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Animals crave sugars because of their energy potential and the pleasurable sensation of tasting sweetness. Yet all sugars are not metabolically equivalent, requiring mechanisms to detect and differentiate between chemically similar sweet substances. Insects use a family of ionotropic gustatory receptors to discriminate sugars 1 , each of which is selectively activated by specific sweet molecules 2 – 6 . Here, to gain insight into the molecular basis of sugar selectivity, we determined structures of Gr9, a gustatory receptor from the silkworm Bombyx mori (BmGr9), in the absence and presence of its sole activating ligand, d -fructose. These structures, along with structure-guided mutagenesis and functional assays, illustrate how d -fructose is enveloped by a ligand-binding pocket that precisely matches the overall shape and pattern of chemical groups in d -fructose. However, our computational docking and experimental binding assays revealed that other sugars also bind BmGr9, yet they are unable to activate the receptor. We determined the structure of BmGr9 in complex with one such non-activating sugar, l -sorbose. Although both sugars bind a similar position, only d -fructose is capable of engaging a bridge of two conserved aromatic residues that connects the pocket to the pore helix, inducing a conformational change that allows the ion-conducting pore to open. Thus, chemical specificity does not depend solely on the selectivity of the ligand-binding pocket, but it is an emergent property arising from a combination of receptor–ligand interactions and allosteric coupling. Our results support a model whereby coarse receptor tuning is derived from the size and chemical characteristics of the pocket, whereas fine-tuning of receptor activation is achieved through the selective engagement of an allosteric pathway that regulates ion conduction. A study reports structures of an insect taste receptor in the absence and presence of different sugars, providing details on the molecular basis of sugar detection and selectivity in insects.

Deep learning has revolutionised de novo protein design, with new models achieving unprecedented success in creating novel proteins with specific functions, including artificial protein binders. However, current workflows remain computationally demanding and challenging to operate without dedicated infrastructure and expertise. To overcome these limitations, we present BinderFlow, an open, structured, and parallelised pipeline that automates end-to-end protein binder design. Its batch-based architecture enables live monitoring of design campaigns, seamless coexistence with other GPU-intensive processes, and minimal user intervention. BinderFlow’s modular design facilitates the integration of new tools, allowing rapid adaptation to emerging methods. We demonstrate its utility by running automated design campaigns that rapidly generate diverse, high-confidence candidates suitable for experimental validation. To complement the pipeline, we developed BFmonitor, a web-based dashboard for real-time campaign monitoring, design evaluation, and hit selection. Together, BinderFlow and BFmonitor make generative protein design more accessible, scalable, and reproducible, streamlining both exploratory and production-level research. The software is freely available at https://github.com/cryoEM-CNIO/BinderFlow under the GNU LGPL v3.0 license.

Collaborative digital games and interfaces are increasingly used in both research and commercial contexts, yet little is known about how the spatial arrangement and interface sharing affect the user experience in dyadic settings. Using a two-player iPad pong game, this study compared user experiences across three collaborative gaming scenarios: face-to-face single-screen (F2F-OneS), face-to-face dual-screen (F2F-DualS), and remote dual-screen (Rmt-DualS) scenarios. Eleven dyads participated in all conditions using a within-subject design. After each session, the participants completed a 21-item user experience questionnaire and took part in brief interviews. The results from a repeated-measure ANOVA and post hoc paired t-tests showed significant scenario effects for several experience items, with F2F-OneS yielding higher engagement, novelty, and accomplishment than remote play, and qualitative interviews supported the quantitative findings, revealing themes of social presence and interaction. These results highlight the importance of spatial and interface design in collaborative settings, suggesting that both technical and social factors should be considered in multi-user interface development.

Background Androgenetic alopecia (AGA) is the most common cause of hair loss globally, affecting millions of people, particularly men. Current treatments include FDA‐approved drugs and devices, but many patients experience side effects or suboptimal results. The niostem device is a new, wearable device that delivers low‐level electrical stimulation to promote hair growth. This pilot study aims to evaluate the efficacy and safety of the niostem device in male AGA patients. Methods A total of 21 male patients with AGA used the niostem device daily for 30 min over 6 months. Participants had not used any anti‐hair loss products within the 6 months preceding the start of the study. Hair density, thickness, and terminal hair counts were assessed at baseline, 3 months, and 6 months using trichoscopic measurements. Patient‐reported outcomes were recorded, and adverse events were monitored. Results The niostem device resulted in significant increases in hair count, with a 12% increase in total hair density at 3 months and a 19.3% increase at 6 months. Hair thickness also increased by 8.8% in 6 months. Terminal hair density improved significantly over time, with visible hair growth observed in the participants. No adverse events were reported. Conclusions The niostem device demonstrated a significant increase in hair density and hair thickness in male AGA patients, with no adverse effects. Further large‐scale studies are warranted.

In the pursuit of developing expressive music performance models using artificial intelligence, this paper introduces DExter, a new approach leveraging diffusion probabilistic models to render Western classical piano performances. The main challenge faced in performance rendering tasks is the continuous and sequential modeling of expressive timing and dynamics over time, which is critical for capturing the evolving nuances that characterize live musical performances. In this approach, performance parameters are represented in a continuous expression space, and a diffusion model is trained to predict these continuous parameters while being conditioned on a musical score. Furthermore, DExter also enables the generation of interpretations (expressive variations of a performance) guided by perceptually meaningful features by being jointly conditioned on score and perceptual-feature representations. Consequently, we find that our model is useful for learning expressive performance, generating perceptually steered performances, and transferring performance styles. We assess the model through quantitative and qualitative analyses, focusing on specific performance metrics regarding dimensions like asynchrony and articulation, as well as through listening tests that compare generated performances with different human interpretations. The results show that DExter is able to capture the time-varying correlation of the expressive parameters, and it compares well to existing rendering models in subjectively evaluated ratings. The perceptual-feature-conditioned generation and transferring capabilities of DExter are verified via a proxy model predicting perceptual characteristics of differently steered performances.

