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Abstract Peri-hilar cholangiocarcinoma (pCCA) is chemorefractory and limited genomic analyses have been undertaken in Western idiopathic disease. We undertook comprehensive genomic analyses of a U.K. idiopathic pCCA cohort to characterize its mutational profile and identify new targets. Whole exome and targeted DNA sequencing was performed on forty-two resected pCCA tumors and normal bile ducts, with Gene Set Enrichment Analysis (GSEA) using one-tailed testing to generate false discovery rates (FDR). 60% of patients harbored one cancer-associated mutation, with two mutations in 20%. High frequency somatic mutations in genes not typically associated with cholangiocarcinoma included mTOR, ABL1 and NOTCH1. We identified non-synonymous mutation (p.Glu38del) in MAP3K9 in ten tumors, associated with increased peri-vascular invasion (Fisher’s exact, p

Recent development in modern wireless applications and services, such as augmented reality, image processing, and network gaming requires persistent computing on average commercial wireless devices to perform complex tasks with low latency. The traditional cloud systems are unable to meet those requirements solely. In the said perspective, Mobile Edge Computing (MEC) serves as a proxy between the things (devices) and the cloud, pushing the computations at the edge of the network. The MEC provides an effective solution to fulfill the demands of low-latency applications and services by executing most of the tasks within the proximity of users. The main challenge, however, is that too many simultaneous service requests created by wireless access produce severe interference, resulting in a decreased rate of data transmission. In this paper, we made an attempt to overcome the aforesaid limitation by proposing a user-centric QoS-aware multi-path service provisioning approach. A densely deployed base station MEC environment has overlapping coverage regions. We exploit such regions to distribute the service requests in a way that avoid hotspots and bottlenecks. Our approach is adaptive and can tune to different parameters based on service requirements. We performed several experiments to evaluate the effectiveness of our approach and compared it with the traditional Greedy approach. The results revealed that our approach improves the network state by 26.95% and average waiting time by 35.56% as compared to the Greedy approach. In addition, the QoS violations were also reduced by the fraction of 16.

The protection and processing of the sensitive data in recommendation system are the major concern. Existing literature, used homomorphic encryption (HE), Reversible Data Transform (RDT), differential privacy (DP) and many more schemes to protect user information. Existing RDT scheme require prior sharing of the parameters and an alternative mechanism e.g., Shamir Threshold Protocol or Diffie-hellman algorithm are used to protect the sharing parameters. In this paper, we proposed a chaotic based RDT approach for privacy-preserving data mining (PPDM) in recommendation system. Using this approach, RDT parameter values will be generated locally and because of this, prior sharing of the parameter values for the recovery process will not be necessary. This approach can be used as an alternative to the standard-RDT algorithm where bandwidth and memory are considered important factors. Our results on the Iris data set clearly show that the proposed chaotic RDT shows similar results as standard-RDT. Secondly, in this paper, we explore the usage of the RDT algorithm on real app usage records in the mobile app recommendation (MAR) domain. Thirdly, we tested the application of the RDT algorithm for the standard MovieLens dataset to ensure the validity of results because app usage dataset is publicly not available. Our results show that the proposed RDT algorithm can replace HE if an adaptive recommendation approach is used. Similarly, we can safely use the RDT approach to any data including user rating, health data or app usage frequency to ensure user privacy before delivering it to the recommender-server.

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction.

Securing communications in vehicle ad hoc networks is crucial for operations. Messages exchanged in vehicle ad hoc network communications hold critical information such as road safety information, or road accident information and it is essential these packets reach their intended destination without any modification. A significant concern for vehicle ad hoc network communications is that malicious vehicles can intercept or modify messages before reaching their intended destination. This can hamper vehicle ad hoc network operations and create safety concerns. The multi-tier trust management system proposed in this paper addresses the concern of malicious vehicles in the vehicle ad hoc network using three security tiers. The first tier of the proposed system assigns vehicles in the vehicle ad hoc network a trust value based on behaviour such as processing delay, packet loss and prior vehicle behavioural history. This will be done by selecting vehicles as watchdogs to observe the behaviour of neighbouring vehicles and evaluate the trust value. The second tier is to protect the watchdogs, which is done by watchdogs’ behaviour history. The third security tier is to protect the integrity of data used for trust value calculation. Results show that the proposed system is successful in identifying malicious vehicles in the VANET. It also improves the packet delivery ratio and end-to-end delay of the vehicle ad hoc network in the presence of malicious vehicles.

