Contact-electrification is a universal effect for all existing materials, but it still lacks a quantitative materials database to systematically understand its scientific mechanisms. Using an ...established measurement method, this study quantifies the triboelectric charge densities of nearly 30 inorganic nonmetallic materials. From the matrix of their triboelectric charge densities and band structures, it is found that the triboelectric output is strongly related to the work functions of the materials. Our study verifies that contact-electrification is an electronic quantum transition effect under ambient conditions. The basic driving force for contact-electrification is that electrons seek to fill the lowest available states once two materials are forced to reach atomically close distance so that electron transitions are possible through strongly overlapping electron wave functions. We hope that the quantified series could serve as a textbook standard and a fundamental database for scientific research, practical manufacturing, and engineering.
Objectives
This study was conducted in order to establish and validate a radiomics model for predicting lymph node (LN) metastasis of intrahepatic cholangiocarcinoma (IHC) and to determine its ...prognostic value.
Methods
For this retrospective study, a radiomics model was developed in a primary cohort of 103 IHC patients who underwent curative-intent resection and lymphadenectomy. Radiomics features were extracted from arterial phase computed tomography (CT) scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method. Multivariate logistic regression analysis was adopted to establish a radiomics model incorporating radiomics signature and other independent predictors. Model performance was determined by its discrimination, calibration, and clinical usefulness. The model was internally validated in 52 consecutive patients.
Results
The radiomics signature comprised eight LN-status–related features and showed significant association with LN metastasis in both cohorts (
p
< 0.001). A radiomics nomogram that incorporates radiomics signature and CA 19-9 level showed good calibration and discrimination in the primary cohort (AUC 0.8462) and validation cohort (AUC 0.8921). Promisingly, the radiomics nomogram yielded an AUC of 0.9224 in the CT-reported LN-negative subgroup. Decision curve analysis confirmed the clinical utility of this nomogram. High risk for metastasis portended significantly lower overall and recurrence-free survival than low risk for metastasis (both
p
< 0.001). The radiomics nomogram was an independent preoperative predictor of overall and recurrence-free survival.
Conclusions
Our radiomics model provided a robust diagnostic tool for prediction of LN metastasis, especially in CT-reported LN-negative IHC patients, that may facilitate clinical decision-making.
Key Points
• The radiomics nomogram showed good performance for prediction of LN metastasis in IHC patients, particularly in the CT-reported LN-negative subgroup.
• Prognosis of high-risk patients remains dismal after curative-intent resection.
• The radiomics model may facilitate clinical decision-making and define patient subsets benefiting most from surgery.
In recent years, deep learning-based person re-identification (Re-ID) methods have made significant progress. However, the performance of these methods substantially decreases when dealing with ...occlusion, which is ubiquitous in realistic scenarios. In this article, we propose a novel semantic-aware occlusion-robust network (SORN) that effectively exploits the intrinsic relationship between the tasks of person Re-ID and semantic segmentation for occluded person Re-ID. Specifically, the SORN is composed of three branches, including a local branch, a global branch, and a semantic branch. In particular, the local branch extracts part-based local features, and the global branch leverages a novel spatial-patch contrastive loss (SPC) to extract occlusion-robust global features. Meanwhile, the semantic branch generates a foreground-background mask for a pedestrian image, which indicates the non-occluded areas of the human body. The three branches are jointly trained in a unified multi-task learning network. Finally, pedestrian matching is performed based on the local features extracted from the non-occluded areas and the global features extracted from the whole pedestrian image. Extensive experimental results on a large-scale occluded person Re-ID dataset (i.e., Occluded-DukeMTMC) and two partial person Re-ID datasets (i.e., Partial-REID and Partial-iLIDS) show the superiority of the proposed method compared with several state-of-the-art methods for occluded and partial person Re-ID. We also demonstrate the effectiveness of the proposed method on two general person Re-ID datasets (i.e., Market-1501 and DukeMTMC-reID).
