dataesr/scientific-paragraphs-categorization huggingface dataset:
Constrained by a lack of nationwide validated data on pollutant concentrations attributable to wildfire smoke, many previous studies on the impact of wildfire smoke on health outcomes have been focusing on episodes with high wildfire smoke exposure (or smoke wave) using binary measures of smoke concentrations  (22). Recently, a machine learning model was developed to estimate daily wildfire smoke PM2. 5 concentrations for the contiguous U. S. , using a combination of meteorological factors, fire variables, aerosol measurements, and land use and elevation data  (23). This new, high-resolution wildfire smoke PM2. 5 dataset (10×10 km 2 ) enabled us to further examine the impact of wildfire smoke over a full range -from the more common, low-level smoke concentrations to the increasingly frequent extremely high concentrations.
In conclusion, our study estimated the detrimental effects of wildfire smoke PM2. 5 on both physical and mental health in the U. S. With wildfire intensity and frequency anticipated to increase in the future driven by climate change (2), more effective wildfire preparedness and mitigation strategies are urgently needed. Public health responses that protect people against wildfire smoke pollution is crucial both in and outside the areas where the wildfires occur. We extracted SPEI data at one-month timescale with a 0. 5 degrees spatial resolution for each county from the Global SPEI database  (51). Based on the severity of drought, we classified each month of each county into no drought (SPEI ≥ -0. 4), mild drought (-1. 2 ≤ SPEI ≤ -0. 5), and severe drought (SPEI ≤ -1. 3)  (50, 52).
Acknowledgments  Dr. E. Zang  received support from the  National Institute on Aging  ( R21AG074238-01 ), the  National Institute on Minority Health and Health Disparities  ( 1R01MD017298-01 ), the  Research Education Core of the Claude D. Pepper Older Americans Independence Center at Yale School of Medicine  ( P30AG021342 ), and the  Institution for Social and Policy Studies at Yale University. Dr. Y. Liu  received support from the  National Institute of Environmental Health Sciences  ( 1R01ES034175 ).
Data analysis We produced two sets of data: the results of the nominal group process, including the voting and prioritisation; and recordings of the discussion of these findings held at subsequent meetings. The first was the series of ideas, ideas grouping and ranking results for each question. The second was a series of discussions to clarify the prioritised ideas and to reach consensus statements based on these. This data was analysed concurrently to data collection. The set of ideas described in 'round robin' stages were transcribed independently by two researchers (M. H. and Y. A. T. H. ), and one team member (Y. A. T. H. ) transcribed and thematically analysed the final discussions. F I G U R E 1 Adapted nominal group technique process. Votes were quantitatively analysed according to McMillan 9,15 and Foth  16  ; we found relative homogeneity in our patient and professional groups, who were asked similar questions but from different perspectives.
After decades of research, discussions related to the link between political events and monetary policy have been ongoing. The purpose of this study is to examine whether electorally induced cycles in monetary policy exist. To achieve this, a unique panel dataset comprising 110 countries over 32 periods  was constructed, incorporating election periods and political regimes. This study provides evidence that elections influence monetary policy in developed and developing countries. Specifically, the study reveals that the growth of monetary mass (measured as the growth rate of M1) is significantly higher during pre-electoral periods. On average, the growth of monetary mass is between 1. 1% and 2% higher during the 12 months prior to a national election. Furthermore, the study conducts an extensive analysis on the type of institutional frameworks that may mitigate these political monetary cycles.
Notable examples include Argentina in 2010  (Vuletin and Zhu, 2011) , post-Brexit England and India in late 2018  (Jones and Matthijs, 2019)  or the former Ukrainian central banker, Yakiv Smoliy who resigned in July 2020 due to ''systematic political pressure''. 3 According to  Binder's (2021)  dataset on the political pressures faced by central banks, from 2010 to 2018, 39% of 118 central banks have faced political pressures. These observations highlight the notable interactions between political phenomena and monetary institutions, despite high levels of de jure independence  (Alesina and Stella, 2010; Fernández-Albertos, 2015; Lockwood, 2016; Binder and Spindel, 2017). The H. Oriola literature has provided several explanations. First, the 2007-2008 financial crisis resulted in a curtailment of political independence for central banks  (Balls et al.
