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Long-term fear memory formation in the hippocampus and neocortex depends upon brain-derived neurotrophic factor (BDNF) signaling after acquisition. Incremental, appetitive odor discrimination learning is thought to depend substantially on the differentiation of adult-born neurons within the olfactory bulb (OB)--a process that is closely associated with BDNF signaling. We sought to elucidate the role of neurotrophin signaling within the OB on odor memory consolidation. Male mice were trained on odor--reward associative discriminations after bilateral infusion of the kinase inhibitor K252a, or vehicle control, into the OB. K252a is a partially selective inhibitor of tyrosine kinase (Trk) receptors, including the TrkB receptor for BDNF, though it also inhibits other plasticity-related kinases such as PKC and CaMKII/IV. K252a infusion into the OB did not impair odor acquisition or short-term (2 h) memory for the learned discriminations, but significantly impaired long-term (48 h) odor memory (LTM). This LTM deficit also was associated with reduced selectivity for the conditioned odorant in a reward-seeking digging task. Infusions of K252a immediately prior to testing did not impair LTM recall. These results indicate that kinase activation in the OB is required for the consolidation of odor memory of incrementally acquired information.

This paper presents the components influencing English as a Foreign Language (EFL) learning demotivation and resilience. Semi-structured interview data from 23 EFL students and nine teachers were thematically analyzed guided by the Grounded Theory approach after systematic reiterative comparative reviewing. The major demotivators were teachers' lack of clear delivery for elementary school students, excessive afterschool learning programs at private institutes for junior high school students, and impractical EFL lessons focusing on grammar rather than spoken English for high school students. In order to curb these demotivators and maintain high EFL learning motivation levels, four distinctive components influencing their resilience were identified: social support, emotional regulation, a clear learning goal, and tenacity in EFL learning. This study illustrates that emotional regulation, not negative emotions such as anxiety, is crucial, and students' clearly stated short-term learning goal is a crucial component in maintaining high academic resilience. Participants cultivated specific components influencing resilience as they advanced academically. Additionally, high-proficiency EFL students exhibited greater tenacity and utilized various coping strategies. We argue for the necessity to refine the sub-constructs of EFL learning demotivation and resilience and recommend the need for a follow-up confirmatory study to prove the conceptual validity of these constructs.

The purpose of the study is to explore the structural relationships among second language (L2) proficiency and the constructs of L2 learners' resilience, motivation, and demotivation. A total of 869 undergraduate students in South Korea participated in the study. The data were analyzed by means of factor analysis, correlations, and structural equation modeling (SEM). The factor analysis identified seven resilience factors (life satisfaction, sociability, communicative efficacy, self-composure, strategic competence, metacognitive adaptation, and realistic optimism), six motivation factors (ought-to L2 self, ideal L2 self, instrumental motivation, parental support, academic motivation, awareness of importance), and six demotivation factors (negative perception of English-speaking countries, compulsory EFL learning, perceived inappropriateness of textbooks or tasks, low self-esteem, unsupportive learning environment, unsupportive teachers). The SEM corroborated that resilience, L2 learning motivation, and L2 learning demotivation contribute to L2 proficiency. L2 learning demotivation had more explanatory power for L2 proficiency than did L2 learning motivation. Resilience had both a direct and an indirect effect on L2 proficiency via L2 learning demotivation. The findings suggest that teachers should equip learners with strategies to manage adversity in L2 learning.

In the process of pyrolyzing waste plastics, the generation of Cl2 gas can pose a problem. During the pyrolysis processing, incomplete combustion of organic compounds containing chlorine can lead to the formation of toxic chemicals, which can cause issues in subsequent processing stages. Therefore, an adsorbent plays an important role in removing Cl2 in the dechlorination process, and alkaline adsorbents and metal oxides are generally used. Waste red mud is composed of Fe metal oxide and alkaline components, so it is intended to be used as a Cl2 adsorbent. The Cl2 removal ability of red mud with different redox status of iron oxides was assessed. Hydrogen treatment was performed at various temperatures to control the reduction potential of the Fe in the metal oxides, and phase changes in the Fe oxide component of red mud were confirmed. In the case of red mud hydrogenated at 700 °C, most of the Fe2O3 structure could be converted to the Fe3O4 structure, and the Fe3O4 structure showed superior results in Cl2 adsorption compared to the Fe2O3 structure. As a result, red mud at an H2 treatment temperature of 700 °C showed about three times higher Cl2 adsorption compared to red mud without H2 treatment. [ABSTRACT FROM AUTHOR]

