A hybrid neural network based model for blood donation forecasting.

OBJECTIVE: To develop a hybrid neural network-based blood donation prediction method, via this predictive model, we can obtain the best estimate of whole blood in Beijing Tongzhou District Central Blood Station and help managers smoothly solve the allocation problem under fluctuating hospital demand and limited resources. METHOD: Inspired by the practical problems faced by blood stations providing transfusion services to several hospitals, a hybrid model based on a time-series prediction method and neural network, SARIMAX-TCN-LSTM is proposed for the prediction of daily whole blood donations. The experiment was performed at the central blood station in Tongzhou district, where we used whole blood donations from January 1, 2015, to November 14, 2021, as the subject, supplemented by meteorological and epidemic factors affecting blood donation, to predict daily blood donations for the next two weeks. RESULT: The hybrid model significantly outperformed the traditional time series forecasting method on multiple regression metrics, with twice as effective fitting as the baseline and a 33% reduction in Root Mean Squared Error (RMSE). Results indicate that the proposed model can improve the prediction accuracy of daily blood donations, and the co-validity of the structure was evidenced in an ablation experiment. CONCLUSION: Development and evaluation of a hybrid neural network-based model structure improve the prediction of daily blood donations. This intelligent forecasting method can help managers to overcome the challenges of sudden blood demand and contribute to the optimization of resource allocation tasks.

A cell image segmentation method based on edge feature residual fusion.

In recent years, cancer has seriously damaged human health, and the morphological structure of cells serves as an important basis for cancer diagnosis and grading. Automatic cell segmentation based on deep learning has become an important means of computer-aided pathological diagnosis. Aiming at the existing problems of rough segmentation boundaries and inaccurate segmentation in cell image segmentation, this paper designs a cell image segmentation network model (ERF-TransUNet) based on edge feature residual fusion from the perspective of mutual complementarity and constraint between edge features and object features. The model uses a hybrid architecture of CNN and Transformer to extract multi-scale features from cell images, and adds independent edge feature extraction modules and residual fusion modules to enhance the extraction of edge features and their constraints when fusing with cell object features, improving the accuracy of cell contour positioning. Through experiments on two gland cell datasets, CRAG and Glas, and comparing the segmentation effects with current popular deep learning models, the network model proposed in this paper has achieved good performance in both Dice coefficient and Hausdorff distance, which can effectively improve the segmentation effect of cell images.

FedMCC: Federated multi-center clustering algorithm to improve privacy healthcare.

In recent years, healthcare data from various sources such as clinical institutions, patients, and pharmaceutical industries have become increasingly abundant. However, due to the complex healthcare system and data privacy concerns, aggregating and utilizing these data in a centralized manner can be challenging. Federated learning (FL) has emerged as a promising solution for distributed training in edge computing scenarios, utilizing on-device user data while reducing server costs. In traditional FL, a central server trains a global model sampled client data randomly, and the server combines the collected model from different clients into one global model. However, for not independent and identically distributed (non-i.i.d.) datasets, randomly selecting users to train server is not an optimal choice and can lead to poor model training performance. To address this limitation, we propose the Federated Multi-Center Clustering algorithm (FedMCC) to enhance the robustness and accuracy for all clients. FedMCC leverages the Model-Agnostic Meta-Learning (MAML) algorithm, focusing on training a robust base model during the initial training phase and better capturing features from different users. Subsequently, clustering methods are used to ensure that features among users within each cluster are similar, approximating an i.i.d. training process in each round, resulting in more effective training of the global model. We validate the effectiveness and generalizability of FedMCC through extensive experiments on public healthcare datasets. The results demonstrate that FedMCC achieves improved performance and accuracy for all clients while maintaining data privacy and security, showcasing its potential for various healthcare applications.

Ultrasensitive CRISPR/Cas13a-Mediated Photoelectrochemical Biosensors for Specific and Direct Assay of miRNA-21.

Sensitive and specific assay of microRNAs (miRNAs) is beneficial to early disease screening. Herein, we for the first time proposed clustered regularly interspaced short palindromic repeats (CRISPR)/Cas13a-mediated photoelectrochemical biosensors for the direct assay of miRNA-21. In this study, compared with traditional nucleic acid-based signal amplification strategies, the CRISPR/Cas13a system can greatly improve the specificity and sensitivity of target determination due to its accurate recognition and high-efficient trans-cleavage capability without complex nucleic acid sequence design. Moreover, compared with the CRISPR/Cas12a-based biosensing platform, the developed CRISPR/Cas13a-mediated biosensor can directly detect RNA targets without signal transduction from RNA to DNA, thereby avoiding signal leakage and distortion. Generally, the proposed biosensor reveals excellent analysis capability with a wider linear range from 1 fM to 5 nM and a lower detection limit of 1 fM. Additionally, it also shows satisfactory stability in the detection of human serum samples and cell lysates, manifesting that it has great application prospects in the areas of early disease diagnosis and biomedical research.

FB-CGANet: filter bank channel group attention network for multi-class motor imagery classification.

Objective.Motor imagery-based brain-computer interface (MI-BCI) is one of the most important BCI paradigms and can identify the target limb of subjects from the feature of MI-based Electroencephalography signals. Deep learning methods, especially lightweight neural networks, provide an efficient technique for MI decoding, but the performance of lightweight neural networks is still limited and need further improving. This paper aimed to design a novel lightweight neural network for improving the performance of multi-class MI decoding.Approach.A hybrid filter bank structure that can extract information in both time and frequency domain was proposed and combined with a novel channel attention method channel group attention (CGA) to build a lightweight neural network Filter Bank CGA Network (FB-CGANet). Accompanied with FB-CGANet, the band exchange data augmentation method was proposed to generate training data for networks with filter bank structure.Main results.The proposed method can achieve higher 4-class average accuracy (79.4%) than compared methods on the BCI Competition IV IIa dataset in the experiment on the unseen evaluation data. Also, higher average accuracy (93.5%) than compared methods can be obtained in the cross-validation experiment.Significance.This work implies the effectiveness of channel attention and filter bank structure in lightweight neural networks and provides a novel option for multi-class motor imagery classification.

Research Progress on the Surface of High-Nickel Nickel-Cobalt-Manganese Ternary Cathode Materials: A Mini Review.

To address increasingly prominent energy problems, lithium-ion batteries have been widely developed. The high-nickel type nickel-cobalt-manganese (NCM) ternary cathode material has attracted attention because of its high energy density, but it has problems such as cation mixing. To address these issues, it is necessary to start from the surface and interface of the cathode material, explore the mechanism underlying the material's structural change and the occurrence of side reactions, and propose corresponding optimization schemes. This article reviews the defects caused by cation mixing and energy bands in high-nickel NCM ternary cathode materials. This review discusses the reasons why the core-shell structure has become an optimized high-nickel ternary cathode material in recent years and the research progress of core-shell materials. The synthesis method of high-nickel NCM ternary cathode material is summarized. A good theoretical basis for future experimental exploration is provided.