Rapid and efficient detection of dopamine is crucial for disease prevention and clinical diagnosis. Herein, ZnCoP nanopolyhedra synthesized via a metal-organic framework (MOF)-derived phosphidation route are employed as an electrocatalyst for electrochemical dopamine sensing. The constructed sensor exhibits outstanding analytical performance, featuring a wide linear range (0.5-1500 μM), high sensitivity (1038 μA mM-1 cm-2), and a low detection limit of 0.19 μM. Additionally, the sensor demonstrates excellent selectivity, long-term stability, and reliable repeatability. The superior performance is attributed to the well-defined polyhedral architecture inherited from the MOF precursor, which offers abundant electroactive sites and facilitates efficient electron transfer. These results establish ZnCoP nanopolyhedra as a promising sensing platform for dopamine detection, with strong potential for applications in medical diagnostics and bioanalytical monitoring.
Although abolishing the Crabtree effect in Saccharomyces cerevisiae through a pyruvate dehydrogenase bypass eliminates carbon loss through ethanol overflow metabolism, it compromises growth rates. While the Crabtree effect has been a valuable natural adaptation, it is energetically inferior to respiration and is generally undesirable in cell factories engineered to produce assimilatory compounds. Restoring growth efficiency in Crabtree-negative strains remains a central challenge. Through adaptive laboratory evolution of the engineered strain (sZJD23) and subsequent reverse engineering, a variant (sZJD28) with markedly improved growth was identified. This improvement is driven primarily by a mutation in MED2 (encoding a Mediator complex subunit) and, to a lesser extent, a mutation in GPD1 (encoding glycerol-3-phosphate dehydrogenase). By integrating quantitative proteomics with enzyme-constrained genome-scale modelling, we demonstrate that these mutations jointly enable a more efficient mode of oxidative stress adaptation and energy utilization. The GPD1 mutation suppresses a protein-costly, suboptimal NAD⁺-recycling strategy reliant on glycerol synthesis, while the MED2 mutation reshapes the oxidative stress response towards peroxisomal detoxification. Collectively, these adjustments optimize metabolic flux distribution and reduce protein costs in energy metabolism, thereby increasing ATP availability. Our findings reveal how coordinated mutations in regulatory and metabolic genes restore growth fitness in engineered Crabtree-negative yeast.
In this study, a hydrophilicity-switchable deep eutectic solvent (HS-DES) was prepared by combining nonanoic acid (C9) with tetrabutylammonium bromide (TBAB) and applied as an efficient extraction medium for the ultrasound-assisted liquid-phase microextraction (UALPME) of verapamil from human plasma samples prior to spectrofluorimetric determination. The hydrophilicity-hydrophobicity transition of the HS-DES was controlled by pH adjustment using NaOH or HCl solutions, enabling reversible phase switching and facilitating effective separation of verapamil from the biological matrix. Key experimental parameters influencing the extraction efficiency were systematically investigated, and further optimization was performed using a central composite design coupled with a desirability function approach. The developed HS-DES-based UALPME (HS-DES-UALPME) method provides a tunable, environmentally friendly, and effective strategy for the preconcentration of verapamil from complex biological samples. The method showed wide linear range (200-2000 ng/mL), a good limit of detection (62.16 ng/mL), and extraction recovery (59.55%). To assess the method's sustainability, the AGREE tool was applied, clearly demonstrated the overall greenness of the developed procedure. A comparison of the analytical performance of the method with previously reported methods demonstrated its superiority or, at minimum, its comparability in key analytical parameters.
1,3,4-Oxadiazoles are privileged heterocyclic scaffolds with broad bioactivities, holding significant importance in pharmaceutical and agrochemical discovery. Herein, we report a novel, efficient, and simple synthetic route to 2-amino-1,3,4-oxadiazoles via NaOH-mediated desulfurative cyclization of hydrazides and isothiocyanates under mild reaction conditions. This developed methodology features broad substrate tolerance, excellent yields, operational simplicity, and mild conditions, providing a straightforward pathway for versatile syntheses of valuable 1,3,4-oxadiazole derivative. Consequently, the present reaction opens an alternative path for the preparation of 1,3,4-oxadiazoles via regioselective cyclization hydrazides and isothiocyanates.
