BACKGROUND:Biofilms are present on almost all surfaces in households, healthcare and medical equipment, foods, industrial and water supply systems, and often contain opportunistic pathogens that represent one of the key public health hazards. The highest risks are imposed by ESKAPEE pathogens, as well as mixed bacterial-fungal communities often exhibiting multiple drug resistance, this way challenging public healthcare. RESULTS:Here we show how recent developments in hyperspectral imaging technology, complemented by advanced image analysis and machine learning methods, create a unique framework for the potential design of non-contact monitoring systems operating at the macroscale. We could successfully identify five key pathogenic bacteria and a common pathogenic yeast, C. albicans, that frequently occur on surfaces in monospecies and mixed biofilms consisting of combinations of various strains using hyperspectral imaging in the visible, near-infrared, and short-wave infrared spectral bands. Our results indicate that the above pathogenic species could be identified in monocultural biofilms with 95-99.5 % accuracy, while in more frequently occurring mixed biofilms obtained by combining different microorganisms, the accuracy ranges from 90 to 92 % for gram-negative E. coli, K. pneumoniae, and P. aeruginosa to 96-99 % for fungi and gram-positive E. faecalis and S. aureus, respectively, under in vitro conditions. SIGNIFICANCE:Based on our results, we believe that hyperspectral imaging represents a promising and highly efficient technology for the rapid, regular, non-contact monitoring of various equipment and surfaces to detect bacterial and fungal pathogens in situ.

Pseudomonas aeruginosa, an opportunistic pathogen, causes various biofilm-associated infections like pneumonia, infections in cystic fibrosis patients, and urinary tract and burn infections with high morbidity and mortality, as well as low treatment efficacy due to the extremely wide spread of isolates with multidrug resistance. Here, we report the new bacteriophage Pseudomonas phage Ka2 isolated from a tributary stream of Lake Baikal and belonging to the Pbunavirus genus. Transmission electron microscopy resolved that Pseudomonas phage Ka2 has a capsid of 57 ± 9 nm and a contractile and inflexible tail of 115 ± 10 nm in the non-contracted state. The genome consists of 66,310 bp with a GC content of 55% and contains 96 coding sequences. Among them, 52 encode proteins have known functions, and none of them are potentially associated with lysogeny. The bacteriophage lyses 21 of 30 P. aeruginosa clinical isolates and decreases the MIC of amikacin, gentamicin, and cefepime up to 16-fold and the MIC of colistin up to 32-fold. When treating the biofilms with Ka2, the biomass was reduced by twice, and up to a 32-fold decrease in the antibiotics MBC against biofilm-embedded cells was achieved by the combination of Ka2 with cefepime for the PAO1 strain, along with a decrease of up to 16-fold with either amikacin or colistin for clinical isolates. Taken together, these data characterize the new Pseudomonas phage Ka2 as a promising tool for the combined treatment of infections associated with P. aeruginosa biofilms.

Introduction. Remote sensing and monitoring of the sea surface are of great importance in such fields, as operational oceanography, environmental monitoring, etc. The ability to quickly assess the state of the sea surface is particularly relevant in areas that pose a danger to shipping, where rapid and accurate response becomes critical. Modern radars represent information as digital image series largely reminiscent to a frame series in a video stream, thus enabling the use of gradient methods originally designed for and proven successful in video analysis.Aim. Determination of sea wave characteristics from radar images using gradient motion estimation methods. The use of gradient methods will allow implementing additional tools for processing radar image series obtained from sea backscatter.Materials and methods. To assess the characteristics of the sea surface from radar images, gradient methods were used. To train the methods, a series of synthetic images of the sea surface obtained by mathematical modeling were used. To evaluate the effectiveness of the gradient methods, two representative experimental radar image series provided by the Institute of Oceanography RAS were employed.Results. Using gradient methods, the direction and speed of waves were calculated from several consecutive radar observations. Regression models of the dependence of calculated values on the specified ones were constructed. The Farneback and TV-L1 methods proved to be effective especially for obtaining the direction of the sea waves.Conclusion. An algorithm for evaluating speed and direction of the sea surface displacement using gradient methods was pre-trained using simulated model data. The implemented methods and algorithms for assessing the speed and direction of sea waves were validated using two experimental image series obtained from shipborne navigational radars.

