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Adsorptive separation technology provides an effective approach for separating gases with similar physicochemical properties, such as the purification of acetylene (C2H2) from carbon dioxide (CO2). The high designability and tunability of metal–organic framework (MOF) adsorbents make them ideal design platforms for this challenging separation. Herein, we employ an isoreticular functionalization strategy to fine-tune the pore environment of Zr- and Th-based UiO-66 by the immobilization of the benzothiadiazole group via bottom-up synthesis. The functionalized UPC-120 exhibits an enhanced C2H2/CO2separation performance, which is confirmed by adsorption isotherms, dynamic breakthrough curves, and theoretical simulations. The synergy of ligand functionalization and metal ion fine-tuning guided by isoreticular chemistry provides a new perspective for the design and development of adsorbents for challenging gas separation processes.

Smart manufacturing has been transforming toward industrial digitalization integrated with various advanced technologies. Metaverse has been evolving as a next-generation paradigm of a digital space extended and augmented by reality. In the metaverse, users are interconnected for various virtual activities. In consideration of advanced possibilities that may be brought by the metaverse, it is envisioned that industrial metaverse should be integrated into smart manufacturing to upgrade industry for more visible, intelligent and efficient production in the future. Therefore, a conceptual model, named IMverse Model, and novel characteristics of the industrial metaverse for smart manufacturing are proposed in this article. Besides, an industrial metaverse architecture, named IMverse Architecture, is proposed involving several key enabling technologies. Typical innovative applications of the industrial metaverse throughout the whole product life cycle for smart manufacturing are presented with insights. Nonetheless, in prospect of future, the industrial metaverse still faces limitations and is far from implementation. Thus, challenges and open issues of the industrial metaverse for smart manufacturing are discussed, then outlook is provided for further research and application.

The rapid growth of IoT (Internet of Things) and smart services facilitate many CPS (Cyber-Physical Systems) such as smart health, smart grid and so on. Nevertheless, the communication security issues in CPS are becoming more and more important with the growing complexity of the CPS network and the increasing dependency of critical network infrastructure on cyber-based technologies. In recent years, deep learning technology has shown its superiority in detecting communication security attacks, but its high computational complexity and the massive amount of data generated by IoT devices have brought challenges to traditional cloud computing technology in terms of bandwidth and computing resources. In this paper, we have analyzed the characteristics of heterogeneity and hierarchy in attacks on CPS. We have also analyzed the role of edge intelligence in handling the security of large-scale data communication in CPS. Furthermore, we proposed a CPS communication attack detection framework based on edge cloud collaboration, aiming to improve the parallel efficiency of hardware resources when executing detection tasks. We aim to enhance the intelligence of physical devices and the degree of cloud collaboration, satisfying the real-time processing requirements of large-scale, hierarchical CPS attack detection. Furthermore, through simple simulation experiments, we verified the effectiveness of the proposed edge cloud collaboration framework in CPS attack detection.

In this paper, we present the Magnetic Resonance Image (MRI) denoising method via nonlocal multidimensional low rank tensor transformation constraint (NLRT). We first design a nonlocal MRI denoising method by non-local low rank tensor recovery framework. Furthermore, a multidimensional low rank tensor constraint is used to obtain low-rank prior information combined with 3-dimensional structure feature of MRI image cubes. Our NLRT can achieve denoising by retaining more image detail information. The optimization and updating process of the model is solved via the alternating direction method of multipliers (ADMM) algorithm. Several state-of-the-art denoising methods are selected for comparative experiments. In order to reflect the performance of the denoising method, Rician noise with different levels is added to the experiment to analyze the results. The experimental results prove that our NLTR has more outstanding denoising ability and can obtain better MRI images.

