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Unique features of a global human ectoparasite identified through sequencing of the bed bug genome
(2016)
The bed bug, Cimex lectularius, has re-established itself as a ubiquitous human ectoparasite throughout much of the world during the past two decades. This global resurgence is likely linked to increased international travel and commerce in addition to widespread insecticide resistance. Analyses of the C. lectularius sequenced genome (650 Mb) and 14,220 predicted protein-coding genes provide a comprehensive representation of genes that are linked to traumatic insemination, a reduced chemosensory repertoire of genes related to obligate hematophagy, host–symbiont interactions, and several mechanisms of insecticide resistance. In addition, we document the presence of multiple putative lateral gene transfer events. Genome sequencing and annotation establish a solid foundation for future research on mechanisms of insecticide resistance, human–bed bug and symbiont–bed bug associations, and unique features of bed bug biology that contribute to the unprecedented success of C. lectularius as a human ectoparasite.
We investigate the theoretical impact of including two empirically-grounded insights in a dynamic life cycle portfolio choice model. The first is to recognize that, when managing their own financial wealth, investors incur opportunity costs in terms of current and future human capital accumulation, particularly if human capital is acquired via learning by doing. The second is that we incorporate age-varying efficiency patterns in financial decisionmaking. Both enhancements produce inactivity in portfolio adjustment patterns consistent with empirical evidence. We also analyze individuals’ optimal choice between self-managing their wealth versus delegating the task to a financial advisor. Delegation proves most valuable to the young and the old. Our calibrated model quantifies welfare gains from including investment time and money costs, as well as delegation, in a life cycle setting.
Background/Aims: Hepatocellular carcinoma (HCC) represents the second most common cause of cancer-related deaths worldwide, not least due to its high chemoresistance. The long non-coding RNA nuclear paraspeckle assembly transcript 1 (NEAT1), localised in nuclear paraspeckles, has been shown to enhance chemoresistance in several cancer types. Since data on NEAT1 in HCC chemosensitivity are completely lacking and chemoresistance is linked to poor prognosis, we aimed to study NEAT1 expression in HCC chemoresistance and its link to HCC prognosis.
Methods: NEAT1 expression was determined in either sensitive, or sorafenib, or doxorubicin resistant HepG2, PLC/PRF/5, and Huh7 cells by qPCR. Paraspeckles were detected by immunostaining of paraspeckle component 1 (PSPC1) in cell culture and in a cohort of HCC patients. PSPC1 expression was correlated with clinical data. The expression of transcript variants of NEAT1 and transcripts encoding the paraspeckle-associated proteins was analysed in the TCGA liver cancer data set.
Results: NEAT1 was overexpressed in all three sorafenib and doxorubicin resistant cell lines. Paraspeckles were present in all chemoresistant cells, whereas no signal was detected in the sensitive cells. Expression of NEAT1 transcripts as well as transcripts encoding PSPC1, NONO, and RBM14 was increased in tumour tissue. Expression of PSPC1, NONO, and RBM14 transcripts was significantly associated with poor survival, whereas NEAT1 expression was not. Immunohistochemical analysis revealed that nuclear and cytoplasmic PSPC1-positivity was significantly associated with shorter overall survival of HCC patients.
Conclusion: Our data show an induction of NEAT1 in HCC chemoresistance and a high correlation of transcripts encoding paraspeckle-associated proteins with poor survival in HCC. Therefore, NEAT1, PSPC1, NONO, and RBM14 might be promising targets for novel HCC therapies, and the paraspeckle-associated proteins might be clinical markers and predictors for poor survival in HCC.
Atmospheric aerosols and their effect on clouds are thought to be important for anthropogenic radiative forcing of the climate, yet remain poorly understood1. Globally, around half of cloud condensation nuclei originate from nucleation of atmospheric vapours2. It is thought that sulfuric acid is essential to initiate most particle formation in the atmosphere3,4, and that ions have a relatively minor role5. Some laboratory studies, however, have reported organic particle formation without the intentional addition of sulfuric acid, although contamination could not be excluded6,7. Here we present evidence for the formation of aerosol particles from highly oxidized biogenic vapours in the absence of sulfuric acid in a large chamber under atmospheric conditions. The highly oxygenated molecules (HOMs) are produced by ozonolysis of α-pinene. We find that ions from Galactic cosmic rays increase the nucleation rate by one to two orders of magnitude compared with neutral nucleation. Our experimental findings are supported by quantum chemical calculations of the cluster binding energies of representative HOMs. Ion-induced nucleation of pure organic particles constitutes a potentially widespread source of aerosol particles in terrestrial environments with low sulfuric acid pollution.
