Tunability-selective lithium niobate light modulators via high-Q resonant metasurface.

Herein, we propose and demonstrate an efficient light modulator by intercalating the nonlinear thin film into the optical resonator cavities, which introduce the ultra-sharp resonances and simultaneously lead to the spatially overlapped optical field between the nonlinear material and the resonators. Differential field intensity distributions in the geometrical perturbation-assisted optical resonator make the high quality-factor resonant modes and strong field confinement. Multiple channel light modulation is achieved in such layered system, which enables the capability for tunability-selective modulation. The maximal modulation tunability is up to 1.968 nm/V, and the figure of merit (FOM) reaches 65.6 V-1, showing orders of magnitude larger than that of the previous state-of-the-art modulators. The electrical switch voltage is down to 0.015 V, the maximal switching ratio is 833%, and the extinction ratio is also up to 9.70 dB. These features confirm the realization of high-performance modulation and hold potential for applications in switches, communication and information, augmented and virtual reality, etc.

Deep learning-based inverse design of multi-functional metasurface absorbers.

A novel approach-integrating a simulated annealing (SA) algorithm with deep learning (DL) acceleration-is presented for the rapid and accurate development of terahertz perfect absorbers through forward prediction and backward design. The forward neural network (FNN) effectively deduces the absorption spectrum based on metasurface geometry, resulting in an 80,000-fold increase in computational speed compared to a full-wave solver. Furthermore, the absorber's structure can be precisely and promptly derived from the desired response. The incorporation of the SA algorithm significantly enhances design efficiency. We successfully designed low-frequency, high-frequency, and broadband absorbers spanning the 4 to 16 THz range with an error margin below 0.02 and a remarkably short design time of only 10 min. Additionally, the proposed model in this Letter introduces a novel, to our knowledge, method for metasurface design at terahertz frequencies such as the design of metamaterials across optical, thermal, and mechanical domains.

Integrating Sensor Ontologies with Niching Multi-Objective Particle Swarm Optimization Algorithm.

Sensor ontology provides a standardized semantic representation for information sharing between sensor devices. However, due to the varied descriptions of sensor devices at the semantic level by designers in different fields, data exchange between sensor devices is hindered. Sensor ontology matching achieves data integration and sharing between sensors by establishing semantic relationships between sensor devices. Therefore, a niching multi-objective particle swarm optimization algorithm (NMOPSO) is proposed to effectively solve the sensor ontology matching problem. As the sensor ontology meta-matching problem is essentially a multi-modal optimization problem (MMOP), a niching strategy is introduced into MOPSO to enable the algorithm to find more global optimal solutions that meet the needs of different decision makers. In addition, a diversity-enhancing strategy and an opposition-based learning (OBL) strategy are introduced into the evolution process of NMOPSO to improve the quality of sensor ontology matching and ensure the solutions converge to the real Pareto fronts (PFs). The experimental results demonstrate the effectiveness of NMOPSO in comparison to MOPSO-based matching techniques and participants of the Ontology Alignment Evaluation Initiative (OAEI).

Outer membrane vesicles (OMVs) enabled bio-applications: A critical review.

Outer membrane vesicles (OMVs) are nanoscale spherical vesicles released from Gram-negative bacteria. The lipid bilayer membrane structure of OMVs consists of similar components as bacterial membrane and thus has attracted more and more attention in exploiting OMVs' bio-applications. Although the endotoxic lipopolysaccharide on natural OMVs may impose potential limits on their clinical applications, genetic modification can reduce their endotoxicity and decorate OMVs with multiple functional proteins. These genetically engineered OMVs have been employed in various fields including vaccination, drug delivery, cancer therapy, bioimaging, biosensing, and enzyme carrier. This review will first briefly introduce the background of OMVs followed by recent advances in functionalization and various applications of engineered OMVs with an emphasis on the working principles and their performance, and then discuss about the future trends of OMVs in biomedical applications.

A whole area scanning-enabled direct-counting strategy for studying blocking efficiency in mitigating protein-solid surface binding.

