Common Structure Research Articles

Construction of diverse hollow MFI zeolites through regulating the micropore filling agents

Constructing hollow structure into microporous zeolites can improve the accessibility of acid sites located at the inner part and the diffusion property. Hence, the development of an efficient synthesis strategy to acquire zeolites with tunable hollow structures and acidity has attracted much attention. In this work, an innovative tandem synthesis route was proposed to prepare MFI zeolites with diverse hollow structure while maintaining solid yields exceeding 90 %. The substitution of ethanol molecules, which previously occupied the micropores, with tetrapropylammonium cations was proved to be the key factor to construct hollow structure. And a crystallization-driven particle dissolution mechanism was proposed. The dimension of the hollow cavity, particle size, and Si/Al ratio can be flexibly regulated. Interestingly, hollow MFI samples featuring the common cavity structure, “eye-like” cavity structure, or double-cavity structure can be directly synthesized by controlling the dissolution of core parts. In the 1-butene catalytic cracking reactions, a much higher conversion of 67.2 % was acquired over hollow ZSM-5 compared with that over conventional ZSM-5 (35.8 %) after 64 h of reaction. This improvement can be attributed to the eightfold increase of diffusivity in hollow ZSM-5. This facile and efficient synthesis method endows accurate regulation of the hollow structure, which is meaningful for both fundamental research and industrial applications of hollow zeolites.

Antifungal drug resistance in Candida: a special emphasis on amphotericin B.

Invasive fungal infections in humans caused by several Candida species, increased considerably in immunocompromised or critically ill patients, resulting in substantial morbidity and mortality. Candida albicans is the most prevalent species, although the frequency of these organisms varies greatly according to geographic region. Infections with C. albicans and non-albicans Candida species have become more common, especially in the past 20 years, as a result of aging, immunosuppressive medication use, endocrine disorders, malnourishment, extended use of medical equipment, and an increase in immunogenic diseases. Despite C. albicans being the species most frequently associated with human infections, C. glabrata, C. parapsilosis, C. tropicalis, and C. krusei also have been identified. Several antifungal drugs with different modes of action are approved for use in clinical settings to treat fungal infections. However, due to the common eukaryotic structure of humans and fungi, only a limited number of antifungal drugs are available for therapeutic use. Furthermore, drug resistance in Candida species has emerged as a result of the growing use of currently available antifungal drugs against fungal infections. Amphotericin B (AmB), a polyene class of antifungal drugs, is mainly used for the treatment of serious systemic fungal infections. AmB interacts with fungal plasma membrane ergosterol, triggering cellular ion leakage via pore formation, or extracting the ergosterol from the plasma membrane inducing cellular death. AmB resistance is primarily caused by changes in the content or structure of ergosterol. This review summarizes the antifungal drug resistance exhibited by Candida species, with a special focus on AmB.

Final design analysis of the optical hinge of the wide angle viewing system for ITER equatorial port 12

The ITER Visible/Infrared Wide Angle Viewing System (WAVS) is a machine protection optical diagnostic that is being developed for ITER. The first two ex-vessel components of WAVS are the Optical Hinge (OH) and Optical Relay Unit (ORU) and both share a common support structure. Equatorial Port 12 (EP12) WAVS should be operational for the first plasma and its facing its Final Design Review (FDR). It is being developed by the Consortium constituted by CEA, CIEMAT, INTA and Bertin Technologies through a Grant Agreement financed by F4E.To compensate vacuum vessel vertical displacements respect to the building during operation, an Optical Hinge (OH) will be placed at the beginning of the ex-vessel area for each Line of Sight (LoS). Each OH is formed by a pair of mirrors one of which is moveable using a piezoactuator. The OHs are attached to the common support which is, in turn, fixed to the Interspace Support Structure.To assure the optical performance, the common support structure has to withstand operational loads with low deviation and tilt. In addition, the structural integrity of the common structure has to be maintained in case of seismic or accidental loads as it is classified as a Safety Relevant component.This work exposes the analyses performed to face the FDR of ex-vessel WAVS in EP12. It comprises the structural analyses performed by CIEMAT for the OHORU common support structure to assure its integrity according to RCC-MR Code and the detailed model of one OH LoS to check also the diagnostic optical requirements.

