A MACHINE LEARNING APPROACH TO QUERY TIME-SERIES MICROARRAY DATA SETS FOR FUNCTIONALLY RELATED GENES USING HIDDEN MARKOV MODELS

Microarray technology captures the rate of expression of genes under varying experimental conditions. Genes encode the information necessary to build proteins; proteins used by cellular functions exhibit higher rates of expression for the associated genes. If multiple proteins are required for a particular function then their genes show a pattern of coexpression during time periods when the function is active within a cell. Cellular functions are generally complex and require groups of genes to cooperate; these groups of genes are called functional modules. Modular organization of genetic functions has been evident since 1999. Detecting functionally related genes in a genome and detecting all genes belonging to particular functional modules are current research topics in this field. The number of microarray gene expression datasets available in public repositories increases rapidly, and advances in technology have now made it feasible to routinely perform whole-genome studies where the behavior of every gene in a genome is captured. This promises a wealth of biological and medical information, but making the amount of data accessible to researchers requires intelligent and efficient computational algorithms. Researchers working on specific cellular functions would benefit from this data if it was possible to quickly extract information useful to their area of research. This dissertation develops a machine learning algorithm that allows one or multiple microarray data sets to be queried with a set of known and functionally related input genes in order to detect additional genes participating in the same or closely related functions. The focus is on time-series microarray datasets where gene expression values are obtained from the same experiment over a period of time from a series of sequential measurements. A feature selection algorithm selects relevant time steps where the provided input genes exhibit correlated expression behavior. Time steps are the columns in microarray data sets, rows list individual genes. A specific linear Hidden Markov Model (HMM) is then constructed to contain one hidden state for each of the selected experiments and is trained using the expression values of the input genes from the microarray. Given the trained HMM the probability that a sequence of gene expression values was generated by that particular HMM can be calculated. This allows for the assignment of a probability score for each gene in the microarray. High-scoring genes are included in the result set (of genes with functional similarities to the input genes.) P-values can be calculated by repeating this algorithm to train multiple individual HMMs using randomly selected genes as input genes and calculating a Parzen Density Function (PDF) from the probability scores of all HMMs for each gene. A feedback loop uses the result generated from one algorithm run as input set for another iteration of the algorithm. This iterated HMM algorithm allows for the characterization of functional modules from very small input sets and for weak similarity signals. This algorithm also allows for the integration of multiple microarray data sets; two approaches are studied: Meta-Analysis (combination of the results from individual data set runs) and the extension of the linear HMM across multiple individual data sets. Results indicate that Meta-Analysis works best for integration of closely related microarrays and a spanning HMM works best for the integration of multiple heterogeneous datasets. The performance of this approach is demonstrated relative to the published literature on a number of widely used synthetic data sets. Biological application is verified by analyzing biological data sets of the Fruit Fly D. Melanogaster and Baker‟s Yeast S. Cerevisiae. The algorithm developed in this dissertation is better able to detect functionally related genes in common data sets than currently available algorithms in the published literature

Tobac 1.2: Towards a flexible framework for tracking and analysis of clouds in diverse datasets

We introduce tobac (Tracking and Object-Based Analysis of Clouds), a newly developed framework for tracking and analysing individual clouds in different types of datasets, such as cloud-resolving model simulations and geostationary satellite retrievals. The software has been designed to be used flexibly with any two-or three-dimensional timevarying input. The application of high-level data formats, such as Iris cubes or xarray arrays, for input and output allows for convenient use of metadata in the tracking analysis and visualisation. Comprehensive analysis routines are provided to derive properties like cloud lifetimes or statistics of cloud properties along with tools to visualise the results in a convenient way. The application of tobac is presented in two examples. We first track and analyse scattered deep convective cells based on maximum vertical velocity and the threedimensional condensate mixing ratio field in cloud-resolving model simulations. We also investigate the performance of the tracking algorithm for different choices of time resolution of the model output. In the second application, we show how the framework can be used to effectively combine information from two different types of datasets by simultaneously tracking convective clouds in model simulations and in geostationary satellite images based on outgoing longwave radiation. The tobac framework provides a flexible new way to include the evolution of the characteristics of individual clouds in a range of important analyses like model intercomparison studies or model assessment based on observational data. © 2019 Author(s)

