Python Script Research Articles

Development of a technique for molecular typing of <i>Bacillus anthracis</i> strains using new VNTR and INDEL markers

Introduction. Bacillus anthracis, the pathogen of a particularly dangerous zoonotic disease known as anthrax, requires strict epidemiological control and is characterized by high genetic homogeneity, which necessitates the development of genotyping methods. The aim of the study were to to find and characterize the VNTR and INDEL loci of B. anthracis and to develop on their basis a genotyping technique by PCR with electrophoretic detection of the results. Materials and methods. Marker search and phylogenetic analysis were performed on a sample of 388 genomes of B. anthracis strains, 322 from the GenBank collection (RefSeq) and 66 from the collection of the Stavropol Anti-Plague Institute of Rospotrebnadzor. Phylogenetic analysis was performed on the basis of SNP crustal alignment using the Parsnp program. The search for markers was carried out using the Mauve program and author's scripts in Python. PCR was performed using a ScreenMix-HS kit (CJSC "Eurogen", Russia). Results. Genomic variations of B. anthracis strains (SNP — 25,664, SNR — 14,387, VNTR — 693, INDEL — 14,667) were found, bioinformatic analysis of which revealed nine new VNTR and six INDEL molecular markers most suitable for genotyping. The genetic (allelic) variants of the markers are described. Primers were selected for the found markers and a PCR protocol with detection by electrophoresis in agarose gel was developed. When typing using VNTR markers was applied, the strains were divided into nine clusters: A.Br.Ames, A.Br.001/002, A.Br.Aust94, A.Br.005/006, A.Br.008/009 (Tsiankovskii), A.Br.008/009 (STI), A.Br.008/009 (A.Br.125), A.Br.008/009 (strain 228/269), B.Br.001/002. When typing using INDEL markers, the strains were divided into six clusters: A.Br.Ames, A.Br.001/002, A.Br.Aust94, A.Br.008/009(Tsiankovskii), B.Br.001/002(B.Br.014), as well as a cluster comprising several genetic lineages: A.Br.008/009 (STI), A.Br.008/009 (A.Br.125), A.Br.005/006 и B.Br.001/002. Conclusion. The use of the developed methodology for the identification of variable VNTR and INDEL loci makes it possible to reliably determine the phylogenetic position of B. anthracis strains and is promising for use in the epidemiological investigation of anthrax outbreaks.

A Deep Learning-based Classification Model for Arabic News Tweets Using Bidirectional Long Short-Term Memory Networks

This research develops a classification model for Arabic news tweets using Bidirectional Long Short-Term Memory networks (BiLSTM). Tweets about Arabic news were gathered between August 2016 and August 2020 and divided into five categories. Custom Python scripts, Twitter API and the GetOldTweets3 Python library were used to collect the data. BiLSTM was used to train and test the model. The results indicated an average accuracy, precision, recall, and f1-score of 0.88, 0.92, 0.88, and 0.89, respectively. The results could have practical implications for Arabic machine learning and NLP tasks in research and practice.

Automated screening of precipitation polymerizations and evaluation using image recognition for divinylbenzene and methacrylic acid

AbstractBy applying automated high‐throughput experimentation, 63 precipitation polymerizations of divinylbenzene and methacrylic acid were performed with a total of 1638 samples analyzed by gas chromatography (GC), nuclear magnetic resonance (NMR) spectroscopy, and scanning electron microscopy (SEM). The conversion of each reaction was investigated revealing the best substrate concentrations within the current setup. The GC evaluation was performed automatically via a new custom‐made Python script significantly reducing the time to evaluate the results. Furthermore, the particle growth was monitored by utilizing an innovative image recognition tool to identify particles and their respective sizes using SEM images. Furthermore, a statistical particle size distribution analysis was performed, which is hardly achievable in reasonable time by classical evaluation methods. Using this new procedure, the highest conversion (70%) as well as the largest particles (3700 nm) have been obtained utilizing a high initial monomer (5 vol%) and initiator (5 mol%) concentration. Accordingly, the smallest particles (245 nm) yielded from the lowest starting concentration (1 vol% monomer and 1 mol% initiator).

