Search

The separation of He/H2 using membrane technology has gained significant interest in the field of He extraction from natural gas. One of the greatest challenges associated with this process is the extremely close kinetic diameters of the two gas molecules, resulting in low membrane selectivity. In this study, we investigated the structure-performance relationship of metal-organic framework (MOF) membranes for He/H2 separation through molecular simulations and machine learning approaches. By conducting molecular simulations, we identified the potential MOF membranes with high separation performance from the Computation-Ready Experimental (CoRE) MOF database, and the diffusion-dominated mechanism was further elucidated. Moreover, random forest (RF)-based machine learning models were established to identify the crucial factors influencing the He/H2 separation performance of MOF membranes. The pore limiting diameter (PLD) and void fraction (φ), are revealed as the most important physical features for determining the membrane selectivity and He permeability, respectively. Additionally, density functional theory (DFT) calculations were carried out to validate the molecular simulation results and suggested that the electronegative atoms on the pore surfaces can enhance the diffusion-based separation of He/H2, which is critical for improving the membrane selectivities of He/H2. This study offers useful insights for designing and developing novel MOF membranes for the separation of He/H2 at the molecular level.

Abstract Learning-based computer-generated hologram (CGH) demonstrates great potential for real-time high-quality holographic displays. However, real-time 4K CGH generation for 3D scenes remains a challenge due to the computational burden. Here, a variant conventional neural network (CNN) is presented for CGH encoding with learned layered initial phases for layered CGH generation. Specifically, the CNN predicts the CGH based on the input complex amplitude on the CGH plane, and the learned initial phases act as a universal phase for any target images at the target depth layer. These phases are generated during the training process of the coding CNN to further optimize the quality. The CNN is trained to learn encoding 3D CGH by randomly selecting the depth layer in the training process, and contains only 938 parameters. The generation time for a 2D 4K CGH is 18 ms, and is increased by 12 ms for each layer in a layered 3D scene. The average Peak Signal to Noise Ratio (PSNR) of each layer is above 30dB in the depth range from 160 to 210 mm. Experiments verify that our method can achieve real-time layered 4K CGH generation.

Abstract The tumor vasculature was different from the normal vasculature in both function and morphology, which caused hypoxia in the tumor microenvironment (TME). Previous anti-angiogenesis therapy had led to a modest improvement in cancer immunotherapy. However, antiangiogenic therapy only benefitted a few patients and caused many side effects. Therefore, there was still a need to develop a new approach to affect tumor vasculature formation. The CD93 receptor expressed on the surface of vascular endothelial cells (ECs) and its natural ligands, MMRN2 and IGFBP7, were now considered potential targets in the antiangiogenic treatment because recent studies had reported that anti-CD93 could normalize the tumor vasculature without impacting normal blood vessels. Here, we reviewed recent studies on the role of CD93, IGFBP7, and MMRN2 in angiogenesis. We focused on revealing the interaction between IGFBP7-CD93 and MMRN2-CD93 and the signaling cascaded impacted by CD93, IGFBP7, and MMRN2 during the angiogenesis process. We also reviewed retrospective studies on CD93, IGFBP7, and MMRN2 expression and their relationship with clinical factors. In conclusion, CD93 was a promising target for normalizing the tumor vasculature.

AbstractNanotechnology has ignited a transformative revolution in disease detection, prevention, management, and treatment. Central to this paradigm shift is the innovative realm of cell membrane-based nanocarriers, a burgeoning class of biomimetic nanoparticles (NPs) that redefine the boundaries of biomedical applications. These remarkable nanocarriers, designed through a top-down approach, harness the intrinsic properties of cell-derived materials as their fundamental building blocks. Through shrouding themselves in natural cell membranes, these nanocarriers extend their circulation longevity and empower themselves to intricately navigate and modulate the multifaceted microenvironments associated with various diseases. This comprehensive review provides a panoramic view of recent breakthroughs in biomimetic nanomaterials, emphasizing their diverse applications in cancer treatment, cardiovascular therapy, viral infections, COVID-19 management, and autoimmune diseases. In this exposition, we deliver a concise yet illuminating overview of the distinctive properties underpinning biomimetic nanomaterials, elucidating their pivotal role in biomedical innovation. We subsequently delve into the exceptional advantages these nanomaterials offer, shedding light on the unique attributes that position them at the forefront of cutting-edge research. Moreover, we briefly explore the intricate synthesis processes employed in creating these biomimetic nanocarriers, shedding light on the methodologies that drive their development.