Advanced deep space communication systems study Final report

Deep space communication system requirements for period 1970 to 198

Patterns and drivers of recent disturbances across the temperate forest biome

Increasing evidence indicates that forest disturbances are changing in response to global change, yet local variability in disturbance remains high. We quantified this considerable variability and analyzed whether recent disturbance episodes around the globe were consistently driven by climate, and if human influence modulates patterns of forest disturbance. We combined remote sensing data on recent (2001-2014) disturbances with in-depth local information for 50 protected landscapes and their surroundings across the temperate biome. Disturbance patterns are highly variable, and shaped by variation in disturbance agents and traits of prevailing tree species. However, high disturbance activity is consistently linked to warmer and drier than average conditions across the globe. Disturbances in protected areas are smaller and more complex in shape compared to their surroundings affected by human land use. This signal disappears in areas with high recent natural disturbance activity, underlining the potential of climate-mediated disturbance to transform forest landscapes.A.S. and R.S. acknowledge support from the Austrian Science Fund (FWF) through START grant Y895-B25. C.S. acknowledges funding from the German Academic Exchange Service (DAAD) with funds from the German Federal Ministry of Education and Research (BMBF) and the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007–2013) under REA grant agreement Nr. 605728 (P.R.I.M.E.—Postdoctoral Researchers International Mobility Experience). T. D. acknowledges funding from the Fonds institutionnel de recherche de l’Universitédu Quebec en Abitibi-Te ́ miscamingue, the Natural Sciences and Engineering Research ́ Council of Canada (NSERC), Tembec, and EACOM Timber Corporation. Á.G.G. was supported by FONDECYT 11150835. S.J.H. and T.T.V. acknowledge NSF Award 1262687. A.H. was partially supported by NSF (award #1738104). D.K. acknowledges support from the US NSF. D.L. was supported by an Australian Research Council Laureate Fellowship. A.S.M. was supported by the Environment Research and Technology Development Fund (S-14) of the Japanese Ministry of the Environment and by the Grants-in-Aid for Scientific Research of the Japan Society for the Promotion of Science (15KK0022). G.L.W.P. acknowledges support from a Royal Society of New Zealand Marsden Fund grant. S.L.S. acknowledges funds from the US Joint Fire Sciences Program (project number 14-1-06-22) and UC ANR competitive grants. M.S. and T.H. acknowledges support from the institutional project MSMT CZ.02.1.01/0.0/0.0/16_019/ 0000803. M.G.T. acknowledges funding from the University of Wisconsin-Madison Vilas Trust and the US Joint Fire Science Program (project numbers 09-1-06-3, 12-3-01-3, and 16-3-01-4). The study used data from the TRY initiative on plant traits (http://www.trydb.org). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. Boenisch (Max Planck Institute for Biogeochemistry, Jena, Germany). TRY is currently supported by Future Earth/bioDISCOVERY and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzi

Factors considered by medical students when formulating their specialty preferences in Japan: findings from a qualitative study

<p>Abstract</p> <p>Background</p> <p>Little research addresses how medical students develop their choice of specialty training in Japan. The purpose of this research was to elucidate factors considered by Japanese medical students when formulating their specialty choice.</p> <p>Methods</p> <p>We conducted qualitative interviews with 25 Japanese medical students regarding factors influencing specialty preference and their views on roles of primary versus specialty care. We qualitatively analyzed the data to identify factors students consider when developing specialty preferences, to understand their views about primary and subspecialty care, and to construct models depicting the pathways to specialization.</p> <p>Results</p> <p>Students mention factors such as illness in self or close others, respect for family member in the profession, preclinical experiences in the curriculum such as labs and dissection, and aspects of patient care such as the clinical atmosphere, charismatic role models, and doctor-patient communication as influential on their specialty preferences. Participating students could generally distinguish between subspecialty care and primary care, but not primary care and family medicine. Our analysis yields a "Two Career" model depicting how medical graduates can first train for hospital-based specialty practice, and then switch to mixed primary/specialty care outpatient practice years later without any requirement for systematic training in principles of primary care practice.</p> <p>Conclusion</p> <p>Preclinical and clinical experiences as well as role models are reported by Japanese students as influential factors when formulating their specialty preferences. Student understanding of family medicine as a discipline is low in Japan. Students with ultimate aspirations to practice outpatient primary care medicine do not need to commit to systematic primary care training after graduation. The Two Career model of specialization leaves the door open for medical graduates to enter primary care practice at anytime regardless of post-graduate residency training choice.</p

Ecosystem Resilience Monitoring and Early Warning Using Earth Observation Data: Challenges and Outlook

As the Earth system is exposed to large anthropogenic interferences, it becomes ever more important to assess the resilience of natural systems, i.e., their ability to recover from natural and human-induced perturbations. Several, often related, measures of resilience have been proposed and applied to modeled and observed data, often by different scientific communities. Focusing on terrestrial ecosystems as a key component of the Earth system, we review methods that can detect large perturbations (temporary excursions from a reference state as well as abrupt shifts to a new reference state) in spatio-temporal datasets, estimate the recovery rate after such perturbations, or assess resilience changes indirectly from stationary time series via indicators of critical slowing down. We present here a sequence of ideal methodological steps in the field of resilience science, and argue how to obtain a consistent and multi-faceted view on ecosystem or climate resilience from Earth observation (EO) data. While EO data offers unique potential to study ecosystem resilience globally at high spatial and temporal scale, we emphasize some important limitations, which are associated with the theoretical assumptions behind diagnostic methods and with the measurement process and pre-processing steps of EO data. The latter class of limitations include gaps in time series, the disparity of scales, and issues arising from aggregating time series from multiple sensors. Based on this assessment, we formulate specific recommendations to the EO community in order to improve the observational basis for ecosystem resilience research

