Population Vector Research Articles

Productivity loss and cost of bovine tuberculosis for the dairy livestock sector in Ethiopia

Bovine tuberculosis (BTB) is endemic in Ethiopia. Although upgraded dairy cattle account for only 1% of the total cattle population, they are the backbone of the marketed milk production in the country. Supported by research data outputs from three years, we report in this paper an estimate of the productivity loss and cost of BTB to the Ethiopian dairy sector in two dairy settings, the urban production system in Central Ethiopia (model 1) and the national upgraded dairy production (model 2). Primary data sources were used (e.g. market survey; three-year longitudinal productivity survey; abattoir survey) as well as secondary data sources. A matrix population model, composed of a population vector representing the herd composition that is repeatedly multiplied with a projection matrix, was developed to simulate the livestock dairy population. The initial herd structure was simulated over 30 years to obtain an equilibrium herd-structure representing an Eigenvector of the projection matrix. We performed an incremental cost of disease analysis by comparing livestock production with and without BTB during a period of 10 years. We assumed a BTB prevalence of 40%. In year ten, the Net present value (NPV) of livestock production in terms of milk, meat and hides was estimated at 154.5 million USD for model 1 and 1.7 billion USD for model 2. Loss of NPV over 10 years was estimated at 12 million USD for model 1 and 131.7 million USD for model 2, representing roughly 7.3% loss in NPV or 219 USD per animal. This is a benchmark against which a national TB control program could be developed in the future to calculate its benefit/cost ratio.

Ephaptic entrainment in hybrid neuronal model

In recent decades, there has been a growing interest in the impact of electric fields generated in the brain. Transmembrane ionic currents originate electric fields in the extracellular space and are capable of affecting nearby neurons, a phenomenon called ephaptic neuronal communication. In the present work, the Quadratic Integrated-and-Fire model (QIF-E) underwent an adjustment/improvement to include the ephaptic entrainment behavior between neurons and electric fields. Indeed, the aim of our study is to validate the QIF-E model, which is a model to estimate the influence of electric fields on neurons. For this purpose, we evaluated whether the main properties observed in an experiment by Anastassiou et al. (Nat Neurosci 14:217–223, 2011), which analyzed the effect of an electric field on cortical pyramidal neurons, are reproduced with the QIF-E model. In this way, the analysis tools are employed according to the neuronal activity regime: (i) for the subthreshold regime, the circular statistic is used to describe the phase differences between the input stimulus signal (electrode) and the modeled membrane response; (ii) in the suprathreshold regime, the Population Vector and the Spike Field Coherence are used to estimate phase preferences and the entrainment intensity between the input stimulus and Action Potentials. The results observed are (i) in the subthreshold regime the values of the phase differences change with distinct frequencies of the input stimulus; (ii) in the supra-threshold regime the preferential phase of Action Potentials changes for different frequencies. In addition, we explore other parameters of the model, such as noise and membrane characteristic-time, in order to understand different types of neurons and extracellular environment related to ephaptic communication. Such results are consistent with results observed in empirical experiments based on ephaptic phenomenon. In addition, the QIF-E model allows further studies on the physiological importance of ephaptic communication in the brain, and its simplicity may open a door to simulate the ephaptic response in neuronal networks and assess the impact of ephaptic communication in such scenarios.

Barn Owl's Auditory Space Map Activity Matching Conditions for a Population Vector Readout to Drive Adaptive Sound-Localizing Behavior.

Space-specific neurons in the owl's midbrain form a neural map of auditory space, which supports sound-orienting behavior. Previous work proposed that a population vector (PV) readout of this map, implementing statistical inference, predicts the owl's sound localization behavior. This model also predicts the frontal localization bias normally observed and how sound-localizing behavior changes when the signal-to-noise ratio varies, based on the spread of activity across the map. However, the actual distribution of population activity and whether this pattern is consistent with premises of the PV readout model on a trial-by-trial basis remains unknown. To answer these questions, we investigated whether the population response profile across the midbrain map in the optic tectum of the barn owl matches these predictions using in vivo multielectrode array recordings. We found that response profiles of recorded subpopulations are sufficient for estimating the stimulus interaural time difference using responses from single trials. Furthermore, this decoder matches the expected differences in trial-by-trial variability and frontal bias between stimulus conditions of low and high signal-to-noise ratio. These results support the hypothesis that a PV readout of the midbrain map can mediate statistical inference in sound-localizing behavior of barn owls.SIGNIFICANCE STATEMENT While the tuning of single neurons in the owl's midbrain map of auditory space has been considered predictive of the highly specialized sound-localizing behavior of this species, response properties across the population remain largely unknown. For the first time, this study analyzed the spread of population responses across the map using multielectrode recordings and how it changes with signal-to-noise ratio. The observed responses support the hypothesis concerning the ability of a population vector readout to predict biases in orienting behaviors and mediate uncertainty-dependent behavioral commands. The results are of significance for understanding potential mechanisms for the implementation of optimal behavioral commands across species.

