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Priyadarshini, Pallavi, Fengqiong Qin, Ee-Peng Lim, and Wee-Keong Ng. "Parameter driven synthetic web database generation." Journal of Systems and Software 69, no. 1-2 (January 2004): 29–42. http://dx.doi.org/10.1016/s0164-1212(03)00002-5.
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Sanghi, Anupam, Shadab Ahmed, and Jayant R. Haritsa. "Projection-compliant database generation." Proceedings of the VLDB Endowment 15, no. 5 (January 2022): 998–1010. http://dx.doi.org/10.14778/3510397.3510398.
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Synthesizing data using declarative formalisms has been persuasively advocated in contemporary data generation frameworks. In particular, they specify operator output volumes through row-cardinality constraints. However, thus far, adherence to these volumetric constraints has been limited to the Filter and Join operators. A critical deficiency is the lack of support for the Projection operator, which is at the core of basic SQL constructs such as Distinct, Union and Group By. The technical challenge here is that cardinality unions in multi-dimensional space, and not mere summations, need to be captured in the generation process. Further, dependencies across different data subspaces need to be taken into account. We address the above lacuna by presenting PiGen , a dynamic data generator that incorporates Projection cardinality constraints in its ambit. The design is based on a projection subspace division strategy that supports the expression of constraints using optimized linear programming formulations. Further, techniques of symmetric refinement and workload decomposition are introduced to handle constraints across different projection subspaces. Finally, PiGen supports dynamic generation, where data is generated on-demand during query processing, making it amenable to Big Data environments. A detailed evaluation on workloads derived from real-world and synthetic benchmarks demonstrates that PiGen can accurately and efficiently model Projection outcomes, representing an essential step forward in customized database generation.
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Pujol, David, Amir Gilad, and Ashwin Machanavajjhala. "PreFair: Privately Generating Justifiably Fair Synthetic Data." Proceedings of the VLDB Endowment 16, no. 6 (February 2023): 1573–86. http://dx.doi.org/10.14778/3583140.3583168.
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When a database is protected by Differential Privacy (DP), its usability is limited in scope. In this scenario, generating a synthetic version of the data that mimics the properties of the private data allows users to perform any operation on the synthetic data, while maintaining the privacy of the original data. Therefore, multiple works have been devoted to devising systems for DP synthetic data generation. However, such systems may preserve or even magnify properties of the data that make it unfair, rendering the synthetic data unfit for use. In this work, we present PreFair, a system that allows for DP fair synthetic data generation. PreFair extends the state-of-the-art DP data generation mechanisms by incorporating a causal fairness criterion that ensures fair synthetic data. We adapt the notion of justifiable fairness to fit the synthetic data generation scenario. We further study the problem of generating DP fair synthetic data, showing its intractability and designing algorithms that are optimal under certain assumptions. We also provide an extensive experimental evaluation, showing that PreFair generates synthetic data that is significantly fairer than the data generated by leading DP data generation mechanisms, while remaining faithful to the private data.
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Pavez, Vicente, Gabriel Hermosilla, Francisco Pizarro, Sebastián Fingerhuth, and Daniel Yunge. "Thermal Image Generation for Robust Face Recognition." Applied Sciences 12, no. 1 (January 5, 2022): 497. http://dx.doi.org/10.3390/app12010497.
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This article shows how to create a robust thermal face recognition system based on the FaceNet architecture. We propose a method for generating thermal images to create a thermal face database with six different attributes (frown, glasses, rotation, normal, vocal, and smile) based on various deep learning models. First, we use StyleCLIP, which oversees manipulating the latent space of the input visible image to add the desired attributes to the visible face. Second, we use the GANs N’ Roses (GNR) model, a multimodal image-to-image framework. It uses maps of style and content to generate thermal imaging from visible images, using generative adversarial approaches. Using the proposed generator system, we create a database of synthetic thermal faces composed of more than 100k images corresponding to 3227 individuals. When trained and tested using the synthetic database, the Thermal-FaceNet model obtained a 99.98% accuracy. Furthermore, when tested with a real database, the accuracy was more than 98%, validating the proposed thermal images generator system.
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Dinges, Laslo, Ayoub Al-Hamadi, Moftah Elzobi, Sherif El-etriby, and Ahmed Ghoneim. "ASM Based Synthesis of Handwritten Arabic Text Pages." Scientific World Journal 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/323575.
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Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system that automatically turns Arabic Unicode text into synthetic images of handwritten documents and detailed ground truth. Active Shape Models (ASMs) based on 28046 online samples were used for character synthesis and statistical properties were extracted from the IESK-arDB database to simulate baselines and word slant or skew. In the synthesis step ASM based representations are composed to words and text pages, smoothed by B-Spline interpolation and rendered considering writing speed and pen characteristics. Finally, we use the synthetic data to validate a segmentation method. An experimental comparison with the IESK-arDB database encourages to train and test document analysis related methods on synthetic samples, whenever no sufficient natural ground truthed data is available.
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Sazonova, Kateryna, Olena Nosovets, Vitalii Babenko, and Olga Averianova. "GENERATION OF SYNTHETICAL MEDICAL DATA BY MDR-ANALYSIS." Proceedings of the National Aviation University 87, no. 2 (July 27, 2021): 31–36. http://dx.doi.org/10.18372/2306-1472.87.15719.
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Purpose: The purpose of this article is to outline an algorithm for generating synthetic medical data in order to augment small samples of data. Methods: To achieve the research goal, methods such as: correlation analysis (to identify significant variables and the relationships between them), MDR analysis (to build logical chains of relationships between medical data), and regression analysis (to model medical data variables to use this to generate synthetic data) were used. Results: A database of heart failure patients that is publicly available was used to test the developed algorithm for generating synthetic medical data in action; as a result, statistical relationships between data were found and used to build linear regression models. Discussion: The proposed algorithm allows, with a few simple, yet important actions, to perform the generation of medical data, which makes it possible to obtain large data sets that can be used to implement machine learning methods in any tasks related to medicine.
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Burman, Nitin, Claudia Manetti, Paulo Tostes, Joost Lumens, and Jan D'hooge. "A pipeline to enable large-scale generation of diverse 2D cardiac synthetic ultrasound recordings corresponding to healthy and heart failure virtual patients." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A279. http://dx.doi.org/10.1121/10.0016267.
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Simulated ultrasound (US) data are widely used in echocardiography to develop and validate rapidly growing convolutional neural networks (CNNs) based learning algorithms for image processing and analysis. In this context, a large and diverse database of synthetic US scans is considered vital for CNN training purposes, as clinical US data are scarce and difficult to access. Major hurdles in creating an extensive database are the long US simulation time and unstable heart models for extreme parameter settings. Here, we developed and implemented a cardiac US simulation pipeline that kinematically connects two state-of-the-art solutions in the field of US simulation (COLE) and cardiac modelling (CircAdapt), benefiting from the fast simulation time of the convolution-based ultrasound simulator and stability of the mechanical heart model to produce 2D synthetic cardiac US recordings. Furthermore, using our pipeline, we generated diverse set of 600 2D synthetic cardiac US recordings of healthy and heart failure virtual patients with variations in the shapes, motion patterns, and functions of the heart, along with their ground truth 2D myocardial velocity profiles and deformation curves. The resulting database is a potential tool for augmenting training databases of machine learning based US image processing algorithms. [Work funded by European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 860745.]
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Kuriki, Mikaele Silva, Francisco Lledo Santos, and Cristiano Poleto. "Small-Scale Wetland Model for Synthetic Sewage Treatment." Ciência e Natura 44 (April 21, 2022): e25. http://dx.doi.org/10.5902/2179460x68834.
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With the demand for electricity growing, the migration to renewable sources is a reality. In distributed generation, photovoltaic systems are a renewable and sustainable alternative to the main energy sources to generate electricity. Monitoring a photovoltaic system over its operating time guarantees its good performance. This requires solar radiation and temperature data measured at the installation site or the use of solarimetric stations databases. However, the differences between the results simulated with databases and with data measured at the installation site are not widely known, which would be the ideal case from a technical point of view. The aim of this study was to verify the feasibility of monitoring the performance of a 2.5 kWp photovoltaic system located in the city of Porto Alegre - Brazil using the System Advisor Model (SAM) modeling tool and a public database. Simulation results were compared using data provided by a station of the National Institute of Meteorology (INMET) with the results obtained with data measured at the site of the photovoltaic system. Differences were verified between the solar radiation measured on site and that of the INMET database, and the difference in accumulated radiation was 9.2% for the entire period analyzed. When comparing the measured and simulated alternating current energy using the radiation and temperature data measured on site for the non-shading time, it was found that the difference between the results was 0.5%. Using the INMET climate file, the monthly differences ranged from -6% to 14% and the difference in accumulated energy for the entire measurement period was 2.5%. The results showed that the use of a database measured by a public solarimetric station close to the site, in this case approximately 6 km away from the installation, is feasible for monitoring photovoltaic systems, since the differences found were not significant. This monitoring can identify system failures and performance loss over time.
