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Amarda Shehu - One of the best experts on this subject based on the ideXlab platform.
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decoy selection for protein structure prediction via extreme gradient boosting and ranking
BMC Bioinformatics, 2020Co-Authors: Nasrin Akhter, Gopinath Chennupati, Hristo Djidjev, Amarda ShehuAbstract:Identifying one or more biologically-active/native Decoys from millions of non-native Decoys is one of the major challenges in computational structural biology. The extreme lack of balance in positive and negative samples (native and non-native Decoys) in a decoy set makes the problem even more complicated. Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity. Recent investigations into energy landscape-based decoy selection approaches show promises. However, lack of generalization over varied test cases remains a bottleneck for these methods. We propose a novel decoy selection method, ML-Select, a machine learning framework that exploits the energy landscape associated with the structure space probed through a template-free decoy generation. The proposed method outperforms both clustering and energy ranking-based methods, all the while consistently offering better performance on varied test-cases. Moreover, ML-Select shows promising results even for the decoy sets consisting of mostly low-quality Decoys. ML-Select is a useful method for decoy selection. This work suggests further research in finding more effective ways to adopt machine learning frameworks in achieving robust performance for decoy selection in template-free protein structure prediction.
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balancing multiple objectives in conformation sampling to control decoy diversity in template free protein structure prediction
BMC Bioinformatics, 2019Co-Authors: Ahmed Bin Zaman, Amarda ShehuAbstract:Computational approaches for the determination of biologically-active/native three-dimensional structures of proteins with novel sequences have to handle several challenges. The (conformation) space of possible three-dimensional spatial arrangements of the chain of amino acids that constitute a protein molecule is vast and high-dimensional. Exploration of the conformation spaces is performed in a sampling-based manner and is biased by the internal energy that sums atomic interactions. Even state-of-the-art energy functions that quantify such interactions are inherently inaccurate and associate with protein conformation spaces overly rugged energy surfaces riddled with artifact local minima. The response to these challenges in template-free protein structure prediction is to generate large numbers of low-energy conformations (also referred to as Decoys) as a way of increasing the likelihood of having a diverse decoy dataset that covers a sufficient number of local minima possibly housing near-native conformations. In this paper we pursue a complementary approach and propose to directly control the diversity of generated Decoys. Inspired by hard optimization problems in high-dimensional and non-linear variable spaces, we propose that conformation sampling for decoy generation is more naturally framed as a multi-objective optimization problem. We demonstrate that mechanisms inherent to evolutionary search techniques facilitate such framing and allow balancing multiple objectives in protein conformation sampling. We showcase here an operationalization of this idea via a novel evolutionary algorithm that has high exploration capability and is also able to access lower-energy regions of the energy landscape of a given protein with similar or better proximity to the known native structure than several state-of-the-art decoy generation algorithms. The presented results constitute a promising research direction in improving decoy generation for template-free protein structure prediction with regards to balancing of multiple conflicting objectives under an optimization framework. Future work will consider additional optimization objectives and variants of improvement and selection operators to apportion a fixed computational budget. Of particular interest are directions of research that attenuate dependence on protein energy models.
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from extraction of local structures of protein energy landscapes to improved decoy selection in template free protein structure prediction
Molecules, 2018Co-Authors: Nasrin Akhter, Amarda ShehuAbstract:Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and cost demands of wet laboratory investigations. Template-free methods can now compute thousands of conformations known as Decoys, but selecting native conformations from the generated Decoys remains challenging. Repeatedly, research has shown that the protein energy functions whose minima are sought in the generation of Decoys are unreliable indicators of nativeness. The prevalent approach ignores energy altogether and clusters Decoys by conformational similarity. Complementary recent efforts design protein-specific scoring functions or train machine learning models on labeled Decoys. In this paper, we show that an informative consideration of energy can be carried out under the energy landscape view. Specifically, we leverage local structures known as basins in the energy landscape probed by a template-free method. We propose and compare various strategies of basin-based decoy selection that we demonstrate are superior to clustering-based strategies. The presented results point to further directions of research for improving decoy selection, including the ability to properly consider the multiplicity of native conformations of proteins.
