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The trade-off between strength and toughness presents a fundamental challenge in engineering material design. Composite materials (CMs) can strategically arrange different materials to enhance both strength and toughness by optimizing the distribution of loads and increasing resistance to crack propagation. However, current data-driven computational modeling approaches for CM configuration optimization suffer from limitations of "substantial computational cost" and "poor predictive power over extrapolation spaces", making it difficult to integrate with global optimization algorithms, and ultimately limiting the discovery of materials with optimal tradeoffs. As a breakthrough, we propose a data-driven design framework with a multi-task DL architecture capable of accurately predicting local fields' spatiotemporal behavior, including stress evolution and crack propagation, alongside homogenized mechanical properties. Our model, trained on datasets generated from crack phase fields simulations of random configurations, demonstrated exceptional predictive performance even for unseen configurations with well organized patterns exploiting nature-inspired morphological features. Importantly, solely from composite material (CM) configurations, our model effectively predicts long-term spatiotemporal fields with an accuracy comparable to FEM but with a substantial reduction in computational time. By coupling the model's predictive power with genetic optimization algorithms, we demonstrated the framework's applicability in two representative inverse design tasks: devising CM configurations with mechanical properties beyond the training set and guiding desired crack pattern formation. Our research highlights the potential of artificial intelligence as a feasible alternative to conventional computational approaches for straightforward configurational and structural optimization.

As natural predators, owls fly with astonishing stealth due to the serrated feather morphology that produces advantageous flow characteristics. Traditionally, these serrations are tailored for airfoil edges with simple two-dimensional patterns, limiting their effect on noise reduction while negotiating tradeoffs in aerodynamic performance. Conversely, the intricately structured wings of cicadas have evolved for effective flapping, presenting a potential blueprint for alleviating these aerodynamic limitations. In this study, we formulate a synergistic design strategy that harmonizes noise suppression with aerodynamic efficiency by integrating the geometrical attributes of owl feathers and cicada forewings, culminating in a three-dimensional sinusoidal serration propeller topology that facilitates both silent and efficient flight. Experimental results show that our design yields a reduction in overall sound pressure levels by up to 5.5 dB and an increase in propulsive efficiency by over 20% compared to the current industry benchmark. Computational fluid dynamics simulations validate the efficacy of the bioinspired design in augmenting surface vorticity and suppressing noise generation across various flow regimes. This topology can advance the multifunctionality of aerodynamic surfaces for the development of quieter and more energy-saving aerial vehicles., (© 2024. The Author(s).)

The utilization of low-density and robust mechanical metamaterials rises as a promising solution for multifunctional electromagnetic wave absorbers due to their structured porous structures, which facilitates impedance matching and structural absorption. However, the various geometrical parameters involved in constructing these metamaterials affect their electromagnetic response, necessitating a comprehensive understanding of underlying absorbing mechanisms. Through experimentally validated numerical analysis, this study delves into the influence of geometrical factors on the electromagnetic response of representative low-density, high strength mechanical metamaterials, namely octet-truss and octet-foam. By juxtaposing electromagnetic response under varying volume fractions, cell lengths, and multilayer configurations of octet-truss and octet-foam, distinct absorption mechanisms emerge as geometrical parameters evolve. These mechanisms encompass diminished reflection owing to porous structures, effective medium approximations within subwavelength limits, and transmission-driven or reflection-driven phenomena originating from the interplay of open- and closed-cell structures. Through analyses on these mechanical metamaterials, we demonstrate the viability of employing them as tunable yet scalable structures that are lightweight, robust, and broadband electromagnetic wave absorption.

Numerical solutions to partial differential equations (PDEs) are instrumental for material structural design where extensive data screening is needed. However, traditional numerical methods demand significant computational resources, highlighting the need for innovative optimization algorithms to streamline design exploration. Direct gradient-based optimization algorithms, while effective, rely on design initialization and require complex, problem-specific sensitivity derivations. The advent of machine learning offers a promising alternative to handling large parameter spaces. To further mitigate data dependency, researchers have developed physics-informed neural networks (PINNs) to learn directly from PDEs. However, the intrinsic continuity requirement of PINNs restricts their application in structural mechanics problems, especially for composite materials. Our work addresses this discontinuity issue by substituting the PDE residual with a weak formulation in the physics-informed training process. The proposed approach is exemplified in modeling digital materials, which are mathematical representations of complex composites that possess extreme structural discontinuity. This article also introduces an interactive process that integrates physics-informed loss with design objectives, eliminating the need for pretrained surrogate models or analytical sensitivity derivations. The results demonstrate that our approach can preserve the physical accuracy in data-free material surrogate modeling but also accelerates the direct optimization process without model pretraining., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

Background: Liver transplantation is the main treatment for cholestatic liver disease and some metabolic liver diseases in children. However, no accurate prediction model to determine the survival probability of grafts prior to surgery exists. This study aimed to develop an effective prognostic model for allograft survival after pediatric liver transplantation., Methods: This retrospective cohort study included 2032 patients who underwent pediatric liver transplantation between January 1, 2006, and January 1, 2020. A nomogram was developed using Cox regression and validated based on bootstrap sampling. Predictive and discriminatory accuracies were determined using the concordance index and visualized using calibration curves; net benefits were calculated for model comparison. An online Shiny application was developed for easy access to the model., Results: Multivariable analysis demonstrated that preoperative diagnosis, recipient age, body weight, graft type, preoperative total bilirubin, interleukin-1β, portal venous blood flow direction, spleen thickness, and the presence of heart disease and cholangitis were independent factors for survival, all of which were selected in the nomogram. Calibration of the nomogram indicated that the 1-, 3-, and 5-year predicted survival rates agreed with the actual survival rate. The concordance indices for graft survival at 1, 3, and 5 years were 0.776, 0.757, and 0.753, respectively, which were significantly higher than those of the Pediatric End-Stage Liver Disease and Child-Pugh scoring systems. The allograft dysfunction risk of a recipient could be easily predicted using the following URL: https://aspelt.shinyapps.io/ASPELT/ / CONCLUSION: The allograft survival after pediatric liver transplantation (ASPELT) score model can effectively predict the graft survival rate after liver transplantation in children, providing a simple and convenient evaluation method for clinicians and patients., (© 2023. The Author(s).)

