dataesr/scientific-paragraphs-categorization huggingface dataset:
Starting with the agent-organization dimensions, JACK  (Winikoff, 2005)  has the concept of team (similar to the JaCaMo concept of group). Although teams can be used to structure agent roles, in this paper we highlighted the benefits of using organizations to coordinate and regulate MAS, features not available in JACK. A closer-related work is 2OPL ('Organization-Oriented Programming Language')  (Dastani et al. , 2008). This platform consists in a rule-based language that allows the programming of multi-agent organizations in terms of norms and which is meant to be exploited in synergy with agent programming languages-2APL in particular  (Dastani, 2008). So far, the work has been given solid theoretical foundations but lacks a clear description of how the approach integrates with the agent level from a practical programming point of view.
We do not aim to cover all that work in this section, please refer to the surveys cited above for further details. On the modelling side, existing work in the Agent-Oriented Software Engineering (AOSE) literature consider the use of Organization and Environment as dimensions to engineer MAS in conjunction with the agent dimension. Our work is clearly related to such approaches in terms of modelling. Much contribution has been given in that direction; interested readers can find comprehensive accounts in  Weyns and Parunak (2007) ,  Sterling and Taveter (2009) ,  Stratulat et al. (2009). These include in particular work that aims at defining a unifying meta-model for developing MAS, integrating concepts belonging to different dimensions, for example, the FAML meta-model  (Beydoun et al. , 2009).
In AOSE, agent, organization, and environment concepts are used as concepts to drive the analysis and high-level design of MAS. However, they do not explore their value from a programming perspective. So in this paper, while recognizing the importance of having conceptual frameworks and methodologies that make it possible to exploit agent, organization, and environment concepts to model MAS, we argue that such concepts can have a key role also at the programming level, in particular to be then exploited as first-class abstractions in agent-oriented programming languages and frameworks. In this way, we aim at contributing to fill an evident gap that exists between the modelling level and the implementation level. Typically, MAS meta-models like FAML or the one described in  Sterling and Taveter (2009)  are rather comprehensive, including concepts that are similar or analogous to the ones that take part in the JaCaMo dimensions.
For instance, the concept of service that appears in  Sterling and Taveter (2009)  or the concept of facet in FAML are strongly related to the notion of artefact which is part of the JaCaMo meta-model. The same applies for the notion of role. However, in  Sterling and Taveter (2009) , for instance, when agent programming platforms and languages are considered for building a MAS concretely, there is an apparent gap, since the considered platforms (Jason  (Bordini et al. , 2007) , 2APL/ 2OPL  (Dastani, 2008) , GOAL  (Hindriks, 2009) , JACK  (Winikoff, 2005) , and JADE  (Bellifemine et al. , 2007) ) are able to deal only (or almost only) with the agent level, so high-level organization and environment concepts cannot be directly mapped  19. Also, typically low-level workarounds are used (such as modelling everything as an agent, even environment or organizational abstractions).
Packet forwarding and routing are the major focus in opportunistic networking because of the intermittent connectivity and long duration disconnection  [16]. Mobile assisted based routing is used as a special kind of routing scheme and termed as a store, carry and forward. Presently, many surveys are addressed on MSN and can be cited in the literature. The classification of survey works correspond to the media and host layers are divided into MSN network later and MSN software system later as presented in Table  1. However, it is observed via Table  1  that an extensive survey in mobile networks considering an overall design and development of protocol design and architectures with the perspective of MSN and mobile computing is still lacking. Hence, it would be quite interesting for researchers, developers, and service providers to exploit issues related to MSNs.
AbsTrACT background Deep brain stimulation (DBs) can be an effective therapy for tics and comorbidities in select cases of severe, treatment-refractory Tourette syndrome (Ts). clinical responses remain variable across patients, which may be attributed to differences in the location of the neuroanatomical regions being stimulated. We evaluated active contact locations and regions of stimulation across a large cohort of patients with Ts in an effort to guide future targeting. Methods We collected retrospective clinical data and imaging from 13 international sites on 123 patients. We assessed the effects of DBs over time in 110 patients who were implanted in the centromedial (cM) thalamus (n=51), globus pallidus internus (Gpi) (n=47), nucleus accumbens/anterior limb of the internal capsule (n=4) or a combination of targets (n=8).
