The genome sequence of the wisent (Bison bonasus)

This work was supported by the Youth Science and Technology Innovation Team of Sichuan Province (2014TD003), Shenzhen Industrial Designation Services Cloud Platform (GGJS20150429172906635), International Collaboration 111 Projects of China, Fundamental Research Funds for the Central Universities, 985 and 211 Projects of Sichuan University.The wisent, also known as the European bison, was rescued from extinction approximately 80 years ago through the conservation of 12 individuals. Here, we present the draft genome sequence of a male wisent individual descended from this founding stock. A total of 366 billion base pairs (Gb) of raw reads from whole-genome sequencing of this wisent were generated using the Illumina HiSeq2000 platform. The final genome assembly (2.58 Gb) is composed of 29,074 scaffolds with an N50 of 4.7 Mb. 47.3% of the genome is composed of repetitive elements. We identified 21,542 genes and 58,385 non-coding RNAs. A phylogenetic tree based on nuclear genomes indicated sister relationships between bison and wisent and between the wisent-bison clade and yak. For 75 genes we obtained evidence of positive evolution in the wisent lineage. We provide the first genome sequence and gene annotation for the wisent. The availability of these resources will be of value for the future conservation of this endangered large mammal and for reconstructing the evolutionary history of the Bovini tribe.Publisher PDFPeer reviewe

Ancient polymorphisms and divergence hitchhiking contribute to genomic islands of divergence within a poplar species complex

How genome divergence eventually leads to speciation is a topic of prime evolutionary interest. Genomic islands of elevated divergence are frequently reported between diverging lineages, and their size is expected to increase with time and gene flow under the speciation-with-gene-flow model. However, such islands can also result from divergent sorting of ancient polymorphisms, recent ecological selection regardless of gene flow, and/or recurrent background selection and selective sweeps in low-recombination regions. It is challenging to disentangle these nonexclusive alternatives, but here we attempt to do this in an analysis of what drove genomic divergence between four lineages comprising a species complex of desert poplar trees. Within this complex we found that two morphologically delimited species, Populus euphratica and Populus pruinosa, were paraphyletic while the four lineages exhibited contrasting levels of gene flow and divergence times, providing a good system for testing hypotheses on the origin of divergence islands. We show that the size and number of genomic islands that distinguish lineages are not associated with either rate of recent gene flow or time of divergence. Instead, they are most likely derived from divergent sorting of ancient polymorphisms and divergence hitchhiking. We found that highly diverged genes under lineage-specific selection and putatively involved in ecological and morphological divergence occur both within and outside these islands. Our results highlight the need to incorporate demography, absolute divergence measurement, and gene flow rate to explain the formation of genomic islands and to identify potential genomic regions involved in speciation.Publisher PDFPeer reviewe

The malnutrition in AECOPD and its association with unfavorable outcomes by comparing PNI, GNRI with the GLIM criteria: a retrospective cohort study

IntroductionThe management of nutritional risk has garnered significant attention in individuals diagnosed with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) due to the high prevalence of malnutrition and its correlation with unfavorable outcomes. While numerous rating scales exist to assist in assessment for both clinical and research purposes, there is considerable variability in the selection of scales based on the characteristics of the study participants and the study design. The objective of this study was to examine the efficacy of the Geriatric Nutritional Risk Index (GNRI) and Prognostic Nutritional Index (PNI) in identifying malnutrition and predicting prognosis in elderly AECOPD patients.MethodsFrom January 2022 to December 2022, a consecutive inclusion of elderly AECOPD patients admitted to the First Affiliated Hospital of Zhengzhou University was conducted. Diagnosing malnutrition in patients using PNI and GNRI, comparing the results with the diagnostic outcomes based on the Global Leadership Initiative on Malnutrition (GLIM) criteria through Receiver Operating Characteristic curves. Logistic regression analysis was employed to assess the risks associated with length of stay (LOS), hospitalization costs, and Charlson Comorbidity Index (CCI) based on GLIM, GNRI, or PNI.ResultsA total of 839 elderly AECOPD patients were investigated in the study. The GNRI and PNI demonstrated a sensitivity of 89.5 and 74.1%, specificity of 77.2 and 66.4%, and an area under the curve of 0.834 and 0.702, respectively. The identification of high malnutrition-risk cases using the GLIM, GNRI and PNI were associated with a significant increase in the risk of LOS over 7 days [odds ratio (95% CI) for GLIM, GNRI, PNI: 1.376 (1.033–1.833); 1.405 (1.070–1.846); 1.875 (1.425–2.468)] and higher hospitalization expenses [OR (95% CI) for GLIM, GNRI: 1.498 (1.080–2.080); 1.510 (1.097–2.079)], but not with the CCI.ConclusionAccording to our study, it is possible to use GNRI and PNI as alternatives to GLIM in the context of AECOPD, which makes it easier to identify malnutrition. The utilization of GNRI and PNI as alternatives to GLIM in the context of AECOPD enables the identification of malnutrition. The presence of malnourished individuals experiencing AECOPD is correlated with higher probabilities of extended hospital stays and escalated in-hospital expenses

