Miracle-dz/UniFAR-FTUs huggingface dataset:
Decreasing trends of particle number and black carbon mass concentrations at 16 observational sites in Germany from 2009 to 2018

Abstract. Anthropogenic emissions are dominant contributors to air pollution. Consequently, mitigation policies have been attempted since the 1990s in Europe to reduce pollution by anthropogenic emissions. To evaluate the effectiveness of these mitigation policies, the German Ultrafine Aerosol Network (GUAN) was established in 2008, focusing on black carbon (BC) and sub-micrometre aerosol particles. In this study, long-term trends of atmospheric particle number concentrations (PNCs) and equivalent BC (eBC) mass concentration over a 10-year period (2009–2018) were determined for 16 GUAN sites ranging from roadside to high Alpine environments. Overall, statistically significant decreasing trends are found for most of these parameters and environments in Germany. The annual relative slope of eBC mass concentration varies between −13.1 % and −1.7 % per year. The slopes of the PNCs vary from −17.2 % to −1.7 %, −7.8 % to −1.1 %, and −11.1 % to −1.2 % per year for 10–30, 30–200, and 200–800 nm size ranges, respectively. The reductions in various anthropogenic emissions are found to be the dominant factors responsible for the decreasing trends of eBC mass concentration and PNCs. The diurnal and seasonal variations in the trends clearly show the effects of the mitigation policies for road transport and residential emissions. The influences of other factors such as air masses, precipitation, and temperature were also examined and found to be less important or negligible. This study proves that a combination of emission mitigation policies can effectively improve the air quality on large spatial scales. It also suggests that a long-term aerosol measurement network at multi-type sites is an efficient and necessary tool for evaluating emission mitigation policies.
Osmotic Conductance of the Peritoneum in Capd Patients with Permanent Loss of Ultrafiltration Capacity

Objective To compare the effectiveness of osmotic pressure in the induction of fluid flow during continuous ambulatory peritoneal dialysis (CAPD) in patients with permanent loss of ultrafiltration capacity (UFC) and clinically stable patients. Design Estimation of osmotic conductance in individual CAPD patients using data from single CAPD dwell studies. Patients Twenty clinically stable CAPD patients with normal ultrafiltration rate (NUR group); 8 CAPD patients with permanent UFC loss due to high diffusion rate for small solutes [high diffusion rate (HDR) group]; 3 CAPD patients with permanent loss of UFC dueto high absorption rate (HAR) of peritoneal dialysate (HAR group). Design Six-hour dwell studies were carried out in each patient using 2 L of Dianeal 3.86% dialysis fluid. Dialysate volume and the peritoneal absorption rate were measured using radioiodinated serum albumin as a marker. The dialysate volume over dwell-time curves were examined using three mathematical models of fluid transport for solutions with a crystalloid osmotic agent: model P, based on a phenomenologically derived exponential function of time; model OS, based on the linear relationship between the rate of net volume change (Qv) to the difference of osmolality in dialysate and blood; and model G, based on the linear relationship between Qv and the difference of glucose concentration in dialysate and blood. Results All three models provided an accurate description of the measured dialysate volume over time curves. The osmotic conductance, defined as the coefficient of proportionality between the rate of ultrafiltration and the osmolality (or, alternatively, glucose) gradient between dialysate and blood plasma, was 30% lower in the HDR group than in the NUR group, but close to the normal value in the HAR group. Conclusion In the HDR group the changes in the peritoneal membrane, which resulted in the increased diffusion rate of small solutes, also yielded a decrease of osmotic conductance. In contrast, the changes in the membrane in the HAR group, which resulted in increased peritoneal absorption, did not change the osmotic con ductance or the solute diffusion rate. The detailed pathophysiological mechanisms for these two different types of UFC loss failure are still unknown.
Nanocomposite Membranes with High Fuel Cell Performance Based on Sulfonated Poly (1,4-phenylene ether ether sulfone) and Ytterbium/Yttrium Doped-Perovskite Nanoparticles

