Introducing π-HelixNovo for practical large-scale de novo peptide sequencing.

De novo peptide sequencing is a promising approach for novel peptide discovery, highlighting the performance improvements for the state-of-the-art models. The quality of mass spectra often varies due to unexpected missing of certain ions, presenting a significant challenge in de novo peptide sequencing. Here, we use a novel concept of complementary spectra to enhance ion information of the experimental spectrum and demonstrate it through conceptual and practical analyses. Afterward, we design suitable encoders to encode the experimental spectrum and the corresponding complementary spectrum and propose a de novo sequencing model $\pi$-HelixNovo based on the Transformer architecture. We first demonstrated that $\pi$-HelixNovo outperforms other state-of-the-art models using a series of comparative experiments. Then, we utilized $\pi$-HelixNovo to de novo gut metaproteome peptides for the first time. The results show $\pi$-HelixNovo increases the identification coverage and accuracy of gut metaproteome and enhances the taxonomic resolution of gut metaproteome. We finally trained a powerful $\pi$-HelixNovo utilizing a larger training dataset, and as expected, $\pi$-HelixNovo achieves unprecedented performance, even for peptide-spectrum matches with never-before-seen peptide sequences. We also use the powerful $\pi$-HelixNovo to identify antibody peptides and multi-enzyme cleavage peptides, and $\pi$-HelixNovo is highly robust in these applications. Our results demonstrate the effectivity of the complementary spectrum and take a significant step forward in de novo peptide sequencing.

TEC-miTarget: enhancing microRNA target prediction based on deep learning of ribonucleic acid sequences.

BACKGROUND: MicroRNAs play a critical role in regulating gene expression by binding to specific target sites within gene transcripts, making the identification of microRNA targets a prominent focus of research. Conventional experimental methods for identifying microRNA targets are both time-consuming and expensive, prompting the development of computational tools for target prediction. However, the existing computational tools exhibit limited performance in meeting the demands of practical applications, highlighting the need to improve the performance of microRNA target prediction models. RESULTS: In this paper, we utilize the most popular natural language processing and computer vision technologies to propose a novel approach, called TEC-miTarget, for microRNA target prediction based on transformer encoder and convolutional neural networks. TEC-miTarget treats RNA sequences as a natural language and encodes them using a transformer encoder, a widely used encoder in natural language processing. It then combines the representations of a pair of microRNA and its candidate target site sequences into a contact map, which is a three-dimensional array similar to a multi-channel image. Therefore, the contact map's features are extracted using a four-layer convolutional neural network, enabling the prediction of interactions between microRNA and its candidate target sites. We applied a series of comparative experiments to demonstrate that TEC-miTarget significantly improves microRNA target prediction, compared with existing state-of-the-art models. Our approach is the first approach to perform comparisons with other approaches at both sequence and transcript levels. Furthermore, it is the first approach compared with both deep learning-based and seed-match-based methods. We first compared TEC-miTarget's performance with approaches at the sequence level, and our approach delivers substantial improvements in performance using the same datasets and evaluation metrics. Moreover, we utilized TEC-miTarget to predict microRNA targets in long mRNA sequences, which involves two steps: selecting candidate target site sequences and applying sequence-level predictions. We finally showed that TEC-miTarget outperforms other approaches at the transcript level, including the popular seed match methods widely used in previous years. CONCLUSIONS: We propose a novel approach for predicting microRNA targets at both sequence and transcript levels, and demonstrate that our approach outperforms other methods based on deep learning or seed match. We also provide our approach as an easy-to-use software, TEC-miTarget, at https://github.com/tingpeng17/TEC-miTarget . Our results provide new perspectives for microRNA target prediction.

Immunosuppression induces regression of fibrosis in primary biliary cholangitis with moderate-to-severe interface hepatitis.

