dataesr/scientific-paragraphs-categorization huggingface dataset:
Different estimation procedures are provided in the AMAPVox software. We used the Free Path Length estimator first developed for single return TLS in Pimont and colleagues  76  and later extended to the multiple return case  77. The common assumption made for all estimation procedures in AMAPvox is to consider vegetation elements as randomly distributed within a voxel (thereby neglecting within voxel clumping) and to express the directional gap probability (or directional transmittance) as a function of the optical path length of laser pulse through a voxel and the local extinction coefficient  78. The extinction coefficient is the product of the Plant Area Density and the projection function G(θ), which is the ratio of plant area projected in direction θ to actual area: Pðθ; lÞ ¼ expðÀλ θ lÞ; ð3Þ where P(θ,l) is the probability of non-interception of a light beam of zenith angle θ (i.
PAI ¼ β 0 þ β 1 *exp ðÀβ2*xÞ þ u i þ ε i ð5Þ where β 0 to β 2 are the model parameters, u i is the random intercept for transect i, and ε i is the normally distributed residual error. This approach has been used to investigate edge effects on forest structure and dynamics  23, 81. The model was fitted using the function nlme in R. The results from this model indicates that the effects of transect account for 6. 5% of the total PAI variability only, and that most of the variance (93. 5%) is explained by the withintransect variability, including the distance from edge and seasonal variations (Supplementary Fig. 2 ). A hockey-stick model consisting of two linear segments was also implemented with the R package "SiZer" using the function piecewise linear. This model identified a "distance from edge" threshold, dividing voxels from the two transects near the fragment margins into edge and interior groups.
Meteorological parameters are derived from NASA's GMAO MERRA-2 assimilation model (https://gmao. gsfc. nasa. gov/reanalysis/MERRA/) and GEOS FP-IT (https://gmao. gsfc. nasa. gov/news/geos_system_news/2016/FP-IT_NRT_G5. 12. 4. php). We then integrated the daily rainfall estimates to accumulated monthly (30 day period) rainfall and classified dry season as the period with running 30-day rainfall below 200 mm as in Maeda and colleagues  86. Microclimate variables. Soil moisture and maximum temperatures are key drivers of species' distributions and affect how species respond to climatic variations  87, 88. We measured air temperature (°C) and electrical conductivity of soil moisture (time-domain transmission; TDT) across a network of 22 data loggers varying in distance from the forest fragment margins (0 and 520 m).
Temperature-Moisture-Sensor (TMS) data loggers measured air temperature at 15 cm above the ground and TDT at 8 cm below ground, and all the data retrieved using TMS Lolly manager software (Tomst, Czech Republic)  89. TDT values were transformed into volumetric soil moisture following calibration curves in Wild and colleagues  89  using as input data soil texture (50% clay, 25% sand and 25% silt contents) and mean soil density of 1100 kg/m 3 measured by Camargo and Kapos 90 in the same forest fragments of our study. Data loggers were shielded from direct solar radiation and recorded data every 15 min. Microclimate data were recorded between 27th April 2019 and 16th October 2019, resulting in a total of 435,798 coupled temperature and volumetric soil moisture readings. TMS device measures microclimate variables affecting many ecological processes, including those related to water and energy balance.
Model explanatory power was assessed in terms of AIC (Supplementary Table  1 ). The LME model was fitted using the lme function in the "nlme" R package. Variations in transect area and monitoring period can influence PAI trends, and thus we used varIdent weights function to account for the noise attributed to sampling effort  91. Performance of the final models was evaluated using an 80/20 split of the data for calibration and validation, respectively, over 200 randomised permutations of the dataset. These analyses generated a distribution of model coefficients and allowed an assessment of model stability and uncertainty of predictions. We calculated 95% confidence intervals from the 2. 5% and 97. 5% quantiles of the distribution of model coefficients. If increasing upper canopy PAI contributes to lower light interception in the lower stratum of the vegetation, we may expect a decreasing leaf development in the understory of forests in the interior of fragments  38, 41.
