dataesr/scientific-paragraphs-categorization huggingface dataset:
Of note, as the FastME program runs fast, the data noising strategy does not require important running times, especially when compared with the ones required for estimating all pairwise Mash dissimilarities. Thanks to its ability to run on multiple threads, JolyTree is quite fast. On an Intel Xeon E5-1660 v4 (16Gb RAM) running under Linux Debian 4. 9. 110-3+deb9u6, 90% of the 187 genome datasets were analyzed in less than 15 and 5 minutes each, on 6 and 12 threads, respectively. The main variable having a negative impact on the overall running times is the  The Aggregatibacter tree inferred by JolyTree (Fig. 5 ) is very similar to the model tree of  Murra et al. (2018). Indeed, both trees recover the same phylogenetic relationships among the four species A. actinomycetemcomitans, A. aphrophilus, A.
2017). Both phylogenetic trees were inferred by ML from large multi-gene datasets and every species clade is assessed by maximum support values  (Nicholson et al. 2017 , Perrin et al. 2017). Interestingly, the tree reconstructed by JolyTree from the Elizabethkingia genome dataset (Fig. 7 ) leads to the diagrammatic species tree ((E. occulta, E. ursungii),((E. bruuniana,E. miricola),E. anophelis)). Agreeing with  Nicholson et al. (2017) , the clade E. occulta + E. ursingii is strongly supported (i. e. rate of elementary quartets = 0. 98). However, contrary to  Nicholson et al. (2017) , the clade E. bruuniana + E. miricola is well supported too (i. e. rate of elementary quartets = 0. 80), whereas the clade E. anophelis + E. bruuniana + E. miricola is quite less supported (0. 65). The phylogenetic tree inferred by JolyTree therefore hilghlights some weakness of the phylogenetic signal induced by the Elizabethkingia dataset, and questions the accuracy of the two model trees inferred by  Nicholson et al.
Conclusions This paper describes a novel bioinformatics procedure, implemented by the Bash script JolyTree (gitlab. pasteur. fr/GIPhy/JolyTree), to perform a complete phylogenetic analysis from unaligned genome sequence sets. Such procedure is quite fast because it takes advantage of the ability of the pairwise distance estimate step to be run on multiple threads. Simulation and real case analyses have shown that JolyTree leads to accurate trees. Some incorrect branching can be observed within the inferred phylogenetic trees, but they are often assessed by weak branch supports. Therefore, JolyTree represents a useful approach for performing phylogenetic analyses with fast running times from hundreds of genome assemblies. Of note, this bioinformatics procedure was used previously for inferring Corynebacterium More generally, this paper confirms the usefulness of the MinHash dissimilarity and its ability to efficiently approximate the pairwise p-distance (and the related ANI similarity) between two genome contig sets.
Acknowledgements This manuscript is dedicated to the memory of  Nicolas Joly , who helped to improve the implementation of the script JolyTree for running on multiple threads. The author thanks  Laetitia Fabre ,  Julien Guglielmini  and  Rachel Legendre  for useful comments on the manuscript. The author is also grateful to  Brian D. Ondov  for reviewing this manuscript. The author is obliged to  Sylvain Brisse  and to the  Hub de Bioinformatique et Biostatistique, Institut Pasteur, Paris (France) , for support.
Considering only genome datasets made up by at least four species, as well as only robust phylogenomics analyses based on core genome data for comparison, this survey led to six genera: Aggregatibacter (Murra et al. 2018), (Zhang and Qiu 2015)7,Perrin et al. 2017)kingia(Nicholson et al. 2017, Perrin et al. 2017), Lactococcus(Yu et al. 2017), Providencia (Galac and Lazzaro 2012), and Ralstonia(Zhang and Qiu 2015).
