Browse Articles

An intelligent controlling in electric vehicle system with integrated DBS and BMS for sustainable solution

A highly potent nanobody-based bispecific therapeutic provides broad-spectrum protection against ebolavirus

DeepDegradome: A structure-aware deep learning framework for PROTAC and ligand generation against protein targets

Targeted protein degradation is a promising strategy for drug discovery, but designing effective PROTACs remains challenging, especially for proteins without well-defined binding sites. Current methods rely on modifying linkers between fixed ligands, which limits the diversity and innovation of the overall molecular architecture of PROTAC. Here, we introduce DeepDegradome, an AI-powered method that automates the structure-aware design of both small-molecule ligands and PROTACs. It employs a large fragment library constructed from public databases and applies an in-house docking method (iFitDock) to obtain initial binding fragments. DeepDegradome builds ligands by assembling these fragments based on the shape and physicochemical features of the target protein pocket. It can further construct PROTACs from these generated ligands, eliminating the dependency on predefined warheads or E3 ligands. Compared to other AI models, DeepDegradome produces more valid, drug-like molecules with higher predicted binding affinity. We demonstrate DeepDegradome’s effectiveness by designing and validating multiple potency inhibitors and PROTACs for two protein targets: WDR5 and CDK9. One synthesized compound showed excellent agreement between predicted and actual binding conformation confirmed by X-ray crystallography. By combining ligand and PROTAC design in one system, DeepDegradome offers a scalable and reliable tool for discovering new drugs against protein targets.

Influence of urban innovation capacity on urban energy transition in China—the moderating role of climate risk

Enhancer-mediated Etv4 activation stimulates osteogenic differentiation

Host ESCRT machinery orchestrates the assembly of tomato spotted wilt virus ribonucleoproteins

The genomic RNA of negative-strand RNA viruses is encapsidated by nucleocapsid proteins and associates with RNA polymerase to form a ribonucleoprotein (RNP) complex. Lacking both a 5’ cap and a 3’ poly (A) tail, viral RNAs are highly unstable and prone to degradation by cellular nucleases. Therefore, newly synthesized genomic and complementary-strand RNAs must be rapidly protected through RNP formation. However, the molecular mechanisms governing RNP assembly in cytoplasm-replicating negative-strand RNA viruses remain largely unknown. Here, we screened a yeast knockout library and isolated mutants in several components of the endosomal sorting complexes required for transport (ESCRT) genes that affected RNA replication of tomato spotted wilt virus (TSWV). In wild-type (WT) yeast cells, TSWV nucleocapsid (N) and RNA polymerase (L) proteins colocalize at the trans-Golgi network (TGN) in a replicon-RNA-dependent manner, suggesting that TSWV RNPs accumulate at the TGN. However, in the snf7 Δ, bro1 Δ, and doa4 Δ mutant cells, N localization to TGN and RNP formation were impaired. Another RNA replication-defective mutant, vps36 Δ, showed normal N localization, and SNF7, BRO1, and DOA4 were recruited to the TGN by TSWV N or L proteins, implying that the ESCRT components have additional roles in TSWV RNA replication beyond facilitating N transport. These findings suggest that ESCRT components play multifaceted roles in TSWV RNA replication, including the intracellular transport of N to the TGN—where RNA replication takes place—thereby ensuring accurate and efficient RNP assembly.

Inhibition mechanisms of gas desorption and pressure inversion in water-bearing coal under stepwise decompression via volume expansion

Unlocking large-area free-standing MOF-glasses for molecular sieving gas separation membranes

Abstract Membranes from MOF glasses hold significant promise for gas separations due to the absence of grain boundary diffusion, liquid processibility, and tunability. The inherent high viscosity of MOF melts renders them prone to cracking and further handling, and their propensity to densify at high temperatures and long time in molten state severely limits their upscaling potential. A solution to overcome these limitations is demonstrated by selecting suitable materials that fit the thermal and mechanical behaviour to MOF-glass, enabling processing and making of large, crack-free MOF-glass sheets. This is demonstrated on the example of the well-known MOF-glass former ZIF-62. By optimizing each step of the process – from melting to performance testing – we successfully fabricate a crack-free, self-supported ZIF-62 glass membrane. The microstructure is investigated using microscopy as well as SEM-EDX analysis, confirming homogeneous boundary-free MOF-glass, while gas permeation experiments prove the applicability of MOF-glass as gas separation membrane. The membrane exhibits exceptionally sharp methane molecular sieving cut-off with such low permeability that gas chromatography is unable to detect CH 4 . We conclude this work by giving a brief outlook of the remaining challenges and perspectives for MOF glasses, envisioning transferability of our approach to other glass-forming systems and their scaling perspectives.

