dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We would like to thank  Richard Lednický ,  Ulrich Heinz ,  Tamás Csörgő ,  Máté Csanád , and  Yuri Sinyukov  for numerous helpful discussions. The ALICE collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE collaboration gratefully acknowledges the resources and support provided by all Grid centres and the  Worldwide LHC Computing Grid (WLCG) collaboration. The ALICE collaboration acknowledges the following funding agencies for their support in building and running the ALICE detector:  State Committee of Science, World Federation of Scientists (WFS)  and  Swiss Fonds Kidagan ,  Armenia ,  ALICE Collaboration Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Financiadora de Estudos e Projetos (FINEP) ,  Fundacão de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ;  National Natural Science Foundation of China (NSFC) , the  Chinese Ministry of Education (CMOE)  and the  Ministry of Science and Technology of China (MSTC) ;  Ministry of Education and Youth of the Czech Republic ;  Danish Natural Science Research Council , the  Carlsberg Foundation  and the  Danish National Research Foundation ; The  European Research Council  under the European Community's  Seventh Framework Programme ;  Helsinki Institute of Physics  and the  Academy of Finland ;  French Grant  No.
C/Population Under Study The sanitary data were collected by the university hospital centre of Pointe-a-Pitre, the main medical structure of the department, which receives more than 65,000 visits in the emergency department each year, including 20,000 children. This centre is equipped with IT tools (IT extraction software) which are used for collecting medical data (visits to the emergency department). The daily visits due to the aggravation of asthma were codified according to the international classification of diseases (CIM 10 th edition) (J45-J46). The information provided by data extraction corresponded to the number of visits per day for asthma conditions to the paediatric emergency department, as well as administrative data concerning the age and gender of the children admitted in the health emergency structure.
The responsible taxa corresponded to the Poaceae family (cereal) for 43% and to the Mimosaceae family for 16% of the cases. The pollination, taking into account the climate, is perennial in Guadeloupe. The responsible taxa represent the majority of emissions. The maximum emissions correspond to a period of around one week over a month. In the absence of a daily count of pollen on our territory, we only considered periods involving maximum emission of taxa. The data on influenza epidemics were obtained by consulting the regional agency for monitoring influenza on the territory. The Regional Flu Monitoring Agency uses the number of flu consultations collected by the network of sentinel physicians practicing in the territory of Guadeloupe and from all the emergency cases at the region's hospitals.
Aknowledgements The authors are grateful to  V. Avetisov ,  P. Krapivsky  and  R. Metzler  for encouraging discussions. This work was partially supported by the grants  ANR-2011-BS04-013-01 WALKMAT  and the  IRSES  project  FP7-PEOPLE-2010-IRSES 269139 DCP-PhysBio. M. T. and S. N. acknowledge the financial support of the  Higher School of Economics program for Basic Research.
Results Plant growth. Statistical results on plant growth dataset are summarized in Table  1. Nitrogen and water inputs affected plant height significantly on the three measurement dates (58 days after seeding [DAS 58], Fig. 1 ; DAS 32 and DAS 40 not shown) whereas effect of nitrogen 3 water interaction was only found at DAS 58. The number of nodes per plant was affected differently by nitrogen and water inputs and their interaction was significant (Fig. 1 ). Plants supplied with the optimal water input increased in height i. e. about 30% higher compared to those subjected to low or high drought. For the optimal water treatment, the plants fertilised with the optimal nitrogen regime attained significantly greater height than those treated with excessive nitrogen input (P , 0. 001), but their height was similar to that of those treated with insufficient nitrogen input (P 5 0.
5 ). The amounts of nutrient inputs and volumes of feeding solution supplied to plants increased daily depending on the plant development stage. Indeed, the amount of nitrogen taken by plants was controlled by the increase in their daily dry mass  45, 46. Moreover, when growth reached steady-state i. e. exponential growth phase, plant N concentration remained constant  45. We therefore calculated the daily amounts of nitrogen required to produce optimal dry mass during approximately 52 days of growth (ON treatment). To proceed, we used data of tomato growth previously obtained in our laboratory conditions  47. We used 3 stock solutions to provide nitrate, phosphate and sulphate salts independently (1 Fe & micronutrients). The following concentrations were used (in M: NO 3 5 1; H 2 PO 4 5 0.
