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Abstract Smart city construction is crucial for regional economic development, and the micro-level impact of such construction on business operations cannot be ignored. Based on the panel data of Shanghai and Shenzhen A-share listed companies from 2008 to 2020, this paper investigates the relationship between smart city construction and firm performance and its mechanism of action. The study findings show that (1) smart city construction significantly improves enterprise performance; (2) the improvements are significant across all industries and regions; and (3) the impact of smart city construction on small and medium-sized enterprises (SMEs) and nonstate enterprises is more significant than that on large enterprises and state-owned enterprises. These findings provide a theoretical framework and fresh perspectives for encouraging coordinated regional development and new urbanization construction.

Abstract Background RAS mutations affect prognosis in patients with metastatic colorectal cancer (mCRC) and have been identified as strong negative predictive markers for anti-epidermal growth factor receptor monoclonal antibody (anti-EGFR mAb) therapy, but many tumors containing wild-type RAS genes still do not respond to these therapies. Some additional biomarkers may have prognostic or predictive roles, but conclusions remain controversial. Methods We performed a meta-analysis and systematic review of randomized controlled trials comparing anti-EGFR mAb therapy with alternative therapy that investigated the prognostic and predictive impact of additional biomarkers in RAS wild-type (wt) mCRC patients. Hazard ratios (HRs) and 95% confidence intervals (CIs) for progression-free survival (PFS) and overall survival (OS) and odds ratios (ORs) for objective response rate (ORR) were calculated. The prognostic value of biomarkers was investigated by separately pooling HR and OR for different treatment groups in an individual study. The predictive value was assessed by pooling study interactions between treatment effects and biomarker subgroups. Results Thirty publications reporting on eighteen trials were selected, including a total of 13,507 patients. In prognostic analysis, BRAF mutations were associated with poorer PFS [HRs = 3.76 (2.47–5.73) and 2.69 (1.82–3.98)] and OS [HRs = 2.66 (1.95–3.65) and 2.45 (1.55–3.88)] in both the experimental and control arms; low miR-31-3p expression appeared to have longer PFS and OS. In terms of predictive effect, a lack of response to anti-EGFR therapy was observed in patients with BRAF mutant tumors (P interaction

Hepatocellular carcinoma (HCC) is a highly aggressive liver malignancy and one of the most lethal cancers globally, with limited effective therapeutic options. Bile acids (BAs), as primary metabolites of hepatic cholesterol, undergo enterohepatic circulation involving secretion into the intestine and reabsorption into the liver, and their composition is modulated in this process. Recent clinical observations have revealed a correlation between alteration in the BAs profile and HCC incidence, and the effect of various species of BAs on HCC development has been investigated. The regulatory effect of different BA species on cell proliferation, migration, and apoptosis in tumor cells, as well as their interaction with gut microbiota, inflammation, and immunity have been identified to be involved in HCC progression. In this review, we summarize the current understanding of the diverse functions of BAs in HCC pathogenesis and therapy, from elucidating the fundamental mechanisms underlying both tumor-promoting and tumor-suppressive consequences of various BA species to exploring potential strategies for leveraging BAs for HCC therapy. We also discuss ongoing efforts to target specific BA species in HCC treatment while highlighting new frontiers in BA biology that may inspire further exploration regarding their connection to HCC.

Cardiovascular disease (CVD), a leading cause of death and disability worldwide, and is associated with a wide range of risk factors, and genetically associated conditions. While many CVDs are preventable and early detection alongside treatment can significantly mitigate complication risks, current prediction models for CVDs need enhancements for better accuracy. Mendelian randomization (MR) offers a novel approach for estimating the causal relationship between exposure and outcome by using genetic variation in quasi-experimental data. This method minimizes the impact of confounding variables by leveraging the random allocation of genes during gamete formation, thereby facilitating the integration of new predictors into risk prediction models to refine the accuracy of prediction. In this review, we delve into the theory behind MR, as well as the strengths, applications, and limitations behind this emerging technology. A particular focus will be placed on MR application to CVD, and integration into CVD prediction frameworks. We conclude by discussing the inclusion of various populations and by offering insights into potential areas for future research and refinement.

