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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]

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]

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]

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]

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]

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]

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]

Background: It is assumed that people with multiple sclerosis (MS) who participate in programs of physical exercise improve their physical fitness. Objective: The aim of this network meta‐analysis (NMA) was to analyze the effect of different types of exercise on muscular fitness and cardiorespiratory fitness (CRF) among people with MS and to determine the best type of exercise according to disease severity. Methods: MEDLINE, the Physiotherapy Evidence Database, the Cochrane Library, SPORTDiscus, Scopus, and Web of Science were searched from inception to April 2022 to identify randomized controlled trials (RCTs) concerning the effect of physical exercise on fitness in people with MS. We ranked the types of physical exercise by calculating the surface under the cumulative ranking (SUCRA). Results: We included 72 RCTs involving 2543 MS patients in this NMA. A ranking of five types of physical exercise (aerobic, resistance, combined [aerobic and resistance], sensorimotor training, and mind–body exercises) was achieved. Combined and resistance training had the highest effect sizes (0.94, 95% CI 0.47, 1.41, and 0.93, 95% CI 0.57, 1.29, respectively) and the highest SUCRA (86.2% and 87.0%, respectively) for muscular fitness. The highest effect size (0.66, 95% CI 0.34, 0.99) and SUCRA (86.9%) for CRF was for aerobic exercise. Conclusions: Combined and resistance training seem to be the most effective exercises to improve muscular fitness and aerobic exercise for CRF in people with MS. [ABSTRACT FROM AUTHOR]

Body fat (BF) and cardiorespiratory fitness (CRF) are important health markers that ought to be considered in screening exams. The aim of this study was to assess the value of six indicators, i.e., tri-ponderal mass index (TMI), relative fat mass (RFM), waist–BMI ratio, waist-to-height ratio (WHtR), waist-to-hip ratio (WHR) and body mass index (BMI) in predicting CRF in school-aged children. The analysis was based on the data coming from the examination of 190 children participating in school physical education (PE) classes. Their body weight (BW) and height (BH), waist and hip circumference (WC; HC) and percentage of body fat (%BF) were measured; the CRF test was performed with the use of the 20 m shuttle run test (20 mSRT); peak heart rate (HRpeak) was measured; TMI, relative fat mass pediatric (RFMp), waist–BMI ratio, WHtR, BMI and WHR were calculated. Statistical analysis was mainly conducted using regression models. The developed regression models, with respect to the sex and age of the children, revealed RFMp as the strongest CRF indicator (R2 = 51.1%) and WHR as well as waist–BMI ratio as the weakest ones (R2 = 39.2% and R2 = 40.5%, respectively). In predicting CRF in school-aged children, RFMp turned out to be comparable to body fat percentage obtained by means of the bioimpedance analysis (BIA) (R2 = 50.3%), and as such it can be used as a simple screening measure in prophylactic exams of school children. All of these models were statistically significant (p < 0.001). [ABSTRACT FROM AUTHOR]

Negative affective aspects of alcohol withdrawal and pain involve converging brain circuits. In this issue of Neuron , Son et al. 1 identify a peripheral mechanism of an alcohol-withdrawal-induced headache-like condition, which is centered on mast-cell-specific receptor MrgprB2 activated by corticotropin-releasing factor (CRF) in dura mater to drive nociception. Negative affective aspects of alcohol withdrawal and pain involve converging brain circuits. In this issue of Neuron , Son et al. identify a peripheral mechanism of an alcohol-withdrawal-induced headache-like condition, which is centered on mast-cell-specific receptor MrgprB2 activated by corticotropin-releasing factor (CRF) in dura mater to drive nociception. [ABSTRACT FROM AUTHOR]

Perinatal high‐fat diet (pHFD) exposure alters the development of vagal neurocircuits that control gastrointestinal (GI) motility and reduce stress resiliency in offspring. Descending oxytocin (OXT; prototypical anti‐stress peptide) and corticotropin releasing factor (CRF; prototypical stress peptide) inputs from the paraventricular nucleus (PVN) of the hypothalamus to the dorsal motor nucleus of the vagus (DMV) modulate the GI stress response. How these descending inputs, and their associated changes to GI motility and stress responses, are altered following pHFD exposure are, however, unknown. The present study used retrograde neuronal tracing experiments, cerebrospinal fluid extraction, in vivo recordings of gastric tone, motility and gastric emptying rates, and in vitro electrophysiological recordings from brainstem slice preparations to investigate the hypothesis that pHFD alters descending PVN–DMV inputs and dysregulates vagal brain–gut responses to stress. Compared to controls, rats exposed to pHFD had slower gastric emptying rates and did not respond to acute stress with the expected delay in gastric emptying. Neuronal tracing experiments demonstrated that pHFD reduced the number of PVNOXT neurons that project to the DMV, but increased PVNCRF neurons. Both in vitro electrophysiology recordings of DMV neurons and in vivo recordings of gastric motility and tone demonstrated that, following pHFD, PVNCRF–DMV projections were tonically active, and that pharmacological antagonism of brainstem CRF1 receptors restored the appropriate gastric response to brainstem OXT application. These results suggest that pHFD exposure disrupts descending PVN–DMV inputs, leading to a dysregulated vagal brain–gut response to stress. Key points: Maternal high‐fat diet exposure is associated with gastric dysregulation and stress sensitivity in offspring.The present study demonstrates that perinatal high‐fat diet exposure downregulates hypothalamic–vagal oxytocin (OXT) inputs but upregulates hypothalamic–vagal corticotropin releasing factor (CRF) inputs.Both in vitro and in vivo studies demonstrated that, following perinatal high‐fat diet, CRF receptors were tonically active at NTS–DMV synapses, and that pharmacological antagonism of these receptors restored the appropriate gastric response to OXT.The current study suggests that perinatal high‐fat diet exposure disrupts descending PVN–DMV inputs, leading to a dysregulated vagal brain–gut response to stress. [ABSTRACT FROM AUTHOR]

