Reproductive Compatibility Within and Among Spruce Budworm (Lepidoptera: Tortricidae) Populations

Spruce bud worm moths collected as larvae from two species of host trees in four populations were mated in single pairs in two years. In 1980 but not 1981, more of the intra-population matings than the inter-population matings were fertile. Host tree origin was not a significant factor in the level of sterility

Spruce Budworm Weight and Fecundity: Means, Frequency Distributions, and Correlations for Two Populations (Lepidoptera: Tortricidae)

Pupal weights and fecundities of spruce budworm from Minnesota had different means, coefficients of variation, and frequency distributions than spruce budworm from New Hampshire. The two variables were correlated in one of the populations but not the other

Mammography facilities serving vulnerable women have longer follow-up times.

ObjectiveTo investigate mammography facilities' follow-up times, population vulnerability, system-based processes, and association with cancer stage at diagnosis.Data sourcesProspectively collected from San Francisco Mammography Registry (SFMR) 2005-2011, California Cancer Registry 2005-2012, SFMR facility survey 2012.Study designWe examined time to biopsy for 17 750 abnormal mammogram results (BI-RADS 4/5), categorizing eight facilities as short or long follow-up based on proportion of mammograms with biopsy at 30 days. We examined facility population vulnerability (race/ethnicity, language, education), and system processes. Among women with a cancer diagnosis, we modeled odds of advanced-stage (≥IIb) cancer diagnosis by facility follow-up group.Data extraction methodsMerged SFMR, Cancer Registry and facility survey data.Principal findingsFacilities (N = 4) with short follow-up completed biopsies by 30 days for 82% of mammograms compared with 62% for facilities with long follow-up (N = 4) (P < 0.0001). All facilities serving high proportions of vulnerable women were long follow-up facilities. The long follow-up facilities had fewer radiologists, longer biopsy appointment wait times, and less communication directly with women. Having the index abnormal mammogram at a long follow-up facility was associated with higher adjusted odds of advanced-stage cancer (OR 1.45; 95% CI 1.10-1.91).ConclusionsProviding mammography facilities serving vulnerable women with appropriate resources may decrease disparities in abnormal mammogram follow-up and cancer diagnosis stage

A novel, nondestructive, dried blood spot-based hematocrit prediction method using noncontact diffuse reflectance spectroscopy

Dried blood spot (DBS) sampling is recognized as a valuable alternative sampling strategy both in research and in clinical routine. Although many advantages are associated with DBS sampling, its more widespread use is hampered by several issues, of which the hematocrit effect on DBS-based quantitation remains undoubtedly the most widely discussed one. Previously, we developed a method to derive the approximate hematocrit from a nonvolumetrically applied DBS based on its potassium content. Although this method yielded good results and was straightforward to perform, it was also destructive and required sample preparation. Therefore, we now developed a nondestructive method which allows to predict the hematocrit of a DBS based on its hemoglobin content, measured via noncontact diffuse reflectance spectroscopy. The developed method was thoroughly validated. A linear calibration curve was established after log/log transformation. The bias, intraday and interday imprecision of quality controls at three hematocrit levels and at the lower and upper limit of quantitation (0.20 and 0.67, respectively) were less than 11%. In addition, the influence of storage and the volume spotted was evaluated, as well as DBS homogeneity. Application of the method to venous DBSs prepared from whole blood patient samples (n = 233) revealed a good correlation between the actual and the predicted hematocrit. Limits of agreement obtained after Bland and Altman analysis were -0.076 and. +0.018. Incurred sample reanalysis demonstrated good method reproducibility. In conclusion, mere scanning of a DBS suffices to derive its approximate hematocrit, one of the most important variables in DBS analysis

Toward a Viable Independence? The Koniambo Project and the Political Economy of Mining in New Caledonia

In New Caledonia, pro-independence leaders perceive economic autonomy as a prerequisite for political independence. The Koniambo Project, a joint venture between a Canadian multinational and a local mining company, is seen by many Kanak as an opportunity to loosen economic ties to metropolitan France. Indeed, unlike cases in which large-scale resource extraction has disadvantaged local groups and intensified demands for political rights, the Koniambo Project resulted from pro-independence activism. This atypical situation can be explained by the French government’s strategy in New Caledonia. Violent uprisings in the mid- 1980s ended with accords that promised economic development. Radical activists believed this would pave the way for independence while their opponents hoped to obviate such aspirations. Similarly, the Koniambo Project is viewed either as an opportunity for greater Kanak autonomy or as yet another in a series of actions that have used economic gains to deter pro-independence efforts

