Browse Articles

Complete preclinical evaluation of the novel antibody mimetic Nanofitin-IRDye800CW for diverse non-invasive diagnostic applications in the management of HER-2 positive tumors

Synthesis, in vitro, and in silico studies of 7-fluorochromone based thiosemicarbazones as α-glucosidase inhibitors

Mining of candidate genes related to body size in Chinese native pig breeds based on public data

A practical generalization metric for deep networks benchmarking

Abstract There is an ongoing and dedicated effort to estimate bounds on the generalization error of deep learning models, coupled with an increasing interest with practical metrics that can be used to experimentally evaluate a model’s ability to generalize. This interest is not only driven by practical considerations but is also vital for theoretical research, as theoretical estimations require practical validation. However, there is currently a lack of research on benchmarking the generalization capacity of various deep networks and verifying these theoretical estimations. This paper aims to introduce a practical generalization metric for benchmarking different deep networks and proposes a novel testbed for the verification of theoretical estimations. Our findings indicate that a deep network’s generalization capacity in classification tasks is contingent upon both classification accuracy and the diversity of unseen data. The proposed metric system is capable of quantifying the accuracy of deep learning models and the diversity of data, providing an intuitive and quantitative evaluation method - a trade-off point. Furthermore, we compare our practical metric with existing generalization theoretical estimations using our benchmarking testbed. It is discouraging to note that most of the available generalization estimations do not correlate with the practical measurements obtained using our testbed. On the other hand, this finding is significant as it exposes the shortcomings of theoretical estimations and inspires new exploration.

Implicit neural representation for potential field geophysics

Influence of experimental variables on spheroid attributes

Effects of biochar and nitrogen fertilizer on microbial communities, CO2 emissions, and organic carbon content in soil

Efficient exploration of reaction pathways using reaction databases and active learning

The fast and accurate simulation of chemical reactions is a major goal of computational chemistry. Recently, the pursuit of this goal has been aided by machine learning interatomic potentials (MLIPs), which provide energies and forces at quantum mechanical accuracy but at a fraction of the cost of the reference quantum mechanical calculations. Assembling the training set of relevant configurations is key to building the MLIP. Here, we demonstrate two approaches to training reactive MLIPs based on reaction pathway information. One approach exploits reaction datasets containing reactant, product, and transition state structures. Using an SN2 reaction dataset, we accurately locate reaction pathways and transition state geometries of up to 170 unseen reactions. In another approach, which does not depend on data availability, we present an efficient active learning procedure that yields an accurate MLIP and converged minimum energy path given only the reaction end point structures, avoiding quantum mechanics driven reaction pathway search at any stage of training set construction. We demonstrate this procedure on an SN2 reaction in the gas phase and with a small number of solvating water molecules, predicting reaction barriers within 20 meV of the reference quantum chemistry method. We then apply the active learning procedure on a more complex reaction involving a nucleophilic aromatic substitution and proton transfer, comparing the results against the reactive ReaxFF force field. Our active learning procedure, in addition to rapidly finding reaction paths for individual reactions, provides an approach to building large reaction path databases for training transferable reactive machine learning potentials.

Fe leaking from orthodontic appliances affects buccal enamel more than lingual during in vitro experiment

Structural effects of the insertion of large rings in two-dimensional networks

The structural effect of inserting large central rings into a two-dimensional network of three-coordinate nodes is investigated using a ring-growth Monte Carlo procedure. The size of the central ring is systematically varied, as is the inherent level of disorder in the surrounding network (as controlled by the Monte Carlo “temperature” and characterized by the fraction of six-membered rings). The effect of the central ring on the overall network topology is analyzed in terms of both topological and geometric distances. For larger central rings, the first topological shell becomes exclusively populated by four- and five-membered rings, which leads to an effective upper limit on the size of the central ring that can effectively be accommodated. The topological shells are found to show ordering at significant distances away from the central ring. The effective correlation lengths are determined as a function of both central ring size and level of network disorder, which allows for an understanding of the potential density of large rings that may be accommodated.

