Modelling post-implantation human development to yolk sac blood emergence.

Implantation of the human embryo begins a critical developmental stage that comprises profound events including axis formation, gastrulation and the emergence of haematopoietic system1,2. Our mechanistic knowledge of this window of human life remains limited due to restricted access to in vivo samples for both technical and ethical reasons3-5. Stem cell models of human embryo have emerged to help unlock the mysteries of this stage6-16. Here we present a genetically inducible stem cell-derived embryoid model of early post-implantation human embryogenesis that captures the reciprocal codevelopment of embryonic tissue and the extra-embryonic endoderm and mesoderm niche with early haematopoiesis. This model is produced from induced pluripotent stem cells and shows unanticipated self-organizing cellular programmes similar to those that occur in embryogenesis, including the formation of amniotic cavity and bilaminar disc morphologies as well as the generation of an anterior hypoblast pole and posterior domain. The extra-embryonic layer in these embryoids lacks trophoblast and shows advanced multilineage yolk sac tissue-like morphogenesis that harbours a process similar to distinct waves of haematopoiesis, including the emergence of erythroid-, megakaryocyte-, myeloid- and lymphoid-like cells. This model presents an easy-to-use, high-throughput, reproducible and scalable platform to probe multifaceted aspects of human development and blood formation at the early post-implantation stage. It will provide a tractable human-based model for drug testing and disease modelling.

De novo construction of T cell compartment in humanized mice engrafted with iPSC-derived thymus organoids.

Hematopoietic humanized (hu) mice are powerful tools for modeling the action of human immune system and are widely used for preclinical studies and drug discovery. However, generating a functional human T cell compartment in hu mice remains challenging, primarily due to the species-related differences between human and mouse thymus. While engrafting human fetal thymic tissues can support robust T cell development in hu mice, tissue scarcity and ethical concerns limit their wide use. Here, we describe the tissue engineering of human thymus organoids from inducible pluripotent stem cells (iPSC-thymus) that can support the de novo generation of a diverse population of functional human T cells. T cells of iPSC-thymus-engrafted hu mice could mediate both cellular and humoral immune responses, including mounting robust proinflammatory responses on T cell receptor engagement, inhibiting allogeneic tumor graft growth and facilitating efficient Ig class switching. Our findings indicate that hu mice engrafted with iPSC-thymus can serve as a new animal model to study human T cell-mediated immunity and accelerate the translation of findings from animal studies into the clinic.

Hummingbird: efficient performance prediction for executing genomic applications in the cloud.

MOTIVATION: A major drawback of executing genomic applications on cloud computing facilities is the lack of tools to predict which instance type is the most appropriate, often resulting in an over- or under- matching of resources. Determining the right configuration before actually running the applications will save money and time. Here, we introduce Hummingbird, a tool for predicting performance of computing instances with varying memory and CPU on multiple cloud platforms. RESULTS: Our experiments on three major genomic data pipelines, including GATK HaplotypeCaller, GATK Mutect2 and ENCODE ATAC-seq, showed that Hummingbird was able to address applications in command line specified in JSON format or workflow description language (WDL) format, and accurately predicted the fastest, the cheapest and the most cost-efficient compute instances in an economic manner. AVAILABILITY AND IMPLEMENTATION: Hummingbird is available as an open source tool at: https://github.com/StanfordBioinformatics/Hummingbird. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Swarm: A federated cloud framework for large-scale variant analysis.

Genomic data analysis across multiple cloud platforms is an ongoing challenge, especially when large amounts of data are involved. Here, we present Swarm, a framework for federated computation that promotes minimal data motion and facilitates crosstalk between genomic datasets stored on various cloud platforms. We demonstrate its utility via common inquiries of genomic variants across BigQuery in the Google Cloud Platform (GCP), Athena in the Amazon Web Services (AWS), Apache Presto and MySQL. Compared to single-cloud platforms, the Swarm framework significantly reduced computational costs, run-time delays and risks of security breach and privacy violation.

