Editorial: Acetic acid bacteria

Acetic acid bacteria (ABB) are strictly aerobic organisms that can be found in a wide variety of natural and industrial environments. Their versatility and metabolic adaptability make them microorganisms of high interest to study the optimization of obtaining their multiple products and the essential mechanisms that allow them to grow under extreme conditions. Their metabolism, particularly the role of membrane-bound and soluble dehydrogenases, may offer new opportunities in the development of innovative processes based on their capability for carrying out the incomplete oxidation of several substrates. On the other hand, the resistance of AAB to some extreme conditions, i.e., low pH values, and adaptability to many different habitats make them highly competitive bacteria; so, their interaction with other organisms and plants is a very important topic to be studied. The ability of AAB in producing exopolysaccharides is also of great interest for both research and industrial purposes. They are considered as model organisms for understanding the mechanisms of cellulose synthesis and they are until now the most efficient organisms for producing it, under controlled conditions. Finally, the current state of omic technologies and efficient genetic modification methods can be applied for a greater understanding of the physiological behavior, the recovery of new strains and/or those occurring in complex environments as well as to exploit the full potential of AAB for oxidative bioconversions

Gamma Ray Burst Neutrinos Probing Quantum Gravity

Very high energy, short wavelength, neutrinos may interact with the space-time foam predicted by theories of quantum gravity. They would propagate like light through a crystal lattice and be delayed, with the delay depending on the energy. This will appear to the observer as a violation of Lorenz invariance. Back of the envelope calculations imply that observations of neutrinos produced by gamma ray bursts may reach Planck-scale sensitivity. We revisit the problem considering two essential complications: the imprecise timing of the neutrinos associated with their poorly understood production mechanism in the source and the indirect nature of their energy measurement made by high energy neutrino telescopes.Comment: 14 pages, 2 figure

First record of Triops gadensis Korn and García-de-Lomas, 2010 (Crustacea: Notostraca) in Córdoba Province, southern Spain

We studied a Triops population from a Mediterranean temporary pond located in Guadalcázar, a township in the western part of Córdoba Province. There are four morphologically similar species of Triops occurring in the SW Iberian Peninsula. In particular, the two species found in southern Andalusia, T. baeticus and T. gadensis, cannot reliably be told apart based on known morphological characters. We thus used sequences of 12S rDNA as a molecular marker to soundly identify the population to species rank. Our results demonstrate that the Triops population studied belongs to T. gadensis. This result is rather unexpected given that the species was thought to be restricted to a small distribution range along the Atlantic coast in Cádiz Province. The population is located at approximately 170 km from previously known sites of T. gadensis and shows a previously unknown haplotype, the sixth 12S haplotype reported for the species. We thus suggest that the population has high conservation value, and its habitat, including the surroundings, should be legally protected. We further propose that an open habitat type as pastureland should be retained for the surroundings of the site in order to support its ecological function in the frame of meta-populations dynamics, and as a possible source population from which new localities could be populated via passive dispersal

Artificial intelligence-based software (AID-FOREST) for tree detection: A new framework for fast and accurate forest inventorying using LiDAR point clouds

Forest inventories are essential to accurately estimate different dendrometric and forest stand parameters. However, classical forest inventories are time consuming, slow to conduct, sometimes inaccurate and costly. To address this problem, an efficient alternative approach has been sought and designed that will make this type of field work cheaper, faster, more accurate, and easier to complete. The implementation of this concept has required the development of a specifically designed software called "Artificial Intelligence for Digital Forest (AID-FOREST)", which is able to process point clouds obtained via mobile terrestrial laser scanning (MTLS) and then, to provide an array of multiple useful and accurate dendrometric and forest stand parameters. Singular characteristics of this approach are: No data pre-processing is required either pre-treatment of forest stand; fully automatic process once launched; no limitations by the size of the point cloud file and fast computations.To validate AID-FOREST, results provided by this software were compared against the obtained from in-situ classical forest inventories. To guaranty the soundness and generality of the comparison, different tree spe-cies, plot sizes, and tree densities were measured and analysed. A total of 76 plots (10,887 trees) were selected to conduct both a classic forest inventory reference method and a MTLS (ZEB-HORIZON, Geoslam, ltd.) scanning to obtain point clouds for AID-FOREST processing, known as the MTLS-AIDFOREST method. Thus, we compared the data collected by both methods estimating the average number of trees and diameter at breast height (DBH) for each plot. Moreover, 71 additional individual trees were scanned with MTLS and processed by AID-FOREST and were then felled and divided into logs measuring 1 m in length. This allowed us to accurately measure the DBH, total height, and total volume of the stems.When we compared the results obtained with each methodology, the mean detectability was 97% and ranged from 81.3 to 100%, with a bias (underestimation by MTLS-AIDFOREST method) in the number of trees per plot of 2.8% and a relative root-mean-square error (RMSE) of 9.2%. Species, plot size, and tree density did not significantly affect detectability. However, this parameter was significantly affected by the ecosystem visual complexity index (EVCI). The average DBH per plot was underestimated (but was not significantly different from 0) by the MTLS-AIDFOREST, with the average bias for pooled data being 1.8% with a RMSE of 7.5%. Similarly, there was no statistically significant differences between the two distribution functions of the DBH at the 95.0% confidence level.Regarding the individual tree parameters, MTLS-AIDFOREST underestimated DBH by 0.16 % (RMSE = 5.2 %) and overestimated the stem volume (Vt) by 1.37 % (RMSE = 14.3 %, although the BIAS was not statistically significantly different from 0). However, the MTLS-AIDFOREST method overestimated the total height (Ht) of the trees by a mean 1.33 m (5.1 %; relative RMSE = 11.5 %), because of the different height concepts measured by both methodological approaches. Finally, AID-FOREST required 30 to 66 min per ha-1 to fully automatically process the point cloud data from the *.las file corresponding to a given hectare plot. Thus, applying our MTLS-AIDFOREST methodology to make full forest inventories, required a 57.3 % of the time required to perform classical plot forest inventories (excluding the data postprocessing time in the latter case). A free trial of AID -FOREST can be requested at [email protected]

The effect of interpolation methods in temperature and salinity trends in the Western Mediterranean

Temperature and salinity data in the historical record are scarce and unevenly distributed in space and time and the estimation of linear trends is sensitive to different factors. In the case of the Western Mediterranean, previous works have studied the sensitivity of these trends to the use of bathythermograph data, the averaging methods or the way in which gaps in time series are dealt with. In this work, a new factor is analysed: the effect of data interpolation. Temperature and salinity time series are generated averaging existing data over certain geographical areas and also by means of interpolation. Linear trends from both types of time series are compared. There are some differences between both estimations for some layers and geographical areas, while in other cases the results are consistent. Those results which do not depend on the use of interpolated or non-interpolated data, neither are influenced by data analysis methods can be considered as robust ones. Those results influenced by the interpolation process or the factors analysed in previous sensitivity tests are not considered as robust results