dataesr/scientific-paragraphs-categorization huggingface dataset:
Effect of degraded input The effect of degraded input was investigated by reducing randomly the number of residues in the graph (left panels) or by increasing the number of connected neighbors described by the hyperparameter k (right panels) introduced in Section 2. 1. In the graph including all backbone heavy atoms, several percentages of residues ablation from the graph network were considered (Figure  4 , upper left panel). It is remarkable that the prediction by Sequoia is reduced from less than 10% for all ablation levels smaller than 20%. For larger ablation levels, the success rate decreases strongly but, for the prediction of α or β elements, is mostly reduced of about 20% for an ablation level of 50%. The two Datasets A and B (continuous and dashed lines) displays similar resistance to ablation for all predictions.
Positions of Sequoia erroneous predictions The error cases in Sequoia prediction were examined for Dataset A in the absence of noise (Figure  5 , upper panel). For each erroneously predicted residue, the distance of the residue to the extremity of the corresponding secondary structure element was determined. For classifications α-Other and β-Other, a large majority of the erroneous predictions (w = 2) were located at the limits within the two first or the two last residues of a secondary structure element, most of them being the first or the last residue (w = 1). These erroneous predictions are the sign of different points of view on the limits of secondary structure elements. DSSP handles a discrete classification, whereas Sequoia is sensitive to the geometrical deformations close to the limits which leads to to exclude the limit residues from the detection of the element.
Examples of Sequoia use Examples of Sequoia predictions are given for three proteins displaying only α helices, only β strands or both types of secondary structures (Figure  6 ). It is visible that the Sequoia predictions of α helices and β strands are in quite good agreement with the DSSP predictions. The missing residues in the prediction of secondary structure elements are mostly located at the extremities of the elements in agreement with the previous analyses of Figure  5. The efficiency of Sequoia prediction was also tested on C α positions determined using Deeptracer  [36, 33]  on three EM maps obtained from the Electronic Microscopy Data Bank (EMDB) 2  : EMD-23927  [16] , EMD-30915  [28] , EMD-30942 (to be published). These entries were chosen as they correspond to different protein complexes (affinity captured human p97 hexamer, Salmonella flagella MS-ring protein FliF 1-456, apo spike protein of SARS-CoV2).
They were obtained by single particle reconstruction and correspond to medium resolution data, for which the determination of atomic positions is not straightforward. The resolutions for the entries EMD-23927, EMD-30915, EMD-30942 were respectively of: 4. 22, 3. 45 and 4. 46 Å, and no corresponding PDB entry has been described in EMDB for these data. The EM maps were uploaded to the Deeptracer Web server  3  and the deep-learning prediction of atoms positions was run using the default parameters. The output containing only C α atoms was downloaded and given to the Sequoia prediction tool trained on the database of C α graphs with the classifications α-Other and α-β-Other. The results of the prediction are displayed in Figure  7. The predicted α helices and β strands are drawn in cartoon whereas the residues predicted to belong to the classification Other are drawn as spheres.
The three classifications α-Other, β-Other and α-β-Other, obtained by Sequoia, are successful at percentages mostly larger than 80% even for the less precise dataset B formed with NMR structures. Sequoia approach is also remarkably resistant to the ablation of protein residues and to the variation of distance threshold between residues. The examination of individual residue errors in Sequoia revealed that most of these errors are located within the two first or last residues of the considered secondary structure elements. The origin of such errors arises from the choice of the method DSSP  [19]  as reference for validating Sequoia. Indeed, DSSP implements a discrete classification of residues among secondary structures in which the prediction jumps from one to another value at the limits of secondary structure elements, without continuous interpolation.