Lipid biosynthesis is recently studied its functions in a range of cellular physiology including differentiation and regeneration. However, it still remains to be elucidated in its precise function. To reveal this, we evaluated the roles of lysophosphatidic acid (LPA) signaling in alveolar bone formation using the LPA type 2 receptor (LPAR2) antagonist AMG‐35 (Amgen Compound 35) using tooth loss without periodontal disease model which would be caused by trauma and usually requires a dental implant to restore masticatory function. In this study, in vitro cell culture experiments in osteoblasts and periodontal ligament fibroblasts revealed cell type‐specific responses, with AMG‐35 modulating osteogenic differentiation in osteoblasts in vitro. To confirm the in vivo results, we employed a mouse model of tooth loss without periodontal disease. Five to 10 days after tooth extraction, AMG‐35 facilitated bone formation in the tooth root socket as measured by immunohistochemistry for differentiation markers KI67, Osteocalcin, Periostin, RUNX2, transforming growth factor beta 1 (TGF‐β1) and SMAD2/3. The increased expression and the localization of these proteins suggest that AMG‐35 elicits osteoblast differentiation through TGF‐β1 and SMAD2/3 signaling. These results indicate that LPAR2/TGF‐β1/SMAD2/3 represents a new signaling pathway in alveolar bone formation and that local application of AMG‐35 in traumatic tooth loss can be used to facilitate bone regeneration and healing for further clinical treatment. [ABSTRACT FROM AUTHOR]

This exploratory study focused on the factors that constitute second language (L2) learners' resilience, and how these factors are related to L2 learning by investigating what relation resilience may have to motivated behaviour and proficiency in English learning. A total of 1620 secondary school learners of English participated in a questionnaire survey. By analysing the quantitative data, five resilience factors were extracted: perceived happiness, empathy, sociability, persistence, and self-regulation. Confirmative factor analysis using AMOS proved that these five factors are distinctive constructs. Among the factors, persistence was identified to play the most influential role in L2 learning. Persistence showed the highest correlations with, and the strongest explanatory power for, motivated behaviour and English proficiency. It is suggested that resilience can be further explored as an individual differences factor in L2 learning, which is involved in L2 learning process and outcomes.

BackgroundData collected by an actigraphy device worn on the wrist or waist can provide objective measurements for studies related to physical activity; however, some data may contain intervals where values are missing. In previous studies, statistical methods have been applied to impute missing values on the basis of statistical assumptions. Deep learning algorithms, however, can learn features from the data without any such assumptions and may outperform previous approaches in imputation tasks. ObjectiveThe aim of this study was to impute missing values in data using a deep learning approach. MethodsTo develop an imputation model for missing values in accelerometer-based actigraphy data, a denoising convolutional autoencoder was adopted. We trained and tested our deep learning–based imputation model with the National Health and Nutrition Examination Survey data set and validated it with the external Korea National Health and Nutrition Examination Survey and the Korean Chronic Cerebrovascular Disease Oriented Biobank data sets which consist of daily records measuring activity counts. The partial root mean square error and partial mean absolute error of the imputed intervals (partial RMSE and partial MAE, respectively) were calculated using our deep learning–based imputation model (zero-inflated denoising convolutional autoencoder) as well as using other approaches (mean imputation, zero-inflated Poisson regression, and Bayesian regression). ResultsThe zero-inflated denoising convolutional autoencoder exhibited a partial RMSE of 839.3 counts and partial MAE of 431.1 counts, whereas mean imputation achieved a partial RMSE of 1053.2 counts and partial MAE of 545.4 counts, the zero-inflated Poisson regression model achieved a partial RMSE of 1255.6 counts and partial MAE of 508.6 counts, and Bayesian regression achieved a partial RMSE of 924.5 counts and partial MAE of 605.8 counts. ConclusionsOur deep learning–based imputation model performed better than the other methods when imputing missing values in actigraphy data.