Miracle-dz/UniFAR-FTUs huggingface dataset:
Interictal stereotactic-EEG functional connectivity in refractory focal epilepsies

Drug-refractory focal epilepsies are network diseases associated with functional connectivity alterations both during ictal and interictal periods. A large majority of studies on the interictal/resting state have focused on functional MRI-based functional connectivity. Few studies have used electrophysiology, despite its high temporal capacities. In particular, stereotactic-EEG is highly suitable to study functional connectivity because it permits direct intracranial electrophysiological recordings with relative large-scale sampling. Most previous studies in stereotactic-EEG have been directed towards temporal lobe epilepsy, which does not represent the whole spectrum of drug-refractory epilepsies. The present study aims at filling this gap, investigating interictal functional connectivity alterations behind cortical epileptic organization and its association with post-surgical prognosis. To this purpose, we studied a large cohort of 59 patients with malformation of cortical development explored by stereotactic-EEG with a wide spatial sampling (76 distinct brain areas were recorded, median of 13.2 per patient). We computed functional connectivity using non-linear correlation. We focused on three zones defined by stereotactic-EEG ictal activity: the epileptogenic zone, the propagation zone and the non-involved zone. First, we compared within-zone and between-zones functional connectivity. Second, we analysed the directionality of functional connectivity between these zones. Third, we measured the associations between functional connectivity measures and clinical variables, especially post-surgical prognosis. Our study confirms that functional connectivity differs according to the zone under investigation. We found: (i) a gradual decrease of the within-zone functional connectivity with higher values for epileptogenic zone and propagation zone, and lower for non-involved zones; (ii) preferential coupling between structures of the epileptogenic zone; (iii) preferential coupling between epileptogenic zone and propagation zone; and (iv) poorer post-surgical outcome in patients with higher functional connectivity of non-involved zone (within- non-involved zone, between non-involved zone and propagation zone functional connectivity). Our work suggests that, even during the interictal state, functional connectivity is reinforced within epileptic cortices (epileptogenic zone and propagation zone) with a gradual organization. Moreover, larger functional connectivity alterations, suggesting more diffuse disease, are associated with poorer post-surgical prognosis. This is consistent with computational studies suggesting that connectivity is crucial in order to model the spatiotemporal dynamics of seizures.10. 1093/brain/awy214_video1awy214media15833456182001.
Patient safety in nursing care during medication administration

ABSTRACT Objective: to evaluate the conformity of care practices of the nursing team during the administration of drugs through central vascular catheter. Method: a descriptive, prospective, observational study conducted in an Intensive Care Unit. The non-probabilistic intentional sample consisted of 3402 observations of drug administrations in patients with central vascular catheters. The previously validated collection instrument was constructed based on the Guideline for Prevention of Intravascular catheter-related infections. Data was collected through direct observations of nursing practices performed by the nursing team. The analysis used analytical, descriptive and inferential statistics (Chi-square test and Fisher’s exact test). Results: a total of 3402 procedures of drug administrations were observed. Female nursing technicians performed the highest number of actions. In none of the procedures did the professional perform all necessary actions. 0.2% of drug administrations were preceded by hand hygiene and 1.3% by disinfection of the multidose vial, ampoule or injectors. Conclusion: the practice evaluated was classified as undesirable. Failure to achieve the desired conformity was probably due to the low adherence of professionals to the practice of hand hygiene and disinfection of materials, injectors and connectors.
A Reinforcement Learning and Recurrent Neural Network Based Dynamic User Modeling System

