Insight into the role of a novel c-di-GMP effector protein in Rhodococcus ruber.

C-di-GMP is a ubiquitous second messenger in bacterium, which regulates cellular functions such as the formation of biofilm membrane, cell mobility, virulence, cell adhesion, cell cycle et al. These functions are associated with an increasing number of c-di-GMP effector proteins and/or riboswitchs. In the study, CEP1 (c-di-GMP effector protein 1), a novel c-di-GMP binding protein, was screened with a combination of affinity pull-down and LC/MS/MS methods. The binding of CEP1 and c-di-GMP was demonstrated by surface plasmon resonance, with the dissociation constants of 127 ± 1.03 µM. Quantitative real time PCR assay showed the mRNA levels of cep1 gene in Rhodococcus ruber SD3 increased to 63.29 times and 71.18 times after toluene and phenol stress, respectively. Furthermore, cep1 gene enhanced strain was constructed using shuttle plasmid pNV18, which showed improved growth compared to the wild-type strain in the presence of different organic solvents. The study provided an insight into a mechanism, by which c-di-GMP was connected with organic solvent tolerance of Rhodococcus ruber SD3.

Is it time to switch your T1W sequence? Assessing the impact of prospective motion correction on the reliability and quality of structural imaging.

New large neuroimaging studies, such as the Adolescent Brain Cognitive Development study (ABCD) and Human Connectome Project (HCP) Development studies are adopting a new T1-weighted imaging sequence with prospective motion correction (PMC) in favor of the more traditional 3-Dimensional Magnetization-Prepared Rapid Gradient-Echo Imaging (MPRAGE) sequence. Here, we used a developmental dataset (ages 5-21, N = 348) from the Healthy Brain Network (HBN) Initiative to directly compare two widely used MRI structural sequences: one based on the Human Connectome Project (MPRAGE) and another based on the ABCD study (MPRAGE+PMC). We aimed to determine if the morphometric measurements obtained from both protocols are equivalent or if one sequence has a clear advantage over the other. The sequences were also compared through quality control measurements. Inter- and intra-sequence reliability were assessed with another set of participants (N = 71) from HBN that performed two MPRAGE and two MPRAGE+PMC sequences within the same imaging session, with one MPRAGE (MPRAGE1) and MPRAGE+PMC (MPRAGE+PMC1) pair at the beginning of the session and another pair (MPRAGE2 and MPRAGE+PMC2) at the end of the session. Intraclass correlation coefficients (ICC) scores for morphometric measurements such as volume and cortical thickness showed that intra-sequence reliability is the highest with the two MPRAGE+PMC sequences and lowest with the two MPRAGE sequences. Regarding inter-sequence reliability, ICC scores were higher for the MPRAGE1 - MPRAGE+PMC1 pair at the beginning of the session than the MPRAGE1 - MPRAGE2 pair, possibly due to the higher motion artifacts in the MPRAGE2 run. Results also indicated that the MPRAGE+PMC sequence is robust, but not impervious, to high head motion. For quality control metrics, the traditional MPRAGE yielded better results than MPRAGE+PMC in 5 of the 8 measurements. In conclusion, morphometric measurements evaluated here showed high inter-sequence reliability between the MPRAGE and MPRAGE+PMC sequences, especially in images with low head motion. We suggest that studies targeting hyperkinetic populations use the MPRAGE+PMC sequence, given its robustness to head motion and higher reliability scores. However, neuroimaging researchers studying non-hyperkinetic participants can choose either MPRAGE or MPRAGE+PMC sequences, but should carefully consider the apparent tradeoff between relatively increased reliability, but reduced quality control metrics when using the MPRAGE+PMC sequence.

Measurement reliability for individual differences in multilayer network dynamics: Cautions and considerations.

Multilayer network models have been proposed as an effective means of capturing the dynamic configuration of distributed neural circuits and quantitatively describing how communities vary over time. Beyond general insights into brain function, a growing number of studies have begun to employ these methods for the study of individual differences. However, test-retest reliabilities for multilayer network measures have yet to be fully quantified or optimized, potentially limiting their utility for individual difference studies. Here, we systematically evaluated the impact of multilayer community detection algorithms, selection of network parameters, scan duration, and task condition on test-retest reliabilities of multilayer network measures (i.e., flexibility, integration, and recruitment). A key finding was that the default method used for community detection by the popular generalized Louvain algorithm can generate erroneous results. Although available, an updated algorithm addressing this issue is yet to be broadly adopted in the neuroimaging literature. Beyond the algorithm, the present work identified parameter selection as a key determinant of test-retest reliability; however, optimization of these parameters and expected reliabilities appeared to be dataset-specific. Once parameters were optimized, consistent with findings from the static functional connectivity literature, scan duration was a much stronger determinant of reliability than scan condition. When the parameters were optimized and scan duration was sufficient, both passive (i.e., resting state, Inscapes, and movie) and active (i.e., flanker) tasks were reliable, although reliability in the movie watching condition was significantly higher than in the other three tasks. The minimal data requirement for achieving reliable measures for the movie watching condition was 20 min, and 30 min for the other three tasks. Our results caution the field against the use of default parameters without optimization based on the specific datasets to be employed - a process likely to be limited for most due to the lack of test-retest samples to enable parameter optimization.

