The Effects of Early Intervention on the Expressive Language Outcomes of Children with Autism Spectrum Disorder: A Systematic Review

Background: Autism Spectrum Disorder (ASD) is characterized by persistent challenges in social communication as well as restricted and repetitive behaviors and is often observable in early childhood. Expressive language delays are common in young children diagnosed with ASD. Expressive language includes any form of communicative output, such as verbal language, sign language, and the use of alternative and augmentative communication (AAC). Early intervention, for the purpose of this systematic review, is defined as speech and language services provided before a child is 5 years (60 months) of age. Evidence suggests that early intervention can lead to positive outcomes in the symptoms of children with ASD. Objective: To determine whether early intervention of ASD in children between 0-59 months of age has positive effects on expressive language development. Methods: A systematic search of the PsychINFO, PubMED, CINAHL, ERIC, and LLBA database was conducted, followed by a qualitative analysis of relevant articles. Studies included monolingual (i.e., English) children who were diagnosed with ASD. Studies were systematically graded and processed using inter-rater procedures. Results: Fourteen articles were included based upon inclusionary criteria. The overall quality of the studies was moderate. The most widely used early intervention techniques included the Early Start Denver Model (ESDM) and Pivotal Response Training (PRT). Conclusions: Consistent high-interval (i.e., 25 hours per week), behaviorally-based early intervention (i.e., before 40 months) may lead to positive outcomes in expressive language development. Clinicians working with young children with ASD should implement behaviorally-based, empirically-supported interventions, such as ESDM or PRT. Future research should prioritize high-quality study designs (e.g., randomized control trials) with larger sample sizes of children diagnosed with ASD, which is necessary to discern a direct relationship between behaviorally-based early intervention and expressive language outcomes for children with ASD.https://scholarworks.uvm.edu/csdms/1008/thumbnail.jp

Calcium transport through the luminal membrane of the distal tubule. I. Interrelationship with sodium

Calcium transport through the luminal membrane of the distal tubule. I. Interrelationship with sodium.Calcium (Ca2+) transport by isolated luminal membranes from rabbit renal distal tubule has been characterized. Ca2+ uptake by these membrane vesicles exhibited saturation kinetics. In the absence of sodium (Na+) in the incubation medium, a low affinity system was observed with a KmCa2+ of 2.83 ± 0.64 mM and Vmax of 3.03 ± 0.48 pmoL/µg/10 sec. A second type of kinetics was also detected with a high affinity and a low velocity (KmCa2+ 0.04 ± 0.01 mM, Vmax 1.18 ± 0.22 pmol/µg/10 sec). The luminal membranes from proximal tubules showed a single system with a KmCa2+ of 0.49 ± 0.20 mM and Vmax of 1.26 ± 0.17 pmol/µg/10 sec. The presence of Na+ sharply decreased Ca2+ uptake by the high affinity system of the membranes from distal tubules, increasing the KmCa2+ to 0.07 mM ± 0.01 (P < 0.01) and decreasing the Vmax to 0.27 pmol/µg/10 sec (P < 0.005). This effect of Na+ was concentration-dependent, with a half-maximal effect at 38 mM Na+ and a Hill coefficient of 0.9. In contrast, Na+ had no effect on Ca2+ transport through the luminal membranes of proximal tubules nor on the low affinity system of the distal tubule. The composition of the intravesicular medium also influenced Ca2+ uptake by the membranes from distal tubules. Compared to mannitol, trans-Na+ or K+ significantly reduced Ca2+ transport. Finally, cis-K+ induced an increase in this transport. As found with Na+, the effect of K+ was concentration-dependent, with a Hill coefficient of 0.42. It is concluded that: 1) the luminal membrane of the distal tubule fundamentally differs from the brush border membrane of the proximal tubule; 2) Na+ has an inhibitory effect on Ca2+ uptake when applied on either side of the distal tubule membrane, and therefore probably binds at two different sites of the carriers in the membrane; 3) trans-K+ inhibits whereas cis-K+ enhances Ca2+ transport in this membrane; and 4) the actions of Na+ and K+ are not dependent upon any exchange mechanism

Predictive Model for Strawberry Bud Weevil (Coleoptera: Curculionidae) Adults in Strawberry Fields