This index provides precise measurement of electoral periods based on the exact date of elections. Second, to overcome the limitation of studies biased towards a limited number of countries  (Franzese, 2002a) , an extensive data collection is conducted, resulting in a unique dataset composed of 110 countries from 1985 to 2016. 5 Moreover, this comprehensive database allows for an in-depth investigation of political and institutional characteristics that may influence PMCs. In a nutshell, PMCs are observed when electoral timing is exogenous, the incumbent is running for his/her own re-election, the country is a presidential regime, or the incumbent does not originate from an identified leftwing party. Additionally, more competitive elections and older central banks tend to dampen electoral fluctuations within monetary policy.
Data The database is composed of 110 countries over 32 periods. The selection of countries in the sample was guided by on several criteria. First, only countries with available data for at least 10 consecutive periods were included. Second, countries with less than two elections over the sample period were excluded. Third, the database was restrained the mentioned period  to mitigate potential biases due to an excessively imbalanced sample. 8 M1_Growth is the main dependent variable. It represents the orientation of monetary policy and is measured as the percentage change in the growth rate of monetary mass. 9 Several control variables are introduced in the regressions. These include CBIE an index of de jure independence of central banks; Ygap, a proxy of the output gap; Inflation, the first difference of the consumer price index; Credit, the credit to GDP ratio and Fix, a dummy variable that takes the value 1 if a fixed exchange rate regime is implemented in a given country.
Orientation of monetary policy As the sample consists of a large number of developing countries, the main variable of interest is the annual growth rate of monetary mass, measured as M1. Specifically, an increase (decrease) in monetary mass is considered an illustration of an expansive (restrictive) monetary policy. The use of the growth of M1 (M1_Growth) as a proxy for the orientation of monetary policy is motivated by several arguments: (i) the limited availability of data on monetary base and policy rates; (ii) low levels of financial development in developing countries resulting in a close gap between M0 and M1 and (iii) the will to ensure comparability with existing literature that study large databases  (Alpanda and Honig, 2009). Although, the growth rate of M0 or the policy rate provide a closer measure of the real orientation of monetary policy compared to M1, these variables would limit the number of countries in the sample.
10  The International Financial Statistics database, developed by the International Monetary Fund (IMF) provides observations used to compute M0_Growth, M1_Growth and M2_Growth. Information on the main interest rate targeted by central banks (𝛥PR) comes from Monetary and Financial Statistics, also provided by the IMF. Furthermore, it is crucial to emphasize that a period of high money growth can weaken the popularity of the incumbent. Indeed, when inflation rates soar, it leads voters to hold the incumbent responsible for the economic instability  (Ashworth, 2012). Consequently, to avoid punishment by the voters, the incumbent may be motivated to call for early elections before the economic situation is out of control  (Smith, 2004). Therefore, extremely high growth rates of M1 could introduce bias in estimations due to In addition, the first difference of the policy rate implemented by central banks (𝛥PR) is used as dependent variable.
This variable takes a value of 1 in year 𝑡 if an election occurs in the first half of year 𝑡. Similarly, if an election takes place in the second half of year 𝑡, the variable takes a value of 1 in 𝑡+1. On the other hand, while the variables Election and ElectionDpre are basic dummies, they may not provide the most relevant measurement of pre-electoral periods. To address this limitation a more refined approach is adopted, following the methodology developed by  Franzese (2000). His approach is based on the computation of an index, called ElectionIpre which takes into account the precise date of an election within yearly database. The ElectionIpre index is specifically designed to capture a 12-month pre-electoral period. More precisely, if an election is takes place in year 𝑡, during month 𝑀 on a specific day 𝑑, the index takes the following value: ElectionIpre 𝑖,𝑡 = (𝑀 -1) + 𝑑∕𝐷 12 ElectionIpre 𝑖,𝑡-1 = 12 -(𝑀 -1) -𝑑∕𝐷 12 where 𝐷 denotes the total number of days contained in month 𝑀.
