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High-throughput deep-sequencing technology and bioinformatics analysis of the small RNA (sRNA) population isolated from plants allows universal virus detection and complete virome reconstruction for a given sample. In the present sRNA deep-sequencing analysis of virus-infected wheat samples in the Czech Republic, samples were firstly tested for barley yellow dwarf viruses (BYDVs), wheat streak mosaic virus (WSMV) and wheat dwarf virus (WDV) using ELISA, RT-PCR and PCR. Subsequent sRNA sequencing of these samples yielded more than ~60 million single-end 50-bp reads with high confidence for nine field samples of wheat. Overall, 16.5% of reads were virus-specific and 83.5% were mapped to the host. More 21-nt reads (~7.7E+06 reads) were found than 24-nt (~6.20E+06 reads) or 22-nt (~4.30E+06 reads) reads. De novo assembly of the high-quality contigs revealed the presence of three earlier reported viruses in the Czech Republic: BYDVs (31.48%), WSMV (24.23%) and WDV (26.66%). We also showed the presence of cereal yellow dwarf virus (14.33%; two species CYDV-RPS and CYDV-RPV (family Luteoviridae/Polerovirus) and wheat yellow dwarf virus (WYDV, 3.30%; Luteoviridae). Phylogenetic analysis showed CYDV and WYDV grouped separately from BYDVs. Furthermore, several recombination breakpoints were found among the groups of yellow dwarf viruses (BYDVs, CYDV, and WYDV). Using RNA deep sequencing, we confirmed the presence of the three known viruses (BYDVs, WSMV, and WDV) and the first record of two species of CYDV and WYDV in wheat in the Czech Republic.

Healthy aging is accompanied by heterogeneous decline of cognitive abilities among individuals, especially during senescence. The mechanisms of this variability are not understood, but have been associated with the reorganization of white matter fiber tracts and the functional co-activations of brain regions. Here, we built a causal inference framework to provide mechanistic insight into the link between structural connectivity and brain function, informed by brain imaging data and network modeling. By applying various degrees of interhemispheric degradation of structural connectivity, we were not only able to reproduce the age-related decline in interhemispheric functional communication and the associated dynamical flexibility, but we obtained an increase of global modulation of structural connectivity over the brain function during senescence. Notably, the increase in modulation between structural connectivity and brian function was higher in magnitude and steeper in its increase in older adults with poor cognitive performance. We independently validated the causal hypothesis of our framework via a Bayesian approach based on deep-learning. The current results might be the first mechanistic demonstration of dedifferentiation and scaffolding during aging leading to cognitive decline demonstrated in a large cohort.

International audience; Spontaneously fluctuating brain activity patterns that emerge at rest have been linked to brain's health and cognition. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a mechanistic description of the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major data features across scales and imaging modalities. These include spontaneous high amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability and characteristic functional connectivity dynamics. When aggregated across cortical hierarchies, these match the profiles from empirical data. The understanding of the brain's resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function such as predictive coding. In addition, it shifts the focus from the single recordings towards brain's capacity to generate certain dynamics characteristic of health and pathology.

SummaryDuring the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO;N= 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread.

Reverse transcription PCR (RT-PCR) is a popular method for detecting RNA viruses in plants. RT-PCR is usually performed in a classical two-step procedure: in the first step, cDNA is synthesized by reverse transcriptase (RT), followed by PCR amplification by a thermostable polymerase in a separate tube in the second step. However, one-step kits containing multiple enzymes optimized for RT and PCR amplification in a single tube can also be used. Here, we describe an RT-PCR single-enzyme assay based on an RTX DNA polymerase that has both RT and polymerase activities. The expression plasmid pET_RTX_(exo-) was transferred to various E. coli genotypes that either compensated for codon bias (Rosetta-gami 2) or contained additional chaperones to promote solubility (BL21 (DE3) with plasmids pKJE8 or pTf2). The RTX enzyme was then purified and used for the RT-PCR assay. Several purified plant viruses (TMV, PVX, and PVY) were used to determine the efficiency of the assay compared to a commercial one-step RT-PCR kit. The RT-PCR assay with the RTX enzyme was validated for the detection of viruses from different genera using both total RNA and crude sap from infected plants. The detection endpoint of RTX-PCR for purified TMV was estimated to be approximately 0.01 pg of the whole virus per 25 µL reaction, corresponding to 6 virus particles/µL. Interestingly, the endpoint for detection of TMV from crude sap was also 0.01 pg per reaction in simulated crude plant extracts. The longest RNA fragment that could be amplified in a one-tube arrangement was 2379 bp long. The longest DNA fragment that could be amplified during a 10s extension was 6899 bp long. In total, we were able to detect 13 viruses from 11 genera using RTX-PCR. For each virus, two to three specific fragments were amplified. The RT-PCR assay using the RTX enzyme described here is a very robust, inexpensive, rapid, easy to perform, and sensitive single-enzyme assay for the detection of plant viruses.

