In vitro dose-dependent effect of melatonin also showed variation during day and night. We explain our observations on the basis of melatonin's antioxidative function and its effect on the fluidity of plasma membrane of red blood cells. Rhythmic modulation of MDA and GSH contents emphasized the role of melatonin as an antioxidant and its function against oxidative stress.The pineal secretory product melatonin  acts as an effective antioxidant and free-radical scavenger and plays an important role in several physiological functions such as sleep induction, immunomodulation, cardiovascular protection, thermoregulation, neuroprotection, tumor-suppression and oncostasis. Membrane lipid-peroxidation in terms of malondialdehyde (MDA) and intracellular glutathione (GSH) is considered to be a reliable marker of oxidative stress. The present work was undertaken to study the modulating effect of melatonin on MDA and GSH in human erythrocytes during day and night. Our observation shows the modulation of these two biomarkers by melatonin, and this may have important therapeutic implications.  Oxidative stress, or the imbalance between oxidant production and antioxidant levels, appears to be favour of the former, consequently resulting in acceleration of neurodegeneration, cognitive impairment, immunosuppression, and ageing . Melatonin vitro and scavenges peroxynitrite. It has also been reported to increase the synthesis of glutathione and of several antioxidant enzymes [1-acetyl-N2-formyl-5-methoxy-kynuramine and N1-acetyl-5-methoxy-kynuramine [Melatonin scavenges hydrochlorous acid, detoxifies highly toxic hydroxyl and peroxyl radicals  enzymes . Upon menuramine . Therefo The present study was undertaken to understand the modulation of intracellular reduced glutathione (GSH) and malondialdehyde (MDA) by melatonin in human red blood cells according to the oscillatory circadian changes in levels of this hormone. We have also studied the dose-dependent effect of melatonin on GSH and MDA in erythrocytes obtained from blood at two different times, subjected to oxidative stress by incubating with tert-butyl hydroperoxide (t-BHP) . We used2HPO4. Supernatant and buffy coat were carefully removed after each wash.The study was carried out on different healthy donors of both sexes who gave informed consent for the use of their blood samples for the study. The criteria for screening of volunteers included nonsmoking individuals having no acute or chronic diseases  or organ dysfunction and had not taken any medication , 9. The ε = 31,500) and is expressed as nmol·mL−1 of packed erythrocytes.Erythrocyte MDA was measured according to the method of Esterbauer and Cheeseman with slight modification . Packed Erythrocyte GSH was measured following the method of Beutler . The met−4 M–10−8 M) were done with PBS. The concentration of ethanol was always <0.01% (v/v) in the final solution. The in vitro effect of melatonin was evaluated by incubating erythrocytes with melatonin at different doses  of melatonin for 30 minutes at 37°C. The erythrocytes were again washed two to three times with PBS, pH 7.4, to remove any amount of the compound, and finally, packed erythrocytes were used for the assay of MDA and GSH. In parallel control experiments, blood was incubated with ethanol ) but without melatonin.Blood was washed two to three times with PBS containing 5 mM glucose (GPBS), pH 7.4. Erythrocytes were then suspended in 4 volumes of GPBS. A stock solution (10 mM) of melatonin was prepared in absolute ethanol; further dilutions  in presence and absence of melatonin in the above experiments. The concentration of t-BHP used in the present study to induce oxidative stress of erythrocytes was in the range of concentrations used in other published reports [ Oxidative stress was induced  reports .The current study was designed to determine whether circadian rhythm-associated variations in the levels of malondialdehyde and glutathione occur in the erythrocytes of healthy adults and to investigate the concentration-dependent effect of melatonin on variation in these two biomarkers of oxidative stress. Malondialdehyde and hydroxynonenal, by-products of lipid peroxidation, are considered as toxic second messengers that can diffuse within or even escape from the cell and attack targets far from the site of production. MDA is considered to be one of the reliable markers of cellular peroxidative damage. P < .05) variation in erythrocyte MDA content in the blood samples collected during night and in the morning. Such a rhythmic variation in MDA is indicative of the relationship between oxidative stress and periodic changes in melatonin synthesis in response to photic and nonphotic signals. The dose-dependent effect of melatonin on erythrocyte MDA content after in vitro oxidative insult by incubating with t-BHP is shown in −4 M) resulted in a significant decrease in MDA content (P < .05), and lower concentrations of melatonin showed slightly greater protection of lipid peroxidation. It was observed that the physiological level (10−9 M) of melatonin exerted maximum protective effect. The effect of melatonin may be due to its free radical-scavenging property. Glutathione, an efficient antioxidant present in almost all living cells, is also considered as a biomarker of redox imbalance at cellular level. in vivo, as suggested by in vitro studies. Melatonin owes its free radical-scavenging property to the methoxy group at position-5 of the indole nucleus and the acetyl group of the side chain of melatonin [Oxidative free radicals resulting from cellular metabolism tend to disturb cellular membrane integrity and enzyme activities in red blood cells. Free radical-induced lipid peroxidation intensifies in the absence of a protective antioxidant defence mechanism resulting in decrease in cell membrane fluidity. Under physiological conditions, the reactive oxygen species are eliminated by enzymatic and nonenzymatic antioxidant defence systems. Knowledge of the absorption and metabolism of exogenous indoleamine drug becomes essential before any conclusions may be drawn regarding its potential to exert biological activity elatonin .in vitro indoleamine treatment indicating suppressed activity of exogenous melatonin in presence of the endogenous hormone.The low concentration of MDA in nocturnal samples is in conformity with previous reports that proposed the periodicity in lipid peroxidation alongwith circadian rhythmicity . Melaton−5 M-10−6 M) may be attributed to the fact that the indoleamine is a weak lipoperoxyl radical scavenger and is least effective in counteracting the accumulation of lipid peroxides in the membrane of RBCs under induced oxidative challenge [in vitro treatment with melatonin. Drug potency of melatonin at higher doses is quite evident from previous reports [The observed decrease of lipid peroxidation cannot be explained merely on the basis of the direct scavenging of lipoperoxyl radicals by melatonin. The less effect of melatonin at a still higher concentration (10hallenge , 18. Zavhallenge  reported reports . The pro reports .γ-glutamyl-cysteine synthase forming a meshwork of an antioxidative system. Melatonin has been found to upregulate cellular glutathione level to check lipid peroxidation in brain cells [γ-glutamyltranspeptidase. The increase in erythrocyte GSH concentration after melatonin administration could be linked to the known stimulation of γ-glutamylcysteine synthase, a rate-limiting enzyme in reduced glutathione synthesis, by melatonin [The recycling of glutathione in the cells depends on an NADPH-dependent glutathione enzyme system which includes glutathione peroxidise, glutathione reductase, and in cells . The stiin cells . Since min cells . The plaelatonin . The stielatonin . The incMelatonin, due to its circadian secretion, causes day and night modulation in the markers of oxidative stress. Erythrocyte MDA and GSH contents are affected by melatonin according to the changes in photic signals, which emphasizes the role of melatonin as an antioxidant and its function against oxidative stress in red blood cells. The effect of exogenous melatonin has been related to plasma-membrane fluidity as well as its free radical-scavenging potential. In addition, exogenous melatonin has effects on these antioxidant-defence systems which may have important therapeutic implications.


