Detailed Structure Research Articles

Addressing the Activity and Selectivity Challenges for CO2 Reduction via Transition‐Metal‐Free Homo‐ and Hetero‐Biatomic Catalysts Embedded in Two‐Dimensional Networks

AbstractDesigning low‐cost and stable electrocatalysts with enhanced activity and selectivity for CO2 valorization to useful chemicals and fuels faces enormous challenges to address the pressing climate change concerns. Synergistic interactions between the atoms in double atom catalysts has emerged as a promising tool to tune and boost CO2 activation and reduction. In this work, we systematically designed highly stable transition‐metal‐free homo‐ and hetero‐biatomic catalysts based on Al, Be, B and Si supported on TCNQ monolayer for CO2 reduction to C1 products using dispersion corrected periodic density functional theory calculations. Our findings reveal that transition‐metal‐free homo‐and hetero‐biatomic TCNQ catalysts can effectively capture and activate the CO2 molecule with binding energies ranging from 0.09 to 2.35 eV. Extensive free energy calculations to screen the favourable reaction pathways to different C1 products (CO, HCOOH, CH3OH and CH4) demonstrate that Al2‐TCNQ, AlBe‐TCNQ and BeSi‐TCNQ stand out as potential candidates for catalyzing CO2RR to methanol in a selective manner, suppressing the competitive HER simultaneously. Remarkably, BeSi‐TCNQ shows the best catalytic activity towards CO2 reduction to methanol at record low limiting potential of −0.29 V with spontaneous desorption of the final product. From the in‐depth examination of the electronic structure details, integrated projected density of states and crystal orbital Hamilton populations are used to understand and rationalize the binding interactions between the adsorbed CO2 molecule and the homo‐ or hetero‐biatomic TCNQ catalysts. Moreover, the ΔG*COOH‐ΔG*CO2 and ΔG*HCOOH‐ΔG* OCHO/*COOH values are found to provide reasonable guidance to evaluate the overall CO2RR performance of homo‐ and hetero‐biatomic TCNQ catalysts, respectively. Therefore, our findings provide significant insights for designing transition metal‐free homo‐ and hetero‐biatomic catalysts with intriguing properties for improving CO2 utilization and conversion.

Scanning electron microscopic investigation on Chicory tribe (Compositae) botanical sources and their antimicrobial potential.

This study discusses the micro-level structural details of Cichorieae pollen sources elucidated by scanning electron microscopy (SEM) and explains their symmetry and morphometry. The in-depth knowledge from the electron ultrastructure of Asteraceae pollen has provided insights into enhanced pollen morphology, and the antimicrobial significance of species under study presents novel avenues for their natural defense mechanisms in the development of antimicrobial agents. In this research, both quantitative and qualitative features of pollen were examined. The pollen grains are prolate-spheroidal and oblate-spheroidal in shape, characterized by a maximum polar diameter of 55.6-61.0 μm and a maximum equatorial distance of 68.3-74.4 μm. SEM reveals various configurations such as echinate perforate-tectate, psilate, and echino-lophate perforate. The Cichorieae species have significant antimicrobial efficacy and are promising sources for the development of novel antimicrobial drugs with potential implications in pharmaceutical and healthcare industries. SEM analysis of Cichorieae pollens has provided remarkable insights into their unique structures, revealing diverse shapes and surface ornamentations, which can be used for accurate Asteraceae species identification. RESEARCH HIGHLIGHTS: SEM provides unique pollen surface structures and patterns of Chicory pollen grains. Chemical composition of Chicory botanical sources provides valuable information on their potential as antimicrobial agents. SEM imaging reveals specialized fenestrate grain structures of taxonomic importance.

