dataesr/scientific-paragraphs-categorization huggingface dataset:
The Argelander Institute for Astronomy of the University of Bonn and the Ludwig Maximilians Universität Munich also participated in the science preparation for eROSITA. This research has made use of data obtained from the Chandra Data Archive and the Chandra Source Catalog, and software provided by the Chandra X-ray Center (CXC) in the application packages CIAO and Sherpa. The Hyper Suprime-Cam (HSC) collaboration includes the astronomical communities of Japan and Taiwan, and Princeton University. The HSC instrumentation and software were developed by the National Astronomical Observatory of Japan (NAOJ), the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU), the University of Tokyo, the High Energy Accelerator Research Organization (KEK), the Academia Sinica Institute for Astronomy and Astrophysics in Taiwan (ASIAA), and Princeton University.
Funding was contributed by the FIRST program from the Japanese Cabinet Office, the Ministry of Education, Culture, Sports, Science and Technology (MEXT), the Japan Society for the Promotion of Science (JSPS), Japan Science and Technology Agency (JST), the Toray Science Foundation, NAOJ, Kavli IPMU, KEK, ASIAA, and Princeton University. This paper makes use of software developed for the Large Synoptic Survey Telescope. We thank the LSST Project for making their code available as free software at http://dm. lsst. org. This paper is based [in part] on data collected at the Subaru Telescope and retrieved from the HSC data archive system, which is operated by Subaru Telescope and Astronomy Data Center (ADC) at National Astronomical Observatory of Japan. Data analysis was in part carried out with the cooperation of Center for Computational Astrophysics (CfCA), National Astronomical Observatory of Japan.
range of our dataset, the longer wavelength data (W4 or MIPS1) are essential to constrain the AGN properties properly. Out of the final 89 high-z sources, 50 sources (56%) have available FIR photometry (Herschel PACS or SPIRE photometric bands). For these sources, we ran X-CIGALE twice. One time including the FIR photometry and a second time without Herschel information, using both times the same parametric grid (Table  3 ). Our goal was to examine whether lack of FIR data affects the estimated SFR. First, in the top panel of Fig. B. 1, we compared the SFR measurements of the two runs (with and without FIR data). In both cases, we found a very good agreement between the measurements. Calculating the Pearson correlation factor, we found ρ SFR = 0. 724, indicating good correlation between the two runs.
We followed the same procedure to examine whether lack of MIR or NIR affects the SFR calculations and found no significant differences between the results. We also made use of the mock catalogues that X-CIGALE can produce. In the middle panel of Fig. B. 1, we show the estimated parameter (mock analysis) of the SFR with and without FIR data compared to the true values, for those sources that satisfied our selection criteria described in Sect. 3. 2. The corresponding linear Pearson correlation coefficients of the combined samples are ρ SFR = 0. 842 when FIR data are included in the SED fitting analysis, and ρ SFR = 0. 916 when we excluded FIR photometry. In the bottom panel of Fig. B. 1, we show the distributions of the difference between the estimated and true values of the physical properties for both runs.
Overall, 71 out 89 have optical, NIR, and MIR (W1 or IRAC1 and W2 or IRAC2) photometry available. 76 out of 89 have NIR photometry and 82 out of 89 have MIR photometry. There are no sources that lack both NIR and MIR photometry. Next, we examined how a lack of NIR bands affects the M * calculations. Following the process described above, for those sources that have NIR photometry (76 AGNs), we run X-CIGALE twice. We also examined if X-CIGALE is able to constrain the AGN component without the longest MIR bands (W4 or MIPS1). There are 26 AGNs in our dataset (20 in XXL and 6 in eFEDS) that lack detection in W4 or MIPS. Florez et al. (2020)  examined if the absence of MIR photometry has an impact on their derived properties and found no significant deviations  (their Fig. A. 2 and A. 3 ). In our case, the inclusion of the X-ray flux in the SED fitting process should provide X-CIGALE an (additional) constraint for the AGN emission.