Acknowledgements This study was funded by the  Academy of Finland  (decision numbers  318252 ,  319905  and  345472 ). This publication is number 831 of the  Technical Series of the Biological Dynamics of Forest Fragment Forest (BDFFP-INPA / STRI). We thank the  Biological Dynamics of Forest Fragment  Project team for the thorough logistical support in the field. We are grateful to  Renann Henrique Paiva Dias Silva ,  Juliane Menezes  and  Vinicius Bertin  for the great and thorough assistance in the field. G. V. received support from Laboratoire d' Excellence CEBA  ( ANR-10-LABX-25 ). K. C. was funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  835398. R. S. O. receives a CNPq productivity scholarship and is supported by a  NERC - FAPESP  grant  2019/07773-1.
Data availability The repeated PAI data collected in Central Amazonia using a terrestrial laser scanner (TLS) between April and October 2019 and generated in this study have been deposited in the national Finnish Fairdata services database under accession code https:// etsin. fairdata. fi/dataset/e488f81b-b927-4bbd-a6f7-2f532f434b2b. PAR estimates were obtained from https://lpdaac. usgs. gov/products/mcd18a2v006/ and meteorological data from https://gmao. gsfc. nasa. gov/reanalysis/MERRA/. Microclimate measurements collected in the field during the current study are available from the corresponding author on reasonable request within 10 days.
Study Population The study population consisted of the pooled DELTA and DELTA 2 registries. The DELTA registry included allcomer patients with ULMCA disease treated in 14 centers with either PCI with first-generation drug-eluting stents (DES) or CABG between April 2002 and April 2006. 15 The DELTA 2 registry included all-comer patients with ULMCA disease treated with PCI with newgeneration DES in 19 centers in 7 countries between April 2006 and December 2015. 16 All data related to hospital admissions, procedures, and outcomes were collected at each center. Information on clinical status at the latest clinical follow-up was obtained by clinical visits, telephone interviews, and referring physicians. Dual-antiplatelet therapy was administered according to hospital and physician practice. Angiographic follow-up was scheduled according to hospital practice or if a noninvasive evaluation or clinical presentation suggested myocardial ischemia.
Moreover, there are relevant data that could not be retrieved from the overall databases. Patients' overall operative risk may not have been adequately assessed given the lack of data on validated risk scores, including the EuroSCORE or the Society of Thoracic Surgeons Score. Among these, most notably detailed information about preprocedural and postprocedural SYNTAX scores. Indeed, in the SYNTAX trials complete revascularization was achieved in similar proportions in men and women, whereas in the EXCEL trial women were more likely than men to undergo complete revascularization, which may account in part for the difference in results between these trials. 13, 14 Finally, there was a substantial lack of data on optimal medical treatment at follow-up. Given the fact that it has been shown that women are less likely to receive guideline-directed optimal medical therapy, an imbalance in terms of treatment cannot be excluded as a factor influencing the observed higher rates of adverse events in women.
Data We used the Performance Monitoring for Action (PMA) survey to implement an abortion module in Burkina Faso (Performance Monitoring for Action (PMA) 2021). Fieldwork occurred from December 2020 through March 2021. The survey design employed a two-stage urban-rural stratified cluster sampling approach with probability proportional to size sampling of geographic areas or clusters in Burkina Faso to identify a nationally representative sample of women aged 15-49 years (Performance Monitoring for Action (PMA) 2022). Interviewers created a master sampling frame of all households within each cluster, randomly selected 35, and interviewed a member of the household to create a household roster with all members' ages and genders. The interviewer then invited all females between the ages of 15 and 49 to participate in the female questionnaire.
We then used the model to predict the likelihood that the respondents without friends/confidantes had an abortion in the prior year. We combined this predicted likelihood of abortion data for the respondents with no friends/confidantes with the existing friend/confidante abortion data to estimate the one-year incidence of abortion in each surrogate sample. The lack of significant differences in abortion rates between respondents who reported having or not having close friends/confidantes supports the use of such a hybrid surrogate sample. Additionally, we included the friend/confidante abortions that respondents reported with some uncertainty (response option "Yes, I think so") to further adjust for incomplete abortion visibility. To address remaining evidence of selection bias in each surrogate sample, we constructed poststratification weights for the adjusted best friend and the confidante sample so the distribution of characteristics would more closely resemble the respondent characteristics.
