Search

Photonics inverse design relies on human experts to search for a design topology that satisfies certain optical specifications with their experience and intuitions, which is relatively labor-intensive, slow, and sub-optimal. Machine learning has emerged as a powerful tool to automate this inverse design process. However, supervised or semi-supervised deep learning is unsuitable for this task due to: (1) a severe shortage of available training data due to the high computational complexity of physics-based simulations along with a lack of open-source datasets and/or the need for a pre-trained neural network model; (2) the issue of one-to-many mapping or non-unique solutions; and (3) the inability to perform optimization of the photonic structure beyond inverse designing. Reinforcement Learning (RL) has the potential to overcome the above three challenges. Here, we propose Learning to Design Optical-Resonators (L2DO) to leverage RL that learns to autonomously inverse design nanophotonic laser cavities without any prior knowledge while retrieving unique design solutions. L2DO incorporates two different algorithms – Deep Q-learning and Proximal Policy Optimization. We evaluate L2DO on two laser cavities: a long photonic crystal (PC) nanobeam and a PC nanobeam with an L3 cavity, both popular structures for semiconductor lasers. Trained for less than 152 hours on limited hardware resources, L2DO has improved state-of-the-art results in the literature by over 2 orders of magnitude and obtained 10 times better performance than a human expert working the same task for over a month. L2DO first learned to meet the required maxima of Q-factors (>50 million) and then proceeded to optimize some additional good-to-have features (e.g., resonance frequency, modal volume). Compared with iterative human designs and inverse design via supervised learning, L2DO can achieve over two orders of magnitude higher sample-efficiency without suffering from the three issues above. This work confirms the potential of deep RL algorithms to surpass human designs and marks a solid step towards a fully automated AI framework for photonics inverse design.

Sodium taurocholate co-transporting polypeptide (NTCP) has been identified as an entry receptor for hepatitis B virus (HBV), but the molecular events of the viral post-endocytosis steps remain obscure. In this study, we discovered that manganese (Mn) could strongly inhibit HBV infection in NTCP-reconstituted HepG2 cells without affecting viral replication. We therefore profiled the antiviral effects of Mn2+ in an attempt to elucidate the regulatory mechanisms involved in early HBV infection. Intriguingly, Mn2+ conspicuously stimulated lysosomal activity, as evidenced by hyperactivation of mTORC1 and increased endo/lysosomal acidity. After HBV-triggered internalization, the NTCP receptor was sorted to late endosomal compartments by the ESCRT machinery in concert with the invading virion. The establishment of HBV infection was found to be independent of lysosomal fusion-driven late endosome maturation; Mn2+-induced lysosomal hyperfunction virtually impaired infection, suggesting that virions may gain cytosolic access directly from late endosomes. In contrast, suppression of lysosomal activity substantially enhanced HBV infection. Prolonged mTORC1 inactivation facilitated viral infection by depleting lysosomes and accelerating endocytic transport of virions. Notably, treatment with the natural steroidal alkaloid tomatidine recapitulated the effects of Mn2+ in stimulating lysosomal activity and exhibited potent anti-HBV activity in HepG2-NTCP cells and in proliferating human hepatocyte organoids. These findings provide new insights into the post-endocytosis events of HBV infection. The negative regulation of early HBV infection by endo/lysosomal activity makes it a promising target for antiviral therapies. Author summary: Despite the identification of NTCP as a functional HBV receptor over a decade ago, the later stages of the HBV entry process remain poorly understood. Inspired by a previous publication, we tested the possible effect of manganese (Mn) on HBV infectivity. To our surprise, although Mn2+ did not induce a type I interferon response, it was still able to inhibit HBV infectivity in HepG2-NTCP cells. Using loss-of-function assays with pharmacological inhibitors and RNA interference, we found that after internalization, HBV and the NTCP receptor are co-sorted via the ESCRT machinery into the late endosome, where the virions achieve endosomal escape. Notably, endosome-lysosome fusion is not required for HBV infection. In contrast, lysosomal hyperfunction, induced by Mn2+ or the natural steroidal alkaloid tomatidine, impairs de novo HBV infection, while the use of lysosomal inhibitors facilitates this process. The suppressive impact of lysosomal activity on the early HBV infection renders it a promising target for antiviral therapies. [ABSTRACT FROM AUTHOR]

