dataesr/scientific-paragraphs-categorization huggingface dataset:
LSTM: We evaluate an LSTM-based regression network with 200 hidden units. The training is done using Adam's optimisation to minimise the mean absolute error  [29]. AR-GP: AR-GP are trained to forecast the response variable using the input and response features until time t. For each subject, the missing observations are filled using the forward filling approach, where data from a previous observation are carried over to the following observation. When the training matrix is completed, the parameters of AR-GPs are learned by minimising the negative log-likelihood  [20]. AR-GP + MICE: Multivariate imputation by chained equations (MICE) is an imputation strategy for matrix completion  [30]. It works by iteratively building predictive models to fill each specified variable in the matrix. Each variable is imputed using other variables in the dataset and the iterations are run until convergence is met.
B. Datasets Health progression modelling requires longitudinal data from a person that can provide long-term predictions for disease status of an individual. Often, this data exists in the form of electronic health records or sequence readings collected over time. Current state-of the-art methods such as deep learning methods provide accurate models of individuals' health status in case of Big Data sets where both the number of individuals and the number of individual measurements through time are large  [31]. However, in the presence of limited training data (small N and t + d ≈ 0), such as when early disease discovery is of utmost importance, such approaches produce sub-optimal results. Our framework for time series-prediction in the absence of missing or limited data can enhance health prediction capabilities.
Hence, we select two datasets from real life presented as follows: 1) Gestational Weight Gain: One health demographic is managing gestational weight gain among women. Approximately 70% of pregnant women gain either too little or too much weight at the end of their pregnancy in accordance with the Institute of Medicine recommended guidelines  [32]. Inappropriate weight gain during pregnancy has been associated with shortand long-term health complications to the mother and baby. Thus, early recognition of signs of weight gain during pregnancy is essential  [22]. In this study, data were collected from diverse subjects in Europe where 80 women in their fifth week of pregnancy or later were recruited from midwife practices in Eindhoven, The Netherlands. The weight data were collected by a WiFi-connected scale, Withings WS30.
2  The dataset is described in Table  I. Note that this is a case of univariate time series data where only one variable (weight gain) is measured with respect to time. A mobile application allowed participants to log their weights weekly, and the weight data was sent to the cloud. The participants provided an informed consent pre-data collection and the study was approved by the Internal Ethics Committee for Biomedical Experiments of the involved organisations (ICBE Reference number 2015-0079 respectively). We model the weight (gain) y as a function of time, (t j 1 , t j 2 ,. , t j m ) using the proposed approach. We achieve this by first normalising measured weight with pre-pregnancy weight to obtain weight gain data and then fitting various forecasting approaches. For the parametric approaches, we utilise the complete data from N -1 subjects to generate a prior to estimate a MAP model.
Around 266 features were extracted based on these modalities and merged together over time to form a coherent numerical multivariate time series feature set. Since the complete dataset has a lot of missing visits, we follow the state-of-the-art approach for Alzheimer's disease marker forecast  [20]  and selected a smaller dataset of 95 subjects such that data from at least ten visits is present and missing data is no more than 82. 5% of the feature set. This helps in benchmarking our proposed approach with the AR-GP approach  [20]. In the case of this multivariate time series data, our experimentation to predict a cognitive score (MMSE, ADAS or CDRSB) using 266 features that vary with time is as follows: 1) Collective temporal realignment: The Alzheimer's study  [38]  recruited patients that were already going through some stage of cognitive decline.
Since the disease progression in every individual differs in their onset, the target time series (y) for each of the patients had a different t 0. Therefore, we calculate the value of τ optimal (j) using the response variable of the target data and the response variable of the j th subject in the training data. This lag is calculated for all the subjects in the training data with respect to a given target subject. Based on the calculated τ optimal (j), lagged/led versions of the predictor (X j ) and the response (y j ) are created to be used for further training. 2) Subset selection based on the response variable y: Based on the available target data (X, y) from a given test subject until month t d , we find the subjects in the training dataset with a similar cognitive decline. This is done by applying the subset selection approach explained in Section V-A on y j.
