Learning Ability Research Articles

Single-view 3D reconstruction via dual attention

Constructing global context information and local fine-grained information simultaneously is extremely important for single-view 3D reconstruction. In this study, we propose a network that uses spatial dimension attention and channel dimension attention for single-view 3D reconstruction, named R3Davit. Specifically, R3Davit consists of an encoder and a decoder, where the encoder comes from the Davit backbone network. Different from the previous transformer backbone network, Davit focuses on spatial and channel dimensions, fully constructing global context information and local fine-grained information while maintaining linear complexity. To effectively learn features from dual attention and maintain the overall inference speed of the network, we do not use a self-attention layer in the decoder but design a decoder with a nonlinear reinforcement block, a selective state space model block, and an up-sampling Residual Block. The nonlinear enhancement block is used to enhance the nonlinear learning ability of the network. The Selective State Space Model Block replaces the role of the self-attention layer and maintains linear complexity. The up-sampling Residual Block converts voxel features into a voxel model while retaining the voxels of this layer. Features are used in the up-sampling block of the next layer. Experiments on the synthetic dataset ShapeNet and ShapeNetChairRFC with random background show that our method outperforms recent state of the art (SOTA) methods, we lead by 1% and 2% in IOU and F1 scores, respectively. Simultaneously, experiments on the real-world dataset Pix3d fully prove the robustness of our method. The code will be available at https://github.com/epicgzs1112/R3Davit.

Application Research of Power Measurement Data Exchange Based on Uninterruptible Power Supply

With the maturity of the smart meter embedded system integration mode, the power consumption information can be fed back to the management center by the intelligent terminal through the power Internet of Things. The smart terminal adopts the narrowband data communication mode. However, the narrowband channel interval is hindered by interference signals, resulting in serious data packet loss. Therefore, a stable and efficient acquisition scheme is needed to ensure its smooth interaction. Based on the application of smart meter data communication, we analyze the power consumption optimization and carrier transmission optimization of the power Internet of Things and propose a data analysis method based on self-learning and external characteristics, which is used to calculate the offline execution strategy of smart meters when they encounter factors such as power outage or intrusion. A production task-driving rule is designed. The external characteristics of deep learning data of the power Internet of Things are collected. The accuracy of data classification is judged according to the parallel Internet of Things of a convolutional neural network and a long-term and short-term memory deep neural network. When dealing with high-dimensional original data, the data network is graded to reduce the work of feature selection and the difficulty of training. Moreover, we improve the autonomous execution ability of an embedded terminal device in an offline state. We also strengthen the ability of abnormal data detection and judgment learning to meet the requirements of efficient and stable operation of the power grid. Simulation results verify that compared with the current mainstream schemes, the performance of the proposed scheme is improved by more than 17%. Besides, the fault tolerance rate is enhanced by 29%, and the power consumption after optimization is reduced by 26.3% and 42.8%. These effects highlight the role of data processing in embedded systems. It solves the problem of abnormal data and energy loss affecting the accuracy of error estimation of electric energy meters, opens up a new way for data acquisition and processing of large-scale smart meters, and further improves the service level of information management of power systems.

Artemisiae Iwayomogii Herba mitigates excessive neuroinflammation and Aβ accumulation by regulating the pro-inflammatory response and autophagy-lysosomal pathway in microglia in 5xFAD mouse model of Alzheimer's disease.

Alzheimer's disease (AD) presents a growing societal challenge, driven by an aging population. It is characterized by neurodegeneration linked to β-amyloid (Aβ) and tau protein aggregation. Reactive glial cell-mediated neuroinflammation exacerbates disease progression by facilitating the accumulation of Aβ and impairing its clearance, thus highlighting potential therapeutic targets. Aerial parts of Artemisia iwayomogi (AIH), a kind of mugwort, has been consumed as a medicinal herb in East Asia for relieving inflammation-related diseases. Previously, AIH was found to exert potent inhibitory effects on neuroinflammation. This study aimed to examine whether AIH mitigates AD pathogenesis by regulating neuroinflammation and reducing Aβ deposition. AIH treatment to primary mixed glial cultures attenuated the pro-inflammatory responses evoked by Aβ stimulation. When treated to 5 × familial AD (5xFAD) mice, AIH improved learning and cognitive ability and reduced Aβ burden in the brain. AIH suppressed glial overactivation, as well as inhibited the expressions of pro-inflammatory mediators in the brain. Moreover, AIH regulated AKT signaling and elevated the expression of autophagy-lysosomal mediators in vitro. It was confirmed that lysosome-associated membrane protein 1 (LAMP1) was increased in the Aβ-associated microglia in the mouse hippocampus. Finally, it was observed that tau phosphorylation was alleviated, and synaptic protein expression was increased in AIH-treated 5xFAD mice. Overall, this study demonstrated that AIH ameliorated excessive neuroinflammation and Aβ accumulation by regulating microglial activation and autophagy-lysosomal pathway, thereby suggesting AIH as a promising therapeutic candidate for AD treatment.

