Actinoplanes sandaracinus sp. nov., a novel ligninase- producing and cellulose-degrading actinobacterium isolated from soil.

A novel ligninase-producing and cellulose-degrading actinobacterium, designated strain NEAU-A12T, was isolated from a soil sample collected from Aohan banner, Chifeng City, Inner Mongolia Autonomous Region, PR China. A polyphasic taxonomic study was used to establish the status of strain NEAU-A12T. 16S rRNA gene sequence analysis revealed that strain NEAU-A12T belonged to the genus Actinoplanes and showed the highest similarity (98.3 %) to Actinoplanes palleronii DSM 43940T, while showing less than 98.3 % similarity to other members of the genus Actinoplanes. The phospholipid profile contained diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, phosphatidylinositol and glycosylphosphatidylinositol. The diagnostic sugars in cell hydrolysates were determined to be arabinose, glucose and xylose. The cell wall contained meso-diaminopimelic acid as the diagnostic diamino acid. The predominant menaquinones were MK-9(H4), MK-9(H6) and MK-9(H2). The major fatty acids were C15 : 0, C16 : 0, C16 : 1 ω7c and C17 : 0. Meanwhile, genomic analysis revealed a genome size of 10 192 524 bp and a DNA G+C content of 70.6 mol%, and indicated that strain NEAU-A12T had the potential to degrade lignin and cellulose, as well as produce bioactive compounds. In addition, the average nucleotide identity values between strain NEAU-A12T and its reference strains A. palleronii DSM 43940T, Actinoplanes regularis DSM 43151T, Actinoplanes philippinensis DSM 43019T, Actinoplanes xinjiangensis DSM 45184T and Actinoplanes italicus DSM 43146T were 80.3, 80.3, 84.1, 84.3 and 84.0 %, respectively. The levels of digital DNA-DNA hybridization between them were found to be 23.6 % (21.3-26.1 %), 23.8 % (21.5-26.3 %), 28.3 % (25.9-30.8 %), 28.6 % (26.0-30.9 %) and 28.4 % (26.2-31.1 %), respectively. Based on phenotypic, chemotaxonomic and genotypic data, strain NEAU-A12T is considered to represent a novel species of the genus Actinoplanes, for which the name Actinoplanes sandaracinus sp. nov. is proposed, with NEAU-A12T (=CCTCC AA 2020039T=DSM 112043T) as the type strain.

Recent Advances in the Reverse Water-Gas Conversion Reaction.

The increase in carbon dioxide emissions has significantly impacted human society and the global environment. As carbon dioxide is the most abundant and cheap C1 resource, the conversion and utilization of carbon dioxide have received extensive attention from researchers. Among the many carbon dioxide conversion and utilization methods, the reverse water-gas conversion (RWGS) reaction is considered one of the most effective. This review discusses the research progress made in RWGS with various heterogeneous metal catalyst types, covering topics such as catalyst performance, thermodynamic analysis, kinetics and reaction mechanisms, and catalyst design and preparation, and suggests future research on RWGS heterogeneous catalysts.

Iridium-Catalysed Transfer Hydrogenation of 1,8-Naphthyridine with Indoline: Access to Functionalized N-Heteroarenes.

An iridium-catalysed hydrogen transfer strategy, enabling straightforward access to tetrahydro pyridine derivatives from aryl-1,8-naphthyridines and indolines, was developed. This method proceeds with unprecedented synthetic effectiveness including high step-economic fashion together with the advantages of having no by-product and no need for external high-pressure H2 gas, offering an important basis for the transformation of 1,8-naphthyridines and indolines into functionalized products.

High-Value Oil-Water Separation Materials Prepared from Waste Polyethylene Terephthalate.

