Shadowed Fading in Indoor Off-Body Communications Channels: A Statistical Characterization using the κ − µ / Gamma Composite Fading Model

This paper investigates the characteristics of the shadowed fading observed in off-body communications channels at 5.8 GHz. This is realized with the aid of the κ-μ/gamma composite fading model, which assumes that the transmitted signal undergoes κ-μ fading, which is subject to multiplicative shadowing. Based on this, the total power of the multipath components, including both the dominant and scattered components, is subject to non-negligible variations that follow the gamma distribution. For this model, we present an integral form of the probability density function (PDF) as well as important analytic expressions for the PDF, cumulative distribution function, moments, and moment generating function. In the case of indoor off-body communications, the corresponding measurements were carried out in the context of four explicit individual scenarios, namely: line of sight (LOS), non-LOS walking, rotational, and random movements. The measurements were repeated within three different indoor environments and considered three different hypothetical body worn node locations. With the aid of these results, the parameters for the κ-μ/gamma composite fading model were estimated and analyzed extensively. Interestingly, for the majority of the indoor environments and movement scenarios, the parameter estimates suggested that dominant signal components existed even when the direct signal path was obscured by the test subject's body. In addition, it is shown that the κ-μ/gamma composite fading model provides an adequate fit to the fading effects involved in off-body communications channels. Using the Kullback-Leibler divergence, we have also compared our results with another recently proposed shadowed fading model, namely, the κ-μ/lognormal LOS shadowed fading model. It was found that the κ-μ/gamma composite fading model provided a better fit for the majority of the scenarios considered in this paper

Impaired in vitro Interferon-gamma production in patients with visceral leishmaniasis is improved by inhibition of PD1/PDL-1 ligation

Visceral leishmaniasis (VL) is a neglected tropical disease that causes substantial morbidity and mortality and is a growing health problem in Ethiopia, where this study took place. Most individuals infected with Leishmania donovani parasites will stay asymptomatic, but some develop VL that, if left untreated, is almost always fatal. This stage of the disease is associated with a profound immunosuppression, characterised by impaired production of Interferonγ (IFNγ), a cytokine that plays a key role in the control of Leishmania parasites, and high expression levels of an inhibitory receptor, programmed cell death 1 (PD1) on CD4+ T cells. Here, we tested the contribution of the interaction between the immune checkpoint PD1 and its ligand PDL-1 on the impaired production of IFNγ in VL patients. Our results show that in the blood of VL patients, not only CD4+, but also CD8+ T cells express high levels of PD1 at the time of VL diagnosis. Next, we identified PDL-1 expression on different monocyte subsets and neutrophils and show that PDL-1 levels were significantly increased in VL patients. PD1/PDL-1 inhibition resulted in significantly increased production of IFNγ, suggesting that therapy using immune checkpoint inhibitors might improve disease control in these patients

Pneumococcal carriage in sub-Saharan Africa--a systematic review.

BACKGROUND: Pneumococcal epidemiology varies geographically and few data are available from the African continent. We assess pneumococcal carriage from studies conducted in sub-Saharan Africa (sSA) before and after the pneumococcal conjugate vaccine (PCV) era. METHODS: A search for pneumococcal carriage studies published before 2012 was conducted to describe carriage in sSA. The review also describes pneumococcal serotypes and assesses the impact of vaccination on carriage in this region. RESULTS: Fifty-seven studies were included in this review with the majority (40.3%) from South Africa. There was considerable variability in the prevalence of carriage between studies (I-squared statistic = 99%). Carriage was higher in children and decreased with increasing age, 63.2% (95% CI: 55.6-70.8) in children less than 5 years, 42.6% (95% CI: 29.9-55.4) in children 5-15 years and 28.0% (95% CI: 19.0-37.0) in adults older than 15 years. There was no difference in the prevalence of carriage between males and females in 9/11 studies. Serotypes 19F, 6B, 6A, 14 and 23F were the five most common isolates. A meta-analysis of four randomized trials of PCV vaccination in children aged 9-24 months showed that carriage of vaccine type (VT) serotypes decreased with PCV vaccination; however, overall carriage remained the same because of a concomitant increase in non-vaccine type (NVT) serotypes. CONCLUSION: Pneumococcal carriage is generally high in the African continent, particularly in young children. The five most common serotypes in sSA are among the top seven serotypes that cause invasive pneumococcal disease in children globally. These serotypes are covered by the two PCVs recommended for routine childhood immunization by the WHO. The distribution of serotypes found in the nasopharynx is altered by PCV vaccination

Identification of a region required for TSC1 stability by functional analysis of TSC1 missense mutations found in individuals with tuberous sclerosis complex

Background: Tuberous sclerosis complex (TSC) is an autosomal dominant disorder characterised by the development of hamartomas in a variety of organs and tissues. The disease is caused by mutations in either the TSC1 gene on chromosome 9q34, or the TSC2 gene on chromosome 16p13.3. The TSC1 and TSC2 gene products, TSC1 and TSC2, form a protein complex that inhibits signal transduction to the downstream effectors of the mammalian target of rapamycin (mTOR). Recently it has been shown that missense mutations to the TSC1 gene can cause TSC. Methods: We have used in vitro biochemical assays to investigate the effects on TSC1 function of TSC1 missense variants submitted to the Leiden Open Variation Database. Results: We identified specific substitutions between amino acids 50 and 190 in the N-terminal region of TSC1 that result in reduced steady state levels of the protein and lead to increased mTOR signalling. Conclusion: Our results suggest that amino acid residues within the N-terminal region of TSC1 are important for TSC1 function and for maintaining the activity of the TSC1-TSC2 complex

