dataesr/scientific-paragraphs-categorization huggingface dataset:
There could be dependence between detections due to individuals forming clusters. However, observers often detected some but not all individuals within clusters. Furthermore, the aggregation of individuals was not very strong at the quadrat scale. Hence, the dependence between detections is probably relatively small, and we consider the binomial model appropriate. We designed the experiment to disentangle the effects on detection probability of species conspicuousness, habitat closure and survey effort. We had precise hypotheses about the mechanisms explaining these effects (detailed in Table  1 ) that included interactions between variables, and we collected a large dataset to test all our hypotheses. Therefore, we fitted a single model including all the variables and interactions corresponding to our hypotheses and used their statistical significance for inference.
To take into account the repeated measurements at each quadrat, we included random intercepts for the observers, the field sessions and the quadrats (nested within the field session). As the majority of observers participated in only one field session, we did not add a random effect interaction between observers and field sessions. Furthermore, we did not add random effects on the slopes because we had no specific hypothesis that would justify it. We standardised all the variables prior to model fitting and assessed the model's goodness-of-fit using the package DHARMa  (Hartig, 2022). We computed the proportion F I G U R E 1 Diagram of the study's experimental design. We conducted 30 field sessions, each focusing on a different species in a habitat with a different closure level, to cover the widest possible range of combinations of species conspicuousness and habitat closure.
DATA AVA I L A B I L I T Y S TAT E M E N T All data and code used for this work are available from the GitHub repository: https://github. com/JanPe rret/Indiv idual_detec tion_in_plant_ counts and are also archived on Zenodo: https://doi. org/10. 5281/ zenodo. 7837541 (Perret, 2023).
A more recent systematic review  [13]  assessed eleven retrospective articles and reported the global AUC of the GAP Score to be 0. 68 ± 0. 2. Nevertheless, it is obvious that the sagittal plane shape and alignment is not the sole factor that drives mechanical complications. Even though several efforts  [14] [15] [16]  were made to include surgical factors and patient characteristics in an attempt to increase predictive ability, considering the complexity and variability of adult spinal deformity, determination of such factors possesses a significant challenge. Nowadays, progress in health care comes from data-driven approaches. Machine Learning (ML) techniques are a subdomain of artificial intelligence applications to identify patterns in a dataset. In an ideal world, clinicians should work closely with data scientists to derive clinical outcomes.
Understanding the mechanisms and relations in a dataset is crucial in model building and that's why we see crossfunctional teams crack complex problems in the healthcare domain. Google's DeepMind team that tackles protein folding problems is a perfect example of an ideal working environment. While clinicians offer their knowledge in the domain, data scientists guide the research by using appropriate AI or machine learning approaches. Even though many sample datasets are crafted to minimize multicollinearity, almost always it is not the case in real-world datasets. When the input variables are highly correlated, it is important to choose an ML approach that is not sensitive to such cases. Decision trees are known to be immune to correlated variables as the algorithm only chooses only one feature when deciding a split, even though feature importance is hard to pinpoint due to the same reason  [17].
Dataset Adult spinal deformity surgery clinical data was collected prospectively in a study group database of adult spinal deformity patients that underwent elective spine surgery between 2010 and 2019. Each of the 6 participating sites obtained an institutional review board approval for patient enrollment and data collection protocols. Database enrollment criteria were 18 and more years-of-age and at least one of the following: coronal Cobb angle ≥20 • , sagittal vertical axis ≥5 cm, pelvic tilt ≥25 • or thoracic kyphosis ≥60 •. For this study, inclusion criteria were to undergo ≥4 levels of posterior instrumented fusion, to have a postoperative GAP Score of 0, 1 or 2 and to have ≥2 years of follow-up. Mechanical complications were recorded as proximal junctional kyphosis and failure, distal junctional kyphosis and failure, rod breakage, and implant-related complications.