Multifunctional Triboelectric Nanogenerator-enabled Structural Elements for Next Generation Civil Infrastructure Monitoring Systems.

There is a critical shortage in research needed to explore a new class of multifunctional structural components that respond to their environment, empower themselves and self-monitor their condition. Here, we propose the novel concept of triboelectric nanogenerator-enabled structural elements (TENG-SEs) to build the foundation for the next generation civil infrastructure systems with intrinsic sensing and energy harvesting functionalities. In order to validate the proposed concept, we develop proof-of-concept multifunctional composite rebars with built-in triboelectric nanogenerator mechanisms. The developed prototypes function as structural reinforcements, nanogenerators and distributed sensing mediums under external mechanical vibrations. Experiential and theoretical studies are performed to verify the electrical and mechanical performance of the developed self-powering and self-sensing composite structural components. We demonstrate the capability of the embedded structural elements to detect damage patterns in concrete beams at multiscale. Finally, we discuss how this new class of TENG-SEs could revolutionize the large-scale distributed monitoring practices in civil infrastructure and construction fields.

Iterative point set registration for aligning scRNA-seq data.

Several studies profile similar single cell RNA-Seq (scRNA-Seq) data using different technologies and platforms. A number of alignment methods have been developed to enable the integration and comparison of scRNA-Seq data from such studies. While each performs well on some of the datasets, to date no method was able to both perform the alignment using the original expression space and generalize to new data. To enable such analysis we developed Single Cell Iterative Point set Registration (SCIPR) which extends methods that were successfully applied to align image data to scRNA-Seq. We discuss the required changes needed, the resulting optimization function, and algorithms for learning a transformation function for aligning data. We tested SCIPR on several scRNA-Seq datasets. As we show it successfully aligns data from several different cell types, improving upon prior methods proposed for this task. In addition, we show the parameters learned by SCIPR can be used to align data not used in the training and to identify key cell type-specific genes.

Detection and characterization of the Isospora lunaris infection from different finch hosts in southern Iran.

This study was conducted to investigate the Isosporoid protozoan infections in finch types. Fecal samples were collected from marketed domestic Java sparrows (Lonchura oryzivora), colored and white Zebra finch (Taeniopygia guttata), and European goldfinch (Carduelis carduelis) in southern Iran. The coccidial oocysts were recovered and investigated according to the morphological features and the ribosomal gene markers. Additionally, a challenge infection was conducted with 5 × 104 and 5 × 103 sporulated oocysts in four java sparrows to estimate the clinical manifestations. Based on the morphology, the oocysts of Isospora lunaris were identified in all sampled bird types; however, the molecular method revealed the isolates had considerable similarities with some of Isospora and systemic Isospora-like organisms named as Atoxoplasma. Phylogenetic data also constructed an Atoxoplasma/Isospora clade with high sequence identities. High dose of the challenge with the parasite led to severe depression and sudden death, but it did not coincide with remarkable lesions and parasitic invasion in visceral organs. Contrary to molecular results, this feature is consistent with the common Isospora infections in passerines and differs from those described for Atoxoplasma species. Because of the prevalence, possibility of transmission, and clinical consequences, preventive measures are necessary to avoid outbreaks of isosporoid infections among finch type birds.

Piezoelectric Sensing Techniques in Structural Health Monitoring: A State-of-the-Art Review.

Recently, there has been a growing interest in deploying smart materials as sensing components of structural health monitoring systems. In this arena, piezoelectric materials offer great promise for researchers to rapidly expand their many potential applications. The main goal of this study is to review the state-of-the-art piezoelectric-based sensing techniques that are currently used in the structural health monitoring area. These techniques range from piezoelectric electromechanical impedance and ultrasonic Lamb wave methods to a class of cutting-edge self-powered sensing systems. We present the principle of the piezoelectric effect and the underlying mechanisms used by the piezoelectric sensing methods to detect the structural response. Furthermore, the pros and cons of the current methodologies are discussed. In the end, we envision a role of the piezoelectric-based techniques in developing the next-generation self-monitoring and self-powering health monitoring systems.

