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Results for “Consistency”

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At least 19 recordsLinked to original sources

PubMed2026

Diffusion MRI radiomics in meningiomas: imaging correlates of tumor grade and intraoperative consistency.

OBJECTIVE: Despite advancements in imaging studies, the preoperative prediction of the biological behavior and intraoperative consistency of intracranial meningiomas remains limited. This study evaluated the association of volumetric diffusion-based and texture-derived radiomic features extracted from routine MRI with histopathological aggressiveness and intraoperative tumor consistency. METHODS: Ninety-seven intracranial meningiomas resected at two tertiary centers were retrospectively analyzed. Volumetric segmentation was performed on contrast-enhanced T1-weighted MRI and coregistered to apparent diffusion coefficient (ADC) maps. Data on first-order diffusion metrics and selected texture features were collected. The associations between World Health Organization (WHO) grade and Ki-67 index were assessed using nonparametric tests and Spearman correlation analysis. Independent factors associated with intraoperative tumor consistency (Zada grades 1-5) were evaluated via multivariate ordinal logistic regression analysis that adjusted for tumor volume, skull base location, calcification status, and WHO grade. Secondary receiver operating characteristic (ROC) curve analyses were performed to differentiate solid (Zada grades 4-5) from soft (Zada grades 1-2) tumors. ROC analyses were performed within the study cohort and were intended as exploratory assessments of discriminative performance. RESULTS: The mean ADC (ADCmean) and the 10th percentile of the ADC decreased significantly with increasing WHO grade (p < 0.001). ADCmean had a moderate inverse correlation with the Ki-67 index (r = -0.42, p < 0.001) and intraoperative tumor consistency (r = -0.45, p < 0.001). In the multivariate analysis, the ADCmean remained independently associated with increasing tumor firmness. Each 0.1 × 10-3 mm2/sec increase corresponded to a 38% reduction in the odds of belonging to a higher consistency category (OR 0.62, 95% CI 0.51-0.74, p < 0.001). The ROC analysis showed good discrimination for solid tumors (area under the curve 0.847, 95% CI 0.742-0.953) and soft tumors (area under the curve 0.824, 95% CI 0.714-0.935). Texture features had weaker associations with intraoperative tumor consistency. CONCLUSIONS: Volumetric diffusion-derived metrics, particularly ADCmean, are associated with both histopathological aggressiveness and intraoperative tumor firmness in meningiomas. Diffusion imaging may reflect a graded microstructural continuum rather than a purely dichotomous property, providing complementary preoperative insights into surgical complexity.

Humans↗

PubMed2025

NanoFilter: enhancing phasing performance by utilizing highly consistent INDELs and SNVs in nanopore sequencing.

MOTIVATION: Nanopore sequencing data offer longer reads compared to other technologies, which is beneficial for phasing and genome assembly. INDELs provide valuable haplotype information and have significant potential to improve phasing performance. However, accurately identifying INDELs with variant callers is challenging, and incorporating INDELs into phasing remains a complex task. To address these issues, we developed NanoFilter, a novel filtering strategy designed to filter out INDELs that contain wrong phasing information based on their consistency. RESULTS: Our assessment using Nanopore R10 simplex data shows that filtering out low-consistency INDELs increases their precision from 88.3% to 98.8%, nearly matching the precision of SNVs. In the phasing results of Margin, incorporating these filtered INDELs leads to a 12.77% increase in N50 length and fewer switch errors. Furthermore, we found that SNVs filtered by NanoFilter will enhance assembly performance. When NanoFilter is integrated into the HapDup assembly pipeline, NanoFilter reduces the Hamming error rate and increases N50 length by 7.8%. AVAILABILITY AND IMPLEMENTATION: NanoFilter is available at https://github.com/Chenshanming-repo/NanoFilter (DOI: 10.5281/zenodo.16777826) and HapDup-NanoFilter is available at https://github.com/Chenshanming-repo/HapDup-NanoFilter (DOI: 10.5281/zenodo.16777890).

Nanopore Sequencing↗

PubMed2015

A consistent muscle activation strategy underlies crawling and swimming in Caenorhabditis elegans.

