Search

Bats from three provinces in Vietnam (Lai Chau, Son La, and Dong Thap) were examined for the presence of pathogenic Leptospira or specific antibodies using polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA), and microscopic agglutination test (MAT). Tissue specimens from 298 bats belonging to 11 species were analyzed using a real-time PCR assay specific for leptospires of pathogenic species. Leptospiral DNA was identified in 40 bats from following species: Rousettus amplexicaudatus (5/9; 55.5 %), Rousettus leschenaultii (17/42; 40.4 %), Myotis hasseltii (8/25; 32 %), Taphozous longimanus (3/12; 25 %), and Eonycteris spelaea (7/32; 21.9 %). Based on secY phylogeny, sequences from M. hasseltii bore a strong resemblance to L. borgpetersenii. Sequences from other species revealed unique lineages: one of them resembled Leptospira sp., previously identified in Rousettus madagascariensis (Madagascar) and Rousettus aegyptiacus (South Africa); the second lineage showed close relation to L. kirshneri; and the third held an intermediary position between L. noguchii and L. interrogans. Through ELISA, anti-Leptospira antibodies were found in 83 of 306 bats, with the highest seroprevalence observed in R. leschenaultii (44/48; 91.6 %), R. amplexicaudatus (6/8; 75 %), and E. spelaea (19/25; 76 %). 66 of these ELISA-positive samples were tested using MAT; 41 of them were confirmed in MAT as positive. The predominant serogroups in our study were Tarassovi and Mini., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Persistent diarrhea is a severe gastroenteric disease with relatively high risk of pediatric mortality in developing countries. We conducted a randomized, double-blind, controlled clinical trial to evaluate the efficacy of liquid-form Bacillus clausii spore probiotics (LiveSpo CLAUSY; 2 billion CFU/5 mL ampoule) at high dosages of 4-6 ampoules a day in supporting treatment of children with persistent diarrhea. Our findings showed that B. clausii spores significantly improved treatment outcomes, resulting in a 2-day shorter recovery period (p < 0.05) and a 1.5-1.6 folds greater efficacy in reducing diarrhea symptoms, such as high frequency of bowel movement of ≥ 3 stools a day, presence of fecal mucus, and diapered infant stool scale types 4-5B. LiveSpo CLAUSY supportive treatment achieved 3 days (p < 0.0001) faster recovery from diarrhea disease, with 1.6-fold improved treatment efficacy. At day 5 of treatment, a significant decrease in blood levels of pro-inflammatory cytokines TNF-α, IL-17, and IL-23 by 3.24% (p = 0.0409), 29.76% (p = 0.0001), and 10.87% (p = 0.0036), respectively, was observed in the Clausy group. Simultaneously, there was a significant 37.97% decrease (p = 0.0326) in the excreted IgA in stool at day 5 in the Clausy group. Overall, the clinical study demonstrates the efficacy of B. clausii spores (LiveSpo CLAUSY) as an effective symptomatic treatment and immunomodulatory agent for persistent diarrhea in children.Trial registration: NCT05812820., (© 2024. The Author(s).)

A new filovirus named Měnglà virus was found in bats in southern China in 2015. This species has been assigned to the new genus Dianlovirus and has only been detected in China. In this article, we report the detection of filoviruses in bats captured in Vietnam. We studied 248 bats of 15 species caught in the provinces of Lai Chau and Son La in northern Vietnam and in the province of Dong Thap in the southern part of the country. Filovirus RNA was found in four Rousettus leschenaultii and one Rousettus amplexicaudatus from Lai Chau Province. Phylogenetic analysis of the polymerase gene fragment showed that three positive samples belong to Dianlovirus , and two samples form a separate clade closer to Orthomarburgvirus . An enzyme-linked immunosorbent assay showed that 9% of Rousettus , 13% of Eonycteris , and 10% of Cynopterus bats had antibodies to the glycoprotein of marburgviruses.

Statin medications, in addition to lifestyle modifications, have been the regimen of choice for addressing dyslipidemia in the general population. Its widespread use has been justified by increasing evidence that hyperlipidemia is a strong risk factor for the development of atherosclerotic disease resulting in myocardial infarction and other cardiovascular events. Unfortunately, this medication is not tolerated by some patients as it causes uncomfortable side effects such as myalgias, arthralgias, and headaches to name a few.  On rare occasions, some patients may develop immunity against the medication itself resulting in a condition known as immune-mediated necrotizing myopathy (IMNM). In such instances, patients carry antibodies against 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) or signal recognition particle (SRP). A small subset of patients may develop IMNM even in the absence of these two antibodies and they are termed seronegative. In this care report, we review the case of a 55-year-old Hispanic male with a history of Hashimoto's thyroiditis and hyperlipidemia who presented to an outpatient rheumatology office for severe proximal muscle weakness after being asymptomatic on rosuvastatin for over 20 years. The patient was stabilized with high-dose steroids and was subsequently given a regimen of mycophenolate and intravenous immunoglobulin (IVIG). He was able to regain approximately 75%-80% of his baseline muscle strength., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Luong et al.)

Objective: There is a research gap in studies that evaluate the effectiveness of a school-embedded mindfulness-based intervention for both students and teachers. To address this gap, the present pilot study reviews relevant literature and investigates whether students and teachers who participate in separate Mindfulness-Based Stress Reduction (MBSR) courses show improvements across a variety of psychological variables including areas of mental health and creativity., Methods: The study applied a controlled waitlist design with three measurement points. A total of 29 students (n = 15 in the intervention and n = 14 in the waitlist group) and 29 teachers (n = 14 in the intervention and n = 15 in the waitlist group) completed questionnaires before and after the MBSR course. The intervention group was also assessed after a 4-month follow-up period., Results: Relative to the control group, significant improvements in self-reported stress, self-regulation, school-specific self-efficacy and interpersonal problems were found among the students who participated in the MBSR course (p < 0.05, Cohens' d ranges from 0.62 to 0.68). Medium effect sizes on mindfulness, anxiety and creativity indicate a realistic potential in those areas. By contrast, teachers in the intervention group showed significantly higher self-reported mindfulness levels and reduced interpersonal problems compared to the control group(p < 0.05, Cohens' d = 0.66 and 0.42, respectively), with medium effect sizes on anxiety and emotion regulation., Conclusion: The present findings contribute to a growing body of studies investigating mindfulness in schools by discussing the similarities and differences in the effects of MBSR on students and teachers as well as stressing the importance of investigating interpersonal effects.

