Metformin and guanylurea in aquatic environments: an overview and improved analysis

Metformin, a biguanide in chemical classification, is widely used as one of the most effective first-line oral drugs for type 2 diabetes. It is difficult to be metabolized by the human body and exists in both urine and faeces samples. Guanylurea is metformin’s biotransformation product. Consequently, significant concentrations of metformin and guanylurea have been reported in wastewater treatment plants (WWTPs) and coastal aquatic environments. In this thesis, a comprehensive overview is conducted to discuss the occurrence, impact, analysis and treatment of metformin and guanylurea in coastal aquatic environments of Canada, USA and Europe. The maximum concentrations of metformin and guanylurea in surface water samples were as high as 59,000 and 4,502 ng L⁻¹, respectively. Metformin can be absorbed in non-target organisms by plants and in Atlantic salmon (Salmo salar). Guanylurea has a confirmed mitotic activity in plant cells. Analysis methods of metformin are currently developed based on high-performance liquid chromatography (HPLC) and gas chromatography–mass spectrometry (GC-MS). The removal of metformin from aquatic environments in the target regions is summarized. The review helps to fill a knowledge gap and provides insights for regulatory considerations. The potential options for managing these emerging pollutants are outlined too. To help better track the occurrence of the two non-volatile biguanide compounds in liquid samples, the improvement of existing GC-MS based methods for reliable metformin and guanylurea analysis is also conducted in this thesis. Derivatization of metformin and guanylurea is the key pre-treatment procedure before the associated GC-MS analysis. Four selected factors affecting for the derivatization were evaluated, and the optimal factors include temperature (90oC), reacting time (40 minutes), solvent (1,4-dioxane), and ratio (1.5:1) of reagent to target component. Buformin and N-methyl-bis(trifluoroacetamide) (MBTFA) were used as the internal standard (IS) and the derivatization reagent, respectively. Calibration curves were made based on the optimal conditions of derivatization for metformin and guanylurea with the R² values of calibration linearity achieved as 99.35% and 99.2%, respectively. The values of relative standard deviation (RSD%) of metformin and guanylurea based on seven repeated trails are 2.67% and 15.37%, respectively. The optimal conditions for enhancing the sensitization of metformin and guanylurea derivatization performance were obtained. The improved GC-MS analysis method was eventually applied for metformin and guanylurea analysis in real water samples. Detection of metformin and guanylurea in other types of real water samples could be conducted in the future like final effluents of wastewater treatment plants

Stability and Generalization for Minibatch SGD and Local SGD

The increasing scale of data propels the popularity of leveraging parallelism to speed up the optimization. Minibatch stochastic gradient descent (minibatch SGD) and local SGD are two popular methods for parallel optimization. The existing theoretical studies show a linear speedup of these methods with respect to the number of machines, which, however, is measured by optimization errors. As a comparison, the stability and generalization of these methods are much less studied. In this paper, we study the stability and generalization analysis of minibatch and local SGD to understand their learnability by introducing a novel expectation-variance decomposition. We incorporate training errors into the stability analysis, which shows how small training errors help generalization for overparameterized models. We show both minibatch and local SGD achieve a linear speedup to attain the optimal risk bounds

Impossible meet-in-the-middle fault analysis on the LED lightweight cipher in VANETs

With the expansion of wireless technology, vehicular ad-hoc networks (VANETs) are emerging as a promising approach for realizing smart cities and addressing many serious traffic problems, such as road safety, convenience, and efficiency. To avoid any possible rancorous attacks, employing lightweight ciphers is most effective for implementing encryption/decryption, message authentication, and digital signatures for the security of the VANETs. Light encryption device (LED) is a lightweight block cipher with two basic keysize variants: LED-64 and LED-128. Since its inception, many fault analysis techniques have focused on provoking faults in the last four rounds to derive the 64-bit and 128-bit secret keys. It is vital to investigate whether injecting faults into a prior round enables breakage of the LED. This study presents a novel impossible meet-in-the-middle fault analysis on a prior round. A detailed analysis of the expected number of faults is used to uniquely determine the secret key. It is based on the propagation of truncated differentials and is surprisingly reminiscent of the computation of the complexity of a rectangle attack. It shows that the impossible meet-in-the-middle fault analysis could successfully break the LED by fault injections

Sample adaptive multiple kernel learning for failure prediction of railway points

