Corporate environmental governance scheme and investment efficiency over the course of COVID-19.

Taking the COVID-19 outbreak as the exogenous shock, we use quarterly reports of Chinese listed firms to examine whether enhanced environmental governance scheme improves corporate investment efficiency over the course of COVID-19. The results show that after the outbreak, firms with greater environmental governance scheme experience more efficient investments, with this effect being more pronounced in non-state-owned enterprises, firms unlisted as key pollution-monitoring units, and firms with higher financial constraints. The results are robust to a battery of robustness checks. These findings provide new evidence on the importance of environmental governance in reaping economic benefits and resilience during crisis times.

Reconstruction of scalar and vectorial components in X-ray dark-field tomography.

Grating-based X-ray dark-field imaging is a novel technique for obtaining image contrast for object structures at size scales below setup resolution. Such an approach appears particularly beneficial for medical imaging and nondestructive testing. It has already been shown that the dark-field signal depends on the direction of observation. However, up to now, algorithms for fully recovering the orientation dependence in a tomographic volume are still unexplored. In this publication, we propose a reconstruction method for grating-based X-ray dark-field tomography, which models the orientation-dependent signal as an additional observable from a standard tomographic scan. In detail, we extend the tomographic volume to a tensorial set of voxel data, containing the local orientation and contributions to dark-field scattering. In our experiments, we present the first results of several test specimens exhibiting a heterogeneous composition in microstructure, which demonstrates the diagnostic potential of the method.

Simultaneously Tailoring Zinc Deposition and Solvation Structure by Electrolyte Additive.

Aqueous zinc ion batteries (AZIBs) have attracted intense attention due to their high safety and low cost. Unfortunately, the serious dendrite growth and side reactions of the Zn metal anode in an aqueous electrolyte result in rapid battery failure, hindering the practical application of AZIBs. Herein, sodium gluconate as a dual-functional electrolyte additive has been employed to enhance the electrochemical performance of AZIBs. Gluconate anions preferentially adsorb on the surface of the Zn anode, which effectively prevents H2 evolution and induces uniform Zn deposition to suppress dendrite growth. Moreover, the gluconate anions can highly coordinate with Zn2+, promoting the dissolution of [Zn(H2O)6]2+ to inhibit side reactions and the water-induced corrosion reaction. As a result, the Zn||Zn symmetric battery exhibits a long-term cycling stability of over 3000 h at 1 mA cm-2/1 mA h cm-2 and 600 h at 10 mA cm-2/10 mA h cm-2. Furthermore, the NH4V4O10||Zn full battery also displays excellent cycling stability and a high reversible capacity of 193 mA h g-1 at 2 A g-1 after 1000 cycles. Given the low-cost advantage of SG, the proposed interface chemistry modulation strategy holds considerable potential for promoting the commercialization of AZIBs.

Constructing Highly Stable Zinc Metal Anodes via Induced Zn(002) Growth.

The nonuniform electric field at the surface of a zinc (Zn) anode, coupled with water-induced parasitic reactions, exacerbates the growth of Zn dendrites, presenting a significant impediment to large-scale energy storage in aqueous Zn-ion batteries. One of the most convenient strategies for mitigating dendrite-related issues involves controlling crystal growth through electrolyte additives. Herein, we present thiamine hydrochloride (THC) as an electrolyte additive capable of effectively stabilizing the preferential deposition of the Zn(002) plane. First-principles calculations reveal that THC tends to adsorb on Zn(100) and Zn(101) planes and is capable of inducing the deposition of Zn ion onto the (002) plane and the preferential growth of the (002) plane, resulting in a flat and compact deposition layer. A THC additive not only effectively suppresses dendrite growth but also prevents the generation of side reactions and hydrogen evolution reaction. Consequently, the Zn||Zn symmetric battery exhibits long-term cycling stability of over 3000 h at 1 mA cm-2/1 mAh cm-2 and 1000 h at 10 mA cm-2/10 mAh cm-2. Furthermore, the NH4V4O10||Zn full battery also displays excellent cycling stability and a high reversible capacity of 210 mAh g-1 after 1000 cycles at 1 A g-1, highlighting a significant potential for practical applications.

Fine-tuning of xylose metabolism in genetically engineered Saccharomyces cerevisiae by scattered integration of xylose assimilation genes.