The training of temporal action localization models relies heavily on a large amount of manually annotated data. Video annotation is more tedious and time-consuming compared with image annotation. Therefore, the semi-supervised method that combines labeled and unlabeled data for joint training has attracted increasing attention from academics and industry. This study proposes a method called pseudo-label refining (PLR) based on the teacher-student framework, which consists of three key components. First, we propose pseudo-label self-refinement which features in a temporal region interesting pooling to improve the boundary accuracy of TAL pseudo label. Second, we design a module named boundary synthesis to further refined temporal interval in pseudo label with multiple inference. Finally, an adaptive weight learning strategy is tailored for progressively learning pseudo labels with different qualities. The method proposed in this study uses ActionFormer and BMN as the detector and achieves significant improvement on the THUMOS14 and ActivityNet v1.3 datasets. The experimental results show that the proposed method significantly improve the localization accuracy compared to other advanced SSTAL methods at a label rate of 10% to 60%. Further ablation experiments show the effectiveness of each module, proving that the PLR method can improve the accuracy of pseudo-labels obtained by teacher model reasoning. [ABSTRACT FROM AUTHOR]

A medical specialty prediction system for remote diagnosis can reduce the unexpected costs incurred by first-visit patients who visit the wrong hospital department for their symptoms. To develop medical specialty prediction systems, several researchers have explored clinical predictive models using real medical text data. Medical text data include large amounts of information regarding patients, which increases the sequence length. Hence, a few studies have attempted to extract entities from the text as concise features and provide domain-specific knowledge for clinical text classification. However, it is still insufficient to inject them into the model effectively. Thus, we propose Entity-enhanced BERT (E-BERT), which utilizes the structural attributes of BERT for medical specialty prediction. E-BERT has an entity embedding layer and entity-aware attention to inject domain-specific knowledge and focus on relationships between medical-related entities within the sequences. Experimental results on clinical questionnaire data demonstrate the superiority of E-BERT over the other benchmark models, regardless of the input sequence length. Moreover, the visualization results for the effects of entity-aware attention prove that E-BERT effectively incorporate domain-specific knowledge and other information, enabling the capture of contextual information in the text. Finally, the robustness and applicability of the proposed method is explored by applying it to other Pre-trained Language Models. These effective medical specialty predictive model can provide practical information to first-visit patients, resulting in streamlining the diagnostic process and improving the quality of medical consultations. [ABSTRACT FROM AUTHOR]

In view of the problems existing in the ESG classification task of Chinese financial texts, such as feature loss caused by excessively long texts, this paper proposes an interactive multi-task model AmultiESG for ESG classification of Chinese financial texts. The model divides Chinese financial text ESG classification and financial sentiment dictionary expansion into primary and secondary tasks. First, BiLSTM model is used to learn the original representation of the text. Then, in the secondary task, the attention mechanism and full connection layers are combined with the domain dictionary to realize the extraction of emotional words. In the main task, in order to prevent feature loss due to the excessively long texts, we process the text again and divide it into blocks according to the period. Meanwhile, we learned new feature representation of the text by combining text label representation, text block representation, BiLSTM output features and domain dictionary features. And we introduce an interactive information transfer mechanism to iteratively improve the predicted results of the two tasks and strengthen the association between them. It has been experimentally demonstrated that the proposed method shows superior performance compared to other baselines for the ESG classification task of Chinese financial text, especially for long-text classification tasks. [ABSTRACT FROM AUTHOR]

The task of named entity recognition (NER) plays a crucial role in extracting cybersecurity-related information. Existing approaches for cybersecurity entity extraction predominantly rely on manual labelling data, resulting in labour-intensive processes due to the lack of a cybersecurity-specific corpus. In this paper, we propose an improved self-training-based distant label denoising method for cybersecurity entity extraction. Firstly, we create two domain dictionaries of cybersecurity. Then, an algorithm that combines reverse maximum matching and part-of-speech tagging restrictions is proposed, for generating distant labels for the cybersecurity domain corpus. Lastly, we propose a high-confidence text selection method and an improved self-training algorithm that incorporates a teacher-student model and weight update constraints, for exploring the true labels of low-confidence text using a model trained on high-confidence text, thereby reducing the noise in the distant annotation data. Experimental results demonstrate that the cybersecurity distantly-labelled data we obtained is of high quality. Additionally, the proposed constrained self-training algorithm effectively improves the F1 score of several state-of-the-art NER models on this dataset, yielding a 3.5% improvement for the Vendor class and a 3.35% improvement for the Product class. [ABSTRACT FROM AUTHOR]

Machine learning models learn low-dimensional representations from complex high-dimensional data. Not only computer science but also social science has benefited from the advancement of these powerful tools. Within such tools, word embedding is one of the most popular methods in the literature. However, we have no particular documentation of this emerging trend because this trend overlaps different social science fields. To well compile this fragmented knowledge, we survey recent studies that apply word embedding models to human behavior mining. Our taxonomy built on the surveyed article provides a concise but comprehensive overview of this emerging trend of intersection between computer science and social science and guides scholars who are going to navigate the use of word embedding algorithms in their voyage of social science research. [ABSTRACT FROM AUTHOR]

Measures of 'humanness', 'coherence' or 'fluency' are the mainstay of dialogue system evaluation, but they don't target system capabilities and rarely offer actionable feedback. Reviewing recent work in this domain, we identify an opportunity for evaluation at the level of action sequences, rather than the more commonly targeted levels of whole conversations or single responses. We introduce interactive probes, an evaluation framework inspired by empirical work on social interaction that can help to systematically probe the capabilities of dialogue systems. We sketch some first probes in the domains of tellings and repair, two sequence types ubiquitous in human interaction and challenging for dialogue systems. We argue interactive probing can offer the requisite flexibility to keep up with developments in interactive language technologies and do justice to the open-endedness of action formation and ascription in interaction. [ABSTRACT FROM AUTHOR]

