Semi batch learning with store management using enhanced conjugate gradient

This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.

Determinants of root-associated fungal communities within Asteraceae in a semi-arid grassland

Summary

1.While plant–fungal interactions are important determinants of plant community assembly and ecosystem functioning, the processes underlying fungal community composition are poorly understood.
2.Here, we studied for the first time the root-associated eumycotan communities in a set of co-occurring plant species of varying relatedness in a species-rich, semi-arid grassland in Germany. The study system provides an opportunity to evaluate the importance of host plants and gradients in soil type and landscape structure as drivers of fungal community structure on a relevant spatial scale. We used 454 pyrosequencing of the fungal internal transcribed spacer region to analyse root-associated eumycotan communities of 25 species within the Asteraceae, which were sampled at different locations within a soil type gradient. We partitioned the variance accounted for by three predictors (host plant phylogeny, spatial distribution and soil type) to quantify their relative roles in determining fungal community composition and used null model analyses to determine whether community composition was influenced by biotic interactions among the fungi.
3.We found a high fungal diversity (156 816 sequences clustered in 1100 operational taxonomic units (OTUs)). Most OTUs belonged to the phylum Ascomycota (35.8%); the most abundant phylotype best-matched Phialophora mustea. Basidiomycota were represented by 18.3%, with Sebacina as most abundant genus. The three predictors explained 30% of variation in the community structure of root-associated fungi, with host plant phylogeny being the most important variance component. Null model analysis suggested that many fungal taxa co-occurred less often than expected by chance, which demonstrates spatial segregation and indicates that negative interactions may prevail in the assembly of fungal communities.
4.Synthesis. The results show that the phylogenetic relationship of host plants is the most important predictor of root-associated fungal community assembly, indicating that fungal colonization of host plants might be facilitated by certain plant traits that may be shared among closely related plant species.

Uncovering the challenges of managing cachexia in advanced cancer: Preliminary results from semi-structured interviews with healthcare professionals in a regional cancer centre

Background: Cancer cachexia is a complex metabolic syndrome characterised by severe and progressive weight loss which is predominantly muscle mass. It is a devastating and distressing complication of advanced cancer with profound bio-psycho-social implications for patients and their families. At present there is no curative treatment for cachexiain advanced cancer therefore the most important healthcare response entails the minimisation of the psycho-social distress associated with it. However the literature suggests healthcare professionals’are missing opportunities to intervene and respond to the multi-dimensional needs of this population.

Objective:The objective of this study was to explore healthcare professionals’ response to cachexia in advanced cancer.

Methods: An interpretative qualitative approach was adopted in this study. A purposive sample of doctors, nurses, specialist nurses and dieticians were recruited from a regional cancer centre between November 2009 and November 2010. Data was collection was twofold: two multi-professional focus groups were conducted first to uncover the main themes and issues in cachexia management. This data then informed the interview schedule for the following 25 individual semi-structured interviews.

Results: Preliminary data analysis of the semi-structured interviews revealed distinct differences between disciplines in their perceptions of cancer cachexia which influenced their response to it in clinical practice. The commonality between disciplines, with the exception of palliative care, was a reliance on the biomedical approach to cancer cachexia management.

Discussion and Conclusions: Cancer cachexia is a complex and challenging syndrome which needs to be addressed from a holistic model of care to reflect the multi-dimensional needs of this patient group. The perspectives of those involved in care delivery is required in order to inform the development of interventions aimed at minimising the distress associated with this devastating syndrome.

The BTA stat test:A tumor marker for the detection of upper tract transitional cell carcinoma

Objectives. To conduct a prospective evaluation to determine the utility of the BTA stat test in the detection of upper tract transitional cell carcinoma (UTTCC). Monitoring for UTTCC currently relies on invasive procedures such as upper tract imaging, ureteral washing cytology (UWC) and/or ureteroscopy, or voided urine cytology (VUC). The BTA stat test is a sensitive qualitative immunoassay that detects human complement factor H-related protein in voided urine.

Methods. A total of 81 patients participated, 27 with histopathologically confirmed UTTCC, 26 with upper tract calculi, and 28 with microscopic hematuria but no evidence of urologic disease. Voided specimens collected before surgery or treatment were tested with the BTA stat test and VUC. UWC was performed in specimens collected by a ureteral catheter.