Feature engineering The GAP parameters (relative pelvic version, relative lumbar lordosis, lordosis distribution index, and relative spinopelvic alignment) were one-hot encoded for each group because of the bimodal nature of their effect on complications (i. e. aligned values for lordosis are in the middle and malaligned values can go in both directions and hyperlordosis or hypolordosis). In total, 32 Boolean features were generated and replaced with the original GAP score evaluation features. All other features were encoded ordinally, so that the splits in the resulting trees can be informative. The missing variables in the dataset remained missing since the XGBoost modeling allows missing values in the dataset. Multiplications of two features were made if their joint occurrence is clinically relevant.
Input variable selection With 191 features in hand, the next step was variable selection to continue with modeling. The dataset was, therefore, split into train and test sets with a 75%-25% ratio with stratified sampling using mechanical complication, the response variable, and age intervals of 20 years because patients' age is already an established factor in predicting mechanical complications  [6]. Then, high dimensionality was reduced by eliminating features that the absolute value of their correlation with our response variable is less than 0. 1 using the Pearson correlation coefficient  [20]. As such, 118 features were eliminated. Since the patient-reported outcome measures are highly correlated, the feature having the highest correlation with mechanical complications was chosen to advance with: the preoperative ODI Walking score.
In the second grid search, the minimum child weight and gamma values were tuned. In the third grid search the close space of the best performing parameter set was selected. Another round of grid search was made with the selected features of the expert-guided model. The classification and regression training (Caret) package of the RStudio was used for cross-validation and training of XGBoost models  [23]  alongside a data manipulation package "dplyr"  [24]. The hyperparameter set was finalized using 9 times 3-fold cross-validation. To assess the models built by a particular hyperparameter set, the mean performance metrics of the validation set for each hyperparameter set were assessed. The hyperparameter set with the best mean F score, which is the harmonic mean of the precision and recall (F = 2 *P *R/(P + R) where P and R stand for precision and recall, respectively.
[25]  predicted major complications within two years with 92% accuracy. On the other hand, Scheer et al. [26]  focused on a specific type of mechanical complication and reported 86% accuracy. A combined dataset of two study groups were used to develop risk stratification models for complications, hospital readmissions and unplanned reoperations  [27] , in which AUCs ranged from 0. 67 to 0. 92. The performance metrics reported in the current study are comparable with the literature. Within a post-operatively well-aligned group of patients (i. e. GAP Score of 0, 1 and 2), the most effective features for predicting mechanical complications were the location of the lower-instrumented vertebra (Iliac and/or S2-iliac-alar fixation vs S1-S2 vs L5 vs L4 vs L3-L2 vs T12-L1 vs T11 or higher), estimated blood loss, pre-operative ODI walking score, age, BMI, the combined effect of the number of levels fused and the construct location (thoracic only vs lumbar only vs thoraco-lumbar vs lumbo-sacral vs thoraco-lumbo-sacral vs cervicothoracic vs cervico-thoraco-lumbar vs cervico-thoraco-lumbo-sacral), and the follow-up duration after surgery, in order of significance.
B. Balaban et al. were among reported factors. Depending on what is available in different datasets, various patient-reported outcome measures were identified in the literature. Comprising the greater part of most commonly used patient-reported outcome measures, the current study pinpointed ODI Walking score as the most correlated one, which was also acknowledged in other reports  [27]. Inherently, the risk of incurring a mechanical complication increases as the duration of follow-up increases, which was well demonstrated by Gupta et al. [10] , where the AUC for the GAP score's ability to predict mechanical complications incrementally increased from 68 to 91 through longer follow-up up to twelve years. In comparison to having 5 trees with 23 leaves that was built using 14 features, some of which were non-explainable or non-actionable (such as features that split data in opposite directions in different splits make it not possible to derive clinical conclusions or features that produce splits that are conflicting with known clinical effects and/or features that yield a threshold value that is considered to be within the normal range in the clinic) in the unguided model, the expert guided model comprised 4 trees with 16 leaves using 7 features all of which have a clear role either in patient selection or pre-operative planning and/or patient counselling.
