Search

In this work, we introduce a differentiable implementation of the local natural orbital coupled cluster (LNO-CC) method within the automatic differentiation framework of the PySCFAD package. The implementation is comprehensively tuned for enhanced performance, which enables the calculation of first-order static response properties on medium-sized molecular systems using coupled cluster theory with single, double, and perturbative triple excitations [CCSD(T)]. We evaluate the accuracy of our method by benchmarking it against the canonical CCSD(T) reference for nuclear gradients, dipole moments, and geometry optimizations. In addition, we demonstrate the possibility of property calculations for chemically interesting systems through the computation of bond orders and Mössbauer spectroscopy parameters for a [NiFe]-hydrogenase active site model, along with the simulation of infrared spectra via ab initio LNO-CC molecular dynamics for a protonated water hexamer. [ABSTRACT FROM AUTHOR]

While the vibrational spectra of semi-rigid molecules can be computed on approaches relying on the Watson Hamiltonian, floppy molecules or molecular clusters are better described by Hamiltonians, which are capable of dealing with any curvilinear coordinates. It is the kinetic energy operator (KEO) of these Hamiltonians, which render the correlated calculations relying on them rather costly. Novel implementation of vibrational self-consistent field theory and vibrational configuration interaction theory on the basis of the Podolsky Hamiltonian are reported, in which the inverse of the metric tensor, i.e., the G matrix, is represented by an n-mode expansion expressed in terms of polynomials. An analysis of the importance of the individual terms of the KEO with respect to the truncation orders of the n-mode expansion is provided. Benchmark calculations have been performed for the cis-HOPO and methanimine, H2CNH, molecules and are compared to experimental data and to calculations based on the Watson Hamiltonian and the internal coordinate path Hamiltonian. [ABSTRACT FROM AUTHOR]

QM/MM methods have been used to study electronic structure properties and chemical reactivity in complex molecular systems where direct electronic structure calculations are not feasible. In our previous work, we showed that non-polarizable force fields, by design, describe intermolecular interactions through pairwise interactions, overlooking many-body interactions involving three or more particles. In contrast, polarizable force fields account partially for many-body effects through polarization, but still handle van der Waals and permanent electrostatic interactions pairwise. We showed that despite those limitations, polarizable and non-polarizable force fields can reproduce relative cooperativity achieved using density functional theory due to error compensation mechanisms. In this contribution, we assess the performance of QM/MM methods in reproducing these phenomena. Our study highlights the significance of the QM region size and force field choice in QM/MM calculations, emphasizing the importance of parameter validation to obtain accurate interaction energy predictions. [ABSTRACT FROM AUTHOR]

Molecular cluster ions, which are stored in an electromagnetic trap under ultra-high vacuum conditions, undergo blackbody infrared radiative dissociation (BIRD). This process can be simulated with master equation modeling (MEM), predicting temperature-dependent dissociation rate constants, which are very sensitive to the dissociation energy. We have recently introduced a multiple-well approach for master equation modeling, where several low-lying isomers are taken into account. Here, we experimentally measure the BIRD of CO4●–(H2O)1,2 and model the results with a slightly modified multiple-well MEM. In the experiment, we exclusively observe loss of water from CO4●–(H2O), while the BIRD of CO4●–(H2O)2 leads predominantly to loss of carbon dioxide, with water loss occurring to a lesser extent. The MEM of two competing reactions requires empirical scaling factors for infrared intensities and the sum of states of the loose transition states employed in the calculation of unimolecular rate constants so that the simulated branching ratio matches the experiment. The experimentally derived binding energies are ΔH0(CO4●––H2O) = 45 ± 3 kJ/mol, ΔH0(CO4●–(H2O)–H2O) = 41 ± 3 kJ/mol, and ΔH0(CO2–O2●–(H2O)2) = 37 ± 3 kJ/mol. Quantum chemical calculations on the CCSD(T)/aug-cc-pVTZ//CCSD/aug-cc-pVDZ level, corrected for the basis set superposition error, yield binding energies that are 2–5 kJ/mol higher than experiment, within error limits of both experiment and theory. The relative activation energies for the two competing loss channels are as well fully consistent with theory. [ABSTRACT FROM AUTHOR]

Helium nanodroplets ("HNDs") are widely used for forming tailor-made clusters and molecular complexes in a cold, transparent, and weakly interacting matrix. The characterization of embedded species by mass spectrometry is often complicated by the fragmentation and trapping of ions in the HNDs. Here, we systematically study fragment ion mass spectra of HND-aggregated water and oxygen clusters following their ionization by charge transfer ionization ("CTI") and Penning ionization ("PEI"). While the efficiency of PEI of embedded clusters is lower than for CTI by about factor 10, both the mean sizes of detected water clusters and the relative yields of unprotonated cluster ions are significantly larger, making PEI a "soft ionization" scheme. However, the tendency of ions to remain bound to HNDs leads to a reduced detection efficiency for large HNDs containing > 1 0 4 helium atoms. These results are instrumental in determining optimal conditions for mass spectrometry and photoionization spectroscopy of molecular complexes and clusters aggregated in HNDs. [ABSTRACT FROM AUTHOR]

The abundance of extraterrestrial methanol makes the reaction between methanol molecules in a molecular cluster a possible key step in the search for mechanisms for the formation of more complex molecules under the conditions of the interstellar medium as well as circumstellar and planetary atmospheres. The reaction leading to the formation of the dimethyl ether ion from a methanol molecule interacting with a protonated methanol ion via the elimination of a water molecule is a basic mechanism for the formation of complex organic molecules. Here, we experimentally examine such reactions in the gas phase, analyzing the production and reactivity of protonated cluster ions formed by the ionization of a supersonic jet of methanol. Focusing especially on the post-collisional relaxation of the protonated methanol dimer and trimer ions after high-energy single collisions, the results indicate a strong size selectivity favoring the occurrence of this reaction only in the dimer ion. To elucidate this behavior, the velocity distribution of the eliminated water molecule was measured using an event-by-event coincidence analysis. These results are interpreted using quantum chemical calculations of the dissociation pathways. It turns out that in the dimer case, two transition states are able to contribute to this intracluster reaction. In the trimer case, methanol evaporation appears as the most energetically favorable relaxation pathway. [ABSTRACT FROM AUTHOR]

This work reports the development of an algorithm for rapid and efficient evaluation of energy gradients for large molecular clusters employing correlated methods viz. second-order Møller–Plesset perturbation theory (MP2) theory and couple cluster singles and doubles (CCSD). The procedure segregates the estimation of Hartree–Fock (HF) and correlation components. The HF energy and gradients are obtained by performing a full calculation. The correlation energy is approximated as the corresponding two-body interaction energy. Correlation gradients for each monomer are approximated from the respective monomer-centric fragments comprising its immediate neighbours. The programmed algorithm is explored for the geometry optimization of large molecular clusters using the BERNY optimizer as implemented in the Gaussian suite of software. The accuracy and efficacy of the method are critically probed for a variety of large molecular clusters containing up to 3000 basis functions, in particular large water clusters. The CCSD level geometry optimization of molecular clusters containing ∼800 basis functions employing a modest hardware is also reported. [ABSTRACT FROM AUTHOR]

A new approach for obtaining interacting quantum atoms-defined components of binding energy of intermolecular interactions, which bypasses the use of standard six-dimensional integrals and two-particle reduced density matrix (2-RDM) reconstruction, is proposed. To examine this approach, three datasets calculated within the density functional theory framework using the def2-TZVP basis have been explored. The first two, containing 53 weakly bound bimolecular associates and 13 molecular clusters taken from the crystal, were used in protocol refinement, and the third one containing other 20 bimolecular and three cluster systems served as a validation reference. In addition, to verify the performance of the proposed approach on an exact 2-RDM, calculations within the coupled cluster formalism were performed for part of the first set systems using the cc-pVTZ basis set. The process of optimization of the proposed parametric model is considered, and the role of various energy contributions in the formation of non-covalent interactions is discussed with regard to the obtained trends. [ABSTRACT FROM AUTHOR]