Background: Chronic renal failure (CRF) is manifested by various skin symptoms. New changes have been described continuously since the advent of hemodialysis, which increases life expectancy and allows time for these changes to occur. Objective: The aim of this study was to determine the frequency of skin manifestations in chronic renal failure (CRF) patients receiving hemodialysis. Methods: it was a cross sectional observational study in which skin changes were studied in a total of 500 CRF patientsreceiving hemodialysis attending dermatology OPD in a tertiary hospital in central India. Results: 80% of the patients complained of some skin problems. However, at examination, all patients had at least one skin condition caused by CRF. The most common finding was xerosis (80%), followed by pallor (70%), itching (50%), and skin hyperpigmentation (40%). Other skin manifestations include Kell's disease (20%), fungal (40%), bacterial (15%), purpura (10%) and dermatitis (2%). Nail changes include koilonychia (25%), onychomycosis (20%), subungual hyperkeratosis (10%), onycholysis (10%) and hemorrhages (5%), Mee's line (5%), Muehrcke lines (5%) and Beau's lines (2%). Hair changes include thinning hair (35%) and brittle and dull hair (15%). Oral changes include striated macroglossia (40%), xerostomia (30%), ulcerative stomatitis (30%), angular cheilitis (15%), and uremic breath (10%). Some rare side effects of CRF have also been observed, such as uremic frostbite, gynecomastia, and pseudoKaposi's sarcoma. Conclusion: CRF is associated with a complex set of cutaneous manifestations caused by disease or treatment. The most common are xerosis and pruritus, and early recognition of skin symptoms can reduce pain and morbidity. [ABSTRACT FROM AUTHOR]

The leading intention of the current paper is to review the research work accomplished by various researchers to achieve sentiment analysis on the text and to elaborate on natural language processing (NLP) and various machine learning algorithms used to evaluate textual sentiments. In this study, primitive cases are considered that used crucial algorithms, and knowledge that can be opted for sentiment analysis. A survey of the work that has been done till now is conducted observing the results and outcomes concerning varying parameters of various researchers who worked on previously existing as well as novel and hybrid algorithms opting legion methodologies. The fundamental algorithms like Support Vector Machine (SVM), Bayesian Networks (BN), Maximum Entropy (MaxEnt), Conditional Random Fields (CRF) and Artificial Neural Networks (ANN) are also discussed to achieve practice percentage and accuracy score in the field of NLP, sentiment analysis and text analytics. Various other novel approaches and algorithms like CNN, LSTM, KNN, K*, K‐means, K‐means++, SOM and ENORA, along with their limitations and the performance metrics providing accuracies for major open data sets are also analyzed. [ABSTRACT FROM AUTHOR]

The mainstream methods for change detection in synthetic-aperture radar (SAR) images use difference images to define the initial change regions. However, methods can suffer from semantic collapse, which makes it difficult to determine semantic information about the changes. In this paper, we proposed a hierarchical fusion SAR image change-detection model based on hierarchical fusion conditional random field (HF-CRF). This model introduces multimodal difference images and constructs the fusion energy potential function using dynamic convolutional neural networks and sliding window entropy information. By using an iterative convergence process, the proposed method was able to accurately detect the change-detection regions. We designed a dynamic region convolutional semantic segmentation network with a two-branch structure (D-DRUNet) to accomplish feature fusion and the segmentation of multimodal difference images. The proposed network adopts a dual encoder–single decoder structure where the baseline is the UNet network that utilizes dynamic convolution kernels. D-DRUNet extracts multimodal difference features and completes semantic-level fusion. The Sobel operator is introduced to strengthen the multimodal difference-image boundary information and construct the dynamic fusion pairwise potential function, based on local boundary entropy. Finally, the final change result is stabilized by iterative convergence of the CRF energy potential function. Experimental results demonstrate that the proposed method outperforms existing methods in terms of the overall number of detection errors, and reduces the occurrence of false positives. [ABSTRACT FROM AUTHOR]

Devices in the Internet of Things (IoT) usually use cloud storage and cloud computing to save storage and computing cost. Therefore, the efficient realization of one-to-many communication of data on the premise of ensuring the security of cloud storage data is a challenge. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) can not only protect the security of data in the cloud and achieve one-to-many communication but also achieve fine-grained access control for data. However, the single-authority CP-ABE faces the crisis of single point of failure. In order to improve security, the Multi-Authority CP-ABE (MA-CP-ABE) is adopted. Although there are provably-secure MA-CP-ABE schemes, Edward Snowden's research shows that provably-secure cryptographic schemes are vulnerable to backdoor attacks, resulting in secret disclosure, and thus threatening security. In addition, ABE requires huge computational overhead in key generation, encryption and decryption, which increase with the increase in the number of attributes and the complexity of the access structure, and there are a large number of resource-constrained devices in the IoT. To mitigate this issue, we construct the Online/Offline MA-CP-ABE with Cryptographic Reverse Firewalls (OO-MA-CP-ABE-CRFs) scheme. This scheme not only uses Cryptographic Reverse Firewall (CRF) to resist backdoor attacks but also uses online/offline key generation, online/offline encryption and outsourcing encryption technology to optimize the efficiency of the MA-CP-ABE scheme with reverse firewall, reducing the storage and computing cost of users. Finally, the security of the OO-MA-CP-ABE-CRFs scheme is proved, and the experimental results indicate that the scheme is efficient and practical. [ABSTRACT FROM AUTHOR]

Named Entity Recognition and Classification (NER) serves as a foundation for many natural language processing tasks such as question answering, text summarization, news/document clustering and machine translation. Manipuri's early NER systems are based on machine learning approaches and employ handcrafted morphological features and domain-specific rules. The domain-specific rules for Manipuri NER are hard to extract as the language is highly agglutinative, inflectional and falls in the category of low resource language. In recent years, deep learning, empowered by continuous vector representation and semantic composition through non-linear processing, has been employed in the various NER task yielding state-of-the accuracy. In this paper, we propose a Manipuri NER model using Bidirectional Long Short Term Memory (BiLSTM) deep neural network in unison with an embedding technique. The embedding technique is a BiLSTM character-level word representation in conjunction with word embedding, which acts as a feature for the Bi-LSTM NER model. The proposed model also employs a Conditional Random Field (CRF) classifier to capture the dependency among output NER tags. Various Gradient Descent (GD) optimizers for the neural model were experimented with to establish an efficient GD optimizer for accurate NER. The NER model with RMSprop GD optimizer achieved an F-Score measure of approximately 98.19% at learning rate η = 0.001 and with decay constant of ρ = 0.9 . Further, while performing an intrinsic evaluation on the word embedding, it is found that the proposed embedding technique as a feature can capture the semantic and syntactic rule of the language with 88.14% average clustering accuracy for all NE classes. [ABSTRACT FROM AUTHOR]

