dataesr/scientific-paragraphs-categorization huggingface dataset:
The rendering code -Mitsuba The Mitsuba rendering code enables simulation of radiance as observed from a perspective camera. Similarly to 3DMCPOL, Mitsuba implements 3D Monte-Carlo calculations. However, Mitsuba back-propagates radiance from each camera to the sun. Mitsuba is a relatively fast rendering software. However, it is not based on physical first-principles, that can be derived for an atmospheric medium. This affects the radiometric reliability of the images. However, this effect is not significant in the context of 3D geometric surface reconstruction and tracking of feature points. Here are the assumptions that underlie Mitsuba, and our use of it: 1. Only one particle type exists in the medium: a cloud droplet. Hence, the effects of scattering by molecules and aerosols are not accounted for.
The vertical section of water content (see Fig. 5a) and the relatively low winds (see Fig. 5band Fig. 5c), of up to 10 m/s, were plotted for a horizontal resolution of 20 m. With a cloud base between 1 and 2 km, this test case is an example of not well-developed cumulus cloud as often found at mid-latitudes. For the liquid phase, which is the only cloud phase taken into account in this case, the optical 215 properties were quantified from the liquid water content and the effective radius using the Mie theory of scattering. Extinction coefficient σ liq and the phase function (PF) were then fed into the rendering code Mitsuba (Wenzel, 2010) to obtain the cloud radiance. In comparison to the deep convective case we notice vertical winds and COT values (see Fig. 5g) up to four and seven times lower, respectively.
Then, the nurse gave the patient the perceived stress-VAS at the beginning (T0) and at the end (T1) of the BNC. The patient was asked to self-assess her/his level of perceived stress using the VAS and then, to turn the VAS over so that the nurse could not see it (to limit a social desirability bias). Neither, the patient nor the nurse saw the T0 score when she/he ticked the line at T1, about 30-40 min afterwards (to limit a memory bias). Also, at the end of the BNC, the 25-question questionnaire was handed to the patient, together with a prepaid envelope. The participant was asked to fill it in and to send it back within 15 days. In cases where the questionnaire was not received by the project team within 15 days, a phone call was made by a nurse, to the patient. The nurse who conducted the BNC collected the patient's clinical data on the same day.
Statistical considerations Based on the prospective primary objective and on the literature data concerning the VAS of perceived stress  (Guerdoux-Ninot and Trouillet, 2019) , the necessary number of patients to be included in the study would have been 32, as a minimum. However, taking into account the retrospective secondary objectives to be assessed, it was decided to include all patients who underwent the BNC during the inclusion period and who consented to study participation after the study was presented to them. All quantitative data was analysed using the lmer function of the lme4 package  (Bates et al. , 2015)  for R (R Core Team, 2013). The Restricted Maximum Likelihood method was used to test the adjustment to data of several mixed-effect models (M ij ) describing the relationship between the perceived stress and fixed effects and a random effect.
Acknowledgements We would like to thank all the nurses who were involved in the definition of this study and in clinical data collection. We also thank  Sophie Gourgou  for her help in data computerised collection,  Aurélie Gastou ,  Vanessa  and  Cédric Guillaumon  for their institutional support,  Aurélie Ruivet  for her contribution in the focus group of 'bad news',  Mary Harries  for her help in English editing, and  Louise Coutant  for her help in daily professional life.
regarding the efficacy of NR in reducing the risk of progression to severe disease in large cohorts of infected KTRs is lacking, because those patients were not represented in clinical trials. 3 Second, due to the inhibitory effect of ritonavir on cytochrome (CYP) P450-3A and P-glycoprotein, NR has significant and potentially harmful interactions with many medications, including CNIs. 4  The aim of the current study is to provide data regarding the management, safety, and efficacy of NR treatment of SARS-CoV-2 infection in a cohort of KTRs.