Fragmentation methods such as MIM (Molecules-in-Molecules) provide a route to accurately model large systems and have been successful in predicting their structures, energies, and spectroscopic properties. However, their use is often limited to systems at equilibrium due to the inherent complications in the choice of fragments in systems away from equilibrium. Furthermore, the presence of charges resulting from any heterolytic bond breaking may increase the fragmentation error. We have previously suggested EE-MIM (Electrostatically Embedded Molecules-In-Molecules) as a method to mitigate the errors resulting from the missing long-range interactions in molecular clusters in equilibrium. Here, we show that the same method can be applied to improve the performance of MIM to solve the longstanding problem of dependency of the fragmentation energy error on the choice of the fragmentation scheme. We chose four widely used acid dissociation reactions (HCl, HClO4, HNO3, and H2SO4) as test cases due to their importance in chemical processes and complex reaction potential energy surfaces. Electrostatic embedding improves the performance at both one and two-layer MIM as shown by lower EE-MIM1 and EE-MIM2 errors. The EE-MIM errors are also demonstrated to be less dependent on the choice of the fragmentation scheme by analyzing the variation in fragmentation energy at the points with more than one possible fragmentation scheme (points where the fragmentation scheme changes). EE-MIM2 with M06-2X as the low-level resulted in a variation of less than 1 kcal/mol for all the cases and 1 kJ/mol for all but three cases, rendering our method fragmentation scheme-independent for acid dissociation processes. [ABSTRACT FROM AUTHOR]

The magnetization processes and magnetocaloric effect (MCE) of molecular magnets are studied using the quantum Heisenberg model with the goal of finding magnetic structures with optimal magnetocaloric properties. To fulfill this goal, we examine the influence of various factors such as quantum fluctuations, the magnitude and distribution of spins, the cluster size and its geometry on the conventional (cooling) and inverse (heating) MCE. We find, surprisingly, that the best cooling and heating effects are observed in the Ising limit on the smallest possible molecular clusters represented by dimers and trimers. The increasing Heisenberg interaction suppresses both the cooling as well as heating effects, but while the heating is reduced very strongly, for relatively small values of the anisotropic Heisenberg constant, the cooling effects are reduced only weakly. Since the heating effect is undesired in low-temperature refrigeration, the Heisenberg limit is also interesting from a practical point of view. Moreover, we find that spin distributions also have a significant influence on the magnetocaloric properties of molecular magnets. Specifically, configurations with large spins on the edges of the finite chain significantly enhance the cooling effect. [ABSTRACT FROM AUTHOR]

Background: Cuproptosis, a recently identified type of programmed cell death triggered by copper, has mechanisms in Wilms tumor (WT) that are not yet fully understood. This research focuses on examining the link between WT and Cuproptosis-related genes (CRGs), with the goal of developing a predictive model for WT. Methods: Four gene expression datasets related to WT were sourced from the GEO database. Subsequently, expression profiles of CRGs were extracted for differential analysis and immune infiltration studies. Utilizing 105 WT samples, clusters related to Cuproptosis were identified. This involved analyzing associated immune cell infiltration and conducting functional enrichment analysis. Disease-characteristic genes were pinpointed using weighted gene co-expression network analysis. Finally, the WT risk prediction model was constructed by four machine learning methods: random forest, support vector machine (SVM), generalized linear and extreme gradient strength model. The best-performing machine learning model was chosen, and a nomogram was created. The effectiveness of this predictive model was validated using methods such as the calibration curve, decision curve analysis, and by appiying it to the TARGET-GTEx dataset. Results: Thirteen differentially expressed Cuproptosis-related genes were identified. The infiltration level of CD8 + T cells in WT children was lower than that in Normal tissue (NT) children, and the level of M0 infiltration of macrophages and T follicular helper cells was higher than that in NT children. In addition, two clusters of cuproptosis-related WT were identified. Enrichment analysis results indicated that genes in cluster 2 were primarily involved in cell division, nuclear division regulation, DNA biosynthesis process, ubiquitin-mediated proteolysis. The SVM model was judged to be the optimal model using 5 genes. Its accuracy was confirmed through a calibration curve and decision curve analysis, demonstrating satisfactory performance on the TARGET-GTEx validation dataset. Additional analysis revealed that these five genes exhibited high expression in both the TARGET-GTEx validation dataset and sequencing data. Conclusion: This research established a link between WT and Cuproptosis. It developed a predictive model for assessing the risk of WT and pinpointed five key genes associated with the disease. [ABSTRACT FROM AUTHOR]

Photocatalytic hydrogen production is one of the most valuable technologies in the future energy system. Here, we designed a metal‐covalent organic frameworks (MCOFs) with both small‐sized metal clusters and nitrogen‐rich ligands, named COF‐Cu3TG. Based on our design, small‐sized metal clusters were selected to increase the density of active sites and shorten the distance of electron transport to active sites. While another building block containing nitrogen‐rich organic ligands acted as a node that could in situ anchor metal atoms during photocatalysis and form interlayer single‐atom electron bridges (SAEB) to accelerate electron transport. Together, they promoted photocatalytic performance. This represented the further utilization of Ru atoms and was an additional application of the photosensitizer. N2‐Ru‐N2 electron bridge (Ru‐SAEB) was created in situ between the layers, resulting in a considerable enhancement in the hydrogen production rate of the photocatalyst to 10.47 mmol g−1 h−1. Through theoretical calculation and EXAFS, the existence position and action mechanism of Ru‐SAEB were reasonably inferred, further confirming the rationality of the Ru‐SAEB configuration. A sufficiently proximity between the small‐sized Cu3 cluster and the Ru‐SAEB was found to expedite electron transfer. This work demonstrated the synergistic impact of small molecular clusters with Ru‐SAEB for efficient photocatalytic hydrogen production. [ABSTRACT FROM AUTHOR]

Context: Exploring potential energy surfaces (PES) is fundamental in computational chemistry, as it provides insights into the relationship between molecular energy, geometry, and chemical reactivity. We introduce Kick-MEP, a hybrid method for exploring the PES of atomic and molecular clusters, particularly those dominated by non-covalent interactions. Kick-MEP computes the Coulomb integral between the maximum and minimum electrostatic potential values on a 0.001 a.u. electron density isosurface for two interacting fragments. This approach efficiently estimates interaction energies and selects low-energy configurations at reduced computational cost. Kick-MEP was evaluated on silicon-lithium clusters, water clusters, and thymol encapsulated within Cucurbit[7]uril, consistently identifying the lowest energy structures, including global minima and relevant local minima. Methods: Kick-MEP generates an initial population of molecular structures using the stochastic Kick algorithm, which combines two molecular fragments (A and B). The molecular electrostatic potential (MEP) values on a 0.001 a.u. electron density isosurface for each fragment are used to compute the Coulomb integral between them. Structures with the lowest Coulomb integral are selected and refined through gradient-based optimization and DFT calculations at the PBE0-D3/Def2-TZVP level. Molecular docking simulations for the thymol-Cucurbit[7]uril complex using AutoDock Vina were performed for benchmarking. Kick-MEP was validated across different molecular systems, demonstrating its effectiveness in identifying the lowest energy structures, including global minima and relevant local minima, while maintaining a low computational cost. [ABSTRACT FROM AUTHOR]

