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Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are suggested to be responsible for drug resistance and cancer relapse due in part to their ability to self-renew themselves and differentiate into heterogeneous lineages of cancer cells. Thus, it is important to understand the characteristics and mechanisms by which CSCs display resistance to therapeutic agents. In this review, we highlight the key features and mechanisms that regulate CSC function in drug resistance as well as recent breakthroughs of therapeutic approaches for targeting CSCs. This promises new insights of CSCs in drug resistance and provides better therapeutic rationales to accompany novel anticancer therapeutics.

Among the various contenders for next-generation sodium-ion battery anodes, hard carbons stand out for their notable reversible capacity, extended cycle life, and cost-effectiveness. Their economic advantage can be further enhanced by using inexpensive precursors, such as biomass waste. Lignin, one of the most abundant natural biopolymers on Earth, which can be readily obtained from wood, possesses a three-dimensional amorphous polymeric structure, making it a suitable candidate for producing carbonaceous materials through appropriate carbonization processes for energy storage applications. In this work, we synthesized hard carbon using lignin containing CaSO4 to facilitate partial catalytic graphitization to improve the microstructural features, such as interlayer spacing, degree of disorder, and surface defects. Partial catalytic graphitization enables hard carbon to develop an ordered structure compared with hard carbon carbonized without CaSO4 as analyzed by X-ray diffraction, Raman spectroscopy, scanning/transmission electron microscopy, and X-ray photoelectron spectroscopy. The CaSO4-aided partially catalytic graphitized hard carbon (CCG-HC) exhibited improved electrochemical performance, showing a larger portion of the low-voltage plateau—an indicator typically associated with a highly ordered structure—compared to simply carbonized hard carbon (HC). Notably, CCG-HC delivered a reversible capacity of 237 mAh g−1, retained 95.6% of its capacity over 100 cycles at 50 mA g−1, and exhibited 127 mAh g−1 at 1.0 A g−1.

Pine trees are more vulnerable to diseases and pests than other trees, so prevention and management are necessary in advance. In this paper, two models of deep learning were mixed to quickly check whether or not to detect pine pests and to perform a comparative analysis with other models. In addition, to select a good performance model of artificial intelligence, a comparison of the recall values, such as Precision (AP), Intersection over Union (IoU) = 0.5, and AP (IoU), of four models including You Only Look Once (YOLOv5s)_Focus+C3, Cascade Region-Based Convolutional Neural Networks (Cascade R-CNN)_Residual Network 50, Faster Region-Based Convolutional Neural Networks, and Faster R-CNN_ResNet50 was performed, and in addition to the mixed model Swin Transformer_Cascade R-CNN proposed in this paper, they were evaluated. As a result of this study, the recall value of the YOLOv5s_Focus+C3 model was 66.8%, the recall value of the Faster R-CNN_ResNet50 model was 91.1%, and the recall value of the Cascade R-CNN_ResNet50 model was 92.9%. The recall value of the model that mixed the Cascade R-CNN_Swin Transformer proposed in this study was 93.5%. Therefore, as a result of comparing the recall values of the performances of the four models in detecting pine pests, the Cascade R-CNN_Swin Transformer mixed model proposed in this paper showed the highest accuracy.

Lithium-ion batteries are widely used in electric vehicles, smartphones, and energy storage devices due to their high power and light weight. The goal of this study is to predict the remaining capacity of a lithium-ion battery and evaluate its performance through three machine learning models: linear regression, decision tree, and random forest, and two deep learning models: neural network and ensemble model. Mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R-squared), and root mean squared error (RMSE) were used to measure prediction accuracy. For the evaluation of the artificial intelligence model, the dataset was downloaded and integrated with measurement data of the CS2 lithium-ion battery provided by the University of Maryland College of Engineering. As a result of the study, the RMSE of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. According to the measured values, the ensemble model showed the best predictive performance, followed by the neural network model. Decision tree and random forest models also showed very good performance, and the linear regression model showed relatively poor predictive performance compared to the other models.