The rotational vestibulo-ocular reflex (rVOR) generates compensatory eye movements in response to rotational head accelerations. The velocity-storage mechanism (VSM), which is controlled by the vestibulo-cerebellar nodulus and uvula, determines the rVOR time constant. In healthy subjects, it has been suggested that self-motion perception in response to earth-vertical axis rotations depends on the VSM in a similar way as reflexive eye movements. We aimed at further investigating this hypothesis and speculated that if the rVOR and rotational self-motion perception share a common VSM, alteration in the latter, such as those occurring after a loss of the regulatory control by vestibulo-cerebellar structures, would result in similar reflexive and perceptual response changes. We therefore set out to explore both responses in patients with vestibulo-cerebellar degeneration.Reflexive eye movements and perceived rotational velocity were simultaneously recorded in 14 patients with chronic vestibulo-cerebellar degeneration (28–81yrs) and 12 age-matched healthy subjects (30–72yrs) after the sudden deceleration (90°/s2) from constant-velocity (90°/s) rotations about the earth-vertical yaw and pitch axes. rVOR and perceived rotational velocity data were analyzed using a two-exponential model with a direct pathway, representing semicircular canal activity, and an indirect pathway, implementing the VSM. We found that VSM time constants of rVOR and perceived rotational velocity co-varied in cerebellar patients and in healthy controls . When constraining model parameters to use the same VSM time constant for rVOR and perceived rotational velocity, moreover, no significant deterioration of the quality of fit was found for both populations (variance-accounted-for >0.8).Our results confirm that self-motion perception in response to rotational velocity-steps may be controlled by the same velocity storage network that controls reflexive eye movements and that no additional, e.g. cortical, mechanisms are required to explain perceptual dynamics. In the absence of visual cues, it is mainly the vestibular system providing the brain with a sense of body and spatial orientation. The semicircular canals detect rotational movements and the otolith organs detect translational movements as well as the orientation of the head relative to gravity. Specifically, the semicircular canals sense angular and the otolith organs sense linear acceleration. Thus, when the head moves at a constant angular velocity, after an initial deflection, the cupula of the sensory organ passively returns to its resting position resulting in a decay of the firing rate of the vestibular nerve and, therefore, in a decrease of movement sensation. The semicircular canal cupular time constant has been estimated to range between 3 and 7 seconds Although the concept of the velocity storage mechanism has been developed based on reflexive eye movements, Okada et al. have suggested that this velocity storage mechanism is also present at higher-level cortical processes such as in rotational self-motion perception The velocity storage mechanism is modulated by the vestibulo-cerebellum, specifically the nodulus and ventral uvula To investigate our hypothesis, namely whether the rVOR and rotational self-motion perception share a common velocity storage mechanism, we decided to explore perceptual and reflexive eye movement responses in patients with midline cerebellar lesions. Even though we could not predict the effect of the cerebellar lesions on the duration of both responses, i.e. whether we would find a prolongation or shortening of the responses, we, nevertheless, expected to find a correlation of both responses in the case of a common velocity storage mechanism. Alternatively, the lack of correlation would imply that vestibular signals for rotational self-motion perception are further processed, possibly at a more rostral level in the central nervous system.14 patients  with chronic degeneration of the vestibulo-cerebellum due to hereditary or sporadic disease see  and 12 aParticipants were seated on a turntable with three servo-controlled motor-driven axes . They were positioned so that the intersection of the inter-aural and naso-occipital axes was at the intersection of the three axes of the turntable. The head was restrained with an individually molded thermoplastic mask . Pillows and safety belts minimized movements of the body.Participants were asked to turn a lever attached to a potentiometer that was fixed to the chair. The instruction was to match the rate of lever spinning with the perceived rotational velocity and to stop spinning when the rotation was not felt anymore  after anesthetizing the conjunctiva with 0.4% Oxybuprocaine. Search coil annuli were calibrated with a method described elsewhere Participants were rotated in complete darkness about the earth-vertical axis while seated upright  or lying on their left side . Two patients  were delivered only in counterclockwise direction . The second component of the function represents the central processing called the velocity storage mechanism and is the difference of two exponentials with two different time constants, Cτ and VSMτ. It is depicted as dashed traces (see inserts B and C).A model previously developed for monkeys representing the step responses of the rotational vestibulo-ocular reflex (rVOR) was fitted to our data Cτ and VSMτ are the semicircular canal and velocity storage time constants, respectively, and Dg and Ig are the gains, i.e. the strength or weight of the peripheral and central contributions τ, and another with a time constant accounting for the central, i.e. velocity storage (VSM)τ, dynamics.As outlined previously see , slow-phVSMτ free to change , indicating a possible link between the central time constant VSMτ of slow-phase eye velocity and perceived rotational velocity. VSMτ of slow-phase eye velocity and perceived rotational velocity  in individual patients.VSMτ for fitting slow-phase eye velocity and perceived rotational velocity traces. Estimated model parameters with constrained VSMτ are presented in VSMτ parameters, time constants of all samples were normally distributed and no statistical differences between patients and healthy controls were found during both yaw and pitch rotations (paired t-test p>0.1).We subsequently constrained the model to use the same VSMτ was additionally evaluated using the Bayesian information criterion .Similar to the unconstraint model condition, we found that cerebellar eye velocity and perceptual curves were fitted well by the model . The eligibility of the model when reducing the number of parameters by using the same VSM,τ such as the one shown in the first column of From both figures one can also infer that the main characteristics of slow-phase eye velocity and perceived rotational velocity responses were similar in our patients and in healthy subjects. Specifically, slow-phase eye velocity showed an almost immediate rise followed by a slower decay approximating a single exponential curve, while perceived rotational velocity rise is followed by a plateau-like phase. The latter is especially evident in patients having a long Values of gain ratios of the velocity storage component to the semicircular canals component in cerebellar and healthy subjects are provided in In patients with chronic degeneration of the vestibulo-cerebellum due to hereditary or sporadic disease, we studied the vestibulo-ocular reflex and self-motion perception after a sudden stop from a sustained rotation about the earth-vertical axis (angular velocity step response) while seated either in the upright position  or lying on the left side . We aimed to investigate whether the loss of the regulatory control of the brainstem velocity-storage mechanism by midline cerebellar structures due to cerebellar atrophy would induce similar changes in rotational self-motion perception and in reflexive eye movements. Such finding would emphasize a possible sharing of the velocity storage mechanism between the rotational vestibulo-ocular reflex (rVOR) and self-motion perception. Results from this study support that hypothesis.We found that reflexive eye movement and self-motion perception responses to an angular velocity step behaved similarly. By using a model originally developed for reflexive eye movements A correlation of vestibulo-ocular and vestibulo-perceptual responses has previously been shown by Bronstein et al. in a similar population of patients with midline cerebellar degeneration We did not find any significant difference in the duration of reflexive eye movements and sensation in our patients compared to healthy age-matched subjects. This contrasts Bronstein et al. findings of a shortening of the vestibulo-ocular and vestibulo-perceptual responses compared to healthy controls. It is conceivable that this discrepancy might originate from subtle differences in the populations of patients, e.g. in etiology, disease duration, and region predominantly affected by cerebellar atrophy. Regarding this latter, in fact, ablation in rhesus monkeys point to a separate control of the horizontal and vertical/torsional rVOR time constants by different regions  of the nodulus and ventral uvula A last remark concerns the finding of a wide spread in the duration of reflexive and perceptual responses observed during earth-vertical pitch rotations in our patients see  and 3. S


S-nitrosylation is believed to be the most important mechanism for transduction of bioactivity of NO. The first proteins found to be nitrosylated were mainly of cytoplasmic origin or isolated from mitochondria and peroxisomes. Interestingly, it was shown that redox-sensitive transcription factors are also nitrosylated and that NO influences the redox-dependent nuclear transport of some proteins. This implies that NO plays a role in regulating transcription and/or general nuclear metabolism which is a fascinating new aspect of NO signaling in plants. In this review, we will discuss the impact of S-nitrosylation on nuclear plant proteins with a focus on transcriptional regulation, describe the function of this modification and draw also comparisons to the animal system in which S-nitrosylation of nuclear proteins is a well characterized concept.Nitric oxide (NO) plays an important role in many different physiological processes in plants. It mainly acts by post-translationally modifying proteins. Modification of cysteine residues termed as  Nitric oxide (NO) is a small, highly reactive gaseous radical. Although it is cytotoxic in high concentrations, NO plays a key role as a biological messenger in all kingdoms. In plants, it is implicated in various physiological processes like flowering, stomatal closure, germination, root development, gravitropism, and responses to abiotic and biotic stresses .2O3 (or higher oxides like NO2), respectively. Moreover, adding or removing one electron from the antibonding highest occupied molecular orbital by reducing or oxidizing chemicals yields nitroxyl anion (NO-) and nitrosonium cation (NO+). Collectively, these species are referred to as reactive nitrogen species (RNS) each having distinct chemical properties leading to numerous reactions with biological molecules like lipids, carbohydrates, nucleic acids, and proteins. Although most of these reactions were assumed to be indicative for nitrosative stress in the past, it has become clear that some of these RNS also function as important redox-signaling molecules in the cell by binding covalently to target proteins  binds to the transition metal (Lewis acid) of metalloproteins yielding a metal–nitrosyl complex. One example from mammals is the binding of NO to the heme center of soluble guanylate cyclase which activates this enzyme by inducing conformational changes and this in turn leads to the production of cyclic GMP .Reactive nitrogen species can modify the activity of proteins by covalently binding to tyrosine and cysteine residues. Tyrosine nitration refers to the addition of a nitro group to susceptible tyrosine residues in ortho position to the hydroxyl group thus leading to 3-nitrotyrosine. The main nitrating species is peroxynitrite which is produced in a diffusion controlled reaction between NO and superoxide . TyrosinS-nitrosylation of protein cysteine residues is believed to be the most important mechanism for transduction of bioactivity of NO in plants. The formation of nitrosothiols is still debated. The direct reaction of thiol groups with NO is too slow to occur in vivo, instead it is assumed that N2O3 is the main nitrosylating species in aerobic conditions although the formation of dinitrogen trioxide is controversially discussed (S-nitrosothiol formation are nitrosonium and nitroxyl ions (S-nitrosylated glutathione GSNO) in animals; in plants, however, evidence for this mechanism is lacking  leads to a decrease in the export rate and a subsequent nuclear accumulation of its target protein Nrf2, an antioxidant transcription factor  not only plays an important role in glycolysis but also participates in nuclear events like regulation of gene transcription, RNA transport and DNA replication. In animal cells, the link between NO signaling and nuclear action of GAPDH is well established. GAPDH lacks a nuclear localization signal and the homotetramer is too large (150 kDa) to pass passively through nuclear pores. Upon stress GAPDH is specifically nitrosylated at Cys150 by inducible NO-synthase (iNOS) leading to complex formation with seven in absentia homolog 1 (Siah1), an E3 ubiquitin ligase. Siah1 has a very rapid turnover in HEK293 cells but binding to GAPDH markedly increases its stability. The nuclear import signal of Siah1 enables the translocation of the GAPDH/Siah1 complex into the nucleus . Interessynthase .Arabidopsis, both GAPDH isoforms GapC1 and GapC2 were shown to be nitrosylated and glutathionylated on Cys155 and Cys159  demonstrating the reversibility of these modifications. A GFP–GAPDH fusion protein was localized in both the cytosol and nucleus in A. thaliana protoplasts indicating partial nuclear localization of GAPDH (Nicotiana tabacum osmotic stress-activated protein kinase) partially localized to the nucleus in BY2 cells after salt stress. Both proteins of this complex seem to be regulated by NO: GAPDH is directly S-nitrosylated, whereas the regulation of NtOSAK is rather indirect, involving the NO-dependent phosphorylation of a serine residue in the activation loop of the kinase , which shares structural features with plant non-expressor of pathogenesis-related genes 1 NPR1; , functioS-nitrosylation , is essential for salicylic acid (SA)-mediated signal transduction . Recentlrization . Trx catrization . Howevercis-elements of the PR1 promoter, promoting PR1 gene expression and defense (The TGACG motif binding transcription factors (TGA) belong to the group of basic leucine zipper (bZIP) proteins and the DNA-binding sites for several bZIP factors were enriched in promoter regions of NO-regulated genes . In the  defense . Redox-d defense . The CysS-nitrosylation (S-nitrosylated in vitro after S-nitrosoglutathione (GSNO) treatment  catalyze the transfer of acetyl groups from acetyl-coenzyme A on lysine residues of histone tails thereby neutralizing the positive charge of the lysine residue. This reduces the affinity of histones for negatively charged DNA resulting in a loose chromatin structure that is easily accessible for the transcriptional machinery. In contrast, histone deacetylases (HDACs) remove the acetyl group of histone tails and condense the chromatin, thereby resulting in reduced gene expression . HistoneS-nitrosylation of HDAC2 in neurons. HDAC2 contains three cysteine residues and only double mutation of Cys262 and Cys274 completely abolished its S-nitrosylation  of HDAC2 in muscle cells and neurons (Brain-derived neurotrophic factor (BDNF) and other neurotrophins play a crucial role in the development of the rat and mouse nervous system by influencing the expression of many specific genes that promote differentiation, cell survival, etc. . Since ssylation . S-nitroand nNos . NO-depeand nNos . Interesactivity  whereas activity . This di neurons .S-nitrosylation of PP2A enforces its binding to HDAC4/14-3-3 leading to dephosphorylation and subsequent nuclear localization of HDAC4  or HDA1 (histone deacetylase 1). Sirtuins (two members in Arabidopsis) are homologous to yeast SIR2 (silent information regulator 2) and have a different catalytic mechanism as they need NADH as a cofactor. The HD2-like family seems to be plant-specific, no homologs have been identified in other organisms so far , making these proteins interesting candidates for further studies. Data from our lab support the idea that histone deacetylases might also be redox regulated in plants  probably participates in regulation of transcription. In animals, several transcription factors are known to be regulated by this post-translational modification: results from studies in neuronal physiology have demonstrated that NO modulates gene expression through the formation of SNO-bonds in multiple transcriptional activators (S-nitrosylation mediates NO-dependent regulation of various zinc-finger-containing transcription factors, including egr-1 and NFκB. As zinc-finger motifs are very sensitive to S-nitrosylation this class of TFs might also be interesting to study in plants. Besides acting on transcription factors, NO also seems to be involved in epigenetic regulation of plant chromatin by modifying key remodeler enzymes like HDACs, which is a new and fascinating aspect of NO-mediated redox signaling in plants. However, important questions are remaining. Work so far has mostly been carried out in vitro, the in vivo relevance as well as the exact molecular mechanism still needs to be determined leaving much space for future investi - gations.e damage . This rativators . For insThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


A bivariate mixture model utilizing information across two species was proposed to solve the fundamental problem of identifying differentially expressed genes in microarray experiments. The model utility was illustrated using a dog and human lymphoma data set prepared by a group of scientists in the College of Veterinary Medicine at North Carolina State University. A small number of genes were identified as being differentially expressed in both species and the human genes in this cluster serve as a good predictor for classifying diffuse large-B-cell lymphoma (DLBCL) patients into two subgroups, the germinal center B-cell-like diffuse large B-cell lymphoma and the activated B-cell-like diffuse large B-cell lymphoma. The number of human genes that were observed to be significantly differentially expressed (21) from the two-species analysis was very small compared to the number of human genes (190) identified with only one-species analysis (human data). The genes may be clinically relevant/important, as this small set achieved low misclassification rates of DLBCL subtypes. Additionally, the two subgroups defined by this cluster of human genes had significantly different survival functions, indicating that the stratification based on gene-expression profiling using the proposed mixture model provided improved insight into the clinical differences between the two cancer subtypes. Diffuse large-B-cell lymphoma (DLBCL), the most common type of non-Hodgkin lymphoma in adults, accounts for 30% to 40% of newly diagnosed lymphomas and has an annual incidence in America of more than 25,000 cases. Combination chemotherapy has transformed DLBCL from a fatal disease into one that is often curable, but only approximately 50% of all patients are cured,2. This Recent developments in microarray technology allow researchers to accurately and precisely measure gene expression patterns in lymphomas which provides the opportunity to revolutionize the way these tumors are grouped and treated. In other words, studying gene expression profiles in lymphomas may provide the opportunity to identify pathways on which the tumor depends and to target the pathways for the development of new drugs. Indeed, gene-expression profiling studies have distinguished three molecular subtypes of DLBCL: germinal-center B-cell-like (GCB) DLBCL, activated B-cell-like (ABC) DLBCL, and primary mediastinal B-cell lymphoma (PMBL),5-8.The first attempt at examining gene expression profiling to identify distinct B-cell malignancies was made by Alizadeh et al.. A hieraA gene-expression signature is a group of genes expressed in a specific cell lineage or stage of differentiation or during a particular biologic response and hence genes within the same gene-expression signature are probably associated with similar biologic aspects of tumor..A gene-eWright et al. formulatA panel of 36 genes whose expression predicts survival in DLBCL was identified by Lossos et al. through limma in  7] and tp < 0.0001). A log-rank test[Figureank test was usedAs determined by gene-expression profiling performed in and7],,7], amonTo validate the relevance between specific genes and phenotypes, a careful search of the literature was undertaken using Entrez Gene. Some ofCD39 [Entrez Gene:953] is a B lymphocyte activation marker and has powerful functions in the immune system[e system.JAW1 [Entrez Gene:4033], a lymphoid-restricted protein, suggested that this protein may have a role in the developmentally regulated trafficking of the antigen receptors in B cells and may influence lymphoid development[Jaw1 mRNA were found in germinal center B-cells and in diffuse large B-cell lymphomas of germinal center subtype.The expression pattern of elopment. Tedoldielopment pointed LMO2 [Entrez Gene:4005], though its function in germinal center cells is unknown, as a candidate marker involved in the development of DLBCL has been discussed in several papers. Natkunam et al.. RGS is a family of proteins acting to limit and modulate heterotrimeric G-protein signaling. Han et al. as marker for the activated B-cell-like signature has been well-established[FOXP1 are significantly different. Goatly et al.[FOXP1 expression in lymphoma development by investigating the FOXP1 translocation, copy number change, and protein expression in mucosa-associated lymphoid tissue lymphoma and DLBCL. Korac and Dominis[FOXP1, BCL2, and BCL6 gene expression in diffuse large B-cell lymphoma tumor cells. FOXP1 protein was detected in 28 patients; genetic abnormalities involving the FOXP1 locus were found in 19 patients, and both were present in 13 patients, among the samples of lymph nodes from 53 patients with newly diagnosed DLBCL. FOXP1 genetic abnormalities have been found to be associated with both BCL2 and BCL6 expression. Though it has been discovered that BCL2 and BCL6 proteins have an impact on diffuse large B-cell lymphoma development and outcome, they may not be good prognostic markers. FOXP1 has played a role in the development of DLBCL. The identified association among FOXP1, BCL2, and BCL6 indicates the possibilities of uncovering the development process in diffuse large B-cell lymphoma tumor cells. In addition, Nyman et al.[FOXP1 and MUM1/IRF4 as activated B-cell-like markers to distinguish patients between the activated B-cell-like and other diffuse large B-cell lymphoma subtypes. Most recently, six common prognostic biomarkers, including FOXP1, were used to conclusively decide the cut-off values calculated by receiver operating curves to predict survival for DLBCL patients[The importance of the transcription factor ablished,9. Banhaablished investigy et al. made an  Dominis exploredn et al. used FOXpatients. All theAmong the 21 selected genes, five  have been carefully examined to explore their association with the development of lymphoma. From the mixture model assumption, genes in the same category should react to the stimulus  in a similar manner. Hence, the implications of these 21 genes (some of them may not have been studied scrupulously) in lymphoma may provide timely and important insight on guiding future investigations of their roles in both B-cell biology and lymphoma development.Since the development of high throughput gene expression technology, the important and difficult task of searching for genes that exhibit differences across species  has been the focus of much research. Simultaneously analyzing gene expression across two species takes into account the biological similarity between different organisms while identifying genes that could be potential prognostic markers and increase the power to detect differences. Identification of the relevant genes and a better understanding of the associated molecular pathways may open new possibilities in cancer diagnosis and treatment. Furthermore, it may become a practical assay for newly diagnosed patients to optimize their clinical management.In this case study, the application of the proposed nine-component mixture model successfully reduced the quantities of variables (genes) needed to be investigated for the study of two types of DLBCL in humans. The dimension of variables decreased from 6,566 to 21, a cluster of genes that were identified as being differentially expressed in both species. On the other hand, an analysis of data from one species that selected genes using a specified FDR led to a much longer list of differentially expressed human genes (935 genes with FDR = 0.01 and 190 with FDR = 0.00001). Furthermore, the misclassification rate for human cancer type classification using clustering with gene expression from these 21 genes identified by the bivariate mixture model was remarkably low. The survivorship of the patients stratified according to this clustering was very different across the two types of cancer, indicating that the stratification based on gene-expression profiling using the proposed nine-component mixture model provided better insight for the clinical differences between the two types of cancer.While validating the relevance of the identified human genes through NCBI’s database, literature, if any, for the corresponding dog orthologs were also searched. Far less research about DLBCL has been conducted for canines. As the model assumption is based on the biological mechanism behind humans and animals, the promising DLBCL classification results based on the human genes may be extended to dogs. Furthermore, currently, direct experiments on humans are not practical. This research provides the possibility for scientists to conduct observational or experimental research on modeling organisms as the first step to understand phenotypes, and then extend the findings to humans for further investigation.DLBCL: diffuse large-B-cell lymphoma; GCB DLBCL: germinal-center B-cell-like diffuse large-B-cell lymphoma; ABC DLBCL: activated B-cell-like diffuse large-B-cell lymphoma.The authors declare that they have no competing interests.All authors read and approved the final manuscript.


