dataesr/scientific-paragraphs-categorization huggingface dataset:
We next turned to the prediction of mutations and genetic signatures across cancer types. Kather et al. showed in a seminal study that many mutations and genetic signatures are predictable from H&E stained tissue slides  [6]. Our objective was to assess whether logistic regression could effectively predict mutations and signatures using Giga-SSL representations, as an alternative to employing a full MIL. The data workflow for these experiments is delineated in Fig. 1D. In total, the prediction tasks are (1) 830 point mutation predictions across 14 tumor types (2) 376 known oncogenic driver mutations and (3) 182 subtyping and genetic signature tasks. Of note, genetic variant prediction tasks are usually imbalanced, with the minority class comprising 8% of the dataset on average. This contrasts with subtyping and genetic signature prediction tasks, where the minority class represents on average 33% of the dataset.
Giga-SSL is computationally efficient. To highlight Giga-SSL's computational efficiency, Fig. 2. C shows the GPU-days required for our experiments. Training a Giga-SSL model for 1000 epochs on the complete TCGA dataset takes only 10 GPU-hours on a single GPU. Once we have obtained the representations, downstream prediction tasks become highly efficient. This is because the classification algorithm fits only logistic regression, and the cross-validated experiments take approximately 1 hour on a laptop CPU -a sharp contrast to the 92 GPU-days reported in  [6]. When applied to external datasets, Giga-SSL showcases good scalability. On average, a WSI can be encoded by Giga-SSL in roughly 5 seconds using a single GPU. This implies that encoding a typical dataset of 1000 WSIs can be completed in less than 1.
Discussion In this article, we present a framework providing generic representations for H&E stained WSI, targeting robust solutions for small datasets and simplified training, thus lowering barriers for morpho-molecular cancer analyses. Addressing these challenges, we introduce Giga-SSL, the first SSL framework able to derive concise yet highly discriminative WSI-level embeddings. Using logistic regression, we highlight the advantages of these features, achieving state-of-the-art classification performances on small datasets, exhibiting robust generalization to external datasets and doubling the number of predictable gene mutations in the TCGA across cancer types. A major advantage of Giga-SSL is its computational efficiency, at training and inference time. This efficiency allows for the potential training of Giga-SSL models on datasets significantly larger than the TCGA at minimal cost and with much lower environmental impact.
Moreover, even scientists without detailed knowledge of image analysis and deep learning can easily utilize this modality, facilitating tests on new outcome variables or experiments with different datasets stratifications. We are releasing the entire encoded TCGA-FFPE dataset to the public, reducing its size from 12 TB to 23 MB. Such readily available embeddings can be integrated into TCGA genomics and transcriptomic analyses without any need for prior image-processing know-how or specialized equipment. We are optimistic that this initiative will spark interest within the bioinformatics sector, encouraging comprehensive integration of pathology and molecular data, and fostering joint exploration of cancer's molecular and morphological landscape.
Giga-SSL architecture The architecture of Giga-SSL combines the ResNet18 framework with SparseConvMIL  [19]. The first four residual convolution blocks independently encode each tile. The resulting tile encodings are aggregated within a SparseMap and processed by 4 sparse convolution blocks  [19]. Together, these components achieve functionality akin to a full ResNet18, as detailed in  [20] , and the final layer of this architectural blocks are the WSI embeddings used in the downstream analysis. A final multi-layer perceptron called projector further encodes the embeddings of the WSI, following  [18]. The CL loss is computed using the output of the projector network. Slide-level transformations aim to generate different views that are to be pulled closer or pushed apart, depending on whether they originate from the same WSI, while maintaining part of the biological information of the WSI.
