Search

Abstract Engineering human tissue with diverse cell types and architectures remains challenging. The cerebral cortex, which has a layered cellular architecture composed of layer-specific neurons organised into vertical columns, delivers higher cognition through intricately wired neural circuits. However, current tissue engineering approaches cannot produce such structures. Here, we use a droplet printing technique to fabricate tissues comprising simplified cerebral cortical columns. Human induced pluripotent stem cells are differentiated into upper- and deep-layer neural progenitors, which are then printed to form cerebral cortical tissues with a two-layer organization. The tissues show layer-specific biomarker expression and develop a structurally integrated network of processes. Implantation of the printed cortical tissues into ex vivo mouse brain explants results in substantial structural implant-host integration across the tissue boundaries as demonstrated by the projection of processes and the migration of neurons, and leads to the appearance of correlated Ca2+ oscillations across the interface. The presented approach might be used for the evaluation of drugs and nutrients that promote tissue integration. Importantly, our methodology offers a technical reservoir for future personalized implantation treatments that use 3D tissues derived from a patient’s own induced pluripotent stem cells.

Abstract Objective This study aimed to establish a cell-free fetal DNA (cffDNA) assay using multiplex digital PCR (dPCR) for identifying fetuses at increased risk of 22q11.2 deletion/duplication syndrome. Methods Six detection sites and their corresponding probes were designed for the 22q11.2 recurrent region. A dPCR assay for the noninvasive screening of 22q11.2 deletion/duplication syndrome was established. A total of 130 plasma samples from pregnant women (including 15 samples with fetal 22q11.2 deletion/duplication syndrome) were blindly tested for evaluating the sensitivity and specificity of the established assay. Results DNA with different sizes of 22q11.2 deletion/duplication was detected via dPCR, indicating that the designed probes and detection sites were reasonable and effective. In the retrospective clinical samples, 11 out of 15 samples of pregnant women with 22q11.2 deletion/duplication were detected during the cffDNA assay, and accurate regional localization was achieved. Among the 115 normal samples, 111 were confirmed to be normal. Receiver operating characteristic curves were used for assessing the cut-off values and AUC for these samples. The sensitivity, specificity, and positive as well as negative predictive values were 73.3%, 96.5%, 73.3%, and 96.5%, respectively. Conclusion The cffDNA assay based on dPCR technology for the noninvasive detection of 22q11.2 recurrent copy number variants in fetuses detected most affected cases, including smaller but relatively common nested deletions, with a low false-positive rate. It is a potential, efficient and simple method for the noninvasive screening of 22q11.2 deletion/duplication syndrome.

The widespread deployment of deep learning models in practice necessitates an assessment of their vulnerability, particularly in security-sensitive areas. As a result, transfer-based adversarial attacks have elicited increasing interest in assessing the security of deep learning models. However, adversarial samples usually exhibit poor transferability over different models because of overfitting of the particular architecture and feature representation of a source model. To address this problem, the Intermediate Layer Attack with Attention guidance (IAA) is proposed to alleviate overfitting and enhance the black-box transferability. The IAA works on an intermediate layer $l$ of the source model. Guided by the model’s attention (i.e., gradients) to the features of layer $l$ , the attack algorithm seeks and undermines the key features that are likely to be adopted by diverse architectures. Significantly, IAA focuses on improving existing white-box attacks without introducing significant visual perceptual quality degradation. Namely, IAA maintains the white-box attack performance of the original algorithm while significantly enhancing its black-box transferability. Extensive experiments on ImageNet classifiers confirmed the effectiveness of our method. The proposed IAA outperformed all state-of-the-art benchmarks in various white-box and black-box settings, i.e., improving the success rate of BIM by 29.65% against normally trained models and 27.16% against defense models.

The spatial arrangement of bacterial strains and species within microbial communities is considered crucial for their ecology. Here, Krishna Kumar et al. use a droplet-based printing method to arrange different bacterial genotypes across a sub-millimetre array, and show that micron-scale changes in spatial distributions can drive major shifts in ecology.

This paper develops a novel equivalent input disturbance (EID)-based adaptive sliding mode control (SMC) method for singularly perturbed systems (SPSs). Firstly, the block diagonalization approach is introduced to decompose the full-order SPSs exactly, and slow and fast subsystems are obtained by solving the upper and lower triangular matrices individually. Secondly, an EID is constructed to estimate the unknown disturbances with the observer gain and error system convergence analyzed. Then, depending on the decoupled reduced-order system models, a Lyapunov equation-based solution is adopted to construct a composite sliding surface. Finally, combined with the EID estimation, an adaptive SMC law is proposed to compensate the adverse effect of disturbances and the reachability condition is proven. The presented control strategy is free of any priori disturbances information while the satisfactory system performance can be guaranteed. Simulation results on two examples illustrate its superiority over the existing methods.

For security and efficiency problems in threshold based deduplication for cloud data,a novel method based on threshold re-encryption was proposed to deal with side channel attacks.A lightweight threshold re-encryption mechanism was presented to transfer the secondary encryption to the cloud for execution and allow clients to generate ciphertext based on key segmentation instead of ciphertext segmentation,both of which largely reduce computational overhead of clients.Also,the proposed mechanism enables clients to decrypt from both one-time encrypted and re-encrypted ciphertext,thus avoiding the overhead of redundant encryption of the same file.Mutual integrity verification between cloud service provider and clients was also supported by the proposed method,which directly ensured the correctness of the correspondence between ciphertext and plaintext on client side.Experiments show that the proposed method not only largely reduces the computational overhead on client side,but also achieves superior storage performance on cloud side simultaneously.

