Biochemical Reaction Processes Research Articles

Fermentation of Soy-based dairy substitutes by lactic acid bacteria: focus on the process of soy protein hydrolysis

Soy-based dairy substitutes (SBDS) made from soybeans are becoming increasingly popular due to the nutritional benefits of soybeans. The use of lactic acid bacteria (LAB) fermentation to enhance the quality of SBDS is a reasonable strategy, in which protein hydrolysis is one of the key biochemical reaction processes in the fermentation process. However, a systematic analysis is yet to be conducted to comprehensively assess the current impact and potential mechanisms of LAB hydrolyzed soy protein on the quality of SBDS. This review summarizes the effects of LAB hydrolyzed soy proteins on the flavor, texture, nutrient digestion and absorption, bioactive peptide production, and allergenicity of SBDS. In addition, measures to improve the ability of LAB to hydrolyze soy protein were presented, including pretreatment of raw materials, screening of suitable strains, optimization of fermentation conditions, and an outlook on future research directions. From the perspective of protein hydrolysis, this review effectively highlights the significant role of LAB in shaping the quality of SBDS during fermentation. It offers an invaluable reference for those involved in the development of high-caliber SBDS production.

Stochastic dynamical behaviors of SOS/ERK signaling pathway perturbated by external noise

The SOS/ERK cascades are key signaling pathways that regulate cellular processes ranging from cellular proliferation, differentiation and apoptosis to tumor formation. However, the properties of these signaling pathways are not well understood. More importantly, how stochastic perturbations of internal and external cellular environment affect these pathways remains unanswered. To answer these questions, we, in this paper, propose a stochastic model according to the biochemical reaction processes of the SOS/ERK pathways, and, respectively, research the dynamical behaviors of this model under the four kinds of noises: Gaussian noise, colored noise, Lévy noise and fraction Brown noise. Some important results are found that Gaussian and colored noises have less effect on the stability of the system when the strength of the noise is small; Lévy and fractional Brownian noises significantly change the trajectories of the system. Power spectrum analysis shows that Lévy noise induces a system with quasi-periodic trajectories. Our results not only provide an understanding of the SOS/ERK pathway, but also show generalized rules for stochastic dynamical systems.

Single-Molecule Catalysis in the Palladium Cross-Coupling Reaction Cycle.

Single-molecule (SM) methods are applied to study various types of catalytic processes in chemical and biochemical reactions. In this study, the Suzuki-Miyaura cross-coupling reaction forming a fluorescent product is investigated within the SM approximation. Stochastic analysis of emission intermittency in selected nanoscopic spots allows us to determine the single-molecule turnover frequency (SM-TOF) of the Pd catalyst in a specific probe reaction. We generate and analyze simulated intermittency time traces of a single catalyst surrounded by reactant molecules to assess the reliability of the method applied to real intermittency time trace data from hundreds of nanoscopic fluorescence spots. The results demonstrate that the proposed method can be used to evaluate the average SM-TOF of Pd in a cross-coupling reaction.

An online soft sensor method for biochemical reaction process based on JS-ISSA-XGBoost

BackgroundA method combining offline techniques and the just-in-time learning strategy (JITL) is proposed, because the biochemical reaction process often encounters changing features and parameters over time.MethodsFirstly, multiple sub-databases in the fermentation process are constructed offline by an improved fuzzy C-means algorithm and the sample data are adaptively pruned by a similarity query threshold. Secondly, an improved eXtreme Gradient Boosting (XGBoost) method is used on the online modeling stage to build soft sensor models, and the multi-similarity-driven just-in-time learning strategy is used to increase the diversity of the model. Finally, to improve the generalization of the whole algorithm, the output of the base learner is fused by an improved Stacking integration model and then the predictive output is performed.ResultsApplying the constructed soft sensor model to the problem of predicting cell concentration and product concentration in Pichia pastoris fermentation process. The experimental results show that the root mean square error of the cell concentration is 0.0260, the coefficient of determination is 0.9945, the root mean square error of the product concentration is 2.6688, and the coefficient of determination is 0.9970. It shows that the proposed method has the advantages of timely prediction and high prediction accuracy, which validates the effectiveness and practicality of the method.ConclusionThe JS-ISSA-XGBoost is an extensive and excellent soft measurement model that meets the practical needs for real-time monitoring of parameters and prediction of control in biochemical reactions.

