Natural Sciences Engineering and Technology Journal

Targeted Metabolic Engineering of Saccharomyces cerevisiae for High-Efficiency Valorization of Lignocellulosic Biomass into Superior-Quality Bioplastics

Nur Diana — 2025-08-28

The global transition towards a sustainable circular bioeconomy urgently requires innovative platforms for converting renewable waste streams into value-added products. Lignocellulosic biomass, particularly agricultural residue like rice straw, stands as a vast, underutilized carbon source. This study details the systematic metabolic engineering of Saccharomyces cerevisiae for the high-efficiency production of poly(3-hydroxybutyrate) (PHB), a biodegradable bioplastic, from rice straw hydrolysate. A multi-faceted synthetic biology approach was implemented in S. cerevisiae CEN.PK2-1C. A robust xylose co-utilization pathway was integrated using codon-optimized genes from Scheffersomyces stipitis. The PHB biosynthesis pathway from Cupriavidus necator was introduced using a cassette of strong, constitutive yeast promoters (pTDH3, pTEF1, pPGK1). To maximize carbon flux towards PHB, key competing pathways were eliminated via CRISPR-Cas9-mediated gene knockouts of the primary alcohol dehydrogenase (ADH1) and glycerol-3-phosphate dehydrogenase (GPD1) genes. The performance of the final engineered strain was evaluated in high-cell-density fed-batch fermentation using detoxified rice straw hydrolysate sourced from Palembang, Indonesia. The final engineered strain, YL-PHB-05 (Δadh1 Δgpd1), demonstrated superior performance. In fed-batch bioreactor cultivation, it achieved a final cell dry weight of 33.8 ± 1.5 g/L and a PHB titer of 15.2 ± 0.7 g/L, with an intracellular PHB accumulation of 45.0 ± 1.2% of cell dry weight. This corresponds to a high yield of 0.28 g PHB per gram of consumed sugars. Crucially, the produced PHB exhibited a superior weight-average molecular weight (Mw) of 1.2 x 10⁶ Da with a polydispersity index of 2.1. In conclusion, this work successfully demonstrates a robust strategy for engineering S. cerevisiae into an efficient cell factory for producing high-quality bioplastics from a globally relevant agricultural waste stream. The high titers, yields, and superior polymer properties achieved present a significant advancement towards establishing an economically viable and sustainable process for bioplastic production within a circular bioeconomy.

An In-Silico Investigation of Machine Learning for Integrating Genomic and Digital Biomarker Data in Cardiovascular Risk Stratification

Immanuel Simbolon — 2025-08-28

Conventional models for stratifying cardiovascular disease (CVD) risk have limitations. The integration of static genomic data and dynamic digital biomarkers from wearable technology holds theoretical promise, but its potential quantitative impact remains poorly defined. This study aimed to develop and validate an in-silico framework to quantify the theoretical maximum predictive gain of an integrated risk model under idealized conditions. We developed a sophisticated data generating process (DGP) to create a synthetic dataset of 5,000 individuals. The DGP incorporated demographic and clinical variables with distributions and correlations based on epidemiological literature. It included a simulated polygenic risk score (PRS) for coronary artery disease and advanced digital biomarkers derived from wireless health monitoring data, such as heart rate variability (HRV) and time in moderate-to-vigorous physical activity (MVPA). The 10-year risk of Major Adverse Cardiovascular Events (MACE) was generated via a defined logistic function incorporating these variables plus stochastic noise. We compared the performance of the ACC/AHA Pooled Cohort Equations (PCE) against several machine learning models (Logistic Regression, Random Forest, XGBoost) using the area under the receiver operating characteristic curve (AUC-ROC), precision, recall, and F1-score. In this simulated environment, the integrated XGBoost model achieved near-optimal predictive performance with an AUC-ROC of 0.92 (95% CI, 0.90-0.94), significantly outperforming the benchmark PCE model (AUC-ROC 0.76; 95% CI, 0.73-0.79; p < 0.001). The inclusion of the PRS and, most notably, dynamic digital biomarkers like HRV, provided substantial incremental improvements in risk discrimination over traditional factors alone. In conclusion, this in-silico study demonstrates the substantial theoretical potential of integrating genomic and advanced digital biomarker data through machine learning for CVD risk stratification. While these idealized results are not directly generalizable, they provide a quantitative rationale for pursuing real-world data collection and validation studies. This work establishes a methodological proof-of-concept and highlights the potential for a paradigm shift toward more dynamic and personalized cardiovascular risk assessment.

Nature-Based Solutions for Climate-Resilient Stormwater Management in Jakarta: A Comparative Modeling of Green Roof and Permeable Pavement Performance