BACKGROUND: In patients with primary biliary cholangitis (PBC) treated with ursodeoxycholic acid (UDCA), the presence of moderate-to-severe interface hepatitis is associated with a higher risk of liver transplantation and death. This highlights the need for novel treatment approaches. In this study, we aimed to investigate whether combination therapy of UDCA and immunosuppressant (IS) was more effective than UDCA monotherapy. METHODS: We conducted a multicenter study involving PBC patients with moderate-to-severe interface hepatitis who underwent paired liver biopsies. Firstly, we compared the efficacy of the combination therapy with UDCA monotherapy on improving biochemistry, histology, survival rates, and prognosis. Subsequently we investigated the predictors of a beneficial response. RESULTS: This retrospective cohort study with prospectively collected data was conducted in China from January 2009 to April 2023. Of the 198 enrolled patients, 32 underwent UDCA monotherapy, while 166 received combination therapy, consisting of UDCA combined with prednisolone, prednisolone plus mycophenolate mofetil (MMF), or prednisolone plus azathioprine (AZA). The monotherapy group was treated for a median duration of 37.6 months (IQR 27.5-58.1), and the combination therapy group had a median treatment duration of 39.3 months (IQR 34.5-48.8). The combination therapy showed a significantly greater efficacy in reducing fibrosis compared to UDCA monotherapy, with an 8.3-fold increase in the regression rate (from 6.3% to 52.4%, P < 0.001). Other parameters, including biochemistry, survival rates, and prognosis, supported its effectiveness. Baseline IgG >1.3 × ULN and ALP <2.4 × ULN were identified as predictors of regression following the combination therapy. A predictive score named FRS, combining these variables, accurately identified individuals achieving fibrosis regression with a cut-off point of ≥ -0.163. The predictive value was validated internally and externally. CONCLUSION: Combination therapy with IS improves outcomes in PBC patients with moderate-to-severe interface hepatitis compared to UDCA monotherapy. Baseline IgG and ALP are the most significant predictors of fibrosis regression. The new predictive score, FRS, incorporating baseline IgG and ALP, can effectively identify individuals who would benefit from the combination therapy.

In Situ Detection of Electrochemical Reaction Surface Area by Optical Weak Measurement.

The surface area is key to electrochemical systems, including those in electrocatalysis and energy storage. Studies have shown that the surface area of the electrocatalyst directly affects the electrochemical activity, adsorption performance, and stability of the electrocatalyst. This paper used an optical weak measurement (WM) method, which has little impact on the analyte, to measure the reaction surface area (RSA) that actually participated in the electrochemical reaction. Then compared the RSA obtained by the WM with the total surface area (TSA) obtained by the standard Brunauer-Emmett-Teller (BET) measurement and the active surface area (ASA) obtained by the electrochemical double-layer capacitance (EDLC) method. Their growth trend was consistent, indicating the reliability of the WM method. Compared with the above two methods, the WM method is an in situ detection and easy to operate experimentally, which can help researchers to consider the effect of surface area on electrocatalyst performance more rationally.

Polyamine-producing bacteria inhibit the absorption of Cd by spinach and alter the bacterial community composition of rhizosphere soil.

Polyamines (PAs) are small aliphatic nitrogenous bases with strong biological activity that participate in plant stress response signaling and the alleviation of damage from stress. Herein, the effects of the PA-producing bacterium Bacillus megaterium N3 and PAs on the immobilization of Cd and inhibition of Cd absorption by spinach and the underlying mechanisms were studied. A solution test showed that strain N3 secreted spermine and spermidine in the presence of Cd. Both strain N3 and the PAs (spermine+spermidine) immobilized Cd and increased the pH of the solution. Untargeted metabolomics results showed that strain N3 secreted PAs, N1-acetylspermidine, 3-indolepropionic acid, indole-3-acetaldehyde, cysteinyl-gamma-glutamate, and choline, which correlated with plant growth promotion and Cd immobilization. A pot experiment showed that rhizosphere soil inoculation with strain N3 and PAs improved spinach dry weight and reduced spinach Cd absorption compared with the control. These positive effects were likely due to the increase in rhizosphere soil pH and NH4+-N and PA contents, which can be attributed primarily to Cd immobilization. Moreover, inoculation with strain N3 more effectively inhibited the absorption of Cd by spinach than spraying PAs, mainly because strain N3 enabled a better relative abundance of bacteria (Microvirga, Pedobacter, Bacillus, Brevundimonas, Pseudomonas, Serratia, Devosid, and Aminobacter), that have been reported to have the ability to resist heavy metals and produce PAs. Strain N3 regulated the structure of rhizosphere functional bacterial communities and inhibited Cd uptake by spinach. These results provide a theoretical basis for the prevention of heavy metal absorption by vegetables using PA-producing bacteria.

Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images.

Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires significant effort. To reduce the burden of manual annotation, we propose a novel weakly supervised segmentation framework based on sparse patch annotation, i.e., only small portions of patches in an image are labeled as 'tumor' or 'normal'. The framework consists of a patch-wise segmentation model called PSeger, and an innovative semi-supervised algorithm. PSeger has two branches for patch classification and image classification, respectively. This two-branch structure enables the model to learn more general features and thus reduce the risk of overfitting when learning sparsely annotated data. We incorporate the idea of consistency learning and self-training into the semi-supervised training strategy to take advantage of the unlabeled images. Trained on the BCSS dataset with only 25% of the images labeled (five patches for each labeled image), our proposed method achieved competitive performance compared to the fully supervised pixel-wise segmentation models. Experiments demonstrate that the proposed solution has the potential to reduce the burden of labeling histopathological images.

Phase Contrast Image-Based Rapid Antimicrobial Susceptibility Testing of Bacteria in Liquid Culture Media.

Currently, the world is facing the problem of bacterial resistance, which threatens public health, and bacterial antimicrobial susceptibility testing (AST) plays an important role in biomedicine, dietary safety and aquaculture. Traditional AST methods take a long time, usually 16-24 h, and cannot meet the demand for rapid diagnosis in the clinic, so rapid AST methods are needed to shorten the detection time. In this study, by using an in-house built centrifuge to centrifuge bacteria in a liquid medium onto the inner wall of the bottom surface of a counting plate, and using a phase contrast microscope to track bacterial growth under the effect of different antibiotic concentrations, the results of the minimum inhibitory concentration (MIC) of bacteria under the effect of antibiotics can be obtained in as early as 4 h. We used a combination of E. coli and tigecycline and obtained MIC results that were consistent with those obtained using the gold standard broth micro-dilution method, demonstrating the validity of our method; due to the time advantage, the complete set can be used in the future for point of care and clinical applications, helping physicians to quickly obtain the MIC used to inhibit bacterial growth.

Imaging Sensor for the Detection of the Flow Battery Via Weak Value Amplification.

Flow battery electrodes are vital for performing redox reactions, and an in-depth understanding of reaction kinetics and spatial distribution differences in electrodes is very important for improving the efficiency of electrochemical reactions. In this study, a reflection-type phase-sensitive weak measurement imaging system was developed for the detection of flow batteries. The phase difference between two polarization components in total internal reflection caused by electrode redox processes was measured by weak value amplification. The resulting refractive index resolution of the imaging system was estimated to be 2.8-4.2 × 10-6 RIU. The real-time monitoring ability of the system was demonstrated by linear sweep voltammetry tests of vanadium redox batteries. Compared to traditional optical methods, the proposed weak measurement imaging sensor did not require coating, as it can be used in acid electrolytes of vanadium flow batteries. Meanwhile, the weak value amplification effect led to a higher resolution than the total internal reflection system shown in our previous work, thereby resulting in more accurate detection of electrochemical reactions. In sum, the proposed sensor looks very promising for the detection of electrochemical reactions in flow batteries, water splitting, electrochemical corrosion, and electrocatalysis.

Real time and online aerosol identification based on deep learning of multi-angle synchronous polarization scattering indexes.