With the exponential growth in areas of machine intelligence, the world has witnessed promising solutions to the personalized content recommendation. The ability of interactive learning agents to take optimal decisions in dynamic environments has been very well conceptualized and proven by Reinforcement Learning (RL). The learning characteristics of Deep-Bidirectional Recurrent Neural Networks (DBRNN) in both positive and negative time directions has shown exceptional performance as generative models to generate sequential data in supervised learning tasks. In this paper, we harness the potential of the said two techniques and strive to create personalized video recommendation through emotional intelligence by presenting a novel context-aware collaborative filtering approach where intensity of users' spontaneous non-verbal emotional response towards recommended video is captured through system-interactions and facial expression analysis for decision-making and video corpus evolution with real-time data streams. We take into account a user's dynamic nature in the formulation of optimal policies, by framing up an RL-scenario with an off-policy (Q-Learning) algorithm for temporal-difference learning, which is used to train DBRNN to learn contextual patterns and generate new video sequences for the recommendation. Evaluation of our system with real users for a month shows that our approach outperforms state-of-the-art methods and models a user's emotional preferences very well with stable convergence.
SDMan: Towards a Software Defined Management Framework

Software Defined Infrastructure (SDI) has become a relevant topic for computing and communication industry. Despite this huge technological movement, Network and Systems Management has been disregarded as one of the main themes in this ecosystem, and Software Defined Infrastructure has been managed by semi-software-defined management solutions. In order to reduce this gap, this paper presents SDMan, a Software Defined Management framework. The SDMan's proof of concept uses the OpenStack cloud platform and aims to demonstrate the feasibility of the proposed solution.
A Grid-Based Coverage Analysis of Urban mmWave Vehicular Ad Hoc Networks

In this letter, a tractable coverage model, specifically designed for urban vehicular ad hoc networks, is presented to aid a better system designer. This is achieved through the use of a model based upon line processes, which simplifies the analysis. It is found that even in crowded interferer scenarios, millimeter-wave vehicular communications can establish reliable links with a signal-to-interference-plus-noise-ratio threshold of around 5 dB, with a coverage probability of approximately 0.8 at the 50-m separation between a typical transmitter and a typical receiver. These results and their inference toward the design and deployment of urban vehicular ad hoc networks may impact the developments of future vehicle-to-vehicle applications and services.
Fine-Grained Traffic Engineering on SDN-Aware Wi-Fi Mesh Networks

Software-defined networking (SDN) has introduced noticeable progresses in the design and development of computer networks. In fact, SDN empowers us to run more advanced algorithms for resource management through the separation of control and data planes using a centralized approach. By applying SDN to different communication platforms, e.g., Wi-Fi mesh networks (WMNs), we gain an unprecedented opportunity to design more efficient and cost-effective solutions. In WMNs, the process of service provisioning incorporates tightly correlated steps, including access point association, gateway selection, and backhaul routing. In most of the prior studies, these steps were investigated as independent NP-hard problems. To the best of our knowledge, there is no thorough formulation that considers all the necessary steps at different tiers of WMNs to construct a unified and fine-grained solution. In this paper, we present a structured scheme to address the demands of end-users over SDN-aware WMNs. In contrast to most of the former works, our proposed formulation takes the key characteristics of wireless networks into account, especially for multichannel multiradio WMNs. In addition, for applying our solution to the large-scale scenarios, we introduce a heuristic algorithm that can find a suboptimal solution in polynomial time. The functionality of the presented work is evaluated through extensive numerical results and real testbed implementation.
Anomaly Detection and Diagnosis Scheme for Mobile Health Applications

Mobile healthcare applications highly depend on healthcare data, which is collected from wearable or implantable sensors. However, sensor readings may be inaccurate due to resource-constrained devices, sensor misplacement, patient with smearing, and other environmental related causes. Analyzing healthcare data is of paramount importance to provide high quality-care services and reduce false medical diagnosis. In this paper, we propose an online approach to detect inaccurate measurements and to raise alerts only when patients seem to be in emergency situations. The proposed approach is based on robust principal component analysis and adaptive threshold for multivariate anomaly detection, and on contribution plots for univariate anomaly diagnosis. We apply our proposed approach on real medical dataset. Our experimental results prove the effectiveness of our approach in detecting and diagnosing anomalous physiological measurements. The reduced time and space complexities of our approach make it useful and efficient for real time mobile health applications.
Loss of CXCR4 in Myeloid Cells Enhances Antitumor Immunity and Reduces Melanoma Growth through NK Cell and FASL Mechanisms