In the present study, new nanocomposite membranes based on sulfonated poly (1,4-phenylene ether-ether sulfone), BaZr0.9Y0. 1O3-δ and SrCe0.9Yb0. 1O3-δ doped-perovskite nanoparticles have been prepared. The BaZr0.9Y0. 1O3-δ and SrCe0.9Yb0. 1O3-δ doped-perovskite nanoparticles enhance the thermal and mechanical stability of the nanocomposite membranes. Besides, the water uptake of the nanocomposite membranes increases and so, the proton conductivity increase. Substitution of Ce4+ by Yb3+ in the SrCe0.9Yb0. 1O3-δ doped-perovskite nanoparticles and the substitution of Zr4+ by Y3+ in the BaZr0.9Y0. 1O3-δ doped-perovskite nanoparticles produce oxygen vacancies and decrease the coulombic repulsion between protons and positive ions. The oxygen vacancies in BaZr0.9Y0. 1O3-δ and SrCe0.9Yb0. 1O3-δ doped-perovskite nanoparticles act as water and protons trap, and so increase the rotational motion of the proton and the proton conductivity in the nanocomposite membranes. The highest proton conductivity for the nanocomposite membranes containing the BaZr0.9Y0. 1O3-δ and SrCe0.9Yb0. 1O3-δ doped-perovskite nanoparticles are 126 mS cm−1 and 117 mS cm−1, respectively, at 80°C and 95% RH. The long lifetime of the proton conductivity and considerable fuel cell performance, 0.61 W cm−2 power density at 0.5 V, of the nanocomposite membranes confirm their high potential for application in the PEM fuel cells.
Bioinformatics Platform for Large Scale Proteomics Liquid Chromatography Tandem Mass Spectrometry Data

Mass spectrometry has emerged to become a state-of-the-art methodology for the analysis of the proteome. In order to manage and extract valuable information from mass spectrometry data, a computational data management platform is indispensable. Furthermore the standardized presentation of data is of the utmost importance. At present however, no proteomics bioinformatics platform exists that fulfills the Minimum Information About a Proteomics Experiment (MIAPE) standard and that provides all the features needed for the effective analysis of mass spectrometry experiments. In view of this we have developed a versatile MIAPE compliant platform for the management and analysis of proteomics mass spectrometry experiments: the MAss SPECTRometry Analysis System (MASPECTRAS). This platform is web-based with a database back-end and relies on the Java 2 Enterprise Edition development platform. The platform is scalable and enables the outsourcing of computationally intensive tasks to a computing cluster. The data model captures data concerning experimental design and at all other subsequent steps leading up to evaluation and result export. The analysis process relies on the output of the major mass spectrometry search engines SEQUEST, Mascot, SpectrumMill, X! Tandem and OMSSA. The data is run through an automated analysis pipeline in the following four steps: 1) import and parsing to a MIAPE compliant representation; 2) validation; 3) protein clustering; 4) peptide quantification. For manual analysis MASPECTRAS provides unique features that include the merging of results originating from different search engines, a chromatogram viewer and export to PRIDE-XML format which can be uploaded directly in a public repository. The functionality of the whole system is furthermore embedded in a multi-user environment providing controlled user access. The platform has been evaluated using large-scale and quantitative data. The MASPECTRAS platform offers researchers an environment for the rapid analysis of large-scale proteomics experiments. Due to its modular design it is flexible enough to easily accommodate future changes in proteomics data management.
Determination of Cobalt(II)-Hydrogen Peroxide-Induced DNA Oxidative Damage and Preventive Antioxidant Activity by CUPRAC Colorimetry

