Probabilistic Memory Model for Visual Images Categorization

During the past decades, numerous memory models have been proposed, which focused mainly on how spoken words are studied, whereas models on how visual images are studied are still limited. In this study, we propose a probabilistic memory model (PMM) for visual images categorization which is able to mimic the workings of the human brain during the image storage and retrieval. First, in the learning phase, the visual images are represented by the feature vectors extracted with convolutional neural network (CNN) and each feature component is assumed to conform to a Gaussian distribution and may be incompletely copied with a certain probability or randomly produced in accordance to an exponential distribution. Then, in the test phase, the likelihood ratio between the test image and each studied image is calculated based on the probabilistic inference theory, and an odd value in favor of an old item over a new one is obtained based on all likelihood values. Finally, if the odd value is above a certain threshold, the Bayesian decision rule is applied for image classification. Experimental results on two benchmark image datasets demonstrate that the proposed PMM can perform well on categorization tasks for both studied and non-studied images

Unraveling the link: environmental tobacco smoke exposure and its impact on infertility among American women (18–50 years)

PurposeThe detrimental effects of environmental tobacco smoke (ETS) on women’s reproductive health have been widely recognized. However, the detailed association between exposure to environmental tobacco smoke and the incidence of infertility remains under-explored. This investigation focuses on exploring this potential connection.MethodsFor this analysis, we extracted data from the US National Health and Nutrition Examination Survey (NHANES) database, covering the years 2013 to 2018, focusing on individuals with recorded serum cotinine levels and infertility information. ETS exposure and fertility status were analyzed as independent and dependent variables, respectively. We applied weighted multivariate logistic regression method to evaluate the impact of ETS on infertility, including subgroup analyses for more detailed insights.ResultsThe study encompassed 3,343 participants. Logistic regression analysis revealed a notable positive correlation between ETS exposure and infertility, with an odds ratio (OR) of 1.64 (95% Confidence Interval [CI]: 1.14–2.36). We observed a non-linear relationship between ETS exposure and infertility risk. Notably, infertility risk increased by 64% in serum cotinine levels above 0.136 compared to that in serum cotinine levels below 0.011. Further, subgroup analysis and interaction tests showed consistent results across different segments, underscoring the robustness of the ETS-infertility link.ConclusionOur findings suggest that environmental tobacco smoke exposure may be a contributing factor to infertility. These results reinforce the recommendation for women in their reproductive years to avoid ETS exposure, especially when planning for pregnancy

Geochemical differences between subduction- and collision-related copper-bearing porphyries and implications for metallogenesis

Porphyry Cu (-Mo-Au) deposits occur not only in continental margin-arc settings (subduction-related porphyry Cu deposits, such as those along the eastern Pacific Rim (EPRIM)), but also in continent-continent collisional orogenic belts (collision-related porphyry Cu deposits, such as those in southern Tibet). These Cu-mineralized porphyries, which develop in contrasting tectonic settings, are characterized by some different trace element (e.g., Th, and Y) concentrations and their ratios (e.g., Sr/Y, and La/Yb), suggesting that their source magmas probably developed by different processes. Subduction-related porphyry Cu mineralization on the EPRIM is associated with intermediate to felsic calc-alkaline magmas derived from primitive basaltic magmas that pooled beneath the lower crust and underwent melting, assimilation, storage, and homogenization (MASH), whereas K-enriched collision-related porphyry Cu mineralization was associated with underplating of subduction-modified basaltic materials beneath the lower crust (with subsequent transformation into amphibolites and eclogite amphibolites), and resulted from partial melting of the newly formed thickened lower crust. These different processes led to the collision-related porphyry Cu deposits associated with adakitic magmas enriched by the addition of melts, and the subduction-related porphyry Cu deposits associated with magmas comprising all compositions between normal arc rocks and adakitic rocks, all of which were associated with fluid-dominated enrichment process. In subduction-related Cu porphyry magmas, the oxidation state (fO2), the concentrations of chalcophile metals, and other volatiles (e.g., S and Cl), and the abundance of water were directly controlled by the composition of the primary arc basaltic magma. In contrast, the high Cu concentrations and fO2 values of collision-related Cu porphyry magmas were indirectly derived from subduction modified magmas, and the large amount of water and other volatiles in these magmas were controlled in part by partial melting of amphibolite derived from arc basalts that were underplated beneath the lower crust, and in part by the contribution from the rising potassic and ultrapotassic magmas. Both subduction- and collision-related porphyries are enriched in potassium, and were associated with crustal thickening. Their high K2O contents were primarily as a result of the inheritance of enriched mantle components and/or mixing with contemporaneous ultrapotassic magmas