Rationally engineered porous structures enable lightweight broadband electromagnetic (EM) wave absorbers for countering radar signals or mitigating EM interference between multiple components. However, the scalability of such structures has been hindered by their limited mechanical properties resulting from low density. Herein, an additively manufactured Kelvin foam-based EM wave absorber (KF-EMA) is reported that exhibits multifunctionality, namely EM wave absorption and light-weighted load-bearing structures with constant relative stiffness made possible using bending-dominated lattice structures. Based on tuning design parameters, such as the backbone structures and constituent materials, the proposed KF-EMA features a multilayered 3D-printed design with geometrically optimized KF structures made of carbon black-based backbone composites. The developed KF-EMA demonstrated an absorbance greater than 90% at frequencies ranging from 5.8 to 18 GHz (average EM wave absorption rates of 95.89% and maximum of 99.1% at 15.8 GHz), while the low-density structures of the absorber (≈200 kg m -3 ) still maintained a compression index between the stiffness and relative density (n = 2) under compression. The design strategy paves the way for using metamaterials as mechanically reinforced EM wave absorbers that enable multifunctionality by optimizing unit-cell parameters through a single and low-density structure., (© 2023 Wiley-VCH GmbH.)

Extensive burns can cause nonnegligible acute and chronic damage to central nervous system of patients. The damage of central nervous system may have a profound impact on patients, including neurobehavioral changes such as post-traumatic stress disorder, depression, anxiety, and sleep disorder. These changes may persist after injury, greatly affecting patients' integration into society and return to work. This paper systematically reviewed the clinical manifestations, pathogenesis, and current intervention methods of mental disorders in patients with extensive burns, aiming to provide a basis for further understanding, prevention, and treatment of patients with mental disorders after burns.

Graphene aerogel (GA), a 3D carbon-based nanostructure built on 2D graphene sheets, is well known for being the lightest solid material ever synthesized. It also possesses many other exceptional properties, such as high specific surface area and large liquid absorption capacity, thanks to its ultra-high porosity. Computationally, the mechanical properties of GA have been studied by molecular dynamics (MD) simulations, which uncover nanoscale mechanisms beyond experimental observations. However, studies on how GA structures and properties evolve in response to simulation parameter changes, which provide valuable insights to experimentalists, have been lacking. In addition, the differences between the calculated properties via simulations and experimental measurements have rarely been discussed. To address the shortcomings mentioned above, in this study, we systematically study various mechanical properties and the structural integrity of GA as a function of a wide range of simulation parameters. Results show that during the in silico GA preparation, smaller and less spherical inclusions (mimicking the effect of water clusters in experiments) are conducive to strength and stiffness but may lead to brittleness. Additionally, it is revealed that a structurally valid GA in the MD simulation requires the number of bonds per atom to be at least 1.40, otherwise the GA building blocks are not fully interconnected. Finally, our calculation results are compared with experiments to showcase both the power and the limitations of the simulation technique. This work may shed light on the improvement of computational approaches for GA as well as other novel nanomaterials.

Additive manufacturing technologies have progressed in the past decades, especially when used to print biofunctional structures such as scaffolds and vessels with living cells for tissue engineering applications. Part quality and reliability are essential to maintaining the biocompatibility and structural integrity needed for engineered tissue constructs. As a result, it is critical to detect for any anomalies that may occur in the 3D-bioprinting process that can cause a mismatch between the desired designs and printed shapes. However, challenges exist in detecting the imperfections within oftentimes transparent bioprinted and complex printing features accurately and efficiently. In this study, an anomaly detection system based on layer-by-layer sensor images and machine learning algorithms is developed to distinguish and classify imperfections for transparent hydrogel-based bioprinted materials. High anomaly detection accuracy is obtained by utilizing convolutional neural network methods as well as advanced image processing and augmentation techniques on extracted small image patches. Along with the prediction of various anomalies, the category of infill pattern and location information on the image patches can be accurately determined. It is envisioned that using our detection system to categorize and localize printing anomalies, real-time autonomous correction of process parameters can be realized to achieve high-quality tissue constructs in 3D-bioprinting processes.

Elastography is a medical imaging technique used to measure the elasticity of tissues by comparing ultrasound signals before and after a light compression. The lateral resolution of ultrasound is much inferior to the axial resolution. Current elastography methods generally require both axial and lateral displacement components, making them less effective for clinical applications. Additionally, these methods often rely on the assumption of material incompressibility, which can lead to inaccurate elasticity reconstruction as no materials are truly incompressible. To address these challenges, a new physics-informed deep-learning method for elastography is proposed. This new method integrates a displacement network and an elasticity network to reconstruct the Young's modulus field of a heterogeneous object based on only a measured axial displacement field. It also allows for the removal of the assumption of material incompressibility, enabling the reconstruction of both Young's modulus and Poisson's ratio fields simultaneously. The authors demonstrate that using multiple measurements can mitigate the potential error introduced by the "eggshell" effect, in which the presence of stiff material prevents the generation of strain in soft material. These improvements make this new method a valuable tool for a wide range of applications in medical imaging, materials characterization, and beyond., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Lattice structures are known to have high performance-to-weight ratios because of their highly efficient material distribution in a given volume. However, their inherently large void fraction leads to low mechanical properties compared to the base material, high anisotropy, and brittleness. Most works to date have focused on modifying the spatial arrangement of beam elements to overcome these limitations, but only simple beam geometries are adopted due to the infinitely large design space associated with probing and varying beam shapes. Herein, we present an approach to enhance the elastic modulus, strength, and toughness of lattice structures with minimal tradeoffs by optimizing the shape of beam elements for a suite of lattice structures. A generative deep learning-based approach is employed, which leverages the fast inference of neural networks to accelerate the optimization process. Our optimized lattice structures possess superior stiffness (+59%), strength (+49%), toughness (+106%), and isotropy (+645%) compared to benchmark lattices consisting of cylindrical beams. We fabricate our lattice designs using additive manufacturing to validate the optimization approach; experimental and simulation results show good agreement. Remarkable improvement in mechanical properties is shown to be the effect of distributed stress fields and deformation modes subject to beam shape and lattice type.

Among various porous solids for gas separation and purification, metal-organic frameworks (MOFs) are promising materials that potentially combine high CO 2 uptake and CO 2 /N 2 selectivity. So far, within the hundreds of thousands of MOF structures known today, it remains a challenge to computationally identify the best suited species. First principle-based simulations of CO 2 adsorption in MOFs would provide the necessary accuracy; however, they are impractical due to the high computational cost. Classical force field-based simulations would be computationally feasible; however, they do not provide sufficient accuracy. Thus, the entropy contribution that requires both accurate force fields and sufficiently long computing time for sampling is difficult to obtain in simulations. Here, we report quantum-informed machine-learning force fields (QMLFFs) for atomistic simulations of CO 2 in MOFs. We demonstrate that the method has a much higher computational efficiency (∼1000×) than the first-principle one while maintaining the quantum-level accuracy. As a proof of concept, we show that the QMLFF-based molecular dynamics simulations of CO 2 in Mg-MOF-74 can predict the binding free energy landscape and the diffusion coefficient close to experimental values. The combination of machine learning and atomistic simulation helps achieve more accurate and efficient in silico evaluations of the chemisorption and diffusion of gas molecules in MOFs.

