High-Resolution Structure of the N-Terminal Endonuclease Domain of the Lassa Virus L Polymerase in Complex with Magnesium Ions

Lassa virus (LASV) causes deadly hemorrhagic fever disease for which there are no vaccines and limited treatments. LASV-encoded L polymerase is required for viral RNA replication and transcription. The functional domains of L–a large protein of 2218 amino acid residues–are largely undefined, except for the centrally located RNA-dependent RNA polymerase (RdRP) motif. Recent structural and functional analyses of the N-terminal region of the L protein from lymphocytic choriomeningitis virus (LCMV), which is in the same Arenaviridae family as LASV, have identified an endonuclease domain that presumably cleaves the cap structures of host mRNAs in order to initiate viral transcription. Here we present a high-resolution crystal structure of the N-terminal 173-aa region of the LASV L protein (LASV L173) in complex with magnesium ions at 1.72 Å. The structure is highly homologous to other known viral endonucleases of arena- (LCMV NL1), orthomyxo- (influenza virus PA), and bunyaviruses (La Crosse virus NL1). Although the catalytic residues (D89, E102 and K122) are highly conserved among the known viral endonucleases, LASV L endonuclease structure shows some notable differences. Our data collected from in vitro endonuclease assays and a reporter-based LASV minigenome transcriptional assay in mammalian cells confirm structural prediction of LASV L173 as an active endonuclease. The high-resolution structure of the LASV L endonuclease domain in complex with magnesium ions should aid the development of antivirals against lethal Lassa hemorrhagic fever

On Laplacian eigenvalue equation with constant Neumann boundary data

Let $\Omega$ be a bounded Lipshcitz domain in $\mathbb{R}^n$ and we study boundary behaviors of solutions to the Laplacian eigenvalue equation with constant Neumann data. \begin{align} \label{cequation0} \begin{cases} -\Delta u=cu\quad &\mbox{in $\Omega$}\\ \frac{\partial u}{\partial \nu}=-1\quad &\mbox{on $\partial \Omega$}. \end{cases} \end{align}First, by using properties of Bessel functions and proving new inequalities on elementary symmetric polynomials, we obtain the following inequality for rectangular boxes, balls and equilateral triangles: \begin{align} \label{bbb} \lim_{c\rightarrow \mu_2^-}c\int_{\partial \Omega}u_c\, d\sigma\ge \frac{n-1}{n}\frac{P^2(\Omega)}{|\Omega|}, \end{align}with equality achieved only at cubes and balls. In the above, $u_c$ is the solution to the eigenvalue equation and $\mu_2$ is the second Neumann Laplacian eigenvalue. Second, let $\kappa_1$ be the best constant for the Poincar\'e inequality with mean zero on $\partial \Omega$, and we prove that $\kappa_1\le \mu_2$, with equality holds if and only if $\int_{\partial \Omega}u_c\, d\sigma>0$ for any $c\in (0,\mu_2)$. As a consequence, $\kappa_1=\mu_2$ on balls, rectangular boxes and equilateral triangles, and balls maximize $\kappa_1$ over all Lipschitz domains with fixed volume. As an application, we extend the symmetry breaking results from ball domains obtained in Bucur-Buttazzo-Nitsch[J. Math. Pures Appl., 2017], to wider class of domains, and give quantitative estimates for the precise breaking threshold at balls and rectangular boxes. It is a direct consequence that for domains with $\kappa_1<\mu_2$, the above boundary limit inequality is never true, while whether it is valid for domains on which $\kappa_1=\mu_2$ remains open

Identifying Latent Causal Content for Multi-Source Domain Adaptation

Multi-source domain adaptation (MSDA) learns to predict the labels in target domain data, under the setting that data from multiple source domains are labelled and data from the target domain are unlabelled. Most methods for this task focus on learning invariant representations across domains. However, their success relies heavily on the assumption that the label distribution remains consistent across domains, which may not hold in general real-world problems. In this paper, we propose a new and more flexible assumption, termed \textit{latent covariate shift}, where a latent content variable $\mathbf{z}_c$ and a latent style variable $\mathbf{z}_s$ are introduced in the generative process, with the marginal distribution of $\mathbf{z}_c$ changing across domains and the conditional distribution of the label given $\mathbf{z}_c$ remaining invariant across domains. We show that although (completely) identifying the proposed latent causal model is challenging, the latent content variable can be identified up to scaling by using its dependence with labels from source domains, together with the identifiability conditions of nonlinear ICA. This motivates us to propose a novel method for MSDA, which learns the invariant label distribution conditional on the latent content variable, instead of learning invariant representations. Empirical evaluation on simulation and real data demonstrates the effectiveness of the proposed method

Bryophyte diversity is related to vascular plant diversity and microhabitat under disturbance in karst caves

