dataesr/scientific-paragraphs-categorization huggingface dataset:
Spectral counting Quantification based on spectral counting approaches are known to be less sensitive than workflows based on MS1 quantification  (Ramus et al. , 2016). Nevertheless, spectral counting metrics can be extracted almost instantly from identification data and work quite well to identify proteins with medium to high fold changes. It thus remains a powerful tool to give a preliminary overview of a dataset. The algorithm implemented in Proline is similar to the one used in a previously published software  (Hesse et al. , 2016)  and can easily discriminate between proteins with a theoretical fold change of 10 and 100, while most UPS1 proteins with an expected fold change of 2 cannot be distinguished from the yeast invariant background (Supplementary Fig. S11 ).
Erroneous or suspect profiles can be individually discarded by the user, and protein quantification can then be recomputed with the remaining validated measurements. Technically, all data are stored in an embedded relational database where each module in the suite reads the required information and writes the produced information with its associated metadata. This type of centralized persistent storage allows relevant information to be extracted at any level of detail upon request from an algorithm or end user. This architecture (see Supplementary Section 'Software architecture') facilitates data visualization and exploration compared with the tabulated text files typically produced as output by other similar software. It is also compatible with the incremental addition of newly acquired MS runs which form part of an ongoing study, or with performing a time-consuming process in stages, as it is possible, e.
Computational efficiency Data-processing efficiency is today a major concern for many labs, and the core facilities that routinely deal with several ongoing largescale label-free studies in parallel. Moreover, as the number of studies increases, the dataset scaling (number of runs to be compared) has also expanded. Consequently, it is now relatively common to generate and analyze datasets containing tens or even hundreds of LC-MS/MS runs. Thus, to assess the relative computational efficiency of Proline and MaxQuant, we benchmarked them on the same computer (see Supplementary Section 'Hardware used to assess computational speed') and recorded the processing time for the corresponding label-free workflows, using Mascot and Andromeda, respectively, for the MS/MS search. The processing speed was measured for two different datasets: one composed of eight LC-MS/MS runs from the standard dataset (UPS1 spiked at 50 and 25 fmol/mg, four replicates for each), and a larger one corresponding to the whole standard dataset (40 LC-MS/MS runs).
Data processing in MaxQuant starts with feature detection and extraction of MS signal from the raw file, whereas the Proline workflow begins with MS/MS data processing (including database search with Mascot, as shown here, or with another search engine). Whatever the order of the different steps, this comparative analysis clearly shows that many steps (peaklist creation, search result validation, quantification) are performed faster by Proline and also that the Mascot search is faster than the Andromeda search (Fig. 6 ). It must be highlighted that the search results import step, absent from the MaxQuant workflow, negatively impacts the final Proline processing time, and that writing of the results in Proline also longer than in MaxQuant. However, even with these drawbacks, Proline remains faster than MaxQuant when processing the two datasets tested, as the time spent on the other steps is significantly reduced, particularly for peaklist creation and extraction of quantitative data.
These two steps avail of the mzDB file format, which provides optimal data access thanks to precise indexation of the MS information  (Bouyssieet al. , 2015; Handy et al. , 2017). Interestingly, previous studies showed that not only the characteristics of the hardware setup are important to speed up the quantitative workflow, but that IO operations are also a major bottleneck in data processing with MaxQuant  (Neuhauser et al. , 2013). To overcome this bottleneck, these authors suggested the use of optimized computers equipped with solid state drives as an efficient way to alleviate memory constraints when accessing raw data. In the evaluation presented here, we used this type of hardware to compare the software in the best conditions. Nevertheless, we still observed a significant increase in speed with Proline, indicating that the use of indexed, dedicated file formats such as mzDB also represents a powerful solution for optimal access to the MS data.
