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Forests

Q: What are the different types of forests?

Forests come in a variety of types, including tropical rainforests, temperate deciduous forests, boreal forests, and mangrove forests. Each type has its own unique characteristics in terms of climate, vegetation, and wildlife. For example, tropical rainforests are known for their high biodiversity and lush, evergreen canopy, while boreal forests are dominated by coniferous trees like spruce and pine.

Q: How do forests impact the global climate?

Forests play a crucial role in regulating the global climate. They act as sinks for carbon dioxide, absorbing and storing vast amounts of carbon from the atmosphere. This helps to mitigate the effects of climate change by reducing greenhouse gas emissions. Additionally, forests influence local and regional weather patterns through their effects on precipitation, temperature, and humidity.

Q: What are some common challenges in forest research?

One of the key challenges in forest research is ensuring the reproducibility and accuracy of study findings. Researchers often need to sift through a vast amount of literature to identify the most relevant protocols and methodologies for their specific forest-focused projects. This can be a time-consuming and error-prone process, particularly when trying to compare the effectiveness of different protocols.

Q: How can PubCompare.ai assist with forest research?

PubCompare.ai is a powerful AI-driven platform that can greatly assist researchers working on forest-related studies. By allowing users to effortlssly locate and compare the most relevant research protocols from literature, pre-prints, and patents, PubCompare.ai helps ensure reproducibility and accuracy in forest research. The platform's intelligent comparisons can highlight key differences in protocol effectiveness, enabling researchers to choose the best option for their specific needs and goals.

Q: What are the benefits of using PubCompare.ai for forest research?

Some of the key benefits of using PubCompare.ai for forest research include: 1. Increased efficiency in screening protocol literature 2. Leveraging AI to pinpoint critical insights that can inform protocol selection 3. Identifying the most effective protocols related to forests for your specific research goals 4. Comparing protocol effectiveness to choose the best option for reproducibility and accuracy By empowering researchers to optimize their forest-related studies, PubCompare.ai helps elevate the quality and impact of forest research.

Forests are complex ecosystems composed of trees, plants, and diverse wildlife.
They play a vital role in regulating the global climate, providing habitats for endangered species, and producing oxygen essential for life.
PubCompare.ai empowers researchers to optimize their forest-related studies by effortlessly locating and comparing the most relevant research protocols from literature, pre-prints, and patents.
This AI-driven platform ensures reproducibility and accuracy, helping users discover the best protocols and products for their forest-focused projects through intelligent comparisons.
Explore the power of PubCompare.ai to elevatte your forest research today.

Most cited protocols related to «Forests»

Comparative Analysis of Mutational Profiles

mafCompare function in Maftools allows comparison of two independent cohorts to identify differentially mutated genes or to perform association between clinical features. A 2 × 2 contingency table of frequencies of mutation is calculated for every gene from the input cohorts followed by Fisher's exact test to identify genes showing significant differences in their mutation frequencies. Similarly, for clinical enrichment analysis, once again contingency tables are generated for every categorical variable followed by Fisher's exact test to calculate P-values. Results from cohort comparison and enrichment analysis are visualized as forest plots or frequency bar plots (Figs. 4A, 5E,F).

Mayakonda A., Lin D.C., Assenov Y., Plass C, & Koeffler H.P. (2018). Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Research, 28(11), 1747-1756.

Publication 2018

Figs Forests Genes

Optimizing Phylogenetic Tree Reconstruction

Edmonds’ algorithm attempts to minimize the sum of the edge lengths in the tree. However, the resulting dMST does not necessarily represent true phylogenetic relationships between strains because allelic distances do not always correlate with divergence time. We therefore implemented a subsequent branch optimization step that accounts for these discrepancies. Algorithm 1 gives an overview over the local branch recrafting (see also Supplemental Fig. S5), starting from the already computed dMST(V,E), where E is a distance matrix sorted in ascending order of allelic distances, and a forest F where each u ∈ V is a single tree t(u) ∈ F.
Optimizations are applied to both ends of each edge in the dMST(V,E) iteratively as shown in Supplemental Figure S5D. The TargetNodes() function picks a subset of the nodes in tree t(u) which are the centroids and the nodes that are directly connected to u (Supplemental Fig. S5D). The ModelSelection() function compares the maximum likelihoods of two models M_A and M_B (Supplemental Fig. S5B,C). Here we describe only the model selection process for u. Given d(u → w), d(w → u), d(u → v) and d(w → v), when assuming d(u → v) ≥ d(w → v), the proportions of invariable sites in branches l_A, k_A, l_B and k_B satisfy:

