Efficient and Deterministic Propagation of Mixed Quantum-Classical Liouville Dynamics

We propose a highly efficient mixed quantum-classical molecular dynamics scheme based on a solution of the quantum-classical Liouville equation (QCLE). By casting the equations of motion for the quantum subsystem and classical bath degrees of freedom onto an approximate set of coupled first-order differential equations for <i>c</i>-numbers, this scheme propagates the composite system in time deterministically in terms of independent classical-like trajectories. To demonstrate its performance, we apply the method to the spin-boson model, a photoinduced electron transfer model, and a Fenna–Matthews–Olsen complex model, and find excellent agreement out to long times with the numerically exact results, using several orders of magnitude fewer trajectories than surface-hopping solutions of the QCLE. Owing to its accuracy and efficiency, this method promises to be very useful for studying the dynamics of mixed quantum-classical systems

Three-dimensional principal coordinate analysis of <i>g20</i> clone sequences of cyanophage communities obtained from paddy floodwaters in NE China (<i>dark green circles</i>) and from Japanese paddy floodwaters (<i>light green circles</i>) and paddy soils (<i>brown circles</i>).

<p>The <i>percentages</i> in the axis labels represent the percentage of variation explained by the principal coordinates.</p

Closest relatives of sequenced <i>g20</i> clones from different paddy floodwaters at the amino acid level.

<p>Closest relatives of sequenced <i>g20</i> clones from different paddy floodwaters at the amino acid level.</p

Neighbor-joining phylogenetic tree showing the relationships of <i>g20</i> amino acid sequence from paddy floodwaters in NE China with from those from lake freshwaters (Dorigo et al. 2004; Short and Suttle 2005; Wilhelm et al. 2006; Zhong and Jacquet 2013; Yeo and Gin, unpublished data which were submitted in Jan 15, 2013), paddy floodwaters in Japan (Wang et al. 2010), paddy field soils in Japan (Wang et al. 2011) and marine waters (Fuller et al. 1998; Zhong et al. 2002; Marston and Sallee 2003; Wang and Chen 2004; Mann et al. 2005; Short and Suttle 2005; Li and Li, unpublished data which were submitted in Jun 16, 2013).

<p><i>Green triangles</i> and <i>blue circles</i> indicate <i>g20</i> clones obtained from lake freshwater and marine water, respectively; <i>Black</i> and <i>white square boxes</i> indicate <i>g20</i> clones obtained from paddy field soils in Japan and paddy floodwaters in Japan, respectively; <i>White triangles</i> indicate <i>g20</i> clones obtained from paddy floodwaters in NE China. The <i>number in parentheses</i> denotes the accession number of amino acid sequences in the NCBI website. Bootstrap values <50 are not shown. The scale bar represents the number of amino acid substitutions per residue.</p

Three-dimensional principal coordinate analysis of <i>g20</i> clone sequences of cyanophage communities obtained from marine waters (<i>blue circles</i>), lake freshwaters (<i>red circles</i>), and paddy fields (<i>green circles</i>).

<p>The <i>percentages</i> in the axis labels represent the percentages of variation explained by the principal coordinates.</p

Neighbor-joining phylogenetic tree showing the relationship of <i>g20</i> amino acid sequences from paddy floodwaters in NE China with those from Japanese paddy floodwaters (Wang et al. 2010) and paddy field soils (Wang et al.2011).

<p><i>Brown</i> and <i>white square boxes</i> indicate <i>g20</i> clones obtained from paddy field soils in Japan and paddy floodwaters in Japan, respectively; <i>green triangles</i> indicate <i>g20</i> clones obtained from paddy floodwaters in NE China; <i>JP</i> and <i>CN</i> represent Japan and China, respectively; <i>PFW</i> and <i>PFS</i> represent paddy floodwater and paddy field soil, respectively. Bootstrap values <50 are not shown. The scale bar represents the number of amino acid substitutions per residue.</p

Image_1_Nucleic acid-sensing-related gene signature in predicting prognosis and treatment efficiency of small cell lung cancer patients.tif

IntroductionNucleic acid-sensing (NAS) pathways could induce innate and adaptive immune responses. However, rare evidence exhibited how the core genes of the NAS pathways affected the immune response and prognosis of small cell lung cancer (SCLC) patients.MethodsWe conducted a comprehensive bioinformatic analysis based on the RNA profiles of 114 SCLC patients, including 79 from cBioPortal, 21 from GSE30219, and 14 from our sequencing data. The multiplex immunohistochemistry (mIHC) was used to characterize the role of NAS related genes in the tumor microenvironment (TME) of SCLC.ResultsA prognostic model (7NAS risk model) was constructed based on 7 NAS-related genes which was demonstrated as an independent prognostic index. The low-risk group was identified to have a better prognosis and an immune-activated microenvironment in both the public datasets and our dataset. Intriguingly, mIHC data showed that CD45+ immune cells, CD8+ T lymphocytes, and CD68+ macrophages were prevalently enriched in low-risk SCLC patients and positively correlated with IRF1 expression. Additionally, Patients in the low-risk group might have superior responses to chemotherapy and immunotherapy.ConclusionConclusively, this study created a new risk model based on genes associated with NAS pathways which could predict the prognosis and response of treatment in patients with SCLC.</p