Three different sampling methods (sweep net, D-Vac, tapping into a carton container) were evaluated for Anthonomus signatus Say in strawberry fields. The results suggest that sampling with a sweep net reflects population numbers best. A predictive model for adult abundance was developed to describe and predict population build-up. The strawberry fields used in the study were in their 2nd yr of production. Overwintering adults generally begin to appear in a strawberry field ≈300 cumulatitive degree-days (DD) calculated from 1 April at temperatures above 0&deg;C. These weevils attain maximum abundance anywhere from 500 to 670 DD. Within that interval, a treatment with cypermethrin or chlorpyriphos was effective against this pest. The summer generation attained maximum abundance anywhere from 1,250 to 1,650 DD. A treatment with chlorpyriphos at 1,679 DD reduced the summer generation of weevils and decreased clipped buds in the field the following yea

NALP1 in vitiligo-associated multiple autoimmune disease.

BACKGROUND: Autoimmune and autoinflammatory diseases involve interactions between genetic risk factors and environmental triggers. We searched for a gene on chromosome 17p13 that contributes to a group of epidemiologically associated autoimmune and autoinflammatory diseases. The group includes various combinations of generalized vitiligo, autoimmune thyroid disease, latent autoimmune diabetes in adults, rheumatoid arthritis, psoriasis, pernicious anemia, systemic lupus erythematosus, and Addison's disease. METHODS: We tested 177 single-nucleotide polymorphisms (SNPs) spanning the 17p13 linkage peak for association with disease and identified a strong candidate gene. We then sequenced DNA in and around the gene to identify additional SNPs. We carried out a second round of tests of association using some of these additional SNPs, thus elucidating the association with disease in the gene and its extended promoter region in fine detail. RESULTS: Association analyses resulted in our identifying as a candidate gene NALP1, which encodes NACHT leucine-rich-repeat protein 1, a regulator of the innate immune system. Fine-scale association mapping with the use of DNA from affected families and additional SNPs in and around NALP1 showed an association of specific variants with vitiligo alone, with an extended autoimmune and autoinflammatory disease phenotype, or with both. Conditional logistic-regression analysis of NALP1 SNPs indicated that at least two variants contribute independently to the risk of disease. CONCLUSIONS: DNA sequence variants in the NALP1 region are associated with the risk of several epidemiologically associated autoimmune and autoinflammatory diseases, implicating the innate immune system in the pathogenesis of these disorders

Fast Fourier Optimization: Sparsity Matters

Many interesting and fundamentally practical optimization problems, ranging from optics, to signal processing, to radar and acoustics, involve constraints on the Fourier transform of a function. It is well-known that the {\em fast Fourier transform} (fft) is a recursive algorithm that can dramatically improve the efficiency for computing the discrete Fourier transform. However, because it is recursive, it is difficult to embed into a linear optimization problem. In this paper, we explain the main idea behind the fast Fourier transform and show how to adapt it in such a manner as to make it encodable as constraints in an optimization problem. We demonstrate a real-world problem from the field of high-contrast imaging. On this problem, dramatic improvements are translated to an ability to solve problems with a much finer grid of discretized points. As we shall show, in general, the "fast Fourier" version of the optimization constraints produces a larger but sparser constraint matrix and therefore one can think of the fast Fourier transform as a method of sparsifying the constraints in an optimization problem, which is usually a good thing.Comment: 16 pages, 8 figure

