Search Results (265)

While there is a consensus that the cytochrome b₆f complex (cytb₆f) in algae and plants is involved in the regulatory mechanism of oxygenic photosynthesis known as light-induced state transitions (STs), no such consensus exists for cyanobacteria. Here, we provide the first direct functional evidence for cytb₆f using single-point mutation data. We introduced a PetD-Phe124Ala substitution in the cyanobacterium Synechocystis sp. PCC 6803 to test the key predictions of the hydrophobic-mismatch (HMM) model for cytb₆f-driven STs in all oxygenic photosynthetic species. These predictions concern the role of the Phe/Tyr124^fg-loop-PetD and the extent and kinetic characteristics of STs. The effects of PetD-F124A mutation on STs were monitored using 77K and Pulse-Amplitude-Modulated (PAM) fluorescence. For comparison, we employed a phycobilisome (PBS)-less Synechocystis mutant and wild-type (WT) strain, as well as the stn7 mutant and WT of Arabidopsis plant. The PetD-F124A mutation reduced the extent of STs and selectively affected the two-exponential kinetics components of the transitions. Under State 1 conditions, the mutant exhibited ~60% less energetic decoupling of PBS from photosystem I (PSI) compared to the WT. It is explainable by the HMM model with the inability of the PetD-F124A mutant, during the induction phase of the State 2→State 1 transition to adopt the cytb₆f conformation with minimal hydrophobic thickness. PAM-derived parameters indicated that PSII electron transport function is not inhibited, and no detectable effect on cyclic electron transport around PSI was observed under low-light conditions. Circular dichroism and differential scanning calorimetry confirmed that both the PSI trimer/monomer ratio and the structural integrity of the PBSs are preserved in the mutant. The compensatory response to the mutation includes decreased PSI content and an increase in PBS rod size. In conclusion, (1) cytb₆f is involved in cyanobacterial STs; (2) evidence is provided supporting the HMM model; (3) the electron transfer and signal transduction functions of cytb₆f are separated into distinct domains; and (4) the signaling pathway regulating STs and pigment-protein composition in Synechocystis involves PetD-Phe124. Full article

A major computational bottleneck in classifying large-scale hyperspectral images (HSI) is the mandatory data decompression prior to processing. Compressed-domain computing offers a solution by enabling deep learning on partially compressed data. However, existing compressed-domain methods are predominantly tailored for the Discrete Cosine Transform (DCT) used in natural images, while HSIs are typically compressed using the Discrete Wavelet Transform (DWT). The fundamental structural mismatch between the block-based DCT and the hierarchical DWT sub-bands presents two core challenges: how to extract features from multiple wavelet sub-bands, and how to fuse these features effectively? To address these issues, we propose a novel framework that extracts and fuses features from different DWT sub-bands directly. We design a multi-branch feature extractor with sub-band feature alignment loss that processes functionally different sub-bands in parallel, preserving the independence of each frequency feature. We then employ a sub-band cross-attention mechanism that inverts the typical attention paradigm by using the sparse, high-frequency detail sub-bands as queries to adaptively select and enhance salient features from the dense, information-rich low-frequency sub-bands. This enables a targeted fusion of global context and fine-grained structural information without data reconstruction. Experiments on three benchmark datasets demonstrate that our method achieves classification accuracy comparable to state-of-the-art spatial-domain approaches while eliminating at least 56% of the decompression overhead. Full article

Background/Objectives: Diabetes mellitus is increasingly prevalent among adults aged over 80 years; however, this population remains substantially underrepresented in clinical research on diabetes complications. This scoping review synthesises current evidence on diabetes-related complications and comorbidities in this older age group (>80 years), reported prevalence, and key evidence gaps. Methods: A systematic search of MEDLINE, Embase, and Web of Science was conducted for studies published between 1992 and 2024 reporting diabetes-related complications in individuals aged ≥80 years. Two reviewers independently screened titles, abstracts, and full texts. Data were extracted and summarised using narrative synthesis, and descriptive statistics (SPSS v29) were conducted. Results: Fifty-one studies were included, comprising 17,630,083 individuals aged ≥80 years. Macrovascular complications were most frequently reported, followed by microvascular and peripheral outcomes. Hypertension was the most reported comorbidity. Macrovascular outcomes were assessed in over 17 million individuals, while microvascular complications were studied in fewer than 400,000. Only five studies focused exclusively on adults aged ≥80 years. Reporting was also limited by retrospective designs, heterogeneity in definitions, and frequent omission of key variables, including diabetes duration, HbA1c, frailty, and cognitive status. Conclusions: There is a critical mismatch between research focus and the complications most relevant to function and quality of life in older populations with diabetes. Easily measurable yet clinically impactful outcomes, such as retinopathy, neuropathy, nephropathy, and foot disease, remain under-investigated in this cohort. Standardised, age-stratified reporting that incorporates functional and geriatric domains is needed to inform person-centred care in this expanding population. Full article

