Data Structure Research Articles

Real-world data to support post-market safety and performance of embolization coils: evidence generation from a medical device manufacturer and data institute partnership

Abstract Background Recognizing the limitations of pre-market clinical data, regulatory authorities have embraced total product lifecycle management with post-market surveillance (PMS) data to assess medical device safety and performance. One method of proactive PMS involves the analysis of real-world data (RWD) through retrospective review of electronic health records (EHR). Because EHRs are patient-centered and focused on providing tools that clinicians use to determine care rather than collecting information on individual medical products, the process of transforming RWD into real-world evidence (RWE) can be laborious, particularly for medical devices with broad clinical use and extended clinical follow-up. This study describes a method to extract RWD from EHR to generate RWE on the safety and performance of embolization coils. Methods Through a partnership between a non-profit data institute and a medical device manufacturer, information on implantable embolization coils’ use was extracted, linked, and analyzed from clinical data housed in an electronic data warehouse from the state of Indiana’s largest health system. To evaluate the performance and safety of the embolization coils, technical success and safety were defined as per the Society of Interventional Radiology guidelines. A multi-prong strategy including electronic and manual review of unstructured (clinical chart notes) and structured data (International Classification of Disease codes), was developed to identify patients with relevant devices and extract data related to the endpoints. Results A total of 323 patients were identified as treated using Cook Medical Tornado, Nester, or MReye embolization coils between 1 January 2014 and 31 December 2018. Available clinical follow-up for these patients was 1127 ± 719 days. Indications for use, adverse events, and procedural success rates were identified via automated extraction of structured data along with review of available unstructured data. The overall technical success rate was 96.7%, and the safety events rate was 5.3% with 18 major adverse events in 17 patients. The calculated technical success and safety rates met pre-established performance goals (≥ 85% for technical success and ≤ 12% for safety), highlighting the relevance of this surveillance method. Conclusions Generating RWE from RWD requires careful planning and execution. The process described herein provided valuable longitudinal data for PMS of real-world device safety and performance. This cost-effective approach can be translated to other medical devices and similar RWD database systems.

A Knowledge Graph-Based Consistency Detection Method for Network Security Policies

Network security policy is regarded as a guideline for the use and management of the network environment, which usually formulates various requirements in the form of natural language. It can help network managers conduct standardized network attack detection and situation awareness analysis in the overall time and space environment of network security. However, in most cases, due to configuration updates or policy conflicts, there are often differences between the real network environment and network security policies. In this case, the consistency detection of network security policies is necessary. The previous consistency detection methods of security policies have some problems. Firstly, the detection direction is single, only focusing on formal reasoning methods to achieve logical consistency detection and solve problems. Secondly, the detection policy field is not comprehensive, focusing only on a certain type of problem in a certain field. Thirdly, there are numerous forms of data structures used for consistency detection, and it is difficult to unify the structured processing and analysis of rule library carriers and target information carriers. With the development of intelligent graph and data mining technology, the above problems have the possibility of optimization. This article proposes a new consistency detection approach for network security policy, which uses an intelligent graph database as a visual information carrier, which can widely connect detection information and achieve comprehensive detection across knowledge domains, physical devices, and detection methods. At the same time, it can also help users grasp the security associations with the real network environment based on the graph algorithm of the knowledge graph and intelligent reasoning. Furthermore, these actual network situations and knowledge bases can help managers improve policies more tailored to local conditions. This article also introduces the consistency detection process of typical cases of network security policies, demonstrating the practical details and effectiveness of this method.

P-Aryl-Substituted Iminopyridine Chromium Complexes as Precatalysts for Ethylene Polymerization: The Question of the Physical Oxidation State Examined by Experimental and Density Functional Study in the Absence of Structural Data

<i>p</i>-Aryl-Substituted Iminopyridine Chromium Complexes as Precatalysts for Ethylene Polymerization: The Question of the Physical Oxidation State Examined by Experimental and Density Functional Study in the Absence of Structural Data

Leveraging gene correlations in single cell transcriptomic data

BackgroundMany approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for rare cell types, subtleties of cell states, and details of gene regulatory networks—there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually).ResultsWe approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization—a step that skews distributions, particularly for sparse data—and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene–gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships.ConclusionsNew insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene–gene correlations.