Attribute-level sentiment element extraction aims to obtain the word pair < opinion target, opinion word > from texts, which mainly obtain fine-grained evaluation information in the attribute level. Due to the information fragmentation and semantic sparseness of product reviews, it is difficult to capture more comprehensive local information from unstructured texts, which leads to the incorrect extraction of some word pairs. Aimed at the problem, this paper proposes a framework for Attribute-Level Sentiment Element Extraction (ALSEE) towards product reviews. Firstly, a small amount of sample data is selected by random sampling, and multiple features (including part of speech, word distance, dependency relationship and semantic role) which are labelled. The labelled data are used as the training set. Then, the Condition Random Field (CRF) model is applied to extract opinion targets (OT) and opinion words (OW). The self-training strategy is used to achieve the semi-supervised learning of CRF model through iterative training. Finally, target-opinion word pairs with modifying relationship are obtained by dependency parsing. Compared with the existing methods, the proposed framework can effectively extract attribute-level sentiment elements though experimental results. [ABSTRACT FROM AUTHOR]

This work proposes a new intelligent disease prediction system for predicting the disease and also knowing the current status of the dead diseases such as diabetic, heart and cancer diseases. More number of people are affecting and losing their life early due to these diseases so that these are also called as dead diseases. The proposed disease prediction and monitoring system consists of two phases such as feature selection and classification phases. In the feature selection phase, a newly proposed feature selection algorithm called conditional random field and mutual information-based feature selection algorithm is used for identifying the most contributed features that are used to enhance the prediction accuracy. In the classification phase, a newly proposed fuzzy-aware multilayer backpropagation neural network is applied for predicting and monitoring the diabetic disease and heart disease effectively. Here, newly generated fuzzy rules are also incorporated for making effective decision on patient records. The proposed prediction and monitoring system is used to predict and monitor the heart, diabetic and cancer diseases. The experiments have been conducted for evaluating the performance of the proposed disease prediction and monitoring system by using UCI Machine Learning Repository datasets and also proved that as better than the existing disease prediction systems in terms of precision, recall, F-measure and prediction accuracy. [ABSTRACT FROM AUTHOR]

Many cancer patients undergoing treatment experience cancer-related fatigue (CRF). Inflammatory markers are correlated with CRF but are not routinely targeted for treatment. We previously demonstrated in an NIH-funded placebo-controlled, double-blind, randomized clinical trial (NCT00878995, closed to follow-up) that seven weekly injections of 100 mg adjunct testosterone preserved lean body mass in cancer patients undergoing standard-of-care treatment in a hospital setting. Because testosterone therapy can reduce circulating proinflammatory cytokines, we conducted an ancillary analysis to determine if this testosterone treatment reduced inflammatory burden and improved CRF symptoms and health-related quality of life. Randomization was computer-generated and managed by the pharmacy, which dispensed testosterone and placebo in opaque syringes to the administering study personnel. A total of 24 patients were randomized (14 placebo, 10 testosterone), and 21 were included in the primary analysis (11 placebo, 10 testosterone). Testosterone therapy did not ameliorate CRF symptoms (placebo to testosterone difference in predicted mean multidimensional fatigue symptom inventory scores: −5.6, 95% CI: −24.6 to 13.3), improve inflammatory markers, or preserve health-related quality of life and functional measures of performance in late-stage cancer patients. [ABSTRACT FROM AUTHOR]

Background: Under the condition of stress, the hypothalamic–pituitary–adrenal axis (HPA axis) is activated and causes the secretion of corticotropin‐releasing factor (CRF). Previous studies have demonstrated that CRF is involved in the regulation of pain and itch. Thus, it remains worthy to explore whether the desensitization of pain and itch under high‐intensity acute stress (such as high fear and tension) is related to the sharp increase of CRF. Methods: Forced swimming was used to simulate acute stress. ELISA and pharmacological methods were conducted to observe the effects of forced swimming on acute pain or itch and the relationship between blood CRF content and itch or pain behavior. Intracerebroventricular (ICV) administration of CRF was conducted to examine the effects of CRF on acute pain or itch. Intrathecal administration of CRF receptor agonist or antagonist was conducted to examine the receptor mechanisms of the regulatory role of CRF in pain and itch. Results: ELISA experiment showed that the serum CRF in mice reached its peak within 5–10 min after acute stress (forced swimming). Behavioral data showed that the scratching behavior induced by itch agents decreased after acute swimming, while the mechanical pain threshold increased significantly. The inhibitory effect of acute stress on pain and itch is mediated by CRF receptor2 (CRFR2). Then, ICV injection of CRF was used to simulate the massive release of CRF under acute stress, and we observed that the scratching behavior induced by histamine or chloroquine was significantly inhibited after ICV injection of CRF. The above effects of CRF are mainly mediated by CRFR2. These results suggest that 5–10 min after acute stress, a large amount of CRF is released into the blood from the hypothalamus, which significantly inhibits acute pain and itch by acting on CRFR2. ICV injection of CRF can replicate the antipruritus effects of acute stress. Conclusions: The present study investigated the mechanism of acute stress‐induced analgesia and antipruritus and provided theoretical support for the treatment of pain and itch. [ABSTRACT FROM AUTHOR]

• An improved deep learning model using CRF and Bi-LSTM is given to achieve named entity recognition with unbalanced labels. • An adaptive G-Score is used to compare the fitting ability of models by evaluating the gap between Precision and Recall. • A multiclassification cross-entropy loss function has been presented for training with additional weights. With the development of cutting-edge IT technologies, e.g. Big Data, Knowledge Engineering, etc., traditional Intellectual Property (IP) services have depicted high redundancy and low efficiency during management of such large-scale of data. Recent advancement of Artificial Intelligence (AI) and Deep Learning (DL) models has been accelerating relevant research activities being investigated on Knowledge Graph (KG) schemes and applications in different domains, such as medical services, social media, etc. However, when IP services and their cyber-social provision are taken into account, relevant approaches suffer from unbalanced labels against training results, and inappropriate evaluation metrics not well reflecting the impact of the unbalance. In this paper, a deep learning model combining Conditional Random Field and Bidirectional LSTM has been proposed, in order to achieve named entity recognition with unbalanced labels. An adaptive metric, G-Score was introduced to compare the fitting ability of models by evaluating the gap between Precision and Recall. According to the results, the proposed model can effectively recognize the potential named entities with outperformance over other relevant models. [ABSTRACT FROM AUTHOR]