As one of the most common forms of waste, waste PET is a serious pollutant in natural and human living environments. There is an urgent need to recycle PET. For this study, the complete degradation of PET was realized at a low temperature. A lipophilic hydrophobic membrane was formed on the surface of a stainless steel mesh (SSM) using a simple dip coating method, and an oil-water separation material was successfully prepared. After loading with degradation products, the surface roughness of SSM increased from 19.09 µm to 62.33 µm. The surface changed from hydrophilic to hydrophobic, and the water contact angle increased to 123°. The oil-water separation flux of the modified SSM was 9825 L/(m2·h), and the separation efficiency was 98.99%. The modified SSM had good reuse performance. This hydrophobic modification method can also be used to modify other porous substrates, such as activated carbon, filter paper, foam, and other materials. The porous substrate modified by the degradation product of waste PET was used to prepare oil-water separation materials, not only solving the problem of white pollution but also reducing the dependence on non-renewable resources in the conventional methods used for the preparation of oil-water separation materials. This study provides new raw materials and methods for the industrial production of oil-water separation materials, which have important application prospects.

Ni3B as p-Block Element-Modulated Cocatalyst for Efficient Photocatalytic CO2 Reduction.

Due to the multiple electron and proton transfer processes involved, the photogenerated charges are easily recombined during the photocatalytic reduction of CO2, making the generation of the eight-electron product CH4 kinetically more difficult. Herein, Ni3B nanoparticles modulated by p-block element were combined with TiO2 nanosheets to construct a novel Schottky junction photocatalyst (Ni3B/TiO2) for the selective photocatalytic conversion of CO2 to CH4. The formed Ni3B/TiO2 photocatalyst with Schottky junction ensures a transfer pathway of photogenerated electrons from TiO2 to Ni3B, which facilitates the accumulation of electrons on the surface of Ni3B and subsequently improves the activity of photocatalytic CO2 reduction to CH4. The optimized Ni3B/TiO2 Schottky junction shows an improved CH4 yield of 30.03 µmol g-1 h-1, which was much higher than those of TiO2 (1.62 µmol g-1 h-1), NiO/TiO2 (2.44 µmol g-1 h-1), and Ni/TiO2 (4.3 µmol g-1 h-1). This work demonstrated that the introduction of p-block elements can alleviate the scaling relationship effect of pure metal cocatalysts to a certain extent, and the modified Ni3B can be used as a promising new cocatalyst to effectively improve the selective photocatalytic of CO2 to CH4.

Epicoccum spp. Causing Maize Leaf Spot in Heilongjiang Province, China.

Maize leaf spot occurs worldwide and affects maize production. Maize can be infected by several pathogens causing leaf spot, such as Bipolaris zeicola, Bipolaris maydis, Curvularia species, Alternaria species, etc. In the current study, 30 Epicoccum isolates recovered from symptomatic maize leaves were identified based on morphological characteristics, pathogenicity, and multilocus sequence analyses of nuLSU, ITS, tub2, and rpb2. These maize isolates were grouped into five Epicoccum species, including E. nigrum, E. layuense, E. sorghinum, E. latusicollum, and E. pneumoniae. Pathogenicity tests showed that all five Epicoccum species could produce small ellipse- and spindle-shaped spots on maize leaves. The lesion center was grayish yellow to dark gray and surrounded by a chlorotic area. Furthermore, the Epicoccum isolates exhibited high pathogenicity to 20 main maize varieties of Heilongjiang Province but showed different sensitivities to the commonly used fungicides carbendazim and tebuconazole. In addition, these Epicoccum isolates showed different production capacity of pectinase, cellulase, protease, amylase, laccase, and gelatinase, but all showed high lipase activity. This is the first report globally of E. layuense, E. latusicollum, and E. pneumoniae as causal agents of maize leaf spot. E. pneumoniae was first reported as a plant pathogen.

Low-temperature grown vertically aligned carbon nanotube array for an optimal infrared bolometer.

