dataesr/scientific-paragraphs-categorization huggingface dataset:
g. between 50 and 20 • S and between 20 and 50 • N (Fig. 10d ). The dataset by  Kindermann et al. (2008)  was better correlated with our estimates, the difference rarely exceeding 30 % in absolute terms. All AGB datasets yielded similar values of total AGB in the wet tropics between 10 • S and 10 • N (Table  3 ), with our estimate being intermediate compared to the others. For the latitude ranges corresponding to the humid and dry tropics, our total AGB was much lower than  Saatchi et al. and Baccini et al. (2011a, 2012)  (87. 1 vs. 120. 8 and 95. 2 Pg, respectively) because of their substantially higher estimates of AGB outside the rainforest region. It was also different from  Liu et al. and Kindermann et al. (2015, 2008) (87. 1 vs. 111. 7 and 122. 9 Pg, respectively) because the coarse spatial resolution of the data did not allow forest fragmentation to be accounted for.
For boreal and temperate forests (Table 3, column: 30-90 • N), the total AGB from our dataset was larger than in  Thurner et al. and the GEOCARBON dataset (160. 8 vs. 114. 7 and 108. 2 Pg, respectively). In both these datasets, a more stringent definition of forest than the CCI Land Cover dataset was used, resulting in a large number of AGB estimates equal to 0 Mg ha -1 in areas labelled as forest by the CCI Land Cover dataset. Our estimate of total AGB was slightly lower than that from  Liu et al. and Kindermann et al. (2015, 2008)  because these datasets did not account for forest fragmentation. The values were difficult to compare for the latitude range including the temperate forests of the Southern Hemisphere (Table  3 , column: 60-30 • S) because of the approximations underlying the AGB values obtained in the other maps (extrapolation from coarse-resolution estimates in GEOCARBON, statistics of large countries in  Kindermann et al.
Instead, values at the level of continents or domains provided a more realistic indication of the quality of the AGB dataset since we could identify a predominant error source in each (Table  S8  in the Supplement). Interpreting Table  S8  in the Supplement, errors were partly a consequence of a simple model relating SAR observations to biomass through three unknown parameters whose estimates relied on several assumptions and generalization rules. In addition, to avoid unrealistic model fits when relying on external data for model training, our model training procedure was forced to follow the average relationship between SAR backscatter and biomass. This reduced the variability in the predicted backscatter with respect to the measured backscatter, which translated to overestimation in the low AGB range and underestimation in the high AGB range, as evidenced by Fig.
5. In spite of these weaknesses, we preferred such an approach to the traditional model training based on a dataset of reference biomass measurements because of the paucity of reference measurements available globally, which would lead to major AGB biases in regions not represented in the training dataset. Underestimation of AGB in dense mature forests (Fig. 5  and Fig. S9  in the Supplement) were further exacerbated by the weak sensitivity of SAR backscatter to forest variables (Table  S8  in the Supplement). The range of AGB beyond 150 Mg ha -1 , in particular in wet biomes, corresponded to a backscatter range at C-and L-band of less than 0. 3 dB  (Cartus and Santoro, 2019; Santoro et al. , 2015a)  and thus had substantial uncertainty. Combining estimates of biomass from multiple C-band Envisat ASAR observations increased the accuracy of the AGB retrieval compared to individual estimates  (Santoro et al.
, 2011). However, the ALOS PALSAR L-band dataset consisted of a single observation so that any contribution to the SAR backscatter not related to the forest itself translated into a bias. This was most relevant in the wet tropics, where AGB was estimated solely on such single observations. Imperfect modelling of the ALOS PAL-SAR backscatter in sloping terrain caused an additional bias, which propagated through to the estimate of AGB (Fig. S14  in the Supplement). Characterization of the spatial variability in wood density and biomass expansion factors was critical. We acknowledge that in less sampled regions, such as savannahs, the estimates of AGB might have been limited by the observations used to estimate WD and BEF. Nonetheless, even if we could not quantify the impact of local BCEF biases on the AGB estimates because GSV observations were unavailable for most of the inventory, our understanding of the retrieval errors (Table S8 in the Supplement) and uncertainties (Fig.
