dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the large range of variation in (Mg + Ca)/ Fe (Fig. 3d ) emphasizes its potential interest as a tracer of the origin of the dust. Despite its limited number of dust samples (11), the dataset collected at El Farafra seems particularly interesting for evidencing the influence of the source region on the dust composition. Indeed, the 11 representative points of this station in Fig. 3a, b, , d  can be sorted into two groups: the first  The reconstruction with the HYSPLIT model of the backtrajectories of the air-masses reaching the sampling site during the 7 periods of G1 shows that they were systematically coming from the north, which is to say from the direction of the Qattara Depression (the left column of Fig. 4  illustrates the case of 5 July 2015). The MERRA-2 reconstruction of the dust concentration close to the surface also indicates the presence of dust plumes in the north of the Western Desert (Fig.
Acknowledgements The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and/or READY website (https:// www. ready. noaa. gov) used in this publication. The authors thank NASA's Giovanni data system (http:// giova nni. sci. gsfc. nasa. gov/ giova nni/) for making the MERRA-2 products easily available and the Barcelona Supercomputing Center for making the data and images from the BSC-DREAM8b model available on http:// www. bsc. es/ ess/ bsc-dust-dailyforec ast/. Funding Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). No funding was received for conducting this study.
Results of clonogenic assays were plotted as survival curves showing the fraction of surviving cells as a function of the dose of ionizing radiation. The experimental data were fitted to a linear quadratic model, according to the equation SF ¼e À ðαDþβD 2 Þ (with SF being the Survival Fraction, D the dose of radiations in Gy and α, β radiobiological curve fitting factors, respectively). Dose modifying factor (DMF) was calculated for an isoeffect corresponding to SF = 75%.
Conventional 3D invasion assays were performed and analysed as previously described,  16  (Supplementary Methods 2). Briefly, each spheroid was placed into individual well of 96-well plate and then embedded with 100 µL of a mix of Matrigel ® and hyaluronic acid (100 µg/mL). Spheroids were maintained at 37°C in a humidified 5% CO2 atmosphere for 4 days, allowing invasion and migration processes to take place. For improved contrast, viable cells of the tumor core and invasion area were stained with MTT (1 mg/mL). Invasion was imaged using the GelCount ® device (Oxford Optronix, UK) and quantified with ImageJ software: the total area of invasion (manual yellow delineation) and the core area (white delineation) as well as the invasion distance (µm) were determined.
Mitotic Catastrophe (MC) Detection After harvesting, counting, and assessing the viability of escaped cells from U87 spheroids (at T15), cells were cytocentrifuged at a concentration of 2. 5×10 4 cells per slide for 1 minute at 500 rpm (Sakura, AutoSmear CF-12DE). The cells were then fixed in 10% neutral buffered formalin for 30 minutes. The slides were rinsed with PBS once. The slides were then stained with hematoxylin/eosin for visualization of nuclei. A mounting solution was deposited on the slides for histological staining preservation (Eukitt ® ). Snapshots of the slides were obtained at x10 magnification using an optical microscope (Nikon, DIAPHOT 300) equipped with a camera (Nikon, Digital sight-DS-Fi1). Ten pictures per slide were taken for data processing.
F-Actin was detected with Alexafluor-488-conjugated phalloidin (Ab176753, Abcam; dilution 1:1000 in 2% PBS-BSA). In practice, 150 µL of a mixed solution containing the Alexa Fluor 555-conjugated secondary antibody and the Alexafluor-488conjugated phalloidin was deposited in Labtek II wells for 1 hour in the dark, at room temperature. The cells were then rinsed for 5 minutes with PBS, 3 times. Nuclei were counterstained with Hoechst 33432 (Thermo Scientific, 62249; dilution 1:1000 in PBS) for 15 minutes. Cells were rinsed once with PBS and then a solution against photobleaching (Ibidi, 50001) was applied before slide-lamellar mounting. Phalloidin and secondary antibody fluorescence were analyzed using an ImageXpress ® Micro Confocal instrument with a 10x and 20x APO Plan objective and MetaXpress software (Molecular Devices, Sunnyvale, CA).
