The Relationship Between Plasma DPP4 Activity to BDNF Ratio and Mild Cognitive Impairment in Elderly Population With Normal Glucose Tolerance

Objective: Since decreased brain-derived neurotrophic factor (BDNF) and increased dipeptidyl peptidase-4 (DPP4) activity have both been implicated in the pathogenesis of mild cognitive impairment (MCI), the aim of our study was to evaluate the association of MCI with plasma DPP4 activity to BDNF ratio (DBR) in an elderly population with normal glucose tolerance.Methods: We cross-sectionally measured C-reactive protein, interleukin-6, nitrotyrosine, 8-iso-PGF2a, DPP4 activity BDNF and calculated the DBR in a total of 1,066 elderly participants in China. MCI was determined by the Montreal Cognitive Assessment and finally confirmed by neurologists.Results: An inverse correlation was found between DPP4 activity and BDNF (r = -0.456, P < 0.001) and this inverse correlation was partly mediated by nitrotyrosine and 8-iso-PGF2a. Across rising quartiles of DBR, nitrotyrosine, 8-iso-PGF2a, C-reactive protein and interleukin-6 progressively increased, whereas the Montreal Cognitive Assessment score progressively decreased. Subjects in the lowest quartile of BDNF and highest quartiles of DBR and DPP4 activity, had higher MCI risk compared with subjects in the highest quartile of the BDNF and lowest quartiles of DBR and DPP4 activity, respectively (all P < 0.05). The odds ratio for MCI became more pronounced with decreased BDNF and increased DPP4.Conclusion: In conclusion, a negative correlation was found between DPP4 activity and BDNF, and this negative correlation was partly mediated by oxidative stress, not inflammation. The DBR was positively associated with MCI and thus may be used as a novel risk biomarker for MCI in an elderly population with normal glucose tolerance

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.Comment: Baichuan 2 technical report. Github: https://github.com/baichuan-inc/Baichuan

Evaluation of Reference Genes for Gene Expression Analysis in <i>Eichhornia crassipes</i>

Eichhornia crassipes is a notorious invasive aquatic weed, causing enormous ecological and economic losses worldwide. However, it has great potential in agriculture, industry, medical care, and other areas. While being such an important plant, it is poorly understood from the molecular perspective. Aiming to select suitable reference genes for gene expression quantification in E. crassipes, this study favors future research at the molecular level. In this work, 12 candidate reference genes were selected. Their expression stability in samples of different tissues, samples treated with various hormones, samples supplied with different levels of phosphorus (P), and pooled samples, were analyzed using GeNorm, NormFinder, BestKeeper, and RefFinder. Meanwhile, the optimal number of reference genes was calculated by GeNorm. The results showed that eIF and ElF1a were the two most stable reference genes in all samples and in tissue samples. In response to hormone treatments, Actin and eIF are the best choices of internal controls. In the case of P treatments, TUA and H2A are recommended to be used as reference genes. Overall, results from this work suggest different reference genes should be applied in qRT-PCR on E. crassipes, according to the specific experimental setup

Decarboxylative thiolation of redox-active esters to free thiols and further diversification

Thiols are important precursors for the synthesis of a variety of pharmaceutically important sulfur-containing compounds. Here, the authors report a visible light-mediated decarboxylative thiolation of alkyl redox-active esters to free thiols and the in situ product diversification of a number of thiol derivatives

Effects of Nonlinear Damping on Vibrations of Microbeam

The present paper develops a new Bernoulli&ndash;Euler theory of microbeams for the consideration of small-scale effects and nonlinear terms, which are induced by the axial elongation of the beam and Kelvin&ndash;Voigt damping. The non-resonance and primary resonance of microbeams are researched through the application of Galerkin and multiple scale methods to the new model. The results suggest the following: (1) Nonlinear damping slightly affects the vibration amplitudes under the non-resonance condition; (2) nonlinear damping can significantly change the bifurcation points that induce a jump in the vibration amplitudes under the primary resonance condition. The current researches indicate that nonlinear damping is necessary for an accurate description of microbeam vibrations

Effect of Microstructure on Micro-Mechanical Properties of Composite Solid Propellant

This study was aimed at determining the effect of microstructure on the macro-mechanical behavior of a composite solid propellant. The microstructure model of a composite solid propellant was generated using molecular dynamics algorithm. The correlation of how microstructural mechanical properties and the effect of initial interface defects in propellant act on the macro-mechanics were studied. Results of this study showed that the mechanical properties of propellant rely heavily on its mesoscopic structure. The grain filling volume fraction mainly influences the propellant initial modulus, the higher the volume fraction, the higher initial modulus. Additionally, it was found that the ratio of particles influences the tensile strength and breaking elongation rate of the propellant. The big particles could also improve the initial modulus of a propellant, but decrease its tensile strength and breaking elongation rate. Furthermore, the initial defects lowered the uniaxial tensile modulus, tensile strength, and the relaxation modulus of propellant, but did not affect the relaxation behavior of the propellant

