ATP-free in vitro biotransformation of starch-derived maltodextrin into poly-3-hydroxybutyrate via acetyl-CoA.

In vitro biotransformation (ivBT) facilitated by in vitro synthetic enzymatic biosystems (ivSEBs) has emerged as a highly promising biosynthetic platform. Several ivSEBs have been constructed to produce poly-3-hydroxybutyrate (PHB) via acetyl-coenzyme A (acetyl-CoA). However, some systems are hindered by their reliance on costly ATP, limiting their practicality. This study presents the design of an ATP-free ivSEB for one-pot PHB biosynthesis via acetyl-CoA utilizing starch-derived maltodextrin as the sole substrate. Stoichiometric analysis indicates this ivSEB can self-maintain NADP+/NADPH balance and achieve a theoretical molar yield of 133.3%. Leveraging simple one-pot reactions, our ivSEBs achieved a near-theoretical molar yield of 125.5%, the highest PHB titer (208.3 mM, approximately 17.9 g/L) and the fastest PHB production rate (9.4 mM/h, approximately 0.8 g/L/h) among all the reported ivSEBs to date, and demonstrated easy scalability. This study unveils the promising potential of ivBT for the industrial-scale production of PHB and other acetyl-CoA-derived chemicals from starch.

In situ preparation and properties of polyvinyl alcohol/synthetic ribbon-like nanocellulose composites.

This study presents a novel approach in which a dual network (DN) composite, comprising polyvinyl alcohol (PVA) and ribbon-like nanocellulose (RC), was synthesized in one step using the volume exclusion effect involved in enzyme-catalyzed cellulose synthesis. Additionally, the impact of PVA as a crowding reagent during enzymatic catalysis on the in situ formation of nanocellulose and its resulting aspect ratio was explored. In contrast, the other two composites were created by incorporating enzyme-catalyzed synthetic block cellulose (BC) and its acid-hydrolyzed regenerated disc-shaped cellulose (DC) into the PVA. Subsequently, the mechanism by which three distinct types of nanocellulose, varying in morphology and size, was explored to elucidate their contributions to enhancing the properties of PVA. The results demonstrated that PVA/RC outperformed PVA/BC and PVA/DC. The elevated aspect ratio and intricate network structure of RCs not only significantly bolster the mechanical robustness of PVA/RC, leading in an 86.40 % surge in tensile strength and a remarkable 277.03 % rise in tensile modulus in comparison to pure PVA, but also induce a slight enhancement in elongation at break. Moreover, the thermal stability and biodegradability of PVA/RC was enhanced. Collectively, this study introduces an innovative strategy for the efficient fabrication of biodegradable composites with enhanced properties.

Biomanufacturing by In Vitro Biotransformation (ivBT) Using Purified Cascade Multi-enzymes.

In vitro biotransformation (ivBT) refers to the use of an artificial biological reaction system that employs purified enzymes for the one-pot conversion of low-cost materials into biocommodities such as ethanol, organic acids, and amino acids. Unshackled from cell growth and metabolism, ivBT exhibits distinct advantages compared with metabolic engineering, including but not limited to high engineering flexibility, ease of operation, fast reaction rate, high product yields, and good scalability. These characteristics position ivBT as a promising next-generation biomanufacturing platform. Nevertheless, challenges persist in the enhancement of bulk enzyme preparation methods, the acquisition of enzymes with superior catalytic properties, and the development of sophisticated approaches for pathway design and system optimization. In alignment with the workflow of ivBT development, this chapter presents a systematic introduction to pathway design, enzyme mining and engineering, system construction, and system optimization. The chapter also proffers perspectives on ivBT development.

Analysis of Corn Yield Prediction Potential at Various Growth Phases Using a Process-Based Model and Deep Learning.

Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid approach using the WOFOST model and deep learning was developed to forecast corn yield, which analysed yield prediction potential at different growth phases and features. The World Food Studies (WOFOST) model was used to build a comprehensive simulated dataset by inputting meteorological, soil, crop and management data. Different feature combinations at various growth phases were designed to forecast yield using machine learning and deep learning methods. The results show that the key features of corn's vegetative growth stage and reproductive growth stage were growth state features and water-related features, respectively. With the continuous advancement of the crop growth stage, the ability to predict yield continued to improve. Especially after entering the reproductive growth stage, corn kernels begin to form, and the yield prediction performance is significantly improved. The performance of the optimal yield prediction model in flowering (R2 = 0.53, RMSE = 554.84 kg/ha, MRE = 8.27%), in milk maturity (R2 = 0.89, RMSE = 268.76 kg/ha, MRE = 4.01%), and in maturity (R2 = 0.98, RMSE = 102.65 kg/ha, MRE = 1.53%) were given. Thus, our method improves the accuracy of yield prediction, and provides reliable analysis results for predicting yield at various growth phases, which is helpful for farmers and governments in agricultural decision making. This can also be applied to yield prediction for other crops, which is of great value to guide agricultural production.

An artificial multi-enzyme cascade biocatalysis for biomanufacturing of nicotinamide mononucleotide from starch and nicotinamide in one-pot.

ß-Nicotinamide mononucleotide (NMN) is an important precursor in the synthesis of nicotinamide adenine dinucleotide (NAD+) and confers multiple health benefits, resulting in the rapid growth of NMN market capacity in the fields of food and health care. To overcome the drawbacks of NMN production by the existing chemical or microbial fermentation method, there is an urgent need to develop a prospective NMN production strategy with low cost, low pollution, and high yield. In this study, we demonstrated an artificial in vitro multi-enzyme cascade biocatalysis using starch and nicotinamide (Nam) as substrates for the synthesis of NMN in one-pot. This multi-enzyme cascade reaction was optimized in terms of pH value, buffer concentration, inorganic phosphate concentration, enzyme composition, and phosphoenolpyruvate concentration. Under optimized conditions, a high molar yield of 87.8% for NMN was achieved using 3.2 mM Nam as substrate, and a molar yield of 55.37% for NMN was also achieved under the initial Nam concentration of 9.21 mM. This in vitro enzymatic platform provides an environmental friendliness biomanufacturing technology for the production of NMN, showing a highly promising alternative approach for NMN production.

Facile biosynthesis of synthetic crystalline cellulose nanoribbon from maltodextrin through a minimized two-enzyme phosphorylase cascade and its application in emulsion.

Nanocellulose has many promising applications such as a green ingredient for Pickering emulsion. Traditional strategies to produce nanocellulose, which are acid or enzymatic hydrolysis and mechanical methods on natural complicated cellulose, are hard to control and can result in significant pollutants during the processes. Herein, we demonstrated a facile and sustainable method for the biocatalytic production of insoluble synthetic crystalline cellulose nanoribbon (CCNR) from cheap maltodextrin by coupling α-glucan phosphorylase (αGP) and cellodextrin phosphorylase (CDP) using cellobiose as a primer. And by optimizing the combination of different αGP and CDP, it turned out that the optimal enzyme combination is αGP from Thermotoga maritime and CDP from Clostridium thermocellum, in which CDP was attached to a family 9 cellulose-binding module. The product yield and degree of polymerization (DP) of insoluble synthetic CCNR was affected by the primer concentration at a fixed concentration of maltodextrin. After optimization of reaction conditions, the highest product yield of insoluble synthetic CCNR was 44.92 % and the highest DP of the insoluble synthetic CCNR was 24 from 50 g 1-1 maltodextrin. This insoluble synthetic CCNR can be used as a Pickering emulsions stabilizer, showing excellent emulsifiability. This study provides a promising alternative for cost-efficient production of insoluble synthetic CCNR which was used as a green emulsion stabilizer.

An ATP-free in vitro synthetic enzymatic biosystem facilitating one-pot stoichiometric conversion of starch to mannitol.

D-Mannitol (hereinafter as mannitol) is a six-carbon sugar alcohol with diverse applications in food and pharmaceutical industries. To overcome the drawbacks of the chemical hydrogenation method commonly used for mannitol production at present, there is a need to search for novel prospective mannitol production strategies that are of high yield and low cost. In this study, we present a novel approach for the stoichiometric synthesis of mannitol via an in vitro synthetic enzymatic biosystem using the low-cost starch as substrate. By dividing the overall reaction pathway into three modules which could be executed sequentially in one pot, our design aimed at the stoichiometric conversion of starch-based materials into mannitol in an ATP-independent and cofactor-balanced manner. At optimized conditions, high product yields of around 95-98% were achieved using both 10 g/L and 50 g/L maltodextrin as substrate, indicating the potential of our designed system for industrial applications. This study not only provides a high-efficient strategy for the synthesis of mannitol but also expands the product scope of sugar alcohols by the in vitro synthetic enzymatic biosystems using low-cost starch-based materials as the input. KEY POINTS : • We described a design-build-test-learn pipeline to construct in vitro biosystems. • The designed system comprised six key enzymes and another three enzymes. • The system converted maltodextrin stoichiometrically to mannitol in one pot.

