Variety Of Architectures Research Articles

Recent Advances in Asymmetric Addition Reactions to Isatins

The 3-substituted-3-hydroxy-2-oxindole scaffolds are not only widely encountered in bioactive compounds but also serve as versatile building blocks for the construction of diverse valuable architectures. Although numerous synthetic methodologies have been developed over the past decades, the asymmetric addition of nucleophiles or their equivalents to isatin substrates has proved to be one of the most efficient strategies for the synthesis of chiral 3-substituted-3-hydroxy-2-oxindoles. This Short Review aims to summarize the significant progress achieved in this field in recent years, with particular attention paid to reaction development, mechanism, and stereo-induction models.

Tailoring Cu-SiO2 Interaction through Nanocatalyst Architecture to Assemble Surface Sites for Furfural Aqueous-Phase Hydrogenation to Cycloketones.

In this contribution, nanocatalysts with rather diverse architectures were designed to promote different intimacy degrees between Cu and SiO2 and consequently tune distinct Cu-SiO2 interactions. Previously synthesized copper nanoparticles were deposited onto SiO2 (NPCu/SiO2) in contrast to ordinarily prepared supported Cu/SiO2. NPCu@SiO2 and SiO2@Cu core-shell nanocatalysts were also synthesized, and they were all bulk and surface characterized by XRD, TGA, TEM/HRTEM, H2-TPR, XANES, and XPS. It was found that Cu0 is the main copper phase in NPCu/SiO2 while Cu2+ rules the ordinary Cu/SiO2 catalyst, and Cu0 and electron-deficient Cuδ+ species coexist in the core-shell nanocatalysts as a consequence of a deeper metal-support interaction. Catalytic performance could not be associated with the physical properties of the nanocatalysts derived from their architectures but was associated with the more refined chemical characteristics tuned by their design. Cu/SiO2 and NPCu/SiO2 catalysts led to the formation of furfuryl alcohol, evidencing that catalysts holding weak or no metal-support interaction have no significant impact on product distribution even in the aqueous phase. The establishment of such interactions through advanced catalyst architecture, allowing the formation of electron-deficient Cuδ+ moieties, particularly Cu2+ and Cu+ as unveiled by spectroscopic investigations, is critical to promoting the hydrogenation-ring rearrangement cascade mechanism leading to cycloketones.

Structured segment rescaling with Gaussian processes for parameter efficient ConvNets

We introduce a novel mechanism for structured pruning on ConvNet blocks and channels. Our mechanism, Structured Segment Rescaling (SSR) down-samples a ConvNet’s dimensions using depth and width modifiers that respectively remove whole blocks and channels. SSR is a systematic approach for constructing ConvNets that can replace arbitrary design heuristics. The SSR modifiers rescale logical partitions (segments) of a ConvNet with grouped layers. Different modifiers on segments yield many different architectures with unique rescales for their blocks. This diversity of architectures is then systemically explored using a Gaussian Process (GP) that optimizes for modifiers that maintain accuracy and reduce parameters. We analyze SSR in the context of resource constrained environments using ResNets trained on the CIFAR datasets. An initial set of depth and width modifiers explore extreme rescales of ResNet segments, where we find up to 70% parameter reduction. The GP then generalizes on these initial rescales by being trained on them and then predicts the accuracy of other rescaled ConvNet given their segment modifiers. SSR produces over 105 ConvNets that can be trained selectively based on their GP predicted accuracy. The GP enabled SSR pushes compression to over 80% with minimal accuracy impact. While both depth and width modifiers can reduce parameters, we show reducing blocks is better for reducing latency with up to 80% faster ConvNets. Using our mechanism, we can efficiently customize ConvNets using their parameter-accuracy trade-offs. SSR only requires 101 GPU hours and modest engineering to yield efficient new ConvNets that can facilitate edge inference.