Cyber–physical–social systems (CPSS), an emerging cross-disciplinary research area, combines cyber–physical systems (CPS) with social networking for the purpose of providing personalized services for humans. CPSS big data, recording various aspects of human lives, should be processed to mine valuable information for CPSS services. To efficiently deal with CPSS big data, artificial intelligence (AI), an increasingly important technology, is used for CPSS data processing and analysis. Meanwhile, the rapid development of edge devices with fast processors and large memories allows local edge computing to be a powerful real-time complement to global cloud computing. Therefore, to facilitate the processing and analysis of CPSS big data from the perspective of multi-attributes, a cloud-edge-aided quantized tensor-train distributed long short-term memory (QTT-DLSTM) method is presented in this article. First, a tensor is used to represent the multi-attributes CPSS big data, which will be decomposed into the QTT form to facilitate distributed training and computing. Second, a distributed cloud-edge computing model is used to systematically process the CPSS data, including global large-scale data processing in the cloud, and local small-scale data processed at the edge. Third, a distributed computing strategy is used to improve the efficiency of training via partitioning the weight matrix and large amounts of input data in the QTT form. Finally, the performance of the proposed QTT-DLSTM method is evaluated using experiments on a public discrete manufacturing process dataset, the Li-ion battery dataset, and a public social dataset.

This preview discusses the emergent new paradigm of structural complexity engineering, featuring two recent studies on an aperiodic metal-organic framework and nanoassembly of a symmetry-breaking chiral continuum bowtie. These structurally complex materials are not ordered but also not completely random, with controlled lowered symmetry.

A linker design strategy is developed to attain novel polynuclear rare-earth (RE) metal–organic frameworks (MOFs) with unprecedented topologies. We uncover the critical role of ortho-functionalized tricarboxylate ligands in directing the construction of highly connected RE MOFs. The acidity and conformation of the tricarboxylate linkers were altered by substituting with diverse functional groups at the ortho position of the carboxyl groups. For instance, the acidity difference between carboxylate moieties resulted in forming three hexanuclear RE MOFs with novel (3,3,3,10,10)-c wxl, (3,12)-c gmx, and (3,3,3,12)-c joetopologies, respectively. In addition, when a bulky methyl group was introduced, the incompatibility between the net topology and ligand conformation guided the co-appearance of hexanuclear and tetranuclear clusters, generating a novel 3-periodic MOF with a (3,3,8,10)-c kywnet. Interestingly, a fluoro-functionalized linker prompted the formation of two unusual trinuclear clusters and produced a MOF with a fascinating (3,8,10)-c lfgtopology, which could be gradually replaced by a more stable tetranuclear MOF with a new (3,12)-c leetopology with extended reaction time. This work enriches the polynuclear clusters library of RE MOFs and unveils new opportunities to construct MOFs with unprecedented structural complexity and vast application potential.

Here, we analyze the characteristics and the formation mechanisms of low-level jets (LLJs) in the middle reaches of the Yangtze River during the 2010 mei-yu season using Wuhan station radiosonde data and the fifth generation of the European Centre for Medium-Range Weather Forecasts (ERA5) reanalysis dataset. Our results show that the vertical structure of LLJs is characterized by a predominance of boundary layer jets (BLJs) concentrated at heights of 900–1200 m. The BLJs occur most frequently at 2300 LST (LST=UTC+ 8 hours) but are strongest at 0200 LST, with composite wind velocities >14 m s−1. Synoptic-system-related LLJs (SLLJs) occur most frequently at 0800 LST but are strongest at 1100 LST, with composite wind velocities >12 m s−1. Both BLJs and SLLJs are characterized by a southwesterly wind direction, although the wind direction of SLLJs is more westerly, and northeasterly SLLJs occur more frequently than northeasterly BLJs. When Wuhan is south of the mei-yu front, the westward extension of the northwest Pacific subtropical high intensifies, and the low-pressure system in the eastern Tibetan Plateau strengthens, favoring the formation of LLJs, which are closely related to precipitation. The wind speeds on rainstorm days are greater than those on LLJ days. Our analysis of four typical heavy precipitation events shows the presence of LLJs at the center of the precipitation and on its southern side before the onset of heavy precipitation. BLJs were shown to develop earlier than SLLJs.

