Search Results (406)

Background: Bacterial adaptation to environmental and chemical stress involves coordinated, system-level responses collectively described as perturbome. Understanding conserved elements within core perturbomes may reveal strategic vulnerabilities for antimicrobial development. Methods: In this study, we implemented an integrative framework combining functional and comparative genomics, drug–target interactions and molecular docking to prioritize conserved stress-response targets in Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus. Results: A total of 147 genes from previously defined core perturbomes were analyzed through interactome reconstruction and functional enrichment. Interactome and functional analyses revealed significant connectivity and functional clustering, primarily associated with molecule biosynthesis, translation, transcriptional regulation, and energy metabolism. Orthology-based comparative genomics identified six conserved orthogroups shared across at least two species, representing key stress-adaptive nodes including fatty acid synthesis initiation, metabolic stress buffering, transcription termination (Rho), ATP synthesis, peptidoglycan remodeling, and UDP-glucose-mediated envelope biosynthesis. Drug–target interaction analyses suggested that these conserved proteins are modulated by enzymatic inhibitors, metabolite analogs, or active-site competitors. Structural and docking analyses focused on a selected protein, FabF (β-ketoacyl-ACP synthase II) and confirmed catalytically coherent binding of cerulenin within the active site, with high concordance between experimentally resolved and AlphaFold-predicted structures, supporting the reliability of structure-based prioritization. Conclusions: Overall, the results demonstrate that bacterial stress responses converge on evolutionarily conserved metabolic and regulatory elements essential for homeostasis and tolerance to perturbations, being the first work integrating core perturbome data from different microorganisms. The proposed perturbome-informed framework provides a rational strategy to identify robust, broad-spectrum antimicrobial targets and highlights opportunities for drug repurposing and future experimental validation. Full article

Viruses cause a great number of infectious diseases with medical, veterinary, agricultural, social and economic impact. Their unique mechanisms to spread, overcome and resist the existing countermeasures require innovative and smart antiviral strategies such as the effective disinfection of enclosed environments with ensured broad-spectrum efficacy and minimized risks associated with handling liquid biocides. Formic acid (FA) is a well-established natural acaricide used in beehives with an antiviral potential; however, its application in a liquid form is hindered by severe corrosiveness and rapid, uncontrolled evaporation. This study describes a novel formulation of FA, using a cryogel carrier for achieving a vapor-phase inactivation of viruses, thus eliminating the need for direct contact between the disinfectant and the pathogen. Firstly, a poly(N-isopropylacrylamide) (PNIPAm) cryogel was synthesized by a procedure involving cryogenic treatment, photochemical crosslinking, and freeze-drying, and then the cryogel was swollen with 65% FA or _ddH₂O as a control. After an exposure of a panel of animal and human viruses to FA, evaporated by the polymer carrier for time intervals between 15 min and 12 h, they were neutralized completely as follows: Poliovirus (PV) as a surrogate for major bee viral pathogens for 60 min by 5.1 ∆lg; Feline calicivirus (FCV) for 60 min by 5.3 ∆lg; Adenovirus 5 (AdV5) for 12 h by 4.0 ∆lg; and Influenza virus A (IAV) for 15 min by 5.1 ∆lg. Results were recorded after titration, 48–72 h incubation, cytopathic effect estimation and NR uptake assay. Our results suggest that 65% FA, when delivered via the PNIPAm cryogel matrix, acts as a powerful agent for fumigation-like disinfection. This “dry” delivery strategy offers significant practical advantages: it eliminates the need for open liquid containers, prevents spill-related hazards, and provides an alternative for controlled, long-term release of active vapors. Full article

