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To address the issues of sparse training data, complex entity structures, and uneven entity distribution in the industrial equipment failure domain, this study constructs an industrial equipment failure named entity recognition corpus. Due to the difficulty of character level named entity recognition models in representing the professional vocabulary information in the field of industrial equipment failure, this study proposes a character enhanced industrial equipment failure named entity recognition model to address this problem. In the embedding layer, professional vocabulary information is directly fused between the Transformer layers of ROBERT WWM (Robustly Optimized BERT Pretraining Approach with Whole Word Masking) to allocate word information to each of its constituent characters for enhanced semantics. The global semantic information is obtained through a BiLSTM (Bidirectional Long Short Term Memory), and the CRF (Conditional Random Field) is used to learn the dependency relationship between adjacent labels to obtain the optimal sentence level label sequence. Experimental results demonstrate that the proposed model has good performance on industrial equipment fault named entity recognition tasks, with an average F1 score of 92.403%. [ABSTRACT FROM AUTHOR]

Abstract Executive function (EF) has a significant impact on career achievement in adolescence and later adulthood, and there are many factors that influence EF. Cardiorespiratory fitness (CRF) is an important factor in the physical fitness of adolescents and is of great significance to healthy development. However, the current association between CRF and EF in Chinese adolescents is still unclear. For this reason, this study analysed the association between CRF and EF. A three-stage stratified cluster sampling method was used to investigate the demographic information, CRF, EF and multiple covariates of 1245 adolescents in China. One-way analysis of variance and chi-square test were used to compare the EF status of different CRFs. The association between CRF and EF was analysed using multiple linear regression analysis and logistic regression analysis. Multiple linear regression analysis showed that, after adjusting for relevant confounding factors, compared with Chinese adolescents with VO2max P 75 decreased by 1.41 ms, 238.73 ms, 273.09 ms, 74.14 ms. Logistic regression analysis showed that compared with Chinese adolescents with VO2max > P 75, Chinese adolescents with VO2max

Executive function (EF) has a significant impact on career achievement in adolescence and later adulthood, and there are many factors that influence EF. Cardiorespiratory fitness (CRF) is an important factor in the physical fitness of adolescents and is of great significance to healthy development. However, the current association between CRF and EF in Chinese adolescents is still unclear. For this reason, this study analysed the association between CRF and EF. A three-stage stratified cluster sampling method was used to investigate the demographic information, CRF, EF and multiple covariates of 1245 adolescents in China. One-way analysis of variance and chi-square test were used to compare the EF status of different CRFs. The association between CRF and EF was analysed using multiple linear regression analysis and logistic regression analysis. Multiple linear regression analysis showed that, after adjusting for relevant confounding factors, compared with Chinese adolescents with VO2max < P25, the inhibition function reaction time, 1back reaction time, 2back reaction time, and cognitive flexibility response time of adolescents with VO2max > P75 decreased by 1.41 ms, 238.73 ms, 273.09 ms, 74.14 ms. Logistic regression analysis showed that compared with Chinese adolescents with VO2max > P75, Chinese adolescents with VO2max < P25 developed inhibitory function dysfunction (OR 2.03, 95% CI: 1.29, 3.20), 1back dysfunction (OR 6.26, 95% CI 3.94, 9.97), 2back dysfunction (OR 8.94, 95% CI 5.40, 14.82), cognitive flexibility dysfunction (OR 2.26, 95% CI 1.44, 3.57) The risk was higher (P < 0.01). There is a positive association between CRF and EF in Chinese adolescents. High-grade CRF adolescents have higher EF levels, that is, shorter response times. This study provides reference and lessons for better promoting adolescents' executive function development in the future. [ABSTRACT FROM AUTHOR]

Background: Drug addiction, characterized by compulsive drug use and high relapse rates, arises from complex interactions between reward and aversion systems in the brain. The paraventricular nucleus (PVN), located in the anterior hypothalamus, serves as a neuroendocrine center and is a key component of the hypothalamic–pituitary–adrenal axis. Objective: This review aimed to explore how the PVN impacts reward and aversion in drug addiction through stress responses and emotional regulation and to evaluate the potential of PVN as a therapeutic target for drug addiction. Methods: We review the current literature, focusing on three main neuron types in the PVN—corticotropin‐releasing factor, oxytocin, and arginine vasopressin neurons—as well as other related neurons, to understand their roles in modulating addiction. Results: Existing studies highlight the PVN as a key mediator in addiction, playing a dual role in reward and aversion systems. These findings are crucial for understanding addiction mechanisms and developing targeted therapies. Conclusion: The role of PVN in stress response and emotional regulation suggests its potential as a therapeutic target in drug addiction, offering new insights for addiction treatment. [ABSTRACT FROM AUTHOR]

Traditional machine learning-based entity extraction methods rely heavily on feature engineering by experts, and the generalization ability of the model is poor. Prototype networks, on the other hand, can effectively use a small amount of labeled data to train models while using category prototypes to enhance the generalization ability of the models. Therefore, this paper proposes a prototype network-based named entity recognition (NER) method, namely the FSPN-NER model, to solve the problem of difficult recognition of sensitive data in data-sparse text. The model utilizes the positional coding model (PCM) to pre-train the data and perform feature extraction, then computes the prototype vectors to achieve entity matching, and finally introduces a boundary detection module to enhance the performance of the prototype network in the named entity recognition task. The model in this paper is compared with LSTM, BiLSTM, CRF, Transformer and their combination models, and the experimental results on the test dataset show that the model outperforms the comparative models with an accuracy of 84.8%, a recall of 85.8% and an F1 value of 0.853. [ABSTRACT FROM AUTHOR]

Purpose: Cardiorespiratory fitness (CRF) is a criticalmarker of overall health and a key predictor of morbidity andmortality, but the existing prediction equations for CRF are primarily derived from general populations and may not be suitable for patients with obesity. Methods: Predicted CRF from different non-exercise prediction equations was compared with measured CRF of patients with obesity who underwent maximal cardiopulmonary exercise testing (CPET). Multiple linear regression was used to develop a population-specific nonexercise CRF prediction model for treadmill exercise including age, sex, weight, height, and physical activity level as determinants. Results: Six hundred sixty patients underwent CPET during the study period. Within the entire cohort, R² values had a range of 0.24 to 0.46. Predicted CRF was statistically different from measured CRF for 19 of the 21 included equations. Only 50% of patients were correctly classified into the measured CRF categories according to predicted CRF. A multiple model for CRF prediction (mL⋅min-1) was generated (R² = 0.78) and validated using two crossvalidation methods. Conclusions: Most used equations provide inaccurate estimates of CRF in patients with obesity, particularly in cases of severe obesity and low CRF. Therefore, a new prediction equation was developed and validated specifically for patients with obesity, offering a more precise tool for clinical CPET interpretation and risk stratification in this population. [ABSTRACT FROM AUTHOR]