Conclusion Proline is an open-source, cross-platform software (running on MacOS, Linux and Windows), written in the Java and Scala programing languages. It is distributed under the CECILL license (https://github. com/profiproteomics). Its core libraries constitute a java virtual machine-based framework that can be used as a starting point for the development of new tools. Proline accurately and efficiently processes MS-based proteomics data, and can handle both small and very large datasets. The highly detailed data and metadata stored and made available combined with its ease of use perfectly fits the needs of mass spectrometry experts working in proteomics as well as core proteomics facilities. The software was designed to be computationally efficient and capable of organizing, linking and storing all MS data in a centralized database.
MS-based proteomics results associated with a particular project can then be navigated and browsed by users through the graphical user interfaces provided; alternatively, the data can be extracted by querying the database. Usability, algorithms and validation rules implemented in Proline were developed in close collaboration with MS researchers to ensure a consistent, reliable and efficient end-user experience. The label-free MS1 quantification algorithm combines a novel signal detection procedure starting from chromatographic peak apexes with a cross-assignment method based on efficient RT alignment computation and identification-based deisotoping. This algorithm demonstrates very good performance levels, with a low MVs rate and a good accuracy of observed versus expected ratios on a standard spiked dataset.
The tool is thus very competitive with respect to existing solutions such as MaxQuant. It is also important to highlight that Proline's architecture, based on a message-oriented middleware, is compatible with moving to a more distributed architecture in which Proline components could be executed across multiple computers to complete a single processing request. We believe that this future capability could be of interest when processing very large-scale datasets, as it would allow the advantages of a cloud infrastructure to be exploited. Proline version 2. 0 is the seventh publicly available release of the software. It can be downloaded from http://www. profiproteomics. fr/proline/#downloads. On the same web page, a user forum, an online documentation and tutorials using a published dataset  (Ramus et al.
Deep convolutional neural networks (CNNs) Recent developments in computing power have reduced the computation time of CNNs  (Schmidhuber, 2015). At the same time, problems such as overfitting  (Hinton et al. , 2012) , where a CNN gives good accuracy on training images but not when applied to new unseen images, and vanishing gradients  (He et al. , 2016a) , where deep networks with many layers do not converge to a solution during training, have been addressed. This progress has allowed the construction of deeper networks (more layers) using larger images (e. g. He et al. , 2016a, b; Zagoruyko and Komodakis, 2016) , and since 2012, the performance of deep CNNs on common eval-uation datasets has surpassed engineered features (e. g. morphology)  (Krizhevsky et al. , 2012)  and is on par with human performance  (Russakovsky et al.
They then applied transfer learning using the VGG network to compare CNN-based classification of this dataset with human performance. At CEREGE, we have been building on the previous work done with the SYRACO system to develop deep CNN classification systems for use in our microfossil sorting machine, MiSo (patent pending). The application is 2-fold; firstly we wish to identify images so that the machine can physically separate any particle into different species or morphotypes for further analysis. Secondly, we want to classify images from large foraminifera datasets to perform species or morphotype counts and abundance calculations. In this study, we detail our method for automated classification of foraminifera images, with application to large image sets obtained from sediment cores. The method is also applicable to other single-particle classification tasks.
It consists of five steps: (i) acquisition of images, (ii) curation of a training image set, (iii) preprocessing the images, (iv) training of a CNN and eventually (v) application of the CNN to classify a larger foraminifera image set. In Sect. 2 we explain the steps of this method, which are applied to the Endless Forams planktonic image set in Sect. 3. 1. We finally compare human-based counting with the CNN approach on large datasets (> 500 000 images) in Sects. 3. 2 and 3. 3 by investigating benthic foraminifera and planktonic foraminifera fauna in a set of late Pleistocene equatorial Pacific sediment cores.
