Necessary prerequisites for evidence-based practice:results of investigating nurses'informatics competency and information literacy skills

This study aimed to evaluate the correlation between nursing informatics(NI)competency and information literacy skills for evidencebased practice(EBP)among intensive care nurses.This cross-sectional study was conducted on 184 nurses working in intensive care units(ICUs).The study data were collected through demographic information,Nursing Informatics Competency Assessment Tool(NICAT),and information literacy skills for EBP questionnaires.The intensive care nurses received competent and low-moderate levels for the total scores of NI competency and information literacy skills,respectively.They received a moderate score for the use of different information resources but a low score for information searching skills,different search features,and knowledge about search operators,and only 31.5%of the nurses selected the most appropriate statement.NI competency and related subscales had a significant direct bidirectional correlation with information literacy skills for EBP and its subscales(P<0.05).Nurses require a high level of NI competency and information literacy for EBP to obtain up-to-date information and provide better care and decision-making.Health planners and policymakers should develop interventions to enhance NI competency and information literacy skills among nurses and motivate them to use EBP in clinical settings.

Decoding bovine coronavirus immune targets:an epitope informatics approach

Bovine coronavirus(BCoV)poses a significant threat to the global cattle industry,causing both respiratory and gastrointestinal infections in cattle populations.This necessitates the development of efficacious vaccines.While several inactivated and live BCoV vaccines exist,they are predominantly limited to calves.The immunization of adult cattle is imperative for BCoV infection control,as it curtails viral transmission to calves and ameliorates the impact of enteric and respiratory ailments across all age groups within the herd.This study presents an in silico methodology for devising a multiepitope vaccine targeting BCoV.The spike glycoprotein(S)and nucleocapsid(N)proteins,which are integral elements of the BCoV structure,play pivotal roles in the viral infection cycle and immune response.We constructed a remarkably effective multiepitope vaccine candidate specifically designed to combat the BCoV population.Using immunoinformatics technology,B-cell and T-cell epitopes were predicted and linked together using linkers and adjuvants to efficiently trigger both cellular and humoral immune responses in cattle.The in silico construct was characterized,and assessment of its physicochemical properties revealed the formation of a stable vaccine construct.After 3D modeling of the vaccine construct,molecular docking revealed a stable interaction with the bovine receptor bTLR4.Moreover,the viability of the vaccine’s high expression and simple purification was demonstrated by codon optimization and in silico cloning expression into the pET28a(+)vector.By applying immunoinformatics approaches,researchers aim to better understand the immune response to bovine coronavirus,discover potential targets for intervention,and facilitate the development of diagnostic tools and vaccines to mitigate the impact of this virus on cattle health and the livestock industry.We anticipate that the design will be useful as a preventive treatment for BCoV sickness in cattle,opening the door for further laboratory studies.

Impact of Informatics on QIPC

Quantum information processing and communication(QIPC) is an area of science that has two main goals: On one side,it tries to explore(still not well known) potential of quantum phenomena for(efficient and reliable) information processing and(efficient,reliable and secure) communication.On the other side,it tries to use quantum information storing,processing and transmitting paradigms,principles,laws,limitations,concepts,models and tools to get deeper insights into the phenomena of quantum world and to find efficient ways to describe and handle/simulate various complex physical phenomena.In order to do that QIPC has to use concepts,models,theories,methods and tools of both physics and informatics.The main role of physics at that is to discover primitive physical phenomena that can be used to design and maintain complex and reliable information storing,processing and transmitting systems.The main role of informatics is,one one side,to explore,from the information processing and communication point of view,limitations and potentials of the potential quantum information processing and communication technology,and to prepare information processing methods that could utilise potential of quantum information processing and communication technologies.On the other side,the main role of informatics is to guide and support,by theoretical tools and outcomes,physics oriented research in QIPC.The paper is to describe and analyse a variety of ways and potential informatics contributes and should/could contribute to the development of QIPC--see also Gruska(1999,2006,2008).

Big Data Bot with a Special Reference to Bioinformatics

There are quintillions of data on deoxyribonucleic acid(DNA)and protein in publicly accessible data banks,and that number is expanding at an exponential rate.Many scientific fields,such as bioinformatics and drug discovery,rely on such data;nevertheless,gathering and extracting data from these resources is a tough undertaking.This data should go through several processes,including mining,data processing,analysis,and classification.This study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human intervention.This software simulates the extraction of data from web-based(point-and-click)resources or graphical user interfaces that cannot be accessed using command-line tools.The software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides(AMP)sequences(46240 hits)from various MARVIN software panels,which can be later utilized to develop novel AMPs.Furthermore,for machine learning research,the program was validated by extracting 10,000 protein tertiary structures from the Protein Data Bank.As a result,data collection from the web will become faster and less expensive,with no need for manual data extraction.The software is critical as a first step to preparing large datasets for subsequent stages of analysis,such as those using machine and deep-learning applications.

