Scientific Reports

The erythrocyte sedimentation rate (ESR) is one of the most common and widely used laboratory diagnostic parameters in connection with inflammatory reactions and it is probable that every reader has already experienced a determination of their ESR. A rapid ESR is a non-specific parameter that provides information about the inflammatory process. Although the origins of this methodology date back to antiquity, the description of the process as the collapse of a percolating gel formed from erythrocytes has only recently been achieved. It was not yet known whether slow ESR has any medically relevant significance. Here we show a variety of clinical pictures that exhibit a systematically slow ESR (e.g., sickle cell disease, neuroacanthocytosis syndromes, chronic mountain sickness). Using a combination of measured data and physical modelling, we show how the accuracy and significance of ESR data can be increased. With this improved ESR (supraESR), we introduce a completely new, cost-effective diagnostic parameter, based on an established and easily automated measurement method, that enables low-cost screening for neuroacanthocytosis syndrome, a group of rare neurodegenerative diseases previously detectable only through complex diagnostic tests.

Dominance hierarchies are a fundamental aspect of social organization, enabling animals to minimize aggression and optimize access to resources. Previous studies have highlighted the energetic and physiological demands of dominant status, as well as the behavioral flexibility required of subordinates to navigate these hierarchies. Despite advancements in automated behavior tracking, limitations persist in tracking fine-scale, real-time interactions within complex social environments. Here, we developed and validated a novel RFID-based system to continuously monitor dominance hierarchies in group-housed male mice over 10 days. This system enabled unbiased behavioral inference across light phases and revealed spatial and temporal patterns of dominance behavior undetectable through traditional live-scored methods. Automated tracking accurately identified alpha individuals and consistently inferred linear hierarchies across cohorts, with greater precision for higher-ranked individuals. Behavioral metrics, such as transition frequencies and proximity to food zones, were consistent with dominance driven activity. Hormonal analyses revealed that higher-ranked mice exhibited increased leptin and peptide YY, consistent with heightened activity and satiety signaling, while lower C-peptide levels reflected greater metabolic demands of dominance. Furthermore, dominance rank was associated with differences in light-dark activity, which were in turn related to circulating hormone profiles. This study demonstrates the utility of automated RFID tracking in capturing dominance hierarchies with temporal and spatial granularity, while revealing links between social rank, metabolic regulation, and activity patterns advancing our understanding of social behavior dynamics.

Reported relative biological effectiveness (RBE) values for low-energy X-rays disagree, assays scoring initial DNA double-strand breaks (DSBs) returning about 1.1 and chromosome-level assays 2 to 4. Whether radiation quality varies within the diagnostic range, and how its comparison with a megavoltage reference depends on target scale, has not been quantified on a tube-potential series. A tungsten-anode tube with 1 mm Be and 2.5 mm Al filtration, with copper added in some cases, was modelled in PHITS for 40 to 200 kV. The spectra were transported into a water phantom in which absorbed dose, lineal-energy densities and cluster size distributions were scored for target diameters of 3 nm to 1 micrometre against a cobalt-60 reference; DSB yields were computed in the electron track-structure mode with the PHITS DNA damage tally. Between 40 and 120 kV the depth-dose ratio changed by a factor of 5.7 and the tube output by a factor of 42, whereas the dose-mean lineal energy varied by 2.5 % at 1 micrometre and 1.2 % at 3 nm against a reproducibility of 0.3 %. Relative to cobalt-60 it was 2.05 times larger at 1 micrometre but only 1.08 times larger at 3 nm, while DSB yields per unit dose were 5 to 7 % higher and constant across the range within the 2 % bound set by the statistics. Tube potential therefore changes the amount and distribution of dose but not its physical quality, and a stated RBE is incomplete without the target scale implied by the endpoint.

