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Brain organoids have been used to recapitulate the processes of brain development and related diseases. However, the lack of vasculatures, which regulate neurogenesis and brain disorders, limits the utility of brain organoids. In this study, we induced vessel and brain organoids, respectively, and then fused two types of organoids together to obtain vascularized brain organoids. The fused brain organoids were engrafted with robust vascular network-like structures and exhibited increased number of neural progenitors, in line with the possibility that vessels regulate neural development. Fusion organoids also contained functional blood–brain barrier-like structures, as well as microglial cells, a specific population of immune cells in the brain. The incorporated microglia responded actively to immune stimuli to the fused brain organoids and showed ability of engulfing synapses. Thus, the fusion organoids established in this study allow modeling interactions between the neuronal and non-neuronal components in vitro, particularly the vasculature and microglia niche. Understanding how the organs form and how their cells behave is essential to finding the causes and treatment for developmental disorders, as well as understanding certain diseases. However, studying most organs in live animals or humans is technically difficult, expensive and invasive. To address this issue, scientists have developed models called ‘organoids’ that recapitulate the development of organs using stem cells in the lab. These models are easier to study and manipulate than the live organs. Brain organoids have been used to recapitulate brain formation as well as developmental, degenerative and psychiatric brain conditions such as microcephaly, autism and Alzheimer’s disease. However, these brain organoids lack the vasculature (the network of blood vessels) that supplies a live brain with nutrients and regulates its development, and which has important roles in brain disorders. Partly due to this lack of blood vessels, brain organoids also do not develop a blood brain barrier, the structure that prevents certain contents of the blood, including pathogens, toxins and even certain drugs from entering the brain. These characteristics limit the utility of existing brain organoids. To overcome these limitations, Sun, Ju et al. developed brain organoids and blood vessel organoids independently, and then fused them together to obtain vascularized brain organoids. These fusion organoids developed a robust network of blood vessels that was well integrated with the brain cells, and produced more neural cell precursors than brain organoids that had not been fused. This result is consistent with the idea that blood vessels can regulate brain development. Analyzing the fusion organoids revealed that they contain structures similar to the blood-brain barrier, as well as microglial cells (immune cells specific to the brain). When exposed to lipopolysaccharide – a component of the cell wall of certain bacteria – these cells responded by initiating an immune response in the fusion organoids. Notably, the microglial cells were also able to engulf connections between brain cells, a process necessary for the brain to develop the correct structures and work normally. Sun, Ju et al. have developed a new organoid system that will be of broad interest to researchers studying interactions between the brain and the circulatory system. The development of brain-blood-barrier-like structures in the fusion organoids could also facilitate the development of drugs that can cross this barrier, making it easier to treat certain conditions that affect the brain. Refining this model to allow the fusion organoids to grow for longer times in the lab, and adding blood flow to the system will be the next steps to establish this system.

The rapid development of neuro-inspired computing demands synaptic devices with ultrafast speed, low power consumption, and multiple non-volatile states, among other features. Here, a high-performance synaptic device is designed and established based on a Ag/PbZr 0.52 Ti 0.48 O 3 (PZT, (111)-oriented)/Nb:SrTiO 3 ferroelectric tunnel junction (FTJ). The advantages of (111)-oriented PZT (~1.2 nm) include its multiple ferroelectric switching dynamics, ultrafine ferroelectric domains, and small coercive voltage. The FTJ shows high-precision (256 states, 8 bits), reproducible (cycle-to-cycle variation, ~2.06%), linear (nonlinearity <1) and symmetric weight updates, with a good endurance of >10 9 cycles and an ultralow write energy consumption. In particular, manipulations among 150 states are realized under subnanosecond (~630 ps) pulse voltages ≤5 V, and the fastest resistance switching at 300 ps for the FTJs is achieved by voltages <13 V. Based on the experimental performance, the convolutional neural network simulation achieves a high online learning accuracy of ~94.7% for recognizing fashion product images, close to the calculated result of ~95.6% by floating-point-based convolutional neural network software. Interestingly, the FTJ-based neural network is very robust to input image noise, showing potential for practical applications. This work represents an important improvement in FTJs towards building neuro-inspired computing systems. Brain-inspired computing demands high-performance synapses. Here, the authors report a subnanosecond ferroelectric tunnel junction with 256 conductance states, 10 9 endurance, and 5.3 fJ/bit energy consumption, satisfactory to build synaptic devices.

