Distribution of the North American Porcupine (Erethizon dorsatum) in Northern California

Western Wildlife 4:17–28, 2017: The North American Porcupine (Erethizon dorsatum) is one of the most widely distributed mammals in North America, but recent reports have suggested declines in parts of its range in the West. In California, little is known about the historical or current status of the porcupine, and maps of its distribution conflict considerably. Nevertheless, the species is of interest to natural resource managers. For much of the 1900s, foresters and others primarily treated porcupines as pests because of the undesirable damage they inflict feeding on trees and gnawing on manmade items in search of salt. More recently, porcupines have been recognized for their role in promoting forest structure and diversity, and as potential prey for the Fisher (Pekania pennanti). We collected records of porcupine occurrence in the northern part of California since the beginning of the 20th Century, relying on government and private databases, reports from the public, and other sources. These records confirm that porcupines may occur in most major regions and habitat types across northern California, in contrast to many published range maps. The contemporary distribution of porcupines in the state most closely resembles the California Wildlife Habitat Relationships System (CWHR) range map, which is based on projections of suitable habitat. We are unable to offer deeper insight into trends of abundance and possible changes in distribution because these records are likely spatiotemporally correlated with observer effort. This work is a first step and we recommend that a broader statewide effort be conducted to better understand the distribution, abundance, and ecology of North American Porcupines in California. See below or click here to view data

Phenotypic Characterization of Chicken Thymic Stromal Elements

Phenotypic profiles of the thymic stromal components provide an excellent approach to elucidating the nature of the microenvironment of this organ. To address this issue in chickens, we have produced an extensive panel of 18 mAb to the thymic stroma. These mAb have been extensively characterized with respect to their phenotypic specificities and reveal that the stromal cells are equally as complex as the T cells whose maturation they direct. They further demonstrate that, in comparison to the mammalian thymus, there is a remarkable degree of conservation in thymic architecture between phylogenetically diverse species. Eleven mAb reacted with thymic epithelial cells: MUI-73 was panepithelium, MUI-54 stained all cortical and medullary epithelium but only a minority of the subcapsule, MUI-52 was specific for isolated stellate cortical epithelial cells, MUI-62, -69, and -71 were specific for the medulla (including Hassall’s corpusclelike structures), MUI-51, -53, -70, and -75 reacted only with the type-I epithelium, or discrete regions therein, lining the subcapsular and perivascular regions and MUI-58 demonstrated the antigenic similarity between the subcapsule and the medulla. Seven other mAb identified distinct isolated stromal cells throughout the cortex and medulla. Large thymocyte-rich regions, which often spanned from the outer cortex to medulla, lacked epithelial cells. These mAb should prove invaluable for determining the functional significance of thymic stromal-cell subsets to thymopoiesis

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Abstract Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials

X-ray holography with a customizable reference

In X-ray Fourier-transform holography, images are formed by exploiting the interference pattern between the X-rays scattered from the sample and a known reference wave. To date, this technique has only been possible with a limited set of special reference waves. We demonstrate X-ray Fourier-transform holography with an almost unrestricted choice for the reference wave, permitting experimental geometries to be designed according to the needs of each experiment and opening up new avenues to optimize signal-to-noise and resolution. The optimization of holographic references can aid the development of holographic techniques to meet the demands of resolution and fidelity required for single-shot imaging applications with X-ray lasers

A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data

The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission from time-series vital signs and laboratory results obtained within the first 24 hours of hospital admission. The stacked platform comprises two components: a) an unsupervised LSTM Autoencoder that learns an optimal representation of the time-series, using it to differentiate the less frequent patterns which conclude with an adverse event from the majority patterns that do not, and b) a gradient boosting model, which relies on the constructed representation to refine prediction, incorporating static features of demographics, admission details and clinical summaries. The model is used to assess a patient's risk of adversity over time and provides visual justifications of its prediction based on the patient's static features and dynamic signals. Results of three case studies for predicting mortality and ICU admission show that the model outperforms all existing outcome prediction models, achieving PR-AUC of 0.891 (95$$ CI: 0.878 - 0.969) in predicting mortality in ICU and general ward settings and 0.908 (95$$ CI: 0.870-0.935) in predicting ICU admission.Comment: 14 page