© 2019 Association for Computing Machinery. Railway points are among the key components of railway infrastructure. As a part of signal equipment, points control the routes of trains at railway junctions, having a significant impact on the reliability, capacity, and punctuality of rail transport. Meanwhile, they are also one of the most fragile parts in railway systems. Points failures cause a large portion of railway incidents. Traditionally, maintenance of points is based on a fixed time interval or raised after the equipment failures. Instead, it would be of great value if we could forecast points' failures and take action beforehand, min-imising any negative effect. To date, most of the existing prediction methods are either lab-based or relying on specially installed sensors which makes them infeasible for large-scale implementation. Besides, they often use data from only one source. We, therefore, explore a new way that integrates multi-source data which are ready to hand to fulfil this task. We conducted our case study based on Sydney Trains rail network which is an extensive network of passenger and freight railways. Unfortunately, the real-world data are usually incomplete due to various reasons, e.g., faults in the database, operational errors or transmission faults. Besides, railway points differ in their locations, types and some other properties, which means it is hard to use a unified model to predict their failures. Aiming at this challenging task, we firstly constructed a dataset from multiple sources and selected key features with the help of domain experts. In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels. We present a robust multiple kernel learning algorithm for predicting points failures. Our model takes into account the missing pattern of data as well as the inherent variance on different sets of railway points. Extensive experiments demonstrate the superiority of our algorithm compared with other state-of-the-art methods

Infection of inbred BALB/c and C57BL/6 and outbred Institute of Cancer Research mice with the emerging H7N9 avian influenza virus

A new avian-origin influenza virus A (H7N9) recently crossed the species barrier and infected humans; therefore, there is an urgent need to establish mammalian animal models for studying the pathogenic mechanism of this strain and the immunological response. In this study, we attempted to develop mouse models of H7N9 infection because mice are traditionally the most convenient models for studying influenza viruses. We showed that the novel A (H7N9) virus isolated from a patient could infect inbred BALB/c and C57BL/6 mice as well as outbred Institute of Cancer Research (ICR) mice. The amount of bodyweight lost showed differences at 7 days post infection (d.p.i.) (BALB/c mice 30%, C57BL/6 and ICR mice approximately 20%), and the lung indexes were increased both at 3 d.p.i. and at 7 d.p.i.. Immunohistochemistry demonstrated the existence of the H7N9 viruses in the lungs of the infected mice, and these findings were verified by quantitative real-time polymerase chain reaction (RT-PCR) and 50% tissue culture infectious dose (TCID50) detection at 3 d.p.i. and 7 d.p.i.. Histopathological changes occurred in the infected lungs, including pulmonary interstitial inflammatory lesions, pulmonary oedema and haemorrhages. Furthermore, because the most clinically severe cases were in elderly patients, we analysed the H7N9 infections in both young and old ICR mice. The old ICR mice showed more severe infections with more bodyweight lost and a higher lung index than the young ICR mice. Compared with the young ICR mice, the old mice showed a delayed clearance of the H7N9 virus and higher inflammation in the lungs. Thus, old ICR mice could partially mimic the more severe illness in elderly patients. </p

Advances in Local Vibrational Mode Theory and Unified Reaction Valley Approach (URVA)

Since the establishment of the Local Vibrational Mode Theory and the Unified Reaction Valley Approach (URVA), these two research pillars have been pushed forward in the CATCO group and played an important role in (i) characterizing the chemical bonds in molecules and (ii) molecular chemical reactions. This dissertation elaborates my contributions to the Local Vibrational Mode Theory and the Unified Reaction Valley Approach (URVA). We have applied the Local Vibrational Mode Theory to hydrogen bonding in liquid water and proposed an explanation for the Mpemba effect. We explored and discovered new directions of applying local vibrational modes majorly in characterizing substituent effect. We have extended the local vibrational mode idea, developing intrinsically comparable normal vibrational modes. My major contribution to the Unified Reaction Valley Approach has been the coding of a standalone URVA analysis program, which is easy to use and maintain and independent of the software used to create the input data. This program will be published in the future as an open-source tool helping chemists to understand chemical reactions in all details

Exploring the Mechanism of Catalysis with the Unified Reaction Valley Approach (URVA)—A Review

