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Nanorod array and planar green-emission InGaN/GaN multi-quantum well (MQW) LEDs were fabricated by lithography, nano-imprinting, and top–down etching technology. The defect-pinning effect of the nanostructure was found for the first time. The ratio of the bright regions to the global area in the panchromatic CL images of green MQW samples increased from 30% to about 90% after nano-fabrication. The overall luminous performance significantly improved. Throughout temperature-dependent photoluminescence (TDPL) and time-resolved PL (TRPL) measurements, the migration and recombination of carriers in the MQWs of green LEDs were analyzed. It was proved that nanostructures can effectively prevent carriers from being captured by surrounding nonradiative recombination centers. The overall PL integral intensity can be enhanced to above 18 times. A much lower carrier lifetime (decreasing from 91.4 to 40.2 ns) and a higher internal quantum efficiency (IQE) (increasing from 16.9% to 40.7%) were achieved. Some disputes on the defect influence were also discussed and clarified.

BACKGROUND Shift work is associated with reduced performance and efficiency, the current study aimed at investigating whether blue-enriched white light could improve workers' performance. METHODS The study, which adopted a randomized controlled trial, was conducted among 48 simulated shift workers. The participants performed sustained attention task, working memory task, and sleepiness task during night shift work. The data was analyzed using two-way repeated measure ANOVA. RESULTS The results indicated that, compared to conventional light, participants' correct responses of the sustained attention significantly increased when they were exposed to blue-enriched white light, correspondingly, the commission errors and omission errors declined. Furthermore, the blue-enriched white light had a significant effect on the decrease of sleepiness. However, the working memory was not significantly affected. CONCLUSION Exposing to blue-enriched white light can improve sustained attention and reduce sleepiness.

The blue-light hazard (BLH) has raised concerns with the increasing applications of white light-emitting diodes (LEDs). Many researchers believed that the shorter wavelength or more light components generally resulted in more severe retinal damage. In this study, based on the conventional phosphor-coated white LED, we added azure (484 nm), cyan (511 nm), and red (664 nm) light to fabricate the low-hazard light source. The low-hazard light sources and conventional white LED illuminated 68 Sprague-Dawley (SD) rats for 7 days. Before and after light exposure, we measured the retinal function, thickness of retinal layers, and fundus photographs. The expression levels of autophagy-related proteins and the activities of oxidation-related biochemical indicators were also measured to investigate the mechanisms of damaging or protecting the retina. With the same correlated color temperature (CCT), the low-hazard light source results in significantly less damage on the retinal function and photoreceptors, even if it has two times illuminance and blue-light hazard-weighted irradiance ([Formula: see text]) than conventional white LED. The results illustrated that [Formula: see text] proposed by IEC 62471 could not exactly evaluate the light damage on rats' retinas. We also figured out that more light components could result in less light damage, which provided evidence for the photobiomodulation (PBM) and spectral opponency on light damage.

Infancy is a critical period for the human brain development, and brain age is one of the indices for the brain development status associated with neuroimaging data. The difference between the predicted age based on neuroimaging and the chronological age can provide an important early indicator of deviation from the normal developmental trajectory. In this study, we utilize the Graph Convolutional Network (GCN) to predict the infant brain age based on resting-state fMRI data. The brain connectivity obtained from rs-fMRI can be represented as a graph with brain regions as nodes and functional connections as edges. However, since the brain connectivity is a fully connected graph with features on edges, current GCN cannot be directly used for it is a node-based method for sparse graphs. Hence, we propose an edge-based Graph Path Convolution (GPC) method, which aggregates the information from different paths and can be naturally applied on dense graphs. We refer the whole model as Brain Connectivity Graph Convolutional Networks (BC-GCN). Further, two upgraded network structures are proposed by including the residual and attention modules, referred as BC-GCN-Res and BC-GCN-SE to emphasize the information of the original data and enhance influential channels. Moreover, we design a two-stage coarse-to-fine framework, which determines the age group first and then predicts the age using group-specific BC-GCN-SE models. To avoid accumulated errors from the first stage, a cross-group training strategy is adopted for the second stage regression models. We conduct experiments on infant fMRI scans from 6 to 811 days of age. The coarse-to-fine framework shows significant improvements when being applied to several models (reducing error over 10 days). Comparing with state-of-the-art methods, our proposed model BC-GCN-SE with coarse-to-fine framework reduces the mean absolute error of the prediction from70 days to 49.9 days. The code is now available at https://github.com/SCUT-Xinlab/BC-GCN.

Functional organization of the human cerebral cortex is highly constrained by underlying brain structures, but how functional activity is associated with different brain structures during development is not clear, especially at the neonatal stage. Since long-range functional connectivity is far from mature in the dynamically developing neonatal brain, it is of great scientific significance to investigate the relationship between different structural and functional features at the local level. To this end, for the first time, correlation and regression analyses were performed to examine the relationship between cortical morphology, cortical myelination, age, and local brain functional activity, as well as functional connectivity strength using high-resolution structural and resting-state functional MRI data of 177 neonates (29–44 postmenopausal weeks, 98 male and 79 female) from both static and dynamic perspectives. We found that cortical myelination was most strongly associated with local brain functional activity across the cerebral cortex than other cortical structural features while controlling the age effect. These findings suggest the crucial role of cortical myelination in local brain functional development at birth, providing valuable insights into the fundamental biological basis of functional activity at this early developmental stage.