Next-generation non-volatile memories with ultrafast speed, low power consumption, and high density are highly desired in the era of big data. Here, we report a high performance memristor based on a Ag/BaTiO 3 /Nb:SrTiO 3 ferroelectric tunnel junction (FTJ) with the fastest operation speed (600 ps) and the highest number of states (32 states or 5 bits) per cell among the reported FTJs. The sub-nanosecond resistive switching maintains up to 358 K, and the write current density is as low as 4 × 10 3  A cm −2 . The functionality of spike-timing-dependent plasticity served as a solid synaptic device is also obtained with ultrafast operation. Furthermore, it is demonstrated that a Nb:SrTiO 3 electrode with a higher carrier concentration and a metal electrode with lower work function tend to improve the operation speed. These results may throw light on the way for overcoming the storage performance gap between different levels of the memory hierarchy and developing ultrafast neuromorphic computing systems. Memristor devices based on ferroelectric tunnel junctions are promising, but suffer from quite slow switching times. Here, the authors report on ultrafast switching times at and above room temperature of 600ps in Ag/BaTiO3/Nb:SrTiO3 based ferroelectric tunnel junctions.

Excessive inflammatory responses induced upon SARS-CoV-2 infection are associated with severe symptoms of COVID-19. Inflammasomes activated in response to SARS-CoV-2 infection are also associated with COVID-19 severity. Here, we show a distinct mechanism by which SARS-CoV-2 N protein promotes NLRP3 inflammasome activation to induce hyperinflammation. N protein facilitates maturation of proinflammatory cytokines and induces proinflammatory responses in cultured cells and mice. Mechanistically, N protein interacts directly with NLRP3 protein, promotes the binding of NLRP3 with ASC, and facilitates NLRP3 inflammasome assembly. More importantly, N protein aggravates lung injury, accelerates death in sepsis and acute inflammation mouse models, and promotes IL-1β and IL-6 activation in mice. Notably, N-induced lung injury and cytokine production are blocked by MCC950 (a specific inhibitor of NLRP3) and Ac-YVAD-cmk (an inhibitor of caspase-1). Therefore, this study reveals a distinct mechanism by which SARS-CoV-2 N protein promotes NLRP3 inflammasome activation and induces excessive inflammatory responses. SARS-CoV-2 infection has been shown to drive NLRP3 inflammasome activation and thereby cytokine storm, but how it does so is unclear. Here the authors show that the viral N protein can bind to NLRP3, resulting in enhanced interaction with ASC and thereby with the NLRP3 inflammasome.

In this paper, the key intention is to present a compact and efficient MATLAB code for the implementation of the isogeometric topology optimization (ITO) method published by Jie Gao et al. (Int J Numer Methods Eng 119: 991–1017, 2019). A main function IgaTop2D with eight inputs in the 56-line MATLAB code is developed, mainly including nine components: (1) Geom_Mod subfunction that uses non-uniform rational B-splines (NURBS) to develop the geometrical model; (2) the preparation of the isogeometric analysis (IGA) that is implemented in Pre_IGA subfunction; (3) the definition of Dirichlet and Neumann boundary conditions in Boun_Cond subfunction; (4) the initialization of control densities and the densities at Gauss quadrature points implemented from lines 11 to 20 of the main function; (5) a Shep_Fun subfunction for the smoothing mechanism; (6) IGA to solve structural responses in three steps: compute IGA element stiffness matrices in Stiff_Ele2D subfunction, assemble all IGA element stiffness matrices in Stiff_Ass2D subfunction, and Solving; (7) calculation of the objective function and sensitivity analysis in lines 32–46 of IgaTop2D; (8) OC to advance design variables; and (9) the representations of the optimized solutions in Plot_Data and Plot_Topy subfunctions. Finally, several numerical examples are shown to demonstrate the effectiveness of the ITO MATLAB implementation IgaTop2D, which are attached in the Appendix, also offering an entry point for newcomers who have an interest in the field of the ITO.

This paper presents the compact and efficient Matlab codes for the concurrent topology optimization of multiscale composite structures not only in 2D scenario but also considering 3D cases. A modified SIMP approach (Sigmund 2007) is employed to implement the concurrent topological design, with an energy-based homogenization method (EBHM) to evaluate the macroscopic effective properties of the microstructure. The 2D and 3D Matlab codes in the paper are developed, using the 88-line 2D SIMP code (Struct Multidisc Optim 43(1): 1–16, 2011) and the 169-line 3D topology optimization code (Struct Multidisc Optim 50(6): 1175–1196, 2014), respectively. This paper mainly contributes to the following four aspects: (1) the code architecture for the topology optimization of cellular composite structures (ConTop2D.m and ConTop3D.m), (2) the code to compute the 3D iso-parametric element stiffness matrix (elementMatVec3D.m), (3) the EBHM to predict the macroscopic effective properties of 2D and 3D material microstructures (EBHM2D.m and EBHM3D.m), and (4) the code to calculate the sensitivities of the objective function with respect to the design variables at two scales. Several numerical examples are tested to demonstrate the effectiveness of the Matlab codes, which are attached in the Appendix, also offering an entry point for new comers in designing cellular composites using topology optimization.