NK-cell antitumor activity is enhanced by myeloid-CXCR4 deletion, revealing a pathway by which this receptor may contribute to tumor surveillance suppression and promotion of metastasis. This pathway provides a rationale for the clinical application of CXCR4 antagonists. The chemokine receptor, CXCR4, is involved in cancer growth, invasion, and metastasis. Several promising CXCR4 antagonists have been shown to halt tumor metastasis in preclinical studies, and clinical trials evaluating the effectiveness of these agents in patients with cancer are ongoing. However, the impact of targeting CXCR4 specifically on immune cells is not clear. Here, we demonstrate that genetic deletion of CXCR4 in myeloid cells (CXCR4MyeΔ/Δ) enhances the antitumor immune response, resulting in significantly reduced melanoma tumor growth. Moreover, CXCR4MyeΔ/Δ mice exhibited slowed tumor progression compared with CXCR4WT mice in an inducible melanocyte BrafV600E/Pten−/− mouse model. The percentage of Fas ligand (FasL)–expressing myeloid cells was reduced in CXCR4MyeΔ/Δ mice as compared with myeloid cells from CXCR4WT mice. In contrast, there was an increased percentage of natural killer (NK) cells expressing FasL in tumors growing in CXCR4MyeΔ/Δ mice. NK cells from CXCR4MyeΔ/Δ mice also exhibited increased tumor cell killing capacity in vivo, based on clearance of NK-sensitive Yac-1 cells. NK cell–mediated killing of Yac-1 cells occurred in a FasL-dependent manner, which was partially dependent upon the presence of CXCR4MyeΔ/Δ neutrophils. Furthermore, enhanced NK cell activity in CXCR4MyeΔ/Δ mice was also associated with increased production of IL18 by specific leukocyte subpopulations. These data suggest that CXCR4-mediated signals from myeloid cells suppress NK cell–mediated tumor surveillance and thereby enhance tumor growth. Systemic delivery of a peptide antagonist of CXCR4 to tumor-bearing CXCR4WT mice resulted in enhanced NK-cell activation and reduced tumor growth, supporting potential clinical implications for CXCR4 antagonism in some cancers. Cancer Immunol Res; 6(10); 1186–98. ©2018 AACR.
Privacy Preservation in Social Networks Sequential Publishing

The proliferation of social networks allowed creating a big quantity of data about users and their relationships. Such data contains much private information. Therefore, anonymization is required before publishing the data for data mining purposes (scientific research, marketing, decision support etc). Most of anonymization works focus on the privacy preserving techniques that allow publishing one instance of the social network without revealing sensitive information. However to analyze the evolution of the social network sequential releases are needed. In this paper we study the problem of privacy in sequential releases of social networks which are represented as labeled bipartite graphs to model the affiliation relationship between users and interests. We propose a solution that allows publishing sequential releases of the same social network while preserving the privacy of data. We consider a set of complex queries to study the utility given by our solution. The experiments demonstrate that the utility of data is preserved as the queries can be answered with reasonable accuracy over the anonymized data.
Modeling Topic Propagation on Heterogeneous Online Social Networks

Online information propagation modeling has attracted researchers' attention on information reliability. Our study presents a new perspective for modeling information propagation. Then, a universal model for tracking topic propagation on heterogeneous online social network is proposed. Experimental results show that our method not only overcomes their drawbacks but also proves precise for all kinds of topic propagation in areas such as information safety and marketing prediction.
A Large Multiethnic Genome-Wide Association Study of Adult Body Mass Index Identifies Novel Loci

