Construction of Facial Skin Temperature-Based Anomaly Detection Model for Daily Fluctuations in Health Conditions

A method for estimating health conditions is required to monitor daily health conditions. Various types of data have been used in healthcare studies; however, imaging data are superior because they allow quick and remote measurements. Thermal face images can be measured safely and economically using infrared thermography. Many physiological and psychological states have been evaluated based on the data from these images. A previous study, using short-term experiments, confirmed that an anomaly detection model constructed using a variational autoencoder enables the detection of anomalous states of thermal face images. A long-term experiment is essential to estimate long-term fluctuating human states, such as health conditions. The purpose of this study is to construct a facial skin temperature-based anomaly detection model for human health conditions. The authors obtained thermal face images with health condition questionnaires for approximately a year. Based on the questionnaire responses, the thermal images in good and poor health conditions were labeled “normal state” and “anomaly state,” respectively. The facial skin temperature-based anomaly detection model for health conditions was constructed using a variational autoencoder with only thermal face images in the normal state. The AUC, which represents anomaly detection performance, was 0.70. In addition, an increasing trend of the performance of the model by learning a wider area of skin temperature was confirmed

EksPy: a new Python framework for developing graphical user interface based PyQt5

This study introduces EksPy Python framework, a novel framework designed for developing graphical user interface (GUI) applications in Python. EksPy framework is built on PyQt5, which is a collection of Python bindings for the Qt libraries, and it provides a user-friendly and intuitive interface. The comparative analysis of EksPy framework with existing frameworks such as Tkinter and PyQt highlights its notable features, including ease of use, rapid development, enhanced performance, effective database management, and the model-view-controller (MVC) concept. The experimental results illustrate that EksPy framework requires less code and enhances code readability, thereby facilitating better understanding and efficient development. Additionally, EksPy framework offers a modern and customizable appearance, surpassing Tkinter’s capabilities. Furthermore, it incorporates a built-in object-relational mapping (ORM) feature to simplify database interactions and adheres to the MVC architectural pattern. In conclusion, EksPy Python framework emerges as a powerful, user-friendly, and efficient framework for GUI application development in Python

A Food Recommender Based on Frequent Sets of Food Mining Using Image Recognition

Food recommendation is an important service in our life. To set a system, we searched a set of food images from social network which were shared or reviewed on the web, including the information that people actually chose in daily life. In the field of representation learning, we proposed a scalable architecture for integrating different deep neural networks (DNNs) with a reliability score of DNN. This allowed the integrated DNN to select a suitable recognition result obtained from the different DNNs that were independently constructed. The frequent set of foods extracted from food images was applied to Apriori data mining algorithm for the food recommendation process. In this study, we evaluated the feasibility of our proposed method

High accumulation of plasminogen and tissue plasminogen activator at the flow surface of mural fibrin in the human arterial system

AbstractPurpose: We assessed the fibrinolytic activity of the organized mural thrombus lining of aneurysms and prosthetic grafts. Methods: Between May 1995 and April 1998, the full-thickness mural thrombi of aneurysms and the pseudointima lining of vascular grafts were obtained from 12 patients, ranging from 55 to 78 years in age, who underwent elective surgery. These included five aortic arch aneurysms, four abdominal aortic aneurysms, and three patent synthetic vascular grafts. The specimens were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE)/immunoblot and immunohistochemistry for human plasmin/plasminogen, tissue plasminogen activator (tPA), and fibrin degradation product (D-dimer). Results: In the SDS-PAGE/immunoblot, 25- and 27-kd bands appeared specifically in experimental fibrin plates after limited digestion by plasmin and were also recognized in the mural thrombi. The presence of bands at 25 and 27 kd, which were most prominent in sections near the flow surface layer, was consistent with the hypothesis that the mural fibrin was digested by the endogenous plasmin. Apparent immunoreactivity was found at the flow surface of the masses at a thickness of 10 to 400 μm, suggesting the presence of a plasminogen and tPA-rich layer, with D-dimer as a consequential product of fibrinolysis. Conclusion: The hypothesis that fibrin surfaces in the arterial system acquire fibrinolytic activity because of digestion by circulating endogenous plasmin was confirmed; this may contribute to the antithrombogenicity of these flow surfaces. (J Vasc Surg 2000;32:374-82.

A Fly-Through Mission Strategy Targeting Peptide as a Signature of Chemical Evolution and Possible Life in Enceladus Plumes

In situ detection of organic molecules in the extraterrestrial environment provides a key step towards better understanding the variety and the distribution of building blocks of life and it may ultimately lead to finding extraterrestrial life within the Solar System. Here we present combined results of two separate experiments that enable us to realize such in situ life signature detection from the deep habitats of the "Ocean World": a hydrothermal reactor experiment simulating complex organic synthesis and a simulated fly-through capture experiment of organic-bearing microparticles using silica aerogels, followed by subsequent analysis. Both experiments employ peptide as a plausible organics existing in Encleadus plume particles produced in its subsurface ocean. Recent laboratory hydrothermal experiments and a theoretical model on silica saturation indicated an on going hydrothermal reactions in subsurface Enceladus ocean. Given the porous chondritic origin of the core, it is likely that organic compounds originated by radiation chemistry such as amino acid precursors could have been provided, leached, and altered through widespread water-rock interactions. By using the same laboratory experimental setup from the latest water-rock interaction study, we performed amino acid polymerization experiments for 144 days and monitored the organic complexity changing over time. So far over 3,000 peaks up to the size of greater than 600 MW were observed through the analysis of capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS) with an indication of amino acid derivatives and short peptides. Generally abiotic polymerization of enantiomeric amino acids results in forming stereoisomeric peptides with identical molecular weight and formula as opposed to homochiral biopolymers. Assuming Enceladus plume particles may contain a mixture of stereoisomeric peptides, we were able to distinguish 16 of the 17 stereoisomeric tripeptides as a test sample using capillary electrophoresis (CE) under optimized conditions. We further conducted Enceladus plume fly-through capture experiment by accelerating peptides soaked in rock particles up to a speed of 5.7 km/s and capturing with originally developed hydrophobic silica aerogels. Direct peptide extraction with acetonitrile-water followed by CE analysis led to detection of only but two stereoisomeric acidic peptide peaks, presenting the first run-through hypervelocuty impact sample analysis targeting peptides as key molecule to to understand the ongoing astrobiology on Enceladus

Overcoming epithelial-mesenchymal transition-mediated drug resistance with monensin-based combined therapy in non-small cell lung cancer

Background The epithelial-mesenchymal transition (EMT) is a key process in tumor progression and metastasis and is also associated with drug resistance. Thus, controlling EMT status is a research of interest to conquer the malignant tumors. Materials and methods A drug repositioning analysis of transcriptomic data from a public cell line database identified monensin, a widely used in veterinary medicine, as a candidate EMT inhibitor that suppresses the conversion of the EMT phenotype. Using TGF-β-induced EMT cell line models, the effects of monensin on the EMT status and EMT-mediated drug resistance were assessed. Results TGF-β treatment induced EMT in non-small cell lung cancer (NSCLC) cell lines and the EGFR-mutant NSCLC cell lines with TGF-β-induced EMT acquired resistance to EGFR-tyrosine kinase inhibitor. The addition of monensin effectively suppressed the TGF-β-induced-EMT conversion, and restored the growth inhibition and the induction of apoptosis by the EGFR-tyrosine kinase inhibitor. Conclusion Our data suggested that combined therapy with monensin might be a useful strategy for preventing EMT-mediated acquired drug resistance