Plant diversity, habitat properties, and their relationships in karst caves remain poorly understood. We surveyed vascular plant and bryophyte diversities and measured the habitat characteristics in six karst caves in south China with different disturbance histories (one had been disturbed by poultry feeding, three had been disturbed by tourism, and two were undisturbed). The plant diversity differences among the six caves were analyzed using cluster analysis, and the relationships of plant diversity and microhabitat were assessed using canonical correspondence analysis. We found a total of 43 angiosperm species from 27 families, 20 lycophyte and fern species from 9 families, and 20 species of bryophytes from 13 families in the six caves. Habitat characteristics including light intensity, air relative humidity, air temperature, and soil properties varied among the caves. The plant diversity in karst caves was not rich, but the species composition was unique. The caves with high disturbance had the lowest species richness, numbers of individuals, and Shannon-Wiener diversity indices but the highest Simpson’s dominance indices. The caves with less disturbance had the highest numbers of species, numbers of individuals, and Shannon-Wiener diversity indices but the lowest Simpson’s dominance indices. The disturbed caves were often dominated by drought-tolerant, tenacious mosses (bryophytes), while the relatively undisturbed caves contained abundant liverworts (bryophytes), which were better adapted to humid environments. Plant diversity in karst caves was closely related to habitat heterogeneity, light and water status, and nutrient availability. Tourism and poultry farming were associated with the degradation of vegetation in some karst caves. Protecting and restoring bryophytes might facilitate the settlement, growth, and succession of vascular plants in karst caves. Bryophytes can be used as indicators of overall plant diversity and restoration status in karst caves

Immunogenicity and Protective Efficacy of a Recombinant Pichinde Viral-Vectored Vaccine Expressing Influenza Virus Hemagglutinin Antigen in Pigs

Influenza A virus of swine (IAV-S) is an economically important swine pathogen. The IAV-S hemagglutinin (HA) surface protein is the main target for vaccine development. In this study, we evaluated the feasibility of using the recombinant tri-segmented Pichinde virus (rPICV) as a viral vector to deliver HA antigen to protect pigs against IAV-S challenge. Four groups of weaned pigs (T01–T04) were included in the study. T01 was injected with PBS to serve as a non-vaccinated control. T02 was inoculated with rPICV expressing green fluorescence protein (rPICV-GFP). T03 was vaccinated with rPICV expressing the HA antigen of the IAV-S H3N2 strain (rPICV-H3). T04 was vaccinated with the recombinant HA protein antigen of the same H3N2 strain. Pigs were vaccinated twice at day 0 and day 21 and challenged at day 43 by intra-tracheal inoculation with the homologous H3N2 IAV-S strain. After vaccination, all pigs in T03 and T04 groups were seroconverted and exhibited high titers of plasma neutralizing antibodies. After challenge, high levels of IAV-S RNA were detected in the nasal swabs and bronchioalveolar lavage fluid of pigs in T01 and T02 but not in the T03 and T04 groups. Similarly, lung lesions were observed in T01 and T02, but not in the T03 and T04 groups. No significant difference in terms of protection was observed between the T03 and T04 group. Collectively, our results demonstrate that the rPICV-H3 vectored vaccine elicited protective immunity against IAV-S challenge. This study shows that rPICV is a promising viral vector for the development of vaccines against IAV-S

Identifiable Latent Polynomial Causal Models Through the Lens of Change

Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of identifying these latent causal models, known as identifiability. A recent breakthrough explores identifiability by leveraging the change of causal influences among latent causal variables across multiple environments \citep{liu2022identifying}. However, this progress rests on the assumption that the causal relationships among latent causal variables adhere strictly to linear Gaussian models. In this paper, we extend the scope of latent causal models to involve nonlinear causal relationships, represented by polynomial models, and general noise distributions conforming to the exponential family. Additionally, we investigate the necessity of imposing changes on all causal parameters and present partial identifiability results when part of them remains unchanged. Further, we propose a novel empirical estimation method, grounded in our theoretical finding, that enables learning consistent latent causal representations. Our experimental results, obtained from both synthetic and real-world data, validate our theoretical contributions concerning identifiability and consistency

Establishment of a Reverse Genetics System for Studying Human Bocavirus in Human Airway Epithelia

Human bocavirus 1 (HBoV1) has been identified as one of the etiological agents of wheezing in young children with acute respiratory-tract infections. In this study, we have obtained the sequence of a full-length HBoV1 genome (including both termini) using viral DNA extracted from a nasopharyngeal aspirate of an infected patient, cloned the full-length HBoV1 genome, and demonstrated DNA replication, encapsidation of the ssDNA genome, and release of the HBoV1 virions from human embryonic kidney 293 cells. The HBoV1 virions generated from this cell line-based production system exhibits a typical icosahedral structure of approximately 26 nm in diameter, and is capable of productively infecting polarized primary human airway epithelia (HAE) from the apical surface. Infected HAE showed hallmarks of lung airway-tract injury, including disruption of the tight junction barrier, loss of cilia and epithelial cell hypertrophy. Notably, polarized HAE cultured from an immortalized airway epithelial cell line, CuFi-8 (originally derived from a cystic fibrosis patient), also supported productive infection of HBoV1. Thus, we have established a reverse genetics system and generated the first cell line-based culture system for the study of HBoV1 infection, which will significantly advance the study of HBoV1 replication and pathogenesis.This work was supported by PHS R21 grant AI085236 and PHS R01 grant AI070723 from NIAID (J Qiu) and PHS R01 grant HL108902 from NHLBI (J Engelhardt)