In this study, we employ our developed instrument to obtain high-throughput multi-angle single-particle polarization scattering signals. Based on experimental results of a variety of samples with different chemical composition, particle size, morphology, and microstructure, we trained a deep convolutional network to identify the polarization signal characteristics during aerosol scattering processes, and then investigate the feasibility of multi-dimensional polarization characterization applied in the online and real-time fine and accurate aerosol recognition. Our model shows a high classification accuracy rate (>98%) and can achieve aerosol recognition at a very low proportion (<0.1%), and shows well generalization ability in the test set and the sample types not included in the training set. The above results indicate that that the time series pulses from multi-angle polarization scattering contain enough information related with microscopic characteristics of an individual particle, and the deep learning model shows its capability to extract features from these synchronous multi-dimensional polarization signals. Our investigations confirm a good prospect of aerosol attribute retrieval and identifying and classifying individual aerosols one by one by the combination of multi-dimensional polarization scattering indexes with deep learning method.

In situ detection of electrochemical reaction by weak measurement.

In the field of electrochemical energy storage systems, the use of in situ detection technology helps to study the mechanism of electrochemical reaction. Our group has previously in situ detected the electrochemical reaction in vanadium flow batteries by total internal reflection (TIR) imaging. In order to further improve the detection resolution, in this study, the weak measurement (WM) method was introduced to in situ detect the electrochemical reaction during the linear sweep voltammetry or the cyclic voltammetry tests with quantitative measurement of the absolute current density, which lays a foundation for replacing the TIR for two-dimensional imaging of electrochemical reactions in vanadium flow batteries, oxygen/hydrogen evolution reaction, surface treatments, electrochemical corrosion and so on.

[Research progress on three-dimensional printed interbody fusion cage].

Spinal fusion is a standard operation for treating moderate and severe intervertebral disc diseases. In recent years, the proportion of three-dimensional printing interbody fusion cage in spinal fusion surgery has gradually increased. In this paper, the research progress of molding technology and materials used in three-dimensional printing interbody fusion cage at present is summarized. Then, according to structure layout, three-dimensional printing interbody fusion cages are classified into five types: solid-porous-solid (SPS) type, solid-porous-frame (SPF) type, frame-porous-frame (FPF) type, whole porous cage (WPC) type and others. The optimization process of three-dimensional printing interbody fusion cage and the advantages and disadvantages of each type are analyzed and summarized in depth. The clinical application of various types of 3D printed interbody fusion cage was introduced and summarized later. Lastly, combined with the latest research progress and achievements, the future research direction of three-dimensional printing interbody fusion cage in molding technology, application materials and coating materials is prospected in order to provide some reference for scholars engaged in interbody fusion cage research and application.

Hydrogel-based microbeads for Raman-encoded suspension array using the reversed-phase suspension polymerization method and ultraviolet light curing.

A one-step synthesis using the reversed-phase suspension polymerization method and ultraviolet light curing is proposed for preparing the Raman-encoded suspension array (SA). The encoded microcarriers are prepared by doping the Raman reporter molecules into an aqueous phase, and then dispersing the aqueous phase in an oil phase and curing by ultraviolet light irradiation. The multiplexed biomolecule detection and various concentration experiments confirm the qualitative and quantitative analysis capabilities of the Raman-encoded SA with a limit of detection of 52.68 pM. The narrow bandwidth of the Raman spectrum can achieve a large number of codes in the available spectral range and the independence between the encoding channel and the fluorescent label channel provides the encoding method with high accuracy. This preparation method is simple and easy to operate, low in cost, and high in efficiency. A large number of hydrogel-based encoding microbeads could be quickly obtained with good biocompatibility. Most importantly, concentrating plenty of Raman reporter molecules inside the microbeads increases the signal intensity and means the molecular assembly is not limited by the functional groups; thus, the types of materials available for Raman encoding method are expanded. Furthermore, the signal intensity-related encoding method is verified by doping different proportions of Raman reporter molecules with our proposed synthesis method, which further increases the detection throughput of Raman-encoded SA. Graphical Abstract.

Detection of Macromolecular Content in a Mixed Solution of Protein Macromolecules and Small Molecules Using a Weak Measurement Linear Differential System.

