Maize (Zea mays), also called corn, is believed to have originated in central Mexico 7000 years ago from a wild grass, and Native Americans transformed maize into a better source of food. Maize contains approximately 72% starch, 10% protein, and 4% fat, supplying an energy density of 365 Kcal/100 g and is grown throughout the world, with the United States, China, and Brazil being the top three maize‐producing countries in the world, producing approximately 563 of the 717 million metric tons/year. Maize can be processed into a variety of food and industrial products, including starch, sweeteners, oil, beverages, glue, industrial alcohol, and fuel ethanol. In the last 10 years, the use of maize for fuel production significantly increased, accounting for approximately 40% of the maize production in the United States. As the ethanol industry absorbs a larger share of the maize crop, higher prices for maize will intensify demand competition and could affect maize prices for animal and human consumption. Low production costs, along with the high consumption of maize flour and cornmeal, especially where micronutrient deficiencies are common public health problems, make this food staple an ideal food vehicle for fortification.
Lactobacillus plantarum (widespread member of the genus Lactobacillus) is one of the most studied species extensively used in food industry as probiotic microorganism and/or microbial starter. The exploitation of Lb. plantarum strains with their long history in food fermentation forms an emerging field and design of added-value foods. Lb. plantarum strains were also used to produce new functional (traditional/novel) foods and beverages with improved nutritional and technological features. Lb. plantarum strains were identified from many traditional foods and characterized for their systematics and molecular taxonomy, enzyme systems (α-amylase, esterase, lipase, α-glucosidase, β-glucosidase, enolase, phosphoketolase, lactase dehydrogenase, etc.), and bioactive compounds (bacteriocin, dipeptides, and other preservative compounds). This review emphasizes that the Lb. plantarum strains with their probiotic properties can have great effects against harmful microflora (foodborne pathogens) to increase safety and shelf-life of fermented foods.
Cofactors are small molecules or ions that participate in enzymatic reactions as essential carriers of electrons, atoms, or functional groups, thereby governing cellular redox balance and energy metabolism. In the yeast <i>Saccharomyces cerevisiae</i>, the availability of cofactors such as NAD(H), NADP(H), CoA, and acetyl-CoA directly influences the flux through biosynthetic pathways leading to aroma-active compounds. Esters and higher alcohols are the two most important families of volatile flavor compounds in fermented alcoholic beverages. Their synthesis is intimately linked to the intracellular levels and ratios of these cofactors. This review summarizes recent progress in cofactor engineering strategies aimed at modulating the production of esters, higher alcohols, and 2,3-butanediol in <i>S. cerevisiae</i>. We discuss the underlying metabolic pathways, highlight key studies that manipulate cofactor pools to redirect carbon flux, and examine emerging tools (e.g., riboswitches, fine-tuned promoter systems) that enable precise cofactor balancing. Finally, we outline future challenges and opportunities for applying cofactor engineering to design yeast cell factories with tailored flavor profiles.
Continuous integration and delivery (CI/CD) pipelines are critical for sustaining the evolution of large software systems. In regulated industries with legacy technologies, however, pipelines themselves can become a source of technical debt. This paper presents an industrial case study of Bankdata, a cooperative IT provider for Danish banks, where a Jenkins-based COBOL CI/CD pipeline had grown fragile, slow, and tightly coupled to platform-specific logic. The original architecture relied on Groovy scripts spread across four repositories with runtime dependency installation, leading to long execution times, high maintenance costs, and vendor lock-in. We report on the migration to a containerized architecture featuring an abstraction layer for platform logic, simplified repository structure, and a pre-built OCI-compliant image containing COBOL tools and dependencies. The new design achieved an 82% runtime reduction. Our experience highlights lessons on abstraction, containerization, and organizational adoption, offering guidance for modernizing pipelines in legacy, high-security environments.