Anthropogenic interventions lead to various direct and indirect impacts on natural ecosystems that are often hindered by natural long-term variability, and thus their detection and evaluation remain challenging. Ecological systems are strongly affected by climate variations that typically exhibit long-term correlations capable of imitating or hindering external trends in finite-time observations, thus complicating their detection and correct attribution to either anthropogenic interventions or natural variability. Here we focus on the quantitative assessment of the alterations in the tree-ring width (TRW) of four tree species in response to the changes in the soil water regime following a drainage experiment in a dwarf-shrub type peatland forest. We consider the long-term effects of the intervention, focusing on two characteristic quantities: the durations of clusters when significant discrepancies with relevant controls could be observed in every single consecutive year, and relative trends in the data reflecting long-term, gradual changes in the ecosystem. By extrapolating pre-drainage TRW dynamics and adjusting for recent climate variations using a multivariate model, we simulate surrogate data series that act as additional controls for the post-drainage time period. By comparing the long-term dynamics of the observational TRW data series against both natural and surrogate controls over several decades following the drainage experiment, we evaluate long-term alterations and gradual trends in the tree growth dynamics and reassess the statistical significance of these effects, taking into account long-term correlations in the natural TRW variations. Our results also indicate pronounced alterations in the drought stress response characterized by significant negative trends in the tree growth dynamics following the 2010 heatwave and associated flash drought in the drained area, while no similar effect could be observed in the undrained area, indicating that the increased productivity of the forest ecosystem following the drainage likely comes at the cost of its reduced drought stress resilience.

Introduction. Analysis of locomotor activity is essential in a number of biomedical and pharmacological research designs, as well as environmental monitoring. The movement trajectories of biological objects can be represented by time series exhibiting a complex multicomponent structure and non-stationary dynamics, thus limiting the effectiveness of conventional correlation and spectral time series analysis methods. Recordings obtained using markerless technologies are typically characterized by enhanced noise levels, including both instrumental noise and anomalous errors associated with false estimates of the location of the points of interest, as well as gaps in the trajectories, promoting an urgent need in the development of robust methods to assess the characteristics of locomotor activity.Aim. Development of robust methods for assessing the characteristics of locomotor activity capable of efficient processing of noisy recordings obtained by markerless video-based motion capture systems.Materials and methods. In order to assess the characteristics of locomotor activity, the relative movements of body parts of laboratory animals were analyzed using the stability metrics of the mutual dynamics of their trajectories, their relative delays, as well as the relative duration of the recording fragments when relatively stable mutual dynamics could be observed. The local maxima of the cross-correlation function of two body fragments, the minima of the standard deviation of the difference between their Hilbert phases, as well as their relative delays, were used as the metrics of mutual dynamics.Results. The considered phase metrics were shown to explicitly reflect changes in locomotor activity, while the assessment of time delays using phase metric was shown to be prone to periodic error. The above limitation could be largely overcome using the correlation metrics, assuming that phase and correlation metrics could be combined.Conclusion. The proposed robust methods provide stable estimates of the characteristics of locomotor activity based on markerless video capture recordings, altogether increasing the efficiency of diagnostic procedures and assessment of the therapeutic effect during rehabilitation.

Modern information and telecommunication, transportation and logistic, economic and financial systems are represented by complex networks exhibiting traffic flows with spatio-temporal long-term persistence. Conventional queuing theory relies largely upon stationary models where traffic flows are assumed independent and are typically characterized by the first two moments of inter-arrival and service time distributions, leading to drastic underestimations of traffic flow delays. Here we extend a recent superstatistical approach focusing on traffic models with variable arrival rates by accounting for interdependent activity patterns on multiple network nodes. We suggest an analytical correction to the conventional stationary queue model given by the Kingman’s formula based on the calculation of aggregated inter-arrival times variability from the variabilities of arrival rates at individual nodes and cross-correlations between them. We confirm our analytical approximations by comparing with computer simulation results and large-batch empirical traffic analysis from the backbone of a major academic network. We believe that our results, in combination with recent data on the effects of long-term temporal persistence in network traffic flow, are applicable to various complex networks not limited to information and telecommunication, transportation, and logistics but also to economics and finance, rainfall and river flow dynamics, water accumulation in reservoirs, and many other research domains exhibiting spatio-temporal interdependence patterns.