Overview of JET results for optimising ITER operation

The JET 2019–2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major neutral beam injection upgrade providing record power in 2019–2020, and tested the technical and procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle (a) physics in the coming D–T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed shattered pellet injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design and operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D–T benefited from the highest D–D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER.This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014–2018 and 2019–2020 under Grant Agreement No. 633053.Peer ReviewedArticle signat per 1223 autors/autores: J. Mailloux1, N. Abid1, K. Abraham1, P. Abreu2, O. Adabonyan1, P. Adrich3, V. Afanasev4, M. Afzal1, T. Ahlgren5, L. Aho-Mantila6, N. Aiba7, M. Airila6, M. Akhtar1, R. Albanese8, M. Alderson-Martin1, D. Alegre9, S. Aleiferis10, A. Aleksa1, A.G. Alekseev11, E. Alessi12, P. Aleynikov13, J. Algualcil14, M. Ali1, M. Allinson1, B. Alper1, E. Alves2, G. Ambrosino8, R. Ambrosino8, V. Amosov15, E.Andersson Sunden16, P. Andrew13, B.M. Angelini17, C. Angioni18, I. Antoniou1, L.C. Appel1, C. Appelbee1, S. Aria1, M. Ariola8, G. Artaserse17, W. Arter1, V. Artigues18, N. Asakura7, A. Ash1, N. Ashikawa19, V. Aslanyan20, M. Astrain21, O. Asztalos22, D. Auld1, F. Auriemma23, Y. Austin1, L. Avotina24, E. Aymerich25, A. Baciero9, F. Bairaktaris26, J. Balbin27, L. Balbinot23, I. Balboa1, M. Balden18, C. Balshaw1, N. Balshaw1, V.K. Bandaru18, J. Banks1, Yu.F. Baranov1, C. Barcellona28, A. Barnard1, M. Barnard1, R. Barnsley13, A. Barth1, M. Baruzzo17, S. Barwell1, M. Bassan13, A. Batista2, P. Batistoni17, L. Baumane24, B. Bauvir13, L. Baylor29, P.S. Beaumont1, D. Beckett1, A. Begolli1, M. Beidler29, N. Bekris30,31, M. Beldishevski1, E. Belli32, F. Belli17, É. Belonohy1, M. Ben Yaala33, J. Benayas1, J. Bentley1, H. Bergsåker34, J. Bernardo2, M. Bernert18, M. Berry1, L. Bertalot13, H. Betar35, M. Beurskens36, S. Bickerton1, B. Bieg37, J. Bielecki38, A. Bierwage7, T. Biewer29, R. Bilato18, P. Bílková39, G. Birkenmeier18, H. Bishop1, J.P.S. Bizarro2, J. Blackburn1, P. Blanchard40, P. Blatchford1, V. Bobkov18, A. Boboc1, P. Bohm39, T. Bohm41, I. Bolshakova42, T. Bolzonella23, N. Bonanomi18, D. Bonfiglio23, X. Bonnin13, P. Bonofiglo43, S. Boocock1, A. Booth1, J. Booth1, D. Borba2,30, D. Borodin44, I. Borodkina39,44, C. Boulbe45, C. Bourdelle27, M. Bowden1, K. Boyd1, I.Bozicevic Mihalic46, S.C. Bradnam1, V. Braic47, L. Brandt48, R. Bravanec49, B. Breizman50, A. Brett1, S. Brezinsek44, M. Brix1, K. Bromley1, B. Brown1, D. Brunetti1,12, R. Buckingham1, M. Buckley1, R. Budny, J. Buermans51, H. Bufferand27, P. Buratti17, A. Burgess1, A. Buscarino28, A. Busse1, D. Butcher1, E.de la Cal9, G. Calabrò52, L. Calacci53, R. Calado2, Y. Camenen54, G. Canal55, B. Cannas25, M. Cappelli17, S. Carcangiu25, P. Card1, A. Cardinali17, P. Carman1, D. Carnevale53, M. Carr1, D. Carralero9, L. Carraro23, I.S. Carvalho2, P. Carvalho2, I. Casiraghi56, F.J. Casson1, C. Castaldo17, J.P. Catalan14, N. Catarino2, F. Causa12, M. Cavedon18, M. Cecconello16, C.D. Challis1, B. Chamberlain1, C.S. Chang43, A. Chankin18, B. Chapman1,57, M. Chernyshova58, A. Chiariello8, P. Chmielewski58, A. Chomiczewska58, L. Chone59, G. Ciraolo27, D. Ciric1, J. Citrin60, Ł. Ciupinski61, M. Clark1, R. Clarkson1, C. Clements1, M. Cleverly1, J.P. Coad1, P. Coates1, A. Cobalt1, V. Coccorese8, R. Coelho2, J.W. Coenen44, I.H. Coffey62, A. Colangeli17, L. Colas27, C. Collins29, J. Collins1, S. Collins1, D. Conka24, S. Conroy16, B. Conway1, N.J. Conway1, D. Coombs1, P. Cooper1, S. Cooper1, C. Corradino28, G. Corrigan1, D. Coster18, P. Cox1, T. Craciunescu63, S. Cramp1, C. Crapper1, D. Craven1, R. Craven1, M.Crialesi