The study of protein-solid surface binding as well as blocking efficiency of blocking agents plays an important role in the development of high-performance immunoassays. Although conventional colorimetric based assays are widely employed to monitor protein non-specific binding on the surface of microplate wells and evaluate the performance of blocking agents, there is still a great need to develop new methods to achieve the same goal from a new perspective. In this study, an innovative whole area scanning (WAS)-enabled direct-counting strategy was developed and validated through studying the blocking efficiency of different blocking agents on the non-specific binding of streptavidin-alkaline phosphatase conjugate (Strep-ALP, a model protein) to the surface of 96-well microplates. After non-specific binding of Strep-ALP in wells with or without blocking agents' treatment and loading of ELF™ 97 phosphate (ELFP), ALP in Strep-ALP conjugates converts ELFP to water-insoluble ELF™ 97 alcohol (ELFA), which precipitates locally, self-assembles into large needle structures, and glows green fluorescence upon excitation. After quenching the reaction, WAS of the whole wells allows us to directly count the number of individual fluorescent precipitates, which can be used to calculate and compare the blocking efficiency of three commonly used blocking agents (BSA, casein, and dry milk) based on mitigating the non-specific binding of Strep-ALP. WAS-enabled counting of individual needle-type precipitates opens a new avenue to investigate protein-solid surface binding as well as the efficiency of blocking agents with high sensitivity.

Modulation of SRSF2 expression reverses the exhaustion of TILs via the epigenetic regulation of immune checkpoint molecules.

The elevated expression of immune checkpoints by the tumor microenvironment is associated with poor prognosis in several cancers due to the exhaustion of tumor-infiltrating lymphocytes (TILs), and the effective suppression of the expression of these genes is key to reversing the exhaustion of TILs. Herein, we determined that serine/arginine-rich splicing factor 2 (SRSF2) is a target for blocking the tumor microenvironment-associated immunosuppressive effects. We found that the expression of SRSF2 was increased in exhausted T cells and that SRSF2 was involved in multiple immune checkpoint molecules mediating TILs' exhaustion. Furthermore, SRSF2 was revealed to regulate the transcription of these immune checkpoint genes by associating with an acyl-transferases P300/CBP complex and altering the H3K27Ac level near these genes, thereafter influencing the recruitment of signal transducer and activator of transcription 3 (STAT3) to these gene promoters. Collectively, our data indicated that SRSF2 functions as a modulator of the anti-tumor response of T cells and may be a therapeutic target for reversing the exhaustion of TILs.

Protecting-group-free total synthesis of aplykurodinone-1.

A concise, stereoselective, and protecting-group-free total synthesis of aplykurodinone-1 from Hajos-Parrish ketone was described. The synthetic approach features a sequence of aerobic allylic oxidation and elimination of alcohol 9. The key intermediate for this synthesis was formed by a stereoselective intramolecular radical cyclization.

High-Throughput Monitoring of Pathogenic Fungal Growth Using Whole Slide Imaging for Rapid Antifungal Susceptibility Assessment.

Invasive fungal infections are a major health threat with high morbidity and mortality, highlighting the urgent need for rapid diagnostic tools to detect antifungal resistance. Traditional culture-based antifungal susceptibility testing (AFST) methods often fall short due to their lengthy process. In our previous research, we developed a whole-slide imaging (WSI) technique for the high-throughput assessment of bacterial antibiotic resistance. Building on this foundation, this study expands the application of WSI by adapting it for rapid AFST through high-throughput monitoring of the growth of hundreds of individual fungi. Due to the distinct "budding" growth patterns of fungi, we developed a unique approach that utilizes specific cell number change to determine fungi replication, instead of cell area change used for bacteria in our previous study, to accurately determine the growth rates of individual fungal cells. This method not only accelerates the determination of antifungal resistance by directly observing individual fungal cell growth, but also yields accurate results. Employing Candida albicans as a representative model organism, reliable minimum inhibitory concentration (MIC) of fluconazole inhibiting 100% cells of Candida albicans (denoted as MIC100) was obtained within 3h using the developed method, while the modified broth dilution method required 72h for the similar reliable result. In addition, our approach was effectively utilized to test blood culture samples directly, eliminating the need to separate the fungi from whole blood samples spiked with Candida albicans. These features indicate the developed method holds great potential serving as a general tool in rapid antifungal susceptibility testing and MIC determination.

A predictive model for determining the nitrite concentration in the effluent of an anammox reactor using ensemble regression tree algorithm.

Anaerobic ammonium oxidation (anammox) is a cost-effective biological nitrogen removal method for treating wastewater. Nitrite has strong negative effect on microbial activity of anammox bacteria, while the conventional equitment available for determining nitrite on-line is challenging due to high price. By knowing the concentration of nitrite in the effluent, its concentration in the reactor can be controlled accordingly. To investigate this, an ensemble regression tree algorithm was used to establish the predictive model proposed in the current work. Moreover, the Bayesian algorithm was adopted to systematically optimize various parameters of machine learning algorithms. The predicted concentrations of nitrite were in good agreement with the observed values, and the coefficient of determination (R2) and root mean squared error (RMSE) values reached 0.91 and 4.81, respectively. Furthermore, the model established by the ensemble regression tree algorithm was compared with models established by commonly used machine learning algorithms. Finally, the established models were applied to another anammox reactor, and the predicted results of ensemble regression tree model were found to be in good agreement with the experimental values with R2 and RMSE values of 0.84 and 6.34, respectively.