Recently, movie-watching fMRI has been recognized as a novel method to explore brain working patterns. Previous researchers correlated natural stimuli with brain responses to explore brain functional specialization by “reverse correlation” methods, which were based on within-group analysis. However, what external stimuli drove significantly different brain responses in two groups of different subjects were still unknown. To address this, sliding time windows technique combined with inter-Subject functional correlation (ISFC) was proposed to detect movie events with significant group differences between autism spectrum disorder (ASD) and typical development (TD) subjects. Then, using inter-Subject correlation (ISC) and ISFC analysis, we found that in three movie events involving character emotions, the ASD group showed significantly lower ISC in the middle temporal gyrus, temporal pole, cerebellum, caudate, precuneus, and showed decreased functional connectivity between large scale networks than that in TD. Under the movie event focusing on objects and scenes shot, the dorsal and ventral attentional networks of ASD had a strong synchronous response. Meanwhile, ASD also displayed increased functional connectivity between the frontoparietal network (FPN) and dorsal attention network (DAN), FPN, and sensorimotor network (SMN) than TD. ASD has its own unique synchronous response rather than being “unresponsive” in natural movie-watching. Our findings provide a new method and valuable insight for exploring the inconsistency of the brain “tick collectively” to same natural stimuli. This analytic approach has the potential to explore pathological mechanisms and promote training methods of ASD.

In this study, red, green, cyan, warm white and cool white (RGCWW) LEDs are individually controlled to simulate dynamic daylight. The spectral power distributions (SPDs) are measured for both the daylight and the mixed white light. The correlated color temperature (CCT), circadian action factor (CAF) curves in the daytime of the mixed white LED almost overlap to those of daylight with a small error of about 1%. The CIE chromaticity coordinates of the mixed white light are distributed along the Planckian locus with a little deviation below 0.003. Also, its color rendering index (CRI) is above 90. The RGCWW five-chip LEDs of high circadian and visual performances will benefit to synchronize circadian rhythm in closed environment during long time without daylight, combined with better mood and alertness. The simulation errors and development trend of the mixed lighting are discussed.

As a country with the highest CO2 emissions and at the turning point of socio-economic transition, China's effort to reduce CO2 emissions will be crucial for climate change mitigation. Yet, due to geospatial variations of CO2 emissions in different cities, it is important to develop city-specific policies and tools to help control and reduce CO2 emissions. The key question is how to identify and quantify these variations so as to provide reference for the formulation of the corresponding mitigation policies. This paper attempts to answer this question through a case study of 26 cities in the Yangtze River Delta. The CO2 emissions pattern of each city is measured by two statistics: Gini coefficient to describe its quantitative pattern and Global Moran's I index to capture its spatial pattern. It is found that Gini coefficients in all these cities are all greater than 0.94, implying a highly polarized pattern in terms of quantity; and the maximum value for Global Moran's I index is 0.071 with a standard deviation of 0.021, indicating a weak spatial clustering trend but strong difference among these cities. So, it would be more efficient for these cities at current stage to reduce CO2 emissions by focusing on the large emission sources at certain small localities, particularly the very built-up areas rather than covering all the emission sources on every plot of the urban prefectures. And by a combination of these two metrics, the 26 cities are regrouped into nine types with most of them are subject to type HL and ML. These reclassification results then can serve as reference for customizing mitigation policies accordingly and positioning these policies in a more accurate way in each city.

Shift workers are mostly suffered from the disruption of circadian rhythm and health problems. In this study, we designed proper light environment to maintain stable circadian rhythm, cognitive performance, and mood status of shift workers. We used five-channel light-emitting diodes to build up the dynamic daylight-like light environment. The illuminance, correlated color temperature, and circadian action factor of light were tunable in the ranges of 226 to 678 lx, 2680 to 7314 K, and 0.32 to 0.96 throughout the day (5:30 to 19:40). During the nighttime, these parameters maintained about 200 lx, 2700 K, and 0.32, respectively. In this light environment, three subjects had engaged in shift work for 38 consecutive days. We measured plasma melatonin, activity counts, continuous performance tests, and visual analogue scale on mood to assess the rhythm, cognitive performance, and mood of subjects. After 38-day shift work, the subjects’ peak melatonin concentration increased significantly. Their physiological and behavioral rhythms maintained stable. Their cognitive performance improved significantly after night work, compared with that before night work. Their mood status had no significant change during the 38-day shift work. These results indicated that the light environment was beneficial to maintain circadian rhythm, cognitive performance and mood status during long-term shift work in closed environment.

The theory of “flow space” presents a new paradigm for understanding the transformation and reconstruction of urban and regional spatial structure in the Internet age, and the advancement of big data acquisition and mining technology paves a new foundation for robust empirical study of the flow space. Talent flow, as an important element in the flow space between cities, can act as a reflection of the spatial pattern of the regional flow network and the talent attraction capacity of different cities. Yet, the characteristics of the flow space from this perspective have not been well studies in existing literature. By analyzing the flow of graduates in 2015 from universities directly administered by the Ministry of Education of China, this paper aims to understand the talent redistribution pattern of 23 major cities within China and each province’s capacity to supply and attract graduates in this process. Therefore, a set of assessment indexes are proposed and spatial analysis by ArcGIS is applied to the evaluation of the regional development potential of the 23 cities and the overall flow patterns of graduates among all the provinces. It is revealed that the flow space is showing a flattening trend, compared with the early period featured by polarization development. The middle part of China is rising in terms of talent attraction, while the Northeast China is showing signs of contraction as a result of the outflow of talents. Then, it further discusses the underlying causes of these new changes by considering China's current development transformation. The analysis indicates that the comparatively bigger scale of higher education and stronger talent contribution capability, lower living cost, better air quality and rising job opportunities in the gradually emerging high technology industries are the key factors contributing to this pattern change.

