High Index Sensitivity Research Articles

Real-time ultrasound sensing with a mode-optimized photonic crystal slab.

Integrated photonic sensors can provide large scale, flexible detection schemes. Photonic crystal slabs (PCSs) offer a miniaturized platform for wideband, sensitive ultrasound detection by exploiting the photoelastic effect in water. However, poor modal overlap with the sensing medium and non-negligible absorption loss of the aqueous medium have previously limited PCS sensor performance. In this study, we detail the development and optimization of a PCS-based acoustic sensor by adding to it a low-loss high-index polymer cladding layer. Exploiting a mode-optimized TM-like optical resonance present in a PCS, with high bulk index sensitivity (>600nm/RIU) and quality factor Q (>8000), we demonstrate real-time ultrasound sensing at a noise equivalent pressure of 170 Pa (1.9Pa/Hz). The PCS sensor is backside-coupled to an optical fiber, which, along with its intensity-based ultrasound-sensing architecture, will allow us to scale up easily to a 2D array. This work paves the way to a sensitive compact ultrasound detector for photoacoustic-based diagnostics and monitoring of tissue.

Potential-Scanning Sensing for Refractive Index Using an Indium Tin Oxide (ITO)-Coated Long-Period Fiber Grating (LPFG)

Here is presented a potential-scanning sensing technique for refractive index detection using a long-period fiber grating (LPFG) coated with indium tin oxide (ITO). The LPFG was produced by an electric arc-induced technique. The sensing is achieved by controlling the electron density of the ITO electrode by potential scanning. The attenuation peak of the LPFG in the transmittance spectra is shifted by the application of a potential. Therefore, a transmittance–potential spectrum with an attenuation peak is obtained during potential scanning, and the refractive index near the sensor can be determined from the peak potential at attenuation. The ITO-LPFG was successfully employed to experimentally perform potential-scanning for refractive index detection in sucrose solutions with concentrations between 0% and 33%. A sensor resolution of 0.0063 ΔRI and a sensitivity of 9.9 V/RIU were obtained using this approach. These values were improved using wavelengths that were experimentally shown to provide high sensitivity and refractive index linearity. A minimum sensor resolution of 0.0017 ΔRI and a maximum sensitivity of 26.7 V/RIU were obtained at 1136 nm. The measurements determined from the peak potential in this work were unaffected by variations in the initial light intensity for individual samples. In addition, this sensing approach can be employed in the near-infrared region and is capable of real-time and remote monitoring.

Enhanced creep resistance of Ti30Al25Zr25Nb20 high-entropy alloy at room temperature

Several previous studies have shown that the dissolution of Al in a solid solution single phase BCC is high unfavorable in TiZrNb system. In this work, a novel Ti30Al25Zr25Nb20 high-entropy alloy (HEA) with a single body-centered cubic (bcc) structure was developed. The Young's modulus and nano-hardness of this HEA measured by continuous stiffness measurement (CSM) technique were 158.2 ± 5.2 GPa and 8.9 ± 0.2 GPa, respectively. The creep behaviors of this HEA at a constant temperature of 20 °C under different loading rates and peak loads were studied by nano-indentation technique. The result indicated that with the increase of loading rates and peak loads, the creep displacement increased, but the creep strain rate sensitive index first decreased and then increased, and the physical mechanisms are interrupted by introducing dislocation activation volume. The reason might attribute to the hysteresis diffusion effect. Compared with other metallic structural materials, Ti30Al25Zr25Nb20 HEA possesses a relatively high dislocation activation volume and low creep strain rate sensitive index value, indicating a significantly high creep resistance.