Vertically aligned carbon nanotube (VACNT) arrays have been explored as an absorber of thermal-type photodetectors. A long and dense VACNT array absorbs a wide spectral range of incident light with high absorption rate, but has a high thermal mass that results in a low response speed. To achieve a small thermal mass, a shorter and less dense VACNT array is needed. In addition, the high temperature needed to grow the VACNTs is detrimental to the functional sensing materials of the photodetector. The height, density, and growth temperature of VACNTs need to be optimized to achieve a working absorber that has high absorption rate and a high response speed. In this work, a low-temperature plasma enhanced chemical vapor deposition process is used to prepare various VACNT arrays with different heights and densities by controlling the CNT growth parameters. The absorption coefficients of the resulting samples are measured with Fourier transform infrared spectroscopy. An effective medium theory (EMT) is adopted to establish a working model of the VACNTs. Using experimentally extracted CNT density and height as fitting parameters, the EMT model is fitted to obtain theoretical absorption coefficients, which are found to be comparable to the experimentally measured absorption coefficients. Our experimental and theoretical investigations pave the way for future studies to integrate CNTs with infrared photodetectors.

Co-N-Si/AC Catalyst for Aerobic Oxidation of Benzyl Alcohols to Esters under Mild Conditions.

A stable, earth-abundant, reusable cobalt-based heterogeneous catalyst is developed for the oxidative esterification of alcohols under ambient conditions, featuring broad substrate scope, providing good to excellent product yields. This protocol enables easy recyclability of the catalyst, measured up to five times without significant loss of efficiency. The active sites of Co-N-Si/AC are proposed to be Co-N species.

Synthesis and interface characterization of CNTs on graphene.

Carbon nanotubes (CNTs) and graphene are potential candidates for future interconnect materials. CNTs are promising on-chip via interconnect materials due to their readily formed vertical structures, their current-carrying capacity, which is much larger than existing on-chip interconnect materials such as copper and tungsten, and their demonstrated ability to grow in patterned vias with sub-50 nm widths; meanwhile, graphene is suitable for horizontal interconnects. However, they both present the challenge of having high-resistance contacts with other conductors. An all-carbon structure is proposed in this paper, which can be formed using the same chemical vapor deposition method for both CNTs and graphene. Vertically aligned CNTs are grown directly on graphene with an Fe or Ni catalyst. The structural characteristics of the graphene and the grown CNTs are analyzed using Raman spectroscopy and electron microscopy techniques. The CNT-graphene interface is studied in detail using transmission electron microscopic analysis of the CNT-graphene heterostructure, which suggests C-C bonding between the two materials. Electrical measurement results confirm the existence of both a lateral conduction path within graphene and a vertical conduction path in the CNT-graphene heterostructure, giving further support to the C-C bonding at the CNT-graphene interface and resulting in potential applications for all-carbon interconnects.

Aerobic Copper-Catalyzed Halocyclization of Methyl N-Heteroaromatics with Aliphatic Amines: Access to Functionalized Imidazo-Fused N-Heterocycles.

A new aerobic copper-catalyzed halocyclization reaction of methyl N-heteroaromatics and aliphatic amines has been developed, which enables straightforward access to functionalized imidazo-fused N-heterocycles with the merits of good functional tolerance, use of easily available copper salts as the catalysts, lithium halides as the halogen sources, and O2 as a sole oxidant. Due to the reaction features' selective introduction of halogen functionalities to the newly formed imidazo ring, further extensions of the developed chemistry toward synthetic diversity, including effective access to functional materials, are easily envisioned.

Effect of improved contact on reliability of sub-60 nm carbon nanotube vias.

Advances in semiconductor technology due to the aggressive downward scaling of on-chip feature sizes have led to rapid rises in the resistivity and current density of interconnect conductors. As a result, current interconnect materials, Cu and W, are subject to performance and reliability constraints approaching or exceeding their physical limits. Therefore, alternative materials are being actively considered as potential replacements to meet such constraints. The carbon nanotube (CNT) is among the leading replacement candidates for on-chip interconnect vias due to its high aspect-ratio nanostructure and superior current-carrying capacity to Cu and W, as well as other potential candidates. Based on the results for 40 nm and 60 nm top-contact metallized CNT vias, we demonstrate that not only are their current-carrying capacities two orders of magnitude higher than their Cu and W counterparts, they are enhanced by reduced via resistance due to contact engineering facilitated by the first reported contact resistance extraction scheme for a 40 nm linewidth.

