Perspectives on Pain: A Visual Interpretation of Chronic Headaches

The action of antithrombotic drugs can be evaluated by measuring the deposition of111In-labelled platelets on peripheral bypass grafts several days after injection. This evaluation can be performed qualitatively (visual interpretation on the daily images) or quantitatively. Four different methods which calculate the ratio of platelet uptake with a reference region are compared: two methods use a gamma camera and two a detector. A blood sample or the region under the sternal angle are used as reference. The daily ratio of the counts, recorded by a gamma camera in a region of interest covering the graft, and the blood radioactivity interpolated from a platelet survival curve appears to be the most reliable method. The information of all the ratios can be combined in a single thrombogenicity index which reflects the daily rise of a linear or exponential regression versus time.

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Données LiDAR aériennes: pertinence de l'interprétation visuelle pour la foresterie

Schweizerische Zeitschrift fur Forstwesen ◽

10.3188/szf.2017.0127 ◽

2017 ◽

Vol 168 (3) ◽

pp. 127-133

Author(s):

Matthew Parkan

Keyword(s):

Cross Sections ◽

Forest Edge ◽

Airborne Lidar ◽

Visual Interpretation ◽

Individual Tree ◽

Small Areas ◽

Interactive 3D ◽

The Individual ◽

Airborne Lidar Data ◽

Stem Position

Airborne LiDAR data: relevance of visual interpretation for forestry Airborne LiDAR surveys are particularly well adapted to map, study and manage large forest extents. Products derived from this technology are increasingly used by managers to establish a general diagnosis of the condition of forests. Less common is the use of these products to conduct detailed analyses on small areas; for example creating detailed reference maps like inventories or timber marking to support field operations. In this context, the use of direct visual interpretation is interesting, because it is much easier to implement than automatic algorithms and allows a quick and reliable identification of zonal (e.g. forest edge, deciduous/persistent ratio), structural (stratification) and point (e.g. tree/stem position and height) features. This article examines three important points which determine the relevance of visual interpretation: acquisition parameters, interactive representation and identification of forest characteristics. It is shown that the use of thematic color maps within interactive 3D point cloud and/or cross-sections makes it possible to establish (for all strata) detailed and accurate maps of a parcel at the individual tree scale.

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Art and Ambiguity

10.1093/oso/9780199666416.003.0003 ◽

2018 ◽

Author(s):

John O’Dea

Keyword(s):

Perceptual Experience ◽

Three Dimensional ◽

Empirical Work ◽

Visual Interpretation ◽

Perceptual Constancy ◽

Multistable Perception ◽

State Of Affairs

This chapter defends a solution to the problem of variable appearances that co-occur with perceptual constancy. In conditions which are non-ideal, yet within the range of perceptual constancy, we see things veridically despite a puzzling “appearance” which is suggestive of a non-veridical state of affairs. For example, a tilted coin is often taken to have an “elliptical appearance”. This chapter defends Gestalt-shift approach, according to which these appearances are in fact illusory, but not part of normal perceptual experience. The experience of ellipticality when viewing a tilted coin, it is argued, arises from something like a brief and unstable Gestalt shift to a different visual interpretation of the scene, of the kind that E. H. Gombrich argued artists invoke when painting a three-dimensional scene on a flat canvas. Recent empirical work on multistable perception is used to show how this might work.

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Agroforestry Systems of the Tehuacán-Cuicatlán Valley: Land Use for Biocultural Diversity Conservation

Land ◽

10.3390/land8020024 ◽

2019 ◽

Vol 8 (2) ◽

pp. 24

Author(s):

Mariana Vallejo ◽

M. Isabel Ramírez ◽

Alejandro Reyes-González ◽

Jairo López-Sánchez ◽

Alejandro Casas

Keyword(s):