Integrated assessment of human vulnerability to extreme climate hazards: emerging outcomes for adaptation finance allocation in Southwest Nigeria

ABSTRACT Efficient allocation of adaptation finance resources hinges on precise indices of human climate vulnerability measurement for targeting the most vulnerable and prioritized institutional development pathways. Climate vulnerability numerical index has the potential to identify vulnerable groups, regions and livelihood sectors in setting adaptation finance flow towards vulnerable households and key economic sectors. We analysed human vulnerability to climate hazards in Southern rainforest of Nigeria, a region that holds rural livelihoods with fragile ecology. Using cross-sectional household data, Human Climate Vulnerability Index was constructed for the five local government areas in this region. Results suggest that the most vulnerable area faces poor wealth and inadequate support institutions while the least vulnerable has an access to the best basic amenities. Socio-economic factors especially the large share of female-headed households, rising disadvantaged aging population and poor infrastructure are major contributors to vulnerability. The highest exposure index is associated with high temperatures and solar radiation. Highest sensitivity index is mostly determined by demographic inequalities and socio-economic, infrastructure, health and environmental indicators while Adaptive Capacity (AC) index is dependent on wealth and assets and social amenities. Adaptation finance should target socio-economic, demographic inequalities, communication services, support institutions and infrastructure to reduce sensitivity but target basic social amenities to strengthen AC.

“ROLE OF FNAC IN THE DIAGNOSIS OF SALIVARY GLAND LESION”

Background: Fine needle aspiration cytology (FNAC) has been used for diagnosis of salivary gland lesions for many years. Various studies in the existing literature have shown a wide range of sensitivity and diagnostic accuracy of cytologic diagnosis. FNAC is a safe, simple, cost effective, 1-4 accurate and minimal invasive procedure for the evaluation of salivary gland lesions. FNAC is not only useful in planning denitive preoperative 2,5-6 diagnosis but also can prevent unnecessary surgical intervention. Salivary gland swelling occur more commonly in 3rd decade of life with equal sex incidence. Parotid is one of the most commonly involved glands in the head & neck region swellings. FNAC appears to be highly sensitive for benign tumours and highly specic for malignant tumors and it should be the rst line of investigation in evaluating the salivary gland pathologies. 7 Early diagnosis and appropriate management carries good prognosis. Methods: Patients with suspected salivary gland enlargements, referred for FNAC, were included in this study. FNAC was performed by using the standard procedure. Cytologic diagnosis was compared with histopathologic diagnosis wherever it was available. Results: In the present study conducted in the department of pathology, Shyam Shah Medical College Rewa MP, over a period of 5 years, 152 Patients with suspected salivary gland enlargements were retrospectively appraised. The benign lesions of salivary gland were 133 (87.5%), out of which Pleomorphic adenoma was diagnosed in 75 (56.39%) cases, clinical correlation was found in 58 (77.33%) cases. 29 cases were subsequently correlated with histopathological examination 26 correctly correlated and the diagnostic accuracy of FNAC was found to be 89.65%. Inammatory lesions were proved correct in 08 out of 10 cases after histopathology. Therefore, accuracy of FNAC was 80.0%. Malignant lesions of salivary gland were found in 19 cases, out of which 15 (78.94%) cases correlated with clinical diagnosis. 13 cases were subjected to histopathology 10 correctly correlated and the diagnostic accuracy of FNAC was found to be 76.92%. Overall diagnostic accuracy of FNAC was 84.61%. Conclusion:In conclusions it can be established that FNAC is an efcient and accurate procedure with high sensitivity index, and its usefulness is enhanced due to it being a relatively easy procedure which can be carried out even on outdoor patients.

Detection of brain lesion location in MRI images using convolutional neural network and robust PCA

Purpose and aim: Detection of brain tumors plays a critical role in the treatment of patients. Before any treatment, tumor segmentation is crucial to protect healthy tissues during treatment and to destroy tumor cells. Tumor segmentation involves the detection, precise identification, and separation of tumor tissues. In this paper, we provide a deep learning method for the segmentation of brain tumors. Material and methods: In this article, we used a convolutional neural network (CNN) to segment tumors in seven types of brain disease consisting of Glioma, Meningioma, Alzheimer's, Alzheimer's plus, Pick, Sarcoma, and Huntington. First, we used the feature-reduction-based method robust principal component analysis to find tumor location and spot in a dataset of Harvard Medical School. Then we present an architecture of the CNN method to detect brain tumors. Results: Results are depicted based on the probability of tumor location in magnetic resonance images. Results show that the presented method provides high accuracy (96%), sensitivity (99.9%), and dice index (91%) regarding other investigations. Conclusion: The provided unsupervised method for tumor clustering and proposed supervised architecture can be potential methods for medical uses.

