Two-Dimensional Controlled Syntheses of Polypeptide Molecular Brushes via N-Carboxyanhydride Ring-Opening Polymerization and Ring-Opening Metathesis Polymerization.

Well-defined molecular brushes bearing polypeptides as side chains were prepared by a "grafting through" synthetic strategy with two-dimensional control over the brush molecular architectures. By integrating N-carboxyanhydride ring-opening polymerizations (NCA ROPs) and ring-opening metathesis polymerizations (ROMPs), desirable segment lengths of polypeptide side chains and polynorbornene brush backbones were independently constructed in controlled manners. The N2 flow accelerated NCA ROP was utilized to prepare polypeptide macromonomers with different lengths initiated from a norbornene-based primary amine, and those macromonomers were then polymerized via ROMP. It was found that a mixture of dichloromethane and an ionic liquid were required as the solvent system to allow for construction of molecular brush polymers having densely-grafted peptide chains emanating from a polynorbornene backbone, poly(norbornene-graft-poly(β-benzyl-l-aspartate)) (P(NB-g-PBLA)). Highly efficient postpolymerization modification was achieved by aminolysis of PBLA side chains for facile installment of functional moieties onto the molecular brushes

Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction

Due to limited communication capacities of edge devices, most existing federated learning (FL) methods randomly select only a subset of devices to participate in training for each communication round. Compared with engaging all the available clients, the random-selection mechanism can lead to significant performance degradation on non-IID (independent and identically distributed) data. In this paper, we show our key observation that the essential reason resulting in such performance degradation is the class-imbalance of the grouped data from randomly selected clients. Based on our key observation, we design an efficient heterogeneity-aware client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS), which can effectively reduce class-imbalance of the group dataset from the intentionally selected clients. In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way. Based on this measure, we also design a computation-efficient client sampling strategy, such that the actively selected clients will generate a more class-balanced grouped dataset with theoretical guarantees. Extensive experimental results demonstrate Fed-CBS outperforms the status quo approaches. Furthermore, it achieves comparable or even better performance than the ideal setting where all the available clients participate in the FL training

Identification of pyroptosis-related subtypes and establishment of prognostic model and immune characteristics in asthma

BackgroundAlthough studies have shown that cell pyroptosis is involved in the progression of asthma, a systematic analysis of the clinical significance of pyroptosis-related genes (PRGs) cooperating with immune cells in asthma patients is still lacking.MethodsTranscriptome sequencing datasets from patients with different disease courses were used to screen pyroptosis-related differentially expressed genes and perform biological function analysis. Clustering based on K-means unsupervised clustering method is performed to identify pyroptosis-related subtypes in asthma and explore biological functional characteristics of poorly controlled subtypes. Diagnostic markers between subtypes were screened and validated using an asthma mouse model. The infiltration of immune cells in airway epithelium was evaluated based on CIBERSORT, and the correlation between diagnostic markers and immune cells was analyzed. Finally, a risk prediction model was established and experimentally verified using differentially expressed genes between pyroptosis subtypes in combination with asthma control. The cMAP database and molecular docking were utilized to predict potential therapeutic drugs.ResultsNineteen differentially expressed PRGs and two subtypes were identified between patients with mild-to-moderate and severe asthma conditions. Significant differences were observed in asthma control and FEV1 reversibility between the two subtypes. Poor control subtypes were closely related to glucocorticoid resistance and airway remodeling. BNIP3 was identified as a diagnostic marker and associated with immune cell infiltration such as, M2 macrophages. The risk prediction model containing four genes has accurate classification efficiency and prediction value. Small molecules obtained from the cMAP database that may have therapeutic effects on asthma are mainly DPP4 inhibitors.ConclusionPyroptosis and its mediated immune phenotype are crucial in the occurrence, development, and prognosis of asthma. The predictive models and drugs developed on the basis of PRGs may provide new solutions for the management of asthma

Multiscale and Multiphysics Modeling of Additive Manufacturing of Advanced Materials

The objective of this proposed project is to research and develop a prediction tool for advanced additive manufacturing (AAM) processes for advanced materials and develop experimental methods to provide fundamental properties and establish validation data. Aircraft structures and engines demand materials that are stronger, useable at much higher temperatures, provide less acoustic transmission, and enable more aeroelastic tailoring than those currently used. Significant improvements in properties can only be achieved by processing the materials under nonequilibrium conditions, such as AAM processes. AAM processes encompass a class of processes that use a focused heat source to create a melt pool on a substrate. Examples include Electron Beam Freeform Fabrication and Direct Metal Deposition. These types of additive processes enable fabrication of parts directly from CAD drawings. To achieve the desired material properties and geometries of the final structure, assessing the impact of process parameters and predicting optimized conditions with numerical modeling as an effective prediction tool is necessary. The targets for the processing are multiple and at different spatial scales, and the physical phenomena associated occur in multiphysics and multiscale. In this project, the research work has been developed to model AAM processes in a multiscale and multiphysics approach. A macroscale model was developed to investigate the residual stresses and distortion in AAM processes. A sequentially coupled, thermomechanical, finite element model was developed and validated experimentally. The results showed the temperature distribution, residual stress, and deformation within the formed deposits and substrates. A mesoscale model was developed to include heat transfer, phase change with mushy zone, incompressible free surface flow, solute redistribution, and surface tension. Because of excessive computing time needed, a parallel computing approach was also tested. In addition, after investigating various methods, a Smoothed Particle Hydrodynamics Model (SPH Model) was developed to model wire feeding process. Its computational efficiency and simple architecture makes it more robust and flexible than other models. More research on material properties may be needed to realistically model the AAM processes. A microscale model was developed to investigate heterogeneous nucleation, dendritic grain growth, epitaxial growth of columnar grains, columnar-to-equiaxed transition, grain transport in melt, and other properties. The orientations of the columnar grains were almost perpendicular to the laser motion's direction. Compared to the similar studies in the literature, the multiple grain morphology modeling result is in the same order of magnitude as optical morphologies in the experiment. Experimental work was conducted to validate different models. An infrared camera was incorporated as a process monitoring and validating tool to identify the solidus and mushy zones during deposition. The images were successfully processed to identify these regions. This research project has investigated multiscale and multiphysics of the complex AAM processes thus leading to advanced understanding of these processes. The project has also developed several modeling tools and experimental validation tools that will be very critical in the future of AAM process qualification and certification

