Complex Architecture Research Articles

Complex genetic architecture of the chicken Growth1 QTL region.

The genetic complexity of polygenic traits represents a captivating and intricate facet of biological inheritance. Unlike Mendelian traits controlled by a single gene, polygenic traits are influenced by multiple genetic loci, each exerting a modest effect on the trait. This cumulative impact of numerous genes, interactions among them, environmental factors, and epigenetic modifications results in a multifaceted architecture of genetic contributions to complex traits. Given the well-characterized genome, diverse traits, and range of genetic resources, chicken (Gallus gallus) was employed as a model organism to dissect the intricate genetic makeup of a previously identified major Quantitative Trait Loci (QTL) for body weight on chromosome 1. A multigenerational advanced intercross line (AIL) of 3215 chickens whose genomes had been sequenced to an average of 0.4x was analyzed using genome-wide association study (GWAS) and variance-heterogeneity GWAS (vGWAS) to identify markers associated with 8-week body weight. Additionally, epistatic interactions were studied using the natural and orthogonal interaction (NOIA) model. Six genetic modules, two from GWAS and four from vGWAS, were strongly associated with the studied trait. We found evidence of both additive- and non-additive interactions between these modules and constructed a putative local epistasis network for the region. Our screens for functional alleles revealed a missense variant in the gene ribonuclease H2 subunit B (RNASEH2B), which has previously been associated with growth-related traits in chickens and Darwin's finches. In addition, one of the most strongly associated SNPs identified is located in a non-coding region upstream of the long non-coding RNA, ENSGALG00000053256, previously suggested as a candidate gene for regulating chicken body weight. By studying large numbers of individuals from a family material using approaches to capture both additive and non-additive effects, this study advances our understanding of genetic complexities in a highly polygenic trait and has practical implications for poultry breeding and agriculture.

Probing the Architecture of Multisubunit Protein Complexes with In-line Disulfide Reduction and Native MS Analysis.

Native mass spectrometry (MS) continues to enjoy growing popularity as a means of providing a wealth of information on noncovalent biopolymer assemblies ranging from composition and binding stoichiometry to characterization of the topology of these assemblies. The latter frequently relies on supplementing MS measurements with limited fragmentation of the noncovalent complexes in the gas phase to identify the pairs of neighboring subunits. While this approach has met with much success in the past two decades, its implementation remains difficult (and the success record relatively modest) within one class of noncovalent assemblies: protein complexes in which at least one binding partner has multiple subunits cross-linked by disulfide bonds. We approach this problem by inducing chemical reduction of disulfide bonds under nondenaturing conditions in solution followed by native MS analysis with online buffer exchange to remove unconsumed reagents that are incompatible with the electrospray ionization process. While this approach works well with systems comprised of thiol-linked subunits that remain stable upon reduction of the disulfide bridges (such as immunoglobulins), chemical reduction frequently gives rise to species that are unstable (prone to aggregation). This problem is circumvented by taking advantage of the recently introduced cross-path reactive chromatography platform (XPRC), which allows the disulfide reduction to be carried out in-line, thereby minimizing the loss of metastable protein subunits and their noncovalent complexes with the binding partners prior to MS analysis. The feasibility of this approach is demonstrated using hemoglobin complexes with haptoglobin 1-1, a glycoprotein consisting of four polypeptide chains cross-linked by disulfide bonds.

