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Phylogeny and bioprospecting: the diversity of medicinal plants used in cancer management
Get PDFSocietal Impact StatementAs the second‐leading cause of mortality worldwide, cancer is a major focus of drug discovery research. Traditional plant knowledge can guide the search for undiscovered compounds, but the efficacy of this approach for cancer, a highly complex disease affecting diverse tissues, is unknown. We investigated the patterns underlying plant selection for cancer treatment globally, finding certain lineages are repeatedly targeted. While this indicates therapeutic value, their relatedness with plants used for unrelated ethnobotanical uses suggests that plants are probably selected to treat cancer‐associated symptoms, rather than addressing tumour growth. Careful re‐examination and scoring of ethnobotanical reports may make the prediction of lineages for drug discovery more informative.Summary Cancer is a highly diverse disease and as the second‐leading cause of death worldwide is a focus of drug discovery research. Natural products have been shown to be a useful source of novel molecules for treating cancer. It is likely there are many plants with undiscovered molecules of therapeutic value, however identifying new leads from the vast diversity of plants is very challenging. Traditional knowledge might inform bioprospecting by predicting lineages of plants rich in therapeutically useful molecules. We characterise the phylogenetic diversity of plants used in traditional cancer management using a comprehensive genus‐level phylogeny of angiosperms, and a list of 597 genera used globally to treat different cancers. We phylogenetically predict which lineages may have elevated potential for drug discovery and assess the quality of the prediction. We demonstrate the independent and repeated targeting of specific lineages of plants by different peoples in different parts of the world. However, the lineages we report here as rich in plants used in traditional cancer management coincide with those for other ethnobotanical applications and contain few plants with proven anti‐cancer activity. That the same lineages are used to treat different cancers is suggestive of independent discovery of therapeutic value. However, it is likely that the traditional knowledge explored here is shaped by the selection of plants conferring milder effects for treating wider symptoms, such as tiredness or nausea, rather than for halting tumour growth. Accurate prediction of useful plant lineages for cancer management requires more nuanced information than is commonly provided in ethnobotanical records
Discriminating foliar adhered from metabolised Pb when monitoring vegetation exposed to windborne contamination
Get PDFMonitoring heavy metals in vegetation near mining or industrial sites is crucial for detecting plant contamination; requiring discrimination between metals adhered to foliar surfaces from the internal concentrations. We investigated key factors that might contribute to lead (Pb) accumulation in leaves of local vegetation near a Pb mine: (i) distance from the pollutant source, (ii) morphological characteristics of leaf surfaces, (iii) their susceptibility to Pb loss by washing, and (iv) the effect of contrasting washing reagents in Pb removal. Native plant species were sampled at three field locations, possessing different leaf surface morphologies: glabrous (smooth), resinous (waxy) and hirsute (hairy). After washing with Citranox, EDTA or deionised water, Pb contents were assessed by ICP-OES and SEM-EDX. We observed an order of Pb (and other metals) retention from hirsute > resinous > glabrous, and found: i) greater Pb accumulation in leaves near the mine due to particulate matter (PM) deposition; ii) hirsute leaves retain the highest PM-Pb; iii) higher Pb removal (10-fold) by Citranox and EDTA compared to water; and iv) hirsute leaves retained considerable PM-Pb underneath trichomes despite washing, leading to Pb overestimation. Therefore, for accurate Pb monitoring, washed glabrous leaves are best indicated due to their negligible PM retention
Dynamics between housing and stock markets: international evidence over 1870 to 2015
Get PDFThis research investigates the dynamic relationship between housing and stock markets across nine countries. Using total return indices from 1870 to 2015, empirical results around the globe consistently show that stock and housing markets are linearly segmented, with fractional integration found in Denmark and the US. A positive lead-lag relationship from stock to housing is observed for most countries, offering support for the wealth effect theory. The results have important implications for portfolio diversification strategy and government policy
Asymmetries in firm-level globalization: the case of Swiss multinational enterprises
Get PDFThis paper addresses the regional and global strategies of multinational enterprises (MNEs), with an application to the largest Swiss companies. We extend Rugman and Verbeke’s (2004) classic approach to measure MNE globalization by adopting a multidimensional lens, whereby we focus on four distinct parameters that evaluate respectively: market success across geographic space (proxied by sales); investments as a response to foreign business opportunities (proxied by assets); human capital (as proxied by the employees’ geographic distribution); and knowledge capital (as measured by patented innovations). We observe substantial discrepancies in globalization levels according to the parameter used. According to this study, the largest segment of companies (42.1%) remains home-regional in terms of sales. Bi-regional firms constitute the second largest category, comprising 28.9% of the sample. Only 21.1% of the companies can be classified as global in terms of sales distribution. Upstream activities such as knowledge capital seem to be more home-region oriented than downstream activities. One critical conclusion of this study is that not a single large Swiss MNE can be considered global in terms of knowledge capital creation
The Role of cluster ecosystems and intellectual capital in achieving high growth entrepreneurship: evidence from Germany
Get PDFPurpose – This paper examines the role played by business cluster ecosystems and intellectual capital in achieving high growth firm (HGF) status.