Octopamine (OA) is structurally and functionally similar to adrenaline/noradrenaline in vertebrates, and OA modulates diverse physiological and behavioral processes in invertebrates. OA exerts its actions by binding to specific octopamine receptors (OARs). Functional and pharmacological characterization of OARs have been investigated in several insects. However, the literature on OARs is scarce for parasitoids. Here we cloned three β-adrenergic-like OARs ( CcOctβRs ) from Cotesia chilonis . CcOctβRs share high similarity with their own orthologous receptors. The transcript levels of CcOctβRs were varied in different tissues. When heterologously expressed in CHO-K1 cells, CcOctβRs induced cAMP production, and were dose-dependently activated by OA, TA and putative octopaminergic agonists. Their activities were inhibited by potential antagonists and were most efficiently blocked by epinastine. Our study offers important information about the molecular and pharmacological properties of β-adrenergic-like OARs from C. chilonis that will provide the basis to reveal the contribution of individual receptors to the physiological processes and behaviors in parasitoids.

Lattice structures are typically made up of a crisscross pattern of beam elements, allowing engineers to distribute material in a more structurally effective way. However, a main challenge in the design of lattice structures is a trade-off between the density and mechanical properties. Current studies have often assumed the cross-sectional area of the beam elements to be uniform for reducing the design complexity. This simplified approach limits the possibility of finding superior designs with optimized weight-to-performance ratios. Here, the optimized shape of the beam elements is investigated using a deep learning approach with high-order Bézier curves to explore the augmented design space. This is then combined with a hybrid neural network and genetic optimization (NN-GO) adaptive method for the generation of superior lattice structures. In our optimized design, the distribution of material is smartly shifted more towards the joint region, the weakest location of lattice structures, to achieve the highest modulus and strength. This design strikes to balance between two modes of deformation: axial and bending. Thus, the optimized design is efficient for load bearing and energy absorption. To validate our simulations, the optimized design is then fabricated by additive manufacturing and its mechanical properties are evaluated through compression testing. A good correlation between experiments and simulations is observed and the optimized design has outperformed benchmark ones in terms of modulus and strength. We show that the extra design flexibility from high-order Bézier curves allows for a smoother transition between the beam elements which reduces the overall stress concentration profile.

Fish scales serve as a natural dermal armor with remarkable flexibility and puncture resistance. Through studying fish scales, researchers can replicate these properties and tune them by adjusting their design parameters to create biomimetic scales. Overlapping scales, as seen in elasmoid scales, can lead to complex interactions between each scale. These interactions are able to maintain the stiffness of the fish's structure with improved flexibility. Hence, it is important to understand these interactions in order to design biomimetic fish scales. Modeling the flexibility of fish scales, when subject to shear loading across a substrate, requires accounting for nonlinear relations. Current studies focus on characterizing these kinematic linear and nonlinear regions but fall short in modeling the kinematic phase shift. Here, we propose an approach that will predict when the linear-to-nonlinear transition will occur, allowing for more control of the overall behavior of the fish scale structure. Using a geometric analysis of the interacting scales, we can model the flexibility at the transition point where the scales start to engage in a nonlinear manner. The validity of these geometric predictions is investigated through finite element analysis. This investigation will allow for efficient optimization of scale-like designs and can be applied to various applications.

Notably known for its extraordinary thermal and mechanical properties, graphene is a favorable building block in various cutting-edge technologies such as flexible electronics and supercapacitors. However, the almost inevitable existence of defects severely compromises the properties of graphene, and defect prediction is a difficult, yet important, task. Emerging machine learning approaches offer opportunities to predict target properties such as defect distribution by exploiting readily available data, without incurring much experimental cost. Most previous machine learning techniques require the size of training data and predicted material systems of interest to be identical. This limits their broader application, because in practice a newly encountered material system may have a different size compared with the previously observed ones. In this paper, we develop a transferable learning approach for graphene defect prediction, which can be used on graphene with various sizes or shapes not seen in the training data. The proposed approach employs logistic regression and utilizes data on local vibrational energy distributions of small graphene from molecular dynamics simulations, in the hopes that vibrational energy distributions can reflect local structural anomalies. The results show that our machine learning model, trained only with data on smaller graphene, can achieve up to 80% prediction accuracy of defects in larger graphene under different practical metrics. The present research sheds light on scalable graphene defect prediction and opens doors for data-driven defect detection for a broad range of two-dimensional materials.

Elastography is an imaging technique to reconstruct elasticity distributions of heterogeneous objects. Since cancerous tissues are stiffer than healthy ones, for decades, elastography has been applied to medical imaging for noninvasive cancer diagnosis. Although the conventional strain-based elastography has been deployed on ultrasound diagnostic-imaging devices, the results are prone to inaccuracies. Model-based elastography, which reconstructs elasticity distributions by solving an inverse problem in elasticity, may provide more accurate results but is often unreliable in practice due to the ill-posed nature of the inverse problem. We introduce ElastNet, a de novo elastography method combining the theory of elasticity with a deep-learning approach. With prior knowledge from the laws of physics, ElastNet can escape the performance ceiling imposed by labeled data. ElastNet uses backpropagation to learn the hidden elasticity of objects, resulting in rapid and accurate predictions. We show that ElastNet is robust when dealing with noisy or missing measurements. Moreover, it can learn probable elasticity distributions for areas even without measurements and generate elasticity images of arbitrary resolution. When both strain and elasticity distributions are given, the hidden physics in elasticity-the conditions for equilibrium-can be learned by ElastNet., Competing Interests: The authors declare no competing interest.

Highly hydrated silk materials (HHSMs) have been the focus of extensive research due to their usefulness in tissue engineering, regenerative medicine, and soft devices, among other fields. However, HHSMs have weak mechanical properties that limit their practical applications. Inspired by the mechanical training-driven structural remodeling strategy (MTDSRS) in biological tissues, herein, engineered MTDSRS is developed for self-reinforcement of HHSMs to improve their inherent mechanical properties and broaden potential utility. The MTDSRS consists of repetitive mechanical training and solvent-induced conformation transitions. Solvent-induced conformation transition enables the formation of β-sheet physical crosslinks among the proteins, while the repetitive mechanical loading allows the rearrangement of physically crosslinked proteins along the loading direction. Such synergistic effects produce strong and stiff mechanically trained-HHSMs (MT-HHSMs). The fracture strength and Young's modulus of the resultant MT-HHSMs (water content of 43 ± 4%) reach 4.7 ± 0.9 and 21.3 ± 2.1 MPa, respectively, which are 8-fold stronger and 13-fold stiffer than those of the as-prepared HHSMs, as well as superior to most previously reported HHSMs with comparable water content. In addition, the animal silk-like highly oriented molecular crosslinking network structure also provides MT-HHSMs with fascinating physical and functional features, such as stress-birefringence responsibility, humidity-induced actuation, and repeatable self-folding deformation., (© 2021 Wiley-VCH GmbH.)

