Reflections on the Innovation of University Scientific Research Management in the Era of Big Data

In China, universities are important centers for SR (scientific research) and innovation, and the quality of SR management has a significant impact on university innovation. The informatization of SR management is a critical component of university development in the big data environment. As a result, it is crucial to figure out how to improve SR management. As a result, this paper builds a four-tier B/W/D/C (Browser/Web/Database/Client) university SR management innovation information system based on big data technology and thoroughly examines the system’s hardware and software configuration. The SVM-WNB (Support Vector Machine-Weighted NB) classification algorithm is proposed, and the improved algorithm runs in parallel on the Hadoop cloud computing platform, allowing the algorithm to process large amounts of data efficiently. The optimization strategy proposed in this paper can effectively optimize the execution of scientific big data applications according to a large number of simulation experiments and real-world multidata center environment experiments.

Analysis of 272 genetic variants in the upgraded interactive FXI web database reveals new insights on FXI deficiency

Coagulation Factor XI (FXI) is a plasma glycoprotein composed of four apple (Ap) domains and a serine protease (SP) domain. FXI circulates as a dimer and activates Factor IX (FIX), promoting thrombin production and preventing excess blood loss. Genetic variants that degrade FXI structure and function often lead to bleeding diatheses, commonly termed FXI deficiency. The first interactive FXI variant database underwent initial development in 2003 at https://www.factorxi.org. Here, based on a much improved FXI crystal structure, the upgraded FXI database contains information regarding 272 FXI variants (including 154 missense variants) found in 657 patients, this being a significant increase from the 183 variants identified in the 2009 update. Type I variants involve the simultaneous reduction of FXI coagulant activity (FXI:C) and FXI antigen levels (FXI:Ag), whereas Type II variants result in decreased FXI:C yet normal FXI:Ag. The database updates now highlight the predominance of Type I variants in FXI. Analysis in terms of a consensus Ap domain revealed the near-uniform distribution of 81 missense variants across the Ap domains. A further 66 missense variants were identified in the SP domain, showing that all regions of the FXI protein were important for function. The variants clarified the critical importance of changes in surface solvent accessibility, as well as those of cysteine residues and the dimer interface. Guidelines are provided below for clinicians who wish to use the database for diagnostic purposes. In conclusion, the updated database provides an easy-to-use web resource on FXI deficiency for clinicians.

Smoking a Dangerous Addiction: A Systematic Review on an Underrated Risk Factor for Oral Diseases

Despite growing knowledge of the adverse effects of cigarette smoking on general health, smoking is one of the most widely prevalent addictions around the world. Globally, about 1.1 billion smokers and over 8 million people die each year because of cigarette smoking. Smoking acts as a source for a variety of oral and systemic diseases. Various periodontal issues such as increased pocket depth, loss of alveolar bone, tooth mobility, oral lesions, ulcerations, halitosis, and stained teeth are more common among smokers. This systematic review was conducted according to the guidelines from PRISMA, and research articles were retrieved from the Web database sources on 31 May 2021. The quality of research articles was ensured by the type of evidence from combined schema incorporating as schema-13 evidence type description, Cochrane health promotion and public health field (CHPPHF), and the health gains notation framework-14 screening question for quality assessment of qualitative and quantitative studies. Smokers have been found to have bleeding on probing, periodontal pockets, and clinical attachment loss compared to nonsmokers. Oral and respiratory cancers are among the most lethal known diseases caused by cigarette smoking and other commonly occurring sequelae such as stained teeth, periodontal diseases, etc.

Who Is Where in Marine Food Webs? A Trait-Based Analysis of Network Positions

Networks of trophic interactions provide a lot of information on the functioning of marine ecosystems. Beyond feeding habits, three additional traits (mobility, size, and habitat) of various organisms can complement this trophic view. The combination of traits and food web positions are studied here on a large food web database. The aim is a better description and understanding of ecological roles of organisms and the identification of the most important keystone species. This may contribute to develop better ecological indicators (e.g., keystoneness) and help in the interpretation of food web models. We use food web data from the Ecopath with Ecosim (EwE) database for 92 aquatic ecosystems. We quantify the network position of organisms by 18 topological indices (measuring centrality, hierarchy, and redundancy) and consider their three, categorical traits (e.g., for mobility: sessile, drifter, limited mobility, and mobile). Relationships are revealed by multivariate analysis. We found that topological indices belong to six different categories and some of them nicely separate various trait categories. For example, benthic organisms are richly connected and mobile organisms occupy higher food web positions.