Microstructural evolution and superplastic behavior of a fine-grained Mg−Gd−Y−Ag alloy processed by simple shear extrusion

Abstract Two magnesium alloys with the nominal compositions of Mg−6Gd−3Y (GW63) and Mg−6Gd−3Y−1Ag (GW63−1Ag) were hot extruded and then processed by 6 passes of the simple shear extrusion (SSE) process at 553 K. Both SEM and EBSD studies confirmed the mean grain size reduction of the extruded base GW63 alloy from 10.1 to 2.9 μm by adding 1 wt% Ag. This decrease in the grain size was due to the formation of the Ag-containing precipitates with network-type morphology at the grain boundaries of the extruded GW63−1Ag alloy. Further grain refinement was achieved after SSE through the occurrence of DRX in both Ag-free and Ag-containing alloys. However, the GW63−1Ag alloy showed a higher fraction of DRX grains and HAGBs after SSE processing compared to the Ag-free alloy. The enhanced DRX behavior was attributed to the smaller initial grain size of the Ag-containing alloy in the as-extruded condition and the role of Ag addition in suppressing the solute drag effect of the segregated Gd and Y elements at the grain boundaries. The superplastic behavior of the alloys was assessed via the shear punch testing (SPT) method performed in the temperature ranges of 623–723 K for the extruded specimens and 573–673 K for the SSE processed alloys. It was revealed that none of the extruded alloys exhibited superplastic flow, because of their large grain sizes. The GW63 alloy processed by 6 passes of SSE displayed a maximum m-value of 0.38 at 648 K, while the high strain rate sensitivity (SRS) index of 0.5 was measured at 623 K for the Ag-containing alloy after SSE. This was accompanied with a superplastic deformation behavior of the latter alloy, for which an activation energy of 105 kJ mol−1 that is close to that of the grain boundary diffusion of pure Mg was obtained. Therefore, grain boundary sliding (GBS) associated with grain boundary diffusion was introduced as the dominant deformation mechanism during the superplastic flow of the Ag-containing alloy processed by SSE.

An Active Contour Method Based on Regularized Kernel Fuzzy C-Means Clustering

This research presents hybrid level set evolution for complex and inhomogeneous image segmentation. Firstly, we develop an adaptive force with level set evolution, which is driven by region information. Adaptive force is produced by consolidating local and global force terms in an altered fashion. Besides, to avoid local fitting terms being stuck into a local minimum, we use the swap function to interchange the fitting terms so that fitting values inside the object are always higher. Later for the elimination of the costly contour initialization that existed in previous level set based evolutions, we integrate kernel based fuzzy c-means clustering and intensity-based thresholding framework with the proposed framework to automate the proposed strategy. Finally, for the level set function regularization and the for the elimination of its re- initialization we have used the Gaussian function in the level set evolution. We demonstrate the results on some complex images to show the strong and exact segmentation results that are conceivable with this new class of adaptive active contour model. We have additionally performed statistical analysis on real images and BRATS dataset using Dice index, accuracy, sensitivity, specificity and Jaccard index metrics. Results show that the proposed method gets high Dice index, accuracy, sensitivity, specificity and Jaccard index values compared to the previous state of art methods.

