Untargeted metabolomics reveals links between Tiger nut (Cyperus esculentus L.) and its geographical origin by metabolome changes associated with membrane lipids

Tiger nuts and tiger nut milk are well-known Valencian products, and step-by-step these tubers and the tuber-based beverage are becoming more and more relevant products in international markets. However, the increasing demand and success of Valencian tiger nuts did not allow protected designation of origin (PDO) tuber to supply the domestic and international markets. Therefore, the verification of the geographical origin is highly required. In this research, the main objective was to combine an advance analytical method and chemometrics tools in order to decipher the geographical origin of 45 tiger nut samples from (i) ‘Xufa de València’ PDO and (ii) African samples. The analytical method, based on solid-liquid extraction followed by ultra-high performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS) metabolomics approach, highlighted sensitivity and wide linear dynamic range in order to simultaneously analyse polar and non-polar metabolites. After data processing, a pronounced sample clustering according to the geographical origin was clearly observed using unsupervised models, and supervised models revealed that tiger nuts lipidome was associated with the geographical origin. As a result, African samples highlighted an overexpression of phospholipids, such as phosphatidylethanolamine 34:1, and triacylgricerols crosslinked to environmental stress and alteration of membrane lipid compositions.     

Effect of tiger nut fibre on quality characteristics of pork burger

Horchata is a refreshing beverage obtained from tiger nut tubers that yields high amount of by-products. These by-products have a high content of fibre that allows its application in the development of dietary fibre rich foods. The utilization of increasing levels (0%-control, 5%, 10% and 15%) of tiger nut fibre (TNF), in the formulation of pork burgers was evaluated. This evaluation was based on: chemical composition, physicochemical, cooking characteristics and sensory properties of burgers. Pork burgers elaborated with TNF had higher nutritional value (higher fibre content) and better cooking characteristics (higher cooking yield, fat retention and moisture retention) than control burgers. Some of the negative changes in colour (a* decrease and b* increase) and texture (chewiness and springiness increase) parameters due to TNF addition observed in raw burgers were masked by the stronger modifications due to the cooking process. Burgers with TNF were perceived as less greasy, less juicy, more grainy and with less meaty flavour than controls; although this perception did not reduce the overall acceptability of burgers. Overall acceptability scores were slightly lower in burgers with 15% TNF, although no significant differences were detected with the scores of control, 5% and 10% TNF burgers. TNF addition to burgers is a promising and convenient application as dietary fibre of burgers was significantly increased without changes in sensory acceptance.     

Prediction of metabolic status of dairy cows in early lactation with on-farm cow data and machine learning algorithms

Metabolic status of dairy cows in early lactation can be evaluated using the concentrations of plasma β-hydroxybutyrate (BHB), free fatty acids (FFA), glucose, insulin, and insulin-like growth factor 1 (IGF-1). These plasma metabolites and metabolic hormones, however, are difficult to measure on farm. Instead, easily obtained on-farm cow data, such as milk production traits, have the potential to predict metabolic status. Here we aimed (1) to investigate whether metabolic status of individual cows in early lactation could be clustered based on their plasma values and (2) to evaluate machine learning algorithms to predict metabolic status using on-farm cow data. Through lactation wk 1 to 7, plasma metabolites and metabolic hormones of 334 cows were measured weekly and used to cluster each cow into 1 of 3 clusters per week. The cluster with the greatest plasma BHB and FFA and the lowest plasma glucose, insulin, and IGF-1 was defined as poor metabolic status; the cluster with the lowest plasma BHB and FFA and the greatest plasma glucose, insulin, and IGF-1 was defined as good metabolic status; and the intermediate cluster was defined as average metabolic status. Most dairy cows were classified as having average or good metabolic status, and a limited number of cows had poor metabolic status (10–50 cows per lactation week). On-farm cow data, including dry period length, parity, milk production traits, and body weight, were used to predict good or average metabolic status with 8 machine learning algorithms. Random Forest (error rate ranging from 12.4 to 22.6%) and Support Vector Machine (SVM; error rate ranging from 12.4 to 20.9%) were the top 2 best-performing algorithms to predict metabolic status using on-farm cow data. Random Forest had a higher sensitivity (range: 67.8–82.9% during wk 1 to 7) and negative predictive value (range: 89.5–93.8%) but lower specificity (range: 76.7–88.5%) and positive predictive value (range: 58.1–78.4%) than SVM. In Random Forest, milk yield, fat yield, protein percentage, and lactose yield had important roles in prediction, but their rank of importance differed across lactation weeks. In conclusion, dairy cows could be clustered for metabolic status based on plasma metabolites and metabolic hormones. Moreover, on-farm cow data can predict cows in good or average metabolic status, with Random Forest and SVM performing best of all algorithms.     