Scalable and accurate method for neuronal ensemble detection in spiking neural networks.

We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. We found that our method outperforms current alternative methodologies. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity could be organized in distinguishable functional ensembles. We provide a Graphic User Interface, which facilitates the use of our method by the scientific community.

Comments on “The Solution of a Mathematical Model for Dengue Fever Transmission Using Differential Transformation Method: J. Nig. Soc. Phys. Sci. 1 (2019) 82-87”

The mathematical model for dengue fever transmission studied by [1], has been re-investigated. The differential transformation method (DTM) is used to compute the semi-analytical solutions of the non-linear differential equations of the compartment (SIR) model of dengue fever. This epidemiology problem is well-posed. The effect of treatment as a control measure is studied through the growth equations of exposed and infected humans. The inadvertent errors in the recurrence relations (DTM) of equations for dengue disease transmission including initial conditions have been removed. Furthermore, the semi-analytic solutions of the model are obtained and verified with the built-in function AsymptoticDSolveValue of Wolfram Mathematica. It has been found that results obtained from the DTM are valid only for small-time t (t < 1.5), as t becomes large, the human population (exposed and recovered) and infected vector population become negative.

The Influence of Clustering Population on Estimation Accuracy of Population Totals Vector

The estimation accuracy of a population totals vector based on a simple cluster sample is considered. The variance-covariance matrix of estimators depends on the intra-cluster spread of variables under study. The spread depends on the partition of the population into clusters. The variance-covariance matrix is evaluated under several variants of clustering algorithm. This lets us find the clustering algorithm providing the most accurate estimation of population vector totals.

Another approach for Leslie model via linear difference equations

We explore here an approach to study the Leslie model, describing the growth of the discrete age structured populations. Our main tool is based upon properties of some linear difference equations. Involving the analytical and combinatorial aspects of solutions of these equations, we establish new formulas for the entries of the population vector and derive their asymptotic behavior. Significant illustrative examples and applications are given.

Studying Viral Populations with Tools from Quantum Spin Chains

We study Eigen's model of quasi-species, characterized by sequences that replicate with a specified fitness and mutate independently at single sites. The evolution of the population vector in time is then closely related to that of quantum spins in imaginary time. We employ multiple perspectives and tools from interacting quantum systems to examine growth and collapse of realistic viral populations, specifically certain HIV proteins. All approaches used, including the simplest perturbation theory, give consistent results.

An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

Objective: Energy efficiency aspect in wireless sensor networks (WSN) can be achieved by small sized rechargeable and easily replaceable batteries. The lifetime of wireless sensor network can be improved by identifying the efficient and reliable nodes as a cluster heads using Hybrid Simulated Annealing algorithm. The proposed algorithm identifies cluster head to reduce overhead and is capable of handling high volume of nodes with minimum node death rate. Methods: This study proposed initialization of population vectors using the opposite point procedure, self-adaptive control approach by node mutation rate, crossover rate, node capacity and cluster head allocation Methods. Findings: A case study in the proposed work is found to be better in throughput, accuracy, efficiency, energy utilization, batteries recharge ability and replacement procedures compared to the conventional methods. By the analysis and comparison of the proposed method with existing methods, it is identified that the reduction of the number of dead nodes gradually increases the throughput and lifetime of the nodes with respect to the number of iterations. Novelty: To overcome the limitations of conventional Low Energy Adaptive Clustering Hierarchy (LEACH), harmony search algorithm (HSA), modified HSA and differential evolution, we propose a hybrid optimal model using simulated annealing algorithm which includes a node capability function. It is used to improve the network lifetime of the cluster heads and sensor nodes. The proposed method have capability of batteries recharge ability and replacement option to improve network throughput and reliability of network. Keywords: Wireless sensor network; Differential Evolution; Low Energy Adaptive Clustering Hierarchy; HAS; modified HAS; simulated annealing algorithm

Learning a New Path to Reward: Hippocampal Signals Modify Orbitofrontal Representations

