LinearSankoff: Linear-time Simultaneous Folding and Alignment of RNA Homologs (preprint)

The classical Sankoff algorithm for the simultaneous folding and alignment of homologous RNA sequences is highly influential, but it suffers from two major limitations in efficiency and modeling power. First, it takes $O(n^6)$ for two sequences where n is the average sequence length. Most implementations and variations reduce the runtime to $O(n^3)$ by restricting the alignment search space, but this is still too slow for long sequences such as full-length viral genomes. On the other hand, the Sankoff algorithm and all its existing implementations use a rather simplistic alignment model, which can result in poor alignment accuracy. To address these problems, we propose LinearSankoff, which seamlessly integrates the original Sankoff algorithm with a powerful Hidden Markov Model-based alignment module. This extension substantially improves alignment quality, which in turn benefits secondary structure prediction quality, confirmed over a diverse set of RNA families. LinearSankoff also applies beam search heuristics and the A$^\star$-like algorithm to achieve that runtime scales linearly with sequence length. LinearSankoff is the first linear-time algorithm for simultaneous folding and alignment, and the first such algorithm to scale to coronavirus genomes (n $\approx$ 30,000nt). It only takes 10 minutes for a pair of SARS-CoV-2 and SARS-related genomes, and outperforms previous work at identifying crucial conserved structures between the two genomes.

Investigation on the spatial and temporal patterns of coupling sustainable development posture and economic development in World Natural Heritage Sites: A case study of Jiuzhaigou, China

To continue directing global sustainable development efforts from 2015 to 2030, the United Nations adopted 17 global development goals known as the Sustainable Development Goals (SDGs) when the Millennium Development Goals (MDGs) from 2000 to 2015 expired. Sustainable development of World Natural Heritage Sites is one of these 17 MDGs and a crucial step toward achieving global sustainability. A scientific and systematic indicator system that can measure the sustainable development of natural World Heritage Sites more objectively and fairly is urgently needed to support the establishment of SDG11.4 on a Chinese scale and to help with the subsequent promotion of the development of natural World Heritage Sites. This study proposes a comprehensive assessment indicator system for the sustainable development of natural heritage sites based on the theoretical framework of "value contribution-environmental effect” to quantify the sustainable development of natural heritage sites. The study is based on the ecological environment and regional economic and social data of Jiuzhaigou World Natural Heritage Site from 2010 to 2020. Finally, the degree of coupling and coordination between the natural environment and economic development is assessed and studied. The results show that tourism to the World Heritage Site drove rapid economic development in Jiuzhaigou County between 2010 and 2020. As the fame of the World Heritage Site Jiuzhaigou has grown, so has the per capita income of local locals, making them unduly reliant on tourists for a living. Meanwhile, both the 2017 earthquake and the COVID-19 epidemic in 2019 have had substantial detrimental effects on the local economy. Furthermore, the Jiuzhaigou sustainable development trend from 2010 to 2020 exhibits a "W-shaped” curve, and there is a high level of positive coupling between the Jiuzhaigou sustainable development trend and economic development, and the two are mutually reinforcing.

Generation and functional analysis of defective viral genomes during SARS-CoV-2 infection (preprint)

Defective viral genomes (DVGs) have been identified in many RNA viruses as a major factor influencing antiviral immune response and viral pathogenesis. However, the generation and function of DVGs in SARS-CoV-2 infection are less known. In this study, we elucidated DVG generation in SARS-CoV-2 and its relationship with host antiviral immune response. We observed DVGs ubiquitously from RNA-seq datasets of in vitro infections and autopsy lung tissues of COVID-19 patients. Four genomic hotspots were identified for DVG recombination and RNA secondary structures were suggested to mediate DVG formation. Functionally, bulk and single cell RNA-seq analysis indicated the IFN stimulation of SARS-CoV-2 DVGs. We further applied our criteria to the NGS dataset from a published cohort study and observed significantly higher DVG amount and frequency in symptomatic patients than that in asymptomatic patients. Finally, we observed unusually high DVG frequency in one immunosuppressive patient up to 140 days after admitted to hospital due to COVID-19, first-time suggesting an association between DVGs and persistent viral infections in SARS-CoV-2. Together, our findings strongly suggest a critical role of DVGs in modulating host IFN responses and symptom development, calling for further inquiry into the mechanisms of DVG generation and how DVGs modulate host responses and infection outcome during SARS-CoV-2 infection.

