Serial Correlations in Single-Subject fMRI with Sub-Second TR

When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences

Emittance measurements of Space Shuttle orbiter reinforced carbon-carbon

The spectral and total normal emittance of the Reinforced Carbon-Carbon (RCC) used on Space Shuttle nose cap and wing leading edges has been measured at room temperature and at surface temperatures of 1200 to 2100 K. These measurements were made on virgin and two flown RCC samples. Room temperature directional emittance data were also obtained and were used to determine the total hemispherical emittance of RCC as a function of temperature. Results of the total normal emittance for the virgin samples showed good agreement with the current RCC emittance design curve; however, the data from the flown samples showed an increase in the emittance at high temperature possibly due to exposure from flight environments

Ultrafiltration for acute decompensated cardiac failure: A systematic review and meta-analysis

Background Ultrafiltration is a method used to achieve diuresis in acute decompensated heart failure (ADHF) when there is diuretic resistance, but its efficacy in other settings is unclear. We therefore conducted a systematic review and meta-analysis to evaluate the use of ultrafiltration in ADHF. Methods We searched MEDLINE and EMBASE for studies that evaluated outcomes following filtration compared to diuretic therapy in ADHF. The outcomes of interest were body weight change, change in renal function, length of stay, frequency of rehospitalization, mortality and dependence on dialysis. We performed random effects meta-analyses to pool studies that evaluated the desired outcomes and assessed statistical heterogeneity using the I2 statistic. Results A total of 10 trials with 857 participants (mean age 68 years, 71% male) compared filtration to usual diuretic care in ADHF. Nine studies evaluated weight change following filtration and the pooled results suggest a decline in mean body weight − 1.8; 95% CI, − 4.68 to 0.97 kg. Pooled results showed no difference between the filtration and diuretic group in change in creatinine or estimated glomerular filtration rate. The pooled results suggest longer hospital stay with filtration (mean difference, 3.70; 95% CI, − 3.39 to 10.80 days) and a reduction in heart failure hospitalization (RR, 0.71; 95% CI, 0.51–1.00) and all-cause rehospitalization (RR, 0.89; 95% CI, 0.43–1.86) compared to the diuretic group. Filtration was associated with a non-significant greater risk of death compared to diuretic use (RR, 1.08; 95% CI, 0.77–1.52)

Cognitive control mechanisms revealed by ERP and fMRI: Evidence from repeated task-switching

We investigated the extent to which a common neural mechanism is involved in task set-switching and response withholding, factors that are frequently confounded in task-switching and go/no-go paradigms. Subjects' brain activity was measured using event-related electrical potentials (ERPs) and event-related functional MRI (fMRI) neuroimaging in separate studies using the same cognitive paradigm. Subjects made compatible left/right keypress responses to left/right arrow stimuli of 1000 msec duration; they switched every two trials between responding at stimulus onset (GO task-green arrows) and stimulus offset (WAIT task-red arrows). Withholding an immediate response (WAIT vs. GO) elicited an enhancement of the frontal N2 ERP and lateral PFC activation of the right hemisphere, both previously associated with the "no-go" response, but only on switch trials. Task-switching (switch vs. nonswitch) was associated with frontal N2 amplification and right hemisphere ventrolateral PFC activation, but only for the WAIT task. The anterior cingulate cortex (ACC) was the only brain region to be activated for both types of task switch, but this activation was located more rostrally for the WAIT than for the GO switch trials. We conclude that the frontal N2 ERP and lateral PFC activation are not markers for withholding an immediate response or switching tasks per se, hut are associated with switching into a response-suppression mode. Different regions within the ACC may be involved in two processes integral to task-switching: processing response conflict (rostral ACC) and overcoming prior response suppression (caudal ACC)

Collateral donor artery physiology and the influence of a chronic total occlusion on fractional flow reserve

Background— The presence of a concomitant chronic total coronary occlusion (CTO) and a large collateral contribution might alter the fractional flow reserve (FFR) of an interrogated vessel, rendering the FFR unreliable at predicting ischemia should the CTO vessel be revascularized and potentially affecting the decision on optimal revascularization strategy. We tested the hypothesis that donor vessel FFR would significantly change after percutaneous coronary intervention of a concomitant CTO. Methods and Results— In consecutive patients undergoing percutaneous coronary intervention of a CTO, coronary pressure and flow velocity were measured at baseline and hyperemia in proximal and distal segments of both nontarget vessels, before and after percutaneous coronary intervention. Hemodynamics including FFR, absolute coronary flow, and the coronary flow velocity–pressure gradient relation were calculated. After successful percutaneous coronary intervention in 34 of 46 patients, FFR in the predominant donor vessel increased from 0.782 to 0.810 (difference, 0.028 [0.012 to 0.044]; P=0.001). Mean decrease in baseline donor vessel absolute flow adjusted for rate pressure product: 177.5 to 139.9 mL/min (difference −37.6 [−62.6 to −12.6]; P=0.005), mean decrease in hyperemic flow: 306.5 to 272.9 mL/min (difference, −33.5 [−58.7 to −8.3]; P=0.011). Change in predominant donor vessel FFR correlated with angiographic (%) diameter stenosis severity (r=0.44; P=0.009) and was strongly related to stenosis severity measured by the coronary flow velocity–pressure gradient relation (r=0.69; P<0.001). Conclusions— Recanalization of a CTO results in a modest increase in the FFR of the predominant collateral donor vessel associated with a reduction in coronary flow. A larger increase in FFR is associated with greater coronary stenosis severity

