Efficacy of immunotherapy in ARID1A-mutant solid tumors: a single-center retrospective study.

BACKGROUND: Immune checkpoint inhibitors (ICIs), especially those targeting programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1), have introduced a new treatment landscape for many types of tumors. However, they only achieve a limited therapeutic response. Hence, identifying patients who may benefit from ICIs is currently a challenge. METHODS: 47 tumor patients harboring ARID1A mutations were retrospectively studied. The genomic profiling data through next-generation sequencing (NGS) and relevant clinical information were collected and analyzed. Additionally, bioinformatics analysis of the expression of immune checkpoints and immune cell infiltration levels was conducted in ARID1A-mutant gastric cancer (GC). RESULTS: ARID1A mutations frequently co-occur with mutations in DNA damage repair (DDR)-associated genes. Among the 35 ARID1A-mutant patients who received immunotherapy, 27 were evaluable., with the objective response rate (ORR) was 48.15% (13/27), and the disease control rate (DCR) was 92.59% (25/27). Moreover, survival assays revealed that ARID1A-mutant patients had longer median overall survival (mOS) after immunotherapy. In ARID1A-mutated GC patients, receiving ICIs treatment indicated longer progressive-free survival (PFS). Additionally, the incidence of microsatellite instability-high (MSI-H), high tumor mutation burden (TMB-H) and Epstein‒Barr virus (EBV) infection was elevated. Bioinformatic analysis showed significant enrichment of immune response and T cell activation pathway within differentially expressed genes in ARID1A-mutant GC group. Finally, ARID1A mutations status was considered to be highly correlated with the level of tumor infiltrating lymphocytes (TILs) and high expression of immune checkpoints. CONCLUSIONS: Patients with tumors harboring ARID1A mutations may achieve better clinical outcomes from immunotherapy, especially in GC. ARID1A mutations can lead to genomic instability and reshape the tumor immune microenvironment (TIME), which can be used as a biomarker for immunotherapy.

Research status of elderly-care robots and safe human-robot interaction methods.

Faced with the increasingly severe global aging population with fewer children, the research, development, and application of elderly-care robots are expected to provide some technical means to solve the problems of elderly care, disability and semi-disability nursing, and rehabilitation. Elderly-care robots involve biomechanics, computer science, automatic control, ethics, and other fields of knowledge, which is one of the most challenging and most concerned research fields of robotics. Unlike other robots, elderly-care robots work for the frail elderly. There is information exchange and energy exchange between people and robots, and the safe human-robot interaction methods are the research core and key technology. The states of the art of elderly-care robots and their various nursing modes and safe interaction methods are introduced and discussed in this paper. To conclude, considering the disparity between current elderly care robots and their anticipated objectives, we offer a comprehensive overview of the critical technologies and research trends that impact and enhance the feasibility and acceptance of elderly care robots. These areas encompass the collaborative assistance of diverse assistive robots, the establishment of a novel smart home care model for elderly individuals using sensor networks, the optimization of robot design for improved flexibility, and the enhancement of robot acceptability.

Reinforcement learning-based integrated active fault diagnosis and tracking control.

Reliable and real-time active diagnosis of system faults with uncertainties is strongly dependent on the input design. This paper establishes a data-driven framework for integrated design of active fault diagnosis and control while ensuring the tracking performance. To be specific, the input design is formulated as a constrained optimization problem that can be solved with the aid of constrained reinforcement learning algorithms. Moreover, based on the maximum mean discrepancy metric, a novel active fault isolation scheme is proposed to implement model discrimination using system outputs. At the end, the effectiveness of the proposed approach is evaluated in two case studies in the presence of probabilistic disturbances and uncertainties.

A Safe and Compliant Noncontact Interactive Approach for Wheeled Walking Aid Robot.

