dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , changepoint) occurs by estimating five separate linear mixed models based on the quintiles of time. The changepoint time is defined as the lower bound of the time interval where the fastest slope occurred. Lastly, the early and final slopes are informed by the slope of cognitive decline estimated using a linear mixed model considering the subsets of cognitive measures collected before and after the approximated changepoint, respectively. The linear mixed models are implemented using the hlme function from the lcmm  [31]  package (version 1. 9. 5) to fit mixed effect models on segments of the longitudinal data.
Overview of the package The nlive package offers three estimation functions (nlive. smm, nlive. pmma, nlive. pmms) to fit the SMM, PMM-abrupt, PMM-smooth models, respectively, relying on the SAEM algorithm implemented in saemix. These functions all require to take as input a dataset that provides information on the longitudinal The first fourth arguments are mandatory: dataset defines the name of the data frame in the longitudinal format; ID defines the name of the variable representing the grouping structure specified with " (e. g. , "ID" representing the unique identifier of participants); outcome corresponds to the name of the time-varying variable representing the longitudinal outcome specified with " (e. g. , "cognition"); time is the name of the variable representing the timescale specified with " (e.
The cohorts are described in detail elsewhere  [17]. The time intervals for each visit of each individual were generated using a uniform distribution of [-2, 2] months. The data generation started randomly from 24 years before death (time=-24) to death (time=0). This allowed the dataset to be more realistic where each individual has a different time from baseline to death, and there is an average of 10 years of follow-up (SD=5). As an inclusion criterion, individuals need at least 4 cognitive observations to enter the model. Each scenario was tested using 100 replications. In the first step, for each model, convergence was successfully reached in the second phase of the SAEM algorithm in all replications under all scenarios. The run time increased with the sample size and the number of covariates and was longer for SMM (Fig.
R > library(nlive) The dataCog dataset contains 1200 individuals with annual cognitive testing for at least 4 years until death (mean follow-up=7 [SD=5] years); a description of the data The fitted SMM model indicates that higher age at death was associated with lower cognitive level at baseline (see term beta_ageDeath90(first. level)) and close to death (see term beta_ageDeath90(last. level)). In addition, higher age at death was associated with an earlier half of cognitive decline (see term beta_ageDeath90(midpoint)). However, age at death was not associated with the Hill slope(see term beta_ ageDeath90(hill. slope)). To facilitate the interpretation of the estimated parameters, it is convenient to visualize the estimated average trajectories over time. In nlive. smm(), users can easily   3 ).
Modeling the abrupt PMM For PMM-abrupt, the call of the function nlive. pmma() is: R > pmm. abrupt. fit <-nlive. pmma(dataset=dataCog, +ID="ID", + outcome="cognition", + time="time", + var. all=c("ageDeath90"), + traj. marg=TRUE, + traj. marg. group=c("ageDeath90")) In this example, as expected, findings are generally similar to those obtained for the PMM-abrupt model. The main difference is that the estimated changepoint parameter represents here the beginning of the transition period. Marginal estimated trajectories in the whole study sample and according to age at death are displayed in Fig. 5 , respectively.
Concluding remarks In this work, we introduce the version 0. 2. 0 of the newly developed R package nlive to fit three non-linear mixed models for Gaussian longitudinal data: the sigmoidal mixed model (SMM) and two piecewise linear mixed models with a random changepoint (PMM-abrupt and PMM-smooth). The SMM includes 4 parameters, which allow for the estimation of early level, half of the decline, Hill slope (the steepness of the curve), and final level of the longitudinal outcome of interest. The two PMM separate the trajectory into two linear phases and allow for estimation of the early slope, changepoint, final slope, and final level. These models were chosen for the implementation as they currently cannot be easily implemented in R and are of importance, especially in aging research. All needed pieces such as functions, covariance matrices, and initials generation were programmed.
The nlive() function allows fitting these models with one line of code that is intuitive enough to the less sophisticated users. The yielding product has only five mandatory arguments. Options are available to readily accommodate user preferences, including manual specification of starting values or diagnostic plots. It was also designed to help interpretation of the output by  providing features such as annotated output, warnings (e. g. small sample, number of covariates), and graphs. This package is the first to provide a seamless user interface to fit the Sigmoidal Mixed Effect Model. Some packages in R can fit the Sigmoid curve but not the mixed effect model. As for PMM, although this is not the first package to provide an interface to fit the model, the nlive package includes the more recent developments in the model structure and the likelihood maximization algorithm.