Lipopolysaccharide (LPS), a potent immunogenic component of the outer membrane of Gram-negative bacteria, triggers severe inflammation and organ failure even at nanomolar concentrations. However, neutralizing LPS in vivo remains challenging due to the high abundance of other biomolecules in biological fluids, which interfere with LPS detoxification. Brassicaceae species produce a transmembrane protein termed lipooligosaccharide-specific reduced elicitation (LORE) that specifically recognizes and binds LPS. Here, we prepare plant-derived nanovesicles naturally presenting LORE on their surface and integrate them with cerium-based nanozymes exhibiting LPS hydrolysis activity. We show that these hybrid nanostructures (Atv@Ce) neutralize LPS through two coordinated mechanisms: LORE captures LPS specifically, while nanozymes chemically degrade the phosphate groups and glycosidic bonds in the lipid A moiety. This dual strategy effectively attenuates both local and systemic inflammation, offering a biocompatible detoxification strategy with translational potential. Our work provides an insight into nanomaterial-mediated detoxification.
Highbush blueberry (Vaccinium corymbosum) is an important horticultural crop of significant nutritional, therapeutic, and economic value. However, the development of elite cultivars via genetic transformation has been severely restricted by labor-intensive tissue culture requirements and low transformation efficiency. Here, a simple, efficient, and tissue culture-independent Agrobacterium rhizogenes-mediated hairy root transformation system was established for the highbush blueberry cultivar "Bluerain." By eliminating the need for aseptic manipulation, new shoots generated from semilignified stem segments were subjected to vacuum-assisted A. rhizogenes strain K599 infiltration. Approximately 3 months post-infiltration, high biomass hairy roots was successfully induced, and the transformation efficiency reached 74.4% following stepwise optimization. This in planta protocol was further successfully applied to the cultivars "O'Neal," "Legacy," and "Emerald," yielding transformation efficiencies range from 26.7% to 40.0%. This optimized genetic transformation approach provides a valuable tool for the functional characterization of root-specific gene and for accelerating clonal propagation and genetic improvement in highbush blueberry.
Object detection in Unmanned Aerial Vehicles (UAVs) is inherently challenging due to the wide variation in altitudes and viewpoints, coupled with the limited computational resources of onboard embedded systems. Traditional Convolutional Neural Networks (CNNs), while accurate, are often over-parameterized and inefficient for real-time UAV deployment due to their high computational and memory demands. Designed for all types of inputs, they perform redundant operations that strain the limited processing power, memory, and energy resources available on UAV platforms. This work addresses these limitations by modeling UAV object detection as a context-aware task, using altitude as a primary adaptive parameter. We train specialized CNN configurations for discrete altitude ranges, exploring the impact of key parameters-input image resolution, network width, and kernel size-on detection performance and efficiency. Dynamic parameter switching based on altitude enables resource-efficient deployment without compromising accuracy. Using two altitude-diverse datasets, we demonstrate that optimal CNN settings vary significantly across altitudes, underscoring the need for altitude-specific adaptation. We introduce a threshold-based switching mechanism and perform a detailed analysis of how varying these altitude thresholds affects both detection accuracy and system efficiency. Our results show that well-chosen thresholds result in minimal accuracy loss and maximal resource savings compared to an equivalent non-dynamic CNN. The proposed dynamic CNN framework offers a scalable, energy-efficient solution for UAV-based applications such as surveillance, search and rescue, and environmental monitoring, where adaptability to context is essential.
Fault diagnosis of taper roller bearings needs to be accurate and efficient to ensure industrial machinery reliability. This research has developed a vibration-based fault diagnosis method which combines statistical feature ranking and machine learning classification. Vibration signals for the five different health conditions (healthy, inner race fault, outer race fault, roller fault, and cage fault) were collected from an SKF 32,206 taper roller bearing with changing speeds and loads. Two types of features, time-domain and frequency-domain, were derived and the features with the highest discriminating power were determined by one-way ANOVA and Kruskal Wallis statistical tests. Different combinations of features were tested with six classifiers: support vector machines (SVM), neural networks, discriminant analysis, naive bayes, decision trees, and nearest neighbor. It was found that Kruskal-Wallis feature-ranking benefits not only the result accuracy but also the computational efficiency, and the best feature set had 18 features. Thus, the linear SVM classifier yielded a classification accuracy of 99% and an AUC of 1, while requiring the least training time, demonstrating that it is suitable for real-time purposes. This work proposes a novel, dependable, and computationally efficient method of identifying faults in taper roller bearings, thus leading to greater automation of condition monitoring in industrial plants.
