dataesr/scientific-paragraphs-categorization huggingface dataset:
Only these bands are required for the following calculations of NBR and RBR. The most commonly used metrics for burned area and burn severity mapping, derived from satellite data, is the normalized burn ratio (NBR) (Equation  2 ). Healthy vegetation has very high near-infrared reflectance and low reflectance in the shortwave infrared portion of the spectrum. Burned areas on the other hand have relatively low reflectance in the nearinfrared and high reflectance in the shortwave infrared band. A high NBR value generally indicates healthy vegetation while a low value indicates bare ground and recently burned areas. After the calculation of NBR pre-and post-fire it was appropriate to merge all the preand post-fire bands and products into a single database (Collocation) (Fig. 6 ). With this merged database of the pre-processed products were calculated the changes in the pre-and post-fire NBR values.
RESULTS AND DISCUSSIONS The delimitation and extraction of the two areas affected by forest fires with the calculation of the RBR index gave different results compared to Copernicus EMS maps. The size of the burned area according to the calculation process of this study in the case of Kineta is 3511 hectares, presenting the following distribution of burn severity: Low Severity: 493 ha (14%), Moderate-low Severity: 1483 ha (42%), Moderate-high Severity: 1370 ha (39%), High Severity: 164 ha (5%) (Fig. 7 ). On the other hand, the size of the affected area in the case of Mati is 942 hectares, presenting the following distribution of burn severity: Low Severity: 76 ha (8%), Moderate-low Severity: 369 ha (39%), Moderatehigh Severity: 271 ha (29%), High Severity: 226 (24%) (Fig. 8 ). EMS data (emergency maps and vector packages) estimates the size of the burned area in Kineta at 5613 hectares and in Mati at 1275 hectares (Copernicus EMS, 2018).
Procedure The solution comprises a set of several minimally invasive sensors installed in the individual's home (see Table  1  and Figure  2 ). The solution transmits the data to a remote storage server via a LIVE Intercom gateway (a commercially available device). Data are then available for remote consultation by authorized users, that is, patients, care partners, or physicians. The LIVE Intercom also allows direct audio communication with the support center (call function) and preliminary data processing before 3G transmission to the remote secure health database. The minimal sensor set consists of 4 passive infrared (PIR) sensors to monitor activity (bedroom, kitchen, living room, and entrance hall) and 1 contact sensor on the main entrance door. A sensor on the refrigerator door, a pendant (or bracelet) with a push-button emergency alarm, and a detector under the bed will be available to several participants to assess the technical feasibility.
The sensor-based alerts and the use of the Live intercom calling function are both directed to a telecare worker on a nationwide telecare platform (IMA/Serena) all 7 days a week and 24 hours a day. The telecare worker can suggest a range of actions, from direct contact with the older individual to a phone call to the care partner or to emergency services. The first month is dedicated to algorithm learning: all sensors are functional, but no automatic alert is generated from the platform. Activity data relating to sleeping, eating, and time spent outdoors, and so on, are recorded and analyzed. After the first month, there will be sufficient data to detect unusual behaviors and eventfully trigger an alert. The algorithm used to perform real-time detection of behavioral anomalies is described elsewhere  [14].
Follow-Up Procedure The first 5 participants are enrolled for a total of 10 months (an initial 4-month period followed by a 6-month follow-up period) and the other 20 participants for 6 months. An installation visit will take place in the days following enrollment. A remote follow-up evaluation will be carried out over the phone by a CRA every 2 months after the baseline assessment (see Table  2 ). The last visit will be conducted by the CRA in the participant's home after 6 months. For the first 5 participants enrolled in the study, the baseline for data analysis is defined as the beginning of the 6-month follow-up period at the end of the first preliminary 4-month period which is dedicated to technical feasibility. The proper regulatory and ethical conduct of the study is monitored by a clinical research technician, acting on behalf of the University Hospital.
