Introduction: A number of previous studies examined a possible association of toxic metals and autism, and over half of those studies suggest that toxic metal levels are different in individuals with Autism Spectrum Disorders (ASD). Additionally, several studies found that those levels correlate with the severity of ASD.

Methods: In order to further investigate these points, this paper performs the most detailed statistical analysis to date of a data set in this field. First morning urine samples were collected from 67 children and adults with ASD and 50 neurotypical controls of similar age and gender. The samples were analyzed to determine the levels of 10 urinary toxic metals (UTM). Autism-related symptoms were assessed with eleven behavioral measures. Statistical analysis was used to distinguish participants on the ASD spectrum and neurotypical participants based upon the UTM data alone. The analysis also included examining the association of autism severity with toxic metal excretion data using linear and nonlinear analysis. “Leave-one-out” cross-validation was used to ensure statistical independence of results.

Results and Discussion: Average excretion levels of several toxic metals (lead, tin, thallium, antimony) were significantly higher in the ASD group. However, ASD classification using univariate statistics proved difficult due to large variability, but nonlinear multivariate statistical analysis significantly improved ASD classification with Type I/II errors of 15% and 18%, respectively. These results clearly indicate that the urinary toxic metal excretion profiles of participants in the ASD group were significantly different from those of the neurotypical participants. Similarly, nonlinear methods determined a significantly stronger association between the behavioral measures and toxic metal excretion. The association was strongest for the Aberrant Behavior Checklist (including subscales on Irritability, Stereotypy, Hyperactivity, and Inappropriate Speech), but significant associations were found for UTM with all eleven autism-related assessments with cross-validation R² values ranging from 0.12–0.48.

Background: The relationship between relative metabolic disturbances and developmental disorders is an emerging research focus. This study compares the nutritional and metabolic status of children with autism with that of neurotypical children and investigates the possible association of autism severity with biomarkers.

Method:Participants were children ages 5-16 years in Arizona with Autistic Spectrum Disorder (n = 55) compared with non-sibling, neurotypical controls (n = 44) of similar age, gender and geographical distribution. Neither group had taken any vitamin/mineral supplements in the two months prior to sample collection. Autism severity was assessed using the Pervasive Development Disorder Behavior Inventory (PDD-BI), Autism Treatment Evaluation Checklist (ATEC), and Severity of Autism Scale (SAS). Study measurements included: vitamins, biomarkers of vitamin status, minerals, plasma amino acids, plasma glutathione, and biomarkers of oxidative stress, methylation, sulfation and energy production.

Results: Biomarkers of children with autism compared to those of controls using a t-test or Wilcoxon test found the following statistically significant differences (p < 0.001): Low levels of biotin, plasma glutathione, RBC SAM, plasma uridine, plasma ATP, RBC NADH, RBC NADPH, plasma sulfate (free and total), and plasma tryptophan; also high levels of oxidative stress markers and plasma glutamate. Levels of biomarkers for the neurotypical controls were in good agreement with accessed published reference ranges. In the Autism group, mean levels of vitamins, minerals, and most amino acids commonly measured in clinical care were within published reference ranges. A stepwise, multiple linear regression analysis demonstrated significant associations between several groups of biomarkers with all three autism severity scales, including vitamins (adjusted R[superscript 2] of 0.25-0.57), minerals (adj. R[superscript 2] of 0.22-0.38), and plasma amino acids (adj. R[superscript 2] of 0.22-0.39).

Conclusion: The autism group had many statistically significant differences in their nutritional and metabolic status, including biomarkers indicative of vitamin insufficiency, increased oxidative stress, reduced capacity for energy transport, sulfation and detoxification. Several of the biomarker groups were significantly associated with variations in the severity of autism. These nutritional and metabolic differences are generally in agreement with other published results and are likely amenable to nutritional supplementation. Research investigating treatment and its relationship to the co-morbidities and etiology of autism is warranted.

Background: Children with autism have often been reported to have gastrointestinal problems that are more frequent and more severe than in children from the general population.

Methods: Gastrointestinal flora and gastrointestinal status were assessed from stool samples of 58 children with Autism Spectrum Disorders (ASD) and 39 healthy typical children of similar ages. Stool testing included bacterial and yeast culture tests, lysozyme, lactoferrin, secretory IgA, elastase, digestion markers, short chain fatty acids (SCFA's), pH, and blood presence. Gastrointestinal symptoms were assessed with a modified six-item GI Severity Index (6-GSI) questionnaire, and autistic symptoms were assessed with the Autism Treatment Evaluation Checklist (ATEC).