Parasitic diseases caused by Cysticercus tenuicollis significantly impact the livestock industry. The quantification of such impacts presents challenges due to the handling of data obtained through modern molecular techniques. This investigation explores the application of neutrosophic cluster analysis to the study of Cysticercus tenuicollis DNA. Neutrosophic clustering and the use of linguistic terms help interpret analyses, employing a five-step neutrosophic clustering algorithm: selection of the clustering algorithm, definition of distance metrics, data preparation, algorithm execution, and examination of results. Samples from 20 sheep exhibiting liver cysts were collected in Cotopaxi Province, Ecuador. The analysis resulted in a neutrosophic cluster where one group displayed low truth and falsity values but a moderate level of indeterminacy. The stability of the clusters was evaluated using the Adjusted Rand Index (ARI) with 100 bootstrapping repetitions, yielding a moderate value of approximately 0.52, which implies that the detected cluster structures are robust and representative of the underlying patterns in the data. The clusters show a clear differentiation based on the initial DNA concentration. Samples with high DNA concentrations tend to group together, suggesting that the quality and quantity of DNA may be critical indicators of the samples' condition and possibly the presence and state of the parasite. [ABSTRACT FROM AUTHOR]

Macroion‐counterion interaction is essential for regulating the solution behaviors of hydrophilic macroions, as simple models for polyelectrolytes. Here, we explore the interaction between uranyl peroxide molecular cluster Li68K12(OH)20[UO2(O2)OH]60 (U60) and multivalent counterions. Different from interaction with monovalent counterions that shows a simple one‐step process, isothermal titration calorimetry, combined with light/X‐ray scattering measurements and electron microscopy, confirm a two‐step process for their interaction with multivalent counterions: an ion‐pairing between U60 and the counterion with partial breakage of hydration shells followed by strong U60‐U60 attraction, leading to the formation of large nanosheets with severe breakage and reconstruction of hydration shells. The detailed studies on macroion‐counterion interaction can be nicely correlated to the microscopic (self‐assembly) and macroscopic (gelation or phase separation) phase transitions in the dilute U60 aqueous solutions induced by multivalent counterions. [ABSTRACT FROM AUTHOR]

Cell adhesion can be significantly influenced by the topography of the substrate surface. However, how the adhesion molecules essentially respond to this topographical stimulus is not fully understood yet. Here, we employ an effective Monte Carlo simulation to systematically investigate a fluctuating membrane interacting with a curved substrate via adhesive proteins. Interestingly, results show that, compared with the flat substrate, curved substrates regulate the membrane adhesion in a bond length dependent manner. The effects of the substrate surface amplitude and wavelength on the number of molecular bonds and adhesion pattern are also extracted from the scaling relationship between the characteristic lateral length of the membrane and the local substrate curvature radius. Furthermore, the local substrate curvature is found to select the bond distribution in terms of the bond length and stiffness. The results suggest that the bond stiffness enhances the clustering of molecular bonds, mainly due to synergistic interactions among these molecular bonds. [ABSTRACT FROM AUTHOR]

Elucidating the speciation (in terms of concentration versus pH) and understanding the formation mechanisms of polyoxometalates remains a significant challenge, both in experimental and computational domains. POMSimulator is a new methodology that tackles this problem from a purely computational perspective. The methodology uses results from quantum mechanics based methods to automatically set up the chemical reaction network, and to build speciation models. As a result, it becomes possible to predict speciation and phase diagrams, as well as to derive new insights into the formation mechanisms of large molecular clusters. In this work we present the main features of the first open‐source version of the software. Since the first report [Chem. Sci. 2020, 11, 8448‐8456], POMSimulator has undergone several improvements to keep up with the growing challenges that were tackled. After four years of research, we recognize that the source code is sufficiently stable to share a polished and user‐friendly version. The Python code, manual, examples, and install instructions can be found at https://github.com/petrusen/pomsimulator. [ABSTRACT FROM AUTHOR]

The experiment was carried out during rabi (September, 2018-January, 2019) season under water deficit experimental fields at Annamalai University, Tamil Nadu, India to evaluate the performance of maize inbreds under water deficient conditions and to identify potential diverse inbreds for hybridization programme. Ten quantitative characters were studied for variability studies, whereas genetic diversity was observed at both molecular and phenotypic levels. The genotype, G34 showed the highest mean for single plant yield (SPY) (135.6 g), this was followed by G11, G32, G38 and G48. While the genotypes G20 and G50 showed early days to 50% tasseling (DT) and silking (<55 days). High heritability and genetic advance as per cent of mean was observed for the traits leaf area, number of kernels cob-1 and SPY, showing the action of additive gene action. Path analysis showed that SPY had positive direct effects from days to DT, plant height, leaf area, cob length, number of kernels and test weight. Cluster analysis based on Mahalanobis (D2) diversity analysis partitioned the 52 genotypes into 5 clusters, whereas mean Euclidean distance based molecular clustering using the six markers partitioned the selected fourteen genotypes into six clusters. Overall based on both these diversity analyses the genotypes G34, G20, G16, G48, G50, G40 and G34 can be selected and used as parents in future hybridization programme to get superior high yielding hybrids. [ABSTRACT FROM AUTHOR]

Context: Revealing the mechanism of intermolecular interactions in dinitroamine ammonium (ADN)-based liquid propellants and exploring the reasons for their performance changes, multi-perspective interaction analyses of ADN and ADN-water (H2O)-methanol (CH3OH) solutions have been conducted via theoretical methods. The band structure, density of states (DOS), surface electrostatic potential (ESP), Hirshfeld surface, reduced density gradient (RDG), AIM topological analysis, and detonation performance were studied and the results showed that both the ADN and ADN-H2O-CH3OH solutions had hydrogen bonds and van der Waals interactions. By introducing the small molecules H2O and CH3OH, the detonation performance of the ADN-H2O-CH3OH solution slightly decreased, but its sensitivity also decreased. Overall, the comprehensive performance of the ADN-H2O-CH3OH solution has improved, and the application range has expanded. These results are helpful for obtaining a deeper understanding of ADN-based liquid propellants at the atomic level and contribute to the development of new liquid propellants. Methods: The ADN and ADN-H2O-CH3OH solutions were constructed by Amorphous cell module and optimized via GGA with PBE methods in the Dmol3 module of the Materials Studio, and their electronic properties were calculated. Hirshfeld surfaces were generated with CrystalExplorer 3.0. A topological analysis of a variety of molecular clusters was performed via QTAIM. The QTAIM and RDG analyses in this work were generated by Multiwfn 3.0. [ABSTRACT FROM AUTHOR]

The fluxional nature of halogen bonds (XBs) in small molecular clusters, supramolecules, and molecular crystals has received considerable attention in recent years. In this work, based on extensive density‐functional theory calculations and detailed electrostatic potential (ESP), natural bonding orbital (NBO), non‐covalent interactions‐reduced density gradient (NCI‐RDG), and quantum theory of atoms in molecules (QTAIM) analyses, we unveil the existence of fluxional halogen bonds (FXBs) in a series of linear (IC6F4I)m(OONC6H4NOO)n (m + n = 2–5) complexes of tetrafluorodiiodobenzene with dinitrobenzene which appear to be similar to the previously reported fluxional hydrogen bonds (FHBs) in small water clusters (H2O)n (n = 2–6). The obtained GS⇌TS⇌GS'$$ \mathrm{GS}\rightleftharpoons \mathrm{TS}\rightleftharpoons {\mathrm{GS}}^{\hbox{'}} $$ fluxional mechanisms involve one FXB in the systems which fluctuates reversibly between two linear CI···O XBs in the ground states (GS and GS') via a bifurcated CI  O2N van der Waals interaction in the transition state (TS). The cohesive energies (Ecoh) of these complexes with up to four XBs exhibit an almost perfect linear relationship with the numbers of XBs in the systems, with the average calculated halogen bond energy of Ecoh/XB = 3.48 kcal·mol−1 in the ground states which appears to be about 55% of the average calculated hydrogen bond energy (Ecoh/HB = 6.28 kcal·mol−1) in small water clusters. [ABSTRACT FROM AUTHOR]