Autism spectrum disorder is heterogeneous across clinical presentation, cognition, development, and biological profile. This heterogeneity complicates multimodal phenotyping when measurements are grouped in different modality blocks: Some blocks are missing, and deployment sites differ from training sites. We introduce a hierarchical latent-summary framework for multimodal autism phenotyping under incomplete observation and domain shift. The model separates a shared global latent summary from block-specific latent summaries. It makes block configurations, missingness patterns, and domain labels explicit. Under compactness, continuity, coupling observability, and inverse-stability assumptions, recovered summaries are well defined, and the error in the global summary can be bounded. This error control propagates to block-specific summaries under Lipschitz coupling maps. Domain variation is handled through a Wasserstein risk envelope in recovered latent-summary space. The guarantee is conditional on the deployment distribution lying inside the prescribed Wasserstein ball. Empirical evaluation has two complementary roles. Two synthetic studies test the structural mechanisms predicted by the theory: The first shows the asymmetric block value, nonuniform missing-block degradation, and a near tie between the full block set and a stable reduced configuration; the second separates practical train-only radius calibration from a certified transport construction. A real-data clinical illustration using the Autism Brain Imaging Data Exchange (ABIDE) phenotypic and preprocessed imaging-derived variables then examines whether the cross-sectional surrogate exposes analogous block-structured phenomena in a multisite autism cohort after excluding direct diagnostic symptom instruments. This illustration shows modest site-held-out diagnostic signals, clear block asymmetry, substantial site-level instability, and limited degradation under moderate additional block removal. These findings support a block-structured view of multimodal autism phenotyping in which prediction, missingness, latent recovery, and transportability must be evaluated jointly. Full article

As unmanned aerial vehicles (UAVs) are increasingly deployed in various fields, their flight safety has become a critical issue. However, limited onboard sensing and computing resources make it difficult to perform intelligent fault monitoring and diagnosis directly on UAVs. To explore an offboard alternative, this paper investigates a drone nest vibration analysis based fault diagnosis framework for a multirotor UAV rotor system using vibration signals measured from a laboratory-scale simulated drone nest. A simplified coupled dynamic model of the UAV–drone nest system is established to analyze the transmission mechanism of rotor fault-induced vibration and to explain the observability of fault-related frequency components under the tested configuration. Considering the weak and attenuated characteristics of the nest-side vibration signals, a multi-domain feature fusion and multi-task learning network is developed to integrate time-domain, frequency-domain, and envelope-spectrum information while jointly learning fault type and rotational speed. Comparative experiments on the constructed quadrotor–drone nest test platform are conducted to validate the feasibility and effectiveness of the proposed method under the tested operating conditions. Full article

Reliable and intelligent fault diagnosis of rotating machinery is crucial for the safety and stability of industrial systems. Nevertheless, the acquisition of labeled fault data is often difficult in practical applications because of the high cost of maintenance, the rarity of fault events, and the inherent safety risks associated with fault induction experiments. As a result, most real-world datasets consist mainly of healthy operating samples, which makes bearing fault diagnosis under fault-free training conditions particularly challenging. The objective of this study was to develop a simulation-driven diagnostic framework capable of identifying real bearing faults without using real fault samples during model training. To achieve this objective, pseudo-fault data were generated by superimposing periodic impulse–resonance responses, governed by theoretical bearing fault characteristic frequencies, onto healthy vibration signals. The synthesized dataset was further analyzed using wavelet packet decomposition and envelope spectrum analysis to extract discriminative time–frequency features. These features were then fed into the proposed Hierarchical Convolutional Attention Network (HCANet), which captured hierarchical multi-scale representations while emphasizing fault-related components. Furthermore, a Central Clustering Loss was employed to encourage intra-class compactness and enhance inter-class separability, thereby improving the generalization capability of the diagnostic model. Experimental validation on two bearing datasets showed that the proposed method achieved high diagnostic accuracy when tested on real fault samples, despite being trained exclusively on healthy signals and synthesized pseudo-fault samples. These results demonstrated the effectiveness of the proposed simulation-driven strategy and highlighted its potential as a practical solution for bearing fault diagnosis in zero-real-fault-data scenarios. Full article

