Visualization Techniques for Tongue Analysis in Traditional Chinese Medicine

Visual inspection of the tongue has been an important diagnostic method of Traditional Chinese Medicine (TCM). Clinic data have shown significant connections between various viscera cancers and abnormalities in the tongue and the tongue coating. Visual inspection of the tongue is simple and inexpensive, but the current practice in TCM is mainly experience-based and the quality of the visual inspection varies between individuals. The computerized inspection method provides quantitative models to evaluate color, texture and surface features on the tongue. In this paper, we investigate visualization techniques and processes to allow interactive data analysis with the aim to merge computerized measurements with human expert's diagnostic variables based on five-scale diagnostic conditions: Healthy (H), History Cancers (HC), History of Polyps (HP), Polyps (P) and Colon Cancer (C)

Using Artificial Intelligence for Model Selection

We apply the optimization algorithm Adaptive Simulated Annealing (ASA) to the problem of analyzing data on a large population and selecting the best model to predict that an individual with various traits will have a particular disease. We compare ASA with traditional forward and backward regression on computer simulated data. We find that the traditional methods of modeling are better for smaller data sets whereas a numerically stable ASA seems to perform better on larger and more complicated data sets.Comment: 10 pages, no figures, in Proceedings, Hawaii International Conference on Statistics and Related Fields, June 5-8, 200

Boosting Semi-Supervised Learning by bridging high and low-confidence predictions

Pseudo-labeling is a crucial technique in semi-supervised learning (SSL), where artificial labels are generated for unlabeled data by a trained model, allowing for the simultaneous training of labeled and unlabeled data in a supervised setting. However, several studies have identified three main issues with pseudo-labeling-based approaches. Firstly, these methods heavily rely on predictions from the trained model, which may not always be accurate, leading to a confirmation bias problem. Secondly, the trained model may be overfitted to easy-to-learn examples, ignoring hard-to-learn ones, resulting in the \textit{"Matthew effect"} where the already strong become stronger and the weak weaker. Thirdly, most of the low-confidence predictions of unlabeled data are discarded due to the use of a high threshold, leading to an underutilization of unlabeled data during training. To address these issues, we propose a new method called ReFixMatch, which aims to utilize all of the unlabeled data during training, thus improving the generalizability of the model and performance on SSL benchmarks. Notably, ReFixMatch achieves 41.05\% top-1 accuracy with 100k labeled examples on ImageNet, outperforming the baseline FixMatch and current state-of-the-art methods.Comment: Accepted to ICCVW2023 (Workshop on representation learning with very limited images: the potential of self-, synthetic- and formula-supervision

Assessing competitiveness of foreign and local supermarket chains in Vietnamese market by using Fuzzy TOPSIS method

Considering the strategic importance for supermarket chains and to understanding the critical elements affecting their competitiveness and their relative level of competitiveness, this study tries to assess competitiveness of foreign and local supermarket chains in Vietnam using the fuzzy TOPSIS method. The results show that, even smaller size Vietnamese supermarket chains, when compared to foreign chains, are still slightly higher in competitiveness.Competitiveness; Supermarket chains; Fuzzy TOPSIS

Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering

The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm that learns from natural protein sequences to infer evolutionarily plausible mutations and predict enzyme fitness. MODIFY co-optimizes predicted fitness and sequence diversity of starting libraries, prioritizing high-fitness variants while ensuring broad sequence coverage. In silico evaluation shows that MODIFY outperforms state-of-the-art unsupervised methods in zero-shot fitness prediction and enables ML-guided directed evolution with enhanced efficiency. Using MODIFY, we engineer generalist biocatalysts derived from a thermostable cytochrome c to achieve enantioselective C-B and C-Si bond formation via a new-to-nature carbene transfer mechanism, leading to biocatalysts six mutations away from previously developed enzymes while exhibiting superior or comparable activities. These results demonstrate MODIFY's potential in solving challenging enzyme engineering problems beyond the reach of classic directed evolution

Liquid-Phase Packaging of a Glucose Oxidase Solution with Parylene Direct Encapsulation and an Ultraviolet Curing Adhesive Cover for Glucose Sensors

We have developed a package for disposable glucose sensor chips using Parylene encapsulation of a glucose oxidase solution in the liquid phase and a cover structure made of an ultraviolet (UV) curable adhesive. Parylene was directly deposited onto a small volume (1 μL) of glucose oxidase solution through chemical vapor deposition. The cover and reaction chamber were constructed on Parylene film using a UV-curable adhesive and photolithography. The package was processed at room temperature to avoid denaturation of the glucose oxidase. The glucose oxidase solution was encapsulated and unsealed. Glucose sensing was demonstrated using standard amperometric detection at glucose concentrations between 0.1 and 100 mM, which covers the glucose concentration range of diabetic patients. Our proposed Parylene encapsulation and UV-adhesive cover form a liquid phase glucose-oxidase package that has the advantages of room temperature processing and direct liquid encapsulation of a small volume solution without use of conventional solidifying chemicals

Efficacy and Safety of a Chinese Herbal Medicine Formula (RCM-104) in the Management of Simple Obesity: A Randomized, Placebo-Controlled Clinical Trial

Objective. This study was to evaluate the efficacy and safety of a Chinese herbal medicine formula (RCM-104) for the management of simple obesity. Method. Obese subjects aged between 18 and 60 years were selected for 12-week, double-blind, randomized, placebo-controlled trial. Subjects were randomly assigned to take 4 capsules of either the RCM-104 formula (n = 59) or placebo (n = 58), 3 times daily for 12 weeks. Measures of BW, BMI and WC, HC, WHR and BF composition were assessed at baseline and once every four weeks during the 12 week treatment period. Results. Of the 117 subjects randomised, 92 were included in the ITT analysis. The weight, BMI and BF in RCM-104 group were reduced by 1.5 kg, 0.6 kg/m2 and 0.9% and those in the placebo group were increased by 0.5 kg, 0.2 kg/m2 and 0.1% respectively. There were significant differences in BW and BMI (P < 0.05) between the two groups. Eleven items of the WLQOQ were significantly improved in the RCM-104 group while only 2 items were significantly improved in the placebo group. Adverse events were minor in both groups. Conclusion. RCM-104 treatment appears to be well tolerated and beneficial in reducing BW and BMI in obese subjects

Social Internet of Things and New Generation Computing -- A Survey

Social Internet of Things (SIoT) tries to overcome the challenges of Internet of Things (IoT) such as scalability, trust and discovery of resources, by inspiration from social computing. This survey aims to investigate the research done on SIoT from two perspectives including application domain and the integration to the new computing models. For this, a two-dimensional framework is proposed and the projects are investigated, accordingly. The first dimension considers and classifies available research from the application domain perspective and the second dimension performs the same from the integration to new computing models standpoint. The aim is to technically describe SIoT, to classify related research, to foster the dissemination of state-of-the-art, and to discuss open research directions in this field.Comment: IoT, Social computing, Surve