dataesr/scientific-paragraphs-categorization huggingface dataset:
Questionnaires The following validated questionnaires were used: FSS (fatigue severity scale), WHODAS 2. 0 (World Health Organization Disability Assessment Schedule), QoL/ SF-36v2 (Quality of life/Short Form Health Survey) and IPAQ (International physical activity questionnaire). Data from the SF-36v2 was analyzed with Quality Metric Health Outcomes Scoring Software 5. 0, data from the WHODAS 2. 0 was converted to IRT (itemresponse-theory)-based scoring  [6, 7]  and data from the IPAQ was calculated to metabolic equivalent of task (MET)-min per week. [8] [9] [10]  We only used the data from the Dutch participants for the analyses of the SF-36v2 and IPAQ since the data of other countries were incomplete or not reliable. We refer to the results section for details of data.
Data analysis Mainly descriptive statistics were used to analyze the data and performed by using IBM SPSS Statistics 25. Absolute numbers and percentages of the number of answers on the item are presented. Correlations between age and disease severity were calculated (Pearson correlation coefficient). Analyses for the open-question sections were performed by narrative analysis using ATLAS. ti 2. 0. Axial coding was used for the analyses to identify relations between the data and categorized when referring to the same phenomenon.
Participants In total 282 participants with confirmed diagnoses of muscle glycogenosis were included between 2015 and 2018. 269 (95%) of them had GSD5. This report only presents the data of the participants with GSD5. Of the 269 GSD5 participants, 196 (73%) completed all mandatory questionnaires; for other participants, data were incomplete. Table  1  in Scalco et al. shows the completeness of data entry for different items  [4]. The number of participants that have provided an answer to a question are provided in the tables and figures. Figure  1  in the first EUROMAC report by Scalco et al. [4]  provides an overview of the geographical origin of the participants. The majority (215; 80%) were from the UK (United Kingdom), Spain, Italy or the Netherlands.
Discussion The EUROMAC registry was created to ensure data collection and its maintenance with the aim to improve diagnosis and care for individuals with rare glycogen storage diseases. The registry increases knowledge from clinical data from individuals with GSD5 and related conditions and has played a positive role in promoting translational research  [3]. Moreover, it is one of few international registry of muscle glycogenosis available. Besides unraveling phenotypic characteristics of GSD5 as seen in the accompanying papers  [3, 4] , the current report provides insight in the previous treatments, functional limitations, fatigue, health disability, quality of life and physical activity. We will discuss the main findings below. The number of participants currently working (67%) is in line with the proportion of the general adult population that is working in various European countries: 72% in the Netherlands [13], 76% in the UK  [25] , and 66% in Spain  [26].
However, caution should be exercised due to the potential overestimation of rehabilitation use stemming from selection bias. Moreover, for comparison of fatigue, the FSS score for individuals with Lyme disease is 4. 8, Parkinson's disease is 4. 1 and for a healthy population it is 2. 3. [11, 12, 30]  Limitations of this study include the collection of retrospective data and the issue of missing data, which  reduces the validity. The data was collected in the prospective EUROMAC registry and had to be entered by various researchers and clinicians. This carries the risk of observation or confirmation bias in case of subjective outcome measures. The data from the WHODAS was not of sufficient quality to be interpreted further. It is also important to note that the present study had a limited number of correlation analyses, which may restrict the generalizability of the findings and the ability to explore potential relationships between additional variables of interest.
Acknowledgements EUROMAC was supported by several patients' associations and large networks of patient registries, such as the Association for Glycogen Storage Disease (https:// agsd. org. uk/) and the EPIRARE Group on Rare Disease Registries (https:// www. euror dis. org/ conte nt/ epira re). The European Neuromuscular Centre (https:// www. enmc. org/) also supported the project by contributing to the organization and funding of a dedicated workshop. We are grateful to all the participants for their participation in EUROMAC. and  Neuromuscular Diseases Unit, Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy. 10 Neurology Department ,  Raymond Poincaré University Hospital, APHP, Garches, France. 11 Pathology Department, Alvaro Cunqueiro Hospital, Vigo, Spain.