Prognostic and reclassification value of serum cathepsin S over the GRACE risk score in patients with non-ST-segment elevation acute coronary syndrome

Abstract Background Cathepsin S is an extracellular matrix degradation enzyme that plays an important role in atherosclerotic cardiovascular disease by inducing vasa vasorum development and atherosclerotic plaque rupture. Purpose To determine the prognostic and reclassification value of baseline serum cathepsin S after adjustment for the Global Registry of Acute Coronary Events (GRACE) score, which is a clinical guideline recommended risk score in non-ST-segment elevation acute coronary syndrome (NSTE-ACS). Methods Serum cathepsin S was measured by ELISA in 1,129 consecutive patients presenting with acute symptoms to the emergency department for whom a final adjudicated diagnosis of NSTE-ACS was made. All-cause mortality or all-cause death/non-fatal myocardial infarction (MI) after a median follow-up of 21 months were evaluated as the primary or secondary study endpoint, respectively. The Net Reclassification Index (NRI) estimated the reclassification predictive value for risk of each end-point of cathepsin S over the GRACE score. Results After a median follow-up of 21 months 101 (8.95%) deaths were reported. The combined endpoint of death or non-fatal MI occurred in 176 (15.6%) patients. Dose-response curve analysis adjusted for the effect of age, gender, diabetes mellitus, high-sensitivity-cardiac troponin T, high-sensitivity C-reactive protein, revascularization and index diagnosis revealed a non-linear association of continuous cathepsin S with all-cause death (P=0.036 for non-linearity; adjusted HR=1.60 for 80th vs. 20th percentiles, P=0.038) or with the combined endpoint (P=0.008 for non-linearity, adjusted HR=1.53 for 80th vs. 20th percentiles, P=0.011). Serum cathepsin S maintained its predictive value for all-cause death (adjusted HR=1.70 highest vs. lowest tertile, 95% CI 1.03–2.82, P=0.039) after adjusting for the GRACE Score. Similarly, cathepsin S predicted the combined endpoint of all-cause death or non-fatal MI (adjusted HR=1.67 highest vs. lowest tertile, 95% CI 1.15–2.42, P=0.007) independently of the GRACE Score. When cathepsin S was added over the GRACE Score it correctly reclassified risk for all-cause death in 20% of the population (P=0.004). Similarly, serum Cathepsin S conferred a significant reclassification value over the GRACE score for all-cause death or non-fatal MI in 15.9% of the population. Conclusions Serum cathepsin S is a predictor of mortality and improves risk stratification over the GRACE score in patients with NSTE-ACS. The clinical application of cathepsin S as a novel biomarker in NSTE-ACS should be further explored and validated. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): German Heart Foundation

Auto-ROIs of bone scan image using thin-plate spline with specific bone landmarks

Bone scan image (BSI) is the most widely available technique for detecting cancer that has spread (metastasized) from the original location, such as breast or prostate cancer. One benefit of this process is as strong prognostic indicator of survival longevity for cancer patient. However, some challenging issues should be considered, i.e. variability in image brightness, abnormal bone shape or structure, asymmetry gesture position, and image noise due to muscle and fat tissue make the process to determine the region of interest (ROI) become more difficult, not to mention the required-time when inspecting the ROI of each bone part manually. The purposes of this study are to provide an effortless and meticulous ROI labeling as prerequired in clinical application. Finally, to propose a novel framework for determination of multiple ROI automatically by utilizing specific bone landmarks, in the hope of that could contribute to clinical observation in metastasis analysis. The results show that the proposed framework is promising method with high sensitivity (0.96), specificity (0.97), precision (0.94), Sorensen-Dice Index (0.96) and image content index (0.96).

