scholarly journals Probabilistic Provenance Detection and Management Pathways for Pseudotsuga menziesii (Mirb.) Franco in Italy Using Climatic Analogues

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 215
Author(s):  
Maurizio Marchi ◽  
Claudia Cocozza
Keyword(s):  
Pseudotsuga Menziesii ◽  
National Forest Inventory ◽  
Douglas Fir ◽  
Classification Model ◽  
Climatic Data ◽  
Modeling Framework ◽  
North West ◽  
Random Forest Classification ◽  
Seed Sources ◽  
Forest Classification

The introduction of Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] in Europe has been one of the most important and extensive silvicultural experiments since the 1850s. This success was mainly supported by the species’ wide genome and phenotypic plasticity even if the genetic origin of seeds used for plantations is nowadays often unknown. This is especially true for all the stands planted before the IUFRO experimentation in the 1960s. In this paper, a methodology to estimate the Douglas-fir provenances currently growing in Italy is proposed. The raw data from the last Italian National Forest Inventory were combined with literature information to obtain the current spatial distribution of the species in the country representing its successful introduction. Afterwards, a random forest classification model was run using downscaled climatic data as predictors and the classification scheme adopted in previous research studies in the Pacific North West of America. The analysis highlighted good matching between the native and the introduction range in Italy. Coastal provenances from British Columbia and the dry coast of Washington were detected as the most likely seed sources, covering 63.4% and 33.8% of the current distribution of the species in the country, respectively. Interior provenances and those from the dry coast of Oregon were also represented but limited to very few cases. The extension of the model on future scenarios predicted a gradual shift in suitable provenances with the dry coast of Oregon in the mid-term (2050s) and afterwards California (2080s) being highlighted as possible new seed sources. However, only further analysis with genetic markers and molecular methods will be able to confirm the proposed scenarios. A validation of the genotypes currently available in Italy will be mandatory as well as their regeneration processes (i.e., adaptation), which may also diverge from those occurring in the native range due to a different environmental pressure. This new information will also add important knowledge, allowing a refinement of the proposed modeling framework for a better support for forest managers.

scholarly journals Fully Automated Detection of Paramagnetic Rims in Multiple Sclerosis Lesions on 3T Susceptibility-Based MR Imaging

2020 ◽  
Author(s):  
Carolyn Lou ◽  
Pascal Sati ◽  
Martina Absinta ◽  
Kelly Clark ◽  
Jordan D. Dworkin ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Severe Disease ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
List Type ◽  
Training Set ◽  
Random Forest Classification ◽  
Automated Method ◽  
Forest Classification

AbstractBackground and PurposeThe presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis (MS) lesion. Increased prevalence of these paramagnetic rim lesions (PRLs) is associated with a more severe disease course in MS. The identification of these lesions is time-consuming to perform manually. We present a method to automatically detect PRLs on 3T T2*-phase images.MethodsT1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 19 subjects with MS. The images were then processed with lesion segmentation, lesion center detection, lesion labelling, and lesion-level radiomic feature extraction. A total of 877 lesions were identified, 118 (13%) of which contained a paramagnetic rim. We divided our data into a training set (15 patients, 673 lesions) and a testing set (4 patients, 204 lesions). We fit a random forest classification model on the training set and assessed our ability to classify lesions as PRL on the test set.ResultsThe number of PRLs per subject identified via our automated lesion labelling method was highly correlated with the gold standard count of PRLs per subject, r = 0.91 (95% CI [0.79, 0.97]). The classification algorithm using radiomic features can classify a lesion as PRL or not with an area under the curve of 0.80 (95% CI [0.67, 0.86]).ConclusionThis study develops a fully automated technique for the detection of paramagnetic rim lesions using standard T1 and FLAIR sequences and a T2*phase sequence obtained on 3T MR images.HighlightsA fully automated method for both the identification and classification of paramagnetic rim lesions is proposed.Radiomic features in conjunction with machine learning algorithms can accurately classify paramagnetic rim lesions.Challenges for classification are largely driven by heterogeneity between lesions, including equivocal rim signatures and lesion location.

scholarly journals Douglas Fir Seed Sources Tested for Christmas Trees in Connecticut

1984 ◽  
Vol 2 (3) ◽  
pp. 93-97
Author(s):  
R.A. Jaynes ◽  
G.R. Stephens ◽  
J.F. Ahrens
Keyword(s):  
New Mexico ◽  
Pseudotsuga Menziesii ◽  
Rocky Mountain ◽  
Douglas Fir ◽  
Christmas Tree ◽  
Seed Sources ◽  
Christmas Trees ◽  
Adelges Cooleyi ◽  
Geographic Sources ◽  
Southern Rocky Mountain