Introduction: The mesolimbic reward system is associated with the promotion and rewarding benefits of social relationships. In the socially monogamous prairie vole (Microtus ochrogaster), the establishment of a pair bond can be displayed by a robust preference for a breeding partner and aggressive rejection of unfamiliar conspecifics. Mesolimbic dopamine signaling influences bondrelated behaviors within the vole through dopamine transmission and receptor activity in the nucleus accumbens. However, only one experiment has examined how the ventral tegmental area (VTA), a region that produces much of the foreand mid-brain dopamine, regulates these social behaviors. Specifically, inhibition of either glutamate or GABA neurons in the VTA during a brief courtship promoted a partner preference formation in male prairie voles. The VTA is a heterogeneous structure that contains dopamine, GABA, and glutamate neurons as well as receives a variety of projections including corticotropin-releasing factor (CRF) suggested to modulate dopamine release. Methods: We used pharmacological manipulation to examine how GABA and CRF signaling in the VTA modulate partner preference formation in male and female prairie voles. Specifically, we used a 3 h partner preference test, a social choice test, to assess the formation of a partner preference following an infused bicuculline and CRF during a 1 h cohabitation and muscimol and CP154526, a CRFR1 antagonist, during a 24 h cohabitation with an opposite-sex conspecific. Results: Our study demonstrated that bicuculline, a GABAA receptor antagonist, and CRF in the VTA promoted a partner preference, whereas low-dose muscimol, a GABAA receptor agonist, and CP154526, a CRFR1 antagonist, inhibited a partner preference in both male and female prairie voles. Conclusion: This study demonstrated that GABA and CRF inputs into the VTA is necessary for the formation of a partner preference in male and female prairie voles. [ABSTRACT FROM AUTHOR]

Introduction: The diversity of edible fungus species and the extent of mycological knowledge pose significant challenges to the research, cultivation, and popularization of edible fungus. To tackle this challenge, there is an urgent need for a rapid and accurate method of acquiring relevant information. The emergence of question and answer (Q&A) systems has the potential to solve this problem. Named entity recognition (NER) provides the basis for building an intelligent Q&A system for edible fungus. In the field of edible fungus, there is a lack of a publicly available Chinese corpus suitable for use in NER, and conventional methods struggle to capture long-distance dependencies in the NER process. Methods: This paper describes the establishment of a Chinese corpus in the field of edible fungus and introduces an NER method for edible fungus information based on XLNet and conditional random fields (CRFs). Our approach combines an iterated dilated convolutional neural network (IDCNN) with a CRF. First, leveraging the XLNet model as the foundation, an IDCNN layer is introduced. This layer addresses the limited capacity to capture features across utterances by extending the receptive field of the convolutional kernel. The output of the IDCNN layer is input to the CRF layer, which mitigates any labeling logic errors, resulting in the globally optimal labels for the NER task relating to edible fungus. Results: Experimental results show that the precision achieved by the proposed model reaches 0.971, with a recall of 0.986 and an F1-score of 0.979. Discussion: The proposed model outperforms existing approaches in terms of these evaluation metrics, effectively recognizing entities related to edible fungus information and offering methodological support for the construction of knowledge graphs. [ABSTRACT FROM AUTHOR]

BackgroundMental health and vulnerabilities to neuropsychiatric disorders involve the interplay of genes and environment, particularly during sensitive developmental periods. Early-life adversity (ELA) and stress promote vulnerabilities to stress-related affective disorders, yet it is unknown how transient ELA dictates lifelong neuroendocrine and behavioral reactions to stress. The population of hypothalamic corticotropin-releasing factor (CRF)-expressing neurons that regulate stress responses is a promising candidate to mediate the long-lasting influences of ELA on stress-related behavioral and hormonal responses via enduring transcriptional and epigenetic mechanisms.MethodsCapitalizing on a well-characterized model of ELA, we examined ELA-induced changes in gene expression profiles of CRF-expressing neurons in the hypothalamic paraventricular nucleus of developing male mice. We used single-cell RNA sequencing on isolated CRF-expressing neurons. We determined the enduring functional consequences of transcriptional changes on stress reactivity in adult ELA mice, including hormonal responses to acute stress, adrenal weights as a measure of chronic stress, and behaviors in the looming shadow threat task.ResultsSingle-cell transcriptomics identified distinct and novel CRF-expressing neuronal populations, characterized by both their gene expression repertoire and their neurotransmitter profiles. ELA-provoked expression changes were selective to specific subpopulations and affected genes involved in neuronal differentiation, synapse formation, energy metabolism, and cellular responses to stress and injury. Importantly, these expression changes were impactful, apparent from adrenal hypertrophy and augmented behavioral responses to stress in adulthood.ConclusionsWe uncover a novel repertoire of stress-regulating CRF cell types differentially affected by ELA and resulting in augmented stress vulnerability, with relevance to the origins of stress-related affective disorders.

Named Entity Recognition (NER) is a crucial component in various domains, including medical and financial fields, as it helps identify text fragments belonging to predefined categories from unstructured text. Over time, NER algorithms have evolved from dictionary-based approaches to machine learning and deep learning techniques. Transfer learning, a novel deep learning method, has shown impressive results in NER tasks. However, transfer learning models still face challenges, such as limited entity labels and the impact of noisy datasets. To address these challenges, this research aims to optimise the application of deep learning models for NER and achieve enhanced results. The research initially applied the BERT+CRF model to the WanChuang dataset, resulting in an F1-measure of 89.1%. This established the feasibility of using transfer learning models for NER on Chinese medical data and served as a baseline for comparison in the project. To address label-related issues in the baseline model, a scheme was proposed to improve the learning rate of the CRF layer, resulting in an increased F1 measure of 91.0%. Additionally, to mitigate the impact of noisy training data, a 10-fold retraining scheme was introduced to optimise the training set. By retraining the model using the optimised training set, an optimal F1 measure of 92.7% was achieved. The experiments demonstrated that the transfer learning model enhances NER entity extraction capabilities while the optimised CRF layer effectively captures the internal relationships of entity tags, thus improving overall performance. This research contributes to advancing NER techniques and their application in various domains. [ABSTRACT FROM AUTHOR]