Background: T cells and natural killer (NK) cells are essential components of the immune system and are regulated by coinhibitory and costimulatory molecules in which the B7 family and CD28 family play significant roles. Previous immune checkpoint studies on B7/CD28 family members, such as PD-1, have led to remarkable success in cancer immunotherapy. However, there is still a need to find new immune checkpoint molecules. Recent studies have demonstrated that HHLA2 exerts inhibitory and stimulatory functions on the immune system by binding to different receptors on different sites. However, the pathways between HHLA2 and its two receptors on T cells and NK cells remain controversial. Aim of Review: Here, we reviewed recent studies about HHLA2 ligand interactions with KIR3DL3 and TMIGD2. We focused on elucidating the pathways between KIR3DL3/TMIGD2 and HHLA2 as well as their function in tumour progression. We also addressed the relationship between HHLA2 expression and the clinical prognosis of cancer patients. Key Scientific Concepts of Review: KIR3DL3/TMIGD2-HHLA2 may represent novel pathways within the tumour microenvironment and serve as crucial immune checkpoints for developing novel therapeutic drugs against human cancer.

Abstract The efficient separation of butadiene (1,3‐C4H6) from C4 hydrocarbons is a critical step in petrochemical processes. However, the traditional cryogenic distillation suffers from energy‐intensity and serious environmental stress, necessitating the development of alternative technologies for efficient 1,3‐C4H6 separation. Herein, a 1,3‐C4H6 recognition mixed matrix membrane is reported via incorporating metal copper encapsulated a metal‐organic framework (CuBTC@Cu) into elastic poly(dimethylsiloxane) (PDMS). The resulting CuBTC@Cu/PDMS membrane can efficient separate 1,3‐C4H6 from various C4 hydrocarbons including 1,3‐C4H6/n‐C4H8, 1,3‐C4H6/iso‐C4H8, 1,3‐C4H6/n‐C4H10 and 1,3‐C4H6/iso‐C4H10, yielding superior selectivity of 5.11, 6.35, 4.78, and 10.30, respectively, with 1,3‐C4H6 permeability of 53240 Barrer. Notably, the appropriate π‐complexation interaction between butadiene molecules and CuBTC@Cu as well as suitable transmission channel size enable the membrane only permeable to 1,3‐C4H6 and block the permeation of other C4 hydrocarbons, showing a unique 1,3‐C4H6 recognition behavior in membrane separation. The concept of affinity‐relying separation combining molecular sieving would open a new direction for designing gas membranes for efficient light hydrocarbon separations.

Polymers of intrinsic microporosity (PIMs), integrating unique microporous structure and solution-processability, are one class of the most promising membrane materials for energy-efficient gas separations. However, the micropores generated from inefficient chain packing often exhibit wide pore size distribution, making it very challenging to achieve efficient olefin/paraffin separations. Here, we propose a coordination-driven reconstruction (CDR) strategy, where metal ions are incorporated into amidoxime-functionalized PIM-1 (AO-PIM) to in situ generate coordination crosslinking networks. By varying the type and content of metal ions, the resulting crosslinking structures can be optimized, and the molecular sieving capability of PIM membranes can be dramatically enhanced. Particularly, the introduction of alkali or alkaline earth metals renders more precise micropores contributing to superior C3H6/C3H8 separation performance. K+ incorporated AO-PIM membranes exhibit a high ideal C3H6/C3H8 selectivity of 50, surpassing almost all the reported polymer membranes. Moreover, the coordination crosslinking structure significantly improves the membrane stability under higher pressure as well as the plasticization resistant performance. We envision that this straightforward and generic CDR strategy could potentially unlock the potentials of PIMs for olefin/paraffin separations and many other challenging gas separations. Public summary: • Coordination-driven reconstruction (CDR) strategy in polymer of intrinsic microporosity (PIM) membranes was proposed • Reduced micropore sizes and narrowed pore size distribution were obtained • CDR strategy enhanced molecular sieving ability and membrane stability remarkably • K+ incorporated amidoxime-functionalized PIM membrane exhibited high ideal C3H6/C3H8 selectivity up to 50