Leaf nitrogen and carbon contents were measured with an elemental analyser (Flash EA1112 Series, ThermoFinnigan, Milan, Italy) at INRA-PSH Avignon. The leaf C/N ratio was calculated from the mass data (in g). Data analyses. We performed two-way ANOVA, followed by Tukey's post hoc test for multiple comparisons when necessary, in order to determine the effects of nitrogen and water inputs on plant height, number of nodes per plant, leaf N content, leaf C/N ratio, T. absoluta pupal weight, development time from egg to pupa and from egg to adult. Shapiro and Bartlett tests were used to assess variance homogeneity and normality, respectively. In addition, survival of T. absoluta was fitted to a log-linear model (using ''water'', ''nitrogen'' and ''stage'' as factors), and further analyzed with permuted Fisher exact test.
In order to test the ''Nitrogen limitation hypothesis'', insufficient and optimal nitrogen data were used to test the relationships between leaf N content and T. absoluta development traits i. e. development time and pupal weight by using regression analysis. The excessive nitrogen dataset was not included in this analysis because nitrogen provided in excess may induce various effects (notably negative ones) beyond the actual ''nitrogen concentration -plant growth'' relationship  17. All these data were analysed using the R software  48.
Autopsy procedures All cases but 3 underwent a complete autopsy performed by foetopathologists according to standardized protocols, including X-rays, photographs, and macroscopic and histological examination of all viscera. Foetal biometric data were assessed according to the morphometric criteria of Guihard-Costa et al. [34]. Twenty out of 26 foetuses were personally re-evaluated by C. F. B. Three were initially examined by one of the expert neuropathologists (F. R or A. L) and therefore not re-examined. Concerning the three remaining patients, parents had refused post-mortem examination, so that these foetuses were diagnosed and evaluated on the basis of foetal MRI performed at 30 weeks of gestation (WG) only.
Acknowledgements We would like to thank  Tania Attie-Bitach ,  Sophie Blesson ,  Francoise Chapon Bérénice Doray ,  Edith Andrini ,  Antoinette Gelot ,  Pierre Simon Jouk ,  Philippe Parent ,  Lucile Pinson ,  Caroline Rouleau  who have volunteered their time to provide data to participate in the research to make this article possible. We would like to thank the patients and their families, as well as their referring physicians and genetic counsellors, without whom this study would not have been possible. We would like to thank  Magalie Percevault  for her help in performing molecular analysis. We thank members of the  Chelly lab  for their thoughtful comments. This work was supported by funding from  INSERM ,  Fondation pour la Recherche Médicale  (FRM ; J Chelly-Equipe FRM 2013:  DEQ2000326477 ),  Fondation JED-Belgique ,  Agence National de Recherche  ( ANR Blanc 1103 01project R11039KK ;  ANR E-Rare Program , convention  2011-RARE-012-01-projet E10107KP ) and the  EU-FP7  project  DESIRE , Grant agreement no:  602531.
Acknowledgments We thank  IDEXX  for providing the assays used for analysis of proANP 31-67 and NT-proBNP. We also thank the  Universities  involved for their efforts that allowed successful completion of the study. We are grateful to all dog owners for their willingness to enroll their dogs, and we thank  Yukihide Momozawa  for his contribution to the study. The study was funded by the  European Commission  ( FP7-LUPA ,  GA-201370 ).
