Emission accounting and drivers in East African countries

East Africa is typical of the less developed economies that have emerged since the 21st century, whose brilliant economic miracle has also triggered the rapid growth of energy consumption and carbon dioxide emissions. However, previous carbon accounting studies have never focused on the region. Based on multi-source data, this paper rebuilt the 45-sectors carbon emission inventories of eight East African countries from 2000 to 2017, and used index decomposition analysis to quantify the drivers of growth. Here we found that overall the CO2 emissions show a 'two-stage exponential growth' pattern, with significant heterogeneity between countries. In terms of the energy mix, technical progress in hydro and geothermal energy was almost offset by a growing appetite for oil and coal, making it the weak and valuable factor driving emissions reduction (−1.4Mt). But it was far from enough to overcome the pressure of economic and population growth, which brought about a 13Mt and 11Mt emission growth respectively from 2000 to 2017. Increasing energy intensity due to industrialization and transport development also contributed to an increment of 6.4Mt. Low-carbon policies should be tailored to local conditions and targeted at the improvement of energy efficiency and use of renewable energy so as to achieve a win-win situation between sustainable economic growth and emission reduction

Formalization of Function Matrix Theory in HOL

Function matrices, in which elements are functions rather than numbers, are widely used in model analysis of dynamic systems such as control systems and robotics. In safety-critical applications, the dynamic systems are required to be analyzed formally and accurately to ensure their correctness and safeness. Higher-order logic (HOL) theorem proving is a promise technique to match the requirement. This paper proposes a higher-order logic formalization of the function vector and the function matrix theories using the HOL theorem prover, including data types, operations, and their properties, and further presents formalization of the differential and integral of function vectors and function matrices. The formalization is implemented as a library in the HOL system. A case study, a formal analysis of differential of quadratic functions, is presented to show the usefulness of the proposed formalization

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Three studies on the Libor Market Model

The purpose of this thesis is to further current knowledge of the Libor Market Model (LMM) in terms of more efficient implementation and extension to include non-lognormally distributed rates. The performance of LMM in pricing and hedging performance of Bermudan swaptions is also compared with Hull-White, Black-Karasinski, and Swap Market Model (SMM) from an Asset-Liability-Management (ALM) perspective. The first study develops an efficient method for LMM implementation and pricing of Bermudan swaption. Following Derick, Stapleton and Stapleton (2005), we constructed recombining binomial trees for the term structure of forward Libor rate using the method developed by Ho, Stapleton and Subrahmanyam (1995). The contribution of this study is twofold: first, we list the assumptions on the volatility under which LMM can be implemented by the recombining tree method. Second, we perform extensive numerical studies I to compare the European and Bermudan swaption prices produced from the rec01pbining tree with those from the Monte Carlo simulation. Our findings lead us to conclude that our recombining tree LMM could be very useful for pricing exotic interest rate derivatives with early exercise feature. Since the Monte Carlo simulation method is more suited for path-dependent options, \~e conclude that the recombining tree method is a useful tool to complement the Monte Carlo simulation method in pricing exotic interest rate derivatives. T~e second study compares the pricing and hedging performance of the LMM against two spot-rate models, namely Hull-White and Black-Karasinski, and the more recent swap market model from an Asset-Liability-Management (ALM) perspective. In contrast to previous studies in the literature, our emphasis here is on ALM and we use hedging performance on Bermudan swaptions to proxy risk management outcome of a portfolio of long term mortgage loans. The focus here is on the differences between the four models (viz. HW, BK, LMM and SMM) instead of the absolute pricing or hedging error of the individual model. The contribution of this study is twofold: first, we are the first to compare the hedging performance of Bemmdan swaptions for a new set of models, viz.,.HW, BK, SMM and LMM. Second, we perform the test in two currencies from an ALM perspective so that our results are valuable in the decision-making of the ALM division of international financial institutions. The third study extends the LMM to relax the assumption of lognormally distributed forward rates. The probabilities of the tree are modified to include different non-lognormal. distributions. The new model is very easy to implement with the addition of one extra parameter to the standardEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Formalization of Matrix Theory in HOL4

