Seahorse Brood Pouch Transcriptome Reveals Common Genes Associated with Vertebrate Pregnancy

Viviparity (live birth) has evolved more than 150 times in vertebrates, and represents an excellent model system for studying the evolution of complex traits. There are at least 23 independent origins of viviparity in fishes, with syngnathid fishes (seahorses and pipefish) unique in exhibiting male pregnancy. Male seahorses and pipefish have evolved specialized brooding pouches that provide protection, gas exchange, osmoregulation, and limited nutrient provisioning to developing embryos. Pouch structures differ widely across the Syngnathidae, offering an ideal opportunity to study the evolution of reproductive complexity. However, the physiological and genetic changes facilitating male pregnancy are largely unknown. We used transcriptome profiling to examine pouch gene expression at successive gestational stages in a syngnathid with the most complex brood pouch morphology, the seahorse Hippocampus abdominalis. Using a unique time-calibrated RNA-seq data set including brood pouch at key stages of embryonic development, we identified transcriptional changes associated with brood pouch remodeling, nutrient and waste transport, gas exchange, osmoregulation, and immunological protection of developing embryos at conception, development and parturition. Key seahorse transcripts share homology with genes of reproductive function in pregnant mammals, reptiles, and other live-bearing fish, suggesting a common toolkit of genes regulating pregnancy in divergent evolutionary lineage

Thermal Behavior in the Lens Process

Direct laser metal deposition processing is a promising manufacturing technology which could significantly impact the length oftime between initial concept and finished part. For adoption ofthis technology in the manufacturing environment, further understanding is required to ensure robust components with appropriate properties are routinelyfabricated. This requires a complete understanding ofthe thermal history.during part fabrication and control ofthis behavior. This paper will describe our research to understand the thermal behavior for the Laser Engineered Net Shaping (LENS) process!, where a component is fabricated by focusing a laser beam onto a substrate to create a molten pool in which powder particles are simultaneously injected to build each layer. The substrate is moved beneath the l~ser beam to deposit a thin cross section, thereby creating the desired geometry for each layer. After deposition of each layer, the powder delivery nozzle and focusing lens assembly is incremented in the positive Z-direction, thereby building a three dimensional component layer additively. It is important to control the thermal behavior to reproducibly fabricate parts. The ultimate intent is to monitor the thermal signatures and to incorporate sensors and feedback algorithms to control part fabrication. With appropriate control, the geometric properties (accuracy, surface finish, low warpage) as well as the materials' properties (e.g. strength, ductility) of a component can be dialed into the part through the fabrication parameters. Thermal monitoring techniques will be described, and their particular benefits highlighted. Preliminary details in correlating thermal behavior with processing results will be discussed.Mechanical Engineerin

Infection-related morbidity and mortality among older patients with DLBCL treated with full- or attenuated-dose R-CHOP

Infection-related morbidity and mortality are increased in older patients with diffuse large B-cell lymphoma (DLBCL) compared with population-matched controls. Key predictive factors for infection-related hospitalization during treatment with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) and deaths as a result of infection in older patients during and after treatment with R-CHOP remain incompletely understood. For this study, 690 consecutively treated patients age 70 years or older who received full-dose or attenuated-dose R-CHOP treatment were analyzed for risk of infection-related hospitalization and infection-related death. Median age was 77 years, and 34.4% were 80 years old or older. Median follow-up was 2.8 years (range, 0.4-8.9 years). Patient and baseline disease characteristics were assessed in addition to intended dose intensity (IDI). Of all patients, 72% were not hospitalized with infection. In 331 patients receiving an IDI ≥80%, 33% were hospitalized with ≥1 infections compared with 23.3% of 355 patients receiving an IDI of 80% across the whole cohort. Primary quinolone prophylaxis independently reduced infection-related admission. A total of 51 patients died as a result of infection. The 6-month, 12-month, 2-year, and 5-year cumulative incidences of infection-related death were 3.3%, 5.0%, 7.2%, and 11.1%, respectively. Key independent factors associated with infection-related death were an International Prognostic Index (IPI) score of 3 to 5, Cumulative Illness Rating Scale for Geriatrics (CIRS-G) score ≥6, and low albumin, which enabled us to generate a predictive risk score. We defined a smaller group (15%) of patients (IPI score of 0-2, albumin >36 g/L, CIRS-G score <6) in which no cases of infection-related deaths occurred at 5 years of follow-up. Whether patients at higher risk of infection-related death could be targeted with enhanced antimicrobial prophylaxis remains unknown and will require a randomized trial

