Hierarchical structure of cascade of primary and secondary periodicities in Fourier power spectrum of alphoid higher order repeats

<p>Abstract</p> <p>Background</p> <p>Identification of approximate tandem repeats is an important task of broad significance and still remains a challenging problem of computational genomics. Often there is no single best approach to periodicity detection and a combination of different methods may improve the prediction accuracy. Discrete Fourier transform (DFT) has been extensively used to study primary periodicities in DNA sequences. Here we investigate the application of DFT method to identify and study alphoid higher order repeats.</p> <p>Results</p> <p>We used method based on DFT with mapping of symbolic into numerical sequence to identify and study alphoid higher order repeats (HOR). For HORs the power spectrum shows equidistant frequency pattern, with characteristic two-level hierarchical organization as signature of HOR. Our case study was the 16 mer HOR tandem in AC017075.8 from human chromosome 7. Very long array of equidistant peaks at multiple frequencies (more than a thousand higher harmonics) is based on fundamental frequency of 16 mer HOR. Pronounced subset of equidistant peaks is based on multiples of the fundamental HOR frequency (multiplication factor <it>n </it>for <it>n</it>mer) and higher harmonics. In general, <it>n</it>mer HOR-pattern contains equidistant secondary periodicity peaks, having a pronounced subset of equidistant primary periodicity peaks. This hierarchical pattern as signature for HOR detection is robust with respect to monomer insertions and deletions, random sequence insertions etc. For a monomeric alphoid sequence only primary periodicity peaks are present. The 1/<it>f</it>^{<it>β </it>}– noise and periodicity three pattern are missing from power spectra in alphoid regions, in accordance with expectations.</p> <p>Conclusion</p> <p>DFT provides a robust detection method for higher order periodicity. Easily recognizable HOR power spectrum is characterized by hierarchical two-level equidistant pattern: higher harmonics of the fundamental HOR-frequency (secondary periodicity) and a subset of pronounced peaks corresponding to constituent monomers (primary periodicity). The number of lower frequency peaks (secondary periodicity) below the frequency of the first primary periodicity peak reveals the size of <it>n</it>mer HOR, i.e., the number <it>n </it>of monomers contained in consensus HOR.</p

Experimental Characterization and Quadratic Programming-Based Control of Brushless-Motors

A new torque control strategy for brushless motors is presented, which results in minimum torque ripple and copper losses. The motor model assumes linear magnetics, but contains a current limit which can delimit the onset of magnetic saturation, or be the motor amplifier current limit, whichever is reached first. The control problem is formulated and solved as a quadratic programming problem with equality and inequality constraints to find the nonlinear mapping from desired torque and position to the motor&apos;s phase currents. The optimal solution is found in closed form using the Kuhn--Tucker theorem. The solution shows that, unlike the conventional commutation with a fixed current-position waveform, the waveforms of the proposed controller vary in order to respect the current limitation in one phase by boosting the current in the other phases. This increases the maximum torque capability of the motor---in our particular system by 20%---compared to fixed waveform commutation. Experimental data from our brushless direct-drive motor demonstrates that the controller produces virtually ripple-free torque and enhances remarkably the tracking accuracy of the motion controller

Potential Inhibitory Effect of Miltefosine against Terbinafine-Resistant <i>Trichophyton indotineae</i>

Several prolonged and significant outbreaks of dermatophytosis caused by Trichophyton indotineae, a new emerging terbinafine-resistant species, have been ongoing in India in recent years, and have since spread to various countries outside Asia. Miltefosine, an alkylphosphocholine, is the most recently approved drug for the treatment of both visceral and cutaneous leishmaniasis. Miltefosine in vitro activity against terbinafine-resistant and susceptible T. mentagrophytes/T. interdigitale species complex, including T. indotineae, is limited. The current study aimed to assess miltefosine’s in vitro activity against dermatophyte isolates, which are the most common causes of dermatophytosis. Miltefosine, terbinafine, butenafine, tolnaftate, and itraconazole susceptibility testing was performed using Clinical and Laboratory Standards Institute broth microdilution methods (CLSI M38-A3) against 40 terbinafine-resistant T. indotineae isolates and 40 terbinafine-susceptible T. mentagrophytes/T. interdigitale species complex isolates. Miltefosine had MIC ranges of 0.063–0.5 µg/mL and 0.125–0.25 µg/mL against both terbinafine-resistant and susceptible isolates. In terbinafine-resistant isolates, the MIC50 and MIC90 were 0.125 µg/mL and 0.25 µg/mL, respectively, and 0.25 µg/mL in susceptible isolates. Miltefosine had statistically significant differences in MIC results when compared to other antifungal agents (p-value 0.05) in terbinafine-resistant strains. Accordingly, the findings suggest that miltefosine has a potential activity for treating infections caused by terbinafine-resistant T. indotineae. However, further studies are needed to determine how well this in vitro activity translates into in vivo efficacy