Abstract The classical Fenton system composed of Fe(II) and H2O2 uses harsh oxidative conditions and cannot realistically simulate physiological oxidations which are less severe. Here, reactive oxygen species (ROS) were generated with a combination of CoSO4 and H2O2 to provide milder conditions. DNA was used as a biologically meaningful probe for monitoring the oxidative conversion. Oxidative hazard on DNA was accomplished in ammonia/ammonium chloride buffer at 37 °C, and the Fenton reaction was stopped with trichloroacetic acid (TCA). A suitable aliquot of this solution was added to cupric ion reducing antioxidant capacity (CUPRAC) reaction mixture, and the absorbance at 450 nm was recorded. The oxidized species derived from DNA were CUPRAC-reactive while intact DNA was not. The protective effects of antioxidants (AOxs), known to have radical scavenging effects, were tested; green tea and a synthetic fetal bovine serum (FBS) were also successfully used as real ROS scavengers. Although the classical iron-based Fenton procedure applied in ethanol medium generated CUPRAC-responsive products, the proposed system was perfectly ethanol-tolerant, enabling the CUPRAC measurement of DNA oxidation products against an unaffected reagent blank. The protective effects of phenolic antioxidants, perfectly solubilized in ethanol, could also be measured.
Audiovisual prosody in interaction

Users may download and print one copy of any publication from the public portal for the purpose of private study or research You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Characterization of Polyphenols and Mineral Contents in Three Medicinal Weeds

Aims: Common weeds Rorippa palustris (L.) Besser, Euphorbia rothiana Spreng. and Schoenoplectiella articulata (L.) Lye are used for food, medicinal, green biofertilizer and biosorbent applications. In this work, their polyphenol and mineral contents have been characterized. Methodology: Samples from aforementioned three plants were manually collected in Raipur city (CG, India) and processed for the analyses. Folin-Ciocalteu and aluminum chloride were used for the spectrophotometric determination of polyphenols. The mineral contents were quantified by X-ray fluorescence. Results: The total concentration of 20 elements (viz. P, S, Cl, As, Se, K, Rb, Mg, Ca, Sr, Ba, Al, Ti, Cr, Mn, Fe, Co, Zn, Mo and Pb), total polyphenol and flavonoid contents in the leaves ranged from 46372 to 71501, from 47877 to 73791 and from 1950 to 9400 mg/kg, respectively. Remarkable concentrations of several nutrients (P, S, Cl, K, Mg, Ca and Fe) were observed. Conclusion: The biomass from medicinal weeds R. palustris, E. rothiana and S. articulata featured very high K, Ca and Fe contents. Other nutrients (polyphenols, flavonoids, P, S, Cl and Mg) were identified at moderate levels. These species may hold promise as bioindicators.
Mixtures of proteins and protein-stabilised droplets: rheology of emulsions and emulsion gels

The soft materials formed from emulsions stabilised by proteins, like yogurt, are referred to as emulsion gels. This designation is however not precise enough to reflect the variety of composition of these materials. Indeed, during emulsification not all the proteins in solution adsorb at the interface and the emulsion is thus a mixture of protein-stabilised droplets and unadsorbed proteins. The composition of this mixture affects the viscosity of the emulsions and the texture of the emulsion gels. The objective of this thesis is to study the rheological properties of proteinstabilised emulsions and the gels they form considering the full range of their composition. A first step has been to characterise separately the purified suspensions of protein-stabilised droplets, and of suspensions of pure proteins and their gelation. These components have then been combined, resulting in emulsions and emulsion gels of well-characterised compositions, thus allowing a rigorous approach to the rheology of these systems. The viscosity of purified suspensions of proteins and of protein-stabilised droplets has been studied. It was found that these systems are conveniently studied in the framework developed for soft colloidal suspensions, for which the viscosity scales with the volume fraction. The properties of the droplet and protein suspensions have then been used to model the behaviour of their mixtures. The viscosity models for the two types of pure suspensions have facilitated the development of a semi-empirical model that relates the viscosity of protein-stabilised emulsions to their composition. The gelation of the pure suspensions has then been characterised. Indeed, at low pH, proteins can aggregate and also form gels, either of protein molecules in solution or of protein-covered droplets. The rheological properties of these fractal networks were found to depend on their volume fraction, in good correspondence
Constant Potential, Electrochemically Active Boundary Conditions for Electrochemical Simulation