Functional network modules overlap and are linked to interindividual connectome differences during human brain development.

The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

Structure‐Based Design and in‐Flow Synthesis of Aromatic Endoperoxides Acting as Oxygen Releasing Agents

AbstractHypoxia occurs in different pathological settings as a consequence of poor vascularization, and it results in reduced efficacy of some current nosocomial treatments i. e. chemotherapy and photodynamic therapy. In order to overcome these boundaries, aromatic endoperoxides (EPOs) have been studied and proposed as oxygen‐releasing agents (ORAs) due to their ability to reversibly bind molecular oxygen (O2), liberating it upon suitable triggering. DFT calculation of the dissociation energy (Ediss) of the intramolecular O−O bridge and structural crystallographic data of synthesized and studied EPOs drove the design of an array of 9,10‐disubstituted anthracenes among which three candidates were carefully selected. Once optimized the synthesis of the aromatic substrates, for the first time the corresponding EPOs have been produced under continuous flow irradiation in the presence of a sub‐stoichiometric amount of a photosensitizer in organic solvents. The release of O2 could be obtained straightforwardly at 37.5 °C by thermolysis. In accordance with the calculated Ediss=3.2 kcal mol−1 and an experimental t1/2=40 minutes, 3,3′‐(anthracene‐9,10‐diyl)bis(prop‐2‐yn‐1‐ol) resulted as the best candidate for the sustained release of O2 under physiologically relevant conditions. Its exploitation as ORA to relieve hypoxia will be evaluated and described in due course.

Using multi-level regression to determine associations and estimate causes and effects in clinical anesthesia due to patient, practitioner and hospital or health system practice variability.

In this research methods tutorial of clinical anesthesia, we will explore techniques to estimate the influence of a myriad of factors on patient outcomes. Big data that contain information on patients, treated by individual anesthesiologists and surgical teams, at different hospitals, have an inherent multi-level data structure (Fig.1). While researchers often attempt to determine the association between patient factors and outcomes, that does not provide clinicians with the whole story. Patient care is clustered together according to clinicians and hospitals where they receive treatment. Therefore, multi-level regression models are needed to validly estimate the influence of each factor at each level. In addition, we will explore how to estimate the influence that variability-for example, one anesthesiologist deciding to do one thing, while another takes a different approach-has on outcomes for patients, using the intra-class correlation coefficient for continuous outcomes and the median odds ratio for binary outcomes. From this tutorial, you should acquire a clearer understanding of how to perform and interpret multi-level regression modeling and estimate the influence of variable clinical practices on patient outcomes in order to answer common but complex clinical questions. Fig.1 Infographics.

Conducting power analysis for meta-analysis with dependent effect sizes: Common guidelines and an introduction to the POMADE R package.

Sample size and statistical power are important factors to consider when planning a research synthesis. Power analysis methods have been developed for fixed effect or random effects models, but until recently these methods were limited to simple data structures with a single, independent effect per study. Recent work has provided power approximation formulas for meta-analyses involving studies with multiple, dependent effect size estimates, which are common in syntheses of social science research. Prior work focused on developing and validating the approximations but did not address the practice challenges encountered in applying them for purposes of planning a synthesis involving dependent effect sizes. We aim to facilitate the application of these recent developments by providing practical guidance on how to conduct power analysis for planning a meta-analysis of dependent effect sizes and by introducing a new R package, POMADE, designed for this purpose. We present a comprehensive overview of resources for finding information about the study design features and model parameters needed to conduct power analysis, along with detailed worked examples using the POMADE package. For presenting power analysis findings, we emphasize graphical tools that can depict power under a range of plausible assumptions and introduce a novel plot, the traffic light power plot, for conveying the degree of certainty in one's assumptions.

