Multiphase Geometric Couplings for the Segmentation of Neural Processes

The ability to constrain the geometry of deformable models for image segmentation can be useful when information about the expected shape or positioning of the objects in a scene is known a priori. An example of this occurs when segmenting neural cross sections in electron microscopy. Such images often contain multiple nested boundaries separating regions of homogeneous intensities. For these applications, multiphase level sets provide a partitioning framework that allows for the segmentation of multiple deformable objects by combining several level set functions. Although there has been much effort in the study of statistical shape priors that can be used to constrain the geometry of each partition, none of these methods allow for the direct modeling of geometric arrangements of partitions. In this paper, we show how to define elastic couplings between multiple level set functions to model ribbon-like partitions. We build such couplings using dynamic force fields that can depend on the image content and relative location and shape of the level set functions. To the best of our knowledge, this is the first work that shows a direct way of geometrically constraining multiphase level sets for image segmentation. We demonstrate the robustness of our method by comparing it with previous level set segmentation methods.Engineering and Applied Science

Promises, facts and challenges for graphene in biomedical applications

The graphene family has captured the interest and the imagination of an increasing number of scientists working in different fields, ranging from composites to flexible electronics. In the area of biomedical applications, graphene is especially involved in drug delivery, biosensing and tissue engineering, with strong contributions to the whole nanomedicine area. Besides the interesting results obtained so far and the evident success, there are still many problems to solve, on the way to the manufacturing of biomedical devices, including the lack of standardization in the production of the graphene family members. Control of lateral size, aggregation state (single vs. few layers) and oxidation state (unmodified graphene vs. oxidized graphenes) is essential for the translation of this material into clinical assays. In this Tutorial Review we critically describe the latest developments of the graphene family materials into the biomedical field. We analyze graphene-based devices starting from graphene synthetic strategies, functionalization and processibility protocols up to the final in vitro and in vivo applications. We also address the toxicological impact and the limitations in translating graphene materials into advanced clinical tools. Finally, new trends and guidelines for future developments are presented

Segmentation fusion for connectomics

We address the problem of automatic 3D segmentation of a stack of electron microscopy sections of brain tissue. Unlike previous efforts, where the reconstruction is usually done on a section-to-section basis, or by the agglomerative clustering of 2D segments, we leverage information from the entire volume to obtain a globally optimal 3D segmentation. To do this, we formulate the segmentation as the solution to a fusion problem. We first enumerate multiple possible 2D segmentations for each section in the stack, and a set of 3D links that may connect segments across consecutive sections. We then identify the fusion of segments and links that provide the most globally consistent segmentation of the stack. We show that this two-step approach of pre-enumeration and posterior fusion yields significant advantages and provides state-of-the-art reconstruction results. Finally, as part of this method, we also introduce a robust rotationally-invariant set of features that we use to learn and enumerate the above 2D segmentations. Our features outperform previous connectomic-specific descriptors without relying on a large set of heuristics or manually designed filter banks.Engineering and Applied Science

Enhanced Cellular Transduction of Nanoparticles Resistant to Rapidly Forming Plasma Protein Coronas

Nanoparticles (NPs) are increasingly being developed as biomedical platforms for drug/nucleic acid delivery and imaging. However, in biological fluids, NPs interact with a wide range of proteins that form a coating known as protein corona. Coronae can critically influence self-interaction and binding of other molecules, which can affect toxicity, promote cell activation, and inhibit general or specific cellular uptake. Glycosaminoglycan (GAG)-binding enhanced transduction (GET) is developed to efficiently deliver a variety of cargoes intracellularly; employing GAG-binding peptides, which promote cell targeting, and cell penetrating peptides (CPPs) which enhance endocytotic cell internalization. Herein, it is demonstrated that GET peptide coatings can mediate sustained intracellular transduction of magnetic NPs (MNPs), even in the presence of serum or plasma. NP colloidal stability, physicochemical properties, toxicity and cellular uptake are investigated. Using label-free snapshot proteomics, time-resolved profiles of human plasma coronas formed on functionalized GET-MNPs demonstrate that coronae quickly form (<1 min), with their composition relatively stable but evolving. Importantly GET-MNPs present a subtly different corona composition to MNPs alone, consistent with GAG-binding activities. Understanding how NPs interact with biological systems and can retain enhanced intracellular transduction will facilitate novel drug delivery approaches for cell-type specific targeting of new nanomaterials