Abstract Krylov complexity measures the spread of the wavefunction in the Krylov basis, which is constructed using the Hamiltonian and an initial state. We investigate the evolution of the maximally entangled state in the Krylov basis for both chaotic and non-chaotic systems. For this purpose, we derive an Ehrenfest theorem for the Krylov complexity, which reveals its close relation to the spectrum. Our findings suggest that neither the linear growth nor the saturation of Krylov complexity is necessarily associated with chaos. However, for chaotic systems, we observe a universal rise-slope-ramp-plateau behavior in the transition probability from the initial state to one of the Krylov basis states. Moreover, a long ramp in the transition probability is a signal for spectral rigidity, characterizing quantum chaos. Also, this ramp is directly responsible for the late-time peak of Krylov complexity observed in the literature. On the other hand, for non-chaotic systems, this long ramp is absent. Therefore, our results help to clarify which features of the wave function time evolution in Krylov space characterize chaos. We exemplify this by considering the Sachdev-Ye-Kitaev model with two-body or four-body interactions.

The GATA gene family belongs to a kind of transcriptional regulatory protein featuring a zinc finger motif, which is essential for plant growth and development. However, the identification of the GATA gene family in tetraploid potato is still not performed. In the present research, a total of 88 GATA genes in the tetraploid potato C88.v1 genome were identified by bioinformatics methods. These StGATA genes had an uneven distribution on 44 chromosomes, and the corresponding StGATA proteins were divided into four subfamilies (I-IV) based on phylogenetic analysis. The cis-elements of StGATA genes were identified, including multiple cis-elements related to light-responsive and hormone-responsive. The collinearity analysis indicates that segmental duplication is a key driving force for the expansion of GATA gene family in tetraploid potato, and that the GATA gene families of tetraploid potato and Arabidopsis share a closer evolutionary relationship than rice. The transcript profiling analysis showed that all 88 StGATA genes had tissue-specific expression, indicating that the StGATA gene family members participate in the development of multiple potato tissues. The RNA-seq analysis was also performed on the tuber flesh of two potato varieties with different color, and 18 differentially expressed GATA transcription factor genes were screened, of which eight genes were validated through qRT-PCR. In this study, we identified and characterized StGATA transcription factors in tetraploid potato for the first time, and screened differentially expressed genes in potato flesh with different color. It provides a theoretical basis for further understanding the StGATA gene family and its function in anthocyanin biosynthesis.

The results of phylogenetic analysis, based on the whole chloroplast genome and morphological study support the transfer of a long ignored bamboo species, Sasa gracilis, to the recently established genus, Sinosasa, in this study. Morphologically, this species differs from all the other known Sinosasa species by having very short (2–3 mm) foliage leaf inner ligules, which is unusual in this genus. A revised description of its morphology and colour photos are also provided.

We developed a novel method based on self-supervised learning to improve the ghost imaging of occluded objects. In particular, we introduced a W-shaped neural network to preprocess the input image and enhance the overall quality and efficiency of the reconstruction method. We verified the superiority of our W-shaped self-supervised computational ghost imaging (WSCGI) method through numerical simulations and experimental validations. Our results underscore the potential of self-supervised learning in advancing ghost imaging.

To improve the imaging speed of ghost imaging and ensure the accuracy of the images, an adaptive ghost imaging scheme based on 2D-Haar wavelets has been proposed. This scheme is capable of significantly retaining image information even under under-sampling conditions. By comparing the differences in light intensity distribution and sampling characteristics between Hadamard and 2D-Haar wavelet illumination patterns, we discovered that the lateral and longitudinal information detected by the high-frequency 2D-Haar wavelet measurement basis could be used to predictively adjust the diagonal measurement basis, thereby reducing the number of measurements required. Simulation and experimental results indicate that this scheme can still achieve high-quality imaging results with about a 25% reduction in the number of measurements. This approach provides a new perspective for enhancing the efficiency of computational ghost imaging.

Neomicrocalamus and Temochloa are closely related to bamboo genera. However, when considered with newly discovered and morphologically similar material from China and Vietnam, the phylogenetic relationship among these three groups was ambiguous in the analyses based on DNA regions. Here, as a means of investigating the relationships among the three bamboo groups and exploring potential sources of genomic conflicts, we present a phylogenomic examination based on the whole plastome, single-nucleotide polymorphism (SNP), and single-copy nuclear (SCN) gene datasets. Three different phylogenetic hypotheses were found. The inconsistency is attributed to the combination of incomplete lineage sorting and introgression. The origin of newly discovered bamboos is from introgressive hybridization between Temochloa liliana (which contributed 80.7% of the genome) and Neomicrocalamus prainii (19.3%), indicating that the newly discovered bamboos are closer to T. liliana in genetics. The more similar morphology and closer distribution elevation also imply a closer relationship between Temochloa and newly discovered bamboos.

