dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome Sequencing, Assembly and Annotation Labrenzia aggregata USBA 371 was grown on 5 mL of Marine Medium 2216 (Difco) at 30 • C for 24 h and 160 rpm. Cells were harvested by centrifugation at 4000 rpm when the mid exponential phase was reached, pelleted and immediately used for DNA extraction. We extracted the genomic DNA using the Wizard ® Genomic DNA Purification Kit (Promega) according to the manufacturer's instructions. Illumina 250PE insert standard shotgun library was designed and sequenced using an Illumina HiSeq. Raw reads were analyzed using FastQC (v0. 11. 2) (http://www. bioinformatics. bbsrc. ac. uk/projects/fastqc/), filtering of data (phred score <30, read length <80bp and PCR-sequencing adapters) was performed using Trimmomatic (v0. 36)  [52]. Remaining reads were assembled using SPADES (v3.
For this reason, our analysis does not consider the hand dominance effect. The hardware for this study was the Myo armband, which contains a nine-axis IMU sensor and eight electromyographic electrodes. Through the nine-axis IMU sensor, the armband captures acceleration, gyroscope, and orientation data at a sample rate of 50 Hz. Using the eight surface electromyographic electrodes, it can also collect sEMG signals to measure users' muscular activity at a frequency of 200 Hz. Physiological data were transmitted to a receiver (in our case, a laptop) via the Bluetooth Low Energy protocol. Data from multiple armbands were simultaneously collected using the Myo SDK  [35]  and modified myo-python library  [36]  and synchronized using the algorithm proposed by Wang et al  [37].
Data Analysis Our first step was to annotate the video recordings manually: we visually inspected all recordings (493 min in total) and annotated each video frame as belonging to one of the 14 handwash steps. All the video recordings were inspected by two data annotators (annotator 1: 16 participants and annotator 2: 4 participants), and annotator 1 checked the annotations made by annotator 2. The interrater reliability was 1. 0 for those samples with two annotators. In total, we made 7180 manual annotations, which represented our ground truth. Using these annotations, along with the frame numbers of the video recordings, we then annotated the armband data with the relevant hand hygiene step label. Next, we were able to measure the accuracy of the algorithm for classifying the steps of the hand hygiene procedures, which operates as follows: (1) preprocessing, (2) feature extraction, (3) classification, (4) postprocessing, and (5) evaluation.
Preprocessing Owing to the different frequencies of IMU (50 Hz) and sEMG (200 Hz) data, we downsampled both types of sensor data to 25 Hz. We then normalized the downsampled data by applying z-score normalization and applied a sliding window procedure with a 0. 2-second time window with 75% overlap. We also measured the classification accuracy for different sliding window parameters, ranging from 0. 1 seconds to 0. 4 seconds and from 50% overlapping to 75% overlapping, and found the sliding window with a 0. 2-second time window and 75% overlap gave the best classification accuracy.
Evaluation We measured the accuracy of both a user-independent model (one model to classify data for all participants) and user-dependent models (one model tuned to each participant). To measure the accuracy of models, we used leave-one-session-out (LOSO) cross-validation for the user-dependent model and leave-one-participant-out (LOPO) cross-validation for the user-independent models, as suggested in the literature  [30]. In LOSO cross-validation, we considered 29 hand hygiene sessions from one participant for training and tested the model on the holdout session. We performed this protocol 30 times per participant (thus, each session became the holdout session once) and calculated the average accuracy per participant. We then repeated this procedure for each participant independently. To evaluate the user-independent model, we used 19 participants' hand hygiene data to train the model and tested the model on the remaining participant.
Placement of Armbands To investigate the performance of the armband, we calculated the accuracy for eight distinct combinations of armband positions: four combinations of two armbands (left arm+right arm, left forearm+right forearm, right arm+right forearm, and left arm+left forearm) and four positions of one armband (right arm, left arm, right forearm, and left forearm). We separately calculated the average accuracy for the classification results (XGBoost) and the smoothed classification results (XGBoost+E. Divisive). At this point, the results are derived using a fusion of all available sensor data (acceleration, gyroscope, orientation, and sEMG) and are shown in Figure  7  (user-dependent models) and Figure  8  (user-independent model). As noted earlier, we used LOSO cross-validation for the user-dependent models and LOPO cross-validation for the user-independent model.
