Incidents of snake fungal disease caused by the fungal pathogen Ophidiomyces ophidiicola in Texas.

The pathogen Ophidiomyces ophidiicola, widely known as the primary cause of snake fungal disease (SFD) has been detected in Texas's naïve snakes. Our team set out to characterize O. ophidiicola's spread in eastern Texas. From December 2018 until November 2021, we sampled and screened with ultraviolet (UV) light, 176 snakes across eastern Texas and detected 27. O. ophidiicola's positive snakes using qPCR and one snake in which SFD was confirmed via additional histological examination. Upon finding the ribbon snake with clear clinical display, we isolated and cultured what we believe to be the first culture from Texas. This cultured O. ophidiicola TX displays a ring halo formation when grown on a solid medium as well as cellular autofluorescence as expected. Imaging reveals individual cells within the septated hyphae branches contain a distinct nucleus separation from neighboring cells. Overall, we have found over 1/10 snakes that may be infected in East Texas, gives credence to the onset of SFD in Texas. These results add to the progress of the disease across the continental United States.

Programs as Polypeptides.

Object-oriented combinator chemistry (OOCC) is an artificial chemistry with composition devices borrowed from object-oriented and functional programming languages. Actors in OOCC are embedded in space and subject to diffusion; since they are neither created nor destroyed, their mass is conserved. Actors use programs constructed from combinators to asynchronously update their own states and the states of other actors in their neighborhoods. The fact that programs and combinators are themselves reified as actors makes it possible to build programs that build programs from combinators of a few primitive types using asynchronous spatial processes that resemble chemistry as much as computation. To demonstrate this, OOCC is used to define a parallel, asynchronous, spatially distributed self-replicating system modeled in part on the living cell. Since interactions among its parts result in the construction of more of these same parts, the system is strongly constructive. The system's high normalized complexity is contrasted with that of a simple composome.

Effects of geomorphology, habitat, and spatial location on fish assemblages in a watershed in Ohio, USA.

In this paper, we evaluate relationships between in-stream habitat, water chemistry, spatial distribution within a predominantly agricultural Midwestern watershed and geomorphic features and fish assemblage attributes and abundances. Our specific objectives were to: (1) identify and quantify key environmental variables at reach and system wide (watershed) scales; and (2) evaluate the relative influence of those environmental factors in structuring and explaining fish assemblage attributes at reach scales to help prioritize stream monitoring efforts and better incorporate all factors that influence aquatic biology in watershed management programs. The original combined data set consisted of 31 variables measured at 32 sites, which was reduced to 9 variables through correlation and linear regression analysis: stream order, percent wooded riparian zone, drainage area, in-stream cover quality, substrate quality, gradient, cross-sectional area, width of the flood prone area, and average substrate size. Canonical correspondence analysis (CCA) and variance partitioning were used to relate environmental variables to fish species abundance and assemblage attributes. Fish assemblages and abundances were explained best by stream size, gradient, substrate size and quality, and percent wooded riparian zone. Further data are needed to investigate why water chemistry variables had insignificant relationships with IBI scores. Results suggest that more quantifiable variables and consideration of spatial location of a stream reach within a watershed system should be standard data incorporated into stream monitoring programs to identify impairments that, while biologically limiting, are not fully captured or elucidated using current bioassessment methods.

Markov random field modeling of the spatial distribution of proteins on cell membranes.

Cell membranes display a range of receptors that bind ligands and activate signaling pathways. Signaling is characterized by dramatic changes in membrane molecular topography, including the co-clustering of receptors with signaling molecules and the segregation of other signaling molecules away from receptors. Electron microscopy of immunogold-labeled membranes is a critical technique to generate topographical information at the 5-10 nm resolution needed to understand how signaling complexes assemble and function. However, due to experimental limitations, only two molecular species can usually be labeled at a time. A formidable challenge is to integrate experimental data across multiple experiments where there are from 10 to 100 different proteins and lipids of interest and only the positions of two species can be observed simultaneously. As a solution, we propose the use of Markov random field (MRF) modeling to reconstruct the distribution of multiple cell membrane constituents from pair-wise data sets. MRFs are a powerful mathematical formalism for modeling correlations between states associated with neighboring sites in spatial lattices. The presence or absence of a protein of a specific type at a point on the cell membrane is a state. Since only two protein types can be observed, i.e., those bound to particles, and the rest cannot be observed, the problem is one of deducing the conditional distribution of a MRF with unobservable (hidden) states. Here, we develop a multiscale MRF model and use mathematical programming techniques to infer the conditional distribution of a MRF for proteins of three types from observations showing the spatial relationships between only two types. Application to synthesized data shows that the spatial distributions of three proteins can be reliably estimated. Application to experimental data provides the first maps of the spatial relationship between groups of three different signaling molecules. The work is an important step toward a more complete understanding of membrane spatial organization and dynamics during signaling.

A rational approach to minimal high-resolution cross-reactive arrays.

We report a rational approach to the construction of cross-reactive arrays for steroids consisting of five to seven sensors incorporating modified oligonucleotides. The sensors for our arrays were selected to maximize their differential responses to the two steroids most different in an arbitrarily chosen parameter named "shape-length". The arrays incorporated three previously unreported types of sensors identified through a massive screening effort: (1) three-way junction sensors with neutralized charges within junction; (2) "self-aggregating sensors"; and (3) sensors incorporating fluorophore directly in a three-way junction as a spacer. The arrays were tested on seven steroids and an alkaloid (cocaine) over a range of concentrations, and achieved 92-96% accuracy in class assignments, despite the close structural similarities between steroids.

A rotation and translation invariant discrete saliency network.

We describe a neural network that enhances and completes salient closed contours in images. Our work is different from all previous work in three important ways. First, like the input provided to primary visual cortex (V1) by the lateral geniculate nucleus (LGN), the input to our computation is isotropic. That is, it is composed of spots, not edges. Second, our network computes a well-defined function of the input based on a distribution of closed contours characterized by a random process. Third, even though our computation is implemented in a discrete network, its output is invariant to continuous rotations and translations of the input image.

Large-scale effects of timber harvesting on stream systems in the Ouachita Mountains, Arkansas, USA.

Using Basin Area Stream Survey (BASS) data from the United States Forest Service, we evaluated how timber harvesting influenced patterns of variation in physical stream features and regional fish and macroinvertebrate assemblages. Data were collected for three years (1990-1992) from six hydrologically variable streams in the Ouachita Mountains, Arkansas, USA that were paired by management regime within three drainage basins. Specifically, we used multivariate techniques to partition variability in assemblage structure (taxonomic and trophic) that could be explained by timber harvesting, drainage basin differences, year-to-year variability, and their shared variance components. Most of the variation in fish assemblages was explained by drainage basin differences, and both basin and year-of-sampling influenced macroinvertebrate assemblages. All three factors modeled, including interactions between drainage basins and timber harvesting, influenced variability in physical stream features. Interactions between timber harvesting and drainage basins indicated that differences in physical stream features were important in determining the effects of logging within a basin. The lack of a logging effect on the biota contradicts predictions for these small, hydrologically variable streams. We believe this pattern is related to the large scale of this study and the high levels of natural variability in the streams. Alternatively, there may be time-specific effects we were unable to detect with our sampling design and analyses.