Note that we apply subset selection on the response variable instead of X since it gives us similar subjects in output space. We also compared the performance of our subset selection approach with a k-means clustering approach. Clusters were obtained using the input features of the multivariate time series. For a given test subject, the subjects in the closest cluster are considered as training data. These training subjects along with the available test subject's data are used to train a non-parametric GP as follows: (1) the missing values are forward filled (2) K clusters with centroids {c k } are created using k-means clustering with training data matrix until month t d (3) calculate distance d k = dist(X + , c k ) of the test subject's predictor variables data X + from each centroid {c k } and the optimal profile (subset) is selected as cluster c opt = c i such that, d i < d j , ∀i = j ∈ [1, K] In what follows, we will refer to this method as the "K-means + GP" approach.
Disease progression Estimation: We perform non-parametric regression using Gaussian processes on the input feature set X. First, given a test subject's response variable y + , a time aligned training data is made that consists of a lagged version of Xs and ys. Subsequently, subset selection is performed by finding subjects in the training data whose response variable (y j ) is close to the test subject's response variable (y + ). Once a subset is selected, GP based regression is performed on [X , X + ] using  (7)  and  (8). We perform leave-one-subject out cross-validation on the dataset. In both cases, developing models to automatically predict Alzheimer disease-related metrics or gestational weight gain is of utmost importance to intervene appropriately and in time. This makes the availability of the target data another challenge.
Also, it can be observed in Fig. 5  that the GP approach performs worse than the SS-GP approach. Based on a paired t-test, which assumes equal variances, we found that all differences between performances of the SS-GP model and the other models are statistically significant at a significance level of 5%. Only for the SS-GP and the MAP model performances, no statistically significant difference was found. This is not unexpected because of the simplicity of the dataset. For t + d > 220, the performance of ARIMA significantly outperforms the performance of all other approaches. However t + d = 220 is too close to the horizon to result in effective intervention. Note that the average delivery day is around day 277. The benefit of using our SS-GP approach is further illustrated in Fig. 6. In Fig.
B. Alzheimer's Disease Prediction Unlike the gestational weight gain use case, where the final objective was to predict the end-of-pregnancy weight gain because the data was recorded daily, we aim to predict the progression of Alzheimer's disease at each visit, since these visits are separated by six months or more. For this purpose, we will study the performance of several methods for predicting three metrics for cognitive decline that are commonly used by clinicians and that were introduced in Section VI-B2: MMSE, ADAS13, and CDRSB. Following our realignment approach, we first calculate the optimal τ for each response time series (cognitive score) in the training dataset with respect to the available response data from the test subject. We compute the standard deviation at a particular time instant for all response time series in the training data that are aligned with respect to the target subject.
This standard deviation should be smaller than when no alignment is performed. We experimented with all the subjects in a leave one Fig. 6. Prediction error (i = 1 th subject) is (a) high (low confidence) when the complete training dataset is considered due to inter-subject differences but (b) reduces using close subset selection based on heuristics. The prediction confidence (grey) also increases using the SS approach. out fashion. In Fig. 7  each line depicts the standard deviation of the ADAS13 matrix created using aligned versions of the time series for a given test subject. By computing the standard deviation without alignment, a baseline was established. We observed that > 80% of the subjects have a standard deviation less (more desirable) than the baseline when adjusted for the alignment using our temporal realignment approach.
VIII. CONCLUSION In this article, we proposed a novel approach, termed the SS-GP approach, for forecasting time series that are not necessarily uniformly sampled. For this purpose, we combined the non-parametric GP regression with a subset selection procedure that selects a set of time series from the data that closely resembles the test subject's data. Our subset selection procedure is robust as it selects the subset size dynamically based on temporal similarities between the time series in the subset and the test time series. The temporal similarity is measured with a DTW distance that can be computed between time series with a different length. We validated this method on two use cases and compared it with several other approaches. Firstly, on the univariate gestational weight gain dataset, our approach performs similar to a parametric polynomial fitting which is not unexpected because of the simplicity of the data set.