Compared to conventional image recognition, fine-grained classification exhibits increased vulnerability to labeling noise due to the presence of closely related categories, resulting in degraded performance on complex and non-representative samples. While existing approaches mitigate these issues through data cleaning, loss modification, and semi-supervised learning techniques, they often overlook the intrinsic attributes within training samples. Instead of designing any network architectures, this study introduces a model-agnostic progressive training strategy comprising of progressive training and label optimization, where the former is to decrease the affect from the noisy samples by facilitating a graduated learning approach in an easy-to-hard manner, while the latter is to denoise the label noises. Theoretical analysis also demonstrates that the proposed method uncovers valuable cues hidden in the training data, thereby enhancing the robustness of any learning-based models. Experimental evaluations on fine-grained classification benchmarks (e.g., CUB-200-2011, DTD, and Food-101) across various mainstream classification networks demonstrate the effectiveness of our training strategy. Code is available at . [ABSTRACT FROM AUTHOR]

The rapid growth of the global population has placed immense pressure on agriculture to enhance food production while addressing environmental and socioeconomic challenges such as biodiversity loss, water scarcity, and climate variability. Addressing these challenges requires adopting modern techniques and advancing agricultural research. Although some techniques, such as machine learning and deep learning, are increasingly used in agriculture, progress is constrained by the lack of large labelled datasets. This constraint arises because collecting data is often time-consuming, labour-intensive, and requires expert knowledge for data annotation. To mitigate data limitations, transfer learning (TL) offers a viable solution by allowing pre-trained models to be adapted for agricultural applications. Many researchers have demonstrated TL's potential to advance agriculture. Despite its importance, there is a lack of a comprehensive review, which could be essential to guide researchers in this field. Given the significance and the lack of a review paper, this paper provides a review dedicated to TL in agriculture, offering three main contributions. First, we provide an in-depth background study on TL and its applications in agriculture. Second, we offer a comprehensive examination of TL-based agricultural applications, covering pre-trained models, dataset sources, input image types, implementation platforms, and TL approaches. Third, based on an exploration of the existing studies, we identify the challenges faced when applying TL in agriculture. Finally, to address the identified challenges, we recommend suggestions for future research directions. [ABSTRACT FROM AUTHOR]

Deep learning networks have been trained using first-order-based methods. These methods often converge more quickly when combined with an adaptive step size, but they tend to settle at suboptimal points, especially when learning occurs in a large output space. When first-order-based methods are used with a constant step size, they oscillate near the zero-gradient region, which leads to slow convergence. However, these issues are exacerbated under nonconvexity, which can significantly diminish the performance of first-order methods. In this work, we propose a novel Boltzmann Probability Weighted Sine with a Cosine distance-based Adaptive Gradient (BSCAGrad) method. The step size in this method is carefully designed to mitigate the issue of slow convergence. Furthermore, it facilitates escape from suboptimal points, enabling the optimization process to progress more efficiently toward local minima. This is achieved by combining a Boltzmann probability-weighted sine function and cosine distance to calculate the step size. The Boltzmann probability-weighted sine function acts when the gradient vanishes and the cooling parameter remains moderate, a condition typically observed near suboptimal points. Moreover, using the sine function on the exponential moving average of the weight parameters leverages geometric information from the data. The cosine distance prevents zero in the step size. Together, these components accelerate convergence, improve stability, and guide the algorithm toward a better optimal solution. A theoretical analysis of the convergence rate under both convexity and nonconvexity is provided to substantiate the findings. The experimental results from language modeling, object detection, machine translation, and image classification tasks on a real-world benchmark dataset, including CIFAR10, CIFAR100, PennTreeBank, PASCALVOC and WMT2014, demonstrate that the proposed step size outperforms traditional baseline methods. [ABSTRACT FROM AUTHOR]

According to World Health Organization statistics, inappropriate medication has become an important factor affecting the safety of rational medication. In the gray area of medical insurance supervision, such as designated drugstores and medical institutions, there are lots of inappropriate medication phenomena regarding "big prescription for minor ailments." A traditional clinical decision support system is mostly based on established rules to regulate inappropriate prescriptions, which are not suitable for clinical environments and require intelligent review. In this study, we model the complex relationships between patients, diseases, and drugs based on medical big data to promote appropriate medication use. More specifically, we first construct the medication knowledge graph based on the historical prescription big data of tertiary hospitals and medical text data. Second, based on the medication knowledge graph, we employ a Gaussian mixture model to group patient population representation as physiological features. For diagnostic features, we employ pre-training word vector Bidirectional Encoder Representations from Transformers to enhance the semantic representation between diagnoses. In addition, to reduce adverse drug interactions caused by drug combinations, we employ a graph convolution network to transform drug interaction information into drug interaction features. Finally, we employ the sequence generation model to learn the complex relationships between patients, diseases, and drugs and provide an appropriate medication evaluation for doctor prescriptions in small hospitals from two aspects: drug list and medication course of treatment. In this study, we utilize the MIMIC III dataset alongside data from a tertiary hospital in Fujian Province to validate our model. The results show that our method is more effective than other baseline methods in the accuracy of the medication regimen prediction of rational medication. In addition, it achieved high accuracy in the appropriate medication detection of prescription in small hospitals. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between risk and protective factors and suicidal ideation among teachers, a group often overlooked in suicide prevention efforts. Using the diathesis-stress model as a partial framework, the research explores how depression and anxiety, as risk factors, and emotional intelligence, resilience, and positive thoughts, as protective factors, influence suicidal ideation. A cross-sectional design was employed, involving 1,270 Spanish teachers (median age: 46, 70.2% female). Key measures included the Positive and Negative Suicidal Ideation scale (PANSI), the Brief Emotional Intelligence Scale (BEIS-10), the Brief Resilient Coping Scale (BRCS) for resilience, and the General Health Questionnaire (GHQ-12) for depression and anxiety. A network approach was used to examine the relationships between these variables. Results showed that depression had a stronger relationship with negative ideation than anxiety, while emotional intelligence and positive thoughts acted as protective factors. Positive ideation was negatively associated with depression and anxiety, and positively linked to emotional intelligence and resilience. Emotional intelligence emerged as the most critical protective factor. These findings provide valuable evidence to inform the development of emotional management and resilience-building programs aimed at enhancing teachers' mental health and performance, which could also indirectly benefit students. [ABSTRACT FROM AUTHOR]