Histogram shifting is an effective manner to achieve reversible watermarking, which works by shifting pixels between the peak point and its nearest zero point in histogram to make room for watermark embedding. However, once zero point is absent, the algorithm suffers from overflowing problem. Even though some works attempt to deal with this risk by introducing auxiliary information, such as a location map, they occupy a lot of embedding capacity inevitably. In this article, in order to deal with overflowing problem efficiently, we propose a border following–based reversible watermarking algorithm for images. With the help of border following algorithm and pre-processing, available regions with at least one zero point are recognized to embed watermark so that auxiliary information is not needed any more. And the algorithm utilized also ensures the same border can be re-recognized from the watermarked image without error, thus the correctness is also guaranteed. The performance of the proposed algorithm is evaluated using classic image datasets in this area, and the results not only validate the effectiveness of the proposed algorithm but also indicate its advantages compared with the classic histogram shifting–based reversible watermarking algorithm as well as the state of the art.

Cloud storage provides an inexpensive and effective means for the storage and management of images, which in turn occupy a huge proportion and are usually stored in an archived mode. Considering the security and efficiency requirements of cloud images, an efficient real-time integrity audit scheme is in urgent need. However, existing solutions cannot be directly applied since they do not take the characteristics of cloud images into account and thus take enormous computations, communications, and storage to generate, transfer, and store authentication data. Moreover, the result of auditing cannot be used as evidence to prove the guilt of cloud service provider since the verifier whom is specified by the client may hide its misbehavior. Reversible watermarking is a potential way to achieve lightweight real-time audit for cloud images without introducing permanent distortion. Nevertheless, existing algorithms cannot provide stable capacity for authentication data of fixed length. In addition, it entails security problems once it is used to solve the fairness problem. This paper proposes an efficient real-time integrity audit scheme specific to cloud images with fair arbitration support. The scheme is based on the presented adaptive reversible watermarking algorithm which provides a fixed embedding capacity for images to embed authentication data. To address fairness problem under the proposed mechanism, we adopt Diffie-Hellman key exchange scheme to design a new challenge-response protocol under the established simplified consensus mechanism, so that replay attack resistance and privacy-preserving fair arbitration are achieved. The security analysis and performance evaluation show that the proposed scheme has provable security and greatly enhanced efficiency comparing with the state of the art.

This paper presents a disturbance observer-based integral sliding mode control (ISMC) for singularly perturbed systems (SPSs) with mismatched disturbances. First, a linear state feedback control law for the slow subsystem of the SPS is designed, and the fast subsystem is established. Second, a disturbance observer and an ISMC incorporating the disturbance estimate are constructed. The gain of ISMC is obtained by applying the H∞ control theory, and the resulting ISMC can effectively attenuate the mismatched disturbances. Finally, the proposed method is applied to an electric system, which illustrates the effectiveness and feasibility. The proposed design methods are based on the reduced-order subsystems and thus free of the high dimensionality and possible numerical ill-conditioned problems.

In this paper, a novel adaptive-gain sliding mode observer (SMO) is designed for sensorless control of permanent magnet linear synchronous motors (PMLSMs). By the proposed adaptive-gain algorithm, the gain can dynamically adapt to a changing operation condition of the PMLSMs and reach an appropriate value in finite time. Stability of the SMO is proved by using Lyapunov stability theory. Compared with the traditional SMO, the adaptive-gain SMO can improve the control precision, especially when the operation condition is not invariant. Simulation results are given to show the advantages of the proposed method.

This paper studies the problems of tip position regulation and vibration suppression of flexible manipulators without using the model. Because of the two-timescale characteristics of flexible manipulators, applying the existing model-free control methods may lead to ill-conditioned numerical problems. In this paper, the dynamics of a flexible manipulator is decomposed into two subsystems which are linear and controllable at different timescales by singular perturbation (SP) theory and a model-free composite controller is designed to alleviate the ill-conditioned numerical problems. To do this, a model-free composite control strategy is constructed which facilitates in designing the controller in slow and fast timescales. In the slow timescale, the slow subsystem controller is designed by adaptive dynamic programming (ADP) based on the measurements of the slow inputs and the position, while the vibration in the slow timescale is estimated by the least square method. In the fast timescale, the vibration is reconstructed based on the measurements of vibration and its estimate in the slow timescale, by which the fast controller is designed using ADP. Stability of the closed-loop system is proved by SP theory. Finally, simulations are given to show the feasibility and effectiveness of the proposed methods.

The energy model of belt conveyors plays a key role in the energy efficiency optimization problem of belt conveyors. However, the existing energy models and parameter identification methods are mainly limited to single-motor-driven belt conveyors and require speed sensors. This paper will present an energy model and a parameter identification method for dual-motor-driven belt conveyors whose speed sensors are not available. Firstly, a new energy model of dual-motor-driven belt conveyors is established by combining the traditional energy model with the dynamic model of a dual-motor-driven system. Then, a parameter identification method based on an extended Kalman filtering algorithm and recursive least square approach is proposed. Finally, the feasibility and effectiveness of the method are demonstrated by simulation experiments.