What Makes Natural Scene Memorable?

Recent studies on image memorability have shed light on the visual features that make generic images, object images or face photographs memorable. However, a clear understanding and reliable estimation of natural scene memorability remain elusive. In this paper, we provide an attempt to answer: "what exactly makes natural scene memorable". Specifically, we first build LNSIM, a large-scale natural scene image memorability database (containing 2,632 images and memorability annotations). Then, we mine our database to investigate how low-, middle- and high-level handcrafted features affect the memorability of natural scene. In particular, we find that high-level feature of scene category is rather correlated with natural scene memorability. Thus, we propose a deep neural network based natural scene memorability (DeepNSM) predictor, which takes advantage of scene category. Finally, the experimental results validate the effectiveness of DeepNSM.Comment: Accepted to ACM MM Workshop

What do we know about multidimensional poverty in China: its dynamics, causes, and implications for sustainability

Poverty is a primary obstacle to achieving sustainable development. Therefore, exploring the spatiotemporal dynamics and causes of poverty is of great significance to the sustainable poverty reduction of the “post poverty alleviation era” in China. This paper used the multisource big data of 2022 counties in China from 2000 to 2015 to establish a comprehensive evaluation framework to explore the multidimensional poverty situation in China. The results showed the following findings: There is an obvious spatiotemporal heterogeneity of multidimensional poverty, showing a typical stair-like gradient from high in the west to low in the east, with the poverty level in state-designated poverty counties higher and intensifying over time. The spatial differentiation of multidimensional poverty is contributed to by multiple factors, in which the geographical condition has a stronger impact on state-designated poverty counties, while natural endowment and human resources have a stronger effect on non-state-designated poverty counties. These things considered, the regional poverty causes were relatively stable before 2015, but the poverty spatial agglomeration of some regions in the Northwest, Northeast, and Yangtze River Economic Belt has undergone significant changes after 2015. These findings can help policymakers better target plans to eliminate various types of poverty in different regions

Ensemble multiboost based on ripper classifier for prediction of imbalanced software defect data

Identifying defective software entities is essential to ensure software quality during software development. However, the high dimensionality and class distribution imbalance of software defect data seriously affect software defect prediction performance. In order to solve this problem, this paper proposes an Ensemble MultiBoost based on RIPPER classifier for prediction of imbalanced Software Defect data, called EMR_SD. Firstly, the algorithm uses principal component analysis (PCA) method to find out the most effective features from the original features of the data set, so as to achieve the purpose of dimensionality reduction and redundancy removal. Furthermore, the combined sampling method of adaptive synthetic sampling (ADASYN) and random sampling without replacement is performed to solve the problem of data class imbalance. This classifier establishes association rules based on attributes and classes, using MultiBoost to reduce deviation and variance, so as to achieve the purpose of reducing classification error. The proposed prediction model is evaluated experimentally on the NASA MDP public datasets and compared with existing similar algorithms. The results show that EMR-SD algorithm is superior to DNC, CEL and other defect prediction techniques in most evaluation indicators, which proves the effectiveness of the algorithm

Mechanism of dissolution and oxidation of stibnite mediated by the coupling of iron and typical antimony oxidizing bacteria

Antimony oxidizing bacteria (SbOB) and iron oxides are the main driving factors to the weathering dissolution and oxidation of stibnite (Sb2S3) waste ore. The characteristics of the dissolution and oxidation process of stibnite in the absence of strain AO-1 and iron oxides, Pseudomonas sp. AO-1-mediated (AO-1-mediated), Fe (Fe, Fe2(SO4)3, and FeS2) -mediated, and coupled-mediated groups (Fe+AO-1, Fe2(SO4)3+AO-1, FeS2+AO-1) under various pH values were examined through sequential batch experiments. The results showed that all the AO-1-mediated, Fe-mediated and coupled-mediated can promote the dissolution and oxidation of stibnite, and the promotion effect increased with the rise of pH. The order of contribution to the dissolution of stibnite under the coupling mediation is as follows: coupling effect (42.4-78.2%) > chemical effect (19.4-56.6%) > biological effect (0.9-2.4%). In addition, the dissolution and oxidation mechanisms of stibnite were further investigated and analyzed in combination with scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). This study has important implications for elucidating the source control and geochemical behavior of antimony pollution in antimony mining areas

Root-specific flavones and critical enzyme genes involved in their synthesis changes due to drought stress on Scutellaria baicalensis

