The influence of different metal atoms on the performance of metalloporphyrin-based sensor reaction with propanol

Density functional theory (DFT) method was carried out to investigate the molecular interaction between metalloporphyrin-based sensor and propanol. The relative energies were used to determine the most stable state of metalloporphyrin and its complexes at three different spin states for further theoretical studies. The low-spin states were found to be the most stable states for cobalt porphyrin (CoP), tin porphyrin (Sn), and zinc porphyrin (ZnP) before exposure to propanol and CoP, SnP, ZnP, iron porphyrin (FeP), ruthenium porphyrin (RuP) after exposure to propanol. The intermediate-spin state was found to be the most stable states for the other metalloporphyrins and their complexes, except for manganese porphyrin (MnP) after exposure to propanol. The calculated binding energies were shown the following order for metalloporphyrin-based sensor-binding propanol: MnP>ZnP>CoP>RuP>SnP>FeP>AgP>CuP. This calculated result may be useful for the theoretical design of metalloporphyrin-based sensor for propanol determination and perhaps other analyte

A density functional theory study of metalloporphyrin derivatives act as fluorescent sensor for rapid evaluation of trimethylamine

A fluorescent sensor based on metalloporphyrin was developed for trimethylamine (TMA) evaluation. Density functional theory (DFT) has been performed to investigate the design mechanism of fluorescent sensor. Fourteen metalloporphyrin (MP) models were selected to find the influence of metal atoms and substituent groups on the binding performance of fluorescent sensor. The optimized geometry structures, relative energies, mulliken charges, spin densities, and four frontier molecular orbitals together with binding energies of these fluorescent sensors were investigated. AgP sensor has the lowest relative energies before and after exposure to TMA, which make AgP sensor better than the others to go through more than one pathway. Binding energy results revealed that the metalloporphyrin sensors with different metal atoms and substituent groups cause remarkable changes in TMA binding performances. Thus, theoretical investigations can be used to extend the fluorescent sensor in to TMA related analytes in different detection requirements, and perhaps other molecule

Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index

Recent genetic association studies have identified 55 genetic loci associated with obesity or body mass index (BMI). The vast majority, 51 loci, however, were identified in European-ancestry populations. We conducted a meta-analysis of associations between BMI and ∼2.5 million genotyped or imputed single nucleotide polymorphisms among 86 757 individuals of Asian ancestry, followed by in silico and de novo replication among 7488–47 352 additional Asian-ancestry individuals. We identified four novel BMI-associated loci near the KCNQ1 (rs2237892, P = 9.29 × 10−13), ALDH2/MYL2 (rs671, P = 3.40 × 10−11; rs12229654, P = 4.56 × 10−9), ITIH4 (rs2535633, P = 1.77 × 10−10) and NT5C2 (rs11191580, P = 3.83 × 10−8) genes. The association of BMI with rs2237892, rs671 and rs12229654 was significantly stronger among men than among women. Of the 51 BMI-associated loci initially identified in European-ancestry populations, we confirmed eight loci at the genome-wide significance level (P < 5.0 × 10−8) and an additional 14 at P < 1.0 × 10−3 with the same direction of effect as reported previously. Findings from this analysis expand our knowledge of the genetic basis of obesity

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Theoretical Study on the Electronic Properties of Two-Dimensional Covalent Triazine Frameworks/As van der Waals Heterostructures

The manuscript substantiates the structural and electronic properties of covalent triazine frameworks (CTF)/As van der Waals heterostructures (vdWh) employing the standard first-principles calculation method. The numerical results designate that the CTF/As vdWh has robust crystal structures, a type-II band alignment (BA), and an indirect bandgap of 1.44 eV. The calculated results demonstrate that the strain could lead to interesting indirect-direct semiconductor transitions, while the external electric field could give rise to type-II to type-I BA and semiconductor-metal transitions. The underlined outcomes present the workability of CTF/As vdWhs in unprecedented high-performance optoelectronic equipment

Solving Large-Scale Multiobjective Optimization Problems With Sparse Optimal Solutions via Unsupervised Neural Networks

