Pancreatic Cancer Data Classification with Quantum Machine Learning

Quantum computing is a promising new approach to tackle the complex real-world computational problems by harnessing the power of quantum mechanics principles.The inherent parallelism and exponential computational power of quantum systems hold the potential to outpace classical counterparts in solving complex optimization problems,which are pervasive in machine learning.Quantum Support Vector Machine(QSVM)is a quantum machine learning algorithm inspired by classical Support Vector Machine(SVM)that exploits quantum parallelism to efficiently classify data points in high-dimensional feature spaces.We provide a comprehensive overview of the underlying principles of QSVM,elucidating how different quantum feature maps and quantum kernels enable the manipulation of quantum states to perform classification tasks.Through a comparative analysis,we reveal the quantum advantage achieved by these algorithms in terms of speedup and solution quality.As a case study,we explored the potential of quantum paradigms in the context of a real-world problem:classifying pancreatic cancer biomarker data.The Support Vector Classifier(SVC)algorithm was employed for the classical approach while the QSVM algorithm was executed on a quantum simulator provided by the Qiskit quantum computing framework.The classical approach as well as the quantum-based techniques reported similar accuracy.This uniformity suggests that these methods effectively captured similar underlying patterns in the dataset.Remarkably,quantum implementations exhibited substantially reduced execution times demonstrating the potential of quantum approaches in enhancing classification efficiency.This affirms the growing significance of quantum computing as a transformative tool for augmenting machine learning paradigms and also underscores the potency of quantum execution for computational acceleration.

Comparative study of CEA and CA19-9 in esophageal,gastric and colon cancers individually and in combination(ROC curve analysis)

Objective:To determine the clinical serum levels of carcinoembryonic antigen(CEA) and carbohydrate antigen 19-9(CA19-9),individually and in combination,for the diagnosis of 50 healthy subjects and 150 cases of esophageal,gastric,and colon cancers.Methods:The sensitivities of the two markers were compared individually and in combination,with specificity set at 100%.Receiver operating characteristic(ROC) curves were plotted.Results:Serum CEA levels were significantly higher in cancer patients than in the control group.The sensitivity of CEA was determined:in esophageal cancer,sensitivity=28%,negative predictive value(NPV)=61.72%,and AUC=0.742(SE=0.05),with a significance level of P<0.0001;in gastric cancer,sensitivity=30%,NPV=58.82%,and AUC=0.734(SE=0.05),with a significance level of P<0.0001;in colon cancer,sensitivity=74%,NPV=79.36%,and AUC=0.856(SE=0.04),with a significance level of P<0.0001.The sensitivity of CA19-9 was also evaluated:in esophageal cancer,sensitivity=18%,NPV=54.94%,and AUC=0.573(SE=0.05),with a significance level of P=0.2054.In gastric cancer,sensitivity=42%,NPV=63.29%,and AUC=0.679(SE=0.05),with a significance level of P<0.0011.In colon cancer,sensitivity=26%,NPV=57.47%,and AUC=0.580(SE=0.05),with a significance level of P=0.1670.The following were the sensitivities of CEA/CA19-9 combined:in esophageal cancer,sensitivity=42%,NPV=63.29%,SE=0.078(95% CI:0.0159-0.322);gastric cancer,sensitivity=58%,NPV=70.42%,SE=0.072(95% CI:-0.0866-0.198);and colon cancer,sensitivity=72%,NPV=78.12%,SE=0.070(95% CI:0.137-0.415).Conclusion:CEA exhibited the highest sensitivity for colon cancer,and CA19-9 exhibited the highest sensitivity for gastric cancer.Combined analysis indicated an increase in diagnostic sensitivity in esophageal and gastric cancer compared with that in colon cancer.

