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Results for “Stochastic Processes”

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At least 19 recordsLinked to original sources
PubMed2024

Seasonal hydrological dynamics affected the diversity and assembly process of the antibiotic resistome in a canal network.

The significant threat of antibiotic resistance genes (ARGs) to aquatic environments health has been widely acknowledged. To date, several studies have focused on the distribution and diversity of ARGs in a single river while their profiles in complex river networks are largely known. Here, the spatiotemporal dynamics of ARG profiles in a canal network were examined using high-throughput quantitative PCR, and the underlying assembly processes and its main environmental influencing factors were elucidated using multiple statistical analyses. The results demonstrated significant seasonal dynamics with greater richness and relative abundance of ARGs observed during the dry season compared to the wet season. ARG profiles exhibited a pronounced distance-decay pattern in the dry season, whereas no such pattern was evident in the wet season. Null model analysis indicated that deterministic processes, in contrast to stochastic processes, had a significant impact on shaping the ARG profiles. Furthermore, it was found that Firmicutes and pH emerged as the foremost factors influencing these profiles. This study enhanced our comprehension of the variations in ARG profiles within canal networks, which may contribute to the design of efficient management approaches aimed at restraining the propagation of ARGs.

Seasons↗
PubMed1978

Compartment model approach to the estimation of tumor incidence and growth: investigation of a model of cancer latency.

Consideration is made of the problems involved in determining the effects of a chronic disease process, such as stomach cancer, on the observed mortality of the U.S. population. Specifically, since the time of initiation of tumor growth is unknown and the tumor becomes clinically manifest only after reaching considerable size, the early rate and pattern of tumor growth is unobserved. As a possible solution to the analysis of such problems, it is proposed to use stochastic compartment modelling techniques which deal with the problems of estimating the transition probabilities of a partially observed stochastic process. Implementation of the stochastic compartment techniques in this case depends on the selection of certain mathematical expressions from theories of carcinogenesis, epidemiologic studies and animal studies which allow the calculation of transition probabilities to unobserved states by making them explicit functions of time or age. Though the selection of the specific functions might be subject to debate, the general strategy of explicitly selecting such functions, and thereby exposing them for review in terms of biologic reasonableness and consistency with the data, seems to be a valid and useful methodology. Furthermore, various ways of viewing the model results (say from its internal behavior, e.g., from implied distributions of waiting times in various disease states) yield different insights into the various factors in carcinogenesis. The model, with parameters representing tumor incidence, time to tumor death given onset, genetic susceptibility to tumor growth and the effects of competing forces of mortality, is fitted to data on deaths due to stomach cancer for male U.S. residents age 25 and over in 1969. Two basic forms of the model, one with a waiting time distribution for occupants of the latent state and another with a single latency time, achieved excellent fits to the data. Examination of parameter estimates and compartment waiting time distributions are consistent with theoretical expectations and intuition. It is concluded that such strategies, involving the integration of clinical, experimental and vital statistics data into a comprehensive model of population carcinogenesis, are potentially powerful tools for investigation of the temporal dimensions of disease development in a human population.

Adult↗
PubMed1978

Mechanism of antigen-induced antibody biosynthesis from antibody precursors, the heavy and light immunoglobulin chains.

The immediate precursors of antibody molecules, the heavy (H) and light (L) peptide chains of the immunoglobulins, combine with each other by means of disulfide bonds formed by dehydrogenation of their cysteine residues. In the absence of an antigen this process yields the heterogeneous mixture of normal immunoglobulins. Antigens or their processed derivatives (Ag) interfere with this stochastic process by noncovalent combination with complementarily fitting H chains. The (Ag.H)(n) complexes thus formed, owing to the loss of rotational and translational freedom, combine preferentially with those L chains whose V(L) regions have some affinity for the determinants of the antigen molecule. Subsequent release of Ag from the (Ag.H.L)(n) complexes yields free antigen and antibody molecules. Each of the released Ag molecules can be used repeatedly for the same reaction cycle and thus can induce the biosynthesis of a large number of antibody molecules. Any macromolecule, natural or synthetic, that has at least a few polar groups and that can penetrate to the nascent H and L chains can thus act as an antigen. Whereas the structure of the H and L chains is genetically determined and transmitted through the germ line, the process induced by the antigen is a phenotypic phenomenon. The antigen acts in this process as a stereospecific cofactor or regulator of the thiol-disulfide transhydrogenation of the combining H and L chains of immunoglobulins.