Data Hospitalization records from 2005 to 2010 in Hong Kong were obtained from the electronic database of the Hospital Authority, which manages 41 public hospitals covering 78% of total hospital bed-days in the whole territory  [7]. These hospitalization data recorded up to 15 discharge diagnoses for each hospitalized patient, but were not linked to any baseline characteristics of lifestyle factors and co-morbidities nor specific medical treatments received by these patients. Weekly numbers of non-accidental hospitalization were aggregated for the age groups of 0-4, 5-14, 15-29, 30-39, 40-49, 50-59 and 60 or over (60+) years by excluding the cases with any of the 15 listed discharge diagnosis of accidental cause (International Classification of Diseases version 9 codes (ICD9), 001-799). Age-stratified laboratory-confirmed hospitalization with A(H1N1)pdm09 infection during the first wave pandemic (26 April 2009 to 2 January 2010) were obtained from the eFlu database managed by the Hospital Authority, which collected the demographic data as well as hospitalization and fatal outcomes of laboratory-confirmed A(H1N1) pdm09 cases in Hong Kong  [5].
Influenza virus surveillance data were obtained from the microbiology laboratory of the Queen Mary Hospital, which is one of the largest public hospitals in Hong Kong. Nasopharyngeal specimens were collected from the patients with influenza-like symptoms and tested for influenza (type A and B), respiratory syncytial virus (RSV), adenovirus and parainfluenza viruses by immunofluorescence tests  [8]. Nearly 90% of influenza A positive specimens were further subtyped into seasonal A(H1N1) and A(H3N2) by viral culture and haemagglutination inhibition tests. During the 2009 pandemic, the specimens were also tested for A (H1N1)pdm09 by RT-PCR. This laboratory tested all the virus samples collected in Hong Kong Island and provided more than 20% of specimens to the Department of Health virology surveillance network from 2005 to 2010.
Excess hospitalization rate was calculated by dividing excess hospitalization with age-specific annual population size, which was estimated from the census data by linear interpolation. Age-specific eIHR was calculated with the age-specific excess hospitalization rate as numerator and the corresponding attack rate estimated from two local serological studies as denominator (eIHR ¼ Excess hospitalization rate Attack rate )  [4, 5]. cIHR had a similar formula, in that the numerator was switched to laboratory confirmed A(H1N1)pdm09 hospitalization which was reported by the eFlu database of Hospital Authority (cIHR ¼ Laboratory confirmed hospitalization rate Attack rate ). Because the age-specific attack rate was only available for the first wave of the 2009 pandemic, we calculated the eIHR and cIHR only for this period.
Results Weekly observed and fitted non-accidental hospitalization number was shown in Figure  1. Significant association of A(H1N1)pdm09 with hospitalization was only found in the age groups below 30 years old (p < 0. 05). According to our point estimate, during the first wave of the 2009 H1N1 pandemic, there were a total of 10,178 excess hospitalization associated with A(H1N1)pdm09, which was markedly higher than the total number of 7,386 laboratory-confirmed cases who were hospitalized and reported by the eFlu database. Excess rate of hospitalization associated with A(H1N1)pdm09 was highest in the 0-4 age group (881. 3 per 100,000 population), followed by the 5-14, 60+, 15-29, 50-59, 30-39 and 40-49 age groups (Table  1 ). The estimated excess rates were comparable to the rates of laboratory confirmed hospitalization in children and young adults, but one to three times higher for the age groups of 50-59 and 60+.
This training strategy included both transient and static data because of the strong transient nature of the signals to be classified after detection of a state change  [12]. During this run, no feedback regarding the EMG activities was provided to the subject. After the training run, the training data were used to train the classifier (see Section "Algorithm"). Once the classifier was trained, the two validation tests with online feedback were performed. The purpose of these two tests was to analyze different aspects of the proposed framework. The 'Target reaching test' was performed in velocity mode, and the 'Grasping test' was performed in position mode. The subjects were given 5 to 10 min to familiarize with the interface and the visual feedback, during which the proportional range of control for each class was optimized for the comfort of the subjects.
Acknowledgments We thank  ChemoSens Platform  ( Centre des Sciences du Gout et de l'Alimentation, Dijon, France ) for technical assistance in APCI-MS analyses,  Laboratoire de Rheologie (UniversiteJoseph Fourier Grenoble, France ) for bolus rheology,  Claude Yven  for electromyography,  Fromageries Bel SA, Soredab (groupe Soparind Bongrain)  and the panelists who participated in this study.
