dataesr/scientific-paragraphs-categorization huggingface dataset:
5. Pterophylla macgillivrayi, Aneityum, Vanuatu. A -B twig with leaves and very young fruits, infructescence composed of bidents visible in B; C under-surface of leaves, note rachis unwinged. Voucher: Balick et al. 4874. photos: michael j. balick. RECOGNITION. Similar to Pterophylla exigua (A. C. Sm. ) Pillon & H. C. DISTRIBUTION. Solomon Islands (Western Province) (Map 3). SPECIMENS EXAMINED. SOLOMON ISLANDS. Western Prov. : New Georgia Group: Vangunu: NW of Vura along ridge to Mt Olasana, 8°42'S 158°01 'E, 680 m, 14  Nov. 1997 (st. ) , Bradford 814 (K ×2, MO); ibid. , 8°42'S ing intact (Katovai et al. 2012). Long term plots here have provided data on forest dynamics (Burslem et al. 2000;  Whitmore 1989 ) and the vegetation here (and presumably also on Vangunu) is subject to cyclones so trees that survive must be capable of regenerating after major disturbances; however, we have no specific information about regeneration in P.
Study populations AD patients and control individuals as well as patients with other neurological disorders (iNPH, vascular dementia (VaD), and mild cognitive impairment (MCI)) are part of the Finnish sample set of the European Alzheimer's Disease DNA BioBank (EADB). Demographics and GWAS-based genetic analysis of this sample set have been described in detail before  (24). Carriers of the APP A673T variant and matching controls for plasma, CSF, and biopsy analysis were selected from the Kuopio University Hospital (KUH) NPH Registry. The sample set and recruitment process have been previously described (36). RNA and protein expression data from Braak stage-classi ed post-mortem temporal cortical samples were obtained from a Finnish neuropathological cohort, which has been described in detail previously  (37).
Proteomics sample preparation For proteomics analysis, we have used six plasma and six CSF samples from each of the three carriers of APP A673T mutation (two male and one female) and three age-and sex-matched control individuals. We have applied a TMT®-based Calibrator approach for multiplexed proteomics (42) using brain lysate as Calibrator tissue. The total protein concentration of plasma and CSF was determined by a modi ed Bradford protein assay and each sample was visualized by Coomassie-stained (Imperial Stain, Pierce) SDS-PAGE 4-20% gradient gels (Criterion, Biorad; data not shown). Three brain cortex tissues (Braak Stage 0) were lysed in 8M urea, 75 mM NaCl, and 80 mM Tris; pH 8. 2 containing phosphatase and protease inhibitors. Tissue debris was removed by centrifugation at 12000g, 4°C for 2x10min.
The protein concentration was determined by the Bradford assay and the protein content was visualized by Coomassie-stained (Imperial Stain, Pierce) SDS-PAGE 4-20% gradient gels (Criterion, Biorad; data not shown). 1. 98 mg of each brain lysate was used to generate a pooled brain lysate that was used as a brain Calibrant sample. A tandem IgY14/Super depletion method was used to deplete high and medium abundant proteins from 6 plasma samples. Plasma volumes corresponding to ~ 12. 5 mg of protein were immuno-a nitydepleted of the 14 most abundant plasma proteins followed by the next ~ 50 moderately abundant proteins (43) using mouse Seppro® IgY14 (LC10) and Seppro® Supermix (LC5) columns (Sigma-Aldrich, St. Louis, MO, USA). In an FPLC-assisted manner (Knauer) plasma samples were loaded on the IgY14 column and the ow-through was directed onto the Supermix column.
Peptide mass spectra were acquired throughout the entire chromatographic run (180 minutes). The mass spectrometer was operated in data dependent mode with full scans at 120. 000 resolution and MS2 fragment scans acquired at 60. 000 resolution with the automatic gain control (AGC) setting set to 1e5. The duty cycle was set to 3 seconds, meaning a full scan was acquired at least every 3 seconds, followed by MS2 scans of fragmented precursors picked from the most abundant targets. After being fragmented once, precursors were excluded from additional fragmentation for 30 seconds. The isolation width for precursor isolation was set to 1. 2 m/z and the fragmentation was performed by higher-energy collision induced dissociation (HCD) at a normalized collision energy of 38. An inclusion list was used to prioritize the fragmentation of precursor masses corresponding to tryptic and non-tryptic peptides derived from APP and tau phosphopeptides.
