Search Results (1,506)

Micro-expressions, subtle and often asymmetric facial movements, play a pivotal role in nonverbal emotional communication. Addressing the core challenges of temporal misalignment, fragmented feature extraction, and slow real-time detection in micro-expression recognition (MER), we propose a novel dual-branch spatiotemporal model for dynamic sequence MER. Leveraging MediaPipe for 3D facial feature extraction and Dynamic Time Warping (DTW) for sequence alignment, our method nonlinearly maps variable-length sequences to a fixed length. A hybrid data augmentation technique enhances model robustness, while the dual-branch network simultaneously captures local spatial features and global temporal dynamics. Experimental results on the CASMEII dataset demonstrate state-of-the-art performance with 99.22% accuracy, along with a significant improvement in real-time detection speed. This approach holds substantial practical value for applications in deception detection, mental health assessment, and human–computer interaction. Full article

Background: Nanopore sequencing produces ionic current signals that are sensitive to chemical modifications in DNA and RNA molecules. However, accurate modification detection remains challenging due to limited labeled data and variability across experimental conditions. Methods: We present a scalable unsupervised framework for modification discovery that learns reference signal distributions from unmodified sequences using a CNN–Transformer variational autoencoder (VAE). The model is trained on large-scale data via streaming sampling and k-mer-aware soft balancing to ensure robust signal representation. At inference, candidate nucleotides are scored using the VAE reconstruction error, and read-level signals are aggregated to produce site-level modification evidence. Results: On controlled DNA oligonucleotide datasets, models trained on unmodified sequences achieve strong discrimination when evaluated on modified oligos. In contrast, performance decreases in cell line samples when models trained on unmodified whole-genome-amplified (WGA) DNA and in vitro-transcribed (IVT) RNA are evaluated on natively modified (5mC/m6A) data, reflecting the impacts of biological noise and heterogeneity. Despite reduced classification accuracy, site-level anomaly score profiles exhibit peak-like patterns that correspond to known modification-enriched regions. Conclusions: These findings demonstrate the feasibility of large-scale unsupervised reference modeling for de novo modification detection, while underscoring the challenges in translating models built from synthetic oligo datasets into robust genome-wide modification detection. Full article

Objective: Deep Brain Stimulation (DBS) has been consolidated as a valid therapeutic option for advanced Parkinson’s disease (PD). The identification of specific targets can be achieved through different methods, including conventional direct and indirect methods. The aim of our multicentric study is to provide a comparison between these traditional methods and artificial intelligence (AI) in the ascertainment of the ideal targets. Materials and Methods: A total of eight patients, who received bilateral STN (subthalamic nucleus) DBS implantation between 2022 and 2023 were analyzed. Target coordinates were calculated based on the Schaltenbrand and Wahren atlases and the AI using the RebrAIn system during the planning phase; intraoperatively, the targets were either confirmed or adjusted according to microelectrode recordings (MERs). The differences in the three Cartesian axes of stereotactic coordinates (X, Y, and Z) according to these methods were evaluated and compared through non-parametric ANOVA Friedman test. Results: The results revealed significant agreement in the lateral–lateral coordinates (X, X′, X″), indicating stability in target determination along this axis across the methods. However, more substantial discrepancies were observed in the antero-posterior and cranio-caudal coordinates, suggesting lower consistency between the examined methodologies. Conclusions: Our preliminary study results suggest that, despite the challenges posed by interindividual anatomical variability and the limitations of imaging techniques, artificial intelligence has shown comparable values on the lateral–lateral X coordinates. The accuracy of predictive targeting using machine learning models needs to be validated by further studies, but the preliminary results appear to indicate a potential promising role for artificial intelligence in integrating the preoperative workflow. Full article