With this approach, the influential nodes can easily be analyzed as the number of them were 12 for the unguided model, and only 7 for the expert-guided model. The two models presented in this paper achieved similar accuracies (77% for the unguided model and 74% for the expert-guided model) and the same sensitivity (80%) on the test set. Further to that, thanks to the systematic expert interference in feature selection, the final model only used half of the features, all of which were clinically-sensible and actionable. There are a few limitations of this study. The retrospective design implies that some vital information related to mechanical complications is bound to be missing. Plus, the size of the cohort is a limiting factor in experimenting with specific conditions, yet the ESSG dataset is one of the largest and high-quality ASD datasets.
As higher sensitivity is essential to allow taking preventive measures in high-risk patients, sensitivity measures were closely examined during the cross-validation process. Doing so, the proposed model minimizes the risk of undetected mechanical complications and incorporates the ability to be used in the planning phase of surgeries. The model results may also serve as a post-operative decision-aid tool for preventive postsurgery care. The authors believe this methodical expert-guided model will be beneficial not only to alert surgeons to possible mechanical complications following adult spinal deformity surgery but also aid the planning phase of the surgeries as well as patient selection and counselling. Future work would benefit from running ML models with larger datasets including a wider range of sites.
Methods Adult spinal deformity surgery clinical data was collected prospectively in a study group database. Preoperative, operative and postoperative patient characteristics, radiographic measurements, technical details as well as patient-reported outcome measures were retrieved and analyzed. Several feature engineering strategies were applied, so that the splits in the resulting trees can be informative and combined effects of several features can be tested. Variable selection process included correlation analysis. The XGBoost model was built using default parameters where the objective function was "binary logistic", and the booster was "gbtree". The model hyperparameters were optimized with 9-times 3-fold repeated cross-validation in three grid search rounds. An expert-guided model was built upon the initial model to make it clinically sensible, and actionable, where a methodical expert interference was carried out regardless of the gain scores of the features.
Acknowledgements This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Clinical data was collected by the authors of Global Alignment and Proportion (GAP) Score in six different sites across Europe and the data is unavailable to access. Barıs ¸ Balaban  is a bursar of the  Scientific and Technological Research Council of Turkey  ( TUB İTAK) 2244 -Industry Doctorate Program  in conjunction with  Bilmed Computer  and Software Company. Prof. Dr. Ugur Sezerman  supervised this work as the department head of  Bioinformatics and Biostatistics Department at Acibadem University, Turkey.
Besides providing a guide to the most likely evolutionary sequence of events, this dataset also provides new insights into how different TP elements contribute to differential targeting and to the efficiency of protein import. We show that some HA-RAMPs natively contain such sequence elements, with some HA-RAMPs being prone to chloroplast targeting while others show a preference for the mitochondria. Furthermore, we provide evidence for a critical evolutionary contribution of a substitution of cationic residues from Lysine (K) in HA-RAMPs to Arginine (R) in TPs.
Discussion How TPs may have evolved from HA-RAMPs In an assessment of the hypothesis that extant TPs are derived from a subclass of antimicrobial peptides that harbor an amphipathic helix, the HA-RAMPs 1,2 , we studied a systematic series of HA-RAMP constructs. This dataset allows us to propose an evolutionary scenario (Fig. 8 ) for the conversion of such peptides from antimicrobial to bona fide targeting peptides whose properties allow differential targeting between mitochondria and chloroplasts. We showed that a K to R switch reduces cytotoxicity of HA-RAMPs, while increasing targeting. This dual effect must produce a strong selection pressure favoring this exchange at an early step of the endosymbiosis process (Fig. 8A ). The underlying mechanism warrants further research, but the finding is in line with the fact that Arginines provide a better uptake efficiency than other cationic residues because of the guanidinium group forming bidentate hydrogen bonds with membrane phosphate groups  27, 28.