Jeffrey Skolnick - One of the best experts on this subject based on the ideXlab platform.
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goap a generalized orientation dependent all atom statistical potential for protein structure prediction
Biophysical Journal, 2011Co-Authors: Hongyi Zhou, Jeffrey SkolnickAbstract:An accurate scoring function is a key component for successful protein structure prediction. To address this important unsolved problem, we develop a generalized orientation and distance-dependent all-atom statistical potential. The new statistical potential, generalized orientation-dependent all-atom potential (GOAP), depends on the relative orientation of the planes associated with each heavy atom in interacting pairs. GOAP is a generalization of previous orientation-dependent potentials that consider only representative atoms or blocks of side-chain or polar atoms. GOAP is decomposed into distance- and angle-dependent contributions. The DFIRE distance-scaled finite ideal gas reference state is employed for the distance-dependent component of GOAP. GOAP was tested on 11 commonly used decoy sets containing 278 targets, and recognized 226 native structures as best from the Decoys, whereas DFIRE recognized 127 targets. The major improvement comes from decoy sets that have homology-modeled structures that are close to native (all within ∼4.0 A) or from the ROSETTA ab initio decoy set. For these two kinds of Decoys, orientation-independent DFIRE or only side-chain orientation-dependent RWplus performed poorly. Although the OPUS-PSP block-based orientation-dependent, side-chain atom contact potential performs much better (recognizing 196 targets) than DFIRE, RWplus, and dDFIRE, it is still ∼15% worse than GOAP. Thus, GOAP is a promising advance in knowledge-based, all-atom statistical potentials. GOAP is available for download at http://cssb.biology.gatech.edu/GOAP.
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goap a generalized orientation dependent all atom statistical potential for protein structure prediction
Biophysical Journal, 2011Co-Authors: Hongyi Zhou, Jeffrey SkolnickAbstract:An accurate scoring function is a key component for successful protein structure prediction. To address this important unsolved problem, we develop a generalized orientation and distance-dependent all-atom statistical potential. The new statistical potential, generalized orientation-dependent all-atom potential (GOAP), depends on the relative orientation of the planes associated with each heavy atom in interacting pairs. GOAP is a generalization of previous orientation-dependent potentials that consider only representative atoms or blocks of side-chain or polar atoms. GOAP is decomposed into distance- and angle-dependent contributions. The DFIRE distance-scaled finite ideal gas reference state is employed for the distance-dependent component of GOAP. GOAP was tested on 11 commonly used decoy sets containing 278 targets, and recognized 226 native structures as best from the Decoys, whereas DFIRE recognized 127 targets. The major improvement comes from decoy sets that have homology-modeled structures that are close to native (all within ∼4.0 A) or from the ROSETTA ab initio decoy set. For these two kinds of Decoys, orientation-independent DFIRE or only side-chain orientation-dependent RWplus performed poorly. Although the OPUS-PSP block-based orientation-dependent, side-chain atom contact potential performs much better (recognizing 196 targets) than DFIRE, RWplus, and dDFIRE, it is still ∼15% worse than GOAP. Thus, GOAP is a promising advance in knowledge-based, all-atom statistical potentials. GOAP is available for download at http://cssb.biology.gatech.edu/GOAP.
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spicker a clustering approach to identify near native protein folds
Journal of Computational Chemistry, 2004Co-Authors: Yang Zhang, Jeffrey SkolnickAbstract:: We have developed SPICKER, a simple and efficient strategy to identify near-native folds by clustering protein structures generated during computer simulations. In general, the most populated clusters tend to be closer to the native conformation than the lowest energy structures. To assess the generality of the approach, we applied SPICKER to 1489 representative benchmark proteins sequence identity; each contains up to 280,000 structure Decoys generated using the recently developed TASSER (Threading ASSembly Refinement) algorithm. The best of the top five identified folds has a root-mean-square deviation from native (RMSD) in the top 1.4% of all Decoys. For 78% of the proteins, the difference in RMSD from native to the identified models and RMSD from native to the absolutely best individual decoy is below 1 A; the majority belong to the targets with converged conformational distributions. Although native fold identification from divergent decoy structures remains a challenge, our overall results show significant improvement over our previous clustering algorithms.