Cumulenes are valuable precursors to an entire series of more complex organic derivatives. Cyclic cumulenes present a special case, since they possess an increased potential to possess a high order of symmetry. The stability of the cumulene molecule can be brought forth through the construction of crossed clusters. In this case, molecular modeling tools were employed in order to provide insights into the construction and stability of 3 C26 triple crossed C26 cyclic cumulene cluster. The study revealed that is a very little change in the total energy per atom (or per electron) of triple crossed 3 C26 when compared with C26 (0.00002 Hartree/e, which is less than 0.01 ‰). Other important parameters, such as the HOMO-LUMO gap and the angles between the planes of the molecular rings, suggest its kinship with fullerenes and the regular tetrahedron. Furthermore, in order to proof the suitability of the approach and of its results a symmetry constrained optimization study was conducted using Z-matrix internal coordinates with Gaussian software. At DFT BLYP 6-311 G* theory level a symmetry constrained optimization of the 3 C26 cluster converged, which suggests the possibility of obtaining this cluster by synthetic means. [ABSTRACT FROM AUTHOR]

Even more fascinating than its bulk parent, a water droplet possesses extraordinary catalytic and hydro‐voltaic capability, elastic adaptivity, hydrophobicity, sensitivity, thermal stability, etc., but the underlying mechanism is still elusive. We emphasize herewith that the H‒O bond follows the universal bond order‒length‒strength correlation and nonbonding electron polarization regulation and the hydrogen bond cooperativity and polarizability notion regulates the performance of the coupling hydrogen bond (O:H‒O). Computational and spectrometric evidence consistently shows that molecular undercoordination shortens the intramolecular H‒O bond by up to 10% while lengthening the intermolecular O:H nonbond by 20% cooperatively with an association of electron polarization, making the 0.3‐nm thick droplet skin of a supersolid phase of self‐electrification. The supersolid skin dictates the performance and functionality of the droplet in chemical, dielectric, electrical, mechanical, optical, and thermal properties as well as the transport dynamics of electrons and phonons. The amplification of these findings could deepen our insight into the undercoordinated aqueous systems, including bubbles and molecular clusters, and promote deep engineering of water and ice. [ABSTRACT FROM AUTHOR]

Pheochromocytoma and paraganglioma (PPGL) are rare tumors derived from the adrenal medulla and extra-adrenal chromaffin cells. Diagnosis is often challenging due to the great variability in clinical presentation; the complexity of management due to the dangerous effects of catecholamine excess and the potentially malignant behavior require in-depth knowledge of the pathology and multidisciplinary management. Nowadays, diagnostic ability has certainly improved and guidelines and consensus documents for treatment and follow-up are available. A major impulse to the development of this knowledge has come from the new findings on the genetic and molecular characteristics of PPGLs. Germline mutation in susceptibility genes is detected in 40% of subjects, with a mutation frequency of 10–12% also in patients with sporadic presentation and genetic testing should be incorporated within clinical care. PPGL susceptibility genes include "old genes" associated with Neurofibromatosis type 1 (NF1 gene), Von Hippel Lindau syndrome (VHL gene) and Multiple Endocrine Neoplasia type 2 syndrome (RET gene), the family of SDHx genes (SDHA, SDHB, SDHC, SDHD, SDHAF2), and genes less frequently involved such as TMEM, MAX, and FH. Each gene has a different risk of relapse, malignancy, and other organ involvement; for mutation carriers, affected or asymptomatic, it is possible to define a tailored long-life surveillance program according to the gene involved. In addition, molecular characterization of the tumor has allowed the identification of somatic mutations in other driver genes, bringing to 70% the PPGLs for which we know the mechanisms of tumorigenesis. This has expanded the catalog of tumor driver genes, which are identifiable in up to 70% of patients Integrated genomic and transcriptomic data over the last 10 years have revealed three distinct major molecular signatures, triggered by pathogenic variants in susceptibility genes and characterized by the activation of a specific oncogenic signaling: the pseudo hypoxic, the kinase, and the Wnt signaling pathways. These molecular clusters show a different biochemical phenotype and clinical behavior; they may also represent the prerequisite for implementing customized therapy and follow-up. [ABSTRACT FROM AUTHOR]

Energies of the atomic and molecular orbitals belonging to one and two atom systems from the fourth and fifth periods of the periodic table have been calculated using ab initio quantum mechanical calculations. The energies of selected occupied and unoccupied orbitals surrounding the highest occupied and lowest unoccupied orbitals (HOMOs and LUMOs) of each system were selected and used as input for our artificial intelligence (AI) software. Using the AI software, correlations between orbital parameters and selected chemical and physical properties of bulk materials composed of these elements were established. Using these correlations, the materials' bulk properties were predicted. The Q2 correlation for the single-atom predictions of first ionization potential, melting point, and boiling point were 0.3589, 0.4599, and 0.1798 respectively. The corresponding Q2 correlations using orbital parameters describing two-atom systems increased the capability to predict the experimental properties to the respective values of 0.8551, 0.8207, and 0.7877. The accuracy in predicting materials' bulk properties was increased up to four-fold by using two atoms instead of one. We also present results of the prediction of molecules for materials relevant to energy systems. [ABSTRACT FROM AUTHOR]

Context. As a refractory material, vanadia (solid V2O5) is likely to be found as a condensate in the atmospheres of substellar objects such as exoplanets and brown dwarfs. However, the nature of the nanometer-sized vanadium oxide clusters that partake in the nucleation process is not well understood. Aims. We aim to understand the formation of cloud condensation nuclei in oxygen-rich substellar atmospheres by calculating the relevant fundamental properties of the energetically most favorable vanadium oxide molecules and clusters and, investigate how they contribute to the formation of condensation seeds. Methods. We applied a hierarchical optimization approach in order to find the most favourable structures for clusters of (VO)N and (VO2)N for N = 1−10, and of (V2O5)N for N = 1−4, and to calculate their thermodynamical potentials. The candidate geometries are initially optimized by applying classical interatomic potentials; these are then refined at the B3LYP/cc-pVTZ level of theory to obtain accurate zero-point energies and thermochemical quantities. Results. We present previously unreported vanadium oxide cluster structures as the lowest-energy isomers. Moreover, we report revised cluster energies and their thermochemical properties. Chemical equilibrium calculations are used to assess the impact of the updated and newly derived thermodynamic potentials on the gas-phase abundances of vanadium-bearing species. In chemical equilibrium, larger clusters from different stoichiometric families are found to be the most abundant vanadium-bearing species for temperatures below ~1000 K, while molecular VO is the most abundant between ~1000 K and ~2000 K. We determine the nucleation rates of each stoichiometric family for a given (Tgas, pgas) profile of a brown dwarf using both classical and non-classical nucleation theory. Conclusions. Small differences in the revised Gibbs free energies of the clusters have a large impact on the abundances of vanadium-bearing species in chemical equilibrium at temperatures below ~1000 K. These abundance changes subsequently have an impact on the nucleation rates of each stoichiometric family. We find that with the revised and more accurate cluster data, non-classical nucleation rates are up to 15 orders of magnitude higher than classical nucleation rates. [ABSTRACT FROM AUTHOR]

Magnetic circularly polarized luminescence (MCPL) is a technique based on the differential emission of left and right circularly polarized light induced by a magnetic field. This technique is the emission counterpart of to the widely explored magnetic circular dichroism. In this work, the first demonstration of MCPL is presented in heterometallic lanthanide(III) systems by using two molecular cluster aggregates, {Eu1Tb19} and {Eu3Tb17}. The results presented here indicate the potential that MCPL holds for obtaining highly tuned magneto‐optical materials for various applications, such as anti‐counterfeiting materials. Additionally, the findings suggest that magnetic interactions between the ions modulate the dissymmetry factor, opening the possibility of the development of highly tunable MCPL materials. [ABSTRACT FROM AUTHOR]

