dataesr/scientific-paragraphs-categorization huggingface dataset:
| Relationships between leaf [Si], plant height and biomass We first tested differences in plant above-ground biomass and plant height across the different treatments (N and growth modality) by ANOVA, followed by post hoc tests. Relationships between above-ground biomass/plant height (dependent variables) and leaf [Si] (independent variable) were then tested through mixed-effect models with genotype identity as a random factor, using the package 'nlme'  (Pinheiro et al. , 2022). Models involving only the single individuals included both N treatments to test if a N-induced decrease in biomass affects leaf [Si], while models considering only plants with a neighbour were run separately for each N treatment. To test whether high-Si genotypes lost more or gain more biomass as a response to plant-plant interactions, we tested the significance of relationships between the logRR of plant biomass in response to plant-plant interactions and genotype-mean leaf  [Si]  in single by regression analyses, and for both N treatments.
The decadal changes of the fugacity of CO2 (fCO2) and pH in surface waters are investigated in the Southern Indian Ocean 10 (45°S-57°S) using repeated summer observations, including measurements of fCO2, total alkalinity (AT) and total carbon (CT) collected over the period 1998-2019 in the frame of the French monitoring program OISO. We used three datasets (underway fCO2, underway AT-CT and station AT-CT) to evaluate the trends of fCO2 and pH and their drivers, including the accumulation of anthropogenic CO2 (Cant). The study region is separated into three domains based on the frontal system and biogeochemical characteristics: (i) High Nutrients Low Chlorophyll (HNLC) waters in the Polar Front Zone (PFZ), 15 (ii) HNLC waters south of the Polar Front (PF) and (iii) the highly productive zones in fertilized waters near Crozet and Kerguelen Islands.
Almost everywhere, we obtained similar trends in surface fCO2 and pH using the fCO2 or AT-CT datasets. Over the period 1998-2019, we observed an increase in surface fCO2 and a decrease in pH ranging from +1. 0 to +4. 0 µatm yr -1 and from -0. 0015 to -0. 0043 yr -1 , respectively. South of the PF, the fCO2 trend is close to the atmospheric CO2 rise (+2. 0 µatm yr -1 ) and the decrease in pH is in the range of the mean trend for the global ocean (around -0. 0020 yr - 20 1 ); these trends are driven by the warming of surface waters (up to +0. 04°C yr -1 ) and the increase in CT, mainly due to the accumulation of Cant (around +0. 6 µmol kg -1 yr -1 ). In the PFZ, our data show slower fCO2 and pH trends (around +1. 3 µatm yr -1 and -0. 0013 yr -1 , respectively) associated with an increase in AT (around +0.
4 µmol kg -1 yr -1 ) that limited the impact of a more rapid accumulation of Cant north of the PF (up to +1. 1 µmol kg -1 yr -1 ). In the fertilized waters near Crozet and Kerguelen Islands, fCO2 increased and pH decreased faster than in the other domains, between +2. 2 and +4. 0 µatm yr -1 25 and between -0. 0023 yr -1 and -0. 0043 yr -1. The fastest trends of fCO2 and pH are found around Kerguelen Island north and south of the PF. These trends result from both a significant warming (up to +0. 07°C yr -1 ) and a rapid increase in CT (up to +1. 4 µmol kg -1 yr -1 ), mainly explained by the uptake of Cant. Our data also show rapid changes on short periods and a relative stability of both fCO2 and pH in recent years at several locations both north and south of the PF, which leaves many open questions, notably the tipping point for the saturation state of carbonate minerals that remains highly uncertain.
5 The fCO2 data syntheses such as the LDEO database  (Takahashi et al. , 2009)  or the SOCAT database  (Bakker et al. , 2016) , were also associated with total alkalinity (AT) reconstructed from salinity (e. g. Lee et al. , 2006)  to calculate pH or total carbon (CT) trends and evaluate their drivers  (Lenton et al. , 2012; Lauvset et al. , 2015; Iida et al. , 2021). For pH, the trends estimated in surface waters of the Southern Ocean range between -0. 0023 yr -1 and -0. 0015 yr -1. However, similarly to fCO2, the observations in the Western Antarctic Peninsula present a different pH trend, i. e. positive of +0. 0020 (±0. 0002) yr -1 , 10 explained by a decrease in CT ranging between -0. 2 and -0. 8 µmol kg -1 yr -1  (Hauri et al. , 2016; Brown et al. , 2019). In the Drake Passage, observations present contrasting pH trends depending on the season and location, ranging between -0.