Implementing these approaches would be an important step towards generating new hypotheses about how the current targeting methods can be improved in order to produce the best therapeutic outcomes in TS DBS patients. The goal of this study was to create PSAs in order characterise how DBS has been applied in patients with TS so far and to investigate whether there are specific regions that are potentially effective in managing tics and comorbidities in order to better predict patient response. In collaboration with the International TS DBS Database and Registry, we have assembled the first retrospective data set on 123 patients with TS from 13 international clinic sites that includes structural imaging, stimulation settings, and pre-DBS and post-DBS clinical outcomes data. We assessed the effects of DBS on TS and OCB severity over time and identified any differences across brain targets, comorbidities and demographics.
Patient cohort This study included data on a subset of patients from the International TS DBS Database and Registry in coordination with the International Neuromodulation Registry at the University of Utah. 18 61 Retrospective data were collected on patients receiving bilateral DBS therapy for treatment-refractory TS. Patients were selected for DBS therapy based on local evaluations in accordance with published recommendations. 62 Quadripolar DBS leads were implanted bilaterally in each patient (lead models of patients with available data: Medtronic 3387 (n=44), Medtronic 3389 (n=21) and NeuroPace DL-330-3. 5 (n=5)). The data collected during follow-up at each clinic site included demographics, comorbidities, brain region(s) targeted with DBS, stimulation settings, preoperative and postoperative clinical rating scale scores, and preoperative and postoperative MRI and/or CT.
Patient-specific models For each patient, we identified the DBS contact locations and constructed computational models to estimate the VTA using previously described methods. 52 Bilateral electrode contacts were localised using the lead artefacts in the postoperative CT or MRI. Preoperative MRI and postoperative MRI or CT volumes were aligned using BRAINSFit rigid registration  65  implemented Neuropsychiatry in 3D Slicer (http://www. slicer. org). 66 We constructed an isotropic finite element model (FEM) of the DBS lead geometry and surrounding conductive tissue using SCIRun software (V. 5. 0, SCI Institute, Salt Lake City, Utah). The FEM was used to solve for the spatial electric field generated by the stimulation settings. The resulting electric field was then applied to multicompartment axon models implemented in NEURON software.
Coregistration to cohort atlas An MRI cohort atlas was constructed to serve as a common coordinate space to facilitate cross-patient comparisons of the DBS electrode and VTA locations. We implemented a hierarchical approach for constructing the atlas to minimise error attributed to differences in image acquisition and quality across sites. 69 70 he subset of 58 patients with high-quality preoperative MRI (approximately 1 mm 3 voxels, high contrast and minimal artefacts) was further divided into 5 subgroups of 11-13 patients according to clinic site. An atlas was generated using skullstripped preoperative MRI for each of the five subgroups using the SyN nonlinear image registration method implemented in Advanced Normalization Tools (ANTs) software. 71 The final cohort atlas was generated using the subgroup atlases as the input image volumes.
For patients whose preoperative MRI were not included in the cohort atlas (n=8) and patients with only postoperative MRI (n=10), nonlinear image registration using SyN in ANTs was used to warp the final MRI cohort atlas to each respective patient's imaging. Using the resulting transformations, DBS electrode locations and VTA were coregistered to the cohort atlas. To visualise detailed segmentations of the target nuclei in cohort atlas space, we performed nonlinear image registration of the MNI ICBM 2009b Nonlinear Asymmetric atlas  72 73  to our cohort atlas to coregister select atlases that were already aligned to the MNI atlas and available from the Lead-DBS software. 74 The Harvard-Oxford atlas  [75] [76] [77] [78]  was used to obtain bilateral segmentations of the thalamus and NA/ ALIC.
