dataesr/scientific-paragraphs-categorization huggingface dataset:
Application of the Best Items Scale that is Cross-validated, Unit-weighted, Informative, and Transparent Algorithm to EMA Data (for Idiographic Predictor Selection and Prediction of Binge Eating) The machine learning algorithm Best Item Scales that are Cross-validated, Unit-weighted, Informative and Transparent (BISCUIT)  [30]  of the bestScales function from the R package psych  [46]  was applied separately to the EMA data of each patient to select the best idiographic predictors of binge-eating episodes. This method was chosen because of its (1) robustness to missing data; (2) use of unit-weighted scoring of predictors, which was found to be more generalizable, especially in the context of prediction; and (3) tendency to select more parsimonious predictor sets compared with other approaches such as Elastic Net regression  [30, 47, 48].
XSL • FO RenderX because of the temporary abandonment of rigid diet rules after eating in the absence of hunger  [52, 53]. In addition to items with conceptual similarity to binge eating, emotional items were selected in 9 patients. This supports the relevance of emotional eating in binge-eating predictions  [4, 11, 19]. However, the selected emotion sets were highly heterogeneous across the 9 patients. In fact, no single emotion item (or specific set of emotion items) was consistently selected across all patients. This speaks against a singular and generalizable emotional eating theory of binge eating. Similarly, because no other nonemotion-related predictor was consistently selected across all patients, our pilot data provide no evidence for other generalizable nomothetic theories of binge eating.
Thus, there was a high possibility of randomly drawn training sets that were unrepresentative of the data set. The results from the validation step might also vary slightly, as the R function set. seed does not apply to the cross tables. Another general drawback of the BISCUIT algorithm is that nonlinear trends and interaction effects among predictors are not considered. In addition, when applied under optimal conditions (ie, big data sets and no missing data), gold standard machine learning approaches, such as random forests  [56]  and XGBoost  [57]  in combination with super learners  [58] , calibrate better to the data. However, for typical EMA data sets, the conditions are rarely optimal for these algorithms. Missing data and a limited number of observations are typical features of high-burden EMA sampling schemes.
Acknowledgments This project received funding from the  European Research Council  under the European Union's  Horizon 2020 Research and Innovation Program  ( ERCStG-2014 639445 NewEat ) and the  Austrian Science Fund  ( KLI 762 ). Publishing open access was supported by the  Paris Lodron University of Salzburg  and  Austrian Science Fund. The funding bodies were not involved in the study design, assessment and analysis of data, interpretation of results, or writing of the manuscript.
It is also supposed that the SGE, which is usually implemented in the gardens of individual homes, is highly dependent on population and land use type. By way of explanation, the estate prices in industrial areas are usually less than in other urban zones. Moreover, the green areas were also supposed to affect the HM capacity of spatial zones, which could, in turn, affect the land surface temperature (LST) in addition to the consumption of energy and the areas of green zones. Figure  4  shows a structure of relationships between the aforementioned factors. Accordingly, the data of (i) population, (ii) land use/land cover (LULC), (iii) SGE, (iv) HM index, (v) EC, (vi) HR from hot wastewater, and (vii) LST index were collected from different sources. The data of population for the year 2017 were collected from the population database of INSEE (2021) website.
The Urban Atlas  [36]  provides the LULC maps Accordingly, the data of (i) population, (ii) land use/land cover (LULC), (iii) SGE, (iv) HM index, (v) EC, (vi) HR from hot wastewater, and (vii) LST index were collected from different sources. The data of population for the year 2017 were collected from the population database of INSEE (2021) website. The Urban Atlas  [36]  provides the LULC maps for the Île-de-France area for the years 2006, 2012, and 2018. The data on wastewater heat recovery for the year 2015 indicates a concentric form of high capacities at the center with low capacity for the neighboring suburban areas. The potential of SGE available only for a small area in the Île-de-France region in the year 2022 was collected by digitizing the published distribution map of the Bureau de Recherches Géologiques et Minières  [33].