Results for Synthetic Dataset The first part of the tests was devoted to the synthetic dataset. The regularization parameters, for HMF-IPNMF  [59, 65] , HySure  [41, 77]  and FuVar  [58] , considered for the synthetic dataset are reported in Table  3. FuVar λ m = 1, λ A = 10 -4 , λ 1 = 0. 01 and λ 2 = 10, 000 For the HMF-IPNMF method, the maximum number of iterations of IP-NMF was set to 100. For the CNMF method, the numbers of iterations for the inner and outer loops were fixed to 100 and 3, respectively. For the FuVar and HySure methods, the blurring kernel was assumed to be known a priori. As an initialization step for all the state-of-the-art methods, the VCA method was applied. The FCLS method was used to initialize the abundance coefficients for the FuVar CNMF and HMF-IPNMF methods. As the CNMF and HySure methods do not consider the spectral variability, they were executed by fixing the number of pure materials to 30 as suggested in  [77].
This allows them to have more flexibility and to somehow manage the spectral variability although they are not designed for it. The HMF-IPNMF and FuVar methods were applied by considering the manually determined number of classes of endmembers equal to 7, because these two methods deal with the spectral variability. For the HSB-SV method, all the fixed parameters are reported in Table  4. The numbers of pure materials and subsets were fixed to perform the spectral reconstruction while preserving the processing time as much as possible. Finally, the CPU used in the conducted experiments was an Intel Core i5-8350U processor running at 1. 70 GHz, with a memory capacity of 16 GB. The results of the quality metrics for the synthetic dataset are reported in Table  5. Figure  3  illustrates the spectra library extracted from the HSI by means of the AEEB method.
53 • and NMSE λ equal to 7. 92%. The HSB-SV method delivered the best overall results denoted by spectral performance criteria with the lowest values of the SAM (2. 65 • ) and the NMSE λ (7. 49%), which clearly demonstrates the superior performance of HSB-SV in terms of spectral fidelity compared to the tested state-of-art-methods. These findings show that considering a spectral library to estimate the hyperspectral spectra is very effective. Furthermore, these findings were confirmed by the PSNR with the highest value (equal to 43. 01) corresponding to a substantial gain of nearly 3 dB as compared with HMF-IPNMF and 10 dB with respect to the FuVar method. To illustrate the notable gain of HSB-SV, Figure  4  represents the PSNR of all methods for each spectral band for the synthetic dataset.
20 s and 12. 89 s, respectively) even though these methods are not considering spectral variability and are supposed to be more efficient in terms of computational cost. These findings prove the effectiveness of the HSB-SV method to deal with spectral variability during the hypersharpening process by providing products with high fidelity reconstruction at a very low processing cost. This significant reduction of the processing time is caused by two main elements. The first one is that the AEEB method is very efficient, in particular a fast algorithm (VCA) is considered for the extraction of endmembers. The second main reason is the efficiency of the SUnSAL method. For the visual inspection of the obtained results, Figure  5  illustrates the true color composite of the synthetic dataset for all tested methods.
Results for Real Dataset The second part of the tests is devoted to the real data. The regularization parameters were identical to those in the tests performed with the synthetic dataset for the state-ofthe-art methods (Table  3 ). For the HSB-SV method, the used parameters are reported in Table  7. The spectra library extracted from HSI by means of the AEEB method is illustrated in Figure  7. Figure  7  clearly illustrates the presence of spectral variability in the real dataset and thus the notion of class of endmembers must be considered. Table  8  reports the performance metrics for the real dataset. The CNMF method obtained the highest values for D λ and D s showing that it provided the lowest spectral and spatial fidelities of this benchmarking. The FuVar method improved this aspect with a better spatial and spectral reconstruction as it considered spectral variability.
