Tissue-specific imaging is a robust methodology to differentiate in vivo T1 black holes with advanced multiple sclerosis-induced damage

BACKGROUND AND PURPOSE: Brains of patients with multiple sclerosis (MS) characteristically have "black holes" (BHs), hypointense lesions on T1-weighted (T1W) spin-echo (SE) images. Although conventional MR imaging can disclose chronic BHs (CBHs), it cannot stage the degree of their pathologic condition. Tissue-specific imaging (TSI), a recently introduced MR imaging technique, allows selective visualization of white matter (WM), gray matter (GM), and CSF on the basis of T1 values of classes of tissue. We investigated the ability of TSI-CSF to separate CBHs with longer T1 values, which likely represent lesions containing higher levels of destruction and unbound water. MATERIALS AND METHODS: Eighteen patients with MS, who had already undergone MR imaging twice (24 months apart) on a 1.5T scanner, underwent a 3T MR imaging examination. Images acquired at 1.5T included sequences of precontrast and postcontrast T1W SE, T2-weighted (T2W) SE, and magnetization transfer (MT). Sequences obtained at 3T included precontrast and postcontrast T1W SE, T2W SE, T1 inversion recovery prepared fast spoiled gradient recalled-echo (IR-FSPGR) and TSI. A BH on the 3T-IR-FSPGR was defined as a CBH if seen as a hypointense, nonenhancing lesion with a corresponding T2 abnormality for at least 24 months. CBHs were separated into 2 groups: those visible as hyperintensities on TSI-CSF (group A), and those not appearing on the TSI-CSF (group B). RESULTS: Mean MT ratios of group-A lesions (0.22 \ub1 0.06, 0.13-0.35) were lower (F 1,13 = 60.39; P < .0001) than those of group-B lesions (0.32 \ub1 0.03, 0.27-0.36). CONCLUSIONS: Group-A lesions had more advanced tissue damage; thus, TSI is a potentially valuable method for qualitative and objective identification

Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI.

Manually segmenting multiple sclerosis (MS) cortical lesions (CLs) is extremely time consuming, and past studies have shown only moderate inter-rater reliability. To accelerate this task, we developed a deep-learning-based framework (CLAIMS: Cortical Lesion AI-Based Assessment in Multiple Sclerosis) for the automated detection and classification of MS CLs with 7 T MRI. Two 7 T datasets, acquired at different sites, were considered. The first consisted of 60 scans that include 0.5 mm isotropic MP2RAGE acquired four times (MP2RAGE×4), 0.7 mm MP2RAGE, 0.5 mm T &lt;sub&gt;2&lt;/sub&gt; *-weighted GRE, and 0.5 mm T &lt;sub&gt;2&lt;/sub&gt; *-weighted EPI. The second dataset consisted of 20 scans including only 0.75 × 0.75 × 0.9 mm &lt;sup&gt;3&lt;/sup&gt; MP2RAGE. CLAIMS was first evaluated using sixfold cross-validation with single and multi-contrast 0.5 mm MRI input. Second, the performance of the model was tested on 0.7 mm MP2RAGE images after training with either 0.5 mm MP2RAGE×4, 0.7 mm MP2RAGE, or alternating the two. Third, its generalizability was evaluated on the second external dataset and compared with a state-of-the-art technique based on partial volume estimation and topological constraints (MSLAST). CLAIMS trained only with MP2RAGE×4 achieved results comparable to those of the multi-contrast model, reaching a CL true positive rate of 74% with a false positive rate of 30%. Detection rate was excellent for leukocortical and subpial lesions (83%, and 70%, respectively), whereas it reached 53% for intracortical lesions. The correlation between disability measures and CL count was similar for manual and CLAIMS lesion counts. Applying a domain-scanner adaptation approach and testing CLAIMS on the second dataset, the performance was superior to MSLAST when considering a minimum lesion volume of 6 μL (lesion-wise detection rate of 71% versus 48%). The proposed framework outperforms previous state-of-the-art methods for automated CL detection across scanners and protocols. In the future, CLAIMS may be useful to support clinical decisions at 7 T MRI, especially in the field of diagnosis and differential diagnosis of MS patients

Resistência de soja a insetos: VIII. IAC 78-2318, linhagem com resistência múltipla Resistance of soybean to insects: VIII. IAC 78-2318 line with multiple insect resistance

