Simple Modular Architecture Research Tool Research Articles

Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops

Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species. Modern resources were used in this study, including the UniProt protein database for crop physiochemical properties associated with specific signaling domains and the SMART database for signaling protein domains. These insights were then applied to deep learning and machine learning techniques after careful data processing. The rigorous metric evaluations and ablation analysis that typified the study’s approach highlighted the algorithms’ effectiveness and dependability in recognizing and classifying stress events. Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. The study observed these successes but also highlights the ongoing obstacles to AI adoption in agriculture, emphasizing the need for creative thinking and interdisciplinary cooperation. In addition to its scholarly value, the collected data has significant implications for improving resource efficiency, directing precision agricultural methods, and supporting global food security programs. Notably, the gradient boosting and LSTM algorithm outperformed the others with an exceptional accuracy of 96% and 97%, demonstrating their potential for accurate stress categorization. This work highlights the revolutionary potential of AI to completely disrupt the agricultural industry while simultaneously advancing our understanding of plant stress responses.

Towards Integrating Information Systems of Statistical Indicators on Traceability, Quality and Safety of Italian Agrifood Systems for Citizens, Institutions and Policy-Makers

This paper arises from an ONRR project on “New technologies and methodologies for traceability, quality, safety, measurements and certifications to enhance the value and protect the typical traits in agrifood chains”. It has a first aim of performing an exhaustive review pertaining to the construction of indicators of the quality and safety of agrifood products, creating information systems dashboards of unidimensional and multidimensional indicators and applying such indicators, in the context of consumer choices and decisions of policy-makers and firms. Since it has been found that the literature offers no single proposal linking all such aspects, a second central and innovative aim is to propose an original step-by-step procedure for integrating information systems of statistical indicators for citizens, institutions and policy-makers with a specific focus on new technologies and methods for traceability, quality and safety of agrifood systems. The final output is a smart and user-friendly online database that is an absolute innovation on the topic.

Pan-cancer multi-omics analysis of PTBP1 reveals it as an inflammatory, progressive and prognostic marker in glioma

PTBP1 is an oncogene that regulates the splicing of precursor mRNA. However, the relationship between PTBP1 expression and gene methylation, cancer prognosis, and tumor microenvironment remains unclear. The expression profiles of PTBP1 across various cancers were derived from the TCGA, as well as the GTEx and CGGA databases. The CGGA mRNA_325, CGGA mRNA_301, and CGGA mRNA_693 datasets were utilized as validation cohorts. Immune cell infiltration scores were approximated using the TIMER 2.0 tool. Functional enrichment analysis for groups with high and low PTBP1 expression was conducted using Gene Set Enrichment Analysis (GSEA). Methylation data were predominantly sourced from the SMART and Mexpress databases. Linked-omics analysis was employed to perform functional enrichment analysis of genes related to PTBP1 methylation, as well as to conduct protein functional enrichment analysis. Single-cell transcriptome analysis and spatial transcriptome analysis were carried out using Seurat version 4.10. Compared to normal tissues, PTBP1 is significantly overexpressed and hypomethylated in various cancers. It is implicated in prognosis, immune cell infiltration, immune checkpoint expression, genomic variation, tumor neoantigen load, and tumor mutational burden across a spectrum of cancers, with particularly notable effects in low-grade gliomas. In the context of gliomas, PTBP1 expression correlates with WHO grade and IDH1 mutation status. PTBP1 expression and methylation play an important role in a variety of cancers. PTBP1 can be used as a marker of inflammation, progression and prognosis in gliomas.

Activity and resolution aware privacy protection for smart water meter databases

Identifying water end-uses from household meter readings yields valuable commercial and environmental insights for both customers and water providers. However, smart water meter databases may expose sensitive information about the activities of metered households, thus raising privacy concerns that limit the possibility of making smart meter data available for research and planning. This paper considers the case where a water provider wishes to publish a database of household water meter traces to be used by water analysts, who are often external entities. This scenario presents privacy risks should an adversary gain access to this database, with the threat of uniquely identifying a household of interest and exposing its water use activities. To mitigate such risks, this paper proposes a process for activity-aware privacy protection and evaluates its effectiveness using real-world and synthetic databases. Our experimental analysis shows that water meter privacy protection is strongly dependent on the type of activity, temporal resolution, and population size. For example, in the case of a high-resolution database, we found that a large base population, small published sample, and 30-second resolution provided optimal trade-offs between privacy and information value. For a low-resolution database with a population of over 3500 households, 1-hour resolution provided strong information value and customisable privacy guarantees depending on the published sample size.