Indeed, the use of the Sparse Unmixing by Variable Splitting and Augmented Lagrangian (SUnSAL) method proves to be very attractive to estimate high-spatial-resolution abundance coefficients while preserving the processing time to a very low level. The proposed technique was tested on synthetic and real datasets along with some recent state-of-the-art methods. The results, based on spatial and spectral performance criteria, show that the introduced strategy is very attractive and efficient in terms of spectral and spatial reconstructions. The new method outperforms the approaches tested in this paper. Besides, the HSB-SV method has the lowest processing time compared to the considered tested state-of-the-art methods, specifically compared to techniques dealing with spectral variability. These findings prove that using a spectral library appears very effective for the hypersharpening process.
Leuenberger  [Leuenberger et al, 2017]  found excellent correlation of the functional use ratio (FUR30) with the box and block (BB) test (r²=0. 9). Lum  [Lum et al 2020]  chose to synchronise accelerometers with video recordings of healthy and hemiparetic subjects performing activities of daily life. The video recordings were used both to accurately measure the amount of functional UL movement in the laboratory over a given period of time and to serve as a basis for labelling actimetric data as functional, non-functional and unknown movement. This labelling was then used to developed several machine learning algorithms to separate functional from non-functional UL movements. While the activity counts show low correlation with the video results (r²=0. 57), the machine learning algorithms show excellent results (r²=0.
Moreover, these algorithms do not yet distinguish between movements of different amplitudes. This makes it difficult to identify the physical capabilities and limitations of post-stroke subjects in their ecological environments while it is necessary to turn accelerometer data into clinical meaningful data  [Hayward, 2016]. In this study we recorded 3D acceleration at each wrist, over a period of 7 days, in the volunteers' home (ecological) environment. We then adapted and compared different accelerometric variables (UH, FUR30, JR50, etc) between a population of 19 stroke patients and 11 healthy subjects in order to determine the most informative variables to guide clinical decision making.
Procedures Accelerometers (Axivity Ax3, Newcastle upon Tyme, UK) were placed on each wrist for all participants. The participants were asked to wear the accelerometers for 7 days without removing them. Data acquisition was performed at a frequency of 50Hz coupled with a cut off of 8g for the measurement of acceleration in the three spatial directions. The accelerometers were recovered at the end of the 7 days to extract the data using the OmGui software provided by Axivity. The data were sliced day by day to obtain daily acceleration data values. The data were then saved in csv format so they can be read by any programming language.
Data Analyses and calculation of variables Data processing was done using the python 3. 7 programming language. The numpy and scipy libraries are notably used for numerical calculation operations (derivation, frequency analysis). The scipy library allows the application of a low pass filter with a cut-off frequency of 10Hz in order to remove noise. The magnitude of the acceleration vector (SVM: scalar vector magnitude) is then calculated for each time step of the two actimeters (via the acceleration data at a given time t : ax(t); ay(t); az(t)).
3) Use hours The calculation of use hours follows the formulation presented by Waddell et al  [Waddell et al, 2017]  and Bailey et al  [Bailey et al, 2014]. The first step was to down-sample the raw data from 50Hz to 30Hz to follow the recommendations of Waddell et al  [Waddell et al, 2017]. Next, the data were bandpass filtered between 0. 25 and 2. 5Hz, down-sampled to 1Hz (i. e. , 1s) and then converted to activity counts (0. 001664g/count). The activity counts on each axis were combined to create a single vector magnitude value (√² + ² + ²) for each second. The number of hours of use (UH) corresponds to the total amount of time the UL is assumed to be active. This variable is calculated by adding all seconds where the activity count is greater than 2. It is then possible to define use hours ratio (UHR) between the paretic (non-dominant) and non-paretic (dominant) UL.
Statistical Analysis The statistical analysis was performed with the programming language python 3. 8, using scipy an pandas packages. The criterion for a significant difference was p<0. 05. All statistical analyses were performed at 95% confidence. Nonparametric tests had to be applied because normality tests (Shapiro-Wilk) showed that some of the data groups do not have normal distribution. For each type of variable (JR50, UH, UHR, FU30, FUR30), a value was calculated for each subject and for each day. This corresponds to 7 values per subject and per variable. For each subject and each variable, the median value of the 7 days of measurements was saved and stored. The visual representations of the variables from the accelerometric data were examined using boxplots for each population. The values shown as dots in the boxplot represent the median of the 7 days of measurements per patient.