Background: Sequencing-based genetic testing is widely used in biomedical research, including pathogenic microorganism detection with metagenomic next-generation sequencing (mNGS). The application of sequencing results to clinical diagnosis and treatment relies on various interpretation knowledge bases. Currently, the existing knowledge bases are primarily built through manual knowledge extraction. This method requires professionals to read extensive literature and extract relevant knowledge from it, which is time-consuming and costly. Furthermore, manual extraction unavoidably introduces subjective biases. In this study, we aimed to automatically extract knowledge for interpreting mNGS results. Method: We propose a novel approach to automatically extract pathogenic microorganism knowledge based on the question-answer (QA) model. First, we construct a MicrobeDB dataset since there is no available pathogenic microorganism QA dataset for training the model. The created dataset contains 3,161 samples from 618 published papers covering 224 pathogenic microorganisms. Then, we fine-tune the selected baseline model based on MicrobeDB. Finally, we utilize ChatGPT to enhance the diversity of training data, and employ data expansion to increase training data volume. Results: Our method achieves an Exact Match (EM) and F1 score of 88.39% and 93.18%, respectively, on the MicrobeDB test set. We also conduct ablation studies on the proposed data augmentation method. In addition, we perform comparative experiments with the ChatPDF tool based on the ChatGPT API to demonstrate the effectiveness of the proposed method. Conclusions: Our method is effective and valuable for extracting pathogenic microorganism knowledge. [ABSTRACT FROM AUTHOR]

Multi-domain NMT aims to develop a parameter-sharing model for translating general and specific domains, such as biology, legal, etc., which often struggle with the parameter interference problem. Existing approaches typically tackle this issue by learning a domain-specific sub-network for each domain equally, but they ignore the significant data imbalance problem across domains. For instance, the training data for the general domain often outweighs the biological domain tenfold. In this paper, we observe a natural similarity between the general and specific domains, including shared vocabulary or similar sentence structure. We propose a novel parameter inheritance strategy to adaptively learn domain-specific child networks from the general domain. Our approach employs gradient similarity as the criterion for determining which parameters should be inherited or discarded between the general and specific domains. Extensive experiments on several multi-domain NMT corpora demonstrate that our method significantly outperforms several strong baselines. In addition, our method exhibits remarkable generalization performance in adapting to few-shot multi-domain NMT scenarios. Further investigations reveal that our method achieves good interpretability because the parameters learned by the child network from the general domain depend on the interconnectedness between the specific domain and the general domain. [ABSTRACT FROM AUTHOR]

People use search engines for various topics and items, from daily essentials to more aspirational and specialized objects. Therefore, search engines have taken over as people's preferred resource. The "How To" prefix has become familiar and widely used in various search styles to find solutions to particular problems. This search allows people to find sequential instructions by providing detailed guidelines to accomplish specific tasks. Categorizing instructional text is also essential for task-oriented learning and creating knowledge bases. This study uses the "How To" articles to determine the multi-label instruction category. We have brought this work with a dataset comprising 11,121 observations from wikiHow, where each record has multiple categories. To find out the multi-label category meticulously, we employ some transformer-based deep neural architectures, such as Generalized Autoregressive Pretraining for Language Understanding (XLNet), Bidirectional Encoder Representation from Transformers (BERT), etc. In our multi-label instruction classification process, we have reckoned our proposed architectures using accuracy and macro f1-score as the performance metrics. This thorough evaluation showed us much about our strategy's strengths and drawbacks. Specifically, our implementation of the XLNet architecture has demonstrated unprecedented performance, achieving an accuracy of 97.30% and micro and macro average scores of 89.02% and 93%, a noteworthy accomplishment in multi-label classification. This high level of accuracy and macro average score is a testament to the effectiveness of the XLNet architecture in our proposed 'InstructNet' approach. By employing a multi-level strategy in our evaluation process, we have gained a more comprehensive knowledge of the effectiveness of our proposed architectures and identified areas for forthcoming improvement and refinement. [ABSTRACT FROM AUTHOR]

Natural disasters leave scenes of devastation in their wake upon arrival. Sometimes, the destruction is so prominent that reaching the area where the disaster happened becomes challenging-rescuing and communicating with the victims and providing relief become nearly impossible. Nevertheless, some people continuously try to post about the situation on social media, like Facebook. Moreover, the Mass Media utilized the latest technology to search for the latest updates on the impacted area. Analyzing the social and mass media to be aware of the situation can help the authorities take necessary steps. In this research, we intend to develop a multilingual (Bangla and English) system that can predict the post-disaster impact by maneuvering the contents from mass and social media. Therefore, we have created a novel multilingual dataset using the disaster news from Newspaper and posts from Facebook and utilized the modern text processing architecture transformers. For Bangla and English news and posts, our proposed mBERT model achieved accuracy of 89.38 and 87.80%, respectively. [ABSTRACT FROM AUTHOR]

Nowadays, video monitoring of farrowing and automatic video evaluation using Deep Learning have become increasingly important in farm animal science research and open up new possibilities for addressing specific research questions like the determination of husbandry relevant indicators. A robust detection performance of newborn piglets is essential for reliably monitoring the farrowing process and to access important information about the welfare status of the sow and piglets. Although object detection algorithms are increasingly being used in various scenarios in the field of livestock farming, their usability for detecting newborn piglets has so far been limited. Challenges such as frequent animal occlusions, high overlapping rates or strong heterogeneous animal postures increase the complexity and place new demands on the detection model. Typically, new data is manually annotated to improve model performance, but the annotation effort is expensive and time-consuming. To address this problem, we propose a Noisy Student approach to automatically generate annotation information and train an improved piglet detection model. By using a teacher-student model relationship we transform the image structure and generate pseudo-labels for the object classes piglet and tail. As a result, we improve the initial detection performance of the teacher model from 0.561, 0.838, 0.672 to 0.901, 0.944, 0.922 for the performance metrics Recall, Precision and F1-score, respectively. The results of this study can be used in two ways. Firstly, the results contribute directly to the improvement of piglet detection in the context of birth monitoring systems and the evaluation of the farrowing progress. Secondly, the approach presented can be transferred to other research questions and species, thereby reducing the problem of cost-intensive annotation processes and increase training efficiency. In addition, we provide a unique dataset for the detection and evaluation of newborn piglets and sow body parts to support researchers in the task of monitoring the farrowing process. [ABSTRACT FROM AUTHOR]

