Graphite-N reinforced sludge biochar electrode: A experimental and DFT theoretical analysis of efficient evolution and in-situ utilization of H2O2.

Rational reuse of municipal sludge to produce electro-Fenton electrode can not only save resources, but also produce superior peroxide and degradation pollutants simultaneously. Herein, a novel electro-Fenton electrode derived from sludge biochar loaded on Ni foam (SBC@Ni) was constructed via high temperature pyrolysis and chemical coating for efficient H2O2 evolution and pollutant degradation. Systematic experiments and density functional theory calculations (DFT calculation) explained that the production of graphite C and graphite N during high-temperature pyrolysis of municipal sludge can greatly enhance the oxygen reduction reaction of SBC@Ni electrode and promote the evolution of H2O2. And the hybrid heterojunctions, such as FeP, also played a key role in electrocatalytic processes. Notably, the electrode still exhibited excellent performance after 1000 linear scans and 12 hours of continuous current stimulation, which demonstrated the excellent stability of the electrode. Moreover, SBC@Ni electrode can not only effectively oxidize 4-chlorophenol through the electro-Fenton effect, but also fully mineralize organic matter, indicating promising environmental application. The free radical quenching experiment also revealed that the ·OH is the main active species for 4-CP degradation in SBC@Ni electro-Fenton system.

Deciphering cell states and genealogies of human haematopoiesis.

The human blood system is maintained through the differentiation and massive amplification of a limited number of long-lived haematopoietic stem cells (HSCs)1. Perturbations to this process underlie diverse diseases, but the clonal contributions to human haematopoiesis and how this changes with age remain incompletely understood. Although recent insights have emerged from barcoding studies in model systems2-5, simultaneous detection of cell states and phylogenies from natural barcodes in humans remains challenging. Here we introduce an improved, single-cell lineage-tracing system based on deep detection of naturally occurring mitochondrial DNA mutations with simultaneous readout of transcriptional states and chromatin accessibility. We use this system to define the clonal architecture of HSCs and map the physiological state and output of clones. We uncover functional heterogeneity in HSC clones, which is stable over months and manifests as both differences in total HSC output and biases towards the production of different mature cell types. We also find that the diversity of HSC clones decreases markedly with age, leading to an oligoclonal structure with multiple distinct clonal expansions. Our study thus provides a clonally resolved and cell-state-aware atlas of human haematopoiesis at single-cell resolution, showing an unappreciated functional diversity of human HSC clones and, more broadly, paving the way for refined studies of clonal dynamics across a range of tissues in human health and disease.

Systematic functional interrogation of SARS-CoV-2 host factors using Perturb-seq.

Genomic and proteomic screens have identified numerous host factors of SARS-CoV-2, but efficient delineation of their molecular roles during infection remains a challenge. Here we use Perturb-seq, combining genetic perturbations with a single-cell readout, to investigate how inactivation of host factors changes the course of SARS-CoV-2 infection and the host response in human lung epithelial cells. Our high-dimensional data resolve complex phenotypes such as shifts in the stages of infection and modulations of the interferon response. However, only a small percentage of host factors showed such phenotypes upon perturbation. We further identified the NF-κB inhibitor IκBα (NFKBIA), as well as the translation factors EIF4E2 and EIF4H as strong host dependency factors acting early in infection. Overall, our study provides massively parallel functional characterization of host factors of SARS-CoV-2 and quantitatively defines their roles both in virus-infected and bystander cells.

Massively parallel base editing to map variant effects in human hematopoiesis.

Systematic evaluation of the impact of genetic variants is critical for the study and treatment of human physiology and disease. While specific mutations can be introduced by genome engineering, we still lack scalable approaches that are applicable to the important setting of primary cells, such as blood and immune cells. Here, we describe the development of massively parallel base-editing screens in human hematopoietic stem and progenitor cells. Such approaches enable functional screens for variant effects across any hematopoietic differentiation state. Moreover, they allow for rich phenotyping through single-cell RNA sequencing readouts and separately for characterization of editing outcomes through pooled single-cell genotyping. We efficiently design improved leukemia immunotherapy approaches, comprehensively identify non-coding variants modulating fetal hemoglobin expression, define mechanisms regulating hematopoietic differentiation, and probe the pathogenicity of uncharacterized disease-associated variants. These strategies will advance effective and high-throughput variant-to-function mapping in human hematopoiesis to identify the causes of diverse diseases.

Functional characterization and transcriptional activity analysis of Dryopteris fragrans farnesyl diphosphate synthase genes.