Behavioral test batteries are increasingly used to characterize multiple functional domains in zebrafish, yet the potential impact of test sequence on behavioral outcomes remains poorly defined. Here, we systematically evaluated whether test order influences behavioral responses in a three-assay battery comprising the novel tank test (NTT), mirror-induced aggression (MIA), and social preference (SP) test. Adult zebrafish (Danio rerio) were exposed to all possible permutations of the three assays in a fully counterbalanced design, allowing assessment of order effects across locomotor, anxiety-like, aggression-related, and social behaviors. Test order produced modest and parameter-specific effects, primarily affecting locomotor activity in the NTT and social proximity in the SP assay. Time-course analysis revealed within-test behavioral dynamics, with limited evidence that test order modulates early adaptation or late engagement with the testing environment but does not alter overall temporal response profiles. Sex-dependent effects were assay-specific and most pronounced in the NTT, with no consistent sex differences observed in MIA or SP. To evaluate the global structure of behavioral variation, Principal Component Analysis (PCA) was performed across assays. Despite localized effects of test order, no clear multivariate separation between test sequences was observed, indicating that sequential testing does not produce distinct baseline phenotypes. Together, these findings support the robustness and reproducibility of multidomain behavioral batteries while highlighting the importance of standardized test-order reporting to improve cross-study comparability.

Radiotherapy is common practice to treat cancer but produces significant side effects such as chronic pain. Cancer survivors report developing chronic pain due to their treatment even long after the cancer is cured. To understand the mechanisms underlying the radiotherapy-induced chronic pain, we assessed how ionising X-ray radiation exposure during 4 consecutive days of 5 Gy (total radiation dose of 20 Gy) affected dorsal root ganglia (DRG) sensory neurons (rodent F11 cell line). On the 5th day, we assessed known impacts of ionising radiation (senescence, oxidative stress, cellular metabolism, mitochondrial copy number, and mitochondrial respiration) followed by assessing expression of genes associated with populations of DRG neuronal fibres. We discovered that fractionated exposure to ionising radiation increased senescence, mitochondrial copy number, and modulated the NAD+/NADH pathway, but did not change the oxygen consumption rate nor induce oxidative stress 24 hours after the last irradiation exposure. Additionally, ionising radiation altered the expression of genes associated with mechanoreceptor fibres, known to have pro-nociceptive properties in the context of injury and chronic pain.

SH2 domains are phosphotyrosine-binding modules that play a critical role in cell signaling by mediating protein-protein interactions. While tyrosine phosphorylation has been shown to impact SH2 domain function in signaling, the specific effects of phosphorylation at different sites within the domain remain poorly understood. In this study, we selected two conserved regions of tyrosine phosphorylation within SH2 domains, near conserved binding interface residues, and developed approaches to evaluate the impact of those sites on ligand binding. Using a modified dot blot assay to screen phosphomimic mutations, we studied specific tyrosine residues within the PTPN11-N, LYN, and SYK-C SH2 domains, finding that the PTPN11 N-terminal site (Y63) modulates the specificity, reducing binding of physiologically relevant substrates. Our findings provide new insights into the regulatory mechanisms governing SH2 domain function and highlight the importance of site-specific phosphorylation in modulating protein-protein interactions in cell signaling pathways.

The morphology of cyanobacterial colonies plays a key role in harmful cyanobacterial blooms, with implications for their vertical migration, resistance against grazing, and light availability. In this study, we introduce the use of Optical Coherence Tomography (OCT) to investigate the three-dimensional morphology of cyanobacterial colonies. The technique enables non-invasive 3D imaging of colonies up to several millimeters in size, providing access to detailed mesoscale morphological features. Gas vesicles inside cells were shown to strongly improve image quality. We describe the sample preparation and image acquisition protocol, as well as an image processing pipeline that extracts mesoscale morphological features and provides a volumetric visualization of colonies. The method was tested for representative colonies of different cyanobacterial species while a dataset of volumetric images and measured mesoscale features was acquired for natural colonies of Microcystis. We demonstrate the utility of 3D imaging by quantifying the effects of irregular colony morphologies on their flotation velocity and the light availability within colonies. We anticipate OCT to become a key imaging technique to monitor populations of cyanobacterial colonies and investigate colony formation, with potential extensions to other colonial and aggregated organisms in freshwater and marine environments.

