ResearchPad - markov-models https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[ToyArchitecture: Unsupervised learning of interpretable models of the environment]]> https://www.researchpad.co/article/elastic_article_15730 Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are often uncomputable, or lack practical implementations. In this paper we attempt to follow a big picture view while also providing a particular theory and its implementation to present a novel, purposely simple, and interpretable hierarchical architecture. This architecture incorporates the unsupervised learning of a model of the environment, learning the influence of one’s own actions, model-based reinforcement learning, hierarchical planning, and symbolic/sub-symbolic integration in general. The learned model is stored in the form of hierarchical representations which are increasingly more abstract, but can retain details when needed. We demonstrate the universality of the architecture by testing it on a series of diverse environments ranging from audio/visual compression to discrete and continuous action spaces, to learning disentangled representations.

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<![CDATA[A non-stationary Markov model for economic evaluation of grass pollen allergoid immunotherapy]]> https://www.researchpad.co/article/elastic_article_14555 Allergic rhino-conjunctivitis (ARC) is an IgE-mediated disease that occurs after exposure to indoor or outdoor allergens, or to non-specific triggers. Effective treatment options for seasonal ARC are available, but the economic aspects and burden of these therapies are not of secondary importance, also considered that the prevalence of ARC has been estimated at 23% in Europe. For these reasons, we propose a novel flexible cost-effectiveness analysis (CEA) model, intended to provide healthcare professionals and policymakers with useful information aimed at cost-effective interventions for grass-pollen induced allergic rhino-conjunctivitis (ARC).MethodsTreatments compared are: 1. no AIT, first-line symptomatic drug-therapy with no allergoid immunotherapy (AIT). 2. SCIT, subcutaneous immunotherapy. 3. SLIT, sublingual immunotherapy. The proposed model is a non-stationary Markovian model, that is flexible enough to reflect those treatment-related problems often encountered in real-life and clinical practice, but that cannot be adequately represented in randomized clinical trials (RCTs). At the same time, we described in detail all the structural elements of the model as well as its input parameters, in order to minimize any issue of transparency and facilitate the reproducibility and circulation of the results among researchers.ResultsUsing the no AIT strategy as a comparator, and the Incremental Cost Effectiveness Ratio (ICER) as a statistic to summarize the cost-effectiveness of a health care intervention, we could conclude that:SCIT systematically outperforms SLIT, except when a full societal perspective is considered. For example, for T = 9 and a pollen season of 60 days, we have ICER = €16,729 for SCIT vs. ICER = €15,116 for SLIT (in the full societal perspective).For longer pollen seasons or longer follow-up duration the ICER decreases, because each patient experiences a greater clinical benefit over a larger time span, and Quality-adjusted Life Year (QALYs) gained per cycle increase accordingly.Assuming that no clinical benefit is achieved after premature discontinuation, and that at least three years of immunotherapy are required to improve clinical manifestations and perceiving a better quality of life, ICERs become far greater than €30,000.If the immunotherapy is effective only at the peak of the pollen season, the relative ICERs rise sharply. For example, in the scenario where no clinical benefit is present after premature discontinuation of immunotherapy, we have ICER = €74,770 for SCIT vs. ICER = €152,110 for SLIT.The distance between SCIT and SLIT strongly depends on under which model the interventions are meta-analyzed.ConclusionsEven though there is a considerable evidence that SCIT outperforms SLIT, we could not state that both SCIT and SLIT (or only one of these two) can be considered cost-effective for ARC, as a reliable threshold value for cost-effectiveness set by national regulatory agencies for pharmaceutical products is missing. Moreover, the impact of model input parameters uncertainty on the reliability of our conclusions needs to be investigated further. ]]> <![CDATA[Predicting change: Approximate inference under explicit representation of temporal structure in changing environments]]> https://www.researchpad.co/article/5c5ca27ed5eed0c48441e4cc