Farnesyl diphosphate synthase (FPS), a key enzyme of the terpene metabolic pathway, catalyzes the precursor of sesquiterpene compounds farnesyl diphosphate (FPP) synthesis, and plays an important role in regulating plant growth and development. Dryopteris fragrans is a medicinal plant rich terpenoids. In this study, the function of the gene was verified in vitro and in vivo, the promoter of the gene was amplified and its transcriptional activity was analyzed. In the present study, we report the molecular cloning and functional characterization of DfFPS1 and DfFPS2, two FPS genes from D. fragrans. We found that the two genes were evolutionarily conserved. Both DfFPS genes were highly expressed in the gametophyte and mature sporophyte leaves, and their expression levels increased in response to methyl jasmonate (MeJA) and high temperature. Both DfFPS proteins were localized in the cytoplasm and could catalyze FPP synthesis in vitro. We also found that the overexpression of DfFPS genes in tobacco plants promoted secondary metabolite accumulation but exhibited negligible effect on plant growth and development. However, the transgenic plants exhibited tolerance to high temperature and drought. The promoters of the two genes were amplified using fusion primer and nested integrated polymerase chain reaction (FPNI-PCR). The promoter sequences were truncated and their activity was examined using the ß-glucuronidase (GUS) gene reporter system in tobacco leaves, and we found that both genes were expressed in the stomata. The transcriptional activity of the promoters was found to be similar to the expression pattern of the genes, and the transcriptional core regions of the two genes were mainly between -943 bp and -740 bp of proDfFPS1. Therefore, we present a preliminary study on the function and transcriptional activity of the FPS genes of D. fragrans and provide a basis for the regulation of terpene metabolism in D. fragrans. The results also provide a novel basis for the elucidation of terpene metabolic pathways in ferns.

Competitive adsorption of sulfamethoxazole and bisphenol A on magnetic biochar: Mechanism and site energy distribution.

Competitive adsorption and complementary adsorption between emerging pollutants has been observed in multiple studies. Investigation of the preference of pollutants for different types of adsorption sites can provide a supplementary perspective for understanding complementary adsorption. In this study, the simultaneous adsorption of two typical emerging pollutants, sulfamethoxazole (SMX) and bisphenol A (BPA), on magnetic biochar (MBC-1) was investigated. The results showed that the modification with ferric chloride optimized the surface properties of biochar (aromaticity, hydrophobicity, and oxygen-containing functional groups, etc.), and helped to remove SMX and BPA through various interactions. The equilibrium adsorption capacity of the two adsorbents was inhibited by competitive adsorption in the mixed solute systems, which was due to the same adsorption mechanism. When pH = 7, the SMX and BPA adsorption mainly involved pore filling, hydrophobic effect, π-π EDA, and hydrogen bonding. In addition, electrostatic force, surface coordination, and ion exchange have also been proven to be related to the adsorption of SMX and BPA. In the co-adsorption system, BPA's competitive advantage might be due to its superior hydrophobicity, charge property, and molecular diameter. In the competitive adsorption experiment, the total adsorption capacity (Qi) of the competitive solute exceeded the adsorption inhibition (△Qi) of the main solute, indicating that the two solutes occupied their preferred adsorption sites, which confirmed the complementary adsorption phenomenon. Complementary adsorption can be explained by the preference of SMX and BPA for different types of adsorption sites. BPA preferentially occupied high-energy sites in the co-adsorption system, such as π-π EDA interaction, ion exchange, and surface coordination. At the same time, SMX tended to be removed by hydrophobic interaction and hydrogen bonding.

Spatiotemporally resolved transcriptomics reveals the subcellular RNA kinetic landscape.

Spatiotemporal regulation of the cellular transcriptome is crucial for proper protein expression and cellular function. However, the intricate subcellular dynamics of RNA remain obscured due to the limitations of existing transcriptomics methods. Here, we report TEMPOmap-a method that uncovers subcellular RNA profiles across time and space at the single-cell level. TEMPOmap integrates pulse-chase metabolic labeling with highly multiplexed three-dimensional in situ sequencing to simultaneously profile the age and location of individual RNA molecules. Using TEMPOmap, we constructed the subcellular RNA kinetic landscape in various human cells from transcription and translocation to degradation. Clustering analysis of RNA kinetic parameters across single cells revealed 'kinetic gene clusters' whose expression patterns were shaped by multistep kinetic sculpting. Importantly, these kinetic gene clusters are functionally segregated, suggesting that subcellular RNA kinetics are differentially regulated in a cell-state- and cell-type-dependent manner. Spatiotemporally resolved transcriptomics provides a gateway to uncovering new spatiotemporal gene regulation principles.