Manipulation of multiple genes is a common experience in metabolic engineering and synthetic biology studies. Chromosome integration of multiple genes in one single position is always performed, however, there is so far no study on the integration of multiple genes separately in various positions (here in after referred to as "scattered integration") and its effect on fine-tuning of cellular metabolism. In this study, scattered integration of the xylose assimilation genes PsXR, PsXDH and ScXK was investigated in Saccharomyces cerevisiae, and transcription analysis of these genes as well as their enzyme activities were compared with those observed when the genes were integrated into one single site (defined as "tandem integration" here). Not only notable differences in transcription levels and enzyme activities were observed when the genes were integrated by the two strategies, but also change of the cofactor preference of PsXR gene was validated. Xylose fermentation was further studied with the strains developed with these strategies, and elevated xylose utilization rate was obtained in the scattered integration strain. These results proved that by positioning multiple genes on different chromosomes, fine-tuning of cellular metabolism could be achieved in recombinant S. cerevisiae.

Executives' ESG cognition and enterprise green innovation: Evidence based on executives' personal microblogs.

Based on cognitive theory, we investigated the influence of executives' ESG cognition on corporate green innovation using data from Chinese manufacturing listed companies from 2010 to 2019. The paper first constructs a metric of ESG cognition of company executives by presenting a quantitative analysis of data from their personal microblogs using textual analysis. The findings show that executive ESG perceptions significantly improve corporate green innovation. After addressing the endogeneity issue through a series of robustness tests, the findings of this paper still held true. Further research found that the enhancement effect of executive ESG perceptions on firms' green innovation level was mainly found in the sample without heavy pollution and with lower financing constraints and a higher marketization process. This study makes an important contribution to the research on corporate green innovation based on the perspective of executive ESG cognition while also providing a theoretical basis and practical reference for corporate green innovation practices.

Production of cellulosic ethanol and value-added products from corn fiber.

Corn fiber, a by-product from the corn processing industry, mainly composed of residual starch, cellulose, and hemicelluloses, is a promising raw material for producing cellulosic ethanol and value-added products due to its abundant reserves and low costs of collection and transportation. Now, several technologies for the production of cellulosic ethanol from corn fiber have been reported, such as the D3MAX process, Cellerate™ process, etc., and part of the technologies have also been used in industrial production in the United States. The ethanol yields range from 64 to 91% of the theoretical maximum, depending on different production processes. Because of the multicomponent of corn fiber and the complex structures highly substituted by a variety of side chains in hemicelluloses of corn fiber, however, there are many challenges in cellulosic ethanol production from corn fiber, such as the low conversion of hemicelluloses to fermentable sugars in enzymatic hydrolysis, high production of inhibitors during pretreatment, etc. Some technologies, including an effective pretreatment process for minimizing inhibitors production and maximizing fermentable sugars recovery, production of enzyme preparations with suitable protein compositions, and the engineering of microorganisms capable of fermenting hexose and pentose in hydrolysates and inhibitors tolerance, etc., need to be further developed. The process integration of cellulosic ethanol and value-added products also needs to be developed to improve the economic benefits of the whole process. This review summarizes the status and progresses of cellulosic ethanol production and potential value-added products from corn fiber and presents some challenges in this field at present.

Efficient recovery of valuable metals from cathode materials of spent LiCoO2 batteries via co-pyrolysis with cheap carbonaceous materials.

Recovery of valuable metals from spent Li-ion batteries has prominent economic and environmental benefits. In this study, a novel approach for recycling valuable metals from spent LiCoO2 batteries via co-pyrolysis with three different carbonaceous materials (waste polyethylene, biomass, and coal)) was proposed and evaluated. The thermodynamic analysis proved that carbonaceous materials (mainly carbon) were theoretically able to facilitate the decomposition process of LiCoO2. The promotion effect on LiCoO2 decomposition was in the following order: coal > biomass > polyethylene, and the decomposition temperature of LiCoO2 could significantly reduce by 400 °C via adding coal. The char produced from the carbonaceous materials, rather than the volatiles, played an important role in LiCoO2 decomposition and reduction. The pyrolysis products of LiCoO2 and coal mixture exhibited typical superparamagnetism and hysteresis behaviours, which benefitted the subsequent magnetic separation. The recovery rates of Co and Li were sensitive to the pyrolysis temperature and residence time, respectively. A high proportion of Co was in the form of CoO below 800 °C and had not been completely reduced, leading to the relatively lower recovery rates of Co below 800 °C. The optimal recovery rates of Co (96.8%) and Li (88.7%) were obtained at the pyrolysis temperature of 800 °C and the residence time of 10 min. The final recovery products were Co and Li2CO3 with rather high crystallinities and purities. Therefore, this study provided a novel approach for the efficient recycling of valuable metals from spent Li-ion batteries with high application prospects.

Corporate Competing Culture and Environmental Investment.

