Three-dimensional periodic structures for enhancing light-matter interaction and energy storage

Three-dimensional (3D) periodic architectures hold great promise for applications ranging from manipulating the flow of light for integrated photonics to high power and high energy batteries. Among the approaches to fabricate 3D meso-structured materials, colloidal self-assembly and holographic lithography are particularly attractive owing to their ability to create large, uniform templates. However, these 3D structures require extrinsic functionalities (e.g. emitters, microcavities or energy materials) to fully utilize their potentials. This thesis focused on additions of functional defects to the 3D networks and studied the enhanced interactions between the embedded defects and the 3D host materials. A method based on epitaxial colloidal opal growth was developed to place fluorescent nanoparticles at specific locations inside 3D silicon inverse opal photonic crystals (PhCs), allowing the coupling between high dielectric contrast PhCs and localized emitters to be investigated. Transfer-printing was next used to assemble a new type of 3D PhC vertical microcavity consisting of a planar defect sandwiched between two silicon inverse opals. This technique was similarly applied to embed pre-defined high-quality defects into 3D holographic PhCs. Objects such as nanoparticle films, spheres, and emitters served as defects and were introduced to well-defined positions. Finally, interdigitated microbatteries were created from templates defined by both 3D holographic lithography and conventional UV lithography. The influence of electrode width on liquid-phase ion diffusion was studied, which provided design parameters of microbatteries for practical applications

Lightweight Structure-aware Transformer Network for VHR Remote Sensing Image Change Detection

Popular Transformer networks have been successfully applied to remote sensing (RS) image change detection (CD) identifications and achieve better results than most convolutional neural networks (CNNs), but they still suffer from two main problems. First, the computational complexity of the Transformer grows quadratically with the increase of image spatial resolution, which is unfavorable to very high-resolution (VHR) RS images. Second, these popular Transformer networks tend to ignore the importance of fine-grained features, which results in poor edge integrity and internal tightness for largely changed objects and leads to the loss of small changed objects. To address the above issues, this Letter proposes a Lightweight Structure-aware Transformer (LSAT) network for RS image CD. The proposed LSAT has two advantages. First, a Cross-dimension Interactive Self-attention (CISA) module with linear complexity is designed to replace the vanilla self-attention in visual Transformer, which effectively reduces the computational complexity while improving the feature representation ability of the proposed LSAT. Second, a Structure-aware Enhancement Module (SAEM) is designed to enhance difference features and edge detail information, which can achieve double enhancement by difference refinement and detail aggregation so as to obtain fine-grained features of bi-temporal RS images. Experimental results show that the proposed LSAT achieves significant improvement in detection accuracy and offers a better tradeoff between accuracy and computational costs than most state-of-the-art CD methods for VHR RS images

Electroplating lithium transition metal oxides.

Materials synthesis often provides opportunities for innovation. We demonstrate a general low-temperature (260°C) molten salt electrodeposition approach to directly electroplate the important lithium-ion (Li-ion) battery cathode materials LiCoO2, LiMn2O4, and Al-doped LiCoO2. The crystallinities and electrochemical capacities of the electroplated oxides are comparable to those of the powders synthesized at much higher temperatures (700° to 1000°C). This new growth method significantly broadens the scope of battery form factors and functionalities, enabling a variety of highly desirable battery properties, including high energy, high power, and unprecedented electrode flexibility

The first rare case of Candida palmioleophila infection reported in China and its genomic evolution in a human host environment

IntroductionCandida palmioleophila is a rare human pathogenic fungus, which has been poorly characterized at the genome level. In this study, we reported the first fatal case of C. palmioleophila infection in China and investigate the microevolution of C. palmioleophila in the human host environment.MethodsA series of C. palmioleophila stains were collected from the patient at different time points for routine microbial and drug sensitivity testing. The first C. palmioleophila isolate 07202534 was identified by de novo whole genome sequencing. The in vitro and in vivo genetic evolutionary characteristics of C. palmioleophila were discussed based on the analysis of bioinformatics data.ResultsThe six C. palmioleophila isolates displayed dose-dependent sensitivity to fluconazole. The C. palmioleophila genome contained homologous genes such as CDR1 and MDR1, which were recognized to be related to azole resistance. In addition, amino acid variation was detected at F105L and other important sites of ERG11. In addition, the mean divergence time between C. palmioleophila and Scheffersomyces stipites CBS 6054 was 406.04 million years, indicating that C. palmioleophila originated earlier than its closest relative. In addition, the six strains of C. palmioleophila isolated form the patient had higher homology and fewer mutation sites, which indicated the stability in C. palmioleophila genome. We also found that C. palmioleophila had a wide natural niche and may evolve slowly.DiscussionWe believe that this study will contribute to improve our understanding of the genetic evolution, pathogenicity, and drug resistance of C. palmioleophila and will aid in the prevention and control of its spread

