dataesr/scientific-paragraphs-categorization huggingface dataset:
An AUC of 0. 83 for predicting transition is promising, but the model must be validated in similar independent CHR datasets. The predictive power of lipidomic data may be enhanced by combining these with measures of other factors that may influence clinical outcomes in CHR subjects, such as neuroimaging data, psychopathology, oxidative stress, proteomic, and inflammatory markers 43. 20 Célia Jantac 17 Marie-Odile Krebs  17  Dorte Nordholm  18  Lasse Randers  18  Kristine Krakauer  18  Louise Glenthøj  18  Birte Glenthøj  19  Merete Nordentoft  18  Stephan Ruhrmann 20 Dominika Gebhard  20  Julia Arnhold  21  Joachim Klosterkötter  21  Gabriele Sachs  22  Iris Lasser  22  Bernadette Winklbaur  22  Philippe A Delespaul  23, 24  Bart P. Rutten  23  Jim van Os  1, 23.
1 minute and a weight of 1 and with no requirement of charge state or ID and no comparison of isotope pattern; (7) Peak list row filter with a minimum of 43 peaks in a row (10% of the samples); (8) Gap filling using the same RT and m/z range gap filler algorithm with an m/z tolerance of 0. 009 m/z or 11. 0 ppm; (9) Identification of lipids using a custom database (based on UHPLC-MS/MS data using the same lipidomics protocol, with RT data, MS and MS/MS) search with an m/z tolerance of 0. 009 m/z or 10. 0 ppm and a RT tolerance of 0. 1 min. ; (10) Normalization using internal standards (PE 17:0/17:0, SM d18:1/17:0, Cer d18:1/17:0, LPC 17:0) TG 17:0/17:0/17:0 and PC 16:0/d30/18:1) for identified lipids and closest internal standard (based on RT) for the unknown lipids, followed by calculation of the concentrations based on lipid-class calibration curves; (11) Imputation of missing values by half of the row's minimum.
0 ppm and a weight of 2, RT tolerance of 0. 1 minute and a weight of 1 and with no requirement of charge state or ID and no comparison of isotope pattern; (7) Peak list row filter with a minimum of 43 peaks in a row (10% of the samples); (8) Gap filling using the same RT and m/z range gap filler algorithm with an m/z tolerance of 0. 009 m/z or 11. 0 ppm; (9) Identification of lipids using a custom database (based on UHPLC-MS/MS data using the same lipidomics protocol, with RT data, MS and MS/MS) search with an m/z tolerance of 0. 009 m/z or 10. 0 ppm and a RT tolerance of 0. 1 min. ; (10) Normalization using internal standards (PE 17:0/17:0, SM d18:1/17:0, Cer d18:1/17:0, LPC 17:0) TG 17:0/17:0/17:0 and PC 16:0/d30/18:1) for identified lipids and closest internal standard (based on RT) for the unknown lipids, followed by calculation of the concentrations based on lipid-class calibration curves; (11) Imputation of missing values by half of the row's minimum.
A minimal duration of 10 min is requested between rest and stress injections so that the generator is fully replenished. The same protocol is followed for both rest and stress PET acquisitions. PET data are acquired in list mode (to allow re-binning for gated and dynamic datasets for MBF calculations) for at least 5 min. The PET acquisition should be ready to initiate on the system when the button to start the elution of the 82 Sr/ 82 Rb generators is pressed, and this should be clearly communicated. Then, the acquisition begins as soon as activity is detected on the PET detectors. In case of [ 13 N]NH 3 , at least 5 half-lives should be left between the two studies (i. e. ≈50 min). To optimise the patient throughput it is reasonable to proceed with the acquisition of the resting study of at least another patient and then come back to the former one for the stress acquisition.