Using Chinese listed companies as research setting, this paper constructs a measure of corporate competing culture through textual analysis on firms' management discussion and analysis (MD&A) disclosures, and examines the impact of corporate competing culture on environmental investment. The results show that competing culture has a significant and positive impact on firms' environmental investment, and the results remain robust to a battery of robustness tests. Moreover, the mediating analysis indicates that competing culture promotes corporate environmental investment through enhancing firms' internal control quality. Furthermore, the heterogeneity results show that the positive impact of corporate competing culture on environmental investment is more pronounced in firms with larger size, stronger corporate governance, in high-polluting industry, and located in less developed regions. Our findings shed light on the importance of corporate competing culture and provide practical implications for corporate sustainable development.

Automatic multi-organ segmentation in dual-energy CT (DECT) with dedicated 3D fully convolutional DECT networks.

PURPOSE: Dual-energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissues compared to conventional single-energy CT (SECT). Recent research shows that automatic multi-organ segmentation of DECT data can improve DECT clinical applications. However, most segmentation methods are designed for SECT, while DECT has been significantly less pronounced in research. Therefore, a novel approach is required that is able to take full advantage of the extra information provided by DECT. METHODS: In the scope of this work, we proposed four three-dimensional (3D) fully convolutional neural network algorithms for the automatic segmentation of DECT data. We incorporated the extra energy information differently and embedded the fusion of information in each of the network architectures. RESULTS: Quantitative evaluation using 45 thorax/abdomen DECT datasets acquired with a clinical dual-source CT system was investigated. The segmentation of six thoracic and abdominal organs (left and right lungs, liver, spleen, and left and right kidneys) were evaluated using a fivefold cross-validation strategy. In all of the tests, we achieved the best average Dice coefficients of 98% for the right lung, 98% for the left lung, 96% for the liver, 92% for the spleen, 95% for the right kidney, 93% for the left kidney, respectively. The network architectures exploit dual-energy spectra and outperform deep learning for SECT. CONCLUSIONS: The results of the cross-validation show that our methods are feasible and promising. Successful tests on special clinical cases reveal that our methods have high adaptability in the practical application.

A 3-D Projection Model for X-ray Dark-field Imaging.

The X-ray dark-field signal can be measured with a grating-based Talbot-Lau interferometer. It measures small angle scattering of micrometer-sized oriented structures. Interestingly, the signal is a function not only of the material, but also of the relative orientation of the sample, the X-ray beam direction, and the direction of the interferometer sensitivity. This property is very interesting for potential tomographically reconstructing structures below the imaging resolution. However, tomographic reconstruction itself is a substantial challenge. A key step of the reconstruction algorithm is the inversion of a forward projection model. In this work, we propose a very general 3-D projection model. We derive the projection model under the assumption that the observed scatter distribution has a Gaussian shape. We theoretically show the consistency of our model with existing, more constrained 2-D models. Furthermore, we experimentally show the compatibility of our model with simulations and real dark-field measurements. We believe that this 3-D projection model is an important step towards more flexible trajectories and, by extension, dark-field imaging protocols that are much better applicable in practice.

UGT85A84 Catalyzes the Glycosylation of Aromatic Monoterpenes in Osmanthus fragrans Lour. Flowers.

The monoterpenes linalool and its oxides are the key aroma-active compounds in Osmanthus fragrans Lour. flowers. The glycosides of these monoterpenes accumulate throughout flowering, leading to considerable storage of potential aroma constituents that account for the majority of non-volatile aroma compounds. However, the UDP-glycosyltransferase (UGT) responsible for the glycosylation of linalool and its oxides has not been clarified. Four candidate OfUGTs (UGT85A82, UGT85A83, UGT85AF3, and UGT85A84) with high homology to the known terpenoid UGTs were screened by transcriptome sequencing. Over-expression of the candidate OfUGTs in tobacco showed that UGT85A84 glycosylated linalool oxides in planta. Since the transcript levels of UGT85A84 were positively correlated with glycoside accumulation, the recombinant UGT85A84 protein was subjected to reactions with aglycones and sugar donors. Two formate adducts were exclusively detected in UDP-Glc with linalool and linalool oxide reactions by liquid chromatography-mass spectrometry (LC-MS), indicating that UDP-Glc was the specific sugar donor. The kinetic parameters demonstrated that UGT85A84 glycosylated both linalool and lianlool oxides in vitro. Further analysis demonstrated that the transcription levels of MEP pathway genes might play an important role in mediating terpenoid glycosylation. Our findings unraveled the mechanism underlying the glycosylation of essential aroma compounds in flowers. This study will facilitate the application of potential aroma contributors in future industries.

A learning-based material decomposition pipeline for multi-energy x-ray imaging.

