dataesr/scientific-paragraphs-categorization huggingface dataset:
Accordingly, most recent guidelines now recommend that patients with arterial hypertension associated with diabetes or chronic kidney disease must be treated to attain the goal of systolic blood pressure 140 mmHg and diastolic blood pressure 90 mmHg. ] [16] [17]  Given these premises, the question arises as whether the current available antihypertensive armamentarium, and particularly monotherapies, may be suitable to achieve modern blood pressure targets in older individuals, regardless of the presence of associated clinical conditions or additional cardiovascular risk factors. The availability of a large database of elderly hypertensive patients enrolled in two randomized, double-blind, parallel group studies with a similar design, comparing the efficacy of an angiotensinreceptor blocker (ARB), olmesartan medoxomil, with that of the angiotensin-converting enzyme (ACE)-inhibitor, ramipril, gave us the possibility to explore such a scenario.
Treatment efficacy according to new blood pressure targets in the whole study group The pooled dataset from the two studies consisted of 1,426 patients (intention-to-treat population) of which 712 were treated with olmesartan at an average dose of 27. 2±12. 6 mg (47. 1% of patients taking the full drug dosage) and 714 treated with ramipril at an average dose of 7. 3±3. 1 mg (55. 3% of patients taking the full drug dosage, P=0. 008 vs olmesartan). As shown in Table  1 , no statistically significant differences existed between the two treatment groups for the main demographic and clinical characteristics at baseline. Notes: Data are shown as means (± sD) or as absolute (n) and relative (%) frequencies. P-values for between-treatment difference are also reported. a Available for 699 patients randomized to olmesartan and for 703 patients randomized to ramipril.
Patients at target according to number and types of previous antihypertensive treatment Patients with hypertension at highest risk of cardiovascular complications, such as older persons, have a greater chance of being resistant to particular classes of drugs or may not adequately respond to monotherapy. 1  In order to gain further insight into the mechanisms behind the efficacy of olmesartan and ramipril in our pooled datasets of aged hypertensives, we evaluated response rate in subgroups of patients according to the number of drugs and type or RAS antagonist (ARB or ACE inhibitor) used at the time of entering the studies. In never-treated patients (24. 8% of the whole study population), olmesartan ability to achieve the blood pressure target, whether it was based on the 140/90 or the 150/90 mmHg threshold, was better than that of ramipril (with statistically significant differences for the 140/90 mmHg target), suggesting that the subgroup of patients of our population previously receiving no antihypertensive drug was more prone to respond to the ARB than to the ACE inhibitor (Figure  4 ).
It is a 64 kb heterozygous deletion affecting exons 9 to 13 of ATRNL1. A 325 kb deletion adjacent to this gene, described by Stark et al. , implicates this gene in cognitive impairment, autism and several dysmorphic features. ATRNL1 is involved in the regulation of energy homeostasis by binding to melanocortins  [41, 42]. The two patients have some common characteristics (Table  3 ), especially autistic traits, skeletal abnormalities, and ID. However, the presence of the deletion in the healthy mother of patient 24 makes it difficult to assess the clinical significance of this CNV. Therefore, sequencing of ATRNL1 was performed but no point mutation was found. This CNV can then be considered pathogenic with an incomplete penetrance or simply classified as benign. A study with a larger number of controls is therefore required because only intronic deletions were found in our control database (10 CNVs, all in intron 26).
We therefore compared the estimated coefficient of inbreeding to the coefficient deduced from the pedigree of each family, and interestingly, we noticed the occurrence of significant deviations from theoritical values. In 17/42 patient (40. 4%), a higher degree of relationship than shown by their pedigree was observed. This variation is due to multiple loops of consanguinity or multiple generations of inbreeding observed within the Lebanese community  [44] , which complicates the estimation of the degree of relationship. These cases were marked for further investigation with the aim of sequencing candidate genes within the ROH regions. Finally, we established an internal database for polymorphic CNVs to help further studies discriminate between rare polymorphisms and disease-associated variants (data not shown).