Peer-to-peer energy trading based on a hybrid blockchain system

The emergence of distributed energy and prosumers has greatly promoted the reform of the energy market. Blockchain technology provides a very potent solution for distributed energy transactions, however, blockchain technology still develops slowly in the energy field due to the unstable performance of the imperfect system. This paper aims to narrow this gap by the following steps. First, a function-driven integration method is proposed to combine the merit of blockchain distributed ledger and traditional centralized databases. Then a hybrid energy blockchain system architecture is designed, including the blockchain layer, smart contract layer, database layer, and client layer. Further, a peer-to-peer market mechanism considering energy storage arbitrage and fully automated matchmaking is developed on the hybrid blockchain system. The case study proves the effectiveness and safety of the system. The proposed architecture of combined blockchain and database technologies effectively paves the way for constructing a more reasonable and feasible distributed energy trading platform.

Isolation, characterization and application of a lytic phage vB_VspM_VS1 against Vibrio splendidus biofilm.

Vibrio splendidus is a common pathogen in the ocean that infects Apostichopus japonicus, Atlantic salmon and Crassostrea gigas, leading to a variety of diseases. In this study, a virulent phage vB_VspM_VS1, which infects V. splendidus, was isolated from aquaculture ponds in Dalian, China, and it belongs to the family Straboviridae in the order Caudoviricetes. vB_VspM_VS1 had an adsorption rate of 96% in 15 min, a latent period of 65 min, and a burst size of 140 ± 6 PFU/cell. The complete genome of phage vB_VspM_VS1 consists of a linear double-stranded DNA that is 248,270 bp in length with an average G + C content of 42.5% and 389 putative protein-coding genes; 116 genes have known functions. There are 4 tail fiber genes in the positive and negative strands of the phage vB_VspM_VS1 genome. The protein domain of the phage vB_VspM_VS1 tail fibers was obtained from the Protein Data Bank and the SMART (http://smart.embl.de) database. Bacterial challenge tests revealed that the growth of V. splendidus HS0 was apparently inhibited (OD600 < 0.01) in 12 h at an MOI of 10. In against biofilms, we also showed that the OD570 value of the vB_VspM_VS1-treated group (MOI = 1) decreased significantly to 0.04 ± 0.01 compared with that of the control group (0.48 ± 0.08) at 24 h. This study characterizes the genome of the phage vB_VspM_VS1 that infects the pathogenic bacterium V. splendidus of A. japonicus.

EDIL3 is a potential prognostic biomarker that correlates with immune infiltrates in gastric cancer.

EDIL3, which contains epidermal growth factor-like repeats and discoidin I-like domains, is a secretory protein that plays an important role in embryonic development and various illnesses. However, the biological function of EDIL3 in gastric cancer (GC) is still unclear. The objective of this research was to explore the role and potential mechanism of EDIL3 in GC. In this study, we used the GEPIA, HPA, MethSurv, SMART, STRING, GeneMANIA, LinkedOmics TIMER, TIMER2.0, TISIDB, and RNAactDrug databases to comprehensively analyze the roles of EDIL3 in GC. To validate the in silico findings, EDIL3 expression was measured in our collected GC tissues. Meanwhile, several in vitro experiments were performed to test the function of EDIL3 in GC. We found that EDIL3 was highly expressed in GC and associated with adverse clinical features. In vitro assays revealed that EDIL3 promoted the proliferation, migration, and invasion of GC cells. The functions of EDIL3 and co-expression genes were significantly associated with extracellular structure organization and matrix receptor interaction. EDIL3 expression was positively associated with numerous tumor-infiltrating immune cells and their biomarkers. This study determined that EDIL3 may function as an oncogene and is associated with immune infiltration in GC. EDIL3 could be used as a potential therapeutic target for GC.

