Prediction of rhinitis with class imbalance based on heterogeneous ensemble learning.

Common clinical rhinitis is characterized by different types of cases and class imbalance. Its prediction belongs to multiple output classification. Low recognition rate and poor generalization performance often occur for minority class. Therefore, we propose a novel integrated classification model, ARF-OOBEE, which transforms the multi-output classification to multi-label classification and multi-class classification. The multi-label classifier automatically adjusts the number and depth of integrated forest learners according to the imbalance ratio of single class label in a subset. It can effectively reduce the impact of class imbalance on classification and improve prediction performance of both majority or minority class concurrently. Also, we build a multi-class classification based on out-of-bag Extra-Tree to accomplish finer classification for the predicted labels. In addition, we calculate the feature importance for rhinitis on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features. We conduct 12 folds cross-validation experiments on 461 cases of clinical rhinitis. The outcomes show that the evaluation indicators of ARF-OOBEE, such as Sensitivity, Specificity, Accuracy, F1-Score, AUC, and G-Mean are 74.9%,86.5%,92.0%,78.3%,95.3%, and 79.9%, respectively. In comparison to the other methods, ARF-OOBEE has better evaluation indicator and is more effective for the early clinical diagnosis of rhinitis.

Risk prediction of pulse wave for hypertensive target organ damage based on frequency-domain feature map.

The application of deep learning to the classification of pulse waves in Traditional Chinese Medicine (TCM) related to hypertensive target organ damage (TOD) is hindered by challenges such as low classification accuracy and inadequate generalization performance. To address these challenges, we introduce a lightweight transfer learning model named MobileNetV2SCP. This model transforms time-domain pulse waves into 36-dimensional frequency-domain waveform feature maps and establishes a dedicated pre-training network based on these maps to enhance the learning capability for small samples. To improve global feature correlation, we incorporate a novel fusion attention mechanism (SAS) into the inverted residual structure, along with the utilization of 3 × 3 convolutional layers and BatchNorm layers to mitigate model overfitting. The proposed model is evaluated using cross-validation results from 805 cases of pulse waves associated with hypertensive TOD. The assessment metrics, including Accuracy (92.74 %), F1-score (91.47 %), and Area Under Curve (AUC) (97.12 %), demonstrate superior classification accuracy and generalization performance compared to various state-of-the-art models. Furthermore, this study investigates the correlations between time-domain and frequency-domain features in pulse waves and their classification in hypertensive TOD. It analyzes key factors influencing pulse wave classification, providing valuable insights for the clinical diagnosis of TOD.

A Hydrogen-Bonded yet Hydrophobic Porous Molecular Crystal for Molecular-Sieving-like Separation of Butane and Isobutane.

Porous molecular crystals sustained by hydrogen bonds and/or weaker connections are an intriguing type of adsorbents, but they rarely demonstrate efficient adsorptive separation because of poor structural robustness and tailorability. Herein, we report a porous molecular crystal based on hydrogen-bonded cyclic dinuclear AgI complex, which exhibits exceptional hydrophobicity with a water contact angle of 134°, and high chemical stability in water at pH 2-13. The seemingly rigid adsorbent shows a pore-opening or nonporous-to-porous type butane adsorption isotherm and complete exclusion of isobutane, indicating potential molecular sieving. Quantitative column breakthrough experiments show slight co-adsorption of isobutane with an experimental butane/isobutane selectivity of 23, and isobutane can be purified more efficiently than for butane. In situ powder/single-crystal X-ray diffraction and computational simulations reveal that a trivial guest-induced structural transformation plays a critical role.

Two Isostructural Flexible Porous Coordination Polymers Showing Contrasting Single-Component and Mixture Adsorption Properties for Propylene/Propane.

Solvothermal reactions of 3-(3-methylpyridin-4-yl)benzoic acid (Hmpba) with Mn(NO3)2 or Co(NO3)2 yielded isostructural porous coordination polymers, [Mn(mpba)2]·guest (MCF-56, 1·g) and [Co(mpba)2]·guest (MCF-57, 2·g), respectively. X-ray diffraction revealed that 1·g and 2·g possess similar one-dimensional ultramicroporous channels, and guest-free [Mn(mpba)2] (1') and [Co(mpba)2] (2') possess significantly and slightly contracted channels, respectively. Single-component C3H6/C3H8 adsorption isotherms and computational simulations showed the typical nonporous-to-porous structural transformations for 1', in which C3H6 exhibits a significantly lower threshold pressure, and the typical small-pore-to-large-pore structural transformations for 2', in which C3H6 exhibits a slightly lower threshold pressure. Mixture column breakthrough experiments showed that the C3H6/C3H8 separation performances of 2' are obviously better than those of 1', because the latter cannot adsorb C3H6 below the threshold pressure for pore opening, and the pore opened by C3H6 can adsorb C3H8.