Laboratory Tests of Metrological Characteristics of a Non-Repetitive Low-Cost Mobile Handheld Laser Scanner

The popularity of mobile laser scanning systems as a surveying tool is growing among construction contractors, architects, land surveyors, and urban planners. The user-friendliness and rapid capture of precise and complete data on places and objects make them serious competitors for traditional surveying approaches. Considering the low cost and constantly improving availability of Mobile Laser Scanning (MLS), mainly handheld surveying tools, the measurement possibilities seem unlimited. We conducted a comprehensive investigation into the quality and accuracy of a point cloud generated by a recently marketed low-cost mobile surveying system, the MandEye MLS. The purpose of the study is to conduct exhaustive laboratory tests to determine the actual metrological characteristics of the device. The test facility was the surveying laboratory of the University of Agriculture in Kraków. The results of the MLS measurements (dynamic and static) were juxtaposed with a reference base, a geometric system of reference points in the laboratory, and in relation to a reference point cloud from a higher-class laser scanner: Leica ScanStation P40 TLS. The Authors verified the geometry of the point cloud, technical parameters, and data structure, as well as whether it can be used for surveying and mapping objects by assessing the point cloud density, noise and measurement errors, and detectability of objects in the cloud.

Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models

The task of localizing distinct anatomical structures in medical image data is an essential prerequisite for several medical applications, such as treatment planning in orthodontics, bone-age estimation, or initialization of segmentation methods in automated image analysis tools. Currently, Anatomical Landmark Localization (ALL) is mainly solved by deep-learning methods, which cannot guarantee robust ALL predictions; there may always be outlier predictions that are far from their ground truth locations due to out-of-distribution inputs. However, these localization outliers are detrimental to the performance of subsequent medical applications that rely on ALL results. The current ALL literature relies heavily on implicit anatomical constraints built into the loss function and network architecture to reduce the risk of anatomically infeasible predictions. However, we argue that in medical imaging, where images are generally acquired in a controlled environment, we should use stronger explicit anatomical constraints to reduce the number of outliers as much as possible. Therefore, we propose the end-to-end trainable Global Anatomical Feasibility Filter and Analysis (GAFFA) method, which uses prior anatomical knowledge estimated from data to explicitly enforce anatomical constraints. GAFFA refines the initial localization results of a U-Net by approximately solving a Markov Random Field (MRF) with a single iteration of the sum-product algorithm in a differentiable manner. Our experiments demonstrate that GAFFA outperforms all other landmark refinement methods investigated in our framework. Moreover, we show that GAFFA is more robust to large outliers than state-of-the-art methods on the studied X-ray hand dataset. We further motivate this claim by visualizing the anatomical constraints used in GAFFA as spatial energy heatmaps, which allowed us to find an annotation error in the hand dataset not previously discussed in the literature.

Molecular Classification with Graph ConvolutionalNetworks: Exploring the MUTAG Dataset for Mutagenicity Prediction

This paper presents the implementation of a Graph Convolutional Network (GCN) for the classification of chemical compounds using the MUTAG dataset, which consists of 188 ni- troaromatic compounds labeled according to their mutagenicity. The GCN model leverages the inherent graph structure of molec-ular data to capture and learn from the relationships between atoms and bonds, represented as nodes and edges, respectively. By utilizing three graph convolutional layers followed by a global mean pooling layer, the model effectively aggregates node features to generate meaningful graph-level representations. The model was trained using the Adam optimizer with a learning rate of 0.01, and cross-entropy loss was employed to supervise the classification task. The results demonstrate the efficacy of GCNs in graph classification tasks, with the model achieving a training accuracy of 79.33% and a test accuracy of 76.32%. This study highlights the potential of GCNs in cheminformatics and other domains where graph-structured data is prevalent, paving the way for further exploration and application of advanced graph neural networks in similar tasks.

