cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/wb09-rs14
title: Measurement and Simulation of Human Sitting and Standing Movement Biomechanics

description: Previous studies of robotic lower-limb exoskeletons and prostheses with regenerative actuators have focused exclusively on level-ground walking applications. Here we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamic simulations to estimate the biomechanical energy available for regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using parameter identification. Joint mechanical power was calculated from net joint torques and rotational velocities and numerically integrated to determine joint biomechanical energy. The hip produced the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee (0.8 ± 0.3 W/kg) and ankle (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively. Assuming known regenerative actuator efficiencies (i.e., maximum 63%), robotic lower-limb exoskeletons and prostheses could theoretically regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules per walking stride. Given that these regeneration performance calculations are based on healthy young adults, future research should involve seniors and/or rehabilitation patients to better estimate the biomechanical energy available for regeneration in individuals with mobility impairments. Reference: Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. bioRxiv. DOI: 10.1101/801258.
DOI: 10.21227/6598-jp59
title: Measurement and Simulation of Human Sitting and Standing Movement Biomechanics

description: Previous studies of robotic lower-limb exoskeletons and prostheses with regenerative actuators have focused exclusively on level-ground walking applications. Here we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamic simulations to estimate the biomechanical energy available for regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using parameter identification. Joint mechanical power was calculated from net joint torques and rotational velocities and numerically integrated to determine joint biomechanical energy. The hip produced the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee (0.8 ± 0.3 W/kg) and ankle (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively. Assuming known regenerative actuator efficiencies (i.e., maximum 63%), robotic lower-limb exoskeletons and prostheses could theoretically regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules per walking stride. Given that these regeneration performance calculations are based on healthy young adults, future research should involve seniors and/or rehabilitation patients to better estimate the biomechanical energy available for regeneration in individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. bioRxiv. DOI: 10.1101/801258.
DOI: 10.21227/t8gb-gh30
title: Measurement and Simulation of Human Sitting and Standing Movement Biomechanics

description: Previous studies of robotic lower-limb exoskeletons and prostheses with regenerative actuators have focused exclusively on level-ground walking applications. Here we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamic simulations to estimate the biomechanical energy available for regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using parameter identification. Joint mechanical power was calculated from net joint torques and rotational velocities and numerically integrated to determine joint biomechanical energy. The hip produced the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee (0.8 ± 0.3 W/kg) and ankle (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively. Assuming known regenerative actuator efficiencies (i.e., maximum 63%), robotic lower-limb exoskeletons and prostheses could theoretically regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules per walking stride. Given that these regeneration performance calculations are based on healthy young adults, future research should involve seniors and/or rehabilitation patients to better estimate the biomechanical energy available for regeneration in individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. bioRxiv. DOI: 10.1101/801258.
DOI: 10.5281/zenodo.4275609
title: Density functional theory calculations of coherent bcc Fe-Cu interfacial energy densities

description: File contains the data required to calculate interfacial energy densities of {100}, {110}, {111}, {210}, {211} and {221} orientated coherence bcc Fe-Cu interfaces. Data produced for the study detailed in: Cu nanoprecipitate morphologies and interfacial energy densities in bcc Fe from density functional theory (DFT) A.M. Garrett and C.P. Race. Submitted to Computational Materials Science. .txt files contain the total energies calculated for relaxed interface-containing and bulk simulation cells at a range of interfacial spacings. This data can be used to calculate the size independent interfacial energy densities for a range of Fe-Cu interface orientations using standard fitting approaches. Columns of the tables in the .txt files are no. atoms, interface-containing simulation cell length, interfacial area, total energy of the relaxed interface-containing simulation cell, total energy of the reference bulk Fe and total energy of the reference bulk Cu. Lengths are in Angstrom and energies are in eV.
DOI: 10.5281/zenodo.4275608
title: Density functional theory calculations of coherent bcc Fe-Cu interfacial energy densities

description: File contains the data required to calculate interfacial energy densities of {100}, {110}, {111}, {210}, {211} and {221} orientated coherence bcc Fe-Cu interfaces. Data produced for the study detailed in: Cu nanoprecipitate morphologies and interfacial energy densities in bcc Fe from density functional theory (DFT) A.M. Garrett and C.P. Race. Submitted to Computational Materials Science. .txt files contain the total energies calculated for relaxed interface-containing and bulk simulation cells at a range of interfacial spacings. This data can be used to calculate the size independent interfacial energy densities for a range of Fe-Cu interface orientations using standard fitting approaches. Columns of the tables in the .txt files are no. atoms, interface-containing simulation cell length, interfacial area, total energy of the relaxed interface-containing simulation cell, total energy of the reference bulk Fe and total energy of the reference bulk Cu. Lengths are in Angstrom and energies are in eV.
DOI: 10.21427/r5pe-fg09
title: A Qualitative Study of E-Cigarette Use Among Young People in Ireland
DOI: 10.25646/7652
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.23644/uu.8947346
title: 50K SNP genotypes of southeast European sheep