The high‐speed impact‐resistanct materials are of great significance while their development is hindered by the intrinsic tradeoff between mechanical strength and energy dissipation capability. Herein, the new chemical system of molecular granular material (MGM) is developed for the design of impact‐resistant materials from the supramolecular complexation of sub‐nm molecular clusters (MCs) and hyper‐branched polyelectrolytes. Their hierarchical aggregation provides the origin of the decoupling of mechanical strengths and structural relaxation dynamics. The MCs' intrinsic fast dynamics afford excellent high‐speed impact‐resistance, up to 5600 s−1 impact in a typical split‐Hopkinson pressure bar test while only tiny boundary cracks can be observed even under 7200 s−1 impact. The high loadings of MCs and their hierarchical aggregates provide high‐density sacrificial bonding for the effective dissipation of the impact energy, enabling the protection of fragile devices from the direct impact of over 200 m s−1 bullet. Moreover, the MGMs can be conveniently processed into protective coatings or films with promising recyclability due to the supramolecular interaction feature. The research not only reveals the unique relaxation dynamics and mechanical properties of MGMs in comparison with polymers and colloids, but also develops new chemical systems for the fabrication of high‐speed impact‐resistant materials. [ABSTRACT FROM AUTHOR]

Single‐atom catalysts with precise structure and extremely high catalytic efficiency remain a fervent focus in the fields of materials chemistry and catalytic science. Herein, a nickel‐substituted polyoxometalate (POM) {NiSb6O4(H2O)3[β‐Ni(hmta)SbW8O31]3}15− (NiPOM) with one extremely exposed nickel site [NiO3(H2O)3] was synthesized using the conventional aqueous method. The uniform dispersion of single nickel center with well‐defined structure was facilely achieved by anchoring nanosized NiPOM on graphene oxide (GO). The resulting NiPOM/GO can couple with CdS photoabsorber for the construction of low‐cost and ultra‐efficient hydrogen evolution system. The H2 yield can reach to 2753.27 mmol gPOM−1 h−1, which represents a record value among all the POM‐based photocatalytic systems. Remarkablely, an extremely high hydrogen yield of 3647.28 mmol gPOM−1 h−1 was achieved with simultaneous photooxidation of commercial waste plastic, representing the first POM‐based photocatalytic system for H2 evolution and waste plastic conversion. This work highlights a straightforward strategy for constructing extremely exposed single‐metal site with precise microenvironment by facilely manipulating nanosized molecular cluster to control individual atom. [ABSTRACT FROM AUTHOR]

Background: Acute myeloid leukemia (AML) is a genetically heterogeneous disease in which glutamine (Gln) contributes to AML progression. Therefore, this study aimed to identify potential prognostic biomarkers for AML based on Gln metabolism-related genes. Methods: Gln-related genes that were differentially expressed between Cancer Genome Atlas-based AML and normal samples were analyzed using the limma package. Univariate, least absolute shrinkage, selection operators, and stepwise Cox regression analyses were used to identify prognostic signatures. Risk score-based prognostic and nomogram models were constructed to predict the prognostic risk of AML. Subsequently, consistent cluster analysis was performed to stratify patients into different subtypes, and subtype-related module genes were screened using weighted gene co-expression network analysis. Results: Through a series of regression analyses, HGF, ANGPTL3, MB, F2, CALR, EIF4EBP1, EPHX1, and PDHA1 were identified as potential prognostic biomarkers of AML. Prognostic and nomogram models constructed based on these genes could significantly differentiate between high- and low-risk AML with high predictive accuracy. The eight-signature also stratified patients with AML into two subtypes, among which Cluster 2 was prone to a high risk of AML prognosis. These two clusters exhibited different immune profiles. Of the subtype-related module genes, the HOXA and HOXB family genes may be genetic features of AML subtypes. Conclusion: Eight Gln metabolism-related genes were identified as potential biomarkers of AML to predict prognostic risk. The molecular subtypes clustered by these genes enabled prognostic risk stratification. Highlights: Eight genes were identified as potential prognostic biomarkers of acute myeloid leukemia (AML). The prognostic and nomogram models can accurately predict the AML prognostic risks. Eight prognostic signatures stratified AML patients into two subtypes with different prognostic patterns and immune profiles. HOXA and HOXB family genes may be genetic features of AML subtypes. [ABSTRACT FROM AUTHOR]

Background: The study aims to comprehensively combine colorectal cancer data cohorts in order to analyze the effects of various DNA methylation-coding genes on colorectal cancer patients. The annual incidence and mortality of colorectal cancer are very high, and there are no effective treatments for advanced colorectal cancer. DNA methylation is a method widely used to regulate epigenetics in the molecular mechanism study of tumors. Method: Three single-cell cohorts GSE166555, GSE146771, and EMTAB8107, and five transcriptome cohorts GSE17536, GSE39582, GSE72970, and TCGA-CRC (TCGA-COAD and TCGA-READ) were applied in this study. 2 erasers (ALKBH5 and FTO), There are 7 writers (METTL3, METTL14, WTAP, VIRMA, RBM15, RBM15B, and ZC3H13) and 11 readers (YTHDC1, IGF2BP1, IGF2BP2, IGF2BP3, YTHDF1, YTHDF3, YTHDC2, and HNRNPA2B1, YTHDF2, HNRNPC and RBMX), a total of 20 M6A regulators, were used as the basis of the dataset in this study and were applied to the construction of molecular typing and prognostic models. Drugs that are differentially sensitive in methylation-regulated gene-related prognostic models were identified using the ConsensusClusterPlus package, which was also used to identify distinct methylation regulatory expression patterns in colorectal cancer and to model the relationship between tissue gene expression profiles and drug IC50 values. Finally, TISCH2 assessed which immune cells were significantly expressed with M6A scores. The immunosuppression of M6A methylation is spatially explained. Results: This study used data from 583 CRC patients in the TCGA-CRC cohort. Firstly, the mutation frequency and CNV variation frequency of 20 m6A modification-related factors were analyzed, and the corresponding histogram and heat map were drawn. The study next analyzed the expression variations between mutant and wild forms of the VIRMA gene and explored differences in the expression of these variables in tumor and normal tissues. In addition, the samples were divided into different subgroups by molecular clustering method based on m6A modification, and each subgroup's expression and clinicopathological characteristics were analyzed. Finally, we compared prognostic differences, tumor microenvironment (TME) characteristics, immune cell infiltration, and gene function enrichment among different subpopulations. We also developed a colorectal cancer m6A-associated gene signature and validated its prognostic effects across multiple cohorts. Finally, using single-cell RNA sequencing data, we confirmed that tumor cells show elevated expression of m6A-related gene signatures. Discussion: This study explored the mutation frequency, expression differences, interactions, molecular clustering, prognostic effect, and association with tumor characteristics of m6A modification-related factors in CRC and validated them at the single-cell level. These results clarify the association between m6A alteration and colorectal cancer (CRC) and offer important insights into the molecular recognition and management of cancer. [ABSTRACT FROM AUTHOR]

$ ({\rm La}_2 {\rm O}_3)_{\rm n} $ (La 2 O 3) n (n=1-5) clusters have been computationally studied using DFT and TDDFT with B3LYP functional under hybrid GGA approximation and LANL2DZ basis set. The doping effect of various elements like Ba, Y and Hf on the structural, electronic and non-linear optical (NLO) properties of $ ({\rm La}_2 {\rm O}_3)_{\rm n} $ (La 2 O 3) n clusters has been studied for finding their optoelectronic applications. Different optimised geometries of $ ({\rm La}_2 {\rm O}_3)_{\rm n} $ (La 2 O 3) n and Ba, Y and Hf doped $ ({\rm La}_2 {\rm O}_3)_{\rm n} $ (La 2 O 3) n clusters have been obtained. HOMO-LUMO gap $ (\Delta \mathcal {E}) $ (Δ E) and chemical hardness (τ) of $ ({\rm La}_2 {\rm O}_3)_{\rm n} $ (La 2 O 3) n clusters decreases significantly with doping of Ba, Y and Hf atoms making these clusters very reactive. Refractive index and the absorption in UV–VIS region of electromagnetic spectra of Ba, Y and Hf doped $ ({\rm La}_2 {\rm O}_3)_{\rm n} $ (La 2 O 3) n clusters increases as compared to $ ({\rm La}_2 {\rm O}_3)_{\rm n} $ (La 2 O 3) n clusters making these clusters suitable to be used as additives in the manufacturing of various types of glasses. Also, dielectric constant (ϵ) of Ba, Y and Hf doped $ ({\rm La}_2 {\rm O}_3)_{\rm n} $ (La 2 O 3) n clusters increases finding their applications to be used in the oxide layer of MOSFETs. [ABSTRACT FROM AUTHOR]