Exploiting both RGB (2D appearance) and Depth (3D geometry) information can improve the performance of semantic segmentation. However, due to the inherent difference between the RGB and Depth ...information, it remains a challenging problem in how to integrate RGB-D features effectively. In this letter, to address this issue, we propose a Non-local Aggregation Network (NANet), with a well-designed Multi-modality Non-local Aggregation Module (MNAM), to better exploit the non-local context of RGB-D features at multi-stage. Compared with most existing RGB-D semantic segmentation schemes, which only exploit local RGB-D features, the MNAM enables the aggregation of non-local RGB-D information along both spatial and channel dimensions. The proposed NANet achieves comparable performances with state-of-the-art methods on popular RGB-D benchmarks, NYUDv2 and SUN-RGBD.
The introduction of trifluoromethyl groups into organic molecules is of paramount importance in modern synthetic chemistry and medicinal chemistry. While methods for constructing C(sp2)−CF3 bonds ...have been well established, the advancement of practical and comprehensive approaches for forming C(sp3)−CF3 bonds remains considerably restricted. In this work, we describe an efficient and site‐specific deaminative trifluoromethylation reaction of aliphatic primary amines to afford the corresponding alkyl trifluoromethyl compounds. The reaction proceeds at room temperature with readily accessible N‐anomeric amide (Levin's reagent) and bench‐stable bpyCu(CF3)3 (Grushin's reagent, bpy=2,2′‐bipyridine) under blue light. The protocol features mild reaction conditions, good functional group tolerance, and moderate to good yields. Remarkably, the method can be applied to the direct, late‐stage trifluoromethylation of natural products and bioactive molecules. Experimental mechanistic studies were conducted, and a radical mechanism is proposed, wherein the dual roles of Grushin's reagent have been elucidated.
A direct deaminative trifluoromethylation of inactivated aliphatic primary amines with N‐anomeric amide (Levin's reagent) and bench‐stable bpyCu(CF3)3 (Grushin's reagent, bpy=2,2′‐bipyridine) under blue light irradiation is reported. The protocol features mild reaction conditions, good functional group tolerance and can be applied to the direct, late‐stage trifluoromethylation of natural products and bioactive molecules.
Abstract
The resistive switching effect in memristors typically stems from the formation and rupture of localized conductive filament paths, and HfO
2
has been accepted as one of the most promising ...resistive switching materials. However, the dynamic changes in the resistive switching process, including the composition and structure of conductive filaments, and especially the evolution of conductive filament surroundings, remain controversial in HfO
2
-based memristors. Here, the conductive filament system in the amorphous HfO
2
-based memristors with various top electrodes is revealed to be with a quasi-core-shell structure consisting of metallic hexagonal-Hf
6
O and its crystalline surroundings (monoclinic or tetragonal HfO
x
). The phase of the HfO
x
shell varies with the oxygen reservation capability of the top electrode. According to extensive high-resolution transmission electron microscopy observations and ab initio calculations, the phase transition of the conductive filament shell between monoclinic and tetragonal HfO
2
is proposed to depend on the comprehensive effects of Joule heat from the conductive filament current and the concentration of oxygen vacancies. The quasi-core-shell conductive filament system with an intrinsic barrier, which prohibits conductive filament oxidation, ensures the extreme scalability of resistive switching memristors. This study renovates the understanding of the conductive filament evolution in HfO
2
-based memristors and provides potential inspirations to improve oxide memristors for nonvolatile storage-class memory applications.
Over the past few years, deep convolutional neural network-based methods have made great progress in semantic segmentation of street scenes. Some recent methods align feature maps to alleviate the ...semantic gap between them and achieve high segmentation accuracy. However, they usually adopt the feature alignment modules with the same network configuration in the decoder and thus ignore the different roles of stages of the decoder during feature aggregation, leading to a complex decoder structure. Such a manner greatly affects the inference speed. In this paper, we present a novel Stage-aware Feature Alignment Network (SFANet) based on the encoder-decoder structure for real-time semantic segmentation of street scenes. Specifically, a Stage-aware Feature Alignment module (SFA) is proposed to align and aggregate two adjacent levels of feature maps effectively. In the SFA, by taking into account the unique role of each stage in the decoder, a novel stage-aware Feature Enhancement Block (FEB) is designed to enhance spatial details and contextual information of feature maps from the encoder. In this way, we are able to address the misalignment problem with a very simple and efficient multi-branch decoder structure. Moreover, an auxiliary training strategy is developed to explicitly alleviate the multi-scale object problem without bringing additional computational costs during the inference phase. Experimental results show that the proposed SFANet exhibits a good balance between accuracy and speed for real-time semantic segmentation of street scenes. In particular, based on ResNet-18, SFANet respectively obtains 78.1% and 74.7% mean of class-wise Intersection-over-Union (mIoU) at inference speeds of 37 FPS and 96 FPS on the challenging Cityscapes and CamVid test datasets by using only a single GTX 1080Ti GPU.