0021 (±0. 0006) yr -1 in the north part in summer and -0. 0008 (±0. 0004) yr -1 in the south part in winter  (Munro et al. , 2015). Given the large range of fCO2 and pH trends depending on the regions, season and periods considered, it is important to 15 document and understand these trends in the various sectors of the Southern Ocean. In this paper, we investigate the surface fCO2 and pH trends and their drivers in the Indian sector of the Southern Ocean using high-quality direct measurements of CT, AT and fCO2 from repeated cruises conducted since 1998. Our dataset enables to compare the pH trends deduced either from fCO2 observations and AT reconstructed from salinity (e. g. method used by  Lauvset et al. , 2015) ) or pH calculated using sea surface AT and CT observations (e. g. method used by  Leseurre et al.
02 µmol kg -1. For the water-column data obtained between 1998 and 2011 secondary quality controls were performed in the frame of the CARINA and GLODAP-v2 projects  (Lo Monaco et al. , 2010; Key et al. , 2015; Olsen et al. , 2016). For OISO cruises 5 conducted since 2012 (not yet included in GLODAP-v2) we proceeded to a data quality control following the same protocol as for GLODAP-v2 (comparison with deep waters) and we found no systematic bias for the properties measured during these cruises as described in detail by  Mahieu et al. (2020). All the data (and metadata) will be delivered to the NCEID/OCADS database and to  GLODAP-v2-2023. 10  The surface underway fCO2, AT and CT data (and metadata) are available at the NCEID/OCADS database (www. ncei. noaa. gov/access/ocean-carbon-data-system/oceans/VOS_Program/OISO.
Data selection To investigate the fCO2 and pH trends and their drivers over the period 1998-2019 we used the observations regularly conducted during summer cruises (between December and February). The sea surface fCO2 and pH trends were evaluated using three datasets: (i) underway temperature, salinity and fCO2 data, (ii) underway temperature, salinity, AT and CT data  To estimate the trends from underway datasets, we first calculated the mean value for each cruise in the gridded boxes depicted in Figure  2. Results at each station in the mixed layer will be then compared to those obtained in the corresponding boxes and region. As we are interested in separating the anthropogenic signal from natural variability for both fCO2 and 30 pH trends, and because anthropogenic CO2 concentrations are not well evaluated in surface waters, we also estimated the trends at each station selecting the data just below the summer mixed layer (a layer referred to as BML).
(1998). We used the MATLAB version  (van Heuven et al. , 2011)  that 10 now includes error propagation  (Orr et al. , 2018). The constants of thermodynamic equilibrium of CO2 in seawater used are K1 (for the dissociation of carbonic acid) and K2 (for the bicarbonate ion) defined by  Mehrbach et al. (1973)  and refitted by  Dickson and Millero (1987). The total boron value is calculated according to  Uppström (1974) , and the KHSO4 dissociation constant is from  Dickson (1990). The adopted pH scale is total scale. When AT data were not available, it was estimated from salinity. The correlation between sea surface AT and salinity in the open ocean is modeled with an empirical 15 linear relationship  (Millero et al. , 1998; Friis et al. , 2003). Here we used the AT-S relationship estimated from the OISO data (underway AT CT dataset) over the period 1998-2019 (summer cruises; Eq.
Long-term trends in surface waters The trends in surface waters estimated using the three datasets are presented in Figure  4  and listed in Table  1. For clarity we separated the western and eastern tracks (west and east of 70°E). As expected in the context of global warming, we observe a warming in surface waters south of 48°S (+0. 05 °C yr -1 on average; Fig. 4a , Table  1 ); on the other hand, there is 5 no significant change in salinity (Fig. 4b ). Thus, AT calculated from salinity (Eq. 1) does not present a long-term change, which is confirmed by AT measurements (underway and stations data) with the exception of a small area in the PFZ near 47-48°S on the western track (Fig. 4c ). In this area, underway AT measurements suggest a slight decrease, whereas the mixed layer data (stations O7, O8) suggest an increase in AT of +0.