The smooth PMM implemented is based on the polynomial transition that was demonstrated to have improved properties over the Bacon-Watts. PMM models were also reparameterized which allows the interpretation based on the estimated value at time zero and not a projection to zero from the first and more distant slope. Here we build upon recently developed tools in R such as the saemix package that utilizes the Stochastic Approximation EM-based algorithm, shown in several tests to have a better convergence rate than the Maximum Likelihood. All models were fitted with the same algorithm. Extensive testing of basic functionality was already performed for saemix development. In this interface, however, convergence adequacy was tested given the particular complexity of these models. Overall the convergence rate was high, the time was reasonable, and the bias was low.
The motivation of this package was aging research including biomarkers of the Alzheimer's pathological cascade (a. k. a. Jack curves)  [33] , natural history of cognition  [4, 34, 35] , retesting effect  [36, 37] , and terminal decline  [38, 39]. These models, however, are nonspecific and the nlive can be used in a wide variety of fields. Many processes were demonstrated to follow a sigmoid trajectory over time (a. k. a. 3 to 5 parameters logistic, Hill, Langmuir, Langmuir-Hill, and Hill-Langmuir equation). Such processes are found in agriculture  [1] , pharmacology  [2]  and marketing  [3] , to cite a few. Similarly, many processes that are initially linear may have an unknown change that may modify the trajectory. Such processes are found in a wide variety of fields from environmental sciences  [40]  to engineering  [41].
Acknowledgements The authors thank  Dr. Emmanuelle Comets  ( Université Paris Cité, Université Sorbonne Paris Nord, Inserm, Université Rennes ) for kindly reviewing and commenting on several versions of this manuscript and as a collaborator on the programming of the package. The authors also thank  Dr. Lisa Barnes  ( Rush Alzheimer's Disease Center [RADC ]) for reviewing examples during the development of the package using the African American Clinical Core (AA Core) (data not shown), as well as the staff of RADC and the participants of the  Minority Aging Research Study (MARS), Religious Orders Study (ROS) , and Rush Memory and Aging Project (MAP). Finally, we thank the reviewers for taking the time to go over this article and package and provide important suggestions.
calls of nlive. pmma and nlive. pmms are: nlive. pmma(dataset, ID, outcome, time, + var. all=NULL, var. last. level=NULL, + var. slope1=NULL, var. slope2=NULL, + var. changepoint=NULL,. ) nlive. pmms(dataset, ID, outcome, time, + var. all=NULL, var. last. level=NULL, + var. slope1=NULL, var. slope2=NULL, + var. changepoint=NULL,. ).
To test this hypothesis, we pretrained the fUS-BMI using a previously recorded session's data and then tested the decoder in an online experiment. To perform this pretraining, we first aligned the data from the previous session's imaging plane to the current session's imaging plane (Fig. S1 ). We used semi-automated rigid body registration to find the transform between the previous and current imaging plane, applied this 2D image transform to each frame of the previous session, and saved the aligned data. This semi-automated pre-registration process took less than 1 minute. To pretrain the model, the fUS-BMI automatically loaded this aligned dataset and trained the initial decoder. The fUS-BMI reached significant performance substantially faster (Fig. 2D ) when we used this pretraining approach.
The resulting Power Doppler images were transferred to a MATLAB instance in real-time and used for the fUS-BMI. The prototype 2 Hz real-time fUS system had approximately an 800 ms latency from the end of the ultrasound pulse sequence to arrival of the beamformed fUS image in MATLAB. Each fUS image and associated timing information were saved for post hoc analyses. Anatomical Doppler neuroimaging -At the start of each recording session, we used a custom plane-wave imaging sequence to acquire an anatomical image of the vasculature. We used a pulse repetition frequency of 7500 Hz and transmitted plane waves at 5 angles ([-6°, -3°, 0°, 3°, 6°]) with 3 accumulations. We coherently compounded these 5 angles from 3 accumulations (15 images) to create one high-contrast ultrasound image. Each high-contrast image was formed in 2 ms, i.
For the real-time experiments, the data recorded during a successful trial was automatically added to the training set. During the initial training phase, successful trials were defined as the NHP performing the movement to the correct target and receiving his juice reward. Once in BMI mode, successful trials were defined as a correct prediction plus the NHP maintaining fixation until juice delivery. For experiments that used a model trained on data from a previous session, we used data from all valid trials from the previous session upon initialization of the fUS-BMI. A valid trial was defined as any trial that reached the prediction phase, regardless of whether the correct class was predicted. The classifier then retrained after each successful trial was added to the training set during the current session.
Across-session alignment At the beginning of each experimental session, we acquired an anatomical image showing the vasculature within the imaging field of view. For sessions where we used previous data as the initial training set for the fUS-BMI, we then performed a semi-automated intensity-based rigid-body registration between the new anatomical image and the anatomical image acquired in a previous session. We used the MATLAB `imregtform` function with the mean square error metric and a regular step gradient descent optimizer to generate an initial automated alignment of the previous anatomical image to the new anatomical image. If the automated alignment had misaligned the two images, the software prompted the proctor to manually shift and rotate the previous session's anatomical image using a custom MATLAB GUI.
