The transition of reported pain in different body regions – a one-year follow-up study

BACKGROUND: The course of pain at a specific region such as the lower back has previously been shown as well as for generalized pain. However we have not found any report on the course of pain from various different specific regions. The aim of this investigation was to study the one-year transition of reported pain in different body locations. METHODS: From a general population 14555 men and women, 46–68 years, responded to an extensive health questionnaire including the standardized Nordic questionnaire. The population represented 27% of the total population within the age group in Malmö, Sweden. At the one year follow-up 12607 responded to the questionnaire, yielding a response rate of 87%. The one year prevalence of long-lasting pain and the pattern of pain reporting from different regions were studied for men and women. RESULTS: The one-year prevalence of long-lasting neck pain was 14% (95% CI 13–15) among men and 25% (95% CI 24–26) among women at baseline and 15% (95% CI 14–16) for the men and 23% (95% CI 22–24) for the women at follow-up. Of those reporting neck pain "all the time" at baseline, 48% of the men and 54% of the women also reported neck pain "all the time" at the one-year follow-up. At the follow-up neck pain was reported as present "often" by 43% of the men and 47% of the women who reported neck pain "often" at baseline. Similar transition pattern were found for neck, shoulders, elbow/wrist/hand and lower back symptoms, as well as consistent prevalence rates. CONCLUSION: The one-year transition pattern of reported pain was similar in different body regions and among men and women. Furthermore the prevalence rates of long-lasting pain in the population were consistent at baseline and the follow-up. The findings of similar transition patterns support the interpretation of long-lasting pain as a generalized phenomenon rather than attributed to specific exposure. This may have implications for future pain research

Pain in the lumbar, thoracic or cervical regions: do age and gender matter? A population-based study of 34,902 Danish twins 20–71 years of age

Background. It is unclear to what extent spinal pain varies between genders and in relation to age. It was the purpose of this study to describe the self-reported prevalence of 1) pain ever and pain in the past year in each of the three spinal regions, 2) the duration of such pain over the past year, 3) pain radiating from these areas, and 4) pain in one, two or three areas. In addition, 5) to investigate if spinal pain reporting is affected by gender and 6) to see if it increases gradually with increasing age. Method. A cross-sectional survey was conducted in 2002 on 34,902 twin individuals, aged 20 to 71 years, representative of the general Danish population. Identical questions on pain were asked for the lumbar, thoracic and cervical regions. Results. Low back pain was most common, followed by neck pain with thoracic pain being least common. Pain for at least 30 days in the past year was reported by 12%, 10%, and 4%, respectively. The one-yr prevalence estimates of radiating pain were 22% (leg), 16% (arm), and 5% (chest). Pain in one area only last year was reported by 20%, followed by two (13%) and three areas (8%). Women were always more likely to report pain and they were also more likely to have had pain for longer periods. Lumbar and cervical pain peaked somewhat around the middle years but the curves were flatter for thoracic pain. Similar patterns were noted for radiating pain. Older people did not have pain in a larger number of areas but their pain lasted longer. Conclusion. Pain reported for and from the lumbar and cervical spines was found to be relatively common whereas pain in the thoracic spine and pain radiating into the chest was much less common. Women were, generally, more likely to report pain than men. The prevalence estimates changed surprisingly little over age and were certainly not more common in the oldest groups, although the pain was reported as more long-lasting in the older group

The beta2 integrin CD11c distinguishes a subset of cytotoxic pulmonary T cells with potent antiviral effects in vitro and in vivo

BACKGROUND: The integrin CD11c is known as a marker for dendritic cells and has recently been described on T cells following lymphotropic choriomeningitis virus infection, a systemic infection affecting a multitude of organs. Here, we characterise CD11c bearing T cells in a murine model of localised pulmonary infection with respiratory syncytial virus (RSV). METHODS: Mice were infected intranasally with RSV and expression of β2 integrins and T lymphocyte activation markers were monitored by flow cytometry. On day 8 post RSV infection CD11c(+ )CD8(+ )and CD11c(- )CD8(+ )T cells were assessed for cytokine production, cytotoxic activity and migration. Expression of CD11c mRNA in CD8(+ )T cells was assessed by quantitative PCR. RESULTS: Following RSV infection CD11c(+ )CD8(+ )T cells were detectable in the lung from day 4 onwards and accounted for 45.9 ± 4.8% of CD8(+ )T cells on day 8 post infection, while only few such cells were present in mediastinal lymph nodes, spleen and blood. While CD11c was virtually absent from CD8(+ )T cells in the absence of RSV infection, its mRNA was expressed in CD8(+ )T cells of both naïve and RSV infected mice. CD11c(+), but not CD11c(-), CD8(+ )T cells showed signs of recent activation, including up-regulation of CD11a and expression of CD11b and CD69 and were recruited preferentially to the lung. In addition, CD11c(+ )CD8(+ )T cells were the major subset responsible for IFNγ production, induction of target cell apoptosis in vitro and reduction of viral titres in vivo. CONCLUSION: CD11c is a useful marker for detection and isolation of pulmonary antiviral cytotoxic T cells following RSV infection. It identifies a subset of activated, virus-specific, cytotoxic T cells that exhibit potent antiviral effects in vivo

Levels and Patterns of Nucleotide Variation in Domestication QTL Regions on Rice Chromosome 3 Suggest Lineage-Specific Selection