PyDFT-QMMM: A modular, extensible software framework for DFT-based QM/MM molecular dynamics.

PyDFT-QMMM is a Python-based package for performing hybrid quantum mechanics/molecular mechanics (QM/MM) simulations at the density functional level of theory. The program is designed to treat short-range and long-range interactions through user-specified combinations of electrostatic and mechanical embedding procedures within periodic simulation domains, providing necessary interfaces to external quantum chemistry and molecular dynamics software. To enable direct embedding of long-range electrostatics in periodic systems, we have derived and implemented force terms for our previously described QM/MM/PME approach [Pederson and McDaniel, J. Chem. Phys. 156, 174105 (2022)]. Communication with external software packages Psi4 and OpenMM is facilitated through Python application programming interfaces (APIs). The core library contains basic utilities for running QM/MM molecular dynamics simulations, and plug-in entry-points are provided for users to implement custom energy/force calculation and integration routines, within an extensible architecture. The user interacts with PyDFT-QMMM primarily through its Python API, allowing for complex workflow development with Python scripting, for example, interfacing with PLUMED for free energy simulations. We provide benchmarks of forces and energy conservation for the QM/MM/PME and alternative QM/MM electrostatic embedding approaches. We further demonstrate a simple example use case for water solute in a water solvent system, for which radial distribution functions are computed from 100ps QM/MM simulations; in this example, we highlight how the solvation structure is sensitive to different basis-set choices due to under- or over-polarization of the QM water molecule's electron density.

Insights into the variations of kinetic and potential energies in a multi-bond reaction: the reaction electronic flux perspective.

The debate over whether kinetic energy (KE) or potential energy (PE) are the fundamental energy components that contribute to forming covalent bonds has been enduring and stimulating over time. However, the supremacy of these energy components in reactions where multiple bonds are simultaneously formed or broken has yet to be explored. In this study, we use the reaction electronic flux (REF), an effective tool for investigating changes in driving electronic activity when bond formation or dissociation occurs in a chemical reaction, to examine the fluctuations in the KE and PE in a multi-bond reaction. To that end, the activation of CO by low-valent group 14 catalysts through a concerted -bond metathesis mechanism is analyzed. The findings of this preliminary study suggest that the REF can be utilized as a tool to rationalize alterations in the KE and PE in a multi-bond reaction. Specifically, analyses across the reaction coordinate reveal that changes in the KE and PE precede activation in the REF, stimulating the electronic activity where bond formation or dissociation processes dominate. The activation of CO by the low-valent LEH catalysts (L = N,N'-bis(2,6-diisopropyl phenyl)- -diketiminate; E = Si, Ge, Sn, and Pb) was studied along the reaction coordinate at the M06-2X/6-31G(d,p)-LANL2DZ(E) level of theory. The respective minimum energy path calculations were obtained using the intrinsic reaction coordinate (IRC) procedure. The reaction electronic flux (REF) was calculated through the computation of the electronic chemical potential using the frontier molecular orbital approximation. Mayer bond orders along the reaction coordinate have been determined using the NBO 3.1 program in Gaussian16. Most of the reaction coordinate quantities reported in this study (REF, KE, PE, among others) have been determined using the Kudi program and custom Python scripts.

In silico-guided discovery and in vitro validation of novel sugar-tethered lysinated carbon nanotubes for targeted drug delivery of doxorubicin