Land Use ◽

Agricultural Land ◽

Agroforestry Systems ◽

World Heritage Site ◽

Biocultural Diversity ◽

Semiarid Region ◽

Agricultural Land Use ◽

Visual Interpretation ◽

Natural Ecosystems ◽

Human Settlements

The Tehuacán-Cuicatlán Valley, Mexico, is the semiarid region with the richest biodiversity of North America and was recently recognized as a UNESCO's World Heritage site. Original agricultural practices remain to this day in agroforestry systems (AFS), which are expressions of high biocultural diversity. However, local people and researchers perceive a progressive decline both in natural ecosystems and AFS. To assess changes in location and extent of agricultural land use, we carried out a visual interpretation of very-high resolution imagery and field work, through which we identified AFS and conventional agricultural systems (CAS) from 1995 to 2003 and 2012. We analyzed five communities, representative of three main ecological and agricultural zones of the region. We assessed agricultural land use changes in relation to conspicuous landscape features (relief, rivers, roads, and human settlements). We found that natural ecosystems cover more than 85% of the territory in each community, and AFS represent 51% of all agricultural land. Establishment and permanence of agricultural lands were strongly influenced by gentle slopes and the existence of roads. Contrary to what we expected, we recorded agricultural areas being abandoned, thus favoring the regeneration of natural ecosystems, as well as a 9% increase of AFS over CAS. Agriculture is concentrated near human settlements. Most of the studied territories are meant to preserve natural ecosystems, and traditional AFS practices are being recovered for biocultural conservation.

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Mangrove Forest Landcover Changes in Coastal Vietnam: A Case Study from 1973 to 2020 in Thanh Hoa and Nghe An Provinces

Forests ◽

10.3390/f12050637 ◽

2021 ◽

Vol 12 (5) ◽

pp. 637

Author(s):

Huong Thi Thuy Nguyen ◽

Giles E. S. Hardy ◽

Tuat Van Le ◽

Huy Quoc Nguyen ◽

Hoang Huy Nguyen ◽

...

Keyword(s):

Mangrove Forest ◽

Monsoon Season ◽

Coastal Development ◽

Northern Coast ◽

Mangrove Forests ◽

Visual Interpretation ◽

Mangrove Restoration ◽

Coast Of Vietnam ◽

Landsat Satellite

Mangrove forests can ameliorate the impacts of typhoons and storms, but their extent is threatened by coastal development. The northern coast of Vietnam is especially vulnerable as typhoons frequently hit it during the monsoon season. However, temporal change information in mangrove cover distribution in this region is incomplete. Therefore, this study was undertaken to detect change in the spatial distribution of mangroves in Thanh Hoa and Nghe An provinces and identify reasons for the cover change. Landsat satellite images from 1973 to 2020 were analyzed using the NDVI method combined with visual interpretation to detect mangrove area change. Six LULC classes were categorized: mangrove forest, other forests, aquaculture, other land use, mudflat, and water. The mangrove cover in Nghe An province was estimated to be 66.5 ha in 1973 and increased to 323.0 ha in 2020. Mangrove cover in Thanh Hoa province was 366.1 ha in 1973, decreased to 61.7 ha in 1995, and rose to 791.1 ha in 2020. Aquaculture was the main reason for the loss of mangroves in both provinces. Overall, the percentage of mangrove loss from aquaculture was 42.5% for Nghe An province and 60.1% for Thanh Hoa province. Mangrove restoration efforts have contributed significantly to mangrove cover, with more than 1300 ha being planted by 2020. This study reveals that improving mangrove restoration success remains a challenge for these provinces, and further refinement of engineering techniques is needed to improve restoration outcomes.

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Paediatric Idiopathic Intracranial Hypertension (IIH)—A Review

Life ◽

10.3390/life11070632 ◽

2021 ◽

Vol 11 (7) ◽

pp. 632

Author(s):

Andrew Malem ◽

Twishaa Sheth ◽

Brinda Muthusamy

Keyword(s):