Yang Zhang - One of the best experts on this subject based on the ideXlab platform.
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3drobot automated generation of diverse and well packed protein structure Decoys
Bioinformatics, 2016Co-Authors: Haiyou Deng, Ya Jia, Yang ZhangAbstract:Motivation Computationally generated non-native protein structure conformations (or Decoys) are often used for designing protein folding simulation methods and force fields. However, almost all the decoy sets currently used in literature suffer from uneven root mean square deviation (RMSD) distribution with bias to non-protein like hydrogen-bonding and compactness patterns. Meanwhile, most protein decoy sets are pre-calculated and there is a lack of methods for automated generation of high-quality Decoys for any target proteins. Results We developed a new algorithm, 3DRobot, to create protein structure Decoys by free fragment assembly with enhanced hydrogen-bonding and compactness interactions. The method was benchmarked with three widely used decoy sets from ab initio folding and comparative modeling simulations. The Decoys generated by 3DRobot are shown to have significantly enhanced diversity and evenness with a continuous distribution in the RMSD space. The new energy terms introduced in 3DRobot improve the hydrogen-bonding network and compactness of Decoys, which eliminates the possibility of native structure recognition by trivial potentials. Algorithms that can automatically create such diverse and well-packed non-native conformations from any protein structure should have a broad impact on the development of advanced protein force field and folding simulation methods. AVAILIABLITY AND IMPLEMENTATION: http://zhanglab.ccmb.med.umich.edu/3DRobot/ Contact jiay@phy.ccnu.edu.cn; zhng@umich.edu Supplementary information Supplementary data are available at Bioinformatics online.
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what is the best reference state for designing statistical atomic potentials in protein structure prediction
Proteins, 2012Co-Authors: Haiyou Deng, Ya Jia, Yanyu Wei, Yang ZhangAbstract:Many statistical potentials were developed in last two decades for protein folding and protein structure recognition. The major difference of these potentials is on the selection of reference states to offset sampling bias. However, since these potentials used different databases and parameter cutoffs, it is difficult to judge what the best reference states are by examining the original programs. In this study, we aim to address this issue and evaluate the reference states by a unified database and programming environment. We constructed distance-specific atomic potentials using six widely-used reference states based on 1022 high-resolution protein structures, which are applied to rank modeling in six sets of structure Decoys. The reference state on random-walk chain outperforms others in three decoy sets while those using ideal-gas, quasi-chemical approximation and averaging sample stand out in one set separately. Nevertheless, the performance of the potentials relies on the origin of decoy generations and no reference state can clearly outperform others in all decoy sets. Further analysis reveals that the statistical potentials have a contradiction between the universality and pertinence, and optimal reference states should be extracted based on specific application environments and decoy spaces. Proteins 2012; © 2012 Wiley Periodicals, Inc.
