dataesr/scientific-paragraphs-categorization huggingface dataset:
Second, the inference function caters to models that use VI and returns samples from the variational distribution of the latent variables. For a VAE, this calculation is done through an encoder neural network (Figure  4b ). Finally, the loss function specifies the learning objective given the inference and generative outputs. For example, the loss function can either return the data likelihood (e. g. , for MAP), or a lower bound thereof (e. g. , for VI or EM). scvi-tools also contains many pre-coded building blocks that can be used in the development of new Module subclasses for new probablistic models. These include popular neural network architectures, as well as distribution classes that are commonly used for single-cell data, like the mean-parameterized negative binomial. scvi-tools also provides an alternative "backend" with Pyro, which has useful properties like automated loss computation and (in some cases) inference procedures like automatic differentiation variational inference (ADVI  [74] ).
g. , warmup  [83] , alternate variational bounds  [84, 85] , adversarial inference  [86] ), we recommend using the PyTorch backend. In the development of scvi-tools, we aimed to bridge the gap that exists between the single-cell software ecosystems and the contemporary machine learning frameworks for constructing and deploying this class of models. Thus, developers can now expect to build models using popular machine learning libraries that are immediately accessible to end users in the single-cell community. On our documentation website, we provide a series of tutorials on building a model with scvi-tools, walking through the steps of data management, module construction, and model development. We also built a template repository on GitHub that enables developers to quickly create a Python package that uses unit testing, automated documentation, and popular code styling libraries.
This repository demonstrates how the scvi-tools building blocks, through a simplified implementation of scVI, can be used externally. We anticipate that most models built with scvi-tools will be deployed in this way as independent packages. scvi-tools remains under active development. Thus, end users can expect that scvi-tools will continually evolve along with the field, adding support for new models, new workflows, and new features. Looking forward, we anticipate that these resources will serve the single-cell community by facilitating the prototyping of new models, creating a standard for the deployment of probabilistic analysis software, and enhancing the scientific discovery pipeline. Therapuetics and has ownership interest in Serqet Therapeutics. F. J. T. reports receiving consulting fees from Roche Diagnostics GmbH and Cellarity Inc.
high-income countries  17. Since many sleep-related environmental factors substantially vary with countries' level of development (e. g. , paid work time, artificial lighting), the results reported in the literature may not generalize to low-and middle-income countries (84% of the world's population). Here, we present the distribution of reported sleep durations of 730,187 participants spread over 63 countries. This data was collected as part of the Sea Hero Quest project, a mobile video game designed to assess navigation ability in the global population  [18] [19] [20]. Navigation ability is a multifaceted construct involving several cognitive processes, and has been strongly associated with many life outcomes such as academic achievement and career success in science  21. Participants were asked several questions including: How much sleep do you get on average each night? A total of 3,881,449 participants played at least one level of the game, and 27.
A linear-mixed model (LMM) was calculated to predict reported sleep duration with age, age 2 (the quadratic component of age, since Fig. 1  shows a U-shaped curve), gender, education, home environment, commute duration and their interaction with age as fixed effects, and random intercepts clustered by countries: sleep duration ~age*(gender + education + home environment + commute duration) + age 2 + (1 | country). We included these variables as their association with sleep duration has been reported in the literature. All variables significantly predicted reported sleep duration, the results of this LMM are detailed in Table  S2 , and the evolution of reported sleep duration as a function of age by gender, education level, commute duration and home environment is displayed in Fig. 1e. Given the magnitude of the dataset, we chose to focus on effect sizes, which are independent of the sample size.
Since participant demographics and sleep disorders are also associated with spatial ability  19, 46  , it will be important to validate our results with objective sleep parameters recorded using polysomnography. We controlled for the effect of age, gender, level of education, commute duration, home environment and country on sleep duration. Other participant-related variables such as ethnicity, socioeconomic status, neurological and psychiatric disorders, and a high level of inflammation also have been associated with sleep duration variation  5, 7, 47, 48. External variables such as the day of the week, the time of the day, and light levels when the cognitive test was performed significantly impact sleep duration and cognitive performance  10, 49, 50. We unfortunately did not collect these variables in our current dataset, but they would be important to include in a future model.
