Innovative Method Research Articles

Improved learning efficiency of deep Monte-Carlo for complex imperfect-information card games

Deep Reinforcement Learning (DRL) has achieved considerable success in games involving perfect and imperfect information, such as Go, Texas Hold’em, Stratego, and DouDiZhu. Nevertheless, training a state-of-the-art model for complex imperfect-information card games like DouDiZhu and Big2 remains resource and time-intensive. To address this challenge, this paper introduces two innovative methods: the Opponent Model and Optimized Deep Monte-Carlo (ODMC). These methods are designed to improve the training efficiency of Deep Monte-Carlo (DMC) for imperfect-information card games. The Opponent Model predicts hidden information, enhancing the agent’s learning speed in DMC compared to the original training that only utilizes observed information as input features. In ODMC, the Minimum Combination Search (MCS) is a heuristic search algorithm based on dynamic programming. It calculates the minimum combination of actions in the current state, and ODMC uses MCS to filter suboptimal actions in each state. This reduces the action space considered by DMC, resulting in faster training that focuses on evaluating the most promising actions. The effectiveness of the proposed approach is evaluated by examining two complex card games with imperfect information: DouDiZhu and Big2. Ablation experiments are conducted to evaluate both the Opponent Model (D+OM and B+OM) and ODMC (D+ODMC and B+ODMC), along with their combined variants (D+OMODMC and B+OMODMC). Furthermore, D+OMODMC and B+OMODMC are compared with state-of-the-art DouDiZhu and Big2 artificial intelligence (AI) programs, respectively. The experimental results demonstrate that the proposed methods achieve comparable performance to the original DMC, but with only 25.5% of the training time on the same device. These findings are valuable for mitigating both the equipment requirements and training time in complex imperfect-information card games.

Machine Learning Based Predictive Modelling of Vehicle CO2 Emissions: An Extensive Examination of Automobile Features

There is a growing interest in discovering novel and inventive methods to decrease the emission of carbon dioxide (CO2) from diverse sources, mostly due to concerns about safeguarding the environment and mitigating the adverse effects of greenhouse gas emissions, especially in the automotive sector. This study article explores the fascinating potential of predicting the quantity of CO2 generated by autos using machine learning (ML) based methodologies. In order to create a system that can predict carbon dioxide emissions depending on a number of factors, we are investigating machine learning. Significant details about the car, such as its construction, kind, number of cylindrical objects, horsepower, gearbox type, fuel type, and fuel consumption, are included in the dataset that forms the basis of our investigation. This project's main objective is to create accurate and dependable prediction models for calculating vehicle-generated CO2 emissions. Legislators that deal with the environment, corporations, and consumers could find great value in these models. We use a range of machine learning (ML) techniques, such as ensemble techniques, neural networks, decision trees, and regression algorithms, to accomplish this goal. This study assesses the prediction accuracy, adaptability to different situations, and data utilisation of several techniques. To determine which predictive models are most suited for predicting CO2 emissions within the limitations of the given information, we employ a comprehensive evaluation process. Additionally, we look at how different feature subsets affect prediction accuracy, providing an indication of the relative significance of different vehicle parameters in relation to CO2 emissions. Our results advance the field of predictive emissions models and provide insight on the primary drivers of vehicle-related greenhouse gas emissions.

Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration

Contrastive learning has emerged as a prevailing paradigm for high-level vision tasks, which, by introducing properly negative samples, has also been exploited for low-level vision tasks to achieve a compact optimization space to account for their ill-posed nature. However, existing methods rely on manually predefined and task-oriented negatives, which often exhibit pronounced task-specific biases. To address this challenge, our paper introduces an innovative method termed 'learning from history', which dynamically generates negative samples from the target model itself. Our approach, named Model Contrastive Learning for Image Restoration (MCLIR), rejuvenates latency models as negative models, making it compatible with diverse image restoration tasks. We propose the Self-Prior guided Negative loss (SPN) to enable it. This approach significantly enhances existing models when retrained with the proposed model contrastive paradigm. The results show significant improvements in image restoration across various tasks and architectures. For example, models retrained with SPN outperform the original FFANet and DehazeFormer by 3.41 and 0.57 dB on the RESIDE indoor dataset for image dehazing. Similarly, they achieve notable improvements of 0.47 dB on SPA-Data over IDT for image deraining and 0.12 dB on Manga109 for a 4x scale super-resolution over lightweight SwinIR, respectively. Code and retrained models are available at https://github.com/Aitical/MCLIR.