Giga-SSL Training Training is performed in 2 steps. First, the tile encoder is pre-trained on histopathology data using MoCo  [18]  and frozen, like in  [20]. This allows to compare Giga-SSL to competing methods based on the same tile encodings. In a second step, the sparse units (in orange 1) are trained on the full TCGA with the WSI-level contrastive pretext task under the slide-level transformations mentioned in the text. Training is performed for 100 GPU-hours on a single V100 GPU. We use the Adam optimizer with a starting learning rate of 0. 003. We use a cosine annealing learning rate scheduler with 10 warming epochs and a final learning rate to 3e -6. As described in the methodological publication  [20] , we approximate the tile-level augmentations by randomly sampling 25 augmentations per WSI.
Statistical procedure To address downstream classification challenges, we deploy an L2-regularized logistic regression (parameters: C=10, class weight='balanced'). We report the performance of these logistic regressions over 10 random dataset splits for benchmarking and generalization experiments, and 3 splits for mutation prediction experiments (to account for the very imbalanced nature of the mutation prediction task). The same splits are used in compared methods. For mutation prediction, the primary criterion is task predictability. As outlined in  [6] , we accumulate the model's posterior probabilities predicted throughout the crossvalidation folds. We then conduct a two-sided t-test between the predictions of the samples belonging to one class and the predictions from the remainder of the dataset.
We adjusted the resulting p-values for multiple testing, accounting for a total of 1388 classification tasks, using the Benjamini-Hochberg correction. We set the significance threshold, pthres, to 0. 01 and compare the predictability of tasks between Giga-SSL and the MIL model implemented in the original publication. Each task of the pancancer experiments employs a patient-level three-fold cross validation strategy. • A Python package for encoding WSIs using Giga-SSL models is available at https: //github. com/trislaz/democratizing WSI. The package was designed to be userfriendly, enabling a wide range of users to utilize GigaSSL and our pre-trained models. A Supplementaries.
Acknowledgements This work was performed using HPC resources from  GENCI-IDRIS  (Grant  2023-AD011011863R3  ). The results in this article are based upon data generated by the TCGA Research Network: https://www. cancer. gov/tcga. TL was supported by a  Q-Life PhD fellowship  ( Q- life  ANR-17-CONV-0005 ). Furthermore, this work was supported by the  French government  under management of  Agence Nationale de la Recherche  as part of the " Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ) and by the  ITMO Cancer  ( 20CM107-00 ).
Data Availability The TCGA-FFPE WSIs are publicly available through https://portal. gdc. cancer. gov/. The in-house Curie datasets (breast cancer slides and UV) consist of confidential medical data not open to the public. The lightweight embeddings of all the TCGA-FFPE slides computed with giga-SSL models trained using MoCo and CtransPath embeddings, as well as the used models, are made publicly available at https://data. mendeley. com/datasets/d573xfd9fg/3 (DOI: 10. 17632/d573xfd9fg. 3). Labels for various datasets are extracted from different sources: • Lung (LUAD/LUSC): GDC Portal • Breast Cancer (Ductal/Lobular): GDC Portal • Kidney (clear/papillary/chromophobe cells): GDC Portal • mhrd Breast Cancer (HRP/HRD): GerkeLab Repository, as in  [22]. HRD score are binarized using mean as threshold.
F. Mutations Drivers Subtype, genetic signatures Union AUC Intersection TCGA project AUC Mutations Drivers Subtypes Genetic signatures Number of predictable variables Fig. 2 Evaluation Results. A. Displays the results for the four benchmark tasks. The first row of graphs presents the ROC-AUC scores of Giga-SSL and CLAM as a function of the training set size. The second row demonstrates the AUC improvement of Giga-SSL over CLAM as a function of the training set size. B. Presents the results on the external validation datasets for both Giga-SSL and CLAM. C. Indicates the time requirements for key computation steps, measured in GPU-days. CV: Cross-validation. D.
