Three-dimensional simultaneous brain mapping of T1, T2, T2∗ and magnetic susceptibility with MR Multitasking.

PURPOSE: To develop a new technique that enables simultaneous quantification of whole-brain T1 , T2 , T2∗ , as well as susceptibility and synthesis of six contrast-weighted images in a single 9.1-minute scan. METHODS: The technique uses hybrid T2 -prepared inversion-recovery pulse modules and multi-echo gradient-echo readouts to collect k-space data with various T1, T2, and T2∗ weightings. The underlying image is represented as a six-dimensional low-rank tensor consisting of three spatial dimensions and three temporal dimensions corresponding to T1 recovery, T2 decay, and multi-echo behaviors, respectively. Multiparametric maps were fitted from reconstructed image series. The proposed method was validated on phantoms and healthy volunteers, by comparing quantitative measurements against corresponding reference methods. The feasibility of generating six contrast-weighted images was also examined. RESULTS: High quality, co-registered T1 , T2 , and T2∗ susceptibility maps were generated that closely resembled the reference maps. Phantom measurements showed substantial consistency (R2 > 0.98) with the reference measurements. Despite the significant differences of T1 (p < .001), T2 (p = .002), and T2∗ (p = 0.008) between our method and the references for in vivo studies, excellent agreement was achieved with all intraclass correlation coefficients greater than 0.75. No significant difference was found for susceptibility (p = .900). The framework is also capable of synthesizing six contrast-weighted images. CONCLUSION: The MR Multitasking-based 3D brain mapping of T1 , T2 , T2∗ , and susceptibility agrees well with the reference and is a promising technique for multicontrast and quantitative imaging.

Glucose Sensor Using U-Shaped Optical Fiber Probe with Gold Nanoparticles and Glucose Oxidase.

In this study, we proposed a U-shaped optical fiber probe fabricated using a flame heating method. The probe was packaged in glass tube to reduce human factors during experimental testing of the probe as a glucose sensor. The U-shaped fiber probe was found to have high sensitivity in detecting the very small molecule. When the sensor was dipped in solutions with different refractive indexes, its wavelength or transmission loss changed. We used electrostatic self-assembly to bond gold nanoparticles and glucose oxidase (GOD) onto the sensor’s surface. The results over five cycles of the experiment showed that, as the glucose concentration increased, the refractive index of the sensor decreased and its spectrum wavelength shifted. The best wavelength sensitivity was 2.899 nm/%, and the linearity was 0.9771. The best transmission loss sensitivity was 5.101 dB/%, and the linearity was 0.9734. Therefore, the proposed U-shaped optical fiber probe with gold nanoparticles and GOD has good potential for use as a blood sugar sensor in the future.

Tilted Fiber Bragg Grating Sensor with Graphene Oxide Coating for Humidity Sensing.

In this study, we propose a tilted fiber Bragg grating (TFBG) humidity sensor fabricated using the phase mask method to produce a TFBG that was then etched with five different diameters of 20, 35, 50, 55 and 60 µm, after which piezoelectric inkjet technology was used to coat the grating with graphene oxide. According to the experimental results, the diameter of 20 µm yielded the best sensitivity. In addition, the experimental results showed that the wavelength sensitivity was -0.01 nm/%RH and the linearity was 0.996. Furthermore, the measurement results showed that when the relative humidity was increased, the refractive index of the sensor was decreased, meaning that the TFBG cladding mode spectrum wavelength was shifted. Therefore, the proposed graphene oxide film TFBG humidity sensor has good potential to be an effective relative humidity monitor.

Recognition of Glaucomatous Fundus Images Using Machine Learning Methods Based on Optic Nerve Head Topographic Features.