Future studies should also assess sleep quality known to be confounded with sleep duration. Our data reveal a set of patterns linked to sleep across the world population. We show two major inflection points in the trajectory of reported sleep duration throughout the human life course and find that geographical and cultural factors can be used to predict the average reported sleep of a nation. We also show that objective memory performance can be associated with self-reported sleep duration, 7 h representing a rather universal optimum self-reported sleep duration for cognition in adults. Importantly, declared sleep duration appears to be associated with memory performance starting with late mid-life. Crossing the geographical, linguistic, and cultural boundaries to generalize empirical evidence on sleep has been curbed by methodological difficulties.
Data and task The Sea Hero Quest app was released on May 2016 on the App Store for iOS and on Google Play for Androids. It was available to the general public in 17 languages: English, French, German, Spanish, Macedonian, Greek, Croatian, Dutch, Albanian, Hungarian, Romanian, Slovak, Czech, Polish, Portuguese, Italian, and Serbian. The Sea Hero Quest task and data collection have been thoroughly described in ref. 18. We set up a portal where researchers can invite a targeted group of participants to play Sea Hero Quest and generate data about their spatial navigation capabilities. Those invited to play the game will be sent a unique participant key, generated by the Sea Hero Quest system according to the criteria and requirements of a specific project. https://seaheroquest. alzheimersresearchuk.
org/Access to the portal will be granted for non commercial purposes. Future publications based on this dataset should add "Sea Hero Quest Project" as co-author. Participants. A total of 3,881,449 participants played at least one level of the game. 60. 8% of the participants provided basic demographics (age, gender, home country) and 27. 6% provided more detailed demographics (home environment, level of education). We removed participants above 70 years old because we have previously shown that they suffer from a strong selection bias, causing their performance to be substantially higher than would be expected in unselected participants of the same age  18. We removed participants with an average sleep duration above 10 h (0. 6%) or below 5 h (0. 9%) for 2 reasons. First, they represent only 1.
008, 0. 016], indicating that there is no meaningful difference in terms of navigation between the two groups. To ensure robust results within countries, we removed participants from countries with fewer than 500 players. This resulted in 730,187 participants from 63 countries included in our analysis, (see Table  S1 ). To provide a reliable estimate of spatial navigation ability, we examined the data from a subset of participants who completed the tutorial levels at least the first four wayfinding levels (up to level 11 in the game, N = 418,152). We only analyzed the first attempt of each level. When the participant retried a level to improve their score, we did not include the associated trajectory in the analysis. Demographics. Among the 730,187 included participants, 381,153 identified as men and 349,034 as women.
Analysis Change-points estimation. The change points of the curve representing reported sleep duration as a function of age (Fig. 1c ) are estimated with a parametric global method, systematically varying the location of the division points until the total residual error attains a minimum  51. This method is implemented in the Matlab function (introduced in R2016a) findchangepts with the linear statistics option. More precisely, this method starts by randomly dividing the age curve into two sections. Then it computes a least-square regression line for each section, and adds the deviations (sum of squared differences between the signal values and the predictions) section-to-section to find the total residual error. This procedure is repeated for all divisions points until the total residual error attains a minimum.
Analysis of intra-and extracellular dynamic like vesicles transport involves particle tracking algorithms. The design of a particle tracking pipeline is a routine but tedious task. Therefore, particle dynamics analysis is often performed by combining several pieces of software (filtering, detection, tracking, etc. ) requiring many manual operations, and thus leading to poorly reproducible results. Given the new segmentation tools based on deep learning, modularity and interoperability between software have become essential in particle tracking algorithms. A good synergy between a particle detector and a tracker is of paramount importance. In addition, a user-friendly interface to control the quality of estimated trajectories is necessary. To address these issues, we developed STracking, a P ython library that allows combining algorithms into standardized particle tracking pipelines.
