Search Results (58)

The considerable HIV-1 genetic diversity has several implications for viral adaptive and evolutionary capabilities. Its genetic diversity is due to its high mutational rates derived from the error-prone viral reverse transcriptase activity, which generates highly heterogeneous viral populations. Moreover, genetic diversity can also increase due to intra- or intersubtype viral genomic recombination following multiple infections. This study examines HIV-1 intersubtype recombinant viruses and their increased genomic diversity over a 12-month period in five individuals from São Paulo state, Brazil. We collected peripheral blood mononuclear cells once every three months from selected participants at five distinct visits. Molecular clones of 1.15 Kbp fragments of the Pol polyprotein, spanning the protease and a portion of the reverse transcriptase (RT) genes, were generated by bulk PCR. Pol sequences were used for evolutionary analysis, including phylogenetics (using TnT), genetic diversity (using Highlighter), and hypermutation frequency (using Hypermut). Recombination detection experiments were conducted with a jumping profile-hidden Markov model (jpHMM), SimPlot++, and RDP5. We observed great genetic diversity and frequent recombination events in all patients. Furthermore, most of the patients presented hypermutations. These findings highlight the dynamic nature of HIV-1 genetic diversity, driven by frequent recombination and hypermutation, which can accelerate viral adaptation and diversification, underscoring the challenges for treatment, prevention, and disease control. Full article

Vietnam faces a hyperendemic burden of hepatitis B virus (HBV) infection, but the prevalence of occult HBV infection (OBI) and its underlying molecular mechanisms in healthy populations remain poorly understood. This study aimed to characterize the serological and molecular HBV profile of a healthy Vietnamese adult cohort in Southern Vietnam. We assessed the prevalence of occult HBV infection (OBI) and HBsAg-positivity (serving as a proxy for probable chronic infection). In this cross-sectional study, 397 healthy adults from Southern Vietnam underwent serological screening for HBsAg, anti-HBs, and anti-HBc. All participants were screened for HBV DNA using a high-sensitivity PCR assay (LOD ≥ 5 IU/mL). For all viremic cases, the full Pre-S/S region was sequenced to determine genotype and characterize escape mutations. We uncovered a high prevalence of both HBsAg-positivity (17.6%) and OBI (9.3% HBsAg-negative, HBV DNA-positive). Serological analysis revealed a massive, age-dependent reservoir of past exposure (63.7% anti-HBc) characterized by a high and increasing prevalence of the anti-HBc only profile (31.5%), a key serological marker for OBI. This trend contrasted sharply with a steep age-related decline in protective anti-HBs. The viral landscape was dominated by genotypes B (73.8%) and C (26.2%), with sub-genotypes B4 and C1 being the most prevalent. Critically, individuals with OBI carried a significantly higher burden of S gene escape mutations compared to those with HBsAg-positivity (p < 0.001). Canonical escape variants, including sG145R (21.6%), sK141R/T/E/Q (24.3%), and sT116N/A/I/S (18.9%), were exclusively or highly enriched in the OBI group. A LASSO-logistic model based on this mutational profile successfully predicted occult infection with high accuracy (AUC = 0.83). A substantial hidden reservoir of occult HBV infection exists within the healthy adult population of Vietnam, driven by a high burden of S gene escape mutations. These findings highlight the significant limitations of conventional HBsAg-only screening. They also underscore the need for comprehensive molecular surveillance to address the true scope of HBV viremia, hopefully enabling a reduction in hidden transmission of clinically significant viral variants. Full article

Background/Objectives: Retrocochlear auditory dysfunctions (RADs), including auditory neuropathy (AN) and auditory processing disorders (APD), encompass disorders characterized by impaired auditory processing beyond the cochlea. This narrative review critically examines their distinguishing features, synthesizing recent advances in classification, pathophysiology, clinical presentation, and treatment. Methods: This narrative review involved a comprehensive literature search across major electronic databases (e.g., PubMed, Scopus) to identify and synthesize relevant studies on the classification, diagnosis, and management of AN and APD. The goal was to update the view on etiologies (genetic/non-genetic) and individualized rehabilitative strategies. Diagnosis relies on a comprehensive assessment, including behavioral, electrophysiological, and imaging tests. Rehabilitation is categorized into bottom-up and top-down approaches. Results: ANSD is defined by neural desynchronization with preserved outer hair cell function, resulting in abnormal auditory brainstem responses and poor speech discrimination. The etiologies (distal/proximal) influence the prognosis for interventions, particularly cochlear implants (CI). APD involves central processing deficits, often with normal peripheral hearing and heterogeneous symptoms affecting speech perception and localization. Rehabilitation is multidisciplinary, utilizing bottom-up strategies (e.g., auditory training, CI) and compensatory top-down approaches. Remote microphone systems are highly effective in improving the signal-to-noise ratio. Conclusions: Accurate diagnosis and personalized, multidisciplinary management are crucial for optimizing communication and quality of life. Evidence suggests that combined bottom-up and top-down interventions may yield superior outcomes. However, methodological heterogeneity limits the generalizability of protocols, highlighting the need for further targeted research. Full article