Esposito48, G. Croci56, D. Croft1, A. Croitoru63, K. Crombe51,64, T. Cronin1, N. Cruz2, C. Crystal32, G. Cseh22, A. Cufar65, A. Cullen1, M. Curuia66, T. Czarski58, H. Dabirikhah1, A.Dal Molin56, E. Dale1, P. Dalgliesh1, S. Dalley1, J. Dankowski38, P. David18, A. Davies1, S. Davies1, G. Davis1, K. Dawson1, S. Dawson1, I.E. Day1, M. De Bock13, G. De Temmerman13, G. De Tommasi8, K. Deakin1, J. Deane1, R. Dejarnac39, D. Del Sarto35, E. Delabie29, D. Del-Castillo-Negrete29, A. Dempsey67, R.O. Dendy1,57, P. Devynck27, A. Di Siena18, C. Di Troia17, T. Dickson1, P. Dinca63, T. Dittmar44, J. Dobrashian1, R.P. Doerner68, A.J.H. Donne´69, S. Dorling1, S. Dormido-Canto70, D. Douai27, S. Dowson1, R. Doyle67, M. Dreval71, P. Drewelow36, P. Drews44, G. Drummond1, Ph. Duckworth13, H. Dudding1,72, R. Dumont27, P. Dumortier51, D. Dunai22, T. Dunatov46, M. Dunne18, I. Duran39, F. Durodie51, R. Dux18, A. Dvornova27, R. Eastham1, J. Edwards1, Th. Eich18, A. Eichorn1, N. Eidietis32, A. Eksaeva44, H. El Haroun1, G. Ellwood13, C. Elsmore1, O. Embreus73, S. Emery1, G. Ericsson16, B. Eriksson16, F. Eriksson74, J. Eriksson16, L.G. Eriksson75, S. Ertmer44, S. Esquembri21, A.L. Esquisabel76, T. Estrada9, G. Evans1, S. Evans1, E. Fable18, D. Fagan1, M. Faitsch18, M. Falessi17, A. Fanni25, A. Farahani1, I. Farquhar1, A. Fasoli40, B. Faugeras45, S. Fazinic46, F. Felici40, R. Felton1, A. Fernandes2, H. Fernandes2, J. Ferrand1, D.R. Ferreira2, J. Ferreira2, G. Ferrò53, J. Fessey1, O. Ficker39, A.R. Field1, A. Figueiredo2, J. Figueiredo2,30, A. Fil1, N. Fil1,20, P. Finburg1, D. Fiorucci23, U. Fischer31, G. Fishpool1, L. Fittill1, M. Fitzgerald1, D. Flammini17, J. Flanagan1, K. Flinders1, S. Foley1, N. Fonnesu17, M. Fontana40, J.M. Fontdecaba9, S. Forbes1, A. Formisano8, T. Fornal58, L. Fortuna28, E. Fortuna-Zalesna61, M. Fortune1, C. Fowler1, E. Fransson74, L. Frassinetti34, M. Freisinger44, R. Fresa8, R. Fridström34, D. Frigione53, T. Fülöp73, M. Furseman1, V. Fusco24, S. Futatani17, D. Gadariya77, K. Gál69, D. Galassi40, K. Gałazka58, S. Galeani53, D. Gallart78, R. Galvao55, Y. Gao44, J. Garcia27, M. García-Muñoz79, M. Gardener1, L. Garzotti1, J. Gaspar80, R. Gatto81, P. Gaudio53, D. Gear1, T. Gebhart29, S. Gee1, M. Gelfusa53, R. George1, S.N. Gerasimov1, G. Gervasini12, M. Gethins1, Z. Ghani1, M. Gherendi63, F. Ghezzi12, J.C. Giacalone27, L. Giacomelli12, G. Giacometti54, C. Gibson1, K.J. Gibson72, L. Gil2, A. Gillgren74, D. Gin4, E. Giovannozzi17, C. Giroud1, R. Glen1, S. Glöggler18, J. Goff1, P. Gohil32, V. Goloborodko82, R. Gomes2, B. Gonçalves2, M. Goniche27, A. Goodyear1, S. Gore1, G. Gorini56, T. Görler18, N. Gotts1, R. Goulding43, E. Gow1, B. Graham1, J.P. Graves40, H. Greuner18, B. Grierson43, J. Griffiths1, S. Griph1, D. Grist1, W. Gromelski58, M. Groth59, R. Grove29, M. Gruca58, D. Guard1, N. Gupta1, C. Gurl1, A. Gusarov83, L. Hackett1, S. Hacquin27,30, R. Hager43, L. Hägg16, A. Hakola6, M. Halitovs24, S. Hall1, S.A. Hall1, S. Hallworth-Cook1, C.J. Ham1, D. Hamaguchi7, M. Hamed27, C. Hamlyn-Harris1, K. Hammond1, E. Harford1, J.R. Harrison1, D. Harting1, Y. Hatano84, D.R. Hatch50, T. Haupt1, J. Hawes1, N.C. Hawkes1, J. Hawkins1, T. Hayashi7, S. Hazael1, S. Hazel1, P. Heesterman1, B. Heidbrink85, W. Helou13, O. Hemming1, S.S. Henderson1, R.B. Henriques2, D. Hepple1, J. Herfindal29, G. Hermon1, J. Hill1, J.C. Hillesheim1, K. Hizanidis26, A. Hjalmarsson16, A. Ho60, J. Hobirk18, O. Hoenen13, C. Hogben1, A. Hollingsworth1, S. Hollis1, E. Hollmann68, M. Hölzl18, B. Homan45, M. Hook1, D. Hopley1, J. Horácek39, D. Horsley1, N. Horsten59, A. Horton1, L.D. Horton30,40, L. Horvath1,72, S. Hotchin1, R. Howell1, Z. Hu56, A. Huber44, V. Huber44, T. Huddleston1, G.T.A. Huijsmans13, P. Huynh27, A. Hynes1, M. Iliasova4, D. Imrie1, M. Imrísek39, J. Ingleby1, P. Innocente23, K. Insulander