Insight into thermal regulation of supercapacitors via surface-engineered phase change microcapsules.

Constructing efficient thermal management system to settle the thermal runaway of energy storage devices via employing phase change microcapsules (MEPCMs) is of great significance. However, it is still a challenge that the conventional MEPCMs go against the electrochemical performance and hardly be homogenously fixed in the electrodes. In order to conquer these long-standing critical issues, we designed a novel electrochemically active double-shell phase change microcapsule by introducing polypyrrole on the surface of dense amine resin shell of the conventional inert MEPCM. The active MEPCMs@PPy are uniformly immobilized on the surface of the electrode material using reduced graphene oxide to ensure the stable and efficient operation of the flexible supercapacitor. The assembled all-solid-state supercapacitor containing MEPCMs@PPy (SCs@MEPCMs@PPy) lagged 103 s to 55 °C than the SCs@00 without the added phase change material. At a high temperature of 55 °C and a scan rate of 50 mV s-1, SCs@MEPCMs@PPy exhibits an areal specific capacitance of 110.6 mA cm-2, which is higher than that of the original SCs@MEPCMs. A capacitance retention of 79.8 % and coulombic efficiency of 98.4 % can be reached after 3000 cycles. This study opens a new avenue for developing applicable microencapsulated phase change materials in temperature-regulated electrode systems for supercapacitors and alkaline-ion batteries.

Chief Executive Officer Collectivism and Corporate Pollution Abatement Behavior: Evidence From Industrial Firms in China.

This study examines the relationship between chief executive officers (CEOs)' collectivistic cultural background and corporate pollution abatement behavior among industrial firms in China. Using hand-collected data on birthplaces of CEOs of the industrial firms, we provided robust evidence that CEOs born in provinces with a higher level of collectivistic culture promote corporate pollution abatement performance. This study further shows that firms exhibit significant differences in their emission reduction behavior when firms are subjected to environmental regulation shocks: firms with collectivistic CEOs tend to reduce more pollution than firms with individualistic CEOs without sacrificing their firms' production.

Corrigendum: Chief executive officer collectivism and corporate pollution abatement behavior: Evidence from industrial firms in China.

[This corrects the article DOI: 10.3389/fpsyg.2022.946111.].

Corporate Competing Culture and Environmental Investment.

Using Chinese listed companies as research setting, this paper constructs a measure of corporate competing culture through textual analysis on firms' management discussion and analysis (MD&A) disclosures, and examines the impact of corporate competing culture on environmental investment. The results show that competing culture has a significant and positive impact on firms' environmental investment, and the results remain robust to a battery of robustness tests. Moreover, the mediating analysis indicates that competing culture promotes corporate environmental investment through enhancing firms' internal control quality. Furthermore, the heterogeneity results show that the positive impact of corporate competing culture on environmental investment is more pronounced in firms with larger size, stronger corporate governance, in high-polluting industry, and located in less developed regions. Our findings shed light on the importance of corporate competing culture and provide practical implications for corporate sustainable development.

Micro Expression Recognition via Dual-Stream Spatiotemporal Attention Network.

Microexpression can manifest the real mood of humans, which has been widely concerned in clinical diagnosis and depression analysis. To solve the problem of missing discriminative spatiotemporal features in a small data set caused by the short duration and subtle movement changes of microexpression, we present a dual-stream spatiotemporal attention network (DSTAN) that integrates dual-stream spatiotemporal network and attention mechanism to capture the deformation features and spatiotemporal features of microexpression in the case of small samples. The Spatiotemporal networks in DSTAN are based on two lightweight networks, namely, the spatiotemporal appearance network (STAN) learning the appearance features from the microexpression sequences and the spatiotemporal motion network (STMN) learning the motion features from optical flow sequences. To focus on the discriminative motion areas of microexpression, we construct a novel attention mechanism for the spatial model of STAN and STMN, including a multiscale kernel spatial attention mechanism and global dual-pool channel attention mechanism. To obtain the importance of each frame in the microexpression sequence, we design a temporal attention mechanism for the temporal model of STAN and STMN to form spatiotemporal appearance network-attention (STAN-A) and spatiotemporal motion network-attention (STMN-A), which can adaptively perform dynamic feature refinement. Finally, the feature concatenate-SVM method is used to integrate STAN-A and STMN-A to a novel network, DSTAN. The extensive experiments on three small spontaneous microexpression data sets of SMIC, CASME, and CASME II demonstrate the proposed DSTAN can effectively cope with the recognition of microexpressions.