Prediction of SARS-CoV-2 hosts among Brazilian mammals and new coronavirus transmission chain using evolutionary bioinformatics

Severe acute respiratory syndrome coronavirus(SARS-CoV)and SARS-CoV-2 are thought to transmit to humans via wild mammals,especially bats.However,evidence for direct bat-to-human transmission is lacking.Involvement of intermediate hosts is considered a reason for SARS-CoV-2 transmission to humans and emergence of outbreak.Large biodiversity is found in tropical territories,such as Brazil.On the similar line,this study aimed to predict potential coronavirus hosts among Brazilian wild mammals based on angiotensin-converting enzyme 2(ACE2)sequences using evolutionary bioinformatics.Cougar,maned wolf,and bush dogs were predicted as potential hosts for coronavirus.These indigenous carnivores are philogenetically closer to the known SARS-CoV/SARS-CoV-2 hosts and presented low ACE2 divergence.A new coronavirus transmission chain was developed in which white-tailed deer,a susceptible SARS-CoV-2 host,have the central position.Cougar play an important role because of its low divergent ACE2 level in deer and humans.The discovery of these potential coronavirus hosts will be useful for epidemiological surveillance and discovery of interventions that can contribute to break the transmission chain.

Applying Double Skin Facade with ETFE Membrane Assembly for Energy Saving and Acoustic Protection for the Building of the Czech Institute of Informatics,Robotics and Cybernetics in Prague

Multidisciplinary, integrated planning approach by architects, engineers, scientists and manufacturers to reduce energy consumption of buildings. The CIIRC Complex, located on the main campus of Czech Technical University in Prague consists of two buildings, newly constructed building and adaptive reuse of existing building. CIIRC—Czech Institute of Informatics, Robotics and Cybernetics is a contemporary teaching facility of new generation and use for scientific research teams. New building has ten above-ground floors, on the bottom 4 floors of laboratories, scientist modules, classrooms, above are offices, meeting rooms, teaching and research modules for professors and students. Offices of the rector are on the last two floors of the building. On the top floor is congress type auditorium, in the basement is fully automatic car park. Double skin pneumatic cushions facade. In the project are introduced series of architectural and technical features and innovations. Probably the most visible is the double skin facade facing south-transparent double layer membrane ETFE (Ethylen-TetraFluorEthylen) cushions with triple glazed modular system assembly. Acting as solar collector, recuperating of hot air on the top floors, saving up to 30% of an energy consumption.

Translational Informatics for Parkinson’s Disease:from Big Biomedical Data to Small Actionable Alterations

Parkinson’s disease(PD)is a common neurological disease in elderly people,and its morbidity and mortality are increasing with the advent of global ageing.The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations.To highlight the use of big data for precision PD medicine,we review PD big data and informatics for the translation of basic PD research to clinical applications.We emphasize some key findings in clinically actionable changes,such as susceptibility genetic variations for PD risk population screening,biomarkers for the diagnosis and stratification of PD patients,risk factors for PD,and lifestyles for the prevention of PD.The challenges associated with the collection,storage,and modelling of diverse big data for PD precision medicine and healthcare are also summarized.Future perspectives on systems modelling and intelligent medicine for PD monitoring,diagnosis,treatment,and healthcare are discussed in the end.

A bioinformatics method for predicting long noncoding RNAs associated with vascular disease

Long noncoding RNAs(lncRNAs)play important roles in human diseases including vascular disease.Given the large number of lncRNAs,however,whether the majority of them are associated with vascular disease remains unknown.For this purpose,here we present a genomic location based bioinformatics method to predict the lncRNAs associated with vascular disease.We applied the presented method to globally screen the human lncRNAs potentially involved in vascular disease.As a result,we predicted 3043 putative vascular disease associated lncRNAs.To test the accuracy of the method,we selected 10 lncRNAs predicted to be implicated in proliferation and migration of vascular smooth muscle cells(VSMCs)for further experimental validation.The results confirmed that eight of the 10 lncRNAs(80%)are validated.This result suggests that the presented method has a reliable prediction performance.Finally,the presented bioinformatics method and the predicted vascular disease associated lncRNAs together may provide helps for not only better understanding of the roles of lncRNAs in vascular disease but also the identification of novel molecules for the diagnosis and therapy of vascular disease.

A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

YPED: An Integrated Bioinformatics Suite and Database for Mass Spectrometry-based Proteomics Research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database（YPED） that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a singlelaboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography–tandem mass spectrometry（LC–MS/MS） database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring（MRM）/selective reaction monitoring（SRM） assay development. We have linked YPED＇s database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.

