Missed opportunities for vaccination in health facilities in Swaziland

Objectives. To determine whether potential exists to increase vaccination coverage in Swaziland by reducing missed opportunities.Design. The standard World Health Organisation Expanded Programme on Immunisation (WHO EPI) protocol forassessing missed opportunities for vaccination was used to conduct this study. It involved client exit interviews and review of health cards.Setting. Selected variety of health facilities in Swaziland.Subjects. Children less than 2 years of age and women of child-bearing age exiting each facility.                           Outcome measures. Children and women eligible for vaccination exiting sampled health facilities.Results. Fifty-four per cent of eligible children less than 2 years of age were missed for vaccination. This constitutes 26% of all children less than 2 years old leaving the facilities studied. Almost 100% of eligible women of childbearing age were missed for vaccination, constituting 88% of women leaving the study facilities. The distribution of the proportion of missed opportunities varied considerably between regions and health facility types. Missed opportunities occurred more frequently among those children requiring the first dose of all antigens and this may be linked to the high proportion of children missed for vaccination who did not possess a health card. Missed opportunities were more likely to occur in facilities providing integrated services.Conclusions. The frequent attendance at hea,lth facilities of the target group presents a valuable opportunity to increase vaccination coverage through avoidance of missed opportunities. All regions need to set vaccination coverage targets and develop plans to increase coverage, which should include strategies to ensure that all health workers routinely screen all clients for eligibility and vaccinate as required

Crossing Statistic: Bayesian interpretation, model selection and resolving dark energy parametrization problem

By introducing Crossing functions and hyper-parameters I show that the Bayesian interpretation of the Crossing Statistics [1] can be used trivially for the purpose of model selection among cosmological models. In this approach to falsify a cosmological model there is no need to compare it with other models or assume any particular form of parametrization for the cosmological quantities like luminosity distance, Hubble parameter or equation of state of dark energy. Instead, hyper-parameters of Crossing functions perform as discriminators between correct and wrong models. Using this approach one can falsify any assumed cosmological model without putting priors on the underlying actual model of the universe and its parameters, hence the issue of dark energy parametrization is resolved. It will be also shown that the sensitivity of the method to the intrinsic dispersion of the data is small that is another important characteristic of the method in testing cosmological models dealing with data with high uncertainties.Comment: 14 pages, 4 figures, discussions extended, 1 figure and two references added, main results unchanged, matches the final version to be published in JCA

Venom costs and optimization in scorpions

Scorpions use venoms as weapons to improve prey capture and predator defense, and these benefits must be balanced against costs associated with its use. Venom costs involve direct energetic costs associated with the production and storage of toxins, and indirect fitness costs arising from reduced venom availability. In order to reduce these costs, scorpions optimize their venom use via evolutionary responses, phenotypic plasticity, and behavioral mechanisms. Over long timescales, evolutionary adaptation to environments with different selection pressures appears to have contributed to interspecific variation in venomcomposition and stingermorphology. Furthermore, plastic responses may allow scorpions to modify and optimize their venom composition as pressures change. Optimal venomuse can vary when facing each prey itemand potential predator encountered, and therefore scorpions display a range of behaviors to optimize their venom use to the particular situation. These behaviors include varying sting rates, employing dry stings, and further altering the volume and composition of venom injected. Whilst these cost-reducing mechanisms are recognized in scorpions, relatively little is understood about the factors that influence them. Here, we review evidence of the costs associated with venom use in scorpions and discuss the mechanisms that have evolved to minimize them

Small molecules in the venom of the scorpion Hormurus waigiensis

Despite scorpion stings posing a significant public health issue in particular regions of the world, certain aspects of scorpion venom chemistry remain poorly described. Although there has been extensive research into the identity and activity of scorpion venom peptides, non-peptide small molecules present in the venom have received comparatively little attention. Small molecules can have important functions within venoms; for example, in some spider species the main toxic components of the venom are acylpolyamines. Other molecules can have auxiliary effects that facilitate envenomation, such as purines with hypotensive properties utilised by snakes. In this study, we investigated some non-peptide small molecule constituents of Hormurus waigiensis venom using LC/MS, reversed-phase HPLC, and NMR spectroscopy. We identified adenosine, adenosine monophosphate (AMP), and citric acid within the venom, with low quantities of the amino acids glutamic acid and aspartic acid also being present. Purine nucleosides such as adenosine play important auxiliary functions in snake venoms when injected alongside other venom toxins, and they may have a similar role within H. waigiensis venom. Further research on these and other small molecules in scorpion venoms may elucidate their roles in prey capture and predator defence, and gaining a greater understanding of how scorpion venom components act in combination could allow for the development of improved first aid

Integrative characterisation of secreted factors involved in intercellular communication between prostate epithelial or cancer cells and fibroblasts

