Bio-manure substitution declines soil N2O and NO emissions and improves nitrogen use efficiency and vegetable quality index.

Substituting mineral fertilizer with manure or a combination of organic amendments plus beneficial soil microorganisms (bio-manure) in agriculture is a standard practice to mitigate N2O and NO emissions while enhancing crop performance and nitrogen use efficiency (NUE). Here, we conducted a greenhouse trial for three consecutive vegetable growth seasons for Spinach, Coriander herb, and Baby bok choy to reveal the response of N2O and NO emissions, NUE, and vegetable quality index (VQI) to fertilization strategies. Strategies included solely chemical nitrogen fertilizer (CN), 20 (M1N4) and 50% (M1N1) substitution with manure, 20 (BM1N4) and 50% (BM1N1) substitution with bio-manure, and no fertilization as a control and were organized in a completely randomized design (n = 3). Manure decreased N2O emissions by 24-45% and bio-manure by 44-53% compared to CN. Manure reduced NO emissions by 28-41% and bio-manure by 55-63%. Bio-manure increased NUE by 0.04-31% and yields by 0.05-61% while improving VQI, attributed to yield growth and reduced vegetable NO3- contents. Improvement of root growth was the main factor that explained the rise of NUE; NUE declined with the increase of N2O emissions, showing the loss of vegetable performance under conditions when denitrification processes prevailed. Under the BM1N1, the highest VQI and the lowest yield-scaled N-oxide emissions were observed, suggesting that substitution with bio-manure can improve vegetable quality and mitigate N-oxide emissions. These findings indicate that substituting 50% of mineral fertilizer with bio-manure can effectively improve NUE and VQI and mitigate N-oxides in intensive vegetable production.

A Symmetrical Siamese Network Framework With Contrastive Learning for Pose-Robust Face Recognition.

Face recognition has achieved remarkable success owing to the development of deep learning. However, most of existing face recognition models perform poorly against pose variations. We argue that, it is primarily caused by pose-based long-tailed data - imbalanced distribution of training samples between profile faces and near-frontal faces. Additionally, self-occlusion and nonlinear warping of facial textures caused by large pose variations also increase the difficulty in learning discriminative features of profile faces. In this study, we propose a novel framework called Symmetrical Siamese Network (SSN), which can simultaneously overcome the limitation of pose-based long-tailed data and pose-invariant features learning. Specifically, two sub-modules are proposed in the SSN, i.e., Feature-Consistence Learning sub-Net (FCLN) and Identity-Consistence Learning sub-Net (ICLN). For FCLN, the inputs are all face images on training dataset. Inspired by the contrastive learning, we simulate pose variations of faces and constrain the model to focus on the consistent areas between the original face image and its corresponding virtual pose face images. For ICLN, only profile images are used as inputs, and we propose to adopt Identity Consistence Loss to minimize the intra-class feature variation across different poses. The collaborative learning of two sub-modules guarantees that the parameters of network are updated in a relatively equal probability between near-frontal face images and profile images, so that the pose-based long-tailed problem can be effectively addressed. The proposed SSN shows comparable results over the state-of-the-art methods on several public datasets. In this study, LightCNN is selected as the backbone of SSN, and existing popular networks also can be used into our framework for pose-robust face recognition.

Post-anthesis supplementary irrigation improves grain yield and nutritional quality of drip-irrigated rice (Oryza sativa L.).

