In this work, we aimed at investigating cell and tissue responses of the apple snail
Pomacea canaliculata
, following the inoculation of the zoonotic pathogen
Mycobacterium marinum
. Different doses ...were tested (10, 20, 65, and 100 M CFU) and the mortality rate was negligible. The histopathogenesis was followed at 4, 9, and 28 days after inoculation. Overt histopathological lesions were consistently observed after the two largest doses only. In the lung, marked hemocyte aggregations, including intravascular nodule formation, were observed within the large blood veins that run along the floor and roof of this organ. Hemocyte aggregations were found occluding many of the radial sinuses supplying the respiratory lamina. Acid-fast bacilli were contained in the different hemocyte aggregations. In addition, hemocytes were observed infiltrating the storage tissue, which makes up most of the lung wall, and the connective tissue of the mantle edge. Additionally, signs of degradation in the storage tissue were observed in the lung wall on day 28. In the kidney, nodules were formed associated with the constitutive hemocyte islets and with the subpallial hemocoelic space, in whose hemocytes the acid-fast structures were found. Electron microscopy analysis revealed the presence of bacteria-containing phagosomes within hemocytes located in the surface zone of the islets. Additionally, electron-dense spheroidal structures, which are likely remnants of digested mycobacteria, were observed in close proximity to the hemocytes’ nuclei. The size attained by the hemocyte nodules varied during the observation period, but there was no clear dependence on dose or time after inoculation. Nodules were also formed subpallially. Some of these nodules showed 2–3 layers with different cellular composition, suggesting they may also form through successive waves of circulating cells reaching them. Nodular cores, including those formed intravascularly in the lung, would exhibit signs of hemocyte dedifferentiation, possibly proliferation, and death. Hemocyte congestion was observed in the hemocoelic spaces surrounding the pallial ends of the renal crypts, and the renal crypts themselves showed de-epithelization, particularly on day 28. The diverse cellular responses of
P. canaliculata
to
M. marinum
inoculation and the high resilience of this snail to the pathogen make it a suitable species for studying mycobacterial infections and their effects on cellular and physiological processes.
According to the World Health Organization (WHO), Diabetes Mellitus (DM) is one of the most prevalent diseases in the world. It is also associated with a high mortality index. Diabetic foot is one of ...its main complications, and it comprises the development of plantar ulcers that could result in an amputation. Several works report that thermography is useful to detect changes in the plantar temperature, which could give rise to a higher risk of ulceration. However, the plantar temperature distribution does not follow a particular pattern in diabetic patients, thereby making it difficult to measure the changes. Thus, there is an interest in improving the success of the analysis and classification methods that help to detect abnormal changes in the plantar temperature. All this leads to the use of computer-aided systems, such as those involved in artificial intelligence (AI), which operate with highly complex data structures. This paper compares machine learning-based techniques with Deep Learning (DL) structures. We tested common structures in the mode of transfer learning, including AlexNet and GoogleNet. Moreover, we designed a new DL-structure, which is trained from scratch and is able to reach higher values in terms of accuracy and other quality measures. The main goal of this work is to analyze the use of AI and DL for the classification of diabetic foot thermograms, highlighting their advantages and limitations. To the best of our knowledge, this is the first proposal of DL networks applied to the classification of diabetic foot thermograms. The experiments are conducted over thermograms of DM and control groups. After that, a multi-level classification is performed based on a previously reported thermal change index. The high accuracy obtained shows the usefulness of AI and DL as auxiliary tools to aid during the medical diagnosis.
Chaotic systems implemented by artificial neural networks are good candidates for data encryption. In this manner, this paper introduces the cryptographic application of the Hopfield and the ...Hindmarsh-Rose neurons. The contribution is focused on finding suitable coefficient values of the neurons to generate robust random binary sequences that can be used in image encryption. This task is performed by evaluating the bifurcation diagrams from which one chooses appropriate coefficient values of the mathematical models that produce high positive Lyapunov exponent and Kaplan-Yorke dimension values, which are computed using TISEAN. The randomness of both the Hopfield and the Hindmarsh-Rose neurons is evaluated from chaotic time series data by performing National Institute of Standard and Technology (NIST) tests. The implementation of both neurons is done using field-programmable gate arrays whose architectures are used to develop an encryption system for RGB images. The success of the encryption system is confirmed by performing correlation, histogram, variance, entropy, and Number of Pixel Change Rate (NPCR) tests.
In this work, we present a novel design for vertical surface contact using a two degree of freedom robotic arm attached to a Micro Air Vehicle. To achieve this, we propose a controller based on a ...Gain-Scheduled Proportional–Integral–Derivative approach. In previous works, the Gain-Scheduled Proportional–Integral–Derivative method was used to control the attitude of the Micro Air Vehicle, thus mitigating the perturbations induced by the movement of the arm. The novel approach of this work focuses on the achievement of an automatized full-contact with a rigid vertical surface using a Micro Air Vehicle with a robotic arm. We have improved the capabilities of the Gain-Scheduled Proportional–Integral–Derivative control to consider the inherent issues of approximating to a flat structure in order to carry out an aerial interaction task successfully. For the Micro Air Vehicle’s position feedback, a motion capture system is used in this work. A paintbrush attached to the end effector of the arm is used to draw over a whiteboard surface to show the full contact of the aerial manipulator. A distance sensor is added to the on-board sensors to measure the distance between the vertical surface and the system to ensure a correct distance and achieve a safe contact. Experimental testing results show that the controller can maintain a stable flight with sufficient accuracy to complete the aerial interaction tasks.
