Multi-Environment Model Estimation for Motility Analysis of Caernorhabditis elegans

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and ‘skeletonizing’ across a wide range of motility assays.

Mechanisms of recognition and binding of α-TTP to the plasma membrane by multi-scale molecular dynamics simulations

We used multiple sets of simulations both at the atomistic and coarse-grained level of resolution to investigate interaction and binding of α-tochoperol transfer protein (α-TTP) to phosphatidylinositol phosphate lipids (PIPs). Our calculations indicate that enrichment of membranes with such lipids facilitate membrane anchoring. Atomistic models suggest that PIP can be incorporated into the binding cavity of α-TTP and therefore confirm that such protein can work as lipid exchanger between the endosome and the plasma membrane. Comparison of the atomistic models of the α-TTP-PIPs complex with membrane-bound α-TTP revealed different roles for the various basic residues composing the basic patch that is key for the protein/ligand interaction. Such residues are of critical importance as several point mutations at their position lead to severe forms of ataxia with vitamin E deficiency (AVED) phenotypes. Specifically, R221 is main residue responsible for the stabilization of the complex. R68 and R192 exchange strong interactions in the protein or in the membrane complex only, suggesting that the two residues alternate contact formation, thus facilitating lipid flipping from the membrane into the protein cavity during the lipid exchange process. Finally, R59 shows weaker interactions with PIPs anyway with a clear preference for specific phosphorylation positions, hinting a role in early membrane selectivity for the protein. Altogether, our simulations reveal significant aspects at the atomistic scale of interactions of α-TTP with the plasma membrane and with PIP, providing clarifications on the mechanism of intracellular vitamin E trafficking and helping establishing the role of key residue for the functionality of α-TTP.

Facial Nerve Image Enhancement from CBCT using Supervised Learning Technique

Facial nerve segmentation plays an important role in surgical planning of cochlear implantation. Clinically available CBCT images are used for surgical planning. However, its relatively low resolution renders the identification of the facial nerve difficult. In this work, we present a supervised learning approach to enhance facial nerve image information from CBCT. A supervised learning approach based on multi-output random forest was employed to learn the mapping between CBCT and micro-CT images. Evaluation was performed qualitatively and quantitatively by using the predicted image as input for a previously published dedicated facial nerve segmentation, and cochlear implantation surgical planning software, OtoPlan. Results show the potential of the proposed approach to improve facial nerve image quality as imaged by CBCT and to leverage its segmentation using OtoPlan.

Long-term hydrological dynamics and fire history over the last 2000 years in CE Europe reconstructed from a high-resolution peat archive

Sphagnum peatlands in the oceanic-continental transition zone of Poland are currently influenced by climatic and anthropogenic factors that lead to peat desiccation and susceptibility to fire. Little is known about the response of Sphagnum peatland testate amoebae (TA) to the combined effects of drought and fire. To understand the relationships between hydrology and fire dynamics, we used high-resolution multi-proxy palaeoecological data to reconstruct 2000 years of mire history in northern Poland. We employed a new approach for Polish peatlands – joint TA-based water table depth and charcoal-inferred fire activity reconstructions. In addition, the response of most abundant TA hydrological indicators to charcoal-inferred fire activity was assessed. The results show four hydrological stages of peatland development: moderately wet (from ∼35 BC to 800 AD), wet (from ∼800 to 1390 AD), dry (from ∼1390 to 1700 AD) and with an instable water table (from ∼1700 to 2012 AD). Fire activity has increased in the last millennium after constant human presence in the mire surroundings. Higher fire activity caused a rise in the water table, but later an abrupt drought appeared at the onset of the Little Ice Age. This dry phase is characterized by high ash contents and high charcoal-inferred fire activity. Fires preceded hydrological change and the response of TA to fire was indirect. Peatland drying and hydrological instability was connected with TA community changes from wet (dominance of Archerella flavum, Hyalosphenia papilio, Amphitrema wrightianum) to dry (dominance of Cryptodifflugia oviformis, Euglypha rotunda); however, no clear fire indicator species was found. Anthropogenic activities can increase peat fires and cause substantial hydrology changes. Our data suggest that increased human fire activity was one of the main factors that influenced peatland hydrology, though the mire response through hydrological changes towards drier conditions was delayed in relation to the surrounding vegetation changes.