To quantify this energy vs performance trade-off, we fed the user-independent model with the data from different combinations of sensors in line with the widely used wearable devices. Specifically, we examined the performance of the user-independent model with six different combinations of sensors through LOPO cross-validation. The six combinations are as follows: three-axis accelerometer, three-axis gyroscope, eight sEMG electrodes, six-axis IMU (accelerometer and gyroscope), nine-axis IMU (accelerometer, gyroscope, and magnetometer), and nine-axis IMU + eight sEMG electrodes. Effectively, these are all subsets of the full dataset we collected, and that is why this analysis is possible. The classification accuracy when using different sensor combinations, with different sensor placement, is shown in Figure  11.
However, for the user-independent models, the performance drops below 70% when the armband is located on the upper arm (80% accuracy for the lower arm). Thus, a user-dependent model is preferred when only using the data from one armband. Furthermore, to reduce the costs of data collection, we measured the amount of training data that is required to achieve a reasonable classification accuracy. With six hand hygiene sessions as the training set, the models in the one armband scenario placed on participants' arm achieve around 80% accuracy for the user-dependent model. After 16 hand hygiene sessions, the accuracy of the models is higher than 90%. However, these models need manually annotated personal training data, which might require considerable costs associated with personnel time and resources.
Limitations In this study, we recruited participants through our university's mailing list and collected data in a controlled laboratory setting and did not conduct a field study in health care settings. However, it was necessary to conduct a laboratory study first to provide a semicontrolled setting for instrument validation. We also provided substantial training to participants, and we did observe their performance to be higher than 90%, suggesting that our training was effective in ensuring they follow the WHO guidelines during the study. Another limitation is that we did not measure the effect of handedness on classifier performance because of the small number of left-handed participants (1/20, 5% participants). However, as mentioned by Galluzzi et al  [34] , hand dominance is not crucial to hand hygiene detection because of symmetric arm movements.
Protein quantification After purification and reconstitution into nanodiscs, samples were loaded on 12% acrylamide SDS-PAGE gels without boiling. After electrophoresis, the gels were stained with Coomassie Blue and digitally scanned. Densitometry was performed using ImageJ software. The linear regions in the densitometry profile were determined by measuring the density of standards with known protein amounts as shown in Supplementary Figures  S1  and S2.
Offline measurements The filters used for the present analysis were investigated by offline AMS PMF in a previous study  (Daellenbach et al. , 2017). Here, to optimize comparison between the offline AMS and offline EESI-TOF techniques, we produced a new aerosol extract, which was then nebulized for new simultaneous AMS and EESI-TOF measurements. In this way, we avoided differences due to extraction or nebulizer performance, filter aging during storage, system background or contamination, handling artifacts, etc. , which might occur if the current EESI-TOF analyses were to be compared with the original offline AMS study. As a consequence, the AMS dataset presented here is not completely identical to that of  Daellenbach et al. (2017) , although the observed differences are small. For each analyzed filter sample, one 16 mm diameter filter punch was subjected to ultrasonic extraction in 10 mL of ultrapure water (18.
Offline AMS analysis The offline AMS analysis followed the methodology developed by  Daellenbach et al. (2016). The offline AMS operation was similar to other AMS measurements  (Hu et al. , 2013; DeCarlo et al. , 2006). HR-TOF-AMS data were processed using the software SQUIRREL (SeQUential Igor data RetRiEvaL; D. Sueper, University of Colorado, Boulder, CO, USA) v. 1. 57 and PIKA (Peak Integration by Key Analysis) v. 1. 16 for the IGOR Pro software package (Wavemetrics, Inc. , Portland, OR, USA). The high-resolution mass spectral analysis was performed for each m/z (mass to charge) in the range of 12-120 at AMS V mode and yielded a dataset consisting of 257 ions (excluding isotopes and CO 2 -dependent ions). The interference of NH 4 NO 3 on the CO + 2 signal was corrected  (Pieber et al. , 2016)  as follows: CO 2,real = CO 2,meas -CO 2,meas NO 3,meas NH 4 NO 3 ,pure • NO 3,real , (1) where the CO 2,meas NO 3,meas NH 4 NO 3 ,pure correction factor was determined based on the measurement period and varied between 1 % and ∼ 5 %.
The AMS data were rescaled to the ambient concentration by normalizing the measured signal to the estimated watersoluble organic matter (WSOM) concentration, which was calculated as the product of the measured water-soluble organic carbon (WSOC) multiplied by the OM : OC ratios determined from the offline AMS results. This method and the associated uncertainties are described in detail by  Daellenbach et al. (2016 Daellenbach et al. ( , 2017)). Note that because we do not attempt to correct for the water extraction efficiency of OM components, the analysis presented herein describes the source apportionment of AMS WSOM.
