dataesr/scientific-paragraphs-categorization huggingface dataset:
| DISCUSSION The present subgroup analysis of SoliMix data suggests that individuals with different baseline characteristics of age, diabetes duration, daily insulin dose, HbA1c, BMI, and renal function were indistinguishable in terms of benefit from iGlarLixi compared to BIAsp 30 in regard to HbA1c, body weight change, and incidence of hypoglycaemia. This is inclusive of participants with characteristics of more challenging management profiles such as a higher baseline HbA1c (>75 mmol/mol [>9. 0 %]; however, >86 mmol/mol [>10. 0 %] was an exclusion criterion), longer duration of T2D (≥10 years), higher baseline insulin dose requirement (≥30 U/d), and obesity (≥30. 0 to <35. 0 and ≥35. 0 kg/m 2 ). 86 Thus, the conclusion of the main SoliMix study that switching to iGlarLixi rather than BIAsp 30 is likely to be beneficial for people with suboptimally controlled T2D on basal insulin and OGLDs 21 is corroborated by this analysis in people with broadly different biomedical characteristics.
0%) versus <64 mmol/mol subgroup, attributed to differences in dosing. 25 Benefits of IDegLira have also been preserved across subgroups in the DUAL VII study with similar HbA1c reductions, less severe or plasma glucose-confirmed hypoglycaemia, lower end-of-trial total daily insulin dose, together with weight loss relative to meal-time plus basal insulin therapy. 26 e main limitations of the present analysis are, firstly, the exploratory nature of the assessments except for the between-treatment differences in HbA1c and body weight changes in the basal insulin dose and BMI subgroups. However, the data used were entirely those of the original database, and the endpoints were those specified for the main study analysis. Secondly, many subgroups had small numbers of participants which reduced statistical power for within-subgroup confidence intervals, which are consequently often wide, but also for heterogeneity comparisons.
ACKNOWLEDGMENTS The authors thank the study participants, trial staff, and investigators for their participation. We thank  Ana Merino-Trigo, PhD  ( Sanofi ) for coordinating the development, facilitating author discussions, and providing a courtesy review of this manuscript. We thank  Rose Dall, PhD ,  Emiliana Jelezarova, PhD , CMPP™ and  Hannah Brown, PhD , CMPP™ of Fishawack Communications Ltd, part of  Fishawack Health , for providing editorial assistance. Sponsorship for this study was funded by  Sanofi, Paris, France. Editorial assistance was provided by  Fishawack Communications Ltd , part of  Fishawack Health , and was funded by  Sanofi.
Methods This is a single center, retrospective study performed at the China Medical University Hospital (CMUH), Taiwan. A total of 10 patients with either wAMD or PCV with persistent SRF or pigment PED who underwent previous treatment with anti-VEGF agents were reviewed. Best-corrected visual acuity (BCVA) assessment, central retinal thickness (CRT), and functionally relevant anatomic findings, such as SRF and PED, were retrospectively retrieved from electronic records. CRT were evaluated via spectral domain OCT (SD-OCT) device (Heidelberg Engineering, Heidelberg, Germany) and defined as the mean thickness of the 1000 µm central diameter area. All OCT findings including maximum height and width of the PED were evaluated using Heidelberg Eye Explorer Software (v. 5. 6. 4. 0; Heidelberg Engineering, Heidelberg, Germany).
Exclusion criteria were diabetes other than T1DM, change in insulin therapy within three months preceding the study, and non-insulin treatment at any time since T1DM diagnosis. All participant provided written informed consent. A screening log form was completed by the physician to document the site's selection process of the study patients. Participating hospitals were General Hospital Pula, General Hospital Varazdin, Vuk Vrhovac University Clinic for Diabetes, Endocrinology and Metabolic Diseases, University Hospital Merkur, and Clinical Hospital Center Rijeka. The Agency for Medicinal Products and Medical Devices of Croatia's Central Ethics Committee's approval was obtained. All experiments were performed in accordance with relevant guidelines and regulations. During the single visit at the time of the study, data (age, gender, level of education, single-person or nonsingle-person household, duration of the T1DM, presence of chronic complications, A1C value, hypoglycemia occurrence) were collected from patient's records and interviews.