To address the problem of poor entity recognition performance caused by the lack of Chinese annotation in clinical electronic medical records, this paper proposes a multi-medical entity recognition method F-MNER using a fusion technique combining BART, Bi-LSTM, and CRF. First, after cleaning, encoding, and segmenting the electronic medical records, the obtained semantic representations are dynamically fused using a bidirectional autoregressive transformer (BART) model. Then, sequential information is captured using a bidirectional long short-term memory (Bi-LSTM) network. Finally, the conditional random field (CRF) is used to decode and output multi-task entity recognition. Experiments are performed on the CCKS2019 dataset, with micro avg Precision, macro avg Recall, weighted avg Precision reaching 0.880, 0.887, and 0.883, and micro avg F1-score, macro avg F1-score, weighted avg F1-score reaching 0.875, 0.876, and 0.876 respectively. Compared with existing models, our method outperforms the existing literature in three evaluation metrics (micro average, macro average, weighted average) under the same dataset conditions. In the case of weighted average, the Precision, Recall, and F1-score are 19.64%, 15.67%, and 17.58% higher than the existing BERT-BiLSTM-CRF model respectively. Experiments are performed on the actual clinical dataset with our MF-MNER, the Precision, Recall, and F1-score are 0.638, 0.825, and 0.719 under the micro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.685, 0.800, and 0.733 under the macro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.647, 0.825, and 0.722 under the weighted avg evaluation mechanism. The above results show that our method MF-MNER can integrate the advantages of BART, Bi-LSTM, and CRF layers, significantly improving the performance of downstream named entity recognition tasks with a small amount of annotation, and achieving excellent performance in terms of recall score, which has certain practical significance. Source code and datasets to reproduce the results in this paper are available at https://github.com/xfwang1969/MF-MNER. Illustration of the proposed MF-MNER. The method mainly includes four steps: (1) medical electronic medical records need to be cleared, coded, and segmented. (2) The semantic representation obtained by dynamic fusion of the bidirectional autoregressive converter (BART) model. (3) The sequence information is captured by a bi-directional short-term memory (Bi-LSTM) network. (4) the multi-task entity recognition is decoded and output by conditional random field (CRF). [ABSTRACT FROM AUTHOR]

Ancient roles of CRF and ACTH in immune-endocrine interactions were obtained early in the development process of fish alimentary canal. The immunolocalization of corticotrophin releasing factor (CRF) and adrenocorticotropic hormone (ACTH) was inspected in the developing gut of Oreochromis niloticus larvae. The aim was to investigate a possible involvement of these molecules early in the integration of immunological and endocrine systems. Immediately after hatching, the gut of O. niloticus is observed as a straight undifferentiated tube, and with the rapid development, it differentiates into four segments: buccopharinx, esophagus, presumptive stomach and intestine. The immunohistochemical investigation showed the immunolocalization of CRF in the growing digestive tract at all stages (from hatching to 42 days post-hatching). Immunoreaction of CRF was detected in the mucosal epithelium of both the undifferentiated gut and the developing esophagus, stomach, and intestine. Furthermore, CRF immunoreactivity was found in the gastric glands of the stomach. The number of CRF-immunoreactive (ir) cells and the strength of immunoreaction gradually increased as the larvae developed, particularly after the exogenous feeding began; 21 days after hatching. Only the goblet cells of the developing intestine exhibited an ACTH immunoreactivity, which increased at 7dph during the yolk sac resorbtion period. A dramatic decrease was recoded in the number and size of ACTH-ir cells associated with the beginning of the exogenous feeding, and at 28 days post hatching, a very weak immunoreaction was produced. The widespread anatomic distribution and early onset of CRF and ACTH activities, in the developing gut, indicate that these molecules play a functional role in food intake, growth, immunological response, and osmoregulation during O. niloticus development, particularly with the start of the exogenous feeding. [ABSTRACT FROM AUTHOR]

Our study focuses on Traditional Chinese Medical (TCM) named entity recognition (NER), which involves identifying and extracting specific entity names from TCM record. This task has significant implications for doctors and researchers, as it enables the automated identification of relevant TCM terms, ultimately enhancing research efficiency and accuracy. However, the current Bidirectional Encoder Representations from Transformers-Long Short Term Memory-Conditional Random Fields (BERT-LSTM-CRF) model for TCM NER is constrained by a traditional structure, limiting its capacity to fully harness the advantages provided by Bidirectional Encoder Representations from Transformers (BERT) and long short term memory (LSTM) models. Through comparative experiments, we also observed that the straightforward superimposition of models actually leads to a decrease in recognition results. To optimize the structure of the traditional BERT-BiLSTM-CRF model and obtain more effective text representations, we propose the Dyn-Att Net model, which introduces dynamic attention and a parallel structure. By integrating BERT and LSTM models with the dynamic attention mechanism, our model effectively captures semantic, contextual, and sequential relations within text sequences, resulting in high accuracy. To validate the effectiveness of our model, we compared it with nine other models in TCM dataset namely the publicly available PaddlePaddle dataset. Our Dyn-Att Net model, based on BERT, outperforms the other models, achieving an F1 score of 81.91%, accuracy of 92.06%, precision of 80.26%, and recall of 83.76%. Furthermore, its robust generalization capability is substantiated through validation on the APTNER, MSRA, and EduNER datasets. Overall, the Dyn-Att Net model not only enhances NER accuracy within the realm of traditional Chinese medicine, but also showcases considerable potential for cross-domain generalization. Moreover, the Dyn-Att Net model's parallel architecture facilitates efficient computation, contributing to time-saving efforts in NER tasks. [ABSTRACT FROM AUTHOR]