Pancancer analysis of oncogenic BARX2 identifying its prognostic value and immunological function in liver hepatocellular carcinoma

The transcription factor BarH-like homeobox 2 (BARX2), a member of the Bar-like homeobox gene family, is involved in cell proliferation, differentiation, immune responses and tumorigenesis. However, the potential role of BARX2 in the development of liver hepatocellular carcinoma (LIHC) remains unclear. Therefore, we aimed to study the biological role of BARX2 in hepatocellular carcinoma. Through the UALCAN, GTEx PORTAL, TIMER 2.0, LinkedOmics, SMART, MethSurv, Metascape, GSEA and STRING public databases, the BARX2 mRNA level, prognostic value, coexpressed genes, associated differentially expressed genes, DNA methylation and functional enrichment of LIHC patients were studied. The relationships between BARX2 expression and various clinical or genetic parameters of LIHC patients were determined using data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and BEAT LIHC databases. In addition, the biological function of BARX2 in LIHC was studied in vitro. Through large-scale data mining, our study showed that BARX2 was differentially expressed between different normal and tumour tissues.BARX2 expression in LIHC tissues was significantly lower than that in corresponding controls, especially in patients with T2-4 stage disease. In patients with LIHC, overexpression of BARX2 was an independent poor prognostic factor associated with poor cytogenetic risk and gene mutations. Genomic hypermethylation of the BARX2 gene was associated with upregulated BARX2 expression and poor overall survival (OS) in LIHC. Functional enrichment analysis showed that BARX2 had an immunomodulatory role and was involved in the inflammatory response in LIHC occurrence. In conclusion, the oncogene BARX2 may serve as a new biomarker and prognostic factor for patients with LIHC. The immunomodulatory function of BARX2 deserves further validation in LIHC.

BarleyExpDB: an integrative gene expression database for barley

BackgroundRNA-sequencing (RNA-seq) has been widely used to study the dynamic expression patterns of transcribed genes, which can lead to new biological insights. However, processing and analyzing these huge amounts of histological data remains a great challenge for wet labs and field researchers who lack bioinformatics experience and computational resources.ResultsWe present BarleyExpDB, an easy-to-operate, free, and web-accessible database that integrates transcriptional profiles of barley at different growth and developmental stages, tissues, and stress conditions, as well as differential expression of mutants and populations to build a platform for barley expression and visualization. The expression of a gene of interest can be easily queried by searching by known gene ID or sequence similarity. Expression data can be displayed as a heat map, along with functional descriptions as well as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Proteins Families Database, and Simple Modular Architecture Research Tool annotations.ConclusionsBarleyExpDB will serve as a valuable resource for the barley research community to leverage the vast publicly available RNA-seq datasets for functional genomics research and crop molecular breeding.

Genetic Analysis of a Chinese Pedigree with Hereditary Spherocytosis Caused by Copy Number Variation Deletion of SPTB Gene

To investigate the molecular mechanism of the disease based on the clinical characterization and genetic mutation analysis in a family with hereditary spherocytosis. The proband with jaundice and anemia was referred to Yidu Central Hospital of Weifang in May 2021. Peripheral blood samples were collected from six members of the family. Second-generation sequencing was used to screen the pathological mutations, and the clinically significant variant sites were selected. Then the relevant databases were used to analyze the variant sites, and RT-qPCR was used to detect the relative mRNA levels of candidate gene. The structure and function of SPTB protein were analyzed by UniProt and SMART databases. We infer that the SPTB gene copy number variation (CNV) deletion was co-segregated with the phenotype of the patients in this family based on the results of second-generation sequencing (about 700 target genes). The UCSC Genome Browser demonstrated that the deleted region was mainly located in exon2-3 of SPTB gene. The results of RT-qPCR showed that the relative SPTB mRNA levels of all patients were lower than the healthy control. UniProt and SMART databases analysis showed that SPTB protein without CH1 and CH2 domains could not bind to erythrocyte membrane actin. The CNV deletion of SPTB gene may be the reason for the hereditary spherocytosis in this family.