Background/Objectives: Developing novel strategies to combat respiratory infections caused by multidrug-resistant “priority pathogens” like the ESKAPEE Pseudomonas aeruginosa and Staphylococcus aureus is an urgent priority. Methods: We investigated two shortened variants of the proline-rich antimicrobial peptide (PrAMP) B7-005, B7-006 (15-mer) and B7-007 (13-mer). Evaluation included MIC assays against laboratory and clinical multidrug-resistant isolates, mechanistic studies of membrane permeabilization, cytotoxicity testing on BEAS-2B bronchial epithelial cells, and proteolytic stability assays in human elastase and sputum. Results: Despite their reduced size, lower positive charge, and decreased proline content, both variants retained full antimicrobial activity against clinical pathogens with consistent MIC values ≤ 25 µM. These variants exhibit membrane permeabilization in P. aeruginosa but may also relay on a hybrid mode of action involving also intracellular targets. Notably, B7-006 and B7-007 displayed low cytotoxicity compared to the lytic peptide BMAP-18. While B7-007 showed greater susceptibility to proteolytic degradation than its parent B7-005, it preserved partial integrity during the initial hours of exposure. Conclusions: Overall, these findings demonstrate that the B7 scaffold tolerates substantial truncation while preserving potency and selectivity, identifying a minimal 13-amino-acid active core. This work provides critical insights into structure–activity relationships and supports the development of compact, mechanistically versatile antimicrobial peptides to address the growing threat of multidrug-resistant respiratory pathogens. Full article

Given the rapid mutation and high transmissibility of coronaviruses, especially SARS-CoV-2, comparative genomic studies are crucial for understanding viral evolution, transmission dynamics, and therapeutic development. In prior work, we analyzed and compared the spectral distribution patterns of various k-mer subsets across 920 genome sequences, spanning from primates to prokaryotes. This revealed an evolutionary mechanism in genome sequences, indicating the presence of both CG and TA-specific selection modes. In the present study, we further investigate the specific selection modes in coronavirus genomic sequences by examining the intrinsic distribution rules of 32 XYi 6-mer subset spectra. Our results show that coronavirus genomes exhibit only the CG-specific selection mode, with no evidence of TA-specific selection. Using the CG-specific selection mode, we identified CG1 6-mers as the fundamental subset underlying coronavirus genome evolution. To validate the CG1 subset, we constructed phylogenetic relationships for a set of coronaviruses and SARS-CoV-2 variant genomes. Comparative analysis confirmed that the resulting phylogenetic relationships align more closely with established knowledge. This study thus provides a theoretical framework for inferring phylogenetic relationships at the whole-genome level. Full article

This study evaluated the performance of Fourier-transform infrared (FTIR) spectroscopy for identifying spectral signatures associated with two key traits of Listeria monocytogenes: serogroup classification and biofilm-forming capacity. A total of 100 strains, previously serogrouped by PCR and categorized as high, intermediate, or low biofilm producers, were analyzed. Whole-genome sequencing was performed, and comparative genomics was conducted at core-genome, pangenome, and whole-genome (k-mer) levels to determine which genomic representation best reflected the phenotypes. Strains were typed using Fourier-Transform Infrared (FTIR Biotyper^® system from Bruker Daltonics GmbH and Co., Bremen, Germany) with five technical replicates. Spectral data from the polysaccharide region (1300–800 cm⁻¹) were extracted and used to train twelve statistical models within a machine learning pipeline combined with cross-validation to predict four serogroups and three biofilm clusters from 501 spectral variables. Genomic analyses showed strong concordance between population structure and serogroup, whereas biofilm formation displayed only weak genomic association, explaining less than 0.1% of genomic variance (PERMANOVA R² ≤ 0.001). Penalized discriminant analysis achieved the highest performance for serogroup prediction (overall accuracy 97.2%), while the k-nearest neighbor model performed best for biofilm prediction (74.8%). Two dedicated R Shiny applications were developed to facilitate model use. Overall, FTIR spectroscopy coupled with machine learning can provide a rapid and cost-effective alternative to PCR, genomic analyses, and in vitro assays for phenotypic trait prediction. Full article

