Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Automated classification of Schistosoma mansoni granulomatous microscopic images of mice liver using Artificial Intelligence (AI) technologies is a key issue for accurate diagnosis and treatment. In this paper, three grey difference statistics-based features, namely three Gray-Level Co-occurrence Matrix (GLCM) based features and fifteen Gray Gradient Co-occurrence Matrix (GGCM) features were calculated by correlative analysis. Ten features were selected for three-level cellular granuloma classification using a Scaled Conjugate Gradient Back-Propagation Neural Network (SCG-BPNN) in the same performance. A cross-entropy is then calculated to evaluate the proposed Sigmoid input and the ten-hidden layer network. The results depicted that SCG-BPNN with texture features performs high recognition rate compared to using morphological features, such as shape, size, contour, thickness and other geometry-based features for the classification. The proposed method also has a high accuracy rate of 87.2% compared to the Back-Propagation Neural Network (BPNN), Back-Propagation Hopfield Neural Network (BPHNN) and Convolutional Neural Network (CNN)

A distance regularized level-set evolution model based MRI dataset segmentation of brain’s caudate nucleus

The caudate nucleus of the brain is highly correlated to the emotional decision-making of pessimism, which is an important process for improving the understanding and treatment of depression; and the segmentation of the caudate nucleus is the most basic step in the process of analysis and research concerning this region. In this paper, Level Set Method (LSM) is applied for caudate nucleus segmentation. Firstly, Distance Regularized Level Set Evolution (DRLSE), Region-Scalable Fitting (RSF) and Local Image Fitting (LIF) models are proposed for segmentation of the caudate nucleus of Magnetic Resonance Imaging (MRI) images of the brain, and the segmentation results are compared by using selected evaluation indices. The average Dice Similarity Coefficient (DSC) values of the proposed three methods all exceed 85%, and the average Jaccard Similarity (JS) values are over 77%, respectively. The results indicate that all these three models can have good segmentation results for medical images with intensity inhomogeneity and meet the general segmentation requirements, while the proposed DRLSE model performs better in segmentation

Autonomous Flight Control for Multi-Rotor UAVs Flying at Low Altitude

Unmanned aerial vehicles (UAVs) at low altitude flight may significantly degrade their performance and the safety under wind disturbances and incorrect operations. This paper presents a robust control strategy for UAVs to achieve good performance of low altitude flight and disturbance rejection. First, a novel second-order hexacopter dynamics is established and the position tracking is translated to the altitude and the rotational angle tracking problem. An integrated control scheme is created to deal with the challenges faced by hexacopter at low altitude flight, in which the influence of near-ground threshold distance and the desired roll, pitch, and yaw are analyzed. Moreover, an improved flying altitude planner and an attitude planner for low altitude conditions are designed respectively to avoid the overturning risk due to the big reaction torque and external disturbances. Second, a sliding-mode-based altitude tracking controller and an attitude tracking controller are designed to reduce the tracking errors and improve the robustness of the system. Finally, the proposed control scheme is tested on simulation and experiment platforms of multi-rotor UAV to show the feasibility and accurate trajectory tracking at low altitude flight

MTANS:Multi-Scale Mean Teacher Combined Adversarial Network with Shape-Aware Embedding for Semi-Supervised Brain Lesion Segmentation

The annotation of brain lesion images is a key step in clinical diagnosis and treatment of a wide spectrum of brain diseases. In recent years, segmentation methods based on deep learning have gained unprecedented popularity, leveraging a large amount of data with high-quality voxel-level annotations. However, due to the limited time clinicians can provide for the cumbersome task of manual image segmentation, semi-supervised medical image segmentation methods present an alternative solution as they require only a few labeled samples for training. In this paper, we propose a novel semi-supervised segmentation framework that combines improved mean teacher and adversarial network. Specifically, our framework consists of (i) a student model and a teacher model for segmenting the target and generating the signed distance maps of object surfaces, and (ii) a discriminator network for extracting hierarchical features and distinguishing the signed distance maps of labeled and unlabeled data. Besides, based on two different adversarial learning processes, a multi-scale feature consistency loss derived from the student and teacher models is proposed, and a shape-aware embedding scheme is integrated into our framework. We evaluated the proposed method on the public brain lesion datasets from ISBI 2015, ISLES 2015, and BRATS 2018 for the multiple sclerosis lesion, ischemic stroke lesion, and brain tumor segmentation respectively. Experiments demonstrate that our method can effectively leverage unlabeled data while outperforming the supervised baseline and other state-of-the-art semi-supervised methods trained with the same labeled data. The proposed framework is suitable for joint training of limited labeled data and additional unlabeled data, which is expected to reduce the effort of obtaining annotated images

