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A familial congenital heart disease with a possible multigenic origin involving a mutation in BMPR1A
(2019)
The genetics of many congenital heart diseases (CHDs) can only unsatisfactorily be explained by known chromosomal or Mendelian syndromes. Here, we present sequencing data of a family with a potentially multigenic origin of CHD. Twelve of nineteen family members carry a familial mutation [NM_004329.2:c.1328 G > A (p.R443H)] which encodes a predicted deleterious variant of BMPR1A. This mutation co-segregates with a linkage region on chromosome 1 that associates with the emergence of severe CHDs including Ebstein’s anomaly, atrioventricular septal defect, and others. We show that the continuous overexpression of the zebrafish homologous mutation bmpr1aap.R438H within endocardium causes a reduced AV valve area, a downregulation of Wnt/ß-catenin signalling at the AV canal, and growth of additional tissue mass in adult zebrafish hearts. This finding opens the possibility of testing genetic interactions between BMPR1A and other candidate genes within linkage region 1 which may provide a first step towards unravelling more complex genetic patterns in cardiovascular disease aetiology.
Ecosystem boundaries, such as the Arctic-Boreal treeline, are strongly coupled with climate and were spatially highly dynamic during past glacial-interglacial cycles. Only a few studies cover vegetation changes since the last interglacial, as most of the former landscapes are inundated and difficult to access. Using pollen analysis and sedimentary ancient DNA (sedaDNA) metabarcoding, we reveal vegetation changes on Bol’shoy Lyakhovsky Island since the last interglacial from permafrost sediments. Last interglacial samples depict high levels of floral diversity with the presence of trees (Larix, Picea, Populus) and shrubs (Alnus, Betula, Ribes, Cornus, Saliceae) on the currently treeless island. After the Last Glacial Maximum, Larix re-colonised the island but disappeared along with most shrub taxa. This was probably caused by Holocene sea-level rise, which led to increased oceanic conditions on the island. Additionally, we applied two newly developed larch-specific chloroplast markers to evaluate their potential for tracking past population dynamics from environmental samples. The novel markers were successfully re-sequenced and exhibited two variants of each marker in last interglacial samples. SedaDNA can track vegetation changes as well as genetic changes across geographic space through time and can improve our understanding of past processes that shape modern patterns.
The SusKat-ABC (Sustainable Atmosphere for the Kathmandu Valley-Atmospheric Brown Clouds) international air pollution measurement campaign was carried out from December 2012 to June 2013 in the Kathmandu Valley and surrounding regions in Nepal. The Kathmandu Valley is a bowl-shaped basin with a severe air pollution problem. This paper reports measurements of two major greenhouse gases (GHGs), methane (CH4) and carbon dioxide (CO2), along with the pollutant CO, that began during the campaign and were extended for 1 year at the SusKat-ABC supersite in Bode, a semi-urban location in the Kathmandu Valley. Simultaneous measurements were also made during 2015 in Bode and a nearby rural site (Chanban) similar to 25 km (aerial distance) to the southwest of Bode on the other side of a tall ridge. The ambient mixing ratios of methane (CH4), carbon dioxide (CO2), water vapor, and carbon monoxide (CO) were measured with a cavity ring-down spectrometer (G2401; Picarro, USA) along with meteorological parameters for 1 year (March 2013-March 2014). These measurements are the first of their kind in the central Himalayan foothills. At Bode, the annual average mixing ratios of CO2 and CH4 were 419.3 (+/- 6.0) ppm and 2.192 (+/- 0.066) ppm, respectively. These values are higher than the levels observed at background sites such as Mauna Loa, USA (CO2: 396.8 +/- 2.0 ppm, CH4: 1.831 +/- 0.110 ppm) and Waliguan, China (CO2: 397.7 +/- 3.6 ppm, CH4: 1.879 +/- 0.009 ppm) during the same period and at other urban and semi-urban sites in the region, such as Ahmedabad and Shadnagar (India). They varied slightly across the seasons at Bode, with seasonal average CH4 mixing ratios of 2.157 (+/- 0.230) ppm in the pre-monsoon season, 2.199 (+/- 0.241) ppm in the monsoon, 2.210 (+/- 0.200) ppm in the post-monsoon, and 2.214 (+/- 0.209) ppm in the winter season. The average CO2 mixing ratios were 426.2 (+/- 25.5) ppm in the pre-monsoon, 413.5 (+/- 24.2) ppm in the monsoon, 417.3 (+/- 23.1) ppm in the postmonsoon, and 421.9 (+/- 20.3) ppm in the winter season. The maximum seasonal mean mixing ratio of CH4 in winter was only 0.057 ppm or 2.6% higher than the seasonal minimum during the pre-monsoon period, while CO2 was 12.8 ppm or 3.1% higher during the pre-monsoon period (seasonal maximum) than during the monsoon (seasonal minimum). On the other hand, the CO mixing ratio at Bode was 191% higher during the winter than during the monsoon season. The enhancement in CO2 mixing ratios during the pre-monsoon season is associated with additional CO2 emissions from forest fires and agro-residue burning in northern South Asia in addition to local emissions in the Kathmandu Valley. Published