PRCIS: Machine learning classifiers are an effective approach to detecting glaucomatous fundus images based on optic disc topographic features making it a straightforward and effective approach. STUDY DESIGN: Retrospective case-control study. OBJECTIVE: The aim was to compare the effectiveness of clinical discriminant rules and machine learning classifiers in identifying glaucomatous fundus images based on optic disc topographic features. METHODS: The study used a total of 800 fundus images, half of which were glaucomatous cases and the other half non-glaucomatous cases obtained from an open database and clinical work. The images were randomly divided into training and testing sets with equal numbers of glaucomatous and non-glaucomatous images. An ophthalmologist framed the edge of the optic cup and disc, and the program calculated five features, including the vertical cup-to-disc ratio and the width of the optic rim in four quadrants in pixels, used to create machine learning classifiers. The discriminative ability of these classifiers was compared with clinical discriminant rules. RESULTS: The machine learning classifiers outperformed clinical discriminant rules, with the extreme gradient boosting method showing the best performance in identifying glaucomatous fundus images. Decision tree analysis revealed that the cup-to-disc ratio was the most important feature for identifying glaucoma fundus images. At the same time, the temporal width of the optic rim was the least important feature. CONCLUSIONS: Machine learning classifiers are an effective approach to detecting glaucomatous fundus images based on optic disc topographic features and integration with an automated program for framing and calculating the required parameters would make it a straightforward and effective approach.

Early Detection of Primary Open Angle, Angle Closure, and Normal Tension Glaucoma in an Asian Population Using Optical Coherence Tomography.

PRCIS: Spectral-domain optical coherence tomography (SD-OCT) facilitates early glaucoma detection in the Chinese population in Taiwan. The best parameters for primary open angle glaucoma (POAG), primary angle closure glaucoma (PACG), normal tension glaucoma (NTG), and suspected glaucoma (GS) detection are temporal inferior Bruch's membrane opening-minimum rim width (BMO-MRW), inner temporal macular ganglion cell layer (mGCL), temporal superior Circumpapillary retinal nerve fiber layer (cpRNFL), and mean global BMO-MRW, respectively. PURPOSE: We investigated the diagnostic capability of SD-OCT for different types of early glaucoma among the Chinese population in Taiwan. PARTICIPANTS AND METHODS: One eye each was assessed from 113 individuals with healthy eyes, 125 individuals with suspected glaucoma (GS), and 156 patients with early glaucoma (POAG, 87; PACG, 50; and NTG, 19). Circumpapillary (cp) RNFL thickness (global and sectoral), BMO-MRW, and macular parameters, including the macular RNFL (mRNFL), mGCL, and macular inner plexiform layer (mIPL), were assessed using SD-OCT. The areas under receiver operator characteristic curves (AUCs) were calculated to evaluate the diagnostic capacity of the parameters to differentiate between healthy and early glaucomatous eyes. RESULTS: The parameters most suitable for detecting early POAG, PACG, NTG, and GS were temporal inferior BMO-MRW (AUC, 0.847), inner temporal mGCL (AUC, 0.770), temporal superior cpRNFL (AUC, 0.861), and mean global BMO-MRW (AUC, 0.768), respectively. Among the macular parameters, the mGCL exhibited the highest diagnostic capacity. The diagnostic capacity of the mGCL was lower than that of cpRNFL and BMO-MRW for POAG and NTG but not PACG. After adjusting for confounding variables in multivariable analysis, the AUC was determined to be 0.935 for POAG and 0.787 for GS. CONCLUSION: SD-OCT facilitates the detection of early POAG, PACG, and NTG. Using a combination of cpRNFL, BMO-MRW, and macular parameters may enhance their diagnostic capacities. Further studies are necessary to validate these findings.

Upper-Limb Amputation Disrupts the Interhemispheric Structural Rather than Functional Connectivity.