The drilling tests were performed on CFRP/Al7175 stacks by recording the thrust force Fz, torque, feed and spindle currents of the ADU electric motor, axial and radial vibrations of the workpiece, axial vibrations of the spindle, with different cutting parameters and at different stages of tool wear. The acquired signals were preprocessed and then one hundred (100) features related to the tool condition and the nature of the material were extracted from the time and frequency domain. The feature importance measure built into the RF model learning process was used to select the RF model Input data with the goal of using only those features potentially correlated to the drilled material signatures. The developed methodology resulted in high classification accuracy for the different configurations tested.
Material recognition model The main objective of this work was to develop a data-driven, real-time adaptive cutting process for drilling multi-material stacks on eADU. To this end, the developed methodology was divided into two main phases. The first phase concerned the off-line establishment of the material recognition model, taking into account tool wear and drilling incidents (lubrication failure, chip clogging). The second phase aimed at developing and integrating the methodology on the eADU operating device (Fig. 1 ). In this paper, only the first phase will be discussed and presented in detail. As shown in Fig. 1 , the initial step of the proposed approach was to build a representative database through multi-material drilling tests on a custom multi-sensor drill rig. Then, a sample of data was extracted, pre-processed, and a set of features was constructed from the signals through time and frequency analysis.
During these tests, the eADU motor currents were sampled at 100 Hz. To initialize the database, two hundred and eight holes arranged in an 8×26 array were drilled (Fig. 5 ). The objective of these tests was to characterize all recorded signals (thrust force Fz, torque, thrust and torque current of the eADU, axial and radial vibrations of the workpiece, axial vibrations of the eADU, air flow and pressure of the MQL) in the time and frequency domain in order to define criteria for reliable material identification while taking into account tool wear. Finally, a third series of tests was performed on two drilling configurations (Al7175/CFRP) and (CFRP/Al7175) with a new tool and the tool used in the previous series of tests (230 holes). The objective was to establish criteria based on the electrical signals from the motors for material identification.
Database During the three sets of tests, the thrust force Fz, torque Mz, feed motor (thrust current) and spindle motor (torque current) currents of the eADU, MQL flow rate and pressure, axial and radial vibrations of the workpiece, and axial vibrations of the eADU are recorded (Fig. 6  and Fig. 7 ). As shown in Fig. 7 , for all tests performed, the pressure and flow rate of the MQL are constant. This is due to the regulation performed by the proportional valve in the eADU control box. Thus, these two physical quantities are not material sensitive and will not be considered further in this study. Detection strategies based on the eADU motor currents can be more easily adapted to industrial constraints by avoiding external sensors subject to reliability issues. The data set extracted from the eADU is pre-processed and a set of features is extracted from the time and frequency domain, serving as input vectors for the RF model.
Feature dimensionality reduction The proposed dataset contains 114 normalized features (100 of them are extracted from the signals and 14 are the most influential parameters in the drilling process). Some of these features do not allow reliable identification of drilled materials, as shown in the example in Fig. 12 , so it is necessary to reduce the dimensionality of the features in order to keep only those that are most significant and reliable for material identification. This process improved the performance of the machine learning models while reducing the computational time.
g. , spindle speed and feed rate), industrially, in the structure of an aircraft, the cross-section varies along the profile and the thickness of different layers at different locations are unknown  [27, 39]. All process parameters, with the exception of part thickness, were found to be insignificant for the prediction of material classes. This can be explained by the fact that all tests in the dataset were performed at iso cutting condition and with fixed operating parameters (lubrication, Mitis frequency and amplitude setup, etc. ). This optimal subset of the eight (8) features that were of greater importance according to RF is used to train and validate the RF model. Beyond the main study with all characteristics (thrust force Fz, torque, thrust current, torque current, axial and radial part vibration, axial spindle vibration), the RF model was also evaluated on: ➢ Features extracted only from the motors currents of the eADU; ➢ Features extracted from the accelerometers and the motors currents of the electric ADU; ➢ Features extracted only from the time domain; ➢ Features extracted only from the frequency domain.