Results. The BTA stat test was significantly more sensitive and specific than VUC or UWC. The overall sensitivity for each was 82%, 11%, and 48%; the specificity was 89%, 54%, and 33%. The positive predictive value for the BTA stat test was 79% and the negative predictive value was 91%, both the highest of the three tests.

Conclusions. The BTA stat test was superior to VUC and UWC in the detection of UTTCC. These results may support the adoption of a less aggressive follow-up policy when monitoring for UTTCC when the BTA stat result is negative. If cystoscopy is negative and the BTA stat test is positive, upper tract investigations should be expedited and, if the bladder is in place, bladder biopsies performed. (C) 2001, Elsevier Science Inc.

Comparison of Microbiomes from Different Niches of Upper and Lower Airways in Children and Adolescents with Cystic Fibrosis

Changes in the airway microbiome may be important in the pathophysiology of chronic lung disease in patients with cystic fibrosis. However, little is known about the microbiome in early cystic fibrosis lung disease and the relationship between the microbiomes from different niches in the upper and lower airways. Therefore, in this cross-sectional study, we examined the relationship between the microbiome in the upper (nose and throat) and lower (sputum) airways from children with cystic fibrosis using next generation sequencing. Our results demonstrate a significant difference in both α and β-diversity between the nose and the two other sampling sites. The nasal microbiome was characterized by a polymicrobial community while the throat and sputum communities were less diverse and dominated by a few operational taxonomic units. Moreover, sputum and throat microbiomes were closely related especially in patients with clinically stable lung disease. There was a high inter-individual variability in sputum samples primarily due to a decrease in evenness linked to increased abundance of potential respiratory pathogens such as Pseudomonas aeruginosa. Patients with chronic Pseudomonas aeruginosa infection exhibited a less diverse sputum microbiome. A high concordance was found between pediatric and adult sputum microbiomes except that Burkholderia was only observed in the adult cohort. These results indicate that an adult-like lower airways microbiome is established early in life and that throat swabs may be a good surrogate in clinically stable children with cystic fibrosis without chronic Pseudomonas aeruginosa infection in whom sputum sampling is often not feasible.

Complexity of Inferences in Polytree-shaped Semi-Qualitative Probabilistic Networks

Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Bayesian networks and qualitative probabilistic networks. They provide a very general modeling framework by allowing the combination of numeric and qualitative assessments over a discrete domain, and can be compactly encoded by exploiting the same factorization of joint probability distributions that are behind the Bayesian networks. This paper explores the computational complexity of semi-qualitative probabilistic networks, and takes the polytree-shaped networks as its main target. We show that the inference problem is coNP-Complete for binary polytrees with multiple observed nodes. We also show that inferences can be performed in linear time if there is a single observed node, which is a relevant practical case. Because our proof is constructive, we obtain an efficient linear time algorithm for SQPNs under such assumptions. To the best of our knowledge, this is the first exact polynomial-time algorithm for SQPNs. Together these results provide a clear picture of the inferential complexity in polytree-shaped SQPNs.

Semi-Qualitative Probabilistic Networks in Computer Vision Problems

This paper explores the application of semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information to computer vision problems. Our version of SQPN allows qualitative influences and imprecise probability measures using intervals. We describe an Imprecise Dirichlet model for parameter learning and an iterative algorithm for evaluating posterior probabilities, maximum a posteriori and most probable explanations. Experiments on facial expression recognition and image segmentation problems are performed using real data.

Computing lower and upper expectations under epistemic independence

This paper investigates the computation of lower/upper expectations that must cohere with a collection of probabilistic assessments and a collection of judgements of epistemic independence. New algorithms, based on multilinear programming, are presented, both for independence among events and among random variables. Separation properties of graphical models are also investigated.

Belief Updating and Learning in Semi-Qualitative Probabilistic Networks

This paper explores semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information. We first show that exact inferences with SQPNs are NPPP-Complete. We then show that existing qualitative relations in SQPNs (plus probabilistic logic and imprecise assessments) can be dealt effectively through multilinear programming. We then discuss learning: we consider a maximum likelihood method that generates point estimates given a SQPN and empirical data, and we describe a Bayesian-minded method that employs the Imprecise Dirichlet Model to generate set-valued estimates.