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23 pages, 1045 KiB  
Article
The In Silico Optimization of a Fed-Batch Reactor Used for the Enzymatic Hydrolysis of Chicory Inulin to Fructose by Employing a Dynamic Approach
by Daniela Gheorghe, Gheorghe Maria, Laura Renea and Crina Muscalu
Dynamics 2025, 5(1), 10; https://doi.org/10.3390/dynamics5010010 - 7 Mar 2025
Abstract
In recent years, inulin enzymatic hydrolysis has become a very promising alternative for producing fructose on a large scale. Genetically modified chicory was used to extract inulin of industrial quality. By using an adequate kinetic model from the literature, this study aimed to [...] Read more.
In recent years, inulin enzymatic hydrolysis has become a very promising alternative for producing fructose on a large scale. Genetically modified chicory was used to extract inulin of industrial quality. By using an adequate kinetic model from the literature, this study aimed to determine the optimal operating alternatives of a batch (BR) or fed-batch (FBR) reactor used for the hydrolysis of inulin to fructose. The operation of the FBR with a constant or variable/dynamic feeding was compared to that of the BR to determine which best maximizes reactor production while minimizing enzyme consumption. Multi-objective optimal solutions were also investigated by using the Pareto-optimal front technique. Our in-silico analysis reveals that, for this enzymatic process, the best alternative is the FBR operated with a constant control variable but using the set-point given by the (breakpoint) of the Pareto optimal front under the imposed technological constraints. This set point reported the best performances, regarding all the considered opposite economic objectives. Also, the FBR with a constant, but NLP optimal feeding, reported fairly good performances. Full article
20 pages, 3287 KiB  
Article
Development of a Pre-Modification Strategy to Overcome Restriction–Modification Barriers and Enhance Genetic Engineering in Lactococcus lactis for Nisin Biosynthesis
by Chen Chen, Yue Zhang, Ruiqi Chen, Ke Liu, Hao Wu, Jianjun Qiao and Qinggele Caiyin
Int. J. Mol. Sci. 2025, 26(5), 2200; https://doi.org/10.3390/ijms26052200 - 28 Feb 2025
Viewed by 81
Abstract
Due to the barriers imposed by the restriction–modification (RM) system, Nisin-producing industrial strains of Lactococcus lactis often encounter low transformation efficiency, which seriously hinders the widespread application of genetic engineering in non-model L. lactis. Herein, we present a novel pre-modification strategy (PMS) [...] Read more.
Due to the barriers imposed by the restriction–modification (RM) system, Nisin-producing industrial strains of Lactococcus lactis often encounter low transformation efficiency, which seriously hinders the widespread application of genetic engineering in non-model L. lactis. Herein, we present a novel pre-modification strategy (PMS) coupled with optimized plasmid delivery systems designed to systematically evade RM barriers and substantially improve Nisin biosynthesis in L. lactis. Through the use of engineered Escherichia coli strains with methylation profiles specifically optimized for L. lactis C20, we have effectively evaded RM barriers, thereby facilitating the efficient introduction of large Nisin biosynthetic gene clusters into L. lactis. The PMS tools, which significantly improve the transformation efficiency (~103 transformants per microgram of DNA), have been further improved in combination with a Rolling Circle Amplification, resulting in a higher enhancement in transformation efficiency (~104 transformants per microgram of DNA). Using this strategy, large Nisin biosynthetic gene clusters and the expression regulation of all genes within the cluster were introduced and analyzed in L. lactis, leading to a highest Nisin titer of 11,052.9 IU/mL through a fed-batch fermentation in a 5 L bioreactor. This is the first systematic report on the expression regulation and application of a complete Nisin biosynthesis gene cluster in L. lactis. Taken together, our studies provide a versatile and efficient strategy for systematic evasion and enhancement of RM barriers and Nisin biosynthesis, thereby paving the way for genetic modification and metabolic engineering in L. lactis. Full article
(This article belongs to the Section Molecular Biology)
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Figure 1
<p>Biosynthesis and regulation mechanism of Nisin.</p>
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<p>The methylome profile of <span class="html-italic">L. lactis</span> C20. (<b>A</b>) The genome profile, detected motifs, and methylated positions in <span class="html-italic">L. lactis</span> C20. The motifs are arranged according to modification type of the RM system and ordered from inner to outer circle, with their occurrence in genomes as shown in the plot legend. (<b>B</b>) MTase specificities are determined based on the detected methylated genomic positions. The degree of conservation (bits) is represented by the height of each stack, while the relative frequency of the base is represented by the height of the letters. The partner motifs C<b>A</b>YNNNNNNTCG and CG<b>A</b>NNNNNNRTG are both methylated on both strands, and all motifs are recognized by N-6 adenine-specific methyltransferases. The pentagram symbol in green represents the methylated bases. (<b>C</b>) Gene structure and recognition motifs of RM systems in <span class="html-italic">L. lactis</span> C20. The genes associated with the REase (Res and HsdR) and methyltransferases (HsdM and Mod) are represented in orange and green, respectively, while the genes encoding the specificity subunit (HsdS) are represented in blue.</p>
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<p>RM recognition sites and transformation efficiency of plasmids by PMS tools. (<b>A</b>) The distribution of RM system target recognition sites on plasmids pLEB124 (4.6 kb), pNZ8148 (3.2 kb), pNZTS-Cas9 (9.6 kb), and pNZTS-cBE (11.3 kb). The RM system target recognition motifs 5′-CAYNNNNNNTCG-3′, 5′-CGANNNNNNRTG-3′, 5′-GCGGAANDVNB-3′, and 5′-GCGGA-3′ are indicated as red, blue, and orange rectangles, respectively. (<b>B</b>) Transformation efficiency of pNZ8148 (CFU/μg DNA) by PMS1, PMS2, and PMS3 tools. (<b>C</b>) Transformation efficiency of pLEB124 (CFU/μg DNA) by PMS1, PMS2, and PMS3 tools. (<b>D</b>) Transformation efficiency of pNZTS-Cas9 (CFU/μg DNA) by PMS1, PMS2, and PMS3 tools. (<b>E</b>) Transformation efficiency of pNZTS-cBE (CFU/μg DNA) by PMS1, PMS2, and PMS3 tools. Data are shown as means ± SD from three biologically independent replicates. ns, no significance; *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Transformation efficiency of plasmids by PMS-RCA tools. (<b>A</b>) Transformation efficiency of p NZ8148 (CFU/μg DNA) by RCA, PMS, and PMS-RCA tools. (<b>B</b>) Transformation efficiency of pLEB24 (CFU/μg DNA) by RCA, PMS, and PMS-RCA tools. (<b>C</b>) Transformation efficiency of pNZTS-Cas9 (CFU/μg DNA) by RCA, PMS, and PMS-RCA tools. (<b>D</b>) Transformation efficiency of pNZTS-cBE (CFU/μg DNA) by RCA, PMS, and PMS-RCA tools. Data are shown as means ± SD from three biologically independent replicates. ns, no significance; *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Growth profiles and Nisin titers of engineered <span class="html-italic">L. lactis</span> strains. (<b>A</b>) Growth curves at different time points. (<b>B</b>) Determination of Nisin titers at different time points. C20, the original strain <span class="html-italic">L. lactis</span> C20, as control group; Nis-1, the engineered strain <span class="html-italic">L. lactis</span> Nis-1 harboring pNZ8148-Nis1; Nis2, the engineered strain <span class="html-italic">L. lactis</span> Nis2 harboring pLEB124-Nis2; Nis-12, the engineered strain <span class="html-italic">L. lactis</span> Nis-12 harboring dual-plasmid system with pNZ8148-Nis1 and pLEB124-Nis2. Data are presented as means ± SD from three parallel replicates. ns, no significance; *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ****, <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Relative expression levels of genes within Nisin biosynthetic gene cluster in <span class="html-italic">L. lactis</span> C20 and engineered <span class="html-italic">L. lactis</span> Nis-1, <span class="html-italic">L. lactis</span> Nis-2, and <span class="html-italic">L. lactis</span> Nis-12 by quantitative Real-Time PCR (qRT-PCR) analysis. Nis-Z, the precursor of Nisin; Nis-B, Nisin biosynthesis protein; Nis-T, ABC transporter ATP-binding protein; Nis-C, Nisin biosynthesis protein; Nis-I, lantibiotic immunity lipoprotein; Nis-P, serine protease; Nis-R, DNA-binding response regulator; Nis-K, sensor histidine kinase; Nis-F, ABC transporter ATP-binding protein; Nis-E, lantibiotic ABC transporter permease, Nis-G, transporter. Expression is presented relative to that of the control genes of <span class="html-italic">L. lactis</span> C20. Data are presented as means ± SD from three parallel replicates. ns, no significance; *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ****, <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Fed-batch fermentation for Nisin production in a 5 L bioreactor by <span class="html-italic">L. lactis</span> C20 and engineered <span class="html-italic">L. lactis</span> Nis-2. Time courses of cell density, concentration of residual glucose, and Nisin titer of <span class="html-italic">L. lactis</span> C20 and <span class="html-italic">L. lactis</span> Nis-2 during the fed-batch fermentation. Glucose C20, residual glucose concentration of fermentation broth in <span class="html-italic">L. lactis</span> C20; Glucose Nis2, residual glucose concentration of fermentation broth in <span class="html-italic">L. lactis</span> Nis-2; OD<sub>600</sub> C20, cell density of <span class="html-italic">L. lactis</span> C20 during fermentation; OD<sub>600</sub> Nis2, cell density of <span class="html-italic">L. lactis</span> Nis-2 during fermentation; Nisin titer C20, Nisin titer of <span class="html-italic">L. lactis</span> C20 during fermentation; Nisin titer Nis2, Nisin titer of <span class="html-italic">L. lactis</span> Nis-2 during fermentation. Data are presented as means ± SD from three parallel replicates.</p>
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22 pages, 2683 KiB  
Article
Alkylpolyglycosides—Based Formulations for Sustainable Remediation of Contaminated Aquifers: Lab-Scale Process Study for NAPL Solubilization Assessment
by Berardino Barbati, Laura Lorini, Marco Bellagamba and Marco Petrangeli Papini
Sustainability 2025, 17(5), 1939; https://doi.org/10.3390/su17051939 - 25 Feb 2025
Viewed by 263
Abstract
In the context of the surfactant-enhanced remediation of polluted sites, this work focuses on the development of non-ionic alkylpolyglucosidic (APG) surfactant formulations with different additives for the solubilization and mobilization of strongly adsorbed organic pollutants. The study involved three consecutive phases to evaluate [...] Read more.