Background: The increasing evidence suggests that necroptosis mediates many behaviors of tumors, as well as the regulation of the tumor microenvironment. Long non-coding RNAs (lncRNAs) are involved in a variety of regulatory processes during tumor development and are significantly associated with patient prognosis. It suggests that necroptosis-related lncRNAs (NRlncRNAs) may serve as biomarkers for the prognosis of hepatocellular carcinoma (HCC). Methods: lncRNA expression profiles of HCC were obtained from TCGA database. LncRNAs associated with necroptosis were extracted using correlation analysis. Prognostic models were constructed based on least absolute shrinkage and selection operator algorithm (LASSO) and multivariate Cox regression analysis. The differences of tumor microenvironment between high-risk and low-risk groups were further analyzed. Single-cell RNA sequencing data of HCC was performed to assess the enrichment of necroptosis-related genes in immune cell subsets. Finally, real-time RT-PCR was used to detect the prognosis-related lncRNAs expression in different HCC cell lines. Results: We constructed a prognostic signature based on 8 NRlncRNAs, which also showed good predictive accuracy. The model showed that the prognosis of patients with high-risk score was significantly worse than that of patients with low-risk score (P

The goal of this work was to explore the total iron burden of cerebral microbleeds (CMBs) using a semi-automatic quantitative susceptibility mapping and to establish its effect on brain atrophy through the mediating effect of white matter hyperintensities (WMH). A total of 95 community-dwelling people were enrolled. Quantitative susceptibility mapping (QSM) combined with a dynamic programming algorithm (DPA) was used to measure the characteristics of 1309 CMBs. WMH were evaluated according to the Fazekas scale, and brain atrophy was assessed using a 2D linear measurement method. Histogram analysis was used to explore the distribution of CMBs susceptibility, volume, and total iron burden, while a correlation analysis was used to explore the relationship between volume and susceptibility. Stepwise regression analysis was used to analyze the risk factors for CMBs and their contribution to brain atrophy. Mediation analysis was used to explore the interrelationship between CMBs and brain atrophy. We found that the frequency distribution of susceptibility of the CMBs was Gaussian in nature with a mean of 201 ppb and a standard deviation of 84 ppb; however, the volume and total iron burden of CMBs were more Rician in nature. A weak but significant correlation between the susceptibility and volume of CMBs was found (r = -0.113, P < 0.001). The periventricular WMH (PVWMH) was a risk factor for the presence of CMBs (number: β = 0.251, P = 0.014; volume: β = 0.237, P = 0.042; total iron burden: β = 0.238, P = 0.020) and was a risk factor for brain atrophy (third ventricle width: β = 0.325, P = 0.001; Evans's index: β = 0.323, P = 0.001). PVWMH had a significant mediating effect on the correlation between CMBs and brain atrophy. In conclusion, QSM along with the DPA can measure the total iron burden of CMBs. PVWMH might be a risk factor for CMBs and may mediate the effect of CMBs on brain atrophy.

BackgroundGastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis.MethodsIn this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score.ResultsThe segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set.ConclusionThe results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.

Pseudosasa was confirmed as polyphyletic by recent phylogenetic analyses, with Chinese species of Pseudosasa distantly related to those from Japan. Among the Chinese species of Pseudosasa, Pseudosasa pubiflora is a morphologically unique as well as taxonomically problematic species endemic to South China, of which the generic designation is still uncertain. Molecular analyses based on both plastid and nuclear genomic data demonstrated that this species is closest to the recently published genus Sinosasa. Morphologically, the two are somewhat similar to each other in flowering branches developing at the nodes of every order of branches, raceme-like units of inflorescence with 3–5 short spikelets, each spikelet with few florets including a rudimentary one at the apex, and each floret with 3 stamens and 2 stigmas. However, P. pubiflora is very different from Sinosasa species in many reproductive and vegetative characters, such as the morphology of paracladia (lateral spikelet “pedicels”), the absence or existence of pulvinus at the base of paracladia, the relative length of the upper glume and the lowest lemma, the shape of lodicules and primary culm buds, the branch complement, the morphology of nodes, culm leaves and dried foliage leaf blades, and the number of foliage leaves per ultimate branchlet. The morphological and molecular evidence warrants recognition of a new genus to accommodate this unique species, which is here named Kengiochloa. After consulting related literature and examination of herbarium specimens or specimen photos, a taxonomic revision of K. pubiflora and its synonyms was made, and it was confirmed that four names, viz. P. gracilis, Yushania lanshanensis, Arundinaria tenuivagina and P. parilis, should be merged with K. pubiflora, while Indocalamus pallidiflorus and Acidosasa paucifolia are distinct species.

