Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration

Segmentation of nodules in thyroid ultrasound imaging plays a crucial role in the detection and treatment of thyroid cancer. However, owing to the diversity of scanner vendors and imaging protocols in different hospitals, the automatic segmentation model, which has already demonstrated expert-level accuracy in the field of medical image segmentation, finds its accuracy reduced as the result of its weak generalization performance when being applied in clinically realistic environments. To address this issue, the present paper proposes ASTN, a framework for thyroid nodule segmentation achieved through a new type co-registration network. By extracting latent semantic information from the atlas and target images and utilizing in-depth features to accomplish the co-registration of nodules in thyroid ultrasound images, this framework can ensure the integrity of anatomical structure and reduce the impact on segmentation as the result of overall differences in image caused by different devices. In addition, this paper also provides an atlas selection algorithm to mitigate the difficulty of co-registration. As shown by the evaluation results collected from the datasets of different devices, thanks to the method we proposed, the model generalization has been greatly improved while maintaining a high level of segmentation accuracy

Association of inflammatory indicators with intensive care unit mortality in critically ill patients with coronary heart disease.

ObjectiveCoronary heart disease (CHD) is one of the major cardiovascular diseases, a common chronic disease in the elderly and a major cause of disability and death in the world. Currently, intensive care unit (ICU) patients have a high probability of concomitant coronary artery disease, and the mortality of this category of patients in the ICU is receiving increasing attention. Therefore, the aim of this study was to verify whether the composite inflammatory indicators are significantly associated with ICU mortality in ICU patients with CHD and to develop a simple personalized prediction model.Method7115 patients from the Multi-Parameter Intelligent Monitoring in Intensive Care Database IV were randomly assigned to the training cohort (n = 5692) and internal validation cohort (n = 1423), and 701 patients from the eICU Collaborative Research Database served as the external validation cohort. The association between various inflammatory indicators and ICU mortality was determined by multivariate Logistic regression analysis and Cox proportional hazards model. Subsequently, a novel predictive model for mortality in ICU patients with CHD was developed in the training cohort and performance was evaluated in the internal and external validation cohorts.ResultsVarious inflammatory indicators were demonstrated to be significantly associated with ICU mortality, 30-day ICU mortality, and 90-day ICU mortality in ICU patients with CHD by Logistic regression analysis and Cox proportional hazards model. The area under the curve of the novel predictive model for ICU mortality in ICU patients with CHD was 0.885 for the internal validation cohort and 0.726 for the external validation cohort. The calibration curve showed that the predicted probabilities of the model matched the actual observed probabilities. Furthermore, the decision curve analysis showed that the novel prediction model had a high net clinical benefit.ConclusionIn ICU patients with CHD, various inflammatory indicators were independent risk factors for ICU mortality. We constructed a novel predictive model of ICU mortality risk in ICU patients with CHD that had great potential to guide clinical decision-making

Ecological vulnerability assessment of coral islands and reefs in the South China Sea based on remote sensing and reanalysis data

Coral reefs are ecosystems that are highly vulnerable to external environmental impacts, including changes associated with ocean acidification and global warming. Assessing the vulnerability of coral reef growth environments over large areas of the sea is a difficult and complex process, as it is influenced by many variables. There are few studies on environmental vulnerability assessment of coral islands and reefs in the South China Sea. It is therefore particularly important to understand the environmental sensitivity of corals and how coral communities respond to changes in climate-related environmental variables. In this study, indicators were selected mainly from natural environmental factors that hinder the development of coral reefs. The sea surface temperature (SST), sea surface salinity (SSS), wind velocity (WV) and direction, sea level height (SL), ocean currents (OC), and chlorophyll concentration (Chl) of coral reefs in South China Sea Island were integrated to calculate the coral reef environmental vulnerability region. In a GIS environment, Spatial Principal Component Analysis (SPCA) was used to develop sensitivity models and evaluate the ecological vulnerability of coral reefs. Based on the Environmental vulnerability indicator (EVI) values, the study area was classified as 5 grades of ecological vulnerability: Potential (0.000–0.577), Light (0.577–0.780), Medium (0.780–0.886), Heavy (0.886–0.993) and Very Heavy (0.993–1.131). Sensitivity models identified regional gradients of environmental stress and found that some coral reefs in western Malaysia and southwestern Philippines have higher vulnerability. Meanwhile, the study found that the reefs of Paracel Islands and Macclesfield Bank areas of medium vulnerability. Future use of high-precision data from long time series will allow better estimates of site-specific vulnerability and allow for the precise establishment of marine protected areas so that the ecological diversity of coral reefs can be sustained

sEMG Signals Characterization and Identification of Hand Movements by Machine Learning Considering Sex Differences

