dataesr/scientific-paragraphs-categorization huggingface dataset:
The biosynthesis of SeNPs using bacteria (both anaerobic and aerobic) has been extensively explored due to their ability to colonize, adapt to, and persist in detrimental environmental niches. However, it is of great interest to search for new bacterial strains capable of producing SeNPs in order to establish a more comprehensive data bank and to identify the factors affecting the properties of SeNPs. Indeed, several studies have established that the biological functions of SeNPs depend upon various factors, such as the synthesis method, surface composition, morphology, and size. These characteristics may cause the intake of plant foods biofortified with SeNPs to have a different effect within the human body compared with other sources of Se that are conventionally used. Amongst the bacterial populations, endophytic bacteria are considered potential candidates for NP bio-production due to their high secretion of active metabolites  [36].
2% (w/v) Poly(ethyleneimine) (Sigma-Aldrich, Dorset, UK) solution to enhance bacterial cell adhesion and to prevent the removal of the bacteria during experiments. Then, the glass coverslips were rinsed three times in Tris buffer to eliminate the excess of cells and left to air dry for 15 min. Samples were imaged in air using AFM (Witec Alpha 300, Ulm, Germany). All images were collected in AC mode at room temperature using Hi'Res-C14/Cr-Au probe of thickness 2. 1 µm, length 125 µm, width 25 µm, resonance frequency 160 kHz, and a force constant of 5 N/m. The data acquired during surface scanning were converted into images of topography and amplitude. In the topography images, the shape, structure, and surface of selenite-tolerant bacteria could be observed. Furthermore, amplitude images allow the visualization of fine surface details of bacterial cells  [42].
TEM TEM analysis was performed to characterize the size and shape of synthesized SeNPs. TEM observations were carried out at the Center for the Development of Nanoscience and Nanotechnology (University of Santiago de Chile) using a HITACHI HT-7700 (Tokyo, Japan) instrument operated at an accelerating voltage of 120 kV with a Tungsten filament, allowing a resolution of 0. 2 nm. Samples (powder) were dispersed in distilled water using an ultrasonic bath and deposited on a copper TEM grid (300 mesh) coated with Formvar/carbon. Water was subsequently evaporated at room temperature. The size of the SeNPs was determined via ImageJ software (version 1. 53t)  [43].
Raman Spectroscopy Raman measurements were performed to explore the crystalline traits and lattice dynamics of the synthesized SeNPs. Solid SeNPs were placed on a clean cover slip. Normal Raman spectra were recorded using Witec Alpha 300 equipment with an excitation wavelength of 633 nm (spectral range 150-2100 cm -1 ). The acquisition interval was 1 s, and all spectra were averaged over 32 independent runs. The acquired digital experimental spectroscopic data were processed and plotted using Origin 8. 5.
X-ray Diffraction XRD patterns were performed using a RIGAKU Multipurpose diffractometer model Smartlab, with Theta-Theta Bragg-Brentano geometry goniometer and coupled to a solidstate detector D/teX Ultra 250 (Rigaku Corporation, Akishima-shi, Japan) for data acquisition. The X-ray beam was generated using a copper tube target (Cu Kalpha λ = 1. 5418Å) at 30 kV/10 mA and filtered with Ni. The measure was recorded at room temperature between 5 and 55 • 2-theta (degrees) with a step of 0. 02 • and a scan speed of 1 • /min. Optical configurations were adjusted by divergent and receiving slits on both sides, with parallel Soller slits of 5 • and 5 mm, respectively. The instrumental resolution was aligned with NIST SRM660c standard (LaB6). While the XRD measurement was carried out, the sample films were positioned on a silicon wafer with an orientation of <100> (about 69 • ).
X-ray Photoelectron Spectroscopy XPS measurements were performed using an X-ray photoelectron spectrometer (SPECS, Berlin, Germany). A FlexPS system from SPECS was equipped with a hemispherical analyzer PHOBIOS 150 and detector 1D-DLD, with a monochromatic X-ray source FOCUS 500 providing Ag K-alpha radiation with a characteristic energy of 2984 eV, using spot size of 500 µm and depth of 10 nm. Analyses were performed using two different points of powdered samples, and the elemental composition was analyzed using CasaXPS software (version 2. 3. 19, Casa Software Ltd, Las Vegas, NV, USA).