Computational Mass Spectrometry All mass spectrometry data les were processed in Proteome Discoverer (PD) v2. 1 (Thermo Scienti c). Spectra were searched against a human UniProtKB reviewed database (version from April 2019) appended with mutant APP variants and a database of common laboratory contaminants using the SEQUEST HT search engine. The enzyme speci city was set to trypsin, with the search allowing for up to 2 missed cleavages. The precursor mass tolerance was set to 10 ppm, while the fragment tolerance was set to 0. 02 Da. TMT modi cation of N-termini and lysines, and carbamidomethylation of cysteines were set as static modi cations, while methionine oxidation was set as a variable modi cation. Phosphorylation of serine, threonine, and tyrosine was considered as a variable modi cation and localization probabilities of phosphorylation sites were determined using ptmRS  (44).
Bioinformatics analysis of mass spectrometry data For CSF and Plasma samples: PSMs with isolation interference values > 50% were removed. TMT® reporter intensities were then corrected for isotopic impurities and median scaling applied to the data matrix. Reporter ion intensities were then re-calculated relative to the median across reference channels and log 2 -transformed. Peptide expression values were computed as the median across all related PSMs. Peptides with > 35% missing values in at least one experimental group were removed. Among the remaining peptides, missing values were imputed using an iterative PCA method (46). Protein expression was calculated as the trimmed mean (trim factor = 0. 2) across all unmodi ed peptides distinct to the protein group. To identify peptides, phosphopeptides, or proteins signi cantly changing in abundance between clinical groups, we applied moderated t-statistics  (47) , which is very suited for small data sets.
Acknowledgments: We thank  Biocenter Kuopio National Virus Vector Laboratory at the University of Eastern Finland (Kuopio, Finland)  for packaging of the lentiviral vectors and the  University of Eastern Finland Cell and Tissue Imaging Unit (Kuopio, Finland) , supported by  Biocenter Kuopio and Biocenter Finland , for maintenance and help with the microscopes. We also thank  Gitte Böhm  and  Jutta Korder  for running sample preparation for LC-MS-experiments,  Stephan Jung ,  Antje Berfelde , and  Claudia Höhle  for running the LC-MS-analyses,  Michael Bremang  for computational proteomics and assistance in the preparation of the manuscript,  Juliane Weisser  for assistance in the preparation of the manuscript.
Funding: This research was supported by  Academy of Finland  (grant numbers  338182 ,  307866 ,  315459 , and  330178 ,  339767 );  Sigrid Jusélius Foundation ; the  Strategic Neuroscience Funding of the University of Eastern Finland ; the  Alfred Kordelin Foundation ; the  Finnish Brain Foundation ; the  Orion Research Foundation  sr;  Kuopio University Hospital VTR Fund. This project is co-funded by the  Horizon 2020 Framework Program  of the  European Union  ( Marie Skłodowska Curie  grant agreement No  740264 ).
MD and QM/MM computations were also carried out on a minimal model of the Fcb2 active site  [44] , on its whole flavodehydrogenase domain  [45]  and on LCHAO  [46]. In some cases, experimental results led to ambiguous conclusions. But in other cases, pieces of evidence were provided that are hardly compatible or even incompatible with a hydride transfer mechanism  [43, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]. 1. hGOX active site with bound glyoxylate, drawn from the crystal structure (Protein Data Bank 2RDU). Atom colouring: red for oxygen, blue for nitrogen, yellow for the flavin carbon atoms and green for the other amino acids and glyoxylate. The side chains in the figure are conserved in other family members, except for Y26 (phenylalanine in LCHAO) and, importantly, for W110 which is specific to GOX; in other family members, it is replaced by a variety of residues.
The hydrogen bonds and other close interaction are shown by dotted lines, as described in  [16]. Glyoxylate is here in its unhydrated form and can be considered as playing the role of an inhibitor. In the Fcb2 crystal structure with bound pyruvate (Protein Data Bank 1FCB), the hydrogen bonding interactions are identical to those shown here, with the exception that here mobile hGOX R167 is in the so-called proximal position and interacts with the ligand carboxylate, while its Fcb2 homologue R289 is in the so-called distal position (Fig. S1 ), interacting with D292, an invariant residue in the family  [21]. In the 1FCB Fcb2 structure, the flavin is in the semiquinone state  [21, 62]  in agreement with the evidence that pyruvate stabilizes the flavin semiquinone  [63]. In this work, we analyse the reactivity of trifluorolactate (F3Lac) with hGOX, and compare it to that of lactate and glycolate.