Unravelling the Effect of Acid-Driven Electron Transfer in High-Valent FeIV =O/MnIV =O Species and Its Implications for Reactivity.

The electron transfer (ET) step is one of the crucial processes in biochemical redox reactions that occur in nature and has been established as a key step in dictating the reactivity of high-valent metal-oxo species. Although metalloenzymes possessing metal-oxo units at their active site are typically associated with outer-sphere electron transfer (OSET) processes, biomimetic models, in contrast, have been found to manifest either an inner-sphere electron transfer (ISET) or OSET mechanism. This distinction is clearly illustrated through the behaviour of [(N4Py)MnIV (O)]2+ (1) and [(N4Py)FeIV (O)]2+ (2) complexes, where complex 1 showcases an OSET mechanism, while complex 2 exhibits an ISET mechanism, especially evident in their reactions involving C-H bond activation and oxygen atom transfer reactions in the presence of a Lewis/Bronsted acid. However, the precise reason for this puzzling difference remains elusive. This work unveils the origin of the perplexing inner-sphere vs outer-sphere electron transfer process (ISET vs OSET) in [(N4Py)MnIV (O)]2+ (1) and [(N4Py)FeIV (O)]2+ (2) species in the presence of Bronsted acid. The calculations indicate that when the substrate (toluene) approaches both 1 and 2 that is hydrogen bonded with two HOTf molecules (denoted as 1-HOTf and 2-HOTf, respectively), proton transfer from one of the HOTf molecules to the metal-oxo unit is triggered and a simultaneous electron transfer occurs from toluene to the metal centre. Interestingly, the preference for OSET by 1-HOTf is found to originate from the choice of MnIV =O centre to abstract spin-down (β) electron from toluene to its δ(dxy ) orbital. On the other hand, in 2-HOTf, a spin state inversion from triplet to quintet state takes place during the proton (from HOTf) coupled electron transfer (from toluene) preferring a spin-up (α) electron abstraction to its σ* (dz 2 ) orbital mediated by HOTf giving rise to ISET. In addition, 2-HOTf was calculated to possess a larger reorganisation energy, which facilitates the ISET process via the acid. The absence of spin-inversion and smaller reorganisation energy switch the mechanism to OSET for 1-HOTf. Therefore, for the first time, the significance of spin-state and spin-inversion in the electron transfer process has been identified and demonstrated within the realm of high-valent metal-oxo chemistry. This discovery holds implications for the potential involvement of high-valent Mn-oxo species in performing similar transformative processes within Photosystem II.

Aeration intensity drives dissolved organic matter transformation and humification during composting by regulating the organics metabolic functions of microbiome

Oxygen levels are critical for composting success and improving humification. Whereas aeration intensity driving the structural heterogeneity and formation mechanism of humus in the dissolved organic matter (DOM) transformation process during kitchen waste with hydrothermal pretreatment composting remains unclear. In this study, the potential mechanisms of aeration intensity on humification were explored by Fourier transform infrared spectroscopy and fluorescence spectroscopy combined with functional microbial prediction analysis. The results showed that moderate aeration intensity (AR0.05, 0.05 L min−1 kg−1 DM) achieved a highly matured fertilizer with a humification index of 0.83 and a humic acid / fulvic acid (HA/FA) of 1.99. Partial least-squares path model demonstrated that environmental factors and humus-like substances were the two most critical factors to improve humification levels. According to the heterogeneous-2DCOS analysis, the biopolymers in DOM were preferentially decomposed into low molecular weight HA precursors through a series of biochemical reaction processes, and then formed the carbon skeleton of HA through condensation reactions, and finally polymerized into complex macromolecular HA. Bugbase analysis showed that AR0.05 treatment reduced 58.52 % of potentially pathogenic bacteria in the thermophilic phase. Moreover, functional bacterial communities in composting systems with AR0.05 were more favorable to drive the organic matter decomposition and HA formation. The mantel test revealed temperature and pH were key drivers in the composition of phenotypic and functional bacterial communities. This study provided unique insights into a deeper understanding of the aeration intensity on the humification pathways of kitchen waste composting.