Dexmedetomidine alleviates the long-term neurodevelopmental toxicity induced by sevoflurane in the developing brain.

Sevoflurane is an extensively used anesthetic for pediatric patients, however, numerous studies showed that sevoflurane (SEVO) may cause long-term neurodevelopmental toxicity. Dexmedetomidine (DEX) has been shown to be protective against SEVO-induced neurotoxicity, but the mechanism remains unclear. The effects and mechanisms of different DEX administration routes on SEVO-induced neurotoxicity and long-term cognitive defects were determined and further investigated the role of sex in these processes. Male and female Sprague Dawley (SD) rats at postnatal day 7 (PND7) received an intraperitoneal injection of DEX (10 μg/kg) before or after exposure to 2.5% SEVO for 6 hours, or before and after SEVO exposure. The respiratory and mortality rates of the pups were recorded during anesthesia. Neuroapoptosis was evaluated by TdT-mediated dUTP Nick-End labeling (TUNEL) staining. Immunohistochemistry and immunofluorescence were employed to detect the expression of caspase-3 in neuronal cells and neurons. The expression of GSK-3β and DISC1 were determined by Western blotting or RT-qPCR. Morris Water Maze (MWM) test was used to evaluate the learning and memory ability of rats until they were 3 weeks and 5 weeks old. Compared with the control group, exposure to 2.5% SEVO resulted in increased neuroapoptosis, and decreased the expression of DISC1 at levels of mRNA and protein and phosphorylated GSK-3β in the developing brain. SEVO exposure during critical neurodevelopmental periods could cause persistent cognitive defects in adolescent male and female rats, and inhibited DISC1 and phosphorylated GSK-3β protein expression. The neurotoxic impacts of SEVO were lessened by the administration of DEX (10 μg/kg) before or after exposure. Our findings suggest that DEX (10 μg/kg) mitigates the neurotoxic effects of SEVO on the developing rat brain as well as postnatal cognitive defects by regulating the DISC1/GSK-3β signaling.

PHƯƠNG PHÁP DẠY HỌC “BẮC GIÀN GIÁO” VÀ VIỆC ỨNG DỤNG VÀO GIẢNG DẠY MÔN KHẨU NGỮ TIẾNG TRUNG QUỐC Ở TRƯỜNG ĐẠI HỌC SƯ PHẠM HÀ NỘI

The “scaffolding” teaching method is a teaching model originating from Constructivist Theory and the Zone of Proximal Development (ZPD) developed by Vygotsky. It offers new insights into dealing with the relationship between “teaching” and “learning”. The changing roles of teachers and students, promoting students' collaborative learning and exploration abilities, and cultivating students' independent problem-solving abilities of this teaching model have helped teachers improve their skills and enhance their educational and teaching abilities. In the process of implementing “scaffolding” teaching, we need to grasp the following key points: teachers need to clarify their roles, arrange a reasonable conceptual framework according to students’ levels, and check control interactive teaching in the student's “ZPD” and gradually reduce the role of the “scaffolder”. In this article, we would like to analyze the birth of the concept of “scaffolding”, its basic characteristics, and teaching steps. Finally, we applied it to teaching Chinese oral courses for students majoring in Chinese language at HNUE.