Matrix theory plays an important role in modeling linear systems in engineering and science. To model and analyze the intricate behavior of complex systems, it is imperative to formalize matrix theory in a metalogic setting. This paper presents the higher-order logic (HOL) formalization of the vector space and matrix theory in the HOL4 theorem proving system. Formalized theories include formal definitions of real vectors and matrices, algebraic properties, and determinants, which are verified in HOL4. Two case studies, modeling and verifying composite two-port networks and state transfer equations, are presented to demonstrate the applicability and effectiveness of our work

Root exudates with low C/N ratios accelerate CO2 emissions from paddy soil

Root exudates can significantly modify microbial activity and soil organic matter (SOM) mineralization. However, how root exudates and their C/N stoichiometric ratios control paddy soil C mineralization is poorly understood. This study used a mixture of glucose, oxalic acid, and alanine as root exudate mimics for three C/N stoichiometric ratios (CN6, CN10, and CN80) to explore the underlying mechanisms involved in SOM mineralization. The input of root exudates enhanced CO2 emissions by 1.8–2.3-fold that of soil with only C additions (C-only). Artificial root exudates with low C/N ratios (CN6 and CN10) increased the metabolic quotient (qCO2) by 12% over those with higher stoichiometric ratios (CN80 and C-only), suggesting a relatively high energy demand for microorganisms to acquire organic N from SOM by increasing N-hydrolase production. The increase of stoichiometric ratios of C- to N-hydrolase (β-1,4-glucosidase to β-1,4-N-acetyl glucosaminidase) promoted SOM degradation compared to those involved in organic C- and N- degradation, which had a significant positive correlation with qCO2. The stoichiometric ratios of microbial biomass (MBC/MBN) were positively correlated with C use efficiency, indicating root exudates with higher C/N ratios provide an undersupply of N for microorganisms that trigger the release of N-degrading extracellular enzymes. Our findings showed that the C/N stoichiometry of root exudates controlled SOM mineralization by affecting the specific response of the microbial biomass through the activity of C- and N-releasing extracellular enzymes to adjust the microbial C/N ratio

Polymer–Doxorubicin Conjugate Micelles Based on Poly(ethylene glycol) and Poly(<i>N</i>‑(2-hydroxypropyl) methacrylamide): Effect of Negative Charge and Molecular Weight on Biodistribution and Blood Clearance

Well-defined water-soluble block copolymers poly­(ethylene glycol)-<i>b</i>-poly­(<i>N</i>-(2-hydroxypropyl) methacrylamide-<i>co</i>-<i>N</i>-methacryloylglycylglycine) (PEG-<i>b</i>-P­(HPMA-<i>co</i>-MAGG)) and their doxorubicin (Dox) conjugates with different composition and molecular weight were synthesized. These Dox conjugates can form micelles in buffer solution. The physicochemical properties, in vivo biodistribution, blood clearance, and especially the tumor accumulation of copolymers and micelles were studied. Severe liver accumulation can be observed for PEG-<i>b</i>-PMAGG copolymers. This was quite different from their Dox conjugate for which decreased RES uptake and elevated kidney accumulation could be observed. When decrease the negative charge to an appropriate amount such as 8–10 mol %, both RES uptake and kidney accumulation could be suppressed. Obvious tumor accumulation could be achieved especially when the molecular weight were increased from ∼40 to ∼80 KDa. These results provided us with a guideline for the design of nanoscaled drug delivery system as well as a potential option for treating kidney-related cancers

Bacterial extracellular vesicles: A position paper by the microbial vesicles task force of the Chinese society for extracellular vesicles

Abstract Recently, the interest in extracellular vesicles released by bacteria has rapidly increased. Bacterial extracellular vesicles (BEVs) have been involved in bacteria‐bacteria and bacteria‐host interactions, which strengthen health or bring about various pathologies. However, BEV separation, characterization, and functional studies require the establishment of guidelines and further optimization in order to stimulate the development of science in BEV research and a following successful transformation into clinical applications. This position paper is authored by the Microbial Vesicles Task Force of the Chinese Society for Extracellular Vesicles (CSEV) composed of experienced medical laboratory specialists, microbiologists, virologists, biologists and material biologists who are actively engaged in BEV research. Herein, we present a concise description of BEV research and discover challenges and critical gaps in current BEV‐based analyses for clinical applications. Finally, we also offer suggestions and considerations to improve experimental reproducibility and interoperability in BEV research to promote progress in the field