Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

PurposeThis study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).MethodsThe MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.ResultsTwenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.ConclusionThe ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model

A Versatile Punch Stroke Correction Model for Trial V-Bending of Sheet Metals Based on Data-Driven Method

During air bending of sheet metals, the correction of punch stroke for springback control is always implemented through repeated trial bending until achieving the forming accuracy of bending parts. In this study, a modelling method for correction of punch stroke is presented for guiding trial bending based on a data-driven technique. Firstly, the big data for the model are mainly generated from a large number of finite element simulations, considering many variables, e.g., material parameters, dimensions of V-dies and blanks, and processing parameters. Based on the big data, two punch stroke correction models are developed via neural network and dimensional analysis, respectively. The analytic comparison shows that the neural network model is more suitable for guiding trial bending of sheet metals than the dimensional analysis model, which has mechanical significance. The actual trial bending tests prove that the neural-network-based punch stroke correction model presents great versatility and accuracy in the guidance of trial bending, leading to a reduction in the number of trial bends and an improvement in the production efficiency of air bending

Tomato Leaf Disease Identification Method Based on Improved YOLOX

In tomato leaf disease identification tasks, the high cost and consumption of deep learning-based recognition methods affect their deployment and application on embedded devices. In this study, an improved YOLOX-based tomato leaf disease identification method is designed. To address the issue of positive and negative sample imbalance, the sample adaptive cross-entropy loss function (LBCE−β) is proposed as a confidence loss, and MobileNetV3 is employed instead of the YOLOX backbone for lightweight model feature extraction. By introducing CBAM (Convolutional Block Attention Module) between the YOLOX backbone and neck network, the model’s feature extraction performance is increased. CycleGAN is used to enhance the data of tomato disease leaf samples in the PlantVillage dataset, solving the issue of an imbalanced sample number. After data enhancement, simulation experiments and field tests revealed that the YOLOX’s accuracy improved by 1.27%, providing better detection of tomato leaf disease samples in complex environments. Compared with the original model, the improved YOLOX model occupies 35.34% less memory, model detection speed increases by 50.20%, and detection accuracy improves by 1.46%. The enhanced network model is quantized by TensorRT and works at 11.1 FPS on the Jetson Nano embedded device. This method can provide an efficient solution for the tomato leaf disease identification system

Letrozole and the Traditional Chinese Medicine, Shaofu Zhuyu Decoction, Reduce Endometriotic Disease Progression in Rats: A Potential Role for Gut Microbiota

We previously showed that the Chinese herbal medicine, Shaofu Zhuyu decoction (SFZYD), shrank the size of endometriotic lesions in rats with endometriosis. We therefore conducted the present study to investigate the effects of letrozole and SFZYD on gut microbiota in endometriotic rats. Rats were divided into four groups: a blank group, model group, letrozole group, and SFZY group. Ectopic lesion size and COX-2 expression in the endometrium and endometriotic lesions were compared, and the community of gut microbiota was detected using 16S rRNA gene sequencing. Both letrozole and SFZYD reduced the size of ectopic lesions as well as lowered the expression of COX-2, thus reducing the inflammatory response. Compared with the blank group, the α-diversity of gut microbiota in endometriotic rats decreased, the Firmicutes/Bacteroidetes ratio increased, and the abundance of Ruminococcaceae was reduced. The α-diversity of gut microbiota in the letrozole group was similar to that in the model group, but the Firmicutes/Bacteroidetes ratio was diminished. The α-diversity in the SFZY group was similar to that in the blank group, the Firmicutes/Bacteroidetes ratio was attenuated, and the abundance of Ruminococcaceae was elevated compared with the model group. These results indicated that the therapeutic mechanisms of both letrozole and SFZYD were related to the restoration of gut microbiota

An Efficient Codebook Search Algorithm for Line Spectrum Frequency (LSF) Vector Quantization in Speech Codec

A high-performance vector quantization (VQ) codebook search algorithm is proposed in this paper. VQ is an important data compression technique that has been widely applied to speech, image, and video compression. However, the process of the codebook search demands a high computational load. To solve this issue, a novel algorithm that consists of training and encoding procedures is proposed. In the training procedure, a training speech dataset was used to build the squared-error distortion look-up table for each subspace. In the encoding procedure, firstly, an input vector was quickly assigned to a search subspace. Secondly, the candidate code word group was obtained by employing the triangular inequality elimination (TIE) equation. Finally, a partial distortion elimination technique was employed to reduce the number of multiplications. The proposed method reduced the number of searches and computation load significantly, especially when the input vectors were uncorrelated. The experimental results show that the proposed algorithm provides a computational saving (CS) of up to 85% in the full search algorithm, up to 76% in the TIE algorithm, and up to 63% in the iterative TIE algorithm. Further, the proposed method provides CS and load reduction of up to 29–33% and 67–69%, respectively, over the BSS-ITIE algorithm

An Efficient Codebook Search Algorithm for Line Spectrum Frequency (LSF) Vector Quantization in Speech Codec

A high-performance vector quantization (VQ) codebook search algorithm is proposed in this paper. VQ is an important data compression technique that has been widely applied to speech, image, and video compression. However, the process of the codebook search demands a high computational load. To solve this issue, a novel algorithm that consists of training and encoding procedures is proposed. In the training procedure, a training speech dataset was used to build the squared-error distortion look-up table for each subspace. In the encoding procedure, firstly, an input vector was quickly assigned to a search subspace. Secondly, the candidate code word group was obtained by employing the triangular inequality elimination (TIE) equation. Finally, a partial distortion elimination technique was employed to reduce the number of multiplications. The proposed method reduced the number of searches and computation load significantly, especially when the input vectors were uncorrelated. The experimental results show that the proposed algorithm provides a computational saving (CS) of up to 85% in the full search algorithm, up to 76% in the TIE algorithm, and up to 63% in the iterative TIE algorithm. Further, the proposed method provides CS and load reduction of up to 29–33% and 67–69%, respectively, over the BSS-ITIE algorithm