Accurate species identification in blackberries (Rubus spp.) is difficult because of morphological similarity and frequent hybridization. We studied 56 wild accessions from the Sirius Federal Territory (Russia), representing coastal and foothill ecosystems of the Black Sea region. Multilocus DNA barcoding with the plastid rbcL gene and nuclear ITS1 and ITS2 regions revealed signals of hybridization and hidden diversity. The rbcL marker showed low variation, grouping most accessions into two clusters with several singletons, which limited its use for distinguishing species. In contrast, ITS1 and ITS2 showed higher variation, forming six clusters and eight singletons, and allowed for clear separation of taxa such as Rubus caesius L., R. irritans Focke, and R. amabilis Focke. Accession 3 carried a raspberry (closely to R. corchorifolius L.fil) plastid haplotype, pointing to a hybrid origin. We also found groups of nearby plants with identical mutations, which likely reflect clonal spread with fixed somatic changes or the persistence of recent hybrid lineages. At the same time, accessions collected up to 140 km apart did not form separate clusters, showing weak geographic structuring along the coast. The results demonstrate that multilocus barcoding can reveal not only species boundaries but also evolutionary processes among Rubus such as hybridization, clonal propagation, and early stages of speciation. Full article

Large background noise, difficulty in feature extraction, and low parameter-optimization efficiency of diagnosis models are key challenges in rolling bearing fault diagnosis. To address these issues, this paper proposes a fault diagnosis framework that combines Singular Spectrum Decomposition (SSD) with a Multi-Strategy Enhanced Cuckoo Search (MS-CS) algorithm to optimize an Extreme Learning Machine (ELM). First, the raw vibration signal is decomposed via SSD and each intrinsic component’s energy contribution is computed; components whose cumulative energy exceeds 90% are retained and reconstructed, thereby effectively suppressing noise while preserving critical fault features. Next, Multiscale Permutation Entropy (MPE) is extracted from the reconstructed signal to form a high-discriminability feature set. To overcome the traditional Cuckoo Search algorithm’s tendency to become trapped in local optima and its slow convergence, Cauchy mutation and adaptive Levy flight strategies are introduced to enhance global exploration and local exploitation. Finally, the improved MS-CS algorithm is employed to optimize the ELM’s input weights and hidden-layer biases, yielding a high-precision diagnostic model. Experimental results on benchmark bearing data demonstrate an average fault recognition rate of 96%, representing improvements of 6.67% over the conventional CS-ELM and 18% over the unoptimized ELM. These findings confirm the proposed method’s effectiveness and robustness in practical engineering applications. Full article

Lung cancer (LC) remains a leading cause of cancer mortality worldwide, where accurate and early identification of gene mutations such as epidermal growth factor receptor (EGFR) is critical for precision treatment. However, machine learning-based radiomics approaches often face challenges due to the small and imbalanced nature of the datasets. This study proposes a comprehensive framework based on Generic Sparse Regularized Autoencoders with Kullback–Leibler divergence (GSRA-KL) to generate high-quality synthetic radiomics data and overcome these limitations. A systematic approach generated 63 synthetic radiomics datasets by tuning a novel kl_weight regularization hyperparameter across three hidden-layer sizes, optimized using Optuna for computational efficiency. A rigorous assessment was conducted to evaluate the impact of hyperparameter tuning across 63 synthetic datasets, with a focus on the EGFR gene mutation. This evaluation utilized resemblance-dimension scores (RDS), novel utility-dimension scores (UDS), and t-SNE visualizations to ensure the validation of data quality, revealing that GSRA-KL achieves excellent performance (RDS > 0.45, UDS > 0.7), especially when class distribution is balanced, while remaining competitive with the Tabular Variational Autoencoder (TVAE). Additionally, a comprehensive statistical correlation analysis demonstrated strong and significant monotonic relationships among resemblance-based performance metrics up to moderate scaling (≤1.0*), confirming the robustness and stability of inter-metric associations under varying configurations. Complementary computational cost evaluation further indicated that moderate kl_weight values yield an optimal balance between reconstruction accuracy and resource utilization, with Spearman correlations revealing improved reconstruction quality (MSE $\rho =-0.78$, $p<0.001$) at reduced computational overhead. The ablation-style analysis confirmed that including the KL divergence term meaningfully enhances the generative capacity of GSRA-KL over its baseline counterpart. Furthermore, the GSRA-KL framework achieved substantial improvements in computational efficiency compared to prior PSO-based optimization methods, resulting in reduced memory usage and training time. Overall, GSRA-KL represents an incremental yet practical advancement for augmenting small and imbalanced high-dimensional radiomics datasets, showing promise for improved mutation prediction and downstream precision oncology studies. Full article

