Author Correction: The Apostasia genome and the evolution of orchids.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Revealing the Denoising Principle of Zero-Shot N2N-Based Algorithm from 1D Spectrum to 2D Image.

Denoising is a necessary step in image analysis to extract weak signals, especially those hardly identified by the naked eye. Unlike the data-driven deep-learning denoising algorithms relying on a clean image as the reference, Noise2Noise (N2N) was able to denoise the noise image, providing sufficiently noise images with the same subject but randomly distributed noise. Further, by introducing data augmentation to create a big data set and regularization to prevent model overfitting, zero-shot N2N-based denoising was proposed in which only a single noisy image was needed. Although various N2N-based denoising algorithms have been developed with high performance, their complicated black box operation prevented the lightweight. Therefore, to reveal the working function of the zero-shot N2N-based algorithm, we proposed a lightweight Peak2Peak algorithm (P2P) and qualitatively and quantitatively analyzed its denoising behavior on the 1D spectrum and 2D image. We found that the high-performance denoising originates from the trade-off balance between the loss function and regularization in the denoising module, where regularization is the switch of denoising. Meanwhile, the signal extraction is mainly from the self-supervised characteristic learning in the data augmentation module. Further, the lightweight P2P improved the denoising speed by at least ten times but with little performance loss, compared with that of the current N2N-based algorithms. In general, the visualization of P2P provides a reference for revealing the working function of zero-shot N2N-based algorithms, which would pave the way for the application of these algorithms toward real-time (in situ, in vivo, and operando) research improving both temporal and spatial resolutions. The P2P is open-source at https://github.com/3331822w/Peak2Peakand will be accessible online access at https://ramancloud.xmu.edu.cn/tutorial.

Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information.

Molecular vibrational spectroscopies, including infrared absorption and Raman scattering, provide molecular fingerprint information and are powerful tools for qualitative and quantitative analysis. They benefit from the recent development of deep-learning-based algorithms to improve the spectral, spatial, and temporal resolutions. Although a variety of deep-learning-based algorithms, including those to simultaneously extract the global and local spectral features, have been developed for spectral classification, the classification accuracy is still far from satisfactory when the difference becomes very subtle. Here, we developed a lightweight algorithm named patch-based convolutional encoder (PACE), which effectively improved the accuracy of spectral classification by extracting spectral features while balancing local and global information. The local information was captured well by segmenting the spectrum into patches with an appropriate patch size. The global information was extracted by constructing the correlation between different patches with depthwise separable convolutions. In the five open-source spectral data sets, PACE achieved a state-of-the-art performance. The more difficult the classification, the better the performance of PACE, compared with that of residual neural network (ResNet), vision transformer (ViT), and other commonly used deep learning algorithms. PACE helped improve the accuracy to 92.1% in Raman identification of pathogen-derived extracellular vesicles at different physiological states, which is much better than those of ResNet (85.1%) and ViT (86.0%). In general, the precise recognition and extraction of subtle differences offered by PACE are expected to facilitate vibrational spectroscopy to be a powerful tool toward revealing the relevant chemical reaction mechanisms in surface science or realizing the early diagnosis in life science.

Rapid Sample Pretreatment Facilitating SERS Detection of Trace Weak Organic Acids/Bases in Complex Matrices.

Fast and efficient sample pretreatment is the prerequisite for realizing surface-enhanced Raman spectroscopy (SERS) detection of trace targets in complex matrices, which is still a big issue for the practical application of SERS. Recently, we have proposed a highly performed liquid-liquid extraction (LLE)-back extraction (BE) for weak acids/bases extraction in drinking water and beverage samples. However, the performance efficiency decreased drastically on facing matrices like food and biological blood. Based on the total interaction energies among target, interferent, and extractant molecules, solid-phase extraction (SPE) with a higher selectivity was introduced in advance of LLE-BE, which enabled the sensitive (µg L-1 level) and rapid (within 10 min) SERS detection of both koumine (a weak base) and celastrol (a weak acid) in different food and biological samples. Further, the high SERS sensitivity was determined unmanned by Vis-CAD (a machine learning algorithm), instead of the highly demanded expert recognition. The generality of SPE-LLE-BE for various weak acids/bases (2 < pKa < 12), accompanied by the high efficiency, easy operation, and low cost, offers SERS as a powerful on-site and efficient inspection tool in food safety and forensics.

Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives.

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.

Signal2signal: Pushing the Spatiotemporal Resolution to the Limit by Single Chemical Hyperspectral Imaging.

There is growing interest in developing a high-performance self-supervised denoising algorithm for real-time chemical hyperspectral imaging. With a good understanding of the working function of the zero-shot Noise2Noise-based denoising algorithm, we developed a self-supervised Signal2Signal (S2S) algorithm for real-time denoising with a single chemical hyperspectral image. Owing to the accurate distinction and capture of the weak signal from the random fluctuating noise, S2S displays excellent denoising performance, even for the hyperspectral image with a spectral signal-to-noise ratio (SNR) as low as 1.12. Under this condition, both the image clarity and the spatial resolution could be significantly improved and present an almost identical pattern with a spectral SNR of 7.87. The feasibility of real-time denoising during imaging was well demonstrated, and S2S was applied to monitor the photoinduced exfoliation of transition metal dichalcogenide, which is hard to accomplish by confocal Raman spectroscopy. In general, the real-time denoising capability of S2S offers an easy way toward in situ/in vivo/operando research with much improved spatial and temporal resolution. S2S is open-source at https://github.com/3331822w/Signal2signal and will be accessible online at https://ramancloud.xmu.edu.cn/tutorial.

Overexpression of NtEXPA7 promotes seedling growth and resistance to root-knot nematode in tobacco.

Root-knot nematode (RKN) is one of the most damaging plant pathogen in the world. They exhibit a wide host range and cause serious crop losses. The cell wall, encasing every plant cell, plays a crucial role in defending of RKN invasion. Expansins are a group of cell wall proteins inducing cell wall loosening and extensibility. They are widely involved in the regulation of plant growth and the response to biotic and abiotic stresses. In this study, we have characterized the biological function of tobacco (Nicotiana tabacum) NtEXPA7, the homologue of Solyc08g080060.2 (SlEXPA18), of which the transcription level was significantly reduced in susceptible tomato upon RKN infection. The expression of NtEXPA7 was up-regulated after inoculation of RKNs. The NtEXPA7 protein resided in the cell wall. Overexpression of NtEXPA7 promoted the seedling growth of transgenic tobacco. Meanwhile the increased expression of NtEXPA7 was beneficial to enhance the resistance against RKNs. This study expands the understanding of biological role of expansin in coordinate plant growth and disease resistance.

Porous Tellurium-Doped Ruthenium Dioxide Nanotubes for Enhanced Acidic Water Oxidation.

Electrocatalysts with high activity and durability for acidic oxygen evolution reaction (OER) play a crucial role in achieving cost-effective hydrogen production via proton exchange membrane water electrolysis. A novel electrocatalyst, Te-doped RuO2 (Te-RuO2) nanotubes, synthesized using a template-directed process, which significantly enhances the OER performance in acidic media is reported. The Te-RuO2 nanotubes exhibit remarkable OER activity in acidic media, requiring an overpotential of only 171 mV to achieve an anodic current density of 10 mA cm-2. Furthermore, they maintain stable chronopotentiometric performance under 10 mA cm-2 in acidic media for up to 50 h. Based on the experimental results and density functional calculations, this significant improvement in OER performance to the synergistic effect of large specific surface area and modulated electronic structure resulting from the doping of Te cations is attributed.

Multifunctional Heterostructured Fe3 O4 -FeTe@MCM Electrocatalyst Enabling High-Performance Practical Lithium-Sulfur Batteries Via Built-in Electric Field.

