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Breast cancer is the most common malignancy in women. Although personalized or targeting molecular cancer therapy is more popular up to now, the cytotoxicity chemotherapy for patients with advanced breast cancer is considered as the alternative option. However, chemoresistance is still the common and critical limitation for breast cancer treatment. Berberine, known as AMPK activator, has shown multiple activities including antitumor effect. In this study, we investigate the chemosensitive effect of different dosages berberine on drug-resistant human breast cancer MCF-7/MDR cell in vitro and in vivo, and the mechanisms underlying AMPK activation on Doxorubicin (DOX) chemosensitivity. Our results showed that berberine could overcome DOX resistance in dose-orchestrated manner: On one hand, low-dose berberine can enhance DOX sensitivity in drug-resistance breast cancer cells through AMPK-HIF-1α-P-gp pathway. On the other hand, high-dose berberine alone directly induces apoptosis through the AMPK-p53 pathway with the independence of HIF-1α expression. Taken together, our findings demonstrate that berberine sensitizes drug-resistant breast cancer to DOX chemotherapy and directly induces apoptosis through the dose-orchestrated AMPK signaling pathway in vitro and in vivo. Berberine appears to be a promising chemosensitizer and chemotherapeutic drug for breast cancer treatment.

Hydroisomerization of long chain n-alkanes has been playing an important role in the petroleum industry, in which heavy distillate and residue are converted into value-added products such as gasoline, jet fuel, other middle distillates and lubricant oils. Herein, 10-ring zeolites, including ZSM-22, ZSM-23, ZSM-35, and ZSM-48 were studied for the process. ZSM-35 and ZSM-48 are relatively less studied zeolites as hydroisomerization reaction catalysts though they are expected to display interesting shape selective properties. A higher conversion was obtained over Pt-ZSM-35 and a higher selectivity was obtained over Pt-ZSM-23 at low temperature and short contact time, and a higher selectivity was obtained over Pt-ZSM-48 at high temperature and long contact time, while the mixed catalysts displayed interesting conversion and selectivity. Small differences in the channel system have a notable influence on the product distribution of hexadecane hydroisomerization over 10-ring zeolites. A combination of the evidence of no absence of multibranched products and the length of hexadecane molecular could infer that hexadecane hydroisomerization includes a pore mouth reaction mechanism.

The existence of pH-dependent surface-enhanced Raman scattering (SERS) of p-aminobenzenethiol (PATP) on Ag nanoparticles has been confirmed by numerous studies, but its mechanism still remains to be clarified. Discussion of the mechanism is at a standstill because of the lack of a systematic investigation of the process behind the pH-induced variation of the PATP behavior. Two-dimensional correlation spectroscopy is one of the most powerful and versatile spectral analysis methods for investigating perturbation-induced variations in dynamic data. Herein, we have analyzed the pH-dependent behavior of PATP using a static buffer solution with pH ranging from 3.0 to 2.0. The order of the variations in the different vibrational intensities was carefully investigated based on 2D correlation SERS spectroscopy. These results have demonstrated that the very first step of the pH-response process involves protonation of the amine group. The pH-response mechanism revealed is an important new component to our understanding of the origin of the b2-type bands of PATP.

The molecular structure and hydrogen bonding of ethylene glycol (EG) and EG-water mixtures in the liquid phase were studied by using near-infrared (NIR) spectroscopy. The spectra were evaluated using a two-dimensional (2D) correlation approach, moving-window 2D correlation analysis and chemometric methods. The minor changes for the CH stretching bands indicate that the structures of pure liquid EG and EG-water mixtures are determined by the intermolecular hydrogen bonding through the OH groups. The analysis of the ν2 + ν3 combination band of water reveals that in EG-rich solutions the molecules of water are predominantly bonded with two molecules of EG and this cooperative hydrogen bonding is stronger than that in bulk water. Further increase in the water content leads to formation of small water clusters around OH groups of EG. Comparing results for the binary mixtures of water with different organic solvents one can conclude that the total amount and distribution of the polar groups are the most important factors determining the solubility of water in the organic phase. The distribution of these groups depends on the length and structure of the hydrocarbon chain. Due to high population and relatively uniform distribution of the OH groups of EG water has unlimited solubility in liquid EG.

Federated learning (FL) is a machine learning paradigm where a shared central model is learned across multiple distributed client devices while the training data remains on edge devices or local clients. Most prior work on federated learning uses Federated Averaging (FedAvg) as an optimization method for training in a synchronized fashion. This involves independent training at multiple edge devices with synchronous aggregation steps. However, the assumptions made by FedAvg are not realistic given the heterogeneity of devices. In particular, the volume and distribution of collected data vary in the training process due to different sampling rates of edge devices. The edge devices themselves also vary in their available communication bandwidth and system configurations, such as memory, processor speed, and power requirements. This leads to vastly different training times as well as model/data transfer times. Furthermore, availability issues at edge devices can lead to a lack of contribution from specific edge devices to the federated model. In this paper, we present an Asynchronous Online Federated Learning (ASOfed) framework, where the edge devices perform online learning with continuous streaming local data and a central server aggregates model parameters from local clients. Our framework updates the central model in an asynchronous manner to tackle the challenges associated with both varying computational loads at heterogeneous edge devices and edge devices that lag behind or dropout. Experiments on three real-world datasets show the effectiveness of ASO-fed on lowering the overall training cost and maintaining good prediction performance.

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