Automated Identification of Skull Fractures With Deep Learning: A Comparison Between Object Detection and Segmentation Approach.

Objective: Skull fractures caused by head trauma can lead to life-threatening complications. Hence, timely and accurate identification of fractures is of great importance. Therefore, this study aims to develop a deep learning system for automated identification of skull fractures from cranial computed tomography (CT) scans. Method: This study retrospectively analyzed CT scans of 4,782 patients (median age, 54 years; 2,583 males, 2,199 females; development set: n = 4,168, test set: n = 614) diagnosed with skull fractures between September 2016 and September 2020. Additional data of 7,856 healthy people were included in the analysis to reduce the probability of false detection. Skull fractures in all the scans were manually labeled by seven experienced neurologists. Two deep learning approaches were developed and tested for the identification of skull fractures. In the first approach, the fracture identification task was treated as an object detected problem, and a YOLOv3 network was trained to identify all the instances of skull fracture. In the second approach, the task was treated as a segmentation problem and a modified attention U-net was trained to segment all the voxels representing skull fracture. The developed models were tested using an external test set of 235 patients (93 with, and 142 without skull fracture). Results: On the test set, the YOLOv3 achieved average fracture detection sensitivity and specificity of 80.64, and 85.92%, respectively. On the same dataset, the modified attention U-Net achieved a fracture detection sensitivity and specificity of 82.80, and 88.73%, respectively. Conclusion: Deep learning methods can identify skull fractures with good sensitivity. The segmentation approach to fracture identification may achieve better results.

A Multifunction Lipid-Based CRISPR-Cas13a Genetic Circuit Delivery System for Bladder Cancer Gene Therapy.

The treatment of bladder cancer has recently shown minimal progress. Gene therapy mediated by CRISPR provides a new option for bladder cancer treatment. In this study, we developed a versatile liposome system to deliver the CRISPR-Cas13a gene circuits into bladder cancer cells. After in vitro studies and intravesical perfusion studies in mice, this system showed five advantages: (1) CRISPR-Cas13a, a transcriptional targeting and cleavage tool for gene expression editing, did not affect the stability of the cell genome; (2) the prepared liposome systems were targeted to hVEGFR2, which is always highly expressed in bladder cancer cells; (3) the CRISPR-Cas13a sequence was driven by an artificial tumor specific promoter to achieve further targeting; (4) a near-infrared photosensitizer released using near-infrared light was introduced to control the delivery system; and (5) the plasmids were constructed with three crRNA tandem sequences to achieve multiple targeting and wider therapeutic results. This tumor cell targeting lipid delivery system with near-infrared laser-controlled ability provided a versatile strategy for CRISPR-Cas13a based gene therapy of bladder cancer.

Wide-field, high-resolution lensless on-chip microscopy via near-field blind ptychographic modulation.

We report a novel lensless on-chip microscopy platform based on near-field blind ptychographic modulation. In this platform, we place a thin diffuser in between the object and the image sensor for light wave modulation. By blindly scanning the unknown diffuser to different x-y positions, we acquire a sequence of modulated intensity images for quantitative object recovery. Different from previous ptychographic implementations, we employ a unit magnification configuration with a Fresnel number of ∼50 000, which is orders of magnitude higher than those of previous ptychographic setups. The unit magnification configuration allows us to have the entire sensor area, 6.4 mm by 4.6 mm, as the imaging field of view. The ultra-high Fresnel number enables us to directly recover the positional shift of the diffuser in the phase retrieval process, addressing the positioning accuracy issue plaguing regular ptychographic experiments. In our implementation, we use a low-cost, DIY scanning stage to perform blind diffuser modulation. Precise mechanical scanning that is critical in conventional ptychography experiments is no longer needed in our setup. We further employ an up-sampling phase retrieval scheme to bypass the resolution limit set by the imager pixel size and demonstrate a half-pitch resolution of 0.78 µm. We validate the imaging performance via in vitro cell cultures, transparent and stained tissue sections, and a thick biological sample. We show that the recovered quantitative phase map can be used to perform effective cell segmentation of a dense yeast culture. We also demonstrate 3D digital refocusing of the thick biological sample based on the recovered wavefront. The reported platform provides a cost-effective and turnkey solution for large field-of-view, high-resolution, and quantitative on-chip microscopy. It is adaptable for a wide range of point-of-care-, global-health-, and telemedicine-related applications.