Fruit wines, produced through the fermentation of various fruits, are well-documented for their distinct flavor profiles. Intelligent sensory analysis, GC-TOF/MS and GC-IMS were used for the analysis of the volatile profile of eight types of fruit wines including 5 grape wine (SJ, LS, HY, TJ, FT), 1 fermented plum wine (FZ), 1 blueberry wine (HZ), as well as 1 configured plum wine (LM). A total of 281 compounds were identified through GC-TOF/MS, with esters and acids constituting over 80% of all samples. GC-IMS identified 60 compounds, predominantly including 16 esters, 11 alcohols, and 6 ketones, and 7 sulfur-containing compounds. This observation leads to the assumption that the IMS and MS data contain different information about the composition of the volatile profile. 37 and 18 differential compounds for TOF/MS data and IMS data were obtained, respectively. Three ranking algorithms combined with five machine learning models Neural Networks (NN), Random Forests (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR) applied and identified both 58 key features from volatiles. LR and KNN achieved an overall classification of 0.95 and an F1 score greater than 0.9. For the IMS data, NN, LR, and KNN models exhibited accuracies and F1 scores greater than 0.9. This study advances fruit wine classification, benefiting the beverage industry and food chemistry research.
CRISPR technology, which is derived from the bacterial adaptive immune system, has transformed traditional genetic engineering techniques, made strain engineering significantly easier, and become a very versatile genome editing system that allows for precise, programmable modifications to a wide range of microbial genomes. The economies of fermentation-based manufacturing are changing because of its quick acceptance in both academic and industry labs. CRISPR processes have been used to modify industrially significant bacteria, including the lactic acid producers, Clostridium spp., Escherichia coli, and Corynebacterium glutamicum, in order to increase the yields of bioethanol, butanol, succinic acid, acetone, and polyhydroxyalkanoate precursors. CRISPR-mediated promoter engineering and single-step multiplex editing have improved inhibitor tolerance, raised ethanol titers, and allowed for the de novo synthesis of terpenoids, flavonoids, and recombinant vaccines in yeasts, especially Saccharomyces cerevisiae and emerging non-conventional species. While enzyme and biopharmaceutical manufacturing use CRISPR for quick strain optimization and glyco-engineering, food and beverage fermentations benefit from starter-culture customization for aroma, texture, and probiotic functionality. Off-target effects, cytotoxicity linked to Cas9, inefficient delivery in specific microorganisms, and regulatory ambiguities in commercial fermentation settings are some of the main challenges. This review provides an industry-specific summary of CRISPR–Cas9 applications in microbial fermentation and highlights technical developments, persisting challenges, and industrial advancements.
The quality of cider is influenced by its phenolic compound content. Apple pomace, an industrial by-product of cider production, is rich in bioactive compounds, including polyphenols. The objective of this study was to determine the potential of apple pomace addition during fermentation to increase the phenolic content in cider. Apple juice from Jonagold apples was divided into a control and three treatment groups. Control cider was fermented with 100% apple juice, while treatments were prepared with different additions of apple pomace to the apple juice. Ciders were fermented for 14 days, followed by chemical and sensory analysis. Ciders with apple pomace addition contained 31–61% higher phenolic compound concentrations than the control. The addition of apple pomace modified the volatile profile of the ciders. Treatment ciders contained higher concentrations of isoamyl alcohol, phenylethyl alcohol, and ethyl acetate, and lower concentrations of acetaldehyde. Ciders with apple pomace addition exhibited lower levels of astringency and sourness, and higher bitterness levels compared to the control. There was no difference in aroma perception and taste acceptance between the ciders. This study demonstrates the potential of apple pomace addition as a cidermaking technique for phenolic compound extraction and sensory profile modification.
