ABSTRACT
Prime editing enables a wide variety of precise genome edits in living cells. Here we use protein evolution and engineering to generate prime editors with reduced size and improved efficiency. Using phage-assisted evolution, we improved editing efficiencies of compact reverse transcriptases by up to 22-fold and generated prime editors that are 516-810 base pairs smaller than the current-generation editor PEmax. We discovered that different reverse transcriptases specialize in different types of edits and used this insight to generate reverse transcriptases that outperform PEmax and PEmaxΔRNaseH, the truncated editor used in dual-AAV delivery systems. Finally, we generated Cas9 domains that improve prime editing. These resulting editors (PE6a-g) enhance therapeutically relevant editing in patient-derived fibroblasts and primary human T-cells. PE6 variants also enable longer insertions to be installed in vivo following dual-AAV delivery, achieving 40% loxP insertion in the cortex of the murine brain, a 24-fold improvement compared to previous state-of-the-art prime editors.
Subject(s)
Bacteriophages , Protein Engineering , Humans , Animals , Mice , Bacteriophages/genetics , Brain , Cerebral Cortex , DNA-Directed RNA PolymerasesABSTRACT
Autozygosity is associated with rare Mendelian disorders and clinically relevant quantitative traits. We investigated associations between the fraction of the genome in runs of homozygosity (FROH) and common diseases in Genes & Health (n = 23,978 British South Asians), UK Biobank (n = 397,184), and 23andMe. We show that restricting analysis to offspring of first cousins is an effective way of reducing confounding due to social/environmental correlates of FROH. Within this group in G&H+UK Biobank, we found experiment-wide significant associations between FROH and twelve common diseases. We replicated associations with type 2 diabetes (T2D) and post-traumatic stress disorder via within-sibling analysis in 23andMe (median n = 480,282). We estimated that autozygosity due to consanguinity accounts for 5%-18% of T2D cases among British Pakistanis. Our work highlights the possibility of widespread non-additive genetic effects on common diseases and has important implications for global populations with high rates of consanguinity.
Subject(s)
Consanguinity , Diabetes Mellitus, Type 2 , Humans , Diabetes Mellitus, Type 2/genetics , Homozygote , Phenotype , Polymorphism, Single Nucleotide , Biological Specimen Banks , Genome, Human , Genetic Predisposition to Disease , United KingdomABSTRACT
Long-lived plasma cells (PCs) secrete antibodies that can provide sustained immunity against infection. High-affinity cells are proposed to preferentially select into this compartment, potentiating the immune response. We used single-cell RNA-seq to track the germinal center (GC) development of Ighg2A10 B cells, specific for the Plasmodium falciparum circumsporozoite protein (PfCSP). Following immunization with Plasmodium sporozoites, we identified 3 populations of cells in the GC light zone (LZ). One LZ population expressed a gene signature associated with the initiation of PC differentiation and readily formed PCs in vitro. The estimated affinity of these pre-PC B cells was indistinguishable from that of LZ cells that remained in the GC. This remained true when high- or low-avidity recombinant PfCSP proteins were used as immunogens. These findings suggest that the initiation of PC development occurs via an affinity-independent process.
Subject(s)
B-Lymphocytes , Germinal Center , Plasma Cells , Cell Differentiation , Precursor Cells, B-LymphoidABSTRACT
Proliferating cells known as neoblasts include pluripotent stem cells (PSCs) that sustain tissue homeostasis and regeneration of lost body parts in planarians. However, the lack of markers to prospectively identify and isolate these adult PSCs has significantly hampered their characterization. We used single-cell RNA sequencing (scRNA-seq) and single-cell transplantation to address this long-standing issue. Large-scale scRNA-seq of sorted neoblasts unveiled a novel subtype of neoblast (Nb2) characterized by high levels of PIWI-1 mRNA and protein and marked by a conserved cell-surface protein-coding gene, tetraspanin 1 (tspan-1). tspan-1-positive cells survived sub-lethal irradiation, underwent clonal expansion to repopulate whole animals, and when purified with an anti-TSPAN-1 antibody, rescued the viability of lethally irradiated animals after single-cell transplantation. The first prospective isolation of an adult PSC bridges a conceptual dichotomy between functionally and molecularly defined neoblasts, shedding light on mechanisms governing in vivo pluripotency and a source of regeneration in animals. VIDEO ABSTRACT.
