Changes of water clarity in large lakes and reservoirs across China observed from long-term MODIS

Water clarity is a well-established first-order indicator of water quality and has been used globally by water regulators in their monitoring and management programs. Assessments of water clarity in lakes over large temporal and spatial scales, however, are rare, limiting our understanding of its variability and the driven forces. In this study, we developed and validated a robust Secchi disk depth (ZSD) algorithm for lakes across China based on two water color parameters, namely Forel-Ule Index (FUI) and hue angle α, retrieved from MODIS data. The MODIS ZSD model shows good results when compared with in-situ measurements from 17 lakes, with a 27.4% mean relative difference (MRD) in the validation dataset. Compared with other empirical ZSD models, our FUI and α-based model demonstrates improved performance and adaptability over a wide range of water clarity and trophic states. This algorithm was subsequently applied to MODIS measurements to provide a comprehensive assessment of water clarity in large lakes (N = 153) across China for the first time. The mean summer ZSD of the studied lakes between 2000 and 2017 demonstrated marked spatial and temporal variations. Spatially, the ZSD of large lakes presented a distinct spatial pattern of “high west and low east” over China. This spatial pattern was found to be associated with the significant differences in lake depth and altitude between west and east China while China's population, GDP, temperature, and precipitation distribution have also contributed to a certain extent. Temporally, the ZSD of most lakes increased during this period, with an overall mean rate of 3.3 cm/yr for all lakes. Here, 38.6% (N = 59) of the lakes experienced a significant increase in their ZSD value during the past 18 years while only 8.5% (N = 13) showed a significant decreasing trend. Significant increases in lake ZSD were observed in west China, which were found to correlate with the increase of air temperature and lake surface area. This is possibly a response of the lakes in west China to climate change. In the lake systems of east China, which are predominately used as a drinking water source, the increase in lake ZSD was found to be strongly correlated with changes in local GDP (gross domestic production), NDVI (normalized difference vegetation index) and lake surface area, suggesting a combined effect of the implemented management practices and climatic variability. The results of this study provide important information for water quality conservation and management in China, and also highlight the value of satellite remote sensing in monitoring water quality over lakes at a large scale and long-term

Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties

This paper is concerned with the problem of designing a non-fragile state estimator for a class of uncertain discrete-time neural networks with time-delays. The norm-bounded parameter uncertainties enter into all the system matrices, and the network output is of a general type that contains both linear and nonlinear parts. The additive variation of the estimator gain is taken into account that reflects the possible implementation error of the neuron state estimator. The aim of the addressed problem is to design a state estimator such that the estimation performance is non-fragile against the gain variations and also robust against the parameter uncertainties. Sufficient conditions are presented to guarantee the existence of the desired non-fragile state estimators by using the Lyapunov stability theory and the explicit expression of the desired estimators is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is given to demonstrate the effectiveness of the proposed design approach

Adapting Multiple Distributions for Bridging Emotions from Different Speech Corpora

In this paper, we focus on a challenging, but interesting, task in speech emotion recognition (SER), i.e., cross-corpus SER. Unlike conventional SER, a feature distribution mismatch may exist between the labeled source (training) and target (testing) speech samples in cross-corpus SER because they come from different speech emotion corpora, which degrades the performance of most well-performing SER methods. To address this issue, we propose a novel transfer subspace learning method called multiple distribution-adapted regression (MDAR) to bridge the gap between speech samples from different corpora. Specifically, MDAR aims to learn a projection matrix to build the relationship between the source speech features and emotion labels. A novel regularization term called multiple distribution adaption (MDA), consisting of a marginal and two conditional distribution-adapted operations, is designed to collaboratively enable such a discriminative projection matrix to be applicable to the target speech samples, regardless of speech corpus variance. Consequently, by resorting to the learned projection matrix, we are able to predict the emotion labels of target speech samples when only the source label information is given. To evaluate the proposed MDAR method, extensive cross-corpus SER tasks based on three different speech emotion corpora, i.e., EmoDB, eNTERFACE, and CASIA, were designed. Experimental results showed that the proposed MDAR outperformed most recent state-of-the-art transfer subspace learning methods and even performed better than several well-performing deep transfer learning methods in dealing with cross-corpus SER tasks

Comprehensive Identification and Expression Analysis of the YTH Family of RNA-Binding Proteins in Strawberry