(Casjens et al. 2018The only difference is the clade B. afzelli + B. spielmanii that groups with the clade B. bisettii + B. burgdorferi + B. finlandensis but not with B. bavariensis + B. garinii (Fig. 6). This conflicting grouping is likely caused by a long branch attraction between B. japonica and B. spielmanii, as a species tree similar to the model tree(Casjens et al. 2018) is inferred by JolyTree when removing these two genomes (not shown). A more accurate phylogenetic tree is therefore expected when more B. japonica and B. spielmanii genomes are available in the RefSeq collection. However, ij ij ij ij ij when considering the present genome dataset, the likely incorrect clade B. afzelli + B. bisettii + B. burgdorferi + B. finlandensis + B. japonica + B. spielmanii could be easily detected as its corresponding internal branch is weakly supported by a rate of elementary quartets of 0.
Healthcare In the literature, several works have been done in order to provide remote healthcare services for the patients in their homes as in Ref. [5] , where the authors present a cloud-based web service-oriented architecture relying on a "home system" for the collection of information from a heterogeneous set of devices, and which includes the core elements of the REST service platform and many features required for the healthcare services to operate. However, the drawback of the work is that they do not deal with the data security issues, which is one of the main concerns of the users at any architecture dealing with health related data. In Ref. [6] , the authors present their research work in a project called SPHERE (Sensor Platform for Healthcare in a Residential Environment) developed in UK, with the aim of providing an interdisciplinary work to build a generic platform that merges complementary sensor data to generate rich datasets that support the detection and management of various health conditions.
The data are gathered from the IoT sensors through the data acquisition layer, and analyzed at the fog layer. A notification is sent to the cloud layer only if an adverse event is triggered. However, they also do not consider the security and the privacy aspects, even though their architecture deals with sensitive health data. The authors of Ref. [10]  presents an emotion-aware connected healthcare system. It captures speech and images of the patient in a smart home environment and then processes them in order to detect the different emotions such as pain. The latter may for instance trigger the intervention of a caregiver. To summarize, the major drawback of the previous works is obviously the lack of deep analysis of the different security aspects, in particular regarding the access control.
The overall system is composed of a HEMS, a Demand Side Management (DSM) system, and a supply side management system, inside a real smart home in Singapore. As for the smart metering systems, Ref. [17]  presents the integration of the advanced smart metering infrastructure (AMI) in the context of the smart building with an EMS in order to enhance the management capabilities of each individual consumer, using the daily energy profiles collected from this architecture. As for the application layer, the authors of Ref. [18]  propose a centralized system implemented in software application to manage the home appliances through a Wireless Sensor Network (WSN). It uses several management algorithms based on consumption models combining timing schedule, power, temperature or ambient light measurements and prioritization.
The Gateway Layer: represented by the smart home gateway. It provides the ability to communicate with the different smart devices in the smart home (energy, health and smart meters), using different communication protocols (e. g. , Bluetooth, WIFI and Zigbee). Details about the gateway will be provided in Section 3. 3. Different stack of communication protocols were deployed in order to enable the interaction with different kinds of IoT devices. The gateway also interacts with databases in order to store the data coming from all the sensors, errors, alerts and operational logs. From this layer a secure web socket is established between the application server and the home gateway in order to exchange data and commands in a secure way. Both components are located in a private local network of the smart home and connected to a secure WiFi.
A Node. js server providing a Web API is deployed. This server can either extract real-time data directly from the gateway for the case of remote tele-consultation as will be explained later, or query a database for historical records. Thanks to the selected development technologies, all these services can be deployed on the Cloud in a seamless way. Moreover, in order to receive notifications and alerts from some particular sensors (in particular from the most critical ones), a simple android application was developed. The application uses Bluetooth Low Energy (BLE) in order to communicate with the gateway, and can either read, write or subscribe to a particular sensor to receive notifications. For the energy part, the EMS is used in the application layer in order to infer control strategies from the measured data (i.