Abstract Background Oligodendrocytes have robust regenerative ability and are key players in remyelination during physiological and pathophysiological states. However, the mechanisms of brain microenvironmental cue in regulation of the differentiation of oligodendrocytes still needs to be further investigated. Results We demonstrated that myelin-associated glycoprotein (MAG) was a novel receptor for angiopoietin-like protein 2 (ANGPTL2). The binding of ANGPTL2 to MAG efficiently promoted the differentiation of oligodendrocytes in vitro, as evaluated in an HCN cell line. Angptl2-null mice had a markedly impaired myelination capacity in the early stage of oligodendrocyte development. These mice had notably decreased remyelination capacities and enhanced motor disability in a cuprizone-induced demyelinating mouse model, which was similar to the Mag-null mice. The loss of remyelination ability in Angptl2-null/Mag-null mice was similar to the Angptl2-WT/Mag-null mice, which indicated that the ANGPTL2-mediated oligodendrocyte differentiation effect depended on the MAG receptor. ANGPTL2 bound MAG to enhance its phosphorylation level and recruit Fyn kinase, which increased Fyn phosphorylation levels, followed by the transactivation of myelin regulatory factor (MYRF). Conclusion Our study demonstrated an unexpected cross-talk between the environmental protein (ANGPTL2) and its surface receptor (MAG) in the regulation of oligodendrocyte differentiation, which may benefit the treatment of many demyelination disorders, including multiple sclerosis.

Ghost imaging (GI) has been received more and more attention in past thirty years. However, it has some limitations in the resolution. In this article, we propose a scheme to improve the resolution of GI by a new imaging information, i.e. the coefficient of skewness coming from the fluctuations of the second-order correlation function, and put forward an enhanced GI protocol exploiting those fluctuations. We demonstrate experimentally that the additional information obtained from the new information makes it possible to improve resolution compared to the traditional GI scheme.

Abstract In this work, we investigate the quantum chaos in various T T ¯ $$ T\overline{T} $$ -deformed SYK models with finite N, including the SYK4, the supersymmetric SYK4, and the SYK2 models. We numerically study the evolution of the spectral form factor (SFF), the out-of-time ordered correlator (OTOC), and the Krylov complexity. We find that the characteristic evolution of the SFF, OTOC and K-complexity of both the SYK4 and SSYK4 models remains unchanged under the deformation, which implies that the properties of quantum chaos is preserved. We also identify a many-body localization behavior in the deformed SYK2 model.

Due to their specialised flowering biology where frequent or even annual flowering is uncommon, reproductive materials of bamboos are not always available, so hampering taxonomic interpretation and research into other aspects. Bambusa contracta, B. corniculata, B. cornigera and B. subtruncata were established only based on vegetative materials and flowering or fruiting material has been hitherto unknown. The floral morphology of these four species is described for the first time and, correspondingly, epitypes are designated to support a more complete interpretation of the species.

We study the phase structure and charge transport at finite temperature and chemical potential in the non-Hermitian $\mathcal{PT}$-symmetric holographic model of [SciPost Phys. 9, 032 (2020)]. The non-Hermitian $\mathcal{PT}$-symmetric deformation is realized by promoting the parameter of a global U(1) symmetry to a complex number. Depending on the strength of the deformation, we find three phases: stable $\mathcal{PT}$-symmetric phase, unstable $\mathcal{PT}$-symmetric phase, and an unstable $\mathcal{PT}$-symmetry broken phase. In the three phases, the square of the condensate and also the spectral weight of the AC conductivity at zero frequency are, respectively, positive, negative, and complex. We check that the Ferrell-Glover-Tinkham sum rule for the AC conductivity holds in all the three phases. We also investigate a complexified U(1) rotor model with $\mathcal{PT}$-symmetric deformation, derive its phase structure and condensation pattern, and find a zero frequency spectral weight analogous to the holographic model.

