Motif-based community detection in heterogeneous multilayer networks.

Multilayer networks composed of intralayer edges and interlayer edges are an important type of complex networks. Considering the heterogeneity of nodes and edges, it is necessary to design more reasonable and diverse community detection methods for multilayer networks. Existing research on community detection in multilayer networks mainly focuses on multiplexing networks (where the nodes are homogeneous and the edges are heterogeneous), but few studies have focused on heterogeneous multilayer networks where both nodes and edges represent different semantics. In this paper, we studied community detection on heterogeneous multilayer networks and proposed a motif-based detection algorithm. First, the communities and motifs of multilayer networks are defined, especially the interlayer motifs. Then, the modularity of multilayer networks based on these motifs is designed, and the community structure of the multilayer network is detected by maximizing the modularity of multilayer networks. Finally, we verify the effectiveness of the detection algorithm on synthetic networks. In the experiments on synthetic networks, comparing with the classical community detection algorithms (without considering interlayer heterogeneity), the motif-based modularity community detection algorithm can obtain better results under different evaluation indexes, and we found that there exists a certain relationship between motifs and communities. In addition, the proposed algorithm is applied in the empirical network, which shows its practicability in the real world. This study provides a solution for the investigation of heterogeneous information in multilayer networks.

FedDUS: Lung tumor segmentation on CT images through federated semi-supervised with dynamic update strategy.

BACKGROUND AND OBJECTIVE: Lung tumor annotation is a key upstream task for further diagnosis and prognosis. Although deep learning techniques have promoted automation of lung tumor segmentation, there remain challenges impeding its application in clinical practice, such as a lack of prior annotation for model training and data-sharing among centers. METHODS: In this paper, we use data from six centers to design a novel federated semi-supervised learning (FSSL) framework with dynamic model aggregation and improve segmentation performance for lung tumors. To be specific, we propose a dynamically updated algorithm to deal with model parameter aggregation in FSSL, which takes advantage of both the quality and quantity of client data. Moreover, to increase the accessibility of data in the federated learning (FL) network, we explore the FAIR data principle while the previous federated methods never involve. RESULT: The experimental results show that the segmentation performance of our model in six centers is 0.9348, 0.8436, 0.8328, 0.7776, 0.8870 and 0.8460 respectively, which is superior to traditional deep learning methods and recent federated semi-supervised learning methods. CONCLUSION: The experimental results demonstrate that our method is superior to the existing FSSL methods. In addition, our proposed dynamic update strategy effectively utilizes the quality and quantity information of client data and shows efficiency in lung tumor segmentation. The source code is released on (https://github.com/GDPHMediaLab/FedDUS).

MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention.

The integration of structural magnetic resonance imaging (sMRI) and deep learning techniques is one of the important research directions for the automatic diagnosis of Alzheimer's disease (AD). Despite the satisfactory performance achieved by existing voxel-based models based on convolutional neural networks (CNNs), such models only handle AD-related brain atrophy at a single spatial scale and lack spatial localization of abnormal brain regions based on model interpretability. To address the above limitations, we propose a traceable interpretability model for AD recognition based on multi-patch attention (MAD-Former). MAD-Former consists of two parts: recognition and interpretability. In the recognition part, we design a 3D brain feature extraction network to extract local features, followed by constructing a dual-branch attention structure with different patch sizes to achieve global feature extraction, forming a multi-scale spatial feature extraction framework. Meanwhile, we propose an important attention similarity position loss function to assist in model decision-making. The interpretability part proposes a traceable method that can obtain a 3D ROI space through attention-based selection and receptive field tracing. This space encompasses key brain tissues that influence model decisions. Experimental results reveal the significant role of brain tissues such as the Fusiform Gyrus (FuG) in AD recognition. MAD-Former achieves outstanding performance in different tasks on ADNI and OASIS datasets, demonstrating reliable model interpretability.

Allosteric Feature Collaboration for Model-Heterogeneous Federated Learning.

