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Human Brain Mapping Jun 2024Free water fraction (FWF) represents the amount of water per unit volume of brain parenchyma, which is not bound to macromolecules. Its excess in multiple sclerosis (MS)...
Free water fraction (FWF) represents the amount of water per unit volume of brain parenchyma, which is not bound to macromolecules. Its excess in multiple sclerosis (MS) is related to increased tissue loss. The use of mcDESPOT (multicomponent driven single pulse observation of T1 and T2), a 3D imaging method which exploits both the T1 and T2 contrasts, allows FWF to be derived in clinically feasible times. However, this method has not been used to quantify changes of FWF and their potential clinical impact in MS. The aim of this study is to investigate the changes in FWF in MS patients and their relationship with tissue damage and cognition, under the hypothesis that FWF is a proxy of clinically meaningful tissue loss. To this aim, we tested the relationship between FWF, MS lesion burden and information processing speed, evaluated via the Symbol Digit Modalities Test (SDMT). In addition to standard sequences, used for T1- and T2-weighted lesion delineation, the mcDESPOT sequence with 1.7 mm isotropic resolution and a diffusion weighted imaging protocol (b = 0, 1200 s/mm, 40 diffusion directions) were employed at 3 T. The fractional anisotropy map derived from diffusion data was used to define a subject-specific white matter (WM) atlas. Brain parenchyma segmentation returned masks of gray matter (GM) and WM, and normal-appearing WM (NAWM), in addition to the T1 and T2 lesion masks (T1L and T2L, respectively). Ninety-nine relapsing-remitting MS patients (age = 43.3 ± 9.9 years, disease duration 12.3 ± 7.7 years) were studied, together with twenty-five healthy controls (HC, age = 38.8 ± 11.0 years). FWF was higher in GM and NAWM of MS patients, compared to GM and WM of HC (both p < .001). In MS patients, FWF was the highest in the T1L and GM, followed by T2L and NAWM, respectively. FWF increased significantly with T1L and T2L volume (ρ ranging from 0.40 to 0.58, p < .001). FWF in T2L was strongly related to both T1L volume and the volume ratio T1L/T2L (ρ = 0.73, p < .001). MS patients performed worse than HC in the processing speed test (mean ± SD: 54.1 ± 10.3 for MS, 63.8 ± 10.8 for HC). FWF in GM, T2L, perilesional tissue and NAWM increased with SDMT score reduction (ρ = -0.30, -0.29, -0.33 respectively and r = -.30 for T2L, all with p < .005). A regional analysis, conducted to determine which NAWM regions were of particular importance to explain the relationship between FWF and cognitive impairment, revealed that FWF spatial variance was negatively related to SDMT score in the corpus callosum and the superior longitudinal fasciculus, WM structures known to be associated with cognitive impairment, in addition to the left corticospinal tract, the sagittal stratum, the right anterior limb of internal capsule. In conclusion, we found excess free water in brain parenchyma of MS patients, an alteration that involved not only MS lesions, but also the GM and NAWM, impinging on brain function and negatively associated with cognitive processing speed. We suggest that the FWF metric, derived from noninvasive, rapid MRI acquisitions and bearing good biological interpretability, may prove valuable as an MRI biomarker of tissue damage and associated cognitive impairment in MS.
Topics: Humans; Female; Male; Adult; Middle Aged; Brain; Multiple Sclerosis; Diffusion Magnetic Resonance Imaging; Water; Cognitive Dysfunction; Parenchymal Tissue; White Matter; Gray Matter; Processing Speed
PubMed: 38895882
DOI: 10.1002/hbm.26761 -
Sensors (Basel, Switzerland) Jun 2024The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products...