Background: CD8+ T lymphocyte infiltration is closely associated with the prognosis and immunotherapy response of gastric cancer (GC). For now, the examination of CD8 infiltration levels relies on endoscopic biopsy, which is invasive and unsuitable for longitude assessment during anti‐tumor therapy. Purpose: This work aims to develop and validate a noninvasive workflow based on contrast‐enhanced CT (CECT) images to evaluate the CD8+ T‐cell infiltration profiles of GC. Methods: GC patients were retrospectively and consecutively enrolled and randomly assigned to the training (validation) or test cohort at a 7:3 ratio. All patients were binary classified into the CD8‐high (infiltrated proportion ≥ 20%) or CD8‐low group (infiltrated proportion < 20%) group. A total of 1170 radiomics features were extracted from each presurgical CECT series. After feature selection, fifteen radiomics features were transmitted to three independent machine‐learning models for the computation of predictive radiological scores. Multilayer perceptron (MLP) was applied to merge the radiological scores with clinical factors. The predictive efficacy of the radiological scores and of the combined model was evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis in both the training and test cohorts. Results: A total of 210 patients were enrolled in this study (mean age: 63.22 ± 8.74 years, 151 men), and were randomly assigned to the training set (n = 147) or the test set (n = 63). The merged radiological score was correlated with CD8 infiltration in both the training (p = 1.8e−10) and test cohorts (p = 0.00026). The combined model integrating the radiological scores and clinical features achieved an area under the curve (AUC) value of 0.916 (95% CI: 0.872–0.960) in the training set and 0.844 (95% CI: 0.742–0.946) in the test set for classifying CD8‐high GCs. The model was well‐calibrated and exhibited net benefit over "treat‐all" and"treat‐none" strategies in decision curve analysis. Conclusions: Artificial intelligent systems combining radiological features and clinical factors could accurately predict CD8 infiltration levels of GC, which may benefit personalized treatment of GC in the context of immunotherapy. [ABSTRACT FROM AUTHOR]

A prevailing animal model currently used to study severe human diseases like obstructive cholestasis, primary biliary or sclerosing cholangitis, biliary atresia, and acute liver injury is the common bile duct ligation (cBDL). Modifications of this model include ligation of the left hepatic bile duct (pBDL) or ligation of the left bile duct with the corresponding left hepatic artery (pBDL+pAL). Both modifications induce cholestasis only in the left liver lobe. After induction of total or partial cholestasis in mice, the well-being of these animals was evaluated by assessing burrowing behavior, body weight, and a distress score. To compare the pathological features of these animal models, plasma levels of liver enzymes, bile acids, bilirubin, and within the liver tissue, necrosis, fibrosis, inflammation, as well as expression of genes involved in the synthesis or transport of bile acids were assessed. The survival rate of the animals and their well-being was comparable between pBDL+pAL and pBDL. However, surgical intervention by pBDL+pAL caused confluent necrosis and collagen depositions at the edge of necrotic tissue, whereas pBDL caused focal necrosis and fibrosis in between portal areas. Interestingly, pBDL animals had a higher survival rate and their well-being was significantly improved compared to cBDL animals. On day 14 after cBDL liver aspartate, as well as alanine aminotransferase, alkaline phosphatase, glutamate dehydrogenase, bile acids, and bilirubin were significantly elevated, but only glutamate dehydrogenase activity was increased after pBDL. Thus, pBDL may be primarily used to evaluate local features such as inflammation and fibrosis or regulation of genes involved in bile acid synthesis or transport but does not allow to study all systemic features of cholestasis. The pBDL model also has the advantage that fewer mice are needed, because of its high survival rate, and that the well-being of the animals is improved compared to the cBDL animal model. [ABSTRACT FROM AUTHOR]

This paper attempts to investigate the effect of various parameters on the axial compressive behavior of nano-silica concrete-filled angle steel reinforced GFRP tubular columns. The proposed new composite column consists of three parts: the outer GFRP tube, the inner angle section steel and the nano-silica concrete filled between GFRP tube and angle section steel. Twenty-seven specimens with different nano-silica concrete compressive strength (20MPa, 30MPa and 40MPa), diameter-to-thickness ratio of GFRP tube (20, 25 and 40) and steel ratio (0.008, 0.022 and 0.034) were tested under axial load. The main purpose of this study is to examine the effect of the three parameters on the following: failure modes, deformation capacity, load bearing capacity, ductility and initial stiffness of the new composite column under axial load. It was found that the load bearing capacity and initial stiffness increased as the nano-silica concrete compressive strength of the specimens increased. But the specimens with higher nano-silica concrete compressive strength showed lower deformation capacity than that of the specimens with lower nano-silica concrete compressive strength. The varieties of the steel ratio have no significant effect on the specimens’ axial deformation behavior. Experimental results also showed that both load bearing capacity and deformation capacity increased with the decrease of diameter-to-thickness ratio of GFRP tube. However, diameter-to-thickness ratio of GFRP tube has no significant effect on the initial stiffness of specimens. The confinement coefficient was proposed to better evaluate the confinement effect of GFRP tube on the inner angle section steel reinforced core nano-silica concrete. The confinement effect of GFRP tube on lower strength concrete was better, and the confinement effect reduced as the diameter-to-thickness ratio of GFRP tube increased. The design formulas for the load bearing capacity of the nano-silica concrete-filled angle steel reinforced GFRP tubular columns under axial load were proposed.

