Large Files Research Articles

FUSE based file system for efficient storage and retrieval of fragmented multimedia files

Multimedia content providers (such as cyberlockers) are overwhelmed by huge volumes and large sizes of multimedia files. Cyberlockers provide Quality-of-Service (QoS) downloading to end users as the files are split into fragments. However, merging these fragments on the fly for streaming harms Quality-of-Experience (QoE) of users and overwhelms computational capacity. As a solution, this paper proposes a FUSE based file system, namely fumy, for efficient storage of large multimedia files as small sized fragments (for downloading) and virtually unifying them for their efficient retrieval (for streaming). We discuss virtual unification, namespace manipulation and operation redirection employed by fumy to achieve its goal. We also discuss the implementation details of fumy as a FUSE file system. We evaluated fumy for its performance on extended file systems in various operational and benchmarking configurations. Our results suggest that fumy adds minimal performance overhead to extended file systems when the fragment size is 512 MiB or higher, imposes least performance tax on ext4 file system, and even enhances the performance of extended file systems for specific workloads and fragments sizes. We recommend using ext4 file system with fragment size of 512 MiB for different workloads to achieve optimal performance of fumy.

Pixel type classification based reversible data hiding for hyperspectral images

The digital files are becoming larger and larger with the development of computer hardware and computing power, and the fast processing for large files, e.g., hyperspectral images, is becoming feasible for not only companies but also individuals. However, the acquirement of hyperspectral images still costs a lot. Therefore, security issues like copyright ownership of hyperspectral images need to be taken seriously. Reversible data hiding (RDH) is a technology that can embed watermarking information into multimedia cover to protect copyright. However, the natural-images-based RDH methods cannot exploit the large amount of inter-band redundancy contained by hyperspectral images, which leads to a low efficiency for copyright protection. In this paper, a novel RDH method specially designed for hyperspectral images is proposed. We use the value information from the pixel of an adjacent band to classify each pixel into one of the five types when predicting it, and an adaptive predictor is matched for the pixels of each type to achieve a high prediction accuracy. Finally, a complexity calculation method containing three components is put forward to further improve the embedding performance. Experiments show that the proposed method outperforms the existing RDH method for hyperspectral images and other state-of-the-art RDH methods for natural images.

Sherlock: an open-source data platform to store, analyze and integrate Big Data for computational biologists

In the era of Big Data, data collection underpins biological research more than ever before. In many cases, this can be as time-consuming as the analysis itself. It requires downloading multiple public databases with various data structures, and in general, spending days preparing the data before answering any biological questions. Here, we introduce Sherlock, an open-source, cloud-based big data platform (https://earlham-sherlock.github.io/) to solve this problem. Sherlock provides a gap-filling way for computational biologists to store, convert, query, share and generate biology data while ultimately streamlining bioinformatics data management. The Sherlock platform offers a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to enable users to analyze, process, query and extract information from extremely complex and large data sets. Furthermore, Sherlock can handle different structured data (interaction, localization, or genomic sequence) from several sources and convert them to a common optimized storage format, for example, the Optimized Row Columnar (ORC). This format facilitates Sherlock’s ability to quickly and efficiently execute distributed analytical queries on extremely large data files and share datasets between teams. The Sherlock platform is freely available on GitHub, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users can easily and quickly create and work with specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, analytics, integration and collaboration through modern big data technologies.

Removal of Giant Choledocholithiasis by Direct Cholangioscopy with Gastroscope and Electrohydraulic Lithotripsy

A 71-year-old woman presented to the emergency department with 3 months of intermittent right upper quadrant pain. Laboratory results showed a total bilirubin of 0.6 mg/dL, aspartate aminotransferase of 52 IU/L, alanine aminotransferase of 90 IU/L, and alkaline phosphatase of 239 IU/L. A computed tomography of the abdomen and pelvis was performed, which revealed a large 9 × 4 × 4 cm hyperdense mass underneath the liver, adjacent to the gallbladder (Figure A, arrowhead). An endoscopic ultrasound showed a very large radiopaque filing defect in a very dilated bile duct, and a cholangiogram during endoscopic retrograde cholangiopancreatography confirmed a very large stone occupying the entire common bile duct (Figure B, arrowheads trace stone outline).