BackgroundGallbladder cancer (GBC) is a mortal malignancy with limited therapeutic strategies. We aimed to develop novel immune scoring systems focusing on B7-H3, B7-H4, and HHLA2. We further investigated their potential clinical effects in predicting survival and immunotherapeutic efficacy for GBC.MethodsThis was a retrospective cohort study in a single center that explored the expression characteristics of B7-H3, B7-H4, and HHLA2. The immune scoring nomograms for prognostic were developed via logistic regression analyses. Their performance was evaluated using the Harrell concordance index (C-index) and decision curves analysis (DCA), and validated with calibration curves.ResultsB7-H3, B7-H4, and HHLA2 manifested with a relatively high rate of co-expression patterns in GBC tissues. They were associated with worse clinicopathological stage, suppression of immune microenvironment, and unfavorable prognosis in postoperative survival. B7 stratification established based on B7-H3, B7-H4, and HHLA2 was an independent prognostic predictor (p

Abstract Immune checkpoint molecules, also known as cosignaling molecules, are pivotal cell-surface molecules that control immune cell responses by either promoting (costimulatory molecules) or inhibiting (coinhibitory molecules) a signal. These molecules have been studied for many years. The application of immune checkpoint drugs in the clinic provides hope for cancer patients. Recently, the poliovirus receptor (PVR)-like protein cosignaling network, which involves several immune checkpoint receptors, i.e., DNAM-1 (DNAX accessory molecule-1, CD226), TIGIT (T-cell immunoglobulin (Ig) and immunoreceptor tyrosine-based inhibitory motif (ITIM)), CD96 (T cell activation, increased late expression (TACLILE)), and CD112R (PVRIG), which interact with their ligands CD155 (PVR/Necl-5), CD112 (PVRL2/nectin-2), CD111 (PVRL1/nectin-1), CD113 (PVRL3/nectin-3), and Nectin4, was discovered. As important components of the immune system, natural killer (NK) and T cells play a vital role in eliminating and killing foreign pathogens and abnormal cells in the body. Recently, increasing evidence has suggested that this novel cosignaling network axis costimulates and coinhibits NK and T cell activation to eliminate cancer cells after engaging with ligands, and this activity may be effectively targeted for cancer immunotherapy. In this article, we review recent advances in research on this novel cosignaling network. We also briefly outline the structure of this cosignaling network, the signaling cascades and mechanisms involved after receptors engage with ligands, and how this novel cosignaling network costimulates and coinhibits NK cell and T cell activation for cancer immunotherapy. Additionally, this review comprehensively summarizes the application of this new network in preclinical trials and clinical trials. This review provides a new immunotherapeutic strategy for cancer treatment.

BackgroundImmunotherapy has achieved great success in cancer. Nevertheless, many patients cannot benefit from immune checkpoint blockade therapy because of the scantiness of CD8+ T cell infiltration in the tumor microenvironment (TME). CXCL11 is known as a regulator that influences T-cell infiltration into tumors. However, the role of CXCL11 in pan-cancer is still unclear.MethodsIn this study, we investigated the expression and function of CXCL11 across 33 types of cancers based on datasets from The Cancer Genome Atlas (TCGA) database and the Genotype-Tissue Expression (GTEx) database. We analyzed the CXCL11 differential expression in tumor tissue and nontumoral tissue and in different stages of cancers. Moreover, the correlations among CXCL11 expression, prognosis, mismatch repair, tumor mutation burden (TMB), microsatellite instability (MSI), tumor microenvironment, and immune-related genes were evaluated.ResultsCXCL11 expression was significantly higher in tumoral tissue than in nontumoral tissue for most types of cancer. Improved CXCL11 expression was related to an inconsistent prognosis in different cancers. CXCL11 was positively associated with CD8+ T cells and T follicular helper cells in the TME. High expression of CXCL11 was positively related to TMB in BLCA, BRCA, CESC, COAD, LGG, LUAD, OV, SKCM, STAD, THYM, and UCEC. A positive correlation between CXCL11 and MSI was found in COAD and UVM. Moreover, functional analysis of CXCL11 showed that high CXCL11 expression was significantly related to immune-relevant pathways.ConclusionsCXCL11 might function as a prognostic and immunotherapy marker across cancers. Further investigation into CXCL11 might provide promising insights to improve cancer therapy.