Enhancing Metaphor Recognition of Literary Works in Applied Artificial Intelligence: A Multi-Level Approach with Bi-LSTM and CNN Fusion

ABSTRACT Understanding metaphorical language is essential for AI to interpret and communicate with humans accurately. However, current methods often struggle with the complexity of metaphors, making it difficult for AI systems to understand human language fully. Recognizing metaphors is challenging because they are frequently ambiguous and depend on context. In this study, we propose a new approach using a combination of Bi-directional Long Short-Term Memory (Bi-LSTM) networks, Convolutional Neural Networks (CNN), and uni-directional LSTM components to create a multi-level model for recognizing metaphors. Our model uses various features, including dependency, semantics, and part-of-speech, to improve its learning ability. Additionally, we introduce a new method for recognizing the emotional context of metaphors using a random walk model to determine the emotional tone of words. Our results show that this model improves performance in recognizing metaphors, enhancing AI’s ability to understand them.

A novel biomimetic nanovesicle containing caffeic acid-coupled carbon quantum dots for the the treatment of Alzheimer's disease via nasal administration.

Alzheimer's disease (AD) is a common neurodegenerative disease characterized by progressive cognitive and physical impairment. Neuroinflammation is related to AD, and the misfolding and aggregation of amyloid protein in the brain creates an inflammatory microenvironment. Microglia are the predominant contributors to neuroinflammation, and abnormal activation of microglia induces the release of a large amount of inflammatory factors, promotes neuronal apoptosis, and leads to cognitive impairment. In this study, we used microglial membranes containing caffeic acid-coupled carbon quantum dots to prepare a novel biomimetic nanocapsule (CDs-CA-MGs) for the treatment of AD. The application of CDs-CA-MGs via nasal administration can bypass the blood‒brain barrier (BBB) and directly target the site of inflammation. After treatment with CDs-CA-MGs, AD mice showed reduced inflammation in the brain, decreased neuronal apoptosis, and significantly improved learning and memory abilities. In addition, CDs-CA-MGs affect inflammation-related JAK-STAT and Toll-like receptor signaling pathways in AD mice. CDs-CA-MGs significantly downregulated interleukins (IL-1β and IL-6) and tumor necrosis factor (TNF-α). This finding suggested that CDs-CA-MGs may improve cognitive impairment by modulating inflammatory responses. In conclusion, the use of CDs-CA-MGs provides a possible therapeutic strategy for the treatment of AD.

The role of the O2O blended teaching model in improving the teaching effectiveness of physical education classes

Abstract The deep fusion of Internet technology and education is constantly pushing forward the reform of university education. Traditional educational ideas, concepts, and models cannot keep pace with the times, and hybrid teaching has become a new way of education in colleges and universities. To improve the teaching effect of physical education classes, the study used a blended teaching model and designed a teaching evaluation and performance prediction model under the blended teaching model based on an improved cluster analysis method and attention mechanism. The lab results indicated that under the blended teaching model, students’ performance increased by 12.89 points, and the level of skill mastery and proficiency increased by 26.52 and 28.55%, respectively, with grades more inclined to high score distribution. “Excellent” grade clustering increased by 77.71%, and “Good” grade clustering increased by 19.01%. The minimum error sum of squares of the improved clustering algorithm was 58.18 and 36.25% lower than the other two algorithms, and the clustering results were more relevant. The two-way attention mechanism algorithm predicted higher accuracy results and performed best on all four evaluation metrics, with a prediction accuracy of 98.23%, an accuracy of 98.42%, and an F1 value of 91.78%. This hybrid teaching model is more in line with the characteristics of the physical education teaching discipline, successfully cultivates students’ independent learning ability, stimulates students’ love for physical education courses, and achieves better teaching results.

Advancing health coaching: A comparative study of large language model and health coaches

