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Aversive stimuli can cause hippocampal place cells to remap their firing fields, but it is not known whether remapping plays a role in storing memories of aversive experiences. Here, we addressed this question by performing in vivo calcium imaging of CA1 place cells in freely behaving rats (n = 14). Rats were first trained to prefer a short path over a long path for obtaining food reward, then trained to avoid the short path by delivering a mild footshock. Remapping was assessed by comparing place cell population vector similarity before acquisition versus after extinction of avoidance. Some rats received shock after systemic injections of the amnestic drug scopolamine at a dose (1 mg/kg) that impaired avoidance learning but spared spatial tuning and shock-evoked responses of CA1 neurons. Place cells remapped significantly more following remembered than forgotten shocks (drug-free versus scopolamine conditions); shock-induced remapping did not cause place fields to migrate toward or away from the shocked location and was similarly prevalent in cells that were responsive versus non-responsive to shocks. When rats were exposed to a neutral barrier rather than aversive shock, place cells remapped significantly less in response to the barrier. We conclude that place cell remapping occurs in response to events that are remembered rather than merely perceived and forgotten, suggesting that reorganization of hippocampal population codes may play a role in storing memories for aversive events.

Human translation always has an incomparable role in the process of English learning, especially in the promotion of English reading comprehension. This paper centers on the research problem of selecting suitable research objects, combining questionnaire surveys and test methods to obtain relevant data on artificial translation and English reading comprehension. Then, it analyzes the correlation between the dimensions of artificial translation and the effect on English reading comprehension, constructs the multiple regression model of artificial intelligence on English reading comprehension, and proposes the corresponding significance test method. The results show that the correlation coefficients between the five dimensions of human translation and English reading comprehension scores are, in descending order: accuracy (0.824) > completeness (0.636) > fitness (0.579) > fluency (0.485) > consistency (0.372), which are all significantly positively correlated with English reading scores. The regression results showed that the level of manual translation dimension explained 26.7% of the variation in reading achievement. Among the human translation dimensions, accuracy, completeness, and consistency have significant positive effects on English reading comprehension. The study suggests that human translation accuracy, completeness, and consistency can be optimized to improve English reading comprehension.

This study attempts to disclose the characteristics of knowledge integration in an interdisciplinary field by looking into the content aspect of knowledge.

Applying hyperspectral remote sensing technology to the prediction of soil moisture content (SMC) during the growth stage of soybean emerges as an effective approach, imperative for advancing the development of modern precision agriculture. This investigation focuses on SMC during the flowering stage under varying nitrogen application levels and film mulching treatments. The soybean canopy's original hyperspectral data, acquired at the flowering stage, underwent 0–2-order differential transformation (with a step size of 0.5). Five spectral indices exhibiting the highest correlation with SMC were identified as optimal inputs. Three machine learning methods, namely support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to formulate the SMC prediction model. The results indicate the following: (1) The correlation between the optimal spectral index of each order, obtained after fractional differential transformation, and SMC significantly improved compared to the original hyperspectral reflectance data. The average correlation coefficient between each spectral index and SMC under the 1.5-order treatment was 0.380% higher than that of the original spectral index, with mNDI showing the highest correlation coefficient at 0.766. (2) In instances of utilizing the same modeling method with different input variables, the SMC prediction model's accuracy follows the order: 1.5 order > 2.0 order > 1.0 order > 0.5 order > original order. Conversely, with consistent input variables and a change in the modeling method, the accuracy order becomes RF > SVM > BPNN. When comprehensively assessing model evaluation indicators, the 1.5-order differential method and RF method emerge as the preferred order differential method and model construction method, respectively. The R2 for the optimal SMC estimation model in the modeling set and validation set were 0.912 and 0.792, RMSEs were 0.005 and 0.004, and MREs were 2.390% and 2.380%, respectively. This study lays the groundwork for future applications of hyperspectral remote sensing technology in developing soil moisture content estimation models for various crop growth stages and sparks discussions on enhancing the accuracy of these different soil moisture content estimation models. [ABSTRACT FROM AUTHOR]

Exploring how functional traits vary along environmental gradients has long been one of the central questions of trait-based community ecology. Variation in functional traits includes both intraspecific trait variation (ITV) and interspecific trait variation (Vinter); however, the effects of ITV on functional diversity and community assembly remain to be explored. In this study, we compared functional diversity among three communities (i.e., riverbank, transition zone, and desert margin communities) at three spatial scales (i.e., 10 m × 10 m, 20 m × 20 m, and 50 m × 50 m) in the desert ecosystem of the Ebinur Lake basin in Xinjiang. We also analyzed the effects of ITV and environmental and spatial factors on functional diversity. Our results showed that incorporating ITV increased measurements of functional richness at the 10 m × 10 m scale in all three communities (p < 0.01). Rao's quadratic entropy (RaoQ) represents the differences in functional traits between different species. ITV significantly increased RaoQ at the 50 m × 50 m scale in the riverbank and desert margin community, whereas it significantly decreased RaoQ in the transitional zone community. Similarly, ITV significantly increased functional β-diversity at the 10 m × 10 m and 20 m × 20 m scales in the transitional zone community. Spatial factors mainly influenced functional diversity at smaller scales, whereas environmental factors were influential mainly at larger scales. After considering ITV, spatial factors had less of an effect on functional β-diversity, except for the 50 m × 50 m scale in the transitional zone and desert margin community, indicating that ITV can reduce the measured effect of dispersal on functional β-diversity. Considering ITV did not change the interpretation of the main ecological processes affecting functional diversity. However, it did change the extent to which environmental filtering and dispersal effects explained functional diversity. [ABSTRACT FROM AUTHOR]