About half of present-day cloud condensation nuclei originate from atmospheric nucleation, frequently appearing as a burst of new particles near midday1. Atmospheric observations show that the growth rate of new particles often accelerates when the diameter of the particles is between one and ten nanometres2,3. In this critical size range, new particles are most likely to be lost by coagulation with pre-existing particles4, thereby failing to form new cloud condensation nuclei that are typically 50 to 100 nanometres across. Sulfuric acid vapour is often involved in nucleation but is too scarce to explain most subsequent growth5,6, leaving organic vapours as the most plausible alternative, at least in the planetary boundary layer7,8,9,10. Although recent studies11,12,13 predict that low-volatility organic vapours contribute during initial growth, direct evidence has been lacking. The accelerating growth may result from increased photolytic production of condensable organic species in the afternoon2, and the presence of a possible Kelvin (curvature) effect, which inhibits organic vapour condensation on the smallest particles (the nano-Köhler theory)2,14, has so far remained ambiguous. Here we present experiments performed in a large chamber under atmospheric conditions that investigate the role of organic vapours in the initial growth of nucleated organic particles in the absence of inorganic acids and bases such as sulfuric acid or ammonia and amines, respectively. Using data from the same set of experiments, it has been shown15 that organic vapours alone can drive nucleation. We focus on the growth of nucleated particles and find that the organic vapours that drive initial growth have extremely low volatilities (saturation concentration less than 10−4.5 micrograms per cubic metre). As the particles increase in size and the Kelvin barrier falls, subsequent growth is primarily due to more abundant organic vapours of slightly higher volatility (saturation concentrations of 10−4.5 to 10−0.5 micrograms per cubic metre). We present a particle growth model that quantitatively reproduces our measurements. Furthermore, we implement a parameterization of the first steps of growth in a global aerosol model and find that concentrations of atmospheric cloud concentration nuclei can change substantially in response, that is, by up to 50 per cent in comparison with previously assumed growth rate parameterizations.
The growth of freshly formed aerosol particles can be the bottleneck in their survival to cloud condensation nuclei. It is therefore crucial to understand how particles grow in the atmosphere. Insufficient experimental data has impeded a profound understanding of nano-particle growth under atmospheric conditions. Here we study nano-particle growth in the CLOUD (Cosmics Leaving OUtdoors Droplets) chamber, starting from the formation of molecular clusters. We present measured growth rates at sub-3 nm sizes with different atmospherically relevant concentrations of sulphuric acid, water, ammonia and dimethylamine. We find that atmospheric ions and small acid-base clusters, which are not generally accounted for in the measurement of sulphuric acid vapour, can participate in the growth process, leading to enhanced growth rates. The availability of compounds capable of stabilizing sulphuric acid clusters governs the magnitude of these effects and thus the exact growth mechanism. We bring these observations into a coherent framework and discuss their significance in the atmosphere.
While it is apparent that rare variation can play an important role in the genetic architecture of autism spectrum disorders (ASDs), the contribution of common variation to the risk of developing ASD is less clear. To produce a more comprehensive picture, we report Stage 2 of the Autism Genome Project genome-wide association study, adding 1301 ASD families and bringing the total to 2705 families analysed (Stages 1 and 2). In addition to evaluating the association of individual single nucleotide polymorphisms (SNPs), we also sought evidence that common variants, en masse, might affect the risk. Despite genotyping over a million SNPs covering the genome, no single SNP shows significant association with ASD or selected phenotypes at a genome-wide level. The SNP that achieves the smallest P-value from secondary analyses is rs1718101. It falls in CNTNAP2, a gene previously implicated in susceptibility for ASD. This SNP also shows modest association with age of word/phrase acquisition in ASD subjects, of interest because features of language development are also associated with other variation in CNTNAP2. In contrast, allele scores derived from the transmission of common alleles to Stage 1 cases significantly predict case status in the independent Stage 2 sample. Despite being significant, the variance explained by these allele scores was small (Vm< 1%). Based on results from individual SNPs and their en masse effect on risk, as inferred from the allele score results, it is reasonable to conclude that common variants affect the risk for ASD but their individual effects are modest.
The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen’s quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model’s Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.
Background: Bipolar disorder is associated with circadian disruption and a high risk of suicidal behavior. In a previous exploratory study of patients with bipolar I disorder, we found that a history of suicide attempts was associated with differences between winter and summer levels of solar insolation. The purpose of this study was to confirm this finding using international data from 42% more collection sites and 25% more countries. Methods: Data analyzed were from 71 prior and new collection sites in 40 countries at a wide range of latitudes. The analysis included 4876 patients with bipolar I disorder, 45% more data than previously analyzed. Of the patients, 1496 (30.7%) had a history of suicide attempt. Solar insolation data, the amount of the sun’s electromagnetic energy striking the surface of the earth, was obtained for each onset location (479 locations in 64 countries). Results: This analysis confirmed the results of the exploratory study with the same best model and slightly better statistical significance. There was a significant inverse association between a history of suicide attempts and the ratio of mean winter insolation to mean summer insolation (mean winter insolation/mean summer insolation). This ratio is largest near the equator which has little change in solar insolation over the year, and smallest near the poles where the winter insolation is very small compared to the summer insolation. Other variables in the model associated with an increased risk of suicide attempts were a history of alcohol or substance abuse, female gender, and younger birth cohort. The winter/summer insolation ratio was also replaced with the ratio of minimum mean monthly insolation to the maximum mean monthly insolation to accommodate insolation patterns in the tropics, and nearly identical results were found. All estimated coefficients were significant at p < 0.01. Conclusion: A large change in solar insolation, both between winter and summer and between the minimum and maximum monthly values, may increase the risk of suicide attempts in bipolar I disorder. With frequent circadian rhythm dysfunction and suicidal behavior in bipolar disorder, greater understanding of the optimal roles of daylight and electric lighting in circadian entrainment is needed.