The performance of neural machine translation models based on the Transformer architecture is contingent upon the quality of the data. When the training data contains a high proportion of noise, the performance of the model deteriorates. This paper addresses the issue of diminished model capability in the presence of noisy datasets by proposing an optimization method based on semantic confidence-weighted alignment. This method integrates alignment metrics and model parameter confidence adjustments to recalibrate loss weights, thereby enhancing the model's ability to identify and process noisy data. Experimental results demonstrate that this approach significantly improves the performance of translation models, particularly in low-resource language pairs such as Malay-Chinese, especially when dealing with noisy datasets. Compared to traditional methods, there is a notable increase in BLEU scores. [ABSTRACT FROM AUTHOR]

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems. However, with the constant evolution of algorithms, data, and computing power, the increasing size and complexity of these models have led to increased training costs and reduced efficiency. This study aims to minimize the inference time of such models while maintaining computational performance. It also proposes a novel Distillation model for PAL-BERT (DPAL-BERT), specifically, employs knowledge distillation, using the PAL-BERT model as the teacher model to train two student models: DPAL-BERT-Bi and DPAL-BERT-C. This research enhances the dataset through techniques such as masking, replacement, and n-gram sampling to optimize knowledge transfer. The experimental results showed that the distilled models greatly outperform models trained from scratch. In addition, although the distilled models exhibit a slight decrease in performance compared to PAL-BERT, they significantly reduce inference time to just 0.25% of the original. This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency. [ABSTRACT FROM AUTHOR]

The off-target activities within the CRISPR-Cas9 system remains a formidable barrier to its broader application and development. Recent advancements have highlighted the potential of deep learning models in predicting these off-target effects, yet they encounter significant hurdles including imbalances within datasets and the intricacies associated with encoding schemes and model architectures. To surmount these challenges, our study innovatively introduces an Efficiency and Specificity-Based (ESB) class rebalancing strategy, specifically devised for datasets featuring mismatches-only off-target instances, marking a pioneering approach in this realm. Furthermore, through a meticulous evaluation of various One-hot encoding schemes alongside numerous hybrid neural network models, we discern that encoding and models of moderate complexity ideally balance performance and efficiency. On this foundation, we advance a novel hybrid model, the CRISPR-MCA, which capitalizes on multi-feature extraction to enhance predictive accuracy. The empirical results affirm that the ESB class rebalancing strategy surpasses five conventional methods in addressing extreme dataset imbalances, demonstrating superior efficacy and broader applicability across diverse models. Notably, the CRISPR-MCA model excels in off-target effect prediction across four distinct mismatches-only datasets and significantly outperforms contemporary state-of-the-art models in datasets comprising both mismatches and indels. In summation, the CRISPR-MCA model, coupled with the ESB rebalancing strategy, offers profound insights and a robust framework for future explorations in this field. Author summary: In the field of gene editing, the application of deep learning technologies holds significant promise for predicting off-target effects in the CRISPR-Cas9 system. Nevertheless, one of the primary challenges encountered is the extreme imbalance among classes within the off-target datasets, which severely hampers the predictive accuracy for certain classes. Furthermore, as an array of sequence encoding methods continue to evolve, there has been a corresponding increase in model complexity. Addressing these issues, we introduce a novel Efficiency and Specificity-Based (ESB) class rebalancing strategy designed to mitigate the impact of class imbalance. Additionally, we assess the influence of six encoding schemes and four distinct architectural approaches on the prediction performance, employing four benchmark datasets for validation. Building upon these insights, we have developed a new hybrid model, termed CRISPR-MCA. Our experimental results demonstrate that the ESB strategy significantly surpasses the performance of existing baseline methods across multiple models. Moreover, the CRISPR-MCA model exhibits robust performance on two distinct types of datasets, affirming its effectiveness in enhancing the accuracy of deep learning predictions for off-target activities. [ABSTRACT FROM AUTHOR]

Copyright of Social Medicine is the property of Social Medicine Publishing Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on document-level RE in English, Korean document-level RE research is still at the very beginning due to the absence of a dataset. To accelerate the studies, we present TREK (Toward Document-Level Relation Extraction in Korean) dataset constructed from Korean encyclopedia documents written by the domain experts. We provide detailed statistical analyses for our large-scale dataset and human evaluation results suggest the assured quality of TREK. Also, we introduce the document-level RE model that considers the named entity-type while considering the Korean language's properties. In the experiments, we demonstrate that our proposed model outperforms the baselines and conduct qualitative analysis. [ABSTRACT FROM AUTHOR]

Predicting human mobility is essential for urban planning and personalized services. The problem addressed in this study is analyzing user behavior patterns and predicting their next destination. Due to the complexity and diversity of human mobility, it's necessary to study user behavior patterns from various angles and leverage diverse context information to construct prediction models. Unfortunately, most previous research often neglects personalized preferences and falls short in offering a comprehensive understanding of user behavior patterns. Furthermore, some studies have not effectively mined and utilized contextual information. To address these shortcomings, this paper introduces a novel Personalized Behavior Modeling Network (PBMN). Compared to existing methods, PBMN provides a more comprehensive modeling of user behavior and utilizes context information more extensively, enabling more accurate prediction. It models user behavior through two parallel channels, taking into account both sequential patterns and personalized preferences, while fully utilizing different contextual information. Ultimately, it generates prediction results by personalized integration of different behavior features. Specifically, PBMN employs a pair of attention-based encoders and decoders to model the overall behavior features. Additionally, it utilizes three parallel recurrent neural networks to model recent behavior features at different levels of context information. The performance of PBMN was evaluated using two real-world datasets. Experimental results demonstrate that PBMN outperforms five mainstream prediction methods concerning three commonly used evaluation metrics, emphasizing the effectiveness of PBMN [ABSTRACT FROM AUTHOR]