In the context of the surfactant-enhanced remediation of polluted sites, this work focuses on the development of non-ionic alkylpolyglucosidic (APG) surfactant formulations with different additives for the solubilization and mobilization of strongly adsorbed organic pollutants. The study involved three consecutive phases to evaluate the effect of the additives on surface behavior and the potential improvement in alkylpolyglycoside surfactant’s capability to solubilize toluene and perchloroethylene (PCE), selected as reference contaminants. After a chemical–physical characterization phase, the APG-based formulations were first used in a batch configuration test, in which the formulations’ solubilization ability was indirectly assessed by observing the effect on pollutants’ adsorption. Lastly, a continuous configuration column experiment was performed to simulate the flushing process of a synthetic matrix previously contaminated with strongly adsorbed toluene or PCE. The results showed that the presence of additives firstly reduced the ability of the surfactant to form micelles, increasing the CMC, but at the same time improved the ability to reduce surface tension. Moreover, the addition of the additives overall resulted in a significant improvement in adsorbed pollutant removal in a minimal volume of fed solution, reaching 96% and 99% efficiencies for toluene and PCE, respectively, compared with 76% and 92%, for toluene and PCE, respectively, in the presence of free-additive APG surfactant. Full article
(This article belongs to the Section Sustainability in Geographic Science)
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<p>Schematic example of the experimental setup for the continuous configuration column experiments.</p>
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<p>Variation in the surface tension with surfactant concentration for the three investigated formulations: (<b>a</b>) APG 2a, (<b>b</b>) APG 2b, and (<b>c</b>) APG 2c.</p>
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<p>Isotherm curves of toluene and PCE on PWB in surfactant formulations at different concentrations: (<b>a</b>) PWB–toluene–APG 2a, (<b>b</b>) PWB–toluene–APG 2b, (<b>c</b>) PWB–toluene–APG 2c; (<b>d</b>) PWB–PCE–APG 2a; (<b>e</b>) PWB–PCE–APG 2b and (<b>f</b>) PWB–PCE–APG 2c.</p>
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<p>Breakthrough curves of both toluene and PCE for (<b>a</b>) PWB-TOL-a, (<b>b</b>) PWB-TOL-b, (<b>c</b>) PWB-TOL-c columns, (<b>d</b>) PWB-PCE-a, (<b>e</b>) PWB-PCE-b, and (<b>f</b>) PWB-PCE-c columns.</p>
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<p>Flushing curves of toluene with (<b>a</b>) APG 2a, (<b>b</b>) APG 2b, (<b>c</b>) APG 2c, and (<b>d</b>) APG 2 (previous work [<a href="#B32-sustainability-17-01939" class="html-bibr">32</a>]). The gray-scale symbols refer to the toluene breakthrough curve of the contamination phase. The colored symbols refer to the toluene solubilization of the flushing phase. All the formulations are used with a total concentration equal to 5× CMC.</p>
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<p>Flushing curves of PCE with (<b>a</b>) APG 2a, (<b>b</b>) APG 2b, (<b>c</b>) APG 2c, and (<b>d</b>) APG 2 (previous work [<a href="#B32-sustainability-17-01939" class="html-bibr">32</a>]). The gray-scale symbols refer to the toluene breakthrough curve of the contamination phase. The colored symbols refer to the toluene solubilization of the flushing phase. All the formulations are used with a total concentration equal to 5× CMC.</p>
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13 pages, 3066 KiB  
Article
Bulk Water Microbes Could Accelerate Chlorine Decay at Low Chlorine Concentrations
by Mohamed Fawaz Fawzer, George Kastl, K. C. Bal Krishna, Ian Fisher and Arumugam Sathasivan
Water 2025, 17(5), 624; https://doi.org/10.3390/w17050624 - 21 Feb 2025
Viewed by 276
Abstract
Using a laboratory-scale system, consisting of a primary disinfection tank (PDT) and three intermittently mixed reactors (R1–R3) in series, bulk water and biofilm contributions to chlorine decay were quantified. The reactors (surface-to-volume ratio: 23.7 m−1; retention time in each reactor: 42.6 [...] Read more.
Using a laboratory-scale system, consisting of a primary disinfection tank (PDT) and three intermittently mixed reactors (R1–R3) in series, bulk water and biofilm contributions to chlorine decay were quantified. The reactors (surface-to-volume ratio: 23.7 m−1; retention time in each reactor: 42.6 ± 1.18 h) were fed with plant-filtered water (PFW). Secondary disinfection was carried out in R1. Free chlorine concentration decreased with travel time (R1: 1.2 mg/L; R2: 0.6 mg/L; and R3: 0.12 mg/L). The bacterial number (ATP) decreased from 67 pg/mL in PFW and remained at ~2–3 pg/mL in R1 and R2 but increased back to 68 pg/mL in R3. First-order chlorine decay rate coefficients decreased from R1 to R2, as expected, but increased by five-fold from R2 to R3. The increased bacterial number (ATP) in R3 and batch chlorine decay tests confirmed that bulk water (soluble compounds, microbes, and sediments) contributed approximately 40% of the decay, and the biofilm contributed 60% in R3. When ATP levels in the reactors were combined with literature data, the bacterial number increased significantly when free chlorine decreased below 0.2 mg/L, but data between 0.2 and 0.5 mg/L are limited. More investigation is needed in the future for chlorine < 0.5 mg/L regarding bacterial regrowth and its effect on bulk water chlorine decay. Full article
(This article belongs to the Section Urban Water Management)
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Graphical abstract

Graphical abstract
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<p>Schematic diagram of the laboratory-scale chlorinated reactor system simulating a drinking water distribution system (DWDS).</p>
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<p>(<b>A</b>) Reactor chlorine profile: Observed data (markers) and model predictions (lines). The model was estimated using two different first-order chlorine decay coefficients for R2 (0.0190 ± 0.0005 h<sup>−1</sup>) and R3 (0.0981 ± 0.0079 h<sup>−1</sup>). (<b>B</b>) First-order chlorine decay coefficients from all batch tests and the model. Chlorine concentration of R3 for batch tests, after topping up given in brackets.</p>
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<p>Results of the fourth batch test (BT4) for the sample from R3 to identify the contributions of bulk water (dissolved compounds), bacteria and sediments, and biofilms regarding accelerated chlorine decay. (<b>A</b>) chlorine decay profiles at 20 °C; (<b>B</b>) relative contributions.</p>
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<p>Live bacterial cells measured by ATP levels.</p>
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<p>ATP levels in typical bulk water-chlorinated systems: Round markers represent reactor data and square markers represent literature data (Delahaye et al. 2003 [<a href="#B25-water-17-00624" class="html-bibr">25</a>], Nescerecka et al. 2014 [<a href="#B27-water-17-00624" class="html-bibr">27</a>], Gora et al. 2020 [<a href="#B28-water-17-00624" class="html-bibr">28</a>], and Pan et al. 2021 [<a href="#B29-water-17-00624" class="html-bibr">29</a>]).</p>
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18 pages, 2592 KiB  
Article
Use of Wastewaters from Ethanol Distilleries and Raw Glycerol for Microbial Oil Production
by Evelyn Faife, Nayra Ochoa, Jingyang Xu, Dehua Liu, Wei Du, Manuel Díaz and María Guadalupe Aguilar-Uscanga
Processes 2025, 13(2), 467; https://doi.org/10.3390/pr13020467 - 8 Feb 2025
Viewed by 445
Abstract
The production of biodiesel from single-cell oils (SCOs) utilizing industrial wastes as feedstock presents an economically viable approach. To date, studies have rarely reported the utilization of vinasse combined with industrial glycerol for the production of SCO. This study aimed to assess the [...] Read more.
