A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures.

A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature exposure durations during day, night, and both day and night. A stand-alone application with Graphical User Interfaces (GUI) for calibrating, preprocessing, and classification of hyperspectral rice seed images is presented. The software application can be used for training two deep learning architectures for the classification of any type of hyperspectral seed images. The average overall classification accuracy of 91.33% and 89.50% is obtained for seed-based classification using 3D-CNN for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The DNN gives an average accuracy of 94.83% and 91% for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The accuracies obtained are higher than those presented in the literature for hyperspectral rice seed image classification. The HSI analysis presented here is on the Kitaake cultivar, which can be extended to study the temperature tolerance of other rice cultivars.

Detection of Target Genes for Drug Repurposing to Treat Skeletal Muscle Atrophy in Mice Flown in Spaceflight.

Skeletal muscle atrophy is a common condition in aging, diabetes, and in long duration spaceflights due to microgravity. This article investigates multi-modal gene disease and disease drug networks via link prediction algorithms to select drugs for repurposing to treat skeletal muscle atrophy. Key target genes that cause muscle atrophy in the left and right extensor digitorum longus muscle tissue, gastrocnemius, quadriceps, and the left and right soleus muscles are detected using graph theoretic network analysis, by mining the transcriptomic datasets collected from mice flown in spaceflight made available by GeneLab. We identified the top muscle atrophy gene regulators by the Pearson correlation and Bayesian Markov blanket method. The gene disease knowledge graph was constructed using the scalable precision medicine knowledge engine. We computed node embeddings, random walk measures from the networks. Graph convolutional networks, graph neural networks, random forest, and gradient boosting methods were trained using the embeddings, network features for predicting links and ranking top gene-disease associations for skeletal muscle atrophy. Drugs were selected and a disease drug knowledge graph was constructed. Link prediction methods were applied to the disease drug networks to identify top ranked drugs for therapeutic treatment of skeletal muscle atrophy. The graph convolution network performs best in link prediction based on receiver operating characteristic curves and prediction accuracies. The key genes involved in skeletal muscle atrophy are associated with metabolic and neurodegenerative diseases. The drugs selected for repurposing using the graph convolution network method were nutrients, corticosteroids, anti-inflammatory medications, and others related to insulin.

An Integrative Network Science and Artificial Intelligence Drug Repurposing Approach for Muscle Atrophy in Spaceflight Microgravity.

Muscle atrophy is a side effect of several terrestrial diseases which also affects astronauts severely in space missions due to the reduced gravity in spaceflight. An integrative graph-theoretic network-based drug repurposing methodology quantifying the interplay of key gene regulations and protein-protein interactions in muscle atrophy conditions is presented. Transcriptomic datasets from mice in spaceflight from GeneLab have been extensively mined to extract the key genes that cause muscle atrophy in organ muscle tissues such as the thymus, liver, and spleen. Top muscle atrophy gene regulators are selected by Bayesian Markov blanket method and gene-disease knowledge graph is constructed using the scalable precision medicine knowledge engine. A deep graph neural network is trained for predicting links in the network. The top ranked diseases are identified and drugs are selected for repurposing using drug bank resource. A disease drug knowledge graph is constructed and the graph neural network is trained for predicting new drugs. The results are compared with machine learning methods such as random forest, and gradient boosting classifiers. Network measure based methods shows that preferential attachment has good performance for link prediction in both the gene-disease and disease-drug graphs. The receiver operating characteristic curves, and prediction accuracies for each method show that the random walk similarity measure and deep graph neural network outperforms the other methods. Several key target genes identified by the graph neural network are associated with diseases such as cancer, diabetes, and neural disorders. The novel link prediction approach applied to the disease drug knowledge graph identifies the Monoclonal Antibodies drug therapy as suitable candidate for drug repurposing for spaceflight induced microgravity. There are a total of 21 drugs identified as possible candidates for treating muscle atrophy. Graph neural network is a promising deep learning architecture for link prediction from gene-disease, and disease-drug networks.

Detection of Genes in Arabidopsis thaliana L. Responding to DNA Damage from Radiation and Other Stressors in Spaceflight.

Ionizing radiation present in extraterrestrial environment is an important factor that affects plants grown in spaceflight. Pearson correlation-based gene regulatory network inferencing from transcriptional responses of the plant Arabidopsis thaliana L. grown in real and simulated spaceflight conditions acquired by GeneLab, followed by topological and spectral analysis of the networks is performed. Gene regulatory subnetworks are extracted for DNA damage response processes. Analysis of radiation-induced ATR/ATM protein-protein interactions in Arabidopsis reveals interaction profile similarities under low radiation doses suggesting novel mechanisms of DNA damage response involving non-radiation-induced genes regulating other stress responses in spaceflight. The Jaccard similarity index shows that the genes AT2G31320, AT4G21070, AT2G46610, and AT3G27060 perform similar functions under low doses of radiation. The incremental association Markov blanket method reveals non-radiation-induced genes linking DNA damage response to root growth and plant development. Eighteen radiation-induced genes and sixteen non-radiation-induced gene players have been identified from the ATR/ATM protein interaction complexes involved in heat, salt, water, osmotic stress responses, and plant organogenesis. Network analysis and logistic regression ranking detected AT3G27060, AT1G07500, AT5G66140, and AT3G21280 as key gene players involved in DNA repair processes. High atomic weight, high energy, and gamma photon radiation result in higher intensity of DNA damage response in the plant resulting in elevated values for several network measures such as spectral gap and girth. Nineteen flavonoid and carotenoid pigment activations involved in pigment biosynthesis processes are identified in low radiation dose total light spaceflight environment but are not found to have significant regulations under very high radiation dose environment.