\underset{0 \leq l_{A} \leq 1, 0 \leq k_{A} \leq 1}{argmax} \log P (M_{A} | l_{A}, k_{A})

= \underset{0 \leq l_{A} \leq 1, 0 \leq k_{A} \leq 1}{argmax} \log P (u \to w | l_{A}) P (u \to v | l_{A}, k_{A}) P (w \to v | l_{A}, k_{A})

= \underset{0 \leq l_{A} \leq 1, 0 \leq k_{A} \leq 1}{argmax} | L | d (u \to w) \log (1 - l_{A}^{2}) + | L | (1 - d (u \to w)) \log (l_{A}^{2})

+ | L | d (u \to v) \log (1 - l_{A} k_{A}) + | L | (1 - d (u \to v)) \log (l_{A} k_{A})

\begin{matrix} + | L | d (w \to v) \log (1 - l_{A} k_{A}) + | L | (1 - d (w \to v)) \log (l_{A} k_{A}) (1) \end{matrix}

\underset{0 \leq l_{B} \leq 1, 0 \leq k_{B} \leq 1}{argmax} \log P (M_{B} | l_{B}, k_{B})

= \underset{0 \leq l_{B} \leq 1, 0 \leq k_{B} \leq 1}{argmax} \log P (w \to u | l_{B}) P (u \to v | l_{B}, k_{B}) P (w \to v | l_{B}, k_{B})

= \underset{0 \leq l_{B} \leq 1, 0 \leq k_{B} \leq 1}{argmax} | L | d (w \to u) \log (1 - l_{B}) + | L | (1 - d (w \to u)) \log (l_{B})

+ | L | d (u \to v) \log (1 - l_{B} k_{B}) + | L | (1 - d (u \to v)) \log (l_{B} k_{B})

\begin{matrix} + | L | d (w \to v) \log (1 - k_{B}) + | L | (1 - d (w \to v)) \log (k_{B}), (2) \end{matrix}

where L is a set of loci in an MLST profile. Note that the direction of the distances between u and w are different in the two equations. Model A assumes u as the centroid node and adopts d(u → w) in Equation 1, whereas model B treats w as the centroid and thus uses d(w → u). We further denote

x = 1 - \frac{(1 - d (w \to u)) (1 - d (w \to v)) + (1 - d (u \to v))}{2} .

Then, the parameters in Equations 1 and 2 are calculated as

l_{A} = \sqrt{1 - d (u \to w)}

k_{A} = \frac{1 - (1 / 2) (d (u \to v) + d (w \to v))}{l_{A}}

l_{B} = 1 + \frac{x d (w \to u)}{d (u \to v) - 2 x}

k_{B} = 1 + \frac{x d (w \to v)}{d (u \to v) - 2 x} .

These parameters can then be used to calculate P(M_A) and P(M_B) using Equations 1 and 2.

Zhou Z., Alikhan N.F., Sergeant M.J., Luhmann N., Vaz C., Francisco A.P., Carriço J.A, & Achtman M. (2018). GrapeTree: visualization of core genomic relationships among 100,000 bacterial pathogens. Genome Research, 28(9), 1395-1404.

Publication 2018

Alleles Forests Strains Trees

Random Forest Classification Protocols

Here we focus on the use of random forests for classification tasks, rather than regression tasks, for instance for predicting the disease status from a set of selected genetic and environmental risk factors, or for predicting whether a site of interest is edited by means of neighboring sites and other predictor variables as in our application example.
Random forests are an ensemble method that combines several individual classification trees in the following way: From the original sample several bootstrap samples are drawn, and an unpruned classification tree is fit to each bootstrap sample. The variable selection for each split in the classification tree is conducted only from a small random subset of predictor variables, so that the "small n large p" problem is avoided. From the complete forest the status of the response variable is predicted as an average or majority vote of the predictions of all trees.
Random forests can highly increase the prediction accuracy as compared to individual classification trees, because the ensemble adjusts for the instability of the individual trees induced by small changes in the learning sample, that impairs the prediction accuracy in test samples. However, the interpretability of a random forest is not as straightforward as that of an individual classification tree, where the influence of a predictor variable directly corresponds to its position in the tree. Thus, alternative measures for variable importance are required for the interpretation of random forests.