Charge-reversal proteolysis polymers enable tissue-specific STING degradation in rheumatoid arthritis

Inhibiting stimulator of interferon genes (STING) is critical for treating rheumatoid arthritis (RA), yet achieving precise suppression with high tissue specificity across protein variants remains challenging. Here, we engineer a multilevel, intelligent STING degrader—charge-reversal proteolysis-targeting chimeras (CreTACs)—that efficiently delivers to RA sites and degrades STING variants in humans, mice, and rats. Unlike traditional degraders with systemic toxicity, this charge reversal platform leverages pH-programmed charge inversion: Electroneutrality in circulation (pH 7.4) minimizes toxicity, while acidic-triggered protonation enables a 7.5-fold increase in arthritic joint accumulation (tissue level), pH-gated cellular internalization (80% uptake at pH 6.5 vs. 50% at pH 7.4; cellular level), and enhanced cytoplasmic STING (proton channel) affinity via charge interactions (protein level). In collagen-induced arthritis models, CreTACs outperformed methotrexate by suppressing synovitis and bone erosion without hematological toxicity. This multilevel charge reversal strategy establishes a blueprint for next-generation proteolysis drug-delivery systems or biomaterials, offering transformative potential for healthcare.

Modeling Clostridioides difficile toxin pathogenesis and antiserum protection in an immunocompetent intestine-on-chip platform

Abstract Clostridioides difficile ( C. difficile ) is a leading cause of nosocomial diarrhea and colitis, including severe pseudomembranous colitis, particularly following antibiotic-induced dysbiosis. The pathogenesis of C. difficile infection (CDI) is primarily driven by the action of two large exotoxins, toxin A (TcdA) and toxin B (TcdB), which compromise intestinal epithelial integrity and trigger strong mucosal inflammation. These toxins lead to the disassembly of epithelial junctions, immune cell infiltration, and release of pro-inflammatory mediators. Despite extensive research, mechanistic insight into C. difficile -host interactions and correlates of protection remain limited, in part due to the physiological constraints of conventional two-dimensional (2D) in vitro models. Here, we present a three-dimensional (3D) microphysiological Intestine-on-Chip (IoC) model as a dynamic and immunocompetent in vitro platform to study toxin-mediated pathogenesis and therapeutic interventions in CDI. In contrast to traditional static cell culture systems composed solely of epithelial monolayers, the immunocompetent IoC (i-IoC) model integrates Caco-2 C2BBe1 epithelial cells, primary human umbilical vein endothelial cells (HUVECs), monocyte-derived macrophages, and circulating neutrophils (polymorphonuclear leukocytes, (PMN)) under continuous perfusion, thus more closely mimicking the tissue architecture and immune microenvironment of the human intestine. Upon stimulation with purified TcdA and TcdB, the i-IoC model exhibited toxin-specific disruption of epithelial junctional proteins, macrophage depletion, elevated cytokine secretion, and recruitment and transmigration of PMN, thereby replicating hallmark features of acute CDI. Notably, the model responded with higher sensitivity and biological complexity than static 2D cultures. Toxin-neutralizing antibody sera effectively attenuated these pathological responses, reducing both structural damage and inflammatory mediator release. Our findings demonstrate that the i-IoC model faithfully recapitulates key aspects of CDI pathophysiology, including epithelial damage, immune cell dynamics, and cytokine-driven inflammation. This platform offers a versatile and translationally relevant tool to study host–pathogen interactions and to evaluate preventive or therapeutic strategies aimed at mitigating C. difficile toxin (CDT)-mediated tissue injury.