Although federated learning (FL) has achieved outstanding results in privacy-preserved distributed learning, the setting of model homogeneity among clients restricts its wide application in practice. This article investigates a more general case, namely, model-heterogeneous FL (M-hete FL), where client models are independently designed and can be structurally heterogeneous. M-hete FL faces new challenges in collaborative learning because the parameters of heterogeneous models could not be directly aggregated. In this article, we propose a novel allosteric feature collaboration (AlFeCo) method, which interchanges knowledge across clients and collaboratively updates heterogeneous models on the server. Specifically, an allosteric feature generator is developed to reveal task-relevant information from multiple client models. The revealed information is stored in the client-shared and client-specific codes. We exchange client-specific codes across clients to facilitate knowledge interchange and generate allosteric features that are dimensionally variable for model updates. To promote information communication between different clients, a dual-path (model-model and model-prediction) communication mechanism is designed to supervise the collaborative model updates using the allosteric features. Client models are fully communicated through the knowledge interchange between models and between models and predictions. We further provide theoretical evidence and convergence analysis to support the effectiveness of AlFeCo in M-hete FL. The experimental results show that the proposed AlFeCo method not only performs well on classical FL benchmarks but also is effective in model-heterogeneous federated antispoofing. Our codes are publicly available at https://github.com/ybaoyao/AlFeCo.

Hypoxia Affects Mitochondrial Stress and Facilitates Tumor Metastasis of Colorectal Cancer Through Slug SUMOylation.

BACKGROUND: Colorectal cancer (CRC) is a malignant tumor. Slug has been found to display a key role in diversified cancers, but its relevant regulatory mechanisms in CRC development are not fully explored. OBJECTIVE: Hence, exploring the function and regulatory mechanisms of Slug is critical for the treatment of CRC. METHODS: Protein expressions of Slug, N-cadherin, E-cadherin, Snail, HIF-1α, SUMO1, Drp1, Opa1, Mfn1/2, PGC-1α, NRF1, and TFAM were measured through western blot. To evaluate the protein expression of Slug and SUMO-1, an immunofluorescence assay was used. Cell migration ability was tested through transwell assay. The SUMOylation of Slug was examined through CO-IP assay. RESULTS: Slug displayed higher expression and facilitated tumor metastasis in CRC. In addition, hypoxia treatment was discovered to upregulate HIF-1α, Slug, and SUMO-1 levels, as well as induce Slug SUMOylation. Slug SUMOylation markedly affected mitochondrial biosynthesis, fusion, and mitogen-related protein expression levels to trigger mitochondrial stress. Additionally, the induced mitochondrial stress by hypoxia could be rescued by Slug inhibition and TAK-981 treatment. CONCLUSION: Our study expounded that hypoxia affects mitochondrial stress and facilitates tumor metastasis of CRC through Slug SUMOylation.

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision.

Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.

[Alzheimer's disease classification based on nonlinear high-order features and hypergraph convolutional neural network].

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that damages patients' memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.

ImageCAS: A large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images.

Cardiovascular disease (CVD) accounts for about half of non-communicable diseases. Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed tomography angiography (CTA) is one of the widely used noninvasive imaging modalities in coronary artery diagnosis due to its superior image resolution. Clinically, segmentation of coronary arteries is essential for the diagnosis and quantification of coronary artery disease. Recently, a variety of works have been proposed to address this problem. However, on one hand, most works rely on in-house datasets, and only a few works published their datasets to the public which only contain tens of images. On the other hand, their source code have not been published, and most follow-up works have not made comparison with existing works, which makes it difficult to judge the effectiveness of the methods and hinders the further exploration of this challenging yet critical problem in the community. In this paper, we propose a large-scale dataset for coronary artery segmentation on CTA images. In addition, we have implemented a benchmark in which we have tried our best to implement several typical existing methods. Furthermore, we propose a strong baseline method which combines multi-scale patch fusion and two-stage processing to extract the details of vessels. Comprehensive experiments show that the proposed method achieves better performance than existing works on the proposed large-scale dataset. The benchmark and the dataset are published at https://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT.