description: Quality-filtered Illumina 50K SNP data from 93 sheep breeds/populations: original data from Balkan, Czech and Polish Zackel breeds, merged with existing data for Norvegian, Finnish, Dutch, German, Swiss, Italian, Spanish, Turkish, Israelian, Egyptian, Iranian and Kazakstan breeds and Asian or European mouflon populations. <br>
DOI: 10.5517/ccdc.csd.cc10qzpd
title: CCDC 945614: Experimental Crystal Structure Determination

description: Related Article: Anna Zieleniewska, Xiaotao Zhao, Stefan Bauroth, Changsheng Wang, Andrei S. Batsanov, Christina Krick Calderon, Axel Kahnt, Timothy Clark, Martin R. Bryce, Dirk M. Guldi|2020|J.Am.Chem.Soc.|142|18769|doi:10.1021/jacs.0c04003
DOI: 10.5517/ccdc.csd.cc10qzqf
title: CCDC 945615: Experimental Crystal Structure Determination

description: Related Article: Anna Zieleniewska, Xiaotao Zhao, Stefan Bauroth, Changsheng Wang, Andrei S. Batsanov, Christina Krick Calderon, Axel Kahnt, Timothy Clark, Martin R. Bryce, Dirk M. Guldi|2020|J.Am.Chem.Soc.|142|18769|doi:10.1021/jacs.0c04003
DOI: 10.5517/ccdc.csd.cc231v78
title: CCDC 1938189: Experimental Crystal Structure Determination

description: Related Article: Abolghasem Bakhoda, Otome E. Okoromoba, Christine Greene, Mahdi Raghibi Boroujeni, Jeffery A. Bertke, Timothy H. Warren|2020|J.Am.Chem.Soc.|142|18483|doi:10.1021/jacs.0c07137
DOI: 10.5517/ccdc.csd.cc231v89
title: CCDC 1938190: Experimental Crystal Structure Determination

description: Related Article: Abolghasem Bakhoda, Otome E. Okoromoba, Christine Greene, Mahdi Raghibi Boroujeni, Jeffery A. Bertke, Timothy H. Warren|2020|J.Am.Chem.Soc.|142|18483|doi:10.1021/jacs.0c07137
DOI: 10.5517/ccdc.csd.cc231v9b
title: CCDC 1938191: Experimental Crystal Structure Determination

description: Related Article: Abolghasem Bakhoda, Otome E. Okoromoba, Christine Greene, Mahdi Raghibi Boroujeni, Jeffery A. Bertke, Timothy H. Warren|2020|J.Am.Chem.Soc.|142|18483|doi:10.1021/jacs.0c07137
DOI: 10.5517/ccdc.csd.cc23ts6y
title: CCDC 1961190: Experimental Crystal Structure Determination

description: Related Article: Ahmet Atilgan, M. Mustafa Cetin, Jierui Yu, Yassine Beldjoudi, Jian Liu, Charlotte L. Stern, Furkan M. Cetin, Timur Islamoglu, Omar K. Farha, Pravas Deria, J. Fraser Stoddart, Joseph T. Hupp|2020|J.Am.Chem.Soc.|142|18554|doi:10.1021/jacs.0c07784
DOI: 10.5517/ccdc.csd.cc23ts7z
title: CCDC 1961191: Experimental Crystal Structure Determination

description: Related Article: Ahmet Atilgan, M. Mustafa Cetin, Jierui Yu, Yassine Beldjoudi, Jian Liu, Charlotte L. Stern, Furkan M. Cetin, Timur Islamoglu, Omar K. Farha, Pravas Deria, J. Fraser Stoddart, Joseph T. Hupp|2020|J.Am.Chem.Soc.|142|18554|doi:10.1021/jacs.0c07784
DOI: 10.5517/ccdc.csd.cc23ts80
title: CCDC 1961192: Experimental Crystal Structure Determination