Background: Recent neuroimaging studies on upper-limb amputation have revealed the reorganization of bilateral sensorimotor cortex after sensory deprivation, underpinning the assumption of changes in the interhemispheric connections. In the present study, using functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), we aim to explore the alterations in the interhemispheric functional and structural connectivity after upper-limb amputation. Methods: Twenty-two upper-limb amputees and 15 age- and sex-matched healthy controls were recruited for MRI scanning. The amputees were further divided into subgroups by amputation side and residual limb pain (RLP). DTI metrics of corpus callosum (CC) subregions and resting-state functional connectivity (FC) between the bilateral sensorimotor cortices were measured for each participant. Linear mixed models were carried out to investigate the relationship of interhemispheric connectivity with the amputation, amputation side, and RLP. Results: Compared with healthy controls, upper-limb amputees showed lower axial diffusivity (AD) in CC subregions II and III. Subgroup analyses showed that the dominant hand amputation induced significant microstructural changes in CC subregion III. In addition, only amputees with RLP showed decreased fractional anisotropy and AD in CC, which was also correlated with the intensity of RLP. No significant changes in interhemispheric FC were found after upper-limb amputation. Conclusion: The present study demonstrated that the interhemispheric structural connectivity rather than FC degenerated after upper-limb amputation, and the degeneration of interhemispheric structural connectivity was shown to be relevant to the amputation side and the intensity of RLP. Impact statement Neuroimaging studies have revealed the functional reorganization of bilateral sensorimotor cortex after amputation, with expanded activation from the intact hemisphere to the deprived hemisphere. Our findings indicated a degeneration of interhemispheric white matter connections in upper-limb amputees, unveiling the underlying structural basis for bilateral functional reorganization after amputation.

Retrospective quantification of clinical abdominal DCE-MRI using pharmacokinetics-informed deep learning: a proof-of-concept study.

Introduction: Dynamic contrast-enhanced (DCE) MRI has important clinical value for early detection, accurate staging, and therapeutic monitoring of cancers. However, conventional multi-phasic abdominal DCE-MRI has limited temporal resolution and provides qualitative or semi-quantitative assessments of tissue vascularity. In this study, the feasibility of retrospectively quantifying multi-phasic abdominal DCE-MRI by using pharmacokinetics-informed deep learning to improve temporal resolution was investigated. Method: Forty-five subjects consisting of healthy controls, pancreatic ductal adenocarcinoma (PDAC), and chronic pancreatitis (CP) were imaged with a 2-s temporal-resolution quantitative DCE sequence, from which 30-s temporal-resolution multi-phasic DCE-MRI was synthesized based on clinical protocol. A pharmacokinetics-informed neural network was trained to improve the temporal resolution of the multi-phasic DCE before the quantification of pharmacokinetic parameters. Through ten-fold cross-validation, the agreement between pharmacokinetic parameters estimated from synthesized multi-phasic DCE after deep learning inference was assessed against reference parameters from the corresponding quantitative DCE-MRI images. The ability of the deep learning estimated parameters to differentiate abnormal from normal tissues was assessed as well. Results: The pharmacokinetic parameters estimated after deep learning have a high level of agreement with the reference values. In the cross-validation, all three pharmacokinetic parameters (transfer constant Ktrans, fractional extravascular extracellular volume ve, and rate constant kep) achieved intraclass correlation coefficient and R2 between 0.84-0.94, and low coefficients of variation (10.1%, 12.3%, and 5.6%, respectively) relative to the reference values. Significant differences were found between healthy pancreas, PDAC tumor and non-tumor, and CP pancreas. Discussion: Retrospective quantification (RoQ) of clinical multi-phasic DCE-MRI is possible by deep learning. This technique has the potential to derive quantitative pharmacokinetic parameters from clinical multi-phasic DCE data for a more objective and precise assessment of cancer.

Does Tibetan Household Livelihood Capital Enhance Tourism Participation Sustainability? Evidence from China's Jiaju Tibetan Village.

