MPC-STANet: Alzheimer’s Disease Recognition Method based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism

Alzheimer\u27s disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer\u27s disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50

MPC-STANet: Alzheimer’s Disease Recognition Method based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism

Alzheimer\u27s disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer\u27s disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

Isolation and Characterization of Novel Microsatellite Markers for Yellow Perch (Perca flavescens)

To perform whole genome scanning for complex trait analysis, we isolated and characterized a total of 21 novel genomic-SSRs and EST-SSRs for yellow perch (Perca flavescens), using the methods of construction of SSR-enrichment libraries and EST database mining of a related species P. fluviatilis. Of 16 genomic-SSR primer pairs examined, eight successfully amplified scorable products. The number of alleles at these informative loci varied from 3 – 14 with an average of 8.5 alleles per locus. When tested on wild perch from a population in Pennsylvania, observed and expected heterozygosities ranged from 0.07 – 0.81 and from 0.37 – 0.95, respectively. Of 2,226 EST sequences examined, only 110 (4.93%) contained microsatellites and for those, 13 markers were tested, 12 of which exhibited polymorphism. Compared with genomic-SSRs, EST-SSRs exhibited a lower level of genetic variability with the number of alleles of averaging only 2.6 alleles per locus. Cross-species utility indicated that three of the genomic-SSRs and eight of the EST-SSRs successfully cross-amplified in a related species, the walleye (Sander vitreus)

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

Background: Previous cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes. Methods: We conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment. Results: Forty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference − 0.40 [95% CI − 0.71 to − 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference − 1.6% [95% CI − 4.3% to 1.2%]; P = 0.42) between groups. Conclusions: In this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness. Trial registration: ISRCTN, ISRCTN12233792. Registered November 20th, 2017

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial.

BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017

Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial (vol 26, 46, 2022)

BackgroundPrevious cluster-randomized controlled trials evaluating the impact of implementing evidence-based guidelines for nutrition therapy in critical illness do not consistently demonstrate patient benefits. A large-scale, sufficiently powered study is therefore warranted to ascertain the effects of guideline implementation on patient-centered outcomes.MethodsWe conducted a multicenter, cluster-randomized, parallel-controlled trial in intensive care units (ICUs) across China. We developed an evidence-based feeding guideline. ICUs randomly allocated to the guideline group formed a local "intervention team", which actively implemented the guideline using standardized educational materials, a graphical feeding protocol, and live online education outreach meetings conducted by members of the study management committee. ICUs assigned to the control group remained unaware of the guideline content. All ICUs enrolled patients who were expected to stay in the ICU longer than seven days. The primary outcome was all-cause mortality within 28 days of enrollment.ResultsForty-eight ICUs were randomized to the guideline group and 49 to the control group. From March 2018 to July 2019, the guideline ICUs enrolled 1399 patients, and the control ICUs enrolled 1373 patients. Implementation of the guideline resulted in significantly earlier EN initiation (1.20 vs. 1.55 mean days to initiation of EN; difference - 0.40 [95% CI - 0.71 to - 0.09]; P = 0.01) and delayed PN initiation (1.29 vs. 0.80 mean days to start of PN; difference 1.06 [95% CI 0.44 to 1.67]; P = 0.001). There was no significant difference in 28-day mortality (14.2% vs. 15.2%; difference - 1.6% [95% CI - 4.3% to 1.2%]; P = 0.42) between groups.ConclusionsIn this large-scale, multicenter trial, active implementation of an evidence-based feeding guideline reduced the time to commencement of EN and overall PN use but did not translate to a reduction in mortality from critical illness.Trial registrationISRCTN, ISRCTN12233792 . Registered November 20th, 2017