ContextMultiwalled carbon nanotubes (MWCNTs) functionalized with lysine via 1,3-dipolar cycloaddition and conjugated to galactose or mannose are potential nanocarriers that can effectively bind to the lectin receptor in MDA-MB-231 or MCF-7 breast cancer cells. In this work, a method based on molecular dynamics (MD) simulation was used to predict the interaction of these functionalized MWCNTs with doxorubicin and obtain structural evidence that allows a better understanding of the drug loading and release process. The MD simulations showed that while doxorubicin only interacted with pristine MWCNTs through π-π stacking interactions, functionalized MWCNTs were also able to establish hydrogen bonds, suggesting that the functionalized groups improve doxorubicin loading. Moreover, the elevated adsorption levels observed for functionalized nanotubes further support this enhancement in loading efficiency. MD simulations also shed light on the intratumoral pH-specific release of doxorubicin from functionalized MWCNTs, which is induced by protonation of the daunosamine moiety. The simulations show that this change in protonation leads to a lower absorption of doxorubicin to the MWCNTs. The MD studies were then experimentally validated, where functionalized MWCNTs showed improved dispersion in aqueous medium compared to pristine MWCNTs and, in agreement with the computational predictions, increased drug loading capacity. Doxorubicin-loaded functionalized MWCNTs demonstrated specific release of doxorubicin in tumor microenvironment (pH = 5.0) with negligible release in the physiological pH (pH = 7.4). Furthermore, doxorubicin-free MWNCT nanoformulations exhibited insignificant cytotoxicity. The experimental studies yielded nearly identical results to the MD studies, underlining the usefulness of the method. Our functionalized MWCNTs represent promising non-toxic nanoplatforms with enhanced aqueous dispersibility and the potential for conjugation with ligands for targeted delivery of anti-cancer drugs to breast cancer cells.MethodsThe computational model of a pristine carbon nanotube was created with the buildCstruct 1.2 Python script. The lysinated functionalized groups were added with PyMOL and VMD. The carbon nanotubes and doxorubicin molecules were parameterized using the general AMBER force field, and RESP charges were determined using Gaussian 09. Molecular dynamics simulations were carried out with the AMBER 20 software package. Adsorption levels were calculated using the water-shell function of cpptraj. Cytotoxicity was evaluated via a MTT assay using MDA-MB-231 and MCF-7 breast cancer cells. Drug uptake of doxorubicin and doxorubicin-loaded MWCNTs was measured by fluorescence microscopy.

A scale-span method to characterize the mechanical property of BCF/PEEK considering uncertain structural characteristics

The focus of this study is exploring a scale-span characterization method to predict the mechanical properties of Braided Carbon Fiber Reinforced Poly Ether Ether Ketone (BCF/PEEK) considering structural uncertainty. Firstly, a complicated micro-scale Representative Volume Element (RVE) model that considers the random position and size of fiber was established via the developed Python script. Analogously, a mathematical expression adopted by the trust region algorithm was proposed to accurately describe the detailed cross-sectional shapes and fluctuation amplitude characteristics of BCF/PEEK for the sake of establishing the precision meso-scale RVE model. Then, the corresponding elastic properties that consider fiber volume fraction and fiber distribution location were characterized via the span-scale characterization method. Meanwhile, the influence of fiber bundle fluctuation amplitude and fiber volume fraction on the mechanical properties was investigated as well. In addition, the mechanical properties of BCF/PEEK with the change of the braid angle between warp and weft yarn were predicted via the established meso-scale RVE model. Finally, a series of experiments have been carried out. The maximum and minimum absolute prediction deviation of all elastic property parameters were only 4.07 % and 1.41 %, respectively, which verified the proposed scale-span characterization method can predict the mechanical properties of composites well.

Process simulation of laser shock-based disassembly of adhesively bonded composite/metallic structural parts: a numerical upscaling

ABSTRACT In previous works, both experimental and numerical investigations have shown the potential of the laser shock technique for disassembling adhesively bonded composite/metal coupons. This study extends this concept to upscale the method, focusing on the full disassembly of a foreign object damage (FOD) panel designed to replicate an aircraft engine fan blade. Utilizing LS-Dyna explicit FE software, the numerical simulation incorporates various material models: a progressive damage model for the 3D CFRP substrate, Johnson–Cook plasticity model combined with Grüneisen equation of state for the titanium layer, and a cohesive zone model with a bilinear traction separation law for the adhesive layer. Investigating the FOD panel’s inclined geometry, a preliminary analysis examines the impact of inclination angles on shock wave propagation and back face velocity, revealing minimal alterations. Initial simulations are conducted to determine double pulse delay times and evaluate spot location effects. Automation of the multi-step process simulation is achieved through a Python script. After approximately 30 shots, complete disassembly of the FOD strip is achieved, with observed damage primarily limited to matrix cracking in the composite substrate. Numerical findings support the efficacy of the double-shot laser shock method for disassembling adhesively bonded metallic/CFRP components, contingent upon appropriate experimental arrangements.

