Allometry and ecology shape eye size evolution in spiders.

Eye size affects many aspects of visual function, but eyes are costly to grow and maintain. The allometry of eyes can provide insight into this trade-off, but this has mainly been explored in species that have two eyes of equal size. By contrast, animals possessing larger visual systems can exhibit variable eye sizes within individuals. Spiders have up to four pairs of eyes whose sizes vary dramatically, but their ontogenetic, static, and evolutionary allometry has not yet been studied in a comparative context. We report variable dynamics in eye size across 1,098 individuals in 39 species and 8 families, indicating selective pressures and constraints driving the evolution of different eye pairs and lineages. Supplementing our sampling with a recently published phylogenetically comprehensive dataset, we confirmed these findings across more than 400 species; found that ecological factors such as visual hunting, web building, and circadian activity correlate with eye diameter; and identified significant allometric shifts across spider phylogeny using an unbiased approach, many of which coincide with visual hunting strategies. The modular nature of the spider visual system provides additional degrees of freedom and is apparent in the strong correlations between maximum/minimum investment and interocular variance and three key ecological factors. Our analyses suggest an antagonistic relationship between the anterior and posterior eye pairs. These findings shed light on the relationship between spider visual systems and their diverse ecologies and how spiders exploit their modular visual systems to balance selective pressures and optical and energetic constraints.

"Using network analysis modularity to group health code systems and decrease dimensionality in machine learning models".

Background: Machine learning (ML) prediction models in healthcare and pharmacy-related research face challenges with encoding high-dimensional Healthcare Coding Systems (HCSs) such as ICD, ATC, and DRG codes, given the trade-off between reducing model dimensionality and minimizing information loss. Objectives: To investigate using Network Analysis modularity as a method to group HCSs to improve encoding in ML models. Methods: The MIMIC-III dataset was utilized to create a multimorbidity network in which ICD-9 codes are the nodes and the edges are the number of patients sharing the same ICD-9 code pairs. A modularity detection algorithm was applied using different resolution thresholds to generate 6 sets of modules. The impact of four grouping strategies on the performance of predicting 90-day Intensive Care Unit readmissions was assessed. The grouping strategies compared: 1) binary encoding of codes, 2) encoding codes grouped by network modules, 3) grouping codes to the highest level of ICD-9 hierarchy, and 4) grouping using the single-level Clinical Classification Software (CCS). The same methodology was also applied to encode DRG codes but limiting the comparison to a single modularity threshold to binary encoding.The performance was assessed using Logistic Regression, Support Vector Machine with a non-linear kernel, and Gradient Boosting Machines algorithms. Accuracy, Precision, Recall, AUC, and F1-score with 95% confidence intervals were reported. Results: Models utilized modularity encoding outperformed ungrouped codes binary encoding models. The accuracy improved across all algorithms ranging from 0.736 to 0.78 for the modularity encoding, to 0.727 to 0.779 for binary encoding. AUC, recall, and precision also improved across almost all algorithms. In comparison with other grouping approaches, modularity encoding generally showed slightly higher performance in AUC, ranging from 0.813 to 0.837, and precision, ranging from 0.752 to 0.782. Conclusions: Modularity encoding enhances the performance of ML models in pharmacy research by effectively reducing dimensionality and retaining necessary information. Across the three algorithms used, models utilizing modularity encoding showed superior or comparable performance to other encoding approaches. Modularity encoding introduces other advantages such as it can be used for both hierarchical and non-hierarchical HCSs, the approach is clinically relevant, and can enhance ML models' clinical interpretation. A Python package has been developed to facilitate the use of the approach for future research.

Coordinated inflammation and immune response transcriptional regulation in breast cancer molecular subtypes.

Breast cancer, characterized by its complexity and diversity, presents significant challenges in understanding its underlying biology. In this study, we employed gene co-expression network analysis to investigate the gene composition and functional patterns in breast cancer subtypes and normal breast tissue. Our objective was to elucidate the detailed immunological features distinguishing these tumors at the transcriptional level and to explore their implications for diagnosis and treatment. The analysis identified nine distinct gene module clusters, each representing unique transcriptional signatures within breast cancer subtypes and normal tissue. Interestingly, while some clusters exhibited high similarity in gene composition between normal tissue and certain subtypes, others showed lower similarity and shared traits. These clusters provided insights into the immune responses within breast cancer subtypes, revealing diverse immunological functions, including innate and adaptive immune responses. Our findings contribute to a deeper understanding of the molecular mechanisms underlying breast cancer subtypes and highlight their unique characteristics. The immunological signatures identified in this study hold potential implications for diagnostic and therapeutic strategies. Additionally, the network-based approach introduced herein presents a valuable framework for understanding the complexities of other diseases and elucidating their underlying biology.

