BRAQUE: Bayesian Reduction for Amplified Quantization in UMAP Embedding.

Single-cell biology has revolutionized the way we understand biological processes. In this paper, we provide a more tailored approach to clustering and analyzing spatial single-cell data coming from immunofluorescence imaging techniques. We propose Bayesian Reduction for Amplified Quantization in UMAP Embedding (BRAQUE) as an integrative novel approach, from data preprocessing to phenotype classification. BRAQUE starts with an innovative preprocessing, named Lognormal Shrinkage, which is able to enhance input fragmentation by fitting a lognormal mixture model and shrink each component towards its median, in order to help further the clustering step in finding more separated and clear clusters. Then, BRAQUE's pipeline consists of a dimensionality reduction step performed using UMAP, and a clustering performed using HDBSCAN on UMAP embedding. In the end, clusters are assigned to a cell type by experts, using effects size measures to rank markers and identify characterizing markers (Tier 1), and possibly characterize markers (Tier 2). The number of total cell types in one lymph node detectable with these technologies is unknown and difficult to predict or estimate. Therefore, with BRAQUE, we achieved a higher granularity than other similar algorithms such as PhenoGraph, following the idea that merging similar clusters is easier than splitting unclear ones into clear subclusters.

In vitro glistening formation in IOLs: automated method for assessing the volumetric density and depth distribution of microvacuoles.

PURPOSE: To develop a method to measure the depth profile of microvacuoles (MVs) in intraocular lenses (IOLs) and to characterize, after accelerated aging, the glistening of an acrylic hydrophobic IOL. SETTING: University of Milano-Bicocca, Milan, Italy. DESIGN: In vitro study. METHODS: A heat treatment was applied in vitro to Basis V IOLs exposed to deionized water (24 hours at 45 ± 1°C, rapid cooling, and 24 hours at 24 ± 1°C). Thirty images (area 1.2 mm) of each IOL were acquired by a microscope, focusing on sequential planes every 23 ± 2 µm. By tracking the traces of each MV in consecutive images, the coordinates of the MV centroids along the IOL thickness were construed by an automated procedure, and in the generated single-focus stacked image, MVs were counted by an automated method. RESULTS: MV density was found normally distributed along the IOL depth profile (Jarque-Bera test). In focus-stacked images, the MV automated counting was found accurate within 5% vs manual counting, and MV volume density of the order of 10 mm was estimated. It was observed that stacks of 15 images provided a 4% lower MV volume density compared with the stacking of 30 images. CONCLUSIONS: The assessment of the number of MVs by the acquisition of a single image of an IOL was influenced by the distance of the selected plane from the IOL surface. The decrease in MV density approaching the IOL edges can be explained as a consequence of the diffusion of water toward the external environment after accelerated aging.

With or without U(K): A pre-Brexit network analysis of the EU ETS.

The European Emission Trading System (EU ETS) is commonly regarded as the key pillar of the European climate policy and as the main unifying tool to create a unique carbon price all over Europe. The UK has always played a crucial role in the EU ETS, being one of the most active national registry and a crucial hub for the exchange of allowances in the market. Brexit, therefore, could deeply modify the number and directions of such exchanges as well as the centrality of the other countries in this system. To investigate these issues, the present paper exploits network analysis tools to compare the structure of the EU ETS market in its first two phases with and without the UK, investigating a few different scenarios that might emerge from a possible reallocation of the transactions that have involved UK partners. We find that without the UK the EU ETS network would become in general much more homogeneous, though results may change focusing on the type of accounts involved in the transactions.

Tissue microarray design and construction for scientific, industrial and diagnostic use.

CONTEXT: In 2013 the high throughput technology known as Tissue Micro Array (TMA) will be fifteen years old. Its elements (design, construction and analysis) are intuitive and the core histopathology technique is unsophisticated, which may be a reason why has eluded a rigorous scientific scrutiny. The source of errors, particularly in specimen identification and how to control for it is unreported. Formal validation of the accuracy of segmenting (also known as de-arraying) hundreds of samples, pairing with the sample data is lacking. AIMS: We wanted to address these issues in order to bring the technique to recognized standards of quality in TMA use for research, diagnostics and industrial purposes. RESULTS: We systematically addressed the sources of error and used barcode-driven data input throughout the whole process including matching the design with a TMA virtual image and segmenting that image back to individual cases, together with the associated data. In addition we demonstrate on mathematical grounds that a TMA design, when superimposed onto the corresponding whole slide image, validates on each and every sample the correspondence between the image and patient's data. CONCLUSIONS: High throughput use of the TMA technology is a safe and efficient method for research, diagnosis and industrial use if all sources of errors are identified and addressed.