Virtual parameter calibration of pod pepper seeds based on discrete element simulation.

In order to achieve numerical optimization of the pod pepper seed sowing device, the contact parameters of pod pepper seeds were calibrated, with the angle of repose used as the response value. A set of discrete element method (DEM) models of pod pepper seeds was developed to simulate the formation of seed repose angles using reverse engineering reconstruction techniques. An eight-factor, three-level response surface experiment based on the Box-Behnken central combination test method was performed to study the effects of various factors on the angle of repose of seeds. The angle of repose obtained from physical experiments with a value of 27.56° was taken as the target value. The optimal combination of parameters is obtained as follows: seed Poisson's ratio of 0.22, seed shear modulus of 15.47 MPa, seed-to-seed static friction coefficient of 0.25, seed-to-seed rolling friction coefficient of 0.67, seed-to-seed collision recovery coefficient of 0.64, seed-to-steel-plate static friction coefficient of 0.55, seed-to-steel-plate rolling friction coefficient of 0.45, and seed-to-steel plate collision recovery coefficient of 0.34. A two-sample t-test of the angle of repose obtained by the cylinder lifting method and the pumping plate method against the target value yielded P > 0.05, indicating the reliability of the simulation experiments.

Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy.

Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary super-resolution imaging conditions, in an unsupervised manner without the need for either ground truths or additional data acquisition. We demonstrate the versatile applicability of ZS-DeconvNet on multiple imaging modalities, including total internal reflection fluorescence microscopy, three-dimensional wide-field microscopy, confocal microscopy, two-photon microscopy, lattice light-sheet microscopy, and multimodal structured illumination microscopy, which enables multi-color, long-term, super-resolution 2D/3D imaging of subcellular bioprocesses from mitotic single cells to multicellular embryos of mouse and C. elegans.

Unsupervised distribution-aware keypoints generation from 3D point clouds.

Keypoints extraction from 3D objects is a fundamental task in point cloud processing. The ideal keypoints should be an ordered and well-aligned set of points that effectively reflect the shape and structure of the object. To this end, this paper proposes an unsupervised 3D point cloud keypoints generation network with the consideration of the probability distribution of keypoints and spatial distribution among keypoints. The network downsamples and groups the 3D point cloud, obtaining local features of the point cloud. The local features are leveraged to explicitly learn the mixture probability distribution of keypoint position. A composite loss function that comprehensively considers shape similarity, point importance, and geometric constraint is proposed to guide the network in generating keypoints with semantic consistency and regular spatial distribution. The experimental results and quantitative comparisons on the ShapeNet and KeypointNet datasets demonstrate that the proposed method achieves ordered, well-aligned, and robust keypoints generation for 3D point clouds. The source code of the proposed method is available at https://github.com/djzgroup/Keypoints.

Wrap-like transfer printing for three-dimensional curvy electronics.

Three-dimensional (3D) curvy electronics has wide-ranging application in biomedical health care, soft machine, and high-density curved imager. Limited by material properties, complex procedures, and coverage ability of existing fabrication techniques, the development of high-performance 3D curvy electronics remains challenging. Here, we propose an automated wrap-like transfer printing prototype for fabricating 3D curvy electronics. Assisted by a gentle and uniform pressure field, the prefabricated planar circuits on the petal-like stamp are integrated onto the target surface intactly with full coverage. The driving pressure for the wrapping is provided by the strain recovery of a prestrained elastic film triggered by the air pressure control. The wrapping configuration and strain distribution of the stamp are simulated by finite element analysis, and the pattern and thickness of the stamps are optimized. Demonstration of this strategy including spherical meander antenna, spherical light-emitting diode array, and spherical solar cell array illustrates its feasibility in the development of complex 3D curvy electronics.

Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit.

A fundamental challenge in fluorescence microscopy is the photon shot noise arising from the inevitable stochasticity of photon detection. Noise increases measurement uncertainty and limits imaging resolution, speed and sensitivity. To achieve high-sensitivity fluorescence imaging beyond the shot-noise limit, we present DeepCAD-RT, a self-supervised deep learning method for real-time noise suppression. Based on our previous framework DeepCAD, we reduced the number of network parameters by 94%, memory consumption by 27-fold and processing time by a factor of 20, allowing real-time processing on a two-photon microscope. A high imaging signal-to-noise ratio can be acquired with tenfold fewer photons than in standard imaging approaches. We demonstrate the utility of DeepCAD-RT in a series of photon-limited experiments, including in vivo calcium imaging of mice, zebrafish larva and fruit flies, recording of three-dimensional (3D) migration of neutrophils after acute brain injury and imaging of 3D dynamics of cortical ATP release. DeepCAD-RT will facilitate the morphological and functional interrogation of biological dynamics with a minimal photon budget.