description: Related Article: Ahmet Atilgan, M. Mustafa Cetin, Jierui Yu, Yassine Beldjoudi, Jian Liu, Charlotte L. Stern, Furkan M. Cetin, Timur Islamoglu, Omar K. Farha, Pravas Deria, J. Fraser Stoddart, Joseph T. Hupp|2020|J.Am.Chem.Soc.|142|18554|doi:10.1021/jacs.0c07784
DOI: 10.5517/ccdc.csd.cc23ts91
title: CCDC 1961193: Experimental Crystal Structure Determination

description: Related Article: Ahmet Atilgan, M. Mustafa Cetin, Jierui Yu, Yassine Beldjoudi, Jian Liu, Charlotte L. Stern, Furkan M. Cetin, Timur Islamoglu, Omar K. Farha, Pravas Deria, J. Fraser Stoddart, Joseph T. Hupp|2020|J.Am.Chem.Soc.|142|18554|doi:10.1021/jacs.0c07784
DOI: 10.5517/ccdc.csd.cc24y77l
title: CCDC 1994299: Experimental Crystal Structure Determination

description: Related Article:  Sebastian Peil, Giovanni Bistoni, Richard Goddard, Alois Fürstner|2020|J.Am.Chem.Soc.|142|18541|doi:10.1021/jacs.0c07808
DOI: 10.5517/ccdc.csd.cc2593rc
title: CCDC 2004762: Experimental Crystal Structure Determination

description: Related Article: Wenhui Niu, Ji Ma, Paniz Soltani, Wenhao Zheng, Fupin Liu, Alexey A. Popov, Jan J. Weigand, Hartmut Komber, Emanuele Poliani, Cinzia Casiraghi, Jörn Droste, Michael Ryan Hansen, Silvio Osella, David Beljonne, Mischa Bonn, Hai I. Wang, Xinliang Feng, Junzhi Liu, Yiyong Mai|2020|J.Am.Chem.Soc.|142|18293|doi:10.1021/jacs.0c07013
DOI: 10.5517/ccdc.csd.cc25jwwh
title: CCDC 2012237: Experimental Crystal Structure Determination

description: Related Article: Ryuhei Okura, Hiromasa Uchiyama, Kazunori Kadota, Yuichi Tozuka|2020|Cryst.Growth Des.|20|7219|doi:10.1021/acs.cgd.0c00896
DOI: 10.5517/ccdc.csd.cc25jwyk
title: CCDC 2012239: Experimental Crystal Structure Determination

description: Related Article: Ryuhei Okura, Hiromasa Uchiyama, Kazunori Kadota, Yuichi Tozuka|2020|Cryst.Growth Des.|20|7219|doi:10.1021/acs.cgd.0c00896
DOI: 10.5517/ccdc.csd.cc25jx0n
title: CCDC 2012241: Experimental Crystal Structure Determination

description: Related Article: Ryuhei Okura, Hiromasa Uchiyama, Kazunori Kadota, Yuichi Tozuka|2020|Cryst.Growth Des.|20|7219|doi:10.1021/acs.cgd.0c00896
DOI: 10.5517/ccdc.csd.cc25jx2q
title: CCDC 2012243: Experimental Crystal Structure Determination

description: Related Article: Ryuhei Okura, Hiromasa Uchiyama, Kazunori Kadota, Yuichi Tozuka|2020|Cryst.Growth Des.|20|7219|doi:10.1021/acs.cgd.0c00896
DOI: 10.5517/ccdc.csd.cc25jwzl
title: CCDC 2012240: Experimental Crystal Structure Determination

description: Related Article: Ryuhei Okura, Hiromasa Uchiyama, Kazunori Kadota, Yuichi Tozuka|2020|Cryst.Growth Des.|20|7219|doi:10.1021/acs.cgd.0c00896
DOI: 10.5517/ccdc.csd.cc25jx1p
title: CCDC 2012242: Experimental Crystal Structure Determination

description: Related Article: Ryuhei Okura, Hiromasa Uchiyama, Kazunori Kadota, Yuichi Tozuka|2020|Cryst.Growth Des.|20|7219|doi:10.1021/acs.cgd.0c00896
DOI: 10.5517/ccdc.csd.cc25pl4m
title: CCDC 2016740: Experimental Crystal Structure Determination

description: Related Article:  Sebastian Peil, Giovanni Bistoni, Richard Goddard, Alois Fürstner|2020|J.Am.Chem.Soc.|142|18541|doi:10.1021/jacs.0c07808
DOI: 10.5517/ccdc.csd.cc25pl5n
title: CCDC 2016741: Experimental Crystal Structure Determination