The production of biodiesel from single-cell oils (SCOs) utilizing industrial wastes as feedstock presents an economically viable approach. To date, studies have rarely reported the utilization of vinasse combined with industrial glycerol for the production of SCO. This study aimed to assess the performance of a Rhodotorula toruloides strain in vinasse from ethanol distilleries supplemented with pure/raw glycerol as an affordable carbon feedstock for SCO production. Several critical factors, including the C/N ratio, the impact of impurities in the crude glycerol, the proper nitrogen source, and the effects of the vinasse compositions, were evaluated. The results showed that the incorporation of urea and raw glycerol increased the lipid content to 51.8 ± 1.6% and the lipid productivity to 0.034 ± 0.001 g L−1h−1. Elevated biomass (42.5 g L−1) and lipid (11.0 g L−1) concentrations indicated that impurities in the raw glycerol positively affected the growth and lipid accumulation of this strain. Notably, supplementing raw glycerol to the vinasse led to a 16.1% increase in biomass concentration and a 25.7% rise in lipid content, significantly enhancing lipid productivity by 59.6%. The fatty acid profile predominantly featured unsaturated fatty acids (96.8%), including high percentages of stearic acid (41.8 ± 2.6%), palmitic acid (21.8 ± 1.5%), and oleic acid (18.3 ± 1.4%), aligning with the standards for vegetable-oil-based biodiesel manufacture. Fed-batch strategies using pulse-feeding turned out to be less effective than the constant-flow feeding strategy with vinasse supplemented with raw glycerol, which achieved a higher lipid productivity of 0.30 g L−1h−1. Full article
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<p>The effect of nitrogen source on (<b>A</b>) biomass concentration, (<b>B</b>) lipid productivity, and (<b>C</b>) lipid content grown in flask culture at initial pH of 5.0, 30 °C, and 150 rpm. Data are shown as mean ± SD from three experiments (<span class="html-italic">n</span> = 3). Glu: glucose, Gly: glycerol, AS: ammonium sulfate (<span class="html-italic">p</span> &lt; 0.05, Fisher’s test). Different letters in the same column in the table indicate that there were significant differences according to the Tukey test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effect of C/N ratio on (<b>A</b>) biomass concentration, (<b>B</b>) lipid productivity, (<b>C</b>) lipid content, and (<b>D</b>) biomass yield of <span class="html-italic">R. toruloides</span> grown in flask culture at initial pH of 5.0, 30 °C, and 150 rpm. Each point represents the mean value ± SD of three samples of a representative experiment of three runs (<span class="html-italic">p</span> &lt; 0.05, Fisher’s test). Different letters in the same column in the table indicate that there were significant differences according to the Tukey test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Effect of glycerol concentration on (<b>A</b>) biomass concentration, (<b>B</b>) lipid productivity, (<b>C</b>) lipid content, (<b>D</b>) biomass yield, and (<b>E</b>) lipid yield of <span class="html-italic">R. toruloides</span> using nitrogen-limited media (C/N 250). Each point represents the mean value ± SD of three samples from a representative experiment of three runs (<span class="html-italic">p</span> &lt; 0.05, Fisher’s test). Different letters in the same column in the table indicate that there were significant differences according to the Tukey test (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Growth kinetics of <span class="html-italic">R. toruloides</span> strain in two vinasse samples. (<b>A</b>) Cell growth in undiluted vinasse A (factor 1) and diluted vinasse A (factor 2). (<b>B</b>) Cell growth in undiluted vinasse B (factor 1) and diluted vinasse B (factor 2). Each point represents the mean ± SD of three samples from a representative experiment.</p>
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<p>Average percentage of major fatty acid compositions of oils extracted from <span class="html-italic">R. toruloides</span> biomass grown on diluted vinasse B supplemented with pure and raw glycerol. Each bar represents the average of each sample.</p>
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<p>Kinetic behavior of biomass concentration (ο; g L<sup>−1</sup>), glycerol concentration (Δ; g L<sup>−1</sup>), and lipid concentration (◊; g L<sup>−1</sup>) of cultures employing fed-batch strategies with raw glycerol and vinasse. (<b>A</b>) Strategy I, (<b>B</b>) strategy II, and (<b>C</b>) strategy III.</p>
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20 pages, 1764 KiB  
Article
A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment
by Jinyin Bai, Wei Zhu, Shuhong Liu, Chenhao Ye, Peng Zheng and Xiangchen Wang
Appl. Sci. 2025, 15(4), 1702; https://doi.org/10.3390/app15041702 - 7 Feb 2025
Viewed by 606
Abstract
Traditional algorithms and single predictive models often face challenges such as limited prediction accuracy and insufficient modeling capabilities for complex time-series data in fault prediction tasks. To address these issues, this paper proposes a combined prediction model based on an improved temporal convolutional [...] Read more.
Traditional algorithms and single predictive models often face challenges such as limited prediction accuracy and insufficient modeling capabilities for complex time-series data in fault prediction tasks. To address these issues, this paper proposes a combined prediction model based on an improved temporal convolutional network (TCN) and bidirectional long short-term memory (BiLSTM), referred to as the TCN-BiLSTM model. This model aims to enhance the reliability and accuracy of time-series fault prediction. It is designed to handle continuous processes but can also be applied to batch and hybrid processes due to its flexible architecture. First, preprocessed industrial operation data are fed into the model, and hyperparameter optimization is conducted using the Optuna framework to improve training efficiency and generalization capability. Then, the model employs an improved TCN layer and a BiLSTM layer for feature extraction and learning. The TCN layer incorporates batch normalization, an optimized activation function (Leaky ReLU), and a dropout mechanism to enhance its ability to capture multi-scale temporal features. The BiLSTM layer further leverages its bidirectional learning mechanism to model the long-term dependencies in the data, enabling effective predictions of complex fault patterns. Finally, the model outputs the prediction results after iterative optimization. To evaluate the performance of the proposed model, simulation experiments were conducted to compare the TCN-BiLSTM model with mainstream prediction methods such as CNN, RNN, BiLSTM, and A-BiLSTM. The experimental results indicate that the TCN-BiLSTM model outperforms the comparison models in terms of prediction accuracy during both the modeling and forecasting stages, providing a feasible solution for time-series fault prediction. Full article
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<p>Dilated convolution architecture diagram.</p>
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<p>LSTM architecture diagram.</p>
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<p>BiLSTM architecture diagram.</p>
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<p>TCN-BiLSTM architecture diagram.</p>
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<p>Historical optimization plot.</p>
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<p>Hyperparameter optimization plot.</p>
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<p>Line chart of experimental results.</p>
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<p>Histogram of experimental results.</p>
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<p>Boxplot of experimental results.</p>
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20 pages, 3628 KiB  
Article
In Vitro Investigation of the Effects of Bacillus subtilis-810B and Bacillus licheniformis-809A on the Rumen Fermentation and Microbiota
by Raphaële Gresse, Bruno Ieda Cappellozza, Didier Macheboeuf, Angélique Torrent, Jeanne Danon, Lena Capern, Dorthe Sandvang, Vincent Niderkorn, Giuseppe Copani and Evelyne Forano
Animals 2025, 15(4), 476; https://doi.org/10.3390/ani15040476 - 7 Feb 2025
Viewed by 617
Abstract
Direct-fed microbials (DFMs) have shown the potential to improve livestock performance and overall health. Extensive research has been conducted to identify new DFMs and understand their mechanisms of action in the gut. Bacillus species are multifunctional spore-forming bacteria that exhibit resilience to harsh [...] Read more.