Targeted sequencing for hereditary breast and ovarian cancer in BRCA1/2-negative families reveals complex genetic architecture and phenocopies

Approximately 20% of breast cancer cases are attributed to increased family risk; yet variation in BRCA1/2 can only explain 20-25% of cases. Historically, only single gene or single variant testing were common in at-risk family members, and further sequencing studies were rarely offered after negative results. In this study, we applied an efficient and inexpensive targeted sequencing approach to provide molecular diagnoses in 245 human samples representing 134 BRCA mutation negative (BRCAX) hereditary breast and ovarian cancer (HBOC) families recruited from 1973-2019 by Dr. Henry Lynch. Sequencing identified 391 variants, which were functionally annotated and ranked based on their predicted clinical impact. Known pathogenic CHEK2 breast cancer variants were identified in five BRCAX families in this study. While BRCAX was an inclusion criterion for this study, we still identified a pathogenic BRCA2 variant (p.Met192ValfsTer13) in one family. A portion of BRCAX families could be explained by other hereditary cancer syndromes that increase HBOC risk: Li-Fraumeni Syndrome (gene: TP53) and Lynch Syndrome (gene: MSH6). Interestingly, many families carried additional Variants of Undetermined Significance (VOUSs) that may further modify phenotypes of syndromic family members. Ten families carried more than one potential VOUS suggesting the presence of complex multi-variant families. Overall, nine BRCAX HBOC families in our study may be explained by known likely-pathogenic/pathogenic variants, and six families carried potential VOUSs, which require further functional testing. To address this, we developed a functional assay where we successfully re-classified one family’s PMS2 VOUS as benign.

DNA-Binding Proteins and Passenger Proteins in Plasma DNA-Protein Complexes: Imprint of Parental Cells or Key Mediators of Carcinogenesis Processes?

Knowledge of the composition of proteins that interact with plasma DNA will provide a better understanding of the homeostasis of circulating nucleic acids and the various modes of interaction with target cells, which may be useful in the development of gene targeted therapy approaches. The goal of the present study is to shed light on the composition and architecture of histone-containing nucleoprotein complexes (NPCs) from the blood plasma of healthy females (HFs) and breast cancer patients (BCPs) and to explore the relationship of proteins with crucial steps of tumor progression: epithelial-mesenchymal transition (EMT), cell proliferation, invasion, cell migration, stimulation of angiogenesis, and immune response. MALDI-TOF mass spectrometric analysis of NPCs isolated from blood samples using affine chromatography was performed. Bioinformatics analysis showed that the shares of DNA-binding proteins in the compositions of NPCs in normal and cancer patients are comparable and amount to 40% and 33%, respectively; in total, we identified 38 types of DNA-binding motifs. Functional enrichment analysis using FunRich 3.13 showed that, in BCP blood, the share of DNA-binding proteins involved in nucleic acid metabolism increased, while the proportion of proteins involved in intercellular communication and signal transduction decreased. The representation of NPC passenger proteins in breast cancer also changes: the proportion of proteins involved in transport increases and the share of proteins involved in energy biological pathways decreases. Moreover, in the HF blood, proteins involved in the processes of apoptosis were more represented in the composition of NPCs and in the BCP blood-in the processes of active secretion. For the first time, bioinformatics approaches were used to visualize the architecture of circulating NPCs in the blood and to show that breast cancer has an increased representation of passenger proteins involved in EMT, cell proliferation, invasion, cell migration, and immune response. Using breast cancer protein data from the Human Protein Atlas (HPA) and DEPC, we found that 86% of NPC proteins in the blood of BCPs were not previously annotated in these databases. The obtained data may indirectly indicate directed protein sorting in NPCs, which, along with extracellular vesicles, can not only be diagnostically significant molecules for liquid biopsy, but can also carry out the directed transfer of genetic material from donor cells to recipient cells.

PlantMine: A Machine-Learning Framework to Detect Core SNPs in Rice Genomics.

As a fundamental global staple crop, rice plays a pivotal role in human nutrition and agricultural production systems. However, its complex genetic architecture and extensive trait variability pose challenges for breeders and researchers in optimizing yield and quality. Particularly to expedite breeding methods like genomic selection, isolating core SNPs related to target traits from genome-wide data reduces irrelevant mutation noise, enhancing computational precision and efficiency. Thus, exploring efficient computational approaches to mine core SNPs is of great importance. This study introduces PlantMine, an innovative computational framework that integrates feature selection and machine learning techniques to effectively identify core SNPs critical for the improvement of rice traits. Utilizing the dataset from the 3000 Rice Genomes Project, we applied different algorithms for analysis. The findings underscore the effectiveness of combining feature selection with machine learning in accurately identifying core SNPs, offering a promising avenue to expedite rice breeding efforts and improve crop productivity and resilience to stress.