The development of capable of simultaneously modulating the sluggish electrochemical kinetics, shuttle effect, and lithium dendrite growth is a promising strategy for the commercialization of lithium-sulfur batteries. Consequently, an elaborate preparation method is employed to create a host material consisting of multi-channel carbon microspheres (MCM) containing highly dispersed heterostructure Fe3 O4 -FeTe nanoparticles. The Fe3 O4 -FeTe@MCM exhibits a spontaneous built-in electric field (BIEF) and possesses both lithophilic and sulfophilic sites, rendering it an appropriate host material for both positive and negative electrodes. Experimental and theoretical results reveal that the existence of spontaneous BIEF leads to interfacial charge redistribution, resulting in moderate polysulfide adsorption which facilitates the transfer of polysulfides and diffusion of electrons at heterogeneous interfaces. Furthermore, the reduced conversion energy barriers enhanced the catalytic activity of Fe3 O4 -FeTe@MCM for expediting the bidirectional sulfur conversion. Moreover, regulated Li deposition behavior is realized because of its high conductivity and remarkable lithiophilicity. Consequently, the battery exhibited long-term stability for 500 cycles with 0.06% capacity decay per cycle at 5 C, and a large areal capacity of 7.3 mAh cm-2 (sulfur loading: 9.73 mg cm-2 ) at 0.1 C. This study provides a novel strategy for the rational fabrication of heterostructure hosts for practical Li-S batteries.

Boosting Polysulfide Redox Kinetics by Temperature-Induced Metal-Insulator Transition Effect of Tungsten-Doped Vanadium Dioxide for High-Temperature Lithium-Sulfur Batteries.

The practical application of Li-S batteries is still severely restricted by poor cyclic performance caused by the intrinsic polysulfides shuttle effect, which is even more severe under the high-temperature condition owing to the inevitable increase of polysulfides' solubility and diffusion rate. Herein, tungsten-doped vanadium dioxide (W-VO2) micro-flowers are employed with first-order metal-insulator phase transition (MIT) property as a robust and multifunctional modification layer to hamper the shuttle effect and simultaneously improve the thermotolerance of the common separator. Tungsten doping significantly reduces the transition temperature from 68 to 35 °C of vanadium dioxide, which renders the W-VO2 easier to turn from the insulating monoclinic phase into the metallic rutile phase. The systematic experiments and theoretical analysis demonstrate that the temperature-induced in-suit MIT property endows the W-VO2 catalyst with strong chemisorption against polysulfides, low energy barrier for liquid-to-solid conversion, and outstanding diffusion kinetics of Li-ion under high temperatures. Benefiting from these advantages, the Li-S batteries with W-VO2 modified separator exhibit significantly improved rate and long-term cyclic performance under 50 °C. Remarkably, even at an elevated temperature (80 °C), they still exhibit superior electrochemical performance. This work opens a rewarding avenue to use phase-changing materials for high-temperature Li-S batteries.

High Performance Thermoelectric Power of Bi0.5Sb1.5Te3 Through Synergistic Cu2GeSe3 and Se Incorporations.

Bi2Te3-based alloys are the benchmark for commercial thermoelectric (TE) materials, the widespread demand for low-grade waste heat recovery and solid-state refrigeration makes it imperative to enhance the figure-of-merits. In this study, high-performance Bi0.5Sb1.5Te3 (BST) is realized by incorporating Cu2GeSe3 and Se. Concretely, the diffusion of Cu and Ge atoms optimizes the hole concentration and raises the density-of-states effective mass (md *), compensating for the loss of "donor-like effect" exacerbated by ball milling. The subsequent Se addition further increases md *, enabling a total 28% improvement of room-temperature power factor (S2σ), reaching 43.6 µW cm-1 K-2 compared to the matrix. Simultaneously, the lattice thermal conductivity is also significantly suppressed by multiscale scattering sources represented by Cu-rich nanoparticles and dislocation arrays. The synergistic effects yield a peak ZT of 1.41 at 350 K and an average ZT of 1.23 (300-500 K) in the Bi0.5Sb1.5Te2.94Se0.06 + 0.11 wt.% Cu2GeSe3 sample. More importantly, the integrated 17-pair TE module achieves a conversion efficiency of 6.4%, 80% higher than the commercial one at ΔT = 200 K. These results validate that the facile composition optimization of the BST/Cu2GeSe3/Se is a promising strategy to improve the application of BST-based TE modules.