Subject(s)
Argonaute Proteins/metabolism , Helminth Proteins/metabolism , Planarians/physiology , Tetraspanins/metabolism , Animals , Argonaute Proteins/antagonists & inhibitors , Argonaute Proteins/genetics , Cell Cycle/radiation effects , Gene Expression Regulation , Helminth Proteins/antagonists & inhibitors , Helminth Proteins/genetics , Pluripotent Stem Cells/cytology , Pluripotent Stem Cells/metabolism , Pluripotent Stem Cells/transplantation , Principal Component Analysis , RNA Interference , RNA, Double-Stranded/metabolism , RNA, Helminth/chemistry , RNA, Helminth/isolation & purification , RNA, Helminth/metabolism , Regeneration/genetics , Sequence Analysis, RNA , Single-Cell Analysis , Tetraspanins/genetics , Whole-Body IrradiationABSTRACT
A complex interplay of environmental factors impacts the metabolism of human cells, but neither traditional culture media nor mouse plasma mimic the metabolite composition of human plasma. Here, we developed a culture medium with polar metabolite concentrations comparable to those of human plasma (human plasma-like medium [HPLM]). Culture in HPLM, relative to that in traditional media, had widespread effects on cellular metabolism, including on the metabolome, redox state, and glucose utilization. Among the most prominent was an inhibition of de novo pyrimidine synthesis-an effect traced to uric acid, which is 10-fold higher in the blood of humans than of mice and other non-primates. We find that uric acid directly inhibits uridine monophosphate synthase (UMPS) and consequently reduces the sensitivity of cancer cells to the chemotherapeutic agent 5-fluorouracil. Thus, media that better recapitulates the composition of human plasma reveals unforeseen metabolic wiring and regulation, suggesting that HPLM should be of broad utility.
Subject(s)
Culture Media/chemistry , Multienzyme Complexes/antagonists & inhibitors , Orotate Phosphoribosyltransferase/antagonists & inhibitors , Orotidine-5'-Phosphate Decarboxylase/antagonists & inhibitors , Uric Acid/metabolism , Aged , Animals , Cell Culture Techniques , Cell Line, Tumor , Fluorouracil/pharmacology , Glucose/metabolism , Humans , Leukemia, Myeloid, Acute/drug therapy , Leukemia, Myeloid, Acute/pathology , Male , Mice , Middle Aged , Multienzyme Complexes/chemistry , Orotate Phosphoribosyltransferase/chemistry , Orotidine-5'-Phosphate Decarboxylase/chemistry , Protein Domains , Pyrimidines/biosynthesisABSTRACT
Bread wheat (Triticum aestivum) is a globally dominant crop and major source of calories and proteins for the human diet. Compared with its wild ancestors, modern bread wheat shows lower genetic diversity, caused by polyploidisation, domestication and breeding bottlenecks1,2. Wild wheat relatives represent genetic reservoirs, and harbour diversity and beneficial alleles that have not been incorporated into bread wheat. Here we establish and analyse extensive genome resources for Tausch's goatgrass (Aegilops tauschii), the donor of the bread wheat D genome. Our analysis of 46 Ae. tauschii genomes enabled us to clone a disease resistance gene and perform haplotype analysis across a complex disease resistance locus, allowing us to discern alleles from paralogous gene copies. We also reveal the complex genetic composition and history of the bread wheat D genome, which involves contributions from genetically and geographically discrete Ae. tauschii subpopulations. Together, our results reveal the complex history of the bread wheat D genome and demonstrate the potential of wild relatives in crop improvement.
Subject(s)
Aegilops , Bread , Crops, Agricultural , Evolution, Molecular , Genome, Plant , Triticum , Aegilops/genetics , Alleles , Crops, Agricultural/genetics , Disease Resistance/genetics , Domestication , Genes, Plant/genetics , Genetic Variation/genetics , Genome, Plant/genetics , Haplotypes/genetics , Phylogeny , Plant Breeding , Plant Diseases/genetics , Polyploidy , Triticum/geneticsABSTRACT
Artificial intelligence (AI) in omics analysis raises privacy threats to patients. Here, we briefly discuss risk factors to patient privacy in data sharing, model training, and release, as well as methods to safeguard and evaluate patient privacy in AI-driven omics methods.