The gateway provides the possibility to interact with various devices using different communication protocols toward an interoperable architecture. Since the scope of the contribution is narrowed down to the health and energy services, the connected devices can be divided into two categories: the health-related medical devices, such as pulse sensor, muscle sensor, temperature sensor and fall detection sensor using an accelerometer, and the energy-related devices, which can be smart plugs, energy harvesting sensors or smart relays. Moreover, for each sensor, an actuator is attached. The idea is that if an abnormality is detected, a command is sent to the actuator in order to perform some actions. For Proof of Concept (PoC) purposes, "Servo Motors" and "LEDs" are used. The gateway is also connected directly to a database in order to store the data coming from the sensors, the errors and the alerts.
Wellbeing Monitoring Sensors For the data collected from these two medical devices, an evaluation is done in order to decide whether the measured data is normal or not. However, a classification of the data needs to be done to contextualize the information. For the pulse sensor, depending on the age and the activity of the user, measured data can have different meanings in each case. For instance, for a person aged more than 12 years, for a normal activity, the normal heart rate is between 60 and 100 bpm (beat per minute)  [37]. As for the thermometer, for babies and children, the average body temperature ranges from 97. 9 °F (36. 6 °C) to 99 °F (37. 2 °C), for adults the average body temperature ranges from 97 °F (36. 1 °C) to 99 °F (37. 2 °C), and for adults over age 65, the average body temperature is lower than 98.
Energy Monitoring and Control The operational pipeline in this smart home platform is the EMS that controls the operation of in-home devices and appliances optimally with regards to different objectives considering the information about task operating status, user's requests and network signals received through the AMI system. The AMI by itself includes two layers namely physical and utility layers. The physical layer includes the smart meters and the data concentrator that collects the measurements periodically and provides a dataset of data with synchronized timestamps using a Network Time Protocol (NTP) server. The communication between the smart meters and the data concentrator is based on the standard EN 13757-5 that implements a radio mesh topology. The second layer in AMI topology integrates the logical software provided by the Kamstrup OMNIA suite, to ensure efficient interoperability with the AMI network.
Reliability Undetected Sensor: the system should detect when a sensor is not connected. In this case, an error is raised and sent to the application, as shown in Figure  6. An unavailable sensor, for certain reasons, can have serious consequences, where for instance in case of home monitoring of a patient, if the fall detection sensor is not available, the fall cannot be detected, or in case of a smart plug which is not working, wrong energy consumption values will provide. In the current state of the work, our system detects if a sensor is missing and send an alert. This alert is displayed on the user visualization interface and registered in the database. Database fault tolerance: the architecture implements the NoSQL database MongoDB. To provide more reliability to the system in particular regarding the data provided by the different sensors of the smart home, we used what is called "replica set" in MongoDB.
As defined in Ref. [41] , "A replica set in MongoDB is a group of mongod processes that maintain the same data set. Replica sets provide redundancy and high availability, and are the basis for all production deployments". In practice, replication provides redundancy and increases data availability, and to be able to recover in case of a disaster. By creating multiple copies of data on different database servers, replication provides a level of fault tolerance against the loss of a single database server, in addition to better performances, increasing data locality and availability in particular for distributed services. The implantation used the master-slave (primary-secondary) replication, as shown in Figure  3 , where the primary server receives all the writes and reads requests, and then replicate the data to the slaves.
Hardware Implementation As far as the energy devices are concerned, the smart home is equipped with several home appliances as shown in Figure  9. Their energy consumption is monitored using smart plugs from the Danish company Develco. These smart plugs use ZigBee as the communication protocol and are connected to the energy home gateway. In addition to the smart plugs, a smart meter from the Danish manufacture Kamstrup was also deployed for measuring the total consumption. This model is widely installed in the Danish homes. The meter is connected to the energy home gateway using a ZigBee module for local measures as well as to a data concentrator, which provides the utility with the demand profiles for billing purposes. The energy home gateway, shown above the smart meter on Figure  10 , was also manufactured by the Danish company Develco and contains the embedded ZigBee coordinator attached to an ARM9 CPU which runs a Linux OS.
