Search Results (154)

Nucleic acid-based gene interfering and editing molecules, such as antisense oligonucleotides, ribozymes, small interfering RNAs (siRNAs), and CRISPR-Cas9-associated guide RNAs, are promising gene-targeting agents for therapeutic applications. Cancer’s heterogeneous and diverse nature demands gene-silencing technologies that are both specific and adaptable. RNase P ribozymes, called M1GS RNAs, are engineered constructs that link the catalytic M1 RNA from bacterial RNase P to a programmable guide sequence. This guide sequence directs the M1GS ribozyme to base-pair with a target RNA, inducing it to fold into a structure resembling pre-tRNA. Catalytic activity can be enhanced through in vitro selection strategies. In this review, we will discuss the application of M1GS ribozymes in targeting cancer-associated RNAs, focusing on the BCR-ABL transcript in leukemia, the internal ribosome entry site (IRES) of hepatitis C virus (HCV), and the replication and transcription activator (RTA) of Kaposi’s sarcoma-associated herpesvirus (KSHV). Together, these examples highlight the versatility of M1GS ribozymes across both viral and cellular oncogenic targets, underscoring their potential as a flexible synthetic biology platform for cancer therapy. Full article

Infrared–visible (IR–VIS) image fusion is becoming central to unmanned aerial vehicle (UAV) perception, enabling robust operation across day–night cycles, backlighting, haze or smoke, and large viewpoint or scale changes. However, for practical applications some challenges still remain: visible images are illumination-sensitive; infrared imagery suffers thermal crossover and weak texture; motion and parallax cause cross-modal misalignment; UAV scenes contain many small or fast targets; and onboard platforms face strict latency, power, and bandwidth budgets. Given these UAV-specific challenges and constraints, we provide a UAV-centric synthesis of IR–VIS fusion. We: (i) propose a taxonomy linking data compatibility, fusion mechanisms, and task adaptivity; (ii) critically review learning-based methods—including autoencoders, CNNs, GANs, Transformers, and emerging paradigms; (iii) compare explicit/implicit registration strategies and general-purpose fusion frameworks; and (iv) consolidate datasets and evaluation metrics to reveal UAV-specific gaps. We further identify open challenges in benchmarking, metrics, lightweight design, and integration with downstream detection, segmentation, and tracking, offering guidance for real-world deployment. A continuously updated bibliography and resources are provided and discussed in the main text. Full article

Silver nanoparticles are metallic particles with very small dimensions and excellent optical, electrical and biological properties. Lately, they have shown promising results in biosensing applications. In the material’s fabrication, the synthesis parameters remain the main aspect to be considered once a certain application is targeted. Therefore, this work presents the synthesis of silver nanoparticles using a chemical reduction based on various volumes of reducing and stabilizing agents. The multiple synthesis methods proposed were tested and optimized in order to achieve the best results for further biosensing applications. In this regard, sodium borohydride (NaBH₄) was used as reducing agent in volumes of 400 μL and 1 mL, while trisodium citrate (TSC) was proposed in much smaller volumes of 10, 20, and 50 μL. The optical and morphological analysis obtained from UV-VIS and TEM microscopy confirmed the formation of nanoparticles in case of all synthesis. The average diameters of silver nanoparticles were in the range between 21 and 27 nm, with high homogeneity for the samples with 20 and 50 μL of TSC. FT-IR analysis confirmed the TSC functionalization on the AgNPs’ surface. SERS analysis and the bulk sensitivity method also showed good surface results, leading to the assumption that both reducing and stabilizing agents can influence the final properties of the material. LSPR biosensing of para-aminothiophenol was tested, and was proven to have detection capabilities at concentrations as low as 10⁻⁷ M. Overall, the results proved that the synthesis method with a smaller amount of reducing agent and a moderate quantity of stabilizing agent has superior properties for biosensing applications. Full article

Selective depression of serpentine remains a major challenge in the flotation of low-grade nickel sulphide ores because serpentine slimes impair concentrate grade and recovery. In this study, four structurally related micromolecular depressants bearing amino and phosphonic functionalities were designed, synthesized and systematically evaluated. Micro-flotation screening (depressant range: 0–20 mg·L⁻¹) and bench-scale tests identified an operational optimum near pH 9 and a reagent dosage of ≈18 mg·L⁻¹; potassium butyl xanthate (PBX) was used as a collector and methyl isobutyl carbinol (MIBC) as a frother. Phosphonate-containing molecules (PMIDA and GLY) delivered the largest gains in pentlandite recovery and concentrate selectivity compared with carboxylate analogues and a benchmark depressant. Mechanistic studies (zeta potential, adsorption isotherms, FT-IR, and XPS) indicated that selective adsorption of amino and phosphonate groups on serpentine occurs via coordination with surface Mg sites and by altering the electrical double layer. The DLVO modelling showed that these reagents generate an increased repulsive barrier, mitigating slime coating and entrainment. Contact-angle measurements confirmed selective hydrophilization of serpentine while pentlandite remained hydrophobic. These findings demonstrate that incorporating targeted phosphonate chelation into small-molecule depressants is an effective strategy to control serpentine interference and to enhance flotation performance. Full article

