Despite the increasing usage of porphyrinic metal-organic frameworks (MOFs) for combination therapy, the controlled encapsulation of inorganic nanoparticle-based therapeutics into such MOFs with specific structures has remained a major obstacle for improved tumor therapy. Here, we report the synthesis of a mesoporous MOF shell on the surface of gold nanorods (AuNRs), wherein a single AuNR is captured individually in single-crystalline MOFs with a controlled crystallographic orientation, for combinational phototherapy against solid tumors. The core-shell heterostructures have the benefits of a mesoporous structure and photoinduced singlet oxygen generation behavior characterized by the porphyrinic MOF shell, together with the plasmonic photothermal conversion characteristic of AuNRs. We demonstrated that the AuNR@MOF nanoplatform enables an efficient tumor treatment strategy by combining photodynamic therapy and photothermal therapy. We should emphasize that such systems could have applications beyond the field of cancer therapy, like plasmonic harvesting of light energy to induce and accelerate catalytic reactions within MOFs and multifunctional nanocarriers for agricultural formulations.
Peroxymonosulfate (PMS)-based advanced oxidation processes (PMS-AOPs) as an efficient strategy for organic degradation are highly dependent on catalyst design and structured active sites. However, the identification of the active sites and their relationship with reaction mechanisms for organic degradation are not fully understood for a composite catalyst due to the complex structure. Herein, we developed a family of Co encapsulated in N-doped carbons (Co-PCN) with tailored types and contents of active sites via manipulated pyrolysis for PMS activation and ciprofloxacin (CIP) degradation, focusing on the correlation of active sites to generated reactive species and degradation routes of organics. The structure–function relationships between the different active sites in Co-PCN catalysts and reactive oxygen species (ROS), as well as bond breaking position of CIP, were revealed through regression analysis and density functional theory calculation. Co–Nx, O–C═O, C═O, graphitic N, and defects in Co-PCN stimulate the generation of 1O2 for oxidizing the C–C bond in the piperazine ring of CIP into C═O. The substitution of F by OH and hydroxylation of the piperazine ring might be induced by SO4•– and •OH, whose formation was affected by C–O, Co(0), Co–Nx, graphitic N, and defects. The findings provided new insights into reaction mechanisms in PMS-AOP systems and rational design of catalysts for ROS-oriented degradation of pollutants.
This paper examines a Sinopean victory list of the boxer Marcus Iutius Marcianus Rufus (French 2004: 76-77 no. 105) and the implications of counting the number of victories he won. Inscribed and set up by the Sinopean boule, the list represents an official recognition of the athlete's successful boxing career, which not only included victories in the four periodoi of mainland Greece, but also the Capitoline and Neapolitan games in Italy. The text has been studied by Theodoré Reinach (1916), George Bean (1953), and David French (2004), and resulting in different ways to count Rufus' victories.The three epigraphists encountered several issues with counting Rufus' victories. How to differentiate between a Bithynian koinon event from a metropolitan event held by Nicaea and Nicomedia is one issue, and whether to count the half-talent victories with the iselastic victories so to fit an ideal number of total victories that Rufus won is another, with the three epigraphists producing different solutions. Perhaps more perplexing of all, however, is how to interpret the Greek letters ΡΝ placed at the end of the victory list. Reinach interpreted them as the remaining letters of ἀνδριατί or "jeux mineurs" (Reinach 1916: 358). Bean and French saw them as Greek numerals, indicating the total tally of all listed victories. While the total tally seems a convincing interpretation on formulaic grounds, the arithmetic does not add up. On the one hand, tabulation indicates that Bean's count of total victories yields 159, with 110 half-talent victories and 49 iselastic victories. He reconciled the number by claiming to have seen signs of reinscribing in the squeeze, and suggested that Rufus initially won 101 half-talent victories, only to have achieved 110 at a later time, upon which occasion an update was applied to his monument (Bean 1953: 176). On the other hand, while French counted the half-talent victories as 110, and his total number of iselastic victories amount to 48, he still maintained that ΡΝ stands for "(In all) 150 victories," leaving the arithmetic issue open for further examination (French 2004: 77).This paper surveys other victory lists to study how koinon and metropolitan victories were differentiated and counted, and how chronographic features were positioned and identified. This paper also proposes to disassociate the number 150 from the total count of victories, and reconsider what was signified by this number. One possibility is the era: the 150th year of the era of Sinope. It has been demonstrated that Sinopean coinage during the imperial period used first the colonial era from 45 BCE, then the so-called Lucullan era of 70 BCE (Leschhorn 1993: 161-162). While era-based chronography is not found on extant imperial period inscriptions from Sinope, Rufus' victory list may be the first surviving example.
A uniform statistical inferential tool in making individualized treatment decisions, which implements the methods of Ma et al. (2017)<doi:10.1177/0962280214541724> and Guo et al. (2021)<doi:10.1080/01621459.2020.1865167>. It uses a flexible semiparametric modeling strategy for heterogeneous treatment effect estimation in high-dimensional settings and can gave valid confidence bands. Based on it, one can find the subgroups of patients that benefit from each treatment, thereby making individualized treatment selection.