Optimizing Surface State Electrons of Topological Semi-Metal by Atomic Doping for Enhanced Hydrogen Evolution Reaction.

Topological materials carrying topological surface states (TSSs) have extraordinary carrier mobility and robustness, which provide a new platform for searching for efficient hydrogen evolution reaction (HER) electrocatalysts. However, the majority of these TSSs originate from the sp band of topological quantum catalysts rather than the d band. Here, based on the density functional theory calculation, it is reported a topological semimetal Pd3Sn carrying TSSs mainly derived from d orbital and proposed that optimizing surface state electrons of Pd3Sn by introduction heteroatoms (Ni) can promote hybridization between hydrogen atoms and electrons, thereby reducing the Gibbs free energy (ΔGH) of adsorbed hydrogen and improving its HER performance. Moreover, this is well verified by electrocatalytic experiment results, the Ni-doped Pd3Sn (Ni0.1Pd2.9Sn) show much lower overpotential (-29 mV vs RHE) and Tafel slope (17 mV dec-1) than Pd3Sn (-39 mV vs RHE, 25 mV dec-1) at a current density of 10 mA cm-2. Significantly, the Ni0.1Pd2.9Sn nanoparticles exhibit excellent stability for HER. The electrocatalytic activity of Ni0.1Pd2.9Sn nanoparticles is superior to that of commercial Pt. This work provides an accurate guide for manipulating surface state electrons to improve the HER performance of catalysts.

Effect of high-fat diet on the fatty acid profiles of brain in offspring mice exposed to maternal gestational diabetes mellitus.

OBJECTIVE: Fatty acids play a critical role in the proper functioning of the brain. This study investigated the effects of a high-fat (HF) diet on brain fatty acid profiles of offspring exposed to maternal gestational diabetes mellitus (GDM). METHODS: Insulin receptor antagonist (S961) and HF diet were used to establish the GDM animal model. Brain fatty acid profiles of the offspring mice were measured by gas chromatography at weaning and adulthood. Protein expressions of the fatty acid transport pathway Wnt3/ß-catenin and the target protein major facilitator superfamily domain-containing 2a (MFSD2a) were measured in the offspring brain by Western blot. RESULTS: Maternal GDM increased the body weight of male offspring (P < 0.05). In weaning offspring, factorial analysis showed that maternal GDM increased the monounsaturated fatty acid (MUFA) percentage of the weaning offspring's brain (P < 0.05). Maternal GDM decreased offspring brain arachidonic acid (AA), but HF diet increased brain linoleic acid (LA) (P < 0.05). Maternal GDM and HF diet reduced offspring brain docosahexaenoic acid (DHA), and the male offspring had higher DHA than the female offspring (P < 0.05). In adult offspring, factorial analysis showed that HF diet increased brain MUFA in offspring, and male offspring had higher brain MUFA than female offspring (P < 0.05). The HF diet increased brain LA in the offspring. Male offspring had higher level of AA than female offspring (P < 0.05). HF diet reduced DHA in the brains of female offspring. The brain protein expression of ß-catenin and MFSD2a in both weaning and adult female offspring was lower in the HF + GDM group than in the CON group (P < 0.05). CONCLUSIONS: Maternal GDM increased the susceptibility of male offspring to HF diet-induced obesity. HF diet-induced adverse brain fatty acid profiles in both male and female offspring exposed to GDM.

Water addition but not reduction alters plant biomass-diversity relationship.

