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Triplet Exciton Management via Solid-State Interactions in Organic Semiconductors
Tightly-bound excitons play an important role in the function of molecular materials for light emission and light harvesting. This thesis investigates the effects of solid-state interactions on triplet excitons in a new family of organometallic light emitters, carbene-metal-amides (CMAs). Triplet excitons are normally silent in luminescence due to the spin-forbidden decay process, whereas effectively harvesting triplet excitons helps to boost the performance of light-emitting devices. As the triplet excitons are sensitive to both molecular properties and external environment, in this thesis we deploy optical spectroscopy techniques to understand the effect of solid-state interactions on triplet excitons.
After introducing the relevant theoretical and experimental background of triplet formation in a single molecule and interactions between molecules, we firstly describe the intermolecular electrostatic interactions and the role of triplet diffusion and find that the combined effects of both blueshift the charge-transfer energy while other photophysical properties remain relatively constant in gold-bridged CMA1.
We then describe the crystallisation of CMA1 thin films, which allows us to experimentally investigate the link between molecular conformations and photophysical properties. A combination of restricted torsional distortion and molecular electronic polarisation greatly blueshifts the charge-transfer emission by around 400 meV in the crystalline versus the amorphous phase. We also discover that the intersystem crossing rate and emission kinetics are unaffected by the extent of torsional distortion.
Finally, we apply electrostatic interactions to the other two coinage metal-bridged CMAs to explore the effect of heavy metal atoms on the intersystem crossing and luminescence mechanism. We show that the photophysical properties do not reflect expected trends based upon the heavy atom effect as both direct coupling between charge-transfers states and spin-vibronic coupling via an intermediate state are present
The effectiveness of ultrasound-guided core needle biopsy in detecting lymph node metastases in the axilla in patients with breast cancer: systematic review and meta-analysis
Objective: This study aimed to perform a meta-analysis to investigate the diagnostic safety and accuracy of Ultrasound-Guided Core Needle Biopsy (US-CNB) Axillary Lymph Nodes (ALNs) region in patients with Breast Cancer (BC).
Methods: The authors searched the electronic databases PubMed, Scopus, Embase, and Web of Science for clinical trials about US-CNB for the detection of ALNs in breast cancer patients. The authors extracted and pooled raw data from the included studies and performed statistical analyses using Meta-DiSc 1.4 and Review Manager 5.3 software. A random effects model was used to calculate the data. At the same time, data from the Ultrasound-guided Fine-Needle Aspiration (US-FNA) were introduced for comparison with the US-CNB. In addition, the subgroup was performed to explore the causes of heterogeneity. (PROSPERO ID: CRD42022369491).
Results: In total, 18 articles with 2521 patients were assessed as meeting the study criteria. The overall sensitivity was 0.90 (95% CI [Confidence Interval], 0.87‒0.91; p = 0.00), the overall specificity was 0.99 (95% CI 0.98‒1.00; p = 0.62), the overall area under the curve (AUC) was 0.98. Next, in the comparison of US-CNB and US-FNA, US-CNB is better than US-FNA in the diagnosis of ALNs metastases. The sensitivity was 0.88 (95% CI 0.84‒0.91; p = 0.12) vs. 0.73 (95% CI 0.69‒0.76; p = 0.91), the specificity was 1.00 (95% CI 0.99‒1.00; p = 1.00) vs. 0.99 (95% CI 0.67‒0.74; p = 0.92), and the AUC was 0.99 vs. 0.98. Subgroup analysis showed that heterogeneity may be related to preoperative Neoadjuvant Chemotherapy (NAC) treatment, region, size of tumor diameter, and the number of punctures.
