Prediction of upcoming urinary tract infection after intracerebral hemorrhage: a machine learning approach based on statistics collected at multiple time points

PurposeAccurate prediction of urinary tract infection (UTI) following intracerebral hemorrhage (ICH) can significantly facilitate both timely medical interventions and therapeutic decisions in neurocritical care. Our study aimed to propose a machine learning method to predict an upcoming UTI by using multi-time-point statistics.MethodsA total of 110 patients were identified from a neuro-intensive care unit in this research. Laboratory test results at two time points were chosen: Lab 1 collected at the time of admission and Lab 2 collected at the time of 48 h after admission. Univariate analysis was performed to investigate if there were statistical differences between the UTI group and the non-UTI group. Machine learning models were built with various combinations of selected features and evaluated with accuracy (ACC), sensitivity, specificity, and area under the curve (AUC) values.ResultsCorticosteroid usage (p < 0.001) and daily urinary volume (p < 0.001) were statistically significant risk factors for UTI. Moreover, there were statistical differences in laboratory test results between the UTI group and the non-UTI group at the two time points, as suggested by the univariate analysis. Among the machine learning models, the one incorporating clinical information and the rate of change in laboratory parameters outperformed the others. This model achieved ACC = 0.773, sensitivity = 0.785, specificity = 0.762, and AUC = 0.868 during training and 0.682, 0.685, 0.673, and 0.751 in the model test, respectively.ConclusionThe combination of clinical information and multi-time-point laboratory data can effectively predict upcoming UTIs after ICH in neurocritical care

Development status and application of neuronavigation system

The neuronavigation system is a combination of navigation technology and neurosurgery. It can be used to assist in neurosurgery through three-dimensional reconstruction of medical image data, extraction of lesions, optimal surgical path planning, tracking and positioning of surgical instruments, and real-time intraoperative display. Accurate and maximal treatment of lesions, while effectively avoiding secondary injuries to patients during surgery. Therefore, the development and application of neuronavigation systems are reviewed

ALMA Observations of a Quiescent Molecular Cloud in the Large Magellanic Cloud

We present high-resolution (sub-parsec) observations of a giant molecular cloud in the nearest star-forming galaxy, the Large Magellanic Cloud. ALMA Band 6 observations trace the bulk of the molecular gas in $^{12}$CO(2-1) and high column density regions in $^{13}$CO(2-1). Our target is a quiescent cloud (PGCC G282.98-32.40, which we refer to as the "Planck cold cloud" or PCC) in the southern outskirts of the galaxy where star-formation activity is very low and largely confined to one location. We decompose the cloud into structures using a dendrogram and apply an identical analysis to matched-resolution cubes of the 30 Doradus molecular cloud (located near intense star formation) for comparison. Structures in the PCC exhibit roughly 10 times lower surface density and 5 times lower velocity dispersion than comparably sized structures in 30 Dor, underscoring the non-universality of molecular cloud properties. In both clouds, structures with relatively higher surface density lie closer to simple virial equilibrium, whereas lower surface density structures tend to exhibit super-virial line widths. In the PCC, relatively high line widths are found in the vicinity of an infrared source whose properties are consistent with a luminous young stellar object. More generally, we find that the smallest resolved structures ("leaves") of the dendrogram span close to the full range of line widths observed across all scales. As a result, while the bulk of the kinetic energy is found on the largest scales, the small-scale energetics tend to be dominated by only a few structures, leading to substantial scatter in observed size-linewidth relationships.Comment: Accepted by ApJ; 21 pages in AASTeX two-column styl

Exergy Transfer Analysis of Biomass and Microwave Based on Experimental Heating Process

