International Trade Reshapes the Decoupling of Emissions from Economic Growth.

Efforts to stabilize the global climate change while also continuing human development depend upon "decoupling" economic growth from fossil fuel CO2 emissions. However, evaluations of such decoupling have typically relied on production-based emissions, which do not account for emissions embodied in international trade. Yet international trade can greatly change emissions accounting and reshape the decoupling between emissions and economic growth. Here, we evaluate decoupling of economic growth from different accounts of emissions in each of the 159 countries and analyze the drivers of decoupling. We find that between 1995 and 2015, although 29 countries exhibited strong decoupling of territorial emissions (growing economies and decreasing emissions), only 19 countries achieved economic growth while their consumption-based emissions decreased. Most developed countries have achieved decoupling of emissions related to domestic goods and services, but have not achieved decoupling of emissions related to imported goods and services. The U-test confirms that the domestic component of consumption-based emissions exhibits a stronger decoupling trend from gross domestic product (GDP) growth than consumption-based emissions, and emissions from imports continue to rise with GDP per capita without a corresponding decline, providing a statistical validation of the decoupling analysis. Moreover, in the countries where economic growth and consumption-based emissions are most decoupled, a key driver is decreasing emissions intensity due to technological progress─and especially reductions in the intensity of imported goods and services. Our results reveal the importance of assessing decoupling using consumption-based emissions; successful decoupling may require international cooperation and coordinated mitigation efforts of trading partners.

THz Metamaterial Sensitivity Enhancement by Reduction of Substrate's Fabry-Pérot Oscillations Using Back Plates as an Optical De-Coupler.

This work presents a new method for the enhancement of sensitivity in Terahertz (THz) spectroscopy on metamaterial (MM) in terms of its resonance frequency shift (ΔF), by attaching the dielectric back plate to the MM's silicon (Si) wafer. The dielectric back plates are designed to minimize the Fresnel reflections at the backside of the substrate, identical to a broadband antireflective (AR) plate tailored for THz. Utilizing broadband AR technology, we demonstrate the concept of decoupling MM resonance from the substrate's Fabry-Pérot (FP) oscillations. This is done by effectively coupling the THz light out of the high-permittivity substrate, resulting in the improved quality factor of the MM resonance and overall plasmonic enhancement on the metasurface. The back plate acts as a surface plasmonic enhancer to the THz MM by increasing the field intensity on the front metasurface, leading to enhancement of dielectric response (MM's ΔF). This makes the MM resonance ultrasensitive to the minor changes of particle size/concentration under test spread on the metasurface, contributing to enhanced resonance ΔF. The plate also makes the Si substrate optically lossless, enabling the full effect of MM resonance shift and increasing the resonance ΔF by 8-fold compared with MM's fabricated on conventional Si substrates. This research is backed-up with system-level CST simulations and experimental THz impedance spectroscopy of the MM. This method and chip structure is CMOS compatible having potential applications for any resonant MM fabricated on a substrate aimed to maximize dielectric sensitivity for biosensing and nanoparticle THz spectroscopy.

Decoupled graph knowledge distillation: A general logits-based method for learning MLPs on graphs.

While Graph Neural Networks (GNNs) have demonstrated their effectiveness in processing non-Euclidean structured data, the neighborhood fetching of GNNs is time-consuming and computationally intensive, making them difficult to deploy in low-latency industrial applications. To address the issue, a feasible solution is graph knowledge distillation (KD), which can learn high-performance student Multi-layer Perceptrons (MLPs) to replace GNNs by mimicking the superior output of teacher GNNs. However, state-of-the-art graph knowledge distillation methods are mainly based on distilling deep features from intermediate hidden layers, this leads to the significance of logit layer distillation being greatly overlooked. To provide a novel viewpoint for studying logits-based KD methods, we introduce the idea of decoupling into graph knowledge distillation. Specifically, we first reformulate the classical graph knowledge distillation loss into two parts, i.e., the target class graph distillation (TCGD) loss and the non-target class graph distillation (NCGD) loss. Next, we decouple the negative correlation between GNN's prediction confidence and NCGD loss, as well as eliminate the fixed weight between TCGD and NCGD. We named this logits-based method Decoupled Graph Knowledge Distillation (DGKD). It can flexibly adjust the weights of TCGD and NCGD for different data samples, thereby improving the prediction accuracy of the student MLP. Extensive experiments conducted on public benchmark datasets show the effectiveness of our method. Additionally, DGKD can be incorporated into any existing graph knowledge distillation framework as a plug-and-play loss function, further improving distillation performance. The code is available at https://github.com/xsk160/DGKD.

