Predicting Influenza Antigenicity by Matrix Completion With Antigen and Antiserum Similarity

The rapid mutation of influenza viruses especially on the two surface proteins hemagglutinin (HA) and neuraminidase (NA) has made them capable to escape from population immunity, which has become a key challenge for influenza vaccine design. Thus, it is crucial to predict influenza antigenic evolution and identify new antigenic variants in a timely manner. However, traditional experimental methods like hemagglutination inhibition (HI) assay to select vaccine strains are time and labor-intensive, while popular computational methods are less sensitive, which presents the need for more accurate algorithms. In this study, we have proposed a novel low-rank matrix completion model MCAAS to infer antigenic distances between antigens and antisera based on partially revealed antigenic distances, virus similarity based on HA protein sequences, and vaccine similarity based on vaccine strains. The model exploits the correlations of viruses and vaccines in serological tests as well as the ability of HAs from viruses and vaccine strains in inferring influenza antigenicity. We also compared the effects of comprehensive 65 amino acids substitution matrices in predicting influenza antigenicity. As a result, we applied MCAAS into H3N2 seasonal influenza virus data. Our model achieved a 10-fold cross validation root-mean-squared error (RMSE) of 0.5982, significantly outperformed existing computational methods like antigenic cartography, AntigenMap and BMCSI. We also constructed the antigenic map and studied the association between genetic and antigenic evolution of H3N2 influenza viruses. Finally, our analyses showed that homologous structure derived amino acid substitution matrix (HSDM) is most powerful in predicting influenza antigenicity, which is consistent with previous studies

Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures

Playing critical roles as post-transcriptional regulators, microRNAs (miRNAs) are a family of short non-coding RNAs that are derived from longer transcripts called precursor miRNAs (pre-miRNAs). Experimental methods to identify pre-miRNAs are expensive and time-consuming, which presents the need for computational alternatives. In recent years, the accuracy of computational methods to predict pre-miRNAs has been increasing significantly. However, there are still several drawbacks. First, these methods usually only consider base frequencies or sequence information while ignoring the information between bases. Second, feature extraction methods based on secondary structures usually only consider the global characteristics while ignoring the mutual influence of the local structures. Third, methods integrating high-dimensional feature information is computationally inefficient. In this study, we have proposed a novel mutual information-based feature representation algorithm for pre-miRNA sequences and secondary structures, which is capable of catching the interactions between sequence bases and local features of the RNA secondary structure. In addition, the feature space is smaller than that of most popular methods, which makes our method computationally more efficient than the competitors. Finally, we applied these features to train a support vector machine model to predict pre-miRNAs and compared the results with other popular predictors. As a result, our method outperforms others based on both 5-fold cross-validation and the Jackknife test

Navigating the Complexity of Describing Chinese Historical Places Using the Wikibase Platform

Chinese historical places, with their extensive history spanning thousands of years, serve as profound symbols of the nation's rich cultural legacy. Capturing their historical changes in a consistent data model for describing cultural heritage, however, poses significant challenges due to shifts in the places’ name, administrative boundaries, administrative units, and geographical locations. Various data models on platforms like Wikidata offer differing approaches: some separate place names and locations into distinct classes, while others consolidate them. This poster showcases the authors’ investigation into different data models for describing cultural heritage historical places and their chosen approach. By leveraging Wikibase as a testbed database, the authors delve into the merits and limitations of each data model, unveiling the techniques employed for bulk data creation, complexities encountered, and valuable insights derived from this exploration.</p

