Extraction of bixin from annatto seeds using supercritical carbon dioxide

The solubility of 93% pure bixin in supercritical carbon dioxide (SC-CO2) and of the bixin present in annatto seeds (Bixa orellana L.) was measured. For the seeds, the measurements were made in a temperature range from 30 to 50ºC and pressure between 10 and 35 MPa and for the pure bixin, at 40ºC from 10 to 35 MPa. The main pigments of annatto seeds are bixin and norbixin, but the extracts only showed the presence of cis and trans-bixin, indicating that norbixin is not soluble in SC-CO2. The annatto seeds used in the experiments contained about 2.7% bixin and 3.1% oil. In the seeds, the crossover point of solubility was at about 28 MPa and values for solubility were about ten times higher than those of the pure bixin, giving evidence that the oil acted as a co-solvent with the CO2.FAPES

Nuove applicazioni per le unità abitative in emergenza: tecnologie e tecniche della tradizione costruttiva andina amazonica

This work reports the result of a research activity conducted in Bolivia in the year 2020, in synergy between Bolivian Polytechnic University School "Josè Maria Nunez del Prado", University of Naples Federico II and Pegaso Telematic University. In particular, the research had as its object the study of a housing unit suitable for coping with the floods that frequently afflict some Bolivian peasant populations. During the research, much attention was initially paid to the historical, political, socio-economic aspects and to the cultural and traditional characteristics of the ethnic groups that make up the population of Bolivia; subsequently, the morphology and hydrography of the Bolivian territory were examined, as well as the climate, the trend of rainfall and the El Niño and La Niña phenomena that often cause floods with serious risks for the populations, especially rural ones. Having completed these first two phases of the research, the authors moved on to the study of the traditional construction characteristics of rural architecture, paying particular attention to the basic construction materials used in the peasant areas of Bolivia, in particular to raw earth and bamboo, as well as the principles and rules that regulate the construction of rural housing in Bolivia. For rural architecture, the traditional Pawichi house and the traditional dwellings of the indigenous Chiquitana population were examined in detail. Only after having acquired all these important aspects, the research became interested in the design of a new rural house which, respecting Bolivian cultural traditions, would be able to cope with floods and reduce the risks associated with them. In the design of this new emergency housing unit, the authors focused their attention not only on the compositional and functional aspects of the housing unit, but also on the constructive aspects by designing new structural elements such as the pillars made with five bamboo canes suitably linked together. with knots of the Andean construction tradition. The composition of the housing unit proposed by the authors is governed by a basic 4x4 module that can be full, or delimited by infill and window frames, or empty, or without surrounding infill elements to allow the creation of the traditional place for socialization, the Punilla. Never as in this case have, I been particularly happy with the invitation formulated by the authors, and in particular by the young researchers, Francesca Volpe and Emanuele La Mantia, to present their work. In fact, I was able to see the procedural quality of the research and the intelligent design procedure that led to the definition of a housing prototype of great interest for the Andean populations

Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model

Recently, intelligent video surveillance applications have become essential in public security by the use of computer vision technologies to investigate and understand long video streams. Anomaly detection and classification are considered a major element of intelligent video surveillance. The aim of anomaly detection is to automatically determine the existence of abnormalities in a short time period. Deep reinforcement learning (DRL) techniques can be employed for anomaly detection, which integrates the concepts of reinforcement learning and deep learning enabling the artificial agents in learning the knowledge and experience from actual data directly. With this motivation, this paper presents an Intelligent Video Anomaly Detection and Classification using Faster RCNN with Deep Reinforcement Learning Model, called IVADC-FDRL model. The presented IVADC-FDRL model operates on two major stages namely anomaly detection and classification. Firstly, Faster RCNN model is applied as an object detector with Residual Network as a baseline model, which detects the anomalies as objects. Besides, deep Q-learning (DQL) based DRL model is employed for the classification of detected anomalies. In order to validate the effective anomaly detection and classification performance of the IVADC-FDRL model, an extensive set of experimentations were carried out on the benchmark UCSD anomaly dataset. The experimental results showcased the better performance of the IVADC-FDRL model over the other compared methods with the maximum accuracy of 98.50% and 94.80% on the applied Test004 and Test007 dataset respectively

Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification

At present times, COVID-19 has become a global illness and infected people has increased exponentially and it is difficult to control due to the non-availability of large quantity of testing kits. Artificial intelligence (AI) techniques including machine learning (ML), deep learning (DL), and computer vision (CV) approaches find useful for the recognition, analysis, and prediction of COVID-19. Several ML and DL techniques are trained to resolve the supervised learning issue. At the same time, the potential measure of the unsupervised learning technique is quite high. Therefore, unsupervised learning techniques can be designed in the existing DL models for proficient COVID-19 prediction. In this view, this paper introduces a novel unsupervised DL based variational autoencoder (UDL-VAE) model for COVID-19 detection and classification. The UDL-VAE model involved adaptive Wiener filtering (AWF) based preprocessing technique to enhance the image quality. Besides, Inception v4 with Adagrad technique is employed as a feature extractor and unsupervised VAE model is applied for the classification process. In order to verify the superior diagnostic performance of the UDL-VAE model, a set of experimentation was carried out to highlight the effective outcome of the UDL-VAE model. The obtained experimental values showcased the effectual results of the UDL-VAE model with the higher accuracy of 0.987 and 0.992 on the binary and multiple classes respectively

Diagnosis of leukemia disease based on enhanced virtual neural network

White Blood Cell (WBC) cancer or leukemia is one of the serious cancers that threaten the existence of human beings. In spite of its prevalence and serious consequences, it is mostly diagnosed through manual practices. The risks of inappropriate, sub-standard and wrong or biased diagnosis are high in manual methods. So, there is a need exists for automatic diagnosis and classification method that can replace the manual process. Leukemia is mainly classified into acute and chronic types. The current research work proposed a computer-based application to classify the disease. In the feature extraction stage, we use excellent physical properties to improve the diagnostic system’s accuracy, based on Enhanced Color Co-Occurrence Matrix. The study is aimed at identification and classification of chronic lymphocytic leukemia using microscopic images of WBCs based on Enhanced Virtual Neural Network (EVNN) classification. The proposed method achieved optimum accuracy in detection and classification of leukemia from WBC images. Thus, the study results establish the superiority of the proposed method in automated diagnosis of leukemia. The values achieved by the proposed method in terms of sensitivity, specificity, accuracy, and error rate were 97.8%, 89.9%, 76.6%, and 2.2%, respectively. Furthermore, the system could predict the disease in prior through images, and the probabilities of disease detection are also highly optimistic

5-Aminolaevulinic Acid (ALA) for the Fluorescence Detection of Bronchial Tumors

At the moment only early detection of lung cancer offers a good prognosis for the patients. Conventional white light endoscopy is mostly insufficient for early diagnosis. Therefore we developed a system of fluorescence diagnosis using 5-aminolaevulinic acid (ALA) exogeneously applied. As precursor of the heme synthesis it is metabolized to protoporphyrin IX – a red fluorescent substance. Therefore protoporphyrin IX accumulates in tumorous and premalignant tissue, and can be directly visualized by fluorescence bronchoscopy. Excitation with blue light (380–435 nm) causes a red fluorescence, which can be detected after filtering most of the blue component with the naked eye or a camera system. After earlier work with laser systems and cold light sources we now use the system D-Light AF for the fluorescence diagnosis using ALA-induced protoporphyrin IX fluorescence

Artificial intelligence with big data analytics-based brain intracranial hemorrhage e-diagnosis using CT images

Due to the fast development of medical imaging technologies, medical image analysis has entered the period of big data for proper disease diagnosis. At the same time, intracerebral hemorrhage (ICH) becomes a serious disease which affects the injury of blood vessels in the brain regions. This paper presents an artificial intelligence and big data analytics-based ICH e-diagnosis (AIBDA-ICH) model using CT images. The presented model utilizes IoMT devices for data acquisition process. The presented AIBDA-ICH model involves graph cut-based segmentation model for identifying the affected regions in the CT images. To manage big data, Hadoop Ecosystem and its elements are mainly used. In addition, capsule network (CapsNet) model is applied as a feature extractor to derive a useful set of feature vectors. Finally, the presented AIBDA-ICH model makes use of the fuzzy deep neural network (FDNN) model to carry out classification process. For validating the superior performance of the AIBDA-ICH method, an extensive set of simulations were performed and the outcomes are examined under diverse aspects. The experimental values pointed out the improved e-diagnostic performance of the AIBDA-ICH model over the other compared methods with the precision and accuracy of 94.96% and 98.59%, respectively