Direct-fed microbials (DFMs) have shown the potential to improve livestock performance and overall health. Extensive research has been conducted to identify new DFMs and understand their mechanisms of action in the gut. Bacillus species are multifunctional spore-forming bacteria that exhibit resilience to harsh conditions, making them ideal candidates for applications in the feed industry and livestock production. This study investigates the mode of action of B. licheniformis and B. subtilis in the rumen using diverse in vitro techniques. Our results revealed that both strains germinated and grew in sterile rumen and intestinal contents from dairy cows and bulls. Gas composition analysis of in vitro cultures in a medium containing 40% rumen fluid demonstrated that germination of B. licheniformis and B. subtilis strains reduced oxygen levels, promoting an anaerobic environment favorable to rumen microbes. Enzymatic activity assays showed that B. licheniformis released sugars from complex substrates and purified polysaccharides in filtered rumen content. Additionally, the combination of B. licheniformis and B. subtilis survived and grew in the presence of a commercial monensin dose in rumen fluid media. The effects of B. licheniformis and B. subtilis on rumen fermentation activity and microbiota were studied using an in vitro batch fermentation assay. In fermenters that received a combination of B. licheniformis and B. subtilis, less CO2 was produced while dry matter degradation and CH4 production was comparable to the control condition, indicating better efficiency of dry matter utilization by the microbiota. The investigation of microbiota composition between supplemented and control fermenters showed no significant effect on alpha and beta diversity. However, the differential analysis highlighted changes in several taxa between the two conditions. Altogether, our data suggests that the administration of these strains of Bacillus could have a beneficial impact on rumen function, and consequently, on health and performance of ruminants. Full article
(This article belongs to the Section Cattle)
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<p>Growth of <span class="html-italic">B. subtilis</span> and <span class="html-italic">B. licheniformis</span> in digestive content, rumen juice and a rich medium containing 40% of rumen fluid (Mean OD 600 nm ± standard deviation).</p>
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<p>Co-incubation of <span class="html-italic">B. licheniformis</span> and <span class="html-italic">B. subtilis</span> (Bovacillus) with an in-feed commercial dose of monensin (“CTRL” = control condition without bacterial treatment) (Mean OD 600 nm ± standard deviation).</p>
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<p>Oxygen percentage in the rumen-containing medium before inoculation (t = 0) and after 24 h incubation (t = 24) of spores of <span class="html-italic">B. licheniformis</span> (green bars) and <span class="html-italic">B. subtilis</span> (blue bars) (Mean percentage of oxygen ± standard deviation).</p>
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<p>Concentration of released reducing sugars after consumption of complex and purified substrates by <span class="html-italic">Bacillus</span> strains cultivated in rich medium containing 40% rumen fluid.</p>
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<p>Short fatty acids relative abundance (%) produced by fermentation activity of the in vitro rumen microbiota in the control and <span class="html-italic">Bacillus</span> condition after 8 or 24 h of fermentation (“CTRL” = control condition without bacterial treatment).</p>
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<p>(<b>a</b>) Relative abundance of neutral detergent fibers (NDF) remaining in the fermentation media, (<b>b</b>) total “TOT” gas produced in the atmosphere of batch rumen fermenters after 8 h of fermentation, (<b>c</b>) hydrogen “H<sub>2</sub>” in the atmosphere of batch rumen fermenters after 8 h of fermentation, (<b>d</b>) carbon dioxide “CO<sub>2</sub>” in the atmosphere of batch rumen fermenters after 8 h of fermentation (“CTRL” = control condition without bacterial treatment). The conditions sharing the same letters are not significantly different from each other (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Significantly differentially abundant ASVs after 8 h (<b>a</b>) and 24 h (<b>b</b>) of fermentation in the <span class="html-italic">Bacillus</span> treatments compared to the control condition highlighted using the METACODER R package. “Bovacillus” corresponds to the cocktail of <span class="html-italic">B. subtilis</span> and <span class="html-italic">B. licheniformis.</span> Positive values relate to taxa more abundant in the <span class="html-italic">Bacillus</span> treatment while negative values relate to taxa more abundant in the control condition (“CTRL”).</p>
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<p>Count of <span class="html-italic">S</span>. Typhimurium after 24 h of co-incubation with a mixture of <span class="html-italic">B. licheniformis</span> and <span class="html-italic">B. subtilis</span> (Bovacillus) or alone as a control condition (“CTRL”). *** <span class="html-italic">p</span> value &lt; 0.0001.</p>
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19 pages, 2821 KiB  
Article
Genetic Code Expansion for Controlled Surfactin Production in a High Cell-Density Bacillus subtilis Strain
by Alexander Hermann, Eric Hiller, Philipp Hubel, Lennart Biermann, Elvio Henrique Benatto Perino, Oscar Paul Kuipers, Rudolf Hausmann and Lars Lilge
Microorganisms 2025, 13(2), 353; https://doi.org/10.3390/microorganisms13020353 - 6 Feb 2025
Viewed by 808
Abstract
Background: In biotechnology, B. subtilis is established for heterologous protein production. In addition, the species provides a variety of bioactive metabolites, including the non-ribosomally produced surfactin lipopeptide. However, to control the formation of the target product-forming enzyme, different expression systems could be introduced, [...] Read more.
Background: In biotechnology, B. subtilis is established for heterologous protein production. In addition, the species provides a variety of bioactive metabolites, including the non-ribosomally produced surfactin lipopeptide. However, to control the formation of the target product-forming enzyme, different expression systems could be introduced, including the principle of genetic code expansion by the incorporation of externally supplied non-canonical amino acids. Methods: Integration of an amber stop codon into the srfA operon and additional chromosomal integration of an aminoacyl-tRNA synthetase/tRNA mutant pair from Methanococcus jannaschii enabled site-directed incorporation of the non-canonical amino acid O-methyl-L-tyrosine (OMeY). In different fed-batch bioreactor approaches, OMeY-associated surfactin production was quantified by high-performance thin-layer chromatography (HPTLC). Physiological adaptations of the B. subtilis production strain were analyzed by mass spectrometric proteomics. Results: Using a surfactin-forming B. subtilis production strain, which enables high cell density fermentation processes, the principle of genetic code expansion was introduced. Accordingly, the biosynthesis of the surfactin-forming non-ribosomal peptide synthetase (NRPS) was linked to the addition of the non-canonical amino acid OMeY. In OMeY-associated fed-batch bioreactor fermentation processes, a maximum surfactin titre of 10.8 g/L was achieved. In addition, the effect of surfactin induction was investigated by mass spectrometric proteome analyses. Among other things, adaptations in the B. subtilis motility towards a more sessile state and increased abundances of surfactin precursor-producing enzymes were detected. Conclusions: The principle of genetic code expansion enabled a precise control of the surfactin bioproduction as a representative of bioactive secondary metabolites in B. subtilis. This allowed the establishment of inducer-associated regulation at the post-transcriptional level with simultaneous use of the native promoter system. In this way, inductor-dependent control of the production of the target metabolite-forming enzyme could be achieved. Full article
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<p>The principle of surfactin biosynthesis based on genetic code expansion. Under control conditions, a canonical amino acid (cAA) is incorporated into the nascent polypeptide chain, while the release factor (RF) recognizes the stop codons, such as the amber stop codon (UAG), which leads to an end of the translation process. By introducing an orthogonal aaRS/tRNA system, both the release factor and the incorporation of a non-canonical amino acid (ncAA) are able to target the amber stop codon.</p>
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<p>Validation of the correlation between surfactin production and the addition of OMeY. Shake flask cultures were performed using a mineral salt medium containing 8 g/L glucose and 0, 0.25, 0.5, 0.75 and 1 mM of OMeY. <span class="html-italic">B. subtilis</span> strain AH2 was cultured for 12 h. Samples were taken regularly for quantitative surfactin measurement. All cultivation approaches were performed in biological triplicates.</p>
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<p>Development of bioreactor processes with feeding strategies allowing OMeY-dependent surfactin production. The engineered <span class="html-italic">B. subtilis</span> surfactin production strain AH2 was cultivated in a batch fermentation until the glucose was depleted and the feeding process was started (black dashed line). When the culture reached an OD<sub>600</sub> of approximately 100, surfactin production was activated by adding OMeY as an inducer (red dashed line) at a final concentration of 0.75 mM (<b>a</b>) followed by volume-associated co-feeding (<b>b</b>). The entire bioreactor process was stopped when the 6-litre glucose feed solution was consumed.</p>
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<p>Proteome analysis regarding physiological adaptations based on OMeY-derived induction of surfactin production. (<b>a</b>) Heatmap without normalization as an overview of the protein signal intensities determined between the OMeY-induced and control time points. Volcano plots represent different groups of proteins, namely SrfA proteins for surfactin biosynthesis (<b>b</b>), enzymes for biosynthesis of branched-chain amino acids (<b>c</b>) and fatty acids (<b>d</b>), proteins associated with motility (<b>e</b>) and iron acquisition (<b>f</b>), and their changes in abundance after 10 (blue), 30 (green) and 60 min (red) of inducting surfactin production.</p>
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<p>Proteome analysis regarding physiological adaptations based on OMeY-derived induction of surfactin production. (<b>a</b>) Heatmap without normalization as an overview of the protein signal intensities determined between the OMeY-induced and control time points. Volcano plots represent different groups of proteins, namely SrfA proteins for surfactin biosynthesis (<b>b</b>), enzymes for biosynthesis of branched-chain amino acids (<b>c</b>) and fatty acids (<b>d</b>), proteins associated with motility (<b>e</b>) and iron acquisition (<b>f</b>), and their changes in abundance after 10 (blue), 30 (green) and 60 min (red) of inducting surfactin production.</p>
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18 pages, 2326 KiB  
Article
Batch-to-Batch Optimization Control of Fed-Batch Fermentation Process Based on Recursively Updated Extreme Learning Machine Models
by Alex Moore and Jie Zhang
Algorithms 2025, 18(2), 87; https://doi.org/10.3390/a18020087 - 6 Feb 2025
Viewed by 449
Abstract
This paper presents a new method of batch-to-batch optimization control for a fed-batch fermentation process. A recursively updated extreme learning machine (ELM) neural network model is used to model a fed-batch fermentation process. ELM models have advantages over other neural networks in that [...] Read more.