Birefringent hydrogels have a strong potential for applications in biomedicine and optics as they can modulate the optical and mechanical anisotropy in confined two-dimensional geometries. However, production of birefringent hydrogels with hierarchical structures, mechanical properties, and biorelated behavior that are analogous to biological tissues is still challenging. Starting from the silk fibroin (SF)-ionic liquid solution system, this study aimed to rationally design a "binary solvent-exchange-induced self-assembly (BSEISA)" strategy to produce birefringent SF hydrogels (SFHs). In this method, the conformational transition rate of SF can be effectively controlled by the exchange rate of the binary solvents. Therefore, this method provides the possibility of controlling the conformation and orientation of SF. Molecular simulations confirmed that methanol is more effective in driving β-sheet formation than other often used solvents, such as formic acid and water. The formed β-sheets act as the physical cross-links that connect disparate protein chains, thereby forming continuous and stable three-dimensional (3D) hydrogel networks. The resultant BSEISA-SFHs are transparent and birefringent with mechanical characteristics similar to those of soft biological tissues, such as lens and cartilage. Interestingly, our results revealed that the evolution of experimental birefringent fringes perfectly matched the changes in stress distribution predicted using finite element analysis. Owing to the unique birefringence of BSEISA-SFHs, together with the advantages in mechanical performance, these hydrogels are anticipated to act as good tissue surrogates for understanding the mechanical response of biological tissues.

Effective osteogenesis remains a challenge in the treatment of bone defects. The emergence of artificial bone scaffolds provides an attractive solution. In this work, a new biomineralization strategy is proposed to facilitate osteogenesis through sustaining supply of nutrients including phosphorus (P), calcium (Ca), and silicon (Si). We developed black phosphorus (BP)-based, three-dimensional nanocomposite fibrous scaffolds via microfluidic technology to provide a wealth of essential ions for bone defect treatment. The fibrous scaffolds were fabricated from 3D poly (l-lactic acid) (PLLA) nanofibers (3D NFs), BP nanosheets, and hydroxyapatite (HA)-porous SiO 2 nanoparticles. The 3D BP@HA NFs possess three advantages: i) stably connected pores allow the easy entrance of bone marrow-derived mesenchymal stem cells (BMSCs) into the interior of the 3D fibrous scaffolds for bone repair and osteogenesis; ii) plentiful nutrients in the NFs strongly improve osteogenic differentiation in the bone repair area; iii) the photothermal effect of fibrous scaffolds promotes the release of elements necessary for bone formation, thus achieving accelerated osteogenesis. Both in vitro and in vivo results demonstrated that the 3D BP@HA NFs, with the assistance of NIR laser, exhibited good performance in promoting bone regeneration. Furthermore, microfluidic technology makes it possible to obtain high-quality 3D BP@HA NFs with low costs, rapid processing, high throughput and mass production, greatly improving the prospects for clinical application. This is also the first BP-based bone scaffold platform that can self-supply Ca 2+ , which may be the blessedness for older patients with bone defects or patients with damaged bones as a result of calcium loss., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors.)

Background: Liver fibrosis is a common pathological change caused by a variety of etiologies. Early diagnosis and timely treatment can reverse or delay disease progression and improve the prognosis. This study aimed to assess the potential utility of two-dimensional shear wave elastography and texture analysis in dynamic monitoring of the progression of liver fibrosis in rat model., Methods: Twenty rats were divided into control group (n = 4) and experimental groups (n = 4 per group) with carbon tetrachloride administration for 2, 3, 4, and 6 weeks. The liver stiffness measurement was performed by two-dimensional shear wave elastography, while the optimal texture analysis subsets to distinguish fibrosis stage were generated by MaZda. The results of elastography and texture analysis were validated through comparing with histopathology., Results: Liver stiffness measurement was 6.09 ± 0.31 kPa in the control group and 7.10 ± 0.41 kPa, 7.80 ± 0.93 kPa, 8.64 ± 0.93 kPa, 9.91 ± 1.13 kPa in the carbon tetrachloride induced groups for 2, 3, 4, 6 weeks, respectively (P < 0.05). By texture analysis, histogram and co-occurrence matrix had the most frequency texture parameters in staging liver fibrosis. Receiver operating characteristic curve of liver elasticity showed that the sensitivity and specificity were 95.0% and 92.5% to discriminate liver fibrosis and non-fibrosis, respectively. In texture analysis, five optimal parameters were selected to classify liver fibrosis and non-fibrosis., Conclusions: Two-dimensional shear wave elastography showed potential applications for noninvasive monitoring of the progression of hepatic fibrosis, even in mild fibrosis. Texture analysis can further extract and quantify the texture features in ultrasonic image, which was a supplementary to further visual information and acquired high diagnostic accuracy for severe fibrosis., (Copyright © 2020. Published by Elsevier B.V.)

Background: To investigate the role of two-dimensional shear wave elastography (2D-SWE) in the preoperative evaluation of pediatric patients with biliary atresia awaiting liver transplantation., Methods: Among a total of 152 pediatric patients enrolled in this single-institution prospective study between March 2018 and August 2019, 143 patients (age range, 4-97 months; median age, 7 months; 84 males, 59 females) who underwent successful routine ultrasound examination, SWE examination, and blood test before liver transplantation were included in the final analysis. The values of liver stiffness measured by SWE were compared with ultrasound and blood test parameters by Spearman's correlation analysis., Results: The overall median liver stiffness with 2D-SWE was 29.0 ± 10.9 kPa, with a range of 9.0-53.3 kPa. The success rate of 2D-SWE measurements was 98.0% (149/152). Liver stiffness measurement (LSMs) had no significant correlation with gender, age, weight, and height of the pediatric recipients. LSMs were correlated with ultrasound parameters including portal vein (PV) maximum velocity, PV direction, hepatic artery resistance index (HARI), spleen diameter, ascites, and blood test parameters (albumin level, platelet count level, and international normalized ratio). In the pediatric recipients with hepatofugal PV flow, high HARI (HARI ≧ 0.90), and ascites, or without Kasai operation, LSMs were significantly higher (P < .05)., Conclusions: SWE is feasible and valuable for assessing liver damage in children with biliary atresia awaiting liver transplantation and might be used as selection criteria for children in need of priority access to liver transplantation., (© 2020 Wiley Periodicals LLC.)