Although undulatory swimming is observed in many organisms, the neuromuscular basis for undulatory movement patterns is not well understood. To better understand the basis for the generation of these movement patterns, we studied muscle activity in the nematode Caenorhabditis elegans. Caenorhabditis elegans exhibits a range of locomotion patterns: in low viscosity fluids the undulation has a wavelength longer than the body and propagates rapidly, while in high viscosity fluids or on agar media the undulatory waves are shorter and slower. Theoretical treatment of observed behaviour has suggested a large change in force-posture relationships at different viscosities, but analysis of bend propagation suggests that short-range proprioceptive feedback is used to control and generate body bends. How muscles could be activated in a way consistent with both these results is unclear. We therefore combined automated worm tracking with calcium imaging to determine muscle activation strategy in a variety of external substrates. Remarkably, we observed that across locomotion patterns spanning a threefold change in wavelength, peak muscle activation occurs approximately 45° (1/8th of a cycle) ahead of peak midline curvature. Although the location of peak force is predicted to vary widely, the activation pattern is consistent with required force in a model incorporating putative length- and velocity-dependence of muscle strength. Furthermore, a linear combination of local curvature and velocity can match the pattern of activation. This suggests that proprioception can enable the worm to swim effectively while working within the limitations of muscle biomechanics and neural control.

Alleles↗

PubMed2026

Beyond Photometric Consistency: Addressing Loss Insensitivity to Depth Noise in Endoscopic Estimation via Error Calibration.

Self-supervised monocular depth estimation in endoscopy is fundamentally constrained by the ill-posed nature of photometric supervision. In this work, we identify a critical yet overlooked cause of this ambiguity: the inherent insensitivity of photometric loss to depth noise. To overcome this intrinsic limitation, we propose Depth Error Calibration Learning (DECL), a two-stage framework that suppresses prediction variance and mitigates residual errors in self-supervised depth estimation. In Stage I (Variance Reduction), a cyclic depth generation strategy produces multiple depth hypotheses for the input image. The per-pixel empirical variance is quantified and integrated into a dedicated variance loss term, which penalizes inconsistent predictions and encourages the network to generate more stable and reliable depth estimates. In Stage II (Bias Calibration), an image-conditioned diffusion model refines the Stage-I depth prior and mitigates structured residuals through iterative denoising, thereby improving geometric accuracy and global consistency. Extensive experiments on three public endoscopic datasets demonstrate that DECL achieves consistent improvements over representative self-supervised monocular depth estimation methods under the evaluated protocols. Moreover, ablation studies on two representative backbones indicate that DECL is not restricted to a single network implementation, while broader validation on additional backbone families remains necessary. The source code is publicly available at https://github.com/DavidLuBit/EndoDenoising.

Journal Article↗

PubMed2026

A multi-modal survival prediction framework with group-based batch training and structural consistency alignment.

OBJECTIVE: Integrating whole-slide images (WSIs) with transcriptomic profiles is pivotal for enhancing cancer survival prediction. However, the intrinsic gigapixel resolution and variable sequence lengths of WSIs create a fundamental trade-off between training efficiency and the preservation of data heterogeneity in existing frameworks. Furthermore, substantial statistical and structural discrepancies between histological and genomic modalities often impede effective cross-modal alignment and fusion, thereby limiting prognostic accuracy. METHODS: We propose PRISM, an efficient multi-modal learning framework for integrating WSIs with transcriptomic profiles. To reconcile training efficiency with full data heterogeneity, PRISM first stochastically partitions variable-length WSI sequences into a main subset and a complementary residual subset, both of which are packed into fixed-length groups for batch training. The main subset is processed in the main branch, utilizing isolation masking to maintain intra-group sequence independence. Simultaneously, the residual subset is consolidated into "hyperslides" within a residual branch that leverages tailored supervision, effectively capturing inter-slide correlations. Furthermore, PRISM integrates an Informative Token Aggregation (ITA) module to reduce redundancy in WSIs and employs Cross-batch Structural Consistency Alignment (CBSCA) mechanism to enhance inter-modal structural connectivity. Finally, efficient cross-modal feature interaction is achieved through a Low-rank Bilinear Gated Fusion (LBGF) module. Code is available at https://github.com/Alisa2080/PRISM. RESULTS: Compared with existing methods, PRISM achieves the best overall C-index across five TCGA cohorts. On the larger TCGA-BRCA dataset, PRISM requires only 6 hours of training time, substantially reducing computational cost relative to strong multimodal baselines. Furthermore, comprehensive evaluations demonstrate that PRISM achieves the best overall IBS ranking and favorable time-dependent AUC performance at 1, 3, and 5 years, thereby delivering a more favorable trade-off between prognostic performance and computational efficiency. CONCLUSION: PRISM provides a favorable balance between predictive performance, calibration quality, and computational efficiency, highlighting its potential for practical deployment in multimodal survival modeling for computational pathology.

