Forensic Science International: Genetics

Shotgun sequencing of DNA mixtures has the potential to become an important tool in forensic genetics, particularly for degraded samples or complex mixtures. However, the analysis of shotgun-sequenced mixtures is challenged by several confounding factors, including sequencing errors, population structure, the potential presence of relatives, linkage, and linkage disequilibrium. Additionally, the statistical methods currently available for such data are limited. Here, we develop likelihood ratios that summarize the evidence for the presence of a suspects DNA in a mixture, assuming genotypes of the suspect and the victim are available, while accounting for DNA from unknown contributors modeled either as general population contamination or as a single contaminating individual. We show that these likelihood ratios are not robust to model assumptions and can falsely support the presence of the suspects DNA when the true contributor is even a distant relative. We therefore propose a combined evidence score, {Lambda}: the likelihood ratio minimized over contamination models, reported only when the evidence favors the suspect over a hypothesized sibling by at least a factor of C (default C = 10) under at least one contamination model, and otherwise set to 1 (log {Lambda} = 0). In simulations based on 1000 Genomes data, {Lambda} retains nearly all power to detect true contributors at mixture proportions as low as 5% while eliminating the false positives produced by model mis-specification, population structure, and by untested relatives. The methods are implemented in the open-source program DNAMIXTURE, providing the forensic and population genetics community a resource for robustly identifying small individual contributions to a DNA mixture.

BACKGROUND: The protein 4.1a/4.1b ratio is an intrinsic molecular marker of mean red blood cell (RBC) age resulting from progressive deamidation of protein 4.1b during RBC circulation. Despite its potential for estimating mean RBC age from a single blood sample, its application has been limited by labor-intensive RBC membrane preparation, and its interindividual variability has not been characterized in a large human population. METHODS: We developed a low-volume method for direct RBC membrane isolation from whole blood using saponin permeabilization and differential centrifugation without prior RBC washing. Membrane proteins were separated by SDS-PAGE, and protein 4.1a/4.1b ratios were determined by densitometric analysis with Gaussian peak fitting. Assay conditions, sample-volume requirements, membrane protein recovery, and analytical reproducibility were evaluated. The method was subsequently applied to 114 anonymized residual samples from routine diagnostic testing selected for complete blood count parameters within the corresponding laboratory reference intervals. RESULTS: The optimized procedure used 50 uL of whole blood, 0.06% saponin, and centrifugation at 9000 g, although protein 4.1a/4.1b remained measurable from starting volumes as low as 5 uL. Mean within-subject CVs were 3.90% for electrophoretic/densitometric determination and 5.39% for membrane protein recovery; whole-procedure CVs in 3 subjects were 1.40% to 3.50%. In the population analysis, the mean protein 4.1a/4.1b ratio was 1.428 (SD 0.146), with an observed range of 1.052 to1.799 and a central 95% interval of 1.151 to 1.695. Ratios did not differ significantly between females and males (P=0.246) or among females aged 18 to 50 years, females aged >50 years, and males (P=0.151). The ratio showed modest positive correlations with chronological age in females (Spearman p=0.26, P=0.047) and males (p=0.33, P=0.015), while substantial interindividual variability remained across the age range. CONCLUSIONS: Direct isolation of RBC membranes from small volumes of whole blood enables reproducible determination of the protein 4.1a/4.1b ratio using a simplified workflow. Application of the assay to a comparatively large human population revealed substantial interindividual variation in this molecular marker of mean RBC age that was not explained by sex or chronological age alone. The method provides a practical approach for investigating biological variation in mean RBC age and its potential contribution to laboratory measurements influenced by RBC survival.

