Deciphering miRNA-lncRNA-mRNA interaction through experimental validation of miRNAs, lncRNAs, and miRNA targets on mRNAs in Cajanus cajan.

Pigeon pea (Cajanus cajan) is widely cultivated for its nutritional and medicinal value yet remains an orphan crop as productivity has not been improved because of a lack of genome and non-coding genome information. Non-coding RNAs, like miRNAs and long non-coding RNAs (lncRNAs), are involved in regulation of growth, metabolism, development, and stress response, and have a critical role in post-transcriptional gene regulation (PTGR). We attempted to elucidate the roles of miRNAs and lncRNAs in pigeon pea through experimental validation of computationally predicted miRNAs and lncRNAs and targets of miRNAs on mRNAs. We experimentally validated 20 miRNAs and 11 lncRNAs. We predicted cleavage sites of three miRNA targets: serine/threonine-protein kinase, polygalacturonase, beta-galactosidase. We identified 469 targets of 265 miRNAs and their functional annotations using computational methods. We built a miRNA-mRNA-lncRNA network model, with the miRNAs targeting both mRNAs and lncRNAs, to obtain information on the interplay of these three molecules. A confirmed interaction through experimental validation was established between miRNA, namely cca-miR1535a targeting the mRNA for beta-galactosidase, as well as the lncRNA cca-lnc-020033. Our findings increase knowledge of the non-coding genome of pigeon pea and their roles in PTGR and in improving agronomic traits of this pulse crop.

Plant-pathogen interactions: what microarray tells about it?

Plant defense responses are mediated by elementary regulatory proteins that affect expression of thousands of genes. Over the last decade, microarray technology has played a key role in deciphering the underlying networks of gene regulation in plants that lead to a wide variety of defence responses. Microarray is an important tool to quantify and profile the expression of thousands of genes simultaneously, with two main aims: (1) gene discovery and (2) global expression profiling. Several microarray technologies are currently in use; most include a glass slide platform with spotted cDNA or oligonucleotides. Till date, microarray technology has been used in the identification of regulatory genes, end-point defence genes, to understand the signal transduction processes underlying disease resistance and its intimate links to other physiological pathways. Microarray technology can be used for in-depth, simultaneous profiling of host/pathogen genes as the disease progresses from infection to resistance/susceptibility at different developmental stages of the host, which can be done in different environments, for clearer understanding of the processes involved. A thorough knowledge of plant disease resistance using successful combination of microarray and other high throughput techniques, as well as biochemical, genetic, and cell biological experiments is needed for practical application to secure and stabilize yield of many crop plants. This review starts with a brief introduction to microarray technology, followed by the basics of plant-pathogen interaction, the use of DNA microarrays over the last decade to unravel the mysteries of plant-pathogen interaction, and ends with the future prospects of this technology.

Imatinib mesylate therapy in patients of chronic myeloid leukemia with Philadelphia chromosome positive: an experience from eastern India.

Imatinib inhibits constitutively active BCR-ABL tyrosine kinase of chronic myeloid leukemia (CML). In a long term study it was found superior to interferon alfa plus cytarabine for newly diagnosed CML in the chronic phase. However, till date there is no major study to evaluate eastern Indian CML patients treated with imatinib mesylate. The aim of our study was to see the efficacy, tolerability, toxicity and safety of imatinib in eastern Indian subset of CML population. The present study enrolled 831 patients with CML out of which 197 were excluded due to various reasons of noncompliance, death and not being fit to receive the drug. The rest, 634 (76% of total enrolled) were selected for the evaluation. In the beginning of the study, 603 patients were in chronic phase, 27 in accelerated phase and 4 patients in blast crisis phase. Among 634 patients, 280 patients (44%) received previously either interferon alpha or hydroxyurea and other 354 patients (56%) were previously untreated. Complete hematological remission and major cytogenetic response were 91 and 67%, respectively after 1 year of treatment. Complete molecular remission was 35% after 1 year of treatment. Sixty-four patients (10.1%) were resistant to imatinib mesylate in 5 years. The disease free and overall survival at 60 months were 72.2 and 76.1% respectively. After 60 months of follow up, continuous treatment of chronic phase CML with imatinib as initial therapy was found to be safe and able to induce durable responses in a high proportion of patients.

Stem cell transplant: An experience from eastern India.