Spatial redundancy transformer for self-supervised fluorescence image denoising.

Fluorescence imaging with high signal-to-noise ratios has become the foundation of accurate visualization and analysis of biological phenomena. However, the inevitable noise poses a formidable challenge to imaging sensitivity. Here we provide the spatial redundancy denoising transformer (SRDTrans) to remove noise from fluorescence images in a self-supervised manner. First, a sampling strategy based on spatial redundancy is proposed to extract adjacent orthogonal training pairs, which eliminates the dependence on high imaging speed. Second, we designed a lightweight spatiotemporal transformer architecture to capture long-range dependencies and high-resolution features at low computational cost. SRDTrans can restore high-frequency information without producing oversmoothed structures and distorted fluorescence traces. Finally, we demonstrate the state-of-the-art denoising performance of SRDTrans on single-molecule localization microscopy and two-photon volumetric calcium imaging. SRDTrans does not contain any assumptions about the imaging process and the sample, thus can be easily extended to various imaging modalities and biological applications.

Enhancing adoptive T cell therapy for solid tumor with cell-surface anchored immune checkpoint inhibitor nanogels.

The efficacy of Adoptive Cell Therapy (ACT) for solid tumor is still mediocre. This is mainly because tumor cells can hijack ACT T cells' immune checkpoint pathways to exert immunosuppression in the tumor microenvironment. Immune Checkpoint Inhibitors such as anti-PD-1 (aPD1) can counter the immunosuppression, but the synergizing effects of aPD1 to ACT was still not satisfactory. Here we demonstrate an approach to safely anchor aPD1-formed nanogels onto T cell surface via bio-orthogonal click chemistry before adoptive transfer. The spatial-temporal co-existence of aPD1 with ACT T cells and the responsive drug release significantly improved the treatment outcome of ACT in murine solid tumor model. The average tumor weight of the group treated by cell-surface anchored aPD1 was only 18 % of the group treated by equivalent dose of free aPD1 and T cells. The technology can be broadly applicable in ACTs employing natural or Chimeric Antigen Receptor (CAR) T cells.

Platelet Pharmacytes for the Hierarchical Amplification of Antitumor Immunity in Response to Self-Generated Immune Signals.

Systemic immunosuppression mediated by tumor-derived exosomes is an important cause for the resistance of immune checkpoint blockade (ICB) therapy. Herein, self-adaptive platelet (PLT) pharmacytes are engineered to mediate cascaded delivery of exosome-inhibiting siRNA and anti-PD-L1 (aPDL1) toward synergized antitumor immunity. In the pharmacytes, polycationic nanocomplexes (NCs) assembled from Rab27 siRNA (siRab) and a membrane-penetrating polypeptide are encapsulated inside the open canalicular system of PLTs, and cytotoxic T lymphocytes (CTLs)-responsive aPDL1 nanogels (NGs) are covalently backpacked on the PLT surface. Upon systemic administration, the pharmacytes enable prolonged blood circulation and active accumulation to tumors, wherein PLTs are activated to liberate siRab NCs, which efficiently transfect tumor cells, silence Rab27a, and inhibit exosome secretion. The immunosuppression is thus relieved, leading to the activation, proliferation, and tumoral infiltration of cytotoxic T cells, which trigger latent aPDL1 release. As such, the competitive aPDL1 exhaustion by PD-L1-expressing exosomes is minimized to sensitize ICB. Synergistically, siRab and aPDL1 induce strong antitumor immunological response and memory against syngeneic murine melanoma. This study reports a bioinspired mechanism to resolve the blood circulation/cell internalization contradiction of polycationic siRNA delivery systems, and renders an enlightened approach for the spatiotemporal enhancement of antitumor immunity.

Polarized endosome dynamics engage cytoplasmic Par-3 that recruits dynein during asymmetric cell division.