Introduction: Approximately 50% of irrigation water is saved during drip-irrigation of rice, which has tremendous potential for water-saving agriculture, particularly in areas where water resources are scarce. However, the grain yield and quality of drip-irrigated rice are adversely affected. Methods: In this study, we investigated the effects of different irrigation strategies on the grain yield and quality of drip-irrigated rice using field experiments. Four irrigation treatments were studied: whole growing season flooding (FI), whole growing season normal drip irrigation (DI, soil relative moisture (RSM) was maintained in the range of 90-100%), pre-anthesis drip irrigation and post-anthesis water stress (SAF, the RSM was maintained in the range of 80-90% after anthesis), pre-anthesis drip irrigation, and post-anthesis flooding (FAF). Results: The results showed that grain yield, harvest index, seed setting rate and 1000 grain weight in DI and SAF were significantly lower than in FI and FAF. These parameters were not significantly different between FI and FAF but were significantly greater in DI than in SAF. Compared with FI and FAF, the source capacity, source activity time, and sink activity of DI and SAF decreased, and the sink-source difference increased. The sink-source difference had a significant negative correlation with rice yield and 1000 grain weight. The activities of ADP-glucose pyrophosphorylase, starch branching enzyme, and amylopectin content in grains in the middle panicles of FAF were significantly higher than those of DI and SAF. SAF resulted in increased amylose/amylopectin ratio and total protein content in grains but decreased proportion of glutenin in total protein. Irrigation after anthesis of drip-irrigated rice narrowed the difference between sink sources in rice plants, increased the grain yield and harvest index by 29.2% and 11%, respectively, compared to DI, increased water productivity by 19% compared to FI, and improved the grain quality of drip-irrigated rice. Discussion: This study highlights that post-anthesis sufficient irrigation of drip-irrigated rice plays a positive role in maintaining the source-sink balance. This study serves as a foundation for the development of more effective rice farming methods that conserve water, while increasing the grain yield and quality of drip-irrigated rice.

Novel Reinforcing Techniques and Bearing Capacity Analysis for Tunnel Lining Structures with Extensive Corrosion.

Affected by the erosive environment, tunnel lining concrete in the long-term service zprocess often exhibits engineering diseases such as concrete corrosion degradation and loss of strength, decreasing the stability of the tunnel lining structure and the traffic safety. Based on HTG tunnel project, the basic distribution rule of tunnel lining corrosion and macro mechanical properties of corroded concrete were explored in this paper through engineering disease site investigation. Then, on this basis, aiming at large-scale corrosion of tunnel lining structure, two reinforcement and repair schemes are proposed, corrugated steel plate reinforcement method and channel steel reinforcement method. Indoor component tests are carried out on the two reinforcement schemes. The failure characteristics and stress and deformation law of tunnel lining members after reinforcement and repair were verified. The analysis showed that the failure process of the reinforced specimens on the tensile side could be divided into the non-cracking stage and the working stage with cracks, and the cracking load and failure load of the specimens were significantly increased. The bearing capacity of the reinforced specimens was divided into the ultimate bearing capacity against cracking and the ultimate bearing capacity during failure. Finally, the calculation methods of the bearing capacity of the channel steel reinforcement method and the corrugated steel plate reinforcement method were derived. Comparative analysis shows that the results of numerical simulation, experimental testing and theoretical simplification methods are close to each other, and the maximum deviation is less than 8%. The established method for calculating the bearing capacity of corroded components after reinforcement is reliable and can be used for the design calculation of corroded lining reinforcement.

Tensile Performance Test Research of Hybrid Steel Fiber-Reinforced Self-Compacting Concrete.

Notched beam specimens were loaded by the three-point bending test device, and the effects of different volume contents and combinations of steel fibers on the tensile properties of hybrid steel fiber-reinforced self-compacting concrete (HSFRSCC) were studied. The failure law and strain field distribution of the specimens were studied by digital image correlation (DIC) technology. Moreover, the curves between the load and crack mouth opening displacement (CMOD) of 18 groups of hybrid steel fiber-reinforced concrete specimens were obtained, and the stress-strain curves of 18 groups of specimens were derived from the load-CMOD curves. The results show that both single and hybrid steel fibers can improve the crack deformation resistance and tensile properties of concrete, but hybrid steel fibers have a more significant improvement effect. Only when the content of steel fiber is more than 0.6% can it have a more obvious postpeak descending section, and hybrid steel fiber has higher postpeak deformation capacity and flexural toughness. The fundamental reason why concrete with hybrid steel fibers has better tensile properties is that micro and macro steel fibers cooperate with each other to resist cracks, improving the toughness of concrete after cracking. Finally, the mechanism of different size and volume content of steel fiber was analyzed from the micro level, which can be used as a reference for the engineering design of HSFRSCC in the future.

Low-temperature aerobic carbonization and activation of cellulosic materials for Pb2+ removal in water source.

