ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion Classification

Skin image datasets often suffer from imbalanced data distribution, exacerbating the difficulty of computer-aided skin disease diagnosis. Some recent works exploit supervised contrastive learning (SCL) for this long-tailed challenge. Despite achieving significant performance, these SCL-based methods focus more on head classes, yet ignoring the utilization of information in tail classes. In this paper, we propose class-Enhancement Contrastive Learning (ECL), which enriches the information of minority classes and treats different classes equally. For information enhancement, we design a hybrid-proxy model to generate class-dependent proxies and propose a cycle update strategy for parameters optimization. A balanced-hybrid-proxy loss is designed to exploit relations between samples and proxies with different classes treated equally. Taking both "imbalanced data" and "imbalanced diagnosis difficulty" into account, we further present a balanced-weighted cross-entropy loss following curriculum learning schedule. Experimental results on the classification of imbalanced skin lesion data have demonstrated the superiority and effectiveness of our method

A short-term hybrid wind speed prediction model based on decomposition and improved optimization algorithm

Introduction: In the field of wind power generation, short-term wind speed prediction plays an increasingly important role as the foundation for effective utilization of wind energy. However, accurately predicting wind speed is highly challenging due to its complexity and randomness in practical applications. Currently, single algorithms exhibit poor accuracy in short-term wind speed prediction, leading to the widespread adoption of hybrid wind speed prediction models based on deep learning techniques. To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization.Methods: To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization. Firstly, the model incorporates an attention mechanism into the LSTM model to extract important temporal slices from each mode component, effectively improving the slice prediction accuracy. Secondly, two different search operators are introduced to enhance the original Salp Swarm Algorithm, addressing the issue of getting trapped in local optima and achieving globally optimal short-term wind speed predictions.Result: Through comparative experiments using multiple-site short-term wind speed datasets, this study demonstrates that the proposed VMD-AtLSTM-ASSA model outperforms other hybrid prediction models (VMD-RNN, VMD-BPNN, VMD-GRU, VMD-LSTM) with a maximum reduction of 80.33% in MAPE values. The experimental results validate the high accuracy and stability of the VMD-AtLSTM-ASSA model.Discussion: Short-term wind speed prediction is of paramount importance for the effective utilization of wind power generation, and our research provides strong support for enhancing the efficiency and reliability of wind power generation systems. Future research directions may include further improvements in model performance and extension into other meteorological and environmental application domains

Photoactivatable nanogenerators of reactive species for cancer therapy

In recent years, reactive species-based cancer therapies have attracted tremendous attention due to their simplicity, controllability, and effectiveness. Herein, we overviewed the state-of-art advance for photo-controlled generation of highly reactive radical species with nanomaterials for cancer therapy. First, we summarized the most widely explored reactive species, such as singlet oxygen, superoxide radical anion (O2●), nitric oxide (●NO), carbon monoxide, alkyl radicals, and their corresponding secondary reactive species generated by interaction with other biological molecules. Then, we discussed the generating mechanisms of these highly reactive species stimulated by light irradiation, followed by their anticancer effect, and the synergetic principles with other therapeutic modalities. This review might unveil the advantages of reactive species-based therapeutic methodology and encourage the pre-clinical exploration of reactive species-mediated cancer treatments

Bio-oil based biorefinery strategy for the production of succinic acid

Background: Succinic acid is one of the key platform chemicals which can be produced via biotechnology process instead of petrochemical process. Biomass derived bio-oil have been investigated intensively as an alternative of diesel and gasoline fuels. Bio-oil could be fractionized into organic phase and aqueous phase parts. The organic phase bio-oil can be easily upgraded to transport fuel. The aqueous phase bio-oil (AP-bio-oil) is of low value. There is no report for its usage or upgrading via biological methods. In this paper, the use of AP-bio-oil for the production of succinic acid was investigated

Data-Driven Modeling of Landau Damping by Physics-Informed Neural Networks

Kinetic approaches are generally accurate in dealing with microscale plasma physics problems but are computationally expensive for large-scale or multiscale systems. One of the long-standing problems in plasma physics is the integration of kinetic physics into fluid models, which is often achieved through sophisticated analytical closure terms. In this study, we successfully construct a multi-moment fluid model with an implicit fluid closure included in the neural network using machine learning. The multi-moment fluid model is trained with a small fraction of sparsely sampled data from kinetic simulations of Landau damping, using the physics-informed neural network (PINN) and the gradient-enhanced physics-informed neural network (gPINN). The multi-moment fluid model constructed using either PINN or gPINN reproduces the time evolution of the electric field energy, including its damping rate, and the plasma dynamics from the kinetic simulations. For the first time, we introduce a new variant of the gPINN architecture, namely, gPINN$p$ to capture the Landau damping process. Instead of including the gradients of all the equation residuals, gPINN$p$ only adds the gradient of the pressure equation residual as one additional constraint. Among the three approaches, the gPINN$p$-constructed multi-moment fluid model offers the most accurate results. This work sheds new light on the accurate and efficient modeling of large-scale systems, which can be extended to complex multiscale laboratory, space, and astrophysical plasma physics problems.Comment: 11 pages, 7 figure

PCR-Based Seamless Genome Editing with High Efficiency and Fidelity in <i>Escherichia coli</i>

Efficiency and fidelity are the key obstacles for genome editing toolboxes. In the present study, a PCR-based tandem repeat assisted genome editing (TRAGE) method with high efficiency and fidelity was developed. The design of TRAGE is based on the mechanism of repair of spontaneous double-strand breakage (DSB) via replication fork reactivation. First, cat-sacB cassette flanked by tandem repeat sequence was integrated into target site in chromosome assisted by Red enzymes. Then, for the excision of the cat-sacB cassette, only subculturing is needed. The developed method was successfully applied for seamlessly deleting, substituting and inserting targeted genes using PCR products. The effects of different manipulations including sucrose addition time, subculture times in LB with sucrose and stages of inoculation on the efficiency were investigated. With our recommended procedure, seamless excision of cat-sacB cassette can be realized in 48 h efficiently. We believe that the developed method has great potential for seamless genome editing in E. coli