Industrial Cyber Physical Systems can use data and information gained from across a variety of different environments to enable robots that are reconfigurable. Custom grasping is a basic operation a robot must be able to carry out for a given task, i.e., finding the best grasping point for emergent behaviors. However, environmental disturbance and limited data degrade the precision and speed of many tailored machine learning models on robot grasping detection. This paper proposes a region-based method to enable fast custom grasping through fewer RGB-D data. The grasping detection problem is simplified as a two-stage prediction problem. At the first stage, a robust grasp candidate generation strategy is proposed based on the Sobel operator. At the second stage, a region-based predictor is designed to locate the best grasping point-pair for an emergent task. The predictor is trained by a modified consistency based self-training method to realize semi-supervised learning. Experimental results show that the success rate of custom grasping of new emergent object can be increased by 3.4% on average using the proposed method. By introducing data augmentation strategies in training, the success rate is further increased by 9.2% on average. A robot is able to grasp new object with 91.5% success rate using less than 100 training samples. The number of training samples required for the proposed method is less than to 1% of which for the previous works. Note to Practitioners—This research was motivated by the problem of robot reconfigurability for various industrial automation processes and focuses mainly on the recognition of grasping point-pair of emergent object for different task. Existing approaches on robotic grasping detection are tailored to a given object and require expensive training with large amount of labeled data. This paper presents a region-based few shot learning approach that enables the robot to detect the best grasping point-pair autonomously and quickly. We show how to generate candidate point-pairs with image distortion and background disturbance. We then demonstrate how the best grasping point-pair can be located with much less training cost. Experiments suggest that this approach is feasible in robot automation for handling a class of objects. In future research, we will construct behavior learning module to enable evolving cyber-physical robotic system for more purposes.

Introduction: Early onset sepsis (EOS) remains a potentially fatal newborn condition, especially in very preterm infants. Data on the pathogen distribution and antibiotic susceptibility patterns of EOS among very preterm infants are scarce but essential for the choice of empirical antibiotic administration. We sought to assess the epidemiologic characteristics and antibiotic susceptibility patterns of pathogens causing EOS among a cohort of very preterm infants in China. Methods: This prospective, observational study included a cohort of infants born at a gestational age (GA) less than 32 weeks of 32 newborn intensive care units (NICUs) in China between January 1, 2018 and December 31, 2020. EOS was defined by isolation of pathogenic species from blood culture within 72 h of birth. Results: A total of 108 EOS cases (18.4 per 1000 admissions) were identified among 5865 very preterm infants. Incidence of EOS increased with the decrease of GA and birthweight. Escherichia coli(n= 44, 40.7%) was the most common pathogen, followed by Klebsiellaspp. (n= 10, 9.3%). The distribution and proportion of pathogenic bacteria varied significantly by GA. E. coliand Klebsiellaspp. showed high resistance to ampicillin and third-generation cephalosporins, while they showed good susceptibility to carbapenem antibiotics and piperacillin–tazobactam. Conclusion: Our data demonstrated that pathogens causing neonatal EOS showed high rates of resistance to ampicillin and third-generation cephalosporins. This raised questions about the best empirical antibiotic choice for preterm infants suspected of having EOS in low- and middle-income countries (LMICs).

The porous scaffold – a substitute for artificial bone – is vital to bone defect repair and bone tissue regeneration in tissue engineering. In consideration of the limitations of few existing design methods, the significant difference between the internal pore structure of porous scaffolds and natural bone microstructure, and the low controllability of design methods. Based on Voronoi-Tessellation (VT) and random function theory, this paper proposed a design method with controllable pore density, adjustable pore shape and size, and heterogeneous pore distribution. The stress change law of porous scaffolds under compression was analyzed using finite element simulation experiments; the relationship between the porosity and the structural strength of porous scaffolds was obtained. Furthermore, the anisotropy of porous scaffolds was studied, and the change law of the macroscopic equivalent elastic modulus with the change in porosity of porous scaffolds was acquired. Under the same porosity, the mechanical properties of the porous scaffold with a uniform pore distribution and the porous scaffold designed in this paper were analyzed and compared; the damage extension laws of two different porous scaffolds were obtained. The results demonstrate that the design method proposed in this paper has high controllability, and the parametric modeling process is both random and controllable. The internal pore structures of porous scaffolds generated by the design method are closer to the microstructure of natural bone tissue – the structural strength satisfies the requirements of artificial implants.Graphical abstract

