Automatic measurement of fetal anterior neck lower jaw angle in nuchal translucency scans.

This study aims at suggesting an end-to-end algorithm based on a U-net-optimized generative adversarial network to predict anterior neck lower jaw angles (ANLJA), which are employed to define fetal head posture (FHP) during nuchal translucency (NT) measurement. We prospectively collected 720 FHP images (half hyperextension and half normal posture) and regarded manual measurement as the gold standard. Seventy percent of the FHP images (half hyperextension and half normal posture) were used to fit models, and the rest to evaluate them in the hyperextension group, normal posture group (NPG), and total group. The root mean square error, explained variation, and mean absolute percentage error (MAPE) were utilized for the validity assessment; the two-sample t test, Mann-Whitney U test, Wilcoxon signed-rank test, Bland-Altman plot, and intraclass correlation coefficient (ICC) for the reliability evaluation. Our suggested algorithm outperformed all the competitors in all groups and indices regarding validity, except for the MAPE, where the Inception-v3 surpassed ours in the NPG. The two-sample t test and Mann-Whitney U test indicated no significant difference between the suggested method and the gold standard in group-level comparison. The Wilcoxon signed-rank test revealed significant differences between our new approach and the gold standard in personal-level comparison. All points in Bland-Altman plots fell between the upper and lower limits of agreement. The inter-ICCs of ultrasonographers, our proposed algorithm, and its opponents were graded good reliability, good or moderate reliability, and moderate or poor reliability, respectively. Our proposed approach surpasses the competition and is as reliable as manual measurement.

Co-exposure to low-dose lead, cadmium, and mercury promotes memory deficits in rats: Insights from the dynamics of dendritic spine pruning in brain development.

Lead (Pb), cadmium (Cd), and mercury (Hg) are environmentally toxic heavy metals that can be simultaneously detected at low levels in the blood of the general population. Although our previous studies have demonstrated neurodevelopmental toxicity upon co-exposure to these heavy metals at these low levels, the precise mechanisms remain largely unknown. Dendritic spines are the structural foundation of memory and undergo significant dynamic changes during development. This study focused on the dynamics of dendritic spines during brain development following Pb, Cd, and Hg co-exposure-induced memory impairment. First, the dynamic characteristics of dendritic spines in the prefrontal cortex were observed throughout the life cycle of normal rats. We observed that dendritic spines increased rapidly from birth to their peak value at weaning, followed by significant pruning and a decrease during adolescence. Dendritic spines tended to be stable until their loss in old age. Subsequently, a rat model of low-dose Pb, Cd, and Hg co-exposure from embryo to adolescence was established. The results showed that exposure to low doses of heavy metals equivalent to those detected in the blood of the general population impaired spatial memory and altered the dynamics of dendritic spine pruning from weaning to adolescence. Proteomic analysis of brain and blood samples suggested that differentially expressed proteins upon heavy metal exposure were enriched in dendritic spine-related cytoskeletal regulation and axon guidance signaling pathways and that cofilin was enriched in both of these pathways. Further experiments confirmed that heavy metal exposure altered actin cytoskeleton dynamics and disturbed the dendritic spine pruning-related LIM domain kinase 1-cofilin pathway in the rat prefrontal cortex. Our findings demonstrate that low-dose Pb, Cd, and Hg co-exposure may promote memory impairment by perturbing dendritic spine dynamics through dendritic spine pruning-related signaling pathways.

Insight into the effect of a heavy metal mixture on neurological damage in rats through combined serum metabolomic and brain proteomic analyses.

The heavy metals lead (Pb), cadmium (Cd), and mercury (Hg) that cause neurocognitive impairment have been extensively studied. These elements typically do not exist alone in the environment; they are often found with other heavy metals and can enter the body through various routes, thereby impacting health. Our previous research showed that low Pb, Cd, and Hg levels cause neurobehavioral impairments in weaning and adult rats. However, little is known about the biomarkers and mechanisms underlying Pb, Cd, and Hg mixture-induced neurological impairments. A combined analysis of metabolomic and proteomic data may reveal heavy metal-induced alterations in metabolic and protein profiles, thereby improving our understanding of the molecular mechanisms underlying heavy metal-induced neurological impairments. Therefore, brain tissue and serum samples were collected from rats exposed to a Pb, Cd, and Hg mixture for proteomic and metabolomic analyses, respectively. The analysis revealed 363 differential proteins in the brain and 206 metabolites in serum uniquely altered in the Pb, Cd, and Hg mixture exposure group, compared to those of the control group. The main metabolic impacted pathways were unsaturated fatty acids biosynthesis, linoleic acid metabolism, phenylalanine metabolism, and tryptophan metabolism. We further identified that the levels of arachidonic acid (C20:4 n-3) and, adrenic acid (C22:4 n-3) were elevated and that kynurenic acid (KA) and quinolinic acid (QA) levels and the KA/QA ratio, were decreased in the group exposed to the Pb, Cd, and Hg mixture. A joint analysis of the proteome and metabolome showed that significantly altered proteins such as LPCAT3, SLC7A11, ASCL4, and KYAT1 may participate in the neurological impairments induced by the heavy metal mixture. Overall, we hypothesize that the dysregulation of ferroptosis and kynurenine pathways is associated with neurological damage due to chronic exposure to a heavy metal mixture.

