Tree Seedling Establishment in Response to Warming and Nitrogen Addition in a Temperate Old Field

Climate change and increased atmospheric nitrogen deposition over the next century may alter the ability of woody species to germinate and compete with grasses and forbs in temperate old fields. To investigate the responses of seed germination and seedling growth to warming and nitrogen addition, I transplanted seeds and seedlings into plots of a field experiment and conducted a greenhouse experiment. The combination of warming and nitrogen allowed seeds to germinate earlier, although there was no effect on final germination. In the greenhouse nitrogen increased seedling growth, and warming had little effect. However, in the field, warming significantly decreased the growth and survival of M. coronaria seedlings. Overall, my results suggest that while warming and nitrogen may have direct effects on germination and establishment of seedlings, these effects may be outweighed by indirect effects via interactions with drought and herbivory

The Classification of Periodic Light Curves from non-survey optimized observational data through Automated Extraction of Phase-based Visual Features

We implement two hidden-layer feedforward networks to classify 3011 variable star light curves. These light curves are generated from a reduction of non-survey optimized observational images gathered by wide-field cameras mounted on the Liverpool Telescope. We extract 16 features found to be highly informative in previous studies but achieve only 19.82% accuracy on a 30% test set, 5.56% above a random model. Noise and sampling defects present in these light curves poison these features primarily by reducing our Periodogram period match rate to fewer than 5%. We propose using an automated visual feature extraction technique by transforming the phase-folded light curves into image based representations. This eliminates much of the noise and the missing phase data, due to sampling defects, should have a less destructive effect on these shape features as they still remain at least partially present. We produced a set of scaled images with pixels turned either on or off based on a threshold of data points in each pixel defined as at minimum one fifth of those of the most populated pixel for each light curve. Training on the same feedforward network, we achieve 29.13% accuracy, a 13.16% improvement over a random model and we also show this technique scales with an improvement to 33.51% accuracy by increasing the number of hidden layer neurons. We concede that this improvement is not yet sufficient to allow these light curves to be used for automated classification and in conclusion we discuss a new pipeline currently being developed that simultaneously incorporates period estimation and classification. This method is inspired by approximating the manual methods employed by astronomers

GRAPE: Genetic Routine for Astronomical Period Estimation

Period estimation is an important task in the classification of many variable astrophysical objects. Here we present GRAPE: A Genetic Routine for Astronomical Period Estimation, a genetic algorithm optimised for the processing of survey data with spurious and aliased artefacts. It uses a Bayesian Generalised Lomb-Scargle (BGLS) fitness function designed for use with the Skycam survey conducted at the Liverpool Telescope. We construct a set of simulated light curves using both regular survey cadence and the unique Skycam variable cadence with four types of signal: sinusoidal, sawtooth, symmetric eclipsing binary and eccentric eclipsing binary. We apply GRAPE and a frequency spectrum BGLS periodogram to the light curves and show that the performance of GRAPE is superior to the frequency spectrum for any signal well modelled by the fitness function. This is due to treating the parameter space as a continuous variable.We also show that the Skycam sampling is sufficient to correctly estimate the period of over 90% of the sinusoidal shape light curves relative to the more standard regular cadence.We note that GRAPE has a computational overhead which makes it slower on light curves with low numbers of observations and faster with higher numbers of observations and discuss the potential optimisations used to speedup the runtime. Finally, we analyse the period dependence and baseline importance of the performance of both methods and propose improvements which will extend this method to the detection of quasi-periodic signals

The Diagnostic Potential of Transition Region Lines under-going Transient Ionization in Dynamic Events

We discuss the diagnostic potential of high cadence ultraviolet spectral data when transient ionization is considered. For this we use high cadence UV spectra taken during the impulsive phase of a solar flares (observed with instruments on-board the Solar Maximum Mission) which showed excellent correspondence with hard X-ray pulses. The ionization fraction of the transition region ion O V and in particular the contribution function for the O V 1371A line are computed within the Atomic Data and Analysis Structure, which is a collection of fundamental and derived atomic data and codes which manipulate them. Due to transient ionization, the O V 1371A line is enhanced in the first fraction of a second with the peak in the line contribution function occurring initially at a higher electron temperature than in ionization equilibrium. The rise time and enhancement factor depend mostly on the electron density. The fractional increase in the O V 1371A emissivity due to transient ionization can reach a factor of 2--4 and can explain the fast response in the line flux of transition regions ions during the impulsive phase of flares solely as a result of transient ionization. This technique can be used to diagnostic the electron temperature and density of solar flares observed with the forth-coming Interface Region Imaging Spectrograph.Comment: 18 pages, 6 figure

Classifying Periodic Astrophysical Phenomena from non-survey optimized variable-cadence observational data

Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. Therefore, the production of methods and systems for the automated classification of time-domain astronomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure, the Small Telescopes Installed at the Liverpool Telescope. These instruments have been in operation since March 2009 gathering data of large areas of sky around the current field of view of the main telescope generating a large dataset containing millions of light sources. The instruments are inexpensive to run as they do not require a separate telescope to operate but this style of surveying the sky introduces structured artifacts into our data due to the variable cadence at which sky fields are resampled. These artifacts can make light sources appear variable and must be addressed in any processing method. The data from large sky surveys can lead to the discovery of interesting new variable objects. Efficient software and analysis tools are required to rapidly determine which potentially variable objects are worthy of further telescope time. Machine learning offers a solution to the quick detection of variability by characterising the detected signals relative to previously seen exemplars. In this paper, we introduce a processing system designed for use with the Liverpool Telescope identifying potentially interesting objects through the application of a novel representation learning approach to data collected automatically from the wide-field instruments. Our method automatically produces a set of classification features by applying Principal Component Analysis on set of variable light curves using a piecewise polynomial fitted via a genetic algorithm applied to the epoch-folded data. The epoch-folding requires the selection of a candidate period for variable light curves identified using a genetic algorithm period estimation method specifically developed for this dataset. A Random Forest classifier is then used to classify the learned features to determine if a light curve is generated by an object of interest. This system allows for the telescope to automatically identify new targets through passive observations which do not affect day-to-day operations as the unique artifacts resulting from such a survey method are incorporated into the methods. We demonstrate the power of this feature extraction method compared to feature engineering performed by previous studies by training classification models on 859 light curves of 12 known variable star classes from our dataset. We show that our new features produce a model with a superior mean cross-validation F1 score of 0.4729 with a standard deviation of 0.0931 compared with the engineered features at 0.3902 with a standard deviation of 0.0619. We show that the features extracted from the representation learning are given relatively high importance in the final classification model. Additionally, we compare engineered features computed on the interpolated polynomial fits and show that they produce more reliable distributions than those fit to the raw light curve when the period estimation is correct

SLC19A1 transports immunoreactive cyclic dinucleotides.

The accumulation of DNA in the cytosol serves as a key immunostimulatory signal associated with infections, cancer and genomic damage1,2. Cytosolic DNA triggers immune responses by activating the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway3. The binding of DNA to cGAS activates its enzymatic activity, leading to the synthesis of a second messenger, cyclic guanosine monophosphate-adenosine monophosphate (2'3'-cGAMP)4-7. This cyclic dinucleotide (CDN) activates STING8, which in turn activates the transcription factors interferon regulatory factor 3 (IRF3) and nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB), promoting the transcription of genes encoding type I interferons and other cytokines and mediators that stimulate a broader immune response. Exogenous 2'3'-cGAMP produced by malignant cells9 and other CDNs, including those produced by bacteria10-12 and synthetic CDNs used in cancer immunotherapy13,14, must traverse the cell membrane to activate STING in target cells. How these charged CDNs pass through the lipid bilayer is unknown. Here we used a genome-wide CRISPR-interference screen to identify the reduced folate carrier SLC19A1, a folate-organic phosphate antiporter, as the major transporter of CDNs. Depleting SLC19A1 in human cells inhibits CDN uptake and functional responses, and overexpressing SLC19A1 increases both uptake and functional responses. In human cell lines and primary cells ex vivo, CDN uptake is inhibited by folates as well as two medications approved for treatment of inflammatory diseases, sulfasalazine and the antifolate methotrexate. The identification of SLC19A1 as the major transporter of CDNs into cells has implications for the immunotherapeutic treatment of cancer13, host responsiveness to CDN-producing pathogenic microorganisms11 and-potentially-for some inflammatory diseases

Melanocytic naevi and melanoma in survivors of childhood cancer.

There is evidence from previous studies of small numbers of children who received cytotoxic therapy for cancer, that they may develop increased numbers of melanocytic naevi (moles), the strongest known risk factors for melanoma. Our aim was to investigate a large number of survivors of childhood cancer in order to test the hypothesis that they have more melanocytic naevi than matched controls. Total-body naevus counts were obtained from 263 oncology patients ascertained in paediatric oncology departments in Queensland, Australia, and from 263 hospital controls matched for age and sex. Additional information was gathered from children's parents about concurrent factors influencing naevus development such as type of complexion and history of sun exposure. Matched analyses, both crude and adjusted for possible confounding factors, revealed no significant difference in overall density of naevi among oncology patients and control subjects, according to diagnosis or to duration or type of chemotherapy. However significantly more oncology patients had atypical naevi (P < 0.05) and acral naevi (P < 0.0001) than controls. One patient developed a malignant melanoma 13 years after chemotherapy and radiotherapy for rhabdomyosarcoma. These findings support an association between treatment for childhood cancer and acral naevi and suggest that atypical naevi may also be associated with chemotherapy in childhood

A Dynamic, Modular Intelligent-Agent framework for Astronomical Light Curve Analysis and Classification

Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. This makes it almost impossible for objects to be identified manually. Therefore the production of methods and systems for the automated classification of time-domain astro-nomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure. These in-struments have been in operation since March 2009 gathering data of multi-degree sized areas of sky around the current field of view of the main telescope. Utilizing a Structured Query Language database established by a pre-processing operation upon the resultant images, which has identified millions of candidate variable stars with multiple time-varying magnitude observations, we applied a method designed to extract time-translation invariant features from the time-series light curves of each object for future input into a classification system. These efforts were met with limited success due to noise and uneven sampling within the time-series data. Additionally, finely surveying these light curves is a processing intensive task. Fortunately, these algorithms are capable of multi-threaded implementations based on available resources. Therefore we propose a new system designed to utilize multiple intelligent agents that distribute the data analysis across multiple machines whilst simultaneously a powerful intelligence service operates to constrain the light curves and eliminate false signals due to noise and local alias periods. This system will be highly scalable, capable of operating on a wide range of hardware whilst maintaining the production of ac-curate features based on the fitting of harmonic models to the light curves within the initial Structural Query Language database