Distributional extensions of Carollia castanea and Micronycteris minuta from Guatemala, Central America

Field expeditions in 2011 that inventoried the terrestrial vertebrate fauna of two wildlife protected areas in the tropical Caribbean of Guatemala have produced the first confirmed records of two bats for the country: the white-bellied big-eared bat, Micronycteris (Schizonycteris) minuta (Gervais 1856) and the Chesnut short-tailed bat Carollia castanea H. Allen, 1890, both of neotropical distribution and with their current northern limit at Lancetilla, Honduras. The record of M. minuta at Sierra de Caral, Guatemala extends the range of this species 137 km to the west, and the record of C. castanea at Cerro San Gil extends its range 147 km to the west

An Entangled Model for Sustainability Indicators.

Nowadays the challenge for humanity is to find pathways towards sustainable development. Decision makers require a set of sustainability indicators to know if the sustainability strategies are following those pathways. There are more than one hundred sustainability indicators but they differ on their relative importance according to the size of the locality and change on time. The resources needed to follow these sustainability indicators are scarce and in some instances finite, especially in smaller regions. Therefore strategies to select set of these indicators are useful for decision makers responsible for monitoring sustainability. In this paper we propose a model for the identification and selection of a set of sustainability indicators that adequately represents human systems. In developing this model, we applied evolutionary dynamics in a space where sustainability indicators are fundamental entities interconnected by an interaction matrix. we used a fixed interaction that simulates the current context for the city of Cuernavaca, México as an example. We were able to identify and define relevant sets indicators for the system by using the Pareto principle. In this case we identified a set of sixteen sustainability indicators with more than 80% of the total strength. This set presents resilience to perturbations. For the Tangled Nature framework we provided a manner of treating different contexts (i.e., cities, counties, states, regions, countries, continents or the whole planet), dealing with small dimensions. This model provides decision makers with a valuable tool to select sustainability indicators set for towns, cities, regions, countries, continents or the entire planet according to a coevolutionary framework. The social legitimacy can arise from the fact that each individual indicator must be selected from those that are most important for the subject community

Biphasic toxicodynamic features of some antimicrobial agents on microbial growth: a dynamic mathematical model and its implications on hormesis

<p>Abstract</p> <p>Background</p> <p>In the present work, we describe a group of anomalous dose-response (DR) profiles and develop a dynamic model that is able to explain them. Responses were obtained from conventional assays of three antimicrobial agents (nisin, pediocin and phenol) against two microorganisms (<it>Carnobacterium piscicola </it>and <it>Leuconostoc mesenteroides</it>).</p> <p>Results</p> <p>Some of these anomalous profiles show biphasic trends which are usually attributed to hormetic responses. But they can also be explained as the result of the time-course of the response from a microbial population with a bimodal distribution of sensitivity to an effector, and there is evidence suggesting this last origin. In light of interest in the hormetic phenomenology and the possibility of confusing it with other phenomena, especially in the bioassay of complex materials we try to define some criteria which allow us to distinguish between <it>sensu stricto </it>hormesis and biphasic responses due to other causes. Finally, we discuss some problems concerning the metric of the dose in connection with the exposure time, and we make a cautionary suggestion about the use of bacteriocins as antimicrobial agents.</p> <p>Conclusions</p> <p>The mathematical model proposed, which combines the basis of DR theory with microbial growth kinetics, can generate and explain all types of anomalous experimental profiles. These profiles could also be described in a simpler way by means of bisigmoidal equations. Such equations could be successfully used in a microbiology and toxicology context to discriminate between hormesis and other biphasic phenomena.</p

Human Immunodeficiency Virus type 1 in seronegative infants born to HIV-1-infected mothers