Effect of Microstructure on Micro-Mechanical Properties of Composite Solid Propellant

This study was aimed at determining the effect of microstructure on the macro-mechanical behavior of a composite solid propellant. The microstructure model of a composite solid propellant was generated using molecular dynamics algorithm. The correlation of how microstructural mechanical properties and the effect of initial interface defects in propellant act on the macro-mechanics were studied. Results of this study showed that the mechanical properties of propellant rely heavily on its mesoscopic structure. The grain filling volume fraction mainly influences the propellant initial modulus, the higher the volume fraction, the higher initial modulus. Additionally, it was found that the ratio of particles influences the tensile strength and breaking elongation rate of the propellant. The big particles could also improve the initial modulus of a propellant, but decrease its tensile strength and breaking elongation rate. Furthermore, the initial defects lowered the uniaxial tensile modulus, tensile strength, and the relaxation modulus of propellant, but did not affect the relaxation behavior of the propellant

Personalized lightweight distributed network intrusion detection system in fog computing

With the continuous development of Internet of Things (IoT) technology, there is a constant emergency of new IoT applications with low latency, high dynamics, and large bandwidth requirements.This has led to the widespread aggregation of massive devices and information at the network edge, promoting the emergence and deep development of fog computing architecture.However, with the widespread and in-depth application of fog computing architecture, the distributed network security architecture deployed to ensure its security is facing critical challenges brought by fog computing itself, such as the limitations of fog computing node computing and network communication resources, and the high dynamics of fog computing applications, which limit the edge deployment of complex network intrusion detection algorithms.To effectively solve the above problems, a personalized lightweight distributed network intrusion detection system (PLD-NIDS) was proposed based on the fog computing architecture.A large-scale complex network flow intrusion detection model was trained based on the convolutional neural network architecture, and furthermore the network traffic type distribution of each fog computing node was collected.The personalized model distillation algorithm and the weighted first-order Taylor approximation pruning algorithm were proposed to quickly compress the complex model, breaking through the limitation of traditional model compression algorithms that can only provide single compressed models for edge node deployment due to the high compression calculation overhead when facing a large number of personalized nodes.According to experimental results, the proposed PLD-NIDS architecture can achieve fast personalized compression of edge intrusion detection models.Compared with traditional model pruning algorithms, the proposed architecture achieves a good balance between computational loss and model accuracy.In terms of model accuracy, the proposed weighted first-order Taylor approximation pruning algorithm can achieve about 4% model compression ratio improvement under the same 0.2% model accuracy loss condition compared with the traditional first-order Taylor approximation pruning algorithm

A Methodology of Image Segmentation for High Resolution Remote Sensing Image Based on Visual System and Markov Random Field

In consideration of the visual system's tremendous ability to perceive and identify the information, a new image segmentation method is presented which simulates the mechanism of visual system for the high resolution remote sensing image segmentation with Markov random field model. Firstly, the characteristics of the visual system have been summarized as: hierarchy, learning ability, feature detection capability and sparse coding property. Secondly, the working mechanism of visual system is simulated by wavelet transform, unsupervised clustering algorithm, feature analysis and Laplace distribution. Then, the segmentation is achieved by the visual mechanism and the Markov random field. Different satellites remote sensing images are adopted as the experimental data, and the segmentation results demonstrate the proposed method have good performance in high resolution remote sensing images

Seawater carbonate chemistry and competition for growth, photosynthetic performance and biochemical composition in Neopyropia yezoensis and Ulva prolifera

The occurrence of various marine macroalgae in the same niche will inevitably lead to interspecific competition due to similar environmental requirements. With the increasing global atmospheric CO2 concentration, the resulting ocean acidification can potentially influence competition among macroalgae in the future. Neopyropia yezoensis (Rhodophyta, formerly Pyropia yezoensis) and the epiphytic alga Ulva prolifera (Chlorophyta) were selected for investigating competition among macroalgae grown under different CO2 conditions. The results showed that when cultured with U. prolifera, N. yezoensis' growth rate was significantly inhibited along with a sharp decrease in net photosynthetic rate. Although CO2 decreased the growth rate of N. yezoensis, it enhanced the resistance of the alga to the allelopathic effect of U. prolifera. While no difference was found between U. prolifera grown in monoculture and biculture, strong competitive ability was observed. CO2 could enhance this ability with higher net photosynthetic rate. However, CO2 significantly inhibited the carotenoid synthesis in both plants. This inhibition in N. yezoensis was more pronounced in the presence of U. prolifera. Biculture promoted the accumulation of soluble protein in N. yezoensis while it inhibited the process in U. prolifera. In addition, it enhanced the inhibitory effect of acidification on soluble carbohydrates of both plants. Elevated CO2 levels alleviated the competition between N. yezoensis and U. prolifera, but the latter can become the more competitive epiphytic alga which can impact the future of nori culture