Exergy transfer and microwave heating performances of wheat straw particles as affected by microwave power (250, 300, and 350 W), feeding load (10, 30, and 50 g), and particle size (0.058, 0.106, and 0.270 mm) were investigated and detailed in this study. The results show that when the microwave power increased from 250 to 350 W, the average heating rate increased in the range of 23.41–56.18 °C/min with the exergy transfer efficiency increased in the range of 1.10–1.89%. When the particle size increased from 0.058 to 0.270 mm, the average heating rate decreased in the range of 20.59–56.18 °C/min with the exergy transfer efficiency decreased in the range of 0.70–1.89%. When the feeding load increased from 10 to 50 g, the average heating rate increased first and then decreased in the range of 5.96–56.18 °C/min with the exergy transfer efficiency increased first and then decreased in the range of 0.07–1.89%. The highest exergy transfer efficiency was obtained at a microwave power of 300 W, feeding load of 30 g, and particle size of 0.058 mm

A Two-Stage Voting-Boosting Technique for Ensemble Learning in Social Network Sentiment Classification

In recent years, social network sentiment classification has been extensively researched and applied in various fields, such as opinion monitoring, market analysis, and commodity feedback. The ensemble approach has achieved remarkable results in sentiment classification tasks due to its superior performance. The primary reason behind the success of ensemble methods is the enhanced diversity of the base classifiers. The boosting method employs a sequential ensemble structure to construct diverse data while also utilizing erroneous data by assigning higher weights to misclassified samples in the next training round. However, this method tends to use a sequential ensemble structure, resulting in a long computation time. Conversely, the voting method employs a concurrent ensemble structure to reduce computation time but neglects the utilization of erroneous data. To address this issue, this study combines the advantages of voting and boosting methods and proposes a new two-stage voting boosting (2SVB) concurrent ensemble learning method for social network sentiment classification. This novel method not only establishes a concurrent ensemble framework to decrease computation time but also optimizes the utilization of erroneous data and enhances ensemble performance. To optimize the utilization of erroneous data, a two-stage training approach is implemented. Stage-1 training is performed on the datasets by employing a 3-fold cross-segmentation approach. Stage-2 training is carried out on datasets that have been augmented with the erroneous data predicted by stage 1. To augment the diversity of base classifiers, the training stage employs five pre-trained deep learning (PDL) models with heterogeneous pre-training frameworks as base classifiers. To reduce the computation time, a two-stage concurrent ensemble framework was established. The experimental results demonstrate that the proposed method achieves an F1 score of 0.8942 on the coronavirus tweet sentiment dataset, surpassing other comparable ensemble methods

Impacts of Climate Change on Snow Avalanche Activity Along a Transportation Corridor in the Tianshan Mountains

Abstract Snow avalanches can repeatedly occur along the same track under different snowpack and meteorological conditions during the snow season in areas of snow avalanche activity. The snowfall, air temperature, and snow cover can change dramatically in a warming climate, causing significant changes in the snow avalanche risk. But how the risk of snow avalanche activity during the snow season will change under a warming climate remains an open question. Based on the observed meteorological and snowpack data from 1968 to 2021 and the snow avalanche activity data during the 2011–2021 snow seasons along a transportation corridor in the central Tianshan Mountains that has a typical continental snow climate, we analyzed the temporal distribution of the snow avalanche activity and the impacts of climate change on it. The results indicate that the frequency of the snow avalanche activity is characterized by a Gaussian bimodal distribution, resulting from interactions between the snowfall, air temperature, and snowpack evolution. In addition, the active period of wet snow avalanches triggered by temperature surges and high solar radiation has gradually moved forward from the second half to the first half of March with climate warming. The frequency and size of snowfall-triggered snow avalanches showed only a slight and insignificant increase. These findings are important for rationally arranging snow avalanche relief resources to improve the risk management of snow avalanche disasters, and highlight the necessity to immediately design risk mitigation strategies and disaster risk policies to improve our adaptation to climate change

An Investigation into the Critical Factors Influencing the Spread of Campylobacter during Chicken Handling in Commercial Kitchens in China