Biogeochemical cycling of trace elements and nutrients in ferruginous waters: Constraints from a deep oligotrophic ancient lake

AbstractIron‐rich, ferruginous waters were the dominant geochemical regime for most of Earth's history. Modern ferruginous waters are found in stratified, sulfur‐poor lakes, and serve as crucial analogs for biogeochemical cycling throughout Earth's past. Here we present the first depth‐resolved data of physical structure, nutrients and trace elements from Lake Poso (Indonesia), a deep oligotrophic ancient lake. Lake Poso is ferruginous, with anoxia below ~ 90 m depth, placing it among the world's largest ferruginous lakes. Physical stratification is weaker than other tropical anoxic lakes, indicating sensitivity for paleoclimate reconstructions. Trace elements and nutrients are predominantly shaped by the oxic–anoxic transition. Manganese– and Fe oxyhydroxide–driven biogeochemical cycling occurs at distinct depth horizons, with Co and Ni controlled by Mn and showing shallow release in anoxic waters, while V, Cr, P, and As are controlled by Fe, with release in surface sediments and diffusive transport. Chromium is nonquantitatively removed in anoxic waters, in contrast to widespread assumptions in Cr‐based paleoreconstructions. Oxycline U and Se removal corresponds to a local N minimum, suggesting biological reduction and/or uptake. These first ferruginous water Se data also show removal in sediments, indicating sediment signals reflect multiple removal processes and informing Se‐based paleoreconstructions, while the absence of sediment U removal contrasts other anoxic basins. A comparison with other ferruginous lakes demonstrates how local influences drive deviations from expectations in other systems, and highlight common, generalizable ferruginous basin features. Therefore, these data will guide research in ferruginous settings across space and time, and improve paleoreconstructions from ferruginous sediment records.

Association between Neural Plasticity and Pain-Related Fear in Chronic Ankle Instability: A Structural Neuroimaging Study.

Pain-related movement fear is a contributing factor to residual pain and functional deficits in chronic ankle instability (CAI), but its underlying neural mechanisms remain unclear. We aimed to (1) delineate whether participants with CAI exhibit discernible differences in specific emotion and pain-related brain regions, compared to a healthy control (HC) cohort and (2) explore potential neural mechanisms underlying pain and fear in participants with CAI, with an emphasis on investigating possible associations with pain-related neural plasticity. Cross-sectional study. University research laboratory. 28 participants with CAI (17males and 11 females; age: 31.28±6.31 years) and 28 HCs (16 males and 12 females; age: 30.18±7.59 years). We analyzed T1 structural imaging data from participants and assessed their fear of movement and pain intensity using the Tampa Scale for Kinesiophobia (TSK) and the Visual Analog Scale (VAS) for pain, respectively. We compared the mean gray matter (GM) density of pain-related area between the two groups and their correlations with the TSK and VAS scores. In comparison with the HC group, participants with CAI showed a significant decrease in the mean GM density in the prefrontal cortex (Cohen's d = -0.808) and periaqueductal gray (Cohen's d = -0.934). In participants with CAI, the mean GM density of the prefrontal cortex (PFC) was negatively correlated with the TSK scores (r = -0.531). During intense exercise, the mean GM density of the periaqueductal gray (PAG) was negatively correlated with the VAS scores (r = -0.484). Additionally, TSK scores were positively correlated with VAS scores (r = 0.455). Our exploratory findings suggest that, in participants with CAI, the atrophy of the PFC and PAG may be associated with pain-related fear. Future clinical diagnosis and treatment for CAI should consider the impact of psychological barriers on functional recovery.

Characterizing the structural complexity of the Earth’s forests with spaceborne lidar

Forest structural complexity is a key element of ecosystem functioning, impacting light environments, nutrient cycling, biodiversity, and habitat quality. Addressing the need for a comprehensive global assessment of actual forest structural complexity, we derive a near-global map of 3D canopy complexity using data from the GEDI spaceborne lidar mission. These data show that tropical forests harbor most of the high complexity observations, while less than 20% of temperate forests reached median levels of tropical complexity. Structural complexity in tropical forests is more strongly related to canopy attributes from lower and middle waveform layers, whereas in temperate forests upper and middle layers are more influential. Globally, forests exhibit robust scaling relationships between complexity and canopy height, but these vary geographically and by biome. Our results offer insights into the spatial distribution of forest structural complexity and emphasize the importance of considering biome-specific and fine-scale variations for ecological research and management applications. The GEDI Waveform Structural Complexity Index data product, derived from our analyses, provides researchers and conservationists with a single, easily interpretable metric by combining various aspects of canopy structure.