Engineering the biomimetic cofactors of NMNH for cytochrome P450 BM3 based on binding conformation refinement.

Cytochrome P450 BM3 (BM3) is an important oxidoreductase that is widely used in drug synthesis, chemical synthesis, and other industries. However, as BM3 unquestionably increases costs by consuming a natural cofactor that unstably provides electrons, an alternative biomimetic cofactor with simpler structures represented by nicotinamide mononucleotide (NMNH) has been utilized. Currently, few reports exist on artificially modified BM3 enzymes using NMNH, especially regarding theoretical simulation and calculation. With the cognition of the mechanism in mind, we propose a strategy that optimizes and refines catalytic conformation. Based on constrained molecular dynamics simulation, the distance between N-5 of FAD flavin and C-4 of NMNH is used as a cue for the determination of improved conformation, and the potential positive mutants are subsequently screened virtually in accordance with binding free energy requirements. As a result, the K cat/K M values of the favorable mutant S848R increased to 205.38% compared to the wild-type BM3 with NMNH. These data indicate that our strategy can be applied for the specific utilization of biomimetic cofactors by oxidoreductases represented by BM3.

Synthetic semicrystalline cellulose oligomers as efficient Pickering emulsion stabilizers.

Nanocellulose are promising Pickering emulsion stabilizers for being sustainable and non-toxic. In this work, semicrystalline cellulose oligomers (SCCO), which were synthesized from maltodextrin using cellobiose as primer by in vitro enzymatic biosystem, were exploited as stabilizers for oil-in-water Pickering emulsions. At first, the morphology, structure, thermal and rheological properties of SCCO suspensions were characterized, showing that SCCO had a sheet morphology and typical cellulose-Ⅱ structure with 56 % crystallinity. Then the kinetic stabilities of emulsions containing various amounts of SCCO were evaluated against external stress such as pH, ionic strength, and temperature. Noting that SCCO-Pickering emulsions exhibited excellent stabilities against changes in centrifugation, pH, ionic strengths, and temperatures, and it was also kinetically stable for up to 6 months. Both SCCO suspensions and their emulsions exhibited gel-like structures and shear-thinning behaviors. These results demonstrated great potential of SCCO to be applied as nanocellulosic emulsifiers in food, cosmetic and pharmaceutical industries.

A Nomogram Model Identifies Eosinophilic Frequencies to Powerfully Discriminate Kawasaki Disease From Febrile Infections.

Background: Kawasaki disease (KD) is a form of systemic vasculitis that occurs primarily in children under the age of 5 years old. No single laboratory data can currently distinguish KD from other febrile infection diseases. The purpose of this study was to establish a laboratory data model that can differentiate between KD and other febrile diseases caused by an infection in order to prevent coronary artery complications in KD. Methods: This study consisted of a total of 800 children (249 KD and 551 age- and gender-matched non-KD febrile infection illness) as a case-control study. Laboratory findings were analyzed using univariable, multivariable logistic regression, and nomogram models. Results: We selected 562 children at random as the model group and 238 as the validation group. The predictive nomogram included high eosinophil percentage (100 points), high C-reactive protein (93 points), high alanine transaminase (84 points), low albumin (79 points), and high white blood cell (64 points), which generated an area under the curve of 0.873 for the model group and 0.905 for the validation group. Eosinophilia showed the highest OR: 5.015 (95% CI:-3.068-8.197) during multiple logistic regression. The sensitivity and specificity in the validation group were 84.1 and 86%, respectively. The calibration curves of the validation group for the probability of KD showed near an agreement to the actual probability. Conclusion: Eosinophilia is a major factor in this nomogram model and had high precision for predicting KD. This report is the first among the existing literature to demonstrate the important role of eosinophil in KD by nomogram.

[Accuracy improvement of spectral classification of crop using microwave backscatter data].