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Baowaly, Mrinal Kanti, Chia-Ching Lin, Chao-Lin Liu, and Kuan-Ta Chen. "Synthesizing electronic health records using improved generative adversarial networks." Journal of the American Medical Informatics Association 26, no. 3 (December 7, 2018): 228–41. http://dx.doi.org/10.1093/jamia/ocy142.
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AbstractObjectiveThe aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method.Materials and MethodsWe modified medGAN to obtain two synthetic data generation models—designated as medical Wasserstein GAN with gradient penalty (medWGAN) and medical boundary-seeking GAN (medBGAN)—and compared the results obtained using the three models. We used 2 databases: MIMIC-III and National Health Insurance Research Database (NHIRD), Taiwan. First, we trained the models and generated synthetic EHRs by using these three 3 models. We then analyzed and compared the models’ performance by using a few statistical methods (Kolmogorov–Smirnov test, dimension-wise probability for binary data, and dimension-wise average count for count data) and 2 machine learning tasks (association rule mining and prediction).ResultsWe conducted a comprehensive analysis and found our models were adequately efficient for generating synthetic EHR data. The proposed models outperformed medGAN in all cases, and among the 3 models, boundary-seeking GAN (medBGAN) performed the best.DiscussionTo generate realistic synthetic EHR data, the proposed models will be effective in the medical industry and related research from the viewpoint of providing better services. Moreover, they will eliminate barriers including limited access to EHR data and thus accelerate research on medical informatics.ConclusionThe proposed models can adequately learn the data distribution of real EHRs and efficiently generate realistic synthetic EHRs. The results show the superiority of our models over the existing model.
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Loisel, Hubert, Daniel Schaffer Ferreira Jorge, Rick A. Reynolds, and Dariusz Stramski. "A synthetic optical database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean." Earth System Science Data 15, no. 8 (August 18, 2023): 3711–31. http://dx.doi.org/10.5194/essd-15-3711-2023.
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Abstract. Radiative transfer (RT) simulations have long been used to study the relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean, from which ocean apparent optical properties (AOPs) can be calculated. For example, inverse models used to estimate IOPs from ocean color radiometric measurements have been developed and validated using the results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range of near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development is the generation of a synthetic dataset of seawater IOPs that serves as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by the probability distributions of IOPs that are consistent with global distributions representative of vast areas of open-ocean pelagic environments and coastal regions, covering a broad range of optical water types. The generation of synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs, assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column (not considered in previous synthetically generated optical databases) and three simulation scenarios associated with the sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0, 30, and 60∘ assuming clear skies, standard atmosphere, and a wind speed of 5 m s−1. Thus, overall 29 880 RT simulations were performed. The output results of these simulations include radiance distributions, plane and scalar irradiances, and a whole set of AOPs, including remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines, where all optical variables are reported in the spectral range of 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving IOPs and AOPs. The database is available at the Dryad open-access repository of research data (https://doi.org/10.6076/D1630T, Loisel et al., 2023).
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Hoffmann, Martin A., Louis-Félix Nothias, Marcus Ludwig, Markus Fleischauer, Emily C. Gentry, Michael Witting, Pieter C. Dorrestein, Kai Dührkop, and Sebastian Böcker. "High-confidence structural annotation of metabolites absent from spectral libraries." Nature Biotechnology 40, no. 3 (October 14, 2021): 411–21. http://dx.doi.org/10.1038/s41587-021-01045-9.
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AbstractUntargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries.
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Alessandrini, M., M. De Craene, O. Bernard, S. Giffard-Roisin, P. Allain, I. Waechter-Stehle, J. Weese, et al. "A Pipeline for the Generation of Realistic 3D Synthetic Echocardiographic Sequences: Methodology and Open-Access Database." IEEE Transactions on Medical Imaging 34, no. 7 (July 2015): 1436–51. http://dx.doi.org/10.1109/tmi.2015.2396632.
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Sauthier, Philippe, Magali Breguet, Alexandre Rozenholc, and Michaël Sauthier. "Quebec Trophoblastic Disease Registry: How to Make an Easy-To-Use Dynamic Database." International Journal of Gynecologic Cancer 25, no. 4 (May 2015): 729–33. http://dx.doi.org/10.1097/igc.0000000000000401.
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ObjectiveTo create an easy-to-use dynamic database designed specifically for the Quebec Trophoblastic Disease Registry (RMTQ).IntroductionIt is now well established that much of the success in managing trophoblastic diseases comes from the development of national and regional reference centers. Computerized databases allow the optimal use of data stored in these centers.MethodsWe have created an electronic data registration system by producing a database using FileMaker Pro 12. It uses 11 external tables associated with a unique identification number for each patient. Each table allows specific data to be recorded, incorporating demographics, diagnosis, automated staging, laboratory values, pathological diagnosis, and imaging parameters.ResultsFrom January 1, 2009, to December 31, 2013, we used our database to register 311 patients with 380 diseases and have seen a 39.2% increase in registrations each year between 2009 and 2012. This database allows the automatic generation of semilogarithmic curves, which take into account β-hCG values as a function of time, complete with graphic markers for applied treatments (chemotherapy, radiotherapy, or surgery). It generates a summary sheet for a synthetic vision in real time.ConclusionsWe have created, at a low cost, an easy-to-use database specific to trophoblastic diseases that dynamically integrates staging and monitoring. We propose a 10-step procedure for a successful trophoblastic database. It improves patient care, research, and education on trophoblastic diseases in Quebec and leads to an opportunity for collaboration on a national Canadian registry.
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Wang, Xiaona, Fengcheng Li, Wenqi Qiu, Binbin Xu, Yanlin Li, Xichen Lian, Hongyan Yu, et al. "SYNBIP: synthetic binding proteins for research, diagnosis and therapy." Nucleic Acids Research 50, no. D1 (October 19, 2021): D560—D570. http://dx.doi.org/10.1093/nar/gkab926.
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Abstract The success of protein engineering and design has extensively expanded the protein space, which presents a promising strategy for creating next-generation proteins of diverse functions. Among these proteins, the synthetic binding proteins (SBPs) are smaller, more stable, less immunogenic, and better of tissue penetration than others, which make the SBP-related data attracting extensive interest from worldwide scientists. However, no database has been developed to systematically provide the valuable information of SBPs yet. In this study, a database named ‘Synthetic Binding Proteins for Research, Diagnosis, and Therapy (SYNBIP)’ was thus introduced. This database is unique in (a) comprehensively describing thousands of SBPs from the perspectives of scaffolds, biophysical & functional properties, etc.; (b) panoramically illustrating the binding targets & the broad application of each SBP and (c) enabling a similarity search against the sequences of all SBPs and their binding targets. Since SBP is a human-made protein that has not been found in nature, the discovery of novel SBPs relied heavily on experimental protein engineering and could be greatly facilitated by in-silico studies (such as AI and computational modeling). Thus, the data provided in SYNBIP could lay a solid foundation for the future development of novel SBPs. The SYNBIP is accessible without login requirement at both official (https://idrblab.org/synbip/) and mirror (http://synbip.idrblab.net/) sites.
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Lykoshin, D. D., V. V. Zaitsev, M. A. Kostromina, and R. S. Esipov. "New-generation osteoplastic materials based on biological and synthetic matrices." Fine Chemical Technologies 16, no. 1 (March 18, 2021): 36–54. http://dx.doi.org/10.32362/2410-6593-2021-16-1-36-54.
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Objectives. The purpose of this analytical review is to evaluate the market for osteoplastic materials and surgical implants, as well as study the features of new-generation materials and the results of clinical applications.Methods. This review summarizes the volumes of research articles presented in the electronic database PubMed and eLIBRARY. A total of 129 scientific articles related to biological systems, calcium phosphate, polymer, and biocomposite matrices as carriers of pharmaceutical substances, primary recombinant protein osteoinductors, antibiotics, and biologically active chemical reagents were analyzed and summarized. The search depth was 10 years.Results. Demineralized bone matrix constitutes 26% of all types of osteoplastic matrices used globally in surgical osteology, which includes neurosurgery, traumatology and orthopedics, dentistry, and maxillofacial and pediatric surgery. Among the matrices, polymer and biocomposite matrices are outstanding. Special attention is paid to the possibility of immobilizing osteogenic factors and target pharmaceutical substances on the scaffold material to achieve controlled and prolonged release at the site of surgical implantation. Polymeric and biocomposite materials can retard the release of pharmaceutical substances at the implantation site, promoting a decrease in the toxicity and an improvement in the therapeutic effect. The use of composite scaffolds of different compositions in vivo results in high osteogenesis, promotes the initialization of biomineralization, and enables the tuning of the degradation rate of the material.Conclusions. Osteoplastic materials of various compositions in combination with drugs showed accelerated regeneration and mineralization of bone tissue in vivo, excluding systemic side reactions. Furthermore, although some materials have already been registered as commercial drugs, a plethora of unresolved problems remain. Due to the limited clinical studies of materials for use on humans, there is still an insufficient understanding of the toxicity of materials, time of their resorption, speed of drug delivery, and the possible long-term adverse effects of using implants of different compositions.
