Modulatory effect of Althaea officinalis L root extract on cisplatin-induced cytotoxicity and cell proliferation in A549 human lung cancer cell line

Purpose: To explore the modulatory effect of an Althaea officinalis root extract (AORE) on cisplatininduced cytotoxicity and cell proliferation in a lung cancer cell line.Methods: Aqueous AORE was obtained from peeled and powdered roots. The effect of cisplatin on cytotoxicity and cell proliferation was studied. The cisplatin concentrations tested ranged from 0 - 30 mg/mL. Cell viability and proliferation were studied using trypan blue and MTT assays, respectively. The cells were also exposed to a combination of cisplatin and the AORE.Results: Cisplatin yielded a 50 % inhibitory concentration at 25 mg/mL and exhibited a dose-dependent cell proliferation loss. Combined use of the root extract and cisplatin had significant (R2 = 0.8305) modulatory effects on cytotoxicity and antiproliferative activities. The best inhibitory effects were observed in cells exposed to a combination of 8 or 10 % AORE and 25 mg/mL cisplatin. The optimal effect on cell proliferation was obtained using 25 mg/mL cisplatin and 10 % v/v AORE.Conclusion: The enhanced activity of cisplatin in combination with AORE was more pronounced for cell proliferation than cytotoxicity, indicating that AOREs may be used to control tumor progression and metastasis.Keywords: A549 cells, Althaea officinalis, lung cancer, cell proliferation, cell viability, cisplatin, modulatory effec

Evolution of the spatiotemporal pattern of PM2.5 concentrations in China – a case study from the Beijing-Tianjin-Hebei region

Atmospheric haze pollution has become a global concern because of its severe effects on human health and the environment. The Beijing-Tianjin-Hebei urban agglomeration is located in northern China, and its haze is the most serious in China. The high concentration of PM2.5 is the main cause of haze pollution, and thus investigating the temporal and spatial characteristics of PM2.5 is important for understanding the mechanisms underlying PM2.5 pollution and for preventing haze. In this study, the PM2.5 concentration status in 13 cities from the Beijing-Tianjin-Hebei region was statistically analyzed from January 2016 to November 2016, and the spatial variation of PM2.5 was explored via spatial autocorrelation analysis. The research yielded three overall results. (1) The distribution of PM2.5 concentrations in this area varied greatly during the study period. The concentrations increased from late autumn to early winter, and the spatial range expanded from southeast to northwest. In contrast, the PM2.5 concentration decreased rapidly from late winter to early spring, and the spatial range narrowed from northwest to southeast. (2) The spatial dependence degree, by season from high to low, was in the order winter, autumn, spring, summer. Winter (from December to February of the subsequent year) and summer (from June to August) were, respectively, the highest and lowest seasons with regard to the spatial homogeneity of PM2.5 concentrations. (3) The PM2.5 concentration in the Beijing-Tianjin-Hebei region has significant spatial spillovers. Overall, cities far from Bohai Bay, such as Shijiazhuang and Hengshui, demonstrated a high-high concentration of PM2.5 pollution, while coastal cities, such as Chengde and Qinhuangdao, showed a low-low concentration

Solar radiation nowcasting through advanced CNN model integrated with ResNet structure

Although a range of solar radiation forecasting methods have been developed for predicting photovoltaic generation, only a few of them focus on solar radiation forecasting for building energy demand. From the perspective of building performance modelling, solar radiation forecasting needs to meet several critical requirements including high spatial resolution (1m-2km) and high temporal resolution (5-60mins), the accurate value of Direct Normal Irradiance (DNI) and Diffuse Horizontal Irradiance (DHI), which differs the requirement for predicting photovoltaic generation. As the geometric sum of DNI and DHI, accurate prediction of Global Horizontal Irradiance (GHI) with high spatial-temporal resolution tends to be the prerequisite for precise prediction of DNI and DHI. This research aims to construct a hybrid nowcasting model to predict GHI in high spatial-temporal resolution. In this article, the authors adopt an advanced Convolutional Neural Network (CNN) model with Residual Neural Network (ResNet) structure to identify the cloud image information and predict the GHI at 10 minutes intervals merely using cloud images captured by a ground-based sky camera. On this basis, several ResNet structures are compared to achieve the optimal nowcasting model for GHI. The results present that the ResNet structure can efficiently capture the cloud information and the ResNet-152 achieves better performance than other alternative structures on the nowcasting of GHI. Finally, the authors discussed the calculation of synchronous DNI and DHI using the predictive GHI and Dirint model, and the application of DNI and DHI as the input for the simulation of building energy management