In this work, many-body effects including band-gap renormalization (BGR), Coulomb enhancement (CE) and carrier collisions (CS) are considered when the micro light emitting diode (micro-LED) operates in the injection level above 100 kA/cm2. Electroluminescence (EL) spectra of 20 micron-diameter LEDs show the redshift and broadening with current density increasing from 50 to 360 kA/cm2. An abnormal increases of external quantum efficiency (EQE) are observed above 100 kA/cm2. BGR effect combined with thermal effect lead to redshift of EL peak wavelength. Carriers collision dominates spectral width. CE enhances radiative recombination and also results in blue shift of peak wavelength.

The three-dimensional thermal characteristics of micro-light-emitting diodes (μLEDs) on GaN and sapphire substrates were studied with forward-voltage methods, thermal transient measurements, and infrared thermal imaging. The μLEDs on the GaN substrate showed an approximately 10 °C lower junction temperature and smaller amplitude of the K factors than those on the sapphire substrate under the current injection level of 4 kA cm−2. The thermal transient measurement showed that the spreading thermal resistances of the mesa, the GaN epilayer, and the interface of the GaN/substrate were reduced significantly for μLEDs on the GaN substrate because of the high-quality GaN crystal and the homogeneous interfaces. The infrared thermal images showed lower total average junction temperatures and more uniform temperature distributions for the μLEDs on the GaN substrate, which were also simulated with APSYS software. The thermal transport mechanisms are discussed for the lateral and vertical directions in the μLEDs.

The spectral transmittance of ocular media on the short-wavelength light is higher for young observers than elders. It is essential to concentrate on the photobiological safety of children and circadian effect of elders when designing the light sources. In this study, we optimized the peak wavelengths and ratios of four-channel light emitting-diodes (LEDs), to fabricate the dynamic white light which performed well on photobiological safety, circadian effect, and color rendition for observers of different ages. Based on the optimal results, we selected the four monochromatic LEDs of 461.0, 525.0, 589.0, and 660.0 nm, and then fabricated the practical white light. The correlated color temperature (CCT) of the dynamic white light is tunable from 2700 to 6500 K. For 1-year-old observers, the blue light hazard efficiency of radiation (BLHER) is less than 0.237. For 70-year-old observers, the melanopic efficacy of luminous radiation (MELR) reaches 0.987 mW/lm and its tunability is optimized to be 2.10. The general color rendering index (Ra), special color rendering index (R9), color fidelity index (Rf), and color gamut index (Rg) are more than 89.3, 79.1,81.7, and 93.6, respectively. At last, we also discussed the impacts of the peak wavelength variation on the performances of hybrid white light.

Ag nanoparticles (NPs) are fabricated on the cross-section of green emitting quantum wells (QWs). The effect of the dipole polarization orientation on the localized surface plasmon (LSP)-QW coupling can be studied by setting the incident direction of the electron beam parallel to the plane of the QWs. Cathodoluminescence (CL) measurements on the QWs show that the intensity with the Ag NPs is enhanced 6.1 times compared with that without the Ag NPs. Total energy loss profiles for an electron beam in the GaN and Ag NP are accurately simulated using a Monte Carlo program (CASINO). The orientations of the in-plane dipoles in the QWs can vary from 0° to 360°. Through a two-step simulation process using the three-dimensional (3D) finite difference time domain (FDTD) method, the weighted average of CL intensities are simulated for QWs with the Ag NPs. The simulation results agree well with the experimental results. Lastly, the dipole orientation dependent LSP-QW coupling process is discussed.

The discovery of ionic current rectification (ICR) phenomena in synthetic nanofluidic systems elicits broad interest from interdisciplinary fields of chemistry, physics, materials science, and nanotechnology; and thus, boosts their applications in, for example, chemical sensing, fluidic pumping, and energy related aspects. So far, it is generally accepted that the ICR effect stems from the broken symmetry either in the nanofluidic structures, or in the environmental conditions. Although this empirical regularity is supported by numerous experimental and theoretical results, great challenge still remains to precisely figure out the correlation between the asymmetric ion transport properties and the degree of symmetry breaking. An appropriate and quantified measure is therefore highly demanded. Herein, taking DNA-stuffed nanopores as a model system, we systematically investigate the evolution of dynamic ICR in between two symmetric states. The fully stuffed and fully opened nanopores are symmetric; therefore, they exhibit linear ion transport behaviors. Once the stuffed DNA superstructures are asymmetrically removed from one end of the nanopore via aptamer-target interaction, the nanofluidic system becomes asymmetric and starts to rectify ionic current. The peak of ICR is found right before the breakthrough of the stuffed DNA forest. After that, the nanofluidic system gradually retrieves symmetry, and becomes non-rectified. Theoretical results by both the coarse-grained Poisson-Nernst-Planck model and the 1D statistic model excellently support the experimental observations, and further establish a quantified correlation between the ICR effect and the degree of asymmetry for different molecular filling configurations. Based on the ICR properties, we develop a proof-of-concept demonstration for sensing ATP, termed the ATP balance. These findings help to clarify the origin of ICR, and show implications to other asymmetric transport phenomena for future innovative nanofluidic devices and materials.

