ADAM22, a Kv1 channel-interacting protein, recruits membrane-associated guanylate kinases to juxtaparanodes of myelinated axons

Clustered Kv1 K+channels regulate neuronal excitability at juxtaparanodes of myelinated axons, axon initial segments, and cerebellar basket cell terminals (BCTs). These channels are part of a larger protein complex that includes cell adhesion molecules and scaffolding proteins. To identify proteins that regulate assembly, clustering, and/or maintenance of axonal Kv1 channel protein complexes, we immunoprecipitated Kv1.2 αsubunits, and then used mass spectrometry to identify interacting proteins.We found that a disintegrin and metalloproteinase 22 (ADAM22) is a component of the Kv1 channel complex and that ADAM22 coimmunoprecipitates Kv1.2 and the membrane-associated guanylate kinases (MAGUKs) PSD-93 and PSD-95. When coexpressed with MAGUKs in heterologous cells, ADAM22 and Kv1 channels are recruited into membrane surface clusters. However, coexpression of Kv1.2 with ADAM22 and MAGUKs does not alter channel properties. Among all the known Kv1 channel-interacting proteins, only ADAM22 is found at every site where Kv1 channels are clustered. Analysis of Caspr-null mice showed that, like other previously described juxtaparanodal proteins, disruption of the paranodal junction resulted in redistribution of ADAM22 into paranodal zones. Analysis of Caspr2-, PSD-93-, PSD-95-, and double PSD-93/PSD-95-null mice showed ADAM22 clustering at BCTs requires PSD-95, but ADAM22 clustering at juxtaparanodes requires neither PSD-93 nor PSD-95. In direct contrast, analysis of ADAM22-null mice demonstrated juxtaparanodal clustering of PSD-93 and PSD-95 requires ADAM22, whereas Kv1.2 and Caspr2 clustering is normal in ADAM22-null mice. Thus, ADAM22 is an axonal component of the Kv1 K+channel complex that recruits MAGUKs to juxtaparanodes. Copyrigh

Simulação das perdas de água por evaporação e arraste, no aspersor NY-7 (4,6 mm x 4,0 mm), em sistemas de aspersão convencional Simulation of evaporation and wind drift losses, in the NY-7 sprinkler (4.6 mm x 4.0 mm), in stationary sprinler irrigation systems

As perdas de água por evaporação e arraste em sistemas de irrigação por aspersão podem assumir valores consideráveis, reduzindo a eficiência do sistema. Os objetivos do presente trabalho foram avaliar a capacidade preditiva de cinco modelos empíricos para estimar perdas de água por evaporação e arraste em aspersores modelo NY-7 (bocais de 4,6 mm x 4,0 mm), trabalhando sob diferentes condições operacionais e ambientais, e ajustar modelos específicos para o aspersor NY-7. Comparando os resultados medidos em ensaios de campo, com os resultados simulados, foi possível concluir que os cinco modelos empíricos considerados apresentaram pouca ou nenhuma adequação, tanto para os ensaios com um único aspersor (quadrado do erro-médio de 5,27; 20,70; 5,07; 6,95 e 7,06% para os modelos empíricos 1; 2; 3; 4 e 5, respectivamente) quanto para os ensaios com linhas laterais contendo aspersores (quadrado do erro-médio de 7,41; 24,43; 6,72; 3,16 e 2,9% para os modelos empíricos 1; 2; 3; 4 e 5, respectivamente). Comparados aos cinco modelos empíricos considerados, os novos modelos ajustados apresentaram menores erros, indicando que a aplicação de modelos empíricos deve ser limitada às condições de operação (diâmetro de bocal, pressão de operação, etc.) similares àquelas em que os modelos foram desenvolvidos.<br>Evaporation and wind drift losses during sprinkler irrigation may reach significant values, cutting system efficiency down. The present work aims: (a) to evaluate the ability of five empirical models in predicting losses of a NY-7 model sprinkler (nozzle of 4.6 mm x 4.0 mm), working under different operational and climatic conditions; and (b) to adjust specific models to the NY-7 sprinkler. By comparing measured values - obtained on field trials - with simulated ones, it was possible to conclude that, in general, the five considered empirical models presented little or no adjustment for the single-sprinkler outdoor tests (root mean square error of 5.27; 20.70; 5.07; 6.95 and 7.06% for empirical models 1; 2; 3; 4 and 5, respectively) as well as for the block irrigation outdoor tests (root mean square error of 7.41; 24.43; 6.72; 3.16 and 2.90% for empirical models 1; 2; 3; 4 and 5, respectively). When compared to the five considered empirical models, the new adjusted models showed lower errors, indicating that the application of empirical models must be limitated to working conditions (nozzle size, operational pressure, etc.) similar to the ones in which they were developed