A cataract is a medical condition causing an opacity in the ocular nucleus due to various factors such as age and diseases. Starting from traditional image processing techniques for processing and ...extracting relevant features, using computational intelligence methods is essential to help experts in the medical pre-diagnosis for automatic classification and grading of the disease. However, the learning capabilities of such automated processes rely considerably upon the availability of adequately-labeled databases approved by medical experts. Considering the shortage of available public databases for implementing potential algorithms such as Deep Learning, this work presents a new nuclear cataract database composed of 1437 labeled images for multi-level classification according to the LOCS III system. The images were obtained and correctly classified by experts from an ophthalmologic medical center in Mexico City. Also, our research compares relevant Machine Learning algorithms employed for medical images like Support Vector Machines, Deep Learning structures like GoogLeNet, and our proposed Deep Learning Structure with the highest classification rates for the two and multiple cataract levels according to LOCS III.
An essential part of cloud computing, IoT, and in general the broad field of digital systems, is constituted by the mechanisms which provide access to a number of services or applications. Biometric ...techniques aim to manage the access to such systems based on personal data; however, some biometric traits are openly exposed in the daily life, and in consequence, they are not secret, e.g., voice or face in social networks. In many cases, biometric data are non-cancelable and non-renewable when compromised. This document examines the vulnerabilities and proposes hardware and software countermeasures for the protection and confidentiality of biometric information using randomly created supplementary information. Consequently, a taxonomy is proposed according to the operating principle and the type of supplementary information supported by protection techniques, analyzing the security, privacy, revocability, renewability, computational complexity, and distribution of biometric information. The proposed taxonomy has five categories: 1) biometric cryptosystems; 2) cancelable biometrics; 3) protection schemes based on machine learning or deep learning; 4) hybrid protection schemes; and 5) multibiometric protection schemes. Furthermore, this document proposes quantitative evaluation measures to compare the performance of protection techniques. Likewise, this research highlights the advantages of injective and linear mapping for the protection of authentication and identification systems, allowing the non-retraining of these systems when the protected biometric information is canceled and renewed. Finally, this work mentions commercial products for cancelable biometric systems and proposes future directions for adaptive and cancelable biometric systems in low-cost IoT devices.
The relevance of the development of monitoring systems for rotating machines is not only the ability to detect failures but also how early these failures can be detected. The purpose of this paper is ...to present an experimental study of partially damaged rotor bar in induction motor under different load conditions based on discrete wavelet transform analysis. The approach is based on the extraction of features from vibration signals at different level of damage and three mechanical load conditions. The proposed analysis is reliable for tracking the damage in rotor bar. The paper presents an analysis and extraction of vibration features for partially damaged rotor bar in induction motors. The experimental analysis shows the change in behavior of vibration due to load condition and progressive damage.
There is an increased interest in developing reliable condition monitoring and fault diagnosis systems of machines like induction motors; such interest is not only in the final phase of the failure ...but also at early stages. In this paper, several levels of damage of rotor bars under different load conditions are identified by means of vibration signals. The importance of this work relies on a simple but effective automatic detection algorithm of the damage before a break occurs. The feature extraction is based on discrete wavelet analysis and autocorrelation process. Then, the automatic classification of the fault degree is carried out by a binary classification tree. In each node, comparing the learned levels of the breaking off correctly identifies the fault degree. The best results of classification are obtained employing computational intelligence techniques like support vector machines, multilayer perceptron, and the k-NN algorithm, with a proper selection of their optimal parameters.
Reservoir computing has shown promising results in predicting chaotic time series. However, the main challenges of time-series predictions are associated with reducing computational costs and ...increasing the prediction horizon. In this sense, we propose the optimization of Echo State Networks (ESN), where the main goal is to increase the prediction horizon using a lower count number of neurons compared with state-of-the-art models. In addition, we show that the application of the decimation technique allows us to emulate an increase in the prediction of up to 10,000 steps ahead. The optimization is performed by applying particle swarm optimization and considering two chaotic systems as case studies, namely the chaotic Hindmarsh–Rose neuron with slow dynamic behavior and the well-known Lorenz system. The results show that although similar works used from 200 to 5000 neurons in the reservoir of the ESN to predict from 120 to 700 steps ahead, our optimized ESN including decimation used 100 neurons in the reservoir, with a capability of predicting up to 10,000 steps ahead. The main conclusion is that we ensured larger prediction horizons compared to recent works, achieving an improvement of more than one order of magnitude, and the computational costs were greatly reduced.
It is difficult to establish a black-box model for sparse data, because not enough data can be applied for training. This paper presents a novel identification approach using multiple fuzzy neural ...networks. It focuses on structure and parameters uncertainty which have been widely explored in the literature. Firstly, the sparse data are used within a fixed time interval to generate model structure. Then kernel regression methods are used to generate training data, a stable updating algorithm is proposed to train the membership functions. To cope structure change, a hysteresis strategy is proposed to enable multiple fuzzy neural identifier switching with guaranteed performance. Both theoretic analysis and simulation example show the efficacy of the proposed method.
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