The relationship between plant aboveground biomass and diversity typically follows a unimodal pattern, showing a positive correlation in resource-poor habitats and a negative correlation in resource-rich environments. Precipitation is a crucial resource for both plant biomass and diversity in terrestrial ecosystems. However, the impact of precipitation changes on the relationship between plant biomass and diversity remains unclear. We conduct a water addition field experiment in a semiarid grassland and identify a unimodal relationship between plant biomass and species richness under ambient conditions. Water addition delays the declining phase of this unimodal curve and shift it upward compared to ambient conditions. Our meta-analysis of water addition experiments conducted across major biomes worldwide (grassland, shrubland, desert, and forest) supports this finding, while water reduction does not alter the biomass-diversity relationship. Water addition increases biomass in all climate but only increases species richness in arid and semiarid climate. Similarly, water reduction decreases biomass in all climate but only reduces species richness in arid and semiarid climate. Species richness in dry subhumid and humid climate does not change significantly. Furthermore, our field experiment shows that water addition increases plant diversity while decreasing soil inorganic nitrogen levels. The increase in one resource, such as water, leads to the scarcity of another, such as nutrient, thus postponing the declining phase of the plant biomass-diversity relationship typically observed in resource-rich habitats. Our research contributes to predicting the plant biomass-diversity relationship under changing precipitation conditions and highlights the complex interplay between water availability, nutrient level, and plant diversity.

Gestational Diabetes Mellitus Remodels the Fetal Brain Fatty Acid Profile Through Placenta-Brain Lipid Axis in C57BL/6J Mice.

BACKGROUND: Polyunsaturated fatty acids (PUFAs), especially docosahexaenoic acid (DHA), are critical for proper fetal brain growth and development. Gestational diabetes mellitus (GDM) could affect maternal-fetal fatty acid metabolism. OBJECTIVE: This study aimed to explore the effect of GDM and high-fat (HF) diet on the DHA transport signaling pathway in the placenta-brain axis and fatty acid concentrations in the fetal brain. METHODS: Insulin receptor antagonist (S961) and HF diet were used to establish an animal model of GDM. Eighty female C57BL/6J mice were randomly divided into control (CON), GDM, HF, and HF+GDM groups. The fatty acid profiles of the maternal liver and fetal brain were analyzed by gas chromatography. In addition, we analyzed the protein amounts of maternal liver fatty acid desaturase (FADS1/3), elongase (ELOVL2/5) and the regulatory factor sterol-regulatory element-binding protein (SREBP)-1c, and the DHA transport signaling pathway (Wnt3/ß-catenin/MFSD2a) of the placenta and fetal brain using western blotting. RESULTS: GDM promoted the decrease of maternal liver ELOVL2, ELOVL5, and SREBP-1c. Accordingly, we observed a significant decrease in the amount of maternal liver arachidonic acid (AA), DHA, and total n-3 PUFA and n-6 PUFA induced by GDM. GDM also significantly decreased the amount of DHA and n-3 PUFA in the fetal brain. GDM downregulated the Wnt3/ß-catenin/MFSD2a signaling pathway, which transfers n-3 PUFA in the placenta and fetal brain. The HF diet increased n-6 PUFA amounts in the maternal liver, correspondingly increasing linoleic acid, gamma-linolenic acid, AA, and total n-6 PUFA in the fetal brain, but decreased DHA amount in the fetal brain. However, HF diet only tended to decrease placental ß-catenin and MFSD2a amounts (P = 0.074 and P = 0.098, respectively). CONCLUSIONS: Maternal GDM could affect the fatty acid profile of the fetal brain both by downregulating the Wnt3/ß-catenin/MFSD2a pathway of the placental-fetal barrier and by affecting maternal fatty acid metabolism.

Stop codon variant in EFEMP1 is associated with primary open-angle glaucoma due to impaired regulation of aqueous humor outflow.

