Making things happen : a model of proactive motivation

Being proactive is about making things happen, anticipating and preventing problems, and seizing opportunities. It involves self-initiated efforts to bring about change in the work environment and/or oneself to achieve a different future. The authors develop existing perspectives on this topic by identifying proactivity as a goal-driven process involving both the setting of a proactive goal (proactive goal generation) and striving to achieve that proactive goal (proactive goal striving). The authors identify a range of proactive goals that individuals can pursue in organizations. These vary on two dimensions: the future they aim to bring about (achieving a better personal fit within one’s work environment, improving the organization’s internal functioning, or enhancing the organization’s strategic fit with its environment) and whether the self or situation is being changed. The authors then identify “can do,” “reason to,” and “energized to” motivational states that prompt proactive goal generation and sustain goal striving. Can do motivation arises from perceptions of self-efficacy, control, and (low) cost. Reason to motivation relates to why someone is proactive, including reasons flowing from intrinsic, integrated, and identified motivation. Energized to motivation refers to activated positive affective states that prompt proactive goal processes. The authors suggest more distal antecedents, including individual differences (e.g., personality, values, knowledge and ability) as well as contextual variations in leadership, work design, and interpersonal climate, that influence the proactive motivational states and thereby boost or inhibit proactive goal processes. Finally, the authors summarize priorities for future researc

Marine pelagic ecosystems: the West Antarctic Peninsula

The marine ecosystem of the West Antarctic Peninsula (WAP) extends from the Bellingshausen Sea to the northern tip of the peninsula and from the mostly glaciated coast across the continental shelf to the shelf break in the west. The glacially sculpted coastline along the peninsula is highly convoluted and characterized by deep embayments that are often interconnected by channels that facilitate transport of heat and nutrients into the shelf domain. The ecosystem is divided into three subregions, the continental slope, shelf and coastal regions, each with unique ocean dynamics, water mass and biological distributions. The WAP shelf lies within the Antarctic Sea Ice Zone (SIZ) and like other SIZs, the WAP system is very productive, supporting large stocks of marine mammals, birds and the Antarctic krill, Euphausia superba. Ecosystem dynamics is dominated by the seasonal and interannual variation in sea ice extent and retreat. The Antarctic Peninsula is one among the most rapidly warming regions on Earth, having experienced a 28C increase in the annual mean temperature and a 68C rise in the mean winter temperature since 1950. Delivery of heat from the Antarctic Circumpolar Current has increased significantly in the past decade, sufficient to drive to a 0.68C warming of the upper 300 m of shelf water. In the past 50 years and continuing in the twenty-first century, the warm, moist maritime climate of the northern WAP has been migrating south, displacing the once dominant cold, dry continental Antarctic climate and causing multi-level responses in the marine ecosystem. Ecosystem responses to the regional warming include increased heat transport, decreased sea ice extent and duration, local declines in icedependent Ade´lie penguins, increase in ice-tolerant gentoo and chinstrap penguins, alterations in phytoplankton and zooplankton community composition and changes in krill recruitment, abundance and availability to predators. The climate/ecological gradients extending along theWAPand the presence of monitoring systems, field stations and long-term research programmes make the region an invaluable observatory of climate change and marine ecosystem response

Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity

Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan

Social cartographies as performative devices in research on higher education

In this article, we review social cartography as a methodological approach to map and collectively engage diverse perspectives within the study of higher education. We illustrate the uses of this approach by drawing on our own experiences engaging it as part of an international research project about the effects of the convergence of globalization and economic crises in higher education. We offer several examples of how social cartography can enable agonistic collaboration amongst existing positions, as well as open up new spaces and possibilities for alternative futures in higher education

A Pre-Landing Assessment of Regolith Properties at the InSight Landing Site

This article discusses relevant physical properties of the regolith at the Mars InSight landing site as understood prior to landing of the spacecraft. InSight will land in the northern lowland plains of Mars, close to the equator, where the regolith is estimated to be ≥3--5 m thick. These investigations of physical properties have relied on data collected from Mars orbital measurements, previously collected lander and rover data, results of studies of data and samples from Apollo lunar missions, laboratory measurements on regolith simulants, and theoretical studies. The investigations include changes in properties with depth and temperature. Mechanical properties investigated include density, grain-size distribution, cohesion, and angle of internal friction. Thermophysical properties include thermal inertia, surface emissivity and albedo, thermal conductivity and diffusivity, and specific heat. Regolith elastic properties not only include parameters that control seismic wave velocities in the immediate vicinity of the Insight lander but also coupling of the lander and other potential noise sources to the InSight broadband seismometer. The related properties include Poisson’s ratio, P- and S-wave velocities, Young’s modulus, and seismic attenuation. Finally, mass diffusivity was investigated to estimate gas movements in the regolith driven by atmospheric pressure changes. Physical properties presented here are all to some degree speculative. However, they form a basis for interpretation of the early data to be returned from the InSight mission.Additional co-authors: Nick Teanby and Sharon Keda

Genome-wide association study identifies two susceptibility loci for osteosarcoma

Osteosarcoma is the most common primary bone malignancy of adolescents and young adults. To better understand the genetic etiology of osteosarcoma, we performed a multistage genome-wide association study consisting of 941 individuals with osteosarcoma (cases) and 3,291 cancer-free adult controls of European ancestry. Two loci achieved genome-wide significance: a locus in the GRM4 gene at 6p21.3 (encoding glutamate receptor metabotropic 4; rs1906953; P = 8.1 × 10⁻⁹) and a locus in the gene desert at 2p25.2 (rs7591996 and rs10208273; P = 1.0 × 10⁻⁸ and 2.9 × 10⁻⁷, respectively). These two loci warrant further exploration to uncover the biological mechanisms underlying susceptibility to osteosarcoma