Analysis of Gene Expression by RT-qPCR RNA extraction and cDNA synthesis were performed as described in Supplementary Methods 3. Among the 5 reference genes selected, GAPDH and RPL32 were identified as the most stable genes according RefFinder web-tool. The list of reference and targeted genes is given in Supplementary Methods 3. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) was performed with qPCRBIO SyGreen Blue Mix according to manufacturer instructions (PCRBIOSYSTEMS), in a Biorad CFX96 Touch thermocycler using the Bio-Rad CFX Maestro 2. 3 software. Relative gene expression was calculated using the 2(-Delta Delta C(T)) Method. 19.
I. INTRODUCTION A RTIFICIAL intelligence (AI) based on deep learning architectures is being increasingly employed in various industries and everyday life  [4] ,  [18]. The next step in this direction is to embed AI on small devices, such as smartphones, with limited computing resources  [2]. However, one of the major challenges in this context is the memory required to train deep architectures based on automatic differentiation  [1]. This process is resource-intensive, particularly during the reverse mode of automatic differentiation  [22] , which is essential for training these types of deep architectures. This issue has been inherent to neural networks since the origins of the field. For example, Stochastic Gradient Descent (SGD)  [16]  is currently the only method suitable for working on huge datasets.
Indeed it was a major breakthrough in addressing theoretically the splitting of large datasets into small batches while guaranteeing convergence. Another way to reduce memory usage is checkpointing  [21]  introduced as a trade-off between execution speed and memory consumption, without affecting gradient computations or updates. Beyond these seminal techniques, current works  [15]  on embedded AI systems still tries to limit the memory consumption with the additional objective to not compromise the accuracy of the model. In addition to the resource consumption issue, overfitting of training data is a common unwanted behavior  [17]  that results in a reduced generalization power of the model. It occurs when the model memorizes the training set and fails to generalize to new data. To mitigate this issue, dropout was introduced in  [7]  on neural networks.
The approach is based on compilation choices made during the automatic differentiation process, ensuring that each parameter is utilized only once, even if this necessitates duplicating variables. Automatic differentiation, the main computational technique for calculating function derivatives, has significantly facilitated the emergence of deep learning techniques by automatically computing gradients for custom-designed models, similar to how SGD enables gradient descent on large-scale datasets. Applying automatic differentiation to this model representation directly decomposes the gradient as a sum of path contributions. In summary, this paper presents two contributions. Firstly, RAD is generalized into SPAD by allowing for alternative probability distributions on the LCG, which reduces overfitting and can be interpreted as a form of dropout during backpropagation.
In other words, the more the observation batch is representative of the dataset, the better the approximation. SPAD is a new gradient estimator dealing with the trade-off of choosing the best component at a time and maintaining it for the following iterations. The set of the LCG paths is denoted as P. m random paths in the backpropagation graph, denoted as P m ⊂ P, are sampled, and the induced gradients g θ,i (with i ∈ [1. m] without loss of generality) restricted to these paths are calculated. Among these m paths and for the next k max iterations, the one yielding the largest gradient i max is associated to an almost one probability with keeping an ϵ > 0 fraction of exploration for all the other paths (not restricted to the m ones). The Almost-Dirac notation D ϵ i (j) in 5 below is conveniently introduced to represent SPAD: ∀ϵ > 0; ∀i, j ≤ N ; D ϵ i (j) = (1 -ϵ)δ j=i + ϵ N -1 δ j≠i.
