Perfectionistic Cognitions as Antecedents of Work Engagement : Personal Resources, Personal Demands, or Both?

Whereas personal resources have been established as a counterpart to external job resources in the Job Demands–Resources Theory, personal demands as a counterpart to job demands have been rather neglected. In this study, we propose that multidimensional perfectionism—in the form of daily perfectionistic cognitions—is a relevant personal characteristic for predicting daily work engagement in addition to and in its interplay with daily time pressure as a common job demand. 157 employees participated in a daily diary study for 15 workdays. As hypothesized, multilevel regression analyses yielded a positive unique effect of perfectionistic strivings cognitions and a negative unique effect of perfectionistic concerns cognitions on daily work engagement. Furthermore, we found that both unique perfectionistic strivings cognitions and perfectionistic concerns cognitions moderated a quadratic relationship between daily time pressure and daily work engagement. Building on the Job Demands–Resources Theory, we propose that the dimension of perfectionistic strivings constitutes a personal resource and the dimension of perfectionistic concerns constitutes a personal demand in the prediction of work engagement

Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants' Well-being: Ecological Momentary Assessment

Background: Sensors embedded in smartphones allow for the passive momentary quantification of people’s states in the context of their daily lives in real time. Such data could be useful for alleviating the burden of ecological momentary assessments and increasing utility in clinical assessments. Despite existing research on using passive sensor data to assess participants’ moment-to-moment states and activity levels, only limited research has investigated temporally linking sensor assessment and self-reported assessment to further integrate the 2 methodologies. Objective: We investigated whether sparse movement-related sensor data can be used to train machine learning models that are able to infer states of individuals’ work-related rumination, fatigue, mood, arousal, life engagement, and sleep quality. Sensor data were only collected while the participants filled out the questionnaires on their smartphones. Methods: We trained personalized machine learning models on data from employees (N=158) who participated in a 3-week ecological momentary assessment study. Results: The results suggested that passive smartphone sensor data paired with personalized machine learning models can be used to infer individuals’ self-reported states at later measurement occasions. The mean R 2 was approximately 0.31 (SD 0.29), and more than half of the participants (119/158, 75.3%) had an R 2 of ≥0.18. Accuracy was only slightly attenuated compared with earlier studies and ranged from 38.41% to 51.38%. Conclusions: Personalized machine learning models and temporally linked passive sensing data have the capability to infer a sizable proportion of variance in individuals’ daily self-reported states. Further research is needed to investigate factors that affect the accuracy and reliability of the inference

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

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

Northward shift of the agricultural climate zone under 21st-century global climate change

As agricultural regions are threatened by climate change, warming of high latitude regions and increasing food demands may lead to northward expansion of global agriculture. While socio-economic demands and edaphic conditions may govern the expansion, climate is a key limiting factor. Extant literature on future crop projections considers established agricultural regions and is mainly temperature based. We employed growing degree days (GDD), as the physiological link between temperature and crop growth, to assess the global northward shift of agricultural climate zones under 21st-century climate change. Using ClimGen scenarios for seven global climate models (GCMs), based on greenhouse gas (GHG) emissions and transient GHGs, we delineated the future extent of GDD areas, feasible for small cereals, and assessed the projected changes in rainfall and potential evapotranspiration. By 2099, roughly 76% (55% to 89%) of the boreal region might reach crop feasible GDD conditions, compared to the current 32%. The leading edge of the feasible GDD will shift northwards up to 1200 km by 2099 while the altitudinal shift remains marginal. However, most of the newly gained areas are associated with highly seasonal and monthly variations in climatic water balances, a critical component of any future land-use and management decisions

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

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

Global forest management data for 2015 at a 100 m resolution

Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services