MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme

Causal inference permits us to discover covert relationships of various variables in time series. However, in most existing works, the variables mentioned above are the dimensions. The causality between dimensions could be cursory, which hinders the comprehension of the internal relationship and the benefit of the causal graph to the neural networks (NNs). In this paper, we find that causality exists not only outside but also inside the time series because it reflects a succession of events in the real world. It inspires us to seek the relationship between internal subsequences. However, the challenges are the hardship of discovering causality from subsequences and utilizing the causal natural structures to improve NNs. To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme. We evaluate the MCNS framework and impregnation NN with MCNS on time series classification tasks. Experimental results illustrate that our impregnation, by refining attention, shape selection classification, and pruning datasets, drives NN, even the data itself preferable accuracy and interpretability. Besides, MCNS provides an in-depth, solid summary of the time series and datasets.Comment: 9 pages, 6 figure

Development of gas sensor based on fractal substrate structures

Gas sensor plays a key role in many applications with sensitivity being a critical performance characteristic. Increasing the surface area of gas sensing material is one approach that can increase sensitivity. Fractal geometries, which have the large specific surface area and special fractal dimension, have previously been successfully used in the design of macrostructure and microstructure of gas sensors to improve their performance. In this article, the influence of geometrical structure of the substrate on the gas sensor performance has been investigated. Two fractal structures (Koch snowflake and Menger sponge) and one traditional structure (Cylinder) were fabricated by 3-D printing and coated in Ag-doped multiwalled carbon nanotube (Ag:MWCNT)-based sensing materials. The fabricated sensors were tested with nitrogen dioxide at different temperatures and humidity. Experimental results show that the sensitivity of gas sensors with fractal structures is increased more than twice that of those with traditional geometrical structures

Resistance-capacitance gas sensor based on fractal geometry

An important component of any chemiresistive gas sensor is the way in which the resistance of the sensing film is interrogated. The geometrical structure of an electrode can enhance the performance of a gas-sensing device and in particular the performance of sensing films with large surface areas, such as carbon nanotubes. In this study, we investigated the influence of geometrical structure on the performance of gas sensors, combining the characteristics of carbon nanotubes with a novel gas sensor electrode structure based on fractal geometry. The fabricated sensors were tested with exposure to nitric oxide, measuring both the sensor resistance and capacitance (RC) of the sensor responses. Experimental results showed that the sensors with fractal electrode structures had a superior performance over sensors with traditional geometrical structures. Moreover, the RC characteristics of these fractal sensors could be further improved by using different test frequencies that could aid in the identification and quantification of a target gas

First-line atezolizumab/durvalumab plus platinum–etoposide combined with radiotherapy in extensive-stage small-cell lung cancer

Abstract Background Immunotherapy has made significant advances in the treatment of extensive-stage small-cell lung cancer (ES-SCLC), but data in combination with radiotherapy are scarce. This study aims to assess the safety and efficacy of chemoimmunotherapy combined with thoracic radiotherapy in patients with ES-SCLC. Methods This single-center retrospective study analyzed patients with ES-SCLC who received standard platinum–etoposide chemotherapy combined with atezolizumab or durvalumab immunotherapy as induction treatment, followed by consolidative thoracic radiotherapy (CTRT) before disease progression in the first-line setting. Adverse events during radiotherapy with or without maintenance immunotherapy and survival outcomes were assessed. Results Between December 2019 and November 2021, 36 patients with ES-SCLC were identified to have received such treatment modality at one hospital. The number of metastatic sites at diagnosis was 1–4. The biological effective dose of CTRT ranged from 52 to 113 Gy. Only two patients (6%) developed grade 3 toxic effect of thrombocytopenia, but none experienced grade 4 or 5 toxicity. Four patients developed immune-related pneumonitis during the induction treatment period but successfully completed later CTRT. The rate of radiation-related pneumonitis was 8% with grades 1–2 and well tolerated. The median progression-free survival (PFS) was 12.8 months, but the median overall survival (OS) was not determined. The estimated 1-year OS was 80.2% and 1-year PFS was 53.4%. Conclusions Immunotherapy combined with CTRT for ES-SCLC is safe and has ample survival benefit

Individual differences in circadian locomotor parameters correlate with anxiety- and depression-like behavior

Disrupted circadian rhythms are a core feature of mood and anxiety disorders. Circadian rhythms are coordinated by a light-entrainable master clock located in the suprachiasmatic nucleus. Animal models of mood and anxiety disorders often exhibit blunted rhythms in locomotor activity and clock gene expression. Interestingly, the changes in circadian rhythms correlate with mood-related behaviours. Although animal models of depression and anxiety exhibit aberrant circadian rhythms in physiology and behavior, it is possible that the methodology being used to induce the behavioral phenotype (e.g., brain lesions, chronic stress, global gene deletion) affect behavior independently of circadian system. This study investigates the relationship between individual differences in circadian locomotor parameters and mood-related behaviors in healthy rats. The circadian phenotype of male Lewis rats was characterized by analyzing wheel running behavior under standard 12h:12h LD conditions, constant dark, constant light, and rate of re-entrainment to a phase advance. Rats were then tested on a battery of behavioral tests: activity box, restricted feeding, elevated plus maze, forced swim test, and fear conditioning. Under 12h:12h LD conditions, percent of daily activity in the light phase and variability in activity onset were associated with longer latency to immobility in the forced swim test. Variability in onset also correlated positively with anxiety-like behavior in the elevated plus maze. Rate of re-entrainment correlated positively with measures of anxiety in the activity box and elevated plus maze. Lastly, we found that free running period under constant dark was associated with anxiety-like behaviors in the activity box and elevated plus maze. Our results provide a previously uncharacterized relationship between circadian locomotor parameters and mood-related behaviors in healthy rats and provide a basis for future examination into circadian clock functioning and mood