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Isaza, Cesar, Ivan Alonso Lujan-Cabrera, Ely Karina Anaya Rivera, Jose Amilcar Rizzo Sierra, Jonny Paul Zavala De Paz, and Cristian Felipe Ramirez-Gutierrez. "Generation of a Synthetic Database for the Optical Response of One-Dimensional Photonic Crystals Using Genetic Algorithms." Mathematics 10, no. 23 (November 28, 2022): 4484. http://dx.doi.org/10.3390/math10234484.
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This work proposes an optimization tool based on genetic algorithms for the inverse design of photonic crystals. Based on target reflectance, the algorithm generates a population of chromosomes where the genes represent the thickness of a layer of a photonic crystal. Each layer is independent of another. Therefore, the sequence obtained is a disordered configuration. In the genetic algorithm, two dielectric materials are first selected to generate the population. Throughout the simulation, the chromosomes are evaluated, crossed over, and mutated to find the best-fitted one based on an error function. The target reflectance was a perfect mirror in the visible region. As a result, it was found that obtaining photonic crystal configurations with a specific stop band with disordered arrangements is possible. The genetic information of the best-fitted individuals (layer sequence, optical response, and error) is stored in an h5 format. This method of generating artificial one-dimensional photonic crystal data can be used to train a neural network for solving the problem of the inverse design of any crystal with a specific optical response.
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Mohammadi Far, Somayeh, Matin Beiramvand, Mohammad Shahbakhti, and Piotr Augustyniak. "Prediction of Preterm Delivery from Unbalanced EHG Database." Sensors 22, no. 4 (February 15, 2022): 1507. http://dx.doi.org/10.3390/s22041507.
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Objective: The early prediction of preterm labor can significantly minimize premature delivery complications for both the mother and infant. The aim of this research is to propose an automatic algorithm for the prediction of preterm labor using a single electrohysterogram (EHG) signal. Method: The proposed method firstly employs empirical mode decomposition (EMD) to split the EHG signal into two intrinsic mode functions (IMFs), then extracts sample entropy (SampEn), the root mean square (RMS), and the mean Teager–Kaiser energy (MTKE) from each IMF to form the feature vector. Finally, the extracted features are fed to a k-nearest neighbors (kNN), support vector machine (SVM), and decision tree (DT) classifiers to predict whether the recorded EHG signal refers to the preterm case. Main results: The studied database consists of 262 term and 38 preterm delivery pregnancies, each with three EHG channels, recorded for 30 min. The SVM with a polynomial kernel achieved the best result, with an average sensitivity of 99.5%, a specificity of 99.7%, and an accuracy of 99.7%. This was followed by DT, with a mean sensitivity of 100%, a specificity of 98.4%, and an accuracy of 98.7%. Significance: The main superiority of the proposed method over the state-of-the-art algorithms that studied the same database is the use of only a single EHG channel without using either synthetic data generation or feature ranking algorithms.
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Chávez-Hernández, Ana L., Norberto Sánchez-Cruz, and José L. Medina-Franco. "Fragment Library of Natural Products and Compound Databases for Drug Discovery." Biomolecules 10, no. 11 (November 6, 2020): 1518. http://dx.doi.org/10.3390/biom10111518.
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Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including coronavirus disease 2019 (COVID-19), which is causing the current pandemic. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent substrates of unique substructures for fragment-based drug discovery. To this end, fragment libraries should be incorporated into automated drug design pipelines. However, public fragment libraries based on extensive collections of natural products are still limited. Herein, we report the generation and analysis of a fragment library of natural products derived from a database with more than 400,000 compounds. We also report fragment libraries of a large food chemical database and other compound datasets of interest in drug discovery, including compound libraries relevant for COVID-19 drug discovery. The fragment libraries were characterized in terms of content and diversity.
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Fu, Bo, Xiangyi Zhang, Liyan Wang, Yonggong Ren, and Dang N. H. Thanh. "A blind medical image denoising method with noise generation network." Journal of X-Ray Science and Technology 30, no. 3 (April 15, 2022): 531–47. http://dx.doi.org/10.3233/xst-211098.
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BACKGROUND: In the process of medical images acquisition, the unknown mixed noise will affect image quality. However, the existing denoising methods usually focus on the known noise distribution. OBJECTIVE: In order to remove the unknown real noise in low-dose CT images (LDCT), a two-step deep learning framework is proposed in this study, which is called Noisy Generation-Removal Network (NGRNet). METHODS: Firstly, the output results of L0 Gradient Minimization are used as the labels of a dental CT image dataset to form a pseudo-image pair with the real dental CT images, which are used to train the noise generation network to estimate real noise distribution. Then, for the lung CT images of the LIDC/IDRI database, we migrate the real noise to the noise-free lung CT images, to construct a new almost-real noisy images dataset. Since dental images and lung images are all CT images, this migration can be achieved. The denoising network is trained to realize the denoising of real LDCT for dental images by using this dataset but can extend for any low-dose CT images. RESULTS: To prove the effectiveness of our NGRNet, we conduct experiments on lung CT images with synthetic noise and tooth CT images with real noise. For synthetic noise image datasets, experimental results show that NGRNet is superior to existing denoising methods in terms of visual effect and exceeds 0.13dB in the peak signal-to-noise ratio (PSNR). For real noisy image datasets, the proposed method can achieve the best visual denoising effect. CONCLUSIONS: The proposed method can retain more details and achieve impressive denoising performance.
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Wang, Yunpeng, Scarlet Ferrinho, Helen Connaris, and Rebecca J. M. Goss. "The Impact of Viral Infection on the Chemistries of the Earth’s Most Abundant Photosynthesizes: Metabolically Talented Aquatic Cyanobacteria." Biomolecules 13, no. 8 (August 4, 2023): 1218. http://dx.doi.org/10.3390/biom13081218.
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Cyanobacteria are the most abundant photosynthesizers on earth, and as such, they play a central role in marine metabolite generation, ocean nutrient cycling, and the control of planetary oxygen generation. Cyanobacteriophage infection exerts control on all of these critical processes of the planet, with the phage-ported homologs of genes linked to photosynthesis, catabolism, and secondary metabolism (marine metabolite generation). Here, we analyze the 153 fully sequenced cyanophages from the National Center for Biotechnology Information (NCBI) database and the 45 auxiliary metabolic genes (AMGs) that they deliver into their hosts. Most of these AMGs are homologs of those found within cyanobacteria and play a key role in cyanobacterial metabolism-encoding proteins involved in photosynthesis, central carbon metabolism, phosphate metabolism, methylation, and cellular regulation. A greater understanding of cyanobacteriophage infection will pave the way to a better understanding of carbon fixation and nutrient cycling, as well as provide new tools for synthetic biology and alternative approaches for the use of cyanobacteria in biotechnology and sustainable manufacturing.
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Pacheco, Fernando, Gabriel Hermosilla, Osvaldo Piña, Gabriel Villavicencio, Héctor Allende-Cid, Juan Palma, Pamela Valenzuela, et al. "Generation of Synthetic Data for the Analysis of the Physical Stability of Tailing Dams through Artificial Intelligence." Mathematics 10, no. 23 (November 22, 2022): 4396. http://dx.doi.org/10.3390/math10234396.
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In this research, we address the problem of evaluating physical stability (PS) to close tailings dams (TD) from medium-sized Chilean mining using artificial intelligence (AI) algorithms. The PS can be analyzed through the study of critical variables of the TD that allow estimating different potential failure mechanisms (PFM): seismic liquefaction, slope instability, static liquefaction, overtopping, and piping, which may occur in this type of tailings storage facilities in a seismically active country such as Chile. Thus, this article proposes the use of four machine learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural networks (ANN), and extreme gradient boosting (XGBoost), to estimate five possible PFM. In addition, due to the scarcity of data to train the algorithms, the use of generative adversarial networks (GAN) is proposed to create synthetic data and increase the database used. Therefore, the novelty of this article consists in estimating the PFM for TD and generating synthetic data through the GAN. The results show that, when using the GAN, the result obtained by the ML models increases the F1-score metric by 30 percentage points, obtaining results of 97.4%, 96.3%, 96.7%, and 97.3% for RF, SVM, ANN, and XGBoost, respectively.
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Mousses, Spyro, David Schneider, Jeff Kiefer, Pieter Derdeyn, Kendyl Douglas, Abhishek Kothari, Daniel D. Von Hoff, and Chris Yoo. "Application of artificial intelligence to predict a new class of novel synthetic lethal targets." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): 2598. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.2598.
