A long-term study (1949–2015) of the aftermaths of the deliberate experimental introduction of an invasive freshwater amphipod (Crustacea) to a small Island

The ability to track changing distributions and long-term inter actions between native and introduced species provides insights into forecasting impacts of invaders. We used data from deliberate field introduction experiments of a freshwater Gammarus amphipod (Crustacea) to the rivers of a small British island, The Isle of Man. The deliberate introductions commenced in 1949 and re-sampling occurred in the ‘60s, ‘70s ‘80s, ‘90s, 200 5 and finally in 2015. The findings generally confirmed the failure of introductions of Gammarus pulex into sites previously dominated by the native Gammarus duebeni celticus even with high propagule pressure in terms of numbers of individual invaders released and repeated introductions. Despite this, the island-wide 2015 survey revealed that G. d. celticus occurred in fewer sites and G. pulex in more sites than a decade earlier, indicating at this island-wide scale, the displacement of G. pulex by G. d. celticus seems to be continuing. In addition, one of the introduction sites, Arbory Stream which contained G. d. celticus from 1951–2005 contained only G. pulex in 2015, showing that even after six decades, assumptions cannot be made as to the limits of invader range expansion. Multivariate analysis of physico-chemical data from the 2015 island-wide sur vey revealed water quality as a crucial environmental gradient that influences the distributions of the native and invader species, with the former in higher organic water quality sites than sites with the invader or where invader and native co-occurred. The North American amp hipod Crangonyx pseudogracilis had expanded its range since 2005, including invading low biological water quality sites previously devoid of both Gammarus spp.. Pollution from historic metal mining is implicated as a potential major factor excluding amphipods. This deliberate ecological experiment, through long-term monitoring, continues to provide insights into the factors determining the distributions of natives and invaders

Responses to threat in a freshwater invader: Longitudinal data reveal personality, habituation and robustness to changing water temperatures in the ‘killer shrimp’ Dikerogammarus villosus (Crustacea: Amphipoda).

Freshwater biodiversity and ecosystem integrity are under threat from biological invasions. The ‘killer shrimp’ Dikerogammarus villosus is a highly predatory amphipod which has spread readily across Central Europe and recently the U.K. and its arrival has been associated with the significant loss of resident species. Despite this, studies of its behavioural ecology are sparse, even though its behaviour may contribute to its invasion success. For the first time we investigated anti-predator ‘fleeing’ behaviour in D. villosus and how this changed with water temperature. Three key patterns emerged from our analysis. First, within a particular temperature condition there are moderate but consistent among-individual differences in behaviour. These are driven by a combination of mean level among-individual differences and within-individual relative consistency in behaviour, and provide the key marker for animal personalities. Second, the fleeing responses were not influenced by temperature and thirdly, regardless of temperature, all individuals appeared to habituate to a repeated non-dangerous stimulus, indicating a capacity for individual-learning. We suggest that the anti-predator behaviour of D. villosus contributes to its rapid spread and that consistent among-individual differences in behaviour may promote biological invasions across heterogeneous conditions. Robustness to changing water temperatures may also be potentially advantageous, particularly in an era of global climate change, where average temperatures could be elevated and less predictabl

Evlauation of Breeds and Breed Crosses for the Production of Weaning Weight in South Dakota

Records on 41,403 calves from 324 contemporary groups were studied to determine the effect of a calf\u27s breed background on its weaning weight. Calves with the heaviest weaning weights were crossbred, typically sired by Continental breed bulls and out of crossbred dams

Simulations of a lattice model of two-headed linear amphiphiles: influence of amphiphile asymmetry

Using a 2D lattice model, we conduct Monte Carlo simulations of micellar aggregation of linear-chain amphiphiles having two solvophilic head groups. In the context of this simple model, we quantify how the amphiphile architecture influences the critical micelle concentration (CMC), with a particular focus on the role of the asymmetry of the amphiphile structure. Accordingly, we study all possible arrangements of the head groups along amphiphile chains of fixed length $N=12$ and 16 molecular units. This set of idealized amphiphile architectures approximates many cases of symmetric and asymmetric gemini surfactants, double-headed surfactants and boloform surfactants. Consistent with earlier results, we find that the number of spacer units $s$ separating the heads has a significant influence on the CMC, with the CMC increasing with $s$ for $s<N/2$. In comparison, the influence of the asymmetry of the chain architecture on the CMC is much weaker, as is also found experimentally.Comment: 30 pages, 17 fgure