Chromosomal microarray analysis-based technologies Cyto 2. 7M Genomic DNA, isolated from peripheral blood samples using the salting-out technique, was amplified, purified, fragmented, denatured, and then hybridized into the Cyto 2. 7 M, following the Affymetrix® standard protocol. A single array has a high density of 2,361,876 nonpolymorphic markers and 400,103 SNP markers, with whole-genome backbone coverage of ~1 kb spacing. The analysis of scanned chips was performed using the Affymetrix Chromosome Analysis Suite software (ChAS v. 1. 0. 1). The software initiates studies on arrays of which the Median Absolute Pairwise Difference score (MAPD), the waviness segment count, and the SNPQC meet the Quality Control (QC) criteria set by the manufacturer: MAPD < 0. 27; SNPQC > 1. 1; Wav Seg Count ≤ 30  [45].
Assessment of array parameters Assessing the existence and causality of the small CNVs identified by this high-resolution platform was very challenging and required the consideration of array parameters. We selected ninety-nine random CNVs, with no threshold, for validation by quantitative PCR (Q-PCR). Our results (data not shown but available upon request) prompted us to select a threshold of 62 kb with at least 49 consecutive markers, which we further utilized to filter the CNV analysis results.
Quantitative PCR (Q-PCR) The array findings were confirmed and their de novo and inherited status were distinguished using Q-PCR with an ABI Prism 7500 system (Applied Biosystems, Foster City, CA, USA) using fluorescent SYBR Green dye (ABI). Specific primers targeting genes or intronic sequences were designed using Primer Express 3 Software (ABI). PCR was performed in a 20 μl reaction volume containing 10 μl Power SYBR-Green PCR Master Mix (ABI), 10 pmol forward and reverse primers, and 10 ng of genomic DNA. The reaction cycling conditions were 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 1 minute. Each sample was run in triplicate for the quantification of the expression level of a target gene and compared to the expression level of two endogenous genes. Data evaluation was carried out using the ABI Prism Sequence Detection System (SDS) using the comparative ΔΔ threshold cycle number (Ct) method.
Workflow for selecting CNVs To assess the clinical significance of the detected CNVs, we followed the recommended steps from Miller et al. and Buysse et al. [10, 47, 48]. All imbalances found at least twice in the Database of Genomic Variants (DGV) and our internal database of healthy individuals were considered to be benign and excluded from further analysis. CNVs under the selected threshold, as well as those that did not involve genes or miRNAs, were also excluded. The remaining CNVs were classified into groups. Group I contains pathogenic CNVs overlapping critical regions of known microdeletions or microduplications and/or involving genes already described as causing a phenotype, especially ID. These CNVs are found in the publicly available DECIPHER (http://decipher. sanger. ac. uk) and ISCA (www.
clinicalgenome. org) databases and in published literature such as the Catalogue of Unbalanced Chromosome Aberrations in Man  [9]. Group II contains genomic imbalances classified as being variants of uncertain clinical significance because of their unclear possible pathogenicity. Parental studies were mandatory for the classification of these CNVs. VOUS were grouped into four categories: Group IIa contains rare, likely pathogenic CNVs that mostly occur de novo and includes genes with a possible correlation to the phenotype (abnormal with a low recurrence risk); Group IIb corresponds to CNVs for which clinical interpretation remains uncertain, even after parental studies, owing to variable expressivity or incomplete penetrance; Group IIc includes all inherited CNVs, also called familial variants, that were considered to be benign; and Group IId contains VOUS that could not be further tested owing to the absence of parental DNA.
Adverse events and safety monitoring AEs and serious AEs (SAEs) were defined and graded according to the 2004 US National Institutes of Health Division of AIDS toxicity table  [25]. All SAEs were to be reported within 24 hours to the study principal investigator and ethics committees. An independent Drug Safety and Monitoring Board (DSMB) monitored study safety; data from G6PDd patients was sent to the DSMB as they became available.