To adapt to changing circumstances, we often recall similar situations and the consequences of past choices. Memory for recent events requires the hippocampus (HPC); using memory to adapt to a new circumstance can require the orbitofrontal cortex (OFC). Spatial learning is impaired by HPC but not by OFC dysfunction. Rather, learning an unfamiliar reversal of a well-established contingency is impaired by OFC dysfunction, suggesting that learning a new spatial path to reward requires both structures and their interactions. To investigate possible interactions, we simultaneously recorded OFC and HPC subregion CA1 ensembles as rats learned spatial reversals in a plus maze. CA1 and OFC units fired differentially across maze locations, and spiking recorded in the start arms in each structure predicted pending choices. Striking interactions between the structures occurred only while rats learned an unfamiliar reversal: (1) OFC spiking phase locked to hippocampal theta oscillations. (2) Mixed pairs of CA1 and OFC neurons fired together within single theta cycles. (3) CA1 population vectors (PVs) predicted changes in OFC activity, but not vice versa. (4) Spatial representations decoded from PVs in the two structures differed: CA1 ensembles stably represented distinct spatial journeys, whereas OFC ensembles developed more generalized goal arm representations in different paths to identical rewards. That these dynamics occurred only during learning an unfamiliar reversal, not during stable performance or familiar reversals, suggests that unique frontotemporal interaction mechanisms are engaged selectively when new task features come to guide decision-making.

Parameter estimation for multivariate population processes: a saddlepoint approach

The setting considered in this paper concerns a discrete-time multivariate population process under Markov modulation. Our objective is to estimate the model parameters, based on periodic observations of the network population vector. These parameters relate to the arrival, routing and departure processes, but also to the (unobservable) Markovian background process. When opting for the classical likelihood-based approach, the evaluation of the likelihood is problematic. We show however, how an accurate saddlepoint approximation can be used. Numerical experiments illustrate our method and show that even under relatively complicated conditions the parameters are estimated relatively precisely.

Assessing the growth rate of endangered Franciscana dolphin in Argentina, South America

Cetacean populations are vulnerable to decline due to anthropogenic threats and life history traits. The Franciscana dolphin (Pontoporia blainvillei) has been considered the most affected small dolphin in the Southwestern Atlantic Ocean. In this study a method is presented for estimating the growth rate of the Franciscana dolphin affected by incidental mortality (bycatch) in coastal marine areas of Argentina, South America. We used a general approach based on vital parameters information such as reproductive rates and survival probabilities for an age-structured population. The Franciscana’s growth rate was estimated using Leslie’s approach through an algorithm implemented in a 14 × 14 matrix model. Then, the population was characterized analysing the discrete-time evolution of the age–population vector. We found that the potential growth rate <1 indicates that Franciscanas in Argentina are susceptible to decline under current levels of incidental mortality.

On real-valued SDE and nonnegative-valued SDE population models with demographic variability.

Population dynamics with demographic variability is frequently studied using discrete random variables with continuous-time Markov chain (CTMC) models. An approximation of a CTMC model using continuous random variables can be derived in a straightforward manner by applying standard methods based on the reaction rates in the CTMC model. This leads to a system of Itô stochastic differential equations (SDEs) which generally have the form [Formula: see text] where [Formula: see text] is the population vector of random variables, [Formula: see text] is the drift vector, and G is the diffusion matrix. In some problems, the derived SDE model may not have real-valued or nonnegative solutions for all time. For such problems, the SDE model may be declared infeasible. In this investigation, new systems of SDEs are derived with real-valued solutions and with nonnegative solutions. To derive real-valued SDE models, reaction rates are assumed to be nonnegative for all time with negative reaction rates assigned probability zero. This biologically realistic assumption leads to the derivation of real-valued SDE population models. However, small but negative values may still arise for a real-valued SDE model. This is due to the magnitudes of certain problem-dependent diffusion coefficients when population sizes are near zero. A slight modification of the diffusion coefficients when population sizes are near zero ensures that a real-valued SDE model has a nonnegative solution, yet maintains the integrity of the SDE model when sizes are not near zero. Several population dynamic problems are examined to illustrate the methodology.