IX. LIMITATIONS & FUTURE WORK Although effective in regression when data is missing, Gaussian Processes (GPs) have a high computational complexity of O(n 3 ). Our subset selection is a local approximation technique that decreases complexity by including only the most useful training points (<< n) that are close to the test point. However, the collective realignment technique has a high time complexity because it determines the ideal alignment for a specific test time series by comparing it to all the time series in the training dataset. In future research, we would like to experiment with another scalable sparse approximation of GPs developed in  [41]  that can further reduce the time complexity. In addition, the proposed approach is only tested on data sets from healthcare considering the necessity that arises in this domain from data acquisition limitations.
The muon detector is one from the Diaphane collaboration, used on the Soufrière of Guadeloupe. First it was placed beside the TBM, directed towards the drilled cylinder from a lateral perspective. Then it was moved directly inside the TBM for the rest of the drilling operation (left of  Fig. 17. 15). These experiments provide a unique dataset to optimize the methodology. Because the telescope is moving forward, the muon flux crossing a particular geological object measured by the telescope has different directions with respect to time, allowing 3D density estimates. An example of apparent density 3D reconstruction distribution of the ground, using inhomogeneous Poisson likelihood, is presented in Fig. 17. 15. The algorithms for this particular reconstruction remove systematic noise from the buildings; caves, etc.
Accounting for Beer-Lambert law in the film, the space-time dependence of the heat source is expressed as: Q = I 0 (t). α. (1 -R cr ). exp (︃ -2x 2 w 2 )︃. exp (︃ exp(-α. z) 1 -exp(-α. d) )︃ (1) where I 0 (t) is the intensity of the pulse, α and R cr the absorption coefficient and reflection coefficient of chromium, w the beam waist and d the thickness of the thin film. The subsequent temperature distributions are computed using the Heat Transfer Module in COMSOL Multiphysics using an approach similar to our previous works  [20]. However, we incorporate in the current model, the fluid dynamical responses to study the effect of surface tension and material properties on the melt pool flow and temperature. The donor substrate (glass) is accounted in the modeled system. Due to its transparency to the wavelength of the QCW laser, it does not influence the heat source but the modest heat losses into it are accounted.
Weekly median atmospheric Hg II concentrations (collected on CEMs at VRS and ZEP, and with a Tekran® 1130-1135 system at ALT) during AMDE weeks were higher at VRS, 243 pg m -3 (IQR 82 to 412) and ALT, 118 ± 46 pg m -3 than at ZEP, 38 pg m -3 (IQR 21 to 53) due to the frequent arrival of air masses at ZEP from the free troposphere. Hg concentrations in 2019 snow samples from Ny-Ålesund ranged from 0. 14 to 21. 3 (5. 2 ± 7. 0) ng L -1 , which is similar to previous observations in the area  54, 55  , but lower than snow Hg levels up to 373 ng L -1 during AMDE events  53. Hg 0 stable isotopes and air mass back trajectories Figures  2b, c , d and 3-5 show the Hg 0 stable isotope variability of the dataset; NH background Hg 0 isotope observations are indicated in Fig. 2b-d  as horizontal shaded red bands, representing the IQR of published data.
2520, 0. 5024, 0. 7520, 1. 493 for the 199, 200, 201, and 204 Hg isotopes respectively. Analytical uncertainty of isotopic analysis was assessed by repeated measurement of the samples, of inhouse standards UM-Almaden, ETH Fluka and JT Baker, and of procedural standards NIST SRM 1632d and 1632e (see Supplementary data file). The results obtained were in agreement with published values  61, 62, 77, 78. The 2 SD uncertainty reported for samples is the largest of the 2 SD's of sample replicates, procedural standards, or in-house standards.
