138 research outputs found
Selection and Recognition of Landmarks Using Terrain Spatiograms
A team of robots working to explore and map an area may need to share information about landmarks so as to register their local maps and to plan effective exploration strategies. In previous papers we have introduced a combined image and spatial representation for landmarks: terrain spatiograms. We have shown that for manually selected views, terrain spatiograms provide an effective, shared representation that allows for occlusion filtering and a combination of multiple views.
In this paper, we present a landmark saliency architecture (LSA) for automatically selecting candidate landmarks. Using a dataset of 21 outdoor stereo images generated by LSA, we show that the terrain spatiogram representation reliably recognizes automatically selected landmarks. The terrain spatiogram results are shown to improve on two purely appearance based approaches: template matching and image histogram matching
Lightweight Multilingual Software Analysis
Developer preferences, language capabilities and the persistence of older
languages contribute to the trend that large software codebases are often
multilingual, that is, written in more than one computer language. While
developers can leverage monolingual software development tools to build
software components, companies are faced with the problem of managing the
resultant large, multilingual codebases to address issues with security,
efficiency, and quality metrics. The key challenge is to address the opaque
nature of the language interoperability interface: one language calling
procedures in a second (which may call a third, or even back to the first),
resulting in a potentially tangled, inefficient and insecure codebase. An
architecture is proposed for lightweight static analysis of large multilingual
codebases: the MLSA architecture. Its modular and table-oriented structure
addresses the open-ended nature of multiple languages and language
interoperability APIs. We focus here as an application on the construction of
call-graphs that capture both inter-language and intra-language calls. The
algorithms for extracting multilingual call-graphs from codebases are
presented, and several examples of multilingual software engineering analysis
are discussed. The state of the implementation and testing of MLSA is
presented, and the implications for future work are discussed.Comment: 15 page
Lightweight Call-Graph Construction for Multilingual Software Analysis
Analysis of multilingual codebases is a topic of increasing importance. In
prior work, we have proposed the MLSA (MultiLingual Software Analysis)
architecture, an approach to the lightweight analysis of multilingual
codebases, and have shown how it can be used to address the challenge of
constructing a single call graph from multilingual software with mutual calls.
This paper addresses the challenge of constructing monolingual call graphs in a
lightweight manner (consistent with the objective of MLSA) which nonetheless
yields sufficient information for resolving language interoperability calls. A
novel approach is proposed which leverages information from a
compiler-generated AST to provide the quality of call graph necessary, while
the program itself is written using an Island Grammar that parses the AST
providing the lightweight aspect necessary. Performance results are presented
for a C/C++ implementation of the approach, PAIGE (Parsing AST using Island
Grammar Call Graph Emitter) showing that despite its lightweight nature, it
outperforms Doxgen, is robust to changes in the (Clang) AST, and is not
restricted to C/C++.Comment: 10 page
Automatic Verification of Autonomous Robot Missions
Before autonomous robotics can be used for dangerous or critical missions, performance guarantees should be made available. This
paper overviews a software system for the verification of behavior-based controllers in context of chosen hardware and environmental models.
Robotic controllers are automatically translated to a process algebra.
The system comprising both the robot and the environment are then
evaluated by VIPARS, a verification software module in development,
and compared to specific performance criteria. The user is returned a
probability that the performance criteria will hold in the uncertainty of
real-world conditions. Experimental results demonstrate accurate verification for a mission related to the search for a biohazard
Exercise-Based Stroke Rehabilitation: Clinical Considerations Following the COVID-19 Pandemic
Background. The COVID-19 pandemic attributable to the severe acute respiratory syndrome virus (SARS-CoV-2) has had a significant and continuing impact across all areas of healthcare including stroke. Individuals post-stroke are at high risk for infection, disease severity, and mortality after COVID-19 infection. Exercise stroke rehabilitation programs remain critical for individuals recovering from stroke to mitigate risk factors and morbidity associated with the potential long-term consequences of COVID-19. There is currently no exercise rehabilitation guidance for people post-stroke with a history of COVID-19 infection. Purpose. To (1) review the multi-system pathophysiology of COVID-19 related to stroke and exercise; (2) discuss the multi-system benefits of exercise for individuals post-stroke with suspected or confirmed COVID-19 infection; and (3) provide clinical considerations related to COVID-19 for exercise during stroke rehabilitation. This article is intended for healthcare professionals involved in the implementation of exercise rehabilitation for individuals post-stroke who have suspected or confirmed COVID-19 infection and non-infected individuals who want to receive safe exercise rehabilitation. Results. Our clinical considerations integrate pre-COVID-19 stroke (n = 2) and COVID-19 exercise guidelines for non-stroke populations (athletic [n = 6], pulmonary [n = 1], cardiac [n = 2]), COVID-19 pathophysiology literature, considerations of stroke rehabilitation practices, and exercise physiology principles. A clinical decision-making tool for COVID-19 screening and eligibility for stroke exercise rehabilitation is provided, along with key subjective and physiological measures to guide exercise prescription. Conclusion. We propose that this framework promotes safe exercise programming within stroke rehabilitation for COVID-19 and future infectious disease outbreaks
Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems
P. 1-15Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.S
SLEPR: A Sample-Level Enrichment-Based Pathway Ranking Method — Seeking Biological Themes through Pathway-Level Consistency
Analysis of microarray and other high throughput data often involves identification of genes consistently up or down-regulated across samples as the first step in extraction of biological meaning. This gene-level paradigm can be limited as a result of valid sample fluctuations and biological complexities. In this report, we describe a novel method, SLEPR, which eliminates this limitation by relying on pathway-level consistencies. Our method first selects the sample-level differentiated genes from each individual sample, capturing genes missed by other analysis methods, ascertains the enrichment levels of associated pathways from each of those lists, and then ranks annotated pathways based on the consistency of enrichment levels of individual samples from both sample classes. As a proof of concept, we have used this method to analyze three public microarray datasets with a direct comparison with the GSEA method, one of the most popular pathway-level analysis methods in the field. We found that our method was able to reproduce the earlier observations with significant improvements in depth of coverage for validated or expected biological themes, but also produced additional insights that make biological sense. This new method extends existing analyses approaches and facilitates integration of different types of HTP data
Expression and purification of recombinant G protein-coupled receptors: A review
Given their extensive role in cell signalling, GPCRs are significant drug targets; despite this, many of these receptors have limited or no available prophylaxis. Novel drug design and discovery significantly rely on structure determination, of which GPCRs are typically elusive. Progress has been made thus far to produce sufficient quantity and quality of protein for downstream analysis. As such, this review highlights the systems available for recombinant GPCR expression, with consideration of their advantages and disadvantages, as well as examples of receptors successfully expressed in these systems. Additionally, an overview is given on the use of detergents and the styrene maleic acid (SMA) co-polymer for membrane solubilisation, as well as purification techniques
Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans
Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have
fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in
25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16
regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of
correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP,
while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in
Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium
(LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region.
Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant
enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the
refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa,
an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of
PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent
signals within the same regio
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