Single-cell RNA sequencing to decipher the immunogenicity of ChAdOx1 nCoV-19/AZD1222 and mRNA-1273 vaccines in patients with autoimmune rheumatic diseases

Objectives To investigate the differences between the vector vaccine ChAdOx1 nCoV-19/AZD1222 (Oxford-AstraZeneca) and mRNA-based vaccine mRNA-1273 (Moderna) in patients with autoimmune rheumatic diseases (AIRD), and to explore the cell-cell interactions between high and low anti-SARS-CoV-2 IgG levels in patients with rheumatic arthritis (RA) using single-cell RNA sequencing (scRNA-seq). Methods From September 16 to December 10, 2021, we consecutively enrolled 445 participants (389 patients with AIRD and 56 healthy controls), of whom 236 were immunized with AZD1222 and 209 with mRNA-1273. The serum IgG antibodies to the SARS-CoV-2 receptor-binding domain was quantified by electrochemiluminescence immunoassay at 4-6 weeks after vaccination. Moreover, peripheral blood mononuclear cells (PBMCs) were isolated from RA patients at 4-6 weeks after vaccination for scRNA-seq and further analyzed by CellChat. ScRNA-seq of PBMCs samples from GSE201534 in the Gene Expression Omnibus (GEO) database were also extracted for analysis. Results The anti-SARS-CoV-2 IgG seropositivity rate was 85.34% for AIRD patients and 98.20% for healthy controls. The anti-SARS-CoV-2 IgG level was higher in patients receiving mRNA-1273 than those receiving AZD1222 (β: 35.25, 95% CI: 14.81-55.68, p=0.001). Prednisolone-equivalent dose >5 mg/day and methotrexate use in AIRD patients, and non-anti-tumor necrosis factor-α biologics and Janus kinase inhibitor use in RA patients were associated with inferior immunogenicity. ScRNA-seq revealed CD16-monocytes were predominant in RA patients with high anti-SARS-CoV2-IgG antibodies, and enriched pathways related to antigen presentation via MHC class II were found. HLA-DRA and CD4 interaction was enhanced in high anti-SARS-CoV2-IgG group. Conclusions mRNA-1273 and AZD1222 vaccines exhibited differential immunogenicity in AIRD patients. Enriched pathways related to antigen presentation via MHC class II in CD16-monocytes might be associated with higher anti-SARS-CoV2-IgG level in RA patients and further study is warranted.

Broad and durable antibody response after vaccination with inactivated SARS-CoV-2 in individuals with a history of 2003 SARS-CoV infection.

In vaccinees who were infected with SARS-CoV in 2003, we observed greater antibody responses against spike and nucleoprotein of both SARS-CoV-2 and SARS-CoV after a single dosage of inactivated SARS-CoV-2 vaccine. After receiving the second vaccination, antibodies against RBD of SARS-CoV-2 Wuhan, Beta, Delta, and recently emerged Omicron are significantly higher in SARS-CoV experienced vaccinees than in SARS-CoV naïve vaccinees. Neutralizing activities measured by authentic viruses and pseudoviruses of SARS-CoV, SARS-CoV-2 Wuhan, Beta, and Delta are greater in SARS-CoV experienced vaccinees. In contrast, only weak neutralizing activities against SARS-CoV-2 and variants were detected in SARS-CoV naïve vaccinees. By 6 months after the second vaccination, neutralizing activities were maintained at a relatively higher level in SARS-CoV experienced vaccinees but were undetectable in SARS-CoV naïve vaccinees. These findings suggested a great possibility of developing a universal vaccine by heterologous vaccination using spike antigens from different SARS-related coronaviruses.

