2,117 research outputs found

    Investigating the effects of palmitoylation on the dopamine 1 receptor (D1)

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    The dopamine D1 receptor (D1) is a G protein-coupled receptor (GPCR) which regulates various key brain functions like attention, movement, reward, and memory. Understanding D1 signalling may open the horizon for novel treatments for neurological disorders. Upon agonist activation, the heterotrimeric G proteins Gαs activate adenylyl cyclase to increase cAMP/PKA signalling. D1 also engages β-arrestin proteins leading to β-arrestin dependent signalling. The D1 has two palmitoylation sites on cysteines 347&351 in its C-tail domain. However, the distinct roles and implications of palmitoylation on the D1 signalling, trafficking and β-arrestins recruitment are still largely unexplored. A palmitoylation D1 mutant was generated and luminescent based techniques such as BRET and split-Nanoluc complementation assay were employed, to delineate D1 palmitoylation effects on its pharmacology and signalling. The D1 agonists induced 50% less cAMP production in the mutant compared to wildtype (WT) and WT showed a more efficient dissociation of its Gαs. Moreover, the mutant receptor failed to recruit β-arrestin1&2, induced less ERK1/2 activation and internalises in an agonist-independent process while showing an altered intracellular Golgi trafficking. Also, in β-arrestin 1&2 KO HEK 293 cells similar cAMP production levels were reported for D1 WT and palmitoylation mutant. β-arrestin 1&2 KO blocked agonist-induced WT D1 plasma membrane trafficking, indicating that these β-arrestins are driving the differences between WT and the palmitoylation mutant D1. Taken together, our studies indicate that Gαs is the main transducer for D1 cAMP and ERK1/2 signalling and that palmitoylation is essential for its β-arrestin 1&2 interactions and modulating D1 signalling cascades in a drug-dependant process

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Face Emotion Recognition Based on Machine Learning: A Review

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    Computers can now detect, understand, and evaluate emotions thanks to recent developments in machine learning and information fusion. Researchers across various sectors are increasingly intrigued by emotion identification, utilizing facial expressions, words, body language, and posture as means of discerning an individual's emotions. Nevertheless, the effectiveness of the first three methods may be limited, as individuals can consciously or unconsciously suppress their true feelings. This article explores various feature extraction techniques, encompassing the development of machine learning classifiers like k-nearest neighbour, naive Bayesian, support vector machine, and random forest, in accordance with the established standard for emotion recognition. The paper has three primary objectives: firstly, to offer a comprehensive overview of effective computing by outlining essential theoretical concepts; secondly, to describe in detail the state-of-the-art in emotion recognition at the moment; and thirdly, to highlight important findings and conclusions from the literature, with an emphasis on important obstacles and possible future paths, especially in the creation of state-of-the-art machine learning algorithms for the identification of emotions

    Adaboost CNN with Horse Herd Optimization Algorithm to Forecast the Rice Crop Yield

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    Over three billion people use rice every day, and it occupies about 12% of the nation's arable land. Since, due to the growing population and the latest climate change projections, it is critical for governments and planners to obtain timely and accurate rice yield estimates. The proposed work develops a rice crop yield forecasting model based on soil nutrients. Soil nutrients and crop production statistics are taken as an input for the proposed method. In ensemble learning, there are three categories, they are Boosting, Bagging and Stacking. In the proposed method, Boosting technique called Adaboost with Convolutional Neural Network is used to achieve the High accuracy by converting weak classifiers to strong classifiers. Adaptive data cleaning and imputation using frequent values are used as pre-processing approaches in the projected technique. A novel technique known as Convolutional neural network with adaptive boosting (Adaboost) technique is projected and can precisely handle more imbalanced datasets. The data weights are initialized; also the initial CNN is trained utilizing original weights of data. The weights of the second CNN are then modified utilizing the first CNN. These actions will be performed sequentially for all weak classifiers. An optimization algorithm called Horse Herd (HOA) is passed down in the proposed technique to find the optimal weights of the links in the classifier. The proposed method attains 95% accuracy, 87% precision, 85% recall, 5% error, 96% specificity, 87% F1-Score, 97% NPV and 12% FNR value.Thus the designed model as predicted the crop yield prediction in the effective manner

