Learning Ability Research Articles

Children’s literacy skills development through non-formal education: a scoping review

The rapid advancement of technology in recent years has brought about profound changes in every aspect of our lives. Such technological advancement has impacted children in a digital age where technology permeates every facet of their existence, from education to entertainment and communication to socialization. The younger generations today appear deficient in motor, emotional, and social abilities. They appear to exhibit higher levels of aggression, anxiety, dependency, and reduced creativity. Integrating non-formal education into children’s learning aims to maximize enjoyment and engagement by capturing children’s interest and inspiring them to continue learning. Thus, non-formal education encompasses a spectrum of curricular activities in the natural environment outside of the school area. This scoping review aims to identify the focus skills development among primary school learners and how non-formal educational activities could boost learners’ learning ability. Four databases, including Scopus, web of science (WoS), Education Resources Information Center (ERIC), and ScienceDirect, were used in this research, which found 36 articles for eligibility. Only 15 articles are eligible for analysis and reference after the exclusion and inclusion process for data collection. The findings show that non-formal education offers learners the opportunity to explore a multitude of interests beyond the structured confines of the classroom.

Effects of Prenatal Methcathinone Exposure on the Neurological Behavior of Adult Offspring.

Our previous research has shown that prenatal methcathinone exposure affects the neurodevelopment and neurobehavior of adolescent offspring, but the study on whether these findings continue into adulthood is limited. This study aims to explore the effects of prenatal methcathinone exposure on anxiety-like behavior, learning and memory abilities, as well as serum 5-hydroxytryptamine and dopamine concentrations in adult offspring. Pregnant rats were injected daily with methcathinone between the 7th and 20th days of gestation. The neurobehavioral performance of both male and female adult offspring rats was evaluated by neurobehavioral tests, including open-field tests, Morris water maze (MWM) tests, and novel object recognition (NOR) tests. The levels of 5-hydroxytryptamine and dopamine concentration in rat serum were detected by ELISA. Significant differences were found in the length of center distance and time of center duration in the open-field test, as well as the times of crossing the platform in the MWM test, between the prenatal methcathinone exposure group and the control group. Results of the NOR test showed that adult offspring rats exposed to methcathinone need more time to discriminate the novel object. No gender differences were detected in the neurobehavioral tests. The serum concentrations of 5-hydroxytryptamine and dopamine in rats exposed to methcathinone prenatally were lower than that in the control group, and the serum dopamine concentration was independent of gender in each group. Prenatal methcathinone exposure affects the neurological behavior in adult offspring, and 5-hydroxytryptamine and dopamine might be involved in the process.

Subjective visual vertical/horizontal and video head impulse test in dyslexic children.

Different studies have tried to establish a relationship between dyslexia and the vestibular system function. Subjective Visual Vertical/Horizontal (SVV and SVH) and Video Head Impulse Test (VHIT) are useful for studying the vestibular system and can be easily performed in children. Our aim was to evaluate the vestibular function in dyslexic children by SVV/SVH and VHIT. We enrolled 18 dyslexic children (10M/8F; mean age 10.7 ± 2.3 years; range 7-14 years) and 18 age-matched children with typical development of learning abilities. All children performed VHIT, SVV and SVH. We found normal gain and symmetry of vestibulo-ocular-reflex both in dyslexic and typically developing children. Fifteen out of 18 dyslexic children (83.3%) showed a difference of at least one amongst SVV or SVH. The mean value of SVV was 2.3° and the mean value of SVH was 2.6°. Statistical analysis showed a significant difference between typically developing and dyslexic children for both SVV and SVH. We confirm a relationship between dyslexia and the alteration of SVV and SVH. Our results could be related to the pathogenetic hypothesis of a visual processing impairment related to a dysfunction of the magnocellular pathway or to a general deficit related to a multimodal cortical network.

