Summary Human functional MRI (fMRI) research primarily focuses on analyzing data averaged across groups, which limits the detail, specificity, and clinical utility of fMRI resting-state functional connectivity (RSFC) and task activation maps. To push our understanding of functional brain organization to the level of individual humans, we assembled a novel MRI dataset containing five hours of RSFC data, six hours of task fMRI, multiple structural MRIs, and neuropsychological tests from each of ten adults. Using these data, we generated ten high fidelity, individual-specific functional connectomes. This individual connectome approach revealed several new types of spatial and organizational variability in brain networks, including unique network features and topologies that corresponded with structural and task-derived brain features. We are releasing this highly-sampled, individual-focused dataset as a resource for neuroscientists, and we propose precision individual connectomics as a model for future work examining the organization of healthy and diseased individual human brains.

Hubs are network components that hold positions of high importance for network function. Previous research has identified hubs in human brain networks derived from neuroimaging data; however, there is little consensus on the localization of such hubs. Moreover, direct evidence regarding the role of various proposed hubs in network function (e.g., cognition) is scarce. Regions of the default mode network (DMN) have been frequently identified as "cortical hubs" of brain networks. On theoretical grounds, we have argued against some of the methods used to identify these hubs and have advocated alternative approaches that identify different regions of cortex as hubs. Our framework predicts that our proposed hub locations may play influential roles in multiple aspects of cognition, and, in contrast, that hubs identified via other methods (including salient regions in the DMN) might not exert such broad influence. Here we used a neuropsychological approach to directly test these predictions by studying long-term cognitive and behavioral outcomes in 30 patients, 19 with focal lesions to six "target" hubs identified by our approaches (high system density and participation coefficient) and 11 with focal lesions to two "control" hubs (high degree centrality). In support of our predictions, we found that damage to target locations produced severe and widespread cognitive deficits, whereas damage to control locations produced more circumscribed deficits. These findings support our interpretation of how neuroimaging-derived network measures relate to cognition and augment classic neuroanatomically based predictions about cognitive and behavioral outcomes after focal brain injury.functional connectivity | neuropsychology | fMRI | brain hubs T he careful description of circumscribed cognitive and behavioral deficits following localized brain damage has provided much of our knowledge of the functional geography of the brain. In some cases, however, relatively small, circumscribed lesions seem to have broader effects than would be predicted from their size and location. Historically, these effects sometimes have been attributed to diaschisis (i.e., effects at a distance) owing to connections between affected and unaffected brain regions. The potential importance of interactivity among brain regions is supported by recent research (1-3); for example, He et al. (4) found that visuospatial inattention after right inferior parietal lesions was best explained by the effects of those lesions on more superior parietal activity.In the broadest sense, interactive explanations of brain function can be thought of as reflecting the organization of the brain as a large-scale network. The advent of large-scale network descriptions of brain structure and function extends the possibility of richer and broader explanations of unusually severe cognitive and behavioral consequences that sometimes follow circumscribed lesions. Some large-scale network studies have focused on "hubs," a term from network science that indicates potential points of impo...

Pigs are considered as important hosts or “mixing vessels” for the generation of pandemic influenza viruses. Systematic surveillance of influenza viruses in pigs is essential for early warning and preparedness for the next potential pandemic. Here, we report on an influenza virus surveillance of pigs from 2011 to 2018 in China, and identify a recently emerged genotype 4 (G4) reassortant Eurasian avian-like (EA) H1N1 virus, which bears 2009 pandemic (pdm/09) and triple-reassortant (TR)-derived internal genes and has been predominant in swine populations since 2016. Similar to pdm/09 virus, G4 viruses bind to human-type receptors, produce much higher progeny virus in human airway epithelial cells, and show efficient infectivity and aerosol transmission in ferrets. Moreover, low antigenic cross-reactivity of human influenza vaccine strains with G4 reassortant EA H1N1 virus indicates that preexisting population immunity does not provide protection against G4 viruses. Further serological surveillance among occupational exposure population showed that 10.4% (35/338) of swine workers were positive for G4 EA H1N1 virus, especially for participants 18 y to 35 y old, who had 20.5% (9/44) seropositive rates, indicating that the predominant G4 EA H1N1 virus has acquired increased human infectivity. Such infectivity greatly enhances the opportunity for virus adaptation in humans and raises concerns for the possible generation of pandemic viruses.

Executive control functions are associated with frontal, parietal, cingulate, and insular brain regions that interact through distributed large-scale networks. Here, we discuss how fMRI functional connectivity can shed light on the organization of control networks and how they interact with other parts of the brain. In the first section of our review, we present convergent evidence from fMRI functional connectivity, activation, and lesion studies that there are multiple dissociable control networks in the brain with distinct functional properties. In the second section, we discuss how graph theoretical concepts can help illuminate the mechanisms by which control networks interact with other brain regions to carry out goal-directed functions, focusing on the role of specialized hub regions for mediating cross-network interactions. Again, we use a combination of functional connectivity, lesion, and task activation studies to bolster this claim. We conclude that a large-scale network perspective provides important neurobiological constraints on the neural underpinnings of executive control, which will guide future basic and translational research into executive function and its disruption in disease. K E Y W O R D Scognitive control, executive control, fMRI, functional connectivity, networks | IN TRO DUCT IO NExecutive control includes the set of processes that allows humans to flexibly adapt their behavior, deftly guiding neural processing to achieve goals at multiple timescales. Executive control (hereafter referred to more simply as control) encompasses processes involved in attentional enhancement of taskrelevant inputs and guided processing of these inputs toward appropriate behavioral responses, as well as suppression of task-irrelevant distractors and inhibition of goal-inappropriate prepotent responses. In the past, two different approaches have provided useful insight into how the brain maintains and executes control-related processing: one is built on understanding the specialized processing within individual brain regions, especially in the prefrontal cortex, while the other is focused on distributed processing across different brain areas that are organized into large-scale networks (also called systems). More recently, the latter approach has evolved to adopt a complex systems view that has been especially fruitful in exploring the interactions among sets of control-related regions. Hence, in the current review, we present recent evidence that suggests that distinct control processes localize not only to different brain areas, but to functionally dissociable brain networks. Furthermore, we emphasize the contribution of specialized hub regions to executive control processes via their role in mediating interactions between control networks and other networks involved in basic processing that are critical for accomplishing complex tasks.Specifically, in the first section of our review, we will present evidence from studies of fMRI functional connectivity, brain lesions, and fMRI task activations t...