Combination of TRAIL and actinomycin D liposomes enhances antitumor effect in non-small cell lung cancer

The intractability of non-small cell lung cancer (NSCLC) to multimodality treatments plays a large part in its extremely poor prognosis. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is a promising cytokine for selective induction of apoptosis in cancer cells; however, many NSCLC cell lines are resistant to TRAIL-induced apoptosis. The therapeutic effect can be restored by treatments combining TRAIL with chemotherapeutic agents. Actinomycin D (ActD) can sensitize NSCLC cells to TRAIL-induced apoptosis by upregulation of death receptor 4 (DR4) or 5 (DR5). However, the use of ActD has significant drawbacks due to the side effects that result from its nonspecific biodistribution in vivo. In addition, the short half-life of TRAIL in serum also limits the antitumor effect of treatments combining TRAIL and ActD. In this study, we designed a combination treatment of long-circulating TRAIL liposomes and ActD liposomes with the aim of resolving these problems. The combination of TRAIL liposomes and ActD liposomes had a synergistic cytotoxic effect against A-549 cells. The mechanism behind this combination treatment includes both increased expression of DR5 and caspase activation. Moreover, systemic administration of the combination of TRAIL liposomes and ActD liposomes suppressed both tumor formation and growth of established subcutaneous NSCLC xenografts in nude mice, inducing apoptosis without causing significant general toxicity. These results provide preclinical proof-of-principle for a novel therapeutic strategy in which TRAIL liposomes are safely combined with ActD liposomes

A self-starting bi-chromatic LiNbO_3 soliton microcomb

The wide range of functions that are possible with lithium niobate (LN) waveguide devices, including phase and intensity modulation, second-harmonic generation, and difference-frequency generation, makes it attractive as a potential microcomb material. LN microcombs would combine essential comb self-referencing and control functions with the pulse generation process in a single microresonator device. Here, we demonstrate a soliton microcomb in a monolithic high-Q LN resonator. Direct frequency doubling of the soliton spectrum is observed inside the same cavity. The LN soliton mode-locking process also self-starts and allows bi-directional switching of soliton states, effects that are shown to result from the LN photorefractive effect. The Kerr solitons exhibit a self-frequency shift resulting from the Raman effect of LN. This microcomb platform can dramatically simplify miniature time keeping, frequency synthesis/division, and spectroscopy systems. Moreover, direct generation of femtosecond timescale pulses within LN microresonators can benefit quantum photonics and signal processing systems

Study on mechanical properties and microstructure of steel-polypropylene fiber coal gangue concrete

Incorporating coal gangue into the concrete matrix can realize the utilization of solid waste and reduce the use of natural aggregate. To improve the mechanical properties of coal gangue concrete, this paper designs four-level and four-factor orthogonal tests with coal gangue ceramide substitution rate, coal gangue ceramide sand substitution rate, steel fiber content, and polypropylene fiber content as independent variables. Through multidimensional data analysis of the test results, The main and secondary factors of compressive strength of hybrid fiber coal gangue concrete from strong to weak are the replacement rate of coal gangue ceramic sand, the replacement rate of coal gangue ceramic grain, the content of steel fiber and the content of polypropylene fiber. The optimal content is 30% coal gangue ceramic particle, 25∼30% coal gangue ceramic sand, 0.75∼1% steel fiber, and 0.2% polypropylene fiber. The grey prediction model GM (1, 5) is obtained, which can predict the concrete strength well within the range selected in this paper. The influence of fiber and coal gangue on the microstructure was studied by scanning electron microscopy, and the influence law of interfacial transition zone on the strength of concrete was explored, which provided a theoretical basis for the study of solid waste utilization of coal gangue

Mechanical properties and microscopic characteristics of steel fiber coal gangue concrete

Incorporation of coal gangue in the concrete mixes can realize utilization of the solid waste and reduce extraction/use of natural aggregates. To improve the mechanical properties of coal gangue concrete, this paper studies use of steel fibre together with coal gangue coarse aggregate, coal gangue fine aggregate/sand in various concrete mixes. The effect of volume dosages of steel fibre and different levels of replacing nature coarse aggregate and river sand with coal gangue aggregates on concrete compressive strength was first investigated. Then, a design of experiment using orthogonal test was adopted to study concrete mixes with 3 factors, namely, coal gangue coarse aggregate, coal gangue sand and steel fibre, and each at 3 levels. Through multidimensional statistical data analysis of the test results, the primary and secondary factors and the optimal composition of the steel fibre reinforced coal gangue concrete were identified, and a grey prediction model for compressive strength of the concrete mixes established. The microstructural characteristics and failure mechanism of steel fiber reinforced coal gangue concrete was also studied and discussed