dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability We have deposited raw FASTQ files from our genome sequencing, RNAseq, and MSG in the NCBI Sequence Read Archive under accession SRP158931. In addition, we have deposited a data package on FigShare that contains the following information, resources and scripts: (1) all supplementary tables and figures described in the text, (2) phenotypes for all individuals, (3) genotypes for all markers associated with linkage groups, (4) genotypes for the consensus, linkage group-specific markers used for QTL mapping, (5) sequences of all markers, all assembled genome scaffolds, and all assembled transcripts, (6) the genome annotation, and (7) custom Python scripts employed. Supplemental material available at FigShare: https://doi. org/10. 25387/g3. 8072405.
Difficulty ordering markers within linkage groups In examining marker order between the genetic maps derived from the two backcross populations (FL-BC and KS-BC) we found considerable inconsistency. While markers at the termini of each linkage group were largely consistent in order between the two backcrosses, markers in the middle of each linkage group were scrambled. Figure  1  highlights this phenomenon for linkage group 1. If marker order were preserved between backcross populations, markers would fall along a line. We observed the same pattern when using the software ALLMAPS  (Tang et al. 2015) , which allowed us to map genetic markers to the set of physical A. grisella scaffolds from our genome assembly. ALLMAPS showed that markers common to both backcross populations always mapped to the same scaffold, but that the marker order defined by each backcross-specific genetic map was distinct (Figure  2  demonstrates this pattern for linkage group 18).
To explore this pattern further we plotted genotypes for all individuals in a population at all markers, in the order defined by the genetic map. As exemplified by linkage group 1 (Figure  3 ), crossovers appear to be relatively rare in the pair of backcross populations, but as expected are absent in the segregant population. Most backcross individuals are either homozygous or heterozygous for an entire linkage group, and when crossovers are evident they are near the ends of the linkage groups. The apparent scarcity of crossover events provided minimal information to Lep-MAP2 to assist with marker ordering, and is likely why markers in the middle of the linkage groups are inconsistently-ordered between backcross populations (Figures  1  and 2 ). Genotyping error could also lead to a spurious reduction in crossover rate and incorrect marker order, and we evaluated the level of genotyping error in our dataset using two strategies.
This is a common issue when applying genotyping-by-sequencing technologies to noninbred diploid organisms such as our backcross individuals, because a true heterozygote may appear as a homozygote if the alternative allele is simply not sampled by a read, whereas for a true homozygote to appear as a heterozygote a specific sequencing error must occur. Our use of a fairly common-cutting restriction enzyme (AseI, AT^TAAT) likely contributed to our biased genotyping error, since it resulted in a very large number of markers, each of which we covered only shallowly with sequencing reads. To test whether greater numbers of crossover events, and enhanced consistency in marker order between backcrosses, could be achieved using a dataset with a reduced potential for genotyping error, we repeated the genotype calling step in all three populations, but increased the read depth requirement from 5 to 8 (using genotypes in Stacks, see "Materials and Methods").
Genome assembly and annotation A goal of any mapping project is to enable the identification of genes contributing to trait variation. Hence, we assembled a draft genome of A. grisella (Table  4 ) using both short-and long-insert sequencing libraries and short-read (100-bp) Illumina sequencing. The total, end-toend length of the assembled scaffolds is 418-Mb, half of the bases are in scaffolds 87. 3-Kb or longer, and the assembly has a GC content of 32. 4%, which is on par with other sequenced lepidopteran genomes (e. g. ,  Zhan et al. 2011). To assess completeness of the set of scaffolds we used CEGMA  (Parra et al. 2007 (Parra et al. , 2009)) , and showed that our assembled scaffolds contain 196/248 (79. 03%) intact core eukaryotic genes. Investigators could likely obtain a more contiguous, and more complete assembly by adding long-read, single molecule sequencing data to our short-read sequencing dataset in the future  (Chakraborty et al.
To facilitate such work we have released all raw and processed genome data associated with this study (see "Data availability"). To annotate scaffolds we used MAKER2 (Holt and Yandell 2011), identifying 15,848 predicted genes. The number of genes we found is not dissimilar to the 12,669 predicted protein-coding genes identified in H. melpomene  (Heliconius Genome Consortium 2012)  or the 12,901 identified in B. mori  (Xia et al. 2004 ). MAKER2 scores annotated gene models on a 0-1 Annotation Edit Distance (AED) scale  (Eilbeck et al. 2009) , where lower values indicate better agreement between the annotation and the supporting evidence. The AED scale provides a useful statistic for understanding the quality of an annotation  (Holt and Yandell 2011). We found that 91. 5% of the predicted genes in our A.
