Development and Validation of the Frost Multidimensional Perfectionism Scale–Brief

Twenty-five years ago, one of the first empirically validated measures of perfectionism, the Frost et al. Multidimensional Perfectionism Scale (F-MPS) was published. Since that time, psychometric studies of the original F-MPS have provided a plethora of evidence to support the potential development of a shorter yet still psychometrically robust version of the measure. Using confirmatory factor analyses across community and clinical samples, the current study identifies an eight-item F-MPS-Brief with two dimensions (i.e., striving and evaluative concerns) that evidences good internal consistency, measurement equivalence across ethnicities, and concurrent and convergent validity. This new, short version of the F-MPS captures well the bidimensional model of perfectionism that has emerged across studies over the past two decades and is suggested for use when a short yet high-performing assessment tool for this model is desired

Towards standardization of human adipose-derived stromal cells secretomes

The secretome of adipose-derived stromal cells (ASC) is a heterogeneous mixture of components with a beneficial influence on cellular microenvironments. As such, it represents a cell-free alternative in regenerative medicine therapies. Pathophysiological conditions increase the therapeutic capacity of ASC and, with this, the benefits of the secretome. Such conditions can be partially mimicked in vitro by adjusting culturing conditions. Secretomics, the unbiased analysis of a cell secretome by mass spectrometry, is a powerful tool to describe the composition of ASC secretomes. In this proteomics databases review, we compared ASC secretomic studies to retrieve persistently reported proteins resulting from the most explored types of culturing conditions used in research, i.e., exposure to normoxia, hypoxia, or cytokines. Our comparisons identified only eight common proteins within ASC normoxic secretomes, no commonalities within hypoxic ASC secretomes, and only nine within secretomes of ASC exposed to proinflammatory cytokines. Within these, and regardless of the culturing condition that stimulated secretion, a consistent presence of extracellular matrix-related pathways associated with such proteins was identified. Confounders such as donors' age, sex, body mass index, the anatomical area where ASC were harvested, secretome collection method, data description, and how the data is shared with the scientific community are discussed as factors that might explain our outcomes. We conclude that standardization is imperative as the currently available ASC secretomic studies do not facilitate solid conclusions on the therapeutic value of different ASC secretomes.</p

Towards standardization of human adipose-derived stromal cells secretomes

The secretome of adipose-derived stromal cells (ASC) is a heterogeneous mixture of components with a beneficial influence on cellular microenvironments. As such, it represents a cell-free alternative in regenerative medicine therapies. Pathophysiological conditions increase the therapeutic capacity of ASC and, with this, the benefits of the secretome. Such conditions can be partially mimicked in vitro by adjusting culturing conditions. Secretomics, the unbiased analysis of a cell secretome by mass spectrometry, is a powerful tool to describe the composition of ASC secretomes. In this proteomics databases review, we compared ASC secretomic studies to retrieve persistently reported proteins resulting from the most explored types of culturing conditions used in research, i.e., exposure to normoxia, hypoxia, or cytokines. Our comparisons identified only eight common proteins within ASC normoxic secretomes, no commonalities within hypoxic ASC secretomes, and only nine within secretomes of ASC exposed to proinflammatory cytokines. Within these, and regardless of the culturing condition that stimulated secretion, a consistent presence of extracellular matrix-related pathways associated with such proteins was identified. Confounders such as donors' age, sex, body mass index, the anatomical area where ASC were harvested, secretome collection method, data description, and how the data is shared with the scientific community are discussed as factors that might explain our outcomes. We conclude that standardization is imperative as the currently available ASC secretomic studies do not facilitate solid conclusions on the therapeutic value of different ASC secretomes.</p

Deep machine learning provides state-of-the art performance in image-based plant phenotyping

Deep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches

Evolutionary dynamics of tree invasions: complementing the unified framework for biological invasions

Evolutionary processes greatly impact the outcomes of biological invasions. An extensive body of research suggests that invasive populations often undergo phenotypic and ecological divergence from their native sources. Evolution also operates at different and distinct stages during the invasion process. Thus, it is important to incorporate evolutionary change into frameworks of biological invasions because it allows us to conceptualize how these processes may facilitate or hinder invasion success. Here, we review such processes, with an emphasis on tree invasions, and place them in the context of the unified framework for biological invasions. The processes and mechanisms described are pre-introduction evolutionary history, sampling effect, founder effect, genotype-by-environment interactions, admixture, hybridization, polyploidization, rapid evolution, epigenetics and second-genomes. For the last, we propose that co-evolved symbionts, both beneficial and harmful, which are closely physiologically associated with invasive species, contain critical genetic traits that affect the evolutionary dynamics of biological invasions. By understanding the mechanisms underlying invasion success, researchers will be better equipped to predict, understand and manage biological invasions