The resolution is an important factor in evaluating image quality. In general, the resolution of correlation imaging is taken to the full width at half maximum (FWHM) of the point spread function (PSF) produced by the second-order correlation function. In this paper, we show that the resolution of correlation imaging can be improved by the fluctuation characteristic of the second-order correlation function. It is demonstrated both experimentally and theoretically that the resolution of the system can be drastically improved. We also prove that the FWHM of the PSF can be narrowed by 2n by extracting the n-order fluctuation information of the second-order correlation function.

Nonalcoholic fatty liver disease (NAFLD) is characterized by excessive accumulation of hepatic lipids and metabolic stress-induced liver injury. There are currently no approved effective pharmacological treatments for NAFLD. Traditional Chinese medicine (TCM) has been used for centuries to treat patients with chronic liver diseases without clear disease types and mechanisms. More recently, TCM has been shown to have unique advantages in the treatment of NAFLD. We performed a systematic review of the medical literature published over the last two decades and found that many TCM formulas have been reported to be beneficial for the treatment of metabolic dysfunctions, including Potentilla discolor Bunge (PDB). PDB has a variety of active compounds, including flavonoids, terpenoids, organic acids, steroids and tannins. Many compounds have been shown to exhibit a series of beneficial effects for the treatment of NAFLD, including anti-oxidative and anti-inflammatory functions, improvement of lipid metabolism and reversal of insulin resistance. In this review, we summarize potential therapeutic effects of TCM formulas for the treatment of NAFLD, focusing on the medicinal properties of natural active compounds from PDB and their underlying mechanisms. We point out that PDB can be classified as a novel candidate for the treatment and prevention of NAFLD.

Abstract We construct three models to describe the scenario where two eternal black holes are separated by a flat space, and can eventually be entangled by exchanging radiation. In the doubly holographic setup, we compute the entanglement entropy and mutual information among subsystems and obtain the dynamic phase structure of the entanglement. The formation of entanglement between these two black holes is delayed by the space which the radiation must travel through. If the black holes exchange sufficient Hawking modes, the final state is characterized by a connected entanglement wedge; otherwise, the final entanglement wedge contains two separate islands. In the former case, the entanglement wedge of the black holes forms at the time scale proportional to the size of the flat space between them. While in both cases, the unitarity of the evolution is preserved. When the sizes of the black holes are not equal, we observe a loss of entanglement between the smaller black hole and the radiation at late times. On the field theory side, we consider two Sachdev-Ye-Kitaev (SYK) clusters coupled to a Majorana chain, which resemble two black holes connected by a radiation region. We numerically compute the same entanglement measures and obtain similar phase structures as the bulk results. In general, a time delay of the entanglement between the SYK clusters is found in cases with a long Majorana chain. In particular, when the SYK clusters are different in size, similar entanglement loss between the smaller SYK cluster and the Majorana chain is observed. Finally, we investigate a chain model composed of EPR clusters with particles exchanging between neighboring clusters and reproduce the features of entanglement observed in the previous models.

Abstract The phenomenon of tumor hierarchy and genetic instability can be explained by the “two‐hits theory” and results in the occurrence of many somatic mutations. The expression of nonsynonymous mutations results in the production of mutant proteins from tumor cells, namely tumor‐specific antigens called neoantigens. Because neoantigens do not exist in healthy cells, they have the potential to stimulate antitumor immune responses by CD4+ and CD8+ T‐cell activation without jeopardizing normal tissues. Immunotherapy has reshaped the cancer treatment paradigm in recent decades with the introduction of immune‐checkpoint blockade therapy and transgenic T‐cell receptor/chimeric antigen receptor T cells. However, these strategies performed poorly in solid tumors because of the obstacles of the immunosuppressive microenvironment caused by regulatory T cells and other suppressor cells. Therefore, other immunotherapeutic strategies are under development, such as personalized vaccines, to trigger de novo T‐cell responses against neoantigens and lead to the amplification of tumor‐specific T‐cell subclones. Neoantigen epitope prediction algorithms have enabled the detection of neoantigens and the creation of tailored neoantigen vaccines as a result of the fast development of next‐generation sequencing and cancer bioinformatics. Here we provide an overview of the current neoantigen cancer vaccines and adoptive T‐cell transfer therapy with neoantigen‐specific lymphocytes. We also discuss the challenges in developing neoantigen‐targeted immunotherapeutic strategies for cancer.