Strobl C., Boulesteix A.L., Zeileis A, & Hothorn T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8, 25.

Publication 2007

Forests Hereditary Diseases Trees

Evaluating Specialization Indices in Plant-Pollinator Networks

Two published plant-pollinator networks were selected to investigate the behavior of different specialization measures [32 (link),33 (link)]. Both articles use their observed interaction matrices as a model to discuss network properties based on the number of links per pollinator or plant species, allowing a comparison of conclusions drawn. Both networks may be compared as they comprise relatively large datasets from temperate ecosystems, reporting interaction frequencies between plants and their floral visitors: the British meadow community studied by Memmott [32 (link)] involved 79 pollinator and 25 plant species (2183 pollinator visits observed), the forests in Argentina studied by Vázquez and Simberloff [33 (link)] involved 90 pollinator and 14 plant species (5285 visits). The datasets can be obtained from the Interaction Web Database [53 ]. We simulated a decreased sampling intensity in both networks using a rarefaction method in order to investigate how sampling effort affects the estimation of specialization indices. Real association matrices were reduced by randomly extracting interactions, e.g. from the total of m = 2183 visits in Memmott's web down to m = 5 visits (in steps of five, repeated ten times for each m).
In order to compare the null model characteristics of the specialization measures, we simulated artificial matrices with randomly associated partners and plotted the indices against an increasing number of partners and/or total number of interactions. We assumed that the total frequency of participating species approximates a lognormal distribution, which is typical for biological communities [21 ,22 (link),24 (link)]. All row and column totals were randomly generated from a lognormal distribution (μ = 50, ∑= 1) that was scaled to the desired total number of interactions. Ten different combinations of row and column totals were obtained for each matrix size and taken as template to randomly associate the partners five times, thus each matrix size was represented by 50 random associations.

Blüthgen N., Menzel F, & Blüthgen N. (2006). Measuring specialization in species interaction networks. BMC Ecology, 6, 9.

Publication 2006

Biological Community Ecosystem Forests Plants

Comparative Analysis of Random Forest Variable Selection Methods

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Publication 2019

Forests Inclusion Bodies

Most recents protocols related to «Forests»

Copper-Infused Carbon Nanotube Composite Wire

Not available on PMC !

Example 4

Although example 4 is provided with regard to copper, it should be understood that aspects herein are not limited as such, and any metal can be used instead of, or in addition to copper.

In this example, a structure comprises a thread infused with copper by electroplating to create a substrate, which can then optionally be further coated with CNT and infused. In several embodiments, the CNT thread includes a metal core.

The carbon nanotube thread can be, for example, floating catalyst chemical vapor deposition grown and formed, pulled form VACNT forests, etc. radially from the substrate, as discussed above.

More carbon nanotubes can be grown radially aligned using NAHF-X IP or deposited by methods such electrophoresis, e.g., as described more fully herein.

An indefinite number of identical concentric layers can be built up until the wire is at a desired diameter or ratio of copper to carbon.

Additionally, in some embodiments, the finished wire can be further mechanically drawn down to resize, reshape, refine, alter, combinations thereof, etc., the properties of the wire.

In some embodiments, multiple wires can be combined into a finished cable or further carbon nanotube coatings and copper electroplating can continue.

US11866839B2. Composite carbon nanotube structures (2024-01-09). University of Dayton Research Institute [US]. Inventors: Paul Kladitis [US], Brian Rice [US], Lingchuan Li [US].

Patent 2024

Carbon Copper Electrophoresis Forests Metals Nanotubes, Carbon

Prognostic 3-Gene Signature Model

The correlations between the risk score and clinical characteristics were visualized by violin plot using the “ggplot2” package. Univariate and multivariate Cox regression analyses were used to evaluate the independent prognostic value of the 3-gene signature model and other clinical characteristics (tumor grade, T stage, N stage, gender, age, and AJCC stage). The results of univariate and multivariate Cox analyses based on the TCGA cohort were displayed as a forest plot by the “forestplot” package.

Liu Q., Li R., Wu H, & Liang Z. (2023). A novel cuproptosis-related gene model predicts outcomes and treatment responses in pancreatic adenocarcinoma. BMC Cancer, 23, 226.

