Key Features Research Articles

Analysis of the characteristics and causes of city gas explosion accidents based on DT-AR algorithm

In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention.

Generating Usage-related Questions for Preference Elicitation in Conversational Recommender Systems

A key distinguishing feature of conversational recommender systems over traditional recommender systems is theirability to elicit user preferences using natural language. Currently, the predominant approach to preference elicitation is to ask questions directly about items or item attributes. Users searching for recommendations may not have deep knowledge of the available options in a given domain. As such, they might not be aware of key attributes or desirable values for them. However, in many settings, talking about the planned use of items does not present any difficulties, even for those that are new to a domain. In this article, we propose a novel approach to preference elicitation by asking implicit questions based on item usage. As one of the main contributions of this work, we develop a multi-stage data annotation protocol using crowdsourcing, to create a high-quality labeled training dataset. Another main contribution is the development of four models for the question generation task: two template-based baseline models and two neural text-to-text models. The template-based models use heuristically extracted common patterns found in the training data, while the neural models use the training data to learn to generate questions automatically. Using common metrics from machine translation for automatic evaluation, we show that our approaches are effective in generating elicitation questions, even with limited training data. We further employ human evaluation for comparing the generated questions using both pointwise and pairwise evaluation designs. We find that the human evaluation results are consistent with the automatic ones, allowing us to draw conclusions about the quality of the generated questions with certainty. Finally, we provide a detailed analysis of cases where the models show their limitations.

Distinct features of the regenerating heart uncovered through comparative single-cell profiling.

Adult humans respond to heart injury by forming a permanent scar, yet other vertebrates are capable of robust and complete cardiac regeneration. Despite progress towards characterizing the mechanisms of cardiac regeneration in fish and amphibians, the large evolutionary gulf between mammals and regenerating vertebrates complicates deciphering which cellular and molecular features truly enable regeneration. To better define these features, we compared cardiac injury responses in zebrafish and medaka, two fish species that share similar heart anatomy and common teleost ancestry but differ in regenerative capability. We used single-cell transcriptional profiling to create a time-resolved comparative cell atlas of injury responses in all major cardiac cell types across both species. With this approach, we identified several key features that distinguish cardiac injury response in the non-regenerating medaka heart. By comparing immune responses to injury, we found altered cell recruitment and a distinct pro-inflammatory gene program in medaka leukocytes, and an absence of the injury-induced interferon response seen in zebrafish. In addition, we found a lack of pro-regenerative signals, including nrg1 and retinoic acid, from medaka endothelial and epicardial cells. Finally, we identified alterations in the myocardial structure in medaka, where they lack primordial layer cardiomyocytes and fail to employ a cardioprotective gene program shared by regenerating vertebrates. Our findings reveal notable variation in injury response across nearly all major cardiac cell types in zebrafish and medaka, demonstrating how evolutionary divergence influences the hidden cellular features underpinning regenerative potential in these seemingly similar vertebrates.

A social inference model of idealization and devaluation.

People often form polarized beliefs, imbuing objects (e.g., themselves or others) with unambiguously positive or negative qualities. In clinical settings, this is referred to as dichotomous thinking or "splitting" and is a feature of several psychiatric disorders. Here, we introduce a Bayesian model of splitting that parameterizes a tendency to rigidly categorize objects as either entirely "Bad" or "Good," rather than to flexibly learn dispositions along a continuous scale. Distinct from the previous descriptive theories, the model makes quantitative predictions about how dichotomous beliefs emerge and are updated in light of new information. Specifically, the model addresses how splitting is context-dependent, yet exhibits stability across time. A key model feature is that phases of devaluation and/or idealization are consolidated by rationally attributing counter-evidence to external factors. For example, when another person is idealized, their less-than-perfect behavior is attributed to unfavorable external circumstances. However, sufficient counter-evidence can trigger switches of polarity, producing bistable dynamics. We show that the model can be fitted to empirical data, to measure individual susceptibility to relational instability. For example, we find that a latent categorical belief that others are "Good" accounts for less changeable, and more certain, character impressions of benevolent as opposed to malevolent others among healthy participants. By comparison, character impressions made by participants with borderline personality disorder reveal significantly higher and more symmetric splitting. The generative framework proposed invites applications for modeling oscillatory relational and affective dynamics in psychotherapeutic contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Development of a multilingual online course with the language support for teaching physics in English