Beyond CREA: Evolutionary patterns of non-allometric shape variation and divergence in a highly allometric clade of murine rodents.

The shared functions of the skull are thought to result in common evolutionary patterns in mammalian cranial shape. Craniofacial evolutionary allometry (CREA) is a particularly prominent pattern where larger species display proportionally elongate facial skeletons and smaller braincases. It was recently proposed that CREA arises from biomechanical effects of cranial scaling when diets are similar. Thus, deviations from CREA should occur with changes in cranial biomechanics, for example due to dietary change. Here, we test this using 3D geometric morphometric analysis in a dataset of Australian murine crania, which are highly allometric. We contrast allometric and non-allometric variation in the cranium by comparing evolutionary mode, allometry, ordinations, as well as allometry, integration, and modularity in functional modules. We found evidence of stabilising selection in allometry-containing and size-free shape, and substantial non-allometric variation aligned with dietary specialisation in parallel with CREA. Integration among cranial modules was higher, and modularity lower, with size included, but integration between rostrum and cranial vault, which are involved in the CREA pattern, dropped dramatically after size removal. Our results thus support the hypothesis that CREA is a composite arising from selection on cranial function, with substantial non-allometric shape variation occurring alongside CREA where dietary specialisation impacts selection on gnawing function. This emphasises the need to research mammalian cranial evolution in the context of allometric and non-allometric selection on biomechanical function.

Reconstruction of mechanical leg axis using non-modular cemented hinged prosthesis in complex primary total knee arthroplasty.

PURPOSE: Modular cementless knee arthroplasty systems are capable of precise reconstruction of the mechanical axis. However, they are considered more susceptible to complications. In contrast, non-modular cemented systems are said to be more forgiving and show good long-term results. The aim of this study was to investigate the resulting leg axis after implantation of a non-modular cemented rotating hinged knee prosthesis. Furthermore, potential risk factors for the occurrence of malalignment and complications should be identified. METHODS: Between 2005 and 2015, 115 patients could be included in this monocentric retrospective cohort study. All patients underwent primary hinged non-modular cemented total knee arthroplasty. Preoperative and postoperative standardized long radiographs were analysed to determine resulting leg axis. Furthermore, epidemiological and intraoperative data as well as perioperative complications were surveyed. RESULTS: Average leg axis was 5.8° varus preoperatively and 0.6° valgus postoperatively. Considering an axis deviation of 3° as the target corridor, 27% of all cases examined were outside the desired range. 21% cases showed a femoral deviation from the target corridor and 15% showed a tibial deviation. There was a significant relationship between the preoperative mLDFA and the mechanical alignment of the femoral component (R = 0.396, p < 0.001) as well as between the preoperative mMPTA and the mechanical alignment of the tibial component (R = 0.187, p = 0.045). The mean operative duration was 96 min. No periprosthetic fractures were observed within the study cohort. CONCLUSION: The main result of the present work is that a non-modular cemented rotating hinged knee arthroplasty system can reconstruct the mechanical leg axis precisely and comparable to modular cementless and unconstrained total knee prostheses. Component malalignment is primarily dependent upon extraarticular deformity preoperatively. Periprosthetic fracture rates and duration of surgery were lower compared with current literature. LEVEL OF EVIDENCE: Level III: Retrospective cohort study.

Community connectivity and local heterogeneity explain animal species co-occurrences within pond communities.