Campylobacteriosis is the most common cause of bacterial gastroenteritis worldwide. Consumption of chicken meat is considered the main route for human infection with Campylobacter. This study aimed to determine the critical factors for Campylobacter cross-contamination in Chinese commercial kitchens during chicken handling. Five commercial kitchens were visited to detect Campylobacter occurrence from 2019 to 2020. Chicken samples (n = 363) and cotton balls from the kitchen surfaces (n = 479) were collected, and total bacterial counts and Campylobacter spp. were detected. Genotypic characterization of 57 Campylobacter jejuni isolates was performed by multilocus sequence typing (MLST). In total, 77.41% of chicken carcass samples and 37.37% of kitchen surfaces showed Campylobacter spp. contamination. Before chicken preparation, Campylobacter spp. were already present in the kitchen environment; however, chicken handling significantly increased Campylobacter spp. prevalence (p < 0.05). After cleaning, boards, hands, and knives still showed high bacterial loads including Campylobacter spp., which related to poor sanitary conditions and ineffective handling practices. Poor sanitation conditions on kitchen surfaces offer greater opportunities for Campylobacter transmission. Molecular typing by MLST revealed that Campylobacter cross-contamination occurred during chicken preparation. The most prevalent sequence types, ST693 and ST45, showed strong biofilm formation ability. Consequently, sanitary condition of surfaces and biofilm formation ability of isolates were the critical points contributing to spread of Campylobacter in kitchen environment. These results provide insight into potential targeted control strategies along the farm-to-plate chain and highlight the necessity for improvements in sanitary conditions. The implementation of more effective cleaning measures should be considered to decrease the campylobacteriosis risk

Short term wind power prediction for regional wind farms based on spatial-temporal characteristic distribution

Accurate regional wind power prediction is of great significance to the wind farm clusters integration and the economic dispatch of the regional power grid. The complex spatiotemporally coupled characteristics between multiple wind farms bring challenges to wind power prediction (WPP) of regional wind farm clusters. In this context, this paper proposes a regional WPP method using spatiotemporally multiple clustering algorithm and hybrid neural network to learn the potential spatial-temporal dependencies of regional wind farms. In which, a long-term daily power curve similarity method is proposed to identify spatially correlative wind power plants in long-term. Furthermore, the spatio-temporal wind farm sub-clusters are dynamically recognized by the similar fluctuation trend of short-term power sequences. On this basis, a spatial-temporal integrated prediction model consisting of the improved convolutional neural network (I-CNN) and the bidirectional long short-term memory (BILSTM) network is established for spatio-temporal sub-cluster based on point clouds distribution. Finally, the effectiveness of the proposed regional wind power forecasting framework is validated by using the Wind Integration National Dataset Toolkit, and the results show that the method improves accuracy effectively. 2022 Elsevier LtdThis work is supported by the National Natural Science Foundation of China (No. 52207121 and No. 52007167 ) and the technology project of Electric Power Research Institute of State Grid Hubei Electric Power Co ., Ltd. (Grant number: B31532225680 ).Scopu

Activation of Ene-Diamido Samarium Methoxide with Hydrosilane for Selectively Catalytic Hydrosilylation of Alkenes and Polymerization of Styrene: an Experimental and Theoretical Mechanistic Study

Samarium methoxide incorporating the ene-diamido ligand L­(DME)­Sm­(μ-OMe)₂Sm­(DME)­L (<b>1</b>; L = [DipNC­(Me)­C­(Me)­NDip]^2–, Dip = 2,6-<i>i</i>Pr₂C₆H₃, and DME = 1,2-dimethoxyethane) has been prepared and structurally characterized. Complex <b>1</b> catalyzed the syndiospecific polymerization of styrene upon activation with phenylsilane and regioselective hydrosilylation of styrenes and nonactivated terminal alkenes. Unprecedented regioselectivity (>99.0%) for both types of alkenes has been achieved with the formation of Markovnikov and anti-Markovnikov products in high yields, respectively, whereas the polymerization of styrene resulted in the formation of syndiotactic silyl-capped oligostyrenes. The kinetic experiments and density functional theory calculations strongly support a samarium hydride intermediate generated by σ-bond metathesis of the Sm–OMe bond in <b>1</b> with PhSiH₃. In addition, the observed regioselectvity for hydrosilylation and polymerization is consistent with the calculated energy profiles, which suggests that the bulky ene-diamido ligand and samarium hydride intermediate have important roles for regio- and stereoselectivity