraduate-education nature of our partner institutions, both public (Sonoma State University) and private (Pomona and Harvey Mudd Colleges), has enabled us to engage physics, astronomy, and engineering undergraduates in all phases of this project. This material is based upon work supported by the National Science Foundation under Grant No. 0960343.Comment: 10 pages and 11 figure

KAPAO First Light: the design, construction and operation of a low-cost natural guide star adaptive optics system

We present the instrument design and first light observations of KAPAO, a natural guide star adaptive optics (AO) system for the Pomona College Table Mountain Observatory (TMO) 1-meter telescope. The KAPAO system has dual science channels with visible and near-infrared cameras, a Shack-Hartmann wavefront sensor, and a commercially available 140-actuator MEMS deformable mirror. The pupil relays are two pairs of custom off-axis parabolas and the control system is based on a version of the Robo-AO control software. The AO system and telescope are remotely operable, and KAPAO is designed to share the Cassegrain focus with the existing TMO polarimeter. We discuss the extensive integration of undergraduate students in the program including the multiple senior theses/capstones and summer assistantships amongst our partner institutions. This material is based upon work supported by the National Science Foundation under Grant No. 0960343

Invasive Plant Suppresses the Growth of Native Tree Seedlings by Disrupting Belowground Mutualisms

The impact of exotic species on native organisms is widely acknowledged, but poorly understood. Very few studies have empirically investigated how invading plants may alter delicate ecological interactions among resident species in the invaded range. We present novel evidence that antifungal phytochemistry of the invasive plant, Alliaria petiolata, a European invader of North American forests, suppresses native plant growth by disrupting mutualistic associations between native canopy tree seedlings and belowground arbuscular mycorrhizal fungi. Our results elucidate an indirect mechanism by which invasive plants can impact native flora, and may help explain how this plant successfully invades relatively undisturbed forest habitat

Insect bioelectrostatics and autodissemination of Metarhizium anisopliae (Metsch.) for the biological control of the house-fly ( Musca domestica L.)

SIGLEAvailable from British Library Document Supply Centre- DSC:DXN059784 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Insect Bioelectrostatics and autodissemination of Metarhizium Anisopliae (Metsch.) for the biological control of the House-Fly (Musca Domestica L.)

The effect of surface material on the electrostatic charge of house-flies (Musca domestica) was investigated. It was found that the charge of M. domestica is dependent on the material upon which they walk. Charge is gained through triboelectrification, the exchange of electrons with a surface through contact and friction. Charge level was found to be a function of distance moved though each different surface material elicits a different maximum charge on M. domestica. This maximum charge is reached after approximately 30 cm. Two methods were developed for measuring the electrical charge of insects. The first involved placing individuals into a Faraday pail, which is calibrated to measure the net charge of objects that are contained within. In the second method, individual M. domestica were connected directly to an electrostatic voltmeter. Electrical charge was found to be proportional to the non-contact pick-up of dielectric particles from a surface by an in vitro M. domestica wing. Experiments with live flies indicated that the charge gained from triboelectrification on a surface affects the pick-up of particles. This could have important implications in the choice of construction material for an autoinoculation trap for the dissemination of entomopathogens. The conidiospores of the entomopathogenic fungus Metarhizium anisopliae were formulated with carnauba wax particles. Carnuaba wax is a strong dielectric and can carry a strong electrostatic charge. Formulation with carnauba wax in a 1:5 ratio was found to conserve conidia in a simulated bait station without significantly affecting mortality. Such formulation also reduces the loss of conidia from inoculated flies to the environment. Because less conidia are lost to the environment, indirect transmission from inoculated flies to the environment to uninfected individuals is reduced, however, this is not seen as an important process in the autodissemination of fungal entomopathogens. Conidia formulated with wax are transmitted horizontally in cage conditions, from fly to fly, but less readily than unformulated conidia.</p

I2S2: Image-To-Scene Sketch Translation Using Conditional Input and Adversarial Networks

Image generation from sketch is a popular and well-studied computer vision problem. However, the inverse problem image-to-sketch (I2S) synthesis still remains open and challenging, let alone image-to-scene sketch (I2S 2 ) synthesis, especially when full-scene sketch generations are highly desired. In this paper, we propose a framework for generating full-scene sketch representations from natural scene images, aiming to generate outputs that approximate hand-drawn scene sketches. Specifically, we exploit generative adversarial models to produce full-scene sketches given arbitrary input images that are actually conditions which are incorporated to guide the distribution mapping in the context of adversarial learning. To advance the use of such conditions, we further investigate edge detection solutions and propose to utilize Holistically-nested Edge Detection (HED) maps to condition the generative model. We conduct extensive experiments to validate the proposed framework and provide detailed quantitative and qualitative evaluations to demonstrate its effectiveness. In addition, we also demonstrate the flexibility of the proposed framework by using different conditional inputs, such as the Canny edge detector

A knowledge brokering framework for integrated landscape management

Sustainable land management is at the heart of some of the most intractable challenges facing humanity in the 21st century. It is critical for tackling biodiversity loss, land degradation, climate change and the decline of ecosystem services. It underpins food production, livelihoods, dietary health, social equity, climate change adaptation, and many other outcomes. However, interdependencies, trade-offs, time lags, and non-linear responses make it difficult to predict the combined effects of land management decisions. Policy decisions also have to be made in the context of conflicting interests, values and power dynamics of those living on the land and those affected by the consequences of land use decisions. This makes designing and coordinating effective land management policies and programmes highly challenging. The difficulty is exacerbated by the scarcity of reliable data on the impacts of land management on the environment and livelihoods. This poses a challenge for policymakers and practitioners in governments, development banks, non-governmental organisations, and other institutions. It also sets demands for researchers, who are under ever increasing pressure from funders to demonstrate uptake and impact of their work. Relatively few research methods exist that can address such questions in a holistic way. Decision makers and researchers need to work together to help untangle, contextualise and interpret fragmented evidence through systems approaches to make decisions in spite of uncertainty. Individuals and institutions acting as knowledge brokers can support these interactions by facilitating the co-creation and use of scientific and other knowledge. Given the patchy nature of data and evidence, particularly in developing countries, it is important to draw on the full range of available models, tools and evidence. In this paper we review the use of evidence to inform multiple-objective integrated landscape management policies and programmes, focusing on how to simultaneously achieve different sustainable development objectives in diverse landscapes. We set out key success factors for evidence-based decision-making, which are summarised into 10 key principles for integrated landscape management knowledge brokering in integrated landscape management and 12 key skills for knowledge brokers. We finally propose a decision-support framework to organise evidence that can be used to tackle different types of land management policy decision.PRIFPRI3; CRP5; 1 Fostering Climate-Resilient and Sustainable Food SupplyEPTDCGIAR Research Program on Water, Land and Ecosystems (WLE