scholarly journals Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features

2019 ◽  
Vol 11 (4) ◽  
pp. 381 ◽  
Author(s):  
Guillaume Brigot ◽  
Marc Simard ◽  
Elise Colin-Koeniguer ◽  
Alexandre Boulch
Keyword(s):  
Machine Learning ◽  
Forest Structure ◽  
Vertical Profile ◽  
Three Dimensional ◽  
Canopy Cover ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Dimensional Structure ◽  
Interferometric Synthetic Aperture Radar ◽  
The Impact

This paper presents a machine learning based method to predict the forest structure parameters from L-band polarimetric and interferometric synthetic aperture radar (PolInSAR) data acquired by the airborne UAVSAR system over the Réserve Faunique des Laurentides in Québec, Canada. The main objective of this paper is to show that relevant parameters of the PolInSAR coherence region can be used to invert forest structure indicators computed from the airborne LIDAR sensor Laser Vegetation and Ice Sensor (LVIS). The method relies on the shape of the observed generalized PolInSAR coherence region that is related to the three-dimensional structure of the scene. In addition to parameters describing the coherence shape, we consider the impact of acquisition parameters such as the interferometric baseline, ground elevation and local surface slope. We use the parameters as input a multilayer perceptron model to infer canopy features as estimated from LIDAR waveform. The output features are canopy height, cover and vertical profile class. Canopy height and canopy cover are estimated with a normalized RMSE of 13%, 15% respectively. The vertical profile was divided into 3 distinct classes with 66% accuracy.

scholarly journals SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

2019 ◽  
Author(s):  
Sushant Kumar ◽  
Arif Harmanci ◽  
Jagath Vytheeswaran ◽  
Mark B. Gerstein
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Mutations ◽  
Dimensional Structure ◽  
Rapid Decline ◽  
Ras Signaling ◽  
Cancer Genes ◽  
Structural Variations ◽  
Pathogenic Variants

AbstractA rapid decline in sequencing cost has made large-scale genome sequencing studies feasible. One of the fundamental goals of these studies is to catalog all pathogenic variants. Numerous methods and tools have been developed to interpret point mutations and small insertions and deletions. However, there is a lack of approaches for identifying pathogenic genomic structural variations (SVs). That said, SVs are known to play a crucial role in many diseases by altering the sequence and three-dimensional structure of the genome. Previous studies have suggested a complex interplay of genomic and epigenomic features in the emergence and distribution of SVs. However, the exact mechanism of pathogenesis for SVs in different diseases is not straightforward to decipher. Thus, we built an agnostic machine-learning-based workflow, called SVFX, to assign a “pathogenicity score” to somatic and germline SVs in various diseases. In particular, we generated somatic and germline training models, which included genomic, epigenomic, and conservation-based features for SV call sets in diseased and healthy individuals. We then applied SVFX to SVs in six different cancer cohorts and a cardiovascular disease (CVD) cohort. Overall, SVFX achieved high accuracy in identifying pathogenic SVs. Moreover, we found that predicted pathogenic SVs in cancer cohorts were enriched among known cancer genes and many cancer-related pathways (including Wnt signaling, Ras signaling, DNA repair, and ubiquitin-mediated proteolysis). Finally, we note that SVFX is flexible and can be easily extended to identify pathogenic SVs in additional disease cohorts.

scholarly journals Susceptibility of protein therapeutics to spontaneous chemical modifications by oxidation, cyclization, and elimination reactions

Amino Acids ◽  
2019 ◽  
Vol 51 (10-12) ◽  
pp. 1409-1431 ◽  
Author(s):  
Luigi Grassi ◽  
Chiara Cabrele
Keyword(s):  
Small Molecules ◽  
Three Dimensional ◽  
Drug Market ◽  
Structure And Function ◽  
Dimensional Structure ◽  
Chemical Modifications ◽  
Small Molecule Drugs ◽  
And Function ◽  
The Impact

Abstract Peptides and proteins are preponderantly emerging in the drug market, as shown by the increasing number of biopharmaceutics already approved or under development. Biomolecules like recombinant monoclonal antibodies have high therapeutic efficacy and offer a valuable alternative to small-molecule drugs. However, due to their complex three-dimensional structure and the presence of many functional groups, the occurrence of spontaneous conformational and chemical changes is much higher for peptides and proteins than for small molecules. The characterization of biotherapeutics with modern and sophisticated analytical methods has revealed the presence of contaminants that mainly arise from oxidation- and elimination-prone amino-acid side chains. This review focuses on protein chemical modifications that may take place during storage due to (1) oxidation (methionine, cysteine, histidine, tyrosine, tryptophan, and phenylalanine), (2) intra- and inter-residue cyclization (aspartic and glutamic acid, asparagine, glutamine, N-terminal dipeptidyl motifs), and (3) β-elimination (serine, threonine, cysteine, cystine) reactions. It also includes some examples of the impact of such modifications on protein structure and function.

scholarly journals SphereCon—a method for precise estimation of residue relative solvent accessible area from limited structural information