The metabolism of anabolic-androgenic steroids in the greyhound

BACKGROUND Effective control of the use of anabolic-androgenic steroids (AASs) in animal sports is essential in order to ensure both animal welfare and integrity. In order to better police their use in Australian and New Zealand greyhound racing, thorough metabolic studies have been carried out on a range of registered human and veterinary AASs available in the region. RESULTS Canine metabolic data are presented for the AASs boldenone, danazol, ethylestrenol, mesterolone, methandriol, nandrolone and norethandrolone. The principal Phase I metabolic processes observed were the reduction of A-ring unsaturations and/or 3-ketones with either 3α,5β- or 3β,5α-stereochemistry, the oxidation of secondary 17β-hydroxyl groups and 16α-hydroxylation. The Phase II β-glucuronylation of sterol metabolites was extensive. CONCLUSION The presented data have enabled the effective analysis of AASs and their metabolites in competition greyhound urine samples.Australian Research Council LP077483

Ready ... Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals

Plasmodium Infection Is Associated with Impaired Hepatic Dimethylarginine Dimethylaminohydrolase Activity and Disruption of Nitric Oxide Synthase Inhibitor/Substrate Homeostasis.

Inhibition of nitric oxide (NO) signaling may contribute to pathological activation of the vascular endothelium during severe malaria infection. Dimethylarginine dimethylaminohydrolase (DDAH) regulates endothelial NO synthesis by maintaining homeostasis between asymmetric dimethylarginine (ADMA), an endogenous NO synthase (NOS) inhibitor, and arginine, the NOS substrate. We carried out a community-based case-control study of Gambian children to determine whether ADMA and arginine homeostasis is disrupted during severe or uncomplicated malaria infections. Circulating plasma levels of ADMA and arginine were determined at initial presentation and 28 days later. Plasma ADMA/arginine ratios were elevated in children with acute severe malaria compared to 28-day follow-up values and compared to children with uncomplicated malaria or healthy children (p<0.0001 for each comparison). To test the hypothesis that DDAH1 is inactivated during Plasmodium infection, we examined DDAH1 in a mouse model of severe malaria. Plasmodium berghei ANKA infection inactivated hepatic DDAH1 via a post-transcriptional mechanism as evidenced by stable mRNA transcript number, decreased DDAH1 protein concentration, decreased enzyme activity, elevated tissue ADMA, elevated ADMA/arginine ratio in plasma, and decreased whole blood nitrite concentration. Loss of hepatic DDAH1 activity and disruption of ADMA/arginine homeostasis may contribute to severe malaria pathogenesis by inhibiting NO synthesis

Online survey insight report: Involving children, young people, and families in our research

Insight report summarising the findings from an online public involvement survey to help shape child, young persons, and family research in North West London

Neuro-symbolic learning of answer set programs from raw data

One of the ultimate goals of Artificial Intelligence is to assist humans in complex decision making. A promising direction for achieving this goal is Neuro-Symbolic AI, which aims to combine the interpretability of symbolic techniques with the ability of deep learning to learn from raw data. However, most current approaches require manually engineered symbolic knowledge, and where end-to-end training is considered, such approaches are either restricted to learning definite programs, or are restricted to training binary neural networks. In this paper, we introduce Neuro-Symbolic Inductive Learner (NSIL), an approach that trains a general neural network to extract latent concepts from raw data, whilst learning symbolic knowledge that maps latent concepts to target labels. The novelty of our approach is a method for biasing the learning of symbolic knowledge, based on the in-training performance of both neural and symbolic components. We evaluate NSIL on three problem domains of different complexity, including an NP-complete problem. Our results demonstrate that NSIL learns expressive knowledge, solves computationally complex problems, and achieves state-of-the-art performance in terms of accuracy and data efficiency. Code and technical appendix: https://github.com/DanCunnington/NSIL