Open Search Research Articles

Value Orientations of the Audiovisual Periodicals Dramaturgy: Reconfiguring Scientific Optics

The article examines the problem of the transition to a new paradigm of media products’ value understanding of audiovisual periodicals in digital age. Relevance of the approach is challenged by the necessity to change the evaluative paradigm of modern aesthetics, which dominated for almost the entire twentieth century, to the criteria of postmodern and metamodern. The article emphasizes the professional competencies that can be positioned as “non-digitizable”, such as creativity and value orientation of creators. The article demonstrates the transformation of singularity and innovativeness of films to the “dramaturgy of seriality” due to TV and web documentaries, and further, to the dramaturgy of open search processes of metamodern. Methodologically, the change of scientific “optics” is proved in the course of revision of traditional screen dramaturgy replaced by the new approaches to the value status of transmedia audiovisual broadcasting. A generative format model is presented as the general-purpose mechanism of the TV and video shows dramatic structure. The value approach changes the methodological discourses of evaluating broadcasting media: from text-centric to polycode, from author’s subordination to viewer’s priority, from the content analysis to the dynamics of show structures. The results of the study move forward the development of professional competencies in media value chain and can be used for the training and retraining of specialists.

Open innovation and information technology use: Towards an operational alignment view

AbstractA centralised open innovation search approach refers to setting up a dedicated and specialised sourcing unit to search external sources for knowledge, while a decentralised open innovation search approach refers to empowering employees in various functional units to search external sources for knowledge during their routine research and development work. This research examines the operational alignment of the open innovation search approach with information technology (IT) use given that open innovation search can be significantly enhanced by IT. We theorise that organisations that have a high extent of employing a centralised (decentralised) open innovation approach and a high IT use intensity for external (internal) information flows are likely to have a high extent of exploratory (exploitative) innovation. We further posit that harnessing exploratory and exploitative innovation can lead to organisational innovation performance. This theory was validated through a field survey involving 186 organisations. The findings from this study advance the IT and open innovation literature by gaining an understanding of the interplay among different open innovation search approaches and IT use, and the resulting variances in innovation performance in terms of new product sales and patents.

The Fields of Interest of Lean Six Sigma through PDCA cycle and 4P Excellence Model

The aim of this paper is to review the focus of research carried out on Lean Six Sigma (LSS). A screening of 116 papers related to LSS from renowned and open database searches including DOAJ, ResearchGate, MDPI on the period of 2006 to 2021 have been done. This paper provides a review of Leans Six Sigma (LSS) in the context of the Plan Do Check Act cycle and the 4P Excellence Model to identify areas for future research and opportunities to deliver business excellence. 21% of the papers (25) contribute on Plan phase of using LSS, 62% of the papers (72) are focused on «DO» phase which is the practical deployment of LSS, 15% of the searches (17) related to Check phase which concerns the questioning of the methodology and 2% of the papers is looking for adapting and adopting the LSS, this is the Act phase. From the 72 papers classified as focused on «DO» phase, 70% of the papers are focused on processes which are using the LSS tools for achieving a specific goal of reducing waste and variation. How to innovate the Products by LSS approach took the second interest, it represents 26% of the various papers. People, Partnership and Leadership which are fundamentals for culture change share the last part. We have also seen a great interest in publications in the tangible parts of using LSS in processes and products during a visible period from 2006 to 2013, then several studies look more into the importance of the non-tangible parts of using LSS as an improvement process after 2013.

To which non-physician health professionals do French general practitioners refer their patients to and what factors are associated with these referrals? Secondary analysis of the French national cross-sectional ECOGEN study