Predicting the severity of the grass pollen season and the effect of climate change in Northwest Europe

Allergic rhinitis is an inflammation in the nose caused by overreaction of the immune system to allergens in the air. Managing allergic rhinitis symptoms is challenging and requires timely intervention. The following are major questions often posed by those with allergic rhinitis: How should I prepare for the forthcoming season? How will the season's severity develop over the years? No country yet provides clear guidance addressing these questions. We propose two previously unexplored approaches for forecasting the severity of the grass pollen season on the basis of statistical and mechanistic models. The results suggest annual severity is largely governed by preseasonal meteorological conditions. The mechanistic model suggests climate change will increase the season severity by up to 60%, in line with experimental chamber studies. These models can be used as forecasting tools for advising individuals with hay fever and health care professionals how to prepare for the grass pollen season

The star formation history of mass-selected galaxies in the COSMOS field

We explore the evolution of the specific star formation rate (SSFR) for 3.6um-selected galaxies of different M_* in the COSMOS field. The average SFR for sub-sets of these galaxies is estimated with stacked 1.4GHz radio continuum emission. We separately consider the total sample and a subset of galaxies (SF) that shows evidence for substantive recent star formation in the rest-frame optical SED. At 0.2<z<3 both populations show a strong and M_*-independent decrease in their SSFR towards z=0.2, best described by a power- law (1+z)^n, where n~4.3 for all galaxies and n~3.5 for SF sources. The decrease appears to have started at z>2, at least above 4x10^10M_Sun where our conclusions are most robust. We find a tight correlation with power-law dependence, SSFR (M_*)^beta, between SSFR and M_* at all z. It tends to flatten below ~10^10M_Sun if quiescent galaxies are included; if they are excluded a shallow index beta_SFG -0.4 fits the correlation. On average, higher M_* objects always have lower SSFRs, also among SF galaxies. At z>1.5 there is tentative evidence for an upper SSFR-limit that an average galaxy cannot exceed. It is suggested by a flattening of the SSFR-M_* relation (also for SF sources), but affects massive (>10^10M_Sun) galaxies only at the highest z. Below z=1.5 there thus is no direct evidence that galaxies of higher M_* experience a more rapid waning of their SSFR than lower M_* SF systems. In this sense, the data rule out any strong 'downsizing'. We combine our results with recent measurements of the galaxy (stellar) mass function in order to determine the characteristic mass of a SF galaxy (M_*=10^(10.6\pm0.4)M_Sun). In this sense, too, there is no 'downsizing'. Our analysis constitutes the most extensive SFR density determination with a single technique to z=3. Recent Herschel results are consistent with our results, but rely on far smaller samples.Comment: 37 pages, 14 figures, 7 tables; accepted for publication in the Astrophysical Journal; High resolution versions of all figures available at www.mpia-hd.mpg.de/homes/karim/research.htm