Conclusions and future works This paper presents a new material identification approach for mechanical assembly of multi-material aerospace stacks by electric ADU. This approach is based on Random Forest (RF) and multi-sensor data fusion (electric ADU motor currents, dynamometer and accelerometers). To develop this approach, a database was built from tests on Al7175/CFRP stacks under isocutting conditions and at different stages of tool wear. A dataset was then extracted to serve as training and performance evaluation data for the RF model. The dataset was pre-processed by dealing with missing values, outliers and signal segmentation. A specific segmentation method based on tool geometry and cutting parameters was developed to separate stable drilling zones from transition zones. Time and frequency analyses were carried out on the stable Al7175 and CFRP drilling zones of the various signals to extract characteristics correlated with the nature of the material and the condition of the tool.
It also aimed to assess the robustness of the time and frequency domains of material identification. In the case of data fusion, the RF model identified Al7175 and CFRP with 100% accuracy. A second model was tested with only electric currents of the ADU motor, an accuracy of 99% was obtained. The combination of the accelerometer characteristics of the part was obtained with those of the current eADU unit with an accuracy of 100%. Finally, a comparison of the model based on the characteristics extracted from the time domain and those extracted separately from the frequency domain was carried out. The frequency range approach proved to be more robust with 100% material identification accuracy. Based on the results obtained, it can be concluded that the method developed facilitates the identification of materials when drilling Al715/CFRP stack in both drilling directions.
Studies have demonstrated that exercise benefits executive function. However, it remains unclear which type of exercise is optimal for preserving executive function among young adults and the cerebral blood flow (CBF) mechanisms that underlie exercise-induced cognitive benefits. Therefore, this study aims to compare the intervention effects of high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) on executive function and the CBF mechanism. This was a double-blinded, randomized, controlled trial study conducted between October 2020 and January 2021 (ClinicalTrials. gov identifier: NCT04830059). Ninety-three healthy young adults (25. 23 ± 2. 18 years old; 49. 82% male) were randomized into the HIIT (N = 33), MICT (N = 32), and control (N = 28) groups. Participants in exercise groups were guided to perform 40 min of HIIT and MICT three times a week for 12 weeks, while the control group received health education for the same period.
Results Descriptive analysis of the demographic information. The descriptive analysis of the study population is indicated in Table  1. Sixty-seven subjects were included in the data analysis (HIIT = 26, MICT = 21, control group = 20), and all participants were analyzed according to the initial grouping. There were no significant differences among groups in the demographic information, including age, height, Body Mass Index (BMI), years of education, and birthplace. No significant difference was found in baseline maximum oxygen uptake (VO 2 max ) between groups. Overall, 49. 82% of the study population was male. Participants across all three groups were 25. 23 ± 2. 18 years old on average, with a mean BMI of 21. 91 ± 2. 92 kg/m 2. The mean VO 2 max of all the participants was 43. 71 ± 6. 98 mL/(kg•min).
Materials and methods Study design and population. We conducted a double-blind, randomized controlled trial (RCT) study from October 2020 to January 2021 using randomization, assignment concealment, and blinding methods (ClinicalTrials. gov identifier: NCT04830059. Data: 02/04/2021). Ninety-three participants met the inclusion criteria and were randomized into three groups: HIIT (N = 32), MICT (N = 33), and control (N = 28). Demographic information was collected, including age, gender, height, weight, family income, education level, and birthplace (rural or urban). Each participant had a unique identification number. They were randomly divided into different groups using a random number generator. The randomization process was carried out by a research staff member who did not participate in the assessment and analysis of the data.
Group allocation was confidential to ensure the successful implementation of randomization. The participants' identification number and grouping information were hidden in an opaque envelope, which was opened by themselves after the baseline assessment. Moreover, participants in the three groups came to the fitness centre on different days of the week, so they did not know the existence of other groups. Participants and evaluators were blinded to the group allocation of intervention assignments throughout the study. Detailed information on the randomization, assignment concealment, and blinding methods is described in the previous article  39. The sample size was calculated by the software of G-Power (Version 3. 1. 9. 7). The effect sizes of exercise interventions on cognition ranges from 0.