In our daily lives timing of our actions plays an essential role when we navigate the complex everyday environment. It is an open question though how the representations of the temporal structure of the world influence our behavior. Here we propose a probabilistic model with an explicit representation of state durations which may provide novel insights in how the brain predicts upcoming changes. We illustrate several properties of the behavioral model using a standard reversal learning design and compare its task performance to standard reinforcement learning models. Furthermore, using experimental data, we demonstrate how the model can be applied to identify participants’ beliefs about the latent temporal task structure. We found that roughly one quarter of participants seem to have learned the latent temporal structure and used it to anticipate changes, whereas the remaining participants’ behavior did not show signs of anticipatory responses, suggesting a lack of precise temporal expectations. We expect that the introduced behavioral model will allow, in future studies, for a systematic investigation of how participants learn the underlying temporal structure of task environments and how these representations shape behavior.

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<![CDATA[Exploring chromatin hierarchical organization via Markov State Modelling]]> https://www.researchpad.co/article/5c79b497d5eed0c4841ea6b0

We propose a new computational method for exploring chromatin structural organization based on Markov State Modelling of Hi-C data represented as an interaction network between genomic loci. A Markov process describes the random walk of a traveling probe in the corresponding energy landscape, mimicking the motion of a biomolecule involved in chromatin function. By studying the metastability of the associated Markov State Model upon annealing, the hierarchical structure of individual chromosomes is observed, and corresponding set of structural partitions is identified at each level of hierarchy. Then, the notion of effective interaction between partitions is derived, delineating the overall topology and architecture of chromosomes. Mapping epigenetic data on the graphs of intra-chromosomal effective interactions helps in understanding how chromosome organization facilitates its function. A sketch of whole-genome interactions obtained from the analysis of 539 partitions from all 23 chromosomes, complemented by distributions of gene expression regulators and epigenetic factors, sheds light on the structure-function relationships in chromatin, delineating chromosomal territories, as well as structural partitions analogous to topologically associating domains and active / passive epigenomic compartments. In addition to the overall genome architecture shown by effective interactions, the affinity between partitions of different chromosomes was analyzed as an indicator of the degree of association between partitions in functionally relevant genomic interactions. The overall static picture of whole-genome interactions obtained with the method presented in this work provides a foundation for chromatin structural reconstruction, for the modelling of chromatin dynamics, and for exploring the regulation of genome function. The algorithms used in this study are implemented in a freely available Python package ChromaWalker (https://bitbucket.org/ZhenWahTan/chromawalker).

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<![CDATA[Utilizing longitudinal microbiome taxonomic profiles to predict food allergy via Long Short-Term Memory networks]]> https://www.researchpad.co/article/5c61e8e9d5eed0c48496f3af

Food allergy is usually difficult to diagnose in early life, and the inability to diagnose patients with atopic diseases at an early age may lead to severe complications. Numerous studies have suggested an association between the infant gut microbiome and development of allergy. In this work, we investigated the capacity of Long Short-Term Memory (LSTM) networks to predict food allergies in early life (0-3 years) from subjects’ longitudinal gut microbiome profiles. Using the DIABIMMUNE dataset, we show an increase in predictive power using our model compared to Hidden Markov Model, Multi-Layer Perceptron Neural Network, Support Vector Machine, Random Forest, and LASSO regression. We further evaluated whether the training of LSTM networks benefits from reduced representations of microbial features. We considered sparse autoencoder for extraction of potential latent representations in addition to standard feature selection procedures based on Minimum Redundancy Maximum Relevance (mRMR) and variance prior to the training of LSTM networks. The comprehensive evaluation reveals that LSTM networks with the mRMR selected features achieve significantly better performance compared to the other tested machine learning models.

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<![CDATA[Kinetic and thermodynamic insights into sodium ion translocation through the μ-opioid receptor from molecular dynamics and machine learning analysis]]> https://www.researchpad.co/article/5c536a92d5eed0c484a47639

The differential modulation of agonist and antagonist binding to opioid receptors (ORs) by sodium (Na+) has been known for decades. To shed light on the molecular determinants, thermodynamics, and kinetics of Na+ translocation through the μ-OR (MOR), we used a multi-ensemble Markov model framework combining equilibrium and non-equilibrium atomistic molecular dynamics simulations of Na+ binding to MOR active or inactive crystal structures embedded in an explicit lipid bilayer. We identify an energetically favorable, continuous ion pathway through the MOR active conformation only, and provide, for the first time: i) estimates of the energy differences and required timescales of Na+ translocation in inactive and active MORs, ii) estimates of Na+-induced changes to agonist binding validated by radioligand measurements, and iii) testable hypotheses of molecular determinants and correlated motions involved in this translocation, which are likely to play a key role in MOR signaling.