In this frame, it is interesting to see that the F1 score of 70-80% observed for Sequoia for many of the test described in the present work, are of similar order of values than the success rates observed for deep learning analysis of EM maps  [30, 36]. q q q aa1[, 1] aa1[, 2] q q q q q q q q q q q q q q q q q q q q q 40 50 60 aa1[, 3] q q q q q q q q q q q q q q q q q q q q q All atoms: Dataset B q q q aa2[, 1] aa2[, 2] q q q q q q q q q q q q q q q q q q q q q 0 0. 05 0. 1 aa2[, 1] aa2[, 3] q q q q q q q q q q q q q q q q q q q q q 0 0. 05 0. 1 Cα: Dataset B Noise η 2 Noise η 2 F1-score F1-score Fig. 3. Sequoia and FOS predictions of secondary structure elements. The predictions (F1score) are displayed for all atoms (upper panels) and atoms C α (lower panels), for datasets A (left panels) and B (right panels) and for Sequoia (continuous lines) and FOS (dashed lines).
, 2018). If chromatic shift correction is needed, download Chromagnon  (Matsuda et al. , 2018)  from https://github. com/macronucleus/Chromagnon and place at your preferred directory. The version used in this protocol is Chromagnon v0. 85. b. Download Icy from http://icy. bioimageanalysis. org/download/ and install according to site instructions. The version used in this protocol is Icy 2. 1. 0. 1. c. Configure Icy to make use of the maximum available RAM: memory settings are automatically set up at the first run of Icy, depending on your system and the memory available. You can change these using the preferences panel (Max Memory in General panel of Preferences) CRITICAL: You will also need Java installed for Icy to work. Download the correct version for your operating system at the link given in the Icy FAQ section: http://icy.
bioimageanalysis. org/faq/. Attention should be paid to the Java version needed (1. 8 is advised and 64-bits is mandatory; use the provided links on the Icy website) Note: Launch Icy: in the output Panel, you should see the correct version of the Java machine installed (e. g. , for Java Oracle Java(TM) SE Runtime Environment 1. 8. 0_231-b11 (64 bit)and the message VTK 6. 3. 0 library successfully loaded. If this latter does not appear, check the   The channels in reconstructed SIM data files (SIR) may be misaligned in x, y, and z with respect to each other due to: (a) mechanical misalignment in the optical path and/or (b) chromatic shift between wavelengths. Both can be corrected using the software Chromagnon  (Matsuda et al. , 2018). Here are two methods for SIM channel correction depending on the data: Method 1: This method will only work if there are common fluorescent features in both channels.
Stage shift correction Timing: 15 min-30 min In some cases, particularly if there is a time lag between acquisitions of successive datasets, there may also be shifts in the microscope stage that need to be accounted for. For example, this can occur if a sample has multiple emitters at different wavelengths to be recorded (the cryoSIM microscope can only record two channels per acquisition). In that case, data are recorded in sets of two channels and each time at least one wavelength is re-collected to act as a reference point against the added channels collected. See Figure  2  for a schematic of the processing steps. 11. Acquire pairs of images, with one common channel in both pairs: a. Designate one image from one channel to be the reference to which all other images will be transformed to.
Always add this image in the 'reference' part of Chromagnon whenever it is used. In Figure  2  this is the green channel 1, from the first image pair. 12. Perform the chromatic shift correction on each channel: a. Follow the same steps as in the Chromatic Shift Correction section of this paper. 13. Calculate the stage shift and apply the matrix to correct for stage shift to the other channel(s): a. Open Icy. b. Open the datasets of the same channel taken at different times. These will contain the same information but will not be entirely co-incident in 3D due to shifts in the microscope setup that can occur between successive data collection cycles. c. Open ec-CLEM. d. Assign the images as 'target' (the reference image) and 'source' (the image that you want to transform). e. Follow steps d-h from the Image Registration section of this paper to co-register the images.
f. A transformation schema file will be saved in the source image folder. g. Open the data corresponding to the second channel from 'source' <filename>. _SIR_ALN. dv. h. In ec-CLEM, go to 'Advanced Usage'/ 'Apply transformation matrix'. i. Choose the channel to transform and the transformation schema file that was just created and click the play button to apply the transform. j. Save the transformed data. k. Repeat the steps if there are other images that require alignment for stage shift. 14. Merge all final aligned channels into one image using Icy (Sequence/Merge) or ImageJ/Fiji.