This research study evaluated the suitability of controlled-release urea (CRU) as an alternate nitrogen (N) fertilizer source to conventional soluble urea (U) for tomato production under a humid, warm climate in coastal plain soils. Tomatoes are typically produced on raised plastic-mulched beds, where U is fertigated through multiple applications. On the other hand, CRU is applied once at planting, incorporated into soil before the raised beds are covered with plastic mulch. N source and management will likely impact tomato yield, N use efficiency (NUE), and apparent recovery of N fertilizer (APR). A 2-year field study was conducted on fall and spring tomato crops in north Florida to determine the crop N requirement and NUE in tomatoes (var. HM 1823) grown in sandy soils under a plastic-mulched bed system. In addition to a no N fertilizer treatment, three urea N sources [one soluble source and two polymer-coated CRU sources with different N release durations of 60 (CRU-60) and 75 (CRU-75) days] were applied at three N rates (140, 168, and 224 kg.ha-1 ). Across all N sources and N rates, fall yields were at least 20% higher than spring seasons. At the 140 kg.ha-1 N rate, APR and NUE were improved, especially when U was applied in fall tomato, whereas preplant CRUs improved N efficiency in spring tomato. Based on the lower APR values found in spring production seasons (0% to 16%) when compared with fall (57.1% to 72.6%), it can be concluded that residual soil N was an important source for tomatoes. In addition, the mean whole-plant N accumulation of tomato was 102.5 kg.ha-1, further indicating that reducing the N rate closer to crop N demand would greatly improve conventional vegetable production systems on sandy soils in north Florida. In conclusion, polymer-coated CRU and fertigation U applications were able to supply the N requirement of spring and fall tomato at a 38% reduction of the recommended N rate for tomato in Florida (224 kg.ha-1 ). Preliminary results show that adoption of CRU fertilizers can be considered a low-risk alternative N source for tomato production and the ease of applying CRU once during the bed preparation period for tomato may be an additional incentive. [ABSTRACT FROM AUTHOR]

The keyword extraction of patents is crucial for technicians to master the trends of technology. Traditional keyword extraction approaches only handle short text like title or claims, but ignore the comprehensive meaning of the description. This paper proposes a novel patent keyword extraction method based on corpus classification (PKECC), which simulates the patent understanding methods of human patent examiners. First of all, a corpus classification model based on multi-level attention mechanism adopts the Bert model and hierarchical attention mechanism to classify the sentences of patent description into four parts including technical field, technical problem, technical solution, and technical effect. Then, the proposed keyword extraction method based on the fusion of BiLSTM and CRF is incorporated to extract keywords from the four parts. The proposed PKECC simulates understanding style of patent examiner by extracting keywords from the description. Meanwhile, PKECC may reduce the complexity of extracting keywords from a long text and improve the accuracy of keyword extraction. The proposed PKECC is compared with 5 traditional or state-of-the-art models and achieves better accuracy, F1 score and recall rate; its recall rate is above 62%, its accuracy reaches over 84%, and the F1 score arrives at 69%. In addition, the experimental results shows the proposed PKECC has a better universality in keyword extraction. [ABSTRACT FROM AUTHOR]

Social media platforms like Twitter have been recognized as a reliable real-time information dissemination and collection medium, especially during disasters when traditional communication media fail. Information access improves situational awareness and is essential for successful disaster management. The response team primarily requires information about the people in danger and the need for and availability of resources, such as food, shelter, and medical supplies. These details can only be actionable with the location information. People's tweets during disasters will be informal and not adhere to standard linguistic rules, causing traditional NLP methods to fail. This study focuses on location reference recognition, in which the system must identify any locations mentioned in tweets. Most existing solutions focus on rule-based systems and gazetteers, which depend on the completeness of the gazetteers and manually defined rules. Since people's writing styles differ significantly, manually defining rules will be complex. This paper introduces a neural network architecture based on BiLSTM, CRF, and attention mechanisms. It exploits statistical linguistic properties also. Compared to state-of-the-art methods, the model demonstrated superior results in both in- and cross-domain scenarios on tweet datasets representing diverse disaster types from different regions and times. Empirical results demonstrate that supervised systems can replace gazetteer-based solutions. BiLSTM and CRF, in conjunction with attention mechanism, improve the sequential modelling in informal text. Our system excels in non-English tweets also. The observations have applications in location-based services like tracking news events, traffic management, and event localization. [ABSTRACT FROM AUTHOR]

Irritable bowel syndrome (IBS) is a persistent functional gastrointestinal disorder characterised by abdominal pain and altered patterns of defecation. This study aims to clarify an increase in the expression and interaction of protein disulfide-isomerase A3 (PDIA3) and Signal Transducer and Activator of Transcription 3 (STAT3) within the membrane of dendritic cells (DCs) from individuals with IBS. Mechanistically, the heightened interaction between PDIA3 and STAT3 at the DC membrane results in reduced translocation of phosphorylated STAT3 (p-STAT3) into the nucleus. The reduction of p-STAT3 to nuclear transport subsequently increased the levels of cathepsin S (CTSS) and major histocompatibility complex class II (MHC-II). Consequently, activated DCs promote CD4+ T cell proliferation and cytokine secretion, including interleukin-4 (IL-4), interleukin-6 (IL-6), interleukin-9 (IL-9), and tumour necrosis factor-alpha (TNF-α), thereby contributing to the development of IBS. Importantly, the downregulation of PDIA3 and the administration of punicalagin (Pun), a crucial active compound found in pomegranate peel, alleviate IBS symptoms in rats, such as increased visceral hypersensitivity and abnormal stool characteristics. Collectively, these findings highlight the involvement of the PDIA3-STAT3 protein complex in IBS, providing a novel perspective on the modulation of immune and inflammatory responses. Additionally, this research advances our understanding of the role and mechanisms of PDIA3 inhibitors, presenting new therapeutic possibilities for managing IBS.

IntroductionThe mesolimbic reward system is associated with the promotion and rewarding benefits of social relationships. In the socially monogamous prairie vole (Microtus ochrogaster), the establishment of a pair bond can be displayed by a robust preference for a breeding partner and aggressive rejection of unfamiliar conspecifics. Mesolimbic dopamine signaling influences bond-related behaviors within the vole through dopamine transmission and receptor activity in the nucleus accumbens. However, only one experiment has examined how the ventral tegmental area (VTA), a region that produces much of the fore- and mid-brain dopamine, regulates these social behaviors. Specifically, inhibition of either glutamate or GABA neurons in the VTA during a brief courtship promoted a partner preference formation in male prairie voles. The VTA is a heterogeneous structure that contains dopamine, GABA, and glutamate neurons as well as receives a variety of projections including corticotropin-releasing factor (CRF) suggested to modulate dopamine release.MethodsWe used pharmacological manipulation to examine how GABA and CRF signaling in the VTA modulate partner preference formation in male and female prairie voles. Specifically, we used a 3 h partner preference test, a social choice test, to assess the formation of a partner preference following an infused bicuculline and CRF during a 1 h cohabitation and muscimol and CP154526, a CRFR1 antagonist, during a 24 h cohabitation with an opposite-sex conspecific.ResultsOur study demonstrated that bicuculline, a GABAA receptor antagonist, and CRF in the VTA promoted a partner preference, whereas low-dose muscimol, a GABAA receptor agonist, and CP154526, a CRFR1 antagonist, inhibited a partner preference in both male and female prairie voles.ConclusionThis study demonstrated that GABA and CRF inputs into the VTA is necessary for the formation of a partner preference in male and female prairie voles.