Objective: To study the difference of long non coding RNA (lncRNA) expression profile in bone marrow specimens of children with acute leukemia (AL) and other hematological disease children with normal bone marrows as controls, to screen the lncRNA related with childhood hematological diseases, and to explore the expression of lncRNA AC002454.1 and its clinical significance in AL children., Methods: The microarray gene chip technology was used to statistically analyze the lncRNA in bone marrow cells of newly diagnose AL children and control children. Ninty-seren differentially expressed lncRNAs were selected. The bone marrow specimens of ALL children (21 cases), AML children (22 cases) and control children (21 cases) were verified and compared by using qRT-PCR; then the lncRNA with maximum differential expression-lncRNA AC002454.1 was selected and used to analyze the relation of relative expression level with clinical indicators., Results: The microarray gene chip detection showed that 1 884 differentially expressed lncRNA were found in ALL children, and 4 289 differentically expressed lncRNA were found in AML children. The results confirming these differentically expressed lncRNA by qRT-PCR showed that 9 lncRNA expression were significantly up-regulated in ALL children, and 12 lncRNA expression were significantly up-regulated in AML children. Among these up-regulated lncRNA, the difference of AC002454.1 expression was most significant in ALL and AML children (P＜0.05, P＜0.01). The detection showed that there was a significant difference, in AC002454.1 relative expression level of newly diagnosed T-ALL and B-ALL children (P＜0.01), moreover, this difference also was found in ALL and AML children (P＜0.05). The detection analysis showed that there was no statistical difference in AC002454.1 relative expression level among the different sex, age, WBC count at initial diagnosis, chromosome, fusion gene, and risk stratification (P＞0.05 for all)., Conclusion: The lncRNA expression profile of AL children has been gained by using the lncRNA microarray gene chip technicology. AC002454.1 the significantly high expression exist in AL children, which relates with immunotyping and prognosis of AL children in a certain degree.

Defects in graphene can profoundly impact its extraordinary properties, ultimately influencing the performances of graphene-based nanodevices. Methods to detect defects with atomic resolution in graphene can be technically demanding and involve complex sample preparations. An alternative approach is to observe the thermal vibration properties of the graphene sheet, which reflects defect information but in an implicit fashion. Machine learning, an emerging data-driven approach that offers solutions to learning hidden patterns from complex data, has been extensively applied in material design and discovery problems. In this paper, we propose a machine learning-based approach to detect graphene defects by discovering the hidden correlation between defect locations and thermal vibration features. Two prediction strategies are developed: an atom-based method which constructs data by atom indices, and a domain-based method which constructs data by domain discretization. Results show that while the atom-based method is capable of detecting a single-atom vacancy, the domain-based method can detect an unknown number of multiple vacancies up to atomic precision. Both methods can achieve approximately a 90% prediction accuracy on the reserved data for testing, indicating a promising extrapolation into unseen future graphene configurations. The proposed strategy offers promising solutions for the non-destructive evaluation of nanomaterials and accelerates new material discoveries.

Atoms are the building blocks of matter that make up the world. To create new materials to meet some of civilization's greatest needs, it is crucial to develop a technology to design materials on the atomic and molecular scales. However, there is currently no computational approach capable of designing materials atom-by-atom. In this study, we consider the possibility of direct manipulation of individual atoms to design materials at the nanoscale using a proposed method coined "Nano-Topology Optimization". Here, we apply the proposed method to design nanostructured materials to maximize elastic properties. Results show that the performance of our optimized designs not only surpasses that of the gyroid and other triply periodic minimal surface structures, but also exceeds the theoretical maximum (Hashin-Shtrikman upper bound). The significance of the proposed method lies in a platform that allows computers to design novel materials atom-by-atom without the need of a predetermined design.

Over the past decades, significant effort has been made to improve the adhesive properties of adhesive pillars, by searching for pillar shapes with optimized interfacial stress distribution. However, the shape optimizations in the previous studies are conducted by considering specific pillar forms with a few parameters, hence with limited design space. In this study, we present a framework to find a free-form optimized adhesive pillar shape out of extensive design space. We generate 200 000 different shapes of adhesive pillars based on the Bézier curve with a few control points by considering two distinct edge shapes, sharp and truncated edges, to account for the limitation in the realistic manufacturing resolution. The resulting interfacial stress distributions from numerical simulations are used to train deep neural networks for each edge type. Our deep learning model shows greater than 99% classification accuracy on a limited data set with orders of magnitude speedup in computation time compared to finite element analyses. On the basis of the trained neural network, we conduct genetic optimization by maximizing a fitness function that prefers the uniform interfacial stress distribution with neither stress peak nor singularity. The optimized adhesive pillar shape is composed of smoothly mixed convex and concave parts and shows improved uniformity in the interfacial stress distribution. Our study also demonstrates that the deep learning can be used for nonparametric curve optimization task with diverse fitness function.

As the counterparts of noradrenaline and adrenaline in vertebrates, octopamine (OA) regulates multiple physiological and behavioral processes in invertebrate. OA mediates its effects via binding to specific octopamine receptors (OARs). Functional and pharmacological characterization of OARs have been reported in several insects. However, little work was documented in hemipteran insects. We cloned a β-adrenergic-like OAR (NcOA2B2) from Nephotettix cincticeps. NcOA2B2 shares high similarity with members of the OA2B2 receptor class. Transcript level of NcOA2B2 varied in various tissues and was highly expressed in the leg. After heterologous expression in CHO-K1 cells, NcOA2B2 was dose-dependently activated by OA (EC 50  = 2.56 nM) and tyramine (TA) (EC 50  = 149 nM). Besides putative octopaminergic agonists, dopaminergic agonists and amitraz and DPMF potently activated NcOA2B2 in a dose-dependent manner. Receptor activity was blocked by potential antagonists and was most efficiently antagonized by asenapine. Phentolamine showed both antagonist and agonist effects on NcOA2B2. Our results offer the important information about molecular and pharmacological characterization of an OAR from N. cincticeps that will provide the basis for forthcoming studies on its roles in physiological processes and behaviors, and facilitate the design of novel insecticides for pest control., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Pteromalus puparum is a gregarious pupal endoparasitoid with a wide host range. It deposits eggs into pierid and papilionid butterfly pupae. Glutathione S-transferases (GSTs) are a family of multifunctional detoxification enzymes that act in xenobiotic metabolism in insects. Insect genome projects have facilitated identification and characterization of GST family members. We identified 20 putative GSTs in the P. puparum genome, including 19 cytosolic and one microsomal. Phylogenetic analysis showed that P. puparum GSTs are clustered into Hymenoptera-specific branches. Transcriptomic data of embryos, larvae, female pupae, male pupae, female adults, male adults, venom glands, carcass, salivary glands, and ovaries revealed stage-, sex-, and tissue-specific expression patterns of GSTs in P. puparum. This is the most comprehensive study of genome-wide identification, characterization, and expression profiling of GST family in hymenopterans. Our results provide valuable information for understanding the metabolic adaptation of this wasp., (© 2019 Wiley Periodicals, Inc.)