Machine Learning to Predict Enzyme-Substrate Interactions in Elucidation of Synthesis Pathways: A Review.

Enzyme-substrate interactions play a fundamental role in elucidating synthesis pathways and synthetic biology, as they allow for the understanding of important aspects of a reaction. Establishing the interaction experimentally is a slow and costly process, which is why this problem has been addressed using computational methods such as molecular dynamics, molecular docking, and Monte Carlo simulations. Nevertheless, this type of method tends to be computationally slow when dealing with a large search space. Therefore, in recent years, methods based on artificial intelligence, such as support vector machines, neural networks, or decision trees, have been implemented, significantly reducing the computing time and covering vast search spaces. These methods significantly reduce the computation time and cover broad search spaces, rapidly reducing the number of interacting candidates, as they allow repetitive processes to be automated and patterns to be extracted, are adaptable, and have the capacity to handle large amounts of data. This article analyzes these artificial intelligence-based approaches, presenting their common structure, advantages, disadvantages, limitations, challenges, and future perspectives.

Tree families and physical structureacross an elevational gradient in a Southern AndeanCloud forest in Ecuador

Because the elevational gradient of the Andes is a fundamental correlate of the structure, function, and dynamics of its Cloud forest, I sampled treespecies, families and physical structure across an 1100 m elevational gradient – at 100 m intervals from 1900 m to 2800 m – in an Andean Cloud forest close to Loja in Southern Ecuador. I found (1) the species Clusia sp., Nectandra membrenacea and Miconia puncta, andthe families Clusiaceae, Lauraceae, Melastomataceae,and Rubiaceae, were most common with Annonaceae, Cyatheaceae, Hypericaceae, Malvaceae and Solanaceae least common and (2) skewed unimodal curves best fit the species and symmetric unimodal (Gaussian) curves best fit the families.I also found (1) totalstem density increased with elevation (772/ha – 984/ha) but not monotonically with the smallest stem size always the most common and (2) linear curves best fit physical structure. Iconclude that while both tree species and tree families were distributed individualistically across this Andean elevational gradient, with two families showing a peak at mid-elevations,stem density increased with elevation as stem sizedecreasedwhich lead to several parameters (e.g., basal area, above-ground biomass) being unchanged while others (e.g., richness, diversity) were always low.

Oriented Growth of Parallel‐Standing Bimetallic Nanosheet Arrays for Enhanced Charge Transfer

AbstractFabricating highly oriented 2D nanosheet arrays is crucial for boosting their performance in electronics, catalysis, optics, and energy conversion, while it remains a challenge due to the high surface energy often leads to random aggregation or interlaced structure. In this study, it is found that the exposed facet of Fe2O3 can greatly influence the interface growth arrangement of bimetallic hydroxide (NiCo(OH)2) nanosheets. The NiCo(OH)2 nanosheets tend to parallel‐standing on the cubic and spindle Fe2O3 while random‐ and interlaced‐standing on the Fe2O3 hexagonal nanoplates and microspheres. The theoretical simulation further indicates the orientation of deposited hydroxide sheets is decided explicitly by the interfacial lattice match/mismatch. Compared to the common interlaced structure of nanosheets, the parallel‐standing nanosheet arrays reduce grain boundaries and improve the charge transfer efficiency. As a result, the derived NiCoP cages exhibit a promising oxygen evolution reaction performance with an overpotential of 255 mV at 10 mA cm−2, and the maximum current density of 400 mA cm−2 with the overpotential of 319 mV.

Current Status of Indole-Derived Marine Natural Products: Synthetic Approaches and Therapeutic Applications.

Indole is a versatile pharmacophore widely distributed in bioactive natural products. This privileged scaffold has been found in a variety of molecules isolated from marine organisms such as algae and sponges. Among these, indole alkaloids represent one of the biggest, most promising family of compounds, having shown a wide range of pharmacological properties including anti-inflammatory, antiviral, and anticancer activities. The aim of this review is to show the current scenario of marine indole alkaloid derivatives, covering not only the most common chemical structures but also their promising therapeutic applications as well as the new general synthetic routes developed during the last years.

