Learning Task Research Articles

A multi-task graph deep learning model to predict drugs combination of synergy and sensitivity scores

BackgroundDrug combination treatments have proven to be a realistic technique for treating challenging diseases such as cancer by enhancing efficacy and mitigating side effects. To achieve the therapeutic goals of these combinations, it is essential to employ multi-targeted drug combinations, which maximize effectiveness and synergistic effects.ResultsThis paper proposes ‘MultiComb’, a multi-task deep learning (MTDL) model designed to simultaneously predict the synergy and sensitivity of drug combinations. The model utilizes a graph convolution network to represent the Simplified Molecular-Input Line-Entry (SMILES) of two drugs, generating their respective features. Also, three fully connected subnetworks extract features of the cancer cell line. These drug and cell line features are then concatenated and processed through an attention mechanism, which outputs two optimized feature representations for the target tasks. The cross-stitch model learns the relationship between these tasks. At last, each learned task feature is fed into fully connected subnetworks to predict the synergy and sensitivity scores.The proposed model is validated using the O’Neil benchmark dataset, which includes 38 unique drugs combined to form 17,901 drug combination pairs and tested across 37 unique cancer cells. The model’s performance is tested using some metrics like mean square error (MSE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$MSE$$\\end{document}), mean absolute error (MAE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$MAE$$\\end{document}), coefficient of determination (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document}), Spearman, and Pearson scores. The mean synergy scores of the proposed model are 232.37, 9.59, 0.57, 0.76, and 0.73 for the previous metrics, respectively. Also, the values for mean sensitivity scores are 15.59, 2.74, 0.90, 0.95, and 0.95, respectively.ConclusionThis paper proposes an MTDL model to predict synergy and sensitivity scores for drug combinations targeting specific cancer cell lines. The MTDL model demonstrates superior performance compared to existing approaches, providing better results.

Better together: Automated app review analysis with deep multi-task learning

Context:User reviews of mobile apps provide an important communication channel between developers and users. Existing approaches to automated app review analysis mainly focus on one task (e.g., bug classification task, information extraction task, etc.) at a time, and are often constrained by the manually defined patterns and the ignorance of the correlations among the tasks. Recently, multi-task learning (MTL) has been successfully applied in many scenarios, with the potential to address the limitations associated with app review mining tasks. Objective:In this paper, we propose MABLE, a deep MTL-based and semantic-aware approach, to improve app review analysis by exploiting task correlations. Methods:MABLE jointly identifies the types of involved bugs reported in the review and extracts the fine-grained features where bugs might occur. It consists of three main phases: (1) data preparation phase, which prepares data to allow data sharing beyond single task learning; (2) model construction phase, which employs a BERT model as the shared representation layer to capture the semantic meanings of reviews, and task-specific layers to model two tasks in parallel; (3) model training phase, which enables eavesdropping by shared loss function between the two related tasks. Results:Evaluation results on six apps show that MABLE outperforms ten commonly-used and state-of-the-art baselines, with the precision of 79.76% and the recall of 79.24% for classifying bugs, and the precision of 79.83% and the recall of 80.33% for extracting problematic app features. The MTL mechanism improves the F-measure of two tasks by 3.80% and 4.63%, respectively. Conclusion:The proposed approach provides a novel and effective way to jointly learn two related review analysis tasks, and sheds light on exploring other review mining tasks.

Expressibility-induced concentration of quantum neural tangent kernels

Quantum tangent kernel methods provide an efficient approach to analyzing the performance of quantum machine learning models in the infinite-width limit, which is of crucial importance in designing appropriate circuit architectures for certain learning tasks. Recently, they have been adapted to describe the convergence rate of training errors in quantum neural networks in an analytical manner. Here, we study the connections between the expressibility and value concentration of quantum tangent kernel models. In particular, for global loss functions, we rigorously prove that high expressibility of both the global and local quantum encodings can lead to exponential concentration of quantum tangent kernel values to zero. Whereas for local loss functions, such issue of exponential concentration persists owing to the high expressibility, but can be partially mitigated. We further carry out extensive numerical simulations to support our analytical theories. Our discoveries unveil a fundamental feature of quantum neural tangent kernels, indicating that the issue of their concentration cannot be bypassed merely by transitioning to a local encoding scheme while maintaining high expressibility. This offers valuable insights for the design of wide quantum variational circuit models in practical applications.