Abstract 1367: PD1 signaling uncovers a pathogenic subset of cytotoxic T cells

Abstract PD-1 drives its fame for serving as a pivotal immune checkpoint receptor on T cells. Within the realm of cancer immunotherapy, blocking this receptor triggers the T cell activation, leading to the immune response against tumors. Conversely, in autoimmunity, PD-1 agonist is a prime candidate for effectively suppressing the T-cell-driven auto-reactivity and self-tissue damage. Despite a decade of exploration and unlike cancer immunotherapy, the efficacy of PD-1 agonists in treating autoimmunity remains a challenge. To overcome this hurdle, innovative methods must be devised to look beyond the PD-1 receptor engagement and uncover the downstream effectors involved in PD-1 signal transduction. A comprehensive genome-wide CRISPR/Cas9 screen was also conducted to pinpoint genes linked to PD-1 signaling. An in-depth exploration of genes associated with PD-1 signaling was undertaken, utilizing publicly accessible bulk and single-cell RNA sequencing datasets of patients with melanoma treated with PD-1 blockade and in patients with inflammatory arthritis. We employed flow cytometry of T cells derived from peripheral blood and synovial fluid to validate the results.Our screening process validated established regulators in the proximal PD-1 signaling pathway and unveiled an additional 1,112 unique genes associated with PD-1's capacity to hinder T-cell functions. These genes demonstrated a robust correlation with the response of cancer patients to PD-1 blockades and exhibited high scores for tumor immune dysfunction and exclusion, affirming their downstream involvement in PD-1 signaling. Functional annotation underscored the significance of these genes in established immune regulation processes. Intriguingly, T cells isolated from patients with inflammatory arthritis exhibited a significant downregulation of the same genes, emphasizing their overarching inhibitory role. Examining single-cell RNA sequencing data revealed that five specific genes, KLRG1, CRTAM, SLAMF7, PTPN2, and KLRD1, were notably downregulated in activated and effector T cells from synovial fluids. Back-gating these genes to canonical cytotoxic T cell signatures identified PD-1+ HLA-DRHIGH KLRGLOW T cells as a novel inflammatory T cell subset that should be targeted with immunotherapies. Our findings suggest that PD-1+ HLA-DRHIGH KLRGLOW T cells are promising targets for prospective PD-1 antagonists and agonists in treating inflammatory diseases. This study not only reveals novel genes linked to PD-1 downstream functions but also serves as a valuable resource, offering insights for further investigations essential to delineate the role of PD-1 within immune responses. Citation Format: Shoiab Bukhari, Shalom Lerrer, Johanna Straube, Robert Winchester, Brian Henick, Matthew Dragovich, Adam Mor. PD1 signaling uncovers a pathogenic subset of cytotoxic T cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1367.

Abstract 868: A novel unsupervised machine learning model applied to neuroblastoma single-cell RNA-seq data reveals a drug-induced mesenchymal-like gene expression program