The development and implementation of scalable and efficient security solutions have become a critical area of focus in cybersecurity research and practice. This trend is receiving more attention lately due to the increasing frequency and sophistication of cyber threats to Internet of Things (IoT) networks. This paper proposes a novel cryptographic co-processor architecture based on RISC-V, designed to offer both high efficiency and flexibility for IoT applications. The design introduces a generic interface for cipher blocks, supports parallel execution of cipher operations, and avoids custom modifications to the RISC-V instruction set architecture. The proposed design extends the RISC-V architecture without new instruction sets to support AES-128 and SHA-256 operations, demonstrating its capabilities through these ciphering blocks. The new processor architecture ensures efficient data transfers between memory and cipher units, without delaying the processor pipeline, by utilizing Memory-Mapped I/O (MMIO) and Direct Memory Access (DMA) modules to optimize data handling. The proposed design is implemented and verified on the Xilinx ZCU-102 FPGA board using the Vivado 2022.2 tool. The proposed design achieves throughput rates of 8.2 Gbps for the AES-128 cipher block and 482 Mbps for the SHA-256 cipher block, operating at a relatively low system clock frequency of 64 MHz. The throughput is calculated based on the core cycle count of each algorithm, as this metric is commonly adopted in the literature. Nevertheless, the total end-to-end cycles of the proposed design equal the core cycles plus the serializer and deserializer cycles. The cycle count of the serializer/deserializer depends only on the processor's data bus width. Also, the total power consumption of the proposed design is 1.575 watts. Achieving such high throughput at this reduced frequency is significant as it helps designers better minimize the power consumption, thereby enhancing the overall energy efficiency and performance of the system.
This study presents a quantitative structure-property relationship (QSPR) framework that integrates graph theory with machine learning to predict key physicochemical properties of diverse organic compounds. A data set of 275 structurally diverse organic compounds, including alkanes, alkenes, alkynes, cyclic systems, and aromatic hydrocarbons-was represented as molecular graphs, and their topological features were encoded using two complementary degree-based indices: the Sombor index (SO) and the Modified inverse degree index (ID). These indices were employed to model the octanol-water partition coefficient (LogP), calculated LogP (CLogP), molar refractivity (MR), and critical pressure (CP). Linear regression established baseline correlations, while Random Forest and XGBoost regression models were implemented to capture nonlinear relationships and enhance predictive accuracy. Models were evaluated using a 70%/30% train-test split and five-fold cross-validation. The predicted values show good correlation with Actual values. Machine learning models are outperforming than linear regression. XGBoost has the highest prediction performance for all properties, with a best [Formula: see text] value larger than 0.97. Well-chosen topological indices and machine learning models, provide a highly efficient and effective way to predict the physicochemical properties of organic compounds with diverse scaffolds.
Glutarimide-containing Cereblon (CRBN) ligands are critical motifs for PROTACs, molecular glue degraders and next-generation Cereblon E3 ligase modulatory drugs (CELMoDs), which represent promising therapeutic modalities in targeted protein degradation. However, the multistep synthetic routes required to access glutarimide scaffolds continue to present formidable challenges for medicinal chemists, limiting rapid structure-activity relationship (SAR) exploration and late-stage diversification. To streamline access to these privileged motifs, modular and efficient methodologies are still highly desirable. Here, we report a unified organocatalytic synthesis platform for the rapid assembly of diverse glutarimide derivatives from readily available nitrogen heterocycles. Employing a sequence of phosphine-catalysed C-N bond formation, metal-free Giese addition and acid-mediated cyclisation, this approach provides high selectivity, broad functional group tolerance and operational simplicity under conditions amenable to both multigram synthesis and high-throughput parallel synthesis. Using this platform, we rapidly prepare CRBN binder libraries, access control analogues (for example, N‑alkylated glutarimides) and perform late‑stage functionalisation of bioactive molecules. This strategy could offer a transformative solution for the efficient and cost-effective synthesis of CRBN-targeted therapeutics and chemical biology probes, overcoming longstanding synthetic bottlenecks in the field.
