A Freestanding 3D Skeleton with Gradationally Distributed Lithiophilic Sites for Realizing Stable Lithium Anodes.

Lithium (Li) metal anodes are drawing considerable attention owing to their ultrahigh theoretical capacities and low electrochemical reduction potentials. However, their commercialization has been hampered by safety hazards induced by continuous dendrite growth. These issues can be alleviated using the ZnO-modified 3D carbon-based host containing carbon nanotubes (CNTs) and carbon felt (CF) fabricated by electroplating in the present study (denoted as ZnO/CNT@CF). The constructed skeleton has lithiophilic ZnO that is gradationally distributed along its thickness. The utilization of an inverted ZnO/CNT@CF-Li anode obtained by flipping over the carbon skeleton after Li electrodeposition is also reported herein. The synergistic effect of the Li metal and lithiophilic sites reduces the nucleation overpotential, thus inducing Li+ to preferentially deposit inside the porous carbon-based scaffold. The composite electrode compels Li to grow away from the separator, thereby significantly improving battery safety. A symmetric cell with the inverted ZnO/CNT@CF-Li electrode operates steadily for 700 cycles at 1 mA cm-2 and 1 mAh cm-2 . Moreover, the ZnO/CNT@CF-Li|S cell exhibits an initial areal capacity of 10.9 mAh cm-2 at a S loading of 10.4 mg cm-2 and maintains a capacity of 3.0 mAh cm-2 after 320 cycles.

Pyramid feature adaptation for semi-supervised cardiac bi-ventricle segmentation.

Cardiac bi-ventricle segmentation (BVS) is an essential task for assessing cardiac indices, such as the ejection fraction and volume of the left ventricle (LV) and right ventricle (RV). However, BVS is extremely challenging due to the high variability of the bi-ventricle structure and lack of labeled data. In this paper, we propose a pyramid feature adaptation based semi-supervised method (PABVS) for cardiac bi-ventricle segmentation. The PABVS first extracts the multiscale pyramid features of bi-ventricle structure to cope with the high variability of bi-ventricle structure. Then, a weighted pyramid feature adaptation strategy is proposed to ensure a smooth feature space among labeled data and unlabeled data. In particular, the PABVS performs weighted feature adaptation at each level of a multiscale pyramid feature based on adversarial learning. It gives less importance to outlier feature layers of labeled data and more importance to representative layers. The experimental results on magnetic resonance images show that our proposed PABVS can achieve Dice values 0.915 for EpiLV with 40% labeled data and the Dice values 0.976 for EpiLV with all labeled data, which outperforms mainstream semi-supervised methods. This endows our PABVS with great potential for the effective clinical application of BVS.

Application of the healthcare failure mode and effects analysis system to reduce the incidence of posture syndrome of thyroid surgery.

This study aimed to investigate the application of the healthcare failure mode and effect analysis (HFMEA) to reduce the incidence of posture syndrome of thyroid surgery (PSTS).Subjects before (n = 78, July 2017-December 2017) and after (n = 114, January 2018-June 2018) HFMEA implementation (The Second Hospital of Nanjing, Nanjing University of Chinese Medicine) were selected. The training for PSTS was optimized using HFMEA.The occurrence of PSTS was reduced from 59% to 18% after HFMEA (P < .001). Symptoms of pain and nausea and vomiting were also decreased after HFMEA (all P < .001). The critical thinking ability of 34 medical personnel to evaluate the reduction of thyroid postoperative posture syndrome increased from 246 ±â€Š19 to 301 ±â€Š14 (P < .001) after HFMEA.HFMEA was used to create preoperative posture training procedures for PSTS, bedside cards for training, innovative preoperative posture training equipment, and a diversified preoperative posture training health education model.

Classifying Incomplete Gene-Expression Data: Ensemble Learning with Non-Pre-Imputation Feature Filtering and Best-First Search Technique.

(1) Background: Gene-expression data usually contain missing values (MVs). Numerous methods focused on how to estimate MVs have been proposed in the past few years. Recent studies show that those imputation algorithms made little difference in classification. Thus, some scholars believe that how to select the informative genes for downstream classification is more important than how to impute MVs. However, most feature-selection (FS) algorithms need beforehand imputation, and the impact of beforehand MV imputation on downstream FS performance is seldom considered. (2) Method: A modified chi-square test-based FS is introduced for gene-expression data. To deal with the challenge of a small sample size of gene-expression data, a heuristic method called recursive element aggregation is proposed in this study. Our approach can directly handle incomplete data without any imputation methods or missing-data assumptions. The most informative genes can be selected through a threshold. After that, the best-first search strategy is utilized to find optimal feature subsets for classification. (3) Results: We compare our method with several FS algorithms. Evaluation is performed on twelve original incomplete cancer gene-expression datasets. We demonstrate that MV imputation on an incomplete dataset impacts subsequent FS in terms of classification tasks. Through directly conducting FS on incomplete data, our method can avoid potential disturbances on subsequent FS procedures caused by MV imputation. An experiment on small, round blue cell tumor (SRBCT) dataset showed that our method found additional genes besides many common genes with the two compared existing methods.

DeepPPI: Boosting Prediction of Protein-Protein Interactions with Deep Neural Networks.

The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many proteins variants statistically associated with human disease, nearly all such variants have unknown mechanisms, for example, protein-protein interactions (PPIs). In this study, we address this challenge using a recent machine learning advance-deep neural networks (DNNs). We aim at improving the performance of PPIs prediction and propose a method called DeepPPI (Deep neural networks for Protein-Protein Interactions prediction), which employs deep neural networks to learn effectively the representations of proteins from common protein descriptors. The experimental results indicate that DeepPPI achieves superior performance on the test data set with an Accuracy of 92.50%, Precision of 94.38%, Recall of 90.56%, Specificity of 94.49%, Matthews Correlation Coefficient of 85.08% and Area Under the Curve of 97.43%, respectively. Extensive experiments show that DeepPPI can learn useful features of proteins pairs by a layer-wise abstraction, and thus achieves better prediction performance than existing methods. The source code of our approach can be available via http://ailab.ahu.edu.cn:8087/DeepPPI/index.html .