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A probabilistic model is proposed with dynamics which naturally leads to a bottleneck in the number of mitochondria transmitted from one host-cell generation to the other. We take into account deleterious mutations during the replication of mitochondria within a cell of a germ-line and introduce selection inside the cell reproduction mechanism. The bottleneck size strongly depends on the selection mechanism and on the maximum number of mitochondria per cell. We obtain that the smaller the maximum allowed number of mitochondria per cell during replication, the tighter the bottleneck. Such a result is in agreement with the fact that species producing small litters provide developing oocytes with a smaller number of mitochondria. This amplifies the differences among oocytes leading to competition and removal of inferior cells.

Current researches give priority to the diffusion of single message, but the diffusion of multi-messages at the same time in the actual network also exists. The diverse correlation of the messages will influence each other in the diffusion. It should be taken into consideration. This paper works to make a definition to the framework of Social–Messages Network. Based on it, a multi-message topic dissemination probabilistic model with memory attenuation is put forward, which introduces the correlation among messages. We adopt a simple learning strategy to gain the diverse correlation of messages. Then, the numerical simulation is utilized to analyze the model, whilst the relationship of the model parameter with the scope of the topic diffusion and the spread speed are studied and analyzed. With the related discussion data on Twitter, an empirical study is made to the model and the diffusion progress of the message is anticipated, which suggested that the anticipation is fundamentally in line with the actual data, and the estimated value of our model is closer to the reality than the classic diffusion model. Study on the topic diffusion will be conducive to the understanding and the anticipation of the multi–messages spread.

Link prediction based on random walks has been widely used. The existing random walk algorithms ignore the probability of a walker visit from the initial node to the destination node for the first time, which makes a major contribution to establish links in some networks. To deal with the problem, we develop a link prediction method named Local Random Walk with Distance (LRWD) based on local random walk and the shortest distance of node pairs. In LRWD, walkers walk with their own steps rather than uniform steps. To evaluate the performance of the LRWD algorithm, we present the concept of distance distribution. The experimental results show that LRWD can improve the prediction accuracy when the distance distribution of the network is relatively concentrated.

We consider the problem of enriching the travelogue associated with a small number (even one) of images with more web images. Images associated with the travelogue always consist of the content and the style of textual information. Relying on this assumption, in this paper, we present a framework of travelogue enriching, exploiting both textual and visual information generated by different users. The framework aims to select the most relevant images from automatically collected candidate image set to enrich the given travelogue, and form a comprehensive overview of the scenic spot. To do these, we propose to build two-layer probabilistic models, i.e. a text-layer model and image-layer models, on offline collected travelogues and images. Each topic (e.g. Sea, Mountain, Historical Sites) in the text-layer model is followed by an image-layer model with sub-topics learnt (e.g. the topic of sea is with the sub-topic like beach, tree, sunrise and sunset). Based on the model, we develop strategies to enrich travelogues in the following steps: (1) remove noisy names of scenic spots from travelogues; (2) generate queries to automatically gather candidate image set; (3) select images to enrich the travelogue; and (4) choose images to portray the visual content of a scenic spot. Experimental results on Chinese travelogues demonstrate the potential of the proposed approach on tasks of travelogue enrichment and the corresponding scenic spot illustration.

This paper proposes a probabilistic approach to recognize handwritten Chinese characters. According to the stroke writing sequence, strokes and interleaved stroke relations are built manually as a 1-D string, called an on-line model, to describe a Chinese character. In an input character, strokes are first extracted by a tree searching method. The recognition problem is then formulated as an optimization matching problem in a multistage directed graph, where the number of stages is the length of the modelled stroke sequence. Nodes in a stage represent extracted strokes that have the same stroke type as defined in the on-line model and the link between two neighboring nodes corresponds to the relationship between the two extracted strokes. The probability that the extracted stroke belongs to the predefined stroke type is calculated from the stroke line segments, and the transition probability between two extracted strokes is the degree of satisfaction of the relationship defined in the on-line model. The Viterbi algorithm, which can handle stroke insertion, deletion, splitting, and merging, is applied to recover the sequence of strokes consisting of the unknown character. The similarity is defined to be the product of stroke probabilities and stroke transition probabilities in the stroke sequence. The unknown character is matched with all modelled characters and is recognized as the one with the highest similarity. Experiments with 540 characters uniformly selected from the database CCL/HCCR1 (250 variations/class) are conducted, and the recognition rate is about 92.8%, which proves the feasibility of the proposed recognition system.