Site of Experimentation Two open field trials were performed on a private Farm, in El-Fayoum, Egypt (29. 5004 N,  30. 8767 E) in 2017 and 2018. Before the initiation of each experiment, soil samples were taken at a depth of 25 cm, and the physicochemical parameters of the experimentation site were measured. Soil samples were analyzed according to the standard published procedures  [37, 38] , and the results are presented in Table  1. According to the aridity index, the research region has a hyper-arid climate  [39]. Information regarding the meteorological data for El-Fayoum during the study months (September-November) is shown in Table  2.
Statistical analysis Data analysis was carried out to describe reaction observed during the different treatment process, but also estimate error in values and nally have a visualization through tables and gures. Statistical analysis was performed using the R software packages 3. 6. 1 (https://www. r-project. org). Spearman's rank correlation, Principal component analysis (PCA) was performed with R 3. 6. 1. Statistical signi cance was determined with a P value < 0. 05.
Acknowledgments: We would like to thank all the partners. University of Bordeaux. Cheikh Anta Diop University. University of Geneva  for the collaboration. Thanks to the  Applied biotechnology and environmental bioprocesses research group (Ecole Supérieure Polytechnique. Université Cheikh Anta Diop)  for making human and material resources available for molecular extraction. Thanks to AFFINISEP (https://www. a nisep. fr/) which provides recommendations and procedure for activation and extraction of the passive samplers.
| Participants Twenty-three individuals with a smoking habit and twenty individuals who never regularly smoked (fifteen women in each group) voluntarily participated in this study, having signed an informed consent before the session. All participants were Spanish from Tenerife (Spain) and were recruited by convenience in the towns of La Laguna and Santa Cruz de Tenerife. They received 15€ after the completion of the study, which was approved by the Ethical Committee of the University of La Laguna (Tenerife, Spain). Each participant first filled out an online questionnaire in which demographic and personal data were collected (gender, age and smoking history) and neuropsychological tests (Barratt Impulsiveness Scale; STAI-R; Beck's Depression Questionnaire; and the Positive Affect and Negative Affect Scale, PANAS)  (Salvo & Castro, 2013; Sandin et al.
An ANOVA performed on the intensity with the Picture type (appetitive, smoking-related, neutral) and the Visual perspective (first-person, third-person) as between-subject factors confirmed no differences between picture types (F 2,114 = 2. 04, p = 0. 14) or visual perspectives (F 1,114 = 0. 03, p = 0. 95). The emotional valence and arousal-inducing properties of each picture were assessed and validated online on an independent pool of 83 participants (76% females, 15% of smokers) using the Psytoolkit software  (Stoet, 2010).
| EEG preprocessing Data were processed offline using the Brain Analyzer software (Brain Products GmbH, Gilching, Germany). The EEG recorded data were filtered with a 0. 1-30 Hz band-pass, re-referenced to the algebraic mean of the activity at the two mastoids and corrected for ocular artefact using independent component analysis (ICA,  Makeig et al. , 1995; Jung et al. , 1998). Raw data, only including correct responses, were then segmented in 2-s epochs centred on the time of picture onset. Manual artefact rejection was carried out, resulting in a similar trial rejection rate between smokers and controls (3. 5% and 3. 2%, respectively, t 38 = 0. 30, p = 0. 77). Data were then subjected to further processing in order to extract the relevant electrophysiological data, with three objectivedriven segmentations carried out as per the following pipeline: 1.
| Statistical analyses Data are presented as means ± 1 SEM or box plots (medians ± 25% and min/max as whiskers) and were analysed using "R" software (version 3. 4. 0) with the ULLRToolbox (https://sites. google. com/site/ullrtoolbox/home). Assumptions of normality and homogeneity of variance were verified using the Shapiro-Wilk's and Cochran test, respectively. The emotional valence and arousing properties of each picture of the bespoke first-and third-person visual perspective-matched picture library created for the purpose of the study was established in an independent cohort of 83 individuals, including 12 smokers, using an analysis of variance (ANOVA) with smoking status (smokers vs. non-smokers) as between-subject factor and the visual perspective (1-VP vs. 3-VP) and picture category (appetitive, smoking-related and neutral) as within-subject factors.