Humans↗

PubMed2025

Consistently processed RNA sequencing data from 50 sources enriched for pediatric data.

Larger cohorts improve the power of tumor gene expression analysis, but the signal is muddied if datasets are processed using different methods or have inaccurate metadata. Here we present five compendia containing consistently processed gene expression data derived from 16,446 diverse RNA sequencing datasets. To create the compendia, we obtained access to RNA sequence data from repositories containing public data as well as clinical partners with access to non-published data. We then assessed the quality, quantified gene expression, harmonized clinical metadata, and released the expression values and metadata without access restrictions. These datasets have been used for diverse projects ranging from identifying similarities between tumor types to assessing how well cell lines recapitulate tumors. They have also been used for n-of-1 analysis to identify genes with unusual expression patterns in a single sample and to infer molecular diagnosis. The comparison to new data is enabled by our dockerized, freely available pipeline. The compendia have been cited in at least 20 publications.

Humans↗

PubMed2026

Extreme climatic events drive consistent and predictable shifts in soil antibiotic resistance genes.

Antimicrobial resistance (AMR) is a growing One Health challenge, and as climate warming intensifies extreme events, it remains unclear how these disturbances affect soil antibiotic resistance genes (ARGs). Here we analyzed the data from a controlled experiment using soils from 30 grassland sites across ten European countries, which simulated drought, flooding, freeze-thaw, and heatwaves to explore ARG dynamics. Overall, ARGs exhibited relatively small but highly consistent shifts across treatments. Heatwaves caused the strongest reductions in ARG abundance and in their linkages with mobile genetic elements (MGEs), a pattern that may reflect a hypothesized metabolic-genetic trade-off, in which microbial investment may shift from core metabolism toward stress signaling and structural maintenance. ARG dynamics during and after disturbance were governed by distinct soil physicochemical properties, with temperature and nutrient status determining acute responses, whereas soil moisture and seasonal variability in temperature and precipitation shaped longer-term legacy effects. Cross-validated random-forest models showed positive predictive performance for Bray-Curtis-based compositional responses within the environmental range represented by the 30 grassland sites. Our findings enhance the understanding of how soil ARGs respond to extreme climatic events and provide a step toward predicting extreme-event impacts on soil resistomes with relevance to One Health.

Soil Microbiology↗

PubMed2025

Identification of a prognostic signature consisting of three macrophage-related genes for glioblastoma based on bulk and single-cell transcriptomes analyses.

BACKGROUND: Tumor-associated macrophages have been implicated in the progression and treatment resistance of glioblastoma (GBM). This study aimed to identify macrophage-related genes associated with prognosis and therapeutic response in GBM. MATERIALS AND METHODS: Bulk RNA-seq data from 533 patients with GBM were downloaded from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Bioinformatic tools were used to detect the co-expression gene modules associated with the infiltration of immune cells, identify a prognostic macrophage-related gene signature, and explore their association with sensitivity to chemotherapeutic drugs and immune checkpoint blockade. Single-cell RNA-seq data and multiplexed immunofluorescence were used to validate ISG20 expression (a member of the identified gene signature) in macrophages. RESULTS: We detected gene modules associated with macrophages and identified a signature consisting of three macrophage-related genes (ISG20, PARP12 and IFIT5) in the discovery set (TCGA-GBM, n = 159), and validated its prognostic value in the validation set (CGGA-GBM, n = 374). This gene signature demonstrated favorable accuracy in predicting prognosis and resistance of immuno- and chemo-therapy. The co-expression of ISG20 and PD-1 in macrophages was verified by single-cell RNA-seq data and multiplex immunofluorescence. CONCLUSIONS: This study presents a macrophage-related gene signature to predict prognosis and therapeutic response in GBM. ISG20, PARP12 and IFIT5 are interferon-stimulated genes, and further investigations may provide new insights into the interplay between macrophages and interferon signaling in GBM.