Sample size Sample size was based on demonstrating a difference in primaquine-related toxicity between the two G6PD groups. Such data are lacking in vivax-infected patients, so, assuming similar Hb dynamics between P. vivax and P. falciparum  [26] , we used a falciparum database of South-East Asian patients treated with artemisinin-based combinations; 374/6,882 (5. 4 %) had a fall in baseline haematocrit ≥25 % by D7. We hypothesised primaquine toxicity rates of 5 % (G6PDn) and 25 % (G6PDd). Using a power of 0. 8, a two-sided alpha of 0. 05, and a 2:1 allocation in favour of the G6PDd arm, the sample size was 92 and 46 patients, rounded up to 100 G6PDd and 50 G6PDn patients. Between ~580 and ~1,200 patients would need to be screened to recruit 100 G6PDd patients, based on reported G6PDd prevalence rates  [19].
Indeed, primaquine was continued in the two G6PDd males with fractional Hb falls >25 % on D7 because they were clinically well and had adequate Hb concentrations of ~9 g/dL. As expected, the G6PDn patients tended to have smaller declines in Hb concentrations and they tolerated weekly primaquine despite some of them experiencing drops in Hb exceeding 2 g/dL. The key to the safe use of primaquine and, in the future, tafenoquine as anti-relapse treatment to achieve P. vivax radical cure is the accurate diagnosis of G6PDd and identifying those with more severe G6PDd. Indeed, tafenoquine registration trials are excluding patients with enzyme activities <70 % of the population median (NCT02216123)  [29]. In our setting, a patient with severe G6PDd who could be misclassified as G6PDn and would thus receive the appropriate primaquine antirelapse dose of 0.
Enzyme activity U/g Hb This study had limitations. The total number of G6PDd patients was only 18, most were hemizygous males, and most had the Viangchan variant; their measured enzyme activities were low (median <1 U/g Hb), placing them at the severe end of the G6PD spectrum. Perhaps surprisingly, this study is currently the largest clinical series of primaquine-treated, vivax-infected patients with mostly severe G6PDd. Our findings are consistent with other smaller primaquine challenge studies (0. 75 mg/kg) in otherwise healthy volunteers with Mediterranean G6PDd and patients who had Hb falls of approximately 20-25 %  [12, [34] [35] [36] [37]. More safety data are needed from larger safety studies in  patients with different G6PDd variants and in heterozygous G6PDd females who represent a therapeutic challenge.
Acknowledgements We wish to express our sincere and profound thanks to the patients who took part in this study and to the nurses and laboratory staff who contributed to its successful and safe execution. We thank our DSMB who reviewed the G6PDd data in real time and provided very valuable and timely responses and advice:  Drs Toby Leslie  (Chairperson),  Chanthap Lon ,  Isabella Ribeiro  and  Professor Lucio Luzzatto. Funding  for this study was obtained by  PR  of  WHO Geneva  via a grant from  UKAid  without which this study would not have taken place. We record our profound gratitude to the taxpayers of the United Kingdom. WRJT was part supported by  France Expertise International  through the 5 % initiative as a consultant to CNM in operational research and he expresses "ses remerciements profonds au peuple français.
"JKB is supported by  Wellcome Trust  grant  B9RJIXO. DM is supported the  French Ministry of Foreign Affairs  and SKim was part funded by a grant from the  Asia Pacific Malaria Elimination Network. None of the funders had any role in study design, data collection, analysis, and interpretation, or writing of the manuscript or in the decision to submit the manuscript for publication. The views expressed in the article are those of the authors and are not to be regarded as official views of the WHO.
II. EXPERIMENT Below 0. 3 GPa, an aluminium gas cell was used with a preformed Bridgeman seal for the neutron diffraction studies of urea and deuterium. Powdered deuterated urea was placed in the gas cell. The cell was then pressurised using D 2 gas; the pressure of which was carefully controlled by an in-house control panel and Hi-Pro capstan 0. 3 GPa pump suitable for use with deuterium. The gas cell was mounted on the PEARL instrument at the ISIS neutron facility, UK. Time-of-fight (t-of) diffraction data were collected in 90 • scattering geometry at room temperature at a series of gas pressures up to 0. 3 GPa. For the higher pressure neutron diffraction experiments above 0. 3 GPa, a Paris-Edinburgh (PE) press was used to generate pressure on a mixture of deuterium and urea. Powdered urea was placed in a deuterium-proof encapsulated copper beryllium gasket (modified from the standard titaniumzirconium alloy encapsulated gasket  8  ) between a pair of standard tungsten-carbide anvils with a toroidal profile.