The mean value of the error on log[L AGN,torus (erg s -1 )] is 0. 27 dex in the former case, compared to ∼0. 15 dex for the latter. The scatter of the log[L AGN,torus (erg s -1 )] also increases when we ignore the longest MIR photometric bands (bottom panel), but X-CIGALE can still reliably recover the parameter (ρ torus = 0. 832). Finally, we examined whether the lack of the W4 or MIPS1 photometric bands affect the reliability of X-CIGALE to measure the SFR. In Fig. B. 4 (top panel), we present the SFR calculations with and without the W4 or MIPS1 photometric bands, from the SED fitting process. The two SFR measurements are consistent (ρ SFR = 0. 934). In the middle panel, we show the estimated parameters of the host galaxy as functions of the true ones, using the mock analysis. The corresponding linear Pearson correlation coefficients are similar for the two runs indicating not strong deviations in the estimated parameters when these MIR data are missing.
One time taking into account the NIR bands in the SED fitting process and the second time ignoring the NIR information. The comparison of the estimated M * for the two runs is shown in the top panel of Fig. B. 2. We also use the mock catalogues to examine how well X-CIGALE can constrain M * in the absence of NIR photometry. The results are shown in middle and bottom panels of Fig. B. 2. Based on these tests, X-CIGALE has reliably measured the M * for the 13 sources in our dataset that lack NIR observations.
Sample size calculation Sample size calculation was performed using G*Power software, V. 3. 1. Based on previous clinical trials exploring the ability of interventions to reduce SI in the mid-term,  36  we estimated a target sample size enough to have 80% power to detect between-group differences of 6-7 points on the SI subscale of the CSSRS  35  (25% decrease in the intensity of SI in the intervention group). The alpha error was set at 5% and the power at 80%. We assumed a dropout rate of 20% and a randomisation imbalance of 10%. With these settings, we estimated that we needed a total of 220 participants, 110 in each arm.
Open access ≥4-active SI with some intent to act-in the CSSRS SI subscale. We will consider that an SA has occurred when the patient meets the criteria for 'actual SA' in the CSSRS suicidal behaviour subscale. Additionally, the CSSRS will be administered at every follow-up visit. After checking the patients' eligibility, the attending psychiatrist will explain the project in detail and invite patients to participate. If patients agree to participate, they will be asked to sign the informed consent, after which they will be randomly assigned to the intervention group (EMI+EMA+TAU) or the control group (EMA+TAU) using a mixed block randomisation scheme generated with NQuery software. Allocation ratio will be 1:1. After enrolment, participants will undergo a baseline interview, which will be divided into two parts: the first part will be carried out by attending psychiatrist who will further assess the characteristics of the SA using the Brief Suicide Questionnaire (BSQ),  38  and set up the MEmind application-which administers EMI and EMA-with the participant.
Non-digital measures Assessments will be carried out by attending psychiatrists and research assistants, all of them previously trained. All questionnaires will be completed on the MEmind website, which allows for easy completion and immediate uploading to databases. Some of the main tools used will be the following: ► The BSQ  38  will be administered by the attending psychiatrist to characterise the index event that motivated inclusion in the study (SA or emergency referral for SI). It includes questions on lethality, method and surrounding circumstances of the attempt and lifetime history of suicidal behaviour. ► The CSSRS scale  35  will be administered by the attending psychiatrist at inclusion to determine eligibility. Additionally, the CSSRS will be administered by the research assistant at every follow-up visit to detect the occurrence of a new suicidal event.
Statistical analysis Traditional statistical analyses will be performed using SPSS V. 24. 0 statistical software. To compare SI at baseline with SI at the end of follow-up (CSSRS score), we will use paired samples t tests. To compare the improvement in SI after follow-up between the intervention group and the control group, we will use an independent samples t-test. Survival curves (time to a new suicidal event) will be calculated using the Kaplan-Meier test. The intervention group's survival curves and the control group will be compared using the log rank test. To assess the project's feasibility, participation rate, retention and percentage of EMA questions answered will be calculated. Binary logistic regression will be used to explore the correlation between the study variables, obtaining crude ORs and age-adjusted and sex-adjusted ORs.