Quantification and statistical analysis Unless reported otherwise, summary statistics reported as XX ± XX are mean ± SEM. Post hoc simulated session -We used the recorded real-time fUS images to simulate the effects of different parameters on fUS-BMI performance, such as using only current session data without pretraining. To do this, we streamed pre-recorded fUS images and behavioral data, frame by frame, to the same fUS-BMI function used for closed-loop, online fUS-BMI. To dynamically build the training set, we added all trials reaching the end of the memory phase regardless of whether the offline fUS-BMI predicted the correct movement direction. This was done because the high possible error rate from bad predictions meant that building the training set from only correctly predicted trials could lead to imbalanced samples across conditions (directions) and possibly contain insufficient trials to train the model; zero correct predictions for certain directions could prevent the model from ever predicting that direction.
ACKNOWLEDGEMENTS We thank  Kelsie Pejsa  for assistance with animal care, surgeries, and training. We thank  Claire Rabut  and  Lydia Lin  for helpful discussions. We thank  Krissta Passanante  for her illustrations. W. S. G was supported by an  NEI F30  ( NEI F30 EY032799 ), the  Josephine de Karman Fellowship , and the  UCLA-Caltech MSTP  ( NIGMS   T32 GM008042 ). S. L. N was supported by the  Della Martin Foundation. This research was supported by the  National Institute of Health BRAIN Initiative  (grant  1R01NS123663-01  to R. A. A. , M. G. S. , and M. T. ), the  T&C Chen Brain-Machine Interface Center , and the  Boswell Foundation  (R. A. A. ).
PV production In this case study, an integrated photovoltaic system has been considered to produce on-site carbon-free energy. The photovoltaic system has a peak power of 4 kWp, oriented south and with a tilt of 30°. Electricity generated from the modules is simulated using EnergyPlus for semi-arid climatic conditions of Morocco. The solar energy plays a key role in reaching Net Zero Energy Buildings, particularly in the urban context. And for our case study, we assess the potential of the installed solar technologies to offset the tota Energy Use Intensity (EUI) of the building. Subsequently, we can assume that we've reached a NZEB when the PV production surpasses the electrical demand of the building. In our case study, the yearly SS, which is the ratio of solar production to Energy Use Intensity, is equal to 1.
Some remarks on complexity As a proof-of-concept, we implemented the closure properties of C 2 -finite sequences in the open source computer algebra system SageMath  [25]. For computations in the base ring of C-finite sequences we use the ore algebra package which provides an efficient implementation of closure properties in this ring  [14]. A preliminary version of the package can be obtained from the authors. In our implementation, we do not use Algorithm 1 verbatim. Instead, we increase the ansatz until the corresponding linear system has a termwise solution for a fixed number of initial terms. We check this using the Rouché-Capelli theorem. This is usually quite fast, since the computation of terms of a C-finite sequence and computations over the field K are cheap in comparison with the operations in Q(R C ).
Background: The EU LifeCycle Project was launched in 2017 to combine, harmonise, and analyse data from more than 250,000 participants across Europe and Australia, involving cohorts participating in the EU-funded LifeCycle Project. The purpose of this cohort description is to provide a detailed overview over the major measures within mental health domains that are available in 17 European and Australian cohorts participating in the LifeCycle Project. Methods: Data on cognitive, behavioural and psychological development has been collected on participants from birth until adulthood through questionnaire and medical data. We developed an inventory of the available data by mapping individual instruments, domain types, and age groups, providing the basis for statistical harmonization across mental health measures.
Results: The mental health data in LifeCycle contain longitudinal and cross-sectional data for ages 0-18+ years, covering domains across a wide range of behavioural and psychopathology indicators and outcomes (including executive function, depression, ADHD and cognition). These data span a unique combination of qualitative data collected through behavioural/cognitive/mental health questionnaires and examination, as well as data from biological samples and indices in the form of brain imaging (MRI, foetal ultrasound) and DNA methylation data. Harmonized variables on a subset of mental health domains have been developed, providing statistical equivalence of measures required for longitudinal meta-analyses across instruments and cohorts.
This cohort description focuses on the extensive work done to catalogue and harmonise variables related to cognitive, behavioural, and psychological development within the broader LifeCycle consortium. 1  It is well-recognised that experiences in early life play an important part in shaping later mental health  9  and the data within the LifeCycle Project permit analyses of these associations. LifeCycle includes many pregnancy and birth cohorts that prospectively collected data on offspring from conception and across different ages of child, adolescent, and adult development. The The mental health studies in LifeCycle aim to investigate epidemiological interrelations between early life exposures, behaviour, and cognition, with later mental and physical health. Towards this end we have harmonised measures from 17 LifeCycle cohorts to enable studies that examine how environmental stressors in utero and in early childhood affect, or are associated with, psychological trajectories, behaviours, and mental outcomes throughout childhood, adolescence and adulthood.