Large-Scale Automatic Reconstruction of Neuronal Processes from Electron Microscopy Images

Automated sample preparation and electron microscopy enables acquisition of very large image data sets. These technical advances are of special importance to the field of neuroanatomy, as 3D reconstructions of neuronal processes at the nm scale can provide new insight into the fine grained structure of the brain. Segmentation of large-scale electron microscopy data is the main bottleneck in the analysis of these data sets. In this paper we present a pipeline that provides state-of-the art reconstruction performance while scaling to data sets in the GB-TB range. First, we train a random forest classifier on interactive sparse user annotations. The classifier output is combined with an anisotropic smoothing prior in a Conditional Random Field framework to generate multiple segmentation hypotheses per image. These segmentations are then combined into geometrically consistent 3D objects by segmentation fusion. We provide qualitative and quantitative evaluation of the automatic segmentation and demonstrate large-scale 3D reconstructions of neuronal processes from a $\mathbf{27,000}$ $\mathbf{\mu m^3}$ volume of brain tissue over a cube of $\mathbf{30 \; \mu m}$ in each dimension corresponding to 1000 consecutive image sections. We also introduce Mojo, a proofreading tool including semi-automated correction of merge errors based on sparse user scribbles

Automated tracing of myelinated axons and detection of the nodes of Ranvier in serial images of peripheral nerves

The development of realistic neuroanatomical models of peripheral nerves for simulation purposes requires the reconstruction of the morphology of the myelinated fibres in the nerve, including their nodes of Ranvier. Currently, this information has to be extracted by semimanual procedures, which severely limit the scalability of the experiments. In this contribution, we propose a supervised machine learning approach for the detailed reconstruction of the geometry of fibres inside a peripheral nerve based on its high-resolution serial section images. Learning from sparse expert annotations, the algorithm traces myelinated axons, even across the nodes of Ranvier. The latter are detected automatically. The approach is based on classifying the myelinated membranes in a supervised fashion, closing the membrane gaps by solving an assignment problem, and classifying the closed gaps for the nodes of Ranvier detection. The algorithm has been validated on two very different datasets: (i) rat vagus nerve subvolume, SBFSEM microscope, 200 × 200 × 200 nm resolution, (ii) rat sensory branch subvolume, confocal microscope, 384 × 384 × 800 nm resolution. For the first dataset, the algorithm correctly reconstructed 88% of the axons (241 out of 273) and achieved 92% accuracy on the task of Ranvier node detection. For the second dataset, the gap closing algorithm correctly closed 96.2% of the gaps, and 55% of axons were reconstructed correctly through the whole volume. On both datasets, training the algorithm on a small data subset and applying it to the full dataset takes a fraction of the time required by the currently used semiautomated protocols. Our software, raw data and ground truth annotations are available at http://hci.iwr.uni-heidelberg.de/Benchmarks/. The development version of the code can be found at https://github.com/RWalecki/ATMA

Rapidly Transducing and Spatially Localized Magnetofection Using Peptide-Mediated Non-Viral Gene Delivery Based on Iron Oxide Nanoparticles

Non-viral delivery systems are generally of low efficiency, which limits their use in gene therapy and editing applications. We previously developed a technology termed glycosaminoglycan (GAG)-binding enhanced transduction (GET) to efficiently deliver a variety of cargos intracellularly; our system employs GAG-binding peptides, which promote cell targeting, and cell penetrating peptides (CPPs), which enhance endocytotic cell internalization. Herein, we describe a further modification by combining gene delivery and magnetic targeting with the GET technology. We associated GET peptides, plasmid (p)DNA, and iron oxide superparamagnetic nanoparticles (MNPs), allowing rapid and targeted GET-mediated uptake by application of static magnetic fields in NIH3T3 cells. This produced effective transfection levels (significantly higher than the control) with seconds to minutes of exposure and localized gene delivery two orders of magnitude higher in targeted over non-targeted cell monolayers using magnetic fields (in 15 min exposure delivering GFP reporter pDNA). More importantly, high cell membrane targeting by GET-DNA and MNP co-complexes and magnetic fields allowed further enhancement to endocytotic uptake, meaning that the nucleic acid cargo was rapidly internalized beyond that of GET complexes alone (GET-DNA). Magnetofection by MNPs combined with GET-mediated delivery allows magnetic field-guided local transfection in vitro and could facilitate focused gene delivery for future regenerative and disease-targeted therapies in vivo