ObjectiveRecent advances in large language models (LLM) offer opportunities to automate health coaching. With zero-shot learning ability, LLMs could revolutionize health coaching by providing better accessibility, scalability, and customization. The aim of this study is to compare the quality of responses to clients' sleep-related questions provided by health coaches and an LLM. Design, setting, and participantsFrom a de-identified dataset of coaching conversations from a pilot randomized controlled trial, we extracted 100 question-answer pairs comprising client questions and corresponding health coach responses. These questions were entered into a retrieval-augmented generation (RAG)-enabled open-source LLM (LLaMa-2-7b-chat) to generate LLM responses. Out of 100 question-answer pairs, 90 were taken out and assigned to three groups of evaluators: experts, lay-users, and GPT-4. Each group conducted two evaluation tasks: (Task 1) a single-response quality assessment spanning five criteria—accuracy, readability, helpfulness, empathy, and likelihood of harm—rated on a five-point Likert scale, and (Task 2) a pairwise comparison to choose the superior response between pairs. A suite of inferential statistical methods, including the paired and independent sample t-tests, Pearson correlation, and chi-square tests, were utilized to answer the study objective. Recognizing potential biases in human judgment, the remaining 10 question-answer pairs were used to assess inter-evaluator reliability among the human evaluators, quantified using the interclass correlation coefficient and percentage agreement metrics. ResultsUpon exclusion of incomplete data, the analysis included 178 single-response evaluations (Task 1) and 83 pairwise comparisons (Task 2). Expert and GPT-4 assessments revealed no discernible disparities in health coach and LLM responses across the five metrics. Contrarily, lay-users deemed LLM responses significantly more helpful than that of human coaches (p < 0.05). LLM responses were preferred in the majority (62.25 %, n = 155) of the aggregate 249 assessments, with all three evaluator groups favoring LLM over health coach inputs. While GPT-4 rated both health coach and LLM responses significantly higher than experts in terms of readability, helpfulness, and empathy, its ratings on accuracy and likelihood of harm aligned with those of experts. Response length positively correlated with accuracy and empathy scores, but negatively affected readability across all evaluator groups. Expert and lay-user evaluators demonstrated moderate to high inter-evaluator reliability. ConclusionOur study showed encouraging findings by demonstrating that RAG-enabled LLM has comparable performance to health coaches in the domain tested. Serving as an initial step towards the creation of more sophisticated, adaptive, round-the-clock automated health coaching systems, our findings call for more extensive evaluation which could assist in the development of the model that could in the future lead to potential clinical implementation.

The application of digital technology in teaching physics: A quantitative study in terrain using Sem-moderating

The formulation of the problem that arises from this research is to find out and analyze how the variables of creativity in delivering subject matter and the effective use of digital media affect the improvement of students' learning abilities through the capacity to use technology as an intervening variable. The subjects of this study are high school teachers and students in Medan City, where the variables in this study are independent variables, namely the creativity of delivering subject matter and the effective use of digital media, the dependent variable, namely the variable of improving students' learning abilities and the intervening variable is the variable capacity to use technology. The results of data analysis used using SEM analysis using SMART PLS 4.0 software. The research method uses a descriptive quantitative method of data analysis using the structural equation model (SEM) method, where the results of data processing with the SEM method are carried out with the PLS 4.0 application. From the results of this study, the conclusions are that partially the variables of creativity in the delivery of learning materials and the effective use of digital media affect student learning abilities and affect the capacity to use technology. Simultaneously, the variable delivery of learning materials and the capacity to use technology affect students' learning ability through the variable capacity to use technology as an intervening variable. The more creative the delivery of learning materials, and the more effective the use of digital media through the creation of the right technology use capacity will have an impact on improving student learning abilities and producing students who are creative and able to translate learning materials significantly.

Mitochondrial DNA copy number and neurocognitive outcomes in children.

Low mitochondria DNA copy number (mtDNAcn) has been linked to cognitive decline. However, the role of mtDNAcn in healthy cognitive development is unclear. We hypothesized early-life mtDNAcn would be associated with children's learning and memory. We quantified mtDNAcn in umbilical cord blood and child blood at ages 5-7 from participants in a prospective birth cohort. We administered the Children's Memory Scale (CMS) at ages 9-14 (N = 342) and the Wechsler Intelligence Scale for Children (WISC-IV) at ages 7 and 9 (N = 457). Associations between mtDNAcn tertiles and CMS and WISC were evaluated with linear regression and linear mixed-effects models, respectively. We examined non-linear associations using generalized additive mixed models. Relative to the middle tertile of mtDNAcn, lower childhood mtDNAcn was associated with lower WISC Working Memory (β = -2.65, 95% CI [-5.24, -0.06]) and Full-Scale IQ (β = -3.71 [-6.42, -1.00]), and higher CMS Visual Memory (β = 4.70 [0.47, 8.93]). Higher childhood mtDNAcn was linked to higher CMS Verbal Memory (β = 7.75 [2.50, 13.01]). In non-linear models, higher childhood mtDNAcn was associated with lower WISC Verbal Comprehension. Our study provides novel evidence that mtDNAcn measured in childhood is associated with children's neurocognitive performance. mtDNAcn may be a marker of healthy child development. Mitochondrial DNA copy number (mtDNAcn) may serve as a biomarker for early-life neurocognitive performances in the children's population. Both low and high mtDNAcn may contribute to poorer neurocognition, reflected through learning and memory abilities. This research elucidated the importance of investigating mitochondrial biomarkers in healthy populations and facilitated advancements of future studies to better understand the associations between mitochondrial markers and adverse children's health outcomes.

