A General Statement of Structured Singular Value Concepts

Some key concepts of strucred singular value theory for the stability and performance-robustness analysis of linear time-invariant multivariable systems are stated. Using a set-invariance principle, the theory is then generalized to allow for nonlinear and/or time-varying nominal systems and uncertainties. The general theory is then re-specialized to the case of nominally linear time-invariant systems subject to L2-induced-norm bounded uncertainties

PopR: Software for Wildlife Managers

It is widely recognized that modern computer software makes wildlife management and research easier and allows increasingly complex tasks to become routine.  Unfortunately, data storage and reporting rarely keep pace with the rapid expansion of data analysis software.  Such disconnects in workflow can lead to missed opportunities where data are not used to their fullest extent and results are slow to emerge.  Here we present a server-based software system, PopR (https://popr.cfc.umt.edu), which merges wildlife management agency databases with state-of-the-art statistical software for real-time wildlife data analysis, population modeling and reporting.  The interface to PopR is a secure website allowing access from any location with internet access and from any platform (personal computer, smartphone, tablet, etc.).  PopR connects to remote data sources through an application program interface (API).  PopR implements Bayesian integrated population models (IPM) combining multiple data sources.  The IPM’s efficiently deal with limited data, overcome missing data and facilitate prediction with error.  PopR also implements individual data source analyses such as survival, sightability and herd composition, among others.  PopR modules are in development or in use in the states of Idaho, Montana and South Dakota where the software is used for a variety of species including deer, elk and mountain lions.  Finally, add-on applications include tools for defining biological populations, checking data integrity and eliciting expert opinion.  The PopR workflow management system promises to streamline data collection, automate routine analyses and generally save managers time while increasing inference from limited data

Visible-Light-Driven Rotation of Molecular Motors in a Dual-Function Metal-Organic Framework Enabled by Energy Transfer

The visible-light-driven rotation of an overcrowded alkene-based molecular motor strut in a dual-function metal-organic framework (MOF) is reported. Two types of functional linkers, a palladium-porphyrin photosensitizer and a bispyridine-derived molecular motor, were used to construct the framework capable of harvesting low-energy green light to power the rotary motion. The molecular motor was introduced in the framework using the postsynthetic solvent-assisted linker exchange (SALE) method, and the structure of the material was confirmed by powder (PXRD) and single-crystal X-ray (SC-XRD) diffraction. The large decrease in the phosphorescence lifetime and intensity of the porphyrin in the MOFs upon introduction of the molecular motor pillars confirms efficient triplet-to-triplet energy transfer between the porphyrin linkers and the molecular motor. Near-infrared Raman spectroscopy revealed that the visible light-driven rotation of the molecular motor proceeds in the solid state at rates similar to those observed in solution

New Crayfish Species Records from the Sipsey Fork Drainage, Including Lewis Smith Reservoir (Alabama, USA): Native or Introduced Species?

As part of a study of aquatic faunal community changes along riverine-lacustrine transition zones upstream of Lewis Smith Reservoir in northwest Alabama, USA, we collected crayfish from 60 sites in the Sipsey Fork, Brushy Creek, and selected tributaries (Black Warrior River system). After finding two unexpected and possibly-introduced crayfish species, we expanded our investigation of crayfish distributions to include crayfish obtained from stomachs of black bass ( Micropterus spp.) caught at seven sites in the reservoir. To explore what crayfish species were in the drainage historically, we examined museum databases as well as stomach and intestinal contents of a variety of preserved fishes that were caught in the Sipsey Fork and Brushy Creek drainages upstream of the reservoir in the early 1990’s. Of the seven crayfish species collected, one, Orconectes ( Procericambarus ) sp. nr ronaldi , was not previously reported from Alabama, and another, O. lancifer , was not reported from the Black Warrior River system prior to the study. Three are known or possibly introduced species. Upstream of the reservoir, the native species Cambarus obstipus, C. striatus , and O. validus were common. The same three species were found in fish collected in the 1990’s. Orconectes perfectus was found only in the reservoir but may be native to the drainage. Orconectes lancifer was in the reservoir and in stream reaches influenced by the reservoir. Evidence points to O. lancifer being introduced in the drainage, but this is uncertain. Orconectes sp. nr ronaldi was found in a relatively small portion of Brushy Creek and its tributaries, in both flowing and impounded habitats, and may be introduced. Orconectes virilis is introduced in Alabama and was found only in stomachs of fish collected in the reservoir