Overall, mHealth can offer significant benefits for both patients and healthcare providers, ensuring enhanced quality, efficiency, flexibility and cost reductions in healthcare delivery. The M2M paradigm in the context of mHealth involves the use of appropriate sensor devices on patients. The medical sensors, placed in the vicinity of, or inside, the human body, are usually interconnected through a short-range wireless technology, thus forming a Wireless Body Area Network (WBAN). An M2M-enabled gateway node collects all the sensory data from the WBAN and forwards them to a remote online server, where processing and integration with medical-related software applications take place. The connection of the gateway to the Internet is generally based on long-range communication access technologies for Wireless Local/Metropolitan Area Networks (WLANs/WMANs).
Actuators perform specific actions according to data received from the sensors or inserted by the end user (e. g. , a pump for the administration of insulin based on blood glucose measurements). Unlike typical sensor networks, the number of devices in a WBAN is relatively small (i. e. , usually restricted within the range of 20-50), with each device having a unique function. ) have been extensively studied. In the context of sensor networks, several studies have been made with respect to the signal propagation in different communication media  [24]. Nevertheless, wireless communication close or inside the human body introduces new challenges due to the different propagation characteristics of the body area environment. Even though the distances between the nodes are small, WBAN links suffer from high propagation losses, mainly due to user activity, in the case on on-body communications, and power absorption by the human tissue, in the case of implants.
By studying the performance of various detection algorithms in the presence of pulse distortion and receiver noise, it has been shown that non-coherent receivers, such as energy detectors and Transmitted-Reference (TR) receivers (In TR systems, two signals-a reference and a data signal-are transmitted per bit. The information is contained in the sign difference of the transmitted signals. ), are suitable for WBANs, due to the fact that they do not require any explicit channel estimation (thus consuming less power and reducing complexity). In  [59] , two non-coherent modulation/detection pairs for IR-UWB systems with and without chirp pulse compression have been investigated. The authors strongly recommend the usage of chirp pulse compression in medical IR-UWB WBAN devices that are expected to use low data rates.
HBC Technologies Unlinke the other two WBAN PHY layer wireless technologies, HBC uses the human body as the communication medium. HBC transceivers enable low-power communication at low frequencies. Previous studies have shown that the optimal operation frequency range for body coupled communications ranges between 10-100 MHz  [68, 69]. The lower bound is imposed by propagation losses while the upper bound is determined by the fact that the RF energy is no longer confined to the body but is radiated into the environment. Therefore, HBC signals must be modulated to a carrier lower than 100 MHz, before being capacitively coupled to the body via electrodes on or near the surface of the skin. Recently published works on HBC transceivers  [70, 71]  have shown energy efficiency in the order of 1 nJ/bit, data rates of 1 Mbps and receiver sensitivity of -60 to -70 dBm.
Energy-Efficient Channel Access and Synchronization Mechanisms In an ideal energy-efficient MAC protocol, nodes would spend most of their time with their radio interface turned off (or in a low-consumption sleep mode) and wake up only to transmit or receive data in a collision-free way and without unnecessary listening times. However, in practice, significant energy is consumed in data collisions and idle listening periods, occurring when the nodes keep listening to the channel, usually after waking up from sleep mode  [77]. To reduce power consumption, most WBAN-oriented protocols employ sleep modes. The challenge, however, is to design mechanisms that handle efficiently the periods during which the nodes remain awake. This is mainly achieved through channel access schemes that reduce data collisions and minimize listening times.
Synchronization is also an important issue, since most energy-efficient access mechanisms require common time reference within the WBAN. The rest of this section summarizes the main mechanisms adopted in the literature with respect to these issues. Collision-free channel access: Many proposals guarantee collision-free data transmission by introducing low-cycle Time Division Multiple Access (TDMA) scheduling schemes where channel access is managed by a central coordinating node. An example of a straightforward TDMA implementation has been proposed in  [79]. The proposed superframe structure starts with the transmission of a beacon by the coordinator, followed by time slots that are allocated to nodes according to a known and predefined schedule that matches their periodic traffic patterns. A more advanced TDMA-based solution for periodic traffic that takes into consideration packet retransmission due to channel errors has been proposed in  [80].