PURPOSE: Benefiting from multi-energy x-ray imaging technology, material decomposition facilitates the characterization of different materials in x-ray imaging. However, the performance of material decomposition is limited by the accuracy of the decomposition model. Due to the presence of nonideal effects in x-ray imaging systems, it is difficult to explicitly build the imaging system models for material decomposition. As an alternative, this paper explores the feasibility of using machine learning approaches for material decomposition tasks. METHODS: In this work, we propose a learning-based pipeline to perform material decomposition. In this pipeline, the step of feature extraction is implemented to integrate more informative features, such as neighboring information, to facilitate material decomposition tasks, and the step of hold-out validation with continuous interleaved sampling is employed to perform model evaluation and selection. We demonstrate the material decomposition capability of our proposed pipeline with promising machine learning algorithms in both simulation and experimentation, the algorithms of which are artificial neural network (ANN), Random Tree, REPTree and Random Forest. The performance was quantitatively evaluated using a simulated XCAT phantom and an anthropomorphic torso phantom. In order to evaluate the proposed method, two measurement-based material decomposition methods were used as the reference methods for comparison studies. In addition, deep learning-based solutions were also investigated to complete this work as a comprehensive comparison of machine learning solution for material decomposition. RESULTS: In both the simulation study and the experimental study, the introduced machine learning algorithms are able to train models for the material decomposition tasks. With the application of neighboring information, the performance of each machine learning algorithm is strongly improved. Compared to the state-of-the-art method, the performance of ANN in the simulation study is an improvement of over 24% in the noiseless scenarios and over 169% in the noisy scenario, while the performance of the Random Forest is an improvement of over 40% and 165%, respectively. Similarly, the performance of ANN in the experimental study is an improvement of over 42% in the denoised scenario and over 45% in the original scenario, while the performance of Random Forest is an improvement by over 33% and 40%, respectively. CONCLUSIONS: The proposed pipeline is able to build generic material decomposition models for different scenarios, and it was validated by quantitative evaluation in both simulation and experimentation. Compared to the reference methods, appropriate features and machine learning algorithms can significantly improve material decomposition performance. The results indicate that it is feasible and promising to perform material decomposition using machine learning methods, and our study will facilitate future efforts toward clinical applications.

An Improved Extrapolation Scheme for Truncated CT Data Using 2D Fourier-Based Helgason-Ludwig Consistency Conditions.

We improve data extrapolation for truncated computed tomography (CT) projections by using Helgason-Ludwig (HL) consistency conditions that mathematically describe the overlap of information between projections. First, we theoretically derive a 2D Fourier representation of the HL consistency conditions from their original formulation (projection moment theorem), for both parallel-beam and fan-beam imaging geometry. The derivation result indicates that there is a zero energy region forming a double-wedge shape in 2D Fourier domain. This observation is also referred to as the Fourier property of a sinogram in the previous literature. The major benefit of this representation is that the consistency conditions can be efficiently evaluated via 2D fast Fourier transform (FFT). Then, we suggest a method that extrapolates the truncated projections with data from a uniform ellipse of which the parameters are determined by optimizing these consistency conditions. The forward projection of the optimized ellipse can be used to complete the truncation data. The proposed algorithm is evaluated using simulated data and reprojections of clinical data. Results show that the root mean square error (RMSE) is reduced substantially, compared to a state-of-the-art extrapolation method.

[Development of genetically stable recombinant Saccharomyces cerevisiae strains using combinational chromosomal integration].

Chromosomal integration enables stable phenotype and therefore has become an important strategy for breeding of industrial Saccharomyces cerevisiae strains. pAUR135 is a plasmid that enables recycling use of antibiotic selection marker, and once attached with designated homologous sequences, integration vector for stable expression can be constructed. Development of S. cerevisiae strains by metabolic engineering normally demands overexpression of multiple genes, and employing pAUR135 plasmid, it is possible to construct S. cerevisiae strains by combinational integration of multiple genes in multiple sites, which results in different ratios of expressions of these genes. Xylose utilization pathway was taken as an example, with three pAUR135-based plasmids carrying three xylose assimilation genes constructed in this study. The three genes were sequentially integrated on the chromosome of S. cerevisiae by combinational integration. Xylose utilization rate was improved 24.4%-35.5% in the combinational integration strain comparing with that of the control strain with all the three genes integrated in one location. Strain improvement achieved by combinational integration is a novel method to manipulate multiple genes for genetic engineering of S. cerevisiae, and the recombinant strains are free of foreign sequences and selection markers. In addition, stable phenotype can be maintained, which is important for breeding of industrial strains. Therefore, combinational integration employing pAUR135 is a novel method for metabolic engineering of industrial S. cerevisiae strains.