U-net model for brain extraction: Trained on humans for transfer to non-human primates.

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

Evaluating fMRI-Based Estimation of Eye Gaze During Naturalistic Viewing.

The collection of eye gaze information during functional magnetic resonance imaging (fMRI) is important for monitoring variations in attention and task compliance, particularly for naturalistic viewing paradigms (e.g., movies). However, the complexity and setup requirements of current in-scanner eye tracking solutions can preclude many researchers from accessing such information. Predictive eye estimation regression (PEER) is a previously developed support vector regression-based method for retrospectively estimating eye gaze from the fMRI signal in the eye's orbit using a 1.5-min calibration scan. Here, we provide confirmatory validation of the PEER method's ability to infer eye gaze on a TR-by-TR basis during movie viewing, using simultaneously acquired eye tracking data in five individuals (median angular deviation < 2°). Then, we examine variations in the predictive validity of PEER models across individuals in a subset of data (n = 448) from the Child Mind Institute Healthy Brain Network Biobank, identifying head motion as a primary determinant. Finally, we accurately classify which of the two movies is being watched based on the predicted eye gaze patterns (area under the curve = 0.90 ± 0.02) and map the neural correlates of eye movements derived from PEER. PEER is a freely available and easy-to-use tool for determining eye fixations during naturalistic viewing.

Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI.

A variety of psychiatric, behavioral and cognitive phenotypes have been linked to brain ''functional connectivity'' -- the pattern of correlation observed between different brain regions. Most commonly assessed using functional magnetic resonance imaging (fMRI), here, we investigate the connectivity-phenotype associations with functional connectivity measured with electroencephalography (EEG), using phase-coupling. We analyzed data from the publicly available Healthy Brain Network Biobank. This database compiles a growing sample of children and adolescents, currently encompassing 1657 individuals. Among a variety of assessment instruments we focus on ten phenotypic and additional demographic measures that capture most of the variance in this sample. The largest effect sizes are found for age and sex for both fMRI and EEG. We replicate previous findings of an association of Intelligence Quotient (IQ) and Attention Deficit Hyperactivity Disorder (ADHD) with the pattern of fMRI functional connectivity. We also find an association with socioeconomic status, anxiety and the Child Behavior Checklist Score. For EEG we find a significant connectivity-phenotype relationship with IQ. The actual spatial patterns of functional connectivity are quite different between fMRI and source-space EEG. However, within EEG we observe clusters of functional connectivity that are consistent across frequency bands. Additionally we analyzed reproducibility of functional connectivity. We compare connectivity obtained with different tasks, including resting state, a video and a visual flicker task. For both EEG and fMRI the variation between tasks was smaller than the variability observed between subjects. We also found an increase of reliability with increasing frequency of the EEG, and increased sampling duration. We conclude that, while the patterns of functional connectivity are distinct between fMRI and phase-coupling of EEG, they are nonetheless similar in their robustness to the task, and similar in that idiosyncratic patterns of connectivity predict individual phenotypes.

The phase of prestimulus alpha oscillations affects tactile perception.

Previous studies have shown that neural oscillations in the 8- to 12-Hz range influence sensory perception. In the current study, we examined whether both the power and phase of these mu/alpha oscillations predict successful conscious tactile perception. Near-threshold tactile stimuli were applied to the left hand while electroencephalographic (EEG) activity was recorded over the contralateral right somatosensory cortex. We found a significant inverted U-shaped relationship between prestimulus mu/alpha power and detection rate, suggesting that there is an intermediate level of alpha power that is optimal for tactile perception. We also found a significant difference in phase angle concentration at stimulus onset that predicted whether the upcoming tactile stimulus was perceived or missed. As has been shown in the visual system, these findings suggest that these mu/alpha oscillations measured over somatosensory areas exert a strong inhibitory control on tactile perception and that pulsed inhibition by these oscillations shapes the state of brain activity necessary for conscious perception. They further suggest that these common phasic processing mechanisms across different sensory modalities and brain regions may reflect a common underlying encoding principle in perceptual processing that leads to momentary windows of perceptual awareness.

Interactions between myoblasts and macrophages under high glucose milieus result in inflammatory response and impaired insulin sensitivity.