The lack of an appropriate preclinical model of metabolic dysfunction-associated steatotic liver disease (MASLD) that recapitulates the whole disease spectrum impedes exploration of disease pathophysiology and the development of effective treatment strategies. Here, we develop a mouse model (Streptozotocin with high-fat diet, STZ + HFD) that gradually develops fatty liver, metabolic dysfunction-associated steatohepatitis (MASH), hepatic fibrosis, and hepatocellular carcinoma (HCC) in the context of metabolic dysfunction. The hepatic transcriptomic features of STZ + HFD mice closely reflect those of patients with obesity accompanying type 2 diabetes mellitus, MASH, and MASLD-related HCC. Dietary changes and tirzepatide administration alleviate MASH, hepatic fibrosis, and hepatic tumorigenesis in STZ + HFD mice. In conclusion, a murine model recapitulating the main histopathologic, transcriptomic, and metabolic alterations observed in MASLD patients is successfully established. Metabolic dysfunction-associated steatotic liver disease (MASLD) characterizes a spectrum of liver disorders initiated by hepatic lipid accumulation associated with metabolic syndrome. Here, the authors generate a mouse model that recapitulates the main histopathologic, transcriptomics, and metabolic alterations observed in MASLD patients.

In this study, we identified the different causes of odor problems and their associated discomfort. We also recognized the significance of public health and environmental concerns. To address odor issues, it is vital to conduct precise analysis and comprehend the root causes. We suggested a hybrid model of a Convolutional Neural Network (CNN) and Transformer called the CNN–Transformer to tackle this challenge and assessed its effectiveness. We utilized a dataset containing 120,000 samples of odor to compare the performance of CNN+LSTM, CNN, LSTM, and ELM models. The experimental results show that the CNN+LSTM hybrid model has an accuracy of 89.00%, precision of 89.41%, recall of 91.04%, F1-score of 90.22%, and RMSE of 0.28, with a large prediction error. The CNN+Transformer hybrid model had an accuracy of 96.21%, precision and recall of 94.53% and 94.16%, F1-score of 94.35%, and RMSE of 0.27, showing a low prediction error. The CNN model had an accuracy of 87.19%, precision and recall of 89.41% and 91.04%, F1-score of 90.22%, and RMSE of 0.23, showing a low prediction error. The LSTM model had an accuracy of 95.00%, precision and recall of 92.55% and 94.17%, F1-score of 92.33%, and RMSE of 0.03, indicating a very low prediction error. The ELM model performed poorly with an accuracy of 85.50%, precision and recall of 85.26% and 85.19%, respectively, and F1-score and RMSE of 85.19% and 0.31, respectively. This study confirms the suitability of the CNN–Transformer hybrid model for odor analysis and highlights its excellent predictive performance. The employment of this model is expected to be advantageous in addressing odor problems and mitigating associated public health and environmental concerns.

Butanol is an important industrial solvent and advanced biofuel that can be produced by biphasic fermentation by Clostridium acetobutylicum . It has been known that acetate and butyrate first formed during the acidogenic phase are reassimilated to form acetone-butanol-ethanol (cold channel). Butanol can also be formed directly from acetyl-coenzyme A (CoA) through butyryl-CoA (hot channel). However, little is known about the relative contributions of the two butanol-forming pathways. Here we report that the direct butanol-forming pathway is a better channel to optimize for butanol production through metabolic flux and mass balance analyses. Butanol production through the hot channel was maximized by simultaneous disruption of the pta and buk genes, encoding phosphotransacetylase and butyrate kinase, while the adhE1 D485G gene, encoding a mutated aldehyde/alcohol dehydrogenase, was overexpressed. The ratio of butanol produced through the hot channel to that produced through the cold channel increased from 2.0 in the wild type to 18.8 in the engineered BEKW(pPthlAAD ** ) strain. By reinforcing the direct butanol-forming flux in C. acetobutylicum , 18.9 g/liter of butanol was produced, with a yield of 0.71 mol butanol/mol glucose by batch fermentation, levels which are 160% and 245% higher than those obtained with the wild type. By fed-batch culture of this engineered strain with in situ recovery, 585.3 g of butanol was produced from 1,861.9 g of glucose, with the yield of 0.76 mol butanol/mol glucose and productivity of 1.32 g/liter/h. Studies of two butanol-forming routes and their effects on butanol production in C. acetobutylicum described here will serve as a basis for further metabolic engineering of clostridia aimed toward developing a superior butanol producer. IMPORTANCE Renewable biofuel is one of the answers to solving the energy crisis and climate change problems. Butanol produced naturally by clostridia has superior liquid fuel characteristics and thus has the potential to replace gasoline. Due to the lack of efficient genetic manipulation tools, however, strain improvement has been rather slow. Furthermore, complex metabolic characteristics of acidogenesis followed by solventogenesis in this strain have hampered development of engineered clostridia having highly efficient and selective butanol production capability. Here we report for the first time the results of systems metabolic engineering studies of two butanol-forming routes and their relative importances in butanol production. Based on these findings, a metabolically engineered Clostridium acetobutylicum strain capable of producing butanol to a high titer with high yield and selectivity could be developed by reinforcing the direct butanol-forming flux. Renewable biofuel is one of the answers to solving the energy crisis and climate change problems. Butanol produced naturally by clostridia has superior liquid fuel characteristics and thus has the potential to replace gasoline. Due to the lack of efficient genetic manipulation tools, however, strain improvement has been rather slow. Furthermore, complex metabolic characteristics of acidogenesis followed by solventogenesis in this strain have hampered development of engineered clostridia having highly efficient and selective butanol production capability. Here we report for the first time the results of systems metabolic engineering studies of two butanol-forming routes and their relative importances in butanol production. Based on these findings, a metabolically engineered Clostridium acetobutylicum strain capable of producing butanol to a high titer with high yield and selectivity could be developed by reinforcing the direct butanol-forming flux.