S10  in the Supplement) indicates that the main contributors to the errors were not associated with the BCEF layer. This interpretation, however, is affected by the opportunistic dataset of plot inventory measurements available to assess the AGB estimates. We agree that further research opportunities should focus on understanding the implications of local wood formation strategies to AGB estimates at local scales and across environmental gradients. Our estimate of the total AGB in forests increased from 522 to 596 Pg when further accounting for the woody biomass in non-forest CCI land cover classes. An additional 4 Pg was estimated in cropland and non-vegetated land cover types, resulting in a terrestrial woody AGB pool of 600 Pg, corresponding to approximately 282 Pg C assuming the mean carbon fraction in woody vegetation of 0.
47  (IPCC, 2006). Based on our dataset and accounting for additional maps of non-woody biomass and auxiliary datasets, a more comprehensive report of the global carbon stock both above-and below-ground was recently published  (Spawn et al. , 2020). The lack of AGB estimates for the western Pacific islands had a negligible effect on our total AGB values. AGB underestimates and substantial uncertainty in our dataset arose from having only a single observation of L-band backscatter and the inability of C-band observations to resolve levels of AGB in forests with high biomass stocks. This effectively led to use of a single radar observation to estimate biomass in dense tropical forests, causing our values to be at the low end of the range of estimates of the terrestrial AGB pool (Table  3  and Table  S1  in the Supplement).
Although our estimates for the wet tropics were affected by bias, our dataset provides new insights into the spatial distribution and levels of AGB in tropical, subtropical, and temperate forests (Table  3 , Fig. 10 ) and provides overall more accurate spatial patterns of the terrestrial carbon pool than previous maps (Fig. 11 ). For the boreal domain, our estimates agreed with previous maps, which were reported to be unbiased (Table  3 , Fig. 10 ), whilst providing a more detailed portrait of the spatial distribution of AGB (Fig. 3 ) because of the higher spatial resolution of the remote sensing datasets used in this study (1 vs. 100 ha). At the country level, we demonstrate the benefit of our map-based estimates of AGB and total AGB in the context of global reporting of biomass pools (Table  2 , Fig.
7 ). Nonetheless, our dataset may not be sufficiently accurate for individual countries to use it as the only basis for reporting biomass and carbon pools, in particular for countries where the AGB estimates may be experiencing systematic retrieval errors (e. g. predominant high AGB, limited sensitivity of the backscatter to biomass, strong topography). However, it may provide support to national reporting in conjunction with a forest inventory system  (Naesset et al. , 2020). Our dataset agreed well with values reported in the 2010 FRA for countries with an established national forest inventory (Fig. 7 , Table S7 in the Supplement). However, for countries reporting AGB on the basis of expert knowledge, best-guess estimates, and default parameters, we identified clear regional patterns of either overestimation (e.
g. in Africa; see Fig. 7 ) or underestimation (e. g. in Asia; see Fig. 7 ), which could be related to the capacity of national forest inventories (Fig. 8 ). Although the global average AGB in our map differs from the FRA value by only 8 % (108 vs. 119 Mg ha -1 ; Table  2 ), the smaller forest area reported in the FRA implies that our total AGB estimate exceeded the value reported in the FRA by 11 %. Our dataset provides new insights on the spatial distribution and magnitude of terrestrial woody AGB as well as a valuable resource for climate and carbon modelling because of its completeness, standardized estimation procedure, and high level of detail. The major caveat of past and current spaceborne remote sensing observations used to estimate biomass is their limited sensitivity to any forest parameter.
Validation of microfluidic real-time PCR results using standard PCR and DNA sequencing To confirm the microfluidic real-time PCR results, all samples positive for infectious agents underwent conventional and nested PCR assays using primers different from those of the BioMark™ system (Table  1 ). Amplicons were sequenced by Eurofins MWG Operon (Ebersberg, Germany) and assembled using BioEdit software (Ibis Biosciences, Carlsbad). The final nucleotide sequences were analyzed to identify the sequenced microorganisms using the GenBank database through the National Center for Biotechnology Information (NCBI; Bethesda, MD) Basic Local Alignment Sequence Tool (BLAST) search engine (www. ncb i. nlm. nih. gov/blast). Nucleotide sequence data reported in the present study are available in GenBank, EMBL and DDBJ databases under accession numbers MZ146328, MZ151073, and MZ146329.