PfgPDI: Pocket feature-enabled graph neural network for protein-drug interaction prediction.

Biomolecular interaction recognition between ligands and proteins is an essential task, which largely enhances the safety and efficacy in drug discovery and development stage. Studying the interaction between proteins and ligands can improve the understanding of disease pathogenesis and lead to more effective drug targets. Additionally, it can aid in determining drug parameters, ensuring proper absorption, distribution, and metabolism within the body. Due to incomplete feature representation or the model's inadequate adaptation to protein-ligand complexes, the existing methodologies suffer from suboptimal predictive accuracy. To address these pitfalls, in this study, we designed a new deep learning method based on transformer and GCN. We first utilized the transformer network to grasp crucial information of the original protein sequences within the smile sequences and connected them to prevent falling into a local optimum. Furthermore, a series of dilation convolutions are performed to obtain the pocket features and smile features, subsequently subjected to graphical convolution to optimize the connections. The combined representations are fed into the proposed model for classification prediction. Experiments conducted on various protein-ligand binding prediction methods prove the effectiveness of our proposed method. It is expected that the PfgPDI can contribute to drug prediction and accelerate the development of new drugs, while also serving as a valuable partner for drug testing and Research and Development engineers.

TransVAE-DTA: Transformer and variational autoencoder network for drug-target binding affinity prediction.

BACKGROUND AND OBJECTIVE: Recent studies have emphasized the significance of computational in silico drug-target binding affinity (DTA) prediction in the field of drug discovery and drug repurposing. However, existing DTA prediction approaches suffer from two major deficiencies that impede their progress. Firstly, while most methods primarily focus on the feature representations of drug-target binding affinity pairs, they fail to consider the long-distance relationships of proteins. Furthermore, many deep learning-based DTA predictors simply model the interaction of drug-target pairs through concatenation, which hampers the ability to enhance prediction performance. METHODS: To address these issues, this study proposes a novel framework named TransVAE-DTA, which combines the transformer and variational autoencoder (VAE). Inspired by the early success of VAEs, we aim to further investigate the feasibility of VAEs for drug structure encoding, while utilizing the transformer architecture for target feature representation. Additionally, an adaptive attention pooling (AAP) module is designed to fuse the drug and target encoded features. Notably, TransVAE-DTA is proven to maximize the lower bound of the joint likelihood of drug, target, and their DTAs. RESULTS: Experimental results demonstrate the superiority of TransVAE-DTA in drug-target binding affinity prediction assignments on two public Davis and KIBA datasets. CONCLUSIONS: In this research, the developed TransVAE-DTA opens a new avenue for engineering drug-target interactions.

Enhanced Pharmacokinetics of Celastrol via Long-Circulating Liposomal Delivery for Intravenous Administration.

Background: Rheumatoid Arthritis (RA) involves prolonged inflammation of the synovium, damaging joints and causing stiffness and deformity. Celastrol (Cel), derived from the Chinese herbal medicine Tripterygium wilfordii Hook F, offers immunosuppressive effects for RA treatment but is limited by poor solubility and bioavailability. Purpose: In this study, long-circulating Cel-loaded liposomes (Cel-LPs) were used to increase the pharmacokinetics of Cel, thereby improving drug delivery and efficacy for the treatment of RA. Methods: Cel-LPs were prepared and administered orally and intravenously to compare the elimination half-life of drugs and bioavailability of Cel. Cel-LPs were prepared using the lipid thin-layer-hydration-extrusion method. Human rheumatoid arthritis synovial (MH7A) cells were used to investigate the compatibility of Cel-LPs. The pharmacokinetic studies were performed on male Sprague-Dawley (SD) rats. Results: The Cel-LPs had an average size of 72.20 ± 27.99 nm, a PDI of 0.267, a zeta potential of -31.60 ± 6.81 mV, 78.77 ± 5.69% drug entrapment efficiency and sustained release (5.83 ± 0.42% drug loading). The cytotoxicity test showed that liposomes had excellent biocompatibility and the fluorescence microscope diagram indicated that liposome entrapment increased intracellular accumulation of Rhodamine B by MH7A cells. Furthermore, the results exhibited that Cel-LPs improved the pharmacokinetics of Cel by increasing the elimination half-life (t1/2) to 11.71 hr, mean residence time (MRT(0-∞)) to 7.98 hr and apparent volume of distribution (Vz/F) to 44.63 L/kg in rats, compared to the Cel solution. Conclusion: In this study, liposomes were demonstrated to be effective in optimizing the delivery of Cel, enabling the formulation of Cel-LPs with prolonged blood circulation and sustained release characteristics. This formulation enhanced the intravenous solubility and bioavailability of Cel, developing a foundation for its clinical application in RA and providing insights on poorly soluble drug management.