This manuscript presents a theoretical model for simulating molecular dynamics at electrode-electrolyte interfaces. The novelty of the model is that it combines a method for simulating constant potential electrodes and a method for simulating stochastic interfacial charge transfer. We combine these methods to simulate model electrochemical systems under driven conditions, where charge is flowing across the electrode-electrolyte interface. The manuscript describes the theoretical formalism and applies it to a model battery system. We highlight the ability of the model to support the formation of electrical double-layers and to provide microscopic physical insight the results of potential jump experiments.
Conductive Copper Niobate: Superior Li+‐Storage Capability and Novel Li+‐Transport Mechanism

Niobates with shear ReO3 crystal structures are remarkably promising anode materials for Li+ batteries due to their large capacities, inherent safety, and high cycling stability. However, they generally suffer from limited rate capabilities rooted in their insufficient electronic and Li+ conductivities. Here, micrometer‐sized copper niobate (Cu2Nb34O87) bulk as a new anode material having a high electronic conductivity of 2.1 × 10−5 S cm−1 and an impressive average Li+ diffusion coefficient of ≈3.5 × 10−13 cm2 s−1 is exploited, which synergistically leads to an excellent rate capability (184 mAh g−1 at 10 C) while remaining a large reversible capacity and superior cycling stability. Moreover, the fast Li+ transport pathways of grain boundary (micrometer scale) → lattice deformation area (nanometer scale) → (010) crystallographic plane (angstrom scale) are demonstrated in Cu2Nb34O87. Therefore, these results could pave the way for practical application of Cu2Nb34O87 in high‐performance Li+ batteries.
Automated quantification of DNA damage via deep transfer learning based analysis of comet assay images

The comet assay is a technique used to assess the DNA damage in individual cells. The extent of the damage is indicated by the ratio between the amount of DNA in the tail of the comet and the amount in the head. This assessment is typically made by the operator manually analyzing the images. This process is inefficient and time consuming. Researchers in the past have used machine learning techniques to automate this process but it required manual feature extraction. In some cases, deep learning was applied but only for damage classification. We have successfully applied Convolutional Neural Networks(CNN) to achieve automated quantification of DNA damage from comet images. Typically deep learning techniques such as CNN require large amounts of labelled training data, which may not always be available. We demonstrate that by applying deep transfer learning, state of the art results can be obtained in the detection of DNA damage, even with a limited number of comet images.
Experimental analysis of a solar thermal hybrid VRF system for maximum energy economy based on Delhi (India) climate

ABSTRACT The main objective of the present study is to develop a solar thermal hybrid variable refrigerant flow (VRF) air-conditioning system and predict energy saving in this system compared to normal VRF air-conditioning system. In R410a based VRF system compressor works partially to increase pressure and temperature up to 27 bar, 55°C from compressor suction pressure and temperature 27 bar, 55°C. Further increase in temperature and pressure from approximate 55°C, 27 bar (Solar heat exchanger inlet) to 80°C, 34.39 bar (Solar heat exchanger outlet) is achieved with the help of evacuated tube type solar collector-based heat exchanger. The calculation of energy saving in solar thermal hybrid VRF system has been compared with fully electric-fired VRF system. The energy saving of approximate 45–50% can be achieved in solar thermal hybrid VRF system as compared to normal VRF system.
Release of bioactive agents from mesoporous silica nanoparticles for biological applications

The high surface area, pore volume, and biocompatible/ biodegradable silica matrix of mesoporous silica nanoparticles (MSN) has led to extensive investigation into these materials as carriers for bioactive agents. These agents can be loaded into and sheltered within the mesopores, then released at the target site either through simple diffusion or in response to specific stimuli. Owing to the facile surface modification of MSN, stimuli responsive MSN is frequently synthesized through the covalent linkage of an organic functionality, capable of changing conformation in response to a given stimuli, to the silica surface. However, this goal can also be accomplished through coating of the particle surface with an enzymatically digestible polymer. In this work, vanillin loaded MSN coated with the linear glucose polymer amylose or branched glycogen is proposed as a salivary α-amylase responsive system for drug delivery. The starch cap is capable of retaining vanillin within the MSN pores until the enzyme is introduced, making delivery specific to the oral cavity. This target offers benefits such as higher patient compliance and the negation of first-pass metabolic effects. In addition, the use of large pore MSN for the release of large, homotetrameric protein and silver-MSN nanocomposites for antimicrobial applications are discussed.
Facile functionalized mesoporous silica using biomimetic method as new matrix for preparation of shape‐stabilized phase‐change material with improved enthalpy

