Membrane protein extraction and purification using partially-esterified SMA polymers.

Styrene maleic acid (SMA) polymers have proven to be very successful for the extraction of membrane proteins, forming SMA lipid particles (SMALPs), which maintain a lipid bilayer around the membrane protein. SMALP-encapsulated membrane proteins can be used for functional and structural studies. The SMALP approach allows retention of important protein-annular lipid interactions, exerts lateral pressure, and offers greater stability than traditional detergent solubilisation. However, SMA polymer does have some limitations, including a sensitivity to divalent cations and low pH, an absorbance spectrum that overlaps with many proteins, and possible restrictions on protein conformational change. Various modified polymers have been developed to try to overcome these challenges, but no clear solution has been found. A series of partially-esterified variants of SMA (SMA 2625, SMA 1440 and SMA 17352) has previously been shown to be highly effective for solubilisation of plant and cyanobacterial thylakoid membranes. It was hypothesised that the partial esterification of maleic acid groups would increase tolerance to divalent cations. Therefore, these partially-esterified polymers were tested for the solubilisation of lipids and membrane proteins, and their tolerance to magnesium ions. It was found that all partially esterified polymers were capable of solubilising and purifying a range of membrane proteins, but the yield of protein was lower with SMA 1440, and the degree of purity was lower for both SMA 1440 and SMA 17352. SMA 2625 performed comparably to SMA 2000. SMA 1440 also showed an increased sensitivity to divalent cations. Thus, it appears the interactions between SMA and divalent cations are more complex than proposed and require further investigation.

Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields.

Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, E(t), that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain (i.e., the Fourier transform of E(t)), and (2) a cross-correlation neural network approach for directly predicting E(t) in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning, particularly deep neural networks, can be employed as cost-effective statistical approaches for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems.

Application of Machine Learning Models for Tracking Participant Skills in Cognitive Training.

A key need in cognitive training interventions is to personalize task difficulty to each user and to adapt this difficulty to continually apply appropriate challenges as users improve their skill to perform the tasks. Here we examine how Bayesian filtering approaches, such as hidden Markov models and Kalman filters, and deep-learning approaches, such as the long short-term memory (LSTM) model, may be useful methods to estimate user skill level and predict appropriate task challenges. A possible advantage of these models over commonly used adaptive methods, such as staircases or blockwise adjustment methods that are based only upon recent performance, is that Bayesian filtering and deep learning approaches can model the trajectory of user performance across multiple sessions and incorporate data from multiple users to optimize local estimates. As a proof of concept, we fit data from two large cohorts of undergraduate students performing WM training using an N-back task. Results show that all three models predict appropriate challenges for different users. However, the hidden Markov models were most accurate in predicting participants' performances as a function of provided challenges, and thus, they placed participants at appropriate future challenges. These data provide good support for the potential of machine learning approaches as appropriate methods to personalize task performance to users in tasks that require adaptively determined challenges.

Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks.

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep.

Event Detection in Continuous Video: An Inference in Point Process Approach.

We propose a novel approach toward event detection in real-world continuous video sequences. The method: 1) is able to model arbitrary-order non-Markovian dependences in videos to mitigate local visual ambiguities; 2) conducts simultaneous event segmentation and labeling; and 3) is time-window free. The idea is to represent a video as an event stream of both high-level semantic events and low-level video observations. In training, we learn a point process model called a piecewise-constant conditional intensity model (PCIM) that is able to capture complex non-Markovian dependences in the event streams. In testing, event detection can be modeled as the inference of high-level semantic events, given low-level image observations. We develop the first inference algorithm for PCIM and show it samples exactly from the posterior distribution. We then evaluate the video event detection task on real-world video sequences. Our model not only provides competitive results on the video event segmentation and labeling task, but also provides benefits, including being interpretable and efficient.

Social Grouping for Multi-Target Tracking and Head Pose Estimation in Video.

Many computer vision tasks are more difficult when tackled without contextual information. For example, in multi-camera tracking, pedestrians may look very different in different cameras with varying pose and lighting conditions. Similarly, head direction estimation in high-angle surveillance video in which human head images are low resolution is challenging. Even humans can have trouble without contextual information. In this work, we couple novel contextual information, social grouping, with two important computer vision tasks: multi-target tracking and head pose/direction estimation in surveillance video. These three components are modeled in a probabilistic formulation and we provide effective solvers.We show that social grouping effectively helps to mitigate visual ambiguities in multi-camera tracking and head pose estimation. We further notice that in single-camera multi-target tracking, social grouping provides a natural high-order association cue that avoids existing complex algorithms for high-order track association. In experiments, we demonstrate improvements with our model over models without social grouping context and several state-of-art approaches on a number of publicly available datasets on tracking, head pose estimation, and group discovery.

Evidence-Based Pediatric Outcome Predictors to Guide the Allocation of Critical Care Resources in a Mass Casualty Event.

OBJECTIVE: ICU resources may be overwhelmed by a mass casualty event, triggering a conversion to Crisis Standards of Care in which critical care support is diverted away from patients least likely to benefit, with the goal of improving population survival. We aimed to devise a Crisis Standards of Care triage allocation scheme specifically for children. DESIGN: A triage scheme is proposed in which patients would be divided into those requiring mechanical ventilation at PICU presentation and those not, and then each group would be evaluated for probability of death and for predicted duration of resource consumption, specifically, duration of PICU length of stay and mechanical ventilation. Children will be excluded from PICU admission if their mortality or resource utilization is predicted to exceed predetermined levels ("high risk"), or if they have a low likelihood of requiring ICU support ("low risk"). Children entered into the Virtual PICU Performance Systems database were employed to develop prediction equations to assign children to the exclusion categories using logistic and linear regression. Machine Learning provided an alternative strategy to develop a triage scheme independent from this process. SETTING: One hundred ten American PICUs SUBJECTS: : One hundred fifty thousand records from the Virtual PICU database. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The prediction equations for probability of death had an area under the receiver operating characteristic curve more than 0.87. The prediction equation for belonging to the low-risk category had lower discrimination. R for the prediction equations for PICU length of stay and days of mechanical ventilation ranged from 0.10 to 0.18. Machine learning recommended initially dividing children into those mechanically ventilated versus those not and had strong predictive power for mortality, thus independently verifying the triage sequence and broadly verifying the algorithm. CONCLUSION: An evidence-based predictive tool for children is presented to guide resource allocation during Crisis Standards of Care, potentially improving population outcomes by selecting patients likely to benefit from short-duration ICU interventions.

