dataesr/scientific-paragraphs-categorization huggingface dataset:
[O ii]λλ3726,3729 doublet) c see table D. 1 for line names d corrected for instrumental velocity dispersion Table A. 5. Description of the columns of the redshift table. Notes. a MXDF,UDF10 or MOSAIC b BALMER,FORBIDDEN,LYALPHA, ABS,CIV548 or MGII2796 c LMFIT Table A. 6. Description of the columns of the line table. DNAME display name for the line (e. g. , Lyα), set to None for close doublets. Col. name Description ID MUSE source identifier DATASET MUSE data set a LINESET line set b Z redshift in vacuum Z_ERR error in redshift VEL velocity offset with respect to reference redshift (km s -1 ) VEL_ERR velocity offset error (km s -1 ) VDISP rest-frame velocity dispersion (km s -1 ) VDISP_ERR error in velocity dispersion (km s -1 ) LINE name of line with the highest S/N SNRMAX maximum S/N for the fitted lines SNRSUM total S/N for all lines SNRSUM_CLIPPED total S/N for lines with S/N > 3 NL number of fitted lines NL_CLIPPED RCHI2 number of fitted lines with S/N > 3 returned reduced χ 2 by the minimization routine STATUS returned status of the fitting function c Col.
14. Column description of source table DR2_CAT PL_ABSFIT pyPlatefit reference spectrum absorption lines fit Col. name Description n n n n n y y y y n n n n n Name MUSE_WHITE MUSE_EXPMAP ORI_CORR_REF ORI_CORR_SEG ORI_CORR_lid ORI_MAXMAP MUSE source identifier exposure map source image (in number of exposure) Description reconstructed white-light source image reference correlation image for ORIGIN reference segmentation image for ORIGIN correlation image for ORIGIN line lid Maximum of correlation image for all ORIGIN sources DATASET ID MUSE data set a Z redshift in vacuum ZCONF redshift confidence (1-3) RA right ascension ( • ) DEC declination ( • ) ORI_ID matched ORIGIN identifier RAF_ID matched RAFELSKI identifier CANDELS_ID matched CANDELS identifier C3DHST_ID matched HST-3D identifier DIST offset from source center (arcsec) HST_ff HST image in filter ff centered on source HST_SEGRAF HST segmentation image for Rafelski catalog and centered on source HST_SEGCAN HST segmentation image for CANDELS catalog and centered on source HST_SEG3D HST segmentation image for 3D-HST catalog and centered on source NB_EMI_lll narrowband source image for lll emission line NB_ABS_lll narrowband source image for lll absorption line SEG_EMI_lll narrowband source segmentation image for lll emission line SEG_ABS_lll narrowband source image for lll absorption line NB_EMI_COMBINED combined emission lines narrowband image NB_ABS_COMBINED combined absorption lines narrowband image SEG_EMI_COMBINED combined emission lines narrowband segmentation image SEG_ABS_COMBINED combined absorption lines narrowband segmentation image Notes.
956  [17]. Schweitzer and Thali used neural networks to diagnose fatal obstructive asphyxia from pulmonary PMCT with a correct recognition rate above 95% based on few cases  [18]. Moreover, the characteristics of an injury may be determined through AI-based models. For instance, in forensic anthropology, Dempsey et al. used machine learning models to determine if a femur bone wound was due to a vertical impact (a fall) or a horizontal impact (vehicle impact) based on femur radiographs. The final model achieved an average precision, recall, and F1-score of 0. 81, 0. 71, and 0. 66, respectively, from a dataset of 103 cases  [19]. AI-based models are also able to determine or assess the severity of an injury or a pathology. For example, Garland et al. examined fatal head injury identification using convolutional neural networks from PMCT images.
For example, Falissard et al. designed a neural network that is able to determine the cause of death from death certificates better than the Iris software  [34]. According to the performance results given by the authors, this neural network outperformed the software by more than 0. 2 accuracy points. Therefore, AI may also be able to perform better than state-of-the-art software. In forensic anthropology, Byeon et al. designed a neural network to discriminate cut marks from trampling marks on bone images. On 20 experimental marks, the neural network achieved an average accuracy of 0. 91 and an average sensitivity and specificity of 0. 9, whereas the average accuracy of taphonomy experts reached 0. 63  [35]. In forensic ballistics, Savakar and Kannur developed an AI-based ensemble classifier for weapon type identification based on skin wound features.