An soft-sensor method for the biochemical reaction process based on LSTM and transfer learning

Due to significant differences in data distribution under different working conditions during Pichia pastoris biochemical reaction process, traditional soft-sensor model suffer from the model failure and deterioration, this paper propose a soft-sensor modeling method combing long short-term memory network (LSTM) and balanced distribution adaptation method (BDA). Firstly, the source domain data is used to establish an accurate source domain LSTM prediction model, and the structure and parameters of the first layer of LSTM are fixed to migrate to the target domain prediction model. Then use the balanced distribution adaptation method to shrink the distribution differences between different domains of data. Finally, data that has been modeled with balanced and adaptive distribution assist the real-time data to train the remaining layer of the network, and the accurate target domain prediction model is finally obtained. The simulation results show that the mentioned method has the preponderance of timely prediction and high prediction accuracy, which validates the effectiveness and practicality of the method. This method solves the problem of soft-sensor modeling under unknown modes of multiple operating conditions in Pichia pastoris biochemical reaction process, achieving the prediction of key parameters under different operating conditions, which can be widely applied, and also providing a new method for soft-sensor modeling of other non system systems.

Activating Main-Group Mg Atomic Sites within Tri-s-triazine for Photocatalytic H2O Overall Splitting: Dynamic Mechanism and Performance

Photocatalysis involving oxygenated-intermediate conversion is rather difficult to achieve over main-group metals owing to the inherent absence of empty and filled host d orbitals. Nevertheless, main-group metals (e.g., Mg) as cofactors within enzymes are highly active in biochemical reaction processes. By theoretical predictions and in situ diffuse reflectance infrared Fourier transform spectroscopy, we confirm that tri-s-triazine-based N-coordinated Mg cofactors can be activated to efficiently complete photocatalytic H2O dissociation to H2 and O2. Photogenerated e– and h+ in the spontaneously formed localized electric field caused by increasing entropy realize the separation in space and migration in the direction to N and single Mg atoms to dehydrogenate and subsequently deoxidize the adjacent hydroxyl group intermediates for H2 and O2 generation, respectively, with a solar-to-H2 efficiency of 0.88% under AM 1.5 G excitation. This work provides a perspective to activate main-group metals and deeply understand their catalytic mechanism of the artificial photocatalytic H2O overall splitting process.

The Design of 2DOF IMC-PID Controller in Biochemical Reaction Networks

DNA molecules can be adopted to design biomolecular control circuits that can effectively control biochemical reaction processes. However, the leak reaction in actual biochemical reactions causes a significant uncertainty for reactions. In this paper, the first-order time-delay system is selected as the controlled object. A two-degree-of-freedom internal model PID controller (2DOF IMC-PID) is constructed for the first time within the framework of chemical reaction networks (CRNs). Under this control strategy, the set-point tracking and disturbance suppression are tuned with individual controllers, respectively. The controller parameters are determined by two filtering parameters that affect the controller’s performance, so the parameter tuning is simpler and more targeted. Then, the 2DOF IMC-PID controller is implemented in DSD reaction networks, with less overshoot in the 2DOF IMC-PID control system than the traditional PID control system and the 2DOF PID control system. Finally, a 2DOF IMC-PID division gate control system is established to effectively inhibit the impacts of leak reactions on the computation results. Although the leak reaction occurs at the division gate, the ideal output can be produced by the 2DOF IMC-PID division gate control system.

Numerical simulation of the interaction and coalescence of inline hydrogen bubbles in biohydrogen production by photofermentation with corncob

The escape rate of hydrogen in photobiological hydrogen production plays an important role in the biochemical reaction process, and the interaction of hydrogen bubbles in bio-hydrogen production material liquid affects the escape rate of hydrogen. In this paper, the Eulerian-Eulerian-Multifluid VOF method is used to simulate the interactions of three types of hydrogen bubbles: coalescence of equal-sized inline hydrogen bubbles, a small leading hydrogen bubble is chased by a large trailing hydrogen bubble and a large leading hydrogen bubble is chased by a small trailing hydrogen bubble. The results show that the interaction strength between hydrogen bubbles is influenced by the initial interval and initial diameter of the hydrogen bubbles and the flow index of the hydrogen-producing material liquid. The hydrogen bubbles exhibit three types of aggregation. In the pre-merger period, the velocity of the trailing hydrogen bubble also shows four stage changes, which are acceleration to the maximum velocity - deceleration - small increase in velocity - deceleration to the merging velocity. There is also a brief acceleration of the leading hydrogen bubble. For the interaction between Type II and Type III hydrogen bubbles of the same size, there is no significant difference in the rise rate of large hydrogen bubbles after the coalescence.

