Assessing the influence of mechanical ventilation on blood gases and blood pressure in rattlesnakes

ObjectiveTo characterize the impact of mechanical positive pressure ventilation on heart rate (HR), arterial blood pressure, blood gases, lactate, glucose, sodium, potassium and calcium concentrations in rattlesnakes during anesthesia and the subsequent recovery period.Study designProspective, randomized trial.AnimalsTwenty one fasted adult South American rattlesnakes (Crotalus durissus terrificus).MethodsSnakes were anesthetized with propofol (15 mg kg⁻¹) intravenously, endotracheally intubated and assigned to one of four ventilation regimens: Spontaneous ventilation, or mechanical ventilation at a tidal volume of 30 mL kg⁻¹ at 1 breath every 90 seconds, 5 breaths minute⁻¹, or 15 breaths minute⁻¹. Arterial blood was collected from indwelling catheters at 30, 40, and 60 minutes and 2, 6, and 24 hours following induction of anesthesia and analyzed for pH, PaO₂, PaCO₂, and selected variables. Mean arterial blood pressure (MAP) and HR were recorded at 30, 40, 60 minutes and 24 hours.ResultsSpontaneous ventilation and 1 breath every 90 seconds resulted in a mild hypercapnia (PaCO₂ 22.4 ± 4.3 mmHg [3.0 ± 0.6 kPa] and 24.5 ± 1.6 mmHg [3.3 ± 0.2 kPa], respectively), 5 breaths minute⁻¹ resulted in normocapnia (14.2 ± 2.7 mmHg [1.9 ± 0.4 kPa]), while 15 breaths minute⁻¹ caused marked hypocapnia (8.2 ± 2.5 mmHg [1.1 ± 0.3 kPa]). Following recovery, blood gases of the four groups were similar from 2 hours. Anesthesia, independent of ventilation was associated with significantly elevated glucose, lactate and potassium concentrations compared to values at 24 hours (p < 0.0001). MAP increased significantly with increasing ventilation frequency (p < 0.001). HR did not vary among regimens.Conclusions and clinical relevanceMechanical ventilation had a profound impact on blood gases and blood pressure. The results support the use of mechanical ventilation with a frequency of 1–2 breaths minute⁻¹ at a tidal volume of 30 mL kg⁻¹ during anesthesia in fasted snakes.     

Population decline in the endemic Atlantic salmon (Salmo salar) in Kapisillit River,Greenland

Atlantic salmon, Salmo salar L., is found throughout the North Atlantic, with thousands of rivers having spawning populations. In Greenland, spawning is limited to one river in West Greenland, the Kapisillit River (64?N), and the salmon are limited to the lower few kilometres of the system. Using mark–recapture, it was estimated the parr population was 5,953 individuals, and that the population size has declined by 52% since 1959. In spite of this decline, parr density remains high, being between 0.26 and 0.62 parr/m². Using a historical age‐length key, an estimated minimum of 635 smolt will have descended to the sea in 2017. These will be caught in a fishery currently subject to no regulatory measures and fishing remains the most likely driver of the population decline. The genetically distinct population is endemic to Greenland, and managers should implement measures to conserve this genetic integrity and local biodiversity.     

Remote sensing and forest inventories in Nordic countries – roadmap for the future

The Nordic countries have long traditions in forest inventory and remote sensing (RS). In sample-based national forest inventories (NFIs), utilization of aerial photographs started during the 1960s, satellite images during the 1980s, laser scanning during the 2000s, and photogrammetric point clouds during the 2010s. In forest management inventories (FMI), utilization of aerial photos started during the 1940s and laser scanning during the 2000s. However, so far, RS has mostly been used for map production and research rather than for estimation of regional parameters or inference on their accuracy. In recent years, the RS technology has been developing very fast. At the same time, the needs for information are constantly increasing. New technologies have created possibilities for cost-efficient production of accurate, large area forest data sets, which also will change the way forest inventories are done in the future. In this study, we analyse the state-of-the-art both in the NFIs and FMIs in the Nordic countries. We identify the benefits and drawbacks of different RS materials and data acquisition approaches with different user perspectives. Based on the analysis, we identify the needs for further development and emerging research questions. We also discuss alternatives for ownership of the data and cost-sharing between different actors in the field.     

Reinventing rural development in Vietnam: Discursive constructions of grassroots democracy during the renovation reform

Over the past decades Vietnam has seen striking efforts to reinvent the exercise of democratic rural development. Promotion of grassroots democracy, notably under the Grassroots Democracy Decree (GDD), has been an acute response by Communist Party and government to large scale unrest among the rural populace owing to dissatisfactions with a felt mismatch between espoused commitments to ‘good governance’ and its actual practice. Through evidence from field work, this paper assesses the implications of the GDD in the central and northern highlands, analyzing how the promotion of grassroots democracy is discursively constructed by rural development professionals. The results outline three dominant discourses, which center on their respective interests in liberalist democratization, improved efficiency in state renovation, and enhanced accountability in governing local policy ambiguities. It argues that ‘grassroots democracy’ is serving as a conceptual mediator, supporting learning between diverging interests associated with rural development and different ideological positions shrouding the notion of democracy itself. Yet, given the extent that discourses are reflective of how professionals relate to grassroots aspirations, grassroots movements, which originally ushered the Party and central government to pass the GDD, have a significant struggle ahead of them to affect concrete changes in professionals' practices.     