Shortened protocols for [ 13 N]NH 3 have been proposed, adjusting for residual activity of the resting injected dosage, but the effectiveness of the correction methods is not yet definitively established  [48, 49]. For [ 13 N]NH 3 , PET acquisitions are the same for the rest and the stress studies. Since patients have to be repositioned on the examination bed for the stress study, a second CT for attenuation is usually required. Tracer injection is ideally performed using an automated injection system and image acquisition is started immediately or a few seconds before the start of tracer injection. Again, clear communication between the involved personnel is mandatory. The optimal acquisition protocol is in list mode for approximately 10 min. The data will be subsequently re-binned to obtain the dynamic image sequence necessary for MBF measurement and gated images of the final tracer uptake for visual assessment and volume calculations.
Rb At the end of the acquisition, the position of the heart on PET and CT acquisitions is visually matched for attenuation correction using dedicated software. From list mode acquisitions, three different PET reconstructed datasets can be obtained for both rest and stress studies: static (non-gated) acquisitions are reconstructed with PET data acquired between 90-120 s and 5-8 min after the injection, once the blood signal is low; gated acquisitions, which are reconstructed from PET data acquired within the same time interval post injection, usually divided in 8 (the best choice to contain noise) or 16 bins (better curve); and dynamic PET acquisitions. Several time frame schemes have been proposed for dynamic PET acquisition (Table  3 ). In patients with LVEF < 30%, the reconstruction of static and gated acquisitions should be started only 150-180 s after the injection to reduce the level of residual blood signal and improve the contrast between the myocardium and the LV cavity.
Presently, most processing software packages also provide polar map displays of myocardial uptake and permit the comparison of the individual patient results with a reference database (or allow the user to create an own normal data base). It is therefore possible, and in case recommended, to integrate the quantitative measurement of MBF with the  assessment of the relative tracer distribution and with the comparison with a normal reference. Visual semi-quantitative image analysis is performed on a regional basis, using 17 segments (AHA model), and each segment is scored using a 5point scale ranging from 0 (normal perfusion), 1 (mildly reduced perfusion), 2 (moderately reduced perfusion), 3 (severely reduced perfusion), to 4 (absent perfusion). This yields a summed perfusion score for both stress and rest myocardial perfusion images.
Hybrid imaging Cardiac hybrid imaging offers the ability to combine strengths of different imaging modalities for anatomical and functional evaluation of CAD  [175] [176] [177]. Hybrid images contain two datasets that are combined into a fused image in which both modalities contribute important information. This can be achieved with a hybrid scanner or with separate imaging systems by software-based image fusion. Hybrid scanners combining PET with a multidetector CT, which can perform CT assessment of coronary anatomy, are becoming the standard for almost all commercially available devices  [175]. In addition to CT, the newest generation of scanners offers the combination of PET with MRI  [177]. CCTA has become an alternative to diagnostic ICA for the evaluation of many patients with suspected CAD.
v. β-blocker to slow down the heart rate before CCTA  [188]. Proper indications and possible contraindications, such as contrast allergy, should be reviewed separately for PET and CT studies  [176]. Analysis software that is able to handle fused images and data should be used in addition to independent analysis of anatomic and functional images. Dedicated cardiac fusion software packages are now commercially available allowing hybrid imaging with an excellent interobserver reproducibility and short processing durations  [189, 190]. These software packages, following automated/semiautomated segmentation, generate 3D reconstructions of myocardial perfusion data that are fused with 3D coronary anatomy datasets resulting in a hybrid 3D visualisation. Verification of adequate co-registration and possible manual correction is needed to avoid misalignment, because of mismatches in the respiratory phases between the two datasets, minor beat-to-beat variations in the heart position or different ventricular size in ECG-gated and non-gated datasets  [175, 176, 191].
97 vs. 0. 92). A limitation of the meta-analysis and current studies is that in many of them angiographic stenosis diameter is used as the reference standard instead of invasive FFR. The recent single-centre prospective PACIFIC trial compared hybrid imaging with stand-alone imaging in 208 patients who underwent CCTA, SPECT perfusion imaging and [ 15 O]water PET perfusion imaging and ICA combined with measurement of FFR in all arteries  [76]. In this study, the addition of functional imaging to CCTA improved specificity, but there was an increase in false negative findings that resulted in no overall incremental diagnostic benefit as compared with stand-alone imaging. However, studies comparing hybrid imaging with either stand-alone or side-by-side interpretation of the datasets have shown that interpretation was changed based on fused images in almost one-third of patients evaluated for suspected obstructive CAD  [198] [199] [200] [201].
