Powerful and interpretable behavioural features for quantitative phenotyping of C. elegans

Behaviour is a sensitive and integrative readout of nervous system function and therefore an attractive measure for assessing the effects of mutation or drug treatment on animals. Video data provide a rich but high-dimensional representation of behaviour, and so the first step of analysis is often some form of tracking and feature extraction to reduce dimensionality while maintaining relevant information. Modern machine-learning methods are powerful but notoriously difficult to interpret, while handcrafted features are interpretable but do not always perform as well. Here, we report a new set of handcrafted features to compactly quantify Caenorhabditis elegans behaviour. The features are designed to be interpretable but to capture as much of the phenotypic differences between worms as possible. We show that the full feature set is more powerful than a previously defined feature set in classifying mutant strains. We then use a combination of automated and manual feature selection to define a core set of interpretable features that still provides sufficient power to detect behavioural differences between mutant strains and the wild-type. Finally, we apply the new features to detect time-resolved behavioural differences in a series of optogenetic experiments targeting different neural subsets

A label-free microfluidic assay to quantitatively study antibiotic diffusion through lipid membranes

PublishedJournal ArticleResearch Support, Non-U.S. Gov'tWith the rise in antibiotic resistance amongst pathogenic bacteria, the study of antibiotic activity and transport across cell membranes is gaining widespread importance. We present a novel, label-free microfluidic assay that quantifies the permeability coefficient of a broad spectrum fluoroquinolone antibiotic, norfloxacin, across lipid membranes using the UV autofluorescence of the drug. We use giant lipid vesicles as highly controlled model systems to study the diffusion through lipid membranes. Our technique directly determines the permeability coefficient without requiring the measurement of the partition coefficient of the antibiotic.This work was supported by a European Research Council (ERC) grant (261101 PassMembrane) to UFK. JC acknowledges support from an Internal Graduate Studentship, Trinity College, Cambridge. CC is supported by the ERC. SP acknowledges the support of the Leverhulme Trust and the Newton Trust through an Early Career Fellowship. AJ is supported by the Mexican National Council of Science and Technology. We thank Thomas Muller for help with the lithography and Tuomas Knowles for the use of his lithography facilitie

Revealing the organic dye and mordant composition of Paracas textiles by a combined analytical approach

The object of this study is a wide selection of dyed cotton and camelid samples from an important collection of 2000-year-old Paracas textiles, now at the Museo Nacional de Arqueología, Antropología e Historia del Perú (MNAAHP; Lima; Peru) and at the National Museum of World Culture (NMWC; Gothenburg; Sweden). The threads, chosen as representative of the whole palette, were selected from eighteen different textiles. A combined spectroscopic and spectrometric analytical approach was selected to characterize the organic and inorganic composition of this wide set of samples. In particular, technical photography was used to gain a general overview of the samples, X-ray fluorescence (XRF) was employed for identifying the mordants and mapping the elemental distribution in the threads, while liquid chromatography coupled with diode array detector and with high-resolution mass spectrometry (HPLC–DAD, HPLC–HRMS) were used for characterizing organic dye composition. This study provides fundamental information on the mordants or other inorganic auxiliaries used in the dyeing processes, rarely investigated up to now, and to the varieties of vegetal sources employed in Paracas textiles. The widening of the Andean dyestuff database is highly important not only to acquire knowledge on Paracas culture, but also to ease the dye characterization of archaeological textiles from the Peruvian region and South American area in general.[Figure not available: see fulltext.]

Semantic filtering through deep source separation on microscopy images

By their very nature microscopy images of cells and tissues consist of a limited number of object types or components. In contrast to most natural scenes, the composition is known a priori. Decomposing biological images into semantically meaningful objects and layers is the aim of this paper. Building on recent approaches to image de-noising we present a framework that achieves state-of-the-art segmentation results requiring little or no manual annotations. Here, synthetic images generated by adding cell crops are sufficient to train the model. Extensive experiments on cellular images, a histology data set, and small animal videos demonstrate that our approach generalizes to a broad range of experimental settings. As the proposed methodology does not require densely labelled training images and is capable of resolving the partially overlapping objects it holds the promise of being of use in a number of different applications

Single-Molecule Localization Microscopy Reconstruction Using Noise2Noise for Super-Resolution Imaging of Actin Filaments

Single-molecule localization microscopy (SMLM) is a super-resolution imaging technique developed to image structures smaller than the diffraction limit. This modality results in sparse and non-uniform sets of localized blinks that need to be reconstructed to obtain a super-resolution representation of a tissue. In this paper, we explore the use of the Noise2Noise (N2N) paradigm to reconstruct the SMLM images. Noise2Noise is an image denoising technique where a neural network is trained with only pairs of noisy realizations of the data instead of using pairs of noisy/clean images, as performed with Noise2Clean (N2C). Here we have adapted Noise2Noise to the 2D SMLM reconstruction problem, exploring different pair creation strategies (fixed and dynamic). The approach was applied to synthetic data and to real 2D SMLM data of actin filaments. This revealed that N2N can achieve reconstruction performances close to the Noise2Clean training strategy, without having access to the super-resolution images. This could open the way to further improvement in SMLM acquisition speed and reconstruction performance

Assessing motor-related phenotypes of Caenorhabditis elegans with the wide field-of-view nematode tracking platform

Caenorhabditis elegans is a valuable model organism in biomedical research that has led to major discoveries in the fields of neurodegeneration, cancer and aging. Because movement phenotypes are commonly used and represent strong indicators of C. elegans fitness, there is an increasing need to replace manual assessments of worm motility with automated measurements to increase throughput and minimize observer biases. Here, we provide a protocol for the implementation of the improved wide field-of-view nematode tracking platform (WF-NTP), which enables the simultaneous analysis of hundreds of worms with respect to multiple behavioral parameters. The protocol takes only a few hours to complete, excluding the time spent culturing C. elegans, and includes (i) experimental design and preparation of samples, (ii) data recording, (iii) software management with appropriate parameter choices and (iv) post-experimental data analysis. We compare the WF-NTP with other existing worm trackers, including those having high spatial resolution. The main benefits of WF-NTP relate to the high number of worms that can be assessed at the same time on a whole-plate basis and the number of phenotypes that can be screened for simultaneously

International Consensus Statement on Rhinology and Allergy: Rhinosinusitis

Background: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR‐RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR‐RS‐2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence‐based findings of the document. Methods: ICAR‐RS presents over 180 topics in the forms of evidence‐based reviews with recommendations (EBRRs), evidence‐based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results: ICAR‐RS‐2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence‐based management algorithm is provided. Conclusion: This ICAR‐RS‐2021 executive summary provides a compilation of the evidence‐based recommendations for medical and surgical treatment of the most common forms of RS