GINI: From ISH Images to Gene Interaction Networks

Accurate inference of molecular and functional interactions among genes, especially in multicellular organisms such as Drosophila, often requires statistical analysis of correlations not only between the magnitudes of gene expressions, but also between their temporal-spatial patterns. The ISH (in-situ-hybridization)-based gene expression micro-imaging technology offers an effective approach to perform large-scale spatial-temporal profiling of whole-body mRNA abundance. However, analytical tools for discovering gene interactions from such data remain an open challenge due to various reasons, including difficulties in extracting canonical representations of gene activities from images, and in inference of statistically meaningful networks from such representations. In this paper, we present GINI, a machine learning system for inferring gene interaction networks from Drosophila embryonic ISH images. GINI builds on a computer-vision-inspired vector-space representation of the spatial pattern of gene expression in ISH images, enabled by our recently developed system; and a new multi-instance-kernel algorithm that learns a sparse Markov network model, in which, every gene (i.e., node) in the network is represented by a vector-valued spatial pattern rather than a scalar-valued gene intensity as in conventional approaches such as a Gaussian graphical model. By capturing the notion of spatial similarity of gene expression, and at the same time properly taking into account the presence of multiple images per gene via multi-instance kernels, GINI is well-positioned to infer statistically sound, and biologically meaningful gene interaction networks from image data. Using both synthetic data and a small manually curated data set, we demonstrate the effectiveness of our approach in network building. Furthermore, we report results on a large publicly available collection of Drosophila embryonic ISH images from the Berkeley Drosophila Genome Project, where GINI makes novel and interesting predictions of gene interactions. Software for GINI is available at http://sailing.cs.cmu.edu/Drosophila_ISH_images/     

Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course

Transcranial magnetic stimulation (TMS) is a safe, non-invasive brain stimulation technique that uses a strong electromagnet in order to temporarily disrupt information processing in a brain region, generating a short-lived “virtual lesion.” Stimulation that interferes with task performance indicates that the affected brain region is necessary to perform the task normally. In other words, unlike neuroimaging methods such as functional magnetic resonance imaging (fMRI) that indicate correlations between brain and behavior, TMS can be used to demonstrate causal brain-behavior relations. Furthermore, by varying the duration and onset of the virtual lesion, TMS can also reveal the time course of normal processing. As a result, TMS has become an important tool in cognitive neuroscience. Advantages of the technique over lesion-deficit studies include better spatial-temporal precision of the disruption effect, the ability to use participants as their own control subjects, and the accessibility of participants. Limitations include concurrent auditory and somatosensory stimulation that may influence task performance, limited access to structures more than a few centimeters from the surface of the scalp, and the relatively large space of free parameters that need to be optimized in order for the experiment to work. Experimental designs that give careful consideration to appropriate control conditions help to address these concerns. This article illustrates these issues with TMS results that investigate the spatial and temporal contributions of the left supramarginal gyrus (SMG) to reading.     

Deep matrix factorization models for estimation of missing data in a low-cost sensor network to measure air quality

According to the WHO, pollution is a worldwide public health problem. In Colombia, low-cost strategies for air quality monitoring have been implemented using wireless sensor networks (WSNs), which achieve a better spatial resolution than traditional sensor networks for a lower operating cost. Nevertheless, one of the recurrent issues of WSNs is the missing data due to environmental and location conditions, hindering data collection. Consequently, WSNs should have effective mechanisms to recover missing data, and matrix factorization (MF) has shown to be a solid alternative to solve this problem. This study proposes a novel MF technique with a neural network architecture (i.e., deep matrix factorization or DMF) to estimate missing particulate matter (PM) data in a WSN in Aburrá Valley, Colombia. We found that the model that included spatial-temporal features (using embedding layers) captured the behavior of the pollution measured at each node more efficiently, thus producing better estimations than standard matrix factorization and other variations of the model proposed here.     

