A mixed methods approach to evaluating community drug distributor performance in the control of neglected tropical diseases

BACKGROUND: Trusted literate, or semi-literate, community drug distributors (CDDs) are the primary implementers in integrated preventive chemotherapy (IPC) programmes for Neglected Tropical Disease (NTD) control. The CDDs are responsible for safely distributing drugs and for galvanising communities to repeatedly, often over many years, receive annual treatment, create and update treatment registers, monitor for side-effects and compile treatment coverage reports. These individuals are 'volunteers' for the programmes and do not receive remuneration for their annual work commitment. METHODS: A mixed methods approach, which included pictorial diaries to prospectively record CDD use of time, structured interviews and focus group discussions, was used to triangulate data on how 58 CDDs allocated their time towards their routine family activities and to NTD Programme activities in Uganda. The opportunity costs of CDD time were valued, performance assessed by determining the relationship between time and programme coverage, and CDD motivation for participating in the programme was explored. RESULTS: Key findings showed approximately 2.5 working weeks (range 0.6-11.4 working weeks) were spent on NTD Programme activities per year. The amount of time on NTD control activities significantly increased between the one and three deliveries that were required within an IPC campaign. CDD time spent on NTD Programme activities significantly reduced time available for subsistence and income generating engagements. As CDDs took more time to complete NTD Programme activities, their treatment performance, in terms of validated coverage, significantly decreased. Motivation for the programme was reported as low and CDDs felt undervalued. CONCLUSIONS: CDDs contribute a considerable amount of opportunity cost to the overall economic cost of the NTD Programme in Uganda due to the commitment of their time. Nevertheless, programme coverage of at least 75 %, as required by the World Health Organisation, is not being achieved and vulnerable individuals may not have access to treatment as a consequence of sub-optimal performance by the CDDs due to workload and programmatic factors

Osteoporosis-related fracture case definitions for population-based administrative data

<p>Abstract</p> <p>Background</p> <p>Population-based administrative data have been used to study osteoporosis-related fracture risk factors and outcomes, but there has been limited research about the validity of these data for ascertaining fracture cases. The objectives of this study were to: (a) compare fracture incidence estimates from administrative data with estimates from population-based clinically-validated data, and (b) test for differences in incidence estimates from multiple administrative data case definitions.</p> <p>Methods</p> <p>Thirty-five case definitions for incident fractures of the hip, wrist, humerus, and clinical vertebrae were constructed using diagnosis codes in hospital data and diagnosis and service codes in physician billing data from Manitoba, Canada. Clinically-validated fractures were identified from the Canadian Multicentre Osteoporosis Study (CaMos). Generalized linear models were used to test for differences in incidence estimates.</p> <p>Results</p> <p>For hip fracture, sex-specific differences were observed in the magnitude of under- and over-ascertainment of administrative data case definitions when compared with CaMos data. The length of the fracture-free period to ascertain incident cases had a variable effect on over-ascertainment across fracture sites, as did the use of imaging, fixation, or repair service codes. Case definitions based on hospital data resulted in under-ascertainment of incident clinical vertebral fractures. There were no significant differences in trend estimates for wrist, humerus, and clinical vertebral case definitions.</p> <p>Conclusions</p> <p>The validity of administrative data for estimating fracture incidence depends on the site and features of the case definition.</p

FoxM1, a Forkhead Transcription Factor Is a Master Cell Cycle Regulator for Mouse Mature T Cells but Not Double Positive Thymocytes

FoxM1 is a forkhead box transcription factor and a known master regulator required for different phases of the cell cycle. In cell lines, FoxM1 deficient cells exhibit delayed S phase entry, aneuploidy, polyploidy and can't complete mitosis. In vivo, FoxM1 is expressed mostly in proliferating cells but is surprisingly also found in non-proliferating CD4+CD8+ double positive thymocytes. Here, we addressed the role of FoxM1 in T cell development by generating and analyzing two different lines of T-cell specific FoxM1 deficient mice. As expected, FoxM1 is required for proliferation of early thymocytes and activated mature T cells. Defective expression of many cell cycle proteins was detected, including cyclin A, cyclin B1, cdc2, cdk2, p27 and the Rb family members p107 and p130 but surprisingly not survivin. Unexpectedly, loss of FoxM1 only affects a few cell cycle proteins in CD4+CD8+ thymocytes and has little effect on their sensitivity to apoptosis and the subsequent steps of T cell differentiation. Thus, regulation of cell cycle genes by FoxM1 is stage- and context-dependent