A multivariate regression model will also be built to assess the unique strength of the association of the variables. Cox regression analysis will be performed to explore variables associated with survival time (time until next event). All tests will be two-tailed, with a significance level of p<0. 05% and 95% CIs. In addition to the traditional analyses, with the Signal Theory Department of the Carlos III University's support, unsupervised analyses will be carried out using machine learning techniques to create behavioural models and patient activity profiles from data collected through active and passive EMA.
The proposed MDE approach is based on the minimization of various distance metrics between the theoretical distribution and the observed data. The major contribution of this paper is threefold: • Novel estimation procedures are developed to estimate the model parameters for the four-parameter GΓ distribution in order to fit data with positive and/or negative values with the four-parameter GΓ distribution; • R package that implements the proposed estimation methods and functions related to the four-parameter GΓ distribution is developed; • The proposed estimation methods are shown to be feasible and effective. The remainder of this article is organized as follows. Section 2 provides the preliminary knowledge about the Amoroso family of distributions, including the functional form of the distribution, special cases, and some basic properties.
R package There are some existing R packages that have the capability to estimate the parameter of different statistical distributions using maximum likelihood estimation method in which suitable modifications of the R programs are needed for applying to the four-parameter GΓ distribution. For example, the fitdistrplus package  [16] , the MASS package  [54] , and the survival package  [53]. There are also some R packages for fitting the three-parameter generalized gamma distribution, a special case of the four-parameter GΓ distribution with µ = 0 such as the fit. GenGamma function in the R package temporal (Parametric Time to Event Analysis)  [32]. However, these existing R functions can only obtain the maximum likelihood estimates of the model parameters for some special cases of the four-parameter GΓ distribution with c > 0 (power shape parameter) and a > 0 (positive scale parameter) or require substantial R programming to modify the code.
Here, we aim to develop an R package to estimate the parameters of the four-parameter GΓ distribution based on different estimation methods proposed in Section 3. The package called AmoRosoDistrib (https://github. com /ccombesGG4/AmoRosoDistrib/) written in R  [41]  is a general package that aims at fitting univariate parametric distributions in the Amoroso family of distributions with the scale parameter a ∈ R\{0}, the location parameter µ ∈ (-∞, ∞) and the shape parameters ℓ ∈ R + and c ∈ R\{0}. The AmoRosoDistrib package also provides the PDF, CDF, quantile function, hazard function for the fourparameter GΓ distribution using the parametrization originating from Amoroso  [4]. The package also includes a random number generation for the four-parameter GΓ distribution based on the property that if Z ∼ Gamma (1, ℓ), then Y = a Z 1 c + µ ∼ GG(a, ℓ, c, µ).
The AmoRosoDistrib package includes different parameter estimation methods based on optimization approach to fit the univariate population. By default, direct optimization is performed using the general-purpose constraint optimization function constrOptim with Nelder-Mead method available in the R package stats  [41]  which minimizes a function subject to linear inequality constraints using an adaptive barrier algorithm. In summary, the nine estimation methods that are provided in the package are: 1. Maximum Likelihood Estimate: fit. mle() (see, Eq. (  4 )); 2. Minimum Kullback-Leibler divergence estimate based on PDF: fit. mkle() (see, Eq. (  5 )); 3. Minimum Jensen-Shannon divergence estimate based on PDF: fit. mjse() (see, Eq. (  6 )); 4. Minimum Hellinger distance estimate based on PDF: fit.
1. Moreover, we also provide the function called mu bias reduced estimate to compute the bias-reduced estimator for parameter µ based on Eq. (  13 ). In addition, the AmoRosoDistrib package also provides functions to compute different theoretical characteristics of the GΓ 4 distributions (i. e. , the values of the parameters are input variables) and different statistics computed from a sample (i. e. , the data are the input variables): • Theoretical mean: mean theo;.