<span lang="EN-US">Distance education offers an alternative to traditional teaching methods, particularly online courses. During the pandemic, the role of information and communication technologies in the field of education has increased and is used to organize both synchronous and asynchronous learning. However, not all information and communication technology (ICT) tools and platforms provide the necessary functionality for developing online courses in the direction of language integration. This article discusses the issues of creating a multilingual online course of physics in English with the necessary language support and additional functions for the development of students’ academic language. The study used such methods as a survey, focus groups, analytical and descriptive methods, and empirical research. The key features of the developed course are the use of various types of content and methods for assessing both subject and language knowledge and skills. The presented survey results, data on summative assessment (SE), and trial testing show the effectiveness of the developed course modules. The conclusions and results described in the article will be useful to teachers of natural sciences in general education and private schools, especially those using international programs, deputy directors, as well as teachers who teach their subjects in a second or third language.</span>

Near-Optimal Scheduling for IC Packaging Operations Considering Processing-Time Variations and Factory Practices

Due to the short life cycles of electronic products, trial run lots of new products are crucial in IC packaging for production verification and engineering adjustments. The processing time of trial run lots may differ significantly from production lots due to engineering adjustments and is difficult to predict without sufficient historical data. The purpose of this paper is to develop a framework for near-optimal scheduling of IC packaging considering processing time variations and factory practices. Using an orthogonal greedy algorithm and recurrent neural network, this framework extracts key features and predicts the processing times of trial run lots and production lots for each operation using these algorithms. After formulating the scheduling problem in an integer linear programming form, an ordinal optimization method is embedded within the decomposition and coordination framework to obtain dynamic and near-optimal schedules in a computationally efficient manner. This approach can improve the efficiency of IC packaging and potentially other operations by optimizing lot scheduling, reducing production tardiness, and increasing productivity in the factory, paving the way for self-optimizing factories in the future.

Driving Alertness System Using Deep Learning

Abstract: Driver fatigue, a major contributor to road accidents, poses a significant challenge to road safety. Numerous fatal collisions could be prevented by promptly alerting drivers experiencing drowsiness. Several drowsiness detection methods have been developed to monitor driver alertness while driving and issue warnings when attention lapses. These systems employ various techniques to assess drowsiness levels, including extracting key features from facial expressions such as yawning, eye closure, and head movements. Evaluating both the driver's physiological state and vehicle behavior is crucial for identifying signs of drowsiness. This research delves into a comprehensive evaluation of prevalent classification methodologies and a thorough examination of contemporary driver drowsiness detection technologies. The study initially categorizes existing techniques into three broad groups: physiological, vehicular, and behavioral parameter-based approaches. It then provides an extensive review of recognized supervised learning algorithms employed for drowsiness detection. Furthermore, the examination conducts a comparative analysis of the advantages and limitations of various methods.

De novo GRIN variants in M3 helix associated with neurological disorders control channel gating of NMDA receptor

N-methyl-d-aspartate receptors (NMDARs) are members of the glutamate receptor family and participate in excitatory postsynaptic transmission throughout the central nervous system. Genetic variants in GRIN genes encoding NMDAR subunits are associated with a spectrum of neurological disorders. The M3 transmembrane helices of the NMDAR couple directly to the agonist-binding domains and form a helical bundle crossing in the closed receptors that occludes the pore. The M3 functions as a transduction element whose conformational change couples ligand binding to opening of an ion conducting pore. In this study, we report the functional consequences of 48 de novo missense variants in GRIN1, GRIN2A, and GRIN2B that alter residues in the M3 transmembrane helix. These de novo variants were identified in children with neurological and neuropsychiatric disorders including epilepsy, developmental delay, intellectual disability, hypotonia and attention deficit hyperactivity disorder. All 48 variants in M3 for which comprehensive testing was completed produce a gain-of-function (28/48) compared to loss-of-function (9/48); 11 variants had an indeterminant phenotype. This supports the idea that a key structural feature of the M3 gate exists to stabilize the closed state so that agonist binding can drive channel opening. Given that most M3 variants enhance channel gating, we assessed the potency of FDA-approved NMDAR channel blockers on these variant receptors. These data provide new insight into the structure–function relationship of the NMDAR gate, and suggest that variants within the M3 transmembrane helix produce a gain-of-function.