Humans↗

PubMed2026

Consistent and idiosyncratic pleiotropy in shaping genetic correlations.

Pleiotropy, the phenomenon where a single mutation influences multiple phenotypic traits, creates genetic correlations that can constrain evolutionary trajectories. Yet genetic correlations differ in their persistence: some remain stable over long evolutionary timescales, whereas others change rapidly across generations or environments. One explanation is that similar values of genetic correlation, rG, can arise from different pleiotropic architectures: broadly aligned effects across many loci, or disproportionate covariance contributions from a few large effect loci. Motivated by the distinction between vertical and horizontal pleiotropy, here, we develop a bivariate marker effect framework for recombinant mapping populations that separates candidate large covariance contributors from the polygenic background correlation, rD. We define rD as the correlation among marker effects after trimming markers with unusually large covariance contributions. rD is a trait-pair summary of how consistently small and moderate effect markers align across the genome; high rD is expected when many perturbations propagate through shared developmental, physiological, causal, or geometric structure. Applying this framework to high-dimensional yeast single-cell morphology, we show that trait pairs with similar rG can differ substantially in rD, and that a small number of candidate outlier regions can strongly influence some marker effect correlations. We then test whether rD predicts the environmental stability of genetic correlations under geldanamycin-mediated Hsp90 perturbation. Trait pairs with stronger rD show smaller absolute changes in rG. These results suggest that genetic correlations supported by a strong polygenic marker effect background are more environmentally stable than correlations shaped primarily by a few large covariance contributors.

Genetic Pleiotropy↗

PubMed2026

Multi-Target HCC Blood Test Demonstrates Consistent Performance Across Subgroups of Patients with Chronic Liver Disease.

BACKGROUND: Early detection of hepatocellular carcinoma (HCC) is critical for improving patient outcomes; however, ultrasound-based surveillance has limited sensitivity and variable performance across patient populations. We evaluated the performance of a multitarget HCC blood test (mt-HBT), incorporating methylated DNA markers, alpha-fetoprotein (AFP), and patient sex, across clinically relevant subgroups. METHODS: We performed a subgroup analysis of a multicenter, prospective case-control study that included 159 patients with early-stage HCC (Barcelona Clinic Liver Cancer Stage 0/A) and 649 control patients with cirrhosis or chronic hepatitis B without HCC. The mt-HBT combined methylated HOXA1, TSPYL5, and B3GALT6 markers with AFP and sex. Sensitivity and specificity were evaluated overall and according to age, sex, obesity, liver disease etiology, Child Pugh class, and tumor size. Performance was compared with AFP and GALAD. RESULTS: Overall sensitivity and specificity of mt-HBT for early-stage HCC detection were 76.7% (95% CI, 69.6-82.6) and 87.5% (95% CI, 84.8-89.8), respectively. Sensitivity was significantly higher than that of AFP (35.2%, p < 0.001) and comparable to that of GALAD (78.6%, p = 0.56), whereas specificity was lower than that of AFP (98.5%, p < 0.001) but higher than that of GALAD (76.9%, p < 0.001). Sensitivity was maintained across key subgroups, including patients with obesity (68.9%), Child Pugh B cirrhosis (75.0%), hepatitis C (80.0%), hepatitis B (72.2%), alcohol-associated liver disease (80.5%), and metabolic dysfunction-associated steatotic disease (69.0%) (all p > 0.05 between subgroups). Specificity exceeded 80% in all examined populations and was significantly higher in women than in men (92.5% vs. 83.9%, p < 0.001). Sensitivity increased with tumor size, ranging from 60.0% for tumors < 2 cm to 100% for tumors > 5 cm. CONCLUSIONS: The mt-HBT demonstrated robust, consistent performance for early-stage HCC detection across diverse patient populations, including subgroups in which ultrasound surveillance commonly underperforms. These findings support the prospective validation of mt-HBT as a blood-based surveillance strategy for HCC.

