Towards the Development of a Diagnostic Test for Autism Spectrum Disorder: Big Data Meets Metabolomics.

Autism spectrum disorder (ASD) is defined as a neurodevelopmental disorder which results in impairments in social communications and interactions as well as repetitive behaviors. Despite current estimates showing that approximately 2.2% of children are affected in the United States, relatively little about ASD pathophysiology is known in part due to the highly heterogenous presentation of the disorder. Given the limited knowledge into the biological mechanisms governing its etiology, the diagnosis of ASD is performed exclusively based on an individual's behavior assessed by a clinician through psychometric tools. Although there is no readily available biochemical test for ASD diagnosis, multivariate statistical methods show considerable potential for effectively leveraging multiple biochemical measurements for classification and characterization purposes. In this work, markers associated with the folate dependent one-carbon metabolism and transulfuration (FOCM/TS) pathways analyzed via both Fisher Discriminant Analysis and Support Vector Machine showed strong capability to distinguish between ASD and TD cohorts. Furthermore, using Kernel Partial Least Squares regression it was possible to assess some degree of behavioral severity from metabolomic data. While the results presented need to be replicated in independent future studies, they represent a promising avenue for uncovering clinically relevant ASD biomarkers.

KIF3A accelerates KIF3C within the kinesin-2 heterodimer to generate symmetrical phosphate release rates for each processive step.

Heterodimeric KIF3AC is a mammalian kinesin-2 that is highly expressed in the central nervous system and associated with vesicles in neurons. KIF3AC is an intriguing member of the kinesin-2 family because the intrinsic kinetics of KIF3A and KIF3C when expressed as homodimers and analyzed in vitro are distinctively different from each other. For example, the single-molecule velocities of the engineered homodimers KIF3AA and KIF3CC are 293 and 7.5 nm/s, respectively, whereas KIF3AC has a velocity of 186 nm/s. These results led us to hypothesize that heterodimerization alters the intrinsic catalytic properties of the two heads, and an earlier computational analysis predicted that processive steps would alternate between a fast step for KIF3A followed by a slow step for KIF3C resulting in asymmetric stepping. To test this hypothesis directly, we measured the presteady-state kinetics of phosphate release for KIF3AC, KIF3AA, and KIF3CC followed by computational modeling of the KIF3AC phosphate release transients. The results reveal that KIF3A and KIF3C retain their intrinsic ATP-binding and hydrolysis kinetics. Yet within KIF3AC, KIF3A activates the rate of phosphate release for KIF3C such that the coupled steps of phosphate release and dissociation from the microtubule become more similar for KIF3A and KIF3C. These coupled steps are the rate-limiting transition for the ATPase cycle suggesting that within KIF3AC, the stepping kinetics are similar for each head during the processive run. Future work will be directed to define how these properties enable KIF3AC to achieve its physiological functions.

Shotgun Metagenomics Study Suggests Alteration in Sulfur Metabolism and Oxidative Stress in Children with Autism and Improvement after Microbiota Transfer Therapy.

Links between gut microbiota and autism spectrum disorder (ASD) have been explored in many studies using 16S rRNA gene amplicon and shotgun sequencing. Based on these links, microbiome therapies have been proposed to improve gastrointestinal (GI) and ASD symptoms in ASD individuals. Previously, our open-label microbiota transfer therapy (MTT) study provided insight into the changes in the gut microbial community of children with ASD after MTT and showed significant and long-term improvement in ASD and GI symptoms. Using samples from the same study, the objective of this work was to perform a deeper taxonomic and functional analysis applying shotgun metagenomic sequencing. Taxonomic analyses revealed that ASD Baseline had many bacteria at lower relative abundances, and their abundance increased after MTT. The relative abundance of fiber consuming and beneficial microbes including Prevotella (P. dentalis, P. enoeca, P. oris, P. meloninogenica), Bifidobacterium bifidum, and a sulfur reducer Desulfovibrio piger increased after MTT-10wks in children with ASD compared to Baseline (consistent at genus level with the previous 16S rRNA gene study). Metabolic pathway analysis at Baseline compared to typically developing (TD) children found an altered abundance of many functional genes but, after MTT, they became similar to TD or donors. Important functional genes that changed included: genes encoding enzymes involved in folate biosynthesis, sulfur metabolism and oxidative stress. These results show that MTT treatment not only changed the relative abundance of important genes involved in metabolic pathways, but also seemed to bring them to a similar level to the TD controls. However, at a two-year follow-up, the microbiota and microbial genes shifted into a new state, distinct from their levels at Baseline and distinct from the TD group. Our current findings suggest that microbes from MTT lead to initial improvement in the metabolic profile of children with ASD, and major additional changes at two years post-treatment. In the future, larger cohort studies, mechanistic in vitro experiments and metatranscriptomics studies are recommended to better understand the role of these specific microbes, functional gene expression, and metabolites relevant to ASD.

