The impact of taxon sampling on phylogenetic inference: a review of two decades of controversy.

Over the past two decades, there has been a long-standing debate about the impact of taxon sampling on phylogenetic inference. Studies have been based on both real and simulated data sets, within actual and theoretical contexts, and using different inference methods, to study the impact of taxon sampling. In some cases, conflicting conclusions have been drawn for the same data set. The main questions explored in studies to date have been about the effects of using sparse data, adding new taxa, including more characters from genome sequences and using different (or concatenated) locus regions. These questions can be reduced to more fundamental ones about the assessment of data quality and the design guidelines of taxon sampling in phylogenetic inference experiments. This review summarizes progress to date in understanding the impact of taxon sampling on the accuracy of phylogenetic analysis.

Structural network analysis of biological networks for assessment of potential disease model organisms.

Model organisms provide opportunities to design research experiments focused on disease-related processes (e.g., using genetically engineered populations that produce phenotypes of interest). For some diseases, there may be non-obvious model organisms that can help in the study of underlying disease factors. In this study, an approach is presented that leverages knowledge about human diseases and associated biological interactions networks to identify potential model organisms for a given disease category. The approach starts with the identification of functional and interaction patterns of diseases within genetic pathways. Next, these characteristic patterns are matched to interaction networks of candidate model organisms to identify similar subsystems that have characteristic patterns for diseases of interest. The quality of a candidate model organism is then determined by the degree to which the identified subsystems match genetic pathways from validated knowledge. The results of this study suggest that non-obvious model organisms may be identified through the proposed approach.

Investigation of Effect of Part-Build Directions and Build Orientations on Tension-Tension Mode Fatigue Behavior of Acrylonitrile Butadiene Styrene Material Printed Using Fused Filament Fabrication Technology.

This article explores the fatigue characteristics of acrylonitrile butadiene styrene (ABS) components fabricated using fused filament fabrication (FFF) additive manufacturing technology. ABS is frequently used as a polymeric thermoplastic material in open-source FFF machines for a variety of engineering applications. However, a comprehensive understanding of the mechanical properties and execution of FFF-processed ABS components is necessary. Currently, there is limited knowledge regarding the fatigue behavior of ABS components manufactured using FFF AM technology. The primary target of this study is to evaluate the results of part-build directions and build orientation angles on the tensile fatigue behavior exhibited by ABS material. To obtain this target, an empirical investigation was carried out to assess the influence of building angles and orientation on the fatigue characteristics of ABS components produced using FFF. The test samples were printed in three distinct directions, including Upright, On Edge, and Flat, and with varying orientation angles ([0°, 90°], [15°, 75°], [30°, 60°], [45°]), using a 50% filling density. The empirical data suggest that, at each printing angle, the On-Edge building orientation sample exhibited the most prolonged vibrational duration before fracturing. In this investigation, we found that the On-Edge printing direction significantly outperformed the other orientations in fatigue life under cyclic loading with 1592 loading cycles when printed with an orientation angle of 15°-75°. The number of loading cycles was 290 and 39 when printed with the same orientation angle for the Flat and Upright printing directions, respectively. This result underscores the importance of orientation in the mechanical performance of FFF-manufactured ABS materials. These findings enhance our comprehension of the influence exerted by building orientation and building angles on the fatigue properties of FFF-produced test samples. Moreover, the research outcomes supply informative perspectives on the selection of building direction and building orientation angles for the design of 3D-printed thermoplastic components intended for fatigue cyclic-loading applications.

Mechanical and Tribological Performance of HDPE Matrix Reinforced by Hybrid Gr/TiO2 NPs for Hip Joint Replacement.

Hip joint collapse is a very common health problem. Many cases need a joint replacement, so nano-polymeric composites are an ideal alternative solution. Due to its mechanical properties and wear resistance, HDPE might be considered a suitable alternative to frictional materials. The current research focuses on using hybrid nanofiller TiO2 NPs and nano-graphene with various loading compositions to evaluate the best loading amount. The compressive strength, modules of elasticity, and hardness were examined via experiments. The COF and wear resistance were evaluated via a pin-on-disk tribometer. The worn surfaces were analyzed based on 3D topography and SEM images. The HDPE samples with various compositions of 0.5%, 1.0%, 1.5%, and 2.0 wt.% filling content of TiO2 NPs and Gr (with a ratio of 1:1) were analyzed. Results revealed that hybrid nanofiller with a composition of 1.5 wt.% exhibits superior mechanical properties compared to other filling compositions. Moreover, the COF and wear rate decreased by 27.5% and 36.3%, respectively.

Unveiling the Potential of Rice Straw Nanofiber-Reinforced HDPE for Biomedical Applications: Investigating Mechanical and Tribological Characteristics.

The efficient utilization of rice waste has the potential to significantly contribute to environmental sustainability by minimizing the waste impact on the environment. Through repurposing such waste, novel materials can be developed for various biomedical applications. This approach not only mitigates waste, but it also promotes the adoption of sustainable materials within the industry. In this research, rice-straw-derived nanofibers (RSNFs) were utilized as a reinforcement material for high-density polyethylene (HDPE). The rice-straw-derived nanofibers were incorporated at different concentrations (1, 2, 3, and 4 wt.%) into the HDPE. The composites were fabricated using twin-screw extrusion (to ensure homogenous distribution) and the injection-molding process (to crease the test samples). Then, the mechanical strengths and frictional performances of the bio-composites were assessed. Different characterization techniques were utilized to investigate the morphology of the RSNFs. Thermal analyses (TGA/DTG/DSC), the contact angle, and XRD were utilized to study the performances of the HDPE/RSNF composites. The study findings demonstrated that the addition of RSNFs as a reinforcement to the HDPE improved the hydrophilicity, strength, hardness, and wear resistance of the proposed bio-composites.

Mining disease fingerprints from within genetic pathways.

Mining biological networks can be an effective means to uncover system level knowledge out of micro level associations, such as encapsulated in genetic pathways. Analysis of human disease genetic pathways can lead to the identification of major mechanisms that may underlie disorders at an abstract functional level. The focus of this study was to develop an approach for structural pattern analysis and classification of genetic pathways of diseases. A probabilistic model was developed to capture characteristic components ('fingerprints') of functionally annotated pathways. A probability estimation procedure of this model searched for fingerprints in each disease pathway while improving probability estimates of model parameters. The approach was evaluated on data from the Kyoto Encyclopedia of Genes and Genomes (consisting of 56 pathways across seven disease categories). Based on the achieved average classification accuracy of up to ~77%, the findings suggest that these fingerprints may be used for classification and discovery of genetic pathways.