Importantly, the same camera exposure, gain and white balance are used for all images, typically 3000 ms exposure, 0 dB gain and a ratio of 1. 8 red : 1. 0 green : 1. 4 blue for the white balance. The depth of field of our telecentric lens is approximately 90 µm and not enough to capture most foraminifera entirely in focus. Therefore fusing of multiple images at different focus depths (Z stack) is employed. Either the HeliconSoft commercial image stacking software (e. g. Helicon Focus 7) or our own custom algorithm is used to fuse the image into a single full-focus image (Fig. 2 ). A separation of 70 µm between images is used for the stack. Each foraminifera particle is then cropped into an individual image. Since foraminifera are generally bright white particles, a mask is found using binary segmentation of the image intensity by comparing it to a fixed background threshold -or a dynamic background model in the case of out MiSo machine (Fig.
CNN selection Once the training set has been labelled, it is used to train a CNN classifier. We use two different CNN topologies: 1. a fast-to-train transfer learning approach that is possible to run on a computer without a high-end graphics processing unit (GPU), and 2. a slower-to-train custom full-depth CNN requiring a computer with a dedicated machine learning GPU that is more accurate and classifies faster. The transfer learning approach is advantageous to get a baseline estimation of the classification accuracy for each class in the training set. From this, any modifications or additions can be made, for example, checking the labelling or adding more image of a class with low accuracy. Once satisfactory results are obtained, the full-depth CNN is then trained as it is more accurate and classifies faster, meaning that large datasets can be processed more quickly.
Transfer learning Transfer learning has been employed in other foraminifera classification methods using CNNs, such as in  Mitra et al. (2019) ,  Zhong et al. (2018)  and  Hsiang et al. (2019). Our method takes a slightly different approach in order to speed up training significantly. Firstly, we create a head network, with a ResNet50  (He et al. , 2016a ) CNN pre-trained on the ImageNet  (Deng et al. , 2009)  database at its core. It consists of a set of transform layers to convert images in the range [0, 1] to the range expected according the preprocessing used in ImageNet pre-training, followed by a cyclic slice layer  (Dieleman et al. , 2016) , then the ResNet50 network with final dense layers removed and replaced with a global average pooling layer, and finally a cyclic pooling layer.
Full-depth CNN We also created a custom compact CNN that adapts to input image size, has only one tuneable parameter and also makes use of cyclic layers. The motivation was that other commonly available topologies are quite large and intensive to train, having been designed with the ImageNet dataset in mind. Our design, called Base-Cyclic, uses convolutional units consisting of a 3 × 3 convolutional layer followed by batch normalization layer and rectified linear activation (ReLU)  (Nair and Hinton, 2010). The convolutional layers are initialized using the method of  He et al. (2015)  as this was found to improve training convergence. Two convolutional units are combined into a block, with a 2 × 2 max pooling layer at the end. A network consists of N sequential blocks, the number of which, N, is proportional to the input image size according to N = log 2 (image width) -2, which is then rounded to the nearest integer.
Training The CNNs are trained using cross-entropy loss on the predicted labels. We randomly select 80 % of the training image set for training, with the remaining 20 % used for validation, and feed the images in batches of 64. Adam (adaptive moment) optimization  (Kingma and Ba, 2014 ) is employed to update the network parameters, using an initial learning rate of 0. 001, as this has been found to be good starting point for other image sets  (Wilson et al. , 2017). Training is performed using code written in the Python programming language and using the TensorFlow v1. 14 library  (Abadi et al. , 2016). Three parameterless preprocessing steps are applied to the images before training (or inference). (i) The image intensity is rescaled into the range to [0,1], i. e. an 8 bit image is divided by 255 and a 16 bit image by 65 535, which removes variance due to bit depth.
The number of epochs and drops are tuned to the training set, and we find that those with large numbers of images per class require fewer epochs. For smaller training sets (< 5000 images with fewer examples per class), we use 40 epochs and four drops. For larger sets, the number of epochs is reduced (e. g. 10 epochs for > 10 000 images). More drops are added if the noise in the validation accuracy is still significant after four drops. Henceforth, we refer to our automatic learning rate scheduler as ALRS. At the end of training, the network is "frozen", whereby trainable variables are replaced with constants and saved in protobuf format. An XML file is created with metadata about the network, such as the input size and class names, so that all the information necessary to be able to use the network for classification is present; thus, the CNN can be readily shared with other users.