63 and especially footnote 42). 15 Hence, ElectionIpre is considered equal to 1 when its value exceeds 1. Second, two or more elections may occur within the same year. To address this issue, the study considers only the last election that takes within the period. Third, several countries experienced national elections in the year after (before) their last (first) occurrence within the dataset. In such cases, the study computes a value for ElectionIpre (ElectionIpost ) in the last (first) year observed in the panel, even if the election is held ''out of sample''. 16 Therefore, computating these electoral indexes requires an extensive data collection. Unfortunately, no single database contains information on the exact election dates of every country worldwide. As a result, data from multiple sources were combined, including the Database of Political Institutions (DPI) developed by  Beck et al.
(2001)   Hyde and Marinov (2012)  and the Free and Fair Elections dataset computed by  Bishop and Hoeffler (2016). Although these data were supplemented with the extensive data collection efforts conducted by  Nohlen et al. (1999 Nohlen et al. ( , 2001a Nohlen et al. ( , 2001b Nohlen et al. ( , 2005a Nohlen et al. ( , 2005b Nohlen et al. ( , 2010)). In addition to the aforementioned electoral measures, a variable PreElection has been computed. It is a dummy equal to 1 when ElectionIpre is greater than 0. Similarly, another dummy called PostElection is assigned a value of 1 when ElectionIpost is different from 0. Table  2  uses these variables to distinguish between pre-electoral and non pre-electoral periods, as well as post-electoral and non post-electoral periods, within the sample. Moreover, PreElection is used as the treatment variable in the average treatment effect (ATE) methodology employed in Section 3.
Consequently, the implementation of a fixed exchange rate regime no longer prevent the appearance of PMCs, as both CBI and capital mobility are weakened by the rise of nationalism and populism. Moreover, the relationship between economic growth and the adoption of a fixed exchange rate regime is intricately linked to the level of financial development  (Aghion et al. , 2009; Slavtcheva, 2015). Countries with limited financial development tend to experience positive effect on economic growth when adopting a fixed exchange rate regime. Given that the dataset is mainly composed of developing countries with relatively low financial development, the adoption of a fixed exchange rate regime may result in higher average economic growth rates. In response to the increased money demand stemming from economic growth, central banks must increase their money supply (i.
e. M0, M1 and M2). These arguments collectively explain the observed positive coefficient for the Fix variable in the estimations. To take into account the exchange rate regime, the variable Fix is introduced within the model. It represents a dummy that takes a value of 1 if the country is classified as a de facto fixed exchange rate regime, and 0 otherwise. The use of a de facto classification is preferred over a de jure classification due to concerns regarding potential biases in countries' declarations on their exchange rate regime  (Obstfeld and Rogoff, 1995; Calvo and Reinhart, 2002). Information on the de facto exchange rate regime is obtained through the dataset developed by  Levy-Yeyati and Sturzenegger (2016). 23 When a country is classified as a fixed exchange rate regime according to Levy-Yeyati and Sturzenegger's (2016) classification, the dummy Fix takes the value 1; otherwise it is equal to 0.
22 More precisely, as stated by  Rey (2015) , CBI is only achievable if capital mobility is managed, whether the exchange rate is flexible or fixed. 23 For missing values, the variable has been completed using the work of  Ilzetzki et al. (2017). 24 More precisely, every observation coded as Fix † Fix 2, Fix 3 or Fix U in Levy-Yeyati and Sturzenegger's (2016) dataset is considered as a fixed exchange rate regime. For more information see Appendix C in Levy-Yeyati and Sturzenegger (2016) (pp. 24-26). In addition, countries for which  Ilzetzki et al. (2017)  work is used are considered as fixed exchange rate regimes if the variable Change is equal to 1, 2 or 3. H. Oriola de jure measure called Fix_[de jure]. 25 Moreover, estimations of the main model are conducted only on countries characterized by a floating exchange rate regime in Table  A.