In Vitro Antifungal Susceptibility Profile of Miltefosine against a Collection of Azole and Echinocandins Resistant Fusarium Strains

Fusarium species are filamentous fungi that cause a variety of infections in humans. Because they are commonly resistant to many antifungal drugs currently available in clinical settings, research into alternative targets in fungal cells and therapeutic approaches is required. The antifungal activity of miltefosine and four comparators, amphotericin B, voriconazole, itraconazole, and caspofungin, were tested in vitro against a collection of susceptible and resistant clinical (n = 68) and environmental (n = 42) Fusarium isolates. Amphotericin B (0.8 &mu;g/mL) had the lowest geometric mean (GM) MICs/MECs values followed by miltefosine (1.44 &mu;g/mL), voriconazole (2.15 &mu;g/mL), caspofungin (7.23 &mu;g/mL), and itraconazole (14.19 &mu;g/mL). Miltefosine was the most effective agent against Fusarium isolates after amphotericin B indicating that miltefosine has the potential to be studied as a novel treatment for Fusarium infections

Social and intelligent applications for future cities: Current advances

[EN] Cities face many challenges concerning their management, security, transportation, public health, the distribution of resources, sustainability, energy efficiency, and many more. As cities grow larger, it is only expected that these problems become more acute and, therefore, they will need solutions to tackle or smooth these problems. With the rise of technologies such as artificial intelligence and the increasing number of social applications that allow citizens to participate in the urban digital ecosystem, researchers and policymakers have seen an opportunity in the application of these technologies to tackle urban challenges. In this editorial article, we review some relevant contributions to this special issue to social and intelligent applications for future cities.Sanchez-Anguix, V.; Chao, K.; Novais, P.; Boissier, O.; Julian Inglada, VJ. (2021). Social and intelligent applications for future cities: Current advances. Future Generation Computer Systems. 114:181-184. https://doi.org/10.1016/j.future.2020.07.055S181184114Diez, C., Palanca, J., Sanchez-Anguix, V., Heras, S., Giret, A., & Julián, V. (2019). Towards a Persuasive Recommender for Bike Sharing Systems: A Defeasible Argumentation Approach. Energies, 12(4), 662. doi:10.3390/en12040662Robu, V., Chalkiadakis, G., Kota, R., Rogers, A., & Jennings, N. R. (2016). Rewarding cooperative virtual power plant formation using scoring rules. Energy, 117, 19-28. doi:10.1016/j.energy.2016.10.077Al Nuaimi, E., Al Neyadi, H., Mohamed, N., & Al-Jaroodi, J. (2015). Applications of big data to smart cities. Journal of Internet Services and Applications, 6(1). doi:10.1186/s13174-015-0041-5Serrano, E., & Bajo, J. (2019). Deep neural network architectures for social services diagnosis in smart cities. Future Generation Computer Systems, 100, 122-131. doi:10.1016/j.future.2019.05.034Wang, L., Zhen, H., Fang, X., Wan, S., Ding, W., & Guo, Y. (2019). A unified two-parallel-branch deep neural network for joint gland contour and segmentation learning. Future Generation Computer Systems, 100, 316-324. doi:10.1016/j.future.2019.05.035Ojagh, S., Malek, M. R., Saeedi, S., & Liang, S. (2020). A location-based orientation-aware recommender system using IoT smart devices and Social Networks. Future Generation Computer Systems, 108, 97-118. doi:10.1016/j.future.2020.02.041Jiao, X., Xiao, Y., Zheng, W., Wang, H., & Hsu, C.