Structural effects of water clusters on viscosity at high shear rates.

In this study, we use molecular dynamics simulations of liquid water to investigate how shear thinning affects the viscosity of liquid water by structural changes of the hydrogen bond network. The effect of shear on viscosity can be divided into two parts: shear-induced destruction of the hydrogen bond network and the influence of the water structure on shear viscosity. First, strong shear destroys tetrahedral structures and thus reduces the connectivity of the hydrogen bond network. It is mainly because shear deformation, characterized by compression and expansion axes, respectively, triggers the destruction and formation of hydrogen bonds, resulting in anisotropic effects on water structures. At the same time, shear destroys large clusters and enhances the formation of small ones, resulting in a decrease in average cluster sizes. Second, the change of viscosity obeys a power law relationship with the change of hydrogen bond structures, highlighting a one-to-one correspondence between structure and property. Meanwhile, in order to explain why the structure affects viscosity, we define hydrogen-bond viscosity and find that the cooperative motion of the water structures can promote momentum transfer in the form of aggregations. Hydrogen-bond viscosity accounts for 5%-50% of the total viscosity. Our results elucidate that water structures are the important structural units to explain the change of water properties.

Self-Propulsion by Directed Explosive Emulsification.

An active droplet system, programmed to repeatedly move autonomously at a specific velocity in a well-defined direction, is demonstrated. Coulombic energy is stored in oversaturated interfacial assemblies of charged nanoparticle-surfactants by an applied DC electric field and can be released on demand. Spontaneous emulsification is suppressed by an increase in the stiffness of the oversaturated assemblies. Rapidly removing the field releases the stored energy in an explosive event that propels the droplet, where thousands of charged microdroplets are ballistically ejected from the surface of the parent droplet. The ejection is made directional by a symmetry breaking of the interfacial assembly, and the combined interaction force of the microdroplet plume on one side of the droplet propels the droplet distances tens of times its size, making the droplet active. The propulsion is autonomous, repeatable, and agnostic to the chemical composition of the nanoparticles. The symmetry-breaking in the nanoparticle assembly controls the microdroplet velocity and direction of propulsion. This mechanism of droplet propulsion will advance soft micro-robotics, establishes a new type of active matter, and introduces new vehicles for compartmentalized delivery.

Resurrected and Tunable Conductivity and Ferromagnetism in the Secondary Growth La0.7Ca0.3MnO3 on Transferred SrTiO3 Membranes.

To avoid the epitaxy dilemma in various thin films, such as complex oxide, silicon, organic, metal/alloy, etc., their stacking at an atomic level and secondary growth are highly desired to maximize the functionality of a promising electronic device. The ceramic nature of complex oxides and the demand for accurate and long-range-ordered stoichiometry face severe challenges. Here, the transport and magnetic properties of the La0.7Ca0.3MnO3 (LCMO) secondary growth on single-crystal freestanding SrTiO3 (STO) membranes are demonstrated. It has been experimentally found that on an only 10 nm thick STO membrane, the LCMO can offer a bulk-like Curie temperature (TC) of 253 K and negative magnetoresistance of -64%, with a weak dependence on the thickness. The resurrected conductivity and ferromagnetism in LCMO confirm the advantages of secondary growth, which benefits from the excellent flexibility and transferability. Additionally, this study explores the integration strategy of complex oxides with other functional materials.

Decoding the guardians of cotton resilience: A comprehensive exploration of the ßCA genes and its role in Verticillium dahliae resistance.