Lactate based caproate production with Clostridium drakei and process control of Acetobacterium woodii via lactate dependent in situ electrolysis.

Syngas fermentation processes with acetogens represent a promising process for the reduction of CO2 emissions alongside bulk chemical production. However, to fully realize this potential the thermodynamic limits of acetogens need to be considered when designing a fermentation process. An adjustable supply of H2 as electron donor plays a key role in autotrophic product formation. In this study an anaerobic laboratory scale continuously stirred tank reactor was equipped with an All-in-One electrode allowing for in-situ H2 generation via electrolysis. Furthermore, this system was coupled to online lactate measurements to control the co-culture of a recombinant lactate-producing Acetobacterium woodii strain and a lactate-consuming Clostridium drakei strain to produce caproate. When C. drakei was grown in batch cultivations with lactate as substrate, 1.6 g·L-1 caproate were produced. Furthermore, lactate production of the A. woodii mutant strain could manually be stopped and reinitiated by controlling the electrolysis. Applying this automated process control, lactate production of the A. woodii mutant strain could be halted to achieve a steady lactate concentration. In a co-culture experiment with the A. woodii mutant strain and the C. drakei strain, the automated process control was able to dynamically react to changing lactate concentrations and adjust H2 formation respectively. This study confirms the potential of C. drakei as medium chain fatty acid producer in a lactate-mediated, autotrophic co-cultivation with an engineered A. woodii strain. Moreover, the monitoring and control strategy presented in this study reinforces the case for autotrophically produced lactate as a transfer metabolite in defined co-cultivations for value-added chemical production.

Attractive deep morphology-aware active contour network for vertebral body contour extraction with extensions to heterogeneous and semi-supervised scenarios.

Automatic vertebral body contour extraction (AVBCE) from heterogeneous spinal MRI is indispensable for the comprehensive diagnosis and treatment of spinal diseases. However, AVBCE is challenging due to data heterogeneity, image characteristics complexity, and vertebral body morphology variations, which may cause morphology errors in semantic segmentation. Deep active contour-based (deep ACM-based) methods provide a promising complement for tackling morphology errors by directly parameterizing the contour coordinates. Extending the target contours' capture range and providing morphology-aware parameter maps are crucial for deep ACM-based methods. For this purpose, we propose a novel Attractive Deep Morphology-aware actIve contouR nEtwork (ADMIRE) that embeds an elaborated contour attraction term (CAT) and a comprehensive contour quality (CCQ) loss into the deep ACM-based framework. The CAT adaptively extends the target contours' capture range by designing an all-to-all force field to enable the target contours' energy to contribute to farther locations. Furthermore, the CCQ loss is carefully designed to generate morphology-aware active contour parameters by simultaneously supervising the contour shape, tension, and smoothness. These designs, in cooperation with the deep ACM-based framework, enable robustness to data heterogeneity, image characteristics complexity, and target contour morphology variations. Furthermore, the deep ACM-based ADMIRE is able to cooperate well with semi-supervised strategies such as mean teacher, which enables its function in semi-supervised scenarios. ADMIRE is trained and evaluated on four challenging datasets, including three spinal datasets with more than 1000 heterogeneous images and more than 10000 vertebrae bodies, as well as a cardiac dataset with both normal and pathological cases. Results show ADMIRE achieves state-of-the-art performance on all datasets, which proves ADMIRE's accuracy, robustness, and generalization ability.

Feature-correlation-aware history-preserving-sparse-coding framework for automatic vertebra recognition.

