TRANSPLANTATION OF SPLEEN (INDICATIONS, SURGICAL ASPECTS, COMPLICATIONS, PROPHYLAXIS)

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INFLUENCE OF IMMUNOREACTIVITY TYPE UPON POSTOPERATIVE STATUS OF PATIENTS WITH COLO-RECTAL CANCER (CRC)

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PHAGOCYTIC ACTIVITY OF POLYMORPHONUCLEAR LEUCOCYTES AFTER EXTRACORPORAL TRANSPLANTATION OF SPLEEN (AUTOTRANSPLANTATION)

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The Machine Learning Landscape of Top Taggers

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.Comment: Yet another tagger included

Emerging threats and opportunities to managed bee species in European agricultural systems: a horizon scan

Managed bee species provide essential pollination services that contribute to food security worldwide. However, managed bees face a diverse array of threats and anticipating these, and potential opportunities to reduce risks, is essential for the sustainable management of pollination services. We conducted a horizon scanning exercise with 20 experts from across Europe to identify emerging threats and opportunities for managed bees in European agricultural systems. An initial 63 issues were identified, and this was shortlisted to 21 issues through the horizon scanning process. These ranged from local landscape-level management to geopolitical issues on a continental and global scale across seven broad themes-Pesticides & pollutants, Technology, Management practices, Predators & parasites, Environmental stressors, Crop modification, and Political & trade influences. While we conducted this horizon scan within a European context, the opportunities and threats identified will likely be relevant to other regions. A renewed research and policy focus, especially on the highest-ranking issues, is required to maximise the value of these opportunities and mitigate threats to maintain sustainable and healthy managed bee pollinators within agricultural systems

Lesion detection in demoscopy images with novel density-based and active contour approaches

<p>Abstract</p> <p>Background</p> <p>Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Automated assessment tools for dermoscopy images have become an important field of research mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is the detection of lesion borders, since many other features, such as asymmetry, border irregularity, and abrupt border cutoff, rely on the boundary of the lesion. </p> <p>Results</p> <p>To automate the process of delineating the lesions, we employed Active Contour Model (ACM) and boundary-driven density-based clustering (BD-DBSCAN) algorithms on 50 dermoscopy images, which also have ground truths to be used for quantitative comparison. We have observed that ACM and BD-DBSCAN have the same border error of 6.6% on all images. To address noisy images, BD-DBSCAN can perform better delineation than ACM. However, when used with optimum parameters, ACM outperforms BD-DBSCAN, since ACM has a higher recall ratio.</p> <p>Conclusion</p> <p>We successfully proposed two new frameworks to delineate suspicious lesions with i) an ACM integrated approach with sharpening and ii) a fast boundary-driven density-based clustering technique. ACM shrinks a curve toward the boundary of the lesion. To guide the evolution, the model employs the exact solution <abbrgrp><abbr bid="B27">27</abbr></abbrgrp> of a specific form of the Geometric Heat Partial Differential Equation <abbrgrp><abbr bid="B28">28</abbr></abbrgrp>. To make ACM advance through noisy images, an improvement of the model’s boundary condition is under consideration. BD-DBSCAN improves regular density-based algorithm to select query points intelligently.</p

A feature selection method for classification within functional genomics experiments based on the proportional overlapping score

Background: Microarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature's relevance to a classification task.Results: We apply POS, along-with four widely used gene selection methods, to several benchmark gene expression datasets. The experimental results of classification error rates computed using the Random Forest, k Nearest Neighbor and Support Vector Machine classifiers show that POS achieves a better performance.Conclusions: A novel gene selection method, POS, is proposed. POS analyzes the expressions overlap across classes taking into account the proportions of overlapping samples. It robustly defines a mask for each gene that allows it to minimize the effect of expression outliers. The constructed masks along-with a novel gene score are exploited to produce the selected subset of genes

Technical design of the phase I Mu3e experiment

The Mu3e experiment aims to find or exclude the lepton flavour violating decay $\mu \rightarrow eee$ at branching fractions above $10^{-16}$. A first phase of the experiment using an existing beamline at the Paul Scherrer Institute (PSI) is designed to reach a single event sensitivity of $2\cdot 10^{-15}$. We present an overview of all aspects of the technical design and expected performance of the phase~I Mu3e detector. The high rate of up to $10^{8}$ muon decays per second and the low momenta of the decay electrons and positrons pose a unique set of challenges, which we tackle using an ultra thin tracking detector based on high-voltage monolithic active pixel sensors combined with scintillating fibres and tiles for precise timing measurements.Comment: 114 pages, 185 figures. Submitted to Nuclear Instruments and Methods A. Edited by Frank Meier Aeschbacher This version has many enhancements for better readability and more detail

Technical design of the phase I Mu3e experiment

The Mu3e experiment aims to find or exclude the lepton flavour violating decay μ→eee at branching fractions above 10−16. A first phase of the experiment using an existing beamline at the Paul Scherrer Institute (PSI) is designed to reach a single event sensitivity of 2⋅10−15. We present an overview of all aspects of the technical design and expected performance of the phase I Mu3e detector. The high rate of up to 108 muon decays per second and the low momenta of the decay electrons and positrons pose a unique set of challenges, which we tackle using an ultra thin tracking detector based on high-voltage monolithic active pixel sensors combined with scintillating fibres and tiles for precise timing measurements

Genomic modelling of the ESR1 Y537S mutation for evaluating function and new therapeutic approaches for metastatic breast cancer

Drugs that inhibit estrogen receptor-α (ER) activity have been highly successful in treating and reducing breast cancer progression in ER-positive disease. However, resistance to these therapies presents a major clinical problem. Recent genetic studies have shown that mutations in the ER gene are found in >20% of tumours that progress on endocrine therapies. Remarkably, the great majority of these mutations localize to just a few amino acids within or near the critical helix 12 region of the ER hormone binding domain, where they are likely to be single allele mutations. Understanding how these mutations impact on ER function is a prerequisite for identifying methods to treat breast cancer patients featuring such mutations. Towards this end, we used CRISPR-Cas9 genome editing to make a single allele knock-in of the most commonly mutated amino acid residue, tyrosine 537, in the estrogen-responsive MCF7 breast cancer cell line. Genomic analyses using RNA-seq and ER ChIP-seq demonstrated that the Y537S mutation promotes constitutive ER activity globally, resulting in estrogen-independent growth. MCF7-Y537S cells were resistant to the anti-estrogen tamoxifen and fulvestrant. Further, we show that the basal transcription factor TFIIH is constitutively recruited by ER-Y537S, resulting in ligand-independent phosphorylation of Serine 118 (Ser118) by the TFIIH kinase, cyclin-dependent kinase (CDK)7. The CDK7 inhibitor, THZ1 prevented Ser118 phosphorylation and inhibited growth of MCF7-Y537S cells. These studies confirm the functional importance of ER mutations in endocrine resistance, demonstrate the utility of knock-in mutational models for investigating alternative therapeutic approaches and highlight CDK7 inhibition as a potential therapy for endocrine-resistant breast cancer mediated by ER mutations