Preliminary data suggested as well a potential value for quantitative assessments including MBF and MFR  [94, 223]. A phase 2 study, BMS747158-201, consisting of 2 cohorts, was conducted to develop and subsequently to evaluate the diagnostic performance of 1-day rest/stress PET imaging protocols in patients with known or suspected CAD. An initial cohort was used to identify the appropriate activity and timing of [ 18 F]flurpiridaz injection for a 1-day rest/stress protocol. In the second cohort, 143 patients presenting with a broad spectrum of pre-test likelihood of CAD were enrolled to evaluate the sensitivity, specificity and accuracy of [ 18 F]flurpiridaz with the optimised 1-day rest/stress protocol; ICA or 3month follow-up for cardiac events was used as the reference standard. Of the 143 subjects enrolled, 125 had an interpretable scan.
PET MPI sensitivity was 76. 9% for all readers, and specificity ranged from 74. 0 to 87. 7%. SPECT MPI sensitivity ranged from 59. 6 to 71. 2%, and specificity ranged from 76. 7 to 89. 0%  [228]. The phase 3 study BMS747158-301 (ClinicalTrials. gov Identifier: NCT01347710) was an open-label, international multicentre study for the assessment of CAD using [ 18 F] flurpiridaz PET MPI compared with SPECT MPI in patients with suspected or known CAD who were referred for ICA. Patients underwent a 1-day rest/stress protocol with ICA used as gold standard. The primary objective of this study was to assess the diagnostic efficacy (sensitivity and specificity) of [ 18 F]flurpiridaz injection PET MPI compared with SPECT MPI. A total of 755 patients had evaluable rest and stress SPECT MPI and [ 18 F]flurpiridaz PET MPI procedures as well as reference ICA.
Although statistically significant superiority in sensitivity was demonstrated for [ 18 F]flurpiridaz PET MPI over SPECT MPI across readers (p < 0. 001), the endpoint of demonstrating the non-inferiority of [ 18 F]flurpiridaz PET MPI to SPECT MPI in specificity was not met, with [ 18 F] flurpiridaz PET MPI reaching a specificity of 76. 2%, compared with SPECT MPI specificity of 86. 8%. Thus, another phase III trial (AURORA, ClinicalTrials. gov Identifier: NCT03354273) is currently ongoing, with the purpose to get the final approval for the clinical use of [ 18 F]flurpiridaz. However, a most recent retrospective evaluation on a cohort of the first phase 3 study has demonstrated that stress MBF quantified using [ 18 F]flurpiridaz and the previously validated two-tissue compartment model are able to accurately identify CAD both on a per-patient and a per-vessel basis  [94, 223, 229].
France The impact of housing hazards on health outcomes is becoming a major issue especially given the recent and on-going reviews of energy legislation in many European countries. A large body of epidemiological literature argues that fuel poverty -a frequently debated example of a housing hazard involving low indoor temperatures -is associated with heightened health risks. By using a large scale nationally representative dataset, I seek in this article to delve into this alleged association and uncover a causal relationship between fuel poverty and self-assessed health. Coming from an economics standpoint, I used a panel approach based on an instrumental fixed-effects model which allow me to analyse causality. I used both objective and subjective fuel-poverty indicators. The results show that there is a significant causal relationship between fuel poverty and self-assessed health status.
In this framework, one way to help determine which type of policy measure should be implemented to fight residential fuel poverty is to identify its consequences on health as precisely as possible. Nevertheless, analysing the impacts of residential fuel poverty and identifying transmission channels through which it can affect physical and mental health outcomes has proven to be an enormous challenge, requiring the development of new suitable research paradigms, methodologies, datasets as well as a willingness for cooperation across different academic disciplines  (Houd, 2005). Despite the considerable body of interdisciplinary literature in the fields of epidemiology, medicine, environmental health, psychology and psychiatry that have begun to tackle this issue, the contribution from the field of economics is still limited.
