Combinações entre cultivares, ambientes, preparo e cobertura do solo em características agronômicas de alface.

Objetivou-se identificar combinações entre cultivares, ambientes de cultivo e preparo e cobertura de solo capazes de melhorar o desempenho agronômico e aumentar a produtividade da cultura da alface em cultivo orgânico. A pesquisa foi conduzida na Universidade Federal do Acre, utilizando o delineamento experimental de blocos casualizados, com parcelas subdivididas para cada experimento (campo e casa de vegetação), com quatro repetições. Em cada experimento, três cultivares de alface (Simpson, Marisa e Vera), constituindo as sub-parcelas, foram sorteadas nas parcelas, representadas por quatro preparos e cobertura do solo (encanteiramento com cobertura de palha de arroz, polietileno prateado, solo descoberto e plantio direto). A produtividade comercial de alface foi de 12,3 t ha-1 em cultivo protegido e de 7,9 t ha-1 em campo. O cultivo protegido promoveu melhor desenvolvimento das plantas, caracterizado por maior massa da matéria fresca e seca da parte aérea, massa da matéria fresca comercial e melhor classificação comercial, além de promover bom desempenho agronômico e maior produtividade em qualquer um dos preparos de solo. As cultivares Simpson e Marisa apresentaram massa da matéria seca da parte aérea semelhante e superior à ‘Vera’, porém, o crescimento do caule da ‘Simpson’ foi elevado, caracterizando pendoamento precoce, fato que reduz sua qualidade comercial. As cultivares Marisa e Vera não alongaram o caule indicando serem tolerantes às condições ambientais de Rio Branco. A cobertura do solo com casca de arroz ou plástico prateado contribuiu para minimizar os efeitos climáticos prejudiciais ao cultivo da alface em campo. O plantio direto orgânico não diferiu do plantio em canteiro descoberto

The effect of acceptance and commitment therapy on insomnia and sleep quality: A systematic review

Background Acceptance and Commitment Therapy (ACT), as a type of behavioral therapy, attempts to respond to changes in people’s performance and their relationship to events. ACT can affect sleep quality by providing techniques to enhance the flexibility of patients’ thoughts, yet maintaining mindfullness. Therefore, for the first time, a systematic review on the effects of ACT on sleep quality has been conducted. Methods This systematic review was performed to determine the effect of ACT on insomnia and sleep quality. To collect articles, the PubMed, Web of Science (WOS), Cochrane library, Embase, Scopus, Science Direct, ProQuest, Mag Iran, Irandoc, and Google Scholar databases were searched, without a lower time-limit, and until April 2020. Results Related articles were derived from 9 research repositories, with no lower time-limit and until April 2020. After assessing 1409 collected studies, 278 repetitive studies were excluded. Moreover, following the primary and secondary evaluations of the remaining articles, 1112 other studies were removed, and finally a total of 19 intervention studies were included in the systematic review process. Within the remaining articles, a sample of 1577 people had been assessed for insomnia and sleep quality. Conclusion The results of this study indicate that ACT has a significant effect on primary and comorbid insomnia and sleep quality, and therefore, it can be used as an appropriate treatment method to control and improve insomnia

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

<p>Abstract</p> <p>Background</p> <p>RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities.</p> <p>Results</p> <p>Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (<it>N</it>-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative.</p> <p>Conclusion</p> <p>The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall <it>t</it>-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at the following site: <url>ftp://scitoolsftp.idtdna.com/SEQ2SVM/</url>.</p