Taste sorting and naming: can taste concepts be misrepresented by traditional psychophysical labelling systems?

Japanese and American subjects were required to sort filterpapers, soaked in taste stimuli, into categories that had conceptuallythe same taste. Both Japanese and Americans sorted in the samemanner, indicating similar conceptualization. Taste names weregiven to the taste categories obtained by using the traditional‘four basic taste’ naming system, common in tastepsychophysics. This method was seen to underestimate the numberof categories actually present. This suggests a re-assessmentof current psychophysical taste-naming techniques.     

The ecological drivers and consequences of wildlife trade

Wildlife trade is a key driver of extinction risk, affecting at least 24% of terrestrial vertebrates. The persistent removal of species can have profound impacts on species extinction risk and selection within populations. We draw together the first review of characteristics known to drive species use – identifying species with larger body sizes, greater abundance, increased rarity or certain morphological traits valued by consumers as being particularly prevalent in trade. We then review the ecological implications of this trade-driven selection, revealing direct effects of trade on natural selection and populations for traded species, which includes selection against desirable traits. Additionally, there exists a positive feedback loop between rarity and trade and depleted populations tend to have easy human access points, which can result in species being harvested to extinction and has the potential to alter source–sink dynamics. Wider cascading ecosystem repercussions from trade-induced declines include altered seed dispersal networks, trophic cascades, long-term compositional changes in plant communities, altered forest carbon stocks, and the introduction of harmful invasive species. Because it occurs across multiple scales with diverse drivers, wildlife trade requires multi-faceted conservation actions to maintain biodiversity and ecological function, including regulatory and enforcement approaches, bottom-up and community-based interventions, captive breeding or wildlife farming, and conservation translocations and trophic rewilding. We highlight three emergent research themes at the intersection of trade and community ecology: (1) functional impacts of trade; (2) altered provisioning of ecosystem services; and (3) prevalence of trade-dispersed diseases. Outside of the primary objective that exploitation is sustainable for traded species, we must urgently incorporate consideration of the broader consequences for other species and ecosystem processes when quantifying sustainability.     

Developmental regulation of pectic polysaccharides in the root meristem of Arabidopsis

JIM 5, an antibody that recognizes a relatively unesterifiedpectic epitope, distinguishes between dividing (meristematic)and non-dividing (central cells of the quiescent centre) cellsin the Arabidopsis root tip, indicating that non-dividing cellwalls contain higher levels of relatively unesterified pectinthan dividing cells. JIM 7, an antibody that recognizes a relativelymethyl esterified epitope, labels all cell walls uniformly throughoutthe root, suggesting that there is little variation in the relativelymethyl esterified pectic component in the two cell types. Theseobservations suggest that the characteristics of cell wallsin the root tip result in part from modulations in the amountof unesterified and non-methyl esterified pectin. Key words: Pectin, quiescent centre, roots, Arabidopsis     

Ethylene is a positive regulator of root hair development in Arabidopsis thaliana

Evidence is provided that ethylene is a positive regulator of hair cell development in the root epidermis of Arabidopsis thaliana. Treatment of seedlings with increasing concentrations of the ethylene precursor, 1-aminocyclopropane-1-carboxylic acid (ACC) results in progressively more root hair cells developing in positions normally occupied by non-hair cells. Consistent with these findings are observations that treatments that block either ethylene synthesis or its perception reduce the number of root hairs. A model is proposed in which either ethylene or ACC is a signal involved in specifying the pattern of cell differentiation in the Arabidopsis root epidermis.     

Global discovery of human-infective RNA viruses: A modelling analysis

RNA viruses are a leading cause of human infectious diseases and the prediction of where new RNA viruses are likely to be discovered is a significant public health concern. Here, we geocoded the first peer-reviewed reports of 223 human RNA viruses. Using a boosted regression tree model, we matched these virus data with 33 explanatory factors related to natural virus distribution and research effort to predict the probability of virus discovery across the globe in 2010–2019. Stratified analyses by virus transmissibility and transmission mode were also performed. The historical discovery of human RNA viruses has been concentrated in eastern North America, Europe, central Africa, eastern Australia, and north-eastern South America. The virus discovery can be predicted by a combination of socio-economic, land use, climate, and biodiversity variables. Remarkably, vector-borne viruses and strictly zoonotic viruses are more associated with climate and biodiversity whereas non-vector-borne viruses and human transmissible viruses are more associated with GDP and urbanization. The areas with the highest predicted probability for 2010–2019 include three new regions including East and Southeast Asia, India, and Central America, which likely reflect both increasing surveillance and diversity of their virome. Our findings can inform priority regions for investment in surveillance systems for new human RNA viruses.     

