True and False Foodplants of \u3ci\u3eCallosamia Promethea\u3c/i\u3e (Lepidoptera: Saturniidae) in Southern Michigan

A survey in 1980 of the associations of over 400 cocoons of Callosamia promethea Drury in vegetation along and adjacent to southern Michigan roadsides gave evidence for seven species of true larval foodplants (not including two others known in the area from other studies) and 17 species of false foodplants, the latter determined by the (1) rarity of their association with cocoons, (2) only one or two cocoons per plant, and (3) their proximity to a well known true foodplant. Three species, sassafras, black cherry, and buttonbush, are evidently the most important true foodplants in this area. Comparisons are made of the foodplants in terms of past literature, geography, and taxonomic relationships

RnaseIII and T4 Polynucleotide Kinase Sequence Biases and Solutions During RNA-Seq Library Construction

Background: RNA-seq is a next generation sequencing method with a wide range of applications including single nucleotide polymorphism (SNP) detection, splice junction identification, and gene expression level measurement. However, the RNA-seq sequence data can be biased during library constructions resulting in incorrect data for SNP, splice junction, and gene expression studies. Here, we developed new library preparation methods to limit such biases. Results: A whole transcriptome library prepared for the SOLiD system displayed numerous read duplications (pile-ups) and gaps in known exons. The pile-ups and gaps of the whole transcriptome library caused a loss of SNP and splice junction information and reduced the quality of gene expression results. Further, we found clear sequence biases for both 5' and 3' end reads in the whole transcriptome library. To remove this bias, RNaseIII fragmentation was replaced with heat fragmentation. For adaptor ligation, T4 Polynucleotide Kinase (T4PNK) was used following heat fragmentation. However, its kinase and phosphatase activities introduced additional sequence biases. To minimize them, we used OptiKinase before T4PNK. Our study further revealed the specific target sequences of RNaseIII and T4PNK. Conclusions: Our results suggest that the heat fragmentation removed the RNaseIII sequence bias and significantly reduced the pile-ups and gaps. OptiKinase minimized the T4PNK sequence biases and removed most of the remaining pile-ups and gaps, thus maximizing the quality of RNA-seq data.National Institute on Alcohol Abuse and Alcoholism (NIAAA) AA12404, AA019382, AA020926, AA016648National Institutes of Health (NIH) R01 GM088344Waggoner Center for Alcohol and Addiction Researc

Long-Billed Curlew Nest Site Selection and Success in the Intermountain West

Grassland birds have experienced steeper population declines between 1966 and 2015 than any other bird group on the North American continent, and migratory grassland birds may face threats in all stages of their annual cycle. The grassland‐associated long‐billed curlew (Numenius americanus) is experiencing population declines in regional and local portions of their North American breeding range. The nesting period is an important portion of the annual cycle when curlews may face demographic rate limitations from a suite of threats including predators and anthropogenic disturbance. We compared nest sites to random sites within breeding territories to examine nest site selection, and modeled correlates of nesting success for 128 curlew nests at 5 Intermountain West sites. Nest sites were 6 times more likely than random sites to be situated adjacent to existing cowpies. Additionally, curlews selected nest sites with shorter vegetation, and less bare ground, grass, and shrub cover than at random sites within their territories. Nest success varied widely among sites and ranged from 12% to 40% in a season with a mean of 27% for all nests during the 2015 and 2016 seasons. Higher nest success probability was associated with higher curlew densities in the area, greater percent cover of conspicuous objects (cowpies, rocks) near the nest, and higher densities of black‐billed magpies (Pica hudsonia) and American crows (Corvus brachyrhynchos) at the site. We also found increased probability of nesting success with increased distance from a nest to the nearest potential perch in that territory. Given the central role of working lands to curlews in much of the Intermountain West, understanding limitations to nesting success in these diverse landscapes is necessary to guide adaptive management strategies in increasingly human‐modified habitats. We suggest some grazing and irrigation practices already provide suitable nesting conditions for curlews, and others may require only minor temporal shifts to improve compatibility

Sorting Permutations: Games, Genomes, and Cycles

Permutation sorting, one of the fundamental steps in pre-processing data for the efficient application of other algorithms, has a long history in mathematical research literature and has numerous applications. Two special-purpose sorting operations are considered in this paper: context directed swap, abbreviated cds, and context directed reversal, abbreviated cdr. These are special cases of sorting operations that were studied in prior work on permutation sorting. Moreover, cds and cdr have been postulated to model molecular sorting events that occur in the genome maintenance program of certain species of single-celled organisms called ciliates. This paper investigates mathematical aspects of these two sorting operations. The main result of this paper is a generalization of previously discovered characterizations of cds-sortability of a permutation. The combinatorial structure underlying this generalization suggests natural combinatorial two-player games. These games are the main mathematical innovation of this paper.Comment: to appear in Discrete Mathematics, Algorithms and Application

Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data

<p>Abstract</p> <p>Background</p> <p>Statistical learning (SL) techniques can address non-linear relationships and small datasets but do not provide an output that has an epidemiologic interpretation.</p> <p>Methods</p> <p>A small set of clinical variables (CVs) for stage-1 non-small cell lung cancer patients was used to evaluate an approach for using SL methods as a preprocessing step for survival analysis. A stochastic method of training a probabilistic neural network (PNN) was used with differential evolution (DE) optimization. Survival scores were derived stochastically by combining CVs with the PNN. Patients (n = 151) were dichotomized into favorable (n = 92) and unfavorable (n = 59) survival outcome groups. These PNN derived scores were used with logistic regression (LR) modeling to predict favorable survival outcome and were integrated into the survival analysis (i.e. Kaplan-Meier analysis and Cox regression). The hybrid modeling was compared with the respective modeling using raw CVs. The area under the receiver operating characteristic curve (Az) was used to compare model predictive capability. Odds ratios (ORs) and hazard ratios (HRs) were used to compare disease associations with 95% confidence intervals (CIs).</p> <p>Results</p> <p>The LR model with the best predictive capability gave Az = 0.703. While controlling for gender and tumor grade, the OR = 0.63 (CI: 0.43, 0.91) per standard deviation (SD) increase in age indicates increasing age confers unfavorable outcome. The hybrid LR model gave Az = 0.778 by combining age and tumor grade with the PNN and controlling for gender. The PNN score and age translate inversely with respect to risk. The OR = 0.27 (CI: 0.14, 0.53) per SD increase in PNN score indicates those patients with decreased score confer unfavorable outcome. The tumor grade adjusted hazard for patients above the median age compared with those below the median was HR = 1.78 (CI: 1.06, 3.02), whereas the hazard for those patients below the median PNN score compared to those above the median was HR = 4.0 (CI: 2.13, 7.14).</p> <p>Conclusion</p> <p>We have provided preliminary evidence showing that the SL preprocessing may provide benefits in comparison with accepted approaches. The work will require further evaluation with varying datasets to confirm these findings.</p

Effects of habitat and livestock on nest productivity of the Asian houbara Chlamydotis macqueenii in Bukhara Province, Uzbekistan

To inform population support measures for the unsustainably hunted Asian houbara Chlamydotis macqueenii (IUCN Vulnerable) we examined potential habitat and land-use effects on nest productivity in the Kyzylkum Desert, Uzbekistan. We monitored 177 nests across different semi-arid shrub assemblages (clay-sand and salinity gradients) and a range of livestock densities (0–80 km-2). Nest success (mean 51.4%, 95% CI 42.4–60.4%) was similar across four years; predation caused 85% of those failures for which the cause was known, and only three nests were trampled by livestock. Nesting begins within a few weeks of arrival when food appears scarce, but later nests were more likely to fail owing to the emergence of a key predator, suggesting foraging conditions on wintering and passage sites may be important for nest productivity. Nest success was similar across three shrub assemblages and was unrelated to landscape rugosity, shrub frequency or livestock density, but was greater with taller mean shrub height (range 13–67 cm) within 50 m. Clutch size (mean = 3.2 eggs) and per-egg hatchability in successful nests (87.5%) did not differ with laying date, shrub assemblage or livestock density. We therefore found no evidence that livestock density reduced nest productivity across the range examined, while differing shrub assemblages appeared to offer similar habitat quality. Asian houbara appear well-adapted to a range of semi-desert habitats and tolerate moderate disturbance by pastoralism. No obvious in situ mitigation measures arise from these findings, leaving regulation and control as the key requirement to render hunting sustainable

A computationally efficient symmetric diagonally dominant matrix projection-based Gaussian process approach

Although kernel approximation methods have been widely applied to mitigate the O(n3) cost of the n × n kernel matrix inverse in Gaussian process methods, they still face computational challenges. The ‘residual’ matrix between the covariance and the approximating component is often discarded as it prevents the computational cost reduction. In this paper, we propose a computationally efficient Gaussian process approach that achieves better computational efficiency, O(mn2), compared with standard Gaussian process methods, when using m n data. The proposed approach incorporates the ‘residual’ matrix in its symmetric diagonally dominant form which can be further approximated by the Neumann series. We have validated and compared the approach with full Gaussian process approaches and kernel approximation based Gaussian process variants, both on synthetic and real air quality data

Artificial drainage of peatlands: hydrological and hydrochemical process and wetland restoration

Peatlands have been subject to artificial drainage for centuries. This drainage has been in response to agricultural demand, forestry, horticultural and energy properties of peat and alleviation of flood risk. However, the are several environmental problems associated with drainage of peatlands. This paper describes the nature of these problems and examines the evidence for changes in hydrological and hydrochemical processes associated with these changes. Traditional black-box water balance approaches demonstrate little about wetland dynamics and therefore the science of catchment response to peat drainage is poorly understood. It is crucial that a more process-based approach be adopted within peatland ecosystems. The environmental problems associated with peat drainage have led, in part, to a recent reversal in attitudes to peatlands and we have seen a move towards wetland restoration. However, a detailed understanding of hydrological, hydrochemical and ecological process-interactions will be fundamental if we are to adequately restore degraded peatlands, preserve those that are still intact and understand the impacts of such management actions at the catchment scale

A Pair of Dopamine Neurons Target the D1-Like Dopamine Receptor DopR in the Central Complex to Promote Ethanol-Stimulated Locomotion in Drosophila

Dopamine is a mediator of the stimulant properties of drugs of abuse, including ethanol, in mammals and in the fruit fly Drosophila. The neural substrates for the stimulant actions of ethanol in flies are not known. We show that a subset of dopamine neurons and their targets, through the action of the D1-like dopamine receptor DopR, promote locomotor activation in response to acute ethanol exposure. A bilateral pair of dopaminergic neurons in the fly brain mediates the enhanced locomotor activity induced by ethanol exposure, and promotes locomotion when directly activated. These neurons project to the central complex ellipsoid body, a structure implicated in regulating motor behaviors. Ellipsoid body neurons are required for ethanol-induced locomotor activity and they express DopR. Elimination of DopR blunts the locomotor activating effects of ethanol, and this behavior can be restored by selective expression of DopR in the ellipsoid body. These data tie the activity of defined dopamine neurons to D1-like DopR-expressing neurons to form a neural circuit that governs acute responding to ethanol