Genetic Correlations of Reproductive and Maternal Traits with Growth and Carcass Traits in Beef Cattle

Some genes may affect more than one trait. Therefore, the traits can be genetically correlated. Knowledge of genetic correlations among traits is useful for efficient selection of replacement bulls and heifers if the breeder considers more than one trait. In designed selection programs, emphasis to be placed on the various traits can depend, in part, on the genetic correlations among them. In addition, genetic correlations can be used to predict what is expected to happen to traits other than those used in selection as a result of that selection. This effect on traits other than those used in selection is referred to as correlated response. The objective of this study was to estimate from experimental data the genetic correlations between reproductive and maternal traits of beef females and growth and carcass traits of paternal half-sib steers. A more detailed account of the methodology and results can be found in the Journal of Animal Science, volume 58, pages 1171 to 1180

Genetic Correlations of Reproductive and Maternal Traits with Growth and Carcass Traits in Beef Cattle

Some genes may affect more than one trait. Therefore, the traits can be genetically correlated. Knowledge of genetic correlations among traits is useful for efficient selection of replacement bulls and heifers if the breeder considers more than one trait. In designed selection programs, emphasis to be placed on the various traits can depend, in part, on the genetic correlations among them. In addition, genetic correlations can be used to predict what is expected to happen to traits other than those used in selection as a result of that selection. This effect on traits other than those used in selection is referred to as correlated response. The objective of this study was to estimate from experimental data the genetic correlations between reproductive and maternal traits of beef females and growth and carcass traits of paternal half-sib steers. A more detailed account of the methodology and results can be found in the Journal of Animal Science, volume 58, pages 1171 to 1180

Are Amphipod invaders a threat to the regional biodiversity? Conservation prospects for the Loire River

The impact of invasions on local biodiversity is well established, but their impact on regional biodiversity has so far been only sketchily documented. To address this question, we studied the impact at various observation scales (ranging from the microhabitat to the whole catchment) of successive arrivals of non-native amphipods on the amphipod assemblage of the Loire River basin in France. Amphipod assemblages were studied at 225 sites covering the whole Loire catchment. Non-native species were dominant at all sites in the main channel of the Loire River, but native species were still present at most of the sites. We found that the invaders have failed to colonize most of tributaries of the Loire River. At the regional scale, we found that since the invaders first arrived 25 years ago, the global amphipod diversity has increased by 33% (from 8 to 12 species) due to the arrival of non-native species. We discuss the possibility that the lack of any loss of biodiversity may be directly linked to the presence of refuges at the microhabitat scale in the Loire channel and in the tributaries, which invasive species have been unable to colonize. The restoration of river quality could increase the number of refuges for native species, thus reducing the impact of invader

Isolation of murine cementoblasts: unique cells or uniquely‐positioned osteoblasts?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109660/1/j.1600-0722.1998.tb02197.x.pd

Anomaly Detection in Social Media Using Recurrent Neural Network

© 2019, Springer Nature Switzerland AG. In today’s information environment there is an increasing reliance on online and social media in the acquisition, dissemination and consumption of news. Specifically, the utilization of social media platforms such as Facebook and Twitter has increased as a cutting edge medium for breaking news. On the other hand, the low cost, easy access and rapid propagation of news through social media makes the platform more sensitive to fake and anomalous reporting. The propagation of fake and anomalous news is not some benign exercise. The extensive spread of fake news has the potential to do serious and real damage to individuals and society. As a result, the detection of fake news in social media has become a vibrant and important field of research. In this paper, a novel application of machine learning approaches to the detection and classification of fake and anomalous data are considered. An initial clustering step with the K-Nearest Neighbor (KNN) algorithm is proposed before training the result with a Recurrent Neural Network (RNN). The results of a preliminary application of the KNN phase before the RNN phase produces a quantitative and measureable improvement in the detection of outliers, and as such is more effective in detecting anomalies or outliers against the test dataset of 2016 US Presidential Election predictions