In an European context astonishingly characterised by the absence of specific databases to analyse the question of the relationship between fuel poverty and health, I relied on a nationally representative dataset, namely the Statistics on Resources and Living Conditions (SRCV) database -which is part of the European Union Statistics on Income and Living Conditions (EU-SILC) database-to conduct this study. Although these statistics are not specifically devoted to the analysis of the relationship between fuel poverty and health (cf. Subsection 3. 1. 1), they nonetheless offer a unique opportunity to address the causality. Thus, I aim through this study to initiate an incipient understanding of this issue and to open the door for new research prospects. To reliably deal with causality, I used an instrumental fixed-effects model based on the identification of individual-specific instruments.
Since, causality is still not unequivocally demonstrated and is still up for discussion even when experimental designs based on based on randomized controlled trial (RCT) are used to clearly identify causal paths, I aim through the use of an instrumental-variables approach and a large scale nationally representative dataset to contribute to this debate. I also aim to point-out the need for more interdisciplinary approach to tackle the more general question of the relationship between housing hazards and health outcomes. The paper unfolds as follows. In Section 2, I give a brief review of the literature on health determinants. In Section 3, I present the empirical analysis and discuss findings. Finally, in Section 4, I present the policy implications and conclude.
Housing hazards, and in particular fuel poverty, can be seen as a transmission mechanism through which income impacts health outcomes  (Kuehnle, 2014). 3 Econometric analysis I present my econometric analysis in two steps. I first present the data and summary statistics (Sub-section 3. 1). Then, I present the econometric estimations used to determine if there any correlations and causality relationships between fuel poverty and self-assessed health status (Subsection 3. 2). First, it provides a cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions. Second, it provides longitudinal data pertaining to individual-level changes over time. The cross-sectional data is collected in two stages. An early subset of variables collected via registers or interviews to assess as early as possible poverty trends.
A full set of variables provided along with the longitudinal data help to produce main key policy indicators on social cohesion. The longitudinal data aim to identify the incidence and dynamic processes of the persistence of poverty and social exclusion among subgroups in the population. For the cross-sectional and longitudinal components, all household and personal data are linkable. I used the longitudinal component of the SRCV, in particular, the waves going from 2004 to 2015 21. Given that the SRCV does not aim to address questions of fuel poverty and its relationship to health status, the quality of data on fuel poverty and health is not as good as one would hope. However, these data still provide the valuable opportunity to conduct a longitudinal study to at least approach this issue and then open new prospects for further investigations.
(2016) ,  Dubois and Meier (2016) , and Bouzarovski and Tirado Herrero (  2017 )). In particular, Thomson and Snell (2013) used the indicator "Can the household afford energy if they want it?" or equivalently "Can they keep the home adequately warm?". For health data, I used the answer to the following question in the EU-SILC database, which represents self-reported health status: -"How is your general health status?" ("Very good", "Good", "Fairly good", "Bad", "Very bad"), According to the ODPM (  2006 ), health status represents the individual's state of physical, mental and social well-being. It is not limited to the presence or absence of disease, infirmity or physical injury, but includes psychological injuries and distress 22. To be consistent with previous studies, I generated a binary indicator for the self-reported health status variable.
The variable dwelling age was used as a proxy for the energy-efficiency level of the dwelling. In particular, dwellings built before the first French thermal regulation in 1974 are assumed to be less energy efficient; For the CLIM HF R variable, I adopted the official classification of France into eight zones having different climate characteristics, i. e. F R1 to F R8 (see Table  1 ). All control variables were extracted from the EU-SILC database. By incorporating them in estimations, I help control for factors that may confound or mediate the relationship between health and fuel poverty. In this context, in addition to the impact of fuel poverty, I paid particular attention to the effect of income on self-assessed health outcomes because such effects have been extensively analysed in the literature.
Conclusion and policy recommendations In this article, I studied the causal relationship between fuel poverty and self-assessed health status by using a large scale nationally representative dataset and an instrumental fixed-effects model. I considered objective as well as subjective indicators of fuel poverty. The results show that there is a significant causal relationship between fuel poverty and self-assessed health status regardless of the fuel-poverty indicator and that this causal effect shows a lag in time. The results also show that the magnitude of causality is greater for subjective measures of fuel poverty. Throughout this study, I aimed to shed light on the need to take overall health into account when defining policy measures devoted to fighting fuel poverty in the residential sector.
