Innovative methods for microplastic characterization and detection: Deep learning supported by photoacoustic imaging and automated pre-processing data.

Plastic products' widespread applications and their non-biodegradable nature have resulted in the continuous accumulation of microplastic waste, emerging as a significant component of ecological environmental issues. In the field of microplastic detection, the intricate morphology poses challenges in achieving rapid visual characterization of microplastics. In this study, photoacoustic imaging technology is initially employed to capture high-resolution images of diverse microplastic samples. To address the limited dataset issue, an automated data processing pipeline is designed to obtain sample masks while effectively expanding the dataset size. Additionally, we propose Vqdp2, a generative deep learning model with multiple proxy tasks, for predicting six forms of microplastics data. By simultaneously constraining model parameters through two training modes, outstanding morphological category representations are achieved. The results demonstrate Vqdp2's excellent performance in classification accuracy and feature extraction by leveraging the advantages of multi-task training. This research is expected to be attractive for the detection classification and visual characterization of microplastics.

Advancing microplastic surveillance through photoacoustic imaging and deep learning techniques.

Microplastic contamination presents a significant global environmental threat, yet scientific understanding of its morphological distribution within ecosystems remains limited. This study introduces a pioneering method for comprehensive microplastic assessment and environmental monitoring, integrating photoacoustic imaging and advanced deep learning techniques. Rigorous curation of diverse microplastic datasets enhances model training, yielding a high-resolution imaging dataset focused on shape-based discrimination. The introduction of the Vector-Quantized Variational Auto Encoder (VQVAE2) deep learning model signifies a substantial advancement, demonstrating exceptional proficiency in image dimensionality reduction and clustering. Furthermore, the utilization of Vector Quantization Microplastic Photoacoustic imaging (VQMPA) with a proxy task before decoding enhances feature extraction, enabling simultaneous microplastic analysis and discrimination. Despite inherent limitations, this study lays a robust foundation for future research, suggesting avenues for enhancing microplastic identification precision through expanded sample sizes and complementary methodologies like spectroscopy. In conclusion, this innovative approach not only advances microplastic monitoring but also provides valuable insights for future environmental investigations, highlighting the potential of photoacoustic imaging and deep learning in bolstering sustainable environmental monitoring efforts.

Loss of tumor suppressor TMEM127 drives RET-mediated transformation through disrupted membrane dynamics.

Internalization from the cell membrane and endosomal trafficking of receptor tyrosine kinases (RTKs) are important regulators of signaling in normal cells that can frequently be disrupted in cancer. The adrenal tumor pheochromocytoma (PCC) can be caused by activating mutations of the rearranged during transfection (RET) receptor tyrosine kinase, or inactivation of TMEM127, a transmembrane tumor suppressor implicated in trafficking of endosomal cargos. However, the role of aberrant receptor trafficking in PCC is not well understood. Here, we show that loss of TMEM127 causes wildtype RET protein accumulation on the cell surface, where increased receptor density facilitates constitutive ligand-independent activity and downstream signaling, driving cell proliferation. Loss of TMEM127 altered normal cell membrane organization and recruitment and stabilization of membrane protein complexes, impaired assembly, and maturation of clathrin-coated pits, and reduced internalization and degradation of cell surface RET. In addition to RTKs, TMEM127 depletion also promoted surface accumulation of several other transmembrane proteins, suggesting it may cause global defects in surface protein activity and function. Together, our data identify TMEM127 as an important determinant of membrane organization including membrane protein diffusability and protein complex assembly and provide a novel paradigm for oncogenesis in PCC where altered membrane dynamics promotes cell surface accumulation and constitutive activity of growth factor receptors to drive aberrant signaling and promote transformation.

Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics.

The latest breakthroughs in spatially resolved transcriptomics technology offer comprehensive opportunities to delve into gene expression patterns within the tissue microenvironment. However, the precise identification of spatial domains within tissues remains challenging. In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current graph neural networks have not extensively addressed. Benchmark testing demonstrates its significant efficacy in elucidating tissue anatomy and interpreting tumor heterogeneity, indicating its potential in advancing spatial transcriptomics research and understanding complex biological phenomena.

Enhancing subcellular protein localization mapping analysis using Sc2promap utilizing attention mechanisms.