In purely organic room‐temperature phosphorescence (RTP) materials, fluorescence radiative transitions generally compete against inter‐system crossing (ISC) transitions to give fluorescence/phosphorescence dual emission. In order to achieve pure phosphorescence in RTP organic light‐emitting diodes (OLEDs), extended π‐conjugation is introduced into Se‐containing donors to suppress the fluorescence radiative transitions by the symmetry excited state orbitals in the introduced π–π* transitions that cancel the overlap of the orbitals, and two novel nonaromatic amine donor containing orange RTP emitters namely 2‐(benzo[c]phenoxaselenin‐10‐yl)‐4,6‐diphenyl‐1,3,5‐triazine (BPXSeDRZ) and 2‐(dibenzo[a,c]phenoxaselenin‐11‐yl)‐4,6‐diphenyl‐1,3,5‐triazine (DBPXSeDRZ) are developed, both of which exhibit efficient orange pure phosphorescence in doped films thanks to the sufficient ISC transitions. As a result, high external quantum efficiencies of 17.2% and 17.9% are respectively achieved for the orange RTP‐OLEDs based on BPXSeDRZ and DBPXSeDRZ for the first time, and they also exhibit stable pure electro‐phosphorescence spectra in a wide driving voltage. Moreover, by adopting DBPXSeDRZ as a sensitizer to harvest the triplet excitons and tetraphenyldibenzoperiflanthene as a conventional fluorescent emitter, the external quantum efficiency of the sensitized red fluorescent OLED can be elevated to 10.1%, which greatly expands the potential applications of the metal‐free phosphors in OLEDs. [ABSTRACT FROM AUTHOR]

Multi‐resonance (MR) based organoboron emitters exhibiting high‐efficiency narrowband thermally activated delayed fluorescence (TADF) have become a critical material component for constructing high‐performance organic light‐emitting diodes (OLEDs) with high color purity and high color gamut. However, most of the MR‐TADF devices suffer from severe efficiency roll‐off at high current density due to the relatively large singlet–triplet splitting energy, long excited state lifetime, and slow reverse intersystem crossing rate (kRISC). Herein, by utilizing a sulfur atom‐fused donor unit, 7H‐benzo[4,5]thieno[2,3‐b]carbazole, an organic boron emitter (BTC‐BNCz) capable of high efficiency and low efficiency roll‐off narrowband TADF emission is developed. Benefitting from the heavy atom effect of the sulfur atom, kRISC up to 1.6 × 105 s−1 is achieved, which could greatly reduce the undesired quenching effect. Consequently, the OLED using BTC‐BNCz as dopant exhibits high external quantum efficiency (EQE) of 27.0% with full width at half maximum (FWHM) of 35 nm. Moreover, by utilizing a TADF material with a high horizontal dipole ratio as assistant host, outstanding device performance is achieved with a maximum EQE of 35.2% and FWHM of 37 nm, featuring slow efficiency roll‐off. [ABSTRACT FROM AUTHOR]

Doping‐free organic light‐emitting diodes (OLEDs) are beneficial to reduce manufacturing costs for mass production, which, however, are limited by the ubiquitous aggregation‐caused quenching (ACQ) effect, especially for thermally activated delayed fluorescence (TADF) emitters with relatively long exciton lifetimes. Herein, two typical TADF emitters pACRS and pACRSO with different sulfur atom valence states are developed, where pACRS with a thioether unit exhibits suppressed intermolecular interaction, alleviated ACQ effect, and higher efficiency in a conventional doping‐free device, in contract to pACRSO with a sulfone unit. It is of interest that comparable efficiencies are achieved for both emitters in doping‐free OLEDs with an ultrathin emission layer (UEML), in which the emitter molecules are deposited onto the surface of the underlying organic layer of host material to form an ultrathin quasi‐host‐guest system with alleviated ACQ effect. Besides, the suppressed trap effect is the key to guarantee the efficiency of UEML‐OLEDs, while different intermolecular interactions of emitters that determine the optimized UEML thickness will affect the efficiency roll‐off. Furthermore, benefiting from the UEML structure, highly efficient doping‐free OLEDs are also successfully achieved for red TADF emitters with stronger intermolecular interactions. Such an UEML strategy meeting efficiency and cost demands holds great promise for doping‐free OLEDs. [ABSTRACT FROM AUTHOR]