Advanced deception countermeasures now enable adversaries to inject false targets into synthetic-aperture-radar (SAR) imagery, generating electromagnetic signatures virtually indistinguishable from genuine targets, thus destroying the separability essential for conventional recognition algorithms. To address this problem, we propose a versatile front-end Multi-Feature Extraction and Spatial Transformation Network (MFE-STN), specifically designed for the task of discriminating between true targets and deceptive false targets created by SAR jamming, which can be seamlessly integrated with existing CNN backbones without architecture modification. MFE-STN integrates three complementary operations: (i) wavelet decomposition to extract the overall geometric features and scattering distribution of the target, (ii) a manifold transformation module for non-linear alignment of heterogeneous feature spaces, and (iii) a lightweight deformable spatial transformer that compensates for local geometric distortions introduced by deceptive jamming. By analyzing seven typical parameter-mismatch effects, we construct a simulated dataset containing six representative classes—four known classes and two unseen classes. Experimental results demonstrate that inserting MFE-STN boosts the average F1-score of known targets by 12.19% and significantly improves identification accuracy for unseen targets. This confirms the module’s capability to capture discriminative signatures to distinguish genuine targets from deceptive ones while exhibiting strong cross-domain generalization capabilities. Full article

Electromagnetic wave interference has escalated into a pervasive global issue, driving intensified research efforts across both civilian and military domains. However, the development of advanced electromagnetic wave (EMW) absorbers with finely tunable dielectric and magnetic loss properties has emerged as a pivotal strategy for mitigating electromagnetic pollution. Herein, we propose a cation engineering strategy to tailor the absorption properties of ZIF-67-derived layered double hydroxide (LDH) composites through systematic substitution of Co²⁺ with Fe, Mn, Zn, or Ni and stoichiometric control (Co/X = 1:4, 1:1). Mn/Zn doping enhances dipole polarization via lattice distortion, while structural analysis confirms that higher Co/X ratios preserve core–shell architectures, optimizing impedance matching. In contrast, Fe incorporation leads to excessive conductivity and impedance mismatch. The optimized CoNi1-4 composite exhibits superior broadband absorption (EAB = 4.52 GHz at 1.8 mm thickness, RL_min = −24.5 dB), attributed to synergistic interface polarization and magnetic coupling. This study delivers a highly tailorable materials platform that enables a deeper fundamental understanding of the synergy between dielectric and magnetic loss processes, thereby offering new pathways for optimizing electromagnetic wave absorption. Full article

Approximately 7% of males globally suffer from male infertility, which is becoming more widely acknowledged as a clinical indicator of potential health hazards as well as a cause of reproductive failure. Among these, cancer has become a significant worry due to mounting evidence that spermatogenesis impairment is associated with increased risk of prostate, testicular, and other cancers. Male infertility may be an early clinical manifestation of systemic genomic instability due to shared biological pathways, such as Y-chromosome microdeletions (AZF regions), germline DNA repair defects, mutations in tumor suppressor genes (e.g., BRCA1/2, TP53), mismatch repair gene mutations (e.g., MLH1, MSH2), and dysregulated epigenetic profiles. This narrative review covers the most recent research on prognostic markers of cancer in infertile men. These include molecular biomarkers such as genetic, epigenetic, and proteomic signatures; endocrine and hormonal profiles; and clinical predictors such as azoospermia, severe oligozoospermia, and a history of cryptorchidism. The possibility of incorporating these indicators into risk stratification models for precision medicine and early cancer surveillance is highlighted. For this high-risk group, bridging the domains of andrology and oncology may allow for better counseling, earlier detection, and focused therapies. Full article