Oryza sativa or Asian cultivated rice is one of the major cereal grass species domesticated for human food use during the Neolithic. Domestication of this species from the wild grass Oryza rufipogon was accompanied by changes in several traits, including seed shattering, percent seed set, tillering, grain weight, and flowering time. Quantitative trait locus (QTL) mapping has identified three genomic regions in chromosome 3 that appear to be associated with these traits. We would like to study whether these regions show signatures of selection and whether the same genetic basis underlies the domestication of different rice varieties. Fragments of 88 genes spanning these three genomic regions were sequenced from multiple accessions of two major varietal groups in O. sativa—indica and tropical japonica—as well as the ancestral wild rice species O. rufipogon. In tropical japonica, the levels of nucleotide variation in these three QTL regions are significantly lower compared to genome-wide levels, and coalescent simulations based on a complex demographic model of rice domestication indicate that these patterns are consistent with selection. In contrast, there is no significant reduction in nucleotide diversity in the homologous regions in indica rice. These results suggest that there are differences in the genetic and selective basis for domestication between these two Asian rice varietal groups

Characteristics of the memory sources of dreams: A new version of the content-matching paradigm to take mundane and remote memories into account

Several studies have demonstrated that dream content is related to the waking life of the dreamer. However, the characteristics of the memory sources incorporated into dreams are still unclear. We designed a new protocol to investigate remote memories and memories of trivial experiences, both relatively unexplored in dream content until now. Upon awakening, for 7 days, participants identified the waking life elements (WLEs) related to their dream content and characterized them and their dream content on several scales to assess notably emotional valence. Thanks to this procedure, they could report WLEs from the whole lifespan, and mundane ones before they had been forgotten. Participants (N = 40, 14 males, age = 25.2 ± 7.6) reported 6.2 ± 2.0 dreams on average. For each participant, 83.4% ± 17.8 of the dream reports were related to one or more WLEs. Among all the WLEs incorporated into dreams dated by the participants (79.3 ± 19%), 40.2 ± 30% happened the day before the dream, 26.1 ± 26% the month before (the day before excluded), 15.8 ± 21% the year before the dream (the month before excluded), and 17.9 ± 24% happened more than one year before the dream. As could be expected from previous studies, the majority of the WLEs incorporated into dreams were scored as important by the dreamers. However, this was not true for incorporated WLEs dating from the day before the dream. In agreement with Freud’s observations, the majority of the day residues were scored as mundane. Finally, for both positive and negative WLEs incorporated into dreams, the dreamt version of the WLE was rated as emotionally less intense than the original WLE. This result, showing that dreams tend to attenuate the emotional tone of waking-life memories towards a more neutral one, argues in favor of the emotional regulation hypothesis of dreaming

Pigmentation plasticity enhances crypsis in larval newts: Associated metabolic cost and background choice behaviour

In heterogeneous environments, the capacity for colour change can be a valuable adaptation enhancing crypsis against predators. Alternatively, organisms might achieve concealment by evolving preferences for backgrounds that match their visual traits, thus avoiding the costs of plasticity. Here we examined the degree of plasticity in pigmentation of newt larvae (Lissotriton boscai) in relation to predation risk. Furthermore, we tested for associated metabolic costs and pigmentation-dependent background choice behaviour. Newt larvae expressed substantial changes in pigmentation so that light, high-reflecting environment induced depigmentation whereas dark, low-reflecting environment induced pigmentation in just three days of exposure. Induced pigmentation was completely reversible upon switching microhabitats. Predator cues, however, did not enhance cryptic phenotypes, suggesting that environmental albedo induces changes in pigmentation improving concealment regardless of the perceived predation risk. Metabolic rate was higher in heavily pigmented individuals from dark environments, indicating a high energetic requirement of pigmentation that could impose a constraint to larval camouflage in dim habitats. Finally, we found partial evidence for larvae selecting backgrounds matching their induced phenotypes. However, in the presence of predator cues, larvae increased the time spent in light environments, which may reflect a escape response towards shallow waters rather than an attempt at increasing crypsisFinancial support was provided by the Spanish Ministry of Science and Innovation (MICINN), Grant CGL2012-40044 to IGM, and by the Universidad Autónoma de Madrid, Short Stay Grant to NPC. Additional financial support was provided by the MICINN, Grant CGL2015-68670-R to NP

Computational Methods for Protein Identification from Mass Spectrometry Data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology

A New Method for Species Identification via Protein-Coding and Non-Coding DNA Barcodes by Combining Machine Learning with Bioinformatic Methods

Species identification via DNA barcodes is contributing greatly to current bioinventory efforts. The initial, and widely accepted, proposal was to use the protein-coding cytochrome c oxidase subunit I (COI) region as the standard barcode for animals, but recently non-coding internal transcribed spacer (ITS) genes have been proposed as candidate barcodes for both animals and plants. However, achieving a robust alignment for non-coding regions can be problematic. Here we propose two new methods (DV-RBF and FJ-RBF) to address this issue for species assignment by both coding and non-coding sequences that take advantage of the power of machine learning and bioinformatics. We demonstrate the value of the new methods with four empirical datasets, two representing typical protein-coding COI barcode datasets (neotropical bats and marine fish) and two representing non-coding ITS barcodes (rust fungi and brown algae). Using two random sub-sampling approaches, we demonstrate that the new methods significantly outperformed existing Neighbor-joining (NJ) and Maximum likelihood (ML) methods for both coding and non-coding barcodes when there was complete species coverage in the reference dataset. The new methods also out-performed NJ and ML methods for non-coding sequences in circumstances of potentially incomplete species coverage, although then the NJ and ML methods performed slightly better than the new methods for protein-coding barcodes. A 100% success rate of species identification was achieved with the two new methods for 4,122 bat queries and 5,134 fish queries using COI barcodes, with 95% confidence intervals (CI) of 99.75–100%. The new methods also obtained a 96.29% success rate (95%CI: 91.62–98.40%) for 484 rust fungi queries and a 98.50% success rate (95%CI: 96.60–99.37%) for 1094 brown algae queries, both using ITS barcodes