Abstract High-risk neuroblastoma is one of the most difficult to treat pediatric cancers, with a survival rate of only about 50% and significant long-term consequences from current chemotherapy. Preclinical models such as cell lines and mice are the backbone of drug development and experimental-mechanistic oncology, but the development of new treatments is hampered in part by a lack of understanding of the direct clinical relevance of such data collected in preclinical models. Despite this, few formal methods have been developed to determine how these various models represent/resemble primary patient tumors. Here, we present the first comprehensive single-cell RNA-seq analysis of neuroblastoma across an extensive cohort of patient tumors and a variety of preclinical model systems (n = 126 total samples assembled - the largest cohort of its kind). By building an innovative unsupervised machine learning method, which we term “automatic consensus nonnegative matrix factorization” (acNMF), we have integrated and contrasted the transcriptional landscapes of patient tumors with those of cell lines, patient-derived xenografts (PDX), and genetic mouse models (GEMM).Using these tools, we discovered the dominant adrenergic gene expression programs found in neuroblastoma patient tumors were preserved across all preclinical models. However, the presumptive chemo-resistant mesenchymal-like programs, while identifiable in cell lines, were primarily restricted to subpopulations of cancer-associated fibroblasts and Schwann-like cells in vivo. Surprisingly, a mesenchymal-like program could be acutely chemotherapy-induced in GEMM and was evident in pre-treated patient and PDX samples, suggesting a previously uncharacterized mechanism of therapy escape. In addition to these core findings, our computational tools were able to further delineate the classical neuroblastoma adrenergic and mesenchymal gene expression programs, discovering for example, novel subpopulations of cancer associated fibroblasts. These behaviors were conserved across tumors and most preclinical models, which we validated by RNA in situ hybridization, which is a high resolution ultra sensitive spatial transcriptomics technology. Our work cautions against overreliance on traditional preclinical models without recognizing their limitations and we offer a nuanced, high-resolution view of neuroblastoma pre-clinical systems for advancing therapeutic development. We have launched an open-source web resource, featuring this integrated map to aid the scientific community in further exploration of these data and hypothesis generation (available at http://pscb.stjude.org). Citation Format: Rich Chapple, Xueying Liu, Sivaraman Natarajan, Margaret I. Alexander, Yuna Kim, Anand Patel, Christy W. LaFlamme, Min Pan, William C. Wright, Hyeong-Min Lee, Yinwen Zhang, Meifen Lu, Selene C. Koo, Courtney Long, John Harper, Chandra Savage, Melissa D. Johnson, Thomas Confer, Walter J. Akers, Michael A. Dyer, Heather Sheppard, John Easton, Paul Geeleher. A novel unsupervised machine learning model applied to neuroblastoma single-cell RNA-seq data reveals a drug-induced mesenchymal-like gene expression program [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 868.

Self‐Powered Vibration Frequency Monitoring Device for the Grid Based on Triboelectric Nanogenerator and Micro Thermoelectric Generator

AbstractThe galloping of transmission lines (GTLs) has a significant impact on the development of smart grids. However, traditional vibration frequency monitoring devices for transmission lines suffer from issues such as low measurement accuracy, high environmental requirements, and the inability to achieve self‐powering. A self‐powered vibration frequency monitoring method is proposed based on frequency‐sensing triboelectric nanogenerator (F‐TENG) and micro thermoelectric generators (MTEG). Models for vibration frequency sensing based on F‐TENG and energy capture based on MTEG are established. A flexible self‐powered sensing prototype, integrating F‐TENG, MTEG, and a Signal processing and Energy harvesting Circuit (SEC), is fabricated. Additionally, an innovative solvothermal method for the preparation of MTEG materials is presented, resulting in Bi2Te3‐based thermoelectric materials with significantly high thermoelectric conversion performance. Experimental results demonstrate that within the range of 0.1–5.1 Hz, F‐TENG can precisely perceive the frequency of GTLs, with a maximum error of 1.274%. MTEG achieves a maximal open‐circuit voltage of 3.282 V. Finally, the SEC unit is designed to couple the outputs of F‐TENG and MTEG for frequency calculation and wireless transmission to a microcontroller. This device provides an efficient solution for monitoring the frequency of GTLs and offers robust support for the stability of the smart grid.

Managerial activity of future physical education teachers aimed at building a positive image using visual technologies

With the integration of technology and social media in education, there is increasing interest in innovative approaches to enhance managerial skills among prospective physical education and sports teachers. The integration of technology and social media into education has prompted interest in exploring innovative methods to enhance the managerial skills of prospective physical education and sports teachers. This study investigates the impact of an intervention on the managerial skills of fourth-year students majoring in Physical Culture and Sports across three educational institutions in Kazakhstan. Out of 564 participants, 355 were eligible for analysis: 167 used chatbots and 188 followed conventional classroom methods. The intervention included a 4-week team management course starting in January 2022. Chatbot users accessed various materials such as short lecture videos, stories, assignments, infographics, and audio sequences, while conventional learners attended standard lectures and practical sessions. Following the course, participants undertook a 12-week teaching practice, with chatbot users also maintaining an active Twitter presence to build a positive image as future physical education teachers. Managerial skills were assessed using a 44-item questionnaire, covering Conceptual, Human, and Technical skills. Additionally, social media popularity was measured based on Twitter activity. Results showed that both groups significantly improved their Conceptual, Human, and Technical skills after the course. Chatbot users demonstrated greater improvement in Technical skills (p = 0.003, d = 1.139), while conventional learners showed stronger Human skills (p = 0.021, d = 1.32). Specifically, the mean score for Technical skills increased from 2.71 to 3.76 for chatbot users and from 2.65 to 3.20 for conventional learners. Moreover, chatbot-aided learners further enhanced their Conceptual and Human skills through social media engagement. Correlation analysis indicated a close relationship between managerial skills and social media popularity. These findings have significant practical implications for the education and development of prospective physical education and sports teachers. Integrating chatbots and social media into educational settings offers innovative approaches to enhancing managerial skills.