CO2 separation performance of polymer membranes can be significantly enhanced by selecting porous fillers with high CO2 affinity. Ionic liquids incorporation has been recognized as an effective strategy for improving the separation ability of pristine porous fillers. However, the influence of the specific functional groups of ILs in IL@MOF composites on separation performance of MMMs still remains unclear. Herein, we designed three microenvironment-tuned IL@ZIF-8 composites in which the three ILs contain different functional groups (-CH3, –SO3H, and –NH2). Molecular simulation results showed that the NH2-IL@ZIF-8 has a commendable CO2 adsorption capacity and CO2/CH4 adsorptive selectivity, and the results were well confirmed by the following experimental data. More importantly, the prepared NH2-IL@ZIF-8 based MMMs also exhibit superior CO2 separation performance among the three IL@ZIF-8 based MMMs owning to its high CO2 affinity. Thus, this work can provide guidance for designing IL@MOF composites for MMMs fabrication towards gas separation, and the research mode combining molecular simulation prediction and experimental verification can afford valuable reference for material development in membrane separation field.

In this paper, we propose a method to generate multi-depth phase-only holograms using stochastic gradient descent (SGD) algorithm with weighted complex loss function and masked multi-layer diffraction. The 3D scene can be represented by a combination of layers in different depths. In the wave propagation procedure of multiple layers in different depths, the complex amplitude of layers in different depths will gradually diffuse and produce occlusion at another layer. To solve this occlusion problem, a mask is used in the process of layers diffracting. Whether it is forward wave propagation or backward wave propagation of layers, the mask can reduce the occlusion problem between different layers. Otherwise, weighted complex loss function is implemented in the gradient descent optimization process, which analyzes the real part, the imaginary part, and the amplitude part of the focus region between the reconstructed images of the hologram and the target images. The weight parameter is used to adjust the ratio of the amplitude loss of the focus region in the whole loss function. The weight amplitude loss part in weighted complex loss function can decrease the interference of the focus region from the defocus region. The simulations and experiments have validated the effectiveness of the proposed method.

Cost effective separation of acetylene (C2H2) and ethylene (C2H4) is of key importance to obtain essential chemical raw materials for polymer industry. Due to the low compression limit of C2H2, there is an urgent demand to develop suitable materials for efficiently separating the two gases under ambient conditions. In this paper, we provided a high-throughput screening strategy to study porous metal-organic frameworks (MOFs) containing open metal sites (OMS) for C2H2/C2H4 separation, followed by a rational design of novel MOFs in-silico. A set of accurate force fields was established from ab initio calculations to describe the critical role of OMS towards guest molecules. From a large-scale computational screening of 916 experimental Cu-paddlewheel-based MOFs, three materials were identified with excellent separation performance. The structure-performance relationships revealed that the optimal materials should have the largest cavity diameter around 5–10 Å and pore volume in-between 0.3-1.0 cm3 g−1. Based on the systematic screening study result, three novel MOFs were further designed with the incorporation of fluorine functional group. The results showed that Cu-OMS and the –F group on the aromatic rings close to Cu sites could generate a synergistic effect on the preferential adsorption of C2H2 over C2H4, leading to a remarkable improvement of C2H2 separation performance of the materials. The findings could provide insight for future experimental design and synthesis of high-performance nanostructured materials for C2H2/C2H4 separation.