Evaluating the Performance of High Spatial Resolution UAV-Photogrammetry and UAV-LiDAR for Salt Marshes: The Cádiz Bay Study Case

Salt marshes are very valuable and threatened ecosystems, and are challenging to study due to their difficulty of access and the alterable nature of their soft soil. Remote sensing methods in unmanned aerial vehicles (UAVs) offer a great opportunity to improve our knowledge in this type of complex habitat. However, further analysis of UAV technology performance is still required to standardize the application of these methods in salt marshes. This work evaluates and tunes UAV-photogrammetry and UAV-LiDAR techniques for high-resolution applications in salt marsh habitats, and also analyzes the best sensor configuration to collect reliable data and generate the best results. The performance is evaluated through the accuracy assessment of the corresponding generated products. UAV-photogrammetry yields the highest spatial resolution (1.25 cm/pixel) orthomosaics and digital models, but at the cost of large files that require long processing times, making it applicable only for small areas. On the other hand, UAV-LiDAR has proven to be a promising tool for coastal research, providing high-resolution orthomosaics (2.7 cm/pixel) and high-accuracy digital elevation models from lighter datasets, with less time required to process them. One issue with UAV-LiDAR application in salt marshes is the limited effectiveness of the autoclassification of bare ground and vegetated surfaces, since the scattering of the LiDAR point clouds for both salt marsh surfaces is similar. Fortunately, when LiDAR and multispectral data are combined, the efficiency of this step improves significantly. The correlation between LiDAR measurements and field values improves from R2 values of 0.79 to 0.94 when stable reference points (i.e., a few additional GCPs in rigid infrastructures) are also included as control points. According to our results, the most reliable LiDAR sensor configuration for salt marsh applications is the nadir non-repetitive combination. This configuration has the best balance between dataset size, spatial resolution, and processing time. Nevertheless, further research is still needed to develop accurate canopy height models. The present work demonstrates that UAV-LiDAR technology offers a suitable solution for coastal research applications where high spatial and temporal resolutions are required.

Construction of Intelligent Search Engine for Big Data Multimedia Resource Subjects Based on Partial Least Squares Structural Equation

Abstract The equivalence of the research model is used as part of least squares to construct an intelligent search engine for large multimedia files. First of all, based on the intelligent design of big data analysis, optimize the search engine mode and create a good search environment. According to the research on big data technology, the application value of big data in intelligent search engine is analyzed, and a better search engine system is built on this basis. The basic theory of comparative modeling is also investigated. Then, on the big data management platform of Siping Fire Bureau, the operation of the big data search engine is identified according to the difference of evolutionary algorithms. Experiments show that the big data search engine, as part of the least squares structural equation algorithm, also improves user satisfaction, which is considered below. From the big data search engine, according to the difference of the evolutionary algorithm, the return time, return value and cost are improved, and the search itself can be effectively and accurately realized.

OCL Queries of Historical Geographic Information

Searching for precise information contained in large textual files is very time consuming and error prone. Investigating past and historical data concerning geography and economy can be facilitated a lot when the data is appropriately stored in a data base or and the corresponding schema has a proper form. Therefore, it is convenient to transform textual data to an ontology and query it in such a form.

Prediction of tidal-driven currents using convolutional neural network

Efficient forecasting models of coastal currents are required for the predictive control of smart vessels with autonomous or remote operating capabilities. Accurate physics-based numerical models demand computational resources that often are unavailable onboard ships; moreover, internet bandwidth at remote locations might not be sufficient to exchange the large computational files with onshore servers. In this case, light and efficient Data-Driven Models (DDM) may be developed to be used as a surrogate for a physics-based model.Currents and surface elevation are hydrodynamic variables driven by astronomic tide potential, allowing for harmonic analysis and synthesis of otherwise irregular solutions of Navier-Stokes equations. Once numerical solutions are obtained and analysed in the Fourier domain, the identified harmonics (tide constituents) can be used as a dataset to train the DDM. One approach to building a DDM is by utilizing a Convolutional Neural Network (CNN).CNN is mainly applied in image recognition processes, where it learns the spatial features of image matrices of pixels arranged in columns and rows and uses the learned features to predict an output. This approach can be applied to 2D or 3D fields of predominantly tidal-driven coastal currents. This paper extends the CNN framework to approximate and predict coastal hydrodynamic state variables in space and time. The depth-averaged Navier-Stokes equations were used to create the dataset necessary to train the adopted CNN autoencoder architecture.Two methods of CNN model development are explored. The first method assesses CNN’s capabilities for time forward prediction, using 2D time series of hydrodynamic variables. The second method investigates CNN’s parameter (using Manning number) prediction ability using 2D spatial plots formed by the top 20 most energetic tidal constituents that were obtained from the numerical solution. The hyperparameters of the CNN model for both methods were carefully optimized and tuned using the loss function, Root Mean Square Error (RMSE). The coefficient of determination, the relative error of the time series, and its amplitude and phase were used to gauge the performance of the CNN model for both methods at the final testing stage.Once accurate solutions are achieved, the trained DDM is almost instantaneous and uses negligible computational resources as compared to traditional fluid solvers. Therefore, trained DDM can be deployed onboard ships where global forecast information (e.g., from Global Forecast System) can be used to provide an accurate prediction of the current forcing. To generate the instantaneous current predictions for navigation and smart ship control, the global data can be pulled or pushed to the ship before the mission.