Research on the Construction Path of IBM’s Learning Team Enterprise

The foundation of a company's success lies in its internal management and team building. With the change in times and economic environment, the company also needs to establish different teams and construct different organizational cultures to adapt to the new environment. As an excellent team can bring more benefits to the company, the construction of an organizational culture is particularly important. However, the organizational culture cannot be established to fit the team at once; it should go through a series of attempts and innovations, constantly finding problems, solving problems, and creating new types that are more suitable for the company. In this article, through the case of how IBM built its learning team, we analyze the challenges IBM has faced and how IBM can solve these problems. Case studies can prove the correctness of the theory. This study is based on the path of IBM's team construction, trying to come up with solutions to problems facing the company. Firstly, introduce the current situation and analyze the challenges the company has faced, then give solutions to the problems in specific situations. There is no common structure that adapts to all organizations, every company should choose the structure that fits its strategy and environment.

Expanding the Spatial Reach and Human Impacts of Critical Zone Science

AbstractTwo major barriers hinder the holistic understanding of subsurface critical zone (CZ) evolution and its impacts: (a) an inability to measure, define, and share information and (b) a societal structure that inhibits inclusivity and creativity. In contrast to the aboveground portion of the CZ, which is visible and measurable, the bottom boundary is difficult to access and quantify. In the context of these barriers, we aim to expand the spatial reach of the CZ by highlighting existing and effective tools for research as well as the “human reach” of CZ science by expanding who performs such science and who it benefits. We do so by exploring the diversity of vocabularies and techniques used in relevant disciplines, defining terminology, and prioritizing research questions that can be addressed. Specifically, we explore geochemical, geomorphological, geophysical, and ecological measurements and modeling tools to estimate CZ base and thickness. We also outline the importance of and approaches to developing a diverse CZ workforce that looks like and harnesses the creativity of the society it serves, addressing historical legacies of exclusion. Looking forward, we suggest that to grow CZ science, we must broaden the physical spaces studied and their relationships with inhabitants, measure the “deep” CZ and make data accessible, and address the bottlenecks of scaling and data‐model integration. What is needed—and what we have tried to outline—are common and fundamental structures that can be applied anywhere and used by the diversity of researchers involved in investigating and recording CZ processes from a myriad of perspectives.

Does the Operation of a Reservoir Alter Its Interactions with the Atmosphere? Investigating the Role of Advective Fluxes on Energy and Hydrological Balances of the Romaine-2 Subarctic Hydropower Reservoir

Abstract The hydrological processes of cascading hydroelectric reservoirs differ from those of lakes, due to the importance of the inflows and outflows that vary with energy demand. These heat and water advection terms are rarely considered in water body energy balance analyses even though reservoirs are common man-made structures, especially in North America, and thus may affect the regional climate. This study provides a comprehensive assessment of the water and energy balance of the 85-km2 Romaine-2 northern reservoir (50.69°N, 63.24°W), mean depth of 44 m, highlighting the significant contribution of the advection heat fluxes. The water balance input was primarily controlled by upstream (turbine) inflows (77.6%), while lateral (natural) inflows and direct precipitation represented 21.2% and 1.2%, respectively. As for the reservoir’s heat budget, the net advection of heat accounted on average for 25.0% of the input, of which net radiation was the largest component (73.3%). After accounting for the absence of energy balance closure, latent heat and sensible heat fluxes represented 73.2% and 25.1% of total energy output from the reservoir, respectively. The thermal regime was influenced by the hydrological flow conditions, which were regulated by reservoir management. This played a major role in the evolution of the thermocline and the temperature of the epilimnion, and ultimately, in the dynamics of the turbulent heat fluxes. This study suggests that the heat advection term represents a large fraction of the heat budget of northern reservoirs and should be properly considered.

Prodigiosin: An In-depth Exploration of a Bioactive Compound from Serratia sp.