Space-based infrared (IR) detection, with wide coverage, all-time operation, and stealth, is crucial for aerial target surveillance. Under low signal-to-noise ratio (SNR) conditions, however, its small target size, limited features, and strong clutters often lead to missed detections and false alarms, reducing stability and real-time performance. To overcome these issues of energy-integration imaging in perceiving dim targets, this paper proposes a biomimetic vision-inspired Infrared Temporal Differential Detection (ITDD) method. The ITDD method generates sparse event streams by triggering pixel-level radiation variations and establishes an irradiance-based sensitivity model with optimized threshold voltage, spectral bands, and optical aperture parameters. IR sequences are converted into differential event streams with inherent noise, upon which a lightweight multi-modal fusion detection network is developed. Simulation experiments demonstrate that ITDD reduces data volume by three orders of magnitude and improves the SNR by 4.21 times. On the SITP-QLEF dataset, the network achieves a detection rate of 99.31%, and a false alarm rate of $1.97\times {10}^{-5}$, confirming its effectiveness and application potential under complex backgrounds. As the current findings are based on simulated data, future work will focus on building an ITDD demonstration system to validate the approach with real-world IR measurements. Full article

The tail boom is a critical structural component of a helicopter, and accurately capturing its dynamic characteristics is essential; however, the inherent geometric and material complexity of the tail boom usually leads to large-scale finite element models whose system matrices are of very high order, and as the matrix order increases the computational effort grows exponentially. To further accelerate the condensation process for a truss-type tail-boom FE model, this paper presents a substructure-based secondary condensation method in which the global structure is partitioned into several substructures, each secondary substructure is first condensed onto its boundary nodes and then assembled into the primary structure, and the primary structure—now enriched with the condensed secondary substructures—is finally reduced to the target degrees of freedom, repeatedly operating on low-order matrices instead of a single high-order one to markedly shorten overall computation time. The proposed method is compared with both overall secondary IRS condensation and overall secondary SEREP condensation. All three secondary-condensation strategies yield six-degree-of-freedom coupled-spring equivalent models whose accuracy errors are very small in modal, frequency-domain, and time-domain analyses: frequency errors remain within 1%, and the goodness-of-fit of the time-history response curves exceeds 0.9, while the computational time is reduced by more than 70%, demonstrating that the substructure-based secondary condensation method is highly effective, delivering much higher computational efficiency without sacrificing accuracy. Full article

Impurities in polypropylene random copolymer (PPR) raw materials can seriously affect the performance of the final product, and efficient and accurate impurity detection is crucial to ensure high production quality. In order to solve the problems of high small-target miss rates, weak anti-interference ability, and difficulty in balancing accuracy and speed in existing detection methods used in complex industrial scenarios, this paper proposes an enhanced machine vision detection algorithm based on YOLOv11. Firstly, the FasterLDConv module dynamically adjusts the position of sampling points through linear deformable convolution (LDConv), which improves the feature extraction ability of small-scale targets on complex backgrounds while maintaining lightweight features. The IR-EMA attention mechanism is a novel approach that combines an efficient reverse residual architecture with multi-scale attention. This combination enables the model to jointly capture feature channel dependencies and spatial relationships, thereby enhancing its sensitivity to weak impurity features. Again, a DC-DyHead deformable dynamic detection head is constructed, and deformable convolutions are embedded into the spatial perceptual attention of DyHead to enhance its feature modelling ability for anomalies and occluded impurities. We introduce an enhanced InnerMPDIoU loss function to optimise the bounding box regression strategy. This new method addresses issues related to traditional CIoU losses, including excessive penalties imposed on small targets and a lack of sufficient gradient guidance in situations where there is almost no overlap. The results indicate that the average precision (mAP@0.5) of the improved algorithm on the self-made PPR impurity dataset reached 88.6%, which is 2.3% higher than that of the original YOLOv11n, while precision (P) and recall (R) increased by 2.4% and 2.8%, respectively. This study provides a reliable technical solution for the quality inspection of PPR raw materials and serves as a reference for algorithm optimisation in the field of industrial small-target detection. Full article