T alk product characteristics will enfold the 50-150 km spatial resolution and imprecision in salinity data; for example, ±0. 2 in satellite-derived salinity translates to ±10-15 µMol kg -1 in T alk. Validation of such first-order surface ocean T alk estimates will make use of growing discrete sampling databases such as the Global Data Analysis Project (GLODAP;  Key et al. , 2004). A likely contribution from satellite-informed total alkalinity products will be the identification of mixing regions and T alk :S dynamics near salinity fronts that are now poorly resolved in climatologies and/or in situ global gridded T alk products. Refining T alk estimates in these regions requires additional information to help resolve nonconservative T alk :S variation due to biochemically diverse freshwater inputs and biological perturbations (see  Friis et al.
Comprehensive quality-controlled data repositories such as the Surface Ocean CO 2 Atlas (SOCAT) and the Lamont-Doherty Earth Observatory (LDEO) databases  (Bakker et al. , 2014; Takahashi et al. , 2014)  facilitate access. The satellite products are meant to complement these detailed ground-truth data sets by providing global and regional views (hotspot areas) of the surface ocean with temporal and spatial resolutions far superior to what can be achieved with more traditional methods of collecting data via ship surveys, time-series stations, buoys, and autonomous vehicles. Moreover, the critically important estimations of air-sea exchange of CO 2 that constrain global carbon budgets depend on the air-to-sea pCO 2 disequilibrium. Air-sea flux estimates can be created by combining pCO 2 disequilibrium with satellite sea state data (often based on wind speed) to estimate CO 2 gas-exchange coefficients  (Wanninkhof, 2014, and    satellite-based predictive approaches for estimating pCO 2 and the air-sea mass flux.
SENSOR SYNERGY There are several untapped avenues where satellite data from multiple sensors and platforms may serve to improve our understanding of ocean carbonate system dynamics and acidification. For example, the recently launched NASA Orbiting Carbon Observatory-2 is now retrieving atmospheric CO 2 , thus providing a new data set for investigations of air-to-sea pCO 2 disequilibrium. New sensors will also help solve the particular problems and take advantage of opportunities presented in ocean basins that receive large freshwater runoff, largely due to spatial and temporal heterogeneity in carbonate constituent concentrations. Examples include ecosystems contained within the northwestern tropical South Atlantic and the Caribbean with Amazon and Orinoco River discharges, the Bay of Bengal, the Gulf of Mexico, and the Arctic.
g. , the Geostationary Operational Environmental Satellite [GOES] Imager collects images every 30 minutes). This capability is necessary for studying large bays and coastal oceans where physical, biological, and chemical processes react on short time scales. Enhanced frequency in coverage will yield an unparalleled number of clear-sky retrievals of ocean color compared to the one to three images per week (at best) for most sites from low Earth orbiting (LEO) sensors such as the MODIS and VIIRS sensors, or the planned Pre-Aerosol Cloud and ocean Ecology (PACE) mission. While increasing analyses of satellite data will aid in our understanding of carbonate system variability and acidification processes, many challenges remain. It is important to reiterate that, presently, information gained from satellite data is generally limited to the surface or mixed layer.
ACKNOWLEDGMENTS. We gratefully acknowledge our sponsors whose grants made this collaboration possible. The  NASA   Ocean Biology & Biogeochemistry program  (particularly  NNX14AL84G ), the  NOAA Ocean Acidification Program  and  Integrated Ocean Observing System programs, including Northeastern Regional Association of Coastal Ocean Observing Systems (NERACOOS)  grants  A002004  and  USM-GR05194-001 ,  Pathfinder ESA-STSE Ocean Acidification , and the  National Science Foundation. Background and satellite images in Figure 1 are courtesy of  NASA , except the  GOCI satellite image , which is courtesy of the  Korea Ocean Satellite Research Center  and the  SMOS satellite image , which is courtesy of the  European Space Agency. Aquarius is a joint mission shared by  NASA  and  CONAE. We appreciate the insightful critiques of  Frank Muller-Karger ,  Nick Hardman-Mountford , and one anonymous reviewer, and thank  Amy Ehntholt  and  Kristy Donahue  for valuable help.