A Novel Data Reduction Approach for Structural Health Monitoring Systems.

The massive amount of data generated by structural health monitoring (SHM) systems usually affects the system's capacity for data transmission and analysis. This paper proposes a novel concept based on the probability theory for data reduction in SHM systems. The beauty salient feature of the proposed method is that it alleviates the burden of collecting and analysis of the entire strain data via a relative damage approach. In this methodology, the rate of variation of strain distributions is related to the rate of damage. In order to verify the accuracy of the approach, experimental and numerical studies were conducted on a thin steel plate subjected to cyclic in-plane tension loading. Circular holes with various sizes were made on the plate to define damage states. Rather than measuring the entire strain response, the cumulative durations of strain events at different predefined strain levels were obtained for each damage scenario. Then, the distribution of the calculated cumulative times was used to detect the damage progression. The results show that the presented technique can efficiently detect the damage progression. The damage detection accuracy can be improved by increasing the predefined strain levels. The proposed concept can lead to over 2500% reduction in data storage requirement, which can be particularly important for data generation and data handling in on-line SHM systems.

In vivo study of the efficacy of the aromatic water of Zataria multiflora on hydatid cysts.

Gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS) were employed to determine the chemical composition of the essential oil (EO) from aromatic water (AW) of Zataria multiflora. Thymol (66.9%), carvacrol (15.2%), and carvone (7.3%) were found to be the major EO constituents. Eighty laboratory BALB/c mice were infected intraperitoneally by injection of 1,500 viable protoscolices and were divided into prevention (40 mice) and therapeutic (40 mice) groups. To prove the preventive effect of the Z. multiflora AW on development of hydatid cysts, the 40 infected mice were allocated into three treatment groups, including the albendazole group (10 mice that received 150 mg/kg body weight/day for 10 days), the Z. multiflora AW group (15 mice that received 20 ml/liter in drinking water for 8 months), and a control group (15 mice that received no treatment). To estimate the therapeutic effect of the Z. multiflora AW on the hydatid cyst, after 8 months of infection, the 15 remaining mice were allocated into three experimental treatment groups of five animals each, including the albendazole group (300 mg/kg/day for 20 days), Z. multiflora AW group (40 ml/liter in drinking water for 30 days), and control group (no treatment). All mice were then euthanized, and the sizes and weights of the cysts as well as their ultrastructural changes were investigated. The weights and sizes of the hydatid cysts significantly decreased upon treatment with the Z. multiflora AW in both the preventive and therapeutic groups (P < 0.05). The results of scanning electron microscopy also showed considerable damage in the germinal layer of the hydatid cysts recovered from the treated animals.

Maximizing Triboelectric Nanogenerators by Physics-Informed AI Inverse Design.

Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought-after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs for real-life applications. Artificial intelligence-enabled inverse design is a powerful tool to realize performance-oriented triboelectric nanogenerators. This is an emerging scientific direction that can address the concerns about the design and optimization of triboelectric nanogenerators leading to a next generation nanogenerator systems. This perspective paper aims at reviewing the principal analysis of triboelectricity, summarizing the current challenges of designing and optimizing triboelectric nanogenerators, and highlighting the physics-informed inverse design strategies to develop triboelectric nanogenerators. Strategic inverse design is particularly discussed in the contexts of expanding the four-mode analytical models by physics-informed artificial intelligence, discovering new conductive and dielectric materials, and optimizing contact interfaces. Various potential development levels of artificial intelligence-enhanced triboelectric nanogenerators are delineated. Finally, the potential of physics-informed artificial intelligence inverse design to propel triboelectric nanogenerators from prototypes to multifunctional intelligent systems for real-life applications is discussed.

Millifluidic Nanogenerator Lab-on-a-Chip Device for Blood Electrical Conductivity Monitoring at Low Frequency.