Enhancing Knowledge Graph Embedding with Hierarchical Self-Attention and Graph Neural Network Techniques for Drug-Drug Interaction Prediction in Virtual Reality Environments

In biomedicine, the critical task is to decode Drug–Drug Interactions (DDIs) from complex biomedical texts. The scientific community employs Knowledge Graph Embedding (KGE) methods, enhanced with advanced neural network technologies, including capsule networks. However, existing methodologies primarily focus on the structural details of individual entities or relations within Biomedical Knowledge Graphs (BioKGs), overlooking the overall structural context of BioKGs, molecular structures, positional features of drug pairs, and their critical Relational Mapping Properties. To tackle the challenges identified, this study presents HSTrHouse an innovative hierarchical self-attention BioKGs embedding framework. This architecture integrates self-attention mechanisms with advanced neural network technologies, including Convolutional Neural Network (CNN) and Graph Neural Network (GNN), for enhanced computational modeling in biomedical contexts. The model bifurcates the BioKGs into entity and relation layers for structural analysis. It employs self-attention across these layers, utilizing PubMedBERT and CNN for position feature extraction, and a GNN for drug pair molecular structure analysis. Then, we connect the position and molecular structure features to integrate them into the self-attention calculation of entity and relation. After that, the output of the self-attention layer is combined with the connected vectors of the position feature and molecular structure feature to obtain the final representation vector, and finally, to model the Relational Mapping Properties (RMPs), the representation vector is embedded into the complex vector space using Householder projections to obtain the BioKGs model. The paper validates HSTrHouse’s efficacy by comparing it with advanced models on three standard BioKGs for DDIs research.

Cdc48/p97 segregase: Spotlight on DNA-protein crosslinks

The ATP-dependent molecular chaperone Cdc48 (in yeast) and its human counterpart p97 (also known as VCP), are essential for a variety of cellular processes, including the removal of DNA-protein crosslinks (DPCs) from the DNA. Growing evidence demonstrates in the last years that Cdc48/p97 is pivotal in targeting ubiquitinated and SUMOylated substrates on chromatin, thereby supporting the DNA damage response. Along with its cofactors, notably Ufd1-Npl4, Cdc48/p97 has emerged as a central player in the unfolding and processing of DPCs. This review introduces the detailed structure, mechanism and cellular functions of Cdc48/p97 with an emphasis on the current knowledge of DNA-protein crosslink repair pathways across several organisms. The review concludes by discussing the potential therapeutic relevance of targeting p97 in DPC repair.

EARTHQUAKE PREDICTION USING ARTIFICIAL INTELLIGENCE IN THE FERGHANA DEPRESSION (UZBEKISTAN)

Solving the problem of predicting earthquakes faces difficulties of both theoretical and practical nature. The reason is that the occurrence of earthquakes depends on many factors, which give rise to various anomalies that are used as precursors. However, because of the complexity of the earthquake process and the unavailability of much information about the detailed structure of the Earth's crust, a small number of them can accurately indicate future seismic events. The results of the application of machine learning and deep learning give hope for the possibility of obtaining more accurate information about future strong earthquakes if disparate factors are combined. To determine the most important signs of an earthquake and determine the spatial location of strong earthquakes in a specific seismically active territory of Uzbekistan, namely, in the Fergana depression, the Cora 3, Cora 4, random forest algorithms of machine learning and LSTM, ANN architectures of deep learning were implemented.

Structure and conformational variability of the HER2-trastuzumab-pertuzumab complex

Single particle analysis from cryogenic transmission electron microscopy (cryo-EM) is particularly attractive for complexes for which structure prediction remains intractable, such as antibody-antigen complexes. Here we obtain the detailed structure of a particularly difficult complex between human epidermal growth factor receptor 2 (HER2) and the antigen-binding fragments from two distinct therapeutic antibodies binding to distant parts of the flexible HER2, pertuzumab and trastuzumab (HTP). We highlight the strengths and limitations of current data processing software in dealing with various kinds of heterogeneities, particularly continuous conformational heterogeneity, and in describing the motions that can be extracted from our dataset. Our HTP structure provides a more detailed view than the one previously available for this ternary complex. This allowed us to pinpoint a previously overlooked loop in domain IV that may be involved both in binding of trastuzumab and in HER2 dimerization. This finding may contribute to explain the synergistic anticancer effect of the two antibodies. We further propose that the flexibility of the HTP complex, beyond the difficulties it causes for cryo-EM analysis, actually reflects regulation of HER2 signaling and its inhibition by therapeutic antibodies. Notably we obtain our best data with ultra-thin continuous carbon grids, showing that with current cameras their use to alleviate particle misdistribution is compatible with a protein complex of only 162 kDa. Perhaps most importantly, we provide here a dataset for such a smallish protein complex for further development of software accounting for continuous conformational heterogeneity in cryo-EM images.