Modeling soil quality in Dakahlia Governorate, Egypt using GIS techniques

Evaluating soil quality (SQ) is essential to track changes in soil performance in response to management practices. The current work aimed at using remote sensing and GIS to assess SQ in Dakahlia Governorate, north Nile Delta of Egypt. Twenty-one soil profiles representing the main geomorphic units in the area were dug and samples were collected from different horizons and analysed. Twenty-nine parameters were used to calculate 4 soil quality indices; chemical, physical, fertility and environmental. The four indices were assembled with the geomorphic units in four thematic layers, which were overlain in accordance with geometric mean algorism in a single map. The proposed model showed that about 29% of the total agricultural lands were classified as very high, 30% as high, 33% as moderate, 3% as low and 5% as very low quality. Results of the proposed model agreed with those calculated by the additive index in 95% of the cases, while they agreed with the weighted additive index in 71% of the cases. The Kappa statics indicated an almost perfect agreement between the proposed model and the additive model, while a moderate agreement with the weighted additive model. The proposed model had the highest sensitivity index, indicating its accuracy for evaluating SQ in the studied area. The developed model would provide a useful tool for proper monitoring of soil performance in the studied area.

Seizure prediction with cross-higher-order spectral analysis of EEG signals

Epilepsy is a neurological disorder that happens because of the propagation of abnormal signals produced by clusters of neurons in the brain. The majority of those with epileptic seizures can be treated by drug therapies and surgery. However, 25% of the patients with diagnosed epilepsy continue to have seizures. Seizures can cause serious injuries and limit the independence and mobility of an individual. Seizure detection and prediction could lead to a better understanding of seizures and with that help preventing patient injury.This paper discusses extraction and evaluation of nonlinear multivariate features using the cross-bispectral method to help predict epileptic seizure occurrences. These ten statistic features were employed to discriminate pre-ictal from interictal states. Therefore, the features were given to the support vector machine classifier as the input. Outputs were then processed in order to evaluate the sensitivity, false positive rate (FPR) and the prediction time. The proposed method obtained sensitivity of 100% and average FPR of 0.044 per hour by using the “Freiburg epileptic seizure prediction” dataset. This high sensitivity index and low FPR index compared with other studies show the ability of cross-higher-order spectral method to analyze epileptic EEG signals. The proposed method is also fast and easy and may be helpful in other applications of EEG analysis such as sleep stage identification and brain–computer interface.

Evaluation of construct and criterion-referenced validity of a systems-thinking based near miss reporting form

The validity of methods is an ongoing issue in ergonomics. Inconsistent definitions and approaches to evaluation exacerbate this challenge. In this study, the construct and criterion-referenced validity of a new near miss reporting form was evaluated to determine the extent to which it comprehensively captures near miss incidents and is aligned with the systems thinking approach to accident causation. Interview data were used as the reference standard in the evaluation. Using signal detection theory (SDT), a high average hit rate (HR), predictive value (PV) and sensitivity index (SI) were found, with an almost perfect ranking for the index of concordance. The findings show that the reporting form has strong construct and criterion-referenced validity. It is proposed that the approach used in this study could be used by researchers and practitioners when testing the validity of incident data collection tools. Practitioner summary: The validity of methods is a key issue in ergonomics. In this study, we test the validity of a near miss reporting form using interview data as a standard. This approach could be used by practitioners when testing the validity of other ergonomics methods.

COMPARISON OF PHOTOTHERMAL RADIOMETRY AND MODULATED LUMINESCENCE, INTRAORAL RADIOGRAPHY, AND CONE BEAM COMPUTED TOMOGRAPHY FOR DETECTION OF NATURAL CARIES UNDER RESTORATIONS

