Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

Recent decades have witnessed the rapid development of autonomous laboratories and artificial intelligence, where experiments can be automatically run and optimized. Although human work is reduced, the total time of experimental optimization is still consuming due to limitations of the current ab metaverse framework, which accurately predicts the future state of the system by receiving and analyzing in situ experimental data. To substitute for traditional simulation methods, we designed a physically endorsed deep learning model to predict the future system picture ranging from atomic image to bulk appearance, intensively using the correlations between properties of the system. Through this framework, we studied the general aqueous system, covering 100+ common ionic solutions. We can accurately simulate properties for a general aqueous system as well as predict the time of solvation of ionic compounds ahead of real experiments. In this way, the experiments can be optimized more efficiently without waiting for the end of a bad iteration. We hope our work offers a fresh direction for the digitization of chemical information, enhancing access to and use of experimental data in advancing the field of physical chemistry.

Whole-exome sequencing (WES) plays a crucial role in diagnosing genetic diseases by identifying germline variants. However, reproducibility issues limit its clinical utility. We conducted a large-scale proficiency test across 89 clinical and commercial labs in China, employing the well-characterized Quartet DNA reference materials, to evaluate the impact of experimental and bioinformatic factors on the performance of small variant detection. We observed significant variability in sequencing data quality and variant calling performance, with higher raw read quality and lower contamination levels improved variant detection. Our findings emphasized the collective influence of multiple factors on variant detection, with capture efficiency metrics, such as fold-80 penalty, on-target rate, and target region coverage, instead of base-by-base quality metrics on raw sequences, emerging as the most critical. Our study not only revealed the nationwide performance of WES in China, but also provided actionable best practices for optimizing the entire WES process, from data generation to analysis, thereby enhancing variant detection quality and reliability.

Rising atmospheric CO2 concentrations urgently call for advanced sustainable energy storage solutions, underlining the pivotal role of renewable energies. This perspective delves into the capabilities of redox flow batteries as potential grid storage contenders, highlighting their benefits over traditional lithium-ion batteries. While all-vanadium flow batteries have established themselves, concerns about vanadium availability have steered interest toward Organic Flow Batteries. The multifaceted nature of organic materials calls for an integrated approach combining artificial intelligence, robotics, and material science to enhance battery efficacy. The union of artificial intelligence and robotics expedites the research and development trajectory, encompassing everything from data assimilation to continuous refinement. With the burgeoning metaverse, a groundbreaking avenue for collaborative research emerges, potentially revolutionizing flow battery research and catalyzing the progression towards sustainable energy resolutions.

This study proposes an innovative paradigm for metaverse-based synthesis experiments, aiming to enhance experimental optimization efficiency through human-guided parameter tuning in the metaverse and augmented artificial intelligence (AI) with human expertise. By integration of the metaverse experimental system with automated synthesis techniques, our goal is to profoundly extend the efficiency and advancement of materials chemistry. Leveraging advanced software algorithms and simulation techniques within the metaverse, we dynamically adjust synthesis parameters in real time, thereby minimizing the conventional trial-and-error methods inherent in laboratory experiments. In comparison fully AI-driven adjustments, this human-intervened approach to metaverse parameter tuning achieves desired results more rapidly. Coupled with automated synthesis techniques, experiments in the metaverse system can be swiftly realized. We validate the high synthesis efficiency and precision of this system through NaYF4:Yb/Tm nanocrystal synthesis experiments, highlighting its immense potential in nanomaterial studies. This pioneering approach not only simplifies the process of nanocrystal preparation but also paves the way for novel methodologies, laying the foundation for future breakthroughs in materials science and nanotechnology.

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.