To capture bus passenger flow fluctuations and address the problems of slow convergence and high error in machine learning parameter optimization, this paper develops an improved Whale Optimization Algorithm (IWOA) integrated with a Bidirectional Gated Recurrent Unit (BiGRU). First, a Logistic–Tent chaotic mapping is introduced to generate a diverse and high-quality initial population. Second, a hybrid mechanism combining elite opposition-based learning and Cauchy mutation enhances population diversity and reduces premature convergence. Third, a cosine-based adaptive convergence factor and inertia weight strategy improve the balance between global exploration and local exploitation. Based on the correlation analysis between bus passenger flow and weather condition data in Harbin, and combined with the fluctuation characteristics of bus passenger flow, the data were divided into windows with a 7-day weekly cycle and processed by fuzzy information granulation to obtain three groups of fuzzy granulated window data, namely LOW, R, and UP, representing the fluctuation trend and spatial characteristics of bus passenger flow. The IWOA was employed to optimize and solve parameters such as the hidden layer weights and bias vectors of the BiGRU, thereby constructing a bus passenger flow fluctuation trend and spatial prediction model based on FIG-IWOA-BiGRU. Simulation experiments with 21 benchmark functions and real bus data verified its effectiveness. Results show that IWOA significantly improves optimization accuracy and convergence speed. For bus passenger flow forecasting, the average MAE, RMSE, and MAPE of LOW, R, and UP data are 2915, 3075, and 8.1%, representing improvements over existing classical models. The findings provide reliable decision support for bus scheduling and passenger travel planning. Full article

Although cannabinoids have proven therapeutic benefits, they are increasingly known for their capacity to disturb cardiac electrophysiology, particularly in individuals with hidden genetic issues such as channelopathies. This review consolidates molecular, clinical, epidemiological, and forensic findings linking cannabinoid exposure to arrhythmias and sudden cardiac death. It examines how phytocannabinoids, synthetic analogs, and endocannabinoids influence calcium and potassium currents through cannabinoid receptor-dependent and -independent pathways, affect autonomic regulation, and contribute to adverse conditions such as oxidative stress and inflammation in heart tissue. Genetic variants in key genes linked to SCD (SCN5A, KCNH2, KCNQ1, RYR2, and NOS1AP) can reduce repolarization reserve, transforming otherwise subclinical mutations into lethal substrates when combined with cannabinoid-induced electrical disruptions. Forensic research highlights the importance of comprehensive toxicological testing and postmortem genetic analysis in distinguishing between actual causes and incidental findings. There is an urgent need to re-evaluate the cardiovascular safety of cannabinoids, and this is underscored by the findings presented. The merging of molecular, clinical, and forensic evidence reveals that cannabinoid exposure—especially from high-potency synthetic analogs—can reveal latent channelopathies and precipitate fatal arrhythmias. Accordingly, this review advocates for a paradigm shift toward personalized risk stratification. If genetic screening is integrated with ECG surveillance and controlled cannabinoid dosing, risk assessment can be personalized. Ultimately, forensic and epidemiological data highlight the heart’s vulnerability, emphasizing its role as a target of cannabinoid toxicity and as a crucial aspect of public health monitoring. Full article

