Plasma Mutation Breeding of High Yield γ-Aminobutyric Acid Lactic Acid Bacteria

γ-amino butyric acid (GABA) is a non-protein amino acid widely distributed in nature. It has analgesic effect,promotes growth hormone secretion and prevents the physiological function of Alzheimer's disease.In order toimprove the yield of GABA, the strain of strain HX-3-6 was tested by Atmospheric and Room Temperature Plasma(ARTP), and the strain with more than 90% lethality was selected for fermentation, and through the gradient plateaccording to the level of GABA concentration and colony size of the screening. Then, use the fermentation mediumfor re-screening and genetic stability test. The yield of the mutant strain L-120-1 was 5.828 g/L, which was 18.84%higher than that before the mutation (4.904). The mutant strain L-120-1 was used as the starting strain for ARTPmutagenesis. The yield of the stable mutant strain 90s-high was 6.178g / L, which was 16.15% higher than that beforethe mutation (5.319g / L). The yield of HX-3-6 strain was higher than that of GABA mutant strain 90s-high 2, whichwas stable and high yield GABA was 25.98%

The influence of bile acids homeostasis by cryptotanshinone-containing herbs

Background: Herbs might affect the homeostasis of bile acids through influence of multiple metabolic pathways of bile acids. Aim: The present study aims to investigate the inhibition of cryptotanshinone towards the glucuronidation of LCA, trying to indicate the possible influence of cryptotanshinone-containing herbs towards the homeostasis of bile acids. Methods: The LCA-3-glucuronidation and LCA-24-glucuronidation reaction was monitored by LC-MS. Results: Initial screening showed that 100 μM of cryptotanshinone inhibited LCA-24-glucuronidation and LCA-3-glucuronidation reaction activity by 82.6% and 79.1%, respectively. This kind of inhibition behaviour exerted cryptotanshinone concentrations-dependent and LCA concentrations-independent inhibition behaviour. Conclusion: All these data indicated the possibility of cryptotanshinone’s influence towards the bile acids metabolism and homeostasis of bile acids.Keywords: herbs, lithocholic acid (LCA), homeostasisAfrican Health sciences Vol 14 No. 1 March 201

DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models

Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters. However, it is ineffective or even detrimental when applied to reasoning tasks in Smaller Language Models (SLMs) with less than 10 billion parameters. To address this limitation, we introduce Dialogue-guided Chain-of-Thought (DialCoT) which employs a dialogue format to generate intermediate reasoning steps, guiding the model toward the final answer. Additionally, we optimize the model's reasoning path selection using the Proximal Policy Optimization (PPO) algorithm, further enhancing its reasoning capabilities. Our method offers several advantages compared to previous approaches. Firstly, we transform the process of solving complex reasoning questions by breaking them down into a series of simpler sub-questions, significantly reducing the task difficulty and making it more suitable for SLMs. Secondly, we optimize the model's reasoning path selection through the PPO algorithm. We conduct comprehensive experiments on four arithmetic reasoning datasets, demonstrating that our method achieves significant performance improvements compared to state-of-the-art competitors.Comment: Accepted to EMNLP 202

Drought-Induced Carbon and Water Use Efficiency Responses in Dryland Vegetation of Northern China

Given the context of global warming and the increasing frequency of extreme climate events, concerns have been raised by scientists, government, and the public regarding drought occurrence and its impacts, particularly in arid and semi-arid regions. In this paper, the drought conditions for the forest and grassland areas in the northern region of China were identified based on 12 years of satellite-based Drought Severity Index (DSI) data. The impact of drought on dryland vegetation in terms of carbon use efficiency (CUE) and water use efficiency (WUE) were also investigated by exploring their correlations with DSI. Results indicated that 49.90% of forest and grassland experienced a dry trend over this period. The most severe drought occurred in 2001. In general, most forests in the study regions experienced near normal and wet conditions during the 12 year period. However, grasslands experienced a widespread drought after 2006. The forest CUE values showed a fluctuation increase from 2000 to 2011, whereas the grassland CUE remained steady over this period. In contrast, WUE increased in both forest and grassland areas due to the increasing net primary productivity (NPP) and descending evapotranspiration (ET). The CUE and WUE values of forest areas were more sensitive to droughts when compared to the values for grassland areas. The correlation analysis demonstrated that areas of DSI that showed significant correlations with CUE and WUE were 17.24 and 10.37% of the vegetated areas, respectively. Overall, the carbon and water use of dryland forests was more affected by drought than that of dryland grasslands

Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition

Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represented by a prototype vector because there are generally a large number of samples in the class that have miscellaneous semantics. To solve the problem, we propose MeTNet, which generates prototype vectors for entity types only but not O-class. We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type. The margin plays as a radius and controls a region with adaptive size in the low-dimensional space. Based on the regions, we propose a new inference procedure to predict the label of a query instance. We conduct extensive experiments in both in-domain and cross-domain settings to show the superiority of MeTNet over other state-of-the-art methods. In particular, we release a Chinese few-shot NER dataset FEW-COMM extracted from a well-known e-commerce platform. To the best of our knowledge, this is the first Chinese few-shot NER dataset. All the datasets and codes are provided at https://github.com/hccngu/MeTNet

Exchanging-based Multimodal Fusion with Transformer

We study the problem of multimodal fusion in this paper. Recent exchanging-based methods have been proposed for vision-vision fusion, which aim to exchange embeddings learned from one modality to the other. However, most of them project inputs of multimodalities into different low-dimensional spaces and cannot be applied to the sequential input data. To solve these issues, in this paper, we propose a novel exchanging-based multimodal fusion model MuSE for text-vision fusion based on Transformer. We first use two encoders to separately map multimodal inputs into different low-dimensional spaces. Then we employ two decoders to regularize the embeddings and pull them into the same space. The two decoders capture the correlations between texts and images with the image captioning task and the text-to-image generation task, respectively. Further, based on the regularized embeddings, we present CrossTransformer, which uses two Transformer encoders with shared parameters as the backbone model to exchange knowledge between multimodalities. Specifically, CrossTransformer first learns the global contextual information of the inputs in the shallow layers. After that, it performs inter-modal exchange by selecting a proportion of tokens in one modality and replacing their embeddings with the average of embeddings in the other modality. We conduct extensive experiments to evaluate the performance of MuSE on the Multimodal Named Entity Recognition task and the Multimodal Sentiment Analysis task. Our results show the superiority of MuSE against other competitors. Our code and data are provided at https://github.com/RecklessRonan/MuSE

Negative symptom dimensions and social functioning in Chinese patients with schizophrenia

ObjectiveNegative symptoms can seriously affect social functioning in patients with schizophrenia. However, the role of various components of negative symptoms in social functioning remains unclear. This study aimed to explore the associations among three different dimensions of negative symptoms (i.e., communication, emotion, and motivation) and social functioning to identify potential therapeutic targets.MethodsThis cross-sectional study enrolled 202 Chinese participants with schizophrenia. Negative symptoms were evaluated using the Negative Symptom Assessment (NSA). Social functioning was represented by the Personal and Social Performance Scale (PSP) total score and employment status. Correlation analysis was conducted to clarify the relationship between negative symptoms and the PSP total score. Regression analysis was performed to explore the determinants of the PSP total score and employment status, considering negative symptoms and possible confounders, such as demographic features, positive symptoms, cognitive symptoms, depressive symptoms, and extrapyramidal side effects.ResultsThe PSP total score was correlated with all three dimensions of negative symptoms (i.e., emotion, motivation, and communication; rs = –0.509, –0.662, and –0.657, respectively). Motivation, instead of emotion or communication, predicted both low PSP total scores and unemployment.ConclusionSocial functioning in patients with schizophrenia was significantly related to motivation. Further studies should focus on motivation and consider it as a therapeutic target to improve patients’ social functioning