This paper presents a new method of batch-to-batch optimization control for a fed-batch fermentation process. A recursively updated extreme learning machine (ELM) neural network model is used to model a fed-batch fermentation process. ELM models have advantages over other neural networks in that they can be trained very fast and have good generalization performance. However, the ELM model loses its predictive abilities in the presence of batch-to-batch process variations or disturbances, which lead to a process–model mismatch. The recursive least squares (RLS) technique takes the model prediction error from the previous batch and uses it to update the model parameters for the next batch. This improves the performance of the model and helps it to respond to any changes in process conditions or disturbances. The updated model is used in an optimization control procedure, which generates an improved control profile for the next batch. The update of the RLS model enables the optimization control strategy to maintain a high final product quality in the presence of disturbances. The proposed batch-to-batch optimization control method is demonstrated on a simulated fed-batch fermentation process. Full article
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<p>A single-hidden-layer feedforward neural network (SLFN) configured appropriately for training by ELM.</p>
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<p>Flow diagram of the proposed batch-to-batch optimization control strategy integrating ELM and RLS: (<b>a</b>). data generation; (<b>b</b>). ELM modeling; (<b>c</b>). batch-to-batch optimization with ELM model updating using RLS.</p>
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<p>Feed profiles of the historical batches used to build the ELM model.</p>
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<p>RMSE of the model prediction for the training and validation data sets.</p>
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<p>Predicted (ELM) vs. actual (fed-batch simulation) final biomass concentration (normalized values) for the training, validation, and unseen testing data sets.</p>
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<p>Prediction error of the ELM model for recursive batches with and without RLS model updating. An unmeasured disturbance is introduced from the 51st batch.</p>
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<p>Actual (fed-batch simulation) and predicted (ELM model) final biomass concentration for the ELM with RLS model updating system. An unmeasured disturbance is introduced from the 11th batch.</p>
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<p>Final mass of biomass from the fed-batch simulation (<b>a</b>) and model prediction error (<b>b</b>) for the system with and without RLS model updating. An unmeasured disturbance is introduced from the 11th batch.</p>
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<p>The optimized feed profiles for batch 10 and batch 30.</p>
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22 pages, 2720 KiB  
Article
Exploiting Mixed Waste Office Paper Containing Lignocellulosic Fibers for Alternatively Producing High-Value Succinic Acid by Metabolically Engineered Escherichia coli KJ122
by Walainud Congthai, Chutchawan Phosriran, Socheata Chou, Kanyarat Onsanoi, Chotika Gosalawit, Kuan-Chen Cheng and Kaemwich Jantama
Int. J. Mol. Sci. 2025, 26(3), 982; https://doi.org/10.3390/ijms26030982 - 24 Jan 2025
Viewed by 535
Abstract
Succinic acid is applied in many chemical industries in which it can be produced through microbial fermentation using lignocellulosic biomasses. Mixed-waste office paper (MWOP) containing lignocellulosic fibers is enormously generated globally. MWOP is recycled into toilet paper and cardboard, but the recovery process [...] Read more.
Succinic acid is applied in many chemical industries in which it can be produced through microbial fermentation using lignocellulosic biomasses. Mixed-waste office paper (MWOP) containing lignocellulosic fibers is enormously generated globally. MWOP is recycled into toilet paper and cardboard, but the recovery process is costly. The reuse of MWOP to alternatively produce succinic acid is highly attractive. In this study, pretreatment of MWOPs with 1% (v/v) H2SO4 at 121 °C for 20 min was found to be optimal. The optimal conditions for the enzymatic hydrolysis of H2SO4-pretreated MWOP (AP-MWOP) were at 50 °C, with cellulase loading at 80 PCU/g AP-MWOP. This resulted in the highest glucose (22.46 ± 0.15 g/L) and xylose (5.11 ± 0.32 g/L). Succinic acid production via separate hydrolysis and fermentation (SHF) by Escherichia coli KJ122 reached 28.19 ± 0.98 g/L (productivity of 1.17 ± 0.04 g/L/h). For simultaneous saccharification and fermentation (SSF), succinic acid was produced at 24.58 ± 2.32 g/L (productivity of 0.82 ± 0.07 g/L/h). Finally, succinic acid at 51.38 ± 4.05 g/L with yield and productivity of 0.75 ± 0.05 g/g and 1.07 ± 0.08 g/L/h was achieved via fed-batch pre-saccharified SSF. This study not only offers means to reuse MWOP for producing succinic acid but also provides insights for exploiting other wastes to high-value succinic acid, supporting environmental sustainability and zero-waste society. Full article
(This article belongs to the Special Issue Lignocellulose Bioconversion and High-Value Utilization)
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<p>A schematic illustration of succinic acid production pathway by <span class="html-italic">E. coli</span> KJ122. The cross signs represent the deletion of certain genes (adapted from [<a href="#B11-ijms-26-00982" class="html-bibr">11</a>]).</p>
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<p>Effects of different H<sub>2</sub>SO<sub>4</sub> concentrations for pretreatment of MWOP at 121 °C for 30 min (<b>A</b>) and effect of pretreatment time for pretreatment of MWOP at 121 °C and 135 °C (<b>B</b>) on total released fermentable sugars (glucose and xylose) from the acid-pretreated MWOP after enzymatic hydrolysis by crude cellulase. Each column represents the mean ± SD from three independent replicates. The different alphabet (a or b) above each column indicates statistical significance of differences between the mean values of the total released sugars among different treatments (<span class="html-italic">p</span> &lt; 0.05). Data considered not significant among different treatments are marked <span class="html-italic">ns</span> (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Effect of strong acid digestion and crude cellulase loadings (PCU: protein centered unit) used during enzymatic hydrolysis of 50 g/L MWOP (untreated) and AP-MWOP on total released sugars (glucose and xylose) (<b>A</b>), and total released sugars from 50 g/L AP-MWOP hydrolyzed with different crude cellulase loadings at different time intervals (<b>B</b>). Each column represents the mean ± SD from three independent replicates. The different alphabet above each column indicates statistical significance of differences between the mean values of the total released sugars among different treatments (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Succinic acid production via SHF by <span class="html-italic">E. coli</span> KJ122 using different concentrations of AP-MWOP. (<b>A</b>) 50 g/L AP-MWOP, (<b>B</b>) 70 g/L AP-MWOP, and (<b>C</b>) 100 g/L AP-MWOP. Three independent replications were performed on each test, and the means were reported with the standard deviation (SD).</p>
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<p>Succinic acid production via SHF by <span class="html-italic">E. coli</span> KJ122 using different concentrations of AP-MWOP. (<b>A</b>) 50 g/L AP-MWOP, (<b>B</b>) 70 g/L AP-MWOP, and (<b>C</b>) 100 g/L AP-MWOP. Three independent replications were performed on each test, and the means were reported with the standard deviation (SD).</p>
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<p>Succinic acid production via the SSF process with different pre-saccharification times. (<b>A</b>) 0 h, (<b>B</b>) 6 h, (<b>C</b>) 12 h, (<b>D</b>) 18 h, (<b>E</b>) 24 h with 50 g/L AP-MWOP, and (<b>F</b>) 18 h pre-saccharified batch SSF with 70 g/L AP-MWOP. Three independent replications were performed on each test, and the means were reported with the standard deviation (SD).</p>
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<p>Succinic acid production via the SSF process with different pre-saccharification times. (<b>A</b>) 0 h, (<b>B</b>) 6 h, (<b>C</b>) 12 h, (<b>D</b>) 18 h, (<b>E</b>) 24 h with 50 g/L AP-MWOP, and (<b>F</b>) 18 h pre-saccharified batch SSF with 70 g/L AP-MWOP. Three independent replications were performed on each test, and the means were reported with the standard deviation (SD).</p>
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<p>Effects of different agitation rates of 70 g/L AP-MWOP with 18 h pre-saccharification time on succinic acid production by <span class="html-italic">E. coli</span> KJ122. (<b>A</b>) 100 rpm, (<b>B</b>) 200 rpm, and (<b>C</b>) 300 rpm. Three independent replications were performed on each test, and the means were reported with the standard deviation (SD).</p>