High potential is attributed to the concomitant use of probiotics and prebiotics in a single food product, called “synbiotics”, where the prebiotic component distinctly favours the growth and activity of probiotic microbes. This study implemented a detailed comparison between the prebiotic effect of Fructooligosaccharides (FOSs) and Raffinose family oligosaccharides (RFOs) on the viable count of bacteria, hydrolysis into monosaccharides, the biosynthesis of short-chain fatty acids and sensory attributes of soymilk fermented with 1% (<i>v</i>/<i>v</i>) co-cultures of <i>Lacticaseibacillus rhamnosus</i> JCM1136 and <i>Weissella confusa</i> 30082b. The highest viable count of 1.21 × 10<sup>9</sup> CFU/mL was observed in soymilk with 3% RFOs added as a prebiotic source compared with MRS broth with 3% RFOs (3.21 × 10<sup>8</sup>) and 3% FOS (6.2 × 10<sup>7</sup> CFU/mL) when replaced against glucose in MRS broth. Raffinose and stachyose were extensively metabolised (4.75 and 1.28-fold decrease, respectively) in 3% RFOs supplemented with soymilk, and there was an increase in glucose, galactose, fructose (2.36, 1.55, 2.76-fold, respectively) in soymilk supplemented with 3% FOS. Synbiotic soymilk with 3% RFOs showed a 99-fold increase in methyl propionate, while the one supplemented with 3% FOS showed an increase in methyl butyrate. The highest acceptability based on the sensory attributes was for soymilk fermented with 2% RFOs + 2% FOS + 2% table sugar + 1% vanillin (7.87 ± 0.52) with high mouth feel, product consistency, taste, and flavour. This study shows that the simultaneous administration of soy with probiotic bacteria and prebiotic oligosaccharides like FOSs and RFOs enhance the synergistic interaction between them, which upgraded the nutritional and sensory quality of synbiotic soymilk.
of spring barley were evaluated in accordance with the ČSN 46 1100-5 standard. The submitted samples were characterised by a low protein content (10.2%) and good values of sieving above the 2.5 mm sieve. A favourable feature was the low proportion of grain admixtures, a high average Falling number, and excellent germination capacity of the barley grain. Based on the results, it can be concluded that the quality of the 2024 malting barley harvest appears to be favourable.
The Transfer Matrix Method (TMM) stands as the ubiquitous computational backbone for analyzing 1D wave propagation in layered media, underpinning critical product designs in photonics, seismology, and acoustics -- industries collectively valued in the tens of USD billions. Despite its essential role, legacy implementations of TMM create significant technical (and therefore strategic) bottlenecks, primarily due to a lack of straightforward differentiability and high computational costs associated with Uncertainty Quantification (UQ). This white paper assesses the current market footprint of TMM, identifies the economic "hidden costs" of traditional workflows, and outlines an emerging industrial alternative -- Differentiable Programming and Neural Surrogates -- and their own limitations.
Mari Ashiga, Vardan Voskanyan, Fateme Dinmohammadi
et al.
Recent advancements in Large Language Models (LLMs) for code optimization have enabled industrial platforms to automate software performance engineering at unprecedented scale and speed. Yet, organizations in regulated industries face strict constraints on which LLMs they can use - many cannot utilize commercial models due to data privacy regulations and compliance requirements, creating a significant challenge for achieving high-quality code optimization while maintaining cost-effectiveness. We address this by implementing a Mixture-of-Agents (MoA) approach that directly synthesizes code from multiple specialized LLMs, comparing it against TurinTech AI's vanilla Genetic Algorithm (GA)-based ensemble system and individual LLM optimizers using real-world industrial codebases. Our key contributions include: (1) First MoA application to industrial code optimization using real-world codebases; (2) Empirical evidence that MoA excels with open-source models, achieving 14.3% to 22.2% cost savings and 28.6% to 32.2% faster optimization times for regulated environments; (3) Deployment guidelines demonstrating GA's advantage with commercial models while both ensembles outperform individual LLMs; and (4) Real-world validation across 50 code snippets and seven LLM combinations, generating over 8,700 variants, addresses gaps in industrial LLM ensemble evaluation. This provides actionable guidance for organizations balancing regulatory compliance with optimization performance in production environments.
