Replacing human interpretation of agricultural land in Afghanistan with a deep convolutional neural network

Afghanistan’s annual opium survey relies upon time-consuming human interpretation of satellite images to map the area of potential poppy cultivation for statistical sample design. Deep Convolutional Neural Networks (CNNs) have shown ground-breaking performance for image classification tasks by encoding local contextual information, in some cases outperforming trained analysts. In this study, we investigate the development of a CNN to automate the classification of agriculture from medium-resolution satellite imagery as an alternative to manual interpretation. The residual network (ResNet50) CNN architecture was trained and validated for delineating the agricultural area using labelled multi-seasonal Disaster Monitoring Constellation (DMC) satellite imagery (32 m) of Helmand and Kandahar provinces. The effect of input image chip size, training sampling strategy, elevation data, and multi-seasonal imagery were investigated. The best-performing single-year classification used an input chip size of 33 × 33 pixels, a targeted sampling strategy and transfer learning, resulting in high overall accuracy (94%). The inclusion of elevation data marginally lowered performance (93%). Multi-seasonal classification achieved an overall accuracy of 89% using the previous two years’ data. Only 25% of the target year’s training samples were necessary to update the model to achieve >94% overall accuracy. A data-driven approach to automate agricultural mask production using CNNs is proposed to reduce the burden of human interpretation. The ability to continually update CNN models with new data has the potential to significantly improve automatic classification of vegetation across year

In the Murine and Bovine Maternal Mammary Gland Signal Transducer and Activator of Transcription 3 is Activated in Clusters of Epithelial Cells around the Day of Birth

Signal transducers and activators of transcription (STAT) proteins regulate mammary development. Here we investigate the expression of phosphorylated STAT3 (pSTAT3) in the mouse and cow around the day of birth. We present localised colocation analysis, applicable to other mammary studies requiring identification of spatially congregated events. We demonstrate that pSTAT3-positive events are multifocally clustered in a non-random and statistically significant fashion. Arginase-1 expressing cells, consistent with macrophages, exhibit distinct clustering within the periparturient mammary gland. These findings represent a new facet of mammary STAT3 biology, and point to the presence of mammary sub-microenvironments.</p

Fast manufacturing of E-ELT mirror segments using CNC polishing

We report on the first-ever demonstration of grinding and polishing full-size, off-axis aspheric, mirror segments as prototypes for an extremely large telescope, processed entirely in the final hexagonal shape. We first describe the overall strategy for controlling form and mid spatial frequencies, at levels in the vicinity of <10nm RMS surface. This relies first on direct CNC grinding of the base-form of these 1.4m segments, using the Cranfield BoX™ machine. The segments are then mounted on a custom designed (Optic Glyndwr Optoelectronic Engineering Group) three segment hydraulic support, and CNC polished on a Zeeko IRP 1600 machine using a variety of custom tooling. We overview the fullaperture and sub-aperture metrology techniques used to close the process-loop and certify quality, all of which operate with the segment in-situ on the IRP1600. We then focus on the pristine edge-definition achieved by the combination of tool-lift and smoothing operations; results never previously demonstrated on full-size pre-cut hexagonal segments. Finally, the paper discusses the feasibility of scaling the process to deliver 931 segments in seven years, as required for the E-ELT project. © 2013 SPIE

A systematic review of the psychometric properties of self-report research utilization measures used in healthcare

<p>Abstract</p> <p>Background</p> <p>In healthcare, a gap exists between what is known from research and what is practiced. Understanding this gap depends upon our ability to robustly measure research utilization.</p> <p>Objectives</p> <p>The objectives of this systematic review were: to identify self-report measures of research utilization used in healthcare, and to assess the psychometric properties (acceptability, reliability, and validity) of these measures.</p> <p>Methods</p> <p>We conducted a systematic review of literature reporting use or development of self-report research utilization measures. Our search included: multiple databases, ancestry searches, and a hand search. Acceptability was assessed by examining time to complete the measure and missing data rates. Our approach to reliability and validity assessment followed that outlined in the <it>Standards for Educational and Psychological Testing</it>.</p> <p>Results</p> <p>Of 42,770 titles screened, 97 original studies (108 articles) were included in this review. The 97 studies reported on the use or development of 60 unique self-report research utilization measures. Seven of the measures were assessed in more than one study. Study samples consisted of healthcare providers (92 studies) and healthcare decision makers (5 studies). No studies reported data on acceptability of the measures. Reliability was reported in 32 (33%) of the studies, representing 13 of the 60 measures. Internal consistency (Cronbach's Alpha) reliability was reported in 31 studies; values exceeded 0.70 in 29 studies. Test-retest reliability was reported in 3 studies with Pearson's <it>r </it>coefficients > 0.80. No validity information was reported for 12 of the 60 measures. The remaining 48 measures were classified into a three-level validity hierarchy according to the number of validity sources reported in 50% or more of the studies using the measure. Level one measures (n = 6) reported evidence from any three (out of four possible) <it>Standards </it>validity sources (which, in the case of single item measures, was all applicable validity sources). Level two measures (n = 16) had evidence from any two validity sources, and level three measures (n = 26) from only one validity source.</p> <p>Conclusions</p> <p>This review reveals significant underdevelopment in the measurement of research utilization. Substantial methodological advances with respect to construct clarity, use of research utilization and related theory, use of measurement theory, and psychometric assessment are required. Also needed are improved reporting practices and the adoption of a more contemporary view of validity (<it>i.e.</it>, the <it>Standards</it>) in future research utilization measurement studies.</p

