Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets

Named Entity Recognition (NER) frequently suffers from the problem of insufficient labeled data, particularly in fine-grained NER scenarios. Although $K$-shot learning techniques can be applied, their performance tends to saturate when the number of annotations exceeds several tens of labels. To overcome this problem, we utilize existing coarse-grained datasets that offer a large number of annotations. A straightforward approach to address this problem is pre-finetuning, which employs coarse-grained data for representation learning. However, it cannot directly utilize the relationships between fine-grained and coarse-grained entities, although a fine-grained entity type is likely to be a subcategory of a coarse-grained entity type. We propose a fine-grained NER model with a Fine-to-Coarse(F2C) mapping matrix to leverage the hierarchical structure explicitly. In addition, we present an inconsistency filtering method to eliminate coarse-grained entities that are inconsistent with fine-grained entity types to avoid performance degradation. Our experimental results show that our method outperforms both $K$-shot learning and supervised learning methods when dealing with a small number of fine-grained annotations.Comment: Accepted to EMNLP 202

Data Augmentation for Neural Machine Translation using Generative Language Model

Despite the rapid growth in model architecture, the scarcity of large parallel corpora remains the main bottleneck in Neural Machine Translation. Data augmentation is a technique that enhances the performance of data-hungry models by generating synthetic data instead of collecting new ones. We explore prompt-based data augmentation approaches that leverage large-scale language models such as ChatGPT. To create a synthetic parallel corpus, we compare 3 methods using different prompts. We employ two assessment metrics to measure the diversity of the generated synthetic data. This approach requires no further model training cost, which is mandatory in other augmentation methods like back-translation. The proposed method improves the unaugmented baseline by 0.68 BLEU score

Rubisco activity and gene expression of tropical tree species under light stress

Tropical rain forests contain an ecologically and physiologically diverse range of vegetation and habitats. Sun-acclimated plants can be divided into two groups, shade-tolerant and shade-intolerant, according to the plant’s physiological and genetic responses. Some tropical species have potential capacity for light damage in a shaded environment as well as shade-tolerance to compensate for the impaired light harvesting complex. In particular, ribulose‐1,5‐bisphosphate carboxylase/oxygenase (Rubisco) is regulated by the Calvin cycle, which participated in protein synthesis. Rubisco plays a role in CO2 fixation, which helps supply the energy to regulate Rubisco for ribulose 1,5-bisphosphate (RuBP) reduction. Light intensity is associated with the photosynthetic rate and genetic response to moderate growth environments.Keywords: Gene expression, growth, light intensity, Rubisco activityAfrican Journal of Biotechnology Vol. 12(20), pp. 2764-276

Metabolic Super Scan in 18F-FDG PET/CT Imaging

A 50-yr-old man presented with intermittent hemoptysis and was diagnosed small cell lung cancer. 18F-FDG PET/CT for staging demonstrated extensive hypermetabolic lesions throughout the skeleton and liver. Interestingly, skeletal muscles of limbs, mediastinum, bowel, and especially brain showed very low FDG uptake. Because of some characteristics in common with super scan on skeletal scintigraphy, this case could be considered as 'metabolic super scan'

Establishing GPCR Targets of hMAO Active Anthraquinones from Cassia obtusifolia Linn Seeds Using In Silico and In Vitro Methods.

The present study examines the effect of human monoamine oxidase active anthraquinones emodin, alaternin (=7-hydroxyemodin), aloe-emodin, and questin from Cassia obtusifolia Linn seeds in modulating human dopamine (hD1R, hD3R, and hD4R), serotonin (h5-HT1AR), and vasopressin (hV1AR) receptors that were predicted as prime targets from proteocheminformatics modeling via in vitro cell-based functional assays, and explores the possible mechanisms of action via in silico modeling. Emodin and alaternin showed a concentration-dependent agonist effect on hD3R with EC50 values of 21.85 ± 2.66 and 56.85 ± 4.59 μM, respectively. On hV1AR, emodin and alaternin showed an antagonist effect with IC50 values of 10.25 ± 1.97 and 11.51 ± 1.08 μM, respectively. Interestingly, questin and aloe-emodin did not have any observable effect on hV1AR. Only alaternin was effective in antagonizing h5-HT1AR (IC50: 84.23 ± 4.12 μM). In silico studies revealed that a hydroxyl group at C1, C3, and C8 and a methyl group at C6 of anthraquinone structure are essential for hD3R agonist and hV1AR antagonist effects, as well as for the H-bond interaction of 1-OH group with Ser192 at a proximity of 2.0 Å. Thus, based on in silico and in vitro results, hV1AR, hD3R, and h5-HT1AR appear to be prime targets of the tested anthraquinones

Comparing generalized and specific problematic smartphone/internet use: longitudinal relationships between smartphone application- based addiction and social media addiction and psychological distress

Background and aims: The literature has proposed two types of problematic smartphone/internet use: generalized problematic use and specific problematic use. However, longitudinal findings on the associations between the two types of problematic use and psychological distress are lacking among East-Asians. The present study examined temporal associations between both generalized and specific problematic use of the smartphone/internet, and psychological distress. Methods: Hong Kong University students (N 5 308; 100 males; mean age 5 23.75 years; SD ± 5.15) were recruited with follow-ups at three, six, and nine months after baseline assessment. All participants completed the Smartphone Application-Based Addiction Scale (for generalized problematic smartphone/internet use), the Bergen Social Media Addiction Scale (for specific problematic smartphone/internet use), and the Hospital Anxiety and Depression Scale (for psychological distress) in each assessment. Latent growth modeling (LGM) was constructed to understand temporal associations between generalized/specific problematic use and psychological distress. Results: The LGM suggested that the intercept of generalized problematic use was significantly associated with the intercept of psychological distress (standardized coefficient [b] 5 0.32; P < 0.01). The growth of generalized problematic use was significantly associated with the growth of psychological distress (b 5 0.51; P < 0.01). Moreover, the intercept of specific problematic use was significantly associated with the intercept of psychological distress (b 5 0.28; P < 0.01) and the growth of psychological distress (b 5 0.37; P < 0.01). Conclusion: The initial level of problematic use of the smartphone/internet may prevent psychological distress

The Effect of Ca-P Coated Bovine Bone Mineral on Bone Regeneration around Dental Implant in Dogs

There are many obstacles to overcome in implant dentistry. The bony defect around implant can be seen in immediate installation procedures. Following tooth extraction, however, a socket often presents dimensions that may be considerably greater than the dimensions of a conventional implant