Aiming at promptly and accurately detecting falls and drag-to gaits induced by asynchronous human-robot movement speed during assisted walking, a noncontact interactive approach with generality, compliance and safety is proposed in this paper, and is applied to a wheeled walking aid robot. Firstly, the structure and the functions of the wheeled walking aid robot, including gait rehabilitation robot (GRR) and walking aid robot (WAR) are illustrated, and the characteristic futures of falls and the drag-to gait are shown by experiments. To obtain gait information, a multichannel proximity sensor array is developed, and a two-dimensional gait information detection system is established by combining four proximity sensors groups which are installed in the robot chassis. Additionally, a node-iterative fuzzy Petri net algorithm for abnormal gait recognition is proposed by generating the network trigger mechanism using the fuzzy membership function. It integrates the walking intention direction vector by taking gait deviation, frequency, and torso angle as input parameters of the system. Finally, to improve the compliance of the robot during human-robot interaction, a PID_SC controller is designed by integrating the gait speed compensation, which enables the WAR to track human gait closely. Abnormal gait recognition and assisted walking experiments are carried out respectively. Experimental results show that the proposed algorithm can accurately identify abnormal gaits of different groups of users with different walking habits, and the recognition rate of abnormal gait reaches 91.2%. Results also show that the developed method can guarantee safety in human robot interaction because of user gate follow-up accuracy and compliant movements. The noncontact interactive approach can be applied to robots with similar structure for usage in walking assistance and gait rehabilitation.

ResSANet: Learning Geometric Information for Point Cloud Processing.

Point clouds with rich local geometric information have potentially huge implications in several applications, especially in areas of robotic manipulation and autonomous driving. However, most point cloud processing methods cannot extract enough geometric features from a raw point cloud, which restricts the performance of their downstream tasks such as point cloud classification, shape retrieval and part segmentation. In this paper, the authors propose a new method where a convolution based on geometric primitives is adopted to accurately represent the elusive shape in the form of a point cloud to fully extract hidden geometric features. The key idea of the proposed approach is building a brand-new convolution net named ResSANet on the basis of geometric primitives to learn hierarchical geometry information. Two different modules are devised in our network, Res-SA and Res-SA-2, to achieve feature fusion at different levels in ResSANet. This work achieves classification accuracy up to 93.2% on the ModelNet40 dataset and the shape retrieval with an effect of 87.4%. The part segmentation experiment also achieves an accuracy of 83.3% (class mIoU) and 85.3% (instance mIoU) on ShapeNet dataset. It is worth mentioning that the number of parameters in this work is just 1.04 M while the network depth is minimal. Experimental results and comparisons with state-of-the-art methods demonstrate that our approach can achieve superior performance.

Error analysis and new dual-cosine window for estimating the sensor frequency response function from the step response data.

Aiming at reducing the estimation error of the sensor frequency response function (FRF) estimated by the commonly used window-based spectral estimation method, the error models of interpolation and transient errors are derived in the form of non-parameter models. Accordingly, window effects on the errors are analyzed and reveal that the commonly used hanning window leads to smaller interpolation error which can also be significantly eliminated by the cubic spline interpolation method when estimating the FRF from the step response data, and window with smaller front-end value can restrain more transient error. Thus, a new dual-cosine window with its non-zero discrete Fourier transform bins at -3, -1, 0, 1, and 3 is constructed for FRF estimation. Compared with the hanning window, the new dual-cosine window has the equivalent interpolation error suppression capability and better transient error suppression capability when estimating the FRF from the step response; specifically, it reduces the asymptotic property of the transient error from O(N-2) of the hanning window method to O(N-4) while only increases the uncertainty slightly (about 0.4 dB). Then, one direction of a wind tunnel strain gauge balance which is a high order, small damping, and non-minimum phase system is employed as the example for verifying the new dual-cosine window-based spectral estimation method. The model simulation result shows that the new dual-cosine window method is better than the hanning window method for FRF estimation, and compared with the Gans method and LPM method, it has the advantages of simple computation, less time consumption, and short data requirement; the actual data calculation result of the balance FRF is consistent to the simulation result. Thus, the new dual-cosine window is effective and practical for FRF estimation.

A Robot-Assisted Cell Manipulation System with an Adaptive Visual Servoing Method.

Robot-assisted cell manipulation is gaining attention for its ability in providing high throughput and high precision cell manipulation for the biological industry. This paper presents a visual servo microrobotic system for cell microinjection. We investigated the automatic cell autofocus method that reduced the complexity of the system. Then, we produced an adaptive visual processing algorithm to detect the location of the cell and micropipette toward the uneven illumination problem. Fourteen microinjection experiments were conducted with zebrafish embryos. A 100% success rate was achieved either in autofocus or embryo detection, which verified the robustness of the proposed automatic cell manipulation system.