Directed inter-domain motions enable the IsdH Staphylococcus aureus receptor to rapidly extract heme from human hemoglobin

Pathogenic Staphylococcus aureus actively acquires iron from human hemoglobin (Hb) using the IsdH surface receptor. Heme extraction is mediated by a tridomain unit within the receptor that contains its second (N2) and third (N3) NEAT domains joined by a helical linker domain. Extraction occurs within a dynamic complex, in which receptors engage each globin chain; the N2 domain tightly binds to Hb, while substantial inter-domain motions within the receptor enable its N3 domain to transiently distort the globin's heme pocket. Using molecular simulations, Markov modeling, and quantitative measurements of heme transfer kinetics, we show that directed inter-domain motions within the receptor play a critical role in the extraction process. The directionality of N3 domain motion and the rate of heme extraction is controlled by amino acids within a short, flexible inter-domain tether that connects the N2 and linker domains. In the wild-type receptor directed motions originating from the tether enable the N3 domain to populate configurations capable of distorting Hb's pocket, whereas mutant receptors containing altered tethers are less able to adopt these conformers and capture heme slowly via indirect processes in which Hb first releases heme into the solvent. Thus, our results show inter-domain motions within the IsdH receptor play a critical role in its ability to extract heme from Hb and highlight the importance of directed motions by the short, unstructured, amino acid sequence connecting the domains in controlling the directionality and magnitude of these functionally important motions.

Prospective Mapping of Schistosomian Risk by Markovian Modelling and Multi-criteria Analysis in Central Côte d'Ivoire

The Bélier region and the autonomous district of Yamoussoukro, is a region of central Côte d'Ivoire that records every year cases of schistosomiasis contamination. Although the figures are low, this area is of interest for epidemiological control. The schistosomiasis infection with schistosoma haematobium or urinary bilharziasis is the most widespread and is important in some areas along the main rivers of the region. The development of maps of areas at risk schistosomiasis by 2027 by Markov modeling using Markov chains observable and by combining layers of sensitivity and vulnerability of 2027 of the infection show a change in the surface risk of contamination from 17% in 2017 to 23% in 2027 of the total area of the region. These areas are mainly located in the departments of Yamoussoukro, Toumodi and Djékanou. 15% of the localities in this region are high-risk areas in 2017 and 23% in 2027. The prediction of risk areas and localities at high risk of contamination by Markov modeling makes any preventive control strategy possible.

The Spatiotemporal Implications of Urbanization for Urban Heat Islands in Beijing: A Predictive Approach Based on CA–Markov Modeling (2004–2050)

The rapid increase in infrastructural development in populated areas has had numerous adverse impacts. The rise in land surface temperature (LST) and its associated damage to urban ecological systems result from urban development. Understanding the current and future LST phenomenon and its relationship to landscape composition and land use/cover (LUC) changes is critical to developing policies to mitigate the disastrous impacts of urban heat islands (UHIs) on urban ecosystems. Using remote sensing and GIS data, this study assessed the multi-scale relationship of LUCC and LST of the cosmopolitan exponentially growing area of Beijing, China. We investigated the impacts of LUC on LST in urban agglomeration for a time series (2004–2019) of Landsat data using Classification and Regression Trees (CART) and a single channel algorithm (SCA), respectively. We built a CA–Markov model to forecast future (2025 and 2050) LUCC and LST spatial patterns. Our results indicate that the cumulative changes in an urban area (UA) increased by about 908.15 km2 (5%), and 11% of vegetation area (VA) decreased from 2004 to 2019. The correlation coefficient of LUCC including vegetation, water bodies, and built-up areas with LST had values of r = −0.155 (p > 0.419), −0.809 (p = 0.000), and 0.526 (p = 0.003), respectively. The results surrounding future forecasts revealed an estimated 2309.55 km2 (14%) decrease in vegetation (urban and forest), while an expansion of 1194.78 km2 (8%) was predicted for a built-up area from 2019 to 2050. This decrease in vegetation cover and expansion of settlements would likely cause a rise of about ~5.74 °C to ~9.66 °C in temperature. These findings strongly support the hypothesis that LST is directly related to the vegetation index. In conclusion, the estimated overall increase of 7.5 °C in LST was predicted from 2019–2050, which is alarming for the urban community’s environmental health. The present results provide insight into sustainable environmental development through effective urban planning of Beijing and other urban hotspots.