Purpose: Low cardiorespiratory fitness (CRF) is a major risk factor for cardiovascular disease (CVD) and a stronger predictor of CVD morbidity and mortality than established risk factors. The genetic component of CRF, quantified as peak oxygen uptake (V̇O2peak), is estimated to be ~60%. Unfortunately, current studies on genetic markers for CRF have been limited by small sample sizes and using estimated CRF. To overcome these limitations, we performed a large-scale systematic screening for genetic variants associated with V̇O2peak. Methods: A genome-wide association study was performed with BOLT-LMM including directly measured V̇O2peak from 4525 participants in the HUNT3 Fitness study and 14 million single-nucleotide polymorphisms (SNP). For validation, similar analyses were performed in the United Kingdom Biobank (UKB), where CRF was assessed through a submaximal bicycle test, including ~60,000 participants and ~60 million SNP. Functional mapping and annotation of the genome-wide association study results was conducted using FUMA. Results: In HUNT, two genome-wide significant SNP associated with V̇O2peak were identified in the total population, two in males, and 35 in females. Two SNP in the female population showed nominally significant association in the UKB. One of the replicated SNP is located in PIK3R5 , shown to be of importance for cardiac function and CVD. Bioinformatic analyses of the total and male population revealed candidate SNP in PPP3CA , previously associated with CRF. Conclusions: We identified 38 novel SNP associated with V̇O2peak in HUNT. Two SNP were nominally replicated in UKB. Several interesting genes emerged from the functional analyses, among them one previously reported to be associated with CVD and another with CRF. [ABSTRACT FROM AUTHOR]

Background: The activation of the hypothalamic–pituitary–adrenal (HPA) axis is essential for metabolic adaptation in response to fasting. However, the neurocircuitry connecting changes in the peripheral energy stores to the activity of hypothalamic paraventricular corticotrophin-releasing factor (CRFPVN) neurons, the master controller of the HPA axis activity, is not completely understood. Our main goal was to determine if hypothalamic arcuate nucleus (ARC) POMC and AgRP neurons can communicate fasting-induced changes in peripheral energy stores, associated to a fall in plasma leptin levels, to CRFPVN neurons to modulate the HPA axis activity in mice. Results: We observed increased plasma corticosterone levels associate with increased CRFPVN mRNA expression and increased CRFPVN neuronal activity in 36 h fasted mice. These responses were associated with a fall in plasma leptin levels and changes in the mRNA expression of Agrp and Pomc in the ARC. Fasting-induced decrease in plasma leptin partially modulated these responses through a change in the activity of ARC neurons. The chemogenetic activation of POMCARC by DREADDs did not affect fasting-induced activation of the HPA axis. DREADDs inhibition of AgRPARC neurons reduced the content of CRFPVN and increased its accumulation in the median eminence but had no effect on corticosterone secretion induced by fasting. Conclusion: Our data indicate that AgRPARC neurons are part of the neurocircuitry involved in the coupling of PVNCRF activity to changes in peripheral energy stores induced by prolonged fasting. [ABSTRACT FROM AUTHOR]

Corticotropin-releasing factor (CRF) regulates the stress response in the hypothalamus and modulates neurotransmission across the brain through CRF receptors. Acute stress increases hypothalamic CRF and the GABAergic neurosteroid (3α,5α)3-hydroxypregnan-20-one (3α,5α-THP). We previously showed that 3α,5α-THP regulation of CRF is sex and brain region dependent. In this study, we investigated 3α,5α-THP regulation of stress-induced hypothalamic CRF, CRF receptor type 1 (CRFR1), CRF binding protein (CRFBP), pro-opiomelanocortin (POMC), and glucocorticoid receptor (GR) by western blot and circulating corticosterone (CORT) by enzyme-linked immunosorbent assay (ELISA) in male and female Sprague Dawley rats. Tissue was collected after rats were injected with 3α,5α-THP (15 mg/kg, IP) or vehicle 15 min prior to 30 min of restraint stress (RS), or 10 min of forced swim stress (FSS) and 20 min recovery. The initial exposure to a stress stimulus increased circulating CORT levels in both males and females, but 3α,5α-THP attenuated the CORT response only in females after RS. 3α,5α-THP reduced GR levels in male and females, but differently between stressors. 3α,5α-THP decreased the CRF stress response after FSS in males and females, but after RS, only in female rats. 3α,5α-THP reduced the CRFR1, CRFBP, and POMC increases after RS and FSS in males, but in females only after FSS. Our results showed different stress responses following different types of stressors: 3α,5α-THP regulated the HPA axis at different levels, depending on sex. [ABSTRACT FROM AUTHOR]

An intelligent question-answering of agricultural knowledge can be one of the most important parts of information agriculture. Among them, named entity recognition has been a key technology for intelligent question-answering and knowledge graph construction in the fields of agricultural domain. It is also a high demand for the accurate identification of named entities. Furthermore, the Chinese named entity recognition can be confined to the location and semantic information of characters, due to the long length of agricultural entity and complex naming. Therefore, it is very necessary to improve the recognition performance in the process of named entity recognition, particularly for the sufficient capture of character position, contextual semantic features, and long-distance dependency information. In this study, a novel Chinese named entity recognition of agriculture was proposed using EmBERT-BiLSTM-CRF model. Firstly, the Bidirectional Encoder Representation from Transformers (BERT) pre-trained language model was applied as the layer of word embedding. The context semantic representation of the model was then improved to alleviate the polysemy, when pre-training the depth bidirectional representation of word vectors. Secondly, the language masking of BERT was enhanced significantly, according to the characteristics of Chinese. An Entity-level Masking strategy was utilized to completely mask the Chinese entities in the sentence with the consecutive tokens. The Chinese semantics was then better represented to alleviate the bias caused by incomplete semantics. Thirdly, the Bidirectional Long Short-Term Memory Network (BiLSTM) model was adopted to learn the semantic features of long-sequence using two LSTM networks (forward and backward), considering the contextual information in both directions at the same time. The long-distance dependency information of text was then captured during this time. Finally, the Conditional Random Field (CRF) was used to learn the labelling constraint in the training data. Among them, the learned constraint rules were used to detect whether the label sequence was legal during prediction. After that, the CRF also utilized the information of adjacent labels to output the globally optimal label sequence. Thus, the output of the model was a dependent label sequence, but an optimal sequence was considered the rules and order. A focal loss function was also used to alleviate the unbalanced sample distribution. A series of experiments were performed to construct the corpus of named entity recognition. As such, the corpus contained a total of 29 790 agricultural entities after BIO labelling, including 11 057 crops, 8 121 pesticides, 4 505 diseases, and 6 107 pest entities, in which the training, validation, and test set were divided, according to the ratio of 7:2:1. Four types of agricultural entities from the text were identified, including the crop varieties, pesticides, diseases, and insect pests, and then to label them. The experimental results show that the recognition accuracy of the EmBERT-BiLSTM-CRF model for the four types of entities was 94.97%, and the F1 score was 95.93%. Which compared with the models based on BiLSTM-CRF and BERT-BiLSTM-CRF, the recognition performance of EmBERT-BiLSTM-CRF is significantly improved, proved that used pre-trained language model as the a word embedding layer can represent the characteristics of characters well and the Entity-level Masking strategy can alleviate the bias caused by incomplete semantics, thereby enhanced the Chinese semantic representation ability of the model, so that enabling the model to more accurately identify Chinese agricultural named entities. This research can not only provide arelatively high entity recognition accuracy for tasks such as agricultural intelligence question answering, but also offer new ideas for the identification of Chinese named entities in fishery, animal husbandry, Chinese medical, and biological fields. [ABSTRACT FROM AUTHOR]