Patients with neurogenic rosacea (NR) frequently demonstrate pronounced neurological manifestations, often unresponsive to conventional therapeutic approaches. A molecular-level understanding and diagnosis of this patient cohort could significantly guide clinical interventions. In this study, we amalgamated our sequencing data (n = 46) with a publicly accessible database (n = 38) to perform an unsupervised cluster analysis of the integrated dataset. The eighty-four rosacea patients were partitioned into two distinct clusters. Neurovascular biomarkers were found to be elevated in cluster 1 compared to cluster 2. Pathways in cluster 1 were predominantly involved in neurotransmitter synthesis, transmission, and functionality, whereas cluster 2 pathways were centered on inflammation-related processes. Differential gene expression analysis and WGCNA were employed to delineate the characteristic gene sets of the two clusters. Subsequently, a diagnostic model was constructed from the identified gene sets using linear regression methodologies. The model's C index, comprising genes PNPLA3, CUX2, PLIN2, and HMGCR, achieved a remarkable value of 0.9683, with an area under the curve (AUC) for the training cohort's nomogram of 0.9376. Clinical characteristics from our dataset (n = 46) were assessed by three seasoned dermatologists, forming the NR validation cohort (NR, n = 18; non-neurogenic rosacea, n = 28). Upon application of our model to NR diagnosis, the model's AUC value reached 0.9023. Finally, potential therapeutic candidates for both patient groups were predicted via the Connectivity Map. In summation, this study unveiled two clusters with unique molecular phenotypes within rosacea, leading to the development of a precise diagnostic model instrumental in NR diagnosis. [ABSTRACT FROM AUTHOR]

The charging behavior of molecular Au clusters protected by alkanethiolate (SCnH2n+1=SCn) is, under electrochemical conditions, significantly affected by the penetration of solvents and electrolytes into the SCn layer. In this study, we estimated the charging energy EC(n) associated with [PtAu24(SCn)18]−+e−→[PtAu24(SCn)18]2− (n=4, 8, 12, and 16) in vacuum using mass‐selected gas‐phase anion photoelectron spectroscopy of [PtAu24(SCn)18]z (z=−1 and −2). The EC(n) values of PtAu24(SCn)18 in vacuum are significantly larger than those in solution and decrease with n in contrast to the behavior reported for Au25(SCn)18 in solution. The effective relative permittivity (ϵm*) of the SCn layer in vacuum is estimated to be 2.3–2.0 based on the double‐concentric‐capacitor model. Much smaller ϵm* values in vacuum than those in solution are explained by the absence of solvent/electrolyte penetration into the monolayer. The gradual decrease of ϵm* with n is ascribed to the appearance of an exposed surface region due to the bundle formation of long alkyl chains. [ABSTRACT FROM AUTHOR]

Terpene synthases (TPSs) play pivotal roles in generating diverse terpenoids through complex cyclization pathways. Protein engineering of TPSs offers a crucial approach to expanding terpene diversity. However, significant potential remains untapped due to limited understanding of the structure‐function relationships of TPSs. In this investigation, using a joint approach of molecular dynamics simulations‐assisted engineering and site‐directed mutagenesis, we manipulated the aromatic residue cluster (ARC) of a bifunctional terpene synthase (BFTPS), Pestalotiopsis fici nigtetraene synthase (PfNS). This led to the discovery of previously unreported catalytic functions yielding different cyclization patterns of sesterterpenes. Specifically, a quadruple variant (F89A/Y113F/W193L/T194W) completely altered PfNS′s function, converting it from producing the bicyclic sesterterpene nigtetraene to the tricyclic ophiobolin F. Additionally, analysis of catalytic profiles by double, triple, and quadruple variants demonstrated that the ARC functions as a switch, unprecedently redirecting the production of 5/11 bicyclic (Type B) sesterterpenes to 5/15 bicyclic (Type A) ones. Molecular dynamics simulations and theozyme calculations further elucidated that, in addition to cation‐π interactions, C−H⋅⋅⋅π interactions also play a key role in the cyclization patterns. This study offers a feasible strategy in protein engineering of TPSs for various industrial applications. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common neurodegenerative disease. Anoikis is a new type of programmed cell death that can lead to the development of many diseases. The purpose of this study is to investigate the role of anoikis-related genes (ANRGs) in AD and establish a prediction model. Based on GSE33000, 1 666 differentially expressed genes are screened, and 10 genes are obtained by intersection with 53 ANRGs. Using the above genes, 310 patients with AD are classified into three subtypes by unsupervised clustering, and the differences of immune microenvironment among different subtypes are further analyzed. After that, WGCNA algorithm is used to screen the characteristic genes associated with AD, and combined with four machine learning models (RF, GLM, SVM and XGB), the AD risk prediction model is constructed and verified in three external cohorts (GSE5281, GSE29378 and GSE122063). Finally, we successfully construct a nomogram based on five AD characteristic genes (TM6SF1, SMYD3, OXCT1, MAP1B and ITPKB) of the XGB model to provide reference for clinical prediction of AD. [ABSTRACT FROM AUTHOR]

Sorghum (S. bicolor) is a great millet that constitutes the staple food in many parts of the world and is cultivated in the semi‐arid tropics. The grain is bestowed with traits that make it amenable for many value‐added products, thus increasing the demand. Considerable progress is achieved in increasing the yield in the past through exploitation of primary gene pool species and secondary gene pool species to a very limited extent, but further increase is challenging and it necessitates bringing in the genetic diversity from other gene pools. Tertiary gene pool of sorghum has not been exploited so far due to limitations of cross‐incompatibility. We accomplished inter‐specific hybridization in sorghum with the wild species of tertiary gene pool. Derivatives from three inter‐specific crosses (ISCs), Sorghum bicolor × S. versicolor (ISC304 and ISC320) and S. bicolor × S. usumbarense (ISC812), with the parental lines of popular Indian sorghum hybrids, 27B and 126B, are developed. Thirteen promising lines are identified, and they are subjected to field evaluation and molecular analysis to develop inter‐specific derivatives that are equivalent to parent progenitors in agronomic traits yet diverse enough genetically. Analysis of phenotypic data in conjunction with genotype data revealed that derivatives of S. usumbarense are promising as compared to derivatives from S. versicolor, derivatives of S. usumbarense are closely clustered as compared to that of S. versicolor, and all the derivatives carry genomic regions from both the parents, with S. bicolor genome being predominant. GGE biplot analysis identified three genotypes (ISC320‐5, ISC320‐2, and ISC812‐2) as highly promising. We successfully generated promising inter‐specific derivatives from the tertiary gene pool in sorghum for ready use in crop improvement programs. Core Ideas: Inter‐specific hybridization followed by a single backcross is used to widen the genetic base of sorghum parental lines.Thirteen derivatives from three inter‐specific crosses are evaluated in the field for six seasons.Morphological data for six seasons in conjunction with molecular data are used for characterization of these lines.Morphological and molecular clustering revealed that Sorghum versicolor derivatives are more dispersed than those of S. usumbarense.Genetically diverse lines with good agronomic performance for use in sorghum breeding programs are identified. [ABSTRACT FROM AUTHOR]

