Redundant regulation of meristem identity and plant architecture by FRUITFULL, APETALA1 and CAULIFLOWER

The transition from vegetative to reproductive phases during Arabidopsis development is the result of a complex interaction of environmental and endogenous factors. One of the key regulators of this transition is LEAFY (LFY), whose threshold levels of activity are proposed to mediate the initiation of flowers. The closely related APETALA1 (AP1) and CAULIFLOWER (CAL) meristem identity genes are also important for flower initiation, in part because of their roles in upregulating LFY expression. We have found that mutations in the FRUITFULL (FUL) MADS-box gene, when combined with mutations in AP1 and GAL, lead to a dramatic non-flowering phenotype in which plants continuously elaborate leafy shoots in place of flowers. We demonstrate that this phenotype is caused both by the lack of LFY upregulation and by the ectopic expression of the TERMINAL FLOWER1 (TFL1) gene. Our results suggest that the FUL, AP1 and CAL genes act redundantly to control inflorescence architecture by affecting the domains of LFY and TFL1 expression as well as the relative levels of their activities

The FRUITFULL MADS-box gene mediates cell differentiation during Arabidopsis fruit development

Fruit morphogenesis is a process unique to flowering plants, and yet little is known about its developmental control, Following fertilization, fruits typically undergo a dramatic enlargement that is accompanied by differentiation of numerous distinct cell types. We have identified a mutation in Arabidopsis called fruitfull (ful-1), which abolishes elongation of the silique after fertilization. The ful-1 mutation is caused by the insertion of a DsE transposable enhancer trap element into the 5' untranslated leader of the AGL8 MADS-box gene. beta-glucuronidase (GUS) reporter gene expression in the enhancer trap line is observed specifically in all cell layers of the valve tissue, but not in the replum, the septum or the seeds, and faithfully mimics RNA in situ hybridization data reported previously, The lack of coordinated growth of the fruit tissues leads to crowded seeds, a failure of dehiscence and, frequently, the premature rupture of the carpel valves, The primary defect of ful-1 fruits is within the valves, whose cells fail to elongate and differentiate. Stomata, which are frequent along the epidermis of wild-type valves, are completely eliminated in the ful mutant valves. In addition to the effect on fruit development, ful cauline leaves are broader than those of wild type and show a reduction in the number of internal cell layers. These data suggest that AGL8/FUL regulates the transcription of genes required for cellular differentiation during fruit and leaf development

Agency, qualia and life: connecting mind and body biologically

Many believe that a suitably programmed computer could act for its own goals and experience feelings. I challenge this view and argue that agency, mental causation and qualia are all founded in the unique, homeostatic nature of living matter. The theory was formulated for coherence with the concept of an agent, neuroscientific data and laws of physics. By this method, I infer that a successful action is homeostatic for its agent and can be caused by a feeling - which does not motivate as a force, but as a control signal. From brain research and the locality principle of physics, I surmise that qualia are a fundamental, biological form of energy generated in specialized neurons. Subjectivity is explained as thermodynamically necessary on the supposition that, by converting action potentials to feelings, the neural cells avert damage from the electrochemical pulses. In exchange for this entropic benefit, phenomenal energy is spent as and where it is produced - which precludes the objective observation of qualia

Position statement for the diagnosis and management of anogenital warts

Background: Anogenital warts (AGW) can cause economic burden on healthcare systems and are associated with emotional, psychological and physical issues. ----- Objective: To provide guidance to physicians on the diagnosis and management of AGW. ----- Methods: Fourteen global experts on AGW developed guidance on the diagnosis and management of AGW in an effort to unify international recommendations. Guidance was developed based on published international and national AGW guidelines and an evaluation of relevant literature published up to August 2016. Authors provided expert opinion based on their clinical experiences. ----- Results: A checklist for a patient's initial consultation is provided to help physicians when diagnosing AGW to get the relevant information from the patient in order to manage and treat the AGW effectively. A number of frequently asked questions are also provided to aid physicians when communicating with patients about AGW. Treatment of AGW should be individualized and selected based on the number, size, morphology, location, and keratinization of warts, and whether they are new or recurrent. Different techniques can be used to treat AGW including ablation, immunotherapy and other topical therapies. Combinations of these techniques are thought to be more effective at reducing AGW recurrence than monotherapy. A simplified algorithm was created suggesting patients with 1-5 warts should be treated with ablation followed by immunotherapy. Patients with >5 warts should use immunotherapy for 2 months followed by ablation and a second 2-month course of immunotherapy. Guidance for daily practice situations and the subsequent action that can be taken, as well as an algorithm for treatment of large warts, were also created. ----- Conclusion: The guidance provided will help physicians with the diagnosis and management of AGW in order to improve the health and quality of life of patients with AGW

Identifying and Seeing beyond Multiple Sequence Alignment Errors Using Intra-Molecular Protein Covariation

