Open-source Analysis Software Research Articles

Assessing data analysis techniques in a high-throughput meiosis-like induction detection system

BackgroundStrategies to understand meiotic processes have relied on cytogenetic and mutant analysis. However, thus far in vitro meiosis induction is a bottleneck to laboratory-based plant breeding as factor(s) that switch cells in crops species from mitotic to meiotic divisions are unknown. A high-throughput system that allows researchers to screen multiple candidates for their meiotic induction role using low-cost microfluidic devices has the potential to facilitate the identification of factors with the ability to induce haploid cells that have undergone recombination (artificial gametes) in cell cultures.ResultsA data analysis pipeline and a detailed protocol are presented to screen for plant meiosis induction factors in a quantifiable and efficient manner. We assessed three data analysis techniques using spiked-in protoplast samples (simulated gametes mixed into somatic protoplast populations) of flow cytometry data. Polygonal gating, which was considered the “gold standard”, was compared to two thresholding methods using open-source analysis software. Both thresholding techniques were able to identify significant differences with low spike-in concentrations while also being comparable to polygonal gating.ConclusionOur study provides details to test and analyze candidate meiosis induction factors using available biological resources and open-source programs for thresholding. RFP (PE.CF594.A) and GFP (FITC.A) were the only channels required to make informed decisions on meiosis-like induction and resulted in detection of cell population changes as low as 0.3%, thus enabling this system to be scaled using microfluidic devices at low costs.

Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease

Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

SpineTool is an open-source software for analysis of morphology of dendritic spines

Dendritic spines form most excitatory synaptic inputs in neurons and these spines are altered in many neurodevelopmental and neurodegenerative disorders. Reliable methods to assess and quantify dendritic spines morphology are needed, but most existing methods are subjective and labor intensive. To solve this problem, we developed an open-source software that allows segmentation of dendritic spines from 3D images, extraction of their key morphological features, and their classification and clustering. Instead of commonly used spine descriptors based on numerical metrics we used chord length distribution histogram (CLDH) approach. CLDH method depends on distribution of lengths of chords randomly generated within dendritic spines volume. To achieve less biased analysis, we developed a classification procedure that uses machine-learning algorithm based on experts’ consensus and machine-guided clustering tool. These approaches to unbiased and automated measurements, classification and clustering of synaptic spines that we developed should provide a useful resource for a variety of neuroscience and neurodegenerative research applications.

Improving INTEGRAL/SPI data analysis of GRBs

The spectrometer on the international gamma-ray astrophysics laboratory (INTEGRAL/SPI) is a coded mask instrument observing since 2002 in the keV to MeV energy range, which covers the peak of the νFν spectrum of most gamma-ray bursts (GRBs). Since its launch in 2008, the gamma-ray burst monitor (GBM) on board the Fermi satellite has been the primary instrument for analysing GRBs in the energy range between ≈10 keV and ≈10 MeV. Here, we show that the spectrometer on board INTEGRAL, named ‘SPI’, which covers a similar energy range, can give equivalently constraining results for some parameters if we use an advanced analysis method. Also, combining the data of both instruments reduces the allowed parameter space in spectral fits. The main advantage of SPI over GBM is the energy resolution of ≈0.2% at 1.3 MeV compared to ≈10% for GBM. Therefore, SPI is an ideal instrument for precisely measuring the curvature of the spectrum. This is important, as it has been shown in recent years that physical models rather than heuristic functions should be fit to GRB data to obtain better insights into their still unknown emission mechanism, and the curvature of the peak is unique to the different physical models. To fit physical models to SPI GRB data and get the maximal amount of information from the data, we developed new open-source analysis software, PySPI. We apply these new techniques to GRB 120711A in order to validate and showcase the capabilities of this software. We show that PySPI improves the analysis of SPI GRB data compared to the INTEGRAL off-line scientific analysis software (OSA). In addition, we demonstrate that the GBM and the SPI data for this particular GRB can be fitted well with a physical synchrotron model. This demonstrates that SPI can play an important role in GRB spectral model fitting.

