Prevalence, incidence and etiology of hyponatremia in elderly patients with fragility fractures

Peer reviewedPublisher PD

Observations on the Overwintering Potential of the Striped Cucumber Beetle (Coleoptera: Chrysomelidae) in Southern Minnesota

The striped cucumber beetle, Acalymma vittatum (Fabricius) (Coleoptera: Chrysomelidae), is an important pest of cucurbit crops. However, the overwinter- ing capacity of this pest in temperate regions is poorly understood. In this study, the in-field survival of A. vittatum was examined during three consecutive winters. In addition, the supercooling points of A. vittatum were determined as an index of cold hardiness for adults. During each winter, the survival of adults decreased significantly through time, with no individuals surviving until spring. By comparing the supercooling points and in-field survival of adults to soil temperatures, it appears that winter temperatures in Minnesota are cold enough to induce freezing of the beetles. Moreover, a considerable amount of mortality occurred before minimum monthly soil temperatures dropped below the supercooling point of overwintering individuals, suggesting the occurrence of prefreeze mortality. An improved understanding of the response of A. vittatum to winter temperatures in temperate regions may aid in early season management of this pest

Evaluation of Experimental Populations and Glandular-Haired Varieties of Alfalfa for Alfalfa Blotch Leafminer (Diptera: Agromyzidae) Feeding Injury

Following the spread of the alfalfa blotch leafminer, Agromyza frontella (Rondani) (Diptera: Agromyzidae), into Minnesota and Wisconsin U.S.A. during 1994-1997, three field trials were conducted in Minnesota to assess the potential for leafminer resistance among several sources of alfalfa (Medicago sativa), germplasm. In 1998, 86 entries were evaluated, most of which were experimental populations. In addition, six commercial varieties of alfalfa were evaluated. Of the six varieties, four had been bred for various levels of glandular-hair expression, specifically for resistance to the potato leafhopper, Empoasca fabae (Harris) (Homoptera: Cicadellidae). In two of three trials, we found no significant differences in leafmining injury to trifoliolates among the 86 entries, or among glandular-haired and traditional commercial varieties. At one location, ‘Arrest,’ ‘Ameriguard 301,’ and ‘DK 121 HG’ incurred significantly less pinhole injury than the glandular-haired variety ‘5347 LH’ or the commercial standard, ‘5454.’ However, after accounting for both pinhole and leafmining injury, only ‘Arrest’ and ‘Ameriguard 301’ had less injury than ‘5347 LH,’ ‘DK 121 HG,’ or the standard ‘5454.’ The low levels of resistance to A. frontella injury, among glandular-haired commercial alfalfa varieties and numerous experimental populations M. sativa, confirm the need for alternative A. frontella management strategies such as biological control and possible manipulation of harvest schedules

Prognosis of hyponatremia in elderly patients with fragility fractures

Funding This work is supported by an NHS Research Scotland (NRS) Career Research Fellowship to Dr Soiza.Peer reviewedPublisher PD

Distribution of an Exotic Pest, \u3ci\u3eAgromyza Frontella\u3c/i\u3e (Diptera: Agromyzidae), in Manitoba, Canada.

Agromyza frontella is an exotic alfalfa pest from Europe that was first detected in North America in 1968 and has since spread westward into Ontario and the north central United States. Informal surveys had detected A. frontella in Manitoba, but its distribution throughout this province was unknown. In 1998 we collected alfalfa stems to detect plant damage and sweep samples to detect adult A. frontella and the parasitoid Dacnusa dryas throughout the alfalfa growing region of Manitoba. In south central Manitoba, 100% of stems were damaged by A. frontella, and\u3e 100 adults/10 sweeps were recorded at several sites. In west central Manitoba, no plants were damaged and \u3c 10 adults/10 sweeps were observed. We believe this region to be near the western edge of A. frontella distribution. The most important introduced parasitoid of A. frontella, D. dryas, was not detected which suggests that D. dryas has not invaded Manitoba

First Report of the Alfalfa Blotch Leafminer (Diptera: Agromyzidae), and Selected Parasites (Hymenoptera: Eulophidae) in Minnesota and Wisconsin, USA

Alfalfa blotch leafminer, Agromyza frontella, has been a serious pest of alfalfa, Medicago sativa, in the northeastern U.S. and in eastern Ontario, Canada. Until recently, the western edge of the A. frontella distribution in the U.S. was limited to eastern Ohio. We document for the first time, the occurrence of A. frontella in Minnesota and Wisconsin. Alfalfa stems damaged by A. frontella, based on adult feeding punctures, obvious blotched leafmining or the presence of larvae, were first found in 3 northern Minnesota coun­ties during October, 1994. Infested counties included Lake of the Woods, Cook and Lake, all bordering western Ontario, Canada. In 1995, A. frontella was again found in Cook and Lake counties, where 99-100% of the stems, and 18-35% of the trifoliates/stem, contained larvae or exhibited obvious feeding damage. In 1996, following a more expanded survey, a total of 11 and 5 counties, in Minnesota and Wisconsin, respectively, showed some level of A. frontella feeding damage (stem samples ranged from \u3c5 to 100% infested). Based on additional counties surveyed 11 October, 1996, where A. frontella was not found, we now have a reasonable estimate of the southern edge of the distribution in Minnesota and Wisconsin. A total of 2 and 6 A. frontella adults were identified from sweep-net samples taken from fields with obvious feeding damage during 1995 (Lake Co.) and 1996 (Cook Co,), respectively. Three eulophid (Hymenoptera) parasites were reared from A. frontella-infested alfalfa stems collected during October, 1994 in Cook Co., Minn., including: Diglyphus begini, D. pulchripes, and Diglyphus sp., prob. isaea, all of which are new records. Our hypothesis is that A. frontella moved into Minnesota from Ontario Canada, via alfalfa hay purchased by northern Minnesota growers