Benefiting from the advantages of unmanned aerial vehicle (UAV) platforms such as low cost, rapid deployment capability, and miniaturization, the application of UAV-borne synthetic aperture radar (SAR) has developed rapidly. Utilizing a self-developed monostatic Miniaturized SAR (MiniSAR) system and a bistatic MiniSAR system, our team conducted multiple imaging missions over the same vehicle equipment display area at different times. However, system disparities and time-varying factors lead to a mismatch between the distributions of the training and test data. Additionally, small ground vehicle targets under complex background clutter exhibit limited size and weak scattering characteristics. These two issues pose significant challenges to the precise detection of small ground vehicle targets. To address these issues, this article proposes a domain-unified adaptive target detection framework (DUA-TDF). The approach consists of two stages: image-to-image translation and feature extraction and target detection. In the first stage, a multi-scale detail-aware CycleGAN (MSDA-CycleGAN) is proposed to align the source and target domains at the image level by achieving unpaired image style transfer while emphasizing both global structure and local details of the generated images. In the second stage, a cross-window axial self-attention target detection network (CWASA-Net) is proposed. This network employs a hybrid backbone centered on the cross-window axial self-attention mechanism to enhance feature representation, coupled with a convolution-based stacked cross-scale feature fusion network to strengthen multi-scale feature interaction. To validate the effectiveness and generalization capability of the proposed algorithm, comprehensive experiments are conducted on both self-developed monostatic/bistatic SAR datasets and public dataset. Experimental results demonstrate that our method achieves an mAP50 exceeding 90% in within-domain tests and maintains over 80% in cross-domain scenarios, demonstrating exceptional and robust detection performance as well as cross-domain adaptability. Full article

Accurate fault localization in rail transit train cables is hindered by impedance mismatch, which induces overshoot interference and attenuates reflected signals, causing traditional peak-detection methods to fail. This study proposes a novel traveling wave dual-peak identification method to address this challenge. The approach employs signal polarity normalization to eliminate phase inversion, Gaussian-weighted filtering to suppress noise and distortion, and local extrema screening to robustly isolate incident and reflected wave peaks amidst complex backgrounds including overshoot oscillations and electromagnetic crosstalk. A dual-Gaussian model is optimized via nonlinear fitting to precisely quantify peak arrival times while compensating for waveform broadening. Fault distance is derived from the optimized time difference and wave velocity. Experimental validation across single-core coaxial, twin-core coaxial, and harness cables with open/short-circuit faults at multiple distances confirms the method’s effectiveness. Results demonstrate strong linear relationships between time differences and fault distances for all cable types, with successful peak identification achieved even under severe signal attenuation or strong coupling interference. This method significantly enhances localization accuracy for rail transit cable systems under impedance mismatch conditions. Full article

One of the challenges in inertia sensor applications is the need for a class of devices that operate at one of the ring resonant frequencies to achieve large amplitudes of vibration. However, large amplitudes tend to produce undesirable nonlinear effects due to geometrical nonlinearities. Hence, a rigorous experimental dynamic analysis of rotating thin circular ring-type structures is considered important to gain a deeper understanding of the device’s nonlinear behavior as well as the potential performance improvements. This study aims to experimentally investigate the nonlinear dynamic behavior of rotating thin circular rings and the effects of angular rate as well as mass mismatch variations on the system natural frequency. A prototype made of a macroscale thin cylindrical structure is employed to study the nonlinear dynamic behavior of rotating thin circular rings. Using a precision rate table equipped with a slip ring as well as non-contact sensors/actuators, experiments that closely represent the actual physical operating conditions of angular rate sensors are developed. Natural frequency variations due to the input angular rate changes are measured in time and frequency domains. Useful experimental observations on the frequency split and mass mismatch effects have been performed. Typical nonlinear behavior, such as jump phenomena of a rotating thin circular cylinder, is noted. The nonlinear dynamic behavior of a ring-type resonator system, which is subjected to external excitations, is experimentally investigated. Results from the present experimental study on the mechanics of the ring structure are expected to provide further insight into the design and operation of ring-type resonators for angular rate sensing applications. Full article