The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products may acquire assembly defects due to personnel oversight. A severe assembly defect could impair the product's normal function and potentially cause loss of life or property for the user. For workpiece defect inspection, there is limited discussion on the simultaneous detection of the primary kinds of assembly anomaly (missing parts, misplaced parts, foreign objects, and extra parts). However, these assembly anomalies account for most customer complaints in the traditional hand tool industry. This is because no equipment can comprehensively inspect major assembly defects, and inspections rely solely on professionals using simple tools and their own experience. Thus, this study proposes an automated visual inspection system to achieve defect inspection in hand tool assembly. This study samples the work-in-process from three assembly stations in the ratchet wrench assembly process; an investigation of 28 common assembly defect types is presented, covering the 4 kinds of assembly anomaly in the assembly operation; also, this study captures sample images of various assembly defects for the experiments. First, the captured images are filtered to eliminate surface reflection noise from the workpiece; then, a circular mask is given at the assembly position to extract the ROI area; next, the filtered ROI images are used to create a defect-type label set using manual annotation; after this, the R-CNN series network models are applied to object feature extraction and classification; finally, they are compared with other object detection models to identify which inspection model has the better performance. The experimental results show that, if each station uses the best model for defect inspection, it can effectively detect and classify defects. The average defect detection rate (1-β) of each station is 92.64%, the average misjudgment rate (α) is 6.68%, and the average correct classification rate (CR) is 88.03%.
PubMed: 38894426
DOI: 10.3390/s24113635 -
Sensors (Basel, Switzerland) Jun 2024Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it...
Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it still faces significant challenges in handling highly dynamic environments. The prevalent method currently used for dynamic object recognition in the environment is deep learning. However, models such as Yolov5 and Mask R-CNN require significant computational resources, which limits their potential in real-time applications due to hardware and time constraints. To overcome this limitation, this paper proposes ADM-SLAM, a visual SLAM system designed for dynamic environments that builds upon the ORB-SLAM2. This system integrates efficient adaptive feature point homogenization extraction, lightweight deep learning semantic segmentation based on an improved DeepLabv3, and multi-view geometric segmentation. It optimizes keyframe extraction, segments potential dynamic objects using contextual information with the semantic segmentation network, and detects the motion states of dynamic objects using multi-view geometric methods, thereby eliminating dynamic interference points. The results indicate that ADM-SLAM outperforms ORB-SLAM2 in dynamic environments, especially in high-dynamic scenes, where it achieves up to a 97% reduction in Absolute Trajectory Error (ATE). In various highly dynamic test sequences, ADM-SLAM outperforms DS-SLAM and DynaSLAM in terms of real-time performance and accuracy, proving its excellent adaptability.
PubMed: 38894374
DOI: 10.3390/s24113578 -
Sensors (Basel, Switzerland) May 2024Considering the complex structure of Chinese characters, particularly the connections and intersections between strokes, there are challenges in low accuracy of Chinese...
Considering the complex structure of Chinese characters, particularly the connections and intersections between strokes, there are challenges in low accuracy of Chinese character stroke extraction and recognition, as well as unclear segmentation. This study builds upon the YOLOv8n-seg model to propose the YOLOv8n-seg-CAA-BiFPN Chinese character stroke fine segmentation model. The proposed Coordinate-Aware Attention mechanism (CAA) divides the backbone network input feature map into four parts, applying different weights for horizontal, vertical, and channel attention to compute and fuse key information, thus capturing the contextual regularity of closely arranged stroke positions. The network's neck integrates an enhanced weighted bi-directional feature pyramid network (BiFPN), enhancing the fusion effect for features of strokes of various sizes. The Shape-IoU loss function is adopted in place of the traditional CIoU loss function, focusing on the shape and scale of stroke bounding boxes to optimize the bounding box regression process. Finally, the Grad-CAM++ technique is used to generate heatmaps of segmentation predictions, facilitating the visualization of effective features and a deeper understanding of the model's focus areas. Trained and tested on the public Chinese character stroke datasets CCSE-Kai and CCSE-HW, the model achieves an average accuracy of 84.71%, an average recall rate of 83.65%, and a mean average precision of 80.11%. Compared to the original YOLOv8n-seg and existing mainstream segmentation models like SegFormer, BiSeNetV2, and Mask R-CNN, the average accuracy improved by 3.50%, 4.35%, 10.56%, and 22.05%, respectively; the average recall rates improved by 4.42%, 9.32%, 15.64%, and 24.92%, respectively; and the mean average precision improved by 3.11%, 4.15%, 8.02%, and 19.33%, respectively. The results demonstrate that the YOLOv8n-seg-CAA-BiFPN network can accurately achieve Chinese character stroke segmentation.
PubMed: 38894271
DOI: 10.3390/s24113480 -
Sensors (Basel, Switzerland) May 2024In order to improve the efficiency and accuracy of multitarget detection of soldering defects on surface-mounted components in Printed Circuit Board (PCB) fabrication,...
In order to improve the efficiency and accuracy of multitarget detection of soldering defects on surface-mounted components in Printed Circuit Board (PCB) fabrication, we propose a sample generation method using Stable Diffusion Model and ControlNet, as well as a defect detection method based on the Swin Transformer. The method consists of two stages: First, high-definition original images collected in industrial production and the corresponding prompts are input to Stable Diffusion Model and ControlNet for automatic generation of nonindependent samples. Subsequently, we integrate Swin Transformer as the backbone into the Cascade Mask R-CNN to improve the quality of defect features extracted from the samples for accurate detection box localization and segmentation. Instead of segmenting individual components on the PCB, the method inspects all components in the field of view simultaneously over a larger area. The experimental results demonstrate the effectiveness of our method in scaling up nonindependent sample datasets, thereby enabling the generation of high-quality datasets. The method accurately recognizes targets and detects defect types when performing multitarget inspection on printed circuit boards. The analysis against other models shows that our improved defect detection and segmentation method improves the Average Recall (AR) by 2.8% and the mean Average Precision (mAP) by 1.9%.
PubMed: 38894263
DOI: 10.3390/s24113473 -
Materials (Basel, Switzerland) Jun 2024GH4169 alloy/Inconel 718 is extensively utilized in aerospace manufacturing due to its excellent high temperature mechanical properties. Micro-structuring on the...
GH4169 alloy/Inconel 718 is extensively utilized in aerospace manufacturing due to its excellent high temperature mechanical properties. Micro-structuring on the workpiece surface can enhance its properties further. Through-mask electrochemical micromachining (TMEMM) is a promising and potential processing method for nickel-based superalloys. It can effectively solve the problem that traditional processing methods are difficult to achieve large-scale, high-precision and efficiency processing of surface micro-structure. This study explores the feasibility of electrochemical machining (ECM) for GH4169 using roll-print mask electrochemical machining with a linear cathode. Electrochemical dissolution characteristics of GH4169 alloy were analyzed in various electrolyte solutions and concentrations. Key parameters including cathode sizes, applied voltage and corrosion time were studied in the roll-print mask electrochemical machining. A qualitative model for micro-pit formation on GH4169 was established. Optimal parameters were determined through experiments: 300 μm mask hole and cathode size, 10 wt% NaNO electrolyte, 12 V voltage, 6 s corrosion time. The results demonstrate that the micro-pits with a diameter of 402.3 μm, depth of 92.8 μm and etch factor (EF) of 1.81 show an excellent profile and localization.
PubMed: 38893993
DOI: 10.3390/ma17112729 -
Diagnostics (Basel, Switzerland) May 2024Pulmonary embolism (PE) refers to the occlusion of pulmonary arteries by blood clots, posing a mortality risk of approximately 30%. The detection of pulmonary embolism...
Pulmonary embolism (PE) refers to the occlusion of pulmonary arteries by blood clots, posing a mortality risk of approximately 30%. The detection of pulmonary embolism within segmental arteries presents greater challenges compared with larger arteries and is frequently overlooked. In this study, we developed a computational method to automatically identify pulmonary embolism within segmental arteries using computed tomography (CT) images. The system architecture incorporates an enhanced Mask R-CNN deep neural network trained on PE-containing images. This network accurately localizes pulmonary embolisms in CT images and effectively delineates their boundaries. This study involved creating a local data set and evaluating the model predictions against pulmonary embolisms manually identified by expert radiologists. The sensitivity, specificity, accuracy, Dice coefficient, and Jaccard index values were obtained as 96.2%, 93.4%, 96.%, 0.95, and 0.89, respectively. The enhanced Mask R-CNN model outperformed the traditional Mask R-CNN and U-Net models. This study underscores the influence of Mask R-CNN's loss function on model performance, providing a basis for the potential improvement of Mask R-CNN models for object detection and segmentation tasks in CT images.
PubMed: 38893629
DOI: 10.3390/diagnostics14111102 -
Animals : An Open Access Journal From... May 2024With increasing efforts to ban surgical castration, it is important to find ways to mask the level of boar taint in meat. The aim of this study was to test the...