2019冠状病毒病(COVID-19)诊疗浙江经验 Management of COVID-19: the Zhejiang experience

当前2019冠状病毒病（COVID-19）疫情仍处于胶着状态。浙江大学医学院附属第一医院是国家感染性疾病临床医学中心，浙江省COVID-19患者救治中心。疫情一线的专家集智攻关，以国家卫生健康委员会和国家中医药管理局发布的COVID-19诊治指南为依据，以抗病毒、抗休克、抗低氧血症、抗继发感染、维持水电解质和酸碱平衡、维持微生态平衡的“四抗二平衡”救治策略为核心，总结完善诊治方案，聚焦临床实践的一些具体问题，为COVID-19患者临床诊治提供借鉴。推荐以多学科协作诊治个性化治疗提高COVID-19患者救治质量。建议病原学检测、炎症指标监测和肺部影像学动态观察指导临床诊治。痰液的病毒核酸检测阳性率最高，约10%的急性期患者血液中检测到病毒核酸，50%的患者粪便中检测到病毒核酸，粪便中可分离出活病毒，须警惕粪便是否具有传染性；开展细胞因子等炎症指标监测有助于发现是否出现细胞因子风暴，判断是否需要人工肝血液净化治疗。通过以“四抗二平衡”为核心的综合治疗提高治愈率、降低病死率；早期抗病毒治疗能减少重症、危重症发生，前期使用阿比多尔联合洛匹那韦/利托那韦抗病毒显示出一定效果。休克和低氧血症多为细胞因子风暴所致，人工肝血液净化治疗能迅速清除炎症介质，阻断细胞因子风暴，对维持水电解质酸碱平衡也有很好的作用，可以提高危重型患者的疗效。重型病例疾病早期可适量、短程应用糖皮质激素。氧疗过程中，患者氧合指数小于200 mmHg时应及时转入重症医学科治疗；采用保守氧疗策略，不推荐常规进行无创通气；机械通气患者应严格执行集束化呼吸机相关性肺炎预防管理策略；氧合指数大于150 mmHg时，及早减、停镇静剂并撤机拔管。不推荐预防性使用抗菌药物，对于病程长，体温反复升高和血降钙素原水平升高的患者可酌情使用抗菌药物；要关注COVID-19患者继发真菌感染的诊治。COVID-19患者有肠道微生态紊乱，肠道乳酸杆菌、双歧杆菌等有益菌减少，推荐对所有患者进行营养和胃肠道功能评估，以营养支持和补充大剂量肠道微生态调节剂，纠正肠道微生态失衡，减少细菌移位和继发感染。COVID-19患者普遍存在焦虑和恐惧心理，应建立动态心理危机干预和处理。提倡中西医结合辨证施治；优化重型患者护理促进康复。严重急性呼吸综合征冠状病毒2（SARS-CoV-2）感染后病毒清除规律仍不明了，出院后仍须居家隔离2周，并定期随访。以上经验和建议在本中心实行，取得较好效果，但COVID-19是一种新的疾病，其诊治方案及策略仍有待进一步探索与完善。

Fuzzy Portfolio Selection in COVID-19 Spreading Period Using Fuzzy Goal Programming Model

While the international lockdown for the COVID-19 pandemic has greatly influenced the global economy, we are still confronted with the dilemma about the economy recession when the stock market hits record highs repeatedly. As we know, since portfolio selection is often affected by many non-probabilistic factors, it is of not easiness to obtain the precise probability distributions of the return rates. Therefore, fuzzy portfolio model is proposed for solving the efficient portfolio when the economy environment remains in vagueness for the future. To manage such an investment, we revise the Chen and Tsaur’s fuzzy portfolio model by using fuzzy goal programming model. Then, two numerical examples are illustrated by the proposed model which shows that the portfolio selection can be solved by the linguistic imprecise goal of the expected return. With different linguistic descriptions for the imprecise goal of expected return for the future stock market, the optimal portfolio selection that can be solved under different investment risks which is considered better than Chen and Tsaur’s model in real world situations. The sensitivity analysis with some parameter groups can be provided for more invested risk selection in the portfolio analysis.

LazySampling and LinearSampling: Fast Stochastic Sampling of RNA Secondary Structure with Applications to SARS-CoV-2 (preprint)

Many RNAs fold into multiple structures at equilibrium. The classical stochastic sampling algorithm can sample secondary structures according to their probabilities in the Boltzmann ensemble, and is widely used. However, this algorithm, consisting of a bottom-up partition function phase followed by a top-down sampling phase, suffers from three limitations: (a) the formulation and implementation of the sampling phase are unnecessarily complicated; (b) the sampling phase repeatedly recalculates many redundant recursions already done during the partition function phase; (c) the partition function runtime scales cubically with the sequence length. These issues prevent stochastic sampling from being used for very long RNAs such as the full genomes of SARS-CoV-2. To address these problems, we first adopt a hypergraph framework under which the sampling algorithm can be greatly simplified. We then present three sampling algorithms under this framework, among which the LazySampling algorithm is the fastest by eliminating redundant work in the sampling phase via on-demand caching. Based on LazySampling, we further replace the cubic-time partition function by a linear-time approximate one, and derive LinearSampling, an end-to-end linear-time sampling algorithm that is orders of magnitude faster than the standard one. For instance, LinearSampling is 176× faster (38.9s vs. 1.9h) than Vienna RNAsubopt on the full genome of Ebola virus (18,959 nt ). More importantly, LinearSampling is the first RNA structure sampling algorithm to scale up to the full-genome of SARS-CoV-2 without local window constraints, taking only 69.2 seconds on its reference sequence (29,903 nt ). The resulting sample correlates well with the experimentally-guided structures. On the SARS-CoV-2 genome, LinearSampling finds 23 regions of 15 nt with high accessibilities, which are potential targets for COVID-19 diagnostics and drug design. See code: https://github.com/LinearFold/LinearSampling

