Navigating vast, rugged biological fitness landscapes to discover high-value functional patterns—such as optimal protein sequences—is a central challenge in health informatics. However, conventional algorithms often struggle with the exploration-exploitation dilemma, failing to synergize global search with deep local refinement, which leads to entrapment in suboptimal solutions. To overcome this barrier, we introduce Octopus Inspired Optimization (OIO), a novel hierarchical metaheuristic that mimics the octopus's unique neural architecture to intrinsically unify centralized global exploration and parallelized local exploitation. We validated OIO on a realworld protein engineering benchmark, where it surpassed 15 competing metaheuristics. This success is underpinned by OIO's architectural suitability for protein-like landscapes, confirmed by its top ranking on the NK-Landscape benchmark, and its powerful optimization engine, demonstrated by its first-place performance on the gold-standard CEC2022 benchmark. OIO thus provides a robust, nature-inspired computational tool for complex optimization problems in drug discovery and personalized medicine.

To address the challenges of accurately predicting vehicle trajectories and prioritizing driving tasks in complex situations such as lane changing or highway ramp merging for autonomous vehicles, this paper introduces a deep reinforcement learning (DRL) merging control method called DRLI-P (DRL for Trajectory Prediction and Task Importance Network Fusion). DRLI-P integrates an LSTM-based vehicle trajectory prediction network with a rule-based task importance network (TIN). The TIN assesses the importance of vehicle action rules and complex driving tasks, alleviating problems associated with sparse reward distribution in DRL and improving sampling efficiency. The LSTM uses historical driving data to predict vehicle trajectories and constructs a state space to mitigate slow training speeds and reduced sensitivity to single state parameter changes due to high state dimensionality in multi-vehicle scenarios. A multi-category weighted reward function is developed that focuses on critical driving features such as target distance, vehicle motion information, and trajectory predictions. The proposed merging control method is applied to three leading DRL algorithms: DDPG, TD3, and SAC, followed by simulation experiments in the CARLA environment. The results show that the DRLI-P method significantly improves the convergence speed and performance of all three algorithms, with the most notable improvement seen in the SAC algorithm, thereby increasing the safety, efficiency, and convenience of DRL algorithms for merging control.

Targeted adversarial attacks in black-box settings are pivotal for uncovering vulnerabilities in neural networks and guiding the development of robust defenses. However, conventional attack methods typically perturb the primary content, leading to a degradation in image quality and highlighting the need for more reasonable optimization strategies. In contrast, we propose a novel algorithm that restricts perturbations to the image boundary regions, thereby preserving content fidelity while enhancing attack effectiveness. Our approach employs an encoder–decoder generative network to craft targeted adversarial examples guided by optimized semantic perturbations derived from the boundaries. Moreover, the boundary signal is jointly optimized with the model parameters, enabling efficient, amortized optimization for multi-class targeted attacks. Extensive experiments demonstrate that the proposed boundary-guided method significantly improves the success rates of targeted black-box attacks and can be seamlessly integrated into existing noise-injection techniques to enhance overall performance.

Quantum computing has gained considerable attention, especially after the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era. Quantum processors and cloud services have been made worldwide increasingly available. Unfortunately, jobs on existing quantum processors are often executed in series, and the workload could be heavy to the processor. Typically, one has to wait for hours or even longer to obtain the result of a single quantum job on public quantum cloud due to long queue time. In fact, as the scale grows, the qubit utilization rate of the serial execution mode will further diminish, causing the waste of quantum resources. In this paper, to our best knowledge for the first time, the Quantum Job Scheduling Problem (QJSP) is formulated and introduced, and we accordingly aim to improve the utility efficiency of quantum resources. Specifically, a noise-aware quantum job scheduler (NAQJS) concerning the circuit width, number of measurement shots, and submission time of quantum jobs is proposed to reduce the execution latency. We conduct extensive experiments on a simulated Qiskit noise model, as well as on the Xiaohong (from QuantumCTek) superconducting quantum processor. Numerical results show the effectiveness in both the QPU time and turnaround time.

Imaging Photoplethysmography (IPPG) is an emerging and efficient optical method for non-contact measurement of pulse waves using an image sensor. While the contactless way brings convenience, the inevitable distance between the sensor and the subject results in massive specular reflection interference on the skin surface, which leads to a low Signal to Interference plus Noise Ratio (SINR) of IPPG. To ease this challenge, this work proposes a novel modulation illumination approach to measure the accurate arterial pulse wave via surface reflection interference isolation from IPPG. Based on the proposed skin reflection model, a specific modulation illumination is designed to separate the surface reflections and obtain the subcutaneous diffuse reflections containing the pulse wave information. Compared with the results under ambient illumination and constant supplemental illumination, the SINR of the proposed method is improved by 4.56 and 3.74 dB, respectively.

Genes have the ability to produce transcript variants that perform specific cellular functions. However, accurately detecting all transcript variants remains a long-standing challenge, especially when working with poorly annotated genomes or without a known genome. To address this issue, we have developed a new computational method, TransIntegrator, which enables transcriptome-wide detection of novel transcript variants. For this, we determined 10 Illumina sequencing transcriptomes and a PacBio full-length transcriptome for consecutive embryo development stages of amphioxus, a species of great evolutionary importance. Based on the transcriptomes, we employed TransIntegrator to create a comprehensive transcript variant library, namely iTranscriptome. The resulting iTrancriptome contained 91 915 distinct transcript variants, with an average of 2.4 variants per gene. This substantially improved current amphioxus genome annotation by expanding the number of genes from 21 954 to 38 777. Further analysis manifested that the gene expansion was largely ascribed to integration of multiple Illumina datasets instead of involving the PacBio data. Moreover, we demonstrated an example application of TransIntegrator, via generating iTrancriptome, in aiding accurate transcriptome assembly, which significantly outperformed other hybrid methods such as IDP-denovo and Trinity. For user convenience, we have deposited the source codes of TransIntegrator on GitHub as well as a conda package in Anaconda. In summary, this study proposes an affordable but efficient method for reliable transcriptomic research in most species.