An Experimental Study on the Effect of AIGC on High School Students’ English Words Memory

Vocabulary memorizing is the foundation of English learning, and improving students’ vocabulary memorizing efficiency occupies an essential place in English learning. However, the current vocabulary teaching activities are stereotyped in design and superficial in practice and fail to achieve the teaching goal of improving students’ vocabulary learning ability. With the development of AI, its function for learning is obvious. Therefore, based on AIGC, this study designs an experiment with 60 students from two parallel classes in grade one of a high school in Pingjiang County as the research object. The author adopts the literature method, investigation method, and experimental method. After referring to lots of related literature at home and abroad and conducting the investigation and analysis of the current situation of English vocabulary learning, the design principles and strategies for this experiment are put forward. At the end of the practice, the English vocabulary test and questionnaire survey were used as research tools to collect experimental data, and the SPSS27.0 software was used for statistics and analysis. The results show that AIGC can stimulate students’ interest in vocabulary memorizing, promote memorizing efficiency, and enhance the ability to use the vocabulary.

Medical image segmentation with UNet-based multi-scale context fusion

Histopathological examination holds a crucial role in cancer grading and serves as a significant reference for devising individualized patient treatment plans in clinical practice. Nevertheless, the distinctive features of numerous histopathological image targets frequently contribute to suboptimal segmentation performance. In this paper, we propose a UNet-based multi-scale context fusion algorithm for medical image segmentation, which extracts rich contextual information by extracting semantic information at different encoding stages and assigns different weights to the semantic information at different scales through TBSFF module to improve the learning ability of the network for features. Through multi-scale context fusion and feature selection networks, richer semantic features and detailed information are extracted. The target can be more accurately segmented without significantly increasing the extra overhead. The results demonstrate that our algorithm achieves superior Dice and IoU scores with a relatively small parameter count. Specifically, on the GlaS dataset, the Dice score is 90.56, and IoU is 83.47. For the MoNuSeg dataset, the Dice score is 79.07, and IoU is 65.98.

Enhancing Education and Teaching Management Through Data Mining and Support Vector Machine Algorithm

This study investigates the effectiveness of the SVM+TLBO approach for predicting student performance and evaluating learning abilities in educational settings. By integrating Support Vector Machines (SVM) with Teaching-Learning-Based Optimization (TLBO), the research aims to enhance predictive accuracy and efficiency compared to traditional methods, including Decision Trees, Ant Colony Algorithms, Clustering Algorithms, Convolutional Neural Networks (CNN), Neural Networks, Support Vector Machine (SVM) without Optimization. Results indicate that the SVM+TLBO model significantly outperforms these methods, providing robust predictions and valuable insights into student learning patterns. The findings underscore the importance of data-informed decision-making in education, enabling educators to identify at-risk students and tailor instructional strategies effectively. This research contributes to the growing field of educational data mining, highlighting the potential of advanced machine learning techniques to optimize educational outcomes and foster personalized learning experiences.

The mediating role of self-directed learning ability in the impact of educational environment, learning motivation, and emotional intelligence on metacognitive awareness in nursing students

PurposeThis study aimed to examine the mechanisms influencing metacognitive awareness among nursing students, encompassing educational environment, learning motivation, emotional intelligence, and self-directed learning ability. These findings could be a theoretical reference for formulating metacognitive awareness intervention strategies specifically designed for nursing students.MethodThrough stratified sampling, 600 nursing students were recruited from two universities in China. Data collection involved five self-report instruments: the Metacognitive Awareness Inventory, Medical Education Environment Evaluation Scale, Learning Motivation Scale, Emotional Intelligence Scale, and Self-Directed Learning Ability Scale. Confirmatory factor analysis was utilized to test the measurement model, while structural equation modeling utilized multiple indices to assess model fit. Bootstrapping was conducted to analyze total, direct, and indirect effects.ResultsAll study variables differed significantly according to age and year level (p < 0.01). Metacognitive awareness positively correlated with the educational environment, learning motivation, emotional intelligence, and self-directed learning ability ( r = 0.493–0.729; all p < 0.01). The test of the measurement model shows good reliability and validity. The model depicting factors influencing metacognitive awareness demonstrated a good fit (χ2/df = 2.42, SRMR = 0.048, RMSEA = 0.049, CFI = 0.947, IFI = 0.935). Path analyses revealed that educational environment (EE), learning motivation (LM), and emotional intelligence (EI) directly impacted self-directed learning ability (SDLA) ( βEE = 0.189, βLM = 0.436; βEI = 0.215; all p < 0.05). Moreover, educational environment and self-directed learning ability directly influenced metacognitive awareness (βEE = 0.224, βSDLA = 0.520; all p < 0.05). Lastly, the educational environment, learning motivation, and emotional intelligence influenced metacognitive awareness by self-directed learning ability.ConclusionMetacognitive awareness in nursing students rises with age and year level. Females exhibit more potent self-directed learning abilities than males. Educational environment, motivation, and emotional intelligence indirectly influence metacognitive awareness via self-directed learning ability. This finding has enriched the ways and perspectives of metacognitive awareness training for nursing students.

Analysis of Studies Based on Türkiye Examining the Relationship between Artificial Intelligence and Education: A Meta Synthesis Study

Artificial intelligence is defined as human-made systems that mimic the thinking, perception, and learning abilities of the human brain, using the information they gather and having the ability to improve themselves. With the changes and developments in the field of technology, the use of artificial intelligence has also expanded. The increasing popularity of artificial intelligence in the field of education has led to a rise in research on this topic. This paper aims to synthesize the existing literature on the applications of artificial intelligence in education within a specific time frame and to develop a holistic perspective. In the paper, 20 studies written in the last five years on artificial intelligence and education were examined using the metasynthesis method. The research question is defined as: "What trends exist in academic research on artificial intelligence and education conducted in Turkey over the past five years?" The data source for the research was determined using the criterion sampling method. For this purpose, a detailed search was conducted in databases such as Google Scholar, Academia, TÜBİTAK ULAKBİM, and Dergipark using the keyword "artificial intelligence and education." As a result of the analyses, it was found that while the study emphasizes the contributions of artificial intelligence to the field of education, there are also some concerns on the subject. The contributions of artificial intelligence, particularly in terms of providing personalized learning experiences, saving time, and improving student performance, were highlighted, but concerns were raised regarding data privacy and the ethical principles in the use of artificial intelligence. Additionally, it was found that there are educational and developmental needs for artificial intelligence users.

Driving Abilities and Wearing-Off in Parkinson’s Disease: A Driving Simulation Study

Background/Objectives: Driving abilities require the synchronized activity of cerebral networks associated with sensorimotor integration, motricity, and executive functions. Drivers with Parkinson’s disease (DwP) have impaired driving ability, but little is known about the impact of “wearing-off” and therapies in addition to L-DOPA on driving capacities. This study aimed to (i) compare driving performance between DwP during different motor states and healthy controls and (ii) assess the impact of add-on therapies on driving abilities. Methods: DwP (n = 26) were enrolled as individuals experiencing wearing-off symptoms and treated (within 6 months before the enrollment) with add-on therapies to L-DOPA, including MAO inhibitors for DwP-A (n = 12) or opicapone for DwP-B (n = 14). Age- and sex-matched controls (CON, n = 12) were also enrolled. DwP received two driving assessments in a driving simulator during their “best-on” time and during their wearing-off time on different days. An anamnestic driving questionnaire was collected with the assistance of partners. A Virtual Driving Rating Scale (VDRS) was calculated, as well as learning curves (LCs) for driving items calculated in minutes. Results: DwP reported worse driving performance than CON at the driving questionnaire. In line with this, DwP showed worse VDRS (p &lt; 0.01) and LC (p = 0.021) than CON. Lower VDRS was associated with wearing-off (p &lt; 0.01), but DwP-B had better driving performance while in their “best-on” time (p = 0.037) and more items improving with LCs (7 vs. 3) than DwP-A. Conclusions: DwP demonstrated impaired driving compared to controls. Wearing-off symptoms can also affect driving ability, but therapies (opicapone more so than MAO inhibitors) may play a role in preserving specific driving skills, possibly through maintaining learning abilities.