liver cancer↗

PubMed2026

Toward consistency in somatic genomic testing.

Explore the source record for details and available documents.

Journal Article↗

PubMed2025

Strain-level differences in Gardnerella urinary tract persistence and pathogenesis are consistent with comparative phylogenomic analyses.

BACKGROUND: Gardnerella is a genus of gram variable anaerobic bacteria that is commonly present in the female urogenital tract, especially during bacterial vaginosis (BV). BV is linked with increased risk of urinary tract infections (UTI) and Gardnerella has been frequently detected in urine collected directly from the bladder. Understanding the contribution of Gardnerella to urogenital pathogenesis has been complicated by its genetic heterogeneity and a shortage of data from in vivo models. Recently, a clinical isolate of Gardnerella displayed covert pathogenesis in a mouse urinary tract inoculation model, triggering urothelial exfoliation and promoting UTI by uropathogenic E. coli. Data from clinical studies suggests differential association of Gardnerella phylogenetic clades with BV or urogenital infections. In vitro data has demonstrated heterogeneity in the presence and expression of putative virulence determinants between Gardnerella strains. This study was designed to compare diverse Gardnerella strains in vivo to identify genomic variation associated with urinary tract persistence and pathogenesis. METHODS: Eighteen Gardnerella clinical isolates from each of the four main phylogenetic clades were individually inoculated transurethrally into female C57BL/6 mice. Bacteriuria was monitored by quantitative culturing of Gardnerella in urine. Pathologic features were assessed by immunofluorescent and histological staining of bladder tissues. Pan-genome phylogenetic analyses were performed on the 18 Gardnerella isolates used for mouse infections to identify accessory genes that were associated with observable in vivo phenotypes, including long and short-term persistence, urothelial exfoliation and bladder edema. Genes that were significantly associated to phenotype were then matched against a pangenome analysis of 291 publicly available Gardnerella genomes to determine the conservation of these putative colonization and virulence factors across the genus. RESULTS: Gardnerella strains displayed clear differences in persistence and pathogenesis in the mouse bladder that were congruent with phylogeny. Clade 2 strains were more persistent in the urinary tract whereas strains from the other three clades either caused transient bacteriuria or were undetectable. Strains from clade 2 and 4 induced urothelial exfoliation while edema was triggered by strains from clades 2, 3 and 4. Pangenome analyses revealed 45 genes that were associated with in vivo persistence and pathogenicity. Among the wider 291 publicly available genomes, clade 2 strains encoded more of the genes associated with bacteriuria phenotypes compared to strains in the other three clades. Exfoliation-associated genes were present in most clade 4 strains. Clade 3 strains lack most of the in vivo-associated genes, whereas clade 1 strains were more heterogenous. CONCLUSIONS: This study provides in vivo evidence for differential urinary tract colonization and pathogenesis by strains from different clades/species within the genus Gardnerella and identifies new putative persistence and virulence factors. Utilizing the in vivo data from tested strains, pangenome analyses predicts that clade 2 Gardnerella are the most likely to persist in the urinary tract and that clades 2 and 4 have the highest uropathogenic potential. These findings inform future targeted screening and treatment approaches aimed at limiting harmful Gardnerella urinary tract exposures.

Journal Article↗

PubMed2026

Genomic and phenotypic diversification of Pseudomonas aeruginosa during sustained exposure to a ciliate predator.