Boosting medical diagnostics by pooling independent judgments

Collective intelligence refers to the ability of groups to outperform individual decision makers when solving complex cognitive problems. Despite its potential to revolutionize decision making in a wide range of domains, including medical, economic, and political decision making, at present, little is known about the conditions underlying collective intelligence in real-world contexts. We here focus on two key areas of medical diagnostics, breast and skin cancer detection. Using a simulation study that draws on large real-world datasets, involving more than 140 doctors making more than 20,000 diagnoses, we investigate when combining the independent judgments of multiple doctors outperforms the best doctor in a group. We find that similarity in diagnostic accuracy is a key condition for collective intelligence: Aggregating the independent judgments of doctors outperforms the best doctor in a group whenever the diagnostic accuracy of doctors is relatively similar, but not when doctors' diagnostic accuracy differs too much. This intriguingly simple result is highly robust and holds across different group sizes, performance levels of the best doctor, and collective intelligence rules. The enabling role of similarity, in turn, is explained by its systematic effects on the number of correct and incorrect decisions of the best doctor that are overruled by the collective. By identifying a key factor underlying collective intelligence in two important real-world contexts, our findings pave the way for innovative and more effective approaches to complex real-world decision making, and to the scientific analyses of those approaches

Spatial Guilds in the Serengeti Food Web Revealed by a Bayesian Group Model

Food webs, networks of feeding relationships among organisms, provide fundamental insights into mechanisms that determine ecosystem stability and persistence. Despite long-standing interest in the compartmental structure of food webs, past network analyses of food webs have been constrained by a standard definition of compartments, or modules, that requires many links within compartments and few links between them. Empirical analyses have been further limited by low-resolution data for primary producers. In this paper, we present a Bayesian computational method for identifying group structure in food webs using a flexible definition of a group that can describe both functional roles and standard compartments. The Serengeti ecosystem provides an opportunity to examine structure in a newly compiled food web that includes species-level resolution among plants, allowing us to address whether groups in the food web correspond to tightly-connected compartments or functional groups, and whether network structure reflects spatial or trophic organization, or a combination of the two. We have compiled the major mammalian and plant components of the Serengeti food web from published literature, and we infer its group structure using our method. We find that network structure corresponds to spatially distinct plant groups coupled at higher trophic levels by groups of herbivores, which are in turn coupled by carnivore groups. Thus the group structure of the Serengeti web represents a mixture of trophic guild structure and spatial patterns, in contrast to the standard compartments typically identified in ecological networks. From data consisting only of nodes and links, the group structure that emerges supports recent ideas on spatial coupling and energy channels in ecosystems that have been proposed as important for persistence.Comment: 28 pages, 6 figures (+ 3 supporting), 2 tables (+ 4 supporting

Preclinical species gene expression database: Development and meta-analysis

The evaluation of toxicity in preclinical species is important for identifying potential safety liabilities of experimental medicines. Toxicology studies provide translational insight into potential adverse clinical findings, but data interpretation may be limited due to our understanding of cross-species biological differences. With the recent technological advances in sequencing and analyzing omics data, gene expression data can be used to predict cross species biological differences and improve experimental design and toxicology data interpretation. However, interpreting the translational significance of toxicogenomics analyses can pose a challenge due to the lack of comprehensive preclinical gene expression datasets. In this work, we performed RNA-sequencing across four preclinical species/strains widely used for safety assessment (CD1 mouse, Sprague Dawley rat, Beagle dog, and Cynomolgus monkey) in ∼50 relevant tissues/organs to establish a comprehensive preclinical gene expression body atlas for both males and females. In addition, we performed a meta-analysis across the large dataset to highlight species and tissue differences that may be relevant for drug safety analyses. Further, we made these databases available to the scientific community. This multi-species, tissue-, and sex-specific transcriptomic database should serve as a valuable resource to enable informed safety decision-making not only during drug development, but also in a variety of disciplines that use these preclinical species