The electrical conductivity of blood is a crucial physiological parameter with diverse applications in medical diagnostics. Here, a novel approach utilizing a portable millifluidic nanogenerator lab-on-a-chip device for measuring blood conductivity at low frequencies, is introduced. The proposed device employs blood as a conductive substance within its built-in triboelectric nanogenerator system. The voltage generated by this blood-based nanogenerator device is analyzed to determine the electrical conductivity of the blood sample. The self-powering functionality of the device eliminates the need for complex embedded electronics and external electrodes. Experimental results using simulated body fluid and human blood plasma demonstrate the device's efficacy in detecting variations in conductivity related to changes in electrolyte concentrations. Furthermore, artificial intelligence models are used to analyze the generated voltage patterns and to estimate the blood electrical conductivity. The models exhibit high accuracy in predicting conductivity based solely on the device-generated voltage. The 3D-printed, disposable design of the device enhances portability and usability, providing a point-of-care solution for rapid blood conductivity assessment. A comparative analysis with traditional conductivity measurement methods highlights the advantages of the proposed device in terms of simplicity, portability, and adaptability for various applications beyond blood analysis.

Identification of Novel Biomarkers for Alzheimer's Disease and Related Dementias Using Unbiased Plasma Proteomics.

Alzheimer's disease (AD) and related dementias (ADRD) is a complex disease with multiple pathophysiological drivers that determine clinical symptomology and disease progression. These diseases develop insidiously over time, through many pathways and disease mechanisms and continue to have a huge societal impact for affected individuals and their families. While emerging blood-based biomarkers, such as plasma p-tau181 and p-tau217, accurately detect Alzheimer neuropthology and are associated with faster cognitive decline, the full extension of plasma proteomic changes in ADRD remains unknown. Earlier detection and better classification of the different subtypes may provide opportunities for earlier, more targeted interventions, and perhaps a higher likelihood of successful therapeutic development. In this study, we aim to leverage unbiased mass spectrometry proteomics to identify novel, blood-based biomarkers associated with cognitive decline. 1,786 plasma samples from 1,005 patients were collected over 12 years from partcipants in the Massachusetts Alzheimer's Disease Research Center Longitudinal Cohort Study. Patient metadata includes demographics, final diagnoses, and clinical dementia rating (CDR) scores taken concurrently. The Proteograph™ Product Suite (Seer, Inc.) and liquid-chromatography mass-spectrometry (LC-MS) analysis were used to process the plasma samples in this cohort and generate unbiased proteomics data. Data-independent acquisition (DIA) mass spectrometry results yielded 36,259 peptides and 4,007 protein groups. Linear mixed effects models revealed 138 differentially abundant proteins between AD and healthy controls. Machine learning classification models for AD diagnosis identified potential candidate biomarkers including MBP, BGLAP, and APoD. Cox regression models were created to determine the association of proteins with disease progression and suggest CLNS1A, CRISPLD2, and GOLPH3 as targets of further investigation as potential biomarkers. The Proteograph workflow provided deep, unbiased coverage of the plasma proteome at a speed that enabled a cohort study of almost 1,800 samples, which is the largest, deep, unbiased proteomics study of ADRD conducted to date.

Genetic characterization and phylogenetic analysis of Trypanosoma evansi in Iranian dromedary camels.

Whole blood samples were collected from 117 male clinically healthy Camelus dromedarius aged between 6 months to 18 years from several farms in Yazd Province of Iran. Trypanosoma evansi-affected camels were detected by Giemsa-stained blood smears, and the positive blood samples (4 out of 117) were submitted to PCR examination and phylogenetic analysis. Basic Local Alignment Search Tool data of the obtained complete internal transcribed spacer (ITS) sequences revealed that they corresponded to those of T. evansi, Thailand cattle isolate (AY912276) with the homology of 99 %. Both phylogenetic trees generated by ITS1 and complete ITS were unable to clearly show inter- and intraspecific genetic diversity of Trypanosoma spp. isolates. The phylogenetic tree inferred from the ITS2 nucleotide sequences (569 bp) clearly showed the genetic diversity of the parasites. Phylogenetic and molecular analyses of this region showed that two distinct genotypes of T. evansi in Iranian dromedary camels are present. In contrast to the ITS1 and ITS2 regions, multiple alignment of the nucleotide sequence of the 5.8S rRNA showed a high degree of sequence conservation during evolution in various Trypanosoma spp.