The physicians, using the patients' medical records, completed a case report form (CRF) with yes/no questions about the patients' microvascular complications, including the presence of neuropathy, retinopathy, or nephropathy. The number of documented symptomatic hypoglycemic episodes (blood glucose ≤ 3. 9 mmol/L and blood glucose ≤ 3. 0 mmol/L) during the last three months was recorded in CRF. After data collection, participants were asked to self-complete the Problem Areas in Diabetes (PAID) questionnaire, which were afterwards collected by the project staff. Questionnaire. For this study, we used the Problem Areas in Diabetes (PAID) questionnaire, more specifically, its Croatian version of February 6, 2018  17. PAID is a 20-item screening instrument designed to measure emotional responsiveness specific to diabetes  10.
The items, as listed in Fig. 1 , are rated on a 5-point Likert scale and respondents indicate the degree to which each of the items is currently a problem for them; 0 (not a problem), 1 (minor problem), 2 (moderate problem), 3 (somewhat serious problem), 4 (a serious problem). The scores for each item are summed, then multiplied by 1. 25 to generate a total score out of 100. Higher total score indicates higher distress. A total PAID score ≥ 40 was considered as high diabetes-related distress  18. Statistical methods. Data was analyzed using statistical software SPSS (IBM, V 25. 0)  19. The normality of distribution was verified using the Shapiro-Wilk test. Descriptive analysis was applied to establish the patients' characteristics and to define the proportion of patients with T1DM with a PAID score ≥ 40.
Model generation The aircraft considered in this work is the 'DLR D150' generic aircraft. This aircraft is similar to a commercial fixed-wing single-aisle aircraft used for short to mid-range missions with a capacity of 150 passengers. The design of this aircraft is based on a multi-disciplinary process chain considering inputs from specific tools for outer geometry, cabin design, aerodynamics, structure, powerplants, etc. For the description of the aircraft design the hierarchical XML-based data format called CPACS (Common Parametric Aircraft Configuration Schema) was used  [7]. This data format provides the basis for the python-based modelling and sizing tool PANDORA (Parametric Numerical Design and Optimization Routines for Aircraft)  [8] , which was used for the generation of the flexible FE fuselage model of the D150 aircraft.
Peptide were separated on an Aurora UHPLC column from IonOpticks (25 cm x 75 μm, C18), using a 30 min gradient from 3 to 32% ACN with 0. 1% formic acid, and analyzed on a timsTOF PRO mass spectrometer (Bruker). Mascot generic files were processed with X!Tandem pipeline (version 0. 2. 36) using the PlasmoDB_PB_39_PbergheiANKA database, as described  [10]. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [48]  partner repository with the dataset identifier PXD031463.
Acknowledgements We thank  Jean-François Franetich ,  Maurel Tefit  and  Thierry Houpert  for rearing of mosquitoes, and  Maryse Lebrun  for helpful discussions. The following reagent was obtained through  BEI Resources, NIAID, NIH: Monoclonal Anti-Plasmodium Apical Membrane Antigen 1 ,  Clone 28G2  (produced in vitro), MRA-897A, contributed by  Alan W. Thomas. This work was funded by grants from  the Laboratoire d'Excellence ParaFrap  ( ANR-11-LABX-0024 ), the  Agence Nationale de la Recherche  ( ANR-16-CE15-0004  and  ANR-16-CE15-0010 ) and the  Fondation pour la Recherche Médicale  ( EQU201903007823 ). The authors acknowledge the  Conseil Régional d'Ile-de-France ,  Sorbonne Université , the  National Institute for Health and Medical Research (INSERM)  and the  Biology, Health and Agronomy Infrastructure (IBiSA)  for funding the timsTOF PRO.
We acknowledge the  ImagoSeine core facility  of the  Institut Jacques Monod , member of the  France BioImaging infrastructure  ( ANR-10-INBS-04 ) and  GIS-IBiSA , and funded by  Région Ile-de-France (TeneoVS). ML was supported by a 'DIM 1Health' doctoral fellowship awarded by the  Conseil Régional d'Ile-de-France. AW is supported by the  ATIP-Avenir program.
Law is territorial Many of us have had experiences of having a user looking for a case and failing to find it because they are looking in the wrong jurisdiction. Although this is less of an issue now that Lexis and Westlaw (and Google) allow us to search across datasets. However, it does mean that many of us are trained to think early on in our reference interview or research process -which court, which country? We know from experience how students and new lawyers can be caught out by the fact that the basis of a law is the same in several jurisdictions but the devil is in the detail. Those subtle differences between different countries' laws can create confusion and/or conflict. Most notably, within the world of copyright, it is not unusual for academics and students to talk about fair use (a US doctrine) which is quite different in scope from UK law's principle of fair dealing.
