The Role of Trait and State Perfectionism in Psychological Detachment From Daily Job Demands

Psychological detachment has been proposed to be a mediator of the relations between an individual's responses to stressful work-related experiences and mid- and long-term health. However, the number of studies that have specifically examined the role that personal characteristics play in these associations is considerably small. One personal characteristic that might specifically interfere with psychological detachment is perfectionism, which has been considered an important vulnerability factor for the development of psychological disorders. Hence, the goal of this registered report was to extend research on psychological detachment by introducing trait and state perfectionism as moderators of the aforementioned relations. We conducted an experience sampling study with three measurement occasions per day over the course of 3 working weeks (N = 158 employees; Mage = 41.6; 67% women). Multilevel path models showed that perfectionistic concerns consistently determined strain responses at between- and within-levels of analyses even after the effects of job demands (i.e., unfinished tasks and role ambiguity) and detachment were accounted for. However, we found no evidence for the proposed moderation effects. The theoretical implications for the understanding of the processes proposed in the stressor-detachment model are discussed

Mrpl36 is important for generation of assembly competent proteins during mitochondrial translation

The complexes of the respiratory chain represent mosaics of nuclear and mitochondrially encoded components. The processes by which synthesis and assembly of the various subunits are coordinated remain largely elusive. During evolution, many proteins of the mitochondrial ribosome acquired additional domains pointing at specific properties or functions of the translation machinery in mitochondria. Here, we analyzed the function of Mrpl36, a protein associated with the large subunit of the mitochondrial ribosome. This protein, homologous to the ribosomal protein L31 from bacteria, contains a mitochondria-specific C-terminal domain that is not required for protein synthesis per se; however, its absence decreases stability of Mrpl36. Cells lacking this C-terminal domain can still synthesize proteins, but these translation products fail to be properly assembled into respiratory chain complexes and are rapidly degraded. Surprisingly, overexpression of Mrpl36 seems to even increase the efficiency of mitochondrial translation. Our data suggest that Mrpl36 plays a critical role during translation that determines the rate of respiratory chain assembly. This important function seems to be carried out by a stabilizing activity of Mrpl36 on the interaction between large and small ribosomal subunits, which could influence accuracy of protein synthesis

Dichloroacetate reverses the hypoxic adaptation to bevacizumab and enhances its antitumor effects in mouse xenografts.

Inhibition of vascular endothelial growth factor increases response rates to chemotherapy and progression-free survival in glioblastoma. However, resistance invariably occurs, prompting the urgent need for identification of synergizing agents. One possible strategy is to understand tumor adaptation to microenvironmental changes induced by antiangiogenic drugs and test agents that exploit this process. We used an in vivo glioblastoma-derived xenograft model of tumor escape in presence of continuous treatment with bevacizumab. U87-MG or U118-MG cells were subcutaneously implanted into either BALB/c SCID or athymic nude mice. Bevacizumab was given by intraperitoneal injection every 3 days (2.5 mg/kg/dose) and/or dichloroacetate (DCA) was administered by oral gavage twice daily (50 mg/kg/dose) when tumor volumes reached 0.3 cm(3) and continued until tumors reached approximately 1.5-2.0 cm(3). Microarray analysis of resistant U87 tumors revealed coordinated changes at the level of metabolic genes, in particular, a widening gap between glycolysis and mitochondrial respiration. There was a highly significant difference between U87-MG-implanted athymic nude mice 1 week after drug treatment. By 2 weeks of treatment, bevacizumab and DCA together dramatically blocked tumor growth compared to either drug alone. Similar results were seen in athymic nude mice implanted with U118-MG cells. We demonstrate for the first time that reversal of the bevacizumab-induced shift in metabolism using DCA is detrimental to neoplastic growth in vivo. As DCA is viewed as a promising agent targeting tumor metabolism, our data establish the timely proof of concept that combining it with antiangiogenic therapy represents a potent antineoplastic strategy

Estimating the Global Distribution of Field Size using Crowdsourcing

There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, e.g. automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture

A crowdsourced global data set for validating built-up surface layers

Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki (https://www.geo-wiki.org/) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas

Estimating the Global Distribution of Field Size using Crowdsourcing

There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, e.g. automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture

Drivers of tropical forest loss between 2008 and 2019

During December 2020, a crowdsourcing campaign to understand what has been driving tropical forest loss during the past decade was undertaken. For 2 weeks, 58 participants from several countries reviewed almost 115 K unique locations in the tropics, identifying drivers of forest loss (derived from the Global Forest Watch map) between 2008 and 2019. Previous studies have produced global maps of drivers of forest loss, but the current campaign increased the resolution and the sample size across the tropics to provide a more accurate mapping of crucial factors leading to forest loss. The data were collected using the Geo-Wiki platform (www.geo-wiki.org) where the participants were asked to select the predominant and secondary forest loss drivers amongst a list of potential factors indicating evidence of visible human impact such as roads, trails, or buildings. The data described here are openly available and can be employed to produce updated maps of tropical drivers of forest loss, which in turn can be used to support policy makers in their decision-making and inform the public

Methodology for generating a global forest management layer

The first ever global map of forest management was generated based on remote sensing data. To collect training data, we launched a series of Geo-Wiki (https://www.geo-wiki.org/) campaigns involving forest experts from different world regions, to explore which information related to forest management could be collected by visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, Sentinel time series and normalized difference vegetation index (NDVI) profiles derived from Google Earth Engine. A machine learning technique was then used with the visually interpreted sample (280K locations) as a training dataset to classify PROBA-V satellite imagery. Finally, we obtained a global wall-to-wall map of forest management at a 100m resolution for the year 2015. The map includes classes such as intact forests; forests with signs of management, including logging; planted forests; woody plantations with a rotation period up to 15 years; oil palm plantations; and agroforestry. The map can be used to deliver further information about forest ecosystems, protected and observed forest status changes, biodiversity assessments, and other ecosystem-related aspects