It is known that the actin cytoskeleton and its associated cellular interactions in the trabecular meshwork (TM) and juxtacanalicular tissues mainly contribute to the formation of resistance to aqueous outflow of the eye. Fibulin-3, encoded by EFEMP1 gene, has a role in extracellular matrix (ECM) modulation, and interacts with enzymatic ECM regulators, but the effects of fibulin-3 on TM cells has not been explored. Here, we report a stop codon variant (c.T1480C, p.X494Q) of EFEMP1 that co-segregates with primary open angle glaucoma (POAG) in a Chinese pedigree. In the human TM cells, overexpression of wild-type fibulin-3 reduced intracellular actin stress fibers formation and the extracellular fibronectin levels by inhibiting Rho/ROCK signaling. TGFß1 up-regulated fibulin-3 protein levels in human TM cells by activating Rho/ROCK signaling. In rat eyes, overexpression of wild-type fibulin-3 decreased the intraocular pressure and the fibronectin expression of TM, however, overexpression of mutant fibulin-3 (c.T1480C, p.X494Q) showed opposite effects in cells and rat eyes. Taken together, the EFEMP1 variant may impair the regulatory capacity of fibulin-3 which has a role for modulating the cell contractile activity and ECM synthesis in TM cells, and in turn may maintain normal resistance of aqueous humor outflow. This study contributes to the understanding of the important role of fibulin-3 in TM pathophysiology and provides a new possible POAG therapeutic approach.

Crenobacter oryzisoli sp. nov., a novel phosphate-solubilizing bacterium isolated from the paddy soil.

Two Gram-staining-negative, facultative anaerobic, rod-shaped and phosphate-solubilizing strains designated SG2303T and SG2305, were isolated from paddy soil in China. Phylogenetic analysis based on 16 S rRNA gene sequences indicated that SG2303T and SG2305 represented a member of the genus Crenobacter within the family Neisseriaceae of the phylum Pseudomonadota. Strain SG2303T displayed higher 16 S rRNA gene sequence similarities with members of the genus Crenobacter ranging from 93.5 to 94.0%. Strains C. luteus YIM 78141T and C. cavernae K1W11S-77T were closest related to the isolated strains and were considered as type strains. Growth of strain SG2303T occurred at 10-55 °C (optimum 37 °C), pH 5.0-9.0 (optimum pH 6.0-7.0) and 0-1% (w/v) NaCl (optimum 0%). The average nucleotide identity (ANI) and digital DNA-DNA hybridization (dDDH) values between strain SG2303T and its closely related taxa were 76.1-78.2% and 20.5-22.1%, respectively. The genomic DNA G + C content was 62.2%. The quinone of strain SG2303T was Q-8. The major fatty acids (> 10%) of strain SG2303T were C16:0 (30.6%), summed feature 3 (C16:1ω7c and/or C16:1ω6c) (26.0%) and C12:0 3OH (12.1%). The polar lipids were phosphatidylglycerol (PG), diphosphatidylglycerol (DPG), phosphatidylethanolamine (PE), phospholipids (PL), glycolipid (GL) and unidentified lipids (UL). Based on the results of the phylogenetic, physiological, biochemical, and morphological analysis, strain SG2303T is recognized as a novel species of the genus Crenobacter, for which the name Crenobacter oryzisoli sp. nov. is proposed. The type strain is SG2303T (= GDMCC 1.3970T = JCM 36468T). In addition, SG2303T was also able of phosphorus solubilization and promoting the growth of rice seeds. Strain SG2303T exhibited a relatively high dissolvable phosphorus content of 2.52 µg·mL- 1.

Comparing Sonazoid contrast-enhanced ultrasound to contrast-enhanced CT and MRI for differentially diagnosing renal lesions: a prospective multicenter study.

