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Silver nanowires (AgNWs) are a promising substitute for indium tin oxide (ITO) transparent conductive films (TCFs). However, AgNWs suffer the insufficiencies in contact resistance between the nanowires and light loss. Here, graphene oxide and moth-eye-like antireflection nanostructure have been introduced to physically "weld" the AgNWs junctions and modulate the AR properties, respectively. The sandwich structure AgNWs TCFs achieved resistance of 26.26 Ω sq−1 and transmittance of 98.67%, exhibiting promoted photoelectronic performance compared with the pure AgNWs films. The stability of sandwich structure AgNWs TCFs was improved, and their wettability was also discussed with water contact angle changed from 92.55° to 69.18°, switched from hydrophobic to hydrophilic. Though a simple solution process without mechanical or heat post-treatment, we proposed a stable sandwich structure to obviously improve the electrical and optical capabilities of AgNWs. The enhanced AgNWs TCFs with superior photoelectronic performance are believed to boost the practical applications and future development of AgNWs-based TCFs. [ABSTRACT FROM AUTHOR]

Sign language recognition and translation can address the communication problem between hearing-impaired and general population, and can break the sign language boundariesy between different countries and different languages. Traditional sign language recognition and translation algorithms use Convolutional Neural Networks (CNNs) to extract spatial features and Recurrent Neural Networks (RNNs) to extract temporal features. However, these methods cannot model the complex spatiotemporal features of sign language. Moreover, RNN and its variant algorithms find it difficult to learn long-term dependencies. This paper proposes a novel and effective network based on Transformer and Graph Convolutional Network (GCN), which can be divided into three parts: a multi-view spatiotemporal embedding network (MSTEN), a continuous sign language recognition network (CSLRN), and a sign language translation network (SLTN). MSTEN can extract the spatiotemporal features of RGB data and skeleton data. CSLRN can recognize sign language glosses and obtain intermediate features from multi-view input sign data. SLTN can translate intermediate features into spoken sentences. The entire network was designed as end-to-end. Our method was tested on three public sign language datasets (SLR-100, RWTH, and CSL-daily) and the results demonstrated that our method achieved excellent performance on these datasets. [ABSTRACT FROM AUTHOR]

With a quark level interaction, we give a unified description of the loosely bound molecular systems composed of the heavy flavor hadrons (D ¯ , D ¯ ∗) , (Λ c , Σ c , Σ c ∗) , and (Ξ c , Ξ c ′ , Ξ c ∗) . Using the P c states as inputs to fix the interaction strength of light quark-quark pairs, we reproduce the observed P cs and T cc + states and predict another narrow T cc ′ + state with quantum numbers [ D ∗ D ∗ ] J = 1 I = 0 . If we require a satisfactory description of the T cc + and P c states simultaneously, our framework prefers the assignments of the P c (4440) and P c (4457) as the [ Σ c D ¯ ∗ ] J = 1 / 2 I = 1 / 2 and [ Σ c D ¯ ∗ ] J = 3 / 2 I = 1 / 2 states, respectively. We propose the isospin criterion to explain naturally why the experimentally observed T cc , P c , and P cs molecular candidates prefer the lowest isospin numbers. We also predict the loosely bound states for the bottom di-hadrons. [ABSTRACT FROM AUTHOR]

To extract strong correlations between different energy loads and improve the interpretability and accuracy for load forecasting of a regional integrated energy system (RIES), an explainable framework for load forecasting of an RIES is proposed. This includes the load forecasting model of RIES and its interpretation. A coupled feature extracting strategy is adopted to construct coupled features between loads as the input variables of the model. It is designed based on multi-task learning (MTL) with a long short-term memory (LSTM) model as the sharing layer. Based on SHapley Additive exPlanations (SHAP), this explainable framework combines global and local interpretations to improve the interpretability of load forecasting of the RIES. In addition, an input variable selection strategy based on the global SHAP value is proposed to select input feature variables of the model. A case study is given to verify the effectiveness of the proposed model, constructed coupled features, and input variable selection strategy. The results show that the explainable framework intuitively improves the interpretability of the prediction model. [ABSTRACT FROM AUTHOR]