Björk73, N. Isernia8, I. Ivanova-Stanik58, E. Ivings1, S. Jablonski58, S. Jachmich13,30,51, T. Jackson1, P. Jacquet1, H. Järleblad86, F. Jaulmes39, J.Jenaro Rodriguez1, I. Jepu63, E. Joffrin27, R. Johnson1, T. Johnson34, J. Johnston1, C. Jones1, G. Jones1, L. Jones1, N. Jones1, T. Jones1, A. Joyce1, R. Juarez14, M. Juvonen1, P. Kalnin¸ a24, T. Kaltiaisenaho6, J. Kaniewski1, A. Kantor1, A. Kappatou18, J. Karhunen5, D. Karkinsky1, Yu Kashchuk87, M. Kaufman29, G. Kaveney1, Ye.O. Kazakov51, V. Kazantzidis26, D.L. Keeling1, R. Kelly1, M. Kempenaars13, C. Kennedy1, D. Kennedy1, J. Kent1, K. Khan1, E. Khilkevich4, C. Kiefer18, J. Kilpeläinen59, C. Kim32, Hyun-Tae Kim1,30, S.H. Kim13, D.B. King1, R. King1, D. Kinna1, V.G. Kiptily1, A. Kirjasuo6, K.K. Kirov1, A. Kirschner44, T. kiviniemi59, G. Kizane24, M. Klas88, C. Klepper29, A. Klix31, G. Kneale1, M. Knight1, P. Knight1, R. Knights1, S. Knipe1, M. Knolker32, S. Knott89, M. Kocan13, F. Köchl1, I. Kodeli65, Y. Kolesnichenko82, Y. Kominis26, M. Kong1, V. Korovin71, B. Kos65, D. Kos1, H.R. Koslowski44, M. Kotschenreuther50, M. Koubiti54, E. Kowalska-Strzeciwilk ˛ 58, K. Koziol3, A. Krasilnikov87, V. Krasilnikov13,15, M. Kresina1,27, K. Krieger18, N. Krishnan1, A. Krivska51, U. Kruezi13, I. Ksia˛zek ˙ 90, A.B. Kukushkin11, H. Kumpulainen59, T. Kurki-Suonio59, H. Kurotaki7, S. Kwak36, O.J. Kwon91, L. Laguardia12, E. Lagzdina24, A. Lahtinen5, A. Laing1, N. Lam1, H.T. Lambertz44, B. Lane1, C. Lane1, E.Lascas Neto40, E. Łaszynska58, K.D. Lawson1, A. Lazaros26, E. Lazzaro12, G. Learoyd1, Chanyoung Lee92, S.E. Lee84, S. Leerink59, T. Leeson1, X. Lefebvre1, H.J. Leggate67, J. Lehmann1, M. Lehnen13, D. Leichtle31,93, F. Leipold13, I. Lengar65, M. Lennholm1,75, E. Leon Gutierrez9, B. Lepiavko82, J. Leppänen6, E. Lerche51, A. Lescinskis24, J. Lewis1, W. Leysen83, L. Li44, Y. Li44, J. Likonen6, Ch. Linsmeier44, B. Lipschultz72, X. Litaudon27,30, E. Litherland-Smith1, F. Liu27,30, T. Loarer27, A. Loarte13, R. Lobel1, B. Lomanowski29, P.J. Lomas1, J.M. Lopez21, R. Lorenzini23, S. Loreti17, U. Losada9, V.P. Loschiavo8, M. Loughlin13, Z. Louka1, J. Lovell29, T. Lowe1, C. Lowry1,75, S. Lubbad1, T. Luce13, R. Lucock1, A. Lukin94, C. Luna95, E.de la Luna9, M. Lungaroni53, C.P. Lungu63, T. Lunt18, V. Lutsenko82, B. Lyons32, A. Lyssoivan51, M. Machielsen40, E. Macusova39, R. Mäenpää59, C.F. Maggi1, R. Maggiora96, M. Magness1, S. Mahesan1, H. Maier18, R. Maingi43, K. Malinowski58, P. Manas18,54, P. Mantica12, M.J. Mantsinen97, J. Manyer78, A. Manzanares98, Ph. Maquet13, G. Marceca40, N. Marcenko87, C. Marchetto99, O. Marchuk44, A. Mariani12, G. Mariano17, M. Marin60, M. Marinelli53, T. Markovicˇ39, D. Marocco17, L. Marot33, S. Marsden1, J. Marsh1, R. Marshall1, L. Martellucci53, A. Martin1, A.J. Martin1, R. Martone8, S. Maruyama13, M. Maslov1, S. Masuzaki19, S. Matejcik88, M. Mattei8, G.F. Matthews1, D. Matveev44, E. Matveeva39, A. Mauriya2, F. Maviglia8, M. Mayer18, M.-L. Mayoral1,69, S. Mazzi54, C. Mazzotta17, R. McAdams1, P.J. McCarthy89 K.G. McClements1, J. McClenaghan32, P. McCullen1, D.C. McDonald1, D. McGuckin1, D. McHugh1, G. McIntyre1, R. McKean1, J. McKehon1, B. McMillan57, L. McNamee1, A. McShee1, A. 