Identification and characterization of a 25-lncRNA prognostic signature for early recurrence in hepatocellular carcinoma

Background Early recurrence is the major cause of poor prognosis in hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) are deeply involved in HCC prognosis. In this study, we aimed to establish a prognostic lncRNA signature for HCC early recurrence. Methods The lncRNA expression profile and corresponding clinical data were retrieved from total 299 HCC patients in TCGA database. LncRNA candidates correlated to early recurrence were selected by differentially expressed gene (DEG), univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. A 25-lncRNA prognostic signature was constructed according to receiver operating characteristic curve (ROC). Kaplan-Meier and multivariate Cox regression analyses were used to evaluate the performance of this signature. ROC and nomogram were used to evaluate the integrated models based on this signature with other independent clinical risk factors. Gene set enrichment analysis (GSEA) was used to reveal enriched gene sets in the high-risk group. Tumor infiltrating lymphocytes (TILs) levels were analyzed with single sample Gene Set Enrichment Analysis (ssGSEA). Immune therapy response prediction was performed with TIDE and SubMap. Chemotherapeutic response prediction was conducted by using Genomics of Drug Sensitivity in Cancer (GDSC) pharmacogenomics database. Results Compared to low-risk group, patients in high-risk group showed reduced disease-free survival (DFS) in the training (p < 0.0001) and validation cohort (p = 0.0132). The 25-lncRNA signature, AFP, TNM and vascular invasion could serve as independent risk factors for HCC early recurrence. Among them, the 25-lncRNA signature had the best predictive performance, and combination of those four risk factors further improves the prognostic potential. Moreover, GSEA showed significant enrichment of “E2F TARGETS”, “G2M CHECKPOINT”, “MYC TARGETS V1” and “DNA REPAIR” pathways in the high-risk group. In addition, increased TILs were observed in the low-risk group compared to the high-risk group. The 25-lncRNA signature negatively associates with the levels of some types of antitumor immune cells. Immunotherapies and chemotherapies prediction revealed differential responses to PD-1 inhibitor and several chemotherapeutic drugs in the low- and high-risk group. Conclusions Our study proposed a 25-lncRNA prognostic signature for predicting HCC early recurrence, which may guide postoperative treatment and recurrence surveillance in HCC patients

Identification of QTNs Controlling Seed Protein Content in Soybean Using Multi-Locus Genome-Wide Association Studies

Protein content (PC), an important trait in soybean (Glycine max) breeding, is controlled by multiple genes with relatively small effects. To identify the quantitative trait nucleotides (QTNs) controlling PC, we conducted a multi-locus genome-wide association study (GWAS) for PC in 144 four-way recombinant inbred lines (FW-RILs). All the FW-RILs were phenotyped for PC in 20 environments, including four locations over 4 years with different experimental treatments. Meanwhile, all the FW-RILs were genotyped using SoySNP660k BeadChip, producing genotype data for 109,676 non-redundant single-nucleotide polymorphisms. A total of 129 significant QTNs were identified by five multi-locus GWAS methods. Based on the 22 common QTNs detected by multiple GWAS methods or in multiple environments, pathway analysis identified 8 potential candidate genes that are likely to be involved in protein synthesis and metabolism in soybean seeds. Using superior allele information for 22 common QTNs in 22 elite and 7 inferior lines, we found higher superior allele percentages in the elite lines and lower percentages in the inferior lines. These findings will contribute to the discovery of the polygenic networks controlling PC in soybean, increase our understanding of the genetic foundation and regulation of PC, and be useful for molecular breeding of high-protein soybean varieties

Reflective plasmonic color filters based on lithographically patterned silver nanorod arrays

10.1039/c3nr01419cNanoscale5146243-624