Genome-wide characterization of intergenic polyadenylation sites redefines gene spaces in Arabidopsis thaliana

Background:Messenger RNA polyadenylation is an essential step for the maturation of most eukaryotic mRNAs.Accurate determination of poly(A) sites helps define the 3’-ends of genes, which is important for genome annotation and gene function research. Genomic studies have revealed the presence of poly(A) sites in intergenic regions, which may be attributed to 3’-UTR extensions and novel transcript units. However, there is no systematically evaluation of intergenic poly(A) sites in plants. Results:Approximately 16,000 intergenic poly(A) site clusters (IPAC) in Arabidopsis thaliana were discovered and evaluated at the whole genome level. Based on the distributions of distance from IPACs to nearby sense and antisense genes, these IPACs were classified into three categories. About 70 % of them were from previously unannotated 3’-UTR extensions to known genes, which would extend 6985 transcripts of TAIR10 genome annotation beyond their 3’-ends, with a mean extension of 134 nucleotides. 1317 IPACs were originated from novel intergenic transcripts, 37 of which were likely to be associated with protein coding transcripts. 2957 IPACs corresponded to antisense transcripts for genes on the reverse strand, which might affect 2265 protein coding genes and 39 non-protein-coding genes, including long non-coding RNA genes. The rest of IPACs could be originated from transcriptional read-through or gene mis-annotations. Conclusions:The identified IPACs corresponding to novel transcripts, 3’-UTR extensions, and antisense transcription should be incorporated into current Arabidopsis genome annotation. Comprehensive characterization of IPACs from this study provides insights of alternative polyadenylation and antisense transcription in plants.Funding supports were in part from US National Science Foundation (No. 1541737 to QQL), the Hundred Talent Plans of Fujian Province and Xiamen City (to QQL). This project was also funded by the National Natural Science Foundation of China (Nos. 61201358 and 61174161), the Natural Science Foundation of Fujian Province of China (No. 2012J01154), and the specialized Research Fund for the Doctoral Program of Higher Education of China (Nos. 20120121120038 and 20130121130004), and the Fundamental Research Funds for the Central Universities in China (Xiamen University: Nos. 2013121025, 201412G009, and 2014X0234)

Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations

Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP, to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between visual and textual representation can not be fully eliminated. In this paper, we propose Expectation-Maximization Contrastive Learning (EMCL) to learn compact video-and-language representations. Specifically, we use the Expectation-Maximization algorithm to find a compact set of bases for the latent space, where the features could be concisely represented as the linear combinations of these bases. Such feature decomposition of video-and-language representations reduces the rank of the latent space, resulting in increased representing power for the semantics. Extensive experiments on three benchmark text-video retrieval datasets prove that our EMCL can learn more discriminative video-and-language representations than previous methods, and significantly outperform previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing approaches either as a jointly training layer or an out-of-the-box inference module with no extra training, making it easy to be incorporated into any existing methods.Comment: Accepted to NeurIPS 202

Identification of influential invaders in evolutionary populations

The identification of the most influential nodes has been a vibrant subject of research across the whole of network science. Here we map this problem to structured evolutionary populations, where strategies and the interaction network are both subject to change over time based on social inheritance. We study cooperative communities, which cheaters can invade because they avoid the cost of contributions that are associated with cooperation. The question that we seek to answer is at which nodes cheaters invade most successfully. We propose the weighted degree decomposition to identify and rank the most influential invaders. More specifically, we distinguish two kinds of ranking based on the weighted degree decomposition. We show that a ranking strategy based on negative-weighted degree allows to successfully identify the most influential invaders in the case of weak selection, while a ranking strategy based on positive-weighted degree performs better when the selection is strong. Our research thus reveals how to identify the most influential invaders based on statistical measures in dynamically evolving cooperative communities

Probing New Physics from Top-charm Associated Productions at Linear Colliders

The top-charm associated productions via $e^+ e^-$, $e^- \gamma$ and $\gamma \gamma$ collisions at linear colliders, which are extremely suppressed in the Standard Model (SM), could be significantly enhanced in some extensions of the SM. In this article we calculate the full contribution of the topcolor-assisted technicolor (TC2) to these productions and then compare the results with the existing predictions of the SM, the general two-Higgs-doublet model and the Minimal Supersymmetric Model. We find that the TC2 model predicts much larger production rates than other models and the largest-rate channel is $\gamma \gamma \to t \bar{c}$, which exceeds 10 fb for a large part of the parameter space. From the analysis of the observability of such productions at the future linear colliders, we find that the predictions of the TC2 model can reach the observable level for a large part of the parameter space while the predictions of other models are hardly accessible.Comment: discussions added (version in Eur. Phys. J. C

Topcolor assisted technicolor models and muon anomalous magnetic moment

We discuss and estimate the contributions of the new particles predicted by topcolor assisted technicolor(TC2) models to the muon anomalous magnetic moment $a_{\mu}$. Our results show that the contributions of Pseudo Goldstone bosons are very small which can be safely ignored. The main contributions come from the ETC gauge boson $x_{\mu}$ and topcolor gauge boson $Z^{\prime}$. If we demand that the mass of $Z^{\prime}$ is consistent with other experimental constrains, its contributions are smaller than that of $x_{\mu}$. With reasonable values of the parameters in TC2 models, the observed BNL results for $a_{\mu}$ could be explained.Comment: latex file, 11 pages, several figures and references adde

Efficacy of intra-arterial chemotherapy with sequential anti-PD-1 antibody in unresectable gastric cancer: A retrospective real-world study

BackgroundThe prognosis of unresectable gastric cancer is poor, while the efficacy of anti-PD antibodies has not been evaluated.MethodsPatients with unresectable gastric cancer who received intra-arterial chemotherapy (IAC) with sequential anti-PD-1 antibody as induction therapy in Jinling Hospital were retrospectively analyzed. The primary outcome is R0 resection rate. The secondary outcomes include safety, conversion surgery rate, overall survival (OS) and progression free survival (PFS) after postoperative IAC and anti-PD-1 treatments. Meanwhile, Tumor immunity in the microenvironment (TIME) before and after IAC was comprehensively dissected with multiplex immunofluorescence in order to detect possible mechanisms favoring anti-PD-1 treatment response.ResultsBetween May 2019 and October 2020, 36 patients received at least one cycle of IAC with sequential anti-PD-1 antibody in our institution. The objective response was achieved in 28 patients (77.8%). Thirty patients (83.3%) successfully underwent conversion surgery, among which R0 resection was managed in 25/30 patients, and 23.3% (7/30) was assessed as pathological complete remission. During the median follow-up period of 19.7 months, patients who underwent R0 resection displayed superior OS (HR 0.14 [95% CI 0.04-0.50], P < 0.0001) and PFS (HR 0.11 [0.03-0.44], P < 0.0001) than those who did not. Grade 3 adverse events (AEs) were only encountered in 19.4% patients, no grade 4 AEs observed. In TIME analysis, the number of tertiary lymphoid structures (TLSs) (P = 0.004) were greatly induced by IAC, as well as CD8+ T cells (P = 0.011) and PD-1+ cells (P = 0.025). Meanwhile, Tumor associated macrophages shifted towards anti-tumor M1-like subtypes, with CD68+CD163+ M2-like subpopulation significantly decreased (P = 0.04).ConclusionPreoperative IAC with sequential anti-PD-1 antibody exhibited promising clinical benefit for unresectable gastric cancer with remarkable conversion rate and R0 resection rate, and also prolonged survival as postoperative regimen. TIME transformation induced by ICA might mediate the additive effect with the immune checkpoint inhibitor