Treatment of Industrial Landfill Leachates By Chemical And Biological Methods: Ozonation,Ozonation + Hydrogen Peroxide,Hydrogen Peroxide And Biological Post-Treatment For Ozonated Water

The objective of this study was to determine a suitable treatment method for variable waters from a forest industry landfill site. The main target was to find out the impact of different chemical treatments on the composition and biodegradability of those waters. Earlier studies have shown that biological treatment alone is not a suitable treatment method for these waters. That is why ozonation, ozonation+hydrogen peroxide and hydrogen peroxide treatment were studied in a laboratory scale. The ozonated waters were also biologically post-treated.

All the methods studied were able to degrade a part of the organic compounds and convert them into a more biodegradable form. Also the BOD/COD -ratio increased significantly. The removal of organic compounds by ozonation was 30 - 50 %. Hydrogen peroxide addition did not improve the degradation. The combination of pre-ozonation and biological post-treatment gave a total TOC removal between 50 - 95 %.     

Characterization of seasonal variation of forest canopy in a temperate deciduous broadleaf forest, using daily MODIS data

In this paper, we present an improved procedure for collecting no or little atmosphere- and snow-contaminated observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The resultant time series of daily MODIS data of a temperate deciduous broadleaf forest (the Bartlett Experimental Forest) in 2004 show strong seasonal dynamics of surface reflectance of green, near infrared and shortwave infrared bands, and clearly delineate leaf phenology and length of plant growing season. We also estimate the fractions of photosynthetically active radiation (PAR) absorbed by vegetation canopy (FAPAR_canopy), leaf (FAPAR_leaf), and chlorophyll (FAPAR_chl), respectively, using a coupled leaf-canopy radiative transfer model (PROSAIL-2) and daily MODIS data. The Markov Chain Monte Carlo (MCMC) method (the Metropolis algorithm) is used for model inversion, which provides probability distributions of the retrieved variables. A two-step procedure is used to estimate the fractions of absorbed PAR: (1) to retrieve biophysical and biochemical variables from MODIS images using the PROSAIL-2 model; and (2) to calculate the fractions with the estimated model variables from the first step. Inversion and forward simulations of the PROSAIL-2 model are carried out for the temperate deciduous broadleaf forest during day of year (DOY) 184 to 201 in 2005. The reproduced reflectance values from the PROSAIL-2 model agree well with the observed MODIS reflectance for the five spectral bands (green, red, NIR₁, NIR₂, and SWIR₁). The estimated leaf area index, leaf dry matter, leaf chlorophyll content and FAPAR_canopy values are close to field measurements at the site. The results also showed significant differences between FAPAR_canopy and FAPAR_chl at the site. Our results show that MODIS imagery provides important information on biophysical and biochemical variables at both leaf and canopy levels.     

Assessment of QuickBird high spatial resolution imagery to detect red attack damage due to mountain pine beetle infestation

High spatial resolution remotely sensed data has the potential to complement existing forest health programs for both strategic planning over large areas, as well as for detailed and precise identification of tree crowns subject to stress and infestation. The area impacted by the current mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in British Columbia, Canada, has increased 40-fold over the previous 5 years, with approximately 8.5 million ha of forest infested in 2005. As a result of the spatial extent and intensity of the outbreak, new technologies are being assessed to help detect, map, and monitor the damage caused by the beetle, and to inform mitigation of future beetle outbreaks. In this paper, we evaluate the capacity of high spatial resolution QuickBird multi-spectral imagery to detect mountain pine beetle red attack damage. ANOVA testing of individual spectral bands, as well as the Normalized Difference Vegetation Index (NDVI) and a ratio of red to green reflectance (Red-Green Index or RGI), indicated that the RGI was the most successful (p < 0.001) at separating non-attack crowns from red attack crowns. Based on this result, the RGI was subsequently used to develop a binary classification of red attack and non-attack pixels. The total number of QuickBird pixels classified as having red attack damage within a 50 m buffer of a known forest health survey point were compared to the number of red attack trees recorded at the time of the forest health survey. The relationship between the number of red attack pixels and observed red attack crowns was assessed using independent validation data and was found to be significant (r² = 0.48, p < 0.001, standard error = 2.8 crowns). A comparison of the number of QuickBird pixels classified as red attack, and a broader scale index of mountain pine beetle red attack damage (Enhanced Wetness Difference Index, calculated from a time series of Landsat imagery), was significant (r² = 0.61, p < 0.001, standard error = 1.3 crowns). These results suggest that high spatial resolution imagery, in particular QuickBird satellite imagery, has a valuable role to play in identifying tree crowns with red attack damage. This information could subsequently be used to augment existing detailed forest health surveys, calibrate synoptic estimates of red attack damage generated from overview surveys and/or coarse scale remotely sensed data, and facilitate the generation of value-added information products, such as estimates of timber volume impacts at the forest stand level.     

