Analysis of a gaussian process and feed-forward neural networks based filter for forecasting short rainfall time series

In this paper, an analysis of kernel (GP) and feed-forward neural networks (FFNN) based filter to forecast short rainfall time series is presented. For the FFNN, the learning rule used to adjust the filter weights is based on the Levenberg-Marquardt method and Bayesian approach by the assumption of the prior distributions. In addition, a heuristic law is used to relate the time series roughness with the tuning process. The input patterns for both NN-based and kernel models are the values of rainfall time series after applying a time-delay operator. Hence, the NN´s outputs will tend to approximate the current value of the time series. The time lagged inputs of the GP and their covariance functions are both determined via a multicriteria genetic algorithm, called NSGA-II. The optimization criteria are the quantity of inputs and the filter´s performance on the known data which leads to Pareto optimal solutions. Both filters -FFNN and GP Kernel- are tested over a rainfall time series obtained from La Sevillana establishment. This work proposed a comparison of well-known filter referenced in early work where the contribution resides in the analysis of the best horizon of the forecasted rainfall time series proposed by Bayesian adjustment. The performance attained is shown by the forecast of the next 15 months values of rainfall time series from La Sevillana establishment located in (-31° 1´22.46"S, 62°40´9.57"O) Balnearia, Cordoba, Argentina.http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6706741&isnumber=6706705Fil: Rodriguez Rivero, C. Universidad Nacional de Córdoba; Argentina.Fil: Pucheta, J. Universidad Nacional de Córdoba; Argentina.Fil: Patiño, H. Universidad Nacional de Córdoba; Argentina.Fil: Baumgartner, J. Universidad Nacional de Córdoba; Argentina.Fil: Laboret, S. Universidad Nacional de Córdoba; Argentina.Fil: Sauchelli, V. Universidad Nacional de Córdoba; Argentina.Sistemas de Automatización y Contro

Electrospinning Technique as a Powerful Tool for the Design of Superhydrophobic Surfaces

The development of surface engineering techniques to tune-up the composition, structure, and function of materials surfaces is a permanent challenge for the scientific community. In this chapter, the electrospinning process is proposed as a versatile technique for the development of highly hydrophobic or even superhydrophobic surfaces. Electrospinning makes possible the fabrication of nanostructured ultra-thin fibers, denoted as electrospun nanofibers (ENFs), from a wide range of polymeric materials that can be deposited on any type of surface with arbitrary geometry. In addition, by tuning the deposition parameters (mostly applied voltage, flow rate, and distance between collector/needle) in combination with the chemical structure of the polymeric precursor (functional groups with hydrophobic behavior) and its resultant viscosity, it is possible to obtain nanofibers with highly porous surface. As a result, functionalized surfaces with water-repellent behavior can be implemented in a wide variety of industrial applications such as in corrosion resistance, high efficient water-oil separation, surgical meshes in biomedical applications, or even in energy systems for long-term efficiency of dye-sensitized solar cells, among others

Smartphone sensors for monitoring cancer-related Quality of Life: App design, EORTC QLQ-C30 mapping and feasibility study in healthy subjects