Lithium-ion batteries (LIBs) currently dominate the energy storage market for electronic devices, thanks to their high energy density, high operating voltage, and good cycling performance. However, commercial LIBs employing organic liquid electrolyte and lithium (Li) salts pose significant safety concerns, including flammability and the risk of thermal runaway. Consequently, the development of solid electrolytes is of paramount importance. Among several solid ion conductors, solid polymer electrolytes (SPEs) can offer numerous advantages, including excellent flexibility and interfacial compatibility with electrodes, good processibility, low cost, and light weight characteristics. However, current SPEs face challenges such as poor thermal stability, inferior electrochemical stability, and low Li-ion conductivity at room temperature (~ 10 -5 S cm -1 at 25 ℃). In this study, we introduce a multifunctional zwitterionic polyurethane-based solid polymer electrolyte (zPU-SPE) for all-solid-state LIBs (SLBs) that addresses the limitations of conventional SPE materials. We synthesized zPU [i.e., poly((diethanolamine ethyl acetate)-co-poly(tetrahydrofuran)-co-(1,6-diisocyanatohexane))] and demonstrated its capability to host high amounts of LiTFSI without phase separation (up to 90 wt% LiTFSI loading). The Li-ion conductivity of zPU-SPE at 25° C is 7.4 × 10 -4 S/cm, nearly 14 times greater than that of poly(ethylene oxide) (PEO) SPE (EO/Li + = 16). Moreover, the high surface energy of zPU-SPE (487.5 J/m 2 vs. 1.39 J/m 2 of PEO) minimizes interfacial resistance. The zPU-SPE also exhibits outstanding elasticity, with a tensile break of 1700%, attributable to its dense inter- and intra-molecular hydrogen bonding. We evaluated the SLB battery performance of PEO and zPU-SPEs using a solid-state Li/SPE/LiFePO 4 cell, which we cycled at a constant current rate of 1 C at 25 °C. The SLB cell with zPU-SPE demonstrates remarkable cycle stability, retaining 86% capacity after 1,000 cycles and 76% capacity after 2,000 cycles at a 1C rate and room temperature, while maintaining nearly 100% coulombic efficiency. In contrast, the capacity of the SLB cell with PEO rapidly decreases to 30% of its initial capacity and fails after 100 cycles. Given that significant capacity loss in LIBs under cold weather conditions poses a challenge for electric vehicles, we also assessed the temperature-dependent battery performance. The SLB with zPU-SPE retains 93.8% capacity at 0 °C, a substantial improvement over state-of-the-art LIBs (< 80% capacity retention). We attribute the high-capacity retention of zPU-SPE at low temperatures to the excellent chain mobility of the soft segment in the zPU matrix, which has a low glass transition temperature (-45 °C) and a high ion transport rate due to the zwitterionic group. We also conducted atomistic molecular modeling to investigate the dissolution and dissociation of Li salts and the Li-ion transport mechanism within the zPU polymer matrix. Furthermore, we employed small-angle X-ray scattering to examine the structural organization of the zPU-SPE matrix and its correlation with ion conductivity. Our work aims to guide the design of novel polymer electrolytes capable of overcoming the trade-offs associated with ion-conducting polymers (i.e., stability and ion conductivity). Figure 1

BACKGROUND:Genomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results is incomplete, as they are limited to benchmark regions. Therefore, it is important to develop DNA reference materials that enable the assessment of variant detection performance across the entire genome. RESULTS:We established a DNA reference material suite from four immortalized cell lines derived from a family of parents and monozygotic twins. Comprehensive reference datasets of 4.2 million small variants and 15,000 structural variants were integrated and certified for evaluating the reliability of germline variant calls inside the benchmark regions. Importantly, the genetic built-in-truth of the Quartet family design enables estimation of the precision of variant calls outside the benchmark regions. Using the Quartet reference materials along with study samples, batch effects are objectively monitored and alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Moreover, the matched RNA and protein reference materials and datasets from the Quartet project enables cross-omics validation of variant calls from multiomics data. CONCLUSIONS:The Quartet DNA reference materials and reference datasets provide a unique resource for objectively assessing the quality of germline variant calls throughout the whole-genome regions and improving the reliability of large-scale genomic profiling.

Molecular subtyping of triple-negative breast cancer (TNBC) is essential for understanding the mechanisms and discovering actionable targets of this highly heterogeneous type of breast cancer. We previously performed a large single-center and multiomics study consisting of genomics, transcriptomics, and clinical information from 465 patients with primary TNBC. To facilitate reusing this unique dataset, we provided a detailed description of the dataset with special attention to data quality in this study. The multiomics data were generally of high quality, but a few sequencing data had quality issues and should be noted in subsequent data reuse. Furthermore, we reconduct data analyses with updated pipelines and the updated version of the human reference genome from hg19 to hg38. The updated profiles were in good concordance with those previously published in terms of gene quantification, variant calling, and copy number alteration. Additionally, we developed a user-friendly web-based database for convenient access and interactive exploration of the dataset. Our work will facilitate reusing the dataset, maximize the values of data and further accelerate cancer research.