In 2024 Kenya had a population of 4.78 million camels that contributed to the livelihoods of pastoralist communities in northern Kenya. Previous studies in Kenya, Saudi Arabia and eastern Africa demonstrated high seroprevalence of Middle East respiratory syndrome coronavirus (MERS-CoV)-specific antibodies in dromedary camels, as well as sporadic transmission of MERS-CoV from camels to humans. Based on the MERS-CoV data and the very close contact between owners and their camels in northern Kenya, we speculated that camels may also transmit other zoonotic viruses, such as Rift Valley fever virus (RVFV). In this study, 493 camel and 197 human sera were collected in Marsabit, Kenya, through a cross-sectional survey in 2018 and analyzed for the presence of RVFV IgG antibodies using a laboratory-developed indirect enzyme-linked immunosorbent assay (ELISA). Overall, 15.6% of camels and 7.6% of humans were RVFV IgG-positive; IgG-positive camels were predominantly females in large population herds and IgG-positive humans were engaged in farming-related activities and were greater than 18 years old. Of the eight location groups sampled, two had high camel (site 2 and site 6) and two had high human (site 5 and site 6) RVFV seropositivity rates. These data suggest that camelids, such as dromedary camels, may serve as amplifying hosts for vector-borne zoonotic diseases, such as RVFV, and that humans with frequent farming and camel meat, milk, or camel product contact may have increased risk for RVFV exposure or infection. Full article

To date, Lyme vaccine development has largely overlooked the vaccinee’s human leukocyte antigen (HLA) genetic makeup on which antibody production critically depends. Here, we evaluated in silico the predicted binding affinities of 192 HLA-II alleles with all 15-mer peptide sequences of five Borrelia burgdorferi proteins to identify peptides with strong binding affinity, as they would be the best candidates for antibody production in response to vaccination. We found the following: (a) 226 of the 1067 peptides tested (21.2%) were found to bind strongly to HLA-II molecules; (b) decorin-binding protein A had the greatest number of strongly binding peptides; and (c) 69 HLA-II alleles (primarily of the DRB1 gene) bound with strong affinity to peptides from Borrelia burgdorferi proteins. Finally, we tested for possible susceptibility to autoimmunity by any one of the 226 peptides above by searching for their occurrence in ~84,000 proteins of the human proteome and found overlap with only two 8-mer peptide sequences (embedded within the 226 15-mer peptides), neither of which was characterized by strong binding to HLA-I, suggesting a reduced likelihood of autoimmunity. These findings emphasize the importance of a personalized vaccine approach based on the vaccinee’s human leukocyte antigen genetic makeup and offer specific vaccine-candidate peptides that are predicted to maximize vaccine effectiveness and safety. The results of this computational study provide novel directions for future development of Lyme vaccines. Full article

Melanoma is characterized by a high mutational burden making it an established model for studying tumor neoantigens and developing strategies for personalized immunotherapy. In this study, a reproducible bioinformatics pipeline was developed and implemented for the identification and prioritization of candidate neoantigens derived from non-synonymous somatic mutations in melanoma, using genomic data from the MSK-IMPACT cohort (mel-mskimpact-2020; n = 696) and comparative reference information from TCGA-SKCM. From the somatic mutation annotation file (MAF), 16,311 non-synonymous mutations were filtered, from which 50,480 mutant 8–11-mer peptides were generated using a sliding-window approach centered on the mutated position. Peptide–HLA class I binding affinity was predicted using MHCflurry 2.0 across six representative alleles (HLA-A*02:01, HLA-A*24:02, HLA-B*35:01, HLA-B*39:05, HLA-C*04:01, and HLA-C*07:02). Candidate prioritization was initially based on predicted binding percentile (rank ≤ 2), identifying 12,209 peptide–HLA combinations with high predicted binding affinity. To refine candidate selection, additional computational analyses were incorporated, including proteasomal cleavage prediction using NetChop 3.1 and estimation of T-cell epitope immunogenicity using the Immune Epitope Database (IEDB) immunogenicity predictor. Furthermore, a direct comparison between mutant (MUT) and corresponding wild-type (WT) peptides was performed using Δaffinity and Δrank metrics to evaluate the predicted impact of somatic mutations on HLA binding. The analysis revealed a predominance of peptides associated with the HLA-B locus, particularly the allele HLA-B*35:01, among the interactions with the lowest predicted binding percentiles. Several high-ranking peptide candidates were derived from genes with known roles in melanoma biology, including PLCG2, GATA3, AKT1, PTEN, PTCH1, and SMO. Overall, the integrative computational framework implemented in this study enables the systematic prioritization of candidate neoantigens derived from non-synonymous mutations in melanoma. This pipeline provides a reproducible strategy for exploring tumor neoantigen repertoires and may serve as a foundation for subsequent experimental validation and for studies related to neoantigen-based immunotherapies and immunopeptidomics. Full article