BACKGROUND: Some individuals repeatedly exposed to Human Immunodeficiency Virus do not seroconvert and are resistant to HIV infection. Here, in a pediatric cohort of HIV seronegative infants born of HIV-infected mothers, we have studied eight non-breastfed children in whom viral DNA was detected in their PBMC. Our objective was to assess whether silent infection in these children can be explained by the presence of integrated viral DNA. METHODS: The presence of viral DNA was corroborated by nested PCR with primers for gag and the nef/LTR regions of HIV-1. Integration of HIV DNA into the host genome was assessed by an Alu-LTR PCR. Amplicons were sequenced and phylogenetic analyzes were done. RESULTS: HIV-1 DNA was detected in the earliest available PBMC sample from all eight infants, and two of them tested positive for HIV DNA at 2 years of age. Nested PCR resulted in the amplification of gag, nef/LTR and Alu-LTR fragments, which demostrated that HIV-1 DNA was integrated in the host cell genome. Each individual has a characteristic sequence pattern and is different from the LTR sequence of HXB2 prototype virus and other Mexican isolates. CONCLUSION: HIV-1 DNA was observed in PBMC from HIV exposed seronegative children in this pediatric cohort

Space-use patterns highlight behavioural differences linked to lameness, parity, and days in milk in barn-housed dairy cows

This is the author accepted manuscript. The final version is available from Public Library of Science (PLoS) via the DOI in this record.Lameness is a key health and welfare issue affecting commercial herds of dairy cattle, with potentially significant economic impacts due to the expense of treatment and lost milk production. Existing lameness detection methods can be time-intensive, and under-detection remains a significant problem leading to delayed or missed treatment. Hence, there is a need for automated monitoring systems that can quickly and accurately detect lameness in individual cows within commercial dairy herds. Recent advances in sensor tracking technology have made it possible to observe the movement, behaviour and space-use of a range of animal species over extended time-scales. However, little is known about how observed movement behaviour and space-use patterns in individual dairy cattle relate to lameness, or to other possible confounding factors such as parity or number of days in milk. In this cross-sectional study, ten lame and ten non-lame barn-housed dairy cows were classified through mobility scoring and subsequently 55 tracked using a wireless local positioning system. Nearly 900,000 spatial locations were recorded in total, allowing a range of movement and space-use measures to be determined for each individual cow. Using linear models, we highlight where lameness, parity, and the number of days in milk have a significant effect on the observed space-use patterns. Non-lame cows spent more time, and had higher site fidelity (on a day-to-day basis they were more likely to revisit areas they had visited previously), in the feeding area. Non-lame cows also had a larger full range size within the barn. In contrast, lame cows spent more time, and had a higher site-fidelity, in the cubicle (resting) areas of the barn than non-lame cows. Higher parity cows were found to spend more time in the right-hand-side area of the barn, closer to the passageway to the milking parlour. The number of days in milk was found to positively affect the core range size, but with a negative interaction effect with lameness. Using a simple predictive model, we demonstrate how it is possible to accurately determine the lameness status of all individual cows within the study using only two observed space-use measures, the proportion of time spent in the feeding area and the full range size. Our findings suggest that differences in individual movement and space-use behaviour could be used as indicators of health status for automated monitoring within a Precision Livestock Farming approach, potentially leading to faster diagnosis and treatment, and improved animal welfare for dairy cattle and other managed animal species

A 15.65 solar mass black hole in an eclipsing binary in the nearby spiral galaxy Messier 33

Stellar-mass black holes are discovered in X-ray emitting binary systems, where their mass can be determined from the dynamics of their companion stars. Models of stellar evolution have difficulty producing black holes in close binaries with masses >10 solar masses, which is consistent with the fact that the most massive stellar black holes known so all have masses within 1 sigma of 10 solar masses. Here we report a mass of 15.65 +/- 1.45 solar masses for the black hole in the recently discovered system M33 X-7, which is located in the nearby galaxy Messier 33 (M33) and is the only known black hole that is in an eclipsing binary. In order to produce such a massive black hole, the progenitor star must have retained much of its outer envelope until after helium fusion in the core was completed. On the other hand, in order for the black hole to be in its present 3.45 day orbit about its 70.0 +/- 6.9 solar mass companion, there must have been a ``common envelope'' phase of evolution in which a significant amount of mass was lost from the system. We find the common envelope phase could not have occured in M33 X-7 unless the amount of mass lost from the progenitor during its evolution was an order of magnitude less than what is usually assumed in evolutionary models of massive stars.Comment: To appear in Nature October 18, 2007. Four figures (one color figure degraded). Differs slightly from published version. Supplementary Information follows in a separate postin

