Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

The performance of species distribution models (SDMs) is known to be affected by analysis grain and positional error of species occurrences. Coarsening of the analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine-scale environmental data in SDMs, it is important to test this assumption. Models using fine-scale environmental data are more likely to be negatively affected by positional error as the inaccurate occurrences might easier end up in unsuitable environment. This can result in inappropriate conservation actions. Here, we examined the trade-offs between positional error and analysis grain and provide recommendations for best practice. We generated narrow niche virtual species using environmental variables derived from LiDAR point clouds at 5 x 5 m fine-scale. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three modelling techniques (MaxEnt, BRT and GLM) and evaluated their discrimination ability, niche overlap with virtual species and change in realized niche. We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance. We recommend fitting models with the finest possible analysis grain and as close to the response grain as possible even when available species occurrences suffer from positional errors. If there are significant positional errors in species occurrences, users are unlikely to benefit from making additional efforts to obtain higher resolution environmental data unless they also minimize the positional errors of species occurrences. Our findings are also applicable to coarse analysis grain, especially for fragmented habitats, and for species with narrow niche breadth

The Very Young Type Ia Supernova 2012cg: Discovery and Early-Time Follow-Up Observations

On 2012 May 17.2 UT, only 1.5 +/- 0.2 d after explosion, we discovered SN 2012cg, a Type Ia supernova (SN Ia) in NGC 4424 (d ~ 15 Mpc). As a result of the newly modified strategy employed by the Lick Observatory SN Search, a sequence of filtered images was obtained starting 161 s after discovery. Utilizing recent models describing the interaction of SN ejecta with a companion star, we rule out a ~1 M_Sun companion for half of all viewing angles and a red-giant companion for nearly all orientations. SN 2012cg reached a B-band maximum of 12.09 +/- 0.02 mag on 2012 June 2.0 and took ~17.3 d from explosion to reach this, typical for SNe Ia. Our pre-maximum brightness photometry shows a narrower-than-average B-band light curve for SN 2012cg, though slightly overluminous at maximum brightness and with normal color evolution (including some of the earliest SN Ia filtered photometry ever obtained). Spectral fits to SN 2012cg reveal ions typically found in SNe Ia at early times, with expansion velocities >14,000 km/s at 2.5 d past explosion. Absorption from C II is detected early, as well as high-velocity components of both Si II 6355 Ang. and Ca II. Our last spectrum (13.5 d past explosion) resembles that of the somewhat peculiar SN Ia 1999aa. This suggests that SN 2012cg will have a slower-than-average declining light curve, which may be surprising given the faster-than-average rising light curve.Comment: re-submitted to ApJL, 4 figures, 1 tabl

A computational framework to emulate the human perspective in flow cytometric data analysis

Background: In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not hold true in real-life samples, which can have populations of arbitrary shapes and considerable inter-sample variation. <p/>Results: To address this, we developed a new framework flowScape for emulating certain key aspects of the human perspective in analyzing flow data, which we implemented in multiple steps. First, flowScape begins with creating a mathematically rigorous map of the high-dimensional flow data landscape based on dense and sparse regions defined by relative concentrations of events around modes. In the second step, these modal clusters are connected with a global hierarchical structure. This representation allows flowScape to perform ridgeline analysis for both traversing the landscape and isolating cell populations at different levels of resolution. Finally, we extended manual gating with a new capacity for constructing templates that can identify target populations in terms of their relative parameters, as opposed to the more commonly used absolute or physical parameters. This allows flowScape to apply such templates in batch mode for detecting the corresponding populations in a flexible, sample-specific manner. We also demonstrated different applications of our framework to flow data analysis and show its superiority over other analytical methods. <p/>Conclusions: The human perspective, built on top of intuition and experience, is a very important component of flow cytometric data analysis. By emulating some of its approaches and extending these with automation and rigor, flowScape provides a flexible and robust framework for computational cytomics

Statistical Analsysis to Evaluate Heavy Metal Pollution in the Air Obatained by Moss Technique in Hanoi and its Surrounding Region

The aim of this paper was the application of statistical analysis including principal component analysis to evaluate heavy metal pollution obtained by moss technique in the air of Ha Noi and its surrounding areas and to evaluate potential pollution sources. The concentrations of 33 heavy metal elements in 27 samples of Barbula Indica moss in the investigated region collected in December of 2016 in the investigated area have been examined using multivariate statistical analysis. Five factors explaining 80% of the total variance were identified and their potential sources have been discussed

The Utility of Data Transformation for Alignment, De Novo Assembly and Classification of Short Read Virus Sequences.

Advances in DNA sequencing technology are facilitating genomic analyses of unprecedented scope and scale, widening the gap between our abilities to generate and fully exploit biological sequence data. Comparable analytical challenges are encountered in other data-intensive fields involving sequential data, such as signal processing, in which dimensionality reduction (i.e., compression) methods are routinely used to lessen the computational burden of analyses. In this work, we explored the application of dimensionality reduction methods to numerically represent high-throughput sequence data for three important biological applications of virus sequence data: reference-based mapping, short sequence classification and de novo assembly. Leveraging highly compressed sequence transformations to accelerate sequence comparison, our approach yielded comparable accuracy to existing approaches, further demonstrating its suitability for sequences originating from diverse virus populations. We assessed the application of our methodology using both synthetic and real viral pathogen sequences. Our results show that the use of highly compressed sequence approximations can provide accurate results, with analytical performance retained and even enhanced through appropriate dimensionality reduction of sequence data