Developing a robust machine-learning algorithm to detect hand motion is one of the most challenging aspects of prosthetic hands and exoskeleton design. Machine-learning methods that considered sex differences were used to identify and describe hand movement patterns in healthy individuals. To this purpose, surface Electromyographic (sEMG) signals have been acquired from muscles in the forearm and hand. The results of statistical analysis indicated that most of the same muscle pairs in the right hand (females and males) showed significant differences during the six hand movements. Time features were used an as input to machine-learning algorithms for the recognition of six gestures. Specifically, two types of hand-gesture recognition methods that considered sex differences(differentiating sex datasets and adding a sex label)were proposed and applied to the k-nearest neighbor (k-NN), support vector machine (SVM) and artificial neural network (ANN) algorithms for comparison. In addition, a t-test statistical analysis approach and 5-fold cross validation were used as complements to verify whether considering sex differences could significantly improve classification performance. It was demonstrated that considering sex differences can significantly improve classification performance. The ANN algorithm with the addition of a sex label performed best in movement classification (98.4% accuracy). In the future, hand movement recognition algorithms considering sex differences could be applied to control systems for prosthetic hands or exoskeletons

Precipitation behaviors of multi-scale precipitation strengthened Al–Mg–Si–Cu–Zn alloys controlled by Mg content

In this paper, the effect of the Mg content and the precipitation behaviors in Al–Mg–Si–Cu–Zn alloys is reported. Also, the low strength of 6xxx aluminum alloys is improved by high alloying degree, in which the Mg and Si contents exceed the range of 6xxx aluminum alloys. As a result, the strengths of the alloys are higher than that of 6xxx aluminum alloys. Compared with peak-aged 6016 aluminum alloy, the yield strength and tensile strength of the Al-1.63Mg-1.20Si-0.20Cu-3.00Zn (wt.%) alloy were increased by 112.6 and 97.2 MPa, respectively, due to micron/nanoscale multi-particle strengthening effects. The microscale phase was Mg2Si, and the nano-scale phases were β'' and Q′ phases. The alloy also exhibited excellent properties when aged at a higher temperature (185 °C), so it is expected to significantly harden during actual baking processes. This was revealed by the APT analysis in which elements were clamped to each other at a low temperature, while they precipitated more easily at a high temperature. Precipitation kinetics calculations showed that the activation energy of the β'' phase decreased upon increasing the Mg content, which promoted precipitation strengthening

sEMG Signals Characterization and Identification of Hand Movements by Machine Learning Considering Sex Differences

Developing a robust machine-learning algorithm to detect hand motion is one of the most challenging aspects of prosthetic hands and exoskeleton design. Machine-learning methods that considered sex differences were used to identify and describe hand movement patterns in healthy individuals. To this purpose, surface Electromyographic (sEMG) signals have been acquired from muscles in the forearm and hand. The results of statistical analysis indicated that most of the same muscle pairs in the right hand (females and males) showed significant differences during the six hand movements. Time features were used an as input to machine-learning algorithms for the recognition of six gestures. Specifically, two types of hand-gesture recognition methods that considered sex differences(differentiating sex datasets and adding a sex label)were proposed and applied to the k-nearest neighbor (k-NN), support vector machine (SVM) and artificial neural network (ANN) algorithms for comparison. In addition, a t-test statistical analysis approach and 5-fold cross validation were used as complements to verify whether considering sex differences could significantly improve classification performance. It was demonstrated that considering sex differences can significantly improve classification performance. The ANN algorithm with the addition of a sex label performed best in movement classification (98.4% accuracy). In the future, hand movement recognition algorithms considering sex differences could be applied to control systems for prosthetic hands or exoskeletons

Landslide dynamic hazard prediction based on precipitation variation trend and backpropagation neural network

