A whole-brain model of the aging brain during slow wave sleep.

Age-related brain changes affect sleep and are reflected in properties of sleep slow-waves, however the precise mechanisms behind these changes are still not completely understood. Here, we adapt a previously established whole-brain model relating structural connectivity changes to resting state dynamics, and extend it to a slow-wave sleep brain state. In particular, starting from a representative connectome at the beginning of the aging trajectory, we have gradually reduced the inter-hemispheric connections, and simulated sleep-like slow-wave activity. We show that the main empirically observed trends, namely a decrease in duration and increase in variability of the slow waves are captured by the model. Furthermore, comparing the simulated EEG activity to the source signals, we suggest that the empirically observed decrease in amplitude of the slow waves is caused by the decrease in synchrony between brain regions.Significance Statement Aging is characterized by changes in slow wave (SW) sleep features, yet the precise mechanisms driving these alterations remain elusive. Employing a connectome-based model, we implement the established age- related reductions in inter-hemispheric connectivity, successfully replicating the SW changes in the simulated activity. Our simulation of EEG activity also suggests that observed decreases in SW amplitude stems from diminished synchrony between brain regions. Our results support the notion that alterations in SW characteristics result from reductions in cortical excitatory drive-here facilitated by the inter-hemispheric connections. Our model serves as a robust foundation for extensions to population studies and interventional work in animal models of aging aimed at disentangling the contributions of network alterations, changes to local neural mass properties, and neuromodulation.

Quantitative Estimation of Promoter Activity in Cannabis sativa Using Agroinfiltration-Based Transient Gene Expression.

To properly assess promoter activity, which is critical for understanding biosynthetic pathways in different plant species, we use agroinfiltration-based transient gene expression assay. We compare the activity of several known promoters in Nicotiana benthamiana with their activity in Cannabis sativa (both hemp and medicinal cannabis), which has attracted much attention in recent years for its industrial, medicinal, and recreational properties. Here we describe an optimized protocol for transient expression in Cannabis combined with a ratiometric GUS reporter system that allows more accurate evaluation of promoter activity and reduces the effects of variable infiltration efficiency.

The virtual aging brain: Causal inference supports interhemispheric dedifferentiation in healthy aging.

The mechanisms of cognitive decline and its variability during healthy aging are not fully understood, but have been associated with reorganization of white matter tracts and functional brain networks. Here, we built a brain network modeling framework to infer the causal link between structural connectivity and functional architecture and the consequent cognitive decline in aging. By applying in-silico interhemispheric degradation of structural connectivity, we reproduced the process of functional dedifferentiation during aging. Thereby, we found the global modulation of brain dynamics by structural connectivity to increase with age, which was steeper in older adults with poor cognitive performance. We validated our causal hypothesis via a deep-learning Bayesian approach. Our results might be the first mechanistic demonstration of dedifferentiation during aging leading to cognitive decline.

Brain simulation as a cloud service: The Virtual Brain on EBRAINS.

The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation.

One-Enzyme RTX-PCR for the Detection of RNA Viruses from Multiple Virus Genera and Crop Plants.

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.

High-resolution virtual brain modeling personalizes deep brain stimulation for treatment-resistant depression: Spatiotemporal response characteristics following stimulation of neural fiber pathways.

Over the past 15 years, deep brain stimulation (DBS) has been actively investigated as a groundbreaking therapy for patients with treatment-resistant depression (TRD); nevertheless, outcomes have varied from patient to patient, with an average response rate of ∼50%. The engagement of specific fiber tracts at the stimulation site has been hypothesized to be an important factor in determining outcomes, however, the resulting individual network effects at the whole-brain scale remain largely unknown. Here we provide a computational framework that can explore each individual's brain response characteristics elicited by selective stimulation of fiber tracts. We use a novel personalized in-silico approach, the Virtual Big Brain, which makes use of high-resolution virtual brain models at a mm-scale and explicitly reconstructs more than 100,000 fiber tracts for each individual. Each fiber tract is active and can be selectively stimulated. Simulation results demonstrate distinct stimulus-induced event-related potentials as a function of stimulation location, parametrized by the contact positions of the electrodes implanted in each patient, even though validation against empirical patient data reveals some limitations (i.e., the need for individual parameter adjustment, and differential accuracy across stimulation locations). This study provides evidence for the capacity of personalized high-resolution virtual brain models to investigate individual network effects in DBS for patients with TRD and opens up novel avenues in the personalized optimization of brain stimulation.

Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics.

During 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.

RateML: A Code Generation Tool for Brain Network Models.

