A PLL control for self-tuning of parallel wireless power transfer receivers utilizing switch-mode gyrator emulated inductors

In multiple receivers wireless power transfer (WPT) systems, it is preferable to retune the resonant frequency of every receiver to the transmitter operating frequency in front of frequency mismatches. This paper discusses a proposal for electronic tuning for WPT receivers by means of a variable active switch-mode inductance. The proposed method benefits from the gyrator concept to emulate a variable inductance. Instead of the conventional approach of linear amplifier based implementation of a gyrator, a switch-mode gyrator circuit is exploited for more efficient operation. Additionally, a PLL-like control is presented to enable self-tuning for the receiver resonant tank. Furthermore, a design-space characterization for the system dynamic behavior has been discussed to show the control robustness and the instabilities (including slow-scale and fast-scale chaotic instabilities) it may undergo.Peer ReviewedPostprint (published version

Microstructural quantification of collagen fiber orientations and its integration in constitutive modeling of the porcine carotid artery

Background Mechanical characteristics of vascular tissue may play a role in different arterial pathologies, which, amongst others, requires robust constitutive descriptions to capture the vessel wall’s anisotropic and non-linear properties.Specifically, the complex 3D network of collagen and its interaction with other structural elements has a dominating effect of arterial properties at higher stress levels.The aim of this study is to collect quantitative collagen organization as well as mechanical properties to facilitate structural constitutive models for the porcine carotid artery.This helps the understanding of the mechanics of swine carotid arteries, being a standard in clinical hypothesis testing, in endovascular preclinical trials for example. Method Porcine common carotid arteries (n = 10) were harvested and used to (i) characterize the collagen fiber organization with polarized light microscopy, and (ii) the biaxial mechanical properties by inflation testing.The collagen organization was quantified by the Bingham orientation density function (ODF), which in turn was integrated in a structural constitutive model of the vessel wall.A one-layered and thick-walled model was used to estimate mechanical constitutive parameters by least-square fitting the recorded in vitro inflation test results.Finally, uniaxial data published elsewhere were used to validate the mean collagen organization described by the Bingham ODF. Results Thick collagen fibers, i.e.the most mechanically relevant structure, in the common carotid artery are dispersed around the circumferential direction.In addition, almost all samples showed two distinct families of collagen fibers at different elevation, but not azimuthal, angles.Collagen fiber organization could be accurately represented by the Bingham ODF (¿1,2,3=[13.5,0.0,25.2] and ¿1,2,3=[14.7,0.0,26.6]; average error of about 5%), and their integration into a structural constitutive model captured the inflation characteristics of individual carotid artery samples.Specifically, only four mechanical parameters were required to reasonably (average error from 14% to 38%) cover the experimental data over a wide range of axial and circumferential stretches.However, it was critical to account for fibrilar links between thick collagen fibers.Finally, the mean Bingham ODF provide also good approximation to uniaxial experimental data. Conclusions The applied structural constitutive model, based on individually measured collagen orientation densities, was able to capture the biaxial properties of the common carotid artery. Since the model required coupling amongst thick collagen fibers, the collagen fiber orientations measured from polarized light microscopy, alone, seem to be insufficient structural information. Alternatively, a larger dispersion of collagen fiber orientations, that is likely to arise from analyzing larger wall sections, could have had a similar effect, i.e. could have avoided coupling amongst thick collagen fibers.Peer ReviewedPostprint (author's final draft

Ultrasound- assisted supercritical CO2 treatment in continuous regime: Appliaction in Sacharomyces cerevisiae inactivation