In the developing embryos, the cortical polarity regulator Par-3 is critical for establishing Notch signaling asymmetry between daughter cells during asymmetric cell division (ACD). How cortically localized Par-3 establishes asymmetric Notch activity in the nucleus is not understood. Here, using in vivo time-lapse imaging of mitotic radial glia progenitors in the developing zebrafish forebrain, we uncover that during horizontal ACD along the anteroposterior embryonic axis, endosomes containing the Notch ligand DeltaD (Dld) move toward the cleavage plane and preferentially segregate into the posterior (subsequently basal) Notchhi daughter. This asymmetric segregation requires the activity of Par-3 and dynein motor complex. Using label retention expansion microscopy, we further detect Par-3 in the cytosol colocalizing the dynein light intermediate chain 1 (Dlic1) onto Dld endosomes. Par-3, Dlic1, and Dld are associated in protein complexes in vivo. Our data reveal an unanticipated mechanism by which cytoplasmic Par-3 directly polarizes Notch signaling components during ACD.

Immune Checkpoint Inhibitor-Based Strategies for Synergistic Cancer Therapy.

Immune checkpoint blockade therapy (ICBT) targeting checkpoints, such as, cytotoxic T-lymphocyte associated protein-4 (CTLA-4), programmed death-1 (PD-1), or programmed death-ligand 1 (PD-L1), can yield durable immune response in various types of cancers and has gained constantly increasing research interests in recent years. However, the efficacy of ICBT alone is limited by low response rate and immune-related side effects. Emerging preclinical and clinical studies reveal that chemotherapy, radiotherapy, phototherapy, or other immunotherapies can reprogramm immunologically "cold" tumor microenvironment into a "hot" one, thus synergizing with ICBT. In this review, the working principle and current development of various immune checkpoint inhibitors are summarized, while the interactive mechanism and recent progress of ICBT-based synergistic therapies with other immunotherapy, chemotherapy, phototherapy, and radiotherapy in fundamental and clinical studies in the past 5 years are depicted and highlighted. Moreover, the potential issues in current studies of ICBT-based synergistic therapies and future perspectives are also discussed.

High-dimensional super-resolution imaging reveals heterogeneity and dynamics of subcellular lipid membranes.

Lipid membranes are found in most intracellular organelles, and their heterogeneities play an essential role in regulating the organelles' biochemical functionalities. Here we report a Spectrum and Polarization Optical Tomography (SPOT) technique to study the subcellular lipidomics in live cells. Simply using one dye that universally stains the lipid membranes, SPOT can simultaneously resolve the membrane morphology, polarity, and phase from the three optical-dimensions of intensity, spectrum, and polarization, respectively. These high-throughput optical properties reveal lipid heterogeneities of ten subcellular compartments, at different developmental stages, and even within the same organelle. Furthermore, we obtain real-time monitoring of the multi-organelle interactive activities of cell division and successfully reveal their sophisticated lipid dynamics during the plasma membrane separation, tunneling nanotubules formation, and mitochondrial cristae dissociation. This work suggests research frontiers in correlating single-cell super-resolution lipidomics with multiplexed imaging of organelle interactome.

Activity Prediction of Small Molecule Inhibitors for Antirheumatoid Arthritis Targets Based on Artificial Intelligence.

Rheumatoid arthritis (RA) is a chronic autoimmune disease, which is compared to "immortal cancer" in industry. Currently, SYK, BTK, and JAK are the three major targets of protein tyrosine kinase for this disease. According to existing research, marketed and research drugs for RA are mostly based on single target, which limits their efficacy. Therefore, designing multitarget or dual-target inhibitors provide new insights for the treatment of RA regarding of the specific association between SYK, BTK, and JAK from two signal transduction pathways. In this study, machine learning (XGBoost, SVM) and deep learning (DNN) models were combined for the first time to build a powerful integrated model for SYK, BTK, and JAK. The predictive power of the integrated model was proved to be superior to that of a single classifier. In order to accurately assess the generalization ability of the integrated model, comprehensive similarity analysis was performed on the training and the test set, and the prediction accuracy of the integrated model was specifically analyzed under different similarity thresholds. External validation was conducted using single-target and dual-target inhibitors, respectively. Results showed that our model not only obtained a high recall rate (97%) in single-target prediction, but also achieved a favorable yield (54.4%) in dual-target prediction. Furthermore, by clustering dual-target inhibitors, the prediction performance of model in various classes were proved, evaluating the applicability domain of the model in the dual-target drug screening. In summary, the integrated model proposed is promising to screen dual-target inhibitors of SYK/JAK or BTK/JAK as RA drugs, which is beneficial for the clinical treatment of rheumatoid arthritis.