Positional cloning of PmCH1357 reveals the origin and allelic variation of the Pm2 gene for powdery mildew resistance in wheat

Powdery mildew, caused by the biotrophic fungus Blumeria graminis f. sp. tritici(Bgt), is a prevalent disease in common wheat(Triticum aestivum L.) and causes serious yield losses worldwide. We used a map-based approach to clone the major broad-spectrum powdery mildew resistance gene Pm CH1357 from wheat breeding line CH1357. Pm CH1357 was mapped to a 526 kb region containing only Traes CS5 D01 G044600. The Traes CS5 D01 G044600 sequence of the susceptibility allele in Taichung 29(TC29) was identical to that in Chinese Spring, whereas the sequence of the resistance allele in CH1357 was identical to Pm2a previously cloned from the germplasm Ulka/*8Cc. The susceptibility allele in TC29 contained a 7 bp deletion in exon 1, resulting in loss of 856 of the 1277 amino acids in the predicted nucleotide-binding domain leucine-rich repeat containing Pm2a protein.Pm CH1357/Pm2a sequence was also isolated from the Chinese wheat landraces and cultivars that were previously reported to possess the resistance gene Pm2b, Pm2c,PmLX66, or PmND399. The Pm CH1357/Pm2a resistance allele was present in 10 of 495 accessions in core germplasm and contemporary cultivars from China and the USA. A newly developed diagnostic marker for the 7 bp In Del in the resistance gene can be used to eliminate the susceptibility allele in wheat breeding programs.

Understanding Brown Planthopper Resistance in Rice: Genetics, Biochemical and Molecular Breeding Approaches

Brown planthopper(BPH,Nilaparvata lugens St自I)is the most devastating pest of rice in Asia and causes significant yield loss annually.Around 37 BPH resistance genes have been identified so far in indica,African rice varieties along with wild germplasms such as Oryza officinalis,O.minuta,O.nivara,O.punctata,O.rufipogon and O,latifolia.Genes/QTLs involved in BPH resistance,including Bph1,bph2/BPH26,Bph3,Bph6,bph7,BPH9,Bph12,Bph14,Bph15,Bph17,BPH18,bph19,Bph20,Bph21(t),Bph27,Bph27©Bph28(t),BPH29,QBph3,QBph4,QBph4.2,Bph30,Bph32,Bph33,Bph35 and Bph36,have been fine-mapped by different researchers across the globe.The majority of genes/QTLs are located on rice chromosomes 1,3,4,6,11 and 12.Rice plants respond to BPH attack by releasing various endogenous metabolites like proteinase inhibitors,callose,secondary metabolites(terpenes,alkaloids,flavonoid,etc.)and volatile compounds.Besides that,hormonal signal pathways mediating(antagonistic/synergistic)resista nee responses in rice have been well studied.Marker-assisted breedi ng and genome editi ng techniq ues can also be adopted for improving resista nee to novel BPH biotypes.

Chloroplast gene matK holds the barcodes for identification of Momordica(Cucurbitaceae) species from Indian subcontinent

DNA barcoding is a supplementary tool in plant systematics,extensively used to resolve the species level controversies.This paper details the identification of DNA barcodes for seven species of Momordica,using the chloroplast gene mat K.Since the species M.cymbalaria has been confused as a member of the genus Luffa,26 accessions of Momordica belonging to seven Indian species and two accessions of Luffa acutangula were included in this study.Analysis of mat K sequences has yielded distinct barcodes in M.charantia var.charantia,M.subangulata subsp.renigera,M.cochinchinensis,M.balsamina,M.cymbalaria and also in Luffa acutangula.Evolutionary status of each species was reflected as nucleotide polymorphisms in each sequence.The wild species M.dioica and M.sahyadrica have yielded one barcode but failed to get differentiated.Further,this study provides conclusive proof that M.cymbalaria is a member of Momordica genus.The phylogram generated was successful to distinguish the monoecious species of this genus,M.charantia,M.balsamina and M.cymbalaria,from the dioecious species M.dioica,M.sahyadrica,M.subangulata subsp.renigera and M.cochinchinensis.Thus,mat K locus,by accumulating the evolutionary sequence variations,is proven efficient to differentiate the Momordica species and to reveal their relatedness.

