A novel 4-(1,3,4-thiadiazole-2-ylthio)pyrimidine derivative inhibits cell proliferation by suppressing the MEK/ERK signaling pathway in colorectal cancer

Colorectal cancer (CRC) is one of the most common types of malignant cancers worldwide. Although molecularly targeted therapies have significantly improved treatment outcomes, most of these target inhibitors are resistant. Novel inhibitors as potential anti-cancer drug candidates are still needed to be discovered. Therefore, in the present study, we synthesized a novel 4-(1,3,4-thiadiazole-2-ylthio)pyrimidine derivative (compound 4) using fragment- and structure-based techniques and then investigated the anti-cancer effect and underlying mechanism of anti-CRC. The results revealed that compound 4 significantly inhibited HCT116 cell proliferation with IC50 values of 8.04 ± 0.94 µmol L–1 after 48 h and 5.52 ± 0.42 µmol L–1 after 72 h, respectively. Compound 4 also inhibited colony formation, migration, and invasion of HCT116 cells in a dose-dependent manner, as well as inducing cell apoptosis and arresting the cell cycle in the G2/M phase. In addition, compound 4 was able to inhibit the activation of the MEK/ERK signaling in HCT116 cells. And compound 4 yielded the same effects as the MEK inhibitor U0126 on cell apoptosis and MEK/ERK-related proteins. These findings suggested that compound 4 inhibited cell proliferation and growth, and induced cell apoptosis, indicating its use as e a novel and potent anti-cancer agent against CRC via the MEK/ERK signaling pathway

On the use of an explicit chemical mechanism to dissect peroxy acetyl nitrate formation.

Peroxy acetyl nitrate (PAN) is a key component of photochemical smog and plays an important role in atmospheric chemistry. Though it has been known that PAN is produced via reactions of nitrogen oxides (NOx) with some volatile organic compounds (VOCs), it is difficult to quantify the contributions of individual precursor species. Here we use an explicit photochemical model--Master Chemical Mechanism (MCM) model--to dissect PAN formation and identify principal precursors, by analyzing measurements made in Beijing in summer 2008. PAN production was sensitive to both NOx and VOCs. Isoprene was the predominant VOC precursor at suburb with biogenic impact, whilst anthropogenic hydrocarbons dominated at downtown. PAN production was attributable to a relatively small class of compounds including NOx, xylenes, trimethylbenzenes, trans/cis-2-butenes, toluene, and propene. MCM can advance understanding of PAN photochemistry to a species level, and provide more relevant recommendations for mitigating photochemical pollution in large cities

Rapid identification of lactic acid bacteria at species/subspecies level via ensemble learning of Ramanomes

Rapid and accurate identification of lactic acid bacteria (LAB) species would greatly improve the screening rate for functional LAB. Although many conventional and molecular methods have proven efficient and reliable, LAB identification using these methods has generally been slow and tedious. Single-cell Raman spectroscopy (SCRS) provides the phenotypic profile of a single cell and can be performed by Raman spectroscopy (which directly detects vibrations of chemical bonds through inelastic scattering by a laser light) using an individual live cell. Recently, owing to its affordability, non-invasiveness, and label-free features, the Ramanome has emerged as a potential technique for fast bacterial detection. Here, we established a reference Ramanome database consisting of SCRS data from 1,650 cells from nine LAB species/subspecies and conducted further analysis using machine learning approaches, which have high efficiency and accuracy. We chose the ensemble meta-classifier (EMC), which is suitable for solving multi-classification problems, to perform in-depth mining and analysis of the Ramanome data. To optimize the accuracy and efficiency of the machine learning algorithm, we compared nine classifiers: LDA, SVM, RF, XGBoost, KNN, PLS-DA, CNN, LSTM, and EMC. EMC achieved the highest average prediction accuracy of 97.3% for recognizing LAB at the species/subspecies level. In summary, Ramanomes, with the integration of EMC, have promising potential for fast LAB species/subspecies identification in laboratories and may thus be further developed and sharpened for the direct identification and prediction of LAB species from fermented food

Photoinduced Production of Chlorine Molecules from Titanium Dioxide Surfaces Containing Chloride