Background: The activation of the hypothalamic–pituitary–adrenal (HPA) axis is essential for metabolic adaptation in response to fasting. However, the neurocircuitry connecting changes in the peripheral energy stores to the activity of hypothalamic paraventricular corticotrophin-releasing factor (CRFPVN) neurons, the master controller of the HPA axis activity, is not completely understood. Our main goal was to determine if hypothalamic arcuate nucleus (ARC) POMC and AgRP neurons can communicate fasting-induced changes in peripheral energy stores, associated to a fall in plasma leptin levels, to CRFPVN neurons to modulate the HPA axis activity in mice. Results: We observed increased plasma corticosterone levels associate with increased CRFPVN mRNA expression and increased CRFPVN neuronal activity in 36 h fasted mice. These responses were associated with a fall in plasma leptin levels and changes in the mRNA expression of Agrp and Pomc in the ARC. Fasting-induced decrease in plasma leptin partially modulated these responses through a change in the activity of ARC neurons. The chemogenetic activation of POMCARC by DREADDs did not affect fasting-induced activation of the HPA axis. DREADDs inhibition of AgRPARC neurons reduced the content of CRFPVN and increased its accumulation in the median eminence but had no effect on corticosterone secretion induced by fasting. Conclusion: Our data indicate that AgRPARC neurons are part of the neurocircuitry involved in the coupling of PVNCRF activity to changes in peripheral energy stores induced by prolonged fasting. [ABSTRACT FROM AUTHOR]

Temporal lobe epilepsy (TLE), the most common focal seizure disorder in adults, can be instigated in experimental animals by convulsant-induced status epilepticus (SE). Principal hippocampal neurons from SE-experienced epileptic male rats (postSE neurons) display markedly augmented spike output compared with neurons from nonepileptic animals (non-SE neurons). This enhanced firing results from a cAMP-dependent protein kinase A-mediated inhibition of KCa3.1, a subclass of Ca2+- gated K+ channels generating the slow afterhyperpolarizing Ca2+-gated K+ current (IsAHP). The inhibition of KCa3.1 in post-SE neurons leads to a marked reduction in amplitude of the IsAHP that evolves during repetitive firing, as well as in amplitude of the associated Ca2+-dependent component of the slow afterhyperpolarization potential (KCa-sAHP). Here we show that KCa3.1 inhibition in post-SE neurons is induced by corticotropin releasing factor (CRF) through its Type 1 receptor (CRF1R). Acute application of CRF1R antagonists restores KCa3.1 activity in post-SE neurons, normalizing KCa-sAHP/IsAHP amplitudes and neuronal spike output, without affecting these variables in non-SE neurons. Moreover, pharmacological antagonism of CRF1Rs in vivo reduces the frequency of spontaneous recurrent seizures in post-SE chronically epileptic rats. These findings may provide a new vista for treating TLE. [ABSTRACT FROM AUTHOR]

Background: Decreased cardiorespiratory fitness (CRF) related to cardiopulmonary function increases the risk of cardiovascular disease in patients with end‐stage kidney disease. Thus, early detection of the cause of impaired cardiopulmonary function in patients undergoing peritoneal dialysis (PD) is of important clinical significance. Methods: In this cross‐sectional study, symptom‐restricted cardiopulmonary exercise testing (CPET) was performed in 30 patients undergoing PD and in 23 age‐ and sex‐matched healthy control subjects. A fixed workload was added every minute until fatigue, and breath‐by‐breath respiratory gas was analysed with an automated gas analyzer at 10‐s intervals. Results: The peak oxygen uptake (16.39 ± 0.83 vs. 25.77 ± 1.33 ml/kg/min p < 0.001) and the oxygen uptake at the anerobic threshold of patients undergoing PD (9.61 ± 0.34 vs. 14.55 ± 0.64 ml/kg/min; p < 0.001) were lower than in healthy control subjects, and both of these parameters correlated with body mass index and left atrial dimension. A steeper minute ventilation/carbon dioxide production slope (27.20 ± 0.68 vs. 24.29 ± 0.69；p < 0.01) and a lower end‐tidal carbon dioxide partial pressure (37.93 ± 0.54 vs. 41.27 ± 0.83 mmHg；p < 0.05) were observed in patients undergoing PD. The oxygen pulse and oxygen uptake efficiency slope was smaller in patients undergoing PD. The Maximum heart rate (126.07 ± 4.01 vs. 149.96 ± 5.29 bpm；p < 0.01) and 1‐min heart rate recovery (13.93 ± 1.52 vs. 24.39 ± 1.61 bpm；p < 0.01) were also lower in patients undergoing PD. Conclusion: Subclinical cardiopulmonary dysfunction may exist in patients with PD, and a reduction in CRF in patients undergoing PD is affected by both central and peripheral functions. CPET has potential value in revealing the mechanism of impaired CRF and in discovering subclinical abnormalities in cardiopulmonary function. [ABSTRACT FROM AUTHOR]