As silicon-based computing approaches fundamental physical limits, neurocomputing offers an energy-efficient alternative by leveraging the intrinsic non-linear dynamics of biological systems. To harness these dynamics, it is vital to understand the structure-function relationship governing how neural cultures process complex spatio-temporal information and how to appropriately decode the resulting neural electrophysiological activity. We investigated this utilizing a closed-loop electrophysiology platform, the CL1, to implement reservoir computing in human iPSC-derived neuronal networks. To systematically evaluate the variables driving neurocomputational capacity, we explored how cellular composition (cortical vs. hippocampal lineages), and the physical architecture (unstructured monolayers, 3D neural organoids, and modular networks confined by microfluidic devices) influenced electrophysiological properties and interacted with different decoding methodologies. Using a spatio-temporal version of a handwritten digit pattern recognition task (MNIST), we analyzed how these biological and analytical factors influenced classification accuracy. To ensure robust interpretation this required us to first demonstrated that reservoir computing decoding methods require strict artifact control and trial-based cross-validation to distinguish network computation from artifactual signal separability or temporal data leakage. Applying this validated frequency-domain pipeline, we suggest a clear functional hierarchy where structural modularity acts as a vital functional regularizer. Modular cortical cultures significantly outperformed unconstrained monolayers and organoids on MNIST. Furthermore, decoding frequency information from raw signals proved superior to typical time-bin decoding implementations. These findings establish that maximizing the computational potential of Synthetic Biological Intelligence, while avoiding false positives, requires a synergistic optimization of cellular identity, structural governance, and rigorous decoding logic. In doing so, this work provides a critical base establishing the criteria under which to evaluate neurocomputing implementations.

Formalin-fixed prostate tissue samples were imaged by propagation-based synchrotron phase contrast micro computed tomography ({micro}CT) with a 3D spatial resolution of ca. 3 {micro}m. Post-{micro}CT, samples were prepared for histology with sections close to coplanar with the transverse {micro}CT image planes. Haematoxylin and eosin stained sections were examined by an expert prostate histopathologist and compared qualitatively with corresponding {micro}CT-visible microstructure features. There is potential for {micro}CT to provide complimentary information to conventional histology and light microscopy without the need for preparation of stained thin sections. For the imaging conditions and spatial resolution of our study, {micro}CT may provide tissue architectural features similar to those used in Gleason grading, albeit without clear subcellular microstructure detail. At the spatial resolution of our study {micro}CT may provide novel 3D microstructure information for validation of diffusion weighted magnetic resonance imaging (MRI) methods. As an example, we demonstrate a qualitative correlation between {micro}CT-derived stromal fibre orientation and preferential water diffusion direction measured by diffusion tensor MRI microscopy of the same sample.