Endothelial cells (ECs) angiogenesis is the process of sprouting new vessels from the existing ones, playing critical roles in physiological and pathological processes such as wound healing, placentation, ischemia/reperfusion, cardiovascular diseases and cancer metastasis. Although mitochondria are not the major sites of energy source in ECs, they function as important biosynthetic and signaling hubs to regulate ECs metabolism and adaptations to local environment, thus affecting ECs migration, proliferation and angiogenic process. The understanding of the importance and potential mechanisms of mitochondria in regulating ECs metabolism, function and the process of angiogenesis has developed in the past decades. Thus, in this review, we discuss the current understanding of mitochondrial proteins and signaling molecules in ECs metabolism, function and angiogeneic signaling, to provide new and therapeutic targets for treatment of diverse cardiovascular and angiogenesis-dependent diseases.

The decision process of different remanufacturing schemes in an electronic control system has great fuzziness and uncertainty. Therefore, it is essential to use an appropriate method to show the characteristics of different schemes and support the users’ decision. Based on the concepts of the artificial neural network theory and the improved comprehensive evaluation method, the decision-making system of the electronic control remanufacturing scheme was constructed in the present study. In the first step, a classification method of parts is proposed from the perspective of manufacturing enterprises. Moreover, an artificial neural network model is used to determine parts of remanufacturing value. Then the pricing strategy is divided according to the users’ needs, and then a decision model is constructed. The combined subjective and objective methods are used to solve the compound weight of different equipment, and a set of improved fuzzy comprehensive decision methods is formed. Then the proposed model was applied to an electronic control transformation project as an example to evaluate the performance of different schemes. The evaluation results were consistent with the results of a third-party organization. It was concluded that the proposed scheme can be used as the theoretical basis to choose the best remanufacturing scheme to ensure the efficient operation of each part in an ECS.

Thermoelectric materials generate electric energy from waste heat, with conversion efficiency governed by the dimensionless figure of merit, ZT. Single-crystal tin selenide (SnSe) was discovered to exhibit a high ZT of roughly 2.2–2.6 at 913 K, but more practical and deployable polycrystal versions of the same compound suffer from much poorer overall ZT, thereby thwarting prospects for cost-effective lead-free thermoelectrics. The poor polycrystal bulk performance is attributed to traces of tin oxides covering the surface of SnSe powders, which increases thermal conductivity, reduces electrical conductivity and thereby reduces ZT. Here, we report that hole-doped SnSe polycrystalline samples with reagents carefully purified and tin oxides removed exhibit an ZT of roughly 3.1 at 783 K. Its lattice thermal conductivity is ultralow at roughly 0.07 W m –1  K –1 at 783 K, lower than the single crystals. The path to ultrahigh thermoelectric performance in polycrystalline samples is the proper removal of the deleterious thermally conductive oxides from the surface of SnSe grains. These results could open an era of high-performance practical thermoelectrics from this high-performance material. SnSe has a very high thermoelectric figure of merit ZT, but uncommonly polycrystalline samples have higher lattice thermal conductivity than single crystals. Here, by controlling Sn reagent purity and removing SnO x impurities, a lower thermal conductivity is achieved, enabling ZT of 3.1 at 783 K.

This study develops and evaluates machine-learning models for predicting the confined ultimate strength ( f cc, u ) and ultimate strain ( ε cc, u ) of a hybrid multi-tube concrete column (MTCC). It is a pioneer structural system that uses a fiber-reinforced polymer (FRP) outer tube, inner steel tubes and concrete with voids. One of the aims of the study is to enhance the accuracy of prediction of the structural performance of this system using machine learning techniques especially using gradient boosting machine (GBM) models. It utilized a database of 283 specimens produced as a result of published experimental studies and it included the geometric and material characteristics that impacted performance. Four example gradient-boosted decision-tree models, including Stochastic Gradient Boosting (SGB), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), and CatBoost (CGB) models were trained and optimized through Bayesian hyperparameter tuning to compare the robustness of the main boosting models. Through the analysis, it was found that SGB model was the most precise with the highest coefficients of determination (R2 = 0.994 when using fcc, u and 0.946 when using ecc, u) and the smallest root mean square error (RMSE). And the logic of the model was interpreted using SHAP analysis, which indicated that concrete compressive strength (f’c) and the thickness of the FRP layer (tf) had the greatest effect on fcc, u. On the contrary, the most crucial factors in calculating ecc, u were the FRP elastic modulus (Ef) and the steel pipe yield strength (fys). An interactive graphical interface was created in an attempt to improve practicality and provide the ability of engineers and researchers to make correct predictions in a simple and efficient manner. The findings confirm the usefulness of machine learning models, especially gradient boosting, in facilitating design and analysis decisions in high-end engineering processes.