Development of a 3D in vitro human-sized model of cervical dysplasia to evaluate the delivery of ethyl cellulose-ethanol injection

Introduction: Cervical cancer, the second leading cause of cancer-related death for women worldwide, remains a preventable yet persistent disease that disproportionately affects women in low and middle-income countries (LMICs). While existing therapies for treating cervical dysplasia are effective, they are often inaccessible in LMICs. Ethanol ablation is an alternative low-cost, accessible therapy that we previously enhanced into ethyl cellulose (EC)-ethanol gel formulation to improve efficacy.Methods: To evaluate the efficacy of EC-ethanol, in this study, we developed a 3D in vitro model of cervical dysplasia featuring a central lesion of cervical cancer cells surrounded by fibroblasts and keratinocytes. Using a GelMA hydrogel formulation (8.7% w/v), we successfully built a 3D model that captured the architectural complexity of cervical dysplasia. We evaluated changes in cell coverage and cell viability. Then, we compared the viscoelastic properties of the GelMA hydrogels to human cervical tissue and using micro-CT imaging, we assessed EC-ethanol injection deposition in the hydrogel, revealing retention of virtually the entire injected volume near the injection site. Finally, we measured changes in cell viability and cell coverage after the EC-ethanol injection.Results: The developed 3D in vitro model successfully replicated the architectural complexity of cervical dysplasia, demonstrating high cell viability and capturing cell responses effectively. The GelMA hydrogel formulation (8.7% w/v) exhibited viscoelastic properties akin to human cervical tissue. Micro-CT imaging revealed efficient deposition of EC-ethanol within the hydrogel, with retention of the injected volume near the injection site. Furthermore, the EC-ethanol injection significantly reduced cervical cancer cell viability and cell coverage while preserving healthy cells within the model.Conclusion: Our findings indicate that our 3D in vitro model mirrored the architecture of cervical dysplasia and demonstrated the potential of EC-ethanol for localized treatment of cervical dysplasia.

A convolutional neural network (Seg-CNN) for recognition, classifi cation, and segmentation of apple fruit branches and stems in images

This article reports the results of research studies conducted in 2023–2024 on transfer learning of Segmentation Convolutional Neural Networks (Seg-CNN) models for classification, recognition, and segmentation of branches with apple fruits and stems in images. State-of-the-art convolutional neural network architectures, i.e., YOLOv8(n,s,m,l,x)-seg, were used for a detailed segmentation of biological objects in images of varying complexity and scale at the pixel level. An image dataset collected in the fi eld using a GoPro HERO 11 camera was marked up for transfer model training. Data augmentation was performed, producing a total of 2500 images. Image markup was performed using the polygon annotation tool. As a result, polygonal contours around objects were created, outlines of branches, apple tree fruits, and stems were outlined, and segments of objects in the images were indicated. The objects were assigned the following classes: Apple branch, Apple fruit, and Apple stem. Binary classification metrics, such as Precision and Recall, as well as Mean Average Precision (mAP), were used to evaluate the performance of the trained models in recognizing branches with apple fruits and stems in images. The YOLOv8x-seg (mAP50 0.758) and YOLOv8l-seg (mAP50 0.74) models showed high performance in terms of all metrics in recognizing branches, apple fruit, and fruit stems in images, outperforming the YOLOv8n-seg (mAP50 0.7) model due to their more complex architecture. The YOLOv8n-seg model has a faster frame processing speed (11.39 frames/s), rendering it a preferred choice for computing systems with limited resources. The results obtained confirm the prospects of using machine learning algorithms and convolutional neural networks for segmentation and pixel-by-pixel classification of branches with apple fruits and stems on RGB images for monitoring the condition of plants and determining their geometric characteristics.