Organic components are detected as adducts with Na +. Spectra are recorded in positive ion mode at 5 s time resolution. In normal operation, the EESI-TOF alternates between direct sampling of aerosol and sampling through a particle filter to provide a background measurement; however, the filter was not used in this study. Instead, the measurement blanks (nebulized ultrapure water) were used to determine the background. The EESI-TOF data were processed in Tofware 2. 5. 7 (Tofwerk AG, Thun, Switzerland). We report the signal measured by the EESI-TOF in terms of mass flux of ions to the microchannel plate detector (ag s -1 , neglecting the mass of Na + ), calculated as shown in Eq. (2). M x = I x • (MW x -MW cc ) (2) Here M x is the mass flux of ions in units of attograms per second, where x represents the measured molecular ion.
I x is the recorded signal measured by EESI-TOF. MW x and MW cc represent the molecular weight of the ion and the charge carrier (e. g. , Na + , H + ), respectively. Note that this measured mass flux can in principle be related to the ambient OA concentration by the instrument flow rate, EESI extraction and ionization efficiency, declustering probability, and ion transmission, where several of these parameters are ion-dependent and currently unknown  (Lopez-Hilfiker et al. , 2019). The EESI-TOF data were normalized to WSOC by using the AMS OM : OC ratios mentioned above. Similar to the AMS, no corrections for the efficiency of the water extraction from the filter samples is applied, and the offline EESI-TOF analysis therefore strictly relates only to the WSOM fraction. Note that because online EESI-TOF operation already requires extraction into the spray droplets (1 : 1 water : acetonitrile mixture), major differences between the measured OA fraction between online and offline analyses are unlikely.
Source apportionment techniques The EESI-TOF PMF input data matrices included 91 filter samples. The input errors σ ij were calculated as the uncertainty related to ion counting statistics and ion-to-ion signal variability at the detector (δ i,j ), which were added in quadrature to the blank variability background (β ij )  (Qi et al. , 2019). σ ij = δ 2 ij + β 2 ij (3) We applied a minimum error corresponding to the measurement of a single ion during the 5 s averaging period. Variables with a low signal-to-noise ratio (SNR <0. 2) were removed, whereas "weak" variables (0. 2< SNR <2) were down-weighted by a factor of 3 rather than 2 (following the recommendations of  Paatero and Hopke, 2009). In total, 1070 fitted ions (1068 adducts with Na + and 2 with H + ) between m/z 135 and 400 were identified.
The PMF source apportionment technique requires the time series of ions from high-resolution mass spectral fitting, along with their corresponding uncertainties, as input. As for the EESI-TOF, the input AMS errors for PMF were calculated as the sum in quadrature of the AMS instrument uncertainties (including ion-counting statistics and ion-to-ion signal variability at the detector (δ i,j )) and the blank variability (β ij )  (Ulbrich et al. , 2009). The offline EESI-TOF and offline AMS source apportionment was performed using positive matrix factorization (PMF)  (Paatero and Tapper, 1994)  as implemented by the Multilinear Engine (ME-2) and with model configuration and analysis executed via the SoFi (Source Finder, version 6. 39) interface  (Canonaco et al. , 2013). PMF is a linear statistical model to describe the variability of a multivariate dataset.
Here this was done by constraining the factor profile elements f i,k for one or more factors  (Canonaco et al. , 2013) , implemented using the a value method, where the output f i,k for each constrained factor is required to satisfy Eq. (  6 ): f i,k = f i,k ± a • f i,k. (6) Here, f i,k represents a predetermined anchor profile and the scalar a (0 ≤ a ≤ 1) determines the tightness of constraint. Anchor profiles may be obtained by several methods, including prior studies, laboratory measurements of known sources, or analysis of a subset of the current dataset, and are discussed on a case-by-case basis in Sect. 3. 1 (AMS) and 3. 2 (EESI-TOF)  (Canonaco et al. , 2015).