Background/Objectives: In the area of pharmacology and clinical research, it is necessary to use versatile technologies able to quantify last-line antibiotic molecules with high specificity and sensitivity. This article describes the development of two types of immunosensors based on amperometric and surface plasmon resonance (SPR) measurements and their applicability in the measurement/assessment of therapeutic drug monitoring (TDM) of four last-line antibiotics such as vancomycin, colistin, daptomycin and meropenem in human plasma. In this study, ligand immobilization by preconcentration assays, sensor surface regeneration, determination of sensitivity and correlation of plasma sample quantification results by HPLC were considered. Results: In the case of the electrochemical biosensor the IC50 values obtained were 3.49 μg/L for vancomycin (VAN), 5.44 μg/L for colistin (COL), 0.82 μg/L for meropenem (MER) and 5.10 μg/L for daptomycin (DAP). For the SPRi biosensor the LODs achieved were 19 ng/mL for VAN, 9 μg/L for COL, 12 μg/L for MER and 12.3 μg/L for DAP. Finally, both electrochemical biosensor and the SPRi optical biosensor showed that for the four antibiotics the standard deviations were less than 10% with respect to the HPLC results, with ranges for VAN between ~5–6 µg/mL, for COL ~0.2–0.7 µg/mL, for MER ~4.5–5.5 µg/mL and for DAP ~0.09–0.65 µg/mL. Conclusions: These kinds of biosensors provide a precise and sensitive strategy, together with real-time determination, to quantify last-line antibiotics, with working ranges like those shown by robust techniques such as HPLC and great potential for the clinic. Full article

Background/Objectives: Coronaviruses are a family of positive-sense RNA viruses that cause respiratory and gastrointestinal disease in mammals and birds. Their zoonotic nature and high mutability make them a pandemic threat. UNICOR-v is a pre-pandemic, pan-coronavirus vaccine composed of an adjuvanted mix of twelve synthetic peptides originating from conserved regions within Nsp12 and M coronavirus proteins containing clusters of predicted T-cell epitopes. Here, we evaluate the immunogenicity of UNICOR-v and its efficacy against Middle East Respiratory Syndrome-related coronavirus (MERS). Methods: Animals were vaccinated with an adjuvanted equimolar mix of UNICOR-v. Humoral and cellular immunogenicity were assessed 28 days later through ELISA and FLUOROSpot. Vaccine efficacy was assessed in a DPP4 knock-in (HDPP4-KI) mouse model where mice were challenged post-vaccination with a lethal or non-lethal dose of MERS-CoV-MA. Results: Vaccination with UNICOR-v induced high IgG titers in both mice and rabbits and cellular secretion of pro-inflammatory cytokines. Vaccination with UNICOR-v, or passive serum transfer, significantly reduced viral lung titers 4 days post-infection compared to placebo. Vaccination induced lower immune cell infiltration in the alveolar space and increased repair of the cells lining the major airways in vaccinated mice, translating to increased survival rate compared to placebo. Conclusions: These data demonstrate the ability of conserved T-cell epitopes to protect against MERS-CoV infection, supporting further characterization of the breadth of protection of UNICOR-v against other coronaviruses that affect humans and livestock, following a One Health approach to control this highly zoonotic family of viruses. Full article

