Chronic obstructive pulmonary disease (COPD) is a chronic disease characterized by a progressive decline in lung function due to airflow limitation, mainly related to IL-1β-induced inflammation. We have hypothesized that single nucleotide polymorphisms (SNPs) in NLRP genes, coding for key regulators of IL-1β, are associated with pathogenesis and clinical phenotypes of COPD. We recruited 704 COPD individuals and 1238 healthy controls for this study. Twenty non-synonymous SNPs in 10 different NLRP genes were genotyped. Genetic associations were estimated using logistic regression, adjusting for age, gender, and smoking history. The impact of genotypes on patients’ overall survival was analyzed with the Kaplan–Meier method with the log-rank test. Serum IL-1β concentration was determined by high sensitivity assay and expression analysis was done by RT-PCR. Decreased lung function, measured by a forced expiratory volume in 1 s (FEV1% predicted), was significantly associated with the minor allele genotypes (AT + TT) of NLRP1 rs12150220 (p = 0.0002). The same rs12150220 genotypes exhibited a higher level of serum IL-1β compared to the AA genotype (p = 0.027) in COPD patients. NLRP8 rs306481 minor allele genotypes (AG + AA) were more common in the Global Initiative for Chronic Obstructive Lung Disease (GOLD) definition of group A (p = 0.0083). Polymorphisms in NLRP1 (rs12150220; OR = 0.55, p = 0.03) and NLRP4 (rs12462372; OR = 0.36, p = 0.03) were only nominally associated with COPD risk. In conclusion, coding polymorphisms in NLRP1 rs12150220 show an association with COPD disease severity, indicating that the fine-tuning of the NLRP1 inflammasome could be important in maintaining lung tissue integrity and treating the chronic inflammation of airways.

Cilj rada bio je ispitati odnos opšteg samopoštovanja, procijenjenog preko Rosenbergove skale samopoštovanja, i prilagodbe na studij, operacionalizirane preko skorova na Baker-Siryk-ovoj skali prilagodbe na studij SACQ. Prilagodba na studij modelirana je preko četiri dimenzije prilagodbe: emocionalne, socijalne, akademske, i institucionalne. Studija je provedena na uzorku od 808 studenata (62.9% ženski; M = 21.88; SD = 2.35). Diskriminativnom analizom preko četiri modaliteta prilagodbe procijenjena je alokacija studenata na grupu visoko/nisko samopoštovanje. Izdvojena je značajna diskriminativna funkcija koja bi se mogla svesti na aspekte emocionalno-socijalne prilagodbe. Ukupno je obuhvaćeno oko 17% preklapanja između studentske prilagodbe i samopoštovanja. Studenti sa višim stepenom samopoštovanja imali su u prosjeku statistički značajno viši nivo prilagodbe na studij u odnosu na studente s nižim samopoštovanjem.

Anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA) is rare, but life-threatening condition. The treatment of choice in patients with ALCAPA is the establishment of a dual coronary artery system with surgical reimplantation of the left coronary artery in the left coronary sinus. Percutaneous coronary intervention is infrequent in the pediatric population but can be a life-saving by promptly restoring flow to an obstructed coronary artery. It is a highly demanding and high-risk procedure in infants due to the technical difficulties and the small coronary artery diameter in infants.

This paper presents a new meta-heuristic algorithm called Playground algorithm. The Playground algorithm is designed to model social interaction amongst children, and the mechanisms and operators of the algorithm are inspired by the model of child interaction and engagement in games during a child's stay at the playground. In order to evaluate the performance of the algorithm, a series of tests were performed over a class of functions selected so that they possess properties such as: multimodality / unimodality, (non) separability, (non) differentiability, (non) convexity, existence of ridges and valleys and multidimensionality. During testing, the values of the algorithm parameters are varied, in order to determine their recommended values. The analysis was carried out with an overview of the effects of the algorithm parameters on the performance of the algorithm in the problem area, performance in the criterion domain, and the execution time.

In order to validate and demonstrate newly developed ranging techniques, a flexible test platform for signal acquisition enabling offline signal processing is generally needed. Developing such a platform becomes challenging when working with wideband (> 100MHz) signals due to the critical timing, the very high sampling rates and the huge data throughput involved. In this paper, we introduce an Ettus X310 SDR platform using custom designed logic allowing for dual-channel 400 Msps data transmission and acquisition for centimeter level ranging applications. Furthermore, we present initial measurement results as a benchmark of the platform, which show that the time delay of a 10 m cable can be estimated with high accuracy, in the order of 50 ps.

For unstructured environments, multi-leg platforms such as hexapods, introduce better stability and adaptation during the moving in a more complex environment. The common hexapod robots, which are controlled by discrete conventional microcontrollers would require new chip replacement due to I/O constraint. With an FPGA, the user can scale design and include new functionality later in the design process without buying new additional hardware. In this paper, we introduce the hybrid architecture as a combination of FPGA and microcontroller design. The goal of this work is to extend context arbitration of a fuzzy behavior-based approach for hexapod robot navigation at the FPGA device. Verilog has been used to develop FLC on FPGA. The controller algorithm is developed and implemented on FPGA Altera Cyclone IV board. An experimental evaluation of hexapod autonomous navigation at a rough terrain presents advantages of this hierarchy approach for real-time robot embedded systems.