Automatic vertebra recognition from magnetic resonance imaging (MRI) is of significance in disease diagnosis and surgical treatment of spinal patients. Although modern methods have achieved remarkable progress, vertebra recognition still faces two challenges in practice: (1) Vertebral appearance challenge: The vertebral repetitive nature causes similar appearance among different vertebrae, while pathological variation causes different appearance among the same vertebrae; (2) Field of view (FOV) challenge: The FOVs of the input MRI images are unpredictable, which exacerbates the appearance challenge because there may be no specific-appearing vertebrae to assist recognition. In this paper, we propose a Feature-cOrrelation-aware history-pReserving-sparse-Coding framEwork (FORCE) to extract highly discriminative features and alleviate these challenges. FORCE is a recognition framework with two elaborated modules: (1) A feature similarity regularization (FSR) module to constrain the features of the vertebrae with the same label (but potentially with different appearances) to be closer in the latent feature space in an Eigenmap-based regularization manner. (2) A cumulative sparse representation (CSR) module to achieve feed-forward sparse coding while preventing historical features from being erased, which leverages both the intrinsic advantages of sparse codes and the historical features for obtaining more discriminative sparse codes encoding each vertebra. These two modules are embedded into the vertebra recognition framework in a plug-and-play manner to improve feature discrimination. FORCE is trained and evaluated on a challenging dataset containing 600 MRI images. The evaluation results show that FORCE achieves high performance in vertebra recognition and outperforms other state-of-the-art methods.

Turn air-captured CO2 with methanol into amino acid and pyruvate in an ATP/NAD(P)H-free chemoenzymatic system.

The use of gaseous and air-captured CO2 for technical biosynthesis is highly desired, but elusive so far due to several obstacles including high energy (ATP, NADPH) demand, low thermodynamic driving force and limited biosynthesis rate. Here, we present an ATP and NAD(P)H-free chemoenzymatic system for amino acid and pyruvate biosynthesis by coupling methanol with CO2. It relies on a re-engineered glycine cleavage system with the NAD(P)H-dependent L protein replaced by biocompatible chemical reduction of protein H with dithiothreitol. The latter provides a higher thermodynamic driving force, determines the reaction direction, and avoids protein polymerization of the rate-limiting enzyme carboxylase. Engineering of H protein to effectively release the lipoamide arm from a protected state further enhanced the system performance, achieving the synthesis of glycine, serine and pyruvate at g/L level from methanol and air-captured CO2. This work opens up the door for biosynthesis of amino acids and derived products from air.

m6 A promotes planarian regeneration.

Regeneration is the regrowth of damaged tissues or organs, a vital process in response to damages from primitive organisms to higher mammals. Planarian possesses active whole-body regenerative capability owing to its vast reservoir of adult stem cells, neoblasts, providing an ideal model to delineate the underlying mechanisms for regeneration. RNA N6 -methyladenosine (m6 A) modification participates in many biological processes, including stem cell self-renewal and differentiation, in particular the regeneration of haematopoietic stem cells and axons. However, how m6 A controls regeneration at the whole-organism level remains largely unknown. Here, we demonstrate that the depletion of m6 A methyltransferase regulatory subunit wtap abolishes planarian regeneration, potentially through regulating genes related to cell-cell communication and cell cycle. Single-cell RNA-seq (scRNA-seq) analysis unveils that the wtap knockdown induces a unique type of neural progenitor-like cells (NP-like cells), characterized by specific expression of the cell-cell communication ligand grn. Intriguingly, the depletion of m6 A-modified transcripts grn, cdk9 or cdk7 partially rescues the defective regeneration of planarian caused by wtap knockdown. Overall, our study reveals an indispensable role of m6 A modification in regulating whole-organism regeneration.

A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups.

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper aims to reveal the complete silent transmission dynamics of COVID-19 by agent-based simulations overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, by inferring individual professions, visited locations and age group, we found that retailing, catering and hotel staff are more likely to get infected than other professions, and elderly and retirees are more likely to get infected at home than outside home.

Predicting the cascading dynamics in complex networks via the bimodal failure size distribution.

