Testing for developmental neurotoxicity using a battery of in vitro assays for key cellular events in neurodevelopment.

Medium- to high-throughput in vitro assays that recapitulate the critical processes of nervous system development have been proposed as a means to facilitate rapid testing and identification of chemicals which may affect brain development. In vivo neurodevelopment is a complex progression of distinct cellular processes. Therefore, batteries of in vitro assays that model and quantify effects on a variety of neurodevelopmental processes have the potential to identify chemicals which may affect brain development at different developmental stages. In the present study, the results of concentration-response screening of 67 reference chemicals in a battery of high content imaging and microplate reader-based assays that evaluate neural progenitor cell proliferation, neural proginitor cell apoptosis, neurite initiation/outgrowth, neurite maturation and synaptogenesis are summarized and compared. The assay battery had a high degree of combined sensitivity (87%) for categorizing chemicals known to affect neurodevelopment as active and a moderate degree of combined specificity (71%) for categorizing chemicals not associated with affects on neurodevelopment as inactive. The combined sensitivity of the assay battery was higher compared to any individual assay while the combined specificity of the assay battery was lower compared to any individual assay. When selectivity of effects for a neurodevelopmental endpoint as compared to general cytotoxicity was taken into account, the combined sensitivity of the assay battery decreased (68%) while the combined specificity increased (93%). The identity and potency of chemicals identified as active varied across the assay battery, underscoring the need for use of a combination of diverse in vitro models to comprehensively screen chemicals and identify those which potentially affect neurodevelopment. Overall, these data indicate that a battery of assays which address many different processes in nervous system development may be used to identify potential developmental neurotoxicants and to distinguish specific from generalized cytotoxic effects with a high degree of success.

A multivariate extension of mutual information for growing neural networks.

Recordings of neural network activity in vitro are increasingly being used to assess the development of neural network activity and the effects of drugs, chemicals and disease states on neural network function. The high-content nature of the data derived from such recordings can be used to infer effects of compounds or disease states on a variety of important neural functions, including network synchrony. Historically, synchrony of networks in vitro has been assessed either by determination of correlation coefficients (e.g. Pearson's correlation), by statistics estimated from cross-correlation histograms between pairs of active electrodes, and/or by pairwise mutual information and related measures. The present study examines the application of Normalized Multiinformation (NMI) as a scalar measure of shared information content in a multivariate network that is robust with respect to changes in network size. Theoretical simulations are designed to investigate NMI as a measure of complexity and synchrony in a developing network relative to several alternative approaches. The NMI approach is applied to these simulations and also to data collected during exposure of in vitro neural networks to neuroactive compounds during the first 12 days in vitro, and compared to other common measures, including correlation coefficients and mean firing rates of neurons. NMI is shown to be more sensitive to developmental effects than first order synchronous and nonsynchronous measures of network complexity. Finally, NMI is a scalar measure of global (rather than pairwise) mutual information in a multivariate network, and hence relies on less assumptions for cross-network comparisons than historical approaches.

BLASST: Band Limited Atomic Sampling With Spectral Tuning With Applications to Utility Line Noise Filtering.

OBJECTIVE: In this paper, we present and test a new method for the identification and removal of nonstationary utility line noise from biomedical signals. METHODS: The method, band limited atomic sampling with spectral tuning (BLASST), is an iterative approach that is designed to 1) fit nonstationarities in line noise by searching for best-fit Gabor atoms at predetermined time points, 2) self-modulate its fit by leveraging information from frequencies surrounding the target frequency, and 3) terminate based on a convergence criterion obtained from the same surrounding frequencies. To evaluate the performance of the proposed algorithm, we generate several simulated and real instances of nonstationary line noise and test BLASST along with alternative filtering approaches. RESULTS: We find that BLASST is capable of fitting line noise well and/or preserving local signal features relative to tested alternative filtering techniques. CONCLUSION: BLASST may present a useful alternative to bandpass, notch, or other filtering methods when experimentally relevant features have significant power in a spectrum that is contaminated by utility line noise, or when the line noise in question is highly nonstationary. SIGNIFICANCE: This is of particular significance in electroencephalography experiments, where line noise may be present in the frequency bands of neurological interest and measurements are typically of low enough strength that induced line noise can dominate the recorded signals. In conjunction with this paper, the authors have released a MATLAB toolbox that performs BLASST on real, vector-valued signals (available at https://github.com/VisLab/blasst).

PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG.

Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints. The resulting procedure, which we call Pairwise Complex Independent Component Analysis (PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space. We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data. On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA. On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods. In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals.

Acute oxalate nephropathy associated with orlistat.

BACKGROUND: Obesity is a major world-wide epidemic which has led to a surge of various weight loss-inducing medical or surgical treatments. Orlistat is a gastrointestinal lipase inhibitor used as an adjunct treatment of obesity and type 2 diabetes mellitus to induce clinically significant weight loss via fat malabsorption. CASE PRESENTATION: We describe a case of a 76-year-old female with past medical history of chronic kidney disease (baseline serum creatinine was 1.5-2.5 mg/dL), hypertension, gout and psoriatic arthritis, who was admitted for evaluation of elevated creatinine, peaking at 5.40 mg/dL. She was started on orlistat 120 mg three times a day six weeks earlier. Initial serologic work-up remained unremarkable. Percutaneous kidney biopsy revealed massive calcium oxalate crystal depositions with acute tubular necrosis and interstitial inflammation. Serum oxalate level returned elevated at 45 mm/l (normal <27). Timed 24-hour urine collection documented increased oxalate excretion repeatedly (54-96 mg/24 hour). After five renal dialysis sessions in eighth days she gradually regained her former baseline kidney function with creatinine around 2 mg/dL. Given coexisting proton-pump inhibitor therapy, only per os calcium-citrate provided effective intestinal oxalate chelation to control hyperoxaluria. CONCLUSIONS: Our case underscores the potential of medically induced fat malabsorption to lead to an excessive oxalate absorption and acute kidney injury (AKI), especially in subjects with pre-existing renal impairment. Further, it emphasizes the importance of kidney biopsy to facilitate early diagnosis and treatment.

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface.

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as outperform traditional within-subject calibration techniques when limited data is available. This method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.

Reliability and accuracy of digital templating for the humeral component of total shoulder arthroplasty.

BACKGROUND: This experimental study evaluated the interobserver reliability and accuracy of pre-operative digital templating for humeral head size, stem size and neck angle for total shoulder arthroplasty. METHODS: Twenty-five patients underwent a total shoulder arthroplasty with a single prosthesis. Four independent, blinded surgeons (two experienced shoulder surgeons and two PGY-6 fellows) used pre-operative radiographs and templating software to generate templates of the humeral head, stem and neck for each patient. Interobserver reliability was calculated using weighted kappa (κ) analysis. Accuracy was assessed by comparing templates to actual implant sizes. RESULTS: Interobserver reliability was fair to substantial (κ = 0.26 to 0.71) for head size, fair to substantial (κ = 0.39 to 0.72) for stem size and slight to fair (κ = 0.16 to 0.34) for neck angle. Templated head size, stem size and neck angle had accuracies of 53%, 77% and 68% within one size variation, respectively. Experience did not affect accuracy (p = 0.11 to 0.48). CONCLUSIONS: Digital templating is not a useful guide for pre-operative surgical planning and should not be used to select a prosthesis.

Structure and dynamics of low Reynolds number turbulent pipe flow.

Using large-scale numerical calculations, we explore the proper orthogonal decomposition of low Reynolds number turbulent pipe flow, using both the translational invariant (Fourier) method and the method of snapshots. Each method has benefits and drawbacks, making the 'best' choice dependent on the purpose of the analysis. Owing to its construction, the Fourier method includes all the flow fields that are translational invariants of the simulated flow fields. Thus, the Fourier method converges to an estimate of the dimension of the chaotic attractor in less total simulation time than the method of snapshots. The converse is that for a given simulation, the method of snapshots yields a basis set that is more optimal because it does not include all of the translational invariants that were not a part of the simulation. Using the Fourier method yields smooth structures with definable subclasses based upon Fourier wavenumber pairs, and results in a new dynamical systems insight into turbulent pipe flow. These subclasses include a set of modes that propagate with a nearly constant phase speed, act together as a wave packet and transfer energy from streamwise rolls. It is these interactions that are responsible for bursting events and Reynolds stress generation. These structures and dynamics are similar to those found in turbulent channel flow. A comparison of structures and dynamics in turbulent pipe and channel flows is reported to emphasize the similarities and differences.