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The high sequencing error rate has impeded the application of long noisy reads for diploid genome assembly. Most existing assemblers failed to generate high-quality phased assemblies using long noisy reads. Here, we present PECAT, aPhasedErrorCorrection andAssemblyTool, for reconstructing diploid genomes from long noisy reads. We design a haplotype-aware error correction method that can retain heterozygote alleles while correcting sequencing errors. We combine a corrected read SNP caller and a raw read SNP caller to further improve the identification of inconsistent overlaps in the string graph. We use a grouping method to assign reads to different haplotype groups. PECAT efficiently assembles diploid genomes using Nanopore R9, PacBio CLR or Nanopore R10 reads only. PECAT generates more contiguous haplotype-specific contigs compared to other assemblers. Especially, PECAT achieves nearly haplotype-resolved assembly onB. taurus(Bison×Simmental) using Nanopore R9 reads and phase block NG50 with 59.4/58.0 Mb for HG002 using Nanopore R10 reads.

 

The quasi-two-dimensional kagome materials AV3Sb5 (A = K, Rb, Cs) were found to be a prime example of kagome superconductors, a new quantum platform to investigate the interplay between electron correlation effects, topology and geometric frustration. In this review, we report recent progress on the experimental and theoretical studies of AV3Sb5 and provide a broad picture of this fast-developing field in order to stimulate an expanded search for unconventional kagome superconductors. We review the electronic properties of AV3Sb5, the experimental measurements of the charge density wave state, evidence of time-reversal symmetry breaking and other potential hidden symmetry breaking in these materials. A variety of theoretical proposals and models that address the nature of the time-reversal symmetry breaking are discussed. Finally, we review the superconducting properties of AV3Sb5, especially the potential pairing symmetries and the interplay between superconductivity and the charge density wave state.

 

RRAM-based in-memory computing (IMC) effectively accelerates deep neural networks (DNNs) and other machine learning algorithms. On the other hand, in the presence of RRAM device variations and lower precision, the mapping of DNNs to RRAM-based IMC suffers from severe accuracy loss. In this work, we propose a novel hybrid IMC architecture that integrates an RRAM-based IMC macro with a digital SRAM macro using a programmable shifter to compensate for the RRAM variations and recover the accuracy. The digital SRAM macro consists of a small SRAM memory array and an array of multiply-and-accumulate (MAC) units. The non-ideal output from the RRAM macro, due to device and circuit non-idealities, is compensated by adding the precise output from the SRAM macro. In addition, the programmable shifter allows for different scales of compensation by shifting the SRAM macro output relative to the RRAM macro output. On the algorithm side, we develop a framework for the training of DNNs to support the hybrid IMC architecture through ensemble learning. The proposed framework performs quantization (weights and activations), pruning, RRAM IMC-aware training, and employs ensemble learning through different compensation scales by utilizing the programmable shifter. Finally, we design a silicon prototype of the proposed hybrid IMC architecture in the 65nm SUNY process to demonstrate its efficacy. Experimental evaluation of the hybrid IMC architecture shows that the SRAM compensation allows for a realistic IMC architecture with multi-level RRAM cells (MLC) even though they suffer from high variations. The hybrid IMC architecture achieves up to 21.9%, 12.65%, and 6.52% improvement in post-mapping accuracy over state-of-the-art techniques, at minimal overhead, for ResNet-20 on CIFAR-10, VGG-16 on CIFAR-10, and ResNet-18 on ImageNet, respectively. 

Protein arginine methyltransferases (PRMTs) are essential epigenetic and post-translational regulators in eukaryotic organisms. Dysregulation of PRMTs is intimately related to multiple types of human diseases, particularly cancer. Based on the previously reported PRMT1 inhibitors bearing the diamidine pharmacophore, we performed virtual screening to identify additional amidine-associated structural analogs. Subsequent enzymatic tests and characterization led to the discovery of a top lead K313 (2-(4-((4-carbamimidoylphenyl)amino)phenyl)-1 H -indole-6-carboximidamide), which possessed low-micromolar potency with biochemical IC 50 of 2.6 μM for human PRMT1. Limited selectivity was observed over some other PRMT isoforms such as CARM1 and PRMT7. Molecular modeling and inhibition pattern studies suggest that K313 is a nonclassic noncompetitive inhibitor to PRMT1. K313 significantly inhibited cell proliferation and reduced the arginine asymmetric dimethylation level in the leukaemia cancer cells. 