Effect of Tip Gap Size on the Tip Flow Structure and Turbulence Generation in a Low Reynolds Number Compressor Cascade

Abstract Efficient and compact axial compressors are currently undergoing rapid development for use in microcooling systems and small-scale vehicles. Limited experimental work concentrates on the inner flow field of the compressors working at such low Reynolds numbers (Re∼104). This study examines the vortical structures and the resulting turbulence production in the transitional flow over a C4 compressor blade at a Reynolds number Re of 24,000, with a specific focus on the impact of tip clearance. The particle image velocimetry measurements reveal the tip flow structures in detail, including the tip leakage vortex (TLV) and its induced complex vortical structures. The tip secondary flow at the low Reynolds number can be divided as the tip leakage flow (TLF)/vortex and transitional boundary layer both at the end walls and the blade surfaces. The TLV propagates at the highest spanwise positions and farthest pitchwise positions at the middle tip gap size (τ/C = 3%) for the three tip gap sizes investigated. The tip flow fluctuations decrease from τ/C = 5% to τ/C = 3% and then increase from τ/C = 3% to τ/C = 1%. The spatial distribution, streamwise evolution, and individual Reynolds normal stress components contributing to the turbulent kinetic energy (TKE) are discussed. The primary contributors to the turbulence generation are examined to elucidate the flow mechanism leading to the distinct anisotropic turbulence structure in the tip region with various tip gap sizes.

Argyreia nervosa-driven biosynthesis of Cu-Ag bimetallic nanoparticles from plant leaves extract unveils enhanced antibacterial properties.

Our study specifically explores the biosynthesis of copper-silver bimetallic nanoparticles (Cu-Ag BMNPs) using Argyreia nervosa (AN) plant leaf green extract as a versatile agent for capping, reducing, and stabilizing. This biosynthesis method is characterized by its simplicity and cost-effectiveness, utilizing silver nitrate (AgNO3) and cupric oxide (CuO) as precursor materials. Our comprehensive characterization of the Cu-Ag BMNPs, employing techniques such as X-ray diffraction (XRD), UV-Vis spectrometry, scanning electron microscopy (SEM), Zetasizer, and Fourier transformed infrared spectrometry (FTIR). FTIR analysis reveals biofunctional groups and chemical bands, while SEM and XRD analyses provide morphological and structural details. To evaluate the antimicrobial properties of the Cu-Ag BMNPs, we conducted disc diffusion and minimum inhibitory concentration (MIC) assays against Escherichia coli (E. coli), with results compared to the standard gentamicin antibiotic. It is observed that the 2% and 5% CuO concentrations of AN Cu-Ag BMNPs exhibit substantial antibacterial activity in comparison to AN extract when tested on EPEC. Among these, the Cu-Ag BMNPs at a 2% concentration demonstrate higher antibacterial activity, potentially attributed to the enhanced dispersion of BMNPs facilitated by the lower CuO doping concentration. These two assays showcased the improved antimicrobial activity of Cu-Ag BMNPs, highlighting their synergistic effect, characterized by high MIC values and a broad zone of inhibition in the disc diffusion tests against E. coli. These results emphasize the significant antibacterial potential of the synthesized BMNPs, with a medicinal plant AN leaf extract playing a pivotal role in enhancing antibacterial activity.