With the proliferation of satellite internet systems, such as Starlink and OneWeb, ground terminals have become critical for ensuring end-user connectivity. However, the security of Satellite Internet Ground Terminals (SIGTs) remains underexplored. These Linux-based embedded systems are vulnerable to advanced attacks due to limited source code access and immature protection mechanisms. This paper presents MAFUZZ, an adaptive fuzzing framework guided by neural network gradients to uncover hidden vulnerabilities in SIGT binaries. MAFUZZ uses a lightweight machine learning model to identify input bytes that influence program behavior and applies gradient-based mutation accordingly. It also integrates an adaptive Havoc mechanism to enhance path diversity. We compare MAFUZZ with NEUZZ, a neural fuzzing tool that uses program smoothing to guide mutation through a static model. Our experiments on real-world Linux binaries show that MAFUZZ improves path coverage by an average of 17.4% over NEUZZ, demonstrating its effectiveness in vulnerability discovery and its practical value for securing satellite terminal software. Full article

According to the guidelines of the American Society of Clinical Oncology (ASCO) and the European Society of Medical Oncology (ESMO), the most significant genetic factor in the diagnosis and treatment of breast cancer is the mutation status of the BRCA1 and BRCA2 genes. Additional genes with a significant influence on cancer risk were selected for genetic panel screening. For these genes, the disease risk score was predicted to be greater than 20%. In clinical practice, it is observed that rare genetic variants have a significant impact in young patients, characterized by increased pathogenesis and a poorer overall prognosis. The ability to predict the potential effects of these rare variants may reveal important information regarding possible phenotypes and may also provide new insights leading to more efficacious treatments and overall improved clinical management. This paper presents the case of a 38-year-old woman with bilateral breast cancer who is likely a carrier of a pathogenic point mutation in the LZTR1 gene (LZTR1: c.1260+1del variant). With this clinical case report herein described, we intend to display the usefulness of performing detailed molecular tests in the field of genetic diagnostics for patients with breast cancer. Understanding the pathogenesis of hereditary cancer development, which is more predictable and reliable than that of sporadic tumors, will allow for the discovery of hitherto hidden intrinsic signaling pathways, facilitating replicable experimentation and thereby expediting the discovery of novel therapeutic treatments. Full article

Recently, it has been proposed that plant melatonin receptors belong to the superfamily of G protein-coupled receptors (GPCRs). However, a detailed description of the phylogeny, protein structure, and binding properties of melatonin, which is still lacking, can help determine the signaling and function of this compound. Melatonin receptor homologs (PMTRs) were identified in 90 Viridiplantae sensu lato proteomes using profile Hidden Markov Models (HMM), which yielded 174 receptors across 87 species. Phylogenetic analysis revealed an expansion of PMTR sequences in angiosperms, which were grouped into three clades. Docking studies uncovered a conserved internal melatonin-binding site in PMTRs, which was analogous to the site in human MT1 receptors. Binding affinity simulations indicated this internal site exhibits stronger melatonin binding compared to a previously reported superficial pocket. Ligand–receptor interaction analysis and alanine scanning highlighted a major role of hydrophobic interactions, with hydrogen bonds contributing predominantly at the internal site, while non-interacting charged residues stabilize the binding pocket. Tunnel and ligand transport simulations suggested melatonin moves favorably through the internal cavity to access the binding site. Also, we presented for the first time details of these pockets in a non-model species, Capsicum chinense. Taken together, the structural analyses presented here illustrate opportunities and theoretical evidence for performing structure–function studies via mutations in specific residues within the proposed new melatonin-binding site in PMTRs, shedding light on their role in plant melatonin signaling. Full article

Recent advancements in agricultural mobile robots (agribots) have enabled the execution of critical tasks such as crop inspection, precision spraying, and selective harvesting. While agribots show significant potential, conventional path-planning algorithms suffer from three limitations: (1) inadequate dynamic obstacle avoidance, which may compromise operational safety, (2) premature convergence to local optima, and (3) excessive energy consumption due to suboptimal trajectories. To overcome these challenges, this study proposes an enhanced Dynamic Genetic Algorithm—Ant Colony Optimization (DGA-ACO) framework. It integrates a 2D risk-penalty mapping model with dynamic obstacle avoidance mechanisms, improves max–min ant system pheromone allocation through adaptive crossover-mutation operators, and incorporates a hidden Markov model for accurately forecasting obstacle trajectories. A multi-objective fitness function simultaneously optimizes path length, energy efficiency, and safety metrics, while genetic operators prevent algorithmic stagnation. Simulations in different scenarios show that DGA-ACO outperforms Dijkstra, A*, genetic algorithm, ant colony optimization, and other state-of-the-art methods. It achieves shortened path lengths and improved motion smoothness while achieving a certain degree of dynamic obstacle avoidance in the global path-planning process. Full article