Results: Gastrointestinal symptoms (assessed by the 6-GSI) were strongly correlated with the severity of autism (assessed by the ATEC), (r = 0.59, p < 0.001). Children with 6-GSI scores above 3 had much higher ATEC Total scores than those with 6-GSI-scores of 3 or lower (81.5 +/- 28 vs. 49.0 +/- 21, p = 0.00002).
Children with autism had much lower levels of total short chain fatty acids (-27%, p = 0.00002), including lower levels of acetate, proprionate, and valerate; this difference was greater in the children with autism taking probiotics, but also significant in those not taking probiotics. Children with autism had lower levels of species of Bifidobacter (-43%, p = 0.002) and higher levels of species of Lactobacillus (+100%, p = 0.00002), but similar levels of other bacteria and yeast using standard culture growth-based techniques. Lysozyme was somewhat lower in children with autism (-27%, p = 0.04), possibly associated with probiotic usage. Other markers of digestive function were similar in both groups.

Conclusions: The strong correlation of gastrointestinal symptoms with autism severity indicates that children with more severe autism are likely to have more severe gastrointestinal symptoms and vice versa. It is possible that autism symptoms are exacerbated or even partially due to the underlying gastrointestinal problems. The low level of SCFA's was partly associated with increased probiotic use, and probably partly due to either lower production (less sacchrolytic fermentation by beneficial bacteria and/or lower intake of soluble fiber) and/or greater absorption into the body (due to longer transit time and/or increased gut permeability).

The electronic structure of eight zinc-centered porphyrin macrocyclic molecules are investigated using density functional theory for ground-state properties, time-dependent density functional theory (TDDFT) for excited states, and Franck-Condon (FC) analysis for further characterization of the UV-vis spectrum. Symmetry breaking was utilized to find the lowest energy of the excited states for many states in the spectra. To confirm the theoretical modeling, the spectroscopic result from zinc phthalocyanine (ZnPc) is used to compare to the TDDFT and FC result. After confirmation of the modeling, five more planar molecules are investigated: zinc tetrabenzoporphyrin (ZnTBP), zinc tetrabenzomonoazaporphyrin (ZnTBMAP), zinc tetrabenzocisdiazaporphyrin (ZnTBcisDAP), zinc tetrabenzotransdiazaporphyrin (ZnTBtransDAP), and zinc tetrabenzotriazaporphyrin (ZnTBTrAP). The two latter molecules are then compared to their phenylated sister molecules: zinc monophenyltetrabenzotriazaporphyrin (ZnMPTBTrAP) and zinc diphenyltetrabenzotransdiazaporphyrin (ZnDPTBtransDAP). The spectroscopic results from the synthesis of ZnMPTBTrAP and ZnDPTBtransDAP are then compared to their theoretical models and non-phenylated pairs. While the Franck-Condon results were not as illuminating for every B-band, the Q-band results were successful in all eight molecules, with a considerable amount of spectral analysis in the range of interest between 300 and 750 nm. The π-π^* transitions are evident in the results for all of the Q bands, while satellite vibrations are also visible in the spectra. In particular, this investigation finds that, while ZnPc has a D_4h symmetry at ground state, a C_4v symmetry is predicted in the excited-state Q band region. The theoretical results for ZnPc found an excitation energy at the Q-band 0-0 transition of 1.88 eV in vacuum, which is in remarkable agreement with published gas-phase spectroscopy, as well as our own results of ZnPc in solution with Tetrahydrofuran that are provided in this paper.

We report a study of the magnetic domain structure of nanocrystalline thin films of nickel-zinc ferrite. The ferrite films were synthesized using aqueous spin-spray coating at low temperature (∼90 °C) and showed high complex permeability in the GHz range. Electron microscopy and microanalysis revealed that the films consisted of columnar grains with uniform chemical composition. Off-axis electron holography combined with magnetic force microscopy indicated a multi-grain domain structure with in-plane magnetization. The correlation between the magnetic domain morphology and crystal structure is briefly discussed.

The tensile stress–strain response of a fiber reinforced concrete dominates the performance under many loading conditions and applications. To represent this property as an average equivalent response, a back-calculation process from flexural testing is employed. The procedure is performed by model fitting of the three-point and four-point bending load deflection data on two types of macro synthetic polymeric fibers, one type of steel fiber and one type of Alkali Resistant (AR) glass fiber. A strain softening tensile model is used to simulate the behavior of different FRC types and obtain the experimental flexural response. The stress–strain model for each age, fiber type and dosage rate is simulated by means of the inverse analysis procedure, using closed-form moment–curvature relationship and load–deflection response of the piecewise-linear material. The method of approach is further applied to one external data set for High Performance Fiber Reinforced Concrete (HPFRC) with two different types of steel fibers and validated by tensile test results reported. Results of back-calculation of stress–strain responses by tri-linear tensile model for all mixtures are compared and correlated with the corresponding standard method parameters used for post crack behavior characterization and a regression analysis for comparative evaluation of test data is presented.

