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Abstract Body mass index (BMI) is a crucial health indicator for obesity. With the progression of socio-economic status and alterations in lifestyle, an increasing number of global populations are at risk of obesity. Given the complexity and severity of neurological diseases, early identification of risk factors is vital for the diagnosis and prognosis of such diseases. In this study, we employed Mendelian randomization (MR) analysis utilizing the most comprehensive genome-wide association study (GWAS) data to date. We selected single nucleotide polymorphisms (SNPs) that are unaffected by confounding factors and reverse causality as instrumental variables. These variables were used to evaluate the genetic and causal relationships between Body Mass Index (BMI) and various neurological diseases, including Parkinson’s Disease (PD), Alzheimer's Disease (AD), Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), Ischemic Stroke (IS), and Epilepsy (EP). The Inverse Variance Weighted (IVW) analysis indicated that there was no significant causal relationship between Body Mass Index (BMI) indicators and PD (P-value = 0.511), AD (P-value = 0.076), ALS (P-value = 0.641), EP (P-value = 0.380). However, a causal relationship was found between BMI indicators and MS (P-value = 0.035), and IS (P-value = 0.000), with the BMI index positively correlated with the risk of both diseases. The Cochran’s Q test for MR-IVW showed no heterogeneity in the MR analysis results between the BMI index and the neurological diseases (P > 0.05). The Egger intercept test for pleiotropy revealed no horizontal pleiotropy detected in any of the neurological diseases studied (P > 0.05). It was found that there was no causal relationship between BMI and PD, AD, ALS, EP, and a genetic causal association with MS, and IS. Meanwhile, the increase in BMI can lead to a higher risk of MS and IS, which reveals the critical role of obesity as a risk factor for specific neurological diseases in the pathogenesis of the diseases.

BackgroundThe most effective approach to managing Alzheimer's disease (AD) lies in identifying reliable biomarkers for AD to forecast the disease in advance, followed by timely early intervention for patients.MethodsTranscriptomic data on peripheral blood mononuclear cells (PBMCs) from patients with AD and the control group were collected, and preliminary data processing was completed using standardized analytical methods. PBMCs were initially segmented into distinct subpopulations, and the divisions were progressively refined until the most significantly altered cell populations were identified. A combination of high-dimensional weighted gene co-expression analysis (hdWGCNA), cellular communication, pseudotime analysis, and single-cell regulatory network inference and clustering (SCENIC) analysis was used to conduct single-cell transcriptomics analysis and identify key gene modules from them. Genes were screened using machine learning (ML) in the key gene modules, and internal and external dataset validations were performed using multiple ML methods to test predictive performance. Finally, bidirectional Mendelian randomization (MR) analysis, regional linkage analysis, and the Steiger test were employed to analyze the key gene.ResultA significant decrease in non-classical monocytes was detected in PMBC of AD patients. Subsequent analyses revealed the inherent connection of non-classical monocytes to AD, and the NAP1L1 gene identified within its gene module appeared to exhibit some association with AD as well.ConclusionThe NAP1L1 gene is a potential predictive biomarker for AD.

Herein, different kinds of methods for buildability quantification of 3D concrete printing are reviewed, including experimental approaches, analytical modelling, and numerical simulations. A brief introduction on printing process is first given. This discusses the material properties in different stages. Material printability, which encompasses pumpability, extrudability and buildability, is then discussed. Subsequently, a brief review of the experimental and analytical models for buildability quantification is presented and they're discussed. An overview on the numerical tools for 3DCP is then given. These numerical models can quantify structural buildability and optimize the printing parameters, therefore, providing a more economical solution for buildability quantification. In the end, a summary and discussion on the limitations of numerical tools for buildability quantification are provided, as well as recommendations for their improvement.

We propose a new numerical method to analyze the early-age creep of 3D printed segments with the consideration of stress history. The integral creep strain evaluation formula is first expressed in a summation form using superposition principle. The experimentally derived creep compliance surface is then employed to calculate the creep strain in the lattice model with a combination of stored stress history. These strains are then converted into element forces and applied to the analyzed object. The entire numerical analysis consists of a sequence of linear analyses, and the viscosity is modelled using imposed local forces. The model is based on the incremental algorithm and one of the main advantages is the straightforward implementation of stress history consideration. The creep test with incremental compressive loading is utilized to validate this model. The modelling results are in good agreement with experimental data, demonstrating the feasibility of the lattice model in early-age creep analysis under incremental compressive loading. To understand the impact of early-age creep on structural viscoelastic deformation during the printing process, additional analyses of a printed segment are carried out. These simulation results highlight the need to consider creep for accurate prediction of viscoelastic deformation during the printing process.