Performing data augmentation in medical named entity recognition (NER) is crucial due to the unique challenges posed by this field. Medical data is characterized by high acquisition costs, specialized terminology, imbalanced distributions, and limited training resources. These factors make achieving high performance in medical NER particularly difficult. Data augmentation methods help to mitigate these issues by generating additional training samples, thus balancing data distribution, enriching the training dataset, and improving model generalization. This paper proposes two data augmentation methods—Contextual Random Replacement based on Word2Vec Augmentation (CRR) and Targeted Entity Random Replacement Augmentation (TER)—aimed at addressing the scarcity and imbalance of data in the medical domain. When combined with a deep learning-based Chinese NER model, these methods can significantly enhance performance and recognition accuracy under limited resources. Experimental results demonstrate that both augmentation methods effectively improve the recognition capability of medical named entities. Specifically, the BERT-BiLSTM-CRF model achieved the highest F1 score of 83.587%, representing a 1.49% increase over the baseline model. This validates the importance and effectiveness of data augmentation in medical NER. [ABSTRACT FROM AUTHOR]

Depth completion is a critical task for extensive applications such as 3D reconstruction and object detection. Recent semi-supervised depth completion techniques based on Stereo-LiDAR fusion has gradually attracted attention due to its ability to realize semi-supervised training under the weakly supervised constraints provided by the sparse LiDAR points as well as the view synthesis consistency provided by the stereo images. However, among these methods, noisy label as a conventional supervised signal for semi-supervised learning have not been taken seriously. To this end, we first propose a semi-supervised learning framework called Recursive Noise Label Learning (RNLL), which is able to maximize the potential of noisy label learning by establishing a recursive noisy label optimization and model learning mechanism. Second, based on the proposed RNLL framework, we come up with a novel semi-supervised depth completion model, UAMD-Net-RNLL, which integrates the CCNN model to accomplish the confidence measure of depth prediction on the basis of the original UAMD-Net model, and realizes the noise filtering of the noisy depth maps and adaptive loss computation by constructing the confidence-guided noisy label loss function. Finally, for the noisy label and photometric consistency loss constraints in multiple rounds of training, we utilize dynamic loss weights to describe their importance changes, and realize the continuous improvement of the prediction performance of the semi-supervised depth completion model. Extensive experimental results conducted on KITTI and DrivingStereo depth completion datasets prove that our technique achieves large performance gain, clearly outperforming competing methods and setting a new state-of-the-art for semi-supervised depth completion. [ABSTRACT FROM AUTHOR]

In ecology and evolutionary biology, the synthesis and modelling of data from published literature are commonly used to generate insights and test theories across systems. However, the tasks of searching, screening, and extracting data from literature are often arduous. Researchers may manually process hundreds to thousands of articles for systematic reviews, meta-analyses, and compiling synthetic datasets. As relevant articles expand to tens or hundreds of thousands, computer-based approaches can increase the efficiency, transparency and reproducibility of literature-based research. Methods available for text mining are rapidly changing owing to developments in machine learning-based language models. We review the growing landscape of approaches, mapping them onto three broad paradigms (frequency-based approaches, traditional Natural Language Processing and deep learning-based language models). This serves as an entry point to learn foundational and cutting-edge concepts, vocabularies, and methods to foster integration of these tools into ecological and evolutionary research. We cover approaches for modelling ecological texts, generating training data, developing custom models and interacting with large language models and discuss challenges and possible solutions to implementing these methods in ecology and evolution. [ABSTRACT FROM AUTHOR]

Prominent works in the space of Natural Language Processing (NLP) have long attempted to create new innovative models by improving upon previous model training approaches, altering model architecture, and developing more in-depth datasets to better their performance. However, with the quickly advancing field of NLP comes increased greenhouse gas emissions, posing concerns over the environmental damage caused by training LLMs. Gaining a comprehensive understanding of the various costs, particularly those pertaining to environmental aspects, that are associated with artificial intelligence serves as the foundational basis for ensuring safe AI models. Currently, investigations into the CO2 emissions of AI models remain an emerging area of research, and as such, we evaluate the CO2 emissions of well-known large language models, which have an especially high carbon footprint due to their significant amount of model parameters. We argue for the training of LLMs in a way that is responsible and sustainable by suggesting measures for reducing carbon emissions. Furthermore, we discuss how the choice of hardware affects CO2 emissions by contrasting the CO2 emissions during model training for two widely used GPUs. Based on our results, we present the benefits and drawbacks of our proposed solutions and make the argument for the possibility of training more environmentally safe AI models without sacrificing their robustness and performance. [ABSTRACT FROM AUTHOR]

The defect detection in the urban underground utility tunnel faces the challenges of low illuminance and large shadow region. The images collected have problems such as noise and uneven illumination, posing higher requirements for the image feature extraction ability of the network model. A residual feature pyramid attention network based on dense connections (RFPADNet) is proposed in the defect detection of the urban underground comprehensive pipe in this paper. The proposed network consists of three dense blocks, and each dense block uses four residual feature pyramid attention models (RFPAM) as the main feature extractors. Its focus is to utilize residual learning to obtain the fused multi-scale feature information. Soft mask branches are added to the multi-scale channels to enhance the network model's ability to extract positive features. The experimental results show that the network model proposed in this paper has a training accuracy of 97.50%. Compared with the existing network models, it could achieve better results in various test sets, which has good feasibility and practicality. [ABSTRACT FROM AUTHOR]

