File Parsing Research Articles

Development of a method for fuzz testing of programs for file parsing using the PolyTracker tool

The article proposes a method of phasing-testing programs for parsing using the PolyTracker tool. During the research, types of parsers are considered and their disadvantages are revealed. The proposed method eliminates the identified drawbacks, increasing code coverage and leveling the problem of state explosion.

SBILib: a handle for protein modeling and engineering.

The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes. The library can handle macromolecular complexes formed by proteins and/or nucleic acid molecules (i.e. DNA and RNA). It is uniquely capable of parsing and calculating protein super-secondary structure and loop geometry. We have compiled a list of example scenarios which SBILib may be applied to and provided access to these within the library. SBILib is made available on Github at https://github.com/structuralbioinformatics/SBILib.

Pile Capacity Checking Tool based on Distributed Fibre Optic Sensing for Instrumented Pile Load Test

Pile load test instrumented using Distributed Optical Fibre Sensing (DOFS) offers a superior ability to assess pile capacity through continuous strain profiles as opposed to the discrete measurement method such as Vibrating Wire Strain gauges. Due to the vast amount of spatial data generated by the sensor, processing the raw data from DOFS is not always straightforward, often done manually with careful interpretation of various factors affecting the measurements. Experts are reconciling with different data processing algorithms to assess pile capacity (t-z curve) from the DOFS data and verify it, leading to a demand for standard practice. Currently, the verification is done by comparing the pile capacity assessed from DOFS data to the analysis from the commercially available numerical software. The software typically requires input parameters such as extensive soil data, structure geometry, and loading intensity. Some of these parameters are limited (due to bureaucracy or trade secrecy) for the pile load test contractor to verify the pile capacity computation. This paper presents a checking tool that has been developed to automate the processes including raw file parsing, initial processes (trimming, positioning, and averaging), and post processes (pile capacity computation and checking). The post processes utilised the simplified Finite Element Method (FEM) to perform the checking which only utilised the DOFS data. In this paper, an application of the developed tool to a DOFS instrumented pile load test in Kuala Lumpur is reported. This exercise provides the information that the tool can be helpful to verify the pile capacity computation from DOFS data with promising performance reliability at 95%.

RabbitFX: Efficient Framework for FASTA/Q File Parsing on Modern Multi-Core Platforms.

The continuous growth of generated sequencing data leads to the development of a variety of associated bioinformatics tools. However, many of them are not able to fully exploit the resources of modern multi-core systems since they are bottlenecked by parsing files leading to slow execution times. This motivates the design of an efficient method for parsing sequencing data that can exploit the power of modern hardware, especially for modern CPUs with fast storage devices. We have developed RabbitFX, a fast, efficient, and easy-to-use framework for processing biological sequencing data on modern multi-core platforms. It can efficiently read FASTA and FASTQ files by combining a lightweight parsing method by means of an optimized formatting implementation. Furthermore, we provide user-friendly and modularized C++ APIs that can be easily integrated into applications in order to increase their file parsing speed. As proof-of-concept, we have integrated RabbitFX into three I/O-intensive applications: fastp, Ktrim, and Mash. Our evaluation shows that the inclusion of RabbitFX leads to speedups of at least 11.6 (6.6), 2.4 (2.4), and 3.7 (3.2) compared to the original versions on plain (gzip-compressed) files, respectively. These case studies demonstrate that RabbitFX can be easily integrated into a variety of NGS analysis tools to significantly reduce associated runtimes. It is open source software available at https://github.com/RabbitBio/RabbitFX.