User-centered design (UCD) commonly requires direct player participation, yet budget limitations or restricted access to users can impede this goal. To address these challenges, this research explores a transformer-based approach coupled with a diffusion process to replicate real player behavior in a 2D side-scrolling action–adventure environment that emphasizes exploration. By collecting an extensive set of gameplay data from real participants in an open-source game, “A Robot Named Fight!”, this study gathered comprehensive state and input information for training. A transformer model was then adapted to generate button-press sequences from encoded game states, while the diffusion mechanism iteratively introduced and removed noise to refine its predictions. The results indicate a high degree of replication of the participant’s actions in contexts similar to the training data, as well as reasonable adaptation to previously unseen scenarios. Observational analysis further confirmed that the model mirrored essential aspects of the user’s style, including navigation strategies, the avoidance of unnecessary combat, and selective obstacle clearance. Despite hardware constraints and reliance on a single observer’s feedback, these findings suggest that a transformer–diffusion methodology can robustly approximate user behavior. This approach holds promise not only for automated playtesting and level design assistance in similar action–adventure games but also for broader domains where simulating user interaction can streamline iterative design and enhance player-centric outcomes.

Background The biguanide drug metformin is a safe and widely prescribed drug for type 2 diabetes. Interestingly, hundreds of clinical trials have been set to evaluate the potential role of metformin in the prevention and treatment of cancer including colorectal cancer (CRC). However, the “metformin signaling” remains controversial. Aims and Methods To interrogate cell signaling induced by metformin in CRC and explore the druggability of the metformin‐rewired phosphorylation network, we performed integrative analysis of phosphoproteomics, bioinformatics, and cell proliferation assays on a panel of 12 molecularly heterogeneous CRC cell lines. Using the high‐resolute data‐independent analysis mass spectrometry (DIA‐MS), we monitored a total of 10,142 proteins and 56,080 phosphosites (P‐sites) in CRC cells upon a short‐ and a long‐term metformin treatment. Results and Conclusions We found that metformin tended to primarily remodel cell signaling in the long‐term and only minimally regulated the total proteome expression levels. Strikingly, the phosphorylation signaling response to metformin was highly heterogeneous in the CRC panel, based on a network analysis inferring kinase/phosphatase activities and cell signaling reconstruction. A “MetScore” was determined to assign the metformin relevance of each P‐site, revealing new and robust phosphorylation nodes and pathways in metformin signaling. Finally, we leveraged the metformin P‐site signature to identify pharmacodynamic interactions and confirmed a number of candidate metformin‐interacting drugs, including navitoclax, a BCL‐2/BCL‐xL inhibitor. Together, we provide a comprehensive phosphoproteomic resource to explore the metformin‐induced cell signaling for potential cancer therapeutics. This resource can be accessed at https://yslproteomics.shinyapps.io/Metformin/. An in‐depth proteomic and phosphoproteomic resource on metformin signaling in colorectal cancer (CRC). The phosphoproteomic response to metformin was highly heterogeneous in CRC cell lines. Establishing a phosphorylation site‐specific MetScore, which identified 55 most significant metformin signature sites. Leveraging the phosphoproteomics to discover potential metformin∼drug interactions in CRC, e.g., navitoclax.

Blueberry is a highly demanded and consumed fruit due to its beneficial effects on human health, because of its bioactive compounds with a high antioxidant capacity. The interest in increasing the yield and quality of blueberries has led to the application of some innovative techniques such as biostimulation. The objective of this research was to assess the effect of the exogenous application of glutamic acid (GLU) and 6-benzylaminopurine (6-BAP) as biostimulants on flower bud sprouting, fruit quality, and antioxidant compounds in blueberry cv. Biloxi. The application of GLU and 6-BAP positively affected bud sprouting, fruit quality, and antioxidant content. The application of 500 and 10 mg L−1 GLU and 6-BAP, respectively, increased the number of flower buds, while 500 and 20 mg L−1 generated fruits with higher content of flavonoids, vitamin C, and anthocyanins and higher enzymatic activity of catalase and ascorbate peroxidase enzymes. Hence, the application of these biostimulants is an effective way to enhance the yield and fruit quality of blueberries.

Generative artificial intelligence (AI) is reshaping creative practices, yet many systems rely on traditional interfaces, limiting intuitive and embodied engagement. This study presents a qualitative observational analysis of participant interactions with a real-time generative AI installation designed to co-create Ukiyo-e-style artwork through embodied inputs. The system dynamically interprets physical presence, object manipulation, body poses, and gestures to influence AI-generated visuals displayed on a large public screen. Drawing on systematic video analysis and detailed interaction logs across 13 sessions, the research identifies core modalities of interaction, patterns of co-creation, and user responses. Tangible objects with salient visual features such as color and pattern emerged as the primary, most intuitive input method, while bodily poses and hand gestures served as compositional modifiers. The system’s immediate feedback loop enabled rapid learning and iterative exploration and enhanced the user’s feeling of control. Users engaged in collaborative discovery, turn-taking, and shared authorship, frequently expressing a positive effect. The findings highlight how embodied interaction lowers cognitive barriers, enhances engagement, and supports meaningful human–AI collaboration. This study offers design implications for future creative AI systems, emphasizing accessibility, playful exploration, and cultural resonance, with the potential to democratize artistic expression and foster deeper public engagement with digital cultural heritage.