AbstractThe assessment of landslide hazards is crucial for disaster prevention and mitigation, but it has not considered the dynamic influencing factors that trigger landslides. The timeliness and practical value of the assessment results still need to be further improved. This study constructed a dynamic landslide hazard assessment system using information value model, dynamic precipitation data, and Backpropagation Neural Network (BPNN) model. Taking the Qingjiang Reservoir landslide in Changyang County, Hubei Province, China as an example, based on dynamic precipitation data and the BPNN model were used to develop a dynamic landslide hazard prediction model, and the temporal assessment and spatial distribution results of slope unit hazards in the study area from the 1980s to the 2010s, 2025, and 2030 were evaluated and predicted. It is predicted that the percentage of very high and high areas in 2025 and 2030 will be 50.5% and 57.5% respectively

Probabilistic short-term power load forecasting based on B-SCN

Grid management and power dispatching rely on accurate short-term power load prediction. Different algorithms have been constantly developed and tested to improve forecast precision. However, these forecasts are constrained by a number of uncertain factors, which are caused by dynamic environment, the nonlinearity and stochasticity of power demand. To obtain more accurate load forecasting value and quantify the uncertainty effectively, this research proposes a boosting stochastic configuration network(B-SCN) based probabilistic forecasting method. First, correlation analysis is taken in multidimensional input parameters. Second, an adaptive B-SCN network architecture is proposed to construct the prediction model and improve the stability of model outputs significantly. The probabilistic forecasting is then used to actualize the model’s uncertainty evaluation by creating the confidence intervals using the Gaussian process. Consequently, experimental results reveal that the proposed boosting-SCN prediction model achieves superior forecasting accuracy than the single SCN model and other commonly used forecasting models. The probabilistic forecasting can efficiently obtain the uncertainties in power load data and provide support for system operation

Growth and carbon sequestration of poplar plantations on the Tibetan Plateau

Tree radial growth has long-term adaptation and rapid responses to climate, manifested as age-dependent low-frequency and climate-sensitive high-frequency signals. Although the former is usually removed in climate-growth analyses, its overall change still profoundly affects forest biomass and carbon sequestration. The iterative growth model (IGM) reveals the underlying links among organism lifespan, growth rate, and respiration, providing a set of theoretical indicators to evaluate or predict growth. Here, IGM was extended to the tree-ring scale (IGMR) to study the low-frequency growth signals of poplar plantations in the Yarlung Tsangpo River, Tibetan Plateau. As predicted by the IGMR, the low-frequency growth signals all follow a unimodal pattern over the diameter at breast height (DBH) gradient while constraining the high-frequency signals. The unimodal growth curves’ length (maximum DBH), height (maximum growth rate of tree DBH), and resulting tree lifespan could be used to assess and predict tree growth. The results showed that the maximum DBH, growth rate and inverse of the longevity of the trees were greater at lower elevations. The indicators of Populus × beijingensis (PB) were better than those of P. alba (PA). Overall, poplars adapted to the plateau climate by reducing growth rates and increasing longevity. Temperature was the key factor affecting these trade-offs, with the best temperature at 14.69 ℃. Combined with stand density, PB plantations (11695.58 ± 1704.98 g/m2) had greater potential maximum biomass than PA plantations (9032.50 ± 2031.21 g/m2). This study highlights that the response of low-frequency growth signals to environments is holistic, and the resulting indicators have important value for evaluating and predicting tree growth and forest carbon sequestration. Moreover, the results have important practical significance for reasonable plantations and proper assessment of the ecological contribution of plantation forests on the Tibetan Plateau

Single-nucleus transcriptomic mapping of blast-induced traumatic brain injury in mice hippocampus

Abstract As a significant type of traumatic brain injury (TBI), blast-induced traumatic brain injury (bTBI) frequently results in severe neurological and psychological impairments. Due to its unique mechanistic and clinical features, bTBI presents diagnostic and therapeutic challenges compared to other TBI forms. The hippocampus, an important site for secondary injury of bTBI, serves as a key niche for neural regeneration and repair post-injury, and is closely associated with the neurological outcomes of bTBI patients. Nonetheless, the pathophysiological alterations of hippocampus underpinning bTBI remain enigmatic, and a corresponding transcriptomic dataset for research reference is yet to be established. In this investigation, the single-nucleus RNA sequencing (snRNA-seq) technique was employed to sequence individual hippocampal nuclei of mice from bTBI and sham group. Upon stringent quality control, gene expression data from 17,278 nuclei were obtained, with the dataset’s reliability substantiated through various analytical methods. This dataset holds considerable potential for exploring secondary hippocampal injury and neurogenesis mechanisms following bTBI, with important reference value for the identification of specific diagnostic and therapeutic targets for bTBI