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Nina2, D. Nishijima105, C. Noble1, C.R. Nobs1, M. Nocente56, D. Nodwell1, K. Nordlund5, H. Nordman13, R. Normanton1, J.M. Noterdaeme18, S. Nowak12, E. Nunn1, H. Nyström34, M. Oberparleiter74, B. Obryk38, J. O’Callaghan1, T. Odupitan1, H.J.C. Oliver1,50, R. Olney1, M. O’Mullane106, J. Ongena51, E. Organ1, F. Orsitto8, J. Orszagh88, T. Osborne32, R. Otin1, T. Otsuka107, A. Owen1, Y. Oya108, M. Oyaizu7, R. Paccagnella23, N. Pace1, L.W. Packer1, S. Paige1, E. Pajuste24, D. Palade63, S.J.P. Pamela1, N. Panadero9, E. Panontin56, A. Papadopoulos26, G. Papp18, P. Papp88, V.V. Parail1, C. Pardanaud54, J. Parisi1,109, F.Parra Diaz109, A. Parsloe1, M. Parsons29, N. Parsons1, M. Passeri53, A. Patel1, A. Pau40, G. Pautasso18, R. Pavlichenko71, A. Pavone36, E. Pawelec90, C.Paz Soldan110, A. Peacock1,75, M. Pearce1, E. Peluso53, C. Penot13, K. Pepperell1, R. Pereira2, T. Pereira2, E.Perelli Cippo12, P. Pereslavtsev107, C. Perez von Thun58, V. Pericoli58, D. Perry1, M. Peterka39, P. Petersson34, G. 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Zychor3 // 1 United Kingdom Atomic Energy Authority, Culham Science Centre, Abingdon, Oxon, OX14 3DB, United Kingdom of Great Britain and Northern Ireland 2 Instituto de Plasmas e Fusao Nuclear, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal 3 National Centre for Nuclear Research (NCBJ), 05-400 Otwock-Swierk, Poland 4 Ioffe Physico-Technical Institute, 26 Politekhnicheskaya, St Petersburg 194021, Russia 5 University of Helsinki, PO Box 43, FI-00014 University of Helsinki, Finland 6 VTT Technical Research Centre of Finland, PO Box 1000, FIN-02044 VTT, Finland 7 National Institutes for Quantum and Radiological Science and Technology, Naka, Ibaraki 311-0193, Japan 8 Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy 9 Laboratorio Nacional de Fusión, CIEMAT, Madrid, Spain 10 NCSR ‘Demokritos’ 153 10, Agia Paraskevi Attikis, Greece 11 NRC Kurchatov Institute, 1 Kurchatov Square, Moscow 123182, Russia 12 Institute for Plasma Science and Technology, CNR, via R. Cozzi 53, 20125 Milano, Italy 13 ITER Organization, Route de Vinon-sur-Verdon, CS 90 046, 13067 Saint Paul Lez Durance Cedex, France 14 Universidad Nacional de Educacion a Distancia, Dept Ingn Energet, Calle Juan del Rosal 12, E-28040 Madrid, Spain 15 Troitsk Insitute of Innovating and Thermonuclear Research (TRINITI), Troitsk 142190, Moscow Region, Russia 16 Department of Physics and Astronomy, Uppsala University, SE-75120 Uppsala, Sweden 17 Dip.to Fusione e Tecnologie per la Sicurezza Nucleare, ENEA C. R. Frascati, via E. Fermi 45, 00044 Frascati (Roma), Italy 18 Max-Planck-Institut für Plasmaphysik, D-85748 Garching, Germany 19 National Institute for Fusion Science, Oroshi, Toki, Gifu 509-5292, Japan 20 MIT Plasma Science and Fusion Center, Cambridge, MA 02139, United States of America 21 Universidad Politécnica de Madrid, Grupo I2A2, Madrid, Spain 22 Centre for Energy Research, POB 49, H-1525 Budapest, Hungary 23 Consorzio RFX, Corso Stati Uniti 4, 35127 Padova, Italy 24 University of Latvia, 19 Raina Blvd., Riga, LV 1586, Latvia 25 Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi 09123 Cagliari, Italy 26 National Technical University of Athens, Iroon Politechniou 9, 157 73 Zografou, Athens, Greece 27 CEA, IRFM, F-13108 Saint Paul Lez Durance, France 28 Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania, 95125 Catania, Italy 29 Oak Ridge National Laboratory, Oak Ridge, TN 37831, TN, United States of America 30 EUROfusion Programme Management Unit, Culham Science Centre, Culham, OX14 3DB, United Kingdom of Great Britain and Northern Ireland 31 Karlsruhe Institute of Technology, PO Box 3640, D-76021 Karlsruhe, Germany 32 General Atomics, PO Box 85608, San Diego, CA 92186-5608, United States of America 33 Department of Physics, University of Basel, Switzerland 34 Fusion Plasma Physics, EECS, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden 35 Institut Jean Lamour, U