Energy balance in nitrogen and sulfur management strategies for oilseed radish

Laser spectroscopic study of the electronic states of palladium monoxide (PdO)

Among the group 10 transition metal monoxides, only the palladium monoxide (PdO) radical has hitherto eluded detection in optical spectra. In this study, we report the first optical spectra of the gas-phase PdO molecule using the laser-induced fluorescence excitation and single-vibronic-level (SVL) emission spectroscopies. Eight rotationally resolved excitation spectra were observed in 17 800–23 650 cm−1, allowing the determination of the vibrational frequencies and rotational constants for the ground and five highly excited electronic states. Six low-energy Ω states below 3500 cm−1 were identified from the SVL emission spectra and assigned to six spin–orbit components of the X 3Σ− and A 3Π electronic states. Furthermore, high-level ab initio calculations were performed on numerous Λ−S and Ω electronic states to support the assignments from the experimental observations. The spectral results elucidate the bonding characteristics of PdO and provide support for verifying the significant relativistic effect on the bonding of PtO.

Study of sand particle transport characteristics and different critical velocities in sand-producing wells via indoor experiments

Ultrafast fragmentation dynamics of carbon dioxide trication induced by an intense laser field: Transient deformation route vs direct Coulomb repulsion

We present a combined experimental and theoretical study of the detailed fragmentation process of CO23+→ CO2+ + O+ induced by an intense laser field. Through multicoincidence fragment measurements together with ab initio molecular dynamics (AIMD) simulations, we find that a transient deformation route appears in competition with the expected Coulomb explosion. The AIMD simulations visually demonstrate that CO23+ undergoes several bending vibrations in ∼50–480 fs, and in the final dissociation stages, the electron density distribution in three-dimensional space migrates from the O ion to the C ion, while the bond strength rapidly decreases to 0, resulting in bond breaking assisted by the asymmetric stretching vibrations. The measured kinetic energy releases are in general agreement with AIMD simulations, and the deduced amount of energy transfer into the vibrational and rotational degrees of freedom of CO2+ is about 3 eV less than that estimated by the Coulomb potential.

Crossing Wallace’s line: an evolutionarily young gibbon ape leukemia virus like endogenous retrovirus identified from the Philippine flying lemur (Cynocephalus volans)

Abstract Wallace’s line is a biogeographical barrier to faunal movements between Southeast Asia and the Australo-Papuan region. There are exceptions among rodents and bats, few of which have crossed Wallace’s line. The gibbon ape leukemia viruses (GALV) and koala retrovirus (KoRV) have only been identified in wildlife on the Australo-Papuan side of Wallaces’s Line with the potential exception of partial sequences identified in two microbat fecal samples from China and a recently described GALV relative in a rodent from Africa. Here we describe a group of GALV-like endogenous retroviral sequences from the Southeast Asian flying lemur (Cynocephalus volans) representing the first known description of a primate relative which has been infected, and the germline colonized, by GALVs on the Southeast Asian side of Wallace’s Line.

Gas-phase reactivity of protonated oxazolone: Chemical dynamics simulations and graph theory-based analysis reveal the importance of ion–molecule complexes

This study delves into the fragmentation mechanisms of the oxazolone form (OXA) of protonated cyclo-di-glycine using chemical dynamics simulations at multiple internal energies. While we focus our in-depth analyses on a representative total energy of 178 kcal/mol, we also performed simulations over the 127–187 kcal/mol range. This broader energy sampling reveals how the population of states evolves with increasing internal energy, enabling us to compute rate constants and then effective energy thresholds using a previously introduced three-state model [Perez Mellor et al., J. Chem. Phys. 155, 124103 (2021)]. By transforming molecular geometries into graph representations, we systematically analyze fragmentation processes and identify key intermediates and ion–molecule complexes (IMCs) that play a crucial role in fragmentation dynamics. The study highlights the distinct isomerization landscapes of OXA, driven by IMC formation, which contrasts with the previously reported behavior of cyclic and linear forms [Perez Mellor et al., J. Chem. Phys. 155, 124103 (2021)]. The resulting fragmentation channels are characterized by their unique energetic thresholds and branching ratios and can provide a molecular explanation of what was observed experimentally. Thanks to an accurate analysis of the trajectories using our graph-theory-based tools, it was possible to point out the particular behavior of OXA fragmentation, which is different from other isomers. In particular, the important role of IMCs is shown, which has an impact on populating different isomeric structures.

