On two-dimensional Bessel functions

The general properties of two-dimensional generalized Bessel functions are discussed. Various asymptotic approximations are derived and applied to analyze the basic structure of the two-dimensional Bessel functions as well as their nodal lines.Comment: 25 pages, 17 figure

Orientia, rickettsia, and leptospira pathogens as causes of CNS infections in Laos: a prospective study

Background Scrub typhus (caused by Orientia tsutsugamushi), murine typhus (caused by Rickettsia typhi), and leptospirosis are common causes of febrile illness in Asia; meningitis and meningoencephalitis are severe complications. However, scarce data exist for the burden of these pathogens in patients with CNS disease in endemic countries. Laos is representative of vast economically poor rural areas in Asia with little medical information to guide public health policy. We assessed whether these pathogens are important causes of CNS infections in Laos. Methods Between Jan 10, 2003, and Nov 25, 2011, we enrolled 1112 consecutive patients of all ages admitted with CNS symptoms or signs requiring a lumbar puncture at Mahosot Hospital, Vientiane, Laos. Microbiological examinations (culture, PCR, and serology) targeted so-called conventional bacterial infections (Streptococcus pneumoniae, Neisseria meningitidis, Haemophilus infl uenzae, S suis) and O tsutsugamushi, Rickettsia typhi/Rickettsia spp, and Leptospira spp infections in blood or cerebrospinal fl uid (CSF). We analysed and compared causes and clinical and CSF characteristics between patient groups. Findings 1051 (95%) of 1112 patients who presented had CSF available for analysis, of whom 254 (24%) had a CNS infection attributable to a bacterial or fungal pathogen. 90 (35%) of these 254 infections were caused by O tsutsugamushi, R typhi/Rickettsia spp, or Leptospira spp. These pathogens were signifi cantly more frequent than conventional bacterial infections (90/1051 [9%] vs 42/1051 [4%]; p<0·0001) by use of conservative diagnostic defi nitions. CNS infections had a high mortality (236/876 [27%]), with 18% (13/71) for R typhi/Rickettsia spp, O tsutsugamushi, and Leptospira spp combined, and 33% (13/39) for conventional bacterial infections (p=0·076). Interpretation Our data suggest that R typhi/Rickettsia spp, O tsutsugamushi, and Leptospira spp infections are important causes of CNS infections in Laos. Antibiotics, such as tetracyclines, needed for the treatment of murine typhus and scrub typhus, are not routinely advised for empirical treatment of CNS infections. These severely neglected infections represent a potentially large proportion of treatable CNS disease burden across vast endemic areas and need more attention

Repurposing rapid diagnostic tests to detect falsified vaccines in supply chains

Substandard (including degraded) and falsified (SF) vaccines are a relatively neglected issue with serious global implications for public health. This has been highlighted during the rapid and widespread rollout of COVID-19 vaccines. There has been increasing interest in devices to screen for SF non-vaccine medicines including tablets and capsules to empower inspectors and standardise surveillance. However, there has been very limited published research focussed on repurposing or developing new devices for screening for SF vaccines. To our knowledge, rapid diagnostic tests (RDTs) have not been used for this purpose but have important potential for detecting falsified vaccines. We performed a proof-in-principle study to investigate their diagnostic accuracy using a diverse range of RDT-vaccine/falsified vaccine surrogate pairs. In an initial assessment, we demonstrated the utility of four RDTs in detecting seven vaccines. Subsequently, the four RDTs were evaluated by three blinded assessors with seven vaccines and four falsified vaccines surrogates. The results provide preliminary data that RDTs could be used by multiple international organisations, national medicines regulators and vaccine manufacturers/distributors to screen for falsified vaccines in supply chains, aligned with the WHO global ‘Prevent, Detect and Respond’ strategy

An elitist quantum-inspired evolutionary algorithm for the flexible job-shop scheduling problem