Image_2_Nucleic acid-sensing-related gene signature in predicting prognosis and treatment efficiency of small cell lung cancer patients.tif

IntroductionNucleic acid-sensing (NAS) pathways could induce innate and adaptive immune responses. However, rare evidence exhibited how the core genes of the NAS pathways affected the immune response and prognosis of small cell lung cancer (SCLC) patients.MethodsWe conducted a comprehensive bioinformatic analysis based on the RNA profiles of 114 SCLC patients, including 79 from cBioPortal, 21 from GSE30219, and 14 from our sequencing data. The multiplex immunohistochemistry (mIHC) was used to characterize the role of NAS related genes in the tumor microenvironment (TME) of SCLC.ResultsA prognostic model (7NAS risk model) was constructed based on 7 NAS-related genes which was demonstrated as an independent prognostic index. The low-risk group was identified to have a better prognosis and an immune-activated microenvironment in both the public datasets and our dataset. Intriguingly, mIHC data showed that CD45+ immune cells, CD8+ T lymphocytes, and CD68+ macrophages were prevalently enriched in low-risk SCLC patients and positively correlated with IRF1 expression. Additionally, Patients in the low-risk group might have superior responses to chemotherapy and immunotherapy.ConclusionConclusively, this study created a new risk model based on genes associated with NAS pathways which could predict the prognosis and response of treatment in patients with SCLC.</p

Table_1_Nucleic acid-sensing-related gene signature in predicting prognosis and treatment efficiency of small cell lung cancer patients.docx

IntroductionNucleic acid-sensing (NAS) pathways could induce innate and adaptive immune responses. However, rare evidence exhibited how the core genes of the NAS pathways affected the immune response and prognosis of small cell lung cancer (SCLC) patients.MethodsWe conducted a comprehensive bioinformatic analysis based on the RNA profiles of 114 SCLC patients, including 79 from cBioPortal, 21 from GSE30219, and 14 from our sequencing data. The multiplex immunohistochemistry (mIHC) was used to characterize the role of NAS related genes in the tumor microenvironment (TME) of SCLC.ResultsA prognostic model (7NAS risk model) was constructed based on 7 NAS-related genes which was demonstrated as an independent prognostic index. The low-risk group was identified to have a better prognosis and an immune-activated microenvironment in both the public datasets and our dataset. Intriguingly, mIHC data showed that CD45+ immune cells, CD8+ T lymphocytes, and CD68+ macrophages were prevalently enriched in low-risk SCLC patients and positively correlated with IRF1 expression. Additionally, Patients in the low-risk group might have superior responses to chemotherapy and immunotherapy.ConclusionConclusively, this study created a new risk model based on genes associated with NAS pathways which could predict the prognosis and response of treatment in patients with SCLC.</p

The distribution characteristics of the major capsid gene (<i>g23</i>) of T4-type phages in paddy floodwater in Northeast China

<p>Our previous study revealed the high diversity of the major capsid gene (<i>g23</i>) of T4-type phages that existed in the paddy field soils in Northeast China. In this study, the phylogeny and genetic diversity of the <i>g23</i> gene in the paddy floodwater samples collected from five sampling sites at three sampling times during the rice (<i>Oryza</i> <i>sativa</i> L.) growth season in Northeast China are reported. In total, 104 different <i>g23</i> clones were isolated, among which 50% of the clones exhibited the highest identities with the clones retrieved in paddy soils and upland black soils. The remaining clones had the highest identities with lake origins. Phylogenetic analysis revealed that 43% of the <i>g23</i> clones grouped into three novel subgroups which included the clones unique to paddy floodwater, and no <i>g23</i> sequences obtained in paddy floodwater fell into the paddy soil groups II, III, IV, V, VI, VII and NPC-A. UniFrac analysis of <i>g23</i> clone assemblages demonstrated that T4-type phage communities in paddy floodwater were changed spatially and temporally, and the communities were different from those in paddy soils. Further comparison of the <i>g23</i> clone assemblages from different environments demonstrated that T4-type phages were biogeographically distributed, and the distribution was both affected by geographical separation and ecological processes across the biomes.</p