Identification of Tomato Leaf Diseases based on LMBRNet

Tomato Disease Image Identification Plays a Very Important Role in the Field of Agricultural Production. Aiming at the Problems of Large Intraclass Differences, Small Inter-Class Differences and Difficult Feature Extraction of Some Tomato Leaf Diseases, This Paper Proposes an Identification of Tomato Leaf Diseases based on LMBRNet. Firstly, a Comprehensive Grouped Differentiated Residual (CGDR) is Built,The Multi-Branch Structure of CGDR is Used to Capture the Diversified Feature Information of Tomato Leaf Diseases in Different Dimensions and Receptive Fields. Then, a Multiple Residual Connection Scheme is Adopted,using Residuals to Connect All Layers, to Ensure the Maximum Information Transmission between Layers in the Network and to Solve the Problems of Network Degradation and Gradient Disappearance in the Network Training Process. Secondly,the Visual Enhancement Effectively Fuses the Results Obtained by Three Different Downsampling Strategies using Average Pooling, Max Pooling, and 1*1 Convolution. Avoid the Loss of Information Caused by Downsampling and Improve the Accuracy of the Network. Moreover, Deep Separable Convolution is Used to Optimize the Network Structure, Reduce the Amount of Model Parameters and Reduce the Computational Resources Occupied by Training and Deploying the Model.we Found that the Depthwise Separable Convolution with a Kernel Size of 1*1 Can Slightly Improve the Efficiency of the Model under the Premise that It Has Little Effect on the Number of Model Parameters. the Application Results of More Than 8000 Images Show that the overall Identification Accuracy is About 99.7%,higher Than ResNet50(97.48%),GoogleNet(98.96%) Etc. Conventional Models. the Parameter Amount of LMBRNet is 4.1M. Less Than ResNet50(23M),GoogleNet(5.7M) Etc. Conventional Models. It is Worth Noting that the Accuracy of LMBRNet(99.7%) is Similar to that of InceptionResNetV2(99.68%), But the Amount of Parameters of LMBRNet(4.1M) is Much Lower Than that of InceptionResNetV2(54M). Moreover, the Parameter Amount of LMBRNet (4.1M) is Slightly Lower Than that of MobileNetV2(2.2M), But the Accuracy Rate of LMBRNet(99.7%) is Higher Than that of MobileNetV2(97.87%). LMBRNet Was Tested on RS, SIW, Plantvillage-Corn Public Datasets, All Obtained High Recognition Accuracy, 82.32% on RS, 88.37% on SIW and 97.25% on Plantvillage-Corn, Indicating that LMBRNet Has Good Generalization. Compare LMBRNet with Advanced Methods. in Four Different Classification Tasks, the Performance of LMBRNet is Similar to ResMLP12 and DCCAM-MRNet, and the Difference of Recognition Accuracy between LMBRNet and ResMLP12 and DCCAM-MRNet is Not More Than 1%. However, the Parameters of LMBRNet (4.1M) Are Lower Than ResMLP12 (14.94M) and DCCAM MRNet (22.8M). LMBRNet is Compared with MobileNetV3, an Advanced Lightweight Classification Model. LMBRNet(88.37% on SIW,82.32% on RS) is Used on Certain Datasets and Performs Better Than MobileNetV3S(83.76% on SIW,75 on RS) and MobileNetV3L(84.34 on SIW,73.39 on RS). the Parameters of LMBRNet(4.1M) Are Lower Than MobileNetV3L(5.4M) and Slightly Higher Than MobileNetV3S(2.9M). This Indicates that LMBRNet Has Good Generality Even Though It Has a Small Number of Parameters

BCMNet: Cross-Layer Extraction Structure and Multiscale Downsampling Network with Bidirectional Transpose FPN for Fast Detection of Wildfire Smoke

At present, the wildfire smoke detection algorithm based on YOLOv3 has problems, such as low accuracy and slow detection speed. In this article, we propose a cross-layer extraction structure and multiscale downsampling network with bidirectional transpose FPN (BCMNet) for fast detection of wildfire smoke. First, a cross-layer extraction module, which combines linear feature multiplexing and receptive field amplification, is designed. It can improve the speed and accuracy of wildfire smoke detection. Second, a multiscale downsampling module with different convolution kernels and maximum pooling operation is designed to preserve the details of the image while downsampling. Then, a bidirectional transposed FPN based on transposed convolution upsampling is designed. It can bidirectionally fuse visual features of shallow layer and semantic features of deep layer on the corresponding scale. The feature information flow between smoke feature maps of different resolution is emphasized. Finally, a wildfire smoke detection system of the Internet of Things based on BCMNet is built by combining the hardware and detection model. The experimental results show that the proposed method achieves 85.50% mAP50 and 79.98% mAP75 at 40 FPS on NVIDIA Geforce RTX 2080 Ti, which is superior to the common smoke detection methods