Publication 2023

Forests Gender Neoplasms

Estimating Intimate Partner Violence

Descriptive statistics were used to summarize participants’ socio-demographic characteristics within each study and in the pooled data. Standard errors for specific studies and pooled estimates took account of any stratification or clustering due to each study’s sampling procedures and pooled estimates were weighted according to the study sample size. Forest plots, I-square and Cochran’s Q statistics were used to assess the consistency of outcomes across the studies and the I-square values showed high heterogeneity in physical IPV experience/perpetration as an outcome (80%, P < 0.001 for men; 62.9%, P = 0.029 for women). Generalized Linear Mixed Effects Models were then used to estimate overall effects and account for any heterogeneity across the studies due to methodological diversity. One-stage Individual Patient Data (IPD) meta-analysis was performed using mixed-effects Poisson regression models was used to account for within-and between-study variances (heterogeneity) across studies for both men and women [30 (link)]. Study-specific estimates were derived from a post-estimation model of the mixed-effects Poisson regression model. Both the main and post-estimation models included participants’ age and childhood trauma experience as fixed effects. To assess the robustness and consistency of the results, we repeated the analysis using mixed-effects logistic regression models. All data were analyzed using Stata Statistical Software: Release 17. College Station, TX: StataCorp LLC. StataCorp. 2019 and all tests were interpreted at a 5% significance level.

Jewkes R., Chirwa E., Alangea D.O., Addo-Lartey A., Christofides N., Dunkle K., Ramsoomar L, & Gibbs A. (2023). Pooled analysis of the association between food insecurity and violence against women: Evidence from low- and middle-income settings. Journal of Global Health, 13, 04021.

Publication 2023

Forests Genetic Heterogeneity Patients Physical Examination Woman

Examining Treatment Effect and Selection Bias in Shoulder Trials

To determine if there was an exaggerated treatment effect at the CI’s site, the outcome data for participants recruited at this site for each trial were averaged, as were the data for participants from the remaining sites. For both trials, the primary outcome was the Oxford Shoulder Score (OSS),¹⁷ a shoulder-specific patient-reported outcome measure (total scores of 0 (worst outcome) to 48 (best outcome)), and therefore was the outcome used for analysis in this study at the one-year follow-up. The target difference between treatment groups for the two trials was set at a threshold of five or four points when testing for differences between surgery and non-surgical options or between surgical options, respectively. Data were analyzed by forest plot. To test for the presence of an early bias in treatment effect over time, the mean outcome for the quintiles of randomized patients was calculated and analyzed by forest plot. For both, a fixed effects model was used and the I² value to determine heterogeneity. As UK FROST had three arms, separate analyses were carried out to compare all treatments. Review Manager (RevMan) 5 was used to undertake these analyses. This was repeated for the first five sites open compared with the remaining sites.
To examine the presence of selection bias, we explored whether there were differences in age or predictors of poor outcome between the patients who were randomized and those who either did not consent or were ineligible to take part. For age, the mean and standard deviation (SD) were calculated for the trial participants and for patients who were ineligible, eligible but did not consent, and the latter groups combined. For the ProFHER trial, the predictor of poor outcome was whether either tuberosity (a rounded prominence) of the humeral bone was involved in the fracture;^{15 (link)} for UK FROST it was diabetic status.^{16 (link)} The percentage of individuals who had tuberosity involved or were diabetic, for the respective trials, was calculated for the following groups: trial participants, ineligible patients, eligible but non-consenting patients, and the latter groups combined. To assess whether these changed over time, the participants were ordered by randomization date and split into quintiles (i.e. five equal groups). Each group was analyzed as above. The non-consenting and ineligible patients were combined and ordered by date of eligibility so that the quintiles matched the same time periods as the recruited group.

Knowlson C.N., Brealey S., Keding A., Torgerson D, & Rangan A. (2023). Examination of early treatment effects and related biases during the conduct of two UK-wide pragmatic orthopaedic surgical trials: ProFHER and UK FROST. Bone & Joint Open, 4(2), 96-103.