[EN] Quality of life (QoL) indicators are now being adopted as clinical outcomes in clinical trials on cancer treatments. Technology-free daily monitoring of patients is complicated, time-consuming and expensive due to the need for vast amounts of resources and personnel. The alternative method of using the patients¿ own phones could reduce the burden of continuous monitoring of cancer patients in clinical trials. This paper proposes monitoring the patients¿ QoL by gathering data from their own phones. We considered that the continuous multiparametric acquisition of movement, location, phone calls, conversations and data use could be employed to simultaneously monitor their physical, psychological, social and environmental aspects. An open access phone app was developed (Human Dynamics Reporting Service (HDRS)) to implement this approach. We here propose a novel mapping between the standardized QoL items for these patients, the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and define HDRS monitoring indicators. A pilot study with university volunteers verified the plausibility of detecting human activity indicators directly related to QoL.Funding for this study was provided by the authors' various departments, and partially by the CrowdHealth Project (Collective Wisdom Driving Public Health Policies (727560)) and the MTS4up project (DPI2016-80054-R).Asensio Cuesta, S.; Sánchez-García, Á.; Conejero, JA.; Sáez Silvestre, C.; Rivero-Rodriguez, A.; Garcia-Gomez, JM. (2019). Smartphone sensors for monitoring cancer-related Quality of Life: App design, EORTC QLQ-C30 mapping and feasibility study in healthy subjects. International Journal of Environmental research and Public Health. 16(3):1-18. https://doi.org/10.3390/ijerph16030461S118163Number of Smartphone Users Worldwide from 2014 to 2020 (in Billions)https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/Mirkovic, J., Kaufman, D. R., & Ruland, C. M. (2014). Supporting Cancer Patients in Illness Management: Usability Evaluation of a Mobile App. JMIR mHealth and uHealth, 2(3), e33. doi:10.2196/mhealth.3359Xing Su, Hanghang Tong, & Ping Ji. (2014). Activity recognition with smartphone sensors. Tsinghua Science and Technology, 19(3), 235-249. doi:10.1109/tst.2014.6838194Schmitz Weiss, A. (2013). Exploring News Apps and Location-Based Services on the Smartphone. Journalism & Mass Communication Quarterly, 90(3), 435-456. doi:10.1177/1077699013493788Higgins, J. P. (2016). Smartphone Applications for Patients’ Health and Fitness. The American Journal of Medicine, 129(1), 11-19. doi:10.1016/j.amjmed.2015.05.038Rivenson, Y., Ceylan Koydemir, H., Wang, H., Wei, Z., Ren, Z., Günaydın, H., … Ozcan, A. (2018). Deep Learning Enhanced Mobile-Phone Microscopy. ACS Photonics, 5(6), 2354-2364. doi:10.1021/acsphotonics.8b00146Priye, A., Ball, C. S., & Meagher, R. J. (2018). Colorimetric-Luminance Readout for Quantitative Analysis of Fluorescence Signals with a Smartphone CMOS Sensor. Analytical Chemistry, 90(21), 12385-12389. doi:10.1021/acs.analchem.8b03521Measuring Quality of Life for Cancer Patients: Where Are We Today and Where Are We Headed Tomorrow?http://blog.mdsol.com/measuring-quality-of-life-for-cancer-patients-where-are-we-today-and-where-are-we-headed-tomorrow/Zulueta, J., Piscitello, A., Rasic, M., Easter, R., Babu, P., Langenecker, S. A., … Leow, A. (2018). Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study. Journal of Medical Internet Research, 20(7), e241. doi:10.2196/jmir.9775Caruso, R., GiuliaNanni, M., Riba, M. B., Sabato, S., & Grassi, L. (2017). Depressive Spectrum Disorders in Cancer: Diagnostic Issues and Intervention. A Critical Review. Current Psychiatry Reports, 19(6). doi:10.1007/s11920-017-0785-7THE WHOQOL GROUP. (1998). Development of the World Health Organization WHOQOL-BREF Quality of Life Assessment. Psychological Medicine, 28(3), 551-558. doi:10.1017/s0033291798006667Basic Issues Concerning Health-Related Quality of Life. (2017). Central European Journal of Urology, 70(2). doi:10.5173/ceju.2017.923Sloan, J. A. (2011). Metrics to Assess Quality of Life After Management of Early-Stage Lung Cancer. The Cancer Journal, 17(1), 63-67. doi:10.1097/ppo.0b013e31820e15dcBordoni, R., Ciardiello, F., von Pawel, J., Cortinovis, D., Karagiannis, T., Ballinger, M., … Rittmeyer, A. (2018). Patient-Reported Outcomes in OAK: A Phase III Study of Atezolizumab Versus Docetaxel in Advanced Non–Small-cell Lung Cancer. Clinical Lung Cancer, 19(5), 441-449.e4. doi:10.1016/j.cllc.2018.05.011Hartkopf, A. D., Graf, J., Simoes, E., Keilmann, L., Sickenberger, N., Gass, P., … Wallwiener, M. (2017). Electronic-Based Patient-Reported Outcomes: Willingness, Needs, and Barriers in Adjuvant and Metastatic Breast Cancer Patients. JMIR Cancer, 3(2), e11. doi:10.2196/cancer.6996Wallwiener, M., Matthies, L., Simoes, E., Keilmann, L., Hartkopf, A. D., Sokolov, A. N., … Brucker, S. Y. (2017). Reliability of an e-PRO Tool of EORTC QLQ-C30 for Measurement of Health-Related Quality of Life in Patients With Breast Cancer: Prospective Randomized Trial. Journal of Medical Internet Research, 19(9), e322. doi:10.2196/jmir.8210Gresham, G., Hendifar, A. E., Spiegel, B., Neeman, E., Tuli, R., Rimel, B. J., … Shinde, A. M. (2018). Wearable activity monitors to assess performance status and predict clinical outcomes in advanced cancer patients. npj Digital Medicine, 1(1). doi:10.1038/s41746-018-0032-6BOHANNON, R. W. (1997). Comfortable and maximum walking speed of adults aged 20—79 years: reference values and determinants. Age and Ageing, 26(1), 15-19. doi:10.1093/ageing/26.1.15Pérez-García, V. M., Fitzpatrick, S., Pérez-Romasanta, L. A., Pesic, M., Schucht, P., Arana, E., & Sánchez-Gómez, P. (2016). Applied mathematics and nonlinear sciences in the war on cancer. Applied Mathematics and Nonlinear Sciences, 1(2), 423-436. doi:10.21042/amns.2016.2.00036Shin, W., Song, S., Jung, S.-Y., Lee, E., Kim, Z., Moon, H.-G., … Lee, J. E. (2017). The association between physical activity and health-related quality of life among breast cancer survivors. Health and Quality of Life Outcomes, 15(1). doi:10.1186/s12955-017-0706-9Wearable Fitness Monitors Useful in Cancer Treatment, Study Findswww.sciencedaily.com/releases/2018/05/180501130856.htmBade, B. C., Brooks, M. C., Nietert, S. B., Ulmer, A., Thomas, D. D., Nietert, P. J., … Silvestri, G. A. (2016). Assessing the Correlation Between Physical Activity and Quality of Life in Advanced Lung Cancer. Integrative Cancer Therapies, 17(1), 73-79. doi:10.1177/1534735416684016Fortner, B. V., Stepanski, E. J., Wang, S. C., Kasprowicz, S., & Durrence, H. H. (2002). Sleep and Quality of Life in Breast Cancer Patients. Journal of Pain and Symptom Management, 24(5), 471-480. doi:10.1016/s0885-3924(02)00500-6Mishra, S. I., Scherer, R. W., Snyder, C., Geigle, P., & Gotay, C. (2014). Are Exercise Programs Effective for Improving Health-Related Quality of Life Among Cancer Survivors? A Systematic Review and Meta-Analysis. Oncology Nursing Forum, 41(6), E326-E342. doi:10.1188/14.onf.e326-e342Ratcliff, C. G., Lam, C. Y., Arun, B., Valero, V., & Cohen, L. (2014). Ecological momentary assessment of sleep, symptoms, and mood during chemotherapy for breast cancer. Psycho-Oncology, 23(11), 1220-1228. doi:10.1002/pon.3525Cox, S. M., Lane, A., & Volchenboum, S. L. (2018). Use of Wearable, Mobile, and Sensor Technology in Cancer Clinical Trials. JCO Clinical Cancer Informatics, (2), 1-11. doi:10.1200/cci.17.00147Brown, W., Yen, P.-Y., Rojas, M., & Schnall, R. (2013). Assessment of the Health IT Usability Evaluation Model (Health-ITUEM) for evaluating mobile health (mHealth) technology. Journal of Biomedical Informatics, 46(6), 1080-1087. doi:10.1016/j.jbi.2013.08.001Darlow, S., & Wen, K.-Y. (2016). Development testing of mobile health interventions for cancer patient self-management: A review. 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Cocreated Smartphone App to Improve the Quality of Life of Adolescents and Young Adults with Cancer (Kræftværket): Protocol for a Quantitative and Qualitative Evaluation. JMIR Research Protocols, 7(5), e10098. doi:10.2196/1009