19 to 0. 59 according to the previous studies  12. For the repeated measures one-way analysis of variance (ANOVA) test, we assumed that the effect size of the exercise intervention was 0. 4 (Cohen's f = 0. 40), 0. 05 for "α error probability, " and 0. 8 for "power (1 -β error probability)"  40. Moreover, the study included three groups (MICT, HIIT, and control group). According to the G-Power software, a total sample size of 64 is needed in this study. Therefore, 100 individuals were initially recruited for this study, of whom 93 were found to meet the inclusion criteria. The trial profile is provided in Fig. 3. The inclusion criteria for this study were as follows: (1) healthy young college students aged 20-30 years with normal BMI, who reported no history of cardiovascular, cerebrovascular, or respiratory disease and were not taking any cardiovascular medications; (2) individuals with normal or corrected vision; (3) participants who reported being physically inactive in the past six months, as determined by the International Physical Activity Questionnaire screening; and (4) individuals without musculoskeletal conditions that could impede participation in physical activity  39.
Some limitations should be acknowledged in this study. Firstly, 12 weeks of intervention is relatively short for improving young adults' CBF and executive function. Future studies with extended intervention periods are preferred to further explore the benefits. Secondly, our sample was small considering the methodological limitations, such as the challenges of CBF assessment with transcranial Doppler ultrasonography due to the thickness of the temporal bone, which even resulted in some participants from whom no signal could be acquired at all. However, all the CBF indexes were assessed by the same researcher and measured in a similar way during the whole process of the research. Additionally, we checked and confirmed data quality during preprocessing to ensure the measurement validity of CBF.
Acknowledgements We appreciate the funding support from the project on on  National Natural Science Foundation of China  (No. 42277419 ),  Ma John 's physical education thought and practice of  Tsinghua University, China Postdoctoral Science Foundation  (No. 2022M711858 ), and Social Project by the  Ministry of Chinese Education  ( 22YJC890024 ). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript. We appreciate the physical training and rehabilitation research centre of  Tsinghua University  for providing venues and professional coaches.
APOLLO: a resource of 247,092 refined genome-scale reconstructions To build genome-scale metabolic reconstructions from the two MAG resources, we retrieved 154,723 MAGs  (Pasolli et al. , 2019)  (Pasolli resource, Table  S1 ) and 92,143 MAGs  (Almeida et al. , 2019)  (Almeida resource, Table  S1 ), as well as 226 genomes from the Human Gastrointestinal Bacteria Culture Collection  (Forster et al. , 2019)  that served as an independent validation dataset (Table  S1 ). For each genome, a draft reconstruction was generated via the KBase online platform  (Arkin et al. , 2018)  and subsequently, refined in a semi-automated manner using the DEMETER pipeline  (Heinken et al. , 2021d) (Figure 1a, Methods). Where possible (57. 0% and 45. 91% of reconstructions for Pasolli and Almeida, respectively, representing 52.
Metabolic features are predictive of taxonomic assignment of the reconstructed strains Beyond reaction and pathway presence, to explore the functional metabolic potential, we predicted the uptake and secretion potential (representing the in silico exometabolome), and the intracellular metabolite production potential (respresenting the in silico endometabolome) of each APOLLO reconstruction using flux balance analysis  (Orth et al. , 2010) , assuming unlimited uptake of nutrients (Methods). In total, 517 could be consumed and 434 secreted (of which 360 overlapped), and 1,248 could be synthesised intracellularly by at least one strain (Table  S5 ). To reduce the dimensionality of the APOLLO dataset, we visualised the separation of strains based on reaction presence, metabolite uptake and secretion potential, and internal metabolite production potential in 2D via linear discriminant analysis (LDA) , and in 3D via uniform manifold approximation and projection for dimension reduction (UMAP)  (McInnes et al.