dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments FIEB ( Ciprian Petrescu ),  INRAE  ( Olivier Lorvelec, Patricia le-Quilliec ),  Madis Podra ,  OFB  ( Christelle Bellanger ,  Maylis Fayet ),  Augustin Granel ,  Alice Mouton ,  Jasmine Veitch ,  Adrien André ,  EEP program  for the European mink,  Tiit Maran , the government of  Navarra , and the  Ministry for the Ecological Transition and the Demographic Challenge (Spain).
Statistical analysis Observations were marked as outlier and excluded from the dataset prior to statistical analyses if the residual (fitted e observed value) > 2. 5 Â standard error on the residuals of the data set (6 pens for performance in T8, T11, T15, T16 [1 pen] and T2 [2 pens]; 2 birds for UA analyses [T8, T16], 2 birds for glucose [T8, T14], 5 birds for TG [T2, T6, T7, T8, T13]). For performance results, pen was the experimental unit, whereas for plasma analyses individual bird was the experimental unit. The experimental data were analysed by linear and quadratic analysis using GenStat statistical software (19. 1st edition). The interaction between Thr Â Gly was analysed for treatments 2 to 17 according to the following equation: Y ijk ¼ m þ Block i þ Thr j þ Gly k þ Thr Â Gly jk þ e ijk , (1) where Y ijk is the response parameter, m is the overall mean, Block i is the random effect of replicated block (i ¼ 1 to 6), Thr j is the effect of AFD Thr-to-Lys ratio (j ¼ 1 to 4), Gly k is the effect of AFD (Gly þ Ser)to-Lys ratio (k ¼ 1 to 4), Thr Â Gly is the interaction effect, and e ijk is the random error term.
DFT calculations We performed the DFT calculations using code HFODD  22, 23  version (3. 01m) and a standard Skyrme density functional UNEDF1  24. The deformed DFT single-particle wave functions were expanded on the spherical harmonic-oscillator basis up to N 0 = 16 quanta. An A-dependent harmonic-oscillator frequency èω was fixed according to the prescription defined in Ref. 25. For the state 9/2 + , the proton configuration (with no pairing correlations) was fixed by occupying 49 lowest axially deformed prolate orbitals, that is, the Nilsson orbital  26  [404]  9  2 with the angular-momentum projection on the symmetry axis Ω = -9/2 was left empty. Similarly, for the state 1/2 -, the hole was kept in the oblate Nilsson orbital [301]  1  2 with Ω = -1/2. It was essential to pick the proton-hole configurations with angular momenta properly aligned along the axial-symmetry axis.
Acknowledgements This work was supported by  ERC  Consolidator Grant No. 648381  (FNPMLS);  STFC  grants  ST/L005794/1 ,  ST/L005786/1 ,  ST/P004423/1 ,  ST/M006433/1 ,  ST/P003885/1  and  Ernest Rutherford Grant  No. ST/L002868/1 ;the  U. S. Department of Energy, Office of Science, Office of Nuclear Physics  under grant  DE-SC0021176 ;  GOA 15/010  from  KU Leuven ,  BriX Research Program  No. P7/12 ; the  FWO-Vlaanderen (Belgium) ; the  European Unions Grant Agreement 654002  ( ENSAR2 );  National Key R&D Program of China  (Contract No:  2018YFA0404403 ); the  National Natural Science Foundation of China  (No: 11875073 ); the  Polish National Science Centre  under Contract No. 2018/31/B/ST2/02220. TRIUMF receives funding via a contribution through the  National Research Council of Canada.
We also observe irreversible long-term trends (over the 6. 5 years data set) of deformation in the Aletsch valley composed of a larger uplift than observed at our reference GNSS station in the Rhone valley, and horizontal displacements of the slopes towards the valley. These observations can be attributed respectively to the elastic bedrock rebound in response to current glacier mass downwasting of the Great Aletsch Glacier and gravitational slope deformations enabled by cyclic groundwater pressure-related rock mass fatigue in the fractured rock slopes. Plain Language Summary Mountain ranges are subject to deformation due to tectonic forces, orogenesis, erosion, and gravity. Other factors can deform the slopes, such as atmospheric-driven thermal expansion and contraction, freeze-thaw in open fractures, and pore pressure variations.