Spatial correlations at different spatial scales are themselves highly correlated in isolation by distance processes

Although many properties of spatial autocorrelation statistics are well characterized, virtually nothing is known about possible correlations among values at different spatial scales, which ultimately would influence how inferences about spatial genetics are made at multiple spatial scales. This article reports the results of stochastic space-time simulations of isolation by distance processes, having a very wide range of amounts of dispersal for plants or animals, and analyses of the correlations among Moran's I-statistics for different mutually exclusive distance classes. In general, the stochastic correlations are extremely large (>0.90); however, the correlations bear a complex relationship with level of dispersal, spatial scale and spatial lag between distance classes. The correlations are so large that any existing or conceived statistical method that employs more than one distance class (or spatial scale) should not ignore them. This result also suggests that gains in statistical power via increasing sample size are limited, and that increasing numbers of assayed loci generally should be preferred. To the extent that sampling error for real data sets can be treated as white noise, it should be possible to account for stochastic correlations in formulating more precise statistical methods. Further, while the current results are for isolation by distance processes, they provide some guidance for some more complex stochastic space-time processes of landscape genetics. Moreover, the results hold for several popular measures other than Moran's I. In addition, in the results, the signal to noise ratios strongly decreased with distance, which also has several implications for optimal statistical methods using correlations at multiple spatial scales.     

A Statistical Approach for Estimation of Process Flow Data from Production of Chemicals of Fossil Origin (6 pp)

Goal, Scope and Background The life cycles of many products including textiles contain chemicals for which process flow data are not known or are too time consuming to collect. Although each chemical may not contribute significantly to the LCA results of the product, which might justify excluding them, but together their contribution could be significant. Similarly, rough estimates of the process flows for the production of a single chemical may be very uncertain and considered meaningless, while the estimates of the cumulative data of process flows for several chemicals may be less uncertain and be a meaningful contribution to the quality of the LCA results. There are methods for estimation of process flows for different types of products, with varying demands regarding input data and time and with varying accuracy of the results. This work contributes to the available methods, focusing on simple estimations for production of chemical substances. The goal was to create a fast method for estimation of emissions, resource and energy flows (process flows) for the production of chemicals, based on easily available data on the properties of the chemicals. The process flows investigated were limited to those normally associated with process industries and contributing most to depletion of resources, to global warming, acidification, eutrophication and photochemical ozone production, i.e. use of energy, crude oil, coal, natural gas, uranium in ore and emissions of CO2, SOx, NOx, NMVOC, methane, BOD, COD and total N. Toxic substances were excluded, since toxic emissions are substance specific and cannot be included in a generalization. Method Available data for the process flows for the production of chemicals of mainly fossil origin were correlated to properties of chemicals such as amount of carbon in the molecule, heat of formation and average number of chemical reaction steps in the production. The production procedures were found in readily available literature. Up to about six reaction steps were evaluated in the correlation study. The variations in the process flows among the chemicals studied were calculated. Results and Discussion There were weak correlations between average number of chemical reaction steps in the production and energy use, COD measured in water emissions, and SOx and NOx emissions to air. For the remaining properties of chemicals and process flows, there were only weak correlations for share of double bonding in the molecule if only molecules containing double bondings were included. Conclusions The precision in estimation of the process flows increases non-significantly when adding information on the number of reaction steps or share of double bonding for chemicals containing double bonding is added. Recommendations and Outlook It seems reasonable to start with a simple grouping method to estimate the process flows for the production of a chemical of fossil origin. Further investigations might investigate whether there is a correlation between process flows and the costs of chemicals, and further study the correlations between process flows and share of double bonding for chemicals containing double bondings.     

Pressure-flow effects on endurance of inspiratory muscles

We examined the effects of varying inspiratory pressures and flows on inspiratory muscle endurance. Four normal subjects performed voluntary forced breathing with various assigned inspiratory tasks. Duty cycle, tidal volume, and mean lung volume were the same in all tasks. Mean esophageal pressure, analogous to a pressure-time integral (PTes), was varied over a wide range. In each task the subject maintained an assigned PTes while breathing on one of a range of inspiratory resistors, and this gave a range of inspiratory flows at any given PTes. Inspiratory muscle endurance for each task was assessed by the length of time the task could be maintained (Tlim). For a given resistor, Tlim increased as PTes decreased. At a given PTes, Tlim increased as the external resistance increased and therefore as mean inspiratory flow rate (VI) decreased. Furthermore, for a given Tlim, PTes and VI were linearly related with a negative slope. We conclude that inspiratory flow, probably because of its relationship to the velocity of muscle shortening, is an independent variable importantly influencing endurance of the inspiratory muscles.     