An ecotype-specific effect of osmopriming and melatonin during salt stress in Arabidopsis thaliana

BackgroundNatural populations of Arabidopsis thaliana exhibit phenotypic variations in specific environments and growth conditions. However, this variation has not been explored after seed osmopriming treatments. The natural variation in biomass production and root system architecture (RSA) was investigated across the Arabidopsis thaliana core collection in response to the pre-sawing seed treatments by osmopriming, with and without melatonin (Mel). The goal was to identify and characterize physiologically contrasting ecotypes.ResultsVariability in RSA parameters in response to PEG-6000 seed osmopriming with and without Mel was observed across Arabidopsis thaliana ecotypes with especially positive impact of Mel addition under both control and 100 mM NaCl stress conditions. Two ecotypes, Can-0 and Kn-0, exhibited contrasted root phenotypes: seed osmopriming with and without Mel reduced the root growth of Can-0 plants while enhancing it in Kn-0 ones under both control and salt stress conditions. To understand the stress responses in these two ecotypes, main stress markers as well as physiological analyses were assessed in shoots and roots. Although the effect of Mel addition was evident in both ecotypes, its protective effect was more pronounced in Kn-0. Antioxidant enzymes were induced by osmopriming with Mel in both ecotypes, but Kn-0 was characterized by a higher responsiveness, especially in the activities of peroxidases in roots. Kn-0 plants experienced lower oxidative stress, and salt-induced ROS accumulation was reduced by osmopriming with Mel. In contrast, Can-0 exhibited lower enzyme activities but the accumulation of proline in its organs was particularly high. In both ecotypes, a greater response of antioxidant enzymes and proline accumulation was observed compared to mechanisms involving the reduction of Na+ content and prevention of K+ efflux.ConclusionsIn contrast to Can-0, Kn-0 plants grown from seeds osmoprimed with and without Mel displayed a lower root sensitivity to NaCl-induced oxidative stress. The opposite root growth patterns, enhanced by osmopriming treatments might result from different protective mechanisms employed by these two ecotypes which in turn result from adaptive strategies proper to specific habitats from which Can-0 and Kn-0 originate. The isolation of contrasting phenotypes paves the way for the identification of genetic factors affecting osmopriming efficiency.

The genetic basis of divergent melanic pigmentation in benthic and limnetic threespine stickleback

Pigmentation is an excellent trait to examine patterns of evolutionary change because it is often under natural selection. Benthic and limnetic threespine stickleback (Gasterosteus aculeatus) exhibit distinct pigmentation phenotypes, likely an adaptation to occupation of divergent niches. The genetic architecture of pigmentation in vertebrates appears to be complex. Prior QTL mapping of threespine stickleback pigmentation phenotypes has identified several candidate loci. However—relative to other morphological phenotypes (e.g., spines or lateral plates)—the genetic architecture of threespine stickleback pigmentation remains understudied. Here, we performed QTL mapping for two melanic pigmentation traits (melanophore density and lateral barring) using benthic-limnetic F2 crosses. The two traits mapped to different chromosomes, suggesting a distinct genetic basis. The resulting QTLs were additive, but explained a relatively small fraction of the total variance (~6%). QTLs maps differed by F1 family, suggesting variation in genetic architecture or ability to detect loci of small effect. Functional analysis identified enriched pathways for candidate loci. Several of the resulting candidate loci for pigmentation, including three loci in enriched pathways (bco1, sulf1, and tyms) have been previously indicated to affect pigmentation in other vertebrates. These findings add to a growing body of evidence suggesting pigmentation is often polygenic.

Benchmarking Test-Time DNN Adaptation at Edge with Compute-In-Memory

The prediction accuracy of deep neural networks (DNNs) deployed at the edge can deteriorate over time due to shifts in the data distribution. For heightened robustness, it’s crucial for DNNs to continually refine and improve their predictive capabilities. However, adaptation in resource-limited edge environments is fraught with challenges: (i) new labeled data might be unavailable; (ii) on-device adaptation is a necessity as cloud connections may be inaccessible; and (iii) the adaptation procedure should prioritize speed, memory efficiency, and energy conservation. Compute-In-Memory (CIM) has recently garnered attention for its computational efficacy and superior operational bandwidth. Additionally, emerging lightweight unsupervised DNN adaptation techniques during test-time have showcased promising results in enhancing model accuracy for data with noise. This article pioneers a holistic benchmarking exploration of these methods, assessing their performance and energy efficacy across diverse CIM architectures in edge and autonomous systems. Our findings reveal that the proposed adaptation strategies can adapt to both environment shifts and inherent hardware noise. Engaging in a thorough cross-layer algorithm-hardware-technology co-design space exploration, we highlight pivotal trade-offs among accuracy, performance, and energy for various DNN adaptation techniques and CIM configurations.

Decoding stomatal characteristics regulating water use efficiency at leaf and plant scales in rice genotypes.