Impact of environmental comfort on urban vitality in small and medium-sized cities: A case study of Wuxi County in Chongqing, China

China's urbanization has exceeded 64% and a large number of small and medium-sized cities are the key development areas in the new stage. In urban planning, it is very important to reveal the influence of environmental comfort on urban vitality to improve the life quality of residents in these towns. Thus, the study investigated the impact of environmental comfort on urban vitality using ordinary least squares regression in Wuxi County. Environmental comfort was assessed through a comprehensive analysis of a built-up area and urban vitality was represented by vitality intensity. In addition, the influence pathways were identified and model validation was verified. The conclusions are as follows: (1) Environmental comfort and urban vitality are distributed spatially similarly, and both gradually decline from the center to the periphery. It is high in the east and low in the west, high in the south and low in the north. (2) Population density, POI mixing degree, building density, and road network density have significant positive effects on urban vitality. Population density has the greatest impact on urban vitality. Building height, building age, and river buffer have significant negative effects on urban vitality. (3) The impact of comprehensive environmental comfort on urban vitality is positive, and in terms of time, the order of impact is afternoon > morning > evening. Finally, a method for assessing the impact of environmental comfort on urban vitality was constructed, and the promoting effect of environmental comfort improvements on the vitality was verified. These findings will fill the gap between urban physical space and social needs in planning practices and provide reference to improve vitality for urban planning in small and medium-sized cities

Transdifferentiation of lung adenocarcinoma in mice with Lkb1 deficiency to squamous cell carcinoma

Lineage transition in adenocarcinoma (ADC) and squamous cell carcinoma (SCC) of non-small cell lung cancer, as implicated by clinical observation of mixed ADC and SCC pathologies in adenosquamous cell carcinoma, remains a fundamental yet unsolved question. Here we provide in vivo evidence showing the transdifferentiation of lung cancer from ADC to SCC in mice: Lkb1-deficient lung ADC progressively transdifferentiates into SCC, via a pathologically mixed mAd-SCC intermediate. We find that reduction of lysyl oxidase (Lox) in Lkb1-deficient lung ADC decreases collagen disposition and triggers extracellular matrix remodelling and upregulates p63 expression, a SCC lineage survival oncogene. Pharmacological Lox inhibition promotes the transdifferentiation, whereas ectopic Lox expression significantly inhibits this process. Notably, ADC and SCC show differential responses to Lox inhibition. Collectively, our findings demonstrate the de novo transdifferentiation of lung ADC to SCC in mice and provide mechanistic insight that may have important implications for lung cancer treatment

Estudio teórico-normativo del grupo de sociedades y del proceso de consolidación de las cuentas anuales

[spa] Este trabajo fin de grado pretende abordar el estudio teórico-normativo del proceso de consolidación de las cuentas anuales del grupo de sociedades y las metodologías que deben ser aplicadas en función de las participaciones, o presunción de control, que se tenga la sociedad dominante sobre las dependientes, asociadas o multigrupos. El trabajo está enfocado, principalmente, al grupo de sociedades que no cotizan en el mercado bursátil (de cualquier estado miembro de la Unión Europea), aunque también se menciona las normativas que rigen el grupo de sociedades que sí cotizan en un mercado bursátil de la Unión Europea. Por tanto, la existencia de un grupo de sociedades es la primera condición para formular las cuentas anuales consolidadas, y para determinar si forman o no grupo de sociedades se tendrá en consideración el concepto de “control”. (Será explicado detalladamente en el desarrollo este trabajo). Sin embargo, parece ser contradictorio a lo dicho anteriormente, las sociedades asociadas y multigrupos sobre las cuales no se ostenta el control también serán incluidas en las cuentas anuales consolidadas.[eng] This final degree project tries to study about process of consolidation and the different methodology that can be applied, and it depends on the participation which the parent company has at the subsidiary, associated company or multigroup company. This project is focused on a group of companies do not listed on any European stock market, but it is mentioned laws that regulate a group of companies listed on at any European stock market. Therefore, the first condition to formulate the consolidated annual accounts is the existence of a group of company. A group of company appears when the parent company has the control at the subsidiary. Nevertheless, associated and multigroup company will be included on the consolidated annual accounts, in spite of the parent company does not have the control, but it is essential the parent company has a subsidiary to formulate the consolidated annual accounts