III. EXPERIMENTS Experiments were conducted on two different types of tasks. Firstly, our novel gradient estimator was applied to a set of functions commonly used for evaluating optimization algorithms. These functions are not well-suited for gradient descent due to their numerous local minima. However, SPAD may overcome this issue by following gradient estimations rather than relying on the exact gradient. Evaluating SPAD on these functions provides further validation of its usefulness beyond the domain of neural networks. The emphasis in these functions lies on the terminal point of the optimization, whether it is in the proximity or not to the globally optimal solution that is known beforehand. Secondly, the estimator was tested 1 on the MNIST and CIFAR10 datasets using standard deep architectures to compare our method to existing ones.
In order to assess the different approach, the considerations are placed on the accuracy achieved on the test data as well as the maximum memory utilization observed during the training process. These experiments vary significantly in several aspects. Firstly, the data varies greatly, as the first search space is 2-dimensional, while our dense architecture for MNIST, as presented in B, has over 410k parameters. Additionally, the minimum of the optimization functions is known, which is not the case for neural networks. The diversity of these tasks provides a deeper understanding of the implications of the proposed method. Remember that the theoretical probability distribution given by SPAD is an Almost-Dirac on the largest gradient contribution. In practice, the exact estimator g I is not utilized.
B. DEEP LEARNING Experiments were conducted on the MNIST and CIFAR10 datasets, comparing SPAD with the standard stochastic gradient estimator (baseline), RAD and the dropout technique. The experimental framework described in  [12]  was employed, which does not involve early stopping to prevent increased memory requirements. However, this framework may result in overfitting, which is intended to be mitigated. This explains why dropout runs were made, to be able to compare with a method that is designed to mitigate overfitting. The objective of our approach is to preserve learning quality while reducing the memory peak in comparison to traditional SGD. Due to the substantial number of parameters in the utilized networks, drawing a single path in the backpropagation graph would lead to negligible updates.
e. , 10%) was applied during dropout runs. Our practical implementation of SPAD is described in Appendix A. The two metrics aimed to be optimized, namely the final accuracy on the testing dataset and the memory peak in percentage required during training, are depicted in Figure  8. The objective is to get the higher accuracy on testing with the lowest memory consumption, i. e. ending in the green zone. On these examples, the many variants of SPAD are competitive with the baseline and RAD, and it achieves strictly superior results on CIFAR10. Note that all the runs share the same neural architectures, which is a fully connected network on MNIST and a convolutional one on CIFAR10. More details are given in B. Concerning overfitting, detailed results on the CIFAR10 dataset are presented in Figure  9 , while more details on the MNIST dataset are given in the appendices.
They tend to confirm that our method effectively reduces it compared to the baseline. While the training loss of the baseline quickly decreases during the first iterations, its test loss quickly increases. On the contrary, SPAD slowly decreases its loss on the training dataset and its testing loss increases slowly compared to the baseline. This observation highlights the similarities between the process of randomly drawing paths during backpropagation and the dropout technique. Turning off a fraction of the network, on the forward pass for dropout and on the backpropagation for SPAD, tends to reduce overfitting. The dropout runs attain the highest test accuracy with significant memory consumption. This approach effectively mitigates overfitting, as the testing loss increases at a much slower rate compared to other heuristics in Figures  11  and 12  from the appendices.
Finding efficient parameter values that strike a balance between memory reduction and learning performance remains a challenge. Additionally, the effectiveness of SPAD may vary depending on the specific model architectures and datasets used, so generalizability across different scenarios would need to be investigated. Overall the main idea is to draw more frequent examples that have a bigger impact on the loss minimization. Concerning this code's stochasticity, the result shows the advantages of a non uniform probability distribution. This is aligned with multiple works  [3] ,  [10]  that use non-uniform distributions on the observations and outperforms the uniform one. Table  3. summarizes the construction of gradient stochasticity based on the chosen stochasticity. The sampling process can be conducted at the observation or code level, with uniform or non-uniform distribution.