Plant extracts are in the focus of the pharmaceutical industry as potential antimicrobials for oral care due to their high antimicrobial activity coupled with low production costs and safety for eukaryotic cells. Here, we show that the extract from Hop (Humulus lupulus L.) exhibits antimicrobial activity against Staphylococcus aureus and Streptococci in both planktonic and biofilm-embedded forms. An extract was prepared by acetone extraction from hop infructescences, followed by purification and solubilization of the remaining fraction in ethanol. The effect of the extract on S. aureus (MSSA and MRSA) was comparable with the reference antibiotics (amikacin, ciprofloxacin, and ceftriaxone) and did not depend on the bacterial resistance to methicillin. The extract also demonstrated synergy with amikacin on six S. aureus clinical isolates, on four of six isolates with ciprofloxacin, and on three of six isolates with ceftriaxone. On various Streptococci, while demonstrating lower antimicrobial activity, an extract exhibited a considerable synergistic effect in combination with two of three of these antibiotics, decreasing their MIC up to 512-fold. Moreover, the extract was able to penetrate S. aureus and S. mutans biofilms, leading to almost complete bacterial death within them. The thin-layer chromatography and LC-MS of the extract revealed the presence of prenylated flavonoids (2′,4′,6′,4-tetrahydroxy-3′-geranylchalcone) and acylphloroglucides (cohumulone, colupulone, humulone, and lupulone), apparently responsible for the observed antimicrobial activity and ability to increase the efficiency of antibiotics. Taken together, these data suggest an extract from H. lupulus as a promising antimicrobial agent for use both as a solely antiseptic and to potentiate conventional antimicrobials.

In the last decades, it has been shown that biofilm-associated infections in most cases are caused by rather two or even more pathogens than by single microorganisms. Due to intermicrobial interactions in mixed communities, bacteria change their gene expression profile, in turn leading to alterations in the biofilm structure and properties, as well as susceptibility to antimicrobials. Here, we report the alterations of antimicrobials efficiency in mixed biofilms of Staphylococcus aureus–Klebsiella pneumoniae in comparison with mono-species biofilms of each counterpart and discuss possible mechanisms of these alterations. In cell clumps detached from dual-species biofilms, S. aureus became insensitive to vancomycin, ampicillin, and ceftazidime compared to solely S. aureus cell clumps. In turn, the increased efficiency of amikacin and ciprofloxacin against both bacteria could be observed, compared to mono-species biofilms of each counterpart. Scanning electron microscopy and confocal microscopy indicate the porous structure of the dual-species biofilm, and differential fluorescent staining revealed an increased number of polysaccharides in the matrix, in turn leading to more loose structure and thus apparently providing increased permeability of the dual-species biofilm to antimicrobials. The qRT-PCR showed that ica operon in S. aureus became repressed in mixed communities, and polysaccharides are produced mainly by K. pneumoniae. While the molecular trigger of these changes remains undiscovered, detailed knowledge of the alterations in antibiotic susceptibility to given drugs opens doors for treatment correction options for S. aureus–K. pneumoniae biofilm-associated infections.

Rapid advancement in computer vision technologies provides increasing opportunities for the quantitative characterization of animal behavior, although reduction of their analysis to several scalar metrics appears a common limitation for the representation of complex behavioral patterns. Here we suggest an alternative approach to the quantitative assessment of animal behavioral patterns by parameterization of a generalized scalable model based on fractional Brownian motion using detrended fluctuation analysis of the observational movement trajectories and validate it using novel tank test data. In a zebrafish model representative movement patterns are characterized by two asymptotic regimes, with persistent increments at small scales and antipersistent increments at large scales. A single crossover between these asymptotic regimes that appears a single free parameter of the animal movement model acts as a complementary behavioral indicator leading to a more explicit characterization of both stimulative and sedative effects. We show explicitly that the model can be also used for a robust estimation of interpretable scalar metrics commonly used in behavioral analysis leading to the emphasized differences between experimental groups. We believe that this approach, due to its universality, robustness and clear physical interpretation, is a perspective tool for the analysis of animal behavior complexity under various experimental and natural conditions.