Body mass index (BMI), a proxy measure for obesity, is determined by both environmental (including ethnicity, age, and sex) and genetic factors, with > 400 BMI-associated loci identified to date. However, the impact, interplay, and underlying biological mechanisms among BMI, environment, genetics, and ancestry are not completely understood. To further examine these relationships, we utilized 427,509 calendar year-averaged BMI measurements from 100,418 adults from the single large multiethnic Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. We observed substantial independent ancestry and nationality differences, including ancestry principal component interactions and nonlinear effects. To increase the list of BMI-associated variants before assessing other differences, we conducted a genome-wide association study (GWAS) in GERA, with replication in the Genetic Investigation of Anthropomorphic Traits (GIANT) consortium combined with the UK Biobank (UKB), followed by GWAS in GERA combined with GIANT, with replication in the UKB. We discovered 30 novel independent BMI loci (P < 5.0 × 10−8) that replicated. We then assessed the proportion of BMI variance explained by sex in the UKB using previously identified loci compared to previously and newly identified loci and found slight increases: from 3.0 to 3.3% for males and from 2.7 to 3.0% for females. Further, the variance explained by previously and newly identified variants decreased with increasing age in the GERA and UKB cohorts, echoed in the variance explained by the entire genome, which also showed gene–age interaction effects. Finally, we conducted a tissue expression QTL enrichment analysis, which revealed that GWAS BMI-associated variants were enriched in the cerebellum, consistent with prior work in humans and mice.
Julunga: A New Large-Scale Distributed Read-Write File Storage System for Cloud Computing Environments

We present in this paper the design of a large distributed file storage system called Julunga for cloud computing environments. Julunga is designed for federated data center environments where multiple numbers of data centers across the globe interconnect to each other and present a coherent single system view to end-users. In Julunga, the metadata, the namespace and the data blocks of the files are completely distributed with no hard limits on the number of files that can be accommodated in a single directory. Also, there is no physical limit on the size of a file. Julunga supports file of exabytes size with ease along with the number of concurrent users updating, reading and writing to the same file or a directory. The location of the data blocks of a file is determined by using functions, thus expunging the need for file allocation tables. Many new data structures and algorithms were designed earlier where locality and preferences of users are considered to provide optimal storage locations for files and metadata. We elaborate, in this paper, the design of fundamental building blocks of the distributed storage system and compare it with the designs of the earlier file storage systems.
Star-BRISE: Energy-Efficient Benchmarking for Interacting Algorithms

Energy-efficient computing is a well-studied and established field. Software energy-efficiency is one of the ways to decrease energy consumption of computing systems. However, contemporary studies on energy-efficient software investigate only individual algorithms, neglecting such an important area as workflow energy-efficiency. In this paper we try to decrease this gap by providing a study which investigates dependencies between software algorithms organized in a workflow. We empirically study the effect of dynamic voltage and frequency scaling and dynamic concurrency throttling on energy consumption of two case studies: workflows combined from (a) compression and encryption algorithms; and (b) matrix transposition and addition. Our findings show, that a suitable structure of a workflow, can significantly reduce energy consumption of the overall system. However, empirical studies of workflow energy-efficiency are themselves very time-and energy-demanding. Therefore, we provide an approach called Star-BRISE that allows to reuse data obtained from benchmarking of individual algorithms to decrease the amount of measurements for resulting workflows. The presented approach can save up to 78% of time and energy effort on finding an optimal configuration for 2-algorithm workflows (and up to 95 % of effort for a single workflow) and is even more efficient with scaling the number of algorithms in a workflow.
Success Probability and Area Spectral Efficiency in SCMA Wireless Networks