In printable electrochromic polymer (ECP) displays, a wide color gamut, precise patterning, and controllable color switching are important. However, it is a significant challenge to achieve such features synergistically. Here, we present a solution-processable ECP stacking scheme, where a crosslinker is co-processed with three primary ECPs (ECP-Cyan, ECP-Magenta, and ECP-Yellow), which endows the primary ECPs with solvent-resistant properties and allows them to be sequentially deposited. Viavarying the film thickness of each ECP layer, a full-color palette can be constructed. The ECP stacking strategy is further integrated with photolithography. Delicate multilayer patterns with overhang and undercut textures can be generated, allowing information displays with spatial dimensionality. In addition, viamodulating the stacking sequence, the electrochemical onset potentials of the ECP components can be synchronized to reduce unwanted intermediate colors that are often found in co-processed ECPs. Should specific color properties be desired, COMSOL modeling could be applied to guide the stacking. We believe that this ECP stacking strategy opens a new avenue for electrochromic printing and displays.

Object detection has been widely used in intelligent video surveillance, robot navigation, industrial detection, and other fields. Object detection can effectively reduce the consumption of human capital and has important practical significance. However, the existing object detection methods lack adaptability to the application environment and need to obtain all images classes at one time to train the model in a static setting, and do not support the incremental learning. Therefore, we propose a novel Class-Incremental Object Detection (CIOD) framework based on deep learning. CIOD divides object detection into two stages: object candidate box generation and selection. In the first stage, we improved the traditional OpenCV cascaded classifier to adapt to class-incremental learning while maintaining accuracy. In the second stage, we use example sets and prototype vectors to construct a class increment-based classifier to identify the generated object candidate box. We verify the effectiveness of the proposed method in terms of object detection effect, efficiency, and memory capacity. Experimental results show that CIOD can detect object in a class-incremental manner and can control the memory capacity not to increase with the number of newly increased classes.

ABSTRACTNeonatal sepsis (NS) is one of the important causes of neonatal death. There are many studies to confirm the role of long non-coding RNA (lncRNA) in neonatal infectious diseases. This study aimed to explore the level of cancer susceptibility 15 (CASC15) and its effect on inflammatory response in NS. Seventy-nine neonatal pneumonia (NP) patients and 80 NS patients were enrolled in this study. Reverse Transcription-quantitative PCR (RT-qPCR) was used to determine the expression levels of CASC15 and miR-144-3p. Receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic value of CASC15 in NS. RAW264.7 cells were stimulated with LPS to simulate the inflammatory response in NS patients, and the regulation and mechanism of CASC15 on the inflammatory response were explored in this in vitro cell model. Serum CASC15 was upregulated in NS patients, and it had the ability to distinguish NS patients from NP patients. LPS stimulation increased the expression of CASC15 and simultaneously stimulated the secretion of inflammatory cytokines, while the knockdown of CASC15 alleviated the inflammatory response induced by LPS stimulation. Besides, serum miR-144-3p was reduced in NS patients, and luciferase reporter genes showed that miR-144-3p was a direct target of CASC15. Overexpression of CASC15 may promote the inflammatory response of NS by targeted regulating the expression of miR-144-3p, which may provide us with new insights in the treatment of NS.

