The multidrug-resistant pathogen Acinetobacter baumannii (A. baumannii) is a global health concern. Its surface capsular polysaccharides and lipopolysaccharides, which are structurally diverse and often contain rare, non-classical sugars, are major virulence factors. These glycans represent promising targets for novel therapeutics. Notably, glycoconjugate vaccines based on these structures elicit protective antibodies and confer effective immunity in animal models, highlighting their potential for combating infections.

Functionalized single-walled carbon nanotubes (SWCNTs) effectively delivered the amh/amhy plasmid into all-female mandarin fish via immersion. At 40 mg/L, plasmid DNA and transcripts were detected in gonads within 7-14 days. By 60-120 days, some fish developed masculinized gonads with downregulated foxl2/cyp19a1a and upregulated amh/dmrt1. The study demonstrates SWCNTs as a viable gene delivery tool in fish and confirms the crucial role of amh/amhy in sex determination via the amhrII/smads pathway activating dmrt1.

Researchers at the University of Windsor developed SH17, a large open-source dataset with 8,099 images and 75,994 labeled instances to improve detection of personal protective equipment (PPE) in manufacturing. Using advanced AI models like YOLOv9, the study achieved over 70% accuracy, offering industries a scalable tool to enhance worker safety and compliance.

A research team from Fudan University has developed a hydrogel technology based on microenvironment-responsive mechanisms. The material can sense pH changes in the wound environment and dynamically release functional agents, enabling a switch from antibacterial action to tissue repair. Constructed from an interpenetrating network of sodium alginate and carboxymethyl chitosan, and loaded with tannic acid and zinc-doped bioactive glass, the hydrogel rapidly releases antibacterial molecules during infection and gradually delivers regenerative ions during healing—achieving, for the first time, precise, stage-specific control of infected wound treatment.

Reactive planning and control capacity for collaborative robots is essential when the tasks change online in an unstructured environment. This is more difficult for collaborative mobile manipulators (CMM) due to high redundancies. To this end, this paper proposed a reactive whole-body locomotion-integrated manipulation approach based on combined learning and optimization. First, human demonstrations are collected, where the wrist and pelvis movements are treated as whole-body trajectories, mapping to the end-effector (EE) and the mobile base (MB) of CMM, respectively. A time-input kernelized movement primitive (T-KMP) learns the whole-body trajectory, and a multi-dimensional kernelized movement primitive (M-KMP) learns the spatial relationship between the MB and EE pose. According to task changes, the T-KMP adapts the learned trajectories online by inserting the new desired point predicted by M-KMP. Then, the updated reference trajectories are sent to a hierarchical quadratic programming (HQP) controller, where the EE and the MB trajectories tracking are set as the first and second priority tasks, generating the feasible and optimal joint level commands. An ablation simulation experiment with CMM of the HQP is conducted to show the necessity of MB trajectory tracking in mimicking human whole-body motion behavior. Finally, the tasks of the reactive pick-and-place and reactive reaching were undertaken, where the target object was randomly moved, even out of the region of demonstrations. The results showed that the proposed approach can successfully transfer and adapt the human whole-body loco-manipulation skills to CMM online with task changes.

Embodied learning for object-centric robotic manipulation is a rapidly developing and challenging area in embodied AI. It is crucial for advancing next-generation intelligent robots and has garnered significant interest recently. Unlike data-driven machine learning methods, embodied learning focuses on robot learning through physical interaction with the environment and perceptual feedback, making it especially suitable for robotic manipulation. In this paper, researchers provide a comprehensive survey of the latest advancements in this field and categorize the existing work into three main branches: 1) Embodied perceptual learning, which aims to predict object pose and affordance through various data representations; 2) Embodied policy learning, which focuses on generating optimal robotic decisions using methods such as reinforcement learning and imitation learning; 3) Embodied task-oriented learning, designed to optimize the robot′s performance based on the characteristics of different tasks in object grasping and manipulation. In addition, researchers offer an overview and discussion of public datasets, evaluation metrics, representative applications, current challenges, and potential future research directions. A project associated with this survey has been established at https://github.com/RayYoh/OCRM_survey.