Background:: The rising interest in natural pigments as alternatives is a result of the expanding usage of synthetic colorants and the negative consequences that go along with them. Noble natural pigments with higher stability and productivity are becoming popular in the food industry, and their diverse biological characteristics make them valuable for pharmaceutical applications. Microbes, especially gram-negative and positive bacteria, are considered attractive sources for replacing synthetic dyes. Prodigiosin, a tripyrrole red pigment produced as secondary metabolites by these bacteria, exhibits unusual properties and has potential as an effective proapoptotic agent against cancer and multi-drug resistant cells. Objective:: This review aims to highlight the characteristics of prodigiosin and explore its potential applications as a therapeutic drug. Results:: The review investigates the biosynthetic cluster genes of prodigiosin identified using the EZ-Tn5 transposon approach in different bacteria, including the pig gene cluster in Serratia sp., red gene cluster in S. coelicolor, and hap gene cluster in Hahella chejuensis. It is also described compound nature for producing host survival physiology. Prodigiosin has a common pyrrolyl Promethean structure and is a member of the tripyrrole family. Numerous tri-pyrrole derivatives have been used in antibiotics and have demonstrated promise as pro-apoptotic agents against cancer and drug-resistant cells. Conclusion:: Prodigiosin is an intriguing subject for investigating biosynthesis and exploitation through biotechnological methods due to its distinctive properties and potential as a medicinal medication. Future investigation and bioengineering on producing strains may synthesize functional derivatives with diverse applications.

Implications of social network structures on socially influenced decision-making

Individuals often make decisions in a social environment where social influence can impact on people’s decision-making domains such as online purchasing, political voting and voluntary vaccine uptake. Social influence can be recognised as the intentional or unintentional change in an individual’s belief, perception, or behaviours caused by an information diffusion process embedded in a social network. However, there is limited research on how this diffusion process is shaped by the topology or structure of the social network. This work provides an exploratory and systematic analysis of how decision-making outcomes in a population can be affected by both the structure of the social network and the starting node of where new information starts to diffuse. Simulation results considering three common network structures highlight how social networks with clear community structures lead to a larger absolute impact on decision-making outcomes and networks where the social connections follow a preferential attachment rule show the largest relative impact than the others. The results also suggest scenarios in which introducing new pieces of information to the social network can facilitate the information diffusion process and produce a more significant impact in terms of the overall population decision-making process.

Structured discriminative Gaussian graph learning for multimode process monitoring

AbstractAiming at the actual industrial process background that different modes share the same system configurations and control structure, this article proposes a novel structured discriminant Gaussian graph learning for multimode process monitoring. The proposed method considers not only the sparsity of graph model but also the measurement of data variation based on a mismatched graph and the common node support between different graphical structures. The objective function involves two sets of regularization terms: the trace terms for mismatched measurements and the ‐norm imposed on the union of decomposed graph matrices. Due to the introduced mismatched trace terms, the cost of matching the data points and graph models that have inconsistent class labels can be expanded, which brings more discrimination for the graph‐based mode identification. While the common structure extracted by the ‐norm forces the estimated graph models to have structural similarities, thus alleviating the negative influence caused by graph discrimination. Once a relatively accurate and discriminative reference graph model is obtained, the downstream test graph learning and analysis can be conducted online by employing the moving window techniques. By comparing the matched and mismatched graph‐based measurements, the process mode can be identified correctly and stably. To grasp the abnormal process changes, the ‐norm for the row sparsity is again applied to the graph difference matrices, the sensitive monitoring statistics and the fault isolation results can be obtained effectively. All the optimization problems in this paper can be solved using the alternating direction multiplier (ADMM) algorithm. The effectiveness of our proposed approach is illustrated by the application to a real blast furnace iron‐making production process.

Identification and Characterization of Synaptic Vesicle Membrane Protein VAT-1 Homolog as a New Catechin-Binding Protein.