Name Entity Recognition (NER) aims to recognize entities in the engine room domain from unstructured engine room domain text. But in the engine room domain, the entities are diverse and complex, and there is a nesting phenomenon, resulting in a low entity recognition rate. In this paper, a deep learning method incorporating language models is proposed to enhance the entity recognition performance within the engine room. domain. Firstly, the Bidirectional Encoder Representation from Transformers (BERT) language model is employed to train text feature extraction, acquiring a matrix of vector representations at the word level. Secondly, the trained word vectors are fed into the Bidirectional Gated Recurrent Unit (BiGRU) for contextual semantic entity feature extraction. Finally, the global optimal sequence is extracted by combining with the Conditional Random Field (CRF) model to obtain the named entities in the ship cabin semantics. The experimental results show that the proposed algorithm can obtain better F1 values for all three types of entity recognition. Compared with BERT-BiGRU, the overall accuracy of entity identification, recall rate and F1 value are improved by 1.35%, 1.45% and 1.40%, respectively.

Named entities are random, like emerging entities and complex entities. Most of the large language model’s tokenizers have fixed vocab; hence, they tokenize out-of-vocab (OOV) words into multiple sub-words during tokenization. During fine-tuning for any downstream task, these sub-words (tokens) make the named entity classification more complex since, for each sub-word, an extra entity type is assigned for utilizing the word embedding of the sub-word. This work attempts to reduce this complexity by aggregating token embeddings of each word. In this work, we have applied Aggregated-CRF (ACRF), where a conditional random field (CRF) is applied at the top of aggregated token embeddings for named entity prediction. Aggregation is done at embeddings of all tokens generated by a tokenizer corresponding to a word. The experiment was done with two Hindi datasets (HiNER and Hindi Multiconer2). This work showed that the ACRF is better than vanilla CRF (where token embeddings are not aggregated). Also, our result outperformed the existing best result at HiNER data, which was done by applying a cross-entropy classification layer. Further, An analysis of the impact of tokenization has been conducted, both generally and according to entity types for each word present in test data, and the results show that ACRF performed better for the words which tokenized in more than one sub-words (OOV) compared to vanilla CRF. In addition, this work conducts a comparative analysis between two transformer-based models, MuRIL-large and XLM-roberta-large and investigates how these models adopt aggregation strategy based on OOV.

One of the important application of natural language processing (NLP) is Name Entity Recognition (NER). It automatically recognise and categorise named entities in a document. Named Entities can be the name of an individual, group, place, etc. It is crucial to the success of NER applications including text summarization, machine translation and information extraction and retrieval. It is one of the most useful application tools for a variety of topics and languages. Despite its widespread use and effectiveness in English, this field is currently under investigation for other Indian languages, such as Bodo. Due to the lack of resources and a high-quality dataset, NER in Bodo is a difficult task. In this research, a deep learning-based NER tagger is investigated for the Bodo language and NER tagged dataset is generated for Bodo language using Docanno and enlarge the dataset size by employing a data augmentation technique. As there is no Bodo NER baseline model to compared with, we employed several deep learning techniques for Bodo NER System and compared their results. We achieved an accuracy of 99.62%, precision of 99.75%, recall of 98.74% and F-score of 99.35% when employed with LSTM and character based. This study also highlights GRU and CNN based models performance in Bodo NER task. [ABSTRACT FROM AUTHOR]

Despite the importance of physiological responses to stress in a short-term, chronically these adjustments may be harmful and lead to diseases, including cardiovascular diseases. The lateral hypothalamus (LH) has been reported to be involved in expression of physiological and behavioral responses to stress, but the local neurochemical mechanisms involved are not completely described. The corticotropin-releasing factor (CRF) neurotransmission is a prominent brain neurochemical system implicated in the physiological and behavioral changes induced by aversive threats. Furthermore, chronic exposure to aversive situations affects the CRF neurotransmission in brain regions involved in stress responses. Therefore, in this study, we evaluated the influence of CRF neurotransmission in the LH on changes in cardiovascular function and baroreflex activity induced by chronic variable stress (CVS). We identified that CVS enhanced baseline arterial pressure and impaired baroreflex function, which were followed by increased expression of CRF2, but not CRF1, receptor expression within the LH. Local microinjection of either CRF1 or CRF2 receptor antagonist within the LH inhibited the baroreflex impairment caused by CVS, but without affecting the mild hypertension. Taken together, the findings documented in this study suggest that LH CRF neurotransmission participates in the baroreflex impairment related to chronic stress exposure. [ABSTRACT FROM AUTHOR]

With the rapid growth of Internet penetration, identifying emergency information from network news has become increasingly significant for emergency monitoring and early warning. Although deep learning models have been commonly used in Chinese Named Entity Recognition (NER), they require a significant amount of well-labeled training data, which is difficult to obtain for emergencies. In this paper, we propose an NER model that combines bidirectional encoder representations from Transformers (BERT), bidirectional long-short-term memory (BILSTM), and conditional random field (CRF) based on adversarial training (ATBBC) to address this issue. Firstly, we constructed an emergency dataset (ED) based on the classification and coding specifications of the national emergency platform system. Secondly, we utilized the BERT pre-training model with adversarial training to extract text features. Finally, BILSTM and CRF were used to predict the probability distribution of entity labels and decode the probability distribution into corresponding entity labels.Experiments on the ED show that our model achieves an F1-score of 85.39% on the test dataset, which proves the effectiveness of our model. [ABSTRACT FROM AUTHOR]