Synthesis and Characterization of the Optical Properties of Pt-TiO 2

Composite Pt-doped TiO2 nanotubes (Pt-TNTs) were synthesized via alkaline fusion-hydrothermal method (AFHM) under ambient atmosphere pressure. Further systematic characterization of Pt-TNTs was performed by using XPS, surface photovoltage spectroscopy (SPS), electric field-induced surface photovoltage spectroscopy (FISPS), UV-Vis diffuse reflectance spectrophotometry (UV-Vis), TEM, and XRD. XPS spectrum showed double peaks which accounted for the presence of platinum dioxide and platinum oxide (PtO2 and PtO, PtOxδ+). Composition analysis showed that the particulate matters on surface of Pt-TNTs were composed of PtOxδ+ and TiO2. The results of SPS and FISPS demonstrated that the bound exciton showed sub-band gap transition characteristics with the asymmetric changes of photoelectric property corresponding to changes in polarity and strength of the external electric field. Furthermore, the influence of the changed microstructure morphology of Pt-doped TNTs on both the photovoltage spectroscopy and the lifetime of photogenerated carriers which occurred at the interfaces of Pt-TNTs was observed. Result of XRD indicated that a mixture of anatase and rutile phases prevailed in Pt-TNTs. Contact potential barriers consisting of PtOxδ+, anatase, rutile, and PtOxδ+ are presumed to form upon PtOxδ+ particle that deposited on the surface of Pt-TNTs

BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network

In recent years, it has been increasingly clear that long noncoding RNAs (lncRNAs) play critical roles in many biological processes associated with human diseases. Inferring potential lncRNA-disease associations is essential to reveal the secrets behind diseases, develop novel drugs, and optimize personalized treatments. However, biological experiments to validate lncRNA-disease associations are very time-consuming and costly. Thus, it is critical to develop effective computational models. In this study, we have proposed a method called BPLLDA to predict lncRNA-disease associations based on paths of fixed lengths in a heterogeneous lncRNA-disease association network. Specifically, BPLLDA first constructs a heterogeneous lncRNA-disease network by integrating the lncRNA-disease association network, the lncRNA functional similarity network, and the disease semantic similarity network. It then infers the probability of an lncRNA-disease association based on paths connecting them and their lengths in the network. Compared to existing methods, BPLLDA has a few advantages, including not demanding negative samples and the ability to predict associations related to novel lncRNAs or novel diseases. BPLLDA was applied to a canonical lncRNA-disease association database called LncRNADisease, together with two popular methods LRLSLDA and GrwLDA. The leave-one-out cross-validation areas under the receiver operating characteristic curve of BPLLDA are 0.87117, 0.82403, and 0.78528, respectively, for predicting overall associations, associations related to novel lncRNAs, and associations related to novel diseases, higher than those of the two compared methods. In addition, cervical cancer, glioma, and non-small-cell lung cancer were selected as case studies, for which the predicted top five lncRNA-disease associations were verified by recently published literature. In summary, BPLLDA exhibits good performances in predicting novel lncRNA-disease associations and associations related to novel lncRNAs and diseases. It may contribute to the understanding of lncRNA-associated diseases like certain cancers

A practical guide to photoacoustic tomography in the life sciences

The life sciences can benefit greatly from imaging technologies that connect microscopic discoveries with macroscopic observations. One technology uniquely positioned to provide such benefits is photoacoustic tomography (PAT), a sensitive modality for imaging optical absorption contrast over a range of spatial scales at high speed. In PAT, endogenous contrast reveals a tissue's anatomical, functional, metabolic, and histologic properties, and exogenous contrast provides molecular and cellular specificity. The spatial scale of PAT covers organelles, cells, tissues, organs, and small animals. Consequently, PAT is complementary to other imaging modalities in contrast mechanism, penetration, spatial resolution, and temporal resolution. We review the fundamentals of PAT and provide practical guidelines for matching PAT systems with research needs. We also summarize the most promising biomedical applications of PAT, discuss related challenges, and envision PAT's potential to lead to further breakthroughs

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Expression Profiling of Exosomal miRNAs Derived from Human Esophageal Cancer Cells by Solexa High-Throughput Sequencing