Cucumber green mottle mosaic virus (CGMMV) represents a serious threat in the production of watermelon. Small RNAs facilitate a mechanism known as RNA interference (RNAi), which regulates gene expression. RNAi technology employs foreign double-stranded RNAs (dsRNAs) to target and reduce the expression levels of specific genes in plants by interfering with their mRNAs. In this study, watermelon plants were treated with dsRNAs of CGMMV MET, IR, and HEL fragments that had been generated in E. coli HT115. We investigated variations in several factors, including viral accumulation, virus-derived small interfering RNAs (vsiRNAs), and symptom severity. MET-dsRNA, IR-dsRNA and HEL-dsRNA dramatically decreased the symptoms of CGMMV in plants in the growth chamber test. Plants treated with viral-derived dsRNA showed a considerable decrease in both virus titers and vsiRNA levels. We also explored the mobility of spray-on dsRNA-derived long dsRNA and discovered that it could be identified in both inoculated leaves and the systemic leaves. IR-dsRNA outperformed MET-dsRNA and HEL-dsRNA in dsRNA therapy. Illumina sequencing of small RNAs from watermelon plants treated with IR-dsRNA and those that were not treated showed that the decreased accumulation of vsiRNAs was consistent with interference with CGMMV infection in systemic leaves. dsRNA-treated plants showed a higher level of 24-nt viral siRNA and lower level of 22-nt viral siRNA accumulation, while 22-nt viral siRNA predominated in untreated plants, indicating that dsRNA treatment improved DCL3 activity. In conclusion, our research provides deeper insights into the mechanism of antiviral RNA interference and confirms the effectiveness of applying dsRNA locally to enhance plant antiviral activity. Full article

Small and medium-sized enterprises (SMEs) are critical contributors to economic growth, innovation, and employment. However, they often struggle in securing external financing. This financial gap mainly arises from perceived risks and information asymmetries creating barriers between SMEs and potential investors. To address this issue, our study proposes a machine learning (ML) framework for predicting the investment readiness (IR) of SMEs. All the models involved in this study are trained using data provided by the European Central Bank’s Survey on Access to Finance of Enterprises (SAFE). We train, evaluate, and compare the predictive performance of nine (9) machine learning algorithms and various ensemble methods. The results provide evidence on the ability of ML algorithms in identifying investment-ready SMEs in a heavily imbalanced and noisy dataset. In particular, the Gradient Boosting algorithm achieves a balanced accuracy of 75.4% and the highest ROC AUC score at 0.815. Employing a relevant cost function economically enhances these results. The approach can offer specific inference to policymakers seeking to design targeted interventions and can provide investors with data-driven methods for identifying promising SMEs. Full article

iPSCs and their derivatives are used to investigate the molecular genetic mechanisms of human diseases, to identify therapeutic targets, and to screen for small molecules. Combining technologies for generating patient-specific iPSC lines and genome editing allows us to create cell models with unique characteristics. We obtained and characterized three iPSC lines by reprogramming peripheral blood mononuclear cells of a patient with Huntington’s disease (HD) using episomal vectors encoding Yamanaka factors. iPSC lines expressed pluripotency marker genes, had normal karyotypes and were capable of differentiating into all three germ layers. The obtained iPSC lines are useful for modeling disease progression in vitro and studying pathological mechanisms of HD, such as ER stress. A transgene of genetically encoded biosensor XBP1-TagRFP was introduced into the iPSCs to visualize ER stress state of cells. The study demonstrated that iPSC-derived medium spiny neurons develop ER stress, though the IRE1-mediated pathway does not seem to be involved in the process. Full article

Background/Objectives: Advances in understanding immune checkpoint pathways and tumor immune biology have enabled the development of immune checkpoint inhibitors (ICIs), particularly targeting the PD-1/PD-L1 axis, which has transformed cancer immunotherapy. While they have shown remarkable success in various cancer types, including melanoma, non-small cell lung cancer, and gastrointestinal malignancies, variability in patient response, immune-related adverse events (irAEs), and resistance mechanisms remain significant. This review aims to evaluate clinical pharmacology, mechanisms of action, resistance pathways, and pharmacogenomic influences shaping interindividual responses to ICIs. Methods: This comprehensive review synthesizes current literature on FDA-approved ICIs, exploring their clinical use, underlying biological mechanisms, and emerging pharmacogenomic data. It also assesses key biomarkers such as tumor mutational burden (TMB), microsatellite instability (MSI), HLA diversity, and epigenetic factors influencing ICI efficacy and safety. Results: We outline key mechanisms contributing to ICI resistance, including T cell dysfunction, altered antigen presentation, and immunosuppressive tumor microenvironment components. Furthermore, we highlight promising pharmacogenomic findings, including single-nucleotide polymorphisms (SNPs) in PD-1/PD-L1 and immune-regulatory genes, offering predictive and prognostic utility. Variability in PD-L1 expression and the role of epigenetic modifications are also addressed as challenges in treatment optimization. Conclusions: Interindividual variability in ICI response underscores the need for biomarker-driven strategies. By integrating pharmacogenomic insights with clinical pharmacology, future approaches may support more personalized and effective use of ICIs. Combination therapies and novel modalities hold promise for overcoming resistance, enhancing therapeutic efficacy, and enabling precision oncology. Full article