Legal research skills and access to resources Unsurprisingly, our legal research skills also prove useful. When searching for particular wording in the legislation (the CDPA is a long act) we know that Westlaw has more functionality than legislation. gov. uk. Someone not working in a law library would not necessarily have access to Westlaw or Lexis or might not be confident with how to search legal databases quickly and accurately. We frequently use Westlaw for the searching but then return to legislation. gov. uk for sending hyperlinks to enquirers who are not in the law faculty but in the wider university. And it is not just a case of needing to look up the copyright legislation -at Cambridge and LSE we get queries that require knowledge of data protection, legal deposit and disability legislation.
Teaching The need to teach specialist legal research skills has led law librarians to embrace the teaching role for many years. Part of that is understanding your audience so that messages can be pitched appropriately. This chimes well with copyright as it is best learnt at the correct level according to pre-existing knowledge. For example, telling a new undergraduate about the requirements for PhD theses to be made available on an open access basis is too much information too soon and irrelevant to their current information needs. The copyright messages need to be tailored so that they are relevant. The best format needs to be considered (in person or online, synchronous or asynchronous). A first-12 [2021] EWHC 697 (IPEC) The case of a self-publisher who wanted a court order to require the British Library and the Bodleian to remove the metadata for her book Twiggles and Squiggles as she believed the institutions were operating illegal licensing schemes (with Google) and affecting the profits from book sales.
However, for rumen microbiota abundance, there is limited information about possible associated genomic regions. Thus, the identification of QTL that are simultaneously associated with rumen bacterial abundance and milk traits can help reveal the basis of the genetic link and the shared metabolic pathways between the rumen microbiota and dairy traits in sheep. We hypothesised that, in Lacaune ewes, rumen microbiota abundance is affected by the host genetics and is genetically linked to dairy traits through shared metabolic pathways. Thus, based on the use of the dataset that was previously described by Martinez Boggio et al. [5] , the objectives of the current study were to  (1)  quantify the effect of the host genetics on rumen bacterial abundance by estimating its heritability, (2) identify and quantify the genetic links between rumen bacteria and dairy traits by estimating the genetic correlations between the two, and (3) identify the QTL and potential underlying mechanisms that determine the genetic variation in rumen bacterial abundance and in dairy traits by identifying shared genomic regions between the two through genome-wide association studies (GWAS).
The genetic structure of the INRAE La Fage flock consists of two independent groups of ewes, which were both divergently selected, as described by Martinez Boggio et al. [5]. Briefly, genetic selection was based on the estimated breeding values (EBV) for milk somatic cell score (SCS)  [8]  or on the EBV for milk persistency (PERS), expressed as the coefficient of variation in milk production (CV milk). Ewes belonging to each of these two lines were studied (N = 700). The genetic difference between the ewes from the divergent lines for SCS (SCS+ /SCS-) was 2. 19 units of SCS EBV [i. e. , a 3. 60 genetic standard deviation (SD)], and that for PERS (PERS+/PERS-) was 5. 52 units of CV milk EBV (i. e. , a 2. 10 genetic SD). Ninety-five additional ewes were included in the dataset, which were derived from the oldest SCS line but are currently selected to increase the frequency of the mutant allele (T) of the suppressor of cytokine signalling 2 (SOCS2) gene in the experimental Lacaune population to investigate possible associations with other traits.
This allele corresponds to a mutation that was identified in 2015  [12]  and explains 12% of the genetic variance in somatic cell count in Lacaune dairy sheep. Thus, the final experimental dataset consisted of data from 795 ewes, including 298 under SCS selection (94 SCS+ and 204 SCS-), 402 under PERS selection (200 PERS+ and 202 PERS-), and 95 under selection for the SOCS2 mutation.
e. , shorter than 300 bp or longer than 500 bp, or that contain at least one ambiguous base; (ii) removal of chimera; (iii) regrouping of sequences by clustering with Swarm in FROGS, and we chose the parameters for a distance equal to 1; (iv) cluster filtering, i. e. , removal of the clusters with abundances lower than 0. 005%  [16] ; and (v) taxonomic assignment to operational taxonomic units (OTU) using the SILVA database (version 138)  [17]  (see Additional file 1: Table  S1 ). The abundance table and the taxonomy files were imported into R (v4. 0. 2)  [18]. The core microbiome was quantified based on the occurrence of OTU across multiple samples, and the proportion of samples over which OTU must occur was set to 90%  [19].
