1,722,600 research outputs found
Model Regresi Hazard Multiplikatif Dan Hazard Aditif Lin-Ying (Studi Kasus: Kecelakaan Lalu Lintas Di Amerika Serikat Tahun 2010 - 2012)
Full text linkAnalisis survival adalah prosedur statistika untuk analisis data dengan peubah yang menjadi fokus adalah waktu sampai terjadi suatu peristiwa. Terdapat beberapa model dalam analisis survival, pada penelitian ini menggunakan model regresi Hazard multiplikatif dan aditif. Model regresi Hazard multiplikatif yang sering digunakan adalah model Cox Proportional Hazard. Salah satu model regresi Hazard aditif adalah model Lin-Ying. Model Cox Proportional Hazard dan model Lin-Ying tidak dapat dibandingkan, hanya sebagai pelengkap agar lebih komprehensif dan memperkuat pemahaman tentang data tersebut. Namun dalam mengestimasi koefisien regresi terdapat kesamaan dalam kedua model tersebut yaitu menggunakan metode estimasi partial likelihood. Berdasarkan hasil analisis dengan model regresi Hazard multiplikatif (Cox Proportional Hazard) dan model regresi Hazard aditif (Lin-Ying) pada data kecelakaan lalu lintas di Amerika Serikat tahun 2010-2012 diketahui memiliki kesamaan dalam menentukan faktor-faktor yang mempengaruhi secara siginifikan resiko kematian pada kecelakaan lalu lintas untuk kedua kalinya. Faktor-faktor yang mempengaruhi signifikan yaitu peubah kepemilikan SIM dan penggunaan sabuk pengaman sedangkan faktor-faktor yang tidak signifikan yaitu peubah jenis kelamin, umur dan pengonsumsian alkohol. Model aditif dan multiplikatif Hazard menyajikan berbagai aspek hubungan antara faktor-faktor risiko dan durasi kejadian tetapi berbeda baik secara analisis maupun interpretasi
Replication Data for: MORDOR Infant Adverse Event Survey Data
No full textMORDOR Infant Adverse Event Survey Dat
Replication files for ‘Community-Level Chlamydial Serology for Assessing Trachoma Elimination in Trachoma-Endemic Niger’, PLOS Neglected Tropical Diseases
No full textAnalytic datasets and data dictionary: PRET study for serologic data
Replication Data for: MORDOR Infant Adverse Event Survey Data
No full textMORDOR Infant Adverse Event Survey Dat
Multi-modal Investigation of Cortical Connectivity at Multiple Scales
No full textLa risonanza magnetica (RM) riveste una grande e crescente importanza nel campo del neuroimaging. Tra le modalità piu interessanti si colloca la RM pesata in diffusione (dMRI) che, insieme alla RM funzionale, alla magneto-encefalografia (MEG), ell'elettro-encefalografia (EEG) e alla spettroscopia funzionale nel vicino infrarosso (fNIRS) contribuisce a costituire una notevole e differenziata mole di informazioni che consentono di analizzare e modellare la struttura e la funzione cerebrale.
La dMRI presenta il grande vantaggio di quantificare la diffusività tissutale in modo non invasivo attraverso la misura dei micromovimenti delle molecole di acqua, consentendo non solo di caratterizzare la struttura della materia bianca con elevata risoluzione, ma anche di supportare le attività cliniche sia quale supporto alla diagnostica sia quale strumento di pianificazione prechirurgica.
Allo stato dell'arte, numerosi aspetti richiedono restano da chiarire determinando un notevole impiego di risorse a livello di ricerca. Tra i principali sono la riproducibità delle misure, la ricostruzione della funzione di distribuzione delle orientazioni (orientation diffusion function, ODF), specialmente in presenza di rumore, la modellazione dei network strutturale e funzionale e lo studio delle rispettive interazioni.