In insects, neuropeptides constitute a group of signaling molecules that act in regulation of multiple physiological and behavioral processes by binding to their corresponding receptors. On the basis of the bioinformatic approaches, we screened the genomic and transcriptomic data of the parasitoid wasp, Pteromalus puparum, and annotated 36 neuropeptide precursor genes and 33 neuropeptide receptor genes. Compared to the number of precursor genes in Bombyx mori (Lepidoptera), Chilo suppressalis (Lepidoptera), Drosophila melanogaster (Diptera), Nilaparvata lugens (Hemiptera), Apis mellifera (Hymenoptera), and Tribolium castaneum (Coleoptera), P. puparum (Hymenoptera) has the lowest number of neuropeptide precursor genes. This lower number may relate to its parasitic life cycle. Transcriptomic data of embryos, larvae, pupae, adults, venom glands, salivary glands, ovaries, and the remaining carcass revealed stage-, sex-, and tissue-specific expression patterns of the neuropeptides, and their receptors. These data provided basic information about the identity and expression profiles of neuropeptides and their receptors that are required to functionally address their biological significance in an endoparasitoid wasp., (© 2019 Wiley Periodicals, Inc.)

All engineering systems that move through fluids can benefit from a reduction in opposing forces, or drag. As a result, there is a significant focus on finding new ways to improve the lift-to-drag ratios of systems that move through fluids. Nature has proven to be an extremely beneficial source of inspiration to overcome current technical endeavors. Shark skin, with its low-drag riblet structure, is a prime example of an evolutionary design that has inspired new implementations of drag reducing technologies. Previously, it has been shown that denticles have drag reducing properties when applied to airfoils and other surfaces moving through fluids. Researchers have been able to mimic the structure of shark skin, but minimal work has been done in terms of optimizing the design of the denticles due to the large number of parameters involved. In this work, we use a combination of computational fluid dynamics simulations and optimization methods to optimize the size and shape of shark skin denticles in order to decrease drag. Results show that by changing the size, shape, and orientation of the denticles, the boundary layer can be altered, and thereby reduce drag. This research demonstrates that denticles play a similar role as vortex generators in energizing the boundary layer to decrease drag. These mechanisms, along with the fundamental knowledge gained through the study of these drag reducing structures can be applied to a vast number of fields including aeronautical, oceanic, and automotive engineering.

In recent years, machine learning (ML) techniques are seen to be promising tools to discover and design novel materials. However, the lack of robust inverse design approaches to identify promising candidate materials without exploring the entire design space causes a fundamental bottleneck. A general-purpose inverse design approach is presented using generative inverse design networks. This ML-based inverse design approach uses backpropagation to calculate the analytical gradients of an objective function with respect to design variables. This inverse design approach is capable of overcoming local minima traps by using backpropagation to provide rapid calculations of gradient information and running millions of optimizations with different initial values. Furthermore, an active learning strategy is adopted in the inverse design approach to improve the performance of candidate materials and reduce the amount of training data needed to do so. Compared to passive learning, the active learning strategy is capable of generating better designs and reducing the amount of training data by at least an order-of-magnitude in the case study on composite materials. The inverse design approach is compared with conventional gradient-based topology optimization and gradient-free genetic algorithms and the pros and cons of each method are discussed when applied to materials discovery and design problems., Competing Interests: The authors declare no conflict of interest., (© 2020 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim.)

The extraordinary properties of graphene have made it an elite candidate for a broad range of emerging applications since its discovery. However, the introduction of structural defects during graphene production often compromises the theoretically predicted performance of graphene-based technologies to a great extent. In this study, a counterintuitive defect enlargement strategy to recover from defect-induced mechanical degradation is explored, of which the realization may lead to an enhanced operating efficiency and manufacturing feasibility. Our molecular-dynamics simulation results show that the enlargement of a preexisting defect to an elliptical shape can potentially recover from the mechanical degradation that the very defect has caused. For a defective graphene sheet having a failure strain of 48% of the pristine graphene sheet, enlarging the defect can enhance the failure strain up to 80% of the pristine graphene sheet. The mechanism of degradation recovery lies in a reduced change in curvature during deformation, which is further solidified by theoretical quantification and stress-field analysis. This theory can also predict and pinpoint the location of the initiation of the fracture-where the curvature changes most significantly during the deformation. In addition, the influence of an elliptical defect on the mechanical properties of a graphene sheet is systematically studied, which is not well understood today. Finally, the degradation recovery potential of defect of various sizes is examined, showing that the initial defect that can create the highest degree of geometric asymmetry has the best potential for degradation recovery. This study investigates the recovery from defect-induced mechanical degradation and the influence of elliptical defects on the mechanical properties of a graphene sheet, which widens our understanding of the possibility of fine-tuning mechanical properties via defect engineering and has the potential to improve materials for emerging technologies such as supercapacitor devices.

Liver fibrosis is a common pathological response in chronic liver injury. In the pathological process of hepatic injury, signaling pathways associated with hepatic fibrosis, which mediates the repair, proliferation and fibrosis of the liver secrete different cytokines. In these pathways, transforming growth factor beta (TGFβ) and signal transducer and activator of transcription 3 (STAT3) play key roles in the proliferation and activation of hepatic stellate cells (HSCs) and promote epithelial mesenchymal transition. In addition, it is also involved in the process of proliferation and transformation of collagen and extracellular matrix molecules into myofibroblasts. TGFβ and STAT3 molecular-related signaling pathways mediate the loss of epithelial phenotype and gene expression in mature epithelial cells, transforming them into mesenchymal cells, and producing anti-apoptosis to hepatocytes and promoting the proliferation of HSCs. However, the mechanisms by which STAT3 and TGFβ molecules are involved in the development and progression of liver fibrosis are not sound distinct. In this review, we attempt to know the mechanisms and interactions of TGFβ and STAT3 molecules that mediate potential liver fibrosis, and promote their role in promoting HSCs production and epithelial mesenchymal transition.