Characterization of initial material Table  1  shows that the alloy composition deviates less than ± 2 at. % from the equimolar state in all three states. The oxygen concentration of the as-cast state is slightly lower than in the rolled and the rolled & annealed state. No significant difference in nitrogen concentration is evident. [  TABLE 1]  As far as assessable with XRD measurements  [24] , all specimens in their initial state are singlephase bcc (not shown here). The XRD patterns are in good agreement with database values for TiZrNbHfTa (ICDD 04-018-5441)  [1]. Figure  1  shows the microstructure of the specimens before oxidation. Despite similar alloy composition and crystal structure, significant differences are apparent. In the as-cast state (Figure  1 a )) and the rolled & annealed state (Figure  1 c )), distinctive, coarse grains are visible in the micrographs, with sub-grain boundaries present in the as-cast state.
[29]. [FIGURE  4 ] Figure  5  a) shows a TiZrNbHfTa phase diagram derived from Calphad calculations using ThermoCalc with the TC-Ni8 database. Above about 1020 °C, the alloy is expected to be singlephase body-centered cubic, i. e. , bcc1. While precipitation of a second body-centered cubic bcc2 phase starts below 1020 °C, at temperatures lower than approx. 700 °C, the initial bcc1 phase is expected to completely decompose into the bcc2 phase and a hexagonal closed-packed hcp1 phase. The decomposition of the bcc1 phase into a NbTa-rich bcc2 phase and a HfZr-rich hcp1 phase at temperatures below 1020 °C has recently been predicted by first principle calculations  [30]  and is experimentally confirmed  [29, 31]. Figure  5 b ) depicts XRD measurements of the non-oxygen-enriched substrates after oxidation (600 °C, 24 h).
Acknowledgments We are grateful for the financial support of the German Research Foundation DFG (project number 270293189; GL 181/41-2 and GA 1704/12-2). The access to the XPS/UPS facility (PHI 5000 VersaProbe III system) at the Device Engineering KeyLab at the Bavarian Polymer Institute, University of Bayreuth, is gratefully acknowledged. E. P. G. was supported by the U. S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division. We thank C. Gadelmeier, University of Bayreuth, for producing the ascast TiZrNbHfTa ingot.
Proteome-wide Mendelian randomization (MR) has emerged as a promising approach in uncovering novel therapeutic targets. However, genetic colocalization analysis has revealed that a third of MR associations lacked a shared causal signal between the protein and disease outcome, raising questions about the effectiveness of this approach. The impact of proteome-wide MR, stratified by cis-trans status, in the presence or absence of genetic colocalization, on therapeutic target identification remains largely unknown. In this study, we conducted genome-wide MR and cis/trans-genetic colocalization analyses using proteomic and complex trait genome-wide association studies. Using two different gold-standard datasets, we found that the enrichment of target-disease pairs supported by MR increased with more p-value stringent thresholds MR p-value, with the evidence of enrichment limited to colocalizing cis-MR associations.
Introduction Many candidate drugs are supported by in vitro evidence and animal models yet fail during human clinical trials, motivating the pursuit of alternative sources of evidence to appraise therapeutic targets  1. A major source of evidence is human genetic data linked to disease traits, which can identify targets more likely to be successfully modulated in clinical trials  2, 3. However, disease-associated genetic signals often span multiple genes, and do not immediately indicate the desirable direction of therapeutic modulation  4, 5. Selection of target genes can be improved by machine learning algorithms trained on curated gold standard target-trait datasets, but such datasets are biased towards the nearest gene and to known mechanisms such as protein-altering variants  [6] [7] [8].
Study overview We used seven publicly available human blood plasma proteogenomic datasets 9,18-23 and genome-wide association studies (GWAS) of 3,766 complex traits from Open Targets that had at least one genome-wide significant locus (Supplementary table  1  and 2 ). We performed two-sample MR using genome-wide significant (5 x 10 -8 ) instruments from proteomic GWAS ('pan-MR') and all available phenotypic GWAS, as well as cis/trans genetic colocalization to connect targets with traits (Figure  1 ). For MR analysis, although the presence of a genome-wide significant locus in the outcome data was a criteria for selection of outcomes, it is not necessarily the same locus that would be MR-analyzed; this would only be determined by the locus that is genome-wide significant in the proteomic GWAS.