Cascading failure as a systematic risk occurs in a wide range of real-world networks. Cascade size distribution is a basic and crucial characteristic of systemic cascade behaviors. Recent research works have revealed that the distribution of cascade sizes is a bimodal form indicating the existence of either very small cascades or large ones. In this paper, we aim to understand the properties and formation characteristics of such bimodal distribution in complex networks and further predict the final cascade size. We first find that the bimodal distribution is ubiquitous under certain conditions in both synthetic and real networks. Moreover, the large cascades distributed in the right peak of bimodal distribution are resulted from either the failure of nodes with high load at the first step of the cascade or multiple rounds of cascades triggered by the initial failure. Accordingly, we propose a hybrid load metric (HLM), which combines the load of the initial broken node and the load of failed nodes triggered by the initial failure, to predict the final size of cascading failures. We validate the effectiveness of HLM by computing the accuracy of identifying the cascades belonging to the right and left peaks of the bimodal distribution. The results show that HLM is a better predictor than commonly used network centrality metrics in both synthetic and real-world networks. Finally, the influence of network structure on the optimal HLM is discussed.

Quantification of the spatial-temporal patterns of great ideas.

Quantitative understanding of the process of knowledge creation is crucial for accelerating the advance of science. Recent years have witnessed a great effort to address this issue by studying the publication data of scientific journals, leading to a variety of surprising discoveries at both individual level and disciplinary level. However, before scientific journals appeared on a large scale and became the mainstream for publishing research results, there are also intellectual achievements that have changed the world, which have usually become classic and are now referred to as the great ideas of great people. So far, little is known about the general law of their birth. In this paper, we reference Wikipedia and academic history books to collect 2001 magnum opuses as representations of great ideas, covering nine disciplines. Using the year and place of publication of these magnum opuses, we show that the birth of great ideas is very concentrated in geography, and more concentrated than other human activities such as contemporary knowledge production. We construct a spatial-temporal bipartite network to study the similarity of output structures between different historical periods and discover the existence of a Great Transformation around the 1870s, which may be associated with the rise of the US in academia. Finally, we re-rank cities and historical periods by employing an iterative approach to study cities' leadership and historical periods' prosperity.

Target area distillation and section attention segmentation network for accurate 3D medical image segmentation.

3D medical image segmentation has an essential role in medical image analysis, while attention mechanism has improved the performance by a large margin. However, existing methods obtained the attention coefficient in a small receptive field, resulting in possible performance limitations. Radiologists usually scan all the slices first to have an overall idea of the target, and then analyze regions of interest in multiple 2D views in clinic practice. We simulate radiologists' recognition process and propose to exploit the 3D context information in a deeper manner for accurate 3D medical images segmentation. Due to the similarity of human body structure, medical images of different populations have highly similar shape and location information, so we use target region distillation to extract the common segmented region information. Particularly, we proposed two optimizations including Target Area Distillation and Section Attention. Target Area Distillation adds positions information to the original input to let the network has an initial attention of the target, while section attention performs attention extraction in three 2D sections thus with large range of receptive field. We compare our method against several popular networks in two public datasets including ImageCHD and COVID-19. Experimental results show that our proposed method improves the segmentation Dice score by 2-4% over the state-of-the-art methods. Our code has been released to the public (Anonymous link).

Magnetic resonance spectroscopy and liquid chromatography-mass spectrometry metabolomics study may differentiate pre-eclampsia from gestational hypertension.