A Piezoelectrically Excited ZnO Nanowire Mass Sensor with Closed-Loop Detection at Room Temperature.

One-dimensional nanobeam mass sensors offer an unprecedented ability to measure tiny masses or even the mass of individual molecules or atoms, enabling many interesting applications in the fields of mass spectrometry and atomic physics. However, current nano-beam mass sensors suffer from poor real-time test performance and high environment requirements. This paper proposes a piezoelectrically excited ZnO nanowire (NW) mass sensor with closed-loop detection at room temperature to break this limitation. It is detected that the designed piezo-excited ZnO NW could operate at room temperature with a resonant frequency of 417.35 MHz, a quality factor of 3010, a mass sensitivity of -8.1 Hz/zg, and a resolution of 192 zg. The multi-field coupling dynamic model of ZnO NW mass sensor under piezoelectric excitation was established and solved. The nonlinear amplitude-frequency characteristic formula, frequency formula, modal function, sensitivity curve, and linear operating interval were obtained. The ZnO NW mass sensor was fabricated by a top-down method and its response to ethanol gas molecules was tested at room temperature. Experiments show that the sensor has high sensitivity, good closed-loop tracking performance, and high linearity, which provides great potential for the detection of biochemical reaction process of biological particles based on mechanics.

The Increase in the Karstification–Photosynthesis Coupled Carbon Sink and Its Implication for Carbon Neutrality

Two of the most important CO2 sequestration processes on Earth are plant photosynthesis and rock chemical dissolution. Photosynthesis is undoubtedly the most important biochemical reaction and carbon sink processes on Earth. Karst geological action does not produce net carbon sinks. Photosynthesis and karstification in nature are coupled. Karstification–photosynthesis coupling can stabilize and increase the capacity of karstic and photosynthetic carbon sinks. Bidirectional isotope tracer culture technology can quantify the utilization of different inorganic carbon sources by plants. Bicarbonate utilization by plants is a driver of karstification–photosynthesis coupling, which depends on plant species and the environment. Carbonic anhydrase, as a pivot of karstification–photosynthesis coupling, can promote inorganic carbon assimilation in plants and the dissolution of carbonate rocks. Karst-adaptable plants can efficiently promote root-derived bicarbonate and atmospheric carbon dioxide use by plants, finally achieving the conjugate promotion of karstic carbon sinks and photosynthetic carbon sinks. Strengthening karstification–photosynthesis coupling and developing karst-adaptable plants will greatly improve the capacity of carbon sinks in karst ecosystems and better serve the “Carbon peak and Carbon neutralization” goals of China.

Monitoring of wastewater treatment process based on multi-stage variational autoencoder

The wastewater treatment process (WWTP) is a physical and biochemical reaction process with multi-stage, non-linear and non-Gaussian characteristics. The multi-stage means that the data has diversity in different substages. In the process monitoring of the WWTP, Affinity Propagation (AP) algorithm is first used to divide the data of the WWTP into stages to process the stage characteristics of data. Next, the capability of Variational Auto-Encoder (VAE) monitoring model to restrict the Gaussian distribution in hidden layers is utilized to address the coexistence of non-Gaussian and nonlinearity. Finally, to illustrate the superiority and feasibility, the proposed model is conducted on the Benchmark Simulation Model 1 (BSM1). The experimental result illustrates that the multistage variational autoencoder (M-VAE) can extract the characteristics of data more comprehensively, with an improvement of average monitoring accuracy by 37.8%, 2.7%, 12.7% and 0.52% in 10/8 process faults compared to the state-of-the-art fault monitoring methods Auto-encoder (AE), VAE, Deep Recurrent Neural Network (DRNN), deep recurrent network with high-order statistic information (HSI-DRNN) respectively.

