A tiny glimpse into the human brain using model-free analysis for resting-state fMRI data

Resting-state functional Magnetic Resonance Imaging (fMRI) acquires four dimensional data that indirectly depicts human brain activity. Within these four dimensional datasets reside resting-state functional connectivity networks (RFNs), depicting how the human brain is organized functionally. This series of studies delve into the use of data-driven analysis methods for resting-state fMRI data. Their strengths were explored and their weaknesses tackled, both in their methodologies and applications, all in hope to gain a better understanding of the data, and thereby how the brain function. The journey begins through the usage of one of the most common data-driven analysis methods in use today: Independent Component Analysis (ICA). ICA requires no user input parameter apart from the input dataset and the number of output Independent Components (NIC). The requirement of the NIC, a priori, is troubling as the inherent number of Independent Components (ICs) that exists within non-simulated datasets is unknown, due to the existence of various noise and artefact sources to differing degrees. Furthermore, comparing datasets using ICA is problematic because of the inherently different dimensionality of different datasets. To investigate the effects of NIC on the ICA output results, a classification framework based on Support Vector Machines (SVM) was implemented to automatically classify ICs as either potential RFNs, or noise/artefact signal. This feature-optimized classification of ICs with SVM, or FOCIS, framework uses features derived from verbal instructions for manual visual inspection of ICs. With only few significant features selected through iterative feature-selection and a small training set, the classification framework performed well with over 98% in overall accuracy for group ICA output results. Analysis of different resting-state fMRI datasets using FOCIS indicated that the specification of NIC can critically affect the ICA results on restingstate fMRI data. These changes are complex and are individually different from one another, irrespective whether the IC is a potential RFN or artefact/noise signals. Applying this knowledge on group comparison studies, ICA was used to study migraine patients undergo kinetic oscillation stimulation treatment. The immediate effects of the treatment allows direct correlation of a patient’s pain levels with changes in their RFNs. Differences in RFNs that include areas in the midbrain and limbic system regulating the central nervous system were discovered in migraine patients compared to healthy control group. Overlapping areas were also shown to be affected by the treatment. These results provide supporting evidence for the hypothesis that the treatment affects and regulates the parasympathetic autonomic reflex, alleviating migraine symptoms. Hierarchical clustering is another data-driven analysis method that is almost devoid of all userinput parameters. The algorithm naturally stratifies data into a hierarchical structure. It is believed that brain function is hierarchically organized, so an algorithm which reflects this aspect is a seemingly excellent choice to use for analyzing the resting-state fMRI data. A hierarchical clustering analysis framework was developed to extract RFNs from resting-state fMRI data with full brain coverage at voxel level. The RFNs identified using hierarchical clustering conforms to those identified previously using other data processing techniques, such as ICA. An innate ability of the clustering algorithm is to naturally organize data into a hierarchical tree (dendrogram). This was fully utilized though extensions in the framework for cluster evaluation. Extending the hierarchical clustering framework with the cluster evaluation pipeline allowed extraction of functional subdivisions of known RFNs. This demonstrated that not only can hierarchical clustering be used to extract the modular organization at the scale of large systems for entire RFNs, but can also be used to derive the functional subdivision of RFNs and provide a consistent method of analysis at different levels of detail. The subnetworks extracted using hierarchical clustering reveals the intrinsic functional connectivity amongst the subnetworks within RFNs and provide clues for further exploring the potential for currently unknown functional junctions within RFNs

Ultra-compact silicon nitride grating coupler for microscopy systems

Grating couplers have been widely used for coupling light between photonic chips and optical fibers. For various quantum-optics and bio-optics experiments, on the other hand, there is a need to achieve good light coupling between photonic chips and microscopy systems. Here, we propose an ultra-compact silicon nitride (SiN) grating coupler optimized for coupling light from a waveguide to a microscopy system. The grating coupler is about 4 by 2 mu m(2) in size and a 116 nm 1 dB bandwidth can be achieved theoretically. An optimized fabrication process was developed to realize suspended SiN waveguides integrated with these couplers on top of a highly reflective bottom mirror. Experimental results show that up to 53% (2.76 dB loss) of the power of the TE mode can be coupled from a suspended SiN waveguide to a microscopy system with a numerical aperture (NA) = 0.65. Simulations show this efficiency can increase up to 75% (1.25 dB loss) for NA = 0.95

Extreme temperature exposure and urolithiasis: A time series analysis in Ganzhou, China

BackgroundAmbient temperature change is a risk factor for urolithiasis that cannot be ignored. The association between temperature and urolithiasis varies from region to region. Our study aimed to analyze the impact of extremely high and low temperatures on the number of inpatients for urolithiasis and their lag effect in Ganzhou City, China.MethodsWe collected the daily number of inpatients with urolithiasis in Ganzhou from 2018 to 2019 and the meteorological data for the same period. The exposure-response relationship between the daily mean temperature and the number of inpatients with urolithiasis was studied by the distributed lag non-linear model (DLNM). The effect of extreme temperatures was also analyzed. A stratification analysis was performed for different gender and age groups.ResultsThere were 38,184 hospitalizations for urolithiasis from 2018 to 2019 in Ganzhou. The exposure-response curve between the daily mean temperature and the number of inpatients with urolithiasis in Ganzhou was non-linear and had an observed lag effect. The warm effects (30.4°C) were presented at lag 2 and lag 5–lag 9 days, and the cold effects (2.9°C) were presented at lag 8 and lag 3–lag 4 days. The maximum cumulative warm effects were at lag 0–10 days (cumulative relative risk, CRR = 2.379, 95% CI: 1.771, 3.196), and the maximum cumulative cold effects were at lag 0–5 (CRR = 1.182, 95% CI: 1.054, 1.326). Men and people between the ages of 21 and 40 were more susceptible to the extreme temperatures that cause urolithiasis.ConclusionExtreme temperature was correlated with a high risk of urolithiasis hospitalizations, and the warm effects had a longer duration than the cold effects. Preventing urolithiasis and protecting vulnerable people is critical in extreme temperature environments