Refinery gas contains abundant H2 and a small amount of C1–C4 hydrocarbons. Here, we propose a condensability sieving strategy to realize preferential permeation of hydrocarbons over H2 by developing halogen-induced porous coordination polymer (PCP) mixed matrix membranes (MMMs) that display condensability sieving gas transport. The 4-Cl-PCP, 5-Cl-PCP and 5-Br-PCP are synthesized by using Zr4+ and X-isophthalic acid (where X ​= ​4-Cl or 5-Cl or 5-Br) exhibit increased gas adsorption capacity with the increase in carbon number of the feed gas, but with almost no H2 adsorption. MMMs containing the PCPs with different charge distributions enhance condensability sieving selectivity and inhibit diffusion selectivity. The permeances of the MMMs originated from condensability sieving are consistent with the polarizability-dependent adsorption of the PCP. The selectivity of the obtained MMMs for n-C4H10/H2, C3H8/H2, C2H6/H2, and CH4/H2 achieve ∼40, ∼15, ∼5, and ∼2, respectively, exhibiting promising applications in refinery gas purification owing to their lower energy consumption compared with H2-preferential permeation membranes.

Limited by the low space-bandwidth product of the spatial light modulator (SLM), it is difficult to realize multiview holographic three-dimensional (3D) display. To conquer the problem, a method based on the holographic optical element (HOE), which is regarded as a controlled light element, is proposed in the study. The SLM is employed to upload the synthetic phase-only hologram generated by the angular spectrum diffraction theory. Digital grating is introduced in the generation process of the hologram to achieve the splicing of the reconstructions and adjust the position of the reconstructions. The HOE fabricated by the computer-generated hologram printing can redirect the reconstructed images of multiview into multiple viewing zones. Thus, the modulation function of the HOE should be well-designed to avoid crosstalk between perspectives. The experimental results show that the proposed system can achieve multiview holographic augmented reality (AR) 3D display without crosstalk. The resolution of each perspective is 4K, which is higher than that of the existing multiview 3D display system.

Long noncoding RNA H19 (H19) is an imprinting gene with only maternal expression that is involved in regulating different processes in various types of cells. Previous studies have shown that abnormal H19 expression is involved in many pathological processes, such as cancer, mainly through sponging miRNAs, interacting with proteins, or regulating epigenetic modifications. Accumulating evidence has shown that several oncogenic signaling pathways lead to carcinogenesis. Recently, the regulatory relationship between H19 and oncogenic signaling pathways in various types of cancer has been of great interest to many researchers. In this review, we discussed the key roles of H19 in cancer development and progression via its regulatory function in several oncogenic signaling pathways, such as PI3K/Akt, canonical Wnt/β-catenin, canonical NF-κB, MAPK, JAK/STAT and apoptosis. These oncogenic signaling pathways regulated by H19 are involved in cell proliferation, proliferation, migration and invasion, angiogenesis, and apoptosis of various cancer cells. This review suggests that H19 may be a novel therapeutic target for cancers treatment by regulating oncogenic signaling pathways.

A holographic function screen (HFS) can recompose the wavefront and re-modulate the light-field distribution from a three-dimensional (3D) light field display (LFD) system. However, the spread function of existing HFSs does not particularly suit integral imaging (II) 3D LFD systems, which causes crosstalk and reduces the sharpness of reconstructed 3D images. An optimized holographic function screen with a flat-top rectangular spread function (FRSF) was designed for an II 3D LFD system. A simulation was carried out through ray tracing, which verified that the proposed diffusion function could suppress crosstalk and improve the overall effect.