Crack Initiation in Compacted Graphite Iron with Random Microstructure: Effect of Volume Fraction and Distribution of Particles.

Thanks to the distinctive morphology of graphite particles in its microstructure, compacted graphite iron (CGI) exhibits excellent thermal conductivity together with high strength and durability. CGI is extensively used in many applications, e.g., engine cylinder heads and brakes. The structural integrity of such metal-matrix materials is controlled by the generation and growth of microcracks. Although the effects of the volume fraction and morphology of graphite inclusions on the tensile response of CGI were investigated in recent years, their influence on crack initiation is still unknown. Experimental studies of crack initiation require a considerable amount of time and resources due to the highly complicated geometries of graphite inclusions scattered throughout the metallic matrix. Therefore, developing a 2D computational framework for CGI with a random microstructure capable of predicting the crack initiation and path is desirable. In this work, an integrated numerical model is developed for the analysis of the effects of volume fraction and nodularity on the mechanical properties of CGI as well as its damage and failure behaviours. Finite-element models of random microstructure are generated using an in-house Python script. The determination of spacings between a graphite inclusion and its four adjacent particles is performed with a plugin, written in Java and implemented in ImageJ. To analyse the orientation effect of inclusions, a statistical analysis is implemented for representative elements in this research. Further, Johnson-Cook damage criteria are used to predict crack initiation in the developed models. The numerical simulations are validated with conventional tensile-test data. The created models can support the understanding of the fracture behaviour of CGI under mechanical load, and the proposed approach can be utilised to design metal-matrix composites with optimised mechanical properties and performance.

Reliability assessment for pipelines corroded by longitudinally aligned defects

Internal corrosion poses a significant threat to offshore pipeline services. Toward offshore pipeline integrity management, this paper aims to use Finite Element Method (FEM) to assess the reliability of corroded pipelines. The FEM analysis was conducted using ABAQUS software, facilitated through Python scripts, to optimize the modeling process on a large scale. Additionally, the input database was generated through a random sampling method using the Latin Hypercube Sampling (LHS) method. The reliability of 32” oil and gas offshore internally corroded pipelines has been evaluated by fragility curve assessment along with Incremental Pressure Analysis (IPA) which is a new method inspired by Incremental Dynamic Analysis (IDA). Multiple corrosion defects pose a higher risk to offshore pipelines during service, with potentially more severe consequences than a single defect alone. Therefore, the interaction of corrosion defects has been considered in this study. Following the reliability assessment the most appropriate Probability Density Function (PDF) for the Maximum Von Mises Stress (MVMS) and failure probability at various Internal Pressure (IP) and longitudinal spacing (Sl) levels has been evaluated.

Bio-based connections and hybrid planar truss: A parallel genetic algorithm approach for model updating

Bolted steel to laminated bio-based material connections experience significant performance challenges due to the nonlinear response and high stress concentrations at their joints. This paper introduces an innovative 3D plasticity-fracture continuum Finite Element (FE) model that significantly advances the simulation of such truss joints by integrating Hill's yielding criteria with an element removal methodology for fracture simulation. This novel approach captures both plastic and fracture behaviors simultaneously, a capability not sufficiently addressed in existing models. We detail the theoretical framework for these models, including the derivation of constitutive equations and the algorithms necessary for their implementation in ABAQUS. Additionally, it is provided a low-fidelity modeling of truss joints, offering a comprehensive analysis of connector elements, joint models, and parametric modeling via Python scripting. The model's efficacy is demonstrated through identification of connection and of hybrid planar trusses under cyclic loading, which validates the practical applicability of the method. To optimize computational efficiency, we developed a Parallel Genetic Algorithm (PGA) that integrates seamlessly with ABAQUS and Python to facilitate parameter calibration. This integration not only enhances the model's accuracy but also reduces computational load, making it feasible for complex engineering applications. Our findings illustrate a significant improvement in modeling accuracy and efficiency, establishing a robust methodology for analyzing truss joints in bio-based construction materials.