Enhance work for java based network analyzer tool used to analyze network simulator files

<span lang="EN-US">The network performance measurement is important in computer networks, and performance measurement may not be effective for installation in peripheral devices resulting in the replacement of those devices and thus increasing cost. In light of this, it is better to have a simulation of the network to see its performance rather than the actual design. NS-2 is one of the most popular and widely used open-source network simulators in many organizations, which generates trace files during the simulation experience. The trace file contains all network events that can be used to calculate performance. Thus, NS-2 does not offer any visualization options for analyzing simulation results (trace files), which is the fundamental problem of trace file parsing difficulty. This paper provides a graphical user interface tool that enables researchers to quickly and efficiently analyze and visualize NS-2 trace files. This tool is a development of the JDNA tool, as it could not analyze more than one trace file at a time. In addition, this work can be a useful guide for network researchers or other programmers to analyze their networks and understand how to calculate network performance metrics.</span>

Highdicom: a Python Library for Standardized Encoding of Image Annotations and Machine Learning Model Outputs in Pathology and Radiology

Machine learning (ML) is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but the lack of interoperability between ML systems and enterprise medical imaging systems has been a major barrier for clinical integration and evaluation. The DICOM® standard specifies information object definitions (IODs) and services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with datasets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface (API) for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library leverages NumPy arrays for efficient data representation and ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers and researchers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source at https://github.com/herrmannlab/highdicom.

Revisiting the Detection of Lateral Movement through Sysmon

This work attempts to answer in a clear way the following key questions regarding the optimal initialization of the Sysmon tool for the identification of Lateral Movement in the MS Windows ecosystem. First, from an expert’s standpoint and with reference to the relevant literature, what are the criteria for determining the possibly optimal initialization features of the Sysmon event monitoring tool, which are also applicable as custom rules within the config.xml configuration file? Second, based on the identified features, how can a functional configuration file, able to identify as many LM variants as possible, be generated? To answer these questions, we relied on the MITRE ATT and CK knowledge base of adversary tactics and techniques and focused on the execution of the nine commonest LM methods. The conducted experiments, performed on a properly configured testbed, suggested a great number of interrelated networking features that were implemented as custom rules in the Sysmon’s config.xml file. Moreover, by capitalizing on the rich corpus of the 870K Sysmon logs collected, we created and evaluated, in terms of TP and FP rates, an extensible Python .evtx file analyzer, dubbed PeX, which can be used towards automatizing the parsing and scrutiny of such voluminous files. Both the .evtx logs dataset and the developed PeX tool are provided publicly for further propelling future research in this interesting and rapidly evolving field.

Charge self-consistent electronic structure calculations with dynamical mean-field theory using Quantum ESPRESSO, Wannier 90 and TRIQS

We present a fully charge self-consistent implementation of dynamical mean field theory (DMFT) combined with density functional theory (DFT) for electronic structure calculations of materials with strong electronic correlations. The implementation uses the Quantum ESPRESSO package for the DFT calculations, the Wannier90 code for the up-/down-folding and the TRIQS software package for setting up and solving the DMFT equations. All components are available under open source licenses, are MPI-parallelized, fully integrated in the respective packages, and use an hdf5 archive interface to eliminate file parsing. We show benchmarks for three different systems that demonstrate excellent agreement with existing DFT + DMFT implementations in other ab initio electronic structure codes.

FlexParser—The adaptive log file parser for continuous results in a changing world

AbstractAny modern system writes events into files, called log files. Those contain crucial information which are subject to various analyses. Examples range from cybersecurity, intrusion detection over usage analyses to trouble shooting. Before data analysis is possible, desired information needs to be extracted first out of the semi‐structured log messages. State‐of‐the‐art event parsing often assumes static log events. However, any modern system is updated consistently and with updates also log file structures can change. We call those changes “mutation” and study parsing performance for different mutation cases. Latest research discovers mutations using anomaly detection post mortem, however, does not cover actual continuous parsing. Thus, we propose a novel and flexible parser, calledFlexParser, which can extract desired values despite gradual changes in the log messages. It implies basic text preprocessing followed by a supervised Deep Learning method. We train a stateful LSTM on parsing one event per data set. Statefulness enforces the model to learn log message structures across several examples. Our model was tested on seven different, publicly available log file data sets and various kinds of mutations. Exhibiting an average F1‐Score of 0.98, it outperforms other Deep Learning methods as well as state‐of‐the‐art unsupervised parsers.