MDSCs have been also detected during infection by various bacteria, parasites and viruses, which can engage different TLRs. These TLR-induced myeloid cells produce different types of mediators to influence immune response and inflammation that can be either beneficial or detrimental to the host. One beneficial function of TLR4/MyD88-triggered MDSCs in the lung is to efferocytose apoptotic neutrophils to help resolve inflammation elicited during bacterial pneumonia. A better understanding of the generation and function of these regulatory cells would be helpful to harness their potential or suppress their function for disease-specific immune regulation.Ligand-mediated activation of toll-like receptors (TLRs) not only induces inflammation but also immune suppression, which is an emerging area of investigation. Multiple negative feedback intracellular mechanisms have been described that are brought into play to prevent uncontrolled TLR activation. However, the identification of TLR-induced regulatory myeloid cells is a relatively recent development that has ramifications in pathogen-induced disease state as well as in cancer. Our efforts to understand how a high dose of lipopolysaccharide (LPS), a ligand of TLR4, suppresses allergic airway inflammation led to the identification of myeloid cells that are CD11b Microorganism-associated molecular patterns are recognized by pattern-recognition receptors such as Toll-like receptors (TLRs), NOD-like receptors and RIG-I-like receptors in different cells of infected hosts to initiate a rapid innate immune response as the first step in defense against the invading pathogen. The innate immune response, in turn, induces an appropriate adaptive immune response to effectively protect the host with the establishment of a memory response for a rapid recall response during subsequent exposure to the pathogen. After the identification of the toll protein in Drosophila as an essential host-defense mechanism in an organism that lacks the adaptive immune system , a very low dose < 1 ng) of LPS does not induce adaptive immunity and instead tolerance to OVA develops  while CD11b+Ly6G+Ly6Clow/− MDSCs consist of multilobed nuclei and represent granulocytic or neutrophilic MDSCs (PMN-MDSC). Additional cell surface molecules including the α-chain of the receptor for IL-4 and IL-13 (CD124), the mouse macrophage-expressed molecule, F4/80, M-CSF-1R or c-fms (CD115) and the costimulatory molecule CD80 have also been identified in different combinations on some subsets of MDSCs  bone marrow progenitor cells into naïve mice, LPS instillation into the lungs promoted the accumulation of GFP+CD11b+Gr1int(Ly6Gint) cells in the lung tissue  within extramedullary tissues in response to LPS  and have been largely identified as tissue-dwelling cells   released. High LPS dose or bacterial infection elicits MDSCs in the lung that play an important role in regulating immune response in the context of diverse pulmonary inflammatory conditions such as allergic airway inflammation and bacterial pneumonia. These TLR4-induced MDSCs accumulate with delayed kinetics after high dose LPS exposure or bacterial infection and have features of PMN-MDSCs. The MDSCs can suppress Th2 effector function and efferocytose apoptotic neutrophils and the common mediator involved is IL-10. The net outcome in each case is resolution of inflammation. Thus, a possible therapeutic approach for non-resolving pneumonia is boosting MDSC numbers via STAT1 inhibition in combination with antibiotics where suppression of unremitting inflammation along with complete elimination of the infectious agent is the desired goal. Unquestionably, a fertile area of future research is to understand context-specific function of MDSCs to suppress or stimulate them for therapeutic benefits.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Endothelial progenitor cells (EPCs) have been implicated in different processes crucial to vasculature repair, which may offer the basis for new therapeutic strategies in cardiovascular disease. Despite advances facilitated by functional genomics, there is a lack of systems-level understanding of treatment response mechanisms of EPCs. In this research we aimed to characterize the EPCs response to adenosine (Ado), a cardioprotective factor, based on the systems-level integration of gene expression data and prior functional knowledge. Specifically, we set out to identify novel biosignatures of Ado-treatment response in EPCs.k-nearest neighbours learning (kNN). The method can be executed by expert- and data-driven input queries to guide the search for biologically meaningful biosignatures. The resulting integrated kNN system identified new candidate EPC biosignatures that can offer high classification performance (areas under the operating characteristic curve > 0.8). We also showed that the proposed models can outperform those discovered by standard gene expression analysis. Furthermore, we report an initial independent in vitro experimental follow-up, which provides additional evidence of the potential validity of the top biosignature.The predictive integration of gene expression data and standardized functional similarity information enabled us to identify new treatment response biosignatures. Gene expression data originated from Ado-treated and -untreated EPCs samples, and functional similarity was estimated with Gene Ontology (GO)-based similarity information. These information sources enabled us to implement and evaluate an integrated prediction approach based on the concept of Response to Ado treatment in EPCs can be accurately characterized with a new method based on the combination of gene co-expression data and GO-based similarity information. It also exploits the incorporation of human expert-driven queries as a strategy to guide the automated search for candidate biosignatures. The proposed biosignature improves the systems-level characterization of EPCs. The new integrative predictive modeling approach can also be applied to other phenotype characterization or biomarker discovery problems. EPCs can differentiate into endothelial cells (ECs), which in turn may lead to regeneration of damaged tissue after a myocardial infarction [The impairment of the endothelium is a key factor driving the initiation and progression of different manifestations of heart disease . Thus, tfarction ,3. EPCs farction . EPCs hafarction . Additiofarction ,7. This Notwithstanding cumulative progress in the functional characterization of EPCs using genome-wide expression profiling ,5, thereTherefore, we reasoned that an integrative computational approach based on the combination of different biological data and information sources could offer new and deeper views of Ado-treatment response of EPCs in a holistic fashion. We also investigated the combination of hypothesis- and data-driven approaches to discovering biologically relevant molecular signatures of treatment response. We implemented these systems-driven, integrative strategies to improve understanding and characterization of EPCs in the context of Ado treatment.The main inputs to our research pipeline were: microarray data from human EPCs, a comprehensive experimentally-validated network of human protein-protein interactions (PPI), human GO annotations, and different sets of research "queries" that represented initial guiding inputs to reduce the search space of potentially novel associations and biomarkers of EPCs activity  algorithm, with k representing the number of putative candidates that are retrieved as functionally related to a query gene. We hypothesized that query genes together with their most relevant kNNs may encode EPC signatures, which can provide a more accurate method to characterize the treatment response of these cells. Afterwards, to assess their potential predictive capacity, we applied the resulting biosignatures as inputs to automated EPCs classification systems.The combination of GO-based similarity and transcriptional association information between pairs of genes was at the centre of the EPCs signature discovery strategy investigated Figure . In thiskNN method was capable to identify potentially relevant query-driven networks linked to EPCs treatment response. The expert-driven queries consisted of a set of 1 chemokine receptor and 6 cytokines with potential significant roles in EPCs development fate: CXCR4, CXCL2, CXCL5, CXCL12, CCL7, CCL2 and CCL23. These choices were based on preliminary experiments recently performed in our laboratory suggesting that Ado may regulate the expression of several members of the chemokines/chemokine receptors superfamily. In addition, the CXCR4/CXCL12 axis is known to be highly implicated in EPCs mobilization and recruitment to injury site [We first investigated whether the integrated ury site -21. In tury site . Thus, tkNN network spaces independently.The data-driven queries consisted of 134 genes highly differential expressed between Ado-treated and -untreated EPC samples . Expert- and data-driven query sets did not share genes in common. Figure kNN method was almost null for all queries. This lack of overlap was estimated by comparing, for each query, the number of shared neighbourhoods detected by each method . This was consistently observed for different neighbourhood sizes (1≤ k ≤ 20).Surprisingly, the overlap between the sub-networks (neighbourhoods) using the PPI network and the integrated kNN methodology can offer complementary predictive capability for detecting candidate biosignatures of treatment response in EPCs. Moreover, we aimed to reveal novel functional relationships to characterize molecular response. Specifically, our objective was to answer the question: can we exploit this knowledge to improve the molecular classification of EPCs in response to Ado treatment?This suggests that, in principle, our integrated kNN method. The classification problem was to distinguish between Ado-treated from untreated samples. We compared its prediction performance against models derived from standard expression data analysis and information encoded in the PPI network. To facilitate comparisons across candidate biosignatures (the inputs to the prediction models) and to minimize the risk of model over-fitting, prediction models were built with Support Vector Machines (SVM) and classification performance was estimated with the Leave-One-Out Cross-Validation data sampling strategy (LOOCV). Areas under the receiver operating characteristic curve  were used to summarize the classification performance of each model (Methods). Candidate biosignatures detected by our integrated kNN method were investigated for k = 1 to 20.To discover biosignatures of treatment response, we built a variety of prediction models based on the genes identified by our integrated kNN and PPI-based methods, with each input representing the mean expression value of the query and its neighbouring genes. Hereafter, we will refer to these input representation schemes as individual gene and integrated gene neighbourhood representations respectively. We also built multiple prediction models for different combinations of the most differentially expressed genes detected by SAM, and different number of (expert- and data-driven) query genes.Also we encoded all candidate biosignatures using two model input representation schemes: 1. each model input represents an individual expression value corresponding to each selected gene, and 2. each model input encodes the integrated gene expression activity detected in the neighbourhood of a query gene, i.e., the expression values of