This paper addresses the stochastic modeling of the stiffness matrix of slender uncertain curved beams that are forced fit into a clamped–clamped fixture designed for straight beams. Because of the misfit with the clamps, the final shape of the clamped–clamped beams is not straight and they are subjected to an axial preload. Both of these features are uncertain given the uncertainty on the initial, undeformed shape of the beams and affect significantly the stiffness matrix associated with small motions around the clamped–clamped configuration. A modal model using linear modes of the straight clamped–clamped beam with a randomized stiffness matrix is employed to characterize the linear dynamic behavior of the uncertain beams. This stiffness matrix is modeled using a mixed nonparametric–parametric stochastic model in which the nonparametric (maximum entropy) component is used to model the uncertainty in final shape while the preload is explicitly, parametrically included in the stiffness matrix representation.

Finally, a maximum likelihood framework is proposed for the identification of the parameters associated with the uncertainty level and the mean model, or part thereof, using either natural frequencies only or natural frequencies and mode shape information of the beams around their final clamped–clamped state. To validate these concepts, three simulated, computational experiments were conducted within Nastran to produce populations of natural frequencies and mode shapes of uncertain slender curved beams after clamping. The three experiments differed from each other by the nature of the clamping condition in the in-plane direction. One experiment assumed a no-slip condition (zero in-plane displacement), another a perfect slip (no in-plane force), while the third one invoked friction. The first two experiments gave distributions of frequencies with similar features while the latter one yielded a strong deterministic dependence of the frequencies on each other, a situation observed and explained recently and thus not considered further here. Then, the application of the stochastic modeling concepts to the no-slip simulated data was carried out and led to a good matching of the probability density functions of the natural frequencies and the modal components, even though this information was not used in the identification process. These results strongly suggest the applicability of the proposed stochastic model.

High-resolution characterizations and predictions are a grand challenge for ecohydrology. Recent advances in flight control, robotics and miniaturized sensors using unmanned aerial vehicles (UAVs) provide an unprecedented opportunity for characterizing, monitoring and modeling ecohydrologic systems at high-resolution (<1 m) over a range of scales. How can the ecologic and hydrologic communities most effectively use UAVs for advancing the state of the art? This Innovative Viewpoints paper introduces the utility of two classes of UAVs for ecohydrologic investigations in two semiarid rangelands of the southwestern U.S. through two useful examples. We discuss the UAV deployments, the derived image, terrain and vegetation products and their usefulness for ecohydrologic studies at two different scales. Within a land-atmosphere interaction study, we utilize high-resolution imagery products from a rotary-wing UAV to characterize an eddy covariance footprint and scale up environmental sensor network observations to match the time-varying sampling area. Subsequently, in a surface and subsurface interaction study within a small watershed, we demonstrate the use of a fixed-wing UAV to characterize the spatial distribution of terrain attributes and vegetation conditions which serve as input to a distributed ecohydrologic model whose predictions compared well with an environmental sensor network. We also point to several challenges in performing ecohydrology with UAVs with the intent of promoting this new self-service (do-it-yourself) model for high-resolution image acquisition over many scales. We believe unmanned aerial vehicles can fundamentally change how ecohydrologic science is conducted and offer ways to merge remote sensing, environmental sensor networks and numerical models.

The intact nervous system has an exquisite ability to modulate the activity of multiple muscles acting at one or more joints to produce an enormous range of actions. Seemingly simple tasks, such as reaching for an object or walking, in fact rely on very complex spatial and temporal patterns of muscle activations. Neurological disorders such as stroke and focal dystonia affect the ability to coordinate multi-joint movements. This article reviews the state of the art of research of muscle synergies in the intact and damaged nervous system, their implications for recovery and rehabilitation, and proposes avenues for research aimed at restoring the nervous system’s ability to control movement.

In this paper, we present an approach to designing decentralized robot control policies that mimic certain microscopic and macroscopic behaviors of ants performing collective transport tasks. In prior work, we used a stochastic hybrid system model to characterize the observed team dynamics of ant group retrieval of a rigid load. We have also used macroscopic population dynamic models to design enzyme-inspired stochastic control policies that allocate a robotic swarm around multiple boundaries in a way that is robust to environmental variations. Here, we build on this prior work to synthesize stochastic robot attachment–detachment policies for tasks in which a robotic swarm must achieve non-uniform spatial distributions around multiple loads and transport them at a constant velocity. Three methods are presented for designing robot control policies that replicate the steady-state distributions, transient dynamics, and fluxes between states that we have observed in ant populations during group retrieval. The equilibrium population matching method can be used to achieve a desired transport team composition as quickly as possible; the transient matching method can control the transient population dynamics of the team while driving it to the desired composition; and the rate matching method regulates the rates at which robots join and leave a load during transport. We validate our model predictions in an agent-based simulation, verify that each controller design method produces successful transport of a load at a regulated velocity, and compare the advantages and disadvantages of each method.