Sleep mode is supported through a three-step handshake, initiated by the transmission of a sleep request packet by the node during the CAP. The coordinator acknowledges reception with an ACK packet and transmits a sleep mode schedule in the downlink period of the following superframe, containing the necessary sleep parameters. A different implementation of the same principle is adopted in Distributed Queuing Body Area Network (DQBAN)  [83]. The superframe structure in DQBAN is again divided into three parts: (i) the access part, further divided into a number of access minislots; (ii) the scheduling part, further dividing into a number of scheduling minislots; and (iii) the data part, in which collision-free data transmission takes place. Nodes compete for channel access by transmitting small control packets in randomly selected access minislots, and a collision resolution algorithm is employed to resolve any collisions among access requests.
g. , power limitations of implantable devices) or battery lifetime levels in the scheduling decisions. In BSN-MAC  [96] , for example, four priority classes are defined to match four energy levels: (i) unconstrained, for the coordinator node that is assumed to have no energy constraints; (ii) constrained, for wearable sensor devices; (iii) highly constrained, for ingestible sensor devices; and (iv) extremely constrained, for implantable devices. The protocol also defines three levels of data criticality (low, normal and high). These parameters, along with the remaining battery lifetime level and the buffer occupancy, are combined to produce a cross-layer metric used to adjust the length of the IEEE 802. 15. 4 superframe with the objective of reducing latency while enhancing energy efficiency.
In order to maximize the idle time between transmissions, nodes may choose to accumulate several packets in their buffers. A decision on whether to transmit in a given time slot is made depending on cross-layer information regarding the battery recovery level, the buffer occupancy and the wireless channel state. Finally, PEH-QoS  [98]  promotes energy-aware management and QoS provisioning in a WBAN with energy harvesting capabilities by proposing a three-module architecture that comprises: (i) the Power-EH Aware Management (PHAM), which ensures the optimal use of the harvested energy, by preventing transmission attempts by nodes with insufficient energy levels and encouraging the detection of all events by the sensor nodes (to ensure the detection of critical medical events); (ii) the Data Queue Aware Control (DQAC), which prevents the saturation of the data packet queue and maintains the clinical validity of the data by discarding outdated packets; (iii) the Packet Aggregator/Scheduling System (PASS) that maximizes throughput and energy efficiency by supporting the transmission of aggregated data packets.
Reliability The reliable data transmission is another key issue addressed in the MAC layer. Since redundancy of devices is not a possibility, given the unique role played by each node in the WBAN, the MAC protocol must provide solutions to increase reliability. This can be achieved by supporting retransmission mechanisms or by increasing diversity through techniques such as cooperative network coding (NC). Retransmission mechanisms: A usual approach is to employ ACK packets to acknowledge the correct data reception (e. g. ,  [82, 83, 87] ). In case of erroneous reception, the data packet can either be discarded or a retransmission can be requested. In  [80] , for example, extra slots are reserved for packet retransmissions and their optimal number is calculated as a function of the packet error rate.
After that, it issues a wakeup command to the next scheduled node on a dedicated secondary control channel, thus waking up the node from its sleep mode and informing it on the data channel selection. Finally, a different interference-related challenge is explored in  [105] , where the proposed scheme aims to reduce the Electromagnetic Interference (EMI) produced by WBANs, which can lead to the malfunctioning of EMI-sensitive life-supporting medical devices (such as defibrillators) in a hospital environment. A context-aware solution is proposed, in which a coordinating device dynamically determines the transmission power limits for each communication in the WBAN, after consulting an inventory of the location and the EMI constraints of all electronic medical devices in a given setting. The nodes must content for access by transmitting an RTS in the dedicated control channel.