Surface Displacement The five cGPS stations installed on both flanks of the valley are equipped with low-cost single-frequency GPS devices. The GPS data processing is based on the Bernese GNSS software, using differential carrier phase techniques. Daily static coordinates are computed with respect to the geodetic dual-frequency GNSS station FIES situated close to Fiescheralp  (Limpach et al. , 2016). The relatively short baselines (5. 0-7. 4 km) between the GPS stations and the reference station FIES allow the mitigation of ionospheric effects by differential processing, and hence the use of single-frequency GPS receivers. The station FIES is part of a regional network of 10 geodetic reference stations operated by the Institute of Geodesy and Photogrammetry of ETH Zurich. This regional dual-frequency GNSS network is continuously processed together with stations of the Swiss national network of GNSS reference stations AGNES (Automated GNSS Network for Switzerland) from Swisstopo.
The reference station FIES is subject to similar cyclic motions as studied here. Its position is continuously computed with respect to the Swisstopo station Hohtenn (HOHT) from the AGNES network, situated in the Rhone valley. This allows to eliminate station FIES's motion and to reference the GPS stations near the glacier to the station HOHT in the Rhone valley. A direct vector between the station HOHT and the stations near the glacier would yield much longer baselines prone to ionospheric delay errors with the single-frequency receivers. The effect of the temperature on the monuments has been investigated by  Frukacz et al. (2017)  and shown to be below the noise level of the stations. At the total stations, interruptions of data acquisition sometimes occur during periods where the station is snow or ice-covered and occasionally because of rockfalls or snow avalanches covering or destroying reflectors.
The station regularly checks its alignment to one of the reflectors, considered as reference, to prevent drift caused by mechanical errors. However, the reference point is not considered as stable and is also included in the analysis here. For ALTD, it is point 39 and for ALTS, point 10 until 2019 and 68 afterward (see Figure  2 ). Point 68 is located close to 10 but is less often snow-covered, which is important to avoid drift of the angular measurements during winter. Therefore, we decided to include reflector 68 as a reference point in 2019. We noticed an increase in instrumental noise when using two reference points and switched to only one (point 68) after a few months. We discard data from this period in our statistical analysis to overcome potential issues related to this instrumental noise.
We average all night-time measurements between 10 p. m. and 6 a. m. to calculate daily solutions of each prism position. Because the total station cannot be considered stable and is subject to a similar motion as the reflectors, we correct the relative position obtained with the low-pass filtered GPS position of the total station to get the absolute position of each prism. For filtering of the position time series, we apply a Butterworth low-pass filter of third order with a cutting frequency of 3. 858 × 10 -7 Hz (corresponding to a period of 1 month, see Figure  3 ). These parameters are used for low-pass filtering of daily sampled data throughout this paper if not specified otherwise. The fast displacement in spring (see Figure  3 ) is calculated for each point with a peak-to-peak amplitude of the seasonality larger than 5 mm.
The points corresponding to the start and end of the spring displacement are identified on the time series for each year in the record. We take the average displacement obtained between these two times for all available years in record, at each point. The results of this analysis is shown in Figure  2. To extract information from the displacement time series and separate the signal coming from different temporally independent sources, we use a statistical method, the Variational Bayesian Independent Component Analysis (vbICA). The vbICA is described by  (Choudrey & Roberts, 2003)  and adapted to the study of geodetic position time series with gaps by  Gualandi et al. (2016). We use the two-dimensional horizontal displacement time series (east, north) for reflectors and cGPS stations that do not have large data gaps (exceeding 20% of the time series).
Strain Analysis Using the distance measurement of the total station, we can directly infer strain between two points on the slopes. This technique has the advantage of increasing the signal-to-noise ratio, as it does not use the angular calculations of the TPS or the absolute positioning from GPS. For points situated far from the total station, a daily solution of distance measurement is used. High-resolution strain measurements are performed between close reflectors and the total stations. Hourly temperature-corrected distance measurements to the total station, including measurements during the day, offer a data set similar to the one we would obtain from low-accuracy extensometers. These datasets are used to analyze smaller and shorter relative displacements of the prisms and compared with environmental variables (e.