Humans can undergo circadian disruption and misalignment when living in closed environments without sufficient daylight. Therefore, it is of great significance to investigate the effects of artificial light on the circadian rhythm. In this work, the red, green, blue, warm white, and cool white (RGBWW) five-channel light-emitting diodes (LEDs) were fabricated as the only light sources in the closed environment. The LED mixed lighting showed a high color rendering index (CRI) all the time. During the day, the light simulated the daylight and increased the tunability of the circadian action factor (CAF) and correlated color temperature (CCT). At night, it maintained low CAF and CCT. Three subjects did irregular shift work in the closed environment for 38 days. Their plasma melatonin and daily activity were measured to assess the circadian rhythm. After 38 days, the subjects' peak melatonin times did not shift significantly (p = 0.676), while their peak melatonin concentrations increased apparently (p = 0.005). The start times of the least active 5-h period (L5) in one day fluctuated in a small range. The standard deviation (SD) was15.11 min in most times. These results demonstrated that the subjects' rhythms maintained stable and were enhanced. The periods of circular cross-correlation between activity and CAF oscillated around 24 h (SD = 15.4 min), indicating the entrainment of light on the stable 24-h rhythm. It was concluded that the daylight-like LED lighting effectively entrained and enhanced the circadian rhythm in the closed environment.

The human brain atlas assists us to enhance our scientific understanding of brain structure and function. The typical anatomical atlases are mainly based on brain morphometry which cannot ensure the consistency of structure and function, and are also hard to cover individual functional differences especially in cerebral cortex. Thus, in recent years, functional atlases for individuals have captured great attention, since they are essential not only for identifying the unique functional organization of individual brains, but also to explore individual variations in behaviors. In this study, a novel approach was proposed to accurately parcellate the whole cerebral cortex at the individual level using resting-state functional magnetic resonance image (rs-fMRI). To examine the functional homogeneity in parcellation, a new evaluation criterion, similarity of cluster (SC) coefficient, was proposed. The parcellation results demonstrated the high consistency between two resting-state sessions (Dice >0.72). The most consistent parcellation appeared in the frontal cortex and the least consistent parcellation appeared in the occipital cortex. The functional homogeneity of subregions was high in frontal cortex and insula whereas low in precentral gyrus. According to SC value, the optimal clustering number was about 1600 per hemisphere. Identification accuracy was 100% between two rs-fMRI sessions, and it was also above 0.97 for rest-task and task-task sessions.

In this work, different sizes of micro-light-emitting diodes (μLEDs) were fabricated on the sapphire and GaN substrates. The thermal characteristics of μLEDs were studied by the forward voltage method, thermal transient measurement, and infrared (IR) thermal imaging. The μLEDs on the GaN substrate showed an approximately 10°C lower junction temperature and smaller amplitude of the K factors than those on the sapphire substrate under the current injection level of 4 kA/cm2. IR thermal imaging results showed the uniform temperature distributed on the GaN substrate. The thermal transient measurement showed that the thermal resistances of the mesa, epilayer, and the interface of GaN/substrate were reduced significantly for μLEDs on the GaN substrate. This means that a high-quality GaN crystal and homogeneous interface corresponded to little scattering for phonons. The APSYS simulation indicated that the high thermal and electrical conductivity of the GaN substrate played a key role in the low junction temperature and uniform temperature distribution. A small-sized μLED combined with a GaN substrate can become a perfect candidate for high-power applications and visible light communication.

Blue light hazard has been the main concern of photobiological safety of light emitting diode (LED) and blue light hazard efficiency of radiation (BLHER) is used to evaluate the extent of blue light hazard. In this work, we used four monochromatic LEDs with the color of purple, azure, green, and red to mix light without blue LED, in order to reduce the BLHER of hybrid white light. Also, the mixed white light kept high level of color fidelity when the correlated color temperature (CCT) ranges from 2700 to 7000 K. Color rendering index (CRI), color fidelity score (Rf), and color gamut score (Rg) are used to evaluate the color fidelity. We simulated the spectrum and parameters of hybrid white light based on different peak wavelengths, full width at half maxima (FWHMs), and relative ratios of four LEDs. Finally, we fabricated a four-channel LED to obtain tunable white light with high level of photobiological safety and good color fidelity, which BLHER is 0.185 at 7110 K, CRI, Rf and Rg are larger than 85.0, 78.7 and 87.4, respectively.

Micro-LEDs can work under an extremely high injection level and are widely used in high-brightness micro-displays and visible light communication. With the increase of carrier concentration, many-body effects gradually become important factors affecting devices’ characteristics. Considering the effects of carrier scattering, bandgap renormalization, and Coulomb enhancement (CE), changes in the electroluminescence spectra of micro-LEDs are analyzed as the current density increases from 49.2 to 358.2 kA/cm2, the latter representing an ultra-high injection level. Affected by plasma screening, CE decreases below about 150 kA/cm2. After that, polarization screening dominates and effectively alleviates the spatial separation of electrons and holes, which results in CE increases to the maximum injection level of 358.2 kA/cm2. It is established that CE promotes radiative recombination processes. Different from the traditional phenomenon of “efficiency droop”, the enhanced attraction between carriers leads to an abnormal increase of external quantum efficiency at high current density.

Referring to the standard of International Electrotechnical Commission (IEC), purple and azure light emitting diodes (LEDs) are used to obtain the blue vision and avoid the blue light hazard (BLH). In order to get the high color fidelity for mixed white LED, the other monochromatic LEDs are chosen according to the CIE 1931 chromaticity diagram and Grassmann Color Law of additive color mixture. Using the mathematical model of monochromatic LEDs and restrictions of chromaticity coordinates on Planckian blackbody locus, there are only 2 independent variables in the simulation to a certain combination of peak wavelengths. The optimized peak wavelengths of the five monochromatic LEDs are 385, 467, 524, 564, and 632 nm. The blue light hazard efficiency of radiation (BLHER) of the practical mixed white light is always less than 0.06 when its correlated color temperature (CCT) changes from 2500 to 7000 K. The BLHER is as low as the incandescent lamp at 2700 K and is much lower than other conventional lighting sources. The measured color rendering index (CRI), color fidelity score (Rf), and color gamut score (Rg) of the mixed white light are more than 84.9, 80.4, and 98.1, respectively. The circadian action factor (CAF) is measured as from 0.23 to 0.89 in the above CCT ranges. At last, the limitations of this work and some potential solutions are discussed.

