The relationship of drought-related gene expression in Arabidopsis thaliana to hormonal and environmental factors

Almost 2000 drought-responsive genes were identified in Arabidopsis thaliana under progressive soil drought stress using whole-genome oligonucleotide microarrays. Most of the drought-regulated genes recovered to normal expression levels by 3 h after rewatering. It has previously been shown that the abscisic acid (ABA) analogue (+)-8′-acetylene-ABA (PBI425) hyperinduces many ABA-like changes in gene expression to reveal a more complete list of ABA-regulated genes, and it is demonstrated here that PBI425 produced a correspondingly increased drought tolerance. About two-thirds of drought-responsive genes (1310 out of 1969) were regulated by ABA and/or the ABA analogue PBI425. Analysis of promoter motifs suggests that many of the remaining drought-responsive genes may be affected by ABA signalling. Concentrations of endogenous ABA and its catabolites significantly increased under drought stress and either completely (ABA) or partially (ABA catabolites) recovered to normal levels by 3 h after rehydration. Detailed analyses of drought transcript profiles and in silico comparisons with other studies revealed that the ABA-dependent pathways are predominant in the drought stress responses. These comparisons also showed that other plant hormones including jasmonic acid, auxin, cytokinin, ethylene, brassinosteroids, and gibberellins also affected drought-related gene expression, of which the most significant was jasmonic acid. There is also extensive cross-talk between responses to drought and other environmental factors including light and biotic stresses. These analyses demonstrate that ABA-related stress responses are modulated by other environmental and developmental factors

Paraneoplastic leukocytosis in a dog following liposarcoma resection

A 10-year-old, female, neutered cocker spaniel presented for surgical debulking of an axillary and cranial thoracic wall liposarcoma. Pre-surgical blood analysis demonstrated anaemia (packed cell volume 17%), leukocytosis (white blood cell count 43.95 × 10 9/L) and thrombocytopenia (15 × 10 9/L), with platelet loss secondary to chronic intra-lesional haemorrhage or immune-mediated destruction, and concomitant Staphylococcus pseudintermedius urinary tract infection. A blood transfusion and antibiotics were administered before surgery. Within 48 hours after surgery, an extreme leukocytosis (white blood cell count 170 × 10 9/L), involving a severe left shift neutrophilia (95 × 10 9/L) was observed; this resolved within 10 days. Serum granulocyte-colony stimulating factor levels were similar to controls. The extreme leukocytosis was suspected to be related to a paraneoplastic leukaemoid reaction combined with an expected postoperative mild leukocytosis. Further investigation into the pathophysiology underlying similar cases is required. One month after surgery, all haematological abnormalities had normalised, and metronomic chemotherapy with chlorambucil commenced.</p

Population Bottlenecks as a Potential Major Shaping Force of Human Genome Architecture

The modern synthetic view of human evolution proposes that the fixation of novel mutations is driven by the balance among selective advantage, selective disadvantage, and genetic drift. When considering the global architecture of the human genome, the same model can be applied to understanding the rapid acquisition and proliferation of exogenous DNA. To explore the evolutionary forces that might have morphed human genome architecture, we investigated the origin, composition, and functional potential of numts (nuclear mitochondrial pseudogenes), partial copies of the mitochondrial genome found abundantly in chromosomal DNA. Our data indicate that these elements are unlikely to be advantageous, since they possess no gross positional, transcriptional, or translational features that might indicate beneficial functionality subsequent to integration. Using sequence analysis and fossil dating, we also show a probable burst of integration of numts in the primate lineage that centers on the prosimian–anthropoid split, mimics closely the temporal distribution of Alu and processed pseudogene acquisition, and coincides with the major climatic change at the Paleocene–Eocene boundary. We therefore propose a model according to which the gross architecture and repeat distribution of the human genome can be largely accounted for by a population bottleneck early in the anthropoid lineage and subsequent effectively neutral fixation of repetitive DNA, rather than positive selection or unusual insertion pressures

Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm

<p>Abstract</p> <p>Background</p> <p>Nowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space.</p> <p>Results</p> <p>We developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (<it>Plasmodium chabaudi</it>), systemic acquired resistance in <it>Arabidopsis thaliana</it>, similarities and differences between inner and outer cotyledon in <it>Brassica napus </it>during seed development, and to <it>Brassica napus </it>whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples.</p> <p>Conclusions</p> <p>Our analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data.</p

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

National identity predicts public health support during a global pandemic

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.publishedVersio

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.Peer reviewe