If all fungal PCR were negative, the date used was the date of the first test. For patients that are PCR-positive for Mucorales, data regarding patients' demographics (biological sex, age), medical history (diabetes), ICU hospitalisation, antiviral therapeutic and biological data (variant SARS-CoV-2, fungal biomarkers in respiratory specimens and serum) were recorded. Antiviral therapeutics (dexamethasone and tocilizumab) were introduced according to the recommendations from the RECOVERY studies  [7, 8].
Statistical Analyses Data were analysed with SIGMA Stat1 (2. 03; Jandel Corporation, San Jose, CA, USA) software. Bivariate analysis was performed using the chi-squared test to compare the proportion of Aspergillus-positive patients or the Aspergillus species proportions according to the period. For continuous parameters, the data distribution was found to be non-Gaussian and values were reported as the median and interquartile range (IQR) [25%; 75%]. The differences were considered significant when p < 0. 05.
Method Participants and experimental protocol. Data were collected from 21 healthy individuals (13 males and 8 females, age: 27 ± 4 years; height: 176 ± 10 cm, body mass: 72 ± 11 kg). All participants had no recent history of lower-limb injury and provided written informed consent to participate in the study. The institutional ethics board at the Norwegian School of Sport Sciences, Norway, approved all protocols and the study was performed in accordance with the Declaration of Helsinki. All participants completed a familiarisation session of isometric and isokinetic knee extensions (IsoMed2000, D&R Ferstl, Hemau, Germany), with their leg randomly selected for the study. The padded interface between the dynamometer arm and the shin was replaced by a stiffer-custom-made interface to stiffen the system.
The order of joint angle positions and velocities was randomised, and participants rested for three minutes between each maximum effort to minimise fatigue. For all contractions, torque was recorded at a sampling frequency of 1500 Hz, and corrected for gravity and passive torque e. g. 26. Using the knee joint angle we calculated the angle dependent knee moment arm based on literature based functions, as suggested in  27. Subsequently, we calculated knee extension force by dividing the joint moment by the moment arm for each data point. Reflective markers were used to estimate sagittal plane knee joint angle on the lateral malleolus, tibial head, lateral epicondyle and trochanter major. Markers were recorded by four cameras at 100 Hz (Qualisys, Gothenburg, Sweden). For the explosive contractions, the onset of torque production was determined by finding the first data point below 1 Nm when going backwards from peak torque (i.
Ultrasound images were sampled at 116 Hz (distal probe) and 82 Hz (proximal probe), which was the maximal sampling frequency of either machine. A square wave analogue pulse from each ultrasound system was used to synchronise all data. Ultrasound images were digitised using UltraTrack, a semi-automated tracking software  28, 29  to quantify vastus lateralis fascicle length, pennation angle and thickness. Specifically, the position and orientation of the upperand lower aponeuroses were tracked from the proximal and distal scans, respectively. Fascicle orientation was tracked in the distal scans, as these were collected in the belly of the muscle with the machine offering the highest spatial resolution. Fascicle length was then determined by calculating the distance between the two aponeuroses in the direction of the fascicle orientation (Fig.
Modelling of muscle properties. Muscle force-length-velocity properties were estimated by combining models to assess muscle force-length and force-velocity properties  31, 32. We used force, fascicle length and fascicle velocity data from the isometric and isokinetic contractions to produce the individual force-length-velocity profiles based on the force-length (Eq. 1) and force-velocity (Eq. 2) relationships. The experimental data were fitted to the equation using nonlinear least square optimisation: where F is the force capacity, F max is isometric force at optimal fascicle length (L 0 ), L is fascicle length, b is the skewness, s is the width of the curve, v is fascicle shortening velocity, v max is the maximum shortening velocity and G is the curvature parameter. We did not constrain v max in our model because we measured fascicle velocity at different isokinetic joint velocities, unlike models which did not measure fascicle velocities e.
g. 33. G was constrained between 3 and 9, similarly to Brennan et al. 32. Four participants were excluded from the force-length-velocity (1) potential data because the model could not be fitted, and this variable was obtained from n = 17 participants. We used L 0 to represent fascicle length in the correlation analyses. F(L) = F max • e - � � � � � �   � L L 0 � b -1 s   � � � � � � 2 (2) F(v) = 1 -v v max 1 + G • v/v max (3) F = F(L) • F(v) Data analyses and statistics. Changes in force, fascicle length, pennation angle and muscle length were calculated for 50 ms time intervals up to 150 ms after torque onset, i. e. 0-50 ms, 50-100 ms, and 100-150 ms. Fascicle velocity was calculated from fascicle length data using the central difference method and averaged over the time intervals (Fig.