(Invited) A Multi-Scale Infrastructure for Automating Materials Science Computations

In this talk, I will present our group’s effort to build a high-throughput infrastructure for the automated execution of materials science computations and analysis. This work is an extension of open-source Python packages that have been developed in Materials Project [1]: (1) pymatgen [2] for structure representation and input/output files generation and handling, (2) FireWorks [3] for managing workflows over computing resources, and (3) Custodian [4] for monitoring inevitable errors during the simulations and applying on-the-fly fixes. In our work, we benefit from these three libraries to build well-tested workflows for speeding up the prediction and screening of materials properties relevant to various applications with a focus on battery systems. At the backend, the infrastructure interfaces with the Gaussian [5] software which enables electronic structure calculations of chemical systems, and LAMMPS [6] open-source code for molecular dynamics (MD) simulations. In addition to its ability to handle the generation of input/data files and parsing of output files from the mentioned computational software, the infrastructure allows management of the collected data and storing it in MongoDB [7], a NoSQL database program using JSON-like documents with flexible schema. Examples of implemented Gaussian-based workflows include the calculation of electrostatic partial charges, NMR chemical shift, binding energy, ionization potential, electron affinity, and bond dissociation energy. Each derived property is saved in its own collection with auxiliary information like molecular metadata (smiles representation, chemical formula, ...), which makes it easy to query and data-mine structure-property relationships. The user can tune the calculations by overriding default workflow parameters, for example, the functional and basis set, by-passing selected steps, or packing many jobs over multiple nodes for supercomputing resources. LAMMPS workflows allow the execution of MD simulations in different ensembles and analysis of the dumped trajectories for various dynamical and structural properties. The infrastructure enables coupling first-principles calculations with classical MD simulations to address chemical and physical phenomena occurring in a given system at different length and time scales. This approach aids in the development of databases required for training machine-learning models to accelerate the design and screening of materials with optimized properties for various materials science applications.References Jain, A., et al., Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. Apl Materials, 2013. 1(1): p. 011002.Ong, S.P., et al., Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis. Computational Materials Science, 2013. 68: p. 314-319.Jain, A., et al., FireWorks: a dynamic workflow system designed for high‐throughput applications. Concurrency and Computation: Practice and Experience, 2015. 27(17): p. 5037-5059.Custodian. <https://github.com/materialsproject/custodian>.Frisch, M.J., et al., Gaussian 16 Rev. C.01. 2016: Wallingford, CT.Plimpton, S., Fast parallel algorithms for short-range molecular dynamics. 1993, Sandia National Labs., Albuquerque, NM (United States).MongoDB Inc., M., 2014.

Gerber File Parsing and the Implementation Method of its Conversion to Bitmap Image

In order to obtain the standard image based on the Gerber file in the AOI (Automatic Optical Inspection), an implementation method is proposed by analysing and converting the Gerber file into a bitmap image. According to the command format rules of Gerber file, the approach is designed to analyse Gerber files by constructing the command regular expression of Gerber files. In order to achieve convenient access to the drawing data, the drawing data processing is performed by defining a class, then the drawing data is stored in a dynamic array and the adjustment factors are added in the drawing data at the same time to meet the further post-processing needs of bitmap image. This method can quickly analyse Gerber files, adjust drawing data and generate bitmap images.

Application Research of File Fingerprint Identification Detection Based on a Network Security Protection System

A DLP (data loss prevention) system usually arranges network monitors at the network boundary to perform network traffic capture, file parsing, and strategy matching procedures. Strategy matching is a key process to prevent corporate secret-related documents from leaking. This paper adopts the document fingerprint similarity detection method based on the SimHash principle and customizes the KbS (Keyword-based SimHash) fingerprint, PbS (Paragraph-based SimHash) fingerprint, and SoP (SimHash of Paragraph) fingerprint, three different feature extraction SimHash algorithms for strategy matching to detect. The parsed unstructured data is stored as a file type in.txt format, and then a file fingerprint is generated. Matching the established sensitive document library to calculate the Hamming distance between the fingerprints, the Hamming distance values under different modification degrees are summarized. The experimental results reveal that the hybrid algorithmic strategy matching rules with different levels and accuracy are established. This paper has a reference role for the leakage prevention research of enterprise sensitive data.