No End in Sight? A Greenwash Review and Research Agenda.

Greenwashing is more virulent than ever. A profusion of environmental, social, and governance and net zero commitments are becoming fraught with questionable and misleading claims. At the same time, we are no closer to solving the pressing environmental and social issues of our time. In this review, we seek to examine this shift and summarize changes in greenwash research into three key phases: (a) 1.0 Static Communication; (b) 2.0 Dynamic Management; and (c) 3.0 Narratives about the Future. We analyze current key areas of developing literature and point to numerous open questions for future research. Next, we go beyond much of the published work to examine emerging tactics and lay out a forward-looking agenda for future research. We also propose a model of Corporate Miscommunication, integrating various streams in greenwash research. In doing so, we seek to lay a pathway for greenwashing researchers to finally find that elusive "end" to greenwashing.

Zero-shot stance detection based on multi-expert collaboration.

Zero-shot stance detection is pivotal for autonomously discerning user stances on novel emerging topics. This task hinges on effective feature alignment transfer from known to unseen targets. To address this, we introduce a zero-shot stance detection framework utilizing multi-expert cooperative learning. This framework comprises two core components: a multi-expert feature extraction module and a gating mechanism for stance feature selection. Our approach involves a unique learning strategy tailored to decompose complex semantic features. This strategy harnesses the expertise of multiple specialists to unravel and learn diverse, intrinsic textual features, enhancing transferability. Furthermore, we employ a gating-based mechanism to selectively filter and fuse these intricate features, optimizing them for stance classification. Extensive experiments on standard benchmark datasets demonstrate that our model significantly surpasses existing baseline models in performance.

DDP-FedFV: A Dual-Decoupling Personalized Federated Learning Framework for Finger Vein Recognition.

Finger vein recognition methods, as emerging biometric technologies, have attracted increasing attention in identity verification due to their high accuracy and live detection capabilities. However, as privacy protection awareness increases, traditional centralized finger vein recognition algorithms face privacy and security issues. Federated learning, a distributed training method that protects data privacy without sharing data across endpoints, is gradually being promoted and applied. Nevertheless, its performance is severely limited by heterogeneity among datasets. To address these issues, this paper proposes a dual-decoupling personalized federated learning framework for finger vein recognition (DDP-FedFV). The DDP-FedFV method combines generalization and personalization. In the first stage, the DDP-FedFV method implements a dual-decoupling mechanism involving model and feature decoupling to optimize feature representations and enhance the generalizability of the global model. In the second stage, the DDP-FedFV method implements a personalized weight aggregation method, federated personalization weight ratio reduction (FedPWRR), to optimize the parameter aggregation process based on data distribution information, thereby enhancing the personalization of the client models. To evaluate the performance of the DDP-FedFV method, theoretical analyses and experiments were conducted based on six public finger vein datasets. The experimental results indicate that the proposed algorithm outperforms centralized training models without increasing communication costs or privacy leakage risks.

Attribute Feature Perturbation-Based Augmentation of SAR Target Data.

Large-scale, diverse, and high-quality data are the basis and key to achieving a good generalization of target detection and recognition algorithms based on deep learning. However, the existing methods for the intelligent augmentation of synthetic aperture radar (SAR) images are confronted with several issues, including training instability, inferior image quality, lack of physical interpretability, etc. To solve the above problems, this paper proposes a feature-level SAR target-data augmentation method. First, an enhanced capsule neural network (CapsNet) is proposed and employed for feature extraction, decoupling the attribute information of input data. Moreover, an attention mechanism-based attribute decoupling framework is used, which is beneficial for achieving a more effective representation of features. After that, the decoupled attribute feature, including amplitude, elevation angle, azimuth angle, and shape, can be perturbed to increase the diversity of features. On this basis, the augmentation of SAR target images is realized by reconstructing the perturbed features. In contrast to the augmentation methods using random noise as input, the proposed method realizes the mapping from the input of known distribution to the change in unknown distribution. This mapping method reduces the correlation distance between the input signal and the augmented data, therefore diminishing the demand for training data. In addition, we combine pixel loss and perceptual loss in the reconstruction process, which improves the quality of the augmented SAR data. The evaluation of the real and augmented images is conducted using four assessment metrics. The images generated by this method achieve a peak signal-to-noise ratio (PSNR) of 21.6845, radiometric resolution (RL) of 3.7114, and dynamic range (DR) of 24.0654. The experimental results demonstrate the superior performance of the proposed method.