BackgroundMultiprofessional practice is a key component in primary care. Examining general practitioner (GP) referral frequency to non-physician health professionals (NPHP) can provide information about how primary care is organised and works which is useful for policymakers. Our study aimed to describe French GP referral frequency to various NPHPs in France and identify associated factors.MethodsThis is an ancillary study to the observational, cross-sectional (ECOGEN) study conducted in 2011/2012 in France among 128 GPs. Data about consultations using the standardised International Classification of Primary Care (ICPC-2), and patient and GP characteristics were collected from 20,613 GP consultations. Referrals were identified through inductive and deductive approaches using ICPC-2 codes, keywords, and deep, open manual searches. Referral frequency was described overall and per NPHP. Patient, GP, and consultation-related factors associated with referral rates were described for the three most frequently identified NPHPs. To minimise potential sources of bias, this observational study followed the STROBE guidelines.ResultsFrench GPs referred 6.8% of patients to NPHPs, with physiotherapists, podiatrists, and nurses accounting for 85.2% of referrals. Older patients, retired patients, multiple health problems managed, and longer consultation durations were found to be associated with higher referral rates (p < 0.001). Specific trends were observed for nurse, physiotherapist, and podiatrist referrals. Women (p < 0.001) and regular patients (p = 0.002) were more likely to receive physiotherapy referrals while people with no professional activity were less likely (p < 0.001). Female GPs and those working in urban practices were more likely to issue a physiotherapy referral (p < 0.001), while GPs working in rural practices (p < 0.001) and those with higher annual consultation numbers (p = 0.002) were more likely to refer to a nurse. Working in multiprofessional centres appeared to have little impact on referral rates, being only slightly associated with podiatrist referrals (p = 0.003).ConclusionsReferral frequency is more associated with patient characteristics and clinical situations than GP-related factors suggesting patients needing referral most are most often referred. Furthermore, the three NPHPs that GPs refer to the most are those for which a referral is required for reimbursement in France, suggesting that health system legislation and NPHP reimbursement are strong determinants for referrals.

A protocol of using PTMiner for quality control and localization of protein modifications identified by open or closed search of tandem mass spectra.

In recent years, an open search of tandem mass spectra has greatly promoted the detection of post-translational modifications (PTMs) in shotgun proteomics. However, post-processing of the results from open searches remains an unsatisfactorily resolved problem, which hinders the open search mode from wide practical use. PTMiner is a software tool based on dedicated statistical algorithms for reliable filtering, localization and annotation of the modifications (mass shifts) detected by open search. Furthermore, PTMiner also supports quality control and re-localization of modifications identified by the traditional closed search. In this protocol, we describe how to use PTMiner for the two search modes. Currently, the search engines supported by PTMiner include pFind, MSFragger, MaxQuant, Comet, MS-GF + and SEQUEST.

Computational Methods for Characterizing Post-translational and Chemical Modifications Found in Open Searches

Computational Methods for Characterizing Post-translational and Chemical Modifications Found in Open Searches

Modeling global real economic activity: Evidence from variable selection across quantiles

We conduct an open search of predictors of global real economic activity. To this end, we apply a predictive quantile regression framework, using four alternative proxies of global real economic activity during February 1997–August 2019 and building on a combination of machine learning algorithms to identify their predictors out of 23 candidate explanatory variables. The contemporaneous level of global real economic activity, the Asian and US financial stress are found the most robust predictors. The effect of US financial shocks appears asymmetric, as they undermine global economic growth when the latter is below the median, but do not matter much when the world economy expands fast. Besides, US shadow interest rates are significantly and positively linked to global real economic activity. This effect holds in a high-growth regime of the world economy and suggests that rising US policy rates, contrary to the conventional wisdom, entail US dollar depreciation rather than appreciation. A weaker US dollar stimulates dollar-denominated cross-border bank inflows to the countries other than the USA, leading to a rise in real investment worldwide and industrial output growth. Thus, our empirical findings inform policymakers which indicators should be monitored more closely to predict future shifts in global economic growth and also provide certain insights about optimal policy responses to such shifts.

A GPU based single-pulse search pipeline (GSP) with database and its application to the Commensal Radio Astronomy FAST Survey (CRAFTS)

We developed a GPU based single-pulse search pipeline (GSP) with a candidate-archiving database. Largely based upon the infrastructure of the open source PulsaR Exploration and Search Toolkit (PRESTO), GSP implements GPU acceleration of the de-dispersion and integrates a candidate-archiving database. We applied GSP to the data streams from the Commensal Radio Astronomy FAST Survey (CRAFTS), which resulted in quasi-real-time processing. The integrated candidate database facilitates synergistic usage of multiple machine-learning tools and thus improves efficient identification of radio pulsars such as rotating radio transients (RRATs) and fast radio bursts (FRBs). We first tested GSP on pilot CRAFTS observations with the FAST Ultra-Wide Band (UWB) receiver. GSP detected all pulsars known from the the Parkes multibeam pulsar survey in the corresponding sky area covered by the FAST-UWB. GSP also discovered 13 new pulsars. We measured the computational efficiency of GSP to be ∼120 times faster than the original PRESTO and ∼60 times faster than an MPI-parallelized version of PRESTO.

