Silicon Waveguide-Integrated Platinum Telluride Midinfrared Photodetector with High Responsivity and High Speed.

The detection of mid-infrared light, covering a variety of molecular vibrational spectra, is critical for both civil and military purposes. Recent studies have highlighted the potential of two-dimensional topological semimetals for mid-infrared detection due to their advantages, including van der Waals (vdW) stacking and gapless electronic structures. Among them, mid-infrared photodetectors based on type-II Dirac semimetals have been less studied. In this paper, we present a silicon waveguide integrated type-II Dirac semimetal platinum telluride (PtTe2) mid-infrared photodetector, and further improve detection performance by using PtTe2-graphene heterostructure. For the fabricated silicon waveguide-integrated PtTe2 photodetector, with an external bias voltage of -10 mV and an input optical power of 86 nW, the measured responsivity is 2.7 A/W at 2004 nm and a 3 dB bandwidth of 0.6 MHz is realized. For the fabricated silicon waveguide-integrated PtTe2-graphene photodetector, as the external bias voltage and input optical power are 0.5 V and 0.13 µW, a responsivity of 5.5 A/W at 2004 nm and a 3 dB bandwidth of 35 MHz are obtained. An external quantum efficiency of 119% can be achieved at an input optical power of 0.376 µW.

Q-BioLiP: A Comprehensive Resource for Quaternary Structure-based Protein-ligand Interactions.

Since its establishment in 2013, BioLiP has become one of the widely used resources for protein-ligand interactions. Nevertheless, several known issues occurred with it over the past decade. For example, the protein-ligand interactions are represented in the form of single chain-based tertiary structures, which may be inappropriate as many interactions involve multiple protein chains (known as quaternary structures). We sought to address these issues, resulting in Q-BioLiP, a comprehensive resource for quaternary structure-based protein-ligand interactions. The major features of Q-BioLiP include: (1) representing protein structures in the form of quaternary structures rather than single chain-based tertiary structures; (2) pairing DNA/RNA chains properly rather than separation; (3) providing both experimental and predicted binding affinities; (4) retaining both biologically relevant and irrelevant interactions to alleviate the wrong justification of ligands' biological relevance; and (5) developing a new quaternary structure-based algorithm for the modelling of protein-ligand complex structure. With these new features, Q-BioLiP is expected to be a valuable resource for studying biomolecule interactions, including protein-small molecule interaction, protein-metal ion interaction, protein-peptide interaction, protein-protein interaction, protein-DNA/RNA interaction, and RNA-small molecule interaction. Q-BioLiP is freely available at https://yanglab.qd.sdu.edu.cn/Q-BioLiP/.

FP-Zernike: An Open-source Structural Database Construction Toolkit for Fast Structure Retrieval.

The release of AlphaFold2 has sparked a rapid expansion in protein model databases. Efficient protein structure retrieval is crucial for the analysis of structure models, while measuring the similarity between structures is the key challenge in structural retrieval. Although existing structure alignment algorithms can address this challenge, they are often time-consuming. Currently, the state-of-the-art approach involves converting protein structures into three-dimensional (3D) Zernike descriptors and assessing similarity using Euclidean distance. However, the methods for computing 3D Zernike descriptors mainly rely on structural surfaces and are predominantly web-based, thus limiting their application in studying custom datasets. To overcome this limitation, we developed FP-Zernike, a user-friendly toolkit for computing different types of Zernike descriptors based on feature points. Users simply need to enter a single line of command to calculate the Zernike descriptors of all structures in customized datasets. FP-Zernike outperforms the leading method in terms of retrieval accuracy and binary classification accuracy across diverse benchmark datasets. In addition, we showed the application of FP-Zernike in the construction of the descriptor database and the protocol used for the Protein Data Bank (PDB) dataset to facilitate the local deployment of this tool for interested readers. Our demonstration contained 590,685 structures, and at this scale, our system required only 4-9 s to complete a retrieval. The experiments confirmed that it achieved the state-of-the-art accuracy level. FP-Zernike is an open-source toolkit, with the source code and related data accessible at https://ngdc.cncb.ac.cn/biocode/tools/BT007365/releases/0.1, as well as through a webserver at http://www.structbioinfo.cn/.

Prevalence and plasma exosome-derive microRNA diagnostic biomarker screening of adolescent idiopathic scoliosis in Yunnan Province, China.