ACKNOWLEDGEMENTS This work was conducted at the  Laboratory of Cognitive Neuroscience and Psycholinguistics, University of La Laguna. J. D. was supported by a  Postdoctoral fellowship  from the  University of La Laguna  ( Programa Agustin de Betancourt ). HAB was supported by the  Spanish Ministry of Science and Innovation  (grant  PID2020-118487GB-I00 ). D. B. was supported by a  Medical Research Council   Programme grant  ( MR/N02530X/1 ) to DB,  Barry Everitt ,  Amy Milton ,  Jeffrey Dalley  and  Trevor Robbins. The authors would to thank  Dr Tristan Hynes  for their careful reading of the proof of this manuscript. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.
Dimensional analysis and Cluster Identi cation based on nicotine and cue omission tests Z-score Normalization of cue omission and nicotine omission variables. Dimensional analysis such as principal component analysis (PCA) and clustering algorithms are sensitive to the scale of the variables. Normalizing the data using z-scoring ensures that each variable contributes equally to the analysis and prevents scale-related biases. Z-score normalization was performed for the 4 variables of interest. The zscore formula for a given individual and a given variable is the following: zi =(xi-µ)/σ , where xi is the value of the data point of the individual for the given variable, µ and σ are respectively the mean and the standard deviation of the all dataset (n = 62 rats) for this variable. The z-score normalization transforms the data in such a way that each variable has a mean of 0 and a standard deviation of 1.
Principal Component Analysis (PCA). Using Bartlett's test of sphericity and Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, we rst assessed that our set of variables justi ed the use of multivariate analysis methods, such as PCA, which we performed to analyze the underlying dimensionality of the normalized CueOm and NicOm variables in our i. v. nicotine + cue group dataset (n = 62). Ascending Hierarchical Clustering (AHC). We investigated the grouping patterns based on the scores of the four normalized variables of interest (CueOm-GE-zscore, NicOm-GE-zscore, CueOm-LE-zscore, and NicOm-LE-zscore) using Agglomerative Hierarchical Clustering (AHC). AHC does not require specifying the number of clusters beforehand and is generally less sensitive to outliers compared to other clustering algorithms like K-means.
Statistics Self-administration behaviour was analyzed using one way or repeated measures ANOVA with Time [sessions or time (min) within session], Hole (active vs inactive), treatment (NicOm vs NicOm + VAR) as within-subject factor, and cluster (cluster A vs cluster B) as between-subject factor. Signi cant main effects or interactions were explored by pairwise comparisons of means using the Duncan post hoc test. A linear regression was performed to test the relationships between the cotinine/nicotine ratio and the number of nicotine self-infusions. Grubbs tests were performed to detect outliers. ANOVAs, linear regression and Grubbs tests were performed using the STATISTICA 13. 3. 0 (2017) data analysis software (TIBCO Software Inc, Palo Alto, CA, USA). XLSTAT was used to run PCA and AHC (Data Analysis and Statistical Solution for Microsoft Excel, Addinsoft, Paris, France, 2017).
Principal Component Analysis (PCA) -components underlying the four variables of interest The suitability of our variables and dataset for PCA was con rmed through the Bartlett test (p < 0. 001) and the Kaiser-Meyer-Olkin (KMO) value (> 0. 5) (Table  S1 ). Initially, a Pearson correlation matrix (Table  S2 ) was employed to explore the relationships between the original variables prior to conducting the PCA itself. The Global Effect (GE) and Loading Effect (LE) variables were identi ed as non-redundant and capable of capturing different aspects of the omission effect. Notably, there was no proportional relationship between CueOm-GE and CueOm-LE (r 2 =-0. 131, ns). Likewise, NicOm-GE and NicOm-LE displayed a weak association (r 2 =-0. 379, p < 0. 05). This observation was further corroborated by the PCA analysis, which revealed four components.
2f right ). F2 seems to encompass the loading effect regardless of the omission type: F2 is primarily associated with Zscore_CueOm-LE, while Zscore_NicOm-LE contributes nearly equally to both F2 and F1. Lastly, F3 seems to represent the global effect of CueOm, as indicated by its stronger correlation with Zscore_CueOm-GE. Individual variations in the respective contribution of nicotine and the cue to self-administration: identi cation and characterization of two clusters (A and B) and relationships with the PCA factors In order to explore whether distinct response patterns to CueOm and NicOm are present, we conducted Ascendant Hierarchical Clustering analysis (AHC) on the four normalized variables of interest. Our nicotine + cue dataset (n = 62) satis ed the requirements for minimum sample for clustering techniques (2 d where d is the number of dimensions)  [35].