Anxiety is an emotional response to a potential threat. It is characterized by worry, feelings of tension, and physical changes. Trinucleotide repeat containing adaptor 6A (TNRC6A) binds to argonaute (AGO) proteins and microRNAs to form the miRNA-induced silencing complex (miRISC), which mediates mRNA degradation, storage, and translational repression functions. However, whether TNRC6A is involved in anxiety regulation remains unknown. In this study, TNRC6A was downregulated in the prefrontal cortex (PFC) of mice exposed to acute restraint stress. Inhibition of TNRC6A in PFC induced anxious behaviour. RNA immunoprecipitation, RNA pull-down and real-time quantitative PCR revealed that TNRC6A directly binds to miR-21-3p and maintains its stability. Intriguingly, miR-21-3p was downregulated in the PFC of acute stress mice, whereas overexpression of miR-21-3p significantly reduced anxiety-like behaviour. Furthermore, miR-21-3p knockdown significantly increased the frequency and amplitude of spontaneous excitatory postsynaptic currents (sEPSCs) in the PFC pyramidal neurons. Dual luciferase assay and western blotting confirmed that miR-21-3p binds to the 3 'UTR region of corticotropin-releasing factor (CRF) mRNA and regulates CRF and cAMP-response element binding protein (CREB) expression. These results confirm that low levels of TNRC6A in the PFC decrease the stability of miR-21-3p which promotes the up-regulation of CRF, leading to the development of anxiety-like behaviours. This research provides insight into a novel molecular mechanism by which TNRC6A regulates anxiety behaviour through the miR-21-3p/CRF signalling axis. [Display omitted] • Prefrontal TNRC6A regulates anxiety behavior in mice. • TNRC6A regulates the stability and function of miR-21-3p. • MiR-21-3p regulates anxiety behavior through CRF signaling and abnormal neurotransmission. • The role of TNRC6A in anxiety behavior is mediated by miR-21-3p/CRF/PKA signaling pathway. [ABSTRACT FROM AUTHOR]

Named Entity Recognition (NER) is a vitally important task of Natural Language Processing (NLP), which aims at finding named entities in natural language text and classifying them into predefined categories such as persons (PER), places (LOC), organizations (ORG), and so on. In the Arabic context, the current NER approaches based on deep learning are mainly based on word embedding or character-level embedding as input. However, using a single granularity representation has problems with out-of-vocabulary (OOV), word embedding errors, and relatively simple semantic content. This paper presents a multi-headed self-attention mechanism implemented in the BiLSTM-CRF neural network structure to recognize Arabic named entities on social media using two embeddings. Unlike other state-of-the-art approaches, this approach combines character and word embedding at the embedding layer, and the attention mechanism calculates the similarity over the entire sequence of characters and captures local context information. The proposed approach better recognized NEs in Dialect Arabic, reaching an F1 value of 74.15% on Darwish's dataset (a publicly available Arabic NER benchmark for social media). According to our knowledge, our findings outperform the current state-of-the-art models for Arabic Named Entity Recognition on social media. [ABSTRACT FROM AUTHOR]

Increased glucocorticoids characterise acute pain responses, but not the chronic pain state, suggesting specific modifications to the hypothalamic–pituitary–adrenal (HPA)‐axis preventing the persistent nature of chronic pain from elevating basal glucocorticoid levels. Individuals with chronic pain mount normal HPA‐axis responses to acute stressors, indicating a rebalancing of the circuits underpinning these responses. Preclinical models of chronic neuropathic pain generally recapitulate these clinical observations, but few studies have considered that the underlying neuroendocrine circuitry may be altered. Additionally, individual differences in the behavioural outcomes of these pain models, which are strikingly similar to the range of behavioural subpopulations that manifest in response to stress, threat and motivational cues, may also be reflected in divergent patterns of HPA‐axis activity, which characterises these other behavioural subpopulations. We investigated the effects of sciatic nerve chronic constriction injury (CCI) on adrenocortical and hypothalamic markers of HPA‐axis activity in the subpopulation of rats showing persistent changes in social interactions after CCI (Persistent Effect) and compared them with rats that do not show these changes (No Effect). Basal plasma corticosterone did not change after CCI and did not differ between groups. However, adrenocortical sensitivity to adrenocorticotropic hormone (ACTH) diverged between these groups. No Effect rats showed large increases in basal plasma ACTH with no change in adrenocortical melanocortin 2 receptor (MC2R) expression, whereas Persistent Effect rats showed modest decreases in plasma ACTH and large increases in MC2R expression. In the paraventricular nucleus of the hypothalamus of Persistent Effect rats, single labelling revealed significantly increased numbers of corticotropin releasing factor (CRF) +ve and glucocorticoid receptor (GR) +ve neurons. Double‐labelling revealed fewer GR +ve CRF +ve neurons, suggesting a decreased hypothalamic sensitivity of CRF neurons to circulating corticosterone in Persistent Effect rats. We suggest that in addition to rebalancing the HPA‐axis, the increased CRF expression in Persistent Effect rats contributes to changes in complex behaviours, and in particular social interactions. [ABSTRACT FROM AUTHOR]

Objectives: Aiming at the problems that current address matching relies heavily on word segmentation dictionary and cannot effectively recognize address elements in addresses and their types, a Chinese address parsing method based on deep learning is proposed. Methods: The model combining robustly optimized bidirectional encoder representations from transformers (BERT） transformers pretraining approach (RoBERTa), bidirectional long short-term memory (BiLSTM), and conditional random field (CRF) is used to parse Chinese addresses. Firstly, the RoBERT a model is used to obtain the word vector representation of the address. Secondly, BiLSTM is used to learn the address model features and contextual in⁃ formation. Finally, CRF is used to construct the constraint relations between the labels. Results: Through the comparison and evaluation of different word vector representations of addresses and different sequence labeling models, the proposed method in this study achieves the maximum value of 0.940 on the generalization ability test dataset, and the precision, recall rate, and F1⁃score of the corresponding test dataset reach 0.962, 0.974, and 0.968.Conclusions：The method proposed in this paper does not need to extract the ad⁃ dress model features, nor does it rely on word segmentation dictionary for address segmentation. Address elements are recognized by learning address context information and address model features. The model generalization ability test dataset used in this study can effectively test whether the model is overfitted. There is little difference between bidirectional gated recurrent unit (BiGRU) and BiLSTM for Chinese ad⁃ dress resolution, and the sparse attention mechanism helps to improve the accuracy of address resolution. [ABSTRACT FROM AUTHOR]