Stavros Vatikiotis, Ioannis Avgerinos, Stathis Plitsos
et al.
Water scarcity and the low quality of wastewater produced in industrial applications present significant challenges, particularly in managing fresh water intake and reusing residual quantities. These issues affect various industries, compelling plant owners and managers to optimise water resources within their process networks. To address this cross-sector business requirement, we propose a Decision Support System (DSS) designed to capture key network components, such as inlet streams, processes, and outlet streams. Data provided to the DSS are exploited by an optimisation module, which supports both network design and operational decisions. This module is coupled with a generic mixed-integer nonlinear programming (MINLP) model, which is linearised into a compact mixed-integer linear programming (MILP) formulation capable of delivering fast optimal solutions across various network designs and input parameterisations. Additionally, a Constraint Programming (CP) approach is incorporated to handle nonlinear expressions through straightforward modeling. This state-of-the-art generalised framework enables broad applicability across a wide range of real-world scenarios, setting it apart from the conventional reliance on customised solutions designed for specific use cases. The proposed framework was tested on 500 synthetic data instances inspired by historical data from three case studies. The obtained results confirm the validity, computational competence and practical impact of our approach both among their operational and network design phases, demonstrating significant improvements over current practices. Notably, the proposed approach achieved a 17.6% reduction in freshwater intake in a chemical industry case and facilitated the reuse of nearly 90% of wastewater in an oil refinery case.
Red dragon fruit (Hylocereus polyrhizus), recognized globally for its substantial nutrient content and health benefits, has been extensively studied; studies have particularly focused on the fruit, while the composition of the stem remains less explored. This research focuses on optimizing fermentation parameters for red dragon fruit wine, specifically examining yeast-strain selection, juice-to-water dilution ratios, and yeast concentrations. Saccharomyces cerevisiae RV002 emerged as the optimal strain due to its robust performance and adaptability under adverse conditions. The study identified a 50% dilution ratio as ideal for maximizing clarity and the sensory attributes of the wine, whereas dilution ratios exceeding 90% significantly reduced ethanol content below acceptable commercial standards. An optimal yeast concentration of 1 g/L was found to balance microbial suppression and alcohol yield effectively; deviations from this concentration led to microbial contamination or impaired fermentation dynamics. Fermentation markedly altered the biochemical properties of Hylocereus polyrhizus, reducing sugar and vitamin C levels while increasing polyphenol content and antioxidant activity, thereby enhancing potential health benefits. These findings underscore the transformative effects of microbial activity on the substrate’s chemical landscape and highlight the potential of tailored fermentation strategies to enhance the utility and value of underutilized fruits in sustainable agricultural practices.
ε-Poly-L-lysine (ε-PL) is a natural and safe food preservative mainly produced by the aerobic, filamentous bacterium <i>Streptomyces albulus</i>. Therefore, it is crucial to breed superior ε-PL-producing strains to enhance fermentation efficiency to reduce production costs. Metabolic engineering is an effective measure for strain modification, but there are few reports on key genes for ε-PL biosynthesis. In this study, metabolic flux analysis was employed to identify potential key genes in ε-PL biosynthesis in <i>S. albulus</i> WG-608. A total of six potential key genes were identified. Three effective key genes (<i>ppc</i>, <i>pyc</i> and <i>pls</i>) were identified for the first time in ε-PL biosynthesis through overexpression experiments. It also presents the first demonstration of the promoting effects of <i>ppc</i> and <i>pyc</i> on ε-PL biosynthesis. Three genes were then co-expressed in <i>S. albulus</i> WG-608 to obtain OE-<i>ppc-pyc-pls</i>, which exhibited an 11.4% increase in ε-PL production compared to <i>S. albulus</i> WG-608, with a 25.5% increase in specific ε-PL production. Finally, the metabolic flux analysis of OE-<i>ppc-pyc-pls</i> compared to <i>S. albulus</i> WG-608 demonstrated that OE-<i>ppc-pyc-pls</i> successfully altered the metabolic flux as expected. This study not only provides a theoretical basis for the metabolic engineering of ε-PL-producing strains but also provides an effective approach for the metabolic engineering of other metabolites.