Introduction. Generalized extreme value (GEV) distributions represent a universal description of the limiting distribution of the normalized local maxima statistics for independent and identically distributed data series. Extreme value distributions are commonly classified into three different types representing different functional forms and thus varying in shape, also known as types I, II, and III. Thus, attribution of some observational data series to a particular type of its local maxima distribution, as well as fitting of the distribution parameters, provides certain information about the laws governing the underlying natural or technogenic process. Radar-based remote sensing techniques represent a ubiquitous tool for analyzing large patterns of the sea surface and determining the parameters of the waves. In turn, understanding the laws governing the extreme values in the rough sea surface obtained from their radar images followed by evaluation of their distribution parameters, depending on the wind speed and direction, as well as the presence of surface currents and swells, can be useful for predicting wave height. Aim. Analysis of the functional forms governing the local extreme value distributions in a rough sea surface for the given wind and swell parameters based on computer simulations. Materials and methods. For the rough sea surface simulated by an additive harmonic synthesis procedure, the local extreme value distribution was fitted using the least-mean-squares technique. The fitted parameters were then used for their classification according to the three predetermined types. Results. Computer simulations of a rough sea surface with combined wind and swell waves were performed. It is shown that the distribution of local maxima in the absence of swell waves could be well approximated by theWeibull (type III GEV) distribution, with the parameters explicitly depending on the wind speed. At the same time, no significant dependence on the sea depth was observed. On the contrary, in the presence of additional swell waves, the distribution of local extrema could be rather attributed to the Fréchet (type II GEV) distribution, with the parameters additionally depending on the angle between the wind and swell waves. Conclusion. The laws governing the distributions of local wave extrema in rough seas are in a good agreement with the theoretical GEV approximations, with the distribution parameters being deductible from the key features of the waves. This indicates the predictability of wave height extrema from sea surface measurements, which can be performed based on remote radar observations.

We suggest an effective approach for the semi-automated segmentation of biomedical images according to their patchiness based on local edge density estimation. Our approach does not require any preliminary learning or tuning, although a couple of free parameters directly controllable by the end user adjust the analysis resolution and sensitivity, respectively. We show explicitly that the local edge density exhibits excellent correlations with the cell monolayer density obtained by manual domain-expert based assessment, characterized by correlation coefficients ρ>0.97. Our results indicate that the proposed algorithm is capable of an efficient segmentation and quantification of patchy areas in various biomedical microscopic images. In particular, the proposed algorithm achieves 95 to 99% median accuracy in the segmentation of image areas covered by the cell monolayer in an in vitro scratch assay. Moreover, the proposed algorithm effectively distinguishes between the native and regenerated tissue fragments in microscopic images of histological sections, indicated by nearly three-fold discrepancy between the local edge densities in the corresponding image areas. We believe that the local edge density estimate could be further applicable as a surrogate image channel characterizing its patchiness either as a substitute or as a complementary source to the conventional cell- or tissue-specific fluorescent staining, in some cases either avoiding or limiting the use of complex experimental protocols. We implemented a simple open-source software tool with for on-the-fly visualization allowing for a straightforward feedback by a domain expert without any specific expertise in image analysis techniques. Our tool is freely available online at https://gitlab.com/digiratory/biomedimaging/bcanalyzer.

In a changing climate, forest ecosystems have become increasingly vulnerable to continuously exacerbating heat and associated drought conditions. Climate stress resilience is governed by a complex interplay of global, regional, and local factors, with hydrological conditions being among the key players. We studied a Scots pine (Pinus sylvestris L.) forest ecosystem located near the southern edge of the boreal ecotone, which is particularly subjected to frequent and prolonged droughts. By comparing the dendrochronological series of pines growing in apparently contrasting hydrological conditions ranging from the waterlogged peat bog area to the dry soil at the surrounding elevations, we investigated how the soil water regime affects the climate response and drought stress resilience of the forest ecosystem. We found that in the dry land area, a significant fraction of the trees were replaced after two major climate extremes: prolonged drought and extremely low winter temperatures. The latter has also been followed by a three- to ten-fold growth reduction of the trees that survived in the next year, whereas no similar effect has been observed in the peat bog area. Multi-scale detrended partial cross-correlation analysis (DPCCA) indicated that tree-ring width (TRW) was negatively correlated with spring and summer temperatures and positively correlated with the Palmer drought severity index (PDSI) for the same year. For the elevated dry land area, the above effect extends to interannual scales, indicating that prolonged heatwaves and associated droughts are among the factors that limit tree growth. In marked contrast, in the waterlogged peat bog area, a reversed tendency was observed, with prolonged dry periods as well as warmer springs and summers over several consecutive years, leading to increasing tree growth with a one- to three-year time lag. Altogether, our results indicate that the pessimal conditions of a warming climate could become favorable through the preservation of the soil water regime.