Subject(s)
Artificial Intelligence , Genomics , Humans , Genomics/methods , Privacy , Information DisseminationABSTRACT
Chromatin loop identification plays an important role in molecular biology and 3D genomics research, as it constitutes a fundamental process in transcription and gene regulation. Such precise chromatin structures can be identified across genome-wide interaction matrices via Hi-C data analysis, which is essential for unraveling the intricacies of transcriptional regulation. Given the increasing number of genome-wide contact maps, derived from both in situ Hi-C and single-cell Hi-C experiments, there is a pressing need for efficient and resilient algorithms capable of processing data from diverse experiments rapidly and adaptively. Here, we propose YOLOOP, a novel detection-based framework that is different from the conventional paradigm. YOLOOP stands out for its speed, surpassing the performance of previous state-of-the-art (SOTA) chromatin loop detection methods. It achieves a 30-fold acceleration compared with classification-based methods, up to 20-fold acceleration compared with the SOTA kernel-based framework, and a fivefold acceleration compared with statistical algorithms. Furthermore, the proposed framework is capable of generalizing across various cell types, multiresolution Hi-C maps, and diverse experimental protocols. Compared with the existing paradigms, YOLOOP shows up to a 10% increase in recall and a 15% increase in F1-score, particularly noteworthy in the GM12878 cell line. YOLOOP also offers fast adaptability with straightforward fine-tuning, making it readily applicable to extremely sparse single-cell Hi-C contact maps. It maintains its exceptional speed, completing genome-wide detection at a 10 kb resolution for a single-cell contact map within 1 min and for a 900-cell-superimposed contact map within 3 min, enabling fast analysis of large-scale single-cell data.
Subject(s)
Algorithms , Chromatin , Chromatin/genetics , Humans , Genomics/methods , Single-Cell Analysis/methods , Chromosome Mapping/methodsABSTRACT
Haematopoietic stem cells (HSCs) reside in specialized microenvironments in the bone marrow-often referred to as 'niches'-that represent complex regulatory milieux influenced by multiple cellular constituents, including nerves1,2. Although sympathetic nerves are known to regulate the HSC niche3-6, the contribution of nociceptive neurons in the bone marrow remains unclear. Here we show that nociceptive nerves are required for enforced HSC mobilization and that they collaborate with sympathetic nerves to maintain HSCs in the bone marrow. Nociceptor neurons drive granulocyte colony-stimulating factor (G-CSF)-induced HSC mobilization via the secretion of calcitonin gene-related peptide (CGRP). Unlike sympathetic nerves, which regulate HSCs indirectly via the niche3,4,6, CGRP acts directly on HSCs via receptor activity modifying protein 1 (RAMP1) and the calcitonin receptor-like receptor (CALCRL) to promote egress by activating the Gαs/adenylyl cyclase/cAMP pathway. The ingestion of food containing capsaicin-a natural component of chili peppers that can trigger the activation of nociceptive neurons-significantly enhanced HSC mobilization in mice. Targeting the nociceptive nervous system could therefore represent a strategy to improve the yield of HSCs for stem cell-based therapeutic agents.
Subject(s)
Autonomic Pathways , Cell Movement , Hematopoietic Stem Cells/cytology , Nociception/physiology , Nociceptors/physiology , Sympathetic Nervous System/cytology , Adenylyl Cyclases/metabolism , Animals , Autonomic Pathways/drug effects , Calcitonin Gene-Related Peptide/metabolism , Calcitonin Receptor-Like Protein/metabolism , Capsaicin/pharmacology , Cell Movement/drug effects , Cyclic AMP/metabolism , Female , GTP-Binding Protein alpha Subunits, Gs/metabolism , Granulocyte Colony-Stimulating Factor/metabolism , Hematopoietic Stem Cells/drug effects , Hematopoietic Stem Cells/metabolism , Male , Mice , Mice, Inbred C57BL , Nociception/drug effects , Nociceptors/drug effects , Receptor Activity-Modifying Protein 1/metabolism , Signal Transduction/drug effects , Stem Cell Niche , Sympathetic Nervous System/drug effectsABSTRACT
Nanomedicine has emerged as a revolutionary strategy of drug delivery. However, fundamentals of the nano-neuro interaction are elusive. In particular, whether nanocarriers can cross the blood-brain barrier (BBB) and release the drug cargo inside the brain, a basic process depicted in numerous books and reviews, remains controversial. Here, we develop an optical method, based on stimulated Raman scattering, for imaging nanocarriers in tissues. Our method achieves a suite of capabilities-single-particle sensitivity, chemical specificity, and particle counting capability. With this method, we visualize individual intact nanocarriers crossing the BBB of mouse brains and quantify the absolute number by particle counting. The fate of nanocarriers after crossing the BBB shows remarkable heterogeneity across multiple scales. With a mouse model of aging, we find that blood-brain transport of nanocarriers decreases with age substantially. This technology would facilitate development of effective therapeutics for brain diseases and clinical translation of nanocarrier-based treatment in general.
