Inositol hexakisphosphate primes syndapin I/PACSIN 1 activation in endocytosis

Endocytosis is controlled by a well-orchestrated molecular machinery, where the individual players as well as their precise interactions are not fully understood. We now show that syndapin I/PACSIN 1 is expressed in pancreatic β cells and that its knockdown abrogates β cell endocytosis leading to disturbed plasma membrane protein homeostasis, as exemplified by an elevated density of L-type Ca(2+) channels. Intriguingly, inositol hexakisphosphate (InsP(6)) activates casein kinase 2 (CK2) that phosphorylates syndapin I/PACSIN 1, thereby promoting interactions between syndapin I/PACSIN 1 and neural Wiskott–Aldrich syndrome protein (N-WASP) and driving β cell endocytosis. Dominant-negative interference with endogenous syndapin I/PACSIN 1 protein complexes, by overexpression of the syndapin I/PACSIN 1 SH3 domain, decreases InsP(6)-stimulated endocytosis. InsP(6) thus promotes syndapin I/PACSIN 1 priming by CK2-dependent phosphorylation, which endows the syndapin I/PACSIN 1 SH3 domain with the capability to interact with the endocytic machinery and thereby initiate endocytosis, as exemplified in β cells. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00018-022-04305-2

Enhanced expression of beta cell Ca(v)3.1 channels impairs insulin release and glucose homeostasis

Voltage-gated calcium 3.1 (Ca(v)3.1) channels are absent in healthy mouse beta cells and mediate minor T-type Ca2+ currents in healthy rat and human beta cells but become evident under diabetic conditions. Whether more active Ca(v)3.1 channels affect insulin secretion and glucose homeostasis remains enigmatic. We addressed this question by enhancing de novo expression of beta cell Ca(v)3.1 channels and exploring the consequent impacts on dynamic insulin secretion and glucose homeostasis as well as underlying molecular mechanisms with a series of in vitro and in vivo approaches. We now demonstrate that a recombinant adenovirus encoding enhanced green fluorescent protein-Ca(v)3.1 subunit (Ad-EGFP-Ca(v)3.1) efficiently transduced rat and human islets as well as dispersed islet cells. The resulting Ca(v)3.1 channels conducted typical T-type Ca2+ currents, leading to an enhanced basal cytosolic-free Ca2+ concentration ([Ca2+](i)). Ad-EGFP-Ca(v)3.1-transduced islets released significantly less insulin under both the basal and first phases following glucose stimulation and could no longer normalize hyperglycemia in recipient rats rendered diabetic by streptozotocin treatment. Furthermore, Ad-EGFP-Ca(v)3.1 transduction reduced phosphorylated FoxO1 in the cytoplasm of INS-1E cells, elevated FoxO1 nuclear retention, and decreased syntaxin 1A, SNAP-25, and synaptotagmin III. These effects were prevented by inhibiting Ca(v)3.1 channels or the Ca2+ -dependent phosphatase calcineurin. Enhanced expression of beta cell Ca(v)3.1 channels therefore impairs insulin release and glucose homeostasis by means of initial excessive Ca2+ influx, subsequent activation of calcineurin, consequent dephosphorylation and nuclear retention of FoxO1, and eventual FoxO1-mediated down-regulation of beta cell exocytotic proteins. The present work thus suggests an elevated expression of Ca(v)3.1 channels plays a significant role in diabetes pathogenesis

Intracameral Microimaging of Maturation of Human iPSC Derivatives into Islet Endocrine Cells

We exploited the anterior chamber of the eye (ACE) of immunodeficient mice as an ectopic site for both transplantation and microimaging of engineered surrogate islets from human induced pluripotent stem cells (hiPSC-SIs). These islets contained a majority of insulin-expressing cells, positive or negative for PDX1 and NKX6.1, and a minority of glucagon- or somatostatin-positive cells. Single, non-aggregated hiPSC-SIs were satisfactorily engrafted onto the iris. They underwent gradual vascularization and progressively increased their light scattering signals, reflecting the abundance of zinc-insulin crystal packaged inside mature insulin secretory granules. Intracameral hiPSC-SIs retrieved from recipients showed enhanced insulin immunofluorescence in correlation with the parallel increase in overall vascularization and light backscattering during the post-transplantation period. This approach enables longitudinal, nondestructive and intravital microimaging of cell fates, engraftment, vascularization and mature insulin secretory granules of single hiPSC-SI grafts, and may offer a feasible and reliable means to screen compounds for promoting in vivo hiPSC-SI maturation