(-)-Epigallocatechin-3-gallate (EGCg), a major constituent of green tea extract, is well-known to exhibit many beneficial actions for human health by interacting with numerous proteins. In this study we identified synaptic vesicle membrane protein VAT-1 homolog (VAT1) as a novel EGCg-binding protein in human neuroglioma cell extracts using a magnetic pull-down assay and LC-tandem mass spectrometry. We prepared recombinant human VAT1 and analyzed its direct binding to EGCg and its alkylated derivatives using surface plasmon resonance. For EGCg and the derivative NUP-15, we measured an association constant of 0.02-0.85 ×103 M-1s-1 and a dissociation constant of nearly 8 × 10-4 s-1. The affinity Km(affinity) of their binding to VAT1 was in the 10-20 µM range and comparable with that of other EGCg-binding proteins reported previously. Based on the common structure of the compounds, VAT1 appeared to recognize a catechol or pyrogallol moiety around the B-, C- and G-rings of EGCg. Next, we examined whether VAT1 mediates the effects of EGCg and NUP-15 on expression of neprilysin (NEP). Treatments of mock cells with these compounds upregulated NEP, as observed previously, whereas no effect was observed in the VAT1-overexpressing cells, indicating that VAT1 prevented the effects of EGCg or NUP-15 by binding to and inactivating them in the cells overexpressing VAT1. Further investigation is required to determine the biological significance of the VAT1-EGCg interaction.

An extension of PARAFAC to analyze multi-group three-way data

This paper introduces a novel methodology for analyzing three-way array data with a multi-group structure. Three-way arrays are commonly observed in various domains, including image analysis, chemometrics, and real-world applications. In this paper, we use a practical case study of process modeling in additive manufacturing, where batches are structured according to multiple groups. Vast volumes of data for multiple variables and process stages are recorded by sensors installed on the production line for each batch. For these three-way arrays, the link between the final product and the observations creates a grouping structure in the observations. This grouping may hamper gaining insight into the process if only some of the groups dominate the controlled variability of the products. In this study, we develop an extension of the PARAFAC model that takes into account the grouping structure of three-way data sets. With this extension, it is possible to estimate a model that is representative of all the groups simultaneously by finding their common structure. The proposed model has been applied to three simulation data sets and a real manufacturing case study. The capability to find the common structure of the groups is compared to PARAFAC and the insights into the importance of variables delivered by the models are discussed.

Web Mining of Online Resources for German Labor Market Research and Education: Finding the Ground Truth?

The labor market is highly dependent on vocational and academic education, training, retraining, and further education in order to master challenges such as advancing digitalization and sustainability. Further training is a key factor in ensuring a qualified workforce, the employability of all employees, and, thus, national competitiveness and innovation. In the contribution at hand, we explore an innovative way to derive knowledge about learning pathways by connecting the dots from different data sources of the German labor market. In particular, we focus on the web mining of online resources for German labor market research and education, such as online advertisements, information portals, and official government websites. A key question for working with different data sources is how to find the ground truth and common data structures that can be used to make the data interoperable. We discuss how to classify and summarize web data from different platforms and which methods can be used for extracting data, entities and relationships from online resources on the German labor market to build a network of educational pathways. Our proposed solution is based on the classification of occupations (KldB) and related document codes (DKZ), and combines natural language processing and knowledge graph technologies. Our research provides the foundation for further investigation into educational pathways and linked data for labor market research. While our work focuses on German data, it is also useful for other German-speaking countries and could easily be extended to other languages such as English.

Image2InChI: Automated Molecular Optical Image Recognition.

The accurate identification and analysis of chemical structures in molecular images are prerequisites of artificial intelligence for drug discovery. It is important to efficiently and automatically convert molecular images into machine-readable representations. Therefore, in this paper, we propose an automated molecular optical image recognition model based on deep learning, called Image2InChI. Additionally, the proposed Image2InChI introduces a novel feature fusion network with attention to integrate image patch and InChI prediction. The improved SwinTransformer as an encoder and the Transformer Decoder as a decoder with patch embedding are applied to predict the image features for the corresponding InChI. The experimental results showed that the Image2InChI model achieves an accuracy of InChI (InChI acc) of 99.8%, a Morgan FP of 94.1%, an accuracy of maximum common structures (MCS acc) of 94.8%, and an accuracy of longest common subsequence (LCS acc) of 96.2%. The experiments demonstrated that the proposed Image2InChI model improves the accuracy and efficiency of molecular image recognition and provided a valuable reference about optical chemical structure recognition for InChI.