From the air temperature and sunshine duration, we calculate the potential evapotranspiration with the Turc method  (Turc, 1961). Modeled snow accumulation and melting rates were validated on measured snow depth at the nearby Eggishorn station. The model is calibrated on the river discharge by minimizing the Nash-Sutcliffe Efficiency index  (Nash & Sutcliffe, 1970). It is computed over the logarithm of the discharge data to focus the importance of the low flows when groundwater is the main contributor to stream discharge. In the case of the Aletsch valley, the Massa river gauge has a large catchment of ∼191 km 2 , with a significant part covered by glaciers (see Figure  1 ). The river discharge dynamics is highly influenced by the meltwater from the glaciers inducing high flow regimes during summer and low flows in winter (see Figure  A1 ).
Geological Mapping Structural field mapping was done using two applications; Fieldmove and Fulcrum. A drone (DJI Phantom v4) was used to acquire a high-resolution surface model around the total station of Chazulecher. The flights were prepared with the software DJI Flight Planner, and Litchi was used to control the drone on-site. Then, the 3D model was computed with Agisoft Metashape, and an orthophoto, digital terrain model (DTM), and mesh were produced. Fractures were then manually digitized in ArcGIS Pro from the orthophoto, and shear zones orientations were computed in Virtual Reality Geological Studio (VRGS), and with a hand-held compass in the field.
Acknowledgments This project is funded by the  Swiss National Science Foundation  (project  172492 ). We thank the  Federal Office for the Environment (FOEN)  for the Massa River monitoring data set, and Mete-oSwiss for the weather data. We thank the two anonymous reviewers and the editor and associate editor for there helpful contribution to improving the manuscript. We thank  Franziska Glueer  and  Reto Seifert  for installing most of the monitoring system and  Andrea Manconi  for helping with the maintenance. We thank the many field helpers who gave some time for this project through the years. There would not be enough space here to name them all. We thank  Bernadette "Berni" Würsch  and  Andrea Gossweiler , as well as colleagues and friends for their support and all the great discussions.
Introduction In the post-genomic era, there has been intense efforts directed toward the reconstruction of genome-scale models of cellular networks. An important portion of these efforts focuses on metabolic networks due to the significance of cellular metabolism for understanding diseases such as cancer  [1] [2] [3] [4]  as well as for metabolic engineering applications in biomanufacturing  [5]. Recent advances in high-throughput sequencing technologies, gene functional annotation, and metabolic pathway databases, and developments of algorithms for mapping gene-proteinreaction (GPR) associations and identifying missing metabolic reactions systematically (gapfilling), have enabled the reconstruction of thousands of genome-scale metabolic models (GEMs), from single cell organisms to human  [6, 7].
Although FBA is effective in handling large networks and predicting cell behavior in many metabolic engineering studies  [12] [13] [14] [15] , considerable uncertainty still remains about the appropriate choice of cellular objective for different conditions and cell types, a choice that typically requires expert knowledge of the cells and their phenotype in a given condition. Such an issue is particularly prominent for complex organisms such as human. Moreover, multiple equivalent flux solutions exist that give the same cellular objective value  [16]. Not to mention, the standard FBA often produces biologically unrealistic flux solutions  [17, 18]. Driven by the increasing ease and availability of whole-genome omics profiling data, a multitude of FBA-based algorithms have been developed to incorporate omics datasets to create context-specific metabolic networks and to improve flux prediction accuracy  [19] [20] [21] [22] [23] [24] [25] [26].
Several of these methods, such as GIMME (Gene Inactivity Moderated by Metabolism and Expression)  [20] , iMAT (integrative Metabolic Analysis Tools)  [21] , and MADE (Metabolic Adjustment by Differential Expression)  [22] , are based on maximizing the consistency between the predicted flux distribution and the mRNA transcript abundance of metabolic genes, where the higher the transcript level of an enzyme, the larger should the flux of the corresponding reactions. Recent methods use data of mRNA transcript abundance for setting the bounds on reaction fluxes, e. g. E-Flux  [23] , or in the biological objective function, e. g. Lee et al. [24]  and RELATCH (RELATive Change)  [25]. Meanwhile, others like GX-FBA  [26]  determine fluxes in a perturbed state using differential gene expression and FBA flux prediction for the control (reference) state.