PTGL: extension to graph-based topologies of cryo-EM data for large protein structures

We provide a software to describe the topology of large protein complexes based mainly on cryo-EM data and stored as macromolecular Crystallographic Information Files (mmCIFs) in the PDB. The software extends the Protein Topology Graph Library and implements an efficient file parser to analyze mmCIFs. The extended Protein Topology Graph Library includes a graph-based representation of the topology of protein complexes on the supersecondary and quaternary structure level. The library holds topology graphs of 151837 PDB files; 921 of them are large structures. The abstraction of protein structure complexes to undirected labeled graphs enables classification and comparison of large protein complexes on quaternary structure level. Online access at http://ptgl.uni-frankfurt.de. Source code in Java under GNU public license 2.0 at https://github.com/MolBIFFM/vplg. Supplementary data are available at Bioinformatics online.

Link Your Sites (LYS) Scripts: Automated Search of Protein Structures and Mapping of Sites Under Positive Selection Detected by PAML

The visualization of the molecular context of an amino acid mutation in a protein structure is crucial for the assessment of its functional impact and the understanding of its evolutionary implications. Currently, searches for fast evolving amino acid positions using codon substitution models like those implemented in PAML (Yang and Nielsen in Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models. Mol Biol Evol 17(1):32–43, 2000; Zhang et al. in Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level. Mol Biol Evol 22(12):2472–2479, 2005) are done in almost complete proteomes, generating large numbers of candidate proteins making the analysis of individual protein structures and models very time-consuming. Here we present the package Link Your Sites (LYS) that can be used to reduce the number of analysed targets to those for which structural information can be retrieved. LYS consists of two python wrapper scripts, where the first one (i) mines the RCSB database (Berman et al. in The protein data bank. Nucleic Acids Res 28(1):235–242, 2000) using the BLAST alignment tool to find the best matching homologous sequences, (ii) fetches their domain positions by using Prosites (Hamelryck and Manderick in Pdb file parser and structure class implemented in python. Bioinformatics 19(17):2308–2310, 2003; Sigrist et al. in Prosite: a documented database using patterns and profiles as motif descriptors. Brief Bioinf 3(3):265–274, 2002; Sigrist et al. in New and continuing developments at prosite. Nucleic Acids Res 41(D1):D344–D347, 2012), (iii) parses the output of PAML extracting the positional information of fast-evolving sites and transforms them into the coordinate system of the protein structure, (iv) outputs one file per gene with the equivalence among the positions in the input sequence and homologous structure. The second script produces figures to be used in publications highlighting the positively selected sites mapped on regions that are known to have functional relevance.

Freud: A software suite for high throughput analysis of particle simulation data

The freud Python package is a library for analyzing simulation data. Written with modern simulation and data analysis workflows in mind, freud provides a Python interface to fast, parallelized C++ routines that run efficiently on laptops, workstations, and supercomputing clusters. The package provides the core tools for finding particle neighbors in periodic systems, and offers a uniform API to a wide variety of methods implemented using these tools. As such, freud users can access standard methods such as the radial distribution function as well as newer, more specialized methods such as the potential of mean force and torque and local crystal environment analysis with equal ease. Rather than providing its own trajectory data structure, freud operates either directly on NumPy arrays or on trajectory data structures provided by other Python packages. This design allows freud to transparently interface with many trajectory file formats by leveraging the file parsing abilities of other trajectory management tools. By remaining agnostic to its data source, freud is suitable for analyzing any particle simulation, regardless of the original data representation or simulation method. When used for on-the-fly analysis in conjunction with scriptable simulation software such as HOOMD-blue, freud enables smart simulations that adapt to the current state of the system, allowing users to study phenomena such as nucleation and growth. Program summaryProgram Title:freudProgram Files doi:http://dx.doi.org/10.17632/v7wmv9xcct.1Licensing provisions: BSD 3-ClauseProgramming language: Python, C++Nature of problem: Simulations of coarse-grained, nano-scale, and colloidal particle systems typically require analyses specialized to a particular system. Certain more standardized techniques – including correlation functions, order parameters, and clustering – are computationally intensive tasks that must be carefully implemented to scale to the larger systems common in modern simulations.Solution method:freud performs a wide variety of particle system analyses, offering a Python API that interfaces with many other tools in computational molecular sciences via NumPy array inputs and outputs. The algorithms in freud leverage parallelized C++ to scale to large systems and enable real-time analysis. The library’s broad set of features encode few assumptions compared to other analysis packages, enabling analysis of a broader class of data ranging from biomolecular simulations to colloidal experiments.Additional comments including restrictions and unusual features:1. freud provides very fast parallel implementations of standard analysis methods like RDFs and correlation functions.2. freud includes the reference implementation for the potential of mean force and torque (PMFT).3. freud provides various novel methods for characterizing particle environments, including the calculation of descriptors useful for machine learning. The source code is hosted on GitHub (https://github.com/glotzerlab/freud), and documentation is available online (https://freud.readthedocs.io/). The package may be installed via pip install freud-analysis or conda install -c conda-forge freud.