Consequences of aberrated DNA methylation in Colon Adenocarcinoma: a bioinformatic-based multi-approach

IntroductionThe biology of colorectal cancer (CRC) is remained to be elucidated. Numerous genetic and epigenetic modifications are in concert to create and progress CRC. DNA methylation as a principal epigenetic factor has gained increased attention and could be utilized for biological studies. This study aims to find novel methylated and downregulated genes with a focus on HAND2 in CRC and decipher the biological consequences.Material and methodData on DNA methylation from GEO and SMART databases and the expression GEPIA2 database were downloaded. Afterward, a set of hypermethylated and downregulated genes in CRC was chosen by overlapping genes. Consequently, HAND2 was selected as a key gene for further investigation and confirmed with cell lines methylation and expression data. The functions of HAND2 were further analyzed using gene ontology analyses and the protein–protein interaction network.ResultsThe methylation (p < 0.01) and expression (p < 0.01) of HAND2 are significantly varied in CRC compared to normal control. The correlation analysis (Pearson's correlation coefficient = -0.44, p = 6.6e-14) conveys that HAND2 significantly downregulated and has a reverse correlation with the methylation status of CpG islands. The biological process analysis of HAND2 target genes conveyed that disruption in HAND2 expression could dysregulate ERK1 and ERK2 signaling pathways.ConclusionTogether, the findings showed that DNA hypermethylation of HAND2 was critical evidence in CRC. Further validation and prospective studies are needed to utilize HAND2 methylation as a promising biomarker.

Smart database design for concrete durability analysis - An application in the Hongkong-Zhuhai-Macau bridge

A complete durability database that provides consistent, reliable information is of great significance for concrete durability analysis. The critical challenge is that most durability databases are quite site-specific. When analyzing a local site, a model developed from the generic database may not be fully applicable to a local site. On the other hand, a model purely developed from limited site-specific data may be very imprecise due to significant statistical uncertainty. This study proposes a novel data-driven method for constructing a smart database for concrete durability analysis. The proposed database design is based on a smart hybridization of site-specific and generic databases, in which site-specific data governs when site-specific data are plentiful, but generic data governs when site-specific data are scarce. This method can handle the missing data which are usually encountered in site investigation and quantify the uncertainties. To demonstrate the effectiveness of the proposed method, a real application that provides the durability data for Hongkong-Zhuhai-Macau (HZM) bridge is presented. The main issues regarding the database design include data preconditioning, missing data recovery, and site-specific data transformation. The results showed that the proposed approach could significantly capture correlation patterns in site-specific data and, moreover, can make meaningful predictions even when data is scarce.

Abstract 14187: Neighborhood Walkability and Cardiovascular Risk in the United States

Walkability may be associated with increased physical activity and thus may confer protection against cardiovascular disease and associated risk factors. We sought to characterize the association between neighborhood-level cardiovascular diseases and risk factors with neighborhood walkability across most US census tracts. Methods: The Centers for Disease Control and Prevention (CDC) PLACES dataset provided census-tract level prevalence of coronary artery disease (CAD) and cardiovascular risk factors (hypertension, high cholesterol, obesity, and diabetes). These data were matched with census tract population-weighted walkability index from the CDC’s smart location database. We calculated the mean prevalence of each cardiovascular health indicator by quartile of walkability score. We also fit a multivariable linear regression model to estimate the association between walkability index and the prevalence of CAD adjusting for age, sex, race, and the CDC'S social vulnerability index, a validated metric of socioeconomic status. Results: 70,123 census tracts were analyzed nationwide. Across walkability quartiles Q1 (least walkable) through Q4 (most walkable), we found a statistically significant decrease in the prevalence of CAD (7.0% to 5.4%). Similarly, there was a decrease in the prevalence of CVD risk factors including hypertension (35.5% to 29.7%), high cholesterol (34.5% to 29.7%), obesity (35.0% to 30.2%), and diabetes (11.6% to 10.6%) across walkability quartiles ( Figure ). After multivariable adjustment, continuous walkability index was negatively and significantly associated with the prevalence of CAD (β = -0.09, p &lt; 0.0001). Conclusions: This nationwide analysis demonstrates that neighborhood walkability is associated with a lower prevalence of cardiovascular risk factors and CAD. Figure: Mean prevalence of cardiovascular health indicators according to walkability index quartile. BP = blood pressure, CAD = coronary artery disease.