Subject(s)
Brain Diseases , Nanomedicine , Animals , Mice , Brain/diagnostic imaging , Blood-Brain Barrier/diagnostic imaging , AgingABSTRACT
Plastics are now omnipresent in our daily lives. The existence of microplastics (1 µm to 5 mm in length) and possibly even nanoplastics (<1 µm) has recently raised health concerns. In particular, nanoplastics are believed to be more toxic since their smaller size renders them much more amenable, compared to microplastics, to enter the human body. However, detecting nanoplastics imposes tremendous analytical challenges on both the nano-level sensitivity and the plastic-identifying specificity, leading to a knowledge gap in this mysterious nanoworld surrounding us. To address these challenges, we developed a hyperspectral stimulated Raman scattering (SRS) imaging platform with an automated plastic identification algorithm that allows micro-nano plastic analysis at the single-particle level with high chemical specificity and throughput. We first validated the sensitivity enhancement of the narrow band of SRS to enable high-speed single nanoplastic detection below 100 nm. We then devised a data-driven spectral matching algorithm to address spectral identification challenges imposed by sensitive narrow-band hyperspectral imaging and achieve robust determination of common plastic polymers. With the established technique, we studied the micro-nano plastics from bottled water as a model system. We successfully detected and identified nanoplastics from major plastic types. Micro-nano plastics concentrations were estimated to be about 2.4 ± 1.3 × 105 particles per liter of bottled water, about 90% of which are nanoplastics. This is orders of magnitude more than the microplastic abundance reported previously in bottled water. High-throughput single-particle counting revealed extraordinary particle heterogeneity and nonorthogonality between plastic composition and morphologies; the resulting multidimensional profiling sheds light on the science of nanoplastics.
Subject(s)
Drinking Water , Microscopy , Humans , Microplastics , Plastics , AlgorithmsABSTRACT
In bacteria, attenuation of protein-tyrosine phosphorylation occurs during oxidative stress. The main described mechanism behind this effect is the H2O2-triggered conversion of bacterial phospho-tyrosines to protein-bound 3,4-dihydroxyphenylalanine. This disrupts the bacterial tyrosine phosphorylation-based signaling network, which alters the bacterial polysaccharide biosynthesis. Herein, we report an alternative mechanism, in which oxidative stress leads to a direct inhibition of bacterial protein-tyrosine kinases (BY-kinases). We show that DefA, a minor peptide deformylase, inhibits the activity of BY-kinase PtkA when Bacillus subtilis is exposed to oxidative stress. High levels of PtkA activity are known to destabilize B. subtilis pellicle formation, which leads to higher sensitivity to oxidative stress. Interaction with DefA inhibits both PtkA autophosphorylation and phosphorylation of its substrate Ugd, which is involved in exopolysaccharide formation. Inactivation of defA drastically reduces the capacity of B. subtilis to cope with oxidative stress, but it does not affect the major oxidative stress regulons PerR, OhrR, and Spx, indicating that PtkA inhibition is the main pathway for DefA involvement in this stress response. Structural analysis identified DefA residues Asn95, Tyr150, and Glu152 as essential for interaction with PtkA. Inhibition of PtkA depends also on the presence of a C-terminal α-helix of DefA, which resembles PtkA-interacting motifs from known PtkA activators, TkmA, SalA, and MinD. Loss of either the key interacting residues or the inhibitory helix of DefA abolishes inhibition of PtkA in vitro and impairs postoxidative stress recovery in vivo, confirming the involvement of these structural features in the proposed mechanism.