An evaluation of the SENTiFIT 270 analyser for quantitation of faecal haemoglobin in the investigation of patients with suspected colorectal cancer.

BACKGROUND: An evaluation of SENTiFIT® 270 (Sentinel Diagnostics, Italy; Sysmex, Spain) analyser for the quantitation of faecal haemoglobin (f-Hb) was performed. METHODS: The analytical imprecision, linearity, carry over and f-Hb stability were determined. Evaluation of the diagnostic accuracy was performed on 487 patients. RESULTS: Within-run and between-run imprecision ranged 1.7%-5.1% and 3.8%-6.2%, respectively. Linearity studies revealed a mean recovery of 101.1% (standard deviation, 6.7%) for all dilutions. No carry over was detected below 7650 μg Hb/g faeces. Decay of f-Hb in refrigerated samples ranged 0.2%-0.5% per day. f-Hb in patients with advanced colorectal neoplasia (ACRN) (colorectal cancer [CRC] plus advanced adenoma [AA]) were significantly higher than from those with a normal colonoscopy. Sensitivity for ACRN at f-Hb cutoffs from 10 to 60 μg Hb/g faeces ranged from 28.9% (95% confidence interval [CI], 21.7%-37.2%) to 46.5% (95% CI, 38.1%-55%), the specificity ranged from 85% (95% CI, 82.3%-87.3%) to 93.2% (95% CI, 91.2%-94.8%), positive predictive values for detecting CRC and AA ranged from 11.6% (95% CI, 7.6%-17.2%) to 20.6% (95% CI, 13.3%-30.3%) and from 34.7% (95% CI, 28.1%-42%) to 42.3% (95% CI, 32.4%-52.7%), respectively, and the negative predictive value for ACRN ranged from 90.2% (95% CI, 87.9%-92.2%) to 88.4% (95% CI, 86%-90.4%). Using two samples per patient sensitivity increased with a slight decrease in specificity. CONCLUSIONS: The analytical and clinical performances of SENTiFIT assay demonstrate a specific and accurate test for detecting ACRN in symptomatic patients and those undergoing surveillance. KEYWORDS: adenoma; analyser evaluation; colorectal cancer; faecal haemoglobin; faecal immunochemical tes