Algorithms to estimate PaCO2 and pH using noninvasive parameters for children with hypoxemic respiratory failure.

BACKGROUND: Ventilator management for children with hypoxemic respiratory failure may benefit from ventilator protocols, which rely on blood gases. Accurate noninvasive estimates for pH or P(aCO2) could allow frequent ventilator changes to optimize lung-protective ventilation strategies. If these models are highly accurate, they can facilitate the development of closed-loop ventilator systems. We sought to develop and test algorithms for estimating pH and P(aCO2) from measures of ventilator support, pulse oximetry, and end-tidal carbon dioxide pressure (P(ETCO2)). We also sought to determine whether surrogates for changes in dead space can improve prediction. METHODS: Algorithms were developed and tested using 2 data sets from previously published investigations. A baseline model estimated pH and P(aCO2) from P(ETCO2) using the previously observed relationship between P(ETCO2) and P(aCO2) or pH (using the Henderson-Hasselbalch equation). We developed a multivariate gaussian process (MGP) model incorporating other available noninvasive measurements. RESULTS: The training data set had 2,386 observations from 274 children, and the testing data set had 658 observations from 83 children. The baseline model predicted P(aCO2) within ± 7 mm Hg of the observed P(aCO2) 80% of the time. The MGP model improved this to ± 6 mm Hg. When the MGP model predicted P(aCO2) between 35 and 60 mm Hg, the 80% prediction interval narrowed to ± 5 mm Hg. The baseline model predicted pH within ± 0.07 of the observed pH 80% of the time. The MGP model improved this to ± 0.05. CONCLUSIONS: We have demonstrated a conceptual first step for predictive models that estimate pH and P(aCO2) to facilitate clinical decision making for children with lung injury. These models may have some applicability when incorporated in ventilator protocols to encourage practitioners to maintain permissive hypercapnia when using high ventilator support. Refinement with additional data may improve model accuracy.

Predicting conserved protein motifs with Sub-HMMs.

BACKGROUND: Profile HMMs (hidden Markov models) provide effective methods for modeling the conserved regions of protein families. A limitation of the resulting domain models is the difficulty to pinpoint their much shorter functional sub-features, such as catalytically relevant sequence motifs in enzymes or ligand binding signatures of receptor proteins. RESULTS: To identify these conserved motifs efficiently, we propose a method for extracting the most information-rich regions in protein families from their profile HMMs. The method was used here to predict a comprehensive set of sub-HMMs from the Pfam domain database. Cross-validations with the PROSITE and CSA databases confirmed the efficiency of the method in predicting most of the known functionally relevant motifs and residues. At the same time, 46,768 novel conserved regions could be predicted. The data set also allowed us to link at least 461 Pfam domains of known and unknown function by their common sub-HMMs. Finally, the sub-HMM method showed very promising results as an alternative search method for identifying proteins that share only short sequence similarities. CONCLUSIONS: Sub-HMMs extend the application spectrum of profile HMMs to motif discovery. Their most interesting utility is the identification of the functionally relevant residues in proteins of known and unknown function. Additionally, sub-HMMs can be used for highly localized sequence similarity searches that focus on shorter conserved features rather than entire domains or global similarities. The motif data generated by this study is a valuable knowledge resource for characterizing protein functions in the future.

Annotating genes of known and unknown function by large-scale coexpression analysis.

About 40% of the proteins encoded in eukaryotic genomes are proteins of unknown function (PUFs). Their functional characterization remains one of the main challenges in modern biology. In this study we identified the PUF encoding genes from Arabidopsis (Arabidopsis thaliana) using a combination of sequence similarity, domain-based, and empirical approaches. Large-scale gene expression analyses of 1,310 publicly available Affymetrix chips were performed to associate the identified PUF genes with regulatory networks and biological processes of known function. To generate quality results, the study was restricted to expression sets with replicated samples. First, genome-wide clustering and gene function enrichment analysis of clusters allowed us to associate 1,541 PUF genes with tightly coexpressed genes for proteins of known function (PKFs). Over 70% of them could be assigned to more specific biological process annotations than the ones available in the current Gene Ontology release. The most highly overrepresented functional categories in the obtained clusters were ribosome assembly, photosynthesis, and cell wall pathways. Interestingly, the majority of the PUF genes appeared to be controlled by the same regulatory networks as most PKF genes, because clusters enriched in PUF genes were extremely rare. Second, large-scale analysis of differentially expressed genes was applied to identify a comprehensive set of abiotic stress-response genes. This analysis resulted in the identification of 269 PKF and 104 PUF genes that responded to a wide variety of abiotic stresses, whereas 608 PKF and 206 PUF genes responded predominantly to specific stress treatments. The provided coexpression and differentially expressed gene data represent an important resource for guiding future functional characterization experiments of PUF and PKF genes. Finally, the public Plant Gene Expression Database (http://bioweb.ucr.edu/PED) was developed as part of this project to provide efficient access and mining tools for the vast gene expression data of this study.