Accelerating urban energy transitions: Harnessing EEET framework dynamics for sustainable development in resource-based cities of Sichuan Basin, China.

This study examines managing urban energy systems in the Sichuan Basin, considering natural gas production. An integrated approach assessed the economy, energy, ecology, and technology (EEET) connections. The study identifies systemic issues and significant differences across locations and emphasizes the interdependent nature of economic expansion, energy innovations, and environmental commitment. Findings offer valuable insights for policymakers, providing strategic directions for overcoming barriers to sustainable city and energy advancements, and nurturing resilient urban environments. The EEET framework sheds light on the complex interactions of urban development, enabling officials to focus on areas for improvement. Addressing conflicts can create more cohesive and sustainable urban landscapes, uncovering serious environmental and energy-use issues in cities with dense industrial zones. Decision-makers could prioritize solutions like enforcing pollution controls and encouraging green energy use. Addressing disputes among urban development participants can craft strategies to encourage cooperation, finding win-win outcomes for sustainable city growth.

Decoupling of the onset of anharmonicity between a protein and its surface water around 200 K.

The protein dynamical transition at ~200 K, where the biomolecule transforms from a harmonic, non-functional form to an anharmonic, functional state, has been thought to be slaved to the thermal activation of dynamics in its surface hydration water. Here, by selectively probing the dynamics of protein and hydration water using elastic neutron scattering and isotopic labeling, we found that the onset of anharmonicity in the two components around 200 K is decoupled. The one in protein is an intrinsic transition, whose characteristic temperature is independent of the instrumental resolution time, but varies with the biomolecular structure and the amount of hydration, while the one of water is merely a resolution effect.

Fall into the pseudo-decoupling trap: Type identification, trend characterization and solution path of carbon decoupling trap in urban agglomerations of China.

This study investigates the stability and sustainability of carbon decoupling in urban agglomerations across China, where the strong coupling between economic growth and carbon emissions poses significant challenges. Despite efforts in energy conservation and emission reduction, urban agglomerations have seen unsatisfactory results. By analyzing the real-pseudo decoupling states in 19 urban agglomerations from 2007 to 2020, the objective of this study is to identify the type and trend characteristics of carbon decoupling traps and to propose solution paths for maintaining decoupling stability. Major Findings: (1) The decoupling state exhibits volatility and instability in urban agglomerations, making them susceptible to decoupling traps. (2) Most urban agglomerations remain un-decoupled, with a few cities achieving real decoupling and gradually shifted from northeast to southeast, while pseudo-decoupling and un-decoupled cities consistently cluster in the southwest and northwest regions. (3) Real-pseudo decoupling is driven by a combination of endogenous and exogenous factors, with energy structure, population density, and environmental regulation intensity emerging as pivotal influencers. (4) Geographical factors exhibit both commonalities and variations in their impact on real-pseudo decoupling. By identifying real-pseudo decoupling states and their driving factors, this study proposes strategic solution paths to overcome carbon constraints and achieve stable decoupling in urban agglomerations, contributing to the broader goals of sustainable economic and environmental development.

Toward structure and metabolism of glycogen C1-C6 in humans at 7 T by localized 13C MRS using low-power bilevel broadband 1H decoupling.