BACKGROUND: Aberrant protein localization is a prominent feature in many human diseases and can have detrimental effects on the function of specific tissues and organs. High-throughput technologies, which continue to advance with iterations of automated equipment and the development of bioinformatics, enable the acquisition of large-scale data that are more pattern-rich, allowing for the use of a wider range of methods to extract useful patterns and knowledge from them. METHODS: The proposed sc2promap (Spatial and Channel for SubCellular Protein Localization Mapping) model, designed to proficiently extract meaningful features from a vast repository of single-channel grayscale protein images for the purposes of protein localization analysis and clustering. Sc2promap incorporates a prediction head component enriched with supplementary protein annotations, along with the integration of a spatial-channel attention mechanism within the encoder to enables the generation of high-resolution protein localization maps that encapsulate the fundamental characteristics of cells, including elemental cellular localizations such as nuclear and non-nuclear domains. RESULTS: Qualitative and quantitative comparisons were conducted across internal and external clustering evaluation metrics, as well as various facets of the clustering results. The study also explored different components of the model. The research outcomes conclusively indicate that, in comparison to previous methods, Sc2promap exhibits superior performance. CONCLUSIONS: The amalgamation of the attention mechanism and prediction head components has led the model to excel in protein localization clustering and analysis tasks. GENERAL SIGNIFICANCE: The model effectively enhances the capability to extract features and knowledge from protein fluorescence images.

Fast Real-Time Brain Tumor Detection Based on Stimulated Raman Histology and Self-Supervised Deep Learning Model.

In intraoperative brain cancer procedures, real-time diagnosis is essential for ensuring safe and effective care. The prevailing workflow, which relies on histological staining with hematoxylin and eosin (H&E) for tissue processing, is resource-intensive, time-consuming, and requires considerable labor. Recently, an innovative approach combining stimulated Raman histology (SRH) and deep convolutional neural networks (CNN) has emerged, creating a new avenue for real-time cancer diagnosis during surgery. While this approach exhibits potential, there exists an opportunity for refinement in the domain of feature extraction. In this study, we employ coherent Raman scattering imaging method and a self-supervised deep learning model (VQVAE2) to enhance the speed of SRH image acquisition and feature representation, thereby enhancing the capability of automated real-time bedside diagnosis. Specifically, we propose the VQSRS network, which integrates vector quantization with a proxy task based on patch annotation for analysis of brain tumor subtypes. Training on images collected from the SRS microscopy system, our VQSRS demonstrates a significant speed enhancement over traditional techniques (e.g., 20-30 min). Comparative studies in dimensionality reduction clustering confirm the diagnostic capacity of VQSRS rivals that of CNN. By learning a hierarchical structure of recognizable histological features, VQSRS classifies major tissue pathological categories in brain tumors. Additionally, an external semantic segmentation method is applied for identifying tumor-infiltrated regions in SRH images. Collectively, these findings indicate that this automated real-time prediction technique holds the potential to streamline intraoperative cancer diagnosis, providing assistance to pathologists in simplifying the process.

Unveiling precision: a data-driven approach to enhance photoacoustic imaging with sparse data.

This study presents the Fourier Decay Perception Generative Adversarial Network (FDP-GAN), an innovative approach dedicated to alleviating limitations in photoacoustic imaging stemming from restricted sensor availability and biological tissue heterogeneity. By integrating diverse photoacoustic data, FDP-GAN notably enhances image fidelity and reduces artifacts, particularly in scenarios of low sampling. Its demonstrated effectiveness highlights its potential for substantial contributions to clinical applications, marking a significant stride in addressing pertinent challenges within the realm of photoacoustic acquisition techniques.

Loss of Tumour Suppressor TMEM127 Drives RET-mediated Transformation Through Disrupted Membrane Dynamics.

Internalization from the cell membrane and endosomal trafficking of receptor tyrosine kinases (RTK) are important regulators of signaling in normal cells that can frequently be disrupted in cancer. The adrenal tumour pheochromocytoma (PCC) can be caused by activating mutations of the RET receptor tyrosine kinase, or inactivation of TMEM127, a transmembrane tumour suppressor implicated in trafficking of endosomal cargos. However, the role of aberrant receptor trafficking in PCC is not well understood. Here, we show that loss of TMEM127 causes wildtype RET protein accumulation on the cell surface, where increased receptor density facilitates constitutive ligand-independent activity and downstream signaling, driving cell proliferation. Loss of TMEM127 altered normal cell membrane organization and recruitment and stabilization of membrane protein complexes, impaired assembly, and maturation of clathrin coated pits, and reduced internalization and degradation of cell surface RET. In addition to RTKs, TMEM127 depletion also promoted surface accumulation of several other transmembrane proteins, suggesting it may cause global defects in surface protein activity and function. Together, our data identify TMEM127 as an important determinant of membrane organization including membrane protein diffusability, and protein complex assembly and provide a novel paradigm for oncogenesis in PCC where altered membrane dynamics promotes cell surface accumulation and constitutive activity of growth factor receptors to drive aberrant signaling and promote transformation.