[EN] Laboratory continuous regime equipment was designed and built for supercritical CO2 microbial inactivation assisted by high power ultrasound (SC-CO2-HPU). Apple juice, previously inoculated with 1-10x107 CFU/ml of Saccharomyces cerevisiae, was treated in the equipment at different juice residence times (3.06-9.2 min), temperatures (31-41 °C) and pressures (100-300 bars). Inactivation ratios were fitted to a hybrid (boolean-real) model in order to study the effect of the process variables. The maximum inactivation achieved by the system was 7.8 log-cycles. The hybrid model demonstrated that HPU has a significant effect on inactivation after shorter residence times. A multi-objective optimization performed with the hybrid model showed that 6.8 log cycles of inactivation could be obtained after a minimum residence time (3.1 min) with HPU application, whereas under the same conditions but without HPU, the inactivation would be 4.3 log-cycles. Therefore, the ultrasound assisted continuous system has shown a great potential for microbial inactivation using SC-CO2 under mild process conditions.This work was supported by the PROMETEOII\2014\005 project financed by the Generalitat Valenciana (Conselleria d'Educacio, Cultura i Esport, Valencia, Spain). The authors acknowledge the Consejo Nacional de Ciencia y Tecnologia (CONACyT/N. 218273) for the scholarship awarded to PhD Student Paniagua-Martinez, I. The authors especially wish to thank Eng. Ramon Pena for his technical assistance in the development of the equipment.Paniagua-Martínez, I.; Mulet Pons, A.; García-Alvarado, M.; Benedito Fort, JJ. (2016). Ultrasound- assisted supercritical CO2 treatment in continuous regime: Appliaction in Sacharomyces cerevisiae inactivation. Journal of Food Engineering. 181:42-49. https://doi.org/10.1016/j.jfoodeng.2016.02.024S424918

Enhance wine production potential by using fresh and dried red grape and blueberry mixtures with different yeast strains for fermentation

Red grapes and blueberries are known for their high content of bioactive compounds and antioxidant properties. In Mediterranean winemaking, traditional sun-drying can be replaced by controlled-airflow-chamber-drying, which provides better quality, higher phenolic content, and increased antioxidants. This study aimed to increase the sugar content and phenolic compounds of the must by drying the fruits to fifty per cent of their original moisture content. Two musts were prepared: the first one was prepared by combining fresh red grapes and dried blueberries (M1), while the other was created using dried red grapes and fresh blueberries (M2), followed by fermentation at 25 °C with M05 Mead and X5 yeast strains. The M2 must showed the highest levels of phenolic compounds, red color (A520), total anthocyanins, and antioxidant activity. During fermentation, the anthocyanin content increased mainly in the dried blueberry macerates, where it increased between 4- to 5.5-fold. More bioactive compounds were extracted from the wines produced using yeast inoculation despite the shorter maceration times. A sensory analysis demonstrated consumers’ acceptance of the wines in terms of color, flavor, and aroma. In conclusion, the use of red grapes in the production of blueberry red wine proved to be effective, providing higher sugar and must yields, while the dried fruits improved the fermentable sugar content obtaining wines with an alcoholic content between 10 and 11% (v/v). The higher levels of bioactive compounds increased the antioxidant capacity of the resulting red fruit wines

Composite resins : A review of the materials and clinical indications

The aim of this work is to present the different components of the composites currently used in dentistry and furnish dentists with a basis that can provide criteria for choosing one or another to suit their therapeutic requirements. Most composites used in dentistry are hybrid materials, so-called because they are composed of polymer groups reinforced by an inorganic phase of glass fillers with different compositions, particle sizes and fill percentages. Flowable or condensable composites have attempted to provide an answer to certain functional requirements, although they have not been too successful at improving properties. Turning to polymerisation initiators, both halogen lamps, whether conventional or high intensity, and LED curing lights which provide a gradual increase in light intensity are very useful for reducing shrinkage of the composite material. The clinical choice of a composite must consider whether priority should be given to mechanical or aesthetic requirements: if mechanical considerations are paramount the material with the greatest volume of filler will be chosen; if aesthetic considerations predominate, particle size will be the most important factor. Additional components such as opaques and tints make it possible to improve the aesthetic results. Equally, the spread of other therapeutic procedures, such as tooth bleaching, has made it necessary to design composite materials in shades that are suitable for the special colour situations found in teeth treated by these methods