Conclusion: US-CNB has a satisfactory diagnostic performance with good specificity and sensitivity in the preoperative diagnosis of ALNs in BC patients
A consensus linkage map of the grass carp (Ctenopharyngodon idella) based on microsatellites and SNPs
<p>Abstract</p> <p>Background</p> <p>Grass carp (<it>Ctenopharyngodon idella</it>) belongs to the family Cyprinidae which includes more than 2000 fish species. It is one of the most important freshwater food fish species in world aquaculture. A linkage map is an essential framework for mapping traits of interest and is often the first step towards understanding genome evolution. The aim of this study is to construct a first generation genetic map of grass carp using microsatellites and SNPs to generate a new resource for mapping QTL for economically important traits and to conduct a comparative mapping analysis to shed new insights into the evolution of fish genomes.</p> <p>Results</p> <p>We constructed a first generation linkage map of grass carp with a mapping panel containing two F1 families including 192 progenies. Sixteen SNPs in genes and 263 microsatellite markers were mapped to twenty-four linkage groups (LGs). The number of LGs was corresponding to the haploid chromosome number of grass carp. The sex-specific map was 1149.4 and 888.8 cM long in females and males respectively whereas the sex-averaged map spanned 1176.1 cM. The average resolution of the map was 4.2 cM/locus. BLAST searches of sequences of mapped markers of grass carp against the whole genome sequence of zebrafish revealed substantial macrosynteny relationship and extensive colinearity of markers between grass carp and zebrafish.</p> <p>Conclusions</p> <p>The linkage map of grass carp presented here is the first linkage map of a food fish species based on co-dominant markers in the family Cyprinidae. This map provides a valuable resource for mapping phenotypic variations and serves as a reference to approach comparative genomics and understand the evolution of fish genomes and could be complementary to grass carp genome sequencing project.</p
CINFormer: Transformer network with multi-stage CNN feature injection for surface defect segmentation
Surface defect inspection is of great importance for industrial manufacture
and production. Though defect inspection methods based on deep learning have
made significant progress, there are still some challenges for these methods,
such as indistinguishable weak defects and defect-like interference in the
background. To address these issues, we propose a transformer network with
multi-stage CNN (Convolutional Neural Network) feature injection for surface
defect segmentation, which is a UNet-like structure named CINFormer. CINFormer
presents a simple yet effective feature integration mechanism that injects the
multi-level CNN features of the input image into different stages of the
transformer network in the encoder. This can maintain the merit of CNN
capturing detailed features and that of transformer depressing noises in the
background, which facilitates accurate defect detection. In addition, CINFormer
presents a Top-K self-attention module to focus on tokens with more important
information about the defects, so as to further reduce the impact of the
redundant background. Extensive experiments conducted on the surface defect
datasets DAGM 2007, Magnetic tile, and NEU show that the proposed CINFormer
achieves state-of-the-art performance in defect detection
Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects
Surface defect inspection is a very challenging task in which surface defects
usually show weak appearances or exist under complex backgrounds. Most
high-accuracy defect detection methods require expensive computation and
storage overhead, making them less practical in some resource-constrained
defect detection applications. Although some lightweight methods have achieved
real-time inference speed with fewer parameters, they show poor detection
accuracy in complex defect scenarios. To this end, we develop a Global Context
Aggregation Network (GCANet) for lightweight saliency detection of surface
defects on the encoder-decoder structure. First, we introduce a novel
transformer encoder on the top layer of the lightweight backbone, which
captures global context information through a novel Depth-wise Self-Attention
(DSA) module. The proposed DSA performs element-wise similarity in channel
dimension while maintaining linear complexity. In addition, we introduce a
novel Channel Reference Attention (CRA) module before each decoder block to
strengthen the representation of multi-level features in the bottom-up path.
The proposed CRA exploits the channel correlation between features at different
layers to adaptively enhance feature representation. The experimental results
on three public defect datasets demonstrate that the proposed network achieves
a better trade-off between accuracy and running efficiency compared with other
17 state-of-the-art methods. Specifically, GCANet achieves competitive accuracy
(91.79% , 93.55% , and 97.35% ) on
SD-saliency-900 while running 272fps on a single gpu
CritiqueLLM: Scaling LLM-as-Critic for Effective and Explainable Evaluation of Large Language Model Generation
Since the natural language processing (NLP) community started to make large
language models (LLMs), such as GPT-4, act as a critic to evaluate the quality
of generated texts, most of them only train a critique generation model of a
specific scale on specific datasets. We argue that a comprehensive
investigation on the key factor of LLM-based evaluation models, such as scaling
properties, is lacking, so that it is still inconclusive whether these models
have potential to replace GPT-4's evaluation in practical scenarios. In this
paper, we propose a new critique generation model called CritiqueLLM, which
includes a dialogue-based prompting method for high-quality referenced /
reference-free evaluation data. Experimental results show that our model can
achieve comparable evaluation performance to GPT-4 especially in system-level
correlations, and even outperform GPT-4 in 3 out of 8 tasks in a challenging
reference-free setting. We conduct detailed analysis to show promising scaling
properties of our model in the quality of generated critiques. We also
demonstrate that our generated critiques can act as scalable feedback to
directly improve the generation quality of LLMs.Comment: 18 pages, 5 figure
An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System
Background. Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method. To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results. The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method
Microwave-Assisted Biosynthesis of Ag/ZrO2 Catalyst with Excellent Activity toward Selective Oxidation of 1,2-Propanediol
通讯作者地址: Huang, JLIn the biorefining process, polyols are important intermediates, and the oxidation of polyols toward other value added products is of great significance. This work describes a green and facile biosynthesis method for the preparation of Ag/ZrO2 catalyst for selective oxidation of 1,2-propanediol (a typical polyol). Cinnamomum comphora (CC) leaf extract was employed as the reducing and capping agent for the preparation of Ag nanoparticles (NPs) with the assistance of microwave irradiation. The main reducing agents were identified as polyphenols by Fourier transform infrared spectroscopic analysis of CC extracts before and after reaction. After electrostatic adsorption, the NPs were anchored onto the support ZrO2. The Ag/ZrO2 catalysts were found with good dispersity and showed excellent activity toward selective oxidation of 1,2-propanediol. The effects of the preparation conditions on catalyst activity were studied; the optimal condition was obtained (microwave time of 4 min, CC concentration of 12 g/L and Ag loading of 5%). Since the natural capping agents are easy to remove, the catalysts need no calcination treatment before catalytic reaction. Thus, the microwave-assisted biosynthesis appears to be environmentally benign as neither expensive chemicals nor intensive energy consumption is engaged.National Nature Science Foundation
21036004
2120614
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