Narrowband Internet of Things (NB-IoT) is the prominent technology that fits the requirements of future IoT networks. However, due to the limited spectrum (i.e., 180 kHz) availability for NB-IoT systems, one of the key issues is how to efficiently use these resources to support massive IoT devices? Furthermore, in NB-IoT, to reduce the computation complexity and to provide coverage extension, the concept of time offset and repetition has been introduced. Considering these new features, the existing resource management schemes are no longer applicable. Moreover, the allocation of frequency band for NB-IoT within LTE band, or as a standalone, might not be synchronous in all the cells, resulting in intercell interference (ICI) from the neighboring cells' LTE users or NB-IoT users (synchronous case). In this paper, first a theoretical framework for the upper bound on the achievable data rate is formulated in the presence of control channel and repetition factor. From the conducted analysis, it is shown that the maximum achievable data rates are 89.2 Kbps and 92 Kbps for downlink and uplink, respectively. Second, we propose an interference aware resource allocation for NB-IoT by formulating the rate maximization problem considering the overhead of control channels, time offset, and repetition factor. Due to the complexity of finding the globally optimum solution of the formulated problem, a sub-optimal solution with an iterative algorithm based on cooperative approaches is proposed. The proposed algorithm is then evaluated to investigate the impact of repetition factor, time offset and ICI on the NB-IoT data rate, and energy consumption. Furthermore, a detailed comparison between the non-cooperative, cooperative, and optimal scheme (i.e., no repetition) is also presented. It is shown through the simulation results that the cooperative scheme provides up to 8% rate improvement and 17% energy reduction as compared with the non-cooperative scheme.

The vision of the Internet of Things (IoT) is to enable systems across the globe to share data using advanced communication technologies. With the recent technological advancements, IoT-based solutions are no longer a challenging vision. IoT will offer numerous and potentially revolutionary benefits to today's digital world. Future personalized and connected healthcare is one of the promising areas to see the benefits of IoT. This paper surveys emerging healthcare applications, including detailed technical aspects required for the realization of a complete end-to-end solution for each application. The survey explores the key application-specific requirements from the perspective of communication technologies. Furthermore, a detailed exploration from the existing to the emerging technologies and standards that would enable such applications is presented, highlighting the critical consideration of short-range and long-range communications. Finally, the survey highlights important open research challenges and issues specifically related to IoT-based future healthcare systems.

With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches.

BackgroundColorectal cancer (CRC) represents a major cause for cancer death and every third patient develops liver metastases (CRLM). Several factors including number and size of metastases and primary tumour lymph-node status have been linked to survival. The primary tumour location along the colo-rectum continuum (sidedness) was analysed in first-line chemotherapy trials, where right-sided CRCs showed decreased survival. This association has not yet been clearly established in patients undergoing resection for CRLM.MethodsClinicopathological differences in CRLM resections according to sidedness in two Austrian centres (2003-2016) are described and survival is compared through Kaplan-Meier and multivariable analysis. A risk-score is presented with time-dependent receiver operating curve analysis and international validation in two major hepatobiliary centres. Furthermore, a systematic meta-analysis of studies on primary tumour location and survival after CRLM resection was performed.Results259 patients underwent hepatectomy. Right-sided CRC patients (n = 59) more often had positive primary tumour lymph-nodes (76.3%/61.3%;p = 0.043) and RAS-mutations (60%/34.9%;p = 0.036). The median overall and disease-free survival was 33.5 and 9.1 months in right-sided versus 55.5 (p = 0.051) and 12.1 months (p = 0.078) in left-sided patients. In multivariable analysis nodal-status (HR 1.52), right-sidedness (HR 1.53), extrahepatic disease (HR 1.71) and bilobar hepatic involvement (HR 1.41) were significantly associated with overall survival. Sidedness was not independently associated with disease-free survival (HR 1.33; p = 0.099). A clinical risk score including right-sidedness, nodal-positivity and extrahepatic involvement significantly predicted overall (p = 0.005) and disease-free survival (p = 0.027), which was confirmed by international validation in 527 patients (p = 0.001 and p = 0.011). Meta-analysis including 10 studies (n = 4312) showed a significant association of right-sidedness with overall survival after resection (HR 1.55;pConclusionsPatients with liver metastases from right-sided CRC experience worse survival after hepatic resection. Sidedness is a simple yet effective factor to predict outcome.