Population Structure of Pseudomonas aeruginosa from Five Mediterranean Countries: Evidence for Frequent Recombination and Epidemic Occurrence of CC235

Several studies in recent years have provided evidence that Pseudomonas aeruginosa has a non-clonal population structure punctuated by highly successful epidemic clones or clonal complexes. The role of recombination in the diversification of P. aeruginosa clones has been suggested, but not yet demonstrated using multi-locus sequence typing (MLST). Isolates of P. aeruginosa from five Mediterranean countries (n = 141) were subjected to pulsed-field gel electrophoresis (PFGE), serotyping and PCR targeting the virulence genes exoS and exoU. The occurrence of multi-resistance (≥3 antipseudomonal drugs) was analyzed with disk diffusion according to EUCAST. MLST was performed on a subset of strains (n = 110) most of them had a distinct PFGE variant. MLST data were analyzed with Bionumerics 6.0, using minimal spanning tree (MST) as well as eBURST. Measurement of clonality was assessed by the standardized index of association (IAS). Evidence of recombination was estimated by ClonalFrame as well as SplitsTree4.0. The MST analysis connected 70 sequence types, among which ST235 was by far the most common. ST235 was very frequently associated with the O11 serotype, and frequently displayed multi-resistance and the virulence genotype exoS−/exoU+. ClonalFrame linked several groups previously identified by eBURST and MST, and provided insight to the evolutionary events occurring in the population; the recombination/mutation ratio was found to be 8.4. A Neighbor-Net analysis based on the concatenated sequences revealed a complex network, providing evidence of frequent recombination. The index of association when all the strains were considered indicated a freely recombining population. P. aeruginosa isolates from the Mediterranean countries display an epidemic population structure, particularly dominated by ST235-O11, which has earlier also been coupled to the spread of ß-lactamases in many countries

Soil Factors and their Influence on Within-Field Crop Variability, I: Field Observation of Soil Variation

A fundamental component of adopting the concept of precision farming in practice is the ability to measure spatial variation in soil factors and assess the influence of this on crop variability in order to apply appropriate management strategies. The aim of this study was to appraise potential methods for measuring spatial variability in soil type, nutrient status and physical properties in practical farming situations. Five fields that are representative of more than 30% of soils used for arable production in England and Wales were selected for use as case studies. Maps of soil type were generated from a conventional hand auger survey on a 100 m grid and the excavation of targeted soil profile pits. These were compared with those refined using a mechanised soil coring device and scans of electromagnetic inductance (EMI) carried out while the soil could reasonably be considered to be at, or near, field capacity moisture content. In addition, soil sampling for nutrient analyses was conducted on a 50 m grid to examine the spatial variation in nutrient status. Conventional methods for sampling soil were found to be appropriate for identifying soil types at specific locations within the field sites, however, they were time- consuming to perform which placed an economic and therefore a practical limitation on the sampling density possible. The resulting data were considered to be too sparse for demarcating soil type boundaries for use in the context of precision farming. The location of soil boundaries were refined by using the mechanised soil corer, however, the limitation of this was found to be the time required to analyse the soil cores produced. Maps of soil variation generated from EMI scans conducted at field capacity appear to reflect the underlying variation in soil type observed in maps generated using the mechanised soil corer. and, therefore, this approach has potential as a cost-effective, data- rich, surrogate for measures of soil variability. Results from analyses of soil samples for measurement of nutrient status indicated that whilst there was considerable variation in macro- and micro-nutrient levels in each field, with the exception of pH, these levels were above commonly accepted agronomic limits. Results did however demonstrate the potential for addressing variation in critical factors such as pH at specific locations, however, there is a need to develop protocols for targeting sampling in order to reduce costs