"Invisible" AI to Help with Machine/Machine Dialogue Finally, there is a dimension of the use of AI which will increase in the years to come and which is not necessarily visible to us on a daily basis: that which allows us to interact with or to help program other machines, to create what is called machine-tomachine-machines or software dialoguing with other machines or software. AIs already make it possible to simplify or improve the speed and reliability of certain intermediate operations in certain tasks. Thus, we can use AI to gather sparse data, "structure" it (put it in the same format, in a table), in order to make it compatible with another standard necessary for the use of a given software, for example. This is typically what we have implemented to facilitate multicenter research in forensic medicine in France, with the ORFeAD network  [41].
The routine use of AI depends on the degree of digitization of medical practices; indeed, the existence of data and information systems is necessary both for the development of AI models and for their integration with existing information systems. AI is also highly dependent on the effective interoperability of different data sources and information systems: in fact, we cannot exclude that in order to function and improve its performance; AI requires the interconnection of separate sources, which do not usually dialogue with each other. The querying of such separate databases, for example, medical data on the one hand and judicial data on the other, of course also raises regulatory questions that ought to be clarified. For example, in France today, it is not legal to interconnect personal data with criminal offense data.
Hence, halogenation is expected to result in reduced biodegradability. In WWTPs, therefore, halogenation may have two simultaneous effects on AOPSs, namely decreased biodegradability and increased sorption to sludge. Further, due to increased lipophilicity, the tendency of halogenated AOPSs to bioaccumulate may increase in parallel  [7]. As a counterexample to halogenation, substitutions that increase hydrophilicity, e. g. , hydroxy, carboxylic-acid or terminal-amino residues, are generally expected to be associated with improved biodegradability  [20]. Thus, low biodegradability seems to be a recurring property of halogenated AOPSs. This contribution aims to investigate the general validity of this statement using a dataset of halogenated (more lipophilic) and non-halogenated (more hydrophilic), specifically terminal-amino-, hydroxy-and carboxylic-acid-substituted AOPSs with empirical data on ready biodegradability.
Basic Data A dataset of 1850 unique small-molecule AOPSs was collated during 2020 and 2021 (Supplemental Information in ref. [23] ), comprising international nonproprietary name, Chemical Abstracts Services (CAS) number, molecular formula, molecular mass, Simplified Molecular Input Line-Entry System (SMILES) code of the molecule, empirical lipophilicity (logKOW) and experimental ready biodegradability where available, as well as in silico modelled properties: lipophilicity, biodegradability and WWTP removal, calculated by EPISuite  [22]. EPISuite contains different estimation programs for quantitative structure-property modelling, based on the substance SMILES code as a minimal input, of various environmentally relevant properties of the molecules under consideration (e. g. , KOWWIN for lipophilicity, HENRYWIN for Henry's Law Constant and BIOWIN for aerobic and anaerobic biodegradability of organic chemicals); EPISuite also has a limited internal database for such properties from the literature.
Experimental biodegradability information was collated for that subset of AOPSs for which documented RB results were found in the above dataset (Supplemental Information in ref. [23] ). All compounds with only inherent or WWTP model biodegradability data were assigned not readily biodegradable (NRB) status, while substances that were proposed to be RB based solely on weight-of-evidence considerations were excluded from the dataset. 'Commodities', i. e. , common, low-molecular-weight organics that are being used and therefore listed as AOPSs, e. g. , simple alcohols, sugars, phenol, etc. [23] , were excluded from the list. Note that all radiocontrast agents, many of which contain iodine, were also excluded from the above dataset, as they have no pharmacological function. The retrieved AOPSs were then scanned for the presence of halogens; their type (Br, Cl, F or I) and number per molecule were entered into a Libre Office  [24]  Calc spreadsheet that already contained the above information.
Hydroxy, carboxylic-acid and terminalamino substitutions were also recorded. In order to model the different removal pathways in WWTPs, EPISuite was run for all AOPSs from the above list (i. e. , with empirical RB/NRB information). Where available, empirical logKOW data were entered, else this field was left blank and EPISuite estimated a logKOW value. For AOPSs known to be RB, half-lives were set manually in both the aeration vessel and the settling tank in the STP (=WWTP) tab in EPISuite to 1 h, corresponding to rapid biodegradation, while the primary clarifier half-life was left blank. For AOPSs known to be NRB, the default 'Use 10,000 h half-life' button was cleared and the 'Use BIOWIN output and EPA draft method for assigning half-lives' option was selected in the STP parameters. The predicted shares in per cent of loss to air, sorption to sludge, biodegradation and resulting total removal were recorded in the same spreadsheet.