Quantification of water content across a cement-clay interface using high resolution neutron radiography

In many designs for radioactive waste repositories, cement and clay will come into direct contact. The geochemical contrast between cement and clay will lead to mass fluxes across the interface, which consequently results in alteration of structural and transport properties of both materials that may affect the performance of the multi-barrier system. We present an experimental approach to study cement-clay interactions with a cell to accommodate small samples of cement and clay. The cell design allows both in situ measurement of water content across the sample using neutron radiography and measurement of transport parameters using through-diffusion tracer experiments. The aim of the high- resolution neutron radiography experiments was to monitor changes in water content (porosity) and their spatial extent. Neutron radiographs of several evolving cement-clay interfaces delivered quantitative data which allow resolving local water contents within the sample domain. In the present work we explored the uncertainties of the derived water contents with regard to various input parameters and with regard to the applied image correction procedures. Temporal variation of measurement conditions created absolute uncertainty of the water content in the order of ±0.1 (m3/m3), which could not be fully accounted for by correction procedures. Smaller relative changes in water content between two images can be derived by specific calibrations to two sample regions with different, invariant water contents.

Automated 3D Lumbar Intervertebral Disc Segmentation from MRI Data Sets

This paper proposed an automated three-dimensional (3D) lumbar intervertebral disc (IVD) segmentation strategy from Magnetic Resonance Imaging (MRI) data. Starting from two user supplied landmarks, the geometrical parameters of all lumbar vertebral bodies and intervertebral discs are automatically extracted from a mid-sagittal slice using a graphical model based template matching approach. Based on the estimated two-dimensional (2D) geometrical parameters, a 3D variable-radius soft tube model of the lumbar spine column is built by model fitting to the 3D data volume. Taking the geometrical information from the 3D lumbar spine column as constraints and segmentation initialization, the disc segmentation is achieved by a multi-kernel diffeomorphic registration between a 3D template of the disc and the observed MRI data. Experiments on 15 patient data sets showed the robustness and the accuracy of the proposed algorithm.

Response profiles of murine spiral ganglion neurons on multi-electrode arrays.

OBJECTIVE Cochlear implants (CIs) have become the gold standard treatment for deafness. These neuroprosthetic devices feature a linear electrode array, surgically inserted into the cochlea, and function by directly stimulating the auditory neurons located within the spiral ganglion, bypassing lost or not-functioning hair cells. Despite their success, some limitations still remain, including poor frequency resolution and high-energy consumption. In both cases, the anatomical gap between the electrode array and the spiral ganglion neurons (SGNs) is believed to be an important limiting factor. The final goal of the study is to characterize response profiles of SGNs growing in intimate contact with an electrode array, in view of designing novel CI devices and stimulation protocols, featuring a gapless interface with auditory neurons. APPROACH We have characterized SGN responses to extracellular stimulation using multi-electrode arrays (MEAs). This setup allows, in our view, to optimize in vitro many of the limiting interface aspects between CIs and SGNs. MAIN RESULTS Early postnatal mouse SGN explants were analyzed after 6-18 days in culture. Different stimulation protocols were compared with the aim to lower the stimulation threshold and the energy needed to elicit a response. In the best case, a four-fold reduction of the energy was obtained by lengthening the biphasic stimulus from 40 μs to 160 μs. Similarly, quasi monophasic pulses were more effective than biphasic pulses and the insertion of an interphase gap moderately improved efficiency. Finally, the stimulation with an external electrode mounted on a micromanipulator showed that the energy needed to elicit a response could be reduced by a factor of five with decreasing its distance from 40 μm to 0 μm from the auditory neurons. SIGNIFICANCE This study is the first to show electrical activity of SGNs on MEAs. Our findings may help to improve stimulation by and to reduce energy consumption of CIs and thereby contribute to the development of fully implantable devices with better auditory resolution in the future.

Dark matter sensitivity of multi-ton liquid xenon detectors

We study the sensitivity of multi ton-scale time projection chambers using a liquid xenon target, e.g., the proposed DARWIN instrument, to spin-independent and spin-dependent WIMP-nucleon scattering interactions. Taking into account realistic backgrounds from the detector itself as well as from neutrinos, we examine the impact of exposure, energy threshold, background rejection efficiency and energy resolution on the dark matter sensitivity. With an exposure of 200 t x y and assuming detector parameters which have been already demonstrated experimentally, spin-independent cross sections as low as 2.5×10−49 cm2 can be probed for WIMP masses around 40 GeV/c2. Additional improvements in terms of background rejection and exposure will further increase the sensitivity, while the ultimate WIMP science reach will be limited by neutrinos scattering coherently off the xenon nuclei.