Ag nanoparticles (NPs) are filled in a photonic crystal (PhC) hole array on green light emitting diodes (LEDs). The localized surface plasmon (LSP)&ndash, quantum well (QW) coupling effect is studied by measuring the cathodoluminescence (CL) spectra impinging at the specific spots on the Ag NPs. Twenty-six percent and fifty-two percent enhancements of the CL intensities are obtained at the center and edge of the Ag NP, respectively, compared to the result that the electron-beam (e-beam) excites the QW directly. To illustrate the coupling process of the three-body system of e-beam&ndash, LSP&ndash, QW, a perturbation theory combining a three-dimensional (3D) finite difference time domain (FDTD) simulation is put forward. The effects of the polarization orientation of the dipole and the field symmetry of the LSP on the LSP&ndash, QW coupling are also discussed.

Background Psychological investigations and functional imaging technology have been used to describe neural correlations of different types of memory with various stimuli. Memory with limited storage capacity and a short retention time can be classified as short-term memory (STM) while long-term memory (LTM) can be life-long without defined capacity. Methods To identify brain activation pattern associated with different modes of memory for numerical figures, we detected brain activities from twenty-two healthy subjects when performing three types of memory tasks for numbers, namely STM, LTM and working memory (WM), by using functional magnetic resonance imaging (fMRI) technique. Results The result revealed variable patterns of activation in different brain regions responding to different types of memory tasks. The activation regions with primary processing and transient maintenance of STM for numerical figures are located in the visual cortex and mainly encoded by visual representations, while LTM was encoded by semantics and mainly recruiting left frontal cortex. We also found that subcortical structures, such as the caudate nucleus and the marginal division of the striatum, plays important roles in working memory. Conclusions Activation of different brain regions in these three kinds of memories, indicating that different kinds of memories rely on different neural correlates and mental processes.

Movie-watching fMRI has been regarded as a novel method to explore the functional brain activity by displaying rich and continuous stimulus. To detect the inter-subject synchrony alteration of brain activity in patients with attention deficit hyperactivity disorder (ADHD), the MRI data of 30 ADHD patients and 30 typically developing (TD) subjects were used in the study, including T1 structural images and functional images (watching the animated film, The present). Two movie clips with significant inter-group difference in state consistency (SC) were extracted from the original movie to explore the effect of the different stimulus on the synchrony alteration of functional brain activity. In the three conditions (entire movie, movie clip 1 and movie clip 2), the inter-subject correlation (ISC) and inter-subject functional correlation (ISFC) of each group were calculated and then compared. The results showed: (a) in the three conditions, the ISC of occipital cortex in ADHD was significantly greater than that in TD; (b) ADHD exhibited decreased ISC in the left postcentral gyrus and right orbital frontal cortex during movie clip 1 and decreased ISC in the inferior frontal gyrus during movie clip 2 compared with TD; (c) the significant inter-group ISFC differences were observed in the regions involved in attention, emotion and cognitive control. This study proves the effectiveness of movie-watching fMRI in exploring the inter-subject synchronization of functional brain activity and promotes the exploration of the neural mechanism of ADHD.

As a country with the highest CO

The human brain is always in the process of constantly changing. Given that the conventional measurements of regional brain activity are less sensitive to changes over time, three dynamic measurements were proposed to capture the temporal variability of regional brain activity. In this study, dynamic amplitude of low frequency fluctuation (d-ALFF), dynamic fractional amplitude of low frequency fluctuation (d-fALFF) and dynamic regional homogeneity (d-ReHo) were obtained from resting-state functional magnetic resonance imaging (rs-fMRI) of both 238 ADHD and 239 typical developing (TD) subjects. Then, they were applied to detecting the regional activity differences between ADHD and TD group. Compared with the conventional measurements (ALFF, fALFF and ReHo), the dynamic measurements were more sensitive in exploring the differences of regional brain activity between ADHD and TD group. The three new measurements not only enrich the diversity of methods for investigating the dynamic variation of regional brain activity, but also emphasize the significance of detecting the temporal variability of regional brain activity.

Quantitative measurement of the dynamic longitudinal cortex development during early postnatal stages is of great importance to understand the early cortical structural and functional development. Conventional methods usually reconstruct the cortical surfaces of longitudinal images from the same subject independently, which often generate longitudinally-inconsistent cortical surfaces and thus lead to inaccurate measurement of cortical changes, especially for vertex-wise mapping of cortical development. This paper aims to address this problem by presenting a method to reconstruct temporally-consistent cortical surfaces from longitudinal infant brain MR images, for accurate and consistent measurement of the dynamic cortex development in infants. Specifically, the longitudinal development of the inner cortical surface is first modeled by a deformable growth sheet with elasto-plasticity property to establish longitudinally smooth correspondences of the inner cortical surfaces. Then, the modeled longitudinal inner cortical surfaces are jointly deformed to locate both inner and outer cortical surfaces with a spatial–temporal deformable surface method. The method has been applied to 13 healthy infants, each with 6 serial MR scans acquired at 2 weeks, 3 months, 6 months, 9 months, 12 months and 18 months of age. Experimental results showed that our method with the incorporated longitudinal constraints can reconstruct the longitudinally-dynamic cortical surfaces from serial infant MR images more consistently and accurately than the previously published methods. By using our method, for the first time, we can characterize the vertex-wise longitudinal cortical thickness development trajectory at multiple time points in the first 18 months of life. Specifically, we found the highly age-related and regionally-heterogeneous developmental trajectories of the cortical thickness during this period, with the cortical thickness increased most from 3 to 6 months (16.2%) and least from 9 to 12 months (less than 0.1%). Specifically, the central sulcus only underwent significant increase of cortical thickness from 6 to 9 months and the occipital cortex underwent significant increase from 0 to 9 months, while the frontal, temporal and parietal cortices grew continuously in this first 18 months of life. The adult-like spatial patterns of cortical thickness were generally present at 18 months of age. These results provided detailed insights into the dynamic trajectory of the cortical thickness development in infants.