    Étude de la résilience des milieux semi-arides à agriculture familiale à l’aide de données d’observation de la Terre

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    Les projections démographiques selon les Nations-Unies, prévoient un doublement de la population en Afrique à partir de 2036, qui atteindra 20 % de la population mondiale en 2050. Cette situation créera davantage de pression pour satisfaire aux besoins de cette population grandissante. Dans ce contexte, la compréhension des interrelations complexes entre le climat, les activités anthropiques et l’environnement bio-géophysique devient essentielle pour minimiser les incertitudes liées aux changements climatiques, en particulier dans les régions semi-arides vulnérables du continent. L’objectif principal de cette thèse est d’explorer la faisabilité de l’utilisation des données de télédétection et des données auxiliaires multi-sources pour comprendre les vecteurs de la résilience des écosystèmes écologiques face aux perturbations des milieux semi-arides à agriculture familiale en Afrique de l’Ouest (Mali et Burkina Faso). Le choix des sites d’étude intègre les dimensions de la diversité écologique et climatique ainsi que les pratiques agroforestières dans cette région semi-aride. Dans ces sites, la compréhension des interrelations entre le climat, notamment les précipitations et l’évolution de la végétation naturelle a été établie grâce à une analyse croisée entre une longue série temporelle du NDVI (AVHRR+MODIS) de 1984 à 2018 et une grille de précipitation extraite de la base de données « Climate Hazards Group InfraRed Precipitation (CHIRP) » couvrant la même période. L’étude part du principe que l’appréciation de l’état de résilience des milieux concernés nécessite une combinaison des facteurs d’influence bio-géophysiques (végétation, érosion, emprise agricole) et des facteurs socioéconomiques (niveau de vie). Les états de la végétation, de l’érosion et de l’emprise agricole ont été extraits des données d’observation de la Terre en utilisant des approches bien établies. Un nouvel indice de pauvreté multidimensionnelle adapté aux régions tropicales semi-arides à agriculture familiale a été proposé dans le cadre de cette thèse. Son développement a été soutenu par une enquête socioéconomique basée sur 68 questions, et conduite auprès de 1248 unités de production agricole. Dans le cadre de cette enquête, une nouvelle stratégie de collecte de vérités terrain a été proposée. Elle est basée sur trois sources différentes : autoévaluation, évaluation par les pairs et évaluation par l’enquêteur. Trois différents algorithmes d’intelligence artificielle ont été évalués afin de mettre au point l’indice de pauvreté adapté au contexte des milieux semi-arides à agriculture familiale. Il s’agit notamment de : Réseaux de Neurones Artificiels (ANN), Support Vecteur Machine (SVM), et Random Forest (RF). L’indice proposé est finalement basé sur le RF, qui a donné les meilleurs résultats de classification, avec des taux variant entre 78 % et 91 % pour les classes de niveau de vie. Par la suite, les principes de seuillage et d’attribution de scores qui déterminent les échelles de niveau de vie ont été appliqués dans un système d’information géographique pour combiner les facteurs d’influence bio-géophysiques avec l’indice de pauvreté proposé, dans le but de caractériser la résilience des terroirs villageois. Le nouvel indicateur résultant de cette combinaison a été désigné comme « Indice Multidimensionnel d’Équilibre du milieu (IME) ». L’appréciation de la résilience des terroirs des villages a été faite suivant trois modalités : résilient, vulnérable et dégradé. Au Mali, les résultats d’évaluation de la résilience montrent qu’aucun des terroirs villageois de la commune de Koury n’a le statut de résilient. En revanche, dans la commune de Sanekuy, deux terroirs villageois sont résilients. Dans le cas des communes concernées au Burkina Faso, la non-disponibilité des données de terrain impose une interprétation conditionnelle des résultats. Ainsi, dans la commune de Boussouma, lorsque l’on considère que tous les villages ont le statut de résilient au point de vue socioéconomique, l’application de l’IME montre que 57% des villages se retrouvent dans un statut dégradé. Lorsque l’on considère que les conditions de vie de tous les villages sont dans le statut vulnérable, l’application de l’IME présente un résultat où 74% des terroirs villageois sont dans un statut dégradé. Dans la commune de Korsimoro, l’application de l’IME utilisant les différents statuts possibles de l’indice de pauvreté multidimensionnelle adapté, présente des résultats à dynamique similaire à celle de Boussouma, où, plus les conditions socioéconomiques sont précaires, plus l’incidence est négative sur le niveau de résilience de l’écosystème écologique. Au regard de ces résultats, l’application de l’IME montre que la résilience des écosystèmes écologiques ruraux est dynamique au rythme des pratiques agroforestières et des variations