3DCNAS: A universal method for predicting the location of fluorescent organelles in living cells in three-dimensional space.

Cellular biology research relies on microscopic imaging techniques for studying the complex structures and dynamic processes within cells. Fluorescence microscopy provides high sensitivity and subcellular resolution but has limitations such as photobleaching and sample preparation challenges. Transmission light microscopy offers a label-free alternative but lacks contrast for detailed interpretation. Deep learning methods have shown promise in analyzing cell images and extracting meaningful information. However, accurately learning and simulating diverse subcellular structures remain challenging. In this study, we propose a method named three-dimensional cell neural architecture search (3DCNAS) to predict subcellular structures of fluorescence using unlabeled transmitted light microscope images. By leveraging the automated search capability of differentiable neural architecture search (NAS), our method partially mitigates the issues of overfitting and underfitting caused by the distinct details of various subcellular structures. Furthermore, we apply our method to analyze cell dynamics in genome-edited human induced pluripotent stem cells during mitotic events. This allows us to study the functional roles of organelles and their involvement in cellular processes, contributing to a comprehensive understanding of cell biology and offering insights into disease pathogenesis.

TMEM127 suppresses tumor development by promoting RET ubiquitination, positioning, and degradation.

The TMEM127 gene encodes a transmembrane protein of poorly known function that is mutated in pheochromocytomas, neural crest-derived tumors of adrenomedullary cells. Here, we report that, at single-nucleus resolution, TMEM127-mutant tumors share precursor cells and transcription regulatory elements with pheochromocytomas carrying mutations of the tyrosine kinase receptor RET. Additionally, TMEM127-mutant pheochromocytomas, human cells, and mouse knockout models of TMEM127 accumulate RET and increase its signaling. TMEM127 contributes to RET cellular positioning, trafficking, and lysosome-mediated degradation. Mechanistically, TMEM127 binds to RET and recruits the NEDD4 E3 ubiquitin ligase for RET ubiquitination and degradation via TMEM127 C-terminal PxxY motifs. Lastly, increased cell proliferation and tumor burden after TMEM127 loss can be reversed by selective RET inhibitors in vitro and in vivo. Our results define TMEM127 as a component of the ubiquitin system and identify aberrant RET stabilization as a likely mechanism through which TMEM127 loss-of-function mutations cause pheochromocytoma.

A membrane-associated MHC-I inhibitory axis for cancer immune evasion.

Immune-checkpoint blockade has revolutionized cancer treatment, but some cancers, such as acute myeloid leukemia (AML), do not respond or develop resistance. A potential mode of resistance is immune evasion of T cell immunity involving aberrant major histocompatibility complex class I (MHC-I) antigen presentation (AP). To map such mechanisms of resistance, we identified key MHC-I regulators using specific peptide-MHC-I-guided CRISPR-Cas9 screens in AML. The top-ranked negative regulators were surface protein sushi domain containing 6 (SUSD6), transmembrane protein 127 (TMEM127), and the E3 ubiquitin ligase WWP2. SUSD6 is abundantly expressed in AML and multiple solid cancers, and its ablation enhanced MHC-I AP and reduced tumor growth in a CD8+ T cell-dependent manner. Mechanistically, SUSD6 forms a trimolecular complex with TMEM127 and MHC-I, which recruits WWP2 for MHC-I ubiquitination and lysosomal degradation. Together with the SUSD6/TMEM127/WWP2 gene signature, which negatively correlates with cancer survival, our findings define a membrane-associated MHC-I inhibitory axis as a potential therapeutic target for both leukemia and solid cancers.

Multi-aspect simulation insight on thermolysis mechanism and interaction of NTO/HMX-based plastic-bonded explosives: a new conception of the mixed explosive model.

Reactive molecular dynamics (RMDs) calculations were used to determine, for the first time, the process of thermolysis of the mixed explosives, including 3-nitro-1,2,4-triazol-5-one (NTO) and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazoline (HMX). Significantly, this is the first time that a layered model for mixed explosives, which is an extreme innovation of mixed explosive models was adopted. It is shown that a large amount of NO2 in the HMX and OH groups generated by the decomposition of HNO2 has a favorable effect on the thermolysis of NTO, as further validated by a reduction in the activation energy of NTO/HMX. The amount of H2O and N2 in the resulting products increased significantly, but the amount of NH3 changed slightly. The analysis results correspond to the change in chemical bonds. Whenever there is an increase in temperature, the time for the maximum number of clusters to appear is shortened accordingly. In addition, the acidity of NTO has been considered. An independent gradient model based on Hirshfeld partition (IGMH) and atoms in molecule (AIM) analysis of NTO/HMX was implemented. The relatively strong hydrogen bonds indicate that HMX can inhibit the acidity of NTO and is beneficial for the wide application of NTO/HMX-based plastic-bonded explosives (PBXs).