Introduction The study of cell biology dynamics, such as intracellular membrane transport inward (i. e. endocytosis) and outward (i. e. exocytosis/ recycling), has been difficult or at least incomplete until recently, due to the heterogeneity of the motion behavior of the analyzed structures. Different tracking software have been published [e. g. utrack  (Jaqaman et al. , 2008) , TrackMate  (Tinevez et al. , 2017) ,  MTT (Serge et al. , 2008) , MSSEF-TSAKF  (Jaiswal et al. , 2015) , maptrack  (Feng et al. , 2011) , Cell Tracking Profiler  (Mitchell et al. , 2020) , btrack  (Ulicna et al. , 2021) ] to track individual biomolecules or extended objects with a shape, such as cells [e. g. TrackMate  (Ershov et al. , 2021)  and CellProfiler  (Stirling et al. , 2021) ], to obtain spatial information and to quantify their kinetics.
Most of them focus on the accuracy and reproducibility of the analysis but the user interfaces remain complex or even limited to a two-dimensional representation. The development of a user-friendly graphical user interface (GUI) therefore appears necessary to facilitate the selection of parameters, the analysis and the visualization of 3D þ time trajectories estimated from complex 3D videos. The use of Python, a versatile and free programming language is growing rapidly within the bioimaging user community  (Fernandez-Gonzalez et al. , 2022). Python tools for visualization [e. g. napari  (Sofroniew et al. , 2021) , ipyvolume  (Breddels et al. , 2018) , SeeVis  (Hattab & Nattkemper, 2019) ] and analysis  [ZeroCostDL4Mic (von Chamier et al. , 2021) , BioImageIT  (Prigent et al. , 2021) , Cellpose  (Stringer et al.
, 2021) ] are widely applied to microscopy images. On the other hand, a lot of particle tracking approaches have been developed over the last decades. Interestingly, although a number of studies aimed at comparing particle tracking performance have been published  (Carter et al. , 2005; Cheezum et al. , 2001; Chenouard et al. , 2014; Ruusuvuori et al. , 2010; Smal & Meijering, 2015; Smal et al. , 2010) , none of the tested methods seems to perform in a generic way, regardless of the type of image data. As a consequence, it is critical for users to have the possibility to test different detectors and/or trackers in order to identify the best solution for their application. In addition, efforts in the Python community includes particle tracking packages such as TrackPy  (Allan et al. , 2021).
TrackPy is a complete particle tracking toolkit, but the code can be a barrier for non-expert user. Here, we present STracking, an open-source Python library for combining algorithms into standardized particles tracking pipelines for biological microscopy images. STracking is distributed under a BSD 3-Clause 'New' or 'Revised' License. STracking combines particle detection, tracking and analysis methods and can be used via a napari plugin. STracking contributes to the recent ecosystem of Python-based plugins for bioimage analysis.
Implementation and application STracking breaks down a particle tracking pipeline into five components: (i) frame-by-frame particle detection; (ii) particle linking; (iii) analysis of particle properties; (iv) design of track features; and (v) filtering of tracks. Each component is represented as a Python object. Each component can be implemented separately. This modular design makes it easy to update and facilitates interoperability with other plugins/algorithms. Whenever a new detection or tracker algorithm is added, compatibility is guaranteed with the particle tracking pipeline, and it is versioned within the STracking library. Several particle detectors are available in STracking: Difference of Gaussian, Determinant of Hessian and Laplacian of Gaussian from the Python library scikit-image (van der  Walt et al.
