Approaches To Improving Detection Of Invasive Fish Species In Western Lake Erie Through Analysis Of Monitoring Efficiencies And Metrics Of Community Distribution

Efficient monitoring programs are essential for the early detection of invasive species. The Ohio Department of Natural Resources (ODNR) monitoring program encompassing 21 years of fish survey data from western Lake Erie was evaluated using Chao biodiversity analysis to determine the efficiency and precision of collection strategies of trawl and gillnet sampling, at detecting rare or non-native species. Overall, ODNR sampling annually accounted for ~80% of extant fish species, leaving gaps in coverage where rare and invasive species may be overlooked and proliferate.Obtaining 90% efficiency would require an estimated doubling of previous sampling effort. Computer simulations calculating different proportions of trawl and gillnet sampling effort indicate an advantage to mixing collection strategies by reducing effort, and reveals a range of effective proportions concerning the two collection techniques. In addition, population trends for several species were evaluated to better elucidate strengths and weakness of current monitoring programs. These results enable an analysis of maximized sampling efficiency to provide earlier detection of future introductions, reduce total costs, and facilitate an improved understanding of native community dynamics. Understanding variations in fish community structure across a lake system can improve efficiency of monitoring programs and better prepares responders to invasive species introductions. Analysis of historic fish data to help designate new areas of concern and sites of future sampling interest were developed by utilizing Chao biodiversity statistics to calculate the odds of sampling new species at these ODNR sampling locations across the western basin. Through comparison of offshore ODNR trawl and gillnet samples, and near shore electrofishing surveys conducted by the University of Toledo both in the 2011 season provide proof that differences in sampling equipment and habitat types lead to variations in sampling efficiency and fish community distribution. Through analysis of spatial trends in species incidence, monitoring programs can selectively target individual species and areas for further study to combat invasive species encroachment into native ecosystems

Curvature Filtrations for Graph Generative Model Evaluation

Graph generative model evaluation necessitates understanding differences between graphs on the distributional level. This entails being able to harness salient attributes of graphs in an efficient manner. Curvature constitutes one such property of graphs, and has recently started to prove useful in characterising graphs. Its expressive properties, stability, and practical utility in model evaluation remain largely unexplored, however. We combine graph curvature descriptors with emerging methods from topological data analysis to obtain robust, expressive descriptors for evaluating graph generative models

Spacesuit Range of Motion Investigations Using Video and Motion Capture Systems at Spaceflight Analogue Expeditions and within the ERAU S.U.I.T. Lab

The Embry-Riddle Aeronautical University (ERAU) Spacesuit Utilization of Innovative Technology Laboratory (S.U.I.T. Lab) is dedicated to the pursuit of advancing human spaceflight by contributing to spacesuit and operations research with experiential programs for students. A significant portion of the S.U.I.T. Lab’s portfolio is dedicated to the design and execution of spacesuit range of motion (ROM) investigations using video and motion capture systems. ROM biomechanical angles were measured using these techniques in conjunction with developing protocols for both simulated extravehicular activity suits at spaceflight analogue expeditions, and on ERAU campus with Final Frontier Design (FFD) intravehicular activity pressure suits. Designing protocols ensures effective communication for the analysis of simulated spacesuit performance to a remote crew. With communication delays to Earth, a self-sufficient spacesuit diagnosis is required to provide future astronauts with immediate action to take when dealing with a malfunctioning spacesuit. The video capture methodology is designed so that any crew would be able to conduct recordings with minimal impact to their schedule and with camera resources that are standard equipment. Spaceflight mission analogues involved in this study include: Hawai\u27i Space Exploration Analog and Simulation (HI-SEAS Mission V, 2017); Mars Desert Research Station (MDRS Crew 188, 2018), and AMADEE-18 in Oman (2018). Video capture can be used to collaborate with several spacesuit manufacturers to offer a snapshot comparison between designs, validate and verify capabilities, and aid with the selection of the right suit for the right job. The analogue locations recorded unsuited and suited data, while the November FFD test focused on motion capture (with video capture taken for validation) of unsuited, suited unpressurized, and suited while pressurized to 3.5 psid conditions. Early results from the motion capture align with values estimated from video capture and future work will compare the accuracy of these techniques

Application of Equilibrium Models of Solution Hybridization to Microarray Design and Analysis

Background: The probe percent bound value, calculated using multi-state equilibrium models of solution hybridization, is shown to be useful in understanding the hybridization behavior of microarray probes having 50 nucleotides, with and without mismatches. These longer oligonucleotides are in widespread use on microarrays, but there are few controlled studies of their interactions with mismatched targets compared to 25-mer based platforms. Principal Findings: 50-mer oligonucleotides with centrally placed single, double and triple mismatches were spotted on an array. Over a range of target concentrations it was possible to discriminate binding to perfect matches and mismatches, and the type of mismatch could be predicted accurately in the concentration midrange (100 pM to 200 pM) using solution hybridization modeling methods. These results have implications for microarray design, optimization and analysis methods. Conclusions: Our results highlight the importance of incorporating biophysical factors in both the design and the analysis of microarrays. Use of the probe ‘‘percent bound’ ’ value predicted by equilibrium models of hybridization is confirmed to be important for predicting and interpreting the behavior of long oligonucleotide arrays, as has been shown for shor