UNLABELLED: Predator-mediated selection is an important ecological force shaping bacterial evolution, but its effects on genomic adaptation and virulence in opportunistic pathogens are not fully understood. Here, we used experimental evolution to study how exposure to the ciliate predator Tetrahymena thermophila affects Pseudomonas aeruginosa. Replicate populations were evolved for 60 days with or without the predator, followed by whole-genome shotgun metagenomic sequencing and phenotypic analyses. Both treatments showed strong selection and evidence of parallel evolution at gene and nucleotide levels, indicating constrained adaptation. However, predator exposure altered evolutionary dynamics. Predator-evolved populations showed a wider distribution of mutation frequencies, with many mutations persisting at intermediate frequencies, consistent with increased clonal interference and ongoing competition among lineages. In contrast, populations evolved without predators showed more high-frequency mutations, consistent with selective sweeps, although some low-frequency variants remained. Despite substantial genomic change, phenotypic outcomes were variable. Virulence in an invertebrate host model did not consistently increase. Instead, evolved isolates showed context-dependent changes, including modest decreases or occasional increases. Competition assays also showed no consistent fitness advantage for predator-evolved isolates, suggesting trade-offs between predator resistance and growth in other environments. Overall, predator-mediated selection reshaped evolutionary dynamics by maintaining diversity and altering the balance of lineages rather than producing uniform increases in virulence. These results highlight how ecological complexity influences adaptive evolution and the context-dependent nature of pathogen traits. IMPORTANCE: Opportunistic pathogens such as Pseudomonas aeruginosa often evolve in environmental settings before infecting hosts, raising questions about how ecological interactions influence virulence. Predator-mediated selection has been suggested to increase virulence via coincidental evolution, but evidence is inconsistent. Here, we show that exposure to a eukaryotic predator does not consistently elevate virulence but does reshape evolutionary dynamics by altering how mutations spread in populations. Predator-exposed populations retained more intermediate-frequency mutations, consistent with increased clonal interference and ongoing competition among lineages, whereas non-predator populations were dominated by selective sweeps. These differences were also reflected in functional targets of adaptation, with predator exposure favoring mutations in genes involved in environmental sensing and interaction. Together, these findings suggest that ecological complexity shapes the dynamics of adaptation rather than driving a single evolutionary outcome, highlighting that virulence is an emergent property influenced by underlying evolutionary processes.

Pseudomonas aeruginosa↗

PubMed2026

Adaptive and degenerative mitochondrial remodeling define distinct redox states in age-related macular degeneration.

Age-related macular degeneration (AMD) is associated with mitochondrial dysfunction and oxidative stress, yet the relationship between mitochondrial remodeling, redox homeostasis, and disease progression remains poorly understood. Nonhuman primates (NHPs) develop spontaneous AMD-related phenotypes, including punctate deposits and soft drusen, providing a unique animal model to investigate mitochondrial pathology in the aging retinal pigment epithelium (RPE). We integrated quantitative mitochondrial ultrastructural profiling with flavoprotein fluorescence imaging, plasma metabolomics, and whole-exome sequencing to characterize mitochondrial and redox alterations in aged rhesus macaques with AMD-related lesions. Flavoprotein fluorescence imaging demonstrated increased metabolic heterogeneity in eyes with soft drusen, consistent with altered mitochondrial redox states and oxidative stress. Morphometric analysis identified distinct mitochondrial remodeling patterns across phenotypes. Normal aging was characterized by concentric cristae and type I paracrystalline inclusions. Eyes with punctate deposits exhibited increased mitochondrial fusion-associated morphology, hyperbranching, and type I paracrystalline inclusions, consistent with a stress-responsive mitochondrial remodeling pattern. In contrast, eyes with soft drusen exhibited reduced fusion-associated morphology, reduced structural complexity, and ultrastructural features consistent with mitochondrial deterioration. These ultrastructural patterns were accompanied by distinct plasma metabolomic signatures. Punctate deposits were associated with altered glycolytic, tricarboxylic acid cycle, and redox-buffering metabolites, consistent with differences in stress-responsive metabolism, whereas soft drusen exhibited metabolomic signatures consistent with altered redox homeostasis. Whole-exome sequencing identified a mitochondrial DNA variant, MT:9582G > A, in cytochrome c oxidase subunit III (COX3) associated with the drusen phenotype. Collectively, these findings identify distinct mitochondrial remodeling patterns associated with AMD-related phenotypes in aged rhesus macaques. The convergence of ultrastructural, imaging, metabolomic, and genetic analyses suggests that punctate deposits and soft drusen are associated with different mitochondrial and redox-related responses to chronic retinal stress. These findings provide a framework for future studies investigating mitochondrial biology and redox-driven mechanisms in AMD.