Machine Translation Testing via Syntactic Tree Pruning

Machine translation systems have been widely adopted in our daily life, making life easier and more convenient. Unfortunately, erroneous translations may result in severe consequences, such as financial losses. This requires to improve the accuracy and the reliability of machine translation systems. However, it is challenging to test machine translation systems because of the complexity and intractability of the underlying neural models. To tackle these challenges, we propose a novel metamorphic testing approach by syntactic tree pruning (STP) to validate machine translation systems. Our key insight is that a pruned sentence should have similar crucial semantics compared with the original sentence. Specifically, STP (1) proposes a core semantics-preserving pruning strategy by basic sentence structure and dependency relations on the level of syntactic tree representation; (2) generates source sentence pairs based on the metamorphic relation; (3) reports suspicious issues whose translations break the consistency property by a bag-of-words model. We further evaluate STP on two state-of-the-art machine translation systems (i.e., Google Translate and Bing Microsoft Translator) with 1,200 source sentences as inputs. The results show that STP can accurately find 5,073 unique erroneous translations in Google Translate and 5,100 unique erroneous translations in Bing Microsoft Translator (400% more than state-of-the-art techniques), with 64.5% and 65.4% precision, respectively. The reported erroneous translations vary in types and more than 90% of them cannot be found by state-of-the-art techniques. There are 9,393 erroneous translations unique to STP, which is 711.9% more than state-of-the-art techniques. Moreover, STP is quite effective to detect translation errors for the original sentences with a recall reaching 74.0%, improving state-of-the-art techniques by 55.1% on average.Comment: Accepted to ACM Transactions on Software Engineering and Methodology 2024 (TOSEM'24

DARA: Domain- and Relation-aware Adapters Make Parameter-efficient Tuning for Visual Grounding

Visual grounding (VG) is a challenging task to localize an object in an image based on a textual description. Recent surge in the scale of VG models has substantially improved performance, but also introduced a significant burden on computational costs during fine-tuning. In this paper, we explore applying parameter-efficient transfer learning (PETL) to efficiently transfer the pre-trained vision-language knowledge to VG. Specifically, we propose \textbf{DARA}, a novel PETL method comprising \underline{\textbf{D}}omain-aware \underline{\textbf{A}}dapters (DA Adapters) and \underline{\textbf{R}}elation-aware \underline{\textbf{A}}dapters (RA Adapters) for VG. DA Adapters first transfer intra-modality representations to be more fine-grained for the VG domain. Then RA Adapters share weights to bridge the relation between two modalities, improving spatial reasoning. Empirical results on widely-used benchmarks demonstrate that DARA achieves the best accuracy while saving numerous updated parameters compared to the full fine-tuning and other PETL methods. Notably, with only \textbf{2.13\%} tunable backbone parameters, DARA improves average accuracy by \textbf{0.81\%} across the three benchmarks compared to the baseline model. Our code is available at \url{https://github.com/liuting20/DARA}.Comment: Accepted by ICME 2024 (Oral

MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling

Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the underlying heterogeneity and non-stationarity implied in the graph streams, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (METR-LA and PEMS-BAY) and a large-scale spatio-temporal dataset that contains a variaty of non-stationary phenomena. Our model outperformed the state-of-the-arts to a large degree on all three datasets (over 27% MAE and 34% RMSE). Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle locations and time slots with different patterns and be robustly adaptive to different anomalous situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.Comment: Preprint submitted to Artificial Intelligence. arXiv admin note: substantial text overlap with arXiv:2211.1470

Single Nanoparticle Translocation Through Chemically Modified Solid Nanopore

The nanopore sensor as a high-throughput and low-cost technology can detect single nanoparticle in solution. In the present study, the silicon nitride nanopores were fabricated by focused Ga ion beam (FIB), and the surface was functionalized with 3-aminopropyltriethoxysilane to change its surface charge density. The positively charged nanopore surface attracted negatively charged nanoparticles when they were in the vicinity of the nanopore. And, nanoparticle translocation speed was slowed down to obtain a clear and deterministic signal. Compared with previous studied small nanoparticles, the electrophoretic translocation of negatively charged polystyrene (PS) nanoparticles (diameter ~100 nm) was investigated in solution using the Coulter counter principle in which the time-dependent nanopore current was recorded as the nanoparticles were driven across the nanopore. A linear dependence was found between current drop and biased voltage. An exponentially decaying function (t(d)  ~ e(−v/v0)) was found between the duration time and biased voltage. The interaction between the amine-functionalized nanopore wall and PS microspheres was discussed while translating PS microspheres. We explored also translocations of PS microspheres through amine-functionalized solid-state nanopores by varying the solution pH (5.4, 7.0, and 10.0) with 0.02 M potassium chloride (KCl). Surface functionalization showed to provide a useful step to fine-tune the surface property, which can selectively transport molecules or particles. This approach is likely to be applied to gene sequencing. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s11671-016-1255-6) contains supplementary material, which is available to authorized users

Research progress of tumor-associated macrophages in immune microenvironment and targeted therapy of osteosarcoma

Osteosarcoma (OS) is a common primary malignant bone tumor in children and adolescents. The high recurrence and metastasis rate have become a common clinical problem to be solved, but there is no effective treatment. In recent years, studies have suggested that targeting the tumor microenvironment will likely become a new treatment direction for OS. Immune cell infiltration in the tumor microenvironment can promote tumor inflammation and angiogenesis. Tumor-associated macrophages (TAMs) are the most important immune cells in the tumor microenvironment, which play important roles in the development and metastasis of OS. The article reviews the effect of TAMs polarization on tumor cells and describes the effect of TAMs on the occurrence and development of OS from five aspects, including TAMs affecting the growth, invasion and metastasis, mediating chemotherapy resistance, stem cell-like phenotype, and immunosuppression of OS. The review summarizes the research progress of targeting TAMs in the treatment of OS in the past years, including influencing the recruitment of TAMs, promoting the polarization of M2 type to M1 type, targeting CD47 to promote the phagocytosis of TAMs, and targeting the immune checkpoint of TAMs, aiming to provide new directions and ideas for targeted therapy of OS