Identifying effective transformations to reduce poverty and approach rural sustainability is at the core of the first sustainable development goal of the United Nations. This article offers scientific support for continued efforts in sustaining rural development and livelihood resilience. Many studies have examined drivers of livelihood transition from farming to non-farm activities, especially participation in tourism against the backdrop of rural tourism development. However, few studies have identified ways to measure the level of tourism participation or have discussed how household-level capital influences decisions regarding tourism participation made by Tibetan ethnic households. This article assesses the role of livelihood capital in the adoption of tourism activities at the household level in Jiaju Tibetan Village, an ethnic region that is experiencing struggling agricultural business and developing tourism sector. Using household survey data, this study presents an ordinal logistic regression model to identify the determinants of the household tourism participation level. The results showed that households' tourism participation was influenced by physical capital (e.g., proximity to major roads, odds ratio = 2.83 at p = 0.024; fixed capitals, odds ratio = 101.19 at p = 0.039), human capital (e.g., availability of family labor, odds ratio = 0.25 at p = 0.004; availability of skilled member, odds ratio = 2.91 at p = 0.002), and social capital (e.g., relatives in governmental sectors, odds ratio = 5.22 at p = 0.044; government payments, odds ratio = 8.78 at p = 0.04), while the influence of financial capital was not significant. The proximity to major roads, availability of skilled members, fixed assets, and direct and indirect support from the government to households were significantly and positively associated with tourism participation level. The effects of household labor availability and annual family income remain unclear. Overall, household livelihood capital plays a critical role in the enhancement of tourism participation in Jiaju Tibetan Village. Our findings have implications for understanding the shift of on-farm occupation to off-farm activities in tourism and for the pursuit of policies contributing to poverty reduction and rural revitalization in China as well as to the Sustainable Development Goals.

Glaucoma Detection Using Support Vector Machine Method Based on Spectralis OCT.

Spectralis optical coherence tomography (OCT) provided more detailed parameters in the peripapillary and macular areas among the OCT machines, but it is not easy to understand the enormous information (114 features) generated from Spectralis OCT in glaucoma assessment. Machine learning methodology has been well-applied in glaucoma detection in recent years and has the ability to process a large amount of information at once. Here we aimed to analyze the diagnostic capability of Spectralis OCT parameters on glaucoma detection using Support Vector Machine (SVM) classification method in our population. Our results showed that applying all OCT features with the SVM method had good capability in the detection of glaucomatous eyes (area under curve (AUC) = 0.82), as well as discriminating normal eyes from early, moderate, or severe glaucomatous eyes (AUC = 0.78, 0.89, and 0.93, respectively). Apart from using all OCT features, the minimum rim width (MRW) may be good feature groups to discriminate early glaucomatous from normal eyes (AUC = 0.78). The combination of peripapillary and macular parameters, including MRW_temporal inferior (TI), MRW_global (G), ganglion cell layer (GCL)_outer temporal (T2), GCL_inner inferior (I1), peripapillary nerve fiber layer thickness (ppNFLT)_temporal superior (TS), and GCL_inner temporal (T1), provided better results (AUC = 0.84). This study showed promise in glaucoma management in the Taiwanese population. However, further validation study is needed to test the performance of our proposed model in the real world.

A U-Shaped Optical Fiber Temperature Sensor Coated with Electrospinning Polyvinyl Alcohol Nanofibers: Simulation and Experiment.

This study describes the fabrication of an electrospun, U-shaped optical fiber sensor for temperature measurements. The sensor is based on single mode fibers and was fabricated into a U-shaped optical fiber sensor through flame heating. This study applied electrospinning to coat PVA, a polymer, onto the sensor layer to reduce its sensitivity to humidity. The sensor is used to measure temperature variations ranging from 30 °C to 100 °C. The objectives of this study were to analyze the sensitivity variation of the sensor with different sensor layer thicknesses resulting from different electrospinning durations, as well as to simulate the wavelength signals generated at different electrospinning durations using COMSOL. The results revealed that the maximum wavelength sensitivity, transmission loss sensitivity, and linearity of the sensor were 25 dBm/°C, 70 pm/°C, and 0.956, respectively. Longer electrospinning durations resulted in thicker sensor layers and higher sensor sensitivity, that wavelength sensitivity of the sensor increased by 42%.