Altered metabolism of mothers of young children with Autism Spectrum Disorder: a case control study.

BACKGROUND: Previous research studies have demonstrated abnormalities in the metabolism of mothers of young children with autism. METHODS: Metabolic analysis was performed on blood samples from 30 mothers of young children with Autism Spectrum Disorder (ASD-M) and from 29 mothers of young typically-developing children (TD-M). Targeted metabolic analysis focusing on the folate one-carbon metabolism (FOCM) and the transsulfuration pathway (TS) as well as broad metabolic analysis were performed. Statistical analysis of the data involved both univariate and multivariate statistical methods. RESULTS: Univariate analysis revealed significant differences in 5 metabolites from the folate one-carbon metabolism and the transsulfuration pathway and differences in an additional 48 metabolites identified by broad metabolic analysis, including lower levels of many carnitine-conjugated molecules. Multivariate analysis with leave-one-out cross-validation allowed classification of samples as belonging to one of the two groups of mothers with 93% sensitivity and 97% specificity with five metabolites. Furthermore, each of these five metabolites correlated with 8-15 other metabolites indicating that there are five clusters of correlated metabolites. In fact, all but 5 of the 50 metabolites with the highest area under the receiver operating characteristic curve were associated with the five identified groups. Many of the abnormalities appear linked to low levels of folate, vitamin B12, and carnitine-conjugated molecules. CONCLUSIONS: Mothers of children with ASD have many significantly different metabolite levels compared to mothers of typically developing children at 2-5 years after birth.

MODELING INTER-KINGDOM REGULATION OF INFLAMMATORY SIGNALING IN HUMAN INTESTINAL EPITHELIAL CELLS.

The human gastrointestinal (GI) tract is colonized by a highly diverse and complex microbial community (i.e., microbiota). The microbiota plays an important role in the development of the immune system, specifically mediating inflammatory responses, however the exact mechanisms are poorly understood. We have developed a mathematical model describing the effect of indole on host inflammatory signaling in HCT-8 human intestinal epithelial cells. In this model, indole modulates transcription factor nuclear factor κ B (NF-κB) and produces the chemokine interleukin-8 (IL-8) through the activation of the aryl hydrocarbon receptor (AhR). Phosphorylated NF-κB exhibits dose and time-dependent responses to indole concentrations and IL-8 production shows a significant down-regulation for 0.1 ng/mL TNF-α stimulation. The model shows agreeable simulation results with the experimental data for IL-8 secretion and normalized NF-κB values. Our results suggest that microbial metabolites such as indole can modulate inflammatory signaling in HTC-8 cells through receptor-mediated processes.

Kinesin-2 heterodimerization alters entry into a processive run along the microtubule but not stepping within the run.

Heterodimeric KIF3AC and KIF3AB, two members of the mammalian kinesin-2 family, generate force for microtubule plus end-directed cargo transport. However, the advantage of heterodimeric kinesins over homodimeric ones is not well-understood. We showed previously that microtubule association for entry into a processive run was similar in rate for KIF3AC and KIF3AB at ∼7.0 µm-1 s-1 Yet, for engineered homodimers of KIF3AA and KIF3BB, this constant is significantly faster at 11 µm-1 s-1 and much slower for KIF3CC at 2.1 µm-1 s-1 These results led us to hypothesize that heterodimerization of KIF3A with KIF3C and KIF3A with KIF3B altered the intrinsic catalytic properties of each motor domain. Here, we tested this hypothesis by using presteady-state stopped-flow kinetics and mathematical modeling. Surprisingly, the modeling revealed that the catalytic properties of KIF3A and KIF3B/C were altered upon microtubule binding, yet each motor domain retained its relative intrinsic kinetics for ADP release and subsequent ATP binding and the nucleotide-promoted transitions thereafter. These results are consistent with the interpretation that for KIF3AB, each motor head is catalytically similar and therefore each step is approximately equivalent. In contrast, for KIF3AC the results predict that the processive steps will alternate between a fast step for KIF3A followed by a slow step for KIF3C resulting in asymmetric stepping during a processive run. This study reveals the impact of heterodimerization of the motor polypeptides for microtubule association to start the processive run and the fundamental differences in the motile properties of KIF3C compared with KIF3A and KIF3B.

Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation.

The number of diagnosed cases of Autism Spectrum Disorders (ASD) has increased dramatically over the last four decades; however, there is still considerable debate regarding the underlying pathophysiology of ASD. This lack of biological knowledge restricts diagnoses to be made based on behavioral observations and psychometric tools. However, physiological measurements should support these behavioral diagnoses in the future in order to enable earlier and more accurate diagnoses. Stepping towards this goal of incorporating biochemical data into ASD diagnosis, this paper analyzes measurements of metabolite concentrations of the folate-dependent one-carbon metabolism and transulfuration pathways taken from blood samples of 83 participants with ASD and 76 age-matched neurotypical peers. Fisher Discriminant Analysis enables multivariate classification of the participants as on the spectrum or neurotypical which results in 96.1% of all neurotypical participants being correctly identified as such while still correctly identifying 97.6% of the ASD cohort. Furthermore, kernel partial least squares is used to predict adaptive behavior, as measured by the Vineland Adaptive Behavior Composite score, where measurement of five metabolites of the pathways was sufficient to predict the Vineland score with an R2 of 0.45 after cross-validation. This level of accuracy for classification as well as severity prediction far exceeds any other approach in this field and is a strong indicator that the metabolites under consideration are strongly correlated with an ASD diagnosis but also that the statistical analysis used here offers tremendous potential for extracting important information from complex biochemical data sets.

Drosophila Ncd reveals an evolutionarily conserved powerstroke mechanism for homodimeric and heterodimeric kinesin-14s.

Drosophila melanogaster kinesin-14 Ncd cross-links parallel microtubules at the spindle poles and antiparallel microtubules within the spindle midzone to play roles in bipolar spindle assembly and proper chromosome distribution. As observed for Saccharomyces cerevisiae kinesin-14 Kar3Vik1 and Kar3Cik1, Ncd binds adjacent microtubule protofilaments in a novel microtubule binding configuration and uses an ATP-promoted powerstroke mechanism. The hypothesis tested here is that Kar3Vik1 and Kar3Cik1, as well as Ncd, use a common ATPase mechanism for force generation even though the microtubule interactions for both Ncd heads are modulated by nucleotide state. The presteady-state kinetics and computational modeling establish an ATPase mechanism for a powerstroke model of Ncd that is very similar to those determined for Kar3Vik1 and Kar3Cik1, although these heterodimers have one Kar3 catalytic motor domain and a Vik1/Cik1 partner motor homology domain whose interactions with microtubules are not modulated by nucleotide state but by strain. The results indicate that both Ncd motor heads bind the microtubule lattice; two ATP binding and hydrolysis events are required for each powerstroke; and a slow step occurs after microtubule collision and before the ATP-promoted powerstroke. Note that unlike conventional myosin-II or other processive molecular motors, Ncd requires two ATP turnovers rather than one for a single powerstroke-driven displacement or step. These results are significant because all metazoan kinesin-14s are homodimers, and the results presented show that despite their structural and functional differences, the heterodimeric and homodimeric kinesin-14s share a common evolutionary structural and mechanochemical mechanism for force generation.

Differences in fecal microbial metabolites and microbiota of children with autism spectrum disorders.

Evidence supporting that gut problems are linked to ASD symptoms has been accumulating both in humans and animal models of ASD. Gut microbes and their metabolites may be linked not only to GI problems but also to ASD behavior symptoms. Despite this high interest, most previous studies have looked mainly at microbial structure, and studies on fecal metabolites are rare in the context of ASD. Thus, we aimed to detect fecal metabolites that may be present at significantly different concentrations between 21 children with ASD and 23 neurotypical children and to investigate its possible link to human gut microbiome. Using 1H-NMR spectroscopy and 16S rRNA gene amplicon sequencing, we examined metabolite profiles and microbial compositions in fecal samples, respectively. Of the 59 metabolites detected, isopropanol concentrations were significantly higher in feces of children with ASD after multiple testing corrections. We also observed similar trends of fecal metabolites to previous studies; children with ASD have higher fecal p-cresol and possibly lower GABA concentrations. In addition, Fisher Discriminant Analysis (FDA) with leave-out-validation suggested that a group of metabolites-caprate, nicotinate, glutamine, thymine, and aspartate-may potentially function as a modest biomarker to separate ASD participants from the neurotypical group (78% sensitivity and 81% specificity). Consistent with our previous Arizona cohort study, we also confirmed lower gut microbial diversity and reduced relative abundances of phylotypes most closely related to Prevotella copri in children with ASD. After multiple testing corrections, we also learned that relative abundances of Feacalibacterium prausnitzii and Haemophilus parainfluenzae were lower in feces of children with ASD. Despite a relatively short list of fecal metabolites, the data in this study support that children with ASD have altered metabolite profiles in feces when compared with neurotypical children and warrant further investigation of metabolites in larger cohorts.