Ischemia-reperfusion injury (IRI) is a major complication in liver transplantation (LT) and it is closely related to the recovery of grafts' function. Researches has verified that both innate and adaptive immune system are involved in the development of IRI and Kupffer cell (KC), the resident macrophages in the liver, play a pivotal role both in triggering and sustaining the sterile inflammation. Damage-associated molecular patterns (DAMPs), released by the initial dead cell because of the ischemia insult, firstly activate the KC through pattern recognition receptors (PRRs) such as toll-like receptors. Activated KCs is the dominant players in the IRI as it can secret various pro-inflammatory cytokines to exacerbate the injury and recruit other types of immune cells from the circulation. On the other hand, KCs can also serve in a contrary way to ameliorate IRI by upregulating the anti-inflammatory factors. Moreover, new standpoint has been put forward that KCs and macrophages from the circulation may function in different way to influence the inflammation. Managements towards KCs are expected to be the effective way to improve the IRI., Competing Interests: Conflict of Interest: The authors declare no potential conflicts of interest.

In the 50 years that succeeded Richard Feynman's exposition of the idea that there is "plenty of room at the bottom" for manipulating individual atoms for the synthesis and manufacturing processing of materials, the materials-by-design paradigm is being developed gradually through synergistic integration of experimental material synthesis and characterization with predictive computational modeling and optimization. This paper reviews how this paradigm creates the possibility to develop materials according to specific, rational designs from the molecular to the macroscopic scale. We discuss promising techniques in experimental small-scale material synthesis and large-scale fabrication methods to manipulate atomistic or macroscale structures, which can be designed by computational modeling. These include recombinant protein technology to produce peptides and proteins with tailored sequences encoded by recombinant DNA, self-assembly processes induced by conformational transition of proteins, additive manufacturing for designing complex structures, and qualitative and quantitative characterization of materials at different length scales. We describe important material characterization techniques using numerous methods of spectroscopy and microscopy. We detail numerous multi-scale computational modeling techniques that complements these experimental techniques: DFT at the atomistic scale; fully atomistic and coarse-grain molecular dynamics at the molecular to mesoscale; continuum modeling at the macroscale. Additionally, we present case studies that utilize experimental and computational approaches in an integrated manner to broaden our understanding of the properties of two-dimensional materials and materials based on silk and silk-elastin-like proteins.

Creating materials with strength and toughness has been a long-sought goal. Conventional engineering materials often face a trade-off between strength and toughness, prompting researchers seeking to overcome these limitations to explore more sophisticated materials, such as composites. This paradigm shift in material design is spurred by nature, which exhibits a plethora of heterogeneous materials that offer outstanding material properties, and many natural materials are widely regarded as examples of high-performing hybrid materials. A classic example is nacre, also known as mother-of-pearl, which boasts a combination of high stiffness, strength, and fracture toughness. Various microstructural features contribute to the toughness of nacre, including mineral bridges (MBs), nano-asperities, and waviness of the constituent platelets. Recent research in biomimicry suggests that MBs contribute to the high strength and toughness observed in nacre and nacre-inspired materials. However, previous work in this area did not allow for complete control over the length scale of the bridges and had limitations on the volume fraction of mineral content. In this work, we present a systematic investigation elucidating the effects of structural parameters, such as volume fraction of mineral phase and density of MBs, on the mechanical response of nacre-inspired additive manufactured composites. Our results demonstrate that it is possible to tune the composite properties by tuning sizes and content of structural features (e.g. MBs and mineral content) in a heterogeneous material. Looking forward, this systematic approach enables materials-by-design of complex architectures to tackle demanding engineering challenges in the future., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Recently a new diluted magnetic semiconductor, (Ba,K)(Zn,Mn) 2 As 2 (BZA), with high Curie temperature was discovered, showing an independent spin and charge-doping mechanism. This makes BZA a promising material for spintronics devices. We report the successful growth of a BZA single crystal for the first time in this study. An Andreev reflection junction, which can be used to evaluate spin polarization, was fabricated based on the BZA single crystal. A 66% spin polarization of the BZA single crystal was obtained by Andreev reflection spectroscopy analysis.

As the counterparts of the vertebrate adrenergic transmitters, octopamine and tyramine are important physiological regulators in invertebrates. They control and modulate many physiological and behavioral functions in insects. In this study, we reported the pharmacological properties of a new α2-adrenergic-like octopamine receptor (CG18208) from Drosophila melanogaster, named DmOctα2R. This new receptor gene encodes two transcripts by alternative splicing. The long isoform DmOctα2R-L differs from the short isoform DmOctα2R-S by the presence of an additional 29 amino acids within the third intracellular loop. When heterologously expressed in mammalian cell lines, both receptors were activated by octopamine, tyramine, epinephrine and norepinephrine, resulting in the inhibition of cAMP production in a dose-dependent manner. The long form is more sensitive to the above ligands than the short form. The adrenergic agonists naphazoline, tolazoline and clonidine can stimulate DmOctα2R as full agonists. Surprisingly, serotonin and serotoninergic agonists can also activate DmOctα2R. Several tested adrenergic antagonists and serotonin antagonists blocked the action of octopamine or serotonin on DmOctα2R. The data presented here reported an adrenergic-like G protein-coupled receptor activated by serotonin, suggesting that the neurotransmission and neuromodulation in the nervous system could be more complex than previously thought., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Objective: To investigate the influence of family environment on developmental coordination disorder (DCD) in preschool children., Methods: Stratified random cluster sampling was used to select 1 727 children (4-6 years old). The Movement Assessment Battery for Children was used to screen out the children with DCD. The Family Environment Scale on Motor Development for Preschool Urban Children and a self-designed questionnaire were used to assess family environment., Results: A total of 117 children were confirmed with DCD. There were significant differences in mother's education level and family structure between the DCD and normal control groups. There were also significant differences in the scores of "Let children manage their daily items" and "Arrange all affairs" between the DCD and normal control groups. The multivariate logistic regression analysis indicated that when children's age and gender were controlled, mother's education level, family structure, "Let children manage their daily items", and "Arrange all affairs" were main factors influencing the development of DCD in children (P<0.05)., Conclusions: Family environment may affect the development of DCD in preschool children. Therefore, parents should not arrange all affairs for children and should provide more opportunities for children to manage their daily life, in order to promote the development of early motor coordination and prevent the development of DCD.