Statistics The halogenated and hydrophilic-substituents datasets were compared using the nonparametric Fisher's Exact Test to investigate whether substitution and ready biodegradability are independent properties. The approach considers the contingency tables reporting, for each substituent under study, the number of substituted or non-substituted substances that are RB or NRB. For the present application, the Fisher's Exact Test was more appropriate than the χ 2 test because (i) for certain halogens, the row (or column) totals were particularly unbalanced, and (ii) the observed counts in some categories were close, if not equal, to zero. The null hypothesis of the test states that there is no association between the two properties, i. e. , the probability of being RB among the substituted molecules is equal to the probability of being RB among the non-substituted ones.
In addition, the EPISuite-predicted removal at different stages of the wastewater treatment process (sorption to sludge, biodegradation and total WWTP removal including losses to air) was compared between groups: (i) RB vs. NRB compounds and (ii) halogenated vs. in silico dehalogenated compounds. Due to the non-Gaussian distribution of most variables under study, non-parametric statistics were preferred over parametric statistics: comparisons between RB and NRB AOPSs were therefore carried out using a Wilcoxon-Mann-Whitney test, while comparisons between paired data from halogenated AOPSs and their dehalogenated analogues were performed with a Wilcoxon signed-rank test. The tests were performed in Python3  [25]  (under Linux ® Mint  [26] ) as well as R, version 4. 2  [27]  (under Windows ® ) to check the consistency of the results.
Dataset Empirical ready biodegradability data have been retrieved for 230 unique AOPSs (Supplementary Table  S1 ), of which 33 (14. 3%) are RB and 197 (85. 7%) are NRB (Table  1 ). Ten commodities comprising acetic acid, benzyl alcohol, ethanol, ethanolamine, glycerin, lactitol, phenol, propyl alcohol, triethanolamine and urea were excluded, all of which are RB and would have biased the share of RB compounds if left in the dataset. Out of these 230 AOPSs, 70 are singly or multiply halogenated: 38 are chlorinated, 39 fluorinated, 3 brominated and 2 iodinated. Of the 70 halogenated compounds, none (0%) proved to be RB. Among the 160 non-halogenated APOSs, 33 (20. 6%) are RB and 127 (79. 4%) are NRB.
Comparing Halogenated and Hydrophilic Pharmaceuticals Comparing the halogenated and the hydrophilic AOPSs with those compounds that do not have the respective substituent using Fisher's Exact Test, results in the finding that for both chlorinated, fluorinated and the sum of all halogenated AOPSs biodegradability is clearly different from the non-halogenated set (Table  1 ; for contingency tables and Py-thon3 program for Fisher's Exact Test see Supplementary Table  S2 ). Based solely on the p-values, no such conclusion can be drawn directly for the brominated and iodinated AOPSs, which is due to their very low numbers among the dataset. However, the ORs computed for all halogenated AOPSs, singly or combined, are all 0, and for chlorinated, fluorinated and the sum of all halogenated AOPSs the computed 95% confidence intervals on these values are quite far from 1 (Table  1 ).
Comparing Wastewater Treatment Plant Removal Pathways EPISuite was run for all 230 AOPSs with RB/NRB information, the WWTP removal results are given in Supplementary Table  S3. The comparison between RB and NRB compounds of the modelled removal pathways sorption to sludge, biodegradation and total removal (including losses to air) by Mann-Whitney-Wilcoxon tests is presented in Table  2. All statistical data suggest clear differences between the two groups. For RB AOPSs, the range of values confirms that, overall, sorption to sludge is a minor removal pathway compared to biodegradation (under the ideal situation of rapid biodegradation in the aeration vessel and settling tank, as entered into EPISuite in paragraph 2. 1); the median predicted total removal among this group of compounds is very high with ~91%.
Comparing Halogenated and In Silico Dehalogenated Pharmaceuticals To further elucidate the role of halogenation, all halogen atoms were removed in silico from the SMILES codes of the 70 halogenated AOPSs and EPISuite was run again with the dehalogenated analogues. A comparison of the halogenated and dehalogenated results regarding predicted ready biodegradability and WWTP removal pathways (sorption to sludge, biodegradation and total removal including losses to air) is given in Table  3  (full data in Supplementary Table  S4 ). Table  3. Pairwise comparison between the wastewater treatment plant removal pathways for the original 70 halogenated active organic pharmaceutical substances (AOPSs) with their dehalogenated analogues, both modelled by EPISuite. The p-value is associated with the (paired) Wilcoxon signed-rank (WSR) test.