4 µmol kg -1 yr -1 (Fig. 4c , Table  1 ). Note that at station O7 we also observed an anomaly in the temperature trend (cooling, Fig. 4a ) that may reflect small-scale variability (due to 10 the intermittent presence of eddies or meanders). Table  1. Trends (per year) evaluated from the fCO2 surface dataset, the AT CT surface dataset and the AT CT data in the mixed layer at each station (O7, O8, O9, O10, O11, A3, O6, and O12). The stations are divided into six areas: the HNLC part of PFZ, the HNLC part of the north POOZ and the south POOZ, the phytoplanktonic bloom of Kerguelen in the POOZ, show a good agreement. Surface CT concentrations increased in all regions (Fig. 4d ) by +0. 4 to +1. 4 µmol kg -1 yr -1 (Table  1 ). The CT trends are rather homogeneous in the south (around +0. 6 µmol kg -1 yr -1 ) and presents more variability in the eastern region (near Kerguelen Island).
As expected, the trend in fCO2 is positive in all regions (from +1. 0 to +4. 0 µatm 25 yr -1 ; Fig. 4e , Table  1 ), and it is generally very similar when using underway fCO2 measurements or fCO2 calculated with AT-CT pairs from both underway and mixed layer data. The same is true for the pH trend calculated from the three datasets: fCO 2 dataset 0. 02 ± 0. 03 0. 000 ± 0. 002 0. 0 ± 0. 1 0. 6 ± 0. 2 1. 8 ± 0. 2 -0. 0018 ± 0. 0002 a A T C T dataset 0. 02 ± 0. 02 -0. 002 ± 0. 002 -0. 1 ± 0. 2 0. 4 ± 0. 2 1. 3 ± 0. 5 -0. 0014 ± 0. 0005 a O7 -0. 05 ± 0. 04 0. 000 ± 0. 002 0. 4 ± 0. 2 1. 4 ± 0. 5 1. 5 ± 0. 7 -0. 0015 ± 0. 0008 b O8 0. 03 ± 0. 05 -0. 001 ± 0. 002 0. 4 ± 0. 2 0. 6 ± 0. 5 1. 0 ± 0. 7 * -0. 0009 ± 0. 0007 b O9 0. 03 ± 0. 03 -0. 001 ± 0. 002 0. 1 ± 0. 1 0. 6 ± 0. 3 1. 6 ± 0. 6 -0. 0016 ± 0.
0006 a fCO 2 dataset 0. 03 ± 0. 02 -0. 001 ± 0. 002 -0. 1 ± 0. 1 0. 5 ± 0. 2 1. 9 ± 0. 3 -0. 0019 ± 0. 0004 a A T C T dataset 0. 04 ± 0. 02 -0. 001 ± 0. 002 0. 1 ± 0. 2 0. 5 ± 0. 3 1. 7 ± 0. 4 -0. 0018 ± 0. 0005 a O10 0. 04 ± 0. 02 -0. 002 ± 0. 002 0. 2 ± 0. 1 * 0. 9 ± 0. 3 2. 3 ± 0. 6 -0. 0023 ± 0. 0006 a fCO 2 dataset 0. 02 ± 0. 02 0. 000 ± 0. 002 0. 0 ± 0. 1 0. 7 ± 0. 2 2. 1 ± 0. 3 -0. 0022 ± 0. 0003 a it is always negative (from -0. 0014 to -0. 0043 yr -1 ; Fig. 4f , Table  1 ) and generally in good agreement. As for temperature and CT, the fCO2 and pH trends are rather homogeneous south of 50°S, and more variable in the vicinity of the Kerguelen Plateau (eastern track) where the variations in pH mirrors those of fCO2 and CT. surface dataset (in grey), the AT CT surface dataset (in black) and the AT CT data in the mixed layer at each station (in orange) along the western and eastern tracks (see Fig.