Human group coordination in a sensorimotor task with neuron-like decision-making

The formation of cooperative groups of agents with limited information-processing capabilities to solve complex problems together is a fundamental building principle that cuts through multiple scales in biology from groups of cells to groups of humans. Here, we study an experimental paradigm where a group of humans is joined together to solve a common sensorimotor task that cannot be achieved by a single agent but relies on the cooperation of the group. In particular, each human acts as a neuron-like binary decision-maker that determines in each moment of time whether to be active or not. Inspired by the population vector method for movement decoding, each neuron-like decision-maker is assigned a preferred movement direction that the decision-maker is ignorant about. From the population vector reflecting the group activity, the movement of a cursor is determined, and the task for the group is to steer the cursor into a predefined target. As the preferred movement directions are unknown and players are not allowed to communicate, the group has to learn a control strategy on the fly from the shared visual feedback. Performance is analyzed by learning speed and accuracy, action synchronization, and group coherence. We study four different computational models of the observed behavior, including a perceptron model, a reinforcement learning model, a Bayesian inference model and a Thompson sampling model that efficiently approximates Bayes optimal behavior. The Bayes and especially the Thompson model excel in predicting the human group behavior compared to the other models, suggesting that internal models are crucial for adaptive coordination. We discuss benefits and limitations of our paradigm regarding a better understanding of distributed information processing.

Memory reactivation in rat medial prefrontal cortex occurs in a subtype of cortical UP state during slow-wave sleep.

Interaction between hippocampal sharp-wave ripples (SWRs) and UP states, possibly by coordinated reactivation of memory traces, is conjectured to play an important role in memory consolidation. Recently, it was reported that SWRs were differentiated into multiple subtypes. However, whether cortical UP states can also be classified into subtypes is not known. Here, we analysed neural ensemble activity from the medial prefrontal cortex from rats trained to run a spatial sequence-memory task. Application of the hidden Markov model (HMM) with three states to epochs of UP-DOWN oscillations identified DOWN states and two subtypes of UP state (UP-1 and UP-2). The two UP subtypes were distinguished by differences in duration, with UP-1 having a longer duration than UP-2, as well as differences in the speed of population vector (PV) decorrelation, with UP-1 decorrelating more slowly than UP-2. Reactivation of recent memory sequences predominantly occurred in UP-2. Short-duration reactivating UP states were dominated by UP-2 whereas long-duration ones exhibit transitions from UP-1 to UP-2. Thus, recent memory reactivation, if it occurred within long-duration UP states, typically was preceded by a period of slow PV evolution not related to recent experience, and which we speculate may be related to previously encoded information. If that is the case, then the transition from UP-1 to UP-2 subtypes may help gradual integration of recent experience with pre-existing cortical memories by interleaving the two in the same UP state. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Using machine learning to reveal the population vector from EEG signals

Objective. Since the discovery of the population vector that directly relates neural spiking activity with arm movement direction, it has become feasible to control robotic arms and neuroprostheses using invasively recorded brain signals. For non-invasive approaches, a direct relation between human brain signals and arm movement direction is yet to be established. Approach. Here, we investigated electroencephalographic (EEG) signals in temporal and spectral domains in a continuous, circular arm movement task. Using machine learning methods that respect the linear mixture of brain activity within EEG signals, we show that directional information is represented in the temporal domain in amplitude modulations of the same frequency as the arm movement, and in the spectral domain in power modulations of the 20–24 Hz frequency band. Main results. In the temporal domain, the directional information was mainly expressed in primary sensorimotor cortex (SM1) and posterior parietal cortex (PPC) contralateral to the moving arm, while in the spectral domain SM1 and PPC of both hemispheres predicted arm movement direction. The different cortical representations suggest distinct neural representations in both domains. Significance. This direct relation between neural activity and arm movement direction in both domains demonstrates the potential of machine learning to reveal neuroscientific insights about the dynamics of human arm movements.

Hippocampal Place Cells Encode Local Surface-Texture Boundaries.

The cognitive map is often assumed to be a Euclidean map that isometrically represents the real world (i.e., the Euclidean distance between any two locations in the physical world should be preserved on the cognitive map). However, accumulating evidence suggests that environmental boundaries can distort the mental representations of physical space. For example, the distance between two locations can be remembered as longer than the true physical distance if the locations are separated by a boundary. While this overestimation is observed under different experimental conditions, even when the boundary is formed by flat surface cues, its physiological basis is not well understood. We examined the neural representation of flat surface cue boundaries, and of the space segregated by these boundaries, by recording place cell activity from CA1 and CA3 while rats foraged on a circular track or square platforms with inhomogeneous surface textures. About 40% of the place field edges concentrated near the boundaries on the circular track (significantly above the chance level 33%). Similarly, place field edges were more prevalent near boundaries on the platforms than expected by chance. In both one- and two-dimensional environments, the population vectors of place cell activity changed more abruptly with distance between locations that crossed cue boundaries than between locations within a bounded region. These results show that the locations of surface boundaries were evident as enhanced decorrelations of the neural representations of locations to either side of the boundaries. This enhancement might underlie the cognitive phenomenon of overestimation of distances across boundaries.