Chronic renal failure patients on dialysis are at a high risk of death due to vascular calcification. This study aimed at investigating the effect of omega-3 fatty acids on the vascular calcification biomarkers fetuin-A and osteoprotegerin (OPG) in patients with chronic renal failure who are undergoing hemodialysis. This prospective, open-label, controlled, parallel study included 60 hemodialysis patients who were randomized to receive either omega-3 fatty acids capsule along with their standard care of treatment (omega-3 group) or their standard care of treatment only (control group). Serum levels of fetuin-A, OPG, calcium, phosphorus, hemoglobin, parathyroid hormone, blood urea nitrogen (BUN), albumin, serum creatinine (SCr), and serum triglycerides (TG) were measured at baseline and after six months of intervention and follow-up of both groups. Significantly increased levels of fetuin-A and OPG (p < 0.001) were observed in the omega-3 group six months after the intervention compared with the control group. Levels of TG, albumin, SCr, BUN, phosphorous, calcium, hemoglobin, and parathyroid hormone were not significantly different in the omega-3 group compared with the control group after six months of intervention. Our study concluded that omega-3 may have a clinically beneficial effect in decreasing cardiovascular events by increasing the levels of the protective vascular calcification inhibitors fetuin-A and osteoprotegerin in chronic renal failure patients who are undergoing hemodialysis. [ABSTRACT FROM AUTHOR]

Among the continuous (CONT) campaigns of very long baseline interferometry (VLBI), CONT17 is a special CONT campaign which comprises three independent networks: two legacy networks, and one VGOS network to mainly check possible biases in network geometry. In this study, we estimate all types of geodetic parameters (station and source coordinates, and Earth orientation parameters (EOP)) within the single networks and compare them to investigate potential network dependent biases. Since the separate networks are technically linked to each other by common parameters, i.e. EOP and common radio source coordinates, they can be regarded as a single network on the Earth and integrated in one common adjustment. Thus, we also estimate the station and source coordinates, and the EOP from the integrated networks and compare them with those of the single networks to check the impact on the geodetic parameters. There are both subtle and large biases (up to 7.1 mm between the legacy networks in the Ty component and 23.3 mm between the legacy and VGOS networks in the D 3 component on the surface of the Earth) between the single networks depending on parameter types. The integrated network eventually compensates those differences. Adding the VGOS network to the legacy networks does not show any significant improvement for the latter. However, the integration can provide the VGOS network with better parameter estimates which is important for the new stations. [ABSTRACT FROM AUTHOR]

Sentence boundary detection (SBD) is an essential step for a very large number of natural language processing applications such as parsing, information retrieval, automatic summarization, machine translation, etc. In this paper, we tackle the problem of SBD of dialectal Arabic, especially for the Tunisian dialect. We compare the efficiency of three learning algorithms: Deep Neuronal Networks (DNN), Support Vector Machines (SVM) and Conditional Random Fields (CRF) to detect the boundaries of sentences written in different types of dialect. The best model achieved an F-measure of 84.37% using CRF which is a popular formalism for structured prediction in NLP and it has been widely applied in text segmentation. [ABSTRACT FROM AUTHOR]

Attribute-level sentiment element extraction aims to obtain the word pair < opinion target, opinion word > from texts, which mainly obtain fine-grained evaluation information in the attribute level. Due to the information fragmentation and semantic sparseness of product reviews, it is difficult to capture more comprehensive local information from unstructured texts, which leads to the incorrect extraction of some word pairs. Aimed at the problem, this paper proposes a framework for Attribute-Level Sentiment Element Extraction (ALSEE) towards product reviews. Firstly, a small amount of sample data is selected by random sampling, and multiple features (including part of speech, word distance, dependency relationship and semantic role) which are labelled. The labelled data are used as the training set. Then, the Condition Random Field (CRF) model is applied to extract opinion targets (OT) and opinion words (OW). The self-training strategy is used to achieve the semi-supervised learning of CRF model through iterative training. Finally, target-opinion word pairs with modifying relationship are obtained by dependency parsing. Compared with the existing methods, the proposed framework can effectively extract attribute-level sentiment elements though experimental results. [ABSTRACT FROM AUTHOR]

Text simplification is a vital work for comprehending patent claims due to its complex syntactic structures and lengthy sentences. Therefore, almost all patent analysis practitioners cannot be able to directly and intuitively understand patent essence even through some common natural language processing (NLP) tools are applied to parse these patent claim paragraph or sentences. Universal text analysis tools above is almost useless, or even crashed when applied to some complex paragraphs of patent claims. Therefore, it is necessary to propose a patent text oriented simplification approach to help patent researchers grasp the essence of patent quickly and intuitively. Motivated by the above reason, we in this article propose a simplification method based on deep learning to segment patent claim into shorter and comprehensible sentences for downstream tasks of patent analysis. The proposed approach contains two stages: on one stage, we use a machine learning approach of conditional random field (CRF) to decompose syntactically complex paragraphs into coarse‐grained level sentences with simplified structures and complete semantics; on another stage, a deep Learning architecture of bidirectional long‐short term memory (Bi‐LSTM)‐CRF is applied to segment coarse‐grained and lengthy sentences of former stage into fined‐grained and shorter sentences. Compared with a series of baselines, our patent segmentation architecture based on deep learning of Bi‐LSTM‐CRF achieves higher performance than any other methods on the evaluation measures of precision, recall, and F1. [ABSTRACT FROM AUTHOR]