Metacommunity processes have the potential to determine most features of the community structure. However, species diversity has been the dominant focus of studies. Nestedness, modularity and checkerboard distribution of species occurrences are main components of biodiversity organisation. Within communities, these patterns emerge from the interaction between functional diversity, spatial heterogeneity and resource availability. Additionally, the connectivity determines the pool of species for community assembly and, eventually, the pattern of species co-occurrence within communities. Despite the recognised theoretical expectations, the change in occurrence patterns within communities along ecological gradients has seldom been considered. Here, we analyse the spatial occurrence of animal species along sampling units within 18 temporary ponds and its relationship with pond environments and geographic isolation. Isolated ponds presented a nested organisation of species with low spatial segregation-modularity and checkerboard-and the opposite was found for communities with high connectivity. A pattern putatively explained by high functional diversity in ponds with large connectivity and heterogeneity, which determines that species composition tracks changes in microhabitats. On the contrary, nestedness is promoted in dispersal-limited communities with low functional diversity, where microhabitat filters mainly affect richness without spatial replacement between functional groups. Vegetation biomass promotes nestedness, probably due to the observed increase in spatial variance in biomass with the mean biomass. Similarly, the richness of vegetation reduced the spatial segregation of animals within communities. This result may be due to the high plant diversity of the pond that is observed similarly along all sampling units, which promotes the spatial co-occurrence of species at this scale. In the study system, the spatial arrangement of species within communities is related to local drivers as heterogeneity and metacommunity processes by means of dispersal between communities. Patterns of species co-occurrence are interrelated with community biodiversity and species interactions, and consequently with most functional and structural properties of communities. These results indicate that understanding the interplay between metacommunity processes and co-occurrence patterns is probably more important than previously thought to understand biodiversity assembly and functioning.

Los procesos metacomunitarios tienen el potencial de determinar la mayoría de las características de la estructura de las comunidades. Sin embargo, los trabajos se han enfocado principalmente en los patrones de diversidad de especies. El anidamiento, la modularidad y la distribución en damero de la ocurrencia espacial de las especies son propiedades básicas de las comunidades. Estos patrones surgen de la interacción entre la diversidad funcional, la heterogeneidad espacial y la disponibilidad de recursos dentro de las comunidades. Además, el pool de especies disponibles para el ensamblaje está determinado por la conectividad de la comunidad, afectando así su patrón de co­ocurrencia de especies. A pesar de las reconocidas expectativas teóricas, el cambio en los patrones de ocurrencia dentro de las comunidades a lo largo de gradientes ecológicos ha sido poco considerado. Aquí, analizamos la ocurrencia espacial de especies animales dentro de 18 charcos temporales y su relación con las características ambientales y el aislamiento geográfico de los charcos. Los charcos aislados presentaron alto anidamiento espacial mientras que los charcos de alta conectividad una distribución de ocurrencias modular y en damero. Por un lado, la baja diversidad funcional en charcos aislados, determinaría que los filtros microambientales afecten la riqueza de especies sin reemplazo espacial entre grupos funcionales, promoviendo un arreglo anidado de ocurrencias. Por otro lado, la alta diversidad funcional en charcos con alta conectividad y heterogeneidad permitiría el reemplazo espacial de especies en gradientes microambientales, determinando los patrones de segregación observados. La biomasa vegetal promueve el anidamiento, probablemente debido al aumento observado en la variación espacial de la biomasa con la biomasa media. La riqueza vegetal también redujo la segregación espacial de los animales dentro de las comunidades. Este resultado puede deberse a que la alta diversidad de plantas de los charcos es también observada a nivel de unidades muestreales, favoreciendo esto la coexistencia espacial de especies. El arreglo espacial de especies dentro de las comunidades estudiadas estaría determinado tanto por factores locales como la heterogeneidad, como por procesos regionales operando a través de la dispersión de individuos entre comunidades. Los patrones de co­ocurrencia de especies están interrelacionados con la diversidad comunitaria y las interacciones bióticas, y consecuentemente con la mayoría de las propiedades estructurales y funcionales de las comunidades. Este estudio evidencia la importancia de la conexión entre procesos metacomunitarios y la co­ocurrencia espacial de especies para comprender el ensamblaje y funcionamiento de la biodiversidad.

A comparative anatomical network analysis of the human and chimpanzee brains.