all genes in a signature are averaged. The latter only applied to models based on the integrated We will name different classification models with acronyms (and their combinations) that encode the characteristics of the models: EDQ (expert-driven queries), NN (our integrated nearest-neighbour technique), PPI (models based on neighbourhoods extracted from the PPI network) and DE .kNN technique, with integrated gene neighbourhood input representation (Table kNN model (k = 15) based on expert-driven queries: Models "EDQ+15NN" . The best model based on individual gene input representation and expert-driven queries reported an AUC = 0.75 . This may partly be explained by the relatively small number of query genes with high quality GO MF annotations. For example, among the expert queries this information was available only for CXCR4, CXCL12, and CCL2. Figure k on the classification performance of our integrated method based on EDQ (6 expert-driven queries).The most powerful prediction models were based on the integrated on Table . A more " Figure . In thisodel k = 5 based oTo further assess the potential predictive relevance of our approach, we implemented classification models based on network neighbourhoods retrieved by the STRING system . We provWe found genes that are shared by different neighbourhoods: CAMK2B (shared by CCL2 and CXCL5's neighbourhoods); CD53 ; and HBS1L, NR3C2 and PSD (shared by CCL23 and CCL7). One may hypothesise that these overlapping genes could encode relevant biological information for treatment response prediction purposes. To test this assumption, we built different treatment response classifiers using their gene expression values as model inputs . These models reported very low classification performance (AUC < 0.5). This emphasises the importance of applying an integrated and synergistic approach to prediction model design, as originally specified in our method.kNN biosignature identification method and the integrated gene neighbourhood input representation scheme. Figure To sum up, this systematic comparison of prediction models indicates that Ado-treatment response in EPCs can be accurately predicted by using models based on: query genes, our integrated kNN method, we measured protein expression levels encoded by one of the genes identified. This was done in 9 independent samples for the protein CCL18, which is known to be implicated in the regulation of immunological responses and inflammation, as well as over-expressed in several diseases [As an initial step towards the independent validation of the predictive potential of the integrated diseases . More rediseases . In the kNN method could detect a candidate biosignature that may be measurable at both gene and protein expression levels, and b. at least one of the members of this signature exhibits consistent differential responses at the transcriptional and post-transcriptional levels. This encourages the future implementation of independent evaluations of the predictive potential of the proposed biosignatures. Moreover, this suggests that different experimental measurement techniques, including qPCR and ELISA, may be applicable.These results indicate that: a. our integrated kNN method can identify candidate biosignatures of Ado-treatment response in EPCs. This biosignatures not only can improve the automated characterization of EPCs, but also can provide insights unobtainable by standard gene expression analysis or "guilt-by-association" methods in PPI networks. This is explained in part by the fact that the integrated kNN method combines predictive evidence, both functional and phenotype-specific, as encoded in GO annotations and whole-genome expression profiling experiments. At this point we consider both features as equally relevant, and we do not have evidence to suggest that a different scheme would provide better predictions. However, as part of future research, it would be important to investigate different feature weighing schemes. Our method also enables the incorporation of prior knowledge through the processing of expert-driven input queries. Additionally, we showed how (less biased) data-driven queries may also drive the discovery of predictive and biologically meaningful biosignatures.We showed that an integrated kNN method highlights additional insights about the biological relevance of the discovered top biosignature , this problem would have represented a more critical influencing factor. Another key aspect to be considered is the relative small number of expert-driven queries analyzed, which entails that other potentially interesting biosignatures may have been missed in our investigation. However, to address the bias and incompleteness of such a hypothesis-driven approach, we also implemented analyses involving large-scale data-driven queries. Our findings showed that the expert-driven queries provide the basis for the most predictive biosignature, though non-redundant biosignatures with lower prediction performance can be obtained with the data-driven queries. Thus, our approach is capable to generate biologically meaningful predictions while minimizing the space of possible false positive associations. As new hypotheses emerge and more data are generated, future research can incorporate additional expert- and data-driven queries. Also we concede that a true independent validation of our approach will ideally consist of the measurement of all the biosignature members to test the classification models and input encoding schemes proposed here. The reported independent experimental follow-up for one of the top biosignature members at the protein expression level opens up a feasible alternative for future validations. Moreover, we are sharing our dataset through the Gene Expression Omnibus  , which mFuture analyses could include comparisons of our technique versus PPI-based models in which the networks are assembled by other PPI integration strategies, such as the iRefWeb system . To expakNN approach, the definition of expert- and data-driven hypotheses represented a guiding principle for implementing a systematic search of candidate biosignatures. Thus, within a systems biology framework, the predictive integration of multiple functional and molecular information resources enabled the discovery of new biosignatures of treatment response in EPCs. This contributes to a more accurate characterization of EPCs and the understanding of their potential impact in clinical applications. Our integrated kNN approach may be suitable to other treatment response investigations, as well as other biomarker discovery applications.We reported the predictive integration of: a. hypothesis and data-driven approaches, and b. gene expression and GO-based similarity information. We showed that such integration can enable the identification of networks of genes that may control the response to Ado-treatment in EPCs. In our integrated EPCs were obtained from peripheral blood mononuclear cells (PBMC) of healthy patients by adhesion techniques as previously described . All patFor microarray experiments, the total RNA was extracted using TriReagent and the RNeasy mini kit according to manufacturer's instructions . The RNA quality and quantity were evaluated with the Bioanalyzer and Nanodrop apparatus (Agilent). 1 μg total RNA was amplified using Amino Allyl MessageAmp kit (Ambion). 5 μg amino allyl-coupled RNA was labeled with Cy3 or Cy5 dyes . Dye coupling yield >5% was a prerequisite for further analysis. 750 ng of labeled RNA was hybridized on 25,000 gene microarrays for 17 hours at 60°C. 4 arrays per sample were hybridized and scanned with the Genepix 4000B Scanner (Molecular Devices). Six independent experiments were performed. For protein expression assessment, cells were harvested and conditioned medium supplemented with protease inhibitors cocktail  were stored at -80°C until use. Nine independent experiments were performed.Microarray data quantification and pre-processing was performed with the MAIA software  and inteConcentration of CCL18 in conditioned medium was measured using the Human CCL18/PARC DuoSet ELISA  according manufacture's instructions.The PPI network was assembled by aggregating experimentally validated human PPIs from the DIP , IntAct i gand gj, annotated to a set of GO terms i Aand j Arespectively, the GO-driven similarity, SIM, is calculated by aggregating maximum inter-set similarity values as follows:The estimation of gene-gene similarity using GO terms requires two main steps: 1. Calculation of the between-term similarity assigned to each gene, and 2. Aggregation of the between-term similarities to estimate the between-gene similarity. In this study GO terms were derived from human annotations downloaded from the GO database, and GO-based similarity was computed using MF and BP independently. We concentrated on non-IEA (non-Inferred from Electronic Annotation) term-gene associations. The estimation of between-term similarity was based on an information theory metric, Lin's semantic similarity measure , which hThese calculations were implemented with the SimTrek system  under thkNN algorithm is summarized in Figure k-most-similar genes were retrieved and defined the query's neighbourhood. We performed analyses for k = 1 to 20. This algorithm was implemented in an adapted version of the open-source SimTrek system [The integrated k system .kNN approach. Different combinations of individual gene and integrated gene neighbourhoods were investigated, including integrated kNN models with k = 1 to 20. The classification performance of the models was estimated using AUC values and LOOCV. SVM-based models were implemented based on their demonstrated classification capability and robustness [We built different classification systems in which the inputs represented gene expression values or mean neighbourhood expression values. These schemes (introduced in Results) represented the individual gene and integrated gene neighbourhood input representation schemes respectively. We evaluated independent models to classify Ado-treated vs. -untreated samples based on inputs detected by SAM and the integrated bustness . To furtkNN approach was implemented with the Java-based, Cytoscape-compatible SimTrek system [Statistica package [Microarray data pre-processing and differential expression analysis were conducted with MAIA  and SAM k system . Networkk system . Other s package . Classif package . GO term package  and PubMkNN: k-nearest neighbors; LOOCV: Leave-one-out cross-validation; MF: GO Molecular function hierarchy; SVM: Support vector machine.AUC: Area under the receiver operating characteristic curve; BP: GO biology process hierarchy; EPCs: Endothelial progenitor cells; GO: Gene Ontology; FA, MRT and YD conceived the study. FA and HW designed the integrated computational approach. HW and HZ performed computational experiments. FL and MRT performed in vitro experiments. FA, HY, HZ and LZ analyzed data. FA, MRT, YD and DW interpreted findings. FA prepared the manuscript assisted by the other authors. All the authors read and approved the manuscript.Gene composition of top biosignatures. Table in PDF format describing top biosignatures.Click here for file


