dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, we compared the models of the complement factor H, a protein which adopts a typical serpentine shape  [21] , as illustrated by the experimental model solved by solution scattering (PDB: 1HAQ) (Figure  7 ). The Robetta algorithm managed to propose an elongated serpentine folding for this protein, while AlphaFold2 proposed a condensed wool ball structure similar to those proposed for EGFR and APP (Figure  7 ). These data indicated that the AlphaFold2 algorithm has difficulties in predicting elongated structures, which is an important feature for the prediction of a correct spatial organization of proteins, such as botulinum neurotoxin and membrane proteins, as we show across our examples. Interestingly, the failure of AlphaFold2 in this case was not due to a low confidence in its prediction, since the pLDDT  [6]  of this model was in the 70-90 range (Figure  S2 ).
TM-Score and Root-Mean-Square Deviation of AlphaFold2 and Robetta Models The reliability of Robetta and AlphaFold2 predictions can be estimated, respectively, by the Å error estimate and pLDDT values. The data for the set of proteins analyzed in the present study are available in Figures  S2-S4. As expected, the level of confidence of both programs was higher for structured vs. unstructured regions. Overall, all the proteins had high pLDDT values, so that the diffences between the predictions of AlphaFold and Robetta are not due to a bias in the selection of the models. Moreover, to further compare the predictions of AlphaFold2 and Robetta with the available experimental structures, we performed a systematic analysis of the TM-score and RMSD values (Table  1 ). Finally, we compared the models of the complement factor H, a protein which adopts a typical serpentine shape  [21] , as illustrated by the experimental model solved by solution scattering (PDB: 1HAQ) (Figure  7 ).
Synthesis Development The radiotracer was produced in a specific radiopharmacy laboratory in a class A high-energy shielded enclosure. Radiolabeling was carried out in a closed system using the miniAIO ®® synthesis device (Trasis, Ans, Belgium). Firstly, we prepared the raw materials in the leaded enclosure. We needed to reconstitute the ascorbate buffer with 2 mL of water (we used a syringe and needle provided by ITM). Then, we transferred the reconstituted ascorbate buffer into the ITM-PSMA peptide vial. Subsequently, we turned on Trasis software and placed the 177 Lu-peptide cassette on the automaton. Fourthly, we connected the reconstituted peptide vial in the second position of the cassette, the 0. 9% NaCl vial in position 5 (via the spike) and the EndolucinBeta ®® vial in position 1.
Finally, we started the synthesis via the Trasis software controlling the device. The reconstituted peptide with ascorbate buffer was transferred to the EndolucinBeta ®® precursor vial. Then, it was transferred to the reaction vial to be heated at 95 • C for 20 min. A cooling step was performed by removing the reaction vial from the reactor by placing it in a leaded vial to let it cool at room temperature for 15 min. Finally, the last stage allowed for the sterilizing filtration and the formulation of the final product.
For each concentration, the average of the integrated peak areas as well as its standard deviation (SD) were calculated using Microsoft Excel 2013. From these values, the relative standard deviation was determined according to the formula: RSD (%) = (SD/mean) × 100. RSD has to be less than 2%  [16, 18]. The calibration curve was performed with GraphPad Prism 5 software by linear regression analysis of the average integrated peak areas versus the known concentration of standard solution. The correlation coefficient (R 2 ) should be greater than 0. 99.
Introduction It is often challenging for machine learning groups to gain access to large-scale annotated medical imaging datasets for training and testing their algorithms. As a result, many researchers rely on smaller proprietary datasets, making it challenging to show the full potential of their algorithms and even more challenging to compare their results against other published methods. Medical image analysis challenges  (Menze et al. , 2015; Bakas et al. , 2018; Simpson et al. , 2019; Antonelli et al. , 2021; Kurc et al. , 2020; Orlando et al. , 2020; Codella et al. , 2019; Bernard et al. , 2018; Sun et al. , 2021; Heller et al. , 2021; Pati et al. , 2021) , therefore, play a pivotal role in the development of machine learning algorithms for medical image analysis by making large-scale, carefully labeled, multi-center, real-world datasets publicly available for training, testing, and comparing machine learning algorithms.