Storage time affects the level and diagnostic efficacy of plasma biomarkers for neurodegenerative diseases

Several promising plasma biomarker proteins,such as amyloid-β(Aβ),tau,neurofilament light chain,and glial fibrillary acidic protein,are widely used for the diagnosis of neurodegenerative diseases.However,little is known about the long-term stability of these biomarker proteins in plasma samples stored at-80°C.We aimed to explore how storage time would affect the diagnostic accuracy of these biomarkers using a large cohort.Plasma samples from 229 cognitively unimpaired individuals,encompassing healthy controls and those experiencing subjective cognitive decline,as well as 99 patients with cognitive impairment,comprising those with mild cognitive impairment and dementia,were acquired from the Sino Longitudinal Study on Cognitive Decline project.These samples were stored at-80°C for up to 6 years before being used in this study.Our results showed that plasma levels of Aβ42,Aβ40,neurofilament light chain,and glial fibrillary acidic protein were not significantly correlated with sample storage time.However,the level of total tau showed a negative correlation with sample storage time.Notably,in individuals without cognitive impairment,plasma levels of total protein and tau phosphorylated protein threonine 181(p-tau181)also showed a negative correlation with sample storage time.This was not observed in individuals with cognitive impairment.Consequently,we speculate that the diagnostic accuracy of plasma p-tau181 and the p-tau181 to total tau ratio may be influenced by sample storage time.Therefore,caution is advised when using these plasma biomarkers for the identification of neurodegenerative diseases,such as Alzheimer's disease.Furthermore,in cohort studies,it is important to consider the impact of storage time on the overall results.

A Survey of the Availability of Primary Bioinformatics Web Resources

The explosive growth of the bioinformatics field has led to a large amount of data and software applications publicly available as web resources. However, the lack of persistence of web references is a barrier to a comprehensive shared access. We conducted a study of the current availability and other features of primary bioinforo matics web resources （such as software tools and databases）. The majority （95%） of the examined bioinformatics web resources were found running on UNIX/Linux operating systems, and the most widely used web server was found to be Apache （or Apache-related products）. Of the overall 1,130 Uniform Resource Locators （URLs） examined, 91% were highly available （more than 90% of the time）, while only 4% showed low accessibility （less than 50% of the time） during the survey. Furthermore, the most common URL failure modes are presented and analyzed.

Special collection of bioinformatics in the era of precision medicine

Precision medicine advocates for the practice of customized disease treatment and prevention such that all clinical decisions are made based on the characteristics of individual patients. The precision medicine framework has been enthusiastically endorsed by the health care community and is set to have a profound impact on the health care practice. However, adopting the precision medicine ideology in the clinics requires solving a series of technical challenges, particularly in informatics and data analytics. To better serve this newly emerging area of research, we present this special issue to showcase the latest research developments in bioinformatics which has been widely regarded as a major stakeholder in the advancement of precision medicine. The ten papers appear in special collection cover a wide range of topics and can be broadly categorized into the following three main areas： statistical method development; informatics method development and algorithms for translational bioinformatics.

Hands-on Workshops as An Efective Means of Learning Advanced Technologies Including Genomics,Proteomics and Bioinformatics

Genomics and proteomics have emerged as key technologies in biomedical research, resulting in a surge of interest in training by investigators keen to incorporate these technologies into their research. At least two types of training can be envisioned in order to produce meaningful results, quality publications and successful grant applications： （1） immediate short-term training workshops and （2） long-term graduate education or visiting scientist programs. We aimed to fill the former need by providing a comprehensive hands-on training course in genomics, proteomics and informatics in a coherent, experimentally-based framework. This was accomplished through a National Heart, Lung, and Blood Institute （NHLBI）-sponsored 10-day Genomics and Proteomics Hands-on Workshop held at National Jewish Health （NJH） and the University of Colorado School of Medicine （UCD）. The course content included comprehensive lectures and laboratories in mass spectrometry and genomics technologies, extensive hands-on experience with instrumentation and software, video demonstrations, optional workshops, online sessions, invited keynote speakers, and local and national guest faculty. Here we describe the detailed curriculum and present the results of short- and long-term evaluations from course attendees. Our educational program consis- tently received positive reviews from participants and had a substantial impact on grant writing and review, manuscript submissions and publications.