Correlation coefficients were considered strong for r ≥ 0. 7, moderate for 0. 7 > r ≥ 0. 4, and weak for r < 0. 4  35. MATLAB (R2018b, https:// se. mathw orks. com/) was used to conduct the statistical tests (with significance level α = 0. 05) and to generate Figs. 1, 2, 3, 4. During the rapid contractions, we calculated the time from contraction onset to 95% of F max, according to Eq. (3).
Additionally, we measured fascicle velocity at five isokinetic joint velocities instead of maximum shortening velocity and added information about operating length due to its influence on force production. Our approach to model muscle properties has several shortcomings that may explain why our RFD data slightly exceeds the force-length-velocity potential (Fig. 4 ). Firstly, force-length properties may be affected by a mismatching of operating lengths between isometric and dynamic contractions. Matching fascicle lengths in different tests is methodologically difficult, because of differences in elastic tissue deformation at different force levels. For contractions with higher forces (i. e. , isometric and slow isokinetic contractions) elastic tissue strain is higher, resulting in shorter fascicle operating lengths.
Immunohistological Evaluation Histological evaluation of kidney samples was on 5 µm thick paraffinembedded serial sections following the protocol of the Leica BOND-Polymer Re-fine Detection automatic immunostainer (Leica, Wetzlar, Germany). All solutions were poured into the bottle-Bond Open Container (Leica) and registered on a computer using the Leica Biosystem program. The immunostaining program protocol included a fixative solution, a bond wash solution, blocking with a common immunohistochemistry blocker, and incubation with the primary antibody for biglycan (Santa Cruz Biotechnology), CD68 (Abcam, Cambridge, MA), CD86 (Santa Cruz Biotechnology), CD206 (Santa Cruz Biotechnology), CCL3 (Santa Cruz Biotechnology), MyD88 (myeloid differentiation factor 88, Santa Cruz Biotechnology), and phospho NF-κB (Cell Signaling).
Statistical Analysis Data are presented as the mean ± SEM. Differences in baseline characteristics, mRNA expression, and protein expression between groups were analyzed with one-way analysis of variance (ANOVA) followed by Tukey multiple comparisons tests. Statistical analysis was performed using GraphPad Prism version 8. 0 (GraphPad Software, San Diego, CA, USA). We considered p-values < 0. 05 to be statistically significant.
C-fos Expression Triggered By Addictive And Natural Rewards To examine which parts of the brain are activated by natural and addictive rewards, we used a wholebrain imaging approach with tissue clearing combined with immunohistochemistry (iDisco+) and lightsheet microscopy  9. To mark cells activated by reward exposure, we holistically labeled the product of one of the IEGs, c-fos. c-Fos is a transcription factor widely used as a marker of neuronal activation that mediates plasticity at the cellular, synaptic, and network levels  [10] [11] [12]. Notably, a c-Fos expression typically follows a time-speci c course, in which protein is detectable in neuronal nuclei within 1-3 h after induction  10. Optically cleared and immunolabelled samples were imaged with a self-made light-sheet microscope  13  and images of the brain were aligned to the Allen Brain Atlas using the ClearMap software  14.
The number of c-Fos + nuclei was automatically detected and associated with speci c brain regions (Fig. 1C ). Finally, the numbers of c-Fos + nuclei corresponding to each structure were divided by the volume of these structures to transform the data into the signal density. Altogether, we analyzed 382 structures, which were classi ed according to the Allen Brain Atlas. For each structure, we calculated the fold increase of c-Fos, where behavioral groups were normalized to control groups (Fig. 1D ). This brief analysis showed the vast activation throughout the brain. To discover brain regions activated by rewards we compared c-Fos signal density for each structure in sucrose-or cocaine-treated groups. Thus, we used the generalized linear model (GLM) followed by Dunnett's correction and Benjamini-Hochberg's correction to avoid false-positive results (type I errors) from a huge amount of data.