Visual Abstract, At rest, mammalian brains display remarkable spatiotemporal complexity, evolving through recurrent functional connectivity (FC) states on a slow timescale of the order of tens of seconds. While the phenomenology of the resting state dynamics is valuable in distinguishing healthy and pathologic brains, little is known about its underlying mechanisms. Here, we identify neuronal cascades as a potential mechanism. Using full-brain network modeling, we show that neuronal populations, coupled via a detailed structural connectome, give rise to large-scale cascades of firing rate fluctuations evolving at the same time scale of resting-state networks (RSNs). The ignition and subsequent propagation of cascades depend on the brain state and connectivity of each region. The largest cascades produce bursts of blood oxygen level-dependent (BOLD) co-fluctuations at pairs of regions across the brain, which shape the simulated RSN dynamics. We experimentally confirm these theoretical predictions. We demonstrate the existence and stability of intermittent epochs of FC comprising BOLD co-activation (CA) bursts in mice and human functional magnetic resonance imaging (fMRI). We then provide evidence for the existence and leading role of the neuronal cascades in humans with simultaneous EEG/fMRI recordings. These results show that neuronal cascades are a major determinant of spontaneous fluctuations in brain dynamics at rest.

International audience; Autotuning, the practice of automatic tuning of applications to provide performance portability, has received increased attention in the research community, especially in high performance computing. Ensuring high performance on a variety of hardware usually means modifications to the code, often via different values of a selected set of parameters, such as tiling size, loop unrolling factor or data layout. However, the search space of all possible combinations of these parameters can be large, which can result in cases where the benefits of autotuning are outweighed by its cost, especially with dynamic tuning. Therefore, estimating the tuning time in advance or shortening the tuning time is very important in dynamic tuning applications. We have found that certain properties of tuning spaces do not vary much when hardware is changed. In this paper, we demonstrate that it is possible to use historical data to reliably predict the number of tuning steps that is necessary to find a wellperforming configuration, and to reduce the size of the tuning space. We evaluate our hypotheses on a number of HPC benchmarks written in CUDA and OpenCL, using several different generations of GPUs and CPUs.

Autotuning, the practice of automatic tuning of code to provide performance portability, has received increased attention in the research community, especially in high performance computing. Ensuring high performance on a variety of hardware usually means modifications to the code, often via different values of a selected set of parameters, such as tiling size, loop unrolling factor or data layout. However, the search space of all possible combinations of these parameters can be enormous. Traditional search methods often fail to find a well-performing set of parameter values quickly.

Computational models of the human brain provide an important tool for studying the principles behind brain function and disease. To achieve whole-brain simulation, models are formulated at the level of neuronal populations as systems of delayed differential equations. In this paper, we show that the integration of large systems of sparsely connected neural masses is similar to well-studied sparse matrix-vector multiplication; however, due to delayed contributions, it differs in the data access pattern to the vectors. To improve data locality, we propose a combination of node reordering and tiled schedules derived from the connectivity matrix of the particular system, which allows performing multiple integration steps within a tile. We present two schedules: with a serial processing of the tiles and one allowing for parallel processing of the tiles. We evaluate the presented schedules showing speedup up to $$2\,\times $$ on single-socket CPU, and $$1.25\,\times $$ on Xeon Phi accelerator.