Neurotransmission in guinea pig brains (paulsen data) The paulsen data frame in R package boot  [11, 15]  has 346 observations of the spontaneous current flowing into individual brain cells in the brain of adult guinea pigs. The currents are measured in pico-amperes. These observations are obtained from an experiment to see if the current flow was quantal in nature  [38]. We fit the paulsen data by using the four-parameter GΓ distribution. First, we use the method proposed in Section 3. 1 with 1000 equal-spaced points in  (-20, 20)  as the sequence of values for parameter c to obtain the initial value of θ in the parameter space Ω + = {a > 0, µ ∈ (y 1:n , ∞), ℓ ∈ R + , c ∈ R\{0}}, and the initial value of θ in the parameter space Ω -= {a < 0, µ ∈ (-∞, y n:n ), ℓ ∈ R + , c ∈ R\{0}}. Then, based on these initial estimates, we obtain the parameter estimates using the estimation methods described in Section 3 in parameter spaces Ω + and Ω -.
Anorexia data on weight change (anorexia data) The anorexia data set in the MASS package  [54]  contains the weight change data for young female anorexia patients. Here, the studied variable is the Weight of the patient after the study period (in lbs. ): Postwt. There are 72 observations in the data set. The objective of this example is to study the behavior of the proposed estimation methods based on a relatively small sample. Following the same approach in the previous numerical example in Section 5. 1, the parameter estimates are presented in Table  3  and the parameter estimates that give a better value of objective function are indicated by an asterisk. The histogram and the fitted PDFs for the variable Postwt in anorexia data set with the two initial estimates, and the estimates obtained from different estimation methods (i.
Although family-based interventions have been suggested as promising approaches for preventing and treating pediatric obesity, available studies failed to include the whole family in its own natural environment and routine. This paper aims to detail the development, implementation, and evaluation phases of the ProxOb home-based, family-centered program and present its feasibility and early results. ProxOb provides families with a 6-month multidisciplinary, home-based, and family-centered intervention followed by an 18-month maintenance phase. A global psychosocial, clinical, and behavior evaluation was conducted at baseline (T0) at the end of the 6-month intervention (T1) and after the 18-month maintenance phase (T2). A total of 130 families with at least one child with obesity completed the ProxOb program so far, and more than 90% of them also presented at least one parent with overweight or obesity.
Origins and Development As part of its National Obesity Plan launched in 2010, the French Minister of Health established 37 Specialized Obesity Centers (CSO) with the mission to structure, coordinate, and develop the prevention and treatment of obesity and related scientific activities at a regional level. In that context, the Auvergne territory CSO, called "CALORIS" (Centre Auvergnat de L'Obésité and de ses RIsques en Santé) centered its activities on the development of innovative multidisciplinary interventions targeting pediatric obesity with a focus on providing access to care for all in need of intervention. After a careful evaluation of the regional situation and the scientific and clinical literature, CALORIS developed a pilot program in 2015 to propose home-based interventions combining physical activity education, nutritional education, and psychological and parenting support to families concerned with pediatric obesity: the ProxOb program.
The ProxOb program aims to provide families with personalized interventions conducted by specialists according to the following steps: (i) specific training programs for health practitioners; (ii) screening of families interested in participating; (iii) baseline clinical evaluation of each family member and evaluation of the family situation (T0); (iv) delivery of a 6-month multidisciplinary, home-based, and family-centered intervention; (v) a second evaluation phase at the end of the 6-month intervention (T1); (vi) an 18-month maintenance phase with regular phone support (with an evaluation session after the first 6 months of independence (12 months from baseline-T2); and (vii) a final evaluation at the end of the overall 18 months of the independence phase (T3). From the first ProxOb campaign (ProxOb I) to the fourth (ProxOb IV), the program stopped at T2, and the following 12 months of independence phase and the T3 assessment point were added from ProxOb V (5th ProxOb cohort).