Plant Carbonic anhydrases (Cas) have been shown to be stress-responsive enzymes that may play a role in adapting to adverse conditions. Cotton is a significant economic crop in China, with upland cotton (Gossypium hirsutum) being the most widely cultivated species. We conducted genome-wide identification of the ßCA gene in six cotton species and preliminary analysis of the ßCA gene in upland cotton. In total, 73 ßCA genes from six cotton species were identified, with phylogenetic analysis dividing them into five subgroups. GHßCA proteins were predominantly localized in the chloroplast and cytoplasm. The genes exhibited conserved motifs, with motifs 1, 2, and 3 being prominent. GHßCA genes were unevenly distributed across chromosomes and were associated with stress-responsive cis-regulatory elements, including those responding to light, MeJA, salicylic acid, abscisic acid, cell cycle regulation, and defence/stress. Expression analysis indicated that GHßCA6, GHßCA7, GHßCA10, GHßCA15, and GHßCA16 were highly expressed under various abiotic stress conditions, whereas GHßCA3, GHßCA9, GHßCA10, and GHßCA18 had higher expression patterns under Verticillium dahliae infection at different time intervals. In Gossypium thurberi, GthßCA1, GthßCA2, and GthßCA4 showed elevated expression across stress conditions and tissues. Silencing GHßCA10 through VIGS increased Verticillium wilt severity and reduced lignin deposition compared to non-silenced plants. GHßCA10 is crucial for cotton's defense against Verticillium dahliae. Further research is needed to understand the underlying mechanisms and develop strategies to enhance resistance against Verticillium wilt.

Pseudomonas fluorescens and L-tryptophan application triggered the phytoremediation potential of sunflower (Heliantus annuus L.) in lead-contaminated soil.

Lead, a toxic heavy metal present in soil, hampers biological activities and affects the metabolism of plants, animals, and human beings. Its higher concentration may disturb the various physio-chemical processes, which result in stunted and poor plant growth. An interactive approach of plant growth promoting rhizobacteria (PGPR) and L-tryptophan can be used to mitigate the lethal effects of lead. A pot experiment was conducted, and two weeks before sowing, the level of lead (300 mg kg-1) was maintained by spiking the PbCl2 salt. Pseudomonas fluorescens and L-tryptophan were applied individually as well as in combination to segregate the effect of both in contaminated soil under a completely Randomized Design (CRD). Statistical analysis revealed that plant growth was significantly reduced up to 22% due to lead contamination. However, the interactive approach of PGPR and L-tryptophan significantly improved the plant growth, physiology, and yield with relative productive index (RPI) under a lead-stressed environment. Moreover, integrated use of PGPR and L-tryptophan demonstrated a considerable increase (22%) in lead removal efficiency (LRE) by improving bioconcentration factor (BCF) and translocation factor (TF) for shoot without increasing the lead concentration in achenes. The reduced lead concentration in achene was due to its immobilization in shoot and root by negatively charged particles and improved the lead sequestration in vegetative parts which abridged the translocation of lead into achenes.

Comparative biotic and abiotic effects on greenhouse gas emissions from agricultural ecosystems: application of straw or biochar?

Farmland has become a significant contributor to greenhouse gas (GHG) emissions, and research has shown that the addition of straw or biochar may be a viable method for mitigating these emissions. However, there is a lack of understanding regarding the comparative biotic and abiotic effects of straw and biochar amendments on GHG emissions. To address this knowledge gap, we conducted a meta-analysis of 100 published papers to quantify the impact of straw and biochar application on GHG emissions. Our findings indicate that straw application significantly increased CO2 and CH4 emissions from agricultural ecosystems by 46.2% and 113.5%, respectively, but did not have a significant effect on N2O emissions. Conversely, biochar amendment significantly reduced CO2, CH4, and N2O emissions by an average of 11.0%, 31.7%, and 22.8%, respectively. We also found that straw and biochar amendments increased soil pH, soil organic carbon (SOC), and C/N ratio, and there were significant differences between them. Moreover, straw application significantly increased the microbial biomass carbon (MBC) content and microbial quotient by 37.1% and 20.1%, respectively, while biochar application increased the MBC content by 25.0% without a significant effect on the microbial quotient. Furthermore, both straw and biochar applications promoted the nitrification process and increased the abundance of ammonia-oxidizing bacteria (AOB) by 50.7% with straw and by 57.5% and 75.1% with biochar for ammonia-oxidizing archaea (AOA) and AOB, respectively. The denitrification process was also stimulated by straw or biochar amendment, resulting in an increase in the abundance of nirK by 22.9% and 16.8%, respectively. Biochar amendment additionally increased the abundance of nosZ by 29.4%, indicating that the main reason for reducing N2O emissions through biochar application is the conversion of NO3--N to N2. Thus, compared to straw application, biochar application is a more effective method for reducing greenhouse gas emissions.