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a novel side chain orientation dependent potential derived from random walk reference state for protein fold selection and structure prediction
PLOS ONE, 2010Co-Authors: J Zhang, Yang ZhangAbstract:Background: An accurate potential function is essential to attack protein folding and structure prediction problems. The key to developing efficient knowledge-based potential functions is to design reference states that can appropriately counteract generic interactions. The reference states of many knowledge-based distance-dependent atomic potential functions were derived from non-interacting particles such as ideal gas, however, which ignored the inherent sequence connectivity and entropic elasticity of proteins. Methodology: We developed a new pair-wise distance-dependent, atomic statistical potential function (RW), using an ideal random-walk chain as reference state, which was optimized on CASP models and then benchmarked on nine structural decoy sets. Second, we incorporated a new side-chain orientation-dependent energy term into RW (RWplus) and found that the side-chain packing orientation specificity can further improve the decoy recognition ability of the statistical potential. Significance: RW and RWplus demonstrate a significantly better ability than the best performing pair-wise distancedependent atomic potentialfunctions in both native and near-native modelselections. It has higherenergy-RMSD and energyTM-score correlations compared with other potentials of the same type in real-life structure assembly Decoys. When benchmarked with a comprehensive list of publicly available potentials, RW and RWplus shows comparable performance to the state-of-the-art scoring functions, including those combining terms from multiple resources. These data demonstrate the usefulness of random-walk chain as reference states which correctly account for sequence connectivity and entropic elasticity of proteins. It shows potential usefulness in structure recognition and protein folding simulations. The RW and RWplus potentials, as well as the newly generated I-TASSER Decoys, are freely available in http://zhanglab.ccmb.med.umich.edu/RW.
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spicker a clustering approach to identify near native protein folds
Journal of Computational Chemistry, 2004Co-Authors: Yang Zhang, Jeffrey SkolnickAbstract:: We have developed SPICKER, a simple and efficient strategy to identify near-native folds by clustering protein structures generated during computer simulations. In general, the most populated clusters tend to be closer to the native conformation than the lowest energy structures. To assess the generality of the approach, we applied SPICKER to 1489 representative benchmark proteins sequence identity; each contains up to 280,000 structure Decoys generated using the recently developed TASSER (Threading ASSembly Refinement) algorithm. The best of the top five identified folds has a root-mean-square deviation from native (RMSD) in the top 1.4% of all Decoys. For 78% of the proteins, the difference in RMSD from native to the identified models and RMSD from native to the absolutely best individual decoy is below 1 A; the majority belong to the targets with converged conformational distributions. Although native fold identification from divergent decoy structures remains a challenge, our overall results show significant improvement over our previous clustering algorithms.
Rosa Carnuccio - One of the best experts on this subject based on the ideXlab platform.
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transcription factor decoy oligodeoxynucleotides to nuclear factor kappab inhibit reverse passive arthus reaction in rat
Naunyn-schmiedebergs Archives of Pharmacology, 2001Co-Authors: Fulvio Dacquisto, Angela Ianaro, Armando Ialenti, Pasquale Maffia, Maria Chiara Maiuri, Rosa CarnuccioAbstract:In the present study we investigated in the reverse passive Arthus reaction elicited in the rat skin the anti-inflammatory effect of double-stranded oligodeoxynucleotides (ODN) with consensus nuclear factor-κB (NF-κB) sequence as transcription factor Decoys (TFD) to inhibit NF-κB binding to native DNA sites. Local administration of wild-type-, but not mutant-decoy ODN, dose-dependently reduced both plasma leakage and neutrophil infiltration in rat skin. Molecular analysis performed on soft tissue obtained from rat skin demonstrated: (1) an inhibition of NF-κB/DNA binding activity; (2) a decreased nuclear level of p50 and p65 NF-κB subunits; (3) an inhibition of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) protein expression, two inflammatory enzymes transcriptionally controlled by NF-κB. Furthermore, SN-50, a cell-permeable peptide capable of inhibiting the nuclear translocation of NF-κB complexes, as well as ammonium pyrrolidine dithiocarbamate, an inhibitor of NF-κB activation, exhibited a similar profile of activity of decoy ODN. Our results indicate that decoy ODN, acting as an in vivo competitor for the transcription factor's ability to bind to cognate recognition sequence, may represent a novel strategy to modulate immune reactions.