Ensembles of human MTL neurons "jump back in time" in response to a repeated stimulus

Episodic memory, which depends critically on the integrity of the medial temporal lobe (MTL), has been described as "mental time travel" in which the rememberer "jumps back in time." The neural mechanism underlying this ability remains elusive. Mathematical and computational models of performance in episodic memory tasks provide a specific hypothesis regarding the computation that supports such a jump back in time. The models suggest that a representation of temporal context, a representation that changes gradually over macroscopic periods of time, is the cue for episodic recall. According to these models, a jump back in time corresponds to a stimulus recovering a prior state of temporal context. In vivo single-neuron recordings were taken from the human MTL while epilepsy patients distinguished novel from repeated images in a continuous recognition memory task. The firing pattern of the ensemble of MTL neurons showed robust temporal autocorrelation over macroscopic periods of time during performance of the memory task. The gradually-changing part of the ensemble state was causally affected by the visual stimulus being presented. Critically, repetition of a stimulus caused the ensemble to elicit a pattern of activity that resembled the pattern of activity present before the initial presentation of the stimulus. These findings confirm a direct prediction of this class of temporal context models and may be a signature of the mechanism that underlies the experience of episodic memory as mental time travel. ? 2012 Wiley Periodicals, Inc.     

基于地理探测器的中国农业生态效率时空分异及其影响因素

研究农业生态效率的时空分异及其影响因素对实现中国农业生态高质量发展具有重要意义。基于2000—2018年中国30个省/区/市的面板数据,采用超效率SBM模型测算省际农业生态效率,在时间序列、空间可视化及趋势面分析揭示农业生态效率时空演变规律的基础上,进一步利用地理探测器模型识别影响农业生态效率空间分异的主导因子及其交互作用。结果表明: 2000—2018年,中国农业生态效率整体呈现稳定上升的趋势,但仍然处于较低水平,存在较大提升空间;中国农业生态效率具有显著的空间分异特征,总体上呈现出东西部地区较高、而中部地区较低的空间分布格局;中国农业生态效率的空间分异受到农业资源禀赋、社会经济、自然生态环境等多种因素的影响,不同因子对农业生态效率空间分异的影响存在明显差异且因子间交互作用会增强其空间分异。综上,要关注农业生态效率时空分异的主导因子,并注重区域间的协同合作,以实现农业的高质量发展。     

Analysis of prehistoric biological variation under a model of isolation by geographic and temporal distance

Biological distances calculated between archeologically recovered human skeletal collections are often used to assess the effects of temporal and spatial distance on subpopulation divergence. Although there are many previous empirical studies that examine skeletal material arrayed across time and/or space, the theoretical expectations for temporally or spatially related variation in biological characteristics have not been formally developed. In this paper I present the infinite island model, the unidimensional stepping-stone model, and the migration matrix method in forms that allow prediction of the genetic distance between groups separated by a given spatial and temporal lag. These models demonstrate that, if there is isolation by geographic distance, then the correlation between genetic and spatial distance (controlling for temporal distance) should be positive and the correlation between genetic and temporal distance (controlling for spatial distance) should be negative. I use observations of nonmetric traits in a sample of prehistoric crania from west-central Illinois to demonstrate the expected relationships among biological, temporal, and spatial distance. The results indicate that, once the effects of temporal trend are removed, biological and spatial distance are positively correlated and biological and temporal distance negatively correlated within this sample.     