Assessment of microbial water treatment by direct solar radiation disinfection approach

Abstract The solar sterilization treatment innovation method can be implemented within households to address water pollution, especially those contaminants caused by microorganisms that may lead to waterborne illnesses. During review, water sources in the local area were assessed for turbidity and microbial quality. It was found that river water had turbidity levels of less than 30 NTU (Nephelometric Turbidity Units) during the dry season and typically up to 50 NTU during the wet season. This review aimed to understand the impact of these findings on the local area and its technological context. This study researched the utilization of sun-based radiation to clean the microbial mass tracked down in untreated water. Temperature and length of the openness are the two normal factors that were distinguished. The first water with a microbial heap of 40–55 (CFU) was presented to solar radiation at a temperature of above 44.5 °C outcome 0 (CFU) at the openness season of 6 h. The size and kind of materials utilized for this process were PET bottles of 1.5 L capacity with a half-dark tone. The factual examination at a 95% certainty information stretch has a p-value of <0.0001.

Research on the System Design and Target Recognition Method of the Rebar-Tying Robot

In the construction industry, the construction process of rebar tying is highly dependent on manual operation, which leads to a wide range of work areas, high labor intensity, and limited efficiency. Therefore, robot technology for automatic rebar tying has become an inevitable trend in on-site construction. This study aims to develop a planar rebar-tying robot that can achieve autonomous navigation, precise positioning, and efficient tying on a plane rebar mesh without boundaries. Our research covers the overall design of the robot control systems, the selection of key hardware, the development of software platforms, and the optimization of core algorithms. Specifically, to address the technical challenges of accurately recognizing the tying position and status, we propose an innovative two-stage identification method that combines a depth camera and an industrial camera to obtain image information about the area to be tied. The effectiveness of the planar rebar-tying robot system, including the recognition method proposed in this study, was verified by experiments on a rebar mesh demonstration platform. The following application of our robot system in the field of the Shenyang Hunnan Science and Technology City Phase IV project achieved satisfactory performance. It is shown that this research has made a unique and significant innovation in the field of automatic rebar tying.

Solar radiation powered biodiesel synthesis using C&D waste catalyst – a novel approach

A significant amount of construction and demolition waste is generated in India every year. The proper management of this waste has become a crucial concern due to fast urban growth, building projects, and increased construction work. The volume of this waste is substantial, which can harm the environment, deplete resources, and pose health risks if not handled well. This study presents an innovative method for biodiesel synthesis, addressing the challenges of construction and demolition (C&D) waste management and renewable energy integration. The research introduces a novel catalyst derived from C&D waste through a calcination process, offering an eco-friendly solution for waste repurposing. This catalyst demonstrates potential for sustainable biodiesel production, marking a significant advancement in waste management practices. Furthermore, a pioneering solar-powered reactor is utilized for biodiesel synthesis, achieving an impressive 92.2% conversion rate under specific conditions. The system's energy efficiency, calculated as [Formula: see text], surpasses conventional methods, highlighting its potential for reducing energy consumption and environmental impact. Moreover, the produced biodiesel meets ASTM D6751 standards, ensuring quality and compatibility with industry requirements.