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2598 Background: Synthetic lethal targets are proteins that are contextually vulnerable. Inhibitors of PARP1, for example, selectively produce a lethal phenotype in the context of cancer cells which have lost BRCA1 or BRCA2 function. As a high mutation rate is a hallmark of many cancers, targeting synthetic lethal interactions to selectively inhibit cancer cells with altered genetic backgrounds may increase the specificity and efficacy of therapeutics. Recently, clinical trials have targeted synthetic lethal pairs such as EGFR and BRAF, TP53 and BCL2, and PTEN and CHD1. Previous attempts to identify synthetic lethal targets have relied on empirical results from published studies of biological pathways perturbed in cancer cells. Developing strategies to rapidly identify synthetic lethals by combining multiple experimental and computational approaches would result in a new class of potential cancer drug targets beyond the existing efforts that rely on single experimental or computational methods alone. Methods: Here we present Expansive AI, an artificial intelligence augmented knowledge network that enables rapid hypothesis generation for accelerated discovery research. Using a purpose-built, hypergraph database of massive, integrated genomic and biomedical data, we can query all synthetic lethals and their component genes, as well as a wealth of data related to these genes. The database of biological data includes 11,000+ cancer genomes from TCGA, prior knowledge resources such as gene ontology and pathway resources, and experimental data including chemical and protein interaction and patent data. The hypergraph’s architecture allows for linking and nesting data, enabling efficient extraction of biologically-relevant features. Results: Using these features, a neural network classified 540 new candidate pairs that have previously not been reported. The candidate pairs were filtered to include only known oncogenes and least-studied genes. This produced a list of gene pairs which may represent the most novel class of synthetic lethal target candidates identified to date. Conclusions: We highlight the results of this AI-based approach and discuss validation efforts of the predicted interactions in specific cancer contexts.
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Wu, Xiaoying, and Dimitri Theodoratos. "Template-Based Bitmap View Selection for Optimizing Queries Over Tree Data." International Journal of Cooperative Information Systems 25, no. 03 (September 2016): 1650005. http://dx.doi.org/10.1142/s0218843016500052.
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Developing and exploiting flexible techniques for optimizing the evaluation of queries over loosely structured data (e.g. tree or graph databases) is of crucial importance for modern database applications. In this context, we consider a new type of views which can be materialized as compressed bitmaps over tree data. We introduce the concept of view structural template to define classes of views. We then define and address a novel view selection problem (called view class selection (VCS) problem) where the goal is to select classes of bitmap views in order to optimize the overall evaluation cost of all tree pattern queries (TPQs) that can be issued against a database while satisfying a space constraint and ensuring that all the TPQs can be answered using exclusively the materialized views. We show that the VCS problem is NP-hard and we design two heuristic greedy algorithms which iteratively generate new batches of candidate view classes and make them available for selection. Each algorithm uses a different view class expansion technique to enable the systematic generation of candidate view classes from classes with smaller templates. We run extensive experiments to evaluate both the effectiveness of the algorithms and their efficiency on real, benchmark and synthetic datasets. Our algorithms are able to suggest high quality selections of view classes in a reasonable amount of time.
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Erland, Lauren A. E., Ryland T. Giebelhaus, Jerrin M. R. Victor, Susan J. Murch, and Praveen K. Saxena. "The Morphoregulatory Role of Thidiazuron: Metabolomics-Guided Hypothesis Generation for Mechanisms of Activity." Biomolecules 10, no. 9 (August 28, 2020): 1253. http://dx.doi.org/10.3390/biom10091253.
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Thidiazuron (TDZ) is a diphenylurea synthetic herbicide and plant growth regulator used to defoliate cotton crops and to induce regeneration of recalcitrant species in plant tissue culture. In vitro cultures of African violet thin petiole sections are an ideal model system for studies of TDZ-induced morphogenesis. TDZ induces de novo shoot organogenesis at low concentrations and somatic embryogenesis at higher concentrations of exposure. We used an untargeted metabolomics approach to identify metabolites in control and TDZ-treated tissues. Statistical analysis including metabolite clustering, pattern and pathway tools, logical algorithms, synthetic biotransformations and hormonomics identified TDZ-induced changes in metabolism. A total of 18,602 putative metabolites with extracted masses and predicted formulae were identified with 1412 features that were found only in TDZ-treated tissues and 312 that increased in response to TDZ. The monomer of TDZ was not detected intact in the tissues but putative oligomers were found in the database and we hypothesize that these may form by a Diels–Alder reaction. Accumulation oligomers in the tissue may act as a reservoir, slowly releasing the active TDZ monomer over time. Cleavage of the amide bridge released TDZ-metabolites into the tissues including organic nitrogen and sulfur containing compounds. Metabolomics data analysis generated six novel hypotheses that can be summarized as an overall increase in uptake of sugars from the culture media, increase in primary metabolism, redirection of terpene metabolism and mediation of stress metabolism via indoleamine and phenylpropanoid metabolism. Further research into the specific mechanisms hypothesized is likely to unravel the mode of action of TDZ and to provide new insights into the control of plant morphogenesis.
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Parate, Shraddha, Vikas Kumar, Danishuddin, Jong Hong, and Keun Lee. "Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics." International Journal of Molecular Sciences 22, no. 10 (May 18, 2021): 5311. http://dx.doi.org/10.3390/ijms22105311.
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Heparanase (Hpse) is an endo-β-D-glucuronidase capable of cleaving heparan sulfate side chains. Its upregulated expression is implicated in tumor growth, metastasis and angiogenesis, thus making it an attractive target in cancer therapeutics. Currently, a few small molecule inhibitors have been reported to inhibit Hpse, with promising oral administration and pharmacokinetic (PK) properties. In the present study, a ligand-based pharmacophore model was generated from a dataset of well-known active small molecule Hpse inhibitors which were observed to display favorable PK properties. The compounds from the InterBioScreen database of natural (69,034) and synthetic (195,469) molecules were first filtered for their drug-likeness and the pharmacophore model was used to screen the drug-like database. The compounds acquired from screening were subjected to molecular docking with Heparanase, where two molecules used in pharmacophore generation were used as reference. From the docking analysis, 33 compounds displayed higher docking scores than the reference and favorable interactions with the catalytic residues. Complex interactions were further evaluated by molecular dynamics simulations to assess their stability over a period of 50 ns. Furthermore, the binding free energies of the 33 compounds revealed 2 natural and 2 synthetic compounds, with better binding affinities than reference molecules, and were, therefore, deemed as hits. The hit compounds presented from this in silico investigation could act as potent Heparanase inhibitors and further serve as lead scaffolds to develop compounds targeting Heparanase upregulation in cancer.
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Mas, Erick, Daniel Felsenstein, Luis Moya, A. Yair Grinberger, Rubel Das, and Shunichi Koshimura. "Dynamic Integrated Model for Disaster Management and Socioeconomic Analysis (DIM2SEA)." Journal of Disaster Research 13, no. 7 (December 1, 2018): 1257–71. http://dx.doi.org/10.20965/jdr.2018.p1257.
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The DIM2SEA research project aims to increase urban resilience to large-scale disasters. We are engaged in developing a prototype Dynamic Integrated Model for Disaster Management and Socioeconomic Analysis (DIM2SEA) that will give disaster officials, stakeholders, urban engineers and planners an analytic tool for mitigating some of the worst excesses of catastrophic events. This is achieved by harnessing state-of-the-art developments in damage assessment, spatial simulation modeling, and Geographic Information System (GIS). At the heart of DIM2SEA is an agent-based model combined with post-disaster damage assessment and socioeconomic impact models. The large amounts of simulated spatial and temporal data generated by the agent-based models are fused with the socioeconomic profiles of the target population to generate a multidimensional database of inherently “synthetic” big data. Progress in the following areas is reported here: (1) Synthetic population generation from census tract data into agent profiling and spatial allocation, (2) developing scenarios of building damage due to earthquakes and tsunamis, (3) building debris scattering estimation and road network disruption, (4) logistics regarding post-disaster relief distribution, (5) the labor market in post-disaster urban dynamics, and (6) household insurance behavior as a reflection of urban resilience.
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Rauf, Abdur, Muhammad Akram, Prabhakar Semwal, Adil A. H. Mujawah, Naveed Muhammad, Zerfishan Riaz, Naveed Munir, et al. "Antispasmodic Potential of Medicinal Plants: A Comprehensive Review." Oxidative Medicine and Cellular Longevity 2021 (November 11, 2021): 1–12. http://dx.doi.org/10.1155/2021/4889719.
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Numerous medicinal plants have been utilized for the treatment of different types of diseases and disorders including gastrointestinal (GI) diseases. GI diseases are the most common complaints that normally affects the largest proportion of children and adolescents with overlapping clinical manifestation in diagnosis and medical needs. Drugs with antispasmodic effects are normally applied for the symptomatic treatment of contraction and cramping of smooth muscles in gastrointestinal diseases as well as in other critical clinical situations. In alternative system of medicines, the antispasmodic herbs played a significant role in the cure of GI diseases. These medicinal plants and their herbal products are used from generation to generation because of multiple nutritional and therapeutic benefits. The multiple uses might be attributed to the presence on biologically active chemical constitutes. The main aim of this review is to focus on the medicinal potential of plants possessing antispasmodic activities with their proposed mechanism of action. Several databases such as Google Scholar, Cochrane database, Scopus, and PubMed were used to search the relevant literature regarding “plants with antispasmodic activities.” This present study highlights the updated and quantified information on several medicinal plants with antispasmodic activity like Zanthoxylum armatum, Matricaria chamomilla, Foeniculum vulgare, Pycnocycla spinosa, Atropa belladonna, Lavandula angustifolia, Mentha pulegium, Glycyrrhiza ularensis, Anethum graveolens, and Origanum majorana. Moreover, recent studies on other medicinal plant species also have been included in this review article. Additionally, the study also revealed that the active compounds of all these plants possess significant spasmolytic effect which is safest, efficacious, and cost effective as compared to the available synthetic drugs.
