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TTK (also known as Mps1) is the core component of the spindle assembly checkpoint, which ensures proper distribution of chromosomes to daughter cells to maintain genome integrity and to balance growth and division. However, the function of TTK in tumorigenesis has not been extensively studied, especially in relation to the development of gastric cancer. In this study, survival and tumor recurrence data related to TTK expression level in gastric cancer patients were collected and analyzed. We observed that TTK expression was negatively correlated with survival and tumor recurrence in vivo. TTK was also upregulated in gastric cancer cells and was observed to be essential for the proliferation and survival of gastric cancer cells. Knockdown of TTK inhibited proliferation and increased apoptosis. Furthermore, we report that TTK regulates the proliferation and apoptosis of tumor cells through the Akt‐mTOR pathway. Knockdown of TTK inhibited activation of Akt‐mTOR signaling. In summary, our data indicate that TTK is involved in the regulation of gastric cancer proliferation and apoptosis.

As a senile chronic, progressive and currently incurable disease, dementia has an enormous impact on society and life quality of the elderly. The development of teleoperation technology has changed the traditional way of care delivery and brought a variety of novel applications for dementia care. In this paper, a telerobotic system is presented which gives the caregivers the capability of assisting dementia elderly remotely. The proposed system is composed of a dual-arm collaborative robot (YuMi) and a wearable motion capture device. The communication architecture is achieved by the robot operation system (ROS). The position-orientation data of the operator’s hand are obtained and used to control the YuMi robot. Besides, a path-constrained mapping method is designed for motion trajectory tracking between the robot and the operator in the progress of teleoperation. Meanwhile, corresponding experiments are conducted to verify the performance of the trajectory tracking using the path-constrained mapping method. Results show that the position tracking deviation between the trajectory of the operator and the robot measured by dynamic time warping distance is 1.05 mm at the sampling frequency of 7.5 Hz. Moreover, the practicability of the proposed system was verified by teleoperating the YuMi robot to pick up a medicine bottle and further demonstrated by assisting an elderly woman in picking up a cup remotely. The proposed telerobotic system has potential utility for improving the life quality of dementia elderly and the care effect of their caregivers.

Regulatory T cells (Tregs) and M2c macrophages have been shown to exert potentially synergistic therapeutic effects in animals with adriamycin-induced nephropathy (AN), a model chronic proteinuric renal disease. M2c macrophages may protect against renal injury by promoting an increase in the number of Tregs in the renal draining lymph nodes of AN mice, but how they do so is unclear. In this study, we used an AN mouse model to analyze how M2c macrophages induce the migration of Tregs. Using flow cytometry, we found that M2c macrophages promoted the migration of Tregs from the peripheral blood to the spleen, thymus, kidney, and renal draining lymph nodes. At the same time, M2c macrophages significantly upregulated chemokine receptors and adhesion molecule in Tregs, including CCR4, CCR5, CCR7, CXCR5, and CD62L. Treating AN mice with monoclonal anti-CD62L antibody inhibited the migration of M2c macrophages and Tregs to the spleen, thymus, kidney, and renal draining lymph nodes. Taken together, our results suggest that M2c macrophages upregulate CD62L in Tregs and thereby promote their migration to inflammatory sites, where they exert renoprotective effects. These insights may aid the development of treatments against chronic kidney disease.