Flexible curved multi-functional sensors for air flow and vibration signal measurement of train bogie

Abstract The fluid-solid coupling effects with the aerodynamic noise and structural vibration signal have great part in measurement accuracy and fault diagnosis of the train bogie. It is important to collect aerodynamic noise and structural vibration data for safety evaluation and on-line structural health monitoring (SHM) of the bogie, which brings new challenges to the integration and installation of the sensors in the limited space and complicated operation condition of the train bogie. Flexible curved multifunctional sensors (FCMFS) integrated with the hot film and structural vibration sensor units is designed to the air flow and structural vibration measurement of the train bogie. Bogie vibration and wind tunnel test platforms with FCMFS are built to the air flow velocity and vibration data measurement on the bogie surface under different operation conditions. Piezoelectric Polyvinylidene fluoride(PVDF) sensors have significant output in the wind tunnel and vibration experiments, and the hot film sensor is only sensitive to the air flow signal. The effect of vibration signal to the hot film sensor could be ignored, which is applied to decouple the aerodynamic noise and structural vibration signals for improving the measurement accuracy of the train bogie. The dynamic performance of FCMFS is verified in Rail Transmit Base of East China Jiaotong University, which is applied to SHM and rapid fault diagnosis of train bogie. The air flow distribution and vibration data are collected to safety assessment and fault diagnosis of train bogie, which would promote the intelligent maintenance level of the train bogie.

Quantification of delamination resistance data of FRP composites and its limits

Quantitative delamination resistance data of fibre-reinforced polymer-matrix (FRP) composites for quasi-static or cyclic fatigue loads are determined under different loading modes and load rates, respectively. Such data find use, e.g., in FRP materials’ development, materials’ selection, assessment of durability, or structural design. Round robins during test development yielded repeatability and reproducibility (coefficients of variation) of roughly 10 to 25%. This scatter has several different sources. Intrinsic material variability amounts to a few percent at best, at least for advanced manufacturing processes. This intrinsic scatter is essential for material comparisons and structural design. Measurement resolution specified in standardised test procedures yields a maximum of 4–5% variability. Most of the remaining scatter comes from other, extrinsic sources. Test operator actions, e.g., choice of test set-up, manual data acquisition or data analysis can yield extrinsic scatter. Damage mechanisms during delamination initiation and propagation act on the micro- and meso-scale, typically a few micrometer to a few hundred micrometer in size, with corresponding time-scales estimated to between a few tens of nanoseconds and a few microseconds. Defect size-scales are hence several orders of magnitudes lower than test specimen and structural scales, respectively. Predictive capability of models using such test data for structures are, therefore, limited. Major issues are up-scaling from straight beam-like specimens to larger shell-like structures, possibly with complex shape and varying thickness as well as from unidirectional fibre orientation to multidirectional lay-ups.

Simona: a comprehensive R package for semantic similarity analysis on bio-ontologies

BackgroundBio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. Semantic similarity analysis on bio-ontologies quantitatively assesses the degree of similarity between biological concepts based on the semantics encoded in ontologies. It plays an important role in structured and meaningful interpretations and integration of complex data from multiple biological domains.ResultsWe present simona, a novel R package for semantic similarity analysis on general bio-ontologies. Simona implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. Moreover, it provides a robust toolbox supporting over 70 methods for semantic similarity analysis. With simona, we conducted a benchmark against current semantic similarity methods. The results demonstrate methods are clustered based on their mathematical methodologies, thus guiding researchers in the selection of appropriate methods. Additionally, we explored annotation-based versus topology-based methods, revealing that semantic similarities solely based on ontology topology can efficiently reveal semantic similarity structures, facilitating analysis on less-studied organisms and other ontologies.ConclusionsSimona offers a versatile interface and efficient implementation for processing, visualization, and semantic similarity analysis on bio-ontologies. We believe that simona will serve as a robust tool for uncovering relationships and enhancing the interoperability of biological knowledge systems.