Fused silica optical components with antireflection (AR) coatings are key components in high-power laser systems. However, their reliability is severely challenged by multi-wavelength irradiation and the presence of unavoidable matrix surface defects. To investigate the coupling effects of electric field modulation between multi-wavelength irradiation, AR coating layers, and defects in AR-coated fused silica, this paper uses the finite-difference time-domain (FDTD) method to simulate the nanoscale electric field intensity distribution in fused silica coated with a double-layer AR coating at three different design wavelengths using multi-wavelength lasers. The effects of electric field coupling between the coating layers and defects are analyzed for three representative scratch geometries. The results show that when the incident wavelength matches the AR design wavelength, the interface field is effectively suppressed, resulting in a smoother field distribution and localized hot spots. Conversely, mismatched wavelengths induce severe field distortion, producing multiple hot spots and lateral interference fringes. Wide, shallow scratches are particularly sensitive to wavelength mismatch, with a 532 nm AR coating exhibiting a global maximum enhancement factor of 1.63442 for 355 nm incident light. These findings highlight the coupling effects of scratch geometry, AR coating dispersion, and laser wavelength on electric field modulation. This research provides valuable insights for optimizing antireflection coatings and improving defect tolerance in multi-wavelength laser applications, helping to improve the reliability of high-power laser systems. Full article

Computation modeling for large thermoplastic deformation of plastic solids is critical for industrial applications like non-invasive assessment of engineering components. While deep learning-based methods have emerged as promising alternatives to traditional numerical simulations, they often suffer from systematic errors caused by geometric mismatches between predicted and ground truth meshes. To overcome this limitation, we propose a novel boundary geometry-constrained neural framework that establishes direct point-wise mappings between spatial coordinates and full-field physical quantities within the deformed domain. The key contributions of this work are as follows: (1) a two-stage strategy that separates geometric prediction from physics-field resolution by constructing direct, point-wise mappings between coordinates and physical quantities, inherently avoiding errors from mesh misalignment; (2) a boundary-condition-aware encoding mechanism that ensures physical consistency under complex loading conditions; and (3) a fully mesh-free approach that operates on point clouds without structured discretization. Experimental results demonstrate that our method achieves a 36–98% improvement in prediction accuracy over deep learning baselines, offering a efficient alternative for high-fidelity simulation of large thermoplastic deformations. Full article

Argonaute (Ago) proteins are ubiquitous across all domains of life. Some prokaryotic Agos (pAgos) function as endonucleases that utilize short nucleic acid guides to recognize and cleave complementary targets. Yet, considerable diversity within pAgos leaves many of their biochemical and functional features insufficiently understood. This study characterizes CalAgo, an pAgo from the mesophilic bacterium Cohnella algarum, which demonstrates DNA-guided DNA endonuclease and RNA endonuclease activities at physiological temperatures. CalAgo’s cleavage activity depends on Mn²⁺ and Mg²⁺ ions and remains effective across a wide range of temperatures and pH levels. CalAgo utilizes only short guides ranging from 15 to 21 nucleotides (nt) in length, in contrast to other reported pAgos that target both DNA and RNA, which often exhibit broad guide selectivity. CalAgo preferentially loads 5′-phosphorylated guides and shows no significant preference among guides with different 5′-end nucleotides. CalAgo is sensitive to guide–target mismatches, and introducing a single mismatch at positions 12 or 15 of the guide strand abolished detectable activity. Structural modeling suggests that this unique guide specificity may originate from structural features in its PAZ domain involved in 3′-guide binding. In summary, this study deepens insight into mesophilic pAgos and supports their potential utility in nucleic acid-based applications. Full article

Accurate bearing fault diagnosis under various operational conditions presents significant challenges, mainly due to the limited availability of labeled data and the domain mismatches across different operating environments. In this study, an adaptive meta-learning framework (AdaMETA) is proposed, which combines dynamic task-aware model-independent meta-learning (DT-MAML) with efficient multi-scale attention (EMA) modules to enhance the model’s ability to generalize and improve diagnostic performance in small-sample bearing fault diagnosis across different load scenarios. Specifically, a hierarchical encoder equipped with C-EMA is introduced to effectively capture multi-scale fault features from vibration signals, greatly improving feature extraction under constrained data conditions. Furthermore, DT-MAML dynamically adjusts the inner-loop learning rate based on task complexity, promoting efficient adaptation to diverse tasks and mitigating domain bias. Comprehensive experimental evaluations on the CWRU bearing dataset, conducted under carefully designed cross-domain scenarios, demonstrate that AdaMETA achieves superior accuracy (up to 99.26%) and robustness compared to traditional meta-learning and classical diagnostic methods. Additional ablation studies and noise interference experiments further validate the substantial contribution of the EMA module and the dynamic learning rate components. Full article