A quasi-experimental approach for evaluating the heat mitigation effects of green roofs in Chicago, Illinois

In the coming decades, millions of urban residents will be exposed to increasingly deadly heat extremes. As an adaptive response to rising temperatures, many cities have begun to install vegetated ”green” roofs, which now vary widely in structure and size, and have generally been shown to have cooling capacity. Yet, little research has been done to differentiate which types of green roofs are most effective at reducing urban heat. We present a method to evaluate the cooling effects associated with green infrastructure that draws on publicly available satellite imagery and open-source software for analysis. Based on a quasi-experimental research design that integrates social and physical science approaches, this technique is able to identify the cooling effects of green infrastructure against background warming trends associated with urbanization and climate change. We demonstrate this method at three green roof sites across the City of Chicago, finding that the study sites with larger, intensive green roofs accompanied by diverse plant species have greater cooling benefits than the extensive, monoculture green roof. Our low-cost method can aid policymakers and planners in empirically evaluating the cooling capacity of green roofs in their own communities. • Green infrastructure can be evaluated with low-cost remote sensing imagery. • Quasi-experimental research designs can identify the cooling effects of green roofs. • Not all green roofs can fully counteract localized warming trends.

Three-dimensional shear-flexure interaction model for analysis of non-planar reinforced concrete walls

This paper presents theoretical formulation and results of experimental validation studies of a novel three-dimensional macroscopic nonlinear model that captures the interaction between axial-flexural and shear behavior in reinforced concrete (RC) walls. The original Shear-Flexure-Interaction Multiple-Vertical-Line-Element-Model (SFI-MVLEM) developed previously by the authors, which is essentially a two-dimensional line element for rectangular walls subjected to in-plane loading, is extended in this study to a three-dimensional model formulation (SFI-MVLEM-3D) by applying geometric transformation of the element degrees of freedom that convert it into a four-node element, as well as by incorporating linear elastic out-of-plane behavior based on Kirchhoff plate theory. The proposed three-dimensional, four-node model element is implemented in the official version of open-source analysis software OpenSees and validated against experimental data obtained for four well-instrumented U-shaped wall specimens tested under complex multidirectional loading protocols. Comprehensive and detailed comparisons were conducted between various experimentally-measured and analytically-predicted wall responses including the load-displacement behavior of the wall specimens in various loading directions, contributions of nonlinear flexural and shear deformations to wall displacements, vertical and horizontal profiles of flexural strains, and crack patterns. Results presented demonstrate that the proposed analytical model captures, with good accuracy, most of the experimentally-measured responses of non-rectangular walls subjected to multidirectional loading. Model predictions of the wall lateral load capacity and local responses (strains) are, although reasonable, relatively less accurate when the walls are subjected to diagonal loading conditions, due to the plane-sections-remain-plane assumption implemented in the proposed model formulation that disregards the experimentally-observed shear lag effect.

Digital (R)Evolution: Open-Source Softwares for Orthodontics

Among the innovations that have changed modern orthodontics, the introduction of new digital technologies in daily clinical practice has had a major impact, in particular the use of 3D models of dental arches. The possibility for direct 3D capture of arches using intraoral scanners has brought many clinicians closer to the digital world. The digital revolution of orthodontic practice requires both hardware components and dedicated software for the analysis of STL models and all other files generated by the digital workflow. However, there are some negative aspects, including the need for the clinician and technicians to learn how to use new software. In this context, we can distinguish two main software types: dedicated software (i.e., developed by orthodontic companies) and open-source software. Dedicated software tend to have a much more user-friendly interface, and be easier to use and more intuitive, due to being designed and developed for a non-expert user, but very high rental or purchase costs are an issue. Therefore, younger clinicians with more extensive digital skills have begun to look with increasing interest at open-source software. The aim of the present study was to present and discuss some of the best-known open-source software for analysis of 3D models and the creation of orthodontic devices: Blue Sky Plan, MeshMixer, ViewBox, and Blender.

Analysis of Off-Target Mutations in CRISPR-Edited Rice Plants Using Whole-Genome Sequencing.