Performance Tuning Of Apache Spark Framework In Big Data Processing with Respect To Block Size And Replication Factor

Apache Spark has recently become the most popular big data analytics framework. Default configurations are provided by Spark. HDFS stands for Hadoop Distributed File System. It means the large files will be physically stored on multiple nodes in a distributed fashion. The block size determines how large files are distributed, while the replication factor determines how reliable the files are. If there is just one copy of each block for a given file and the node fails, the data in the files become unreadable. The block size and replication factor are configurable per file. The results and analysis of the experimental study to determine the efficiency of adjusting the settings of tuning Apache Spark for minimizing application execution time as compared to standard values are described in this paper. Based on a vast number of studies, we employed a trial-anderror strategy to fine-tune these values. We chose two workloads to test the Apache framework for comparative analysis: Wordcount and Terasort. We used the elapsed time to evaluate the same.

APOGEE-centric Ananke Simulations in a SciServer SQL Database

Modern galaxy simulations have reached the complexity required to create sophisticated mock catalogs. Ananke is a set of nine mock catalogs constructed from synthetic surveys of three Milky Way-like galaxies from the Latte suite of FIRE simulations. Ananke provides observed quantities for comparison with modern large-scale stellar surveys. In SDSS-IV DR17, mock catalogs for the Apache Point Galactic Evolution Experiment (APOGEE) were built from Ananke synthetic surveys as a Value-Added Catalog, but were only provided as large flat files (∼>10's GB). Here we announce an >40 Tb SQL database for nine APOGEE-specific mock catalogs and describe additions to the data model necessary for effective user queries. The catalogs can be accessed on the free, science platform, SciServer—supported by the Institute for Data Intensive Engineering and Science at the Johns Hopkins University (IDIES); SciServer supports server-side analysis with commonly used coding languages and tools.

Informatics Research on Mental Health Functioning: Decision Support for the Social Security Administration Disability Program.

The disability determination process of the Social Security Administration's (SSA's) disability program requires assessing work-related functioning for individual claimants alleging disability due to mental impairment. This task is particularly challenging because the determination process involves the review of a large file of information, including objective medical evidence and self-reports from claimants, families, and former employers. To improve this decision-making process, SSA entered an interagency agreement with the Rehabilitation Medicine Department, Epidemiology and Biostatistics Section, in the Clinical Center of the National Institutes of Health, intending to use data science and informatics to develop decision support tools. This collaborative effort over the past decade has led to the development of the Work Disability-Functional Assessment Battery and has initiated an approach to applying natural language processing to the review of claimants' files for information on mental health functioning. This informatics research collaboration holds promise for improving the process of disability determination for individuals with mental impairments who make claims at the SSA.

Large-File Raw Data Synchronization for openBIS Research Repositories.

In a systems medicine research consortium, openBIS is used as a research data repository. To facilitate efficient upload of large files, openBIS is complemented by a Nextcloud data cloud system. Using a Nextcloud client, raw mass spectrometry data is automatically imported into the repository in the background, enabling comprehensive data provenance.

An Additive Manufacturing Direct Slicing Algorithm Based on a STEP Model

The Standard Template Library (STL) file is the most common data format for the description of an additive manufacturing (AM) geometric model, but it has some disadvantages, such as large errors of the geometric model description, the easy loss of topology information, data duplication, large file sizes, and so on. Aiming at these problems, a direct slicing algorithm based on a Standard for the Exchange of Product Model Data (STEP) model was proposed. For the parts composed of basic types of surfaces such as boundary curves, spherical surfaces and cylindrical surfaces, the traditional geometric method was used to calculate the intersection. For the parts with complex surfaces, the three-dimensional models were described based on Non-Uniform Rational B-Spline (NURBS) surfaces. The NURBS surfaces were layered using a discrete tracking algorithm, the tracking starting point was determined, the intersection line between the tangent plane and each NURBS sub-surface was obtained, and the closed layer contour was formed. Finally, the slicing simulations and printing experiments of solid parts were carried out using the direct slicing algorithm based on the STEP model. It was shown that the dimensional accuracy and surface quality of the printed parts from this algorithm had been significantly improved.