A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis

<p>Abstract</p> <p>Background</p> <p>Protein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences.</p> <p>Results</p> <p>In this paper, a novel building block of proteins called Top-<it>n</it>-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-<it>n</it>-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-<it>n</it>-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-<it>n</it>-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-<it>n</it>-grams and LSA gives significantly better results compared to related methods.</p> <p>Conclusion</p> <p>The method based on Top-<it>n</it>-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-<it>n</it>-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.</p

The Things You Do:Internal Models of Others' Expected Behaviour Guide Action Observation

Predictions allow humans to manage uncertainties within social interactions. Here, we investigate how explicit and implicit person models-how different people behave in different situations-shape these predictions. In a novel action identification task, participants judged whether actors interacted with or withdrew from objects. In two experiments, we manipulated, unbeknownst to participants, the two actors action likelihoods across situations, such that one actor typically interacted with one object and withdrew from the other, while the other actor showed the opposite behaviour. In Experiment 2, participants additionally received explicit information about the two individuals that either matched or mismatched their actual behaviours. The data revealed direct but dissociable effects of both kinds of person information on action identification. Implicit action likelihoods affected response times, speeding up the identification of typical relative to atypical actions, irrespective of the explicit knowledge about the individual's behaviour. Explicit person knowledge, in contrast, affected error rates, causing participants to respond according to expectations instead of observed behaviour, even when they were aware that the explicit information might not be valid. Together, the data show that internal models of others' behaviour are routinely re-activated during action observation. They provide first evidence of a person-specific social anticipation system, which predicts forthcoming actions from both explicit information and an individuals' prior behaviour in a situation. These data link action observation to recent models of predictive coding in the non-social domain where similar dissociations between implicit effects on stimulus identification and explicit behavioural wagers have been reported

Hip fractures in a city in Northern Norway over 15 years: time trends, seasonal variation and mortality: The Harstad Injury Prevention Study

Introduction The aim of the present population-based study was to describe age- and sex-specific incidence of hip fractures in a Northern Norwegian city, compare rates with the Norwegian capital Oslo, describe time trends in hip fracture incidence, place of injury, seasonal variation and compare mortality after hip fracture between women and men. Methods Data on hip fractures from 1994 to 2008 in women and men aged 50 years and above were obtained from the Harstad Injury Registry. Results There were altogether 603 hip fractures in Harstad between 1994 and 2008. The annual incidenc rose exponentially from 5.8 to 349.2 per 10,000 in men, and from 8.7 to 582.2 per 10,000 in women from the age group 50–54 to 90+ years. The age-adjusted incidence rates were 101.0 and 37.4 in women and men, respectively, compared to 118.0 in women (p=0.005) and 44.0 in men (p=0.09) in Oslo. The age-adjusted incidence rates did not increase between 1994–1996 and 2006–2008. The majority of hip fractures occurred indoors and seasonal variation was significant in fractures occurring outdoors only. After adjusting for age at hip fracture, mortality after fracture was higher in men than in women 3, 6 and 12 months (p≤0.002) after fracture. Conclusions There are regional differences in hip fracture incidence that cannot be explained by a north–south gradient in Norway. Preventive strategies must be targeted to indoor areas throughout the year and to outdoor areas in winter