This work aims to develop and implement a pulse-acquire sequence for three-dimensional (3D) single-voxel localized 13C MRS in humans at 7 T, in conjunction with bilevel broadband 1H decoupling, and to test its feasibility in vitro and in vivo in human calf muscle with emphasis on the detection of glycogen C1-C6. A localization scheme suitable for measuring fast-relaxing 13C signals in humans at 7 T was developed and implemented using the outer volume suppression (OVS) and one-dimensional image selected in vivo spectroscopy (ISIS-1D) schemes, similar to that which was previously reported in humans at 4 T. The 3D 13C localization scheme was followed by uniform 13C adiabatic excitation, all complemented with an option for bilevel broadband 1H decoupling to improve both 13C sensitivity and spectral resolution at 7 T. The performance of the pulse-acquire sequence was investigated in vitro on phantoms and in vivo in the human calf muscle of three healthy volunteers, while measuring glycogen C1-C6. In addition, T1 and T2 of glycogen C1-C6 were measured in vitro at 7 T, as well as T1 of glycogen C1 in vivo. The glycerol C2 and C1,3 lipid resonances were efficiently suppressed in vitro at 7 T using the OVS and ISIS-1D schemes, allowing distinct detection of glycogen C2-C6. While some glycerol remained in calf muscle in vivo, the intense lipid at 130 ppm was efficiently suppressed. The 13C sensitivity and spectral resolution of glycogen C1-C6 in vitro and glycogen C1 in vivo were improved at 7 T using bilevel broadband 1H decoupling. The T1 and T2 of glycogen C1-C6 in vitro at 7 T were consistent compared with those at 8.5 T, while the T1 of glycogen C1 in vivo at 7 T resulted similar to that in vitro. Localized 13C MRS is feasible in human calf muscle in vivo at 7 T, and this will allow further extension of this method for 13C MRS measurements such as in the brain.

MIST: Multi-instance selective transformer for histopathological subtype prediction.

Accurate histopathological subtype prediction is clinically significant for cancer diagnosis and tumor microenvironment analysis. However, achieving accurate histopathological subtype prediction is a challenging task due to (1) instance-level discrimination of histopathological images, (2) low inter-class and large intra-class variances among histopathological images in their shape and chromatin texture, and (3) heterogeneous feature distribution over different images. In this paper, we formulate subtype prediction as fine-grained representation learning and propose a novel multi-instance selective transformer (MIST) framework, effectively achieving accurate histopathological subtype prediction. The proposed MIST designs an effective selective self-attention mechanism with multi-instance learning (MIL) and vision transformer (ViT) to adaptive identify informative instances for fine-grained representation. Innovatively, the MIST entrusts each instance with different contributions to the bag representation based on its interactions with instances and bags. Specifically, a SiT module with selective multi-head self-attention (S-MSA) is well-designed to identify the representative instances by modeling the instance-to-instance interactions. On the contrary, a MIFD module with the information bottleneck is proposed to learn the discriminative fine-grained representation for histopathological images by modeling instance-to-bag interactions with the selected instances. Substantial experiments on five clinical benchmarks demonstrate that the MIST achieves accurate histopathological subtype prediction and obtains state-of-the-art performance with an accuracy of 0.936. The MIST shows great potential to handle fine-grained medical image analysis, such as histopathological subtype prediction in clinical applications.

Instrumented Mouthguard Decoupling Affects Measured Head Kinematic Accuracy.

Many recent studies have used boil-and-bite style instrumented mouthguards to measure head kinematics during impact in sports. Instrumented mouthguards promise greater accuracy than their predecessors because of their superior ability to couple directly to the skull. These mouthguards have been validated in the lab and on the field, but little is known about the effects of decoupling during impact. Decoupling can occur for various reasons, such as poor initial fit, wear-and-tear, or excessive impact forces. To understand how decoupling influences measured kinematic error, we fit a boil-and-bite instrumented mouthguard to a 3D-printed dentition mounted to a National Operating Committee on Standards for Athletic Equipment (NOCSAE) headform. We also instrumented the headform with linear accelerometers and angular rate sensors at its center of gravity (CG). We performed a series of pendulum impact tests, varying impactor face and impact direction. We measured linear acceleration and angular velocity, and we calculated angular acceleration from the mouthguard and the headform CG. We created decoupling conditions by varying the gap between the lower jaw and the bottom face of the mouthguard. We tested three gap conditions: 0 mm (control), 1.6 mm, and 4.8 mm. Mouthguard measurements were transformed to the CG and compared to the reference measurements. We found that gap condition, impact duration, and impact direction significantly influenced mouthguard measurement error. Error was higher for larger gaps and in frontal (front and front boss) conditions. Higher errors were also found in padded conditions, but the mouthguards did not collect all rigid impacts due to inherent limitations. We present characteristic decoupling time history curves for each kinematic measurement. Exemplary frequency spectra indicating characteristic decoupling frequencies are also described. Researchers using boil-and-bite instrumented mouthguards should be aware of their limitations when interpreting results and should seek to address decoupling through advanced post-processing techniques when possible.