Optimization of Electron Transport Pathway: A Novel Strategy to Solve the Photocorrosion of Ag-Based Photocatalysts.

Although Ag-containing photocatalysts exhibit excellent photocatalytic ability, they present great challenges owing to their photocorrosion and ease of reduction. Herein, an electron acceptor platform of Ag2O/La(OH)3/polyacrylonitrile (PAN) fiber was constructed using a heterojunction strategy and electrospinning technology to develop a novel photocatalytic membrane with a redesigned electron transport pathway. Computational and experimental results demonstrate that the optimized electron transport pathway included intercrystal electron transfer induced by the La-O bond between Ag2O and La(OH)3 as well as electron transfer between the catalyst crystal and electrophilic PAN membrane interface. In addition, the photocatalytic performance of the Ag2O/La(OH)3 membrane for tetracycline (TC) removal was still above 97% after five photocatalytic reaction cycles. Furthermore, the carrier life was greatly extended. Mechanistic study revealed that photogenerated holes on the Ag2O/La(OH)3 membrane were the main reactive species in TC degradation. Overall, this study proposes a novel electron transport pathway strategy that effectively solves the problems of photocatalyst photocorrosion and structural instability.

Resilient Model Free Adaptive Distributed LFC for Multi-Area Power Systems Against Jamming Attacks.

This article is concerned with distributed resilient load frequency control (LFC) for multi-area power interconnection systems against jamming attacks. First, considering uncertainties and high dimension nonlinearity, the model-free adaptive control (MFAC) model is adopted for the power system, in which only input and output (I/O) data are used. Second, jamming attacks are modeled in a stochastic process, and a multistep predictive compensation algorithm is developed to mitigate the impact of jamming attacks. Then, the distributed MFAC protocol with predictive compensation algorithm is designed such that the frequency tracking errors under the predictive compensation algorithm of multi-area power interconnection systems converge consensually into a small neighborhood of origin in the mean square sense. Simulation results show the effectiveness of the approach.

Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration.

The molecular mechanism underlying brain regeneration in vertebrates remains elusive. We performed spatial enhanced resolution omics sequencing (Stereo-seq) to capture spatially resolved single-cell transcriptomes of axolotl telencephalon sections during development and regeneration. Annotated cell types exhibited distinct spatial distribution, molecular features, and functions. We identified an injury-induced ependymoglial cell cluster at the wound site as a progenitor cell population for the potential replenishment of lost neurons, through a cell state transition process resembling neurogenesis during development. Transcriptome comparisons indicated that these induced cells may originate from local resident ependymoglial cells. We further uncovered spatially defined neurons at the lesion site that may regress to an immature neuron-like state. Our work establishes spatial transcriptome profiles of an anamniote tetrapod brain and decodes potential neurogenesis from ependymoglial cells for development and regeneration, thus providing mechanistic insights into vertebrate brain regeneration.

Inferring gene regulation from stochastic transcriptional variation across single cells at steady state.

Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.

Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution.

Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.

Mapping transcriptomic vector fields of single cells.

Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo (https://github.com/aristoteleo/dynamo-release), which infers absolute RNA velocity, reconstructs continuous vector fields that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo's power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.

Fabrication of Pd/Sludge-biochar electrode with high electrochemical activity on reductive degradation of 4-chlorophenol in wastewater.

Effective treatment and utilization of sludge contribute to achieve conventional carbon emission reduction and resource recovery, which is of great significance to realize carbon neutralization of WWTPs. Sludge carbonization derived biochar has attracted more interest because of high potential as catalytic materials. Therein, sludge-derived electrode exhibits a promising potential in the case of sludge utilization for electrocatalysis, however, electrocatalytic performance of the already reported sludge-derived electrode is unsatisfactory due to insufficient active sites. In this study, an efficient Pd/sludge-biochar loaded foam nickel (Pd-SAC@Ni) was successfully fabricated using simple pyrolysis and solidification method, and exhibited remarkable electrocatalytic performance for 4-chlorophenol (4-CP) degradation. Furthermore, the morphology, element distribution and crystal composition were characterized by SEM, EDS, XPS and XRD. The Pd-SAC@Ni electrode exhibited superior electrocatalytic performance than Ni, SAC@Ni, Pd-Ni electrodes. The reduction rate of 98.9% was achieved at current density of 5 mA cm-2, 4-CP concentration of 0.8 mM and initial pH of 7.0. Also, Pd-SAC@Ni electrode showed desirable reusability and achieved 98% of 4-CP removal after multiple runs of experiments. Moreover, the active hydrogen species (H*) generation capacity of electrodes was determined using tert-butanol (TBA) as trapping agent. The mechanism analysis demonstrated that direct reduction process and indirect reduction process both involved in the 4-CP degradation process, and their contribution were 19.5% and 80.5%, respectively. Then, the intermediates formed in the electrochemical degradation of 4-CP were revealed by HPLC and the plausible degradation pathway was proposed. This study provides a cost-effective approach for preparing sludge biochar electrode, and explored a novel way to promote resourceful utilization of sludge for carbon neutrality.