Patient Selection NR has been available in Belgium since April 15, 2022. Since then, this treatment was applied to KTRs followed-up in our center, with the following inclusion criteria : (i) confirmed SARS-CoV-2 infection with positive polymerase-chain reaction (PCR) on nasopharyngeal swab; (ii) symptoms evolving in <5 days; (iii) baseline estimated glomerular filtration rate (by Chronic Kidney Disease Epidemiology Collaboration) >30 ml/min per 1. 73 m 2 ; (iv) no dyspnea, and oxygen saturation >95% at room air; and (v) no pregnancy or breast feeding. This study retrospectively includes all KTRs followed in our kidney transplant center who actually received the treatment between April 28, 2022 and June 3, 2022. Patients were followed-up with until June 16, 2022 (data cutoff) for a median time of 34 days (ranging from 14 to 51 days).
6 However, 4 patients experienced a supratherapeutic tacrolimus concentration after restarting tacrolimus. In our study, therapeutic drug monitoring data were available for 12 patients. Using the proposed recommendations  5  of discontinuation 12 hour before  the start of NR for tacrolimus and administering 20% of the usual daily dosage for cyclosporine, CNI trough concentrations at the end of the treatment were mostly satisfactory. However, we also observed important individual variability. These data suggest that CNI clearance might present a large inter-individual variability among transplant recipients and that some patients may display a longer CYP3A4 and/or Pglycoprotein inhibition leading to increased exposure on restarting immunosuppressive treatment. Therapeutic drug monitoring is thus particularly recommended in this population of patients.
These primers amplify a short (~120 bp) region of the 18S rDNA (V7), and are thus suitable for the analysis of degraded or poor quality DNA  [36, 37]. DNA was amplified following the same protocol of  [37]  and sequenced using an Illumina Hiseq 2000 platform (see  [37]  for complete details on sequencing and bioinformatics processing). The detected sequences were taxonomically assigned using the Ecotag program of the OBITOOLS package, on the basis of NCBI database  [38]. We analysed two DNA aliquots, each with four PCR replicates. We also ran 9 PCR controls, each replicated four times, including PCR mix but no template DNA to identify environmental contaminants  [39]. To avoid the risk of false positives we only considered taxa detected in >50% of PCR replicates with >10 reads [40].
DNA Analyses None of several DNA amplifications and sequencing attempts clarified the identity of the organisms. Using genomic DNA extracted from whole cyst samples as a template, no rDNA fragment was amplified employing either protists primers [29] or protozoan-specific ones  [30] [31] [32]. DNA amplification from in vitro cell isolates was also not successful. Sequencing analysis of the only amplified DNA fragment revealed the presence of fungi belonging to the genus Cladosporium, which was probably due to contamination. Using metabarcoding, we detected only one molecular taxonomic unit in >50% of PCR replicates (sequence: ctcaaacttccatctactaaacgtagatagtccctctaagaagccaaaaaagccaaccaaagtcgacccggctatttagcaggttaaggtctcgttcgttat). The Ecotag program assigned this sequence to the fungal genus Meira (Brachybasidiaceae).
We here introduce z-GAL, a Large Program using NOEMA, aimed at a comprehensive 3 and 2-mm spectroscopic redshift survey of a large sample of 137 bright (S 500µm > 80mJy) Herschel-selected high-z dusty star forming galaxies, probing the peak of cosmic evolution. The results highlight the nature of the sources, including lenses and rare hyper-luminous infrared galaxies, as well as, in some cases, their multiplicity and will serve as a foundation for future detailed follow-up observations.
In many of those cases, the relative intensities of the CO emission lines do provide additional useful constraints. With all the 137 Herschel-selected sources detected in the continuum and in at least two emission lines at 3 and 2 mm, the results of the z-GAL Large Program demonstrate the ability to efficiently derive redshifts and global properties of high-z galaxies using the new correlator Polyfix and the broad bandwidth receivers on NOEMA. In addition to the redshifts, the NOEMA data provide a richness of information on the sources' morphology, revealing in many cases sources that are complex and multiple, spanning the range from lensed galaxies to genuine binary or triple systems, and providing useful additional information on their properties. For the gravitationally amplified sources, ancillary data from the Hubble Space Telescope and the Keck provide additional information on both the foregound lensing galaxies and, in some cases, the backgound high-z system.
