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We show the existence of additive kinematic formulas for general flag area measures, which generalizes a recent result by Wannerer. Building on previous work by the second named author, we introduce an algebraic framework to compute these formulas explicitly. This is carried out in detail in the case of the incomplete flag manifold consisting of all (p+1)-planes containing a unit vector.
Clean water is fundamental to human health and ecosystem integrity. However, water quality deteriorates due to novel anthropogenic pollutants present at microgram per liter concentrations in urban water cycles (termed micropollutants). Wastewater treatment plants (WWTP) have been identified as major point sources for aquatic (micro-)pollutants. Chemical and ecotoxicological analyses have shown that conventional biological WWTPs do not fully remove micropollutants and associated toxicities, which is often because of mobile, polar and/or recalcitrant compounds and transformation products (TPs). To minimize possible environmental risks, advanced wastewater treatment (AWWT) technologies could be a promising mitigation measure. Multiple processes are therefore being developed and evaluated such as ozonation and ozonation followed by granulated activated carbon (GAC) or biological filtration. Assessing the performance of these combined AWWTs was the focus the TransRisk project. Within this project, this thesis accomplished four major goals.
Firstly, the preparation of (waste)water samples was optimised for in vitro bioassays. Acidification, filtration and solid phase extraction (SPE) were tested for their impact on environmentally relevant in vitro endocrine activities, mutagenicity, genotoxicity and cytotoxicity. Significantly different outcomes of these assays were detected comparing neutral and acidified samples. Sample filtration had a lesser impact, but in some cases retention of particle-bound compounds could have caused significant toxicity losses. Out of three SPE sorbents the Telos C18/ENV at sample pH 2.5 extracted highest toxicity, some undetected in aqueous samples. These results indicate that sample preparation needs to be optimised for specific sample matrices and bioassays to avoid false-positive or -negative detects in effect-based analyses.
Secondly, the above listed in vitro toxicities were monitored in a protected region for drinking water production in South-West Germany (2012-2015). Out of 30 sampling sites surface water and groundwater were the least polluted. Nonetheless, a few groundwater samples induced high anti-estrogenic activity that prompted further monitoring. The latter included a waterworks in which no toxicity was detected. Hospital wastewater also had elevated in vitro toxicities and hospitals are, thus, relevant intervention points for source control. The biological WWTPs were effective in removing most of the detected toxicity, and the selected bioassays proved to be pertinent tools for water quality assessment and prioritisation of pollution hotspots.
Thirdly, the in vivo bioassay ISO10872 based on Caenorhabditis elegans (C. elegans) was adapted for this thesis. Using this model, a median effect concentration (EC50) for reproductive toxicity of the polycyclic aromatic hydrocarbon β-naphthoflavone (β- NF) of 114 µg/L was computed which is slightly lower than reported in the scientific literature. β-NF induced cyp-35A3::GFP (a biomarker in transgenic animals) in a time and concentration dependent manner (≤ 21.3–24 fold above controls). β-NF spiked wastewater samples supported earlier hypotheses on particle-bound pollutants. Reproductive toxicity (96 h) and cyp-35A3 induction (24 h) of biologically treated and/or ozonated wastewater extracts and growth promoting effects of GAC/biologically filtered ozonated wastewater extracts were observed. This suggested the presence of residual bioactive/toxic chemicals not included in the targeted chemical analysis. It also highlighted the importance of integrating multiple (apical and molecular) endpoints in wastewater assessments.
Fourthly, five in vitro and the adapted C. elegans bioassay were integrated into a wastewater quality evaluation (developed within TransRisk). Out of the five AWWT options, ozonation (at 1 g O3,applied/g DOC, HRT ~ 18 min) combined with nonaerated GAC filtration was rated most effective for toxicity removal. All five AWWTs largely removed estrogenic and (anti-)androgenic activities, but not anti-estrogenic activity and mutagenicity, which even increased during ozonation. This has been observed in related studies and points towards toxic TPs. These results also emphasized the need for implementing an effective post-treatment for ozonation. The results from a parallel in vivo study with Lumbriculus variegatus and Potamopyrgus antipodarum conducted on site at the WWTP (using flow through systems) were in accordance with the C. elegans results. In this context, it is suggested to further implement C. elegans as sensitive, feasible and ecologically relevant model.
In conclusion, this thesis shows how optimised sample preparation, long-term (in vitro) environmental monitoring, sensitive and ecologically relevant (in vivo) bioassays as well as innovative evaluation concepts, are pivotal in improving the removal of micropollutants and their toxicities with AWWTs. Future research should further develop and evaluate measures at sewer systems, conventional biological, tertiary and other advanced treatment technologies, as well as sociopolitical strategies (e.g., source control or natural conservation) and restoration projects. The effect-based tools optimised in this thesis will support assessing their success.