Identification of source-specific ions To determine ions characteristic of individual factors (or groups of related factors), agglomerative hierarchical clustering was conducted on the EESI-TOF matrix of PMF profiles. A dendrogram is used to show relationships between members of a group. A more detailed description is found in  Qi et al. (2019). Here, we summarize the steps as follows: (1) calculation of the standardized value (z score) is performed along the ions by using Eq. (  7 ): z = x -µ σ. ( 7 ) The µ is the mean value, σ is the standard deviation, and Z represents the distance between the raw score and the mean value in units of the standard deviation. (2) Formation of groups of the new calculated data is performed by using the Euclidean distance (Eq. 8): σ dist x i , x j = n m=1 x im -x j m σ m 2.
3), although the number of cellulose measurements is limited to only 12 filters. Nevertheless, like the factor mass spectrum this correlation is consistent with the assignment of this factor to PBOA. We also considered cooking-related emissions as an alternative assignment for the PBOA EESI factor. Viewed broadly, these two emissions sources are somewhat similar in that they are both expected to have strong contributions from fatty acids, consistent with the salient features of the PBOA EESI mass spectrum. Indeed, a COA AMS factor is resolved, but no COA is retrieved from the EESI-TOF dataset despite previous studies identifying online cooking emissions in EESI-TOF data during summer and winter (Fig. S10 ) at the same site. However, offline AMS analyses have previously shown COA to have a low extraction efficiency  (Daellenbach et al.
, 2016) , resulting in low contribution in the EESI-TOF dataset. Further, the highest COA AMS concentrations occur during the period of 14 April-8 May, during which no EESI-TOF data are available. Without this strong temporal feature, COA may contribute too little to the variability of the EESI-TOF dataset to be resolved. A detailed comparison of the retrieved PBOA EESI profile with previously obtained EESI-TOF COA factors shows that dominant PBOA EESI ions are different from the major components of cooking-related EESI-TOF factors obtained from source apportionment of online summer and winter mass data, e. g. , C 16 H 30 O 3 , C 18 H 34 O 2  (Stefenelli et al. , 2019; Qi et al. , 2019). Further, we note that the time series of the PBOA EESI and COA AMS factors are not well correlated, suggesting different sources and thus a unique source for PBOA EESI unrelated to cooking emissions.
The offline EESI-TOF PMF retrieved a PBOA EESI factor, separated less aged and more aged factors from biomass burning, and presented winter-and summer-dominated emissions, features that are not possible for AMS PMF analysis. We performed cluster analysis of the EESI-TOF ions followed by correlation with the resolved factors, which identifies factor-specific ions of each factor. These characteristic ions represent potential markers for future studies. Overall, the EESI-TOF analysis was supported and corroborated by the AMS PMF analysis. This work highlights the potential of offline, highly chemically resolved data provided by an EESI-TOF for identification of the key sources over a long time period.
Acknowledgements. Carlo Bozzetti  is acknowledged for SoFi training. Felipe Lopez-Hilfiker  and  Veronika Pospisilova  are acknowledged for initial EESI training. Financial support. This research has been supported by the  Swiss National Science Foundation  (grant no. BSSGI0_155846 ), the  Swiss National Science Foundation  (grant no. 200021_169787 ), the  International ST cooperation program of China  (grant no. 2014DFA90780 ), and the  National Natural Science Foundation of China  (grant no. 21976093 ). Review statement. This paper was edited by  John Liggio  and reviewed by two anonymous referees.
(L) Glucose tolerance test performed in SOD1 G93A and in their wild-type control littermates (WT) at 55, 90, and 120 days of age, respectively. Data in (B), (C), (E), (F), (G), and (H) are presented as mean G SEM, ***p < 0. 0001, unpaired Student's t test. observed in early presymptomatic mice and the specific impairment of Complex I activity, we analyzed the extent of AMPK activation in ALS mice.
GAPDH was used as loading control. (B) Densitometric analysis of data obtained in (A). Data are presented as mean G SEM, *p < 0. 05, **p < 0. 001 when compared with wild-type untreated mice (arbitrarily set on at 1) and # p < 0. 05 ## p < 0. 001 when compared with SOD1 G93A untreated mice (n = 6 independent experiments), parametric one-way ANOVA and a Bonferroni post hoc test. (C) Expression of mRNAs coding for Nor1, Glut4, Serca1, Serca2a, Serca2b, MyHCI, MyHCIIa, MyHCIIx, MyHCIIb in the TA of SOD1 G93A mice and wild-type control littermates treated with RAN (SOD1 G93A, RAN and WT RAN) or not treated with RAN (SOD1 G93A and WT) at 120 days of age (n = 4 for each genotype/ treatment). Data are presented as mean G SEM, **p < 0. 001 when compared with wild-type untreated mice (arbitrarily set on at 1) and # p < 0.