PAFPT: Progressive aggregator with feature prompted transformer for underwater image enhancement

The optical characteristics of underwater environments often result in distorted underwater images, which makes it difficult to meet the needs of complex underwater environment perception. The demand for obtaining high-quality underwater images presents challenges for existing underwater image enhancement methods. In this paper, a progressive aggregator with feature prompted transformer for underwater image enhancement is presented. First, a feature prompted transformer module consisting of a feature convolutional transposed attention and a prompted feedforward neural network is proposed. It integrates feature attention mechanisms and adjusts input features through prompt weights to better focus on global information while enhancing local details, enriching texture, removing blurring, and correcting color deviation. Concurrently, a progressive aggregator consisting of four stages of up-sampling aggregation blocks is constructed to transfer features effectively and facilitate the aggregation of features of different scales at different stages to realize comprehensive attention to image details and semantic information. Besides, a region reconstruction unit is designed, and a reconstruction attention mechanism is introduced to pay attention to the bottleneck localized region to suppress noise. The performance of our method is thoroughly evaluated on public underwater image datasets: EUVP, UIEB, RUIE and SQUID. The quantitative and qualitative results indicate that our method outperforms nine state-of-the-art methods in both subjective perception and evaluation metrics, demonstrating excellent learning and generalization abilities. Additionally, the excellent results reflect the significant performance gains it brings to downstream visual applications.

Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia

ABSTRACT Missing streamflow data is a common issue in Peninsular Malaysia, as the technologies used in hydrological studies often fail to collect data accurately. Additionally, conventional methods are still widely used in the region, which are less accurate compared to artificial intelligence (AI) methods in estimating missing streamflow data. Therefore, this study aims to estimate the missing streamflow data from 11 stations in Peninsular Malaysia by using different AI methods and determine the most appropriate method. Four homogeneity tests were applied to check the quality of data, and the results of the tests indicated that the streamflow data in most stations were homogenous. Two AI methods were applied in this study, which were artificial neural network and artificial neuro-fuzzy inference systems (ANFIS). The proposed AI methods were compared with five different conventional methods. All streamflow missing data, constituting 30% of data from each year were estimated on a daily time scale, and evaluated using root mean square error, mean absolute error and correlation coefficient values. The results indicated that ANFIS was the best due to its learning abilities and the fuzzy inference systems, which enable it to handle complicated input–output patterns and provide highly accurate estimation results.

A Study on the Cultivation of Autonomous Learning Ability in College English Teaching

This paper aims to explore the cultivation of autonomous learning ability in college English teaching. Through literature review, current situation analysis, and empirical research, the article deeply analyzes the current problems and influencing factors of cultivating autonomous learning ability in college English teaching, and proposes targeted cultivation strategies and methods. Research results show that optimizing curriculum and teaching methods, enhancing students' autonomous learning motivation and interest, and cultivating students' autonomous learning habits and abilities are important ways to effectively improve college students' English autonomous learning abilities. The research results of this paper provide a useful reference for the reform and development of college English teaching.

Electroacupuncture improves cognitive function by modulating hippocampal lactate-mediated HIF-1α signaling pathway and inhibiting inflammation response in vascular dementia rats