Anies Fatmawati — 2025-08-28

Rapid urbanization and projected climate change impacts pose severe challenges to stormwater management in tropical megacities like Jakarta, Indonesia. Nature-Based Solutions (NBS) are critical for enhancing urban resilience, yet quantitative, context-specific performance data under future climate scenarios are scarce. This study provides a comprehensive, model-based comparative analysis of green roofs and permeable pavements for managing urban stormwater in Jakarta. An archetypal 1-hectare, medium-density urban catchment was developed in the Storm Water Management Model (SWMM). The model was rigorously calibrated and validated against published empirical data from analogous tropical regions (Nash-Sutcliffe Efficiency > 0.78). We evaluated the hydrological (runoff volume, peak flow) and water quality (TSS, TN) performance of green roofs and permeable pavements under partial and full implementation scenarios (25%, 50%, 75%, 100%) for current and two future climate scenarios (RCP4.5, RCP8.5 for 2050). Permeable pavements consistently demonstrated superior hydrological control, achieving up to 82% runoff volume reduction and 88% peak flow attenuation under full implementation for a 2-year baseline storm. Green roofs achieved 48% and 55%, respectively. Under an extreme 25-year storm in the RCP8.5 scenario, performance diminished but remained substantial, with permeable pavements (100% implementation) reducing runoff by 68%. Green roofs provided more consistent pollutant removal, particularly for total nitrogen (approx. 52% removal across scenarios), due to biological processes. In conclusion, both NBS technologies significantly enhance stormwater management capacity, though a clear trade-off exists between the superior hydrological control of permeable pavements and the balanced performance and co-benefits of green roofs. These findings provide a quantitative basis for integrating NBS into urban planning policy in Indonesia to foster climate-adaptive and resilient cities.

A Solvent-Free, Mechanochemical Process for Sustainable Recycling of Neodymium and Dysprosium from E-Waste Magnets

Khairul Raziqin — 2025-08-28

The escalating demand for rare earth elements (REEs), particularly neodymium (Nd) and dysprosium (Dy), for high-performance NdFeB magnets, has created significant supply chain vulnerabilities and environmental concerns associated with primary mining. End-of-life electronic waste (e-waste) represents a substantial secondary resource for these critical materials. This study introduces a novel, environmentally benign approach for recovering Nd and Dy from waste NdFeB magnets. A solvent-free mechanochemical process was developed and optimized. Waste NdFeB magnet powder, sourced from discarded hard disk drives collected in Indonesia, was co-milled with ammonium chloride (NH₄Cl) in a high-energy planetary ball mill. The influence of key process parameters, including milling time (60-360 min), milling speed (200-500 rpm), and the mass ratio of NH₄Cl to magnet powder (1:1 to 5:1), on the extraction efficiency of Nd and Dy was systematically investigated. The structural and morphological transformations were characterized using X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) with Energy-Dispersive X-ray Spectroscopy (EDS). Metal recovery was quantified via subsequent water leaching and analysis by Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES). The mechanochemical treatment successfully converted the insoluble rare earth phases within the magnet matrix into water-soluble rare earth chlorides. Under optimal conditions—a milling time of 240 minutes, a speed of 400 rpm, and a NH₄Cl-to-magnet mass ratio of 3:1—the process achieved remarkable extraction efficiencies of 98.6% for Nd and 96.2% for Dy. XRD analysis confirmed the transformation of the Nd₂Fe₁₄B phase into REE chlorides, alongside iron and iron boride phases. SEM imaging revealed a significant reduction in particle size and the formation of agglomerated composite particles, crucial for the solid-state reaction. In conclusion, this study demonstrates that solvent-free mechanochemistry is a highly effective and sustainable alternative to conventional hydrometallurgical and pyrometallurgical recycling methods. The process operates at ambient temperature, eliminates the need for corrosive acids and organic solvents, and exhibits high recovery rates, presenting a viable pathway towards a circular economy for critical rare earth elements from e-waste.

Elicitation of Systemic Acquired Resistance by a Novel Plant-Derived Biostimulant Composition Confers Robust Protection Against Botrytis cinerea in Tomato (Solanum lycopersicum L.)

Aleisha Wulandari — 2025-08-28

Gray mold, caused by the necrotrophic fungus Botrytis cinerea, is a devastating disease in tomato production worldwide, necessitating the development of sustainable and effective control strategies. Plant-derived biostimulants offer a promising eco-friendly alternative to synthetic fungicides by enhancing the plant's innate immune system. This study, conducted in greenhouse facilities in Palembang, Indonesia, evaluated the efficacy of a novel plant-derived biostimulant (PDB-MX7), a composition of Ascophyllum nodosum and Moringa oleifera extracts, in controlling gray mold in tomato (Solanum lycopersicum L. cv. 'Mutiara'). Tomato plants were treated with PDB-MX7 and subsequently inoculated with a virulent B. cinerea isolate. We assessed disease progression, plant growth parameters, and a suite of underlying defense mechanisms. These included the quantification of oxidative stress markers (H₂O₂, MDA), the activity of key defense-related enzymes (PAL, PPO, SOD, CAT), the accumulation of defense phytohormones (salicylic acid, jasmonic acid), and the expression levels of pathogenesis-related genes (PR-1, PDF1.2) via RT-qPCR. Pre-treatment with PDB-MX7 significantly reduced gray mold disease severity by 76.4% and lesion diameter by 71.8% compared to untreated, inoculated plants. This protective effect was associated with a significant priming of the plant's defense system. PDB-MX7-treated plants exhibited lower levels of H₂O₂ and MDA upon infection, indicating reduced oxidative stress. Furthermore, these plants showed a rapid and potent induction of PAL and PPO activity (3.1-fold and 2.8-fold higher than controls at 48 hpi, respectively). This was corroborated by a significant accumulation of salicylic acid and a more than 5-fold upregulation in the expression of the SA-responsive gene PR-1, indicating the activation of Systemic Acquired Resistance (SAR). In conclusion, the novel biostimulant composition PDB-MX7 confers substantial resistance against B. cinerea in tomato by priming the plant's innate immunity, primarily through the activation of the SA-mediated SAR pathway. This study highlights the potential of PDB-MX7 as a powerful tool for integrated pest management programs in sustainable tomato cultivation.