Gene therapy in the management of oral cancer: Review of the literature

Gene therapy essentially consists of introducing specific genetic material into target cells without producing toxic effects on surrounding tissue. Advances over recent decades in the surgical, radiotherapeutic and chemotherapeutic treatment of oral cancer patients have not produced a significant improvement in patient survival. Increasing interest is being shown in developing novel therapies to reverse oral epithelial dysplastic lesions. This review provides an update on transfer techniques, therapeutic strategies, and the clinical applications and limitations of gene therapy in the management of oral cancer and precancer. We highlight the combination of gene therapy with chemotherapy (e.g., 5-Fluoracil) and immunotherapy, given the promising results obtained in the use of adenovirus to act at altered gene level (e.g., p53). Other techniques such as suicide gene therapy, use of oncolytic viruses or the use of antisense RNA have shown positive although very preliminary results. Therefore, further research into these promising gene therapy techniques is required to assess their true efficacy and safety in the management of these lesions

Deep learning for agricultural land use classification from Sentinel-2

[ES] En el campo de la teledetección se ha producido recientemente un incremento del uso de técnicas de aprendizaje profundo (deep learning). Estos algoritmos se utilizan con éxito principalmente en la estimación de parámetros y en la clasificación de imágenes. Sin embargo, se han realizado pocos esfuerzos encaminados a su comprensión, lo que lleva a ejecutarlos como si fueran “cajas negras”. Este trabajo pretende evaluar el rendimiento y acercarnos al entendimiento de un algoritmo de aprendizaje profundo, basado en una red recurrente bidireccional de memoria corta a largo plazo (2-BiLSTM), a través de un ejemplo de clasificación de usos de suelo agrícola de la Comunidad Valenciana dentro del marco de trabajo de la política agraria común (PAC) a partir de series temporales de imágenes Sentinel-2. En concreto, se ha comparado con otros algoritmos como los árboles de decisión (DT), los k-vecinos más cercanos (k-NN), redes neuronales (NN), máquinas de soporte vectorial (SVM) y bosques aleatorios (RF) para evaluar su precisión. Se comprueba que su precisión (98,6% de acierto global) es superior a la del resto en todos los casos. Por otra parte, se ha indagado cómo actúa el clasificador en función del tiempo y de los predictores utilizados. Este análisis pone de manifiesto que, sobre el área de estudio, la información espectral y espacial derivada de las bandas del rojo e infrarrojo cercano, y las imágenes correspondientes a las fechas del período de verano, son la fuente de información más relevante utilizada por la red en la clasificación. Estos resultados abren la puerta a nuevos estudios en el ámbito de la explicabilidad de los resultados proporcionados por los algoritmos de aprendizaje profundo en aplicaciones de teledetección.[EN] The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. 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Disponible en http://www.aet.org.es/?q=revista8-4González-Guerrero, O., y Pons, X., 2020. The 2017 Land Use/Land Cover Map of Catalonia based on Sentinel-2 images and auxiliary data. Revista de Teledetección, 55, 81-92. https://doi.org/10.4995/raet.2020.13112Gregrio, A., Jansen, J. 2000. Land cover classification system (LCCS); Classification concepts and user manual for software version 2. Roma: FAO.Griffiths, P., Nendel, C., Hostert, P. 2019. Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sensing of Environment, 220, 135-151. https://doi.org/10.1016/j.rse.2018.10.031Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.Z. 2019. XAI-Explainable artificial intelligence. Science Robotics, 4(37). https://doi.org/10.1126/scirobotics.aay7120Haralick, R.M., Shanmugam, K., Dinstein, I.H. 1973. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, 3(6), 610-621. https://doi.org/10.1109/TSMC.1973.4309314Haykin, S. 1994. Neural networks: a comprehensive foundation. River: Prentice Hall.Immitzer, M., Vuolo, F., Atzberger, C. 2016. First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sensing, 8(3), 166. https://doi.org/10.3390/rs8030166Hochreiter, S., Schmidhuber, J. 1997. Long short-term memory. Neural computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A. 2017. Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778-782. https://doi.org/10.1109/LGRS.2017.2681128Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., Johnson, B.A. 2019. Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166-177. https://doi.org/10.1016/j.isprsjprs.2019.04.015Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P. 2016. Convolutional neural networks for large-scale remotesensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(2), 645-657. https://doi.org/10.1109/TGRS.2016.2612821Mikolov, T., Kombrink, S., Burget, L., Černocký, J., Khudanpur, S. 2011. Extensions of recurrent neural network language model. En 2011 IEEE International Conference on acoustics, speech and signal processing (ICASSP). Praga, República Checa, 22-27 Mayo. pp. 5528-5531. https://doi.org/10.1109/ICASSP.2011.5947611Montavon, G., Samek, W., Müller, K.R. 2018. Methods for interpreting and understanding deep neural networks. Digital Signal Processing, 73, 1-15. https://doi.org/10.1016/j.dsp.2017.10.011Mou, L., Ghamisi, P., Zhu, X.X. 2017. Deep recurrent neural networks for hyperspectral image classification. 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Automated classification of crop types and condition in a mediterranean area using a fine-tuned convolutional neural network. En The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. Niza, Francia, 31 Agosto - 2 Septiembre (en línea). pp. 1061-1068. https://doi.org/10.5194/isprs-archivesXLIII-B3-2020-1061-2020Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., Muller, K.R. (Eds.). 2020. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Cham: Springer Nature. https://doi.org/10.1007/978-3-030-28954-6Schmedtmann, J., Campagnolo, M.L. 2015. Reliable crop identification with satellite imagery in the context of common agriculture policy subsidy control. Remote Sensing, 7(7), 9325-9346. https://doi.org/10.3390/rs70709325Schuster, M., Paliwal, K.K. 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673-2681. https://doi.org/10.1109/78.650093Sitokonstantinou, V., Papoutsis, I., Kontoes, C., Lafarga Arnal, A., Armesto Andrés, A.P., Garraza Zurbano, J.A. 2018. Scalable parcel-based crop identification scheme using sentinel-2 data time-series for the monitoring of the common agricultural policy. Remote Sensing, 10(6), 911. https://doi.org/10.3390/rs10060911Story, M., Congalton, R.G. 1986. Accuracy assessment: a user's perspective. Photogrammetric Engineering and Remote Sensing, 52(3), 397-399.Tapsall, B., Milenov, P., Tasdemir, K. 2010. Analysis of RapidEye imagery for annual landcover mapping as an aid to European Union (EU) common agricultural polic. En ISPRS Technical Commission VII Symposium - 100 Years ISPRS. Viena, Austria, 5-7 Julio. pp. 568-573.Van Den Oord, A., Kalchbrenner, N., Kavukcuoglu, K. 2016. Pixel recurrent neural networks. En Proceedings of the 33rd International Conference on International Conference on Machine Learning. New York, EEUU., 20-22 Junio. pp. 1747-1756.Vuolo, F., Neuwirth, M., Immitzer, M., Atzberger, C., Ng, W.T. 2018. How much does multi-temporal Sentinel-2 data improve crop type classification? International Journal of Applied Earth Observation and Geoinformation, 72, 122-130. https://doi.org/10.1016/j.jag.2018.06.007Wardlow, B.D., Egbert, S.L., Kastens, J.H. 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sensing of Environment, 108(3), 290-310. https://doi.org/10.1016/j.rse.2006.11.021Watson, R.T., Noble, I.R., Bolin, B., Ravindranath, N.H., Verardo, D.J., Dokken, D.J. 2000. Land use, land-use change and forestry: a special report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.Zhan, X., Defries, R., Townshend, J.R.G., Dimiceli, C., Hansen, M., Huang, C., Sohlberg, R. 2000. The 250 m global land cover change product from the Moderate Resolution Imaging Spectroradiometer of NASA's Earth Observing System. International Journal of Remote Sensing, 21(6-7), 1433-1460. https://doi.org/10.1080/014311600210254Zhang, L., Zhang, L., Du, B. 2016. Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2), 22-40. https://doi.org/10.1109/MGRS.2016.2540798Zhong, L., Hu, L., Zhou, H. 2019. Deep learning based multi-temporal crop classification. Remote sensing of environment, 221, 430-443. https://doi.org/10.1016/j.rse.2018.11.032Zhu, Z., Woodcock, C.E. 2014. Continuous change detection and classification of land cover using all available Landsat data. 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Hábitos de sueño y problemas relacionados con el sueño en adolescentes: relación con el rendimiento escolar