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<p>Succinic acid production by <span class="html-italic">E. coli</span> KJ122 via a pre-saccharified fed-batch SSF process with an initial concentration of 70 g/L AP-MWOP. Three independent replications were performed on each test, and the means were reported with the standard deviation (SD).</p>
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7 pages, 200 KiB  
Communication
Zoonotic and Qualitative Aspects of Raw Meat-Based Diets for Dogs in The Netherlands: A Follow-Up Study
by Ronald Jan Corbee, Patrick van Hoorn and Paul A. M. Overgaauw
Pets 2025, 2(1), 4; https://doi.org/10.3390/pets2010004 - 23 Jan 2025
Viewed by 1215
Abstract
Background: The Dutch branch organization for pet products promised the public that it will improve the quality of raw meat-based diets (RMBDs) for dogs after several diagnoses of tuberculosis, brucellosis, and hyperthyroidism in dogs fed RMBDs. Objective: The objective of this study was [...] Read more.
Background: The Dutch branch organization for pet products promised the public that it will improve the quality of raw meat-based diets (RMBDs) for dogs after several diagnoses of tuberculosis, brucellosis, and hyperthyroidism in dogs fed RMBDs. Objective: The objective of this study was to re-evaluate the risk factors of commercially available raw meat diets for dogs in The Netherlands. Methods: Seven commercial brands of RMBDs that were previously investigated were re-tested, as well as a newly introduced high-pressure processing (HPP) product. Raw beef sausage for humans was included for comparison. In total, 40 animal RMBDs (five batches per product) were tested for the presence of colony-forming units (CFUs), Salmonella spp., and Escherichia coli directly after defrosting and 4 h later, as well as thyroid hormone. Results: Exceeded EU standards for CFUs and Salmonella bacteria were present in several samples. In the HPP product, bacteria were still present; however, the counts were lower. There were no differences in CFUs directly after defrosting and 4 h later. The human raw meat product was negative for bacteria. Thyroid hormone could be detected in 20 out of 37 samples. In seven of these samples, the levels were >0.75 µg/g, which have been associated with hyperthyroidism. Conclusions: The hygiene (including the use of HPP production) and accurate removal of thyroid tissue during the production of RMBDs still need attention to prevent the presence of zoonotic bacteria, high CFUs, and diet-induced hyperthyroidism. Full article
14 pages, 8768 KiB  
Article
Modifications of Constitutive Promoter to Large-Scale Synthesize Porcine Myoglobin in Komagataella phaffii
by Danni Sun, Yunpeng Wang, Jingwen Zhou, Jianghua Li, Jian Chen, Guocheng Du and Xinrui Zhao
Fermentation 2025, 11(2), 49; https://doi.org/10.3390/fermentation11020049 - 22 Jan 2025
Viewed by 588
Abstract
Myoglobin (MG) is a heme-binding protein and can be used as a color and flavor additive for artificial meat. After the selection of stable constitutive expression, although the synthesis of porcine myoglobin (pMG) was achieved through the application of a modified GAP promoter [...] Read more.
Myoglobin (MG) is a heme-binding protein and can be used as a color and flavor additive for artificial meat. After the selection of stable constitutive expression, although the synthesis of porcine myoglobin (pMG) was achieved through the application of a modified GAP promoter (G1 promoter) in Komagataella phaffii, the lower titer of pMG cannot meet the requirements of commercial production. Herein, another powerful constitutive promoter (GCW14 promoter) was chosen and modified through randomizing its core region for the first time, leading to an increase of 1.18 to 6.01 times in strength. In addition, under the control of a mutated PGCW14 promoter (PGCWm-121), the titer of pMG was further enhanced by optimizing the integrated copy numbers of the pMG gene and knocking out the Yps1-1 protease. Applying the best engineered strain and suitable fermentation conditions, the highest titer of pMG (547.59 mg/L) was achieved in fed-batch fermentation using a cheap and chemically synthesized medium. Furthermore, the obtained pMG had similar peroxide-specific activity (427.50 U/mg) with the extracted natural product after the food-grade purification. The applied strategy can be utilized to synthesize other high value-added hemoproteins, enriching the applications of functional components in the field of artificial meat. Full article
(This article belongs to the Section Fermentation for Food and Beverages)
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<p>The schematic diagram of constructing recombinant <span class="html-italic">K. phaffii</span> strains by CRISPR/Cas9 technology.</p>
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<p><b>Comparison of pMG expression in recombinant strains for different promoters and copy numbers.</b> (<b>A</b>) Results of SDS-PAGE electrophoresis. The molecular weight of band marked with an arrow is 17kDa. (<b>B</b>) GCW14-1: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG. GCW14-2: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG*2. GCW14-3: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG*3. G1-1: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-G1-pMG. G1-2: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-G1-pMG*2. G1-3: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-G1-pMG*3. “****” <span class="html-italic">p</span> &lt; 0.0001 vs. GCW14-2, “***” <span class="html-italic">p</span> &lt; 0.001 vs. GCW14-2.</p>
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<p><b>The construction of the P<sub>GCW14</sub> mutant library.</b> (<b>A</b>) The schematic diagram of mutation. In the step of flow cytometry detection, color dots were used to represent the relative fluorescence ratios (EGFP/mCherry) of single cells. High ratios of single cells were represented by green dots, while low ratios of single cells are represented by red dots. In the step of flow analysis, single cells with higher ratios (green columns) than the control group (red column) were collected, and other cells (grey columns) were discarded. (<b>B</b>) The relative strengths of 141 mutant promoters were normalized to the strength obtained by the wild-type P<sub>GCW14</sub>. The serial numbers of each column are sequentially labeled below the figure, corresponding to regions I, II, and III on the horizontal axis.</p>
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<p><b>The application of the P<sub>GCW14</sub> mutant libraries.</b> (<b>A</b>) The results of SDS-PAGE electrophoresis. The molecular weight of band marked with an arrow is 17 kDa. (<b>B</b>) The application of P<sub>GCW14</sub> mutants for pMG expression at the shaking-flask scale. “***” <span class="html-italic">p</span> &lt; 0.001 vs. control, “*” <span class="html-italic">p</span> &lt; 0.05 vs. control, and “ns” stands for “not significant” (<span class="html-italic">p</span> ≥ 0.05). (<b>C</b>) Representative flow cytometry histograms for the nine mutant promoters and the wild-type P<sub>GCW14</sub>.</p>
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<p><b>The construction of a proper <span class="html-italic">K. phaffii</span> host for the efficient pMG expression at the shaking-flask scale.</b> (<b>A</b>) The results of SDS-PAGE electrophoresis for deleting proteases. The molecular weight of band marked with an arrow is 17kDa. (<b>B</b>) The comparison of pMG expression in recombinant strains for knocking out proteases. Δyps1: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-Δ<span class="html-italic">yps1</span>-GCW14-pMG. Δpep4: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-Δ<span class="html-italic">pep4</span>-GCW14-pMG. Δprb1: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-Δ<span class="html-italic">prb1</span>-GCW14-pMG. DKO: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-Δ<span class="html-italic">pep4</span>-Δ<span class="html-italic">yps1</span>-GCW14-pMG. TKO: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-Δ<span class="html-italic">pep4</span>-Δ<span class="html-italic">yps1</span>-Δ<span class="html-italic">prb1</span>-GCW14-pMG. Control: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG. (<b>C</b>) The results of SDS-PAGE electrophoresis for different proteins of anti-stress response systems. The molecular weight of band marked with an arrow is 17 kDa. (<b>D</b>) The comparison of pMG expression in recombinant strains for proteins of anti-stress response systems. ERO1: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG-GAP-ERO1. PDI: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG-GAP-PDI. HAC1: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG-GAP-HAC1. KAR2: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG-GAP-KAR2. IRE1: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG-GAP-IRE1. AHA1: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG-GAP-AHA1. PRX1: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG-GAP-PRX1. YPT6: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG-GAP-YPT6. YAP1: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG-GAP-YAP1. Control: <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">ku70</span>-GCW14-pMG. “**” <span class="html-italic">p</span> &lt; 0.01 vs. control, “*” <span class="html-italic">p</span> &lt; 0.05 vs. control, and “ns” stands for “not significant” (<span class="html-italic">p</span> ≥ 0.05).</p>