Sequence Data Set TPs with experimentally-confirmed cleavage sites were recovered from proteomic studies: Chlamydomonas reinhardtii cTP (Terashima 2011), mTP (Tardif 2012); Arabidopsis thaliana cTP (Ge 2008, Rowland 2015), mTP (Huang 2009). For each peptide, we recovered the full-length protein sequence from NCBI and UNIPROT. Cytoplasmic control sequences were generated by taking Nterminal sequence stretches of random length, matching the distribution of peptide lengths observed in our Chlamydomonas TP dataset, from a random subset of Chlamydomonas proteins with a validated cytoplasmic location in UNIPROT. For calculating amino acid frequencies, the same HA-RAMP sequences were used as before  5.
Acknowledgments Thank you to  Tiffina Benhamou  and  Gabriel Chemin , who worked on aspects of this study with ODC during their summer internships. We also thank  Prakitchai Chotwutmontri  and  Barry Bruce  for their help in correctly replicating their Hsp70-binding and FGLK-motif finding algorithms. Financial support by annual funding from the  Centre National de la Recherche Scientifique and Sorbonne University  to UMR 7141, by the  ChloroMitoRAMP ANR  grant ( ANR-19-CE13-0009 ) and by  LabEx Dynamo  ( ANR-LABX-011 ), as well as by the  Rothschild Foundation , is gratefully acknowledged.
Case study at GOW In order to evaluate the impact of the used network adjustment approach onto the reference points, the data set of the 2021 local tie campaign carried out at the Geodetic Observatory Wettzell (GOW) is chosen. GOW is a multitechnique station and operates instruments of all basic space geodetic techniques  (Hugentobler et al. 2011). The local site network depicted in Fig. 6  consists of about 30 concrete pillars, surrounding the hosted space geodetic techniques, that are one DORIS beacon (WEUC), two SLR telescopes (SOS-W, WLRS), three VLBI radio telescopes (RTW, TTW-1, TTW-2), and several GNSS antennas (WTZA, WTZR, WTZS, WTZZ, WLRG). Moreover, GOW supports SAR missions by two corner cube reflectors (SAR1, SAR2). In this investigation, we restrict ourselves to the analysis of the DORIS beacon WEUC, the corner cube reflector SAR2, the radio telescope TTW-2, as well as the SLR telescopes SOS-W and WLRS, because indirect measurements were performed for these techniques, which allow the determination of conventional reference points by means of datum-independent geometrical conditions.
Data set The measurement campaign was carried out in the fall of 2021. The survey data form the basis for the local tie vectors for the upcoming International Terrestrial Reference Frame. The measurements were carried out using the Leica total stations TS50 and TS60. Both instruments are specified identically by 0. 6 mm + 1 mm km -1 and 0. 5 for distance and angle measurements, respectively  (Leica 2013; 2015). Moreover, the novel multi-lateration system DistriMetre was used for data acquiring at GOW for the first time. This instrument is a prototype developed by CNAM. In controlled environments, the uncertainty of the distance measurement is about 5 um  (Guillory et al. 2020b; 2022). However, under the environmental conditions of the measurement campaign at GOW, the uncertainties on the measured distances were larger.
5. 8 ). Due to its smaller impact on the first velocity correction, carbon dioxide was measured stationary close to pillar 34 using a CO 2 -Meter GC-2028 (Lutron). Table  1  summarizes the manufacturer specifications of the meteorological measurement sensors. Using these meteorological parameters, the distances of the total stations were corrected using the procedure given by  Pollinger (2020). Furthermore, all measurements of the total stations were carried out in both faces, to reduce systematical deviations caused by mechanical deficiencies of the total stations. For data analysis mean values from repeated measurements of full sets of polar observations were calculated and introduced to the network adjustment. The distances of the DistriMetre were corrected for mechanical misalignment and systematical errors using apriori values taken from laboratory calibrations.
Analysis and results The network adjustment is realized by the in-house software package Java•Applied•Geodesy•3D (JAG3D 2022) using the functional model described in the "Analysis procedure for tie measurements" section. The datum of the network adjustment is defined by the tangent plane of P 0 , which is equal to the centroid of the closed polygon defined by the outer vertices of the network. In Fig. 6 , a green hexagon symbolizes the position of the principal point P 0. The concrete pillars of the network realizing this datum within the free network adjustment are depicted by red triangles. The reference ellipsoid is GRS80. According to  Kallio et al. (2022) , the deflections of the vertical are ζ DOV x = -5. 85 and ζ DOV y = +2. 18 , and a uniform tilt of the points of the entire network is assumed.