Taste, sustainability, and nutrition: Consumers' attitude toward innovations in aquaculture products

Marine aquaculture is one of the solutions to provide sustainable nutritious food. While technological and product development in the marine aquaculture industry is challenging, the industry is also facing consumer skepticism. Therefore, it is crucial to understand consumers' food decisions to deliver the benefits of marine aquaculture on environmental sustainability and human nutrition. This study aims to test how a sheer cognition of innovative or traditional production methods influences the USA consumers' acceptance of marine aquaculture products.In an online experimental study, 186 consumers from the USA evaluated their perceptions of a series of marine aquaculture products that vary in processing levels. Participants were randomly assigned to one of the three conditions: Tradition, Technology, and Control condition. In the Tradition condition, participants were told the products are produced in a traditional way. In contrast, a high-tech production system is emphasized in the Technology condition. In the Control condition, the producer uses a conventional method to farm seafood. The results of a set of general linear models for repeated measures reveal that emphasizing traditional production methods is associated with better perceptions of tastefulness, healthiness, and sustainability. Further analysis of a structural equation model found that the difference between the Tradition versus Technology brand image mainly lies in the perceived sincerity of the producer. The image of a traditional producer contributes to better consumer trust, and it further leads to a higher perceived value of healthiness and sustainability.The results of the study shed light on the strategies to accelerate technological advances and the social acceptance of innovative nutritious food. The research is one of the early attempts in the aquaculture industry to collect consumer feedback on tech/product development. The process can support future tech/product research projects by enhancing the societal embeddedness of marine aquaculture innovation.

PROFIL KWASÓW TŁUSZCZOWYCH I ZAWARTOŚĆ TŁUSZCZU W RYBACH, METODY MODYFIKACJI I MOŻLIWOŚCI WYKORZYSTANIA W ŻYWIENIU PSÓW I KOTÓW

Fish are rich in high-quality protein. Additionally, they are known for their high content of polyunsaturated fatty acids such as EPA and DHA. This review aims to provide an overview of the lipid fraction of selected fish species, exploring methods of manipulating fatty acid content and discovering the resulting health benefits for dogs and cats. The article discusses innovative methods used to manipulate fatty acid content in fish, including nutritional strategies, selective breeding and new technologies. Based on the current scientific literature, the article synthesizes knowledge on the impact of these manipulation methods on the nutritional quality of fish. Additionally, literature data were presented indicating the positive aspects of including fish in the diet of dogs and cats. EPA and DHA are responsible for a number of health benefits, including supporting the circulatory system, cognitive functions and the learning process, an element of diet therapy in the treatment of cancer, and alleviating pain associated with degenerative joint diseases.

Strategies for Preparing Future Public Health Leaders: Learning from Trailblazing Women in Health Education

Many female health education pioneers made seminal contributions to public health and social justice, yet these examples are not well documented in textbooks or integrated into public health curricula as pedagogical learning opportunities. Providing biographies and lessons learned from the past leaders within undergraduate health education curricula can help develop students’ skills as future leaders and address gender inequities in the field. Gender inequities in higher education as well as many public health practice settings result in lower salaries, slower career advancement opportunities, fewer supervisory positions, increased work-life conflicts, and heavier workloads. Developing women’s competencies in leadership and professionalism early in their careers is critical to meeting public health challenges such as COVID-19. We present four female health education trailblazers from different historical eras and various workforce sectors who exemplify leadership attributes (i.e., courage/risk taking, vision, advocacy, mentorship) and can be highlighted as a part of future health education curriculum. Innovative methods and approaches are provided for integrating these leadership lessons into undergraduate public health courses, in alignment with the HESPA II 2020 competencies. Learning from the past to inform the future is critical to inspiring and shaping the careers of emerging health educators.