CoBRA: Containerized Bioinformatics Workflow for Reproducible Ch IP/ATAC-seq Analysis

Chromatin immunoprecipitation sequencing(Ch IP-seq)and the Assay for Transposase-Accessible Chromatin with high-throughput sequencing(ATAC-seq)have become essential technologies to effectively measure protein–DNA interactions and chromatin accessibility.However,there is a need for a scalable and reproducible pipeline that incorporates proper normalization between samples,correction of copy number variations,and integration of new downstream analysis tools.Here we present Containerized Bioinformatics workflow for Reproducible Ch IP/ATAC-seq Analysis(Co BRA),a modularized computational workflow which quantifies Ch IP-seq and ATAC-seq peak regions and performs unsupervised and supervised analyses.Co BRA provides a comprehensive state-of-the-art Ch IP-seq and ATAC-seq analysis pipeline that can be used by scientists with limited computational experience.This enables researchers to gain rapid insight into protein–DNA interactions and chromatin accessibility through sample clustering,differential peak calling,motif enrichment,comparison of sites to a reference database,and pathway analysis.Co BRA is publicly available online at https://bitbucket.org/cfce/cobra.

Rice Information GateWay： A Comprehensive Bioinformatics Platform for Indica Rice Genomes

Dear Editor,Oryza sativa subsp, indica and japonica are two subspecies of Asian cultivated rice, among which indica rice is much more widely grown and genetically diverse. Over the past years, the Rice Annotation Project Database （RAP-DB） （Ohyanagi et al., 2006） and Michigan State University Rice Genome Annotation Project （MSU-RGAP） （Ouyang et al., 2007） are two popular databases that have been developed to manage rice genomic and transcriptomic data based on the unified reference genome of japonica cultivar Nipponbare （International Rice Genome Sequencing Project, 2005）. Beijing Genomics Institute Rice Information System （BGI-RIS） （Zhao et ai., 2004） is an available resource for indica rice cultivar 93-11;

Stage at diagnosis of colorectal cancer through diagnostic route:Who should be screened?

BACKGROUND Colorectal cancer(CRC)is a global health concern,with advanced-stage diagnoses contributing to poor prognoses.The efficacy of CRC screening has been well-established;nevertheless,a significant proportion of patients remain unscreened,with>70%of cases diagnosed outside screening.Although identifying specific subgroups for whom CRC screening should be particularly recommended is crucial owing to limited resources,the association between the diagnostic routes and identification of these subgroups has been less appreciated.In the Japanese cancer registry,the diagnostic routes for groups discovered outside of screening are primarily categorized into those with comorbidities found during hospital visits and those with CRC-related symptoms.AIM To clarify the stage at CRC diagnosis based on diagnostic routes.METHODS We conducted a retrospective observational study using a cancer registry of patients with CRC between January 2016 and December 2019 at two hospitals.The diagnostic routes were primarily classified into three groups:Cancer screening,follow-up,and symptomatic.The early-stage was defined as Stages 0 or I.Multivariate and univariate logistic regressions were exploited to determine the odds of early-stage diagnosis in the symptomatic and cancer screening groups,referencing the follow-up group.The adjusted covariates were age,sex,and tumor location.RESULTS Of the 2083 patients,715(34.4%),1064(51.1%),and 304(14.6%)belonged to the follow-up,symptomatic,and cancer screening groups,respectively.Among the 2083 patients,CRCs diagnosed at an early stage were 57.3%(410 of 715),23.9%(254 of 1064),and 59.5%(181 of 304)in the follow-up,symptomatic,and cancer screening groups,respectively.The symptomatic group exhibited a lower likelihood of early-stage diagnosis than the follow-up group[P<0.001,adjusted odds ratio(aOR),0.23;95%confidence interval(95%CI):0.19-0.29].The likelihood of diagnosis at an early stage was similar between the follow-up and cancer screening groups(P=0.493,aOR for early-stage diagnosis in the cancer screening group vs follow-up group=1.11;95%CI=0.82-1.49).CONCLUSION CRCs detected during hospital visits for comorbidities were diagnosed earlier,similar to cancer screening.CRC screening should be recommended,particularly for patients without periodical hospital visits for comorbidities.

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

Understandings of Urban Sustainability in China:a Bibliometric Analysis Using Chinese Literatures

Cities hold a critical responsibility for achieving the Sustainable Development Goals(SDGs)due to their high population density,extensive resource consumption,and significant economic contributions.To examine the present state of understandings regarding urban sustainability(SDG 11:Sustainable Cities and Communities)within Chinese research communities,this study collected 15950 papers from 1994 to 2022 on the 12 indicators of SDG 11,from the China National Knowledge Infrastructure(CNKI),a hub of Chinese academic papers,that directly relate to policymaking.Significant research topics on SDG 11 were identified for each indicator using bibliometrics analysis approaches.The high-frequency keywords and clusters of keywords over the last three decades reveal that existing studies primarily concentrated on the physical aspects,such as transportation and environment,while there is a lack of consideration of societal aspects.This indicates a limited and biased understanding of the urban sustainability within the Chinese academic community.Hence,it is crucial to prioritize the societal aspects in order to develop a research agenda that further advances urban sustainability.