An interesting future work would be about non-uniform distributions on the observations and on the code, which could hopefully get better learning results without increasing the training memory needs. Such direction would help parameters updates on embedded artificial intelligence, which would open many industrial applications, like embedded machine learning on devices with constrained computational resources. the iterations. These hyperparameters were used throughout the training process and were not modified. Similarly, the CIFAR-10 models were trained for 100,000 iterations using gradient descent with a fixed mini-batch size. The learning rate was reduced by 0. 6 every 10,000 iterations, and we used the Adam optimizer. Data augmentation was not used, but the images were centered. For each experiment reported in the main text, we tuned the initial learning rate and weight decay parameters for the feedforward networks.
We generated 20 pairs of weight decay and learning rate values from specific distribution ranges. To ensure consistent results, each experiment was trained five times using separate bootstrapped resamplings of the full training dataset (50,000 for CIFAR-10 and 60,000 for MNIST). The models were evaluated on the test dataset (10,000 for both). The repetition of these experiments were used to create the memory versus test accuracy plots. On the MNIST dataset, the dropout backpropagation techniques perform slightly worse than the baseline and dropout. We do not observe any overfitting in this task, as shown in Figure  11 , where the test accuracy does not decrease over the iterations even though this data is never used for updating the parameters. In contrast, on the CIFAR10 dataset (Figure  12 ), we observe that while training accuracies consistently increase, the test accuracies tend to decrease at some point for many techniques.
The International Commission on Radiological Protection has classified cardiovascular disease as a tissue reaction (formerly termed a deterministic effect), with an approximate nominal threshold dose of 0. 5 Gy independent of dose rate. 24 This level is determined by linear models fitted to epidemiological data that yield less than a 1% lifetime risk. As such, this threshold is a practical one but is not a true no effect dose threshold. In this systematic review and meta-analysis, we research the risks of radiation associated cardiovascular disease that have been observed in therapeutically or diagnostically exposed cohorts. Risks among groups exposed to generally lower levels of radiation dose (with maximum dose <0. 5 Gy) or low dose rate (<5 mGy/h) are also assessed, specifically in the Life Span Study and in groups that are occupationally and environmentally exposed.
selection of studies We conducted a systematic review and reported according to PRISMA and registered in PROSPERO (CRD42020202036) on 1 November 2020. Thereafter, we made a few small changes in the course of progress with the screening, which are detailed in PROSPERO. We used PubMed/Medline, Embase, Scopus, and Web of Science's Core Collection to systematically search the literature, with no limits applied (date, language), on 6 October 2022. Cardiovascular disease is defined as those causes of mortality and incidence with International Classification of Diseases 10th revision (ICD-10) cardiovascular disease codes I00-I99 (or equivalently the ICD 8th revision codes 390-458 or ICD 9th revision (ICD-9) codes 390-459). We excluded animal studies and any study without an abstract. The database search was conducted by AL with input from MPL and NH, and yielded a total of 15 098 articles; these were loaded into Covidence by AL and then subjected to joint review by MPL and NH.
In the first stage, we used only title and abstract to determine eligibility. Later stages of the search used much more complete information. Among other things, the reviewers (MPL, NH) independently ascertained if the organ dosimetry was adequate to estimate radiation risk and to determine if they were potentially informative on the desired outcome measure, ie, excess relative risk per unit dose. Two reviewers (MPL, KA) independently coded the information from the final 93 papers into a Microsoft Excel spreadsheet, and various semi-automated procedures were used to prepare the analytical database from these data; the coding was also checked by a third author (NH). Further details, including search criteria and dosimetry ascertainment, are given in supplement S1. Information coded included whether the exposure was chronic (ie, low dose rate exposure (<5 mGy/h)),  28  or acute (ie, at dose rates above this level).
34 35 More formal tests of selection or publication bias were also conducted using the test statistic suggested by Egger and colleagues. 34 e also used the trim-and-fill method of Duval and Tweedie 36 to assess the likely extent of the change in excess relative risk that can result from selection bias. The Risk Of Bias In Non-randomised Studiesof Interventions (ROBINS-I) framework 37 was used to assess risk of bias associated with various characteristics of each study. A separate and objectively defined study quality score was also determined (supplement S1). Both scores were used to exclude lower quality studies. All statistical models were fitted using the metafor package 38 39 in R. 40  Forest plots were prepared by use of the forestplot package 41 in R. 40  Results of the metaanalysis were based on the data given in supplement S5.