description: Related Article:  Sebastian Peil, Giovanni Bistoni, Richard Goddard, Alois Fürstner|2020|J.Am.Chem.Soc.|142|18541|doi:10.1021/jacs.0c07808
DOI: 10.5517/ccdc.csd.cc25pl6p
title: CCDC 2016742: Experimental Crystal Structure Determination

description: Related Article:  Sebastian Peil, Giovanni Bistoni, Richard Goddard, Alois Fürstner|2020|J.Am.Chem.Soc.|142|18541|doi:10.1021/jacs.0c07808
DOI: 10.5517/ccdc.csd.cc25pl7q
title: CCDC 2016743: Experimental Crystal Structure Determination

description: Related Article:  Sebastian Peil, Giovanni Bistoni, Richard Goddard, Alois Fürstner|2020|J.Am.Chem.Soc.|142|18541|doi:10.1021/jacs.0c07808
DOI: 10.5517/ccdc.csd.cc25pl8r
title: CCDC 2016744: Experimental Crystal Structure Determination

description: Related Article:  Sebastian Peil, Giovanni Bistoni, Richard Goddard, Alois Fürstner|2020|J.Am.Chem.Soc.|142|18541|doi:10.1021/jacs.0c07808
DOI: 10.5517/ccdc.csd.cc26gxsd
title: CCDC 2040134: Experimental Crystal Structure Determination

description: Related Article: Michael M. Wade Wolfe, James P. Shanahan, Jeff W. Kampf, Nathaniel K. Szymczak|2020|J.Am.Chem.Soc.|142|18698|doi:10.1021/jacs.0c09505
DOI: 10.5517/ccdc.csd.cc26gxtf
title: CCDC 2040135: Experimental Crystal Structure Determination

description: Related Article: Michael M. Wade Wolfe, James P. Shanahan, Jeff W. Kampf, Nathaniel K. Szymczak|2020|J.Am.Chem.Soc.|142|18698|doi:10.1021/jacs.0c09505
DOI: 10.5517/ccdc.csd.cc26gxvg
title: CCDC 2040136: Experimental Crystal Structure Determination

description: Related Article: Michael M. Wade Wolfe, James P. Shanahan, Jeff W. Kampf, Nathaniel K. Szymczak|2020|J.Am.Chem.Soc.|142|18698|doi:10.1021/jacs.0c09505
DOI: 10.5517/ccdc.csd.cc26gxwh
title: CCDC 2040137: Experimental Crystal Structure Determination

description: Related Article: Charles H. Arnett, Isabel Bogacz, Ruchira Chatterjee, Junko Yano, Paul H. Oyala, Theodor Agapie|2020|J.Am.Chem.Soc.|142|18795|doi:10.1021/jacs.0c05920
DOI: 10.6084/m9.figshare.13241423
title: Data on soil and plant samples

description: Soil and plant samples were investigated in a downtown area in order to to assess the possible use of three ornamental plant for the phytoextraction of some metals.
DOI: 10.6084/m9.figshare.13241423.v1
title: Data on soil and plant samples

description: Soil and plant samples were investigated in a downtown area in order to to assess the possible use of three ornamental plant for the phytoextraction of some metals.
DOI: 10.21227/c9za-ep10
title: 6.66 kVA Solar Optimizer

description: This dataset is in support of my original research paper " 6.66kVA Solar Optimizer " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60). Paper is under review.This is not connected to the grid. The other related dataset " " (https://dx.doi.org/10.21227/v7s8-pk56 ), where this will be connected to the inverter which in turn will be connected to grid.Though few similar kind papers are there, this paper is original and different from them as evident from the abstract and the transaction paper , and is based on the actual submitted project for funding. ( project didn't got any funding ; though from self finances, submission was acceptable !)Abstract of the above submitted paper
DOI: 10.5281/zenodo.4275683
title: Pitfalls of Computed Tomography 3D Reconstruction Models in Cranial Nonmetric Analysis