Spatial interactions among anatomical elements help to identify topological factors behind morphological variation and can be investigated through network analysis. Here, a whole-brain network model of the chimpanzee (Pan troglodytes, Blumenbach 1776) is presented, based on macroanatomical divisions, and compared with a previous equivalent model of the human brain. The goal was to contrast which regions are essential in the geometric balance of the brains of the two species, to compare underlying phenotypic patterns of spatial variation, and to understand how these patterns might have influenced the evolution of human brain morphology. The human and chimpanzee brains share morphologically complex inferior-medial regions and a topological organization that matches the spatial constraints exerted by the surrounding braincase. These shared topological features are interesting because they can be traced back to the Chimpanzee-Human Last Common Ancestor, 7-10 million years ago. Nevertheless, some key differences are found in the human and chimpanzee brains. In humans, the temporal lobe, particularly its deep and medial limbic aspect (the parahippocampal gyrus), is a crucial node for topological complexity. Meanwhile, in chimpanzees, the cerebellum is, in this sense, more embedded in an intricate spatial position. This information helps to interpret brain macroanatomical change in fossil hominids.

Properties of vertebrate predator-prey networks in the high Arctic.

Predation is an important ecological process that can significantly impact the maintenance of ecosystem services. In arctic environments, the relative ecological importance of predation is thought to be increasing due to climate change, partly because of increased productivity with rising temperatures. Therefore, understanding predator-prey interactions in arctic ecosystems is vital for the sustainable management of these northern regions. Network theory provides a framework for quantifying the structures of ecological interactions. In this study, we use dietary observations on mammalian and avian predators in a high arctic region, including isolated peninsulas on Ellesmere Island and north Greenland, to construct bipartite trophic networks. We quantify the complexity, specialization, and nested as well as modular structures of these networks and also determine if these properties varied among the peninsulas. Mammal prey remains were the dominant diet item for all predators, but there was spatial variation in diet composition among peninsulas. The predator-prey networks were less complex, had more specialized interactions, and were more nested and more modular than random expectations. However, the networks displayed only moderate levels of modularity. Predator species had less specialized interactions with prey than prey had with predators. All network properties differed among the peninsulas, which highlights that ecosystems often show complex responses to environmental characteristics. We suggest that gaining knowledge about spatial variation in the characteristics of predator-prey interactions can enhance our ability to manage ecosystems exposed to environmental perturbations, particularly in high arctic environments subject to rapid environmental change.

Functional modularity and mechanical stress shape plastic responses during fish development.

The adaptive potential of plastic phenotypes relies on combined developmental responses. We investigated how manipulation of developmental conditions related to foraging mode in the fish Megaleporinus macrocephalus induces plastic responses at different levels: 1) functional modularity of skull bones, 2) biomechanical properties of the chondrocranium using Finite Element Models, 3) bmp4 expression levels, used as a proxy for molecular pathways involved in bone responses to mechanical load. We identified new modules in experimental groups, suggesting increased integration in specific head bone elements associated with the development of subterminal and upturned mouths, which are major features of Megaleporinus plastic morphotypes released in the lab. Plastic responses in head shape involved differences in the magnitude of mechanical stress, which seem restricted to certain chondrocranium regions. Three bones represent a 'mechanical unit' related to changes in mouth position induced by foraging mode, suggesting that functional modularity might be enhanced by the way specific regions respond to mechanical load. Differences in bmp4 expression levels between plastic morphotypes indicate associations between molecular signaling pathways and biomechanical responses to load. Our results offer a multilevel perspective of epigenetic factors involved in plastic responses, expanding our knowledge about mechanisms of developmental plasticity that originate novel complex phenotypes.

Single-cell transcriptomics reveals stage- and side-specificity of gene modules in colorectal cancer.

BACKGROUND: An understanding of mechanisms underlying colorectal cancer (CRC) development and progression is yet to be fully elucidated. This study aims to employ network theoretic approaches to analyse single cell transcriptomic data from CRC to better characterize its progression and sided-ness. METHODS: We utilized a recently published single-cell RNA sequencing data (GEO-GSE178341) and parsed the cell X gene data by stage and side (right and left colon). Using Weighted Gene Co-expression Network Analysis (WGCNA), we identified gene modules with varying preservation levels (weak or strong) of network topology between early (pT1) and late stages (pT234), and between right and left colons. Spearman's rank correlation (ρ) was used to assess the similarity or dissimilarity in gene connectivity. RESULTS: Equalizing cell counts across different stages, we detected 13 modules for the early stage, two of which were non-preserved in late stages. Both non-preserved modules displayed distinct gene connectivity patterns between the early and late stages, characterized by low ρ values. One module predominately dealt with myeloid cells, with genes mostly enriched for cytokine-cytokine receptor interaction potentiallystimulating myeloid cells to participate in angiogenesis. The second module, representing a subset of epithelial cells, was mainly enriched for carbohydrate digestion and absorption, influencing the gut microenvironment through the breakdown of carbohydrates. In the comparison of left vs. right colons, two of 12 modules identified in the right colon were non-preserved in the left colon. One captured a small fraction of epithelial cells and was enriched for transcriptional misregulation in cancer, potentially impacting communication between epithelial cells and the tumor microenvironment. The other predominantly contained B cells with a crucial role in maintaining human gastrointestinal health and was enriched for B-cell receptor signalling pathway. CONCLUSIONS: We identified modules with topological and functional differences specific to cell types between the early and late stages, and between the right and left colons. This study enhances the understanding of roles played by different cell types at different stages and sides, providing valuable insights for future studies focused on the diagnosis and treatment of CRC.