Age and sex estimation from dental radiographs supports forensic identification and age-dependent legal assessment. Multi-input models may combine global and regional representations, but when these inputs are derived from the same radiograph, they may repeat anatomical information rather than provide complementary signals. We developed HMA-Net, a hierarchical multi-stream anatomical network, together with a task-specific input audit that evaluates whether each candidate representation improves at least one prediction task without materially degrading the other. The model was developed using a retrospective single-centre cohort of 4,293 panoramic radiographs from individuals aged 3-18 years and combined a panoramic-image stream with graph-linked tooth representations. On the held-out test set, the final panoramic-plus-permanent-tooth model achieved a mean absolute age error of 0.51 years and a sex-classification accuracy of 85.2%; 99.4% of age estimates were within 2 years of chronological age. Across three independent training runs, removing the jaw-region stream reduced mean absolute age error from 0.68 +/- 0.01 to 0.52 +/- 0.01 years and increased sex accuracy from 81.3+/- 1.7% to 85.8 +/- 0.8%. Adding 20 deciduous-tooth classes provided no age benefit and reduced sex accuracy by 7.9% relative to the permanent-only representation. The mandibular first molars received the greatest tooth-level attention, and their occlusion impaired both prediction tasks. These findings show that anatomically plausible inputs do not necessarily provide incremental predictive value. Task-specific input auditing can improve joint model performance, reduce input redundancy and preserve anatomically traceable predictions. More broadly, this framework provides a testable approach for selecting correlated inputs in medical-imaging systems in which regional views re-present anatomy already contained in the whole image. Keywords: Forensic odontology; Dental age estimation; Sex classification; Panoramic radiography; Multi-input learning; Anatomical input auditing

During biogeographical ancestry (BGA) analysis, an unknown individual for whom ancestry informative markers (AIMs) were analyzed is often assigned to one of a few reference populations. For some individuals, such an assignment is not meaningful: the two parents of the individual may come from different populations, or the individual may come from a population not included in the reference database. In this study, we examined classification methods in combination with 104 AIMs from the VISAGE Enhanced Tool to provide one of the following three possible outcomes: (i) in-reference (IR), with an assignment to one of the reference populations; (ii) admixed (AD), with parents from two in-reference populations; and (iii) out-of-reference (OR), where the individual's ancestry is not sufficiently represented by the reference populations. We therefore compared seven classifiers in combination with class-specific acceptance thresholds for the OR decision. The best-performing method according to our selection criterion was a naive Bayes classifier over pairs of populations, which we call the recent naive Bayes (RNB) classifier. Populations held out of the reference panel were correctly rejected in $84.9\%$ of cases. We further tested the method on $800$ individuals from seven additional groups not included in the reference panel. Only $29.4\%$ of individuals from four non-admixed populations were rejected, resembling joint ancestry with reference populations. In addition, we investigated the prediction accuracy in a newly collected dataset of 28 individuals with self-reported ancestry by analyzing AIMs using massively parallel sequencing.

Detecting dugongs at low densities remains a major challenge for conservation monitoring, particularly in remote or turbid coastal habitats where conventional surveys are difficult. Environmental DNA (eDNA) offers a complementary approach, but robust species-specific tools are needed before it can be confidently applied to dugong surveillance. We developed and analytically validated a quantitative PCR TaqMan assay for detecting dugong (Dugong dugon) eDNA and evaluated its performance across controlled aquarium conditions in Australia and Abu Dhabi, and wild populations in northern Australia and the Arabian/Persian Gulf. The assay detected dugong eDNA in aquarium and field samples, with Sanger sequencing confirming amplification of dugong mitochondrial DNA from positive field detections. Our assay provides a robust and geographically transferable tool for targeted dugong eDNA detection, performing reliably across contrasting coastal environments and widely separated populations. This approach can strengthen local-scale surveillance of low-density or difficult-to-detect dugong populations.

Background: Determining the cause of death in electrocution is often challenging at autopsy, particularly when characteristic external electrical marks are absent. Reliable internal markers are scarce, and the neuropathological changes associated with electrocution remain underexplored. Objective: To identify and characterize histopathological alterations in the brain in cases of fatal electrocution and evaluate their diagnostic utility. Methods: A prospective observational study was conducted on 12 autopsy cases, including 7 confirmed electrocution deaths and 5 non-electrocution deaths (control). Brain tissue samples from multiple regions were processed using H&E and Golgi Cox staining. Histopathological parameters, including neuronal morphology, vascular changes, haemorrhage, and dendritic architecture, were evaluated. Results: Electrocution cases demonstrated neuronal destruction, axonal ballooning, focal hemorrhages, and dendritic disruption. Cerebellar involvement with Purkinje cell damage. Conclusion: Comprehensive neurohistological technique may helpful in evaluation neuronal damage in electrocution death. Neuropathological findings, particularly neuronal and dendritic injury, may serve as supportive evidence in determining cause of death when external signs are absent. Keywords: Electrocution, Neuropathology, Golgi Cox stain, Forensic histopathology, death.