Whole brain network models are now an established tool in scientific and clinical research, however their use in a larger workflow still adds significant informatics complexity. We propose a tool, RateML, that enables users to generate such models from a succinct declarative description, in which the mathematics of the model are described without specifying how their simulation should be implemented. RateML builds on NeuroML's Low Entropy Model Specification (LEMS), an XML based language for specifying models of dynamical systems, allowing descriptions of neural mass and discretized neural field models, as implemented by the Virtual Brain (TVB) simulator: the end user describes their model's mathematics once and generates and runs code for different languages, targeting both CPUs for fast single simulations and GPUs for parallel ensemble simulations. High performance parallel simulations are crucial for tuning many parameters of a model to empirical data such as functional magnetic resonance imaging (fMRI), with reasonable execution times on small or modest hardware resources. Specifically, while RateML can generate Python model code, it enables generation of Compute Unified Device Architecture C++ code for NVIDIA GPUs. When a CUDA implementation of a model is generated, a tailored model driver class is produced, enabling the user to tweak the driver by hand and perform the parameter sweep. The model and driver can be executed on any compute capable NVIDIA GPU with a high degree of parallelization, either locally or in a compute cluster environment. The results reported in this manuscript show that with the CUDA code generated by RateML, it is possible to explore thousands of parameter combinations with a single Graphics Processing Unit for different models, substantially reducing parameter exploration times and resource usage for the brain network models, in turn accelerating the research workflow itself. This provides a new tool to create efficient and broader parameter fitting workflows, support studies on larger cohorts, and derive more robust and statistically relevant conclusions about brain dynamics.

Neuronal Cascades Shape Whole-Brain Functional Dynamics at Rest.

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.

Connectivity Between Brain Networks Dynamically Reflects Cognitive Status of Parkinson's Disease: A Longitudinal Study.

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.

Spatial constraints on the diffusion of religious innovations: The case of early Christianity in the Roman Empire.

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 cultural spreading phenomena.

Robustness of Representative Signals Relative to Data Loss Using Atlas-Based Parcellations.

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.

Large-scale cortico-subcortical functional networks in focal epilepsies: The role of the basal ganglia.

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.

Updating algal evolutionary relationships through plastid genome sequencing: did alveolate plastids emerge through endosymbiosis of an ochrophyte?

Algae with secondary plastids of a red algal origin, such as ochrophytes (photosynthetic stramenopiles), are diverse and ecologically important, yet their evolutionary history remains controversial. We sequenced plastid genomes of two ochrophytes, Ochromonas sp. CCMP1393 (Chrysophyceae) and Trachydiscus minutus (Eustigmatophyceae). A shared split of the clpC gene as well as phylogenomic analyses of concatenated protein sequences demonstrated that chrysophytes and eustigmatophytes form a clade, the Limnista, exhibiting an unexpectedly elevated rate of plastid gene evolution. Our analyses also indicate that the root of the ochrophyte phylogeny falls between the recently redefined Khakista and Phaeista assemblages. Taking advantage of the expanded sampling of plastid genome sequences, we revisited the phylogenetic position of the plastid of Vitrella brassicaformis, a member of Alveolata with the least derived plastid genome known for the whole group. The results varied depending on the dataset and phylogenetic method employed, but suggested that the Vitrella plastids emerged from a deep ochrophyte lineage rather than being derived vertically from a hypothetical plastid-bearing common ancestor of alveolates and stramenopiles. Thus, we hypothesize that the plastid in Vitrella, and potentially in other alveolates, may have been acquired by an endosymbiosis of an early ochrophyte.

Growth and stress response in Arabidopsis thaliana, Nicotiana benthamiana, Glycine max, Solanum tuberosum and Brassica napus cultivated under polychromatic LEDs.

BACKGROUND: The use of light emitting diodes (LEDs) brings several key advantages over existing illumination technologies for indoor plant cultivation. Among these are that LEDs have predicted lifetimes from 50-100.000 hours without significant drops in efficiency and energy consumption is much lower compared to traditional fluorescent tubes. Recent advances allow LEDs to be used with customized wavelengths for plant growth. However, most of these LED growth systems use mixtures of chips emitting in several narrow wavelengths and frequently they are not compatible with existing infrastructures. This study tested the growth of five different plant species under phosphor coated LED-chips fitted into a tube with a standard G13 base that provide continuous visible light illumination with enhanced blue and red light. RESULTS: The LED system was characterized and compared with standard fluorescence tubes in the same cultivation room. Significant differences in heat generation between LEDs and fluorescent tubes were clearly demonstrated. Also, LED lights allowed for better control and stability of preset conditions. Physiological properties such as growth characteristics, biomass, and chlorophyll content were measured and the responses to pathogen assessed for five plant species (both the model plants Arabidopsis thaliana, Nicotiana bentamiana and crop species potato, oilseed rape and soybean) under the different illumination sources. CONCLUSIONS: We showed that polychromatic LEDs provide light of sufficient quality and intensity for plant growth using less than 40% of the electricity required by the standard fluorescent lighting under test. The tested type of LED installation provides a simple upgrade pathway for existing infrastructure for indoor plant growth. Interestingly, individual plant species responded differently to the LED lights so it would be reasonable to test their utility to any particular application.

Genome Sequence of Rhodococcus erythropolis Strain CCM2595, a Phenol Derivative-Degrading Bacterium.

We announce the completion of the genome sequence of a phenol derivative-degrading bacterium, Rhodococcus erythropolis strain CCM2595. This bacterium is interesting in the context of bioremediation for its capability to degrade phenol, catechol, resorcinol, hydroxybenzoate, hydroquinone, p-chlorophenol, p-nitrophenol, pyrimidines, and sterols.