Targeting the removal of Pb2+ in wastewater, cellulosic materials were carbonized in an aerobic environment and activated via ion exchange. The maximum adsorption capacity reached 243.5 mg/g on an MCC-derived adsorbent activated with sodium acetate. The modified porous properties improved the adsorption capacity. The capacity could be completely recovered five times through elution with EDTA. Because of the negative effects of Ni, Mg, and Ca elements, the adsorption capacities of activated carbonized natural materials were lower than that of pure cellulose. N2 adsorption measurement showed that the adsorbent had a large specific surface area as well as abundant micropores and 4-nm-sized mesopores. FTIR and surface potential results proved that carboxyl group was generated in the aerobic carbonization, and was deprotonated during ion exchange. This adsorbent consisted of C-C bonds as the building blocks and hydrophilic groups on the surface. XPS results demonstrated that the Pb 4f binding energies were reduced by 0.7-0.8 eV due to the interaction between Pb2+ and the activated adsorbent, indicating that the carboxylate groups bonded with Pb2+ through coordination interactions. Pseudo-second-order and Elovich kinetic models were well fitted with the adsorption processes on the pristine and activated carbonized adsorbents, indicative of chemisorption on heterogeneous surfaces. The Freundlich expression agreed well with the data measured, and the pristine and activated adsorbents had weak and strong affinities for Pb2+, respectively. The Pb2+ adsorption process was exothermic and spontaneous, and heat release determined the spontaneity. The adsorption capacity is attributed to the carboxylate groups and pores generated in the aerobic oxidation and ion exchange procedures.

Governing effects of melt viscosity on fire performances of polylactide and its fire-retardant systems.

Extreme flammability of polylactide (PLA) has restricted its real-world applications. Traditional research only focuses on developing new effective fire retardants for PLA without considering the effect of melt viscosity on its fire performances. To fill the knowledge gap, a series of PLA matrices of varied melt flow index (MFI) with and without fire retardants are chosen to examine how melt viscosity affects its fire performances. Our results show that the MFI has a governing impact on fire performances of pure PLA and its fire-retardant systems if the samples are placed vertically during fire testing. PLA with higher MFI values achieves higher limiting oxygen index (LOI) values, and a lower loading level of fire retardants is required for PLA to pass a UL-94 V-0 rating. This work unveils the correlation between melt viscosity and their fire performance and offers a practical guidance for creating flame retardant PLA to extend its applications.

Inhibition Mechanism of PinX1 Gene on Cancer Stem Cells of Nasopharyngeal Carcinoma.

The recurrence and long-term metastasis of these tumors are important causes of treatment failure and death. On the other hand, PinX1 is a nucleolar protein found in recent years that can interact with telomere/telomerase simultaneously, and it is highly conserved in human and yeast. Some studies have shown that the PinX1 gene can inhibit the tumor stem cells of NPC. Therefore, the mechanism of inhibition of the PinX1 gene on the tumor stem cells of NPC has been studied in this paper. In this paper, CNE2 cells of NPC were used as experimental materials, CD133 as a marker, PinX1 overexpression plasmids and their corresponding empty plasmids were respectively transfected in CD133+ cells, PinX1 siRNA and their corresponding NC siRNA were respectively transfected in CD133- cells for control experiments. In this study, we found that the telomerase activity of the CD133 - + NC group was 1.001 ± 0.086, the CD133 - + pinx1sirna group was 0.974 ± 0.046, CD133+ + vector group was 0.928 ± 0.102, CD133+ + over PinX1 group was 0.703 ± 0.086. Therefore, the PinX1 gene can inhibit NPC stem cells by inhibiting telomerase activity.

Enhancing photo-fermentative biohydrogen production using different zinc salt additives.