In our former paper, based on a published 3D reactive transport model at microscale with the capability of simulating the chemical reactions involved in ASR, the location of expansive ASR gel related to the reactivity of aggregate, temperature, aggregate porosity and silica content in aggregate, is clarified. Based on the simulation results, in this paper, the cracking process at mesoscale in concrete induced by ASR in the early stage is investigated. The results show that the cracking process can be divided into four stages and three cracking routes are generalized with the behind chemical exposed environments specified. The cracking routes are found to be comparable with the experimental observed routes. For the first time, the cracking patterns induced by ASR in concrete at mesoscale is linked with the chemical reactions at microscale, which is the first step towards building a complete computational tool to predict ASR as realistic as possible.

This paper employs computer vision techniques to predict the micromechanical properties (i.e., elastic modulus and hardness) of cement paste based on an input of Backscattered Electron (BSE) images. A dataset comprising 40,000 nanoindentation tests and 40,000 BSE micrographs was built by express nanoindentation test and Scanning Electron Microscopy (SEM). A Residual Convolutional Neural Network (Res-Net) model, which differs from a typical Convolutional Neural Network (CNN) architecture by a shortcut connection, was employed and compared with a simple table model. The models were trained, tuned, and tested over a training, validation and testing set comprising 70%, 15% and 15% of the 40,000 data pairs, respectively. The following conclusions were drawn: 1) Express nanoindentation tests can provide reliable information for cement paste. Deconvolution based on Gaussian Mixture Model (GMM) can obtain almost invariant statistics for each phase; 2) Based on averaged greyscale values of each BSE image, a table model can predict the elastic modulus and hardness with R2 of 0.80 and 0.83, respectively; 3) Based on the intensity of each pixel as well as their patterns in each BSE image, the Res-Net model can predict the elastic modulus and hardness with a R2 of 0.85 and 0.88, respectively. Deconvolution of the Res-Net prediction obtains similar invariant statistics as derived by the nanoindentation tests, which gives strong evidence of the applicability of the Res-Net model.

Abstract Background Shenzhi Jiannao (SZJN) prescription is a type of herbal formula adopted in the management of cognitive impairment and related disorders. However, its effects and related regulatory mechanisms on vascular dementia (VD) are elusive. Herein, network pharmacology prediction was employed to explore the pharmacological effects and molecular mechanisms of SZJN prescription on VD using network pharmacology prediction, and validated the results through in vitro experiments. Methods Through a search in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, chemical composition and targets for SZJN prescription were retrieved. The potential targets for VD were then obtained from the GeneCards and DisGeNET databases. The network was constructed that depicted the interactions between putative SZJN prescription and known therapeutic targets for VD using Cytoscape 3.7.1. Analysis of protein-protein interaction was achieved via STRING 11.0 software, followed by Gene Ontology (GO) functional enrichment and Kyoto Gene and Genome Encyclopedia (KEGG) pathway analyses. To validate the computer-predicted results, in vitro experiments based on an excitotoxic injury model were designed using glutamate-exposed PC12 cells, and treated with varying concentrations (low, 0.05; medium, 0.1 and high, 0.2 mg/mL) of SZJN prescription. Cell viability and cell death were detected using the IncuCyte imaging system. Moreover, the expression profiles of Caspase-3 were analyzed through qRT-PCR. Results Twenty-eight potentially active ingredients for SZJN prescription, including stigmasterol, beta-sitosterol, and kaempferol, plus 21 therapeutic targets for VD, including PTGS2, PTGS1, and PGR were revealed. The protein-protein interaction network was employed for the analysis of 20 target proteins, including CASP3, JUN, and AChE. The enrichment analysis demonstrated candidate targets of SZJN prescription were more frequently involved in neuroactive ligand-receptor interaction, calcium, apoptosis, and cholinergic synaptic signaling pathways. In vitro experiments revealed that SZJN prescription could significantly reverse glutamate-induced cell viability loss and cell death, and lower the levels of Caspase-3 mRNA in glutamate-induced PC12 cells. Conclusions Collectively, this study demonstrated that SZJN prescription exerted the effect of treating VD by regulating multi-targets and multi-channels with multi-components through the method of network pharmacology. Furthermore, in vitro results confirmed that SZJN prescription attenuated glutamate-induced neurotoxicity.