Sarcoma de Ewing extraóseo en la columna dorsal con compresión medular al momento del diagnóstico: Reporte de un caso / Primary extra-osseous Ewing sarcoma of the dorsal spine with spinal cord compression at diagnosis: Case report

Los tumores neuroectodérmicos primitivos a nivel extraóseo son una neoplasia muy rara, con pocas publicaciones en la literatura. Se reporta un caso de una paciente de nueve años quien se presentó con signos de compresión medular. Se estableció un grupo de diagnósticos diferenciales en el abordaje, entre los que debió incluirse la patología oncológica. En la paciente, se observó la presencia de una masa en la columna dorsal con compromiso del cordón medular, que requirió cirugía descompresiva de urgencia y, posteriormente, se diagnosticó sarcoma de Ewing. Cabe resaltar la importancia del caso debido a la baja incidencia del origen extraóseo en esta neoplasia y su rara localización en la columna dorsal, y destacar la presencia de la compresión medular como forma de presentación del cáncer en pediatría, además de la poca información que, hasta el momento, se posee referida al mejor abordaje terapéutico en esta patología, en esta localización.

Extraosseous primitive neuroectodermal tumors are very rare neoplasms. Only a few cases have been published in the literature. This report is about a 9-year-old female patient whose clinical manifestations showed spinal cord compression, so different diagnosis should be considered, including oncology disease. The patient showed a mass of the dorsal spine with spinal cord compression. She was diagnosed with Ewing Sarcoma after surgical intervention and pathology study. It is necessary to highlight the importance of the present case due to the low incidence of the extraosseous Ewing Sarcoma, and its infrequent location at the dorsal spine and the spinal cord compression as initial presentation of pediatric cancer, as well as the poor information related to the best therapeutic strategy to treat this disease at this location.

[Primary extra-osseous Ewing sarcoma of the dorsal spine with spinal cord compression at diagnosis: Case report]. / Sarcoma de Ewing extraóseo en la columna dorsal con compresión medular al momento del diagnóstico: Reporte de un caso.

Extraosseous primitive neuroectodermal tumors are very rare neoplasms. Only a few cases have been published in the literature. This report is about a 9-year-old female patient whose clinical manifestations showed spinal cord compression, so different diagnosis should be considered, including oncology disease. The patient showed a mass of the dorsal spine with spinal cord compression. She was diagnosed with Ewing Sarcoma after surgical intervention and pathology study. It is necessary to highlight the importance of the present case due to the low incidence of the extraosseous Ewing Sarcoma, and its infrequent location at the dorsal spine and the spinal cord compression as initial presentation of pediatric cancer, as well as the poor information related to the best therapeutic strategy to treat this disease at this location.

Los tumores neuroectodérmicos primitivos a nivel extraóseo son una neoplasia muy rara, con pocas publicaciones en la literatura. Se reporta un caso de una paciente de nueve años quien se presentó con signos de compresión medular. Se estableció un grupo de diagnósticos diferenciales en el abordaje, entre los que debió incluirse la patología oncológica. En la paciente, se observó la presencia de una masa en la columna dorsal con compromiso del cordón medular, que requirió cirugía descompresiva de urgencia y, posteriormente, se diagnosticó sarcoma de Ewing. Cabe resaltar la importancia del caso debido a la baja incidencia del origen extraóseo en esta neoplasia y su rara localización en la columna dorsal, y destacar la presencia de la compresión medular como forma de presentación del cáncer en pediatría, además de la poca información que, hasta el momento, se posee referida al mejor abordaje terapéutico en esta patología, en esta localización.

Ascaris pulmonar en paciente politraumatizado / Pulmonary ascariasis in a polytraumatized patient

La parasitosis pulmonar por Áscaris es una entidad poco frecuente asociada a factores predisponentes relacionados con la edad, condiciones ambientales y de higiene. Es generalmente asintomática, pero cuando la larva entra en el ciclo pulmonar se puede volver sintomática caracterizándose por fiebre, disnea, dolor torácico, tos con expectoración hemoptoica, eosinofilia e infiltrados alveolares en la radiografía de tórax. Su diagnóstico es la comprobación del parásito. Se describe el caso de un paciente politraumatizado que durante su hospitalización presentó infiltrados alveolares asociado a eosinofilia y se documentó una larva de áscaris en pulmón.