MMDAE-HGSOC: A novel method for high-grade serous ovarian cancer molecular subtypes classification based on multi-modal deep autoencoder.

High-grade serous ovarian cancer (HGSOC) is a type of ovarian cancer developed from serous tubal intraepithelial carcinoma. The intrinsic differences among molecular subtypes are closely associated with prognosis and pathological characteristics. At present, multi-omics data integration methods include early integration and late integration. Most existing HGSOC molecular subtypes classification methods are based on the early integration of multi-omics data. The mutual interference among multi-omics data is ignored, which affects the effectiveness of feature learning. High-dimensional multi-omics data contains genes unassociated with HGSOC molecular subtypes, resulting in redundant information, which is not conducive to model training. In this paper, we propose a multi-modal deep autoencoder learning method, MMDAE-HGSOC. MiRNA expression, DNA methylation, and copy number variation (CNV) are integrated with mRNA expression data to construct a multi-omics feature space. The multi-modal deep autoencoder network is used to learn the high-level feature representation of multi-omics data. The superposition LASSO (S-LASSO) regression algorithm is proposed to fully obtain the associated genes of HGSOC molecular subtypes. The experimental results show that MMDAE-HGSOC is superior to the existing classification methods. Finally, we analyze the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which are discovered during the gene selection process.

CDA-SKAG: Predicting circRNA-disease associations using similarity kernel fusion and an attention-enhancing graph autoencoder.

Circular RNAs (circRNAs) constitute a category of circular non-coding RNA molecules whose abnormal expression is closely associated with the development of diseases. As biological data become abundant, a lot of computational prediction models have been used for circRNA-disease association prediction. However, existing prediction models ignore the non-linear information of circRNAs and diseases when fusing multi-source similarities. In addition, these models fail to take full advantage of the vital feature information of high-similarity neighbor nodes when extracting features of circRNAs or diseases. In this paper, we propose a deep learning model, CDA-SKAG, which introduces a similarity kernel fusion algorithm to integrate multi-source similarity matrices to capture the non-linear information of circRNAs or diseases, and construct a circRNA information space and a disease information space. The model embeds an attention-enhancing layer in the graph autoencoder to enhance the associations between nodes with higher similarity. A cost-sensitive neural network is introduced to address the problem of positive and negative sample imbalance, consequently improving our model's generalization capability. The experimental results show that the prediction performance of our model CDA-SKAG outperformed existing circRNA-disease association prediction models. The results of the case studies on lung and cervical cancer suggest that CDA-SKAG can be utilized as an effective tool to assist in predicting circRNA-disease associations.

Enhanced OER Performance and Dynamic Transition of Surface Reconstruction in LaNiO3 Thin Films with Nanoparticles Decoration.

In an electrocatalytic process, the cognition of the active phase in a catalyst has been regarded as one of the most vital issues, which not only boosts the fundamental understanding of the reaction procedure but also guides the engineering and design for further promising catalysts. Here, based on the oxygen evolution reaction (OER), the stepwise evolution of the dominant active phase is demonstrated in the LaNiO3 (LNO) catalyst once the single-crystal thin film is decorated by LNO nanoparticles. It is found that the OER performance can be dramatically improved by this decoration, and the catalytic current density at 1.65 V can be enhanced by ≈1000% via ≈109 cm-2 nanoparticle adhesion after extracting the contribution of surface enlargement. Most importantly, a transition of the active phase from LNO to NiOOH via surface reconstruction with the density of LNO nanoparticles is demonstrated. Several mechanisms in terms of this active phase transition are discussed involving lattice orientation-induced change of the surface energy profile, the lattice oxygen participation, and the A/B-site ions leaching during OER cycles. This study suggests that the active phases in transition metal-based OER catalysts can transform with morphology, which should be corresponding to distinct engineering strategies.

