Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Sequence-specific inhibition of human immunodeficiency virus (HIV) reverse transcription by antisense oligonucleotides: comparative study in cell-free assays and in HIV-infected cells.

We have investigated two regions of the viral RNA of human immunodeficiency virus type 1 (HIV-1) as potential targets for antisense oligonucleotides. An oligodeoxynucleotide targeted to the U5 region of the viral genome was shown to block the elongation of cDNA synthesized by HIV-1 reverse transcriptase in vitro. This arrest of reverse transcription was independent of the presence of RNase H activity associated with the reverse transcriptase enzyme. A second oligodeoxynucleotide targeted to a site adjacent to the primer binding site inhibited reverse transcription in an RNase H-dependent manner. These two oligonucleotides were covalently linked to a poly(L-lysine) carrier and tested for their ability to inhibit HIV-1 infection in cell cultures. Both oligonucleotides inhibited virus production in a sequence- and dose-dependent manner. PCR analysis showed that they inhibited proviral DNA synthesis in infected cells. In contrast, an antisense oligonucleotide targeted to the tat sequence did not inhibit proviral DNA synthesis but inhibited viral production at a later step of virus development. These experiments show that antisense oligonucleotides targeted to two regions of HIV-1 viral RNA can inhibit the first step of viral infection--i.e., reverse transcription--and prevent the synthesis of proviral DNA in cell cultures.

Endothelin axis expression is markedly different in the two main subtypes of renal cell carcinoma

BACKGROUND. The endothelin axis has been implicated in cancer growth, angiogenesis, and metastasis, but to the authors' knowledge the expression of endothelin genes has not been defined in renal cell carcinoma (RCC). METHODS. Tissue specimens were harvested from both normal and tumor-affected regions at the time of radical nephrectomy from 35 patients with RCC (22 with clear cell RCC [ccRCC] and 13 with papillary RCC [PRCC]). Real-time reverse transcriptase-polymerase chain reaction analysis determined the expression profile of the preproendothelins (PPET-1, PPET-2, and PPET-3), the endothelin receptors (ETA and ETB), and the endothelin-converting enzymes (ECE-1 and ECE-2). RESULTS. PPET-1 was found to be up-regulated in ccRCC tumor specimens and down-regulated in PRCC tumor specimens. ETA was significantly down-regulated in PRCC tumor specimens. ECE-1 was expressed in all tissue specimens at comparable levels, with moderate but significant elevation in normal tissue specimens associated with PRCC. Of the other genes, PPET-2 and ETB were expressed in all tissue specimens and no differences were observed between tumor subtypes or tumor-affected and normal tissue specimens, whereas PPET-3 and ECE-2 were present in all tissue specimens but were barely detectable. CONCLUSIONS. The endothelin axis was expressed differently in the two main subtypes of RCC and appeared to match macroscopic features commonly observed in these tumors (i.e., high expression of PPET-I in hypervascular ccRCC contrasted against low PPET-1 and ETA expression in hypovascular PRCC). The presence of ECE-1 mRNA in these tissue specimens suggested that active endothelin ligands were present, indicating endothelin axis activity was elevated in ccRCC compared with normal kidney, but impaired in PRCC. The current study provided further evidence that it is not appropriate to consider ccRCC and PRCC indiscriminately in regard to treatment. (C) 2004 American Cancer Society.

The inhibitory effect of pentobarbitone on reverse transcription-PCR

Pentobarbitone sodium (Sodium 5-ethyl-5[1-methylbutyl]-pentobarbitone) is a short-acting barbiturate that is commonly used to euthanase animals. As part of our studies into the molecular genetics of copper toxicosis in Bedlington terrier dogs, reverse-transcription (RT)-PCR was noted to always fail on RNA samples collected from livers of dogs sacrificed by pentobarbitone injection. When samples were collected without pentobarbitone, however, RTPCR was always successful. We suspected the possible inhibition by pentobarbitone sodium of either reverse transcriptase or Taq polymerase. In vitro studies showed that pentobarbitone interference of PCR occurred at >4 mug/mul. To identify if pentobarbitone produced competitive inhibition, each components (Taq polymerase, MgCl2, dNTP, etc.) of the PCR was individually altered. However, inhibition still persisted, suggesting that multiple PCR components may be affected. Also it was shown that pentobarbitone interference was not dependent on the PCR product size. Simple dilution of pentobarbitone contaminated DNA solutions, and the addition of bovine serum albumin (BSA) to the PCR mix overcame pentobarbitone interference. In vivo, PCR by pentobarbitone was found to be compounded by high DNA concentration and pentobarbitone contamination. In addition, both high DNA concentration and pentobarbitone contamination could be overcome through dilution and the addition of BSA. Further work is required to quantify pentobarbitone concentration in the liver-extracted DNA and RNA samples before this inhibition effect on PCR can be fully elucidated. (C) 2004 Elsevier B.V. All rights reserved.