CSEFuzz: Fuzz Testing Based on Symbolic Execution

Fuzz testing has been successful in finding defects of various software packages. These defects include file parsing, image processing, Internet browsers, and network protocols. However, the quality of the initial seed test cases greatly influences the coverage and defect detection capability of fuzz testing. To address this issue, we propose CSEFuzz, a fuzz testing approach based on symbolic execution for defect detection. First, CSEFuzz generates candidate test cases by symbolic execution and collects coverage information of the test cases. Then, CSEFuzz extracts the test-case templates of the test cases and selects a set of test-case templates according to specific coverage criteria. Finally, CSEFuzz selects test cases according to the selected test-case templates, and the selected test cases are used as initial seed test cases for fuzz testing. Experiments are conducted on 11 open-source programs. The results show that in comparison with afl-cmin, which is the test-case selection command of Kelinci, CSEFuzz with a path coverage criterion reduces the time costs of the initial seed test selection and verification by 94.26%. In addition, compared with afl-cmin, 32 more paths are covered and 16 more defects are detected by CSEFuzz.

With Semantics and Hidden Markov Models to an Adaptive Log File Parser

With Semantics and Hidden Markov Models to an Adaptive Log File Parser

FindTargetsWEB: A User-Friendly Tool for Identification of Potential Therapeutic Targets in Metabolic Networks of Bacteria.

Background: Healthcare-associated infections (HAIs) are a serious public health problem. They can be associated with morbidity and mortality and are responsible for the increase in patient hospitalization. Antimicrobial resistance among pathogens causing HAI has increased at alarming levels. In this paper, a robust method for analyzing genome-scale metabolic networks of bacteria is proposed in order to identify potential therapeutic targets, along with its corresponding web implementation, dubbed FindTargetsWEB. The proposed method assumes that every metabolic network presents fragile genes whose blockade will impair one or more metabolic functions, such as biomass accumulation. FindTargetsWEB automates the process of identification of such fragile genes using flux balance analysis (FBA), flux variability analysis (FVA), extended Systems Biology Markup Language (SBML) file parsing, and queries to three public repositories, i.e., KEGG, UniProt, and DrugBank. The web application was developed in Python using COBRApy and Django. Results: The proposed method was demonstrated to be robust enough to process even non-curated, incomplete, or imprecise metabolic networks, in addition to integrated host-pathogen models. A list of potential therapeutic targets and their putative inhibitors was generated as a result of the analysis of Pseudomonas aeruginosa metabolic networks available in the literature and a curated version of the metabolic network of a multidrug-resistant P. aeruginosa strain belonging to a clone endemic in Brazil (P. aeruginosa ST277). Genome-scale metabolic networks of other gram-positive and gram-negative bacteria, such as Staphylococcus aureus, Klebsiella pneumoniae, and Haemophilus influenzae, were also analyzed using FindTargetsWEB. Multiple potential targets have been found using the proposed method in all metabolic networks, including some overlapping between two or more pathogens. Among the potential targets, several have been previously reported in the literature as targets for antimicrobial development, and many targets have approved drugs. Despite similarities in the metabolic network structure for closely related bacteria, we show that the method is able to selectively identify targets in pathogenic versus non-pathogenic organisms. Conclusions: This new computational system can give insights into the identification of new candidate therapeutic targets for pathogenic bacteria and discovery of new antimicrobial drugs through genome-scale metabolic network analysis and heterogeneous data integration, even for non-curated or incomplete networks.