Typhlodromalus aripo for pubescent versus glabrous cassava cultivars infested with the cassava green mite Mononychellus tanajoa and also the preference for cultivars within each of the two groups. We found that when offered a choice between pubescent and glabrous cassava cultivars (either apex or leaves), T. aripo was significantly more attracted to pubescent cultivars. For each cultivar, M. tanajoa infested leaves and apices were equally attractive to T. aripo. There was however some variation in the response of T. aripo to M. tanajoa-infested plant parts within the group of pubescent cultivars, as well as within the group of glabrous cultivars. Our study confirms not only that T. aripo uses herbivore-induced plant volatiles to search for prey in cassava fields, but it also shows that it can discriminate between glabrous and pubescent cultivars and prefers the latter. This knowledge can be useful in selecting cultivars that are attractive and suitable to T. aripo, which, in turn, may promote biological control of the cassava green mite.It is well known that plant-inhabiting predators use herbivore-induced plant volatiles to locate herbivores being their prey. Much less known, however, is the phenomenon that genotypes of the same host plant species vary in the attractiveness of these induced chemical signals, whereas they also differ in characteristics that affect the predator’s foraging success, such as leaf pubescence. In a series of two-choice experiments (using a Y-tube olfactometer) we determined the preference of  It is now widely accepted that plants can promote the effectiveness of the natural enemies of their herbivores . Takabayashi et al. (Tetranychus urticae Koch and Panonychus ulmi (Koch) differed mainly in quantitative composition for some compounds. In contrast, they observed that the blends emitted by leaves of two apple cultivars infested by the same spider mite species—T. urticae—differed significantly both quantitatively and qualitatively  infested by the two-spotted spider mite T. urticae showed a significant difference in their attractiveness to the predatory mite P. persimilis. More recent studies on pear cultivars  Mononychellus tanajoa (Bondar). This predatory mite inhabits the apex (growing point) of cassava plant branches during day-time, but during night-time it commutes between apex and leaves to forage for CGMs, its prey , for 3 days under continuous light provided by two fluorescent light sources placed above the cages. Cassava plant parts from different cultivars were incubated in separate cages (but in the same room). For each set of olfactometer experiments, odor source consisted of four M. tanajoa-infested leaves or 16 M. tanajoa-infested apices to obtain matching amounts of plant biomass , or each with their stem end (10 cm below the apex) in a larger water-filled plastic vial (2.7 cm in diameter and 6.5 cm deep). The vials were sealed with parafilm to keep the plant tissue fresh during the experiment  without food for 2 h prior to the olfactometer bioassays  were compared. Thus, the three pubescent cultivars were compared in two-choice tests as follows: (1) apex of Oko-Iyawo versus apex of TMS 92/0326; (2) apex of Oko-Iyawo versus apex of Agric; and (3) apex of Agric versus apex of TMS 92/0326. Similarly the four glabrous cultivars were compared in two-choice tests as follows: (1) Apex of Gbeze versus apex of TMS 30572; (2) apex of Gbeze versus apex of Odongbo; (3) apex of Gbeze versus apex of Amala; (4) apex of TMS 30572 versus apex of Odongbo; (5) apex of TMS 30572 versus apex of Amala; and (6) apex of Amala versus apex of Odongbo.In a second series of Y-tube experiments with the same cultivars as in the first, we tested whether the different pubescent (or glabrous) cultivars were equally attractive to T. aripo to leaves and apices of pubescent and glabrous cassava cultivars. The objective was to determine whether attraction of T. aripo is to the apex alone or to cassava leaves alone or to both. In this experiment, apices and leaves of the glabrous cultivar Gbeze or those of the pubescent cultivar Agric were used in two-choice tests in the Y-tube olfactometer as follows: (1) apex of Gbeze versus apex of Agric; (2) leaf of Gbeze versus leaf of Agric; (3) leaf of Gbeze versus apex of Agric; (4) apex of Gbeze versus leaf of Agric; and (5) apex of Agric versus leaf of Agric. Because the cultivar Gbeze was relatively less colonized by T. aripo, we decided not to test the combination of apex of Gbeze versus leaf of Gbeze.In a third series of Y-tube experiments, we compared the attraction of T. aripo females were tested sequentially in two-choice tests in the Y-tube olfactometer and each experiment was replicated three times, taking place on consecutive days. New odor sources were used each day.Each of the two arms of the Y-tube olfactometer had an air stream coming from one of the two boxes, each with a different odor source. Each individual predator was placed at the base of the iron wire positioned in the middle of the Y-shaped glass tube and parallel to the tube walls . If the GH-test is significant, however, the significance of GT may be due to heterogeneity among replicates. In that case the variation among replicates has to be scrutinized before drawing conclusions  , suggesting that the tested predators had no preference   Table . These rf) Table . Since tf) Table .Fig. 4ReT. aripo preferred pubescent cultivars and that the predator uses HIPV to locate its preferred host plant. This general preference of T. aripo for pubescent rather than glabrous cassava cultivars provides at least a partial explanation for the greater abundance of T. aripo in the field on pubescent rather than glabrous cultivars . Whereas it is known from previous studies that residing in the apex provides to T. aripo protection against UV-induced mortality  to Gbeze compared with Odongbo. This may be due to differences in the relative composition or in the amount of volatiles produced by these three glabrous cultivars. Further studies are needed to compare the chemical composition of HIPV emitted by different cassava cultivars under attack by M. tanajoa to determine which compounds or which blend of compounds (see van Wijk et al. T. aripo. Variation in quantity and quality of odor may well explain within- cultivar group variability in T. aripo attraction to pubescent and glabrous cassava cultivars.The differential response of h et al.  and Tamih et al.  for planMorphological plant characteristics may play an important role in the biological control of agricultural crop pests (Skirvin and Fenlon


Saccharomyces cerevisiae now, this motivates us to construct the YTRP  database. For each TF-gene regulatory pair under different experimental conditions, all possible TRPs in two underlying networks  for the specified experimental conditions were automatically enumerated by TRP mining procedures developed from the graph theory. The enumerated TRPs of a TF-gene regulatory pair provide experimentally testable hypotheses for the molecular mechanisms behind a TF and its regulatory target. YTRP is available online at http://cosbi3.ee.ncku.edu.tw/YTRP/. We believe that the TRPs deposited in this database will greatly improve the usefulness of TFPE data for yeast biologists to study the regulatory mechanisms between a TF and its knocked-out targets.Regulatory targets of transcription factors (TFs) can be identified by the TF perturbation experiments, which reveal the expression changes owing to the perturbation (deletion or overexpression) of TFs. But the identified targets of a given TF consist of both direct and indirect regulatory targets. It has been shown that most of the TFPE-identified regulatory targets are indirect, indicating that TF-gene regulation is mainly through transcriptional regulatory pathways (TRPs) consisting of intermediate TFs. Without identification of these TRPs, it is not easy to understand how a TF regulates its indirect targets. Because there is no such database depositing the potential TRPs for Database URL: http://cosbi3.ee.ncku.edu.tw/YTRP/ Cells respond to environmental changes mainly through reorganizing their genomic expressions. This kind of regulation is realized by transcriptional regulatory networks controlled by transcription factors (TFs). Identifying the sophisticated architecture of transcriptional regulatory networks would reveal the fundamental aspects of the mechanisms involved in the cellular adaptation to new environments .The first step toward reconstructing transcriptional regulatory networks is to identify regulatory targets of known TFs. Transcription factor perturbation experiments (TFPEs), a powerful high-throughput experimental procedure, are widely used to achieve this goal , 3. By uIntuitive overlapping of the regulatory target genes inferred from TFPEs and the binding targets from the ChIP experiments can extract the direct regulatory targets of TFs. Unfortunately, direct overlap of these two lists of target genes has been shown to be scarce, indicating that most of the identified regulatory targets by TFPEs are indirect , 5, 6. Thttp://cosbi3.ee.ncku.edu.tw/YTRP/.In this study, we constructed the pioneer database depositing the comprehensive TRP information for TFPE-identified TF-gene regulatory pairs. We retrieved the literature-curated binding and regulatory TF-gene pairs from the YEASTRACT database. Although YEASTRACT provided a graphical tool to visualize the deposited TF-gene pairs as a network, this only plotted the edges between the queried TF list and gene list. The intermediate regulatory pathways were not provided by YEASTRACT. Hence, by applying the developed genome-wide TRP mining procedures on these raw pairs, we extracted the TRPs for specified experimental conditions as potential transcriptional regulatory mechanisms for the given TFPE-inferred TF-gene pairs under different experimental conditions. To provide an easy-to-use and user-friendly searching/browsing interface, we constructed the YTRP  database for yeast biologists with these comprehensive TRP information and the accompanying literature supports for the TRPs. This will facilitate biologists to design subsequent network dynamic analysis experiments. YTRP is available online at TF-gene binding pairs and TF-gene regulatory pairs were used to construct the two underlying networks used for enumerating TRPs. These TF-gene pairs were retrieved from the YEASTRACT database , 8, whicTo get the TRPs, we constructed two transcriptional regulatory networks for each of the nine experimental conditions: the TF-gene direct regulatory network and the TF-gene binding network. Because manual enumeration of TRPs from complex networks requires tremendous amount of time, we mined out the TRP information for the gathered TFPE-identified TF-gene regulatory target pairs under different experimental conditions by TRP mining procedures designed from the graph theory . For theFirst, two underlying networks used for enumerating TRPs were constructed for each experimental condition. The first network, called the TF-gene direct regulatory network, was constructed using the TF-gene direct regulatory pairs. A directed edge in the TF-gene direct regulatory network was added when there are curated TF-gene pairs showing that the TF binds to the promoter region of the target gene and the perturbation of the TF affects the target gene’s expression significantly under the specified experimental condition. The second one, called the TF-gene binding network, was constructed using the TF-gene binding pairs. Two nodes connected by a directed edge in the TF-gene binding network represent a TF-gene binding pair with literature supports under the specified experimental condition. Obviously, the constructed TF-gene direct regulatory network is much less complex but with much more biological support than the constructed TF-gene binding network.Then, for each of the condition-specific TFPE-identified TF-gene regulatory pairs retrieved from