Experimentation in cosmology: Intervening on the whole universe.

There are many arguments against the possibility of experimenting on the whole universe. This system seems to be too big to be manipulated, it exists in only one exemplar and its evolution is a non-repeatable process. In this paper, I claim that we can nonetheless talk about experimentation in cosmology if we use Woodward's non-anthropocentric notion of intervention. However, Woodward and other interventionists argued that an intervention was necessarily an exogenous causal process and thus that no intervention on a closed system such as the universe was possible. I discuss their argument and I determine the conditions under which a consistent notion of endogenous intervention on the universe can be defined. Then, I show that there is at least one cosmic phenomenon satisfying these conditions: the photon decoupling. Finally, I draw some conclusions from this analysis regarding a realist approach of cosmology.

All-in-One: Plasmonic Janus Heterostructures for Efficient Cooperative Photoredox Catalysis.

Janus heterostructures consisting of multiple jointed components with distinct properties have gained growing interest in the photoredox catalytic field. Herein, we have developed a facile low-temperature method to gain anisotropic one-dimensional Au-tipped CdS (Au-CdS) nanorods (NRs), followed by assembling Ru molecular co-catalyst (RuN5) onto the surface of the NRs. The CdS NRs decorated with plasmonic Au nanoparticles (NPs) and RuN5 complex harness the virtues of metal-semiconductor and inorganic-organic interface, giving directional charge transfer channels, spatially separated reaction sites, and enhanced local electric field distribution. As a result, the Au-CdS-RuN5 can act as an efficient dual-function photocatalyst for simultaneous H2 evolution and valorization of biomass-derived alcohols. Benefiting from the interfacial charge decoupling and selective chemical bond activation, the optimal all-in-one Au-CdS-RuN5 heterostructure shows greatly enhanced photoactivity and selectivity as compared to bare CdS NRs, along with a remarkable apparent quantum yield of 40.2 % at 400 nm. The structural evolution and working mechanism of the heterostructures are systematically analyzed based on experimental and computational results.

Low power supercycled TPPM decoupling.

Improving the spectral sensitivity and resolution of biological solids is one of the long-standing problems in nuclear magnetic resonance (NMR) spectroscopy. In this report, we introduce low-power supercycled variants of two-pulse phase-modulated (TPPM) sequence for heteronuclear decoupling. The utility of the sequence is shown by improvements in the transverse relaxation time of observed nuclei (with 1H decoupling) with its application to different samples (uniformly 13C, 15N, 2H-labeled GB1 back-exchanged with 25% H2O and 75% D2O, uniformly 13C, 15N, 2H-labeled human derived Asyn fibril back-exchanged with 100% H2O and uniformly 13C, 15N -labeled human derived Asyn fibril) at fast MAS using low radiofrequency (RF) fields. To understand the effect of spinning speed, the transverse relaxation time is monitored under different spinning frequencies. In comparison to existing heteronuclear decoupling sequences, the supercycled TPPM (sTPPM) sequence significantly improves the spectral sensitivity and resolution and is robust towards B1 inhomogeneity and decoupler offset.

Analysis of the carbon emissions trend in the Indian manufacturing sector: a decomposition and decoupling approach.

There is a growing emphasis on fostering green growth and lowering carbon emissions in order to achieve sustainable economic development. This study uses the Tapio decoupling model and analyzes the factors influencing changes in carbon emissions from manufacturing in India utilizing the log mean Divisia index (LMDI) techniques. Furthermore, the nexus between carbon emission intensity, information and communication technology (ICT), total factor productivity (TFP), skill, and energy intensity has been analyzed using the system-GMM approach. It is based on the plant-level Annual Survey of Industries (ASI) datasets for the organized manufacturing sector of India from 2001 to 2002 to 2019 2020 for the major 21 Indian states/UT. The findings reflect the presence of weak decoupling in the manufacturing sector both at the aggregate level and in states. This indicates that both output and emissions are increasing; however, output growth surpasses emission growth, which signifies an effort to transition towards more environmentally friendly production methods and enhanced energy efficiency. The output and population effect are found to be leading factors in carbon emissions, while energy intensity is found to be reducing the effect. Further, the system-GMM estimates show that ICT and energy intensity positively affect total factor productivity, while with an increase in carbon emission intensity, productivity declines. The study confirms the existence of an inverted N-shaped Kuznets curve in the sector. This present study will contribute to formulating energy and environmental strategies to reduce emissions and promote adopting cleaner energy sources. These efforts will facilitate the attainment of carbon neutrality and enhance energy efficiency within the sector.