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<![CDATA[Triplet-pore structure of a highly divergent TOM complex of hydrogenosomes in Trichomonas vaginalis]]> https://www.researchpad.co/article/5c390bb5d5eed0c48491df0f

Mitochondria originated from proteobacterial endosymbionts, and their transition to organelles was tightly linked to establishment of the protein import pathways. The initial import of most proteins is mediated by the translocase of the outer membrane (TOM). Although TOM is common to all forms of mitochondria, an unexpected diversity of subunits between eukaryotic lineages has been predicted. However, experimental knowledge is limited to a few organisms, and so far, it remains unsettled whether the triplet-pore or the twin-pore structure is the generic form of TOM complex. Here, we analysed the TOM complex in hydrogenosomes, a metabolically specialised anaerobic form of mitochondria found in the excavate Trichomonas vaginalis. We demonstrate that the highly divergent β-barrel T. vaginalis TOM (TvTom)40-2 forms a translocation channel to conduct hydrogenosomal protein import. TvTom40-2 is present in high molecular weight complexes, and their analysis revealed the presence of four tail-anchored (TA) proteins. Two of them, Tom36 and Tom46, with heat shock protein (Hsp)20 and tetratricopeptide repeat (TPR) domains, can bind hydrogenosomal preproteins and most likely function as receptors. A third subunit, Tom22-like protein, has a short cis domain and a conserved Tom22 transmembrane segment but lacks a trans domain. The fourth protein, hydrogenosomal outer membrane protein 19 (Homp19) has no known homology. Furthermore, our data indicate that TvTOM is associated with sorting and assembly machinery (Sam)50 that is involved in β-barrel assembly. Visualisation of TvTOM by electron microscopy revealed that it forms three pores and has an unconventional skull-like shape. Although TvTOM seems to lack Tom7, our phylogenetic profiling predicted Tom7 in free-living excavates. Collectively, our results suggest that the triplet-pore TOM complex, composed of three conserved subunits, was present in the last common eukaryotic ancestor (LECA), while receptors responsible for substrate binding evolved independently in different eukaryotic lineages.

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<![CDATA[Segmenting accelerometer data from daily life with unsupervised machine learning]]> https://www.researchpad.co/article/5c3fa5d4d5eed0c484ca916d

Purpose

Accelerometers are increasingly used to obtain valuable descriptors of physical activity for health research. The cut-points approach to segment accelerometer data is widely used in physical activity research but requires resource expensive calibration studies and does not make it easy to explore the information that can be gained for a variety of raw data metrics. To address these limitations, we present a data-driven approach for segmenting and clustering the accelerometer data using unsupervised machine learning.

Methods

The data used came from five hundred fourteen-year-old participants from the Millennium cohort study who wore an accelerometer (GENEActiv) on their wrist on one weekday and one weekend day. A Hidden Semi-Markov Model (HSMM), configured to identify a maximum of ten behavioral states from five second averaged acceleration with and without addition of x, y, and z-angles, was used for segmenting and clustering of the data. A cut-points approach was used as comparison.

Results

Time spent in behavioral states with or without angle metrics constituted eight and five principal components to reach 95% explained variance, respectively; in comparison four components were identified with the cut-points approach. In the HSMM with acceleration and angle as input, the distributions for acceleration in the states showed similar groupings as the cut-points categories, while more variety was seen in the distribution of angles.

Conclusion

Our unsupervised classification approach learns a construct of human behavior based on the data it observes, without the need for resource expensive calibration studies, has the ability to combine multiple data metrics, and offers a higher dimensional description of physical behavior. States are interpretable from the distributions of observations and by their duration.