Climate change promotes transitions to tall evergreen vegetation in tropical Asia

Vegetation in tropical Asia is highly diverse due to large environmental gradients and heterogeneity of landscapes. This biodiversity is threatened by intense land use and climate change. However, despite the rich biodiversity and the dense human population, tropical Asia is often underrepresented in global biodiversity assessments. Understanding how climate change influences the remaining areas of natural vegetation is therefore highly important for conservation planning. Here, we used the adaptive Dynamic Global Vegetation Model version 2 (aDGVM2) to simulate impacts of climate change and elevated CO₂ on vegetation formations in tropical Asia for an ensemble of climate change scenarios. We used climate forcing from five different climate models for representative concentration pathways RCP4.5 and RCP8.5. We found that vegetation in tropical Asia will remain a carbon sink until 2099, and that vegetation biomass increases of up to 28% by 2099 are associated with transitions from small to tall woody vegetation and from deciduous to evergreen vegetation. Patterns of phenology were less responsive to climate change and elevated CO₂ than biomes and biomass, indicating that the selection of variables and methods used to detect vegetation changes is crucial. Model simulations revealed substantial variation within the ensemble, both in biomass increases and in distributions of different biome types. Our results have important implications for management policy, because they suggest that large ensembles of climate models and scenarios are required to assess a wide range of potential future trajectories of vegetation change and to develop robust management plans. Furthermore, our results highlight open ecosystems with low tree cover as most threatened by climate change, indicating potential conflicts of interest between biodiversity conservation in open ecosystems and active afforestation to enhance carbon sequestration.     

The phylogenetic range of bacterial and viral pathogens of vertebrates

Many major human pathogens are multihost pathogens, able to infect other vertebrate species. Describing the general patterns of host–pathogen associations across pathogen taxa is therefore important to understand risk factors for human disease emergence. However, there is a lack of comprehensive curated databases for this purpose, with most previous efforts focusing on viruses. Here, we report the largest manually compiled host–pathogen association database, covering 2,595 bacteria and viruses infecting 2,656 vertebrate hosts. We also build a tree for host species using nine mitochondrial genes, giving a quantitative measure of the phylogenetic similarity of hosts. We find that the majority of bacteria and viruses are specialists infecting only a single host species, with bacteria having a significantly higher proportion of specialists compared to viruses. Conversely, multihost viruses have a more restricted host range than multihost bacteria. We perform multiple analyses of factors associated with pathogen richness per host species and the pathogen traits associated with greater host range and zoonotic potential. We show that factors previously identified as important for zoonotic potential in viruses—such as phylogenetic range, research effort, and being vector‐borne—are also predictive in bacteria. We find that the fraction of pathogens shared between two hosts decreases with the phylogenetic distance between them. Our results suggest that host phylogenetic similarity is the primary factor for host‐switching in pathogens.     

Analysis of chemometric models applied to Raman spectroscopy for monitoring key metabolites of cell culture

The Food and Drug Administration (FDA) initiative of Process Analytical Technology (PAT) encourages the monitoring of biopharmaceutical manufacturing processes by innovative solutions. Raman spectroscopy and the chemometric modeling tool partial least squares (PLS) have been applied to this aim for monitoring cell culture process variables. This study compares the chemometric modeling methods of Support Vector Machine radial (SVMr), Random Forests (RF), and Cubist to the commonly used linear PLS model for predicting cell culture components—glucose, lactate, and ammonia. This research is performed to assess whether the use of PLS as standard practice is justified for chemometric modeling of Raman spectroscopy and cell culture data. Model development data from five small-scale bioreactors (2 × 1 L and 3 × 5 L) using two Chinese hamster ovary (CHO) cell lines were used to predict against a manufacturing scale bioreactor (2,000 L). Analysis demonstrated that Cubist predictive models were better for average performance over PLS, SVMr, and RF for glucose, lactate, and ammonia. The root mean square error of prediction (RMSEP) of Cubist modeling was acceptable for the process concentration ranges of glucose (1.437 mM), lactate (2.0 mM), and ammonia (0.819 mM). Interpretation of variable importance (VI) results theorizes the potential advantages of Cubist modeling in avoiding interference of Raman spectral peaks. Predictors/Raman wavenumbers (cm⁻¹) of interest for individual variables are X1139–X1141 for glucose, X846–X849 for lactate, and X2941–X2943 for ammonia. These results demonstrate that other beneficial chemometric models are available for use in monitoring cell culture with Raman spectroscopy.