Output gap and inflation The inclusion of output gap and inflation rate in the model serves primarily to control for cyclical variations in the economic context. While it is true that a significant number of countries in the sample not follow the Taylor rule  (Carporale et al. , 2018) , monetary policy is still heavily influenced by the overall macroeconomic conditions in both developed and developing countries. Furthermore, introducing the output gap into the model remains essential to control for potential endogenous election timing, 26 and the significant number of countries that have adopted inflation targeting. The variable Ygap represents the log-difference between real GDP and its trend component obtained through the use of a Hamilton (2018) filter. Data on real GDP and inflation are provided by the WDI dataset.
Summary statistics Table  1  presents summary statistics for each variable used in the different specifications. As mentioned earlier, the sample is composed of 110 countries over the period 1985-2016, resulting in a total of 2099 observations. The summary statistics encompass the available number of observations in the database. To ensure that the analysis avoids economic situations directly influencing election timing, extreme values of M1_Growth and Inflation were excluded from the sample as discussed in Section 3. 1. 2. Specifically, 24 observations that were outliers based on these two variables were excluded from the sample. This adjustment leads to M1_Growth and Inflation values ranging between -75% and 75%. It is important to mention that this threshold may be more constraining; however, more limited values could have necessitated the exclusion of additional countries from the sample.
Mean differences Table  2  presents the summary statistics of the six main variables divided into different sub-samples. The upper part of the table separates the sample into pre-electoral periods (when 𝐸𝑙𝑒𝑐𝑡𝑖𝑜𝑛𝐼𝑝𝑟𝑒 > 0) and non pre-electoral periods (𝐸𝑙𝑒𝑐𝑡𝑖𝑜𝑛𝐼𝑝𝑟𝑒 = 0). The last column of the table computes the difference in means between these sub-samples for each variable, and their significance using several t-tests. 27 Moving on the second and third sections of Table  2 , the sample is further divided between developed and developing countries. Given the large number of in the sample, a significant proportion consists of developing countries. To classify developed countries, the study utilizes the 2020 World Bank classification. 28 considering high-income economies as developed. Consequently, the dataset is composed of 39 developed countries.
Furthermore, more sophisticated estimation techniques render these post-electoral results insignificant, and this, further interpretation is not pursued in this study. Furthermore, Table  2  highlights a significantly higher credit to GDP 25 This measure is provided by the IMF through the Annual Report on Exchange Arrangements and Exchange Restrictions database. 26 As underlined by  Alpanda and Honig (2009)  in parliamentary regimes, a high inflation rate increases the probability of an early election. See  Inoguchi (1981)  and the concept of ''political surfing'' for more information. This issue is investigated further in Section 5. 3. 27 Using  Welch's (1947)  methodology enables a comparison of the means of samples with unequal variances. 28 The complete classifications of countries used by the World Bank are available here: https://datahelpdesk.
31 Due to stationarity issues, 𝛥PR is introduced within the model as its first difference. On the contrary, M0_Growth M1_Growth and M2_Growth are introduced in levels. As previously mentioned, the study uses a dynamic model throughout its analysis. This choice is motivated by four main arguments: (i) the high correlation between M1_Growth and its past values as previously detailed, (ii) the existence of endogenous variables (mainly Inflation and Ygap), (iii) the presence of time invariant country characteristics in the error term (country fixedeffects), and (iv) a dataset subject to the  Nickell (1981)  bias (i. e. , a dataset with a small 𝑇 [32 periods] and a large 𝑁 [110 countries]). The main estimation method used in this study is the difference-GMM estimator  (Arellano and Bond, 1991).
Alternative measures of monetary policy orientation To ensure the robustness of the hypothesis regarding opportunistic PMCs, the study conducts estimations of the main model using alternative measures of monetary policy orientation. As developed in Section 3. 1. 1, M1_Growth is chosen as the main dependent variable to maximize the number of country included in the dataset. While M0_Growth or 𝛥PR could be used for a larger number of countries, merging them with other data sources results in a relatively smaller sample size (65 and 52 countries respectively). Therefore, Table  6  presents regressions results using three alternative explained variables. Specifically, regressions (  6 ), (  7 ) and (  8 ) from Table  4 , which employs ElectionIpre and ElectionIpost as electoral measures, are now reported with M2_Growth, M0_Growth and 𝛥PR as dependent variables.