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<![CDATA[An evolutionary game perspective on quantised consensus in opinion dynamics]]> https://www.researchpad.co/article/5c390ba9d5eed0c48491dbf3

Quantised consensus has been used in the context of opinion dynamics. In this context agents interact with their neighbours and they change their opinion according to their interests and the opinions of their neighbours. We consider various quantised consensus models, where agents have different levels of susceptibility to the inputs received from their neighbours. The provided models share similarities with collective decision making models inspired by honeybees and evolutionary games. As first contribution, we develop an evolutionary game-theoretic model that accommodates the different consensus dynamics in a unified framework. As second contribution, we study equilibrium points and extend such study to the symmetric case where the transition probabilities of the evolutionary game dynamics are symmetric. Symmetry is associated with the case of equally favourable options. As third contribution, we study stability of the equilibrium points for the different cases. We corroborate the theoretical results with some simulations to study the outcomes of the various models.

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<![CDATA[High mutual cooperation rates in rats learning reciprocal altruism: The role of payoff matrix]]> https://www.researchpad.co/article/5c36680fd5eed0c4841a6ff8

Cooperation is one of the most studied paradigms for the understanding of social interactions. Reciprocal altruism -a special type of cooperation that is taught by means of the iterated prisoner dilemma game (iPD)- has been shown to emerge in different species with different success rates. When playing iPD against a reciprocal opponent, the larger theoretical long-term reward is delivered when both players cooperate mutually. In this work, we trained rats in iPD against an opponent playing a Tit for Tat strategy, using a payoff matrix with positive and negative reinforcements, that is food and timeout respectively. We showed for the first time, that experimental rats were able to learn reciprocal altruism with a high average cooperation rate, where the most probable state was mutual cooperation (85%). Although when subjects defected, the most probable behavior was to go back to mutual cooperation. When we modified the matrix by increasing temptation rewards (T) or by increasing cooperation rewards (R), the cooperation rate decreased. In conclusion, we observe that an iPD matrix with large positive reward improves less cooperation than one with small rewards, shown that satisfying the relationship among iPD reinforcement was not enough to achieve high mutual cooperation behavior. Therefore, using positive and negative reinforcements and an appropriate contrast between rewards, rats have cognitive capacity to learn reciprocal altruism. This finding allows to infer that the learning of reciprocal altruism has early appeared in evolution.

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<![CDATA[Healthcare value of implementing hepatitis C screening in the adult general population in Spain]]> https://www.researchpad.co/article/5c084202d5eed0c484fcb735

Background

Elimination of hepatitis C virus (HCV) infection requires high diagnostic rates and universal access to treatment. Around 40% of infected individuals are unaware of their infection, which indicates that effective screening strategies are needed. We analyzed the efficiency (incremental cost-utility ratio, ICUR) of 3 HCV screening strategies: a) general population of adults, b) high-risk groups, and c) population with the highest anti-HCV prevalence plus high-risk groups.

Methods

An analytical decision model, projecting progression of the disease over a lifetime, was used to establish the candidate population for HCV screening. HCV data were obtained from the literature: anti-HCV prevalence (0.56%-1.54%), viremic patients (31.5%), and percentage of undiagnosed persons among those with viremia (35%). It was assumed that most patients would be treated and have HCV therapy response (98% SVR); transition probabilities, utilities, and disease management annual costs were obtained from the literature. Efficiency over the life of patients under the National Health System perspective was measured as quality-adjusted life years (QALY) and total cost (screening, diagnosis, pharmacological and disease management). A discount rate of 3% was applied to costs and outcomes.

Results

Screening of the adult population would identify a larger number of additional chronic hepatitis C cases (N = 52,694) than screening the highest anti-HCV prevalence population plus high-risk groups (N = 42,027) or screening high-risk groups (N = 26,128). ICUR for the general population vs. high-risk groups was €8914/QALY gained per patient (€18,157 incremental cost and 2.037 QALY). ICUR for the general population vs. population with highest anti-HCV prevalence plus high-risk groups was €7,448/QALY gained per patient (€7,733 incremental cost and 1.038 QALY). These ICUR values are below the accepted efficiency threshold (€22,000-€30,000).