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Molecular gut-content analysis reveals high frequency of \u3ci\u3eHelicoverpa zea\u3c/i\u3e (Lepidoptera: Noctuidae) consumption by \u3ci\u3eOrius insidiosus\u3c/i\u3e (Hemiptera: Anthocoridae) in sweet corn

Management of corn earworm Helicoverpa zea in sweet corn grown for processing can be challenging due to the lack of effective transgenic and chemical control options. However, biological control by generalist predators can provide a significant impact on pests in this cropping system. One of the most ubiquitous predators of H. zea and other lepidopterans is the insidious flower bug, Orius insidiosus. This small hemipteran has been observed as an important mortality agent of H. zea in several cropping systems, but the strength of the trophic connection between these species has not been documented in sweet corn. Molecular gut-content analysis was conducted to test field-collected O. insidiosus for the presence of H. zea DNA using species-specific PCR primers developed and optimized for this project. Controlled feeding trials determined that the detectability half-life of this technique was 2.32 h. At peak predation in late August, 32% of O. insidiosus tested positive for H. zea DNA. The date of peak predation also corresponded with peak silking of sweet corn plants, which is the most attractive crop growth stage to both H. zea and O. insidiosus. These results indicate that within a short window prior to collection from the field, on the peak date of predation, approximately one third of O. insidiosus in sweet corn had consumed one to two H. zea eggs and/or first instar larvae. The demonstration of this high frequency of predation allows for the assertion that O. insidiosus is a critical mortality agent of H. zea in sweet corn, and conservation biological control practices should be explored to protect and promote this key predator

Hybrid self-organizing feature map (SOM) for anomaly detection in cloud infrastructures using granular clustering based upon value-difference metrics

We have witnessed an increase in the availability of data from diverse sources over the past few years. Cloud computing, big data and Internet-of-Things (IoT) are distinctive cases of such an increase which demand novel approaches for data analytics in order to process and analyze huge volumes of data for security and business use. Cloud computing has been becoming popular for critical structure IT mainly due to cost savings and dynamic scalability. Current offerings, however, are not mature enough with respect to stringent security and resilience requirements. Mechanisms such as anomaly detection hybrid systems are required in order to protect against various challenges that include network based attacks, performance issues and operational anomalies. Such hybrid AI systems include Neural Networks, blackboard systems, belief (Bayesian) networks, case-based reasoning and rule-based systems and can be implemented in a variety of ways. Traffic in the cloud comes from multiple heterogeneous domains and changes rapidly due to the variety of operational characteristics of the tenants using the cloud and the elasticity of the provided services. The underlying detection mechanisms rely upon measurements drawn from multiple sources. However, the characteristics of the distribution of measurements within specific subspaces might be unknown. We argue in this paper that there is a need to cluster the observed data during normal network operation into multiple subspaces each one of them featuring specific local attributes, i.e. granules of information. Clustering is implemented by the inference engine of a model hybrid NN system. Several variations of the so-called value-difference metric (VDM) are investigated like local histograms and the Canberra distance for scalar attributes, the Jaccard distance for binary word attributes, rough sets as well as local histograms over an aggregate ordering distance and the Canberra measure for vectorial attributes. Low-dimensional subspace representations of each group of points (measurements) in the context of anomaly detection in critical cloud implementations is based upon VD metrics and can be either parametric or non-parametric. A novel application of a Self-Organizing-Feature Map (SOFM) of reduced/aggregate ordered sets of objects featuring VD metrics (as obtained from distributed network measurements) is proposed. Each node of the SOFM stands for a structured local distribution of such objects within the input space. The so-called Neighborhood-based Outlier Factor (NOOF) is defined for such reduced/aggregate ordered sets of objects as a value-difference metric of histogrammes. Measurements that do not belong to local distributions are detected as anomalies, i.e. outliers of the trained SOFM. Several methods of subspace clustering using Expectation-Maximization Gaussian Mixture Models (a parametric approach) as well as local data densities (a non-parametric approach) are outlined and compared against the proposed method using data that are obtained from our cloud testbed in emulated anomalous traffic conditions. The results—which are obtained from a model NN system—indicate that the proposed method performs well in comparison with conventional techniques