Minimizing total costs of forest roads with computer-aided design model

Advances in personal computers (PCs) have increased interest in computer-based road-design systems to provide rapid evaluation of alternative alignments. Optimization techniques can provide road managers with a powerful tool that searches for large numbers of alternative alignments in short spans of time. A forest road optimization model, integrated with two optimization techniques, was developed to help a forest road engineer in evaluating alternative alignments in a faster and more systematic manner. The model aims at designing a path with minimum total road costs, while conforming to design specifications, environmental requirements, and driver safety. To monitor the sediment production of the alternative alignments, the average sediment delivered to a stream from a road section was estimated by using a road erosion/delivery model. The results indicated that this model has the potential to initiate a new procedure that will improve the forest road-design process by employing the advanced hardware and software capabilities of PCs and modern optimization techniques.     

Participatory landscape analysis to guide restoration of ponderosa pine ecosystems in the American Southwest

Over the past 5 decades, stand-replacing crown fires have increased in size and frequency throughout the long-needled pine forests of the American Southwest. Suppression of frequent, low-intensity ground fires has resulted in dense stands of fire-prone trees over large areas. Efforts to restore forest structure to conditions that would permit a return to historical fire regimes, characterized by frequent ground fire, are hindered by the inability of managers and the public to compare the effects of alternative forest management practices on fire behavior and a host of other issues. Currently, forest management is mired in controversy, endangering ecosystem function, biodiversity, public safety, and municipal watersheds. Our research program, motivated by the need to examine cumulative effects of many independent management decisions over large planning areas, focuses on the development of spatial data and modeling tools that enable diverse stakeholders to work together to guide landscape-scale planning efforts. Data layers describing forest composition and structure facilitate modeling of fire threat and wildlife habitat over areas of several hundred thousand hectares. Modeling alternative forest management scenarios via a collaborative, public process fosters informed discourse and helps conflicting parties forge appropriate policy and identify management responses that meet restoration objectives.     

Evaluation of the suitability of data base management systems: a case study at the US Forest Service

A methodology for assessing the feasibility of a data base management system is described, and its use demonstrated in a specific situation: the U.S. Forest service. A series of questions to be answered at the test organization is formulated, aimed at quantifying the benefits of a DBMS. Results of attempting to answer these questions at two test sites are provided. Implications of these results are given for both forest and higher level management.     

The Hydrogeochemistry of Arsenic in the Clara Mine,Germany

Abstract.   The oxidative dissolution of primary arsenic-bearing sulfide minerals in barite-fluorite veins is a potential source of arsenic in the Clara Mine. Geological structures, especially the mineral veins, provide potential pathways for the water. The highest arsenic concentrations are found in ground water within the eastern part of the mine. Arsenic and major ions are positively correlated and provide evidence that arsenic is likely derived locally from the water-vein/water-rock interaction. Geochemical modeling with PHREEQC shows all the arsenate mineral phases to be significantly undersaturated, although secondary arsenate minerals are common in the oxidized part of the deposit. The mine waters plot near the boundary of Fe(OH)₃ and Fe²⁺ in the pH-Eh diagram for the As-Fe-S-H₂O-system. Arsenic occurs as the 5-valent species and . Statistical analysis illustrates a strong association between As, Fe, pH, and HCO₃. Oxidation of the primary As-bearing minerals, such as pyrite and arsenopyrite, and the subsequent behavior of the oxyanion, arsenate, generally controls the distribution and speciation of arsenic. The low concentrations of dissolved As is due to co-precipitation and adsorption of arsenate by Fe, Al, and Mn (oxy)hydroxides.