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The introduction of liquid crystals into microcavities has garnered considerable attention for their exceptional tunability and high sensitivity to external perturbation factors within their distinct phase states. Here, a novel light source with both wavelength tunability and an exceptionally narrow linewidth is presented. This innovation is realized by strategically manipulating LC molecules, transitioning them from a well‐aligned state to a disordered state with increasing temperature. The microcavity is tailored to support bound states in the continuum, a cutting‐edge concept in photonic research that allows for light localization with minimal loss. In the pursuit of potential biocompatibility and to reduce cytotoxicity, indium phosphide colloid quantum dots are opted to serve as the emissive carriers within the system. An ultra‐narrow linewidth light emission of 0.039 nm is observed, corresponding to a quality factor reaching 16668, along with a tunable range of 1.21 nm and a temperature sensitivity of 33.52 pm K−1. The invention's compact size and tunable character make it an ideal candidate for a variety of potential applications, such as eco‐friendly sensors with minimal ecological impact, optical modulators with precise control over light, and adaptable photonic devices that can be integrated with a diverse array of materials and configurations. [ABSTRACT FROM AUTHOR]

Liquid crystals are widely used in photonics because of their profound electro–optic properties. However, the slow switching speeds (milliseconds) and the fluid nature of liquid crystals restrict their potential use in integrated photonics. In this work, polymer‐stabilized liquid crystals are utilized as the functional material to improve the response time with a polymer network that helps pre‐orientate the liquid crystal. Additionally, plasmonic slot structures are simultaneously employed to minimize the switch voltage by confining the optical field within the electrode spacing at subwavelength scales. Thanks to the large overlap of the optical and electric fields, the scattering states of the polymer‐stabilized liquid crystal can be effectively manipulated to modulate the loss of the propagating wave, resulting in strong optical attenuation in the integrated photonic platform. Specifically, a plasmonic enhanced polymer‐stabilized liquid crystal photonic device for operation at 1550 nm, which has a length of only 10 µm and shows an extinction ratio of 0.38 dB µm−1, with a power consumption of less than 6 µW and a response time ≈20 µs, resulting in a figure‐of‐merit of 0.012 mW. [ABSTRACT FROM AUTHOR]

This study investigates the competitive strategies of the international Big 4 audit firms when faced with regional competition from the local Big 6 audit firms. We find that for the Big 4, competition reduces their audit fee premiums and forces them to recruit high-risk clients, while does not affect their audit quality. In further analyses, we find that competition led to greater changes for the Big 4 after the CICPA's proposal in 2007 and when the local Big 6 are in leading positions. We also find that the local Big 6 compromise on audit quality in addition to audit fee premiums and client criteria when faced with competition from small local audit firms. Overall, this study reveals the competitive strategies of the Big 4 under competitive pressure from the local Big 6, which suggests that local audit firms can compete with international audit firms for audit services by scale development. [ABSTRACT FROM AUTHOR]

Notch characteristics significantly affect the fatigue performance of engineered components, for which the stress gradient effect is worth careful consideration. The traditional stress gradient analysis method based on the Coffin–Manson equation does not take into account the stress gradient influence range regarding the definition of the stress gradient correction factor, nor the high-stress gradient region, which has a greater influence on fatigue life. To address the aforementioned problems, a new notch fatigue life model is proposed in this paper. First, the stress–strain field at the root of the notch is analyzed to define the depth of stress gradient influence, following which the influence of the low-stress gradient region is reduced by a weighting function in the calculation of the stress gradient correction factor. Finally, to validate the method, three sets of experimental data, including TC4, GH4169, and EN8B, were used and compared with three other models. The results demonstrate that the predicted lifetimes of the new model are all within a 2-fold dispersion band, and the prediction ability is better than that of the other three models. [ABSTRACT FROM AUTHOR]