Community Education on MTM Services

Background: Medication nonadherence, defined as “the number of doses not taken or taken incorrectly that jeopardizes the patient’s therapeutic outcome,” is a major health problem with about 43% of the general population nonadherent to their medications. Medication nonadherence accounts for an estimated 125,000 deaths per year in the US, 33-69% of medication-related hospital readmissions, and an estimated $100 to$300 billion in direct and indirect medical costs. Medication therapy management (MTM), defined as “a distinct service or group of services that optimize therapeutic outcomes for individual patients,” has been found to reduce medication nonadherence. However, many individuals eligible for MTM services are not aware of the resource available to them and do not believe the service will be beneficial to them. Objectives: A pre post observational study design will be used to determine the effects of two types of educational interventions on MTM of patient’s perceptions of MTM and enrollment in MTM services. Methodology: Participants will be divided into two intervention groups. All participants will complete a pre survey to assess current perceptions of MTM services. One group will attend a community educational event on MTM, and the second group will receive an educational brochure in the mail. All participants will complete a post survey to reassess perceptions of MTM after the educational intervention. In addition, all participants will be tracked to determine future enrollment in an MTM service. Analysis: Descriptive tests and paired t-tests/Wilcoxon Signed Rank tests will be run on data acquired from pre and post surveys. Unpaired t-tests/Mann Whitney and chi-square tests will be run to compare data between intervention groups. Descriptive tests will be run on data acquired from tracking enrollment