Disentangled Dynamic Deviation Transformer Networks for Multivariate Time Series Anomaly Detection.

Graph neural networks have been widely used by multivariate time series-based anomaly detection algorithms to model the dependencies of system sensors. Previous studies have focused on learning the fixed dependency patterns between sensors. However, they ignore that the inter-sensor and temporal dependencies of time series are highly nonlinear and dynamic, leading to inevitable false alarms. In this paper, we propose a novel disentangled dynamic deviation transformer network (D3TN) for anomaly detection of multivariate time series, which jointly exploits multiscale dynamic inter-sensor dependencies and long-term temporal dependencies to improve the accuracy of multivariate time series prediction. Specifically, to disentangle the multiscale graph convolution, we design a novel disentangled multiscale aggregation scheme to better represent the hidden dependencies between sensors to learn fixed inter-sensor dependencies based on static topology. To capture dynamic inter-sensor dependencies determined by real-time monitoring situations and unexpected anomalies, we introduce a self-attention mechanism to model dynamic directed interactions in various potential subspaces influenced by various factors. In addition, complex temporal correlations across multiple time steps are simulated by processing the time series in parallel. Experiments on three real datasets show that the proposed D3TN significantly outperforms the state-of-the-art methods.

Assessing electrocardiogram changes after ischemic stroke with artificial intelligence.

OBJECTIVE: Ischemic stroke (IS) with subsequent cerebrocardiac syndrome (CCS) has a poor prognosis. We aimed to investigate electrocardiogram (ECG) changes after IS with artificial intelligence (AI). METHODS: We collected ECGs from a healthy population and patients with IS, and then analyzed participant demographics and ECG parameters to identify abnormal features in post-IS ECGs. Next, we trained the convolutional neural network (CNN), random forest (RF) and support vector machine (SVM) models to automatically detect the changes in the ECGs; Additionally, We compared the CNN scores of good prognosis (mRS ≤ 2) and poor prognosis (mRS > 2) to assess the prognostic value of CNN model. Finally, we used gradient class activation map (Grad-CAM) to localize the key abnormalities. RESULTS: Among the 3506 ECGs of the IS patients, 2764 ECGs (78.84%) led to an abnormal diagnosis. Then we divided ECGs in the primary cohort into three groups, normal ECGs (N-Ns), abnormal ECGs after the first ischemic stroke (A-ISs), and normal ECGs after the first ischemic stroke (N-ISs). Basic demographic and ECG parameter analyses showed that heart rate, QT interval, and P-R interval were significantly different between 673 N-ISs and 3546 N-Ns (p < 0.05). The CNN has the best performance among the three models in distinguishing A-ISs and N-Ns (AUC: 0.88, 95%CI = 0.86-0.90). The prediction scores of the A-ISs and N-ISs obtained from the all three models are statistically different from the N-Ns (p < 0.001). Futhermore, the CNN scores of the two groups (mRS > 2 and mRS ≤ 2) were significantly different (p < 0.05). Finally, Grad-CAM revealed that the V4 lead may harbor the highest probability of abnormality. CONCLUSION: Our study showed that a high proportion of post-IS ECGs harbored abnormal changes. Our CNN model can systematically assess anomalies in and prognosticate post-IS ECGs.

Deep Self-Taught Hashing for Image Retrieval.

Hashing algorithm has been widely used to speed up image retrieval due to its compact binary code and fast distance calculation. The combination with deep learning boosts the performance of hashing by learning accurate representations and complicated hashing functions. So far, the most striking success in deep hashing have mostly involved discriminative models, which require labels. To apply deep hashing on datasets without labels, we propose a deep self-taught hashing algorithm (DSTH), which generates a set of pseudo labels by analyzing the data itself, and then learns the hash functions for novel data using discriminative deep models. Furthermore, we generalize DSTH to support both supervised and unsupervised cases by adaptively incorporating label information. We use two different deep learning framework to train the hash functions to deal with out-of-sample problem and reduce the time complexity without loss of accuracy. We have conducted extensive experiments to investigate different settings of DSTH, and compared it with state-of-the-art counterparts in six publicly available datasets. The experimental results show that DSTH outperforms the others in all datasets.

2D Tsallis Entropy for Image Segmentation Based on Modified Chaotic Bat Algorithm.

Image segmentation is a significant step in image analysis and computer vision. Many entropy based approaches have been presented in this topic; among them, Tsallis entropy is one of the best performing methods. However, 1D Tsallis entropy does not consider make use of the spatial correlation information within the neighborhood results might be ruined by noise. Therefore, 2D Tsallis entropy is proposed to solve the problem, and results are compared with 1D Fisher, 1D maximum entropy, 1D cross entropy, 1D Tsallis entropy, fuzzy entropy, 2D Fisher, 2D maximum entropy and 2D cross entropy. On the other hand, due to the existence of huge computational costs, meta-heuristics algorithms like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization algorithm (ACO) and differential evolution algorithm (DE) are used to accelerate the 2D Tsallis entropy thresholding method. In this paper, considering 2D Tsallis entropy as a constrained optimization problem, the optimal thresholds are acquired by maximizing the objective function using a modified chaotic Bat algorithm (MCBA). The proposed algorithm has been tested on some actual and infrared images. The results are compared with that of PSO, GA, ACO and DE and demonstrate that the proposed method outperforms other approaches involved in the paper, which is a feasible and effective option for image segmentation.