Psychopharmacological features of Clusters A and B Response to the reinforcing effects of nicotine: Progressive ratio and dose-response curve for nicotine To evaluate the primary reinforcing effects of nicotine, we conducted progressive ratio (PR) and FR3 dose-response tests in CueOm conditions. Rats from ClusterB exhibited a higher breakpoint for nicotine self-administration (Fig. 4a -left) [Cluster, F(1,59) = 16. 34, p < 0. 0005] and sustained responding throughout the PR session (Fig. 4a-right ). Data was averaged over the two sessions of PR as the difference between clusters was similar in the two sessions [Cluster x Session, F(1,59) = 0. 37, p = 0. 54, not shown]. Additionally, the dose-response curve for nicotine self-administration at FR3 tended to be shifted upward in rats from cluster B with maintenance of self-administration for the lower nicotine dose (Fig.
Data collection Information collected from the initial geriatric evaluation and used by this research project are listed in table  1. Data from the geriatric and medical evaluation are variables of interest (secondary outcomes). Variables have been chosen to match with already known risk factors previously described, or to explore geriatric syndromes such as mobility and functional independence, falls, cognitive function, malnutrition and feeding issues, polypathology and polypharmacy. Social context information is also collected. If the written consent is obtained, a unique inclusion number will be assigned to each patient. Data will be collected from the institutional electronic patient record, and anonymously reported on an electronical case report form (eCRF). The eCRF developed for this study uses the REDCAP software (V.
The sample is vortexed for 10 s and kept frozen at -20°C. 30 31  Samples are analysed by the reference method using high-performance liquid chromatography coupled to Open access electrochemical detection with H-Class Waters chromatographic system (Waters, St Quentin en Yvelines, France) and Empower3_HF1_Enterprise software (V. 7. 30. 00. 00, Waters). After defrosting, stabilised plasma are centrifuged 10 min at 15000 g. The supernatant is vortexed in equal proportions with the vitamin C mobile phase buffer (Chromsystems, Gräfelfing, Germany) and injected into the chromatographic system. The separation is run at 37° over a reverse column using isocratic elution with vitamin C mobile phase (Chromsystems) containing KCl 3M (Merck KGaA, Darmstadt, Germany). AA is oxidised at a potential of +0.
Purpose There is increasing concern about the detrimental health effects of added sugar in food and drink products. Sweeteners are seen as a viable alternative. Much work has been done on health and safety of using sweeteners as a replacement for added sugar, but very little on their sustainability. This work aims to bridge that gap with a life cycle assessment (LCA) of sucralose derived from cane sugar grown in the United States of America (USA). Methods An attributional, cradle-to-gate LCA was conducted on sucralose production in the USA. Primary data were derived from literature for the chlorination process, and all other data from background sources. Results are reported via the ReCiPe 2016 (H) method, with focus given to land use, global warming potential (GWP), marine eutrophication, mineral resource scarcity, and water consumption.
Extended author information available on the last page of the article In parallel, there is great concern regarding the environmental impact of food production and consumption (e. g. ,  Behrens et al. 2017; Ibarrola-Rivas and Nonhebel 2022 ). Yet, to date, there has been very little study of the sustainability of ingredients which may replace the sweet taste of sugar. For instance, there is only one relating to a sweetness enhancer, thaumatin  (Suckling et al. 2023a). In terms of NNSs, studies are limited to six life cycle assessments (LCAs); five studies for steviol glycosides produced in various ways  (PureCircle 2015; Cargill 2021; Gantelas et al. 2022; Milovanoff and Kicak 2022; Suckling et al. 2023b) , and one for aspartame produced by fermentation, in the World Food LCA Database  (Nemecek et al.
Therefore, it was necessary to find an alternative approach to deriving the life cycle inventory. Examples of such approaches have been described previously by  Hischier et al. (2005)  and Mila i  Canals et al. (2011). A more recent method developed for deriving process data for chemicals has been developed by  Huber et al. (2022) ; the RREM. In that study,  Huber et al. (2022)  developed a four-step methodology for building up inventory for chemicals which are not present in databases, and proposed methods for dealing with assumptions arising from data gaps. This study took a similar approach wherein sucralose synthesis data were derived from patent  US 7,884,203 (Wang et al. 2011)  and uncertainties or gaps in data accounted for where possible. The results provide insight into the environmental impact of, and uncertainty relating to, producing sucralose, and the challenges associated with conducting an LCA of highly refined food additives in this manner.