RT-qPCR analysis was performed at different stages of disease in SOD1 G93A mice and their wild-type control littermates. (B) PGC1α expression in skeletal muscle of SOD1 G93A mice. PGC1α protein expression in tibialis anterior (TA) of SOD1 G93A (+) and wild type mice (-) at the indicated ages (left panel). β-Actin was used as loading control. Densitometric analysis of data (right panel), presented as mean ± SEM, *P<0,05, **P<0,001, when compared with wild-type mice at the same age (arbitrarily set on at 1), n = 4 independent experiments, unpaired Student's t test. (C) Expression of mRNAs coding for Serca1, Serca2a, Serca2b in the TA of SOD1 G93A mice. RT-qPCR analysis was performed at different stages of disease in SOD1 G93A mice and their wild-type control littermates (at least n=3 for each stage).
DNA-RNA isolation and Real Time qPCR Mitochondrial and genomic DNA were isolated according to Miller  (Miller, 2003) , whereas total RNA was extracted with Trizol and retro-transcribed with a SensiFASTtm cDNA synthesis kit (Bioline Cat# BIO-65054). Mitochondrial DNA content and mRNA expression levels were determined by Real Time qPCR reactions using a Light Cycler 480 SYBR Green System (Roche ETC). Cp values were calculated using the 'second derivative max' algorithm of the Lightcycler software. Relative mitochondrial DNA content was normalized to the genomic genes TNF-alfa and Interleukin 2 (IL2), whereas mRNA relative expression values were normalized to the housekeeping gene TATA box binding protein. Results, where not specifically expressed, were reported as the ratio between the average of values from wild-type control mice and SOD1 G93A mice, normalized to the average values of genomic genes or the housekeeping gene.
ACKNOWLEDGMENTS This work was supported by  AriSLA  through  HyperALS  project (A. F. ); AFM-Telethon  project n. 2018; AFM- Telethon  project n. 21021 (J. P. L. and A. F. ); the  National Health and Medical Research Council  ( 1101085  and  1185427  to F. J. S. and S. T. N. );  Scott Sullivan MND Research Fellowship (Queensland Brain Institute , the  Royal Brisbane & Women's Hospital Foundation , The  MND and Me Foundation ) (S. T. N. ); and  Australian Institute for Bioengineering and Nanotechnology  (S. T. N. ). This work is dedicated to the memory of our dear Mentor and  Friend Maria Teresa Carrı  `.
43. 1. Count tables were generated in R using the package tximport together with DESeq2. Differential expression of genes was conducted using DESeq2 with default options. Genes were considered differentially expressed if the adjusted P-value was<0. 05. RNA-seq data of this study have been deposited in the Sequence Read Archive (SRA) under the accession code: PRJNA623256 (https://www. ncbi. nlm. nih. gov/Traces/study/?acc=PRJNA623256).
In Section 4, the wavelet-based homogenized and multiscale filtering of the fluid fields is introduced, in order to build a set of macroscale variables describing the relevant information contained in local fields, whereas the capabilities of the CWT filtering in the proposed context are thoroughly tested and discussed in Section 5. The filtering technique is thus applied to a reference pressure field obtained numerically at local scale using EUROPLEXUS software for Euler equations (http://www-epx. cea. fr), and the accuracy of the approach in terms of conserved scales in the wavelet approximation, directly related to the number of variables kept at macroscale, is evaluated through the computation of the fluid force applied to the microstructure. The last section draws some conclusions and prospects of the proposed work.
There was no difference in overall response rate at day 62 between the two groups (P = 0. 79). Disease progression or death related to the disease was reported in all dogs. The median PFS for all dogs was 86 days (range: 4224). The median PFS for dogs treated with F14512 was 86 days (range: 7224), and 91 days (range: 4202) for those treated with etoposide phosphate, with no statistical difference between the two groups (P = 0. 30). Five dogs were still alive at the time of data analysis (3 dogs in F14512 group and 2 dogs in etoposide phosphate group) with a median time of follow-up of 139 days (range: 84-153). Analysis of the 28 randomly assigned dogs with Pgp-overexpressing lymphoma (Pgp+), 10 (10/15, 67%) dogs treated with F14512 achieved an objective response (6 CR and 4 PR) at day 62 compared to 5 (5/13, 38%) dogs treated with etoposide phosphate (3 CR and 2 PR).