Polychlorinated (hetero)arenes have shown great promise for organic optoelectronics applications. However, the harsh synthetic routes for polychlorinated compounds and the possible luminescence quenching from the compact intermolecular π–π stacking induced by chlorine atoms limit their investigations and applications in luminescent materials. Herein, two isomeric polychlorinated polycyclic aromatic hydrocarbon (PAH) compounds JY‐1‐Cl and JY‐2‐Cl consisting of rigidified aryl ketones and amine are designed and synthesized under mild conditions through nucleophilic chlorination intermediated by an electron donor‐acceptor complex. Among them, as a result of the strong π–π interactions induced by chlorine atoms, JY‐2‐Cl exhibits bright monomer and dimer emissions with dual thermally activated delayed fluorescence (TADF) characters. Notably, compared with the non‐chlorinated compounds, a high photoluminescence quantum yield is maintained after introducing multiple chlorine atoms into JY‐2‐Cl. The first dual‐TADF organic light‐emitting diodes are also successfully fabricated with maximum external quantum efficiency as high as 29.1 % by employing JY‐2‐Cl as emitter. This work presents a new paradigm and synthesis of polychlorinated amine‐carbonyl PAHs and demonstrates the great potential of the chlorinated materials for luminescent applications. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: Programmed Death-Ligand 1 (PD-L1) is an important biomarker for patient selection of immunotherapy in gastric cancer (GC). This study aimed to construct and validate a non-invasive virtual biopsy system based on radiological features and clinical factors to predict the PD-L1 expression level in GC.Materials and Methods: 217 patients who received gastrectomy for GC were consecutively enrolled in this study, with 157 patients from center 1 as the training cohort and 60 patients from center 2 as the external validation cohort. 1205 quantitative radiomics features were extracted from preprocessed pre-operative contrast-enhanced CT images of enrolled patients. A radiological signature was computed using a regression random forest model and was integrated with clinical factors in a multilayer perceptron. The performance of the digital biopsy system was evaluated by the receiver operating characteristic (ROC) curve and calibration curve in both the training and validation cohort.Results: 15 features were selected for the construction of radiological signature, which was significantly associated with expression levels of PD-L1 in both the training cohort (p<0.0001) and the external validation cohort (p<0.01). The hybrid deep learning model integrating the radiological signature and clinical factor could accurately distinguish GCs with high PD-L1 expression levels in both the training cohort (AUC = 0.806, 95%CI: 0.736-0.875) and the validation cohort (AUC = 0.784, 95%CI: 0.668-0.901).Conclusions: Our results indicate that the combination of deep learning and quantitative radiological features are potential approaches for the non-invasive evaluation of PD-L1 expression levels in GC. The digital biopsy system could provide valuable suggestive information for clinical decision-making of immunotherapy in GC. [ABSTRACT FROM AUTHOR]

Objectives: This study aims to develop and validate a virtual biopsy model to predict microsatellite instability (MSI) status in preoperative gastric cancer (GC) patients based on clinical information and the radiomics of deep learning algorithms. Methods: A total of 223 GC patients with MSI status detected by postoperative immunohistochemical staining (IHC) were retrospectively recruited and randomly assigned to the training (n = 167) and testing (n = 56) sets in a 3:1 ratio. In the training set, 982 high-throughput radiomic features were extracted from preoperative abdominal dynamic contrast-enhanced CT (CECT) and screened. According to the deep learning multilayer perceptron (MLP), 15 optimal features were optimized to establish the radiomic feature score (Rad-score), and LASSO regression was used to screen out clinically independent predictors. Based on logistic regression, the Rad-score and clinically independent predictors were integrated to build the clinical radiomics model and visualized as a nomogram and independently verified in the testing set. The performance and clinical applicability of hybrid model in identifying MSI status were evaluated by the area under the receiver operating characteristic (AUC) curve, calibration curve, and decision curve (DCA). Results: The AUCs of the clinical image model in training set and testing set were 0.883 [95% CI: 0.822–0.945] and 0.802 [95% CI: 0.666–0.937], respectively. This hybrid model showed good consistency in the calibration curve and clinical applicability in the DCA curve, respectively. Conclusions: Using preoperative imaging and clinical information, we developed a deep-learning-based radiomics model for the non-invasive evaluation of MSI in GC patients. This model maybe can potentially support clinical treatment decision making for GC patients. Key points: MSI is an important biomarker for immunotherapy in gastric cancer. Quantitative radiomics features were closely related to MSI in gastric cancer. Combining clinical and radiomics features with deep learning could evaluate MSI noninvasively. [ABSTRACT FROM AUTHOR]