description: Many studies in the literature have highlighted the utility of virtual 3D databanks as a substitute for real skeletal collections and the important application of radiological records in personal identification. However, none have investigated the accuracy of virtual material compared to skeletal remains in nonmetric variant analysis using 3D models. The present study investigates the accuracy of 20 computed tomography (CT) 3D reconstruction models compared to the real crania, focusing on the quality of the reproduction of the real crania and the possibility to detect 29 dental/cranial morphological variations in 3D images. An interobserver analysis was performed to evaluate trait identification, number, position, and shape. Results demonstrate a false bone loss in 3D models in some cranial regions, specifically the maxillary and occipital bones in 85% and 20% of the samples. Additional analyses revealed several difficulties in the detection of cranial nonmetric traits in 3D models, resulting in incorrect identification in circa 70% of the traits. In particular, pitfalls included the detection of erroneous position, error in presence/absence rates, in number, and in shape. The lowest percentages of correct evaluations were found in traits localized in the lateral side of the cranium and for the infraorbital suture, mastoid foramen, and crenulation. The present study highlights important pitfalls in CT scan when compared with the real crania for nonmetric analysis. This may have crucial consequences in cases where 3D databanks are used as a source of reference population data for nonmetric traits and pathologies and during bone-CT comparisons for identification purposes.
DOI: 10.5281/zenodo.4275682
title: Pitfalls of Computed Tomography 3D Reconstruction Models in Cranial Nonmetric Analysis

description: Many studies in the literature have highlighted the utility of virtual 3D databanks as a substitute for real skeletal collections and the important application of radiological records in personal identification. However, none have investigated the accuracy of virtual material compared to skeletal remains in nonmetric variant analysis using 3D models. The present study investigates the accuracy of 20 computed tomography (CT) 3D reconstruction models compared to the real crania, focusing on the quality of the reproduction of the real crania and the possibility to detect 29 dental/cranial morphological variations in 3D images. An interobserver analysis was performed to evaluate trait identification, number, position, and shape. Results demonstrate a false bone loss in 3D models in some cranial regions, specifically the maxillary and occipital bones in 85% and 20% of the samples. Additional analyses revealed several difficulties in the detection of cranial nonmetric traits in 3D models, resulting in incorrect identification in circa 70% of the traits. In particular, pitfalls included the detection of erroneous position, error in presence/absence rates, in number, and in shape. The lowest percentages of correct evaluations were found in traits localized in the lateral side of the cranium and for the infraorbital suture, mastoid foramen, and crenulation. The present study highlights important pitfalls in CT scan when compared with the real crania for nonmetric analysis. This may have crucial consequences in cases where 3D databanks are used as a source of reference population data for nonmetric traits and pathologies and during bone-CT comparisons for identification purposes.
DOI: 10.6084/m9.figshare.13241456
title: Dataset #1 ACC Complete

description: Dataset #1 ACC Complete
DOI: 10.6084/m9.figshare.13241456.v1
title: Dataset #1 ACC Complete

description: Dataset #1 ACC Complete
DOI: 10.6084/m9.figshare.13241474
title: Dataset #1 ACC Significant

description: Dataset #1 ACC Significant
DOI: 10.6084/m9.figshare.13241474.v1
title: Dataset #1 ACC Significant

description: Dataset #1 ACC Significant
DOI: 10.6084/m9.figshare.13241477
title: Dataset #1 KIRP Complete

description: Dataset #1 KIRP Complete
DOI: 10.6084/m9.figshare.13241477.v1
title: Dataset #1 KIRP Complete

description: Dataset #1 KIRP Complete
DOI: 10.6084/m9.figshare.13241480
title: Dataset #1 KIRP Significant

description: Dataset #1 KIRP Significant
DOI: 10.6084/m9.figshare.13241480.v1
title: Dataset #1 KIRP Significant

description: Dataset #1 KIRP Significant
DOI: 10.5281/zenodo.4275717
title: Muscle MRI in two SMA patients on nusinersen treatment: A two years follow-up

description: <strong>Introduction: </strong>The effects of nusinersen in adults with SMA rely on neuromotor function scales and qualitative assessments. There are limited clinical or imaging data on muscle changes over time. <strong>Methods: </strong>Two adult SMA patients underwent clinical assessments including measures of upper and lower limb function with Revised Upper Limb Module (RULM) and Hammersmith Function Motor Scale Expanded (HFMSE); both patients were also studied with whole-body muscle MRI (T1-weighted and Diffusion Tensor Imaging/DTI sequences), at baseline and after 10 and 24 months from the beginning of treatment with nusinersen. <strong>Results: </strong>After two years of treatment, HFMSE and RULM scores were stable in both patients. DTI sequences revealed an increased number, length and organization of muscle fiber tracks, and Fractional Anisotropy (FA) values showed a significant reduction after 10 and 24 months from baseline, in their corresponding maps. <strong>Discussion: </strong>Muscle DTI imaging seems to play an interesting role to monitor treatment effects over time in adult SMA patients.