Background: Protein-coding DNA exhibits characteristic three-base periodicity. In unaligned DNA libraries, however, this signal should cancel if fragment boundaries are uniformly distributed across codon phases. Yet, we find that in multiple metagenomic libraries, base frequencies depended systematically on distance from fragment boundaries, producing a period-3 pattern. We investigated the origin of this unexpected signal. Results: The signal was observed in modern metagenomic datasets and widely across ancient-DNA datasets. We developed a new alignment-free algorithm to infer latent triplet-phase structure from nucleotide composition. The inferred shifts were strongly nonuniform at fragment boundaries, explaining the read-coordinate periodicity. We initially suspected a protocol artifact. To test an alternative, we derived a probabilistic framework linking nucleotide-dependent boundary selection to codon-phase frequencies. The framework showed that, because nucleotide composition differs among codon positions, preferential breakage at particular nucleotides biases the phases represented at fragment boundaries. Simulations confirmed this mechanism: purine-associated fragmentation produced strong period-3 coherence in a coding-dense bacterial genome, whereas no comparable genome-wide effect appeared in the human reference genome. Conclusions: The observed read-coordinate three-base periodicity does not require an intrinsic phase bias in the source DNA. Instead, sequence-dependent fragment formation or recovery can couple boundaries to codon phase. Purine-associated post-mortem fragmentation plausibly explains the widespread phenomenon in ancient-DNA libraries, although PCR- or library-specific mechanisms may contribute to it in modern datasets. Our alignment-free algorithm provides a practical method for estimating and normalizing phase shifts before searching for positional nucleotide patterns, preventing boundary-associated bias from being mistaken for an intrinsic biological pattern.

GeneCapture is developing a novel, point-of-care, rapid AST technique that, when fully automated, can accomplish AST within 2 hours directly from a raw sample, representing a significant advancement in diagnostic capabilities. For this study, a panel of uropathogen isolates was collected from clinical specimens positive for urinary tract infection from various laboratories in the southern US. A selection of these isolates were then spiked into fresh clean-catch urine samples to produce mock samples covering a variety of antibiotic responses. Testing was performed on 20 strains of Escherichia coli, the cause of over 80% of UTIs, and 6 strains each of Klebsiella pneumoniae and Proteus mirabilis, the next most common causes. Each strain was tested against the susceptible, intermediate and resistant (S-I-R) breakpoints of up to 5 appropriate antibiotics including Amoxicillin-Clavulanate, Cefpodoxime, Ciprofloxacin, Nitrofurantoin and Trimethoprim-Sulfamethoxazole. The new method uses a phenotypic assessment of bacterial growth, comparing responses from bacteria treated with antibiotics at S-I-R breakpoint concentrations to untreated controls as the metric for determining susceptibility. Exposure to the drugs for only 90 minutes gave a Categorical Agreement of 98% to gold standard culture results across the resulting 247 bacteria/antibiotic pairs. Testing on separate days of nine of these strains in triplicate gave a reproducibility of 97%. The sequential steps of this novel AST - sample processing, growth, labeling, and counting - can readily be performed on an automated cartridge that is currently under development. The presented work, which took place in a laboratory setting, demonstrates the efficacy of the assay.