PValid 2: A deep learning based validation method for peptide identification in shotgun proteomics with increased discriminating power

Tandem mass spectrometry has been the principal method in shotgun proteomics for peptide and protein identification. However, incorrect identifications reported by proteome search engines are still unknown, and further validation methods are needed. We have proposed a validation method pValid before, but its scope of application is limited because two features used in pValid are related to open database search and sub-optimal peptide candidates for tandem mass spectra, and the performance on complex datasets still has room for improvement. In this study, we developed a more comprehensive validation method, pValid 2, to break these limitations by removing the two features and bringing in a new feature related to the retention time predicted by a deep learning-based method pPredRT. pValid 2 yielded an average false positive rate of 0.03% and an average false negative rate of 1.37% on three testing datasets, better than those of pValid, and flagged 8.47% to 11.31% more incorrect identifications than pValid on two complex datasets. Moreover, pValid 2 flagged almost all decoy identifications in validating the open-search datasets. In addition, the function of validating identifications given by MaxQuant and MS-GF+ was implemented in pValid 2, and the validation results showed that pValid 2 performed dramatically better than three metabolic labeling validation methods. Further considering its cost-effectiveness as a pure computational approach, pValid 2 has the potential to be a widely used validation tool for peptide identifications of any proteome search engines in shotgun proteomics. SignificanceIdentification results given by shotgun proteomics are vital to life science research. The correctness of identifications deeply affects the precision of the subsequent studies about protein structures and functions, protein-protein interactions, pathogenic mechanism, and targeted drugs. Thus, validating the correctness of identifications is crucial and urgent. In 2019, we developed an identification credibility validation method named pValid, whose false positive rate (FPR) is 0.03% and false negative rate (FNR) is 1.79%, comparable to those of the gold standard, i.e., the Synthetic-peptide validation method. However, pValid can only be used for validating the results from pFind, and its validation performance on a few complex datasets still has room for improvement. So, in this submission, we proposed pValid 2, a more comprehensive computational validation method that can validate identifications from any proteome search engines with increased discriminating power.

DeepLC can predict retention times for peptides that carry as-yet unseen modifications.

The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography-mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We present DeepLC, a deep learning peptide retention time predictor using peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides and, more importantly, accurately predicts retention times for modifications not seen during training. Moreover, we show that DeepLC's ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open search of a wide variety of proteome data.

Oral Infections and Systemic Health - More than Just Links to Cardiovascular Diseases.

During the past 20 years, a plethora of research reports has been published showing a statistical association between poor oral health and cardiovascular diseases. The aim of this narrative review was to focus on associations between oral infections and non-atherosclerosis-related systemic diseases. An open literature search and evaluation of articles were conducted on Medline and Cochrane databases with the key words 'oral infection', 'periodontitis', 'pneumonia', 'osteoarthritis', 'rheumatic diseases', 'inflammatory bowel disease', 'kidney disease', 'liver diseases', 'metabolic syndrome', 'diabetes', 'cancer', 'Alzheimer's disease'. Cardiovascular diseases were excluded from the analysis. The scarcity of controlled studies did not allow conducting a systematic review with meta-analysis on the topics, but dental infections have been shown be associated with several general diseases also beyond the atherosclerosis paradigm. However, there is no causal evidence of the role of dental infections in this regard. Poor oral health has nevertheless often been observed to be associated with worsening of the diseases and may also affect treatments. Maintaining good oral health is imperative regarding many diseases, and its importance in the daily life of any patient group cannot be over emphasised.

How does negative performance feedback affect a firm’s openness in its innovation search behaviour? The moderating role of organisational slack

ABSTRACT Using the behavioural theory of the firm as a theoretical lens, an integrated framework for this study was developed to examine how negative performance relative to aspiration as well as organisational slack shapes a firm’s openness in its innovation search behaviour. Furthermore, the study investigates whether the extent to which slack is committed to specific organisational procedures determines its moderating effect on the relationship between negative performance feedback and open innovation search behaviour. Patent archives and financial data from 1688 manufacturing firms publicly listed in the US were collected to construct a panel dataset of 13,685 observations. By examining the open innovation search behaviours of these manufacturers from 1986 to 2006, it was found that negative performance feedback encourages firms to be more open to external knowledge sources. The findings also revealed that the absorption degree of slack indeed determines its moderating role. Specifically, underperforming firms with more unabsorbed slack exhibited more openness in their innovation search behaviours, whereas absorbed slack failed to show significance. Possible explanations for the insignificance of absorbed slack are further discussed.

CaAtlas: An immunopeptidome atlas of human cancer

SummaryComprehensive characterization of tumor antigens is essential for the design of cancer immunotherapies, and mass spectrometry (MS)-based immunopeptidomics enables high-throughput identification of major histocompatibility complex (MHC)-bound peptide antigens in vivo. Here we construct an immunopeptidome atlas of human cancer through an extensive collection of 43 published immunopeptidomic datasets and standardized analysis of 81.6 million MS/MS spectra using an open search engine. Our analysis greatly expands the current knowledge of MHC-bound antigens, including an unprecedented characterization of post-translationally modified antigens and their cancer-association. We also perform systematic analysis of cancer-testis antigens, cancer-associated antigens, and neoantigens. We make all these data together with annotated MS/MS spectra supporting identification of each antigen in an easily browsable web portal named cancer antigen atlas (caAtlas). caAtlas provides a central resource for the selection and prioritization of MHC-bound peptides for in vitro HLA binding assay and immunogenicity testing, which will pave the way to eventual development of cancer immunotherapies.

Text-based multi-dimensional medical images retrieval according to the features-usage correlation.

Emerging medical imaging applications in healthcare, the number and volume of medical images is growing dramatically. Information needs of users in such circumstances, either for clinical or research activities, make the role of powerful medical image search engines more significant. In this paper, a text-based multi-dimensional medical image indexing technique is proposed in which correlation of the features-usages (according to the user’s queries) is considered to provide an off-the content indexing while taking users’ interestingness into account. Assuming that each medical image has some extracted features (e.g., based on the DICOM standard), correlations of the features are discovered by performing data mining techniques (i.e., quantitative association pattern discovery), on the history of users’ queries as a data set. Then, based on the pairwise correlation of the features of medical images (a.k.a. Affinity), set of the all features is fragmented into subsets (using method like the vertical fragmentation of the tables in distribution of relational DBs). After that, each of these subsets of the features turn into a hierarchy of the features (by applying a hierarchical clustering algorithm on that subset), subsequently all of these distinct hierarchies together make a multi-dimensional structure of the features of medical images, which is in fact the proposed text-based (feature-based) multi-dimensional index structure. Constructing and using such text-based multi-dimensional index structure via its specific required operations, medical image retrieval process would be improved in the underlying medical image search engine. Generally, an indexing technique is to provide a logical representation of documents in order to optimize the retrieval process. The proposed indexing technique is designed such that can improve retrieval of medical images in a medical image search engine in terms of its effectiveness and efficiency. Considering correlation of the features of the image would semantically improve precision (effectiveness) of the retrieval process, while traversing them through the hierarchy in one dimension would try to optimize (i.e., minimize) the resources to have a better efficiency. The proposed text-based multi-dimensional indexing technique is implemented using the open source search engine Lucene, and compared with the built-in indexing technique available in the Lucene search engine, and also with the Terrier platform (available for the benchmarking of information retrieval systems) and other the most related indexing techniques. Evaluation results of memory usage and time complexity analysis, beside the experimental evaluations efficiency and effectiveness measures show that the proposed multi-dimensional indexing technique significantly improves both efficiency and effectiveness for a medical image search engine.Graphical abstract

Systematic Review of Plants Used Against Respiratory Diseases Related to COVID-19 in Africa