Evaluation of the influence of athlete neural activity patterns on the dynamic index of leaping ability using data mining techniques

There are many dynamic indicators of jumping ability, and the athlete’s neural activity pattern is an important factor in regulating limb activities. This article uses data mining technology to collect, preprocess, data modeling and analysis, and data visualization of dynamic index data of athletes’ neural activity patterns of jumping ability. The results show that the jumping ability of athletes with higher neural activity intensity increased significantly after training to around 40 cm–50 cm, while athletes with lower neural activity intensity did not change significantly and remained around 30 cm–35 cm. The overall learning ability of athletes with higher levels of neural activity improved by about 10 cm, and the base of neural activity also increased significantly. It shows that there is a significant correlation between the intensity of neural activity and dynamic indicators of jumping ability, which is the main driving force for athletes’ jumping explosive power. The research results can help formulate reasonable and scientific training methods to improve athletes’ jumping ability and overall sports level.

Investigation on Understanding the Numeracy Capacity of Intellectual Disabled Students using Enabling Technology Tools: Web Application, AR and UI/UX

The population of individuals with intellectual disabilities (ID) is increasing, necessitating assistance with a wide range of daily activities. Acquiring and assessing numeracy and communication skills are critical for this demographic, requiring tools and techniques tailored to their specific needs. Effective educational tools must employ multi-modal and multi-sensory approaches to cater to diverse learning styles and incorporate assistive technological solutions. Despite the availability of numerous tools, there is a need to enhance their utility and effectiveness. This study aims to identify and refine the requirements for an innovative educational tool that employs Two-dimensional (2D) and Augmented Reality (AR) technologies. To achieve this, we conducted semi-structured interviews and surveys with teachers working with students with ID, gaining insights into the current solutions, advantages, and limitations. Additionally, we used physical props as design probes in a co-design methodology to better understand and elicit the true needs of individuals with ID. The findings from this research will inform the development of a 2D/AR tool designed to make learning mathematics more engaging and effective for individuals with ID, contributing to the advancement of inclusive education practices. Enabling Technology plays a significant role in the numeracy ability among people with ID. Generative AI and Explainable AI shall further improve learning ability in the years to come.

Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures

Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample size. These efforts unveil new questions about how to integrate data across distinct sources and instruments. The goal of this study was to link scores across common auditory verbal learning tasks (AVLTs). This international secondary analysis aggregated multisite raw data for AVLTs across 53 studies totaling 10,505 individuals. Using the ComBat-GAM algorithm, we isolated and removed the component of memory scores associated with site effects while preserving instrumental effects. After adjustment, a continuous item response theory model used multiple memory items of varying difficulty to estimate each individual’s latent verbal learning ability on a single scale. Equivalent raw scores across AVLTs were then found by linking individuals through the ability scale. Harmonization reduced total cross-site score variance by 37% while preserving meaningful memory effects. Age had the largest impact on scores overall (− 11.4%), while race/ethnicity variable was not significant (p > 0.05). The resulting tools were validated on dually administered tests. The conversion tool is available online so researchers and clinicians can convert memory scores across instruments. This work demonstrates that global harmonization initiatives can address reproducibility challenges across the behavioral sciences.

Invasive Ant Detection: Evaluating Honeybee Learning and Discrimination Abilities for Detecting Solenopsis invicta Odor.