Data-Driven-Based Event-Triggered Control for Nonlinear CPSs Against Jamming Attacks.

This brief considers the security control problem for nonlinear cyber-physical systems (CPSs) against jamming attacks. First, a novel event-based model-free adaptive control (MFAC) framework is established. Second, a multistep predictive compensation algorithm (PCA) is developed to make compensation for the lost data caused by jamming attacks, even consecutive attacks. Then, an event-triggering mechanism with the dead-zone operator is introduced in the adaptive controller, which can effectively save communication resources and reduce the calculation burden of the controller without affecting the control performance of systems. Moreover, the boundedness of the tracking error is ensured in the mean-square sense, and only the input/output (I/O) data are used in the whole design process. Finally, simulation comparisons are provided to show the effectiveness of our method.

Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq.

Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal RNA dynamics. Here we present single-cell metabolically labeled new RNA tagging sequencing (scNT-seq), a method for massively parallel analysis of newly transcribed and pre-existing mRNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking newly transcribed mRNAs with T-to-C substitutions. Using scNT-seq, we jointly profiled new and old transcriptomes in ~55,000 single cells. These data revealed time-resolved transcription factor activities and cell-state trajectories at the single-cell level in response to neuronal activation. We further determined rates of RNA biogenesis and decay to uncover RNA regulatory strategies during stepwise conversion between pluripotent and rare totipotent two-cell embryo (2C)-like stem cell states. Finally, integrating scNT-seq with genetic perturbation identifies DNA methylcytosine dioxygenase as an epigenetic barrier into the 2C-like cell state. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms.

Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe.

Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for "pseudotime"-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as "RNA velocity" restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.

A pooled single-cell genetic screen identifies regulatory checkpoints in the continuum of the epithelial-to-mesenchymal transition.

Integrating single-cell trajectory analysis with pooled genetic screening could reveal the genetic architecture that guides cellular decisions in development and disease. We applied this paradigm to probe the genetic circuitry that controls epithelial-to-mesenchymal transition (EMT). We used single-cell RNA sequencing to profile epithelial cells undergoing a spontaneous spatially determined EMT in the presence or absence of transforming growth factor-ß. Pseudospatial trajectory analysis identified continuous waves of gene regulation as opposed to discrete 'partial' stages of EMT. KRAS was connected to the exit from the epithelial state and the acquisition of a fully mesenchymal phenotype. A pooled single-cell CRISPR-Cas9 screen identified EMT-associated receptors and transcription factors, including regulators of KRAS, whose loss impeded progress along the EMT. Inhibiting the KRAS effector MEK and its upstream activators EGFR and MET demonstrates that interruption of key signaling events reveals regulatory 'checkpoints' in the EMT continuum that mimic discrete stages, and reconciles opposing views of the program that controls EMT.

Wavelength detection of model-sharing fiber Bragg grating sensor networks using long short-term memory neural network.

In this paper, an effective wavelength detection approach based on long short-term memory (LSTM) network is proposed for fiber Bragg grating (FBG) sensor networks. The FBG sensor network utilizes a model-sharing mechanism, where the whole spectral wavelength is divided into several shareable regions and spectral overlap is allowed in each region. LSTM, a representative recurrent neural network in deep learning, is applied to learn the features directly from the spectra of FBGs and build the wavelength detection model. By feeding the spectra sequentially into the well-trained model, the Bragg wavelengths of FBGs can be quickly determined under overlap. The obtained LSTM model can be repeatedly used without re-training to improve the multiplexing capability. The results demonstrate that the LSTM-based method can realize high-accuracy and high-speed wavelength detection in the spectral overlapping situations. The proposed approach offers a flexible tool to enhance the sensing capacity of FBG sensor networks.