ObjetivoConocer la prevalencia de trastornos de sueño en los adolescentes. Describir los hábitos de sueño de los adolescentes y su relación con los trastornos del sueño y los factores asociados. Conocer la relación entre los trastornos del sueño y/o los hábitos de sueño inadecuados con el rendimiento escolar.DiseñoEstudio observacional, descriptivo y transversal.EmplazamientoInstitutos de enseñanza secundaria obligatoria (ESO) de la ciudad de Cuenca.ParticipantesUn total de 1.293 alumnos escolarizados en primero y cuarto cursos de ESO.Mediciones principalesHábitos de sueño en días lectivos y fines de semana y prevalencia de trastornos del sueño medidos mediante un cuestionario estructurado con preguntas abiertas y cerradas, autoadministrado y anónimo. Se determinó el rendimiento escolar de los alumnos y su relación con los hábitos y trastornos de sueño.ResultadosDe los 1.293 alumnos matriculados, completaron la encuesta 1.155 (89,33%), 537 (45,9%) chicos y 618 (54,1%) chicas, con una media de edad de 14 años (rango, 11-18 años). Los días laborables se acuestan en promedio a las 23.17 y se levantan a las 7.46 (tiempo medio, 8 h y 18 min) y los fines de semana se acuestan a la 1.02 y se levantan a las 10.42 (tiempo medio, 9 h y 40 min). El 45,4% declara dormir mal la noche del domingo al lunes. El promedio de asignaturas suspendidas es mayor en los adolescentes con queja de sueño (2,28 frente a 1,91; p = 0,04), los que se levantan cansados (2,17 frente a 1,97; p = 0,048) y los que tienen somnolencia diurnal (2,17 frente a 1,75; p = 0,004).ConclusionesEl horario escolar conlleva deuda de sueño durante la semana que se recupera parcialmente el fin de semana. En los fines de semana se produce una rotura en los hábitos de sueño de los adolescentes. Los adolescentes con problemas relacionados con el sueño muestran peor rendimiento escolar.ObjectiveTo determine the prevalence of sleep disorders in adolescence.To describe sleeping habits of adolescents in relation to sleep disorders and associated factors. To determine the relation between sleep disorders/inappropiate sleeping habits and school performance.DesignObservational, descriptive, crosssectional study.SettingSecondary school of Cuenca (city in Spain).Participants1293 school children of first and fourth curses of secondary education.Main measuresStructured questionnaire with opened and closed questions on sleeping habits during weekdays and at weekends and sleep disorders to be answered by the adolescents anonymously and on their own. Student's school performance with relation with to sleeping habits and sleep disorders were determined.Results1155 students out of 1293 (response rate 89.33%) answered the questionnaire, 537 (45.9%) boys and 618 (54.1%) girls, 14 years old on average (between 11-18 years). On weekdays students went to bed at 23.17 h and got up at 7.46 h (average sleeping time =8 hours and 18 minutes). At weekends they went to bed at 1.02 h and got up at 10.42 h (average sleeping time =9 hours and 40 minutes). 45.4% of students said to sleep badly on Sunday night's.On average the number of subjects failed in class is higher with adolescents who complain about sleep (2.28 vs 1.91; P=.04), who are tired at waking up time (2.17 vs 1.97; P=.048) and who have morning sleepiness (2.17 vs 1.75; P=.004).ConclusionsSchools hours cause deficitsleeping time during weekdays which is partly made up for at weekend. At weekends there is an interruption of the adolescent's sleeping habits. School performance of adolescents with sleep disorders is lower