CO = CO2 ratios of different emission sources in Nepal and India were compared with the observed CO = CO2 ratios in this study. This comparison suggested that the major sources in the Kathmandu Valley were residential cooking and vehicle exhaust in all seasons except winter. In winter, brick kiln emissions were a major source. Simultaneous measurements in Bode and Chanban (15 July-3 October 2015) revealed that the mixing ratios of CO2, CH4, and CO were 3.8, 12, and 64% higher in Bode than Chanban. The Kathmandu Valley thus has significant emissions from local sources, which can also be attributed to its bowl-shaped geography that is conducive to pollution build-up. At Bode, all three gas species (CO2, CH4, and CO) showed strong diurnal patterns in their mixing ratios with a pronounced morning peak (ca. 08:00), a dip in the afternoon, and a gradual increase again through the night until the next morning. CH4 and CO at Chanban, however, did not show any noticeable diurnal variations. These measurements provide the first insights into the diurnal and seasonal variation in key greenhouse gases and air pollutants and their local and regional sources, which is important information for atmospheric research in the region.
The advance of high-throughput RNA-Sequencing techniques enables researchers to analyze the complete gene activity in particular cells. From the insights of such analyses, researchers can identify disease-specific expression profiles, thus understand complex diseases like cancer, and eventually develop effective measures for diagnosis and treatment. The high dimensionality of gene expression data poses challenges to its computational analysis, which is addressed with measures of gene selection. Traditional gene selection approaches base their findings on statistical analyses of the actual expression levels, which implies several drawbacks when it comes to accurately identifying the underlying biological processes. In turn, integrative approaches include curated information on biological processes from external knowledge bases during gene selection, which promises to lead to better interpretability and improved predictive performance. Our work compares the performance of traditional and integrative gene selection approaches. Moreover, we propose a straightforward approach to integrate external knowledge with traditional gene selection approaches. We introduce a framework enabling the automatic external knowledge integration, gene selection, and evaluation. Evaluation results prove our framework to be a useful tool for evaluation and show that integration of external knowledge improves overall analysis results.
The Sun’s atmosphere is frequently disrupted by coronal mass ejections (CMEs), coupled with flares and energetic particles. The coupling is usually attributed to magnetic reconnection at a vertical current sheet connecting the flare and CME, with the latter embedding a helical magnetic structure known as flux rope. However, both the origin of flux ropes and their nascent paths toward eruption remain elusive. Here, we present an observation of how a stellar-sized CME bubble evolves continuously from plasmoids, mini flux ropes that are barely resolved, within half an hour. The eruption initiates when plasmoids springing from a vertical current sheet merge into a leading plasmoid, which rises at increasing speeds and expands impulsively into the CME bubble, producing hard x-ray bursts simultaneously. This observation illuminates a complete CME evolutionary path capable of accommodating a wide variety of plasma phenomena by bridging the gap between microscale and macroscale dynamics.
Simulating Fiber-Reinforced Concrete Mechanical Performance Using CT-Based Fiber Orientation Data
(2019)
The main hindrance to realistic models of fiber-reinforced concrete (FRC) is the local materials property variation, which does not yet reliably allow simulations at the structural level. The idea presented in this paper makes use of an existing constitutive model, but resolves the problem of localized material variation through X-ray computed tomography (CT)-based pre-processing. First, a three-point bending test of a notched beam is considered, where pre-test fiber orientations are measured using CT. A numerical model is then built with the zone subjected to progressive damage, modeled using an orthotropic damage model. To each of the finite elements within this zone, a local coordinate system is assigned, with its longitudinal direction defined by local fiber orientations. Second, the parameters of the constitutive damage model are determined through inverse analysis using load-displacement data obtained from the test. These parameters are considered to clearly explain the material behavior for any arbitrary external action and fiber orientation, for the same geometrical properties and volumetric ratio of fibers. Third, the effectiveness of the resulting model is demonstrated using a second, control experiment. The results of the control experiment analyzed in this research compare well with the model results. The ultimate strength was predicted with an error of about 6%, while the work-of-load was predicted within 4%. It demonstrates the potential of this method for accurately predicting the mechanical performance of FRC components.
Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (n = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state-of-the-art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network-based model BN-FLEMOps and the rule-based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management.
Ecosystem services inherently involve people, whose values help define the benefits of nature's services. It is thus important for researchers to involve stakeholders in ecosystem services research. However, a simple and practicable framework to guide such engagement, and in particular to help researchers anticipate and consider key issues and challenges, has not been well explored. Here, we use experience from the 12 case studies in the European Operational Potential of Ecosystem Research Applications (OPERAs) project to propose a stakeholder engagement framework comprising three key elements: creating space, aligning motivations, and building trust. We argue that involving stakeholders in research demands thoughtful reflection from the researchers about what kind of space they want to create, including if and how they want to bring different interests together, how much space they want to allow for critical discussion, and whether there is a role for particular stakeholders to serve as conduits between others. In addition, understanding their own motivations—including values, knowledge, goals, and desired benefits—will help researchers decide when and how to involve stakeholders, identify areas of common ground and potential disagreement, frame the project appropriately, set expectations, and ensure each party is able to see benefits of engaging with each other. Finally, building relationships with stakeholders can be difficult but considering the roles of existing relationships, time, approach, reputation, and belonging can help build mutual trust. Although the three key elements and the paths between them can play out differently depending on the particular research project, we suggest that a research design that considers how to create the space in which researchers and stakeholders will meet, align motivations between researchers and stakeholders, and build mutual trust will help foster productive researcher–stakeholder relationships.
IMPORTANCE Inflammatory processes have been suggested to have an important role in colorectal cancer (CRC) etiology. Chemerin is a recently discovered inflammatory biomarker thought to exert chemotactic, adipogenic, and angiogenic functions. However, its potential link with CRC has not been sufficiently explored. OBJECTIVE To evaluate the prospective association of circulating plasma chemerin concentrations with incident CRC. DESIGN, SETTING, AND PARTICIPANTS Prospective case-cohort study based on 27 548 initially healthy participants from the European Prospective Investigation Into Cancer and Nutrition (EPIC)-Potsdam cohort who were followed for up to 16 years. Baseline study information and samples were collected between August 23, 1994, and September 25, 1998. Recruitment was according to random registry sampling from the geographical area of Potsdam, Germany, and surrounding municipalities. The last date of study follow-up was May 10, 2010. Statistical analysis was conducted in 2018. MAIN OUTCOMES AND MEASURES Incident CRC, colon cancer, and rectal cancer. Baseline chemerin plasma concentrations were measured by enzyme-linked immunosorbent assay. CONCLUSIONS AND RELEVANCE This study found that the association between chemerin concentration and the risk of incident CRC was linear and independent of established CRC risk factors. Further studies are warranted to evaluate chemerin as a novel immune-inflammatory agent in colorectal carcinogenesis.
Bottom-up and top-down as well as low-level and high-level factors influence where we fixate when viewing natural scenes. However, the importance of each of these factors and how they interact remains a matter of debate. Here, we disentangle these factors by analyzing their influence over time. For this purpose, we develop a saliency model that is based on the internal representation of a recent early spatial vision model to measure the low-level, bottom-up factor. To measure the influence of high-level, bottom-up features, we use a recent deep neural network-based saliency model. To account for top-down influences, we evaluate the models on two large data sets with different tasks: first, a memorization task and, second, a search task. Our results lend support to a separation of visual scene exploration into three phases: the first saccade, an initial guided exploration characterized by a gradual broadening of the fixation density, and a steady state that is reached after roughly 10 fixations. Saccade-target selection during the initial exploration and in the steady state is related to similar areas of interest, which are better predicted when including high-level features. In the search data set, fixation locations are determined predominantly by top-down processes. In contrast, the first fixation follows a different fixation density and contains a strong central fixation bias. Nonetheless, first fixations are guided strongly by image properties, and as early as 200 ms after image onset, fixations are better predicted by high-level information. We conclude that any low-level, bottom-up factors are mainly limited to the generation of the first saccade. All saccades are better explained when high-level features are considered, and later, this high-level, bottom-up control can be overruled by top-down influences.