Background: Idiopathic scoliosis significantly affects the physical and mental health of children and adolescents, with varying prevalence rates in different regions. The occurrence of idiopathic scoliosis is associated with genetic regulation and biochemical factors, but the changes in exosome-derived miRNA profiles among idiopathic scoliosis patients remain unclear. This study aimed to determine the prevalence of idiopathic scoliosis in Yunnan Province, China, and identify key exosome-derived miRNAs in idiopathic scoliosis through a cohort study. Methods: From January 2018 to December 2020, a cross-sectional study on idiopathic scoliosis in children and adolescents was conducted in Yunnan Province. A total of 84,460 students from 13 cities and counties in Yunnan Province participated in a scoliosis screening program, with ages ranging from 7 to 19 years. After confirmation through screening and imaging results, patients with severe idiopathic scoliosis and normal control individuals were selected using propensity matching. Subsequently, plasma exosome-derived miRNA sequencing and RT-qPCR validation were performed separately. Based on the validation results, diagnostic performance analysis and target gene prediction were conducted for differential plasma exosome-derived miRNAs. Results: The overall prevalence of idiopathic scoliosis in children and adolescents in Yunnan Province was 1.10%, with a prevalence of 0.87% in males and 1.32% in females. The peak prevalence was observed at age 13. Among patients diagnosed with idiopathic scoliosis, approximately 12.8% had severe cases, and there were more cases of double curvature than of single curvature, with thoracolumbar curvature being the most common in the single-curvature group. Sequencing of plasma exosome-derived miRNAs associated with idiopathic scoliosis revealed 56 upregulated and 153 downregulated miRNAs. Further validation analysis confirmed that hsa-miR-27a-5p, hsa-miR-539-5p, and hsa-miR-1246 have potential diagnostic value. Conclusions: We gained insights into the epidemiological characteristics of idiopathic scoliosis in Yunnan Province and conducted further analysis of plasma exosome-derived miRNA changes in patients with severe idiopathic scoliosis. This study has provided new insights for the prevention and diagnosis of idiopathic scoliosis, paving the way for exploring clinical biomarkers and molecular regulatory mechanisms. However, further validation and elucidation of the detailed biological mechanisms underlying these findings will be required in the future.

FAT1 upregulation is correlated with an immunosuppressive tumor microenvironment and predicts unfavorable outcome of immune checkpoint therapy in non-small cell lung cancer.

Background: Previous studies found that FAT1 was recurrently mutated and aberrantly expressed in multiple cancers, and the loss function of FAT1 promoted the formation of cancer-initiating cells in several cancers. However, in some types of cancer, FAT1 upregulation could lead to epithelial-mesenchymal transition (EMT). The role of FAT1 in cancer progression, which appears to be cancer-type-specific, is largely unknown. Methods: QRT-PCR and immunochemistry were used to verify the expression of FAT1 in non-small cell lung cancer (NSCLC). QRT-PCR and Western blot were used to detect the influence of siFAT1 knockdown on the expression of potential targets of FAT1 in NSCLC cell lines. GEPIA, KM-plotter, CAMOIP, and ROC-Plotter were used to evaluate the association between FAT1 and clinical outcomes based on expression and clinical data from TCGA and immune checkpoint inhibitors (ICI) treated cohorts. Results: We found that FAT1 upregulation was associated with the activation of TGF-ß and EMT signaling pathways in NSCLC. Patients with a high FAT1 expression level tend to have a poor prognosis and hard to benefit from ICI therapy. Genes involved in TGF-ß/EMT signaling pathways (SERPINE1, TGFB1/2, and POSTN) were downregulated upon knockdown of FAT1. Genomic and immunologic analysis showed that high cancer-associated fibroblast (CAF) abundance, decreased CD8+ T cells infiltration, and low TMB/TNB were correlated with the upregulation of FAT1, thus promoting an immunosuppressive tumor microenvironment (TME) which influence the effect of ICI-therapy. Conclusion: Our findings revealed the pattern of FAT1 upregulation in the TME of patients with NSCLC, and demonstrated its utility as a biomarker for unfavorable clinical outcomes, thereby providing a potential therapeutic target for NSCLC treatment.

Compact and low-insertion-loss polarization beam-splitting multimode filter using pixelated waveguides.