A parallel model of DenseCNN and ordered-neuron LSTM for generic and species-specific succinylation site prediction.

Lysine succinylation (Ksucc) regulates various metabolic processes, participates in vital life processes, and is involved in the occurrence and development of numerous diseases. Accurate recognition of succinylation sites can reveal underlying functional mechanisms and pathogenesis. However, most remain undetected. Moreover, a deep learning architecture focusing on generic and species-specific predictions is still lacking. Thus, we proposed a deep learning-based framework named Deep-Ksucc, combining a dense convolutional network and ordered-neuron long short-term memory in parallel, which took the cascading characteristics of sequence information and physicochemical properties as the input. The results of the generic and species-specific predictions indicated that Deep-Ksucc can identify sequence patterns of different organisms and recognize plenty of succinylation sites. The case study showed that Deep-Ksucc can serve as a reliable tool for biology verification and computer-aided recognition of succinylation sites.

Biochar promotes soil organic carbon sequestration and reduces net global warming potential in apple orchard: A two-year study in the Loess Plateau of China.

The Loess Plateau is China's primary apple-growing area, and the orchard is a significant source of greenhouse gases (GHGs) emissions due to high nitrogen fertilizer input. Thus, a two-year field study was carried out to investigate the effects of apple wood derived biochar on GHGs emissions during apple orchard production, including soil organic carbon sequestration (SOCSR) and net global warming potential (NGWP) assessments. There are four treatments in this study: 20 t ha-1 biochar in a non-fertilized plot (B); no biochar in a fertilized plot (F); 20 t ha-1 biochar in a fertilized plot (FB); no biochar in a non-fertilized plot (CK). Results showed that the combined application of biochar and fertilizer stimulated CO2 emissions by 9.25% and 8.39% than either biochar or fertilizer alone. Meanwhile, biochar in fertilized plot increased annual N2O emissions by 32.6% as compared to fertilized plot without biochar amendment. Compared with CK, biochar had no significant effect on GHG emissions in unfertilized plot. The N2O emission factor of FB and F were 0.91% and 0.45% respectively in 2017-2018 and they were both 0.34% in 2018-2019. Moreover, compared with CK, the FB and B treatments increased the SOCSR by 316.52% and 354.78%, while, decreased the NGWP by 368.93% and 480.91%, respectively. Thus, biochar application may help reduce the impact of apple production on climate change by sequestering more soil organic carbon and decreasing the NGWP.

MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network.

Lysine succinylation is an important post-translational modification, whose abnormalities are closely related to the occurrence and development of many diseases. Therefore, exploring effective methods to identify succinylation sites is helpful for disease treatment and research of related drugs. However, most existing computational methods for the prediction of succinylation sites are still based on machine learning. With the increasing volume of data and complexity of feature representations, it is necessary to explore effective deep learning methods to recognize succinylation sites. In this paper, we propose a multilane dense convolutional attention network, MDCAN-Lys. MDCAN-Lys extracts sequence information, physicochemical properties of amino acids, and structural properties of proteins using a three-way network, and it constructs feature space. For each sub-network, MDCAN-Lys uses the cascading model of dense convolutional block and convolutional block attention module to capture feature information at different levels and improve the abstraction ability of the network. The experimental results of 10-fold cross-validation and independent testing show that MDCAN-Lys can recognize more succinylation sites, which is consistent with the conclusion of the case study. Thus, it is worthwhile to explore deep learning-based methods for the recognition of succinylation sites.

Regional Features of MuSK Antibody-Positive Myasthenia Gravis in Northeast China.