Element Concentration in the Prepared and Commercial Feed as Well Their Status in the Breast Muscle of Chicken after Prolonged Feeding

Quality poultry meat depends upon the feed and as such there are many commercially available feeds. However, their composition and standard by and large throughout the year may not remain same due to obvious reasons. Moreover, there is no mention of locally produced feed particularly in the north eastern part of India. The major objective of this study was to prepare mesh feed E1 with the available ingredients as well as their effect were compared with that of the two commercially available feed Amrit and Godrej (E2 and E3) in terms of Crude protein, fats and element composition. The findings showed that the protein content (240 g/kg) and fats (105 g/kg) in the breast muscle of female was higher in the E3 received against the broiler chicken received local feed. Element analysis of the E1, E2 and E3 depicted significantly higher value of Ca, K, Cu Zn and Se against the commercial feed. Other elements like Mg, Na, Fe, P, and Mn showed no variation while compared E1, E2 and E3 together. Thus the present findings suggest that the local feed E1 could be accepted at per with that of the commercial feed for poultry.

An Application of the ABS Algorithm for Modeling Multiple Regression on Massive Data, Predicting the Most Influencing Factors

Linear Least Square (LLS) is an approach for modeling regression analysis, applied for prediction and quantification of the strength of relationship between dependent and independent variables. There are a number of methods for solving the LLS problem but as soon as the data size increases and system becomes ill conditioned, the classical methods become complex at time and space with decreasing level of accuracy. Proposed work is based on prediction and quantification of the strength of relationship between sugar fasting and Post-Prandial (PP) sugar with 73 factors that affect diabetes. Due to the large number of independent variables, presented problem of diabetes prediction also presented similar complexities. ABS method is an approach proven better than other classical approaches for LLS problems. ABS algorithm has been applied for solving LLS problem. Hence, separate regression equations were obtained for sugar fasting and PP severity.

Discovering the cellular-localized functional modules and modular interactions in response to liver cancer

In this paper,we firstly identify the functional modules enriched with differentially expressed genes(DEGs) and characterized by biological processes in specific cellular locations,based on gene ontology(GO) and microarray data.Then,we further define and filter disease relevant signature modules accord-ing to the ranking of the disease discriminating abilities of the pre-seleeted functional modules.At last,we analyze the potential way by which they cooperate towards human disease.Application of the proposedmethod to the analysis of a liver cancer dataset shows that,using the same false discovery rate (FDR)threshold,we can find more biologically meaningful and detailed processes by using the cellular localiza-tion information.Some biological evidences support the relevancy of our biological modules to the diseasemechanism.

Silencing of Astrocyte Elevated Gene-1 (AEG-1) inhibits the proliferative and invasive potential through interaction with Exostosin-1 (EXT-1) in primary and metastatic colon cancer cells

Colon cancer is the third major cause of cancer deaths,accounting for about 8%in terms of mortality globally.The present study aims to explore the effect of silencing Astrocyte Elevated Gene-1(AEG-1),a metastasis mediating factor,and how it interacts with Exostosin-1(EXT-1)protein to inhibit the proliferative and invasive potential in colon cancer cells.Forward siRNA transfection was performed using AEG-1 siRNA in SW480 and SW620 colon cancer cell lines,and the expression levels of mRNA and protein were analyzed by Real-time PCR and Immunofluorescence.A simple bioinformatics approach was carried out to identify the possible interactions between AEG-1 and EXT-1 using Easy Networks and Pathway Commons Database.Cell survival and clonal efficiency were determined using Cell Counting Kit-8 assay and clonogenic assay,apoptosis using flow cytometry analysis,migration and invasion using scratch and Transwell assays,respectively.Forward siRNA transfection significantly suppressed the expression of AEG-1 in mRNA and protein levels on SW480 and SW620 colon cancer cells.From our results,we found that EXT-1 mRNA and protein level was significantly upregulated in AEG-1 siRNA transfected cells.Moreover,treatment with AEG-1 siRNA inhibited the proliferation,clonogenic ability,migration,and invasion and also induced apoptosis.Through the bioinformatic approach,our data analyses pointed towards the crosstalk between AEG-1 and EXT-1 mediated through Patched-1(PTCH-1)protein.Our current results demonstrated that silencing AEG-1 can restrain cell proliferation,migration,and invasion,ultimately leading to apoptosis.In AEG-1 siRNA transfected cells,PTCH-1 activity might be modulated by several genes and,in turn,affects the EXT-1 activity.Collectively,these observations not only provide insight into the interplay between AEG-1 and EXT-1 but also suggest that AEG-1 may represent a possible candidate therapeutic target through interaction with EXT-1 in colon cancer.