The CRISPR/Cas systems have become the most widely used tool for genome editing in plants and beyond. However, CRISPR/Cas systems may cause unexpected off-target mutations due to sgRNA recognizing highly homologous DNA sequence elsewhere in the genome. Whole-genome sequencing (WGS) can be used to identify on- and off-target mutation. Here, we describe a pipeline of analyzing WGS data using a series of open source software for analysis of off-target mutations in CRISPR-edited rice plants. In this pipeline, the adapter is trimmed using SKEWER. Then, the cleaned reads are mapped to reference genome by applying BWA. To avoid mapping bias, the GATK is used to realign reads near indels (insertions and deletions) and recalibrate base quality controls. Whole-genome single nucleotide variations (SNVs) and indels are detected by LoFreq*, Mutect2, VarScan2, and Pindel. Last, SNVs and indels are compared with in silico off-target sites using Cas-OFFinder.

Escher-Trace: a web application for pathway-based visualization of stable isotope tracing data

BackgroundStable isotope tracing has become an invaluable tool for probing the metabolism of biological systems. However, data analysis and visualization from metabolic tracing studies often involve multiple software packages and lack pathway architecture. A deep understanding of the metabolic contexts from such datasets is required for biological interpretation. Currently, there is no single software package that allows researchers to analyze and integrate stable isotope tracing data into annotated or custom-built metabolic networks.ResultsWe built a standalone web-based software, Escher-Trace, for analyzing tracing data and communicating results. Escher-Trace allows users to upload baseline corrected mass spectrometer (MS) tracing data and correct for natural isotope abundance, generate publication quality graphs of metabolite labeling, and present data in the context of annotated metabolic pathways. Here we provide a detailed walk-through of how to incorporate and visualize 13C metabolic tracing data into the Escher-Trace platform.ConclusionsEscher-Trace is an open-source software for analysis and interpretation of stable isotope tracing data and is available at https://escher-trace.github.io/.

Developing an All-Digital Workflow for Dental Skills Assessment: Part II, Surface Analysis, Benchmarking, and Grading.

In Part I of this study, evidence was presented that visual inspection (VI) of dental student waxups exhibited low precision (ICC=0.332) and accuracy, resulting in questionable grade validity. VI inappropriately assesses morphology in part due to oversimplified grading rubrics and inappropriate application of ratio measurement to ordinal data. The aim of Part II of this study was to develop, apply, and compare with VI a digital assessment workflow and report the outcomes. After inclusion criteria were met, 67 (83%) student waxups were scanned with an intraoral scanner and analyzed using open source surface comparison software. Each was digitally compared to the homologous standard typodont tooth. Percentage contour variation within a stipulated tolerance was computed. The acceptable tolerance was derived from clinical literature reporting how contour deviations impact the biological and biomechanical stability of teeth and periodontium. Surface roughness of each waxup was also computed and compared with the reference teeth. A formula for digital assessment was developed and applied to each waxup. On average, digital grades exceeded the VI assessments, while correcting individual errors for greater accuracy and precision (ICC=0.866). Students were able to easily apply the digital workflow for visualization and accurate self-evaluation. In this study, a digital grading workflow was developed that used portable data formats and freely available open source analysis software, so the method could be introduced at any dental school where intraoral scanning is available.

The Future Is Open: Open-Source Tools for Behavioral Neuroscience Research.