Narrowband internet of things (NB-IoT) is a recent cellular radio access technology based on Long-Term Evolution (LTE) introduced by Third-Generation Partnership Project (3GPP) for Low-Power Wide-Area Networks (LPWAN). The main aim of NB-IoT is to support massive machine-type communication (mMTC) and enable low-power, low-cost, and low-data-rate communication. NB-IoT is based on LTE design with some changes to meet the mMTC requirements. For example, in the physical (PHY) layer only single-antenna and low-order modulations are supported, and in the Medium Access Control (MAC) layers only one physical resource block is allocated for resource scheduling. The aim of this survey is to provide a comprehensive overview of the design changes brought in the NB-IoT standardization along with the detailed research developments from the perspectives of Physical and MAC layers. The survey also includes an overview of Evolved Packet Core (EPC) changes to support the Service Capability Exposure Function (SCEF) to manage both IP and non-IP data packets through Control Plane (CP) and User Plane (UP), the possible deployment scenarios of NB-IoT in future Heterogeneous Wireless Networks (HetNet). Finally, existing and emerging research challenges in this direction are presented to motivate future research activities.

Cancer cachexia is a debilitating condition characterized by a combination of anorexia, muscle wasting, weight loss, and malnutrition. This condition affects an overwhelming majority of patients with pancreatic cancer and is a primary cause of cancer-related death. However, few, if any, effective therapies exist for both treatment and prevention of this syndrome. In order to develop novel therapeutic strategies for pancreatic cancer cachexia, appropriate animal models are necessary. In this study, we developed and validated a syngeneic, metastatic, murine model of pancreatic cancer cachexia. Using our model, we investigated the ability of transforming growth factor beta (TGF-β) blockade to mitigate the metabolic changes associated with cachexia. We found that TGF-β inhibition using the anti-TGF-β antibody 1D11.16.8 significantly improved overall mortality, weight loss, fat mass, lean body mass, bone mineral density, and skeletal muscle proteolysis in mice harboring advanced pancreatic cancer. Other immunotherapeutic strategies we employed were not effective. Collectively, we validated a simplified but useful model of pancreatic cancer cachexia to investigate immunologic treatment strategies. In addition, we showed that TGF-β inhibition can decrease the metabolic changes associated with cancer cachexia and improve overall survival.

Combined intra-operative ablation and resection (CARe) is proposed to treat extensive colorectal liver metastases (CLM). This multicenter study was conducted to evaluate overall survival (OS), local recurrence-free survival (LRFS), hepatic recurrence-free survival (HRFS) and progression-free survival (PFS), to identify factors associated with survival, and to report complications.Four centers combined retropectively their clinical experiences regarding CLM treated by CARe. CLM characteristics, pre- and post-operative chemotherapy regimens, surgical procedures, complications and survivals were analyzed.Of the 288 patients who received CARe, 210 (73%) had synchronous and 255 (88%) had bilateral CLM. Twenty-two patients (8%) had extrahepatic disease. Median follow-up was 3.17 years (95%CI 2.83-4.08). Median OS was 3.33 years (95%CI 3.08-4.17) and 5-year OS was 37% (95%CI 29-45). One- and 5-year LRFS from ablated lesions were 87.9% (95%CI 83.3-91.2) and 78.0% (95%CI 71-83), respectively. Median HRFS and PFS were 14 months (95%CI 11-18) and 9 months (95%CI 8-11), respectively. One hundred patients experienced complications: 29 grade I, 68 grade II-III-IV, and three deaths. In the multivariate models adjusted for center, the occurrence of complications was confirmed as a major independent factor associated with 3-year OS (HR 1.80; P = 0.008). Five-year OS was 25.6% (95%CI 14.9-37.6) for patients with complications and 45% (95%CI 33.3-53.4) for patients without.Recent strategies facing advanced CLM include non-anatomic resections, portal-induced hypertrophy of the future remnant liver and aggressive medical preoperative treatments. CARe has the qualities of an approach that allows effective tumor clearance while maintaining good tolerance for the patient.

For a number of decades, Materials Science and Engineering an Introduction, has been an essential and trusted source of literature, as an introduction to Materials Science and Engineering. Now revised and updated the sixth edition is now available.