OBJECTIVE: To investigate the findings of magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and serum metabolomics for differentiating pre-eclampsia (PE) from gestational hypertension (GH). METHODS: This prospective study enrolled 176 subjects including a primary cohort with healthy non-pregnant women (HN, n = 35), healthy pregnant women (HP, n = 20), GH (n = 27), and PE (n = 39) and a validation cohort with HP (n = 22), GH (n = 22), and PE (n = 11). T1 signal intensity index (T1SI), apparent diffusion coefficient (ADC) value, and the metabolites on MRS were compared. The differentiating performances of single and combined MRI and MRS parameters for PE were evaluated. Serum liquid chromatography-mass spectrometry (LC-MS) metabolomics was investigated by sparse projection to latent structures discriminant analysis. RESULTS: Increased T1SI, lactate/creatine (Lac/Cr), and glutamine and glutamate (Glx)/Cr and decreased ADC value and myo-inositol (mI)/Cr in basal ganglia were found in PE patients. T1SI, ADC, Lac/Cr, Glx/Cr, and mI/Cr yielded an area under the curves (AUC) of 0.90, 0.80, 0.94, 0.96, and 0.94 in the primary cohort, and of 0.87, 0.81, 0.91, 0.84, and 0.83 in the validation cohort, respectively. A combination of Lac/Cr, Glx/Cr, and mI/Cr yielded the highest AUC of 0.98 in the primary cohort and 0.97 in the validation cohort. Serum metabolomics analysis showed 12 differential metabolites, which are involved in pyruvate metabolism, alanine metabolism, glycolysis, gluconeogenesis, and glutamate metabolism. CONCLUSIONS: MRS is expected to be a noninvasive and effective tool for monitoring GH patients to avoid the development of PE. KEY POINTS: • Increased T1SI and decreased ADC value in the basal ganglia were found in PE patients than in GH patients. • Increased Lac/Cr and Glx/Cr, and decreased mI/Cr in the basal ganglia were found in PE patients than in GH patients. • LC-MS metabolomics showed that the major differential metabolic pathways between PE and GH were pyruvate metabolism, alanine metabolism, glycolysis, gluconeogenesis, and glutamate metabolism.

Novel synthetic co-culture of Acetobacterium woodii and Clostridium drakei using CO2 and in situ generated H2 for the production of caproic acid via lactic acid.

Acetobacterium woodii is known to produce mainly acetate from CO2 and H2, but the production of higher value chemicals is desired for the bioeconomy. Using chain-elongating bacteria, synthetic co-cultures have the potential to produce longer-chained products such as caproic acid. In this study, we present first results for a successful autotrophic co-cultivation of A. woodii mutants and a Clostridium drakei wild-type strain in a stirred-tank bioreactor for the production of caproic acid from CO2 and H2 via the intermediate lactic acid. For autotrophic lactate production, a recombinant A. woodii strain with a deleted Lct-dehydrogenase complex, which is encoded by the lctBCD genes, and an inserted D-lactate dehydrogenase (LdhD) originating from Leuconostoc mesenteroides, was used. Hydrogen for the process was supplied using an All-in-One electrode for in situ water electrolysis. Lactate concentrations as high as 0.5 g L-1 were achieved with the AiO-electrode, whereas 8.1 g L-1 lactate were produced with direct H2 sparging in a stirred-tank bioreactor. Hydrogen limitation was identified in the AiO process. However, with cathode surface area enlargement or numbering-up of the electrode and on-demand hydrogen generation, this process has great potential for a true carbon-negative production of value chemicals from CO2.

Electrochemically mediated bioconversion and integrated purification greatly enhanced co-production of 1,3-propanediol and organic acids from glycerol in an industrial bioprocess.

In this study, we show how electrochemically mediated bioconversion can greatly increase the co-production of 1,3-propanediol and organic acids from glycerol in an industrial bioprocess using a Clostridum pasteurianum mutant. Remarkably, an enhanced butyrate formation was observed due to a weakened butanol pathway of the mutant. This allowed the strain to have a higher ATP generation for an enhanced growth, higher glycerol consumption and PDO production. The PDO titer reached as high as 120.67 g/L at a cathodic current of -400 mA, which is 33% higher than that without electricity, with a concurrent increase of butyric acid by 80%. To fully recover the increased PDO and organic acids, a novel downstream process combining thin film evaporation of PDO and esterification of organic acids with ethanol was developed. This enables the efficient co-production of PDO, ethyl acetate and ethyl butyrate with a high overall carbon use of 87%.