To assess structural sealant damage in hidden frame glass curtain walls (HFGCWs) during service, damage states were simulated by controlled cutting with varying incision lengths. Quantitative identification challenges were investigated through natural frequency and curvature modal difference (CMD) analyses at multiple test points. The results indicate that natural frequency decreases with increasing damage severity, while the first-order curvature mode difference (FCMD) exhibits localized abrupt changes in damaged regions. Boundary modes provide more targeted and accurate damage identification. The peak value of the FCMD mutation region enables precise damage localization. A quantitative damage identification threshold of 0.1205 was derived from FCMD distribution characteristics in boundary regions. By leveraging boundary mode features, modal testing efficiency is optimized, reducing the required acquisition nodes and effectively guiding structural sealant damage detection in engineering applications. Full article

In the past, only Oguchi disease was reported as a hereditary retinal disease from Japan. Dr. Chuuta Oguch was a Professor of Nagoya University in Japan. During the past 40 years, four new clinical entities in hereditary retinal disorders have been detected by the Miyake group from Nagoya, Japan. All disorders show essentially normal fundi, and the diagnosis was made mainly by the analysis of an electroretinogram (ERG). Gene mutations are detected in three of them. Bipolar cell (BP) dysfunction syndrome: Congenital stationary night blindness (CSNB) with negative ERG (a-wave is larger than b-wave) was named as the Schubert–Bornschein type in 1952 and considered to be an independent clinical entity. In 1986, Miyake group classified ninety patients with the Schubert–Bornschein type into two types (complete and incomplete type). The complete type of CSNB (CSNB1) showed no rod function, but the incomplete type CSNB (CSNB2) showed remaining rod function in both subjective dark adaptation and rod ERG. In order to investigate the pathogenesis, these two types of CSNB were analyzed by comparing the monkey ERGs using different glutamate analogs to the retina. The ERG analysis demonstrated that CSNB1 has a complete functional defect in the ON type BP, while CSNB2 has incomplete functional defects in the ON and OFF type BP in both rod and cone visual pathways. Evidence of several different genetic heterogeneities was reported in both diseases, indicating CSNB1 and CSNB2 are independent clinical entities. Another entity, showing total complete defect of both ON and OFF BP, was detected in 1974 and was reported by Miyake group in a brother and younger sister, showing severe photophobia, nystagmus, extremely low visual acuity, and disappearance of color vision (total color blindness). This disorder is a congenital stational condition, and subjective visual functions were severely deteriorated from birth but remained unchanged through life. This disease was termed “Total complete bipolar cell dysfunction syndrome (CSNB3)”. The relationship between BP and subjective visual function was unknown. These three kinds of BP diseases can provide information on how BP relates to subjective visual functions. Occult macular dystrophy (OMD): Occult macular dystrophy (OMD) was discovered by Miyake group in 1989. This disease shows an unusual, inherited macular dystrophy characterized by progressive decrease visual acuity due to macular dysfunction, but the fundus and fluorescein angiography are essentially normal. The full-field rod and cone ERG do not show any abnormality, but the focal macular ERG (FERG) or multifocal ERG is abnormal and the only method for diagnosis. Many pedigrees of this disorder suggest autosomal dominant heredity, showing a genetic mutation of RP1L1. This disease was termed “occult macular dystrophy”. “Occult” means “hidden from sight”. Recently, it has been called “Miyake disease”. Full article

According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model. This algorithm includes the introduction of a mutation operator in the gray wolf algorithm to improve the search ability of the algorithm, and, at the same time, we introduce the PSO algorithm idea. The improved fusion algorithm is used as a learning algorithm to optimize the BP neural network, and the optimized network is used to process and predict the data collected from temperature and gas sensors, which effectively improves the accuracy of fire prediction. The sensor measurements were compared with the actual values to verify the effectiveness of the GWO-PSO-optimized BP neural network model. Once experimentally verified, the improved GWO-PSO algorithm achieves a correlation coefficient R of 0.96929, a prediction accuracy rate of 96.10%, and a prediction error rate of only 3.9%, while the system monitors an accurate early warning rate of 96.07%, and the false alarm and omission rates are both less than 5%. This study can detect cotton picker fires in real time and provide timely warnings, which provides a new method for the accurate detection of fires during the field operation of cotton pickers. Full article