Empirical modeling of T cell activation predicts interplay of host cytokines and bacterial indole.

Adoptive transfer of anti-inflammatory FOXP3+ Tregs has gained attention as a new therapeutic strategy for auto-inflammatory disorders such as Inflammatory Bowel Disease. The isolated cells are conditioned in vitro to obtain a sufficient number of anti-inflammatory FOXP3+ Tregs that can be reintroduced into the patient to potentially reduce the pathologic inflammatory response. Previous evidence suggests that microbiota metabolites can potentially condition cells during the in vitro expansion/differentiation step. However, the number of combinations of cytokines and metabolites that can be varied is large, preventing a purely experimental investigation which would determine optimal cell therapeutic outcomes. To address this problem, a combined experimental and modeling approached is investigated here: an artificial neural network model was trained to predict the steady-state T cell population phenotype after differentiation with a variety of host cytokines and the microbial metabolite indole. This artificial neural network model was able to both reliably predict the phenotype of these T cell populations and also uncover unexpected conditions for optimal Treg differentiation that were subsequently verified experimentally. Biotechnol. Bioeng. 2017;114: 2660-2667. © 2017 Wiley Periodicals, Inc.

Mathematical modeling of the methionine cycle and transsulfuration pathway in individuals with autism spectrum disorder.

Previous research has shown a connection between metabolic abnormalities in the methionine cycle and transsulfuration pathway and autism spectrum disorder. Using clinical data from a case-control study investigating measurements of transmethylation and transsulfuration metabolites, a steady-state model of these metabolites in liver cells was developed and participant-specific parameters were identified. Comparison of mean parameter values and parameter distributions between neurotypical study participants and those on the autism spectrum revealed significant differences for four model parameters. Sensitivity analysis identified the parameter describing the rate of glutamylcysteine synthesis, the rate-limiting step in glutathione production, to be particularly important in determining steady-state metabolite concentrations. These results may provide insight into key reactions to target for potential intervention strategies relating to autism spectrum disorder.

Continuous Glucose Monitoring Enables the Detection of Losses in Infusion Set Actuation (LISAs).

Reliable continuous glucose monitoring (CGM) enables a variety of advanced technology for the treatment of type 1 diabetes. In addition to artificial pancreas algorithms that use CGM to automate continuous subcutaneous insulin infusion (CSII), CGM can also inform fault detection algorithms that alert patients to problems in CGM or CSII. Losses in infusion set actuation (LISAs) can adversely affect clinical outcomes, resulting in hyperglycemia due to impaired insulin delivery. Prolonged hyperglycemia may lead to diabetic ketoacidosis-a serious metabolic complication in type 1 diabetes. Therefore, an algorithm for the detection of LISAs based on CGM and CSII signals was developed to improve patient safety. The LISA detection algorithm is trained retrospectively on data from 62 infusion set insertions from 20 patients. The algorithm collects glucose and insulin data, and computes relevant fault metrics over two different sliding windows; an alarm sounds when these fault metrics are exceeded. With the chosen algorithm parameters, the LISA detection strategy achieved a sensitivity of 71.8% and issued 0.28 false positives per day on the training data. Validation on two independent data sets confirmed that similar performance is seen on data that was not used for training. The developed algorithm is able to effectively alert patients to possible infusion set failures in open-loop scenarios, with limited evidence of its extension to closed-loop scenarios.

Experimental and computational optimization of an Escherichia coli co-culture for the efficient production of flavonoids.