Multi-Scale LBP Texture Feature Learning Network for Remote Sensing Interpretation of Land Desertification

Land desertification is a major challenge to global sustainable development. Therefore, the timely and accurate monitoring of the land desertification status can provide scientific decision support for desertification control. The existing automatic interpretation methods are affected by factors such as “same spectrum different matter”, “different spectrum same object”, staggered distribution of desertification areas, and wide ranges of ground objects. We propose an automatic interpretation method for the remote sensing of land desertification that incorporates multi-scale local binary pattern (MSLBP) and spectral features based on the above issues. First, a multi-scale convolutional LBP feature extraction network is designed to obtain the spatial texture features of remote sensing images and fuse them with spectral features to enhance the feature representation capability of the model. Then, considering the continuity of the distribution of the same kind of ground objects in local space, we designed an adaptive median filtering method to process the probability map of the extreme learning machine (ELM) classifier output to improve the classification accuracy. Four typical datasets were developed using GF-1 multispectral imagery with the Horqin Left Wing Rear Banner as the study area. Experimental results on four datasets show that the proposed method solves the problem of ill classification and omission in classifying the remote sensing images of desertification, effectively suppresses the effects of “homospectrum” and “heterospectrum”, and significantly improves the accuracy of the remote sensing interpretation of land desertification

Multi-Scale LBP Texture Feature Learning Network for Remote Sensing Interpretation of Land Desertification

Land desertification is a major challenge to global sustainable development. Therefore, the timely and accurate monitoring of the land desertification status can provide scientific decision support for desertification control. The existing automatic interpretation methods are affected by factors such as “same spectrum different matter”, “different spectrum same object”, staggered distribution of desertification areas, and wide ranges of ground objects. We propose an automatic interpretation method for the remote sensing of land desertification that incorporates multi-scale local binary pattern (MSLBP) and spectral features based on the above issues. First, a multi-scale convolutional LBP feature extraction network is designed to obtain the spatial texture features of remote sensing images and fuse them with spectral features to enhance the feature representation capability of the model. Then, considering the continuity of the distribution of the same kind of ground objects in local space, we designed an adaptive median filtering method to process the probability map of the extreme learning machine (ELM) classifier output to improve the classification accuracy. Four typical datasets were developed using GF-1 multispectral imagery with the Horqin Left Wing Rear Banner as the study area. Experimental results on four datasets show that the proposed method solves the problem of ill classification and omission in classifying the remote sensing images of desertification, effectively suppresses the effects of “homospectrum” and “heterospectrum”, and significantly improves the accuracy of the remote sensing interpretation of land desertification

温度对坏鳃指环虫产卵、孵化和发育的影响

实验研究了离体条件下温度对坏鳃指环虫（Dactylogyrus vastator）产卵和孵化的影响,以及在20℃、在体条件下坏鳃指环虫的产卵和发育过程。在离体条件下,坏鳃指环虫的平均产卵量随着温度的升高而增加,在4、10、20、30和35℃时,其平均产卵量分别为0.25、5.9、9.1、9.2和13.4枚/虫;除4℃外,绝大多数虫卵是在离体后的前5h内产出;然而,在体条件下虫体的产卵是连续且稳定的,在20℃条件下平均产卵量为6.5枚/（虫·d）。虫卵的孵化时间和孵化持续的时间随着温度的升高而减少,在10、20、30和35℃条件下,孵化时间和孵化持续时间分别为19d、3d、2d、36h和24d、5d、5d、3d,而最高的孵化率（65.5%）却出现在30℃。在20℃条件下,纤毛幼虫在感染7d后90%的虫体都已成熟,因此,在此温度条件下坏鳃指环虫由虫卵发育到成虫大约需要8—10d。为了有效控制指环虫病的暴发,在第一次用药1周后要进行第二次用药