Simple and Effective Visual Models for Gene Expression Cancer Diagnostics

In the paper we show that diagnostic classes in cancer gene expression data sets, which most often include thousands of features (genes), may be effectively separated with simple two-dimensional plots such as scatterplot and radviz graph. The principal innovation proposed in the paper is a method called VizRank, which is able to score and identify the best among possibly millions of candidate projections for visualizations. Compared to recently much applied techniques in the field of cancer genomics that include neural networks, support vector machines and various ensemble-based approaches, VizRank is fast and finds visualization models that can be easily examined and interpreted by domain experts. Our experiments on a number of gene expression data sets show that VizRank was always able to find data visualizations with a small number of (two to seven) genes and excellent class separation. In addition to providing grounds for gene expression cancer diagnosis, VizRank and its visualizations also identify small sets of relevant genes, uncover interesting gene interactions and point to outliers and potential misclassifications in cancer data sets

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Distributed Control of Microscopic Robots in Biomedical Applications

Current developments in molecular electronics, motors and chemical sensors could enable constructing large numbers of devices able to sense, compute and act in micron-scale environments. Such microscopic machines, of sizes comparable to bacteria, could simultaneously monitor entire populations of cells individually in vivo. This paper reviews plausible capabilities for microscopic robots and the physical constraints due to operation in fluids at low Reynolds number, diffusion-limited sensing and thermal noise from Brownian motion. Simple distributed controls are then presented in the context of prototypical biomedical tasks, which require control decisions on millisecond time scales. The resulting behaviors illustrate trade-offs among speed, accuracy and resource use. A specific example is monitoring for patterns of chemicals in a flowing fluid released at chemically distinctive sites. Information collected from a large number of such devices allows estimating properties of cell-sized chemical sources in a macroscopic volume. The microscopic devices moving with the fluid flow in small blood vessels can detect chemicals released by tissues in response to localized injury or infection. We find the devices can readily discriminate a single cell-sized chemical source from the background chemical concentration, providing high-resolution sensing in both time and space. By contrast, such a source would be difficult to distinguish from background when diluted throughout the blood volume as obtained with a blood sample

UK-India Centre for Advanced Technology for Minimizing Indiscriminate Use of Antibiotics:"Exploring biology of antibiotic resistance and potential targets for early diagnosis and effective management of infectious diseases”

YesDuring January 15th – 17th, 2017 a group of scientists met, under the auspices of the UK-India Centre for Advanced Technology for Minimizing Indiscriminate Use of Antibiotics, to discuss the further developments and potential solutions to antimicrobial resistance. This was the third work shop under this funding stream held in Hyderabad. The presentations and outcomes of the workshop are released here. Key out comes included the need to address improved treatment and detection of TB, delivery of antimicrobial peptides, potential strategies for combating beta-lactam resistance.Medical Research Counci

Microarrays, Empirical Bayes and the Two-Groups Model

The classic frequentist theory of hypothesis testing developed by Neyman, Pearson and Fisher has a claim to being the twentieth century's most influential piece of applied mathematics. Something new is happening in the twenty-first century: high-throughput devices, such as microarrays, routinely require simultaneous hypothesis tests for thousands of individual cases, not at all what the classical theory had in mind. In these situations empirical Bayes information begins to force itself upon frequentists and Bayesians alike. The two-groups model is a simple Bayesian construction that facilitates empirical Bayes analysis. This article concerns the interplay of Bayesian and frequentist ideas in the two-groups setting, with particular attention focused on Benjamini and Hochberg's False Discovery Rate method. Topics include the choice and meaning of the null hypothesis in large-scale testing situations, power considerations, the limitations of permutation methods, significance testing for groups of cases (such as pathways in microarray studies), correlation effects, multiple confidence intervals and Bayesian competitors to the two-groups model.Comment: This paper commented in: [arXiv:0808.0582], [arXiv:0808.0593], [arXiv:0808.0597], [arXiv:0808.0599]. Rejoinder in [arXiv:0808.0603]. Published in at http://dx.doi.org/10.1214/07-STS236 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Cancer theranostics: multifunctional gold nanoparticles for diagnostics and therapy

Doctorate in Biology, Specialty in BiotechnologyThe use of gold nanoparticles (AuNPs) has been gaining momentum in molecular diagnostics due to their unique physico-chemical properties these systems present huge advantages, such as increased sensitivity, reduced cost and potential for single-molecule characterisation. Because of their versatility and easy of functionalisation, multifunctional AuNPs have also been proposed as optimal delivery systems for therapy (nanovectors). Being able to produce such systems would mean the dawn of a new age in theranostics (diagnostics and therapy)driven by nanotechnology vehicles. Nanotechnology can be exploit for cancer theranostics via the development of diagnostics systems such as colorimetric and imunoassays, and in therapy approaches through gene therapy, drug delivery and tumour targeting systems. The unique characteristics of nanoparticles in the nanometre range, such as high surface-tovolume ratio or shape/size-dependent optical properties, are drastically different from those of their bulk materials and hold pledge in the clinical field for disease therapeutics This PhD project intends to optimise a gold-nanoparticle based technique for the detection of oncogenes’ transcripts (c-Myc and BCR-ABL) that can be used for the evaluation of the expression profile in cancer cells, while simultaneously developing an innovative platform of multifunctional gold nanoparticles (tumour markers, cell penetrating peptides, fluorescent dyes) loaded with siRNA capable of silencing the selected proto-oncogenes, which can be used to evaluate the level of expression and determine the efficiency of silencing. This work is a part of an ongoing collaboration between Research Centre for Human Molecular Genetics, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Portugal and Biofunctional Nanoparticles and Surfaces Group, Instituto de Nanociencia de Aragón, Spain within a European project [NanoScieE+ - NANOTRUCK]. In order to achieve this goal we developed effective conjugation strategies to combine, in a highly controlled way, biomolecules to the surface of AuNPs with specific functions such as: ssDNA oligos to detect specific sequences and for mRNA quantification; Biofunctional spacers: Poly(ethylene glycol) (PEG) spacers used to increase solubility and biocompatibility and confer chemical functionality; Cell penetrating peptides: to overcome the lipophilic barrier of the cellular membranes and deliver molecules into cells using TAT peptide to achieve cytoplasm and nucleus; Quaternary ammonium: to introduce stable positively charged in gold nanoparticles surface; and RNA interference: siRNA complementary to a master regulator gene, the proto-oncogene c-Myc, that is implicated in cell growth, proliferation, loss of differentiation, and cell death. In order to establish that they are viable alternatives to the available methods, these innovative nanoparticles were extensively characterized on their chemical functionalization, ease of uptake, cellular toxicity and inflammation, and knockdown of MYC protein expression in several cancer cell lines and in in vivo models.Fundação para a Ciência e Tecnologia - (SFRH/BD/62957/2009); PTDC/BIO/66514/2006; NANOLIGHT-PTDC/QUI-QUI/112597/2009; Silencing the silencers via multifunctional gold nanoconjugates towards cancer therapy - PTDC/BBB-NAN/1812/201

Deep learning-enabled technologies for bioimage analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases