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Quorum sensing peptides (QSPs) are the signaling molecules used by the Gram-positive bacteria in orchestrating cell-to-cell communication. In spite of their enormous importance in signaling process, their detailed bioinformatics analysis is lacking. In this study, QSPs and non-QSPs were examined according to their amino acid composition, residues position, motifs and physicochemical properties. Compositional analysis concludes that QSPs are enriched with aromatic residues like Trp, Tyr and Phe. At the N-terminal, Ser was a dominant residue at maximum positions, namely, first, second, third and fifth while Phe was a preferred residue at first, third and fifth positions from the C-terminal. A few motifs from QSPs were also extracted. Physicochemical properties like aromaticity, molecular weight and secondary structure were found to be distinguishing features of QSPs. Exploiting above properties, we have developed a Support Vector Machine (SVM) based predictive model. During 10-fold cross-validation, SVM achieves maximum accuracy of 93.00%, Mathew's correlation coefficient (MCC) of 0.86 and Receiver operating characteristic (ROC) of 0.98 on the training/testing dataset (T200p+200n). Developed models performed equally well on the validation dataset (V20p+20n). The server also integrates several useful analysis tools like \"QSMotifScan\", \"ProtFrag\", \"MutGen\" and \"PhysicoProp\". Our analysis reveals important characteristics of QSPs and on the basis of these unique features, we have developed a prediction algorithm \"QSPpred\" (freely available at: http://crdd.osdd.net/servers/qsppred).

Manufacturing firms generate a massive amount of data points because of higher than ever connected devices and sensor technology adoption. These data points could be from varied sources, ranging from flow time and cycle time through different machines in an assembly line to shop floor data collected from sensors viz. temperature, stress capability, pressure, etc. Analysis of this data can help manufacturers in many ways, viz. predict breakdown—reduction in downtime and waste, optimal inventory level—resource optimization, etc. The data may be highly voluminous, highly unstructured, coming from varied sources at a higher speed. Thus, big data analytics has become more critical than ever for the manufacturing industry to have the capability of effectively deriving business value from the vast amount of generated data. Manufacturing firms face hindrances and failures in the implementation of big data analytics. It is, therefore, necessary for the companies in the Indian manufacturing sector to identify and examine the reason and nature of barriers resisting the implementation of Big Data Analytics (BDA) to their organization. This paper explores the existing literature available to identify the barriers, categorized based on different functions of an organization. A total of 16 barriers are determined from the rigorous review of existing research. A survey is conducted on the industry experts from automobile, steel, automotive parts manufacturer, and electrical equipment industries to obtain a contextual relationship between the barriers. Interpretive Structural Modeling and MICMAC (Cross-impact matrix multiplication applied to classification) are the analytical techniques used in this research to classify the barriers into different impact levels and importance. Independent factors (barriers) have high driving power and are the key factors that were further analyzed using Fuzzy AHP to determine their comparative priority/importance. The result of this research shows that barriers related to Management and Infrastructure & Technology are the main hurdles in the implementation of big data analytics in the manufacturing industry. Six critical barriers (based on high driving power) are; lack of long-term vision, lack of commitment from top management, lack of infrastructure facility, lack of funding, lack of availability of specific data tools, and lack of training facility. Lack of commitment from top management is the most critical barrier. Research focuses on a comprehensive analysis of the barriers in implementing big data analytics (BDA) in manufacturing firms. The novelty lies in (a) finding an extensive list of barriers, (b) application domain and geography, and (c) the multi-criteria decision making technique used for finding the critical barriers to the implementation of big data analytics. The findings of this research will help industry leaders to formulate a better plan before the application of BDA in their organizations.

KCI Citation Count: 1

Electroencephalogram (EEG) signals are progressively growing data widely known as biomedical big data, which is applied in biomedical and healthcare research. The measurement and processing of EEG signal result in the probability of signal contamination through artifacts which can obstruct the important features and information quality existing in the signal. To diagnose the human neurological diseases like epilepsy, tumors, and problems associated with trauma, these artifacts must be properly pruned assuring that there is no loss of the main attributes of EEG signals. In this paper, the latest and updated information in terms of important key features are arranged and tabulated extensively by considering the 60 published technical research papers based on EEG artifact removal method. Moreover, the paper is a review vision about the works in the area of EEG applied to healthcare and summarizes the challenges, research gaps, and opportunities to improve the EEG big data artifacts removal more precisely.

PurposeAs digital procurement continues to transform heavily as a value center and create new business models by linking businesses with a web of external partners, the full path to achieving such an all-encompassing thing is unknown. Thus, the study aims to explore the research gap through an exhaustive bibliometric and systematic literature review on the Digital procurement theme in the supply chain domain.Design/methodology/approachThis study is a qualitative and quantitative analysis of this field, using performance analysis and science mapping to examine 583 articles published from 2002 to 2021.FindingsA systematic literature review indicated core topics on “sustainable or green procurement” and “emerging landscape of technology” in the field of study.Research limitations/implicationsThough the Scopus database used for the analysis is the largest, it may not have complete coverage of all published articles in the field of study; thus, this study is a representation of only a sample rather than its entire population.Originality/valueOutcome is based on the review of the past 20 years’ contribution on the topic starting from 2002 to 2021.

Background Colorectal cancer (CRC) remains a significant global health burden, ranked among the most common causes of cancer-related fatalities. While insulin resistance (IR) biomarkers have been associated with CRC prognosis, their role in predicting metastasis remains unclear. Metastasis remains a critical determinant of prognosis and treatment planning in CRC. Identifying precise biomarkers can improve CRC management. This study evaluates the prognostic efficacy of lipid-based IR biomarkers in predicting metastasis in treatment-naïve CRC patients and selects the most appropriate one. We also explore their association with clinicopathological characteristics. Method Eighty-seven CRC patients (metastatic, n  = 24; non-metastatic, n  = 63) from four tertiary hospitals in India were analysed. Clinical data included TNM staging, ECOG-PS, KPS, CEA, and lipid profiles. Statistical tests included Fisher’s exact test, Mann-Whitney U-test, ROC curve analysis, Spearman’s correlation, multiple linear regression, and binary logistic regression. Results Statistically significant differences were observed in job status, diet, smoking, alcohol use, diabetes, BMI, and IR markers between metastatic and non-metastatic CRC patients. Among the IR biomarkers, the ratio of LDL to HDL (LHR) demonstrated the highest diagnostic accuracy with an AUC of 0.867 ( p  < 0.05, CI: 0.79–0.94), a sensitivity of 83.3%, and a specificity of 74.6%. Spearman correlation analysis unveiled a moderate-positive relationship between IR biomarkers and carcinoembryonic antigen (CEA) levels, except for the triglyceride glucose index (TyG). Binary logistic regression identified LHR as the sole significant predictor of metastasis, with a one-unit increase in LHR corresponding to a 19.35% higher likelihood of metastasis. Multiple linear regression confirmed a moderate, significant combined effect of TNM staging, ECOG-PS, KPS, and LHR. Conclusion LHR strongly predicts metastasis in CRC patients, with high sensitivity and specificity among IR biomarkers. Its significant association with TNM staging, ECOG-PS, and CEA levels highlights its potential for early detection of metastasis and improved risk stratification. Larger studies are needed to validate its clinical utility for personalised treatment planning.

Human papillomaviruses (HPVs) are extremely associated with different carcinomas. Despite consequential accomplishments, there is still need to establish more promising biomarkers to discriminate cancerous progressions. Therefore, we have developed HPVbase ( http://crdd.osdd.net/servers/hpvbase/ ), a comprehensive resource for three major efficacious cancer biomarkers i.e. integration and breakpoint events, HPVs methylation patterns and HPV mediated aberrant expression of distinct host microRNAs (miRNAs). It includes clinically important 1257 integrants and integration sites from different HPV types i.e. 16, 18, 31, 33 and 45 associated with distinct histological conditions. An inclusive HPV integrant and breakpoints browser was designed to provide easy browsing and straightforward analysis. Our study also provides 719 major quantitative HPV DNA methylation observations distributed in 5 distinct HPV genotypes from higher to lower in numbers namely HPV 16 (495), HPV 18 (113), HPV45 (66), HPV 31 (34) and HPV 33 (11). Additionally, we have curated and compiled clinically significant aberrant expression profile of 341 miRNAs including their target genes in distinct carcinomas, which can be utilized for miRNA therapeutics. A user-friendly web interface has been developed for easy data retrieval and analysis. We foresee that HPVbase an integrated and multi-comparative platform would facilitate reliable cancer diagnostics and prognosis.

Splenic artery embolization (SAE) is increasingly favored for adult blunt splenic injury management. We compared SAE to other splenic injury management strategies using robust statistical techniques. Univariate analyses of demographics and outcomes were performed for four patient groups: observation, SAE, splenic surgery, splenic surgery + SAE in the American College of Surgeons Trauma Quality Program (TQIP) database. To address nonlinear associations of ED vital signs with mortality, multivariable spline-based logistic regression models with interaction terms between hemodynamic status and management strategy and either splenic Abbreviated Injury Score (AIS) or Injury Severity Score (ISS), were generated. In 44,187 splenic injury patients meeting study inclusion criteria, the most common management strategy was observation alone (77.9%). The observation group had median spleen AIS of 2, ISS 20, with 6.3% mortality; SAE (2.6%) had median spleen AIS3, ISS 24, with 6.6% mortality; splenic surgery (22.4%) AIS4, ISS 29, with 15.4% mortality; and splenic surgery + SAE (0.04%) AIS4, ISS 29, with 15.2% mortality. In multivariable models, SAE had lower predicted probability of mortality than surgery over most initial ED systolic blood pressures (SBPs). At all spleen AIS, SAE had lower predicted mortality than surgery. SAE had lower mortality than surgery except at very high ISS, where it was comparable. SAE had lower predicted mortality than observation management at spleen AIS≥3. In subgroup analysis of patients without severe multi-system injuries, predicted mortality did not differ by management strategy. SAE is associated with decreased mortality at spleen AIS 3-5. The benefits of SAE appear to be largely for spleen AIS 3-5 in the setting of severe (AIS≥3) multi-system injuries.

The histone modifiers (HMs) are crucial for chromatin dynamics and gene expression; however, their dysregulated expression has been observed in various abnormalities including cancer. In this study, we have analyzed the expression of HMs in microarray profiles of head and neck cancer (HNC), wherein a highly significant overexpression of p21-activated kinase 2 (PAK2) was identified which was further validated in HNC patients. The elevated expression of PAK2 positively correlated with enhanced cell proliferation, aerobic glycolysis and chemoresistance and was associated with the poor clinical outcome of HNC patients. Further, dissection of molecular mechanism revealed an association of PAK2 with c-Myc and c-Myc-dependent PKM2 overexpression, wherein we showed that PAK2 upregulates c-Myc expression and c-Myc thereby binds to PKM promoter and induces PKM2 expression. We observed that PAK2–c-Myc–PKM2 axis is critical for oncogenic cellular proliferation. Depletion of PAK2 disturbs the axis and leads to downregulation of c-Myc and thereby PKM2 expression, which resulted in reduced aerobic glycolysis, proliferation and chemotherapeutic resistance of HNC cells. Moreover, the c-Myc complementation rescued PAK2 depletion effects and restored aerobic glycolysis, proliferation, migration and invasion in PAK2-depleted cells. The global transcriptome analysis of PAK2-depleted HNC cells revealed the downregulation of various genes involved in active cell proliferation, which indicates that PAK2 overexpression is critical for HNC progression. Together, these results suggest that the axis of PAK2–c-Myc–PKM2 is critical for HNC progression and could be a therapeutic target to reduce the cell proliferation and acquired chemoresistance and might enhance the efficacy of standard chemotherapy which will help in better management of HNC patients.

Background: The supplier selection process (SSP) has grown as a crucial mechanism in organizations’ supply chain management (SCM) strategies and as a foundation for continuously gaining a competitive advantage. The concept of the circular economy has garnered significant interest due to its ability to address both environmental and social criteria. It is highly important to carefully choose suppliers across all industries that take into account circular and sustainability issues, as well as traditional criteria. There is very limited research involving the supplier selection process in the Indian HVAC manufacturing sector. Design/Methodology/Approach: Thus, this study aimed to determine the critical factors for sustainable supplier selection for HVAC manufacturing firms using a mixed research method with three stages: a secondary study, the Delphi method, and the fuzzy analytical hierarchy process (FAHP). Thirty-two critical sub-factors were identified and grouped into eight major factors: delivery, economic, environmental, social, management and organization, quality, services, and supplier relationship. Results/Conclusions: For HVAC manufacturing firms, the major factors of delivery, quality, and economics were found to be top-ranked among the factors, followed by environmental factors. Studies in developing countries using sustainable factors are still nascent, especially in India. Originality/Value: This study’s novelty lies with the proposed eight major factors, comprising all facets of organizations, including sustainability factors. Supplier selection in HVAC manufacturing firms is exhaustively dealt with in this study, filling a gap in the existing literature. This is important because HVAC products are high-energy-consuming, high-energy-releasing, and costly.