Invasive red imported fire ants (Solenopsis invicta) create a serious threat to public safety, agriculture, biodiversity, and the local economy, necessitating early detection and surveillance, which are currently time-consuming and dependent on the inspector's expertise. This study marks an initial investigation into the potential of honeybees (Apis mellifera) to detect and discriminate the odor of S. invicta through the olfactory conditioning of proboscis extension responses. Deceased S. invicta were used as conditioned stimuli to ensure relevance to non-infested areas. The results showed that the bees rapidly learned to respond to deceased ant odors, with response levels significantly increasing at higher odor intensities. Bees exhibited generalization across the odors of 25 minor workers, 21 median workers, 1 major worker, and 1 female alate. When conditioned with deceased ant odors, bees effectively recognized live ants, particularly when trained on a single minor worker. Discrimination abilities varied by species and were higher when S. invicta was paired with Polyrhachis dives and Nylanderia yaeyamensis, and lower with S. geminata, Pheidole rabo, and Pheidole fervens. Notably, discrimination improved significantly with the application of latent inhibition. These findings suggest that trained honeybees have the potential to detect S. invicta. Further refinement of this approach could enhance its effectiveness for detection and surveillance.

Upaya Meningkatkan Kompetensi Guru, Konsentrasi, dan Kemampuan Bahasa Anak melalui Metode Bermain Tepuk di Kelompok Bermain XYZ

Student learning concentration, student language skills, and teacher competency are very important in achieving student development in early childhood education. These three competencies in the XYZ Playgroup Class in Jakarta have not been well developed. This research aims to analyze the effectiveness of the clapping playing method on teacher competence, the development of student learning concentration, improving students' language skills, as well as the implementation of the learning process in the classroom. The research method used is classroom action research which includes three cycles, each cycle includes the stages of planning, observation, action and reflection. The research subjects were 16 students, two observers. The research instrument is an assessment rubric to measure teacher competency, student learning concentration, and student learning ability. The research results showed that teacher competency increased from Cycle 1 to Cycle 2 with an N-Gain of 03, but from Cycle 2 to Cycle 3 there was no increase because the N-Gain value was still the same (0.3). Meanwhile, the comparison of teacher competency from Cycle 1 to Cycle 3 achieved good development, because the N-Gain value reached 0.6. Student learning concentration has increased, as can be seen from the N-Gain from Cycle 1 to Cycle 2, an increase of 0.4 was obtained. Then the N-Gain from Cycle 2 to Cycle 3 obtained an increased value of 0.6. Students' language skills experienced an increase in N-Gain from Cycle 1 to Cycle 2, resulting in an increase of 0.4. Then the N-Gain from Cycle 2 to Cycle 3 obtained an increased value of 0.5.

문제중심학습 교육이 간호대학생의 문제해결능력, 자기주도 학습능력, 임상추론역량에 미치는 효과

Objectives The purpose of this study was to determine the effect of problem-based learning education on problem-solving ability, self-directed learning ability, and clinical reasoning competency of nursing students. Methods The research design is a quasi-experimental study with a single-group before/after design. A preliminary survey was conducted on April 3 and 4, 2024, targeting 55 fourth-year nursing students, and a follow-up survey was conducted on April 24 and 25. Data analysis included descriptive statistics, paired t-test, and Pearson correlation analysis using the SPSS Statistics 22.0 program. Results After problem-based learning education, nursing students' problem-solving ability(t=-5.91, p&lt;.001), self-directed learning ability(t=-6.02, p&lt;.001), and clinical reasoning ability(t=-7.39, p&lt;.001) was statistically significantly improved. Conclusions Through this study, it was confirmed that problem-based learning is an effective teaching and learning method in improving nursing students' problem-solving ability, self-directed learning ability, and clinical reasoning competency. Therefore, efforts should be made to strengthen the capabilities of nursing students by expanding subjects that apply problem-based learning in nursing education.