Streamlining Research Metrics Compilation Reports: An Automated Approach Using Scopus and Clarivate APIs

This article examines the development and implementation of a customized Python script utilizing the Elsevier Scopus and Clarivate Web of Science Journal Citation Reports Application Programming Interfaces (APIs). The aim was to streamline and expedite the labor-intensive process of collecting research metrics, which were traditionally compiled manually by librarians at the University of Miami Miller School of Medicine Louis Calder Memorial Library. The script significantly reduces the time and effort required to generate comprehensive reports on research productivity, thereby enabling more efficient resource allocation and aiding in faculty evaluations.

Imaging the Fault Plane Structure of Palu-Koro Fault from Earthquake Data Using Python

Abstract Palu-Koro Fault is one of the active faults located in Sulawesi and positioned in the middle of the Island, dividing the west and east-southeast arms. Its long history of earthquakes provides sufficient focal mechanisms data to be analyzed. This research used seven focal mechanism data of shallow magnitude earthquakes surrounding the pull-apart basin area of the Palu-Koro Fault. The focal mechanism data used is secondary data obtained containing the information needed. This research aims to create a 3D model of the pull-apart basin displaying the geometries of the fault planes and providing a closer look at the fault system surrounding the bounding fault zones. Python script was made to classify the mechanism of each earthquake based on its correlation to the trend of the Palu-Koro Fault. Nodal planes are categorized as whether it is presumed to be the fault plane or the auxiliary plane of the focal mechanism. The result found that not all earthquake data was classified through the assumed parameter set. Only five of seven earthquake data managed to be classified, while the rest showed a different focal mechanism. It is found from the python script created revealed the presence of three fault types: normal fault, normal left lateral oblique fault, and left lateral strike-slip. The 3D fault plane interpolation showed two distinct dipping directions, suggesting their association with the basin’s bounding fault, either the west or the east side. From the gathered information, previous research, and geological interpretation, the earthquake data can be concluded into three fault associations to the bounding fault: synthetic extension fault, antithetic extension fault, and sinistral strike-slip fault.

Simple Method to Assess Foam Structure and Stability using Hydrophobin and BSA as Model Systems.

The properties and arrangement of surface-active molecules at air-water interfaces influence foam stability and bubble shape. Such multiscale-relationships necessitate a well-conducted analysis of mesoscopic foam properties. We introduce a novel automated and precise method to characterize bubble growth, size distribution and shape based on image analysis and using the machine learning algorithm Cellpose. Studying the temporal evolution of bubble size and shape facilitates conclusions on foam stability. The addition of two sets of masks, for tiny bubbles and large bubbles, provides for a high precision of analysis. A python script for analysis of the evolution of bubble diameter, circularity and dispersity is provided in the Supporting Information. Using foams stabilized by bovine serum albumin (BSA), hydrophobin (HP), and blends thereof, we show how this technique can be used to precisely characterize foam structures. Foams stabilized by HP show a significantly increased foam stability and rounder bubble shape than BSA-stabilized foams. These differences are induced by the different molecular structure of the two proteins. Our study shows that the proposed method provides an efficient way to analyze relevant foam properties in detail and at low cost, with higher precision than conventional methods of image analysis.

IDeRare: a lightweight and extensible open-source phenotype and exome analysis pipeline for germline rare disease diagnosis.

Diagnosing rare diseases is an arduous and challenging process in clinical settings, resulting in the late discovery of novel variants and referral loops. To help clinicians, we built IDeRare pipelines to accelerate phenotype-genotype analysis for patients with suspected rare diseases. IDeRare pipeline is separated into phenotype and genotype parts. The phenotype utilizes our handmade Python library, while the genotype part utilizes command line (bash) and Python script to combine bioinformatics executable and Docker image. We described various implementations of IDeRare phenotype and genotype parts with real-world clinical and exome data using IDeRare, accelerating the terminology conversion process and giving insight on the diagnostic pathway based on disease linkage analysis until exome analysis and HTML-based reporting for clinicians. IDeRare is freely available under the BSD-3 license, obtainable via GitHub. The portability of IDeRare pipeline could be easily implemented for semi-technical users and extensible for advanced users.