Phytoestrogens modulate breast cancer resistance protein expression and function at the blood-cerebrospinal fluid barrier

PURPOSE: Breast cancer resistance protein (BCRP/ABCG2) is a drug efflux transporter expressed at the blood cerebrospinal fluid barrier (BCSFB), and influences distribution of drugs into the central nervous systems (CNS). Current inhibitors have failed clinically due to neurotoxicity. Novel approaches are needed to identify new modulators to enhance CNS delivery. This study examines 18 compounds (mainly phytoestrogens) as modulators of the expression/function of BCRP in an in vitro rat choroid plexus BCSFB model. METHODS: Modulators were initially subject to cytotoxicity (MTT) assessment to determine optimal non-toxic concentrations. Reverse-transcriptase PCR and confocal microscopy were used to identify the presence of BCRP in Z310 cells. Thereafter modulation of the intracellular accumulation of the fluorescent BCRP probe substrate Hoechst 33342 (H33342), changes in protein expression of BCRP (western blotting) and the functional activity of BCRP (membrane insert model) were assessed under modulator exposure. RESULTS: A 24 hour cytotoxicity assay (0.001 µM-1000 µM) demonstrated the majority of modulators possessed a cellular viability IC50 > 148 µM. Intracellular accumulation of H33342 was significantly increased in the presence of the known BCRP inhibitor Ko143 and, following a 24 hour pre-incubation, all modulators demonstrated statistically significant increases in H33342 accumulation (P < 0.001), when compared to control and Ko143. After a 24 hour pre-incubation with modulators alone, a 0.16-2.5-fold change in BCRP expression was observed for test compounds. The functional consequences of this were confirmed in a permeable insert model of the BCSFB which demonstrated that 17-β-estradiol, naringin and silymarin (down-regulators) and baicalin (up-regulator) can modulate BCRP-mediated transport function at the BCSFB. CONCLUSION: We have successfully confirmed the gene and protein expression of BCRP in Z310 cells and demonstrated the potential for phytoestrogen modulators to influence the functionality of BCRP at the BCSFB and thereby potentially allowing manipulation of CNS drug disposition.

The comparative utility of fluorescence in situ hybridization and reverse transcription-polymerase chain reaction in the diagnosis of alveolar rhabdomyosarcoma.

Fluorescence in situ hybridization (FISH) for FOXO1 gene rearrangement and reverse transcription-polymerase chain reaction (PCR) for PAX3/7-FOXO1 fusion transcripts have become routine ancillary tools for the diagnosis of alveolar rhabdomyosarcomas (ARMS). Here we summarize our experience of these adjunct diagnostic modalities at a tertiary center, presenting the largest comparative series of FISH and PCR for suspected or possible ARMS to date. All suspected or possible ARMS tested by FISH or PCR for FOXO1 rearrangement or PAX3/7-FOXO1 fusion transcripts over a 7-year period were included. FISH and PCR results were correlated with clinical and histologic findings. One hundred samples from 95 patients had FISH and/or PCR performed. FISH had higher rates of technical success (96.8 %) compared with PCR (88 %). Where both tests were utilized successfully, there was high concordance rate between them (94.9 %). In 24 histologic ARMS tested for FISH or PCR, 83.3 % were translocation-positive (all for PAX3-FOXO1 by PCR) and included 3 histologic solid variants. In 76 cases where ARMS was excluded, there were 3 potential false-positive cases with FISH but none with PCR. PCR had similar sensitivity (85.7 %) and better specificity (100 %) in aiding the diagnosis of ARMS, compared with FISH (85 and 95.8 %, respectively). All solid variant ARMS harbored FOXO1 gene rearrangements and PAX3-FOXO1 ARMS were detected to the exclusion of PAX7-FOXO1. In comparative analysis, both FISH and PCR are useful in aiding the diagnosis of ARMS and excluding its sarcomatous mimics. FISH is more reliable technically but has less specificity than PCR. In cases where ARMS is in the differential diagnosis, it is optimal to perform both PCR and FISH: both have similar sensitivities for detecting ARMS, but FISH may confirm or exclude cases that are technically unsuccessful with PCR, while PCR can detect specific fusion transcripts that may be useful prognostically.