Quantitative assessment of maps of the cortical background potentials during the adaptation of freely moving animals to environmental situations

Utilization of spectral-correlation methods of EEG analysis, due to necessary averaging in time, does not make it possible to detect dynamic phenomena in spatial-temporal organization of brain potentials. Non-traditional method is presented of quantitative evaluation of the changes of spatial-temporal correlation of cortical potentials--comparison of successive momentary potentials relief (electroencephalotopograms--EETG). Joint utilization of dynamics of similarity coefficients of successive EETG (Ket), mean values of EETG (Yet) and some other parameters allows to judge about dynamic properties of spatial correlations of potentials of freely moving animals in adaptation process to the environment during which alternation is established of the active and passive phases in decaminute intervals. Quantitative evaluation of spatial-temporal organization of the parameters of rabbits cortical potentials in these phases and in transition periods has revealed their informative possibilities.     

Object-oriented methods for habitat mapping at multiple scales – Case studies from Northern Germany and Wye Downs, UK

This paper presents an application of object-oriented techniques for habitat classification based on remotely sensed images and ancillary data. The study reports the results of habitat mapping at multiple scales using Earth Observation (EO) data at various spatial resolutions and multi temporal acquisition dates. We investigate the role of object texture and context in classification as well as the value of integrating knowledge from ancillary data sources. Habitat maps were produced at regional and local scales in two case studies; Schleswig-Holstein, Germany and Wye Downs, United Kingdom. At the regional scale, the main task was the development of a consistent object-oriented classification scheme that is transferable to satellite images for other years. This is demonstrated for a time series of Landsat TM/ETM+ scenes. At the local scale, investigations focus on the development of appropriate object-oriented rule networks for the detailed mapping of habitats, e.g. dry grasslands and wetlands using very high resolution satellite and airborne scanner images. The results are evaluated using statistical accuracy assessment and visual comparison with traditional field-based habitat maps. Whereas the application of traditional pixel-based classification result in a pixelised (salt and pepper) representation of land cover, the object-based classification technique result in solid habitat objects allowing easy integration into a vector-GIS for further analysis. The level of detail obtained at the local scale is comparable to that achieved by visual interpretation of aerial photographs or field-based mapping and also retains spatially explicit, fine scale information such as scrub encroachment or ecotone patterns within habitats.     

Spatial-typological structure of bird communities in coniferous and deciduous forests of the Northern Volga region

Based on materials collected during continuous year-round strip censuses, the spatial heterogeneity of bird communities in coniferous and deciduous forests of the Northern Volga region was analyzed for the first time and the key factors determining it were identified. A spatial-temporal classification of bird communities of the study area was made, and a quantitative estimate of the strength and generality of the relationship between the spatial heterogeneity of bird communities and environmental factors is given. The spatial-temporal structure of bird communities is mainly determined by forest cover, seasonal variation in heat supply, tree species composition, build-up area, and relief.     

Temporal–spatial reach parameters derived from inertial sensors: Comparison to 3D marker-based motion capture

Reaching is a well-practiced functional task crucial to daily living activities, and temporal–spatial measures of reaching reflect function for both adult and pediatric populations with upper-extremity motor impairments. Inertial sensors offer a mobile and inexpensive tool for clinical assessment of movement. This research outlines a method for measuring temporal–spatial reach parameters using inertial sensors, and validates these measures with traditional marker-based motion capture. 140 reaches from 10 adults, and 30 reaches from nine children aged 18–20 months, were recorded and analyzed using both inertial-sensor and motion-capture methods. Inertial sensors contained three-axis accelerometers, gyroscopes, and magnetometers. Gravitational offset of accelerometer data was measured when the sensor was at rest, and removed using sensor orientation measured at rest and throughout the reach. Velocity was calculated by numeric integration of acceleration, using a null-velocity assumption at reach start. Sensor drift was neglected given the 1–2 s required for a reach. Temporal–spatial reach parameters were calculated independently for each data acquisition method. Reach path length and distance, peak velocity magnitude and timing, and acceleration at contact demonstrated consistent agreement between sensor- and motion-capture-based methods, for both adult and toddler reaches, as evaluated by intraclass correlation coefficients from 0.61 to 1.00. Taken together with actual difference between method measures, results indicate that these functional reach parameters may be reliably measured with inertial sensors.     