Coral sand presents significant challenges to ground improvement design due to its high void ratio, high compressibility, and particle breakage. Traditional bearing capacity theories for vibro-replacement stone columns are typically based on the assumption of soil shear dilation or constant volume. However, these assumptions fail to account for the particle breakage-induced energy dissipation and volume contraction characteristic of coral sand under high stress, leading to significant discrepancies in calculation results. Based on an analysis of the physical and mechanical properties and the breakage mechanism of coral sand, this paper proposes a novel three-step method for in-situ porosity measurement. Furthermore, a dual-mechanism coupling model, incorporating both energy-equivalent additional confining pressure and breakage-friction coupling, is proposed. By introducing the particle breakage energy dissipation coefficient [Formula: see text] and the energy-confining pressure conversion efficiency [Formula: see text], a modified limit equilibrium model for bulging failure is established to dynamically couple the void ratio evolution and the mobilized internal friction angle. Validation through an airport runway project in coral sand geological conditions demonstrates that the proposed method accurately captures the lateral restraint enhancement of coral sand particle breakage. The calculated lateral ultimate stress (166.3 kPa) is consistent with the field measured range of 158.0-175.0 kPa from nine parallel plate load tests. Considering the full range of measured data, the relative error of the prediction ranges from - 5.3% to + 5.0%. This approach yields significantly higher accuracy compared to traditional standard methods, providing a reliable theoretical basis for engineering design under the conditions of coral sand geology.
Targeted radionuclide therapy (TRT) is a cancer treatment method that delivers radiation to specific tumor cells, enabling efficient tumor cell killing. Radionuclides emitting short-range beta or alpha particles have previously been the primary focus. TRT approaches utilizing low-energy electrons, such as Auger and internal conversion electrons, have attracted interest because of their highly localized tumor-killing potential. 134Ce is an imaging surrogate in the form of a 134Ce/134La pair for positron emission tomography (PET) imaging in 225Ac targeted alpha therapy, and it has recently exhibited promising therapeutic properties. This study employs TOPAS-nBio, a Monte Carlo simulation tool, to investigate the radiation damage effects of 134Ce from dosimetric and DNA-scale perspectives. The DNA damage yield analysis incorporates both physical and chemical processes. In the water sphere geometry, the overall dose contribution of 134Ce shows patterns generally similar to those of other Auger-emitting radionuclides, and 134Ce shows damage yields per decay close to those of 161Tb on the DNA scale. Although 134Ce induces fewer DNA double-strand breaks per decay in the nucleus than 125I and 161Tb, it exhibits a higher double-strand break yield when normalized to absorbed dose. Such damage outcome predictions suggest that further research on radionuclide therapy using 134Ce is worthwhile.
The reverse water-gas shift (RWGS) reaction efficiently transforms CO2 into CO at high temperatures, ensuring high CO selectivity while preventing the formation of methane as a byproduct. Nanocrystalline ceria-based materials are synthesised by using a modified co-precipitation method. This method utilizes the molecular water in the precursor materials to aid the hydroxylation process, which helps minimize the risk of agglomeration resulting from hydrogen bonding. The as prepared catalysts were calcined at high temperature to get the crystalline materials. The freshly synthesized and calcined catalysts were analysed using various characterisation techniques such as X-ray diffraction (XRD), Raman spectroscopy, high-resolution transmission electron microscopy (HR-TEM), and X-ray photoelectron spectroscopy (XPS). The results suggest that enhancing the copper concentration in Cu-doped CeO2 significantly improves its catalytic activity at low and moderate temperatures. Among all the catalysts 25 mol % copper doped CeO2 gives extraordinary activity for RWGS reaction having CO2 conversion of ~ 50% at 800 ˚C.