This uniquely rich collection of information has allowed a detailed study of HerBS-89a. The NOEMA data reach a spatial resolution of 0. 55 ⇥ 0. 33 arcsec at 254 GHz, revealing a partial Einstein ring (Fig. 3 ). Thanks to the very high S/N of the continuum emission, a detailed model of the gravitational lens was built, resulting in a picture of the galaxy in the image plane, prior to any beam convolution, and allowing the object reconstruction in the source plane. HerBS-89a is a powerful star forming galaxy, whose flux is magnified by a factor ⇠ 5 by a foreground spiral galaxy at z phot = 0. 6 -0. 9. Its intrinsic infrared luminosity is L IR = (4. 6 ± 0. 4) ⇥ 10 12 L � , its rate of star formation SFR > 600 M � /yr, and its depletion timescale ⌧ depl = (3. 4 ± 1. 0) ⇥ 10 8 years. These values potentially position HerBS-89a in the Figure  2.
The main red-shifted absorption lines (∆(V) ⇠ +100kms -1 clearly reveal infall activity in HerBS-89a. Adapted from  [16] ). The gravitational lensing model mentioned above was applied to the NOEMA spectral cubes of HerBS-89a, with the aim to reconstruct its velocity field and study the inflow in the source plane. The limited angular resolution and S/N ratio per spectral element of the NOEMA data does not lead to a consensus picture of the kinematics of the system and with the data in hand, it is not possible to be conclusive on whether HerBS-89a is a single rotating galaxy or a merging pair. Consequently, di↵erent interpretations of the gas inflow could hold. In the least likely scenario, NOEMA might have detected a flow channeling gas from the intergalactic medium onto the galaxy. While such flow would in principle consist of pristine gas, the detected OH + and CH + transitions testify to the presence of enriched material.
Conclusions and Perspectives Using NOEMA, the z-GAL survey has enabled the first systematic measurements of redshifts of a large and complete sample of high-z galaxies selected from the Herschel northern and equatorial surveys. These data provide a unique database for exploring, through dedicated follow-up observations, the properties of these sources, from the gas and dust properties to the feedback and fueling activity, down to linear scales of 100 pc in the case of gravitational amplification. The results of the z-GAL comprehensive redshift survey will be reported in a forthcoming first series of papers that will cover the following aspects: (i) Overview of the z-GAL survey (Cox et al. ); (ii) The dust continuum and the evolution of dust properties with redshift (Ismail et al. ); (iii) The molecular gas properties (Berta et al); (iv) The nature of the sources -extended/lensed/companions/HyLIRGs  (Bakx et al.
ACKNOWLEDGMENTS This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Grant  Agreement No. No. 101033047  "NWACOMPLEX", and from the  French National Research Agency  ( PIA2/ISITE-BFC , Grant No. ANR-15-IDEX-03 , " Breathing Light " project). The authors thank participants of  Prof. V. E. Zakharov 's seminar " Nonlinear Waves " and, especially,  Dr. Anton Raskovalov  for fruitful discussions.
Objective: In psychiatric literature, few original studies exist on the topic of demoralization in suicide. In this review, we aim to identify original studies on suicidal ideation (SI)/suicidal behavior (SB) and demoralization in populations of community-dwellers and patients with somatic or psychiatric disorders. Methods: We employed a systematic approach that followed PRISMA guidelines, searching through four major electronic databases (PubMed/MEDLINE, Scopus, Science Direct, and PsychINFO) for relevant titles/abstracts published from January 1980-June 2021. We included original studies that explicitly mentioned the investigation of a potential association between SI/SB and demoralization. Results: A total of 18 studies met our inclusion criteria. Their synthesis revealed that demoralization can be associated with SI/SB in a wide range of populations (community dwellers, patients with somatic or psychiatric disorders) and lead to significantly higher suicide risk (odds ratios of >6 were encountered in community dwellers experiencing financial hardship or isolation).
Information sources and search strategy We performed a systematic search in four major electronic databases (PubMed/MEDLINE, Scopus, Science Direct, and PsychINFO) for relevant titles and abstracts published between January 1980 and June 2021. Although J. D. Frank  [1]  introduced the construct of demoralization in the 1970s, we limited our search to studies published after January 1980 by which time the construct had become more standardized and established. Additional articles were retrieved from the reference lists of relevant publications. Additional articles were retrieved from the reference lists of relevant publications. The following terms were combined to search the Pubmed/MEDLINE database either in Medical Subject Headings terms  [MeSHt]  or in titles/abstract [TA]: "Demoralization" AND "Suicidal ideation" [MeSHt] OR "Suicide, attempted" [MeSHt] OR "Suicide" [MeSHt] OR "Suicidal Behavior" [TA] OR "Suicidality" [TA].
