Magnetic resonance tumor regression grade (MR-TRG) to assess pathological complete response following neoadjuvant radiochemotherapy in locally advanced rectal cancer

This study aims to evaluate the feasibility of a magnetic resonance (MR) automatic method for quantitative assessment of the percentage of fibrosis developed within locally advanced rectal cancers (LARC) after neoadjuvant radiochemotherapy (RCT). A total of 65 patients were enrolled in the study and MR studies were performed on 3.0 Tesla scanner; patients were followed-up for 30 months. The percentage of fibrosis was quantified on T2-weighted images, using automatic K-Means clustering algorithm. According to the percentage of fibrosis, an optimal cut-off point for separating patients into favorable and unfavorable pathologic response groups was identified by ROC analysis and tumor regression grade (MR-TRG) classes were determined and compared to histopathologic TRG. An optimal cut-off point of 81% of fibrosis was identified to differentiate between favorable and unfavorable pathologic response groups resulting in a sensitivity of 78.26% and a specificity of 97.62% for the identification of complete responders (CRs). Interobserver agreement was good (0.85). The agreement between P-TRG and MR-TRG was excellent (0.923). Significant differences in terms of overall survival (OS) and disease free survival (DFS) were found between favorable and unfavorable pathologic response groups. The automatic quantification of fibrosis determined by MR is feasible and reproducible

Skeletal anomalies in dusky grouper Epinephelus marginatus (Lowe 1834) juveniles reared with different methodologies and larval densities

The first attempts to reproduce dusky grouper (Epinephelus marginatus, Lowe 1834) under controlled conditions started in 1995, but the egg and larvae quality was very low. Mass production is still encountering many difficulties, mainly concentrated in the larval period when very high mortality rates are observed, confirming what has been observed in the rearing of other grouper species. The main bottlenecks have been identified as the difficulty to properly nourish the larvae, stress shock syndrome, and the high deformation rates. We analysed 633 dusky grouper larvae and juveniles (0.2–7.2 cm total length, TL), sampled during two larval rearing cycles carried out in 2001 and 2002 in Italy. The specimens at different development stages were stained in toto for bone and cartilage and examined for skeletal anomalies during dusky grouper ontogenesis. The incidence of anomalies in groupers hatched from the same egg batch but reared using two different methods (green waters and semi-intensive rearing) and three stocking densities (8, 16 and 28 larvae/l) was compared, with a view to providing tools for identifying the most appropriate larval rearing method in order to at least limit the onset of skeletal anomalies. Our results suggest that during development no particular skeletal anomaly patterns (or fate) can be clearly identified as a high variability was observed in malformation typologies and the regions affected. No significant differences in the morphological quality between groupers reared using semi-intensive (LV02 lot) and green water (GW02-01 lot) methodologies were observed, whilst groupers reared at the highest stocking density (28 larvae/l) showed the highest frequency of deformed individuals (75.8%), the highest malformation charge (average of 5.5 anomalies per deformed individual), the largest range of anomaly typologies (38), and the highest incidence of individuals with at least one severe anomaly (30.9%). Whilst in green waters no evident effects of larvae density were observed on survival rates, the survival rate in large volume reared individuals (17.5%) was considerably higher with respect to those reared in green waters (0.2%) at 7–8 larvae/l. This indicates that the semi-intensive methodology should be considered more effective in enhancing the survival rate of dusky grouper larvae

Fractal dimension of cerebral white matter : A consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment

Patients with cerebral small vessel disease (SVD) frequently show decline in cognitive performance. However, neuroimaging in SVD patients discloses a wide range of brain lesions and alterations so that it is often difficult to understand which of these changes are the most relevant for cognitive decline. It has also become evident that visually-rated alterations do not fully explain the neuroimaging correlates of cognitive decline in SVD. Fractal dimension (FD), a unitless feature of structural complexity that can be computed from high-resolution T1-weighted images, has been recently applied to the neuroimaging evaluation of the human brain. Indeed, white matter (WM) and cortical gray matter (GM) exhibit an inherent structural complexity that can be measured through the FD. In our study, we included 64 patients (mean age \ub1 standard deviation, 74.6 \ub1 6.9, education 7.9 \ub1 4.2 years, 53% males) with SVD and mild cognitive impairment (MCI), and a control group of 24 healthy subjects (mean age \ub1 standard deviation, 72.3 \ub1 4.4 years, 50% males). With the aim of assessing whether the FD values of cerebral WM (WM FD) and cortical GM (GM FD) could be valuable structural predictors of cognitive performance in patients with SVD and MCI, we employed a machine learning strategy based on LASSO (least absolute shrinkage and selection operator) regression applied on a set of standard and advanced neuroimaging features in a nested cross-validation (CV) loop. This approach was aimed at 1) choosing the best predictive models, able to reliably predict the individual neuropsychological scores sensitive to attention and executive dysfunctions (prominent features of subcortical vascular cognitive impairment) and 2) identifying a features ranking according to their importance in the model through the assessment of the out-of-sample error. For each neuropsychological test, using 1000 repetitions of LASSO regression and 5000 random permutations, we found that the statistically significant models were those for the Montreal Cognitive Assessment scores (p-value =.039), Symbol Digit Modalities Test scores (p-value =.039), and Trail Making Test Part A scores (p-value =.025). Significant prediction of these scores was obtained using different sets of neuroimaging features in which the WM FD was the most frequently selected feature. In conclusion, we showed that a machine learning approach could be useful in SVD research field using standard and advanced neuroimaging features. Our study results raise the possibility that FD may represent a consistent feature in predicting cognitive decline in SVD that can complement standard imaging

Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set

Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS

Infrared absorption from Charge Density Waves in magnetic manganites

The infrared absorption of charge density waves coupled to a magnetic background is first observed in two manganites La{1-x}Ca{x}MnO{3} with x = 0.5 and x = 0.67. In both cases a BCS-like gap 2 Delta (T), which for x=0.5 follows the hysteretic ferro-antiferromagnetic transition, fully opens at a finite T{0} < T{Neel}, with 2 Delta(T{0})/kT{c} close to 5. These results may also explain the unusual coexistence of charge ordering and ferromagnetism in La{0.5}Ca{0.5}MnO{3}.Comment: File revtex + 3 figs. in epsf. To appear on Phys. Rev. Let

A methodology for the capture and analysis of hybrid data: a case study of program debugging

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Split-Brain: what we know now and why this is important for understanding consciousness

Recently, the discussion regarding the consequences of cutting the corpus callosum (“split-brain”) has regained momentum (Corballis, Corballis, Berlucchi, & Marzi, 2018; Pinto et al., 2017; Pinto, Lamme, & de Haan, 2017; Volz & Gazzaniga, 2017; Volz, Hillyard, Miller, & Gazzaniga, 2018). This collective review paper aims to summarize the empirical common ground, to delineate the different interpretations, and to identify the remaining questions. In short, callosotomy leads to a broad breakdown of functional integration ranging from perception to attention. However, the breakdown is not absolute as several processes, such as action control, seem to remain unified. Disagreement exists about the responsible mechanisms for this remaining unity. The main issue concerns the first-person perspective of a split-brain patient. Does a split-brain harbor a split consciousness or is consciousness unified? The current consensus is that the body of evidence is insufficient to answer this question, and different suggestions are made to how future studies might address this paucity. In addition, it is suggested that the answers might not be a simple yes or no but that intermediate conceptualization need to be considered

Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative

The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates

Optical conductivity of the nonsuperconducting cuprate La(8-x)Sr(x)Cu(8)O(20)

La(8-x)Sr(x)Cu(8)O(20) is a non-superconducting cuprate, which exhibits a doubling of the elementary cell along the c axis. Its optical conductivity sigma (omega) has been first measured here, down to 20 K, in two single crystals with x = 1.56 and x = 2.24. Along c, sigma (omega) shows, in both samples, bands due to strongly bound charges, thus confirming that the cell doubling is due to charge ordering. In the ab plane, in addition to the Drude term one observes an infrared peak at 0.1 eV and a midinfrared band at 0.7 eV. The 0.1 eV peak hardens considerably below 200 K, in correspondence of an anomalous increase in the sample dc resistivity, in agreement with its polaronic origin. This study allows one to establish relevant similarities and differences with respect to the spectrum of the ab plane of the superconducting cuprates.Comment: Revised version submitted to Phys. Rev. B, including the elimination of Fig. 1 and changes to Figs. 4 and