Additionally, we are examining the nature and degree of mediation of these associations through epigenetic changes and brain development (Figure  1 ). To our knowledge, the data compiled for these studies within LifeCycle represents the largest ongoing consolidation of childhood behaviour, psychopathology and cognition data to date, encompassing more than 200 multidimensional and multi-informant established mental health measures collected from at least 250,000 participants. The geographic coverage is broad, spanning across much of northern, western, central and southern Europe as well as Western Australia (Figure  2 ). Mental health data from more than 250,000 children are available (as of June 2021), including either mother-child or mother-father-child cohorts, and the study population is diverse with respect to the age of the participants, cohort types, and data collection periods (Table  1  The participating cohorts include child participants with follow-up data ranging from birth until adulthood (Table  2 ).
A c c e p t e d V e r s i o n 12 There are a number of approaches to harmonise data and several of these have been described and successfully implemented in large collaborations. 31, 32, 33, 10 The LifeCycle Project has developed a protocol to generate harmonised variables across a selection of important cognitive and mental health domains. This harmonisation approach creates standardised scores and percentiles for important domains such as internalising and externalising symptoms, ADHD and ASD symptoms and diagnosis, and language and motor functions. Percentiles and standardised scores were used as they allow the pooling of mental health outcome data collected using different scales or instruments. One of the biggest harmonisation challenges this project faced was obtaining a thorough inventory of the available mental health data in individual cohorts, which was overcome by mapping the available data by instrument, measure, age group, and domain.
Early-life exposures -lifestyle, migration, socioeconomic, and urban environment The LifeCycle online catalogue 10 also contains information on harmonised data on diverse measures of exposures early in life. These will enable the analysis of risk models for mental health that assess the nature and impact of indirect and direct exposures experienced in early life, and comorbidities with adverse mental health symptoms and other health conditions. Comprehensive exposure-outcome analyses will also be used to develop predictive markers for mental health in children and adolescents, which may help shape the prediction of mental disorders, allowing for targeted early intervention.
In addition, structural and functional connectivity metrics have been assessed. Data have been collected through neuroimaging techniques, such as foetal ultrasound and Magnetic Resonance Imaging (MRI) in childhood and /adulthood MRI. These data enable LifeCycle to describe changes in structural and functional development of the brain from foetal life and infancy, and to subsequently associate this brain development in early life with psychopathology outcomes in childhood, adolescence and adulthood.
Framework for collaborative analyses LifeCycle aims to perform most of the analyses through DataSHIELD. 44, 45 With the recent launch of the platform and its analytical features for use with LifeCycle harmonised data, a number of novel collaborative studies have begun to form within the theme of mental health. Examples of planned and ongoing exposure-outcome analyses include infant feeding patterns and school-age externalising behaviours, maternal smoking in pregnancy and adverse child behaviours, associations between sleep, behaviour and cognition, sibling effects and prematurity, and socioeconomic inequalities and general mental health trajectories. Results from these studies are currently pending, but they have already shown that independent participant data resources have been successfully harmonised and can be coanalysed.
The quantity and breadth of mental health and cognitive data available that have been mapped and harmonised by the LifeCycle mental health research group is a singular resource to enable developmental studies of mental health. These data will play an important role in replicating previous findings with an enhanced statistical power, expanding upon previous associations through larger and more diverse samples, and in the development of novel models to describe how multi-faceted earlylife exposures can shape and influence the landscape of mental health in later life.
Strengths and limitations There are many strengths inherent in large consortia such as LifeCycle. 1 Key among these is that LifeCycle is building the EU Child Cohort Network, a sustainable research network that will enable continued exploitation of the LifeCycle data, metadata and collaborative progress beyond the usual timelines of a funded grant. Another important strength is the ability to study age differences and agerelated mental health and cognitive changes; this developmental aspect will help to understand the long-and short-term consequences of early life exposures, and how other factors such as epigenetic changes may mediate later health outcomes. Geographic diversity is also a key feature; it provides enhanced location coverage and generalizability of results, and also facilitates intra-and interpopulation comparisons.
This makes it possible to make more reliable causal inferences due to different confounding structures. The number of critical mental health domains covered is another strength, allowing for exposureoutcome research into many important and well-studied areas within this field. The availability of the harmonisation protocols, coupled with the extensive overview of mental health measures, including detailed information on the dimensions and age ranges across cohorts, provides users with an integrated catalogue of psychological, cognitive, and psychomotor data in participating cohorts. Furthermore, the use of DataSHIELD enables a flexible and data-secure approach that allows new cohorts and centres to link into the analysis network and contribute with their own data, as well as the addition of newly harmonised data as these are collected and updated.