Both risks were addressed using information criteria and principal component analysis. To evaluate the impact of the analysis strategy on the reference points of space geodetic techniques, both approaches are applied to the identical data set of a local tie campaign performed at the Geodetic Observatory Wettzell in 2021. The network was analyzed using the in-house software package JAG3D. The information criteria AIC and BIC evaluate both approaches differently. However, one can conclude that the observations are better represented by the 6DOF approach, but this approach is not closest to the true model. The estimated vertical deflections of the 6DOF approach agree well with gravimetrically obtained values. Moreover, neither approach has an essential first principal component, and both approaches yield stiff networks.
Study Design and Participants We conducted a multicentre, international, retrospective cohort study of patients of any age and performance status with histopathologically confirmed aNET 1-2cm that underwent complete resection of the primary tumour between January Study data were collected and centrally managed by C. N. using REDCap electronic data capture tools hosted at CTU Bern, University of Bern. 15, 16 ients were summarized in two subgroups based on surgical approach: (1) Simple appendectomy or appendectomy during another primary surgery (subsequently named appendectomy), and (2) appendectomy with oncological right-sided hemicolectomy or ileocecal resection in one or two stages (subsequently named right-sided hemicolectomy). All available tissue blocks with missing histopathological risk factors defined by the ENETS guidelines were reviewed locally by an experienced NET pathologist.
Only anonymized data were shared with the coordinating institution Inselspital Bern, University of Bern, Switzerland. No written informed consent was necessary. The study is registered with ClinicalTrials. gov, NCT03852693. Procedures Demographic, clinical, pathologic, treatment, and outcome data were extracted from electronic medical records at each institution either by C. N. during an on-site visit or by local investigators with expertise in the treatment of NET. Missing follow-up data were completed by contacting the family doctor and/or the patients directly. Standardized data collection templates were used.
Acknowledgements: This study was funded by the  Swiss Cancer Research foundation  (https://gap. swisscancer. ch/publicportal# /details/KFS-4741-02-2019 ). We are grateful to  Kristien Dumon  of the  University Hospitals Leuven ,  Angelika Kogut  of the  Department of Endocrinology and Neuroendocrine Neoplasms, Department of Endocrinology and Pathophysiology, Medical University of Silesia  and  Giuseppe Quero  of the Medical and  Surgical Sciences Department, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Università Cattolica del Sacro Cuore ,  Rome , and  Josephine Schenk  of the  Charité University Medicine Berlin  for their help in data compilation at their respective centres. Furthermore, we thank  Karine Atlan  of the  Pathology Department at Hadassah Medical Organization and Faculty of Medicine , The  Hebrew University of Jerusalem , Israel, and  Fiona Rodepeter  of the  Institute of Pathology, Marburg  for revising pathological samples of patients included at their institutions.
Background: PRSS1 was the first reported chronic pancreatitis (CP) gene. The existence of both gain-offunction (GoF) and gain-of-proteotoxicity (GoP) pathological PRSS1 variants, together with the fact that PRSS1 variants have been identified in CP subtypes spanning the range from monogenic to multifactorial, has made the classification of PRSS1 variants very challenging. Methods: All currently reported PRSS1 variants (derived primarily from two databases) were manually reviewed with respect to their clinical genetics, functional analysis and population allele frequency. They were classified by variant type and pathological mechanism within the framework of our recently proposed ACMG/AMP guidelines-based seven-category system. Results: The total number of distinct germline PRSS1 variants included for analysis was 100, comprising 3 copy number variants (CNVs), 12 5 0 and 3 0 variants, 19 intronic variants, 5 nonsense variants, 1 frameshift deletion variant, 6 synonymous variants, 1 in-frame duplication, 3 gene conversions and 50 missense variants.