ACKNOWLEDGMENTS Phenotyping and DNA isolation was supported by  NSF   IOB-0516634  to M. D. G. and  J. M. G. Sequencing data  for this project was generated by the  KU Genome Sequencing Core  (supported by  NIGMS  award  P20GM103638 ), and data analysis was facilitated by the  Kansas IDeA Network of Biomedical Research Excellence  (supported by  NIGMS  award  P20GM103418 ). Additional support for the project was provided by  NIH   R01 GM085260  to S. J. M. and  NIH   R01 OD010974  to S. J. M. and  Anthony D. Long  ( UC Irvine ).
The combination of an optimal mastery of the technique of the use of nets -through training imparted to the producers -with the choice of regions of production best suited to this technology should make it possible to reduce the environmental impacts of market garden production in sub-Saharan Africa. A synergy of action between experts and researchers in the disciplines of agronomy, economics, socio-technical analysis and the environmental assessment of systems will be necessary to achieve an optimal coherence and the best representation possible of these joint approaches. An example of this synergy can currently be found in two ongoing projects on the continent: ANR-Eco-Plus in  Kenya (2017 Kenya ( -2020) )  and HortiNet in  Côte-d'Ivoire (2018 -2021). conclusion Our first observations in Benin and Kenya suggest that the physical control of insects through the use of nets leads to profound changes.
This platform has helped design banana systems based on multi-species and multifunctional cover crops of service plants. It relies on a toolbox developed by CIRAD researchers, which mainly consists of a collection of service plants, databases on the functional traits of these service plants and the associated ecosystem services, and models standardizing some of this knowledge (models of crop functioning, models of assessment of service plants, simulation and optimization models of cropping practices, etc. )  (Dorel et al. , 2008; Tixier et al. , 2008; Tixier et al. , 2011; Ripoche et al. , 2012). This platform is also working on multicriteria assessment of innovative systems within the framework of a partnership between CIRAD and the Tropical Technical Institute. It thus benefits from the integration of knowledge and know-how originating from research on the functioning of innovating systems, and from expert knowledge from different disciplinary fields or originating from ecological engineering.
This can be observed in pure line varieties that are less adapted to environmental constraints  (Toniutti et al. , 2017 (Toniutti et al. , , 2019)). Before the BREEDCAFS project, adaptation to future climatic constraints and low light intensities characteristic of agroforestry systems had never really been a breeding objective and homeostasis of hybrids was used to cultivate them in agroforestry (Figure  9. 1). Significant progress in the adaptation of Arabica hybrids to agroforestry systems, and to biotic and abiotic stresses, seems all the more attainable as we can now rely on new tools (genomic, transcriptomic, metabolomic) and on genome sequencing  (Denoeud et al. , 2014). Coffea arabica is a polyploid species with reduced genetic diversity. BREEDCAFS offers software for the analysis of polymorphisms of polyploid species.
Thus, in 2013, FARM launched an innovation partnership for a two-year period that brought together the producer organization (UGCPA), the research community (CIRAD) and a communications agency ( Jade Productions) with which previous collaborations had been successful. The objective of these partners was to design this approach together, with the partners being selected for their complementary skills and viewpoints on the issues of communication and ecological intensification. The design of the communications approach spanned several months. It was a reflexive, iterative and participatory process and alternated phases of collective work, field data collection and restitution, and internal reflection at UGCPA. The process was guided by the need for the organization to formulate its requirements and expectations from the agri-environmental policy it wished to implement.
Choice of Segmentation Software The choice of segmentation software depends on the expectations of the user as well as on his confidence in 3D segmentation and modeling. Two different categories of 3D modelers can be identified. The first category of users usually work with a turn-key product predefined and automatic segmentation, and including all processes permitting to obtain the final 3D model within a single software. This easy-to-use solution, as proposed by Materialize (Mimics), represents a quick and easy way to perform 3D modeling in medicine. The limits of this type of practice are the cost of the license (more than 10,000$ per year) and the lack of independence for the user during the 3D modeling, the post-processing, and the pre-printing step. Conversely, the second group is that of self-taught users.
Using open-source softwares (Itk-snap, slicer), they can control each step of the segmentation by manually modifying the primary DICOM contrast or the threshold. They can either easily manage the propagation of the segmentation and the postprocessing steps. As these software are open-source, they can be downloaded for free and they are frequently upgraded by the developers. The main limit of this approach is the need for a learning curve and the risk of potential subjective bias in the modeling.
Introduction The 2019 "World Economic Forum" global risk report 1 has nominated cyber attacks and data breaches as the fourth and fifth deliberate risks facing the world today. It is the second year in a row that these threats feature in the top five list of risks. Healthcare, among others, was offended with more cybersecurity breaches, in which several situations can lead to these breaches, for example, credential-stealing malware, an insider who either systematically or accidentally unveils patient data, or lost laptops or other mobile devices. On the illegal market, "Protected Health Information" (PHI) is more important than credit card credentials or even personally identifiable information. Hence, there is a higher motivation for cybercriminals to target medical databases, and so they can sell the PHI or adapt it for their benefits.
