Prototype of an affordable pressure-controlled emergency mechanical ventilator for COVID-19

We present a viable prototype of a simple mechanical ventilator intended as a last resort to ventilate COVID-19 patients. The prototype implements the pressure-controlled continuous mandatory ventilation mode (PC-CMV) with settable breathing rates, inspiration/expiration time ratios and FiO2 modulation. Although safe, the design aims to minimize the use of technical components and those used are common in industry, so its construction may be possible in times of logistical shortage or disruption or in areas with reduced access to technical materials and at a moderate cost, affordable to lower income countries. Most of the device can be manufactured by modest technical means and construction plans are provided.Comment: This version differs from version 2 in that it includes toxicological and bio-safety tests and updated electronic

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Amendments to model frameworks to optimize the anaerobic digestion and support the green transition

The current world energy system is still heavily dependent on fossil resources (non-renewable and depletable). Anaerobic digestion (AD) has been pointed out as a great strategy for waste and wastewater management while producing biogas that can be upgraded to biomethane. Mathematical models can provide insights into understanding and analyzing important aspects of any process, while minimizing experimental effort, risk, and cost. However, modeling as means to predict, control, and optimize the performance of biological processes on pilot or higher scale is rather scarce. The so-called “BioModel” and Anaerobic Digestion Model No. 1 (ADM1) are well-known model frameworks to understand, characterize, and simulate the anaerobic digestion (AD) processes. Multiple amendments, modifications, and additions occurred during the past years in both frameworks. Therefore. the present article aims to review the most relevant updates made to these models and enlighten the perspectives on the role of kinetic modeling in bio-based gas production. The potential of the existing highly efficient AD models to serve as a basis to develop, predict, and finally support the biogas and bio-methanation processes at a higher scale is discussed

Recent advances and perspectives in the use of conductive materials to improve anaerobic wastewater treatment: a systematic review approached

In recent years, many researches have reported that anaerobic digestion and methane production can be significantly improved with the addition of conductive materials to the process. Despite advances, a number of questions about this strategy remain unsolved, including the mechanism and impact of material properties on methanogenic pathways. In order to provide an update on the current state of knowledge and future application perspectives, this work analyzed some of the most recent studies using conductive materials in the anaerobic digestion of effluents through a systematic review of the literature and statistical analysis, as well as the enriched microorganisms, the influence of the dosage, size and conductivity of the materials used. It was found that the change in the microbial community is associated with the use of conductive material (p < 0.05). Additionally, a slight trend was suggested that millimeter-scale materials appear to be more effective in increasing methane production; in contrast to expectations, it was not possible to find a correlation between electrical conductivity and an increase in methane production, supporting the idea that other properties may also perform important roles in the process and require further research. In general, the use of conductive materials is a promising approach to improve anaerobic digestion and methane production, however, the need for future research was indicated to expand the scale of application of this technology.Peer ReviewedPostprint (author's final draft