Conclusion

HCV screening and treatment of the general adult population is cost-effective compared to screening of high-risk groups or the population with the highest anti-HCV prevalence plus high-risk groups.

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<![CDATA[Macrophage activation by IFN-γ triggers restriction of phagosomal copper from intracellular pathogens]]> https://www.researchpad.co/article/5bfc623ed5eed0c484ec7a25

Copper toxicity and copper limitation can both be effective host defense mechanisms against pathogens. Tolerance of high copper by fungi makes toxicity as a defense mechanism largely ineffective against fungal pathogens. A forward genetic screen for Histoplasma capsulatum mutant yeasts unable to replicate within macrophages showed the Ctr3 copper transporter is required for intramacrophage proliferation. Ctr3 mediates copper uptake and is required for growth in low copper. Transcription of the CTR3 gene is induced by differentiation of H. capsulatum into pathogenic yeasts and by low available copper, but not decreased iron. Low expression of a CTR3 transcriptional reporter by intracellular yeasts implies that phagosomes of non-activated macrophages have moderate copper levels. This is further supported by the replication of Ctr3-deficient yeasts within the phagosome of non-activated macrophages. However, IFN-γ activation of phagocytes causes restriction of phagosomal copper as shown by upregulation of the CTR3 transcriptional reporter and by the failure of Ctr3-deficient yeasts, but not Ctr3 expressing yeasts, to proliferate within these macrophages. Accordingly, in a respiratory model of histoplasmosis, Ctr3-deficient yeasts are fully virulent during phases of the innate immune response but are attenuated after the onset of adaptive immunity. Thus, while technical limitations prevent direct measurement of phagosomal copper concentrations and copper-independent factors can influence gene expression, both the CTR3 promoter induction and the attenuation of Ctr3-deficient yeasts indicate activation of macrophages switches the phagosome from a copper-replete to a copper-depleted environment, forcing H. capsulatum reliance on Ctr3 for copper acquisition.

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<![CDATA[Rosetta FunFolDes – A general framework for the computational design of functional proteins]]> https://www.researchpad.co/article/5bfc6223d5eed0c484ec6c7f

The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability. The low success rates of computational design protocols and the extensive in vitro optimization often required, highlight the challenge of designing proteins that perform essential biochemical functions, such as binding or catalysis. One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins. The structural complexity of the functional motif largely determines how readily one can find host protein structures that are “designable”, meaning that are likely to present the functional motif in the desired conformation. One promising route to enhance the “designability” of protein structures is to allow backbone flexibility. Here, we present a computational approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins—Rosetta Functional Folding and Design (FunFolDes). We performed extensive computational benchmarks, where we observed that the enforcement of functional requirements resulted in designs distant from the global energetic minimum of the protein. An observation consistent with several experimental studies that have revealed function-stability tradeoffs. To test the design capabilities of FunFolDes we transplanted two viral epitopes into distant structural templates including one de novo “functionless” fold, which represent two typical challenges where the designability problem arises. The designed proteins were experimentally characterized showing high binding affinities to monoclonal antibodies, making them valuable candidates for vaccine design endeavors. Overall, we present an accessible strategy to repurpose old protein folds for new functions. This may lead to important improvements on the computational design of proteins, with structurally complex functional sites, that can perform elaborate biochemical functions related to binding and catalysis.

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<![CDATA[Tangled history of a multigene family: The evolution of ISOPENTENYLTRANSFERASE genes]]> https://www.researchpad.co/article/5b6da1ae463d7e4dccc5faea