Background: In Burkina Faso, red blood cell (RBC) transfusion remains the crucial anaemia treatment following chronic renal failure (CRF) as erythropoietin and its analogues are unavailable. However, blood group matching beyond the ABO and Rhesus is not common in Burkina Faso. Thus, alloimmunisation is a potential issue for transfused patients. Objective: Our study aimed to identify anti-erythrocyte antibodies in multi-transfused CRF patients at the Yalgado Ouedraogo Teaching Hospital, Ouagadougou, Burkina Faso. Methods: This cross-sectional study, conducted from October 2018 to November 2019, included CRF patients who had received at least two RBC units. We screened patients for the presence of RBC antibodies using three commercial Cells panels and identified antibody specificities for positive screenings using 11 Cells panels for an indirect antiglobulin test (IAT) in a low ionic strength microcolumn gel-card system. Results: Two hundred and thirty-five patients (45.1% female; average age: 41.5 years) were included. The median number of blood units received per patient was 10 (interquartile range: 5–20). The overall alloimmunisation rate was 5.9% (14/235). Antibodies identified included: anti-D (1 case), anti-C (1 case), anti-D+C (4 cases), anti-CW (1 case), anti-E (1 case), anti-S (1 case) and anti-Lea (1 case). In four positive patients, the specificity of the antibodies was indeterminate. No risk factors were associated with alloimmunisation. Conclusion: In Burkina Faso, screening for RBC alloantibodies should be mandated for patients at risk. The high rate of indeterminate antibodies suggests the need to develop a local RBC antibody panel adapted to the local population. [ABSTRACT FROM AUTHOR]

In recent times, the term artificial intelligence and its various applications have spread, such as machine learning, deep learning, natural language processing, and computer vision, and it has been used in many sectors and has resulted in business development in terms of performance, speed and quality. This development has also extended to libraries and information centers as one of the largest institutions that provide knowledge services, and the research sheds light on the process of extracting bibliographic data from information sources, especially text materials that include (books and scientific articles), Where the search is to encourage enterprises and institutions of information industry knowledge, namely publishers, libraries and information centers to adopt the use of bibliographic data extraction tools, The proposed model provides a general framework for extracting bibliographic data from information sources - text - Arabic, And to facilitate catalogers works and not cancel their role fully, Although the technology could limit the role of the catalogers, The research provides an explanation of what artificial intelligence is and its applications, And the definition of what is descriptive cataloging, clarifying the role of the publisher in the process of creating bibliographic records, and making use of the capabilities of machine learning in extracting bibliographic data from textual information sources, in addition to presenting the structure and components of the proposed model for extracting bibliographic data out of a number of results and recommendations. [ABSTRACT FROM AUTHOR]

Neural network algorithm based on weakly supervised label is a hot research topic in medical field. In view of the lack of labeling data and the inaccurate segmentation of cytoplasm and nucleus, this study proposes a semantic segmentation algorithm based on weakly supervised cervical cell images. First, the algorithm uses unsupervised K-means as the labeling function to generate cell image segmentation labels. Then, training is conducted through an improved Encoder-Decoder network. Subsequently, CRF is introduced as the last layer of the network to integrate the global information of the image and optimize the segmentation results. The label and prediction images are optimized and trained in three times to achieve pixel level classification of cell images. Finally, the algorithm is verified using the cervical cell image dataset. The experimental results show that the algorithm has high generalization ability, and the accuracy rate is up to 96.7%. [ABSTRACT FROM AUTHOR]

Methods based on Graph Convolutional Networks(GCN) for skeleton-based action recognition have achieved great success due to their ability to exploit graph structural information from skeleton data. Recently, the bone information has attracted considerable attention as an effective modality which complements the more conventional joint information for action recognition. However, most existing GCN-based methods extract the joint and bone features with two separate GCN networks, ignoring the dependencies between them. In this paper, a novel GCN model is proposed to exploit the information across joints, bones and their relationship collaboratively on a single undirected graph instead of two separate networks. We call the proposed model Vertex-Edge Graph Convolutional Network (VE-GCN) since it conducts the graph convolution operation on the sampling area containing the designated vertexes from joints and edges from bones, respectively. In addition to conducting the Vertex-Edge graph convolution based on the physical connections of the skeleton, we further apply the Vertex-Edge graph convolution to the non-physical joint-joint and joint-bone connections to capture the distal dependencies, and then the convolution results on the non-physical connections are incorporated into the VE-GCN. Moreover, the Conditional Random Field (CRF) is adopted as the loss function to achieve the task of action recognition. Experimental results on four challenging benchmarks (NTU RGB+D, NTU RGB+D 120, N-UCLA, SYSU) show that the proposed model achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Chinese word segmentation is an important research direction in related research on elementary mathematics knowledge extraction. The speed of segmentation directly affects subsequent applications, and the accuracy of segmentation directly affects corresponding research in the next step. In the machine learning methods for extracting basic mathematical knowledge points, the Conditional Random Field (CRF) model implements new word discovery well, and is increasingly used in knowledge extraction of basic mathematics. This article first introduces the traditional CRF process of named entity recognition. Then, an improved algorithm CRF++for conditional field model is proposed. Since the recognition rate of named entities based on traditional machine learning methods is not high, a post-processing method for entity recognition that automatically generates a dictionary is proposed. After identifying mathematical entities, a pruning strategy combining Viterbi algorithm and rules is proposed to achieve a higher recognition rate of elementary mathematical entities. Finally, several methods of disambiguation after entity recognition are introduced. [ABSTRACT FROM AUTHOR]

Early life adversity can disrupt development leading to emotional and cognitive disorders. This study investigated the effects of social isolation after weaning on anxiety, body weight and locomotion, and on extracellular dopamine (DA) and glutamate (GLU) in the nucleus accumbens (NAc) and their modulation by corticotropin releasing factor receptor 1. On the day of weaning, male rats were housed singly or in groups for 10 consecutive days. Anxiety‐like behaviors were assessed by an elevated plus maze (EPM) and an open field test (OF). Neurotransmitter levels were measured by in vivo microdialysis. Single‐housed rats spent less time, and entered more, into the closed arms of an EPM than group‐housed rats. They also spent less time in the center of an OF, weighed more and showed greater locomotion. In the NAc, no differences in CRF, or in basal extracellular DA or GLU between groups, were observed. A depolarizing stimulus increased DA release in both groups but to higher levels in isolated rats, whereas GLU increased only in single‐housed rats. Blocking CRF‐R1 receptors with CP‐154,526 decreased DA release in single‐housed but not in group‐housed rats. The corticotropin releasing factor receptor type 1 receptor antagonist also decreased GLU in group‐housed animals. These results show that isolating adolescent rats increases anxiety, body weight and ambulation, as well as the sensitivity of dopaminergic neurons to a depolarizing stimulus. This study provides further evidence of the detrimental effects of social isolation during early development and indicates that dysregulation of the CRF system in the NAc may contribute to the pathologies observed. [ABSTRACT FROM AUTHOR]