Background/Objectives: The aquaculture industry represents a fundamental food sector. One of the main limiting factors for this sector is related to bacterial diseases, for which antibiotics have been widely used worldwide for decades. In recent years, a more conscious approach to the use of antimicrobials within the framework of the One Health approach has increased the need for alternatives capable of helping with disease management while avoiding the onset of antimicrobial resistance phenomena. Antimicrobial peptides, which have a broad spectrum of action against pathogens, are a promising solution. Methods: In this work, we investigated the capability of three peptides (Trematocine, Chionodracine, and Cnd-m3) isolated from Antarctic fish to target bacterial and viral pathogens affecting aquaculture. Successively, we investigated their cytotoxicity versus a continuous embryonic cell line (DLEC) derived from European sea bass and their haemolytic activity against fish erythrocytes. Moreover, we evaluated their immunomodulatory effect. Results: Regarding antibacterial properties, Cnd-m3 was identified as the best peptide, demonstrating good bactericidal and bacteriostatic activity against various bacterial strains, including Lactococcus garvieae. Concerning this bacterium, ANS permeability assays showed that the Cnd-m3 peptide has a great ability to interact with its outer membrane, while TEM analysis revealed that the peptide, after destabilization of the cell membrane, interacts with nucleic structures. Considering the antiviral activity, Trematocine was effective against two tested pathogenic enveloped viruses. Moreover, the toxicity of Trematocine and Cnd-m3 was evaluated by investigating their cytotoxicity against a cell line derived from Dicentrarchus labrax and haemolysis against sea bass erythrocytes. Both revealed good selectivity towards pathogens at the lowest concentration. Finally, Cnd-m3 manifested light in vitro immunomodulatory properties. Conclusions: Overall, these data provide a solid basis for future studies assessing the potential applications of two of the tested peptides in aquaculture. Full article

We present the integration of an automated polarization control system into a figure-eight fiber laser with the aim of self-tuning noise-like pulses (NLPs). The system optimizes polarization adjustments by using adaptive control and predictive neural networks (NNs), enhancing temporal and spectral behavior. This approach enables precise control over pulse characteristics, achieving an average output power of 275.25 mW (302.8 nJ) for signal emission at ~1567 nm; adjustable NLP envelope durations from 13 ns to 48 ns, corresponding to spectral widths from 50 to more than 200 nm; and the ability to increase in a controllable way the repetition frequency up to 100 times the fundamental frequency, which corresponds to 909 kHz, through cavity harmonic pulse generation. A multiparameter pulsed regime-seeking algorithm stabilizes high-energy NLPs in the fundamental or harmonic regime while predictive networks optimize the cavity response, and the process is completed in an average time of 11.5 s. The automated polarization control system enables high cavity harmonic pulse generation, as well as broadband supercontinuum (SC) spectrum with high flatness. Full article

As a critical element of the drive motor, rolling bearings are susceptible to localized defects under complex loads and varying operating conditions. Such defects typically generate periodic transient shocks, which reflect bearing fault features. However, the accurate extraction of fault-related transient components becomes challenging due to strong noise influence. To address this issue, a concave sparsity-assisted generalized dispersive mode decomposition (CSA-GDMD) method is developed to enhance fault feature extraction. This method introduces a non-convex sparse model based on generalized mini-max concave (GMC) regularization to preprocess the vibration signal. The GMC penalty effectively suppresses background noise while better preserving the amplitude characteristics of the transient shocks. Subsequently, GDMD is applied to progressively extract transient shock components from the preprocessed signal and reconstruct the signal, resulting in more prominent fault-related transient components. The simulation results show that CSA-GDMD significantly improves the signal-to-noise ratio (SNR), from 6.5905 dB at −15 dB to 9.5122 dB at 5 dB, and reduces the root mean square error (RMSE) from 0.0280 to 0.0196. Consequently, the fault feature frequencies can be identified more clearly in the envelope spectrum, further confirming the accurate fault diagnosis capability of the proposed method for bearing faults under strong noise conditions. Full article