The final dataset included 773 genotyped individuals and 35,492 autosomal SNPs. Markers were positioned on the 26 Ovis aries (OAR) autosomes and mapped to the Ovis aries genome assembly Oar_v3. 1  [23]. The SNP corresponding to the mutation in the SOCS2 gene was included in the map of OAR3 (129,722,200 bp).
Statistical analyses Microbiota abundance data are compositional data  [24]. The abundances of each sample are constrained to a total sum imposed by the sequencing technology used. Therefore, the information is contained in the ratios between OTU abundances, and the raw counts are irrelevant. This produces an interdependence between abundances, and they need to be considered as compositional data  [25]. Thus, we applied an approach based on compositional data theory  [26]  which consisted in transforming the counts into centered log-ratios. As zeros are not compatible with the log-ratios transformation, the OTU abundance data were zero imputed with the geometric Bayesian-multiplicative method  [27]  through the cmul-tRepl function of the zCompositions package  [28]  in R (v4. 0. 2)  [18]. Then, they were centered log-ratio (CLR) transformed with the function clr of the compositions package  [29]  in R (v4.
The analyses were performed using the BLUPF90+ software with the OPTION method VCE  [31]  and by including 100 initial rounds of EM-REML to obtain initial variance components. We used a convergence criterion of 1e -10 which is the value set by default in the BLUPF90 software. To test the significance of the heritability estimates for the 2059 OTU, an empirical significance threshold for the null hypothesis of no genetic control was estimated. For OTU, the null hypothesis was obtained by randomly shuffling their abundances among the individuals. We selected two OTU (one with many and one with few zeros) and performed the random shuffling 10,000 times. For each permutation and OTU, we estimated the heritability of OTU abundance using a three-trait model, as presented in Eq. (  1 ). To define an error rate of 5%, we arranged the heritability estimates by increasing order, and retained the lower value of the upper 5% yielding a significance threshold obtained for both OTU of 0.
The SNP effects and P values were computed using BLUPF90+ and POSTGSF90  [31]. We corrected for multiple testing by using the false discovery rate (FDR) in the p. adjust package in R (v4. 0. 2)  [18]. A genome-wide SNP significance threshold of P < 0. 10 and a suggestive threshold of P < 0. 30 were applied to each trait. After identification of the lead SNP in a given region, significant SNPs that were located less than 1000 mega base pairs (Mbp) apart and were included in the upper third of the peak were grouped within the same QTL region. The CMplot package in R (v4. 0. 2)  [18]  was used to generate Manhattan plots showing thelog 10 (P value) on the y-axis. We estimated the allele substitution effect for significant SNPs that were detected on the same chromosome and at the same position.
Linkage disequilibrium (LD) along the genome was calculated as the squared correlation of allele counts for two SNPs on each chromosome. The proportion of phenotypic variance explained was calculated for each SNP by defining a window variance of 20 adjacent SNPs and for each QTL region defined in this study. The LD and variance explained were calculated using POSTGSF90  [31]. Genes that were identified for the significant SNPs were retrieved from the Ensembl database using the BioMart web interface based on the Ovis aries genome assembly Oar_v3. 1  [39]. Then the DAVID functional annotation tool  [40]  was used to analyse the overrepresented Gene Ontology (GO) biological terms, including biological processes, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways  [41].
OAR5 On OAR5, we detected three QTL for Prevotella OTU1843, Rikenellaceae_RC9_gut_group OTU546 and Ruminococcus OTU1191. The QTL for OTU1843 includes two significant SNPs, of which one (rs402307868) showed a colocalized signal with the Possible_genus_sk018 OTU1631. The allele substitution effects for the SNP with colocalized signals showed contrasting signs for the Prevotella and Possible_genus_ sk018 OTU. The QTL for Rikenellaceae_RC9_gut_group  OTU546 includes two significant SNPs in high LD (LD score of 0. 74). The QTL for Ruminococcus OTU1191 includes two significant SNPs separated by 0. 04 Mbp, but the LD score was very low in that region (< 0. 10). The SNP rs418870684 was associated with the novel gene ENSOARG00000025325.
