Adults with autism spectrum disorders exhibit decreased sensitivity to reward parameters when making effort-based decisions.

BACKGROUND: Efficient effort expenditure to obtain rewards is critical for optimal goal-directed behavior and learning. Clinical observation suggests that individuals with autism spectrum disorders (ASD) may show dysregulated reward-based effort expenditure, but no behavioral study to date has assessed effort-based decision-making in ASD. METHODS: The current study compared a group of adults with ASD to a group of typically developing adults on the Effort Expenditure for Rewards Task (EEfRT), a behavioral measure of effort-based decision-making. In this task, participants were provided with the probability of receiving a monetary reward on a particular trial and asked to choose between either an "easy task" (less motoric effort) for a small, stable reward or a "hard task" (greater motoric effort) for a variable but consistently larger reward. RESULTS: Participants with ASD chose the hard task more frequently than did the control group, yet were less influenced by differences in reward value and probability than the control group. Additionally, effort-based decision-making was related to repetitive behavior symptoms across both groups. CONCLUSIONS: These results suggest that individuals with ASD may be more willing to expend effort to obtain a monetary reward regardless of the reward contingencies. More broadly, results suggest that behavioral choices may be less influenced by information about reward contingencies in individuals with ASD. This atypical pattern of effort-based decision-making may be relevant for understanding the heightened reward motivation for circumscribed interests in ASD.

Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography

Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.

Introduction of novel video-based tasks to clinical fMRI: Comparing traditional fMRI tasks with novel video-based tasks for mapping brain language areas

Recently, blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has become a routine clinical procedure for localization of language and motor brain regions and has been replacing more invasive preoperative procedures. However, the fMRI results from these tasks are not always reproducible even from the same patient. Evaluating the reproducibility of language and speech mapping is especially complicated due to the complex brain circuitry that may become activated during the functional task. Non-language areas such as sensory, attention, decision-making, and motor brain regions may also be activated in addition to the specific language regions during a traditional sentence-completion task. In this study, I test a new approach, which utilizes 4-minute video-based tasks, to map language and speech brain regions for patients undergoing brain surgery. Results from 35 subjects have shown that the video-based task activates Wernicke’s area, as well as Broca’s area in most subjects. The computed laterality indices, which indicate the dominant hemisphere from that functional task, have indicated left dominance from the video-based tasks. This study has shown that the video-based task may be an alternative method for localization of language and speech brain regions for patients who are unable to complete the sentence-completion task.

High susceptibility of MDR and XDR Gram-negative pathogens to biphenyl-diacetylene-based difluoromethyl-allo-threonyl-hydroxamate LpxC inhibitors.

OBJECTIVES: Inhibitors of uridine diphosphate-3-O-(R-3-hydroxymyristoyl)-N-acetylglucosamine deacetylase (LpxC, which catalyses the first, irreversible step in lipid A biosynthesis) are a promising new class of antibiotics against Gram-negative bacteria. The objectives of the present study were to: (i) compare the antibiotic activities of three LpxC inhibitors (LPC-058, LPC-011 and LPC-087) and the reference inhibitor CHIR-090 against Gram-negative bacilli (including MDR and XDR isolates); and (ii) investigate the effect of combining these inhibitors with conventional antibiotics. METHODS: MICs were determined for 369 clinical isolates (234 Enterobacteriaceae and 135 non-fermentative Gram-negative bacilli). Time-kill assays with LPC-058 were performed on four MDR/XDR strains, including Escherichia coli producing CTX-M-15 ESBL and Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii producing KPC-2, VIM-1 and OXA-23 carbapenemases, respectively. RESULTS: LPC-058 was the most potent antibiotic and displayed the broadest spectrum of antimicrobial activity, with MIC90 values for Enterobacteriaceae, P. aeruginosa, Burkholderia cepacia and A. baumannii of 0.12, 0.5, 1 and 1 mg/L, respectively. LPC-058 was bactericidal at 1× or 2× MIC against CTX-M-15, KPC-2 and VIM-1 carbapenemase-producing strains and bacteriostatic at ≤4× MIC against OXA-23 carbapenemase-producing A. baumannii. Combinations of LPC-058 with β-lactams, amikacin and ciprofloxacin were synergistic against these strains, albeit in a species-dependent manner. LPC-058's high efficacy was attributed to the presence of the difluoromethyl-allo-threonyl head group and a linear biphenyl-diacetylene tail group. CONCLUSIONS: These in vitro data highlight the therapeutic potential of the new LpxC inhibitor LPC-058 against MDR/XDR strains and set the stage for subsequent in vivo studies.