Metagenomic Surveys of Gut Microbiota

Gut microbiota of higher vertebrates is host-specific. The number and diversity of the organisms residing within the gut ecosystem are defined by physiological and environmental factors, such as host genotype, habitat, and diet. Recently, culture-independent sequencing techniques have added a new dimension to the study of gut microbiota and the challenge to analyze the large volume of sequencing data is increasingly addressed by the development of novel computational tools and methods. Interestingly, gut microbiota maintains a constant relative abundance at operational tax- onomic unit （OTU） levels and altered bacterial abundance has been associated with complex diseases such as symptomatic atherosclerosis, type 2 diabetes, obesity, and colorectal cancer. Therefore, the study of gut microbial population has emerged as an important field of research in order to ulti- mately achieve better health. In addition, there is a spontaneous, non-linear, and dynamic interac- tion among different bacterial species residing in the gut. Thus, predicting the influence of perturbed microbe-microbe interaction network on health can aid in developing novel therapeutics. Here, we summarize the population abundance of gut microbiota and its variation in different clinical states, computational tools available to analyze the pyrosequencing data, and gut microbe-microbe inter- action networks.

Coronatine-lnsensitive 1 （COI1） Mediates Transcriptional Responses of Arabidopsis thaliana to External Potassium Supply

The ability to adjust growth and development to the availability of mineral nutrients in the soil is an essential life skill of plants but the underlying signaling pathways are poorly understood. In Arab/dops/s thal/ana, shortage of po- tassium （K） induces a number of genes related to the phytohormone jasmonic acid （JA）. Using comparative microarray analysis of wild-type and coi1-16 mutant plants, we classified transcriptional responses to K with respect to their depen- dence on COI1, a central component of oxylipin signaling. Expression profiles obtained in a short-term experiment clearly distinguished between COil-dependent and COil-independent K-responsive genes, and identified both known and novel targets of JA-COIl-signaling. During long-term K-deficiency, coi-16 mutants displayed de novo responses covering similar functions as COil-targets except for defense. A putative role of JA for enhancing the defense potential of K-deficient plants was further supported by the observation that plants grown on low K were less damaged by thrips than plants grown with sufficient K.

SMS 2.0: An Updated Database to Study the Structural Plasticity of Short Peptide Fragments in Non-redundant Proteins

The function of a protein molecule is greatly influenced by its three-dimensional （3D） structure and therefore structure prediction will help identify its biological function. We have updated Sequence, Motif and Structure （SMS）, the database of structurally rigid peptide fragments, by combining amino acid sequences and the corre- sponding 3D atomic coordinates of non-redundant （25%） and redundant （90%） protein chains available in the Protein Data Bank （PDB）. SMS 2.0 provides information pertaining to the peptide fragments of length 5-14 resi- dues. The entire dataset is divided into three categories, namely, same sequence motifs having similar, intermedi- ate or dissimilar 3D structures. Further, options are provided to facilitate structural superposition using the pro- gram structural alignment of multiple proteins （STAMP） and the popular JAVA plug-in （Jmol） is deployed for visualization. In addition, functionalities are provided to search for the occurrences of the sequence motifs in other structural and sequence databases like PDB, Genome Database （GDB）, Protein Information Resource （PIR） and Swiss-Prot. The updated database along with the search engine is available over the World Wide Web through the following URL http：//cluster.physics.iisc.ernet.in/sms/.