Uremic toxin accumulation is one possible reason for alterations in hepatic drug metabolism in patients with chronic kidney disease (CKD). However, the types of uremic toxins and underlying mechanisms are poorly understood. In this study, we report the role of advanced oxidation protein products (AOPPs), a modified protein uremic toxin, in the downregulation of cytochromes P450 1A2 (CYP1A2) and P450 3A4 (CYP3A4) expression levels and activities. We found that AOPP accumulation in plasma in a rat CKD model was associated with decreased protein levels of CYP1A2 and CYP3A4. CYP1A2 and CYP3A4 metabolites (acetaminophen and 6β-hydroxytestosterone, respectively,) in liver microsomes were also significantly decreased. In human hepatocytes, AOPPs significantly decreased CYP1A2 and CYP3A4 protein levels in a dose- and time-dependent manner and downregulated their activities; however, bovine serum albumin (BSA), a synthetic precursor of AOPPs, had no effect on these parameters. The effect of AOPPs was associated with upregulation of p-IKKα/β, p-IκBα, p-NF-κB, and inflammatory cytokines protein levels and increases in p-IKKα/β/IKKα, p-IκBα/IκBα, and p-NF-κB/NF-κB phosphorylation ratios. Further, NF-kB pathway inhibitors BAY-117082 and PDTC abolished the downregulatory effects of AOPPs. These findings suggest that AOPPs downregulate CYP1A2 and CYP3A4 expression and activities by increasing inflammatory cytokine production and stimulating NF-κB-mediated signaling. Protein uremic toxins, such as AOPPs, may modify the nonrenal clearance of drugs in patients with CKD by influencing metabolic enzymes.

Uremic toxin accumulation is one possible reason for alterations in hepatic drug metabolism in patients with chronic kidney disease (CKD). However, the types of uremic toxins and underlying mechanisms are poorly understood. In this study, we report the role of advanced oxidation protein products (AOPPs), a modified protein uremic toxin, in the downregulation of cytochromes P450 1A2 (CYP1A2) and P450 3A4 (CYP3A4) expression levels and activities. We found that AOPP accumulation in plasma in a rat CKD model was associated with decreased protein levels of CYP1A2 and CYP3A4. CYP1A2 and CYP3A4 metabolites (acetaminophen and 6β-hydroxytestosterone, respectively,) in liver microsomes were also significantly decreased. In human hepatocytes, AOPPs significantly decreased CYP1A2 and CYP3A4 protein levels in a dose- and time-dependent manner and downregulated their activities; however, bovine serum albumin (BSA), a synthetic precursor of AOPPs, had no effect on these parameters. The effect of AOPPs was associated with upregulation of p-IKKα/β, p-IκBα, p-NF-κB, and inflammatory cytokines protein levels and increases in p-IKKα/β/IKKα, p-IκBα/IκBα, and p-NF-κB/NF-κB phosphorylation ratios. Further, NF-kB pathway inhibitors BAY-117082 and PDTC abolished the downregulatory effects of AOPPs. These findings suggest that AOPPs downregulate CYP1A2 and CYP3A4 expression and activities by increasing inflammatory cytokine production and stimulating NF-κB-mediated signaling. Protein uremic toxins, such as AOPPs, may modify the nonrenal clearance of drugs in patients with CKD by influencing metabolic enzymes.

The development of novel adsorption materials is of significance for the efficient and low-energy purification of acetylene (C2H2). Emerging metal–organic framework (MOF) adsorbents demonstrate great application prospects in the field of gas adsorption and separation. Herein, we synthesized a Eu-MOF asymmetrically modified with cyclopentadienyl cobalt exhibiting two different types of cages, denoted as UPC-119. Adsorption isotherms and dynamic breakthrough curves confirm its potential in C2H2/CO2separation, which is further evidenced by theoretical simulations. The high adsorption capacity and low adsorption enthalpy render UPC-119 as a promising adsorbent for C2H2/CO2separation with ease of regeneration.

Chemiluminescence microreactors (CLMR) integrate catalytic centers, luminescent centers, and open channels into an atomic-scale platform, which can provide significantly enhanced light emission compared with usual homogeneous solvent systems. Herein, we report a novel metal–organic framework (MOF), UPC-88, which is constructed by a lophinyl-functionalized H4LIM-2Hligand (4,4′,4″,4‴-((naphthalene-1,4-diylbis(4,1-phenylene))bis(1H-imidazole-2,4,5-triyl))tetrabenzoic acid) with the first double (metal/organic) H2O2catalytic center for CLMR. Due to the fixed chromophore and integrated dual catalytic sites, the relaxation phenomenon is greatly reduced and the energy transfer efficiency is significantly improved, resulting in the outstanding light emission performance of UPC-88. The visible luminous time of the UPC-88 system up to 1100 min was recorded as one of the highest ever reported for MOF systems. We linearly fitted the fluorescence intensity and fluorescence power for the first time, and the results show that UPC-88 is the MOF chemiluminescent material with the highest fluorescence power reported so far. The exploration of the CL reaction mechanism reveals the key role of the lophine base center in the decomposition of hydrogen peroxide, enabling the efficient conversion of chemical energy to light energy. This platform will provide a theoretical and experimental basis for next-generation CLMR systems and improved CL performance.

