Browse Articles

Extracting organs of interest from medical images based on convolutional neural network with auxiliary and refined constraints

Causality-driven candidate identification for reliable DNA methylation biomarker discovery

Deep eutectic solvent enhances antibacterial activity of a modular lytic enzyme against Acinetobacter baumannii

Abstract In this study, we evaluated the combined effect between MLE-15, a modular lytic enzyme composed of four building blocks, and reline, a natural deep eutectic solvent. The bioinformatic analysis allowed us to determine the spatial architecture of MLE-15, whose components were bactericidal peptide cecropin A connected via a flexible linker to the cell wall binding domain (CBD) of mesophilic 201ϕ2 − 1 endolysin and catalytic domain (EAD) of highly thermostable Ph2119 endolysin. The modular enzyme showed high thermostability with the melting temperature of 93.97 ± 0.38 °C, significantly higher than their natural counterparts derived from mesophilic sources. The minimum inhibitory concentration (MIC) of MLE-15 was 100 µg/mL for a panel of Gram-positive and Gram-negative bacteria, while the MIC of reline ranged from 6.25 to 25% v/v for the same strains. The addition of reline effectively reduced the MIC of MLE-15 from 100 µg/mL to 3.15–50 µg/mL. This combination displayed additive effects for most strains and synergism for extensively antibiotic-resistant Acinetobacter baumannii and Bacillus subtilis. The subsequent evaluation revealed that MLE-15 eliminated planktonic cells of A. baumannii RUH134, but was ineffective against matured biofilms. However, combined with reline, MLE-15 reduced the bacterial load in the matured biofilm by 1.39 log units. Confocal fluorescence microscopy indicated that reline damaged the structure of the biofilm, allowing MLE-15 to penetrate it. Additionally, MLE-15 and its combination with reline eradicated meropenem-persistent cells of A. baumannii RUH134. Effectiveness in lowering the MIC value of MLE-15 as well as protection against antibiotic-tolerant persister cells, indicate that MLE-15 and reline combination is a promising candidate for effective therapies in bacterial infections, which is especially important in the light of the global crisis of antimicrobial resistance.

Photoreceptor metabolic window unveils eye–body interactions

Environmental gradients shape genetic variation in the desert moss, Syntrichia caninervis Mitt. (Pottiaceae)

Airborne observations reveal the fate of the methane from the Nord Stream pipelines

Abstract The Nord Stream pipeline leaks on 26 September 2022 released 465 ± 20 kt of methane into the atmosphere, which is the largest recorded transient anthropogenic methane emission event. While most of the gas escaped directly to the atmosphere, a fraction dissolved in the water. So far, studies on the fate of this dissolved methane rely on pipeline volumetric estimates or spatially sparse concentration measurements and ocean models. Here, we use atmospheric measurements with broad spatial coverage obtained from an airborne platform to estimate outgassing of 19-48 t h−1 on 5 October 2022. Our results broadly agree with ocean models but reveal uncertainties such as inaccuracies in their spatial emission distribution. Thus, we provide a data-driven constraint on the fate of the methane from the Nord Stream pipelines in the Baltic Sea. These results demonstrate the benefit of a fast-response airborne mission to track a dynamic methane emission event.

Study on the effect of acid fracturing fluid on pore structure of middle to high rank coal

Injectable supramolecular hydrogel co-loading abemaciclib/NLG919 for neoadjuvant immunotherapy of triple-negative breast cancer

Resting state BOLD-perfusion coupling patterns using multiband multi-echo pseudo-continuous arterial spin label imaging

Visualizing stepwise evolution of carbon hybridization from sp3 to sp2 and to sp

Abstract Regulating carbon hybridization states lies at the heart of engineering carbon materials with tailored properties but orchestrating the sequential transition across three states has remained elusive. Here, we visiualize stepwise evolution in carbon hybridizations from sp³ to sp² and to sp states via dehydrogenation and elimination reactions of methylcyano-functionalized molecules on surfaces. Utilizing scanning probing microscopy, we distinguish three distinct carbon-carbon bond types within polymers induced by annealing at elevated temperatures. Density-functional-theory calculations unveil the pivotal role of the electron-withdrawing cyano group in activating neighboring methylene to form C(sp 3 )–C(sp 3 ) bonds, and in facilitating subsequent stepwise HCN eliminations to realize the transformation across three carbon-carbon bond types. We also demonstrate the applicability of this strategy on one-dimensional molecular wires and two-dimensional covalent organic framework on different substrates. Our work expands the scope of carbon hybridization evolution and serves as an advance in flexibly engineering carbon-material by employing cyanomethyl-substituted molecules.

A network meta-analysis of therapies for hyperphosphatemia in CKD based on randomized trials

Bearing characteristics and damage rules of regenerated rock mass

Collaborative control technology and application of support and modification for soft-rock roadway in a kilometer-deep coal mine

Antibacterial and antioxidant potentials, detection of host origin compounds, and metabolic profiling of endophytic Bacillus spp. isolated from Rauvolfia serpentina (L.) Benth. ex Kurz

A novel dynamic vibration absorber for vibration control of piping systems under multi-frequency harmonic excitations

Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength

Biosynthesis and activity of Zn-MnO nanocomposite in vitro with molecular docking studies against multidrug resistance bacteria and inflammatory activators

Reverse design of broadband sound absorption structure based on deep learning method

Abstract This research presents a method based on deep learning for the reverse design of sound-absorbing structures. Traditional methods require time-consuming individual numerical simulations followed by cumbersome calculations, whereas the deep learning design method significantly simplifies the design process, achieving efficient and rapid design objectives. By utilizing deep neural networks, a mapping relationship between structural parameters and the sound absorption coefficient curve is established. The forward network predicts the sound absorption coefficient curve, while the reverse network enables the on-demand design of structural parameters for broadband high sound absorption. During the design process, a mean squared error (MSE) below 0.0001 is achieved. The accuracy of the proposed design method is validated through examples. The results demonstrate that the trained deep learning neural network could effectively replace the complex physical mechanisms between structural parameters and sound absorption coefficient curves. This deep learning design method could also be extended to other types of metamaterial reverse designs, significantly enhancing the efficiency of complex metamaterial designs. Lightweight design is crucial for energy saving and emission reduction. With the total mass and average sound absorption coefficient of sound-absorbing materials as targets, the NSGA-II algorithm has been used for multi-objective optimization design. The optimized average sound absorption coefficient increased by 4.84%, and the total material mass was reduced by 18.98%.

Solar-driven production of renewable chemicals via biomass hydrogenation with green methanol

Petrogenesis and geodynamic implication of the newly recorded Cryogenian Suwaj adakitic magmatism at the eastern Arabian-Shield