2 ). The computation method is explained in the section 2. 3. A T C T dataset 0. 02 ± 0. 02 0. 000 ± 0. 002 0. 1 ± 0. 2 0. 6 ± 0. 3 1. 7 ± 0. 4 -0. 0018 ± 0. 0004 a O11 0. 04 ± 0. 03 0. 000 ± 0. 002 0. 1 ± 0. 1 0. 5 ± 0. 3 1. 7 ± 0. 3 -0. 0017 ± 0. 0003 a fCO 2 dataset 0. 04 ± 0. 02 * -0. 002 ± 0. 002 -0. 1 ± 0. 1 0. 4 ± 0. 7 1. 5 ± 1. 1 * -0. 0018 ± 0. 0013 * e A T C T dataset 0. 05 ± 0. 02 * -0. 005 ± 0. 004 0. 2 ± 0. 4 0. 6 ± 0. 4 1. 6 ± 1. 0 * -0. 0018 ± 0. 0012 * e A3 0. The three datasets generally show similar changes, which indicates that the trends evaluated locally (in the mixed layer) are representative of the trends observed in wider areas (underway data). This does not hold in the Kerguelen bloom because 10 stations O12 and A3 are generally in high production areas while the underway data averaged in the boxes also includes lower production areas (Fig.
In conclusion, although there are large uncertainties on the fCO2 and pH trends at station A3 above the Kerguelen Plateau due to the variability in summer and the short time-series  (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) ,we believe that the rapid trends derived in the surface layer from the station data (in summer) are somehow real, but they result from a rapid increase in fCO2 (decrease in pH) 25 between 2007 and 2008 (detected in the station and underway datasets, Fig. S1 A3 ), that was followed by the stagnation of fCO2 and pH values in recent years (according to the station dataset) or a reversal of the trends (according to the underway datasets). The reasons for such changes are still unknown, but this result highlights the need for pursuing this time-series in order to properly predict the evolution of the carbonate system and the impact of ocean acidification in this region known as a biological hotspot.
35 To estimate the trends of the properties at a regional scale in the PFZ HNLC waters we averaged the data along the repeated track between Crozet and Kerguelen Islands (Fig. 2 ). In this domain, the fCO2 and pH trends deduced from the underway and mixed layer (station) datasets appeared slightly lower than at higher latitudes (Fig. 4 , Table  1 ). Because the Cant trends are higher in the PFZ than in the POOZ, one would expect to observe higher fCO2 and pH trends in the PFZ, but this is the opposite. This region is influenced by the southern branch of the strong SAF current (observed in the band 43-47°S) with a part deflected southward downstream of Crozet Island  (Park et al. , 1993). This circulation imprints zonal variability in surface properties, most pronounced in the western part of the track around 55-58°E where a signal of warmer water and lower fCO2 was regularly observed in all seasons  (Poisson et al.
10 Previous analyses showed that this region experienced high spatial variability of fCO2 (up to 100 µatm over a few miles) depending on the location of the frontal system and the bloom  (Poisson et al. , 1993; Metzl et al. , 2006; Bakker et al. , 2007). During several summer cruises we locally observed very low fCO2 (< 280 µatm) and low CT (near or < 2060 µmol kg -1 ) in the region 44°S-46°S. However, such local signals were smoothed when averaging the data in the 1x1 degree grid box, so 15 they will not strongly impact the observed long-term trends derived from the underway datasets. Occasionally, we also observed low AT concentrations probably linked to intermittent coccolithophorids blooms occurring in this region  (Balch et al. , 2016; Terrats et al. , 2020). These AT variations that drive higher fCO2 were rather local and had no impact on the long-term AT trend that could result from a progressive increase or decline of calcifying species production in this region over 20 years as was suggested in other Southern Ocean sectors (e.
As opposed to station O6, both surface temperature and salinity appear relatively stable from one year to the other, but a 10 warming around +0. 07°C yr -1 is detected from the three datasets, in agreement with the analysis by  Auger et al. (2021). Our driver analysis shows that the fast fCO2 and pH trends around station O12 are explained by both the warming of the surface layer and a fast increase in CT (+1. 4 µmol kg -1 yr -1 ). These rapid trends are no longer found below the mixed layer (representing winter conditions), where the increase in CT is directly related to the increase in Cant. The fast CT increase observed in surface waters could be related to a change in biological processes (e. g. decrease in primary production or 15 community change, but this was not detected in the OISO data for the moment).