A polarization beam-splitting multimode filter using pixelated waveguides has been presented and experimentally demonstrated in this paper. Finite difference time domain method and direct binary search optimization algorithm are employed to optimize pixelated waveguides to realize compact size, broad bandwidth, large extinction ratio, low insertion loss, and good polarization extinction ratio. Measurement results show that, in a wavelength range from 1520 to 1560 nm, for the fabricated device working at transverse-electric polarization, the measured insertion loss is less than 1.23 dB and extinction ratio is larger than 15.14 dB, while for transverse-magnetic polarization, the corresponding insertion loss lower than 0.74 dB and extinction ratio greater than 15.50 dB are realized. The measured polarization extinction ratio larger than 15.02 dB is achieved. The device's length is only 15.4 µm.

PLMSearch: Protein language model powers accurate and fast sequence search for remote homology.

Homologous protein search is one of the most commonly used methods for protein annotation and analysis. Compared to structure search, detecting distant evolutionary relationships from sequences alone remains challenging. Here we propose PLMSearch (Protein Language Model), a homologous protein search method with only sequences as input. PLMSearch uses deep representations from a pre-trained protein language model and trains the similarity prediction model with a large number of real structure similarity. This enables PLMSearch to capture the remote homology information concealed behind the sequences. Extensive experimental results show that PLMSearch can search millions of query-target protein pairs in seconds like MMseqs2 while increasing the sensitivity by more than threefold, and is comparable to state-of-the-art structure search methods. In particular, unlike traditional sequence search methods, PLMSearch can recall most remote homology pairs with dissimilar sequences but similar structures. PLMSearch is freely available at https://dmiip.sjtu.edu.cn/PLMSearch .

Sub-gigahertz bandwidth all-optical tunable differentiator with high-energy efficiency based on a micro-ring resonator.

We propose and demonstrate a tunable fractional-order photonic differentiator (DIFF) that can process input pulses with a sub-gigahertz bandwidth. Our scheme utilizes the self-induced optical modulation effect observed in a silicon-on-insulator micro-ring resonator. Gaussian-like pulses with varying pulse widths between 7.5 and 20 ns are employed for differentiation, achieving an energy efficiency over 45%, to the best of our knowledge, which surpasses all previously reported schemes for input pulses with a sub-gigahertz bandwidth. We simulate the temporal dynamics of pulses to gain insight into the physical mechanisms underlying the differentiated outputs and provide a method for differentiation order adjustment, which is experimentally realized using an all-optical pump-probe technique.

Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features.

Advances in cryo-electron microscopy (cryo-EM) imaging technologies have led to a rapidly increasing number of cryo-EM density maps. Alignment and comparison of density maps play a crucial role in interpreting structural information, such as conformational heterogeneity analysis using global alignment and atomic model assembly through local alignment. Here, we present a fast and accurate global and local cryo-EM density map alignment method called CryoAlign, that leverages local density feature descriptors to capture spatial structure similarities. CryoAlign is a feature-based cryo-EM map alignment tool, in which the employment of feature-based architecture enables the rapid establishment of point pair correspondences and robust estimation of alignment parameters. Extensive experimental evaluations demonstrate the superiority of CryoAlign over the existing methods in terms of both alignment accuracy and speed.

RNA threading with secondary structure and sequence profile.

MOTIVATION: RNA threading aims to identify remote homologies for template-based modeling of RNA 3D structure. Existing RNA alignment methods primarily rely on secondary structure alignment. They are often time- and memory-consuming, limiting large-scale applications. In addition, the accuracy is far from satisfactory. RESULTS: Using RNA secondary structure and sequence profile, we developed a novel RNA threading algorithm, named RNAthreader. To enhance the alignment process and minimize memory usage, a novel approach has been introduced to simplify RNA secondary structures into compact diagrams. RNAthreader employs a two-step methodology. Initially, integer programming and dynamic programming are combined to create an initial alignment for the simplified diagram. Subsequently, the final alignment is obtained using dynamic programming, taking into account the initial alignment derived from the previous step. The benchmark test on 80 RNAs illustrates that RNAthreader generates more accurate alignments than other methods, especially for RNAs with pseudoknots. Another benchmark, involving 30 RNAs from the RNA-Puzzles experiments, exhibits that the models constructed using RNAthreader templates have a lower average RMSD than those created by alternative methods. Remarkably, RNAthreader takes less than two hours to complete alignments with ∼5000 RNAs, which is 3-40 times faster than other methods. These compelling results suggest that RNAthreader is a promising algorithm for RNA template detection. AVAILABILITY AND IMPLEMENTATION: https://yanglab.qd.sdu.edu.cn/RNAthreader.

Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks.

Optical neural networks (ONNs) herald a new era in information and communication technologies and have implemented various intelligent applications. In an ONN, the activation function (AF) is a crucial component determining the network performances and on-chip AF devices are still in development. Here, we first demonstrate on-chip reconfigurable AF devices with phase activation fulfilled by dual-functional graphene/silicon (Gra/Si) heterojunctions. With optical modulation and detection in one device, time delays are shorter, energy consumption is lower, reconfigurability is higher and the device footprint is smaller than other on-chip AF strategies. The experimental modulation voltage (power) of our Gra/Si heterojunction achieves as low as 1 V (0.5 mW), superior to many pure silicon counterparts. In the photodetection aspect, a high responsivity of over 200 mA/W is realized. Special nonlinear functions generated are fed into a complex-valued ONN to challenge handwritten letters and image recognition tasks, showing improved accuracy and potential of high-efficient, all-component-integration on-chip ONN. Our results offer new insights for on-chip ONN devices and pave the way to high-performance integrated optoelectronic computing circuits.

trRosettaRNA: automated prediction of RNA 3D structure with transformer network.

RNA 3D structure prediction is a long-standing challenge. Inspired by the recent breakthrough in protein structure prediction, we developed trRosettaRNA, an automated deep learning-based approach to RNA 3D structure prediction. The trRosettaRNA pipeline comprises two major steps: 1D and 2D geometries prediction by a transformer network; and 3D structure folding by energy minimization. Benchmark tests suggest that trRosettaRNA outperforms traditional automated methods. In the blind tests of the 15th Critical Assessment of Structure Prediction (CASP15) and the RNA-Puzzles experiments, the automated trRosettaRNA predictions for the natural RNAs are competitive with the top human predictions. trRosettaRNA also outperforms other deep learning-based methods in CASP15 when measured by the Z-score of the Root-Mean-Square Deviation. Nevertheless, it remains challenging to predict accurate structures for synthetic RNAs with an automated approach. We hope this work could be a good start toward solving the hard problem of RNA structure prediction with deep learning.

Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15.

We present the monomer and multimer structure prediction results of our methods in CASP15. We first designed an elaborate pipeline that leverages complementary sequence databases and advanced database searching algorithms to generate high-quality multiple sequence alignments (MSAs). Top MSAs were then selected for the subsequent step of structure prediction. We utilized trRosettaX2 and AlphaFold2 for monomer structure prediction (group name Yang-Server), and AlphaFold-Multimer for multimer structure prediction (group name Yang-Multimer). Yang-Server and Yang-Multimer are ranked at the top and the fourth, respectively, for monomer and multimer structure prediction. For 94 monomers, the average TM-score of the predicted structure models by Yang-Server is 0.876, compared to 0.798 by the default AlphaFold2 (i.e., the group NBIS-AF2-standard). For 42 multimers, the average DockQ score of the predicted structure models by Yang-Multimer is 0.464, compared to 0.389 by the default AlphaFold-Multimer (i.e., the group NBIS-AF2-multimer). Detailed analysis of the results shows that several factors contribute to the improvement, including improved MSAs, iterated modeling for large targets, interplay between monomer and multimer structure prediction for intertwined structures, etc. However, the structure predictions for orphan proteins and multimers remain challenging, and breakthroughs in this area are anticipated in the future.

Traction Machine State Recognition Method Based on DPCA Algorithm and Convolution Neural Network.

It is important to improve the identification accuracy of the operating status of elevator traction machines. The distribution difference of the time-frequency signals utilized to identify operating circumstances is modest, making it difficult to extract features from the vibration signals of traction machines under various operating conditions, leading to low recognition accuracy. A novel method for identifying the operating status of traction machines based on signal demodulation method and convolutional neural network (CNN) is proposed. The original vibration time-frequency signals are demodulated by the demodulation method based on time-frequency analysis and principal component analysis (DPCA). Firstly, the signal demodulation method based on principal component analysis is used to extract the modulation features of the experimentally measured vibration signals. Then, The CNN is used for feature vector extraction, and the training model is obtained through multiple iterations to achieve automatic recognition of the running state. The experimental results show that the proposed method can effectively extract feature parameters under different states. The diagnostic accuracy is up to 96.94%, which is about 16.61% higher than conventional methods. It provides a feasible solution for identifying the operating status of elevator traction machines.