Dendrobium officinale phenolic extract maintains proteostasis by regulating autophagy in a Caenorhabditis elegans model of Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease, and accumulating evidence suggested that proteostatic imbalance is a key feature of the disease. Traditional Chinese medicine exhibits a multi-target therapeutic effect, making it highly suitable for addressing protein homeostasis imbalance in AD. Dendrobium officinale is a traditional Chinese herbs commonly used as tonic agent in China. In this study, we investigated protection effects of D. officinale phenolic extract (SH-F) and examined its underlying mechanisms by using transgenic Caenorhabditis elegans models. We found that treatment with SH-F (50 μg/mL) alleviated Aβ and tau protein toxicity in worms, and also reduced aggregation of polyglutamine proteins to help maintain proteostasis. RNA sequencing results showed that SH-F treatment significantly affected the proteolytic process and autophagy–lysosomal pathway. Furthermore, we confirmed that SH-F showing maintainance of proteostasis was dependent on bec-1 by qRT-PCR analysis and RNAi methods. Finally, we identified active components of SH-F by LC-MS method, and found the five major compounds including koaburaside, tyramine dihydroferulate, N-p-trans-coumaroyltyramine, naringenin and isolariciresinol are the main bioactive components responsible for the anti-AD activity of SH-F. Our findings provide new insights to develop a treatment strategy for AD by targeting proteostasis, and SH-F could be an alternative drug for the treatment of AD.

Abstract 6753: Bria-OTS immunotherapy platform: Harnessing gene-modified tumor cells to reinvigorate the cancer immunity cycle for precision anti-tumor responses

Abstract Background: For an immune response to lead to an effective anti-tumor response, a series of stepwise events involving a complex network of iterative cellular and molecular interactions must take place. Cancer, however, often derails this immune cycle, prompting contemporary cancer immunotherapy approaches to seek its reinstatement. Despite significant advances, the inherent disruption of the cancer immunity cycle at multiple junctures, coupled with current therapeutic strategies focusing on isolated stages within this intricate process, has left the attainment of curative therapies as an aspirational goal. We believe that targeting multiple immune processes can restore natural anti-cancer immunity and yield sustained responses. To this end, BriaCell is utilizing gene-modified tumor cells as an immunotherapeutic platform. Our initial version, SV-BR-1-GM, a breast cancer cell line expressing GM-CSF, demonstrates promising clinical outcomes by directly activating CD4+ T-cells in an antigen-specific, HLA-restricted manner. Building on our findings, we hypothesize that tumor cells not only supply antigens but also directly stimulate the immune system. Presenting our second-generation platform, we enhance antigen cross-presentation and direct immune activation through the expression of cytokines, co-stimulatory factors, and HLA alleles. Methods: To achieve this objective, we have developed Bria-OTS cell lines derived from various cancer types with the following genetic modifications: 1) Expression of costimulatory molecules (CD80, CD86, 4-1BBL, CD40) and immunomodulatory cytokines (GM-CSF, IFNα, IL-12 and IL-7). This modification aims to improve the cell's ability to present antigens and activate the host immune system. 2) Expanding HLA alleles to create semi-allogeneic cell lines matching at least one HLA allele across the population aims to enhance the host immune response, enabling recognition of tumor-associated antigens in the context of syngeneic HLA molecules Results: We generated four cell lines for each tumor type, and these cell lines secreted GM-CSF, IFNα, IL12 & IL7, while also expressing CD80, CD86, CD40, and different combinations of both Class I and Class II HLA alleles. In functional assays, our cell lines demonstrated potent antigen presentation, effectively triggering anti-tumor immune responses across diverse immune populations, including T-, NK, and NKT cells. Conclusions: The Bria-OTS cell lines will offer the following key features: 1) multimodal mechanisms of action including thwarting some mechanisms of cancer immune escape. 2) Precision Therapy; Bria-OTS will be precisely matched to individual patients based on their HLA alleles, encompassing over 99% of the U.S. population. 3) rapid, cost-effective treatment as our 'off-the-shelf' cell lines eliminate the need for personalized manufacturing. Citation Format: Miguel A. Lopez-Lago, Vikas Bhardwaj, Pravin Kesarwani, Xiaoyi Zheng, Sagarika Pachhal, Patience Cournoo, Renee Cortez, Charles L. Wiseman, William V. Williams. Bria-OTS immunotherapy platform: Harnessing gene-modified tumor cells to reinvigorate the cancer immunity cycle for precision anti-tumor responses [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 6753.