태블릿PC와 노트필기 사용 집단 간 학습효과 분석

Objectives This study analyzes the learning effects between groups using Tablet PCs and handwritten notes methods among early childhood education freshmen. The focus is on examining learning engagement, enthusiasm, satisfaction with university life, and academic achievement.
 Methods The study participants consisted of 45 freshmen majoring in early childhood education at A university in Chungcheong province, all of whom had completed courses in early childhood music education. I conducted frequency(%) distributions and performed trend, normality, and reliability coefficient analyses of the research variables using SPSS 25.0. Since the normality assumptions were not met, I utilized the Mann-Whitney U test to assess the differences between the group using tablet PCs and the group employing handwritten notes. The statistical significance of the mean difference in pre-post scores between two groups was analyzed at the p<.001, p<.01, and p<.05 levels.
 Results The main results are as follows: First, the preliminary homogeneity test between the groups using tablet PCs and handwritten notes revealed no statistically significant differences, indicatingthat homogeneity was established between the two groups. Second, the tablet PC group scored higher than the handwritten notes group on variables related to learning engagement and its sub-domains(cognitive, behavioral, and emotional engagement), academic enthusiasm and its sub-domains(energetic, committed, and engaged), and university life satisfaction and its sub-domain(administrative and welfare services), and these differences were statistically significant. On the other hand, there were no statistically significant differences in the variables related to overall university life satisfaction and the quality of university education, which are sub-domains of university life satisfaction. Academic achievement was higher in the tablet PC user group compared to the handwritten note user group, and this difference was statistically significant.
 Conclusions The research results suggest the need for contemplation on teaching and learning methods in response to the changes in the digital educational environment in higher education. I aim to propose a way to operate courses by applying AI-based innovative teaching methods, focusing on both theoretical and practical courses inthe field of early childhood education.

Development and effectiveness of a BOPPPS teaching model-based workshop for community pharmacists training.

With the pharmaceutical innovation and clinical knowledge updating, the continuing education and on-the-job training are extremely important for improving community pharmacists' professional competence. Previous training often adopted traditional lecture-based teaching, and the efficacy was limited. The aim of this study is to develop a new strategy for community pharmacist training. Based on the BOPPPS (Bridge-in, Objective, Pre-assessment, Participatory Learning, Post-assessment and Summary) teaching model and workshop method, a continuing on-the-job training program was constructed. Participates were randomly and evenly divided into two groups by random number table method. Twenty-four community pharmacists in total completed all training contents and evaluation components in this study. Twelve pharmacists in experimental group were trained via this new BOPPPS-based workshop, while others still adopted traditional didactic lecture-based approaches. After training, quantitative examination combined with clinical pharmacy practice tests were carried out to evaluate the effectiveness and outcomes of two training groups. For written exam, the total scores from the BOPPPS-based workshop group (82.67 ± 4.70) was higher than that of traditional lectured-base group (73.75 ± 6.15) (P < 0.001). Encouragingly, compared with the results of practical ability assessment from traditional training group (71.75 ± 4.75), the pharmacists receiving BOPPPS-based workshop training presented more excellent performance (78.25 ± 5.03), which displayed statistically significant differences (P < 0.01). In addition, an anonymous questionnaire was used to survey trainees' feelings after completing this continuing education program. The results revealed that the BOPPPS-based workshop can bring a better learning experience than traditional lecture-based training, and the percentages of positive response to each item were more than 91.7%. Through multi-dimensional evaluation, it was suggested that our BOPPPS-based workshop achieved desired training effects. Moreover, our research also demonstrated that this strategy had advantages of stimulating inspiration, autonomous learning, team-work spirit and pharmacy practice improvement. It may provide a reference of innovative training method for community pharmacists.

Caged xanthone derivatives to promote mitochondria-mediated apoptosis in breast cancer cells

Caged xanthones represent a class of natural secondary metabolites exhibiting significant potential as antitumor agents. These compounds are characterized by their distinct cage-like structures, which offer novel and compelling frameworks for drug design. Nonetheless, there exists a dearth of research focused on the structural modification of these compounds, particularly in relation to their cage-like architectures. This study aims to address this gap by introducing an innovative synthetic method for constructing a novel caged structure that incorporates a widely employed maleimide group. Drawing upon the well-established synthetic approach for dihydroxanthones previously developed within our research group, we successfully synthesized 13 new caged xanthones using the Diels-Alder reaction. Subsequently, we evaluated their anti-proliferative activity against HepG2, A549, and MDA-MB-231 cell lines. The results revealed that compound 10i exhibited IC50 values of 15.86 µM ± 1.29, 19.27 µM ± 1.58, and 12.96 µM ± 0.09 against these cell lines, respectively. Further investigations into the mechanism of action of 10i demonstrated its ability to induce G2/M cell cycle arrest and initiate mitochondria-mediated apoptosis in breast cancer cells.

