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1. Semi-blind sparse affine spectral unmixing of autofluorescence-contaminated micrographs

   https://doi.org/10.1093/bioinformatics/btz674  

   Rossetti, Blair J. ; Wilbert, Steven A. ; Mark Welch, Jessica L. ; Borisy, Gary G. ; Nagy, James G. ; Murphy, ed., Robert ( August 2019 , Bioinformatics)

   Spectral unmixing methods attempt to determine the concentrations of different fluorophores present at each pixel location in an image by analyzing a set of measured emission spectra. Unmixing algorithms have shown great promise for applications where samples contain many fluorescent labels; however, existing methods perform poorly when confronted with autofluorescence-contaminated images.

   We propose an unmixing algorithm designed to separate fluorophores with overlapping emission spectra from contamination by autofluorescence and background fluorescence. First, we formally define a generalization of the linear mixing model, called the affine mixture model (AMM), that specifically accounts for background fluorescence. Second, we use the AMM to derive an affine nonnegative matrix factorization method for estimating fluorophore endmember spectra from reference images. Lastly, we propose a semi-blind sparse affine spectral unmixing (SSASU) algorithm that uses knowledge of the estimated endmembers to learn the autofluorescence and background fluorescence spectra on a per-image basis. When unmixing real-world spectral images contaminated by autofluorescence, SSASU greatly improved proportion indeterminacy as compared to existing methods for a given relative reconstruction error.

   The source code used for this paper was written in Julia and is available with the test data at https://github.com/brossetti/ssasu.

    

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2. The macroecology and evolution of avian competence for Borrelia burgdorferi

   https://doi.org/10.1111/geb.13256  

   Becker, Daniel J. ; Han, Barbara A. ; Kamath, ed., Pauline ( January 2021 , Global Ecology and Biogeography)

   Prediction of novel reservoirs of zoonotic pathogens would be improved by the identification of interspecific drivers of host competence (i.e., the ability to transmit pathogens to new hosts or vectors). Tick‐borne pathogens can provide a useful model system, because larvae become infected only when feeding on a competent host during their first blood meal. For tick‐borne diseases, competence has been studied best forBorrelia burgdorferisensu lato (Bbsl), which causes Lyme borreliosis. Major reservoirs include several small mammal species, but birds might play an under‐recognized role in human risk given their ability to disperse infected ticks across large spatial scales. Here, we provide a global synthesis of the ecological and evolutionary factors that determine the ability of bird species to infect larval ticks withBbsl.

   Global.

   1983–2019.

   Birds.

   We compiled a dataset ofBbsl competence across 183 bird species and applied meta‐analysis, phylogenetic factorization and boosted regression trees to describe spatial and temporal patterns in competence, characterize its phylogenetic distribution across birds, reconstruct its evolution and evaluate the trait profiles associated with competent avian species.

   Half of the sampled bird species show evidence of competence forBbsl. Competence displays moderate phylogenetic signal, has evolved multiple times across bird species and is pronounced in the genusTurdus. Trait‐based analyses distinguished competent birds with 80% accuracy and showed that such species have low baseline corticosterone, exist on both ends of the pace‐of‐life continuum, breed and winter at high latitudes and have broad migratory movements into their breeding range. We used these trait profiles to predict various likely but unsampled competent species, including novel concentrations of avian reservoirs within the Neotropics.

   Our results can generate new hypotheses for how birds contribute to the dynamics of tick‐borne pathogens and help to prioritize surveillance of likely but unsampled competent birds. Our findings also emphasize that birds display under‐recognized variation in their contributions to enzootic cycles ofBbsl and the broader need to consider competence in ecological and predictive studies of multi‐host pathogens.

    

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3. MetaBMF: a scalable binning algorithm for large-scale reference-free metagenomic studies

   https://doi.org/10.1093/bioinformatics/btz577  

   Ma, Terry ; Xiao, Di ; Xing, Xin ; Birol, ed., Inanc ( July 2019 , Bioinformatics)

   Metagenomics studies microbial genomes in an ecosystem such as the gastrointestinal tract of a human. Identification of novel microbial species and quantification of their distributional variations among different samples that are sequenced using next-generation-sequencing technology hold the key to the success of most metagenomic studies. To achieve these goals, we propose a simple yet powerful metagenomic binning method, MetaBMF. The method does not require prior knowledge of reference genomes and produces highly accurate results, even at a strain level. Thus, it can be broadly used to identify disease-related microbial organisms that are not well-studied.

   Mathematically, we count the number of mapped reads on each assembled genomic fragment cross different samples as our input matrix and propose a scalable stratified angle regression algorithm to factorize this count matrix into a product of a binary matrix and a nonnegative matrix. The binary matrix can be used to separate microbial species and the nonnegative matrix quantifies the species distributions in different samples. In simulation and empirical studies, we demonstrate that MetaBMF has a high binning accuracy. It can not only bin DNA fragments accurately at a species level but also at a strain level. As shown in our example, we can accurately identify the Shiga-toxigenic Escherichia coli O104: H4 strain which led to the 2011 German E.coli outbreak. Our efforts in these areas should lead to (i) fundamental advances in metagenomic binning, (ii) development and refinement of technology for the rapid identification and quantification of microbial distributions and (iii) finding of potential probiotics or reliable pathogenic bacterial strains.