Controlling the Hydro-Swelling of Smectite Clay Minerals by Fe(III) Reducing Bacteria for Enhanced Oil Recovery from Low-Permeability Reservoirs

The hydro-swelling of smectite clay minerals in low-permeability reservoirs further decreases the reservoir permeability and results in low oil recovery. Currently, the traditional chemical anti-swelling agents are widely used, but most of them are only effective in the short term and are not environmentally friendly. Here, we report the use of Fe(III) reducing microorganisms (FeRM) as a novel green anti-swelling agent to enhance oil recovery from low-permeability reservoirs. The results showed that FeRM (Proteus hauserifective) inhibited/reduced the hydro-swelling of smectite clay minerals through a three-step biochemical mineralization reaction process. The structural Fe(III) reduction in minerals by FeRM can be an important driving force for illitization. The maximum inhibition efficiency (36.6%) and shrinkage efficiency (69.3%) were achieved at 35 °C and 0.1 Mpa. Furthermore, core displacement tests showed that FeRM reduced the waterflooding injection pressure by 61.1%, increased the core permeability by 49.6%, and increased the oil recovery by 8.1%. Finally, the mechanism of FeRM-enhanced oil recovery was revealed. This study demonstrates that using FeRM to inhibit/reduce the hydro-swelling of clay minerals holds great potential to enhance the oil recovery from low-permeability reservoirs.

Robust network topologies for temperature-inducible bioswitches

BackgroundThermoinducible bioswitches are unique in that the all-or-none switch response is triggered by temperature, which is a global factor that impacts all biochemical reaction processes. To date, temperature-inducible bioswitches rely exclusively on special thermal sensing biomolecules of DNA, RNA, proteins and lipids whose conformations are critically temperature dependent.MethodThis paper extends the traditional thermal switch by utilizing purposely designed network topologies of biomolecular interactions to achieve the switching function. By assuming the general Arrhenius law for biochemical reactions, we explore the full space of all three-node genetic interaction networks to screen topologies capable of thermal bioswitches. Three target bioswitches, i.e., thermal-inducible Off–On, cold-inducible On–Off, and hybrid Off–On-Off double switches, are considered separately.ConclusionsWe identify the minimal and core network skeletons that are basic and essential for building robust high-performance bioswitches: three Off–On motifs, three On–Off motifs, and an incoherent feedforward motif for an Off–On-Off double switch. Functional topologies are implicitly preferential in choosing parameter values to achieve the target functions. The scenario of the topology-based bioswitch we propose here is an extension of molecule-based bioswitches and would be valuable in aiding the rational design and synthesis of efficient high-performance thermal bioswitches.

Development of miniature self-powered single-chamber microbial fuel cell and its response mechanism to copper ions in high and trace concentration

Copper ions are widely present in water environment and are involved in various biochemical reaction processes, causing irreversible damage to the human body. In this study, we design and establish a self-powered miniature single-chamber microbial fuel cell (SCMFC) reactor using xurography technology. Optimal volume of 188 μL is obtained by controlling the distance between the anode and cathode. Copper ions in two concentration gradients are tested and good linear response curves are obtained. The opposite responses to copper ions in the trace concentration range (0–0.4 mg/L) and high concentration range (1.0–8.0 mg/L) are observed. The results show that at trace concentration range, the inhibitory effect of copper ions on the biofilm activity of micro-SCMFC is dominant; while high concentration copper ions are involved in chemical reactions that produce Cu2O, which may act as a catalyst and promote electron transfer. A good linear response to trace concentration (0–0.4 mg/L) of copper ions with detection limits of 0.05 mg/L is obtained in this study. It could be used in drinking water for trace copper ion detection. The investigation of the mechanisms provides the scientific basis for the design of the efficient detection of copper ions by SCMFC.

Development and Challenge of Fluorescent Probes for Bioimaging Applications: From Visualization to Diagnosis.