A Kaplan-Meier analysis was performed to estimate the median time to reach clinical response to DBS. Stepwise cumulative probability curves and 95% CIs were calculated to estimate the median time (in months) to response. Log-rank tests were used to compare response curves for patients implanted in the CM thalamus versus the GPi, patients with TS and OCB versus patients without OCB, and patients grouped by age at implantation (<21 years, 21-30 years, 31-40 years and >40 years). Two-sided p values less than 0. 05 were considered statistically significant. All statistical analyses were performed using SAS V. 9. 4.
Patient cohort Retrospective data were collected from 123 patients from 13 different institutions across North America (39 patients), Europe (63 patients) and Asia (21 patients). Clinical data were available for a subset of 110 patients to be included in the statistical analysis of long-term clinical outcomes (table  1 ). The mean (SD, range) follow-up duration was 33. 7 (24. 1, 1. 0-120. 0) months, and the mean (SD, range) number of follow-up time points per patient was 3. 8 (1. of the variations in YGTSS total scores were due to variations by patients and 3. 8% were due to variations by clinic site. Statistical models performed for the secondary outcomes showed that YGTSS subscores also significantly decreased over time (motor: β=-0. 2, 95% CI (-0. 2 to -0. 1), p<0. 0001; phonic: β=-0. 1, 95% CI (-0.
dIsCussIoN effects of dbs for Ts over time The long-term clinical outcomes analysis showed that the YGTSS motor, phonic, impairment and total scores and Y-BOCS total scores significantly improved over time in response to DBS, indicating that DBS may be effective in reducing tic severity and OCB (figure  1 ). These findings are consistent with the results previously reported by meta-analyses and the International TS DBS Database and Registry, from which the present data set includes a subset of patients. 18 51 The data in this study were drawn from mostly open-label studies that were performed at multiple sites that differed in surgical techniques and treatment approaches. This may have biased the results, but our statistical analysis showed that only 3. 8% of the variability in YGTSS total scores was attributed to differences across sites.
Although our results indicate that DBS may be effective for TS, additional randomised controlled trials in large cohorts are needed to definitively conclude that DBS is effective in reducing tics and comorbidities in patients with TS. We observed that the median time was 13 months for patients to reach a 40% reduction in tic severity across all targets (figure  2 ). We varied the criteria for response from 40% improvement to other thresholds, and the time to response remained on the order of multiple months. One limitation of this analysis was that this data set included follow-up time points at the resolution of months, so a detailed analysis at smaller time intervals was not possible. Our results indicate that clinicians may need to be more patient with TS DBS compared with other indications, and detailed data of any stimulation setting changes should be recorded to facilitate careful reconstruction of the acute and long-term effects of stimulation.
Comparison of clinical outcomes across brain targets We found no significant differences in YGTSS scores or Y-BOCS scores over time among patients implanted in the CM thalamus versus amGPi, which suggests that the CM thalamus and amGPi both may be effective in improving TS symptoms and OCB. These results are in agreement with the results reported by meta-analyses and the International TS DBS Database and Registry. 18 51 he finding that multiple nuclei may be effective targets for DBS has also been shown in other indications; for example, both the subthalamic nucleus and GPi have been shown to be effective DBS targets for Parkinson's disease. 95 There were also no significant differences in the median time to response among patients Neuropsychiatry Figure  4  psas of clinical outcomes in Gpi DBs patients.
In our PSAs, we used data from only the final time point per patient to compare VTA across the greatest number of patients. However, using data from a single time point for each patient provides only a 'snapshot' in time of the patient's response to one set of stimulation parameters. Given the complex clinical time course of TS symptoms and in light of our Kaplan-Meier results, time is an important variable to be considered in future studies. Incorporating multiple VTA from several time points per patient could be a way to capture the variability in outcomes over time and investigate the relationship between stimulation regions and the long-term effects of DBS. The data set used to generate the PSAs was limited to only patients with imaging that met the strict quality control criteria. We collected structural imaging on 123 patients, but 53 patients (43%) were excluded from the imaging-based analysis, often due to low-resolution postoperative imaging (n=24 patients).