BACKGROUND: Skeletal muscle handles about 80% of insulin-stimulated glucose uptake and become the major organ occurring insulin resistance (IR). Many studies have confirmed the interactions between macrophages and skeletal muscle regulated the inflammation and regeneration of skeletal muscle. However, despite of the decades of research, whether macrophages infiltration and polarization in skeletal muscle under high glucose (HG) milieus results in the development of IR is yet to be elucidated. C2C12 myoblasts are well-established and excellent model to study myogenic regulation and its responses to stimulation. Further exploration of macrophages' role in myoblasts IR and the dynamics of their infiltration and polarization is warranted. AIM: To evaluate interactions between myoblasts and macrophages under HG, and its effects on inflammation and IR in skeletal muscle. METHODS: We detected the polarization status of macrophages infiltrated to skeletal muscles of IR mice by hematoxylin and eosin and immunohistochemical staining. Then, we developed an in vitro co-culture system to study the interactions between myoblasts and macrophages under HG milieus. The effects of myoblasts on macrophages were explored through morphological observation, CCK-8 assay, Flow Cytometry, and enzyme-linked immunosorbent assay. The mediation of macrophages to myogenesis and insulin sensitivity were detected by morphological observation, CCK-8 assay, Immunofluorescence, and 2-NBDG assay. RESULTS: The F4/80 and co-localization of F4/80 and CD86 increased, and the myofiber size decreased in IR group (P < 0.01, g = 6.26). Compared to Mc group, F4/80+CD86+CD206- cells, tumor necrosis factor-α (TNFα), inerleukin-1ß (IL-1ß) and IL-6 decreased, and IL-10 increased in McM group (P < 0.01, g > 0.8). In McM + HG group, F4/80+CD86+CD206- cells, monocyte chemoattractant protein 1, TNFα, IL-1ß and IL-6 were increased, and F4/80+CD206+CD86- cells and IL-10 were decreased compared with Mc + HG group and McM group (P < 0.01, g > 0.8). Compered to M group, myotube area, myotube number and E-MHC were increased in MMc group (P < 0.01, g > 0.8). In MMc + HG group, myotube area, myotube number, E-MHC, GLUT4 and glucose uptake were decreased compared with M + HG group and MMc group (P < 0.01, g > 0.8). CONCLUSION: Interactions between myoblasts and macrophages under HG milieus results in inflammation and IR, which support that the macrophage may serve as a promising therapeutic target for skeletal muscle atrophy and IR.

Downhill running and caloric restriction attenuate insulin resistance associated skeletal muscle atrophy via the promotion of M2-like macrophages through TRIB3-AKT pathway.

BACKGROUD: Downhill running has recently become a promising exercise modality for metabolic syndrome, but the effect and precise mechanism of downhill running training on insulin resistance (IR) induced skeletal muscle atrophy remains unclear. The current study aimed to explore the benefits of downhill running training accompanied by a low-fat diet on skeletal muscle atrophy in IR mice and its possible mechanisms. METHODS: For in vivo study, high fat diet (HFD) -induced IR mice were submitted to the downhill running training or/and caloric restriction for 8 weeks. In vitro study was performed using co-cultured RAW264.7 macrophages and C2C12 myoblasts model. Glucose tolerance test (GTT), insulin tolerance test (ITT), immunofluorescence staining, Western blot analysis, hematoxylin and eosin (H&E) staining, enzyme-linked immunosorbent assay (ELISA), Cell counting kit-8 (CCK-8) assays and glucose uptake assays were employed to explore the benefits and possible mechanisms of downhill running training accompanied by a low-fat diet on IR mice. RESULTS: Our data revealed that HFD induces IR, which leading to skeletal muscle atrophy. Downhill running accompanied by caloric restriction mitigated HFD-induced IR and improve skeletal muscle atrophy. Further study suggested that descended TRIB3 mediated the favorable impact of downhill running on IR induced skeletal muscle atrophy by suppressing M1-like macrophages and promoting M2-like macrophages. Macrophages-specific knockdown of TRIB3 exerted similar effects on the macrophage polarization and IR related myogenesis to downhill running training accompanied by caloric restriction. In contrast, macrophages-specific overexpression of TRIB3 descended phosphorylation of AKT, further activated M1-like macrophages and aggravated IR related inhibition of myogenesis. CONCLUSIONS: This finding demonstrated the beneficial effects of downhill running training and caloric restriction on IR related skeletal muscle atrophy by promoting M2-like macrophages through TRIB3-AKT pathway.

Predicting central cervical lymph node metastasis in papillary thyroid microcarcinoma using deep learning.

Background: The aim of this study is to design a deep learning (DL) model to preoperatively predict the occurrence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). Methods: This research collected preoperative ultrasound (US) images and clinical factors of 611 PTMC patients. The clinical factors were analyzed using multivariate regression. Then, a DL model based on US images and clinical factors was developed to preoperatively predict CLNM. The model's efficacy was evaluated using the receiver operating characteristic (ROC) curve, along with accuracy, sensitivity, specificity, and the F1 score. Results: The multivariate analysis indicated an independent correlation factors including age ≥55 (OR = 0.309, p < 0.001), tumor diameter (OR = 2.551, p = 0.010), macrocalcifications (OR = 1.832, p = 0.002), and capsular invasion (OR = 1.977, p = 0.005). The suggested DL model utilized US images achieved an average area under the curve (AUC) of 0.65, slightly outperforming the model that employed traditional clinical factors (AUC = 0.64). Nevertheless, the model that incorporated both of them did not enhance prediction accuracy (AUC = 0.63). Conclusions: The suggested approach offers a reference for the treatment and supervision of PTMC. Among three models used in this study, the deep model relied generally more on image modalities than the data modality of clinic records when making the predictions.