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Kuschert, Sarah, Martin Stroet, Yanni Ka-Yan Chin, Anne Claire Conibear, Xinying Jia, Thomas Lee, Christian Reinhard Otto Bartling, et al. "Facilitating the structural characterisation of non-canonical amino acids in biomolecular NMR." Magnetic Resonance 4, no. 1 (February 24, 2023): 57–72. http://dx.doi.org/10.5194/mr-4-57-2023.
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Abstract. Peptides and proteins containing non-canonical amino acids (ncAAs) are a large and important class of biopolymers. They include non-ribosomally synthesised peptides, post-translationally modified proteins, expressed or synthesised proteins containing unnatural amino acids, and peptides and proteins that are chemically modified. Here, we describe a general procedure for generating atomic descriptions required to incorporate ncAAs within popular NMR structure determination software such as CYANA, CNS, Xplor-NIH and ARIA. This procedure is made publicly available via the existing Automated Topology Builder (ATB) server (https://atb.uq.edu.au, last access: 17 February 2023) with all submitted ncAAs stored in a dedicated database. The described procedure also includes a general method for linking of side chains of amino acids from CYANA templates. To ensure compatibility with other systems, atom names comply with IUPAC guidelines. In addition to describing the workflow, 3D models of complex natural products generated by CYANA are presented, including vancomycin. In order to demonstrate the manner in which the templates for ncAAs generated by the ATB can be used in practice, we use a combination of CYANA and CNS to solve the structure of a synthetic peptide designed to disrupt Alzheimer-related protein–protein interactions. Automating the generation of structural templates for ncAAs will extend the utility of NMR spectroscopy to studies of more complex biomolecules, with applications in the rapidly growing fields of synthetic biology and chemical biology. The procedures we outline can also be used to standardise the creation of structural templates for any amino acid and thus have the potential to impact structural biology more generally.
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Heesch, Mateusz, Michał Dziendzikowski, Krzysztof Mendrok, and Ziemowit Dworakowski. "Diagnostic-Quality Guided Wave Signals Synthesized Using Generative Adversarial Neural Networks." Sensors 22, no. 10 (May 19, 2022): 3848. http://dx.doi.org/10.3390/s22103848.
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Guided waves are a potent tool in structural health monitoring, with promising machine learning algorithm applications due to the complexity of their signals. However, these algorithms usually require copious amounts of data to be trained. Collecting the correct amount and distribution of data is costly and time-consuming, and sometimes even borderline impossible due to the necessity of introducing damage to vital machinery to collect signals for various damaged scenarios. This data scarcity problem is not unique to guided waves or structural health monitoring, and has been partly addressed in the field of computer vision using generative adversarial neural networks. These networks generate synthetic data samples based on the distribution of the data they were trained on. Though there are multiple researched methods for simulating guided wave signals, the problem is not yet solved. This work presents a generative adversarial network architecture for guided waves generation and showcases its capabilities when working with a series of pitch-catch experiments from the OpenGuidedWaves database. The network correctly generates random signals and can accurately reconstruct signals it has not seen during training. The potential of synthetic data to be used for training other algorithms was confirmed in a simple damage detection scenario, with the classifiers trained exclusively on synthetic data and evaluated on real signals. As a side effect of the signal reconstruction process, the network can also compress the signals by 98.44% while retaining the damage index information they carry.
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Li, Haiyan, Yan Ma, Lei Guo, Haijiang Li, Jianhua Chen, and Hongsong Li. "Image restoration for irregular holes based on dual discrimination generation countermeasure network." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 2 (April 2021): 423–29. http://dx.doi.org/10.1051/jnwpu/20213920423.
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In order to solve the problem that the global and local generated countermeasure network cannot inpaint the random irregular large holes, and to improve the standard convolution generator, which demonstrates the defects of color difference and blur, a network architecture of inpainting irregular large holes in an image based on double discrimination generation countermeasure network is proposed. Firstly, the image generator is a U-net architecture defined by partial convolution. The normalized partial convolution only completes the end-to-end mask update for the effective pixels. The skip link in U-net propagates the context information of the image to the higher resolution, and optimizes the training results of the model with the weighted loss function of reconstruction loss, perception loss and wind grid loss. Subsequently, the adversary loss function, the dual discrimination network including the synthetic discriminator and the global discriminator are trained separately to judge the consistency between the generated image and the real image. Finally, the weighted loss functions are trained together with generating network and double discrimination network to further enhance the detail and overall consistency of the inpainted area and make the inpainted results more natural. The simulation experiment is carried out on the Place 365 standard database. The subjective and objective experimental results show that the results of the proposed method has reasonable overall and detail semantic consistency than those of the existing methods when they are used to repair random, irregular and large-area holes. The proposed method effectively overcomes the defects of blurry details, color distortion and artifacts.
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Kelkar, Bhagyashri A., Sunil F. Rodd, and Umakant P. Kulkarni. "A Novel Parameter-Light Subspace Clustering Technique Based on Single Linkage Method." Journal of Information & Knowledge Management 18, no. 01 (March 2019): 1950007. http://dx.doi.org/10.1142/s0219649219500072.
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Subspace clustering is a challenging high-dimensional data mining task. There have been several approaches proposed in the literature to identify clusters in subspaces, however their performance and quality is highly affected by input parameters. A little research is done so far on identifying proper parameter values automatically. Other observed drawbacks are requirement of multiple database scans resulting into increased demand for computing resources and generation of many redundant clusters. Here, we propose a parameter light subspace clustering method for numerical data hereafter referred to as CLUSLINK. The algorithm is based on single linkage clustering method and works in bottom up, greedy fashion. The only input user has to provide is how coarse or fine the resulting clusters should be, and if not given, the algorithm operates with default values. The empirical results obtained over synthetic and real benchmark datasets show significant improvement in terms of accuracy and execution time.
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de Oliveira, Mariana Campello, Mariana Capelo Vides, Dângela Layne Silva Lassi, Julio Torales, Antonio Ventriglio, Henrique Silva Bombana, Vilma Leyton, et al. "Toxicity of Synthetic Cannabinoids in K2/Spice: A Systematic Review." Brain Sciences 13, no. 7 (June 24, 2023): 990. http://dx.doi.org/10.3390/brainsci13070990.
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(1) Background: Synthetic cannabinoids (SCs) are emerging drugs of abuse sold as ‘K2’, ‘K9’ or ‘Spice’. Evidence shows that using SCs products leads to greater health risks than cannabis. They have been associated with greater toxicity and higher addiction potential unrelated to the primary psychoactive component of marijuana, Δ9-tetrahydrocannabinol (Δ9-THC). Moreover, early cases of intoxication and death related to SCs highlight the inherent danger that may accompany the use of these substances. However, there is limited knowledge of the toxicology of Spice ingredients. This systematic review intends to analyze the toxicity of SCs compounds in Spice/K2 drugs. (2) Methods: Studies analyzing synthetic cannabinoid toxicity and dependence were included in the present review. We searched the PubMed database of the US National Library of Medicine, Google Scholar, CompTox Chemicals, and Web of Science up to May 2022. (3) Results: Sixty-four articles reporting the effects of synthetic cannabinoids in humans were included in our review. Ten original papers and fifty-four case studies were also included. Fourteen studies reported death associated with synthetic cannabinoid use, with AB-CHMINACA and MDMB-CHMICA being the main reported SCs. Tachycardia and seizures were the most common toxicity symptoms. The prevalence of neuropsychiatric symptoms was higher in third-generation SCs. (4) Conclusion: SCs may exhibit higher toxicity than THC and longer-lasting effects. Their use may be harmful, especially in people with epilepsy and schizophrenia, because of the increased risk of the precipitation of psychiatric and neurologic disorders. Compared to other drugs, SCs have a higher potential to trigger a convulsive crisis, a decline in consciousness, and hemodynamic changes. Therefore, it is crucial to clarify their potential harms and increase the availability of toxicology data in both clinical and research settings.
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Zhu, Yanfei, and Yousheng Li. "Liver X Receptors as potential therapeutic targets in atherosclerosis." Clinical & Investigative Medicine 32, no. 5 (October 1, 2009): 383. http://dx.doi.org/10.25011/cim.v32i5.6927.
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Purpose: Atherosclerosis is the primary independent risk factor of cardiovascular disease, and Liver X Receptors (LXR? and LXR?) activation may play an anti-atherosclerosis effect. In this article, we summarize the current state of knowledge of roles of LXRs in physiology and homeostasis as well as the links between LXR action and atherosclerosis, and discuss the potential therapeutic effects of LXR agonists. Source: A MEDLINE database search was performed to identify relevant articles using the keywords “liver X receptors”, “LXRs”, and “atherosclerosis”. Additional papers were identified by a manual research of the references from the key articles. Principle findings: Both LXR isoforms promote reverse cholesterol transport (RCT) and have anti-inflammatory activity. LXR? is the predominant receptor in the liver regulating triglyceride synthesis. The antiatherosclerotic ability of LXRs makes them attractive targets for drugs for the treatment of cardiovascular disease. However, LXR activation induces lipogenesis and hypertriglyceridemia. The first-generation synthetic ligands of LXR increase hepatic lipogenesis and plasma triglyceride levels. New LXR ligands need to be designed without undesirable side effects. Conclusion: LXR ?-selective agonists and LXR modulators, which act as agonists in macrophages and induce cholesterol efflux while as antagonists of lipogenesis in the liver, are two critical and attractive approaches to treat atherosclerosis and cardiovascular diseases.
