Quinoa whole grain diet compromises the changes of gut microbiota and colonic colitis induced by dextran Sulfate sodium in C57BL/6 mice

A plethora of evidence highlights that the dysbiosis of gut microbiota is a critical factor for inflammatory bowel disease (IBD). Both in vivo and in vitro studies have demonstrated that quinoa possesses potential prebiotic effects. The present study aims to examine the potential in using quinoa to ameliorate the dysbiosis and colitis induced by dextran sodium sulfate (DSS). A total of 40 C57BL/6 mice were fed either an AIN-93M diet or a quinoa-based diet, separately. Colitis was induced for 10 animals/dietary group with a 5-days exposure to 2.5% DSS. The clinical symptoms were monitored every other day, and the gut microbiota was characterized by 165 rRNA gene sequencing. The results indicated that consumption of quinoa lessened clinical symptoms as indicated by the reduced disease activity index and the degree of histological damage (P \u3c 0.05). As expected, the DSS treatment induced significant dysbiosis of gut microbiota in mice on an AIN-93M diet. However, compared to mice fed the AIN-93M diet, the consumption of quinoa alleviated the DSS-induced dysbiosis remarkably, as indicated by increased species richness and diversity, decreased abnormal expansion of phylum Proteobacteria, and decreased overgrowth of genera Escherichia/Shigella and Peptoclostridium (P \u3c 0.05). The relative abundances of Firmicutes and Bacteroidetes were less altered in mice fed with quinoa comparing to those mice fed the AIN-93M diet. In summary, the consumption of quinoa suppressed the dysbiosis of gut microbiota and alleviated clinical symptoms induced by DSS, indicating the potential to utilize quinoa as a dietary approach to improve intestinal health

Patterns of deep fine root and water utilization amongst trees, shrubs and herbs in subtropical pine plantations with seasonal droughts

IntroductionSeasonal droughts will become more severe and frequent under the context of global climate change, this would result in significant variations in the root distribution and water utilization patterns of plants. However, research on the determining factors of deep fine root and water utilization is limited.MethodsWe measured the fine root biomass and water utilization of trees, shrubs and herbs, and soil properties, light transmission, and community structure parameters in subtropical pine plantations with seasonal droughts.Results and DiscussionWe found that the proportion of deep fine roots (below 1 m depth) is only 0.2-5.1%, but that of deep soil water utilization can reach 20.9-38.6% during the dry season. Trees improve deep soil water capture capacity by enhancing their dominance in occupying deep soil volume, and enhance their deep resource foraging by increasing their branching capacity of absorptive roots. Shrubs and herbs showed different strategies for deep water competition: shrubs tend to exhibit a “conservative” strategy and tend to increase individual competitiveness, while herbs exhibited an “opportunistic” strategy and tend to increase variety and quantity to adapt to competitions.ConclusionOur results improve our understanding of different deep fine root distribution and water use strategies between overstory trees and understory vegetations, and emphasize the importance of deep fine root in drought resistance as well as the roles of deep soil water utilization in shaping community assembly

Frequency-domain MLPs are More Effective Learners in Time Series Forecasting

Time series forecasting has played the key role in different industrial, including finance, traffic, energy, and healthcare domains. While existing literatures have designed many sophisticated architectures based on RNNs, GNNs, or Transformers, another kind of approaches based on multi-layer perceptrons (MLPs) are proposed with simple structure, low complexity, and {superior performance}. However, most MLP-based forecasting methods suffer from the point-wise mappings and information bottleneck, which largely hinders the forecasting performance. To overcome this problem, we explore a novel direction of applying MLPs in the frequency domain for time series forecasting. We investigate the learned patterns of frequency-domain MLPs and discover their two inherent characteristic benefiting forecasting, (i) global view: frequency spectrum makes MLPs own a complete view for signals and learn global dependencies more easily, and (ii) energy compaction: frequency-domain MLPs concentrate on smaller key part of frequency components with compact signal energy. Then, we propose FreTS, a simple yet effective architecture built upon Frequency-domain MLPs for Time Series forecasting. FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components. The above stages operated on both inter-series and intra-series scales further contribute to channel-wise and time-wise dependency learning. Extensive experiments on 13 real-world benchmarks (including 7 benchmarks for short-term forecasting and 6 benchmarks for long-term forecasting) demonstrate our consistent superiority over state-of-the-art methods