Douglas fir, Pseudotsuga Menziesii (Mirb.) Franco, is a popular Christmas tree in the Northeast. In 1976 trees from 11 geographic sources ranging from British Columbia to southern Arizona and New Mexico were planted in a replicated design and managed as a commercial plantation. Information was also obtained on 10 seed sources grown on a commercial tree farm. All sources were hardy in the Connecticut plantings. In general, trees from southern Rocky Mountain sources were bluer, and grew faster than those from northern sources, but they were also more susceptible to attack by Cooley gall aphid, Adelges cooleyi (Gill), and rhabdocline needle cast fungus, Rhabdocline pseudotsugae (Syd.)

scholarly journals Epigenetic Analyses of Alcohol Consumption in Combustible and Non-Combustible Nicotine Product Users

Epigenomes ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 18
Author(s):  
Kelsey Dawes ◽  
Luke Sampson ◽  
Rachel Reimer ◽  
Shelly Miller ◽  
Robert Philibert ◽  
...  
Keyword(s):  
Alcohol Consumption ◽  
Self Report ◽  
Classification Model ◽  
Public And Private ◽  
Random Forest Classification ◽  
Epigenetic Biomarkers ◽  
Forest Classification ◽  
Heavy Alcohol Consumption ◽  
Relationship Of ◽  
The Relationship

Alcohol and tobacco use are highly comorbid and exacerbate the associated morbidity and mortality of either substance alone. However, the relationship of alcohol consumption to the various forms of nicotine-containing products is not well understood. To improve this understanding, we examined the relationship of alcohol consumption to nicotine product use using self-report, cotinine, and two epigenetic biomarkers specific for smoking (cg05575921) and drinking (Alcohol T Scores (ATS)) in n = 424 subjects. Cigarette users had significantly higher ATS values than the other groups (p < 2.2 × 10−16). Using the objective biomarkers, the intensity of nicotine and alcohol consumption was correlated in both the cigarette and smokeless users (R = −0.66, p = 3.1 × 10−14; R2 = 0.61, p = 1.97 × 10−4). Building upon this idea, we used the objective nicotine biomarkers and age to build and test a Balanced Random Forest classification model for heavy alcohol consumption (ATS > 2.35). The model performed well with an AUC of 0.962, 89.3% sensitivity, and 85% specificity. We conclude that those who use non-combustible nicotine products drink significantly less than smokers, and cigarette and smokeless users drink more with heavier nicotine use. These findings further highlight the lack of informativeness of self-reported alcohol consumption and suggest given the public and private health burden of alcoholism, further research into whether using non-combustible nicotine products as a mode of treatment for dual users should be considered.

scholarly journals Keeping it classy: classification of live fish and ghost PIT tags detected with a mobile PIT tag interrogation system using an innovative analytical approach

2020 ◽  
Vol 77 (9) ◽  
pp. 1564-1573
Author(s):  
J. Benjamin Stout ◽  
Mary Conner ◽  
Phaedra Budy ◽  
Peter Mackinnon ◽  
Mark McKinstry
Keyword(s):  
Error Rate ◽  
Classification Model ◽  
Parameter Estimates ◽  
Pit Tag ◽  
Endangered Fish ◽  
Random Forest Classification ◽  
Management Actions ◽  
Live Fish ◽  
Forest Classification ◽  
Input Structure

The ability of passive integrated transponder (PIT) tag data to improve demographic parameter estimates has led to the rapid advancement of PIT tag systems. However, ghost tags create uncertainty about detected tag status (i.e., live fish or ghost tag) when using mobile interrogation systems. We developed a method to differentiate between live fish and ghost tags using a random forest classification model with a novel data input structure based on known fate PIT tag detections in the San Juan River (New Mexico, Colorado, and Utah, USA). We used our model to classify detected tags with an overall error rate of 6.8% (1.6% ghost tags error rate and 21.8% live fish error rate). The important variables for classification were related to distance moved and response to monsoonal flood flows; however, habitat variables did not appear to influence model accuracy. Our results and approach allow the use of mobile detection data with confidence and allow for greater accuracy in movement, distribution, and habitat use studies, potentially helping identify influential management actions that would improve our ability to conserve and recover endangered fish.