Small α-dicarbonyl compounds like succinic acid are found abundantly in the atmosphere, thereby contributing to the formation of secondary organic aerosols through solid or liquid particles and gas-phase molecular clusters. The vibrational spectrum of succinic acid was measured in two different environments to evidence the role of the medium on the geometry adopted by the acid. Different structures were observed. The planar and extended structures of succinic acid were favored in the solid state whereas the non-planar gauche conformer was the predominant form in an argon matrix at 20 K, similar to the gas phase. Aided by quantum chemistry calculations and the non-covalent interactions analysis, we investigated the conformational diversity of succinic acid to understand the factors that govern the stability of the acid. [ABSTRACT FROM AUTHOR]

Abstract Background Cuproptosis, a recently identified type of programmed cell death triggered by copper, has mechanisms in Wilms tumor (WT) that are not yet fully understood. This research focuses on examining the link between WT and Cuproptosis-related genes (CRGs), with the goal of developing a predictive model for WT. Methods Four gene expression datasets related to WT were sourced from the GEO database. Subsequently, expression profiles of CRGs were extracted for differential analysis and immune infiltration studies. Utilizing 105 WT samples, clusters related to Cuproptosis were identified. This involved analyzing associated immune cell infiltration and conducting functional enrichment analysis. Disease-characteristic genes were pinpointed using weighted gene co-expression network analysis. Finally, the WT risk prediction model was constructed by four machine learning methods: random forest, support vector machine (SVM), generalized linear and extreme gradient strength model. The best-performing machine learning model was chosen, and a nomogram was created. The effectiveness of this predictive model was validated using methods such as the calibration curve, decision curve analysis, and by appiying it to the TARGET-GTEx dataset. Results Thirteen differentially expressed Cuproptosis-related genes were identified. The infiltration level of CD8 + T cells in WT children was lower than that in Normal tissue (NT) children, and the level of M0 infiltration of macrophages and T follicular helper cells was higher than that in NT children. In addition, two clusters of cuproptosis-related WT were identified. Enrichment analysis results indicated that genes in cluster 2 were primarily involved in cell division, nuclear division regulation, DNA biosynthesis process, ubiquitin-mediated proteolysis. The SVM model was judged to be the optimal model using 5 genes. Its accuracy was confirmed through a calibration curve and decision curve analysis, demonstrating satisfactory performance on the TARGET-GTEx validation dataset. Additional analysis revealed that these five genes exhibited high expression in both the TARGET-GTEx validation dataset and sequencing data. Conclusion This research established a link between WT and Cuproptosis. It developed a predictive model for assessing the risk of WT and pinpointed five key genes associated with the disease.

Abstract This study aims to explore the expression profile of PANoptosis‐related genes (PRGs) and immune infiltration in Alzheimer's disease (AD). Based on the Gene Expression Omnibus database, this study investigated the differentially expressed PRGs and immune cell infiltration in AD and explored related molecular clusters. Gene set variation analysis (GSVA) was used to analyze the expression of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes in different clusters. Weighted gene co‐expression network analysis was utilized to find co‐expressed gene modules and core genes in the network. By analyzing the intersection genes in random forest, support vector machine, generalized linear model, and extreme gradient boosting (XGB), the XGB model was determined. Eventually, the first five genes (Signal Transducer and Activator of Transcription 3, Tumor Necrosis Factor (TNF) Receptor Superfamily Member 1B, Interleukin 4 Receptor, Chloride Intracellular Channel 1, TNF Receptor Superfamily Member 10B) in XGB model were selected as predictive genes. This research explored the relationship between PANoptosis and AD and established an XGB learning model to evaluate and screen key genes. At the same time, immune infiltration analysis showed that there were different immune infiltration expression profiles in AD.

The vibrational self-consistent field (VSCF) method yields anharmonic states and spectra for molecular vibrations, and it serves as the starting point for more sophisticated correlated-vibration methods. Convergence of the iterative, non-linear optimization in VSCF calculations can be erratic or altogether unsuccessful, particularly for chemical systems involving low-frequency motions. In this work, a vibrational formulation of the Direct Inversion of the Iterative Subspace method of Pulay is presented and investigated. This formulation accounts for distinct attributes of the vibrational and electronic cases, including the expansion of each single-mode vibrational wavefunction in its own basis set. The resulting Direct Inversion of the Iterative Subspace method is shown to substantially accelerate VSCF convergence in all convergent cases as well as rectify many cases where Roothaan-based methods fail. Performance across systems ranging from small, rigid molecules to weakly bound molecular clusters is investigated in this analysis. [ABSTRACT FROM AUTHOR]

A comprehensive genetic algorithm is used to perform a global search for Cl−(H2O)1–9 and NaCl(H2O)1–9. The structural optimization, energy calculations, vibrational characteristics, and charge distribution were performed at an ab initio high-level theory. Combined with the calculation results of Na+(H2O)1–6 by Wang et al. [Front. Chem. 7, 624 (2019)] in our group, we systematically investigate these three systems at the same theoretical level. A comparison of bond lengths reveals that in Cl−(H2O)n, the inclusion of Na+ to form NaCl(H2O)n reduces the average distance between Cl− and H2O, indicating that Na+ has a stabilizing effect on ionic hydrogen bonds. Conversely, in Na+(H2O)n, the introduction of Cl− weakens the interactions between Na+ and H2O. In the NaCl(H2O)1–9 structures searched by the genetic algorithm, the ground-state configurations correspond to contact ion pairs, and the solvent-separated ion pair structures appear when n = 7. Furthermore, the anharmonic corrected infrared spectra of Cl−(H2O)1–5 and NaCl(H2O)1–4 exhibit good agreement with the experimental results. According to charge analysis of NaCl(H2O)n, it is observed that charge transfer primarily occurs from Cl− to H2O, resulting in the presence of negative charges on the water molecules. These findings are helpful to understand the effects of Na+ and Cl− on hydrated clusters at the molecular level. [ABSTRACT FROM AUTHOR]

The ion–molecule reaction is one of the most important pathways for the formation of new interstellar chemical species. Herein, infrared spectra of cationic binary clusters of acrylonitrile (AN) with methanethiol (CH3SH) and dimethyl sulfide (CH3SCH3) are measured and compared to those previous studies of AN and methanol (CH3OH) or dimethyl ether (CH3OCH3). The results suggest that the ion–molecular reactions of AN with CH3SH and CH3SCH3 only yield products with S...HN H-bonded or S∴N hemibond structures, rather than the cyclic products as observed in AN-CH3OH and AN-CH3OCH3 studied previously. The Michael addition-cyclization reaction between acrylonitrile and sulfur-containing molecules does not occur due to the weaker acidity of CH bonds in sulfur-containing molecules, which results from their weaker hyperconjugation effect compared to oxygen-containing molecules. The reduced propensity for the proton transfer from the CH bonds hinders the formation of the Michael addition-cyclization product that follows. [ABSTRACT FROM AUTHOR]

The ion–molecule reaction is one of the most important pathways for the formation of new interstellar chemical species. Herein, infrared spectra of cationic binary clusters of acrylonitrile (AN) with methanethiol (CH3SH) and dimethyl sulfide (CH3SCH3) are measured and compared to those previous studies of AN and methanol (CH3OH) or dimethyl ether (CH3OCH3). The results suggest that the ion–molecular reactions of AN with CH3SH and CH3SCH3 only yield products with S...HN H-bonded or S∴N hemibond structures, rather than the cyclic products as observed in AN-CH3OH and AN-CH3OCH3 studied previously. The Michael addition-cyclization reaction between acrylonitrile and sulfur-containing molecules does not occur due to the weaker acidity of CH bonds in sulfur-containing molecules, which results from their weaker hyperconjugation effect compared to oxygen-containing molecules. The reduced propensity for the proton transfer from the CH bonds hinders the formation of the Michael addition-cyclization product that follows. [ABSTRACT FROM AUTHOR]

