MELK-a conserved kinase: functions, signaling, cancer, and controversy.

Maternal embryonic leucine zipper kinase (MELK) is a highly conserved serine/threonine kinase initially found to be expressed in a wide range of early embryonic cellular stages, and as a result has been implicated in embryogenesis and cell cycle control. Recent evidence has identified a broader spectrum of tissue expression pattern for this kinase than previously appreciated. MELK is expressed in several human cancers and stem cell populations. Unique spatial and temporal patterns of expression within these tissues suggest that MELK plays a prominent role in cell cycle control, cell proliferation, apoptosis, cell migration, cell renewal, embryogenesis, oncogenesis, and cancer treatment resistance and recurrence. These findings have important implications for our understanding of development, disease, and cancer therapeutics. Furthermore understanding MELK signaling may elucidate an added dimension of stem cell control

Pre-mining pattern of soils on Nauru, Central Pacific

The environment of Nauru, a raised atoll located in the central Pacific Ocean (0° 32' S, 166° 56' W), was devastated by mining of phosphate "rock" during the twentieth century. Some 100 million tonnes of phosphate material has been removed, leaving more than 80% of the island as a dolomite pinnacle-dominated karrenfeld. Based on fieldwork examining sites unmined at that time, laboratory studies on undisturbed profiles, aerial photographs, and old mining maps, a picture of what the soil pattern on Nauru was before mining has been developed. Four major soil associations were identified: the coastal fringe carbonate-dominated soils set on a recent fringing reef; deep and relatively deep phosphate-dominated soils free of substantial influence from the underlying dolomite pinnacles occurring on the NE, NW, SE, and SW sectors of the uplifted section of the island (known locally as "Topside"); a complex set of soils found on Topside and on the scarp where the pinnacle influence is important but containing some deep soils where phosphate material accumulated between the pinnacles; and a complex set of soils in low-lying areas around the old lagoon at Buada. Distribution of soils is discussed and physical, chemical, and mineralogical properties of the soils are presented. Soils were basically AC profiles, with coarse textures, free drainage, and limited moisture-retention capacities. Organic matter accumulated to various depths from about 5 to 50 cm. Soil pH was generally above 6, cation exchange capacities were closely aligned to organic matter contents, but trace element deficiencies would have been common. Cadmium concentrations were relatively high in Nauru soils. The soils are likely to have been of limited fertility, with moisture being a major limitation in many years. Classification of the soils indicated a dominance of Ustropepts and Ustolls, with smaller areas of Ustipsamments and Ustorthents, and very small areas of soils showing aquic features. The postmining situation is also discussed; only very limited areas of three of the original soil associations remain (the relatively deep profiles free of pinnacle influence have completely disappeared). Limitations to rehabilitation are also briefly reviewed

Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)

Reactive gases and aerosols are produced by terrestrial ecosystems, processed within plant canopies, and can then be emitted into the above-canopy atmosphere. Estimates of the above-canopy fluxes are needed for quantitative earth system studies and assessments of past, present and future air quality and climate. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) is described and used to quantify net terrestrial biosphere emission of isoprene into the atmosphere. MEGAN is designed for both global and regional emission modeling and has global coverage with ~1 km² spatial resolution. Field and laboratory investigations of the processes controlling isoprene emission are described and data available for model development and evaluation are summarized. The factors controlling isoprene emissions include biological, physical and chemical driving variables. MEGAN driving variables are derived from models and satellite and ground observations. Tropical broadleaf trees contribute almost half of the estimated global annual isoprene emission due to their relatively high emission factors and because they are often exposed to conditions that are conducive for isoprene emission. The remaining flux is primarily from shrubs which have a widespread distribution. The annual global isoprene emission estimated with MEGAN ranges from about 500 to 750 Tg isoprene (440 to 660 Tg carbon) depending on the driving variables which include temperature, solar radiation, Leaf Area Index, and plant functional type. The global annual isoprene emission estimated using the standard driving variables is ~600 Tg isoprene. Differences in driving variables result in emission estimates that differ by more than a factor of three for specific times and locations. It is difficult to evaluate isoprene emission estimates using the concentration distributions simulated using chemistry and transport models, due to the substantial uncertainties in other model components, but at least some global models produce reasonable results when using isoprene emission distributions similar to MEGAN estimates. In addition, comparison with isoprene emissions estimated from satellite formaldehyde observations indicates reasonable agreement. The sensitivity of isoprene emissions to earth system changes (e.g., climate and land-use) demonstrates the potential for large future changes in emissions. Using temperature distributions simulated by global climate models for year 2100, MEGAN estimates that isoprene emissions increase by more than a factor of two. This is considerably greater than previous estimates and additional observations are needed to evaluate and improve the methods used to predict future isoprene emissions

In the Fabric of Research: Racial and Gender Stereotypes in Survey Items Assessing Attitudes about Abortion