the YEASTRACT database, all possible TRPs in the two underlying networks under the specified experimental conditions were enumerated. In the graph theory, it is known that enumerating all possible paths between two nodes in a complex network is an NP-complete problem, which means that no algorithm can solve it in polynomial time when the network is dense . Fortunaet al.’s work  user-defined TF list against all genes in the database; (ii) all TFs in the database against user-defined gene list; and (iii) user-defined TF list against user-defined gene list. The search results are presented as a table containing all the TFPE-identified TF-gene regulatory pairs in the input list with mined-out TRP information a. For eaGZF3 queried under the experimental condition of nitrogen source quality/availability, as an example to demonstrate the contents in the detail page. The detail page consists of four parts. The first part explains why we are interested in the TF-gene pair of Gln3-GZF3  as an example. There are three TF-gene pairs along the direction of this TRP: Gln3-GAT1, Gat1-GZF3 and Gln3-GZF3. If these three TF-gene pairs all have TFR evidence, i.e. the confidence score equals to 100%, then this TRP is highly experimentally supported and is prone to be biologically true. The rationale is as following. Because Gln3 is in the upstream of the TRP, the perturbation of Gln3 should influence the expression of both GAT1 and GZF3. Similarly, the perturbation of Gat1 should influence the expression of GZF3.We use the TF-gene regulatory pair, Gln3-ln3-GZF3 a under tF3 (GZF3 . Howeverat1→GZF3 b. MoreovGZF3)  should exist and be functional in yeast cells under the condition of nitrogen source quality/availability. Finally, the fourth part provides a link that allows users to download the whole detail page as a text file  c. For Glext file d.In the browse mode, users can browse the YTRP database by TFs. This returns a table listing all direct and indirect regulatory targets of the queried TFs with mined-out TRP information. Users can also browse YTRP by genes. This returns a table listing all the direct and indirect transcriptional regulators of the queried genes with mined-out TRP information. In the table listing all direct and indirect targets of the queried TFs or the table listing all the transcriptional regulators of the queried genes, users can further filter these TFs or genes by their cellular functions classified by the MIPS database b 15). B. B15). BThe strategies of elucidating the response-to-perturbation mechanisms of genes that are differentially expressed upon knockout of the causative gene are mainly through the integration of different high-throughput assays, such as the protein–DNA binding data or the physical protein–protein interaction data . The YTRThe first type of method was based on the forming of optimization problems on the constructed regulatory and/or metabolic networks . This tyBecause of the computational complexity, the second type of method used a different criterion from optimization. They enumerated TRPs from the constructed network with some constrains on the TRP length between the TFPE-inferred TF-gene indirect pairs. Some considered only the TRPs of length two , 22. TRPYTRP specifically aims to detour these obstacles and extract possible TRPs between TFs and their indirect regulatory targets in yeast cells. YTRP provides an advance on the insight of deposited data compared with the original YEASTRACT database . AlthougIn summary, YTRP serves as a database that provides comprehensive TRP information extracted from constructed underlying transcription regulatory networks. The TRPs mined out by our procedures are not limited to path lengths or confidence restrictions of the searching algorithms. Notice that all TRPs deposited in YTRP are the results of graph data mining on the data provided by YEASTRACT, and no computational prediction or confidence selection was applied to the TRPs. Users can browse YTRP to find the possible TRPs of their interest and choose the TRP hypotheses provided by YTRP for further analysis on their own expert knowledge.P-value threshold of different integrated high-throughput data  as an example, we can find various experimental supports from the literature showing that (i) Gln3 directly binds (to the promoter region of DAL80) and regulates DAL80 and indirectly regulates GAT1 and GZF3; (ii) Dal80 directly binds (to the promoter region of GAT1) and regulates GAT1 and indirectly regulates GZF3; and (iii) Gat1 directly binds (to the promoter region of GZF3) and regulates GZF3. From the subsequent condition-specific mRNA expression analysis, it is further shown that Dal80 represses GAT1 and GZF3 (Taking the second TRP (Gln3→Dal80→Gat1→and GZF3 , 28 (Figand GZF3 b. These In this article, we present the YTRP database that provides comprehensive TRP information for TF-gene regulatory pairs inferred from TFPEs. The enumerated TRPs provide experimentally testable hypotheses of the molecular mechanisms behind TFs and their regulatory targets. We believe that the TRPs deposited in YTRP will greatly improve the usefulness of TFPE data for yeast biologists to study gene transcriptional regulation. YTRP has an easy-to-use interface for biologists to search and browse for TRP information of the TF-gene regulatory pairs of interest. YTRP will be regularly updated based on the newly published literature on TRPs and the latest release of the YEASTRACT database.Taiwan National Science Council [NSC 099-2628-B-006-015-MY3]. And the funding of open access charge was supported by Taiwan National Science Council [NSC 102-2218-E-010-005-MY2].This work was funded by Conflict of interest. None declared.


A bottom-up approach to nanofabricate metallic glasses from metal clusters as building blocks is presented. Considering metallic glasses as a subclass of cluster-assembled materials, the relation between the two lively fields of metal clusters and metallic glasses is pointed out. Deposition of selected clusters or collections of them, generated by state-of-the-art cluster beam sources, could lead to the production of a well-defined amorphous material. In contrast to rapidly quenched glasses where only the composition of the glass can be controlled, in cluster-assembled glasses, one can precisely control the structural building blocks. Comparing properties of glasses with similar compositions but differing in building blocks and therefore different in structure will facilitate the study of structure–property correlation in metallic glasses. This bottom-up method provides a novel alternative path to the synthesis of glassy alloys and will contribute to improving fundamental understanding in the field of metallic glasses. It may even permit the production of glassy materials for alloys that cannot be quenched rapidly enough to circumvent crystallization. Additionally, gaining deeper insight into the parameters governing the structure–property relation in metallic glasses can have a great impact on understanding and design of other cluster-assembled materials. Metal clusters have been the subject of intensive investigations in the last three decades not only because they exhibit fascinating properties that largely differ from their atomic and bulk counterparts but also their size dependence and structure dependence provide unthinkable possibilities. Addition of a single atom may cause property alteration of appreciable magnitude -5. Altho75Si25[Metallic glasses (MG) have also been studied extensively since the first amorphous metallic alloy was introduced more than half a century ago. By cooling with a high rate, Klement et al. observed the formation of glassy structure in a binary alloy Au75Si25. They al75Si25-14. DespCluster-based models such as efficient cluster packing, cluster-plus-glue atom, and cluster resonance have already been suggested to describe the arrangement of atoms in metallic glasses. Many research groups have demonstrated the appositeness of these models through theoretical simulations in combination with experimental structure analysis -39. In tHere, nanofabrication of metallic glasses through the bottom-up approach incorporating size-controlled metallic clusters is proposed.0.5Zr0.5 film can be fabricated by deposition of CuZr dimers, Cu2Zr2 tetramers, or equal numbers of Cu6Zr7 and Cu7Zr6 clusters just to name some of the numerous possibilities. All these films have the same composition and, however, different structures. A schematic view of the sample preparation approach is depicted in Figure Metal clusters of various compositions and sizes can be produced by a state-of-the-art cluster beam source. Recent advances in the field of cluster science enable us to overcome the quantity gap and create a well-defined cluster films of several monolayer thickness with atomic precision within few hours. Interestingly, altering the set of the mass-selected clusters while keeping the overall composition the same would lead to the formation of a potentially different material. For example, a CuThe first experiment of the kind should be performed on CuZr system based on the following reasoning. This system has been the subject of many experimental and theoretical studies in the past. Consequently, much is known about this binary system. Since only two metals are involved, generation of suitable binary clusters and their mass selection is easier compared to other multicomponent systems. In addition, CuZr alloys are known to be good glass formers over a range of compositions with glass transition temperature well above the room temperature -42. The mZrn, n + m = 13) form icosahedra and thus benefit from enhanced structural stability. The composition of the most abundant mixed cluster may vary for different cluster sources and with source conditions. Particular care should be taken to avoid oxidation of metal clusters prior and during deposition. To assure the latter, cluster deposition should be performed under ultra-high vacuum conditions. Finally, the sample should be handled under controlled environment  and below room temperature  throughout the analysis process. The properties of the specific metal cluster or clusters (if a combination of them is used to produce the cluster film) can be investigated to gain knowledge on the structural building blocks. The optical, electronic, geometric, magnetic, and binding energies of metal clusters can be determined both theoretically and experimentally by state-of-the-art scientific instruments. In parallel experiments, a film of conventional metallic glass prepared through rapid quenching processes but with an identical composition as cluster film should be analyzed for comparison purposes. A constructive feedback loop between these two types of metallic glasses synthesized through bottom-up approach and conventional methods is of great importance to unravel fundamental uncertainties associated with structure-dependent properties of metallic glasses.Binary metal clusters can be generated using alloy targets. Ion beam techniques employed in the production of the metal clusters facilitate the use of high-resolution size selection filters. On the basis of the recorded mass spectra, the most intense mixed cluster should be isolated and deposited on a support material, which is kept at a temperature low enough to avoid crystallization of the film during deposition. It is expected that clusters with 13 atoms (CuFigure CAM: Cluster-assembled materials; CAMG: Cluster-assembled metallic glass; MG: Metallic glasses.The author declares that he has no competing interests.