INTRODUCTION: Osteogenesis imperfecta (OI) is a genetic disorder primarily due to mutations in collagen type I-encoding genes, resulting in fragile bones, frequent fractures, pain, and mobility issues. Disease severity and phenotype vary widely, even with the same mutation, suggesting the importance of other factors within the bone microenvironment that influence disease severity. To study the role of such factors, we analyzed bone samples from OI patients and healthy controls using single-cell RNA sequencing to reveal if RNA expression profiles may uncover mechanisms behind OI phenotype. METHODS: Bone samples from surgeries of OI patients and healthy individuals isolated and RNA single-cell sequencing was performed, followed by quality control and bioinformatics analysis. Two healthy and three OI patients were included: two with type-I OI, characterized by a mutation in COL1A1 (collagen type I), and another with type-VIII OI, associated with LEPRE1 mutations, which disrupt the 3-hydroxylation of type I collagen. RESULTS: Clustering and differential expression analysis showed distinct subpopulations in mesenchymal and immune cells. In all OI samples, mesenchymal stromal cell (MSC) proportions were reduced compared to healthy controls. OI type-I patients showed decreased osteoblast numbers alongside an increase in osteoclast precursor cells. Whereas in OI type-VIII, all bone turnover-related cells (osteoblast, osteoclast precursor, and osteoclast) were elevated. Notably, BMP5 and RUNX1 were downregulated in MSCs from both OI types. DISCUSSION: This study demonstrates that the bone marrow microenvironment in OI is significantly altered beyond the known collagen defects. Single-cell RNA sequencing revealed reduced MSC numbers and downregulated osteogenic gene expression. Furthermore, alterations are patient-specific: OI type-I is characterized by reduced osteoblast counts, whereas OI type-VIII exhibits increased osteoblasts and osteoclasts. These findings highlight the critical role of impaired osteogenic differentiation and an abnormal bone remodeling environment in the pathology of OI.

Cardiac tissue is primarily made up of cardiomyocytes, which are regulated by the autonomic nervous system. We have used and developed approaches such as patch clamping and electrical stimulation-combined calcium imaging, computer modeling, optogenetics and chemogenetics combining with video-based Short-Time Fourier transformation (STFT) method to study the physiological activities of cardiomyocytes. The action potential of cardiomyocytes was found to be synchronized with calcium signals, which can be grouped into two categories by STFT. A mathematical model was developed to simulate the changes in electrical activities within cardiomyocytes caused by energy depletion, especially for 2-deoxy-D-glucose (2DG) treatment. Optogenetic and chemogenetics tools, such as ChR2(H134R), OptoXR-{beta}2AR and hM3Dq accelerated beating, while GR, ACR1 and hM4Di inhibited cardiomyocytes beating. A video-based STFT method was developed to visualize the beating frequency during these manipulations. An in vitro co-culture method was developed to study the relationship between sympathetic neuronal firing and calcium dynamics in cardiomyocytes. In vivo, electrocardiograph (ECG) measurements showed that Clozapine N-oxide (CNO) caused heart rates increasement in cTnT-hM3Dq virus injected mouse. However, it had no impact on cTnT-hM4Di virus injected mouse. This study provides comprehensive methodologies for studying cardiomyocyte physiology and manipulating heart rates in vitro and in vivo.

Background Chronic postsurgical pain (CPSP) affects between 7-23% and 13-44% of patients after hip and knee arthroplasty, respectively. Standardised methods of pain assessment provide superior evaluation of pain, including the Oxford Joint Score Pain Subscale (OJS-PS). We aim to estimate the proportion of patients with a phenotype consistent with CPSP through a k-medoids clustering technique and identify a threshold on the OJS-PS to highlight such patients at a population level. Methods In this cross-sectional study Patient Reported Outcomes Measures data 6-months after hip and knee arthroplasty from 2017 to 2025 were examined. An adapted k-medoid clustering technique utilising subsampling, batch assignment and probabilistic consensus allocated clusters. A receiver operator characteristic analysis identified a threshold on the OJS-PS noting the lowest scoring cluster. Our categorisation was compared to self-reported severe or moderate pain; sensitivity, specificity and accuracy of this categorisation were calculated. Results We analysed 109,542 hip and 113,799 knee arthroplasty patients; three clusters were used in each analysis. After hip arthroplasty: 14.4% of patients were assigned to the cluster with the lowest median OJS-PS of 11 [IQR 8 - 13]. A threshold of 15.5 classified patients as severe or moderate pain with 60.6% sensitivity, 91.0% specificity and 85.7% accuracy. Similarly, after knee arthroplasty, 25.3% were assigned to the cluster with the lowest median OJS-PS of 14 [IQR 11 - 16]. A threshold of 18.5 on the OJS-PS had an 85.4% sensitivity, 88.4% specificity and 87.8% accuracy for classifying patients with self-reported severe or moderate pain. Conclusions This robust and scalable clustering technique on ordinal clinical data estimates the proportion of patients reporting a phenotype consistent with CPSP. On a population level the thresholds identified on the OJS-PS could aid screening for potential CPSP patients 6 months after hip and knee arthroplasties.

