Rosmarinus officinalis L., a member of the family Lamiaceae, is regarded as the spice with the highest antioxidant activity. In this study, the transmission electron microscopy, X-ray diffraction, and fourier transform infrared spectroscopy (FTIR) physicochemical characteristics of the nanostructure of gamma-irradiated rosemary were investigated. The particle size distribution of the gamma-irradiated rosemary prepared under irradiation at 30 kGy in a Cobalt-60 irradiator exhibited a very narrow size distribution with average size of 70 nm. The results showed that irradiated (30 kGy) and crude rosemary had similar patterns of FTIR spectra, typical of phenol compound, without any notable changes in the key bands and functional groups status. Rosemary irradiated with 50 kGy and 10 kGy showed the highest and lowest crystallinity, respectively. Rosemary crystallinity of irradiated samples was lower compared with the nonirradiated sample. Therefore, 30 kGy can be optimum for the synthesis of nanoparticles, average size of 70 nm, with low crystallinity and without any notable change in key bands compared to nonirradiated samples.

This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive L₁ sparsity to decompose a noisy single-channel mixture. The proposed adaptive L₁ sparsity CMF algorithm encodes the spectra pattern and estimates the phase of the original signals in time-frequency representation. Their features enhance the temporal decomposition process efficiently. The support vector machine (SVM) based one versus one (OvsO) strategy was applied with a mean supervector to categorize the demixed sound into the matching sound-event class. The first step of the multi-class MSVM method is to segment the separated signal into blocks by sliding demixed signals, then encoding the three features of each block. Mel frequency cepstral coefficients, short-time energy, and short-time zero-crossing rate are learned with multi sound-event classes by the SVM based OvsO method. The mean supervector is encoded from the obtained features. The proposed method has been evaluated with both separation and classification scenarios using real-world single recorded signals and compared with the state-of-the-art separation method. Experimental results confirmed that the proposed method outperformed the state-of-the-art methods.

Background: Medulloblastoma is the most common malignant brain tumor in children, classified into four primary molecular subgroups: WNT, SHH, Group 3, and Group 4, each exhibiting significant molecular heterogeneity and varied survival outcomes. Accurate classification of these subgroups is crucial for optimizing treatments and improving patient outcomes. DNA methylation profiling is a promising approach for subgroup classification; however, its application is still evolving, with ongoing efforts to improve accessibility and develop more accurate classification methods. Objectives: This study aims to develop a supervised machine learning-based framework using Illumina 450K methylation data to classify medulloblastoma into seven molecular subgroups: WNT, SHH-Infant, SHH-Child, Group3-LowRisk, Group3-HighRisk, Group4-LowRisk, and Group4-HighRisk, incorporating age and risk factors for enhanced subgroup differentiation. Methods: The proposed model leverages six metagenes, capturing the underlying patterns of the top 10,000 probes with the highest variances from Illumina 450K data, thus enhancing methylation data representation while reducing computational demands. Results: Among the models evaluated, the SVM achieved the highest performance, with a mean balanced accuracy 98% and a macro-averaged AUC of 0.99 in an independent validation. This suggests that the model effectively captures the relevant methylation patterns for medulloblastoma subgroup classification. Conclusions: The developed SVM-based model provides a robust framework for accurate classification of medulloblastoma subgroups using DNA methylation data. Integrating this model into clinical decision making could enhance subgroup-directed therapies and improve patient outcomes.

Gene enhancers control when and where genes switch on, yet their sequence diversity and tissue specificity make them hard to pinpoint in colorectal cancer. We take a sequence-only route and fine-tune DNABERT-2, a transformer genomic language model that uses byte-pair encoding to learn variable-length tokens from DNA. Using assays curated via the Johnston Cancer Research Centre at Queen's University Belfast, we assembled a balanced corpus of 2.34 million 1 kb enhancer sequences, applied summit-centered extraction and rigorous de-duplication including reverse-complement collapse, and split the data stratified by class. With a 4096-term vocabulary and a 232-token context chosen empirically, the DNABERT-2-117M classifier was trained with Optuna-tuned hyperparameters and evaluated on 350742 held-out sequences. The model reached PR-AUC 0.759, ROC-AUC 0.743, and best F1 0.704 at an optimized threshold (0.359), with recall 0.835 and precision 0.609. Against a CNN-based EnhancerNet trained on the same data, DNABERT-2 delivered stronger threshold-independent ranking and higher recall, although point accuracy was lower. To our knowledge, this is the first study to apply a second-generation genomic language model with BPE tokenization to enhancer classification in colorectal cancer, demonstrating the feasibility of capturing tumor-associated regulatory signals directly from DNA sequence alone. Overall, our results show that transformer-based genomic models can move beyond motif-level encodings toward holistic classification of regulatory elements, offering a novel path for cancer genomics. Next steps will focus on improving precision, exploring hybrid CNN-transformer designs, and validating across independent datasets to strengthen real-world utility.