Heritability of rumen bacteria and dairy traits Our results obtained in Lacaune dairy ewes demonstrate that rumen bacterial abundance is partially controlled by the host genetics. The OTU heritabilities showed an asymmetric distribution with most values close to 0, but with 15% of the OTU (306 of 2059 OTU) having a low to moderate heritability (0. 10 to 0. 29), which is similar to the results reported for rumen bacteria in other ruminants  [2, 4, 6, 42, 43]. However, there are some differences in the heritability range and in the percentage of OTU with a significant heritability. For example, Difford et al. [2]  and Zhang et al. [6] , using the same dataset of 750 dairy cows, obtained significant heritability estimates for 5 to 10% of genera, with heritabilities ranging from 0. 17 to 0. 25, and 2 to 6% of OTU with heritabilities ranging from 0.
Genetic correlations between rumen bacteria and dairy traits Rumen bacteria are undeniably of crucial importance to their host, providing FA and microbial amino acids that contribute to milk fat and protein production  [45]. However, only 96 heritable OTU from the most abundant families Prevotellaceae, Lachnospiraceae and Ruminococcaceae were moderately to highly genetically correlated (absolute values ranging from 0. 33 to 0. 99) with one or more milk FA and proteins. The overall quantification of these correlations is of interest to understand the relationship between rumen microbiota and phenotypes, in spite of the limitations of our study due to the low precision of 16S rRNA gene sequencing at the species level and the large number of estimated genetic correlations (more than 5500) based on a small dataset of 800 ewes, which, even if those with large standard errors are discarded, may lead to some spurious correlations.
In addition, although we did not detect SNPs for milk FA on OAR11, Marina et al. [10]  reported major genes for milk fat synthesis (ACACA and FASN) on this chromosome. In the GWAS of rumen bacterial abundances, we detected signals that were distributed across the host genome, as reported by Li et al. [4] , Abbas et al. [50]  and Zhang et al. [6]. However, we detected three main regions on OAR3, 15 and 26 with signals for Prevotellaceae OTU that colocalized with the same SNP, i. e. , colocalizations for Prevotellaceae and Lachnospiraceae OTU on OAR5, and colocalizations for Prevotellaceae and F082 OTU on OAR26. This suggests that some regions of the host genome are associated with rumen bacterial abundance, but given the limitations raised by Pérez-Enciso et al. [51]  about the difficulty in identifying causative SNPs for microbiota abundance, the results should be confirmed with a larger dataset.
From the genomic regions in which QTL were detected for OTU abundances, we recovered several potential candidate genes, such as TIA1, IL18R1, W5PCZ6_SHEEP and PDE3B, which are involved in host immune system processes, regulation and catabolic processes, as reported by Abbas et al. [50]. However, it was not possible to identify shared metabolic pathways between OTU and dairy traits because we did not detect genomic regions where pleiotropic or closely-related QTL affected both traits simultaneously. One explanation may be that although the dataset is large for a microbial analysis, the number of records is too small for the detection of genome-associated regions and pleiotropic effects compared to other studies  [52, 53].
Conclusions Our findings, based on a large and unique microbiome database, demonstrate that a small proportion of the total bacterial abundance in the rumen of Lacaune ewes is partially controlled by the host genome, for both the total and core microbiome, and that a few of the bacteria are associated with specific genomic regions on OAR3, 5, 15 and 26. Only very few of the genetic correlations between milk fine composition and rumen bacteria were significant, and it was not possible to identify genomic regions and metabolic pathways shared between rumen bacterial abundance and dairy traits. The use of 16S rRNA gene sequencing in this study did not allow for specieslevel affiliation, and additional work is needed to identify which microbial functions are involved and to associate the functions with milk traits.
Acknowledgements We are grateful to the technical staff at  INRAE experimental unit of La Fage  (DOI: https:// doi. org/ 10. 15454/1. 54832 55234 66425 E12 ) for animal care and help with rumen sampling. We thank  Béatrice Gabinaud  for preparing microbiota samples and Get-Plage platform in Toulouse for DNA sequencing, and  Hélène Larroque  and  Rachel Rupp  for access to the genetic resources produced at  INRAE La Fage. Martinez Boggio  et al. Genetics Selection Evolution (2022) 54:77.