BACKGROUND: Hematopoietic stem cell transplant using human leukocyte antigen (HLA)-matched sibling or unrelated bone marrow, or related or unrelated cord blood has been performed successfully to treat patients with different types of hematological malignancies, genetic disorders and hereditary immune deficiencies. Since 1983, stem cell transplantation has been carried out in different institutes of India. But, till then, no transplantation was performed in eastern India. MATERIALS AND METHODS: Our present study is reporting for the first time stem cell transplantation in eastern India. From August 2000 to June 2011 (with a 3-year gap for up-gradation), we have performed a total of 22 transplants. Thirteen patients (M:F:9:4) with indications of aplastic anemia, thalassaemia, acute myeloid leukemia and chronic myeloid leukemia underwent allogenic transplant, whereas autologous transplant was performed for nine patients (M:F:2:1) of multiple myeloma, Hodgkin's and non-Hodgkin's lymphoma and neuroblastoma. The median age of the patients was 19.6 years, with a range of 5 years 8 months to 52 years. Fourteen patients received myeloablative conditioning regime whereas eight patients received immunosuppressive and less myeloablative protocol. Sources of stem cells in case of allogenic transplant are bone marrow and related or unrelated umbilical cord blood and in case of autologous transplant, these are peripheral blood stem cells or self-bone marrow. Standard prophylactic medication was followed prior to transplants. RESULTS: A disease-free survival of 68.18% and overall survival of 86.3% were seen at the median follow-up period of 4.6 years. Common post-transplant complications were mucositis, infection, venoocclusive disease, graft versus host disease, hemorrhagic cystitis, etc. CONCLUSION: The use of cord blood as a source of stem cells has been proved inferior as compared with the bone marrow stem cell source in cases of thalassaemia in our institute and thus is not recommended for thalassaemia. But, it has been proved to be a very useful and effective stem cell source (both related and unrelated cord blood) in cases of aplastic anemia and other immunological disorders.

Hough transform network: learning conoidal structures in a connectionist framework.

A two-layer neural-network model is designed which accepts image coordinates as the input and learns the parametric form of conoidal shapes (lines/circles/ellipses) adaptively. It provides an efficient representation of visual information embedded in the connection weights and the parameters of the processing elements. It not only reduces the large space requirements of the classical Hough transform (HT), but also represents parameters with a higher precision. The performance of the methodology is compared with other existing algorithms and has been found to excel over those algorithms in many cases.

Learning Hough transform: a neural network model.

A single-layered Hough transform network is proposed that accepts image coordinates of each object pixel as input and produces a set of outputs that indicate the belongingness of the pixel to a particular structure (e.g., a straight line). The network is able to learn adaptively the parametric forms of the linear segments present in the image. It is designed for learning and identification not only of linear segments in two-dimensional images but also the planes and hyperplanes in the higher-dimensional spaces. It provides an efficient representation of visual information embedded in the connection weights. The network not only reduces the large space requirement, as in the case of classical Hough transform, but also represents the parameters with high precision.

Unsupervised feature evaluation: a neuro-fuzzy approach.

The present article demonstrates a way of formulating neuro-fuzzy approaches for both feature selection and extraction under unsupervised learning. A fuzzy feature evaluation index for a set of features is defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces. A concept of flexible membership function incorporating weighted distance is introduced for computing membership values in the transformed space. Two new layered networks are designed. The tasks of membership computation and minimization of the evaluation index, through unsupervised learning process, are embedded into them without requiring the information on the number of clusters in the feature space. The network for feature selection results in an optimal order of individual importance of the features. The other one extracts a set of optimum transformed features, by projecting -dimensional original space directly to n'-dimensional (n' < n) transformed space, along with their relative importance. The superiority of the networks to some related ones is established experimentally.

A connectionist model for corner detection in binary and gray images.

A connectionist model along with its state dynamics is developed for detecting corner points in binary and gray images. For a given binary/gray image, each pixel in the image is assigned with some initial cornerity (our measurable quantity) which is a vector representing the direction and strength of the corner. These cornerities are then mapped onto a neural-network model which is essentially designed as a cooperative computational framework. The cornerity at each pixel is updated depending on the neighborhood information. After the network dynamics settles to stable state, the dominant points are obtained by finding out the local maxima in the cornerities. Theoretical investigations are made to ensure the stability and convergence of the network. It is found that the network is able to detect corner points even in the noisy images and for open object boundaries. The dynamics of the network is extended to accept the edge information from gray images also. The effectiveness of the model is experimentally demonstrated in synthetic and real-life binary and gray images.

Blind separation of a mixture of uniformly distributed source signals: A novel approach.

A new, efficient algorithm for blind separation of uniformly distributed sources is proposed. The mixing matrix is assumed to be orthogonal by prewhitening the observed signals. The learning rule adaptively estimates the mixing matrix by conceptually rotating a unit hypercube so that all output signal components are contained within or on the hypercube. Under some ideal constraints, it has been theoretically shown that the algorithm is very similar to an ideal O(1/T2) convergent algorithm, which is much faster than the existing O(1/T) convergent algorithms. The algorithm has been generalized to take care of the noisy signals by adaptively dilating the hypercube in conjunction with its rotation.

Neuro-fuzzy feature evaluation with theoretical analysis.