There has been a recent and substantial increase in the use of open-source tools for conducting research studies in neuroscience. The OpenBehavior Project was created to disseminate open-source projects specific to the study of behavior. In this commentary, we emphasize the benefits of adopting an open-source mindset and highlight current methods and projects that give promise for open-source tools to drive advancement of behavioral measurement and ultimately understanding the neural basis of behavior. Over the past decade, there has been an explosion in the use of new neurobiological tools for measuring and controlling brain cell activity. Recent developments in optogenetics, chemogenetics, cellular imaging, and fiber photometry have spiked publications across cellular, systems, and behavioral neuroscience. Researchers with expertise in molecular biology or cellular physiology are now carrying out behavioral studies, and often bring a fresh approach to the fine-grained study of behavior that has led to the development of many new assays for measuring behavior and cognition in animal models (mice, flies, worms, etc.). Thanks to a revolution in low-cost methods for 3D printing and off-the-shelf microcontrollers (e.g., Arduino, Teensy, microPython) and single-board computers (Raspberry Pi), many of these research groups are able to create complex behavioral tasks quite easily. The R and Python languages, specialized computing libraries (e.g., numpy, OpenCV, TensorFlow), and the Anaconda Python distribution have been crucial for the development of open source analysis software for neuroscience projects. In parallel, these developments in neuroscience research have occurred during a time when there is a simultaneous movement toward sharing computer code (Eglen et al., 2017; Gleeson et al., 2017), through websites like GitHub, and opening up the process of software and hardware design to non-experts through hackerspaces and makerspaces. Despite these developments, there is still room for …

Behavior of a spherical deformable rolling seismic isolator for lightweight residential construction

This paper presents a study for the development and qualification of a practical low-cost seismic isolation system for lightweight residential construction. The study concludes that single concave rolling isolators cast in high strength concrete with a deformable steel-reinforced plastic rolling ball and a displacement restraint system represent a promising isolation system. A full size isolator suitable for application to typical reinforced concrete houses in Turkey was built and tested. The vertical stiffness, creep characteristics under gravity load and the lateral force–displacement characteristics have been studied. Models for behavior have been developed and validated. These models are useful for response history analysis in commonly available commercial and open source analysis software.

ElectroMap: High-throughput open-source software for analysis and mapping of cardiac electrophysiology

The ability to record and analyse electrical behaviour across the heart using optical and electrode mapping has revolutionised cardiac research. However, wider uptake of these technologies is constrained by the lack of multi-functional and robustly characterised analysis and mapping software. We present ElectroMap, an adaptable, high-throughput, open-source software for processing, analysis and mapping of complex electrophysiology datasets from diverse experimental models and acquisition modalities. Key innovation is development of standalone module for quantification of conduction velocity, employing multiple methodologies, currently not widely available to researchers. ElectroMap has also been designed to support multiple methodologies for accurate calculation of activation, repolarisation, arrhythmia detection, calcium handling and beat-to-beat heterogeneity. ElectroMap implements automated signal segmentation, ensemble averaging and integrates optogenetic approaches. Here we employ ElectroMap for analysis, mapping and detection of pro-arrhythmic phenomena in silico, in cellulo, animal model and in vivo patient datasets. We anticipate that ElectroMap will accelerate innovative cardiac research and enhance the uptake, application and interpretation of mapping technologies leading to novel approaches for arrhythmia prevention.

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline.

Experimental bioreactors, such as those treating wastewater, contain particles whose size and shape are important parameters. For example, the size and shape of activated sludge flocs can indicate the conditions at the microscale, and also directly affect how well the sludge settles in a clarifier. Particle size and shape are both misleadingly 'simple' measurements. Many subtle issues, often unaddressed in informal protocols, can arise when sampling, imaging, and analyzing particles. Sampling methods may be biased or not provide enough statistical power. The samples themselves may be poorly preserved or undergo alteration during immobilization. Images may not be of sufficient quality; overlapping particles, depth of field, magnification level, and various noise can all produce poor results. Poorly specified analysis can introduce bias, such as that produced by manual image thresholding and segmentation. Affordability and throughput are desirable alongside reproducibility. An affordable, high throughput method can enable more frequent particle measurement, producing many images containing thousands of particles. A method that uses inexpensive reagents, a common dissecting microscope, and freely-available open source analysis software allows repeatable, accessible, reproducible, and partially-automated experimental results. Further, the product of such a method can be well-formatted, well-defined, and easily understood by data analysis software, easing both within-lab analyses and data sharing between labs. We present a protocol that details the steps needed to produce such a product, including: sampling, sample preparation and immobilization in agar, digital image acquisition, digital image analysis, and examples of experiment-specific figure generation from the analysis results. We have also included an open-source data analysis pipeline to support this protocol.