Therapeutic peptides have been widely concerned, but their efficacy is limited by the inability to penetrate cell membranes, which is a key bottleneck in peptide drugs delivery. Herein, an in vivo ...self‐assembly strategy is developed to induce phase separation of cell membrane that improves the peptide drugs internalization. A phosphopeptide KYp is synthesized, containing an anticancer peptide KLAKLAK2 (K) and a responsive moiety phosphorylated Y (Yp). After interacting with alkaline phosphatase (ALP), KYp can be dephosphorylated and self‐assembles in situ, which induces the aggregation of ALP and the protein‐lipid phase separation on cell membrane. Consequently, KYp internalization is 2‐fold enhanced compared to non‐responsive peptide, and IC50 value of KYp is approximately 5 times lower than that of free peptide. Therefore, the in vivo self‐assembly induced phase separation on cell membrane promises a new strategy to improve the drug delivery efficacy in cancer therapy.
An in vivo self‐assembly strategy is developed to induce phase separation of cell membrane that improves the peptide drugs internalization and anticancer efficacy. KYp self‐assembles in situ, which induces the aggregation of ALP and the protein‐lipid phase separation and leakage on the cell membrane. The peptide drugs internalization is 2‐fold enhanced compared to non‐responsive peptide nanoparticle.
•Two-stage PN-anammox processes were efficient to treat reject water.•53% COD was removed in PN process including oxygen-containing volatile organics, proteins and humic acids.•The effluent COD ...consisted mainly of recalcitrant hydrocarbons.•Organics in reject water stimulated the growth of denitrifying bacteria, Ottowiaand Denitratisoma.
Reject water contains complex components of organic compounds, which have significant influences on the nitrogen removal performance when treated using biological autotrophic nitrogen removal technology. In this study, a two-stage partial nitritation (PN)-anammox (floc-granule) system was established to treat reject water (COD/NH4+-N = 0.97 ± 0.15), and the evolution of organic compounds along PN and annamox bioreactors was investigated using gas chromatography–mass spectrometry and excitation-emission matrix. Also, the variation of PN and anammox sludge properties relating to COD reduction was examined. The PN-anammox system removed approximately 80% of total inorganic nitrogen and COD with hydraulic reaction time of 16 h. The influent organics (330–600 mg COD/L) in reject water were primarily composed of volatile, protein-like and humic acid-like organic compounds. PN process contributed 53 ± 18% of the overall COD removal, primarily including oxygen-containing organics (e.g. phenol), proteins and humic acids. Anammox process contributed 22 ± 15% of the overall COD removal, but large molecule acids (e.g. lactic acid) and small molecule alcohols (e.g. glycerol) were reoccurred, contributing to the effluent COD with recalcitrant hydrocarbons (e.g. n-Octadecane). Reject water increased the extracellular proteins/polysaccharides ratio of PN and anammox sludge, promoting the adsorption and degradation of organic compounds. High-throughput sequencing results showed that denitrifying bacteria of Ottowia increased from 0.03% to 14.4% in PN reactor, and of Denitratisoma increased from 9.6% to 15.4% in anammox reactor. The occurrence of these denitrifiers might mitigate the negative impact of organics to functional organisms. This study highlights the organics fate during PN-anammox treatment system, which is important to maintain the robust nitrogen removal when treating organics-containing and high ammonium concentration wastewater.
Display omitted
PDF