The first phase of this prediction process is the validation of the data used. This is conducted by (a) employing the available mapped data of 2006 and 2012 to predict the LULC for the year 2018 via the CAMCM simulations performed in TerrSet 18 software, and by (b) comparing the actual and predicted LULC maps for the year 2018. The prediction model was validated through Cohen's kappa test conducted in TerrSet 18. The validation process showed high accuracy values of the predicted LULC for the year 2018. For instance, the calculated kappa values were all above 98%, indicating an almost perfect prediction according to Al-Shaar et al. [38]. In detail, the kappa values were 0. 9919 for "Kappa for no information (Kno)", 0. 9955 for "Kappa of grid cells level location (Klocation)", 0. 9999 for "Kappa of stratum level location (KlocationStrata)" and 0.
9896 for the "Overall kappa (Kstandard)". Subsequently, the LULC maps of the years 2006 and 2018 were the inputs to predict the LULC for the year 2054. Figure  6  shows the process of predicting the LULC maps for the year 2054. Urban Sci. 2022, 6, x FOR PEER REVIEW 13 of 31 The LULC maps of the years 2006, 2012, and 2018 were used to predict the LULC for the year 2054 using the Cellular Automata Markov Chain Model (CAMCM). The time span of the prediction is 36 years and is commonly used in the scientific literature for LULC predictions mainly because 36 is a multiple of 12 (the period extended between the years 2006-2018) and because it constitutes a relatively long-term horizon. The first phase of this prediction process is the validation of the data used. This is conducted by (a) employing the available mapped data of 2006 and 2012 to predict the LULC for the year 2018 via the CAMCM simulations performed in TerrSet 18 software, and by (b) comparing the actual and predicted LULC maps for the year 2018.
Selection of Variables Ordinary Least Squares The OLS regression model is the model used to select the variables. A derived OLS module called the exploratory regression (ER) was developed in ArcMap software. The ER runs all OLS models to cover all possible combinations of the explanatory variables, representing the input of the model. This ER model carried out in ArcMap 10. 8 software indicates the coefficients of determination (R 2 ) for all combinations. The selected model is the one having the highest R 2 with the least number of variables. All the selected models have an R 2 above 70% except the HM and the night LST models, with 62% and 60%, respectively. In the second stage, the OLS regression is applied to the selected model to determine the variance inflation factor (VIF) for each variable as well as for the overall model.
HPLC purification was performed using an Äkta™ pure system (GE Healthcare) equipped with Thermo Scientific™ DNAPac™ PA100 preparative ion exchange column (13 µm, 250 x 22. 0 mm). Unmodified DNA oligonucleotides were purchased from IDT. All the DNA polymerases (TdT, Phusion, Hemo KlenTaq, Taq, Bst, Q5, Therminator, Vent (exo -), Dpo4, Deep Vent and Kf exo -) were purchased from New England Biolabs as well as the natural dNTPs. Acrylamide/bisacrylamide (29:1, 40%) was obtained from Fisher Scientific. Visualization of PAGE gels was performed by fluorescence imaging using a using a Typhoon Trio phosphorimager with the ImageQuant software (both from GE Healthcare).
Statistical analyses All statistical analyses were conducted using R version 3. 5. 3 (R Core Team 2020). We used negative binomial generalised mixed-effects models using the glmmTMB package  (Brooks et al. 2017)  to assess if anemone patch status (unbleached/ bleached), moon phase (new moon/full moon), the abundance of juveniles and the interactions between the abundance of juveniles and the categorical factors influenced the abundance of newly settled fish in anemone patches. Anemone patch identity was included as a random factor to account for potential inter-patch differences (e. g. number of anemones in the patch, patch surface area). Similarly, a first-order auto-regression covariance structure (AR1) was included to control for residual serial dependence between temporally observed counts  (Zuur et al.
Background. Brachymetatarsia is defined by an abnormal shortening of the metatarsal bone. This rare condition is mostly primary and congenital. Consequences of this malformation are both esthetic and functional, due to pain and mechanical problems in the forefoot. Surgical management is an important part of patient care. There are two main options: gradual lengthening by progressive callotosis distraction using an external fixator and one stage lengthening using bone graft and osteotomy of the bone. This review presents two cases using the one stage lengthening surgical management method. We also discuss some reports in the literature with the aim to compare the advantages and disadvantages of the two surgical methods. Literature concerning the surgical management of brachymetatarsia was identified using the PubMed and Google Scholar databases.