Individual differences in parasympathetic functioning across social stressor tasks: Relations with child and parent anxiety

AbstractChildren with elevated behavioural inhibition (BI) show context‐inappropriate fear and dysregulated RSA across stressor tasks. However, few studies have examined dynamic RSA within tasks and relations to parent and child anxiety. Using piecewise growth modelling and multimethod baseline data from an intervention study of 151 3.5–5‐year‐old children and their parents, we examined relations between child social anxiety (SA), parent anxiety and their interaction in predicting children's RSA across social stressor tasks (e.g. learning about unfamiliar peers, Trier Social Stress). Within the sample, 49.63% of children were reported to be White, non‐Hispanic/Latine (n = 67), 22% multiracial (n = 31), 14.81% Asian/Pacific Islander (n = 20) and 12.59% Black/African‐American (n = 17). Furthermore, 64.44% of the parents were reported to be White, non‐Hispanic/Latine (n = 87), 20% Asian/Pacific Islander (n = 27), 13.33% Black/African‐American (n = 18) and 2.22% multiracial (n = 3). Children showed differentiated RSA reactivity and recovery within an anticipatory social learning task based on their level of clinically appraised SA. Relations between child SA and RSA across tasks was moderated by parent anxiety, specifically for dyads matched in anxiety. Findings provide support for the potential influence of both child and parent anxiety on children's parasympathetic nervous system (PNS) responses across specific self‐regulatory tasks.

Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst.

Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner's mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner's cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann-Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI's potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts.

Children’s Expressed Emotions During Playful Learning Games

Studies on software tutoring systems for complex learning have shown that confusion has a beneficial relationship with the learning experience and student engagement (Arguel et al., 2017). Causing confusion can prevent boredom while signs of confusion can serve as a signal of genuine learning and as a predecessor for frustration. There is little to no research on the role of confusion in early childhood education and playful learning, as these studies primarily focus on high school and university students during complex learning tasks. Despite that, the field acknowledges that confusion may be caused by inconsistency between information and a student’s internal model referred to as cognitive disequilibrium known from the theory of cognitive development, which was originally theorized based on observational studies on young children (D’Mello and Graesser, 2012). Therefore, there is reason to expect that the virtues of confusion also apply to young children engaging in learning activities, such as playful learning. To investigate the role of confusion in playful learning, we conducted an observational study, in which the behavior and expressed emotions of young children were collected by familiar pedagogues, using a web app, while they engaged with playful learning games designed for kindergartens. The expressed emotions were analyzed using a likelihood metric to determine the likely transitions between emotions (D’Mello and Graesser, 2012). The preliminary results showed that during short play sessions, children express confusion, frustration, and boredom. Furthermore, the observed emotional transitions were matched with previously established models of affect dynamics during complex learning. We argue that games with a learning objective can benefit by purposely confusing the player and how the player’s confusion may be managed to improve the learning experience.

EXPLORING LEARNING OPPORTUNITIES FOR PRIMARY TEACHERS: THE CASE OF KNOWLEDGE FOR TEACHING EARLY ALGEBRA

Understanding how to constitute and develop opportunities for primary teachers teach early algebra to younger children is still an important research gap. In this paper, we bring results of a research program developed in Brazil over the past five years. We aim to discuss how professional learning opportunities emerged when teachers collectively planned, discussed, and analyzed lessons involving different meanings of the equality symbol and the development of functional thinking. Developed from the perspective of a qualitative-interpretative research, data analyzed consists of curriculum documents, protocols for the resolution of formative tasks, audios and videos collected during teacher education processes for in-service teachers. The results highlight that professional learning tasks, combined with the actions of teacher educators during collective discussions, favored in-service teachers to differentiate and understand the students’ reasoning. Some implications for teacher education as well as the professional development of primary teachers are discussed, especially regarding early algebra thinking, because teachers do not normally have the opportunity to study these contents on their own experiences in school.