All major medical events, defined as any event resulting in in-home physician or paramedic intervention, or a call to the emergency services, are collected retrospectively by the CRA every 2 months (by phone). Sociodemographic and health data are collected at baseline. The participants' cognitive and functional parameters are assessed at baseline and at the end of follow-up: MMSE (ranging from 0 to 30; the greater the score, the greater the global cognition)  [15] ; ADL (ranging from 0 to 6, the greater the score; the greater the functional autonomy in daily life)  [13] ; Instrumental activities of daily living (ranging from 0 to 8; the greater the score, the greater the functional autonomy in daily life, eg, ability to use the telephone)  [16] ; Cardiovascular Health Study frailty index criteria (unintentional weight loss, self-reported exhaustion, weakness, slow walking speed, and low physical activity)  [12] ; Short Physical Performance Battery (consisting of a balance test, a 3-m walking test, and a 5 chair-rises test; score ranging from 0 to 12 with 12 indicating the highest degree of functioning)  [17].
Acknowledgments The authors wish to extend their thanks to the following companies, namely,  Telemedecine Technologies ,  Orange ,  Telegrafik , and  Serena  who developed the solution and provide assistance during the trial. This study is funded by a  European Union  ( European Regional Development Fund)  and a  French South-West Regional Administrative grant  through the EASYNOV-SILVER JMIR Res Protoc 2019 | vol. 8 | iss. 11 | e14245 | p. 6 http://www. researchprotocols. org/2019/11/e14245/(page number not for citation purposes) Piau et al  JMIR RESEARCH PROTOCOLS XSL • FO RenderX ECONOMY Program  (Project Number  EASYNOV-SILVER ECONOMY 201-15060378 ). The funder has not played any role in designing this study, implementing the trial, or analyzing and interpreting trial data and publishing research.
6C ; P < 10 -5 , Fisher test). RNA-seq and ChIP-seq for RNA Pol II revealed an increased transcription at H3K9-hyperacetylated distal regions in TRIM28 -/-, compared to WT CD4 + T cells (SI Appendix, Fig. S6B ). This is exemplified at the TNFRII, Snai3/Rnf166, and NRP1 loci, which showed increased H3K9ac signals at a distal, regulatory upstream regions (E) that also correlated with decrease of the H3K9me3 signal (Fig. 6D  and SI Appendix, Fig. S6C ). Reanalysis of a published ChIP-seq dataset using TRIM28-specific antibodies on thymocytes revealed significant enrichment of TRIM28 peaks around transcriptionally deregulated genes (SI Appendix, Fig. S6D ). We further confirmed the presence of TRIM28 at transcriptionally deregulated genes using TRIM28-specific ChIP-PCR and primers designed for TRIM28 peaks identified in SI6C, negative and published positive controls (SI Appendix, Fig.
It is unclear at this point which genes are direct targets of TRIM28-HP1β/γ regulation, and which genes are overexpressed as a consequence of indirect effects, as TRIM28 ChIP-seq proved to be irreproducible (in our hands) and unconvincing (in published datasets). It is also unclear whether the defective glycolytic switch is due to deregulation of a complex combination of genes, or to one individual gene among this list. While decreased H3K9me3 levels at some gene regulatory regions were decreased in TRIM28 -/- cells, neither TRIM28 nor HP1 possess H3K9 trimethylation activity, suggesting that the TRIM28-HP1β/γ complex recruits a histone-methyltransferase (HMT) of H3K9. HP1 proteins can recruit the HMTs Suv39h1 or Suv39h2, while TRIM28 can recruit another H3K9 HMT, SETDB1  (33) (34) (35). Our preliminary results indicate that neither Suv39h1-nor Suv39h2-defective T cells phenocopy the observed defect in effector T cell differentiation or the increased expression of the Treg signature.
For qPCR, 200 ng of RNA was transcribed into cDNA using reverse transcriptase (Thermo Fisher Scientific) according to the manufacturer's instructions. qPCR was performed using 5 ng of template cDNA, H 2 O, Sybr Green (Roche), and specific primers on a LightCycler 480 (Roche) (45 cycles, 60 °C annealing temperature). The following primer sequences were used  (9, 42, 43). Chromatin Immunoprecipitation. Chromatin immunoprecipitation was performed similar as previously published  (9). Data is deposited in the GEO database (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE140448). CRISPR/Cas9 Deletion in Naive CD4 T Cells. CRISPR/Cas9-based deletion of NRP1 and Snai3 in naive CD4 T cells was performed as described in ref. 44. Transduction and Protein Overexpression in CD4 T Cells. T cell transduction protocol was performed similar to Singh et al.