   The software is available at https://github.com/didi10384/MetaBMF.

    

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4. Demystifying autofluorescence with excitation scanning hyperspectral imaging

   https://doi.org/10.1117/12.2290818  

   Deal, Joshua ; Harris, Bradley ; Martin, Will ; Lall, Malvika ; Lopez, Carmen ; Boudreaux, Carole ; Rich, Thomas ; Leavesley, Silas ; Rider, Paul ( February 2018 , Proc. SPIE 10497, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVI)

   Autofluorescence has historically been considered a nuisance in medical imaging. Many endogenous fluorophores, specifically, collagen, elastin, NADH, and FAD, are found throughout the human body. Diagnostically, these signals can be prohibitive since they can outcompete signals introduced for diagnostic purposes. Recent advances in hyperspectral imaging have allowed the acquisition of significantly more data in a shorter time period by scanning the excitation spectra of fluorophores. The reduced acquisition time and increased signal-to-noise ratio allow for separation of significantly more fluorophores than previously possible. Here, we propose to utilize excitation-scanning of autofluorescence to examine tissues and diagnose pathologies. Spectra of autofluorescent molecules were obtained using a custom inverted microscope (TE-2000, Nikon Instruments) with a Xe arc lamp and thin film tunable filter array (VersaChrome, Semrock, Inc.) Scans utilized excitation wavelengths from 360 nm to 550 nm in 5 nm increments. The resultant spectra were used to examine hyperspectral image stacks from various collaborative studies, including an atherosclerotic rat model and a colon cancer study. Hyperspectral images were analyzed with ENVI and custom Matlab scripts including linear spectral unmixing (LSU) and principal component analysis (PCA). Initial results suggest the ability to separate the signals of endogenous fluorophores and measure the relative concentrations of fluorophores among healthy and diseased states of similar tissues. These results suggest pathology-specific changes to endogenous fluorophores can be detected using excitationscanning hyperspectral imaging. Future work will expand the library of pure molecules and will examine more defined disease states. 

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5. Optimal decoding of neural dynamics occurs at mesoscale spatial and temporal resolutions

   https://doi.org/10.3389/fncel.2024.1287123  

   Samiei, Toktam ; Zou, Zhuowen ; Imani, Mohsen ; Nozari, Erfan ( February 2024 , Frontiers in Cellular Neuroscience)

   Jonathan R. Whitlock (Ed.)

   Understanding the neural code has been one of the central aims of neuroscience research for decades. Spikes are commonly referred to as the units of information transfer, but multi-unit activity (MUA) recordings are routinely analyzed in aggregate forms such as binned spike counts, peri-stimulus time histograms, firing rates, or population codes. Various forms of averaging also occur in the brain, from the spatial averaging of spikes within dendritic trees to their temporal averaging through synaptic dynamics. However, how these forms of averaging are related to each other or to the spatial and temporal units of information representation within the neural code has remained poorly understood.

   In this work we developed NeuroPixelHD, a symbolic hyperdimensional model of MUA, and used it to decode the spatial location and identity of static images shown ton= 9 mice in the Allen Institute Visual Coding—NeuroPixels dataset from large-scale MUA recordings. We parametrically varied the spatial and temporal resolutions of the MUA data provided to the model, and compared its resulting decoding accuracy.

   For almost all subjects, we found 125ms temporal resolution to maximize decoding accuracy for both the spatial location of Gabor patches (81 classes for patches presented over a 9×9 grid) as well as the identity of natural images (118 classes corresponding to 118 images) across the whole brain. This optimal temporal resolution nevertheless varied greatly between different regions, followed a sensory-associate hierarchy, and was significantly modulated by the central frequency of theta-band oscillations across different regions. Spatially, the optimal resolution was at either of two mesoscale levels for almost all mice: the area level, where the spiking activity of all neurons within each brain area are combined, and the population level, where neuronal spikes within each area are combined across fast spiking (putatively inhibitory) and regular spiking (putatively excitatory) neurons, respectively. We also observed an expected interplay between optimal spatial and temporal resolutions, whereby increasing the amount of averaging across one dimension (space or time) decreases the amount of averaging that is optimal across the other dimension, and vice versa.

   Our findings corroborate existing empirical practices of spatiotemporal binning and averaging in MUA data analysis, and provide a rigorous computational framework for optimizing the level of such aggregations. Our findings can also synthesize these empirical practices with existing knowledge of the various sources of biological averaging in the brain into a new theory of neural information processing in which theunit of informationvaries dynamically based on neuronal signal and noise correlations across space and time.

    

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