How to Meet the Diverse Needs of Consumers: Big Data Mining based on Online Review

This article applied Word2vec and image mining on OCRs analysis. Data from Dianping.com showed that in Beijing, good taste is the primary factor for customers to choose a restaurant. Unlike the general opinion, careers and locations have little influence on cuisine choice in Beijing. Hot pot is the most popular one all over the city. Warm color, medium dark light and saturation with certain amount of grey are three key aspects for an enjoyable dining environment. Offline mouth to mouth recommendation is the most useful way to spread a restaurants reputation. So making the antecedent consumer satisfy is the most applied way to appeal new ones. This findings can help restaurant owners to run a better business and promote the satisfactory

Prognostic value of osteopontin splice variant-c expression in breast cancers: a meta-analysis

Objectives. Osteopontin (OPN) is overexpressed in breast cancers, while its clinical and prognostic significance remained unclear. This study aimed to assess the prognostic value of OPN, especially its splice variants, in breast cancers. Methods. Data were extracted from eligible studies concerning the OPN and OPN-c expression in breast cancer patients and were used to calculate the association between OPN/OPN-c and survival. Two reviewer teams independently screened the literatures according to the inclusion and exclusion criteria based on quality evaluation. Following the processes of data extraction, assessment, and transformation, meta-analysis was carried out via RevMan 5.3 software. Results. A total of ten studies involving 1,567 patients were included. The results demonstrated that high level OPN indicated a poor outcome in the OS (HR = 2.22, 95% CI: 1.23–4.00, and ; random-effects model) with heterogeneity (%) of breast cancer patients. High level OPN-c appeared to be more significantly associated with poor survival (HR = 2.14, 95% CI: 1.51–3.04, and ; fixed-effects model) with undetected heterogeneity (%). Conclusions. Our analyses indicated that both OPN and OPN-c could be considered as prognostic markers for breast cancers. The high level of OPN-c was suggested to be more reliably associated with poor survival in breast cancer patients

Lattice Enumeration with Discrete Pruning: Improvement, Cost Estimation and Optimal Parameters

Lattice enumeration is a linear-space algorithm for solving the shortest lattice vector problem(SVP). Extreme pruning is a practical technique for accelerating lattice enumeration, which has mature theoretical analysis and practical implementation. However, these works are still remain to be done for discrete pruning. In this paper, we improve the discrete pruned enumeration (DP enumeration), and give a solution to the problem proposed by Leo Ducas et Damien Stehle about the cost estimation of discrete pruning. Our contribution is on the following three aspects: First, we refine the algorithm both from theoretical and practical aspects. Discrete pruning using natural number representation lies on a randomness assumption of lattice point distribution, which has an obvious paradox in the original analysis. We rectify this assumption to fix the problem, and correspondingly modify some details of DP enumeration. We also improve the binary search algorithm for cell enumeration radius with polynomial time complexity, and refine the cell decoding algorithm. Besides, we propose to use a truncated lattice reduction algorithm -- k-tours-BKZ as reprocessing method when a round of enumeration failed. Second, we propose a cost estimation simulator for DP enumeration. Based on the investigation of lattice basis stability during reprocessing, we give a method to simulate the squared length of Gram-Schmidt orthogonalization basis quickly, and give the fitted cost estimation formulae of sub-algorithms in CPU-cycles through intensive experiments. The success probability model is also modified based on the rectified assumption. We verify the cost estimation simulator on middle size SVP challenge instances, and the simulation results are very close to the actual performance of DP enumeration. Third, we give a method to calculate the optimal parameter setting to minimize the running time of DP enumeration. We compare the efficiency of our optimized DP enumeration with extreme pruning enumeration in solving SVP challenge instances. The experimental results in medium dimension and simulation results in high dimension both show that the discrete pruning method could outperform extreme pruning. An open-source implementation of DP enumeration with its simulator is also provided

NHERF1 regulates the progression of colorectal cancer through the interplay with VEGFR2 pathway

The oncogenic role of ectopic expression of Na+/H+ exchanger regulatory factor 1 (NHERF1) was recently suggested in colorectal cancer, where it was implicated in playing a role in the tumor hypoxia microenvironment. Here we showed that a high level expression of NHERF1 was found in colorectal cancer tissues and that the expression of NHERF1 was positively correlated with VEGFR2 expression. The prognostic value of VEGFR2 expression in colorectal cancer relied on the expression of NHERF1. The up-regulation of NHERF1 induced by the exposure to hypoxia in colon cancer cells depended on the activation of VEGFR2 signaling. NHERF1 in turn inhibited the activation of VEGFR2 signaling which could be regulated by the interaction between NHERF1 and VEGFR2, resulting in the reduction of migration and invasion of colon cancer cells. These results suggest a dynamic interplay between NHERF1 and VEGFR2 signaling in colorectal cancer, which could explain the contribution of NHERF1 to the regulation of tumor cell responses to the hypoxia microenvironment