Among the 70 halogenated AOPSs, none are experimentally RB. EPISuite correctly predicts 66 (94. 3%) to be NRB and falsely estimates 4 (5. 7%) to be RB. The latter (bronopol, chlorphenesin, epichlorohydrin and ethyl chloride) are all relatively small molecules with masses ranging from 64. 5 to 202. 6 Da, while the average molecular mass of the 230 AOPSs with RB data is 357. 2 Da and the average mass of the 1850 compounds in  [23]  is 368. 1 Da (not shown). The EPISuite RB predictions were previously shown to be reasonably accurate: sensitivity = 0. 77, 90% confidence interval (90% CI) = 0. 64-0. 86; specificity = 0. 89, 90% CI = 0. 85-0. 92 [23]. Among the dehalogenated forms, six (8. 6%) are predicted by EPISuite to be RB. This comparison further supports the finding that halogenation does impair ready biodegradability.
However, the total removal in WWTPs is not as clearly different between the two groups, which reflects the complementarity of the removal pathways of higher sorption for halogenated vs. higher biodegradation for dehalogenated forms. However, as found above, sorption to sludge of halogenated AOPSs cannot fully compensate for their lack of ready biodegradability. Ethyl chloride was earlier predicted to be the only AOPS in the dataset with significant losses to air (29%), while its dehalogenated analogue, ethane, is calculated to reach 81% losses to air. Considering a Henry's Law constant KH of 0. 011 atm × m 3 × mol -1 at 24. 8 °C for ethyl chloride  [30]  and a 45 times higher KH of 0. 5 atm × m 3 × mol -1 for ethane (both KHs empirical), as cited by EPISuite  [22] , this strong increase in losses to air is plausible.
Experimental Ready Biodegradability Dataset A total of 230 small-molecule AOPSs, excluding commodities, with empirical ready biodegradability information was investigated for the presence of halogen or hydrophilic substituents and their likely influence on ready biodegradability. While 230 compounds may not be very much, considering at least 1850 different small-molecule AOPSs are on the market that were collated from DrugBank  [23] , it is still more than one-tenth of that total. To our knowledge, no dataset of such a size has been collated so far in order to elucidate the influence of simple substituents on ready biodegradability. Out of these 230 AOPSs, 33 are experimentally RB, while 197 are NRB. The RB fraction of 14. 3% (Figure  1 ) most probably overestimates the actual share of RB compounds among all AOPSs, which was recently estimated between 3.
9% (experimental data only, without commodities) and 7. 2% (EPISuite-predicted data) among the larger dataset with 1850 AOPSs  [23]. In the present entire dataset, the predicted RB fraction is slightly higher at 7. 9%. Considering the results detailed in Sections 3. 2 and 3. 4, while halogenation is certainly not the only property of a molecule to impact biodegradability, our data support the conclusion that it is an important one. This is further underpinned by the comparison of the EPISuite-predicted WWTP removal pathways between the original halogenated AOPSs and their in silico dehalogenated counterparts. Overall, for most of the latter, sorption to sludge is predicted to be lower than for the halogenated ones while the biodegradation share is plainly higher. Increased sorption to sludge for the halogenated AOPSs, however, cannot fully compensate for the much higher removal through biodegradation for the dehalogenated forms.
Representativity of the Ready Biodegradability plus Halogenated Dataset Another question concerns the representativity of the 230 AOPSs with empirical RB data, of which 70 are halogenated; among the latter, 38 (54. 3%) are chlorinated, 39 (55. 7%) fluorinated, 3 (4. 3%) brominated and 2 (2. 9%) iodinated. Is the ratio of 70 halogenated out of 230 (30. 4%) realistic for all pharmaceuticals? Among the 1850 AOPSs in  [23] , 566 (30. 6%) are singly or multiply halogenated: 282 (49. 8%) are chlorinated, 243 (42. 9%) fluorinated, 30 (5. 3%) brominated and 11 (1. 9%) iodinated. The shares of halogenated AOPSs among the two totals are very close together, while the fractions of the different halogens show a higher variability. Thus, the figure of just over 30% halogenated AOPSs overall seems reasonable, while the shares of the different substituents vary somewhat more.
