Sampling Weed Spatial Variability on a Fieldwide Scale

Site-specific weed management recommendations require knowledge of weed species, density, and location in the field. This study compared several sampling techniques to estimate weed density and distribution in two 65-ha no-till Zea mays–Glycine max rotation fields in eastern South Dakota. The most common weeds (Setaria viridis, Setaria glauca, Cirsium arvense, Ambrosia artemisiifolia, and Polygonum pensylvanicum) were counted by species in 0.1-m2 areas on a 15- by 30-m (1,352 points in each field) or 30- by 30-m (676 points in each field) grid pattern, and points were georeferenced and data spatially analyzed. Using different sampling approaches, weed populations were estimated by resampling the original data set. The average density for each technique was calculated and compared with the average field density calculated from the all-point data. All weeds had skewed population distributions with more than 60% of sampling points lacking the specific weed, but very high densities (i.e., \u3e 100 plants m−2) were also observed. More than 300 random samples were required to estimate densities within 20% of the all-point means about 60% of the time. Sampling requirement increased as average density decreased. The W pattern produced average species densities that often were similar to the field averages, but information on patch location was absent. Weed counts taken on the 15- by 30-m grid were dependent spatially and weed contour maps were developed. Kriged maps presented both density and location of weed patches and could be used to establish management zones. However, grid-sampling production fields on a small enough scale to obtain spatially dependent data may have limited usefulness because of time, cost, and labor constraints

Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload.

Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin & eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories. The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 different vendors. The system\u27s use of deep-learning-based confidence scoring as a criterion to consider the result as accurate yields an accuracy of up to 98%, and makes it adoptable in a real-world setting. Without confidence scoring, the system achieved an accuracy of 78%. We anticipate that our deep learning system will serve as a foundation enabling faster diagnosis of skin cancer, identification of cases for specialist review, and targeted diagnostic classifications

Spatiotemporal variability of modern precipitation δ18O in the central Andes and implications for paleoclimate and paleoaltimetry estimates

Understanding the patterns of rainfall isotopic composition in the central Andes is hindered by sparse observations. Despite limited observational data, stable isotope tracers have been commonly used to constrain modern‐to‐ancient Andean atmospheric processes, as well as to reconstruct paleoclimate and paleoaltimetry histories. Here, we present isotopic compositions of precipitation (δ18Op and δDp) from 11 micrometeorological stations located throughout the Bolivian Altiplano and along its eastern flank at ~21.5°S. We collected and isotopically analyzed 293 monthly bulk precipitation samples (August 2008 to April 2013). δ18Op values ranged from −28.0‰ to 9.6‰, with prominent seasonal cycles expressed at all stations. We observed a strong relationship between the δ18Op and elevation, though it varies widely in time and space. Constraints on air sourcing estimated from atmospheric back trajectory calculations indicate that continental‐scale climate dynamics control the interannual variability in δ18Op, with upwind precipitation anomalies having the largest effect. The impact of precipitation anomalies in distant air source regions to the central Andes is in turn modulated by the Bolivian High. The importance of the Bolivian High is most clearly observed on the southern Bolivian Altiplano. However, monthly variability among Altiplano stations can exceed 10‰ in δ18Op on the plateau and cannot be explained by elevation or source variability, indicating a nontrivial role for local scale effects on short timescales. The strong influence of atmospheric circulation on central Andean δ18Op requires that paleoclimate and paleoaltimetry studies consider the role of South American atmospheric paleocirculation in their interpretation of stable isotopic values as proxies.Key PointsFive‐year record of central Andes precipitation isotopic compositionPrecipitation isotopes are elevation dependent, but vary in space and timePrecipitation isotope variability is related to large‐scale climate dynamicsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111974/1/jgrd52161.pd

Substructure Versus Property-Level Dispersed Modes Calculation

This paper calculates the effect of perturbed finite element mass and stiffness values on the eigenvectors and eigenvalues of the finite element model. The structure is perturbed in two ways: at the "subelement" level and at the material property level. In the subelement eigenvalue uncertainty analysis the mass and stiffness of each subelement is perturbed by a factor before being assembled into the global matrices. In the property-level eigenvalue uncertainty analysis all material density and stiffness parameters of the structure are perturbed modified prior to the eigenvalue analysis. The eigenvalue and eigenvector dispersions of each analysis (subelement and property-level) are also calculated using an analytical sensitivity approximation. Two structural models are used to compare these methods: a cantilevered beam model, and a model of the Space Launch System. For each structural model it is shown how well the analytical sensitivity modes approximate the exact modes when the uncertainties are applied at the subelement level and at the property level

