Search

Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. The above matrices can be automatically generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity.

Due to several excellent properties, electromagnetic terahertz waves may be very useful for imaging applications, provided sufficiently high-resolution can be achieved. A tightly focused beam can be produced using a two-dimensional metasurface and catenary structure that enables phase-tuning of electromagnetic waves. We modified the catenary structure to produce a metasurface and a non-diffracting 0th-order Bessel beam. According to our calculation, the formed wideband focal spots can be smaller than Abbe’s diffraction limit, between 350 GHz and 450 GHz, with a maximum gain of 15.1 dB. Such a metasurface could open new doors for future high-resolution medical imaging research and technology.

Lead acid batteries are often used, and they produce an abundant amount of lead acid battery waste, which is a source of lead carbonate. Lead oxide was manufactured via thermal decomposition in a vacuum from recycled lead carbonate that was extracted from lead acid battery waste. The decomposed products were characterized by X-ray diffraction and decomposition rate. The impact of the decomposition conditions, including temperature, preservation time, particle size and system pressure, on the lead carbonate decomposition rate were evaluated. The results show that the newly developed lead carbonate vacuum decomposition technique does a good job, and that the decomposition conditions strongly affect the lead carbonate decomposition rate and phase compositions. For instance, at a temperature range of 310–580 °C, a pressure ranging from 10 to 60 Pa, and a preservation time of 30 min, decomposition rates reached as high as 95.77%. At the temperature of 310 °C, when the preservation time increased from 30 min to 180 min, the decomposition rate increased from 29.62% to 78.40%. In terms of the effect of particle size, the mixed particles (≤20 mesh and ≤400mesh) worked best. Furthermore, the lower pressure can explain the lower temperature obtained, and the vacuum system is suited for decomposition, coinciding with a higher decomposition rate.

Aquifers with quasi-linear flow pattern are frequently envisaged in fractured zones, in oil, gas or enhanced geothermal reservoirs, or in civil engineering where cut-off walls are constructed. The water flow towards a well in this linear aquifer system has been long investigated under Darcian flow condition, but remains an open issue for non-Darcian flow. In this study, a general linearization approximation strategy is suggested for the Forchheimer equation, and an analytical solution is proposed by using Laplace transform for non-Darcian flow towards a well in aquifers laterally bounded by no-flow barriers. Numerical simulations using the finite volume method prove that the linearization approximation performs best when it takes the mean of two commonly-used strategies, and the analytical model is sufficiently accurate at late times for observation wells located moderately far from the source. The proposed model was applied to data interpretation of the pumping tests at the Changheba dam foundation bounded by two cut-off walls in Southwest China, where the drawdown curves can be divided into 1D flow, transitional flow and 2D flow stages as a result of lateral flow through weathered bedrocks at late times. The proposed model provides a valuable tool for characterizing the hydraulic properties of aquifers and reservoirs with a linear flow pattern and for assessing the possible leakage through the lateral barriers by type curve matching.

Hexafluoropropylene oxide dimer acid, also known as GenX, is a poly- and perfluoroalkyl substance (PFAS). PFASs are nonvolatile synthetic substances that can be readily disseminated into the environment during processing and use, making them easy to implement in the soil, drinking water, and air. Compared to other PFASs, GenX has a comparatively short carbon chain length and is expected to have a lower tendency to accumulate in humans; therefore, GenX has recently been used as a substitute to other PFASs. However, the mechanisms underlying GenX action and intoxication in humans remains unclear. In this study, the apoptotic capacity of GenX in human liver cells was investigated. When representative human-derived liver cells (HepG2 cells) were treated with GenX for 12 h, cell viability was reduced, and apoptosis was greatly increased. In addition, GenX increased the generation of intracellular reactive oxygen species (ROS), indicating the induction of oxidative stress in a dose-dependent manner. GenX treatment increased the expression of major apoptosis-related genes relative to the untreated control group. This research indicates that GenX causes apoptosis through ROS mediation in HepG2 cells, which may expand our knowledge of the molecular and toxicological mechanisms of GenX., Graphical abstract Image 1, Highlights • Hexafluoropropylene oxide dimer acid (GenX) can be harmful to the liver. • Through the mediation of ROS, GenX causes apoptosis in HepG2 cells. • GenX activates Bax, caspase 3/9, CHOP, and p53 via the apoptosis. • The intrinsic pathway links GenX mode of action to apoptosis-related stimuli., Poly- and perfluoroalkyl substance; Hexafluoropropylene oxide dimer acid; Oxidative stress; Apoptosis; Liver cells.