Ablation study on Endless Forams An ablation study was performed to investigate different CNN topologies and their parameters for foraminifera classification, using the large, publicly available Endless Forams core-top planktonic foraminifera image set  (Hsiang et al. , 2019). All images of this database have been congruently assigned to one species by a set of independent taxonomists, providing a unique benchmark for recent planktonic foraminifera. The image set consists of 27 729 colour images in 35 species classes, ranging from four specimens (Globigerinella adamsi) to 5914 specimens (Globigerinoides ruber) in each class. We excluded five classes, Globigerinella adamsi (4), Globigerinita uvula (7), Tenuitella iota (8), Hastigerina pelagica (13) and Globorotalia ungulata (25) because they had less than 40 images, meaning that only one to eight images are available for validation and, thus, are likely not reliable measurements.
Each image was preprocessed to remove the white metadata panel at the bottom of the image and the black border around the particle, so that only the real photographic part of the image remained, then padded to make them square using the method outlined in Sect. 2. 1. The processed images are available for download from https://github. com/microfossil/ datasets-and-models (last access: 2 October 2020). Both the transfer learning (Sect. 2. 3. 1) and full network training (Sect. 2. 3. 2) approaches were investigated. Training was run using TensorFlow 1. 14 and Python 3. 7, on a Windows 10 desktop computer with NVIDIA RTX 2080 Ti GPU, AMD Ryzen 2700X CPU, Sandisk 970 EVO SSD and 32 GB of RAM.
Initially, greyscale images were used; however, this reduced the accuracy to 79. 9 %, so colour images were used for the rest of the comparison. It is likely that the reduction in performance was because colour is an important discriminating factor for some of the images in this dataset, perhaps due to seemingly different image acquisition settings being used for some of the classes. Interestingly, increasing the image size improved accuracy, up to 85. 2 % for 416 × 416 images. However, this significantly increased memory requirements, with the training set (represented as 16 bit floating point numbers) using approximately 28 GB of memory. The inference time also increased with the image size, taking more than 3 times longer to process 416 × 416 images (6. 48 ms) than 224 × 224 images (1. 95 ms).
5 h for one configuration). The long training time is why larger image sizes were not investigated further; however, it may be feasible for smaller sets of just a few thousand images. The inference time was low for all configurations, ranging from 0. 09 to 0. 68 ms, except for the standard ResNet18 network with was 2. 05 ms. We use the Base-Cyclic network with eight filters in our workflow, as it provides a faster training time with only a small (0. 2 % for this dataset) decrease in accuracy compared with 16 filters. The full network training gave better accuracy than the transfer learning methods at the expense of much longer training times; however, the inference times of the transfer learning networks were around 2 to 10 times longer than the largest Base-Cyclic full network. Therefore, despite their long training times, the shorter inference times and higher accuracy of the Base-Cyclic and ResNet-Cyclic design make them more suitable for processing large image sets.
MD02-2508 sediment core dataset A large image set (73 544 images) was acquired for core MD02-2508 using the imaging system described in Sect. 2. Some images were taken with the particles on a micropalaeontological slide, and some images were taken using the MiSo particle sorting machine at CEREGE. Individual particles were segmented from these larger images as per our method. Images were taken of 41 samples from 40 to 642 cm deep. These samples were chosen to cover the Holocene and the deglaciation (0-16 000 years ago) as given by the age model for this core (see  Tetard et al. , 2017). Manual counting of benthic species in the core had already been performed for 37 samples in this depth range  (Tetard et al. , 2017)  and were used for comparison.