74 Excess relative risk reduced with increasing age at exposure for stroke and all cardiovascular disease in the Life Span Study, 21 but not for heart disease. 50 he excess relative risks that we derived (table 2, table  3 ) were generally consistent with those of a previous systematic review and meta-analysis, published over a decade ago, of moderate to low dose studies,  21  and with those of a subsequent nonsystematic review. 27 Given the overlap in the moderate to low dose studies considered in our paper and previously, this consistency is perhaps unsurprising. However, as suggested by the results of the metaregression analysis (table 2, table  3 ), even if the differences between risks at low and moderate to high table 6 | estimated excess absolute risk of radiation exposure induced death (% per gy), years life lost per gy, and years life lost per radiation induced death, and 95% confidence intervals for various subtypes of cardiovascular disease by country, using subtype specific restricted maximum likelihood estimates for all data from   49  This finding has considerable implications for the system of radiological protection, assuming that the extrapolation is permissible, even, for example, over the restricted dose range 0-0.
13  19 20 54 56 57 59-63 67 90 91 93  95 97-99 The types of chemotherapy that were likely to have been administered are more problematic because some (eg, anthracyclines) are known to be cardiotoxic. As discussed above, the data  [56] [57] [58] [59] [60] [61]  do not suggest that these modify radiation risk. Nevertheless, such factors could confound the associations observed in relation to radiation exposure, although such confounding is unlikely. In some of these studies such information was collected and used in the analysis, and we give this information in supplement S3 tables S3. 4-S3. 6. The heterogeneity in outcomes, and how they are defined and aggregated, is a potential problem in conducting reviews of this sort. We used both incidence and mortality data, in some cases within the same cohort.
SAXS experiments The SAXS experiments were conducted on the BM29 beamline at the European synchrotron radiation facility (ESRF)  40, 41. Following two different procedures. The first experiment was performed in batch, by loading 50 μL of sample in the dedicated BM29 sample changer after centrifugation at 10,000 rpm for 10 minutes. For each sample measured, a series of 20 frames was acquired with an integration time of 1 s for each frame. The azimuthal integration of the images was done automatically from the beamline control software and the 1D scattering curves were subsequently used for data analysis. These datasets were used to extract the radius of gyration and the Flory exponent. In a second experiment, a size-exclusion chromatography (SEC) S200 column of 3 mL volume was used to separate possible contaminants and aggregates from the monomers.
Supporting Information available. Supplementary figures S1 to S12 present additional data and complementary plots detailing the data treatment procedures. SAXS data acquired at the European Synchrotron Radiation Facility (ESRF) are stored under the DOI 10. 15151/ESRF-ES-771376723. Scripts for SAXS data analysis are deposited and available on Github (DOI 10. 5281/zenodo. 7893438). All other data, data analysis scripts and figure-making scripts are available under 10. 5281/zenodo. 7986281. 10. 5281/zenodo. 7986281 6.
The extended dataset of 56 Fe(n,n'γ) cross sections measured by our group more than a decade ago at GELINA (Geel Linear Accelerator) was used in many recent evaluations like ENDF, JEFF and CIELO. Despite the special measures we took to ensure reliability and accuracy, concerns were raised by various groups with regard to several features of this dataset (absolute normalization and/or shape) and therefore the 56 Fe(n,inl) cross section is still under the evaluation by the International Nuclear Data Evaluation Network (INDEN). Consequently, a new experiment is now under preparation aiming to take advantage of the numerous experimental improvements of the GAINS (Gamma Array for Inelastic Neutron Scattering) setup implemented over the years. While γ spectroscopy combined with the time-of-flight method will remain the main technique involved, several other experimental details will differ substantially.