Alternative manufacturing locations for both sugar and chemicals are explored in scenario modelling. The LCA environmental impact assessment was conducted using SimaPro 9. 3 software and the ReCiPe 2016 Midpoint (Hierarchist) method  (Huijbregts et al. 2016). Environmental impact within all impact categories of the ReCiPe 2016 method is reported, with focus given to global warming potential, land use, water consumption, marine eutrophication, and mineral resource scarcity.
Functional unit, goal, and scope The goal of the LCA was to assess the environmental impact of producing 1 kg sucralose (E955) from sugar derived from sugarcane. As sucralose is a popular NNS used in many food and drink formulations, its use in each formulation is out of scope for this LCA, as is transport away from the factory. Therefore, the study is cradle-to-factory-gate. The functional unit is defined as "production of 1 kg purified sucralose". The LCA was undertaken in line with the  ISO 14040:2006 (ISO 2010a ) and ISO 14044:2006 (ISO 2010b)  guidelines. The study included all synthesis steps outlined in US 7,884,203 and background process data were taken from the Ecoinvent 3. 8 and Agri-footprint 6. 1 databases. Where an input material was not available in the databases, proxy processes were developed based upon literature sources.
Therefore, the mass of DCM was estimated using a reference for sucralose solubility in methanol and the assumption that sucralose-6-acetate has the same solubility in DCM  (Li et al. 2010). For activated carbon, the source used was commercial data (SAS 2022), with mass derived from an absorption efficiency of 33. 3% activated carbon mass and 1% of reaction solution mass being absorbed. Effect on the LCA results of varying consumption of both DCM and activated carbon are explored in Section 5. 2. TCA was not in available LCI databases; therefore, a proxy was created from data given in  Kabisch (1956) , and reproduced in Supplementary Material, Section 1, Table  S3. 3. Sucralose-6-acetate purification: 1 kg sucralose-6-acetate is purified using 10% methanol in water (net consumption of 5. 94 g methanol and 1.
Reactivation of 128. 0 g activated carbon is also modelled using the process from the Ecoinvent 3. 8 database. Transport of chemicals is modelled as per previous steps and is 521. 5 kg. km. Unrecovered catalysts and solvents are emitted to water: 478. 9 g methanol; 487. 8 g water; potassium compounds 4. 88 g (as a proxy for potassium hydroxide); and 50. 3 g organic chlorine compounds (as a proxy for unreacted sucralose-6-acetate). From stochiometric equations in Supplementary Materials, Section 3, it is understood that methyl acetate is a by-product of the reaction and 1. 86 g unrecovered methyl acetate is emitted to water. No masses of activated carbon or potassium hydroxide were stated in  US 7, 884, 203 ; therefore, these were estimated, with mass of activated carbon derived as per step 2.
The results show that although sucralose tends to reduce the environmental impact of providing 1 kg SE by 80% or more across most impact categories, this is not true of all impact categories. The impact in GWP of sucralose on a sweetness basis is approx. 10% to 15% that of a global mix of sugar and US cane sugar respectively; MEu is between 0. 3% and 0. 4% respectively; LU between 0. 5% and 0. 8% respectively; and WC between 1 and 2% respectively. In contrast, the impact of sucralose is greater than sugar in MRS at 149% to 155%, respectively, because of demands placed by excess PCl 3 and sodium bicarbonate consumption. Finally, HnCT has a negative relative impact (not shown in Fig. 4 , instead numerical data in Supplementary Material, Section 5, Table  S5 ), which is due to background process data for sugar in the Agri-footprint database, showing a net absorption of certain toxic materials during sugarcane growth and reflects the cradle-to-gate nature of the study.
Limitations and further research This LCA study is for sucralose production, and several instances of estimated or assumed data have been highlighted. Some materials had no existing background LCA processes and needed proxy processes. Further research into these materials would help in reducing uncertainty in the results. The LCA study is only cradle-to-factory-gate and does not include use of sucralose in formulations. Therefore, further work to explore the impacts of sucralose in context of whole food or drink products, and within a dietary context, would allow better understanding of the whole life cycle impacts of producing and consuming sucralose. Life cycle inventory data used in this study were derived from patent literature, and representativeness of the process chosen (  US 7, 884, 203)  was verified by industrial experts (private communication).