ZnIn2S4-based multi-interface coupled photocatalyst for efficient photothermal synergistic catalytic hydrogen evolution.

Photothermal synergistic catalysis is a novel technology that converts energy. In this study, ZnIn2S4 with S-vacancy (ZIS-Vs) is combined with Nickel, Nickle Oxide and Carbon Nanofiber aggregates (Ni-NiO@CNFs) to create a multi-interface coupled photocatalyst with double Schottky barrier, double channel and mixed photothermal conversion effect. Theoretical calculation confirms that the Gibbs free energy (ΔG*H) of the S-scheme heterojunction in the composite material is -0.07 eV, which is close to 0. This promotes the adsorption of H* and accelerates the formation of H2. Internal photothermal catalysis is achieved by visible-near infrared (Vis-NIR, RT) irradiation. The internal photothermal catalytic hydrogen production rate of the best sample (0.9Ni-NiO@CNFs/ZIS-Vs) is as high as 17.24 mmol·g-1·h-1, and its photothermal conversion efficiency (η) is as high as 61.42 %. Its hydrogen production efficiency is 20.52 times that of ZIS-Vs (0.84 mmol·g-1·h-1) under visible light (Vis, RT) conditions. When the Vis-NIR light source is combined with external heating (75 ℃), the hydrogen production efficiency is further improved, and the hydrogen production efficiency (29.16 mmol·g-1·h-1) is 26.75 times that of ZIS-Vs (1.09 mmol·g-1·h-1, Vis-NIR, RT). Further analysis shows that the increase in hydrogen production resulted from the apparent activation energy (Ea) of the catalyst decreasing from 16.7 kJ·mol-1 to 9.28 kJ·mol-1. This study provides a valuable prototype for the design of an efficient photothermal synergistic catalytic system.

Laser directed lithography of asymmetric graphene ribbons on a polydimethylsiloxane trench structure.

Recently, manipulating heat transport by asymmetric graphene ribbons has received significant attention, in which phonons in the carbon lattice are used to carry energy. In addition to heat control, asymmetric graphene ribbons might also have broad applications in renewable energy engineering, such as thermoelectric energy harvesting. Here, we transfer a single sheet of graphene over a 5 µm trench of polydimethylsiloxane (PDMS) structure. By using a laser (1.77 mW, 1 µm diameter spot size, 517 nm wavelength) focusing on one side of the suspended graphene, a triangular shaped graphene ribbon is obtained. As the graphene has a negative thermal expansion coefficient, local laser heating could make the affected graphene area shrink and eventually break. Theoretical calculation shows that the 1.77 mW laser could create a local hot spot as high as 1462.5 °C, which could induce an asymmetric shape structure. We also find the temperature coefficient (-13.06 cm(-1) mW) of suspended graphene on PDMS trench substrate is ten times higher than that reported on SiO2/Si trench substrate. Collectively, our results raise the exciting prospect that the realization of graphene with asymmetric shape on thermally insulating substrate is technologically feasible, which may open up important applications in thermal circuits and thermal management.

An ultra-high element density pMUT array with low crosstalk for 3-D medical imaging.