YTHDF2 promotes the metastasis of oral squamous cell carcinoma through the JAK-STAT pathway

Correlation between entropy fluctuations and the dielectric relaxation of glass-forming systems: The central role of dipolar–dipolar cross correlations

The premise that the dielectric α relaxation has a one-to-one correspondence with entropy fluctuations in equilibrium near the glass transition was experimentally verified in a systematic and quantitative manner for glass-forming systems in general. Validation of this relation was structured at different levels, taking into account various ingredients as the apolar–polar character, macromolecular structure, the presence of hydrogen bonds, or complex structure and dynamics. The results reclaim the suitability of dielectric spectroscopy to echo the primary structural relaxation of glass-forming systems, demonstrating that the dielectric response effectively captures the structural relaxation by reliably correlating with entropy fluctuations. The correlation with entropy fluctuations holds even when the dielectric strength of the systems is high and the dielectric response is narrow and dominated by cross correlations, proving that dipolar intermolecular interactions are fundamental to the structural relaxation and not a particularity of the dielectric probe. This one-to-one correspondence between structural and dielectric α relaxation does not support the existence of a generic spectral shape for the primary structural relaxation valid for all kinds of susceptibility functions.

Research on the decoupling effect and driving factors of industrial carbon emissions in Hubei province

Abstract Under the “dual carbon” goal, the new quality productivity in the industrial energy sector will become an important force in promoting the green and high-quality development of Hubei Province’s economy and society. The article comprehensively uses the Tapio decoupling model and LMDI decomposition method, and empirically analyzes the decoupling effect and driving factors of industrial carbon emissions in Hubei Province from 2006 to 2022 using panel data of industrial industries. Research has found that the growth of Hubei’s industrial economy and carbon emissions have undergone a fluctuating process of "strong decoupling → weak decoupling → expansion negative decoupling". For a considerable period of time in the future, the industrial economic growth and carbon emissions in Hubei will still be in a weak or expanding negative decoupling state. From the decomposition results, it can be seen that the trend of changes in the energy intensity index and carbon intensity index shows a high degree of consistency, and the energy intensity index has become the main factor driving the decrease in Hubei’s industrial carbon intensity index. However, the impact of energy structure effects and industrial structure effects on industrial carbon emissions is relatively weak, and the dividends brought by structural effects are still not significant. On this basis, the article proposes relevant policy recommendations, providing theoretical basis and policy basis for empowering high-quality industrial development in Hubei Province with new quality productivity in the post epidemic era.

Pressure-induced structural and dielectric changes in liquid water at room temperature

Understanding the pressure-dependent dielectric properties of water is crucial for a wide range of scientific and practical applications. In this study, we employ a deep neural network trained on density functional theory data to investigate the dielectric properties of liquid water at room temperature across a pressure range of 0.1–1000 MPa. We observe a nonlinear increase in the static dielectric constant ɛ0 with increasing pressure, a trend that is qualitatively consistent with experimental observations. This increase in ɛ0 is primarily attributed to the increase in water density under compression, which enhances collective dipole fluctuations within the hydrogen-bonding network as well as the dielectric response. Despite the increase in ɛ0, our results reveal a decrease in the Kirkwood correlation factor GK with increasing pressure. This decrease in GK is attributed to pressure-induced structural distortions in the hydrogen-bonding network, which weaken dipolar correlations by disrupting the ideal tetrahedral arrangement of water molecules.