Reciprocal interactions between prostate cancer cells and carcinomaassociated fibroblasts (CAFs) mediate cancer development and progression; however, our understanding of the signalling pathways mediating these cellular interactions remains incomplete. To address this, we defined secretome changes upon co-culture of prostate epithelial or cancer cells with fibroblasts that mimic bi-directional communication in tumours. Using antibody arrays, we profiled conditioned media from mono- and cocultures of prostate fibroblasts, epithelial and cancer cells, identifying secreted proteins that are upregulated in co-culture compared to monoculture. Six of these (CXCL10, CXCL16, CXCL6, FST, PDGFAA, IL17B) were functionally screened by siRNA knockdown in prostate cancer cell/fibroblast co-cultures, revealing a key role for follistatin (FST), a secreted glycoprotein that binds and bioneutralises specific members of the TGF-b superfamily, including activin A. Expression of FST by both cell types was required for the fibroblasts to enhance prostate cancer cell proliferation and migration, whereas FST knockdown in co-culture grafts decreased tumour growth in mouse xenografts. This study highlights the complexity of prostate cancer cell–fibroblast communication, demonstrates that co-culture secretomes cannot be predicted from individual cultures, and identifies FST as a tumour-microenvironment-derived secreted factor that represents a candidate therapeutic target.Yunjian Wu, Kimberley C. Clark, Birunthi Niranjan, Anderly C. Chueh, Lisa G. Horvath, Renea A. Taylor, and Roger J. Dal

Proteomic characterisation of prostate cancer intercellular communication reveals cell type-selective signalling and TMSB4X-dependent fibroblast reprogramming

Background: In prostate cancer, the tumour microenvironment (TME) represents an important regulator of disease progression and response to treatment. In the TME, cancer-associated fbroblasts (CAFs) play a key role in tumour progression, however the mechanisms underpinning fbroblast-cancer cell interactions are incompletely resolved. Here, we address this by applying cell type-specifc labelling with amino acid precursors (CTAP) and mass spectrometry (MS)-based (phospho) proteomics to prostate cancer for the frst time. Methods: Reciprocal interactions between PC3 prostate cancer cells co-cultured with WPMY-1 prostatic fbroblasts were characterised using CTAP-MS. Signalling network changes were determined using Metascape and Enrichr and visualised using Cytoscape. Thymosin β4 (TMSB4X) overexpression was achieved via retroviral transduction and assayed by ELISA. Cell motility was determined using Transwell and random cell migration assays and expression of CAF markers by indirect immunofuorescence. Results: WPMY-1 cells co-cultured with PC3s demonstrated a CAF-like phenotype, characterised by enhanced PDGFRB expression and alterations in signalling pathways regulating epithelial-mesenchymal transition, cytoskeletal organisation and cell polarisation. In contrast, co-cultured PC3 cells exhibited more modest network changes, with alterations in mTORC1 signalling and regulation of the actin cytoskeleton. The expression of the actin binding protein TMSB4X was signifcantly decreased in co-cultured WPMY-1 fbroblasts, and overexpression of TMSB4X in fbroblasts decreased migration of cocultured PC3 cells, reduced fbroblast motility, and protected the fbroblasts from being educated to a CAF-like phenotype by prostate cancer cells. Conclusions: This study highlights the potential of CTAP-MS to characterise intercellular communication within the prostate TME and identify regulators of cellular crosstalk such as TMSB4X.Yunjian Wu, Kimberley C. Clark, Elizabeth V. Nguyen, Birunthi Niranjan, Lisa G. Horvath, Renea A. Taylor, Roger J. Dal

A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer

Precision medicine has emerged as an important paradigm in oncology, driven by the significant heterogeneity of individual patients' tumour. A key prerequisite for effective implementation of precision oncology is the development of companion biomarkers that can predict response to anti-cancer therapies and guide patient selection for clinical trials and/or treatment. However, reliable predictive biomarkers are currently lacking for many anti-cancer therapies, hampering their clinical application. Here, we developed a novel machine learning-based framework to derive predictive multi-gene biomarker panels and associated expression signatures that accurately predict cancer drug sensitivity. We demonstrated the power of the approach by applying it to identify response biomarker panels for an Hsp90-based therapy in prostate cancer, using proteomic data profiled from prostate cancer patient-derived explants. Our approach employs a rational feature section strategy to maximise model performance, and innovatively utilizes Boolean algebra methods to derive specific expression signatures of the marker proteins. Given suitable data for model training, the approach is also applicable to other cancer drug agents in different tumour settings.Sung-Young Shin, Margaret M. Centenera, Joshua T. Hodgson, Elizabeth V. Nguyen, Lisa M. Butler, Roger J. Daly and Lan K. Nguye

The Crossing Statistic: Dealing with Unknown Errors in the Dispersion of Type Ia Supernovae