The vaginal microbiome is associated with reproductive and sexual health, yet its viral component remains understudied. A recent vaginal genome catalogue reported that 85.8% of its species-level viral groups were absent from all five viral databases examined. Viral identification tools depend on reference sequences, either directly or through their training data. Previous benchmarks used simulated or non-vaginal datasets. How well these tools generalise to vaginal metagenomes therefore remains unclear. We benchmarked 14 tools spanning five methodological approaches. Evaluations used controlled genome fragments, viral spike-in co-assemblies and virus-like particle (VLP)-enriched co-assemblies. A multi-evidence benchmark assessed 13 vaginal shotgun assemblies (primary cohort). An independent 30-sample cohort provided external validation. geNomad ranked first or second by Matthews correlation coefficient (MCC) in every ranked evaluation. VIBRANT achieved similar MCC to geNomad on the primary assemblies. It made eight false-positive calls among 11,577 negatives but recovered only 22% of viral contigs. VIBRANT ranked first in the independent cohort. Tool rankings agreed closely between cohorts (Spearman rho = 0.952; 95% CI 0.921 - 0.960). Detection of sequences lacking reference homologues differed substantially between tools. Two phage genomes without detectable reference homologues provided a controlled novelty test. Each evaluated composition-based tool recovered at least 74% of their 1,500-bp fragments. Marker- or reference-dependent tools recovered at most 31%. A separate analysis examined 48 unconfirmed candidate viral contigs lacking detectable nucleotide or protein homologues. Jaeger flagged 41, compared with four for VIBRANT and one for geNomad. On short fragments, attention-based tools achieved 4.0-5.5 times the mean area under the precision-recall curve of feature-based tools. ViraLM accounted for most of this difference. ViraLM also had the highest recall (sensitivity) of eukaryote-infecting viruses in the evaluated panel. VirSorter2 achieved the highest MCC on VLP-enriched contigs. These results support geNomad when prioritising overall classification performance and VIBRANT when minimising false-positive phage calls is the priority. ViraLM supports short-contig and eukaryote-infecting-virus detection. VirSorter2 supports identification in VLP-enriched assemblies, and Jaeger supports screening for candidate novel phages. These findings guide tool selection for studies investigating the vaginal virome in reproductive and sexual health.

Motivation: Real-time qPCR data are commonly analyzed to obtain a Cq values, which are used to compare the relative abundance of a targeted template. However, improper background subtraction distorts Cq and changing the assigned threshold can alter experimental conclusions. In addition, a Cq values provide no information on the quality of the qPCR for a given sample. Alternatively, global fitting of qPCR data provides agnostic quantification without the use of Cq or thresholds, and the fitting terms provide a useful metric of reaction performance. Current implementations of global fitting are limited to custom in-house programs. Results: qPyCR is comprehensive analysis tool written in Python that performs background signal subtractions, computes a threshold, and reports Cq values in a traditional manner. The program also performs global fitting and reports template abundances along with reaction quality values. The quality values can be used to assign performance standards, which allow researchers to distinguish aberrant reactions from true changes in template abundance. Availability and Implementation: Code and example data are freely available on GitHub (https://github.com/sdmoore-labs/qPyCR) and on Zenodo (https://doi.org/10.5281/zenodo.21115342).

This paper predicts antimicrobial resistance (AMR) from matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectra using DRIAMS, evaluated across 13 species-antibiotic datasets spanning sensitive/resistant imbalance ratios of 2.95:1 to 141.21:1 and raw spectral similarities of 0.9166-0.9946. We propose HetSAGE, a heterogeneous graph neural network (GNN) that combines full-spectrum "raw" edges with consensus "biomarker" edges derived from a 4-method feature-selection vote and applies per-edge-type dropout to regularize the two views separately. HetSAGE outperforms a plain multilayer perceptron (MLP) only when spectral similarity is high, consistent with oversmoothing effects reported in the broader GNN literature; the biomarker view converges on features close to independently established clinical markers: within {+/-}1 Da in Staphylococcus aureus, Oxacillin, and within range in Klebsiella pneumoniae, Meropenem, confirming it is not a black box. At the same time, per-edge-type dropout protects this smaller signal from dilution by regularization tuned for the noisier raw spectrum. Together, these results indicate that heterogeneous graph structure is not a universal win for MALDI-TOF AMR prediction but a conditional one, tied to a measurable property of the data, spectral similarity, rather than to architecture alone. This caveat is largely unaddressed in current DRIAMS-based GNN work. It provides a concrete signal for practitioners deciding when a graph model is worth the added complexity over a plain MLP.