As is well known, the quantity of labeled samples determines the success of a convolutional neural network (CNN). However, creating the labeled dataset is a difficult and time-consuming process. In contrast, unlabeled data is cheap and easy to access. Semi-supervised methods incorporate unlabeled data into the training process, which allows the model to learn from unlabeled data as well. We propose a semi-supervised method based on the ensemble approach and the pseudo-labeling method. By balancing the unlabeled dataset with the labeled dataset during training, both the decision diversity between base-learner models and the individual success of base-learner models are high in our proposed training strategy. We show that using multiple CNN models can result in both higher success and a more robust model than training a single CNN model. For inference, we propose using both stacking and voting methodologies. We have shown that the most successful algorithm for the stacking approach is the Support Vector Machine (SVM). In experiments, we use the STL-10 dataset to evaluate models, and we increased accuracy by 15.9% over training using only labeled data. Since we propose a training method based on cross-entropy loss, it can be implemented combined with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The growth of social networks has provided a platform for individuals with prejudiced views, allowing them to spread hate speech and target others based on their gender, ethnicity, religion, or sexual orientation. While positive interactions within diverse communities can considerably enhance confidence, it is critical to recognize that negative comments can hurt people's reputations and well-being. This emergence emphasizes the need for more diligent monitoring and robust policies on these platforms to protect individuals from such discriminatory and harmful behavior. Hate speech is often characterized as an intentional act of aggression directed at a specific group, typically meant to harm or marginalize them based on certain aspects of their identity. Most of the research related to hate speech has been conducted in resource-aware languages like English, Spanish, and French. However, low-resource European languages, such as Irish, Norwegian, Portuguese, Polish, Slovak, and many South Asian, present challenges due to limited linguistic resources, making information extraction labor-intensive. In this study, we present deep neural networks with FastText word embeddings using regularization methods for multi-class hate speech detection in the Norwegian language, along with the implementation of multilingual transformer-based models with hyperparameter tuning and generative configuration. FastText outperformed other deep learning models when stacked with Bidirectional LSTM and GRU, resulting in the FAST-RNN model. In the concluding phase, we compare our results with the state-of-the-art and perform interpretability modeling using Local Interpretable Model-Agnostic Explanations to achieve a more comprehensive understanding of the model's decision-making mechanisms. [ABSTRACT FROM AUTHOR]

Code assistance refers to the utilization of various tools, techniques, and models to help developers in the process of software development. As coding tasks become increasingly complex, code assistant plays a pivotal role in enhancing developer productivity, reducing errors, and facilitating a more efficient coding workflow. This assistance can manifest in various forms, including code autocompletion, error detection and correction, code generation, documentation support, and context-aware suggestions. Language models have emerged as integral components of code assistance, offering developers the capability to receive intelligent suggestions, generate code snippets, and enhance overall coding proficiency. In this paper, we propose new hybrid models for code generation by leveraging pre-trained language models BERT, RoBERTa, ELECTRA, and LUKE with the Marian Causal Language Model. Selecting these models based on their strong performance in various natural language processing tasks. We evaluate the performance of these models on two datasets CoNaLa and DJANGO and compare them to existing state-of-the-art models. We aim to investigate the potential of pre-trained transformer language models to revolutionize code generation, offering improved precision and efficiency in navigating complex coding scenarios. Additionally, conducting error analysis and refining the generated code. Our results show that these models, when combined with the Marian Decoder, significantly improve code generation accuracy and efficiency. Notably, the RoBERTaMarian model achieved a maximum BLEU score of 35.74 and an exact match accuracy of 13.8% on CoNaLa, while LUKE-Marian attained a BLEU score of 89.34 and an exact match accuracy of 78.50% on DJANGO. Implementation of this work is available at https://github.com/AhmedSSoliman/Leveraging-Pretrained-Language-Models-for-Code-Generation. [ABSTRACT FROM AUTHOR]

The presence of antibiotics in the environment is of significant concern due to their adverse effects on aquatic ecosystems. This study provides an assessment of potential ecological risks (RQ) associated with the concentrations of eight antibiotics and antiparasitics (amoxicillin-AMO, azithromycin-AZI, ciprofloxacine-CIP, ofloxacine-OFL, oxfendazole-OXF, lincomycin-LIN, sulfacetamide-SCE and sulfamethoxazole-SME) in the surface water of 13 urban lakes in Hanoi city, Vietnam during the period 2021-2023. The findings revealed considerable variations in the total concentrations of these 8 substances (T AB ), ranging from below the method detection limit (< MDL) to 2240 ng L -1 with an average of 330.4 ng L -1 . Among the 8 antibiotics and antiparasitics examined, OXF, AMO, and SCE were undetectable, while the others were present at a range of concentrations (in ng L -1 ): OFL: 129 (< MDL-1530); CIP: 87.1 (< MDL-608); LIN: 72.7 (< MDL-676); SME: 41.5 (< MDL-504); AZI: 0.03 (< MDL-1). The calculated RQ values for these antibiotics in the Hanoi lakes indicated a high ecological risk for OFL and CIP to bacteria, a medium to high risk for SME for phytoplankton, a high risk for LIN to phytoplankton, while the risk for invertebrates was deemed negligible for all antibiotics across all lakes. The strong, positive correlation between T AB concentrations and different microbial and environmental variables (Escherichia coli, ammonium, phosphate, and chemical oxygen demand) suggests that untreated domestic wastewater is the primary pollution source in these Hanoi lakes. These results should be used to raise public awareness and to encourage the implementation of strategies targeted at managing antibiotic use. They also underscore the need to reduce inputs of untreated, antibiotic-containing wastewater into urban lakes, such as those in Hanoi and advocate for the establishment of national limits for antibiotic concentrations in surface water., Competing Interests: Declarations. Ethics approval: Not applicable. Consent to participate: Not applicable. Consent for publication: All authors have given consent for publication. Conflict of interest: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Dynamic computed tomography (CT)-based brain perfusion imaging is a non-invasive technique that can provide quantitative measurements of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). However, due to high radiation dose, dynamic CT scan with a low tube voltage and current protocol is commonly used. Because of this reason, the increased noise degrades the quality and reliability of perfusion maps. In this study, we aim to propose and investigate the feasibility of utilizing a convolutional neural network and a bi-directional long short-term memory model with an attention mechanism to self-supervisedly yield the impulse residue function (IRF) from dynamic CT images. Then, the predicted IRF can be used to compute the perfusion parameters. We evaluated the performance of the proposed method using both simulated and real brain perfusion data and compared the results with those obtained from two existing methods: singular value decomposition and tensor total-variation. The simulation results showed that the overall performance of parameter estimation obtained from the proposed method was superior to that obtained from the other two methods. The experimental results showed that the perfusion maps calculated from the three studied methods were visually similar, but small and significant differences in perfusion parameters between the proposed method and the other two methods were found. We also observed that there were several low-CBF and low-CBV lesions (i.e., suspected infarct core) found by all comparing methods, but only the proposed method revealed longer MTT. The proposed method has the potential to self-supervisedly yield reliable perfusion maps from dynamic CT images., Competing Interests: Declarations. Ethics Approval: No institutional review board is required. Consent to Participate: Written informed consent was not required for this study because this is a retrospective study. Consent for Publication: Not applicable. Competing Interests: The authors declare no competing interests., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