Robust Multiplexed Clustering and Denoising of Digital PCR Assays by Data Gridding.

Digital PCR (dPCR) relies on the analysis of individual partitions to accurately quantify nucleic acid species. The most widely used analysis method requires manual clustering through individual visual inspection. Some automated analysis methods have emerged but do not robustly account for multiplexed targets, low target concentration, and assay noise. In this study, we describe an open source analysis software called Calico that uses "data gridding" to increase the sensitivity of clustering toward small clusters. Our workflow also generates quality score metrics in order to gauge and filter individual assay partitions by how well they were classified. We applied our analysis algorithm to multiplexed droplet-based digital PCR data sets in both EvaGreen and probes-based schemes, and targeted the oncogenic BRAF V600E and KRAS G12D mutations. We demonstrate an automated clustering sensitivity of down to 0.1% mutant fraction and filtering of artifactual assay partitions from low quality DNA samples. Overall, we demonstrate a vastly improved approach to analyzing ddPCR data that can be applied to clinical use, where automation and reproducibility are critical.

MOSAIC: A Modular Single-Molecule Analysis Interface for Decoding Multistate Nanopore Data.

Biological and solid-state nanometer-scale pores are the basis for numerous emerging analytical technologies for use in precision medicine. We developed Modular Single-Molecule Analysis Interface (MOSAIC), an open source analysis software that improves the accuracy and throughput of nanopore-based measurements. Two key algorithms are implemented: ADEPT, which uses a physical model of the nanopore system to characterize short-lived events that do not reach their steady-state current, and CUSUM+, a version of the cumulative sum statistical method optimized for longer events that do. We show that ADEPT detects previously unreported conductance states that occur as double-stranded DNA translocates through a 2.4 nm solid-state nanopore and reveals new interactions between short single-stranded DNA and the vestibule of a biological pore. These findings demonstrate the utility of MOSAIC and the ADEPT algorithm, and offer a new tool that can improve the analysis of nanopore-based measurements.

FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET

Single-molecule Förster Resonance Energy Transfer (smFRET) allows probing intermolecular interactions and conformational changes in biomacromolecules, and represents an invaluable tool for studying cellular processes at the molecular scale. smFRET experiments can detect the distance between two fluorescent labels (donor and acceptor) in the 3-10 nm range. In the commonly employed confocal geometry, molecules are free to diffuse in solution. When a molecule traverses the excitation volume, it emits a burst of photons, which can be detected by single-photon avalanche diode (SPAD) detectors. The intensities of donor and acceptor fluorescence can then be related to the distance between the two fluorophores. While recent years have seen a growing number of contributions proposing improvements or new techniques in smFRET data analysis, rarely have those publications been accompanied by software implementation. In particular, despite the widespread application of smFRET, no complete software package for smFRET burst analysis is freely available to date. In this paper, we introduce FRETBursts, an open source software for analysis of freely-diffusing smFRET data. FRETBursts allows executing all the fundamental steps of smFRET bursts analysis using state-of-the-art as well as novel techniques, while providing an open, robust and well-documented implementation. Therefore, FRETBursts represents an ideal platform for comparison and development of new methods in burst analysis. We employ modern software engineering principles in order to minimize bugs and facilitate long-term maintainability. Furthermore, we place a strong focus on reproducibility by relying on Jupyter notebooks for FRETBursts execution. Notebooks are executable documents capturing all the steps of the analysis (including data files, input parameters, and results) and can be easily shared to replicate complete smFRET analyzes. Notebooks allow beginners to execute complex workflows and advanced users to customize the analysis for their own needs. By bundling analysis description, code and results in a single document, FRETBursts allows to seamless share analysis workflows and results, encourages reproducibility and facilitates collaboration among researchers in the single-molecule community.

An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited.