WellInverter: a web application for the analysis of fluorescent reporter gene data

BackgroundFluorescent reporter genes have become widely used for monitoring gene expression in living cells. When a microbial strain carrying a reporter gene is grown in a microplate reader, the fluorescence and the absorbance (optical density) of the culture can be automatically measured every few minutes in a highly parallelized way. The extraction of useful information from the resulting large amounts of data is not easy to achieve, because the fluorescence and absorbance measurements are only indirectly related to promoter activities and protein concentrations, requiring mathematical models of the expression of reporter genes for their interpretation. Although the principles of the analysis of reporter gene data are well-established today, there is a lack of general-purpose bioinformatics tools based on generic measurement models and sound inference procedures. This has motivated the development of WellInverter, a web application based on well-known methods for regularized linear inversion.ResultsWe present a new version of WellInverter that considerably improves the performance and usability of the original application. In particular, we have put in place a parallel computing architecture with a load balancer to distribute analysis queries over several back-end servers, we have completely redesigned the graphical user interface to better support the different analysis steps, and we have developed a plug-in system for the parsing of data files produced by microplate readers from different manufacturers. We illustrate the functioning of WellInverter by analyzing data of the expression of a fluorescent reporter gene controlled by a phage promoter in growing Escherichia coli populations. We show that the expression pattern in different growth media, supporting different growth rates, corresponds to the pattern expected for a constitutive gene.ConclusionsThe new version of WellInverter is a robust, easy-to-use and scalable web application, which has been deployed on two publicly accessible web servers and which can also be installed locally. A demo version of the application with two sample datasets is available on-line.

Genetic analysis of Ximenia americana based on the cross-species DNA-DNA hybridization technique

Abstract. Ekandjo A, Naomand E, Kahaka G. 2018. Genetic analysis of Ximenia americana based on DNA-DNA based on the cross-species hybridization technique. Cell Biol Dev 2: 1-7. Genetic analysis of species without the availability of complete genome arrays can be studied using the cross-species hybridization technique. The preliminary microarray studies have created DNA-DNA hybridization of X. americana to the GeneChip of Arabidopsis thaliana (ATH1). Based on the perfect-match (PM) probe signal, the probe pairs of A. thaliana that hybridized with the genomic DNA of X. americana were selected and analyzed using a cel file parser script. This selection was to generate new high-density probe mask files. The X. americana DNA-DNA hybridization data were effectively represented by this file. In addition, this study carried out gene ontology analysis of hybridization data of X. americana/A. thaliana. This gene ontology analysis showed that genes for abiotic stress response were over-expressed in respective comparison to model species of A. thaliana under natural conditions. This analysis was confirmed independently with PCR amplification of the orthologous genes using genomic DNA of X. americana, such as AT4G15910.1, SAD2, HXK1, ACC, and ERF/AP2. Due to the lack of genomic sequence data in X. americana, the primers for genomic amplification were designed using a genomic sequence of A. thaliana. The primers were designed to produce a genomic PCR product of 100 bp. Each selected gene was successfully amplified. Therefore, it gave evidence of homology within primer binding sites. However, the genomic amplification of these crucial abiotic factors in X. americana confirms the response type, which supports the adaptation of X. americana under natural conditions with stress associated (with heat and drought stress). Although transcript levels of these unique abiotic response factors could not be observed in absolute or relative terms, the study showed the inherent existence of such genes at the genomic level of X. americana. Further studies are needed to confirm that some modification of the factors of these abiotic responses or other abiotic responses within the genome of X. americana gives the key basis for its adaptation to the comparatively dry and hot climatic conditions.