Metabolic engineering and synthetic biology have enabled the use of microbial production platforms for the renewable production of many high-value natural products. Titers and yields, however, are often too low to result in commercially viable processes. Microbial co-cultures have the ability to distribute metabolic burden and allow for modular specific optimization in a way that is not possible through traditional monoculture fermentation methods. Here, we present an Escherichia coli co-culture for the efficient production of flavonoids in vivo, resulting in a 970-fold improvement in titer of flavan-3-ols over previously published monoculture production. To accomplish this improvement in titer, factors such as strain compatibility, carbon source, temperature, induction point, and inoculation ratio were initially optimized. The development of an empirical scaled-Gaussian model based on the initial optimization data was then implemented to predict the optimum point for the system. Experimental verification of the model predictions resulted in a 65% improvement in titer, to 40.7±0.1mg/L flavan-3-ols, over the previous optimum. Overall, this study demonstrates the first application of the co-culture production of flavonoids, the most in-depth co-culture optimization to date, and the first application of empirical systems modeling for improvement of titers from a co-culture system.

Entity linking for biomedical literature.

BACKGROUND: The Entity Linking (EL) task links entity mentions from an unstructured document to entities in a knowledge base. Although this problem is well-studied in news and social media, this problem has not received much attention in the life science domain. One outcome of tackling the EL problem in the life sciences domain is to enable scientists to build computational models of biological processes with more efficiency. However, simply applying a news-trained entity linker produces inadequate results. METHODS: Since existing supervised approaches require a large amount of manually-labeled training data, which is currently unavailable for the life science domain, we propose a novel unsupervised collective inference approach to link entities from unstructured full texts of biomedical literature to 300 ontologies. The approach leverages the rich semantic information and structures in ontologies for similarity computation and entity ranking. RESULTS: Without using any manual annotation, our approach significantly outperforms state-of-the-art supervised EL method (9% absolute gain in linking accuracy). Furthermore, the state-of-the-art supervised EL method requires 15,000 manually annotated entity mentions for training. These promising results establish a benchmark for the EL task in the life science domain. We also provide in depth analysis and discussion on both challenges and opportunities on automatic knowledge enrichment for scientific literature. CONCLUSIONS: In this paper, we propose a novel unsupervised collective inference approach to address the EL problem in a new domain. We show that our unsupervised approach is able to outperform a current state-of-the-art supervised approach that has been trained with a large amount of manually labeled data. Life science presents an underrepresented domain for applying EL techniques. By providing a small benchmark data set and identifying opportunities, we hope to stimulate discussions across natural language processing and bioinformatics and motivate others to develop techniques for this largely untapped domain.

Identifying Progression-Specific Alzheimer's Subtypes Using Multimodal Transformer.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can be used to classify subtypes during later stages of AD or related disorders, but making predictions in the asymptomatic or prodromal stage is challenging. Furthermore, the classifications of most existing models lack explainability, and these models rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into progression-specific subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (slow progressing = 177, intermediate = 302, and fast = 15) were used to train and evaluate Tri-COAT using a 10-fold stratified cross-testing approach. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by known biological mechanisms. The multimodal design behind Tri-COAT allows it to achieve the highest classification area under the receiver operating characteristic curve while simultaneously providing interpretability to the model predictions through the co-attention mechanism.

Impact of Suramin on Key Pathological Features of Sporadic Alzheimer's Disease-Derived Forebrain Neurons.

Background: Alzheimer's disease (AD) is characterized by disrupted proteostasis and macroautophagy (hereafter "autophagy"). The pharmacological agent suramin has known autophagy modulation properties with potential efficacy in mitigating AD neuronal pathology. Objective: In the present work, we investigate the impact of forebrain neuron exposure to suramin on the Akt/mTOR signaling pathway, a major regulator of autophagy, in comparison with rapamycin and chloroquine. We further investigate the effect of suramin on several AD-related biomarkers in sporadic AD (sAD)-derived forebrain neurons. Methods: Neurons differentiated from ReNcell neural progenitors were used to assess the impact of suramin on the Akt/mTOR signaling pathway relative to the autophagy inducer rapamycin and autophagy inhibitor chloroquine. Mature forebrain neurons were differentiated from induced pluripotent stem cells (iPSCs) sourced from a late-onset sAD patient and treated with 100µM suramin for 72 h, followed by assessments for amyloid-ß, phosphorylated tau, oxidative/nitrosative stress, and synaptic puncta density. Results: Suramin treatment of sAD-derived neurons partially ameliorated the increased p-Tau(S199)/Tau ratio, and fully remediated the increased glutathione to oxidized nitric oxide ratio, observed in untreated sAD-derived neurons relative to healthy controls. These positive results may be due in part to the distinct increases in Akt/mTOR pathway mediator p-p70S6K noted with suramin treatment of both ReNcell-derived and iPSC-derived neurons. Longer term neuronal markers, such as synaptic puncta density, were unaffected by suramin treatment. Conclusions: These findings provide initial evidence supporting the potential of suramin to reduce the degree of dysregulation in sAD-derived forebrain neurons in part via the modulation of autophagy.