Sparse code multiple access (SCMA) is emerging as a promising multiple access technology for the fifth-generation wireless networks. By leveraging user-specific SCMA codebooks, SCMA possesses the potential to enable non-orthogonal resource access, and thus leading to the overloading gain. Benefiting from this, SCMA can accommodate more users and achieve a higher throughput than orthogonal frequency division multiple access. In this correspondence, we propose a tractable analytical framework using tools from stochastic geometry to investigate the performance of SCMA wireless networks. In particular, we obtain explicit closed-form expressions for the success probability and the area spectral efficiency (ASE). Based on the derived results, we prove that the ASE is a quasi-concave function of the transmitter density. Moreover, we shed light on the impact of SCMA codeword dimension <inline-formula><tex-math notation="LaTeX"> $K$</tex-math></inline-formula> and the number of non-zero entries in SCMA codeword <inline-formula> <tex-math notation="LaTeX">$N$</tex-math></inline-formula> on the network performance. Specifically, we demonstrate that the optimal transmitter density and the maximum ASE scale super-linearly with rate <inline-formula> <tex-math notation="LaTeX">$K^{N}$</tex-math></inline-formula>. Simulation results validate the accuracy of the presented analysis and exhibit the superiority of SCMA.
An Efficient Online Cache Replacement Algorithm for 5G Networks

In recent years, 5G cellular networks utilization has rapidly increased and is expected to grow even more in the near future. This will put the current cellular networks operators in a challenge to overcome the network’s limits to satisfy the increasing mobile data traffic and the proliferation of user demands in deploying mobile applications. The deployment of cache-enabled small base stations (Femtocells) is a promising solution to reduce the backhaul traffic loading and the file-access latency and therefore decrease the cellular network operational costs. Due to the limited cache capacity when compared with the number of files that can be requested by users, in this paper, we formulate the problem of minimizing the cost paid by the cellular network while satisfying the cache capacity as an integer linear program (ILP). Due to the NP-completeness of the ILP formulation and the difficulty of obtaining the file request sequence apriori in real-life scenarios, we propose an online algorithm that decides which file to remove from cache in order to allocate capacity to the newly-requested file. The algorithm works on a per-request basis and does not require the knowledge of the file request sequence in advance. We prove that for a cache that can store up to $k$ files, the algorithm achieves a competitive ratio of $\mathcal {O}(\log (k))$ , which is the best competitive ratio achieved by any online algorithm as shown in the literature. The simulations conducted considering a single cache show that while the proposed algorithm achieves a similar hit ratio compared with widely-used replacement schemes, it can reduce the cost of the cellular network by 25%.
Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients

BACKGROUND Pseudoprogression is a diagnostic challenge in early posttreatment glioblastoma. We therefore developed and validated a radiomics model using multiparametric MRI to differentiate pseudoprogression from early tumor progression in patients with glioblastoma. METHODS The model was developed from the enlarging contrast-enhancing portions of 61 glioblastomas within 3 months after standard treatment with 6472 radiomic features being obtained from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery imaging, and apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps. Imaging features were selected using a LASSO (least absolute shrinkage and selection operator) logistic regression model with 10-fold cross-validation. Diagnostic performance for pseudoprogression was compared with that for single parameters (mean and minimum ADC and mean and maximum CBV) and single imaging radiomics models using the area under the receiver operating characteristics curve (AUC). The model was validated with an external cohort (n = 34) imaged on a different scanner and internal prospective registry data (n = 23). RESULTS Twelve significant radiomic features (3 from conventional, 2 from diffusion, and 7 from perfusion MRI) were selected for model construction. The multiparametric radiomics model (AUC, 0.90) showed significantly better performance than any single ADC or CBV parameter (AUC, 0.57-0.79, P < 0.05), and better than a single radiomics model using conventional MRI (AUC, 0.76, P = 0.012), ADC (AUC, 0.78, P = 0.014), or CBV (AUC, 0.80, P = 0.43). The multiparametric radiomics showed higher performance in the external validation (AUC, 0.85) and internal validation (AUC, 0.96) than any single approach, thus demonstrating robustness. CONCLUSIONS Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improved diagnostic performance for identifying pseudoprogression and showed robustness in a multicenter setting.