A Versatile Pipeline for Analyzing Dynamic Changes in Nuclear Bodies in a Variety of Cell Types.

Various nuclear processes, such as transcriptional control, occur within discrete structures known as foci that are discernable through the immunofluorescence technique. Investigating the dynamics of these foci under diverse cellular conditions via microscopy yields valuable insights into the molecular mechanisms governing cellular identity and functions. However, performing immunofluorescence assays across different cell types and assessing alterations in the assembly, diffusion, and distribution of these foci present numerous challenges. These challenges encompass complexities in sample preparation, determination of parameters for analyzing imaging data, and management of substantial data volumes. Moreover, existing imaging workflows are often tailored for proficient users, thereby limiting accessibility to a broader audience. In this study, we introduce an optimized immunofluorescence protocol tailored for investigating nuclear proteins in different human primary T cell types that can be customized for any protein of interest and cell type. Furthermore, we present a method for unbiasedly quantifying protein staining, whether they form distinct foci or exhibit a diffuse nuclear distribution. Our proposed method offers a comprehensive guide, from cellular staining to analysis, leveraging a semi-automated pipeline developed in Jython and executable in Fiji. Furthermore, we provide a user-friendly Python script to streamline data management, publicly accessible on a Google Colab notebook. Our approach has demonstrated efficacy in yielding highly informative immunofluorescence analyses for proteins with diverse patterns of nuclear organization across differentcontexts.

Application of the Multi-Threading Method and Python Script for the Automate of Network

In recent times, there has been a noticeable surge in the inclination towards the implementation of network automation solutions. This trend is driven by the objective of optimizing network availability within the context of hybrid data center networks, which are becoming increasingly prevalent. Reliability, performance, scalability, and minimal resource overhead are crucial solution design characteristics that significantly impact the decision- making process regarding adoption. Recent research has shown that 90% of network outages happen because of human factors and 69% of respondents manage their network with manual method. That high human error of up to 90%, occurs when configuring network devices using manual method. These problems had a negative impact on the functioning of the organization and were harmful to the user. In this research, we investigate solutions to reduce network outage that caused human error using network automation using the Python programming language and multi-threading method. This network automation will reduce configuration time, eliminate human error, and significantly increase efficiency. The aim of this research is to investigate the efficacy of network automation using Python and multi-threading to reduce network outages.The method used in this research is a parallel or multi-tread execution process method using GNS3 as network simulator. Based on experimental results, the multi- thread automation approach is significantly faster than both the serial automation method (67 seconds) and the manual method (248 seconds), this method requiring only 41 seconds to configure all Cisco router devices. If you're looking for speed, look no farther than the multi- thread automation approach, which is 6 times quicker than the manual method and 3.7 times quicker than the serial automation approach. The findings of this study have substantial and immediate implications for the ability of network engineers to speed up the configuration of networks and lower the rate of human error in doing so.

Auxetic Two‐Phase Chevron Mechanical Metamaterial

This investigation explores novel two‐phase chevron mechanical metamaterials that exhibit auxetic properties. Unlike traditional foam‐like cellular or porous auxetic materials, these designs are composed of chevron patterned layers embedded in anisotropic matrix. This innovation design allows for auxeticity in two orthogonal in‐plane directions (bi‐auxeticity) or in all in‐plane directions (complete auxeticity), providing not only a general strategy but also detailed guidelines for designing non‐porous auxetic mechanical metamaterials with tunable auxetic behaviors. One goal of this work is to explore the mechanical behavior, specifically effective stiffness and Poisson's ratio, of these new designs and to identify the design space for auxetic behavior using numerical and experimental methods. Systematic finite element (FE) simulations are conducted using ABAQUS and Python scripts to quantify effective stiffness and Poisson's ratio within a small strain range. To validate the numerical predictions, three representative designs are selected and fabricated via multi‐material polymer jetting. Uniaxial tension experiments are conducted on these specimens. Design spaces for non‐auxeticity, partial‐auxeticity, and complete auxeticity are identified through an integrated numerical approach. Theoretical criteria for determining the completeness of auxeticity are proposed and verified via FE simulations.