ISOPENTENYLTRANSFERASE (IPT) genes play important roles in the initial steps of cytokinin synthesis, exist in plant and pathogenic bacteria, and form a multigene family in plants. Protein domain searches revealed that bacteria and plant IPT proteins were to assigned to different protein domains families in the Pfam database, namely Pfam IPT (IPTPfam) and Pfam IPPT (IPPTPfam) families, both are closely related in the P-loop NTPase clan. To understand the origin and evolution of the genes, a species matrix was assembled across the tree of life and intensively in plant lineages. The IPTPfam domain was only found in few bacteria lineages, whereas IPPTPfam is common except in Archaea and Mycoplasma bacteria. The bacterial IPPTPfam domain miaA genes were shown as ancestral of eukaryotic IPPTPfam domain genes. Plant IPTs diversified into class I, class II tRNA-IPTs, and Adenosine-phosphate IPTs; the class I tRNA-IPTs appeared to represent direct successors of miaA genes were found in all plant genomes, whereas class II tRNA-IPTs originated from eukaryotic genes, and were found in prasinophyte algae and in euphyllophytes. Adenosine-phosphate IPTs were only found in angiosperms. Gene duplications resulted in gene redundancies with ubiquitous expression or diversification in expression. In conclusion, it is shown that IPT genes have a complex history prior to the protein family split, and might have experienced losses or HGTs, and gene duplications that are to be likely correlated with the rise in morphological complexity involved in fine tuning cytokinin production.

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<![CDATA[SAFlex: A structural alphabet extension to integrate protein structural flexibility and missing data information]]> https://www.researchpad.co/article/5b4a196a463d7e428027f8b1

In this paper, we describe SAFlex (Structural Alphabet Flexibility), an extension of an existing structural alphabet (HMM-SA), to better explore increasing protein three dimensional structure information by encoding conformations of proteins in case of missing residues or uncertainties. An SA aims to reduce three dimensional conformations of proteins as well as their analysis and comparison complexity by simplifying any conformation in a series of structural letters. Our methodology presents several novelties. Firstly, it can account for the encoding uncertainty by providing a wide range of encoding options: the maximum a posteriori, the marginal posterior distribution, and the effective number of letters at each given position. Secondly, our new algorithm deals with the missing data in the protein structure files (concerning more than 75% of the proteins from the Protein Data Bank) in a rigorous probabilistic framework. Thirdly, SAFlex is able to encode and to build a consensus encoding from different replicates of a single protein such as several homomer chains. This allows localizing structural differences between different chains and detecting structural variability, which is essential for protein flexibility identification. These improvements are illustrated on different proteins, such as the crystal structure of an eukaryotic small heat shock protein. They are promising to explore increasing protein redundancy data and obtain useful quantification of their flexibility.

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<![CDATA[View-Invariant Visuomotor Processing in Computational Mirror Neuron System for Humanoid]]> https://www.researchpad.co/article/5989da1cab0ee8fa60b7d5bf

Mirror neurons are visuo-motor neurons found in primates and thought to be significant for imitation learning. The proposition that mirror neurons result from associative learning while the neonate observes his own actions has received noteworthy empirical support. Self-exploration is regarded as a procedure by which infants become perceptually observant to their own body and engage in a perceptual communication with themselves. We assume that crude sense of self is the prerequisite for social interaction. However, the contribution of mirror neurons in encoding the perspective from which the motor acts of others are seen have not been addressed in relation to humanoid robots. In this paper we present a computational model for development of mirror neuron system for humanoid based on the hypothesis that infants acquire MNS by sensorimotor associative learning through self-exploration capable of sustaining early imitation skills. The purpose of our proposed model is to take into account the view-dependency of neurons as a probable outcome of the associative connectivity between motor and visual information. In our experiment, a humanoid robot stands in front of a mirror (represented through self-image using camera) in order to obtain the associative relationship between his own motor generated actions and his own visual body-image. In the learning process the network first forms mapping from each motor representation onto visual representation from the self-exploratory perspective. Afterwards, the representation of the motor commands is learned to be associated with all possible visual perspectives. The complete architecture was evaluated by simulation experiments performed on DARwIn-OP humanoid robot.

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<![CDATA[Degradation Parameters from Pulse-Chase Experiments]]> https://www.researchpad.co/article/5989dab1ab0ee8fa60bab897

Pulse-chase experiments are often used to study the degradation of macromolecules such as proteins or mRNA. Considerations for the choice of pulse length include the toxicity of the pulse to the cell and maximization of labeling. In the general case of non-exponential decay, varying the length of the pulse results in decay patterns that look different. Analysis of these patterns without consideration to pulse length would yield incorrect degradation parameters. Here we propose a method that constructively includes pulse length in the analysis of decay patterns and extracts the parameters of the underlying degradation process. We also show how to extract decay parameters reliably from measurements taken during the pulse phase.