Characterization of human trabecular bone across multiple length scales using a correlative approach combining X-ray tomography with LaserFIB and plasma FIB-SEM

Three-dimensional correlative multimodal and multiscale imaging is an emerging method for investigating the complex hierarchical structure of biological materials such as bone. This approach synthesizes images acquired across multiple length scales, for the same region of interest, to provide a comprehensive view of the material structure of a sample. Here, we develop a workflow for the structural analysis of human trabecular bone using a femtosecond laser to produce a precise grid to facilitate correlation between imaging modalities and identification of structures of interest, in this case, a single trabecula within a volume of trabecular bone. Through such image registration, high resolution X-ray microscopy imaging revealed fine architectural details, including the cement sheath and bone cell lacunae of the selected bone trabecula. The selected bone volume was exposed with a combination of manual polishing and site-specific femtosecond laser ablation and then examined with plasma focused ion beam-scanning electron microscopy. This reliable and versatile correlation approach has the potential to be applied to a variety of biological tissues and traditional engineered materials. The proposed workflow has the enhanced capability for generating highly resolved and broadly contextualized structural data for a better understanding of the architectural features of a material spanning its macroscopic to nanoscopic levels.

Comparison of human breast milk vs commercial formula-induced early trophic enteral nutrition during postoperative prolonged starvation in an animal model

The present study aimed to characterize the changes in macromolecular composition and structure in ileal tissue induced by postoperative prolonged starvation (PS), human breast milk feeding (HM) and commercial formula feeding (CF) for 48 and 72 h (h). Forty-two Wistar albino rats underwent an ileal transection and primary anastomosis and were then divided into six subgroups. Two groups of seven rats were food-deprived for 48 and 72 h with free access to water only in metabolic cages (48 h PS, 72 h PS). Then, two groups of seven rats received early enteral trophic nutrition (EEN) either using HM, and CF at 48 h post-operation (48 h HM, 48 h CF). The other two groups of seven rats received the same trophic enteral nutrition at 72 h post-operation (72 h HM, 72 h CF). An additional seven rats were fed normal rat chow (control), after which the ileal tissues were harvested and freeze-dried overnight. Then sample spectra were recorded by Fourier transform infrared (FTIR) spectroscopy. PS at 48 and 72 h resulted in an increase in the concentration of lipids and a decrease in the concentration of proteins. CF and HM trophic feeding induced a decrease in membrane fluidity and an increase in lipid order. Ileal tissues showed similar compositional and structural changes in lipids and proteins in the PS and CF groups after 48 and 72 h. A marked decrease in nucleic acid concentration was seen in CF at 48 h compared to HM. The human milk feeding groups did not induce any significant alterations and showed compositional and structural data similar to the controls. In conclusion, EEN application seems to be safer when introduced at 48 h rather than 72 h and time of this nutrition is crucial to maintain ileum structure and therefore immunity and well-being. HM-induced trophic nutrition is seen to protect the ileal tissue from significant alterations within lipid and protein compositions, whereas CF caused notable changes. HM is absolutely the best nutritional source for gut health in this animal model.

Spider silk tensile performance does not correlate with web use.

Spider silk is amongst the toughest materials produced by living systems, but its tensile performance varies considerably between species. Despite the extensive sampling of the material properties and composition of dragline silk, the understanding why some silks perform better than others is still limited. Here, I adopted a phylogenetic comparative approach to re-analyse structural and mechanical data from the Silkome database and the literature across 164 species to (a) provide an extended model of silk property evolution, (b) test for correlations between structural and mechanical properties, and (c) to test if silk tensile performance differs between web-building and non-web-building species. Unlike the common notion that orb-weavers have evolved the best performing silks, outstanding tensile properties were found both in and outside the araneoid clade. Phylogenetic linear models indicated that the mechanical and structural properties of spider draglines poorly correlate, but silk strength and toughness correlated better with birefringence (an indicator of the material anisotropy) than crystallinity. Furthermore, in contrast to previous ideas, silk tensile performance did not differ between ecological guilds. These findings indicate multiple unknown pathways towards the evolution of spider silk tensile super-performance, calling for a better integration of non-orb-weaving spiders in spider silk studies.