Background: Cognitive impairment in Parkinson’s disease (PD) is associated with altered connectivity of the resting state networks (RSNs). Longitudinal studies in well cognitively characterized PD subgroups are missing. Objectives: To assess changes of the whole-brain connectivity and between-network connectivity (BNC) of large-scale functional networks related to cognition in well characterized PD patients using a longitudinal study design and various analytical methods. Methods: We explored the whole-brain connectivity and BNC of the frontoparietal control network (FPCN) and the default mode, dorsal attention, and visual networks in PD with normal cognition (PD-NC, n = 17) and mild cognitive impairment (PD-MCI, n = 22) as compared to 51 healthy controls (HC). We applied regions of interest-based, partial least squares, and graph theory based network analyses. The differences among groups were analyzed at baseline and at the one-year follow-up visit (37 HC, 23 PD all). Results: The BNC of the FPCN and other RSNs was reduced, and the whole-brain analysis revealed increased characteristic path length and decreased average node strength, clustering coefficient, and global efficiency in PD-NC compared to HC. Values of all measures in PD-MCI were between that of HC and PD-NC. After one year, the BNC was further increased in the PD-all group; no changes were detected in HC. No cognitive domain z-scores deteriorated in either group. Conclusion: As compared to HC, PD-NC patients display a less efficient transfer of information globally and reduced BNC of the visual and frontoparietal control network. The BNC increases with time and MCI status, reflecting compensatory efforts.

Objectives The aim was to describe the contribution of basal ganglia (BG) thalamo-cortical circuitry to the whole-brain functional connectivity in focal epilepsies. Methods Interictal resting-state fMRI recordings were acquired in 46 persons with focal epilepsies. Of these 46, 22 had temporal lobe epilepsy: 9 left temporal (LTLE), 13 right temporal (RTLE); 15 had frontal lobe epilepsy (FLE); and 9 had parietal/occipital lobe epilepsy (POLE). There were 20 healthy controls. The complete weighted network was analyzed based on correlation matrices of 90 and 194 regions. The network topology was quantified on a global and regional level by measures based on graph theory, and connection-level changes were analyzed by the partial least square method. Results In all patient groups except RTLE, the shift of the functional network topology away from random was observed (normalized clustering coefficient and characteristic path length were higher in patient groups than in controls). Links contributing to this change were found in the cortico-subcortical connections. Weak connections (low correlations) consistently contributed to this modification of the network. The importance of regions changed: decreases in the subcortical areas and both decreases and increases in the cortical areas were observed in node strength, clustering coefficient and eigenvector centrality in patient groups when compared to controls. Node strength decreases of the basal ganglia, i.e. the putamen, caudate, and pallidum, were displayed in LTLE, FLE, and POLE. The connectivity within the basal ganglia–thalamus circuitry was not disturbed; the disturbance concerned the connectivity between the circuitry and the cortex. Significance Focal epilepsies affect large-scale brain networks beyond the epileptogenic zones. Cortico-subcortical functional connectivity disturbance was displayed in LTLE, FLE, and POLE. Significant changes in the resting-state functional connectivity between cortical and subcortical structures suggest an important role of the BG and thalamus in focal epilepsies., Highlights • Focal epilepsies affect large-scale brain networks beyond the epileptogenic zones. • The functional network topology shifted away from random in focal epilepsies. • Subcortico-cortical connectivity decreased in epilepsy due to changes in weak links. • Basal ganglia–thalamus circuitry connectivity was not disturbed in focal epilepsy. • The connectivity between basal ganglia-thalamus circuitry and cortex was affected.

Brain functional connectivity measured by functional magnetic resonance imaging was shown to be influenced by preprocessing procedures. We aim to describe this influence separately for different preprocessing factors and in 20 different most used preprocessing pipelines. We evaluate the effects of slice-timing correction and physiological noise filtering by RETROICOR, diverse levels of motion correction, and white matter, cerebrospinal fluid, and global signal filtering. With usage of three datasets, we show the impact on global metrics of resting-state functional brain networks and their reliability. We show negative effect of RETROICOR on reliability of metrics and disrupting effect of global signal regression on network topology. We do not support the use of slice-timing correction because it does not significantly influence any of the measured features. We also show that the selected types of preprocessing may affect averaged node strength, normalized clustering coefficient, normalized characteristic path length and modularity.

In this paper, we present a workflow for evaluating resting-state brain functional connectivity with different community detection algorithms and their strengths to discriminate between health and Parkinson's disease (PD) and mild cognitive impairment preceding Alzheimer's disease (AD-MCI). We further analyze the complexity of particular pipeline steps aiming to provide guidelines for both execution on computing infrastructure and further optimization efforts. On a dataset of 50 controls and 70 patients we measured an increased modularity coefficient with 81.8% accuracy of classifying PD versus controls and 76.2% accuracy of classifying AD-MCI versus controls. Significantly higher modularity coefficient values were measured when the random matrix theory decomposition was adapted for network construction. These results were observed on networks of 82 nodes based on AAL atlas and 317 nodes based on multimodal parcellation atlas.

