The biological effects of IL-21 signaling on B-cell-mediated responses in organ transplantation

Antibody-mediated rejection has emerged as one of the major issues limiting the success of organ transplantation. It exerts a highly negative impact on graft function and outcome, and effective treatment is lacking. The triggers for antibody development, and the mechanisms leading to graft dysfunction and failure, are incompletely understood. The production of antibodies is dependent on instructions from various immunocytes including CD4 T-helper cells that secrete interleukin (IL)-21 and interact with antigen-specific B-cells via costimulatory molecules. In this article, we discuss the role of IL-21 in the activation and differentiation of B-cells and consider the mechanisms of IL-21 and B-cell interaction. An improved understanding of the biological mechanisms involved in antibody-mediated complications after organ transplantation could lead to the development of novel therapeutic strategies, which control humoral alloreactivity, potentially preventing and treating graft-threatening antibody-mediated rejection

Serum Biochemical Reference Values for Adult and Non-adult Chinese Alligators during the Deep and Late Hibernation Periods

Background: The Chinese alligator (Alligator sinensis) is a critically endangered species. Due to the rapid growth of the captive population, the susceptibility to disease during the recovery period after winter hibernation, especially in young alligators, have detrimentally affected Chinese alligator populations. Serum biochemistry, which relates to metabolism, nutritional status and disease, is enormously helpful in evaluating physical conditions in reptile. Many studies have reported the serum biochemical reference values of various reptilian species, including several crocodilians. However, reference values for Chinese alligators have not yet been reported. For captive Chinese alligators, hibernation is a crucial period because winter management has a direct influence on the survival rate of juveniles and the reproduction rate of adults. The main object of the present study refore was to measure the serum biochemical values of captive Chinese alligators during hibernation.Materials, Methods & Results: As such, this study investigates the serum biochemistry as a factor of age and hibernation stage. During the deep and late hibernation periods blood samples were drawn from 30 healthy captive Chinese alligators (adults, sub-adults, and juveniles) at the Anhui Research Center of Chinese Alligator Reproduction (ARCCAR). Serum biochemical measurements were performed using an automated biochemical analyzer and compared based on the age group and hibernation stage via two-way ANOVA. During late hibernation, serum lactate dehydrogenase, alkaline phosphatase, and aspartate aminotransferase activity increased in all age groups in comparison to that in deep hibernation, while the concentration of calcium decreased. Meanwhile, the concentration of serum phosphorus, uric acid, total protein, and globulin in sub-adults and juveniles considerably increased in comparison to that in deep hibernation, while cholesterol and albumin declined. However, in adults only slight changes were noted. Based on comprehensive statistical analysis, our results indicate that sub-adults and juveniles are at risk of developing renal disease during artificial hibernation.Discussion: Chinese alligators, especially sub-adults and juveniles, are particularly vulnerable to disease when they wake from hibernation. They often display symptoms such as depression, anorexia, lethargy, sluggish movement, slow, incremental weight gain, progressive muscle wasting, and even death. The high rate of morbidity in non-adult Chinese alligators may be associated with the high density of UA and other changes in multiple biochemical markers that occur during late hibernation. These altered serum biochemical profiles may indicate kidney damage. One of the most common diseases among reptiles is nephropathy, the symptoms of which are non-specific and tend to agree with those observed post-hibernation. In summary, this study has reported the serum biochemical values of Chinese alligators of varying ages in the deep and late hibernation phases. Based on statistical analyses, interesting differences between the serum biochemical values of adults and non-adults during the deep and late hibernation have been found. The observed changes suggest that, under an artificial hibernation environment, the kidneys of sub-adults and juveniles may become impaired. We believe that the data reported in this study will provide clinical guidance to facilitate more appropriate artificial wintering conditions for Chinese alligators, and assist the breeding and management of these reptiles, as well as disease prevention, during hibernation and recovery

Unsupervised Chunking with Hierarchical RNN

In Natural Language Processing (NLP), predicting linguistic structures, such as parsing and chunking, has mostly relied on manual annotations of syntactic structures. This paper introduces an unsupervised approach to chunking, a syntactic task that involves grouping words in a non-hierarchical manner. We present a two-layer Hierarchical Recurrent Neural Network (HRNN) designed to model word-to-chunk and chunk-to-sentence compositions. Our approach involves a two-stage training process: pretraining with an unsupervised parser and finetuning on downstream NLP tasks. Experiments on the CoNLL-2000 dataset reveal a notable improvement over existing unsupervised methods, enhancing phrase F1 score by up to 6 percentage points. Further, finetuning with downstream tasks results in an additional performance improvement. Interestingly, we observe that the emergence of the chunking structure is transient during the neural model's downstream-task training. This study contributes to the advancement of unsupervised syntactic structure discovery and opens avenues for further research in linguistic theory

A New Race (X12) of Soybean Cyst Nematode in China

The soybean cyst nematode (SCN), Heterodera glycines, is a serious economic threat to soybean-producing regions worldwide. A new SCN population (called race X12) was detected in Shanxi province, China. Race X12 could reproduce on all the indicator lines of both race and Heterodera glycines (HG) type tests. The average number of females on Lee68 (susceptible control) was 171.40 with the lowest Female Index (FI) 61.31 on PI88788 and the highest FI 117.32 on Pickett in the race test. The average number of females on Lee68 was 323.17 with the lowest FI 44.18 on PI88788 and the highest FI 97.83 on PI548316 in the HG type test. ZDD2315 and ZDD24656 are elite resistant germplasms in China. ZDD2315 is highly resistant to race 4, the strongest infection race in the 16 races with FI 1.51 while being highly sensitive to race X12 with FI 64.32. ZDD24656, a variety derived from PI437654 and ZDD2315, is highly resistant to race 1 and race 2. ZDD24656 is highly sensitive to race X12 with FI 99.12. Morphological and molecular studies of J2 and cysts confirmed the population as the SCN H. glycines. This is a new SCN race with stronger virulence than that of race 4 and is a potential threat to soybean production in China

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

IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions

Retrieval-Augmented Generation (RAG), by incorporating external knowledge with parametric memory of language models, has become the state-of-the-art architecture for open-domain QA tasks. However, common knowledge bases are inherently constrained by limited coverage and noisy information, making retrieval-based approaches inadequate to answer implicit reasoning questions. In this paper, we propose an Induction-Augmented Generation (IAG) framework that utilizes inductive knowledge along with the retrieved documents for implicit reasoning. We leverage large language models (LLMs) for deriving such knowledge via a novel prompting method based on inductive reasoning patterns. On top of this, we implement two versions of IAG named IAG-GPT and IAG-Student, respectively. IAG-GPT directly utilizes the knowledge generated by GPT-3 for answer prediction, while IAG-Student gets rid of dependencies on GPT service at inference time by incorporating a student inductor model. The inductor is firstly trained via knowledge distillation and further optimized by back-propagating the generator feedback via differentiable beam scores. Experimental results show that IAG outperforms RAG baselines as well as ChatGPT on two Open-Domain QA tasks. Notably, our best models have won the first place in the official leaderboards of CSQA2.0 (since Nov 1, 2022) and StrategyQA (since Jan 8, 2023)

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