In the present study, VV polarization microwave backscatter data used for improving accuracies of spectral classification of crop is investigated. Classification accuracy using different classifiers based on the fusion data of HJ satellite multi-spectral and Envisat ASAR VV backscatter data are compared. The results indicate that fusion data can take full advantage of spectral information of HJ multi-spectral data and the structure sensitivity feature of ASAR VV polarization data. The fusion data enlarges the spectral difference among different classifications and improves crop classification accuracy. The classification accuracy using fusion data can be increased by 5 percent compared to the single HJ data. Furthermore, ASAR VV polarization data is sensitive to non-agrarian area of planted field, and VV polarization data joined classification can effectively distinguish the field border. VV polarization data associating with multi-spectral data used in crop classification enlarges the application of satellite data and has the potential of spread in the domain of agriculture.

Opium poppy monitoring with remote sensing in North Myanmar.

BACKGROUND: Myanmar has long been a focus of the international community as a major opium poppy cultivation region. METHOD: This study used remote sensing technology and ground verification to monitor opium poppy cultivation for three opium poppy growth seasons in North Myanmar. RESULTS: The study found that opium poppy cultivation has remained high. In 2005-6, 2006-7 and 2007-8 growing seasons the total areas monitored were 52,482 km(2), 178,274 km(2) and 236,342 km(2) and the total cultivated area of opium poppy was 8959 ha, 18,606 ha and 22,300, respectively. This was significantly less than cultivation levels reported during the 1990s. The major cultivation regions were located in Shan State, producing 88% of total poppy cultivation in North Myanmar in 2007-8. The opium poppy was mainly cultivated in the interlocking regions controlled by the local armed forces in Shan State. The field survey noted that most households in this area were poor and poppy cultivation was a main source of income. There were also differences between our figures on poppy cultivation and those reported by United Nations Office on Drugs and Crime. CONCLUSION: Our study shows that although the opium poppy cultivation in North Myanmar has reduced over recent years, it remains a major producer of opium and to which the international community needs to pay attention, especially in those areas controlled by local armed forces.

[Review of crop residue fractional cover monitoring with remote sensing].

Crop residue, as an important element of agro-ecosystem, can influence the flow of nutrients, carbon, water, and energy in agro-ecosystem. As a crucial indicator of distribution of crop residue, crop residue fractional cover is a key parameter of agro-ecosystem carbon cycle process model. Since remote sensing can easily obtain quantities of data, many researches were carried out on monitoring crop residue fractional cover with remote sensing. The present paper summarizes crop residue fractional cover estimation methods and latest progress in remote sensing, and these methods are classified into five categories according to the differences in methodologies and data sources. The principle of every method is described and compared. The advantages and shortages are also discussed and analyzed. Eventually, this paper points out some methods that should be improved, and presents the prospects of crop residue fractional cover estimation in the future.

[A review of classification methods of remote sensing imagery].

Remote sensing data classification is an important way of information extraction and a hot research topic of remote sensing technique. Classification method of remote sensing data is an important issue, and effective selection of appropriate classifier is especially significant for improving classification accuracy. Along with the development of remote sensing technique, traditional parametric classifier is difficult to meet accuracy requirement, leading to the rapid development of intelligent algorithm based non-parametric classifiers. Recently, combined classifiers become a new hot topic for its ability of utilizing complement information of single classifier. In the present paper, characters and advantages of different classifiers as well as the research prospect are analyzed. The paper provides a scientific reference for the development of remote sensing data classification technique.

[Study on spectral reflectance characteristics of hemp canopies].

Hemp (Cannabis sativa L.) is a special economic crop and widely used in many field. It is significative for the government to master the information about planting acreage and spatial distribution of hemp for hemp industrial policy decision in China. Remote sensing offers a potential way of monitoring large area for the cultivation of hemp. However, very little study on the spectral properties of hemp is available in the scientific literature. In the present study, the spectral reflectance characteristics of hemp canopy were systematically analyzed based on the spectral data acquired with ASD FieldSpec portable spectrometer. The wavebands and its spectral resolution for discriminating hemp from other plants were identified using difference analysis. The major differences in canopy reflectance of hemp and other plants were observed near 530, 552, 734, 992, 1 213, 1 580 and 2 199 nm, and the maximal difference is near 734 nm. The spectral resolution should be 30 nm or less in visible and near infrared regions, and 50 nm or less in middle infrared regions.