Plant growth and development processes are tightly regulated at multiple levels, including transcriptional and post-transcriptional levels, and the RNA-binding protein YTH regulates gene expression during growth and development at the post-transcriptional level by regulating RNA splicing, processing, stability, and translation. We performed a systematic characterization of YTH genes in diploid forest strawberry and identified a total of nine YTH genes. With the help of phylogenetic analysis, these nine genes were found to belong to two different groups, YTHDC and YTHDF, with YTHDF being further subdivided into three subfamilies. Replication analysis showed that YTH3 and YTH4 are a gene pair generated by tandem repeat replication. These two genes have similarities in gene structure, number of motifs, and distribution patterns. Promoter analysis revealed the presence of multiple developmental, stress response, and hormone-response-related cis-elements. Analysis of available transcriptome data showed that the expression levels of most of the YTH genes were stable with no dramatic changes during development in different tissues. However, YTH3 maintained high expression levels in all tissues and during fruit development, and YTH4 was expressed at higher levels in tissues such as flowers, leaves, and seedlings, while it was significantly lower than YTH3 in white fruits and ripening fruits with little fluctuation. Taken together, our study provides insightful and comprehensive basic information for the study of YTH genes in strawberry

Differential Gene Expression Caused by the F and M Loci Provides Insight Into Ethylene-Mediated Female Flower Differentiation in Cucumber

In cucumber (Cucumis sativus L.), the differentiation and development of female flowers are important processes that directly affect the fruit yield and quality. Sex differentiation is mainly controlled by three ethylene synthase genes, F (CsACS1G), M (CsACS2), and A (CsACS11). Thus, ethylene plays a key role in the sex differentiation in cucumber. The “one-hormone hypothesis” posits that F and M regulate the ethylene levels and initiate female flower development in cucumber. Nonetheless, the precise molecular mechanism of this process remains elusive. To investigate the mechanism by which F and M regulate the sex phenotype, three cucumber near-isogenic lines, namely H34 (FFmmAA, hermaphroditic), G12 (FFMMAA, gynoecious), and M12 (ffMMAA, monoecious), with different F and M loci were generated. The transcriptomic analysis of the apical shoots revealed that the expression of the B-class floral homeotic genes, CsPI (Csa4G358770) and CsAP3 (Csa3G865440), was immensely suppressed in G12 (100% female flowers) but highly expressed in M12 (∼90% male flowers). In contrast, CAG2 (Csa1G467100), which is an AG-like C-class floral homeotic gene, was specifically highly expressed in G12. Thus, the initiation of female flowers is likely to be caused by the downregulation of B-class and upregulation of C-class genes by ethylene production in the floral primordium. Additionally, CsERF31, which was highly expressed in G12, showed temporal and spatial expression patterns similar to those of M and responded to the ethylene-related chemical treatments. The biochemical experiments further demonstrated that CsERF31 could directly bind the promoter of M and promote its expression. Thus, CsERF31 responded to the ethylene signal derived from F and mediated the positive feedback regulation of ethylene by activating M expression, which offers an extended “one-hormone hypothesis” of sex differentiation in cucumber

Arabidopsis G-Protein β Subunit AGB1 Interacts with BES1 to Regulate Brassinosteroid Signaling and Cell Elongation

In Arabidopsis, brassinosteroids (BR) are major growth-promoting hormones, which integrate with the heterotrimeric guanine nucleotide-binding protein (G-protein) signals and cooperatively modulate cell division and elongation. However, the mechanisms of interaction between BR and G-protein are not well understood. Here, we show that the G-protein β subunit AGB1 directly interacts with the BR transcription factor BES1 in vitro and in vivo. An AGB1-null mutant, agb1-2, displays BR hyposensitivity and brassinazole (BRZ, BR biosynthesis inhibitor) hypersensitivity, which suggests that AGB1 positively mediates the BR signaling pathway. Moreover, we demonstrate that AGB1 synergistically regulates expression of BES1 target genes, including the BR biosynthesis genes CPD and DWF4 and the SAUR family genes required for promoting cell elongation. Further, Western blot analysis of BES1 phosphorylation states indicates that the interaction between AGB1 and BES1 alters the phosphorylation status of BES1 and increases the ratio of dephosphorylated to phosphorylated BES1, which leads to accumulation of dephosphorylated BES1 in the nucleus. Finally, AGB1 promotes BES1 binding to BR target genes and stimulates the transcriptional activity of BES1. Taken together, our results demonstrate that AGB1 positively regulates cell elongation by affecting the phosphorylation status and transcriptional activity of BES1