Electronic health records (EHRs) are typically stored in relational databases, making them difficult to query for nontechnical users, especially under privacy constraints. We evaluate two practical clinical NLP workflows, natural language to SQL (NL2SQL) for EHR querying and retrieval-augmented generation for clinical question answering (RAG-QA), with a focus on privacy-preserving deployment. We benchmark nine large language models, spanning open-weight options (DeepSeek V3/V3.1, Llama-3.3-70B, Qwen2.5-32B, Mixtral-8 × 22B, BioMistral-7B, and GPT-OSS-20B) and proprietary APIs (GPT-4o and GPT-5). The models were chosen to represent a diverse cross-section spanning sparse MoE, dense general-purpose, domain-adapted, and proprietary LLMs. On MIMICSQL (27,000 generations; nine models × three runs), the best NL2SQL execution accuracy (EX) is 66.1% (GPT-4o), followed by 64.6% (GPT-5). Among open-weight models, DeepSeek V3.1 reaches 59.8% EX, while DeepSeek V3 reaches 58.8%, with Llama-3.3-70B at 54.5% and BioMistral-7B achieving only 11.8%, underscoring a persistent gap relative to general-domain benchmarks. We introduce SQL-EC, a deterministic SQL error-classification framework with adjudication, revealing string mismatches as the dominant failure (86.3%), followed by query-join misinterpretations (49.7%), while incorrect aggregation-function usage accounts for only 6.7%. This highlights lexical/ontology grounding as the key bottleneck for NL2SQL in the biomedical domain. For RAG-QA, evaluated on 100 synthetic patient records across 20 questions (54,000 reference–generation pairs; three runs), BLEU and ROUGE-L fluctuate more strongly across models, whereas BERTScore remains high on most, with DeepSeek V3.1 and GPT-4o among the top performers; pairwise t-tests confirm that significant differences were observed among the LLMs. Cost–performance analysis based on measured token usage shows per-query costs ranging from USD 0.000285 (GPT-OSS-20B) to USD 0.005918 (GPT-4o); DeepSeek V3.1 offers the best open-weight cost–accuracy trade-off, and GPT-5 provides a balanced API alternative. Overall, the privacy-conscious RAG-QA attains strong semantic fidelity, whereas the clinical NL2SQL remains brittle under lexical variation. SQL-EC pinpoints actionable failure modes, motivating ontology-aware normalization and schema-linked prompting for robust clinical querying. Full article

Aim: As the global population ages, the number of bilingual individuals living with dementia is increasing, yet their communication needs remain underrepresented in both clinical practice and research. This evidence review examines the intersection of language regression, communication challenges, and cultural–linguistic identity in bilingual dementia, with a particular focus on the role of speech–language pathologists (SLPs). Methods: Twelve peer-reviewed studies were critically reviewed and thematically analysed across four domains: (1) language regression and retention in bilingual dementia, (2) communication challenges in bilingual dementia care, (3) the marginal role of speech–language pathology, and (4) cultural–linguistic identity and health equity. The included studies span clinical case reports, experimental research, qualitative caregiver studies, and systematic reviews, with bilingual populations across Asia, Europe, North America, and the Middle East. Results: Findings reveal that language deterioration in bilingual dementia is dynamic and highly individualised, often influenced by language history, emotional context, and usage patterns. Caregivers and clinicians face persistent communication breakdowns, particularly in linguistically mismatched settings. Despite their specialised expertise in communication, SLPs remain largely peripheral in dementia care, constrained by systemic, educational, and methodological barriers. Moreover, linguistic and cultural identity play a critical role in how dementia is experienced and managed, yet are rarely integrated into care frameworks. Conclusions: This review highlights a significant knowledge–practice gap in bilingual dementia care and underscores the need to embed culturally and linguistically responsive communication practices, especially through speech–language therapy, at the centre of bilingual dementia care and support. It outlines key research and practice directions to advance equity, accuracy, and relational care in this growing population. Full article