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Phytochemical characterization and anti-inflammatory activity of a water extract of Gentiana purpurea roots
No full textEthnopharmacological relevance
Gentiana purpurea was one of the most important medicinal plants in Norway during the 18th and 19th centuries, and the roots were used against different types of gastrointestinal and airway diseases.
Aim of the study
To explore the content of bioactive compounds in a water extract from the roots, a preparation commonly used in traditional medicine in Norway, to assess the anti-inflammatory potential, and furthermore to quantify the major bitter compounds in both roots and leaves.
Materials and methods
G. purpurea roots were boiled in water, the water extract applied on a Diaion HP20 column and further fractionated with Sephadex LH20, reverse phase C18 and normal phase silica gel to obtain the low molecular compounds. 1D NMR, 2D NMR, and ESI-MS were used for structure elucidation. HPLC-DAD analysis was used for quantification. The inhibition of TNF-α secretion in ConA stimulated peripheral blood mononuclear cells (PBMCs) was investigated.
Results
Eleven compounds were isolated and identified from the hot water extract of G. purpurea roots. Gentiopicrin, amarogentin, erythrocentaurin and gentiogenal showed dose-dependent inhibition of TNF-α secretion. Gentiopicrin is the major secondary metabolite in the roots, while sweroside dominates in the leaves.
Conclusions
The present work gives a comprehensive overview of the major low-molecular weight compounds in the water extracts of G. purpurea, including metabolites produced during the decoction process, and show new anti-inflammatory activities for the native bitter compounds as well as the metabolites produced during preparation of the crude drug
Entrapment
No full textThe word ‘entrapment’ has three common usages in legal discourse. First, it is used in
connection with acts of entrapment: it applies, at least, to a class of acts in which a party,
whom we call the ‘agent’, intentionally brings it about that another party, whom we call the
‘target’, performs a distinct act that is of a criminal type. Secondly, it is used to refer to a
method of proactive law enforcement: the use of acts of entrapment to secure convictions.
Thirdly, it is used, predominantly in the USA, to refer to the entrapment defence: under
this usage, it is argued that the offence with which the defendant is charged resulted from an
act of entrapment. In most jurisdictions, and in the literature, the focus has been, as it is
here, on acts of entrapment, called ‘legal’, ‘state’, ‘government’ or ‘police’ entrapment, in
which the agent is, or is a deputy of, a law-enforcement officer. We mention entrapment by
other agents, called ‘civil’, ‘non-state’ or ‘private’ entrapment, only in passing
Collaborative display of competence: A case study of process-oriented video-based assessment in schools
No full textThis paper presents a case study of process-oriented assessment in a Norwegian secondary school. We investigate the teachers' design of a process-oriented and video-based assessment, shedding light on how student collaboration and competence was displayed and made assessable in video-recorded group assessments. The results reveal that, although this is a highly complex assessment format, student group videos can be integrated within process-oriented assessment in ways that allow for assessing students’ collaborative work
Flow loop study of a cold and cohesive slurry. Pressure drop and formation of plugs
No full textSlurries of cohesive particles constitute a significant risk during subsea petroleum production due to their potential to plug the flow. This article describes a flow loop study of a slurry consistent with 0.23-mm ice particles in decane. The experiments were conducted for the concentration of particles up to 20.3% vol. and Re 25000. The cohesion of ice was suggested by controlling the temperature of the slurry. The relative viscosity of the slurry was computed as a function of particle concentration using pressure drop measurements. The relative viscosity was 3.1 for the concentration of 20.3%. The Bingham-fluid model agreed with the empirical calculations within the discrepancy of 15.5%. Increased viscosity of slurry led to a higher pressure drop in the flow loop compared to the single-phase case. Pressure drops for 20.3% slurry flow were 5.2% and 44.4% higher than for pure decane at Reynolds numbers of 24778 and 4956, respectively. The test section of the loop was equipped with an orifice to induce the formation of plugs. The plugs were observed at particle concentrations below 7.0%. The article presents detailed experimental logs depicting the process of plug formation. The observed blocking cases partially agreed with flow maps from the literature. In addition, we note the applicability of the blockage risk evaluation technique from the Colorado School of Mines
Mobile money as a driver of digital financial inclusion
No full textMeeting the mobile money needs of the less privileged in developing and emerging markets opens up enormous possibilities for banks and newly emerged financial-technology firms. Many consider mobile money services a separate domain within the banking and payment sector, different from its siblings: automated teller machines, net banking, point-of-sale banking, etc. This study was conducted to investigate how mobile money services act as a reliable driver of digital financial inclusion and to determine the role of mobile money agents in the transformation from the traditional services to mobile money services. This paper presents a conceptual model based on the stimulus-organism-response paradigm. We propose that the mobile money agent characteristics are the stimuli, that the mobile money customer is the organism, and that the response of the organism to the stimuli is continuous usage, which leads to financial inclusion in the developing country of Ghana. The continuous usage of mobile money services by customers encourages more engagement experiences and advocacy intentions. We provide empirical evidence suggesting that mobile money agent credibility and service quality stimulate customer empowerment. Furthermore, we argue that for the less financially empowered customer segment, mobile money agent credibility provides the needed impetus for the continuous usage of mobile money services
Global sensitivity analysis and optimal design of heat recovery ventilation for zero emission buildings
No full textEnergy-efficient building services are necessary to realise zero-emission buildings while maintaining adequate indoor environmental quality. As the share of ventilation heating needs grow in well-insulated and airtight buildings, heat recovery in mechanical ventilation systems is increasingly common. Ventilation heat recovery is one of the most efficient and viable means to reduce ventilation heat losses and save energy. Highly efficient heat exchangers are being developed or applied to maximise the energy-saving potential of heat recovery ventilation. Nevertheless, the effects of practical operating conditions and the constraints of heat recovery – such as variations in ventilation rates, frost protection, and the prevention of an overheated air supply over a long-term period, which may significantly influence realistic recovery rates – have been less considered in efforts to maximise the energy savings. It is unclear which design parameters for heat recovery devices have the greatest effect on the annual energy savings from ventilation.