Pan-cancer analysis of oncogenic TNFAIP2 identifying its prognostic value and immunological function in acute myeloid leukemia

BackgroundTumor necrosis factor alpha-induced protein 2 (TNFAIP2), a TNFα-inducible gene, appears to participate in inflammation, immune response, hematopoiesis, and carcinogenesis. However, the potential role of TNFAIP2 in the development of acute myeloid leukemia (AML) remains unknow yet. Therefore, we aimed to study the biological role of TNFAIP2 in leukemogenesis.MethodsTNFAIP2 mRNA level, prognostic value, co-expressed genes, differentially expressed genes, DNA methylation, and functional enrichment analysis in AML patients were explored via multiple public databases, including UALCAN, GTEx portal, Timer 2.0, LinkedOmics, SMART, MethSurv, Metascape, GSEA and String databases. Data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Beat AML database were used to determine the associations between TNFAIP2 expression and various clinical or genetic parameters of AML patients. Moreover, the biological functions of TNFAIP2 in AML were investigated through in vitro experiments.ResultsBy large-scale data mining, our study indicated that TNFAIP2 was differentially expressed across different normal and tumor tissues. TNFAIP2 expression was significantly increased in AML, particularly in French–American–British (FAB) classification M4/M5 patients, compared with corresponding control tissues. Overexpression of TNFAIP2 was an independent poor prognostic factor of overall survival (OS) and was associated with unfavorable cytogenetic risk and gene mutations in AML patients. DNA hypermethylation of TNFAIP2 at gene body linked to upregulation of TNFAIP2 and inferior OS in AML. Functional enrichment analysis indicated immunomodulation function and inflammation response of TNFAIP2 in leukemogenesis. Finally, the suppression of TNFAIP resulted in inhibition of proliferation by altering cell-cycle progression and increase of cell death by promoting early and late apoptosis in THP-1 and U937AML cells.ConclusionCollectively, the oncogenic TNFAIP2 can function as a novel biomarker and prognostic factor in AML patients. The immunoregulation function of TNFAIP2 warrants further validation in AML.

Retraction Note: Smart indoor crop grower based on smart database using IoT

Retraction Note: Smart indoor crop grower based on smart database using IoT

Idea, Architecture, and Applications of 5G Enabled IoMT Systems for Smart Health Care System

The health sector is one of the important sectors of the Indian economy. The average expenditure on smart healthcare can be saved using the smart healthcare systems. Using Smart Healthcare System, death rate can be reduced and life span can be increased. Smart healthcare includes eHealth, mHealth services, smart home services, electronic database, and intelligent medical devices. Use of IoT based smart healthcare systems is increasing rapidly for disease diagnosis, patient’s treatment, and better quality of life. However, it produces a massive amount of data from different applications. It is the need of an hour to process and monitor real time data remotely with accuracy. As there are some limitations of IoT in healthcare like the massive amount of data generated, data security/privacy concerns, and more. This issue can be resolved by 5G technology. It has the capability to tackle the large amount of data with minimum delay and fulfills the requirement, such as high bandwidth, ultra-low latency, and ultra-high energy efficiency. Both 5G and IoT have great utility in the health sector. This research paper presents components, architecture, and applications of 5G enabled Internet of Medical Things (IoMT). This paper also covers how the 5G enabled IoMT supports the smart healthcare system and comparison of available wireless communication technologies for 5G enabled IoT. Requirements and technologies involved in 5G enabled IoMT Systems are presented along with their impact.