Research on the grinding performance of high pressure sintering SiCp/Al matrix composites

The two-dimensional vertical grinding test equipment was used to grind SiCp/Al composites which were prepared by high pressure sintering method. The SEM observation of grinding morphology showed that grinding damage can be prevented by SiC particles reinforcement, 60% volume fraction of SiC particles of SiCp/Al composite can hinder grinding depth and grinding performance was improved with the sintering pressure and temperature increasing. In addition, some scratches and exfoliated pits of SiC particles were observed on the surface of 60% volume SiCp/Al composite as the increase of grinding grain, while the depth of these scratches was shallower, there was no large area exfoliated pits of SiC reinforcements

SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling

Synthetic data has emerged as a promising source for 3D human research as it offers low-cost access to large-scale human datasets. To advance the diversity and annotation quality of human models, we introduce a new synthetic dataset, SynBody, with three appealing features: 1) a clothed parametric human model that can generate a diverse range of subjects; 2) the layered human representation that naturally offers high-quality 3D annotations to support multiple tasks; 3) a scalable system for producing realistic data to facilitate real-world tasks. The dataset comprises 1.2M images with corresponding accurate 3D annotations, covering 10,000 human body models, 1,187 actions, and various viewpoints. The dataset includes two subsets for human pose and shape estimation as well as human neural rendering. Extensive experiments on SynBody indicate that it substantially enhances both SMPL and SMPL-X estimation. Furthermore, the incorporation of layered annotations offers a valuable training resource for investigating the Human Neural Radiance Fields (NeRF).Comment: Accepted by ICCV 2023. Project webpage: https://synbody.github.io

Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1

A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular fingerprints. The dataset, consisting of 931 molecules with LSD1 inhibition activity, was obtained from the ChEMBL database. An evaluation of several candidate algorithms on the main dataset revealed that the support vector regressor gave the best model, with a coefficient of determination (R2) of 0.703. Virtual screening, using this model, identified five predicted potent inhibitors from the ZINC database comprising more than 300,000 molecules. The virtual screening recovered a known inhibitor, RN1, as well as four compounds where activity against LSD1 had not previously been suggested. Thus, we performed a machine-learning-enabled virtual screening of LSD1 inhibitors using only the structural information of the molecules

Search for light dark matter from atmosphere in PandaX-4T

We report a search for light dark matter produced through the cascading decay of $\eta$ mesons, which are created as a result of inelastic collisions between cosmic rays and Earth's atmosphere. We introduce a new and general framework, publicly accessible, designed to address boosted dark matter specifically, with which a full and dedicated simulation including both elastic and quasi-elastic processes of Earth attenuation effect on the dark matter particles arriving at the detector is performed. In the PandaX-4T commissioning data of 0.63 tonne$\cdot$year exposure, no significant excess over background is observed. The first constraints on the interaction between light dark matter generated in the atmosphere and nucleus through a light scalar mediator are obtained. The lowest excluded cross-section is set at $5.9 \times 10^{-37}{\rm cm^2}$ for dark matter mass of $0.1$ MeV$/c^2$ and mediator mass of 300 MeV$/c^2$. The lowest upper limit of $\eta$ to dark matter decay branching ratio is $1.6 \times 10^{-7}$

Synthesis and characterization of novel porphyrin Schiff bases

Novel porphyrin Schiff bases were synthesized by a simple Schiff base condensation in refluxing toluene between 5-(4-aminophenyl)-10,15,20-triphenylporphyrin (ATTP) 3 and styryl aldehydes 4–6 or p-halobenzaldehydes 7–9. The newly synthesized porphyrin Schiff bases were characterized on the basis of their chemical properties and spectral data. A good intramolecular energy transfer from the styryl unit to the porphyrin moiety was found

Recent Developments in Food Packaging Based on Nanomaterials

The increasing demand for high food quality and safety, and concerns of environment sustainable development have been encouraging researchers in the food industry to exploit the robust and green biodegradable nanocomposites, which provide new opportunities and challenges for the development of nanomaterials in the food industry. This review paper aims at summarizing the recent three years of research findings on the new development of nanomaterials for food packaging. Two categories of nanomaterials (i.e., inorganic and organic) are included. The synthetic methods, physical and chemical properties, biological activity, and applications in food systems and safety assessments of each nanomaterial are presented. This review also highlights the possible mechanisms of antimicrobial activity against bacteria of certain active nanomaterials and their health concerns. It concludes with an outlook of the nanomaterials functionalized in food packaging