The high labor cost of image captioning datasets limits the application scenarios of image captioning methods. Therefore, the semi-supervised image captioning research that utilizes partially labeled datasets and a large amount of unlabeled data has gained widespread attention in recent years. The key issue of current semi-supervised image captioning research is how to obtain pseudo-labels that well match unlabeled images, providing valuable training samples for semi-supervised model training. To this end, we propose a semi-supervised image captioning method improved by incorporating semantic consistency (Semi-SC), which adopts both self-training and adversarial training for Teacher and Student models. Semi-SC constructs a semantic consistency discriminator to evaluate data of two modalities with global and local semantic similarity, which helps to filter out high-quality paired pseudo-samples from Teacher model to optimize the training of for Student model. To improve the semantic consistency between the generated captions and original images, a semantic confidence loss is designed to inject important semantic information of images into the generated captions with the global semantic content. Extensive experiments on the MSCOCO dataset and Unlabeled-COCO dataset verify the effectiveness of Semi-SC, which shows significant advantages in CIDEr and SPICE metrics, achieving 78.1 % and 15.8 % in the Scarcely-paired COCO setting and outperforming other existing semi-supervised image captioning methods. [ABSTRACT FROM AUTHOR]

Which kinds of grievances garner support from the public on online platforms? Focusing on national online petitioning, one of the forms of direct democracy in contemporary politics, we examine the content and characteristics of petitions that succeeded in attracting public attention and support. Using our comprehensive data on online petitions that were submitted to the executive office between 2017 and 2022 in South Korea, our analysis yields three important findings. First, a mix of post-materialist topics such as human rights and gender equality and materialist topics such as safety and environment turn out to be salient among petitions that meet the signature threshold. Second, online petitions the contents of which reveal either moral emotions or Confucian attitudes are more likely to gain public support compared to others. Third, keywords that are related to moral claims asking for the apprehension of perpetrators on behalf of victims, such as 'victim,' 'perpetrator,' 'kid,' and 'punishment,' appear most frequently inside the petitions that cross the signature threshold. Such findings provide implications for understanding both the potentials and limitations of national online petitioning in contemporary democracies. [ABSTRACT FROM AUTHOR]

Fake news is a growing challenge for social networks and media. Detection of fake news always has been a problem for many years, but the evolution of social networks and increasing speed of news dissemination in recent years has been considered again. There are several approaches to solving this problem, one of which is to detect fake news based on its text style using deep neural networks. In recent years, transfer learning with transformers is one of the most used forms of deep neural networks for natural language processing. BERT is one of the most promising transformers that outperforms other models in many NLP benchmarks. In this article, we introduce MWPBert, which uses two parallel BERT networks to perform veracity detection on full-text news articles. One of the BERT networks encodes news headline, and another encodes news bodies. Since the input length of the BERT network is limited and constant and the news body is usually a long text, we cannot feed the whole text into the BERT. Therefore, using the MaxWorth algorithm, we selected the part of the news text that is more valuable for fact-checking, and fed it into the BERT network. Finally, we encode the output of the two BERT networks to an output network to classify the news. The experiment results showed that the proposed model outperformed previous models regarding accuracy and other performance measures. [ABSTRACT FROM AUTHOR]

Network-based intrusion detection (N-IDS) is an essential system inside an organization in a smart healthcare enterprise system to prevent the system and its networks from network attacks. A survey of the literature shows that in recent days deep learning approaches are employed successfully for N-IDS using network connections. However, finding the right features from a network connection is a daunting task. This work proposes a multidimensional attention-based deep learning approach for N-IDS that extracts the optimal features for intrusion detection using network payload. The proposed approach includes an embedding that transforms every word in the payload into a 100-dimensional feature vector representation and embedding follows deep learning layers such as a convolutional neural network (CNN) and long short-term memory (LSTM) with attention to extracting optimal features for attack classification. Next, the features of CNN and LSTM layers are concatenated and passed into fully connected layers for intrusion detection. The proposed approach showed 99% accuracy on the KISTI enterprise network payload dataset. In addition, the proposed approach showed 98% accuracy and 99% accuracy on network-based datasets such as KDDCup-99, CICIDS-2017, and WSN-DS and UNSW-NB15 respectively. The good experimental results on various network-based datasets suggest that the proposed N-IDS in smart healthcare enterprise systems is robust and generalizable to detect attacks from different network environments. The proposed approach performed better in all the experiments than the other deep learning-based methods. The model showed a 5% accuracy performance improvement compared to the existing study using the KISTI dataset. In addition, the proposed model has shown similar performances on the other intrusion datasets. The proposed approach serves as a network monitoring tool for efficient and accurate detection of attacks inside an organization on a healthcare enterprise network system. [ABSTRACT FROM AUTHOR]

In the transformer architecture, as self-attention reads entire image patches at once, the context of the sequence between patches is omitted. Therefore, the position embedding method is employed to assist the self-attention layers in computing the ordering information of tokens. While many papers simply add the position vector to the corresponding token vector rather than concatenating them, few papers offer a thorough explanation and comparison beyond dimension reduction. However, the addition method is not meaningful because token vectors and position vectors are different physical quantities that cannot be directly combined through addition. Hence, we investigate the disparity in learnable absolute position information between the two embedding methods (concatenation and addition) and compare their performance on models. Experiments demonstrate that the concatenation method can learn more spatial information (such as horizontal, vertical, and angle) than the addition method. Furthermore, it reduces the attention distance in the final few layers. Moreover, the concatenation method exhibits greater robustness and leads to a performance gain of 0.1–0.5% for existing models without additional computation overhead. [ABSTRACT FROM AUTHOR]

Copyright of Teaching Innovations / Inovacije u Nastavi is the property of University of Belgrade, Faculty of Teacher Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)