Pre-Turkic Burial in the Northern Altai: Cultural and Chronological Interpretation

Purpose. One of the problems in the archeology of Altai of the Great Migration period remains the chronology of funerary sites. The article concerns the materials of one of representative burials of the Choburak-I necropolis in the Northern Altai, demonstrating the wide possibilities of research in this direction.Methods and Results. A man of 25–30 years old was buried in the grave of barrow № 38. The key features of the object are a small mound with a crepe, the western orientation of the deceased person, as well as the burial of a horse «at the feet» of the man. The accompanying inventory included numerous weapons, tools, equipment for a man and a horse. The analysis of the finds testifies to the period of the construction of mound № 38 not earlier than the middle of the 4th century AD. The results of radiocarbon dating were obtained, which supplement this information and indicate the upper chronological boundary no later than the beginning of the 5th century AD.Conclusion. The correlation of the available data makes it possible to determine the chronology of mound no. 38 with-in the middle – second half of the 4th century AD. The recorded characteristics of the funeral rite indicate that this object belongs to the Dialyan tradition of ritual practice of the Bulan-Koby culture. The set of items found with the de-ceased man testifies to his high status in the nomadic society during his lifetime.

Developing and testing a reflection method for implementation of the informal care guideline in community nursing: Design-based research.

To develop a reflection method for community nurses and certified nursing assistants to support the implementation of the Dutch Informal Care guideline in daily care. Design-based research. A design group and four test groups of community nurses and nursing assistants were formed to develop a reflection method that aligns with the needs and preferences of its end-users. The design and test group meetings were video recorded. The video data were iteratively discussed and analysed thematically to adapt and refine the method and to identify its key features. A final reflection method was developed. Five main themes were identified from the analysis: the group, reflective triggers, knowledge about the guidelines, the coach and preconditions. The themes are linked to nine key features representing the building blocks of the reflection method. The key features are group size, participants with different (educational) backgrounds, pairs of participants, expressing thoughts, video feedback, reflection game, making the connection with the guideline, coaching as a process facilitator and meeting organizational and contextual conditions for implementation. An evidence- and practice-based reflection method for community nurses and certified nursing assistants is developed to support the implementation. By involving community nurses and certified nursing assistants, the method closely matches their needs and preferences. Critical elements of the reflection method are a game element, video feedback and working in pairs in a group of participants from different (educational) backgrounds. Guidance is needed to make the transfer from theory to practice. A reflection method for community nurses and certified nursing assistants was developed to enhance care work according to guideline recommendations, aiming to improve the care provided by informal caregivers. The COREQ guideline was used. This reflection method was developed in close collaboration with all stakeholders during the entire study.

Lameness detection system for dairy cows based on instance segmentation

Lameness is one of the major health problems on dairy farms, which seriously affects dairy cow welfare and increases the risk of premature culling. Accuracy lameness detection ensures timely treatment of hooves and improves the level of health management on dairy farms. Most of the existing lameness detection methods detect one dairy cow, and it is difficult to detect lameness in multiple dairy cows at the same time. In this paper, a lameness detection system based on instance segmentation is constructed to extract the lameness features of multiple dairy cows and automatically detect lameness. First, an improved SOLOv2 network is designed to reduce the semantic gap between low-level and high-level features and improve the precision of dairy cow segmentation. Second, individual matching of dairy cows in different video frames is performed using the Hungarian algorithm. Third, the Canny algorithm is used to extract the neck and back contour features and key gait features of dairy cows. Finally, a deep learning model is constructed, and multiple lameness features are fused to detect the lameness of dairy cows. To evaluate the performance of the constructed system, 172 videos were randomly selected from 246 videos as training videos, and the remaining 74 videos were selected as the test videos. The lameness detection accuracy of the constructed system was 98.65%. The experimental results showed that the constructed system can extract the lameness features of multiple dairy cows at the same time and accurately detect the lameness of dairy cows.