Obtaining absolute pose based on pre-loaded satellite images is one of the important means of autonomous navigation for small Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System (GNSS) denied environments. Most of the previous works have tended to build Convolutional Neural Networks (CNNs) to extract features and then directly regress the pose, which will fail when solving the challenges caused by the huge viewpoint and size differences between “UAV-satellite” image pairs in real-world scenarios. Therefore, this paper proposes a probability distribution/regression integrated deep model with the attention-guided triple fusion mechanism, which estimates discrete distributions in pose space and three-dimensional vectors in translation space. In order to overcome the shortage of the relevant dataset, this paper simulates image datasets captured by UAVs with forward-facing cameras during target searching and autonomous attacking. The effectiveness, superiority, and robustness of the proposed method are verified by simulated datasets and flight tests.

Acute myeloid leukaemia (AML) is a malignant disease of myeloid hematopoietic stem/progenitor cells. Despite improved understanding of the pathogenesis of AML since the 1980s, the standard treatment for AML has remained virtually unchanged. Numerous studies have found poor survival rates and high relapse rates among older patients with AML. Several novel therapies for AML, including cytotoxic drugs, genetically- and epigenetically-targeted drugs, and immunotherapies, have been developed in recent years. Alternative treatments with improved efficacy are required for AML because many patients cannot tolerate the toxic effects of chemotherapy. Non-coding RNAs, including microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs), are attractive treatment targets for cancers and several other diseases. LncRNAs, miRNAs and circRNAs regulate DNA transcription and translation. Over the past decade, significant efforts have been made to develop RNA-based therapies, mainly antisense oligonucleotides and small interfering RNA, some of which have been approved for clinical use. Here we reviewed the mechanisms underlying the in vitroand in vivoeffects of promising targets and potential drugs, focusing on the drugs most likely to be used for clinical treatment, to aid in the development of precision therapy for AML.

Inflammatory bowel diseases (IBDs) are increasingly recognized as a pressing global health concern. The gut microbiome emerges as both a potential therapeutic target and a repository for pharmacological interventions in IBDs management. This perspective aims to elucidate the pivotal findings from recent studies concerning the anti-inflammatory properties of gut microbiota-derived metabolites (GMDMs), dissect the strengths and challenges of GMDMs as treatment strategies for IBDs, and highlight the integral role of nuclear receptors in mediating the interplay between IBD pathogenesis and GMDMs. Through the integration of these perspectives, our objective is to deepen the understanding of the therapeutic promise of nuclear receptor-targeted GMDMs, thus propelling forward the exploration and formulation of new pharmacological treatments for IBDs.

A Ba-MOF was obtained based on an in-situpartial hydrolysate of ligand and exhibited high separation selectivity for CO2/CH4and CO2/N2at ambient conditions.

Organic mixed ionic–electronic conductors (OMIECs) are an emerging family of materials crucial in the development of flexible, bio-, and optoelectronics. In electrochromic polymers, the cyclic redox reaction is associated with a mechanical breathing strain, which deforms the OMIECs and degrades the device reliability. We set forth an in situ nanoindentation approach to measure the breathing strain of a poly(3,4-propylenedioxythiophene) (PProDOT) thin film in a customized liquid cell during electrochromic cycles. A breathing volumetric strain of 12–25% is persistent in different sets of electrolytes of various solvents, salts, and salt molarities. The electrochemical conditioning, intermittence time, and cyclic protocol have minor effects on the mechanical response of PProDOT. The mechanical behavior and anion diffusivity measurement further infer the redox kinetics. Heavily cycled PProDOT films show reduced volumetric strain and accumulated mechanical damage of channel cracks and dysfunctional regions of slow and inhomogeneous electrochromic switching. This work is a systematic characterization of mechanical deformation and damage in a model OMIEC and informs the mechanical reliability of organic electrochromic devices.