These data were excluded from further analysis. In the analysis of the fold increase of c-Fos level, behavioral groups are normalized to home cage data. Individual values indicate the quotient of the c-Fos level after reward treatment divided by its level in corresponding control groups. Identi cation of structure co-activation and hierarchical clustering was performed using RStudio software. Separate Pearson correlations were calculated across each group to compare c-Fos data from each brain region to each of the other brain regions. Data were arranged into anatomical groups based on the Allen Mouse Brain Atlas. The total Euclidean distance between structures, which is a square root of the squared differences between structures, was calculated for the hierarchical clustering of each group separately.
Then, the order of structures was rearranged for each group based on hierarchical clustering to nally merge groups back together into a list of structures. The correlation or distance heat maps were then applied to all treatments  16. RStudio packages pheatmap and dendextend were used for heatmap generation, clustering, and modularity analysis. A graph network was created on data from separate Pearson correlations calculated across regions activated by reward treatment with the highest probability (p-value < 0. 01 cut-off after Benjamini-Hochberg's false discovery correction; see statistical analysis). For graph network visualization we used a pyvis library for Python. Fold indexes were calculated with the following equation: , where µ is the number of activated neurons for individual behavioral groups.
Electrophysiology Recordings Slice preparation and silent synapses measurements were performed as previously described  74, 75. For measuring the decay time of NMDAR-mediated responses, excitatory postsynaptic currents, measured at + 45 mV were recorded in the presence of 10 µm NBQX to block AMPAR component. Data were analyzed using Clamp t 10. 3 software. D1 and D2 cells of the ACB shell were distinguished visually based on their uorescence (green for D2 and red for D1). versus D2 cells  24. Similarly, in our studies, in some cases, where only one population was uorescently labeled, cells without uorescence were counted as the other population.
Statistical analysis For analyzing the whole-brain data R script written by us, statistic_binomial. Rmd was used. First, the generalized linear model (GLM) was tted to the number of detected c-Fos positive cells in each brain region in every animal group using a negative binomial to model data points' distribution. Next, Dunnett's correction was performed. We compared a high number of structures, so to avoid false-positive discoveries, Benjamini-Hochberg's false discovery rate was performed on p-values with a cut-off of 0. 1 for the number of structures with elevated c-Fos levels after natural or addictive treatment 76. For graph analysis, we used stricter statistics and the p-value cut-off was set at 0. 01. For the data from immunohistochemistry on brain slices and electrophysiology, statistical analysis was performed using GraphPad Prism software.
S. ; and recommendations to address gaps in knowledge and skills for those in the government, academic, and civilian sectors involved in HIDM. The online supplemental materials contain the workshop agenda (Appendix A) and country-specific HIDM summaries (Appendix B). MeetingSphere (MeetingSphere Inc, 2020, Norfolk, VA) interactive meeting software was used during the workshop. Small breakout groups discussed the agenda topics and reconvened with the larger group to reach consensus. MeetingSphere also enabled attendees to discuss issues over chat; chat text was exported. Additionally, three notetakers transcribed live conversations to capture all discussions. Dialogue from the chat and notes was integrated into the consensus recommendations. This authorship group refined the final versions of the recommendations.
Acknowledgments We would like to thank everyone who prepared for this conference and made this workshop possible including  Sarah Dunsmore ,  Project Manager , for this funding opportunity; the staff at the  National Strategic Institute (NSRI) at the University of Nebraska ; the staff at the  University of Nebraska Medical Center Global Center for Health Security National Training, Simulation and Quarantine Center ; and all the participants of the workshop who-some of whom traveled significant distances-contributed to the rich and necessary discussion on HIDM. We would also like to extend our deep appreciation to  Theresa Tonozzi, MPH , who served as a contractor on the mortuary affairs and helped us develop all the curriculum materials necessary for the workshop, curate existing literature and evidence-based guidance, as well as organize and analyze key findings from the workshop.