Pre-processing of images for registration Timing: 5 min-30 min SIR and SXT 3D image stacks are converted to 2D z axis maximum/minimum intensity projections for ease of processing during the first 2D registration step. This can be done in either Icy or Fiji/ImageJ. Data pairs should be chosen with a view of enabling the calculation of stage drift between successive data acquisition runs. First, the chromatic shift between fluorescence channels is corrected and then the stage-induced drift is calculated from the same channel taken at different time points. The stage drift transformation matrix is then applied to the chromatically corrected channels and finally all channels are merged to obtain a cumulative internally aligned dataset with all the available channels.
Note: Steps one and two can be done on any standard computer but the 3D registration in step 3 is more computationally intensive and requires a system with enhanced RAM capacities (at least 16 Gb). Note: Any files generated by ec-CLEM will be saved in the same folder as the input source image. 17. Compute the 2D image registrations: a. Ensure the following images are saved in the same directory: i. 2D SIR (image obtained from Z-projection of maximum intensity, fluorescence microscopy). ii. 2D brightfield (image, minimum intensity z-projection). iii. 2D X-ray (mosaic image, typically annotated with the regions of interest during data acquisition). iv. 2D X-ray tomogram (minimum intensity z-projection from 3D X-ray tomograms). b. Open Icy and an image pair from each row shown in Table  2. CRITICAL: For each image opened in Icy, check that the metadata, such as pixel size and image dimensions, are correct.
The metadata are shown in the right-hand bar of the Icy main display. Inspect the metadata and edit if needed (occasionally metadata are captured or read incorrectly by software and ec-CLEM will not be able to compute the transformation correctly if these details are incorrect). Note: Records of all metadata are saved in <filename>. xml files in the working directory (generated automatically when a file is first opened in Icy). Deleting an. xml file will allow Icy to read the metadata anew next time the corresponding dataset is read if needed. c. Open eC-CLEM and assign the source/target images according to Table  2. d. Choose the transformation model to be 'Rigid' and the noise model to be 'Isotropic'. e. Press the play button. f. Add fiducial markers to each of the images, moving paired points to corresponding features in each image.
Note Note: Lock both volumes (they are now pre-aligned in 2D) using Lock 3. Note that the datasets are not aligned in z at this stage of the process and common features should be present at similar x and y but different 'heights' in z. c. Place between 5-10 points or until any further improvement cannot be seen, using features such as nanoparticles as your fiducials. d. Click stop. All process parameters are saved within. csv and. transformation_schema files. e. Save the transformed image stacks and/or the merged images. This step can be time consuming and you can consider the 'SaveAsTifFast plugin' (simply search for it in the Icy search bar). This uses a faster ImageJ version of saving. 20. The fully aligned datasets can now be viewed in any relevant 3D visualization package such as ChimeraX  (Goddard et al.
, 2018; Pettersen et al. , 2021). CRITICAL: Ensure you use 'Lock 3' when doing z channel registration in ec-CLEM. Each dataset needs to be at the corresponding z slice that features a shared feature before a fiducial marker is placed there (shared features will be located at different depths in the respective stacks before alignment). To retrospectively change the position of a fiducial in z, either switch to 3D VTK view or use the ROI panel, select an ROI and change its Z position. Optional: Z-alignment can also be done by observing the SIR image stack as an orthogonal view. In Icy click on the drop-down arrow in the top left of the image and change it from 2D to OrthoViewer. This may help to more accurately place the point in the center of the point spread function of fluorescent points but active rendering is computationally demanding and is therefore likely to slow hardware performance.
Figure2. Schematic illustrating the steps in image registration for correcting for chromatic and stage shift for representative 4-channel SIR data Data pairs should be chosen with a view of enabling the calculation of stage drift between successive data acquisition runs. First, the chromatic shift between fluorescence channels is corrected and then the stage-induced drift is calculated from the same channel taken at different time points. The stage drift transformation matrix is then applied to the chromatically corrected channels and finally all channels are merged to obtain a cumulative internally aligned dataset with all the available channels.
txrm/. tiff/. mrc ole/tiff/mrc unsigned 16- 10 nm or 16nm controller/ bit integer Data Explorer (Zeiss) Tilt series Raw data cryoTXM XRM. txrm/. tiff/. mrc ole/tiff/mrc unsigned 16- 10 nm or 16nm (referenced) (referenced) controller/ bit integer Data Explorer (Zeiss) Tomogram/s of 3D stack of cryoTXM IMOD or. rec* mrc Variable 10 nm or 16nm 100031000 or area of images similar 160031600 interest* a The grid map is generated using StitchM, developed at Diamond Light Source, available at: https://github. com/DiamondLightSource/StitchM. Matsuda et al. (Matsuda et al. , 2020)se of the software that will be employed in this process. a. Chromagnon -see the documentation at https://github. com/macronucleus/Chromagnonand in the article byMatsuda et al. (Matsuda et al. , 2020). b.