A CRISPR-Cas12a-based specific enhancer for more sensitive detection of SARS-CoV-2 infection.

BACKGROUND: Real-time reverse transcription-PCR (rRT-PCR) has been the most effective and widely implemented diagnostic technology since the beginning of the COVID-19 pandemic. However, fuzzy rRT-PCR readouts with high Ct values are frequently encountered, resulting in uncertainty in diagnosis. METHODS: A Specific Enhancer for PCR-amplified Nucleic Acid (SENA) was developed based on the Cas12a trans-cleavage activity, which is specifically triggered by the rRT-PCR amplicons of the SARS-CoV-2 Orf1ab (O) and N fragments. SENA was first characterized to determine its sensitivity and specificity, using a systematic titration experiment with pure SARS-CoV-2 RNA standards, and was then verified in several hospitals, employing a couple of commercial rRT-PCR kits and testing various clinical specimens under different scenarios. FINDINGS: The ratio (10 min/5 min) of fluorescence change (FC) with mixed SENA reaction (mix-FCratio) was defined for quantitative analysis of target O and N genes, and the Limit of Detection (LoD) of mix-FCratio with 95% confidence interval was 1.2≤1.6≤2.1. Totally, 295 clinical specimens were analyzed, among which 21 uncertain rRT-PCR cases as well as 4 false negative and 2 false positive samples were characterized by SENA and further verified by next-generation sequencing (NGS). The cut-off values for mix-FCratio were determined as 1.145 for positive and 1.068 for negative. INTERPRETATION: SENA increases both the sensitivity and the specificity of rRT-PCR, solving the uncertainty problem in COVID-19 diagnosis and thus providing a simple and low-cost companion diagnosis for combating the pandemic. FUNDING: Detailed funding information is available at the end of the manuscript.

Genetically Engineered Bacterial Outer Membrane Vesicles with Expressed Nanoluciferase Reporter for in Vivo Bioluminescence Kinetic Modeling through Noninvasive Imaging.

Outer membrane vesicles (OMVs) produced by Gram-negative bacteria play significant roles in the biomedical field as they can be facilely functionalized using genetic engineering tools and thus often serve as a versatile multifunctional nanoparticles for a variety of applications. In this study, we investigated the multifaceted bioluminescence kinetics of a NanoLuc luciferase-expressed outer membrane vesicle produced by E. coli. This multifunctional OMV emits strong blue luminescence at 460 nm after mixing with the substrate furimazine, which potentially can be used for bioluminescence-based optical imaging. Characterization of the vesicles was performed via dynamic light scattering and nanoparticle tracking analysis. A murine animal model was used to observe the in vivo behavior of the bioluminescence produced by outer membrane vesicles through post subcutaneous administration. The bioluminescence signal was tracked by noninvasive in vivo optical imaging, while in vitro cytotoxicity and ex vivo tissue histopathology were studied to demonstrate the biocompatibility of the engineered OMVs. A theoretical model was also developed to simulate the relevant enzyme-substrate reaction kinetics along with absorption of the in vivo system. The interplay of the reaction and absorption is in good agreement with the experimental results. The study shows a great potential of the genetically engineered vesicles as an interesting class of functional nanomaterials for imaging-related biomedical applications.

Preparation, characterization and application of a protein hydrogel with rapid self-healing and unique autofluoresent multi-functionalities.

A smart hydrogel with dual self-healing and autofluoresent functionalities is presented. The protein hydrogel is fabricated by denaturing bovine serum albumin in a basic environment. Upon gelation, autofluorescence is induced and the protein hydrogel can be excited by a wide range of spectrum, ranging from 320 to 520 nm. It was also found that the as-prepared autofluorescent protein hydrogel possessed rapid and repetitive self-healing capability. Without any external stimulus, more than 90% recovery of the mechanical strength can be obtained within 10 min after destruction. Moreover, the as-prepared hydrogel exhibits excellent biocompatibility and cell attachment property after its pH adjustment to neutral pH, while both autofluorescence and self-healing properties were still retained. This study suggests a promising means to prepare multi-functional protein hydrogel with dual physicochemical functionalities, which holds great potential in biomedical related applications. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 107A: 81-91, 2019.