Deglycosylation of Excretory-Secretory Antigens of the Second-Stage Larvae of Toxocara cati Improves Its Efficacy in the Diagnosis of Human Toxocariasis.

Background: Toxocariasis is an important zoonotic infection, especially in tropical areas. One of the significant challenges in the serodiagnosis of human toxocariasis is the cross-reaction of Toxocara antigens with other parasites due to their relatively similar glycan structures. Removing the glycan structure from Toxocara excretory-secretory (TES) antigens may increase the efficacy of these antigens in the diagnosis of toxocariasis. The current study aimed to assess the efficacy of deglycosylated Toxocara cati excretory-secretory (dTES) antigens for the serodiagnosis of human toxocariasis. Methods: Toxocara ES antigens were prepared from T. cati second-stage larvae and deglycosylated using sodium hydroxide (NaOH). The TES antigens, along with the dTES antigens, were used in an ELISA as well as a western blotting system for the detection of anti-Toxocara antibodies. Sera samples collected from 30 confirmed cases of toxocariasis, 30 patients with other diseases, and 30 healthy subjects were evaluated by both systems. Results: The sensitivity of TES and dTES ELISA for the diagnosis of human toxocariasis was 96.67% (95% CI = 82.78-99.92) and 93.33% (95% CI = 77.93-99.18), respectively, while the specificity of dTES (88.33%; 95% CI = 77.43-95.18) increased significantly compared to the TES (80.00%; 95% CI = 67.67-89.22). The sensitivity of both antigens was 100% (95% CI = 88.43-100) by the western blotting system. Moreover, the specificity of TES and dTES antigens was 95% (95% CI = 86.08-98.96) and 98.33% (95% CI = 91.06-99.96), respectively, when using the western blotting system. Conclusion: Results of the current study indicate that the chemical removal of the glycan epitopes of T. cati ES antigens significantly reduces cross-reactivity rates with other parasitic infections. Considering the findings of the present study, the dTES antigens seem to be suitable antigens for the serodiagnosis of human toxocariasis.

Mechanical metamaterials and beyond.

Mechanical metamaterials enable the creation of structural materials with unprecedented mechanical properties. However, thus far, research on mechanical metamaterials has focused on passive mechanical metamaterials and the tunability of their mechanical properties. Deep integration of multifunctionality, sensing, electrical actuation, information processing, and advancing data-driven designs are grand challenges in the mechanical metamaterials community that could lead to truly intelligent mechanical metamaterials. In this perspective, we provide an overview of mechanical metamaterials within and beyond their classical mechanical functionalities. We discuss various aspects of data-driven approaches for inverse design and optimization of multifunctional mechanical metamaterials. Our aim is to provide new roadmaps for design and discovery of next-generation active and responsive mechanical metamaterials that can interact with the surrounding environment and adapt to various conditions while inheriting all outstanding mechanical features of classical mechanical metamaterials. Next, we deliberate the emerging mechanical metamaterials with specific functionalities to design informative and scientific intelligent devices. We highlight open challenges ahead of mechanical metamaterial systems at the component and integration levels and their transition into the domain of application beyond their mechanical capabilities.

Multifunctional Nanogenerator-Integrated Metamaterial Concrete Systems for Smart Civil Infrastructure.