An order of magnitude tougher than nacre, conch shells are known for being one of the toughest body armors in nature. However, the complexity of the conch shell architecture creates a barrier to emulating its cross-lamellar structure in synthetic materials. Here, a 3D biomimetic conch shell prototype is presented, which can replicate the crack arresting mechanisms embedded in the natural architecture. Through an integrated approach combining simulation, additive manufacturing, and drop tower testing, the function of hierarchy in conch shell's multiscale microarchitectures is explicated. The results show that adding the second level of cross-lamellar hierarchy can boost impact performance by 70% and 85% compared to a single-level hierarchy and the stiff constituent, respectively. The overarching mechanism responsible for the impact resistance of conch shell is the generation of pathways for crack deviation, which can be generalized to the design of future protective apparatus such as helmets and body armor., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Dopamine is an important neurotransmitter and neuromodulator in both vertebrates and invertebrates and is the most abundant monoamine present in the central nervous system of insects. A complement of functionally distinct dopamine receptors mediate the signal transduction of dopamine by modifying intracellular Ca 2+ and cAMP levels. In the present study, we pharmacologically characterized three types of dopamine receptors, CsDOP1, CsDOP2 and CsDOP3, from the rice striped stem borer, Chilo suppressalis. All three receptors show considerable sequence identity with orthologous dopamine receptors. The phylogenetic analysis also clusters the receptors within their respective groups. Transcript levels of CsDOP1, CsDOP2 and CsDOP3 were all expressed at high levels in the central nervous system, indicating their important roles in neural processes. After heterologous expression in HEK 293 cells, CsDOP1, CsDOP2 and CsDOP3 were dose-dependently activated by dopamine and synthetic dopamine receptor agonists. They can also be blocked by different series of antagonists. This study offers important information on three dopamine receptors from C. suppressalis that will provide the basis for forthcoming studies investigating their roles in behaviors and physiology, and facilitate the development of new insecticides for pest control., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

In insects, neuropeptides play important roles in the regulation of multiple physiological processes by binding to their corresponding receptors, which are primarily G protein-coupled receptors (GPCRs). The genes encoding neuropeptides and their associated GPCRs in the rice stem borer Chilo suppressalis were identified by a transcriptomic analysis and were used to identify potential targets for the disruption of physiological processes and the protection of crops. Forty-three candidate genes were found to encode the neuropeptide precursors for all known insect neuropeptides except for arginine-vasopressin-like peptide (AVLP), CNMamide, neuropeptide-like precursors 2-4 (NPLP2-4), and proctolin. In addition, novel alternative splicing variants of three neuropeptide genes (allatostatin CC, CCHamide 1, and short neuropeptide F) are reported for the first time, and 51 putative neuropeptide GPCRs were identified. Phylogenetic analyses demonstrated that 44 of these GPCRs belong to the A-family (or rhodopsin-like), 5 belong to the B-family (or secretin-like), and 2 are leucine-rich repeat-containing GPCRs. These GPCRs and their likely ligands were also described. qRT-PCR analyses revealed the expression profiles of the neuropeptide precursors and GPCR genes in various tissues of C. suppressalis. Our study provides fundamental information that may further our understanding of neuropeptidergic signaling systems in Lepidoptera and aid in the design of peptidomimetics, pseudopeptides or small molecules capable of disrupting the physiological processes regulated by these signaling molecules and their receptors.

Long non-coding RNA (lncRNA) plays a role in gene transcription, protein expression and epigenetic regulation; and altered expression results in cancer development. Acute myeloid leukemia (AML) is rare in children; and thus, this study profiled lncRNA expression in bone marrow samples from pediatric AML patients. Arraystar Human LncRNA Array V3.0 was used to profile differentially expressed lncRNAs in three bone marrow samples obtained from each pediatric AML patient and normal controls. Quantitative polymerase chain reaction (qRT-PCR) was performed to confirm dysregulated lncRNA expressions in 22 AML bone marrow samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to construct the lncRNA-mRNA co-expression network. A total of 372 dysregulated lncRNAs (difference ≥10-fold) were found in pediatric AML patients compared to normal controls. Fifty-one mRNA levels were significantly upregulated, while 85 mRNA levels were significantly downregulated by >10-fold in pediatric AML, compared to normal controls. GO terms and KEGG pathway annotation data revealed that cell cycle pathway-related genes were significantly associated with pediatric AML. As confirmed by qRT-PCR, expression of 24 of 97 lncRNA was altered in pediatric AML compared to normal controls. In pediatric AML, ENST00000435695 was the most upregulated lncRNA, while ENST00000415964 was the most downregulated lncRNA. Data from this study revealed dysregulated lncRNAs and mRNAs in pediatric AML versus normal controls that could form gene pathways to regulate cell cycle progression and immunoresponse. Further studies are required to determine whether these lncRNAs could serve as novel therapeutic targets and bbdiagnostic biomarkers in pediatric AML.

Objective: The aim of this study was to investigate the correlation of twisting motion phase and infantile spasms in high risk infants., Materials and Methods: One hundred seventy-eight high-risk newborns experiencing follow-up in the rehabilitation phase were selected and full-body motion quality assessment was performed in the twisting motion phase. The occurrence of infants with infantile spasms after 12 months (corrected age) was statistically analyzed., Results: No clear correlation was found between monotonous movement twisting motion phase and infantile spasms, and spasm synchronized movement had no definite prediction for infantile spasms. The incidence of infant spasm with movement form having spastic synchronized characteristics had significant difference compared with monotonous systemic movement (p < 0.01). The sensitivity of predictive rate for spasm-synchronous movement of infantile spasms was 90.9%, the specificity was 96.8%, the positive predictive value was 80%, and the negative predictive value was 98.7%., Conclusions: Spasm synchronized movement had some predictive value for infantile spasms in twisting motion stage. The newborns with this kind of movement form should be checked by regularly ambulatory EEG.

Background: Neurotransmitter signaling systems play crucial roles in multiple physiological and behavioral processes in insects. Genome wide analyses of de novo transcriptome sequencing and gene specific expression profiling provide rich resources for studying neurotransmitter signaling pathways. The rice striped stem borer, Chilo suppressalis is a destructive rice pest in China and other Asian countries. The characterization of genes involved in neurotransmitter biosynthesis and transport could identify potential targets for disruption of the neurochemical communication and for crop protection., Results: Here we report de novo sequencing of the C. suppressalis central nervous system transcriptome, identification and expression profiles of genes putatively involved in neurotransmitter biosynthesis, packaging, and recycling/degradation. A total of 54,411 unigenes were obtained from the transcriptome analysis. Among these unigenes, we have identified 32 unigenes (31 are full length genes), which encode 21 enzymes and 11 transporters putatively associated with biogenic aminergic signaling, acetylcholinergic signaling, glutamatergic signaling and GABAergic signaling. RT-PCR and qRT-PCR results indicated that 12 enzymes were highly expressed in the central nervous system and all the transporters were expressed at significantly high levels in the central nervous system. In addition, the transcript abundances of enzymes and transporters in the central nervous system were validated by qRT-PCR. The high expression levels of these genes suggest their important roles in the central nervous system., Conclusions: Our study identified genes potentially involved in neurotransmitter biosynthesis and transport in C. suppressalis and these genes could serve as targets to interfere with neurotransmitter production. This study presents an opportunity for the development of specific and environmentally safe insecticides for pest control.