BackgroundPerfusion imaging has become an important image based tool to derive the physiological information in various applications, like tumor diagnostics and therapy, stroke, (cardio-) vascular diseases, or functional assessment of organs. However, even after 20 years of intense research in this field, perfusion imaging still remains a research tool without a broad clinical usage. One problem is the lack of standardization in technical aspects which have to be considered for successful quantitative evaluation; the second problem is a lack of tools that allow a direct integration into the diagnostic workflow in radiology.ResultsFive compartment models, namely, a one compartment model (1CP), a two compartment exchange (2CXM), a two compartment uptake model (2CUM), a two compartment filtration model (2FM) and eventually the extended Toft’s model (ETM) were implemented as plugin for the DICOM workstation OsiriX. Moreover, the plugin has a clean graphical user interface and provides means for quality management during the perfusion data analysis. Based on reference test data, the implementation was validated against a reference implementation. No differences were found in the calculated parameters.ConclusionWe developed open source software to analyse DCE-MRI perfusion data. The software is designed as plugin for the DICOM Workstation OsiriX. It features a clean GUI and provides a simple workflow for data analysis while it could also be seen as a toolbox providing an implementation of several recent compartment models to be applied in research tasks. Integration into the infrastructure of a radiology department is given via OsiriX. Results can be saved automatically and reports generated automatically during data analysis ensure certain quality control.

Easy and Accurate Mechano-profiling on Micropost Arrays

Cell culture substrates with integrated flexible microposts enable a user to study the mechanical interactions between cells and their immediate surroundings. Particularly, cell-substrate interactions are the main interest. Today micropost arrays are a well-characterized and established method with a broad range of applications that have been published over the last decade. However, there seems to be a reservation among biologists to adapt the technique due to the lengthy and challenging process of micropost manufacture along with the lack of easily approachable software for analyzing images of cells interacting with microposts. The force read-out from microposts is surprisingly easy. A micropost acts like a spring with the cell ideally attached at its tip. Depending on size a cell applies force from its cytoskeleton through one or multiple focal adhesion points to the micropost, thus deflecting the micropost. The amount of deflection correlates directly to the applied force in direction and in magnitude. The number of microposts covered by a cell and the post deflection patterns are characteristic and allow determination of values like force per post and many biologically relevant parameters that allow “mechano-profiling” of cell phenotypes. A convenient method for mechano-profiling is described here combining the first generation of ready-to-use commercially available microposts with an in-house developed software package that is now accessible to all researchers. As a demonstration of typical application, single images of bone cancer cells were taken in bright-field microscopy for mechano-profiling of cell line models of metastasis. This combination of commercial traction force sensors and open source software for analysis allows for the first time a rapid implementation of the micropost array technique into routine lab work done by non-expert users. Furthermore, a robust and streamlined analysis process enables a user to analyze a large number of micropost images in a highly time-efficient manner.

Easy and Accurate Mechano-profiling on Micropost Arrays.

Cell culture substrates with integrated flexible microposts enable a user to study the mechanical interactions between cells and their immediate surroundings. Particularly, cell-substrate interactions are the main interest. Today micropost arrays are a well-characterized and established method with a broad range of applications that have been published over the last decade. However, there seems to be a reservation among biologists to adapt the technique due to the lengthy and challenging process of micropost manufacture along with the lack of easily approachable software for analyzing images of cells interacting with microposts. The force read-out from microposts is surprisingly easy. A micropost acts like a spring with the cell ideally attached at its tip. Depending on size a cell applies force from its cytoskeleton through one or multiple focal adhesion points to the micropost, thus deflecting the micropost. The amount of deflection correlates directly to the applied force in direction and in magnitude. The number of microposts covered by a cell and the post deflection patterns are characteristic and allow determination of values like force per post and many biologically relevant parameters that allow "mechano-profiling" of cell phenotypes. A convenient method for mechano-profiling is described here combining the first generation of ready-to-use commercially available microposts with an in-house developed software package that is now accessible to all researchers. As a demonstration of typical application, single images of bone cancer cells were taken in bright-field microscopy for mechano-profiling of cell line models of metastasis. This combination of commercial traction force sensors and open source software for analysis allows for the first time a rapid implementation of the micropost array technique into routine lab work done by non-expert users. Furthermore, a robust and streamlined analysis process enables a user to analyze a large number of micropost images in a highly time-efficient manner.