Hierarchical Agent-based Adaptation for Self-Aware Embedded Computing Systems

Siirretty Doriast

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.

BackgroundSingle-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.ResultsWe introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods.ConclusionsSlingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression

Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

Tackling Rapid Radiations With Targeted Sequencing

In phylogenetic studies across angiosperms, at various taxonomic levels, polytomies have persisted despite efforts to resolve them by increasing sampling of taxa and loci. The large amount of genomic data now available and statistical tools to analyze them provide unprecedented power for phylogenetic inference. Targeted sequencing has emerged as a strong tool for estimating species trees in the face of rapid radiations, lineage sorting, and introgression. Evolutionary relationships in Cyperaceae have been studied mostly using Sanger sequencing until recently. Despite ample taxon sampling, relationships in many genera remain poorly understood, hampered by diversification rates that outpace mutation rates in the loci used. The C4 Cyperus clade of the genus Cyperus has been particularly difficult to resolve. Previous studies based on a limited set of markers resolved relationships among Cyperus species using the C3 photosynthetic pathway, but not among C4 Cyperus clade taxa. We test the ability of two targeted sequencing kits to resolve relationships in the C4 Cyperus clade, the universal Angiosperms-353 kit and a Cyperaceae-specific kit. Sequences of the targeted loci were recovered from data generated with both kits and used to investigate overlap in data between kits and relative efficiency of the general and custom approaches. The power to resolve shallow-level relationships was tested using a summary species tree method and a concatenated maximum likelihood approach. High resolution and support are obtained using both approaches, but high levels of missing data disproportionately impact the latter. Targeted sequencing provides new insights into the evolution of morphology in the C4 Cyperus clade, demonstrating for example that the former segregate genus Alinula is polyphyletic despite its seeming morphological integrity. An unexpected result is that the Cyperus margaritaceus-Cyperus niveus complex comprises a clade separate from and sister to the core C4 Cyperus clade. Our results demonstrate that data generated with a family-specific kit do not necessarily have more power than those obtained with a universal kit, but that data generated with different targeted sequencing kits can often be merged for downstream analyses. Moreover, our study contributes to the growing consensus that targeted sequencing data are a powerful tool in resolving rapid radiationsEspaña Ministry of Economy and Competitiveness (project CGL2016- 77401-P

Dissecting the genetic basis for seed coat mucilage heteroxylan biosynthesis in plantago ovata using gamma irradiation and infrared spectroscopy

Seeds from the myxospermous species Plantago ovata release a polysaccharide-rich mucilage upon contact with water. This seed coat derived mucilage is composed predominantly of heteroxylan (HX) and is utilized as a gluten-free dietary fiber supplement to promote human colorectal health. In this study, a gamma-irradiated P. ovata population was generated and screened using histological stains and Fourier Transform Mid Infrared (FTMIR) spectroscopy to identify putative mutants showing defects in seed coat mucilage HX composition and/or structure. FTMIR analysis of dry seed revealed variation in regions of the IR spectra previously linked to xylan structure in Secale cereale (rye). Subsequent absorbance ratio and PCA multivariate analysis identified 22 putative mutant families with differences in the HX IR fingerprint region. Many of these showed distinct changes in the amount and subtle changes in structure of HX after mucilage extrusion, while 20% of the putative HX mutants identified by FTMIR showed no difference in staining patterns of extruded mucilage compared to wild-type. Transcriptional screening analysis of two putative reduced xylan in mucilage (rxm) mutants, rxm1 and rxm3, revealed that changes in HX levels in rxm1 correlate with reduced transcription of known and novel genes associated with xylan synthesis, possibly indicative of specific co-regulatory units within the xylan biosynthetic pathway. These results confirm that FTMIR is a suitable method for identifying putative mutants with altered mucilage HX composition in P. ovata, and therefore forms a resource to identify novel genes involved in xylan biosynthesis.Matthew R. Tucker, Chao Ma, Jana Phan, Kylie Neumann, Neil J. Shirley, Michael G. Hahn, Daniel Cozzolino and Rachel A. Burto

An Energy Efficient Cooperative Hierarchical MIMO Clustering Scheme for Wireless Sensor Networks

In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO) communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO) clustering scheme and traditional multihop Single-Input-Single-Output (SISO) routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes

Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

Deep learning-based instance segmentation for the precise automated quantification of digital breast cancer immunohistochemistry images

After the 24 months embargo, this version of the article was accepted for publication, after peer review and does not reflect post-acceptance improvements, or any corrections. The published version is available online (2022-01-14) at: https://doi.org/10.1016/j.eswa.2021.116471.The quantification of biomarkers on immunohistochemistry breast cancer images is essential for defining appropriate therapy for breast cancer patients, as well as for extracting relevant information on disease prognosis. This is an arduous and time-consuming task that may introduce a bias in the results due to intra- and inter-observer variability which could be alleviated by making use of automatic quantification tools. However, this is not a simple processing task given the heterogeneity of breast tumors that results in non-uniformly distributed tumor cells exhibiting different staining colors and intensity, size, shape, and texture, of the nucleus, cytoplasm and membrane. In this research work we demonstrate the feasibility of using a deep learning-based instance segmentation architecture for the automatic quantification of both nuclear and membrane biomarkers applied to IHC-stained slides. We have solved the cumbersome task of training set generation with the design and implementation of a web platform, which has served as a hub for communication and feedback between researchers and pathologists as well as a system for the validation of the automatic image processing models. Through this tool, we have collected annotations over samples of HE, ER and Ki-67 (nuclear biomarkers) and HER2 (membrane biomarker) IHC-stained images. Using the same deep learning network architecture, we have trained two models, so-called nuclei- and membrane-aware segmentation models, which, once successfully validated, have revealed to be a promising method to segment nuclei instances in IHC-stained images. The quantification method proposed in this work has been integrated into the developed web platform and is currently being used as a decision support tool by pathologists