RNA viruses exhibit high mutation rates, necessitating antivirals targeting conserved infection mechanisms. In this study, viral hemorrhagic septicemia virus (VHSV), a non-human pathogenic negative-sense RNA virus, was used as a surrogate model to enable high-throughput antiviral screening under reduced biosafety conditions. A recombinant VHSV expressing enhanced green fluorescent protein was used to screen 17,265 compounds, 2000 plant extracts, and 100 marine extracts. Among the candidates, the leaf extract of Phellodendron amurense Rupr. (PL extract) exhibited antiviral activity with low cytotoxicity (selectivity index ≈ 10). The extract reduced viral infectivity in a dose-dependent manner and showed cross-activity against snakehead rhabdovirus. Mechanistic analyses indicated that the PL extract acts primarily at early stages of infection. Virucidal assays demonstrated direct, time-dependent inactivation of viral particles, while pre-treatment reduced host cell susceptibility. Time-of-addition experiments confirmed that antiviral activity was restricted to early infection, suggesting interference with viral attachment or entry rather than intracellular replication. Fractionation revealed that activity was associated with the non-polar n-hexane fraction, implicating lipophilic compounds that may disrupt viral envelope integrity or membrane interactions. These findings suggest that P. amurense leaf extract is a promising candidate for broad-spectrum antivirals targeting conserved entry processes in enveloped RNA viruses. Full article

In most amorphous materials, the concentration of Urbach tail states is larger than the concentration of dangling bond states. However, absorption accounting for the Urbach tail while disregarding the dangling bonds is commonly used or derived by spectroscopic characterizations of amorphous films from a single spectrum, mostly due to the insufficient accuracy of such characterizations. This paper proposes an advanced envelope method (AEM) for transmittance spectrum T(λ), aiming to resolve this problem. The novelties in AEM are: improved preprocessing of T(λ), extending the envelopes deeper into the region of strong absorption (RSA), enhanced determination of the refractive index n(λ) in the region of weak absorption, optimization of both n(λ) and the extinction coefficient k(λ) in RSA, as well as analysis of the types of electron transitions and calculation of their energy gaps. Three single magnetron sputtered a-Si films deposited on glass substrates are characterized by AEM, and three other relevant methods that disregard deep-levels. The best accuracy is achieved when these films are characterized by AEM. It is demonstrated that the absorption coefficient α(λ) of each of these films distinguishes electron transitions via dangling bond states from those via tails states, and the DOS corresponds to the Mott–Davis model of amorphous materials. Full article

Emery–Dreifuss muscular dystrophy (EDMD) is a rare inherited neuromuscular disorder within the spectrum of nuclear envelope diseases, classically characterized by early musculo-tendinous contractures, slowly progressive myopathy, and cardiac involvement dominated by conduction disease and arrhythmias, with variable evolution toward cardiomyopathy and heart failure. This narrative review provides a comprehensive and clinically actionable synthesis of cardiovascular manifestations across EDMD genotypes and phenotypes, outlining pragmatic diagnostic and therapeutic pathways for real-world care. A targeted literature search was performed in PubMed, Embase, and Web of Science, focusing on studies addressing cardiovascular involvement in EDMD. Relevant original studies, case series, registries, guideline documents, and high-quality reviews were selected and synthesized narratively, with particular emphasis on diagnostic strategies, risk stratification, and management approaches. Cardiac involvement in EDMD encompasses a broad and heterogeneous spectrum, including atrial disease and conduction disturbances, ventricular arrhythmias, dilated cardiomyopathy, thromboembolic complications, and sudden cardiac death. Phenotypic expression varies according to the underlying genetic substrate, with distinct atrial- and ventricular-dominant trajectories. Early recognition and structured cardiovascular surveillance are essential to guide timely intervention, including anticoagulation, device therapy, and heart failure management. Despite growing awareness, significant gaps remain in risk prediction and standardized management strategies. EDMD represents a paradigmatic model of cardiomyopathy characterized by prominent electrical instability and systemic involvement. A structured, genotype- and phenotype-informed approach centered on early surveillance, proactive arrhythmia and thromboembolic risk management and timely device therapy may improve clinical decision-making in real-world settings. Future perspectives include the integration of precision medicine and the development of gene- and pathway-targeted therapies, with the potential to shift from symptomatic management toward disease-modifying strategies. Full article