LinearTurboFold: Linear-Time RNA Structural Alignment and Conserved Structure Prediction with Applications to Coronaviruses (preprint)

Many functional RNA structures are conserved across evolution, and such conserved structures provide critical targets for diagnostics and treatment. TurboFold II is a state-of-the-art software that can predict conserved structures and alignments given homologous sequences, but its cubic runtime and quadratic memory usage with sequence length prevent it from being applied to most full-length viral genomes. As the COVID-19 outbreak spreads, there is a growing need to have a fast and accurate tool to identify conserved regions of SARS-CoV-2. To address this issue, we present LinearTurboFold, which successfully accelerates TurboFold II without sacrificing accuracy on secondary structure and multiple sequence alignment prediction. LinearTurboFold is orders of magnitude faster than TurboFold II, e.g., 372 times faster (12 minutes vs. 3.1 days) on a group of five HIV-1 homologs with average length 9,686 nt. LinearTurboFold is able to scale up to the full sequence of SARS-CoV-2, and identifies conserved structures that have been supported by previous studies. Additionally, LinearTurboFold finds a list of novel conserved regions, including long-range base pairs, which may be useful for better understanding the virus.

CoV-Seq: SARS-CoV-2 Genome Analysis and Visualization (preprint)

SummaryCOVID-19 has become a global pandemic not long after its inception in late 2019. SARS-CoV-2 genomes are being sequenced and shared on public repositories at a fast pace. To keep up with these updates, scientists need to frequently refresh and reclean datasets, which is ad hoc and labor-intensive. Further, scientists with limited bioinformatics or programming knowledge may find it difficult to analyze SARS-CoV-2 genomes. In order to address these challenges, we developed CoV-Seq, a webserver to enable simple and rapid analysis of SARS-CoV-2 genomes. Given a new sequence, CoV-Seq automatically predicts gene boundaries and identifies genetic variants, which are presented in an interactive genome visualizer and are downloadable for further analysis. A command-line interface is also available for high-throughput processing. Availability and ImplementationCoV-Seq is implemented in Python and Javascript. The webserver is available at http://covseq.baidu.com/ and the source code is available from https://github.com/boxiangliu/covseq. Contactjollier.liu@gmail.com Supplementary informationSupplementary information are available at bioRxiv online.

LinearDesign: Efficient Algorithms for Optimized mRNA Sequence Design (preprint)

A messenger RNA (mRNA) vaccine has emerged as a promising direction to combat the current COVID-19 pandemic. This requires an mRNA sequence that is stable and highly productive in protein expression, features which have been shown to benefit from greater mRNA secondary structure folding stability and optimal codon usage. However, sequence design remains a hard problem due to the exponentially many synonymous mRNA sequences that encode the same protein. We show that this design problem can be reduced to a classical problem in formal language theory and computational linguistics that can be solved in O(n^3) time, where n is the mRNA sequence length. This algorithm could still be too slow for large n (e.g., n = 3, 822 nucleotides for the spike protein of SARS-CoV-2), so we further developed a linear-time approximate version, LinearDesign, inspired by our recent work, LinearFold. This algorithm, LinearDesign, can compute the approximate minimum free energy mRNA sequence for this spike protein in just 11 minutes using beam size b = 1, 000, with only 0.6% loss in free energy change compared to exact search (i.e., b = +infinity, which costs 1 hour). We also develop two algorithms for incorporating the codon optimality into the design, one based on k-best parsing to find alternative sequences and one directly incorporating codon optimality into the dynamic programming. Our work provides efficient computational tools to speed up and improve mRNA vaccine development.