Cryo-electron tomography (cryo-ET) provides 3D visualization of subcellular components in the near-native state and at sub-molecular resolutions in single cells, demonstrating an increasingly important role in structural biology in situ . However, systematic recognition and recovery of macromolecular structures in cryo-ET data remain challenging as a result of low signal-to-noise ratio (SNR), small sizes of macromolecules, and high complexity of the cellular environment. Subtomogram structural classification is an essential step for such task. Although acquisition of large amounts of subtomograms is no longer an obstacle due to advances in automation of data collection, obtaining the same number of structural labels is both computation and labor intensive. On the other hand, existing deep learning based supervised classification approaches are highly demanding on labeled data and have limited ability to learn about new structures rapidly from data containing very few labels of such new structures. In this work, we propose a novel approach for subtomogram classification based on few-shot learning. With our approach, classification of unseen structures in the training data can be conducted given few labeled samples in test data through instance embedding. Experiments were performed on both simulated and real datasets. Our experimental results show that we can make inference on new structures given only five labeled samples for each class with a competitive accuracy (> 0.86 on the simulated dataset with SNR = 0.1), or even one sample with an accuracy of 0.7644. The results on real datasets are also promising with accuracy > 0.9 on both conditions and even up to 1 on one of the real datasets. Our approach achieves significant improvement compared with the baseline method and has strong capabilities of generalizing to other cellular components. 

The physical realization of Chern insulators is of fundamental and practical interest, as they are predicted to host the quantum anomalous Hall (QAH) effect and topologically protected chiral edge states which can carry dissipationless current. Current realizations of the QAH state often require complex heterostructures and sub-Kelvin temperatures, making the discovery of intrinsic, high temperature QAH systems of significant interest. In this work we show that time-reversal symmetry breaking Weyl semimetals, being essentially stacks of Chern insulators with inter-layer coupling, may provide a new platform for the higher temperature realization of robust chiral edge states. We present combined scanning tunneling spectroscopy and theoretical investigations of the magnetic Weyl semimetal, Co₃Sn₂S₂. Using modeling and numerical simulations we find that depending on the strength of the interlayer coupling, chiral edge states can be localized on partially exposed kagome planes on the surfaces of a Weyl semimetal. Correspondingly, our dI/dVmaps on the kagome Co₃Sn terraces show topological states confined to the edges which display linear dispersion. This work provides a new paradigm for realizing chiral edge modes and provides a pathway for the realization of higher temperature QAH effect in magnetic Weyl systems in the two-dimensional limit.

 

Abstract Biochar is a promising soil additive for use in support of sustainable crop production. However, the high level of heterogeneity in biochar properties and the variations in soil composition present significant challenges to the successful uptake of biochar technologies in diverse agricultural soils. An improved understanding of the mechanisms that contribute to biochar–soil interactions is required to address issues related to climate change and cultivation practices. This review summarizes biochar modification approaches (physical, chemical, and biochar-based organic composites) and discusses the potential role of biochar in sustainable crop production and soil resiliency, including the degradation of soil organic matter, the improvement of soil quality, and reductions in greenhouse gas emissions. Biochar design is crucial to successful soil remediation, particularly with regard to issues arising from soil structure and composition related to crop production. Given the wide variety of feedstocks for biochar production and the resultant high surface heterogeneity, greater efforts are required to optimize biochar surface functionality and porosity through appropriate modifications. The design and establishment of these approaches and methods are essential for the future utilization of biochar as an effective soil additive to promote sustainable crop production. 

The possible realization of Majorana fermions as quasiparticle excitations in condensed-matter physics has created much excitement. Most studies have focused on Majorana bound states; however, propagating Majorana states with linear dispersion have also been predicted. Here, we report scanning tunneling spectroscopic measurements of crystalline domain walls (DWs) in FeSe 0.45 Te 0.55 . We located DWs across which the lattice structure shifts by half a unit cell. These DWs have a finite, flat density of states inside the superconducting gap, which is a hallmark of linearly dispersing modes in one dimension. This signature is absent in DWs in the related superconductor, FeSe, which is not in the topological phase. Our combined data are consistent with the observation of dispersing Majorana states at a π-phase shift DW in a proximitized topological material. 

The path from a Mott insulating phase to high temperature superconductivity encounters a rich set of unconventional phenomena involving the insulator-to-metal transition (IMT), such as emergent electronic orders and pseudogaps, that ultimately affect the condensation of Cooper pairs. A huge hindrance to understanding the origin of these phenomena is the difficulty in accessing doping levels near the parent state. TheJ_eff = 1/2 Mott state of the perovskite strontium iridates has revealed intriguing parallels to the cuprates, with the advantage that it provides unique access to the Mott transition. Here, we exploit this accessibility to study the IMT and the possible nearby electronic orders in the electron-doped bilayer iridate (Sr_1 − xLa_x)₃Ir₂O₇. Using spectroscopic imaging scanning tunneling microscopy, we image the La dopants in the top as well as the interlayer SrO planes. Surprisingly, we find a disproportionate distribution of La between these layers with the interlayer La being primarily responsible for the IMT. This reveals the distinct site-dependent effects of dopants on the electronic properties of bilayer systems. Electron doping also results in charge reordering. We find unidirectional electronic order concomitant with the structural distortion known to exist in this system. Intriguingly, similar to the single layer iridate, we also find local resonant states forming a checkerboard-like pattern trapped by La. This suggests that multiple charge orders may exist simultaneously in Mott systems, even with only one band crossing the Fermi energy.