The article provides a fuzzy set theoretic feature evaluation index and a connectionist model for its evaluation along with their theoretical analysis. A concept of weighted membership function is introduced which makes the modeling of the class structures more appropriate. A neuro-fuzzy algorithm is developed for determining the optimum weighting coefficients representing the feature importance. It is shown theoretically that the evaluation index has a fixed upper bound and a varying lower bound, and it monotonically increases with the lower bound. A relation between the evaluation index, interclass distance and weighting coefficients is established. Effectiveness of the algorithms for evaluating features both individually and in a group (considering their independence and dependency) is demonstrated along with comparisons on speech, Iris, medical and mango-leaf data. The results are also validated using scatter diagram and k-NN classifier.

Blind separation of uniformly distributed signals: a general approach.

A general algorithm for blind separation of uniformly distributed signals is presented. First maximum likelihood equations are obtained for dealing with this task. It is difficult to obtain a closed form maximum likelihood solution for arbitrary mixing matrix. The learning rules are obtained based on the geometric interpretation of the maximum likelihood estimator. The algorithm, under special constraint of orthogonal mixing matrix, is the same as the O(1/T2) convergent algorithm. Special noise correction mechanisms are incorporated in the algorithm, and it has been found that the algorithm exhibits stable performance even in the presence of large amount of noise.

Beta-carotene inhibits rat liver chromosomal aberrations and DNA chain break after a single injection of diethylnitrosamine.

Beta-carotene (BC) has recently been found to possess potent anti-tumour activity in chemically induced rat liver carcinogenesis. In the present study, attempts have been made to understand the basic cytogenetic and molecular mechanism of the anti-tumour effect of BC by monitoring its effect on rat liver chromosomal aberrations (CAs) and DNA chain breaks during the early preneoplastic stage of diethylnitrosamine (DEN)-induced hepatocarcinogenesis in male rats. DNA chain breaks, however, can be detected with great sensitivity by exposing crude cell lysates to alkaline solutions and monitoring the rate of strand unwinding so that one strand break per chromosome can easily be detected. Supplementary BC, in basal diet (120 mg kg[-1]), was given to rats 15 days before carcinogenic threat with DEN. Under these experimental conditions, BC was found to afford a unique protection against DEN-induced CAs 96 h after DEN injection. Long-term treatment with BC also triggered a protective effect on induction of CAs 15, 30 or 45 days after DEN treatment, which was maximal on structural aberrations followed by numerical and physiological types. BC treatment for 15 days before DEN injection was found to offer a significant (P < 0.001) protection in the generation of single-strand breaks compared with DEN control. Thus, BC ranks as a potential chemopreventive agent for the future so far as chemical rat liver carcinogenesis is concerned.

Characterization of the adaptive response to ionizing radiation induced by low doses of X-rays to Vibrio cholerae cells.

Pretreatment with sublethal doses of X-rays induced an adaptive response in Vibrio cholerae cells as indicated by their greater resistance to the subsequent challenging doses of X-irradiation. The adaptive response was maximum following a pre-exposure dose of 1.7 Gy X-rays and an optimum incubation period of 40 min at 37 degrees C. Pre-exposure to a sublethal dose of 1.7 Gy X-rays made the Vibrio cholerae cells 3.38-fold more resistant to the subsequent challenge by X-rays. Pretreatment with a sublethal dose of hydrogen peroxide offered a similar degree of protection to the bacterial cells against subsequent treatment with challenging doses of X-ray radiation. However, exposure of Vibrio cholerae cells to mild heat (42 degrees C for 10 min) before X-ray irradiation decreased their survival following X-irradiation.

On the nature of the adaptive response induced by mitomycin C in Vibrio cholerae OGAWA 154 cells.

The induction of an adaptive response in Vibrio cholerae OGAWA 154 cells was obtained using an alkylating agent, mitomycin C, as both stimulating and challenging agent. Cross-adaptive response was observed in V. cholerae cells when pretreated with a sublethal dose of another alkylating agent, N-methyl-N'nitro-N-nitrosoguanidine, followed by challenging treatment with mitomycin C. The dose of mitomycin C for 50% survival (D50) became almost double for mitomycin C pretreated cells and 1.5 times for N-methyl-N-'nitro-N-nitrosoguanidine pretreated cells, compared to nonpretreated cells. It was also shown that pretreatment with a sublethal dose of oxidative DNA damaging agents, viz, hydrogen peroxide or nitrofurantoin, did not show any cross-adaptive response against subsequent challenge by mitomycin C.

Estimation of single-strand breaks induced in the dried film of DNA by high energy alpha particle from a cyclotron.

DNA extracted and purified from Vibrio cholerae OGAWA 154 cells and prepared in the form of a dry thin film was exposed in air to a beam of alpha particles obtained from a Variable Energy Cyclotron. The number of single-strand breaks per DNA unit exhibited a linear dose-effect relationship indicating the occurrence of single-hit kinetics. The efficiency and yield of alpha-induced single-strand breaks were approximately 72 eV/break and 1.39 respectively.