The mitochondrion-like organelle of Trimastix pyriformis contains the complete glycine cleavage system.

All eukaryotic organisms contain mitochondria or organelles that evolved from the same endosymbiotic event like classical mitochondria. Organisms inhabiting low oxygen environments often contain mitochondrial derivates known as hydrogenosomes, mitosomes or neutrally as mitochondrion-like organelles. The detailed investigation has shown unexpected evolutionary plasticity in the biochemistry and protein composition of these organelles in various protists. We investigated the mitochondrion-like organelle in Trimastix pyriformis, a free-living member of one of the three lineages of anaerobic group Metamonada. Using 454 sequencing we have obtained 7 037 contigs from its transcriptome and on the basis of sequence homology and presence of N-terminal extensions we have selected contigs coding for proteins that putatively function in the organelle. Together with the results of a previous transcriptome survey, the list now consists of 23 proteins - mostly enzymes involved in amino acid metabolism, transporters and maturases of proteins and transporters of metabolites. We have no evidence of the production of ATP in the mitochondrion-like organelle of Trimastix but we have obtained experimental evidence for the presence of enzymes of the glycine cleavage system (GCS), which is part of amino acid metabolism. Using homologous antibody we have shown that H-protein of GCS localizes into vesicles in the cell of Trimastix. When overexpressed in yeast, H- and P-protein of GCS and cpn60 were transported into mitochondrion. In case of H-protein we have demonstrated that the first 16 amino acids are necessary for this transport. Glycine cleavage system is at the moment the only experimentally localized pathway in the mitochondrial derivate of Trimastix pyriformis.

The plastid genome of Eutreptiella provides a window into the process of secondary endosymbiosis of plastid in euglenids.

Euglenids are a group of protists that comprises species with diverse feeding modes. One distinct and diversified clade of euglenids is photoautotrophic, and its members bear green secondary plastids. In this paper we present the plastid genome of the euglenid Eutreptiella, which we assembled from 454 sequencing of Eutreptiella gDNA. Comparison of this genome and the only other available plastid genomes of photosynthetic euglenid, Euglena gracilis, revealed that they contain a virtually identical set of 57 protein coding genes, 24 genes fewer than the genome of Pyramimonas parkeae, the closest extant algal relative of the euglenid plastid. Searching within the transcriptomes of Euglena and Eutreptiella showed that 6 of the missing genes were transferred to the nucleus of the euglenid host while 18 have been probably lost completely. Euglena and Eutreptiella represent the deepest bifurcation in the photosynthetic clade, and therefore all these gene transfers and losses must have happened before the last common ancestor of all known photosynthetic euglenids. After the split of Euglena and Eutreptiella only one additional gene loss took place. The conservation of gene content in the two lineages of euglenids is in contrast to the variability of gene order and intron counts, which diversified dramatically. Our results show that the early secondary plastid of euglenids was much more susceptible to gene losses and endosymbiotic gene transfers than the established plastid, which is surprisingly resistant to changes in gene content.

Sound-speed image reconstruction in sparse-aperture 3-D ultrasound transmission tomography.

The paper is focused on sound-speed image reconstruction in 3-D ultrasound transmission tomography. Along with ultrasound reflectivity and the attenuation coefficient, sound speed is an important parameter which is related to the type and pathological state of the imaged tissue. This is important in the intended application, breast cancer diagnosis. In contrast to 2-D ultrasound transmission tomography systems, a 3-D system can provide an isotropic spatial resolution in the x-, y-, and z-directions in reconstructed 3-D images of ultrasound parameters. Several challenges must, however, be addressed for 3-D systems-namely, a sparse transducer distribution, low signal-to-noise ratio, and higher computational complexity. These issues are addressed in terms of sound-speed image reconstruction, using edge-preserving regularized algebraic reconstruction in combination with synthetic aperture focusing. The critical points of the implementation are also discussed, because they are crucial to enable a complete 3-D image reconstruction. The methods were tested on a synthetic data set and on data sets measured with the Karlsruhe 3-D ultrasound computer tomography (USCT) I prototype using phantoms. The sound-speed estimates in the reconstructed volumes agreed with the reference values. The breast-phantom outlines and the lesion-mimicking objects were also detectable in the resulting sound-speed volumes.

An unexpected gene cluster for downstream degradation of alkylphenols in Sphingomonas sp. strain TTNP3.

In silico analysis of nucleotide sequences flanking the recently found hydroquinone dioxygenase in Sphingomonas sp. strain TTNP3 revealed a gene cluster that encodes a hydroquinone catabolic pathway. In addition to the two open-reading frames encoding the recently characterized hydroquinone dioxygenase, the cluster consisted of six open-reading frames. We were able to express the three open-reading frames, hqdC, hqdD, and hqdE, and demonstrated that the three gene products, HqdC, HqdD, and HqdE had 4-hydroxymuconic semialdehyde dehydrogenase, maleylacetate reductase, and intradiol dioxygenase activity, respectively. Surprisingly, the gene cluster showed similarities to functionally related clusters found in members of the ß- and γ-proteobacteria rather than to those found in other members of the genus Sphingomonas sensu latu.