Quantitative imaging of extrachromosomal DNA (ecDNA) is increasingly important for studying cancer heterogeneity and adaptation, yet automated analysis has been limited by the absence of accessible imaging data, gold standard annotations and adaptable computational tools. Here we establish an open resource for computational ecDNA imaging, integrating 2,986 native-resolution metaphase FISH image sets with manual annotations, standardized benchmarks and open-source quantification frameworks. We use this resource to systematically compare existing and newly developed approaches spanning rule-based computer vision, deep-learning-based segmentation and probabilistic localization. This comparison reveals count-dependent underestimation that distorts ecDNA copy-number distributions and motivates ecCount, a probabilistic localization method developed here to preserve individual ecDNA signals and quantitative burden. ecCount achieves an object-level F1 score of 0.939 on held-out images with minimal count bias. Together, the images, annotations, retrainable models, evaluation tools and guided workflows provide community infrastructure for applying, adapting and improving automated ecDNA quantification across experimental systems.

Maya peoples have undergone several largescale demographic transformations over the past 3000 years, including the formation and depopulation of large urban centers, climatic events, and multiple colonial regimes. Archaeological and stable isotope evidence from across the Maya lowlands throughout these periods paints a picture of a demographically mobile population with complex and dynamic relationships among some of the largest urban polities in antiquity. The nature of the genomic relationship between ancient Maya urban centers, as well as their relationship with present-day Maya communities, however, remains under-characterized. Here, we present mitogenomic data of 16 ancient Maya Ancestors recovered from interments in the ancient Maya cities of Caracol and Santa Rita Corozal located in Western and Northern Belize. These ancient data were combined with 110 newly collected contemporary individuals from Northern Belize, along with nearly 600 previously published ancient and contemporary comparative mitogenomes recovered from sites spanning Mesoamerica and the Caribbean to reconstruct paleodemographic and mobility patterns of maternal lineages through time. Our results are consistent with broadly interconnected Maya populations that have maintained maternal genetic continuity across much of the Holocene, and which are not structured by linguistic family.

Per- and polyfluoroalkyl substances (PFAS) and phthalate metabolites are understudied in follicular fluid of women undergoing assisted reproduction. We characterized these in previously collected follicular fluid from patients at a Nebraska fertility clinic (August 2022 to June 2025) and retrieved demographics and clinical characteristics from fertility records. Spearman correlations and nonparametric tests evaluated PFAS and phthalate correlations and exposure differences by age, race, BMI, and urbanicity. Seven PFAS and four phthalate metabolites were detected in at least half of participants. PFOS and PFHxS concentrations were higher in younger women, and mono-isobutyl phthalate (MiBP) was higher in younger and non-White women. PFAS and phthalate correlations were weak, and exposures did not differ by urbanicity, indicating multiple chemical exposures in follicular fluid with sociodemographic disparities.

Open genomic repositories have become critical infrastructure for pathogen surveillance, variant tracking, outbreak reconstruction, and public health decision making. However, many of these repositories were designed to support rapid scientific data sharing rather than adversarial verification, creating potential security gaps. This paper introduces Advanced Persistent Biological Threats (APBTs), as a framework for understanding how malicious cyber biological actors can gradually disrupt genomic surveillance without entering a laboratory or releasing a biological agent. Instead of altering biological material, an attacker targets the sequence data, metadata, reference datasets, and analytical models used by surveillance systems. We present, to our knowledge, the first integrated multilayer taxonomy of APBTs, structured around a four phase lifecycle, and investigate whether the metadata weaknesses identified by this framework are present in an operational genomic repository. To assess this, we audited 100,000 SARS-CoV-2 BioSample records obtained from the NCBI. A framework of 23 checks was applied across five metadata layers: temporal, geographic, host and specimen, provenance, and technical metadata. Every record triggered at least one APBT relevant metadata vulnerability indicator, with a mean of 7.34 indicators per record (SD = 1.34). Overall, 97.3% of records were classified in the High or Critical severity categories. Every record also contained at least one indicator in both the provenance and technical metadata layers. These results largely reflect the optional status of several fields in the current BioSample submission model rather than isolated errors by individual data contributors. Temporal and geographic information was comparatively complete, while fields needed for provenance reconstruction, chain of custody assessment, technical validation, and analytical interpretation were frequently missing. These findings do not indicate deliberate manipulation. However, the structural incompleteness of BioSample metadata creates vulnerabilities that an adversary can exploit to introduce poisoned, misleading, or weakly traceable metadata into genomic repositories. As a result, maliciously altered metadata can appear plausible, remain undetected, and influence downstream genomic analysis and surveillance. The findings therefore show that widely used pathogen genomic repositories already contain structural weaknesses that can support metadata poisoning as part of an APBT operation.