Study selection and data collection The authors independently extracted and reviewed the studies using a two-step process: (1) screening and selecting based on title and abstract, (2) screening and selecting based on the full-text. Author(s), publication year, study design, sample characteristics (population type, sample size, and psychiatric diagnosis when appropriate), instrument(s) used to assess demoralization and SI/SB, and impact of demoralization on suicide hopelessness-related variables (SI, SA, completed suicide, other SB-related variables, and/or main commentaries) were entered into a spreadsheet  [33]. Potential disagreements regarding article inclusion and data collection were resolved by consensus meetings of all senior authors.
This dataset reveals angular variations of less than 0. 06°yr -1 and a relatively steady paleointensity record (after calibration with geomagnetic field model) contrasting with the fast variations observed in younger speleothems from the same region under influence of the South Atlantic Anomaly. These results point to a quiescent period of the geomagnetic field during the mid-to-late Holocene in the area now comprised by the South Atlantic Anomaly, suggesting an intermittent or an absent behavior at the multi-millennial timescale. T he magnetic field of the Earth and its variations through time have been investigated with a panoply of instruments, from simple magnetic compasses aboard 16th century ships, to a network of geomagnetic observatories implemented in the 19th century  1  and lately by satellites covering the whole globe  2.
1 ) and imply a relatively high error in age determinations (2σ error of ~434 years). From the 14 U-Th analyses, only 2 age points were discarded in the geochronology model due to age inversions (detected by StalAge algorithm) (Supplementary Information, Table  S1 ), and the main age model was generated with the remaining age estimations using the StalAge algorithm  34. Also, to test whether major changes could occur by a different selection of dating points, different scenarios were generated (Supplementary Information, Figures S1) using StalAge  34  and COPRA  35  algorithms, and a major linear trend is observed, so the exclusion of only 2 inversion points was a better choice (Supplementary Data 1). Rock magnetism. Rock magnetic low-temperature experiments included field-cooled (FC) and zero field-cooled (ZFC) measurements, and room-temperature saturation remanent magnetization (RTSIRM) were performed on magnetic extracts obtained from the samples.
4 ), which agrees with the tendency of all models and remarkably matches the BIGMUDI4k. 1 (from 4000 BP onwards). Declination results (Fig. 4b ) shows a westward trend at the beginning of the stalagmite record, between 5500 BP to 4200 BP, which is not in perfect agreement with the geomagnetic models that tend to be flat at the same time interval. Then, a short-term (800 year) eastward trend is observed from 4500 BP to 3800 BP, followed by a westward trend from 3800 BP to 3000 BP. This pattern agrees with the amplitudes observed in the BIGMUDI4k. 1 model and follows the HFM. OL1. A1 as well, but it is not predicted by the CALS10k. 2 nor the SHA. DIF. 14k (yellow circle). Also represented are the records from lakes (purple circles)  [10] [11] [12]  , archeointensity and volcanic rocks for ages between 2000 BP and 7000 BP available in the GEOMAGIA50 database v3.
4  50  derived from Ecuador (blue)  51, 52  , Peru (pink)  [53] [54] [55] [56] [57]  , Bolivia (green)  51  and Uruguay (brown)  58  (Map generated with GMT software  76  ). b Stalagmite DBE50 from Dona Benedita cave and c age model obtained through U/Th dating; data in red are inverse ages that were discarded in the age model. The solid cyan line represents the age model obtained with the StalAge algorithm  34  and dashed lines represent 95% confidence intervals. Arrows represent growth-rates with minimum and maximum values in solid black and average value in dashed cyan. Relative paleointensity (RPI) estimates were obtained using the pseudo-Thellier method applied to an average fraction of 0. 58 ± 0. 07 of the natural remanence, typically encompassing eight demagnetization steps. The mean best-fit slope to the resulting Arai diagrams was -0.
