Sequential Mechanism Research Articles

Catalytic divergence of O-methyltransferases shapes the chemo-diversity of polymethoxylated bibenzyls in Dendrobium catenatum.

Erianin, crepidatin, and chrysotobibenzyl are typical medicinal polymethoxylated bibenzyls (PMBs) that are commercially produced in Dendrobium species. PMBs' chemo-diversity is mediated by the manifold combinations of O-methylation and hydroxylation in a definite order, which remains unsolved. To unequivocally elucidate the methylation mechanism of PMBs, 15 possible intermediates in the biosynthetic pathway of PMBs were chemically synthesized. DcOMT1-5 were highly expressed in tissues where PMBs were biosynthesized, and their expression patterns were well-correlated with the accumulation profiles of PMBs. Moreover, cell-free orthogonal tests based on the synthesized intermediates further confirmed that DcOMT1-5 exhibited distinct substrate preferences and displayed hydroxyl-group regiospecificity during the sequential methylation process. The stepwise methylation of PMBs was discovered from SAM to dihydro-piceatannol (P) in the following order: P → 3-MeP → 4-OH-3-MeP → 4-OH-3,5-diMeP → 3,3'(4'),5-triMeP → 3,4,4',5-tetraMeP (erianin) or 3,3',4,5-tetraMeP (crepidatin) → 3,3',4,4',5-pentaMeP (chrysotobibenzyl). Furthermore, the regioselectivities of DcOMTs were investigated by ligand docking analyses which corresponded precisely with the catalytic activities. In summary, the findings shed light on the sequential catalytic mechanisms of PMB biosynthesis and provide a comprehensive PMB biosynthetic network in D.catenatum. The knowledge gained from this study may also contribute to the development of plant-based medicinal applications and the production of high-value PMBs.

Customized decentralized autonomous organization based optimal energy management for smart buildings

In recent years, the unprecedented rising urbanization level and proliferation of distributed energy sources (DES) are motivating the transition of smart building from consumer to prosumer. In the context of mitigating upstream emission via reducing reliance on utility grids, the key challenge becomes realizing autonomous and economically optimal energy management within smart buildings. This paper innovatively proposes a Decentralized Autonomous Organization (DAO)-based energy management framework for smart buildings, where the mechanism of day-ahead scheduling DAO considering peer-to-peer (P2P) energy sharing is specifically investigated. Besides, a sequential preference satisfaction mechanism is designed to customize participants’ special demands, thereby achieving effective energy matching. Considering potential communication failures between buildings in practical operations, this study develops a communication failure robust distributed algorithm to minimize operational costs and safeguard building’s privacy. Extensive numerical studies for highly DES penetrated smart buildings successfully offers an operation cost reduction of 33.7% when considering preference satisfaction and P2P energy sharing in the day-ahead scheduling problem. Simulation results also demonstrates that the algorithm in this work excels in resisting potential communication failure during operation.

Time-Resolved Mechanism of Positive Aging in InP Quantum-Dot Light-Emitting Diodes.

Positive aging has been reported to be effective for enhancing electroluminescence characteristics of quantum dot (QD) based optoelectrical devices. This study investigated the intricate mechanisms underlying the positive aging effect in quantum-dot light-emitting diodes (QLEDs) influenced by encapsulation with ultraviolet-curable resin. A 120-h analysis assessed the impact of the resin on the electron transport layer and emission layer, utilizing a strategically positioned perfluorinated ionomer (PFI) interlayer. The PFI layer effectively delayed the Al2O3 formation at the zinc magnesium oxide (ZMO)/Al interface and further reduced the interactions within the QD/ZMO interface, thereby curtailing exciton quenching at the interfaces. The time-sequential effect of positive aging demonstrated that resin encapsulation effectively passivates the ZMO surfaces after 12 h. The positive aging facilitated the reaction between aluminum and oxygen from ZMO, contributing to Al2O3 formation within 48 h of aging. Furthermore, positive aging passivated the defect states of the QD surface and the QD/ZMO interface, reducing exciton quenching at the QD or QD/ZMO interface. The enhanced electron injection and reduced exciton quenching resulted in aged InP QLEDs, exhibiting an external quantum efficiency of 12.04%. This is a significant increase from the 3.16% observed in the control device. Finally, a sequential mechanism of positive aging in InP QLEDs was devised, providing new insights into the time-related operation of aging agents. This study elucidates an advanced time-resolved mechanism of positive aging, thereby offering valuable insights into the intricate dynamics of excitons within the domain of QLED physics.

