Non Homogeneous Rough Finite State Automaton

Objective: The study of finite state automaton is an essential tool in machine learning and artificial intelligence. The class of rough finite state automaton captures the uncertainty using the rough transition map. The need to generalize this concept arises to adhere the dynamical behaviour of the system. Hence this paper focuses on defining non-homogeneous rough finite state automaton. Methodology: With the aid of Rough finite state automata we define the concept of non-homogeneous rough finite state automata. Findings: Non homogeneous Rough Finite State Automata (NRFSA) Mt is defined by a tuple (Q,Σ,δt,q0 (t),F(t)) The dynamical behaviour of any system can be expressed in terms of an information system at time t. This leads us to define non-homogeneous rough finite state automaton. For each time ‘t’ we generate lower approximation rough finite state automaton Mt_ and the upper approximation rough finite state automaton Mt- and the defined concepts are elaborated with suitable examples. The ordered pair , Mt=(M(t)-,M(t)-) is called as the non-homogeneous rough finite state automaton. Conclusion: Over all our study reveals the characterization of the system which changes its behaviour dynamically over a time ‘t’. Novelty: The novelty of the proposed article is that it clearly immense the system behaviour over a time ‘t’. Using this concept the possible and the definite transitions in the system can be calculated in any given time ‘t’.

Phenotype stability under dynamic brain-tumor environment stimuli maps glioblastoma progression in patients

Although tumor invasiveness is known to drive glioblastoma (GBM) recurrence, current approaches to treatment assume a fairly simple GBM phenotype transition map. We provide new analyses to estimate the likelihood of reaching or remaining in a phenotype under dynamic, physiologically likely perturbations of stimuli (“phenotype stability”). We show that higher stability values of the motile phenotype (Go) are associated with reduced patient survival. Moreover, induced motile states are capable of driving GBM recurrence. We found that the Dormancy and Go phenotypes are equally represented in advanced GBM samples, with natural transitioning between the two. Furthermore, Go and Grow phenotype transitions are mostly driven by tumor-brain stimuli. These are difficult to regulate directly, but could be modulated by reprogramming tumor-associated cell types. Our framework provides a foundation for designing targeted perturbations of the tumor-brain environment, by assessing their impact on GBM phenotypic plasticity, and is corroborated by analyses of patient data.

High-resolution mapping of tropical moist forest cover dynamics over the last 30 years

<p>Tropical moist forest (TMF) provide essential ecosystem services<sub>1,2</sub>. Fine-scale mapping and characterization of their disturbances are needed to support global conservation policies<sub>3</sub> and to accurately quantify their contribution to global carbon fluxes<sub>4</sub>. However, limited information exists on their remaining extent and long-term historical changes.</p><p>We produced a wall-to-wall map of TMF cover dynamics at 30-meter resolution from 1990 to 2019. Each individual image of the full Landsat archive (~1 200 000 scenes) has been mapped using an expert system to allow all disturbances in the forest cover - including from selective logging activities and fires that are visible during a short period - to be depicted and characterized in terms of timing (dates and duration), sequential dynamics, intensity, and extent.</p><p>The performance of our disturbance classifier has been validated against 12 235 reference sample plots resulting in 9.4% omissions, 8.1% false detections and 91.4% overall accuracy. </p><p>Our dataset depicts the TMF extent and patterns of disturbances through two complementary layers: a transition map and an annual change dataset. The transition map captures the resulting disturbance dynamics over the 30 years by depicting (i) remaining undisturbed forests, (ii) two types of degraded forests (corresponding mostly to either logged or burned forests), (iii) young forest regrowth, and (iv) deforested land that includes four subcategories of converted land cover: (a) water bodies (new dams and river flow changes); (b) tree plantations; and (c) other land cover that includes infrastructure, agriculture, and mining. The annual change dataset is a collection of 30 maps depicting - for each year between 1990 and 2019 - the spatial extents of undisturbed forests and disturbances.</p><p><br>We found that pan-tropical forest disturbances have been underestimated so far. For the first time at this scale, we discriminate deforestation from degradation and we underline the importance of the degradation process in tropical forest ecosystems. Our analysis shows the trends of deforestation and degradation by country, sub-region, and continent. Finally, we extrapolated the recent average rates of disturbances to predict the extent of the undisturbed TMF by 2050.<br><br></p><p>We will continue to update the TMF dataset with future Landsat data and intend to adapt the methodology to Sentinel 2 data (available since 2015) towards near real-time monitoring of TMF with a higher frequency of observations and finer spatial resolution.<br><br>1. Gibson et al. 2011 doi:10.1038/nature10425<br>2. Watson et al. 2018 Doi:10.1038/s41559-018-0490-x<br>3. Mackey et al. 2015 doi:10.1111/conl.12120<br>4. Mitchard E.T.A. 2018 doi </p>