Globally consistent climate sensitivity of natural disturbances across boreal and temperate forest ecosystems

Disturbance regimes are changing in forests across the world in response to global climate change. Despite the profound impacts of disturbances on ecosystem services and biodiversity, assessments of disturbances at the global scale remain scarce. Here, we analyzed natural disturbances in boreal and temperate forest ecosystems for the period 2001-2014, aiming to 1) quantify their within- and between-biome variation and 2) compare the climate sensitivity of disturbances across biomes. We studied 103 unmanaged forest landscapes with a total land area of 28.2 x 10(6) ha, distributed across five continents. A consistent and comprehensive quantification of disturbances was derived by combining satellite-based disturbance maps with local expert knowledge of disturbance agents. We used Gaussian finite mixture models to identify clusters of landscapes with similar disturbance activity as indicated by the percent forest area disturbed as well as the size, edge density and perimeter-area-ratio of disturbed patches. The climate sensitivity of disturbances was analyzed using Bayesian generalized linear mixed effect models and a globally consistent climate dataset. Within-biome variation in natural disturbances was high in both boreal and temperate biomes, and disturbance patterns did not vary systematically with latitude or biome. The emergent clusters of disturbance activity in the boreal zone were similar to those in the temperate zone, but boreal landscapes were more likely to experience high disturbance activity than their temperate counterparts. Across both biomes high disturbance activity was particularly associated with wildfire, and was consistently linked to years with warmer and drier than average conditions. Natural disturbances are a key driver of variability in boreal and temperate forest ecosystems, with high similarity in the disturbance patterns between both biomes. The universally high climate sensitivity of disturbances across boreal and temperate ecosystems indicates that future climate change could substantially increase disturbance activity.Peer reviewe

The at wavelength metrology facility for UV and XUV reflection and diffraction optics at BESSY II

A technology center for the production of high precision reflection gratings has been established. Within this project a new optics beamline and a versatile reflectometer for at wavelength characterization of UV and XUV reflection gratings and other nano optical elements has been set up at BESSY II. The Plane Grating Monochromator beamline operated in collimated light c PGM is equipped with an SX700 monochromator, of which the blazed gratings 600 and 1200 lines mm 1 have been recently exchanged for new ones of improved performance produced in house. Over the operating range from 10 to 2000 eV this beamline has very high spectral purity achieved by i a four mirror arrangement of different coatings which can be inserted into the beam at different angles and ii by absorber filters for high order suppression. Stray light and scattered radiation is removed efficiently by double sets of in situ exchangeable apertures and slits. By use of in and off plane bending magnet radiation the beamline can be adjusted to either linear or elliptical polarization. One of the main features of a novel 11 axes reflectometer is the possibility to incorporate real life sized gratings. The samples are adjustable within six degrees of freedom by a newly developed UHV tripod system carrying a load up to 4 kg, and the reflectivity can be measured between 0 and 90 deg incidence angle for both s and p polarization geometry. This novel powerful metrology facility has gone into operation recently and is now open for external users. First results on optical performance and measurements on multilayer gratings will be presented her

Factors affecting medical students in formulating their specialty preferences in Jordan

<p>Abstract</p> <p>Background</p> <p>In recent years there has been a growing appreciation of the issues of career preference in medicine as it may affect student learning and academic performance. However, no such studies have been undertaken in medical schools in Jordan. Therefore, we carried out this study to investigate the career preferences of medical students at Jordan University of Science and Technology and determine factors that might influence their career decisions.</p> <p>Methods</p> <p>A cross-sectional questionnaire-based survey was carried out among second, fourth and sixth year medical students at the Jordan University of Science and Technology, Irbid, Jordan during the academic year 2006/2007. A total of 440 students answered the questionnaire which covered demographic characteristics, specialty preferences, and the factors that influenced these career preferences. Possible influences were selected on the basis of a literature review and discussions with groups of medical students and physicians. Students were asked to consider 14 specialty options and select the most preferred career preference.</p> <p>Results</p> <p>The most preferred specialty expressed by male students was surgery, followed by internal medicine and orthopaedics, while the specialty most preferred by female students was obstetrics and gynaecology, followed by pediatrics and surgery. Students showed little interest in orthopedics, ophthalmology, and dermatology. While 3.1% of females expressed interest in anesthesiology, no male students did. Other specialties were less attractive to most students.</p> <p>Intellectual content of the specialty and the individual's competencies were the most influential on their preference of specialty. Other influential factors were the "reputation of the specialty", "anticipated income", and "focus on urgent care".</p> <p>Conclusion</p> <p>Surgery, internal medicine, pediatrics, and obstetrics and gynaecology were the most preferred specialty preferences of medical students at Jordan University of Science and Technology.</p