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local administration of transcription factor decoy oligonucleotides to nuclear factor kappab prevents carrageenin induced inflammation in rat hind paw
Gene Therapy, 2000Co-Authors: Fulvio Dacquisto, Angela Ianaro, Armando Ialenti, R Di Vaio, Rosa CarnuccioAbstract:The transcription factor nuclear factor-kappaB (NF-kappaB) plays a key role in the expression of several genes involved in the inflammatory process. In the present study we investigated in an acute model of inflammation, the carrageenin-induced hind paw edema, the anti-inflammatory effect of double stranded oligodeoxynucleotides (ODN) with consensus nuclear factor-kappaB (NF-kappaB) sequence as transcription factor Decoys (TFD) to inhibit NF-kappaB binding to native DNA sites. Local administration of wild-type, but not mutant-ODN decoy, dose-dependently inhibited edema formation induced by carrageenin in rat paw. Molecular analysis performed on soft tissue obtained from inflamed paw demonstrated: (1) an inhibition of NF-kappaB DNA binding activity; (2) a decreased nuclear level of p50 and p65 NF-kappaB subunits; (3) an inhibition of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) protein expression, two inflammatory enzymes transcriptionally controlled by NF-kappaB. Furthermore, SN-50, a cell-permeable peptide capable of inhibiting the nuclear translocation of NF-kappaB complexes, exhibited a similar profile of activity of ODN decoy. Our results indicate for the first time that ODN decoy, acting as an in vivo competitor for the transcription factor's ability to bind to cognate recognition sequence, may represent a novel strategy to modulate acute inflammation.
Thomas C Baker - One of the best experts on this subject based on the ideXlab platform.
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host condition effects upon agrilus planipennis coleoptera buprestidae captures on decoy baited branch traps
European Journal of Entomology, 2016Co-Authors: Michael J. Domingue, Jennifer Berkebile, Kim C Steiner, Loyal P Hall, Kevin R Cloonan, David R Lance, Thomas C BakerAbstract:We deployed branch traps in an ash (Fraxinus) plantation to investigate how Agrilus planipennis behavior is associated with Fraxinus pennsylvanica condition and dispersal patterns. Data were collected from traps with or without the presence of beetle visual Decoys, and from a yearly survey of exit holes. The traps were placed on trees that were either clearly declining, with most foliage arising from epicormic sprouting, or on apparently healthy trees, with little evidence of damage or decline. We calculated correlations of exit holes among neighboring tree rings and also between exit holes and male trap captures. The damaged trees the traps were hung upon had more cumulative exit holes observed than the corresponding healthy trees. However, there was otherwise no evidence that the experiment was biased by differences in exit hole patterns of the surrounding trees. Male captures were greater on decoy-baited traps than controls and this decoy effect was most clearly apparent late in the season when traps were placed on healthy trees. There were also patterns of correlations between male captures and exit hole numbers that may be indicative of short-range mate finding-and dispersal behaviors. Female captures were sparser, but were positively affected by Decoys on healthy and declining trees early in the season. Thus, the results suggest that the placement of such traps on healthier trees will maximize detection, and the branch traps also show promise for further use in dispersal studies.
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interaction of visual and chemical cues in promoting attraction of agrilus planipennis
Journal of Chemical Ecology, 2016Co-Authors: Michael J. Domingue, Peter J Silk, Krista Ryall, Stefanos S Andreadis, Thomas C BakerAbstract:Female emerald ash borers, Agrilus planipennis (Coleoptera: Buprestidae), emit a macrocyclic lactone, (Z)-3-dodecen-12-olide, that increases field trap captures on large-panel prism traps when co-emitted with the green leaf volatile (Z)-3-hexenol. We assessed attraction to these compounds by using visual decoy-baited branch traps, which attract males by mimicking a living female resting upon a leaf. Pairs of branch traps, with and without visual decoy beetles, were placed on green ash, Fraxinus pennsylvanica, trees, which were assigned different odor treatments: 1) no odor, 2) (Z)-3-hexenol alone, and 3) (Z)-3-hexenol-plus-lactone. Male captures were positively affected by the presence of Decoys and the emission of either (Z)-3-hexenol or (Z)-3-hexenol plus lactone. The decoy-baited traps with the combination of (Z)-3-hexenol plus lactone caught more males than any other treatment. Greater male captures were associated with continuing captures later in the season, suggesting that decoy and odor attractants remain attractive throughout the flight period. Female captures were not affected by the visual Decoys, but odors did influence captures, with the (Z)-3-hexenol plus lactone treatment catching the greatest number of females. The rare female trap captures were negatively correlated with the more common male captures on the odorless and (Z)-3-hexenol-baited traps, but were not correlated with male captures when the lactone was added. Thus, in the absence of the lactone, the visual signal of other conspecifics can inhibit female attraction. However, the pheromone attracts both sexes independently of the visual signal on the trap.