Objective: To summarize the characteristics of muscle-specific receptor tyrosine kinase antibody-positive myasthenia gravis (MuSK-MG) in Northeast China. Methods: We retrospectively collected 183 confirmed MG patients and divided them into three groups based on the type of serum antibodies: MuSK-MG (14 cases), acetylcholine receptor (AChR)-MG (130 cases), and double-seronegative (DSN)-MG (39 cases). The clinical, diagnostic, therapeutic, and prognosis data were analyzed. Results: MuSK antibody was detected in 26.7% of seronegative MG. The mean age of onset in MuSK-MG was 53.2 ± 13.6 years. Fifty percent of MuSK-MG patients with an onset symptom of pure ocular muscle weakness. The time from onset to other muscle groups' involvement and the time from onset to myasthenic crisis had no significant difference among the three groups (P > 0.05). The proportion of Osserman classification I in MuSK-MG group was lower than that in DSN-MG group. The proportion of Osserman classification IV in MuSK-MG group was higher than that in the other two groups. The incidences of other coexisting autoimmune diseases in MuSK-MG group were higher. Prognosis after the treatment of steroid combined with tacrolimus for MuSK-MG was similar to AChR-MG treated with steroid combined with an immunosuppressant agent (P > 0.05). Conclusion: Patients with MuSK-MG in Northeast China have a modestly later onset age and a proportion of patients may have a mild form of the disease with delayed disease progression. We confirmed the existence of a rare ocular MuSK-MG phenotype, a high proportion of coexisting with other autoimmune diseases, and a good response to steroids combined with tacrolimus for our MuSK-MG series.

Multiview Clustering by Joint Latent Representation and Similarity Learning.

Subspace learning-based multiview clustering has achieved impressive experimental results. However, the similarity matrix, which is learned by most existing methods, cannot well characterize both the intrinsic geometric structure of data and the neighbor relationship between data. To consider the fact that original data space does not well characterize the intrinsic geometric structure, we learn the latent representation of data, which is shared by different views, from the latent subspace rather than the original data space by linear transformation. Thus, the learned latent representation has a low-rank structure without solving the nuclear-norm. This reduces the computational complexity. Then, the similarity matrix is adaptively learned from the learned latent representation by manifold learning which well characterizes the local intrinsic geometric structure and neighbor relationship between data. Finally, we integrate clustering, manifold learning, and latent representation into a unified framework and develop a novel subspace learning-based multiview clustering method. Extensive experiments on benchmark datasets demonstrate the superiority of our method.

[Characteristics of CO2 and N2O Emissions Under Two Land Use Types in the Loess Plateau of China].

To evaluate the emission characteristics of carbon dioxide (CO2) and nitrous oxide (N2O) in the Loess Plateau, a field in situ study was conducted from July 2017 to July 2018 under two land-use types (15 year old apple orchard and wheat field) using static chamber-gas chromatographic techniques. Four treatments were conducted in this experiment as follows:apple orchard with fertilization (AF), apple orchard without fertilization (ACK), wheat field with fertilization (WF), and wheat field without fertilization (WCK). The results showed that CO2 and N2O emissions varied significantly with the season, and the emission peaks appeared after rainfall and fertilization. The cumulative amount of CO2 and N2O emissions from the AF treatment were 7.14% and 461.4% higher than that of the WF treatment, respectively. However, the cumulative amount of CO2 emissions under the ACK treatment was 10.41% lower than that of the WCK treatment, whereas the cumulative amount of N2O emissions was 109.5% higher than that of the WCK treatment. The N2O emission flux from the orchard was significantly positively correlated with soil temperature and moisture (P<0.01). The CO2 emission fluxes from the orchard and wheat field were significantly positively correlated with topsoil temperature (P<0.05) but were not correlated with topsoil moisture. Thus, the combination of field management and environmental factors affected soil CO2 and N2O emissions. The fertilizer regime and soil hydrothermal conditions were the main factors influencing the characteristics of CO2 and N2O emissions under different land-use types.

Spectroscopic monitoring of transport processes during loading of ovarian tissue with cryoprotective solutions.