In questa tesi, alcuni di questi aspetti sono stati analizzati e sono state proposte alcune soluzioni a livello sia metodologico che clinico. In particolare, a partire da dati diffusion spectrum imaging (DSI), è stato proposto un metodo di denoising del segnale basato sulla multirisoluzione che ha consentito la ricostruzione piu precisa della ODF e quindi della trattografia, è stato sviluppato un metodo di analisi della rimodellazione del network corticale motorio in pazienti affetti da stroke basato sulla tract-based quantification di parametri estratti dalla dMRI e dalla RM a trasferimento di magnetizzazione (MTR), ed è stato analizzato il network funzionale attivato dallo svolgimento di task motori predefiniti in vista dell'integrazione delle informazioni strutturale e funzionale in un modello corticale globale focalizzato sul loop motorio.In neuroimaging, a great interest is currently being directed to diffusion magnetic resonance imaging (dMRI) which, in addition to functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), electroencephalography (EEG), functional near-infrared-spectroscopy (fNIRS) provides a large spectrum of measurements to enlighten the brain structure and function. The success of dMRI is deeply rooted in the powerful concept that during their random, diffusion-driven displacements molecules probe tissue structure at a microscopic scale well beyond the usual image resolution. Diffusion imaging opens several perspectives for what concerns the development of new non invasive techniques not only to optimize the diagnosis and therapy planning for oncological patients but also to discover the anatomical structure of the human cortex.Though, many issues still remains to be solved. Among the most striking are the reconstruction of the ODF (orientation distribution function) in noisy conditions, its reproducibility over time points acquisitions, the intra and inter-subject registration and the integration of functional information about the cortical activity within the reconstruction of the fiber network from raw data. This is of paramount importance as it would allow to link the functional information to the structural anatomical substrate.
This thesis aims at investigating a subset of such issues in order to trace the path to the overall solution. In particular, it aims at integrating multiscale space-scale processing, diffusion imaging and cortical signals to (i) improve the orientation diffusion function (ODF) reconstruction, reproducibility and robustness to noise; (ii) contribute new methods for the registration of intra and inter-modality multidimensional data (tensors, probability distributions); (iii) explore the possibility of integrating functional signals in the processing pipeline in order to guide the fiber reconstruction and as a potential mean of validation of the proposed methods.From the clinical point of view, the goal of this thesis is to make tractography exploitable in daily practice for surgical planning and follow-up, assessment of degenerative pathologies as well as of pharmacological treatments
Multiscale representations for ODF denoising in diffusion spectrum imaging
No full textThe established methods for today's clinical applications include the use of the diffusion Magnetic Resonance Imaging (dMRI). The proposed work concerns wavelet-based denoising of the Diffusion Spectrum Imaging (DSI) data. Both simulated data and real brain data are considered. Diffusion data are first reconstructed by inverse Fourier transform and then projected to the multiscale domain by 3D wavelet transform. The 3D extensions of both critically sampled (Discrete Wavelet Transformation, DWT) and overcomplete representations (Stationary Wavelet Transformation, SWT) have been considered and applied to the 3D reconstructed diffusion propagator. Then, denoising has been performed by (soft/hard) thresholding. The two-fiber crossing case has been considered for both the synthetic DSI data and real data. Simulation data for fiber crossings with different fiber-crossing angles (45, 60, 90 degree), Rician-noise SNR (10, 15, 20, 30, 50, 100 db) using 514-point grid sampling scheme and the maximum b-value of 6000 s/mm-square were generated. Simulations were repeated 100 times. The Kullback-LeiblerDivergence (KLD) has been used to evaluate the performance of the denoising algorithm after signal recovery. Real data were acquired on a healthy volunteer using a 3T scanner (TIM Trio, Siemens, Erlangen, Germany). The maximum b-value was 8000 s/mm-square with 514 diffusion directions. In this case, the KLD was used to quantify the difference between the two reconstruction strategies since the ground truth was not available. Visual inspection confirmed that SWT providesbetter ODF (Orientation Distribution Function) recovery with respect to DWT. Overall, results show that the SWT algorithm provides a more reliable reconstruction of the ODF with respect to the DWT and improves DSI data denoising in sparse domains. Ongoing work includes the assessment of the improvement in terms of angular resolution
An Equivalent Transform Method for Evaluating the Effect of Water Column Mismatch in Geoacoustic Inversion
No full text- …