Enhanced reconstruction of structured illumination microscopy on a polarized specimen.

Structured illumination microscopy (SIM) requires polarization control to guarantee the high-contrast illumination pattern. However, this modulated polarization will induce artifacts in SIM when imaging fluorescent dipoles. Here we proposed the polarization weighted recombination of frequency components to reconstruct SIM data with suppressed artifacts and better resolving power. Both the simulation results and experimental data demonstrate that our algorithm can obtain isotropic resolution on dipoles and resolve a clearer structure in high-density sections compared to the conventional algorithm. Our work reinforces the SIM theory and paves the avenue for the application of SIM on a polarized specimen.

Discovery of Dual FGFR4 and EGFR Inhibitors by Machine Learning and Biological Evaluation.

Kinase inhibitors are widely used in antitumor research, but there are still many problems such as drug resistance and off-target toxicity. A more suitable solution is to design a multitarget inhibitor with certain selectivity. Herein, computational and experimental studies were applied to the discovery of dual inhibitors against FGFR4 and EGFR. A quantitative structure-property relationship (QSPR) study was carried out to predict the FGFR4 and EGFR activity of a data set consisting of 843 and 5088 compounds, respectively. Four different machine learning methods including support vector machine (SVM), random forest (RF), gradient boost regression tree (GBRT), and XGBoost (XGB) were built using the most suitable features selected by the mutual information algorithm. As for FGFR4 and EGFR, SVM showed the best performance with R2test-FGFR4 = 0.80 and R2test-EGFR = 0.75, demonstrating excellent model stability, which was used to predict the activity of some compounds from an in-house database. Finally, compound 1 was selected, which exhibits inhibitory activity against FGFR4 (IC50 = 86.2 nM) and EGFR (IC50 = 83.9 nM) kinase, respectively. Furthermore, molecular docking and molecular dynamics simulations were performed to identify key amino acids for the interaction of compound 1 with FGFR4 and EGFR. In this paper, the machine-learning-based QSAR models were established and effectively applied to the discovery of dual-target inhibitors against FGFR4 and EGFR, demonstrating the great potential of machine learning strategies in dual inhibitor discovery.

Advances of super-resolution fluorescence polarization microscopy and its applications in life sciences.

Fluorescence polarization microscopy (FPM) analyzes both intensity and orientation of fluorescence dipole, and reflects the structural specificity of target molecules. It has become an important tool for studying protein organization, orientational order, and structural changes in cells. However, suffering from optical diffraction limit, conventional FPM has low orientation resolution and observation accuracy, as the polarization information is averaged by multiple fluorescent molecules within a diffraction-limited volume. Recently, novel super-resolution FPMs have been developed to break the diffraction barrier. In this review, we will introduce the recent progress to achieve sub-diffraction determination of dipole orientation. Biological applications, based on polarization analysis of fluorescence dipole, are also summarized, with focus on chromophore-target molecule interaction and molecular organization.

Controlled Sputtering Pressure on High-Quality Sb2Se3 Thin Film for Substrate Configurated Solar Cells.

Magnetron sputtering has become an effective method in Sb2Se3 thin film photovoltaic. Research found that post-selenization treatments are essential to produce stoichiometric thin films with desired crystallinity and orientation for the sputtered Sb2Se3. However, the influence of the sputtering process on Sb2Se3 device performance has rarely been explored. In this work, the working pressure effect was thoroughly studied for the sputtered Sb2Se3 thin film solar cells. High-quality Sb2Se3 thin film was obtained when a bilayer structure was applied by sputtering the film at a high (1.5 Pa) and a low working pressure (1.0 Pa) subsequently. Such bilayer structure was found to be beneficial for both crystallization and preferred orientation of the Sb2Se3 thin film. Lastly, an interesting power conversion efficiency (PCE) of 5.5% was obtained for the champion device.

In Silico Design and Analysis of a Kinase-Focused Combinatorial Library Considering Diversity and Quality.