There is an increasing demand for female fertility preservation. Cryopreservation of ovarian cortex tissue by means of vitrification can be done ad-hoc and for pre-pubertal individuals. Obtaining a homogeneous distribution of protective agents in tissues is one of the major hurdles for successful preservation. Therefore, to rationally design vitrification strategies for tissues, it is needed to determine permeation kinetics of cryoprotective agents; to ensure homogeneous distribution while minimizing exposure time and toxicity effects. In this study, Fourier transform infrared spectroscopy (FTIR) was used to monitor diffusion of different components into porcine ovarian cortex tissue. Water fluxes and permeation kinetics of dimethyl sulfoxide (DMSO), glycerol (GLY), ethylene glycol (EG), and propylene glycol (PG) were investigated. Diffusion coefficients derived from FTIR data, were corroborated with differential scanning calorimetry and osmometer measurements. FTIR allowed real-time spectral fingerprinting of tissue during loading with mixtures of protective agents, while discriminating between different components and water. Exposure to vitrification solutions was found to cause drastic initial weight losses, which could be correlated with spectral features. Use of heavy water allowed distinguishing water fluxes associated with dehydration and permeation, both of which were found to precede permeation of cryoprotective agents. Overall, DMSO and EG were found to permeate faster than GLY and PG. In mixtures, however, solutes behave differently. The non-invasive spectroscopic method described here to study permeation of vitrification solution components into ovarian tissue can be applied to many other types of engineered constructs, tissues, and possibly organs.

Factors Affecting the Membrane Permeability Barrier Function of Cells during Preservation Technologies.

Cellular membranes are exposed to extreme conditions during the processing steps involved in cryopreservation (and freeze-drying) of cells. The first processing step involves adding protective agents. Exposing cells to protective agents causes fluxes of both water and solutes (i.e., permeating cryoprotective agents) across the cellular membrane, resulting in cell volume changes and possibly osmotic stress. In addition, protective molecules may interact with lipids, which may lead to membrane structural changes and permeabilization. After loading with protective agents, subsequent freezing exposes cells to severe osmotic and mechanical stresses, caused by extra and/or intracellular ice formation and a drastically increased solute concentration in the unfrozen fraction. Furthermore, cellular membranes undergo thermotropic and lyotropic phase transitions during cooling and freezing, which drastically alter the membrane permeability and its barrier function. In this article, it is shown that membrane permeability to water and solutes is dependent on the temperature, medium osmolality, types of solutes present, cell hydration level, and absence or presence of ice. Freezing most drastically alters the membrane permeability barrier function, which is reflected as a change in the activation energy for water transport. In addition, membranes become temporarily leaky during freezing-induced fluid-to-gel membrane phase transitions, resulting in the uptake of impermeable solutes.

In situ growth of TiO2 in interlayers of expanded graphite for the fabrication of TiO2-graphene with enhanced photocatalytic activity.

We present a facile route for the preparation of TiO(2)-graphene composites by in situ growth of TiO(2) in the interlayer of inexpensive expanded graphite (EG) under solvothermal conditions. A vacuum-assisted technique combined with the use of a surfactant (cetyltrimethylammonium bromide) plays a key role in the fabrication of such composites. Firstly, the vacuum environment promotes full infusion of the initial solution containing Ti(OBu)(4) and the surfactant into the interlayers of EG. Subsequently, numerous TiO(2) nanoparticles uniformly grow in situ in the interlayers with the help of the surfactant, which facilitates the exfoliation of EG under the solvothermal conditions in ethanol, eventually forming TiO(2)-graphene composites. The as-prepared samples have been characterized by Raman and FTIR spectroscopies, SEM, TEM, AFM, and thermogravimetic analysis. It is shown that a large number of TiO(2) nanoparticles homogeneously cover the surface of high-quality graphene sheets. The graphene exhibits a multi-layered structure (5-7 layers). Notably, the TiO(2)-graphene composite (only 30 wt % of which is TiO(2)) synthesized by subsequent thermal treatment at high temperature under nitrogen shows high photocatalytic activity in the degradation of phenol under visible and UV lights in comparison with bare Degussa P25. The enhanced photocatalytic performance is attributed to increased charge separation, improved light absorbance and light absorption width, and high adsorptivity for pollutants.