In the data link layer, two parallel tasks are carried out simultaneously. On one side, the gateway collects all the information of the smart plugs and sensors in the MongoDB database. It also provides the necessary interfaces for the actuation commands. On the smart meters side, the data concentrator establishes an encrypted communication with the metering head-end which in this case was tested locally but that usually belongs to the utility company. This head-end system is part of the Kamstrup Omnia Suite and provides a short-term and a long-term storage for the energy information. Another MongoDB database is used to store the on-demand measures as well as auto-collection data for a period up to 3 days. Behind this, a Microsoft SQL database records all the historical measurements. Nevertheless, these measures are always accessed by means of an API provided by the manufacturer.
• Forecasting pipeline composed of two sub-modules: -Weather forecasting pipeline, which use meteorological information in the household and external service to estimate the Wind Turbine (WT) and Photovoltaic (PV) production. -Load forecasting pipeline. Based on the historical information recorded by the Smart Meters as well as stochastic models, it estimates the behavior of the consumers in terms of demand for evaluation the most beneficial control actions to be taken. • Electricity price pipeline, where the utility provides real-time prices for the energy that are included in the optimization process. It will provide the current information regarding consumption and load state when requesting the HEMS an optimum scheduling. Therefore, after the computation the HEMS API will provide a response with the control action that will be transmitted to the gateway and finally be carried out in each device.
First, the proposed architecture adopts a WoT paradigm in order to interact with the different heterogeneous sensors and actuators in the smart home, by implementing different communication stacks such as Bluetooth, WiFi and Zigbee, in order to guarantee the interoperability inside the smart home. Then, the designed architecture aimed at facilitating the deployment of the two services, eventually in future works all the other services, by having a control and management layer on top of centralized home gateway that aggregates all the home data and interacts with the heterogeneous IoT devices. The collected data will be then exploited at the management and control layer by the two services. However, a remaining challenge is related to the propitiatory sensors that uses propitiatory gateways, which makes it hard to achieve a centralized control over all the sensors inside the smart home.
These data are then sent both the application server and to the databases. Depending on the targeted use, the data can be either directly retrieved from the sensors, in particular in case of remote medical consultation which require real-time collection of the data, or from the database. Finally these data are visualized using a user friendly interface. Further research needs to be done in order to standardize the most suitable data format for the smart homes. Last but not least, one of the most pressing concerns for the smart home technologies is regarding the security and the privacy of the collected, stored and transmitted data. In our case, the data may contain sensitive, protected or confidential information, such as the health data, that may endanger residents' privacy and safety, if breached.
Therefore, ensuring strong data encryption, database security, secured communication channels, and so on and so forth is required for the next generation smart homes. In Ref. [43] , the authors investigate these issues, by providing a holistic approach of security together with recommendations and good practices for all the stakeholders involved in the smart home environment. The study focuses mainly on the IoT devices inside the smart home (which can be either constrained or with high capability), the interaction and data exchange with remote services and finally the interaction and data exchange with mobile applications. Even though this work implements a security layer in order to guarantee the fundamental security requirement, which are: the confidentiality and integrity, the authentication, and the access control.
Acknowledgments: Concerning S. E. J. , much of the work presented in this article was done during a 3-month stay at  Aalborg University  funded by a mobility grant of  EIT Digital Doctoral School ; This work is also part of S. E. J's PhD thesis globally funded by  IMT Atlantique. As for the authors affiliated to the  Aalborg University , this research was funded by  Aalborg University Talent  Programme 2016 with the Research Project: The  Energy Internet-Integrating Internet of Things  into the Smart Grid http://www. iot-energy. et. aau. dk/.