Stomatal traits in rice genotypes affect water use efficiency. Low-frequency small-size stomata correlate with whole plant efficiency, while low-frequency large-size stomata show intrinsic efficiency and responsiveness to vapour pressure deficit. Leaf surface and the patterning of the epidermal layer play a vital role in determining plant growth. While the surface helps in determining radiation interception, epidermal pattern of stomatal factors strongly regulate gas exchange and water use efficiency (WUE). This study focuses on identifying distinct stomatal traits among rice genotypes to comprehend their influence on WUE. Stomatal frequency ranged from 353 to 687 per mm2 and the size varied between 128.31 and 339.01 μm2 among 150 rice germplasm with significant variability in abaxial and adaxial surfaces. The cumulative water transpired and WUE determined at the outdoor phenomics platform, over the entire crop growth period as well as during specific hours of a 24h-day did not correlate with stomatal frequency nor size. However, genotypes with low-frequency and large-size stomata recorded higher intrinsic water use efficiency (67.04μmol CO2 mol-1 H2O) and showed a quicker response to varying vapour pressure deficit that diurnally ranged between 0.03 and 2.17kPa. The study demonstrated the role of stomatal factors in determining physiological subcomponents of WUE both at single leaf and whole plant levels. Differential expression patterns of stomatal regulatory genes among the contrasting groups explained variations in the epidermal patterning. Increased expression of ERECTA, TMM and YODA genes appear to contribute to decreased stomatal frequency in low stomatal frequency genotypes. These findings underscore the significance of stomatal traits in breeding programs and strongly support the importance of these genes that govern variability in stomatal architecture in future crop improvement programs.

Pattern Recognition of Bima Script Handwritting using Convolutional Neural Network Method

Bima is one of the regions in West Nusa Tenggara Province. The Bima script is a cultural heritage used as a means of communication by the Bima community in the past. The decline in the use of the Bima script threatens cultural heritage. The government has addressed this issue by providing training to teachers to teach it in schools, but this has still been insufficient due to the limited number of teachers participating in the training. Therefore, one efficient method to assist with this issue is by leveraging modern technology, particularly through machine learning for handwriting recognition. This study aims to find the best CNN model for recognizing the Bima script with diacritics to help preserve Bima's cultural heritage through handwriting recognition. The CNN model is combined with hyperparameter tuning, and then testing is conducted in four different scenarios to evaluate the performance of each model architecture and hyperparameter variation to find the best combination. The dataset used is sourced from the Kaggle platform, and augmentation is performed to increase the total number of images to 6,750, with each image containing 75 images in 90 different classes. In this study, testing is done by dividing the dataset into training and testing sets in an 80:20 ratio. The test results show high performance, achieving an accuracy of 98.00%, precision of 98.19%, recall of 98.00%, and f1-score of 98.00% in scenario 4.

Time-modulated planar frequency diverse array for multi-target detection

The frequency diverse array (FDA) is a very interesting technique in wireless communications. Many research papers are introduced in this field. A lot of them is concerned with linear point sources array. In this paper, a design of a time-modulated planar frequency diverse array architecture is proposed for tracking single or multi-target for the first time. The time-modulation array (TMA) and FDA are combined for detecting single and multi-target. The effectiveness of this combined scheme is demonstrated through simulation results that compared with a point source case to show its superior performance. Authors introduce planar FDA array instead of linear FDA array and realize the planar array using cylindrical dielectric resonator antenna array (CDRA). A planar array consists of 8 × 8 CDRA is used to enable the detection of single target/multi-target. The operating frequency of the antenna is set at 10 GHz, aligning with its applications in the FDA radar. The radiation characteristics of the CDRA antenna element are introduced. High maximum gain of 6 dBi is achieved with a remarkably high efficiency. A set of representative results is reported and analyzed to assess the effectiveness of the proposed approach.

Total syntheses of (–)-macrocalyxoformins A and B and (–)-ludongnin C

The complex and diverse molecular architectures along with broad biological activities of ent-kauranoids natural products make them an excellent testing ground for the invention of synthetic methods and strategies. Recent efforts notwithstanding, synthetic access to the highly oxidized enmein-type ent-kauranoids still presents considerable challenges to synthetic chemists. Here, we report the enantioselective total syntheses of C-19 oxygenated enmein-type ent-kauranoids, including (–)-macrocalyxoformins A and B and (–)-ludongnin C, along with discussion and study of synthetic strategies. The enabling feature in our synthesis is a devised Ni-catalyzed decarboxylative cyclization/radical-polar crossover/C-acylation cascade that forges a THF ring concomitantly with the β-keto ester group. Mechanistic studies reveal that the C-acylation process in this cascade reaction is achieved through a carboxylation followed by an in situ esterification. Biological evaluation of these synthetic natural products reveals the indispensable role of the ketone on the D ring in their anti-tumor efficacy.

