The dynamics of apparent horizons in Robinson-Trautman spacetimes

We present an alternative scheme of finding apparent horizons based on spectral methods applied to Robinson-Trautman spacetimes. We have considered distinct initial data such as representing the spheroids of matter and the head-on collision of two non-rotating black holes. The evolution of the apparent horizon is presented. We have obtained in some cases a mass gap between the final Bondi and apparent horizon masses, whose implications were briefly commented in the light of the thermodynamics of black holes.Comment: 9 pages, 7 figure

Machine learning and feature selection methods for egfr mutation status prediction in lung cancer

The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.This work is financed by the ERDF—European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation—COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263

Comprehensive perspective for lung cancer characterisation based on AI solutions using CT images

Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.This work is financed by the ERDF–European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation–COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT–Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263

Adsorption mechanism of hexavalent chromium onto layered double hydroxides-based adsorbents: A systematic in-depth review

© 2019 Elsevier B.V. An attempt has been made in this review to provide some insights into the possible adsorption mechanisms of hexavalent chromium onto layered double hydroxides-based adsorbents by critically examining the past and present literature. Layered double hydroxides (LDH) nanomaterials are typical dual-electronic adsorbents because they exhibit positively charged external surfaces and abundant interlayer anions. A high positive zeta potential value indicates that LDH has a high affinity to Cr(VI) anions in solution through electrostatic attraction. The host interlayer anions (i.e., Cl−, NO3−, SO42−, and CO32−) provide a high anion exchange capacity (53–520 meq/100 g) which is expected to have an excellent exchangeable capacity to Cr(VI) oxyanions in water. Regarding the adsorption-coupled reduction mechanism, when Cr(VI) anions make contact with the electron-donor groups in the LDH, they are partly reduced to Cr(III) cations. The reduced Cr(III) cations are then adsorbed by LDH via numerous interactions, such as isomorphic substitution and complexation. Nonetheless, the adsorption-coupled reduction mechanism is greatly dependent on: (1) the nature of divalent and trivalent salts utilized in LDH preparation, and the types of interlayer anions (i.e., guest intercalated organic anions), and (3) the adsorption experiment conditions. The low Brunauer–Emmett–Teller specific surface area of LDH (1.80–179 m2/g) suggests that pore filling played an insignificant role in Cr(VI) adsorption. The Langmuir maximum adsorption capacity of LDH (Qomax) toward Cr(VI) was significantly affected by the natures of used inorganic salts and synthetic methods of LDH. The Qomax values range from 16.3 mg/g to 726 mg/g. Almost all adsorption processes of Cr(VI) by LDH-based adsorbent occur spontaneously (ΔG° 0) and increase the randomness (ΔS° >0) in the system. Thus, LDH has much potential as a promising material that can effectively remove anion pollutants, especially Cr(VI) anions in industrial wastewater

Comparative chromosome painting discloses homologous Segments in distantly related mammals

Comparative chromosome painting, termed ZOO-FISH, using DNA libraries from flow sorted human chromosomes 1,16,17 and X, and mouse chromosome 11 discloses the presence of syntenic groups in distantly related mammalian Orders ranging from primates (Homo sapiens), rodents (Mus musculus), even-toed ungulates (Muntiacus muntjak vaginalis and Muntiacus reevesi) and whales (Balaenoptera physalus). These mammalian Orders have evolved separately for 55-80 million years (Myr). We conclude that ZOO-FISH can be used to generate comparative chromosome maps of a large number of mammalian species

EGFR Assessment in Lung Cancer CT Images: Analysis of Local and Holistic Regions of Interest Using Deep Unsupervised Transfer Learning

Statistics have demonstrated that one of the main factors responsible for the high mortality rate related to lung cancer is the late diagnosis. Precision medicine practices have shown advances in the individualized treatment according to the genetic profile of each patient, providing better control on cancer response. Medical imaging offers valuable information with an extensive perspective of the cancer, opening opportunities to explore the imaging manifestations associated with the tumor genotype in a non-invasive way. This work aims to study the relevance of physiological features captured from Computed Tomography images, using three different 2D regions of interest to assess the Epidermal growth factor receptor (EGFR) mutation status: nodule, lung containing the main nodule, and both lungs. A Convolutional Autoencoder was developed for the reconstruction of the input image. Thereafter, the encoder block was used as a feature extractor, stacking a classifier on top to assess the EGFR mutation status. Results showed that extending the analysis beyond the local nodule allowed the capture of more relevant information, suggesting the presence of useful biomarkers using the lung with nodule region of interest, which allowed to obtain the best prediction ability. This comparative study represents an innovative approach for gene mutations status assessment, contributing to the discussion on the extent of pathological phenomena associated with cancer development, and its contribution to more accurate Artificial Intelligence-based solutions, and constituting, to the best of our knowledge, the first deep learning approach that explores a comprehensive analysis for the EGFR mutation status classification.The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health for the free publicly available LIDC-IDRI Database used in this work. They also acknowledge The Cancer Imaging Archive (TCIA) for the open-access NSCLC-Radiogenomics dataset publicly available. This work was supported in part by the European Regional Development Fund (ERDF) through the Operational Program for Competitiveness and Internationalization—COMPETE 2020 Program, and in part by the National Funds through the Portuguese Funding Agency, Fundação para a Ciência e a Tecnologia (FCT), under Project POCI-01-0145-FEDER-030263

Balanço e análise da sustentabilidade energética na produção orgânica de hortaliças.

Os insumos e serviços utilizados na produção vegetal representam custo energético. Dependendo desses fatores e das produtividades obtidas, a conversão da produção em energia determinará a eficiência energética do sistema. A agricultura orgânica somente atingirá a missão de preservação ambiental se tiver comprovada sustentabilidade energética. Neste trabalho, objetivou-se caracterizar os balanços energéticos dos cultivos orgânicos e analisar sua sustentabilidade, em comparação aos sistemas convencionais. Monitoraram-se campos de produção de dez culturas, de 1991 a 2000 em Domingos Martins-ES. Os dados do sistema convencional foram obtidos pelas médias dos coeficientes técnicos da região. Quantificaram-se os coeficientes técnicos, convertendo suas grandezas físicas em equivalentes energéticos, expressos em kcal. O sistema orgânico gastou 4.571.159 kcal ha-1 e apresentou 12.696.712 kcal ha-1 de energia inserida na colheita, mostrando balanço médio de 2,78. Esse valor foi similar ao obtido no sistema convencional (1,93). As participações dos componentes nos gastos do sistema orgânico foram embalagem (35,8%), composto orgânico (17,2%), irrigação (12,6%), sementes/mudas (12,4%) e mão-de-obra (11,0%), serviços mecânicos (5,0%) e frete (4,5%). Se os custos com embalagens fossem eliminados, os gastos do sistema orgânico seriam reduzidos para 2.930.113 kcal ha-1, aumentando sua eficiência. A maioria dos cultivos orgânicos pode ser considerada sustentável em transformação de energia, com balanços superiores a 1,00 e produção média diária de 80.421 kcal ha-1 por dia, superior à necessidade mínima de 58.064 kcal ha-1