In particular, the Brain Tumor Segmentation (BraTS) challenge has provided the community with a benchmarking platform to compare segmentation methods for over ten years, increasing the dataset size each year  (Menze et al. , 2015; Bakas et al. , 2017c Bakas et al. , , 2018;; Baid et al. , 2021). The availability of the dataset, and the challenge itself, has permitted the development of many new successful deep learning based approaches such as the DeepMedic  (Kamnitsas et al. , 2016 (Kamnitsas et al. , , 2017) )  and the nnU-Net  (Isensee et al. , 2018 (Isensee et al. , , 2021)). Despite their success in many medical image analysis challenges, the resulting deep learning algorithms are typically not translated in the clinical setting for a variety of reasons. One issue is that most deep learning models produce deterministic outputs, that is, they do not communicate the uncertainties associated with their predictions.
Uncertainty Evaluation Metric This work focuses on the publicly available BraTS challenge dataset  (Menze et al. , 2015; Bakas et al. , 2018 Bakas et al. , , 2017c)) , which consists of both High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) cases, as described in the previous BraTS manuscripts and on The Cancer Imaging Archive (TCIA)  (Clark et al. , 2013). However this naming convention is now obsolete and following the 2021 World Health Organization (WHO) classification of tumors of the central nervous system (CNS)  (Louis et al. , 2021)  the data provided by the BraTS challenge should be described as including: 1) adult-type diffuse gliomas, 2) pediatric-type diffuse low-grade gliomas, and 3) pediatric-type high-grade gliomas. Note that the adult-type diffuse gliomas included in the BraTS dataset comprise both Glioblastoma (IDH-wildtype, CNS WHO grade 4) and Astrocytoma (IDH-mutant, CNS WHO grades 2-4).
A 3D U-Net Based Experiment A set of experiments were devised, in order to show the workings of the derived uncertainty evaluations and rankings. A modified 3D U-Net architecture  (C ¸içek et al. , 2016; Mehta and Arbel, 2018)  generates the segmentation outputs and corresponding uncertainties. The network was trained (n = 228), validated (n = 57), and tested (n = 50) based on the publicly available BraTS 2019 training dataset (n = 335)  (Menze et al. , 2015; Bakas et al. , 2018 Bakas et al. , , 2017c,a,b),a,b). The performances of WT segmentation with the entropy uncertainty measure  (Gal et al. , 2017) , which captures the average amount of information contained in the predictive distribution, are shown in Figure  2. Here uncertainties are estimated using MC-Dropout  (Gal and Ghahramani, 2016), Deep Ensemble (Lakshminarayanan et al.
Dataset The BraTS 2020 challenge dataset  (Menze et al. , 2015; Bakas et al. , 2018 Bakas et al. , , 2017c,a,b) ,a,b)  is divided into three separate cohorts: Training, Validation, and Testing. The Training dataset is composed of multi-parametric MRI (mpMRI) scans from 369 diffuse glioma patients. Each mpMRI set contains 4 different sequences: native T1-weighted (T1), post-contrast T1weighted (T1ce), T2-weighted (T2), and T2 Fluid-Attenuated-Inversion-Recovery (FLAIR). Each MRI volume is skull-stripped (also known as brain extraction)  (Thakur et al. , 2020) , co-aligned to a common anatomical atlas (i. e. , SRI24  (Rohlfing et al. , 2010) ), and resampled to 1mm 3  voxel resolution. Expert human annotators provided GT tumor labels, consisting of 3 classes as described previously. Note that there is no "ground-truth" uncertainty label.
The BraTS 2020 Validation cohort is composed of 125 cases of patients with diffuse gliomas. Similar to the training dataset, this also contains 4 different mpMRI sequences for each case. The validation dataset allows participants to obtain preliminary results in unseen data, in addition to their cross-validated results on the training data. The GT labels for the validation data are not provided to the participants. The BraTS 2020 Testing cohort is then used for the final ranking of the participating team. It is comprised of a total of 166 cases. The exact type of glioma is not revealed to the participating teams. Each team gets a window of 48 hours to upload their results to the challenge's evaluation platform (https://ipp. cbica. upenn. edu/)  (Davatzikos et al. , 2018).