Concluding comments and perspectives Given the importance of the Southern Ocean in climate (past, present and future) it is crucial to document and understand the changes occurring in this remote region. In this study, we investigated the evolution of CO2 and pH in the surface layer of the Indian sector during summer using measurements of fCO2, AT and CT obtained almost every year since 1998. This 10 new analysis extends in time and for pH the previous work of  Metzl (2009)  and  Lourantou and Metzl (2011)  who estimated fCO2 changes over 1991-2011 in the same region. It also extends their analyses using additional datasets (AT-CT) to quantify the drivers of the fCO2 and pH trends including the contributions of Cant evaluated here in subsurface waters. To our knowledge such a regular biogeochemical monitoring in the Southern Ocean is only conducted in the Drake Passage (since 2002;  Munro et al.
7 to +2. 1 µatm yr -1 depending on the data sets, Table  1 ). Below, we further investigate these data to evaluate whether the fCO2 increase from winter 2000 to winter 2019 can also be mainly attributed to the accumulation of anthropogenic CO2. Based on the USV data  (Sutton et al. , 2021)  available in SOCAT-v2021  (Bakker et al. , 2021)  we calculated the CT concentrations from SST, fCO2 and AT (using a similar approach to that of Eq. 1); in June 2019 we estimated surface CT concentrations between 2160 and 2185 µmol kg -1 in the Indian POOZ from the USV data. This is higher than our observations in January 2019 (CT= 2153. 7 ± 0. 6 µmol kg -1 in the mixed layer at station O11) and coherent with what we know on the CT seasonality of around 15-20 µmol kg -1 in this region  (Metzl et al. , 2006; Takahashi et al.
Acknowledgments. The OISO program was supported by the  French institutes INSU (Institut National des Sciences de l'Univers)  and  IPEV (Institut Polaire français, Paul-Emile Victor) ,  OSU Ecce-Terra  (at  Sorbonne Université ), and the  30 French program  SOERE/Great-Gases. We thank the  French oceanographic fleet ("Flotte océanographie française")  for financial and logistic support to the  OISO program  and cruises. We thank the captains and crew of  R. V Marion Dufresne  and the staff at IFREMER, GENAVIR and IPEV. We gratefully thank the numerous scientific who worked at sea for the  OISO program. We also thank  Justine Taugourdeau  and  Apolline Samin  for their help during their internship. The Surface Ocean  CO2  Atlas (SOCAT, www. socat. info) is an international effort, endorsed by the  International Ocean Carbon 35 Coordination Project (IOCCP) , the  Surface Ocean Lower Atmosphere Study (SOLAS)  and the  Integrated Marine Biogeochemistry and Ecosystem Research program  (IMBER), to deliver a uniformly quality controlled surface ocean CO2 database.
Data availability. The datasets are freely available and are accessible in SOCAT (www. socat. info) for fCO2 surface dataset, in GLODAP (www. glodap. info) for water column dataset, at NCEI/OCADS (www. ncei. noaa. gov/acces/ocean-carbon-20 data-system/oceans/VOS_Program/OISO. htlm) for AT and CT surface dataset.
://doi. org/10. 5194/bg-2022-22 Preprint. Discussion started: 28 January 2022 c Author(s) 2022. CC BY 4. 0 License. Temperature Salinity A T C T fCO 2 pH °C yr -1 yr -1 µmol kg -1 yr -1 µmol kg -1 yr -1 µatm yr -1 (TS) yr -1 PFZ HNLC POOZ north HNLC POOZ south HNLC POOZ bloom Kerguelen 05 ± 0. 03 * 0. 000 ± 0. 002 -0. 2 ± 0. 3 1. 1 ± 0. 3 3. 2 ± 0. 7 -0. 0039 ± 0. 0008 e PFZ bloom Crozet fCO 2 dataset -0. 01 ± 0. 05 A T C T dataset -0. 03 ± 0. 05 -0. 001 ± 0. 003 -0. 002 ± 0. 003 0. 0 ± 0. 2 0. 1 ± 0. 3 1. 2 ± 0. 7 2. 6 ± 0. 7 1. 3 ± 0. 7 2. 2 ± 0. 7 -0. 0028 ± 0. 0007 -0. 0024 ± 0. 0008 d d O6 -0. 02 ± 0. 06 -0. 001 ± 0. 003 0. 2 ± 0. 3 1. 3 ± 0. 7 2. 2 ± 0. 8 -0. 0023 ± 0. 0008 d PFZ bloom Kerguelen fCO 2 dataset 0. 07 ± 0. 03 A T C T dataset 0. 06 ± 0. 03 -0. 001 ± 0. 003 -0. 002 ± 0. 003 -0.