Publication 2023

Arm, Upper BAD protein, human Bones Eligibility Determination Forests Fracture, Bone Genetic Heterogeneity Humerus Operative Surgical Procedures Patients Plant Tubers Shoulder

We obtained ten cuproptosis-related genes (including FDX1, LIPT1, DLD, LIAS, DLAT, PDHA1, PDHB, MTF1, GLS, and CDKN2A) from previous studies (Tsvetkov et al., 2022 (link)), and explored their expression levels between tumor samples and normal samples with cut-off criteria of |log₂FC|≥1 and p. adj. <0.05 using the “DESeq2” R package and prognostic values in PCa patients by the univariate Cox regression analysis (p < 0.05). Pearson’s correlation analysis was applied to identify cuproptosis-related lncRNAs (crlncRNAs) with the filter criteria |Pearson R| > 0.3 and p < 0.001. Then, we screened differentially expressed lncRNAs between tumor samples and normal samples with cut-off criteria of |log₂FC|≥1 and p. adj. <0.05 using the “DESeq2” R package via the Wilcoxon test (Liu et al., 2021 (link)). Ultimately, the overlapped lncRNAs were recognized as differentially expressed crlncRNAs (DE-crlncRNAs), and the forest plot was drawn.

Ren L., Yang X., Wang W., Lin H., Huang G., Liu Z., Pan J, & Mao X. (2023). A cuproptosis-related LncRNA signature: Integrated analysis associated with biochemical recurrence and immune landscape in prostate cancer. Frontiers in Genetics, 14, 1096783.

Publication 2023

CDKN2A Gene Forests Genes Neoplasms Patients RNA, Long Untranslated

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What are the different types of forests?

Forests come in a variety of types, including tropical rainforests, temperate deciduous forests, boreal forests, and mangrove forests. Each type has its own unique characteristics in terms of climate, vegetation, and wildlife. For example, tropical rainforests are known for their high biodiversity and lush, evergreen canopy, while boreal forests are dominated by coniferous trees like spruce and pine.

How do forests impact the global climate?

Forests play a crucial role in regulating the global climate. They act as sinks for carbon dioxide, absorbing and storing vast amounts of carbon from the atmosphere. This helps to mitigate the effects of climate change by reducing greenhouse gas emissions. Additionally, forests influence local and regional weather patterns through their effects on precipitation, temperature, and humidity.

What are some common challenges in forest research?

One of the key challenges in forest research is ensuring the reproducibility and accuracy of study findings. Researchers often need to sift through a vast amount of literature to identify the most relevant protocols and methodologies for their specific forest-focused projects. This can be a time-consuming and error-prone process, particularly when trying to compare the effectiveness of different protocols.

How can PubCompare.ai assist with forest research?

PubCompare.ai is a powerful AI-driven platform that can greatly assist researchers working on forest-related studies. By allowing users to effortlssly locate and compare the most relevant research protocols from literature, pre-prints, and patents, PubCompare.ai helps ensure reproducibility and accuracy in forest research. The platform's intelligent comparisons can highlight key differences in protocol effectiveness, enabling researchers to choose the best option for their specific needs and goals.

What are the benefits of using PubCompare.ai for forest research?

Some of the key benefits of using PubCompare.ai for forest research include: 1. Increased efficiency in screening protocol literature 2. Leveraging AI to pinpoint critical insights that can inform protocol selection 3. Identifying the most effective protocols related to forests for your specific research goals 4. Comparing protocol effectiveness to choose the best option for reproducibility and accuracy By empowering researchers to optimize their forest-related studies, PubCompare.ai helps elevate the quality and impact of forest research.

More about "Forests"

Forests are complex, dynamic ecosystems composed of diverse flora and fauna.
These lush, wooded habitats play a vital role in regulating the global climate, providing critical refuge for endangered species, and producing the essential oxygen that sustains life on Earth.
Researchers studying these precious natural resources can leverage the power of AI-driven platforms like PubCompare.ai to optimize their forest-related research protocols.
PubCompare.ai empowers scientists to effortlessly locate and compare the most relevant research protocols from a vast corpus of literature, preprints, and patents.
This intelligent platform ensures the reproducibility and accuracy of forest-focused studies, helping users discover the best protocols and products for their projects through sophisticated comparisons.
Whether you're investigating the impact of Xylazine on forest ecosystems, analyzing Stata version 14 data from field surveys, or using Prism 8 to visualize the effects of Tropicamide on tree growth, PubCompare.ai can elevate your forest research by providing access to the most robust and reliable protocols available.
Explore the capabilities of this AI-driven tool to optimize your forest-related studies and make groundbreaking discoveries in this vital field of study.