Cultivating historical heritage area vitality using urban morphology approach based on big data and machine learning

The conservation of historical heritage can bring social benefits to cities by promoting community economic development and societal creativity. In the early stages of historical heritage conservation, the focus was on the museum-style concept for individual structures. At present, heritage area vitality is often adopted as a general conservation method to increase the vibrancy of such areas. However, it remains unclear whether urban morphological elements suitable for urban areas can be applied to heritage areas. This study uses ridge regression and LightGBM with multi-source big geospatial data to explore whether urban morphological elements that affect the vitality of heritage and urban areas are consistent or have different spatial distributions and daily variations. From a sample of 12 Chinese cities, our analysis shows the following results. First, factors affecting urban vitality differ from those influencing heritage areas. Second, factors influencing urban and heritage areas' vitality have diurnal variations and differ across cities. The overarching contribution of this study is to propose a quantitative and replicable framework for heritage adaptation, combining urban morphology and vitality measures derived from big geospatial data. This study also extends the understanding of forms of heritage areas and provides theoretical support for heritage conservation, urban construction, and economic development

π\pi Phase Interlayer Shift and Stacking Fault in the Kagome Superconductor CsV3_3Sb5_5

The stacking degree of freedom is a crucial factor in tuning material properties and has been extensively investigated in layered materials. The kagome superconductor CsV$_3$Sb$_5$ was recently discovered to exhibit a three-dimensional CDW phase below TCDW ~94 K. Despite the thorough investigation of in-plane modulation, the out-of-plane modulation has remained ambiguous. Here, our polarization- and temperature-dependent Raman measurements reveal the breaking of C$_6$ rotational symmetry and the presence of three distinct domains oriented at approximately 120{\deg}to each other. The observations demonstrate that the CDW phase can be naturally explained as a 2c staggered order phase with adjacent layers exhibiting a relative ${\pi}$ phase shift. Further, we discover a first-order structural phase transition at approximately 65 K and suggest that it is a stacking order-disorder phase transition due to stacking fault, supported by the thermal hysteresis behavior of a Cs-related phonon mode. Our findings highlight the significance of the stacking degree of freedom in CsV$_3$Sb$_5$ and offer structural insights to comprehend the entanglement between superconductivity and CDW.Comment: This manuscript was published in Phys. Rev. Let

Polyclonal antibody against the DPV UL46M protein can be a diagnostic candidate

<p>Abstract</p> <p>Background</p> <p>The duck plague virus (DPV) UL46 protein (VP11/12) is a 739-amino acid tegument protein encoded by the <it>UL46 </it>gene. We analyzed the amino acid sequence of UL46 using bioinformatics tools and defined the main antigenic domains to be between nucleotides 700-2,220 in the <it>UL46 </it>sequence. This region was designated UL46M. The DPV <it>UL46 </it>and <it>UL46M </it>genes were both expressed in <it>Escherichia coli </it>Rosetta (DE3) induced by isopropy1-β-<smcaps>D</smcaps>-thiogalactopyranoside (IPTG) following polymerase chain reaction (PCR) amplification and subcloning into the prokaryotic expression vector pET32a(+). The recombinant proteins were purified using a Ni-NTA spin column and used to generate the polyclonal antibody against UL46 and UL46M in New Zealand white rabbits. The titer was then tested using enzyme-linked immunosorbent assay (ELISA) and agar diffusion reaction, and the specificity was tested by western blot analysis. Subsequently, we established Dot-ELISA using the polyclonal antibody and applied it to DPV detection.</p> <p>Results</p> <p>In our study, the DPV UL46M fusion protein, with a relative molecular mass of 79 kDa, was expressed in <it>E. coli </it>Rosetta (DE3). Expression of the full <it>UL46 </it>gene failed, which was consistent with the results from the bioinformatic analysis. The expressed product was directly purified using Ni-NTA spin column to prepare the polyclonal antibody against UL46M. The titer of the anti-UL46M antisera was over 1:819,200 as determined by ELISA and 1:8 by agar diffusion reaction. Dot-ELISA was used to detect DPV using a 1:60 dilution of anti-UL46M IgG and a 1:5,000 dilution of horseradish peroxidase (HRP)-labeled goat anti-rabbit IgG.</p> <p>Conclusions</p> <p>The anti-UL46M polyclonal antibody reported here specifically identifies DPV, and therefore, it is a promising diagnostic tool for DPV detection in animals. UL46M and the anti-UL46M antibody can be used for further clinical examination and research of DPV.</p

A prognostic binary classifier comprised of five critical mRNAs stratified pancreatic cancer patients following resection