Constraining the dark energy equation of state using Bayes theorem and the Kullback–Leibler divergence

Data-driven model-independent reconstructions of the dark energy equation of state $w$($z$) are presented using $\textit{Planck}$ 2015 era cosmic microwave background, baryonic acoustic oscillations (BAO), Type Ia supernova (SNIa) and Lyman $\alpha$ (Ly$\alpha$) data. These reconstructions identify the $w$($z$) behaviour supported by the data and show a bifurcation of the equation of state posterior in the range 1.5 < $z$ < 3. Although the concordance $\Lambda$ cold dark matter ($\Lambda$CDM) model is consistent with the data at all redshifts in one of the bifurcated spaces, in the other, a supernegative equation of state (also known as ‘phantom dark energy’) is identified within the 1.5$\sigma$ confidence intervals of the posterior distribution. To identify the power of different data sets in constraining the dark energy equation of state, we use a novel formulation of the Kullback–Leibler divergence. This formalism quantifies the information the data add when moving from priors to posteriors for each possible data set combination. The SNIa and BAO data sets are shown to provide much more constraining power in comparison to the Ly$\alpha$ data sets. Further, SNIa and BAO constrain most strongly around redshift range 0.1–0.5, whilst the Ly$\alpha$ data constrain weakly over a broader range. We do not attribute the supernegative favouring to any particular data set, and note that the $\Lambda$CDM model was favoured at more than 2 log-units in Bayes factors over all the models tested despite the weakly preferred $w$($z$) structure in the data.This work was performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk), provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and funding from the Science and Technology Facilities Council (STFC). Parts of this work were undertaken on the COSMOS Shared Memory system at DAMTP, University of Cambridge operated on behalf of the STFC DiRAC HPC Facility; this equipment is funded by BIS National E-infrastructure capital grant ST/J005673/1 and STFC grants ST/H008586/1, ST/K00333X/1. SH and WJH thank STFC for fi- nancial support

Predicting the onset and persistence of episodes of depression in primary health care. The predictD-Spain study: Methodology

Background: The effects of putative risk factors on the onset and/or persistence of depression remain unclear. We aim to develop comprehensive models to predict the onset and persistence of episodes of depression in primary care. Here we explain the general methodology of the predictD-Spain study and evaluate the reliability of the questionnaires used. Methods: This is a prospective cohort study. A systematic random sample of general practice attendees aged 18 to 75 has been recruited in seven Spanish provinces. Depression is being measured with the CIDI at baseline, and at 6, 12, 24 and 36 months. A set of individual, environmental, genetic, professional and organizational risk factors are to be assessed at each follow-up point. In a separate reliability study, a proportional random sample of 401 participants completed the test-retest (251 researcher-administered and 150 self-administered) between October 2005 and February 2006. We have also checked 118,398 items for data entry from a random sample of 480 patients stratified by province. Results: All items and questionnaires had good test-retest reliability for both methods of administration, except for the use of recreational drugs over the previous six months. Cronbach's alphas were good and their factorial analyses coherent for the three scales evaluated (social support from family and friends, dissatisfaction with paid work, and dissatisfaction with unpaid work). There were 191 (0.16%) data entry errors. Conclusion: The items and questionnaires were reliable and data quality control was excellent. When we eventually obtain our risk index for the onset and persistence of depression, we will be able to determine the individual risk of each patient evaluated in primary health car

Tailoring hyper-heuristics to specific instances of a scheduling problem using affinity and competence functions

Hyper-heuristics are high level heuristics which coordinate lower level ones to solve a given problem. Low level heuristics, however, are not all as competent/good as each other at solving the given problem and some do not work together as well as others. Hence the idea of measuring how good they are (competence) at solving the problem and how well they work together (their affinity). Models of the affinity and competence properties are suggested and evaluated using previous information on the performance of the simple low level heuristics. The resulting model values are used to improve the performance of the hyper-heuristic by tailoring it not only to the specific problem but the specific instance being solved. The test case is a hard combinatorial problem, namely the Hybrid Flow Shop scheduling problem. Numerical results on randomly generated as well as real-world instances are included