Antibody Formation↗
PubMed1978

Eye movements of African chameleons: spontaneous saccade timing.

Despite asynchrony, saccades of left and right eyes of African chameleons had similar timing statistics. Prominent qualitative aspects of these statistics did not change if one or both eyes were masked. Evidently, an internal stochastic process regulated chameleon saccade generation.

Animals↗
PubMed2026

Climate and soil shape Daqu wheat quality and seed microbiome via rhizosphere taxa and microbial assembly.

The grain quality and seed microbiome of Daqu wheat are fundamental determinants of Daqu fermentation performance; however, the mechanisms by which cultivation environments influence these traits via rhizosphere microbial communities remain unclear. Bacterial and fungal communities across the bulk soil-rhizosphere-seed continuum of three wheat cultivars grown in four ecoregions were characterized using absolute quantitative amplicon sequencing. The rhizosphere microbiome was treated as a central intermediary, while the response variables were seed microbial diversity and grain-quality traits, including starch content, protein content, and grain hardness. Twelve physicochemical properties of soil and 11 climatic factors were integrated into a multidimensional association framework. Environmental conditions exerted stronger influences on both seed quality traits and microbial diversity than cultivar identity. Distinct regional signatures were also evident in rhizosphere microbiomes, with environmental gradients explaining community variation more effectively than geographic distance. Bacterial communities exhibited greater sensitivity to environmental fluctuations than fungi. Mantel analyses identified available nitrogen, precipitation, and atmospheric pressure as significant drivers of core rhizosphere taxa (P&#xa0;<&#xa0;0.05). iCAMP revealed that stochastic processes predominantly governed rhizosphere bacterial assembly, whereas stochastic and deterministic mechanisms jointly shaped fungal assembly. Partial least squares path modeling further uncovered a rhizosphere-mediated environment-seed cascade, wherein sunlight intensity and duration, atmospheric pressure, and soil nitrogen directly or indirectly affected seed wet gluten content, grain hardness, and seed microbial diversity through their influences on rhizosphere microbiota. Rhizosphere bacterial diversity was negatively associated with seed bacterial diversity (path coefficient&#xa0;=&#xa0;-0.118, P&#xa0;<&#xa0;0.05), indicating that rhizosphere communities may shape seed endophytic bacterial assemblages via environmental filtering and competitive interactions. Collectively, these findings elucidate how environments shape the quality and seed microbiomes of Daqu wheat, providing scientific guidance for optimal site selection and the standardized production of high-quality brewing wheat for industrial Baijiu.

Triticum↗
PubMed2026

Reconstructing the early spatial spread of pandemic respiratory viruses in the United States.

Understanding the geographic spread of emerging respiratory viruses is critical for pandemic preparedness, yet the early spatiotemporal dynamics of the 2009 H1N1 pandemic influenza and severe acute respiratory syndrome coronavirus 2 in the United States remain unclear. While mobility and genomic data have revealed important aspects of pandemic spatial spread, several key questions remain: Did the two pandemics follow similar spatial transmission routes? How rapidly did they spread across the United States? What role did stochastic processes play in early spatial transmission? To address these questions, we integrated high-resolution disease data with a robust, data-efficient inference framework combining air travel, commuting flows, and pathogen superspreading potentials to reconstruct their spatial spread across US metropolitan areas. The two pandemics exhibited distinct transmission pathways across locations; however, both pandemics established local circulation in most metropolitan areas within weeks, driven by several shared transmission hubs. Early spatial spread was more strongly associated with air travel than with commuting, though stochastic dynamics introduced substantial uncertainty in transmission routes, creating challenges for timely detection and control. Simulations indicate that broad wastewater surveillance coverage beyond top transmission hubs coupled with effective infection control may slow initial spatial expansion. Our findings highlight the rapid, stochastic spread of pandemic respiratory pathogens and the difficulties of early outbreak containment.