Participants Non-demented non-disabled (MMSE score ≥24 and modified Rankin's scale ≤1) adult CADASIL patients at the early clinical stage of the disease were recruited from the CADASIL cohort followed in the National Referral Center for this disorder. Controls were drawn from a local database of healthy volunteers free of any known history of neurological disorder and without symptoms of cognitive impairment or disability as evaluated by a structured interview. Twenty-one patients and 28 controls having good image and cortical surface reconstruction qualities were included in this study. This study was carried out in accordance with the recommendations of the ethics committee of DRCD (Département de la Recherche Clinique et du Développement) of AP-HP (Assistance Publique-Hôpitaux de Paris) with written informed consent from all subjects.
FUNDING This work was founded by Leducq foundation, Avenir foundation, PLAGNIOL foundation, NRJ foundation, CADASIL France association and by the  Agence Nationale de la Recherche  ( Commissariat General à l'Investissement) RHU TRT_cSVD. This work was also partially funded by  ANR-12-JS03-001-01  (MODEGY) and by the  Human Brain Project , funded from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the Specific Grant Agreements No. 785907  ( SGA2 ) and No:  604102  ( SGA1 ), and by the  FRM DIC20161236445.
For this reason source attribution methods have required data on dates of infection  [15] [16] [17]  or modelling assumptions on the epidemic  9, 10, 12, 18, 19. An advantage of source attribution methods based on additional modelling assumptions is that they may be applied with relatively small sample sizes, although it can be hard to disentangle assumptions from conclusions. For example, in ref. 12  , it was assumed that young women are predominantly infected by older men in KwaZulu-Natal, South Africa, and it is unclear to what extent the same conclusion is based on data  20. There is consequently a need for broadly applicable source attribution methods that are not dependent on external modelling assumptions to provide independent evidence. Here, we demonstrate that HIV-1 transmission networks and the direction of transmission within them can be reconstructed from deep-sequence data of a large population-based sample of infected individuals with phyloscanner  21  , a recently developed software package for viral phylogenetic inference from deepsequence data.
Our population-level analysis is based on deep-sequence data that was cross-sectionally collected from 40 communities in the Rakai region of Southern Uganda. Rakai communities are predominantly small agrarian and semi-urban trading centres as well as fishing communities alongside Lake Victoria. The area was the initial epicentre of the HIV-1 epidemic in Eastern Africa, and today remains among the highest burdened districts in Uganda with an overall adult HIV prevalence that ranges from 9-26% among inland trading and agrarian communities to 38-43% among lakeside fishing communities  24, 25. We report first that it is feasible to obtain population-based samples of HIV-1 deep-sequence data that represent a large proportion of infected individuals with unsuppressed virus in a local setting in Africa.
In addition, ancestry missed 118 (14. 5%, 118/(694 + 118)) phylogenetically close pairs that had intermingled or sibling subgraphs in most of their deepsequence phylogenies. Therefore, we used all types of subgraph topologies in combination with subgraph distance for inference of Fig. 1  Inferring the direction of transmission from HIV-1 deep-sequence data. a The principles of deep-sequence viral phylogenetic analysis are illustrated on data from male M1 (turquoise) who initially reported partnership with female F1 (green), and later with female F2 (blue). We also included data from another male M2 whose virus was genetically close to that of F1, although a partnership was not reported (see Supplementary Figure  2 ). b Viral genomes from all individuals were deep-sequenced, generating short viral sequence fragments (reads) that cover the genome.
The other individuals were phylogenetically distant or disconnected from the two females by HIV-1 reference sequences, with no relationship to the two females inferred. Deep-sequence phylogenies of all other windows are shown in Supplementary Data 1. b Phyloscan plot of subgraph distances (y-axis) and subgraph topologies (colour) across the genome for both female-female pairs. In the majority of deep-sequence phylogenies, both pairs had intermingled subgraphs that were also near identical Our study has several weaknesses. First, sequence sampling of the infected population in RCCS communities remained incomplete. Phylogenetic inferences are expected to improve with higher sampling fraction  45  , though in practice, complete sequence sampling is hard to achieve. This study enrolled participants before immediate provision of ART was recommended in national guidelines, so that a relatively large proportion of infected individuals did not report ART use at first study visit, and could be sequenced.