des précipitations. Au-delà de la possibilité de cartographier quantitativement et qualitativement l’état de résilience du milieu pour chaque facteur d’influence, cette étude innove par l’établissement d’un indice original d’équilibre du milieu permettant de caractériser la résilience des écosystèmes écologiques des zones tropicales semi-arides à agriculture familiale.Abstract : Population projections, according to the United Nations, predict a doubling of the population in Africa by 2036, which will reach 20% of the world's population in 2050. This situation will lead to important pressure in order to satisfy the needs of this growing population. Understanding the complex interrelationships between climate, anthropogenic activities and the bio-geophysical environment is essential to minimize the uncertainties associated to climate change, especially in vulnerable semi-arid regions of African continent. The main objective of this thesis is to explore the feasibility of using remote sensing and multi-source data to understand the vectors of ecological ecosystems resilience in the semi-arid family farming environments in West Africa (Mali and Burkina Faso). The choice of the study sites takes into account the ecological dimension and climatic diversity of the tropical semi-arid areas. At these sites, an understanding of the interrelationships between climate and vegetation change, was established through a cross-analysis between a long term time series of the NDVI (AVHRR+MODIS) from 1984 to 2018 and, the precipitation grid extracted from the Climate Hazards Group InfraRed Precipitation (CHIRP) database covering the same period. The study assumes that the ecological ecosystem resilience assessment requires a combination of bio geophysical influencing factors (vegetation, erosion, agricultural footprint) and socio-economic factors (standard of living). The state of vegetation, erosion and land covering by agricultural were extracted from earth observation data using well-established approaches. A new multidimensional poverty index adapted to semi-arid tropical areas with family farming system was proposed as part of this thesis. A socio-economic survey involving 1248 agricultural production units based on 68 questions was conducted. A new strategy for collecting ground truth data was proposed based on three different sources (self-assessment, peer assessment and assessment by investigator). Three different algorithms were evaluated to develop a poverty index. These include Artificial Neural Networks (ANN); Support Vector Machine (SVM) and Random Forest (RF). The proposed index is ultimately based on the RF, which gave the best results of classification, with rates varying between 78% and 91% for the standard of living classes. Subsequently, the principles of thresholding and scoring were applied in a geographic information system (GIS) to combine bio geophysical influencing factors with the proposed poverty index, with the aim of characterizing the resilience status of target village’s areas. The new indicator resulting from this combination has been designated as the Multidimensional Middle Equilibrium Index (IME). Applied on Mali commune’s data, the ecological resilience assessment results show that none of the villages in Koury commune has the resilient status. On the other hand, in the commune of Sanekuy, 2 villages are resilient. In the case of Burkina Faso communes, the non-availability of data has conducted to a conditional interpretation of the results. Thus, in the commune of Boussouma, when we consider that all villages have the status of resilient from a socio-economic point of view, the application of the IME shows that 57% of villages find themselves in a degraded status. When we consider that the living conditions of all the villages are in the status of vulnerable, the application of the IME presents a result where 74% of the village are in a degraded status. In the commune of Korsimoro, the application of socio-economic resilience status scenarios shows the results with similar dynamics to Boussouma. Meaning that, the ecological ecosystems resilience of rural areas in semi-arid tropical zones is dynamic and linked to the agroforestry practices and to the rainfall variations. Beyond the possibility for mapping the state of ecological ecosystems resilience with regard to each influencing factor, this study innovates by establishing an original index of family farming environmental balance in the semi-arid tropical areas. In the process of the IME establishing, the study developed a new multidimensional poverty indicator, specifically adapted for semi-arid tropical areas with family farming, which is an innovation and an original contribution to science. Finally, a network learning approach of farmers interacting with agricultural research at the local level was experimented and conceptualized as an exploration of recommendations of methodological tools for adapting the agricultural production system to the ecological ecosystems resilience