Graph analysis uncovers an opposing impact of methylphenidate on connectivity patterns within default mode network sub-divisions.

BACKGROUND: The Default Mode Network (DMN) is a central neural network, with recent evidence indicating that it is composed of functionally distinct sub-networks. Methylphenidate (MPH) administration has been shown before to modulate impulsive behavior, though it is not yet clear whether these effects relate to MPH-induced changes in DMN connectivity. To address this gap, we assessed the impact of MPH administration on functional connectivity patterns within and between distinct DMN sub-networks and tested putative relations to variability in sub-scales of impulsivity. METHODS: Fifty-five right-handed healthy adults underwent two resting-state functional MRI (rs-fMRI) scans, following acute administration of either MPH (20 mg) or placebo, via a randomized double-blind placebo-controlled design. Graph modularity analysis was implemented to fractionate the DMN into distinct sub-networks based on the impact of MPH (vs. placebo) on DMN connectivity patterns with other neural networks. RESULTS: MPH administration led to an overall decreased DMN connectivity, particularly with the auditory, cinguloopercular, and somatomotor networks, and increased connectivity with the parietomedial network. Graph analysis revealed that the DMN could be fractionated into two distinct sub-networks, with one exhibiting MPH-induced increased connectivity and the other decreased connectivity. Decreased connectivity of the DMN sub-network with the cinguloopercular network following MPH administration was associated with elevated impulsivity and non-planning impulsiveness. CONCLUSION: Current findings highlight the intricate effects of MPH administration on DMN rs-fMRI connectivity, uncovering its opposing impact on distinct DMN sub-divisions. MPH-induced dynamics in DMN connectivity patterns with other neural networks may account for some of the effects of MPH administration on impulsive behavior.

Sleep and physical activity measures are associated with resting-state network segregation in non-demented older adults.

Greater physical activity and better sleep are associated with reduced risk of cognitive decline and dementia among older adults, but little is known about their combined associations with measures of brain function and neuropathology. This study investigated potential independent and interactive cross-sectional relationships between actigraphy-estimated total volume of physical activity (TVPA) and sleep patterns [i.e., total sleep time (TST), sleep efficiency (SE)] with resting-state functional magnetic resonance imaging (rs-fMRI) measures of large scale network connectivity and positron emission tomography (PET) measures of amyloid-ß. Participants were 135 non-demented older adults from the BIOCARD study (116 cognitively normal and 19 with mild cognitive impairment; mean age = 70.0 years). Using multiple linear regression analyses, we assessed the association between TVPA, TST, and SE with connectivity within the default-mode, salience, and fronto-parietal control networks, and with network modularity, a measure of network segregation. Higher TVPA and SE were independently associated with greater network modularity, although the positive relationship of SE with modularity was only present in amyloid-negative individuals. Additionally, higher TVPA was associated with greater connectivity within the default-mode network, while greater SE was related to greater connectivity within the salience network. In contrast, longer TST was associated with lower network modularity, particularly among amyloid-positive individuals, suggesting a relationship between longer sleep duration and greater network disorganization. Physical activity and sleep measures were not associated with amyloid positivity. These data suggest that greater physical activity levels and more efficient sleep may promote more segregated and potentially resilient functional networks and increase functional connectivity within specific large-scale networks and that the relationship between sleep and functional networks connectivity may depend on amyloid status.

Personal Income Performance Correlates with Brain Structural Network Modularity but Not Intelligence Quotient.

