Unraveling the relationship between key aroma components and sensory properties of fragrant peanut oils based on flavoromics and machine learning.

Key aroma components of 33 fragrant peanut oils with different aroma types were screened by combined using flavoromics and machine learning. A total of 108 volatile compounds were identified and 100 kinds of them were accurately quantified, and 38 compounds out of them were with odorant activity value ≥1. The 33 peanut oils presented varied intensity of 'fresh peanuts', 'roasted nut', 'burnt', 'over-burnt', 'sweet', 'peanut butter-like', 'puffed food' and 'exotic flavor', and could be classified into four aroma types, namely raw, light, thick and salty. Partial least squares regression analysis, random forest and classification regression tree revealed that 2-acetyl pyrazine had a negative effect on 'fresh peanuts' and could distinguish raw flavor samples well; 2-methylbutanal and 4-vinylguaiacol were key compounds of 'roasted nut' and had significant differences (P < 0.0001) in thick and raw flavor samples; furfural contributed to the 'puffed food' as well as key compound of salty flavor.

Investigation of aroma characteristics of seven Chinese commercial sunflower seed oils using a combination of descriptive Analysis, GC-quadrupole-MS, and GC-Orbitrap-MS.

The aroma characteristics of seven commercial Chinese sunflower seed oils were investigated in this study using descriptive analysis, headspace solid-phase microextraction coupled with GC-quadrupole-MS (LRMS, low-resolution mass spectrometry), and GC-Orbitrap-MS (HRMS, high-resolution mass spectrometry). GC-Orbitrap-MS quantified 96 compounds, including 18 alcohols, 12 esters, 7 ketones, 20 terpenoids, 11 pyrazines, 6 aldehydes, 6 furans, 6 benzene ring-containing compounds, 3 sulfides, 2 alkanes, and 5 nitrogen-containing compounds. Moreover, 22 compounds including 5 acids, 1 amide, and 16 aldehydes were quantified using GC-Quadrupole-MS. To our knowledge, 23 volatile compounds were reported for the first time in sunflower seed oil. All the seven samples were found to have a 'roasted sunflower seeds' note, 'sunflower seeds aroma' note and 'burnt aroma' note and only five of them had 'fried instant noodles' note, three had 'sweet' note and two had 'puffed food' note. Partial least squares regression was used to screen the candidate key volatiles that caused the aroma differences among these seven samples. It was observed that 'roasted sunflower seeds' note was positively correlated with 1-octen-3-ol, n-heptadehyde and dimethyl sulfone, whereas the 'fried instant noodles' and 'puffed food' demonstrated a positive correlation with pentanal, 3-methylbutanal, hexanal, (E)-2-hexenal and 2-pentylfuran. Our findings provide information to the producers and developers for quality control and improvement of sunflower seed oil.

On enabling collaborative non-intrusive load monitoring for sustainable smart cities.

Improving energy efficiency is a crucial aspect of building a sustainable smart city and, more broadly, relevant for improving environmental, economic, and social well-being. Non-intrusive load monitoring (NILM) is a computing technique that estimates energy consumption in real-time and helps raise energy awareness among users to facilitate energy management. Most NILM solutions are still a single machine approach and do not fit well in smart cities. This work proposes a model-agnostic hybrid federated learning framework to collaboratively train NILM models for city-wide energy-saving applications. The framework supports both centralised and decentralised training modes to provide a cluster-based, customisable and optimal learning solution for users. The proposed framework is evaluated on a real-world energy disaggregation dataset. The results show that all NILM models trained in our proposed framework outperform the locally trained ones in accuracy. The results also suggest that the NILM models trained in our framework are resistant to privacy leakage.

A New Machine Learning Algorithm for Numerical Prediction of Near-Earth Environment Sensors along the Inland of East Antarctica.

Accurate short-term small-area meteorological forecasts are essential to ensure the safety of operations and equipment operations in the Antarctic interior. This study proposes a deep learning-based multi-input neural network model to address this problem. The newly proposed model is predicted by combining a stacked autoencoder and a long- and short-term memory network. The self-stacking autoencoder maximises the features and removes redundancy from the target weather station's sensor data and extracts temporal features from the sensor data using a long- and short-term memory network. The proposed new model evaluates the prediction performance and generalisation capability at four observation sites at different East Antarctic latitudes (including the Antarctic maximum and the coastal region). The performance of five deep learning networks is compared through five evaluation metrics, and the optimal form of input combination is discussed. The results show that the prediction capability of the model outperforms the other models. It provides a new method for short-term meteorological prediction in a small inland Antarctic region.

Effect of Hsp90 Inhibitor KW-2478 on HepG2 Cells.

OBJECTIVE: The objectives of this study were to investigate the effects of proliferation, apoptosis, cell cycle, invasion, and senescence of KW-2478 on HepG2 cells, and to explore the related mechanism of apoptosis and the cell cycle. METHODS: HepG2 cells (hepatocellular carcinoma cells) were cultured with KW-2478, at different doses and for different times, in vitro. The MTT assay was used to detect the effect of KW-2478 on proliferation of HepG2 cells. Flow cytometry was used to determine the effects of KW-2478 on the cell cycle and apoptosis of HepG2 cells. The Transwell assay was used to determine the effect of KW-2478 on cell invasion. The ß-galactosidase assay tested the effect of low-dose KW-2478 on the senescence of HepG2 cells. Western blotting and the quantitative polymerase chain reaction were used respectively to assess changes in protein and mRNA levels of related factors in HepG2 cells after the KW-2478 treatment. RESULTS: KW-2478 significantly inhibited proliferation of HepG2 cells. KW-2478 induced apoptosis and cell cycle arrest of HepG2 cells, and inhibited the invasion of HepG2 cells; low dose KW-2478 promoted HepG2 senescence. CONCLUSION: KW-2478 inhibited the proliferation of HepG2 cells, induced apoptosis and cell cycle arrest, inhibited invasion, and promoted senescence. KW-2478 affected the expression of related factors in the mitochondrial apoptotic signaling and cell cycle-related regulatory pathways. KW-2478 downregulated the expression of STAT3, which is a key factor in the JAK-STAT pathway, indicating that KW-2478 may affect the function of HepG2 cells by downregulating STAT3.