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<p><b>The fermentation analysis of pMG from the final strain <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">yps1</span>-GCWm-121-pMG*2 at the shaking-flask scale.</b> (<b>A</b>) The results of SDS-PAGE electrophoresis. The molecular weight of band marked with an arrow is 17 kDa. (<b>B</b>) The comparison of optimal strains for various strategies. “***” <span class="html-italic">p</span> &lt; 0.001 vs. GCW14-1; “ns” stands for “not significant” (<span class="html-italic">p</span> ≥ 0.05).</p>
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<p>The fermentation analysis of pMG from the final strain, <span class="html-italic">K. phaffii</span> X33-Δ<span class="html-italic">yps1</span>-GCWm-121-pMG*2, in 5 L of the fermenter. (<b>A</b>) The results of SDS-PAGE electrophoresis. The molecular weight of band marked with an arrow is 17 kDa. (<b>B</b>) The expression of pMG from the final strain by fed-batch fermentation.</p>
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<p>(<b>A</b>) The results of SDS-PAGE electrophoresis for purified pMG by a Q anion exchanger. 1: Fermentation supernatant. 2: Desalinated and concentrated sample. 3: Purified protein sample (desalination). The molecular weight of band marked with an arrow is 17 kDa. (<b>B</b>) The analysis of the purification effect. 1: Fermentation supernatant. 2: Desalinated and concentrated sample. 3: Purified protein sample (no desalination). “****” <span class="html-italic">p</span> &lt; 0.0001, “***” <span class="html-italic">p</span> &lt; 0.001. (<b>C</b>) The titer of purified pMG (desalination) and the biochemical properties including POD activity, and POD-specific activity.</p>
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21 pages, 3606 KiB  
Article
Beyond Microbial Variability: Disclosing the Functional Redundancy of the Core Gut Microbiota of Farmed Gilthead Sea Bream from a Bayesian Network Perspective
by Federico Moroni, Fernando Naya-Català, Ahmed Ibrahem Hafez, Ricardo Domingo-Bretón, Beatriz Soriano, Carlos Llorens and Jaume Pérez-Sánchez
Microorganisms 2025, 13(1), 198; https://doi.org/10.3390/microorganisms13010198 - 17 Jan 2025
Viewed by 726
Abstract
The significant microbiota variability represents a key feature that makes the full comprehension of the functional interaction between microbiota and the host an ongoing challenge. To overcome this limitation, in this study, fish intestinal microbiota was analyzed through a meta-analysis, identifying the core [...] Read more.
The significant microbiota variability represents a key feature that makes the full comprehension of the functional interaction between microbiota and the host an ongoing challenge. To overcome this limitation, in this study, fish intestinal microbiota was analyzed through a meta-analysis, identifying the core microbiota and constructing stochastic Bayesian network (BN) models with SAMBA. We combined three experiments performed with gilthead sea bream juveniles of the same hatchery batch, reared at the same season/location, and fed with diets enriched on processed animal proteins (PAP) and other alternative ingredients (NOPAP-PP, NOPAP-SCP). Microbiota data analysis disclosed a high individual taxonomic variability, a high functional homogeneity within trials and highlighted the importance of the core microbiota, clustering PAP and NOPAP fish microbiota composition. For both NOPAP and PAP BNs, >99% of the microbiota population were modelled, with a significant proportion of bacteria (55–69%) directly connected with the diet variable. Functional enrichment identified 11 relevant pathways expressed by different taxa across the different BNs, confirming the high metabolic plasticity and taxonomic heterogeneity. Altogether, these results reinforce the comprehension of the functional bacteria–host interactions and in the near future, allow the use of microbiota as a species-specific growth and welfare benchmark of livestock animals, and farmed fish in particular. Full article
(This article belongs to the Special Issue Host–Bacteria Interactions in Aquaculture Systems, 2nd Edition)
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<p>This is a stacked bar chart representing the taxonomic profile (<b>a</b>), reported as relative abundance of bacterial genera, and the inferred functional profile (<b>b</b>), reported as the level 3 KEGG metabolic pathways. Each column represents each sample considered. Samples are grouped according to the feeding trials (NOPAP-PP, PAP, NOPAP-SCP).</p>
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<p>The two-dimensional PLS-DA score plot represents the distribution of the samples between the first two components in the model of the NOPAP-PP, PAP and NOPAP-SCP feeding trials. Four samples belonging to the NOPAP trial were excluded from the model because considered as outliers (<b>a</b>). Venn diagram reporting unique and shared taxa considering the total intestinal microbiota datasets of the three feeding trials (<b>b</b>). Concentric circle nested diagram representing the core microbiota within the 227 common taxa identified, divided by the rank of restrictiveness of the applied filter (<b>c</b>).</p>
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<p>Bayesian network constructed using the three feeding trial merged datasets. The model only reports the microbial interactions, obtained by the taxa counts distribution. Green arrows represent positive interactions between nodes, while red arrows represent negative dependences. The core microbiota nodes are represented by the circles with the outline in bold in the figure.</p>
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<p>Dot plot representing the core microbiota taxa which actively participate in the edges of the BN reported in <a href="#microorganisms-13-00198-f003" class="html-fig">Figure 3</a>. In the table, the core sub-groups are reported, as indicated in <a href="#microorganisms-13-00198-f002" class="html-fig">Figure 2</a>c, the belonging cluster in the BN, the centrality degree (C. Degree), which represents the total number of edges where the node is involved and the percentage of parent (Par %), which indicates the percentage that the node plays in the role of parent compared to the total of its connections. Taxa are colored depending on the core sub-groups, as indicated in <a href="#microorganisms-13-00198-f002" class="html-fig">Figure 2</a>c. In the dot plot, color scale represents the mean relative abundance, in percentage, of each taxon within each group. The size of the dots represents normalized counts in each group.</p>
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<p>Bayesian networks representing NOPAP (<b>a</b>) and PAP (<b>b</b>) models. Circles represent bacterial taxa and squares represent the experimental variable (Diet). The tables report numbers and colors of the clusters (cluster 0 and the clusters colored in grey are not represented in the figure), the relative abundance of the taxa composing each cluster (Abun %) and the sum of the relative abundances according to the following groups: cluster 0; clusters connected to the variable, clusters not connected to the variable (Sum %). The bar plot representing the number of core microbial taxa compared to the total number of nodes belonging to the three categories already defined, for both the models, NOPAP and PAP BNs (<b>c</b>). The bar plot representing the number of edges in which the core microbiota is involved or not, compared to the total number of dependencies, for both the models (<b>d</b>).</p>
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<p>Venn diagram and table reporting the common functions expressed by both NOPAP and PAP models (<b>a</b>). Venn diagrams reporting the number of taxa responsible for each function in the two BNs of NOPAP (NP) and PAP (P) (<b>b</b>).</p>
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<p>Sankey diagrams reporting the list of genera and Phyla which express the 11 inferred metabolic functions, associated to the NOPAP (<b>a</b>) and PAP (<b>b</b>) conditions.</p>
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34 pages, 8577 KiB  
Article
Uranium Mineral Transport in the Peña Blanca Desert: Dissolution or Fragmentation? Simulation in Sediment Column Systems
by Victoria Pérez-Reyes, Rocio M. Cabral-Lares, Jesús G. Canche-Tello, Marusia Rentería-Villalobos, Guillermo González-Sánchez, Blanca P. Carmona-Lara, Cristina Hernández-Herrera, Fabián Faudoa-Gómez, Yair Rodríguez-Guerra, Gregorio Vázquez-Olvera, Jorge Carrillo-Flores, Ignacio A. Reyes-Cortés, Daniel Hernández-Cruz, René Loredo-Portales and María E. Montero-Cabrera
Appl. Sci. 2025, 15(2), 609; https://doi.org/10.3390/app15020609 - 10 Jan 2025
Viewed by 649
Abstract
The Sierra Peña Blanca (SPB) region in Chihuahua, Mexico contains a significant uranium deposit representing about 40% of the country’s reserves. Common uranium minerals in this area include uranophane, schoepite, and weeksite/boltwoodite, with several superficial occurrences. Mining activities in the 1980s left unprocessed [...] Read more.
