Vibrational branching ratios in photoionization of CO and N2

We report results of experimental and theoretical studies of the vibrational branching ratios for CO 4sigma(-1) photoionization from 20 to 185 eV. Comparison with results for the 2sigma(u)(-1) channel of the isoelectronic N-2 molecule shows the branching ratios for these two systems to be qualitatively different due to the underlying scattering dynamics: CO has a shape resonance at low energy but lacks a Cooper minimum at higher energies whereas the situation is reversed for N-2

Star and Stellar Cluster Formation: ALMA-SKA Synergies

© 2015 The Author(s). This work is made available under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license https://creativecommons.org/licenses/by-nc-sa/3.0/.Over the next decade, observations conducted with ALMA and the SKA will reveal the process of mass assembly and accretion onto young stars and will be revolutionary for studies of star formation. Here we summarise the capabilities of ALMA and discuss recent results from its early science observations. We then review infrared and radio variability observations of both young low-mass and high-mass stars. A time domain SKA radio continuum survey of star forming regions is then outlined. This survey will produce radio light-curves for hundreds of young sources, providing for the first time a systematic survey of radio variability across the full range of stellar masses. These light-curves will probe the magnetospheric interactions of young binary systems, the origins of outflows, trace episodic accretion on the central sources and potentially constrain the rotation rates of embedded sources

Stakeholder involvement in systematic reviews: a protocol for a systematic review of methods, outcomes and effects

Background There is an expectation for stakeholders (including patients, the public, health professionals, and others) to be involved in research. Researchers are increasingly recognising that it is good practice to involve stakeholders in systematic reviews. There is currently a lack of evidence about (A) how to do this and (B) the effects, or impact, of such involvement. We aim to create a map of the evidence relating to stakeholder involvement in systematic reviews, and use this evidence to address the two points above. Methods We will complete a mixed-method synthesis of the evidence, first completing a scoping review to create a broad map of evidence relating to stakeholder involvement in systematic reviews, and secondly completing two contingent syntheses. We will use a stepwise approach to searching; the initial step will include comprehensive searches of electronic databases, including CENTRAL, AMED, Embase, Medline, Cinahl and other databases, supplemented with pre-defined hand-searching and contacting authors. Two reviewers will undertake each review task (i.e., screening, data extraction) using standard systematic review processes. For the scoping review, we will include any paper, regardless of publication status or study design, which investigates, reports or discusses involvement in a systematic review. Included papers will be summarised within structured tables. Criteria for judging the focus and comprehensiveness of the description of methods of involvement will be applied, informing which papers are included within the two contingent syntheses. Synthesis A will detail the methods that have been used to involve stakeholders in systematic reviews. Papers from the scoping review that are judged to provide an adequate description of methods or approaches will be included. Details of the methods of involvement will be extracted from included papers using pre-defined headings, presented in tables and described narratively. Synthesis B will include studies that explore the effect of stakeholder involvement on the quality, relevance or impact of a systematic review, as identified from the scoping review. Study quality will be appraised, data extracted and synthesised within tables. Discussion This review should help researchers select, improve and evaluate methods of involving stakeholders in systematic reviews. Review findings will contribute to Cochrane training resources

Prioritising references for systematic reviews with RobotAnalyst: A user study

Screening references is a time-consuming step necessary for systematic reviews and guideline development. Previous studies have shown that human effort can be reduced by using machine learning software to prioritise large reference collections such that most of the relevant references are identified before screening is completed. We describe and evaluate RobotAnalyst, a Web-based software system that combines text-mining and machine learning algorithms for organising references by their content and actively prioritising them based on a relevancy classification model trained and updated throughout the process. We report an evaluation over 22 reference collections (most are related to public health topics) screened using RobotAnalyst with a total of 43 610 abstract-level decisions. The number of references that needed to be screened to identify 95% of the abstract-level inclusions for the evidence review was reduced on 19 of the 22 collections. Significant gains over random sampling were achieved for all reviews conducted with active prioritisation, as compared with only two of five when prioritisation was not used. RobotAnalyst's descriptive clustering and topic modelling functionalities were also evaluated by public health analysts. Descriptive clustering provided more coherent organisation than topic modelling, and the content of the clusters was apparent to the users across a varying number of clusters. This is the first large-scale study using technology-assisted screening to perform new reviews, and the positive results provide empirical evidence that RobotAnalyst can accelerate the identification of relevant studies. The results also highlight the issue of user complacency and the need for a stopping criterion to realise the work savings

On the biocompatibility and teat retention of in situ gelling intramammary formulations : cattle mastitis prevention and treatment