Basolateral amygdala inputs to the nucleus accumbens shell modulate the consolidation of cued-response and inhibitory avoidance learning

The basolateral amygdala (BLA) modulates different types of memory consolidation via distinct projections to downstream brain regions in multiple memory systems. Prior studies indicate that the BLA projects to the nucleus accumbens shell (NAshell) and that these regions interact to influence some types of behavior. Moreover, previous pharmacological work suggests the BLA and NAshell interact to influence memory. However, the precise role of the BLA-NAshell pathway has never been directly investigated in the consolidation of different types of memory including cued-response, spatial, or inhibitory avoidance (IA) learning. To address this, male and female Sprague-Dawley rats received optogenetic manipulations of the BLA or BLA-NAshell pathway immediately following training in different learning tasks. An initial experiment found that optogenetically inhibiting the BLA itself immediately after training impaired cued-response retention in a Barnes maze task in males and females, confirming earlier pharmacological work in males alone. Subsequent experiments found that BLA-NAshell pathway inhibition impaired retention of cued-response and IA learning but had no effect on retention of spatial learning. However, the present work did not observe any effects of pathway stimulation immediately after cued-response or IA learning. Together, the present findings suggest the BLA modulates the consolidation of cued-response and IA, but not spatial, memory consolidation via NAshell projections.

SNN using color-opponent and attention mechanisms for object recognition

The current spiking neural network (SNN) relies on spike-timing-dependent plasticity (STDP) primarily for shape learning in object recognition tasks, overlooking the equally critical aspect of color information. To address this gap, our study introduces an unsupervised variant of STDP that incorporates principles from color-opponency mechanisms (COM) and classical receptive fields (CRF) found in the biological visual system, facilitating the integration of color information during parameter updates within the SNN architecture. Our approach initially preprocesses images into two distinct feature maps: one for shape and another for color. Then, signals derived from COM and intensity concurrently drive the STDP process, thereby updating parameters associated with both color and shape feature maps. Furthermore, we propose a channel-wise attention mechanism to enhance differentiation among objects sharing similar shapes or colors. Specifically, this mechanism utilizes convolution to generate an output spike-wave, identifying a winner based on earliest spike timing and maximal potential. The winning kernel computes attention, which is then applied via convolution to each input image feature map, generating post-feature maps. A STDP-like normalization rule compares firing times between pre- and post-feature maps, dynamically adjusting channel weights to optimize object recognition during the training phase.We assessed the proposed algorithm using SNN with both single-layer and multi-layer architectures across three datasets. Experimental findings highlight its efficacy and superiority in complex object recognition tasks compared to state-of-the-art (SOTA) algorithms. Notably, our approach achieved a significant 20% performance improvement over the SOTA on the Caltech-101 dataset. Moreover, the algorithm is well-suited for hardware implementation and energy efficiency, leveraging a winner-selection mechanism based on the earliest spike time.

6631 A Week With Diabetes: Using Real-Time Continuous Glucose Monitoring In A Simulation Activity With Residents And Fellows

Abstract Disclosure: T. Gallagher: None. C. Donelly: None. R.E. Amori: None. Background: Continuous glucose monitor (CGM) use in managing diabetes mellitus is increasing rapidly. Fellows and residents often prescribe these devices, but most do not themselves have diabetes mellitus and do not use these devices personally. A real-time simulation activity can help physicians in training learn about using the technology in diabetes management and the patient experience living with diabetes mellitus. Objective: Assessing learner’s experiences using a one week real-time CGM simulation. Methods: A real-time simulation using CGM and elements of role playing can enhance knowledge of CGM use and overall diabetes care. Voluntary participants included endocrinology fellows, internal and family medicine residents, and one health professions student. One common structured learning task per day was assigned to all participants over a single seven day period of use in academic year 2022-23. Assessment of each task occurred via personal observations and reflections. Data was collected in an anonymous form and in a group debrief. Results: Eight of 10 participants completed the deidentified post simulation survey and 8/10 participated in the live debrief. Participants reported ease of CGM placement and use. They reported improved insight into the impacts of diabetes on daily activities and increased awareness of diet and exercise impact on glucose levels. Insulin dose calculations were largely correct but errors were seen. All felt the activity was beneficial to their learning. Conclusions: Real-time CGM simulation improves student, resident, and fellow knowledge of the technology and allows the learner to gain insight into a patient’s diabetes self-management. Presentation: 6/3/2024