Background: Respiratory disorders are known to affect the airways including the nasal passages, bronchi and lungs causing blockages. The advent of COVID-19 has further aggravated the complications of the respiratory systems where conventional medicine is not reachable or affordable by the majority poor in Africa. Hence, the over 80% of the African population who turn to traditional medicine for their primary health care.&#x0D; Objective: This review is aimed to identify plants directed against respiratory diseases which can be useful in the fight against COVID-19.&#x0D; Methodology: Scientific articles selected in this study span the last ten years (2011-2021). Keywords such as “ethnobotany in Africa”, “ethnobotany and respiratory diseases” “medicinal plants and respiratory diseases”, “traditional medicine and COVID-19” were searched in open access search engines such as: Science Direct, Research Gate, Google Scholar, Pubmed, Web of Science, Scopus. The ethnobotanical indices were then calculated using Microsoft excel to determine the plants with the most therapeutic potential to be considered for the local management of COVID-19.&#x0D; Results: Data obtained were classified according to country of origin of the author of the publication, the botanical family and the respiratory pathology being treated. Thirteen (13) references were finally selected with the represented country being Benin Republic. One hundred and forty-three plant species belonging to 60 families were registered. The most recurrent families were the Fabaceae and Lamiaceae (18 citations each). The plants in these families were used for the management of fifteen diseases and / or symptoms relating to the respiratory system. The most cited plants were: Ocimum gratissimum L. (4 citations), Entandrophragma cylindrium (Sprague), Scyphocephalium ochocoa Warb., Rubia cordifolia and Allium sativum L. (3 citations each).&#x0D; Conclusion: Based on the data obtained in this review Ocimum gratissimum stands out as the most used plant for the treatment of respiratory disorders.&#x0D; Keywords: Respiratory diseases, Medicinal plants, COVID-19

AA_stat: Intelligent profiling of in vivo and in vitro modifications from open search results

Characterization of post-translational modifications is among the most challenging tasks in tandem mass spectrometry-based proteomics which has yet to find an efficient solution. The ultra-tolerant (open) database search attempts to meet this challenge. However, interpretation of the mass shifts observed in open search still requires an effective and automated solution. We have previously introduced the AA_stat tool for analysis of amino acid frequencies at different mass shifts and generation of hypotheses on unaccounted in vitro modifications. Here, we report on the new version of AA_stat, which now complements amino acid frequency statistics with a number of new features: (1) MS/MS-based localization of mass shifts and localization scoring, including shifts which are the sum of modifications; (2) inferring fixed modifications to increase method sensitivity; (3) inferring monoisotopic peak assignment errors and variable modifications based on abundant mass shift localizations to increase the yield of closed search; (4) new mass calibration algorithm to account for partial systematic shifts; (5) interactive integration of all results and a rated list of possible mass shift interpretations. With these options, we improve interpretation of open search results and demonstrate the utility of AA_stat for profiling of abundant and rare amino acid modifications. AA_stat is implemented in Python as an open-source tool available at https://github.com/SimpleNumber/aa_stat. SignificanceMass spectrometry-based PTM characterization has a long history, yet most of the methods rely on a priori knowledge of modifications of interest and do not provide a whole proteome modification landscape in a blind manner. The open database search is an efficient attempt to address this challenge by identifying peptides with mass shifts corresponding to possible modifications. Then, interpreting these mass shifts is required. Therefore, development of bioinformatics software for post-processing of the open search results, which is capable of detection and accurate annotation of new or unexpected modifications, from characterization of sample preparation efficiency and quality control to discovery of rare post-translational modifications, is of high importance.

Bcl-2-associated transcription factor 1 Ser290 phosphorylation mediates DNA damage response and regulates radiosensitivity in gastric cancer