Determination of rate constants For determining the rate constants, in order to avoid simplifying approximations, we globally fitted the total time course of the transients from a few milliseconds to several hundred seconds using numerical data analysis. This provided the evaluation of the dependence on substrate concentration of all the rate constants involved in the formation of the flavin semiquinone. For each substrate, the spectra of the different species were averaged over the spectra derived from the global fit, regardless of the substrate concentration. The question was whether the interflavin electron transfer was taking place within a tetramer or between tetramers. Each dataset was fitted by distinct mechanisms assuming an electron transfer either between two different tetramers or between two subunits in the same tetramer.
Whatever the substrate and its concentration, for mechanisms involving an inter-tetramers electron transfer, the quality of the fits was better than for those involving intra-tetramer electron transfer. In the following, only the results obtained by fitting the data with the inter-tetramers mechanism are presented and discussed. In the Supporting Information, Figs S5-S7 compare the fits assuming either an intra-tetramer or an intertetramers electron transfer for the three substrates. For lactate and F3Lac, the inter-tetramers mechanism A was used as the kinetic model to globally fit the datasets. Mechanism A FMNox þ Sred > k 1 k 2 Step 1 FMNox-Sred > k 3 k r3 Step 2 FMNred þ Sox FMNox þ FMNred ! k4 Step 3 2FMNsq The reversibility of the flavin reduction step (step 2) had to be introduced in order to enable the formation of a flavin semiquinone at long times in combination with flavin reduction.
Mechanism B For this substrate, an additional reversibility step had to be added for the dissociation of the FMNred-Fig. 3. First and last spectra recorded during enzyme reduction by 3. 9 mM DL-F3Lac, 5 mM L-lactate and 5 mM glycolate. FMNox þ Sred > k1 k 2 Step 1 FMNox-Sred > k3 k r3 Step 2 FMNred-Sox > k5 k r5 Step 3 FMNred þ Sox FMNox þ FMNred ! k 4 Step 4 2 FMNsq Sox complex; this suggests that this product of the physiological substrate has a higher affinity for the active site than have the products of lactate and F3Lac oxidation. This is supported by the crystallization of an hGOX-glyoxylate complex when the enzyme was mixed with glycolate under proper conditions  [16]. The introduction of an intermediate species in the kinetic model clearly improves the residuals, and mechanism B satisfactorily matches the data (Fig.
FMN red /E. FMN sq and E. FMN sq /E. FMN ox couples are close. For the pig liver enzyme at pH 7. 1 and 10 °C in 100 mM phosphate buffer, they were determined to be -30 mV and -17 mV respectively  [14]. There is about 92% sequence identity between the pig liver and the human enzyme; the crystal structure of the latter (Protein Data Bank 2NZL for ex. ) indicates that all the substitutions are located on the surface, except one which is not close to the flavin. Thus, one may assume that the human enzyme has similar redox potentials at the same pH in our 50 mM phosphate buffer at 30 °C.
Global fit analysis Prior to the analysis of the selected absorption spectra, a baseline correction was performed in order to eliminate the instrumental drift over time. The kinetics of absorbance evolution over time were analysed at 19 wavelengths between 340 and 520 nm, at intervals of 10 nm. Each dataset consisting of these 19 series of traces was globally fitted according to mechanisms A, B, IntraA and IntraB. The associated differential rate laws are given in the Supporting Information. The global analysis was performed using MATHEMATICA software (Version 13. 0, 2021; Wolfram Research, Inc. , Champaign, IL, USA). The MultiNonli-nearModelFit function was used to fit the data by numerically solving the differential equations and sharing the different wavelength-independent rate constant parameters, while the molar extinction coefficients of the different species were estimated for each wavelength.
As the number of degrees of freedom (i. e. the number of parameters) are different between the kinetic models, AIC allows to compare the models and to estimate the relevance of the improvement of the fit following an increase in the number of parameters. The value of AIC was estimated according to the following equation AIC ¼ N Á ln SSQ N þ 2 Á p þ 1 ð Þ, where N designates the number of data points, SSQ the sum of squares and p the number of parameters to be fitted. For each dataset, the best kinetic model chosen will be the one with the lowest AIC value. For each substrate concentration, all rate constants presented in this work are an average value of the rate constants estimated from the global analysis of at least three datasets.