Design/methodology/approach – We draw our insights from the knowledge-based perspective and economic geography as theoretical lens, which combined offers a more unifying understanding of how business cluster ecosystems and intellectual capital foster high growth entrepreneurship.
Findings – Drawing on a sample of 11,360 German incorporated firms across 80 clusters over the period 2010-2013, we find that cluster ecosystems play a significant role in supporting firms to become high-growth firms. More specifically, being located in business clusters increases the likelihood of becoming high growth firms (HGFs) by 2.2 percent - 4.49 percent. We also find that clusters with more productive firms in the ecosystems provide favourable conditions for member firms to achieve HGF status, while the impact of other cluster-specific conditions (High-tech cluster membership and MNE share in clusters) are less clear. Additional insights suggest that firm intellectual capital (investments in intangible assets) enables firms to achieve high growth status.
Research limitations/implications – The findings of this paper hold theoretical and managerial relevance and shed more light on the impact of cluster-specific factors in the ecosystems and firm intellectual capital in achieving high growth entrepreneurship.
Originality/value – This paper is among the first of its kind to bring together three distinct literatures (HGFs, business clusters and intellectual capital) and utilize insights from each to derive a conceptual framework that links them in explaining high growth entrepreneurship.
Keywords - Business Cluster; Ecosystems; Intellectual capital, High growth firms; Germany
Paper type Research pape
Impact of different carbon labels on consumer inference
Get PDFCarbon labelling of food products serves as a demand-side tool with the potential to drive the essential shift in consumption patterns toward reducing climate impact. For carbon labels to influence food choices, they must enable consumers to recognize and adopt purchasing behaviour that lower their climate footprint. While inference plays a critical role in facilitating behavioural change, evidence remains sparse regarding how specific characteristics of carbon labels affect consumers' ability to accurately identify low-carbon products.
This study investigates how different carbon labels affect consumers' efficiency in identifying low-carbon-emitting food products. Three labels are evaluated: (i) ‘Digit’ specifies the amount of CO2e-emissions from the production of the product, (ii) ‘Colour-Coded’ label indicates the overall climate impact from A to E, (iii) ‘Logo’ identifies the lowest-emitting products within each product category.
Respondents in a survey in the United Kingdom were asked to identify the lowest-emitting food product in a set of tasks. All labels improved accuracy in the tasks when products from the same food category were included. Importantly, in the tasks that included products from different categories, the Digit outperformed both the Colour-Coded and the Logo labels. Notably, the Logo did not improve accuracy compared to no-label tasks. It is important that a carbon label informs about the overall climate impact rather than the within-category performance, should the label help consumers identify changes that contribute to significant reductions in climate impact
Ecosystem-atmosphere exchanges of carbon dioxide, water vapour and energy in India: a synthesis of insights from eddy covariance measurements
No full textIndia is a large country characterised by diverse bioclimatic regions and semi-natural and managed ecosystems, with some of the largest areas of arable land and mangroves, globally. Eddy covariance represents the state-of-the-art for directly quantifying the exchange of mass and energy between land surface and atmosphere. Here, we collate eddy covariance flux observations from several sites across India, covering major land use and vegetation types and
spanning twenty-seven site-years. The pattern of maximum and minimum CO2 exchange differ widely among the sites and ecosystems. Croplands exhibit maximum CO2 uptake during the
monsoon in response to rainfall. Some forests, croplands, and mangroves behave as well-watered ecosystems, whereas others oscillate between well-watered and water-stressed states, due to temperature and moisture dynamics. Respiration changes commensurately with photosynthetic CO2 uptake, primarily comprising growth respiration. Grasslands have a higher carbon retention capacity, followed by croplands, forests, and mangroves. CO2, water, and