Animals↗

PubMed2026

Perseus: Lineage-Aware Refinement of Kraken2 Taxonomic Classification for Long Read Metagenomes.

MOTIVATION: Long-read metagenomic sequencing improves assembly contiguity and enables genome-resolved analysis of complex microbial communities, but accurate taxonomic classification of long reads and assembled contigs remains challenging. Highly scalable k-mer-based classifiers such as Kraken2 frequently over-assign fine-rank taxonomic labels when applied to long-read data, producing high false positive classification rates driven by sparse or localized k-mer matches, particularly in microbiomes with extensive taxonomic novelty. RESULTS: We present Perseus, a lineage-aware confidence estimation framework for taxonomic classification that models the spatial distribution and hierarchical consistency of k-mer evidence along sequences. This formulation reframes taxonomic classification as a hierarchical confidence estimation problem rather than a single-rank prediction task. Perseus refines k-mer-level taxonomic signals from Kraken2 using a multi-headed convolutional neural network that estimates calibrated confidence scores for taxonomic correctness at each canonical rank. Using these estimates, Perseus confirms assignments, backs off to higher taxonomic ranks, or abstains when evidence is insufficient, prioritizing correctness and lineage consistency over overly specific assignments. Across simulations of taxonomic novelty and real-world metagenomic datasets, Perseus consistently and substantially reduces the false assignment rate while improving precision and lineage-consistent accuracy. These improvements are most pronounced for long reads and assembled contigs, where spatial context enables reliable discrimination between consistent taxonomic signal and spurious matches. AVAILABILITY AND IMPLEMENTATION: Perseus integrates with existing Kraken2 workflows and is available at https://github.com/matnguyen/perseus.

Journal Article↗

PubMed2026

Detection of short tandem repeats in the cattle genome: a comparison of bioinformatic tools.

BACKGROUND: Short tandem repeats (STRs) are repetitive DNA sequences with 1–6 nucleotide repeat units, exhibiting high polymorphism due to varying repeat counts. STRs are more variable than SNPs and can cause genetic disorders. With population-scale cattle whole-genome sequencing data available, whole-genome STR identification has attracted new interest, but challenges remain due to the lack of standardized methods, sequencing data limitations, and the diversity of STR-calling tools. This study compared six STR-calling tools: HipSTR, GangSTR, and ExpansionHunter for short-read data, and Straglr, RepeatHMM, and LongTR for Oxford Nanopore (ONT) long-read data—using sequences from five Holstein cattle (two parent–offspring trios with a shared sire). This is the first cattle study to evaluate short- and long-read STR callers using both data types from the same animals. RESULTS: In short-read data, ExpansionHunter identified the highest number of polymorphic STRs (pSTRs) (327,690), followed by HipSTR (205,900) and GangSTR (110,680), with 93,023 loci detected by all three tools. In long-read data, LongTR detected 470,250 pSTRs, RepeatHMM 224,185, and Straglr 90,275, with only 33,253 loci shared among them. Mendelian consistency of STR genotypes in the trio offspring was high (> 0.8) for all short-read tools, with HipSTR and GangSTR highest at 0.98. LongTR was the only long-read tool with high consistency (0.88). Short-read tools also showed higher concordance in STR genotypes among themselves than was observed among long-read tools. However, long-read tools had a clear advantage in detecting large STRs. Relative to computational efficiency, HipSTR and GangSTR (short-reads), and LongTR (long-reads) required less memory and shorter runtimes than the other tools. CONCLUSIONS: Tool selection is critical for accurate whole-genome STR identification in cattle. For short-read data, HipSTR showed relatively high Mendelian consistency and concordance compared to the other tools, while ExpansionHunter was able to detect longer STRs but with lower Mendelian consistency. For long-read data, LongTR demonstrated higher consistency and computational efficiency relative to the other tools. Based on these results, HipSTR and LongTR are suggested as preferred options for short-read and ONT long-read datasets, respectively, in cattle STR analysis. These recommendations are based on the metrics observed in this study, and confirmatory analyses across additional breeds, larger sample sizes, and validated truth sets are encouraged.