Oriola one stipulated by the constitution is considered as endogenously timed. 38 To classify election dates, the study constructed the variables ElectionIpre_  [Endogenous]  and ElectionIpre_[Exogenous] using the yrcurnt variable from the DPI database. 39 When introducing exogenous electoral periods (ElectionIpre_[Exogenous]), PMCs are observable. On the contrary, when considering endogenous electoral periods (ElectionIpre_[Endogenous]), PMCs are not observed. This findings aligns with expectations, as incumbents and the central bankers lack the incentive to manipulate voters through fiscal or monetary policy when they have already manipulated political agendas to their advantage  (Inoguchi, 1981). In addition, the re-election process of the incumbent is expected to have a significant impact on the presence of PMCs.
The idea behind this approach is that an election in which the incumbent is not running cannot be characterized by a PMC, and vice versa. When these measures are introduced into the model, PMCs are observed only when the incumbent is running for re-election. Therefore, it is evident that when the incumbent will not participate in the subsequent election, PMCs are not implemented. Moreover, this study posits that the obtained results are influenced by countries where governments have a higher capacity to manipulate electoral results. The database used in this study is composed of democratic and non-democratic countries. To tackle this issue, an interaction term between pre-electoral periods and electoral competitiveness is introduced in column 35 (CEI X Elec. ). 38 Every election is then classified as endogenous or exogenous and cannot be considered under both of these categories.
39 yrcurnt measures the number of years prior to the next constitutionally planned national election. When information was unavailable, the study referred to each country electoral laws. Within the dataset, 155 elections out of the 478 are considered endogenously timed. 40 In the case of a presidential regime, defining which election the incumbent president is running or not is easy. On the contrary, certain difficulties can be encountered for parliamentary regimes. For the sake of simplicity, a political party is considered running if it is part of the previous ruling majority.
Acknowledgments I would like to thank the three anonymous reviewers that helped to improve significantly the quality of this study. Moreover, I would like to thank  Alexandru Minea  and  Patrick Villieu  for their academic support. I would also like to thank all the participants of the  WEAI 2021 Virtual International Conference  and the  Doctoral Seminar on Economic Development held in Paris Dauphine , and especially  Marion Mercier  and  Zeynep Yom  for their valuable comments and suggestions. Finally, I am grateful to  Pauline Avril  for her careful rereadings and her valuable tips.
To create an affordable clinical test for use in more decentralized settings, we are developing a multispectral imaging system based on a filter wheel and a smartphone. Our application is the label-free identification of uropathogens from images of bacterial colonies directly on a non-chromogenic culture medium. Using feature selection techniques, we have previously shown through calculations, the possibility to move from hyper-to multi-spectral imaging by exploiting less than 8 spectral bands. Here, we confirm our findings by testing a database of true multispectral images acquired using a filter wheel holding up to 22 dichroic bandpass filters with a 10 nm bandwidth. Performance is reported for 5 species using only 6 filters. Probabilistic SVM algorithms were implemented to allow to reject species other than the targeted most prevalent uropathogens as it is crucial to keep the false-positive rate low.
Materials & methods Hardware. The multispectral imaging hardware system is constituted of a black & white 5MP CMOS (e-con systems, India), free of infrared-blocking coatings, plug & play USC type, actionable through any Android based smartphone. Light source is made of white light LEDs (Yujileds, China) with an emission spectrum extending to 780 nm. To build our reference database, we have used a prototype ('DB') based on a motorized filter wheel, holding 22 optical filters and a CMOS (e-con Systems, India), both piloted by a PC. A second prototype ('alpha'), closer to the aim product, is based on a 8-slot manual filter wheel piloted by a smartphone. Both prototypes (Fig. 1 ) used dichroic bandpass filters with a 10-nm bandwidth. Biological samples. Frozen bacteria originated from bioMérieux strain collection (aka 'pure strain').