-H. (2019). A novel next new point-of-interest recommendation system based on simulated user travel decision-making process. Future Generation Computer Systems, 100, 982-993. doi:10.1016/j.future.2019.05.065Hosseinpour, M., Malek, M. R., & Claramunt, C. (2019). Socio-spatial influence maximization in location-based social networks. Future Generation Computer Systems, 101, 304-314. doi:10.1016/j.future.2019.06.024Palanca, J., Jordán, J., Bajo, J., & Botti, V. (2020). An energy-aware algorithm for electric vehicle infrastructures in smart cities. Future Generation Computer Systems, 108, 454-466. doi:10.1016/j.future.2020.03.001Rodríguez, L., Palanca, J., del Val, E., & Rebollo, M. (2020). Analyzing urban mobility paths based on users’ activity in social networks. Future Generation Computer Systems, 102, 333-346. doi:10.1016/j.future.2019.07.072Saberi, Z., Saberi, M., Hussain, O., & Chang, E. (2019). Stackelberg model based game theory approach for assortment and selling price planning for small scale online retailers. Future Generation Computer Systems, 100, 1088-1102. doi:10.1016/j.future.2019.05.066Güngör, O., Akşanlı, B., & Aydoğan, R. (2019). Algorithm selection and combining multiple learners for residential energy prediction. Future Generation Computer Systems, 99, 391-400. doi:10.1016/j.future.2019.04.018Luo, H., Cai, H., Yu, H., Sun, Y., Bi, Z., & Jiang, L. (2019). A short-term energy prediction system based on edge computing for smart city. Future Generation Computer Systems, 101, 444-457. doi:10.1016/j.future.2019.06.030Levinger, C., Hazon, N., & Azaria, A. (2020). Human satisfaction as the ultimate goal in ridesharing. Future Generation Computer Systems, 112, 176-184. doi:10.1016/j.future.2020.05.028Sánchez, A. J., Rodríguez, S., de la Prieta, F., & González, A. (2019). Adaptive interface ecosystems in smart cities control systems. Future Generation Computer Systems, 101, 605-620. doi:10.1016/j.future.2019.06.029Aghili, S. F., Mala, H., Kaliyar, P., & Conti, M. (2019). SecLAP: Secure and lightweight RFID authentication protocol for Medical IoT. Future Generation Computer Systems, 101, 621-634. doi:10.1016/j.future.2019.07.004Sittón-Candanedo, I., Alonso, R. S., Corchado, J. M., Rodríguez-González, S., & Casado-Vara, R. (2019). A review of edge computing reference architectures and a new global edge proposal. Future Generation Computer Systems, 99, 278-294. doi:10.1016/j.future.2019.04.016Liu, W., Guo, J., Yao, F., & Chen, D. (2020). Adaptive protocol generation for group collaborative in smart medical waste transportation. Future Generation Computer Systems, 110, 167-180. doi:10.1016/j.future.2020.04.003De la Prieta, F., Rodríguez-González, S., Chamoso, P., Corchado, J. M., & Bajo, J. (2019). Survey of agent-based cloud computing applications. Future Generation Computer Systems, 100, 223-236. doi:10.1016/j.future.2019.04.037Vahdat-Nejad, H., Asani, E., Mahmoodian, Z., & Mohseni, M. H. (2019). Context-aware computing for mobile crowd sensing: A survey. Future Generation Computer Systems, 99, 321-332. doi:10.1016/j.future.2019.04.052Raza, M., Hussain, F. K., Hussain, O. K., Zhao, M., & Rehman, Z. ur. (2019). A comparative analysis of machine learning models for quality pillar assessment of SaaS services by multi-class text classification of users’ reviews. Future Generation Computer Systems, 101, 341-371. doi:10.1016/j.future.2019.06.022Costa, D. G., & de Oliveira, F. P. (2020). A prioritization approach for optimization of multiple concurrent sensing applications in smart cities. Future Generation Computer Systems, 108, 228-243. doi:10.1016/j.future.2020.02.067Ahuja, K., & Khosla, A. (2019). A novel framework for data acquisition and ubiquitous communication provisioning in smart cities. Future Generation Computer Systems, 101, 785-803. doi:10.1016/j.future.2019.07.029Qin, P., & Guo, J. (2020). A novel machine natural language mediation for semantic document exchange in smart city. Future Generation Computer Systems, 102, 810-826. doi:10.1016/j.future.2019.07.028Ma, S.-P., Fan, C.-Y., Chuang, Y., Liu, I.-H., & Lan, C.-W. (2019). Graph-based and scenario-driven microservice analysis, retrieval, and testing. Future Generation Computer Systems, 100, 724-735. doi:10.1016/j.future.2019.05.04