Synthesis, in vitro stability, and antiproliferative effect of d-cysteine modified GnRH-doxorubicin conjugates.

Overexpression of gonadotropin-releasing hormone (GnRH) receptor in many tumors but not in normal tissues makes it possible to use GnRH analogs as targeting peptides for selective delivery of cytotoxic agents, which may help to enhance the uptake of anticancer drugs by cancer cells and reduce toxicity to normal cells. The GnRH analogs [d-Cys6 , desGly10 , Pro9 -NH2 ]-GnRH, [d-Cys6 , desGly10 , Pro9 -NHEt]-GnRH, and [d-Cys6 , α-aza-Gly10 -NH2 ]-GnRH were conjugated with doxorubicin (Dox), respectively, through N-succinimidyl-3-maleimidopropionate as a linker to afford three new GnRH-Dox conjugates. The metabolic stability of these conjugates in human serum was determined by RP-HPLC. The antiproliferative activity of the conjugates was examined in GnRH receptor-positive MCF-7 human breast cancer cell line by MTT assay. The three GnRH-Dox conjugates showed improved metabolic stability in human serum in comparison with AN-152. The antiproliferative effect of conjugate II ([d-Cys6 , desGly10 , Pro9 -NHEt]-GnRH-Dox) on MCF-7 cells was higher than that of conjugate I ([d-Cys6 , desGly10 , Pro9 -NH2 ]-GnRH-Dox) and conjugate III ([d-Cys6 , α-aza-Gly10 -NH2 ]-GnRH-Dox), and the cytotoxicity of conjugate II against GnRH receptor-negative 3T3 mouse embryo fibroblast cells was decreased in comparison with free Dox. GnRH receptor inhibition test suggested that the antiproliferative activity of conjugate II might be due to the cellular uptake mediated by the targeting binding of [d-Cys6 -des-Gly10 -Pro9 -NHEt]-GnRH to GnRH receptors. Our study indicates that targeting delivery of conjugate II mediated by [d-Cys6 -des-Gly10 -Pro9 -NHEt]-GnRH is a promising strategy for chemotherapy of tumors that overexpress GnRH receptors.

Design and Performance Analysis of a Multilayer Sea Ice Temperature Sensor Used in Polar Region.

Temperature profiles of sea ice have been recorded more than a few decades. However, few high-precision temperature sensors can complete the observation of temperature profile of sea ice, especially in extreme environments. At present, the most widely used sea ice observation instruments can reach an accuracy of sea ice temperature measurement of 0.1 °C. In this study, a multilayer sea ice temperature sensor is developed with temperature measurement accuracy from -0.0047 °C to 0.0059 °C. The sensor system composition, structure of the thermistor string, and work mode are analyzed. The performance of the sensor system is evaluated from -50 °C to 30 °C. The temperature dependence of the constant current source, the amplification circuit, and the analog-to-digital converter (ADC) circuit are comprehensive tested and quantified. A temperature correction algorithm is designed to correct any deviation in the sensor system. A sea-ice thickness discrimination algorithm is proposed in charge of determining the thickness of sea ice automatically. The sensor system was field tested in Wuliangsuhai, Yellow River on 31 January 2018 and the second reservoir of Fen River, Yellow River on 30 January 2018. The integral practicality of this sensor system is identified and examined. The multilayer sea ice temperature sensor will provide good temperature results of sea ice and maintain stable performance in the low ambient temperature.

Genetic variants of nuclear factor erythroid-derived 2-like 2 associated with the complications in Han descents with type 2 diabetes mellitus of Northeast China.

The transcription factor nuclear factor erythroid 2-like 2 (NFE2L2) is essential for preventing type 2 diabetes mellitus (T2DM)-induced complications in animal models. This case and control study assessed genetic variants of NFE2L2 for associations with T2DM and its complications in Han Chinese volunteers. T2DM patients with (n = 214) or without (n = 236) complications, or healthy controls (n = 359), were genotyped for six NFE2L2 single nucleotide polymorphisms (SNPs: rs2364723, rs13001694, rs10497511, rs1806649, rs1962142 and rs6726395) with TaqMan Pre-Designed SNP Genotyping and Sequence System. Serum levels of heme oxygenase-1 (HMOX1) were determined through enzyme-linked immunosorbent assay. Informative data were obtained for 341 cases and 266 controls. Between T2DM patients and controls, the genotypic and allelic frequencies and haplotypes of the SNPs were similar. However, there was a significant difference in genotypic and allelic frequencies of rs2364723, rs10497511, rs1962142 and rs6726395 between T2DM patients with and without complications, including peripheral neuropathy, nephropathy, retinopathy, foot ulcers and microangiopathy. Furthermore, HMOX1 levels were significantly higher in T2DM patients with complications than in controls. Multiple logistic regression analysis, however, showed that only rs2364723 significantly reduced levels of serum HMOX1 in T2DM patients for the GG genotype carriers compared with participants with CG+CC genotype. The data suggest that although NFE2L2 rs2364723, rs10497511, rs1962142 and rs6726395 were not associated with T2DM risk, they were significantly associated with complications of T2DM. In addition, only for rs2364723 higher serum HMOX1 levels were found in the T2DM patients with CG+CC than those with GG genotype.