(45). Cytokine production was measured 2 d after transduction. See SI Appendix for detailed information. Statistical Analysis. Student's t test and one-way ANOVA were used for normally distributed data, while Mann-Whitney U or Kruskal-Wallis tests were used for not normally distributed data. For paired samples (e. g. , data from mixed BM chimera), paired t test or Wilcoxon test was used, depending on the type of distribution. Data from kinetic experiments were analyzed using 2-way ANOVA using Bonferroni's multiple-test correction. *P < 0. 05, **P < 0. 01, ***P < 0. 001, and ****P < 0. 0001. Statistical analysis was performed using GraphPad software (GraphPad Software).
2. 3. Reconstruction of Regional Trends in Fire and Vegetation during the Holocene (ca. 10,000 cal yr BP-Present) Charcoal and pollen data publicly available in the Global Charcoal Database (www. paleofire. org;  [21, 27] ) and the European Pollen Database (www. europeanpollendatabase. net;  [28] ), respectively, were used to estimate long-term trends in fire and vegetation. Reconstructions draw on well-dated, high-resolution time series. Only the 334 sites whose chronological control and resolution are equal or higher than the median of all records in the databases are included in this study (i. e. , at least one radiocarbon sample per 3000 years, and at least one charcoal sample every 56 years, or one or more pollen samples every 290 years) (Figure  1 ; Table  S1 ). Past fire activity was inferred from trends in the accumulation of micro-or macro-charcoal particles in lake-sediment cores (<125 and ≥125 µm;  [29] ).
We applied constrained cluster analysis (CONISS;  [43] ) to a Manhattan-distance matrix computed from the estimated regional trends in fire, vegetation and anthropogenic impact to define stages of socio-environmental development. The optimal number of stages was determined with a broken-stick model  [44]. To aid in the description of the dataset, the onset of the decline of forest cover was constrained through break-point detection. The optimal number and location of break-points was estimated with the Cross-Entropy method, which is a model based on stochastic optimization  [45]. The algorithm was implemented by simulating the location of break-points on sets of 200 samples (M = 200). A subset comprising 5% of all samples was used to obtain a best performing set of sample solutions (ρ = 0.
Reconstruction of Sub-Regional Trends in Ecosystem Dynamics during the Holocene Defining ecological sub-regions is a non-trivial task that involves converting a four-dimensional continuum defined by latitude, longitude, elevation and time to discrete units. To tackle this objective, we used a classification tree (i. e. , a non-parametric technique employed in the partitioning of large datasets)  [46]  to recursively divide the space defined by the pollen samples from all sites into homogeneous vegetation zones. In particular, we modeled woody taxa pollen percentages from all samples as a function of latitude, longitude and elevation. Given that classification trees tend to overfit, the preliminary trees were cross-validated and 'pruned' to allow the generalization of the results to independent pollen datasets  [47].
Although vegetation reconstructions and models agree on the overall deforestation trend, they differ in the rates at which it occurred. The pollen-based reconstruction suggests a steady decline in woody biomass between ca. 7000 and 3000 cal yr BP, followed by a millennium of rapid loss of trees, and slower deforestation rates coupled with shrub encroachment in the last ca. 2000 years. Conversely, the KK10-derived scenario indicates relatively slow forest loss prior to ca. 3000, and very high rates thereafter, with the exception of around a 500-year period of stability centered at 1000 cal yr BP. Uncertainty propagation in the merging of datasets (i. e. , uncertainties inherent to the reconstruction and interpretation of environmental conditions, population size and timing of events) could explain the discrepancy.