Inhalation of airborne per- and polyfluoroalkyl substances (PFAS) represents an important yet understudied exposure route. While volatile organic compounds (VOCs) have been widely explored as biomarkers of toxicant exposure, their potential for early detection of PFAS-induced pulmonary toxicity remains largely unexplored. This study investigated VOC profiles in human alveolar (A549) and bronchial (BEAS-2B) epithelial cells following exposure to environmentally relevant PFAS concentrations (1, 5, and 10 ppb) for 12, 24, and 48 h. VOCs were analyzed using headspace solid-phase microextraction coupled with gas chromatography-Orbitrap mass spectrometry. We observed significant concentration- and time-dependent alterations in VOC profiles in both cell models. The principal VOCs associated with PFAS exposure include butanal, acetone, 2-butanone, 2-nonanone, (E)-2-nonenal, (E)-2-octenal, (E)-2-heptenal, 2,4-heptadienal, 2,4-nonadienal, 2,4-decadienal, 1-pentanol, and 1-hexanol. Cell type-specific regulations were observed, reflecting differential metabolic responses between transformed and non-transformed lung epithelial cells. Metabolite set enrichment analysis indicated perturbations in ketone body metabolism, consistent with altered cellular energy metabolism. Complementary qPCR analysis revealed altered expressions of CYP1A1, CYP2E1, ALDH1A1, and ALDH2, supporting a mechanistic link between PFAS exposure and oxidative stress, lipid peroxidation, and impaired aldehyde detoxification. These findings demonstrate that VOC profiling, combined with transcriptional analysis, can reveal PFAS-induced metabolic perturbations in pulmonary epithelial cells.

Context: Cytological specimens allow minimally invasive, repeated sampling for diagnosis and molecular testing. However, cytology biobanking protocols have primarily been optimized for morphology, and the effects of preanalytical handling and storage on intracellular proteins remain unclear. Objective: To assess how fixation, staining, storage temperature, and duration affect intracellular protein preservation in cytological specimens and to identify conditions suitable for in situ proximity ligation assay (isPLA). Design: Human cancer cell lines were processed under different preanalytical conditions. Intracellular proteins and protein protein interactions were assessed by isPLA, fluorescence microscopy, and quantitative image analysis. Slides were stored at room temperature or minus 20 Celsius, with or without MayGrunwaldGiemsa (MGG) staining. Results: Storage temperature had the greatest effect on protein signal. Unstained slides stored at minus 20 Celsius retained strong signals for up to 24 months, whereas room temperature storage reduced detectable protein interactions by approximately 95%. MGG staining further reduced signal intensity during prolonged storage. Nevertheless, stained slides remained suitable for isPLA when staining, microscopic evaluation, destaining, and antibody labeling were completed on the same day. Prolonged room temperature storage of stained slides caused loss of antigenicity, increased autofluorescence, and false negative findings. Conclusions: Low temperature storage and standardized preanalytical handling preserve intracellular protein signals in cytological specimens. These conditions support reliable single cell protein profiling of archived samples and their use in biomarker research and translational pathology.