Introduction: This study aims to use diffusion tensor imaging (DTI) in conjunction with brain graph techniques to define brain structural connectivity and investigate its association with personal income (PI) in individuals of various ages and intelligence quotients (IQ). Methods: MRI examinations were performed on 55 male subjects (mean age: 40.1 ± 9.4 years). Graph data and metrics were generated, and DTI images were analyzed using tract-based spatial statistics (TBSS). All subjects underwent the Wechsler Adult Intelligence Scale for a reliable estimation of the full-scale IQ (FSIQ), which includes verbal comprehension index, perceptual reasoning index, working memory index, and processing speed index. The performance score was defined as the monthly PI normalized by the age of the subject. Results: The analysis of global graph metrics showed that modularity correlated positively with performance score (p = 0.003) and negatively with FSIQ (p = 0.04) and processing speed index (p = 0.005). No significant correlations were found between IQ indices and performance scores. Regional analysis of graph metrics showed modularity differences between right and left networks in sub-cortical (p = 0.001) and frontal (p = 0.044) networks. TBSS analysis showed greater axial and mean diffusivities in the high-performance group in correlation with their modular brain organization. Conclusion: This study showed that PI performance is strongly correlated with a modular organization of brain structural connectivity, which implies short and rapid networks, providing automatic and unconscious brain processing. Additionally, the lack of correlation between performance and IQ suggests a reduced role of academic reasoning skills in performance to the advantage of high uncertainty decision-making networks.

An inclusive anatomical network analysis of human craniocerebral topology.

The human brain's complex morphology is spatially constrained by numerous intrinsic and extrinsic physical interactions. Spatial constraints help to identify the source of morphological variability and can be investigated by employing anatomical network analysis. Here, a model of human craniocerebral topology is presented, based on the bony elements of the skull at birth and a previously designed model of the brain. The goal was to investigate the topological components fundamental to the craniocerebral geometric balance, to identify underlying phenotypic patterns of spatial arrangement, and to understand how these patterns might have influenced the evolution of human brain morphology. Analysis of the craniocerebral network model revealed that the combined structure of the body and lesser wings of the sphenoid bone, the parahippocampal gyrus, and the parietal and ethmoid bones are susceptible to sustain and apply major spatial constraints that are likely to limit or channel their morphological evolution. The results also showcase a high level of global integration and efficient diffusion of biomechanical forces across the craniocerebral system, a fundamental aspect of morphological variability in terms of plasticity. Finally, community detection in the craniocerebral system highlights the concurrence of a longitudinal and a vertical modular partition. The former reflects the distinct morphogenetic environments of the three endocranial fossae, while the latter corresponds to those of the basicranium and calvaria.

Elevated Blood Tantalum Concentrations in Patients Following Reconstruction of Severe Acetabular Defects in Total Hip Arthroplasty Using Modular Tantalum Augments in Combination With Uncemented Tantalum Cups.

INTRODUCTION: The reconstruction of acetabular defects in total hip arthroplasty (THA) can be challenging. An option to treat uncontained acetabular defects is to use modular tantalum augments in combination with cementless press-fit cups. However, modularity is associated with an increased risk of debonding and mechanical failure. In addition, metal wear particles can be released due to micromotions at the implant interface. Clinical data on the long-term results of this treatment strategy is limited. The purposes of this study were: (1) to evaluate the clinical and radiological outcome of complex THA using modular trabecular metal augments and uncemented revision cups; (2) to investigate the blood tantalum concentrations in these patients at mid-term (mean 4.5 year) follow-up; and (3) to report complications and mechanisms of failure related to this procedure. MATERIALS AND METHODS: In this single-center study, we retrospectively reviewed data from a consecutive cohort of 27 patients who underwent complex acetabular defect reconstruction using a modular tantalum acetabular augment in combination with an uncemented tantalum cup. We evaluated the implant survival, and the radiological and clinical outcomes after a mean follow-up of 4.5 years (SD 2.1; range 2.5 to 10.6 years) using patient-reported outcome scores (PROMs). Blood samples were analyzed regarding tantalum concentration and compared with a control group. RESULTS: The cumulative survival rate at 4.5 years with the endpoint "revision of the acetabular component for aseptic loosening" was 94.4% (95% confidence interval (CI) 71.6 to 99.2) and 82.9% (95 % CI 60.5 to 93.3) for the endpoint "revision for any reason." The PROMs improved significantly up to the latest follow-up, and radiographic data showed no signs of loosening or implant migration. Median blood tantalum concentrations were significantly higher in the study group (0.15 µg/L) compared to the control group (0.002 µg/L) (P < 0.001). CONCLUSIONS: This study demonstrated acceptable clinical and radiological results of cementless revision THA using modular trabecular metal implants for the reconstruction of large acetabular defects. Tantalum concentrations were significantly higher in patients who had tantalum implants compared to the control group, however, the systemic and local effects of an increased tantalum exposure are not yet fully understood and have to be further investigated.