Titanium dioxide (TiO2) is extensively used with the process of urbanization and potentially influences atmospheric chemistry, which is yet unclear. In this work, we demonstrated strong production of Cl-2 from illuminated KCl-coated TiO2 membranes and suggested an important daytime source of chlorine radicals. We found that water and oxygen were required for the reactions to proceed, and Cl-2 production increased linearly with the amount of coated KCl, humidity of the carrier gas, and light intensity. These results suggested that water promotes the reactivity of coated KCl via interaction with the crystal lattice to release free chloride ions (Cl-). The free Cl- transfer charges to O-2 via photoactivated TiO2 to form Cl-2 and probably the O-2(-) radical. In addition to Cl-2, ClO and HOCl were also observed via the complex reactions between Cl/Cl-2 and HOx. An intensive campaign was conducted in Shanghai, during which evident daytime peaks of Cl-2 were observed. Estimated Cl-2 production from TiO2 photocatalysis can be up to 0.2 ppb/h when the TiO2-containing surface reaches 20% of the urban surface, and highly correlated to the observed Cl-2. Our results suggest a non-negligible role of TiO2 in atmospheric photochemistry via altering the radical budget.Peer reviewe

Polluted dust promotes new particle formation and growth

Peer reviewe

Predicting Temperature of Permanent Magnet Synchronous Motor Based on Deep Neural Network

The heat loss and cooling modes of a permanent magnet synchronous motor (PMSM) directly affect the its temperature rise. The accurate evaluation and prediction of stator winding temperature is of great significance to the safety and reliability of PMSMs. In order to study the influencing factors of stator winding temperature and prevent motor insulation ageing, insulation burning, permanent magnet demagnetization and other faults caused by high stator winding temperature, we propose a computer model for PMSM temperature prediction. Ambient temperature, coolant temperature, direct-axis voltage, quadrature-axis voltage, motor speed, torque, direct-axis current, quadrature-axis current, permanent magnet surface temperature, stator yoke temperature, and stator tooth temperature are taken as the input, while the stator winding temperature is taken as the output. A deep neural network (DNN) model for PMSM temperature prediction was constructed. The experimental results showed the prediction error of the model (MAE) was 0.1515, the RMSE was 0.2368, the goodness of fit (R2) was 0.9439 and the goodness of fit between the predicted data and the measured data was high. Through comparative experiments, the prediction accuracy of the DNN model proposed in this paper was determined to be better than other models. This model can effectively predict the temperature change of stator winding, provide technical support to temperature early warning systems and ensure safe operation of PMSMs

Determination of ifenprodil by LC–MS/MS and its application to a pharmacokinetic study in healthy Chinese volunteers

This paper reports the development and validation of an assay for ifenprodil based on liquid chromatography–tandem mass spectrometry (LC–MS/MS) and its application to a pharmacokinetic study involving single and multiple intravenous infusions to healthy Chinese volunteers. After sample preparation of plasma by liquid–liquid extraction with ethyl acetate, the analyte and internal standard, urapidil, were separated by reversed phase chromatography in a run time of 4 min and detected by positive ion electrospray ionization followed by multiple reaction monitoring of the precursor-to-product ion transitions at m/z 326.2→308.1 for ifenprodil and m/z 388.4→205.3 for IS. The assay was linear in the concentration range 0.2–50.0 ng/mL with recovery >76.4%. In the pharmacokinetic study of single intravenous infusions of 5, 10 and 15 mg ifenprodil, peak plasma concentrations and areas under the plasma concentration–time curve were both linearly related to dose. In the pharmacokinetic study of multiple once daily intravenous infusions of 10 mg ifenprodil for 7 days, pharmacokinetic parameters were similar to those after the single dose showing that ifenprodil does not accumulate on repeated administration

A Review on the Methods for Observing the Substance and Energy Exchange between Atmosphere Boundary Layer and Free Troposphere

Atmosphere boundary layer (ABL or BL) acts as a pivotal part in the climate by regulating the vertical exchange of moisture, aerosol, trace gases and energy between the earth surface and free troposphere (FT). However, compared with research on the exchange between earth surface and ABL, there are fewer researches on the exchange between ABL and FT, especially when it comes to the quantitative measurement of vertical exchange flux between them. In this paper, a number of various methodologies for investigating the exchange of the substance and energy between ABL and FT are reviewed as follows: (1) methods to obtain entrainment rate, which include method by investigating the height of inversion layer, method of flux-jump, estimating with dataset from the ASTEX Lagrangian Experiments and method of using satellite observations and Microwave Imager; (2) mass budget method, which can yield quantitative measurements of exchange flux between ABL and FT; (3) qualitative measurements: method based on Rayleigh distillation and mixing processes, methods of ground-based remote sensing and airborne tracer-tracer relationship/ratio method