AMI-IoT Deployed Platform The Ami-lab has been developing several IoT architectures for the past decade and following; we described how we addressed the previously discussed security issues. We have mainly focused on privacy, data profiling, the man-in-themiddle-attack, data corruption, which can undermine the end-to-end communication from the environmental nodes to the database. The AMI-IoT platform as depicted in Fig. 5  is composed of three main components, which are, end-users environment, network, and cloud architecture. Based on the work of Mendonça et al. [2] , we assume that the three elements, Sensing Approach (SA), Awareness & Security (AS) and Responsibility & Actions (RA), are essentials to address the IoT security concerns.
Sensing Approach (SA) The SA element is the entry point of the architecture. It ensures data detection and its migration from environmental nodes to the database. It also represents the listening state of other components as well as the architecture. At this point, making sure of the working state of environmental nodes is crucial. Data gathered by nodes will be sent through a network path built by the node and its peer. This element is the foundation of IoT architecture, enabling endpoint sleep trackers and allowing them to submit information through the entire network, giving the opportunity to experts to process the data. Sleep tracker such as a smart mat has been used, transmitting data to a node that will serve as a broker and publish the information. On the other hand, a unique peer will be subscribing to that broker getting the data in time through a canal.
Those firewalls have been added, preventing external attacks and allowing just one communication at a time. Regarding privacy, we concluded that even the node should be identified by their ID and not the users. Thus, yet if the attacker has the identifier, he won't be able to know whose information he has access to. Moreover, rules have also been applied so that the user will have limited access to the node. It will restrict phishing attacks, which can compromise the system. Also, all incoming connections are blocked, accepting just the one responsible for collecting data. These techniques lead to securing the End-users environment component. Cloud Architecture. It represents the core of the Ami-lab system. It's all the technologies and methods put together to enable a peer for each environmental node and the storing in the database.
Internet (Network). Named in IoT architecture, the weak link, due to its public nature, it can be subject to many attacks mentioned in the previous sections. It relates the end-users environment to the cloud environment serving as a bridge. While an attack cannot reach the first component of the architecture, there is still a chance to intercept the data while it's been sent. Then, botnet attacks, man-in-the-middle attacks, which will block the transaction or worst prevent data from storing in the database. To avoid this weak point, we created a secure tunnel known just by our peers. The Internet will serve to, will be retrieving the certificates and then establishing a secured channel between the environmental node and the cloud node. Every communication has been made to guarantee that each environmental node has its peer and can communicate just with that peer.
Introduction Aging is often related to significant changes in physical activities, mobility, nutrition, social life and cognitive status. These changes considerably affect elderly people quality of life. According to the World Health Organization (WHO)  [1] , the biggest health risk for seniors is the adoption of a sedentary lifestyle that causes isolation, depression and many other diseases such as cardiovascular disease, obesity, high blood pressure, etc. In this paper, we present our experience in deploying an Internet of Things (IoT) solution for the continuous monitoring and detection of elderly people's behavior changes. The objective is to help geriatricians detect sedentary lifestyle and health related problems at early stage, without the need to perform classical psycho-geriatric tests that have many limitations like assessment inaccuracies Supported by H2020 European Project City4Age.
Dataset This research uses SIMADL  [21]  dataset generated by OpenSHS  [22]  which is an open source simulation tool that offered the flexibility needed to generate the inhabitant's data for classification of ADLs. OpenSHS was used to generate several synthetic datasets that includes 29 columns of binary data representing the sensor values, each binary sensor has two states, on (1) and off (0). The sensors can be divided into two groups, passive and active. The passive sensors react without explicitly the participants interact with them. Instead, they react to the participant movements and positions. The sampling was done every second. Seven participants were asked to perform their simulations using OpenSHS. Each participant generated six datasets resulting in forty-two datasets in total. The participants self-labelled their activities during the simulation.
Imbalanced Data The distribution of the classes (that represent the different ADL) is not uniform, leads to imbalanced classes. This situation appears because of rare abnormal behavior which is clear in the Fig. 2. As shown in Fig. 2 , the class anomaly represent a minority. We tackle this problem in order to improve our classification performance. Dealing with imbalanced datasets requires strategies such as oversampling techniques before providing the data as input to the LSTM model. Oversampling strategy consists in augmenting the minority class samples to reach a balanced level with the majority class.
Oversampling We deal with abnormality, called also anomaly, detection problem as a supervised learning that refers to correctly classifying rare class samples as compared to majority samples. Therefore, anomalies are a minority in the whole behavior, which create an imbalanced data problem. Therefore, we have to oversample our data and after that, we can classify correctly. A subset of data is taken from the minority samples as an example and then new synthetic similar data points are created. These synthetic data points are then added to the original dataset. The new dataset is used to train the classification models. The main objective of balancing classes is to either increasing the samples of the minority class or decreasing the samples of the majority class. In oversampling, we increase the minority class samples.