Multiple diagnosis methods using Bayesian networks are rooted in numerous research projects about model-based diagnosis. Some of this research exploits probabilities to make a diagnosis. Many Bayesian network applications are used for medical diagnosis or for the diagnosis of technical problems in small or moderately large devices. This paper explains in detail the advantages of using Bayesian networks as graphic probabilistic models for diagnosing complex devices, and then compares such models with other probabilistic models that may or may not use Bayesian networks.

We investigate the average-case scalability of parallel algorithms executing on multicomputer systems whose static networks are k-ary d-cubes. Our performance metrics are isoefficiency function and isospeed scalability. For the purpose of average-case performance analysis, we formally define the concepts of average-case isoefficiency function and average-case isospeed scalability. By modeling parallel algorithms on multicomputers using task interaction graphs, we are mainly interested in the effects of communication overhead and load imbalance on the performance of parallel computations. We focus on the topology of static networks whose limited connectivities are constraints to high performance. In our probabilistic model, task computation and communication times are treated as random variables, so that we can analyze the average-case performance of parallel computations. We derive the expected parallel execution time on symmetric static networks and apply the result to k-ary d-cubes. We characterize the maximum tolerable communication overhead such that constant average-case efficiency and average-case average-speed could be maintained and that the number of tasks has a growth rate Θ(P log P), where P is the number of processors. It is found that the scalability of a parallel computation is essentially determined by the topology of a static network, i.e., the architecture of a parallel computer system. We also argue that under our probabilistic model, the number of tasks should grow at least in the rate of Θ(P log P), so that constant average-case efficiency and average-speed can be maintained.

Reconstruction of data loss in structural seismic responses is important for structural health monitoring to evaluate the safety of structures. A physics-guided neural network that leverages the prior knowledge was proposed for reconstructing structural seismic responses that were inaccessible to measure or missing during earthquakes. The presented methodology consisted of convolutional neural networks with dilated kernel and fully connected neural networks, which were developed to achieve a multitask learning that involved the regression task with measured labeled displacement data and the reconstruction task of seismic response without any labels. To better balance the loss gradient across different tasks, a probabilistic model was introduced to optimize the weight coefficient for each task by quantifying the task-dependent uncertainty based on Bayesian statistics. The weight coefficient for each task can be dynamically updated during the training process, thereby improving the learning efficacy and performance accuracy of the neural networks. The probabilistic model with task-dependent uncertainty was validated to outperform the equal-weighted model (i.e. equal weight for each task) in reconstructing the structural seismic responses based on numerical data, even when the relevant physical information (i.e. Bouc–Wen model) was not complete.

This commentary is concerned with a formula for the false discovery rate (FDR) which frequently serves as a basis for its estimation. This formula is valid under some quite special conditions, motivating us to further discuss probabilistic models behind the commonly accepted FDR concept with a special focus on problems arising in microarray data analysis. We also present a simulation study designed to assess the effects of inter-gene correlations on some theoretical results based on such models.

Data availability is not a major issue at present times in view of the widespread use of Internet; however, information and knowledge availability are the issues. Due to data overload and time-critical nature of information need, automatic summarization of documents plays a significant role in information retrieval and text data mining. This paper discusses the design of a multi-document summarizer that uses Katz's K-mixture model for term distribution. The model helps in ranking the sentences by a modified term weight assignment. Highly ranked sentences are selected for the final summary. The sentences that are repetitive in nature are eliminated, and a tiled summary is produced. Our method avoids redundancy and produces a readable (even browsable) summary, which we refer to as an event-specific tiled summary. The system has been evaluated against the frequently occurring sentences in the summaries generated by a set of human subjects. Our system outperforms other auto-summarizers at different extraction levels of summarization with respect to the ideal summary, and is close to the ideal summary at 40% extraction level.

This study theoretically investigates receptor–ligand-mediated endocytosis of nanoparticles (NPs) in wall shear flow. The endocytosis is modeled as a birth–death process and relationships between coefficients in the model and the wall shear rate have been derived to deal with the effects of the shear flow. Model predictions show that flow-induced alteration in bond formation rates does not affect the endocytosis significantly, and the suppression of hydrodynamic load on endocytosis is eminent only when diameters of NPs are large (around 700nm) and the shear rate is sufficiently high. In the latter case, it is shown that the hydrodynamic load suppresses the initial attachment of NPs to cells more than the following internalization. The model also predicts that shear-promoted expression of certain ligands can lead to observable increase in the number of endocytozed NPs in typical flow-chamber experiments, and the promotion can also cause selective endocytosis of NPs by cells at high shear rate regions if the ligand surface density on NPs or the original expression of receptors on cells in the absence of flow is low.