Angiomatoid fibrous histiocytoma: comparison of fluorescence in situ hybridization and reverse transcription polymerase chain reaction as adjunct diagnostic modalities

Angiomatoid fibrous histiocytoma (AFH) is a rare soft tissue neoplasm of intermediate biologic potential and uncertain differentiation, most often arising in the extremities of children and young adults. Although it has characteristic histologic features of a lymphoid cuff surrounding nodules of ovoid cells with blood-filled cystic cavities, diagnosis is often difficult due to its morphologic heterogeneity and lack of specific immunoprofile. Angiomatoid fibrous histiocytoma is associated with recurrent chromosomal translocations, leading to characteristic EWSR1-CREB1, EWSR1-ATF1, and, rarely, FUS-ATF1 gene fusions; fluorescence in situ hybridization (FISH), detecting EWSR1 or FUS rearrangements, and reverse transcription-polymerase chain reaction (RT-PCR) for EWSR1-CREB1 and EWSR1-ATF1 fusion transcripts have become routine ancillary tools. We present a large comparative series of FISH and RT-PCR for AFH. Seventeen neoplasms (from 16 patients) histologically diagnosed as AFH were assessed for EWSR1 rearrangements or EWSR1-CREB1 and EWSR1-ATF1 fusion transcripts. All 17 were positive for either FISH or RT-PCR or both. Of 16, 14 (87.5%) had detectable EWSR1-CREB1 or EWSR1-ATF1 fusion transcripts by RT-PCR, whereas 13 (76.5%) of 17 had positive EWSR1 rearrangement with FISH. All 13 of 13 non-AFH control neoplasms failed to show EWSR1-CREB1 or EWSR1-ATF1 fusion transcripts, whereas EWSR1 rearrangement was present in 2 of these 13 cases (which were histopathologically myoepithelial neoplasms). This study shows that EWSR1-CREB1 or EWSR1-ATF1 fusions predominate in AFH (supporting previous reports that FUS rearrangement is rare in AFH) and that RT-PCR has a comparable detection rate to FISH for AFH. Importantly, cases of AFH can be missed if RT-PCR is not performed in conjunction with FISH, and RT-PCR has the added advantage of specificity, which is crucial, as EWSR1 rearrangements are present in a variety of neoplasms in the histologic differential diagnosis of AFH, that differ in behavior and treatment.

Cross-resistance to elvitegravir and dolutegravir in 502 patients failing on raltegravir: a French national study of raltegravir-experienced HIV-1-infected patients

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Apoptotic properties of Citrus sudachi Hort, ex Shirai (Rutaceae) extract on humanA549 and HepG2 cancer cells

Purpose: To investigate whether Citrus sudachi harvested at two stages of maturity can induce toxicity in a cell-specific manner and to determine the possible mechanisms of Citrus sudachi-induced cytotoxic responses in two types of cancer cells (human lung adenocarcinoma A549 and hepatocellular carcinoma HepG2 cells) and two normal cell lines (lung 16HBE140- and liver CHANG cells). Methods: 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and annexin V/propidium iodidle assay were used to test the antiproliferative activity and apoptosis of methanol extract of Citrus sudachi, respectively. Griess reaction and reverse transcriptase-polymerase chain reaction (RT-PCR) were carried out to evaluate nitric oxide (NO•) production and the mRNA levels of inhibitors of apoptosis (IAP). Results: Citrus sudachi exerted cytotoxicity in a time-dependent manner in cancer cells which increased with increase in maturity but did not affect normal cells. Citrus sudachi was found to induce accumulation of cells in the sub-G1 cell cycle phase, fragmentation of DNA and cell death with characteristics of apoptosis, in both types of cancer cells. Moreover, Citrus sudachi upregulated cellular NO• produced by activation of nitric oxide synthase (NOS), while it suppressed the levels of IAP mRNA in both types of cancer cells. Conclusion: The results obtained suggest that Citrus sudachi induces apoptosis in A549 and HepG2 cells, which may be mediated by NO•. There is need for further studies on the role of Citrus sudachi in cancer treatment.

Enhanced human bone marrow stromal cell affinity for modified poly(L-lactide) surfaces by the upregulation of adhesion molecular genes