Distribution network power flow (DNPF) is a core application of distribution management system (DMS). Two methods to implement fast DNPF are using Newton-Raphson (NR) approach and current iteration (CI) approach. In distribution systems with high penetration of renewable energy sources both methods must be able to model PV nodes correctly and efficiently. This paper explores implementations of NR DNPF and CI DNPF, their execution time and performance in networks with numerous distributed generators.

As memories are becoming a ubiquitous and indispensable part of electronic devices across all industrial domains, the importance of their reliability and fault-tolerance increases. This especially holds for safety-critical applications, which exhibit different levels of data criticality. As a consequence, recent research aims to proactively engage environmentally induced soft errors, by developing new methods for error detection, mitigation, and data recovery in the mixed-critical memories. This article presents a flexible soft error correction strategy called Redundant Parity (RP), designed to enhance existing 1oo2 architectures. RP extends a 1oo2 system's ability of fault detection by enabling the recovery of faulty data utilizing the parity bit concept. An initial evaluation of the strategy in terms of its runtime performance and memory overhead is performed and compared with other software-based mitigation strategies. The preliminary results suggest that RP is indeed a suitable soft error mitigation strategy in existing 1oo2 fail-safe systems.

The success of fundamental network tasks of traffic delivery from a source to a destination node is mainly dependent on the efficiency of the routing protocol. In mobile ad hoc networks, the effectiveness of routing protocols is additionally demanding due to the dynamic nature of network nodes. In this paper, we dealt with the exploitation of the routes generated using DSDV bellman-ford routing protocol. Through a total of 3960 network simulations with different topologies, network loads and mobility nodes, various parameters of the DSDV were considered. Our results show that there are a large number of unused routes, and techniques for improving the efficiency of routing and reducing routing overhead can be implemented.

The vision of the smart-city environment is based on a large number of sensors, actuators, devices connected to the Internet. As interest in the practical implementation of the smart city environment increases, so does the interest in examining network connectivity which can be useful for investigating security vulnerabilities, identifying or blocking traffic accessibility (when needed), and other. In this paper, we analyze the network connectivity of smart-home Xiaomi solutions based on measurements made over 30 days. We analyze the installation phase, the usage phase, and identify key Xiaomi network nodes using geolocation techniques.

The scope and scale of biological data are increasing at an exponential rate, as technologies like next-generation sequencing are becoming radically cheaper and more prevalent. Over the last two decades, the cost of sequencing a genome has dropped from $100 million to nearly $100—a factor of over 106—and the amount of data to be analyzed has increased proportionally. Yet, as Moore’s Law continues to slow, computational biologists can no longer rely on computing hardware to compensate for the ever-increasing size of biological datasets. In a field where many researchers are primarily focused on biological analysis over computational optimization, the unfortunate solution to this problem is often to simply buy larger and faster machines. Here, we introduce Seq, the first language tailored specifically to bioinformatics, which marries the ease and productivity of Python with C-like performance. Seq starts with a subset of Python—and is in many cases a drop-in replacement—yet also incorporates novel bioinformatics- and computational genomics-oriented data types, language constructs and optimizations. Seq enables users to write high-level, Pythonic code without having to worry about low-level or domain-specific optimizations, and allows for the seamless expression of the algorithms, idioms and patterns found in many genomics or bioinformatics applications. We evaluated Seq on several standard computational genomics tasks like reverse complementation, k-mer manipulation, sequence pattern matching and large genomic index queries. On equivalent CPython code, Seq attains a performance improvement of up to two orders of magnitude, and a 160× improvement once domain-specific language features and optimizations are used. With parallelism, we demonstrate up to a 650× improvement. Compared to optimized C++ code, which is already difficult for most biologists to produce, Seq frequently attains up to a 2× improvement, and with shorter, cleaner code. Thus, Seq opens the door to an age of democratization of highly-optimized bioinformatics software.

Many users need social media platforms to improve business. The usage of those platforms is usually focused on the marketing and customer targeting. Platforms like Facebook, Instagram or YouTube give their users a large number of reports and analytic tools. Public figures and organizations have a large number of followers who generate a significant number of activities. This paper focuses on the use of Facebook's geography analytic in the process of events planning. The problem is formulated as a combinatorial optimization problem. Data from social media platforms are used as an input to nature-inspired optimization algorithm. A public data set has been created with cities from 20 European countries. An adjusted genetic algorithm (AGA) is proposed. The greedy approach and AGA are compared on real data from several Facebook pages and on the created public dataset. The genetic algorithm shows better results and it gives the same solution as an exhaustive search for smaller instances.

The planning of concert tours can be a challenging process which requires a large amount of data to be analyzed. The greatest profit cannot be obtained only by maximizing the expected number of visitors. However, most of the organizers mainly focus on that part of planning. To achieve the maximum profit possible, organizers must include other data in their analysis. Social media play a powerful role in music industry. Most of the mentioned data can be found online on social media like Facebook, YouTube or Instagram. Such data can be found in analytic sections of fan or event pages. In this paper, algorithms for tour planning have been introduced by using above mentioned data. Proposed algorithms are based on heuristic methods such as simulated annealing and genetic algorithm. A clustering based method is also implemented. Aforementioned algorithms were tested on real-world instances from Facebook fan page analytics and use number of fans and distance between cities.