Cases The research findings related to brachymetatarsia were identified using the PubMed and Google Scholar databases, using "congenital" and "brachymetatarsia" as keywords. Published data in any language describing surgical techniques of gradual lengthening or single-stage lengthening were selected. We describe two female patients aged 20 and 26 years, who underwent one stage lengthening surgery with an iliac bone graft. We retrospectively reviewed the two cases with a one-year follow-up. Patients provided written informed consent for the case details to be published. All data have been anonymized. Patient 1 had an isolated shortening of 10 mm of the fourth toe on the right foot. The main complaint was esthetic, with an important mental effect. Patient 2 had bilateral brachymetatarsia of the fourth toes.
Discussion Brachymetatarsia is a rare condition. The data collected from the literature describe the surgical techniques, complications, advantages, and disadvantages regarding the correction of the malformation. Surgical treatment is commonly performed in patients older than 12-14 years, because the malformation is not obvious before this age  [10, 11]. Most of the surgeries were performed at the request of the patient due to cosmetic concerns. The mechanical pain caused by the malformation can be treated using conservative treatment. The two most widely used techniques for treatment are gradual lengthening using an external fixator and single stage lengthening. The gradual lengthening method is well documented in the literature and is used by most surgeons  [8, 10]. Our patients underwent one stage lengthening using a bone graft and plate fixation after osteotomy.
Briefly, each qPCR amplification was performed in a final volume of 25 µL reaction mixture including LightCycler 480 Probes Master (2×) (Roche Diagnostics GmbH, Mannheim, Germany), 0. 3 µM of each primer, 0. 1 µM of the probe, and 9 µL of sample extract. The qPCR program consisted of 50 cycles at 95 • C for 15 s and at 60 • C for 60 s each and was performed in a LightCycler 480 Real-Time PCR System (Roche Diagnostics GmbH, Mannheim, Germany) and analyzed using LightCycler 480 Software version 1. 5. Each run contained a positive control and a negative extraction control. This process has been referred to as the Qiasymphony procedure for clarity in the present study. After thawing, the 25 clinical serum samples were not extracted using our in-house procedure but using the Janus Chemagic 360 ® system (Perkin-Elmer, Zaventem, Belgium).
Lichtheimia corymbifera included in the panel kit was undetectable. Rocchi, S et al. have already shown the lack of sensitivity of this kit for Lichtheimia corymbifera in a previous study  [19] , where the authors performed interlaboratory evaluations of qPCR assays for Mucorales to assess reproducibility and performance  [7, 20, 21]. A total of 24 spiked sera, six samples comprising three serial dilutions of two different Mucorales DNAs (Rhizomucor pusillus and Lichtheimia corymbifera) in four medical laboratories, were tested. Three samples (one Lichtheimia corymbifera and two Rhizomucor pusillus) were missed with the MucorGenius ® assay. For this interlaboratory study, Rocchi, S et al. used serum spiked with DNAs extracted from culture (the concentrations of these samples ranged from 10-2 to 10-1 pg/mL (data provided by Rocchi, S.
The calculated dimensions are then optimized on the CST software for the purpose of miniaturization. For a ground plane length of less than 6 mm, the resonant frequency f r m,n,p is reduced to half, which allows the miniaturization of the antenna dimensions. The proposed antenna structure excites the dominant TM 1,0,0 , TM 2,0,0 and TM 3,0,0 modes resulting on a triple-band antenna, which is not discussed in the available literature. This antenna is simulated and realized on the Teflon glass substrate with a relative electric permittivity of 2. 1 and a thickness of 0. 67 mm. The input impedance of this antenna is 50 Ω. Figure  8  and Table  3  show the proposed antenna shape and it optimized dimensions, respectively. Figure  9  shows the area equivalence of the proposed antenna allowin resonant frequencies for the three fundamental modes TMm,n,0 as a function dimensions.
Simulation Results Figure  12  shows the simulated S11 parameters of the antennas (Ant-1, Ant-2 a proposed antenna (Ant-3)) under CST software. The antenna electrical equivalent circuit is based on the antenna structure and the number of resonators. In our case, the proposed antenna consists of three triangular resonators, electrically equivalent to three cascaded RLC resonators  [30] , as shown in Figure  11. The antenna electrical equivalent circuit is based on the antenna structure and the number of resonators. In our case, the proposed antenna consists of three triangular resonators, electrically equivalent to three cascaded RLC resonators  [30] , as shown in Figure  11. The values of the elements R, L and C of the electrical equivalent circuit are calculated in Section 3 by exploiting the simulation results of the S11 parameter (bandwidths and resonant frequencies), the quality factor Q and the expression of the resonant frequency as a function of L and C for a parallel RLC resonator.