A brain organoid/ALL coculture model reveals the AP-1 pathway as critically associated with CNS involvement of BCP-ALL

AbstractCentral nervous system (CNS) involvement remains a clinical hurdle in treating childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL). The disease mechanisms of CNS leukemia are primarily investigated using 2-dimensional cell culture and mouse models. Given the variations in cellular identity and architecture between the human and murine CNS, it becomes imperative to seek complementary models to study CNS leukemia. Here, we present a first-of-its-kind 3-dimensional coculture model combining human brain organoids and BCP-ALL cells. We noticed significantly higher engraftment of BCP-ALL cell lines and patient-derived xenograft (PDX) cells in cerebral organoids than non-ALL cells. To validate translatability between organoid coculture and in vivo murine models, we confirmed that targeting CNS leukemia–relevant pathways such as CD79a/Igα or C-X-C motif chemokine receptor 4–stromal cell-derived factor 1 reduced the invasion of BCP-ALL cells into organoids. RNA sequencing and functional validations of organoid-invading leukemia cells compared with the noninvaded fraction revealed significant upregulation of activator protein 1 (AP-1) transcription factor–complex members in organoid-invading cells. Moreover, we detected a significant enrichment of AP-1 pathway genes in PDX ALL cells recovered from the CNS compared with spleen blasts of mice that had received transplantation with TCF3::PBX1+ PDX cells, substantiating the role of AP-1 signaling in CNS disease. Accordingly, we found significantly higher levels of the AP-1 gene, jun proto-oncogene, in patients initially diagnosed as CNS-positive BCP-ALL compared with CNS-negative cases as well as CNS-relapse vs non–CNS-relapse cases in a cohort of 100 patients with BCP-ALL. Our results suggest CNS organoids as a novel model to investigate CNS involvement and identify the AP-1 pathway as a critical driver of CNS disease in BCP-ALL.

Variability in sleep architecture and alterations in circadian rhythms in patients with acute cerebral infarction accompanied by sleep-disordered breathing.

To continuously and dynamically monitor the sleep status of patients in the acute phase of cerebral infarction, and to investigate the characteristics of acute cerebral infarction(ACI)associated with sleep-disordered breathing (SDB), variations in sleep structure, and changes in sleep circadian rhythms. Patients with ACI within 48h of onset who were admitted to the Department of Neurology at Kailuan General Hospital from November 2020 to December 2022 were selected. Detailed baseline information such as age, gender, smoking history, drinking history, were recorded for the selected participants. From the beginning of their hospitalization, the selected participants were monitored for their sleep status continuously for 5 days using the Intelligent Mattress-based Sleep Monitoring Platform System(IMSMPS). Based on the heart rate data obtained from the monitoring, the interdaily stability (IS) and intradaily variability (IV) of the sleep circadian rhythm were calculated. 1,367 patients with ACI were selected. Monitoring results over 5 days indicated 147 cases (10.75%) without SDB, and 1,220 cases (89.25%) with SDB. Among the group with SDB, there were 248 cases (18.14%) with continuous mild SDB, 395 cases (28.90%) with moderate SDB, 295 cases (21.58%) with severe SDB, and 282 cases (20.63%) that fluctuated between different severity levels. Within this fluctuating group, 152 cases (53.90%) fluctuated between two severity levels, 120 cases (42.55%) between three levels, and 10 cases (3.55%) among all four levels. There were statistically significant differences (P < 0.05) in the sleep latency, sleep efficiency, non-rapid eye movement stages 1-2, rapid eye movement, proportion of non-rapid eye movement, proportion of rapid eye movement, wake after sleep onset, time out of bed, number of awakenings, respiratory variability index, and heart rate variability index among patients with ACI monitored from day 1 to 5. However, other monitored sleep structure parameters did not show statistically significant differences (P > 0.05). The coefficient of variation for all sleep monitoring parameters ranged between 14.54 and 36.57%. The IV in the SDB group was higher than in the group without SDB (P < 0.05), and the IS was lower than in the group without SDB (P < 0.05). Patients in the acute phase of cerebral infarction have a high probability of accompanying SDB. The sleep structure of these patients shows significant variability based on the onset time of the stroke, and some patients experience fluctuations among different severity levels of SDB. ACI accompanied by SDB can further reduce the IS of a patient's sleep circadian rhythm and increase its IV.