Evaluation framework The University of Pennsylvania's Image Processing Portal (https://ipp. cbica. upenn. edu/) is used to quantitatively evaluate all BraTS participating algorithms. This portal allows both the registration of new teams to get access to the BraTS datasets, and the framework for automatically evaluating all participating algorithms on all three (i. e. , training, validation, and testing) cohorts 1. In addition to the IPP, and in favor of reproducibility and transparency, we make the implementation of the quantitative evaluation of uncertainty publicly available through GitHub 2. As mentioned previously, the evaluation framework expects the challenge participants to provide multi-class brain tumor segmentation labels and their associated voxel-wise uncertainties for three tumor entities: whole tumor (WT), tumor core (TC), and enhancing tumor (ET).
Functional simulation-based methods The simulation-based power consumption estimation techniques are commonly used in Computer-Aided Design (CAD) tools. This is mainly done by applying various sets of inputs for a given system to be simulated and subsequently collecting and capturing sets of desired outputs leading to power consumption values. Current and voltage values, capacitance, operating frequency and switching activity are the main required set of information in order to obtain power consumption estimation. The accuracy of such techniques is proportional to both processing time and computer memory resources' usage  [28]. Here we list some of this technique's target models based on related work: ACCEPTED MANUSCRIPT / CLEAN COPY The simulation-based modeling technique described in  [36]  is initiated by an iterative optimization step during model generation phase.
The methodology described in  [15]  aims at providing power estimates of communication systems ahead of implementation. The proposed approach is solely related to hardware with no software considerations assuming that the wireless communication system is entirely constructed using interconnected hardware IP cores set. This process can be done in two phases: 1) IP characterization using Xilinx Power Analyzer (XPA) and modeling using SystemC and 2) building the system by IP interconnections (previously developed in phase one). The aforementioned technique has been applied to an Orthogonal Frequency Division Multiple Access (OFDMA) IP part of the Long-Term Evolution (LTE), the 4 th generation of radio technology for mobile communications. As for the results, the maximal absolute error was found to be less than 4% compared to estimated values by XPA.
In comparision with  [7] ,  [27]  took one step forward and included thermal analysis. Several tools were presented and compared in order to simulate the power and thermal behavior of a chip along with its functionality. The aim was to augment a functional SystemC/TLM model with ACCEPTED MANUSCRIPT / CLEAN COPY non-functional information and subsequently perform power and thermal analysis. The focus was on co-simulating an existing thermal model with a functional SystemC/TLM simulation. Experiments were conducted on both a soft-core processor (MicroBlaze) and an IP (VGA controller); no fundamental issues were detected with the co-simulation techniques on both the SystemC/TLM simulation and the external power/thermal solvers. The methodology described in  [32]  is used to estimate energy dissipation in hardware at any level of abstraction in SystemC with Powersim.
In  [42]  the High-Level power estimation model for SoC is based on extended and optimized event signatures. The aforementioned estimation model has three primary parts: 1) the summation of every section's power consumption of the SoC design, 2) power consumption produced by event ACCEPTED MANUSCRIPT / CLEAN COPY signatures consisting of bus, memory and communication power sub-models and 3) power consumption disparity due to the subtrahend of the number of LUTs of the complete design and subsequent summation of each part. This proposed model takes into consideration the effect of experiential power estimation data, performance events in addition to the LUT changes. The validation results revealed an error rate of 1. 185%. The multi-core processor (dynamic) power consumption estimation tool presented in  [14]  extracts information at the instruction level as well as at the architectural level.
The MIPS32-based processor has been created using Open Virtual Platform (OVP) and virtual machine interface libraries of Imperas' Instruction Set Simulator (ISS). Two, four and eight processor cores were simulated using ISS where different benchmarks were used as application. As a result, it was noted that the energy consumption is inverse-proportional to the number of cores used on the platform. The reason behind is that the same application takes more time on a platform having less processor cores. On another hand, a formal description of a power consumption estimation approach of embedded systems is presented in  [39]. An embedded system consisting of a hardware and a software is denoted as a System Model (SM). The hardware part of the system is reflected in the Operational Model (OM) while the software part is described in the Application Model (AM); UML extended with MARTE profile elements, was used for this purpose.