The computational processing and analysis of small molecules is at heart of cheminformatics and structural bioinformatics and their application in e.g. metabolomics or drug discovery. Pipelining or workflow tools allow for the Lego™-like, graphical assembly of I/O modules and algorithms into a complex workflow which can be easily deployed, modified and tested without the hassle of implementing it into a monolithic application. The CDK-Taverna project aims at building a free open-source cheminformatics pipelining solution through combination of different open-source projects such as Taverna, the Chemistry Development Kit (CDK) or the Waikato Environment for Knowledge Analysis (WEKA). A first integrated version 1.0 of CDK-Taverna was recently released to the public.The CDK-Taverna project was migrated to the most up-to-date versions of its foundational software libraries with a complete re-engineering of its worker's architecture (version 2.0). 64-bit computing and multi-core usage by paralleled threads are now supported to allow for fast in-memory processing and analysis of large sets of molecules. Earlier deficiencies like workarounds for iterative data reading are removed. The combinatorial chemistry related reaction enumeration features are considerably enhanced. Additional functionality for calculating a natural product likeness score for small molecules is implemented to identify possible drug candidates. Finally the data analysis capabilities are extended with new workers that provide access to the open-source WEKA library for clustering and machine learning as well as training and test set partitioning. The new features are outlined with usage scenarios.CDK-Taverna 2.0 as an open-source cheminformatics workflow solution matured to become a freely available and increasingly powerful tool for the biosciences. The combination of the new CDK-Taverna worker family with the already available workflows developed by a lively Taverna community and published on myexperiment.org enables molecular scientists to quickly calculate, process and analyse molecular data as typically found in e.g. today's systems biology scenarios. Current problems in the biosciences typically involve several domains of research. They require a scientist to work with different and diverse sets of data. The reconstruction of a metabolic network from sequencing data, for example, employs many of the data types found along the axis of the central dogma, including reconstruction of genome sequences, gene prediction, determination of encoded protein families, and from there to the substrates of enzymes, which then form the metabolic network. In order to work with such a processing pipeline, a scientist has to copy/paste and often transform the data between several bioinformatics web portals by hand. The manual approach involves repetitive tasks and cannot be considered effective or scalable.Especially the processing and analysis of small molecules comprises tasks like filtering, transformation, curation or migration of chemical data, information retrieval with substructures, reactions, or pharmacophores as well as the analysis of molecular data with statistics, clustering or machine learning to support chemical diversity requirements or to generate quantitative structure activity/property relationships (QSAR/QSPR models). These processing and analysis procedures itself are of increasing importance for research areas like metabolomics or drug discovery. The power and flexibility of the corresponding computational tools become essential success factors for the whole research process.The workflow paradigm addresses the above issues with the supply of sets of elementary workers (activities) that can be flexibly assembled in a graphical manner to allow complex procedures to be performed in an effective manner - without the need of specific code development or software programming skills. Scientific workflows allow the combination of a wide spectrum of algorithms and resources in a single workspace -3. EarliThe CDK-Taverna project aims at building a free open-source cheminformatics pipelining solution through combination of different open-source projects such as Taverna , the Chehttp://www.ts-concepts.de/cdk-taverna2/plugin/. The information provided therein contains the name of the plug-in, its version, the repository location and the required Taverna version. Upon submitting the URL to the plug-in manager it downloads all necessary dependencies automatically from the web. After a subsequent restart the plug-in is enabled and the workers are visible in the services. The plug-in uses Taverna version 2.2.1  where A is a freely selectable identifier, B are numbers from an Atom-to-Atom-Mapping defining the atoms to which the generic group can be attached and C is the chemical group identifier which can be any number. The Atom Alias feature offers the possibility to define a wild card for preconfigured elements. The syntax is  where A is a freely selectable identifier and B are the string representations of the possible elements. The Expandable Atom feature enables the definition of freely sizeable rings or aliphatic chains with syntax [A:B] where A is a freely selectable identifier and B is the maximum number of atoms to insert. Figure CDK-Taverna 1.0 provided basic functions for combinatorial chemistry related reaction enumeration: They supported the use of two reactants, a single product and one generic group per reactant. The new enumeration options used by CDK-Taverna 2.0 offer major enhancements like multi-match detection, any number of reactants, products or generic groups as well as variable R-groups, ring sizes and atom definitions. The extended functionality was developed and applied in industrial cooperation projects. Advanced reaction enumeration features are illustrated in Figure In recent years, computer assisted drug design studies use natural product (NP) likeness as a criterion to screen compound libraries for potential drug candidates ,15. The Ertl et al to score small molecules for NP-likeness [Ertl et al [http://www.myexperiment.org/users/10069/packs. The curation and signature scoring workers may not only be applied in evaluating the NP-likeness of compound libraries but also in evaluating the metabolite-likeness of theoretical metabolites for predicting whole metabolomes. The latter application was the original purpose for the worker development and corresponding results will be presented in a subsequent publication.Version 2.0 of CDK-Taverna provides two groups of workers that re-implement the work of likeness . The worlikeness  are genetl et al . This scUnsupervised clustering tries to partition input data into a number of groups smaller than the number of data whereas supervised machine learning tries to construct model functions that map the input data onto their corresponding output data. If the output codes continuous quantities a regression task is defined. Alternatively the output may code classes so that a classification task is addressed. Molecular data sets for clustering consist of input vectors where each vector represents a molecular entity and consists of a set of molecular descriptors itself. Molecular data sets for machine learning add to each input vector a corresponding output vector with features to be learned - thus they consist of I/O pairs of input and output vectors.The clustering and machine learning workers of CDK-Taverna 2.0 allow the use of distinct WEKA functionality. As far as clustering is concerned the ART-2a worker of version 1.0 is supplemented with five additional WEKA-based workers which offerExpectation Maximisation (EM): Expectation maximisation algorithm for iterative maximum likelihood estimation of cluster memberships [• berships .Farthest First: Heuristic 2-approximation algorithm for solving the k-center problem [•  problem .Hierarchical Clusterer: Hierarchical clustering methods: The distance function and the linkage type are freely selectable [• lectable .Simple KMeans: Simple k-means clustering algorithm [• lgorithm .XMeans: Extended k-means clustering with an efficient estimation of the number of clusters [• clusters .Machine learning workers support the significance analysis of single components (i.e. features) of an input vector to obtain smaller inputs with a reduced set of components/features, the partitioning of machine learning data into training and test sets, the construction of input/output mapping model functions and model based predictions as well as result visualization. There is a total of six WEKA-based machine learning methods available: Two workers allow regression as well as classification procedures...Three-Layer Perceptron-Type Neural Networks: Neural network implementation using the backpropagation algorithm for weight optimisation [• misation .Support Vector Machines: Support Vector Machine implementation using the LibSVM library [•  library .... two workers do only support regression...Multiple Linear Regression: Multiple linear regression algorithm.• M5P regression trees: M5 regression algorithm for constructing tree-based linear models [• r models ,25.... and two workers are restricted to classification tasks:Naive Bayes: Bayesian classifier for the estimation of continuous variables [• ariables .J46 C4.5 decision tree: Decision tree implementation based on the C4.5 classification algorithm [• lgorithm .GA Attribute Selection worker generates an optimum reduced set of input components of predefined length  on the basis of a genetic algorithm. The initial random population is refined by mutation and cross-over steps plus Roulette Wheel selection in each generation. A mutation switches an input component between an "on" or "off" state and a cross-over interchanges a random interval of "on/off" states between two randomly chosen chromosomes (where the number of attributes with "on" state remains fixed). As a fitness function the inverse square root mean squared error Leave-One-Out Attribute Selection worker uses a "Leave-One-Out" strategy for evaluating the significance of each input vector component [For selection of an optimum reduced set of input vector components there are two workers available. The omponent . In eachSplit Dataset Into Train-/Testset worker is available which offers three strategies [For training and test set partitioning the rategies :Random: Data are split randomly into a training and test set of defined sizes.• Cluster Representatives: First the input data of the I/O pairs are clustered with the number of clusters to be equal to the number of training data by application of the Simple KMeans algorithm. Then a single input point of each cluster is chosen randomly as a representative and the corresponding I/O pair is inserted into the training set. The remaining I/O pairs are transferred to the test set.• Single Global Max: Cluster representatives are evaluated in a first step. These representatives are then re ned by an iterative procedure that exchanges data between training and test set that belong to the same cluster. The latter constraint assures that the input data of training and test set have a similar spatial diversity. A single iteration determines the test set I/O pair with the largest deviation between data and model. This I/O pair is then transferred to the training set while the best predicted I/O pair of the same cluster in the training set is transferred to the test set in exchange. Oscillations during the refinement steps may be suppressed by blacklisting exchanged I/O pairs.• Split Dataset Into Train-/Testset worker. The Weka Regression worker is used to build machine learning models which may be evaluated and visualized by the Evaluate Regression Results as PDF worker. The Weka Regression worker provides a configuration menu as shown in Figure QSAR Descriptor Threaded worker. Afterwards the GA Attribute Selection worker was used to determine an optimized minimum subset of 75 molecular descriptors  with maximum predictability. For machine learning a three-layer perceptron type neural network worker with three hidden neurons was used. The diagrams shown for the regression analysis are a scatter plot with experimental versus predicted output values and two kinds of residual plots. In addition characteristic quantities like the root mean squared error or the correlation coefficient are calculated for the generated model.Figure http://cdk-taverna-2.ts-concepts.de/wiki/index.php?title=Main_Page.Based on the free MediaWiki framework a Wiki was developed for the CDK-Taverna 2.0 project . The webCDK-Taverna 2.0 provides an enhanced and matured free open cheminformatics workflow solution for the biosciences. It was successfully applied and tested in academic and industrial environments with data volumes of hundreds of thousands of small molecules. Combined with available workers and workflows from bioinformatics, image analysis or statistics CDK-Taverna supports the construction of complex systems biology oriented workflows for processing diverse sets of biological data.The authors declare that they have no competing interests.EW initiated the integration of Taverna and CDK and supported deployment and architecture. CS and AZ conceived the project and lead the further development. SN supported the reaction enumeration enhancements. KV provided workers for molecular fragmentation. AT did the majority of CDK-Taverna re-engineering and enhancements and developed the project to its current state. All co-authors contributed to the manuscript. All authors read and approved the final manuscript.