Inspection of charcoal records, woody-to-herbaceous taxa ratios and anthropogenic indices, reveals that all sub-regions featured sustained an increase in human impact and forest loss over time (Figures  3 4 5 6 ). (A) Regional biomass burning as estimated by a GAM applied to the charcoal data from the sub-region. (B) Vegetation openness inferred from woody-to-herbaceous pollen taxa. The relative proportion of conifers, broad-leaved trees and shrubs is shown. (C) Anthropogenic index as a function of time. The pink shading represents the total anthropogenic index-to-ruderal taxa ratio. The arrow shows the onset of pronounced deforestation depicted in Figure  7. In all cases, the black shading depicts 95% confidence intervals for the estimated trends. (A) Regional biomass burning as estimated by a GAM applied to the charcoal data from the sub-region (B) Vegetation openness inferred from woody-to-herbaceous pollen taxa.
The relative proportion of conifers, broad-leaved trees and shrubs is shown. (C) Anthropogenic index as a function of time. The pink shading represents the total anthropogenic index-to-ruderal taxa ratio. The arrow shows the onset of pronounced deforestation depicted in Figure  7. In all cases, the black shading depicts 95% confidence intervals for the estimated trends. (A) Regional biomass burning as estimated by a GAM applied to the charcoal data from the sub-region (B) Vegetation openness inferred from woody-to-herbaceous pollen taxa. The relative proportion of conifers, broad-leaved trees and shrubs is shown. (C) Anthropogenic index as a function of time. The pink shading represents the total anthropogenic index-to-ruderal taxa ratio. The arrow shows the onset of pronounced deforestation depicted in Figure  7.
In all cases, the black shading depicts 95% confidence intervals for the estimated trends. (A) Regional biomass burning as estimated by a GAM applied to the charcoal data from the sub-region (B) Vegetation openness inferred from woody-to-herbaceous pollen taxa. The relative proportion of conifers, broad-leaved trees and shrubs is shown. (C) Anthropogenic index as a function of time. • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Conifers Broad- The pink shading represents the total anthropogenic index-to-ruderal taxa ratio.
Figure6. Holocene trends in fire, vegetation and anthropogenic impact in the Southern Mediterranean. (A) Regional biomass burning as estimated by a GAM applied to the charcoal data from the sub-region (B) Vegetation openness inferred from woody-to-herbaceous pollen taxa. The relative proportion of conifers, broad-leaved trees and shrubs is shown. (C) Anthropogenic index as a function of time. The pink shading represents the total anthropogenic index-to-ruderal taxa ratio. The arrow shows the onset of pronounced deforestation depicted in Figure7. In all cases, the black shading depicts 95% confidence intervals for the estimated trends.
10, 11 In contrast to NC100668, ENC2015 is labelled with the PET radioisotope fluorine-18 thus allowing for clinical imaging datasets with higher resolution and improved quantification compared with SPECT. Furthermore, ENC2015 was developed as both an optical near-infrared fluorescent probe and an  18  F positron-emitting radiotracer to image thrombosis in vivo. A dual optical-PET labelled probe would hold great promise for in vivo diagnosis and histological tissue validation with a single chemical moiety. Moreover, ENC2015 offers the potential to allow identification of disease before clinical manifestation, track thrombus activity over time, and monitor therapeutic efficacy. Here, we report our pre-clinical assessment of ENC2015 in an ex vivo model of human thrombosis and an in vivo rodent carotid thrombosis model.
6 ± 0. 5 MBq/mL, n = 10); (ii) infusion of Cy5-ENC2015 (250 nM, n = 6) or  18  F-ENC2015 (5. 5 ± 0. 9 MBq/mL, n = 10) plus factor XIIIa inhibitor (Iodoacetamide, Sigma-Aldrich, UK) at a concentration 10 mg/mL; and (iii) blood control without infusion of optical or radiolabelled tracer (n = 6). For the optical probe, further control experimental groups included: (iv) the administration of ENC2015 in the absence of blood, (v) the infusion of free Cy5 fluorophore, and (vi) the infusion of a scrambled peptide sequence (Supplementary data online, Figure  S2B ) in the presence of thrombus. In one volunteer, we collected blood through the chamber for HPLC radiometabolite analysis to assess tracer stability in ex vivo human blood at 0 and 8 h after radiotracer incubation. Furthermore, paraffin-embedded cross-sections of ex vivo human thrombi (n = 3) were prepared for autoradiographic experiments performed in quadruplicate; (i)  18  F-ENC2015; (ii)  18  F-ENC2015 co-incubated with homologous unlabelled 'cold' ENC2015 (5 lM); (iii)  18  F-ENC2015 co-incubated with heterologous scrambled tracer (5 lM); and (iv)  18  F-ENC2015 co-incubated with iodoacetamide (5 lM), a non-selective irreversible inhibitor of all cysteine peptidases, which in this experimental design works as negative control.