Gene ontology based transfer learning for protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sources for exploiting multi-aspect protein feature information. Gene ontology, hereinafter referred to as <it>GO</it>, uses a controlled vocabulary to depict biological molecules or gene products in terms of biological process, molecular function and cellular component. With the rapid expansion of annotated protein sequences, gene ontology has become a general protein feature that can be used to construct predictive models in computational biology. Existing models generally either concatenated the <it>GO </it>terms into a flat binary vector or applied majority-vote based ensemble learning for protein subcellular localization, both of which can not estimate the individual discriminative abilities of the three aspects of gene ontology.</p> <p>Results</p> <p>In this paper, we propose a Gene Ontology Based Transfer Learning Model (<it>GO-TLM</it>) for large-scale protein subcellular localization. The model transfers the signature-based homologous <it>GO </it>terms to the target proteins, and further constructs a reliable learning system to reduce the adverse affect of the potential false <it>GO </it>terms that are resulted from evolutionary divergence. We derive three <it>GO </it>kernels from the three aspects of gene ontology to measure the <it>GO </it>similarity of two proteins, and derive two other spectrum kernels to measure the similarity of two protein sequences. We use simple non-parametric cross validation to explicitly weigh the discriminative abilities of the five kernels, such that the time & space computational complexities are greatly reduced when compared to the complicated semi-definite programming and semi-indefinite linear programming. The five kernels are then linearly merged into one single kernel for protein subcellular localization. We evaluate <it>GO-TLM </it>performance against three baseline models: <it>MultiLoc, MultiLoc-GO </it>and <it>Euk-mPLoc </it>on the benchmark datasets the baseline models adopted. 5-fold cross validation experiments show that <it>GO-TLM </it>achieves substantial accuracy improvement against the baseline models: 80.38% against model <it>Euk-mPLoc </it>67.40% with <it>12.98% </it>substantial increase; 96.65% and 96.27% against model <it>MultiLoc-GO </it>89.60% and 89.60%, with <it>7.05% </it>and <it>6.67% </it>accuracy increase on dataset <it>MultiLoc plant </it>and dataset <it>MultiLoc animal</it>, respectively; 97.14%, 95.90% and 96.85% against model <it>MultiLoc-GO </it>83.70%, 90.10% and 85.70%, with accuracy increase <it>13.44%</it>, <it>5.8% </it>and <it>11.15% </it>on dataset <it>BaCelLoc plant</it>, dataset <it>BaCelLoc fungi </it>and dataset <it>BaCelLoc animal </it>respectively. For <it>BaCelLoc </it>independent sets, <it>GO-TLM </it>achieves 81.25%, 80.45% and 79.46% on dataset <it>BaCelLoc plant holdout</it>, dataset <it>BaCelLoc plant holdout </it>and dataset <it>BaCelLoc animal holdout</it>, respectively, as compared against baseline model <it>MultiLoc-GO </it>76%, 60.00% and 73.00%, with accuracy increase <it>5.25%</it>, <it>20.45% </it>and <it>6.46%</it>, respectively.</p> <p>Conclusions</p> <p>Since direct homology-based <it>GO </it>term transfer may be prone to introducing noise and outliers to the target protein, we design an explicitly weighted kernel learning system (called Gene Ontology Based Transfer Learning Model, <it>GO-TLM</it>) to transfer to the target protein the known knowledge about related homologous proteins, which can reduce the risk of outliers and share knowledge between homologous proteins, and thus achieve better predictive performance for protein subcellular localization. Cross validation and independent test experimental results show that the homology-based <it>GO </it>term transfer and explicitly weighing the <it>GO </it>kernels substantially improve the prediction performance.</p

A Putative Transcription Factor MYT1 Is Required for Female Fertility in the Ascomycete Gibberella zeae

Gibberella zeae is an important pathogen of major cereal crops. The fungus produces ascospores that forcibly discharge from mature fruiting bodies, which serve as the primary inocula for disease epidemics. In this study, we characterized an insertional mutant Z39P105 with a defect in sexual development and identified a gene encoding a putative transcription factor designated as MYT1. This gene contains a Myb DNA-binding domain and is conserved in the subphylum Pezizomycotina of Ascomycota. The MYT1 protein fused with green fluorescence protein localized in nuclei, which supports its role as a transcriptional regulator. The MYT1 deletion mutant showed similar phenotypes to the wild-type strain in vegetative growth, conidia production and germination, virulence, and mycotoxin production, but had defect in female fertility. A mutant overexpressing MYT1 showed earlier germination, faster mycelia growth, and reduced mycotoxin production compared to the wild-type strain, suggesting that improper MYT1 expression affects the expression of genes involved in the cell cycle and secondary metabolite production. This study is the first to characterize a transcription factor containing a Myb DNA-binding domain that is specific to sexual development in G. zeae