Background: The Nerve Growth Factor (NGF) is essential for neuronal survival and function and represents a key therapeutic target for pain and inflammation-related disorders, as well as for neurodegenerative diseases. Small-molecule antagonists of human NGF (hNGF) offer advantages over monoclonal antibodies, including oral availability and reduced immunogenicity. However, their development is often hindered by solubility challenges, necessitating the use of solvents like dimethyl sulfoxide (DMSO). This study investigates whether DMSO directly interacts with hNGF and affects its receptor-binding properties. Methods: Integrative/hybrid computational and experimental biophysical approaches were used to assess DMSO-NGF interaction by combining machine-learning tools and Nuclear Magnetic Resonance (NMR), Fourier Transform Infrared (FT-IR) spectroscopy, Differential Scanning Fluorimetry (DSF) and Grating-Coupled Interferometry (GCI). These techniques evaluated binding affinity, conformational stability, and receptor-binding dynamics. Results: Our findings demonstrate that DMSO binds hNGF with low affinity in a specific yet non-disruptive manner. Importantly, DMSO does not induce significant conformational changes in hNGF nor affect its interactions with its receptors. Conclusions: These results highlight the importance of considering solvent–protein interactions in drug discovery, as these low-affinity yet specific interactions can affect experimental outcomes and potentially alter the small molecules binding to the target proteins. By characterizing DMSO-NGF interactions, this study provides valuable insights for the development of NGF-targeting small molecules, supporting their potential as effective alternatives to monoclonal antibodies for treating pain, inflammation, and neurodegenerative diseases. Full article

The robust detection of small targets is crucial in infrared (IR) search and tracking applications. Considering that many state-of-the-art (SOTA) methods are still unable to suppress various edges satisfactorily, especially under complex backgrounds, an effective infrared small target detection algorithm inspired by modified fast saliency and the weighted guided image filter (WGIF) is presented in this paper. Initially, the fast saliency map modulated by the steering kernel (SK) is calculated. Then, a set of edge-preserving smoothed images are produced by WGIF using different filter radii and regularization parameters. After that, utilizing the fuzzy sets technique, the background image is predicted reasonably according to the results of the saliency map and smoothed or non-smoothed images. Finally, the differential image is calculated by subtracting the predicted image from the original one, and IR small targets are detected through a simple thresholding. Experimental results on four sequences demonstrate that the proposed method can not only suppress background clutter effectively under strong edge interference but also detect targets accurately with a low false alarm rate. Full article

Infrared small target detection (ISTD) means distinguishing small and faint targets from IR images. Small targets typically span only a handful of pixels, lacking distinct texture and clear structural details. For the past few years, deep learning has made big strides in the field of ISTD. Yet, a persistent challenge remains: the lack of high-level semantic information may cause the disappearance of small target features in the network’s deep layers, ultimately impairing detection accuracy. To tackle this problem, we introduce WT-HMFF, an innovative network architecture that combines the Wavelet Transform Convolution (WTConv) module with the Hierarchical Multi-Scale Feature Fusion (HMFF) module to enhance the ISTD algorithm’s performance. WTConv expands the receptive field through wavelet convolution, effectively capturing global contextual information while preserving target shape characteristics. The HMFF module enables the efficient fusion of shallow and deep features, maintaining the high resolution of deep feature maps and preventing the disappearance of small target features. We have tested it out on public datasets, SIRST and IRSTD-1k, and validated the superiority and robustness of WT-HMFF compared to other methods. Full article

With the rapid development of the unmanned aerial vehicle (UAV) industry and applications, the integration of UAVs into daily life has increased significantly. However, this growing presence raises security concerns, leading to the emergence of anti-UAV technologies. Most existing anti-UAV systems rely on object detection techniques. Yet, these methods often struggle to detect small-sized UAVs accurately. Semantic segmentation, which predicts object locations at the pixel level, offers improved localization for such small targets. Due to the lack of existing datasets for anti-UAV semantic segmentation, we propose a new dataset comprising both infrared (IR) and visible light images. Our dataset includes a total of 605,045 paired UAV images and corresponding segmentation masks. To enhance object diversity and improve model robustness, the dataset integrates multiple existing sources. In addition to the dataset, we evaluate the performance of several baseline models on the semantic segmentation task. We also propose a lightweight model to demonstrate the feasibility of real-time UAV localization using semantic segmentation on VL and IR data. Full article