Estudou-se, em comparação com outros genótipos de soja, o comportamento da linhagem IAC 78-2318, em relação à oviposição e colonização da mosca-branca Bemisia tabaci (Genn.) e à área foliar consumida por besouros crisomelídeos e lagartas. Em Campinas, SP, em 1981, em casa de vegetação, submeteram-se os cultivares Santa Rosa, Paraná, BR-1, Bossier, IAC 8 e IAC 12 e as linhagens IAC 73-228, IAC 78-2318, D72-9601-1, PI 171451, PI 229358 e PI 274454 à infestação artificial de adultos da mosca-branca. IAC 78-2318, embora apresentando alto número de ovos, teve colonização baixa, próxima aos materiais mais resistentes (PI 171451 e PI 229358). Em Santo Antonio de Posse, SP, em 1985, em campo, IAC 78-2318, quando comparado com IAC 80-596-2, 'Santa Rosa', 'IAC 8' e 'IAC 11', mostrou a menor perda de área foliar devida à alimentação de coleópteros crisomelídeos, principalmente Cerotoma arcuata (Oliv.) e Diphaulaca viridipennis Clark, e de lagartas, com predominância de Anticarsia gemmatalis (Hubn.). Como já havia sido registrado anteriormente baixo dano de Epinotia aporema (Wals.) e de percevejos pentatomideos em IAC 78-2318, com as observações presentes essa linhagem fica caracterizada como portadora de resistência múltipla a insetos.<br>The performance of the soybean line IAC 78-2318 in relation to oviposition and colonization by the whitefly Bemisia tabaci (Genn.) and to defoliation by caterpillars and chrysomelidae was studied in comparison to other varieties. At Campinas, State of São Paulo - Brazil, in greenhouse, the cultivars Santa Rosa, Paraná, BR-1, Bossier, IAC 8 and IAC 12, and the lines IAC 73-228, IAC 78-2318, D72-9601-1, PI 171451, PI 229358 e PI 274454 were submitted to artificial infestation of whitefly adults from tomato plants highly infested. Despite the high number of eggs in the IAC 78-2318 folioles, this line had a low colonization, comparable to the more resistants lines (PI 171451 and PI 229358). At Santo Antonio de Posse, SP - Brazil, in the field, when compared with 'IAC 11', 'IAC 8', 'Santa Rosa' and IAC 80-596-2, the IAC 78-2318 showed the lowest defoliation due to the attack of chrysomelidae, mainly Cerotoma arcuata (Oliv.) and Diphaulaca viridipennis Clark, and caterpillars, mainly Anticarsia gemmatalis (Hubn.). The IAC 78-2318 has been cited as a line with low damage by Epinotia aporema (Wals.) and by stink bugs; so, with the present results, it was characterized the presence of multiple insect resistance in this line

Resistência de soja a insetos: IV. Comportamento de cultivares e linhagens em relação a Hedilepta indicata (Fabr.) Resistance of soybean to insects: IV. Performance of cultivars and lines in relation to Hedilepta indicata (Fabr.)

Em condições de campo e de telado, estudou-se o comportamento das seguintes linhagens e cultivares de soja em relação à lagarta-enroladeira, Hedilepta indicata (Fabr., 1775): PI 227687, IAC 73-228, IAC 79-1823, 'Santa Rosa', IAC 80-596-2, 'IAC 12', 'IAC 8', IAC 78-2318, D 72-9601-1, PI 171451, 'IAC São Carlos' e PI 229358, quanto ao número de pontos de ataque do inseto. Tanto no campo como em telado, IAC 73-228 e PI 229358 apresentaram, respectivamente, o menor e o maior valor. Em condições de campo, PI 227687 e IAC 73-228 sofreram as menores perdas de área foliar devidas ao ataque de H. indicata, enquanto 'IAC São Carlos', PI 171451 e PI 229358 se mostraram como os mais suscetíveis. Em outro experimento conduzido em campo, utilizando-se PI 274454, PI 274453, IAC 73-228, 'IAC 12', IAC 80-596-2, 'UFV-1', IAC 78-2318 e 'Paraná', os dois primeiros apresentaram alto nível de resistência ao inseto, superior ao exibido por IAC 73-228, sendo que 'Paraná' teve a maior perda de área foliar. Ainda em condições de campo, em área de seleção de plantas F2 descendentes do cruzamento de 'Paraná' com PI 274453, foram avaliadas, individualmente, plantas quanto à área foliar comida: os resultados obtidos sugerem que a resistência da PI 274453 a H. indicata seja devida a um gene dominante.<br>Performance of soybean cultivars and lines in relation to H. indicata was studied under field and screen house conditions. Differences in the number of points of attack (characterized by the rolling or junction of the folioles by means of silk secretion) were evaluated in PI 227687, IAC 73-228, IAC 79-1823, 'Santa Rosa', IAC 80-596-2, 'IAC 12', 'IAC 8', IAC 78-2318, D 72-9601-1, PI 171451, 'IAC São Carlos' and PI 229358; in both, field and screen house conditions, IAC 73-228 and PI 229358 showed, respectively, the lowest and the highest values. In the field, PI 227687 and IAC 73-228 presented low defoliation, while 'IAC São Carlos', PI 171451 and PI 229358 were highly defoliated. In another field experiment with PI 274454, PI 274453, IAC 73-228, 'IAC 12', IAC 80-596-2, 'UFV-1', IAC 78-2318 and 'Paraná', the first two varieties were highly resistant to H indicata The level of resistance of the PI's was superior to that of IAC 73-228. 'Paraná' was the most susceptible variety. Under field conditions, in an area of selection of F2 plants descendents of crosses between 'Paraná' and PI 274453, 63 plants were evaluated in relation to the eaten leaf area; the results suggest that the PI 274453 resistance to H. indicata is inherited in this cross as dominant simple, being possible to transfer it to commercial varieties