Intracranial Hypertension ◽

Idiopathic Intracranial Hypertension ◽

Adult Population ◽

Underlying Disease ◽

Diagnostic Process ◽

Paediatric Patients ◽

Disease Aetiology ◽

Sight Loss ◽

Chronic Headaches ◽

Work Up

Paediatric idiopathic intracranial hypertension (IIH), is a rare but important differential diagnosis in children presenting with papilloedema. It is characterised by raised intracranial pressure in the absence of an identifiable secondary structural or systemic cause and is, therefore, a diagnosis of exclusion. In the adult population, there is a strong predilection for the disease to occur in female patients who are obese. This association is also seen in paediatric patients with IIH but primarily in the post-pubertal cohort. In younger pre-pubertal children, this is not the case, possibly reflecting a different underlying disease aetiology and pathogenesis. Untreated IIH in children can cause significant morbidity from sight loss, chronic headaches, and the psychological effects of ongoing regular hospital monitoring, interventions, and medication. The ultimate goal in the management of paediatric IIH is to protect the optic nerve from papilloedema-induced optic neuropathy and thus preserve vision, whilst reducing the morbidity from other symptoms of IIH, in particular chronic headaches. In this review, we will outline the typical work-up and diagnostic process for paediatric patients with suspected IIH and how we manage these patients.

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Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach

Remote Sensing ◽

10.3390/rs13061060 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1060

Author(s):

Luc Baudoux ◽

Jordi Inglada ◽

Clément Mallet

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Time Frame ◽

Superior Performance ◽

Land Cover Mapping ◽

Visual Interpretation ◽

Remote Sensing Images ◽

Corine Land Cover ◽

Land Cover Map ◽

By Products

CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.

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Visual interpretation of [18F]Florbetaben PET supported by deep learning–based estimation of amyloid burden

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-020-05044-x ◽

2020 ◽

Author(s):

Ji-Young Kim ◽

Dongkyu Oh ◽

Kiyoung Sung ◽

Hongyoon Choi ◽

Jin Chul Paeng ◽

...

Keyword(s):

Deep Learning ◽

Amyloid Burden ◽

Visual Interpretation

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Building Multi-Feature Fusion Refined Network for Building Extraction from High-Resolution Remote Sensing Images

Remote Sensing ◽

10.3390/rs13142794 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2794

Author(s):

Shuhao Ran ◽

Xianjun Gao ◽

Yuanwei Yang ◽

Shaohua Li ◽

Guangbin Zhang ◽

...

Keyword(s):

Feature Fusion ◽

Small Scale ◽

Automatic Extraction ◽

Learning Approaches ◽

Building Extraction ◽

Visual Interpretation ◽

Learning Capacity ◽

Convolutional Networks ◽

Fully Convolutional Networks ◽

Great Progress

Deep learning approaches have been widely used in building automatic extraction tasks and have made great progress in recent years. However, the missing detection and wrong detection causing by spectrum confusion is still a great challenge. The existing fully convolutional networks (FCNs) cannot effectively distinguish whether the feature differences are from one building or the building and its adjacent non-building objects. In order to overcome the limitations, a building multi-feature fusion refined network (BMFR-Net) was presented in this paper to extract buildings accurately and completely. BMFR-Net is based on an encoding and decoding structure, mainly consisting of two parts: the continuous atrous convolution pyramid (CACP) module and the multiscale output fusion constraint (MOFC) structure. The CACP module is positioned at the end of the contracting path and it effectively minimizes the loss of effective information in multiscale feature extraction and fusion by using parallel continuous small-scale atrous convolution. To improve the ability to aggregate semantic information from the context, the MOFC structure performs predictive output at each stage of the expanding path and integrates the results into the network. Furthermore, the multilevel joint weighted loss function effectively updates parameters well away from the output layer, enhancing the learning capacity of the network for low-level abstract features. The experimental results demonstrate that the proposed BMFR-Net outperforms the other five state-of-the-art approaches in both visual interpretation and quantitative evaluation.

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Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Climate ◽

10.3390/cli9020028 ◽

2021 ◽

Vol 9 (2) ◽

pp. 28

Author(s):

Anurag Malik ◽

Anil Kumar ◽

Priya Rai ◽

Alban Kuriqi

Keyword(s):

Artificial Intelligence ◽

Fuzzy Inference ◽

Standardized Precipitation Index ◽

Meteorological Drought ◽

Vital Role ◽

Precipitation Index ◽

Visual Interpretation ◽

Inference System ◽

Drought Prediction ◽

Optimal Functioning

Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.

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