Heat Adaptive Capacity: What Causes the Differences Between Residents of Xiamen Island and Other Areas?

Extreme heat events caused by climate change have serious adverse effects on residents' health in many coastal metropolises in southeast China. Adaptive capacity (AC) is crucial to reduce heat vulnerability in the human-environment system. However, it is unclear whether changes in individual characteristics and socioeconomic conditions likely amplify or attenuate the impacts of residents' heat adaptive capacity (HAC) changes. Moreover, which public policies can be implemented by the authorities to improve the HAC of vulnerable groups remains unknown. We conducted a questionnaire survey of 630 residents of Xiamen, a typical coastal metropolis, in 2018. The effects of individual and household characteristics, and government actions on the residents' HAC were examined by using ordinal logistic regression analysis. Results show that the majority (48.10%) of Xiamen residents had a "medium" HAC level, followed by a "high" level (37.14%). On Xiamen Island, residents who settled locally for one-three years and spent less than one hour outdoors might report weaker HAC, and their HAC would not improve with increased air conditioning units in household. In other areas of Xiamen, residents with more rooms in their households, no educational experience, and building areas <50 m2 might report better HAC. Further, vulnerable groups, such as local residents and outdoor workers on Xiamen Island, people lacking educational experience and renters in other areas of Xiamen, showed better AC to hot weather than those in previous studies. Low-income groups should be given more attention by local governments and community groups as monthly household income played a positive role in improving Xiamen residents' HAC. Rational green spaces planning and cooling services, such as street sprinkling operations, provided by municipal departments can effectively bring benefits to Xiamen residents. Identification of basic conditions of AC has significant implications for practical promoting targeted measures or policies to reduce health damages and livelihood losses of urban residents during extreme heat events.

Urban heat vulnerability: A dynamic assessment using multi-source data in coastal metropolis of Southeast China.

Extreme heat caused by global climate change has become a serious threat to the sustainable development of urban areas. Scientific assessment of the impacts of extreme heat on urban areas and in-depth knowledge of the cross-scale mechanisms of heat vulnerability forming in urban systems are expected to support policymakers and stakeholders in developing effective policies to mitigate the economic, social, and health risks. Based on the perspective of the human-environment system, this study constructed a conceptual framework and index system of "exposure-susceptibility-adaptive capacity" for urban heat vulnerability (UHV) and proposed its assessment methods. Taking Xiamen City, a coastal metropolis, as an example, spatial analysis and Geodetector were used to explore the spatial and temporal changes, spatial characteristics, and patterns of UHV under multiple external disturbances from natural to anthropological factors, and to reveal the main factors influencing UHV forming and spatial differentiation. Results showed that the exposure, susceptibility, adaptive capacity, and UHV in Xiamen City had a spatial structure of "coastal-offshore-inland". On the hot day, both the exposure and UHV showed a temporal pattern of "rising and then falling, peaking at 14:00" and a spatial pattern of "monsoonal-like" movement between coast and inland. Coastal zoning with favorable socioeconomic conditions had less magnitude of changes in UHV, where the stability of the urban system was more likely to be maintained. During the hot months, the high UHV areas were mainly distributed in the inland, while coastal areas showed low UHV levels. Further, coastal UHV was mainly dominated by "heat exposure", offshore by "comprehensive factors", and inland in the northern mountainous areas by "lack of adaptive capacity". Multi-scale urban adaptive capacity was confirmed to alter spatial distribution of exposure and reshape the spatial pattern of UHV. This study promotes the application of multi-scale vulnerability framework to disaster impact assessment, enriches the scientific knowledge of the urban system vulnerability, and provides scientific references for local targeted cooling policy development and extreme heat resilience building programs.