Iron is a key nutrient for bacterial growth. The source can be either heme or siderophore-Fe complexes. Siderophores are small molecules synthesized by bacteria to scavenge iron from the bacterial environment. The pathogen Pseudomonas aeruginosa can express at least 15 different iron uptake pathways and all but one involve a TonB-dependent transporter (TBDT) for the uptake of iron across the outer membrane. Little is known about how bacteria modulate and adapt the expression of their different iron import pathways according to their environment. Here, we have developed fluorescent reporters between the promoter region of genes encoding a TBDT and the fluorescent reporter mCherry. With these constructs, we can follow the expression of TBDTs under different growth conditions. Mathematical modelling of the data obtained showed the transcription and expression of the gene encoding the TBDT PfeA to have a sigmoidal shape, whereas it was logarithmic for the TBDT gene foxA.
We used the same fitting algorithm and found a = 15. 58, x E = 0. 42 µM for foxA and a = 13. 58, x E = 0. 56 µM for mCherry. The level of transcription of the transporter gene foxA was also higher than that of mCherry. Both the pfeA and mCherry RT-qPCR fold-change data sets followed a sigmoid-shaped curve, but their Hill equation parameters showed differences. The parameters y 0 , n , and K from the two equations were relatively close to each other, but y max was much higher for pfeA than mCherry. To quantitatively assess the likelihood of the expression of pfeA and mCherry, we first normalized the data and then compared them. The method was the following: we (i) normalized the data ( FC pfeA and FC mCh ) between 0 and 1 and put them together in a single dataset we named pfeA + mCherry, (ii) fit this dataset with a single Hill equation H(x) , and (iii) compared the estimated root mean square error (RMSE) RMSE pfeA and RMSE mCh between this model, which corresponds to the common trend, and the normalized data of each experimental set taken individually (that for pfeA and mCherry).
Each concentration of ENT or NOCA was tested using biological triplicates. The error bars represent the standard errors calculated by CFX Maestro™ Software (Bio-Rad). The reference genes used were clpX and rpoD. The data for prompfeA-mCherry were fitted using the Hill Eq. (  1 ), as the two data sets showed a sigmoid shape. The two data sets for promfoxA-mCherry showed a linear shape in the logarithmic domain and, thus, a logarithmic Eq. (  2 ) was used for each fit. The normalized and pooled data sets are presented in Fig. 3A  with the fitted Hill curves. We calculated the RMSE between the normalized pfeA and mCherry whole data sets and the pfeA + mCherry Hill equation. We found RMSE values of 0. 052 and 0. 051 for the comparison of the pfeA data set versus that of pfeA + mCherry and the mCherry data set versus that of pfeA + mCherry, respectively.
We also calculated the RMSE solely for the transition phase by reducing the range of the dataset on the y-axis, first between 10 and 90% of maximal expres- sion and then between 20 and 80% of maximal expression. We found RMSE values of 0. 065 and 0. 070 for pfeA and mCherry, respectively, for the first range and 0. 070 and 0. 071, respectively, for the second. Therefore, the error of our pfeA + mCherry model is approximately 7%. To obtain an idea of what such an error represents, we calculated the dispersion, which intrinsically exists in the data, by calculating the RMSE between the data and the Hill dataset-specific model for both genes. All RMSE values were of the same order of magnitude, regardless of the domain. Thus, modeling both datasets using a unique model did not appear to introduce any additional error (3) H(x) = x n K n + x n , (4) FC pfeA = FC pfeA -y 0pfeA y maxpfeA -y 0pfeA , (5) FC mCh = FC mCh -y 0mCh y maxmCh -y 0mCh.
to that intrinsically present in the dataset, suggesting that the model is valid. In conclusion, pfeA and mCherry behave in the same manner with respect to the siderophore concentration in qRT-PCR. We normalized the data from promfoxA-mCherry using the same method and created the pooled data set foxA + mCherry. The logarithmic models of the normalized transcriptional expression data of foxA and mCherry and those of foxA + mCherry all had the same parameters ( a = 0. 5 and x E = 1 µM, Fig. 3B ). We calculated the RMSE between the normalized foxA and mCherry data sets and the foxA + mCherry logarithmic equation and found RMSE values of 0. 123 and 0. 151 for the comparison of the foxA data set versus that of foxA + mCherry and the mCherry data set versus that of foxA + mCherry, respectively. The error of our foxA + mCherry model is aproximately 15%.
8 of the normalized fold change. The RMSE values were 0. 083 and 0. 099 for the data between 0. 1 and 0. 9 and 0. 100 and 0. 103 for the data between 0. 2 and 0. 8 for RT-qPCR and Fluo respectively. Thus, the error of our RT-qPCR + Fluo model is approximately 10%. Applying a similar methodology as that used for the RT-qPCR data previously, we determined the inherent dispersion between the RT-qPCR or Fluo data and the RT-qPCR + Fluo model. The RMSE between the data and the model showed a comparable magnitude. The utilization of a single model for both datasets did not introduce any additional errors beyond the inherent variability in the data. Therefore, the validity of the model remains unaltered. Thus, the RT-qPCR and Fluo data, in other words, the transcriptional expression of mCherry and mCherry synthesis, appear to show analogous behavior in relation to the siderophore concentration.
