Phase-shifted Bragg grating inscription in PMMA microstructured POF using 248 nm UV radiation

In this work we experimentally validate and characterize the first phase-shifted polymer optical fiber Bragg gratings (PS-POFBGs) produced using a single pulse from a 248 nm krypton fluoride laser. A single-mode poly (methyl methacrylate) optical fiber with a core doped with benzyl dimethyl ketal for photosensitivity improvement was used. A uniform phase mask customized for 850 nm grating inscription was used to inscribe these Bragg structures. The phase shift defect was created directly during the grating inscription process by placing a narrow blocking aperture in the center of the UV beam. The produced high-quality Bragg grating structures, presenting a double dips, reject 16.3 dB (97.6% reflectivity) and 13.2 dB (95.2% reflectivity) of the transmitted power, being therefore appropriate for sensing or other photonic applications. Its transmission spectrum possesses two sharp transmission notches, allowing a significant increase in measurement resolution compared to direct interrogation of a single grating. The reflection and transmission spectra when multiple phase shifts are introduced in the FBG structure are also shown. The PS-POFBG's strain, temperature, pressure, and humidity characteristics have been experimentally analyzed in detail to assess their potential usage as sensors

A novel enhancer of Agouti contributes to parallel evolution of cryptically colored beach mice

Identifying the genetic basis of repeatedly evolved traits provides a way to reconstruct their evolutionary history and ultimately investigate the predictability of evolution. Here, we focus on the oldfield mouse (Peromyscus polionotus), which occurs in the southeastern United States, where it exhibits considerable coat-color variation. Dorsal coats range from dark brown in mice inhabiting mainland habitat to near white on the white-sand beaches of the southeastern US, where light pelage has evolved independently on Florida’s Gulf and Atlantic coasts as an adaptation to visually hunting predators. To facilitate genomic analyses in this species, we first generated a high-quality, chromosome-level genome assembly of P. polionotus subgriseus. Next, in a uniquely variable mainland population that occurs near beach habitat (P. p. albifrons), we scored 23 pigment traits and performed targeted resequencing in 168 mice. We find that variation in pigmentation is strongly associated with a ~2 kb region approximately 5 kb upstream of the Agouti-signaling protein (ASIP) coding region. Using a reporter-gene assay, we demonstrate that this regulatory region contains an enhancer that drives expression in the dermis of mouse embryos during the establishment of pigment prepatterns. Moreover, extended tracts of homozygosity in this region of Agouti indicate that the light allele has experienced recent and strong positive selection. Notably, this same light allele appears fixed in both Gulf and Atlantic coast beach mice, despite these populations being separated by >1,000km. Given the evolutionary history of this species, our results suggest that this newly identified Agouti enhancer allele has been maintained in mainland populations as standing genetic variation and from there has spread to, and been selected in, two independent beach mouse lineages, thereby facilitating their rapid and parallel evolution

Automated workflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles

Background: Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) that associate with clinical phenotypes, but these SNPs usually explain just a small part of the heritability and have relatively modest effect sizes. In contrast, SNPs that associate with metabolite levels generally explain a higher percentage of the genetic variation and demonstrate larger effect sizes. Still, the discovery of SNPs associated with metabolite levels is challenging since testing all metabolites measured in typical metabolomics studies with all SNPs comes with a severe multiple testing penalty. We have developed an automated workflow approach that utilizes prior knowledge of biochemical pathways present in databases like KEGG and BioCyc to generate a smaller SNP set relevant to the metabolite. This paper explores the opportunities and challenges in the analysis of GWAS of metabolomic phenotypes and provides novel insights into the genetic basis of metabolic variation through the re-analysis of published GWAS datasets. Results: Re-analysis of the published GWAS dataset from Illig et al. (Nature Genetics, 2010) using a pathway-based workflow (http://www.myexperiment.org/packs/319.html), confirmed previously identified hits and identified a new locus of human metabolic individuality, associating Aldehyde dehydrogenase family1 L1 (ALDH1L1) with serine/glycine ratios in blood. Replication in an independent GWAS dataset of phospholipids (Demirkan et al., PLoS Genetics, 2012) identified two novel loci supported by additional literature evidence: GPAM (Glycerol-3 phosphate acyltransferase) and CBS (Cystathionine beta-synthase). In addition, the workflow approach provided novel insight into the affected pathways and relevance of some of these gene-metabolite pairs in disease development and progression. Conclusions: We demonstrate the utility of automated exploitation of background knowledge present in pathway databases for the analysis of GWAS datasets of metabolomic phenotypes. We report novel loci and potential biochemical mechanisms that contribute to our understanding of the genetic basis of metabolic variation and its relationship to disease development and progression

Extent and Causes of Chesapeake Bay Warming

Coastal environments such as the Chesapeake Bay have long been impacted by eutrophication stressors resulting from human activities, and these impacts are now being compounded by global warming trends. However, there are few studies documenting long-term estuarine temperature change and the relative contributions of rivers, the atmosphere, and the ocean. In this study, Chesapeake Bay warming, since 1985, is quantified using a combination of cruise observations and model outputs, and the relative contributions to that warming are estimated via numerical sensitivity experiments with a watershed–estuarine modeling system. Throughout the Bay’s main stem, similar warming rates are found at the surface and bottom between the late 1980s and late 2010s (0.02 +/- 0.02C/year, mean +/- 1 standard error), with elevated summer rates (0.04 +/- 0.01C/year) and lower rates of winter warming (0.01 +/- 0.01C/year). Most (~85%) of this estuarine warming is driven by atmospheric effects. The secondary influence of ocean warming increases with proximity to the Bay mouth, where it accounts for more than half of summer warming in bottom waters. Sea level rise has slightly reduced summer warming, and the influence of riverine warming has been limited to the heads of tidal tributaries. Future rates of warming in Chesapeake Bay will depend not only on global atmospheric trends, but also on regional circulation patterns in mid-Atlantic waters, which are currently warming faster than the atmosphere. Supporting model data available at: https://doi.org/10.25773/c774-a36

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost