Ca2+ signatures in symbiosis : another level of dynamism for this key messenger

This article comments on: Binci F, Offer E, Crosino A, Sciascia I, Kleine-Vehn J, Genre A, Giovannetti M, Navazio L. 2024. Spatially and temporally distinct Ca2+ changes in Lotus japonicus roots orient fungal-triggered signalling pathways towards symbiosis or immunity. Journal of Experimental Botany 75,605–619

Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks

With the recent prevalence of reinforcement learning (RL), there have been tremendous interests in utilizing RL for ads allocation in recommendation platforms (e.g., e-commerce and news feed sites). For better performance, recent RL-based ads allocation agent makes decisions based on representations of list-wise item arrangement. This results in a high-dimensional state-action space, which makes it difficult to learn an efficient and generalizable list-wise representation. To address this problem, we propose a novel algorithm to learn a better representation by leveraging task-specific signals on Meituan food delivery platform. Specifically, we propose three different types of auxiliary tasks that are based on reconstruction, prediction, and contrastive learning respectively. We conduct extensive offline experiments on the effectiveness of these auxiliary tasks and test our method on real-world food delivery platform. The experimental results show that our method can learn better list-wise representations and achieve higher revenue for the platform.Comment: arXiv admin note: text overlap with arXiv:2109.04353, arXiv:2204.0037

Regulation of Polysaccharide in Wu-Tou Decoction on Intestinal Microflora and Pharmacokinetics of Small Molecular Compounds in AIA Rats

Wu-tou decoction (WTD), a traditional Chinese medicine prescription, is used to treat rheumatoid arthritis (RA). It works by controlling intestinal flora and its metabolites, which in turn modulates the inflammatory response and intestinal barrier function. Small molecular compounds (SM) and polysaccharides (PS) were the primary constituents of WTD extract. In this work, a model of adjuvant-induced arthritis (AIA) in rats was established and treated with WTD, SM, and PS, respectively. 16S rRNA gene sequencing was used to examine the regulatory impact of the various groups on the disturbance of the gut flora induced by RA. Further, since PS cannot be absorbed into the blood, the influence of PS on the absorption and metabolism of SM was studied by examining their pharmacokinetic (PK) parameters of 23 active components in SM by UPLC-MS/MS. WTD was found to be more effective than PS and SM in alleviating arthritis in AIA rats, which may be related to changes in gut flora. The PK properties of 13 active compounds were altered after PS intervene. Based on the findings, PS may be able to manage the disruption of intestinal microbiota, enhance the intestinal environment of model animals, and hence influence SM absorption and metabolism

MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed

Nowadays, the mainstream approach in position allocation system is to utilize a reinforcement learning model to allocate appropriate locations for items in various channels and then mix them into the feed. There are two types of data employed to train reinforcement learning (RL) model for position allocation, named strategy data and random data. Strategy data is collected from the current online model, it suffers from an imbalanced distribution of state-action pairs, resulting in severe overestimation problems during training. On the other hand, random data offers a more uniform distribution of state-action pairs, but is challenging to obtain in industrial scenarios as it could negatively impact platform revenue and user experience due to random exploration. As the two types of data have different distributions, designing an effective strategy to leverage both types of data to enhance the efficacy of the RL model training has become a highly challenging problem. In this study, we propose a framework named Multi-Distribution Data Learning (MDDL) to address the challenge of effectively utilizing both strategy and random data for training RL models on mixed multi-distribution data. Specifically, MDDL incorporates a novel imitation learning signal to mitigate overestimation problems in strategy data and maximizes the RL signal for random data to facilitate effective learning. In our experiments, we evaluated the proposed MDDL framework in a real-world position allocation system and demonstrated its superior performance compared to the previous baseline. MDDL has been fully deployed on the Meituan food delivery platform and currently serves over 300 million users.Comment: 4 pages, 2 figures, accepted by SIGIR 202

Volatiles from cotton aphid (Aphis gossypii) infested plants attract the natural enemy Hippodamia variegata

The Aphis gossypii is a major threat of cotton worldwide due to its short life cycle and rapid reproduction. Chemical control is the primary method used to manage the cotton aphid, which has significant environmental impacts. Therefore, prioritizing eco-friendly alternatives is essential for managing the cotton aphid. The ladybird, Hippodamia variegata, is a predominant predator of the cotton aphid. Its performance in cotton plantation is directly linked to chemical communication, where volatile compounds emitted from aphid-infested plants play important roles in successful predation. Here, we comprehensively studied the chemical interaction between the pest, natural enemy and host plants by analyzing the volatile profiles of aphid-infested cotton plants using gas chromatography-mass spectrometry (GC-MS). We then utilized the identified volatile compounds in electrophysiological recording (EAG) and behavioral assays. Through behavioral tests, we initially demonstrated the clear preference of both larvae and adults of H. variegata for aphid-infested plants. Subsequently, 13 compounds, namely α-pinene, cis-3-hexenyl acetate, 4-ethyl-1-octyn-3-ol, β-ocimene, dodecane, E-β-farnesene, decanal, methyl salicylate, β-caryophyllene, α-humulene, farnesol, DMNT, and TMTT were identified from aphid-infested plants. All these compounds were electrophysiologically active and induced detectable EAG responses in larvae and adults. Y-tube olfactometer assays indicated that, with few exceptions for larvae, all identified chemicals were attractive to H. variegata, particularly at the highest tested concentration (100 mg/ml). The outcomes of this study establish a practical foundation for developing attractants for H. variegata and open avenues for potential advancements in aphid management strategies by understanding the details of chemical communication at a tritrophic level

Monitoring the Process of Endostar-Induced Tumor Vascular Normalization by Non-contrast Intravoxel Incoherent Motion Diffusion-Weighted MRI

Tumor vascular normalization has been proposed as a new concept in anti-tumor angiogenesis, and the normalization window is considered as an opportunity to increase the effect of chemoradiotherapy. However, there is still a lack of a non-invasive method for monitoring the process of tumor vascular normalization. Intravoxel incoherent motion diffusion-weighted magnetic resonance imaging (IVIM DW-MRI) is an emerging approach which can effectively assess microperfusion in tumors, without the need for exogenous contrast agents. However, its role in monitoring tumor vascular normalization still needs further study. In this study, we established a tumor vascular normalization model of CT26 colon-carcinoma-bearing mice by means of Endostar treatment. We then employed IVIM DW-MRI and immunofluorescence to detect the process of tumor vascular normalization at different times after treatment. We found that the D* values of the Endostar group were significantly higher than those of the control group on days 4, 6, 8, and 10 after treatment, and the f values of the Endostar group were significantly higher than those of the control group on days 6 and 8. Furthermore, we confirmed through analysis of histologic parameters that Endostar treatment induced the CT26 tumor vascular normalization window starting from day 4 after treatment, and this window lasted for 6 days. Moreover, we found that D* and f values were well correlated with pericyte coverage (r = 0.469 and 0.504, respectively; P < 0.001, both) and relative perfusion (r = 0.424 and 0.457, respectively; P < 0.001, both). Taken together, our findings suggest that IVIM DW-MRI has the potential to serve as a non-invasive approach for monitoring Endostar-induced tumor vascular normalization