Unsupervised low-dose CT denoising using bidirectional contrastive network

Background and Objective:Low-dose computed tomography (LDCT) scans significantly reduce radiation exposure, but introduce higher levels of noise and artifacts that compromise image quality and diagnostic accuracy. Supervised learning methods have proven effective in denoising LDCT images, but are hampered by the need for large, paired datasets, which pose significant challenges in data acquisition. This study aims to develop a robust unsupervised LDCT denoising method that overcomes the reliance on paired LDCT and normal-dose CT (NDCT) samples, paving the way for more accessible and practical denoising techniques. Methods:We propose a novel unsupervised network model, Bidirectional Contrastive Unsupervised Denoising (BCUD), for LDCT denoising. This model innovatively combines a bidirectional network structure with contrastive learning theory to map the precise mutual correspondence between the noisy LDCT image domain and the clean NDCT image domain. Specifically, we employ dual encoders and discriminators for domain-specific data generation, and use unique projection heads for each domain to adaptively learn customized embedded representations. We then align corresponding features across domains within the learned embedding spaces to achieve effective noise reduction. This approach fundamentally improves the model’s ability to match features in latent space, thereby improving noise reduction while preserving fine image detail. Results:Through extensive experimental validation on the AAPM-Mayo public dataset and real-world clinical datasets, the proposed BCUD method demonstrated superior performance. It achieved a peak signal-to-noise ratio (PSNR) of 31.387 dB, a structural similarity index measure (SSIM) of 0.886, an information fidelity criterion (IFC) of 2.305, and a visual information fidelity (VIF) of 0.373. Notably, subjective evaluation by radiologists resulted in a mean score of 4.23, highlighting its advantages over existing methods in terms of clinical applicability. Conclusions:This paper presents an innovative unsupervised LDCT denoising method using a bidirectional contrastive network, which greatly improves clinical applicability by eliminating the need for perfectly matched image pairs. The method sets a new benchmark in unsupervised LDCT image denoising, excelling in noise reduction and preservation of fine structural details.

Application of BIM+GIS Integration Technology in the Construction of Urban Intelligent Water Informatization

Urban intelligent water informatization is the process of managing and operating urban water systems with the use of contemporary information and communication technology in order to increase productivity, security, and sustainability. Establishing the appropriate infrastructure is the initial stage in building an intelligent water system so that management can choose wisely how to manage and operate the water system. Building information modeling (BIM) is a digital depiction of a building's or infrastructure's structural and functional details. In most metropolitan regions, a fast development phase is predicted to cause several water issues, including water resource shortages, floods, poor drainage, and water resource contamination. As a result, a practical approach is required for urban intelligent water information technology with the help of artificial intelligence techniques. Intelligent water systems may be built using Building Information Modeling (BIM) software to increase the system's effectiveness, stability, and durability. By offering a potent tool for storing and analyzing geographical data connected to the water distribution system, a geographic information system (GIS) can play a crucial role in the development of urban intelligent water informatization using ArcGIS mapping Software. The Internet of Things (IoT) system is used to implement intelligent water informatization, hardware implementation using ESP32, and software development using MQTT Broker and Node-Red. As a result, we recommended utilizing BIM+GIS integration technology for building urban intelligent water informatization. The suggested system is evaluated using management efficiency, urban flood control, innovative Construction, intensive management, and computation cost. In this research, the main objective is to identify where there are water leaks broken pipes and early warning system and monitoring system for flash flood disasters in urban areas. The result findings show that the integration of BIM and GIS provides a comprehensive platform for managing complex water informatization infrastructure projects and maximizes efficiency and sustainability.

Functional and structural characterization of an IclR family transcription factor for the development of dicarboxylic acid biosensors.