The purpose of this study was to examine the daily social pressure and socioeconomic factors related to women’s alcohol consumption in China. Cross-sectional data were obtained from the 2012 China Family Panel Studies. A multivariate logistic regression analysis of a sample of 16 339 female adults with the mean age of 45.3 years was used to examine the relationships between dependent and independent variables. According to the results, first, the greater the daily social pressure, the more likely women were to engage in general alcohol consumption (odds ratio = 1.061) and risk drinking (odds ratio = 1.057). Second, while there is a positive relationship between the general level of social pressure and women’s alcohol consumption, the relationship between the severe level of social pressure and women’s alcohol consumption was not significant. Finally, women in the Central region were less likely to engage in risk drinking than women in the Western region; women with secondary school education were more likely to engage in risk drinking than women with primary school education or below; and age was significantly positively associated with both general and risk drinking. In conclusion, increasing alcohol consumption among women may be due to increased social pressure.
Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatial-temporal dynamics. Considering the huge success of ResNet in deep learning, it would be natural to train deep SNNs with residual learning. Previous Spiking ResNet mimics the standard residual block in ANNs and simply replaces ReLU activation layers with spiking neurons, which suffers the degradation problem and can hardly implement residual learning. In this paper, we propose the spike-element-wise (SEW) ResNet to realize residual learning in deep SNNs. We prove that the SEW ResNet can easily implement identity mapping and overcome the vanishing/exploding gradient problems of Spiking ResNet. We evaluate our SEW ResNet on ImageNet, DVS Gesture, and CIFAR10-DVS datasets, and show that SEW ResNet outperforms the state-of-the-art directly trained SNNs in both accuracy and time-steps. Moreover, SEW ResNet can achieve higher performance by simply adding more layers, providing a simple method to train deep SNNs. To our best knowledge, this is the first time that directly training deep SNNs with more than 100 layers becomes possible. Our codes are available at https://github.com/fangwei123456/Spike-Element-Wise-ResNet.
Pharmaceuticals and personal care products (PPCPs) are of great concern due to their increasing health effects, so advanced treatment technologies for PPCPs removal are urgently needed. In this study, titanate nanotubes decorated Co(OH)2 hollow microsphere (CoM/TNTs) composites were synthesized by a two-step solvothermal method, and used to activate peroxymonosulfate (PMS) through heterogenous catalysis for acetaminophen (ACE) degradation in water. The optimum material (CoM/TNTs0.5) activated PMS system exhibited high ACE removal efficiency and quick kinetic, as 93.0% ACE was degraded even within 10 min. The two components in CoM/TNTs showed a synergetic effect on PMS activation for radicals production: Co(OH)+ from CoM was the primary active species to active PMS, while TNTs could offer abundant –OH groups for Co(OH)+ formation. Density functional theory (DFT) calculation further interpreted the mechanism of Co(OH)+ for PMS activation by means of reaction potential energy surface (PES) analysis. Both the scavenger quenching tests and electron paramagnetic resonance analysis revealed that the sulfate radical (SO4-·) played a dominant role in ACE degradation. Moreover, DFT calculation also suggested that the ACE atoms with high Fukui index (f -) represented the active sites for electrophilic attack by SO4-·. The toxicity analysis based on quantitative structure-activity relationship (QSAR) verified the reduced toxicity of transformation products. Furthermore, CoM/TNTs also had good reusability and stability over five cycles. This work provides deep insights into the reaction mechanisms of radical production and organics attack in cobalt-based PMS activation system.
Degradation pathway is important for the study of carbamazepine (CBZ) removal in advanced oxidation processes (AOPs). Generally, degradation pathways are speculated based on intermediate identification and basic chemical rules. However, this semiempirical strategy is sometimes time-consuming and baseless. To improve the situation, a mini meta-analysis was first conducted for the degradation pathways of CBZ in AOPs. Then, the rationality of the pathways was analyzed by Density Functional Theory (DFT) calculation. Results show that the degradation pathways of CBZ in various AOPs has high similarity, and the reactive sites predicted by Fukui function fitted well with the data retrieved from literatures. In addition, molecule configuration of degradation intermediates was found to play a very important roles on degradation pathway. The study reveals that computational chemistry is a useful tool for degradation pathway speculation in AOPs.
Accurate forecasts are vital for supporting the decisions of modern companies. Forecasters typically select the most appropriate statistical model for each time series. However, statistical models usually presume some data generation process while making strong assumptions about the errors. In this paper, we present a novel data-centric approach — ‘forecasting with cross-similarity’, which tackles model uncertainty in a model-free manner. Existing similarity-based methods focus on identifying similar patterns within the series, i.e., ‘self-similarity’. In contrast, we propose searching for similar patterns from a reference set, i.e., ‘cross-similarity’. Instead of extrapolating, the future paths of the similar series are aggregated to obtain the forecasts of the target series. Building on the cross-learning concept, our approach allows the application of similarity-based forecasting on series with limited lengths. We evaluate the approach using a rich collection of real data and show that it yields competitive accuracy in both points forecasts and prediction intervals.