Theoretical study on the influence of different solvents on the growth morphology of 3,4-bis(3-nitrofurazan-4-yl)furoxan (DNTF).

CONTEXT: 3,4-bis(3-nitrofurazan-4-yl)furoxan (DNTF) is a new energetic compound with high energy and high density, and it is an important component of propellant and melt cast explosive. In order to study the effect of solvent on the growth morphology of DNTF, the growth plane of DNTF in vacuum is predicted by attachment energy (AE) model, and then the modified attachment energy of each growth plane in different solvents is calculated by molecular dynamics simulation. The morphology of crystal in solvent is predicted by modified attachment energy (MAE) model. The factors affecting crystal growth in solvent environment are analyzed by mass density distribution, radial distribution function and diffusion coefficient. The results show that the growth morphology of crystal in solvent is not only related to the adsorption strength of solvent to crystal plane, but affected by the attraction of crystal plane to solute. The hydrogen bond plays an important role in the adsorption strength between solvent and crystal plane. The polarity of solvent has a great influence on the crystal morphology, and the interaction between the solvent with stronger polarity and the crystal plane is stronger. The morphology of DNTF in n-butanol solvent is closer to spherical, which can effectively reduce the sensitivity of DNTF. METHODS: The molecular dynamics simulation is carried out under the COMPASS force field of Materials Studio software. Gaussian software is used to calculate the electrostatic potential of DNTF at B3LYP-D3/6-311 + G (d, p) theoretical level.

Electrochemical biosensor based on antibody-modified Au nanoparticles for rapid and sensitive analysis of influenza A virus.

To cope with the easy transmissibility of the avian influenza A virus subtype H1N1, a biosensor was developed for rapid and highly sensitive electrochemical immunoassay. Based on the principle of specific binding between antibody and virus molecules, the active molecule-antibody-adapter structure was formed on the surface of an Au NP substrate electrode; it included a highly specific surface area and good electrochemical activity for selective amplification detection of the H1N1 virus. The electrochemical test results showed that the BSA/H1N1 Ab/Glu/Cys/Au NPs/CP electrode was used for the electrochemical detection of the H1N1 virus with a sensitivity of 92.1 µA (pg/mL)-1 cm2, LOD of 0.25 pg/ml, linear ranges of 0.25-5 pg/mL, and linearity of (R 2 = 0.9846). A convenient H1N1 antibody-based electrochemical electrode for the molecular detection of the H1N1 virus will be of great use in the field of epidemic prevention and raw poultry protection. Supplementary Information: The online version contains supplementary material available at 10.1007/s11581-023-04944-w.

Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging.

Complex intracellular organizations are commonly represented by dividing the metabolic process of cells into different organelles. Therefore, identifying sub-cellular organelle architecture is significant for understanding intracellular structural properties, specific functions, and biological processes in cells. However, the discrimination of these structures in the natural organizational environment and their functional consequences are not clear. In this article, we propose a new pixel-level multimodal fusion (PLMF) deep network which can be used to predict the location of cellular organelle using label-free cell optical microscopy images followed by deep-learning-based automated image denoising. It provides valuable insights that can be of tremendous help in improving the specificity of label-free cell optical microscopy by using the Transformer-Unet network to predict the ground truth imaging which corresponds to different sub-cellular organelle architectures. The new prediction method proposed in this article combines the advantages of a transformer's global prediction and CNN's local detail analytic ability of background features for label-free cell optical microscopy images, so as to improve the prediction accuracy. Our experimental results showed that the PLMF network can achieve over 0.91 Pearson's correlation coefficient (PCC) correlation between estimated and true fractions on lung cancer cell-imaging datasets. In addition, we applied the PLMF network method on the cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new way for the time-resolved study of subcellular components in different cells, especially for cancer cells.

Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells.