To observe the effects of electroacupuncture (EA) stimulation of "Sishencong"(EX-HN1) and "Fengchi"(GB20) on lactate (Lac) content, expression of proline hydroxylase 2 (PHD2), hypoxia-inducible factor-1α (HIF-1α)/nuclear transcription factor- κB (NF- κB)/NOD-like receptor thermoprotein structural domain-associated protein 3 (NLRP3) signaling pathway, and inflammatory factors in hippocampal tissue of vascular dementia (VD) rats, so as to explore its mechanisms underlying improvement of VD. Male SD rats screened by Morris water maze tests were randomly divided into blank control, sham-operation, VD model and EA groups (12 rats in each group). The VD model was replicated using the 4-vessel occlusion (VO) method. EA (2 Hz, 1 mA) was applied to EX-HN1 and bilateral GB20 for 30 min, once daily for consecutive 21 days. Morris water maze test was employed to test the rat's memory learning ability before and after modeling and after the intervention. The hippocampal tissue was sampled for observing histopathologic changes with H.E. staining；and detecting Lac content with colorimetric method, and the contents of tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, and IL-18 (also in serum) by using ELISA, respectively. The immunoactivity levels of PHD2, HIF-1α, and p-NF-κB p65 in hippocampal tissue were detected by immunohistochemistry, and the expression levels of PHD2, HIF-1α, NF-κB p65, p-NF-κB p65 and NLRP3 proteins in hippocampal tissue were detected by Western blot. Compared with the blank control and sham-operation groups, the escaping latency, Lac content in hippocampus, the TNF-α, IL-1β, and IL-18 contents in both hippocampus and serum, the immunoactivity of HIF-1α and p-NF-κB p65 and expression levels of HIF-1α, NF-κB p65, p-NF-κB p65, and NLRP3 proteins were significantly increased (P<0.01), while the number of original platform crossing, and PHD2 immunoactivity and protein expression level were significantly decreased (P<0.05, P<0.01) in the model group. Following EA intervention, modeling induced increase and decrease of the indexes mentioned above were all reversed in the EA group (P<0.05, P<0.01). H.E. staining showed disordered arrangement of neurons, uneven cytoplasm stain, blurred nucleolus or disappearance of nucleolus, dilated capillaries, many apoptotic bodies and increased inflammatory cells in the hippocampus tissue of the model group, which was improved to a certain extent in the EA group, including relatively regular arrangement of neurons, reduced apoptotic bodies and inflammatory cells, etc. in the hippocampus. EA stimulation of EX-HN1 and GB20 can improve the cognitive function in VD rats, which may be related to its functions in reducing Lac content, regulating the expression of HIF-1α pathway related proteins, and inhibiting inflammatory responses in the hippocampus tissue.

Designing Effective Pedagogical Activities to Enhance Grammar Understanding and Communicative Competence in Intermediate-Level Adult SL Learners

This article explores strategies to enhance the grammatical understanding and communicative competence of intermediate-level adult second language learners by designing targeted pedagogical activities. It focuses on how pre-task, while-task, and post-task activities can effectively support learners in grasping complex grammatical concepts and applying them in real-world communicative scenarios. The study emphasizes that using grammar in authentic contexts significantly improves learners ability to communicate fluently in English, bridging the gap between theoretical knowledge and practical application.By integrating theory with practice, the article underlines the importance of designing teaching activities that are not only engaging but also aligned with the specific learning needs of adult students. The pre-task phase activates prior knowledge and introduces essential terms, while the while-task phase deepens understanding through critical discussion and note-taking. The post-task phase reinforces the application of learned grammar through role-plays and reflective writing, ensuring that learners can transfer their grammatical knowledge to everyday communication situations.Furthermore, the article provides practical insights into teaching strategies that foster active learning and critical thinking, encouraging learners to not only memorize grammar rules but also use them creatively in meaningful interactions. These strategies offer valuable guidance for educators in creating more effective grammar instruction plans tailored to adult learners' unique needs, ultimately enhancing both their linguistic proficiency and problem-solving skills.

Combined Restraint Stress and Metal Exposure Paradigms in Rats: Unravelling Behavioural and Neurochemical Perturbations.

Accumulation of heavy metals (Mn and Ni) and prolonged exposure to stress are associated with adverse health outcomes. Various studies have shown the impacts of stress and metal exposures on brain function. However, no study has examined the effects of co-exposure to stress, Mn, and Ni on the brain. This study addresses this gap by evaluating oxidative and glial responses, apoptotic activity, as well as cognitive processes in a rat model. Adult Wistar rats were exposed to vehicle (control), restraint stress, 25mg/kg of manganese (Mn) or nickel (Ni), or combined restraint stress plus Mn or Ni. Following treatment, rats were subjected to several behavioural paradigms to assess cognitive function. Enzyme activity, as well as ATPase levels, were evaluated. Thereafter, an immunohistochemical procedure was utilised to evaluate neurochemical markers of glial function, myelination, oxidative stress, and apoptosis in the hippocampus, prefrontal cortex (PFC), and striatum. Results showed that stress and metal exposure increased oxidative stress markers and reduced antioxidant levels. Further, combined stress and metal exposure reduced various forms of learning and memory ability in rats. In addition, there were alterations in Iba1 activity and Nrf2 levels, reduced Olig2 and myelin basic protein (MBP) levels, and increased caspase-3 expression. These neurotoxic outcomes were mostly exacerbated by co-exposure to stress and metals. Overall, our findings establish that stress and metal exposures impaired cognitive performance, induced oxidative stress and apoptosis, and led to demyelination effects which were worsened by combined stress and metal exposure.