EXPLORING BENEFITS AND CHALLENGES OF USING BLACKBOARD COLLABORATE AS A REMOTE LEARNING TOOL AMONG QASSIM UNIVERSITY EFL INTENSIVE COURSE PROGRAM STUDENTS DURING COVID-19 PANDEMIC

Due to the outbreak of COVID-19 pandemic, using Blackboard platform as a learning tool has dramatically increased in Saudi universities. Still, learners lack effective knowledge of using Blackboard platform. Accordingly, there is a pressing need to concentrate on using Blackboard platform during the COVID-19 pandemic to improve learning outcomes. Therefore, this study was conducted to explore the benefits and challenges of using Blackboard Collaborate as a learning tool by EFL ICP students during the COVID-19 pandemic. Focus group interviews were used to collect data from a purposive sample of 15 Saudi male ICP students who took part willingly in this study. Inductive thematic analysis was used to analyze the interview data. The findings showed that using Blackboard Collaborate as a learning tool can help the students enhance online cooperative learning, flexible learning in a variety of ways other than traditional classes, having access to a wide range of information for academic purposes and ability to use technology effectively. Additionally, the findings showed that the challenges faced by ICP students include insufficient technical knowledge and training, limited and poor internet connection, inability to upload and download large size files and problems with logging in password. It was intended that this research would be useful to all higher education institutions stakeholders in general. However, with only a few participants, this study was limited to EFL ICP students at Qassim University. Further research may use a quantitative or mixed-method approach to provide generalizable results.

An Elastic-Segment-Based V2V/V2I Cooperative Strategy for Throughput Enhancement

The stochastic properties of vehicles’ arrival process and the high-speed mobility of vehicles lead to dynamic changes in the topology of vehicular networks, affecting the data download experience of users, especially for large files. In this paper, a novel elastic-segment-based vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) cooperative strategy (ESCS) is proposed to reduce the impact of the density of vehicles on achievable throughput. The achievable throughput and optimal parameters of the proposed system are analyzed. The strategy takes the impact of the density of vehicles on the data download process as the key element for consideration. The density of vehicles has a great influence on the distance between vehicles, and further affects the transmission data rate. To solve the problem of low transmission data rate induced by the density of vehicles, the sizes of the elastic-segments are optimized according to the distance between the vehicles. Owing to the stochastic process of the distance between vehicles, it is particularly challenging to derive a closed-form expression for the achievable throughput. To tackle this problem, we develop a new V2I/V2V cooperative model to adapt to the general two-way lanes, analyze the distribution and mobility of vehicles, and derive a closed-form expression for the achievable throughput, as a function of V2I effective transmission range, the V2V effective transmission range, and other system parameters. We then optimize V2V effective connection range and the vehicle density to maximize the achievable throughput. Simulation results show that the proposed strategy significantly improves the throughput compared with the existing cooperative strategy, especially in the scenario of high vehicle density.

Rancang Bangun Aplikasi Duplicate Docx Scanner Menerapkan Metode SHA-256

Hard drives are one of the core components of a computer in various types and sizes. Large hard disks can become full, even though they don't feel like storing large files. The possibility of saving the same file in the directory is very difficult and will take a long time. The Duplicate Docx Scanner application is able to solve this problem. The Duplicate Docx Scanner application is able to find out the same file is located in a different directory by determining the directory on the hard disk and other storage media. This application can calculate the hash value so that it can find the same file. The Duplicate Docx Scanner application uses the SHA-256 hash method. SHA-256 is used to perform file integrity checks in a variety of situations. This Duplicate Docx Scanner application is designed to use the Microsoft Visual Basic.Net 2008 programming language

Compression of Multibeam Echosounders Bathymetry and Water Column Data

Over the past decade, Multibeam Echosounders (MBES) have become one of the most used techniques in sea exploration. Modern MBES are capable of acquiring both bathymetric information on the seafloor and the reflectivity of the seafloor and water column. Water column imaging MBES surveys acquire significant amounts of data with rates that can exceed several GB/h depending on the ping rate. These large file sizes obtained from recording the full water column backscatter make remote transmission difficult if not prohibitive with current technology and bandwidth limitations. In this paper, we propose an algorithm to decorrelate water column and bathymetry data, focusing on the KMALL format released by Kongsberg Maritime in 2019. The pre-processing stage is integrated into FAPEC, a data compressor originally designed for space missions. Here, we test the algorithm with three different datasets: two of them provided by Kongsberg Maritime and one dataset from the Gulf of Mexico provided by Fugro USA Marine. We show that FAPEC achieves good compression ratios at high speeds using the pre-processing stage proposed in this paper. We also show the advantages of FAPEC over other lossless compressors as well as the quality of the reconstructed water column image after lossy compression at different levels. Lastly, we test the performance of the pre-processing stage, without the constraint of an entropy encoder, by means of the histograms of the original samples and the prediction errors.