BackgroundDNA damage response plays critical roles in tumor pathogenesis and radiotherapy resistance. Protein phosphorylation is a critical mechanism in regulation of DNA damage response; however, the key mediators for radiosensitivity in gastric cancer still needs further exploration.MethodsA quick label-free phosphoproteomics using high-resolution mass spectrometry and an open search approach was applied to paired tumor and adjacent tissues from five patients with gastric cancer. The dysregulated phosphoproteins were identified and their associated-pathways analyzed using Gene Set Enrichment Analysis (GSEA). The mostly regulated phosphoproteins and their potential functions were validated by the specific antibodies against the phosphorylation sites. Specific protein phosphorylation was further analyzed by functional and clinical approaches.Results832 gastric cancer-associated unique phosphorylated sites were identified, among which 25 were up- and 52 down-regulated. Markedly, the dysregulated phosphoproteins were primarily enriched in DNA-damage-response-associated pathways. Particularly, the phosphorylation of Bcl-2-associated transcription factor 1 (BCLAF1) at Ser290 was significantly upregulated in tumor. The upregulation of BCLAF1 Ser290 phosphorylation (pBCLAF1 (Ser290)) in tumor was confirmed by tissue microarray studies and further indicated in association with poor prognosis of gastric cancer patients. Eliminating the phosphorylation of BCLAF1 at Ser290 suppressed gastric cancer (GC) cell proliferation. Upregulation of pBCLAF1 (Ser290) was found in association with irradiation-induced γ-H2AX expression in the nucleus, leading to an increased DNA damage repair response, and a marked inhibition of irradiation-induced cancer cell apoptosis.ConclusionsThe phosphorylation of BCLAF1 at Ser290 is involved in the regulation of DNA damage response, indicating an important target for the resistance of radiotherapy.

Ppx: Programmatic Access to Proteomics Data Repositories.

The volume of proteomics and mass spectrometry data available in public repositories continues to grow at a rapid pace as more researchers embrace open science practices. Open access to the data behind scientific discoveries has become critical to validate published findings and develop new computational tools. Here, we present ppx, a Python package that provides easy, programmatic access to the data stored in ProteomeXchange repositories, such as PRIDE and MassIVE. The ppx package can either be used as a command line tool or a Python package to retrieve the files and metadata associated with a project when provided its identifier. To demonstrate how ppx enhances reproducible research, we used ppx within a Snakemake workflow to reanalyze a published dataset with the open modification search tool ANN-SoLo and compared our reanalysis to the original results. We show that ppx readily integrates into workflows and our reanalysis produced results consistent with the original analysis. We envision that ppx will be a valuable tool for creating reproducible analyses, providing tool developers easy access to data for development, testing, and benchmarking, and enabling the use of mass spectrometry data in data-intensive analyses. The ppx package is freely available and open source under the MIT license at: https://github.com/wfondrie/ppx

Determination of Diversity, Distribution and Host Specificity of Korean Laccaria Using Four Approaches

The genus Laccaria (Hydnangiaceae, Agaricales) plays an important role in forest ecosystems as an ectomycorrhizal fungus, contributing to nutrient cycles through symbiosis with many types of trees. Though understanding Laccaria diversity and distribution patterns, as well as its association with host plants, is fundamental to constructing a balanced plant diversity and conducting effective forest management, previous studies have not been effective in accurately investigating, as they relied heavily on specimen collection alone. To investigate the true diversity and distribution pattern of Laccaria species and determine their host types, we used four different approaches: specimen-based analysis, open database search (ODS), NGS analysis, and species-specific PCR (SSP). As a result, 14 Laccaria species have been confirmed in Korea. Results regarding the species distribution pattern were different between specimen-based analysis and SSP. However, when both were integrated, the exact distribution pattern of each Laccaria species was determined. In addition, the SSP revealed that many Laccaria species have a wide range of host types. This study shows that using these four different approaches is useful in determining the diversity, distribution, and host of ECM fungi. Furthermore, results obtained for Laccaria will serve as a baseline to help understand the role of ECM fungi in forest management in response to climate change.

IOT Smart Guidance Parking Search System for Open Space Parking Area

Open parking facilities can be automated and parking spaces can be easily operated by the implementation of IoT technology (Internet of Things). In this article, we present the evolution and prototyping of the open space smart guidance-parking search system, an IoT-based smart parking search system. The Smart Guidance Parking Searches System consists of i) An IOT module to monitor the availability of a parking slot and to update the parking lot status; and (ii) A web-based software allows users to view parking spaces available for a specific open space area. This paper addresses the existing system, device description, its functional specifications, the methods, and technologies used, the development/deployment of prototypes, along with the findings from the demonstration. This device serves as a guide for the user/driver to search for the parking slot occupancy in open/outdoor environments.