<h3>Background</h3> Intraoral radiographs (IR) are commonly used to evaluate for caries but require approximately 30% to 40% mineral loss before detection, thereby necessitating techniques with a high sensitivity index for early detection. Photothermal radiometry and modulated luminescence (PTR/LUM) offers a potentially more sensitive and safe method for detecting recurrent caries; however, there are no published studies comparing its use in detection of recurrent caries under composite restorations relative to IR and cone beam computed tomography (CBCT). <h3>Objective(s)</h3> The aim of this in vitro study was to compare the sensitivity, specificity, and receiver operating characteristic area under the curve (ROC AUC) of a caries diagnostic system based on PTR/LUM to digital IR and CBCT in detecting simulated recurrent decay under restorations. <h3>Study Design</h3> The study used 54 teeth with Class II composite restorations on 70 proximal surfaces; 35 had caries lesions remaining, and 35 were sound. The gingival floor of the restored surfaces was assessed for presence or absence of caries under composite restorations using each of the 3 modalities. Statistical calculations and analysis were performed using the R statistical computing environment. Sensitivity and specificity were calculated for each device, for each rater and image combination, and ROC analysis was performed. <h3>Results</h3> Average sensitivity across the 6 experienced dentists was 0.40 for both IR and CBCT. However, the sensitivity for the PTR/LUM was 0.89. Specificity of PTR/LUM and IR was 0.80, whereas the CBCT method was 0.70. The intrarater agreement varied for different observers with the use of IR and CBCT, ranging between moderate and substantial with only 1 observer having poor intrarater agreement for both methods. PTR/LUM showed only moderate agreement. <h3>Discussion/Conclusions</h3> PTR/LUM, which involves nonionizing radiation, can serve as a sensitive adjunct in early caries detection and monitoring.

Properties of self-compacting concrete (SCC) with recycled tire rubber aggregate: A comprehensive study

Abstract Disposal of massive scrap tires in an eco-friendly way has become a worldwide concern due to the booming automobile industry. The introduction of waste tire rubber particles into concrete as an alternative aggregate provides a promising way to solve the problem. It is noteworthy that the substitute of natural aggregates with rubber particles can mitigate environmental destruction and resource consumption caused by mining activities of natural aggregates. In this study, a series of experiments were conducted to evaluate both fresh and hardened properties of self-compacting concrete (SCC) with recycled tire rubber aggregate (RA), aiming at providing fundamental insight of its performance for the potential large-scale applications. Results show that the addition of RA leads to a slight decline in workability and an increase in air content of fresh SCC. Increasing RA content results in the improvement of the damping ratio, fracture energy and ductility index while a loss of compressive strength and dynamic modulus. SCC with RA also presents high strain rate sensitivity and toughness index under impact load, indicating improved impact resistance. In addition, use of RA enables SCC to achieve low water absorption and high resistance to chloride ion penetration. From the above, this study provides a comprehensive investigation of the clean and effective utilization of recycled tire rubber in concrete.

Assessing Livelihood Vulnerability to Climate Change Based on Livelihood Zone: Evidence from Mixed Farming System in Kembata Tembaro Zone, Southern Ethiopia

Climate change vulnerability depends upon various factors and differs between places, sectors and communities. This study is aimed at analyzing smallholder farmers’ livelihood vulnerability to climate change and variability in Kembatta Tembaro zone, Southern Ethiopia using the IPCC Livelihood Vulnerability Index approach. The approach estimates vulnerabilities by grouping nine major components into three categories of exposure, sensitivity and adaptive capacity, using data collected from 508 randomly selected farm households based on five livelihood zones. The result score of Livelihood Vulnerability Index revealed that Coffee livelihood zone with high exposure index coupled with limited adaptive capacity made it the most vulnerable among the five livelihood zones; whereas, Ginger livelihood zone with very high adaptive capacity index and low sensitivity, coupled with medium level exposure index to climate change has greatly contributed for its least vulnerability score. In line with the results, people-centered strengthening of adaptive capacity based on the local geographical and socio-economic profiles as well as widening opportunities for off-farm livelihood strategies is essential. Keywords: Climate Change, Livelihood Zone, Livelihood Vulnerability DOI : 10.7176/JEES/9-6-06 Publication date :June 30 th 2019

Real-Time Performance of a Tactile Neuroprosthesis on Awake Behaving Rats.