Simulation Results Figure  12  shows the simulated S11 parameters of the antennas (Ant-1, Ant-2 and the proposed antenna (Ant-3)) under CST software. The values of the elements R, L and C of the electrical equivalent circuit are calculated in Section 3 by exploiting the simulation results of the S 11 parameter (bandwidths and resonant frequencies), the quality factor Q and the expression of the resonant frequency as a function of L and C for a parallel RLC resonator.
Simulation Results Figure  12  shows the simulated S 11 parameters of the antennas (Ant-1, Ant-2 and the proposed antenna (Ant-3)) under CST software. The quality factor Q of each RLC parallel resonator can be calculated for each frequency band (2. 45 GHz, 5. 2 GHz and 8. 2 GHz) by Equation (  8 ): Q = R ⋅ L c = f Δf (8) f = 1 2π√LC (9) R, L and C are the resistance, inductance and capacitance of the resonators, respectively. fr and Δf are the resonant frequency and bandwidth for S11 ≤ -15 dB as shown in Figure  13. Table  4  summarizes the values of Δf and Q for the three resonant frequencies. From Equation (  9 ), we fix one parameter (L) and deduce the other (C) for each resonance frequency. These values of inductance L, capacitance C and the passband for each resonant frequency are then used in Equation  (8)  to determine the values of the resistance R for each resonant frequency, i.
Specific Absorption Rate (SAR) Investigation The study of the specific absorption rate of the undeformed and the deformed antenna bounded on the human body (arm) for the frequencies of 2. 4 GHz, 5. 2 GHz and 8. 2 GHz is presented. Only the bending in the E-plane has been taken into account. The SAR analysis is performed with the CST software, using the IEEE C95. 3 averaging method. SAR calculations were performed on 10 g of tissue volume (ICNIRP). The SAR standard safety level is 2 W/kg for 10 g of tissue  [36]. The human body is a conductive and dielectric material. When the antenna is stuck to the human body, it is mismatched due to the impedance change and some of the available power is absorbed by the human body, resulting in higher Specific Absorption Rate (SAR) values. The SAR can be calculated using the expression  (11).
SAR = σ|E|2 ρ (11) where, σ is the tissue electrical conductivity, ρ is the tissue density (kg/m 3 ) and E is the root-mean-square electric field induced in the tissue (V/m). Figure  34  presents the SAR simulation of the flexible antenna bounded on human body (arm) for 10 g of tissue and 100 mW input power at 2. 45 GHz, 5. 2 GHz and 8. 2 GHz. The arm is modelized in cylindrical form on CST software  [37]. The input power of 100 mW is the maximum authorized for wearable applications  [36]. The undeformed and deformed antennas are omnidirectional for 2. 45 GHz and quasi-omnidirectional for 5. 2 GHz and 8. 2 GHz. The deformation of the waterproof paper antenna does not change the radiation pattern of the antenna much, as shown in Figure  33. The maximum measured gains for the undeformed antenna are 2.
SAR = | | (11) where, σ is the tissue electrical conductivity,  is the tissue density (kg/m 3 ) and E is the root-mean-square electric field induced in the tissue (V/m). Figure  34  presents the SAR simulation of the flexible antenna bounded on human body (arm) for 10 g of tissue and 100 mW input power at 2. 45 GHz, 5. 2 GHz and 8. 2 GHz. The arm is modelized in cylindrical form on CST software  [37]. The input power of 100 mW is the maximum authorized for wearable applications  [36]. It can be seen that the peaks of SAR10g are of 1. 1 W/kg, 0. 71 W/kg and 0. 45 W/kg for the three frequencies 2. 45 GHz, 5. 2 GHz and 8. 2 GHz, respectively. The European standard of 2 W/kg is thus respected. It can be seen that the peaks of SAR 10g are of 1. 1 W/kg, 0. 71 W/kg and 0. 45 W/kg for the three frequencies 2.