In mammals, the central extended amygdala is critical for the regulation of the stress response. This regulation is extremely complex, involving multiple subpopulations of GABAergic neurons and complex networks of internal and external connections. Two neuron subpopulations expressing corticotropin‐releasing factor (CRF), located in the central amygdala and the lateral bed nucleus of the stria terminalis (BSTL), play a key role in the long‐term component of fear learning and in sustained fear responses akin to anxiety. Very little is known about the regulation of stress by the amygdala in nonmammals, hindering efforts for trying to improve animal welfare. In birds, one of the major problems relates to the high evolutionary divergence of the telencephalon, where the amygdala is located. In the present study, we aimed to investigate the presence of CRF neurons of the central extended amygdala in chicken and the local connections within this region. We found two major subpopulations of CRF cells in BSTL and the medial capsular central amygdala of chicken. Based on multiple labeling of CRF mRNA with different developmental transcription factors, all CRF neurons seem to originate within the telencephalon since they express Foxg1, and there are two subtypes with different embryonic origins that express Islet1 or Pax6. In addition, we demonstrated direct projections from Pax6 cells of the capsular central amygdala to BSTL and the oval central amygdala. We also found projections from Islet1 cells of the oval central amygdala to BSTL, which may constitute an indirect pathway for the regulation of BSTL output cells. Part of these projections may be mediated by CRF cells, in agreement with the expression of CRF receptors in both Ceov and BSTL. Our results show a complex organization of the central extended amygdala in chicken and open new venues for studying how different cells and circuits regulate stress in these animals. [ABSTRACT FROM AUTHOR]

Electricity theft presents a substantial threat to distributed power networks, leading to non-technical losses (NTLs) that can significantly disrupt grid functionality. As power grids supply centralized electricity to connected consumers, any unauthorized consumption can harm the grids and jeopardize overall power supply quality. Detecting such fraudulent behavior becomes challenging when dealing with extensive data volumes. Smart grids provide a solution by enabling two-way electricity flow, thereby facilitating the detection, analysis, and implementation of new measures to address data flow issues. The key objective is to provide a deep learning-based amalgamated model to detect electricity theft and secure the smart grid. This research introduces an innovative approach to overcome the limitations of current electricity theft detection systems, which predominantly rely on analyzing one-dimensional (1-D) electric data. These approaches often exhibit insufficient accuracy when identifying instances of theft. To address this challenge, the article proposes an ensemble model known as the RNN-BiLSTM-CRF model. This model amalgamates the strengths of recurrent neural network (RNN) and bidirectional long short-term memory (BiLSTM) architectures. Notably, the proposed model harnesses both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data, thereby enhancing the effectiveness of the theft detection process. The experimental results showcase an impressive accuracy rate of 93.05% in detecting electricity theft, surpassing the performance of existing models in this domain. [ABSTRACT FROM AUTHOR]

Alterations in the various neuropeptide systems in the mesocorticolimbic circuitry have been implicated in negative effects associated with drug withdrawal. The corticotropin-releasing factor (CRF) and α-melanocyte-stimulating hormone are two peptides that may be involved. This study investigated the regulatory effects of chronic nicotine exposure and withdrawal on the mRNA levels of melanocortin receptors (MC3R, MC4R), CRF, and CRF receptors (CRFR1 and CRFR2) expressed in the mesocorticolimbic system. Rats were given drinking water with nicotine or without nicotine (control group) for 12 weeks, after which they continued receiving nicotine (chronic exposure) or were withdrawn from nicotine for 24 or 48 h. The animals were decapitated following behavioral testing for withdrawal signs. Quantitative real-time PCR analysis demonstrated that nicotine exposure (with or without withdrawal) increased levels of CRF and CRFR1 mRNA in the amygdala, CRF mRNA in the medial prefrontal cortex, and CRFR1 mRNA in the septum. Nicotine withdrawal also enhanced MC3R and MC4R mRNA levels in different brain regions, while chronic nicotine exposure was associated with increased MC4R mRNA levels in the nucleus accumbens. These results suggest that chronic nicotine exposure and withdrawal regulate CRF and melanocortin signaling in the mesocorticolimbic system, possibly contributing to negative affective state and nicotine addiction. [ABSTRACT FROM AUTHOR]

Background: Central Orexin‐A (OXA) modulates gastrointestinal (GI) functions and stress response. This study aimed to investigate whether OXA and CRF interact at hypothalamic level. Methods: Solid gastric emptying (GE), fecal output (FO), plasma corticosterone (CORT), and postprandial antro‐pyloric motility were assessed in rats that underwent acute restraint stress (ARS) and pretreated with central OX1R and/or CRF receptor antagonists SB‐334867 and alpha‐helical CRF9,41. Microdialysis was performed to assess ARS‐induced release of OXA and CRF in PVN and LHA, respectively. Immunofluorescence labeling was performed to detect the stress‐induced changes in OXA and to assess the hypothalamic distribution of OX1R and CRF1/2 receptors. ARS‐induced c‐Fos immunoreactivity was evaluated in PVN and LHA of rats received OX1R and CRF receptor antagonists. Key Results: ARS delayed GE by disturbing the coordination of antro‐pyloric contractions while stimulating FO and CORT secretion. ARS‐induced alterations in GE, FO, plasma CORT, and antro‐pyloric motility were attenuated by OX1R and/or CRF receptor antagonists, however, these changes were completely restored in rats received both antagonists. ARS stimulated release of OXA and CRF which were significantly attenuated by α‐CRF9,41 and SB‐334867, respectively. The OX1R was detected in CRF‐immunoreactive cells, whereas dense expression of CRF2 receptor but not CRF1 was observed in LHA. ARS remarkably increased OXA immunoreactivity in LHA. ARS‐induced c‐Fos expression in LHA and PVN was abolished by α‐CRF9,41 and SB‐334867, respectively. Conclusions & Inferences: Our findings suggest a reciprocal contribution of OXA and CRF which seems to be involved in the mediation of stress‐induced alterations in neuroendocrine and GI motor functions. [ABSTRACT FROM AUTHOR]

Our study focuses on Traditional Chinese Medical (TCM) named entity recognition (NER), which involves identifying and extracting specific entity names from TCM record. This task has significant implications for doctors and researchers, as it enables the automated identification of relevant TCM terms, ultimately enhancing research efficiency and accuracy. However, the current Bidirectional Encoder Representations from Transformers-Long Short Term Memory-Conditional Random Fields (BERT-LSTM-CRF) model for TCM NER is constrained by a traditional structure, limiting its capacity to fully harness the advantages provided by Bidirectional Encoder Representations from Transformers (BERT) and long short term memory (LSTM) models. Through comparative experiments, we also observed that the straightforward superimposition of models actually leads to a decrease in recognition results. To optimize the structure of the traditional BERT-BiLSTM-CRF model and obtain more effective text representations, we propose the Dyn-Att Net model, which introduces dynamic attention and a parallel structure. By integrating BERT and LSTM models with the dynamic attention mechanism, our model effectively captures semantic, contextual, and sequential relations within text sequences, resulting in high accuracy. To validate the effectiveness of our model, we compared it with nine other models in TCM dataset namely the publicly available PaddlePaddle dataset. Our Dyn-Att Net model, based on BERT, outperforms the other models, achieving an F1 score of 81.91%, accuracy of 92.06%, precision of 80.26%, and recall of 83.76%. Furthermore, its robust generalization capability is substantiated through validation on the APTNER, MSRA, and EduNER datasets. Overall, the Dyn-Att Net model not only enhances NER accuracy within the realm of traditional Chinese medicine, but also showcases considerable potential for cross-domain generalization. Moreover, the Dyn-Att Net model’s parallel architecture facilitates efficient computation, contributing to time-saving efforts in NER tasks.