Cell trajectory inference is very important to understand the details of tissue cell development, state differentiation and gene dynamic regulation. However, due to the high noise and heterogeneity of the single-cell data, it is challenging to infer cell trajectory in complex biological processes. Here, we proposed a new trajectory inference method, called Metric Learning Bhattacharyya Kernel Feature Decomposition (MLBKFD). In MLBKFD, a statistical model was used to infer cell trajectory by calculating the Markov transition matrix and Bhattacharyya kernel matrix between cells. Before that, to expedite the matrix calculation in the statistical model, a deep feedforward neural network was used to perform dimensionality reduction on single-cell data. The MLBKFD was evaluated on four typical datasets as well as seven recent human fetal lung datasets. Comparisons with the two outstanding methods (i.e., DTFLOW and MARGARET) demonstrate that the MLBKFD is capable of accurately inferring cell development and differentiation trajectories from single-cell data with different sizes and sources. Notably, MLBKFD exhibits nearly twice the speed of DTFLOW while maintaining high precision, particularly when dealing with large datasets. MLBKFD provides accurate and efficient trajectory inference, empowering researchers to gain deeper insights into the complex dynamics of cell development and differentiation.

The understanding of complex biological processes whose function requires the interaction of a large number of components is strongly improved by the construction of mathematical models able to capture the underlying regulatory wirings and to predict the dynamics of the process in a variety of conditions. Iterative rounds of simulations and experimental analysis generate models of increasing accuracy, what is called the systems biology approach. The cell cycle is one of the complex biological processes that benefit from this approach, and in particular budding yeast is an established model organism for these studies. The recent publication about the modeling of the G₁/S transition of the budding yeast cell cycle under a systems biology analysis has highlighted in particular the implications of the cell size determination that impinge the events driving DNA replication. During the life cycle of eukaryotic cells, DNA replication is restricted to a specific time window, called the S phase, and several control mechanisms ensure that each DNA sequence is replicated once, and only once, in the period from one cell division to the next. Here we extend the analysis of the G₁/S transition model by including additional aspects concerning the DNA replication process, in order to give a reasonable explanation to the experimental dynamics, as well as of specific cell cycle mutants. Moreover, we show the mathematical description of the critical cell mass (P_s) that cells have to reach to start DNA replication, which value is modulated depending on the different activation of the replication origins. The sensitivity analysis of the influence that the kinetic parameters of the G₁/S transition model have on the setting of the P_s value is also reported.

In this paper a new method is presented to solve a series of fault detection problems using Probabilistic Model (PM) in Genetic Programming (GP). Fault detection can be seen as a problem of multi-class classification. GP methods used to solve problems have a great advantage in their power to represent solutions to complex problems, and this advantage remains true in the domain of fault detection. Moreover, diagnosis accuracy can be improved by using PM. In the end of this paper, we use this method to solve the fault detection of electro-mechanical device. The results show that the method uses GP with PM to solve fault detection of electro-mechanical device outperforms the artificial neural network (ANN).

Accelerated sea level rise in the 21st century and beyond will result in unprecedented rates of coastal recession which will threaten $ billions worth of coastal developments and infrastrucure. Therefore, we cannot continue to depend on the controversial Bruun rule for estimating coastal recession due to sea level rise. Furthermore, the emergence of risk management style coastal planning frameworks is now requiring probabilistic estimates of coastal recession. This paper describes the development and application of an innovative process based, probabilistic model for the estimation of coastal recession due to sea level rise. The method requires as input long term water level and wave data which are now available via widespread tide gauges and global hind cast models respectively. This method is proposed as a more appropriate and defensible alternative for the determination coastal recession due to SLR for planning purposes.

Cell trajectory inference is very important to understand the details of tissue cell development, state differentiation and gene dynamic regulation. However, due to the high noise and heterogeneity of the single-cell data, it is challenging to infer cell trajectory in complex biological processes. Here, we proposed a new trajectory inference method, called Metric Learning Bhattacharyya Kernel Feature Decomposition (MLBKFD). In MLBKFD, a statistical model was used to infer cell trajectory by calculating the Markov transition matrix and Bhattacharyya kernel matrix between cells. Before that, to expedite the matrix calculation in the statistical model, a deep feedforward neural network was used to perform dimensionality reduction on single-cell data. The MLBKFD was evaluated on four typical datasets as well as seven recent human fetal lung datasets. Comparisons with the two outstanding methods (i.e., DTFLOW and MARGARET) demonstrate that the MLBKFD is capable of accurately inferring cell development and differentiation trajectories from single-cell data with different sizes and sources. Notably, MLBKFD exhibits nearly twice the speed of DTFLOW while maintaining high precision, particularly when dealing with large datasets. MLBKFD provides accurate and efficient trajectory inference, empowering researchers to gain deeper insights into the complex dynamics of cell development and differentiation.