In fact, because of the dynamic character of fuel poverty, there is a need for reliable estimation of scarring effects to obtain a reliable assess its health impacts. To explore these avenues of research, improvements are sorely needed in data collection to enhance the quality and quantity of data on residential fuel poverty and health. When I started working on the question of the relationship between residential fuel poverty and health, the chal-lenge was to find empirical data. Although the EU-SILC database has valuable longitudinal data, it still suffers from a dearth of data on health and housing conditions and is generally not designed to explore the relationship between fuel poverty and health. The medium-and long-term effects need to be analyzed in a more relevant way by using realistic data to monitor the fuel poverty and health conditions of households over several years and to study different populations according to age.
Ideally, this database should be interdisciplinary and combine economic, epidemiological, medical, geographical setting and location data on households' fuel poverty and health situation. The development of this type of database is needed not only on a national, but also a European level and should be developed using general harmonized rules that can be adapted to national specificities to allow for international comparisons and feedback. A Summary statistics of central variables -Self-assessed health status and fuel poverty The EU-SILC database does not provide data on individual-specific energy prices. We need to calculate these prices by merging data from three databases namely EU-SILC, PHEBUS 33 , and PEGASE 34. To do so, we considered two crucial points. First, the energy tariff in France depends on the power needed for space and water heating, appliances, lighting and cooking, etc.
This power itself depends on the structure of the energy mix in the dwelling (share of gas and electricity) and the size of the dwelling (surface area). For instance, the electricity tariff is not the same for a dwelling using gas for heating and a dwelling using electricity for a given surface area. Such information on tariffs is available in PHEBUS database. Second, we needed to associate the energy tariff (divided by the price of the base fee and the unit cost of kWh) with each dwelling (household), which depends on its surface area and its energy mix. For each household, we must determine an electricity tariff and a gas tariff. From a practical point of view, to include energy prices in the EU-SILC database, we used the three following steps: -First, we split the EU-SILC database into categories according to the surface of the dwelling (10 classes), the share of electricity expenditures (10 classes from 0% to 100%) and the share of gas expenditures (10 classes from 0% to 100% ).
-Second, we split the PHEBUS database into the same categories. Because for each category of household, the tariff for electricity and gas is given in PHEBUS database, we incorporated this information (tariffs) into the EU-SILC database. This step let us attribute an electricity and gas tariff for each housing unit in the EU-SILC database. -Finally, we used information provided in the PEGASE database to assign to each energy tariff in the EU-SILC database an energy price covering base fees and consumption. More details about the merging process can be found in  Charlier and Kahouli (2019). 33. The "PHEBUS" database ("Performance de l'Habitat, Equipements, Besoins et Usages de l'énergie") is especially devoted to the in-depth analysis of the fuel poverty issue in France. This database was compiled from April to October 2013 by the Ministry of Ecology, Sustainable Development and Energy ("Ministère de l'Ecologie, du Développement durable et de l'Energie"; MEDDE)), the General Commission for Sustainable Development ("Commissariat Général au Développement Durable" (CGDD)), and the Department of Observation and Statistics ("Service de l'observation et des statistiques"; SOeS).
It has two parts: (1) a face-to-face interview with the occupants of the home about their energy consumption, expenditures and attitudes and (2) an energy-efficiency diagnosis of the dwelling. In particular, "PHEBUS" contains information describing the household, i. e. the amount of energy expenditures, attitudes toward energy consumption, disposable income, age, etc. , and dwelling characteristics, i. e. surface, type of heating system, level of energy efficiency, etc. Therefore, it can study households' energy consumption in detail and the associated question of fuel poverty. The "PHEBUS" database covers the year 2013. 34. The PEGASE database ("Petrole, Electricite, Gaz et Autres Statistiques de l'Energie") stores and distributes French energy statistics collected by the Department of Observation and Statistics ("Service de l'Observation et des Statistiques"; SOeS)).