Therefore, like our approach, relational verification with constraint solving requires no additional separation hypothesis in presence of aliasing. Certified Verification Condition Generation. In a broad sense, this work continues previous efforts in formalization and mechanized proof of program language semantics, analyzers and compilers, such as  [29, 25, 17, 7, 19, 20, 35, 24, 11, 28]. Generation of certificates (in Isabelle) for the Boogie verifier is presented in  [28]. The certified deductive verification tool WhyCert  [17]  comes with a similar soundness result for its verification condition generator. Its formalization follows an alternative proof approach, based on co-induction, while our proof relies on induction. WhyCert is syntactically closer to the C language and the ACSL specification language  [4] , while our proof uses a simplified language, but with a richer aliasing model.
⋆ Part of this work was funded by the  AESC  project supported by the  Ministry of Science, Research and Arts Baden-Württemberg  (Ref:  33-7533. -9-10/20/1 ). 5Termination can be assumed (partial correctness) or proved separately (full correctness) in a well-known way [15]; for the purpose of this paper we can assume it. //C program Csw1 : x3 = * x1 ; * x1 = * x2 ; * x2 = x3 ; //C program Csw2 : * x1 = * x1 + * x2 ; * x2 = * x1 -* x2 ; * x1 = * x1 -* x2 ; //Composed C program Csw3 : x3_1 = * x1_1 ; * x1_1 = * x2_1 ; * x2_1 = x3_1 ; * x1_2 = * x1_2 + * x2_2 ; * x2_2 = * x1_2 -* x2_2 ; * x1_2 = * x1_2 -* x2_2 ; c sw1 ≜ x 3 := * x 1 ;.
Introduction PPFIBP1 (GenBank: NM_003622. 4; MIM: 603141) encodes for the PPFIA binding protein 1, also known as liprin-β1. 2] [3]  In mammals, the liprin family comprises four liprin-α proteins (liprin-α1-4) and two liprin-β proteins (liprin-β1 and -β2). Liprin-β1 has the ability to homodimerize and to heterodimerize with the homologous α-liprins. 1 In addition, liprin-β1 and liprin-α1 co-localize to the cell membrane and to the periphery of focal adhesions in fibroblast cell cultures (COS cells). 1, 4 Liprin-α proteins are major scaffold proteins involved in synapse formation, synaptic signaling and axonal transport processes via the assembly of large protein complexes. 5, 6 Although yet to be confirmed, it has been suggested, that liprin-β1 could play a role in the regulation of liprin-α mediated protein assemblies.
Patient recruitment and consent The study was approved by the ethics committee of the University of Leipzig (402/16-ek). Written informed consent for molecular testing and permission for publication of the data was obtained from all individuals and/or their legal representatives by the referring physicians according to the guidelines of the ethics committees and institutional review boards of the respective institute. All individuals were ascertained in the context of local diagnostic protocols followed by research evaluation of the sequencing data. The compilation of the cohort was supported by international collaboration and online matchmaking via GeneMatcher  11  in the case of family 2, 4, 5, 6, 7, 8, 9, 10 and 11. Family 3 was recently published as part of a larger cohort of individuals with congenital microcephaly.
Variant prioritization We first analysed single nucleotide variants annotated in local and public mutation databases, as well as rare (MAF < 1%) potentially protein-damaging variants in known diseaseassociated genes (e. g. by using in silico panels like MorbidGenes  13  ). Variants were prioritised based on the mode of inheritance, impact on the gene product, minor allele frequency and in silico predicted pathogenicity. Since no pathogenic or likely pathogenic variants in known disease genes could be found and the families gave consent for further research, we then evaluated the sequencing data in a research setting aiming to identify potentially causative variants in novel candidate genes. For this purpose, variants in potential candidate genes were prioritised according to the parameters described above and by considering further data such as mutational constraint parameters from gnomAD  14  or expression patterns in GTEx.