Metabolic impact of adenovirus-mediated overexpression of the glucose-6-phosphatase catalytic subunit in hepatocytes

Glucose-6-phosphatase (G6Pase) catalyzes the hydrolysis of glucose 6-phosphate (Glu-6-P) to free glucose and, as the last step in gluconeogenesis and glycogenolysis in liver, is thought to play an important role in glucose homeostasis. G6Pase activity appears to be conferred by a set of proteins localized to the endoplasmic reticulum, including a glucose-6-phosphate translocase, a G6Pase phosphohydrolase or catalytic subunit, and glucose and inorganic phosphate transporters in the endoplasmic reticulum membrane. In the current study, we used a recombinant adenovirus containing the cDNA encoding the G6Pase catalytic subunit (AdCMV-G6Pase) to evaluate the metabolic impact of overexpression of the enzyme in primary hepatocytes. We found that AdCMV-G6Pase-treated liver cells contain significantly less glycogen and Glu-6-P, but unchanged UDP-glucose levels, relative to control cells. Further, the glycogen synthase activity state was closely correlated with Glu-6-P levels over a wide range of glucose concentrations in both G6Pase-overexpressing and control cells. The reduction in glycogen synthesis in AdCMV-G6Pase-treated hepatocytes is therefore not a function of decreased substrate availability but rather occurs because of the regulatory effects of Glu-6-P on glycogen synthase activity. We also found that AdCMV-G6Pase-treated-cells had significantly lower rates of lactate production and [3-3H]glucose usage, coupled with enhanced rates of gluconeogenesis and Glu-6-P hydrolysis. We conclude that overexpression of the G6Pase catalytic subunit alone is sufficient to activate flux through the G6Pase system in liver cells. Further, hepatocytes treated with AdCMV-G6Pase exhibit a metabolic profile resembling that of liver cells from patients or animals with non-insulin-dependent diabetes mellitus, suggesting that dysregulation of the catalytic subunit of G6Pase could contribute to the etiology of the disease.     

Choline Acetyltransferase Induces the Functional Regeneration of the Salivary Gland in Aging SAMP1/Kl -/- Mice

Salivary gland dysfunction induces salivary flow reduction and a dry mouth, and commonly involves oral dysfunction, tooth structure deterioration, and infection through reduced salivation. This study aimed to investigate the impact of aging on the salivary gland by a metabolomics approach in an extensive aging mouse model, SAMP1/Klotho -/- mice. We found that the salivary secretion of SAMP1/Klotho -/- mice was dramatically decreased compared with that of SAMP1/Klotho WT (+/+) mice. Metabolomics profiling analysis showed that the level of acetylcholine was significantly decreased in SAMP1/Klotho -/- mice, although the corresponding levels of acetylcholine precursors, acetyl-CoA and choline, increased. Interestingly, the mRNA and protein expression of choline acetyltransferase (ChAT), which is responsible for catalyzing acetylcholine synthesis, was significantly decreased in SAMP1/Klotho -/- mice. The overexpression of ChAT induced the expression of salivary gland functional markers (α–amylase, ZO-1, and Aqua5) in primary cultured salivary gland cells from SAMP1/Klotho +/+ and -/- mice. In an in vivo study, adeno-associated virus (AAV)-ChAT transduction significantly increased saliva secretion compared with the control in SAMP1/Klotho -/- mice. These results suggest that the dysfunction in acetylcholine biosynthesis induced by ChAT reduction may cause impaired salivary gland function     

Soluble Prokaryotic Overexpression and Purification of Human GM-CSF Using the Protein Disulfide Isomerase b′a′ Domain