Subject(s)
Bacillus subtilis , Bacterial Proteins , Oxidative Stress , Bacillus subtilis/metabolism , Bacillus subtilis/genetics , Phosphorylation , Bacterial Proteins/metabolism , Bacterial Proteins/genetics , Protein-Tyrosine Kinases/metabolism , Hydrogen Peroxide/metabolism , Amidohydrolases/metabolismABSTRACT
Although changes in alternative splicing have been observed in cancer, their functional contributions still remain largely unclear. Here we report that splice isoforms of the cancer stem cell (CSC) marker CD44 exhibit strikingly opposite functions in breast cancer. Bioinformatic annotation in patient breast cancer in The Cancer Genome Atlas (TCGA) database reveals that the CD44 standard splice isoform (CD44s) positively associates with the CSC gene signatures, whereas the CD44 variant splice isoforms (CD44v) exhibit an inverse association. We show that CD44s is the predominant isoform expressed in breast CSCs. Elimination of the CD44s isoform impairs CSC traits. Conversely, manipulating the splicing regulator ESRP1 to shift alternative splicing from CD44v to CD44s leads to an induction of CSC properties. We further demonstrate that CD44s activates the PDGFRß/Stat3 cascade to promote CSC traits. These results reveal CD44 isoform specificity in CSC and non-CSC states and suggest that alternative splicing provides functional gene versatility that is essential for distinct cancer cell states and thus cancer phenotypes.
Subject(s)
Alternative Splicing , Breast Neoplasms/genetics , Hyaluronan Receptors/genetics , Hyaluronan Receptors/metabolism , Neoplastic Stem Cells/pathology , Animals , Cell Line, Tumor , Disease Models, Animal , Female , Gene Expression Regulation, Neoplastic , Humans , Mice , Protein Isoforms , Signal Transduction/geneticsABSTRACT
Alternative polyadenylation (APA) enables a gene to generate multiple transcripts with different 3' ends, which is dynamic across different cell types or conditions. Many computational methods have been developed to characterize sample-specific APA using the corresponding RNA-seq data, but suffered from high error rate on both polyadenylation site (PAS) identification and quantification of PAS usage (PAU), and bias toward 3' untranslated regions. Here we developed a tool for APA identification and quantification (APAIQ) from RNA-seq data, which can accurately identify PAS and quantify PAU in a transcriptome-wide manner. Using 3' end-seq data as the benchmark, we showed that APAIQ outperforms current methods on PAS identification and PAU quantification, including DaPars2, Aptardi, mountainClimber, SANPolyA, and QAPA. Finally, applying APAIQ on 421 RNA-seq samples from liver cancer patients, we identified >540 tumor-associated APA events and experimentally validated two intronic polyadenylation candidates, demonstrating its capacity to unveil cancer-related APA with a large-scale RNA-seq data set.
Subject(s)
Neoplasms , Transcriptome , Humans , Polyadenylation , RNA-Seq , Sequence Analysis, RNA/methods , Neoplasms/genetics , 3' Untranslated RegionsABSTRACT
Cryo-electron microscopy (cryo-EM) visualizes the atomic structure of macromolecules that are embedded in vitrified thin ice at their close-to-native state. However, the homogeneity of ice thickness, a key factor to ensure high image quality, is poorly controlled during specimen preparation and has become one of the main challenges for high-resolution cryo-EM. Here we found that the uniformity of thin ice relies on the surface flatness of the supporting film, and developed a method to use ultraflat graphene (UFG) as the support for cryo-EM specimen preparation to achieve better control of vitreous ice thickness. We show that the uniform thin ice on UFG improves the image quality of vitrified specimens. Using such a method we successfully determined the three-dimensional structures of hemoglobin (64 kDa), α-fetoprotein (67 kDa) with no symmetry, and streptavidin (52 kDa) at a resolution of 3.5 Å, 2.6 Å and 2.2 Å, respectively. Furthermore, our results demonstrate the potential of UFG for the fields of cryo-electron tomography and structure-based drug discovery.