Astrocytes contribute to the functional differentiation of the hippocampal longitudinal axis during reward and aversion processing in the adult male rat

This study aims to investigate whether glial cells, in particular putative astrocytes, contribute to functional distinctions between the dorsal (DH), intermediate (IH), and ventral (VH) hippocampus. To evaluate this, we performed three different behavioral tasks (i.e., Morris water maze; MWM, Passive avoidance; PA, T-maze place preference; TPP) to determine whether the DH, IH, and VH are necessary for each task. Sensitivity of behavioral tasks was confirmed using lidocaine (2 %, 1 μl) reversible inactivation. Subsequently, we examined the effects of silencing astrocytes, using fluorocitrate (FC, 1 mM/1 μl), into the DH, IH, and VH on these tasks. The effects of drugs were examined separately. We observed that injection of FC into the DH resulted in a significant impairment in MWM performance. In contrast, while FC injections into the IH or VH did not prevent platform localization during the acquisition phase, rats showed difficulty recalling the target zone during the retrieval phase. In the PA test, FC injection into the VH impaired task learning and memory. During the acquisition phase, FC injection into the DH or IH did not differ from the control in the number of shocks; however, during retrieval, there was a significant decrease in the latency before entering the dark chamber. The TPP test performance was impaired by FC injection in the IH. In sum, we show that glial cells, especially astrocytes in specific functional regions of the hippocampus, play distinct roles in processing aversive and rewarding experiences and contribute to the functional organization of the hippocampal longitudinal axis.

Reinforcement Learning with Decoupled State Representation for Robot Manipulations

Deep reinforcement learning has significantly advanced robot manipulations by providing an alternative solution for designing control strategies using raw images as direct inputs. While images offer additional environmental information, the end-to-end policy training manner (from image to action) requires simultaneous representation and task learning by the agent. This often necessitates a substantial number of interaction samples to achieve satisfactory policy performance. Previous works has attempted to address this challenge by learning a visual representation model that encodes the entire image into a low-dimensional vector before the policy training. However, since this vector contains both robot and object information, it inevitably introduces coupling within the state, which can mislead the policy training process. In this study, a novel method called Reinforcement Learning with Decoupled State Representation is proposed to effectively decouple robot and object information within the state representation. Experimental results demonstrate that the proposed method exhibits faster learning speed and achieves superior performance compared to previous methods across various robot manipulation tasks. Moreover, with only 3096 offline images, the proposed method successfully applies to real-world robot pushing tasks, which demonstrates its high practicability.

A unified active learning framework for annotating graph data for regression tasks

In many domains, effectively applying machine learning models requires a large number of annotations and labelled data, which might not be available in advance. Acquiring annotations often requires significant time, effort, and computational resources, making it challenging. Active learning strategies are pivotal in addressing these challenges, particularly for diverse data types such as graphs. Although active learning has been extensively explored for node-level classification, its application to graph-level learning, especially for regression tasks, is not well-explored. We develop a unified active learning framework specializing in graph annotating and graph-level learning for regression tasks on both standard and expanded graphs, which are more detailed representations. We begin with graph collection and construction. Then, we construct various graph embeddings (unsupervised and supervised) into a latent space. Given such an embedding, the framework becomes task agnostic and active learning can be performed using any regression method and query strategy suited for regression. Within this framework, we investigate the impact of using different levels of information for active and passive learning, e.g., partially available labels and unlabelled test data. Despite our framework being domain agnostic, we validate it on a real-world application of software performance prediction, where the execution time of the source code is predicted. Thus, the graph is constructed as an intermediate source code representation. We support our methodology with a real-world dataset to underscore the applicability of our approach. Our real-world experiments reveal that satisfactory performance can be achieved by querying labels for only a small subset of all the data. A key finding is that Graph2Vec (an unsupervised embedding approach for graph data) performs the best, but only when all train and test features are used. However, Graph Neural Networks (GNNs) are the most flexible embedding techniques when used for different levels of information with and without label access. In addition, we find that the benefit of active learning increases for larger datasets (more graphs) and when the graphs are more complex, which is arguably when active learning is the most important.