Control of goal-directed tasks is putatively carried out via the cinguloopercular (CO) and frontoparietal (FP) systems. However, it remains unclear whether these systems show dissociable moment-to-moment processing during distinct stages of a trial. Here, we characterize dynamics in the CO and FP networks in a meta-analysis of 5 decision-making tasks using fMRI, with a specialized "slow reveal" paradigm which allows us to measure the temporal characteristics of trial responses. We find that activations in left FP, right FP, and CO systems form separate clusters, pointing to distinct roles in decision-making. Left FP shows early "accumulator-like" responses, suggesting a role in pre-decision processing. CO has a late onset and transient response linked to the decision event, suggesting a role in performance reporting. The majority of right FP regions show late onsets with prolonged responses, suggesting a role in post-recognition processing. These findings expand upon past models, arguing that the CO and FP systems relate to distinct stages of processing within a trial. Furthermore, the findings provide evidence for a heterogeneous profile in the FP network, with left and right FP taking on specialized roles. This evidence informs our understanding of how distinct control networks may coordinate moment-to-moment components of complex actions.

IntroductionSystemic lupus erythematosus (SLE) is an autoimmune disease involving multiple organ systems. Previous studies have suggested that interferon-lambda 1 (IFN-λ1), a type III interferon, plays an immunomodulatory role. In this study we investigated its role in SLE, including its correlation with disease activity, organ disorder and production of chemokines.MethodsWe determined levels of IFN-λ1 mRNA in peripheral blood mononuclear cells (PBMC) and serum protein levels in patients with SLE using real-time polymerase chain reaction (real-time PCR) and enzyme-linked immunoassay (ELISA). Further, we detected the concentration of IFN-inducible protein-10 (IP-10), monokine induced by IFN-γ (MIG) and interleukin-8 (IL-8) secreted by PBMC under the stimulation of IFN-λ1 using ELISA.ResultsIFN-λ1 mRNA and serum protein levels were higher in patients with SLE compared with healthy controls. Patients with active disease showed higher IFN-λ1 mRNA and serum protein levels compared with those with inactive disease as well. Serum IFN-λ1 levels were positively correlated with Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), anti-dsDNA antibody, C-reactive protein (CRP) and negatively correlated with complement 3. Serum IFN-λ1 levels were higher in SLE patients with renal involvement and arthritis compared with patients without the above-mentioned manifestations. IFN-λ1 with different concentrations displayed different effects on the secretion of the chemokines IP-10, MIG and IL-8.ConclusionsThese findings indicate that IFN-λ1 is probably involved in the renal disorder and arthritis progression of SLE and associated with disease activity. Moreover, it probably plays an important role in the pathogenesis of SLE by stimulating secretion of the chemokines IP-10, MIG and IL-8. Thus, IFN-λ1 may provide a novel research target for the pathogenesis and therapy of SLE.

Pandemic influenza, typically caused by reassortment of human and avian influenza viruses, can result in severe or fatal infections in humans. Timely identification of potential pandemic viruses must be a priority in influenza virus surveillance. However, the range of host species responsible for the generation of novel pandemic influenza viruses remain unclear. In this study, we conducted serological surveys for avian and human influenza virus infections in farmed mink and determined the susceptibility of mink to prevailing avian and human virus subtypes. The results showed that farmed mink were commonly infected with human (H3N2 and H1N1/pdm) and avian (H7N9, H5N6, and H9N2) influenza A viruses. Correlational analysis indicated that transmission of human influenza viruses occurred from humans to mink, and that feed source was a probable route of avian influenza virus transmission to farmed mink. Animal experiments showed that mink were susceptible and permissive to circulating avian and human influenza viruses, and that human influenza viruses (H3N2 and H1N1/pdm), but not avian viruses, were capable of aerosol transmission among mink. These results indicate that farmed mink could be highly permissive "mixing vessels" for the reassortment of circulating human and avian influenza viruses. Therefore, to reduce the risk of emergence of novel pandemic viruses, feeding mink with raw poultry by-products should not be permitted, and epidemiological surveillance of influenza viruses in mink farms should be urgently implemented.

We present a microwave method for the dielectric characterization of small liquids based on a metamaterial-based sensor The proposed sensor consists of a micro-strip line and a double split-ring resonator (SRR). A large electric field is observed on the two splits of the double SRRs at the resonance frequency (1.9 GHz). The dielectric property data of the samples under test (SUTs) were obtained with two measurements. One is with the sensor loaded with the reference liquid (REF) and the other is with the sensor loaded with the SUTs. Additionally, the principle of extracting permittivity from measured changes of resonance characteristics changes of the sensor loaded with REF and SUTs is given. Some measurements were carried out at 1.9 GHz, and the calculated results of methanol–water mixtures with different molar fractions agree well with the time-domain reflectometry method. Moreover, the proposed sensor is compact and highly sensitive for use of sub-wavelength resonance. In comparison with literature data, relative errors are less than 3% for the real parts and 2% for the imaginary parts of complex permittivity.