Halide Superionic Conductors with Non‐Close‐Packed Anion Frameworks

AbstractHalide superionic conductors (SICs) are drawing significant research attention for their potential applications in all‐solid‐state batteries. A key challenge in developing such SICs is to explore and design halide structural frameworks that enable rapid ion movement. In this work, we show that the close‐packed anion frameworks shared by traditional halide ionic conductors face intrinsic limitations in fast ion conduction, regardless of structural regulation. Beyond the close‐packed anion frameworks, we identify that the non‐close‐packed anion frameworks have great potential to achieve superionic conductivity. Notably, we unravel that the non‐close‐packed UCl3‐type framework exhibit superionic conductivity for a diverse range of carrier ions, including Li+, Na+, K+, and Ag+, which are validated through both ab initio molecular dynamics simulations and experimental measurements. We elucidate that the remarkable ionic conductivity observed in the UCl3‐type framework structure stems from its significantly more distorted site and larger diffusion channel than its close‐packed counterparts. By employing the non‐close‐packed anion framework as the key feature for high‐throughput computational screening, we also identify LiGaCl3 as a promising candidate for halide SICs. These discoveries provide crucial insights for the exploration and design of novel halide SICs.

Halide Superionic Conductors with Non-Close-Packed Anion Frameworks.

Halide superionic conductors (SICs) are drawing significant research attention for their potential applications in all-solid-state batteries. A key challenge in developing such SICs is to explore and design halide structural frameworks that enable rapid ion movement. In this work, we show that the close-packed anion frameworks shared by traditional halide ionic conductors face intrinsic limitations in fast ion conduction, regardless of structural regulation. Beyond the close-packed anion frameworks, we identify that the non-close-packed anion frameworks have great potential to achieve superionic conductivity. Notably, we unravel that the non-close-packed UCl3-type framework exhibit superionic conductivity for a diverse range of carrier ions, including Li+, Na+, K+, and Ag+, which are validated through both ab initio molecular dynamics simulations and experimental measurements. We elucidate that the remarkable ionic conductivity observed in the UCl3-type framework structure stems from its significantly more distorted site and larger diffusion channel than its close-packed counterparts. By employing the non-close-packed anion framework as the key feature for high-throughput computational screening, we also identify LiGaCl3 as a promising candidate for halide SICs. These discoveries provide crucial insights for the exploration and design of novel halide SICs.

Tricarboxylic acid cycle metabolites: new players in macrophage.

Metabolic remodeling is a key feature of macrophage activation and polarization. Recent studies have demonstrated the role of tricarboxylic acid (TCA) cycle metabolites in the innate immune system. In the current review, we summarize recent advances in the metabolic reprogramming of the TCA cycle during macrophage activation and polarization and address the effects of these metabolites in modulating macrophage function. Deciphering the crosstalk between the TCA cycle and the immune response might provide novel potential targets for the intervention of immune reactions and favor the development of new strategies for the treatment of infection, inflammation, and cancer.

Fabrication and Characterization of Quad-Component Bioinspired Hydrogels to Model Elevated Fibrin Levels in Central Nervous Tissue Scaffolds.