Parcellation-based approaches are an important part of functional magnetic resonance imaging data analysis. They are a necessary processing step for sorting data in structurally or functionally homogenous regions. Real functional magnetic resonance imaging datasets usually do not cover the atlas template completely; they are often spatially constrained due to the physical limitations of MR sequence settings, the inter-individual variability in brain shape, etc. When using a parcellation template, many regions are not completely covered by actual data. This paper addresses the issue of the area coverage required in real data in order to reliably estimate the representative signal and the influence of this kind of data loss on network analysis metrics. We demonstrate this issue on four datasets using four different widely used parcellation templates. We used two erosion approaches to simulate data loss on the whole-brain level and the ROI-specific level. Our results show that changes in ROI coverage have a systematic influence on network measures. Based on the results of our analysis, we recommend controlling the ROI coverage and retaining at least 60% of the area in order to ensure at least 80% of explained variance of the original signal.

In this paper we present a computational environment called LINUM for agent-based modelling, based on a geospatial transport model similar to the one implemented in ORBIS, The Stanford Geospatial Network Model of the Roman World. We provide the implementation of the transport model, and tools for creating and analyzing agent-based models around it. We also provide web-based visualization of the results of the simulation, and enable the user to generate time-collapsed static network for further analysis by the means of complex networks measures. The functionality of the environment is demonstrated on a model of diffusion process on the transport network.

The numerous studies of Local Exchange Trading Systems (LETS) have so far been based mostly on qualitative approaches. The aim of this paper is to introduce the method of transaction network analysis as an important complement to these studies, enabling one to quantitatively describe functioning of LETS in terms of the actual flows of currency, materialized transactions and topology of the exchange network, the types of information markedly missing in most of the existing LETS studies. We demonstrate the potentials of the method on the case of the LETS initiative “RozLEŤSe” based in Brno, Czech Republic. Looking at the transaction flows using the network analysis approach, we identify the key members of the group, network characteristics of the exchange system and its development over time. This allows us not only to provide a complex description of the system, but also to simulate certain scenarios (e.g. removal of a key person from the network). Within the discussion of potentials and limitations of applying transaction network analysis for studying LETS and other community currency initiatives (e.g. time banks) on a larger scale, we also provide a free accessible software tool for doing so and invite other researchers to cooperate on the task.

A theoretical study of a dielectrophoretic (DEP) force, which is produced by 180° ferroelectric antiparallel hexagonal domains, is presented. The source of the DEP force is a strong and highly inhomogeneous (stray) electric field, which exists in the vicinity of the ferroelectric domain walls at the surface of the ferroelectric film. Finite element method simulations are used for the calculation of spatial distribution of the stray field in the ionic liquid above the top surface of the ferroelectric film. The numerical simulations indicate that the intersection of the ferroelectric domain wall and the surface of the ferroelectric film represents a trap for dielectric nanoparticles in the case of so-called positive dielectrophoresis. The effect of electrical neutrality of dielectric nanoparticles is discussed.

Christianity emerged as a small and marginal movement in the first century Palestine and throughout the following three centuries it became highly visible in the whole Mediterranean. Little is known about the mechanisms of spreading innovative ideas in past societies. Here we investigate how well the spread of Christianity can be explained as a diffusive process constrained by physical travel in the Roman Empire. First, we combine a previously established model of the transportation network with city population estimates and evaluate to which extent the spatio-temporal pattern of the spread of Christianity can be explained by static factors. Second, we apply a network-theoretical approach to analyze the spreading process utilizing effective distance. We show that the spread of Christianity in the first two centuries closely follows a gravity-guided diffusion, and is substantially accelerated in the third century. Using the effective distance measure, we are able to suggest the probable path of the spread. Our work demonstrates how the spatio-temporal patterns we observe in the data can be explained using only spatial constraints and urbanization structure of the empire. Our findings also provide a methodological framework to be reused for studying other distant past cultural spreading phenomena.

Early-stage detection of different kinds of dementia and cognitive impairments is an extremely important task. The neuroscience community is devoting great effort into finding potential markers of disease that would be noninvasive, easy to establish, and stable. In this paper, we evaluate different community detection algorithms and their strengths to discriminate between health and Parkinson’s disease (PD) and mild cognitive impairment preceding Alzheimer’s disease (AD-MCI). On a dataset of 50 controls (34 women; 66.74 ± 7.35 years) and 70 patients (35 women; 66.71 ± 9.44 years), the resting-state fMRI was measured. The data were preprocessed by 16 different variants of preprocessing, functional network for each subject and each preprocessing variant constructed and evaluated by diverse algorithms for modularity computation. The modularity coefficients reflect the ability of a network to form clusters. They were used as a classifier. We measured an increased modularity coefficient with 81.8% accuracy of classifying PD versus controls and 76.2% accuracy of classifying AD-MCI versus controls. Significantly higher modularity coefficient values were measured when the random matrix theory decomposition was adapted for network construction. These results were observed on networks of 82 nodes based on AAL atlas and 317 nodes based on multimodal parcellation atlas.