Improving the prediction of disease-related variants using protein three-dimensional structure

Background: Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability. Non-synonymous SNPs occurring in coding regions result in single amino acid polymorphisms (SAPs) that may affect protein function and lead to pathology. Several methods attempt to estimate the impact of SAPs using different sources of information. Although sequence-based predictors have shown good performance, the quality of these predictions can be further improved by introducing new features derived from three-dimensional protein structures.Results: In this paper, we present a structure-based machine learning approach for predicting disease-related SAPs. We have trained a Support Vector Machine (SVM) on a set of 3,342 disease-related mutations and 1,644 neutral polymorphisms from 784 protein chains. We use SVM input features derived from the protein's sequence, structure, and function. After dataset balancing, the structure-based method (SVM-3D) reaches an overall accuracy of 85%, a correlation coefficient of 0.70, and an area under the receiving operating characteristic curve (AUC) of 0.92. When compared with a similar sequence-based predictor, SVM-3D results in an increase of the overall accuracy and AUC by 3%, and correlation coefficient by 0.06. The robustness of this improvement has been tested on different datasets and in all the cases SVM-3D performs better than previously developed methods even when compared with PolyPhen2, which explicitly considers in input protein structure information.Conclusion: This work demonstrates that structural information can increase the accuracy of disease-related SAPs identification. Our results also quantify the magnitude of improvement on a large dataset. This improvement is in agreement with previously observed results, where structure information enhanced the prediction of protein stability changes upon mutation. Although the structural information contained in the Protein Data Bank is limiting the application and the performance of our structure-based method, we expect that SVM-3D will result in higher accuracy when more structural date become available. \ua9 2011 Capriotti; licensee BioMed Central Ltd

Cataloging Coding Sequence Variations in Human Genome Databases

BACKGROUND: With the recent growth of information on sequence variations in the human genome, predictions regarding the functional effects and relevance to disease phenotypes of coding sequence variations are becoming increasingly important. The aims of this study were to catalog protein-coding sequence variations (CVs) occurring in genetic variation databases and to use bioinformatic programs to analyze CVs. In addition, we aim to provide insight into the functionality of the reference databases. METHODOLOGY AND FINDINGS: To catalog CVs on a genome-wide scale with regard to protein function and disease, we investigated three representative databases; the Human Gene Mutation Database (HGMD), the Single Nucleotide Polymorphisms database (dbSNP), and the Haplotype Map (HapMap). Using these three databases, we analyzed CVs at the protein function level with bioinformatic programs. We proposed a combinatorial approach using the Support Vector Machine (SVM) to increase the performance of the prediction programs. By cataloging the coding sequence variations using these databases, we found that 4.36% of CVs from HGMD are concurrently registered in dbSNP (8.11% of CVs from dbSNP are concurrent in HGMD). The pattern of substitutions and functional consequences predicted by three bioinformatic programs was significantly different among concurrent CVs, and CVs occurring solely in HGMD or in dbSNP. The experimental results showed that the proposed SVM combination noticeably outperformed the individual prediction programs. CONCLUSIONS: This is the first study to compare human sequence variations in HGMD, dbSNP and HapMap at the genome-wide level. We found that a significant proportion of CVs in HGMD and dbSNP overlap, and we emphasize the need to use caution when interpreting the phenotypic relevance of these concurrent CVs. Combining bioinformatic programs can be helpful in predicting the functional consequences of CVs because it improved the performance of functional predictions

Identification of a human neonatal immune-metabolic network associated with bacterial infection

Understanding how human neonates respond to infection remains incomplete. Here, a system-level investigation of neonatal systemic responses to infection shows a surprisingly strong but unbalanced homeostatic immune response; developing an elevated set-point of myeloid regulatory signalling and sugar-lipid metabolism with concomitant inhibition of lymphoid responses. Innate immune-negative feedback opposes innate immune activation while suppression of T-cell co-stimulation is coincident with selective upregulation of CD85 co-inhibitory pathways. By deriving modules of co-expressed RNAs, we identify a limited set of networks associated with bacterial infection that exhibit high levels of inter-patient variability. Whereas, by integrating immune and metabolic pathways, we infer a patient-invariant 52-gene-classifier that predicts bacterial infection with high accuracy using a new independent patient population. This is further shown to have predictive value in identifying infection in suspected cases with blood culture-negative tests. Our results lay the foundation for future translation of host pathways in advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis

Computational Models of Classical Conditioning guest editors’ introduction

In the present special issue, the performance of current computational models of classical conditioning was evaluated under three requirements: (1) Models were to be tested against a list of previously agreed-upon phenomena; (2) the parameters were fixed across simulations; and (3) the simulations used to test the models had to be made available. These requirements resulted in three major products: (a) a list of fundamental classical-conditioning results for which there is a consensus about their reliability; (b) the necessary information to evaluate each of the models on the basis of its ordinal successes in accounting for the experimental data; and (c) a repository of computational models ready to generate simulations. We believe that the contents of this issue represent the 2012 state of the art in computational modeling of classical conditioning and provide a way to find promising avenues for future model development

Duplex ventral pancreas

Complete duplication of the ventral pancreatic ductal system in 2 patients is reported. Both patients, during evaluation for recurrent abdominal pain, underwent endoscopic retrograde cholangiopancreatography that revealed typical changes of chronic pancreatitis and pseudocysts confined to 1 ductal system with the other ductal system completely normal. Both ductal systems filled with contrast medium via a common opening at the major papilla. A rudimentary minor papilla was present, but cannulations were unsuccessful. This unusual anomaly of the ventral pancreas with its embryologic basis, diagnosis, and clinical implications is discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48132/1/261_2005_Article_BF01885095.pd