The LEAFY floral regulator displays pioneer transcription factor properties

Pioneer transcription factors(TFs)are a special category of TFs with the capacity to bind to closed chromatin regions in which DNA is wrapped around histones and may be highly methylated.Subsequently,pioneer TFs are able to modify the chromatin state to initiate gene expression.In plants,LEAFY(LFY)is a master floral regulator and has been suggested to act as a pioneer TF in Arabidopsis.Here,we demonstrate that LFY is able to bind both methylated and non-methylated DNA using a combination of in vitro genomewide binding experiments and structural modeling.Comparisons between regions bound by LFY in vivo and chromatin accessibility data suggest that a subset of LFY bound regions is occupied by nucleosomes.We confirm that LFY is able to bind nucleosomal DNA in vitro using reconstituted nucleosomes.Finally,we show that constitutive LFY expression in seedling tissues is sufficient to induce chromatin accessibility in the LFY direct target genes APETALA1 and AGAMOUS.Taken together,our study suggests that LFY possesses key pioneer TF features that contribute to launching the floral gene expression program.

Construction, characterization and application of a genomewide promoter library in Saccharomyces cerevisiae

Promoters are critical elements to control gene expression but could behave differently under various growth conditions. Here we report the construction of a genome-wide promoter library, in which each native promoter in Saccharomyces cerevisiae was cloned upstream of a yellow fluorescent protein （YFP） reporter gene. Nine libraries were arbitrarily defined and assembled in bacteria. The resulting pools of promoters could be prepared and transformed into a yeast strain either as centromeric plasmids or integrated into a genomic locus upon enzymatic treatment. Using fluorescence activated cell sorting, we classified the yeast strains based on YFP fluorescence intensity and arbitrarily divided the entire library into 12 bins, representing weak to strong promoters. Several strong promoters were identified from the most active bins and their activities were assayed under different growth conditions. Finally, these promoters were applied to drive the expression of genes in xylose utilization to improve fermentation efficiency. Together, this library could provide a quick solution to identify and utilize desired promoters under user-defined growth conditions.

Identification of Potential Leptospira Phosphoheptose Isomerase Inhibitors Through Virtual High-Throughput Screening

The life-threatening infections caused by Leptospira serovars demand the need for designing anti-leptospirosis drugs. The present study encompasses exploring inhibitors against phosphoheptose isomerase （GmhA） of Leptospira, which is vital for lipopolysaccharide （LPS） biosynthesis and is identified as a common drug target through the subtractive genomic approach. GmhA model was built in Modeller 9v7. Structural refinement and energy minimization of the predicted model was carried out using Maestro 9.0. The refined model reliability was assessed through Procheck, ProSA, ProQ and Profile 3D. The substrate-based virtual high-throughput screening （VHTS） in Ligand.Info Meta-Database tool generated an in-house library of 354 substrate structural analogs. Furthermore, structure-based VHTS from the in-house library with different conformations of each ligand provided 14 novel competitive inhibitors. The model together with insight gained from the VHTS would be a promising starting point for developing anti-leptospirosis competitive inhibitors targeting LPS biosynthesis pathway.

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.

PsRNA:A Computing Engine for the Comparative Identification of Putative Small RNA Locations within Intergenic Regions

Small RNAs （sRNAs） are non-coding transcripts exerting their functions in the cells directly. Identification of sRNAs is a difficult task due to the lack of clear sequence and structural biases. Most sRNAs are identified within genus specific intergenic regions in related genomes. However, several of these regions remain un-annotated due to lack of sequence homology and/or potent statistical identification tools. A computational engine has been built to search within the intergenic regions to identify and roughly annotate new putative sRNA regions in Enterobacteriaceae genomes. It utilizes experimentally known sRNA data and their flanking genes/KEGG Orthology （KO） numbers as templates to identify similar sRNA regions in related query genomes. The search engine not only has the capability to locate putative intergenic regions for specific sRNAs, but also has the potency to locate conserved, shuffled or deleted gene clusters in query genomes. Because it uses the KO terms for locating functionally important regions such as sRNAs, any further KO number assignment to additional genes will increase the sensitivity. The PsRNA server is used for the identification of putative sRNA regions through the information retrieved from the sRNA of interest. The computing engine is available online at http：//bioserver 1 .physics.iisc.ernet.in/psrna/and http：//bicmku.in： 8081/psrna/.