Prokaryotic transcription factors (TFs) regulate gene expression in response to small molecules, thus representing promising candidates as versatile small molecule-detecting biosensors valuable for synthetic biology applications. The engineering of such biosensors requires thorough in vitro and in vivo characterization of TF ligand response as well as detailed molecular structure information. In this work, we functionally and structurally characterize the Pca regulon regulatory protein (PcaR) transcription factor belonging to the IclR transcription factor family. Here, we present in vitro functional analysis of the ligand profile of PcaR and the construction of genetic circuits for the characterization of PcaR as an in vivo biosensor in the model eukaryote Saccharomyces cerevisiae. We report the crystal structures of PcaR in the apo state and in complex with one of its ligands, succinate, which suggests the mechanism of dicarboxylic acid recognition by this transcription factor. This work contributes key structural and functional insights enabling the engineering of PcaR for dicarboxylic acid biosensors, in addition to providing more insights into the IclR family of regulators.

Enhancing adversarial robustness for deep metric learning via neural discrete adversarial training

Due to the security concerns arising from adversarial vulnerability in deep metric learning models, it is essential to enhance their adversarial robustness for secure neural network software development. Existing defense strategies utilize adversarial triplets to enhance adversarial robustness but sacrifice benign performance. This paper proposes a novel framework for enhancing adversarial robustness and maintaining benign performance by introducing the concept of Neural Discrete Adversarial Training (NDAT) for deep metric learning. NDAT employs VQGAN to transform the adversarial triplets into discrete inputs and then minimizes metric loss function on discrete adversarial triplets. NDAT aligns discrete adversarial examples more closely with clean samples, significantly reducing distribution deviation from their clean counterparts. Moreover, the visual explanations reveal that NDAT maintains consistent attention maps between benign and adversarial triplets and concentrates on structure details and object location perturbations. To demonstrate the effectiveness of our approach, we combine NDAT with popular adversarial methods under various perturbation iterations and intensities. Experiment evaluations on three benchmark databases illustrate that our proposed framework for deep metric learning significantly outperforms state-of-the-art defense approaches in terms of both adversarial robustness and benign performance.

Electronic and magnetic properties of Me-Graphene nanoribbons: A DFT investigation

In the search for semiconducting materials at the nanoscale, theoretical methods have been used to propose a series of nanocarbon sheets featuring a mixture of sp2 and sp3 hybridized carbon atoms, with Me-Graphene as one example. In addition to interesting physical properties shown by these 2D forms, nanoribbon variations of these structures can feature new properties. In this work, we show that the electronic properties of Me-Graphene nanoribbons can be modulated through structural details such as chirality, width, and edge details. Not only the metallic or semiconducting behavior can be switched by choosing selected geometries, but the direct or indirect character of the gap of the semiconducting cases can be triggered by selecting specific chiralities. In particular combinations of chirality and edge structure, we further point out that multiple spin-polarized states emerge, turning these systems into potential materials for spintronics.

The OEIS National Registry: Rationale, Context, and Current Status

Office based interventional procedures continue to increase in number and scope. An overview of the Outpatient Endovascular and Interventional Society (OEIS) National Registry (OEISNR) is described in this article. Currently there is one production module enrolling peripheral artery interventional procedures, and a new cardiac module encompassing both diagnostic catheterization/percutaneous coronary interventions and electrophysiology procedures is undergoing beta testing. A new embolization module with uterine fibroid embolization (UFE) as the initial modality is nearly initial development completion and expected to enter beta in 2024. The OEISNR has over 338 participating physicians and has enrolled over 42,000 cases since inception in 2017 with an extremely high rate of technical success (97%) and very low complication rates (2.15% overall, 1.65% minor complications, 0.53% major complications). More detailed data breakdowns including patient demographics and comorbidities, indications, lesion characteristics, treatment modalities and device utilization, complication details, and data integrity data are contained in this report. Sample analytics, sample dashboards, and structural details are illustrated and included.