In this paper, we propose a linear differential detection system based on a frequency domain weak measurement. The system can be used for detecting optical substances. Moreover, we completed an experiment to detect himan serum albumin (HSA) content in a mixture of human serum albumin and l-proline via dialysis. This work also proves the differential function of the system. This experiment can be further extended to detecting protein content in a mixed solution that contains protein macromolecules and various small molecules. It is very important for detecting molecules without photomarking in solutions of complex biological samples. In this paper, the system has an optical resolution of 1.39 × 10-5, and resolution of 4.06 × 10-8 mol/L for himan serum protein solution.

Study on polarization scattering applied in aerosol recognition in the air.

In this work, we present an in situ online aerosol recognition scheme by synchronized parallel polarization scattering analysis. By theoretical simulations, we select the feasible scattering angles and evaluate the potential of Stokes parameters to identify aerosols. Correspondingly, we develop a measurement system based on multi-angle optical scattering and multidimensional polarization analyzing technique. We construct two index groups based on non-normalized and normalized polarization parameters respectively, and employ their frequency distribution histograms instead of the simple average values to identify and classify different types of aerosols. The experimental verification confirms a future way of a multi-dimensional polarization parameter group applied in a fast and effective air pollutants monitoring.

Dual-wavelength digital holographic phase and fluorescence microscopy combining with Raman spectroscopy for micro-quartz pieces-based dual-channel encoded suspension array.

Dual-wavelength digital holographic phase and fluorescence microscopy (DW-DHPFM), combining with Raman spectroscopy, is designed to achieve the detection and analysis of biomolecules with a new dual-channel encoding method. This employs the Raman reporter molecules assembled micro-quartz pieces (MQPs) as microcarriers of suspension array (SA). The dual-wavelength digital holographic phase microscopy (DW-DHPM) and Raman spectroscopy are served as the decoding platforms, and the fluorescence microscopy is used to quantify target analytes. Considering the independence between encoding and label signal, the above two encoding channels could effectively avoid the crosstalk in immunoassay process, and the combination of two encoding methods expand the encoding capacity with a considerable magnitude. Accurate and stable decoding abilities are verified by multiplexed immunoassay experiment and the quantitative analysis of targets with high-sensitivity is confirmed by concentration gradient experiments.

Estimation and extraction of the aerosol complex refractive index based on Stokes vector measurements.

We present a way to estimate the aerosol complex refractive indices (ACRI) based on a real-time Stokes vector measurement technology. First, we introduce how to simultaneously get the multi-polarization signals of multi-scattering angles. Then we demonstrate the numerical inversion to retrieve an ACRI based on an iterative Mie algorithm. Meanwhile, we show the experimental results of several types of aerosol samples. Our optimal estimation of ACRI inversion shows a good agreement with the references, which confirms the feasibility and then implies a good prospect of multidimensional polarization characterization applied in the future aerosol recognition, especially suitable for near-spherical scatters.

Fast and accurate decoding of Raman spectra-encoded suspension arrays using deep learning.

A deep learning network called "residual neural network" (ResNet) was used to decode Raman spectra-encoded suspension arrays (SAs). With narrow bandwidths and stable signals, Raman spectra have ideal encoding properties. The different Raman reporter molecules assembled micro-quartz pieces (MQPs) were grafted with various biomolecule probes, which enabled simultaneous detection of numerous target analytes in a single sample. Multiple types of mixed MQPs were measured by Raman spectroscopy and then decoded by ResNet to acquire the type information of analytes. The good classification performance of ResNet was verified by a t-distributed stochastic neighbor embedding (t-SNE) diagram. Compared with other machine learning models, these experiments showed that ResNet was obviously superior in terms of classification stability and training convergence to different datasets. This method simplified the decoding process and the classification accuracy reached 100%.

Ion-chelation based digital barcodes for multiplexing of a suspension array.