Multicomponent interpenetrating polymer network (mIPN) hydrogels are promising tissue-engineering scaffolds that could closely resemble key characteristics of native tissues. The mechanical and biochemical properties of mIPNs can be finely controlled to mimic key features of target cellular microenvironments, regulating cell-matrix interactions. In this work, we fabricated hydrogels made of collagen type I (Col I), fibrin, hyaluronic acid (HA), and poly (ethylene glycol) diacrylate (PEGDA) using a network-by-network fabrication approach. With these mIPNs, we aimed to develop a biomaterial platform that supports the in vitro culture of human astrocytes and potentially serves to assess the effects of the abnormal deposition of fibrin in cortex tissue and simulate key aspects in the progression of neuroinflammation typically found in human pathologies such as Alzheimer's disease (AD), Parkinson's disease (PD), and tissue trauma. Our resulting hydrogels closely resembled the complex modulus of AD human brain cortex tissue (~7.35 kPa), promoting cell spreading while allowing for the modulation of fibrin and hyaluronic acid levels. The individual networks and their microarchitecture were evaluated using confocal laser scanning microscopy (CLSM) and scanning electron microscopy (SEM). Human astrocytes were encapsulated in mIPNs, and negligible cytotoxicity was observed 24 h after the cell encapsulation.

Deindustrialisation and reindustrialisation patterns in V4 countries – industry 4.0 as a way forward?

ABSTRACT This contribution aims at: (i) mapping the deindustrialisation and reindustrialisation tendencies over the economic history of the Visegrad countries (Czechia, Hungary, Poland and Slovakia, henceforth, V4) since 1918 to 2022 by creating a cadastre of applied policy measures and evaluating their effectiveness; (ii) exploring whether the economic mentality of V4 countries allows current reindustrialisation policies to be effectively geared towards Industry 4.0? In order to understand the key features of industrialisation process in the V4s, a broad literature overview places emphasis on the economic characteristics of the region. It is also important to analyse the correlation between industrial policy in the XXth century and the new tendencies at the turn of the millennium by examining whether the current industrial policy mixes are serving economic resilience or grounding critical instability as a form of middle-income trap. Finally, it draws lessons for general- and V4-specific industrial policy in time of Industry 4.0.

Explainable artificial intelligence in the design of selective carbonic anhydrase I-II inhibitors via molecular fingerprinting.

Inhibiting the enzymes carbonic anhydrase I (CA I) and carbonic anhydrase II (CA II) presents a potential avenue for addressing nervous system ailments such as glaucoma and Alzheimer's disease. Our study explored harnessing explainable artificial intelligence (XAI) to unveil the molecular traits inherent in CA I and CA II inhibitors. The PubChem molecular fingerprints of these inhibitors, sourced from the ChEMBL database, were subjected to detailed XAI analysis. The study encompassed training 10 regression models using IC50 values, and their efficacy was gauged using metrics including R2, RMSE, and time taken. The Decision Tree Regressor algorithm emerged as the optimal performer (R2: 0.93, RMSE: 0.43, time-taken: 0.07). Furthermore, the PFI method unveiled key molecular features for CA I inhibitors, notably PubChemFP432 (C(O)N) and PubChemFP6978 (C(O)O). The SHAP analysis highlighted the significance of attributes like PubChemFP539 (C(O)NCC), PubChemFP601 (C(O)OCC), and PubChemFP432 (C(O)N) in CA I inhibitiotable n. Likewise, features for CA II inhibitors encompassed PubChemFP528(C(O)OCCN), PubChemFP791 (C(O)OCCC), PubChemFP696 (C(O)OCCCC), PubChemFP335 (C(O)NCCN), PubChemFP580 (C(O)NCCCN), and PubChemFP180 (C(O)NCCC), identified through SHAP analysis. The sulfonamide group (S), aromatic ring (A), and hydrogen bonding group (H) exert a substantial impact on CA I and CA II enzyme activities and IC50 values through the XAI approach. These insights into the CA I and CA II inhibitors are poised to guide future drug discovery efforts, serving as a beacon for innovative therapeutic interventions.

Biomimetic Total Synthesis of Bimagnolignan: A Natural Anti-Breast Cancer Agent.

A short scalable biomimetic route to bioactive natural product bimagnolignan (1) was accomplished. Compound 1 was successfully prepared through a three-step metal-free synthesis from honokiol (2). Alternatively, 1 was also synthesized by biomimetic transformations that mimic tyrosinase in four steps. The key reactions feature a regioselective acetylation, a highly efficient C(sp2)-H oxidation, a cascade aerobic oxidative cyclization/coupling, and a Cu-catalyzed direct oxidative coupling. In addition, cell-based assays validate that 1 is a promising natural lead for HER2-positive breast cancer treatment.