With increasing efforts to ban surgical castration, it is important to find ways to mask the level of boar taint in meat. The aim of this study was to test the possibility of masking boar taint or skatole levels by adding dried or and to evaluate consumer sensory preferences towards the skatole concentration in different carcass parts (; ; neck chop and subcutaneous fat) and the masking strategy (addition of or ). In the first experiment, the effect of the masking strategy was evaluated at three different skatole concentrations (0.069, 0.269 and 0.463 µg/g). The results showed that the samples with low and medium skatole levels were significantly different between the control group and the groups treated with or . In both cases, the addition of and had a positive effect on the parameters of abnormal odour and pleasantness of odour ( < 0.05). According to the results of the second experiment, meat samples from leaner parts, such as the neck chop and , not treated with and for masking, were significantly ( < 0.05) worse in terms of the occurrence of boar taint or abnormal odour than the masked samples. No significant differences were found between the two masking methods.
PubMed: 38891591
DOI: 10.3390/ani14111544 -
Polymers Jun 2024As the global facial mask market continues to grow, consumers have put forward higher requirements for the functionality and ingredients of mask products. Ordinary...
As the global facial mask market continues to grow, consumers have put forward higher requirements for the functionality and ingredients of mask products. Ordinary facial masks mostly use ordinary non-woven fabrics as the mask base fabric and are used with essence. Preservatives are generally added. At the same time, they are susceptible to the influence of the external environment and are easily oxidized, causing the mask to deteriorate and cause skin allergic reactions. In addition, traditional facial masks have problems such as poor fit with the skin, poor breathability, insufficient absorption of nutrient solutions, and easy dripping. The high specific surface area and high porosity of a nanofiber mask prepared by electrospinning technology are beneficial to the skin's absorption of nutrients, and it has good fit with the skin and strong breathability. A unique advantage of this nanofiber mask is that it uses spray. After the mask is sprayed with water or essence, the water-soluble polymer within it can be quickly dissolved, saving a lot of time. Nanofiber facial mask products can effectively solve consumer pain points and are conducive to the high-end development of facial masks. Therefore, this article combines needleless electrospinning technology to develop a new solid-state, preservative-free, quick-dissolving nanofiber facial mask that can be prepared on a large scale. Based on needleless electrospinning technology, this article deeply explores the process parameters and their influencing mechanisms for preparing nanofiber, quick-dissolving facial masks to achieve the stable preparation of nanofiber facial masks with the best morphology; a comprehensive analysis of the structure and influence of nanofiber facial masks from micro and macro perspectives demonstrates their performance and allows evaluation of them. The experimental results show that the mask morphology is optimal under the process conditions of using a spinning liquid of 20% collagen peptide solution, a spinning voltage of 30 kV, a collection distance of 19 cm, and a liquid supply speed of 130 mL/h.
PubMed: 38891550
DOI: 10.3390/polym16111602 -
Early Human Development Jun 2024It is thought that digit ratios (2D:4D) are a correlate of 1st trimester maternal and foetal sex steroids. Here we consider the relationship of 2D:4D to the former.
BACKGROUND
It is thought that digit ratios (2D:4D) are a correlate of 1st trimester maternal and foetal sex steroids. Here we consider the relationship of 2D:4D to the former.
METHOD
Digit lengths were directly measured with a calliper at infant age 13 months. Measures of T and E were obtained from mother's blood at 6-8 weeks, 10-11 weeks and 1st trimester means were calculated.
RESULTS
There were 69 mother-infant pairs (33 boys). Sex differences in 2D:4D (boys
boys) were found. For mothers of girls: there were negative relationships between 2D:4D and T at 6-8 weeks, 10-11 weeks and 1st trimester means. For infants: girls showed more correlations between 2D:4D and hormones than boys. For boys, there was one positive association between 2D:4D and E and two positive associations for E/T. For girls, 2D:4D was negatively related to T (four correlations) and positively related to E/T (four correlations). Considering associations in the total sample and controlling for sex, at 6-8 weeks right and left 2D:4D were positively related to E. At 10-11 weeks, right and left 2D:4D were negatively related to T. For 1st trimester means, 2D:4D's were positively related to E (right and left) and negatively related to T (right). CONCLUSION
Infant 2D:4D was correlated with first trimester maternal sex steroids, particularly at 10-11 weeks. The correlations were negative for T, and positive for E and E/T with weaker effects for male infants. The latter pattern may arise because in boys T produced by foetal testes masks the effect of maternal T.
PubMed: 38889565
DOI: 10.1016/j.earlhumdev.2024.106067