Mid-Term Outcomes of a Short Modular Neck-Preserving Cementless Hip Stem: A Retrospective Study With a 6-Year Minimum Follow-Up.

Background: The neck-preserving cementless short stem represents a valid therapeutic option for total hip replacement in high-functional-demand patients, but few studies are available about the use of modularity in the last-generation short stem. The aim of the study was to evaluate the mid-term survival of a specific implant design that combines partial collum short hip stem with neck modularity; assessing the functional status was the second endpoint. Methods: A retrospective single-center cohort study was conducted on 75 patients aged 35 to 80 years, with a minimum 6-year follow-up. Patients with neurological/rheumatic pathologies and previous hip surgeries were excluded. All the patients underwent total hip replacement with a short modular neck-preserving cementless hip stem. Clinical outcomes, complications, revisions, and the Western Ontario and McMaster Universities Osteoarthritis Index, Harris hip score, and Short Form 12-Item Health Survey (SF-12) questionnaires were evaluated. The results were compared with healthy population's data extracted from the literature, stratified by age. Results: The Kaplan-Meier analysis revealed a 10-year implant survival rate of 96.7%, coupled with a revision rate of 1.3%. Results showed a Harris hip score and physical SF-12 significantly lower and a mental SF-12 higher when compared to healthy population. No statistically significant differences emerged when comparing groups based on neck modularity. Conclusions: The short modular neck-preserving cementless hip stem emerged as a reasonable choice for patients with elevated functional demands, ensuring good clinical outcomes while preserving bone integrity. The use of a modular neck in short stems didn't show any mechanical problems in the mid-term.

Correlated evolution of beak and braincase morphology is present only in select bird clades.

Complex morphological structures, such as skulls or limbs, are often composed of multiple morphological components (e.g., bones, sets of bones) that may evolve in a covaried manner with one another. Previous research has reached differing conclusions on the number of semi-independent units, or modules, that exist in the evolution of structures and on the strength of the covariation, or integration, between these hypothesized modules. We focus on the avian skull as an example of a complex morphological structure for which highly variable conclusions have been reached in the numerous studies analyzing support for a range of simple to complex modularity hypotheses. We hypothesized that past discrepancies may stem from both the differing densities of data used to analyze support for modularity hypotheses and the differing taxonomic levels of study. To test these hypotheses, we applied a comparative method to 3D geometric morphometric data collected from the skulls of a diverse order of birds (the Charadriiformes) to test support for 11 distinct hypotheses of modular skull evolution. Across all Charadriiformes, our analyses suggested that charadriiform skull evolution has been characterized by the semi-independent, but still correlated, evolution of the beak from the rest of the skull. When we adjusted the density of our morphometric data, this result held, but the strength of the signal varied substantially. Additionally, when we analyzed subgroups within the order in isolation, we found support for distinct hypotheses between subgroups. Taken together, these results suggest that differences in the methodology of past work (i.e., statistical method and data density) as well as clade-specific dynamics may be the reasons past studies have reached varying conclusions.

A comparative study of supervised and unsupervised machine learning algorithms applied to human microbiome.