A ~1 MHz piezoelectric micromachined ultrasonic transducer (pMUT) array with ultra-high element density and low crosstalk is proposed for the first time. This novel pMUT array is based on a nano-layer spin-coating lead zirconium titanium film technique and can be fabricated with high element density using a relatively simple process. Accordingly, key fabrication processes such as thick piezoelectric film deposition, low-stress Si-SOI bonding and bulk silicon removal have been successfully developed. The novel fine-pitch 6 × 6 pMUT arrays can all work at the desired frequency (~1 MHz) with good uniformity, high performance and potential IC integration compatibility. The minimum interspace is ~20 µm, the smallest that has ever been achieved to the best of our knowledge. These arrays can be potentially used to steer ultrasound beams and implement high quality 3-D medical imaging applications.

Bismuth nanoparticles and oxygen vacancies synergistically modified HNb3O8 nanosheets for enhanced photocatalytic N2 reduction towards NH3.

Due to the high stability of the N2 molecule and the low charge separation efficiency, the photocatalytic reduction of N2 to high-value chemicals (NH3) under mild conditions remains a great challenge. Herein, a composite photocatalyst (Bi/HNb3O8-Vo nanosheets) with Bi nanoparticles modified the HNb3O8-Vo nanosheets are designed for the conversion of N2 into NH3. In this design, the introduction of oxygen vacancies on the catalyst surface facilitates the formation of defective energy levels within the band gap of HNb3O8-Vo NS, which promotes the absorption of visible light, and enhances the charge carrier transport and separation. Bi nanoparticles co-catalyst not only facilitates the separation and migration of photogenerated charges, but also acts as reaction sites to adsorb and activate N2 molecule. Consequently, the optimized 5 % Bi/HNb3O8-Vo photocatalysts show a NH3 yield of 372.7 µmol/L g-1h-1 under full spectral irradiation without sacrificial agent, which is much higher than that of HNb3O8 NS (92.2 µmol/L g-1h-1). This work provides a new way for the design of efficient N2 reduction photocatalysts through the synergistic effect of surface vacancies and metal co-catalysts.

Photocatalytic reduction of CO2 into CH4 over Ru-doped TiO2: Synergy of Ru and oxygen vacancies.

Photocatalytic conversion of CO2 and H2O into CH4 is an intriguing approach to achieve solar energy utilization and CO2 conversion, yet remains challenging in conversion efficiency. In this study, we present a synthesis of defected TiO2 nanocrystal with oxygen vacancies (Vo) by a facile Ru doping-induced strategy under hydrothermal condition. The synergistic effect of Ru and oxygen vacancies contributed to the enhanced photocatalytic reduction of CO2 toward CH4. Oxygen vacancies and doped Ru not only can synergistically promote the separation of photogenerated carriers, but also promote the CO2 adsorption, thus enhancing the photocatalytic activities. The optimal Ru-doped TiO2 (denoted as 1% Ru-TiO2-x) exhibited a remarkable enhanced photocatalytic performance with a CH4 yield of 31.63 µmol·g-1·h-1, which is significantly higher than Ru-TiO2 and TiO2-x counterparts. This study systematically investigates the multiple roles of Ru in CO2 reduction and provides new insights for the construction of metal oxide photocatalysts with oxygen vacancies by simple doping of metal ions.

COVID-19 Detection Based on Image Regrouping and Resnet-SVM Using Chest X-Ray Images.

As the COVID-19 spread worldwide, countries around the world are actively taking measures to fight against the epidemic. To prevent the spread of it, a high sensitivity and efficient method for COVID-19 detection is necessary. By analyzing the COVID-19 chest X-ray images, a combination method of image regrouping and ResNet-SVM was proposed in this study. The lung region was segmented from the original chest X-ray images and divided into small pieces, and then the small pieces of lung region were regrouped into a regular image randomly. Furthermore the regrouped images were fed into the deep residual encoder block for feature extraction. Finally the extracted features were as input into support vector machine for recognition. The visual attention was introduced in the novel method, which paid more attention to the features of COVID-19 without the interference of shapes, rib and other related noises. The experimental results showed that the proposed method achieved 93% accuracy without large number of training data, outperformed the existing COVID-19 detection models.