Back-trajectory analysis The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT, v. 4. 2. 0; https://www. arl. noaa. gov/hysplit/) developed by NOAA  79. was driven with 3 hourly meteorological input data from the Global Data Analysis System (GDAS; 1°latitude-longitude 360 by 181 grid) to identify the potential Hg source regions. The model was run in backward mode for 240 h every 2 h throughout the sampling periods at ZEP, VRS and ALT. In total, ~84 backward trajectories were calculated for each sampling period. An analysis of the spatial (horizontal and vertical) residence time of the air mass history has been accomplished. The back-trajectory model results have been analyzed with respect to the air residence time above five surface types (land without snow cover, open water, permanent ice/snow, sea ice, and land based snow).
Acknowledgements This study was funded by the  French Polar Institute IPEV  ( Program 1207 MESSI) , the  EC H2020 ERA-PLANET  (grant No. 689443 )  iCUPE program , the  EC H2020   INTERACT  project (grant No  730938 ), the  Swiss National Science Foundation (SNSF)  project  P400P2_180796 , the  NSF  project  1700711 , the  Chantier Arctique Français (Pollution in the Arctic System)  project, the  AXA Research Fund  and a  Make Our Planet Great Again (MOPGA) postdoctoral scholarship  from the  French Ministry of Science and Education. We thank the following persons for assistance in the field and laboratory:  Jerome Chmeleff  from  GET ,  Alban Thollot  from  IGE ,  Ove Hermansen  and  Are Bäcklund  from  NILU ,  Bjarne Jensen ,  Christel Christoffersen , and  Keld Mortensen  from  AU ,  Christelle Guesnon  from  NPI ,  Melody Fraser  and  Kevin Rawlins  at Alert and all personnel from  IPEV  and  Alfred Wegener Institute  involved in this study.
Data availability The research data that support the findings of this study are available at https://doi. org/10. 3929/ethz-b-000549236. Meteorological and field data used in HYSPLIT simulations from 2018 and 2019 are available from NOAA (https://www. ready. noaa. gov/archives. php). Sea ice concentration data from April to December 2018 and 2019, respectively, displayed in the Supplement, were obtained from https://www. meereisportal. de (grant: REKLIM-2013-04).
However, viruses in the families Paramyxoviridae or Rhabdoviridae (e. g. , vesicular stomatitis virus  [VSV] ) appear to have a single initiation site at 1U. These data from the biochemical assays examining RSV initiation mechanisms were further analysed at the structural level. During the formation of the initiation complex, the priming loop contains an aromatic or proline residue that interacts through stacking interactions with the incoming NTP. The VSV priming loop extends into the active site, whereas the putative RSV priming loop does not. Thus, Rachel's group performed mutational analysis of the aromatic and proline residues in this presumed RSV priming loop. The data indicated that Pro1261 indeed stabilizes the initiation complex and may also function as a priming residue. When mutated to Ala, the L P1261A defect was overcome with high concentrations of NTPs or by changing the divalent cation from Mg 2+ to Mn 2+.
Initial safety data from phase 1 informed phase 2 study design, and dose determination from phase 1 triggered implementation of the phase 2 studies. Phase 1 studies demonstrated a tolerable safety profile of the vaccine and showed consistent development of neutralizing antibody titers after a single dose, with titers increasing even further after the second dose. The response was independent of age and, on average, postvaccination antibody titers were higher than antibody levels in convalescent serum. Phase 3 studies had a simple design and were executed with strong collaboration with NIH and FDA. Entry criteria included individuals at high risk of infection, encompassing a significant proportion of patients over 65 and those with comorbid conditions, and who were representative of US demographics.
Tal noted the difficulty of balancing the desire for a rapid trial with including individuals that fully reflect the diversity of the US population. Analysis of the study data revealed an overall 94. 1% efficacy rate, with data at the time of analysis demonstrating 100% protection against severe disease. Of note, data from Pfizer's independently developed mRNA-based vaccine phase 3 trial, released a week later, demonstrated efficacy within 1% of the Moderna results. Local adverse events were mostly associated with pain at the injection site. Systemic adverse events included fatigue, headache, myalgia, and arthralgia; these effects increased with the second dose, but were transient, expected, and correlated with the immune response. Thus, the safety profile was determined to be tolerable enough to warrant use for clinical benefit.