Developing Time Series Forecasting Models with Generative Large Language Models

Nowadays, Generative Large Language Models (GLLMs) have made a significant impact in the field of Artificial Intelligence (AI). One of the domains extensively explored for these models is their ability as generators of functional source code for software projects. Nevertheless, their potential as assistants to write the code needed to generate and model Machine Learning (ML) or Deep Learning (DL) architectures has not been fully explored to date. For this reason, this work focuses on evaluating the extent to which different tools based on GLLMs, such as ChatGPT or Copilot, are able to correctly define the source code necessary to generate viable predictive models. The use case defined is the forecasting of a time series that reports the indoor temperature of a greenhouse. The results indicate that, while it is possible to achieve good accuracy metrics with simple predictive models generated by GLLMs, the composition of predictive models with complex architectures using GLLMs is still far from improving the accuracy of predictive models generated by human data scientists.

How an embryonic magma feeding system evolves: Insights from the primordial pulses of Mt. Etna volcano

Volcanism at Mt. Etna (Italy) started with an early tholeiitic stage dating back to 542 ka during which subaqueous to subaerial magmas were emitted chiefly through fissure-type eruptions on widespread areas located on the southern flank of the modern volcano edifice. Volcanic products belonging to the earlier Aci Trezza Synthem (542–496 ka) and those of the later Adrano Synthem (332–320 ka) are basalts within a narrow range of variation. Despite the rather homogeneous geochemical characteristics, zoning patterns and FeMg diffusion chronometry on olivine crystals from lavas of both the Synthemic Units have evidenced different dynamics and kinetics of storage and transfer before eruptions. Specifically, one dominant, normally-zoned, Fo83–86 olivine population makes peculiar lavas of the Aci Trezza Synthem, whose patterns can be interpreted as due to simple upward migration from deep storage reservoirs directly to the surface with timescales of 109–200 days. Volcanic rocks of the Adrano Synthem have at least three additional olivine populations (i.e., Fo78-Fo81, Fo73-Fo74, Fo64-Fo70) bearing more complex normal and reverse zoning patterns, features revealing that magmas ascended from the deeper storage zones and then intruded and stalled in shallower reservoirs before being erupted. Transfers throughout these magma reservoirs record both short (<46 days) and long timescales (>106 days), suggesting that tectonics could have accelerated or inhibited magma supply during this later stage of volcanic activity. This new dataset points out that the embryonic plumbing system of Mt. Etna developed a more complex architecture throughout the first ~200 ka of volcanism as a consequence of a declining effect of transtensional tectonics over time.

Data-driven approach for air pollutant concentrations forecasting: A window-based multi-output GBRT approach

Air pollutant concentrations forecasting is essential for detrimental public exposure prevention, air quality enhancement, and effective environmental policy shaping. Recently, air pollutant concentrations forecasting based on machine learning has been considerably advanced with the development of data-driven approaches. However, multi-source data constraints and architectural complexity in machine learning approaches limit the use of machine learning in actual scenarios. Herein we present a window-based multi-output gradient boosting regression tree (WM-GBRT) approach to predict air pollutant concentrations for the next 48 h at each monitoring station, considering air pollutant monitoring data, meteorological monitoring data, NECP-GFS data, and GEOS-CF data. The key innovation of our approach lies in the flexible input window configuration and the implementation of an multi-step independent strategy. The experiments were conducted in China and compared to existing data-driven baselines, we show that our approach achieved the superior performance in forecasting concentrations of six air pollutants, with a considerable improvement of 6.94 % (PM2.5), 12.3 % (PM10), 3.84 % (O3), 30.6 % (SO2), 9.84 % (NO2), and 93.9 % (CO) when compared to the second-best baselines. This simple yet efficient approach can be easily applied, providing a reliable technical method for predicting air pollutant concentrations.