Physics-informed deep neural network for rigid-body protein docking

International audienceProteins are biological macromolecules that perform many essential roles within all living organisms. Many protein functions arise from physical interactions between them and also with other biomolecules (e.g. DNA, metabolites). Being able to predict whether and how two proteins interact is an important problem in fundamental biological research and translational drug discovery. In this work, we present an energy-based model for generating ensembles of rigid-body transformations to predict the configuration of protein complexes. The method incorporates strong, interpretable physical priors. By construction, it is also SE(3) equivariant and fully-differentiable back to the atomic structure. We rely on the observation that bound protein-protein complexes show high geometric and chemical complementarity at the interface of the two proteins. Our method efficiently makes use of this prior by generating on-the-fly point cloud representations of the solvent-excluded surfaces of the proteins. Through learned point descriptors, we can infer regions of high complementarity between the two proteins and compute a proxy for the binding energy. By sampling transformations expected to adopt low energy states, we generate ensembles of bound poses where the two protein surfaces are brought into contact. We expect that the strong physical priors enforced by the network construction will aid in generalization to other related tasks and lead to a richer human understanding of the process behind the generation and scoring of the docked poses. The fact that the method is also fully differentiable allows for gradient-based modifications of the atomic structure which could be critical in tasks related to unbound docking or protein design which remain outstanding problems in protein modelling

Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography

Abstract Here, we have developed a deep learning method to fully automatically detect and quantify six main clinically relevant atrophic features associated with macular atrophy (MA) using optical coherence tomography (OCT) analysis of patients with wet age-related macular degeneration (AMD). The development of MA in patients with AMD results in irreversible blindness, and there is currently no effective method of early diagnosis of this condition, despite the recent development of unique treatments. Using OCT dataset of a total of 2211 B-scans from 45 volumetric scans of 8 patients, a convolutional neural network using one-against-all strategy was trained to present all six atrophic features followed by a validation to evaluate the performance of the models. The model predictive performance has achieved a mean dice similarity coefficient score of 0.706 ± 0.039, a mean Precision score of 0.834 ± 0.048, and a mean Sensitivity score of 0.615 ± 0.051. These results show the unique potential of using artificially intelligence-aided methods for early detection and identification of the progression of MA in wet AMD, which can further support and assist clinical decisions

Genomic-driven nutritional interventions for radiotherapy-resistant rectal cancer patient

Abstract Radiotherapy response of rectal cancer patients is dependent on a myriad of molecular mechanisms including response to stress, cell death, and cell metabolism. Modulation of lipid metabolism emerges as a unique strategy to improve radiotherapy outcomes due to its accessibility by bioactive molecules within foods. Even though a few radioresponse modulators have been identified using experimental techniques, trying to experimentally identify all potential modulators is intractable. Here we introduce a machine learning (ML) approach to interrogate the space of bioactive molecules within food for potential modulators of radiotherapy response and provide phytochemically-enriched recipes that encapsulate the benefits of discovered radiotherapy modulators. Potential radioresponse modulators were identified using a genomic-driven network ML approach, metric learning and domain knowledge. Then, recipes from the Recipe1M database were optimized to provide ingredient substitutions maximizing the number of predicted modulators whilst preserving the recipe’s culinary attributes. This work provides a pipeline for the design of genomic-driven nutritional interventions to improve outcomes of rectal cancer patients undergoing radiotherapy

Holocaust commemoration and the creation of living memory: How the Jewish Museum Berlin, the Contemporary Jewish Museum, and the Memorial to the Murdered Jews of Europe assert the past in the fabric of the present

Author Andreas Huyssen recognizes a contemporary fascination with "memory politics," while Ruth Ellen Gruber finds that a growing interest in "things Jewish" has spread throughout Europe in the recent past. Both of these claims help to promote the need and desire for greater Holocaust commemoration, especially in the city of Berlin where the Nazi regime began. Through the investigation of the architectural and programmatic design of Daniel Libeskind's Jewish Museum Berlin and Contemporary Jewish Museum, as well as Peter Eisenman's Memorial to the Murdered Jews of Europe, this thesis explores numerous interpretations of the representation of the past in the present. Multiple "memory discourses" are examined as a means of understanding the larger process of commemoration. The continued questioning of these devices broadens the discussion to include the need for both a "space for memory," as well as a "space for history" when wishing to create a sufficient commemorative experience.Thesis (M.A.)--University of Southern California, 2009.School code: 0208