Research on underwater target recognition based on auditory EEG signal features and deep learning

Target recognition is a key technical link in hydroacoustic detection, which has a broad application prospect in the fields of marine safety and resource exploration. In recent years, artificial involvement of underwater target recognition methods based on analysis of line spectra, auditory spectra and other characteristics are broadly utilized in actual engineering applications, with their unique characteristics. Meanwhile, the learning tasks of human-computer interaction have been widely used, for machine learning and deep learning are also developing rapidly. Therefore, in this paper, auditory EEG signals under the excitation of underwater targets' signals are used to carry out recognition research by combining human brain perception, artificial analysis, SVM and ResNet with atrous convolution. The results show that the recognition rate of four types of ship radiated noise can reach 94.35% using the ResNet with atrous convolution, which can effectively classify and recognize underwater targets.

IMPLEMENTATION OF THE CRT APPROACH ASSISTED BY WORDWALL MEDIA IN IMPROVING THE POETRY WRITING SKILLS OF CLASS XE STUDENTS OF SMA NEGERI 5 KUPANG

Abstract The aim of this research is to improve students' ability to write in class using a Culturally Responsive Teaching (CRT) approach with the help of Wordwall media. The CRT approach is a learning method used to appreciate and respect the diversity of students. Wherever, this approach will increase the effectiveness of writing poetry in the learning process. The method used in this research is called Classroom Action Research (PTK), which was carried out over two semesters of lectures. This research consists of four stages: preparation, action, evaluation, and reflection. The object of the research was 36 students in class X E of SMA Negeri 5 Kupang. The data collection methods used are observation and checking learning tasks. Qualitative descriptive analysis was used in collecting research data. The results of this research were obtained by applying the Culturally Responsive Teaching (CRT) approach. Keywords: Culturally Responsive Teaching (CRT), Writing poetry, and Wordwall.

A category incremental continuous learning model for imbalance arrhythmia detection

Abstract The development of efficient arrhythmia detection systems is crucial for physiological measurements and computer-aided diagnosis. Existing systems rely mainly on offline learning methods and lack the ability to assimilate new data or recognize emerging categories. To address these challenges, this study introduces an arrhythmia detection model that is resistant to data imbalance and has continuous learning capabilities, specifically for incremental learning of new ECG data categories. The system incorporates constraints derived from the new class data and implements a dynamic mechanism for updating connection weights, facilitating the incremental continual learning of classes. Confronted with the problem of models forgetting the original data and overfitting with the added data in continuous learning, we introduce a data balancing method by regularization to balance the model's memory and learning of the two types of data. Facing the data imbalance problem in continuous learning, we introduce a posteriori probability weighting strategy. This strategy assigns greater importance to high-value samples based on the model's posterior residual kernel density estimates. Comprehensive testing of the model using various datasets from the MIT-BIH database indicates superior performance in incremental learning tasks. The results reveal that the model not only excels in class incremental learning but also ensures effective balancing across different data classes.

Penerapan Manajemen Balance Scorecard (BSC): Studi Kasus di MIS Miftahul Ulum Kec. Serpong Utara Kota Tangerang Selatan