Towards a general model for predicting minimal metal concentrations co-selecting for antibiotic resistance plasmids

© 2021 Elsevier Ltd Many antibiotic resistance genes co-occur with resistance genes for transition metals, such as copper, zinc, or mercury. In some environments, a positive correlation between high metal concentration and high abundance of antibiotic resistance genes has been observed, suggesting co-selection due to metal presence. Of particular concern is the use of copper and zinc in animal husbandry, leading to potential co-selection for antibiotic resistance in animal gut microbiomes, slurry, manure, or amended soils. For antibiotics, predicted no effect concentrations have been derived from laboratory measured minimum inhibitory concentrations and some minimal selective concentrations have been investigated in environmental settings. However, minimal co-selection concentrations for metals are difficult to identify. Here, we use mathematical modelling to provide a general mechanistic framework to predict minimal co-selective concentrations for metals, given knowledge of their toxicity at different concentrations. We apply the method to copper (Cu), zinc (Zn), mercury (Hg), lead (Pb) and silver (Ag), predicting their minimum co-selective concentrations in mg/L (Cu: 5.5, Zn: 1.6, Hg: 0.0156, Pb: 21.5, Ag: 0.152). To exemplify use of these thresholds, we consider metal concentrations from slurry and slurry-amended soil from a UK dairy farm that uses copper and zinc as additives for feed and antimicrobial footbath: the slurry is predicted to be co-selective, but not the slurry-amended soil. This modelling framework could be used as the basis for defining standards to mitigate risks of antimicrobial resistance applicable to a wide range of environments, including manure, slurry and other waste streams. We provide a general framework to predict minimal co-selective concentrations for metals as environmental co-selective agents for antibiotic resistance, using mechanistic differential equations, and apply the method to copper, zinc, mercury, lead and silver

Selenium speciation and bioaccessibility in Se-fertilised crops of dietary importance in Malawi

The purpose of this research was to explore the speciation and bioaccessibility of native soil-derived selenium (Se) versus Se applied via fertiliser in the edible portions of maize, groundnut and cowpea grown in Malawi. Fertiliser-derived Se, applied as isotopically labelled selenate, contributed 88�97% of the total Se in the edible portions. Both soil and fertiliser-derived Se were transformed into similar species, with more than 90% of the extracted Se in an organic form. The main form of fertiliser-derived Se in grain was selenomethionine with an abundance of 92.0 ± 7.6% in maize, 63.7 ± 6.2% in cowpea and 85.2 ± 1.9% in groundnut. In addition, cowpea contained 32.7 ± 6.2% of Se-methyl-selenocysteine. The mean bioaccessibility of fertiliser-derived Se was 73.9 ± 8.5% with no statistically-significant difference across all crops despite some variation in speciation. Understanding the contribution of fertiliser-derived Se to the formation of organic forms of Se in crops is crucial, given that organic Se species are more bioaccessible than inorganic forms

Video Segmentation with Superpixels

Due to its importance, video segmentation has regained interest recently. However, there is no common agreement about the necessary ingredients for best performance. This work contributes a thorough analysis of various within- and between-frame affinities suitable for video segmentation. Our results show that a frame-based superpixel segmentation combined with a few motion and appearance-based affinities are sufficient to obtain good video segmentation performance. A second contribution of the paper is the extension of [1] to include motion-cues, which makes the algorithm globally aware of motion, thus improving its performance for video sequences. Finally, we contribute an extension of an established image segmentation benchmark [1] to videos, allowing coarse-to-fine video segmentations and multiple human annotations. Our results are tested on BMDS [2], and compared to existing methods