In a changing climate, forest ecosystems become increasingly vulnerable to the continuously exacerbating heat and drought stress conditions. Climate stress resilience is governed by a complex interplay of global, regional and local factors, with hydrological conditions among the key roles. Using a modified detrended partial cross-correlation analysis (DPCCA), we analyse the interconnections between long-term tree-ring width (TRW) data and regional climate variations at various scales and time lags. By comparing dendrochronological series of Scots pine trees near the southern edge of the boreal ecotone, we investigate how local hydrological conditions affect heat- and drought stress resilience of the forest ecosystem. While TRW are negatively correlated with spring and summer temperatures and positively cor-related with the Palmer drought severety index (PDSI) in the same year indicating that heatwaves and droughts represent the limiting factors, at interannual scales remarkable contrasts can be observed between areas with different local hydrological conditions. In particular, for the sphagnum bog area positive TRW trends over several consecutive years tend to follow negative PDSI trends and positive spring and summer temperature trends of the same duration with a time lag between one and three years, indicating that prolonged dry periods, as well as warmer springs and summers appear beneficial for the increased annual growth. In contrast, for the surrounding elevated dry land area a reversed tendency can be observed, with pronounced negative long-term correlations with temperature and positive correlations with PDSI. Moreover, by combining detrending models and partial correlation analysis, we show expicitly that the long-term temperature dependence could be partially attributed to the spurious correlations induced by coinciding trends of the trees ageing and climate warming, while contrasts in correlations between TRW and PDSI become only further highlighted, indicating the major impact of the local hydrological conditions on the drought stress resilience. ![Figure][1]</img> Highlights ### Competing Interest Statement The authors have declared no competing interest. [1]: pending:yes

Internet traffic intensity variations contain significant information on the access pattern dynamics. On the one hand, variability in access patterns is a direct manifestation of the end users' and IoT devices behavior. On the other hand, a better understanding of the access pattern dynamics provides essential information for an early redistribution of traffic, leading to potentially more efficient dynamic routing algorithms. Traffic in large networks is typically governed by a complex interplay of auto-and cross-correlation patterns that largely determine its non-stationary nature. Here we have considered two approaches to the identification of the traffic variation model. The first approach is parametric and focuses on fitting the parameters of Seasonal Auto Regressive Integrated Moving Average with exogenous factors (SARIMAX). The second approach is based on training of a recurrent neural network (RNN). Both approaches have been validated explicitly using traffic data records over several days of monitoring at the uplink of a local campus network.

Candida albicans and Staphylococcus aureus are human pathogens that are able to form mixed biofilms on the surface of mucous membranes, implants and catheters. In biofilms, these pathogens have increased resistance to antimicrobials, leading to extreme difficulties in the treatment of mixed infections. The growing frequency of mixed infections caused by S. aureus and C. albicans requires either the development of new antimicrobials or the proposal of alternative approaches to increase the efficiency of conventional ones. Here, we show the antimicrobial, biofilm-preventing and biofilm-eradicating activity of 2(5H)-furanone derivative F131, containing an l-borneol fragment against S. aureus–C. albicans mixed biofilms. Furanone F131 is also capable of inhibiting the formation of monospecies and mixed biofilms by S. aureus and C. albicans. The minimal biofilm-prevention concentration (MBPC) of this compound was 8–16 μg/mL for S. aureus and C. albicans mono- and two-species biofilms. While the compound demonstrates slightly lower activity compared to conventional antimicrobials (gentamicin, amikacin, fluconazole, terbinafine and benzalkonium chloride), F131 also increases the antimicrobial activity of fluconazole–gentamicin and benzalkonium chloride against mixed biofilms of S. aureus–C. albicans, thus reducing MBPC of fluconazole–gentamicin by 4–16 times and benzalkonium chloride twofold. F131 does not affect the transcription of the MDR1, CDR1 and CDR2 genes, thus suggesting a low risk of micromycete resistance to this compound. Altogether, combined use of antibiotics with a F131 could be a promising option to reduce the concentration of fluconazole used in antiseptic compositions and reduce the toxic effect of benzalkonium chloride and gentamicin. This makes them an attractive starting point for the development of alternative antimicrobials for the treatment of skin infections caused by S. aureus–C. albicans mixed biofilms.