Tian Y, Lu C, Zhang X, Tan KC, Jin Y. Solving Large-Scale Multiobjective Optimization Problems With Sparse Optimal Solutions via Unsupervised Neural Networks. IEEE Transactions on Cybernetics. 2021;51(6):3115-3128.Due to the curse of dimensionality of search space, it is extremely difficult for evolutionary algorithms to approximate the optimal solutions of large-scale multiobjective optimization problems (LMOPs) by using a limited budget of evaluations. If the Pareto-optimal subspace is approximated during the evolutionary process, the search space can be reduced and the difficulty encountered by evolutionary algorithms can be highly alleviated. Following the above idea, this article proposes an evolutionary algorithm to solve sparse LMOPs by learning the Pareto-optimal subspace. The proposed algorithm uses two unsupervised neural networks, a restricted Boltzmann machine, and a denoising autoencoder to learn a sparse distribution and a compact representation of the decision variables, where the combination of the learnt sparse distribution and compact representation is regarded as an approximation of the Pareto-optimal subspace. The genetic operators are conducted in the learnt subspace, and the resultant offspring solutions then can be mapped back to the original search space by the two neural networks. According to the experimental results on eight benchmark problems and eight real-world problems, the proposed algorithm can effectively solve sparse LMOPs with 10000 decision variables by only 100000 evaluations

Solving Large-Scale Multi-Objective Optimization Problems with Sparse Optimal Solutions via Unsupervised Neural Networks

Due to the curse of dimensionality of search space, it is extremely difficult for evolutionary algorithms to approximate the optimal solutions of large-scale multiobjective optimization problems (LMOPs) by using a limited budget of evaluations. If the Pareto optimal subspace is approximated during the evolutionary process, the search space can be reduced and the difficulty encountered by evolutionary algorithms can be highly alleviated. Following the above idea, this paper proposes an evolutionary algorithm to solve sparse LMOPs by learning the Pareto optimal subspace. The proposed algorithm uses two unsupervised neural networks, a restricted Boltzmann machine and a denoising autoencoder to learn a sparse distribution and a compact representation of the decision variables, where the combination of the learnt sparse distribution and compact representation is regarded as an approximation of the Pareto optimal subspace. The genetic operators are conducted in the learnt subspace, and the resultant offspring solutions then can be mapped back to the original search space by the two neural networks. According to the experimental results on eight benchmark problems and eight real-world problems,the proposed algorithm can effectively solve sparse LMOPs with 10,000 decision variables by only 100,000 evaluations

Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

Tian Y, Zhang X, He C, Tan KC, Jin Y. Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics. Chinese Journal of Electronics. 2023;32(1):111-129.A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed, new operators are automatically designed in this work, which are expected to be search space independent and thus exhibit robust performance on different problems. This work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. This work then deduces the generic form of translation, scale, and rotation invariant operators, and proposes a principled approach for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000 decision variables

Mechanistic insight of metalloporphyrin-based fluorescence sensor reacting with volatile organic compounds

The theoretical design of a metalloporphyrin-based fluorescence sensor was developed for the discrimination of volatile organic compounds (VOCs). Molecular models of cobalt tetraphenylporphine (CoTPP) with small VOCs were investigated by the density functional theory (DFT) method at the Becke, 3-parameter, Lee–Yang–Parr (B3LYP) and LAN2DZ level. The relative energies of CoTPP and its complexes were calculated at three spin states (low, intermediate, and high) to obtain their optimized geometry structures. Singlets were found to be the most stable states for CoTPP and its complexes, except for CoTPP-oxygen and CoTPP-propanol at triplets. The binding performance was represented by the binding energy (BE), which showed the following order based on the most stable states: propane (L3) < oxygen (O2) < propanol (L2) < hydrogen sulfide (H2S) < propionaldehyde (L6) < nitrogen (N2) < butanone (L5) < ethyl acetate (L4) < trimethylamine (L1). We suggest that the CoTPP-based fluorescence sensor is sensitive to trimethylamine and ethyl acetate, and avoids the interference from propane and oxygen