PURPOSE: To evaluate the diagnostic performance of contrast-enhanced (CE) ultrasound using Sonazoid (SNZ-CEUS) by comparing with contrast-enhanced computed tomography (CE-CT) and contrast-enhanced magnetic resonance imaging (CE-MRI) for differentiating benign and malignant renal masses. MATERIALS AND METHODS: 306 consecutive patients (from 7 centers) with renal masses (40 benign tumors, 266 malignant tumors) diagnosed by both SNZ-CEUS, CE-CT or CE-MRI were enrolled between September 2020 and February 2021. The examinations were performed within 7 days, but the sequence was not fixed. Histologic results were available for 301 of 306 (98.37%) lesions and 5 lesions were considered benign after at least 2 year follow-up without change in size and image characteristics. The diagnostic performances were evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and compared by McNemar's test. RESULTS: In the head-to-head comparison, SNZ-CEUS and CE-MRI had comparable sensitivity (95.60 vs. 94.51%, P = 0.997), specificity (65.22 vs. 73.91%, P = 0.752), positive predictive value (91.58 vs. 93.48%) and negative predictive value (78.95 vs. 77.27%); SNZ-CEUS and CE-CT showed similar sensitivity (97.31 vs. 96.24%, P = 0.724); however, SNZ-CEUS had relatively lower than specificity than CE-CT (59.09 vs. 68.18%, P = 0.683). For nodules > 4 cm, CE-MRI demonstrated higher specificity than SNZ-CEUS (90.91 vs. 72.73%, P = 0.617) without compromise the sensitivity. CONCLUSIONS: SNZ-CEUS, CE-CT, and CE-MRI demonstrate desirable and comparable sensitivity for the differentiation of renal mass. However, the specificity of all three imaging modalities is not satisfactory. SNZ-CEUS may be a suitable alternative modality for patients with renal dysfunction and those allergic to gadolinium or iodine-based agents.

Three Fe(III)-reducing and nitrogen-fixing bacteria, Anaeromyxobacter terrae sp. nov., Anaeromyxobacter oryzisoli sp. nov. and Anaeromyxobacter soli sp. nov., isolated from paddy soil.

Three microaerophilic bacterial strains, designated SG22T, SG63T and SG29T were isolated from paddy soils in PR China. Cells of these strains were Gram-staining-negative and long rod-shaped. SG22T, SG63T and SG29T showed the highest 16S rRNA gene sequence similarities with the members of the genus Anaeromyxobacter. The results of phylogenetic and phylogenomic analysis also indicated that these strains clustered with members of the genus Anaeromyxobacter. The main respiratory menaquinone of SG22T, SG63T and SG29T was MK-8 and the major fatty acids were iso-C15 : 0, iso-C17 : 0 and C16 : 0. SG22T, SG29T and SG63T not only possessed iron reduction ability but also harboured genes (nifHDK) encoding nitrogenase. The genomic DNA G+C contents of SG22T, SG63T and SG29T ranged from 73.3 to 73.5 %. The average nucleotide identity (ANI) and digital DNA-DNA hybridisation (dDDH) values between SG22T, SG63T and SG29T and the closely related species of the genus Anaeromyxobacter were lower than the cut-off values (dDDH 70 % and ANI 95-96 %) for prokaryotic species delineation. On the basis of these results, strains SG22T, SG63T and SG29T represent three novel species within the genus Anaeromyxobacter, for which the names Anaeromyxobacter terrae sp. nov., Anaeromyxobacter oryzisoli sp. nov. and Anaeromyxobacter soli sp. nov., are proposed. The type strains are SG22T (= GDMCC 1.3185T = JCM 35581T), SG63T (= GDMCC 1.2914T = JCM 35124T) and SG29T (= GDMCC 1.2911T = JCM 35123T).

Sodium Carbonate-Promoted Formation of 5-Amino-1,2,4-thiadiazoles and 5-Amino-1,2,4-selenadiazoles with Elemental Sulfur and Selenium.

Sodium carbonate-promoted facile synthesis of 5-amino-1,2,4-thiadiazoles and 5-amino-1,2,4-selenadiazoles with elemental sulfur and selenium, respectively, was developed. This method was carried out with O2 in the air as the green oxidant, and it has several advantages, including low cost, low toxicity, and stable sulfur and selenium sources, good to excellent yields with water as the sole byproduct, simple operation, and a broad substrate scope. Preliminary mechanistic studies indicate that the formation of the 1,2,4-thiadiazole ring and the 1,2,4-selenadiazole ring undergoes different processes.