Low-loss and broadband polarization-insensitive high-order mode pass filter based on photonic crystal and tapered coupler.

In this Letter, a polarization-insensitive high-order mode pass filter is presented, designed, and experimentally demonstrated. When TE0, TM0, TE1, and TM1 modes are injected into the input port, TM0 and TE0 modes are filtered, and TE1 and TM1 modes exit from the output port. To attain compactness, broad bandwidth, low insertion loss, excellent extinction ratio, and polarization-insensitive property, the finite difference time domain method and direct-binary-search or particle swarm optimization algorithm are employed for the optimization of structural parameters of the photonic crystal region and the coupling region in the tapered coupler. Measurement results reveal that, for the fabricated filter working at TE polarization, the extinction ratio and insertion loss are 20.42 and 0.32 dB at 1550 nm. In the case of TM polarization, the corresponding extinction ratio and insertion loss are 21.43 and 0.30 dB. Within a bandwidth from 1520 to 1590 nm, insertion loss smaller than 0.86 dB and extinction ratio larger than 16.80 dB are obtained for the fabricated filter working at TE polarization, while in the case of TM polarization, insertion loss lower than 0.79 dB and extinction ratio greater than 17.50 dB are realized.

Sparse 2-D optical phased array with large grating-lobe-free steering range based on an aperiodic grid.

Two-dimensional (2-D) optical phased arrays (OPAs) usually suffer from limited scan ranges and small aperture sizes. To overcome these bottlenecks, we utilize an aperiodic 32 × 32 grid to increase the beam scanning range and furthermore distribute 128 grating antennas sparsely among 1024 grid points so as to reduce the array element number. The genetic algorithm is used to optimize the uneven grid spacings and the sparse distribution of grating antennas. With these measures, a 128-channel 2-D OPA operating at 1550 nm realizes a grating-lobe-free steering range of 53° × 16°, a field of view of 24° × 16°, a beam divergence of 0.31° × 0.49°, and a sidelobe suppression ratio of 9 dB.

A unified approach to protein domain parsing with inter-residue distance matrix.

MOTIVATION: It is fundamental to cut multi-domain proteins into individual domains, for precise domain-based structural and functional studies. In the past, sequence-based and structure-based domain parsing was carried out independently with different methodologies. The recent progress in deep learning-based protein structure prediction provides the opportunity to unify sequence-based and structure-based domain parsing. RESULTS: Based on the inter-residue distance matrix, which can be either derived from the input structure or predicted by trRosettaX, we can decode the domain boundaries under a unified framework. We name the proposed method UniDoc. The principle of UniDoc is based on the well-accepted physical concept of maximizing intra-domain interaction while minimizing inter-domain interaction. Comprehensive tests on five benchmark datasets indicate that UniDoc outperforms other state-of-the-art methods in terms of both accuracy and speed, for both sequence-based and structure-based domain parsing. The major contribution of UniDoc is providing a unified framework for structure-based and sequence-based domain parsing. We hope that UniDoc would be a convenient tool for protein domain analysis. AVAILABILITY AND IMPLEMENTATION: https://yanglab.nankai.edu.cn/UniDoc/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Polarization-insensitive antisymmetric multimode waveguide Bragg grating filter based on an SiN-Si dual-layer stack.

A polarization-insensitive multimode antisymmetric waveguide Bragg grating (MASWBG) filter based on an SiN-Si dual-layer stack is demonstrated. Carefully optimized grating corrugations patterned on the sidewall of a silicon waveguide and SiN overlay are used to perturbate TE and TM modes, respectively. Furthermore, the lateral-shift apodization technique is utilized to improve the sidelobe suppression ratio (SLSR). A good overlap between the passbands measured in TE and TM polarization states is obtained. Insertion losses, SLSRs, and 3-dB bandwidths of measured passbands in TE/TM polarizations are 1/1.72 dB, 18.5/19.1 dB, and 5.1/3.5 nm, respectively.