    Detection and diabetic retinopathy grading using digital retinal images

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    Diabetic Retinopathy is an eye disorder that affects people suffering from diabetes. Higher sugar levels in blood leads to damage of blood vessels in eyes and may even cause blindness. Diabetic retinopathy is identified by red spots known as microanuerysms and bright yellow lesions called exudates. It has been observed that early detection of exudates and microaneurysms may save the patient’s vision and this paper proposes a simple and effective technique for diabetic retinopathy. Both publicly available and real time datasets of colored images captured by fundus camera have been used for the empirical analysis. In the proposed work, grading has been done to know the severity of diabetic retinopathy i.e. whether it is mild, moderate or severe using exudates and micro aneurysms in the fundus images. An automated approach that uses image processing, features extraction and machine learning models to predict accurately the presence of the exudates and micro aneurysms which can be used for grading has been proposed. The research is carried out in two segments; one for exudates and another for micro aneurysms. The grading via exudates is done based upon their distance from macula whereas grading via micro aneurysms is done by calculating their count. For grading using exudates, support vector machine and K-Nearest neighbor show the highest accuracy of 92.1% and for grading using micro aneurysms, decision tree shows the highest accuracy of 99.9% in prediction of severity levels of the disease

    Automated Distinct Bone Segmentation from Computed Tomography Images using Deep Learning

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    Large-scale CT scans are frequently performed for forensic and diagnostic purposes, to plan and direct surgical procedures, and to track the development of bone-related diseases. This often involves radiologists who have to annotate bones manually or in a semi-automatic way, which is a time consuming task. Their annotation workload can be reduced by automated segmentation and detection of individual bones. This automation of distinct bone segmentation not only has the potential to accelerate current workflows but also opens up new possibilities for processing and presenting medical data for planning, navigation, and education. In this thesis, we explored the use of deep learning for automating the segmentation of all individual bones within an upper-body CT scan. To do so, we had to find a network architec- ture that provides a good trade-off between the problem’s high computational demands and the results’ accuracy. After finding a baseline method and having enlarged the dataset, we set out to eliminate the most prevalent types of error. To do so, we introduced an novel method called binary-prediction-enhanced multi-class (BEM) inference, separating the task into two: Distin- guishing bone from non-bone is conducted separately from identifying the individual bones. Both predictions are then merged, which leads to superior results. Another type of error is tack- led by our developed architecture, the Sneaky-Net, which receives additional inputs with larger fields of view but at a smaller resolution. We can thus sneak more extensive areas of the input into the network while keeping the growth of additional pixels in check. Overall, we present a deep-learning-based method that reliably segments most of the over one hundred distinct bones present in upper-body CT scans in an end-to-end trained matter quickly enough to be used in interactive software. Our algorithm has been included in our groups virtual reality medical image visualisation software SpectoVR with the plan to be used as one of the puzzle piece in surgical planning and navigation, as well as in the education of future doctors

    Coincident Learning for Unsupervised Anomaly Detection

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    Anomaly detection is an important task for complex systems (e.g., industrial facilities, manufacturing, large-scale science experiments), where failures in a sub-system can lead to low yield, faulty products, or even damage to components. While complex systems often have a wealth of data, labeled anomalies are typically rare (or even nonexistent) and expensive to acquire. Unsupervised approaches are therefore common and typically search for anomalies either by distance or density of examples in the input feature space (or some associated low-dimensional representation). This paper presents a novel approach called CoAD, which is specifically designed for multi-modal tasks and identifies anomalies based on \textit{coincident} behavior across two different slices of the feature space. We define an \textit{unsupervised} metric, F^β\hat{F}_\beta, out of analogy to the supervised classification FβF_\beta statistic. CoAD uses F^β\hat{F}_\beta to train an anomaly detection algorithm on \textit{unlabeled data}, based on the expectation that anomalous behavior in one feature slice is coincident with anomalous behavior in the other. The method is illustrated using a synthetic outlier data set and a MNIST-based image data set, and is compared to prior state-of-the-art on two real-world tasks: a metal milling data set and a data set from a particle accelerator

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    20th SC@RUG 2023 proceedings 2022-2023

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