Currently, lithium-ion batteries (LIBs) are considered to be one of the most popular energy storage systems for electronic devices supported by high energy density, high operating voltage, and favorable cycling performance. However, commercial LIBs with the organic liquid electrolyte and lithium (Li) salts are associated with critical safety issues such as uncontrollable side reactions, toxic liquid electrolyte leakage, flammability of electrolytes, and poor thermal stability. Therefore, replacing the liquid electrolyte with solid electrolytes is quite necessary. Among several solid ion conductors, solid polymer electrolytes (SPEs) can offer excellent flexibility, interfacial compatibility with electrodes, good processibility, low cost, and light weights that can overcome the limitations of ceramic ion conductors. However, current SPEs often encounter limitations such as poor mechanical strength and dimensional thermal stability, inferior electrochemical stability, and low Li+ ion conductivity at room temperature (~10-5 S cm-1 at 25 ℃). Here we present a multifunctional solid polymer electrolyte based on zwitterionic polyurethanes (zPU-SPE) for all-solid-state LIBs (SLBs). Our zPU-SPE exhibits a great potential to overcome current technical limitations of conventional SPE materials in SLB applications (e.g., low Li+ ion conductivity, inferior electrochemical/mechanical stabilities, unsatisfactory suppression of Li dendrite growth). We designed and synthesized a series of zPU [i.e., poly ((diethanolamine ethyl acetate)-co-poly(tetrahydrofuran)-co-(1,6-diisocyanatohexane))]. Our zPU-SPE can host an equal amount of lithium bis(trifluoromethanesulfonyl) imide (LiTFSI) without phase separation (up to 90 wt% of LiTFSI loading). The Li-ion conductivity value of zPU exponentially increases with the addition of LiTFSI and reaches 7.4 × 10-4 S/cm at 25° C, almost 14 times higher than that of poly(ethylene oxide) (PEO) SPE with ethylene oxide/Li+ ratio = 16. In addition, its superior adhesion energy (487.5 J/m2 of zPU-SPE vs. 150 J/m2 of commercial 3M Scotch Tape) can minimize interfacial resistance between electrode and SPE, and thus cell resistance of 100-µm-thick zPU SPE is as low as 280 W/cm2 compared to 1230 W/cm2 of the cell with a similar thickness of PEO SPE. Our zPU-SPE also showed an excellent elastic property with a tensile break of 1700% owing to its high density of inter- and intra-molecular hydrogen bonding in polymer matrix. The SLB battery performance of PEO and zPU-SPEs was evaluated using a solid-state Li/SPE/LiFePO4 cell; the assembled SLB cell was cycled at a constant current rate of 1 C at 25 °C. After a discharge capacity of the cell with zPU-SPE stabilized at 100 mAh g-1 after 15 cycles, there was a negligible capacity decrease (only 3% capacity decrease after 500 cycles), delivering a discharge capacity of 97 mAh g-1 with stable Coulombic efficiency (100%) over entire cycles. However, the discharge capacity of Li/PEO/LiFePO4 cells drops rapidly to 3 mAh g-1 after 100 cycles. These results demonstrate that the SLB cell assembled with PCB-PTHFU shows high discharge/charge capacity and excellent capacity retention with stable Coulombic efficiency. The good electrochemical performance of the zPU SPE can be attributed to good compatibility with electrodes, low charge transfer resistance at the interface of electrode/electrolyte, and high Li-ion conductivity, strongly suggesting that zPU SPEs are potential candidates for development of high performance of SLBs. Figure 1

Currently, computational materials science involves human-computer interaction through coding in software or neural networks. There is still no direct way for human intelligence endorsement. The digitalization of human intelligence should be the ultimate goal for many disciplines. In materials science, human intelligence is still irreplaceable from machine learning techniques, where humans can deal with complex correlations in the real world. We design the framework of Mateverse, a materials science computation platform based on Metaverse, which unifies human intelligence, experiment data, and theoretical simulations. In Mateverse, we intensively study the properties of H2O, including the liquid and solid phases. We show that we can optimize a new water force field (which we name TIP4P-Meta) directly from the interactions between human and visible properties of H2O. This force field is validated to be better than the conventional water model, and new ice polymorphs can be generated. We believe our platform can provide valuable hints in the paradigm upgrade in future computational materials science development.

Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the “ratio”-based omics profiling data, i.e ., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies “absolute” feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from “absolute” to “ratio"-based multiomics profiling with universal reference materials.

A consecutive training process of Moiré computation framework from classification to sampling tasks is shown. In article number 2100063, Xi Zhu and co-workers demonstrate that the optical properties of Moiré pattern are related to the twist angle, which can be controlled by advanced laser techniques. Rich choices of 2D materials as well as the programmable, non-replacing features imply a promising development potential for Moiré computation.