Material basis and core chemical structure of Dendrobium officinale polysaccharides against colitis-associated cancer based on anti-inflammatory activity

It has been claimed that Dendrobium officinale polysaccharides (PSs) can degrade into oligosaccharide and then transform into short-chain fatty acids in the intestine after oral administration, and play an anti-colitis-associated cancer (CAC) effect by inhibiting intestinal inflammation. However, the material basis and core chemical structure underlying the anti-colon cancer properties of PSs have not yet been elucidated. In this study, PSs were degraded into enzymatic oligosaccharides (OSs) using β-mannanase. The results of in vivo experiments revealed that PSs and OSs administered by gastric lavage had similar antitumor effects in CAC mice. OS-1 (Oligosaccharide compounds 1) and OS-2 (Oligosaccharide compounds 2) were further purified and characterized from OSs, and it was found that OS-1, OS-2, OSs, and PSs had similar and consistent anti-inflammatory activities in vitro. Chemical structure comparison and evaluation revealed that the chemical structure of β-D-Manp-(1 → 4)-β-D-Glcp corresponding to OS-1 was the least common PS structure with anti-colitic activity. Therefore, our findings suggest that OSs are the material basis for PSs to exert anti-CAC activity and that the chemical structure of β-D-Manp-(1 → 4)-β-D-Glcp corresponding to OS-1 is the core chemical structure of PSs against CAC.

Estimation of multiple networks with common structures in heterogeneous subgroups

Network estimation has been a critical component of high-dimensional data analysis and can provide an understanding of the underlying complex dependence structures. Among the existing studies, Gaussian graphical models have been highly popular. However, they still have limitations due to the homogeneous distribution assumption and the fact that they are only applicable to small-scale data. For example, cancers have various levels of unknown heterogeneity, and biological networks, which include thousands of molecular components, often differ across subgroups while also sharing some commonalities. In this article, we propose a new joint estimation approach for multiple networks with unknown sample heterogeneity, by decomposing the Gaussian graphical model (GGM) into a collection of sparse regression problems. A reparameterization technique and a composite minimax concave penalty are introduced to effectively accommodate the specific and common information across the networks of multiple subgroups, making the proposed estimator significantly advancing from the existing heterogeneity network analysis based on the regularized likelihood of GGM directly and enjoying scale-invariant, tuning-insensitive, and optimization convexity properties. The proposed analysis can be effectively realized using parallel computing. The estimation and selection consistency properties are rigorously established. The proposed approach allows the theoretical studies to focus on independent network estimation only and has the significant advantage of being both theoretically and computationally applicable to large-scale data. Extensive numerical experiments with simulated data and the TCGA breast cancer data demonstrate the prominent performance of the proposed approach in both subgroup and network identifications.

Conformational Dynamics of Lipoxygenases and Their Interaction with Biological Membranes.

Lipoxygenases (LOXs) are a family of enzymes that includes different fatty acid oxygenases with a common tridimensional structure. The main functions of LOXs are the production of signaling compounds and the structural modifications of biological membranes. These features of LOXs, their widespread presence in all living organisms, and their involvement in human diseases have attracted the attention of the scientific community over the last decades, leading to several studies mainly focused on understanding their catalytic mechanism and designing effective inhibitors. The aim of this review is to discuss the state-of-the-art of a different, much less explored aspect of LOXs, that is, their interaction with lipid bilayers. To this end, the general architecture of six relevant LOXs (namely human 5-, 12-, and 15-LOX, rabbit 12/15-LOX, coral 8-LOX, and soybean 15-LOX), with different specificity towards the fatty acid substrates, is analyzed through the available crystallographic models. Then, their putative interface with a model membrane is examined in the frame of the conformational flexibility of LOXs, that is due to their peculiar tertiary structure. Finally, the possible future developments that emerge from the available data are discussed.