Deep learning tools are increasingly used today, particularly in medical segmentation. A gap nonetheless remains in automating segmentation for insects. This work addresses the following question: can a generalist segmentation model, trained on several phylogenetically related orthopteran species, reliably automate head tissue segmentation from micro-CT images? To answer this, we used nnU-Net, a self-configuring 3D deep learning segmentation framework originally developed for medical imaging, whose core function, learning to recognize tissues of interest, applies directly to this context. Six anatomical classes were automated, comparing two training strategies: sequential fine-tuning, which adds species one at a time under the assumption that progressive learning would strengthen predictive power, and from-scratch training, in which the model learns the entire dataset simultaneously. The fine-tuning model (ModelB) reached a Dice coefficient (a measure of overlap between automated segmentation and manual ground truth, ranging from 0 to 1) of 0.7715, compared to 0.7664 for the from-scratch model (ModelC). Although both models produced accurate automated segmentations, no significant difference was found between the two training strategies (paired Wilcoxon test, n = 24, p = 0.243). Despite a dataset limited to 20 individuals and the absence of one method clearly outperforming the other, the models remain usable across the three species studied (Gryllus bimaculatus, Loxoblemmus equestris, L. doenitzi), including in the presence of pronounced sexual dimorphism. It reduces a 20 hour segmentation task to under a minute.

How water intake is initiated and maintained following V2 vasopressin receptor antagonism remains poorly understood. To elucidate the role of the V1b receptor in managing dehydration stress induced by V2 antagonism, we used deep learning-based computer vision to analyze drinking behavior in V1b knockout (V1bKO) and wild-type (WT) mice. While total water access and intake volume were comparable between genotypes, V1bKO mice exhibited distinct temporal dynamics. Modeling cumulative intake with the Hill equation revealed that the time required to reach 50\% of maximal water access was significantly shorter in V1bKO mice than in WT mice. This accelerated drinking effectively mitigated increases in serum osmolality and body weight loss. A reduced Hill's coefficient in V1bKO mice indicates a reduction of the rapid, cooperative-like water accumulation seen in WT mice. Furthermore, elevated basal hemoglobin levels in V1bKO mice were independent of dehydration, as confirmed via bone marrow transplant. Analysis of movement trajectories revealed that V1bKO mice exhibit a lower proportion of vertical movement (required for nozzle access) despite similar total distances traveled. Collectively, our results demonstrate that the V1b receptor critically regulates water-seeking behavior and osmotic homeostasis.

Background: Body composition is recognized as a major determinant of health outcomes, but its multidimensional nature makes clinical adoption challenging. We sought to develop and validate a body composition index (BCI) for all-cause mortality risk assessment, integrating variables of six body composition tissues. Methods: We analyzed 28509 consecutive patients undergoing myocardial perfusion imaging with routine low-dose chest CT attenuation correction (CTAC) scans acquired during myocardial perfusion imaging (MPI) at 12 centers across four countries. An artificial intelligence-based BCI was developed in a cohort of 15037 patients CTACs by integrating the CT-derived metrics of bone, skeletal muscle, and four adipose tissue compartments, coronary artery calcium score, and basic demographic variables (age, sex, BMI). The performance of BCI for mortality prediction was validated in an internal cohort of 6444 patients and an external cohort of 7028 patients by prognosis, calibration, net benefit, and explainability. Model-based simulation of tissue metrics modification was performed to evaluate estimated mortality risk reduction. Findings: During a median of 3.5 (IQR [1.9, 5.1]) years, 4697 (16%) patients died. In the external testing cohort, the BCI demonstrated excellent discrimination for mortality (area under receiver operating characteristic curve 0.78 (95% CI [0.76, 0.79]) and Harrell concordance index 0.75 [0.73, 0.76]), calibration, and net benefit overall and across pre-specified subgroups stratified by patient characteristics and imaging protocols. Visceral adipose tissue attenuation was the most influential body composition measure, followed by skeletal muscle volume. Simulated improvement in body composition was associated with significant mortality risk reduction. Interpretation: An index combining six body composition measures obtained opportunistically from routine chest CT provides robust mortality risk stratification. By converting complex body composition information into a single interpretable score, the BCI can facilitate clinical implementation of opportunistic CT biomarkers and guide individualized preventive strategies.