Background: Pancreatic cancer is characterized by an extremely poor prognosis, even following potentially curative resection. Classical prognostic markers such as histopathological or clinical parameters have limited predictive power. The present study aimed to establish a prognostic model combining mRNA expression data with histopathological and clinical data to better predict survival and stratify pancreatic cancer patients following resection. We pioneered three models in one study and systematically evaluated the clinical benefits of all three models. Methods: To identify differentially expressed genes in pancreatic cancer, mRNA data from normal (GTEx database) and pancreatic cancer (TCGA database) tissues were used. Survival analysis was carried out to identify prognosis-relevant genes from the identified differentially expressed genes and LASSO regression was used to filter out hub genes. The risk score of several hub genes was calculated according to gene expression and coefficients. Validation was carried out using an independent set of GEO microarray data. Multivariate COX regression was used for identifying independent clinical and pathological risk factors related to patient's survival in the TCGA database and a prognostic model combining mRNA expression data with histopathological and clinical data was established. Another prognostic model using clinicopathological factors from the SEER database was conceived based on multivariate COX regression. NRI (net reclassification improvement) and IDI (integrated discrimination index) were used to compare the predictive capabilities of the different models. Results: We identified 1589 differentially expressed genes (DEGs) through the comparison of normal and pancreatic cancer tissues, of whom 317 were associated with prognosis(p < 0.05). LASSO regression identified five hub genes, MYEOV, ANXA2P2, MET, CEP55, and KRT7, that were used for the five-mRNA-classifier prognostic model. The classifier could stratify patients into a short and long survival group: 5-year overall survival in the training set (TCGA, 6 % vs 52 %, p < 0.001), test set (TCGA, 18 % vs 55 %,p < 0.01) and external validation set (GEO, 0 % vs 25 %, p < 0.05). Sensitivity analysis showed that the mRNA model (model 1) was better than the clinicopathological no-mRNA model (model 2) in predicting 5-year survival in the TCGA database (AUC: 0.877 vs 0.718, z = 3.165, p < 0.01) and better than the multi-factor prognostic model (model 3) from the SEER database (AUC: 0.754, z = 2.637, p < 0.01). On predictive performance, model 1 improved model 2 (NRI = 0.084, z = 1.288, p = 0.198; IDI = 0.055, z = 1.041,p = 0.298) and model 3 (NRI = 0.167,z = 1.961,p = 0.05; IDI = 0.086, z = 1.427, p = 0.154). Conclusion: The five-mRNA-classifier is a reliable and feasible instrument to predict the prognosis of pancreatic cancer patients following resection. It might help in patiens counseling and assist clinicians in providing individualized treatment for patients in different risk groups

Effect of Shape on Mesoporous Silica Nanoparticles for Oral Delivery of Indomethacin

The use of mesoporous silica nanoparticles (MSNs) in the field of oral drug delivery has recently attracted greater attention. However, there is still limited knowledge about how the shape of MSNs affects drug delivery capacity. In our study, we fabricated mesoporous silica nanorods (MSNRs) to study the shape effects of MSNs on oral delivery. MSNRs were characterized by transmission electron microscopy (TEM), nitrogen adsorption/desorption, Fourier transform infrared spectroscopy (FTIR), and small-angle X-ray diffraction (small-angle XRD). Indomethacin (IMC), a non-steroidal anti-inflammatory agent, was loaded into MSNRs as model drug, and the drug-loaded MSNRs resulted in an excellent dissolution-enhancing effect. The cytotoxicity and in vivo pharmacokinetic studies indicated that MSNRs can be applied as a safe and efficient candidate for the delivery of insoluble drugs. The use of MSNs with a rod-like shape, as a drug delivery carrier, will extend the pharmaceutical applications of silica materials

Evaluation of the Solid Dispersion System Engineered from Mesoporous Silica and Polymers for the Poorly Water Soluble Drug Indomethacin: In Vitro and In Vivo

This work explored absorption efficacy via an in vivo imaging system and parallel artificial membrane penetration in indomethacin (IMC) solid dispersion (SD) systems. Two different polymer excipients&mdash;hydroxypropyl methylcellulose (HPMC) and Kollicoat IR as precipitation inhibitors (PIs)&mdash;combined with mesoporous silica nanoparticles (MSNs) as carriers were investigated. The IMC&ndash;SDs were prepared using the solvent evaporation method and characterized by solubility analysis, infrared (IR) spectroscopy, powder X-ray diffraction (PXRD), field emission scanning electron microscopy (FESEM), and differential scanning calorimetry (DSC). It was confirmed that IMC successfully changed into an amorphous state after loading into the designed carriers. The in vitro release and stability experiments were conducted to examine the in vitro dissolution rates of IMC&ndash;SDs combined with HPMC and Kollicoat IR as PIs which both improved approximately three-fold to that of the pure drug. Finally, in vivo studies and in vitro parallel artificial membrane penetration (PAMPA) experiments ensured the greater ability of enhancing the dissolution rates of pure IMC in the gastrointestinal tract by oral delivery. In brief, this study highlights the prominent role of HPMC and Kollicoat IR as PIs in MSN SD systems in improving the bioavailability and gastrointestinal oral absorption efficiency of indomethacin