The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the backbone of the functional network. The integration of the functional network with structural information can help us better understand functional communication in the brain. This paper proposed a method to accurately estimate the brain functional network enriched by the structural network from diffusion magnetic resonance imaging. First, we adopted a simplex regression model with graph-constrained Elastic Net to construct the functional networks enriched by the structural network. Then, we compared the constructed network characteristics of this approach with several state-of-the-art competing functional network models. Furthermore, we evaluated whether the structural enriched functional network model improves the performance for predicting the cognitive-behavioral outcomes. The experiments have been performed on 218 participants from the Human Connectome Project database. The results demonstrated that our network model improves network consistency and its predictive performance compared with several state-of-the-art competing functional network models.

Packer test is a tool that has been long developed and widely used for characterizing the permeability of formation. The flow in the tested formation tends to transition from laminar to non-Darcian regime with increasing flow velocity and hydraulic gradient. But as the non-Darcian effect becomes important, how the borehole inclination and groundwater level influence the interpretation of packer test data remains unclear. In this study, a Forchheimer's law-based analytical model incorporating the effects of groundwater level and borehole inclination was presented by image method and velocity integration, which can be immediately reduced to Zangar's (1953) equation for vertical borehole under laminar flow condition and approximately reduced to Chen's (2015b) model when the test interval is at a moderate depth below the groundwater level. The proposed model was validated by numerical simulations. Parametric and error analyses indicate that the hydraulic conductivity will be overestimated while the non-Darcy coefficient will be underestimated if the effects of groundwater level and borehole inclination are neglected. The proposed model was applied to data interpretation of packer tests performed in fractured sedimentary rocks in Hainan Province, China. The results show that significant non-Darcian flow occurred in the tests. The borehole inclination has little influence on the estimation of hydraulic parameters, but demonstrates the anisotropy and heterogeneity of the rocks.

A rapid gas chromatography-tandem mass spectrometric (GC-MS) method was developed for the analysis of alkaline and neutral volatile metabolites of feces from human and rats. Feces were extracted by 75% methanol solution (V/V). After addition of ammonia solution (pH 10, final concentration of 1%), the supernatant was subjected to GC-MS analysis. The interesting compounds were separated on a CP-Sil 5 (25 m × 0.25 mm × 0.12 μm) capillary column. Helium was employed as a carrier gas at a constant flow rate of 1 mL min−1. Oven temperature was programmed as follows: the initial temperature was set at 50 °C and held for 1 min, then increased at 25 °C min−1 to 150 °C, sustained for 1 min; increased at 20 °C min−1 to 200 °C, held for 2 min; and finally increased at 10 °C min−1 to 270 °C, maintained for 5 min. The MS was operated in the electron impact ionization mode at −70 eV. The injector, ion source and transfer line temperatures were maintained at 220 °C, 230 °C and 280 °C, respectively. Mass data were acquired in full scan mode of m/z 10–600. The solvent delay time was set 3 min. A total of eleven volatile components were identified in the feces samples from human and seven in the feces samples from rats by retrieving the NIST library, comparing with the standards and analyzing the MS data. The method was simple and sensitive, and could be used to detect alkaline and neutral volatile metabolites of feces from human and rats.

Background and Objective Patients with migraine show an increased presence of white matter hyperintensities (WMHs), especially deep WMHs. Segmentation of small, deep WMHs is a critical issue in managing migraine care. Here, we aim to develop a novel approach to segmenting deep WMHs using deep neural networks based on the U-Net. Methods 148 non-elderly subjects with migraine were recruited for this study. Our model consists of two networks: the first identifies potential deep WMH candidates, and the second reduces the false positives within the candidates. The first network for initial segmentation includes four down-sampling layers and four up-sampling layers to sort the candidates. The second network for false positive reduction uses a smaller field-of-view and depth than the first network to increase utilization of local information. Results Our proposed model segments deep WMHs with a high true positive rate of 0.88, a low false discovery rate of 0.13, and F1 score of 0.88 tested with ten-fold cross-validation. Our model was automatic and performed better than existing models based on conventional machine learning. Conclusion We developed a novel segmentation framework tailored for deep WMHs using U-Net. Our algorithm is open-access to promote future research in quantifying deep WMHs and might contribute to the effective management of WMHs in migraineurs.