Subject(s)
Graphite , Cryoelectron Microscopy/methods , Graphite/chemistry , Macromolecular Substances , Electron Microscope TomographyABSTRACT
Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.
Subject(s)
Breast Neoplasms , Cell Differentiation , Neoplastic Stem Cells , Humans , Neoplastic Stem Cells/metabolism , Neoplastic Stem Cells/drug effects , Neoplastic Stem Cells/pathology , Breast Neoplasms/genetics , Breast Neoplasms/metabolism , Breast Neoplasms/pathology , Breast Neoplasms/drug therapy , Cell Differentiation/drug effects , Female , Artificial Intelligence , Gene Expression Regulation, Neoplastic/drug effects , MCF-7 Cells , Cell Line, Tumor , Neural Networks, Computer , Gene Expression ProfilingABSTRACT
While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.
Subject(s)
Deep Learning , Histocompatibility Antigens Class II , Humans , Histocompatibility Antigens Class II/immunology , Epitopes/immunology , Computational Biology/methods , Epitopes, T-Lymphocyte/immunologyABSTRACT
Progress in microwave (MW) energy application technology has stimulated remarkable advances in manufacturing and high-quality applications of ionic liquids (ILs) that are generally used as novel media in chemical engineering. This Review focuses on an emerging technology via the combination of MW energy and the usage of ILs, termed microwave-assisted ionic liquid (MAIL) technology. In comparison to conventional routes that rely on heat transfer through media, the contactless and unique MW heating exploits the electromagnetic wave-ions interactions to deliver energy to IL molecules, accelerating the process of material synthesis, catalytic reactions, and so on. In addition to the inherent advantages of ILs, including outstanding solubility, and well-tuned thermophysical properties, MAIL technology has exhibited great potential in process intensification to meet the requirement of efficient, economic chemical production. Here we start with an introduction to principles of MW heating, highlighting fundamental mechanisms of MW induced process intensification based on ILs. Next, the synergies of MW energy and ILs employed in materials synthesis, as well as their merits, are documented. The emerging applications of MAIL technologies are summarized in the next sections, involving tumor therapy, organic catalysis, separations, and bioconversions. Finally, the current challenges and future opportunities of this emerging technology are discussed.
ABSTRACT
Space heating and cooling consume ~13% of global energy every year. The development of advanced materials that promote energy savings in heating and cooling is gaining increasing attention. To thermally isolate the space of concern and minimize the heat exchange with the outside environment has been recognized as one effective solution. To this end, here, we develop a universal category of colorful low-emissivity paints to form bilayer coatings consisting of an infrared (IR)-reflective bottom layer and an IR-transparent top layer in colors. The colorful visual appearance ensures the aesthetical effect comparable to conventional paints. High mid-infrared reflectance (up to ~80%) is achieved, which is more than 10 times as conventional paints in the same colors, efficiently reducing both heat gain and loss from/to the outside environment. The high near-IR reflectance also benefits reducing solar heat gain in hot days. The advantageous features of these paints strike a balance between energy savings and penalties for heating and cooling throughout the year, providing a comprehensive year-round energy-saving solution adaptable to a wide variety of climatic zones. Taking a typical midrise apartment building as an example, the application of our colorful low-emissivity paints can realize positive heating, ventilation, and air conditioning energy saving, up to 27.24 MJ/m2/y (corresponding to the 7.4% saving ratio). Moreover, the versatility of the paint, along with its applicability to diverse surfaces of various shapes and materials, makes the paints extensively useful in a range of scenarios, including building envelopes, transportation, and storage.
ABSTRACT
Improving Coulombic efficiency (CE) is key to the adoption of high energy density lithium metal batteries. Liquid electrolyte engineering has emerged as a promising strategy for improving the CE of lithium metal batteries, but its complexity renders the performance prediction and design of electrolytes challenging. Here, we develop machine learning (ML) models that assist and accelerate the design of high-performance electrolytes. Using the elemental composition of electrolytes as the features of our models, we apply linear regression, random forest, and bagging models to identify the critical features for predicting CE. Our models reveal that a reduction in the solvent oxygen content is critical for superior CE. We use the ML models to design electrolyte formulations with fluorine-free solvents that achieve a high CE of 99.70%. This work highlights the promise of data-driven approaches that can accelerate the design of high-performance electrolytes for lithium metal batteries.