Bioinspired maskless structural colour patterning via tunable nanoparticle segregation

Abstract Structural colouration arises from the interaction of light with nanoscale structures and offers sustainable alternatives to pigment-based colours. However, current structural colour patterning methods rely on multi-step lithographic processes or multiple ink formulations, limiting scalability and spatial resolution. Inspired by melanosome self-assembly in bird feathers, we develop a one-step, mask-free strategy to generate high-resolution structural colour patterns via tunable nanoparticle segregation. During photocuring, silica nanoparticles dispersed in acrylic resin migrate toward oxygen-permeable substrates, forming a nanoparticle-enriched disordered layer. Such segregation is driven by interfacial oxygen inhibition and kinetically governed by the photocuring rate. Using grayscale digital light processing printing, we programmably control the local segregation thickness to create high-resolution structural colour patterns for visual display and information encryption. The segregation structure also affects mid-infrared reflectivity, allowing for infrared camouflage. This scalable approach establishes a mechanistically guided route to multifunctional photonic materials.

A <i>KCNC1</i> variant linked to Rett syndrome disrupts ER to Golgi trafficking of Kv3.1 channel

Intrinsic neuronal excitability, defined by the balance between input and output signals, is crucial to neural function, and its disruption underlies various neurological diseases. Kv3.1 channels, encoded by KCNC1 , are essential for high-frequency action potential firing. Variants in these channels are associated with several subtypes of epilepsy. We report a patient with developmental regression and epilepsy, meeting Rett syndrome criteria, who carries a KCNC1 variant encoding the S474C substitution in Kv3.1 (Kv3.1 S474C ). Electrophysiological and biochemical assays reveal that Kv3.1 S474C reduces channel presence in the plasma membrane and is retained in the endoplasmic reticulum. In murine primary cortical neuron cultures expressing Kv3.1 S474C , we observed reduced neuronal firing frequency and exclusion of the channel from the axon initial segment. Consistently, we found a decreased firing frequency using a conductance-based computational neuronal model. In summary, this study identifies a link between a KCNC1 variant and Rett syndrome, highlighting the importance of S474 residue in Kv3.1 channel trafficking and function in neurons.

Pyrolysis temperature effects of tomato stems biochar on leaching dynamics of ammonium, nitrate, and dissolved organic carbon in sandy soil

Abstract The study objectives are to examine the effect of doses of tomato stems biochar (TSB) produced at different pyrolysis temperatures (250, 400, and 600 °C) on the leaching of nitrate, ammonium, and dissolved organic carbon, as well as quality indicators of sandy soil. The column experiment was including these treatments; control (no biochar added), 1% TSB250, 2.5% TSB250, 5% TSB250, 1% TSB400, 2.5% TSB400, 5% TSB400, 1% TSB600, 2.5% TSB600, and 5% TSB600. Each plastic column was filled with 1 kg of sandy soil. Tomato stems biochar was applied at three doses (1%, 2.5%, and 5% w/w). Soil available nitrogen increased significantly relative to the control treatment by 9.50%, 31.69%, 46.71%, 69.07%, 15.24%, 37.43%, and 75.57% under applying 1% TSB250, 1% TSB400, 2.5% TSB400, 5% TSB400, 1% TSB600, 2.5% TSB600, and 5% TSB600 treatments, respectively. Results showed significant decreases in cumulative leached ammonium over the control treatment by 20.77%, 27.04%, 37.09%, 34.04%, 40.43%, 48.61%, 18.26%, 25.26%, and 32.94% for 1% TSB250, 2.5% TSB250, 5% TSB250, 1% TSB400, 2.5% TSB400, 5% TSB400, 1% TSB600, 2.5% TSB600, and 5% TSB600 treatments, respectively. The amount of cumulative leached nitrate decreased significantly relative to the control treatment by 8.27%, 8.56%, 8.91%, 8.61%, 8.42%, 8.66%, 28.37%, 31.63%, and 34.40% for 1% TSB250, 2.5% TSB250, 5% TSB250, 1% TSB400, 2.5% TSB400, 5% TSB400, 1% TSB600, 2.5% TSB600, and 5% TSB600 treatments, respectively. The effectiveness of biochar treatments in reducing the cumulative leaching of ammonium decreased in the order TSB400 &gt; TSB250 &gt; TSB600. However, the effectiveness of biochar treatments on the cumulative leaching nitrate was in the order of TSB600 &gt; TSB400 ≈ TSB250. Applying TSB at all pyrolysis temperatures and levels in sandy soil led to a significant increase in the cumulative leaching of dissolved organic carbon compared to the control treatment. Utilizing tomato stems biochar as a soil amendment is a promising strategy for significantly enhancing the quality indicators of sandy soil and reducing the leaching of ammonium and nitrate. This would reduce the loss of nitrogen fertilizers added to the soil and preserve groundwater from pollution.