The unified reaction valley approach (URVA) differs from mainstream mechanistic studies, as it describes a chemical reaction via the reaction path and the surrounding reaction valley on the potential energy surface from the van der Waals region to the transition state and far out into the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting molecules is registered by a change in their normal vibrational modes and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction with curvature minima reflecting minimal change and curvature maxima, the location of important chemical events such as bond breaking/forming, charge polarization and transfer, rehybridization, etc. A unique decomposition of the path curvature into internal coordinate components provides comprehensive insights into the origins of the chemical changes taking place. After presenting the theoretical background of URVA, we discuss its application to four diverse catalytic processes: (i) the Rh catalyzed methanol carbonylation&mdash;the Monsanto process; (ii) the Sharpless epoxidation of allylic alcohols&mdash;transition to heterogenous catalysis; (iii) Au(I) assisted [3,3]-sigmatropic rearrangement of allyl acetate; and (iv) the Bacillus subtilis chorismate mutase catalyzed Claisen rearrangement&mdash;and show how URVA leads to a new protocol for fine-tuning of existing catalysts and the design of new efficient and eco-friendly catalysts. At the end of this article the pURVA software is introduced. The overall goal of this article is to introduce to the chemical community a new protocol for fine-tuning existing catalytic reactions while aiding in the design of modern and environmentally friendly catalysts

Equilibrium Geometries, Adiabatic Excitation Energies and Intrinsic C=C/C–H Bond Strengths of Ethylene in Lowest Singlet Excited States Described by TDDFT

Seventeen singlet excited states of ethylene have been calculated via time-dependent density functional theory (TDDFT) with the CAM-B3LYP functional and the geometries of 11 excited states were optimized successfully. The local vibrational mode theory was employed to examine the intrinsic C=C/C&ndash;H bond strengths and their change upon excitation. The natural transition orbital (NTO) analysis was used to further analyze the C=C/C&ndash;H bond strength change in excited states versus the ground state. For the first time, three excited states including &pi;y&prime; &rarr; 3s, &pi;y&prime; &rarr; 3py and &pi;y&prime; &rarr; 3pz were identified with stronger C=C ethylene double bonds than in the ground state

Local Vibrational Mode Analysis of π–Hole Interactions between Aryl Donors and Small Molecule Acceptors

11 aryl&ndash;lone pair and three aryl&ndash;anion &pi; &ndash;hole interactions are investigated, along with the argon&ndash;benzene dimer and water dimer as reference compounds, utilizing the local vibrational mode theory, originally introduced by Konkoli and Cremer, to quantify the strength of the &pi; &ndash;hole interaction in terms of a new local vibrational mode stretching force constant between the two engaged monomers, which can be conveniently used to compare different &pi; &ndash;hole systems. Several factors have emerged which influence strength of the &pi; &ndash;hole interactions, including aryl substituent effects, the chemical nature of atoms composing the aryl rings/ &pi; &ndash;hole acceptors, and secondary bonding interactions between donors/acceptors. Substituent effects indirectly affect the &pi; &ndash;hole interaction strength, where electronegative aryl-substituents moderately increase &pi; &ndash;hole interaction strength. N-aryl members significantly increase &pi; &ndash;hole interaction strength, and anion acceptors bind more strongly with the &pi; &ndash;hole compared to charge neutral acceptors (lone&ndash;pair donors). Secondary bonding interactions between the acceptor and the atoms in the aryl ring can increase &pi; &ndash;hole interaction strength, while hydrogen bonding between the &pi; &ndash;hole acceptor/donor can significantly increase or decrease strength of the &pi; &ndash;hole interaction depending on the directionality of hydrogen bond donation. Work is in progress expanding this research on aryl &pi; &ndash;hole interactions to a large number of systems, including halides, CO, and OCH3&minus; as acceptors, in order to derive a general design protocol for new members of this interesting class of compounds

Quasi-periodical 3D hierarchical silver nanosheets with sub-10 nm nanogap applied as an effective and applicable SERS substrate

Surface-enhanced Raman scattering (SERS) is considered as the most promising trace detection method since its first discovery on rough silver electrodes in 1974.[1] After years of development, it has exhibited great potential as a nondamaging single molecule analysis method applied in a wide range of areas, including biological labeling,[2] chemistry,[3] agriculture,[ 4] environmental science,[5] food safety,[6] and so forth.[7] The origin of the SERS effect is still in dispute but there is an agreement on an electromagnetic enhancement mechanism (EM)[8] due to the excitation of the localized surface plasmon resonance (LSPR) on the nanostructures with existance of nanogaps.[9] Enhancement efficiency and applicability are the two factors that evaluate the SERS substrate. The effeciency means the substrate can trace molecules sensitively, reproducibly, and precisely, while the applicability indicates the SERS substrate can be prepared easily at low cost