Animals↗

PubMed2026

Critical insights on the application of the theory of planned behaviour to food handlers' food safety practices.

Foodborne diseases remain a significant public health concern, often linked to unsafe food-handling practices. The Theory of Planned Behaviour (TPB) is widely used to predict and explain food safety behaviours, yet its application in this field has not been systematically and in-depth evaluated. This review evaluated how the TPB has been applied to study food handlers' behaviour, focusing on methodological approaches, use of the TACT (Target, Action, Context, and Time) framework, validity, elicitation studies, and reliability. Seventeen studies were included following a systematic search of four databases (Scopus, Web of Science, Wiley Online Library, and Taylor & Francis Online). Data were extracted on behaviour definition, aim of study, main findings, use of indirect and direct TPB measures, use of elicitation studies, internal consistency, content validation, analytical methods used, and any extensions to the original TPB framework. Key elements related to adherence to core TPB principles and measurement practices were extracted using a Checklist. Most studies used direct measures of TPB constructs, and only a few reported procedures for content validation. Considerable variability was found in the reporting of key measurement and psychometric practices. Five studies fully applied the TACT framework, while nine incorporated additional factors such as knowledge and moral norms. Elicitation studies were conducted in five cases where indirect measures were employed. Analytical approaches were mainly based on multiple linear regression, with limited use of more advanced techniques such as structural equation modeling. Twelve studies reported internal consistency results. Overall, the review highlights opportunities to strengthen methodological practices in future TPB research on food safety. Greater attention to conducting and reporting content validation, full application of the TACT framework, reporting of internal consistency, and consistent inclusion of elicitation studies when using indirect measures may enhance transparency, reinforcing the credibility and trustworthiness of research findings. A major methodological limitation of this review was that screening and data extraction were conducted by a single reviewer and no formal quality or risk-of-bias assessment of the included studies was performed. Despite these limitations, the findings provide practical guidance for the development and validation of TPB-based questionnaires and may support more robust food safety research, interventions, and policy initiatives aimed at improving food handlers' practices.

Humans↗

PubMed2022

Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies.

BACKGROUND: DNA methylation (DNAm)-based predictors hold great promise to serve as clinical tools for health interventions and disease management. While these algorithms often have high prediction accuracy, the consistency of their performance remains to be determined. We therefore conduct a systematic evaluation across 101 different DNAm data preprocessing and normalization strategies and assess how each analytical strategy affects the consistency of 41 DNAm-based predictors. RESULTS: Our analyses are conducted in a large EPIC DNAm array dataset from the Jackson Heart Study (N = 2053) that included 146 pairs of technical replicate samples. By estimating the average absolute agreement between replicate pairs, we show that 32 out of 41 predictors (78%) demonstrate excellent consistency when appropriate data processing and normalization steps are implemented. Across all pairs of predictors, we find a moderate correlation in performance across analytical strategies (mean rho = 0.40, SD = 0.27), highlighting significant heterogeneity in performance across algorithms. Successful or unsuccessful removal of technical variation furthermore significantly impacts downstream phenotypic association analysis, such as all-cause mortality risk associations. CONCLUSIONS: We show that DNAm-based algorithms are sensitive to technical variation. The right choice of data processing strategy is important to achieve reproducible estimates and improve prediction accuracy in downstream phenotypic association analyses. For each of the 41 DNAm predictors, we report its degree of consistency and provide the best performing analytical strategy as a guideline for the research community. As DNAm-based predictors become more and more widely used, our work helps improve their performance and standardize their implementation.

DNA Methylation↗