Another related method MOOMIN  [33]  uses a Bayesian approach to integrate differential gene expression profiles with GEMs to predict the qualitative change in the metabolic fluxes-increased, decreased or no change. In this work, we developed ΔFBA (deltaFBA) for predicting the metabolic flux difference given a GEM and differential transcriptomic data between two conditions. ΔFBA relies on a constrained-based model that governs the balance of flux difference in the GEM, while maximizing the consistency and minimizing inconsistency between the flux alterations and the gene expression changes. ΔFBA is developed as a MATLAB package that works seamlessly with the COnstraint-Based Reconstruction and Analysis (COBRA) toolbox  [34]. We applied the ΔFBA to analyze the metabolic changes of Escherichia coli in response to environmental and genetic perturbations using data from the studies of Ishii et al.
An alternative to L2-norm minimization is L1-norm minimization, which is analogous to maximizing sparsity of Δv. The L1-norm minimization was previously used in parsimonious FBA (pFBA) method  [19] , but such an approach often still leads to multiple degenerate solutions. On the other hand, the L2-norm minimization will produce a unique solution. However, the mixed integer quadratic optimization that is required to find the minimum L2-norm solution may have high computational requirement. ΔFBA is available as MATLAB scripts and are compatible with the COBRA toolbox  [34]. ΔFBA requires Gurobi optimizer (http://www. gurobi. com) as a pre-requisite. ΔFBA has been tested on a Windows PC using a 6-core Intel Xeon (2146G) Processor with 16 GB RAM.
coli cultured at a dilution rate of 0. 2 h -1. The differential (foldchange) reaction expressions were subsequently evaluated based on the fold-change gene expression using the GPR Max/Min rule in the COBRA toolbox (MATLAB)  [40]. For samples with only RT-PCR mRNA abundance data, the set of up-and downregulated reactions included all reactions with fold-change reaction expressions higher than 1 and those with foldchange lower than 1, respectively. In the additional analyses for samples with whole-genome transcriptome data, the set of up-and downregulated reactions were taken from the top and bottom 5 th percentile of the differential reaction expressions. The differences of the measured cell specific glucose uptake rates between perturbed and control experiments were used as constraints. ΔFBA was applied using the two-step optimization with the L2 norm minimization, as described above.
The second case study came from a study of E. coli growth on 8 different carbon sources performed by Gerosa et al. [36]. Unprocessed global transcriptomic data were obtained from ArrayExpress (E-MTAB-3392), and differential expression analyses between every pair of carbon sources were evaluated using the Limma package in R  [42]. We only included the set of genes with significance fold-change expression at FDR < 0. 05. As before, the fold-change reaction expressions were computed based on fold-change in the global gene expression using the Max-Min GPR rule using COBRA toolbox  [40]. The up-and downregulated set of reactions were taken from the top and bottom 5 th percentile of the differential reaction expressions. In addition, cell culture data on specific growth rates were used to compute the bounds for flux difference for biomass production rate.
The uptake rates of the carbon source changes were also incorporated as constraints. We implemented the two-step optimization of ΔFBA using L2 norm minimization. The third case study came from two studies of skeletal muscle tissue metabolism in type-2 diabetes (T2D) patients by van Tienen et al. [43]  and Jin et al. [44]. The microarray gene expression datasets were obtained from GEO (GSE19420  [43]  and GSE25462  [44, 45] , respectively) and the differential (fold-change) expression of genes for each dataset were computed using the Limma package in R  [42]. We only included the set of genes with significance foldchange expression at FDR < 0. 05. The fold-change reaction expressions were computed based on the differential gene expression using the Max/Min GPR rule  [40]. In the absence of additional constraints in the form of exchange fluxes or growth characteristics, we set the up-and downregulated reactions from the top and bottom 25 th percentile in differential reaction expressions, rather than the 5 th percentile threshold used in E.
Implementation of comparative methods Among the comparative methods in this work, the method Relative Expression and Metabolomic Integrations (REMI) was specifically developed for predicting individual flux distributions of a pair of conditions (v P and v C ) using multi-omics dataset, and thus more comparable to ΔFBA. The toolbox was downloaded from https://github. com/EP-LCSB/remi. The differential gene expressions in each case study were obtained as described above. The mapping from differential gene expression to the corresponding reaction expressions were done using the procedure detailed in REMI  [31]. Briefly, the authors followed the implementation of Fang et al. [34]  to translate gene expression ratios to obtain reaction expression ratios. When several enzyme subunits are required for a reaction, a geometric mean of expression ratios is chosen to represent the reaction ratio.