Medicinal plants represent a big reservoir for discovering new drugs against all kinds of diseases including inflammation. In spite the large number of promising anti-inflammatory plant extracts and isolated components, research on medicinal plants proves to be very difficult. Based on that background this review aims to provide a summarized insight into the hitherto known pharmacologically active concentrations, bioavailability, and clinical efficacy of boswellic acids, curcumin, quercetin and resveratrol. These examples have in common that the achieved plasma concentrations were found to be often far below the determined IC50 values in vitro. On the other hand demonstrated therapeutic effects suggest a necessity of rethinking our pharmacokinetic understanding. In this light this review discusses the value of plasma levels as pharmacokinetic surrogates in comparison to the more informative value of tissue concentrations. Furthermore the need for new methodological approaches is addressed like the application of combinatorial approaches for identifying and pharmacokinetic investigations of active multi-components. Also the physiological relevance of exemplary in vitro assays and absorption studies in cell-line based models is discussed. All these topics should be ideally considered to avoid inaccurate predictions for the efficacy of herbal components in vivo and to unlock the “black box” of herbal mixtures.
Artificial Intelligence (AI) and Machine Learning (ML) are currently hot topics in industry and business practice, while management-oriented research disciplines seem reluctant to adopt these sophisticated data analytics methods as research instruments. Even the Information Systems (IS) discipline with its close connections to Computer Science seems to be conservative when conducting empirical research endeavors. To assess the magnitude of the problem and to understand its causes, we conducted a bibliographic review on publications in high-level IS journals. We reviewed 1,838 articles that matched corresponding keyword-queries in journals from the AIS senior scholar basket, Electronic Markets and Decision Support Systems (Ranked B). In addition, we conducted a survey among IS researchers (N = 110). Based on the findings from our sample we evaluate different potential causes that could explain why ML methods are rather underrepresented in top-tier journals and discuss how the IS discipline could successfully incorporate ML methods in research undertakings.
We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump’s tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump’s decision to tweet about the economy.
Using 10.1 × 109 J/ψ events produced by the Beijing Electron Positron Collider (BEPCII) at a center-of-mass energy √s = 3.097 GeV and collected with the BESIII detector, we present a search for the rare semi-leptonic decay J/ψ → D−e+νe + c.c. No excess of signal above background is observed, and an upper limit on the branching fraction ℬ(J/ψ → D−e+νe + c. c.) < 7.1 × 10−8 is obtained at 90% confidence level. This is an improvement of more than two orders of magnitude over the previous best limit.
Based on an e+e− collision data sample corresponding to an integrated luminosity of 2.93 fb−1 collected with the BESIII detector at √s=3.773 GeV, the first amplitude analysis of the singly Cabibbo-suppressed decay D+→K+K0Sπ0 is performed. From the amplitude analysis, the K∗(892)+K0S component is found to be dominant with a fraction of (57.1±2.6±4.2)%, where the first uncertainty is statistical and the second systematic. In combination with the absolute branching fraction B(D+→K+K0Sπ0) measured by BESIII, we obtain B(D+→K∗(892)+K0S)=(8.69±0.40±0.64±0.51)×10−3, where the third uncertainty is due to the branching fraction B(D+→K+K0Sπ0). The precision of this result is significantly improved compared to the previous measurement. This result also differs from most of theoretical predictions by about 4σ, which may help to improve the understanding of the dynamics behind.
Using data samples collected with the BESIII detector operating at the BEPCII storage ring at center-of-mass energies from 4.178 to 4.600 GeV, we study the process eþe− → π0Xð3872Þγ and search for Zcð4020Þ0 → Xð3872Þγ. We find no significant signal and set upper limits on σðeþe− → π0Xð3872ÞγÞ · BðXð3872Þ → πþπ−J=ψÞ and σðeþe− → π0Zcð4020Þ0Þ · BðZcð4020Þ0 → Xð3872ÞγÞ · BðXð3872Þ → πþπ−J=ψÞ for each energy point at 90% confidence level, which is of the order of several tenths pb.
We measure the inclusive semielectronic decay branching fraction of the D+s meson. A double-tag technique is applied to e+e− annihilation data collected by the BESIII experiment at the BEPCII collider, operating in the center-of-mass energy range 4.178–4.230 GeV. We select positrons fromD+s→Xe+νe with momenta greater than 200 MeV/c and determine the laboratory momentum spectrum, accounting for the effects of detector efficiency and resolution. The total positron yield and semielectronic branching fraction are determined by extrapolating this spectrum below the momentum cutoff. We measure the D+s semielectronic branching fraction to be(6.30±0.13(stat.)±0.09(syst.)±0.04(ext.))%, showing no evidence for unobserved exclusive semielectronic modes. We combine this result with external data taken from literature to determine the ratio of the D+s and D0 semielectronic widths, Γ(D+s→Xe+νe)Γ(D0→Xe+νe)=0.790±0.016(stat.)±0.011(syst.)±0.016(ext.). Our results are consistent with and more precise than previous measurements.