Background/Objectives: Bacteriophage-based display has been utilized for a variety of purposes, such as to assemble protein libraries and conduct biopanning. We have created a modified lambda (λ) bacteriophage platform, ideal for the display and delivery of proteins. Our system utilizes counter-selection recombineering for versatile modification, temperature-sensitive induction for timely lysate production, and an arabinose-inducible mechanism for high-titer, stable yield. Here, we investigated the ability of this specialized λ phage display platform to stimulate highly specific antibodies in mice against the displayed cancer-variant cell-surface receptor EGFRvIII, demonstrating its potential in cancer immunotherapy and broader vaccine development. Methods: λ display immunogenicity was explored by generating fusion proteins between the λ head protein D and a 13-mer peptide from the N terminus of glioblastoma variant cell-surface receptor, EGFRvIII. The 13-mer peptide was fused to either the N or C terminus of the λD protein while λ remained a dormant lysogen in the bacterial host chromosome. Recombinant phage lysates were then generated with ~420 displayed fusion proteins per phage particle. Mice were injected with purified recombinant λ phage without an adjuvant via both intraperitoneal and intramuscular routes, and sera harvested at various timepoints were profiled for immunogenicity. Results: Analysis of serum samples by ELISA and Western blotting demonstrated the ability of the λD~EGFRvIII phage display, especially in the C-terminal fusion construction, to elicit a robust anti-EGFRvIII humoral response by either injection route. Notably, the antibody response was highly specific to EGFRvIII without exhibiting cross-reactivity to wild-type EGFR. Conclusions: The data generated in this study demonstrate the λ system’s immunotherapeutic potential as a high-titer, stable, self-adjuvanting vector for the stimulation of robust antibody titers with defined specificity. Full article

Understanding and developing habits in complex systems thinking using STEM-integrated perspectives is essential in addressing education and workforce needs in society. In this study, we investigated a learning intervention that incorporated multiple models designed to improve engineering students’ understanding of complex systems through investigating the mechanobiology of the Venus flytrap. Mechanobiology is a transdisciplinary field that integrates biology, engineering, chemistry, and physics to explore how cells and tissues sense and respond to forces in their environment. We used an exploratory, mixed-methods approach to examine the impact of this new curriculum on investigating flytrap closure and prey digestion. We then evaluated students’ understanding of complex systems characteristics (i.e., many interacting parts, decentralization, non-linear interactions, emergence, and adaptation) and in their ability to transfer these principles to other systems. Qualitative analyses demonstrate that students articulated key systems principles in relation to their understanding of flytrap mechanobiology, while descriptive summaries of pre- and post-surveys suggest broader conceptual gains. Furthermore, students demonstrated the transfer of systems thinking to other contexts and reported an enhanced understanding of real-world STEM research. Full article

In this study, the phenotypic consequences of naturally occurring single nucleotide polymorphisms (SNPs) in the Middle East respiratory syndrome coronavirus (MERS-CoV) Spike protein were investigated. The impact of Spike mutations on the syncytia formation and neutralisation of contemporary MERS-CoV strains is not currently well understood. Mutations were identified by aligning 584 MERS-CoV Spike sequences from either human clinical isolates collected between 2012 and 2024 or from a clinical isolate that had been passaged in human cells. Fifteen SNPs of interest occurring in the N-terminal domain (NTD), receptor binding domain (RBD) and adjacent to the S1/S2 cleavage site were selected for further characterisation based on their location in the Spike protein, frequency and identification in previous studies. A contemporary clade B, lineage 5 wildtype Spike sequence, obtained from a human MERS-CoV clinical isolate, was used as the backbone in this study. The mutations of interest were introduced to the wildtype backbone to generate Spike variants. Spike variants were characterised via cell–cell fusion assays, and a lentiviral pseudotyping system was used to investigate the impact of these Spike mutations on neutralisation. The I529T, E536K and L745F mutations were shown to increase fusion and syncytia formation. The L411F, T424I, L506F, L745F and T746K mutations were found to increase resistance to neutralisation by pooled patient sera. This study has identified novel naturally occurring Spike mutations that resulted in phenotypic differences in the syncytia formation and neutralisation of contemporary MERS-CoV strains. Continued investigation of the phenotypic consequences of MERS-CoV Spike mutations is essential for assessing the risk to public health, especially given the pandemic potential of this virus. Full article