Near-ultraviolet micro-LEDs with different diameters were fabricated on GaN substrates. The electroluminescence and the light output power–current density and current density–voltage relationships were measured. A saturated current density of 358 kA/cm2 was achieved with a 20 µm LED. The ideality factor curves showed steps and peaks when the injection current density was increased from 20 to 150 kA/cm2 and an abnormal efficiency increase. The transport and recombination processes of micro-LEDs at high injection current densities were simulated, and the many-body effect and phase space filling in the integrated quantum drift-diffusion model were considered. Serious current crowding was observed above 100 kA/cm2, even for the 20 µm LED.

Quantitatively characterizing the development of cortical anatomical networks during the early stage of life plays an important role in revealing the relationship between cortical structural connection and high-level functional development. The development of correlation networks of cortical-thickness, cortical folding, and fiber-density is systematically analyzed in this article to study the relationship between different anatomical properties during the first 2 years of life. Specifically, longitudinal MR images of 73 healthy subjects from birth to 2 year old are used. For each subject at each time point, its measures of cortical thickness, cortical folding, and fiber density are projected to its cortical surface that has been partitioned into 78 cortical regions. Then, the correlation matrices for cortical thickness, cortical folding, and fiber density at each time point can be constructed, respectively, by computing the inter-regional Pearson correlation coefficient (of any pair of ROIs) across all 73 subjects. Finally, the presence/ absence pattern (i.e., binary pattern) of the connection network is constructed from each inter-regional correlation matrix, and its statistical and anatomical properties are adopted to analyze the longitudinal development of anatomical networks. The results show that the development of anatomical network could be characterized differently by using different anatomical properties (i.e., using cortical thickness, cortical folding, or fiber density).

Background and Purpose— Alterations of cerebral glucose metabolism are well anticipated during cerebral ischemia. However, detailed spatiotemporal characteristics of disturbed cerebral glucose metabolism during acute ischemia remain largely elusive. This study aims to delineate spatiotemporal distributions of [ 18 ]F-2-fluoro-2-deoxy- d -glucose (FDG) uptake using positron emission tomography imaging, particularly at the peri-ischemic zone, and its correlation with tissue outcome. Methods— The intraluminal suture middle cerebral artery occlusion model was used to induce focal cerebral ischemia in rats (n=48). All animals underwent sequential MRI and FDG positron emission tomography imaging at different times (30–150 minutes) after middle cerebral artery occlusion. MR and positron emission tomography images were coregistered. FDG uptake in the peri-ischemic zone was assessed in relation to middle cerebral artery occlusion duration, cerebral blood flow, apparent diffusion coefficient, and 24-hour T2 lesions. Results— Elevated FDG uptake was consistently observed at the peri-ischemic zone surrounding the presumed ischemic core with low FDG uptake. Both the spatial volume and the uptake level of the hyper-uptake region were inversely correlated with the duration of middle cerebral artery occlusion. The hyper-uptake regions exhibited a mild reduction of cerebral blood flow (28.2±3.2%) and apparent diffusion coefficient (9.1±1.4%) when compared with that in the contralateral hemisphere. Colocalization analysis revealed that, with reperfusion, an average of 12.1±1.7% of the hyper-uptake volume was recruited into final infarction. Conclusions— Elevated FDG uptake at the peri-ischemic zone is consistently observed during acute cerebral ischemia. The region with elevated FDG uptake likely reflects viable tissues that can be salvaged with reperfusion. Therefore, acute FDG positron emission tomography imaging might hold promise in the management of patients with acute stroke.

The prevalence rate of ADHD varies from age to age. To better understand the development of ADHD from childhood to adolescence, different age groups of ADHD from large dataset are needed to explore the development pattern of brain activities. In this study, amplitude of low frequency fluctuation (ALFF), fractional amplitude of low frequency fluctuation (fALFF) and regional homogeneity (ReHo) were extracted from resting-state functional magnetic resonance imaging (rs-fMRI) of both ADHD subjects and typical developing (TD) subjects from 7 to 16 years old. The result showed that the different areas mainly appear at the bilateral superior frontal cortex, anterior cingulate cortex (ACC), precentral gyrus, right superior occipital lobe, cerebellum and parts of basal ganglia between all ADHD subjects and all TD subjects. Besides, compared with TD, there were different brain activity patterns at different ages in ADHD, which appear at the left ACC and left occipital lobe. The result can inspire more studies on comparisons between functional connectivity methods.

The geometric complexity and variability of the human cerebral cortex has long intrigued the scientific community. As a result, quantitative description of cortical folding patterns and the understanding of underlying folding mechanisms have emerged as important research goals. This paper presents a computational 3-dimensional geometric model of cerebral cortex folding initialized by MRI data of a human fetal brain and deformed under the governance of a partial differential equation modeling cortical growth. By applying different simulation parameters, our model is able to generate folding convolutions and shape dynamics of the cerebral cortex. The simulations of this 3D geometric model provide computational experimental support to the following hypotheses: 1) Mechanical constraints of the skull regulate the cortical folding process. 2) The cortical folding pattern is dependent on the global cell growth rate of the whole cortex. 3) The cortical folding pattern is dependent on relative rates of cell growth in different cortical areas. 4) The cortical folding pattern is dependent on the initial geometry of the cortex.