sanguineus removed from dog V. The detection of R. helvetica 23S -5S ITS in the tick samples from dogs II, III and V concurred with positive signals for the Rickettsia spp. gltA gene in the microfluidic system. The other pathogen detected was Hepatozoon canis, present only in the R. sanguineus sample from dog V. The presence of H. canis was confirmed by positive detection of H. canis 18S rRNA, Apicomplexa 18S rRNA in the microfluidic system and by sequencing a fragment of H. canis 18S rRNA (accession number MZ146329). The tick infesting dog V tested positive for a mixed infection with R. helvetica and H. canis. None of the pooled H. punctata samples collected from the house walls tested positive for TBPs included in our assay. Regarding the blood samples, Anaplasma phagocytophilum (confirmed by detection of A.
phagocytophilum msp2, and Anaplasma spp. 16S rRNA in the microfluidic system) and Rickettsia monacensis (confirmed by detection of Rickettsia spp. gltA in the microfluidic system and sequencing of R. monacensis OmpB fragment (accession number MZ151073)) were detected in two of the dogs, whereas Rickettsia felis (confirmed by detection of Rickettsia spp. gltA and R. felis orfB in the microfluidic system) was detected in only one of the dogs. Anaplasma bovis was the only pathogen found in the goats tested. The presence of A. bovis was confirmed by detection of Anaplasma spp. 16S rRNA in the microfluidic system and by sequencing an A. bovis 16S rRNA fragment (accession number MZ146328). Dog II was diagnosed with the  concomitant infection of A. phagocytophilum and R. felis, and dogs III and V were PCR-positive for R.
Background: During the coronavirus disease 2019 (COVID-19) pandemic, concerns have been arisen on the use of renin-angiotensin system inhibitors (RASI) due to the potentially increased expression of Angiotensinconverting-enzyme (ACE)2 and patient's susceptibility to SARS-CoV2 infection. Diabetes mellitus have been recognized favoring the coronavirus infection with consequent increase mortality in COVID-19. No data have been so far reported in diabetic patients suffering from ST-elevation myocardial infarction (STEMI), a very high-risk population deserving of RASI treatment. Methods: The ISACS-STEMI COVID-19 registry retrospectively assessed STEMI patients treated with primary percutaneous coronary intervention (PPCI) in March/June 2019 and 2020 in 109 European high-volume primary PCI centers.
Study design and population Our study population is represented by patients enrolled in the ISACS-STEMI COVID-19 (NCT 04412655), a retrospective multicenter registry including STEMI patients enrolled by 109 high-volume primary percutaneous coronary intervention (PCI) centers from Europe, Latin America, South-East Asia and North-Africa. This study was conducted to compare STEMI patients treated from March 1st until June 30th of 2019 with those admitted within the same period of 2020. We collected demographic, clinical, procedural data, data on total ischemia time, door-to-balloon time, referral to primary PCI facility, PCI procedural data, in-hospital mortality. The study was approved by the Ethical Committee of AOU Maggiore della Carit a. Novara. Detailed data have previously been provided  [18, 19].
Statistics Data analysis was performed by using SPSS Statistics Software 23. 0 (IBM SPSS Inc. , Chicago, Illinois). Quantitative variables were described using median and interquartile range. Absolute frequencies and percentages were used for qualitative variables. ANOVA or Mann-Whitney and chi-square test were used for continuous and categorical variables, respectively. Normal distribution of continuous variables was tested by the Kolmogorov-Smirnov test. A propensity score analysis was performed in order to overcome the potential bias related to differences in baseline characteristics. For each patient, a propensity score indicating the likelihood of having chronic RASI or in-hospital RASI administration was calculated by use of forward logistic regression analysis that identified variables independently associated with RASI prescription.
