A system of industrialized housing for developing countries

Thesis (M. Arch)--Massachusetts Institute of Technology, Dept. of Architecture, 1962.Accompanying drawings held by MIT Museum.Includes bibliographical references (leaf 28).by Julio Alberto Silva Perrone.M.Arc

Antigen-Induced IL-1RA Production Discriminates Active and Latent Tuberculosis Infection

The IGRA (Interferon Gamma Release Assays) test is currently the standard specific test for Mycobacterium tuberculosis infection status. However, a positive test cannot distinguish between active tuberculosis disease (ATBD) and latent tuberculosis infection (LTBI). Developing a test with this characteristic is needed. We conducted longitudinal studies to identify a combination of antigen peptides and cytokines to discriminate between ATBD and LTBI. We studied 54 patients with ATBD disease and 51 with LTBI infection. Cell culture supernatant from cells stimulated with overlapping Mycobacterium tuberculosis novel peptides and 40 cytokines/chemokines were analyzed using the Luminex technology. To summarize longitudinal measurements of analyte levels, we calculated the area under the curve (AUC). Our results indicate that in vitro cell stimulation with a novel combination of peptides (Rv0849-12, Rv2031c-14, Rv2031c-5, and Rv2693-06) and IL-1RA detection in culture supernatants can discriminate between LTBI and ATBD

Demetallization of Enterococcus faecalis Biofilm: A preliminary study

Objectives: To determine the concentration of calcium, iron, manganese and zinc ions after the application of chelator to Enterococcus faecalis biofilms. Material and Methods: Fifty bovine maxillary central incisors were prepared and inoculated with E. faecalis for 60 days. The following were used as irrigation solutions: 17% EDTA (pH 3, 7 and 10), 2.5% sodium hypochlorite (NaOCl) combined with 17% EDTA (pH 3, 7 and 10), distilled water (pH 3, 7 and 10), and 2.5% NaOCl. Each solution was kept in the root canal for five minutes. Fifteen uncontaminated root canals were irrigated with 17% EDTA (pH 3, 7 and 10). Six teeth were used as bacterial control. The number of calcium, iron, manganese and zinc ions was determined using flame atomic absorption spectrometry. Mean ± standard deviation (SD) values were used for descriptive statistics. Results: Calcium chelation using 17% EDTA at pH 7 was higher than at pH 3 and 10, regardless of whether bacterial biofilm was present. The highest concentration of iron occurred at pH 3 in the presence of bacterial biofilm. The highest concentration of manganese found was 2.5% NaOCl and 17% EDTA at pH 7 in the presence of bacterial biofilm. Zinc levels were not detectable. Conclusions: The pH of chelating agents affected the removal of calcium, iron, and manganese ions. The concentration of iron ions in root canals with bacterial biofilm was higher after the use of 17% EDTA at pH 3 than after the use of the other solutions at all pH levels

Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection

[EN] Background and Objective: Prostate cancer is one of the most common diseases affecting men worldwide. The Gleason scoring system is the primary diagnostic and prognostic tool for prostate cancer. Further-more, recent reports indicate that the presence of patterns of the Gleason scale such as the cribriform pattern may also correlate with a worse prognosis compared to other patterns belonging to the Glea-son grade 4. Current clinical guidelines have indicated the convenience of highlight its presence during the analysis of biopsies. All these requirements suppose a great workload for the pathologist during the analysis of each sample, which is based on the pathologist's visual analysis of the morphology and or-ganisation of the glands in the tissue, a time-consuming and subjective task. In recent years, with the development of digitisation devices, the use of computer vision techniques for the analysis of biopsies has increased. However, to the best of the authors' knowledge, the development of algorithms to automatically detect individual cribriform patterns belonging to Gleason grade 4 has not yet been studied in the literature. The objective of the work presented in this paper is to develop a deep-learning-based system able to support pathologists in the daily analysis of prostate biopsies. This analysis must include the Gleason grading of local structures, the detection of cribriform patterns, and the Gleason scoring of the whole biopsy. Methods: The methodological core of this work is a patch-wise predictive model based on convolutional neural networks able to determine the presence of cancerous patterns based on the Gleason grading system. In particular, we train from scratch a simple self-design architecture with three filters and a top model with global-max pooling. The cribriform pattern is detected by retraining the set of filters of the last convolutional layer in the network. Subsequently, a biopsy-level prediction map is reconstructed by bi-linear interpolation of the patch-level prediction of the Gleason grades. In addition, from the re-constructed prediction map, we compute the percentage of each Gleason grade in the tissue to feed a multi-layer perceptron which provides a biopsy-level score. Results: In our SICAPv2 database, composed of 182 annotated whole slide images, we obtained a Cohen's quadratic kappa of 0.77 in the test set for the patch-level Gleason grading with the proposed architec-ture trained from scratch. Our results outperform previous ones reported in the literature. Furthermore, this model reaches the level of fine-tuned state-of-the-art architectures in a patient-based four groups cross validation. In the cribriform pattern detection task, we obtained an area under ROC curve of 0.82. Regarding the biopsy Gleason scoring, we achieved a quadratic Cohen's Kappa of 0.81 in the test subset. Shallow CNN architectures trained from scratch outperform current state-of-the-art methods for Gleason grades classification. Our proposed model is capable of characterising the different Gleason grades in prostate tissue by extracting low-level features through three basic blocks (i.e. convolutional layer + max pooling). The use of global-max pooling to reduce each activation map has shown to be a key factor for reducing complexity in the model and avoiding overfitting. Regarding the Gleason scoring of biopsies, a multi-layer perceptron has shown to better model the decision-making of pathologists than previous simpler models used in the literature.This work was supported by the Spanish Ministry of Economy and Competitiveness through project DPI2016-77869. The Titan V used for this research was donated by the NVIDIA Corporation.Silva-Rodríguez, J.; Colomer, A.; Sales, MA.; Molina, R.; Naranjo Ornedo, V. (2020). Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection. Computer Methods and Programs in Biomedicine. 195:1-18. https://doi.org/10.1016/j.cmpb.2020.105637S118195Gordetsky, J., & Epstein, J. (2016). Grading of prostatic adenocarcinoma: current state and prognostic implications. Diagnostic Pathology, 11(1). doi:10.1186/s13000-016-0478-2Epstein, J. I., Egevad, L., Amin, M. B., Delahunt, B., Srigley, J. R., & Humphrey, P. A. (2016). The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma. American Journal of Surgical Pathology, 40(2), 244-252. doi:10.1097/pas.0000000000000530Sharma, M., & Miyamoto, H. (2018). Percent Gleason pattern 4 in stratifying the prognosis of patients with intermediate-risk prostate cancer. Translational Andrology and Urology, 7(S4), S484-S489. doi:10.21037/tau.2018.03.20Kweldam, C. F., van der Kwast, T., & van Leenders, G. J. (2018). On cribriform prostate cancer. Translational Andrology and Urology, 7(1), 145-154. doi:10.21037/tau.2017.12.33Remotti, H. (2012). Tissue Microarrays: Construction and Use. Pancreatic Cancer, 13-28. doi:10.1007/978-1-62703-287-2_2KHOUJA, M. H., BAEKELANDT, M., SARAB, A., NESLAND, J. M., & HOLM, R. (2010). Limitations of tissue microarrays compared with whole tissue sections in survival analysis. Oncology Letters, 1(5), 827-831. doi:10.3892/ol_00000145Gertych, A., Ing, N., Ma, Z., Fuchs, T. J., Salman, S., Mohanty, S., … Knudsen, B. S. (2015). Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Computerized Medical Imaging and Graphics, 46, 197-208. doi:10.1016/j.compmedimag.2015.08.002Ren, J., Sadimin, E., Foran, D. J., & Qi, X. (2017). Computer aided analysis of prostate histopathology images to support a refined Gleason grading system. Medical Imaging 2017: Image Processing. doi:10.1117/12.2253887Esteban, Á. E., López-Pérez, M., Colomer, A., Sales, M. A., Molina, R., & Naranjo, V. (2019). A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes. Computer Methods and Programs in Biomedicine, 178, 303-317. doi:10.1016/j.cmpb.2019.07.003Lucas, M., Jansen, I., Savci-Heijink, C. D., Meijer, S. L., de Boer, O. J., van Leeuwen, T. G., … Marquering, H. A. (2019). Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies. Virchows Archiv, 475(1), 77-83. doi:10.1007/s00428-019-02577-xArvaniti, E., Fricker, K. S., Moret, M., Rupp, N., Hermanns, T., Fankhauser, C., … Claassen, M. (2018). Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Scientific Reports, 8(1). doi:10.1038/s41598-018-30535-1G. Nir, S. Hor, D. Karimi, L. Fazli, B.F. Skinnider, P. Tavassoli, D. Turbin, C.F. Villamil, G. Wang, R.S. Wilson, K.A. Iczkowski, M.S. Lucia, P.C. Black, P. Abolmaesumi, S.L. Goldenberg, S.E. Salcudean, Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts, 2018. 10.1016/j.media.2018.09.005Nir, G., Karimi, D., Goldenberg, S. L., Fazli, L., Skinnider, B. F., Tavassoli, P., … Salcudean, S. E. (2019). Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images. JAMA Network Open, 2(3), e190442. doi:10.1001/jamanetworkopen.2019.0442García, G., Colomer, A., & Naranjo, V. (2019). First-Stage Prostate Cancer Identification on Histopathological Images: Hand-Driven versus Automatic Learning. Entropy, 21(4), 356. doi:10.3390/e21040356Ma, Y., Jiang, Z., Zhang, H., Xie, F., Zheng, Y., Shi, H., … Shi, J. (2018). Generating region proposals for histopathological whole slide image retrieval. Computer Methods and Programs in Biomedicine, 159, 1-10. doi:10.1016/j.cmpb.2018.02.020Li, W., Li, J., Sarma, K. V., Ho, K. C., Shen, S., Knudsen, B. S., … Arnold, C. W. (2019). Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images. IEEE Transactions on Medical Imaging, 38(4), 945-954. doi:10.1109/tmi.2018.2875868Openseadragon, (http://openseadragon.github.io/), Accessed: 10-07-2018.Cohen, J. (1968). Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213-220. doi:10.1037/h0026256Swets, J. A. (1988). Measuring the Accuracy of Diagnostic Systems. Science, 240(4857), 1285-1293. doi:10.1126/science.3287615Kweldam, C. F., Nieboer, D., Algaba, F., Amin, M. B., Berney, D. M., Billis, A., … van Leenders, G. J. L. H. (2016). Gleason grade 4 prostate adenocarcinoma patterns: an interobserver agreement study among genitourinary pathologists. Histopathology, 69(3), 441-449. doi:10.1111/his.1297