Georectifying drone image data over water surfaces without fixed ground control: Methodology, uncertainty assessment and application over an estuarine environment

Light-weight consumer-grade drones have the potential to provide geospatial image data to study a broad range of oceanic processes. However, rigorously tested methodologies to effectively and accurately geolocate and rectify these image data over mobile and dynamic water surfaces, where temporally fixed points of reference are unlikely to exist, are limited. We present a simple to use automated workflow for georectifying individual aerial images using position and orientation data from the drone's on-board sensor (i.e. direct-georectification). The presented methodology includes correcting for camera lens distortion and viewing angle and exploits standard mathematics and camera data processing techniques. The method is used to georectify image datasets from test flights with different combinations of altitude and camera angle. Using a test site over land, directly-georectified images, as well as the same images georectified using standard photogrammetry software, are evaluated using a network of known ground control points. The novel methodology performs well with the camera at nadir (both 10 m and 25 m above ground level) and exhibits a mean spatial accuracy of ±1 m. The same accuracy is achieved when the camera angle is 30° at 10 m above ground level but decreases to ±2.9 m at 30° and 25 m. The accuracy changes because the uncertainties are a function of the altitude and angle of the camera versus the ground. Drone in-flight positioning errors can reduce the accuracy further to ±5 m with the camera at 30° and 25 m. An ensemble approach is used to map the uncertainties within the camera field-of-view to show how they change with viewing distance and drone position and orientation. The complete approach is demonstrated over an estuarine environment that includes the shoreline and open water, producing results consistent with the land-based field-tests of accuracy. Overall, the workflow presented here provides a low cost and agile solution for direct-georectification of drone-captured image data over water surfaces. This approach could be used for collecting and processing image data from drones or ship-mounted cameras to provide observations of ocean colour, sea-ice, ocean glitter, sea surface roughness, white-cap coverage, coastal water quality, and river plumes. The Python scripts for the complete image georectification workflow, including uncertainty map generation, are available from https://github.com/JamieLab/SArONG.

Polypy: A Framework to Interpret Polymer Properties from Mass Spectroscopy Data.

Mass spectroscopy (MS) is a robust technique for polymer characterization, and it can provide the chemical fingerprint of a complete sample regarding polymer distribution chains. Nevertheless, polymer chemical properties such as polydispersity (Pd), average molecular mass (Mn), weight average molecular mass (Mw) and others are not determined by MS, as they are commonly characterized by gel permeation chromatography (GPC). In order to calculate polymer properties from MS, a Python script was developed to interpret polymer properties from spectroscopic raw data. Polypy script can be considered a peak detection and area distribution method, and represents the result of combining the MS raw data filtered using Root Mean Square (RMS) calculation with molecular classification based on theoretical molar masses. Polypy filters out areas corresponding to repetitive units. This approach facilitates the identification of the polymer chains and calculates their properties. The script also integrates visualization graphic tools for data analysis. In this work, aryl resin (poly(2,2-bis(4-oxy-(2-(methyloxirane)phenyl)propan) was the study case polymer molecule, and is composed of oligomer chains distributed mainly in the range of dimers to tetramers, in some cases presenting traces of pentamers and hexamers in the distribution profile of the oligomeric chains. Epoxy resin has Mn = 607 Da, Mw = 631 Da, and polydispersity (Pd) of 1.015 (data given by GPC). With Polypy script, calculations resulted in Mn = 584.42 Da, Mw = 649.29 Da, and Pd = 1.11, which are consistent results if compared with GPC characterization. Additional information, such as the percentage of oligomer distribution, was also calculated and for this polymer matrix it was not possible to retrieve it from the GPC method. Polypy is an approach to characterizing major polymer chemical properties using only MS raw spectra, and it can be utilized with any MS raw data for any polymer matrix.