Inference latency prediction for CNNs on heterogeneous mobile devices and ML frameworks

Due to the proliferation of inference tasks on mobile devices, state-of-the-art neural architectures are typically designed using Neural Architecture Search (NAS) to achieve good tradeoffs between machine learning accuracy and inference latency. While measuring inference latency of a huge set of candidate architectures during NAS is not feasible, latency prediction for mobile devices is challenging, because of hardware heterogeneity, optimizations applied by machine learning frameworks, and diversity of neural architectures. Motivated by these challenges, we first quantitatively assess the characteristics of neural architectures (specifically, convolutional neural networks for image classification), ML frameworks, and mobile devices that have significant effects on inference latency. Based on this assessment, we propose an operation-wise framework which addresses these challenges by developing operation-wise latency predictors and achieves high accuracy in end-to-end latency predictions, as shown by our comprehensive evaluations on multiple mobile devices using multicore CPUs and GPUs. To illustrate that our approach does not require expensive data collection, we also show that accurate predictions can be achieved on real-world neural architectures using only small amounts of profiling data.

A Transfer Learning Approach: Early Prediction of Alzheimer’s Disease on US Healthy Aging Dataset

Alzheimer’s disease (AD) is a growing public health crisis, a very global health concern, and an irreversible progressive neurodegenerative disorder of the brain for which there is still no cure. Globally, it accounts for 60–80% of dementia cases, thereby raising the need for an accurate and effective early classification. The proposed work used a healthy aging dataset from the USA and focused on three transfer learning approaches: VGG16, VGG19, and Alex Net. This work leveraged how the convolutional model and pooling layers work to improve and reduce overfitting, despite challenges in training the numerical dataset. VGG was preferably chosen as a hidden layer as it has a more diverse, deeper, and simpler architecture with better performance when dealing with larger datasets. It consumes less memory and training time. A comparative analysis was performed using machine learning and neural network algorithm techniques. Performance metrics such as accuracy, error rate, precision, recall, F1 score, sensitivity, specificity, kappa statistics, ROC, and RMSE were experimented with and compared. The accuracy was 100% for VGG16 and VGG19 and 98.20% for Alex Net. The precision was 99.9% for VGG16, 96.6% for VGG19, and 100% for Alex Net; the recall values were 99.9% for all three cases of VGG16, VGG19, and Alex Net; and the sensitivity metric was 96.8% for VGG16, 97.9% for VGG19, and 98.7% for Alex Net, which has outperformed when compared with the existing approaches for the classification of Alzheimer’s disease. This research contributes to the advancement of predictive knowledge, leading to future empirical evaluation, experimentation, and testing in the biomedical field.

Enhancing COVID-19 detection through integrated CNN-LSTM models: Addressing the challenge of varied radiological presentations

In an era where COVID-19's varied radiological presentations challenge diagnostic precision, this paper introduces a novel fusion of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to refine detection capabilities. The paper capitalizes on an expansive radiography database, utilizing lung segmentation and data augmentation to bolster the training set. Advanced CNN models extract salient features, which are then classified through meticulously optimized machine learning algorithms. The synergy between diverse CNN architectures and Bayesian Optimization markedly enhances hyperparameter tuning, culminating in a significant uplift in diagnostic accuracy. The integration of LSTM models further allows for nuanced severity assessments, paving the way for more personalized and effective pandemic management. This research heralds a new frontier in medical diagnostics, promising a leap in the accuracy of pandemic detection.

O/S Exchange Reaction in Synthesizing Sulfur-Containing Polymers.

Using carbon disulfide (CS2) and carbonyl sulfide (COS) as sulfur-containing and one-carbon feedstocks to make value-added products is paramount for both pure and applied chemistry and environmental science. One of the practical strategies is to copolymerize these bulk chemicals with epoxides to produce sulfur-containing polymers. This approach contributes to improving the sustainability of polymer manufacturing, provides highly desired functional polymer materials, and has attracted much attention. However, these copolymerizations invariably exhibit the intensely complicated chemistry of O/S exchange reaction, leading to sulfur-containing polymers with diverse architectures. As the understanding of O/S exchange continues to deepen, recent efforts have guided significant advances in the synthesis of CS2- and COS-based polymers. This review examines the O/S exchange chemistry and summarizes the recent progress in this field to promote the further advance of synthesizing sulfur-containing polymers from CS2 and COS.