Pathological Eye Growth in Myopia: a Limited Review on Endocrine Physiology for Future Research

Purpose: Myopia or short-sightedness: first manifests in school-children (early-onset), and also in young adults (late-onset). Pathological myopia can lead to stretching of the eyeball to the point of blindness from a detached retina. This title presents a preliminary understanding of endocrine, juxtacrine, and paracrine factors: hoping to stimulate further research and enable early therapeutic intervention. Background: The optical stimulus for eye growth is a blurred image on the retina that results from under-focused accommodation of the internal crystalline lens. Neural signals and biochemical messaging from the retina and vascular choroid provide local impetus to alter collagen structure in the outer white sclera of the eye. In persons that develop highly myopic refractive error, additional factors from blood and tissue chemistry, might influence intra-ocular pressure (IOP), posited to influence eyeball elongation. New research shows that GABA and dopamine are involved, but other factors remain unexplored. Eye Physiology: Human sclera has developmental origination same as any other connective tissue derived from the neural crest and mesoderm. Bundles of collagen fibers exist in the white opaque sclera and are more stringently oriented in the dome of the clear, transparent cornea. “Deformation compliance” of the sclera is governed by complexity of the collagen architecture, and “reduced scleral rigidity” is a known factor for myopic elongation, but developmental physiology is not well understood. Muscular contraction is required for sustained eye focusing to objects located at normal working distances. Short-term contractions can reduce IOP but increased muscle density from sustained near accommodation can promote progression of myopia. This implicates nitric oxide and calcium channels, and a role for associated biochemical and nutritional agonists and antagonists. Endocrine Influences: In his 1954 lecture to the Royal College of Surgeons, Dr. Howard C. Naffziger pointed to all forms of “ocular protrusion,” not just “thyroid exophthalmos,” as being physiologically related. A 2016 review paper on autonomic nervous physiology, aside from sympathetic (adrenergic) and parasympathic (collinergic) control, also implicates Substance P (SP), Vasoactive Intestinal Peptide (VIP), and Phospholipase C (PC). These topics are further described in limited detail. SP is a neuropeptide of the tachykinin family, is released from the terminals of sensory nerves, and regulates emotion (hypothalamus, amygdala, and the periaqueductal gray). It is also a vasodilator dependent on release of nitric oxide. Experimental eye physiology and clinical research pertaining to these topics appears warranted. VIP regulates cardiac contractions, relaxes smooth muscle of the trachea, and affects prolactin secretion by the pituitary gland. Cardiac contractions lead to pulsations of eye pressure about three times in two seconds, and pulmonary effects on IOP occur once every five seconds. Research into these mechanisms requires cross-disciplinary collaborations. PC is a membrane-associated enzyme that cleaves dietary phospholipids and has downstream effects on calcium channels. Binding to blood platelets, of agonists such as thrombin, epinephrine, or collagen: activates PC to release arachidonic acid from cleavage of phosphatidylinositol and phosphatidyl choline. Behavioral effects include visual attention-span and memory: both of which also concern education of children, and effectiveness of adults at work. Conclusions: The author is pleased to report growing interest and evidence for endocrine influences upon the outer coat of the eye (cornea and sclera). A 2022 study on “keratoconus” from Texas and Oklahoma provides in-vitro as well as in-vivo data, suggesting the existence of what they call, the Hypothalamic-Pituitary-Adrenal-Corneal axis. Effective communication across university disciplines and clinic doctors appears necessary for further progress. Funding: The author received no funds from Federal or State granting mechanisms, and no wages from corporate employers. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Stop Making Sense: Complexity and Contradiction in AI and Architecture

Stop Making Sense: Complexity and Contradiction in AI and Architecture

Computational design of myoglobin-based carbene transferases for monoterpene derivatization