Implementasi Algoritma Set Partitioning in Hierarchical Trees (SPIHT) dan Algoritma Huffman Untuk Kompresi File Citra

As technology develops, the process of sending files no longer needs to be done manually. Data or files can be sent electronically via email. The image file size is sometimes very large, where the better the quality of the resulting image, the bigger the pixel size needed to record the image. With a very large image file size, the transfer process may fail, because the media storage space exceeds its limit. The solution to this problem is how the image file can be compressed in order to speed up the transfer and storage of image files. Image compression is done by reducing the size of the image file by reducing the bits in the image file, but not eliminating the information data in it. By compressing, large data will be reduced in size so as to save storage space. In this study, the algorithm used is the Swiss army knife data compression, using this method, the compression results from the value of k have different compression results for each value, and the compression results will be beneficial in sending and moving image files more easily

IS-HBase: An In-Storage Computing Optimized HBase with I/O Offloading and Self-Adaptive Caching in Compute-Storage Disaggregated Infrastructure

Active storage devices and in-storage computing are proposed and developed in recent years to effectively reduce the amount of required data traffic and to improve the overall application performance. They are especially preferred in the compute-storage disaggregated infrastructure. In both techniques, a simple computing module is added to storage devices/servers such that some stored data can be processed in the storage devices/servers before being transmitted to application servers. This can reduce the required network bandwidth and offload certain computing requirements from application servers to storage devices/servers. However, several challenges exist when designing an in-storage computing- based architecture for applications. These include what computing functions need to be offloaded, how to design the protocol between in-storage modules and application servers, and how to deal with the caching issue in application servers. HBase is an important and widely used distributed Key-Value Store. It stores and indexes key-value pairs in large files in a storage system like HDFS. However, its performance especially read performance, is impacted by the heavy traffics between HBase RegionServers and storage servers in the compute-storage disaggregated infrastructure when the available network bandwidth is limited. We propose an I n- S torage-based HBase architecture, called IS-HBase , to improve the overall performance and to address the aforementioned challenges. First, IS-HBase executes a data pre-processing module ( I n- S torage S can N er, called ISSN ) for some read queries and returns the requested key-value pairs to RegionServers instead of returning data blocks in HFile. IS-HBase carries out compactions in storage servers to reduce the large amount of data being transmitted through the network and thus the compaction execution time is effectively reduced. Second, a set of new protocols is proposed to address the communication and coordination between HBase RegionServers at computing nodes and ISSNs at storage nodes. Third, a new self-adaptive caching scheme is proposed to better serve the read queries with fewer I/O operations and less network traffic. According to our experiments, the IS-HBase can reduce up to 97% network traffic for read queries and the throughput (queries per second) is significantly less affected by the fluctuation of available network bandwidth. The execution time of compaction in IS-HBase is only about 6.31% – 41.84% of the execution time of legacy HBase. In general, IS-HBase demonstrates the potential of adopting in-storage computing for other data-intensive distributed applications to significantly improve performance in compute-storage disaggregated infrastructure.

Candidate genes for polycystic ovary syndrome are regulated by TGFβ in the bovine foetal ovary.