Fluorescent probes have been used widely in bioimaging, including biological substance detection, cell imaging, in vivo biochemical reaction process tracking, and disease biomarker monitoring, and have gradually occupied an indispensable position. Compared with traditional biological imaging technologies, such as positron emission tomography (PET) and nuclear magnetic resonance imaging (MRI), the attractive advantages of fluorescent probes, such as real-time imaging, in-depth visualization, and less damage to biological samples, have made them increasingly popular. Among them, ultraviolet-visible (UV-vis) fluorescent probes still occupy the mainstream in the field of fluorescent probes due to the advantages of available structure, simple synthesis, strong versatility, and wide application. In recent years, fluorescent probes have become an indispensable tool for bioimaging and have greatly promoted the development of diagnostics. In this review, we focus on the structure, design strategies, advantages, representative probes and latest discoveries in application fields of UV-visible fluorescent probes developed in the past 3-5years based on several fluorophores. We look forward to future development trends of fluorescent probes from the perspective of bioimaging and diagnostics. This comprehensive review may facilitate the development of more powerful fluorescent sensors for broad and exciting applications in the future.

Analytical modeling of the approximate solution behavior of multi‐dimensional reaction–diffusion Brusselator system

This article presents an effective and simple approximation scheme to analytically approximate solution behavior of a multi‐dimensional reaction–diffusion Brusselator system. This system has applications in cooperative processes of chemical kinetics and biochemical reaction processes. The proposed residual power series method is based on multiple power series expansion and residual error function formulation. The convergence properties and error bounds of the proposed scheme are theoretically established and numerically verified. Two benchmark problems in two‐ and three‐dimensional space are considered to validate the efficiency and accuracy of the proposed scheme. The obtained results verified reliability, accuracy, and simplicity of the proposed scheme against the available results in literature.

Vertical Transport Timescale of Surface‐Produced Particulate Material in the Chesapeake Bay

AbstractAccumulation and remineralization of surface‐produced particulate organic matter (POM) in the water column and seabed link closely to hypoxia and the health of aquatic ecosystems. The POM retention time provides a key timescale to interpret biochemical reaction processes. In this study, we investigated the spatiotemporal variations in the vertical particulate age (VPA) of surface‐produced POM, which is the mean time elapsed since the particulates last contact the surface, by incorporating major physical processes including sinking, resuspension, and deposition in the Chesapeake Bay. It was found that the vertical transport time for the particulates (i.e., VPA) is much longer than the dissolved counterparts as the former consists of new material from the surface and the resuspended aged material that has elongated resting on the seabed after deposition. The VPA is sensitive to settling velocity, especially in low‐frequent resuspension environments, and varies over 2 orders of magnitude with settling velocity from 0 to 10 m/day. Slow‐sinking material can remain in suspension and seldom settle to the seabed, thus mainly contribute to pelagic processes, while the fast‐sinking material connects closely with benthic processes. The seasonality of VPA decreases as the settling velocity increases. No significant difference in VPA was found between wet and dry years, yet the episodic strong flood events entrain old materials from the depositional lateral shoals to increase VPA in the channel. The transport age bridges cross disciplinaries by providing the fourth‐dimensional age information as a common currency to compare the physical transport timescale with the timescales for biochemical reactions.

Effect of adjusting inlet/outlet location on the power performance of a continuous type of microbial fuel cells

Microbial fuel cells (MFCs) are an essential technology in the treatment of wastewater through a series of biochemical reaction processes while generating bioenergy. The advancement of MFCs has been extensively noted, however, certain limitations in performance such as low power density and subpar wastewater treatment hinder its up-scale and commercialization. In addition, the prototyping of MFCs and experimental approach in enhancing its performance is time-consuming and costly. To address these concerns, this study proposes a three-dimensional (3D) numerical model of the continuous type of dual-chambers MFCs to redesign and investigate the effect of changing the inlet and outlet locations. By evaluating the locations of the inlet and outlet, the overall performance of the MFC is projected to improve by addressing the uneven biomass distribution in the electrodes and the uneven concentration of acetic in the solution. With the appropriate changes in the input and output locations, the results revealed that the total current density was increased by 5%. In addition, the maximum percentage of the gap between model A and model B is increased from the original 5% to 8% under 1.5 times the flow rate. This fact implies that an increase in flow rate results in a greater influence of flow field in vortex zone on the performance of MFC. The findings of this work will be useful for the up-scale usage of MFCs for wastewater treatment in the future.