NIMBUS: The Near-Infrared Multi-Band Ultraprecise Spectroimager for SOFIA

We present a new and innovative near-infrared multi-band ultraprecise spectroimager (NIMBUS) for SOFIA. This design is capable of characterizing a large sample of extrasolar planet atmospheres by measuring elemental and molecular abundances during primary transit and occultation. This wide-field spectroimager would also provide new insights into Trans-Neptunian Objects (TNO), Solar System occultations, brown dwarf atmospheres, carbon chemistry in globular clusters, chemical gradients in nearby galaxies, and galaxy photometric redshifts. NIMBUS would be the premier ultraprecise spectroimager by taking advantage of the SOFIA observatory and state of the art infrared technologies. This optical design splits the beam into eight separate spectral bandpasses, centered around key molecular bands from 1 to 4 microns. Each spectral channel has a wide field of view for simultaneous observations of a reference star that can decorrelate time-variable atmospheric and optical assembly effects, allowing the instrument to achieve ultraprecise calibration for imaging and photometry for a wide variety of astrophysical sources. NIMBUS produces the same data products as a low-resolution integral field spectrograph over a large spectral bandpass, but this design obviates many of the problems that preclude high-precision measurements with traditional slit and integral field spectrographs. This instrument concept is currently not funded for development.Comment: 14 pages, 9 figures, SPIE Astronomical Telescopes and Instrumentation 201

Stability of corn (\u3ci\u3eZea mays\u3c/i\u3e)- foxtail (\u3ci\u3eSetaria\u3c/i\u3e spp.) interference relationships

Variation in interference relationships have been shown for a number of crop-weed associations and may have an important effect on the implementation of decision support systems for weed management. Multiyear field experiments were conducted at eight locations to determine the stability of corn-foxtail interference relationships across years and locations. Two coefficients (I and A) of a rectangular hyperbola equation were estimated for each data set using nonlinear regression procedures. The I and A coefficients represent percent corn yield loss as foxtail density approaches zero and maximum percent corn yield loss, respectively. The coefficient I was stable across years at two locations and varied across years at four locations. Maximum yield loss (A) varied between years at one location. Both coefficients varied among locations. Although 3 to 4 foxtail plants m-1 row was a conservative estimate of the single-year economic threshold (Te) of foxtail density, variation in I and A resulted in a large variation in Te. Therefore, the utility of using common coefficient estimates to predict future crop yield loss from foxtail interference between years or among locations within a region is limited

Stability of corn (\u3ci\u3eZea mays\u3c/i\u3e)- foxtail (\u3ci\u3eSetaria\u3c/i\u3e spp.) interference relationships

Variation in interference relationships have been shown for a number of crop-weed associations and may have an important effect on the implementation of decision support systems for weed management. Multiyear field experiments were conducted at eight locations to determine the stability of corn-foxtail interference relationships across years and locations. Two coefficients (I and A) of a rectangular hyperbola equation were estimated for each data set using nonlinear regression procedures. The I and A coefficients represent percent corn yield loss as foxtail density approaches zero and maximum percent corn yield loss, respectively. The coefficient I was stable across years at two locations and varied across years at four locations. Maximum yield loss (A) varied between years at one location. Both coefficients varied among locations. Although 3 to 4 foxtail plants m-1 row was a conservative estimate of the single-year economic threshold (Te) of foxtail density, variation in I and A resulted in a large variation in Te. Therefore, the utility of using common coefficient estimates to predict future crop yield loss from foxtail interference between years or among locations within a region is limited

Recurrent Tissue-Specific Mtdna Mutations are Common in Humans

Mitochondrial DNA (mtDNA) variation can affect phenotypic variation; therefore, knowing its distribution within and among individuals is of importance to understanding many human diseases. Intra-individual mtDNA variation (heteroplasmy) has been generally assumed to be random. We used massively parallel sequencing to assess heteroplasmy across ten tissues and demonstrate that in unrelated individuals there are tissue-specific, recurrent mutations. Certain tissues, notably kidney, liver and skeletal muscle, displayed the identical recurrent mutations that were undetectable in other tissues in the same individuals. Using RFLP analyses we validated one of the tissue-specific mutations in the two sequenced individuals and replicated the patterns in two additional individuals. These recurrent mutations all occur within or in very close proximity to sites that regulate mtDNA replication, strongly implying that these variations alter the replication dynamics of the mutated mtDNA genome. These recurrent variants are all independent of each other and do not occur in the mtDNA coding regions. The most parsimonious explanation of the data is that these frequently repeated mutations experience tissue-specific positive selection, probably through replication advantage