Biodegradable polycarbonates from lignocellulose based 4-pentenoic acid and carbon dioxide

The production of biodegradable polycarbonate by copolymerizing CO2 with epoxides has emerged as an effective method to utilize CO2 in response to growing concerns about CO2 emissions and plastic pollution. Previous studies have mainly focused on the preparation of CO2-based polycarbonates from petrochemical-derived propylene oxide (PO) or cyclohexene oxide (CHO). However, to reduce dependence on fossil fuels, the development of 100% bio-based polymers has gained attention in polymer synthesis. Herein, we reported the synthesis of glycidyl 4-pentenoate (GPA) from lignocellulose based 4-pentenoic acid (4-PA), which was further copolymerized with CO2 using a binary catalyst SalenCoCl/PPNCl to produce bio-based polycarbonates with vinyl side chains and molecular weights up to 17.1 kg/mol. Introducing a third monomer, PO, allows for the synthesis of the GPA/PO/CO2 terpolymer, and the glass transition temperature (Tg) of the terpolymer can be adjusted from 2°C to 19°C by controlling the molar feeding ratio of GPA to PO from 7:3 to 3:7. Additionally, post-modification of the vinyl side chains enables the production of functional polycarbonates, providing a novel approach to the preparation of bio-based materials with diverse side chains and functions

Black-Box Quantum State Preparation with Inverse Coefficients

Black-box quantum state preparation is a fundamental building block for many higher-level quantum algorithms, which is applied to transduce the data from computational basis into amplitude. Here we present a new algorithm for performing black-box state preparation with inverse coefficients based on the technique of inequality test. This algorithm can be used as a subroutine to perform the controlled rotation stage of the Harrow-Hassidim-Lloyd (HHL) algorithm and the associated matrix inversion algorithms with exceedingly low cost. Furthermore, we extend this approach to address the general black-box state preparation problem where the transduced coefficient is a general non-linear function. The present algorithm greatly relieves the need to do arithmetic and the error is only resulted from the truncated error of binary string. It is expected that our algorithm will find wide usage both in the NISQ and fault-tolerant quantum algorithms.Comment: 11 pages, 3 figure

Hybrid quantum-classical convolutional neural network for phytoplankton classification

The taxonomic composition and abundance of phytoplankton have a direct impact on marine ecosystem dynamics and global environment change. Phytoplankton classification is crucial for phytoplankton analysis, but it is challenging due to their large quantity and small size. Machine learning is the primary method for automatically performing phytoplankton image classification. As large-scale research on marine phytoplankton generates overwhelming amounts of data, more powerful computational resources are required for the success of machine learning methods. Recently, quantum machine learning has emerged as a potential solution for large-scale data processing by harnessing the exponentially computational power of quantum computers. Here, for the first time, we demonstrate the feasibility of using quantum deep neural networks for phytoplankton classification. Hybrid quantum-classical convolutional and residual neural networks are developed based on the classical architectures. These models strike a balance between the limited function of current quantum devices and the large size of phytoplankton images, making it possible to perform phytoplankton classification on near-term quantum computers. Our quantum models demonstrate superior performance compared to their classical counterparts, exhibiting faster convergence, higher classification accuracy and lower accuracy fluctuation. The present quantum models are versatile and can be applied to various tasks of image classification in the field of marine science

Diabetes with kidney injury may change the abundance and cargo of urinary extracellular vesicles

BackgroundUrinary extracellular vesicles (uEVs) are derived from epithelia facing the renal tubule lumen in the kidney and urogenital tract; they may carry protein biomarkers of renal dysfunction and structural injury. However, there are scarce studies focusing on uEVs in diabetes with kidney injury.Materials and methodsA community-based epidemiological survey was performed, and the participants were randomly selected for our study. uEVs were enriched by dehydrated dialysis method, quantified by Coomassie Bradford protein assay, and adjusted by urinary creatinine (UCr). Then, they identified by transmission electron microscopy (TEM), nanoparticle track analysis (NTA), and western blot of tumor susceptibility gene 101.ResultsDecent uEVs with a homogeneous distribution were finally obtained, presenting a membrane-encapsulated structure like cup-shaped or roundish under TEM, having active Brownian motion, and presenting the main peak between 55 and 110 nm under NTA. The Bradford protein assay showed that the protein concentrations of uEVs were 0.02 ± 0.02, 0.04 ± 0.05, 0.05 ± 0.04, 0.07 ± 0.08, and 0.11 ± 0.15 μg/mg UCr, respectively, in normal controls and in prediabetes, diabetes with normal proteinuria, diabetes with microalbuminuria, and diabetes with macroproteinuria groups after adjusting the protein concentration with UCr by calculating the vesicles-to-creatinine ratio.ConclusionThe protein concentration of uEVs in diabetes with kidney injury increased significantly than the normal controls before and after adjusting the UCr. Therefore, diabetes with kidney injury may change the abundance and cargo of uEVs, which may be involved in the physiological and pathological changes of diabetes