A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

Recently, frequency transformation (FT) has been increasingly incorporated into deep learning models to significantly enhance state-of-the-art accuracy and efficiency in time series analysis. The advantages of FT, such as high efficiency and a global view, have been rapidly explored and exploited in various time series tasks and applications, demonstrating the promising potential of FT as a new deep learning paradigm for time series analysis. Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT. It is also unclear why FT can enhance time series analysis and what its limitations in the field are. To address these gaps, we present a comprehensive review that systematically investigates and summarizes the recent research advancements in deep learning-based time series analysis with FT. Specifically, we explore the primary approaches used in current models that incorporate FT, the types of neural networks that leverage FT, and the representative FT-equipped models in deep time series analysis. We propose a novel taxonomy to categorize the existing methods in this field, providing a structured overview of the diverse approaches employed in incorporating FT into deep learning models for time series analysis. Finally, we highlight the advantages and limitations of FT for time series modeling and identify potential future research directions that can further contribute to the community of time series analysis

Function of the auxin-responsive gene TaSAUR75 under salt and drought stress

Small auxin-upregulated RNAs (SAURs) are genes regulated by auxin and environmental factors. In this study, we identified a SAUR gene in wheat, TaSAUR75. Under salt stress, TaSAUR75 is downregulated in wheat roots. Subcellular localization revealed that TaSAUR75 was localized in both the cytoplasm and nucleus. Overexpression of TaSAUR75 increased drought and salt tolerance in Arabidopsis. Transgenic lines showed higher root length and survival rate and higher expression of some stress-responsive genes than control plants under salt and drought stress. Less H2O2 accumulated in transgenic lines than in control plants under drought stress. Our findings reveal a positive regulatory role of the auxin-responsive gene TaSAUR75 in plant responses to drought and salt stress and provide a candidate gene for improvement of abiotic stress tolerance in crop breeding

Comparative Transcriptome Analysis Identifies Key Defense Genes and Mechanisms in Mulberry (<i>Morus alba</i>) Leaves against Silkworms (<i>Bombyx mori</i>)

As a consequence of long-term coevolution and natural selection, the leaves of mulberry (Morus alba) trees have become the best food source for silkworms (Bombyx mori). Nevertheless, the molecular and genomic basis of defense response remains largely unexplored. In the present study, we assessed changes in the transcriptome changes of mulberry in response to silkworm larval feeding at 0, 3, and 6 h. A total of 4709 (up = 2971, down = 1738) and 3009 (up = 1868, down = 1141) unigenes were identified after 3 and 6 h of silkworm infestation, respectively. MapMan enrichment analysis results show structural traits such as leaf surface wax, cell wall thickness and lignification form the first physical barrier to feeding by the silkworms. Cluster analysis revealed six unique temporal patterns of transcriptome changes. We predicted that mulberry promoted rapid changes in signaling and other regulatory processes to deal with mechanical damage, photosynthesis impairment, and other injury caused by herbivores within 3–6 h. LRR-RK coding genes (THE1, FER) was predicted participated in perception of cell wall perturbation in mulberry responding to silkworm feeding. Ca2+ signal sensors (CMLs), ROS (OST1, SOS3), RBOHD/F, CDPKs, and ABA were part of the regulatory network after silkworm feeding. Jasmonic acid (JA) signal transduction was predicted to act in silkworm feeding response, 10 JA signaling genes (such as OPR3, JAR1, and JAZ1) and 21 JA synthesis genes (such as LOX2, AOS, and ACX1) were upregulated after silkworm feeding for 3 h. Besides, genes of “alpha-Linolenic acid metabolism” and “phenylpropanoid biosynthesis” were activated in 3 h to reprogram secondary metabolism. Collectively, these findings provided valuable insights into silkworm herbivory-induced regulatory and metabolic processes in mulberry, which might help improve the coevolution of silkworm and mulberry

Identification of Salt Stress Responding Genes Using Transcriptome Analysis in Green Alga Chlamydomonas reinhardtii

Salinity is one of the most important abiotic stresses threatening plant growth and agricultural productivity worldwide. In green alga Chlamydomonas reinhardtii, physiological evidence indicates that saline stress increases intracellular peroxide levels and inhibits photosynthetic-electron flow. However, understanding the genetic underpinnings of salt-responding traits in plantae remains a daunting challenge. In this study, the transcriptome analysis of short-term acclimation to salt stress (200 mM NaCl for 24 h) was performed in C. reinhardtii. A total of 10,635 unigenes were identified as being differently expressed by RNA-seq, including 5920 up- and 4715 down-regulated unigenes. A series of molecular cues were screened for salt stress response, including maintaining the lipid homeostasis by regulating phosphatidic acid, acetate being used as an alternative source of energy for solving impairment of photosynthesis, and enhancement of glycolysis metabolism to decrease the carbohydrate accumulation in cells. Our results may help understand the molecular and genetic underpinnings of salt stress responses in green alga C. reinhardtii