Limitations Sumpter et al. [62]  have recently argued that scientific papers should include a 'Limitations' section, where shortcomings in the sources, methods or conclusions should be listed by the authors. The first limitation of this work is the (still) comparatively low number of AOPSs, with or without halogenation, with ready biodegradability data. Additionally, there is some inherent uncertainty about the equivalence of the tests, as discussed above. Apart from the tests, other properties of compounds besides halogenation will undoubtedly influence biodegradability. In addition, both WWTP characteristics and realistic influent concentrations will have an impact on substance removal. Further, while representativeness has been checked regarding the occurrence of halogenated compounds in the dataset, it is not clear whether this dataset is representative of all human pharmaceuticals regarding disease areas covered; on scanning the list, several salicylates (pain and inflammation), "-tinib" tyrosine kinase inhibitors (cancer) and benzodiazepines (neurology and anaesthesia) catch the eye, all of which may be overrepresented, while there are not too many antibiotics or specific ophthalmologicals, for instance.
Relationships of halogen and hydrophilic substituents in a set of 230 active organic pharmaceutical substances (AOPSs) with experimental ready biodegradability data. Considering the contingency table comparing the readily biodegradable fraction of the halogen-or hydrophilic-substituted AOPSs against the respective non-substituted AOPSs, the pvalue is the probability of all allocations equally or more extreme than observed, under the null hypothesis (absence of association between substitution and ready biodegradability); note, p = 1 indicates too small a sample size to compute a probability. For Odds Ratios, please refer to the text. CI = Confidence interval; NA = not applicable; NRB = not readily biodegradable; RB = readily biodegradable; ∞ = infinite (for practical purposes) Odds Ratio. In the dataset of 230 compounds with experimental RB data, 142 AOPSs containing one or more hydroxy, carboxylic-acid or terminal-amine substituents, but no halogens, were identified, out of which 33(23.
In the dataset of 230 compounds with experimental RB data, 142 AOPSs containing one or more hydroxy, carboxylic-acid or terminal-amine substituents, but no halogens, were identified, out of which 33(23. 2%)are RB and 109 (76. 8%) are NRB. Regarding the single substituents, out of 102 hydroxylated AOPSs, 24 (23. 5%) are RB; among 37 with carboxylic acid groups, 12 (32. 4%) are RB; and out of 42 compounds with terminal amine substituents, 8 (19. 0%) are RB. Molecular Fragment Empirical Biodegradability Fisher's Exact Test Groups Substituents Fragments per molecule, n AOPSs, n RB, n RB, % NRB, n NRB, % p-Value Odds Ratio (95% CI) All AOPSs NA 230 33 14. 3 197 85. 7 NA NA Non-halogenated AOPSs NA 160 33 20. 6 127 79. 4 NA NA Halogenated AOPSs Fluorine 1-7 39 0 0 39 100 0. 0020 0 (0-0. 51) Chlorine 1-6 38 0 0 38 100 0.
Statistical analysis Data analysis was performed with SPSS 23. 0 1 software (Armonk, NY, USA). Quantitative parameters were presented as means and standard deviations while qualitative parameters were expressed as absolute values and percentages. The normality of the tested parameters was assessed by a Kolmogorov-Smirnov test. At baseline, to compare the two groups (AKP+ and AKP-), either a t-test was performed after having performed a Levene's test for the comparison of variances in case of quantitative variables, or a χ2 test in case of qualitative variables. The time effect between the two isokinetic tests was measured by comparing the AKP+ and AKP-groups using an analysis of variance for repeated measures (2 times x 2 groups). A generalized linear multivariate (GLM) model analysis was carried out to assess the weight of potential inter-subject explanatory parameters of the evolution of the knee strength LSI.
monsoonal trough. CAMP 2 Ex's preliminary results indicate 1) increasing aerosol loadings tend to invigorate congestus convection in height and increase liquid water paths; 2) lidar, polarimetry, and geostationary Advanced Himawari Imager remote sensing sensors have skill in quantifying diverse aerosol and cloud properties and their interaction; and 3) high resolution remote sensing technologies are able to greatly improve our ability to evaluate the radiation budget in complex cloud systems. Through the development of innovative informatics technologies, CAMP 2 Ex provides a benchmark dataset of an environment of extremes for the study of aerosol, cloud and radiation processes as well as a crucible for the design of future observing systems.