A CT scan (semi-circular full trajectory, maximum field of view, 480 projections, 50 kVp, 300 ms, and 1:4 binning) was acquired at the end of each emission scan to correct for attenuation and allow PET and CT image coregistration. All PET images were reconstructed using Mediso's iterative Tera-Tomo 3D reconstruction algorithm and the following settings: four iterations, six subsets, full detector model, low regularization, spike filter on, voxel size 0. 4 mm and 400-600 keV energy window. PET data were corrected for random coincidences, scatter, and attenuation.
405 software (PMOD Technologies, Switzerland). Equally sized volumes of interest (VOIs) were drawn over the thrombus and uptake quantified as kBq/mL (Figure  2B ). To account for variation in infused radioactivity between experiments tracer uptake was normalized by dividing the total uptake within the VOI by the total infused activity in MBq to give kBq/mL/MBq. Autoradiographic images of human and rodent thrombus sections were exported into Fiji software  17  where equally sized region of interest's were drawn on serial sections across each treatment group and greyscale pixel intensity was calculated and compared between groups. Percentage specific binding (%SB) was calculated by subtracting measured activity in tissue sections incubated with different inhibitors (non-labelled ENC2015, scrambled peptide and iodoacetamide) from sections exposed to  18  F-ENC2015 only.
, 2010). Irrespective of whether the intended purpose is to facilitate tinnitus subtyping or profiling, a number of data repositories have been created by individual clinics or research centres such as the Tinnitus Data Registry (Oregon Hearing Research Centre) and the Swedish Tinnitus Outreach Project (Karolinska Institutet). Multinational, multi-centre data repositories for tinnitus would provide greater leverage to addressing these scientific issues, but they are rare. The Tinnitus Research Initiative (TRI) database is unique in that it includes data from many centres around the world, collected according to a standardised protocol  (Landgrebe et al. , 2010). Within the TRI database project, the Tinnitus Sample Case History Questionnaire (TSCHQ) is an important instrument, used for the standardised collection of information regarding patient demographics, tinnitus history, tinnitus characteristics and modulating factors, and comorbidities.
The items in the TSCHQ are based on 'a considerable variety of case history questionnaires' and were developed as part of a consensus for tinnitus patient assessment  (Langguth et al. , 2007, p. 529). The TSCHQ is the mainstay of subtyping analyses using the TRI database  (Kreuzer et al. , 2012; Vielsmeier et al. , 2012 Vielsmeier et al. , , 2015;; Schecklmann et al. , 2014; Langguth et al. , 2017a Langguth et al. , , 2017b) )  and has even been adopted for independent studies not involving the TRI database project  (Kojima et al. , 2017; Ward et al. , 2015). It has therefore been a valuable tool to the tinnitus research community. However, it includes only a few questions about co-existing medical conditions and is, therefore, not suitable for acquiring information about the tinnitus-relevant medical history (e.
g. presence of otological or tinnitus-related comorbid systemic diseases, history of medical treatments that can cause tinnitus). Inspired by preceding projects and the importance of big-data analysis for profiling tinnitus, a new tinnitus-oriented database has been developed, namely the European School for Interdisciplinary Tinnitus Research (ESIT) database  (Schlee et al. , 2017; European School for Interdisciplinary Tinnitus Research, 2018). One of the main objectives of the ESIT project is to contribute to our understanding of tinnitus heterogeneity and to create a tinnitus profiling framework. Towards this aim, data from the population will be collected using standardised measures across the different projects in order to create a large multinational database. The ESIT Screening Questionnaire (ESIT-SQ) is a self-reported tinnitus-relevant history questionnaire for standardised collection of information from both tinnitus and non-tinnitus populations that has been created for this purpose.