2020 ◽  
Vol 36 (11) ◽  
pp. 3372-3378
Author(s):  
Alexander Gress ◽  
Olga V Kalinina
Keyword(s):  
Protein Function ◽  
Structural Information ◽  
Solvent Accessibility ◽  
Three Dimensional ◽  
Structural Data ◽  
Supplementary Information ◽  
Dimensional Structure ◽  
Relative Solvent Accessibility ◽  
Precise Measure ◽  
The Impact

Abstract Motivation In proteins, solvent accessibility of individual residues is a factor contributing to their importance for protein function and stability. Hence one might wish to calculate solvent accessibility in order to predict the impact of mutations, their pathogenicity and for other biomedical applications. A direct computation of solvent accessibility is only possible if all atoms of a protein three-dimensional structure are reliably resolved. Results We present SphereCon, a new precise measure that can estimate residue relative solvent accessibility (RSA) from limited data. The measure is based on calculating the volume of intersection of a sphere with a cone cut out in the direction opposite of the residue with surrounding atoms. We propose a method for estimating the position and volume of residue atoms in cases when they are not known from the structure, or when the structural data are unreliable or missing. We show that in cases of reliable input structures, SphereCon correlates almost perfectly with the directly computed RSA, and outperforms other previously suggested indirect methods. Moreover, SphereCon is the only measure that yields accurate results when the identities of amino acids are unknown. A significant novel feature of SphereCon is that it can estimate RSA from inter-residue distance and contact matrices, without any information about the actual atom coordinates. Availability and implementation https://github.com/kalininalab/spherecon. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Rapid response of habitat structure and above-ground carbon storage to altered fire regimes in tropical savanna

2019 ◽  
Vol 16 (7) ◽  
pp. 1493-1503 ◽  
Author(s):  
Shaun R. Levick ◽  
Anna E. Richards ◽  
Garry D. Cook ◽  
Jon Schatz ◽  
Marcus Guderle ◽  
...  
Keyword(s):  
Carbon Storage ◽  
Vegetation Structure ◽  
Habitat Structure ◽  
Canopy Structure ◽  
Three Dimensional ◽  
Canopy Cover ◽  
Fire Regimes ◽  
Dry Season ◽  
Airborne Lidar ◽  
Climate Conditions

Abstract. Fire regimes across the globe have been altered through changes in land use, land management, and climate conditions. Understanding how these modified fire regimes impact vegetation structure and dynamics is essential for informed biodiversity conservation and carbon management in savanna ecosystems. We used a fire experiment at the Territory Wildlife Park (TWP), northern Australia, to investigate the consequences of altered fire regimes for vertical habitat structure and above-ground carbon storage. We mapped vegetation three-dimensional (3-D) structure in high spatial resolution with airborne lidar across 18 replicated 1 ha plots of varying fire frequency and season treatments. We used lidar-derived canopy height and cover metrics to extrapolate field-based measures of woody biomass to the full extent of the experimental site (R2=0.82, RMSE = 7.35 t C ha−1) and analysed differences in above-ground carbon storage and canopy structure among treatments. Woody canopy cover and biomass were highest in the absence of fire (76 % and 39.8 t C ha−1) and lowest in plots burnt late in the dry season on a biennial basis (42 % and 18.2 t C ha−1). Woody canopy vertical profiles differed among all six fire treatments, with the greatest divergence in height classes <5 m. The magnitude of fire effects on vegetation structure varied along the environmental gradient underpinning the experiment, with less reduction in biomass in plots with deeper soils. Our results highlight the large extent to which fire management can shape woody structural patterns in savanna landscapes, even over time frames as short as a decade. The structural profile changes shown here, and the quantification of carbon reduction under late dry season burning, have important implications for habitat conservation, carbon sequestration, and emission reduction initiatives in the region.

scholarly journals Two Pathogenic Gene Mutations Identified Associating with Congenital Cataract and Iris Coloboma Respectively in a Chinese Family

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Bin Li ◽  
Bin Lu ◽  
Xuewen Guo ◽  
Shenghui Hu ◽  
Guihu Zhao ◽  
...  
Keyword(s):  
Congenital Cataract ◽  
Three Dimensional ◽  
Gene Mutations ◽  
Dimensional Structure ◽  
Chinese Family ◽  
Control Group ◽  
Three Dimensional Structure ◽  
Han Ethnicity ◽  
The Family ◽  
The Impact