We investigated the content of survey items to assess whether and how racist and sexist stereotypes are woven into the fabric of research on attitudes about abortion in the United States. We collected and analyzed a comprehensive set of survey items (456 items from 80 studies) used in peer‐reviewed research published from 2008 to 2018 in representative and nonrepresentative studies of U.S. respondents. Our analysis was guided by historical narratives that have been influential in shaping representations of women and reproduction in the United States (e.g., the Moynihan Report). With this background, we developed three themes pertaining to how individuals’ attitudes about abortion are measured: we found that items rely on (1) moral, (2) sexual, and (3) financial evaluations of women seeking abortion care. These themes highlighted implicit and explicit judgments of women, including representations of them as unwilling to partner with men and as fiscally and sexually irresponsible. We argue that survey items meant to objectively assess abortion attitudes draw on negative racial and gender stereotypes and that these stereotypes then travel widely under the veneer of scientific objectivity. Critical methods, such as the item bank analysis described in this study, are crucial to discern how inequality, prejudice, and discrimination can be reproduced in the fabric of research methods. In our discussion, we offer suggestions for researchers to reduce these and related forms of bias in survey‐based abortion research.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155945/1/josi12367_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155945/2/josi12367.pd

Identification of an Efficient Gene Expression Panel for Glioblastoma Classification.

We present here a novel genetic algorithm-based random forest (GARF) modeling technique that enables a reduction in the complexity of large gene disease signatures to highly accurate, greatly simplified gene panels. When applied to 803 glioblastoma multiforme samples, this method allowed the 840-gene Verhaak et al. gene panel (the standard in the field) to be reduced to a 48-gene classifier, while retaining 90.91% classification accuracy, and outperforming the best available alternative methods. Additionally, using this approach we produced a 32-gene panel which allows for better consistency between RNA-seq and microarray-based classifications, improving cross-platform classification retention from 69.67% to 86.07%. A webpage producing these classifications is available at http://simplegbm.semel.ucla.edu

Directions for long-term research in traditional agricultural systems of Micronesia and the Pacific Islands

Abstract-Following a review of the major systems of traditional agriculture, this paper briefly considers the role of traditional agriculture in environmental modification, and the related concepts of sustainability, agricultural intensification, carrying capacity, biodiversity and agricultural disintensification (abandonment). Research interest in traditional agricultural systems of the Pacific Islands has waned considerably since the 1960s and there are few current studies of these systems in Micronesia. Both species and cultivar diversity of the traditional agricultural systems of the region are high and may contribute significantly to their sustainability. However, there is little ongoing research on the role of biodiversity in these traditional systems. Some reasons for the apparent lack of research in the ecology of traditional agricultural systems are presented. The Pacific-Asia Biodiversity Transect (PABITRA) network emphasizes investigation of the function of biodiversity and the health of ecosystems in the tropical Pacific Islands. Accordingly, PABITRA's focus should be on long-term monitoring of traditional agricultural systems in order to define their structural and functional characteristics and the relationship between diversity and sustainability. Given the rapid rate of modernization, the documentation of traditional agricultural knowledge and resources is imperative as this knowledge is rapidly being lost

Monitoring long-term evolution of engineered barrier systems using magnets:Magnetic response

International audienc

High angular resolution mm- and submm-observations of dense molecular gas in M82

Researchers observed CO(7-6), CO(3-2), HCN(3-2) and HCO+(3-2) line emission toward the starburst nucleus of M82 and have obtained an upper limit to H13CN(3-2). These are the first observations of the CO(7-6), HCN(3-2) and HCO+(3-2) lines in any extragalactic source. Researchers took the CO(7-6) spectrum in January 1988 at the Infrared Telescope Facility (IRTF) with the Max Planck Institute for Extraterrestrial Physics/Univ. of California, Berkeley 800 GHz Heterodyne Receiver. In March 1989 researchers used the Institute for Radio Astronomy in the Millimeter range (IRAM) 30 m telescope to observe the CO(3-2) line with the new MPE 350 GHz Superconductor Insulator Superconductor (SIS) receiver and the HCN(3-2) and HCO+(3-2) lines with the (IRAM) 230 GHz SIS receiver (beam 12" FWHM, Blundell et al. 1988). The observational parameters are summarized

Clutter modeling in infrared images using genetic programming

Background clutter characterization in infrared imagery has become an actively researched field, and several clutter models have been reported. These models attempt to evaluate the target detection and recognition probabilities that are characteristic of a certain scene when specific target and human visual perception features are known. The prior knowledge assumed and required by these models is a severe limitation. Furthermore, the attempt to model subjective and intricate mechanisms such as human perception with general mathematical formulas is controversial, in this paper, we introduce the idea of adaptive models that are dynamically derived from a set of examples by a supervised learning mechanism based on genetic programming foundations. A set of characteristic scene and target features with a demonstrated influence on the human visual perception mechanism is first extracted from the original images. Then, the correlations between these features and detection performance results obtained by visual observer tests on the same set of images are captured into models by a learning algorithm. The effectiveness of the adaptive modeling principle is discussed in the final part of the paper