Functions for estimating belowground and whole tree biomass of birch in Norway

Obtaining accurate estimates of national belowground and whole tree biomass is important to better understand the global carbon cycle and to quantify biomass stocks and changes. However, the availability of individual tree belowground biomass functions is generally low due to the difficulty of extracting roots. Allometric birch (Betula pubescens Ehrh. and Betula pendula Roth) biomass functions were derived from 67 trees for belowground and whole tree biomass using diameter at breast height (dbh) and height as the independent variables. The sampled trees spanned a dbh range from 4.0 to 45.5?cm and the functions provided a good fit to the data (RMSE?=?14.2?kg for BG and 40.7?kg for whole tree with dbh as predictor). Belowground, total stem, live crown, and dead branch biomass comprised 29.2%, 52.2%, 18.1%, and 0.5% of the whole tree biomass, respectively. Observed root-to-shoot ratios were between 0.21 and 0.88 with a mean of 0.42. Comparisons with existing belowground birch biomass functions from Fennoscandia indicated considerable differences in estimates between existing functions. The derived data-set for belowground birch biomass is the largest in Fennoscandia and the developed functions are likely the best available for estimating national birch biomass stock and stock change in Norway.     

Correlating phosphorus extracted by simple soil extraction methods with foliar phosphorus concentrations of Picea abies (L.) H. Karst. and Fagus sylvatica (L.)

Phosphorus (P) concentrations in needles and leaves of forest trees are declining in the last years in Europe. For a sustainable forest management the knowledge of site specific P nutrition/availability in forest soils is vital, but we are lacking verified simple methods for the estimation of plant available P. Within this study, four soil P extraction methods [water ( ), double‐lactate (P_lac), citric acid (P_cit), and sodium bicarbonate ( )], as well as total P content of the soil (P_tot) were tested to investigate which method is best correlated with foliar P concentrations of spruce [Picea abies (L.) H. Karst.] and beech [Fagus sylvatica (L.)]. Mineral soil samples from 5 depth levels of 48 forest sites of the Bavarian sample set of the second National Forest Soil Inventory (BZE II) were stratified according to tree species (spruce and beech) and soil pH (pH < 6.2 and > 6.2), covering the whole range of P nutrition. The extractable amount of P per mass unit of soil increased in the order << P_lac < < P_cit, decreased with soil depth, and was higher in soils with pH < 6.2. Citric acid extracted up to 10% of P_tot in acidic soils. Whereas P_cit delivers adequate regression models for P nutrition in the case of spruce (R² up to 0.53) and beech (R² up to 0.58) for acidic soils, shows good results for spruce growing on acidic soils (R² up to 0.66) and for beech on soils with pH > 6.2 (R² up to 0.57). P_lac produces adequate models only for beech on high pH soils (R² up to 0.64), while did not produce acceptable regression models. P_tot seems suitable to explain the P nutrition status of beech on acidic (R² up to 0.62) and alkaline soils (R² up to 0.61). Highest R²s are obtained mostly in soil depths down to 40 cm. As and P_cit showed good results for both investigated tree species, they should be considered preferentially in future studies.     

Finding the appropriate level of complexity for a simulation model: An example with a forest growth model

The topic of model complexity is fundamental to model developers and model users. In this study, we investigate how over- and under-fitting of a driving function in a simulation model influences the predictive ability of the model. Secondly, we investigate whether model selection approaches succeed in selecting driving functions with the best predictive ability. We address these issues through an example with the forest simulator SORTIE-ND. Utilizing maximum likelihood methods and individual tree growth data we parameterize five growth functions of increasing complexity. We then incorporate each growth function into the simulation model SORTIE-ND and test predicted growth against independent data. Compared to the independent data, the simplest and the most complex growth functions had the poorest predictive ability while functions of intermediate complexity had the best predictive ability. The poor predictive ability of the simplest model is caused by poor approximation of the system while the poor predictive ability of the most complex model is caused by biased parameter estimates. A growth function of intermediate complexity was the most parsimonious model where error due to approximation and error due to estimation were simultaneously minimized. The model selection criteria AIC and BIC were found to select complex functions that were over-fitted according to the independent data comparison. BIC was closer to choosing the model that minimized prediction error than AIC. In this example, BIC is the more appropriate model selection criterion. It is important that both model developers and models users remember that more complex models do not always result in better predictive models.     

Analysis of pregerminated barley using hyperspectral image analysis

Pregermination is one of many serious degradations to barley when used for malting. A pregerminated barley kernel can under certain conditions not regerminate and is reduced to animal feed of lower quality. Identifying pregermination at an early stage is therefore essential in order to segregate the barley kernels into low or high quality. Current standard methods to quantify pregerminated barley include visual approaches, e.g. to identify the root sprout, or using an embryo staining method, which use a time-consuming procedure. We present an approach using a near-infrared (NIR) hyperspectral imaging system in a mathematical modeling framework to identify pregerminated barley at an early stage of approximately 12 h of pregermination. Our model only assigns pregermination as the cause for a single kernel's lack of germination and is unable to identify dormancy, kernel damage etc. The analysis is based on more than 750 Rosalina barley kernels being pregerminated at 8 different durations between 0 and 60 h based on the BRF method. Regerminating the kernels reveals a grouping of the pregerminated kernels into three categories: normal, delayed and limited germination. Our model employs a supervised classification framework based on a set of extracted features insensitive to the kernel orientation. An out-of-sample classification error of 32% (CI(95%): 29-35%) is obtained for single kernels when grouped into the three categories, and an error of 3% (CI(95%): 0-15%) is achieved on a bulk kernel level. The model provides class probabilities for each kernel, which can assist in achieving homogeneous germination profiles. This research can further be developed to establish an automated and faster procedure as an alternative to the standard procedures for pregerminated barley.