1H NMR based metabolites analysis of murine melanoma cells and primary melanocytes

Melanoma is one of the most life-threatening skin cancer characterized by ineffective therapies and rising incidence. Here we applied 1H NMR to acquire details of metabolic rewiring between primary melanocytes and murine melanoma cells. A total of 29 metabolites were assigned and identified. The principal component analysis (PCA) illustrated a distinct separation along the first component. A constructed orthogonal partial least squares-discriminant (OPLS-DA) model obtained intrinsic variations as PCA analysis did. The corresponding S-plot and loading plot revealed some significant variations of metabolites in melanoma compared with the control group, including the obvious increases of isoleucine, leucine, valine, 3-hydroxybutyrate, lactate, alanine, 2-oxoglutarate, glutathione, creatine, glycine, tyrosine, phenylalanine, histidine and remarkable decreases of lysine, acetate, n-acetyl-CH3, n-acetyl cysteine, glutamine, glutamate, methionine, choline, taurine, glucose and formate. The down regulation of glucose and the accumulated lactate indicated enhanced aerobic glycolysis for energy requirements in melanoma cells. Decreased taurine acted to fight against reactive oxygen species, as evidenced by an active glutathione system in melanoma cells. Amino acid profiles altered different from any other cancer cells. Tumor-related amino acids identified by NMR might be helping advance the field of therapeutic intervention in melanoma. Keywords: melanocytes, melanoma, NMR, multivariate analysis, pathway DOI: 10.7176/JBAH/15-1-01 Publication date: January 30th 202

Comparative study on the mycelial fermentation of a wild type and domesticated cultivation type of Ganoderma lucidum.

Ganoderma lucidum is a traditional and important medicinal fungus with a long history. It has attracted attention due to its bioactive components such as Ganoderma polysaccharides and triterpenes, and is known for its potential effects in regulating intestinal flora, lowering blood sugar, and exhibiting anticancer properties. The limited quantity of wild Ganoderma lucidum cannot meet the daily needs of people or the demands of the industry. Therefore, the artificial cultivation of Ganoderma lucidum can increase its production quantity. This study investigates the fermentation characteristics of wild and cultivated Ganoderma lucidum mycelia through comparative analysis. The differences in fermentation process between the two types were explored by utilizing different culture media and fermentation conditions, focusing on parameters such as growth rate, enzyme production capability, and content of bioactive components. Results indicate notable disparities between wild and cultivated strains during fermentation, including variations in growth rate, enzyme production, and content of bioactive constituents. Additionally, analysis of fermentation products revealed differences in medicinal value and nutritional composition between the two types. This research provides a theoretical basis for the production and utilization of Ganoderma lucidum, offering valuable insights for further investigation into its fermentation characteristics and optimization of production processes

LSTM-based Flow Prediction

In this paper, a method of prediction on continuous time series variables from the production or flow -- an LSTM algorithm based on multivariate tuning -- is proposed. The algorithm improves the traditional LSTM algorithm and converts the time series data into supervised learning sequences regarding industrial data's features. The main innovation of this paper consists in introducing the concepts of periodic measurement and time window in the industrial prediction problem, especially considering industrial data with time series characteristics. Experiments using real-world datasets show that the prediction accuracy is improved, 54.05% higher than that of traditional LSTM algorithm.Comment: 8 pages, 11 figure

Approximate Analyzing of Labeled Transition Systems

As the most important formal semantic model, labeled transition systems are widely used, which can describe the general concurrent systems or control systems without disturbance. However, under normal circumstance, transition systems are complex and difficult to use due to large amount of calculation and the state space explosion problems. In order to overcome these problems, approximate equivalent labeled transition systems are proposed by means of incomplete low-up matrix decomposition factorization. This technique can reduce the complexity of computation and calculate under the allowing errors. As for continuous-time linear systems, we develop a modeling method of approximated transition system based on the approximate solution of matrix, which provides a facility for approximately formal semantic modeling for linear systems and to effectively analyze errors. An example of application in the context of linear systems without disturbances is studied

InviCloak: An End-to-End Approach to Privacy and Performance in Web Content Distribution

In today's web ecosystem, a website that uses a Content Delivery Network (CDN) shares its Transport Layer Security (TLS) private key or session key with the CDN. In this paper, we present the design and implementation of InviCloak, a system that protects the confidentiality and integrity of a user and a website's private communications without changing TLS or upgrading a CDN. InviCloak builds a lightweight but secure and practical key distribution mechanism using the existing DNS infrastructure to distribute a new public key associated with a website's domain name. A web client and a website can use the new key pair to build an encryption channel inside TLS. InviCloak accommodates the current web ecosystem. A website can deploy InviCloak unilaterally without a client's involvement to prevent a passive attacker inside a CDN from eavesdropping on their communications. If a client also installs InviCloak's browser extension, the client and the website can achieve end-to-end confidential and untampered communications in the presence of an active attacker inside a CDN. Our evaluation shows that InviCloak increases the median page load times (PLTs) of realistic web pages from 2.0s to 2.1s, which is smaller than the median PLTs (2.8s) of a state-of-the-art TEE-based solution