Intelligent identification of effective reservoirs based on the random forest classification model

2020 ◽  
Vol 591 ◽  
pp. 125324 ◽  
Author(s):  
Jieyu Li ◽  
Ping-an Zhong ◽  
Minzhi Yang ◽  
Feilin Zhu ◽  
Juan Chen ◽  
...  
Keyword(s):  
Random Forest ◽  
Classification Model ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals Nutrient Diagnosis of Eucalyptus at the Factor-Specific Level Using Machine Learning and Compositional Methods

Plants ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1049 ◽  
Author(s):  
Betania Vahl de Paula ◽  
Wagner Squizani Arruda ◽  
Léon Etienne Parent ◽  
Elias Frank de Araujo ◽  
Gustavo Brunetto
Keyword(s):  
Area Under The Curve ◽  
Local Scale ◽  
Classification Model ◽  
High Yield ◽  
Specific Level ◽  
Random Forest Classification ◽  
Yield Level ◽  
Forest Classification ◽  
Factor Interactions ◽  
Log Ratio

Brazil is home to 30% of the world’s Eucalyptus trees. The seedlings are fertilized at plantation to support biomass production until canopy closure. Thereafter, fertilization is guided by state standards that may not apply at the local scale where myriads of growth factors interact. Our objective was to customize the nutrient diagnosis of young Eucalyptus trees down to factor-specific levels. We collected 1861 observations across eight clones, 48 soil types, and 148 locations in southern Brazil. Cutoff diameter between low- and high-yielding specimens at breast height was set at 4.3 cm. The random forest classification model returned a relatively uninformative area under the curve (AUC) of 0.63 using tissue compositions only, and an informative AUC of 0.78 after adding local features. Compared to nutrient levels from quartile compatibility intervals of nutritionally balanced specimens at high-yield level, state guidelines appeared to be too high for Mg, B, Mn, and Fe and too low for Cu and Zn. Moreover, diagnosis using concentration ranges collapsed in the multivariate Euclidean hyper-space by denying nutrient interactions. Factor-specific diagnosis detected nutrient imbalance by computing the Euclidean distance between centered log-ratio transformed compositions of defective and successful neighbors at a local scale. Downscaling regional nutrient standards may thus fail to account for factor interactions at a local scale. Documenting factors at a local scale requires large datasets through close collaboration between stakeholders.

scholarly journals Humboldtian Diagnosis of Peach Tree (Prunus persica) Nutrition Using Machine-Learning and Compositional Methods

Agronomy ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 900 ◽  
Author(s):  
Debora Leitzke Betemps ◽  
Betania Vahl de Paula ◽  
Serge-Étienne Parent ◽  
Simone P. Galarça ◽  
Newton A. Mayer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Euclidean Distance ◽  
Prunus Persica ◽  
Nutrient Status ◽  
Classification Model ◽  
Data Sets ◽  
Local Conditions ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Euclidean Distances

Regional nutrient ranges are commonly used to diagnose plant nutrient status. In contrast, local diagnosis confronts unhealthy to healthy compositional entities in comparable surroundings. Robust local diagnosis requires well-documented data sets processed by machine learning and compositional methods. Our objective was to customize nutrient diagnosis of peach (Prunus persica) trees at local scale. We collected 472 observations from commercial orchards and fertilizer trials across eleven cultivars of Prunus persica and six rootstocks in the state of Rio Grande do Sul (RS), Brazil. The random forest classification model returned an area under curve exceeding 0.80 and classification accuracy of 80% about yield cutoff of 16 Mg ha−1. Centered log ratios (clr) of foliar defective compositions have appropriate geometry to compute Euclidean distances from closest successful compositions in “enchanting islands”. Successful specimens closest to defective specimens as shown by Euclidean distance allowed reaching trustful fruit yields using site-specific corrective measures. Comparing tissue composition of low-yielding orchards to that of the closest successful neighbors in two major Brazilian peach-producing regions, regional diagnosis differed from local diagnosis, indicating that regional standards may fail to fit local conditions. Local diagnosis requires well-documented Humboldtian data sets that can be acquired through ethical collaboration between researchers and stakeholders.

Frost hardiness as acquired by Douglas-fir seedlings in three Pacific Northwest nurseries

1989 ◽  
Vol 19 (2) ◽  
pp. 192-197 ◽  
Author(s):  
Ursula K. Schuch ◽  
Mary L. Duryea ◽  
L. H. Fuchigami
Keyword(s):  
Pacific Northwest ◽  
Douglas Fir ◽  
Regression Equation ◽  
Frost Hardiness ◽  
Climatic Data ◽  
Seed Sources ◽  
Whole Plant ◽  
Over Time

Two-year-old Douglas-fir (Pseudotsugamenziesii (Mirb.) Franco) seedlings from two seed sources raised in three nurseries in Oregon and Washington were tested for differences in frost hardiness between September 1985 and January 1986. The objective was to determine whether nursery location significantly influenced hardiness. Seedlings were tested by whole-plant freezing to various temperatures, followed by six evaluations of frost hardiness of needle, bud, and stem tissues. Seedlings at the highest nursery had the hardiest needles and those at the coastal nursery the least hardy needles. Bud hardiness, calculated over time, differed between seed sources but not among nurseries. Stem acclimation followed the pattern of needle hardening from November to January. A regression equation calculated to predict frost hardiness from climatic data and elevation of the nurseries showed that elevation, photoperiod, and number of days of frost were the most important independent factors (R2 = 0.29).

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