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detecting emerald ash borers agrilus planipennis using branch traps baited with 3d printed beetle Decoys
Journal of Pest Science, 2015Co-Authors: Michael J. Domingue, Jonathan P Lelito, Drew P. Pulsifer, Akhlesh Lakhtakia, Jennifer Berkebile, Kim C Steiner, Loyal P Hall, Thomas C BakerAbstract:Small visual-decoy-baited traps for the emerald ash borer (EAB), Agrilus planipennis Fairmaire (Coleoptera: Buprestidae), have been researched as an alternative to current technologies, but have relied on real beetles rather than synthetic materials. We hypothesized that visual Decoys created by three-dimensional (3D) printing can provide such a substitute. Branch traps displaying Decoys consisting of real EAB females or 3D-printed Decoys were compared to controls without Decoys. Traps of the three varieties were placed on neighboring branches along with one (Z)-3-hexen-1-ol lure per tree and checked daily. Both real and 3D-printed Decoys similarly increased EAB trap captures compared to controls. The numbers of both sexes were higher on the decoy-baited traps, but the increase in male captures was more pronounced. Males were also ensnared closer to the Decoys than females. Daily trap–capture patterns showed sparse activity of EAB adults before June 18, 2013 followed by a peak in captures of both males and females until June 28, 2013. Beginning at approximately July 1, 2013, there was a second peak of EAB captures, which consisted almost entirely of males caught on the decoy-baited traps. The native ash borer Agrilus subcinctus was found earlier in the season and was also significantly attracted to both the real EABs and the 3D-printed Decoys compared with control traps. Four purple prism traps were also deployed concurrently and captures tallied on three different days within the season. The results demonstrate efficacy of a small, inexpensive, and fully synthetic decoy-based branch trap system for EAB.
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visual and chemical cues affecting the detection rate of the emerald ash borer in sticky traps
Journal of Applied Entomology, 2013Co-Authors: Michael J. Domingue, Jonathan P Lelito, Ivich Fraser, Victor C Mastro, J H Tumlinson, Thomas C BakerAbstract:Using sticky traps, we compared the efficacy of chemical and visual lures, both alone and in combination, for improving the detection of populations of the emerald ash borer (EAB), Agrilus planipennis. Ash leaflets to which EAB visual Decoys were pinned and coated with sticky material were able to trap EAB with as high a rate of detection as large sticky visually unbaited ‘prism traps’ currently used in wide-scale EAB surveillance programs in North America, in a high-density area. Both the sticky leaf traps and prism traps captured more EAB when a point source of plant odours, either manuka or phoebe oil, was deployed with the trap. For the sticky leaf traps, the shape of the EAB visual decoy lure was found to be important in optimizing the detection rate. Either an entire dead beetle or else two elytra placed side by side to mimic a resting beetle resulted in optimal trap performance. When two elytra were placed end to end or else other body parts were deployed, the traps lost their efficacy. Small green plastic surfaces to which EAB visual Decoys were pinned were found to be fairly good substitutes for live ash leaflets, but the rate of beetle detection was reduced significantly from that of the ash leaflet plus EAB decoy. Throughout all experiments, a clear male bias occurred in sticky leaf traps when EAB visual Decoys were placed on the traps. The implications of these findings for developing new trapping designs for EAB and other forest buprestids are discussed. J. Appl. Entomol.