Prediction of Cascading Failures in Spatial Networks

Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems. Accurate cascading failure prediction can provide useful information to help control networks. However, for a large, gradually growing network with increasing complexity, it is often impractical to explore the behavior of a single node from the perspective of failure propagation. Fortunately, overload failures that propagate through a network exhibit certain spatial-temporal correlations, which allows the study of a group of nodes that share common spatial and temporal characteristics. Therefore, in this study, we seek to predict the failure rates of nodes in a given group using machine-learning methods.We simulated overload failure propagations in a weighted lattice network that start with a center attack and predicted the failure percentages of different groups of nodes that are separated by a given distance. The experimental results of a feedforward neural network (FNN), a recurrent neural network (RNN) and support vector regression (SVR) all show that these different models can accurately predict the similar behavior of nodes in a given group during cascading overload propagation.     

Statistical and Economic Techniques for Site-specific Nematode Management

Recent advances in precision agriculture technologies and spatial statistics allow realistic, site-specific estimation of nematode damage to field crops and provide a platform for the site-specific delivery of nematicides within individual fields. This paper reviews the spatial statistical techniques that model correlations among neighboring observations and develop a spatial economic analysis to determine the potential of site-specific nematicide application. The spatial econometric methodology applied in the context of site-specific crop yield response contributes to closing the gap between data analysis and realistic site-specific nematicide recommendations and helps to provide a practical method of site-specifically controlling nematodes.     

One-dimensional dynamics of attention and decision making in LIP

Where we allocate our visual spatial attention depends upon a continual competition between internally generated goals and external distractions. Recently it was shown that single neurons in the macaque lateral intraparietal area (LIP) can predict the amount of time a distractor can shift the locus of spatial attention away from a goal. We propose that this remarkable dynamical correspondence between single neurons and attention can be explained by a network model in which generically high-dimensional firing-rate vectors rapidly decay to a single mode. We find direct experimental evidence for this model, not only in the original attentional task, but also in a very different task involving perceptual decision making. These results confirm a theoretical prediction that slowly varying activity patterns are proportional to spontaneous activity, pose constraints on models of persistent activity, and suggest a network mechanism for the emergence of robust behavioral timing from heterogeneous neuronal populations.     

Protein modeling and structure prediction with a reduced representation

Protein modeling could be done on various levels of structural details, from simplified lattice or continuous representations, through high resolution reduced models, employing the united atom representation, to all-atom models of the molecular mechanics. Here I describe a new high resolution reduced model, its force field and applications in the structural proteomics. The model uses a lattice representation with 800 possible orientations of the virtual alpha carbon-alpha carbon bonds. The sampling scheme of the conformational space employs the Replica Exchange Monte Carlo method. Knowledge-based potentials of the force field include: generic protein-like conformational biases, statistical potentials for the short-range conformational propensities, a model of the main chain hydrogen bonds and context-dependent statistical potentials describing the side group interactions. The model is more accurate than the previously designed lattice models and in many applications it is complementary and competitive in respect to the all-atom techniques. The test applications include: the ab initio structure prediction, multitemplate comparative modeling and structure prediction based on sparse experimental data. Especially, the new approach to comparative modeling could be a valuable tool of the structural proteomics. It is shown that the new approach goes beyond the range of applicability of the traditional methods of the protein comparative modeling.     

Optimal control of substrate concentrations in bioreactors with separated sensors

Many of the sophisticated sensors desirable for monitoring bioreactors cannot be placed in the bioreactor either because they are not steam sterilizable or because they require nonphysiological operating conditions. Such sensors can be used if they are separated from the bioreactor. Separation of the sensor from the bioreactor causes a time lag in data acquisition. This results in several complexities in the development of an appropriate and stable feedback control system based on a separated sensor. This paper analyzes the optimal control of a bioreactor with a separated sensor without a time lag and analyzes the feedback control (but not necessarily the optimal control) with a time lag. Simulation results indicate that this type of analysis could be extended to more general bioreactor operating conditions.