This study proposes annual efficiency and annual net energy saving models for heat recovery ventilation that consider ventilation rate variations, the longitudinal heat conduction effect and operating controls. We use a global sensitivity analysis to quantify the contributions of various design input parameters to the variation in annual recovery efficiency and annual net energy savings. We identify the most influential parameters and their significant interaction effects for the annual energy performance of heat recovery ventilation. More attention should be paid to these most influential parameters during the design process. Furthermore, the optimal designs for rotary heat exchangers (as identified by a pattern-search optimisation algorithm) can improve annual net energy savings in demand-controlled ventilation by 33–48%, depending on the building areas. In combination with the reference year analysis presented in this study, heat recovery and demand-controlled ventilation can help to meet the need for highly efficient ventilation systems and zero-emission buildings
Strategies for successful Suzuki-Miyaura cross-couplings with thienylboronic acids: From model studies to dye structures
No full textSuzuki-Miyaura cross-coupling is a convenient way for preparing thienyl containing dyes and bioactive molecules. However, reactions with thienylboronic acids can be troublesome. To understand these reactions in more depth, we herein present our investigation into the cross-coupling of (5-formylthiophen-2-yl)boronic acid with 4-bromoanisole as a model reaction. The study includes variations of catalyst, base, aryl halide type, temperature and reaction medium. A key to succeed with such cross-couplings is a highly active catalyst, and sufficient solubility of both the boronic acid and the aryl halide. XPhos precatalysts proved to be the best system for the model reaction, while other catalysts might be applicable provided that more boronic acid is added, or an aryl iodide is used as cross-coupling partner. The XPhos 4th generation precatalyst was also efficient for the construction of mono- and di-thiophene-2-carbaldehyde substituted phenothiazines and a triarylamine dye precursor. However, each cross-coupling pair needs fine tuning of the reaction medium to maximize yield
Explainability in subgraphs-enhanced Graph Neural Networks
No full textRecently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance
the expressive power of Graph Neural Networks
(GNNs), which was proved to be not higher than
the 1-dimensional Weisfeiler-Leman isomorphism
test. The new paradigm suggests using subgraphs
extracted from the input graph to improve the
model’s expressiveness, but the additional complexity exacerbates an already challenging problem in
GNNs: explaining their predictions. In this work,
we adapt PGExplainer, one of the most recent explainers for GNNs, to SGNNs. The proposed explainer accounts for the contribution of all the different subgraphs and can produce a meaningful explanation that humans can interpret. The experiments that we performed both on real and synthetic datasets show that our framework is successful in explaining the decision process of an SGNN
on graph classification tasks
Provincial variations and entrepreneurialism in the development of China’s Distant Water Fisheries (2011–2020)
No full textThis study rescales the current state-centric understanding of the development of China’s distant water fishing (DWF) and explores the instrumental role of Chinese provinces in actualizing and shaping this development. The rapid growth of China’s DWF during 2011–2020 can be attributed primarily to five subnational provinces and actors. As a case study, this article shows that the Fujian provincial government proactively carved out development space for boosting its DWF industry, despite Beijing’s growing efforts to tighten central control out of concern over environmental externalities. Central–Local relations remain a critical perspective for those who seek to understand the challenges faced by China’s central government as it tries to rein in the rapid expansion of the country’s DWF activities
Stochastic Cluster Embedding
No full textNeighbor embedding (NE) aims to preserve pairwise similarities between data items and has been shown to yield an effective principle for data visualization. However, even the best existing NE methods such as stochastic neighbor embedding (SNE) may leave large-scale patterns hidden, for example clusters, despite strong signals being present in the data. To address this, we propose a new cluster visualization method based on the Neighbor Embedding principle. We first present a family of Neighbor Embedding methods that generalizes SNE by using non-normalized Kullback–Leibler divergence with a scale parameter. In this family, much better cluster visualizations often appear with a parameter value different from the one corresponding to SNE. We also develop an efficient software that employs asynchronous stochastic block coordinate descent to optimize the new family of objective functions. Our experimental results demonstrate that the method consistently and substantially improves the visualization of data clusters compared with the state-of-the-art NE approaches. The code of our method is publicly available at https://github.com/rozyangno/sce