A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.

Reliable cell nuclei segmentation is an important yet unresolved problem in biological imaging studies. This paper presents a novel computerized method for robust cell nuclei segmentation based on gradient flow tracking. This method is composed of three key steps: (1) generate a diffused gradient vector flow field; (2) perform a gradient flow tracking procedure to attract points to the basin of a sink; and (3) separate the image into small regions, each containing one nucleus and nearby peripheral background, and perform local adaptive thresholding in each small region to extract the cell nucleus from the background. To show the generality of the proposed method, we report the validation and experimental results using microscopic image data sets from three research labs, with both over-segmentation and under-segmentation rates below 3%. In particular, this method is able to segment closely juxtaposed or clustered cell nuclei, with high sensitivity and specificity in different situations.

Reconstruction of the central surface representation of the cerebral cortex is an important means to study the structure and function of the human brain. In this paper, we propose a novel method based on an elastic transform vector field to drive a deformable model for the reconstruction of the central cortical surface. Both simulated brain cortexes and real brain images are used to evaluate this approach. We applied the surface reconstruction method and a hybrid volumetric and surface registration algorithm to detect simulated brain atrophy. Experimental results show that the central cortical surface representation has better performance in detecting simulated atrophy than the traditionally used inner or outer cortical surface representations.

Quantification of cells is a critical step towards the assessment of cell fate in neurological disease or developmental models. Here, we present a novel cell detection method for the automatic quantification of zebrafish neuronal cells, including primary motor neurons, Rohon-Beard neurons, and retinal cells. Our method consists of four steps. First, a diffused gradient vector field is produced. Subsequently, the orientations and magnitude information of diffused gradients are accumulated, and a response image is computed. In the third step, we perform non-maximum suppression on the response image and identify the detection candidates. In the fourth and final step the detected objects are grouped into clusters based on their color information. Using five different datasets depicting zebrafish cells, we show that our method consistently displays high sensitivity and specificity of over 95%. Our results demonstrate the general applicability of this method to different data samples, including nuclear staining, immunohistochemistry, and cell death detection.

The zebrafish has become an important vertebrate animal model for the study of developmental biology, functional genomics, and disease mechanisms. It is also being used for drug discovery. Computerized detection of blob objects has been one of the important tasks in quantitative phenotyping of zebrafish. We present a new automated method that is able to detect blob objects, such as nuclei or cells in microscopic zebrafish images. This method is composed of three key steps. The first step is to produce a diffused gradient vector field by a physical elastic deformable model. In the second step, the flux image is computed on the diffused gradient vector field. The third step performs thresholding and nonmaximum suppression based on the flux image. We report the validation and experimental results of this method using zebrafish image datasets from three independent research labs. Both sensitivity and specificity of this method are over 90%. This method is able to differentiate closely juxtaposed or connected blob objects, with high sensitivity and specificity in different situations. It is characterized by a good, consistent performance in blob object detection.

Accurate and consistent skull stripping of serial brain MR images is of great importance in longitudinal studies that aim to detect subtle brain morphological changes. To avoid inconsistency and the potential bias introduced by independently performing skull-stripping for each time-point image, we propose an effective method that is capable of skull-stripping serial brain MR images simultaneously. Specifically, all serial images of the same subject are first affine aligned in a groupwise manner to a common space to avoid any potential bias introduced by asymmetric transforms. A brain probability map, which encapsulates prior information gathered from a population of real brain MR images, is then warped to the aligned serial images for guiding skull-stripping via a deformable surface method. In particular, the same initial surface meshes representing the initial brain surfaces are first placed on all aligned serial images, and then all these surface meshes are simultaneously evolved to the respective target brain boundaries, driven by the intensity-based force, the force from the probability map, as well as the force from the spatial and temporal smoothness. Especially, imposing the temporal smoothness helps achieve longitudinally consistent results. Evaluations on 20 subjects, each with 4 time points, from the ADNI database indicate that our method gives more accurate and consistent result compared with 3D skull-stripping method. To better show the advantages of our 4D brain extraction method over the 3D method, we compute the Dice ratio in a ring area (±5mm) surrounding the ground-truth brain boundary, and our 4D method achieves around 3% improvement over the 3D method. In addition, our 4D method also gives smaller mean and maximal surface-to-surface distance measurements, with reduced variances.

Quantitative measurement of dynamic cortex development during early postnatal stages is of great importance to understand early cortical structural and functional development. Conventional methods usually independently reconstruct cortical surfaces of longitudinal images from the same infant, which often generates longitudinallyinconsistent cortical surfaces and leads to inconsistence in cortex development measurement. This paper aims to address this problem by presenting a method to reconstruct consistent cortical surfaces from longitudinal brain MR images in the first-year infants for accurate and consistent measurement of dynamic cortex development. Specifically, longitudinal development of the inner cortical surface is first modeled by a deformable sheet with elasto-plasticity property to establish longitudinally smooth correspondences of inner cortical surfaces. Then, the modeled longitudinal inner cortical surfaces are jointly deformed to locate inner and outer cortical surfaces with a spatial-temporal deformable surface. The method has been applied on 10 infants, each with 5 or 6 scans acquired at every 3 months from birth. Experimental results show that our method can accurately and consistently reconstruct dynamic cortical surfaces from longitudinal infant images, with the average surface distance as low as 0.2mm. By using our method, we can quantitatively characterize longitudinally dynamic cortical thickness development in the first-year infants.