Humans↗
PubMed1978

Origin and nature of correlations in the Ia feedback pathway of the muscle control system.

Mammalian primary muscle spindle endings receive two inputs from fusimotor fibres and length changes of their extrafusal environment. Both inputs are also powerful noise sources disturbing the discharge patterns of the receptors. In this paper emphasis is laid on the extrafusal component. It is shown that extrafusal muscle activity can be viewed as a stochastic process which, acting as a common source, can correlate discharge patterns of adjacent muscle spindles. Some quantitative characteristics of these correlations and their relation to the underlying mechanical events are investigated in some detail in order to provide data for more theoretical considerations on their possible physiological importance.

Action Potentials↗
PubMed1992

Coloured noise or low-dimensional chaos?

Devising a method capable of distinguishing a low-dimensional chaotic signal that might be embedded in a noisy stochastic process has become a major challenge for those involved in time-series analysis. Here a null hypothesis approach is used in conjunction with a known nonlinear predictive test, to probe for the presence of chaos in epidemiological data. A probabilistic set of rules is used to stimulate a historic record of New York City measles outbreaks, generally understood to be governed by a chaotic attractor. The simulated runs of 'surrogate data' are carefully constructed so as to be free from any underlying low-dimensional chaotic process. They therefore serve as a useful null model against which to test the observed time series. However, despite the assumed differences between the dynamics of measles outbreaks and the null model, a nonlinear predictive scheme is found to be unable to differentiate between their characteristic time series. The methodology confirms that, if there is in fact a chaotic signal in the measles data, it is extremely difficult to detect in time series of such limited length. The results have general relevance to the analysis of physical, ecological and environmental time series.

Biometry↗
PubMed1979

A stochastic compartment model of stomach cancer with correlated waiting time distributions.

The incidence and growth rate of stomach cancer in the US population is modelled, for each sex, as a partially observed, discrete state stochastic process. Explicit evaluation of the transition rates between the states of the model is made possible by identifying them as specific functions of the time spent within each state. The functions used in the model were selected from the medical and epidemiological literature. With the model it was found possible to obtain fits to the age distribution of deaths due to stomach cancer for white males in 1975 and for selected age ranges for white females. These results suggested that the natural history of stomach cancer is different for females above and below age 65.

Adult↗
PubMed2026

Increased precipitation decelerates temporal succession of grassland soil microbial communities.

Global precipitation regimes have been shifted in recent decades, imposing significant consequences in water-limited grassland ecosystems. However, the effects of increased precipitation on the succession of soil microbial communities remain unclear, mainly due to the scarcity of long-term experiments with time-series data. Here, we examined temporal succession of grassland soil microbial communities in a long-term increased precipitation experiment. Both soil microbial taxonomic and functional structures were significantly altered by increased precipitation. Increased precipitation significantly decelerated the succession rates of soil microbial functional structure (i.e. time-decay relationships). Consistent with the increased microbial decomposition and heterotrophic respiration, the abundances of soil microbial carbon decomposition genes were markedly enhanced by increased precipitation. Furthermore, increased precipitation stimulated genes involved in nutrient cycling processes, potentially promoting plant growth. Collectively, the contributions of stochastic processes in shaping microbial communities were increased under increased precipitation, suggesting that microbial successional trajectories may shift toward multiple alternative states characterized by greater stochasticity under future altered precipitation regimes.

Soil Microbiology↗