Generative networks are effective tools for digital materials (DM) inverse design. However, the optimization performance of generative networks is restricted by the increasing discrepancy between the optimized input and the prescribed input domain as the design loop increases. Herein, a correction technique is incorporated into generative deep neural network (GDNN) and generative deep convolutional neural network (GDCNN). The correction is performed by pulling the machine learning (ML)‐optimized inputs back to the prescribed domain at certain interval during the optimization process instead of only postprocessing at the end. A DM system with two phases, i.e., the matrix phase and the pore phase, is used for the structural design. The datasets are produced using a numerical model and describe the relationship between the material structure and the elastic modulus and tensile strength. The results show that the optimization effectiveness of corrected GDNN/GDCNN significantly improves given the fact that more structures converge to the best structures and fewer nonrepetitive structures are left after optimization, which helps to search for the best structures and decreases the computational burden when verifying the ML‐recommended structures. The corrected GDNN and GDCNN also manage to find structures with higher tensile strength in the new inverse design.

Abstract Background Synaptic damage and glutamate excitotoxicity have been implicated in the pathogenesis of vascular dementia (VD). Clathrin, RAB5B and N-methyl-d-aspartic acid receptor 1 (NMDAR1) proteins play a vital role in endocytosis of synaptic vesicles in neurons and glutamate over accumulation. Previous researches have been confirmed that Shenzhi Jiannao (SZJN) formula has an anti-apoptotic and neuroprotective effect in VD, but the underlying mechanisms are still unclear. In this study, we aimed to explore the effect of SZJN formula on cognitive impairment and glutamate excitotoxicity via clathrin-mediated endocytosis (CME) in vivo and in vitro. Methods SZJN formula consists of Panax ginseng C.A.Mey., Anemarrhena asphodeloides Bunge, and Paeonia anomala subsp. veitchii (Lynch) D.Y.Hong & K.Y.Pan. All herbs were prepared into granules. Both common carotid arteries were permanent occluded (2‐vessel occlusion, 2VO) in male Sprague Dawley (SD) rats to model VD. One day after operation, the rats began daily treatment with SZJN formula for 2 weeks. The neuroprotective effects of SZJN formula was subsequently assessed by the novel object recognition test, Morris water maze, hematoxylin–eosin (HE) staining and Nissl staining. Glutamate cytotoxicity was assessed by detecting cell viability and cell death of PC12 cells. Immunohistochemistry, immunofluorescence, Western blot, and quantitative real‐time PCR were used to detect the expression levels of clathrin, RAB5B, and NMDAR1. Results Administration of SZJN formula effectively improved short-term memory and spatial memory. SZJN formula treatment significantly reduced hippocampal neuronal loss, and recovered the arrangement and morphology of neurons and Nissl bodies. Moreover, SZJN formula promoted the proliferation of PC12 cells and inhibited glutamate-induced cell death. The down-regulation of clathrin and RAB5B, as well as the upregulation of NMDAR1 in the brain induced by 2VO or glutamate was also notably reversed by SZJN formula at both the protein and mRNA levels, which may contribute to SZJN formula induced improved neurological function. Conclusions Taken together, our findings provide evidence that the neuroprotective effects of SZJN formula in experimental VD maybe mediated through promoting the expression of clathrin-mediated endocytosis and reducing NMDARs‐associated glutamate excitotoxicity. SZJN formula serves as a promising alternative therapy and may be a useful herbal medicine for preventing progression of VD. Graphic abstract

Aiming at the problems of low following accuracy and weak anti-disturbance ability in the three-dimensional path-following control of small fixed-wing Unmanned Aerial Vehicles (UAV), a Globally Stable Integral Sliding Mode Radial Basis Function S-Plane (GSISM+RBF S-Plane) controller is designed. The controller adopts the inner and outer loop mode, the outer loop adopts the Globally Stable Integral Sliding Mode (GSISM) control, and the inner loop adopts the S-Plane control. At the same time, the unknown disturbance in the model is estimated via an RBF neural network. Firstly, the outer loop controller is designed based on the GSISM, and its stability is proved using the Lyapunov theory. Then, the S-Plane controller is designed for the instruction signal of the inner loop. Considering the complexity of the derivation in the S-Plane controller, a second-order differentiator is introduced. Finally, considering the problem of external wind disturbance, the controller is modeled, studied, and processed in order to better reflect the impact of real external wind on UAV path following. Finally, the Globally Stable Sliding Mode (GSSM) control and Globally Stable Integral Sliding Mode S-Plane (GSISM S-Plane) control are used for a comparative experiment. The simulation results show that the designed GSISM+RBF S-Plane controller can accurately track the ideal path compared with the GSSM and GSISM S-Plane controller, and it has good control performance and anti-disturbance performance.