A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor

Carbon monoxide (CO) is an important atmospheric constituent affecting air quality, and methane (CH$_{4}$) is the second most important greenhouse gas contributing to human-induced climate change. Detailed and continuous observations of these gases are necessary to better assess their impact on climate and atmospheric pollution. While surface and airborne measurements are able to accurately determine atmospheric abundances on local scales, global coverage can only be achieved using satellite instruments. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which was successfully launched in October 2017, is a spaceborne nadirviewing imaging spectrometer measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the shortwave infrared (SWIR), combining a high spatial resolution with daily global coverage. These characteristics enable the determination of both gases with an unprecedented level of detail on a global scale, introducing new areas of application. Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH$_{4}$ are simultaneously retrieved from TROPOMI’s radiance measurements in the 2:3 μm spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMDOAS). This algorithm is intended to be used with the operational algorithms for mutual verification and to provide new geophysical insights. We introduce the algorithm in detail, including expected error characteristics based on synthetic data, a machine-learning-based quality filter, and a shallow learning calibration procedure applied in the post-processing of the XCH$_{4}$ data. The quality of the results based on real TROPOMI data is assessed by validation with ground-based Fourier transform spectrometer (FTS) measurements providing realistic error estimates of the satellite data: the XCO data set is characterised by a random error of 5:1 ppb (5:8 %) and a systematic error of 1:9 ppb (2:1 %); the XCH$_{4}$ data set exhibits a random error of 14:0 ppb (0:8 %) and a systematic error of 4:3 ppb (0:2 %). The natural XCO and XCH$_{4}$ variations are well-captured by the satellite retrievals, which is demonstrated by a high correlation with the validation data (R = 0:97 for XCO and R D 0:91 for XCH$_{4}$ based on daily averages). We also present selected results from the mission start until the end of 2018, including a first comparison to the operational products and examples of the detection of emission sources in a single satellite overpass, such as CO emissions from the steel industry and CH$_{4}$ emissions from the energy sector, which potentially allows for the advance of emission monitoring and air quality assessments to an entirely new level

Functional Annotation and Identification of Candidate Disease Genes by Computational Analysis of Normal Tissue Gene Expression Data

Background: High-throughput gene expression data can predict gene function through the ‘‘guilt by association’ ’ principle: coexpressed genes are likely to be functionally associated. Methodology/Principal Findings: We analyzed publicly available expression data on normal human tissues. The analysis is based on the integration of data obtained with two experimental platforms (microarrays and SAGE) and of various measures of dissimilarity between expression profiles. The building blocks of the procedure are the Ranked Coexpression Groups (RCG), small sets of tightly coexpressed genes which are analyzed in terms of functional annotation. Functionally characterized RCGs are selected by means of the majority rule and used to predict new functional annotations. Functionally characterized RCGs are enriched in groups of genes associated to similar phenotypes. We exploit this fact to find new candidate disease genes for many OMIM phenotypes of unknown molecular origin. Conclusions/Significance: We predict new functional annotations for many human genes, showing that the integration of different data sets and coexpression measures significantly improves the scope of the results. Combining gene expression data, functional annotation and known phenotype-gene associations we provide candidate genes for several geneti

Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003-2018) for carbon and climate applications

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO₂) and methane (CH₄), denoted XCO₂ and XCH₄, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO₂) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5∘×5∘ data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO₂ or XCH₄, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO₂ Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1σ): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1σ): 0.66 ppm (0.70 ppm). The corresponding values for the XCH₄ products are single-observation random error (1σ): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb (−2.9 ppb); and spatiotemporal bias (1σ): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO₂ and XCH₄ growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/, last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/, last access: 10 January 2020)

A Seriation Approach for Visualization-Driven Discovery of Co-Expression Patterns in Serial Analysis of Gene Expression (SAGE) Data

Background: Serial Analysis of Gene Expression (SAGE) is a DNA sequencing-based method for large-scale gene expression profiling that provides an alternative to microarray analysis. Most analyses of SAGE data aimed at identifying co-expressed genes have been accomplished using various versions of clustering approaches that often result in a number of false positives. Principal Findings: Here we explore the use of seriation, a statistical approach for ordering sets of objects based on their similarity, for large-scale expression pattern discovery in SAGE data. For this specific task we implement a seriation heuristic we term ‘progressive construction of contigs ’ that constructs local chains of related elements by sequentially rearranging margins of the correlation matrix. We apply the heuristic to the analysis of simulated and experimental SAGE data and compare our results to those obtained with a clustering algorithm developed specifically for SAGE data. We show using simulations that the performance of seriation compares favorably to that of the clustering algorithm on noisy SAGE data. Conclusions: We explore the use of a seriation approach for visualization-based pattern discovery in SAGE data. Using both simulations and experimental data, we demonstrate that seriation is able to identify groups of co-expressed genes more accurately than a clustering algorithm developed specifically for SAGE data. Our results suggest that seriation is a usefu