T1TAdb: the database of Type I Toxin-Antitoxin systems

Type I toxin-antitoxin (T1TA) systems constitute a large class of genetic modules with antisense RNA (asRNA)-mediated regulation of gene expression. They are widespread in bacteria and consist of an mRNA coding for a toxic protein and a noncoding asRNA that acts as an antitoxin preventing the synthesis of the toxin by directly basepairing to its cognate mRNA. The co- and post-transcriptional regulation of T1TA systems is intimately linked to RNA sequence and structure, therefore it is essential to have an accurate annotation of the mRNA and asRNA molecules to understand this regulation. However, most T1TA systems have been identified by means of bioinformatic analyses solely based on the toxin protein sequences, and there is no central repository of information on their specific RNA features. Here we present the first database dedicated to type I TA systems, named T1TAdb. It is an open-access web database (https://d-lab.arna.cnrs.fr/t1tadb) with a collection of ~1,900 loci in ~500 bacterial strains in which a toxin-coding sequence has been previously identified. RNA molecules were annotated with a bioinformatic procedure based on key determinants of the mRNA structure and the genetic organization of the T1TA loci. Besides RNA and protein secondary structure predictions, T1TAdb also identifies promoter, ribosome-binding, and mRNA-asRNA interaction sites. It also includes tools for comparative analysis, such as sequence similarity search and computation of structural multiple alignments, which are annotated with covariation information. To our knowledge, T1TAdb represents the largest collection of features, sequences, and structural annotations on this class of genetic modules.

Classification of Biological Interactions: Challenges in the field and in analysis

Within biological communities, species interact in a wide variety of ways. Species interactions have always been noted and classified by naturalists in describing living organisms and their ways. Moreover, they are essential to characterize ecological communities as functioning entities. Biodiversity databases, as a rule, are comprised of species records in certain localities and times. Many, if not most, originated as databases of museum specimens and/or published records. As such, they provide data on species occurrences and distribution, with little functional information. Currently, online databases for species interaction data are being formed or proposed. Usually, these databases set out to compile data from actual field studies, and their design reflects the singularities of particular studies that seed their development. In two online databases: the Web of Life (2021) and the Interaction Web DataBase (2020) (IWDB), the categories of interactions are quite heterogeneous (Table 1). For instance, they may refer explicitly to certain taxonomic groups (e.g., anemone-fish), or do so implicitly (host-parasitoid; parasitoids are all holometabolous insects with arthropod hosts); conversely, they may encompass almost any taxon (food webs). In another example, the Global Biotic Interactions database (Poelen et al. 2014) (GloBI) offers a choice of relational attributes when entering data, ranging from undefined to quite restricted (Table 2). Here we intend to contribute to the development of interaction databases, from two different points of view. First, what categories can be effectively applied to field observations of biotic interactions? Second, what theoretical and applied questions do we expect to address with interaction databases? These should be equally applicable to comparisons of studies of the same kind or mode of interaction, and to contrasts between interactions in multimodal studies.

From Text-based Genome, Population Variations, and Transcriptome Datafiles to SQLite Database and Web Application: A Bioinformatical Study on Alfalfa

In this study, a web database application with the Flask framework was developed to implement three types of queries and visualize the results over a bioinformatical dataset from Alfalfa (Medicago sativa). A backend SQLite database was constructed from genome FASTA, population variations, transcriptome, and annotation files with extensions ".fasta", ".gff", "vcf", ".annotate", etc. Further, a supplementary command-line-based Java application was also developed for faster access to the database without direct SQL programming. Overall, Python, Java, and HTML were the main programming languages used in this application. Those scripts and the development procedures are valuable for bioinformaticians to build online databases from similar raw datasets of other species.