Should Contact Bans Have Been Lifted More in Germany? : A Quantitative Prediction of Its Effects
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ABSTRACT:
SUBMITTER: Donsimoni J
PROVIDER: S-EPMC7337731 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
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Project description:BackgroundGermany's 16 states regulate smoking differently within health protection principles laid down in the federal law. All state smoke-free laws in Germany have undergone at least one change since taking effect.MethodsWe systematically review federal and state laws regulating smoking, as well as petitions, popular initiatives and referenda that aimed at changing statutory smoking bans. Data generated through the systematic review were correlated with state smoking rates.ResultsThe protection from the dangers of secondhand smoke is the primary motive for smoking bans in Germany. The first smoke-free laws affecting smoking in pubs, restaurants and several other public places were introduced in 2007. In 2008, the Federal Constitutional Court of Germany ruled in a leading decision on the smoke-free laws of two states that some common smoking ban exemptions of the introduced smoke-free laws violate the basic right to freely exercise a profession and mandated revisions. All states but Bavaria and Saarland, whose smoking bans were more and less comprehensive than those judged by the constitutional court, respectively, needed to change the smoking ban exemptions to reconcile their smoke-free laws with the constitution. Direct democracy initiatives to change smoking bans were only successful in Bavaria in 2010, but a total of 15 initiatives by citizens' or interest groups attempted to influence non-smokers protection legislation through direct democratic procedures. Early ratification of a smoking ban in a federal state correlates with a higher reduction in the smoking rate from 2005 to 2009 (Spearman's ρ = 0.51, p = 0.04).ConclusionsThe federal government structure and direct democratic participation in smoke-free legislation in Germany has produced a diversity of local smoking bans and exemptions.
| S-EPMC3924468 | biostudies-literature
Project description:There is a need to characterize biomechanical cell-cell interactions, but due to a lack of suitable experimental methods, relevant in vitro experimental data are often masked by cell-substrate interactions. This study describes a novel method to generate partially lifted substrate-free cell sheets that engage primarily in cell-cell interactions, yet are amenable to biological and chemical perturbations and, importantly, mechanical conditioning and characterization. A polydimethylsiloxane (PDMS) mold is used to isolate a patch of cells, and the patch is then enzymatically lifted. The cells outside the mold remain attached, creating a partially lifted cell sheet. This simple yet powerful tool enables the simultaneous examination of lifted and adherent cells. This tool was then deployed to test the hypothesis that the lifted cells would exhibit substantial reinforcement of key cytoskeletal and junctional components at cell-cell contacts, and that such reinforcement would be enhanced by mechanical conditioning. Results demonstrate that the mechanical strength and cohesion of the substrate-free cell sheets strongly depend on the integrity of the actomyosin cytoskeleton and the cell-cell junctional protein plakoglobin. Both actin and plakoglobin are significantly reinforced at junctions with mechanical conditioning. However, total cellular actin is significantly diminished on dissociation from a substrate and does not recover with mechanical conditioning. These results represent a first systematic examination of mechanical conditioning on cells with primarily intercellular interactions.
| S-EPMC4029044 | biostudies-literature
Project description:ObjectiveWith more teeth retained for longer in an ageing population, population-wide periodontal treatment needs may increase. We assessed and projected periodontal treatment needs from 1997 to 2030 in Germany.MethodsPartial-mouth probing-pocket depths (PPDs) from repeated waves (1997, 2005, 2014) of the nationally representative German Oral Health Studies were transformed into full-mouth PPDs via decision-tree-based ensemble-modelling. In line with German healthcare-regulations, teeth with PPD ≥ 4 mm were regarded as needing periodontal treatment. Weighted means were interpolated cross-sectionally by fitting spline-curves and then regressed longitudinally 1997-2030.ResultsIn 1997, younger adults (35-44 years old) had a mean of 7.4 teeth needing treatment (overall 93.8 million teeth); this decreased to 4.8 teeth (47.3 million teeth) in 2014. For 2030, we project 3.2 teeth (33.7 million teeth). In seniors, an increase was recorded (1997: 4.5 teeth, 33.5 million teeth; 2014: 7.5 teeth, 63.4 million teeth); this is expected to continue until 2030 (to 12.2 teeth, 140.8 million teeth). The cumulative number of teeth needing treatment increased from 2000 (355 million) to 2015 (365 million), and will increase further to 2030 (464 million).ConclusionsPopulation-wide periodontal treatment needs may increase until 2030, mainly in the elderly. Concepts for addressing, these growing needs are required.