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<![CDATA[Modeling and Control of Colorectal Cancer]]> https://www.researchpad.co/article/5989daedab0ee8fa60bbfeee

Colorectal Cancer (CRC) is becoming a major threat to people’s life in China. Screening methods adopted by many other countries as effective counter-cancer methods have not been explicitly explored for people there. Thus, we present a Markov model with detailed precancerous adenoma states and then evaluate various screening strategies in this paper. Different from current researches, our model considers the population’s heterogeneous risk of developing adenomas and observation-based screening strategies. Furthermore, we also give a new cost-effectiveness metric. After calibrating, the model is simulated using the Monte Carlo method. Numerical results show that there are threshold values of compliance rates below which strategy with every ten-year colonoscopy becomes the most cost-effective method; otherwise, an observation-based screening strategy is the most cost-effective. We also find that strategy with single colonoscopy for adenoma-free individuals and every three-year colonoscopy for those with adenoma is recommended when the observation-based strategy is not considered. Our findings give an explicit and complete instruction in CRC screening protocol in average-risk Chinese.

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<![CDATA[A rough set approach for determining weights of decision makers in group decision making]]> https://www.researchpad.co/article/5989db50ab0ee8fa60bdbd78

This study aims to present a novel approach for determining the weights of decision makers (DMs) based on rough group decision in multiple attribute group decision-making (MAGDM) problems. First, we construct a rough group decision matrix from all DMs’ decision matrixes on the basis of rough set theory. After that, we derive a positive ideal solution (PIS) founded on the average matrix of rough group decision, and negative ideal solutions (NISs) founded on the lower and upper limit matrixes of rough group decision. Then, we obtain the weight of each group member and priority order of alternatives by using relative closeness method, which depends on the distances from each individual group member’ decision to the PIS and NISs. Through comparisons with existing methods and an on-line business manager selection example, the proposed method show that it can provide more insights into the subjectivity and vagueness of DMs’ evaluations and selections.

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<![CDATA[Malaria Incidence Rates from Time Series of 2-Wave Panel Surveys]]> https://www.researchpad.co/article/5989dab4ab0ee8fa60bac63f

Methodology to estimate malaria incidence rates from a commonly occurring form of interval-censored longitudinal parasitological data—specifically, 2-wave panel data—was first proposed 40 years ago based on the theory of continuous-time homogeneous Markov Chains. Assumptions of the methodology were suitable for settings with high malaria transmission in the absence of control measures, but are violated in areas experiencing fast decline or that have achieved very low transmission. No further developments that can accommodate such violations have been put forth since then. We extend previous work and propose a new methodology to estimate malaria incidence rates from 2-wave panel data, utilizing the class of 2-component mixtures of continuous-time Markov chains, representing two sub-populations with distinct behavior/attitude towards malaria prevention and treatment. Model identification, or even partial identification, requires context-specific a priori constraints on parameters. The method can be applied to scenarios of any transmission intensity. We provide an application utilizing data from Dar es Salaam, an area that experienced steady decline in malaria over almost five years after a larviciding intervention. We conducted sensitivity analysis to account for possible sampling variation in input data and model assumptions/parameters, and we considered differences in estimates due to submicroscopic infections. Results showed that, assuming defensible a priori constraints on model parameters, most of the uncertainty in the estimated incidence rates was due to sampling variation, not to partial identifiability of the mixture model for the case at hand. Differences between microscopy- and PCR-based rates depend on the transmission intensity. Leveraging on a method to estimate incidence rates from 2-wave panel data under any transmission intensity, and from the increasing availability of such data, there is an opportunity to foster further methodological developments, particularly focused on partial identifiability and the diversity of a priori parameter constraints associated with different human-ecosystem interfaces. As a consequence there can be more nuanced planning and evaluation of malaria control programs than heretofore.

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