Application to planktonic foraminifera dataset (core MD97-2138) The method was also applied to planktonic foraminifera to create a high-resolution analysis of the last climatic cycle within sediment core MD97-2138, retrieved from the western Pacific during the IPHIS cruise in 1997 on the R/V Marion-Dufresne  (Beaufort, 1997; de Garidel-Thoron et al. , 2007).
MD97-2138 sediment core dataset A very large image set (562 363 images) was acquired for core MD97-2138 using the imaging system described in Sect. 2. All images were taken using the MiSo particle sorting machine at CEREGE, and individual particles were segmented from these larger images as per our method. Images were taken of 49 samples from 1 to 945 cm deep, with an av-erage of 11 477 particles (foraminifera, aggregate, etc. ) imaged per sample. These samples were chosen to cover the whole last climatic cycle, from the Holocene to Marine Isotope Stage 6, as given by the age model for this core. Manual counting of planktonic species in this core had already been performed for 123 samples in the same depth range, averaging 342 specimens identified per sample, and counting of fragmented and whole foraminifera for 99 samples, averaging 568 per sample (de  Garidel-Thoron et al.
MD97-2138 training set A training set was constructed from 13 001 images of particles randomly selected from the larger MD97-2138 dataset that was to be classified. The same taxonomy (35 species classes) as used in the Endless Forams dataset was used to label the images, with the addition of five extra classes (aggregate, background, benthics, double and fragment) to cover additional images that did not fit into one of the planktonic species classes. To speed up the labelling process, the training set was pre-labelled using the best-performing CNN trained on the Endless Forams set from Sect. 3. 1 and manually checked and corrected where necessary. Images ranged from 171 pixels × 171 pixels to approximately 2000 pixels × 2000 pixels in size, corresponding to particles 140 to 1500 µm in diameter, with a median of 310 µm.
MD97-2138 class abundance Manual counting of planktonic species and fragmented shells had previously been performed for samples from  MD97-2138 (de Garidel-Thoron et al. , 2007). To compare the results of both manual and CNN counting, we separated out the six most common species and calculated the relative abundance compared to all planktonic foraminifera, as well as the percentage of fragmented particles to all foraminiferal particles, for both methods. The number of images analysed using the CNN approach typically exceeds 2000 images and reaches more than 18 000 compared with the typical 300-350 foraminifera usually counted by a human taxonomist. We also calculated the abundance using automatic counting with a CNN trained on the Endless Forams dataset. The signals obtained using both the Endless Forams network and the MD97-2138 CNN network classification show similar dynamic characteristics to those from manual counting (Fig.
9 ) but with more consistency in results from sample to sample. The CNN trained on MD97-2138 images always gives results closer to the human count, whereas the Endless Forams-trained classifier systematically underestimates the percentage of those main species. However, the general trends are similar when comparing the Endless Forams and MD97-2138 networks. We focus now on the comparison of -Counts for G. ruber are consistently within the same range as the human counts, with the same counts from the recent data and a dip between 60 000 and 110 000 a BP. -Globigerinita glutina is slightly underestimated compared with human counting. -There is a close alignment of N. dutertrei with a peak at 15 000 a BP that matches the human counts. -Pulleniatina obliquiloculata also matches human counts; however, the CNN abundance does not exhibit the same three-peak structure during the previous interglacial.
Discussion 4. 1 Benthic foraminifera dataset (core MD02-2508) The dynamics of each abundance signal calculated for the benthic foraminifera dataset using our automated CNN method were similar to that obtained from manual counting. However, we noticed that the strongest bias in most species is likely caused by false positives. The misclassified images were inspected to find the source of the errors, and as with the training results for this dataset, the various species of the Bolivina genus were generally confused with each other. In particular, many specimens of B. subadvena were misclassified as B. spissa, causing the nonzero counts for this species during the Holocene. One possible explanation is that the intraspecific morphometric variability for species B. argentea, B. spissa and B. subadvena can be higher than the interspecific variability be-tween these species.