The lattice fracture model is a discrete model that can simulate the fracture process of cementitious materials. In this work, the Delft lattice fracture model is reviewed and utilized for fracture analysis. First, a systematic calibration procedure that relies on the combination of two uniaxial tensile tests is proposed to determine the input parameters of lattice elements—tensile strength, compressive strength and elastic modulus. The procedure is then validated by simulating concrete fracture under complex loading and boundary conditions: Uniaxial compression, three-point bending, tensile splitting, and double-edge-notch beam shear. Simulation results are compared to experimental findings in all cases. The focus of this publication is therefore not only on summarizing existing knowledge and showing the capabilities of the lattice fracture model; but also to fill in an important gap in the field of lattice modeling of concrete fracture; namely, to provide a recommendation for a systematic model calibration using experimental data. Through this research, numerical analyses are performed to fully understand the failure mechanisms of cementitious materials under various loading and boundary conditions. While the model presented herein does not aim to completely reproduce the load-displacement curves, and due to its simplicity results in relatively brittle post-peak behavior, possible solutions for this issue are also discussed in this work.

The title complex, [La2(C15H9O2)6(C10H8N2)2], has a centrosymmetric binuclear cage structure in which two LaIII atoms are both nine-coordinated and bridged by four anthracene-9-carboxylate ligands, with an La...La separation of 4.0880 (4) Å. The remaining coordination sites are occupied by two N atoms of a 2,2′-bipyridine (bipy) and two O atoms of an anthracene-9-carboxylate ligand. The six anthracene-9-carboxylate groups coordinate each LaIII atom in three different ways. Adjacent discrete dinuclear units are arranged into a one-dimensional chain along the [111] direction by intermolecular π–π stacking interactions, with a centroid–centroid separation of 3.704 (7) Å.

In order to improve the oxidative desulfurization (ODS) performance of MOF materials, an effective way is to convert a microporous MOF into a hierarchical porous MOF (HP-MOF) by utilizing the linker selective retention strategy. Herein, UiO-66 with the introduction of an unstable linker ligand (dihydro-1,2,4,5-tetrazine-3,6-dicarboxylate, dhtz) can selectively remove dhtz ligands to form HP-MOF (HP-UiO-66-dhtz) through heat treatment at high temperature. While maintaining the original structure of UiO-66, HP-UiO-66-dhtz features mesopores and abundant Lewis acid sites, showing excellent ODS performance for diphenylthiophene (DBT). [ABSTRACT FROM AUTHOR]

Although surgical resection could provide better survival for patients with colorectal cancer liver metastases (CRLM), the recurrence rate after resection of CRLM remains high. The progress of genome sequencing technologies has greatly improved the molecular understanding of colorectal cancer. In the era of genomics and targeted therapy, genetic mutation analysis is of great significance to guide systemic treatment and identify patients who can benefit from resection of CRLM. RAS and BRAF mutations and microsatellite instability/deficient deoxyribonucleic acid (DNA) mismatch repair status have been incorporated into current clinical practice. Other promising molecular biomarkers such as coexisting gene mutations and circulating tumor DNA are under active investigation. This study aimed to review the prognostic significance of molecular biomarkers in patients with CRLM undergoing metastasectomy based on the current evidence. [ABSTRACT FROM AUTHOR]

Rechargeable aqueous zinc-ion batteries (ZIBs) are highly promising energy storage devices due to their advantages of high energy density, low cost, environmental friendliness, and excellent safety. Investigation of advanced cathode materials featuring high capacity is desired for their applications in high-capacity ZIBs. In this study, a porous N-doped carbon-coated manganese oxide/zinc manganate (MZM@N-C) composite was successfully prepared as an advanced cathode material for aqueous ZIBs. The MZM@N-C cathode demonstrated a superior specific capacity of 772.8 mA h g-1 at 50 mA g-1 and maintained a high specific capacity of 205 mA h g-1 after 300 cycles at a high current density of 500 mA g-1. As compared to the unmodified MnOx cathode, MZM@N-C has a higher reversible capacity and cycling stability which could be assigned to the robust one-dimensional (1D) structure and the synergistic effect of MZM@N-C, providing instructive insight into the design of high-capacity manganese-based cathodes for rechargeable aqueous ZIBs. Furthermore, a soft-pack battery was assembled using the MZM@N-C cathode, demonstrating its potential applications in various devices. [ABSTRACT FROM AUTHOR]