4 GHz as seen in Section 2 (page 8). In order to improve the simulation results on CST software, the capacitances CC are added between the first triangle and the feed line corresponding to the two maximums of the E field intensity points for each tilted antenna, as shown in Figure  45. In order to improve the simulation results on CST software, the capacitances C C are added between the first triangle and the feed line corresponding to the two maximums of the E field intensity points for each tilted antenna, as shown in Figure  45. f = R × C + C C L ( 23 ) is given by the formula (23) as a function of Cc. The quality factor increases as func the capacitance Cc.
The TRICL GWAS was originally designed to profiled genotype information of 12,651 participants. All participants provided informed written consent. All studies were reviewed and approved by institutional ethics review committees. All the duplicated samples between ILCCO-OncoArray and TRICL have been removed from ILCCO-OncoArray data set. UK Biobank. The UK Biobank is a large prospective study of individuals aged 40 to 70 years at assessment,  25  who attended assessment centers between 2006 and 2010 and contributed blood samples for genotyping and blood analysis and answered questionnaires about medical history and environmental exposures. In the years since assessment, health outcome data for these individuals (e. g. , diagnoses of cancer) have been accruing through UK national registries and hospital records.
SNPs were removed if meeting any of the following criteria: (1) sex chromosome, (2) minor allele frequency less than 0. 05, (3) call rate less than 95%, and (4) Hardy-Weinberg equilibrium (HWE) test p less than 1. 00 Â 10 À7 in controls or p less than 1. 00 Â 10 À12 in cases. Finally, a total of 28,353 participants (15,157 cases and 13,196 controls) with 340,958 qualified SNPs remained in the subsequent association analysis. To explore the potential functional variants, we extracted the genotyped data in the flanking regions with the imputed data. 27 he genotype data of TRICL were generated from the Affymetrix Axiom Array, which contained 414,504 markers. To estimate missing genotype information, we phased haplotypes with Eagle version 2. 3 using 1000 Genomes Project data (phase 3) as a reference panel  28  and then performed imputations using the Minimac (version 3) software.
SNPs with an imputation quality score R 2 less than 0. 4, minor allele frequency less than 0. 01, or p less than 1 Â 10 À6 for the HWE test were excluded from the analyses. We analyzed the imputed genetic data from the full UK Biobank cohort, consisting of 488,377 individuals genotyped on the Affymetrix UK BiLEVE and UK Biobank Axiom arrays, and applied the same quality control procedures. We included 409,615 European participants (3017 cases and 406,598 controls) as the independent validation set.
Screening Step. BiForce is an entropy-based method, and it is implemented in a Java program that integrates bitwise computing with multithreaded parallelization and allows rapid full pairwise genome scans. 29 BiForce was applied to scan billions of G Â G interactions exhaustively in ILCCO-OncoArray and select potential G Â G interactions by using the criterion of the loglikelihood difference between two log-linear models with and without the interaction term, defined as n P i;j;k b p ijk logðb p ijk =b p ijk Þ, where n is the sample size, b p ijk is the observed frequency of subjects with SNP 1 coded i (0, 1, and 2), SNP 2 coded j (0, 1, and 2), and disease status coded k (0 and 1). In addition, b p ijk was the expected frequency under null hypothesis. BiForce used Kirkwood superposition approximation (KSA) instead of likelihood estimation to calculate b p ijk.
In Silico Functional Validation of the SNPs With G Â G Interaction We used an in silico approach through SNPinfo,  32  RegulomeDB,  33  and HaploReg version 4. 1,  34  to predict potential functions of the identified SNPs. Expression quantitative trait loci (eQTL) was analyzed using the 578 lung tissues in the GTEx project. 35 To concordantly analyze expression profiles from tumor and healthy lung tissues, we collected data from Gene Expression Omnibus (GEO) repository, including GSE43458 (80 cases and 30 controls) and GSE12428 (34 cases and 28 controls). We statistically normalized the data before analysis and used Student's t test to compare the differences between tumor and normal tissues.
Gene Enrichment Pathway Analysis We collected the pathway information with gene sets from the KEGG database, containing a total of 186 pathways up to July 2021. All enrichment analyses were performed using the R package clusterProfiler. 38 ll statistical analyses were performed using R version 3. 6. 3 (The R Foundation for Statistical Computing, Vienna, Austria). p values were two-sided, and p less than 0. 05 was considered statistically significant, unless otherwise specified.