Mitochondrion plays a central role in diverse biological processes in most eukaryotes, and its dysfunctions are critically involved in a large number of diseases and the aging process. A systematic identification of mitochondrial proteomes and characterization of functional linkages among mitochondrial proteins are fundamental in understanding the mechanisms underlying biological functions and human diseases associated with mitochondria. Here we present a database MitProNet which provides a comprehensive knowledgebase for mitochondrial proteome, interactome and human diseases. First an inventory of mammalian mitochondrial proteins was compiled by widely collecting proteomic datasets, and the proteins were classified by machine learning to achieve a high-confidence list of mitochondrial proteins. The current version of MitProNet covers 1124 high-confidence proteins, and the remainders were further classified as middle- or low-confidence. An organelle-specific network of functional linkages among mitochondrial proteins was then generated by integrating genomic features encoded by a wide range of datasets including genomic context, gene expression profiles, protein-protein interactions, functional similarity and metabolic pathways. The functional-linkage network should be a valuable resource for the study of biological functions of mitochondrial proteins and human mitochondrial diseases. Furthermore, we utilized the network to predict candidate genes for mitochondrial diseases using prioritization algorithms. All proteins, functional linkages and disease candidate genes in MitProNet were annotated according to the information collected from their original sources including GO, GEO, OMIM, KEGG, MIPS, HPRD and so on. MitProNet features a user-friendly graphic visualization interface to present functional analysis of linkage networks. As an up-to-date database and analysis platform, MitProNet should be particularly helpful in comprehensive studies of complicated biological mechanisms underlying mitochondrial functions and human mitochondrial diseases. MitProNet is freely accessible at . [ABSTRACT FROM AUTHOR]

Controlling light‐matter interactions via cavity systems manifested by Rabi splitting is paramount important for nanophotonics. However, achieving conveniently accessible and active tuning of light‐matter interactions remains a formidable challenge. Traditional approaches often necessitate either sophisticated design or meticulous nanofabrication to address this issue. Here, a handy strategy is experimentally demonstrated to build an adjustable coupling system featuring reversibly modulated responses based on dielectric‐hydrogel‐metal resonators. By controlling the top tungsten disulfide layer thickness, the flexible manipulation of weak‐intermediate‐strong transitions in exciton‐cavity interactions is revealed on a large‐scale hydrogel membrane without nanopositioning or lithography. Crucially, by leveraging the inflation sensitivity of the hydrogel, the coupling strength can be reversibly tailored with excellent reproducibility by modulating the resonator's dry/immersed states. The combined merits of captivating design and daily stimulus render the novel hydrogel‐based nanocavities as a groundbreaking step toward the development of active and practical integrated optical devices, such as polariton lasing, switches, and sensors. [ABSTRACT FROM AUTHOR]

Lycium barbarum, a member of the Solanaceae family, represents an important eudicot lineage with homology of food and medicine. Lycium barbarum pectin polysaccharides (LBPPs) are key bioactive ingredients of Lycium barbarum, and are among the few polysaccharides with both biocompatibility and biomedical activity. While previous studies have primarily focused on the functional properties of LBPPs, the mechanisms of biosynthesis and transport by key enzymes remain poorly understood. Here, we reported the completion of a 2.18-gigabase reference genome of Lycium barbarum, reconstructed the first entire biosynthesis of pectin polysaccharides and sugar transport, and characterized the important genes responsible for backbone extending, sidechain synthesis, and modification of pectin polysaccharides. Additionally, we characterized long non-coding RNAs (lncRNAs) associated with polysaccharide metabolism and identified a specific rhamnogalacturonan I (RG-I) rhamnosyltransferase, RRT3020, which enhances RG-I biosynthesis in LBPPs. These newly identified enzymes and pivotal genes endow L. barbarum with specific pectin biosynthesis capabilities, distinguishing it from other Solanaceae species. Our findings provide a foundation for evolutionary studies and molecular breeding to enhance the diverse applications of L. barbarum., (© The Author(s) 2024. Published by Oxford University Press and Science Press on behalf of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China.)

Flexible photodetectors are pivotal in contemporary optoelectronic technology applications, such as data reception and image sensing, yet their performance and yield are often hindered by the challenge of heterogeneous integration between photoactive materials and flexible substrates. Here, we showcase the potential of an electrostatic force-assisted transfer printing technique for integrating Si PIN photodiodes onto flexible substrates. This clean and dry process eliminates the need for chemical etchants, making it a highly desirable method for manufacturing high-performance flexible photodetector arrays, expanding their widespread applications in electronic eyes, robotics, and human-machine interaction. As a demonstration, a 5 × 5 flexible Si photodetector focal plane array is constructed for imaging sensors and shaped into a convex semicylindrical form to achieve a π field of view with long-term mechanical and thermal stability. Such an approach provides a high yield rate and consistent performance, with the single photodetector demonstrating exceptional characteristics, including a responsivity of 0.61 A/W, a response speed of 39.77 MHz, a linear dynamic range of 108.53 dB, and a specific detectivity of 2.75 × 10 12 Jones at an applied voltage of -3 V at 940 nm.