Constraints on Brane Inflation and Cosmic Strings

By considering simple, but representative, models of brane inflation from a single brane-antibrane pair in the slow roll regime, we provide constraints on the parameters of the theory imposed by measurements of the CMB anisotropies by WMAP including a cosmic string component. We find that inclusion of the string component is critical in constraining parameters. In the most general model studied, which includes an inflaton mass term, as well as the brane-antibrane attraction, values n_s < 1.02 are compatible with the data at 95 % confidence level. We are also able to constrain the volume of internal manifold (modulo factors dependent on the warp factor) and the value of the inflaton field to be less than 0.66M_P at horizon exit. We also investigate models with a mass term. These observational considerations suggest that such models have r < 2*10^-5, which can only be circumvented in the fast roll regime, or by increasing the number of antibranes. Such a value of r would not be detectable in CMB polarization experiment likely in the near future, but the B-mode signal from the cosmic strings could be detectable. We present forecasts of what a similar analysis using PLANCK data would yield and find that it should be possible to rule out G\mu > 6.5*10^-8 using just the TT, TE and EE power spectra.Comment: 11 pages, 3 figures, revtex4, typos corrected, references adde

A novel algorithmic approach to generate consensus treatment guidelines in adult Acute Myeloid Leukaemia

Induction therapy for acute myeloid leukaemia (AML) has changed with the approval of a number of new agents. Clinical guidelines can struggle to keep pace with an evolving treatment and evidence landscape and therefore identifying the most appropriate front-line treatment is challenging for clinicians. Here, we combined drug eligibility criteria and genetic risk stratification into a digital format, allowing the full range of possible treatment eligibility scenarios to be defined. Using exemplar cases representing each of the 22 identified scenarios, we sought to generate consensus on treatment choice from a panel of nine aUK AML experts. We then analysed >2500 real-world cases using the same algorithm, confirming the existence of 21/22 of these scenarios and demonstrating that our novel approach could generate a consensus AML induction treatment in 98% of cases. Our approach, driven by the use of decision trees, is an efficient way to develop consensus guidance rapidly and could be applied to other disease areas. It has the potential to be updated frequently to capture changes in eligibility criteria, novel therapies and emerging trial data. An interactive digital version of the consensus guideline is available

Coherent diffraction of single Rice Dwarf virus particles using hard X-rays at the Linac Coherent Light Source

Single particle diffractive imaging data from Rice Dwarf Virus (RDV) were recorded using the Coherent X-ray Imaging (CXI) instrument at the Linac Coherent Light Source (LCLS). RDV was chosen as it is a wellcharacterized model system, useful for proof-of-principle experiments, system optimization and algorithm development. RDV, an icosahedral virus of about 70 nm in diameter, was aerosolized and injected into the approximately 0.1 mu m diameter focused hard X-ray beam at the CXI instrument of LCLS. Diffraction patterns from RDV with signal to 5.9 angstrom ngstrom were recorded. The diffraction data are available through the Coherent X-ray Imaging Data Bank (CXIDB) as a resource for algorithm development, the contents of which are described here.11Ysciescopu

Soil hydrologic grouping guide which soil and weather properties best estimate corn nitrogen need

Nitrogen fertilizer recommendations in corn (Zea mays L.) that match the economically optimal nitrogen fertilizer rate (EONR) are imperative for profitability and minimizing environmental degradation. However, the amount of soil N available for the crop depends on soil and weather factors, making it difficult to know the EONR from year-to-year and from field-to-field. Our objective was to explore, within the framework of hydrologic soil groups and drainage classifications (HGDC), which site-specific soil and weather properties best estimated corn N needs (i.e., EONR) for two application timings (at-planting and side-dress). Included in this investigation was a validation step using an independent dataset. Forty-nine N response trials conducted across the U.S. Midwest Corn Belt over three growing seasons (2014–2016) were used for recommendation model development, and 181 independent site-years were used for validation. For HGDC models, soil organic matter (SOM), clay content, and evenness of rainfall distribution before side-dress N application were the properties generally most helpful in predicting EONR. Using the validation data, model recommendations were within 34 kg N ha–1 of EONR for 37 and 42% of the sites with a root mean square error (RMSE) of 70 and 68 kg N ha–1 for at-planting and side-dress applications, respectively. Compared to state-specific recommendations, sites needing ha–1 or no N were better estimated with HGDC models. In contrast, for sites where EONR was \u3e150 kg N ha–1, HGDC models underestimated N needs compared to state specific. These results show HGDC groupings could aid in developing tools for N fertilizer recommendations