DOT: A flexible multi-objective optimization framework for transferring features across single-cell and spatial omics

Single-cell RNA sequencing (scRNA-seq) and spatially-resolved imaging/sequencing technologies have revolutionized biomedical research. On one hand, scRNA-seq provides information about a large portion of the transcriptome for individual cells, but lacks the spatial context. On the other hand, spatially-resolved measurements come with a trade-off between resolution and gene coverage. Combining scRNA-seq with different spatially-resolved technologies can thus provide a more complete map of tissues with enhanced cellular resolution and gene coverage. Here, we propose DOT, a novel multi-objective optimization framework for transferring cellular features across these data modalities. DOT is flexible and can be used to infer categorical (cell type or cell state) or continuous features (gene expression) in different types of spatial omics. Our optimization model combines practical aspects related to tissue composition, technical effects, and integration of prior knowledge, thereby providing flexibility to combine scRNA-seq and both low- and high-resolution spatial data. Our fast implementation based on the Frank-Wolfe algorithm achieves state-of-the-art or improved performance in localizing cell features in high- and low-resolution spatial data and estimating the expression of unmeasured genes in low-coverage spatial data across different tissues. DOT is freely available and can be deployed efficiently without large computational resources; typical cases-studies can be run on a laptop, facilitating its use.Comment: 36 pages, 6 figure

Search-Based Evolution of XML Schemas

The use of schemas makes an XML-based application more reliable, since they contribute to avoid failures by defining the specific format for the data that the application manipulates. In practice, when an application evolves, new requirements for the data may be established, raising the need of schema evolution. In some cases the generation of a schema is necessary, if such schema does not exist. To reduce maintenance and reengineering costs, automatic evolution of schemas is very desirable. However, there are no algorithms to satisfactorily solve the problem. To help in this task, this paper introduces a search-based approach that explores the correspondence between schemas and context-free grammars. The approach is supported by a tool, named EXS. Our tool implements algorithms of grammatical inference based on LL(1) Parsing. If a grammar (that corresponds to a schema) is given and a new word (XML document) is provided, the EXS system infers the new grammar that: i) continues to generate the same words as before and ii) generates the new word, by modifying the original grammar. If no initial grammar is available, EXS is also capable of generating a grammar from scratch from a set of samples