The average trend defined by the speleothem record agrees with the models between 5500 BP and 3500 BP. Nevertheless, it is significantly lower for the younger record segment between 3500 BP and 3000 BP. Fig. 3  Summary of paleomagnetic directions and paleointensity obtained. Paleomagnetic results of DBE50 stalagmite; a orthogonal vector plot for specimen DBE50_40, with respective horizontal (red) and vertical (blue) components and the characteristic direction in cyan shade (from 9 mT to 30 mT). Directional data was obtained using the PMAGPY software  73. b Relative paleointensity for the same specimen calculated with the pseudo-Thellier technique along the same steps (n = 9), corresponding to a magnetization fraction of 63% (f = 0. 63). The slope of the Arai-plot was obtained in Paleointensity.
05 mT was applied along with an alternating field following the pseudo-Thellier protocol  72. The analysis of directions was made with the PmagPy software  73  to obtain the characteristic remanent magnetization direction (ChRM) using a routine for principal component analysis (PCA)  74. For relative paleointensity estimates, the slope obtained with ARM gained by NRM left (Arai plot) was calculated using the line fitting method with the software Paleointensity. org  75. Because the sample was not azimuthally oriented in the field, the procedure adopted is to calculate the Fisher mean for the directional results and compare with the geomagnetic field model CALS10k. 1b for the same period, then the difference in mean declination between them was used to rotate the declination results. To compare the relative paleointensity (RPI) results with the absolute paleointensity record, the data is normalized following the calibration method used in the CALS7k.
Acknowledgements We are thankfull to  Centro Nacional de Pesquisa e Conservação de Cavernas (CECAV/ICMBio)  for providing permission to collect stalagmite samples. We are grateful to  Augusto Auler  and  Bruna M. Cordeiro  for guiding the first field trip to collect speleothems in Dona Benedita Cave. This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil (CAPES) -Finance Code 001. This work was supported by the  São Paulo Research Foundation  (Grants # 2016/24870-2 , # 2016/15807-5 , # 2017/50085-3 , # 2018/15774-5 , # 2018/ 07410-3  and # 2019/06709-8 ) the  Serrapilheira Institute  (grant number:  Serra-1812-27990 ) and the  National Council for Scientific and Technological Development (CNPq)  (Grant numbers:  308769/2018-0  to N. M. S and  426258/2016-9  to ITP).
This work was supported by  National Natural Science Foundation of China ,  NSFC 41888101  to HC. This work was also supported by the  National Science Foundation  grant  EAR-2044535  and  US-Israel Binational Science Foundation  grant # 2016402  to JMF. The IRM is a US National Multi-user Facility supported through the  Instrumentation and Facilities Program  of the  National Science Foundation ,  Earth Sciences Division , and by funding from the  University of Minnesota. This work has been partially performed at  USPMag laboratory at Instituto de Astronomia, Geofísica e Ciências Atmosféricas (IAG)  from  Universidade de São Paulo (USP)  funded by  CAPES/FAPESP/CNPQ. Certain commercial equipment, instruments, materials, and software are identified in this paper to foster understanding. Such identification does not imply recommendation or endorsement by the  National Institute of Standards and Technology , nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.
The number of input connectors is the number-of-layers-by-number-of-inputs represented as a Boolean matrix. For layer connectors, it is the number of-layers-by-number-of-layers. A 1-bynumber of layers Boolean vector represents the output connectors. On the other hand, realtime simulations with a high sequence of data and non-linear behavior provided us with other categories of NNs, especially the long short-term memory (LSTM) NN. LSTM functionality relies on a network with the ability to learn long-term dependencies between time steps in time series and sequence data. It can predict long patterns with unidentified lengths and thus is known for its memory capabilities. By these categories, NNs can provide interesting solutions to modeling and predicting the characteristics of smart materials.
We start modeling with a single SMA wire to choose the best NN among the four stated NNs: LSTM, fitting NN (shallow NN with one hidden layer), feedforward NN, cascade forward NN, depending on the experimental stress-strain data provided by an Ansys platform during the preparation stage (Section 4. 1). After that, we model an SMA actuator of higher complexity from its experimental stress-strain diagram. Here, the difficulty is that the modeling can no longer be considered as an assembly of lines with given slopes (Section 5).