HLA class II antigens positively and negatively associated with hepatosplenic schistosomiasis in a Chinese population

To identify possible associations between host genetic factors and the onset of liver fibrosis following Schistosoma japonicum infection, the major histocompatibility class II alleles of 84 individuals living on an island (Jishan) endemic for schistosomiasis japonica in the Poyang Lake Region of Southern China were determined. Forty patients exhibiting advanced schistosomiasis, characterised by extensive liver fibrosis, and 44 age and sex-matched control subjects were assessed for the class II haplotypes HLA-DRBI and HLA-DQB1. Two HLA-DRB1 alleles, HLA-DRB1*0901 (P = 0.012) and *1302 (P = 0.039), and two HLA-DQB1 alleles, HLA-DQB1*0303 (P = 0.012) and *0609 (P = 0.037), were found to be significantly associated with susceptibility to fibrosis. These associated DRB1 and DQB1 alleles are in very strong linkage disequilibrium, with DRB1*0901-DQB1*0303 and DRB1*1302-DQB1*0609 found as: common haplotypes in this population. In contrast, the alleles HLA-DRB1*1501 (P = 0.025) and HLA-DQB 1*0601 (P = 0.022) were found to be associated with resistance to hepatosplenic disease. Moreover, the alleles DQB1*0303 and DRB1*0901 did not increase susceptibility in the presence of DQB1*0601, indicating that DQB1*0601 is dominant over DQB1*0303 and DRB1*0901. The study has thus identified both positive and negative associations between HLA class II alleles and the risk of individuals developing moderate to severe liver fibrosis following schistosome infection. (C) 2001 Australian Society for Parasitology Inc. Published by Elsevier Science Ltd. All rights reserved

Using proximal sensing parameters linked to the photosynthetic capacity to assess the nutritional status and yield potential in quinoa

Proximal sensing has been used extensively for decades to assess crop nitrogen (N) status using either a hand-held chlorophyll meter or vegetation indices such as the normalized difference vegetation index (NDVI) for various crops. However, little has been done on quinoa (Chenopodium quinoa Willd.). In this study, we investigated how the SPAD chlorophyll meter values and NDVI could be used as indicators for N status and how they can be linked to quinoa performance in terms of photosynthesis and yield. The objectives of this study were to: 1) evaluate SPAD values and NDVI as indicators of N status, 2) assess their relevance over the crop cycle, and 3) investigate their link to the performance in terms of net CO2 assimilation and grain yield at harvest. A pot experiment based on varying nitrogen and phosphorus (P) input conditions was conducted in the glasshouse at Cranfield University, UK. The results showed that both SPAD and NDVI correlated similarly with the leaf N content (%) (R2=0.76, R2=0.82, p<0.001, respectively). High correlations between SPAD and NDVI were also observed at 58 DAS (R2=0.67) and across the entire crop cycle (R2=0.84), validating the utility of both parameters for N status monitoring. Furthermore, significant differences between treatments were observed at different growth stages when SPAD and NDVI were measured across the crop cycle. Strong significant correlations between SPAD and NDVI with the net CO2 assimilation (Anet) (R2=0.86, R2=0.81, p<0.001, respectively) were recorded. SPAD values and NDVI significantly correlated with grain yield at harvest (R2=0.68, R2=0.80, p<0.001, respectively). While SPAD and NDVI are potentially useful tools to improve N fertilizer management and develop in-season yield predictions in quinoa at relatively low-cost, alternative non-saturating spectral indices need to be explored to improve accuracy

In the Murine and Bovine Maternal Mammary Gland Signal Transducer and Activator of Transcription 3 is Activated in Clusters of Epithelial Cells around the Day of Birth

Signal transducers and activators of transcription (STAT) proteins regulate mammary development. Here we investigate the expression of phosphorylated STAT3 (pSTAT3) in the mouse and cow around the day of birth. We present localised colocation analysis, applicable to other mammary studies requiring identification of spatially congregated events. We demonstrate that pSTAT3-positive events are multifocally clustered in a non-random and statistically significant fashion. Arginase-1 expressing cells, consistent with macrophages, exhibit distinct clustering within the periparturient mammary gland. These findings represent a new facet of mammary STAT3 biology, and point to the presence of mammary sub-microenvironments