Neural-network-based molecular dynamics simulations reveal that proton transport in water is doubly gated by sequential hydrogen-bond exchange.

The transport of excess protons in water is central to acid-base chemistry, biochemistry and energy production. However, elucidating its mechanism has been challenging. Recent nonlinear vibrational spectroscopy experiments could not be explained by existing models. Here we use both vibrational spectroscopy calculations and neural-network-based molecular dynamics simulations that account for nuclear quantum effects for all atoms to determine the proton transport mechanism. Our simulations reveal an equilibrium between two stable proton-localized structures with distinct Eigen-like and Zundel-like hydrogen-bond motifs. Proton transport follows a three-step mechanism gated by two successive hydrogen-bond exchanges: the first reduces the proton-acceptor water coordination, leading to proton transfer, and the second, the rate-limiting step, prevents rapid back-transfer by increasing the proton-donor coordination. This sequential mechanism is consistent with experimental characterizations of proton diffusion, explaining the low activation energy and the prolonged intermediate lifetimes in vibrational spectroscopy. These results are crucial for understanding proton dynamics in biochemical and technological systems.

Manganese-catalyzed cyclopropanation of allylic alcohols with sulfones

Cyclopropanes are among the most important structural units in natural products, pharmaceuticals, and agrochemicals. Herein, we report a manganese-catalyzed cyclopropanation of allylic alcohols with sulfones as carbene alternative precursors via a borrowing hydrogen strategy under mild conditions. Various allylic alcohols and arylmethyl trifluoromethyl sulfones work efficiently in this borrowing hydrogen transformation and thereby deliver the corresponding cyclopropylmethanol products in 58% to 99% yields. Importantly, a major benefit of this transformation is that the versatile free alcohol moiety is retained in the resultant products, which can undergo a wide range of downstream transformations to provide access to a series of functional molecules. Mechanistic studies support a sequential reaction mechanism that involves catalytic dehydrogenation, Michael addition, cyclization, and catalytic hydrogenation.

Neural Collaborative Learning for User Preference Discovery From Biased Behavior Sequences

The rapid increase of the data of user behaviors on the Internet brings a promising chance to better discover user preferences. Recommender systems have become a popular tool for the discovery of user preferences. One key issue is how to employ user behavior sequences to develop effective sequential recommendations, especially when behavior sequences are biased. The current sequential recommendation methods either can only mine data dependencies but ignores bias or only can learn bias but cannot mine data dependencies. To solve these problems, in this article, we propose a neural collaborative sequential learning mechanism, which learns sequential information from user behavior sequences that contain bias. We propose a neural collaborative filtering (NCF) model that fully takes advantage of all data dependencies among users, items, and biased sequential behaviors. Our sequential learning mechanism employs a self-attention mechanism to learn sequential features into an embedding space and inputs this sequential embedding into the generalized matrix factorization (GMF) model and the multilayer perceptron (MLP) model. We performed experiments on two real-world datasets and compared our model with many well-known baselines. The experimental results demonstrate that our model achieves superior performance. We also give a thorough analysis through ablation experiments and sensitivity experiments.

Tab-Cox: An Interpretable Deep Survival Analysis Model for Patients With Nasopharyngeal Carcinoma Based on TabNet.