How to obtain an integrated picture of the molecular networks involved in adaptation to microgravity in different biological systems?

Periodically, the European Space Agency (ESA) updates scientific roadmaps in consultation with the scientific community. The ESA SciSpacE Science Community White Paper (SSCWP) 9, "Biology in Space and Analogue Environments", focusses in 5 main topic areas, aiming to address key community-identified knowledge gaps in Space Biology. Here we present one of the identified topic areas, which is also an unanswered question of life science research in Space: "How to Obtain an Integrated Picture of the Molecular Networks Involved in Adaptation to Microgravity in Different Biological Systems?" The manuscript reports the main gaps of knowledge which have been identified by the community in the above topic area as well as the approach the community indicates to address the gaps not yet bridged. Moreover, the relevance that these research activities might have for the space exploration programs and also for application in industrial and technological fields on Earth is briefly discussed.

How are cell and tissue structure and function influenced by gravity and what are the gravity perception mechanisms?

Progress in mechanobiology allowed us to better understand the important role of mechanical forces in the regulation of biological processes. Space research in the field of life sciences clearly showed that gravity plays a crucial role in biological processes. The space environment offers the unique opportunity to carry out experiments without gravity, helping us not only to understand the effects of gravitational alterations on biological systems but also the mechanisms underlying mechanoperception and cell/tissue response to mechanical and gravitational stresses. Despite the progress made so far, for future space exploration programs it is necessary to increase our knowledge on the mechanotransduction processes as well as on the molecular mechanisms underlying microgravity-induced cell and tissue alterations. This white paper reports the suggestions and recommendations of the SciSpacE Science Community for the elaboration of the section of the European Space Agency roadmap "Biology in Space and Analogue Environments" focusing on "How are cells and tissues influenced by gravity and what are the gravity perception mechanisms?" The knowledge gaps that prevent the Science Community from fully answering this question and the activities proposed to fill them are discussed.

Inner membrane dynamics in mitochondria.

Combining the use of cells with sparse cristae marked with IMP-EGFP and short pulsed sub-saturating fluorescence excitation (non-saturation fluorescence microscopy/NSFM) revealed inhomogeneous fluorescence distribution along mitochondria in living cells. Also the matrix located TMRE was distributed non-uniformly and at least in part filling the gaps between the IMP-EGFP fluorescence: fluorescence intensities are modulated in space and time in part in an antidromic manner. The spatial modulations can be interpreted to represent cristae/matrix distributions. The temporal fluctuations of fluorescence vary within 0.3-3s. Because most peak positions of IMP fluorescence remain stationary up to at least several minutes, temporal intensity modulations may result from varying emissions related to the degree of excitation and/or represent wobbling of cristae, i.e. lateral movements, bending or size changes. Modulations by noise and non-saturated excitation have been reduced by 3 steps of deconvolution followed by averaging 4 images. This allowed a final temporal resolution of 150ms. Disappearance of cristae or formation of new ones takes place within a few seconds, but these are rare events. Thus position of cristae seems to be rather stable, but they regularly disassemble close to fission sites. Treatment with oligomycin strongly reduces "wobbling" activity.

Predicting Problematic Behavior in Autism Spectrum Disorder Using Medical History and Environmental Data.

Autism spectrum disorder (ASD), characterized by social, communication, and behavioral abnormalities, affects 1 in 36 children according to the CDC. Several co-occurring conditions are often associated with ASD, including sleep and immune disorders and gastrointestinal (GI) problems. ASD is also associated with sensory sensitivities. Some individuals with ASD exhibit episodes of challenging behaviors that can endanger themselves or others, including aggression and self-injurious behavior (SIB). In this work, we explored the use of artificial intelligence models to predict behavior episodes based on past data of co-occurring conditions and environmental factors for 80 individuals in a residential setting. We found that our models predict occurrences of behavior and non-behavior with accuracies as high as 90% for some individuals, and that environmental, as well as gastrointestinal, factors are notable predictors across the population examined. While more work is needed to examine the underlying connections between the factors and the behaviors, having reasonably accurate predictions for behaviors has the potential to improve the quality of life of some individuals with ASD.