The effect of three different ranitidine dosage regimens on reducing gastric acidity and volume in ambulatory surgical patients.

STUDY OBJECTIVE: To evaluate three different preoperative oral dosing regimens of ranitidine in ambulatory patients who had significant risk of aspiration pneumonitis (gastric pH < or = 2.5 or volume > or = 25 ml at intubation or extubation). DESIGN: Double-blind, placebo-controlled, randomized trial. SETTING: St. Francis Hospital of Buffalo, New York. PATIENTS: Two hundred seventy-one ambulatory patients about to undergo a surgical procedure under general anesthesia, of whom 241 (89%) completed the trial and were considered evaluable. INTERVENTIONS: Patients were randomly assigned to receive one of four regimens administered orally before surgery: placebo at bedtime the night before and in the morning on the day of surgery; ranitidine 150 mg at bedtime and in the morning; ranitidine 150 mg at bedtime and placebo in the morning; or ranitidine 300 mg at bedtime and placebo in the morning. MEASUREMENTS AND MAIN RESULTS: Patients who received ranitidine 150 mg twice/day, ranitidine 150 mg at bedtime, or ranitidine 300 mg at bedtime had a significantly (p < 0.05) lower frequency of a gastric pH 2.5 or below at intubation or extubation than those taking placebo twice/day (3%, 45%, and 31%, respectively, vs 86%). In addition, gastric volume at intubation or extubation was 25 ml or above in significantly fewer patients receiving ranitidine 150 mg at bedtime than placebo (37% vs 13%, p < 0.05). Overall, the number of patients with risk factors for aspiration pneumonitis was significantly lower with ranitidine 150 mg twice/day (20%), ranitidine 150 mg at bedtime (48%), and ranitidine 300 mg at bedtime (35%) than placebo (86%) (p < 0.001), and significantly lower with ranitidine 150 mg twice/day than ranitidine 150 mg at bedtime (p < 0.05). CONCLUSIONS: Ranitidine 150 mg twice/day preoperatively reduced to the greatest degree the percentage of patients who developed significant risk factors for aspiration pneumonitis after surgery under general anesthesia.

Inter-strand cross-linking of Vibrio cholerae DNA induced by furazolidone: a quantitative assay by four simple methods.

Four simple methods, i.e., (i) UV absorption spectrophotometry, (ii) hydroxyapatite chromatography, (iii) fluorescence analysis of ethidium bromide bound to DNA and (iv) assay of S1 endonuclease action, were used in parallel for the estimation of furazolidone-induced inter-strand cross-links in Vibrio cholerae DNA. The data produced by the four methods were in reasonable agreement with each other and provided similar linear dose-response relations, the correlation (between dose and response) coefficient being in any case numerically greater than 0.98. When the data obtained by four independent methods were plotted in a single graph, the resulting dose-response relation could be described by the equation log NR = 1.41 - 0.54 log D, where NR is the % non-reversible DNA remaining in the cells treated by furazolidone at dose D micrograms/ml x h. The correlation coefficient in this plot was -0.98 and significant to a level better than 0.1%. This study thus brings out that any one of these four methods can be used with reasonable confidence for the diagnosis and assay of inter-strand cross-links in DNA.

X-tron: an incremental connectionist model for category perception.

A connectionist model for categorization (self-organization) even in the presence of multiple or mixed patterns has been presented. During self-organization, the network automatically adjusts the number of nodes in the hidden and output layers, depending on the complexity or nature of overlap between the patterns. An ambiguity measure is given based on how well the features are being interpreted by the network. From the ambiguity measure a certainty factor about the decision of the network is derived. The effect of noise on the certainty factor is investigated. A vigilance threshold is used to decide whether the network's decision is correct or not. Functionally the network consists of two parts, one of them categorizes the incoming patterns and the other monitors the performance of categorization. The characteristics of the model has also been demonstrated experimentally on both 1D binary strings and image patterns even when they are corrupted by additive, subtractive, and mixed noise.

PsyCOP-a psychologically motivated connectionist system for object perception.

A connectionist system has been designed for learning and simultaneous recognition of flat industrial objects (based an the concepts of conventional and structured connectionist computing) by integrating the psychological hypotheses with the generalized Hough transform technique. The psychological facts include the evidence of separation of two regions for identification ("what it is") and pose estimation ("where it is"). The system uses the mechanism of selective attention for initial hypotheses generation. A special two-stage training paradigm has been developed for learning the structural relationships between the features and objects and the importance values of the features with respect to the objects. The performance of the system has been demonstrated on real-life data both for single and mixed (overlapped) instances of object categories. The robustness of the system with respect to noise and false alarming has been theoretically investigated.