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Project description:BackgroundPredicting the effect of drug-drug interactions (DDIs) precisely is important for safer and more effective drug co-prescription. Many computational approaches to predict the effect of DDIs have been proposed, with the aim of reducing the effort of identifying these interactions in vivo or in vitro, but room remains for improvement in prediction performance.ResultsIn this study, we propose a novel deep learning model to predict the effect of DDIs more accurately.. The proposed model uses autoencoders and a deep feed-forward network that are trained using the structural similarity profiles (SSP), Gene Ontology (GO) term similarity profiles (GSP), and target gene similarity profiles (TSP) of known drug pairs to predict the pharmacological effects of DDIs. The results show that GSP and TSP increase the prediction accuracy when using SSP alone, and the autoencoder is more effective than PCA for reducing the dimensions of each profile. Our model showed better performance than the existing methods, and identified a number of novel DDIs that are supported by medical databases or existing research.ConclusionsWe present a novel deep learning model for more accurate prediction of DDIs and their effects, which may assist in future research to discover novel DDIs and their pharmacological effects.
| S-EPMC6685287 | biostudies-literature
Project description:We developed a targeted chromosome conformation capture (4C) approach that uses unique molecular identifiers (UMI) to derive high complexity quantitative chromosome contact profiles with controlled signal to noise ratios. We demonstrate that the method improves the sensitivity and specificity for detection of long-range chromosomal interactions, and that it allows the design of interaction screens with predictable statistical power. UMI-4C robustly quantifies contact intensity changes between cell types and conditions, opening the way toward incorporation of long-range interactions in quantitative models of gene regulation. We constructed UMI-4C profiles of 13 different genomic loci (viewpoints) in five different cell lines, in order to study the 3D chromatin contact maps of these selected loci. The coordinates for these viewpoints are: G1p1 chrX:48646542; baitG1_3_5kb chrX:48641393; bait_50kb chrX:48595987; bait_165kb chrX:48476525; ANK1 chr8:41654693; hbb_3HS chr11:5221346; hbb_HBB chr11:5248714; hbb_HBBP1_G1 chr11:5266532; HBB_HBE chr11:5292159; HBB_HS2 chr11:5301345; HBB_HS3 chr11:5306690; HBB_HS5 chr11:5313539; HBB_HBD chr11:5256597
2016-06-15 | E-GEOD-76763 | biostudies-arrayexpress
Project description:We recently showed that constraining eye contact leads to exaggerated increase of amygdala activation in autism. Here, in a proof of concept pilot study, we demonstrate that administration of bumetanide (a NKCC1 chloride importer antagonist that restores GABAergic inhibition) normalizes the level of amygdala activation during constrained eye contact with dynamic emotional face stimuli in autism. In addition, eye-tracking data reveal that bumetanide administration increases the time spent in spontaneous eye gaze during in a free-viewing mode of the same face stimuli. In keeping with clinical trials, our data support the Excitatory/Inhibitory dysfunction hypothesis in autism, and indicate that bumetanide may improve specific aspects of social processing in autism. Future double-blind placebo controlled studies with larger cohorts of participants will help clarify the mechanisms of bumetanide action in autism.
| S-EPMC5827728 | biostudies-literature
Project description:Motivation:Apart from meta-predictors, most of today's methods for residue-residue contact prediction are based entirely on Direct Coupling Analysis (DCA) of correlated mutations in multiple sequence alignments (MSAs). These methods are on average ?40% correct for the 100 strongest predicted contacts in each protein. The end-user who works on a single protein of interest will not know if predictions are either much more or much less correct than 40%, which is especially a problem if contacts are predicted to steer experimental research on that protein. Results:We designed a regression model that forecasts the accuracy of residue-residue contact prediction for individual proteins with an average error of 7 percentage points. Contacts were predicted with two DCA methods (gplmDCA and PSICOV). The models were built on parameters that describe the MSA, the predicted secondary structure, the predicted solvent accessibility and the contact prediction scores for the target protein. Results show that our models can be also applied to the meta-methods, which was tested on RaptorX. Availability and implementation:All data and scripts are available from http://comprec-lin.iiar.pwr.edu.pl/dcaQ/. Contact:malgorzata.kotulska@pwr.edu.pl. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC5860164 | biostudies-literature
Project description:Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members. The typical pipeline to address this task, which we in this paper refer to as the three dimensions of contact prediction, is to (i) filter and align the raw sequence data representing the evolutionarily related proteins; (ii) choose a predictive model to describe a sequence alignment; (iii) infer the model parameters and interpret them in terms of structural properties, such as an accurate contact map. We show here that all three dimensions are important for overall prediction success. In particular, we show that it is possible to improve significantly along the second dimension by going beyond the pair-wise Potts models from statistical physics, which have hitherto been the focus of the field. These (simple) extensions are motivated by multiple sequence alignments often containing long stretches of gaps which, as a data feature, would be rather untypical for independent samples drawn from a Potts model. Using a large test set of proteins we show that the combined improvements along the three dimensions are as large as any reported to date.
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Project description: Not available
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Project description:Though epistasis has long been postulated to have a critical role in genetic regulation of important pathways as well as provide a major source of variation in the process of speciation, the importance of epistasis for genomic selection in the context of plant breeding is still being debated. In this paper, we report the results on the prediction of genetic values with epistatic effects for 280 accessions in the Nebraska Wheat Breeding Program using adaptive mixed least absolute shrinkage and selection operator (LASSO). The development of adaptive mixed LASSO, originally designed for association mapping, for the context of genomic selection is reported. The results show that adaptive mixed LASSO can be successfully applied to the prediction of genetic values while incorporating both marker main effects and epistatic effects. Especially, the prediction accuracy is substantially improved by the inclusion of two-locus epistatic effects (more than onefold in some cases as measured by cross-validation correlation coefficient), which is observed for multiple traits and planting locations. This points to significant potential in using non-additive genetic effects for genomic selection in crop breeding practices.
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