DMFGAN: a multifeature data augmentation method for grape leaf disease identification.

The use of deep learning techniques to identify grape leaf diseases relies on large, high-quality datasets. However, a large number of images occupy more computing resources and are prone to pattern collapse during training. In this paper, a depth-separable multifeature generative adversarial network (DMFGAN) was proposed to enhance grape leaf disease data. First, a multifeature extraction block (MFEB) based on the four-channel feature fusion strategy is designed to improve the quality of the generated image and avoid the problem of poor feature learning ability of the adversarial generation network caused by the single-channel feature extraction method. Second, a depth-based D-discriminator is designed to improve the discriminator capability and reduce the number of model parameters. Third, SeLU activation function was substituted for DCGAN activation function to overcome the problem that DCGAN activation function was not enough to fit grape leaf disease image data. Finally, an MFLoss function with a gradient penalty term is proposed to reduce the mode collapse during the training of generative adversarial networks. By comparing the visual indicators and evaluation indicators of the images generated by different models, and using the recognition network to verify the enhanced grape disease data, the results show that the method is effective in enhancing grape leaf disease data. Under the same experimental conditions, DMFGAN generates higher quality and more diverse images with fewer parameters than other generative adversarial networks. The mode breakdown times of generative adversarial networks in training process are reduced, which is more effective in practical application.

Implementasi Model Project Based Learning Menggunakan Mind Map terhadap Keterampilan 4C Geografi Siswa Kelas X Fase E SMAN 15 Padang

Geography, as a discipline that studies the interaction between humans and the environment, as well as physical phenomena on Earth, plays an important role in developing 4C skills (Communication, Collaboration, Critical Thinking, and Creativity). However, many students still face challenges in applying 4C skills in geography learning. These challenges include a lack of student participation, as seen from their minimal involvement in class discussions, which hinders the development of communication and collaboration skills. This issue is often caused by low self-confidence or insufficient understanding of the material. Additionally, students frequently struggle with critical thinking, particularly in identifying and analyzing complex geographical problems. The method used in this research is descriptive qualitative, with the subjects selected using purposive sampling, resulting in a sample of Class X Phase E 8. Data collection was carried out using assessment rubrics in terms of attitudes, knowledge, and skills to evaluate student activities in creating Mind Maps. The results of this study show that the assessment of Mind Mapping, using the Project-Based Learning model, yielded an overall average score of 81.72, categorized as good, and positively impacted the development of students' 4C skills. Students demonstrated creativity (C6) by creating Mind Maps, which reflect their ability to think critically and creatively, core aspects of Higher Order Thinking Skills (HOTs). Furthermore, they showed improved communication and collaboration abilities in learning, thus achieving the 4C skills.

Maternal exposure to nanopolystyrene induces neurotoxicity in offspring through P53-mediated ferritinophagy and ferroptosis in the rat hippocampus