Background: The human microbiome, consisting of diverse bacte-rial, fungal, protozoan and viral species, exerts a profound influence on various physiological processes and disease susceptibility. However, the complexity of microbiome data has presented significant challenges in the analysis and interpretation of these intricate datasets, leading to the development of specialized software that employs machine learning algorithms for these aims. Methods: In this paper, we analyze raw data taken from 16S rRNA gene sequencing from three studies, including stool samples from healthy control, patients with adenoma, and patients with colorectal cancer. Firstly, we use network-based methods to reduce dimensions of the dataset and consider only the most important features. In addition, we employ supervised machine learning algorithms to make prediction. Results: Results show that graph-based techniques reduces dimen-sion from 255 up to 78 features with modularity score 0.73 based on different centrality measures. On the other hand, projection methods (non-negative matrix factorization and principal component analysis) reduce dimensions to 7 features. Furthermore, we apply supervised machine learning algorithms on the most important features obtained from centrality measures and on the ones obtained from projection methods, founding that the evaluation metrics have approximately the same scores when applying the algorithms on the entire dataset, on 78 feature and on 7 features. Conclusions: This study demonstrates the efficacy of graph-based and projection methods in the interpretation for 16S rRNA gene sequencing data. Supervised machine learning on refined features from both approaches yields comparable predictive performance, emphasizing specific microbial features-bacteroides, prevotella, fusobacterium, lysinibacillus, blautia, sphingomonas, and faecalibacterium-as key in predicting patient conditions from raw data.

Comparing the Clique Percolation algorithm to other overlapping community detection algorithms in psychological networks: A Monte Carlo simulation study.

In psychological networks, one limitation of the most used community detection algorithms is that they can only assign each node (symptom) to a unique community, without being able to identify overlapping symptoms. The clique percolation (CP) is an algorithm that identifies overlapping symptoms but its performance has not been evaluated in psychological networks. In this study, we compare the CP with model parameters chosen based on fuzzy modularity (CPMod) with two other alternatives, the ratio of the two largest communities (CPRat), and entropy (CPEnt). We evaluate their performance to: (1) identify the correct number of latent factors (i.e., communities); and (2) identify the observed variables with substantive (and equally sized) cross-loadings (i.e., overlapping symptoms). We carried out simulations under 972 conditions (3x2x2x3x3x3x3): (1) data categories (continuous, polytomous and dichotomous); (2) number of factors (two and four); (3) number of observed variables per factor (four and eight); (4) factor correlations (0.0, 0.5, and 0.7); (5) size of primary factor loadings (0.40, 0.55, and 0.70); (6) proportion of observed variables with substantive cross-loadings (0.0%, 12.5%, and 25.0%); and (7) sample size (300, 500, and 1000). Performance was evaluated through the Omega index, Mean Bias Error (MBE), Mean Absolute Error (MAE), sensitivity, specificity, and mean number of isolated nodes. We also evaluated two other methods, Exploratory Factor Analysis and the Walktrap algorithm modified to consider overlap (EFA-Ov and Walk-Ov, respectively). The Walk-Ov displayed the best performance across most conditions and is the recommended option to identify communities with overlapping symptoms in psychological networks.

Modular organization of functional brain networks in patients with degenerative cervical myelopathy.

Previous studies have indicated that brain functional plasticity and reorganization in patients with degenerative cervical myelopathy (DCM). However, the effects of cervical cord compression on the functional integration and separation between and/or within modules remain unclear. This study aimed to address these questions using graph theory. Functional MRI was conducted on 46 DCM patients and 35 healthy controls (HCs). The intra- and inter-modular connectivity properties of the whole-brain functional network and nodal topological properties were then calculated using theoretical graph analysis. The difference in categorical variables between groups was compared using a chi-squared test, while that between continuous variables was evaluated using a two-sample t-test. Correlation analysis was conducted between modular connectivity properties and clinical parameters. Modules interaction analyses showed that the DCM group had significantly greater inter-module connections than the HCs group (DMN-FPN: t = 2.38, p = 0.02); inversely, the DCM group had significantly lower intra-module connections than the HCs group (SMN: t = - 2.13, p = 0.036). Compared to HCs, DCM patients exhibited higher nodal topological properties in the default-mode network and frontal-parietal network. In contrast, DCM patients exhibited lower nodal topological properties in the sensorimotor network. The Japanese Orthopedic Association (JOA) score was positively correlated with inter-module connections (r = 0.330, FDR p = 0.029) but not correlated with intra-module connections. This study reported alterations in modular connections and nodal centralities in DCM patients. Decreased nodal topological properties and intra-modular connection in the sensory-motor regions may indicate sensory-motor dysfunction. Additionally, increased nodal topological properties and inter-modular connection in the default mode network and frontal-parietal network may serve as a compensatory mechanism for sensory-motor dysfunction in DCM patients. This could provide an implicative neural basis to better understand alterations in brain networks and the patterns of changes in brain plasticity in DCM patients.