Stimulated Raman Scattering Microscopy (SRS) is a powerful tool for label-free detailed recognition and investigation of the cellular and subcellular structures of living cells. Determining subcellular protein localization from the cell level of SRS images is one of the basic goals of cell biology, which can not only provide useful clues for their functions and biological processes but also help to determine the priority and select the appropriate target for drug development. However, the bottleneck in predicting subcellular protein locations of SRS cell imaging lies in modeling complicated relationships concealed beneath the original cell imaging data owing to the spectral overlap information from different protein molecules. In this work, a multiple parallel fusion network, MPFnetwork, is proposed to study the subcellular locations from SRS images. This model used a multiple parallel fusion model to construct feature representations and combined multiple nonlinear decomposing algorithms as the automated subcellular detection method. Our experimental results showed that the MPFnetwork could achieve over 0.93 dice correlation between estimated and true fractions on SRS lung cancer cell datasets. In addition, we applied the MPFnetwork method to cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new method for the time-resolved study of subcellular components in different cells, especially cancer cells.

A RET::GRB2 fusion in pheochromocytoma defies the classic paradigm of RET oncogenic fusions.

The RET kinase receptor is a target of mutations in neural crest tumors, including pheochromocytomas, and of oncogenic fusions in epithelial cancers. We report a RET::GRB2 fusion in a pheochromocytoma in which RET, functioning as the upstream partner, retains its kinase domain but loses critical C-terminal motifs and is fused to GRB2, a physiological RET interacting protein. RET::GRB2 is an oncogenic driver that leads to constitutive, ligand-independent RET signaling; has transforming capability dependent on RET catalytic function; and is sensitive to RET inhibitors. These observations highlight a new driver event in pheochromocytomas potentially amenable for RET-driven therapy.

Quantitative insights into tightly and loosely bound water in hydration shells of amino acids.

The hydration of amino acids closely correlates the hydration of peptides and proteins and is critical to their biological functions. However, complete and quantitative understanding about the hydration of amino acids is lacking. Here, tightly and loosely bound water of 20 zwitterionic amino acids are quantitatively distinguished and determined by Raman spectroscopy with multivariate curve resolution (Raman-MCR) and differential scanning calorimetry (DSC). The total hydration water obtained from Raman-MCR and the tightly bound water determined by DSC have certain relevance, but they do not exactly correspond. In particular, Pro, Arg and Lys exhibit larger number of tightly bound water molecules (4.02-6.59), showing a significant influence on the onset transition temperature and the melting enthalpy values of water molecules, which provides direct evidence for their unique functions associated with biological water. Asn, Ser, Thr, Met, His and Glu have a smaller number of tightly bound water molecules (0.30-1.31), whilst the other remaining 11 amino acids only contain loosely bound water molecules. Four exceptional amino acids Ile, Leu, Phe and Val show fewer tightly bound water molecules but a higher number of loosely bound water molecules. As for the hydration shell structure, most amino acids except Pro and Trp enhance tetrahedral water structure and H-bonds relative to pure water and at least 1.9% of the hydration water molecules associated with the amino acids show non-hydrogen-bonded OH defects. This work combines two effective experimental techniques to reveal the hydration water structure and quantitatively analyze two kinds of bound water molecules of 20 amino acids.

Insights into the Structure and Dynamics of Imidazolium Ionic Liquid and Tetraethylene Glycol Dimethyl Ether Cosolvent Mixtures: A Molecular Dynamics Approach.

In this work, the effect of molecular cosolvents tetraethylene glycol dimethyl ether (TEGDME) on the structure and versatile nature of mixtures of these compounds with imidazolium-based ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate ([bmim][PF6]) is analyzed and discussed at a molecular level by means of all-atom molecular dynamics (MD) simulations. In the whole concentration range of the binary mixtures, the structures and properties evolution was studied by means of systematic molecular dynamics simulations of the fraction of hydrogen bonds, the radial and spatial distribution functions for the various molecular ions and molecular species in the system, together with the snapshots visualization of equilibrated simulation boxes with a color-coding scheme and the rotational dynamics of coumarin 153 (C153) in the binary mixtures. The goal of the work is to provide a molecular-level understanding of significant improvement of ionic conductivity and self-diffusion with the presence of TEGDME as a cosolvent, which causes an enhancement to the ion translational motion and fluidity in the [bmim][PF6] ionic liquids (ILs). Under a mixture concentration change, the microstructure changes of [bmim][PF6] with the TEGDME molar fraction (XTEG) above 0.50 show a slight difference from that of neat [bmim][PF6] IL and concentrated [bmim][PF6]/TEGDME mixture in terms of the radial and spatial distribution functions. The relative diffusivities of solvent molecules to cations as a function of concentration were found to depend on the solvent but not on the anion. A TEGDME increase is found to be advantageous to the dissipation of the polar regions as well as the nonpolar regions in the [bmim][PF6] ionic liquids. These conclusions are consistent with the experimental results, which verified that the unique, complex, and versatile nature of [bmim][PF6]/TEGDME mixture can be correctly modeled and discussed at a molecular level using MD simulation data.