Results Of the 16,077 STEMI patients undergoing mechanical reperfusion included in the study, 3812 (23. 7%) were diabetic, in whom complete demographic, clinical, procedural, and outcome data were available as well as information regarding in-hospital RASI therapy. Patients with informative data on RASI treatment at admission were 3761 (98. 7%). Higher RASI discontinuation rate after hospital admission was observed was observed in 2019 compared to 2020 (46. 2% in 2019 vs 39. 0% in 2020, p = 0. 001). Table  1  shows baseline characteristics of patients in overall cohort and with versus without chronic RASI therapy at admission, according to the year of treatment. Almost all differences detected were consistent both in 2019 and 2020 cohort. In particular, patients on RASI were older, more often female, with higher burden of comorbidities including hypertension, hypercholesterolemia, and had already experienced a STEMI, and coronary revascularization, but displayed lower prevalence of active smoking.
As we did not collect information on the type and dosage of RASI, we cannot perform a further in-depth assessment of prognostic implications. Finally, although our database comprised almost 3800 diabetic STEMI patients with known in-hospital pharmacological therapy, procedural features and clinical outcome following mechanical reperfusion procedure, the evaluation of the prognostic impact of RASI treatment among SARS-CoV2-positive patients was limited due to the low prevalence of the infection.
The International Mouse Phenotyping Consortium (IMPC) is generating and phenotyping null mutations for every protein-coding gene in the mouse 1,2. The IMPC now uses Cas9, a programmable RNA-guided nuclease that has revolutionized mouse genome editing 3 and increased capacity and flexibility to efficiently generate null alleles in the C57BL/6N strain. In addition to being a valuable novel and accessible research resource, the production of >3,300 knockout mouse lines using comparable protocols provides a rich dataset to analyze experimental and biological variables affecting in vivo null allele engineering with Cas9. Mouse.
The deletion approach resembles the embryonic stem cell-based approaches used for decades to produce null alleles, albeit without selection cassette insertion. To evaluate experimental variables affecting mutant mouse production, we analysed data from 4,473 production attempts on 3,973 unique genes from eight different centres (Supplemental Table  1 ) recorded in the IMPC's international microinjection tracking system (iMITS; downloaded 2020 Oct 11 from https://www. idcc. org/imits/). Four experimental parameters were assessed for their effects on success: Cas9 delivery method, number of guide RNAs (gRNAs) used, deletion size, and number of founders selected for breeding, as well as changes to parameters on repeated production attempts. Gene editing reagents were delivered by microinjection (pronuclear or cytoplasmic) 9,10 or electroporation  [11] [12] [13] [14]  to target specific genes in C57BL/6N zygotes (Supplemental Figure  S1b ).
To assess the influence of additional biological variables on founder and GLT rates, we applied a general linear model (GLM) to test the association of several factors including embryonic expression (GEO GSE11224) 22 , observed/expected loss-of-function (o/e) and probability of lossof-function (pLI) scores 23 , chromosome position, histone methylation and acetylation (as a proxy for chromatin accessibility), cytogenic banding (a second proxy for chromatin accessibility), and OMIM annotation human orthologs 24 (Supplemental Table  4 ). When gene essentiality was excluded from analysis, only embryonic expression was significantly associated with a low number of founders (odds ratio (OR)=0. 95, p=0. 011). However, when essentiality was included, it was the only predictor of experimental failure (OR=0.
Moreover, transmission of only wild-type alleles could indicate the presence of haplo-insufficient alleles for some genes causing mutant progeny to die before genotyping. These data support the hypothesis that a substantial subset of mutations may fail GLT due to negative effects on viability or fertility and are consistent with the significant effect of gene essentiality on founder production (Table  2 ). For quality control (QC) of mutant alleles, the IMPC requires sequence data with ≥100 bp flanking DNA on both sides of the deletion junction. Alleles with deletions that do not result in frameshift mutations after sequencing fail QC. In a pilot study conducted at three production centres, the copy number of the deleted sequence in 6,653 N1 mice representing 703 uniquegene experiments was assessed.