Electrical properties of yttrium calcium oxyborate crystal annealed at high temperature and low oxygen partial pressure

The yttrium calcium oxyborate crystal (YCa 4 O(BO 3 ) 3 , YCOB) has been actively studied for high-temperature piezoelectric sensing applications. In this work, the stability of electric properties of YCOB crystal annealed in critical conditions (high-temperatures of 900-1100 °C with a low oxygen partial pressure of 4 x 10 −6 atm for 24 h) was investigated and the recovery mechanism for the electrical resisitivity, dielectric permittivity and dielectric loss were studied, taking advantage of the X-ray photoelectron spectra and the first principle calculations. The electrical resistivity of the annealed YCOB crystal was slightly decreased when compared to the pristine counterpart, being (2-5) x 10 7 Ω cm at 850 °C. The dielectric permittivity and dielectric loss were found to increase after annealing, showing recoverable behaviours after thermal treatment above 650 °C in air. The calculated vacancy formation energy indicate that the oxygen vacancy is the dominant defects in YCOB. The formation of oxygen vacancy weakens the chemical bonding strength between B (Ca or Y) and O atoms, introduces extra donor levels in the band gap, which excites the electrons to conduction band more easily thus enhances the electrical conductivity and dielectric loss. The recovered electrical properties are believed to be associated with the reduced vacancy defects at elevated temperatures in air

Genomic and Proteomic Analyses of the Fungus Arthrobotrys oligospora Provide Insights into Nematode-Trap Formation

Nematode-trapping fungi are “carnivorous” and attack their hosts using specialized trapping devices. The morphological development of these traps is the key indicator of their switch from saprophytic to predacious lifestyles. Here, the genome of the nematode-trapping fungus Arthrobotrys oligospora Fres. (ATCC24927) was reported. The genome contains 40.07 Mb assembled sequence with 11,479 predicted genes. Comparative analysis showed that A. oligospora shared many more genes with pathogenic fungi than with non-pathogenic fungi. Specifically, compared to several sequenced ascomycete fungi, the A. oligospora genome has a larger number of pathogenicity-related genes in the subtilisin, cellulase, cellobiohydrolase, and pectinesterase gene families. Searching against the pathogen-host interaction gene database identified 398 homologous genes involved in pathogenicity in other fungi. The analysis of repetitive sequences provided evidence for repeat-induced point mutations in A. oligospora. Proteomic and quantitative PCR (qPCR) analyses revealed that 90 genes were significantly up-regulated at the early stage of trap-formation by nematode extracts and most of these genes were involved in translation, amino acid metabolism, carbohydrate metabolism, cell wall and membrane biogenesis. Based on the combined genomic, proteomic and qPCR data, a model for the formation of nematode trapping device in this fungus was proposed. In this model, multiple fungal signal transduction pathways are activated by its nematode prey to further regulate downstream genes associated with diverse cellular processes such as energy metabolism, biosynthesis of the cell wall and adhesive proteins, cell division, glycerol accumulation and peroxisome biogenesis. This study will facilitate the identification of pathogenicity-related genes and provide a broad foundation for understanding the molecular and evolutionary mechanisms underlying fungi-nematodes interactions

Analysis of whole-brain resting-state FMRI data using hierarchical clustering approach.

BACKGROUND: Previous studies using hierarchical clustering approach to analyze resting-state fMRI data were limited to a few slices or regions-of-interest (ROIs) after substantial data reduction. PURPOSE: To develop a framework that can perform voxel-wise hierarchical clustering of whole-brain resting-state fMRI data from a group of subjects. MATERIALS AND METHODS: Resting-state fMRI measurements were conducted for 86 adult subjects using a single-shot echo-planar imaging (EPI) technique. After pre-processing and co-registration to a standard template, pair-wise cross-correlation coefficients (CC) were calculated for all voxels inside the brain and translated into absolute Pearson's distances after imposing a threshold CC≥0.3. The group averages of the Pearson's distances were then used to perform hierarchical clustering with the developed framework, which entails gray matter masking and an iterative scheme to analyze the dendrogram. RESULTS: With the hierarchical clustering framework, we identified most of the functional connectivity networks reported previously in the literature, such as the motor, sensory, visual, memory, and the default-mode functional networks (DMN). Furthermore, the DMN and visual system were split into their corresponding hierarchical sub-networks. CONCLUSION: It is feasible to use the proposed hierarchical clustering scheme for voxel-wise analysis of whole-brain resting-state fMRI data. The hierarchical clustering result not only confirmed generally the finding in functional connectivity networks identified previously using other data processing techniques, such as ICA, but also revealed directly the hierarchical structure within the functional connectivity networks

A taxonomic study of diatom plants in Yading Nature Reserve of Sichuan

In August 2015, diatom diversity of the Yading Nature Reserve of Sichuan was investigated. Diatom samples were observed using light microscope and scanning electron microscope. Totally, 81 species (including varieties) were identified, belonging to 3 classes and 19 families and 31 genera, of which 2 species were newly recorded from China:Cocconeis placentula var. klinorahis Geitler and Psammothidium daonense (Lange-Bertalot) Lange-Bertalot. For two newly reported species, morphological characters, habits and the distributions were described in detail