Núria Ferrer-Bustins, Jean Carlos Correia Peres Costa, Fernando Pérez-Rodríguez
et al.
<i>Listeria monocytogenes</i>, the causative agent of listeriosis, is a relevant pathogen in dry fermented sausages (DFSs), and the application of antilisterial starter cultures is an effective intervention strategy to control the pathogen during DFS production. The effect of factors in relation to DFS formulation and production, NaCl (0–40 g/L), Mn (0.08–0.32 g/L), glucose (0–40 g/L) and temperature (3–37 °C), on the behaviour of <i>L</i>. <i>monocytogenes</i> when cocultured with <i>Latilactobacillus sakei</i> 23K (non-bacteriocinogenic) and CTC494 (bacteriocinogenic) strains was studied through a central composite design in meat simulation media. <i>L. sakei</i> and <i>L. monocytogenes</i> counts, pH, lactic acid production and bacteriocin activity were determined in mono and coculture. The pH decrease and lactic acid production were highly influenced by glucose, while production of sakacin K by <i>L. sakei</i> CTC494 was observed at moderate (10 and 20 °C), but not at the lowest (3 °C) and highest (37 °C), temperatures. Coculture growth had no effect on the acidification and bacteriocin production but inhibited and inactivated <i>L. monocytogenes</i> when <i>L. sakei</i> 23K entered the early stationary phase and when <i>L. sakei</i> CTC494 produced sakacin K. Optimal conditions for achieving a 5-log units reduction of <i>L. monocytogenes</i> were at 20 °C, 20 g/L of NaCl, 0.20 g/L of Mn and 40 g/L of glucose, those highlighting the importance of considering product formulation and fermentation conditions for bioprotective starter cultures application.
Tita Alissa Bach, Aleksandar Babic, Narae Park
et al.
The maritime industry requires effective communication among diverse stakeholders to address complex, safety-critical challenges. Industrial AI, including Large Language Models (LLMs), has the potential to augment human experts' workflows in this specialized domain. Our case study investigated the utility of LLMs in drafting replies to stakeholder inquiries and supporting case handlers. We conducted a preliminary study (observations and interviews), a survey, and a text similarity analysis (LLM-as-a-judge and Semantic Embedding Similarity). We discover that while LLM drafts can streamline workflows, they often require significant modifications to meet the specific demands of maritime communications. Though LLMs are not yet mature enough for safety-critical applications without human oversight, they can serve as valuable augmentative tools. Final decision-making thus must remain with human experts. However, by leveraging the strengths of both humans and LLMs, fostering human-AI collaboration, industries can increase efficiency while maintaining high standards of quality and precision tailored to each case.
Assessing covariate balance (CB) is a common practice in various types of evaluation studies. Two-sample descriptive statistics, such as the standardized mean difference, have been widely applied in the scientific literature to assess the goodness of CB. Studies in health policy, health services research, built and social environment research, and many other fields often involve a finite number of units that may be subject to different treatment levels. Our case study, the California Sugar Sweetened Beverage (SSB) Tax Study, include 332 study cities in the state of California, among which individual cities may elect to levy a city-wide excise tax on SSB sales. Evaluating the balance of covariates between study cities with and without the tax policy is essential for assessing the effects of the policy on health outcomes of interest. In this paper, we introduce the novel concepts of the pseudo p-value and the standardized pseudo p-value, which are descriptive statistics to assess the overall goodness of CB between study arms in a finite study population. While not meant as a hypothesis test, the pseudo p-values bear superficial similarity to the classic p-value, which makes them easy to apply and interpret in applications. We discuss some theoretical properties of the pseudo p-values and present an algorithm to calculate them. We report a numerical simulation study to demonstrate their performance. We apply the pseudo p-values to the California SSB Tax study to assess the balance of city-level characteristics between the two study arms.