Estudio epidemiológico del virus de la hepatitis C en nuestra población y cobertura vacunal

ObjetivoEstudio epidemiológico de los pacientes con hepatitis C crónica y su estado serológico en cuanto a los virus de la hepatitis A (VHA) y B (VHB).DiseñoEstudio transversal descriptivo.EmplazamientoDos centros de salud urbanos.ParticipantesUn total de 291 pacientes afectados de hepatitis C crónica.Mediciones principalesVariables: edad, sexo, año y motivo del diagnóstico, antecedentes personales, enolismo, estado serológico de los VHA, VHB y VIH, y concentración de transaminasas inicial.ResultadosEdad media: 55 ± 16. Sexo: 52% mujeres. Prevalencia: 0,98%. Motivo del diagnóstico: 41% estudio de salud, 15% estudio de enfermedad hepática, 18% estudio de otras enfermedades.Antecedentes personales: intervención quirúrgica 37,5%, usuarios de drogas por vía parenteral 21,4%, transfusión 14%, conducta de riesgo sexual 2,4%, material sanitario de más de un uso 2,4%, familiar VHC positivo 1,4%, no consta ningún antecedente personal 26,5%. Enolismo: 17,9%. Media de transaminasas: AST 79,7 ± 100 (9–920), ALT 114,8 ± 160 (6–1.640). Estado serológico VHB: inmunidad natural 22%, crónica 9%, inmunidad vacunal 3%, negativo 44%, no consta 21%. Estado serológico VHA: inmunidad natural 2%, inmunidad vacunal 2,5%, negativo 9%, no consta 87%. VIH-positivos: 4,5%.ConclusionesLa prevalencia fue inferior a la esperada. Es necesario mejorar el conocimiento del estado serológico, sobre todo del VHA. Sería importante aumentar el grado de cobertura vacunal de los VHB y VHA en estos pacientes.ObjectiveEpidemiological study of patients with chronic hepatitis C and its serological status in relation to the hepatitis A (HA) and B (HB) viruses.DesignDescriptive cross-sectional study.SettingTwo urban health centres.Participants291 patients with chronic hepatitis C.Main measurementsVariables: age, sex, year and reason for diagnosis, personal histories, alcohol intake, serological status of the HA and HB viruses and HIV, and initial level of transaminases.ResultsMean age, 55±16. Sex, 52% women. Prevalence, 0.98%. Reason for diagnosis, 41% health study, 15% study of hepatic pathology, 18% study of other pathologies. Personal histories, surgical intervention, 37.5%; intravenous drug users, 21.4%; transfusion, 14%; high-risk sexual conduct, 2.4%; health material used more than once, 2.4%; family member HC positive, 1.4%; no personal history recorded, 26.5%. Alcoholism, 17.9%. Mean transaminases: AST, 79.7±100 (9–920); ALT, 114.8±160 (6–1640). HB serological status: natural immunity, 22%; chronic, 9%; vaccine immunity, 3%; negative, 44%; not recorded, 21%. HA serological status: natural immunity, 2%; vaccine immunity, 2.5%; negative, 9%; not recorded, 87%. HIV-positive: 4.5%.ConclusionsPrevalence was below the expected level. Knowledge of serological status needs to be improved, especially for HA. The degree of vaccine coverage in these patients for HA and HB should be increased