Nanorobots can adapt to unstructured environments, operate in confined spaces, and interact with various robots. They are ideal robots for exploration and biomedical applications. However, nanorobots' precise control and improved nanomagnetic robots' material quality are still challenging. Artificial intelligence (AI) and robotics breakthroughs have opened up historic opportunities for nanomagnetic robots' fabrication and synthesis. Here, we provide a method for fabricating magnetic nanorobots with an intelligent robotic system updated from a conventional autonomous experimental platform. The nanorobots synthesized by robotics feature uniformly sized samples and can significantly reduce time costs. This work provides new ideas as well as methods for the development of nanorobots.

Redox flow batteries (RFBs) based on lithium polysulfide (Li-PS) chemistry present great opportunities for large-scale energy storage and electric vehicles because of their use of abundant raw materials and their higher energy density compared with traditional flow batteries. However, to successfully implement Li-PS RFBs, issues related to the crossover of PS species through a membrane separator must be resolved. In this work, we demonstrate a facile method for fabricating a novel multifunctional electrochemical membrane (mECM) consisting of an organic ion exchange membrane reinforced with a porous carbon nanotube layer and a boron nitride layer. This rational design endows the membrane with remarkable ion selectivity and dimensional stability in organic electrolyte, leading to a greatly enhanced Li+/PS ion selectivity, which exceeds that of a commercial polyolefin separator (i.e., Celgard 2325) by three orders of magnitude. A Li-PS RFB with the mECM exhibited stable electrochemical performance (0.05% capacity decay per cycle after 40 cycles) with 78% capacity retention over 100 cycles at 0.75C, while a reference cell with a Celgard 2325 membrane rapidly lost its capacity (0.33% capacity decay per cycle and 33% capacity at 100 cycles). Our results strongly suggest that the mECM with its high Li+/PS ion selectivity is a promising membrane separator for developing high-performance Li-PS RFB systems.

Recent advances in optical quantum computation set up a broad discussion on quantum supremacy and its practicability. Lack of programmability and extreme working conditions remain the challenges, calling for a programmable computation scheme. The quasi‐2D layered materials introduce new architectures for the optical neural networks (ONNs), which support various programmable computations following the on‐demand layer design. Compared with the traditional ONNs, Moiré ONNs architectures are more flexible to manufacture via layer number or twist angle control. A general Penn's model to demonstrate the mechanism inside is developed: the dielectric constant control through the layer and twisted bilayer angle dependence, respectively. Theoretically, this device can conduct demo computations ranging from boson sampling to image classification, where quantum computing shows its significant advantages. Instead of redundant 3D‐printing and lithography in traditional ONNs, the Moiré computation framework can train different tasks through programmable twists on single layers without replacing materials.

The magic-angle twisted bilayer graphene (MATBG) recently attracted intensive research attention because of its fascinating and unconventional electronic properties. Herein, we claim the magic-angle phenomenon originates from the Heisenberg uncertainty principle, which can provide intensive explanations on finite size effect and twist-dependent low energy band variations. We showed that flat bands could exist only near the AA stacking structure rather than AB. The finite-size effect gives the minimal size of graphene quantum dots (R ≳ 4 nm) for the emergence of the Dirac point, and the uncertainty relation provides the upper bound for moiré supercells (R ≲ 23.5 nm) in twisted bilayer graphene, which is the quantum mechanical boundary for the emergence of flat bands. Combining the twist dependence of moiré supercell size, we proved that there is only one possible magic angle in MATBG at θ ≈ 1.1°. Our result implies that the unconventional phenomena in MATBG originate from the fundamental feature of condensed matter physics.

In the research field of material science, quantum chemistry database plays an indispensable role in determining the structure and properties of new material molecules and in deep learning in this field. A new quantum chemistry database, the QM-sym, has been set up in our previous work. The QM-sym is an open-access database focusing on transition states, energy, and orbital symmetry. In this work, we put forward the QM-symex with 173-kilo molecules. Each organic molecular in the QM-symex combines with the C n h symmetry composite and contains the information of the first ten singlet and triplet transitions, including energy, wavelength, orbital symmetry, oscillator strength, and other quasi-molecular properties. QM-symex serves as a benchmark for quantum chemical machine learning models that can be effectively used to train new models of excited states in the quantum chemistry region as well as contribute to further development of the green energy revolution and materials discovery.

Novel ion exchange membrane with just the right width of selective aqueous ionic domain (<0.6 nm) and unique functionalities show extraordinary ion selectivity. These unique ion transport properties of our membrane is reflected in a remarkable flow battery performance.