Dimer interaction energies have been well studied in computational chemistry, but they can offer an incomplete understanding of molecular binding depending on the system. In the current study, we present a dataset of focal-point coupled-cluster interaction and deformation energies (summing to binding energies, De) of 28 organic molecular dimers. We use these highly accurate energies to evaluate ten density functional approximations for their accuracy. The best performing method (with a double-ζ basis set), B97M-D3BJ, is then used to calculate the binding energies of 104 organic dimers, and we analyze the influence of the nature and strength of interaction on deformation energies. Deformation energies can be as large as 50% of the dimer interaction energy, especially when hydrogen bonding is present. In most cases, two or more hydrogen bonds present in a dimer correspond to an interaction energy of −10 to −25 kcal mol−1, allowing a deformation energy above 1 kcal mol−1 (and up to 9.5 kcal mol−1). A lack of hydrogen bonding usually restricts the deformation energy to below 1 kcal mol−1 due to the weaker interaction energy. [ABSTRACT FROM AUTHOR]

Sampling reference data is crucial in machine learning potential (MLP) construction. Inadequate coverage of local configurations in reference data may lead to unphysical behaviors in MLP‐based molecular dynamics (MLP‐MD) simulations. To address this problem, this study proposes a new on‐the‐fly reference data sampling method called radial distribution function (RDF)‐based data sampling for MLP construction. This method detects and extracts anomalous structures from the trajectories of MLP‐MD simulations by focusing on the shapes of RDFs. The detected structures are added to the reference data to improve the accuracy of the MLP. This method allows us to realize a reasonable MLP construction for liquid water with minimal additional data. We prepare data from an H2O molecular cluster system and verify whether the constructed MLPs are practical for bulk water systems. MLP‐MD simulations without RDF‐based data sampling show unphysical behaviors, such as atomic collisions. In contrast, after applying this method, we obtain MLP‐MD trajectories with features, such as RDF shapes and angle distributions, that are comparable to those of ab initio MD simulations. Our simulation results demonstrate that the RDF‐based data sampling approach is useful for constructing MLPs that are robust to extrapolations from molecular cluster systems to bulk systems without any specialized know‐how. [ABSTRACT FROM AUTHOR]

A system associated with several number of weak interactions supports numerous number of stable structures within a narrow range of energy. Often, a deterministic search method fails to locate the global minimum geometry as well as important local minimum isomers for such systems. Therefore, in this work, the stochastic search technique, namely parallel tempering, has been executed on the quantum chemical surface of the CNO(‐)(H2O)n system for n=1–8 to generate global minimum as well as several number of local minimum isomers. IR spectrum can act as the fingerprint property for such system to be identified. Thus, IR spectroscopic features have also been included in this work. Vertical detachment energy has also been calculated to obtain clear information about number of water molecules in several spheres around the central anion. In addition, in a real experimental scenario, not only the global but also the local minimum isomers play an important role in determining the average value of a particular physically observable property. Therefore, the relative conformational populations have been determined for all the evaluated structures for the temperature range between 20K and 400K. Further to understand the phase change behavior, the configurational heat capacities have also been calculated for different sizes. [ABSTRACT FROM AUTHOR]

In this work, we present coordinated molecular dynamics, ion cluster acceleration, and retarding potential analysis simulations to determine cluster fragmentation behavior in a realistic emitter geometry for electrosprays operating in the pure ionic regime. Molecular dynamics simulations are used to determine the fragmentation rates of ionic liquid clusters as a function of internal energy, electric field strength, and cluster size. A simplified model of electrospray cluster acceleration is developed from previous electrohydrodynamic emission models and used to simulate retarding potential analysis curves. Fragmentation rates and beam composition are inferred for experimental data based on the molecular dynamics and cluster acceleration simulations. We find that for these experimental data, temperatures of EMI-BF 4 dimers likely range between 590 and 687 K while trimer temperatures are larger between 989 and 1092 K. The percentage of monomers, dimers, and trimers in the beam is approximately 45%, 30%–43%, and 13%–25%, respectively. Both ionic liquid cluster temperatures and beam composition agree with previous analysis of this experimental work, supporting the use of coordinated molecular dynamics and retarding potential analysis as a method of inferring electrospray beam parameters. Insights gained from this simulation process are discussed in the context of currently unexplained electrospray emitter behavior and experimental results including the presence of tetramers and trimers in the beam and fragmentation rates in high electric field regions. [ABSTRACT FROM AUTHOR]

We present an unsupervised data processing workflow that is specifically designed to obtain a fast conformational clustering of long molecular dynamics simulation trajectories. In this approach, we combine two dimensionality reduction algorithms (cc_analysis and encodermap) with a density-based spatial clustering algorithm (hierarchical density-based spatial clustering of applications with noise). The proposed scheme benefits from the strengths of the three algorithms while avoiding most of the drawbacks of the individual methods. Here, the cc_analysis algorithm is applied for the first time to molecular simulation data. The encodermap algorithm complements cc_analysis by providing an efficient way to process and assign large amounts of data to clusters. The main goal of the procedure is to maximize the number of assigned frames of a given trajectory while keeping a clear conformational identity of the clusters that are found. In practice, we achieve this by using an iterative clustering approach and a tunable root-mean-square-deviation-based criterion in the final cluster assignment. This allows us to find clusters of different densities and different degrees of structural identity. With the help of four protein systems, we illustrate the capability and performance of this clustering workflow: wild-type and thermostable mutant of the Trp-cage protein (TC5b and TC10b), NTL9, and Protein B. Each of these test systems poses their individual challenges to the scheme, which, in total, give a nice overview of the advantages and potential difficulties that can arise when using the proposed method. [ABSTRACT FROM AUTHOR]

• Rodent grooming is an important key behavior. • The study analyzed mouse genes linked to aberrant hetero-grooming behavior. • The genes formed a molecular network with several gene clusters. • They can help clarify mechanisms of rodent grooming behavior. Highly prevalent in laboratory rodents, 'social' hetero-grooming behavior is translationally relevant to modeling a wide range of neuropsychiatric disorders. Here, we comprehensively evaluated all known to date mouse genes linked to aberrant hetero-grooming phenotype, and applied bioinformatics tools to construct a network of their established protein–protein interactions (PPI). We next identified several distinct molecular clusters within this complex network, including neuronal differentiation, cytoskeletal, WNT-signaling and synapsins-associated pathways. Using additional bioinformatics analyses, we further identified 'central' (hub) proteins within these molecular clusters, likely key for mouse hetero-grooming behavior. Overall, a more comprehensive characterization of intricate molecular pathways linked to aberrant rodent grooming may markedly advance our understanding of underlying cellular mechanisms and related neurological disorders, eventually helping discover novel targets for their pharmacological or gene therapy interventions. [ABSTRACT FROM AUTHOR]

Deciphering nature's remarkable way of encoding functions in its biominerals holds the potential to enable the rational development of nature‐inspired materials with tailored properties. However, the complex processes that convert solution‐state precursors into solid biomaterials remain largely unknown. In this study, an unconventional approach is presented to characterize these precursors for the diatom‐derived peptides R5 and synthetic Silaffin‐1A1(synSil‐1A1). These molecules can form defined supramolecular assemblies in solution, which act as templates for solid silica structures. Using a tailored structural biology toolbox, the structure‐function relationships of these self‐assemblies are unveiled. NMR‐derived constraints are employed to enable a recently developed fractal‐cluster formalism and then reveal the architecture of the peptide assemblies in atomistic detail. Finally, by monitoring the self‐assembly activities during silica formation at simultaneous high temporal and residue resolution using real‐time spectroscopy, the mechanism is elucidated underlying template‐driven silica formation. Thus, it is demonstrated how to exercise morphology control over bioinorganic solids by manipulating the template architectures. It is found that the morphology of the templates is translated into the shape of bioinorganic particles via a mechanism that includes silica nucleation on the solution‐state complexes' surfaces followed by complete surface coating and particle precipitation. [ABSTRACT FROM AUTHOR]