Creating multifunctional concrete materials with advanced functionalities and mechanical tunability is a critical step toward reimagining the traditional civil infrastructure systems. Here, the concept of nanogenerator-integrated mechanical metamaterial concrete is presented to design lightweight and mechanically tunable concrete systems with energy harvesting and sensing functionalities. The proposed metamaterial concrete systems are created via integrating the mechanical metamaterial and nano-energy-harvesting paradigms. These advanced materials are composed of reinforcement auxetic polymer lattices with snap-through buckling behavior fully embedded inside a conductive cement matrix. We rationally design their composite structures to induce contact-electrification between the layers under mechanical excitations/triggering. The conductive cement enhanced with graphite powder serves as the electrode in the proposed systems, while providing the desired mechanical performance. Experimental studies are conducted to investigate the mechanical and electrical properties of the designed prototypes. The metamaterial concrete systems are tuned to achieve up to 15% compressibility under cycling loading. The power output of the nanogenerator-integrated metamaterial concrete prototypes reaches 330 µW. Furthermore, the self-powered sensing functionality of the nanogenerator concrete systems for distributed health monitoring of large-scale concrete structures is demonstrated. The metamaterial concrete paradigm can possibly enable the design of smart civil infrastructure systems with a broad range of advanced functionalities.

Modelling Human Post-Implantation Development via Extra-Embryonic Niche Engineering.

Implantation of the human embryo commences a critical developmental stage that comprises profound morphogenetic alteration of embryonic and extra-embryonic tissues, axis formation, and gastrulation events. Our mechanistic knowledge of this window of human life remains limited due to restricted access to in vivo samples for both technical and ethical reasons. Additionally, human stem cell models of early post-implantation development with both embryonic and extra-embryonic tissue morphogenesis are lacking. Here, we present iDiscoid, produced from human induced pluripotent stem cells via an engineered a synthetic gene circuit. iDiscoids exhibit reciprocal co-development of human embryonic tissue and engineered extra-embryonic niche in a model of human post-implantation. They exhibit unanticipated self-organization and tissue boundary formation that recapitulates yolk sac-like tissue specification with extra-embryonic mesoderm and hematopoietic characteristics, the formation of bilaminar disc-like embryonic morphology, the development of an amniotic-like cavity, and acquisition of an anterior-like hypoblast pole and posterior-like axis. iDiscoids offer an easy-to-use, high-throughput, reproducible, and scalable platform to probe multifaceted aspects of human early post-implantation development. Thus, they have the potential to provide a tractable human model for drug testing, developmental toxicology, and disease modeling.

Multiset multicover methods for discriminative marker selection.

Markers are increasingly being used for several high-throughput data analysis and experimental design tasks. Examples include the use of markers for assigning cell types in scRNA-seq studies, for deconvolving bulk gene expression data, and for selecting marker proteins in single-cell spatial proteomics studies. Most marker selection methods focus on differential expression (DE) analysis. Although such methods work well for data with a few non-overlapping marker sets, they are not appropriate for large atlas-size datasets where several cell types and tissues are considered. To address this, we define the phenotype cover (PC) problem for marker selection and present algorithms that can improve the discriminative power of marker sets. Analysis of these sets on several marker-selection tasks suggests that these methods can lead to solutions that accurately distinguish different phenotypes in the data.

Magnetic capsulate triboelectric nanogenerators.

Triboelectric nanogenerators have received significant research attention in recent years. Structural design plays a critical role in improving the energy harvesting performance of triboelectric nanogenerators. Here, we develop the magnetic capsulate triboelectric nanogenerators (MC-TENG) for energy harvesting under undesirable mechanical excitations. The capsulate TENG are designed to be driven by an oscillation-triggered magnetic force in a holding frame to generate electrical power due to the principle of the freestanding triboelectrification. Experimental and numerical studies are conducted to investigate the electrical performance of MC-TENG under cyclic loading in three energy harvesting modes. The results indicate that the energy harvesting performance of the MC-TENG is significantly affected by the structure of the capsulate TENG. The copper MC-TENG systems are found to be the most effective design that generates the maximum mode of the voltage range is 4 V in the closed-circuit with the resistance of 10 GΩ. The proposed MC-TENG concept provides an effective method to harvest electrical energy from low-frequency and low-amplitude oscillations such as ocean wave.