An Improved Neural Particle Method for Complex Free Surface Flow Simulation Using Physics-Informed Neural Networks

The research on free surface flow is of great interest in fluid mechanics, with the primary task being the tracking and description of the motion of free surfaces. The development of numerical simulation techniques has led to the application of new methods in the study of free surface flow problems. One such method is the Neural Particle Method (NPM), a meshless approach for solving incompressible free surface flow. This method is built on a Physics-Informed Neural Network (PINN), which allows for training and solving based solely on initial and boundary conditions. Although the NPM is effective in dealing with free surface flow problems, it faces challenges in simulating more complex scenarios due to the lack of additional surface recognition algorithms. In this paper, we propose an improved Neural Particle Method (INPM) to better simulate complex free surface flow. Our approach involves incorporating alpha-shape technology to track and recognize the fluid boundary, with boundary conditions updated constantly during operation. We demonstrate the effectiveness of our proposed method through three numerical examples with different boundary conditions. The result shows that: (1) the addition of a surface recognition module allows for the accurate tracking and recognition of the fluid boundary, enabling more precise imposition of boundary conditions in complex situations; (2) INPM can accurately identify the surface and calculate even when particles are unevenly distributed. Compared with traditional meshless methods, INPM offers a better solution for dealing with complex free surface flow problems that involve random particle distribution. Our proposed method can improve the accuracy and stability of numerical simulations for free surface flow problems

Vibration Tendency Prediction Approach for Hydropower Generator Fused with Multiscale Dominant Ingredient Chaotic Analysis, Adaptive Mutation Grey Wolf Optimizer, and KELM

Accurate vibrational tendency forecasting of hydropower generator unit (HGU) is of great significance to guarantee the safe and economic operation of hydropower station. For this purpose, a novel hybrid approach combined with multiscale dominant ingredient chaotic analysis, kernel extreme learning machine (KELM), and adaptive mutation grey wolf optimizer (AMGWO) is proposed. Among the methods, variational mode decomposition (VMD), phase space reconstruction (PSR), and singular spectrum analysis (SSA) are suitably integrated into the proposed analysis strategy. First of all, VMD is employed to decompose the monitored vibrational signal into several subseries with various frequency scales. Then, SSA is applied to divide each decomposed subseries into dominant and residuary ingredients, after which an additional forecasting component is calculated by integrating the residual of VMD with all the residuary ingredients orderly. Subsequently, the proposed AMGWO is implemented to simultaneously adapt the intrinsic parameters in PSR and KELM for all the forecasting components. Ultimately, the prediction results of the raw vibration signal are obtained by assembling the results of all the predicted prediction components. Furthermore, six relevant contrastive models are adopted to verify the feasibility and availability of the modified strategies employed in the proposed model. The experimental results illustrate that (1) VMD plays a positive role for the prediction accuracy promotion; (2) the proposed dominant ingredient chaotic analysis based on the realization of time-frequency decomposition can further enhance the capability of the forecasting model; and (3) the appropriate parameters for each forecasting component can be optimized by the proposed AMGWO effectively, which can contribute to elevating the forecasting performance distinctly

Numerical modeling of the propagation process of landslide surge using physics-informed deep learning

Abstract The landslide surge is a common secondary disaster of reservoir bank landslides, which can cause more serious damage than the landslide itself in many cases. With the development of large-scale scientific and engineering computing, many new techniques have been applied to the study of hydrodynamic problems to make up for the shortcomings of traditional methods. In this paper, we use the physics-informed neural network (PINN) to simulate the propagation process of surges caused by landslides. We study different characteristics of landslide surges by changing water depth and particle density. We find that: (1) the landslide surge propagation process simulation method based on the physics-informed neural network has good applicability, and the stages of landslide surge propagation can be well presented; (2) the depth of water influences the landslide surge propagation as the amplitude of the surge increases with deeper water; (3) the particle density of water influences the landslide surge propagation as the fluctuation of the surge is more obvious with larger particle density. Our study is helpful to understand the propagation process of landslide surges more clearly and provides new ideas for the follow-up study of this kind of complex fluid–structure interaction problem