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Kamanna, Kantharaju, S. Y. Khatavi, and P. B. Hiremath. "Microwave-assisted One-pot Synthesis of Amide Bond using WEB." Current Microwave Chemistry 7, no. 1 (June 23, 2020): 50–59. http://dx.doi.org/10.2174/2213335606666190828114344.
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Background: Amide bond plays a key role in medicinal chemistry, and the analysis of bioactive molecular database revealed that the carboxamide group appears in more than 25% of the existing database drugs. Typically amide bonds are formed from the union of carboxylic acid and amine; however, the product formation does not occur spontaneously. Several synthetic methods have been reported for amide bond formation in literature. Present work demonstrated simple and eco-friendly amide bond formation using carboxylic acid and primary amines through in situ generation of O-acylurea. The reaction was found to be more efficient, faster reaction rate; simple work-up gave pure compound isolation in moderate to excellent yield using microwave irradiation as compared to conventional heating. Methods: Developed one-pot synthesis of amide compounds using agro-waste derived greener catalyst under microwave irradiation. Results: Twenty amide bond containing organic compounds are synthesized from carboxylic acid with primary amine catalyzed by agro-waste derived medium under microwave irradiation. First, the reaction involved carboxylic acid activation using EDC.HCl, which is the required base for the neutralization and coupling. The method employed natural agro-waste derived from banana peel ash (WEB) for the coupling gave target amide product without the use of an external organic or inorganic base. Conclusion: In the present work, we demonstrated that agro-waste extract is an alternative greener catalytic medium for the condensation of organic carboxylic acid and primary amine under microwave irradiation. The method found several advantages compared to reported methods like solventfree, non-toxic, cheaper catalyst, and simple reaction condition. The final isolated product achieved chromatographically pure by simple recrystallization and did not require further purification.
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Goldschmidt, Jens, Leonard Nitzsche, Sebastian Wolf, Armin Lambrecht, and Jürgen Wöllenstein. "Rapid Quantitative Analysis of IR Absorption Spectra for Trace Gas Detection by Artificial Neural Networks Trained with Synthetic Data." Sensors 22, no. 3 (January 23, 2022): 857. http://dx.doi.org/10.3390/s22030857.
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Infrared absorption spectroscopy is a widely used tool to quantify and monitor compositions of gases. The concentration information is often retrieved by fitting absorption profiles to the acquired spectra, utilizing spectroscopic databases. In complex gas matrices an expanded parameter space leads to long computation times of the fitting routines due to the increased number of spectral features that need to be computed for each iteration during the fit. This hinders the capability of real-time analysis of the gas matrix. Here, an artificial neural network (ANN) is employed for rapid prediction of gas concentrations in complex infrared absorption spectra composed of mixtures of CO and N2O. Experimental data is acquired with a mid-infrared dual frequency comb spectrometer. To circumvent the experimental collection of huge amounts of training data, the network is trained on synthetically generated spectra. The spectra are based on simulated absorption profiles making use of the HITRAN database. In addition, the spectrometer’s influence on the measured spectra is characterized and included in the synthetic training data generation. The ANN was tested on measured spectra and compared to a non-linear least squares fitting algorithm. An average evaluation time of 303 µs for a single measured spectrum was achieved. Coefficients of determination were 0.99997 for the predictions of N2O concentrations and 0.99987 for the predictions of CO concentrations, with uncertainties on the predicted concentrations between 0.04 and 0.18 ppm for 0 to 100 ppm N2O and between 0.05 and 0.18 ppm for 0 to 60 ppm CO.
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Chambi, Diego, Jenny Lundqvist, Erik Nygren, Luis Romero-Soto, Katherine Marin, András Gorzsás, Mattias Hedenström, et al. "Production of Exopolysaccharides by Cultivation of Halotolerant Bacillus atrophaeus BU4 in Glucose- and Xylose-Based Synthetic Media and in Hydrolysates of Quinoa Stalks." Fermentation 8, no. 2 (February 14, 2022): 79. http://dx.doi.org/10.3390/fermentation8020079.
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A halotolerant, exopolysaccharide-producing bacterium isolated from the Salar de Uyuni salt flat in Bolivia was identified as Bacillus atrophaeus using next-generation sequencing. Comparisons indicate that the genome most likely (p-value: 0.0024) belongs to a subspecies previously not represented in the database. The growth of the bacterial strain and its ability to produce exopolysaccharides (EPS) in synthetic media with glucose or xylose as carbon sources, and in hydrolysates of quinoa stalks, was investigated. The strain grew well in all synthetic media, but the growth in glucose was better than that in xylose. Sugar consumption was better when initial concentrations were low. The growth was good in enzymatically produced cellulosic hydrolysates but was inhibited in hemicellulosic hydrolysates produced using hydrothermal pretreatment. The EPS yields were up to 0.064 g/g on initial glucose and 0.047 g/g on initial xylose, and was higher in media with relatively low sugar concentrations. The EPS was isolated and purified by a sequential procedure including centrifugation, cold ethanol precipitation, trichloroacetic acid treatment, dialysis, and freeze-drying. Glucose and mannose were the main sugars identified in hydrolyzed EPS. The EPS was characterized by size-exclusion chromatography, Fourier-transform infrared (FTIR) spectroscopy, heteronuclear single-quantum coherence nuclear magnetic resonance (HSQC NMR) spectroscopy, scanning electron microscopy, X-ray diffraction, and thermogravimetric analysis. No major differences were elucidated between EPS resulting from cultivations in glucose- or-xylose-based synthetic media, while some divergences with regard to molecular-weight averages and FTIR and HSQC NMR spectra were detected for EPS from hydrolysate-based media.
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Carper, Dana L., Travis J. Lawrence, Alyssa A. Carrell, Dale A. Pelletier, and David J. Weston. "DISCo-microbe: design of an identifiable synthetic community of microbes." PeerJ 8 (February 27, 2020): e8534. http://dx.doi.org/10.7717/peerj.8534.
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Background Microbiomes are extremely important for their host organisms, providing many vital functions and extending their hosts’ phenotypes. Natural studies of host-associated microbiomes can be difficult to interpret due to the high complexity of microbial communities, which hinders our ability to track and identify individual members along with the many factors that structure or perturb those communities. For this reason, researchers have turned to synthetic or constructed communities in which the identities of all members are known. However, due to the lack of tracking methods and the difficulty of creating a more diverse and identifiable community that can be distinguished through next-generation sequencing, most such in vivo studies have used only a few strains. Results To address this issue, we developed DISCo-microbe, a program for the design of an identifiable synthetic community of microbes for use in in vivo experimentation. The program is composed of two modules; (1) create, which allows the user to generate a highly diverse community list from an input DNA sequence alignment using a custom nucleotide distance algorithm, and (2) subsample, which subsamples the community list to either represent a number of grouping variables, including taxonomic proportions, or to reach a user-specified maximum number of community members. As an example, we demonstrate the generation of a synthetic microbial community that can be distinguished through amplicon sequencing. The synthetic microbial community in this example consisted of 2,122 members from a starting DNA sequence alignment of 10,000 16S rRNA sequences from the Ribosomal Database Project. We generated simulated Illumina sequencing data from the constructed community and demonstrate that DISCo-microbe is capable of designing diverse communities with members distinguishable by amplicon sequencing. Using the simulated data we were able to recover sequences from between 97–100% of community members using two different post-processing workflows. Furthermore, 97–99% of sequences were assigned to a community member with zero sequences being misidentified. We then subsampled the community list using taxonomic proportions to mimic a natural plant host–associated microbiome, ultimately yielding a diverse community of 784 members. Conclusions DISCo-microbe can create a highly diverse community list of microbes that can be distinguished through 16S rRNA gene sequencing, and has the ability to subsample (i.e., design) the community for the desired number of members and taxonomic proportions. Although developed for bacteria, the program allows for any alignment input from any taxonomic group, making it broadly applicable. The software and data are freely available from GitHub (https://github.com/dlcarper/DISCo-microbe) and Python Package Index (PYPI).
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Aniel-Quiroga, Íñigo, Omar Quetzalcóatl, Mauricio González, and Louise Guillou. "Tsunami run-up estimation based on a hybrid numerical flume and a parameterization of real topobathymetric profiles." Natural Hazards and Earth System Sciences 18, no. 5 (May 29, 2018): 1469–91. http://dx.doi.org/10.5194/nhess-18-1469-2018.