EDITORIAL article Front. Neuroinform., 20 July 2023 Volume 17 - 2023 | https://doi.org/10.3389/fninf.2023.1229945

Increased anthropogenic pressure including intensification of agricultural activities leads to long-term decline of natural biotopes, with planted forests often considered as promising compensatory response, although reduced biodiversity and ecosystem stability represent their common drawbacks. Here we present a complex investigation of the impact of a large Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation in a planted Scots pine forest representing a natural in situ experiment on an engineered ecosystem. After settling around 2006, the colony expanded for 15 years, leading to the intensive deposition of nutrients with feces, food remains and feather thereby considerably altering the local soil biogeochemistry. Thus, lower pH levels around 4.5, 10- and 2-fold higher concentrations of phosphorous and nitrogen, as well as 1.2-fold discrepancies in K, Li, Mn, Zn and Co., respectively, compared to the surrounding control forest area could be observed. Unaltered total organic carbon (Corg) suggests repressed vegetation, as also reflected in the vegetation indices obtained by remote sensing. Moreover, reduced soil microbial diversity with considerable alternations in the relative abundance of Proteobacteria, Firmicutes, Acidobacteriota, Actinobacteriota, Verrucomicrobiota, Gemmatimonadota, Chujaibacter, Rhodanobacter, and Bacillus has been detected. The above alterations to the ecosystem also affected climate stress resilience of the trees indicated by their limited recovery from the major 2010 drought stress, in marked contrast to the surrounding forest (p = 3∙10−5). The complex interplay between geographical, geochemical, microbiological and dendrological characteristics, as well as their manifestation in the vegetation indices is explicitly reflected in the Bayesian network model. Using the Bayesian inference approach, we have confirmed the predictability of biodiversity patterns and trees growth dynamics given the concentrations of keynote soil biogeochemical alternations with correlations R > 0.8 between observations and predictions, indicating the capability of risk assessment that could be further employed for an informed forest management.

Introduction:Complex gait disturbances represent one of the prominent manifestations of various neurophysiological conditions, including widespread neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Therefore, instrumental measurement techniques and automatic computerized analysis appears essential for the differential diagnostics, as well as for the assessment of treatment effectiveness from experimental animal models to clinical settings.Methods:Here we present a marker-free instrumental approach to the analysis of gait disturbances in animal models. Our approach is based on the analysis of video recordings obtained with a camera placed underneath an open field arena with transparent floor using the DeeperCut algorithm capable of online tracking of individual animal body parts, such as the snout, the paws and the tail. The extracted trajectories of animal body parts are next analyzed using an original computerized methodology that relies upon a generalized scalable model based on fractional Brownian motion with parameters identified by detrended partial cross-correlation analysis.Results:We have shown that in a mouse model representative movement patterns are characterized by two asymptotic regimes characterized by integrated 1/f noise at small scales and nearly random displacements at large scales separated by a single crossover. More detailed analysis of gait disturbances revealed that the detrended cross-correlations between the movements of the snout, paws and tail relative to the animal body midpoint exhibit statistically significant discrepancies in the Alzheimer's disease mouse model compared to the control group at scales around the location of the crossover.Discussion:We expect that the proposed approach, due to its universality, robustness and clear physical interpretation, is a promising direction for the design of applied analysis tools for the diagnostics of various gait disturbances and behavioral aspects in animal models. We further believe that the suggested mathematical models could be relevant as a complementary tool in clinical diagnostics of various neurophysiological conditions associated with movement disorders.