Granulocyte-macrophage colony-stimulating factor (GM-CSF) is a member of the colony-stimulating factor (CSF) family, which functions to enhance the proliferation and differentiation of hematopoietic stem cells and other hematopoietic lineages such as neutrophils, dendritic cells, or macrophages. These proteins have thus generated considerable interest in clinical therapy research. A current obstacle to the prokaryotic production of human GM-CSF (hGM-CSF) is its low solubility when overexpressed and subsequent complex refolding processes. In our present study, the solubility of hGM-CSF was examined when combined with three N-terminal fusion tags in five E. coli strains at three different expression temperatures. In the five E. coli strains BL21 (DE3), ClearColi BL21 (DE3), LOBSTR, SHuffle T7 and Origami2 (DE3), the hexahistidine-tagged hGM-CSF showed the best expression but was insoluble in all cases at each examined temperature. Tagging with the maltose-binding protein (MBP) and the b′a′ domain of protein disulfide isomerase (PDIb′a′) greatly improved the soluble overexpression of hGM-CSF at 30 °C and 18 °C. The solubility was not improved using the Origami2 (DE3) and SHuffle T7 strains that have been engineered for disulfide bond formation. Two conventional chromatographic steps were used to purify hGM-CSF from the overexpressed PDIb′a′-hGM-CSF produced in ClearColi BL21 (DE3). In the experiment, 0.65 mg of hGM-CSF was isolated from a 0.5 L flask culture of these E. coli and showed a 98% purity by SDS-PAGE analysis and silver staining. The bioactivity of this purified hGM-CSF was measured at an EC₅₀ of 16.4 ± 2 pM by a CCK8 assay in TF-1 human erythroleukemia cells.     

Enhanced security in internet voting protocol using blind signature and dynamic ballots

In this paper, we introduce an internet voting protocol which satisfies desired security requirements of electronic voting. In the newly proposed protocol, we allow the adversaries to get more power than in any previous works. They can be coercers or vote buyers outside, and corrupted parties inside our system. These adversaries also have ability to collude with each other to ruin the whole system. Our main contribution is to design an internet voting protocol which is unsusceptible to most of sophisticated attacks. We employ the blind signature technique and the dynamic ballots instead of complex cryptographic techniques to preserve privacy in electronic voting. Moreover, we also aim at the practical system by improving the blind signature scheme and removing physical assumptions which have often been used in the previous works.     

From big smartphone data to worldwide research: The Mobile Data Challenge

This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC), an initiative to collect unique longitudinal smartphone dataset for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC, describe the specific datasets used in each of them, discuss the key design and implementation aspects introduced in order to generate privacy-preserving and scientifically relevant mobile data resources for wider use by the research community, and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.     

Human interaction discovery in smartphone proximity networks

Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.     

Metal Single-Site Molecular Complex–MXene Heteroelectrocatalysts Interspersed Graphene Nanonetwork for Efficient Dual-Task of Water Splitting and Metal–Air Batteries

Development of multifunctional electrocatalysts with high efficiency and stability is of great interest in recent energy conversion technologies. Herein, a novel heteroelectrocatalyst of molecular iron complex (Fe_MC)-carbide MXene (Mo₂TiC₂T_x) uniformly embedded in a 3D graphene-based hierarchical network (GrH) is rationally designed. The coexistence of Fe_MC and MXene with their unique interactions triggers optimum electronic properties, rich multiple active sites, and favorite free adsorption energy for excellent trifunctional catalytic activities. Meanwhile, the highly porous GrH effectively promotes a multichannel architecture for charge transfer and gas/ion diffusion to improve stability. Therefore, the Fe_MC–MXene/GrH results in superb performances towards oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) in alkaline medium. The practical tests indicate that Zn/Al–air batteries derived from Fe_MC–MXene/GrH cathodic electrodes produce high power densities of 165.6 and 172.7 mW cm⁻², respectively. Impressively, the liquid-state Zn–air battery delivers excellent cycling stability of over 1100 h. In addition, the alkaline water electrolyzer induces a low cell voltage of 1.55 V at 10 mA cm⁻² and 1.86 V at 0.4 A cm⁻² in 30 wt.% KOH at 80 °C, surpassing recent reports. The achievements suggest an exciting multifunctional electrocatalyst for electrochemical energy applications.     

Stock-Price Forecasting Based on XGBoost and LSTM

Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we fed selected features into a deep long short-term memory (LSTM) network to forecast stock prices. The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future contextual information. The complex structure enables this network to capture more stochasticity within the stock price. The method does not change when applied to stock data or Forex data. Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error, mean squared error, and root-mean-square error.     

A decision tree using ID3 algorithm for English semantic analysis

Natural language processing has been studied for many years, and it has been applied to many researches and commercial applications. A new model is proposed in this paper, and is used in the English document-level emotional classification. In this survey, we proposed a new model by using an ID3 algorithm of a decision tree to classify semantics (positive, negative, and neutral) for the English documents. The semantic classification of our model is based on many rules which are generated by applying the ID3 algorithm to 115,000 English sentences of our English training data set. We test our new model on the English testing data set including 25,000 English documents, and achieve 63.6% accuracy of sentiment classification results.