BACKGROUND: There is currently no way to verify the quality of a multiple sequence alignment that is independent of the assumptions used to build it. Sequence alignments are typically evaluated by a number of established criteria: sequence conservation, the number of aligned residues, the frequency of gaps, and the probable correct gap placement. Covariation analysis is used to find putatively important residue pairs in a sequence alignment. Different alignments of the same protein family give different results demonstrating that covariation depends on the quality of the sequence alignment. We thus hypothesized that current criteria are insufficient to build alignments for use with covariation analyses. METHODOLOGY/PRINCIPAL FINDINGS: We show that current criteria are insufficient to build alignments for use with covariation analyses as systematic sequence alignment errors are present even in hand-curated structure-based alignment datasets like those from the Conserved Domain Database. We show that current non-parametric covariation statistics are sensitive to sequence misalignments and that this sensitivity can be used to identify systematic alignment errors. We demonstrate that removing alignment errors due to 1) improper structure alignment, 2) the presence of paralogous sequences, and 3) partial or otherwise erroneous sequences, improves contact prediction by covariation analysis. Finally we describe two non-parametric covariation statistics that are less sensitive to sequence alignment errors than those described previously in the literature. CONCLUSIONS/SIGNIFICANCE: Protein alignments with errors lead to false positive and false negative conclusions (incorrect assignment of covariation and conservation, respectively). Covariation analysis can provide a verification step, independent of traditional criteria, to identify systematic misalignments in protein alignments. Two non-parametric statistics are shown to be somewhat insensitive to misalignment errors, providing increased confidence in contact prediction when analyzing alignments with erroneous regions because of an emphasis on they emphasize pairwise covariation over group covariation

Protein Sequence Alignment Analysis by Local Covariation: Coevolution Statistics Detect Benchmark Alignment Errors

The use of sequence alignments to understand protein families is ubiquitous in molecular biology. High quality alignments are difficult to build and protein alignment remains one of the largest open problems in computational biology. Misalignments can lead to inferential errors about protein structure, folding, function, phylogeny, and residue importance. Identifying alignment errors is difficult because alignments are built and validated on the same primary criteria: sequence conservation. Local covariation identifies systematic misalignments and is independent of conservation. We demonstrate an alignment curation tool, LoCo, that integrates local covariation scores with the Jalview alignment editor. Using LoCo, we illustrate how local covariation is capable of identifying alignment errors due to the reduction of positional independence in the region of misalignment. We highlight three alignments from the benchmark database, BAliBASE 3, that contain regions of high local covariation, and investigate the causes to illustrate these types of scenarios. Two alignments contain sequential and structural shifts that cause elevated local covariation. Realignment of these misaligned segments reduces local covariation; these alternative alignments are supported with structural evidence. We also show that local covariation identifies active site residues in a validated alignment of paralogous structures. Loco is available at https://sourceforge.net/projects/locoprotein/files

Validation of differential gene expression algorithms: Application comparing fold-change estimation to hypothesis testing

<p>Abstract</p> <p>Background</p> <p>Sustained research on the problem of determining which genes are differentially expressed on the basis of microarray data has yielded a plethora of statistical algorithms, each justified by theory, simulation, or ad hoc validation and yet differing in practical results from equally justified algorithms. Recently, a concordance method that measures agreement among gene lists have been introduced to assess various aspects of differential gene expression detection. This method has the advantage of basing its assessment solely on the results of real data analyses, but as it requires examining gene lists of given sizes, it may be unstable.</p> <p>Results</p> <p>Two methodologies for assessing predictive error are described: a cross-validation method and a posterior predictive method. As a nonparametric method of estimating prediction error from observed expression levels, cross validation provides an empirical approach to assessing algorithms for detecting differential gene expression that is fully justified for large numbers of biological replicates. Because it leverages the knowledge that only a small portion of genes are differentially expressed, the posterior predictive method is expected to provide more reliable estimates of algorithm performance, allaying concerns about limited biological replication. In practice, the posterior predictive method can assess when its approximations are valid and when they are inaccurate. Under conditions in which its approximations are valid, it corroborates the results of cross validation. Both comparison methodologies are applicable to both single-channel and dual-channel microarrays. For the data sets considered, estimating prediction error by cross validation demonstrates that empirical Bayes methods based on hierarchical models tend to outperform algorithms based on selecting genes by their fold changes or by non-hierarchical model-selection criteria. (The latter two approaches have comparable performance.) The posterior predictive assessment corroborates these findings.</p> <p>Conclusions</p> <p>Algorithms for detecting differential gene expression may be compared by estimating each algorithm's error in predicting expression ratios, whether such ratios are defined across microarray channels or between two independent groups.</p> <p>According to two distinct estimators of prediction error, algorithms using hierarchical models outperform the other algorithms of the study. The fact that fold-change shrinkage performed as well as conventional model selection criteria calls for investigating algorithms that combine the strengths of significance testing and fold-change estimation.</p