With the advancement of electrode and equipment technology, neuroprosthetics have become a promising alternative to partially compensate for the loss of sensorimotor function in amputees and patients with neurological diseases. Cortical neural interfaces are suitable especially for spinal cord injuries and amyotrophic lateral sclerosis. Although considerable success has been achieved in the literature by spike decoding of motor signals from the human brain, somatosensory feedback is essential for better motor control, interaction with objects, and the embodiment of prosthetic devices. In this paper, we present a tactile neuroprosthesis for rats based on intracortical microstimulation (ICMS). The rats wore mechanically-isolated boots covered with tactile sensors while performing a psychophysical detection task. The vibrotactile stimuli were measured by the artificial sensors and by using a real-time processor, this information was converted to electrical current pulses for ICMS. Some parameters of the real-time processor algorithm were specific to individual rats and were based on psychometric equivalence functions established earlier. Rats could detect the effects of the vibrotactile stimuli better (i.e., higher sensitivity indices) when the tactile neuroprosthesis was switched on compared to the boot only condition during active movement. In other words, the rats could decode the tactile information embedded in ICMS and use that in a behaviorally relevant manner. The presented animal model without peripheral nerve injury or amputation is also a promising tool to test various hardware and software components of neuroprosthetic systems in general.

Thin-Core Fiber Taper-Based Multi-Mode Interferometer for Refractive Index Sensing

We demonstrate a thin-core fiber (TCF)-based hybrid modal interferometer for high-sensitivity refractive index (RI) sensing. It mainly consists of a tapered TCF with an air bubble fabricated by the arc discharge technique using a fusion splicer machine. Due to mode mismatch and the tapering of the TCF, high-order cladding modes are excited, which can be used for high-sensitivity index sensing. The selected two interference dips of the sensor can obtain the index sensitivities of 370.37 and −72.22 dB/RIU in the RI range from 1.333 to 1.3799. Using the differential intensity demodulation method, a total index sensitivity as high as 442.59 dB/RIU can be achieved. Featured with high sensitivity, linear response, small size, ease of fabrication, and low cost, the proposed sensor may have various applications in biochemical sensing field.

Plasmonic filter and sensor based on a subwavelength end-coupled hexagonal resonator.

In this paper, an end-coupled hexagonal resonator inserted with dual parallel metallic blocks is proposed based on subwavelength metal-insulator-metal waveguides. When the blocks are vertically inserted into the resonator, more transmission channels (three peaks) with symmetrical spectral shapes than that (one peak) of the perfect hexagonal resonator are achieved in the same wavelength range. The transmission peaks all have high transmittances; thus, the structure can be performed as an on-chip optical filter. When the blocks are horizontally distributed in the resonator, the antinode and node of the magnetic field for the expected mode will arise inside and outside the blocks, leading to the mode interactions. Subsequently, Fano resonance with an asymmetrical peak is achieved in the structure. High index sensitivity and high figure of merit, which are significant factors for optical sensors, are investigated by using the finite-difference time-domain method. The proposed structure can highly support the development of integrated photonics and find wide applications in the on-chip optical filtering and sensing areas.

Theta Coherence Asymmetry in the Dorsal Stream of Musicians Facilitates Word Learning

Word learning constitutes a human faculty which is dependent upon two anatomically distinct processing streams projecting from posterior superior temporal (pST) and inferior parietal (IP) brain regions toward the prefrontal cortex (dorsal stream) and the temporal pole (ventral stream). The ventral stream is involved in mapping sensory and phonological information onto lexical-semantic representations, whereas the dorsal stream contributes to sound-to-motor mapping, articulation, complex sequencing in the verbal domain, and to how verbal information is encoded, stored, and rehearsed from memory. In the present source-based EEG study, we evaluated functional connectivity between the IP lobe and Broca’s area while musicians and non-musicians learned pseudowords presented in the form of concatenated auditory streams. Behavioral results demonstrated that musicians outperformed non-musicians, as reflected by a higher sensitivity index (d’). This behavioral superiority was paralleled by increased left-hemispheric theta coherence in the dorsal stream, whereas non-musicians showed stronger functional connectivity in the right hemisphere. Since no between-group differences were observed in a passive listening control condition nor during rest, results point to a task-specific intertwining between musical expertise, functional connectivity, and word learning.