Background: Autophagy plays a vital role in cancer development. However, there is currently no comprehensive study regarding the effects of autophagy-related genes (ARGs) on pancreatic cancer prognosis. Thus, this study aimed to establish an autophagy-related signature for predicting the prognosis of patients with pancreatic cancer. Methods: We identified and validated differentially-expressed ARGs using data from The Cancer Genome Atlas (TCGA) database, Genotype-Tissue Expression project (GTEx) and Expression Omnibus (GEO) database. We performed Cox proportional hazards regression analysis on the differentially-expressed ARGs to develop an autophagy-related signature. We tested the expression of these genes through western blotting and verified their prognostic values through gene expression profiling and interactive analyses (GEPIA). Results: We identified a total of 21 differentially-expressed ARGs and screened 4 OS-related ARGs (TP63, RAB24, APOL1, and PTK6). Both the training and validation sets showed that the autophagy-related signature was more accurate than the Tumor Node Metastasis (TNM) staging system. Moreover, the western blotting result showed that the expression of TP63, APOL1, and PTK6 was high, whereas that of RAB24 was low in cancer tissues. Conclusion: This 4-ARG signature might potentially help in providing personalized therapy to patients with cancer.

The discovery of new covalent organic framework (COF) topologies is often led by trial-and-error experiments. Here, the authors present a methodology for high throughput construction of COFs based on a materials genomics strategy and demonstrate the synthesis of the generated 2D and 3D-COFs.

MOF/polymer interface in mixed-matrix membranes (MMMs) has been considered as a crucial issue for achieving highly efficient gas separation performance. In this research, aluminum fumarate framework (A520) based mixed matrix hydrogel self-supported membranes (A520-MMHMs) were prepared by a facile UV photopolymerization. The hydrophilic A520 incorporated into hydrogel polymer effectively avoided the formation of interfacial defects and improved the compatibility between MOF and hydrogel matrix. As a result, the A520-MMHM possessed enhanced CO2 permeability (∼432.87 Barrer) and CO2/CH4 selectivity (∼51.05) in comparison with pure hydrogel membrane. This performance data is very close to the updated McKeown 2019 upper bound, far exceeding the 2008 Robeson upper bound. Therefore, the design of hydrogel membrane incorporated with water-stable MOF may open up a new way for optimizing self-supported hydrogel membrane performance in gas separation.

The globally increasing concentrations of greenhouse gases in atmosphere after combustion of coal- or petroleum-based fuels give rise to tremendous interest in searching for porous materials to efficiently capture carbon dioxide (CO2) and store methane (CH4), where the latter is a kind of clean energy source with abundant reserves and lower CO2 emission. Hundreds of thousands of porous materials can be enrolled on the candidate list, but how to quickly identify the really promising ones, or even evolve materials (namely, rational design high-performing candidates) based on the large database of present porous materials? In this context, high-throughput computational techniques, which have emerged in the past few years as powerful tools, make the targets of fast evaluation of adsorbents and evolving materials for CO2 capture and CH4 storage feasible. This review provides an overview of the recent computational efforts on such related topics and discusses the further development in this field. Keywords: High-throughput computation, Screening and design, Nanoporous materials, CO2 capture, CH4 storage.

Heavy metal removal from polluted water is of global significance, but current adsorbents are limited by their specificity towards certain metals. Here the authors incorporate a non-specific and strong chelating group into a robust metal-organic framework and demonstrate its versatility for heavy metal adsorption.

The separation of He/H2 using membrane technology has gained significant interest in the field of He extraction from natural gas. One of the greatest challenges associated with this process is the extremely close kinetic diameters of the two gas molecules, resulting in low membrane selectivity. In this study, we investigated the structure-performance relationship of metal-organic framework (MOF) membranes for He/H2 separation through molecular simulations and machine learning approaches. By conducting molecular simulations, we identified the potential MOF membranes with high separation performance from the Computation-Ready Experimental (CoRE) MOF database, and the diffusion-dominated mechanism was further elucidated. Moreover, random forest (RF)- based machine learning models were established to identify the crucial factors influencing the He/H2 separation performance of MOF membranes. The pore limiting diameter (PLD) and void fraction (), are revealed as the most important physical features for determining the membrane selectivity and He permeability, respectively. Additionally, density functional theory (DFT) calculations were carried out to validate the molecular simulation results and suggested that the electronegative atoms on the pore surfaces can enhance the diffusion-based separation of He/H2, which is critical for improving the membrane selectivities of He/H2. This study offers useful insights for designing and developing novel MOF membranes for the separation of He/H2 at the molecular level. [ABSTRACT FROM AUTHOR]