To enhance and regulate cell affinity for poly (l-lactic acid) (PLLA) based materials, two hydrophilic ligands, poly (ethylene glycol) (PEG) and poly (l-lysine) (PLL), were used to develop triblock copolymers: methoxy-terminated poly (ethylene glycol)-block-poly (l-lactide)-block-poly (l-lysine) (MPEG-b-PLLA-b-PLL) in order to regulate protein absorption and cell adhesion. Bone marrow stromal cells (BMSCs) were cultured on different composition of MPEG-b-PLLA-b-PLL copolymer films to determine the effect of modified polymer surfaces on BMSC attachment. To understand the molecular mechanism governing the initial cell adhesion on difference polymer surfaces, the mRNA expression of 84 human extracellular matrix (ECM) and adhesion molecules was analysed using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). It was found that down regulation of adhesion molecules was responsible for the impaired BMSC attachment on PLLA surface. MPEG-b-PLLA-b-PLL copolymer films improved significantly the cell adhesion and cytoskeleton expression by upregulation of relevant molecule genes significantly. Six adhesion genes (CDH1, ITGL, NCAM1, SGCE, COL16A1, and LAMA3) were most significantly influenced by the modified PLLA surfaces. In summary, polymer surfaces altered adhesion molecule gene expression of BMSCs, which consequently regulated cell initial attachment on modified PLLA surfaces.

Molecular characterisation of six badnavirus species associated with leaf streak disease of banana in East Africa

Banana leaf streak disease, caused by several species of Banana streak virus (BSV), is widespread in East Africa. We surveyed for this disease in Uganda and Kenya, and used rolling-circle amplification (RCA) to detect the presence of BSV in banana. Six distinct badnavirus sequences, three from Uganda and three from Kenya, were amplified for which only partial sequences were previously available. The complete genomes were sequenced and characterised. The size and organisation of all six sequences was characteristic of other badnaviruses, including conserved functional domains present in the putative polyprotein encoded by open reading frame (ORF) 3. Based on nucleotide sequence analysis within the reverse transcriptase/ribonuclease H-coding region of open reading frame 3, we propose that these sequences be recognised as six new species and be designated as Banana streak UA virus, Banana streak UI virus, Banana streak UL virus, Banana streak UM virus, Banana streak CA virus and Banana streak IM virus. Using PCR and species-specific primers to test for the presence of integrated sequences, we demonstrated that sequences with high similarity to BSIMV only were present in several banana cultivars which had tested negative for episomal BSV sequences.

Genetic Diversity of Rice Tungro Spherical Virus in Tungro-Endemic Provinces of the Philippines and Indonesia

The two adjacent genes of coat protein 1 and 2 of rice tungro spherical virus (RTSV) were amplified from total RNA extracts of serologically indistinguishable field isolates from the Philippines and Indonesia, using reverse transcriptase polymerase chain reaction (RT-PCR). Digestion with HindIII and BstYI restriction endonucleases differentiated the amplified DNA products into eight distinct coat protein genotypes. These genotypes were then used as indicators of virus diversity in the field. Inter- and intra-site diversities were determined over three cropping seasons. At each of the sites surveyed, one or two main genotypes prevailed together with other related minor or mixed genotypes that did not replace the main genotype over the sampling time. The cluster of genotypes found at the Philippines sites was significantly different from the one at the Indonesia sites, suggesting geographic isolation for virus populations. Phylogenetic studies based on the nucleotide sequences of 38 selected isolates confirm the spatial distribution of RTSV virus populations but show that gene flow may occur between populations. Under the present conditions, rice varieties do not seem to exert selective pressure on the virus populations. Based on the selective constraints in the coat protein amino acid sequences and the virus genetic composition per site, a negative selection model followed by random-sampling events due to vector transmissions is proposed to explain the inter-site diversity observed

Occurrence of LINE, gypsy-like, and copia-like retrotransposons in the clonally propagated sweet potato (Ipomoea batatas L.)

Retrotransposons are a class of transposable elements that represent a major fraction of the repetitive DNA of most eukaryotes. Their abundance stems from their expansive replication strategies. We screened and isolated sequence fragments of long terminal repeat (LTR), gypsy-like reverse transcriptase (rt) and gypsy-like envelope (env) domains, and two partial sequences of non-LTR retrotransposons, long interspersed element (LINE), in the clonally propagated allohexaploid sweet potato (Ipomoea batatas (L.) Lam.) genome. Using dot-blot hybridization, these elements were found to be present in the ~1597 Mb haploid sweet potato genome with copy numbers ranging from ~50 to ~4100 as observed in the partial LTR (IbLtr-1) and LINE (IbLi-1) sequences, respectively. The continuous clonal propagation of sweet potato may have contributed to such a multitude of copies of some of these genomic elements. Interestingly, the isolated gypsy-like env and gypsy-like rt sequence fragments, IbGy-1 (~2100 copies) and IbGy-2 (~540 copies), respectively, were found to be homologous to the Bagy-2 cDNA sequences of barley (Hordeum vulgare L.). Although the isolated partial sequences were found to be homologous to other transcriptionally active elements, future studies are required to determine whether they represent elements that are transcriptionally active under normal and (or) stressful conditions.