PurposeWe developed a deep learning model to achieve automatic multitarget delineation on planning CT (pCT) and synthetic CT (sCT) images generated from cone-beam CT (CBCT) images. The geometric and dosimetric impact of the model was evaluated for breast cancer adaptive radiation therapy.MethodsWe retrospectively analyzed 1,127 patients treated with radiotherapy after breast-conserving surgery from two medical institutions. The CBCT images for patient setup acquired utilizing breath-hold guided by optical surface monitoring system were used to generate sCT with a generative adversarial network. Organs at risk (OARs), clinical target volume (CTV), and tumor bed (TB) were delineated automatically with a 3D U-Net model on pCT and sCT images. The geometric accuracy of the model was evaluated with metrics, including Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95). Dosimetric evaluation was performed by quick dose recalculation on sCT images relying on gamma analysis and dose-volume histogram (DVH) parameters. The relationship between ΔD95, ΔV95 and DSC-CTV was assessed to quantify the clinical impact of the geometric changes of CTV.ResultsThe ranges of DSC and HD95 were 0.73–0.97 and 2.22–9.36 mm for pCT, 0.63–0.95 and 2.30–19.57 mm for sCT from institution A, 0.70–0.97 and 2.10–11.43 mm for pCT from institution B, respectively. The quality of sCT was excellent with an average mean absolute error (MAE) of 71.58 ± 8.78 HU. The mean gamma pass rate (3%/3 mm criterion) was 91.46 ± 4.63%. DSC-CTV down to 0.65 accounted for a variation of more than 6% of V95 and 3 Gy of D95. DSC-CTV up to 0.80 accounted for a variation of less than 4% of V95 and 2 Gy of D95. The mean ΔD90/ΔD95 of CTV and TB were less than 2Gy/4Gy, 4Gy/5Gy for all the patients. The cardiac dose difference in left breast cancer cases was larger than that in right breast cancer cases.ConclusionsThe accurate multitarget delineation is achievable on pCT and sCT via deep learning. The results show that dose distribution needs to be considered to evaluate the clinical impact of geometric variations during breast cancer radiotherapy.

Abstract Cyber physical system (CPS) is facing enormous security challenges because of open and interconnected network and the interaction between cyber components and physical components, the development of cyber physical systems is constrained by security and privacy threats. A feasible solution is to combine the fully homomorphic encryption (FHE) technique to realize the efficient operation of ciphertext without decryption. However, most current homomorphic encryption algorithms only support limited data types, making it difficult to be widely applied in actual environment. To address this limitation, we propose a parallel fully homomorphic encryption algorithm that supports floating-point numbers. The proposed algorithm not only expands the data types supported by the existing fully homomorphic encryption algorithms, but also utilizes the characteristics of multi-nodes in cloud environment to conduct parallel encryption through simultaneous group-wise ciphertext computations. The experimental results show that, in a 16-core 4-node cluster with MapReduce environment, the proposed encryption algorithm achieves the maximum speed-up exceeding 5, which not only solves the limited application problem of the existing fully homomorphic encryption algorithm, but also meets the requirements for the efficient homomorphic encryption of floating-point numbers in cloud computing environment.

The physics-based fractional-order model (FOM) for lithium-ion batteries has shown good application prospects due to its mechanisms and simplicity. To adapt the model to higher-level applications, this paper proposes an improved FOM considering electrolyte-phase diffusion (FOMe) and then proposes a complete method for parameter identification based on three characteristic SOC intervals: the positive solid phase, negative solid phase, and electrolyte phase. The method mainly determines the above three characteristic intervals and identifies four thermodynamic parameters and five dynamic parameters. Furthermore, the paper describes a framework, which first verifies the model and parameter identification method separately based on pseudo two-dimensional model simulations, and secondly verifies FOMe and its parameters as a whole based on the experiments. The results, which are based on simulations and actual Li0.8Co0.1Mn0.1O2 lithium-ion batteries under multiple typical operating profiles and comparisons with other parameter identification methods, show that the proposed model and parameter identification method is highly accurate and efficient.

In human–robot collaboration, the wrapping material on robots is not only required to have the sensing ability to adapt to the external environment but also need to have the function of cushioning the collision between human and robot. Herein, a fluid‐driven soft robot skin with sensing and actuating function is successfully applied to a collaborative robot and working well with the host robot. The skin is an integration of sponge force sensors and pneumatic actuators. By altering the internal air pressure in pneumatic actuators, the developed robot skin can provide more than ten times tunable stiffness and sensitivity. In addition, the skin can reduce the peak force of the collision and achieve the actuating function. Using three‐dimensional printing and computer‐aided design, the skin is fabricated and attached to a collaborative robot conformally. Drawing upon the data acquisition and control system, the experiment for illustrating the applications of the CoboSkin is successfully performed. The skin provides the robot with multi‐functions, which are similar to the human muscle and skin attached to human bones. By mimicking human skin and muscle with tactile sensing function and stiffness tuning function, CoboSkin can enhance the adaptability of the robot to human daily life.