ABSTRACTAberrant DNA methylation is significantly associated with the prognosis of patients with colorectal cancer (CRC). Therefore, the aim of this study was to develop a CpG-methylation-based nomogram for prognostic prediction in CRC. First, 378 CRC patients with methylation data from The Cancer Genome Atlas were randomly divided into training cohort (n = 249) and test cohort (n = 129). A multistep screening strategy was performed to identify six CpG sites that were significantly associated with overall survival in the training cohort. Then, Cox regression modelling was performed to construct a prognostic signature based on the candidate CpG sites. The six-CpG signature successfully separated patients into high-risk and low-risk groups in both training and test cohorts, and its performance was superior to that of previously published methylation markers (P< 0.05). Furthermore, we established a prognostic nomogram incorporating this signature, TNM stage, and age. The nomogram exhibited better prediction for overall survival in comparison with the three independent prognostic factors in the training cohort (C-index: 0.798 vs 0.620 to 0.737; P< 0.001). In the test cohort, the performance of nomogram was also superior to that of the three independent prognostic factors (C-index: 0.715 vs 0.590 to 0.665; P< 0.05). Meanwhile, the calibration curves for survival probability showed good agreement between prediction by nomogram and actual observation in both training and test cohorts. Together, the present study provides a novel CpG-methylation-based nomogram as a promising predictor for overall survival of CRC patients, which may help improve decision-making regarding the personalized treatments of patients with CRC.

To complement established rational and evolutionary protein design approaches, significant efforts are being made to utilize computational modeling and the diversity of naturally occurring protein sequences. Here, we combine structural biology, genomic mining, and computational modeling to identify structural features critical to aldehyde deformylating oxygenases (ADOs), an enzyme family that has significant implications in synthetic biology and chemoenzymatic synthesis. Through these efforts, we discovered latent ADO-like function across the ferritin-like superfamily in various species of Bacteria and Archaea. We created a machine learning model that uses protein structural features to discriminate ADO-like activity. Computational enzyme design tools were then utilized to introduce ADO-like activity into the small subunit of Escherichia coliclass I ribonucleotide reductase. The integrated approach of genomic mining, structural biology, molecular modeling, and machine learning has the potential to be utilized for rapid discovery and modulation of functions across enzyme families.

The design of MOFs with functionalized groups as well as suitable pore space plays a momentous part in gas adsorption and separation. Herein, a new Zn-MOF (Zn4(BTB-NH2)3·xsolvent, UPC-98, H3BTB-NH2= 2′-amino-5′-(4-carboxyphenyl)-[1,1′:3′,1″-terphenyl]-4,4″-dicarboxylic acid) was synthesized successfully by an amino-functionalized ligand (H3BTB-NH2) for light hydrocarbon adsorption and separation application. UPC-98with twofold interpenetrating structure retains relatively high thermal stability and acid–base stability, which can be stable in aqueous solutions (pH = 4–9) and air environment at 250 °C. Interestingly, UPC-98with micropore of 4–5 Å displays a high uptake of C3H8(118.4 cm3·g–1) and C3H6(120.5 cm3·g–1) at 273 K and exhibits excellent adsorption selectivity of C3/C1and C2/C1. The effective separation performance for C2H2/C2H4is confirmed via breakthrough experiments.

Product quality prediction, as an important issue of industrial intelligence, is a typical task of industrial process analysis, in which product quality will be evaluated and improved as feedback for industrial process adjustment. Data-driven methods, with predictive model to analyze various industrial data, have been received considerable attention in recent years. However, to get an accurate prediction, it is an essential issue to extract quality features from industrial data, including several variables generated from supply chain and time-variant machining process. In this article, a data-driven method based on wide-deep-sequence (WDS) model is proposed to provide a reliable quality prediction for industrial process with different types of industrial data. To process industrial data of high redundancy, in this article, data reduction is first conducted on different variables by different techniques. Also, an improved wide-deep (WD) model is proposed to extract quality features from key time-invariant variables. Meanwhile, an long short-term memory (LSTM)-based sequence model is presented for exploring quality information from time-domain features. Under the joint training strategy, these models will be combined and optimized by a designed penalty mechanism for unreliable predictions, especially on reduction of defective products. Finally, experiments on a real-world manufacturing process data set are carried out to present the effectiveness of the proposed method in product quality prediction.

