Escherichia coli, un petit gran organisme

Els processos essencials que tenen lloc dins les cèl·lules, tant fisiològics com genètics o bioquímics, estan altament conservats entre tots els éssers vius. Els bacteris són microorganismes d'estructura cel·lular senzilla en comparació de les cèl·lules dels organismes superiors. A més, molts bacteris es divideixen molt ràpidament, són fàcilment manipulables i es poden fer créixer en una àmplia varietat de medis de cultiu. Tot això ha fet que els bacteris s'hagin fet servir molt extensament com a model biològic per respondre qüestions sobre processos bàsics de la biologia que són comuns a tots els éssers vius. Per exemple, l'estat actual del coneixement biològic, el desenvolupament de la biologia molecular i moltes de les aplicacions biotecnològiques derivades són resultat, en gran mesura, de l'estudi en bacteriologia. Entre tots els bacteris utilitzats en laboratoris de recerca Escherichia coli ha estat, sens dubte, el més utilitzat, i ha donat lloc a molts dels coneixements de què disposem avui dia sobre el funcionament, l'estructura i l'evolució dels éssers vius. En aquest capítol fem una visió sobre l'ús passat, present i futur com a model biològic dels bacteris en general i d'E. coli en particular.Escherichia coli, a little big organism. The basic physiological, biochemical and genetic processes that take place inside a cell are highly conserved among the different organisms. When compared with high organisms, bacteria are microorganisms that have a simple cell structure. Moreover, many bacteria can divide very quickly, are easy to handle and can grow in a broad variety of culture media. All these properties promoted that bacteria were used as model organisms to answer relevant biological questions common in all living beings. Present knowledge in biology, development of molecular biology and many biotechnological applications are mainly due to bacteriological studies. Among all, there is one bacterium that has been very extensively used in biological studies, Escherichia coli. Most of knowledge we have nowadays on the structure, physiology and evolution of the living creatures is based in data from E. coli based studies. In this chapter, an overview on the pass, present and future use of bacteria, and more specifically of E. coli, as a model organism is done

Use of machine-learning and load–velocity profiling to estimate 1-Repetition maximums for two variations of the bench-press exercise

The purpose of the current study was to compare the ability of five different methods to estimate eccentric–concentric and concentric-only bench-press 1RM from load–velocity profile data. Smith machine bench-press tests were performed in an eccentric–concentric (n= 192) and a concentric-only manner (n= 176) while mean concentric velocity was registered using a linear position transducer. Load–velocity profiles were derived from incremental submaximal load (40–80% 1RM) tests. Five different methods were used to calculate 1RM using the slope, intercept, and velocity at 1RM (minimum velocity threshold—MVT) from the load–velocity profiles: calculation with individual MVT, calculation with group average MVT, multilinear regression without MVT, regularized regression without MVT, and an artificial neural network without MVT. Mean average errors for all methods ranged from 2.7 to 3.3 kg. Calculations with individual or group MVT resulted in significant overprediction of eccentric–concentric 1RM (individual MVT: difference = 0.76 kg, p= 0.020, d= 0.17; group MVT: difference = 0.72 kg, p= 0.023, d= 0.17). The multilinear and regularized regression both resulted in the lowest errors and highest correlations. The results demonstrated that bench-press 1RM can be accurately estimated from load–velocity data derived from submaximal loads and without MVT. In addition, results showed that multilinear regression can be used to estimate bench-press 1RM. Collectively, the findings and resulting equations should be helpful for strength and conditioning coaches as they would help estimating 1RM without MVT data

The implementation of velocity-based training paradigm for team sports: Framework, technologies, practical recommendations and challenges

While velocity-based training is currently a very popular paradigm to designing and monitoring resistance training programs, its implementation remains a challenge in team sports, where there are still some confusion and misinterpretations of its applications. In addition, in contexts with large squads, it is paramount to understand how to best use movement velocity in different exercises in a useful and time-efficient way. This manuscript aims to provide clarifications on the velocity-based training paradigm, movement velocity tracking technologies, assessment procedures and practical recommendations for its application during resistance training sessions, with the purpose of increasing performance, managing fatigue and preventing injuries. Guidelines to combine velocity metrics with subjective scales to prescribe training loads are presented, as well as methods to estimate 1-Repetition Maximum (1RM) on a daily basis using individual load–velocity profiles. Additionally, monitoring strategies to detect and evaluate changes in performance over time are discussed. Finally, limitations regarding the use of velocity of execution tracking devices and metrics such as “muscle power” are commented upon. Funding: This research received no external funding

Reciclagem de nutrientes pelas fezes em áreas de Brachiaria decumbens.

bitstream/item/110631/1/Reciclagem-de-nutrientes-pelas-fezes-em-areas-de-Brachiaria-decumbens.pd