The nutritional status of cancer patients is closely associated with the clinical progression of the disease. A survival analysis model combined with a neural network can predict future disease trends in patients, facilitating early prevention and assisting physicians in making diagnoses. However, the complexity of neural networks and their incompatibility with medical tabular data can reduce the interpretability of the model. To address this issue, thr paper propose a novel survival analysis model called Tab-Cox, which combines TabNet and Cox models. This model is specifically designed to predict the survival outcomes of patients with nasopharyngeal carcinoma. The model utilizes TabNet's sequential attention mechanism to extract more interpretable features, providing an interpretable method for identifying disease risk factors. Consequently, the model ensures accurate survival prediction while also making the results more comprehensible for both patients and doctors. The paper tested the efficacy of the model by conducting experiments on various diverse datasets in comparison with other commonly used survival models. The results showed that the proposed model delivered the highest or second-highest accuracy across all datasets. Furthermore, the paper conducted a comparative interpretability analysis against the classical Cox model. In addition and compare the interpretability of the Tab-Cox model with the classical Cox model and discuss the advantages and disadvantages of its interpretability. This demonstrates that Tab-Cox can assist doctors in identifying risk factors that are challenging to capture using artificial methods.

Physiological, transcriptomic and metabolomic insights of three extremophyte woody species living in the multi-stress environment of the Atacama Desert.

In contrast to Neltuma species, S. tamarugo exhibited higher stress tolerance, maintaining photosynthetic performance through enhanced gene expression and metabolites. Differentially accumulated metabolites include chlorophyll and carotenoids and accumulation of non-nitrogen osmoprotectants. Plant species have developed different adaptive strategies to live under extreme environmental conditions. Hypothetically, extremophyte species present a unique configuration of physiological functions that prioritize stress-tolerance mechanisms while carefully managing resource allocation for photosynthesis. This could be particularly challenging under a multi-stress environment, where the synthesis of multiple and sequential molecular mechanisms is induced. We explored this hypothesis in three phylogenetically related woody species co-occurring in the Atacama Desert, Strombocarpa tamarugo, Neltuma alba, and Neltuma chilensis, by analyzing their leaf dehydration and freezing tolerance and by characterizing their photosynthetic performance under natural growth conditions. Besides, the transcriptomic profiling, biochemical analyses of leaf pigments, and metabolite analysis by untargeted metabolomics were conducted to study gene expression and metabolomic landscape within this challenging multi-stress environment. S. tamarugo showed a higher photosynthetic capacity and leaf stress tolerance than the other species. In this species, a multifactorial response was observed, which involves high photochemical activity associated with a higher content of chlorophylls and β-carotene. The oxidative damage of the photosynthetic apparatus is probably attenuated by the synthesis of complex antioxidant molecules in the three species, but S. tamarugo showed the highest antioxidant capacity. Comparative transcriptomic and metabolomic analyses among the species showed the differential expression of genes involved in the biosynthetic pathways of key stress-related metabolites. Moreover, the synthesis of non-nitrogen osmoprotectant molecules, such as ciceritol and mannitol in S. tamarugo, would allow the nitrogen allocation to support its high photosynthetic capacity without compromising leaf dehydration tolerance and freezing stress avoidance.

Bi-SeqCNN: A Novel Light-weight Bi-directional CNN Architecture for Protein Function Prediction.

Deep learning approaches, such as convolution neural networks (CNNs) and deep recurrent neural networks (RNNs), have been the backbone for predicting protein function, with promising state-of-the-art (SOTA) results. RNNs with an in-built ability (i) focus on past information, (ii) collect both short-and-long range dependency information, and (iii) bi-directional processing offers a strong sequential processing mechanism. CNNs, however, are confined to focusing on short-term information from both the past and the future, although they offer parallelism. Therefore, a novel bi-directional CNN that strictly complies with the sequential processing mechanism of RNNs is introduced and is used for developing a protein function prediction framework, Bi-SeqCNN. This is a sub-sequence-based framework. Further, Bi-SeqCNN + is an ensemble approach to better the prediction results. To our knowledge, this is the first time bi-directional CNNs are employed for general temporal data analysis and not just for protein sequences. The proposed architecture produces improvements up to +5.5% over contemporary SOTA methods on three benchmark protein sequence datasets. Moreover, it is substantially lighter and attain these results with (0.50-0.70 times) fewer parameters than the SOTA methods.

Proton-Coupled Electron Transfer Mechanisms for CO2 Reduction to Methanol Catalyzed by Surface-Immobilized Cobalt Phthalocyanine.