Carbene transfer reactions have emerged as pivotal methodologies for the synthesis of complex molecular architectures. Heme protein-catalyzed carbene transfer reactions have shown promising results on model compounds. However, their limited substrate scope has hindered their application in natural product functionalization. Building upon the foundation of previously published work on a carbene transferase-myoglobin variant, this study employs computer-aided protein engineering to design myoglobin variants, using either docking or the deep learning-based LigandMPNN method. These variants were utilized as catalysts in carbene transfer reactions with a selection of monoterpene substrates featuring C–C double bonds, leading to seven target products. This cost-effective methodology broadens the substrate scope for heme protein-catalyzed reactions, thereby opening novel pathways for research in heme protein functionalities and offering fresh perspectives in the synthesis of bioactive molecules.

Biomimetic laminated fiber-reinforced composites with a co-enhancement of strength and toughness

The co-enhancement of strength and toughness (damage-tolerant) is an urgent demand for most fiber-reinforced materials. Unfortunately, these properties are generally mutually exclusive. Hence, it is highly desirable yet challenging to develop new fiber-reinforced structural materials with high strength and toughness. Herein, inspired by the dual-stage rigid-flexible coupling structures (RFCSs) of abalone shell, a biomimetic carbon fiber reinforced composite (BCFRC) was prepared using a method combing droplet-based additive manufacturing and infrared heating-assisted thermoforming. Remarkably, the BCFRC features a unique coordination of high strength (1204 MPa), high toughness (6.29 MJ·m−3), and high stiffness (104 GPa) in tensile tests, benefiting from the co-effect of energy dissipation of brick-and-mortar architected laminates (first-stage RFCS) and the resistance to stress concentration of resin matrix embedded with silanized particles (second-stage RFCS). This work highlights the potential of harnessing the rich designability of fiber prepreg and resin matrix to build high-performance fiber-reinforced materials with bioinspired complex architectures.

Classification of Simple CNN Model and ResNet50

Abstract: In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification tasks, achieving state-of-the-art performance in various domains. Among the plethora of CNN architectures, the Simple CNN model and ResNet50 stand out as widely used architectures with distinct characteristics. In this study, we present a comparative analysis of these two architectures in terms of their performance, computational efficiency, and robustness for classification tasks.The Simple CNN model represents a straightforward convolutional neural network architecture with a sequential arrangement of convolutional, pooling, and fully connected layers. On the other hand, ResNet50 introduces the concept of residual learning, leveraging skip connections to address the vanishing gradient problem and facilitate the training of deeper networks.We conduct experiments on benchmark datasets, evaluating the classification accuracy, training time, and model complexity of both architectures. Our findings reveal insights into the strengths and weaknesses of each model. While the Simple CNN model demonstrates simplicity and ease of implementation, ResNet50 exhibits superior performance, particularly in scenarios with a large amount of training data and complex feature representations.

Tert‐Butyl as a Functional Group: Non‐Directed Catalytic Hydroxylation of Sterically Congested Primary C−H Bonds

tert‐Butyl as a Functional Group: Non‐Directed Catalytic Hydroxylation of Sterically Congested Primary C−H Bonds

Moving lab-grown tissues into the clinic: organ-on-a-chip and bioengineered skin systems

For patients with end stage organ failure, organ transplant is frequently the only curative option available. However, organs available for transplant are in critically short supply around the world, which has led to lengthy wait times and increased mortality. Increased global life expectancy, coupled with raised age thresholds for recipients, has heightened demand and further compounded the need for alternative strategies. Bioengineering substitutes including organ-on-a-chip and 3D bioprinting technologies have made considerable strides toward whole organ generation. Skin is the organ where the most advances have been made thus far, due to the relatively less complex spatial architecture and industry interest in the development of sophisticated models for pharmaceutical and cosmetics testing. Here, we discuss the challenges of recapitulating the complexity of native skin, including a stratified structure, vascularization, and inclusion of skin appendages, such as hair follicles and sweat glands. We discuss current technological and biological progress in the field of tissue and organ bioengineering as well as highlight future challenges to generate de novo tissue for skin grafting.