Tailoring the atomic structure of graphene nanoribbons by STM lithography

The practical realization of nano-scale electronics faces two major challenges: the precise engineering of the building blocks and their assembly into functional circuits. In spite of the exceptional electronic properties of carbon nanotubes only basic demonstration-devices have been realized by time-consuming processes. This is mainly due to the lack of selective growth and reliable assembly processes for nanotubes. However, graphene offers an attractive alternative. Here we report the patterning of graphene nanoribbons (GNRs) and bent junctions with nanometer precision, well-defined widths and predetermined crystallographic orientations allowing us to fully engineer their electronic structure using scanning tunneling microscope (STM) lithography. The atomic structure and electronic properties of the ribbons have been investigated by STM and tunneling spectroscopy measurements. Opening of confinement gaps up to 0.5 eV, allowing room temperature operation of GNR-based devices, is reported. This method avoids the difficulties of assembling nano-scale components and allows the realization of complete integrated circuits, operating as room temperature ballistic electronic devices.Comment: 8 pages text, 5 figures, Nature Nanotechnology, in pres

Genomic regions that underlie soybean seed isoflavone content

Soy products contain isoflavones (genistein, daidzein, and glycitein)that display biological effects when ingested by humans and animals, these effects are species, dose and age dependent. Therefore, the content and quality of isoflavones in soybeans is a key to their biological effect. Our objective was to identify loci that underlie isoflavone content in soybean seeds. The study involved 100 recombinant inbred lines (RIL)fr om the cross of ‘Essex’ by ‘Forrest,’ two cultivars that contrast for isoflavone content. Isoflavone content of seeds fromeach RIL was determined by high performance liquid chromatography (HPLC). The distribution of isoflavone content was continuous and unimodal. The heritability estimates on a line mean basis were 79% for daidzein, 22% for genistein, and 88% for glycitein. Isoflavone content of soybean seeds was compared against 150 polymorphic DNA markers in a one-way analysis of variance. Four genomic regions were found to be significantly associated with the isoflavone content of soybean seeds across both locations and years. Molecular linkage group B1 contained a major QTL underlying glycitein content (P = 0.0001,R2 = 50.2%), linkage group N contained a QTL for glycitein (P = 0.0033,R2 = 11.1%)and a QTL for daidzein (P = 0.0023,R2 = 10.3%) and linkage group A1 contained a QTL for daidzein (P = 0.0081,R2 = 9.6%). Selection for these chromosomal regions in a marker assisted selection program will allow for the manipulation of amounts and profiles of isoflavones (genistein, daidzein, and glycitein)c ontent of soybean seeds. In addition, tightly linked markers can be used in map based cloning of genes associated with isoflavone content

Performance of Monolayer Graphene Nanomechanical Resonators with Electrical Readout

The enormous stiffness and low density of graphene make it an ideal material for nanoelectromechanical (NEMS) applications. We demonstrate fabrication and electrical readout of monolayer graphene resonators, and test their response to changes in mass and temperature. The devices show resonances in the MHz range. The strong dependence of the resonant frequency on applied gate voltage can be fit to a membrane model, which yields the mass density and built-in strain. Upon removal and addition of mass, we observe changes in both the density and the strain, indicating that adsorbates impart tension to the graphene. Upon cooling, the frequency increases; the shift rate can be used to measure the unusual negative thermal expansion coefficient of graphene. The quality factor increases with decreasing temperature, reaching ~10,000 at 5 K. By establishing many of the basic attributes of monolayer graphene resonators, these studies lay the groundwork for applications, including high-sensitivity mass detectors

Oldest known pantherine skull and evolution of the tiger

The tiger is one of the most iconic extant animals, and its origin and evolution have been intensely debated. Fossils attributable to extant pantherine species-lineages are less than 2 MYA and the earliest tiger fossils are from the Calabrian, Lower Pleistocene. Molecular studies predict a much younger age for the divergence of modern tiger subspecies at <100 KYA, although their cranial morphology is readily distinguishable, indicating that early Pleistocene tigers would likely have differed markedly anatomically from extant tigers. Such inferences are hampered by the fact that well-known fossil tiger material is middle to late Pleistocene in age. Here we describe a new species of pantherine cat from Longdan, Gansu Province, China, Panthera zdanskyi sp. nov. With an estimated age of 2.55–2.16 MYA it represents the oldest complete skull of a pantherine cat hitherto found. Although smaller, it appears morphologically to be surprisingly similar to modern tigers considering its age. Morphological, morphometric, and cladistic analyses are congruent in confirming its very close affinity to the tiger, and it may be regarded as the most primitive species of the tiger lineage, demonstrating the first unequivocal presence of a modern pantherine species-lineage in the basal stage of the Pleistocene (Gelasian; traditionally considered to be Late Pliocene). This find supports a north-central Chinese origin of the tiger lineage, and demonstrates that various parts of the cranium, mandible, and dentition evolved at different rates. An increase in size and a reduction in the relative size of parts of the dentition appear to have been prominent features of tiger evolution, whereas the distinctive cranial morphology of modern tigers was established very early in their evolutionary history. The evolutionary trend of increasing size in the tiger lineage is likely coupled to the evolution of its primary prey species

Directed self-organization of graphene nanoribbons on SiC

Realization of post-CMOS graphene electronics requires production of semiconducting graphene, which has been a labor-intensive process. We present tailoring of silicon carbide crystals via conventional photolithography and microelectronics processing to enable templated graphene growth on 4H-SiC{1-10n} (n = 8) crystal facets rather than the customary {0001} planes. This allows self-organized growth of graphene nanoribbons with dimensions defined by those of the facet. Preferential growth is confirmed by Raman spectroscopy and high-resolution transmission electron microscopy (HRTEM) measurements, and electrical characterization of prototypic graphene devices is presented. Fabrication of > 10,000 top-gated graphene transistors on a 0.24 cm2 SiC chip demonstrates scalability of this process and represents the highest density of graphene devices reported to date.Comment: 13 pages, 5 figure