Intrahepatic cholangiocarcinoma (iCCA) is a highly heterogeneous cancer with limited understanding of its classification and tumor microenvironment. Here, by performing single-cell RNA sequencing on 144,878 cells from 14 pairs of iCCA tumors and non-tumor liver tissues, we find that S100P and SPP1 are two markers for iCCA perihilar large duct type (iCCAphl) and peripheral small duct type (iCCApps). S100P + SPP1− iCCAphl has significantly reduced levels of infiltrating CD4+ T cells, CD56+ NK cells, and increased CCL18+ macrophages and PD1+CD8+ T cells compared to S100P-SPP1 + iCCApps. The transcription factor CREB3L1 is identified to regulate the S100P expression and promote tumor cell invasion. S100P-SPP1 + iCCApps has significantly more SPP1+ macrophage infiltration, less aggressiveness and better survival than S100P + SPP1− iCCAphl. Moreover, S100P-SPP1 + iCCApps harbors tumor cells at different status of differentiation, such as ALB + hepatocyte differentiation and ID3+ stemness. Our study extends the understanding of the diversity of tumor cells in iCCA. The molecular classification and tumour microenvironment in intrahepatic cholangiocarcinoma (iCCA) need further characterisation. Here, the authors perform single cell RNA-sequencing from 14 pairs of iCCA tumours and non-tumour liver tissues and propose S100P and SPP1 as markers for patient classification. [ABSTRACT FROM AUTHOR]

We propose an alternative approach to Lüscher's formula for extracting two-body scattering phase shifts from finite volume spectra with no reliance on the partial wave expansion. We use an effective-field-theory-based Hamiltonian method in the plane wave basis and decompose the corresponding matrix elements of operators into irreducible representations of the relevant point groups. The proposed approach allows one to benefit from the knowledge of the long-range interaction and avoids complications from partial wave mixing in a finite volume. We consider spin-singlet channels in the two-nucleon system and pion-pion scattering in the ρ-meson channel in the rest and moving frames to illustrate the method for non-relativistic and relativistic systems, respectively. For the two-nucleon system, the long-range interaction due to the one-pion exchange is found to make the single-channel Lüscher formula unreliable at the physical pion mass. For S-wave dominated states, the single-channel Lüscher method suffers from significant finite-volume artifacts for a L = 3 fm box, but it works well for boxes with L > 5 fm. However, for P-wave dominated states, significant partial wave mixing effects prevent the application of the single-channel Lüscher formula regardless of the box size (except for the near-threshold region). Using a toy model to generate synthetic data for finite-volume energies, we show that our effective-field-theory-based approach in the plane wave basis is capable of a reliable extraction of the phase shifts. For pion-pion scattering, we employ a phenomenological model to fit lattice QCD results at the physical pion mass. The extracted P-wave phase shifts are found to be in a good agreement with the experimental results. [ABSTRACT FROM AUTHOR]

Kidney cancer is the third most common malignancy of the urinary system, of which, kidney renal clear cell carcinoma (KIRC) accounts for the vast majority. Runt-related transcription factors (RUNX) are involved in multiple cellular functions. However, the diverse expression patterns and prognostic values of RUNX genes in kidney cancer remained to be elucidated. In our study, we mined the DNA methylation, transcriptional and survival data of RUNX genes in patients with different kinds of kidney cancer through Oncomine, Gene Expression Profiling Interactive Analysis, UALCAN, Kaplan–Meier Plotter, cBioPortal and LinkedOmics. We found that RUNX1 and RUNX3 were upregulated in KIRC tissues compared with those in normal tissues. The survival analysis results indicated a high transcription level of RUNX1 was associated with poor overall survival (OS) in KIRC patients. Furthermore, KIRC tumor tissues had significantly lower levels of RUNX1 promoter methylation than that in paracancerous tissues, with decreased DNA methylation of RUNX1 notably associated with poor OS in KIRC. In conclusion, our results revealed that RUNX1 may be a potential therapeutic target for treating KIRC, and RUNX1 promoter methylation level shows promise as a novel diagnostic and prognostic biomarker, which laid a foundation for further study. [ABSTRACT FROM AUTHOR]

These authors contributed equally: Guohe Song, Yang Shi, Lu Meng, Jiaqiang Ma. The original version of this Article contained an error in the title, which was previously incorrectly given as 'Single-cell transcriptomic analysis suggests two molecularly subtypes of intrahepatic cholangiocarcinoma'. [Extracted from the article]