Of the remaining cis-target-trait pairs with H4 < 0. 8 with, the majority (98. 8%) had higher H3 (evidence for independent signals) than H1 (genetic association with protein, but not with outcome) supporting reports from previous cis-MR-only studies of widespread genetic confounding  14. However, since only significant loci from protein and outcome GWAS were analyzed for credible set overlap in our genetics portal to determine if a genetic colocalization test would be done, we cannot completely exclude the possibility that insufficient statistical power in the cis-MR results was responsible for the lack of genetic colocalization. For validation, we investigated evidence of enrichment in two different gold standard datasets of different pQTL-based trait associations (i. e. cis, trans, and mixed cis-trans) at different MR p-values with and without genetic colocalization evidence.
Phenome-wide proteogenetic colocalization and Mendelian randomization identifies new high confidence potential drug targets and uncovers novel biological pathways The results from MR and genetic colocalization (MR-coloc) were used to curate a high confidence target-trait dataset (MR p-value < 0. 0005 corresponding to 5% FDR and colocalization H4 > 0. 8) of 774 unique target-trait pairs, of which 285 pairs were from cis-coloc associations (of which half are linked to protein-altering variants), and 489 pairs were from trans-coloc associations (these are proteins with trans or mixed MR associations with a trait where there is evidence of genetic colocalization for at least one of the trans-pQTL signals and the trait) (Figure  3 , Supplementary Table  9 ). Of the 774 target-trait pairs, 202 pairs show evidence of association in more than one outcome dataset, 69 pairs in more than one proteomic dataset, and 32 pairs replicate in both proteomic and outcome datasets.
Of the 774 target-trait pairs, 188 pairs (68 pairs being cis) involved 45 known drug targets while the remainder (586 pairs involving 190 targets) were classified as 'novel' targets. 255 of both cis or trans pairs out of the 586 'novel' target-trait pairs were also supported by other sources of evidence from the Open Targets platform which includes the machine-learning 'locus-to-gene' (L2G) method  6  , animal models, RNA expression atlas, Mendelian disease evidence, and text-mined literature. Of the remaining 331 target-trait pairs that were not supported by other evidence sources, the majority of these were trans target-trait pairs. Using the IntAct database of physical protein-protein interaction , we identified 18 targets from the 190 novel targets where the protein they interacted (molecular interaction score > 0.
Caspase 6 is one of the targets of the pan-caspase inhibitor emricasan that was recently in Phase 1 trials for COVID-19 but now terminated due to problems in recruitment (NCT04803227). Nevertheless, the CD209-CASP6 link implicates the apoptosis pathway as therapeutically relevant for COVID-19, and supports our previous observation of genetic colocalization of soluble Fas with COVID-19 disease  13  ; the binding of Fas with Fas ligand on cells activates the caspase cascade that initiates apoptosis. These examples suggest that by harnessing protein-protein interactions, it is possible to not only uncover new biological insights but also effectively broaden the range of potential druggable targets.
Colocalizing target-trait pairs recapitulates mechanism of action of drug targets To determine whether the high confidence target-trait dataset can reliably inform therapeutic direction, we examined whether the direction of the genetically predicted therapeutic effect of the proteins matches the mechanism of action of drugs at different phases of drug development (Supplementary Table  9 ). Of the 188 pairs representing 45 drug targets, there were 8 targets where the associated trait EFO code directly matched the indication EFO (Figure  3 , Supplementary Table  9 ). Six out of the 8 targets were supported by cis-coloc associations (IL12B, IL2RA, PCSK9, TNFSF11, F2, APOB) and only two targets (IL2RB, INSR) were supported by trans-coloc (SH2B3, ABO). For cis-targets, we found that the genetically predicted therapeutic direction was consistent with the mechanism of action for 5 out of the 6 targets, except for the association of apolipoprotein B with hypercholesterolemia.