Immobilized cobalt phthalocyanine (CoPc) is a highly promising architecture for the six-proton, six-electron reduction of CO2 to methanol. This electroreduction process relies on proton-coupled electron transfer (PCET) reactions that can occur by sequential or concerted mechanisms. Immobilization on a conductive support such as carbon nanotubes or graphitic flakes can fundamentally alter the PCET mechanisms. We use density functional theory (DFT) calculations of CoPc adsorbed on an explicit graphitic surface model to investigate intermediates in the electroreduction of CO2 to methanol. Our calculations show that the alignment of the CoPc and graphitic electronic states influences the reductive chemistry. These calculations also distinguish between charging the graphitic surface and reducing the CoPc and adsorbed intermediates as electrons are added to the system. This analysis allows us to identify the chemical transformations that are likely to be concerted PCET, defined for these systems as the mechanism in which protonation of a CO2 reduction intermediate is accompanied by electron abstraction from the graphitic surface to the adsorbate without thermodynamically stable intermediates. This work establishes a mechanistic pathway for methanol production that is consistent with experimental observations and provides fundamental insight into how immobilization of the CoPc impacts its CO2 reduction chemistry.

Making Arrangements: A Sketch of a ‘Big Package’

ABSTRACT This article examines the making of arrangements for future joint activities as a “big package” of talk in interaction. Arrangement-making is here characterized as having a segmented structure, a series of sequences in which the details of time, place, and manner are addressed, often discretely, in sequences that are boundaried off from one another and variable in their ordering. The analysis also addresses the launching of the package and the varying ways it can emerge out of a prior remote proposal sequence or an already presumed activity, also, some of the contingencies impacting the body of the arrangement, and the sequential mechanisms through which segments and the overall package are closed. It is suggested that the segmented “sequence of sequences” structure may apply to the analysis of other big packages. Data are in English.

Unraveling the Photodissociation Branching and Pathways of Methane at 118 nm by Imaging the CH3, CH2, and CH Fragments.

Under irradiation of a vacuum ultraviolet (VUV) photon, methane dissociates and yields multiple fragments. This photochemical behavior is not only of fundamental importance but also with wide-ranging implications in several branches of science. Despite that and numerous previous investigations, the product channel branching is still under debate, and the underlying dissociation mechanisms remain elusive. In this study, the photofragment imaging technique was exploited for the first time to map out the momentum and anisotropy parameter distributions of the CH3, CH2, and CH fragments at the 118 nm photolysis wavelength (10.48 eV photon energy). In conjunction with previously reported results of the H atom fragment at 121.6 nm (10.2 eV), a complete set of product channel branching in both two-body and three-body fragmentations is accurately determined. We concluded that extensive nonadiabatic transitions partake in the processes with two-body fragmentations accounting for more than 90% of overall photodissociation, for which the channel branching values for CH2 + H2 and CH3 + H are about 0.66 and 0.25, respectively. Careful kinematic analysis enables us to untangle the intertwined triple fragmentations into the CH2(X̃ 3B1 and ã 1A1) + H + H and CH(X2Π) + H + H2 channels and to evidence their underlying sequential (or stepwise) mechanisms. With the aid of electronic correlation and prior theoretical calculations of the potential energy surfaces, the most probable or dominant dissociation pathways are elucidated. Comparisons with fragmentary reports in the literature on various photochemical aspects are also documented, and discrepancies are clarified. This comprehensive study benchmarks the VUV photochemistry of methane and advances our understanding of this important process.

Investigation of the synergistic photocatalytic activity of a ternary ZrTiO4/TiO2/Mn3O4 (ZTM) nanocomposite in a typical water treatment process