Network Architecture and Hyper-parameters Tuning The crucial task is to find a suitable network structure for training the data, specifically to choose the right amount of nodes and layers. Many experiments were run by varying LSTM networks architecture as shown in Table  2  to find the suitable units number. We varied this number from 20, 30, 50, 60, 100 to 200. The number of layers was experimentally fixed to four layers. To compile and fit the model, we experimentally fixed mini-batch size to 128 samples, with ADAM  [25]  as optimizer, which is an algorithm that can used instead of the classical stochastic gradient descent procedure to update the network weights iterative based on training data. Our experiment with LSTM were implemented in Python language using Keras library  [26]  with Tensorflow  [27].
Performance Metrics Analysis High performance system should have less false positive and false negative rates. The performance of our proposed method is evaluated in terms of precision, recall and f-score  [28]. Table  2  presents the obtained results. 2  refers to the results obtained on SIMADL dataset for abnormal behavior detection and shows that LSTM with 20 units gives the best precision of 0. 91%, recall of 0. 91% and f-score of 0. 91%. The rest of the results is expressed in the Table  2  for which we have varied the unit number to 30, 50, 60, 100 and 200. To demonstrate the superiority of the proposed method, we conducted comparison with existing state-of-the-art methods. The results are summarized in Table  3. According to Table  3 , we note that LSTM gives a good result by comparing to machine learning methods such as: SVM, NB, KNN and NN.
Conclusion We proposed an LSTM based abnormal behavior prediction method. Our method identifies and predicts abnormal behaviors with a high degree of accuracy and with less user intervention in order to automate the identification and prediction process. We note that before to classify the activities, we have checked the distribution of classes, we detected an imbalanced classes, to deal with this problem we have applied SMOTE for oversampling classes. The future work can focus on using real dataset from environmental and physiological sensors to understand the health condition of the elderly person's for better wellbeing. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material.
UFOV results Participant Subtest (T1) Subtest (T2) Subtest (T3) 1 1 1 1 2 1 1 1 3 1 1 1 4 3 1 1 5 1 1 1 6 1 1 2 Once the classes are defined, we will be able to label the dataset accordingly. Therefore, with driving speed data, we try to predict the corresponding class using the LSTM model described in the next section.
The goal is to examine, using a more representative and bigger dataset, the performances of these methods and of the smart mattress in general. Results of applying these three estimators on the BCG show that MODWT is more accurate with an average mean absolute error (MAE) of 1. 97 ± 2. 12 BPM. However, the HNM estimator has space for improvements with estimation errors of 2. 91 ± 4. 07 BPM. The smart mattress works well within a standard RR range of 10-20 breaths-per-minute (BPM) but gets less accurate with a bigger range of estimation. These results highlight the need to test these sensors in much more realistic contexts.
7. Dataset. We have recruited 100 patients that were admitted for the night at the CICU. 20 patients were removed from the study because of (1) missing data or (2) unforeseen leave from the CICU. When a patient leaves the hospital, his recordings are automatically deleted from their systems. Thus, it was impossible to compare the recorded BCG with the reference. In total, 80 patients with a maximum of 24 h of recorded data (53 male, 27 female; age 65. 58 ± 12. 58 years old; height 168. 65 ± 9. 35 cm; weight 77. 94 ± 18. 31 kg) are used for RR detection. The available data compiles 1520 h of recordings. Filtering and Artefacts Removal. The data went first through a bidirectional Butterworth bandpass filter of 5 th order with bandpass frequencies of 0. 1 to 2 Hz. Moreover, the motions from a subject may quickly decrease the quality of the signal.
It is, however, less accurate than other vital signs tools used in bigger studies  [4, 16]  on the HR. This is most probably due to the subjects of these studies being healthy and younger. It would still be interesting to add a way of selecting the right level of wavelet coefficients to further improve the estimation of the RR with MODWT. Future work will be on the characterization of this database and its relation to the performances of estimators such as MODWT. We will explore the effects of anthropomorphic parameters on the quality of the estimations.
Furthermore, the physiological signals are very noisy, being susceptible to factors such as posture, mood, physical movement, and external noise  [8]. Lack of comparability between experiments is another issue that can be faced in this field. Unlike in image classification, there are no standard time series datasets used as performance benchmarks  [16, 20]. Some approaches use models for individuals, while others try to make a general model, training, and testing with samples from all individuals at one time. In this paper, we propose a method for classifying sleep stages based on the CNN, LSTM and DNN with the help of transfer learning. More specifically, we.