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Abstract. Tsunami run-up is a key value to determine when calculating and assessing the tsunami hazard in a tsunami-prone area. Run-up can be accurately calculated by means of numerical models, but these models require high-resolution topobathymetric data, which are not always available, and long computational times. These drawbacks restrict the application of these models to the assessment of small areas. As an alternative method, to address large areas empirical formulae are commonly applied to estimate run-up. These formulae are based on numerical or physical experiments on idealized geometries. In this paper, a new methodology is presented to calculate tsunami hazard at large scales. This methodology determines the tsunami flooding by using a coupled model that combines a nonlinear shallow water model (2D-H) and a volume-of-fluid model (RANS 2D-V) and applies the optimal numerical models in each phase of the tsunami generation–propagation–inundation process. The hybrid model has been widely applied to build a tsunami run-up database (TRD). The aim of this database is to form an interpolation domain with which to estimate the tsunami run-up of new scenarios without running a numerical simulation. The TRD was generated by simulating the propagation of parameterized tsunami waves on real non-scaled profiles. A database and hybrid numerical model were validated using real and synthetic scenarios. The new methodology provides feasible estimations of the tsunami run-up; engineers and scientists can use this methodology to address tsunami hazard at large scales.
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Khimdas, S., K. L. Visscher, and C. M. L. Hutnik. "Besifloxacin Ophthalmic Suspension: Emerging Evidence of its Therapeutic Value in Bacterial Conjunctivitis." Ophthalmology and Eye Diseases 3 (January 2011): OED.S4102. http://dx.doi.org/10.4137/oed.s4102.
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Objective To outline the pharmacodynamics, efficacy and safety of besifloxacin ophthalmic suspension 0.6% in the treatment of bacterial conjunctivitis. Quality of Evidence MEDLINE database was searched to review recent pharmacodynamic and clinical studies evaluating besifloxacin and comparing besifloxacin to other topical antibiotics for ophthalmic use. Findings were limited to full-text articles from clinical journals in the English language. Main Message Bacterial resistance is a common source for treatment failure in bacterial conjunctivis. Besifloxacin, a novel fourth generation synthetic fluoroquinolone is likely to show lower resistance rates due to its mechanism of action and its short-term use for ocular infections only (decreased systemic exposure). Besifloxacin displays improved pharmacodynamic properties compared to other commonly used fluoroquinolones and has shown to be efficacious and safe in clinical studies. Conclusion Besifloxacin ophthalmic suspension 0.6% provides safe and efficacious treatment for bacterial conjunctivitis. The factors leading to bacterial resistance are diminished, which allows besifloxacin to be a favorable treatment option.
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MccCane, Brendan, Terry Caelli, and Olivier de Vel. "Learning to Recognize 3D Objects using Sparse Depth and Intensity Information." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 06 (September 1997): 909–31. http://dx.doi.org/10.1142/s021800149700041x.
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In this paper we further explore the use of machine learning (ML) for the recognition of 3D objects in isolation or embedded in scenes. Of particular interest is the use of a recent ML technique (specifically CRG — Conditional Rule Generation) which generates descriptions of objects in terms of object parts and part-relational attribute bounds. We show how this technique can be combined with intensity-based model and scene–views to locate objects and their pose. The major contributions of this paper are: the extension of the CRG classifier to incorporate fuzzy decisions (FCRG), the application of the FCRG classifier to the problem of learning 3D objects from 2D intensity images, the study of the usefulness of sparse depth data in regards to recognition performance, and the implementation of a complete object recognition system that does not rely on perfect or synthetic data. We report a recognition rate of 80% for unseen single object scenes in a database of 18 non-trivial objects.
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Edith Belise, Kenmogne, Nkambou Roger, Tadmon Calvin, and Engelbert Mephu Nguifo. "A heuristic to predict the optimal pattern-growth direction for the pattern growth-based sequential pattern mining approach." Journal of Advanced Computer Science & Technology 6, no. 2 (June 4, 2017): 20. http://dx.doi.org/10.14419/jacst.v6i2.7011.
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Sequential pattern mining is an efficient technique for discovering recurring structures or patterns from very large datasets, with a very large field of applications. It aims at extracting a set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either vertical or horizontal data formats represented respectively by GSP and SPADE, and the pattern-growth approach represented by FreeSpan, PrefixSpan and their further extensions. The performances of these algorithms depend on how patterns grow. Because of this, we introduce a heuristic to predict the optimal pattern-growth direction, i.e. the pattern-growth direction leading to the best performance in terms of runtime and memory usage. Then, we perform a number of experimentations on both real-life and synthetic datasets to test the heuristic. The performance analysis of these experimentations show that the heuristic prediction is reliable in general.
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Wang, Nan, Kim B. Olsen, and Steven M. Day. "A frequency-dependent ground-motion spatial correlation model of within-event residuals for Fourier amplitude spectra." Earthquake Spectra 37, no. 3 (January 7, 2021): 2041–65. http://dx.doi.org/10.1177/8755293020981995.
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Ground motion time series recorded at stations separated by up to about 50 km show a frequency-dependent spatial coherency structure, and the corresponding ground motion intensity measures are found to be correlated. As omitting this correlation can result in underestimation of seismic losses in risk analysis, it is critical to quantify the spatial correlation structure for ground motion Fourier spectra estimated at different sites during a single event within a region. Toward this goal, we have developed an empirical frequency-dependent spatial correlation model for the within-event residuals of effective Fourier amplitude spectra from the Pacific Earthquake Engineering Research Center (PEER) Next Generation Attenuation (NGA) West2 database. The correlation model shows slower decrease of the spatial correlation with distance at lower frequencies compared with higher frequencies, in agreement with the underlying ground motion data, and no significant dependence on the magnitude of the earthquakes is observed. We use this empirical model to incorporate frequency-dependent spatial correlation into a hybrid deterministic-stochastic broadband ground motion generation module, which successfully generates synthetic time series for seven western US earthquakes with frequency-dependent spatial correlation that closely mimics that of the empirical model. Furthermore, the method also significantly improves the correlation for spectral accelerations, cumulative absolute velocities, and Arias intensities, compared with that derived from the original broadband module.
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Elvira-Ortiz, David A., Juan J. Saucedo-Dorantes, Roque A. Osornio-Rios, Daniel Morinigo-Sotelo, and Jose A. Antonino-Daviu. "Power Quality Monitoring Strategy Based on an Optimized Multi-Domain Feature Selection for the Detection and Classification of Disturbances in Wind Generators." Electronics 11, no. 2 (January 17, 2022): 287. http://dx.doi.org/10.3390/electronics11020287.
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Wind generation has recently become an essential renewable power supply option. Wind generators are integrated with electrical machines that require correct functionality. However, the increasing use of non-linear loads introduces undesired disturbances that may compromise the integrity of the electrical machines inside the wind generator. Therefore, this work proposes a five-step methodology for power quality disturbance detection in grids with injection of wind farm energy. First, a database with synthetic signals is generated, to be used in the training process. Then, a multi-domain feature estimation is carried out. To reduce the problematic dimensionality, the features that provide redundant information are eliminated through an optimized feature selection performed by means of a genetic algorithm and the principal component analysis. Additionally, each one of the characteristic feature matrices of every considered condition are modeled through a specific self-organizing map neuron grid so they can be shown in a 2-D representation. Since the SOM model provides a pattern of the behavior of every disturbance, they are used as inputs of the classifier, based in a softmax layer neural network that performs the power quality disturbance detection of six different conditions: healthy or normal, sag or swell voltages, transients, voltage fluctuations and harmonic distortion. Thus, the proposed method is validated using a set of synthetic signals and is then tested using two different sets of real signals from an IEEE workgroup and from a wind park located in Spain.
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Bargiacchi, Eleonora, Nils Thonemann, Jutta Geldermann, Marco Antonelli, and Umberto Desideri. "Life Cycle Assessment of Synthetic Natural Gas Production from Different CO2 Sources: A Cradle-to-Gate Study." Energies 13, no. 17 (September 3, 2020): 4579. http://dx.doi.org/10.3390/en13174579.
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Fuel production from hydrogen and carbon dioxide is considered an attractive solution as long-term storage of electric energy and as temporary storage of carbon dioxide. A large variety of CO2 sources are suitable for Carbon Capture Utilization (CCU), and the process energy intensity depends on the separation technology and, ultimately, on the CO2 concentration in the flue gas. Since the carbon capture process emits more CO2 than the expected demand for CO2 utilization, the most sustainable CO2 sources must be selected. This work aimed at modeling a Power-to-Gas (PtG) plant and assessing the most suitable carbon sources from a Life Cycle Assessment (LCA) perspective. The PtG plant was supplied by electricity from a 2030 scenario for Italian electricity generation. The plant impacts were assessed using data from the ecoinvent database version 3.5, for different CO2 sources (e.g., air, cement, iron, and steel plants). A detailed discussion on how to handle multi-functionality was also carried out. The results showed that capturing CO2 from hydrogen production plants and integrated pulp and paper mills led to the lowest impacts concerning all investigated indicators. The choice of how to handle multi-functional activities had a crucial impact on the assessment.
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Giebelhaus, Ryland T., Lauren A. E. Erland, and Susan J. Murch. "HormonomicsDB: a novel workflow for the untargeted analysis of plant growth regulators and hormones." F1000Research 11 (October 18, 2022): 1191. http://dx.doi.org/10.12688/f1000research.124194.1.