Diversification of the Rho transcription termination factor in bacteria.

Correct termination of transcription is essential for gene expression. In bacteria, factor-dependent termination relies on the Rho factor, that classically has three conserved domains. Some bacteria also have a functional insertion region. However, the variation in Rho structure among bacteria has not been analyzed in detail. This study determines the distribution, sequence conservation, and predicted features of Rho factors with diverse domain architectures by analyzing 2730 bacterial genomes. About half (49.8%) of the species analyzed have the typical Escherichia coli like Rho while most of the other species (39.8%) have diverse, atypical forms of Rho. Besides conservation of the main domains, we describe a duplicated RNA-binding domain present in specific species and novel variations in the bicyclomycin binding pocket. The additional regions observed in Rho proteins exhibit remarkable diversity. Commonly, however, they have exceptional amino acid compositions and are predicted to be intrinsically disordered, to undergo phase separation, or have prion-like behavior. Phase separation has recently been shown to play roles in Rho function and bacterial fitness during harsh conditions in one species and this study suggests a more widespread role. In conclusion, diverse atypical Rho factors are broadly distributed among bacteria, suggesting additional cellular roles.

NF-κB inhibitor alpha controls SARS-CoV-2 infection in ACE2-overexpressing human airway organoids

As SARS-CoV-2 continues to spread worldwide, tractable primary airway cell models that recapitulate the cell-intrinsic response to arising viral variants are needed. Here we describe an adult stem cell-derived human airway organoid model overexpressing the ACE2 receptor (ACE2-OE) that supports robust viral replication while maintaining 3D architecture and cellular diversity of the airway epithelium. ACE2-OE organoids were infected with SARS-CoV-2 variants and subjected to single-cell RNA-sequencing. Interferon-lambda was upregulated in cells with low-level infection while the NF-kB inhibitor alpha gene (encoding IkBa) was consistently upregulated in infected cells, and its expression positively correlated with infection levels. Confocal microscopy showed more IkBa expression in infected than bystander cells, but found concurrent nuclear translocation of NF-kB that IkBa usually prevents. Overexpressing a nondegradable IkBa mutant reduced NF-kB translocation and increased viral infection. These data demonstrate the functionality of ACE2-OE organoids in SARS-CoV-2 research and underscore that the strength of the NF-kB feedback loop in infected cells controls viral replication.

Enhancing urban identity through the refined management of architectural styles: insights from Wuhan

ABSTRACT The shaping of urban features is profoundl influenced by the underlying urban context, and architectural features and style management are important parts of the protection and cultivation of urban identity. However, in China, oversight in managing the urban context often results in neglect of the cultural values embedded in architectural aspects at the micro level. This, in turn, contributes to the convergence and disorder of urban architectural features and styles. This study builds upon the significance of the urban context and takes the typical Chinese megacity Wuhan as a case study. First, the research systematically outlines the diachronic development of architectural features and styles in Wuhan and meticulously analyses the prevailing problems. Subsequently, an evaluation is conducted on the efficacy of existing architectural features and style management policies and the approval processes for architectural schemes. Finally, the research formulates a comprehensive architectural feature and style management and implementation system that harmonizes Wuhan’s urban cultural heritage and urban identification characteristics. This paper aims to contribute to the refinement of urban identity management and the preservation of architectural diversity to foster a more cohesive and culturally resonant urban environment in Wuhan.

Current Developments in Michael Addition Reaction using Heterocycles as Convenient Michael Donors

The Michael addition reaction, a cornerstone of contemporary organic synthesis, has witnessed a resurgence of interest owing to its ability to forge intricate carbon‐carbon and carbon‐heteroatom bonds. In the past few years, heterocyclic compounds have been rigorously used as Michael donors, owing to their architectural diversity and distinct reactivity with or without the presence of base/transition metals/organocatalysts. This review encapsulates the latest breakthroughs in chemistry involving Michael addition reaction using heterocyclic compounds as Michael donors. It delivers a comprehensive update on developments in Michael addition reaction triggered by potent heterocycles since 2017, highlighting novel and innovative methodologies, with strategic insights.