opensciencedatacloud. org). All of these data sets represent Billions of dollars of primary research that could be reused. Access to research data in the public domain and the computational resources to analyse them, will give LMIC researchers the infrastructure to enhance world-class research that is relevant to their society. These data sets are often too large to download on high-speed connections in high income countries, let alone LMIC networks. However, distributed computing techniques, for example cloud computing, present the possibility to sidestep this and the infrastructural difficulties outlined above. By moving the analysis to the data, data storage costs can be eliminated. Limited internet connectivity can be circumvented by the use of light weight workflows. Power intermittency can be mitigated as only the connection to the computing would be dropped rather than the computing itself.
The potential impact for SME's of cloud computing in sub-Saharan Africa has already been discussed elsewhere [6] and the reasoning is similar for researchers as well. The key issue then is the training of researchers to raise the awareness and routes to exploitation of these data sets. By training individuals in data science and cloud computing, who will ultimately pass on their training, the level of understanding of this field can be built up and impact research over a comparatively short period of time. The creation of a cadre of individuals who can analyse, maintain and curate data sets will be an important skill which will also positively impact the local society as enterprises based on data analysis will expand. Teaching these topics in LMIC is not trivial and it is instructive to consider a number of case studies from a variety countries.
Software: In the first year of the school extensive use was made of a commercial tool, GeneSpring  [7] , to carry out the analysis of the data. This tool has an intuitive GUI which allowed the students to focus on the analysis of the data. A number of licenses of the software were made available during the school. However, it was clear that this tool was not used by any of the students afterwards because it was comparatively expensive. An equivalent set of tools based on R  [8]  and Bioconductor  [9]  (libraries that sit on top of R for analysing Biological data set) was used in subsequent schools, along with web-based tools such as DAVID  [10]  and another open source package called Cytoscape  [11]  for visualisation. Typically R is used via the command line but as a com-promise GUI packages (affylm GUI  [12]  and oneChannel GUI  [13]  were used.
In each case a detailed explanation of how the data was gathered, what is known about the species what they should expect or consider. On the last day of the school each team gives a short presentation on their findings. A prize is given for the winning team. Hardware, connectivity and support: In each year the school had access to standard PC's that ran Windows along with a master PC where presentations could be made. Technical support can be very variable. In some cases technical support was available and could be called upon to install software or even to make changes to the hardware, as in one year the main memory of all the PC's was extended overnight. At other times, support was close to non-existent. A key issue is connectivity. As we note below, specific Bioconductor libraries that we used caused the local Chinese network to grind to a halt.
In addition, the comparative speed of response to alternative providers can be used as a teaching tool and a mechanism for introducing remote access in preference to local software. The short-course transcriptomics practicals were normally split into two separate practical sessions using Affymetrix arrays. In Jan 2014 we also added RNAseq analysis using cloud-based resources. The first array practical was very similar to the CCC R/Bioconductor practical described above and required a local install of R followed by installation of packages for OneChannelGUI. Given our experiences with slow network delivery of large packages at UoN-SB and within CCC, we tested local networks for live download prior to the practical and found the download impractical in every case (including at UoN-SB). In order to give the students an experience as close as possible to future local installation on their own systems, we simply provided the zip file tarball of packages for local install into a fresh network delivered / installed instance of R.
This required us to pre-prepare and transport a current version of all necessary packages compatible with the R version. In most cases the negotiation of a shared drive for package distribution was operationally difficult (Windhoek, UNIM 2014) or slow (HAU, UoN-SB) but in one case the shared drive solution was ideal (UNIM 2013). In most cases we compromised to a default state of package distribution by USB. The second array practical applies commercial software, Partek  [16]  on a comparable dataset. Our short-course participants are less likely to become career bioinformaticians than the average CCC student such that their future analysis evaluation may be based on contrasting criteria. In Windhoek we performed an evaluation demonstration on a central laptop, but in Malaysia (and UoN-SB) we obtained licences for all machines (in HAU there was a Genespring demonstration by another group so we did not perform this demonstration).