Performance evaluation The agreement between the predicted flux changes Δv � and the ground truth 13 C-based flux difference Δv M was assessed by using two accuracy metrics: uncentered Pearson correlation coefficient and normalized root mean square error (NRMSE). The uncentered Pearson correlation coefficient ρ was computed as follows r ¼ Dv M � Dv � kDv M k 2 kDv � k 2 ð16Þ Meanwhile, the NRMSE was according to the following equation-using tdStats package in R: NRMSE ¼ 1 Dv M max À Dv M min ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi kDv M À Dv � k 2 2 n M s ð17Þ where n M is the number of measured fluxes. Besides the quantitative agreement in flux changes, we also evaluated the qualitative agreement by comparing the signs of the flux changes between experimental measurements and predictions.
Dysregulation of skeletal muscle metabolism in type-2 diabetes In this case study, we looked at metabolic alterations of human muscle using the myocyte GEM iMyocyte2419  [37]  and gene expression datasets from two type-2 diabetes (T2D) studies, one by van Tienen et al. [43]  and another by Jin et al. [44]. The study by van Tienen et al. [43]  compared long term T2D patients with age-matched cohort, and reported the downregulation of gene expression related to substrate transport into mitochondria, conversion of pyruvate into acetyl-CoA, aspartate-malate shuttle in mitochondria, glycolysis, TCA cycle, and electron transport chain. Similarly, Jin et al. [44]  reported a significant enrichment of pathways involved the oxidative phosphorylation among the downregulated genes in their T2D cohort when compared to control.
Many of such methods, such as GIMME  [20]  and iMAT  [21] , transform gene expression data to a binary state (active/inactive, high/low) and produce metabolic flux prediction for a single condition. But, as shown in Fig  1 , pFBA often works as well, if not better, without using gene expression data. When deciding the reference (control) condition, the more well-characterized metabolic state (e. g. , more experimental data, more obvious metabolic objective) should be used to generate the reference flux distribution ΔFBA flux differences can be combined with the reference flux values by simple algebra to evaluate metabolic fluxes of the other (perturbed) condition. Such a strategy may be advantageous since once the metabolic flux distribution for the baseline condition is accurately determined (and ideally experimentally validated), one can use ΔFBA and differential gene expression datasets for various perturbation experiments to generate accurate prediction for metabolic fluxes of the perturbed conditions.
Note that for many gene expression profiling technologies the relative (differential) expressions are often more reliable and informative of the underlying cellular alterations than the absolute expression because of technical and biological considerations. Finally, while in the formulation and the application of ΔFBA we considered only differential gene expression data, the method can also accommodate other omics dataset, such as proteomics, by appropriate mapping of the data to changes in reaction expressions. Metabolomics data can also be accommodated in ΔFBA via thermodynamics constraints, as done in REMI  [31] , in which certain reactions can only proceed in one direction.
Data Collection and Definition of PVL The following data were collected for each patient: age; body mass index; sex; body surface area; aortic valve characteristics (mean valve gradient [mmHg]), aortic peak velocity [cm/s], aortic surface [cm 2 ], and left ventricular ejection fraction); length of stay in the intensive care unit; and overall length of hospital stay (days). For SAVR, the durations of CPB and aortic clamping (in minutes) were noted. The following postoperative outcomes were documented: new-onset atrial fibrillation, acute kidney injury according to the KDIGO criteria (diuresis <0. 5 mL/kg/h or an increase in plasma creatinine >26. 5 mmol/L), stroke, vascular complications (acute lower limb ischemia or vascular access bleeding requiring vascular surgery), and atrial-ventricular block requiring implantation of a pacemaker.
Description of studies: The result of the literature search and study selections is shown in the PRISMA flow diagram in Figure  1. An initial database search identified 614 records, 564 studies were manually screened after duplicate removal, and only 5 RCT fitting the inclusion criteria were included  [23] [24] [25] [26] [27]. The baseline characteristics of the five included studies, encompassing 215 neonates are reported in Table  1.