This work is devoted to solving the problem of recognizing named entities for Russian-language texts based on the CRF model. Two sets of data were considered: documents on refinancing with a good document structure, semi-structured texts of court records. The model was tested under various sets of text features and CRF parameters (optimization algorithms). In average for all entities, the best F-measure value for structured documents was 0.99, and for semi-structured ones 0.86.

Abstract Background Chronic renal failure (CRF) is defined by a significant decline in renal function that results in decreased salt filtration and inhibition of tubular reabsorption, which ultimately causes volume enlargement. This study evaluated the potential renopreventive effects of the NLRP3 inflammasome inhibitor MCC950 in adenine-induced CRF in rats due to conflicting evidence on the effects of MCC950 on the kidney. Methods Since the majority of the kidney tubular abnormalities identified in people with chronic renal disease are comparable to those caused by adding 0.75 percent of adenine powder to a rat's diet each day for four weeks, this method has received broad approval as a model for evaluating kidney damage. Throughout the test, blood pressure was checked weekly and at the beginning. Additionally, oxidative stress factors, urine sample examination, histological modifications, and immunohistochemical adjustments of caspase-3 and interleukin-1 beta (IL-1) levels in renal tissues were carried out. Results Results revealed that MCC950, an inhibitor of the NLRP3 inflammasome, had a renopreventive effect, which was demonstrated by a reduction in blood pressure readings and an improvement in urine, serum, and renal tissue indicators that indicate organ damage. This was also demonstrated by the decrease in neutrophil gelatinase-associated lipocalin tubular expression (NGAL). The NLRP3 inflammasome inhibitor MCC950 was found to significantly alleviate the worsening renal cellular alterations evidenced by increased expression of caspase-3 and IL-1, according to immunohistochemical tests. Conclusion The NLRP3 inflammasome inhibitor MCC950 demonstrated renopreventive effects in the CRF rat model, suggesting that it might be used as a treatment strategy to stop the progression of CRF.

The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and Conditional Random Fields (CRF) with Data Augmentation Technique (DAT). DAT integrates spam YouTube video comments into the traditional TF-IDF algorithm and generates a weighted word vector. The weighted word vector is fed into BiLSTM CRF to capture context information effectively. The result of this study is a new classification model to spam YouTube comment videos and increase the computational value of its performance. This research conducted two experiments: the first using BiLSTM CRF without DAT and the second using BiLSTM CRF with DAT. The experimental results state that the evaluation score using BiLSTM CRF with DAT shows outstanding performance in text classification, especially in spam YouTube video comment texts, with accuracy = 83.3%, precision = 83.6%, recall = 83.3%, and F-measure = 83.3%. So the combination of the BiLSTM-CRF method and the Data Augmentation Technique is very precise, so it can be used to increase the accuracy of classification texts for spam YouTube video comments

Traditional machine learning-based entity extraction methods rely heavily on feature engineering by experts, and the generalization ability of the model is poor. Prototype networks, on the other hand, can effectively use a small amount of labeled data to train models while using category prototypes to enhance the generalization ability of the models. Therefore, this paper proposes a prototype network-based named entity recognition (NER) method, namely the FSPN-NER model, to solve the problem of difficult recognition of sensitive data in data-sparse text. The model utilizes the positional coding model (PCM) to pre-train the data and perform feature extraction, then computes the prototype vectors to achieve entity matching, and finally introduces a boundary detection module to enhance the performance of the prototype network in the named entity recognition task. The model in this paper is compared with LSTM, BiLSTM, CRF, Transformer and their combination models, and the experimental results on the test dataset show that the model outperforms the comparative models with an accuracy of 84.8%, a recall of 85.8% and an F1 value of 0.853.

This research study evaluated the suitability of controlled-release urea (CRU) as an alternate nitrogen (N) fertilizer source to conventional soluble urea (U) for tomato production under a humid, warm climate in coastal plain soils. Tomatoes are typically produced on raised plastic-mulched beds, where U is fertigated through multiple applications. On the other hand, CRU is applied once at planting, incorporated into soil before the raised beds are covered with plastic mulch. N source and management will likely impact tomato yield, N use efficiency (NUE), and apparent recovery of N fertilizer (APR). A 2-year field study was conducted on fall and spring tomato crops in north Florida to determine the crop N requirement and NUE in tomatoes (var. HM 1823) grown in sandy soils under a plastic-mulched bed system. In addition to a no N fertilizer treatment, three urea N sources [one soluble source and two polymer-coated CRU sources with different N release durations of 60 (CRU-60) and 75 (CRU-75) days] were applied at three N rates (140, 168, and 224 kg⋅ha−1). Across all N sources and N rates, fall yields were at least 20% higher than spring seasons. At the 140 kg⋅ha−1 N rate, APR and NUE were improved, especially when U was applied in fall tomato, whereas preplant CRUs improved N efficiency in spring tomato. Based on the lower APR values found in spring production seasons (0% to 16%) when compared with fall (57.1% to 72.6%), it can be concluded that residual soil N was an important source for tomatoes. In addition, the mean whole-plant N accumulation of tomato was 102.5 kg⋅ha−1, further indicating that reducing the N rate closer to crop N demand would greatly improve conventional vegetable production systems on sandy soils in north Florida. In conclusion, polymer-coated CRU and fertigation U applications were able to supply the N requirement of spring and fall tomato at a 38% reduction of the recommended N rate for tomato in Florida (224 kg⋅ha−1). Preliminary results show that adoption of CRU fertilizers can be considered a low-risk alternative N source for tomato production and the ease of applying CRU once during the bed preparation period for tomato may be an additional incentive.