The Sierra Peña Blanca (SPB) region in Chihuahua, Mexico contains a significant uranium deposit representing about 40% of the country’s reserves. Common uranium minerals in this area include uranophane, schoepite, and weeksite/boltwoodite, with several superficial occurrences. Mining activities in the 1980s left unprocessed uranium ore exposed to weathering, with potential transport towards Laguna del Cuervo. This study presents an experimental simulation of uranium transport in SPB sediments using three approaches: (i) a batch experiment to evaluate the ideal adsorption of (UO2)2+ by fine sediment; (ii) a column system fed with 569 mgU L−1 UO2(NO3)2 to simulate adsorption by different sediment particle sizes; (iii) a column system with an upper horizon of uranophane from the area, fed with deionized water, to simulate uranium weathering and transport in particulate material, determined by liquid scintillation counting, revealed that the clay fraction had the highest adsorption capacity for U. X-ray Absorption Fine Structure (XAFS) analysis at the U L3 edge confirmed the U(IV) oxidation state and the fittings of the extended XAFS spectra confirmed the presence of the uranophane group of minerals. X-ray tomography further corroborated the distribution of particulate minerals along the column. The results suggest that the primary transport mechanism in SPB involves the fragmentation of uranium minerals, accompanied by eventual dissolution and subsequent adsorption of U onto sediments. Full article
(This article belongs to the Special Issue Advances in Environmental Radioactivity Monitoring and Measurement)
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<p>Location of the study area and sampling points at SPB.</p>
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<p>Schematic diagram of eastern section of Boca La Colorada stream alluvial fan.</p>
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<p>(<b>A</b>) Scheme UN experiment; (<b>B</b>) Scheme URP experiment; yellow color represents the horizon of uraniferous minerals; (<b>C</b>) Photograph of packed columns. All the columns were packed with the granulometries described above; the arrow shows the flow of the feed solutions.</p>
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<p>Backscattered electron images of fractions (<b>C</b>) FSD, (<b>D</b>) CSC, (<b>E</b>) FSC before experiments. After the experiment’s conclusion, secondary electron images of fractions (<b>A</b>) C<sub>1</sub>, (<b>B</b>) CS, (<b>F</b>) C<sub>2</sub>, and (<b>G</b>) FS were obtained from UN<b>6</b>.</p>
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<p>Adsorption isotherms for U(VI) at pH = 5 (<b>A</b>) and pH = 7 (<b>B</b>).</p>
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<p>SEM backscattered electron micrographs at 1000× magnification showing (<b>A</b>) U(VI) adsorption (C<sub>0</sub> = 120 mg·L<sup>−1</sup>) at pH = 5; and (<b>B</b>) U(VI) adsorption (C<sub>0</sub> = 120 mg·L<sup>−1</sup>) at pH = 7.</p>
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<p>Computer tomography images, showing the raw image (left side) and the probability of the material’s occurrence as a function of density for the distribution distance in the column profile (right side). (<b>A</b>) In sample URP<b>6</b>FSD, the highest concentration of particles is observed between 10 and 55 mm, showing a coincidence with the uranophane horizon. (<b>B</b>) For sample URP<b>12</b>FSD, the particle concentration is shown with a greater dispersion, up to 110 mm, which indicates the mobility of the particulate mineral of uranophane in the direction of the flow of distilled water (solution). In raw images, colors are according to the particle size; fine particles are in red, and coarse particles are in green and black colors. UV and visible light photography corroborate the particle-dragging behavior (see <a href="#secBdot3-applsci-15-00609" class="html-sec">Appendix B.3</a>, <a href="#applsci-15-00609-f0B9" class="html-fig">Figure B9</a>).</p>
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<p>5 XANES spectra for UO<sub>2</sub> (gray line), UN<b>6</b>CSC (red line), URP<b>6</b>FSC (blue line), URP<b>6</b>CSC (green line), and UN<b>6</b>FSC (wine line).</p>
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<p>Left: (<b>A</b>) the Fourier transforms in radial distribution function, (<b>B</b>) k<sup>3</sup>-weighted spectra of experimental (black solid line) and fitted (blue solid line with crossed) for U L<sub>3</sub> EXAFS, and (<b>C</b>) linear combination fitting for URP<b>6</b>CSC. Right: (<b>D</b>) the Fourier transforms in radial distribution function, (<b>E</b>) k<sup>3</sup>-weighted spectra of experimental (black solid line) and fitted (blue solid line with crossed) for U L<sub>3</sub> EXAFS, and (<b>F</b>) linear combination fitting for URP<b>6</b>FSC.</p>
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13 pages, 2030 KiB  
Article
High-Titer L-lactic Acid Production by Fed-Batch Simultaneous Saccharification and Fermentation of Steam-Exploded Corn Stover
by Liheng Deng, Changsheng Su, Yilu Wu, Qiang Xue, Changwei Zhang, Yong Wang, Bin Wang and Di Cai
Fermentation 2025, 11(1), 25; https://doi.org/10.3390/fermentation11010025 - 9 Jan 2025
Viewed by 661
Abstract
Steam explosion (SE) is an effective lignocellulose pretreatment technology for second-generation L-lactic acid (L-LA) production. In this study, targeted to produce high-concentration L-LA from corn stover (CS), the fed-batch simultaneous saccharification and fermentation (SSF) of acidic, SE-pretreated CS was developed and demonstrated in [...] Read more.
Steam explosion (SE) is an effective lignocellulose pretreatment technology for second-generation L-lactic acid (L-LA) production. In this study, targeted to produce high-concentration L-LA from corn stover (CS), the fed-batch simultaneous saccharification and fermentation (SSF) of acidic, SE-pretreated CS was developed and demonstrated in a 5 L scale bioreactor under non-strict conditions (without detoxification and sterilization). The results indicated that the fed-batch SSF, with a simple pH control, realized a higher tolerance of the strains to the toxic by-products of hydrolysate, in comparison to the conventional sequential hydrolysis and fermentation (SHF), allowing for 153.8 g L−1 of L-LA production, along with a productivity of 1.83 g L−1 h−1 in a system with a total of 40% (w/v) solid loading. The mass balance indicated that up to 449 kg of L-LA can be obtained from 1 t of dried CS. It exhibited obvious superiorities and laid down a solid foundation for the industrialization of second-generation L-LA production. Full article
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<p>The time course of the SHF for L-LA using the undetoxified fed-batch SECSH with an overall (<b>a</b>) 10% (<span class="html-italic">w</span>/<span class="html-italic">v</span>), (<b>b</b>) 20% (<span class="html-italic">w</span>/<span class="html-italic">v</span>), (<b>c</b>) 30% (<span class="html-italic">w</span>/<span class="html-italic">v</span>), and (<b>d</b>) 40% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) solid loading.</p>
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<p>The time course of the SHF for L-LA using the detoxified fed-batch SECSH with an overall 40% (<span class="html-italic">w</span>/<span class="html-italic">v</span>) solid loading. Also, 10 g L<sup>−1</sup> peptone was added as an additional nutrient and a nitrogen source.</p>
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<p>L-LA production by <span class="html-italic">B. coagulans</span> LA2301, based on the fed-batch SSF of SECS without autoclaving and detoxification.</p>
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<p>Mass balance of L-LA based on different fermentation strategies. The overall SECS loadings were 40% (<span class="html-italic">w</span>/<span class="html-italic">v</span>).</p>
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