In-vivo kinetics of inhaled 5-Aminolevulinic acid-Induced Protoporphyrin IX fluorescence in bronchial tissue

BACKGROUND: In the diagnosis of early-stage lung cancer photosensitizer-enhanced fluorescence bronchoscopy with inhaled 5-aminolevolinic acid (5-ALA) increases sensitivity when compared to white-light bronchoscopy. This investigation was to evaluate the in vivo tissue pharmacokinetics of inhaled 5-ALA within the bronchial mucosa in order to define the time optimum for its application prior to bronchoscopy. METHODS: Patients with known or suspected bronchial carcinoma were randomized to receive 200 mg 5-ALA via inhalation 1, 2, 3, 4 or 6 hours before flexible fluorescence bronchoscopy was performed. Macroscopically suspicious areas as well as areas with visually detected porphyrin fluorescence and normal control sites were measured spectroscopically. Biopsies for histopathology were obtained from suspicious areas as well as from adjacent normal areas. RESULTS: Fluorescence bronchoscopy performed in 19 patients reveals a sensitivity for malignant and premalignant changes (moderate dysplasia) which is almost twice as high as that of white-light bronchoscopy, whereas specificity is reduced. This is due to false-positive inflammatory lesions which also frequently show increased porphyrin fluorescence. Malignant and premalignant alterations produced fluorescence values that are up to 5 times higher than those of normal tissue. According to the pharmacokinetics of porphyrin fluorescence measured by spectroscopy, the optimum time range for 5-ALA application is 80–270 min prior to fluorescence bronchoscopy, with an optimum at 160 min. CONCLUSION: According to our results we propose inhalation of 5-ALA 160 min prior to fluorescence bronchoscopy, suggesting that this time difference provides the best tumor/normal tissue fluorescence ratio

Stem cells labeled with superparamagnetic iron oxide nanoparticles in a preclinical model of cerebral ischemia: a systematic review with meta-analysis

Introduction: Although there is an increase in clinical trials assessing the efficacy of cell therapy in structural and functional regeneration after stroke, there are not enough data in the literature describing the best cell type to be used, the best route, and also the best nanoparticle to analyze these stem cells in vivo. This review analyzed published data on superparamagnetic iron oxide nanoparticle (SPION)-labeled stem cells used for ischemic stroke therapy.Method: We performed a systematic review and meta-analysis of data from experiments testing the efficacy of cellular treatment with SPION versus no treatment to improve behavioral or modified neural scale outcomes in animal models of stroke by the Cochrane Collaboration and indexed in EMBASE, PubMed, and Web of Science since 2000. To test the impact of study quality and design characteristics, we used random-effects meta-regression. in addition, trim and fill were used to assess publication bias.Results: the search retrieved 258 articles. After application of the inclusion criteria, 24 reports published between January 2000 and October 2014 were selected. These 24 articles were analyzed for nanoparticle characteristics, stem cell types, and efficacy in animal models.Conclusion: This study highlights the therapeutic role of stem cells in stroke and emphasizes nanotechnology as an important tool for monitoring stem cell migration to the affected neurological locus.Instituto Israelita de Ensino e Pesquisa Albert EinsteinCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)FAPEGHosp Israelita Albert Einstein, BR-05651901 São Paulo, BrazilUniversidade Federal de São Paulo, BR-04021001 São Paulo, SP, BrazilSanta Casa Misericordia São Paulo, BR-01221020 São Paulo, SP, BrazilUniv São Paulo, Inst Matemat & Estat, BR-05508090 São Paulo, SP, BrazilUniv São Paulo, LIM44, BR-05403000 São Paulo, SP, BrazilUniversidade Federal de São Paulo, BR-04021001 São Paulo, SP, BrazilWeb of Scienc