The methodology presented here, while being robust, is rather simplistic and will benefit from the rise of deep learning and artificial intelligence applications to NMR spectra  (Karunanithy et al. , 2021)  to provide a new entry point into metabolite databases. Code and data availability. The code written in Python for virtual decoupling and compatible within TopSpin 4 can be found on GitHub: https://github. com/NMRTeamTBI/VirtualDecoupling (https://doi. org/10. 5281/zenodo. 5163919,  Charlier et al. , 2021). A readme file guides potential users through the data used in the paper. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/mr-2-619-2021-supplement. Author contributions. CC was responsible for software, conceptualization and writing (review and editing); NC, AG, JMN and SMP were responsible for resources, review and editing; BL was responsible for software, review and editing; GL was responsible for conceptualization, project administration, supervision and writing (review and editing).
The treatment system is designed to be modifiable by the experts and automatically adaptable to patients' needs. The probabilistic diagnosis performance has been evaluated based on two public datasets regarding symptoms and risk factors of two chronic diseases: chronic kidney disease and dermatologic disease. The results show that our solution outperforms a classical classifier specifically when more than 40% of the data are missing. The proposed framework is also validated using four scenarios. The evaluation results demonstrate the ability of the proposed framework to help patients and doctors diagnose and treat medical conditions and episodes.
Answer Set Programming Answer Set Programming (ASP) is a form of declarative programming that focuses on severe search problems. ASP represents knowledge using logical phrases, and it derives new knowledge using automated reasoning. The concept of ASP is to represent a particular computational problem through a logic program and find the solutions, called answer sets, for that problem using automated reasoning performed by an ASP solver. ASP syntax is derived from Prolog, and its semantics are described using stable model semantics introduced by Michael Gelfond and Vladimir Lifschitz  (Erdem et al. , 2016). An ASP rule consists of two main parts: (i) Head and (ii) Body; if the body of an ASP rule is true, the ASP solver concludes that the head of that rule is also true. An ASP rule is formalized as follows: 𝑅𝑢𝑙𝑒 ∶ 𝐻𝑒𝑎𝑑 ← 𝐵𝑜𝑑𝑦.
In data-driven training, the BN is trained using data. In the hybrid training, experts provide the structure of the BN. At the same time, CPTs are extracted using data records of different patients regarding each medical condition/episode. In Extended Hapicare, data-driven training is not used in order to assure the expected structure that symptoms and causes of a medical condition/episode are respectively represented as descendants and parents of that condition in the BN. Therefore, in Extended Hapicare, the structure of BN is created by experts with respect to the description given in Section 3. 5. 2. Since the medical conditions are numerous and usually complex to be modeled by experts, the CPTs of BNs used in the screening phase are created using datasets. Moreover, as the accuracy of the diagnosis of episodes is critical, the CPTs of BNs used in the monitoring phase are created by experts (doctors) according to their experiences on the intended patient or other similar patients.
Each cause-effect relationship can hold a probability, and each event can have a probabilistic value. Hence, Bayesian inference can deduce the probability of this episode with respect to any information on the other events. The second step in modeling an episode is the creation of the CPTs. For modeling medical conditions in BN, the creation of CPTs can be done using the datasets, i. e. , training a BN with the given structure using the existing datasets. In the case of modeling episodes in BN, experts provide CPTs. However, the expert might benefit from the explicit probabilities provided in the medical studies. For instance, regarding the background causes of AKI,  Khadzhynov et al. (2019)  have reported that  (ℎ   ∩ ) = 6. 12%,  ( ∩ ) = 8. 81%,  (ℎ  ) = 11. 49%, and  () = 18. 50%, which result in the following conditional probabilities: 𝑃 (𝐴𝐾𝐼|ℎ𝑒𝑎𝑟𝑡 𝑓 𝑎𝑖𝑙𝑢𝑟𝑒) = 53.