Tert-Butyl as a Functional Group: Non-Directed Catalytic Hydroxylation of Sterically Congested Primary C-H Bonds.

The tert-butyl group is a common aliphatic motif extensively employed to implement steric congestion and conformational rigidity in organic and organometallic molecules. Because of the combination of a high bond dissociation energy (~ 100 kcal mol-1) and limited accessibility, in the absence of directing groups, neither radical nor organometallic approaches are effective for the chemical modification of tert-butyl C-H bonds. Herein we overcome these limits by employing a highly electrophilic manganese catalyst, [Mn(CF3bpeb)(OTf)2], that operates in the strong hydrogen bond donor solvent nonafluoro-tert-butyl alcohol (NFTBA) and catalytically activates hydrogen peroxide to generate a powerful manganese-oxo species that effectively oxidizes tert-butyl C-H bonds. Leveraging on the interplay of steric, electronic, medium and torsional effects, site-selective and product chemoselective hydroxylation of the tert-butyl group is accomplished with broad reaction scope, delivering primary alcohols as largely dominant products in preparative yields. Late-stage hydroxylation at tert-butyl sites is demonstrated on 6 densely functionalized molecules of pharmaceutical interest. This work uncovers a novel disconnection approach, harnessing tert-butyl as a potential functional group in strategic synthetic planning for complex molecular architectures.

MPH: fast REML for large-scale genome partitioning of quantitative genetic variation.

Genome partitioning of quantitative genetic variation is useful for dissecting the genetic architecture of complex traits. However, existing methods, such as Haseman-Elston regression and linkage disequilibrium score regression, often face limitations when handling extensive farm animal datasets, as demonstrated in this study. To overcome this challenge, we present MPH, a novel software tool designed for efficient genome partitioning analyses using restricted maximum likelihood. The computational efficiency of MPH primarily stems from two key factors: the utilization of stochastic trace estimators and the comprehensive implementation of parallel computation. Evaluations with simulated and real datasets demonstrate that MPH achieves comparable accuracy and significantly enhances convergence, speed, and memory efficiency compared to widely used tools like GCTA and LDAK. These advancements facilitate large-scale, comprehensive analyses of complex genetic architectures in farm animals. The MPH software is available at https://jiang18.github.io/mph/.

Engineering Large-Scale Self-Mineralizing Bone Organoids with Bone Matrix-Inspired Hydroxyapatite Hybrid Bioinks.

Addressing large bone defects remains a significant challenge owing to the inherent limitations in self-healing capabilities, resulting in prolonged recovery and suboptimal regeneration. Although current clinical solutions are available, they have notable shortcomings, necessitating more efficacious approaches to bone regeneration. Organoids derived from stem cells show great potential in this field; however, the development of bone organoids has been hindered by specific demands, including the need for robust mechanical support provided by scaffolds and hybrid extracellular matrices (ECM). In this context, bioprinting technologies have emerged as powerful means of replicating the complex architecture of bone tissue. The research focused on the fabrication of a highly intricate bone ECM analog using a novel bioink composed of gelatin methacrylate/alginate methacrylate/hydroxyapatite (GelMA/AlgMA/HAP). Bioprinted scaffolds facilitate the long-term cultivation and progressive maturation of extensive bioprinted bone organoids, foster multicellular differentiation, and offer valuable insights into the initial stages of bone formation. The intrinsic self-mineralizing quality of the bioink closely emulates the properties of natural bone, empowering organoids with enhanced bone repair for both in vitro and in vivo applications. This trailblazing investigation propels the field of bone tissue engineering and holds significant promise for its translation into practical applications.