Exploring the energy landscape of a SAM-I riboswitch

SAM-I riboswitches regulate gene expression through transcription termination upon binding a S-adenosyl-L-methionine (SAM) ligand. In previous work, we characterized the conformational energy landscape of the full-length Bacillus subtilis yitJ SAM-I riboswitch as a function of Mg2+ and SAM ligand concentrations. Here, we have extended this work with measurements on a structurally similar ligand, S-adenosyl-l-homocysteine (SAH), which has, however, a much lower binding affinity. Using single-molecule Förster resonance energy transfer (smFRET) microscopy and hidden Markov modeling (HMM) analysis, we identified major conformations and determined their fractional populations and dynamics. At high Mg2+ concentration, FRET analysis yielded four distinct conformations, which we assigned to two terminator and two antiterminator states. In the same solvent, but with SAM added at saturating concentrations, four states persisted, although their populations, lifetimes and interconversion dynamics changed. In the presence of SAH instead of SAM, HMM revealed again four well-populated states and, in addition, a weakly populated ‘hub’ state that appears to mediate conformational transitions between three of the other states. Our data show pronounced and specific effects of the SAM and SAH ligands on the RNA conformational energy landscape. Interestingly, both SAM and SAH shifted the fractional populations toward terminator folds, but only gradually, so the effect cannot explain the switching action. Instead, we propose that the noticeably accelerated dynamics of interconversion between terminator and antiterminator states upon SAM binding may be essential for control of transcription.

Hidden Markov Modeling for Maximum Probability Neuron Reconstruction

Recent advances in brain clearing and imaging have made it possible to image entire mammalian brains at sub-micron resolution. These images offer the potential to assemble brain-wide atlases of neuron morphology, but manual neuron reconstruction remains a bottleneck. Several automatic reconstruction algorithms exist, but most focus on single neuron images. In this paper, we present a probabilistic reconstruction method, ViterBrain, which combines a hidden Markov state process that encodes neuron geometry with a random field appearance model of neuron flourescence. Our method utilizes dynamic programming to compute the global maximizers of what we call the ``most probable'' neuron path. Our most probable estimation method models the task of reconstructing neuronal processes in the presence of other neurons, and thus is applicable in images with several neurons. Our method operates on image segmentations in order to leverage cutting edge computer vision technology. We applied our algorithm to imperfect image segmentations where false negatives severed neuronal processes, and showed that it can follow axons in the presence of noise or nearby neurons. Additionally, it creates a framework where users can intervene to, for example, fit start and endpoints. The code used in this work is available in our open-source Python package brainlit.

The Evaluation of Regional Spatial Plan for 2011-2031 Based on Land Use Changes Prediction Using Cellular Automata-Markov Model in Sleman

In the past years, the development of Sleman Regency has been considered rapid as evidenced by the emergence of built up areas including expansion of the university areas, shopping malls, and housing. Along with the increase in the total population, university students and workers from other regions coming to this regency, the land use in Sleman Regency has started to shift. Land use changes need to be controlled by predicting land use using the CA-Markov model. CA-Markov modeling has dynamic properties that integrate the dimensions of space and time, where the occurrence of events is determined by events that directly precede them and can be used to predict the next event. The accuracy of the CA-Markov concept can be determined by validation and expressed in the Kappa coefficient value (≥ 0.70). This CA-Markov concept has been developed since the 1940s in the field of computers by Von Neumann and Ulam. In this concept it is assumed that pixels are the beginning of the mathematical concept. When a pixel changes, its new status is only affected by its old status and the neighbor status. This research was conducted to predict the land use in 2031 using the Cellular Automata-Makov model, evaluate the use of land in 2031 in relation to RTRW or city plan, and create a scenario of the direction for land use control in 2031 for disaster-prone areas. Based on the prediction of land use in Sleman Regency in 2031, Kappa coefficient was obtained at 0.7399, implying that the suitability of spatial area and distribution reached 73.99% which is considered good. The results of the prediction also showed that in 2031, the land use would be dominated by building area which was predicted to reach 43.53% out of the total area. The evaluation of land use prediction in 2031 based on RTRW method showed that as large as 40.137,39 ha land would be used according to the RTRW, while 17.411,00 ha would not be used accordingly. The improper use of land might be due to the shift in the use of 4.659,18 ha of rice fields into buildings.

Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies

The year 2020 saw the covid-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world have been faced with the challenge of protecting public health while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date, even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) and Bayesian inference can be used to optimize mitigation policies that minimize economic impact without overwhelming hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; (2) an RLbased methodology for optimizing fine-grained mitigation policies within this simulator; and (3) a Hidden Markov Model for predicting infected individuals based on partial observations regarding test results, presence of symptoms, and past physical contacts. This article is part of the special track on AI and COVID-19.