The implementation of Balanced Scorecard (BSC) management at MIS Miftahul Ulum, Serpong Utara District, South Tangerang City is still facing some obstacles. The lack of understanding and accurate data, structured systems, resources, and accountability culture are the main barriers. This has resulted in the suboptimal achievement of strategic goals, such as learning, internal process, customer, and financial indicators that are not yet maximized. The purpose of this study is to determine the extent to which the implementation of Balanced Scorecard (BSC) management at MIS Miftahul Ulum, reviewed from four perspectives, namely: financial, customer, internal process, and learning and growth. This research is qualitative, with data collection methods including interviews, observations, and documentation. Data validity is ensured through triangulation, and data analysis techniques include data collection, data reduction, data presentation, and conclusion drawing. The research shows that the quality of education at MIS Miftahul Ulum, Serpong Utara District, South Tangerang City has improved compared to previously. However, some adjustments need to be made in the implementation of Balanced Scorecard (BSC). Conclusion: MIS Miftahul Ulum focuses on diversifying funding sources, increasing the efficiency of fund management, and increasing income from voluntary donations. Customer satisfaction and making MIS Miftahul Ulum the main school of choice for students and parents. Progress in its internal processes by implementing a quality curriculum, an effective teaching and learning process, and timely completion of teaching and learning tasks, and a quality curriculum provides a strong foundation for students to learn and grow. Effectiveness of the teaching and learning process: An effective teaching and learning process allows students to reach their full learning potential. Timely completion of teaching and learning tasks: Timely completion of teaching and learning tasks ensures that students have enough time to learn and understand the subject matter.

Knowledge enhanced multi-task learning for simultaneous optimization of human parsing and pose estimation

Human parsing and pose estimation are both closely related to the human body structure, and it is advantageous to deal with both together in order to assist each other’s learning. Nevertheless, the two tasks have their own unique characteristics, and it is therefore difficult to efficiently and simultaneously obtain a good performance for both tasks in one single multi-task learning (MTL) network. This paper proposes a new MTL network (named “KDNet”) to enhance the communication between human parsing and pose estimation through knowledge distillation so as to improve the simultaneous learning of both tasks. In the proposed KDNet, a consistent representation module is used to enhance the related information sharing between the tasks, and a single-task model-based loss weighting method is developed to balance the loss levels. The proposed KDNet has been verified across three benchmark datasets, and in comparison to other state-of-the-art methods it achieves substantial performance gains without increasing model parameters during inference. The codes are shared at https://github.com/Yhdian/KDNet.git.

Medial prefrontal cortex acetylcholine signaling mediates the ability to learn an active avoidance response following learned helplessness training.

Increased brain levels of acetylcholine (ACh) have been observed in patients with depression, and increasing ACh levels pharmacologically can precipitate stress-related behaviors in humans and animals. Conversely, optimal ACh levels are required for cognition and memory. We hypothesize that excessive ACh signaling results in strengthening of negative encoding in which memory formation is aberrantly strengthened for stressful events. The medial prefrontal cortex (mPFC) is critical for both top-down control of stress-related circuits, and for encoding of sensory experiences. We therefore evaluated the role of ACh signaling in the mPFC in a learned helplessness task in which mice were exposed to repeated inescapable stressors followed by an active avoidance task. Using fiber photometry with a genetically-encoded ACh sensor, we found that ACh levels in the mPFC during exposure to inescapable stressors were positively correlated with later escape deficits in an active avoidance test in males, but not females. Consistent with these measurements, we found that both pharmacologically- and chemogenetically-induced increases in mPFC ACh levels resulted in escape deficits in both male and female mice, whereas chemogenetic inhibition of ACh neurons projecting to the mPFC improved escape performance in males, but impaired escape performance in females. These results highlight the adaptive role of ACh release in stress response, but also support the idea that sustained elevation of ACh contributes to maladaptive behaviors. Furthermore, mPFC ACh signaling may contribute to stress-based learning differentially in males and females.

A critical discourse analysis of cultural representation in language learning tasks: The case of an English language textbook used in Indonesian secondary schools

ABSTRACT The present study aims to examine the extent to which language learning tasks promote intercultural awareness. Drawing on Kachru’s (1985) circles framework, the analysis of 15 chapters and 107 tasks shows that more than fifty percent of the analyzed tasks do not belong to any of Kachru’s circles. In terms of Baker’s (2011) ICA levels, the majority of the tasks demonstrate the basic level (Level 1); none belongs to the advanced level (Level 2); and only fourteen percent exhibit the top level of intercultural awareness (Level 3). These findings suggest the need to incorporate more intercultural dimensions into language learning tasks to enhance students’ intercultural awareness.