Triboelectric nanogenerators (TENGs) can efficiently convert weak mechanical energy into electricity, making them suitable for small-scale and distributed power supply demanded by the Internet of Things. However, the inevitable friction and wear not only cause high energy dissipation and low triboelectrification efficiency but also severely limit the device's lifetime and reliability. Here, a wear-free structural superlubricity TENG (SSL-TENG) is designed using the graphite-SiO2 pair. By achieving an atomic-level friction interface via micromachining, the device operates in a stable SSL state with a near-zero coefficient of friction (0.0034). As compared to mosaic charge distribution in previous studies, the intimate contact at the SSL interface suppresses air breakdown, enabling a unipolar charge distribution, which yields a high charge density of 0.47 mC/m2 and a 106-fold enhancement in triboelectrification efficiency. Simultaneously, the SSL-TENG demonstrates stable output performance for over 1.1×105 cycles without wear. This work provides a fundamental strategy to eliminate interfacial friction and the air-breakdown limit, paving the way for ultra-reliable and high-output energy harvesting.
Data scarcity limits the characterization of protein fitness landscapes and the development of accurate variant effect prediction models. To address this challenge, we introduce fitness translocation, a data augmentation strategy that generates synthetic variants for a target protein by leveraging variant fitness data previously measured in homologous proteins. Using embeddings from protein language models, the method computes the difference between each homolog variant and its wild type and applies these offsets to the target wild-type embedding to create synthetic variants in embedding space. We illustrate the utility of fitness translocation in the context of variant effect prediction on three protein families: IGPS, GFP, and SARS-CoV-2 spike proteins, across different models and training data sizes. Fitness translocation consistently improves predictive performance, particularly under limited training data, and is effective even when augmenting with remote homologs sharing as little as 35% sequence identity. These results illustrate how biologically grounded data augmentation can expand and diversify protein fitness landscapes, supporting more data-efficient protein engineering. The code and datasets are available at https://github.com/adrienmialland/ProtFitTrans. Supplementary data are available at Bioinformatics online.
The high-level integration of generative artificial intelligence (AI) in edge computing systems has raised the question of the integrity and reliability of deploying Model-as-a-Service. Edge servers are not required to follow the so-called generative model to minimize computational cost, whereas users and service providers want validation mechanisms that do not compromise proprietary model information. To address this challenge, this study proposes a cooperative unmanned aerial vehicle (UAV)-swarm-enabled zero-knowledge verification framework for secure, privacy-preserving verification of edge-based generative artificial intelligence inference. The proposed framework involves edge servers producing an interactive cryptographic zero-knowledge proof to verify the execution of generative AI, and UAV swarms that fly freely to confirm verification operations, subject to mobility and energy constraints. The age of verification metric is proposed to trust verification information, jointly reflecting the unverified server reliability and verification freshness, and to provide dynamic priority to risky edge servers. To effectively plan the behaviour of a UAV swarm, a trust-based multi-agent reinforcement learning approach is developed that enables decentralized decision-making while training is centralized. Extensive simulation results show that the proposed framework significantly improves the state-of-the-art baseline schemes in verification timeliness, malicious server detection delay, energy efficiency, and scalability. The findings validate that integrating cooperative UAV swarms, trust-aware verification, and multi-agent learning is an efficient approach to providing reliable generative AI services in dynamic edge computing environments.
Early identification of patients at risk of severe pneumonia during Omicron SARS-CoV-2 infection is critical for optimizing care and allocating resources. While clinical markers provide insights, imaging-derived radiomics features may enhance prognostic accuracy. We developed a multimodal predictive model combining Delta Radiomics features from serial chest CT scans with clinical data, including blood biochemical markers and lymphocyte subsets. The primary prediction target was severe/critical Omicron pneumonia during hospitalization. Mild and moderate cases were grouped as non-severe disease, whereas severe and critical cases were defined as the severe class for binary classification. The model was trained on 91 patients from the first center, internally validated on 23 patients, and externally tested on 32 patients from a second center. Machine learning algorithms including Logistic Regression, Random Forest, and MLP were applied, and a nomogram was constructed for individualized risk prediction. The combined model showed high discrimination in the training cohort and maintained favorable performance in the internal validation and independent external test cohorts, achieving AUCs of 0.885 and 0.875, respectively. The Delta Radiomics signature, particularly with MLP, showed comparatively stable predictive performance. These findings suggest the added value of temporal CT-derived radiomics when integrated with clinical biomarkers, although further validation in larger prospective cohorts is required. Integrating temporal imaging features with clinical data offers a non-invasive method for early prediction of severe/critical Omicron pneumonia, supporting individualized triage and more efficient allocation of medical resources.