STUDY QUESTIONCould changes in transforming growth factor β (TGFβ) signalling during foetal ovary development alter the expression of polycystic ovary syndrome (PCOS) candidate genes leading to a predisposition to PCOS?SUMMARY ANSWERTGFβ signalling molecules are dynamically expressed during foetal ovary development and TGFβ1 inhibits expression of the androgen receptor (AR) and 7 (INSR, C8H9orf3, RAD50, ERBB3, NEIL2, IRF1 and ZBTB16) of the 25 PCOS candidate genes in foetal ovarian fibroblasts in vitro, whilst increasing expression of the AR cofactor TGFβ-induced transcript 1 (TGFB1I1 or Hic5).WHAT IS KNOWN ALREADYThe ovarian stroma arises from the mesonephros during foetal ovary development. Changes in the morphology of the ovarian stroma are cardinal features of PCOS. The ovary is more fibrous and has more tunica and cortical and subcortical stroma. It is not known why this is and when this arises. PCOS has a foetal origin and perhaps ovarian stroma development is altered during foetal life to determine the formation of a polycystic ovary later in life. PCOS also has a genetic origin with 19 loci containing 25 PCOS candidate genes. In many adult tissues, TGFβ is known to stimulate fibroblast replication and collagen deposition in stroma, though it has the opposite effect in the non-scaring foetal tissues. Our previous studies showed that TGFβ signalling molecules [TGFβs and their receptors, latent TGFβ binding proteins (LTBPs) and fibrillins, which are extracellular matrix proteins that bind LTBPs] are expressed in foetal ovaries. Also, we previously showed that TGFβ1 inhibited expression of AR and 3 PCOS candidate genes (INSR, C8H9orf3 and RAD50) and stimulated expression of TGFB1I1 in cultured foetal ovarian fibroblasts.STUDY DESIGN, SIZE, DURATIONWe used Bos taurus for this study as we can ethically collect foetal ovaries from across the full 9-month gestational period. Foetal ovaries (62–276 days, n = 19) from across gestation were collected from pregnant B. taurus cows for RNA-sequencing (RNA-seq) analyses. Foetal ovaries from B. taurus cows were collected (160–198 days, n = 6) for culture of ovarian fibroblasts.PARTICIPANTS/MATERIALS, SETTING, METHODSRNA-seq transcriptome profiling was performed on foetal ovaries and the data on genes involved in TGFβ signalling were extracted. Cells were dispersed from foetal ovaries and fibroblasts cultured and treated with TGFβ1. The effects of TGFβ regulation on the remaining eight PCOS candidate genes not previously studied (ERBB3, MAPRE1, FDFT1, NEIL2, ARL14EP, PLGRKT, IRF1 and ZBTB16) were examined.MAIN RESULTS AND THE ROLE OF CHANCEMany TGFβ signalling molecules are expressed in the foetal ovary, and for most, their expression levels increased accross gestation (LTBP1/2/3/4, FBN1, TGFB2/3, TGFBR2/3 and TGFB1I1), while a few decreased (FBN3, TGFBR3L, TGFBI and TGFB1) and others remained relatively constant (TGFBRAP1, TGFBR1 and FBN2). TGFβ1 significantly decreased expression of PCOS candidate genes ERBB3, NEIL2, IRF1 and ZBTB16 in cultured foetal ovarian fibroblasts.LARGE SCALE DATAThe FASTQ files, normalized data and experimental information have been deposited in the Gene Expression Omnibus (GEO) accessible by accession number GSE178450.LIMITATIONS, REASONS FOR CAUTIONRegulation of PCOS candidate genes by TGFβ was carried out in vitro and further studies in vivo are required. This study was carried out in bovine where foetal ovaries from across all of the 9-month gestational period were available, unlike in the human where it is not ethically possible to obtain ovaries from the second half of gestation.WIDER IMPLICATIONS OF THE FINDINGSFrom our current and previous results we speculate that inhibition of TGFβ signalling in the foetal ovary is likely to (i) increase androgen sensitivity by enhancing expression of AR, (ii) increase stromal activity by stimulating expression of COL1A1 and COL3A1 and (iii) increase the expression of 7 of the 25 PCOS candidate genes. Thus inhibition of TGFβ signalling could be part of the aetiology of PCOS or at least the aetiology of polycystic ovaries.STUDY FUNDING/COMPETING INTEREST(S)Funding was received from Adelaide University China Fee Scholarship (M.L.), Australian Research Training Program (R.A.) and the Faculty of Health and Medical Science Divisional Scholarship (R.A.), Adelaide Graduate Research Scholarships (R.A. and N.A.B.), Australia Awards Scholarship (M.D.H.), Robinson Research Institute Career Development Fellowship (K.H.) and Building On Ideas Grant (K.H.), National Health and Medical Research Council of Australia Centre for Research Excellence in the Evaluation, Management and Health Care Needs of Polycystic Ovary Syndrome (N.A.B., M.D.H. and R.J.R.; GTN1078444) and the Centre for Research Excellence on Women’s Health in Reproductive life (R.A., R.J.R. and K.H.; GTN1171592) and the UK Medical Research Council (R.A.A.; grant no. G1100357). The funders did not play any role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors of this manuscript have nothing to declare and no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.