Dynamic Occupancy of Wild Asian Elephant: A Case Study Based On the SMART Database from the Western Forest Complex in Thailand

Understanding distribution patterns is essential for the long-term conservation of megafauna, particularly the Asian elephant (Elephas maximus). We investigated the dynamic occupancy of Asian elephants in the Thung Yai Naresuan West Wildlife Sanctuary in Thailand. Asian elephant occurrences were recorded during patrol activities from 2012 to 2019. We applied a single-species dynamic occupancy model to examine the environmental factors influencing habitat occupancy of Asian elephant across multiple seasons. The best-supported model, based on the Akaike information criterion (AIC), indicated that the normalized difference vegetation index and elevation positively influenced the probability of colonization. In contrast, the distance to the nearest population source sites showed a negative association. The probability of local extinction was positively correlated with the distance to the nearest villages and population source sites. The predictive map indicated a higher probability of colonization in a remote mountainous region of the center of the protected area. Higher extinction probability was associated with areas of dense human activity and far from population source sites connecting the Asian elephant population to the east. This is the first study to utilize a patrol database for assessing the dynamic occupancy of Asian elephants across multiple years. Our model provides insight into the dynamic distribution patterns of Asian elephants within the wildlife sanctuary and the factors that most influence these patterns. Long-term ecological data provide crucial information for assessing biodiversity, population status, and the ecological processes of focal wildlife species and are valuable for both protected area management and conservation efforts.

Evolution of two-component quorum sensing systems.

Quorum sensing (QS) is a cell-to-cell communication system that enables bacteria to coordinate their gene expression depending on their population density, via the detection of small molecules called autoinducers. In this way bacteria can act collectively to initiate processes like bioluminescence, virulence and biofilm formation. Autoinducers are detected by receptors, some of which are part of two-component signal transduction systems (TCS), which comprise of a (usually membrane-bound) sensor histidine kinase (HK) and a cognate response regulator (RR). Different QS systems are used by different bacterial taxa, and their relative evolutionary relationships have not been extensively studied. To address this, we used the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to identify all the QS HKs and RRs that are part of TCSs and examined their conservation across microbial taxa. We compared the combinations of the highly conserved domains in the different families of receptors and response regulators using the Simple Modular Architecture Research Tool (SMART) and KEGG databases, and we also carried out phylogenetic analyses for each family, and all families together. The distribution of the different QS systems across taxa, indicates flexibility in HK–RR pairing and highlights the need for further study of the most abundant systems. For both the QS receptors and the response regulators, our results indicate close evolutionary relationships between certain families, highlighting a common evolutionary history which can inform future applications, such as the design of novel inhibitors for pathogenic QS systems.

Building a smart database for predictive maintenance in already implemented manufacturing systems

Predictive analytics methods have become increasingly important in Manufacturing Organization in the context of Smart Maintenance. Standardized process models for data mining already known to search existing data stocks for patterns, trends and correlations. Sensors are progressively implemented in production machines to create a database for data mining processes. But the risk of Big Data, thus the risk of low quality data is probably high. For an economic consideration, the amount of investment in new measurement technology and infrastructure should be assessed. Organizations are confronted with the challenge of how much they have to invest to obtain a meaningful database. For this reason, it is important to research which existing approaches support the development of a sufficient database for predictive maintenance in manufacturing systems and provide a methodical framework.

Database workload characterization with query plan encoders

Smart databases are adopting artificial intelligence (AI) technologies to achieve instance optimality , and in the future, databases will come with prepackaged AI models within their core components. The reason is that every database runs on different workloads, demands specific resources, and settings to achieve optimal performance. It prompts the necessity to understand workloads running in the system along with their features comprehensively, which we dub as workload characterization. To address this workload characterization problem, we propose our query plan encoders that learn essential features and their correlations from query plans. Our pretrained encoders captures the structural and the computational performance of queries independently. We show that our pretrained encoders are adaptable to workloads that expedites the transfer learning process. We performed independent assessments of structural encoder and performance encoders with multiple downstream tasks. For the overall evaluation of our query plan encoders, we architect two downstream tasks (i) query latency prediction and (ii) query classification. These tasks show the importance of feature-based workload characterization. We also performed extensive experiments on individual encoders to verify the effectiveness of representation learning, and domain adaptability.