As previously, for promfoxA-mCherry, both the transcriptional expression of mCherry and mCherry synthesis could be normalized and modeled by the logarithmic equation of RT-qPCR + Flu. The normalized models of the transcriptional expression of mCherry and mCherry synthesis and RT-qPCR + Fluo all had the same parameters ( a = 0. 5 and x E = 1 µM, Fig. 6B ). For the comparison of RT-qPCR/RT-qPCR + Fluo, the RMSE value was the same as that previously calculated, 0. 151. We calculated the RMSE for the comparison of Fluo/RT-qPCR + Fluo and found a value of 0. 087. Here, we also assessed the intrinsic dispersion between the RT-qPCR or Fluo data and the RT-qPCR + Fluo model. The RMSE between the data and the model were of similar magnitude. Employing a single model for both datasets did not introduce additional errors beyond the inherent variability.
Discussion The use of mathematical equations allows the quantification and prediction of biological processes, which can provide insights that may not be immediately apparent from experimental data alone. For example, mathematical modeling can be used to make predictions about the relationships between different variables, such as the relationship between mRNA and protein levels. Additionally, mathematical models can also help to identify potential sources of error or uncertainty in the data and can be used to simulate different scenarios and test the robustness of a model. However, mathematical modeling usually requires large datasets. Here, we developed  two biological constructs that are very helpful for the rapid and low-cost generation of datasets to monitor the expression of the two TBDTs FepA and FoxA in P.
Gene expression was measured with a CFX Opus 96 Real-Time PCR Instrument (Bio-Rad) using iTaq Universal SYBR Green Supermix (Bio-Rad) and the appropriate primers (listed in Supporting Information Table  S2 ). Five genes (rpoD, clpX, rpsL, proC and uvrD) were tested as reference genes using the Reference Gene Selector Tool based on GeNorm in CFX Maestro™ Software (Bio-Rad). PAO1, prompfeA-mCherry and promfoxA-mCherry strains were grown for 8 h in the absence and presence of siderophores (10 µM ENT for PAO1 and prompfeA-mCherry, 100 µM of NOCA for PAO1 and promfoxA-mCherry). We have chosen to use clpX and rpoD in our assays because their stability across the different conditions were the highest (Fig. S4 ). For the data analysis, the quantification cycles were determined by regression and the fold-change calculated with the Pfaffl Method.
Acknowledgements The presented work was supported by a Grant from the  CNRS  ( 80/Prime 2020 ), as well as with a grant from the  Agence Nationale de la Recherche (ANR , Grant Number:  ANR-22-CE44-0024-01 ). The authors also acknowledge the  Interdisciplinary Thematic Institute (ITI) InnoVec (Innovative Vectorization of Biomolecules, IdEx ,  ANR-10-IDEX-0002 ).
Background. The aim of this study was to identify trends in total, deceased donor (DD) and living donor (LD) kidney transplantation (KT) rates in European countries. Methods. The European Renal Association (ERA) Registry and the Global Observatory on Donation and Transplantation (GODT) databases were used to obtain the number of KTs in individual European countries between 2010 and 2018. General population counts were obtained from Eurostat or the national bureaus of statistics. The KT rate per million population (p. m. p. ) and the average annual percentage change (APC) were calculated. Results. The total KT rate in the 40 participating countries increased with 1. 9% annually [95% confidence interval (CI) 1. 5, 2. 2] from 29. 6 p. m. p. in 2010 to 34. 7 p. m. p. in 2018, reflecting an increase of 3.
Patient data The European Renal Association (ERA) Registry  [14]  and the Global Observatory on Donation and Transplantation (GODT)  [15]  databases were used to obtain the number of performed KTs in individual European countries between 2010 and 2018. The ERA Registry annually collects individual and aggregated data on patients with ESKD receiving kidney replacement therapy via the national and regional renal registries in Europe and countries bordering the Mediterranean Sea  [14]. The GODT collects data on organ transplantation worldwide on an annual basis  [15]. The choice of data source was based on the following criteria: (i) where possible one data source was used for a country and (ii) data from the ERA Registry database was preferred except where data for the complete study period from 2010 until 2018 were not available in the ERA Registry database or when the geographical coverage of the country was higher in the GODT database.