Exploring the diversity, bioactivity of endophytes, and metabolome in Synsepalum dulcificum

Synsepalum dulcificum exhibits high edible and medicinal value; however, there have been no reports on the exploration of its endophyte resources. Here, we conducted analyses encompassing plant metabolomics, microbial diversity, and the biological activities of endophytic metabolites in S. dulcificum. High-throughput sequencing identified 4,913 endophytic fungal amplicon sequence variants (ASVs) and 1,703 endophytic bacterial ASVs from the roots, stems, leaves, flowers, and fruits of S. dulcificum. Fungi were classified into 5 phyla, 24 classes, 75 orders, 170 families, and 313 genera, while bacteria belonged to 21 phyla, 47 classes, 93 orders, 145 families, and 232 genera. Furthermore, there were significant differences in the composition and content of metabolites in different tissues of S. dulcificum. Spearman’s correlation analysis of the differential metabolites and endophytes revealed that the community composition of the endophytes correlated with plant-rich metabolites. The internal transcribed spacer sequences of 105 isolates were determined, and phylogenetic analyses revealed that these fungi were distributed into three phyla (Ascomycota, Basidiomycota, and Mucoromycota) and 20 genera. Moreover, 16S rDNA sequencing of 46 bacteria revealed they were distributed in 16 genera in three phyla: Actinobacteria, Proteobacteria, and Firmicutes. The antimicrobial activities (filter paper method) and antioxidant activity (DPPH and ABTS assays) of crude extracts obtained from 68 fungal and 20 bacterial strains cultured in different media were evaluated. Additionally, the α-glucosidase inhibitory activity of the fungal extracts was examined. The results showed that 88.6% of the strains exhibited antimicrobial activity, 55.7% exhibited antioxidant activity, and 85% of the fungi exhibited α-glucosidase inhibitory activity. The research suggested that the endophytes of S. dulcificum are highly diverse and have the potential to produce bioactive metabolites, providing abundant species resources for developing antibiotics, antioxidants and hypoglycemic drugs

Numerical and Experimental Study on Thermal Damage Induced by Medium—Infrared Laser

We studied the laser-induced thermal damage on the surface of a single crystal silicon mirror illuminated by a mid-infrared intense laser. We used mid−infrared quasi-continuous wave lasers to irradiate the surface of the single−crystal silicon mirror. The power density of the irradiation process is 1 kW/cm2 to 17 kW/cm2, and the transient temperature field and thermal stress field under different laser fluxes were obtained. The simulation results show that we can calculate the thermal stress and temperature under laser irradiation. In addition, irradiance exceeding the corresponding breaking strength and melting point limit was obtained by the model. We can predict the irradiance that causes cracking and melting. There is little difference between experimental results and simulation results. On this basis, the thermal damage to the surface of the silicon wafer caused by continuous mid−infrared laser irradiation was studied

Numerical and Experimental Study on Thermal Damage Induced by Medium—Infrared Laser

We studied the laser-induced thermal damage on the surface of a single crystal silicon mirror illuminated by a mid-infrared intense laser. We used mid−infrared quasi-continuous wave lasers to irradiate the surface of the single−crystal silicon mirror. The power density of the irradiation process is 1 kW/cm2 to 17 kW/cm2, and the transient temperature field and thermal stress field under different laser fluxes were obtained. The simulation results show that we can calculate the thermal stress and temperature under laser irradiation. In addition, irradiance exceeding the corresponding breaking strength and melting point limit was obtained by the model. We can predict the irradiance that causes cracking and melting. There is little difference between experimental results and simulation results. On this basis, the thermal damage to the surface of the silicon wafer caused by continuous mid−infrared laser irradiation was studied