3D‐SRDM: 3D super‐resolution of MRI volumes based on diffusion model

AbstractBrain magnetic resonance imaging (MRI) is crucial for diagnosing and understanding neurological disorders, but inherent limitations hinder the visualization of fine details in brain structures. The emergence of super‐resolution techniques, especially deep‐learning methods, has improved imaging quality of MRI, by increasing MRI spatial resolution. At present, deep‐learning algorithms mostly performed super‐resolution on 2D MRI images. However, considering 3D nature of MRI, 3D models are more suitable for brain MRI super‐resolution. To achieve finer brain structural details, this study proposes a 3D brain MRI super‐resolution method based on diffusion model (3D‐SRDM), which is a fast and easily trainable neural network for the generation of high‐resolution brain MRI images. In our 3D‐SRDM model, the self‐attention module in U‐Net is replaced with 3D spatial attention mechanism. The network structure of 3D‐SRDM is optimized to reduce training parameters. Moreover, accelerated sampling from denoising diffusion implicit model is also incorporated to reduce time consumption. By these optimizations, compared with original diffusion model, the proposed model can achieve about 10‐ and 5‐fold speed increase at 4× and 8× super‐resolution of 3D brain MRI volumes, respectively, almost without affecting image quality. Thus, 3D‐SRDM has potential application value in efficiently generating high‐resolution 3D brain MRI images, thus facilitating the doctors' diagnosis.

Structure and ionic conduction enhancement mechanisms at CeO2/SrTiO3 heterointerfaces

Fluorite-perovskite heterointerfaces garner great interest for enhanced ionic conductivity for application in electronic and energy devices. However, the origin of observed enhanced ionic conductivity as well as the details of the atomic structure at these interfaces remain elusive. Here, systematic, multi-stoichiometry computational searches and experimental investigations are performed to obtain stable and exact atomic structures of interfaces between CeO2 and SrTiO3—two archetypes of the corresponding structural families. Local reconstructions take place at the interface because of mismatched lattices. TiO2 terminated SrTiO3 causes a buckled rock salt CeO interface layer to emerge. In contrast, SrO terminated SrTiO3 maintains the fluorite structure at the interface compensated by a partially occupied anion lattice. Moderate enhancement in oxygen diffusion is found along the interface by simulations, yet evidence to support further significant enhancement is lacking. Our findings demonstrate the control of interface termination as an effective pathway to achieve desired device performance.

A two-scale method to include essential behavior of bolted connections in structures including elevated temperatures

A two-scale method is proposed to simulate the essential behavior of bolted connections in structures including elevated temperatures. It is presented, verified, and validated for the structural behavior of two plates, connected by a bolt, under a variety of loads and elevated temperatures. The method consists of a global-scale model that simulates the structure (here the two plates) by volume finite elements, and in which the bolt is modelled by a spring. The spring properties are provided by a small-scale model, in which the bolt is modelled by volume elements, and for which the boundary conditions are retrieved from the global-scale model. To ensure the small-scale model to be as computationally efficient as possible, simplifications are discussed regarding the material model and the modelling of the threads. For the latter, this leads to the experimentally validated application of a non-threaded shank with its stress area. It is shown that a non-linear elastic spring is needed for the bolt in the global-scale model, so the post-peak behavior of the structure can be described efficiently. All types of bolted connection failure as given by design standards are simulated by the two-scale method, which is successfully validated (except for net section failure) by experiments, and verified by a detailed system model, which models the structure in full detail. The sensitivity to the size of the part of the plate used in the small-scale model is also studied. Finally, multi-directional load cases, also for elevated temperatures, are studied with the two-scale method and verified with the detailed system model. As a result, a computationally efficient finite element modelling approach is provided for all possible combined load actions (except for nut thread failure and net section failure) and temperatures. The two-scale method is shown to be insightful, for it contains a functional separation of scales, revealing their relationships, and consequently, local small-scale non-convergence can be handled. Not presented in this paper, but the two-scale method can be used in e.g. computationally expensive two-way coupled fire-structure simulations, where it is beneficial for distributed computing and densely packed bolt configurations with stiff plates, for which a single small-scale model may be representative of several connections.