The kinetic properties of the hydrogen yield of photosynthetic bacteria were investigated using Han-Levenspiel and modified Gompertz models to determine the effects of different zinc salts on the growth and hydrogen production of the photosynthetic bacterium HAU-M1. Inorganic zinc salts (zinc standard solution and zinc sulfate) inhibited bacterial growth by 1-4-fold higher than organic zinc salts (zinc lactate and zinc gluconate). Among these four zinc salts, 5 mg/L zinc lactate displayed the weakest inhibition performance. This compound increased cumulative hydrogen production by approximately 57.81% (80.44 mL/g) and maximum hydrogen production rate by 58.27% (3.43 mL/[g·h]). The Han-Levenspiel model with parameters m > n > 0 indicated that the addition of zinc salts influenced the hydrogen production process of the bacterium in a noncompetitive manner. Compared with the inorganic zinc, the organic zinc salts were more suitable as exogenous zinc supplements to promote bacterial growth and its hydrogen production.

Rubik-Net: Learning Spatial Information via Rotation-Driven Convolutions for Brain Segmentation.

The accurate segmentation of brain tissue in Magnetic Resonance Image (MRI) slices is essential for assessing neurological conditions and brain diseases. However, it is challenging to segment MRI slices because of the low contrast between different brain tissues and the partial volume effect. 2-Dimensional (2-D) convolutional networks cannot handle such volumetric image data well because they overlook spatial information between MRI slices. Although 3-Dimensional (3-D) convolutions capture volumetric spatial information, they have not been fully exploited to enhance representative ability of deep networks; moreover, they may lead to overfitting with insufficient training data. In this paper, we propose a novel convolutional mechanism, termed Rubik convolution, to capture multi-dimensional information between MRI slices. Rubik convolution rotates the axis of a set of consecutive slices, enabling 2-D convolution kernels to extract features of each axial plane simultaneously. Next, feature maps are rotated back to fuse multidimensional information by the Max-View-Maps. Furthermore, we propose an efficient 2-D convolutional network, namely Rubik-Net, where the residual connections and the bottleneck structure are used to enhance information transmission and reduce the number of network parameters. The Rubik-Net shows promising results on iSeg2017, iSeg2019, IBSR and BrainWeb datasets in terms of segmentation accuracy. In particular, we achieved the best results in 95th percentile Hausdorff distance and average surface distance in cerebrospinal fluid segmentation on the most challenging iSeg2019 dataset. The experiments indicate that Rubik-Net improves the accuracy and efficiency of medical image segmentation. Moreover, Rubik convolution can be easily embedded into existing 2-D convolutional networks.

Microenvironment modulation of single-atom catalysts and their roles in electrochemical energy conversion.

Single-atom catalysts (SACs) have become the most attractive frontier research field in heterogeneous catalysis. Since the atomically dispersed metal atoms are commonly stabilized by ionic/covalent interactions with neighboring atoms, the geometric and electronic structures of SACs depend greatly on their microenvironment, which, in turn, determine the performances in catalytic processes. In this review, we will focus on the recently developed strategies of SAC synthesis, with attention on the microenvironment modulation of single-atom active sites of SACs. Furthermore, experimental and computational advances in understanding such microenvironment in association to the catalytic activity and mechanisms are summarized and exemplified in the electrochemical applications, including the water electrolysis and O2/CO2/N2 reduction reactions. Last, by highlighting the prospects and challenges for microenvironment engineering of SACs, we wish to shed some light on the further development of SACs for electrochemical energy conversion.

Elucidating the Electrocatalytic CO2 Reduction Reaction over a Model Single-Atom Nickel Catalyst.

Designing effective electrocatalysts for the carbon dioxide reduction reaction (CO2 RR) is an appealing approach to tackling the challenges posed by rising CO2 levels and realizing a closed carbon cycle. However, fundamental understanding of the complicated CO2 RR mechanism in CO2 electrocatalysis is still lacking because model systems are limited. We have designed a model nickel single-atom catalyst (Ni SAC) with a uniform structure and well-defined Ni-N4 moiety on a conductive carbon support with which to explore the electrochemical CO2 RR. Operando X-ray absorption near-edge structure spectroscopy, Raman spectroscopy, and near-ambient X-ray photoelectron spectroscopy, revealed that Ni+ in the Ni SAC was highly active for CO2 activation, and functioned as an authentic catalytically active site for the CO2 RR. Furthermore, through combination with a kinetics study, the rate-determining step of the CO2 RR was determined to be *CO2 - +H+ →*COOH. This study tackles the four challenges faced by the CO2 RR; namely, activity, selectivity, stability, and dynamics.