Antibiotic resistance is an increasingly serious problem worldwide. Identifying the bacterial species can help guide antibiotic selection and improve patient outcomes. In sepsis, where every minute counts, rapid identification is particularly important, and new approaches are needed. In this study, deep learning models were trained to identify bacterial species from phase-contrast time-lapse images of cells growing in microfluidic traps, without fluorescent labels. We demonstrated this approach in two previous studies using laboratory strains. Here, the previous work is extended to clinical bacterial isolates, evaluating performance on isolates not seen during training. Furthermore, it was tested whether autofluorescence could aid species identification. Two validation sets were acquired in separate experiments from the training data, the first containing clinical-isolate traps with five species (Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae, Staphylococcus aureus and Enterobacter cloacae), imaged every two minutes, and the second containing clinical isolates of two species (E. coli and K. pneumoniae) recovered from spiked donor blood, imaged every minute. Training with the best previous CNN method achieved unsatisfactory results. Therefore, vision transformers were explored, and a self-supervised adaptation procedure was developed for pretrained DINOv3 using a frozen teacher, followed by supervised classification using time-lapse montages. Mean macro-F1 was 93.6 {+/-} 1.3% for isolates precultured in Mueller-Hinton medium using 60 frames (118 minutes) and 87.2 {+/-} 2.7% for isolates precultured in blood using 55 frames (54 minutes) (mean {+/-} sample standard deviation across training seeds). Performance was evaluated over time by increasing the number of frames available to the model during testing, and Fourier-domain filtering approximated reduced numerical aperture. The results are promising and suggest that the aperture can be decreased while retaining good results. However, further evaluation on independent isolates, experiments and patient samples is needed to establish generalization. Together with previous findings, these results bring us closer to rapid image-based diagnostics that could transform the initial treatment of acute bacterial infections by enabling better-informed antibiotic selection.

Raman spectroscopy (RS) has been shown to differentiate various disease states in tissue samples, yet the impact of sample substrate on spectral quality and downstream machine-learning (ML) performance is under-explored. We collected 960 spectra from 36 formalin-fixed, paraffin-embedded colonic specimens mounted in parallel on calcium fluoride (CaF2) and mirrored stainless-steel (SS) slides. Raw SS spectra were more intense than CaF2, and routine dewaxing left prominent paraffin peaks on CaF2 that were largely absent on SS. Extending CaF2 xylene immersion to 48 h reduced key paraffin peaks by 11 to 23%, aligning its biochemical profile with SS. Nested cross-validated classifiers were built to distinguish true disease (T1) from epithelial misplacement (EM). The highest accuracies were observed on SS (0.65 - 0.66), while CaF2 improved from 0.41 - 0.57 to 0.65 - 0.69 after extended dewaxing, chiefly via gains in recall. Although optimal dewaxing renders CaF2 diagnostically non-inferior, SS still offers superior accuracy, faster preparation, at approximately 4% of the cost, supporting its integration into scalable, AI-enabled RS pathology workflows.

Background: Ancient microbial evidence informs research on past health, diet, and environments, but its scarcity and susceptibility to contamination demand care in selecting and processing data. Language models can now turn a research request into an analysis plan, but an unconstrained planner may reference files that do not exist, apply operations a format forbids, or answer a question the data cannot settle. A planner used on such evidence therefore needs to be bounded, and its decisions recoverable afterwards. Results: We developed PaleoRigor around three commitments. Agent: a language model plans the analysis. Constraint: a validator checks every file reference, format and step dependency before anything runs, execution is restricted to predefined local modules, and a planning contract directs the model to refuse out-of-scope requests with a named reason code rather than answer them. Reproducibility: each run is exported with its plan, intermediate files, checksums and decisions. In a preregistered, frozen held-out evaluation, PaleoRigor passed 23 of 24 runs (95.8%; Wilson 95% confidence interval, 79.8-99.3%): 11 of 12 supported runs and all 12 prespecified boundary decisions. An ablation that removed this planning contract from the same model passed 19 of 24 (79.2%). Repeating the frozen design with a second model configuration yielded 24 of 24 successes for PaleoRigor and 19 of 24 for the ablated arm. Pooled post hoc across both configurations, PaleoRigor passed 47 of 48 runs (97.9%), compared with 38 of 48 (79.2%) for the ablated arms. All 96 run labels were recomputed from retained records and matched. Dataset-level tests matched read counts and compressed-file sizes for six public sequencing records and documented the removal of 114 duplicate sequence entries. A retraction-associated audit illustrated how expert review limited conclusions to file-level evidence. Conclusions: For paleomicrobiome data, pairing a language-model agent with explicit constraints and a reproducible record makes the agent's proposed analysis reviewable before its outputs support a biological claim. PaleoRigor delivers checked inputs, a recorded transformation history and a named reason for each request it refuses, and runs as a local desktop application. It prepares data for specialist analysis; ancient-DNA authentication and biological interpretation remain with the paleobiologist.