, 2014). In addition, STracking includes a mask detector, called SSegDetector. This detector takes a label or binary mask as input image and returns a list of object positions (centroids of connected components). Moreover, STracking includes a tracker  (Matov et al. , 2011)  that estimates the optimal tracks as follows: first, a connection graph is created with all the possible connections. Second, tracks are iteratively extracted from the graph using shortest path and graph pruning. STracking library uses two data structures: 'SParticles' to manage the set of detected particles and 'STracks' to manage the collection of trajectories. These data structures contain SciPy  (Virtanen et al. , 2020)  objects to store the particles and the tracks. The particles are represented with a 2D numpy array where each row is dedicated to a specific particle and columns are [T, Z, Y, X] for 3D data and [T, Y, X] for 2D data.
The properties of particles are stored in a dictionary. Similarly, tracks are stored in a 2D numpy array where each row is dedicated to specific particle and columns are [trackID, T, Z, Y, X] for 3D data and [trackID, T, Y, X] for 2D data. Tracks features and split/merge events are stored using dictionaries. This data representation is the same as napari  (Sofroniew et al. , 2021)  points and tracks layers, making STracking natively compatible with the napari viewer. We thus implemented a STracking napari plugin suite (napari-tracks-reader, naparistracking). It provides a graphical interface to create a STracking pipeline without writing Python code. STracking could be used as script in Python or napari plugin. The STracking library can be combined with other Python packages to extend STracking functionalities.
The napari plugin allows one to perform a full STracking pipeline, or to load detections or tracks from another software such as StarDist  (Schmidt et al. , 2018) , TrackMate  (Tinevez et al. , 2017)  or u-track  (Jaqaman et al. , 2008)  and continue the analysis with STracking and napari. Documentation on STracking library with examples is available at https://sylvainprigent. github. io/stracking/. STracking documentation was created using sphinx and the autodoc extension. STracking pipeline using the napari plugin is illustrated with data obtained in Lattice Light-Sheet Structured Illumination Microscopy  (Chen et al. , 2014) (Fig. 1  and Supplementary Video S1). The STracking workflow could also be implemented using Jupyter notebook (Supplementary Note S1). Additionally, STracking library can be used for cell migration experiments (Supplementary Note S2) using label mask images produced by other software such as CellPose  (Stringer et al.
Conclusions The STracking library simplifies the design of single particle tracking workflows through a graphical interface using napari and a comprehensive Python library of functions. Unlike previous single particle tracking tools in Python ecosystem, it provides a very flexible solution for processing and visualizing the tracks taking advantage of Napari  (Sofroniew et al. , 2021)  viewer for 3D þ time representation. A similar approach was introduced in TrackMate software  (Tinevez et al. , 2017)  for the visualization and validation of 2D tracks in Fiji  (Schindelin et al. , 2012)  java-based environment. Thus, reproducible analysis can be performed without being an expert programmer. For this purpose, STracking library includes a pipeline class to allow executing a tracking pipeline recorded as a json file.
We would point out that this plugin-implemented recording technique is not an optimal software architecture since it should be done by the host platform. To overcome this difficulty, we recommend using a powerful data management software such as the recent BioImageIT platform  (Prigent et al. , 2021). In summary, the STracking library greatly simplifies the inspection and optimization of single particle tracking algorithms and thus allows the evaluation of new detection and tracker algorithms in this context, which are constantly being developed.
STracking is available as a Python library using 'pip install' and the source code is publicly available on GitHub (https://github. com/sylvainprigent/stracking). A graphical interface is available using two napari plugins: napari-stracking and napari-tracks-reader. These napari plugins can be installed via the napari plugins menu or The napari plugin source codes are available on GitHub (https://github. com/syl vainprigent/napari-tracks-reader, https://github. com/sylvainprigent/napari-stracking.
Code Availability The Google Earth Engine Java script code is available to run using the links below for the traditional dust emission model (TEM) and the albedo-based dust emission model (AEM). TEM -https://code. earthengine. google. com/97aaaad02da2af9b914fff8d9cd1bf5d 460 AEM -https://code. earthengine. google. com/9726348d2fc3e81381e8a9229667afdd The code is archived as a text file using Zenodo (where the code will not run without access to the Google Earth Engine) using the DOI below https://doi. org/ 10. 5281/zenodo. 5626825.