The flexible job shop scheduling problem (FJSP) is vital to manufacturers especially in today’s constantly changing environment. It is a strongly NP-hard problem and therefore metaheuristics or heuristics are usually pursued to solve it. Most of the existing metaheuristics and heuristics, however, have low efficiency in convergence speed. To overcome this drawback, this paper develops an elitist quantum-inspired evolutionary algorithm. The algorithm aims to minimise the maximum completion time (makespan). It performs a global search with the quantum-inspired evolutionary algorithm and a local search with a method that is inspired by the motion mechanism of the electrons around an atomic nucleus. Three novel algorithms are proposed and their effect on the whole search is discussed. The elitist strategy is adopted to prevent the optimal solution from being destroyed during the evolutionary process. The results show that the proposed algorithm outperforms the best-known algorithms for FJSPs on most of the FJSP benchmarks

A new method for 2D gel spot alignment: application to the analysis of large sample sets in clinical proteomics

<p>Abstract</p> <p>Background</p> <p>In current comparative proteomics studies, the large number of images generated by 2D gels is currently compared using spot matching algorithms. Unfortunately, differences in gel migration and sample variability make efficient spot alignment very difficult to obtain, and, as consequence most of the software alignments return noisy gel matching which needs to be manually adjusted by the user.</p> <p>Results</p> <p>We present Sili2DGel an algorithm for automatic spot alignment that uses data from recursive gel matching and returns meaningful Spot Alignment Positions (SAP) for a given set of gels. In the algorithm, the data are represented by a graph and SAP by specific subgraphs. The results are returned under various forms (clickable synthetic gel, text file, etc.). We have applied Sili2DGel to study the variability of the urinary proteome from 20 healthy subjects.</p> <p>Conclusion</p> <p>Sili2DGel performs noiseless automatic spot alignment for variability studies (as well as classical differential expression studies) of biological samples. It is very useful for typical clinical proteomic studies with large number of experiments.</p

Socioeconomic status and health in the second half of life: findings from the German Ageing Survey

This study examined social inequalities in health in the second half of life. Data for empirical analyses came from the second wave of the German Ageing Survey (DEAS), an ongoing population-based, representative study of community dwelling persons living in Germany, aged 40–85 years (N = 2,787). Three different indicators for socioeconomic status (SES; education, income, financial assets as an indicator for wealth) and health (physical, functional and subjective health) were employed. It could be shown that SES was related to health in the second half of life: Less advantaged persons between 40 and 85 years of age had worse health than more advantaged persons. Age gradients varied between status indicators and health dimensions, but in general social inequalities in health were rather stable or increasing over age. The latter was observed for wealth-related absolute inequalities in physical and functional health. Only income-related differences in subjective health decreased at higher ages. The amount of social inequality in health as well as its development over age did not vary by gender and place of residence (East or West Germany). These results suggest that, in Germany, the influence of SES on health remains important throughout the second half of life

A low mortality, high morbidity Reduced Intensity Status Epilepticus (RISE) model of epilepsy and epileptogenesis in the rat

Animal models of acquired epilepsies aim to provide researchers with tools for use in understanding the processes underlying the acquisition, development and establishment of the disorder. Typically, following a systemic or local insult, vulnerable brain regions undergo a process leading to the development, over time, of spontaneous recurrent seizures. Many such models make use of a period of intense seizure activity or status epilepticus, and this may be associated with high mortality and/or global damage to large areas of the brain. These undesirable elements have driven improvements in the design of chronic epilepsy models, for example the lithium-pilocarpine epileptogenesis model. Here, we present an optimised model of chronic epilepsy that reduces mortality to 1% whilst retaining features of high epileptogenicity and development of spontaneous seizures. Using local field potential recordings from hippocampus in vitro as a probe, we show that the model does not result in significant loss of neuronal network function in area CA3 and, instead, subtle alterations in network dynamics appear during a process of epileptogenesis, which eventually leads to a chronic seizure state. The model’s features of very low mortality and high morbidity in the absence of global neuronal damage offer the chance to explore the processes underlying epileptogenesis in detail, in a population of animals not defined by their resistance to seizures, whilst acknowledging and being driven by the 3Rs (Replacement, Refinement and Reduction of animal use in scientific procedures) principles

Modularization of biochemical networks based on classification of Petri net t-invariants

<p>Abstract</p> <p>Background</p> <p>Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.</p> <p>With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system.</p> <p>Methods</p> <p>Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied.</p> <p>Results</p> <p>We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in <it>Saccharomyces cerevisiae</it>) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability.</p> <p>Conclusion</p> <p>We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.</p

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

14 p.Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change