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Background: Metabolomics is the simultaneous determination of all metabolites in a system. Despite significant advances in the field, compound identification remains a challenge. Prior knowledge of the compound classes of interest can improve metabolite identification. Hormones are a small signaling molecules, which function in coordination to direct all aspects of development, function and reproduction in living systems and which also pose challenges as environmental contaminants. Hormones are inherently present at low levels in tissues, stored in many forms and mobilized rapidly in response to a stimulus making them difficult to measure, identify and quantify. Methods: An in-depth literature review was performed for known hormones, their precursors, metabolites and conjugates in plants to generate the database and an RShiny App developed to enable web-based searches against the database. An accompanying liquid chromatography – mass spectrometry (LC-MS) protocol was developed with retention time prediction in Retip. A meta-analysis of 14 plant metabolomics studies was used for validation. Results: We developed HormonomicsDB, a tool which can be used to query an untargeted mass spectrometry (MS) dataset against a database of more than 200 known hormones, their precursors and metabolites. The protocol encompasses sample preparation, analysis, data processing and hormone annotation and is designed to minimize degradation of labile hormones. The plant system is used a model to illustrate the workflow and data acquisition and interpretation. Analytical conditions were standardized to a 30 min analysis time using a common solvent system to allow for easy transfer by a researcher with basic knowledge of MS. Incorporation of synthetic biotransformations enables prediction of novel metabolites. Conclusions: HormonomicsDB is suitable for use on any LC-MS based system with compatible column and buffer system, enables the characterization of the known hormonome across a diversity of samples, and hypothesis generation to reveal knew insights into hormone signaling networks.
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Costa, Fabrizzio Rodrigues, Cleyton de Carvalho Carneiro, and Carina Ulsen. "Imputation of Gold Recovery Data from Low Grade Gold Ore Using Artificial Neural Network." Minerals 13, no. 3 (February 28, 2023): 340. http://dx.doi.org/10.3390/min13030340.
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In a multivariate database, the missing data can be obtained through several imputation techniques, which are particularly useful for data that are difficult to obtain, for any reason, or have high uncertainties or scarce variables. A Self-Organizing Maps (SOM) neural network is an effective tool for the analysis of multidimensional data applied for the imputation of data. In this paper, data from drilling were used for training, testing, and validation using the variables: total Au recovery (%), which means gold recovery from a gravity concentration plus hydrometallurgical process, Au (g/t), As (ppm), S (%), Al2O3 (%), CaO (%), K2O (%), and MgO (%). After training, the partial omission of Au content and recovery was carried out, from 10% to 50%, to evaluate the data imputation performance for those variables. The results imputed by the SOM were compared with the original data values and evaluated according to descriptive statistics; the results indicated a determination coefficient of 85% when 50% of the data were omitted and 93% when 10% of the data were omitted. Once demonstrated, the correlation between the original data and SOM imputation analysis can help geologists and metallurgists to obtain results with a high degree of reliability of metallurgical recovery through related chemical variables, making it possible to implement SOM analysis as a powerful tool to input analytical data. One of the practical applications of the proposed model is to produce a pattern of imputed data that can be a good alternative in the construction or generation of a synthetic geometallurgical database with missing data.
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Wang, Cong, Zian Zhang, Yongqiang Zhang, Rui Tian, and Mingli Ding. "GMSRI: A Texture-Based Martian Surface Rock Image Dataset." Sensors 21, no. 16 (August 10, 2021): 5410. http://dx.doi.org/10.3390/s21165410.
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CNN-based Martian rock image processing has attracted much attention in Mars missions lately, since it can help planetary rover autonomously recognize and collect high value science targets. However, due to the difficulty of Martian rock image acquisition, the accuracy of the processing model is affected. In this paper, we introduce a new dataset called “GMSRI” that is a mixture of real Mars images and synthetic counterparts which are generated by GAN. GMSRI aims to provide a set of Martian rock images sorted by the texture and spatial structure of rocks. This paper offers a detailed analysis of GMSRI in its current state: Five sub-trees with 28 leaf nodes and 30,000 images in total. We show that GMSRI is much larger in scale and diversity than the current same kinds of datasets. Constructing such a database is a challenging task, and we describe the data collection, selection and generation processes carefully in this paper. Moreover, we evaluate the effectiveness of the GMSRI by an image super-resolution task. We hope that the scale, diversity and hierarchical structure of GMSRI can offer opportunities to researchers in the Mars exploration community and beyond.
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Hudu, Shuaibu Abdullahi, Saadatu Haruna Shinkafi, and Shuaibu Umar. "AN OVERVIEW OF RECOMBINANT VACCINE TECHNOLOGY, ADJUVANTS AND VACCINE DELIVERY METHODS." International Journal of Pharmacy and Pharmaceutical Sciences 8, no. 11 (October 28, 2016): 19. http://dx.doi.org/10.22159/ijpps.2016v8i11.14311.
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Development of an effective vaccine is of paramount important in disease prevention and control. As such, recombinant technology can serve as a gateway for the development of safe and effective vaccines that can be delivered effectively with an appropriate adjuvant. Therefore, this paper aimed to review the role of recombinant vaccine technology, new adjuvants and the challenge of vaccine delivery. Related peer-reviewed journal article searches were conducted using a subscribed database at the Universiti Putra Malaysia library, involving areas of Health Sciences and Medicine via Medline, SCOPUS and Google Scholar. New generation vaccines include highly purified synthetic or recombinant antigens that stimulate effective cell-mediated immune and mucosal immunity. In order to enhance their efficacy, a number of adjuvants are used. Efforts have also been made to explore the usage of non-invasive routes of administration, devices and equipment for optimized antigen and immune-potentiator delivery of the immune system. Recombinant vaccine technology is rapid, compared to the traditional method of vaccine development and does not require the handling of live viruses. It is, therefore, a promising technology for developing a future vaccine to curb emerging and re-emerging viral infections that may be life-threatening or teratogenic.
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Lei, Wenjie, Youyi Ruan, Ebru Bozdağ, Daniel Peter, Matthieu Lefebvre, Dimitri Komatitsch, Jeroen Tromp, Judith Hill, Norbert Podhorszki, and David Pugmire. "Global adjoint tomography—model GLAD-M25." Geophysical Journal International 223, no. 1 (May 21, 2020): 1–21. http://dx.doi.org/10.1093/gji/ggaa253.
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SUMMARY Building on global adjoint tomography model GLAD-M15, we present transversely isotropic global model GLAD-M25, which is the result of 10 quasi-Newton tomographic iterations with an earthquake database consisting of 1480 events in the magnitude range 5.5 ≤ Mw ≤ 7.2, an almost sixfold increase over the first-generation model. We calculated fully 3-D synthetic seismograms with a shortest period of 17 s based on a GPU-accelerated spectral-element wave propagation solver which accommodates effects due to 3-D anelastic crust and mantle structure, topography and bathymetry, the ocean load, ellipticity, rotation and self-gravitation. We used an adjoint-state method to calculate Fréchet derivatives in 3-D anelastic Earth models facilitated by a parsimonious storage algorithm. The simulations were performed on the Cray XK7 ‘Titan’ and the IBM Power 9 ‘Summit’ at the Oak Ridge Leadership Computing Facility. We quantitatively evaluated GLAD-M25 by assessing misfit reductions and traveltime anomaly histograms in 12 measurement categories. We performed similar assessments for a held-out data set consisting of 360 earthquakes, with results comparable to the actual inversion. We highlight the new model for a variety of plumes and subduction zones.
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Vera-Olmos, Javier, Angel Torrado-Carvajal, Carmen Prieto-de-la-Lastra, Onofrio A. Catalano, Yves Rozenholc, Filomena Mazzeo, Andrea Soricelli, Marco Salvatore, David Izquierdo-Garcia, and Norberto Malpica. "How To Pseudo-CT: A Comparative Review of Deep Convolutional Neural Network Architectures for CT Synthesis." Applied Sciences 12, no. 22 (November 15, 2022): 11600. http://dx.doi.org/10.3390/app122211600.
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This paper provides an overview of the different deep convolutional neural network (DCNNs) architectures that have been investigated in the past years for the generation of synthetic computed tomography (CT) or pseudo-CT from magnetic resonance (MR). The U-net, the Atrous-net and the Residual-net architectures were analyzed, implemented and compared. Each network was implemented using 2D filters and 3D filters with 2D slices and 3D patches respectively as inputs. Two datasets were used for training and evaluation. The first one is composed by pairs of 3D T1-weighted MR and Low-dose CT images from the head of 19 healthy women. The second database contains dual echo Dixon-VIBE MR images and CT images from the pelvis of 13 colorectal and 6 prostate cancer patients. Bone structures in the target anatomy were key in choosing the right deep learning approach. This work provides a deep explanation of the architectures in order to know which DCNN fits better each medical application. According to this study, the 3D U-net architecture would be the best option to generate head pseudo-CTs while the 2D Residual-net provides the most accurate results for the pelvis anatomy.