Dynamic power and output data depend only on the input data for a fixed technology and frequency. Bit width, packet length and number of packets represent the stimuli parameters. This technique is divided into two sections: power and behavioral characterization, and, power and behavioral modeling. The final step is to construct the library based on the two previously ACCEPTED MANUSCRIPT / CLEAN COPY mentioned ANNs: ANNP to predict power consumption and ANNB to predict the output feature vector. This learning-based estimation methodology applies for both component and system level. As for the results, an RMSE of +/-1. 5% was recorded for the component level and 8. 5% for the system level with an estimation speed up of about 2490x compared to traditional tools.
Microprocessors/CISA/(MP)SoC In contrast with  [14] ,  [33]  is a power estimation prediction technique based on a statistical model. Its portability is analyzed on new generations of Raspberry Pi Single Board Computer (SBC). In such systems, the power consumption does not depend on the hardware only but also on the use of the software and its internal characteristics. Two different benchmarking tools have been used having different computational behaviors, parallel applications source code compilations, collection of performance counters, linear regression model training and model validation. The final statistical model is based on linear regression and subsequent generation of 8 power coefficients leading to one unified model formula. As for the results, the maximum prediction error was about 18.
The chart shows only simulation-and learning-based methods as the first method is widely used and the second method is the most recent. This graphical representation reveals the distribution of the cluster-based power estimation methodologies applied to specific target models v/s their respective estimation efforts. The following analysis was noted: Simulation-based techniques: The simulation-based power consumption estimation techniques are best suitable for µP/CISA/(MP)SoC target models with estimation error <5% in specific cases, especially when software applications targeting (soft-and hard-core) µPs are involved. This is usually done with moderate efforts. The component/circuit target models come next in the classification ACCEPTED MANUSCRIPT / CLEAN COPY Fig. 2  Estimation Effort v/s Average % Error -Clusters Chart under the same estimation methods with also moderate efforts, leaving behind the white/blackbox IP models, unless considerable efforts were exerted.
Conclusion In this overview paper many power consumption estimation-related topics were covered. High-Level power estimation techniques and target models were presented as the main concern and purpose of this paper. The covered literature was extensive with a considerable variety of evaluation methods and contribution levels applied to many sets of target models. Knowing that, the papers' classification had to be done in a tabular format (Table  1 ) where some of the metrics were quantitative and some others qualitative. A global cluster-based chart (Figure  2 ) representing the power estimation techniques and their respective target models v/s the ACCEPTED MANUSCRIPT / CLEAN COPY estimation efforts has been generated. The purpose of that representation was to have a general and wide understanding of which estimation technique applied to which target model results in a better estimation accuracy while keeping an eye on required estimation efforts.
Study cohort Patients with CTEPH were enrolled in the PRIN-CEPT study as part of an approved IRB protocol (NCT03205085, IRB approval 2017-A00785-48) at the Marie Lannelongue Hospital where the PEAs were performed. A total of 73 patients were enrolled initially, of which, 20 patients had CMR including a 4D flow CMR immediately before the PEA and at least 6 months after the PEA (average 7. 5 ± 1. 3 months) with clear anatomic image resolution of the PAs which were used for volume and area metrics. From these patients, 15 had high quality velocity resolution for both pre-PEA and post-PEA scans that satisfied conservation of flow from the main PA (MPA) to the left and right PAs (LPA, RPA) with a < 20% error which were used for velocity-based metrics capturing velocity flow profiles and secondary flow profiles.
Of the 20 patients with satisfactory CMR, 18 had pre-and post-PEA measurements of mean PA pressure (mPAP) and total pulmonary resistance (TPR) by right heart catheter (RHC) procedures performed 68 ± 93 and 22 ± 46 days before the CMR scan at pre-and post-PEA timepoints. A group of 8 patients with mild to moderate valvular disease, but with no evidence of PH, RV systolic dysfunction (RV ejection fraction, RVEF > 50%), left ventricular (LV) systolic dysfunction (LV ejection fraction, LVEF > 50%) were used from the SUBCLAR study (NCT03549091) for comparison. Details and results of this control group are included in Additional Files 13-17 for reference. Additional patient details can be found in Table  1.