DF3Net: Dual frequency feature fusion network with hierarchical transformer for image inpainting

In recent years, researchers have made significant strides in computer vision by leveraging transformers, achieving remarkable breakthroughs in low-level vision tasks. The inherent long-range dependency of transformers grants them potent remote modeling capabilities, surpassing those of Convolutional Neural Networks (CNNs) and enabling the extraction of global features and accurate semantic structures. However, it has been observed that single transformer frameworks lack sensitivity to high-frequency information in images, resulting in the generation of blurry reconstructed regions. To address this limitation, this paper proposes a novel two-branch Dual Frequency Feature Fusion Network (DF3Net) for image inpainting based on the hierarchical atrous transformer (HAT). Specifically, the head of the dual-frequency convolution (DFC) module decouples the feature maps into low and high-frequency components. The low-frequency factor goes through the proposed HAT branch, while the high-frequency component is input into the gated convolution module branch, effectively capturing both global structural information and local texture details. Finally, the DFC tail fuses the high and low-frequency features to output the reconstructed image. Moreover, the feature fusion between high and low-frequency branches is performed layer-wise within the network, enabling mutual learning between the two branches interactively, ensuring coherence in image semantic structure and texture details. Experimental evaluations on Places2, Paris StreetView, and CelebA-HQ with different mask ratios demonstrate that the proposed method outperforms state-of-the-art methods in enhancing the structural accuracy of image inpainting. It generates semantically reasonable images with fine texture details.

The three-dimensional structure of DapE from Enterococcus faecium reveals new insights into DapE/ArgE subfamily ligand specificity

DapE is a Zn2+-metallohydrolase recognized as a drug target for bacterial control. It is a homodimer that requires the exchange of interface strands by an induced fit essential for catalysis. Identifying novel anti-DapE agents requires greater structural details. Most of the characterized DapEs are from the Gram-negative group. Here, two high-resolution DapE crystal structures from Enterococcus faecium are presented for the first time with novel aspects. A loosened enzyme intermediate between the open and closed conformations is observed. Substrates may bind to loose state, subsequently it closes, where hydrolysis occurs, and finally, the change to the open state leads to the release of the products. Mutation of His352 suggests a role, along with His194, in the oxyanion stabilization in the mono-metalated Zn2+ isoform, while in the di-metalated isoform the metal center 2 complements it function. An aromatic-π box potentially involved in the interaction of DapE with other proteins, and a peptide flip could determine the specificity in the Gram-positive ArgE/DapE group. Finally, details of two extra-catalytic cavities whose geometry changes depending on the conformational state of the enzyme are presented. These cavities could be a target for developing non-competitive agents that trap the enzyme in an inactive state.

Super-targeting of non-minimal WHEN systems

Energy conservation and emission reduction have become critical issues due to increasing environmental policy pressures and high energy prices. Work and heat exchange network (WHEN) synthesis is a promising approach to enhance the energy efficiency of energy-intensive processes. WHEN considering single-stage compression/expansion can be divided into two classes by structure complexity: minimal structure and non-minimal structure. The synthesis problem of minimal structure has been addressed in our previous work, where the global optimal solution can be guaranteed. In contrast, the compressors/expanders are not at the beginning or end of the streams' paths in the non-minimal structure and therefore the inlet/outlet temperatures are now variables of the problem. Instead of solving this complicated problem directly, we propose a super-targeting model to screen and scope the solution space for primal promising options before detailed structure design which can be designed using regular HEN synthesis methods. The super-target is estimated by regulating the thermodynamic paths of hot and cold streams where the objective function uses classical HEN super-target to evaluate the costs of the heat exchanger network. Benefiting from the unimodal nature of non-minimal WHEN super-targeting problems, we develop two deterministic algorithms: one relies on derivatives and one is derivative-free. Case studies show the efficiency of the proposed algorithm. The resulting TAC can be 23.1% lower than the literature report and the solution of super-targeting problems are proven to be globally optimal. The derivative optimization algorithm has been found to exhibit superior efficiency in solving problems of relatively large scale.