A hybrid piezoelectric resonator-based DC-DC converter

Abstract Piezoelectric resonators are becoming attractive alternatives to conventional magnetics in DC-DC converters due to their favorable scaling and manufacturing properties. However, the efficiency and current handling capabilities of baseline piezoelectric resonator-based DC-DC converters degrade at higher voltage conversion ratios due to charge utilization limitations imposed by topological operation. Here we present an Always-Multi-Path Embedded Flying Capacitor Piezoelectric Resonator-based DC-DC converter that uses flying capacitors to add both hybrid multi-path output power delivery features and to reduce the internal charge redistribution losses within the piezoelectric resonator. Specifically, the proposed integrated circuit modifies the optimal voltage conversion of the piezo network from 2:1 to 3:1 while adding a switched-capacitor output network that enables multi-path operation at all times, resulting in a net optimal voltage conversion ratio of 9:1 for the converter, with 4x improved output current. Fabricated in a 180 nm high-voltage CMOS process, the developed chip achieves a peak efficiency of 96.2% at a 48-to-4.8 V conversion ratio.

Atomistic simulations reveal the photoactivation mechanism of a carotenoid-binding photoreceptor

The orange carotenoid protein (OCP) is a key photoreceptor in the photoprotection of cyanobacterial antenna complexes. Its peculiar activity is governed by a bound keto-carotenoid that functions both as a light sensor and an energy quencher. Upon blue light absorption, OCP transitions from an orange resting state (OCP O ) to a red active state (OCP R ) through carotenoid translocation into the N-terminal domain, followed by domain separation. Despite extensive studies, the molecular mechanism underlying photoactivation has remained unresolved. Here, we integrate excited-state nonadiabatic dynamics with enhanced sampling molecular dynamics, to reveal the entire photoactivation pathway at atomistic resolution. Our simulations identify a trans -to- cis photoisomerization of the bound keto-carotenoid as the critical photochemical event that initiates translocation. This cis isomer not only matches transient spectroscopic signatures observed experimentally but also exhibits the specific interactions with the protein required to enable translocation and domain separation. Finally, we uncover a multiphoton mechanism responsible for regenerating the all- trans configuration observed in the OCP R –antenna complex. These findings provide a mechanistic framework for OCP photoactivation, linking photochemistry with large-scale conformational changes. Our work highlights the central role of carotenoid photoisomerization and demonstrates the power of advanced atomistic simulations in dissecting the functional dynamics of photoreceptor proteins.

Sustainable synthesis of MgO nanoparticles from Persea americana for cultivar dependent nanostructure, environmental remediation and bioactivity supported by molecular docking

Pathways for sustainable reaction kinetics in Li-CO2 batteries

A multifunctional bi-anisotropic metasurface with reflection-transmission polarization conversion and narrow bandpass transmission characteristics

Programmable nanomicelles rewire myeloid immunity for durable control of primary and metastatic breast cancer

Online supervised learning of temporal patterns in biological neural networks under feedback control

In vitro biological neural networks (BNNs) provide well-defined model systems for constructively investigating how living cells interact with their environments to shape high-dimensional dynamics that can be used to generate coherent temporal outputs, such as those required for motor control. Here, we develop a real-time closed-loop BNN system that is capable of generating periodic and chaotic temporal signals by integrating cultured cortical neurons with microfluidic devices and high-density microelectrode arrays. We show that training a simple linear decoder with fixed feedback weights enables the system to learn and autonomously generate diverse temporal patterns. When feedback is switched on, the irregular activity in the BNNs is transformed into low-dimensional, structured dynamics, producing coherent trajectories that are characterized by stable transitions between different neural states. BNNs trained on various target frequencies—ranging from 4 to 30 s—can be trained to sustain oscillations at distinct frequencies, demonstrating their adaptability. Importantly, top–down control of the self-organized network formation with microfluidic devices is the key to suppressing excessive synchronization and increasing dynamic complexity in BNNs, facilitating the training process and the generation of robust outputs. This work offers a biologically inspired platform for understanding the physical basis of cortical computations and for advancing energy-efficient neuromorphic computing paradigms.