16 112 113 gh-quality preoperative imaging of multiple modalities (T1-weighted MRI, diffusion-weighted imaging (DWI) and resting-state functional MRI) should be acquired in each patient. Multiple imaging modalities are important for creating detailed patient-specific models that incorporate connectivity data in order to study how fibre pathways and functional networks are being modulated in a single patient with TS and how this may contribute to response. TS and its associated comorbidities are highly heterogeneous from patient to patient. Detailed patient characteristics, such as symptom profiles (tic types and comorbid behaviours), medications, time course of symptoms and severity of comorbidities, need to be collected in order to study their role in the response to DBS and to further characterise the pathology that contributes to different symptoms.
114 115 Patient-specific clinical characteristics combined with high-quality multimodal preoperative imaging would give important insight into how differences in connectivity may affect response to DBS. This could lead to better stratification of patients into subtypes that could guide future treatment strategies. It would be immensely powerful to be able to select stimulation targets for individual patients based on the combination of their TS and comorbidity symptoms, structural neuroanatomy and functional connectivity. The present data set will continue to grow alongside the International TS DBS Database and Registry and will serve as an important resource for future centres to compare potential TS DBS candidates' profiles against the predicted response rates and the reported efficacy of different DBS targets.
5X TAE buffer and separated by electrophoresis at 100 Volts. 1 μg of DNA ladder (GeneRuler 50bp, Fisher Scientific, Illkirch France) serves as a reference for the migration. Gels were revealed by Blue LED GelPicBox at 430nm (Nippongenetics, Du ¨ren Germany). • 10 μl of the PCR reactions were analysed by microfluidic capillary electrophoresis systems (Capillary LifeSciences, France) controlled by the Labchip GX version 4. 1. 1619. 0 SP1 software. Capillary electrophoresis is the high throughput format of the traditional gel electrophoresis. These assays allow a rapid size-based separation and sensitive detection of specific DNA fragments via UV absorption or fluorescent labelling  [17, 18]. The CE system is more advantageous over the conventional slab gel electrophoresis in terms of speed, high-throughput applicability, automated workflow, resolution, and sensitivity.
Robustness assessment of PCR and Q-PCR sexing assays 176 ISA-brown egg embryo tissues at day 9 of incubation were lysed for PCR and Q-PCR analysis. Lysates were not neither purified nor quantified in order to assess the robustness of the PCR and Q-PCR assays on heterogeneous samples in terms of DNA concentration and contaminants. As reference, we used previously described primers CHD 2250F/2718R  [6]  for sex determination. All samples were tested separately using our new PCR (with the primers SWIM/12S) and Q-PCR (with the primers Xho-I and D57) protocols. PCR samples were analysed by capillary electrophoresis (LabChip GX) and the data were processed by an R studio program (Version 0. 99. 903) that we developed for automatization. Each electropherogram profile was normalized in time with two markers and the baseline was subtracted.
Chromosome painting and fineSTRUCTURE algorithms were applied to the 7618 core SNPs and their positions in the alignment of the prophage genome sequences. If the number of SNPs were too small, the co-ancestry matrix (heatmap) would become uniform because each recipient genome would have similar, and small, numbers of fragments (chunks) of DNA inferred to be donated by other donor individuals. Clearly, that is not the case, as observed in the co-ancestry matrix in Fig. 2 , which shows variation in the number of fragments (chunks) of DNA for each recipient genome and population structure. Therefore, even though the prophage genomes are much smaller than those of the host, the current dataset has sufficient information for the algorithms to work. Although no genomic study has yet quantified recombination, phages have generally been considered as actively engaged with horizontal genetic exchange (recombination between different individuals), and their genomes are pervasively mosaic in their architectures  [39].