Workflow in screening phase A typical workflow of screening phase in Extended Hapicare is presented in Fig. 7. First, the data are collected from the patient, either directly from the IoT sensors or manually input by the patient or his/her caregiver. Later, the data are processed using ontology-based reasoning to yield contextual information. Afterward, all the data and their context are processed within the probabilistic diagnosis to estimate the probability of different medical conditions. For each medical condition, the sensing action state is defined as when the probability of that medical condition is between    and    , where the former is a predefined threshold for suspecting a medical condition, while the latter is a predefined threshold for diagnosing a medical condition. Sensing action state signifies that the medical condition is possible, but more evidence is required to confirm or reject this diagnosis.
Comparative study Since the use of probabilistic diagnosis is similar in both phases, in the rest of this section, without losing generality, we focus on the use of probabilistic diagnosis in the screening phase, i. e. , for detecting plausible medical conditions. The performance of the probabilistic diagnosis component is evaluated based on a classical classifier method, namely random forest, to demonstrate how the loss of data affects their respective performance. First, the numeric features were processed using the ontology-based reasoning component of Extended Hapicare to produce a nominal context. Then both random forest and BN are trained using the dataset. We have created BN using hybrid creation for a valid comparison (see Section 3. 5. 1). The evaluation is made by comparing the performance of both methods to predict while increasingly removing random data features.
Dataset description In order to demonstrate the performance of the probabilistic diagnosis in Extended Hapicare, we have implemented the evaluation using two datasets on medical conditions, namely, chronic kidney disease and dermatology datasets. Chronic kidney disease Dataset: The first dataset is for the prediction of chronic kidney diseases; this dataset includes 11 numeric and 13 nominal features to predict chronic kidney disease, from which 4 are causes and 20 are symptoms. The causes in this model are age, hypertension, DM, and coronary artery disease; while the symptoms include the measurements of blood pressure, hemoglobin, red blood cell count, and white blood cell count. The dataset is collected from 400 patients in a hospital in India and labeled regarding the presence of chronic kidney disease.
This dataset is available on the machine learning repository of the University of California, Irvine  (Dua & Graff, 2017). Dermatology Dataset: The second dataset focuses on the diagnosis of different types of erythemato-squamous illnesses in dermatological patients. They all share clinical characteristics of erythema and scaling, and hence it is challenging to distinguish them. Psoriasis, seborrheic dermatitis, lichen planus, rosea pityriasis, chronic dermatitis, and pityriasis rubra pilaris are the illnesses in this group. Unfortunately, the exact diagnosis requires biopsy in most cases. To this end,  Güvenir et al. (1998)  have collected this dataset to decrease the cost of prediction among these illnesses. The dataset has 33 linear and one nominal features and include 366 records, of which 2 are causes, and the rest are symptoms.
Comparison Since the random forest model obtains a well-established prediction performance for the aforementioned datasets, it was selected as a rival for comparison. Fig. 9  depicts how removing the data from the records affects the performance of Extended Hapicare and random forest. They perform almost similarly when the data are complete. However, as the number of missing data increases, the drop of performance in the random forest model is enormous, which confirms the selection of BN for handling missing data in the core of reasoning in Extended Hapicare.
Robustness analysis A BN is robust when the values of its target class slightly depend on the inputs of its causes  (Surhone et al. , 2011). In a robust BN, the changes in one of the causes hardly result in dramatic changes in the target class. Chan and Darwiche (2004)  have implemented a tool integrating multiple approaches for quantification of robustness, also known as sensitivity analysis. We have used their tool to conduct sensitivity analysis based on the algorithm of  Shenoy and Shafer (1986) , on the target class, representing the medical condition. The results show that the three causes can raise the probability of the disease to 37. 04% in the case of the chronic kidney diseases dataset. For the dermatology dataset, there are only two causes and they can raise the probability to 68.