A New Species of \u3ci\u3ePediobius\u3c/i\u3e (Hymenoptera: Eulophidae) Parasitizing \u3ci\u3eChyliza Apicalis\u3c/i\u3e (Diptera: Psilidae) in Ash Trees Attacked by \u3ci\u3eAgrilus Planipennis\u3c/i\u3e (Coleoptera: Buprestidae)

Pediobius chylizae, spec. nov. (Hymenoptera: Eulophidae), is described as new and illustrated. This parasitoid has been reared from the puparia of Chyliza apicalis Loew (Diptera: Psilidae) collected from under the bark of ash trees (Oleaceae: Fraxinus spp.) dying after attack by the emerald ash borer, Agrilus planipennis Fairmaire (Coleptera: Buprestidae), an invasive beetle from Asia. This species is compared with related species of Pediobius from the Holarctic Region

Intraspecific and interspecific variation in the xylem functional traits of Callitris species growing along an aridity gradient

More severe and prolonged drought events as a result of climate change, have the potential to cause broad scale forest and woodland dieback worldwide. The Australian continent is primarily comprised of arid biomes. However, rapid climate change-induced desertification threatens these surprisingly diverse ecosystems. Callitris is Australia’s most successful conifer genus, yet they remain they remain vulnerable to drought-induced decline. Given Callitris are the primary structural component of vegetation in many Arid-Australian ecosystems, their persistence is the most important factor preventing the collapse of these ecosystems. Resistance to drought-induced xylem cavitation has emerged as a key physiological trait determining the survival of tree species under water-limited conditions. Under the influence of aridity, Callitris have evolved the world’s most cavitation resistant xylem, yet little is known about the xylem anatomy liable to convey this. The main objective of this thesis was to identify the anatomical xylem traits and attributes associated with cavitation resistance in Callitris. The main body of work in this thesis involved analysis of microscopic anatomical traits through the use and development of several microscopy techniques. An inter-specific study produced a complementary dataset of xylem anatomical traits for branches of 15 Callitris and closely related species, building on the physiological dataset by Larter et al. (2017). An intraspecific study among five C. glaucophylla populations required the physiological and anatomical traits measurements. An intraspecific increase in cavitation resistance with aridity was found among the five populations in both the primary branches and roots. To understand whole plant hydraulic function, variability in xylem anatomical traits in the tertiary branches, secondary branches and trunks, of C. glaucophylla, in relation to the primary branches and roots was also explored. A greenhouse experiment tested the plasticity of anatomical traits in C. glaucophylla seedlings grown under contrasting water treatments. Mainly, among seedlings grown under well-watered conditions, height growth and more hydraulically efficient roots are prioritised, while more mechanically reinforced tracheids and safer but less efficient pit traits are favoured among seedling grown under water deficit

Automated Quality Control for In-Situ Water Temperature Sensors

The identification of data not representative of the target subject for outdoor (in-situ) environmental sensors (bad data) is a topic that has been explored in the past. Many tools (such as data filters and computer models) have succeeded in providing an end user with properly identified incorrect data over 95% of the time. However, with the continuous increase in the use of automated data collection, a simple indication of the bad data may no longer provide the end user with enough information to reduce the amount of time that must be spent for manual quality control. The purpose of this research was to devise and test a data classification technique capable of determining when and why water quality data are incorrect in an environment that experiences seasonal and daily fluctuations. This should reduce or eliminate the need for manual quality control (QC) in a large-volume data system where the range of good data is wide and changes often. The objectives this project sought to achieve were; training a learning machine that could identify local maximum and minimum values as well as dulled signals, and forming a multi-class classifier that accurately placed sensor temperature data into three categories; good, bad (because of exposure of the temperature probe to ambient air temperature), and bad (because the sensor has become buried in sediment). This involved the development of a model using a Multi-Class Relevance Vector Machine (MCRVM), and identification of its parameters that would provide at least 90% removal of false negatives for Classes 2 and 3 (the bad data) using only 100 data points from each class for purposes of training the learning machine. These objectives were met using the following methods: (1) QC completion on water temperature sensors manually, (2) an iterative process that involved the selection of inputs for the model and then the optimization of these values based on the RVMs performance, and (3) evaluation of the best performing machines testing a small group of data and then a full year