Automatic segmentation of nuclei in low-light microscopy images remains a difficult task, especially for highthroughput experiments where need for automation is strong. Low saliency of nuclei with respect to the background, variability of their intensity together with low signal-to-noise ratio in these images constitute a major challenge for mainstream algorithms of nuclei segmentation. In this work we introduce SalienceNet, an unsupervised deep learning-based method that uses the style transfer properties of cycleGAN to transform low saliency images into high saliency images, thus enabling accurate segmentation by downstream analysis methods, and that without need for any parameter tuning. We have acquired a novel dataset of organoid images with soSPIM, a microscopy technique that enables the acquisition of images in low-light conditions.
, 2018)  have greatly reduced the necessity to choose specific parameters. However, despite these important methodological advances, no single combination of methods and parameters can be adopted to automatically perform nuclei segmentation in all images, due to the aforementioned heterogeneity of biological samples and technical artifacts  (Hollandi et al. , 2022). In particular, live-cell imaging represents a stumbling block for these techniques, since these images are often acquired with low light levels and thus have very low SNR and artifacts. Moreover, current supervised Deep Learning models for nuclei segmentation follow the supervised paradigm and thus require well annotated datasets, which (i) engenders bias due to inaccuracy and incompleteness of available segmentation, where nuclei are improperly annotated and unevenly distributed across images  (He et al.
, 2020)  and (ii) limits application to datasets with different image characteristics. In this paper, instead of focusing on the segmentation itself, we propose to tackle this problem by enhancing the nuclei prior to segmentation step, making the task easy for classical nuclei segmentation tools. Specifically, we take advantage of recent advances in the field of unsupervised generative adversarial networks, aiming to translate images from the source domain to the target domain and alleviating the image annotation requirement. For nuclei enhancement task, the target domain corresponds to images with highly salient nuclei, where strong signal difference between the nuclei and background make segmentation straightforward. In this work, we introduce SalienceNet, a novel unsupervised Deep Learning-based approach for nuclei saliency enhancement in microscopy images that does not require image annotation when there is need to train the network on new data with different characteristics.
Nuclei segmentation Nucleus segmentation methods can be partitioned in two major groups: those that rely on classical image processing approaches and those that propose Deep Learning models. For a thorough review, we refer the reader to  (Hollandi et al. , 2022). Image processing pipelines usually contain a number of filtering and thresholding steps combined, if needed, with basic morphological operators to differentiate nuclei  (Malpica et al. , 1997; Li et al. , 2007). A number of such methods are available as plugins of the main biological analyses open-source software tools such as Fiji  (Schindelin et al. , 2012) , ICY  (De Chaumont et al. , 2012) , QuPath  (Bankhead et al. , 2017)  or CellProfiler  (McQuin et al. , 2018). The development of classical image processing methods for nuclei segmentation is still an active field.
, 2021)  and StarDist  (Schmidt et al. , 2018). Another successful architecture is Mask R-CNN, that has been recently adapted for nuclei segmentation by the authors of nucleAIzer  (Hollandi et al. , 2020). ImageJ has recently proposed Deep Learning-based segmentation plugins, and pre-trained models are available through DeepImageJ (Gómez-de  Mariscal et al. , 2021). The success of the aforementioned Deep Learning methods for nuclei segmentation is particularly due to the use of large and relatively varied training datasets, with images acquired using different microscopy modalities. Nevertheless, establishing a general solution is still an unmet need, especially for images acquired with novel microscopy techniques, such as for example the live-cell imaging  (Ettinger and Wittmann, 2014)  that reduces the intensity of the light sources of the microscope to a minimum in order to limit the photo-damage to the cells thus be able to observe them over long periods of time.