Gitor: Scalable Code Clone Detection by Building Global Sample Graph

Code clone detection is about finding out similar code fragments, which has drawn much attention in software engineering since it is important for software maintenance and evolution. Researchers have proposed many techniques and tools for source code clone detection, but current detection methods concentrate on analyzing or processing code samples individually without exploring the underlying connections among code samples. In this paper, we propose Gitor to capture the underlying connections among different code samples. Specifically, given a source code database, we first tokenize all code samples to extract the pre-defined individual information. After obtaining all samples individual information, we leverage them to build a large global sample graph where each node is a code sample or a type of individual information. Then we apply a node embedding technique on the global sample graph to extract all the samples vector representations. After collecting all code samples vectors, we can simply compare the similarity between any two samples to detect possible clone pairs. More importantly, since the obtained vector of a sample is from a global sample graph, we can combine it with its own code features to improve the code clone detection performance. To demonstrate the effectiveness of Gitor, we evaluate it on a widely used dataset namely BigCloneBench. Our experimental results show that Gitor has higher accuracy in terms of code clone detection and excellent execution time for inputs of various sizes compared to existing state-of-the-art tools. Moreover, we also evaluate the combination of Gitor with other traditional vector-based clone detection methods, the results show that the use of Gitor enables them detect more code clones with higher F1.Comment: 12 pages, 5 figure

Fast and accurate extraction of ultra-high quality factor from cavity ring-down measurement

Cavity ring-down is an essential test to measure ultra-high quality factor (UHQ) optical cavities, which is, however, frequently misinterpreted due to lacking of a specified analysis guideline. Here we clarify the basic property of cavity ring down and present a step-by-step method that enables extraction of the overall quality factor, as well as the intrinsic loss and coupling state of UHQ cavities with better fidelity and simplicity than prior schemes. Our work can facilitate acurrate design and characterization of UHQ cavities for ultra-low noise lasers, high finesse reference cavities, and ultra-narrow optical filters

CC2Vec: Combining Typed Tokens with Contrastive Learning for Effective Code Clone Detection

With the development of the open source community, the code is often copied, spread, and evolved in multiple software systems, which brings uncertainty and risk to the software system (e.g., bug propagation and copyright infringement). Therefore, it is important to conduct code clone detection to discover similar code pairs. Many approaches have been proposed to detect code clones where token-based tools can scale to big code. However, due to the lack of program details, they cannot handle more complicated code clones, i.e., semantic code clones. In this paper, we introduce CC2Vec, a novel code encoding method designed to swiftly identify simple code clones while also enhancing the capability for semantic code clone detection. To retain the program details between tokens, CC2Vec divides them into different categories (i.e., typed tokens) according to the syntactic types and then applies two self-attention mechanism layers to encode them. To resist changes in the code structure of semantic code clones, CC2Vec performs contrastive learning to reduce the differences introduced by different code implementations. We evaluate CC2Vec on two widely used datasets (i.e., BigCloneBench and Google Code Jam) and the results report that our method can effectively detect simple code clones. In addition, CC2Vec not only attains comparable performance to widely used semantic code clone detection systems such as ASTNN, SCDetector, and FCCA by simply fine-tuning, but also significantly surpasses these methods in both detection efficiency.Comment: 21 pages, 7 figure

A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature

This study presents a novel approach that synergizes community detection algorithms with various Graph Neural Network (GNN) models to bolster link prediction in scientific literature networks. By integrating the Louvain community detection algorithm into our GNN frameworks, we consistently enhance performance across all models tested. For example, integrating Louvain with the GAT model resulted in an AUC score increase from 0.777 to 0.823, exemplifying the typical improvements observed. Similar gains are noted when Louvain is paired with other GNN architectures, confirming the robustness and effectiveness of incorporating community-level insights. This consistent uplift in performance reflected in our extensive experimentation on bipartite graphs of scientific collaborations and citations highlights the synergistic potential of combining community detection with GNNs to overcome common link prediction challenges such as scalability and resolution limits. Our findings advocate for the integration of community structures as a significant step forward in the predictive accuracy of network science models, offering a comprehensive understanding of scientific collaboration patterns through the lens of advanced machine learning techniques