Three main genotypes of rhinovirus exist, RV-A, RV-B, and RV-C, with approximately 170 associated types that co-circulate in the human population causing a spectrum of respiratory disease from asymptomatic infection or mild upper respiratory disease to pneumonia. RV-A and RV-C are most often associated with clinical disease. The antigenic diversity associated with rhinoviruses is a primary reason rhinovirus infections are maintained in the human population causing seasonal infections year after year. The antigenic diversity of rhinovirus strains presents a challenge for developing effective vaccines to prevent or reduce disease associated with rhinovirus infection. Longitudinal data from the Childhood Origins of Asthma (COAST) birth cohort study were analysed to determine the relationships between age and RV-C infections.
Neutralizing antibodies specific for RV-A and RV-C were determined using a novel PCR-based assay. Data were pooled from 14 study cohorts in the United States, Finland, and Australia, and mixed-effects logistic regression was used to identify factors related to the proportion of RV-C versus RV-A detection. RV-A and RV-C infections were common in infancy, whereas RV-C was detected much less often than RV-A in older children. The prevalence of neutralizing antibodies to RV-A or RV-C types was low in children 2 years of age and increased through the teen years. At each age, RV-C seropositivity was 3-5 times more prevalent. The ratio of RV-C to RV-A titers during illnesses was significantly related to age, CDHR3 genotype (putative RV-C receptor and asthma susceptibility allele), and wheezing illnesses.
Replication was quantified using a rabies reporter virus expressing mCherry protein and by immunohistochemical staining for the rabies virus N protein. These data suggest that lectins could be an alternative treatment for rabies virus infection. Arbovirus infectivity is significantly reduced by components of the bacterial cell wall. Lana Langendries, M. S. , Rega Institute for Medical Research at KU Leuven, Leuven, Belgium While transmission of arboviruses (e. g. , Zika, dengue, yellow fever, and chikungunya viruses) through mosquito bites at the skin has been well characterized, little is known about the interactions of arboviruses with skin microbiota present at the site of infection. To investigate this question, Lana Langendries studied the effects of bacterial cell wall components on the infectivity of such arboviruses.
Acknowledgments. We would like to thank  Steve Muanza  for his collaboration and input at an early stage of this study, as well as the PESBLADE working group of the  OCEVU Labex  for useful discussions. We also acknowledge instructive exchanges with Pietro Slavich. This work benefited from partial support of the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French government program  managed by the  ANR.
Data analysis SMS measures were analyzed on 64 beats for each condition using circular statistics  18  with the CircStat Matlab Toolbox,  19  which is a robust tool to quantify SMS when there is not a perfect match between the number of taps and the number of the pacing stimuli. 20, 21 Thus, it is appropriate when participants tend to miss taps or tap more than once in response to one beat. The analysis provides a measure of participants' consistency and accuracy in each condition. Consistency ranges from 0 to 1, with 1 corresponding to perfect consistency (all taps occurred at the same delay to the beat) and 0 corresponding to an absence of synchronization (the taps were randomly distributed between the beats). We applied an arcsine transformation to the consistency (sin -1 √ X) before the analysis to normalize the distribution.
to the mean delay to the beats of participants tapping. Accuracy was calculated only if a participant's taps were not randomly distributed. Random distribution of taps was tested using the Rayleigh test for circular uniformity. 18, 22  analyzed participants' socioemotional reactions by measuring the frequency (number of behaviors per minute) of positive emotional facial expressions (EFEs+) according to the Facial Action Coding System. 23 The coding was computer-assisted and performed using Behavioral Observation Research Interactive software. 24 Two independent observers, who were blind to the condition, analyzed the videos (without the audio recording) and recorded the frequency of EFE+ per minute through the use of a keyboard. Interobserver (r = 0. 95 in the day hospital study, r = 0. 72 in the nursing home study) agreement was checked against literature criteria.