A tailored phytosomes based nose-to-brain drug delivery strategy: Silver bullet for Alzheimer's disease

With the aging of the population, the incidence of Alzheimer's disease (AD) has increased dramatically, causing severe medical, care, and economic burdens on society and families. The efficacy of rivastigmine hydrogen tartrate (RHT), the first-line clinical treatment, is severely limited by the complex and multiple pathogenesis of AD and low brain bioavailability caused by the blood-brain barrier (BBB). Confronting such two bottlenecks, the development of multi-target agents encapsulated BBB-bypassing drug delivery systems offer tremendous therapeutics possibilities for AD. In this study, a tailored phytosomes based nose-to-brain drug delivery system with appropriate plume was successfully designed and developed. On the one hand, Ginseng RG3-based phytosomes loaded with RHT was designed for the co-delivery of GRg3 and RHT, achieving the multi-target pharmacology for AD treatment. On the other hand, a tailored nose-to-brain drug delivery system was established for the satisfactory nose-to-brain delivery efficiency, avoiding the obstacle of BBB through bypassing it. In the pharmacodynamic study based on AD rat model, GRg3@RHT exhibited obviously synergic effect, effectively break the vicious cycle of AD progression, ultimately markedly ameliorating learning and memory ability as well as behavioral dysfunctions, and delaying the neurodegenerative process associated with AD. In addition, the strong correlation of viscosity-droplet size-plume geometry-olfactory deposition was also established, and further proved by the in vivo pharmacokinetic study, which is proposed to provide evidence to enhance nose-to-brain delivery efficiency. This study is anticipated to provide novel insights into AD treatment strategies while offering innovative ideas for drug delivery approaches targeting nervous system disorders.

Implications of Lead (Pb)-Induced Transcriptomic and Phenotypic Alterations in the Aged Zebrafish (Danio rerio).

Lead (Pb) is a well-known neurotoxin with established adverse effects on the neurological functions of children and younger adults, including motor, learning, and memory abilities. However, its potential impact on older adults has received less attention. Using the zebrafish model, our study aims to characterize the dose-response relationship between environmentally relevant Pb exposure levels and their effects on changes in behavior and transcriptomics during the geriatric periods. We exposed two-year-old zebrafish to waterborne lead acetate (1, 10, 100, 1000, or 10,000 µg/L) or a vehicle (DMSO) for 5 days. While lower concentrations (1-100 µg/L) reflect environmentally relevant Pb levels, higher concentrations (1000-10,000 µg/L) were included to assess acute toxicity under extreme exposure scenarios. We conducted adult behavior assessment to evaluate the locomotor activity following exposure. The same individual fish were subsequently sacrificed for brain dissection after a day of recovery in the aquatic system. RNA extraction and sequencing were then performed to evaluate the Pb-induced transcriptomic changes. Higher (1000-10,000 ug/L) Pb levels induced hyperactive locomotor patterns in aged zebrafish, while lower (10-100 ug/L) Pb levels resulted in the lowest locomotor activity compared to the control group. Exposure to 100 µg/L led to the highest number of differentially expressed genes (DEGs), while 10,000 µg/L induced larger fold changes in both directions. The neurological pathways impacted by Pb exposure include functions related to neurotransmission, such as cytoskeletal regulation and synaptogenesis, and oxidative stress response, such as mitochondrial dysfunction and downregulation of heat shock protein genes. These findings emphasize a U-shape dose-response relationship with Pb concentrations in locomotor activity and transcriptomic changes in the aging brain.

Identification of water-cooled wall ash accumulation based on AWGAM-YOLOv8n

Identifying the ash accumulation generated on the water-cooled walls of the waste incinerator is essential for the cleanup by the robotic arm. This paper improves a new algorithm based on YOLOv8n, which can identify the ash accumulation position on the water-cooled wall quickly and accurately. Firstly, the multi-scale fusion image enhancement algorithm is used to improve the sharpness and contrast of the image and enrich the details of the image. Secondly, the backbone feature extraction network of YOLOv8n is replaced by Mobilenetv3 network, which reduces the parameters in the model greatly. Finally, this paper improves a new attention mechanism AWGAM (Add Weight Global Attention Mechanism) based on GAM (Global Attention Mechanism), which can better integrate the feature information between different dimensions and improve the learning ability of the model. AWGAM is added to the backbone of the model. The experimental results show that compared with the original YOLOv8n model, the improved YOLOv8n model has 59.9% fewer parameters, 4.4% higher precision, 8.8% higher recall, 3.2% higher mAP50 (mean Average Precision) and 8.8% higher mAP50-95. This model has made remarkable progress on the basis of the original algorithm, and has strong competitiveness compared with other advanced target detection models. The lightweight and high accuracy of ash accumulation detection offered by the proposed model presents promising applications in ash accumulation detection tasks of water-cooled walls.