Purpose. To screen out pathogenic genes in a Chinese family with congenital cataract and iris coloboma. Material and Methods. A three-generation family with congenital cataract and iris coloboma from a Han ethnicity was recruited. DNA was extracted from peripheral blood samples collected from all individuals in the family. Whole exon sequencing was employed for screening the disease-causing gene mutations in the proband, and Sanger sequencing was used for other members of the family and a control group of 500 healthy individuals. Bioinformatics analysis and three-dimensional structure predictions were used to predict the impact of amino acid changes on protein structure and function. Results. The candidate genes of cataract and iris coloboma were successfully screened out. A heterozygote mutation, CRYGD c.70C>A (p.P24T), was identified as cosegregating with congenital cataracts, while another heterozygous mutation, WFS1 c.1514G>C (p.C505S), which had not been reported previously, cosegregated with congenital iris coloboma. Bioinformatic analyses and three-dimensional structure prediction proved that the three-dimensional structures of WFS1 p.C505S and CRYGD p.P24T changed markedly and may contribute significantly to iris coloboma and congenital cataract, respectively. Conclusions. We report a novel mutation, WFS1 p.C505S, and a known mutation, CRYGD p.P24T, that cosegregate with iris coloboma and congenital cataract, respectively, in a Chinese family. This is the first time the association of WFS1 p.C505S with iris coloboma has been demonstrated, although CRYGD p.P24T has been widely reported as being associated with congenital cataract, especially in the Eastern Asian population. These findings may have future therapeutic benefit for the diagnosis of iris coloboma and congenital cataract. The results may also be relevant in further studies aiming to investigate the molecular pathogenesis of iris coloboma and congenital cataract.

Vertically aligned VO2(B) nanobelt forest and its three-dimensional structure on oriented graphene for energy storage

2015 ◽  
Vol 3 (20) ◽  
pp. 10787-10794 ◽  
Author(s):  
Guofeng Ren ◽  
Md Nadim Ferdous Hoque ◽  
Xuan Pan ◽  
Juliusz Warzywoda ◽  
Zhaoyang Fan
Keyword(s):  
Energy Storage ◽  
Lithium Ion Batteries ◽  
Forest Structure ◽  
High Performance ◽  
Three Dimensional ◽  
Lithium Ion ◽  
Dimensional Structure ◽  
Two Dimensional ◽  
Three Dimensional Structure ◽  
Vertically Aligned

Assembling two-dimensional graphene and VO2(B) nanomaterials into an ordered three-dimensional forest structure for high performance lithium ion batteries.

The impacts of Amazon forest degradation and fragmentation on energy, water, and carbon cycles

2020 ◽  
Author(s):  
Marcos Longo ◽  
Sassan Saatchi ◽  
Michael Keller ◽  
Kevin Bowman ◽  
António Ferraz ◽  
...  
Keyword(s):  
Water Stress ◽  
Land Surface Temperature ◽  
Forest Structure ◽  
Land Surface ◽  
Forest Degradation ◽  
Dry Season ◽  
Airborne Lidar ◽  
Carbon Cycles ◽  
The Impact

&lt;p&gt;Tropical forest degradation through selective logging, fragmentation, and understory fires substantially changes forest structure and composition.&amp;#160; In the Amazon, degradation is as widespread as deforestation; however, studies addressing the effects of forest degradation on tropical ecosystem functions are scarce. Here, we integrate small-footprint airborne lidar over the Brazilian Amazon (&gt; 250,000 ha), collected between 2016&amp;#8211;2018, with recent ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) land surface temperature and evapotranspiration products (70-m resolution, data acquired in 2018&amp;#8211;2019) to investigate the role of forest structure, forest fragmentation, and disturbance history on dry-season land surface temperature and evapotranspiration.&amp;#160; During the dry season, degraded forests, especially those affected by multiple degradation events, are significantly warmer (up to 9.3&amp;#176;C) and show reduced evapotranspiration (10% less than intact forests). Likewise, forest near the edges (&lt; 350m) experience the greatest warming (up to 6.5&amp;#176;C) and the greatest reduction (9%) in evapotranspiration. We also used the airborne lidar dataset to initialize the Ecosystem Demography Model (ED-2.2) to investigate the impact of degradation on the gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H) under a broader range of climate conditions, including severe droughts. Consistent with ECOSTRESS, the simulations during the dry season in typical years showed that severely degraded forests experienced water-stress with declines in ET (34% reduction), GPP (35% reduction), and increases of H (43% increases) and daily mean ground temperatures (up to 6.5&amp;#176;C) relative to intact forests.&amp;#160; In the model, the simulated changes are mostly driven by increased below-ground water stress, which can be attributed to the shallower rooting profile of degraded forests. However, relative to intact forest, the impact of degradation on energy, water, and carbon cycles markedly diminishes under extreme droughts such as 2015&amp;#8211;2016, when all forests experience severe stress. Our results indicate the potentially important role of tropical forest degradation changing the carbon, water, and energy cycles in the Amazon, and consequently a much broader influence of land use activities on functioning of tropical ecosystems.&lt;/p&gt;

scholarly journals PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables

2011 ◽  
Vol 16 (2) ◽  
Author(s):  
Piyali Chatterjee ◽  
Subhadip Basu ◽  
Mahantapas Kundu ◽  
Mita Nasipuri ◽  
Dariusz Plewczynski
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Three Dimensional ◽  
Prediction Method ◽  
Protein Sequences ◽  
Dimensional Structure ◽  
Support Vector ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
Protein Functions

AbstractProtein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: http://code.google.com/p/cmater-bioinfo/.

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