Semi-automated 4D flow CMR post-processing pipeline To analyze the velocity and anatomic data of the PAs from the 4D flow CMR, we developed a semi-automated 4D flow CMR post-processing pipeline using custom Python scripts (Fig. 1 ). All 4D flow CMR data were uploaded and stored using a medical imaging cloud platform, Arterys Cardio AI (Arterys Inc. ), where phase offset and background corrections were applied. The corrected velocity and 4D CMR images were downloaded and further post-processed for image and velocity analysis using custom Python scripts. To confine the 4D flow velocity to the PAs, we created 3D models of the proximal lobar PAs using the open-source software, SimVascular (simvascular. org) [24]. Models extended from the pulmonary valve to the LPA and RPA and were truncated at the first segmental branching point on both the left and right PAs from the 4D CMR magnitude images.
2 mm tetrahedral elements to match the minimum resolution of the 4D flow images. Velocities from the 4D flow CMR dataset were resampled onto the mesh with a linear interpolation. To automate extraction of hemodynamic metrics from the PAs, centerlines were generated for each PA geometry at each timepoint using the Vascular Modeling ToolKit (vmtk. org) [26]. The MPA, LPA, and RPA branch regions were identified from the centerlines, and two cross-sectional slices perpendicular to the centerlines were taken 10% and 50% down the length of each branch to capture the flows coming directly into each branch and to compute hemodynamics in the middle of each branch. Branch regions were defined as the volume region between the caps of the PA geometry to the bifurcation region where the centerlines from the LPA and RPA intersected the MPA.
Hemodynamic metrics computed Within the semi-automated post-processing pipeline, we computed metrics describing volumetric data, velocity flow profiles, and secondary flow profiles of the PAs. Details of the metrics computed are shown in Fig. 1  with illustrations of some 4D flow CMR-derived PA metrics. A table of all metrics computed is listed and derived in Table  2. Maximums, minimums, and time averages were computed for each metric throughout the cardiac cycle and during the systolic and diastolic periods, defined by the end-diastolic and end-systolic timepoints. Percent predicted RV metrics were computed to adjust for sex, age, weight, and height (Table  2 )  [27]. Volumetric data including MPA, LPA, and RPA vessel volumes and midvessel cross-sectional areas as well as relative area change (RAC), compliance, and distensibility capturing PA wall stiffness were computed.
We found increased vorticity with independent decreases in volume and increases in flow analogous to the vorticity trends in the 4D flow CMR data (Additional file 1). A correlation matrix between 4D flow-derived metrics (Additional file 6) showed MPA vorticity was strongly correlated with velocity (r = 0. 80, P < < 0. 001) and moderately correlated with volume (r = -0. 60, P < 0. 001) for all pre-PEA and post-PEA timepoints. There were no significant changes in the mean MPA fraction of positive helicity (Fig. 5e ) or the maximum RPA HFI from pre-PEA to post-PEA. MPA fraction of positive helicity was normally distributed and varied up to 16% and RPA HFI varied up to 30% from pre-PEA to post-PEA. The fraction of positive helicity remained at ~ 0. 5 from the pre-PEA to post-PEA timepoint indicating approximately equal volumes of positive and negative helicity but with variation between patients.
Setup A commercially available trike (CATrike 700, Big Cat HPV LLC, Orlando, FL, USA) was modified to allow for FES-Cycling in individuals with a complete spinal cord injury (see Fig. 1 ). The original crank set was replaced by a crank power-meter (2INPOWER, Rotor Bike Components, Madrid, Spain), without the need for further mechanical adaptations. Each leg was equipped with an inertial measurement unit (BNO055, Bosch GmbH, Gerlingen, Germany) located at the thigh. The IMUs processed (Kalman filter) inertial data at a sampling rate of 100 Hz, and were polled via its I2C-interface. Orthotic special pedals with calf support (Hase pedals version 1, Hase Bikes, Waltrop, Germany) were used to restrain movements of the ankle joint. The velocity was determined by a reed-switch which detected a magnet mounted on the rear-wheel of the trike.