Objective . Physiological signals based emotion recognition is a prominent research domain in the field of human-computer interaction. Previous studies predominantly focused on unimodal data, giving limited attention to the interplay among multiple modalities. Within the scope of multimodal emotion recognition, integrating the information from diverse modalities and leveraging the complementary information are the two essential issues to obtain the robust representations. Approach . Thus, we propose a intermediate fusion strategy for combining low-rank tensor fusion with the cross-modal attention to enhance the fusion of electroencephalogram, electrooculogram, electromyography, and galvanic skin response. Firstly, handcrafted features from distinct modalities are individually fed to corresponding feature extractors to obtain latent features. Subsequently, low-rank tensor is fused to integrate the information by the modality interaction representation. Finally, a cross-modal attention module is employed to explore the potential relationships between the distinct latent features and modality interaction representation, and recalibrate the weights of different modalities. And the resultant representation is adopted for emotion recognition. Main results . Furthermore, to validate the effectiveness of the proposed method, we execute subject-independent experiments within the DEAP dataset. The proposed method has achieved the accuracies of 73.82% and 74.55% for valence and arousal classification. Significance . The results of extensive experiments verify the outstanding performance of the proposed method., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective. Extracting discriminative spatial information from multiple electrodes is a crucial and challenging problem for electroencephalogram (EEG)-based emotion recognition. Additionally, the domain shift caused by the individual differences degrades the performance of cross-subject EEG classification. Approach. To deal with the above problems, we propose the cerebral asymmetry representation learning-based deep subdomain adaptation network (CARL-DSAN) to enhance cross-subject EEG-based emotion recognition. Specifically, the CARL module is inspired by the neuroscience findings that asymmetrical activations of the left and right brain hemispheres occur during cognitive and affective processes. In the CARL module, we introduce a novel two-step strategy for extracting discriminative features through intra-hemisphere spatial learning and asymmetry representation learning. Moreover, the transformer encoders within the CARL module can emphasize the contributive electrodes and electrode pairs. Subsequently, the DSAN module, known for its superior performance over global domain adaptation, is adopted to mitigate domain shift and further improve the cross-subject performance by aligning relevant subdomains that share the same class samples. Main Results. To validate the effectiveness of the CARL-DSAN, we conduct subject-independent experiments on the DEAP database, achieving accuracies of 68.67% and 67.11% for arousal and valence classification, respectively, and corresponding accuracies of 67.70% and 67.18% on the MAHNOB-HCI database. Significance. The results demonstrate that CARL-DSAN can achieve an outstanding cross-subject performance in both arousal and valence classification., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective . To enhance the accuracy of heart sound classification, this study aims to overcome the limitations of common models which rely on handcrafted feature extraction. These traditional methods may distort or discard crucial pathological information within heart sounds due to their requirement of tedious parameter settings. Approach. We propose a learnable front-end based Efficient Channel Attention Network (ECA-Net) for heart sound classification. This novel approach optimizes the transformation of waveform-to-spectrogram, enabling adaptive feature extraction from heart sound signals without domain knowledge. The features are subsequently fed into an ECA-Net based convolutional recurrent neural network, which emphasizes informative features and suppresses irrelevant information. To address data imbalance, Focal loss is employed in our model. Main results. Using the well-known public PhysioNet challenge 2016 dataset, our method achieved a classification accuracy of 97.77%, outperforming the majority of previous studies and closely rivaling the best model with a difference of just 0.57%. Significance. The learnable front-end facilitates end-to-end training by replacing the conventional heart sound feature extraction module. This provides a novel and efficient approach for heart sound classification research and applications, enhancing the practical utility of end-to-end models in this field., (© 2023 Institute of Physics and Engineering in Medicine.)