The phytopathogenic fungus Sclerotinia sclerotiorum forms dormant structures (termed sclerotia) that germinate myceliogenically under certain environmental conditions. During myceliogenic germination, sclerotia produce hyphae, which can infect leaves or stems of host plants directly from the ground; this is termed basal infection. This study determined which abiotic conditions were most important for promoting myceliogenic germination of sclerotia in vitro. A high sclerotium hydration level and low incubation temperature (15°C) improved mycelial growth in the presence of a nutrient source. Sclerotia incubated without a nutrient source on moist sand, vigorously myceliogenically germinated most frequently (63%) when they had been previously imbibed and then conditioned at −20°C. By far the most consistent amount of vigorous myceliogenic germination (>75%) was produced when sclerotia were heat-dried before being submerged in water. The hyphae of these sclerotia were shown to infect and proliferate on leaves of intact Brassica napus plants. This research provides a better understanding of the abiotic conditions that are likely to increase the risk of basal infection by S. sclerotiorum.

Domain geometry engineering is one of the most essential application aspects of ferroic materials. Up to now domain geometries have been considered represented by 180° domain walls in samples of infinite size. Here a new possibility is addressed theoretically: periodic domain systems based on 90° domain pairs in a thin film. This case is realistic, as shown by bending plate-like samples of ferroelectric/ferroelastic materials. A symmetry-based analysis is presented of macroscopic properties of plate-like samples in which a 90° domain pattern similar to the Halbach array has been produced. The conclusions can lead to new application related properties of ferroelectric thin films.

Anomalous pyroelectric effect has been repeatedly observed in ferroelectric films which were compositionally graded or in which strain gradient or temperature gradient was realized. The effect is represented by the shift of the hysteresis P(E) loop center due to the change of temperature. Here we propose a new interpretation of this property. It is based on the assumption that the concentration gradient induces additional polarization. This is characterized by macroscopic description based on the modified Landau-Devonshire thermodynamic potential f(P). The graphical representation of this modified potential is an asymmetric double-well. The hysteresis phenomenon is described by the motion of the “representative Brown particle” in this double-well; the shape of the latter is modulated by the time-dependent electric field. The observed anomalous pyroelectric effect is based on the coherence of the asymmetry and kinetic aspect of the phenomenon.

So far many studies have addressed the issue of connectivity changes in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) using different methods, including whole-brain network analyses describing structural and functional topology. In this study we address an association between network topology and brain pathology in MCI. We investigated functional connectivity of MCI subjects ( n = 84) and age-matched healthy controls (HC; n = 32) defined on AAL atlas. In the MCI group we assessed how changes in brain topology correlate with cognitive performance and CSF levels of AD biomarkers in order to shed further light on underlying pathophysiological mechanisms. We used following parameters to describe brain topology: clustering coefficient, path length, node strength, and eigenvector centrality. We found shorter global path length, changes in frontal, occipital and subcortical regions, and more homogeneous hub distribution in MCI as compared to HC. Worse cognitive performance was related to topological changes in mentioned regions. Network properties correlated with CSF biomarkers in frontal and occipital regions. We observed a shift in MCI networks towards random topology with extensive regional changes, parallels can be found in literature. Network results are supported by cognitive results and CSF biomarkers levels, combined bringing unique information to current knowledge of MCI changes.

Domain-related properties of ferroic materials offer a number of application aspects. However, some of the involved domain characteristics have not been fully explained and may initiate an interesting field of further research. Here we specify and discuss several such still unresolved problems related to single crystalline and ceramic samples. Those concerning static domain pattern include the topic of domain wall thickness and of possible macroscopic wall properties, macroscopic properties of ceramic samples depoled in different ways and application of multidomain systems in surface acoustic wave devices. Discussed problems related to dynamic domain phenomena include the issue of visibility and thickness of moving domain walls, the effect of nucleation and the topic of extrinsic contributions to macroscopic properties of multidomain samples.