We propose a new statistic that has been designed to be used in situations where the intrinsic dispersion of a data set is not well known: The Crossing Statistic. This statistic is in general less sensitive than `chi^2' to the intrinsic dispersion of the data, and hence allows us to make progress in distinguishing between different models using goodness of fit to the data even when the errors involved are poorly understood. The proposed statistic makes use of the shape and trends of a model's predictions in a quantifiable manner. It is applicable to a variety of circumstances, although we consider it to be especially well suited to the task of distinguishing between different cosmological models using type Ia supernovae. We show that this statistic can easily distinguish between different models in cases where the `chi^2' statistic fails. We also show that the last mode of the Crossing Statistic is identical to `chi^2', so that it can be considered as a generalization of `chi^2'.Comment: 14 pages, 5 figures. Paper restructured and extended and new interpretation of the method presented. New results concerning model selection. Treatment and error-analysis made fully model independent. References added. Accepted for publication in JCA

Profiling the tyrosine phosphoproteome of different mouse mammary tumour models reveals distinct, model-specific signalling networks and conserved oncogenic pathways

Introduction Although aberrant tyrosine kinase signalling characterises particular breast cancer subtypes, a global analysis of tyrosine phosphorylation in mouse models of breast cancer has not been undertaken to date. This may identify conserved oncogenic pathways and potential therapeutic targets. Methods We applied an immunoaffinity/mass spectrometry workflow to three mouse models: murine stem cell virus-Neu, expressing truncated Neu, the rat orthologue of human epidermal growth factor receptor 2, Her2 (HER2); mouse mammary tumour virus-polyoma virus middle T antigen (PyMT); and the p53?/? transplant model (p53). Pathways and protein¿protein interaction networks were identified by bioinformatics analysis. Molecular mechanisms underpinning differences in tyrosine phosphorylation were characterised by Western blot analysis and array comparative genomic hybridisation. The functional role of mesenchymal¿epithelial transition factor (Met) in a subset of p53-null tumours was interrogated using a selective tyrosine kinase inhibitor (TKI), small interfering RNA (siRNA)¿mediated knockdown and cell proliferation assays. Results The three models could be distinguished on the basis of tyrosine phosphorylation signatures and signalling networks. HER2 tumours exhibited a protein¿protein interaction network centred on avian erythroblastic leukaemia viral oncogene homologue 2 (Erbb2), epidermal growth factor receptor and platelet-derived growth factor receptor ?, and they displayed enhanced tyrosine phosphorylation of ERBB receptor feedback inhibitor 1. In contrast, the PyMT network displayed significant enrichment for components of the phosphatidylinositol 3-kinase signalling pathway, whereas p53 tumours exhibited increased tyrosine phosphorylation of Met and components or regulators of the cytoskeleton and shared signalling network characteristics with basal and claudin-low breast cancer cells. A subset of p53 tumours displayed markedly elevated cellular tyrosine phosphorylation and Met expression, as well as Met gene amplification. Treatment of cultured p53-null cells exhibiting Met amplification with a selective Met TKI abrogated aberrant tyrosine phosphorylation and blocked cell proliferation. The effects on proliferation were recapitulated when Met was knocked down using siRNA. Additional subtypes of p53 tumours exhibited increased tyrosine phosphorylation of other oncogenes, including Peak1/SgK269 and Prex2. Conclusion This study provides network-level insights into signalling in the breast cancer models utilised and demonstrates that comparative phosphoproteomics can identify conserved oncogenic signalling pathways. The Met-amplified, p53-null tumours provide a new preclinical model for a subset of triple-negative breast cancers

iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization

Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate understanding of the sequence(-structure)-function paradigm for DNAs, RNAs and proteins. Rapid accumulation of sequences requires equally pervasive development of new predictive models, which depends on the availability of effective tools that support these efforts. We introduce iLearnPlus, the first machine-learning platform with graphical- and web-based interfaces for the construction of machine-learning pipelines for analysis and predictions using nucleic acid and protein sequences. iLearnPlus provides a comprehensive set of algorithms and automates sequence-based feature extraction and analysis, construction and deployment of models, assessment of predictive performance, statistical analysis, and data visualization; all without programming. iLearnPlus includes a wide range of feature sets which encode information from the input sequences and over twenty machine-learning algorithms that cover several deep-learning approaches, outnumbering the current solutions by a wide margin. Our solution caters to experienced bioinformaticians, given the broad range of options, and biologists with no programming background, given the point-and-click interface and easy-to-follow design process. We showcase iLearnPlus with two case studies concerning prediction of long noncoding RNAs (lncRNAs) from RNA transcripts and prediction of crotonylation sites in protein chains. iLearnPlus is an open-source platform available at https://github.com/Superzchen/iLearnPlus/ with the webserver at http://ilearnplus.erc.monash.edu/.Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yong-Zi Chen, Tatsuya Akutsu, Roger J. Daly, Geoffrey I. Webb, Quanzhi Zhao, Lukasz Kurgan, and Jiangning Son