An extremely low rate of re-insertion of the deleted fragment distal to the deletion junction was detected (14/6,653 mice, 0. 21%, from 8/703 genes, 1. 1%, data not shown). The founder screening methods employed by the IMPC explicitly exclude inversions, translocations, and deletions that extend more than several hundred base pairs from the targeted cut sites, all of which are known to occur after Cas9 treatment  [25] [26] [27]. These types of alleles, while indicative of Cas9 activity, are not identified nor bred in the IMPC and are therefore part of the failed experimental group. Coupled with gene essentiality negatively impacting founder rate, the dataset analyzed herein was not appropriate for analysis of Cas9 activity. As of October 2020, the IMPC has produced null alleles for 9,503 unique genes (this report and 2 ), 4,182 of which were produced using Cas9 endonuclease (Figure  5a ; Supplemental Table  8 ).
With accessible mouse lines enriched by comprehensive phenotyping data, the IMPC fills a critical resource gap in the biomedical community providing tools to identify disease gene candidates (e. g. 20 5] [36] [37]  ). The analysis of our large, multi-centre dataset identified several variables that affect the success of Cas9 mutagenesis experiments and provides the basis for recommendations for genome editing in mouse zygotes. For deletion alleles, electroporation of Cas9 ribonucleoprotein (RNP) is the most accessible and scalable method, providing equivalent performance to cytoplasmic microinjection of Cas9 mRNA and gRNAs. GLT rates are high for Cas9 generated founders and there is no apparent advantage to breeding more than three founders. For attempts that fail to produce founders, changing guide sequences may be beneficial when repeating production for a given gene.
A critical region was defined as one or more exons that when frameshifted or deleted resulted in the open reading frames of all known full-length protein-coding transcripts (per Ensembl build 38) being out of frame 4. For the majority of designs, the premature stop codon was predicted to be introduced in the first half of the protein-coding open reading frame and to target transcripts for nonsense-mediated decay  5, 39. Such alleles are considered presumptive null alleles. Four major categories of alleles were generated. Indel alleles resulting from non-homologous end joining (NHEJ)-mediated repair of a single Cas9-induced DSB within the critical region of a gene. Exon deletion (exdel) alleles resulting from NHEJ-mediated repair of Cas9-induced DSBs flanking the exon(s) within a gene's critical region.
Data download and filtering: A complete data set of Cas9-mediated mouse production attempts was downloaded on October 11, 2020 from the International Microinjection Tracking System (iMITS; 'Cas9 Micro-Injection Excel download') used to record International Mouse Phenotyping Consortium (IMPC) data. This data included all Cas9-based production attempts. A production attempt was defined as the treatment of embryos to introduce Cas9 and guide RNAs to direct genome editing, subsequent embryo transfer, birth and screening of pups born from the embryo transfer, and subsequent breeding of mutant founders to obtain germline transmission of the desired edited allele. The data was filtered to remove attempts labeled as "private", as "experimental", or producing an allele other than a null allele, those with a status "Microinjection in Progress", embryo transfer day of "Next Day", none or >1000 embryos injected, incomplete information (e.
If all attempts had a Elrick et al. Page 23 of 50 status of "Founder obtained", the most recent was kept. However, if none were successful in this filtered dataset, this could falsely give the impression that no successful knockout was produced by the IMPC using Cas9 for the gene. Thus, genes with a successful attempt in the pre-filtered dataset (e. g. at another production centre) were marked as such, but excluded from this analysis (Supplemental Table  2 ). For repeat attempt analysis, all attempts at the same production centre for genes that had more than one attempt and whose first production attempt failed to produce a null mouse line were included. This data set was then filtered to remove attempts in progress (Status = "Microinjection in progress" or "Founders obtained"). The remaining attempts were sorted in chronological order by microinjection [electroporation] date and if the first attempt for a given gene was successful, the set of attempts for that gene at that Centre was removed.
Similarly, if an attempt was aborted within 6 weeks of a successful germline transmission attempt, it was removed since it may have been aborted because germline transmission had already been obtained, rather than having "failed". Finally, if there was no GLT in any attempt at one centre, but successful GLT at another centre, it was removed from the repeat dataset. The resulting data set comprised the repeat dataset (Supplemental Table  3 ). The set of non-IMPC produced mouse null alleles, including conditional-ready null alleles and reporter alleles, was obtained from Mouse Genome Informatics as in Birling et al. 2. The complete production data set including attempts to produce both ES cell-and Cas9-derived mouse lines, was downloaded from iMITS (All Microinjection Attempts) on October 2, 2020.