Science communication revision requires writers to dynamically balance scientific exposition and narrative engagement - a process where writers often struggle with competing directions. Existing LLM-assisted tools help with co-writing, but offer limited support for navigating this iterative, multi-directional revision process. To address this gap, we designed Spatial Balancing, an exploratory revision environment that maps rhetorical goals and revision strategies onto a two-dimensional spatial canvas for experienced science communication creators with domain expertise but lacking formal professional training. By building a design space of communication strategies and embedding them into a spatial exploratory canvas, our system treats feedback as navigational cues rather than prescriptive judgments. Our findings show that this integrated revision environment helps writers stay focused on writing goals, reason about revision as trajectories, and explore alternatives, which supports greater metacognitive control and confidence without increasing workload. This work highlights the value of spatially externalized revision environments for supporting iterative, reflective thinking during LLM-assisted writing.

Humans think visually—we remember in images, dream in pictures, and use visual metaphors to communicate. Yet, most creative writing tools remain text-centric, limiting how writers plan and translate ideas. We present Vistoria, a system for synchronized image-text co-editing in fictional story writing. A formative Wizard-of-Oz co-design study with 10 story writers revealed how sketches, images, and text serve as essential elements for ideation and organization. Drawing on theories of Instrumental Interaction, Vistoria introduces instrumental operations—Lasso, Collage, Perspective Shift, and Filter that enable seamless narrative exploration across modalities. A controlled study with 12 participants shows that co-editing enhances expressiveness, immersion, and collaboration, opening space for writers to follow divergent story directions and craft more vivid, detailed narratives. While multimodality increased cognitive demand, participants reported stronger senses of ownership and agency. These findings demonstrate how multimodal co-editing expands creative potential by balancing abstraction and concreteness in narrative development.

This paper considers the joint optimization of resource allocation and power control for rate-splitting multiple access (RSMA) based low earth orbits (LEO) satellite-terrestrial networks, where resource sharing between terrestrial and LEO satellite communications is optimized and the LEO satellite serves multiple ground stations (GSs) simultaneously through RSMA technique. Particularly, to make full use of RSMA technique, the LEO satellite needs to appropriately schedule transmitting power to common and private streams. Therefore, the key issue is to jointly optimize the resource allocation and power control to fully utilize the benefits of resource sharing and RSMA. However, the combination of continuous power control and discrete resource allocation becomes the bottleneck for providing an effective solution with limited system information. To deal with this problem, we propose a deep reinforcement learning (DRL)-based framework which jointly employs deep Q-network (DQN) algorithm for discrete resource allocation and proximal policy optimization (PPO) algorithm for continuous power control to maximize a joint objective. Simulation experiments evaluate the performance of the proposed scheme compared with several baseline schemes and the results show the advantages of the proposed scheme.

Device-to-device (D2D) communication is regarded as a promising technology to support spectral-efficient Internet of Things (IoT) in beyond fifth-generation (5G) and sixth-generation (6G) networks. This article investigates the spectrum access problem for D2D-assisted cellular networks based on deep reinforcement learning (DRL), which can be applied to both the uplink and downlink scenarios. Specifically, we consider a time-slotted cellular network, where D2D nodes share the cellular spectrum resources (CUEs) with cellular users in a time-splitting manner. Besides, D2D nodes could reuse time slots preoccupied by CUEs according to a location-based spectrum access (LSA) strategy on the premise of cellular communication quality. The key challenge lies in that D2D nodes have no information on the LSA strategy and the access principle of CUEs. Thus, we design a DRL-based spectrum access scheme such that the D2D nodes can autonomously acquire an optimal strategy for efficient spectrum access without any prior knowledge to achieve a specific objective such as maximizing the normalized sum throughput. Moreover, we adopt a generalized double deep$Q$-network (DDQN) algorithm and extend the objective function to explore the resource allocation fairness for D2D nodes. The proposed scheme is evaluated under various conditions and our simulation results show that it can achieve the near-optimal throughput performance with different objectives compared to the benchmark, which is the theoretical throughput upper bound derived from a genius-aided scheme with complete system knowledge available.

BACKGROUND: Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis. RESULTS: Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA). CONCLUSION: FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.

An amendment to this paper has been published and can be accessed via the original article.

MOTIVATION: The discovery of therapeutic targets is important for cancer treatment. Although dozens of targets have been used in cancer therapies, cancer remains a serious disease with a high mortality rate. Owing to the expansion of cancer-related data, we now have the opportunity to infer therapeutic targets using computational biology methods. RESULTS: Here, we describe a method, termed anticancer activity enrichment analysis, used to determine genes that could be used as therapeutic targets. The results show that these genes have high likelihoods of being developed into clinical targets (>60%). Combined with gene expression data, we predicted 50 candidate targets for lung cancer, of which 19 of the top 20 genes are targeted by approved drugs or drugs used in clinical trials. A hexokinase family member, hexokinase domain-containing protein 1 (HKDC1), is the only one of the top 20 genes that has not been targeted by either an approved drug or one being used in clinical trials. Further investigations indicate that HKDC1 is a novel potential therapeutic target for lung cancer. CONCLUSION: We developed a protocol to identify potential therapeutic targets from heterogeneous data. We suggest that HKDC1 is a novel potential therapeutic target for lung cancer. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Summary: Cancer is the leading cause of death worldwide. Screening anticancer candidates from tens of millions of chemical compounds is expensive and time-consuming. A rapid and user-friendly web server, known as CDRUG, is described here to predict the anticancer activity of chemical compounds. In CDRUG, a hybrid score was developed to measure the similarity of different compounds. The performance analysis shows that CDRUG has the area under curve of 0.878, indicating that CDRUG is effective to distinguish active and inactive compounds. Availability: The CDRUG web server and the standard-alone version are freely available at http://bsb.kiz.ac.cn/CDRUG/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: The rapid development of structural genomics has resulted in many "unknown function" proteins being deposited in Protein Data Bank (PDB), thus, the functional prediction of these proteins has become a challenge for structural bioinformatics. Several sequence-based and structure-based methods have been developed to predict protein function, but these methods need to be improved further, such as, enhancing the accuracy, sensitivity, and the computational speed. Here, an accurate algorithm, the CMASA (Contact MAtrix based local Structural Alignment algorithm), has been developed to predict unknown functions of proteins based on the local protein structural similarity. This algorithm has been evaluated by building a test set including 164 enzyme families, and also been compared to other methods. RESULTS: The evaluation of CMASA shows that the CMASA is highly accurate (0.96), sensitive (0.86), and fast enough to be used in the large-scale functional annotation. Comparing to both sequence-based and global structure-based methods, not only the CMASA can find remote homologous proteins, but also can find the active site convergence. Comparing to other local structure comparison-based methods, the CMASA can obtain the better performance than both FFF (a method using geometry to predict protein function) and SPASM (a local structure alignment method); and the CMASA is more sensitive than PINTS and is more accurate than JESS (both are local structure alignment methods). The CMASA was applied to annotate the enzyme catalytic sites of the non-redundant PDB, and at least 166 putative catalytic sites have been suggested, these sites can not be observed by the Catalytic Site Atlas (CSA). CONCLUSIONS: The CMASA is an accurate algorithm for detecting local protein structural similarity, and it holds several advantages in predicting enzyme active sites. The CMASA can be used in large-scale enzyme active site annotation. The CMASA can be available by the mail-based server (http://159.226.149.45/other1/CMASA/CMASA.htm).