Treatment and prevention of cattle mastitis remains a formidable challenge due to the anatomical and physiological constraints of the cow udder. In this study, we investigated polymeric excipients and solvents that can form, (when combined) novel, non-toxic and biocompatible in situ gelling formulations in the mammary gland of bovine cattle. We also report on a new approach to screen intramammary formulations using fresh excised cow teats. Fourteen hydrophilic polymers and six solvents were evaluated for in vitro cytotoxicity and biocompatibility towards cultured bovine mammary epithelial cells (MAC-T), microscopic and macroscopic examination upon contact with excised cow teats. No significant cytotoxicity (p > 0.05) was observed with polyethylene oxides, hydroxypropyl methylcellulose, carboxymethyl cellulose, sodium alginate and xanthan gum. Polycarbophil and carbopol polymers showed significantly higher cytotoxicity (p p < 0.05). In situ gelling formulations comprising hydroxypropyl methylcellulose or carboxymethyl cellulose and solvents in specific ratios were biocompatible at higher concentrations with MAC-T cells compared to alginates. All investigated formulations could undergo in situ sol-to-gel phase transformation, forming non-toxic gels with good biocompatibility in excised cow teats hence, showing potential for use as intramammary carriers for sustained drug delivery

Uncovering the molecular mechanisms of lignocellulose digestion in shipworms

Abstract Lignocellulose forms the structural framework of woody plant biomass and represents the most abundant carbon source in the biosphere. Turnover of woody biomass is a critical component of the global carbon cycle, and the enzymes involved are of increasing industrial importance as industry moves away from fossil fuels to renewable carbon resources. Shipworms are marine bivalve molluscs that digest wood and play a key role in global carbon cycling by processing plant biomass in the oceans. Previous studies suggest that wood digestion in shipworms is dominated by enzymes produced by endosymbiotic bacteria found in the animal’s gills, while little is known about the identity and function of endogenous enzymes produced by shipworms. Using a combination of meta-transcriptomic, proteomic, imaging and biochemical analyses, we reveal a complex digestive system dominated by uncharacterized enzymes that are secreted by a specialized digestive gland and that accumulate in the cecum, where wood digestion occurs. Using a combination of transcriptomics, proteomics, and microscopy, we show that the digestive proteome of the shipworm Lyrodus pedicellatus is mostly composed of enzymes produced by the animal itself, with a small but significant contribution from symbiotic bacteria. The digestive proteome is dominated by a novel 300 kDa multi-domain glycoside hydrolase that functions in the hydrolysis of β-1,4-glucans, the most abundant polymers in wood. These studies allow an unprecedented level of insight into an unusual and ecologically important process for wood recycling in the marine environment, and open up new biotechnological opportunities in the mobilization of sugars from lignocellulosic biomass

Machine learning algorithms for systematic review: reducing workload in a preclinical review of animal studies and reducing human screening error

BACKGROUND: Here, we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies. The aim is to achieve a high-performing algorithm comparable to human screening that can reduce human resources required for carrying out this step of a systematic review. METHODS: We applied ML approaches to a broad systematic review of animal models of depression at the citation screening stage. We tested two independently developed ML approaches which used different classification models and feature sets. We recorded the performance of the ML approaches on an unseen validation set of papers using sensitivity, specificity and accuracy. We aimed to achieve 95% sensitivity and to maximise specificity. The classification model providing the most accurate predictions was applied to the remaining unseen records in the dataset and will be used in the next stage of the preclinical biomedical sciences systematic review. We used a cross-validation technique to assign ML inclusion likelihood scores to the human screened records, to identify potential errors made during the human screening process (error analysis). RESULTS: ML approaches reached 98.7% sensitivity based on learning from a training set of 5749 records, with an inclusion prevalence of 13.2%. The highest level of specificity reached was 86%. Performance was assessed on an independent validation dataset. Human errors in the training and validation sets were successfully identified using the assigned inclusion likelihood from the ML model to highlight discrepancies. Training the ML algorithm on the corrected dataset improved the specificity of the algorithm without compromising sensitivity. Error analysis correction leads to a 3% improvement in sensitivity and specificity, which increases precision and accuracy of the ML algorithm. CONCLUSIONS: This work has confirmed the performance and application of ML algorithms for screening in systematic reviews of preclinical animal studies. It has highlighted the novel use of ML algorithms to identify human error. This needs to be confirmed in other reviews with different inclusion prevalence levels, but represents a promising approach to integrating human decisions and automation in systematic review methodology