Junker: An Intergenic Explorer for Bacterial Genomes

In the past few decades, scientists from all over the world have taken a keen interest in novel functional units such as small regulatory RNAs, small open reading frames, pseudogenes, transposons, integrase binding attB/attP sites, repeat elements within the bacterial intergenic regions （IGRs） and in the analysis of those ＂junk＂ regions for ge- nomic complexity. Here we have developed a web server, named Junker, to facilitate the in-depth analysis of IGRs for examining their length distribution, four-quadrant plots, GC percentage and repeat details. Upon selection of a particular bacterial genome, the physical genome map is displayed as a multiple loci with options to view any loci of interest in detail. In addition, an IGR statistics module has been created and implemented in the web server to analyze the length distribution of the IGRs and to understand the disordered grouping of IGRs across the genome by generating the four-quadrant plots. The proposed web server is freely available at the URL http：//pranag.physics.iisc.ernet.in/junker/.

Molecular evolution of methanogens based on their metabolic facets

The information provided by completely sequenced genomes of methanogens can yield insights into a deeper molecular understanding of evolutionary mechanisms.This review describes the advantages of using metabolic pathways to clarify evolutionary correlation of methanogens with archaea and prokaryotes.Metabolic trees can be used to highlight similarities in metabolic networks related to the biology of methanogens.Metabolic genes are among the most modular in the cell and their genes are expected to travel laterally,even in recent evolution.Phylogenetic analysis of protein superfamilies provides a perspective on the evolutionary history of some key metabolic modules of methanogens.Phage-related genes from distantly related organisms typically invade methanogens by horizontal gene transfer.Metabolic modules in methanogenesis are phylogenetically aligned in closely related methanogens.Reverse order reactions of methanogenesis are achieved in methylotrophic methanogens using metabolic and structural modules of key enzymes.A significant evolutionary process is thought to couple the utilization of heavy metal ions with energetic metabolism in methanogens.Over 30 of methanogens genomes have been sequenced to date,and a variety of databases are being developed that will provide for genome annotation and phylogenomic analysis of methanogens.Into the context of the evolutionary hypothesis,the integration of metabolomic and proteomic data into large-scale mathematical models holds promise for fostering rational strategies for strain improvement.

Prediction of the next highly pathogenic avian influenza pandemic that can cause illness in humans

Background:In recent years,avian influenza viruses(AIVs)have seriously threatened human health.Questions such as:why do AIVs infect humans?,how quickly can an AIV become pandemic?,and which virus is the most dangerous?cannot be sufficiently answered using current bioinformatic studies.Method:Secondary structures and energies of representative 5′-untranslated region(UTR)of the HA gene were calculated.Then their secondary structures and energies were re-calculated after one or two nucleotide substitutions were introduced into the HA 5′-UTR.Phylogenetic trees on the basis of hemagglutinin(HA)and polymerase basic protein 2(PB2)amino acid sequences and HA 5′-UTR nucleotide sequences were constructed.The connection between the energy and translation efficiency of 5′-UTR was confirmed by in vitro coupled transcription/translation assay.Results:The simplicity of the secondary structure of the 5′-UTR of the HA gene determines the overall virus replication rate and transmission potential.Point mutation assays show that the 5′-UTR sequences of the HA gene in the influenza subtypes H2N2,H3N2,and H7N9 have greater variation potentials than other virus subtypes.Conclusion:Some high-virulent strains of avian influenza might emerge in the next two to three years.The H2N2 subtype,once disappeared in humans,may stage a comeback.The current outbreak of H7N9 may become pandemic and cause even more deaths,if one or two bases are substituted in the 5′-UTR sequence of the HA gene.