Data bibliography 1. Vale FF. Sequence Read Archive (SRA), SRP064706 to SRP064710, SRP071062,SRP071067, SRP071271, SRP071274, SRP071276 to SRP071280, SRP071282, SRP071284, SRP071289 to SRP071296 and SRP072438 to SRP072441 (2016). 2. Vale FF. Genbank, KX119174 to KX119206 (2016).
Network slicing is a new paradigm for future 5G networks where the network infrastructure is divided into slices devoted to different services and customized to their needs. With this paradigm, it is essential to allocate to each slice the needed resources, which requires the ability to forecast their respective demands. To this end, we present DeepCog, a novel data analytics tool for the cognitive management of resources in 5G systems. DeepCog forecasts the capacity needed to accommodate future traffic demands within individual network slices while accounting for the operator's desired balance between resource overprovisioning (i. e. , allocating resources exceeding the demand) and service request violations (i. e. , allocating less resources than required). To achieve its objective, DeepCog hinges on a deep learning architecture that is explicitly designed for capacity forecasting.
Comparative evaluations with real-world measurement data prove that DeepCog's tight integration of machine learning into resource orchestration allows for substantial (50% or above) reduction of operating expenses with respect to resource allocation solutions based on state-of-theart mobile traffic predictors. Moreover, we leverage DeepCog to carry out an extensive first analysis of the trade-off between capacity overdimensioning and unserviced demands in adaptive, sliced networks and in presence of real-world traffic.
Contribution. In this paper, we present DeepCog, a new mobile traffic data analytics tool that is explicitly tailored to solve the capacity forecast problem exposed above. The design of DeepCog yields multiple novelties, summarized as follows: • it hinges on a deep learning architecture inspired by recent advances in image processing, which exploits space-independent correlations typical of mobile traffic and computes outputs at a datacenter level;  • it leverages a customized loss function that targets capacity forecast rather than plain mobile traffic prediction, and that lets the operator tune the balance between overprovisioning and demand violations; • it operates on a per-service basis in accordance with network slicing requirements, and at datacenters located at different points of the mobile network.
Overall, these design principles jointly solve the different problems discussed before: in fact, Fig. 1b  is an example of the required capacity forecast by DeepCog in a real-world case study. We remark that DeepCog is one of the very first examples of rigorous integration of machine learning into a cognitive network management process, and marks a difference from the common practice of embedding vanilla deep learning structures into network operation  [9]. Extensive performance evaluations with substantial measurement data collected in an operational metropolitan-scale mobile network demonstrate the superiority of our approach over a wide range of benchmarks based on traditional and state-of-the-art mobile traffic predictors. In addition, DeepCog lets us investigate the fundamental trade-off between overprovisioning of resources and denied service demands, in practical case studies where network slicing and a sensible capacity forecast are in place.
II. DEEPCOG OVERVIEW The design of DeepCog is outlined in Fig. 2. Its organization is that typical of deep learning systems, and it stems from (i) properly formatted input data used to build the forecast, which is fed to (ii) a deep neural network architecture that extrapolates and processes input features to provide (iii) an output value, i. e. , the capacity forecast. During the training phase, the output is used to evaluate (iv) a loss function that quantifies the error with respect to the ground truth, and, in DeepCog, accounts for the costs associated to resource overprovisioning and service request denial. Below, we present each component, and discuss its mapping to the elements of a 5G network architecture with cognitive resource management. Input. The input is composed by measurement data generated in a specific network slice, and recorded by dedicated probes deployed within the network infrastructure.
Depending on the type and location of the probe, the nature of the measurement data may vary, describing the demands in terms of, e. g. , signal quality, occupied resource blocks, bytes of traffic, or computational load on VNFs. DeepCog leverages a set of transformations to map any type of slice traffic measurements into a tensor format that can be processed by the learning algorithm. Details are provided in Section III-C. Neural network. DeepCog leverages a deep neural network structure composed of suitably designed encoding and decoding phases, and that performs a next-step prediction. The structure is general enough that it can be trained to solve the capacity forecast problem for different network slices dedicated to services with significantly diverse demand patterns. The neural network structure is described in Section III-B.