Binocular Encoding in the Damselfly Pre-motor Target Tracking System.

Akin to all damselflies, Calopteryx (family Calopterygidae), commonly known as jewel wings or demoiselles, possess dichoptic (separated) eyes with overlapping visual fields of view. In contrast, many dragonfly species possess holoptic (dorsally fused) eyes with limited binocular overlap. We have here compared the neuronal correlates of target tracking between damselfly and dragonfly sister lineages and linked these changes in visual overlap to pre-motor neural adaptations. Although dragonflies attack prey dorsally, we show that demoiselles attack prey frontally. We identify demoiselle target-selective descending neurons (TSDNs) with matching frontal visual receptive fields, anatomically and functionally homologous to the dorsally positioned dragonfly TSDNs. By manipulating visual input using eyepatches and prisms, we show that moving target information at the pre-motor level depends on binocular summation in demoiselles. Consequently, demoiselles encode directional information in a binocularly fused frame of reference such that information of a target moving toward the midline in the left eye is fused with information of the target moving away from the midline in the right eye. This contrasts with dragonfly TSDNs, where receptive fields possess a sharp midline boundary, confining responses to a single visual hemifield in a sagittal frame of reference (i.e., relative to the midline). Our results indicate that, although TSDNs are conserved across Odonata, their neural inputs, and thus the upstream organization of the target tracking system, differ significantly and match divergence in eye design and predatory strategies. VIDEO ABSTRACT.

A Multi-objective Improved Squirrel Search Algorithm based on Decomposition with External Population and Adaptive Weight Vectors Adjustment

In order to further improve the performance of the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) in solving multi-objective optimization problems, this paper constructs a multi-objective optimization algorithm by taking MOEA/D as the multi-objective framework and the Squirrel Search Algorithm (SSA) as the core evolutionary strategy. Besides, both of them are modified and the Multi-objective Improved Squirrel Search Algorithm based on Decomposition with External Population and Adaptive Weight Vectors Adjustment (MOEA/D-EWA-ISSA) is proposed. MOEA/D-EWA-ISSA establishes an external population for every individual to retain the evolutionary information and maintain the population diversity, external individuals participate in evolution and produce better offspring, the convergence and distribution of Pareto Front (PF) are improved. As for SSA, the jumping search method and the progressive search method are introduced to it, different evolutionary strategies are provided to solve subproblems, which further improves the ability of core evolutionary strategy to solve subproblems and the convergence of the obtained PF. Furthermore, MOEA/D-EWA-ISSA adjusts every weight vector adaptively according to the population’s actual evolutionary direction and the representative neighbor weight vectors, the distribution of the obtained PF is improved as well. The experimental results on multi-objective test functions show that the convergence and distribution of PF have obvious improvement when the improved SSA, the improved MOEA/D and the whole MOEA/D-EWA-ISSA are used to solve multi-objective optimization problems.

Geo-climatic factors in a newly emerging focus of zoonotic visceral leishmaniasis in rural areas of north-eastern Iran.

Visceral leishmaniasis (VL) is the fatal form of leishmaniasis. A newly emerging focus of zoonotic VL (ZVL) including 13 villages has been reported from Maraveh Tappeh County, Golestan province, north-eastern Iran. We investigated the effect of climatic and environmental factors on the occurrence of disease in this focus by geographical information systems (GIS) approaches and logistic regression models. The incidence rate of ZVL in human and dog population was estimated 1.2% and 27.3%, respectively, in the studied areas. Rock soil type (OR=11.7), rainfall (OR=1.04) and elevation (OR=0.99) were found as determinants of ZVL. Also, almost all infected villages were in close proximity of rivers. Cavities of rock soil around rivers are assumed as suitable areas for sandfly resting and growth by providing shadow and moisture. Rainfall also provides appropriate moisture in semi-arid regions for survival of sandflies. The negative effect of higher altitude can be explained by some sandfly life-limiting conditions like freezing weather. Accordingly, regions with lower altitude and higher rainfall, covered by rock and in close proximity of rivers, are the hazard zones for ZVL in the Maraveh Tappeh. Air humidity, temperature, land cover type and slope were not predictors of disease in the current study. Further investigations on human population movement, probable reservoirs and vectors of disease can provide valuable data for modelling the future distribution of ZVL in the Iranian province.