Diffusion tensor imaging (DTI) could provide convenient and crucial insights into the underlying age-related biological maturation of human brains, including myelination, axonal density changes, fiber tract reorganization, and synaptic pruning processes. Fractional anisotropy (FA) derived from DTI has been commonly used to characterize cellular morphological changes associated with the development of human brain, due to its sensitivity to microstructural changes. In this paper, we aim to discern the longitudinal neurodevelopmental patterns in typically maturing human brains using 200 healthy subjects from 5 to 22 years of age, based on the FA in cortical gray matter (GM). Specifically, FA image is first aligned with the corresponding T1 image, which has been parcellated into different cortical ROIs, and then the average FA in each ROI is computed. Linear mixed model is used to analyze the FA developmental pattern in each cortical ROI. The developmental trajectory of FA in each ROI across ages is delineated, and the best-fitting models of age-related changes in FA were linear for all ROIs. FA generally increases with the age from 5 to 22 years of age. In addition, males and females follow the similar pattern, with the FA of females being generally lower than that of males in most ROIs. This provides us some insights into the microstructural changes in the longitudinal cerebral cortex development.

The in vitro and in vivo characterization of an optimized formulation of nanoparticles (NPs) loaded with a high content of dexamethasone palmitate (DEX-P), a chemotherapeutic adjuvant that decreases interstitial fluid pressure in tumors, and (111) In, a signaling agent, is described. These NPs are uniform in size and composition. Single photon emission computed tomography imaging demonstrates significant tumor uptake of (111) In-labeled DEX-P NPs in tumor-bearing mice. As with many nanoparticle-based drug delivery systems, significant liver accumulation is observed. Assessment of liver histology and blood tests show no apparent hepatic or renal toxicity of the DEX-P NPs. Conversion of DEX-P to DEX occurs when DEX-P NPs are incubated with mouse plasma, human tumor homogenate and ascites from tumor bearing mice, but not with human plasma. This conversion is slower in plasma from Es1(e) ((-/-)) /SCID mice, a potential alternative animal model that better mimics humans; however, plasma from these mice are not completely devoid of esterase activity. The difference between blood and tumor esterase activity in humans facilitates the delivery of DEX-P NPs to tumors and the release of dexamethasone by an esterase trigger.

Many problems in computer vision and biomedical image analysis benefit from representing an image as a set of superpixels or supervoxels. Inspired by this, we propose to partition a cortical surface into a collection of small patches, namely supervertices, with quasi-uniform size and coverage, compactness, and also smooth boundaries that align sulcal fundi or gyral crest curves on cortical surfaces. The ultimate goal of supervertices partition of the cortical surfaces is to use supervertices as primitives for cortical surface analysis, such as the extraction of sulcal fundi or gyral crest curves by linking boundaries of supervertices, and also the parcellation of cortical surfaces by labeling supervertices instead of vertices. We formulate the supervertices partition as an energy minimization problem and optimize it with graph cuts. Specifically, our energy function encourages the supervertices with compact shapes and smooth boundaries at flat cortical regions and also the supervertices with boundaries aligned with the sulcal fundi or gyral crest curves at highly bended cortical regions. The method has been successfully applied to cortical surfaces of brain MR images in NAMIC and MSC datasets. Both qualitative and quantitative evaluation results demonstrate its validity. We also show an application, i.e., extraction of gyral crest curves on cortical surfaces by linking boundaries of supervertices.

Convoluted cortical folding and neuronal wiring are 2 prominent attributes of the mammalian brain. However, the macroscale intrinsic relationship between these 2 general cross-species attributes, as well as the underlying principles that sculpt the architecture of the cerebral cortex, remains unclear. Here, we show that the axonal fibers connected to gyri are significantly denser than those connected to sulci. In human, chimpanzee, and macaque brains, a dominant fraction of axonal fibers were found to be connected to the gyri. This finding has been replicated in a range of mammalian brains via diffusion tensor imaging and high–angular resolution diffusion imaging. These results may have shed some lights on fundamental mechanisms for development and organization of the cerebral cortex, suggesting that axonal pushing is a mechanism of cortical folding.

Skull-stripping refers to the separation of brain tissue from non-brain tissue, such as the scalp, skull, and dura. In large-scale studies involving a significant number of subjects, a fully automatic method is highly desirable, since manual skull-stripping requires tremendous human effort and can be inconsistent even after sufficient training. We propose in this paper a robust and effective method that is capable of skull-stripping a large number of images accurately with minimal dependence on the parameter setting. The key of our method involves an initial skull-stripping by co-registration of an atlas, followed by a refinement phase with a surface deformation scheme that is guided by prior information obtained from a set of real brain images. Evaluation based on a total of 831 images, consisting of normal controls (NC) and patients with mild cognitive impairment (MCI) or Alzheimer's Disease (AD), from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database indicates that our method performs favorably at a consistent overall overlap rate of approximately 98% when compared with expert results. The software package will be made available to the public to facilitate neuroimaging studies.

Skull-stripping refers to the separation of brain tissue from nonbrain tissue, such as the scalp, skull, and dura. In large-scale studies involving a significant number of subjects, a fully automatic method is highly desirable, since manual skull-stripping requires tremendous human effort and can be inconsistent even after sufficient training. We propose in this paper a robust and effective method that is capable of skull-stripping a large number of images accurately with minimal dependence on the parameter setting. The key of our method involves an initial skull-stripping by co-registration of an atlas, followed by a refinement phase with a surface deformation scheme that is guided by prior information obtained from a set of real brain images. Evaluation based on a total of 831 images, consisting of normal controls (NC) and patients with mild cognitive impairment (MCI) or Alzheimer's Disease (AD), from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database indicates that our method performs favorably at a consistent overall overlap rate of approximately 98% when compared with expert results. The software package will be made available to the public to facilitate neuroimaging studies.