Because of the exploration of the increase in the aqueous environment caused by various organic pollutants, an urgent need is sensed to develop novel photocatalysts with high efficiency. Here, ZrTiO4/TiO2/Mn3O4 (ZTM) was in-situ synthesized and relatively well-characterized by XRD, FTIR, UV–Vis DRS, EIS, TG/DTG, TPD, SEM, TEM, and BET characterization techniques. Diffraction data showed the crystallite sizes of about 22 and 13 nm obtained by the Scherrer and Williamson-Hall formulas, respectively, with a d-spacing of about 0.21 nm. Then, the composite catalyst was applied in the photodegradation of Metoclopramide (MCP) and showed a boosted photocatalytic activity concerning ZrTiO4, TiO2, and Mn3O4 alone. The effective surface area of 105, 54, and 93 m2/g were obtained in BET analysis of ZTM, Mn3O4, and TiO2. ZTM, Mn3O4, TiO2, and ZrTiO4 band gap energies were obtained via the DRS spectra as 2.22, 1.88, 3.05, and 3.31 eV The scavenging agent's study confirmed that hydroxyl radical and photoinduced electrons have higher roles in the MCP photodegradation process, accordingly confirmed by a sequential direct Z-scheme charge transfer mechanism in the composite.

Optimistic sequential multi-agent reinforcement learning with motivational communication

Centralized Training with Decentralized Execution (CTDE) is a prevalent paradigm in the field of fully cooperative Multi-Agent Reinforcement Learning (MARL). Existing algorithms often encounter two major problems: independent strategies tend to underestimate the potential value of actions, leading to the convergence on sub-optimal Nash Equilibria (NE); some communication paradigms introduce added complexity to the learning process, complicating the focus on the essential elements of the messages. To address these challenges, we propose a novel method called Optimistic Sequential Soft Actor Critic with Motivational Communication (OSSMC). The key idea of OSSMC is to utilize a greedy-driven approach to explore the potential value of individual policies, named optimistic Q-values, which serve as an upper bound for the Q-value of the current policy. We then integrate a sequential update mechanism with optimistic Q-value for agents, aiming to ensure monotonic improvement in the joint policy optimization process. Moreover, we establish motivational communication modules for each agent to disseminate motivational messages to promote cooperative behaviors. Finally, we employ a value regularization strategy from the Soft Actor Critic (SAC) method to maximize entropy and improve exploration capabilities. The performance of OSSMC was rigorously evaluated against a series of challenging benchmark sets. Empirical results demonstrate that OSSMC not only surpasses current baseline algorithms but also exhibits a more rapid convergence rate.

How does citizen-centric co-production lead to value co-creation? A perspective of interactive governance

ABSTRACT This study contributes to co-production research by enhancing the less-focused citizen-centric perspective, traditionally overshadowed by a government-centric view that prioritizes tangible outcomes, while the mechanisms that lead to intangible results like value co-creation are less focused. Through a detailed analysis of Shanghai’s community service regeneration projects, this study elucidates the structured sequential mechanisms of citizen-centric co-production, stemming from the institutional and relational incentives that drive co-production to value co-creation. This research enriches the theoretical discourse on co-production and offers practical insights for citizen-centric public service delivery, highlighting the importance of the interplay of institutional and relational dynamics in value co-creation.

Human mitochondrial carriers of the SLC25 family function as monomers exchanging substrates with a ping-pong kinetic mechanism

Members of the SLC25 mitochondrial carrier family link cytosolic and mitochondrial metabolism and support cellular maintenance and growth by transporting compounds across the mitochondrial inner membrane. Their monomeric or dimeric state and kinetic mechanism have been a matter of long-standing debate. It is believed by some that they exist as homodimers and transport substrates with a sequential kinetic mechanism, forming a ternary complex where both exchanged substrates are bound simultaneously. Some studies, in contrast, have provided evidence indicating that the mitochondrial ADP/ATP carrier (SLC25A4) functions as a monomer, has a single substrate binding site, and operates with a ping-pong kinetic mechanism, whereby ADP is imported before ATP is exported. Here we reanalyze the oligomeric state and kinetic properties of the human mitochondrial citrate carrier (SLC25A1), dicarboxylate carrier (SLC25A10), oxoglutarate carrier (SLC25A11), and aspartate/glutamate carrier (SLC25A13), all previously reported to be dimers with a sequential kinetic mechanism. We demonstrate that they are monomers, except for dimeric SLC25A13, and operate with a ping-pong kinetic mechanism in which the substrate import and export steps occur consecutively. These observations are consistent with a common transport mechanism, based on a functional monomer, in which a single central substrate-binding site is alternately accessible.

Comprehensive Study of Artificial Light-Harvesting Systems with a Multi-Step Sequential Energy Transfer Mechanism.