Synthesis of rice husk activated carbon by fermentation osmotic activation method for hydrogen storage at room temperature

In the present study, an innovative method of fermentation osmotic activation was employed to produce activated carbon derived from rice husk. The fermentation procedure dismantles the robust lignin present on the rice husk surface, thereby liberating amorphous SiO2, enhances osmotic activation, and promotes pore development. Following the high-temperature osmosis treatment utilizing a KOH solution, a significant portion of the amorphous SiO2 on the husk's surface is successfully dissolved and removed, leading to the formation of numerous carbon skeletons. This procedure culminates in the production of activated carbon characterized by a high specific surface area and substantial pore volume, attributed to the role of K ions during carbonization. The sample DFRO1-AC exhibited the most effective hydrogen gravimetric storage capacity of 1.21 wt% at room temperature (25 °C) and 80 bar pressure, featuring a BET specific surface area of 2270 m2/g. Compared to other activated carbons used for hydrogen storage, our materials demonstrate superior hydrogen storage capacity and employ renewable agricultural waste, indicating high feasibility and broad application prospects.

Queen bee gut microbiota extends honeybee lifespan by inhibiting insulin signaling.

Queen and worker bees share the same genetic background but have vastly different lifespans. The gut microbiome regulates host health, suggesting that differences in lifespan between queen and worker bees could be related to gut bacteria. Herein, we used an innovative method to transplant gut microbiota from adult queen or worker bees to microbiota-free bees. The transplantation of queen gut microbiota to microbiota-free bees extended their lifespan. Insulin/insulin-like growth factor signaling, a highly conserved metabolic pathway related to lifespan, displayed identical expression profiles in natural queen bees and microbiota-free bees transplanted with queen microbiota. This finding significantly expands our understanding of the relationships between intestinal bacteria, host health, and the biology of aging.

Deep-NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data.

Noncompartmental analysis (NCA) is a model-independent approach for assessing pharmacokinetics (PKs). Although the existing NCA algorithms are very well-established and widely utilized, they suffer from low accuracies in the setting of sparse PK samples. In response, we developed Deep-NCA, a deep learning (DL) model to improve the prediction of key noncompartmental PK parameters. Our methodology utilizes synthetic PK data for model training and uses an innovative patient-specific normalization method for data preprocessing. Deep-NCA demonstrated adequate performance across six previously unseen simulated drugs under multiple dosing, showcasing effective generalization. Compared to traditional NCA, Deep-NCA exhibited superior performance for sparse PK data. This study advances the application of DL to PK studies and introduces an effective method for handling sparse PK data. With further validation and refinement, Deep-NCA could significantly enhance the efficiency of drug development by providing more accurate NCA estimates while requiring fewer PK samples.

Nitric Acid‐Mediated Artificial Urea Photo‐Synthesis With N2 And CO2

AbstractPhotocatalytic urea synthesis, utilizing N2 and CO2 as feedstock and sustainable solar energy, represents an environmentally friendly and promising alternative strategy. However, the conventional unidirectional gas phase co‐reduction techniques typically employes pose stringent demands for the design of key catalysts, reaction control, mass transfer, and other aspects, due to the disparity in physical properties, structure, and catalytic kinetics between N2 and CO2. Herein, Ru‐TiO2 is synthesized and employed as an effective catalyst for urea photo‐synthesis through a nitric acid‐mediated pathway combining nitrogen oxidation and subsequent kinetically advantageous nitrate and CO2 co‐reduction. A urea yield of 24.95 µmol h−1 g−1cat. is obtained through an innovative intermittent illumination method, AQE values of 4.7% and 6.3% at 380 and 420 nm, respectively. The remarkable photo‐activity is attributed to its unique oxygen vacancy‐anchored Ru nanostructure (Ru‐O4Ti1), which effectively activates inert N2 molecules to minish the disparity of orbital energy levels, facilitates the formation of crucial *NN(OH) intermediates, and serves as an “electronic pump” to avoid electronegativity effect for facilitating electron transfer from nitrogen to TiO2 support for urea photosynthesis. This study presents groundbreaking insights for nitrogen oxidation activation and provides brand‐new ideas for the direct resource utilization of nitrogen.