IntroductionScutellaria baicalensis is rich in bioactive flavonoid, which are widely used in clinical therapy. Many environmental factors, such as water and temperature, affect gene expression and secondary metabolites accumulation in plants.MethodsIn this study, to explore the effect of drought stress on the accumulation of flavonoids and gene expression in S. baicalensis seedlings, 4-week-old Scutellaria baicalensis seedlings were treated with different concentrations of PEG6000 to simulate drought stress. The contents of four root-specific flavones (baicalein, wogonin, baicalin, and wogonoside) in samples under different treatments were quantitatively analyzed by high performance liquid chromatography (HPLC). The expression levels of flavonoid biosynthesis-related genes (PAL1, PAL2, CHS, and UBGAT) were determined by real-time quantitative PCR (qRT-PCR). Also, a correlation analysis between flavonoid contents and gene expression levels was made.ResultsThe HPLC results revealed that 5 and 10% PEG6000 treatments significantly increased the content of four flavonoids, with 5% PEG 6000 treatment being the most beneficial to the flavonoids accumulation. The qRT-PCR results showed that PAL2 and CHS gene expressions differed significantly in different organs, while PAL1 and UBGAT had poor organ-specific. For genes in roots, the expression of PAL1 and UBGAT was the highest in 5% PEG6000 treatment, and PAL2 and CHS were the highest in 10% PEG6000 treatment. Compared with other concentrations of PEG6000, 5 and 10% PEG6000 were more advantageous for gene expression. Collectively, PEG6000 at a low concentration promoted the accumulation of flavonoids and the expression of related genes. Additionally, the correlation results demonstrated that PAL1, PAL2, CHS, and UBGAT genes in roots stimulated the formation and accumulation of the four flavonoids to varying degrees, while the exception of PAL2 gene expression in roots was negatively correlated with wogonin content.DiscussionThis study for the first time investigated the effect of drought stress on the downstream gene UBGAT in S.baicalensis seedlings as well as the correlation between gene expression and flavonoid content in S. baicalensis seedlings under drought stress, providing a new sight for studying the effects of drought stress on flavonoid accumulation and related gene expression in S. baicalensis

Uncoupling bacterial attachment on and detachment from polydimethylsiloxane surfaces through empirical and simulation studies

Bacterial infections related to medical devices can cause severe problems, whose solution requires in-depth understanding of the interactions between bacteria and surfaces. This work investigates the influence of surface physicochemistry on bacterial attachment and detachment under flow through both empirical and simulation studies. We employed polydimethylsiloxane (PDMS) substrates having different degrees of crosslinking as the model material and the extended Derjaguin - Landau - Verwey - Overbeek model as the simulation method. Experimentally, the different PDMS materials led to similar numbers of attached bacteria, which can be rationalized by the identical energy barriers simulated between bacteria and the different materials. However, different numbers of residual bacteria after detachment were observed, which was suggested by simulation that the detachment process is determined by the interfacial physicochemistry rather than the mechanical property of a material. This finding is further supported by analyzing the bacteria detachment from PDMS substrates from which non-crosslinked polymer chains had been removed: similar numbers of residual bacteria were found on the extracted PDMS substrates. The knowledge gained in this work can facilitate the projection of bacterial colonization on a given surface

Metabolomics Analysis of Colostrum and Mature Milk from Saanen Goats

The differences in metabolites and related metabolism pathways in colostrum and mature milk from Saanen goats at different lactation stages were explored by untargeted metabolomics based on ultra-high performance liquid chromatography-quadrupole electrostatic field orbitrap mass spectrometry (UPLC-QE-orbitrap-MS). The results showed that a total of 118 differential metabolites were found between colostrum and mature milk, among which 62 had higher relative contents in colostrum than in mature milk and 56 had lower relative contents in colostrum than in mature milk. These metabolites were mainly lipids, amino acids, and nucleosides. Nine key metabolic pathways most associated with these metabolites were selected, which jointly regulated the lactation process of Saanen goats, and the citric acid cycle could act as a bridge connecting other metabolic pathways. The number of differential metabolites involved in these metabolic pathways was 12. The differential metabolites with relatively high contents in colostrum were taurine, hypotaurine, taurocholic acid, L-phenylalanine, L-tyrosine, succinic acid, isocitrate, D-maltose, α-lactose, 4-hydroxyphenylpyruvate and glycine. The differential metabolite with relatively high contents in mature milk was N1-methyl-4-pyridone-3-carboxamide. They could be used as potential marker metabolites in the colostrum and mature milk of Saanen goats. Metabolomics technology can also be used for identifying differential metabolites in milk from other dairy species at different lactation stages

Multi-level characteristics recognition of cancer core therapeutic targets and drug screening for a broader patient population

Introduction: Target therapy for cancer cell mutation has brought attention to several challenges in clinical applications, including limited therapeutic targets, less patient benefits, and susceptibility to acquired due to their clear biological mechanisms and high specificity in targeting cancers with specific mutations. However, the identification of truly lethal synthetic lethal therapeutic targets for cancer cells remains uncommon, primarily due to compensatory mechanisms.Methods: In our pursuit of core therapeutic targets (CTTs) that exhibit extensive synthetic lethality in cancer and the corresponding potential drugs, we have developed a machine-learning model that utilizes multiple levels and dimensions of cancer characterization. This is achieved through the consideration of the transcriptional and post-transcriptional regulation of cancer-specific genes and the construction of a model that integrates statistics and machine learning. The model incorporates statistics such as Wilcoxon and Pearson, as well as random forest. Through WGCNA and network analysis, we identify hub genes in the SL network that serve as CTTs. Additionally, we establish regulatory networks for non-coding RNA (ncRNA) and drug-target interactions.Results: Our model has uncovered 7277 potential SL interactions, while WGCNA has identified 13 gene modules. Through network analysis, we have identified 30 CTTs with the highest degree in these modules. Based on these CTTs, we have constructed networks for ncRNA regulation and drug targets. Furthermore, by applying the same process to lung cancer and renal cell carcinoma, we have identified corresponding CTTs and potential therapeutic drugs. We have also analyzed common therapeutic targets among all three cancers.Discussion: The results of our study have broad applicability across various dimensions and histological data, as our model identifies potential therapeutic targets by learning multidimensional complex features from known synthetic lethal gene pairs. The incorporation of statistical screening and network analysis further enhances the confidence in these potential targets. Our approach provides novel theoretical insights and methodological support for the identification of CTTs and drugs in diverse types of cancer