Electricity theft presents a substantial threat to distributed power networks, leading to non-technical losses (NTLs) that can significantly disrupt grid functionality. As power grids supply centralized electricity to connected consumers, any unauthorized consumption can harm the grids and jeopardize overall power supply quality. Detecting such fraudulent behavior becomes challenging when dealing with extensive data volumes. Smart grids provide a solution by enabling two-way electricity flow, thereby facilitating the detection, analysis, and implementation of new measures to address data flow issues. The key objective is to provide a deep learning-based amalgamated model to detect electricity theft and secure the smart grid. This research introduces an innovative approach to overcome the limitations of current electricity theft detection systems, which predominantly rely on analyzing one-dimensional (1-D) electric data. These approaches often exhibit insufficient accuracy when identifying instances of theft. To address this challenge, the article proposes an ensemble model known as the RNN-BiLSTM-CRF model. This model amalgamates the strengths of recurrent neural network (RNN) and bidirectional long short-term memory (BiLSTM) architectures. Notably, the proposed model harnesses both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data, thereby enhancing the effectiveness of the theft detection process. The experimental results showcase an impressive accuracy rate of 93.05% in detecting electricity theft, surpassing the performance of existing models in this domain.

Chinese literary texts contain a sizeable vivid imagery vocabulary, which makes it difficult for average readers to judge the boundaries between words, and the current pre-trained language model is also difficult for them to learn its implicit knowledge effectively, which brings troubles to machine semantic analysis. The study uses CRF training to obtain a semantic analysis model of Chinese literary texts that recognizes the semantic relationship between two words. SVM is used to train classifiers for confusing categories, and the two semantic relations in the output of the CRF model are further recognized to determine the final semantic relations between word pairs. Finally, the LCQMC dataset is used as the experimental data, and the semantic analysis technique based on CRF and SVM is employed to obtain the participle, lexical, and dependent syntactic annotations. According to the results, the model’s correct rates on the LAS for paraphrase recognition and dependency analysis of Chinese literary texts are 74.83% and 92.05%, respectively. The study enhances the efficiency of semantic analysis of relevant Chinese texts and is crucial for the study on the semantic analysis of terms.

Background: Chronic renal failure (CRF) is defined by a significant decline in renal function that results in decreased salt filtration and inhibition of tubular reabsorption, which ultimately causes volume enlargement. This study evaluated the potential renopreventive effects of the NLRP3 inflammasome inhibitor MCC950 in adenine-induced CRF in rats due to conflicting evidence on the effects of MCC950 on the kidney. Methods: Since the majority of the kidney tubular abnormalities identified in people with chronic renal disease are comparable to those caused by adding 0.75 percent of adenine powder to a rat's diet each day for four weeks, this method has received broad approval as a model for evaluating kidney damage. Throughout the test, blood pressure was checked weekly and at the beginning. Additionally, oxidative stress factors, urine sample examination, histological modifications, and immunohistochemical adjustments of caspase-3 and interleukin-1 beta (IL-1) levels in renal tissues were carried out. Results: Results revealed that MCC950, an inhibitor of the NLRP3 inflammasome, had a renopreventive effect, which was demonstrated by a reduction in blood pressure readings and an improvement in urine, serum, and renal tissue indicators that indicate organ damage. This was also demonstrated by the decrease in neutrophil gelatinase-associated lipocalin tubular expression (NGAL). The NLRP3 inflammasome inhibitor MCC950 was found to significantly alleviate the worsening renal cellular alterations evidenced by increased expression of caspase-3 and IL-1, according to immunohistochemical tests. Conclusion: The NLRP3 inflammasome inhibitor MCC950 demonstrated renopreventive effects in the CRF rat model, suggesting that it might be used as a treatment strategy to stop the progression of CRF. [ABSTRACT FROM AUTHOR]

Background There is a strong link between chronic stress and vulnerability to drug abuse and addiction. Corticotropin releasing factor (CRF) is central to the stress response that contributes to continuation and relapse to heroin abuse. Chronic heroin exposure can exacerbate CRF production, leading to dysregulation of the midbrain CRF-dopamine-glutamate interaction. Methods Here we investigated the role of midbrain CRF1 receptors in heroin self-administration and assessed neuroplasticity in CRF1 receptor expression in key opioid addiction brain regions. Results Infusions of antalarmin (a CRF1 receptor antagonist) into the ventral tegmental area (VTA) dose dependently reduced heroin self-administration in rats but had no impact on food reinforcement or locomotor activity in rats. Using RNAscope in situ hybridization, we found that heroin, but not saline, self-administration upregulated CRF1 receptor mRNA in the VTA, particularly on dopamine neurons. AMPA GluR1 and dopamine reuptake transporter mRNA in VTA neurons were not affected by heroin. The western-blot assay showed that CRF1 receptors were upregulated in the VTA and nucleus accumbens. No significant changes in CRF1 protein expression were detected in the prefrontal cortex, insula, dorsal hippocampus, and substantia nigra. In addition, we found that 15 days of environmental enrichment implemented after heroin self-administration does not reverse upregulation of VTA CRF1 receptor mRNA but it downregulates dopamine transporter mRNA. Conclusions Overall, these data suggest that heroin self-administration requires stimulation of VTA CRF1 receptors and upregulates their expression in brain regions involved in reinforcement. Such long-lasting neuroadaptations may contribute to continuation of drug use and relapse due to stress exposure and are not easily reversed by EE exposure. [ABSTRACT FROM AUTHOR]

The vase tunicate, Ciona intestinalis (Linnaeus, 1767), is a social but non-colonial ascidian that is implicated in biofouling of aquatic structures and destruction of the shellfish industry through competition for planktonic nutrients and substrate settling habitats. The sequencing of the C. intestinalis genome has provided insight into the phylogenetic origins of this species, indicating that this lineage and its allies represent a sister taxon to the chordates. Although the practical use of this genomic information at controlling this invasive species is equivocal, a number of new studies on the neurological and neuroendocrine aspects of C. intestinalis have suggested new molecular targets that may be exploited for practical applications on the control of C. intestinalis to protect and enhance the shellfish industry from this invasive species. As a result, we have developed a novel behavioural assay for C. intestinalis, which can be employed to investigate novel agents that inhibit growth and development in this species. [ABSTRACT FROM AUTHOR]