A structurally diverse, high-quality, and kinase-focused database plays a critical role in finding hits or leads in kinase drug discovery. Here, we propose a workflow for designing a virtual kinase-focused combinatorial library using existing structures. Based on the analysis of known protein kinase inhibitors (PKIs), detailed fragment optimization, fragment selection, fragment linking, and a molecular filtering scheme were defined. Quick recognition of core fragments that can possibly form dual hydrogen bonds with the hinge region of the ATP-pocket was proposed. Furthermore, three diversity and four quality metrics were chosen for compound library analysis, which can be applied to databases with over 30 million structures. Compared with 13 commercial libraries, our protocol demonstrates a special advantage in terms of good skeleton diversity, acceptable fingerprint diversity, balanced scaffold distribution, and high quality, which can work well not only on existing PKIs, but also on four chosen commercial libraries. Overall, the strategy can greatly facilitate the expansion of a desirable chemical space for kinase drug discovery.

Protein-ligand interaction-guided discovery of novel VEGFR-2 inhibitors.

As an effective target in abnormal angiogenesis-related tumor treatment, VEGFR-2 has small-molecule inhibitors of various scaffolds being approved for treating diseases such as renal carcinoma, non-small cell lung cancer, etc. However, endogenous and acquired drug resistance are still considered to be the main contributors for the failure of VEGFR-2 clinical candidates. Therefore, development of novel VEGFR-2 inhibitors is still urgently needed in the market but also challenging. In this work, residues including Asp1046, Ile1025, HIS1026, Cys919 and Lys868 were identified as the most important residues for Hbonded interaction, while His1026, Asp1046, Glu885, Ile1025 and Leu840 exhibited critical role for the nonbonded interactions through a comprehensive analysis of protein-ligand interactions, which plays critical roles in the binding of compounds and targets. Guided by the analysis of binding interactions, a total of 10 novel VEGFR-2 inhibitors based on N-methyl-4-oxo-N-propyl-1,4-dihydroquinoline-2-carboxamide scaffold were discovered through fragment-based drug design and structure-based virtual screening, which expands the chemical space of current VEGFR-2 inhibitors. Biological activity evaluation showed that even though the enzymatic activity of these compounds against VEGFR-2 were inferior to that of the positive controls sorafenib and motesanib, compound I-10 showed moderate HepG2 cell inhibitory activity with an IC50 value of 33.65 µM and eight compounds exhibited moderate or higher HUVEC inhibitory activity in the range of 19.54-57.98 µM compared to the controls. Particularly, the HUVEC inhibitory activity of compound I-6 (IC50 = 19.54 µM) outperformed motesanib and can be used as starting points for further optimization and development for cancer treatment.Communicated by Ramaswamy H. Sarma.

Discovery of potent apoptosis signal-regulating kinase 1 inhibitors via integrated computational strategy and biological evaluation.

Apoptosis signal-regulating Kinase 1 (ASK1) has been confirmed as a potential therapeutic target for the treatment of non-alcoholic steatohepatitis (NASH) disorder and the discovery of ASK1 inhibitors has attracted increasing attention. In this work, a series of in silico methods including pharmacophore screening, docking binding site analysis, protein-ligand interaction fingerprint (PLIF) similarity investigation and molecular docking were applied to find the potential hits from commercial compound databases. Five compounds with potential inhibitory activity were purchased and submitted to biological activity validation. Thus, one hit compound was discovered with micromolar IC50 value (10.59 µM) against ASK1. Results demonstrated that the integration of computation methods and biological test was quite reliable for the discovery of potent ASK1 inhibitors and the strategy could be extended to other similar targets of interest.

Single-Source Vapor-Deposited Cs2AgBiBr6 Thin Films for Lead-Free Perovskite Solar Cells.

Lead-free double perovskites have been considered as a potential environmentally friendly photovoltaic material for substituting the hybrid lead halide perovskites due to their high stability and nontoxicity. Here, lead-free double perovskite Cs2AgBiBr6 films are initially fabricated by single-source evaporation deposition under high vacuum condition. X-ray diffraction and scanning electron microscopy characterization show that the high crystallinity, flat, and pinhole-free double perovskite Cs2AgBiBr6 films were obtained after post-annealing at 300 °C for 15 min. By changing the annealing temperature, annealing time, and film thickness, perovskite Cs2AgBiBr6 solar cells with planar heterojunction structure of FTO/TiO2/Cs2AgBiBr6/Spiro-OMeTAD/Ag achieve an encouraging power conversion efficiency of 0.70%. Our preliminary work opens a feasible approach for preparing high-quality double perovskite Cs2AgBiBr6 films wielding considerable potential for photovoltaic application.