Dopamine‐functionalized mesoporous silica (DPMS), which was prepared by using a facile, easy to operate, and environmentally friendly biomimetic method, was synthesized as a new supporter of polyethylene glycol (PEG) to prepare shape‐stabilized phase‐change material (ss‐PCM) of PEG/DPMS with improved enthalpy. The thermal properties of PEG/DPMS were investigated, and the study results demonstrated that dopamine functionalization could regulate the crystallization property of PEG molecules immobilized in PEG/DPMS, and then the enthalpy of PEG/DPMS could be improved effectively. Compared with the ss‐PCM of PEG/SBA‐15 that was synthesized by using unfunctionalized silica molecular sieve (SBA‐15) as the matrix, after dopamine modification, the fusion and solidification enthalpies of 70‐wt% PEG/DPMS increased from 47.25 and 34.96 J/g to 69.77 and 67.54 J/g, respectively. The interaction mechanism study results suggested that the amino groups in the molecule of poly‐dopamines on the surface of DPMS could interact with the oxygen atoms in the molecules of PEG to form hydrogen bonds. Additionally, the PEG/DPMS had favorable thermal reliability and prominent thermal stability. Hence, the DPMS could be applied as a promising supporter for the fabrication of ss‐PCM.
Method optimisation for the simultaneous determination of legacy and emerging halogenated flame retardants in particulate matter collected in an electronic waste recycling facility

ABSTRACT The diffusion and the recycling of electric and electronic equipment waste (WEEE) are an important issue for industrialised countries. During treatment operations of this equipment, legacy and emerging halogenated flame retardant can be released in the environment, causing great concern for their toxicity. For this purpose, a method to simultaneously determine polybrominated biphenyl ethers, hexabromocyclododecane, 10 emerging brominated flame retardants and in parallel-polychlorinated biphenyls was optimised and applied to particulate matter samples collected within a WEEE facility. In this paper, starting from a previously published method, we drastically simplified the sample preparation, reducing the overall completion time, solvent consumption, costs and achieving adequate selectivity and sensitivity for all the target compounds. The multi–analyte method was evaluated in terms of reproducibility, linearity, recovery, limits of detection, limit of quantification, and matrix effect and compared to the previous method. Quantitative analyses were carried out by gas chromatography-mass spectrometry in negative chemical ionisation, using matrix-matched calibration curves. The reliability and robustness of the method were demonstrated through the analysis of certified and reference compounds in Standard Reference Material 2585. The most abundant compounds in particulate matter samples, collected in an electric and electronic equipment waste treatment plant, were BDE 47 (3.9 ng m−3), CB 138 (2.7 ng m−3), and 1,2-bis(2,4,6-tribromophenoxy) ethane (4.2 ng m−3).
A mesoscopic DPD simulation study on long chain quaternary ammonium gemini surfactant solution

Abstract Dissipative particle dynamics (DPD) simulation method is applied to study the aggregation process of quaternary ammonium gemini surfactants in three systems including pure water, water-ethanol and water-oil mixture solution. The results show that DPD simulation can directly observe the formation and evolution of spherical surfactant clusters. In pure water system, higher surfactant concentration leads to form large spherical cluster. In water-ethanol system, additional ethanol molecules can destroy the stable structure of the hydrophobic radical and inhibit the aggregation of surfactant. In water-oil mixture, we find that gemini surfactants distribute in the oil-water interface, and the interfacial tension reduces as surfactant concentration increases. We studied the effect of temperature on critical micelle concentration (CMC) as well as the distribution of surfactants micelle at water-oil interface. Graphical Abstract