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications. Full article

Nuclear lamins are the core molecular bridge linking the extracellular mechanical microenvironment to intranuclear gene regulation, and play a central regulatory role in cellular mechanosensation and mechanotransduction. Here, we systematically integrate the latest global research progress on nuclear lamins, delineating the cascade regulatory mechanism by which lamins mediate the transmission of mechanical signals across the nuclear envelope and the subsequent regulation of chromatin remodeling and epigenetic modification, with a focus on the molecular characteristics and functional specificity of distinct nuclear lamin subtypes and their interaction modes with the Linker of Nucleoskeleton and Cytoskeleton complex (LINC complex) and chromatin. Existing studies have established that nuclear lamins are mainly divided into three categories: A-type lamins (Lamin A/C), B-type lamins (Lamin B1, B2), and germ cell-specific subtypes. Among these, A-type lamins directly determine the mechanical stiffness of the nucleus and serve as the core mediators of intranuclear mechanical signal transduction. Each subtype of B-type nuclear lamins has a well-defined, non-redundant functional division: Lamin B1 and Lamin B2 indirectly maintain nuclear structural stability and regulate epigenetic status by anchoring facultative heterochromatin and constitutive heterochromatin, respectively. Notably, Lamin A/C distributed in the nucleoplasm also bears significant mechanical tension, which challenges the long-standing view that the mechanical functions of nuclear lamins are restricted to the nuclear envelope region. After mechanical force is transmitted across the nuclear envelope to nuclear lamins via the LINC complex, it can regulate the spatial conformation of chromatin and epigenetic modifications, thereby determining core cellular life activities including proliferation, differentiation, and migration. Dysregulation of this pathway is closely associated with a wide spectrum of human diseases, including cardiovascular diseases, progeria, muscular dystrophy, and neurodevelopmental disorders. Taken together, this review systematically delineates the hierarchical regulatory network of the “LINC complex–nuclear lamina–chromatin” axis, advances our understanding of the fundamental principles of cellular mechanobiology, and provides a theoretical framework for deciphering the pathological mechanisms and developing targeted therapeutic drugs for related diseases. Full article

Early fault vibration signals from rolling bearings are typically nonlinear, non-stationary, and heavily obscured by background noise, which severely impedes the accurate extraction of fault features. To overcome the limitations of traditional stochastic resonance (SR)—specifically the small-parameter restriction for high-frequency signals and the subjectivity in parameter selection—this paper proposes an adaptive SR envelope spectroscopy method based on particle swarm optimization (PSO) and local mean decomposition (LMD). First, a variable-scale transformation is introduced to compress the high-frequency fault signals into the effective frequency band required by the adiabatic approximation theory. Second, utilizing the global search capability of PSO, the potential well parameters of the bistable system are adaptively optimized by maximizing the output signal-to-noise ratio (SNR), thereby achieving optimal matching between the nonlinear system and the input signal. Finally, the enhanced signal is decomposed by LMD, and the sensitive components are selected for envelope spectrum analysis to identify fault characteristics. Experimental validation using the Case Western Reserve University bearing dataset demonstrates that the proposed method effectively amplifies weak fault signals under strong noise conditions, exhibiting superior feature extraction accuracy and noise robustness compared to traditional methods. Full article

This paper proposes a novel integrated spectral kurtosis (ISK) technology, which is a new conceptualization for fault diagnosis, and compares it with conventional spectral kurtosis technology. The vibration signals from a gearbox are processed by time synchronous averaging (TSA) and analysed using the spectral kurtosis (SK). The ISK feature is estimated across the entire frequency domain, while the envelope is obtained through SK-based filtering and a Hilbert demodulation. The ISK technology demonstrates the ability to distinguish between healthy and defected gearbox cases, achieving a total probability of correct diagnosis (TPCD) of 91.5% for pinions and 96.1% for gears, whereas the SK-based squared envelope technology provides a limited diagnosis effectiveness, with a maximum TPCD of 80%. The motor current signals are also analysed through harmonic amplitude tracking within the current spectrum. A comparison of the ISK and motor current technologies is also made, showing that the motor current technology reaches a maximum of 90% TPCD for gears, which remains lower than the TPCD for the ISK technology. Full article