The continually increasing volume of sequence data results in a growing demand for fast implementations of core algorithms. Computation of pairwise alignments based on dynamic programming is an important part in many bioinformatics pipelines and a major contributor to overall runtime due to the associated quadratic time complexity. This motivates the need for a library of efficient implementations on modern GPUs for a variety of alignment algorithms for different types of sequence data including DNA, RNA, and proteins. Accelign is a library of accelerated pairwise sequence alignment algorithms for CUDA-enabled GPUs. Its parallelization strategy is based on a common wavefront design that can be adapted to support a variety of dynamic programming algorithms: local, global, and semi-global alignment of genomic and protein sequences with a variety of commonly used scoring schemes supporting one-to-one, one-to-many or all-to-all pairwise sequence alignments. This leads to a peak performance between 16.1 TCUPS and 9.1 TCUPS for computing optimal global alignment scores with linear gaps and affine gap penalties on a single RTX PRO 6000 Blackwell GPU, respectively. In addition, our library demonstrates significant speedups in several real-world case studies over prior CPU-based (SeqAn, Parasail, BSalign, EdLib, KSW2, WFA2, A*PA2) and GPU-based libraries (ADEPT, GASAL2), and can even outperform highly customized algorithms (WFA-GPU, CUDASW++4.0). Furthermore, the performance of our approach scales linearly with the number of employed GPUs, which makes it feasible to exploit multi-GPU nodes for increased processing speeds. Accelign provides significant speedups for commonly used pairwise alignment algorithms compared to prior implementations. It is freely available at https://github.com/fkallen/Accelign.
Rheumatoid arthritis (RA) is a chronic inflammatory systemic disease, and the use of biological agents in the treatment of RA in recent years has significantly improved RA disease activities and clinical outcomes. However, the greatly increased medical costs due to the high costs of biologics are a major concern. We aimed to investigate healthcare utilization and costs in patients with RA pre- to post-initiation of biologics or tofacitinib. We conducted a nationwide, population-based study from 1996 to 2017 using Taiwan's National Health Insurance Research Database (NHIRD). In total, 57,084 newly diagnosed RA patients aged ≥ 20 years were identified, of whom 10,566 patients using biologics or tofacitinib were selected and included in the final analysis. The dose adjustments of anti-rheumatic drugs and healthcare utilization and costs among RA patients 3 months before and 6 months after use of biologics were compared. Additionally, a sensitivity analysis evaluating healthcare utilization and costs over a 12-month period pre- to post-initiation of biologics or tofacitinib was conducted. RA patients had more frequent all-cause and RA-related outpatient department (OPD) visits after receiving biologics or tofacitinib, but fewer RA-related emergency room (ER) visits (0.00 ± 0.04 times/month, p = 0.005). There were fewer OPD visits and lower OPD healthcare costs in RA patients using tocilizumab (OPD visits: β - 0.20, p = 0.013; OPD costs: β - 16,366.92, p < 0.001) and abatacept (OPD visits: β - 0.41, p < 0.001; OPD costs: β - 4436.24, p < 0.001), compared with etanercept users. Moreover, significant dose reductions of concomitant anti-rheumatic drugs were observed in RA patients after biologics or tofacitinib, including corticosteroid, leflunomide, hydroxychloroquine, and cyclosporin. Between 10.8 and 47.0% of RA patients experienced a reduction in the dose of anti-rheumatic drugs. This nationwide, population-based study revealed that the dose of concomitant anti-rheumatic drugs and RA-related ER visits significantly reduced after initiating biologics or tofacitinib. Compared with etanercept users, patients treated with tocilizumab or abatacept had significantly lower outpatient care-related visit numbers and costs. Key Points • This nationwide population-based study investigated healthcare utilization and costs in RA patients pre- to post-initiation of biologics or tofacitinib. • RA-related emergency room visits and doses of concomitant anti-rheumatic drugs significantly declined after starting biologic or tofacitinib therapy, suggesting improved disease control. • Tocilizumab and abatacept use were associated with fewer outpatient visits and lower costs than etanercept, offering more resource-efficient options for certain patients. • Older age, male sex, and higher comorbidity burden predicted more dose reduction of conventional anti-rheumatic medications, supporting individualized treatment planning and de escalation strategies in clinical practice.