Fabrication of highly stable microfiber structures via high-substituted hydroxypropyl cellulose coating for device and sensor applications.

We demonstrate a cost-effective approach to fabricate stable microfiber structures taking advantage of high-substituted hydroxypropyl cellulose (H-HPC) coating. The microfibers are bent, twisted, or coiled to form desired geometries, and then the segments in contact are coated to resist the existing bending or twisting stresses to stabilize the structures. Coated devices including a twisted Sagnac interferometer, a ring resonator, and a Fabry-Perot cavity have been exhibited. Measured result suggests that the coated structures are highly stable over 20 days. The thin, low-index coating material allows for a strong evanescent-field interaction between the mode field and ambient solution. As an example, the coated twisted Sagnac interferometer can act as a refractive-index sensor with a sensitivity as high as 2600 nm/RIU. The present technique is greatly beneficial for long-term working of microfiber devices and sensors and offers a possible approach for the development of integrated microphotonic devices and platforms.

The mRNA level of the galectin-10 of Angiostrongylus cantonensis induced by reactive oxygen stress.

Galectin plays an important role in host-parasite interactions. In this study, we identified a novel gene encoding galectin-10 (AcGal-10) from the cDNA library of Angiostrongylus cantonensis and characterized its biological role in the parasite. Sequence and phylogeny analysis showed that AcGal-10 is related to other galectin family members with the conserved loci (H(84)-D(86)-R(88)-V(96)-N(98)-W(105)-E(108)-R(110)). The mRNA level of AcGal-10 was expressed in reactive oxygen stress radicals. We have identified two proteins of A. cantonensis galectin-10 gene, one of which was reported (AcGAL10-W) and the others is AcGAL-10-M. In addition, recombinant AcGal-10 (rAcGal-10) was constructed into the pGEX-4T-1 plasmid, purified, and finally confirmed by SDS-PAGE and LC-MS. Hemagglutination assay showed that the minimum concentration of rAcGAL10-W and rAcGAL10-M required for the hemagglutination of BALB/c mice erythrocyte was 25 µg/mL, and the carbohydrate-binding ability showed no difference between rAcGAL10-W and rAcGAL10-M. The mRNA levels of AcGal-10 were indeed expressed higher after stimulation with H(2)O(2) and recombinant A. cantonensis galectin-10. A mutation of AcGal-10 was also found, but there was no significant difference compared with the wild type. Furthermore, we also confirmed that recombinant AcGal-10 plays a role in the activation of the microglia. In conclusion, the report here showed that AcGal-10 may be an important molecule related to infection of A. cantonensis.

Segmentation of computer tomography image using local robust statistics and region-scalable fitting.

Intensity inhomogeneity may cause considerable difficulties in segmentation of CT image. In order to overcome the difficulties caused by intensity inhomogeneity, the region-scalable fitting (RSF) model was put forward. RSF model draws upon intensity information in local regions with a controllable scale. But only using intensity information may lead to slow convergence rate and poor denoise ability. Combining the method of robust statistics, RSF model is improved in this paper. In the improved model, the intensity in RSF model is replaced with local robust statistics which is the weighted combination of inter-quartile range, mean absolute deviation and intensity median in local region. Inter-quartile range and mean absolute deviation in local region are introduced to sharpen object boundaries, and intensity median in local region is introduced to reduce image noise. The contrast experiments between RSF model and the improved model are provided, which demonstrate the fast convergence rate and robustness to noise of the improved model.

A new iterative reconstruction algorithm for 2D exterior fan-beam CT.

The exterior computed tomography (CT) problem is one kind of truncation problem. It is very ill-posed, so that accurate reconstruction of the attenuation function is hardly possible from real data. Based on projection onto convex sets (POCS) algorithm, total variation minimization (TVM) methods, and C-V model, we develop and investigate a new iterative reconstruction algorithm, which is referred to as subregion-averaged-TVM-POCS (SA-TVM-POCS). Numerical simulations are presented to illustrate the efficiency of the algorithm. The results of this paper can be easily applied to other x-ray CT reconstruction problems.