Ion-chelated microbeads (ICMs) for suspension arrays can be prepared by chelating metal ions (MIs), which are used as encoding materials. Stimulating the ICMs, laser induced breakdown spectra (LIBs) can be obtained and the atomic spectra of the chelated ions are chosen as the decoding signals. Our ICMs show digital characteristics with high stability due to the properties of LIBs. And, since there are many available coding materials and different kinds of coding materials can be easily combined, the coding capacity can be considerably enlarged. Further, the background interference in fluoroimmunoassay detection could be avoided, because the ICMs contain no fluorescence emission. In our studies, we achieved a total of 15 types of barcodes by taking full advantage of 4 kinds of ions, then a fluoroimmunoassay was performed to demonstrate the specificity and detection performance of our ICMs in multiplexing and the detection limit could reach 1.49 × 10-10 M, showing promising potential in applications.

Gold-nanorod-enhanced Raman spectroscopy encoded micro-quartz pieces for the multiplex detection of biomolecules.

The rapid analysis and detection of biomolecules has become increasingly important in biological research. Hence, here we propose a novel suspension array method that is based on gold nanorod (AuNR)-enhanced Raman spectroscopy and uses micro-quartz pieces (MQPs) as microcarriers. AuNRs and Raman reporter molecules are coupled together by Au-S bonds to obtain surface-enhanced Raman scattering labels (SERS labels). The SERS labels are then assembled on the surfaces of the MQPs via electrostatic interactions, yielding encoded MQPs. Experimental results showed that the encoded MQPs could be decoded using a Raman spectrometer. A multiplex immunoassay experiment demonstrated the validity and specificity of these encoded MQPs when they were used for bioanalysis. In concentration gradient experiments, the proposed method was found to give a linear concentration response to the target biomolecule at target concentrations of 0.46875-30 nM, and the detection limit was calculated to be 1.78 nM. The proposed method utilizes MQPs as carriers rather than conventional microbeads, which allows the interference caused by the background fluorescence of microbeads to be eliminated. The fluorescence of the encoded MQPs can be simply, rapidly, and inexpensively quantified using fluorescence microscopy. By dividing the quantitative and qualitative detection of biomolecules into two independent channels, crosstalk between the encoded signal and the labeled signal is averted and high decoding accuracy and detection sensitivity are guaranteed. Graphical abstract.

Image-guided vibrometry system integrated with spectral- and time-domain optical coherence tomography.

Vibrometry using optical coherence tomography (OCT) can provide valuable information for investigating either the mechanical properties or the physiological function of biological tissues, especially the hearing organs. Real-time imaging of the measured tissues provides structure imaging and spatial guidance for and is thus highly demanded by such vibrometry. However, the traditional time-domain OCT (TD-OCT) systems, although capable of subnanometric vibrometry at large ranges of frequencies, are unable to offer an imaging speed that is high enough to acquire depth-resolved images for guidance. The spectral-domain OCT (SD-OCT) systems, although allowing image-guided vibrometry, are challenged in measuring vibration at high frequencies, particularly for scattering tissue specimens that require longer exposure time to ensure imaging and vibrometry performance. This is because of their limit in the line-scan rate of the CCD, in which the maximum resolvable frequency measured by the SD-OCT is about 1/4 of the CCD line-scan rate in practice. In the present study, we have developed a dual-mode OCT system combining both SD-OCT and TD-OCT modalities for image-guided vibrometry, as the SD-OCT can provide guiding structural images in real-time and, moreover, the TD-OCT can guarantee vibrometry at large ranges of frequencies, including high frequencies. The efficacy of the developed system in image-guided vibrometry has been experimentally demonstrated using both piezoelectric ceramic transducer (PZT) and ex vivo middle-ear samples from guinea pigs. For the vibrometry of PZT, the minimum detectable vibration amplitude was reached at ∼0.01 nm. For the vibrometry of the sound-evoked biological samples, both real-time two-dimensional imaging and subnanometric vibrometry were performed at the frequency ranging from 1 to 40 kHz. These results indicate that our dual-mode OCT system is able to act as an excellent vibrometer enabling image-guided high-frequency measurement.