Microplicae are ridge-like membrane projections that are prominent features of many epithelial surfaces, yet little is known about the principles governing their spatial organisation. Determining whether microplicae represent stochastic membrane folds or biologically organised surface architectures is essential for understanding their formation and functional roles. Here, differential interference contrast images of human buccal epithelial cells were analysed using quantitative image-processing approaches. Ridge networks were segmented and characterised using complementary measurements of characteristic wavelength, including medial-axis and nearest-neighbour Voronoi analyses, together with skeleton-based metrics describing network architecture. Analysis of n=100 buccal epithelial cells sampled from n=10 donors revealed a reproducible sub-micron characteristic wavelength. Mean medial-axis spacing was 0.511 {+/-} 0.042 {micro}m and mean Voronoi nearest-neighbour spacing was 0.588 {+/-} 0.057 {micro}m. Characteristic wavelength exhibited CV of between only 8.26% and 9.67% across the dataset. However, metrics describing network architecture, including ridge density, branching and connectivity, varied by up to 109%. Donor-level analysis reported the same overall trends, with conservation of the characteristic wavelength while network parameters had considerably greater variation. These findings identify a previously unrecognised organising principle of microplical architecture, suggesting that epithelial membrane organisation is regulated through conservation of an intrinsic geometric length scale while network topology remains comparatively free to remodel.

This study aims to quantify the genetic similarity of different species (from fish to humans) to the human reference genome (pp6, Homo sapiens.GRCh38) based on the allele presence/absence patterns of 33 language/cognition related gene SNV loci, identify key breakpoints during evolution, and evaluate the enrichment of language and cognition genes at these breakpoints. We designed a similarity calculation method relying on binary features (four columns for A/T/C/G), adopted five difference/distance measures (Sorensen, Rogers, Nei, Reynolds, and Hellinger), and converted them into similarity values (1/(1+distance)). For each method, samples were independently ranked, the first derivative of similarity was computed, and the top 12 peaks were selected as candidate breakpoints. Results show that the similarity curves from the five methods are highly consistent (correlation coefficients >0.9), with major peaks concentrated at positions 355, 363, 381, 382, 390, 400, etc., where the corresponding samples are predominantly ancient hominins and primates. Furthermore, we defined 13 peak groups (starting positions 355-401). For each peak within a group, pairwise SNV differences between the peak apex sample and its immediate left neighbor were compared, and the intersection F_INTERSECTION (shared differential loci) was obtained. For each F_INTERSECTION, we calculated the proportions of language genes and cognition genes. In addition, we computed the differential sets between adjacent groups' F_INTERSECTION to trace the gradual emergence of new loci. In F_INTERSECTION, language genes accounted for an average of 59.5%, and cognition genes for an average of 62.9%. The proportion of language genes reached a peak at position 383 (61.2%), while cognition genes peaked at position 386 (64.9%). High frequency peak samples include c25, c27, and ja2, suggesting that language cognition genes may have undergone independent intensification during Eurasian evolution. Differential analysis between adjacent F_INTERSECTION revealed a stepwise acquisition of new loci from position 355 to 401, with three bursts of newly added loci along the entire evolutionary axis. This study provides a quantitative framework based on similarity curves, offers a novel molecular perspective for understanding the evolution of language and cognitive abilities, and highlights the potential importance of East Asian archaic hominins in the evolution of language cognition genes.