sensible and latent heat fluxes peaked during different times of the day across ecosystems, imprinting phase-lags that vary by site and season. Water-limited ecosystems register the highest ecosystem water use efficiency (WUE), whereas the irrigated croplands have the lowest WUE. Forests have intermediate WUE of these two; however, Indian forests (predominantly tropical and subtropical) have lower WUE than their temperate and boreal counterparts. Canopy-atmosphere coupling is tightest during the dry periods, with their physiological controls regulating the properties of the surface atmosphere. This is reversed during the
monsoon when environmental control dominates physiological control. This information is essential for the long-term monitoring of these ecosystems and climate studies and will be useful to different communities, including scientists, economists, resource managers, and policymakers
The Galician MultiPic: a picture dataset that captures lexical variation
Get PDFThe Multilingual Picture (MultiPic) database has been instrumental in advancing psycholinguistic research by providing standardized norms for colored images across multiple languages. However, many lesser-studied languages remain underrepresented. This study introduces the Galician MultiPic dataset, which provides norms for naming agreement and conceptual familiarity for 500 colored images. Galician, a Western Ibero-Romance language spoken in northwestern Spain, represents a distinct linguistic context due to its bilingual coexistence with Spanish. Data were collected from 85 Galician speakers (all Galician-Spanish bilinguals). Results indicate an average name agreement (H statistic) of 0.71, reflecting the linguistic variability inherent to Galician. This new resource broadens the scope of MultiPic, enabling cross-linguistic comparisons and facilitating psycholinguistic studies in Galician. The dataset is publicly accessible, offering researchers a valuable tool for exploring cognitive processes within bilingual environments
Global River Topology (GRIT): a bifurcating river hydrography
Get PDFExisting global river networks underpin a wide range of hydrological applications but do not represent channels with divergent river flows (bifurcations, multi-threaded channels, canals), as these features defy the convergent flow assumption that elevation-derived networks (e.g. HydroSHEDS, MERIT Hydro) are based on. Yet, bifurcations are important features of the global river drainage system, especially on large floodplains and river deltas, and are also often found in densely populated regions. Here we developed the first raster and vector-based Global RIver Topology (GRIT) that not only represents the tributaries of the global drainage network but also the distributaries, including multi-threaded rivers, canals and deltas. We achieve this by merging a 30m Landsat-based river mask with elevation-generated streams to ensure a homogeneous drainage density outside of the river mask for rivers narrower than approximately 30m. Crucially, we employ the new 30 m digital terrain model, FABDEM, based on TanDEM-X, which shows greater accuracy over the traditionally used SRTM derivatives. After vectorisation and pruning, directionality is assigned by a series of elevation, flow angle and continuity approaches. The new global network and its attributes are validated using gauging stations, comparison with existing networks, and randomized manual checks. The new network represents 19.6 million km of streams
and rivers with drainage areas greater than 50 km2 and includes 67,495 bifurcations. With the advent of hyper-resolution modelling and artificial intelligence, GRIT is expected to greatly improve the accuracy of many river-based applications such as flood forecasting, water availability and quality simulations, or riverine habitat mapping
Efficient prediction of the local electronic structure of ionic liquids from low-cost calculations
Get PDFUnderstanding and predicting ionic liquid (IL) electronic structure is crucial for their development, as local, atomic-scale electrostatic interactions control both the ion-ion and ion-dipole interactions that underpin all applications of ILs. Core-level binding energies, EB(core), from X-ray photoelectron spectroscopy (XPS) experiments capture the electrostatic potentials at nuclei, thus offering significant insight into IL local electronic structure. However, our ability to measure XPS for the many thousands of possible ILs is limited. Here we use an extensive experimental XPS dataset comprised of 44 ILs to comprehensively validate the ability of a very low-cost and technically accessible calculation method, lone-ion-SMD (Solvation Model based on Density) density functional theory (DFT), to produce high quality core-level binding energies, EB(core) for 14 cations and 30 anions. Our method removes the need for expensive and technically challenging calculation methods to obtain EB(core), thus giving the possibility to predict local electronic structure and understand electrostatic interactions at the atomic scale. We demonstrate the ability of the lone-ion SMD method to predict the speciation of halometallate anions in ILs