Weeds Poisonous to Livestock

This archival publication may not reflect current scientific knowledge or recommendations. Current information available from University of Minnesota Agricultural Experiment Station: http://www.maes.umn.edu

Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma

PURPOSE: To determine whether differences in modeling implementation will impact the correction of leakage effects (from blood brain barrier disruption) and relative cerebral blood volume (rCBV) calculations as measured on T2*-weighted dynamic susceptibility-weighted contrast-enhanced (DSC)-MRI at 3T field strength. MATERIALS AND METHODS: This HIPAA-compliant study included 52 glioma patients undergoing DSC-MRI. Thirty-six patients underwent both non Preload Dose (PLD) and PLD-corrected DSC acquisitions, with sixteen patients undergoing PLD-corrected acquisitions only. For each acquisition, we generated two sets of rCBV metrics using two separate, widely published, FDA-approved commercial software packages: IB Neuro (IBN) and NordicICE (NICE). We calculated 4 rCBV metrics within tumor volumes: mean rCBV, mode rCBV, percentage of voxels with rCBV > 1.75 (%>1.75), and percentage of voxels with rCBV > 1.0 (Fractional Tumor Burden or FTB). We determined Pearson (r) and Spearman (ρ) correlations between non-PLD- and PLD-corrected metrics. In a subset of recurrent glioblastoma patients (n=25), we determined Receiver Operator Characteristic (ROC) Areas-Under-Curve (AUC) for FTB accuracy to predict the tissue diagnosis of tumor recurrence versus post-treatment effect (PTRE). We also determined correlations between rCBV and microvessel area (MVA) from stereotactic biopsies (n=29) in twelve patients. RESULTS: Using IBN, rCBV metrics correlated highly between non-PLD- and PLD-corrected conditions for FTB (r=0.96, ρ=0.94), %>1.75 (r=0.93, ρ=0.91), mean (r=0.87, ρ=0.86) and mode (r=0.78, ρ=0.76). These correlations dropped substantially with NICE. Using FTB, IBN was more accurate than NICE in diagnosing tumor vs PTRE (AUC=0.85 vs 0.67) (p<0.01). The highest rCBV-MVA correlations required PLD and IBN (r=0.64, ρ=0.58, p=0.001). CONCLUSIONS: Different implementations of perfusion MRI software modeling can impact the accuracy of leakage correction, rCBV calculation, and correlations with histologic benchmarks

Variação de matéria seca e de nutrientes nas folhas e nos frutos, produção de ácido ascórbico e suco, em seis cultivares de citros, durante um ciclo

De uma plantação de citros, com os cultivares T. Cravo (Citrus reticulata Blanco), L.Hamlin (Citrus sinensis (L.) Osbeck), T. Murcott (Citrus reticulata Blanco x Citrus sinensis (L.) Osbeck), L. Natal (Citrus sinensis (L.) Osbeck, L. Valencia (Citrus sinensis (L.) Osbeck) e L. Pera (Citrus sinensis (L.) Osbeck), situada na "Fazenda Sete Lagoas", no município de Mogi-Guaçu (22&deg; 22% 46&deg; 56'W.Gr.), em Latossolo Vermelho amarelo, fase arenosa, foram coletados frutos 30 dias após florescimento, até a idade da coleta comercial. No material coletado, foram determinadas a variação da matéria seca, a concentração dos macro e micronutrientes nas folhas adjacentes ao fruto, a extração de macro e micronutríentes pelos frutos, a produção de suco (ml) por fruto e a concentração de ácido ascórbico (mg/100 ml de suco). Concluiu-se que: 1. O aumento da matéria seca, intensifica-se a partir do segundo mês apos o florescimento; 2. Com exceção da T. Cravo, ocorre uma diminuição na produção de matéria seca no final do ciclo; 3. A concentração dos macro e micronutrientes nas folhas apresenta oscilações durante o desenvolvimento do fruto; 4. A ordem decrescente de extração de nutrientes é: K, N, Ca, Mg, P = S, Fe, B, Zn, Mn, Cu; 5. A capacidade de exportação de nutrientes pelos cultivares é, em ordem decrescente: L. Pera, L. Hamlin = T. Cravo, T. Murcott, L. Valencia, L. Natal; 6. A quantidade de suco produzido por fruto, oscila entre 43 a 95 ml; 7. A concentração de ácido ascórbico (mg/100 ml de suco), varia entre 30 a 95

Pesky Plants: Identification and Control of Obnoxious, Irritating and Poisonous Plants of Parks, Resorts and Beaches

This archival publication may not reflect current scientific knowledge or recommendations. Current information available from University of Minnesota Agricultural Experiment Station: http://www.maes.umn.edu