This study investigates inflow turbulence strategies for large-eddy simulations (LES) of urban boundary layers under time-varying atmospheric conditions. A combined approach integrating a digital-filter-based synthetic turbulence generator (STG) with the cell perturbation method (CPM) is proposed to reduce turbulence adjustment distance and improve vertical mixing. Using the PALM model, 24 h simulations were conducted over a real urban domain in Seoul, capturing diurnal transitions in stability and wind direction. Six experiments were compared: two reference runs with extended upstream fetch, and four analysis runs without fetch, applying different inflow strategies (NOT, STG, CPM, and CPM + STG). Results indicate that CPM + STG mitigates abrupt structural transitions and sustains turbulence kinetic energy (TKE) more consistently than STG alone, while requiring lower computational cost than extended-fetch configurations. Under unstable daytime conditions, CPM + STG enhanced vertical mixing and preserved local boundary-layer height closer to background values, whereas nighttime performance was dominated by building-induced shear regardless of inflow strategy. These findings suggest that the combined CPM + STG approach achieves a balance between physical realism and computational efficiency, demonstrating its potential as a robust inflow strategy for time-varying urban LES within limited domain sizes.

This study proposes a deep learning-based long short-term memory (LSTM) model to predict the state of charge (SOC) of lithium-ion batteries. The purpose of the research is to accurately model the complex nonlinear behavior that occurs during the charging and discharging processes of batteries to predict the SOC. The LSTM model was trained using battery data collected under various temperature and load conditions. To evaluate the performance of the artificial intelligence model, measurement data from the CS2 lithium-ion battery provided by the University of Maryland College of Engineering was utilized. The LSTM model excels in learning long-term dependencies from sequence data, effectively modeling temporal patterns in battery data. The study trained the LSTM model based on battery data collected from various charge and discharge cycles and evaluated the model’s performance by epoch to determine the optimal configuration. The proposed model demonstrated high SOC estimation accuracy for various charging and discharging profiles. As training progressed, the model’s predictive performance improved, with the predicted SOC moving from 14.8400% at epoch 10 to 12.4968% at epoch 60, approaching the actual SOC value of 13.5441%. Simultaneously, the mean absolute error (MAE) and root mean squared error (RMSE) decreased from 0.9185% and 1.3009% at epoch 10 to 0.2333% and 0.5682% at epoch 60, respectively, indicating continuous improvement in predictive performance. In conclusion, this study demonstrates the effectiveness of the LSTM model for predicting the SOC of lithium-ion batteries and its potential to enhance the performance of battery management systems.

Hard carbons are one of the most promising anode materials for next-generation sodium-ion batteries due to their high reversible capacity, long cycle life, and low cost. The advantage in terms of price of hard carbons can be further improved by using cheaper resources such as biomass waste as precursors. Lignin is one of the richest natural bio-polymer in the earth which can be obtained from woods. As the lignin has three-dimensional amorphous polymeric structure, it is considered as good precursor for producing carbonaceous materials under proper carbonization processes for energy storage devices. In this study, structural properties of lignin-derived hard carbons such as interlayer spacing, degree of disorder and surface defects are controlled. Specifically, lignin-derived hard carbons were synthesized at 1000 °C, 1250 °C, and 1500 °C, and it was confirmed that the structure gradually changed from a disordered structure to ordered structure through X-ray diffraction, Raman spectroscopy, and X-ray photoelectron spectroscopy. Hard carbons exhibit sloping regions at high voltage and plateau region at low voltage during the electrochemical processes for sodium ions. As the heat treatment temperature increases, the contribution to the overall reversible capacity of the sloping region decreases and the contribution of the plateau region increases. This trend confirms that it affects reversible capacity, rate-capability, and cycling stability, meaning that an understanding of structural properties and related electrochemical properties is necessary when developing hard carbon as a negative electrode material for sodium ion batteries.