The ultrastructure of endothelial cells (ECs) "in situ" is of great interest due to their involvement in many physiological processes. In some organs, these cells form transcellular pores or fenestrae, allowing for the rapid exchange of molecules between blood and interstitium. Despite their importance, no optical images of these dynamic morphological structures have yet been acquired in situ. Major obstacles to their in-situ imaging are the lack of specifical labels for fenestrae and their size well below the optical diffraction limit. Here, we report how we have overcome these challenges and managed to visualize the EC ultrastructure in situ in 25 {micro}m thick liver sections. To enable this, a lipophilic, fluorescent membrane dye was infused into the portal vein of murine livers to stain the sinusoidal ECs before the organ was harvested. Tissue sections were subsequently imaged using a novel, super-resolution optical-sectioning structured illumination microscope (OS-SIM), providing approx. 170 nm spatial resolution with significantly faster image acquisition compared to confocal microscopy.

Light microscopy is one of the most powerful tools for understanding living systems, but the opacity of tissue prevents visualization of all but superficial layers. Several methods to clarify tissue have been developed, but most require fixed specimens. To address the challenge of improving resolution in functioning neuronal circuits, we developed a biocompatible clearing agent, iodixanol-ACSF, which is capable of increasing the transparency of living neuronal tissue. Brain-cleared mice were motile and unimpaired on a variety of behavioral tasks, and extracellular recordings showed that many cellular and circuit phenomena were well-preserved. In live iodixanol-ACSF cleared mouse brain tissue, both transmission and cellular-resolution fluorescence microscopy indicate improvements of 150-200% in penetration depth with one-third to one-half the laser intensity when compared to untreated tissue. Our results show that iodixanol-ACSF clearing will enable deeper imaging and extend our understanding of neuronal circuit function.

Language genes can be tentatively considered as a subset of cognitive genes, although they are often discussed separately. During the evolution of SNVs (single nucleotide variations) in cognition-related genes, do language genes and cognitive genes exhibit significantly different intensities of change at several key evolutionary moments--namely, the inflection points or derivative peak positions of similarity curves drawn from multi-SNV locus bases across samples? In this study, nine distance/similarity metrics (Bray-Curtis, Cosine, Pearson, Spearman, Hamming, Jaccard, Matching, Kulczynski, and Gower) were employed to analyze 413 samples from 11 taxonomic groups, targeting SNV loci in language/cognition-related genes (13,415 effective loci, approximately 400 loci per gene), with pp6 (Homo_sapiens.GRCh38) as the reference. For each method, sample similarities (defined as 1/(1+distance)) were independently sorted in ascending order to generate raw similarity scatterplots. Due to the large sample size and representativeness, the scatter density on the similarity curves was high, and no smoothing was applied. Derivative values were calculated from adjacent similarity differences to identify peaks of evolutionary rate change (top 10 peaks per method). Combined with functional annotations of 33 language/cognition-related genes, we quantified the difference scores and occurrence frequencies of the two gene categories at the peak positions. The results indicate that cognitive-related genes exhibit slightly higher occurrence frequencies in peak windows and higher average difference scores per gene than language genes. Comparative analysis of SNVs at the peak samples and their left-side windows revealed that at positions 381-382, all nine methods shared three intersecting mutation loci, involving language genes (NFXL1, SRGAP2, SRGAP2C); at positions 355-356, there was one intersecting mutation locus, involving a language gene (SRGAP2). This suggests that certain mutations in language genes may have played a distinctive role at critical junctures in the evolution of cognitive abilities.