There are increasing concerns regarding the rapid expansion of polystyrene nanoplastics (PS-NPs), which could impact human health. Previous studies have shown that nanoplastics can be transferred from mothers to offspring through the placenta and breast milk, resulting in cognitive deficits in offspring. However, the neurotoxic effects of maternal exposure on offspring and its mechanisms remain unclear. In this study, PS-NPs (50 nm) were gavaged to female rats throughout gestation and lactation to establish an offspring exposure model to study the neurotoxicity and behavioral changes caused by PS-NPs on offspring. Neonatal rat hippocampal neuronal cells were used to investigate the pathways through which NPs induce neurodevelopmental toxicity in offspring rats, using iron inhibitors, autophagy inhibitors, reactive oxygen species (ROS) scroungers, P53 inhibitors, and NCOA4 inhibitors. We found that low PS-NPs dosages can cause ferroptosis in the hippocampus of the offspring, resulting in a decline in the cognitive, learning, and memory abilities of the offspring. PS-NPs induced NOCA4-mediated ferritinophagy and promoted ferroptosis by inciting ROS production to activate P53-mediated ferritinophagy. Furthermore, the levels of the antioxidant factors glutathione peroxidase 4 (GPX4) and glutathione (GSH), responsible for ferroptosis, were reduced. In summary, this study revealed that consumption of PS-NPs during gestation and lactation can cause ferroptosis and damage the hippocampus of offspring. Our results can serve as a basis for further research into the neurodevelopmental effects of nanoplastics in offspring.Graphical

Effect of supplementation to offspring during early gestation and the growing phase with different sources of fatty acids on learning and memory ability of post-weaning lambs

Abstract The objective of the current experiment was to evaluate the effects of supplementation with different dietary fatty acid profiles on the dam during the first third of gestation and on the offspring during growth on the offspring’s cognitive behavior. Seventy-nine post-weaning lambs were blocked by body weight and sex using a 2×2 factorial arrangement of treatments. The first factor (maternal supplementation; MS) was supplementation to the ewes in the first third of gestation with 1.61% Ca salts of palm fatty acid distillate (PFAD) or Ca salts enriched with eicosapentaenoic (EPA) and docosahexaenoic acids (DHA) to early pregnant ewes. The second factor (offspring supplementation; OS) was to supplement the offspring during their growing phase with diets that contained 1.48% of PFAD or Ca salts of PFAD or EPA and DHA. Ewes were housed in groups, three ewes per pen and 12 pens per treatment, with different treatments until day 50 of gestation. From day 51 of gestation until weaning, all the animals (ewes and lambs) were housed in a common pen. After weaning, lambs were housed in group pens (5 pens per treatment, three to five per pen). The lambs ran maze tests on weeks five and seven after weaning to evaluate cognitive ability. The maze contained two trap zones and had the pen conspecific lambs at the end of the maze. The measurements were the times to solve the traps and the total time to complete the maze. Data were analyzed using a mixed procedure considering the 2×2 factorial arrangement of treatments. There was a MS × OS × time interaction for the time to complete the maze (P = 0.02). Lambs receiving a different type of fatty acid supplementation during gestation than post-weaning took less time to complete the maze on the second relative to the first day compared with the lambs fed the same type of fatty acids during gestation and growing. In conclusion, combining different fatty acids during different life stages may improve lambs' cognitive abilities.

Generation and Characterization of Three Novel Mouse Mutant Strains Susceptible to Audiogenic Seizures

The mechanisms of epileptogenesis after brain injury, ischemic stroke, or brain tumors have been extensively studied. As a result, many effective antiseizure drugs have been developed. However, there are still many patients who are resistant to therapy. The molecular and genetic bases regarding such drug-resistant seizures have been poorly elucidated. In many cases, heavy seizures are instigated by brain development malformations and often caused by gene mutations. Such malformations can be demonstrated in mouse models by generating mutant strains. One of the most potent mutagens is ENU (N-ethyl-N-nitrosourea). In the present study, we describe three novel mutant strains generated by ENU-directed mutagenesis. Two of these strains present a very strong epileptic phenotype triggered by audiogenic stimuli (G9-1 and S5-1 strains). The third mouse strain is characterized by behavioral disorders and hyperexcitation of neuronal networks. We identified changes in the expression of those genes encoding neurotransmission proteins in the cerebral cortexes of these mice. It turned out that the G9-1 strain demonstrated the strongest disruptions in the expression of those genes encoding plasma membrane channels, excitatory glutamate receptors, and protein kinases. On the other hand, the number of GABAergic neurons was also affected by the mutation. All three lines are characterized by increased anxiety, excitability, and suppressed motor and orientational–exploratory activities. On the other hand, the strains with an epileptic phenotype—G9-1 and S5-1ave reduced learning ability, and the A9-2 mice line retains high learning ability.