Artificial light-harvesting systems (LHSs) with a multi-step sequential energy transfer mechanism significantly enhance light energy utilization. Nonetheless, most of these systems exhibit an overall energy transfer efficiency below 80%. Moreover, due to challenges in molecularly aligning multiple donor/acceptor chromophores, systems featuring ≥3-step sequential energy transfer are rarely reported. Here, a series of artificial LHSs is introduced featuring up to 4-step energy transfer mechanism, constructed using a cyclic peptide-based supramolecular scaffold. These LHSs showed remarkably high energy transfer efficiencies (≥90%) and satisfactory fluorescence quantum yields (ranging from 17.6% to 58.4%). Furthermore, the structural robustness of the supramolecular scaffold enables a comprehensive study of these systems, elucidating the associated energy transfer pathways, and identifying additional energy transfer processes beyond the targeted sequential energy transfer. Overall, this comprehensive investigation not only enhances the understanding of these LHSs, but also underscores the versatility of cyclic peptide-based supramolecular scaffolds in advancing energy harvesting technologies.

Five-fold twin formation in face-centered cubic metals under impact loading

Five-fold twins (FFTs) are unique microstructural features in face-centered cubic (FCC) metals that significantly influence their mechanical strength. This study examines FFT formation in Cu, Au, and Ni under high-strain rate conditions induced by cold spray and compression shock impact loading, using atomistic simulations. We reveal a sequential twinning mechanism for FFT formation under impact loading, where the specific growth path varies, leading to inherently unstable FFTs that decompose post-formation. Interestingly, impact velocity had minimal influence, while elevated temperatures suppressed FFT formation, suggesting a dependency on the twinning mechanism. The stacking fault energy of the FCC metal greatly affected FFT propensity; metals with lower stacking fault energy (e.g., Au) readily formed FFTs, while those with higher stacking fault energy (e.g., Ni) encountered greater difficulty. These findings provide new insights into the behavior of FFTs under dynamic loading conditions, with implications for the design of high-strength nanocrystalline materials.

Predicting performance of students by optimizing tree components of random forest using genetic algorithm

Prediction of student academic performance is still a problem because of the limitations of the existing methods specifically low generalizability and lack of interpretability. This study suggests a new approach that deals with the current problems and provides more reliable predictions. The proposed approach combines the information gain (IG) and Laplacian score (LS) for feature selection. In this feature selection scheme, combination of IG and LS is used for ranking features and then, Sequential Forward Selection mechanism is used for determining the most relevant indicators. Also, combination of random forest algorithm with a genetic algorithm for is introduced for multi-class classification. This approach strives to attain more accuracy and reliability than current techniques. The case study shows the proposed strategy can predict performance of students with average accuracy of 93.11 % which shows a minimum improvement of 2.25 % compared to the baseline methods. The findings were further confirmed by the analysis of different evaluation metrics (Accuracy, Precision, Recall, F-Measure) to prove the efficiency of the proposed mechanism.

Deep Fuzzy Min-Max Neural Network: Analysis and Design.

Fuzzy min-max neural network (FMNN) is one kind of three-layer models based on hyperboxes that are constructed in a sequential way. Such a sequential mechanism inevitably leads to the input order and overlap region problem. In this study, we propose a deep FMNN (DFMNN) based on initialization and optimization operation to overcome these limitations. Initialization operation that can solve the input order problem is to design hyperboxes in a simultaneous way, and side parameters have been proposed to control the size of hyperboxes. Optimization operation that can eliminate overlap region problem is realized by means of deep layers, where the number of layers is immediately determined when the overlap among hyperboxes is eliminated. In the optimization process, each layer consists of three sections, namely, the partition section, combination section, and union section. The partition section aims to divide the hyperboxes into a nonoverlapping hyperbox set and an overlapping hyperbox set. The combination section eliminates the overlap problem of overlapping hyperbox set. The union section obtains the optimized hyperbox set in the current layer. DFMNN is evaluated based on a series of benchmark datasets. A comparative analysis illustrates that the proposed DFMNN model outperforms several models previously reported in the literature.