Search

Abstract Although thermal waves are ubiquitous in nature and engineering, the development of diagnostic tools capable of elucidating the roles of reaction and transport remains an unmet need. This limits our comprehension of the physics and ability to predict wave dynamics. Here we demonstrate that thermal properties and chemical kinetics can be learned directly from observing thermal wave dynamics, using partial differential equation-constrained optimization. This enables the determination of unobserved reaction rates without the need for a comprehensive measurement of all state variables, given the model space constrained by governing equations. Examples include steady planar waves and unsteady pulsating waves of which dynamics are commonly observed in nature. We show successful learning of thermal properties and chemical kinetics and reconstruction of wave dynamics with the inferred properties, which enables the comprehension of the intricate reaction-transport coupling from thermal data.

Background: Since to the prognosis of lung squamous cell carcinoma is generally poor, there is an urgent need to innovate new prognostic biomarkers and therapeutic targets to improve patient outcomes. Objectives: Our goal was to develop a novel multi-gene prognostic model linked to neutrophils for predicting lung squamous cell carcinoma prognosis. Methods: We utilized messenger RNA expression profiles and relevant clinical data of lung squamous cell carcinoma patients from the Cancer Genome Atlas database. Through K-means clustering, least absolute shrinkage and selection operator regression, and univariate/multivariate Cox regression analyses, we identified 12 neutrophil-related genes strongly related to patient survival and constructed a prognostic model. We verified the stability of the model in the Cancer Genome Atlas database and gene expression omnibus validation set, demonstrating the robust predictive performance of the model. Results: Immunoinfiltration analysis revealed remarkably elevated levels of infiltration for natural killer cells resting and monocytes in the high-risk group compared to the low-risk group, while macrophages had considerably lower infiltration in the high risk group. Most immune checkpoint genes, including programmed cell death protein 1 and cytotoxic T-lymphocyte-associated antigen 4, exhibited high expression levels in the high risk group. Tumor immune dysfunction and exclusion scores and immunophenoscore results suggested a potential inclination toward immunotherapy in the “RIC” version V2 revised high risk group. Moreover, prediction results from the CellMiner database revealed great correlations between drug sensitivity (e.g., Vinorelbine and PKI-587) and prognostic genes. Conclusion: Overall, our study established a reliable prognostic risk model that possessed significant value in predicting the overall survival of lung squamous cell carcinoma patients and may guide personalized treatment strategies.

The study aims to evaluate the relationships between characteristics of regional rice raw material and resulting quality of rice noodles. Four of most commonly used rice cultivars in Guangxi for noodles production were investigated. The results showed that compositions of rice flour primarily affected gelatinization and retrogradation, which then influenced the textural and sensory properties of rice noodles. Amylose content had strong positive correlation with peak viscosity (PV) and trough viscosity (TV) of rice flour (P < 0.01). PV and TV had strong negative correlations with adhesive strength (P < 0.01) and positive correlations with chewiness (P < 0.05), hardness, peak load and deformation at peak of rice noodles (P < 0.01). Protein content had positive correlation with the Setback of rice flour (P < 0.05), which is known to have influences on retrogradation. In addition, solubility had positive correlations with cooking loss (P < 0.01) and broken rate (P < 0.05) of rice noodles and strong negative correlation with its springiness (P < 0.01). Swelling power had negative correlation with broken rate (P < 0.05). As sensory score of rice noodles was negatively correlated with broken rate (P < 0.05) and cooking loss (P < 0.01) and positively correlated with springiness (P < 0.01), solubility and swelling power of rice flours were presumed to be useful for predicting consumer acceptability of rice noodles.

The scale-up of the coupling of water electroreduction (HER) with organic electrooxidation remains challenging. Here the authors address this challenge by coupling HER with electrooxidation of raw biomass chitin, cogenerating acetate and green hydrogen safely at high current density.

In everyday activities, humans move alike to manipulate objects. Prior works suggest that hand movements are built by a limited set of basic building blocks consisting of a set of common postures. However, how the low dimensionality of hand movements supports the adaptability and flexibility of natural behavior is unknown. Through a sensorized glove, we collected kinematics data from thirty-six participants preparing and having breakfast in naturalistic conditions. By means of an unbiased analysis, we identified a repertoire of hand states. Then, we tracked their transitions over time. We found that manual behavior can be described in space through a complex organization of basic configurations. These, even in an unconstrained experiment, recurred across subjects. A specific temporal structure, highly consistent within the sample, seems to integrate such identified hand shapes to realize skilled movements. These findings suggest that the simplification of the motor commands unravels in the temporal dimension more than in the spatial one., (© 2023. The Author(s).)

The use of surface electromyography (sEMG) is rapidly spreading, from robotic prostheses and muscle computer interfaces to rehabilitation devices controlled by residual muscular activities. In this context, sEMG-based gesture recognition plays an enabling role in controlling prosthetics and devices in real-life settings. Our work aimed at developing a low-cost, print-and-play platform to acquire and analyse sEMG signals that can be arranged in a fully customized way, depending on the application and the users' needs. We produced 8-channel sEMG matrices to measure the muscular activity of the forearm using innovative nanoparticle-based inks to print the sensors embedded into each matrix using a commercial inkjet printer. Then, we acquired the multi-channel sEMG data from 12 participants while repeatedly performing twelve standard finger movements (six extensions and six flexions). Our results showed that inkjet printing-based sEMG signals ensured significant similarity values across repetitions in every participant, a large enough difference between movements (dissimilarity index above 0.2), and an overall classification accuracy of 93-95% for flexion and extension, respectively., (© 2021. The Author(s).)