Implicitly Aligning Joint Distributions for Cross-Corpus Speech Emotion Recognition

In this paper, we investigate the problem of cross-corpus speech emotion recognition (SER), in which the training (source) and testing (target) speech samples belong to different corpora. This case thus leads to a feature distribution mismatch between the source and target speech samples. Hence, the performance of most existing SER methods drops sharply. To solve this problem, we propose a simple yet effective transfer subspace learning method called joint distribution implicitly aligned subspace learning (JIASL). The basic idea of JIASL is very straightforward, i.e., building an emotion discriminative and corpus invariant linear regression model under an implicit distribution alignment strategy. Following this idea, we first make use of the source speech features and emotion labels to endow such a regression model with emotion-discriminative ability. Then, a well-designed reconstruction regularization term, jointly considering the marginal and conditional distribution alignments between the speech samples in both corpora, is adopted to implicitly enable the regression model to predict the emotion labels of target speech samples. To evaluate the performance of our proposed JIASL, extensive cross-corpus SER experiments are carried out, and the results demonstrate the promising performance of the proposed JIASL in coping with the tasks of cross-corpus SER

Straw Strip Return Increases Soil Organic Carbon Sequestration by Optimizing Organic and Humus Carbon in Aggregates of Mollisols in Northeast China

In agroecosystems, effective straw return modes are one of the key practices for increasing soil fertility and carbon (C) availability. Although they improve soil quality, there is currently little information available regarding the influence of distinct straw return modes with respect to potential soil organic carbon (SOC) sequestration. In this study, we established a five-year (2015–2019) field experiment in Mollisols of Northeast China, which included four straw return modes, plow tillage with straw return as the control (PTS), rotary tillage with straw return (RTS), rotary tillage with straw strip return (RSS), and plow tillage with straw strip return (PSS), to investigate the impact on soil physicochemical properties, aggregates, and C sequestration. The results reveal that RSS effectively improved the soil physicochemical properties. Such responses increased the contents of SOC, fulvic acid carbon (FAC), and humin carbon (HMC) in all soil layers (0–30 cm). The proportion of macroaggregates was higher in RSS, whereas the proportion of silt/clay was the lowest at depths of 0–20 cm; consequently, the mean weight diameter (MWD) and geometric mean diameter (GMD) of RSS were higher at depths of 0–20 cm due to the improved physical soil structure. In the 0–10 cm and 20–30 cm layers, the highest humic acid carbon (HAC) concentrations associated with all aggregate sizes were found for RSS, in contrast to 10–20 cm, which had increased HMC. Structural equation modeling (SEM) revealed that C transformation was mainly mediated through silt/clay-associated FAC, HMC, and SOC, ultimately determining HAC (81%) and HMC (85%) as the primary humus fractions for SOC sequestration. Therefore, this study shows that RSS is the suitable straw return mode for effectively improving soil quality, aggregate stability, and C sequestration in Mollisols of Northeast China

Implicitly Aligning Joint Distributions for Cross-Corpus Speech Emotion Recognition

In this paper, we investigate the problem of cross-corpus speech emotion recognition (SER), in which the training (source) and testing (target) speech samples belong to different corpora. This case thus leads to a feature distribution mismatch between the source and target speech samples. Hence, the performance of most existing SER methods drops sharply. To solve this problem, we propose a simple yet effective transfer subspace learning method called joint distribution implicitly aligned subspace learning (JIASL). The basic idea of JIASL is very straightforward, i.e., building an emotion discriminative and corpus invariant linear regression model under an implicit distribution alignment strategy. Following this idea, we first make use of the source speech features and emotion labels to endow such a regression model with emotion-discriminative ability. Then, a well-designed reconstruction regularization term, jointly considering the marginal and conditional distribution alignments between the speech samples in both corpora, is adopted to implicitly enable the regression model to predict the emotion labels of target speech samples. To evaluate the performance of our proposed JIASL, extensive cross-corpus SER experiments are carried out, and the results demonstrate the promising performance of the proposed JIASL in coping with the tasks of cross-corpus SER