Hybrid Double Network Cryogels Scaffold for Repair of Local Cartilage Defect

Trauma or repeated injury to the joint can result in focal cartilage defects, which significantly increase the risk of osteoarthritis. Cartilage being avascular has limited self-healing capacity. The current method for treating cartilage defects at early stages involves implantation of autologous chondrocytes with or without a scaffold through invasive surgery; however, invasive surgery can bring pain to patients and post-operative infection risks. Therefore, injectable and biodegradable biomaterials have piqued people\u27s interest. In the current research, we designed a hybrid double network (DN) cryogel by combining multi-arm PEG acrylate and alginate as two networks and crosslinking them at -20°C. Through different characterizations (SEM, mechanical strength, swelling test, etc.), we found that the DN cryogel has a macroporous interconnected structure, high water uptake capacity, and can support chondrocyte growth and extracellular matrix synthesis. These features make it possible for the cryogel to be used as a scaffold for cartilage to treat cartilage defects. Key words: Cartilage defect, double network cryogels, macroporous interconnectionstructur

Dimension Reduction for Efficient Data-Enabled Predictive Control

The recent data-enabled predictive control (DeePC) paradigm directly utilizes offline input/output data from an unknown system to predict its future trajectory and compute optimal control inputs online. In this scheme, the pre-collected input/output data needs to be sufficiently rich to represent the system behavior. This generally leads to an excessive amount of offline data, which consequently results in a high-dimension optimization problem in online predictive control. In this paper, we propose a simple yet effective singular value decomposition (SVD) based strategy to reduce the optimization problem dimension in DeePC. Specifically, in the case of linear time-invariant systems, the excessive input/output measurements can be rearranged into a smaller data library for the non-parametric representation of system behavior. Based on this observation, we use an SVD-based strategy to pre-process the offline data that achieves dimension reduction in DeePC. Numerical experiments confirm that the proposed method significantly enhances the computation efficiency without compromising the control performance.Comment: 9 pages, 4 figure

Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control

Connected and autonomous vehicles (CAVs) promise next-gen transportation systems with enhanced safety, energy efficiency, and sustainability. One typical control strategy for CAVs is the so-called cooperative adaptive cruise control (CACC) where vehicles drive in platoons and cooperate to achieve safe and efficient transportation. In this study, we formulate CACC as a multi-agent reinforcement learning (MARL) problem. Diverging from existing MARL methods that use centralized training and decentralized execution which require not only a centralized communication mechanism but also dense inter-agent communication, we propose a fully-decentralized MARL framework for enhanced efficiency and scalability. In addition, a quantization-based communication scheme is proposed to reduce the communication overhead without significantly degrading the control performance. This is achieved by employing randomized rounding numbers to quantize each piece of communicated information and only communicating non-zero components after quantization. Extensive experimentation in two distinct CACC settings reveals that the proposed MARL framework consistently achieves superior performance over several contemporary benchmarks in terms of both communication efficiency and control efficacy.Comment: 11 pages, 7 figure

High-Precision Fruit Localization Using Active Laser-Camera Scanning: Robust Laser Line Extraction for 2D-3D Transformation

Recent advancements in deep learning-based approaches have led to remarkable progress in fruit detection, enabling robust fruit identification in complex environments. However, much less progress has been made on fruit 3D localization, which is equally crucial for robotic harvesting. Complex fruit shape/orientation, fruit clustering, varying lighting conditions, and occlusions by leaves and branches have greatly restricted existing sensors from achieving accurate fruit localization in the natural orchard environment. In this paper, we report on the design of a novel localization technique, called Active Laser-Camera Scanning (ALACS), to achieve accurate and robust fruit 3D localization. The ALACS hardware setup comprises a red line laser, an RGB color camera, a linear motion slide, and an external RGB-D camera. Leveraging the principles of dynamic-targeting laser-triangulation, ALACS enables precise transformation of the projected 2D laser line from the surface of apples to the 3D positions. To facilitate laser pattern acquisitions, a Laser Line Extraction (LLE) method is proposed for robust and high-precision feature extraction on apples. Comprehensive evaluations of LLE demonstrated its ability to extract precise patterns under variable lighting and occlusion conditions. The ALACS system achieved average apple localization accuracies of 6.9 11.2 mm at distances ranging from 1.0 m to 1.6 m, compared to 21.5 mm by a commercial RealSense RGB-D camera, in an indoor experiment. Orchard evaluations demonstrated that ALACS has achieved a 95% fruit detachment rate versus a 71% rate by the RealSense camera. By overcoming the challenges of apple 3D localization, this research contributes to the advancement of robotic fruit harvesting technology

Reduction of Hox Gene Expression by Histone H1 Depletion

The evolutionarily conserved homeotic (Hox) genes are organized in clusters and expressed collinearly to specify body patterning during embryonic development. Chromatin reorganization and decompaction are intimately connected with Hox gene activation. Linker histone H1 plays a key role in facilitating folding of higher order chromatin structure. Previous studies have shown that deletion of three somatic H1 subtypes together leads to embryonic lethality and that H1c/H1d/H1e triple knockout (TKO) embryonic stem cells (ESCs) display bulk chromatin decompaction. To investigate the potential role of H1 and higher order chromatin folding in the regulation of Hox gene expression, we systematically analyzed the expression of all 39 Hox genes in triple H1 null mouse embryos and ESCs by quantitative RT-PCR. Surprisingly, we find that H1 depletion causes significant reduction in the expression of a broad range of Hox genes in embryos and ESCs. To examine if any of the three H1 subtypes (H1c, H1d and H1e) is responsible for decreased expression of Hox gene in triple-H1 null ESCs, we derived and characterized H1c−/−, H1d−/−, and H1e−/− single-H1 null ESCs. We show that deletion of individual H1 subtypes results in down-regulation of specific Hox genes in ESCs. Finally we demonstrate that, in triple-H1- and single-H1- null ESCs, the levels of H3K4 trimethylation (H3K4me3) and H3K27 trimethylation (H3K27me3) were affected at specific Hox genes with decreased expression. Our data demonstrate that marked reduction in total H1 levels causes significant reduction in both expression and the level of active histone mark H3K4me3 at many Hox genes and that individual H1 subtypes may also contribute to the regulation of specific Hox gene expression. We suggest possible mechanisms for such an unexpected role of histone H1 in Hox gene regulation