Spatiotemporal evolution and decoupling effects of sustainable water resources utilization in the Yellow River Basin: Based on three-dimensional water ecological footprint.

Clarifying the spatiotemporal evolution of sustainable water resources utilization (SWU) and its decoupling effects from economic growth (EG) is essential for the effective management of water ecosystems and sustainable development of basins. However, the traditional Ecological Footprint model limits the ability to compare SWU within a basin, and existing studies need to pay more attention to the importance of water renewability in quantifying SWU. Based on the capital flow and capital stock perspectives, this study constructed an evaluation method for SWU and its decoupling effect from EG by combining the three-dimensional Water Ecological Footprint (WEF), sustainable reclassification, and the Tapio model, and explored different types of SWU enhancement strategies. The results indicate that: (1) From 2010 to 2022, the SWU of the Yellow River Basin (YRB) shows a decreasing and then increasing trend and is generally in water ecological deficit, with a lower SWU in the middle and lower reaches. Overall, the per capita WEFsize decreased by 0.73% per year, while the WEFdepth increased by 0.26% per year, the pressure and stress on the SWU of the YRB are still significant. (2) Agricultural freshwater use and domestic greywater discharge dominate the WEF of the basin, and the problem of inversion of the water use structure with the industrial structure is evident. (3) Spatial differentiation within the basin is apparent, and SWU shows a spatial distribution of western strength and eastern weakness, with significant consumption of water capital stock due to insufficient water capital flow as the main reason. (4) Topio decoupling analysis shows that WEF and EG are mainly strongly decoupled, with WEF lagging behind EG; the decoupling relationship between SWU and EG evolves from END-SD-WD, reduces the consumption of water capital stock and increasing water capital flow is a reasonable way to realise its stable strong decoupling. This study is essential for SWU studies of large river basins in arid and semi-arid regions. It provides insights into the sustainable management and rational allocation of water resources in the YRB and other similar basins worldwide.

Structure-function coupling reveals the brain hierarchical structure dysfunction in Alzheimer's disease: A multicenter study.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative condition characterized by cognitive decline. To date, the specific dysfunction in the brain's hierarchical structure in AD remains unclear. METHODS: We introduced the structural decoupling index (SDI), based on a multi-site data set comprising functional and diffusion-weighted magnetic resonance imaging data from 793 subjects, to assess their brain hierarchy. RESULTS: Compared to normal controls (NCs), individuals with AD exhibited increased SDI within the posterior superior temporal sulcus, insular gyrus, precuneus, hippocampus, amygdala, postcentral gyrus, and cingulate gyrus; meanwhile, the patients with AD demonstrated decreased SDI in the frontal lobe. The SDI in those regions also showed a significant correlation with cognitive ability. Moreover, the SDI was a robust AD neuroimaging biomarker capable of accurately distinguishing diagnostic status (area under the curve [AUC] = 0.86). DISCUSSION: Our findings revealed the dysfunction of the brain's hierarchical structure in AD. Furthermore, the SDI could serve as a promising neuroimaging biomarker for AD. HIGHLIGHTS: This study utilized multi-center, multi-modal data from East Asian populations. We found an increased spatial gradient of the structure decoupling index (SDI) from sensory-motor to higher-order cognitive regions. Changes in SDI are associated with energy metabolism and mitochondria. SDI can identify Alzheimer's disease (AD) and further uncover the disease mechanisms of AD.

Modulating metabolism through synthetic biology: Opportunities for two-stage fermentation.

Bio-based production of fuels, chemicals and materials is needed to replace current fossil fuel based production. However, bio-based production processes are very costly, so the process needs to be as efficient as possible. Developments in synthetic biology tools has made it possible to dynamically modulate cellular metabolism during a fermentation. This can be used towards two-stage fermentations, where the process is separated into a growth and a production phase, leading to more efficient feedstock utilization and thus potentially lower costs. This article reviews the current status and some recent results in application of synthetic biology tools towards two-stage fermentations, and compares this approach to pre-existing ones, such as nutrient limitation and addition of toxins/inhibitors.