Cellular genetic materials, such as microRNAs (miRNAs), mRNAs and proteins, are packaged inside exosomes, small membrane vesicles of endocytic origin that are released into the extracellular environment. These cellular genetic materials can be delivered into recipient cells, where they exert their respective biological effects. However, the miRNA profiles and biological functions of exosomes secreted by cancer cells remain unknown. The present study explored the miRNA expression profile and distribution characteristics of exosomes derived from human esophageal cancer cells through Solexa high-throughput sequencing. Results showed that 56,421 (2.94%) unique sequences in cells and 7727 (0.63%) in exosomes matched known miRNAs. A total of 342 and 48 known miRNAs were identified in cells and exosomes, respectively. Moreover, 64 and 32 novel miRNAs were predicted in cells and exosomes, respectively. Significant differences in miRNA expression profiles were found between human esophageal cancer cells and exosomes. These findings provided new insights into the characteristics of miRNAs in exosomes derived from human esophageal cancer cells and the specific roles of miRNAs in intercellular communication mediated by exosomes in esophageal cancer

Intracavitary electrocardiography for peripherally inserted central catheters tip location and nursing for a patient with persistent atrial fibrillation and acute left heart failure (1例持续性房颤伴急性左心衰患者腔内心电图定位下PICC置管的护理)

This paper summarized the experience of using intracavitary electrocardiography (ECG) for peripherally inserted central catheters (PICC) tip location and related nursing management for a patient with persistent atrial fibrillation and acute left heart failure. A multidisciplinary team was established to evaluate the feasibility and risk of PICC placement, and provide corresponding measures. In order to reduce the risk of catheter misplacement, the intracavitary ECG was used to guide the PICC tip location. The blunt dissection method was used for skin expansion to reduce the risk of puncture site bleeding after PICC catheter placement. Pressure dressing was carried out to prevent bleeding at local puncture area, and regular assessment was conducted to ensure effective use of PICC catheter. The catheter was successfully placed fir 162 d and no catheter-related complication was reported. (总结1例持续性房颤伴急性左心衰患者腔内心电图定位下经外周静脉置入中心静脉导管(PICC)的护理经验。护理重点: 术前多学科合作, 团队成员讨论PICC置管可行性, 评估置管风险, 提出应对措施; 术中使用超声联合腔内心电图定位技术实时监测导管尖端位置, 减少导管异位风险, 采用钝性分离方法代替传统扩皮刀扩皮以减少术后穿刺点出血; 术后通过局部加压包扎预防局部出血; 带管期间定期评估确保导管功能正常。导管成功留置162d, 无相关并发症发生, 完成治疗后拔除导管。

The Appropriate Combination of Hemagglutinin and Neuraminidase Prompts the Predominant H5N6 Highly Pathogenic Avian Influenza Virus in Birds

Haemagglutinin (HA) and neuraminidase (NA) are two vital surface glycoproteins of influenza virus. The HA of H5N6 highly pathogenic avian influenza virus is divided into Major/H5 and Minor/H5, and its NA consists of short stalk NA and full-length stalk NA. The strain combined with Major/H5 and short stalk NA account for 76.8% of all strains, and the proportion was 23.0% matched by Minor/H5 and full-length stalk NA. Our objective was to investigate the influence of HA–NA matching on the biological characteristics and the effects of the epidemic trend of H5N6 on mice and chickens. Four different strains combined with two HAs and two NAs of the represented H5N6 viruses with the fixed six internal segments were rescued and analyzed. Plaque formation, NA activity of infectious particles, and virus growth curve assays, as well as a saliva acid receptor experiment, with mice and chickens were performed. We found that all the strains can replicate well on Madin–Darby canine kidney (MDCK) cells and chicken embryo fibroblasts (CEF) cells, simultaneously, mice and infection group chickens were complete lethal. However, the strain combined with Major/H5 and short stalk N6 formed smaller plaque on MDCK, showed a moderate replication ability in both MDCK and CEF, and exhibited a higher survival rate among the contact group of chickens. Conversely, strains with opposite biological characters which combined with Minor/H5 and short stalk N6 seldom exist in nature. Hence, we drew the conclusion that the appropriate combination of Major/H5 and short stalk N6 occur widely in nature with appropriate biological characteristics for the proliferation and transmission, whereas other combinations of HA and NA had a low proportion and even have not yet been detected