Implementation 2. 2. 1. Specific Training of the Practitioners The CALORIS team, together with specialized trainers from the National Association for Therapeutic Education, created a specific training curriculum leading to an official qualification, particularly focusing on obesity. All the practitioners (as detailed below) involved in the ProxOb program followed a 40 h course describing the etiology of obesity and its prevention and treatment and the elements of a home-based and family-centered intervention and detailing all the specific tools and activities specifically developed for the interventions. The training phase is a highlight of the program since the presence and quality of training have been identified in the literature as an important factor contributing to the success of family-based obesity intervention  [11].
Families with at least one parent presenting with a BMI above 45 kg/m 2 without individual treatment or refusing to follow recommended treatment were excluded. Similarly, families whose child suffered from a syndromic cause of obesity without designated healthcare and who presented with mental and cognitive disorders were not included. Importantly, the overweight and obesity status in children were calculated using the CALIMCO software developed by the French National Nutrition and Health Plan (PNSS), using French adapted curves.
Intervention The ProxOb program rests on an educational approach (therapeutic education intervention) where families received adapted and individualized counseling (there was no prescribed nutritional intervention nor physical training). Each family was assigned to three practitioners composed of one physical activity and health educator, one nutrition and dietetics specialist, and one psychologist and/or social worker. Each practitioner separately visited the home for an introductory 2 h visit, and thereafter, each one visited the family over 6 months for six sessions. The three practitioners were in constant contact concerning the families, and a multidisciplinary meeting with the pediatrician was held on three occasions during the 6-month period. Importantly, the usual general practitioners of the families were all involved in the process and were constantly informed of their evaluation.
Statistical Considerations Statistical analysis was performed using STATA (version 15; StataCorp, College Station, TX, USA). All tests were two-sided, with a Type I error set at 0. 05. Categorical data were expressed as frequencies and associated percentages and continuous data as mean ± standard deviation or median (1st quartile; 3rd quartile), according to statistical distribution. The Gaussian distribution was checked with the Shapiro-Wilk test and/or by histogram. Families were compared based on whether they completed the program using the chi-squared test or Fisher's exact test for categorical variables and Mann-Whitney test for quantitative variables. The evolution of z-BMI (for children) and BMI (for parents) over time (T0, T1, T2) was evaluated by linear mixed-effects models (T3 was not yet available at the time of the analysis), with ProxOb edition, with family and subject considered as random effects.
Only Appelhans et al. intervened at home but only for some measurements and not during their intervention  [14]. Similarly, Varagannis et al. performed a family intervention using a home-based online program, missing the proper inclusion of the home setting in their approach  [13]. As part of its missions, the Auvergne territory Obesity Specialized Center, CALORIS, and in line with the objectives of the French National Obesity Plan, developed and implemented a home-based, family-centered intervention targeting childhood obesity (the ProxOb program). The ProxOb program includes the main key levers suggested by the literature for effective family-based pediatric obesity interventions. First, ProxOb uses a multidisciplinary approach combining therapeutic education programs in physical activity and movement behaviors, eating habits, psychological supports, and parenting, which was described as essential for such interventions by some authors  [17] [18] [19].
In their study, Janicky et al. (2008)  included training sessions to the practitioners and caregivers (two full days + one 6 h boost at mid-intervention), which has also been shown to strengthen the intervention and enhance its effects  [17]. In that sense, all the practitioners and professionals involved in the evaluation and intervention during ProxOb follow specific training before the program and attend three collective staff meetings. Importantly, the home-based intervention lasts 6 months, with a subsequent 12-month assisted maintenance phase, in line with the recent meta-analysis from Arnasan et al. (2020) that highlighted the need for at least 6 months of intervention to favor significant effect of the children's z-BMI  [11]. Based on the results of their systematic analysis of the available evidence regarding family-interventions, Arnasan et al.