Ordered mesoporous carbon (OMC) is considered to be a prospective carbon-based material for microwave absorption because of its abundant well-ordered mesoporous structures, high specific surface area, numerous active sites, and facile preparation process. However, its development has been seriously hindered by its poor impedance-matching characteristic. Herein, silica-modified OMC composites with a designable impedance-matching transition layer are successfully fabricated via a self-assembly method and succeeding calcination treatment. In addition, the silica in OMC@SiO2composites can maintain the mesoporous structure, which facilitates the scattering and reflection of microwaves in the tunnel structure. The as-prepared sample OMC-5@SiO2exhibits a minimum reflection loss (RL) value of −40.7 dB at 10.8 GHz with 2 mm and an effective absorption bandwidth (RL ≤ −10 dB) of 4.8 GHz with a thinner absorber thickness of 1.5 mm. We believe that the as-prepared OMC@SiO2composites can be prospective candidates as high-efficiency and lightweight microwave absorbers.

Two alkynyl functionalized 3D frameworks for selective and sensitive detection of Fe3+ions and nitroaromatic compounds through fluorescence quenching.

The structural diversity of highly connected metal–organic frameworks (MOFs) has long been limited due to the scarcity of highly connected metal clusters and the corresponding available topology. Herein, we deliberately chose a series of tritopic linkers with multiple substituents to construct a series of highly connected rare-earth (RE) MOFs. The steric hindrance of these substituents can be systematically tuned to generate various linker rotamers with tunable configurations and symmetries. For example, the methyl-functionalized linker (L-CH3) with C2vsymmetry exhibits larger steric hindrance, forcing two peripheral phenyl rings perpendicular to the central one. The combination of C2vlinkers and 9-connected RE6clusters leads to the formation of a new fascinating (3,9)-c septopology. Unlike Zr-MOFs exhibiting Zr6clusters in various linker configurations and corresponding different structures, the adaptable RE6clusters can undergo metal insertion and rearrange into new RE9clusters when connected to an unfunctionalized linker (L–H) with C1symmetry, giving rise to a new (3,3,18)-c ytwtopology. More interestingly, by judiciously combining the linkers with both small and bulky substituents through mixed-linker strategies, an RE9-based MOF with an engaging (3,3,12)-c flgtopology could be obtained as a result of continuous steric hindrance control. In this case, the two mixed linkers adopt configurations with moderate steric hindrances. Molecular simulation demonstrates that the combination of substituents with various steric hindrances dictates the resulting MOF structures. This work provides insights into the discovery of unprecedented topologies through systematic and continuous steric tuning, which can further serve as a blueprint for the design and construction of highly complicated porous structures for sophisticated applications.

The functionalized modification of targeted porous metal–organic frameworks (MOFs) is important to improve the adsorption capacity of light hydrocarbon gases. Five isomorphic MOFs with different functional groups (F, Cl, NH2, CH3, and OCH3) are successfully synthesized by systematic functional modification of UMCM-151. By studying the adsorption properties of C2and C3hydrocarbons (C2H2, C2H4, C2H6, C3H6, and C3H8), it is found that the electron-withdrawing group functional UMCM-151-Fand UMCM-151-Clexhibit strong affinity for C2H2(98.71 cm3g–1for UMCM-151-Fand 90.29 cm3g–1for UMCM-151-Cl), while the electron-donating group functional UMCM-151-NH2, UMCM-151-CH3, and UMCM-151-OCH3have strong affinity for C2H4(97.89 cm3g–1for UMCM-151-NH2, 90.22 cm3g–1for UMCM-151-CH3and 94.13 cm3g–1for UMCM-151-OCH3). The differences in affinity of electron effects for light hydrocarbon provide an experimental basis for porous MOFs to improve their light hydrocarbon storage capacity.