Systems metabolic engineering (SME) integrates systems biology, synthetic biology, and evolutionary engineering to design high-performance tailor-made microbial cells. SME advances environmental biotechnology by delivering innovative, scalable, and sustainable solutions for green bioprocesses, waste valorization, pollutant degradation, and resource management. Recent advances in artificial intelligence and machine learning, dynamic metabolic control systems, and metabolite-responsive regulatory elements have further accelerated strain development and expanded the functional scope of engineered microbes and microbial consortia. Here, we discuss key conceptual foundations, technological advances, emerging research directions and current limitations in SME, highlighting its transformative potential to drive innovation toward environmentally resilient, resource-efficient, and sustainable biotechnological solutions.
Wet clutches are critical components in mechanical power transmission systems, enabling controlled torque transfer between rotating parts. However, when disengaged, viscous drag torque is generated, reducing the transmission efficiency. Surface texturing, particularly groove patterns, is commonly employed to modulate flow dynamics and aeration process, ultimately decreasing drag torque. Despite their practical importance, systematic approaches for optimising groove design in wet clutches remain limited in the literature. To address this gap, this study proposes an integrated experimental-Gaussian Process Regression-based, multi-objective optimisation methodology aiming to minimise cumulative power loss while maximising torque capacity. To achieve that, a single-disc test rig was designed to evaluate various groove geometries and operating conditions for the GPR model. Conventional radial grooves were benchmarked against new topologies, and exhaustive search optimisation was used to explore the design space and find the optimal solution. The findings demonstrate the potential for groove design optimisation, with quantified benefits such as up to 23% reduction in cumulative power loss for arc-bow grooves and up to 21.8% for mid-relief designs, highlighting their broader applicability in wet clutch and lubrication systems.
Downy mildew (Plasmopara viticola) and gray mold (Botrytis cinerea) are among the most destructive grapevine diseases worldwide, causing substantial yield losses and compromising fruit quality. Traditional diagnostic methods based on visual assessment and microscopic examination are time-consuming, labor-intensive, and require considerable expertise. This study presents a novel computer vision approach for automated grape disease detection by combining instance and semantic segmentation techniques on multispectral imagery. A dataset of 451 captures comprising RGB and five-band multispectral images (460, 540, 640, 780, 880 nm) was collected from open-field vineyards, including healthy, gray mold-symptomatic, and downy mildew-symptomatic leaves. Two complementary approaches were developed: (i) a YOLOv11-based instance segmentation model for rapid leaf identification, and (ii) a dual-head SegFormer architecture for semantic segmentation incorporating 15 input channels, including RGB, multispectral bands, derived vegetation indices, YOLO-generated masks, and depth information. The dual-head SegFormer includes a primary multiclass segmentation head and a secondary binary head for leaf-background discrimination, with consistency regularization between heads to enhance performance. The YOLOv11 model achieved 89.7% mAP50 for leaf segmentation. The SegFormer-based model achieved an overall mean IoU of 75.22%, an F1-Score of 83.53% and a single-class leaf segmentation IoU of 91.79%. Disease-specific segmentation performance was high, with gray mold achieving 94.32% IoU and an F1-score of 97.08%, while downy mildew achieved 75.5% IoU and an F1-score of 86.04%. The integration of multispectral channels and derived indices improved mean IoU by 3-5%, while the inclusion of YOLO-derived masks and depth information from the Depth Anything V2 model increased single-class IoU by more than 11%. The proposed framework demonstrates strong capability for disease-specific detection and is well suited for UAV- and ground robotics-based precision agriculture applications.
Abstract Splenic marginal zone lymphoma (SMZL) is a rare B-cell malignancy with notable genetic, epigenetic, and clinical heterogeneity. In this study, we used coding and noncoding sequencing (n = 74), including whole-genome sequencing (WGS) of 24 paired tumor-normal samples, targeted sequencing (n = 55), and DNA methylation in 126 patients to characterize the disease. From WGS, we identified recurrent, predominantly clonal coding mutations in KLF2 (50%), KMT2D (25%), and NOTCH2 (25%), alongside rare mutations in FLNC (8%), novel mutations in FAM135B (17%), and noncoding mutational hot spots in BCL6, PAX5, and BACH2 linked to aberrant somatic hypermutation. At least 1 noncoding hot spot was detected in 69% of patients. Copy number aberrations were present in 73% of patients, including del(7q) (27%), gain(3q) (17%), and trisomy 12 (13%). DNA methylation profiling revealed 2 epigenetic subgroups: high-risk (HR) SMZL (n = 67) and low-risk SMZL (n = 59). SMZL-HR was associated with adverse features, including female sex, IGHV1-2∗04 usage, KLF2 mutations, del(7q), shorter telomeres, and elevated epigenetically determined cumulative mitoses scores. Transcriptomic analysis highlighted enhanced cell proliferation in SMZL-HR, with enrichment of E2F and G2M checkpoint pathways and epigenetic regulation via EZH2. Patients with SMZL-HR had significantly shorter time to first treatment (TTFT) (hazard ratio, 1.9; P = .003) and reduced overall survival (hazard ratio, 2.5; P = .039): 85% of patients with SMZL-HR required treatment and showed a higher frequency of transformation (P = .007) and mortality (P< .001). Multivariate analysis confirmed SMZL-HR as an independent predictor of shorter TTFT (hazard ratio, 2.4; P = .001). These findings demonstrate the role of DNA methylation and molecular profiling in SMZL risk stratification.
The fungal pathogen Botrytis cinerea (B. cinerea) attacks over 1400 plant species and results in estimated annual losses of $10–100 billion worldwide. In precision agriculture, deep learning (DL) provides reliable tools for rapid and objective plant disease detection. This study presents a unified two-stage DL solution for the automated detection of visible B. cinerea across three major vegetable crops—tomato, pepper, and cucumber—using standard RGB imagery. In the first stage, a YOLOv11-based instance segmentation model accurately localizes leaf regions, achieving a localization accuracy of 87.3% as measured by mAP50. In the second stage, an ensemble of 13 MobileViT variant models analyzes the segmented leaf regions and performs per-crop classification into healthy and infected leaves. The proposed system achieves an overall detection accuracy of 84.05%, with per-class detection of infected leaves at 88.61% for pepper, 82.68% for tomato, and 70.55% for cucumber, measured using the F1-score. These results demonstrate that the proposed approach can reliably detect B. cinerea symptoms across different crops using only RGB data, offering a practical path toward smartphone-based field deployment and integration into decision support systems for timely, symptom-based disease management.