Rikhiya Ghosh, Oladimeji Farri, Hans-Martin von Stockhausen
et al.
The healthcare industry is currently experiencing an unprecedented wave of cybersecurity attacks, impacting millions of individuals. With the discovery of thousands of vulnerabilities each month, there is a pressing need to drive the automation of vulnerability assessment processes for medical devices, facilitating rapid mitigation efforts. Generative AI systems have revolutionized various industries, offering unparalleled opportunities for automation and increased efficiency. This paper presents a solution leveraging Large Language Models (LLMs) to learn from historical evaluations of vulnerabilities for the automatic assessment of vulnerabilities in the medical devices industry. This approach is applied within the portfolio of a single manufacturer, taking into account device characteristics, including existing security posture and controls. The primary contributions of this paper are threefold. Firstly, it provides a detailed examination of the best practices for training a vulnerability Language Model (LM) in an industrial context. Secondly, it presents a comprehensive comparison and insightful analysis of the effectiveness of Language Models in vulnerability assessment. Finally, it proposes a new human-in-the-loop framework to expedite vulnerability evaluation processes.
Anuj Ranjan, Vishnu D. Rajput, Evgeniya Valeryevna Prazdnova
et al.
Non-ribosomal peptides (NRPs) are a diverse group of bioactive compounds synthesized by microorganisms, and their antimicrobial properties make them ideal candidates for use as biocontrol agents against pathogens. Non-ribosomal peptides produced by Plant-Growth-Promoting Bacteria (PGPB) have gained interest for the biocontrol of plants’ bacterial and fungal pathogens. In this review, the structure and mode of action of NRPs, including their characterization and the characterization of NRP-producing microorganisms, are discussed. The use of NRPs in soilless agriculture and their potential as part of a sustainable plant disease control strategy are also highlighted. In addition, the review debates the commercial aspects of PGPB’s formulations and their potential as a biocontrol agent. Overall, this review emphasizes the importance of NRPs derived from PGPB in the biocontrol of plant pathogens and their potential to be used as an environmentally friendly and sustainable plant disease control strategy.
<i>Trichoderma reesei</i> is widely applied as the major industrial fungus for the production of cellulases used for the conversion of lignocellulosic biomass to biofuels and other biobased products. The protein secretion pathway is vital for cellulase secretion, but few reports are related to the role of the vacuole in cellulase production. Here, we identified a novel vacuolar serine protease gene <i>spt1</i> and investigated the ability of <i>T. reesei</i> to secrete cellulases by disrupting, complementing and overexpressing the <i>spt1</i> gene. Amino acid sequence analysis of the Spt1 protein showed that it belongs to the subtilisin S8 family and has the conserved catalytic triples (Asp, His, Ser) of the serine protease. The deletion of <i>spt1</i> did not lead to a decrease in extracellular protease activity, and the observation of mycelia with the Spt1–eGFP fusion expression and the vacuolar membrane dye FM4-64 staining confirmed that Spt1 was an intracellular protease located in the vacuoles of <i>T. reesei</i>. However, the <i>spt1</i> gene deletion significantly reduced spore production and cellulase secretion, while the <i>spt1</i> complementation recovered these traits to those of the parental strain. When <i>spt1</i> was overexpressed by using its native promoter and introducing multiple copies, the cellulase secretion was improved. Furthermore, a strong promoter, P<i>cdna1</i>, was used to drive the <i>spt1</i> overexpression, and it was found that the cellulase production was significantly enhanced. Specifically, the filter paper activity of the <i>spt1</i> overexpression strain SOD-2 reached 1.36 U/mL, which was 1.72 times higher than that of the parental strain. These findings demonstrated that the <i>spt1</i> gene can be a powerful target for increasing cellulase production in <i>T. reesei</i>, which suggests a possible important role of the vacuole in the cellulase secretion pathway and provides new clues for improving strains for efficient cellulase production.