Multitasking dynamic contrast enhanced magnetic resonance imaging can accurately differentiate chronic pancreatitis from pancreatic ductal adenocarcinoma.

Background and aims: Accurate differentiation of chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) is an area of unmet clinical need. In this study, a novel Multitasking dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) technique was used to quantitatively evaluate the microcirculation properties of pancreas in CP and PDAC and differentiate between them. Methods: The Multitasking DCE technique was able to acquire one 3D image per second during the passage of MRI contrast agent, allowing the quantitative estimation of microcirculation properties of tissue, including blood flow Fp, plasma volume fraction vp, transfer constant Ktrans, and extravascular extracellular volume fraction ve. Receiver operating characteristic (ROC) analysis was performed to differentiate the CP pancreas, PDAC pancreas, normal control pancreas, PDAC tumor, PDAC upstream, and PDAC downstream. ROCs from quantitative analysis and conventional analysis were compared. Results: Fourteen PDAC patients, 8 CP patients and 20 healthy subjects were prospectively recruited. The combination of Fp, vp, Ktrans, and ve can differentiate CP versus PDAC pancreas with good AUC (AUC [95% CI] = 0.821 [0.654 - 0.988]), CP versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), PDAC pancreas versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC tumor with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC downstream with excellent AUC (0.917 [0.795 - 1.000]), and CP versus PDAC upstream with fair AUC (0.722 [0.465 - 0.980]). This quantitative analysis outperformed conventional analysis in differentiation of each pair. Conclusion: Multitasking DCE MRI is a promising clinical tool that is capable of unbiased quantitative differentiation between CP from PDAC.

Retinal artery occlusion following cosmetic injection of poly-L-lactic acid.

Ophthalmic vascular occlusion is an infrequent but devastating complication following cosmetic facial filler injection. We report a case of developing retinal artery occlusion after poly-L-lactic acid (PLLA) injection. A 49-year-old woman with multiple chronic diseases experienced sudden central visual loss and severe ocular pain in the right eye immediately after PLLA injection in the temporal region. Her best-corrected visual acuity in the right eye dropped from 20/20 to 20/200. Fundus photography showed marked optic disc edema and localized retinal whitening in the territory of the blocked vessels. Spectral-domain optical coherence tomography revealed localized hyperreflectivity of the inner retina and retinal edema. Fluorescein angiography showed delayed filling of the retinal arteries and absence of retinal perfusion in the affected areas. Despite prompt aggressive management of the condition with ocular massage, topical brimonidine eyedrops, and hyperbaric oxygen therapy, the patient suffered permanent visual loss due to optic atrophy. Among all the subcutaneous filler materials, PLLA has not been a common cause of vascular complications, especially when injected in the temporal region, as this area has not been considered dangerous in the previous literature. Practitioners should be aware of the risk of visual loss, and extra care should be given on those who originally have a higher risk for vascular complications.

Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT.

Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating reliable diagnoses in glaucoma practice. Here we aim to compare different MLCs based on Spectralis OCT parameters, including circumpapillary retinal nerve fiber layer (cRNFL) thickness, Bruch's membrane opening-minimum rim width (BMO-MRW), Early Treatment Diabetes Retinopathy Study (ETDRS) macular thickness, and posterior pole asymmetry analysis (PPAA), in discriminating normal from glaucomatous eyes. Five MLCs were proposed, namely conditional inference trees (CIT), logistic model tree (LMT), C5.0 decision tree, random forest (RF), and extreme gradient boosting (XGBoost). Logistic regression (LGR) was used as a benchmark for comparison. RF was shown to be the best model. Ganglion cell layer measurements were the most important predictors in early glaucoma detection and cRNFL measurements were more important as the glaucoma severity increased. The global, temporal, inferior, superotemporal, and inferotemporal sites were relatively influential locations among all parameters. Clinicians should cautiously integrate the Spectralis OCT results into the entire clinical picture when diagnosing glaucoma.

Regulation of cellular response to oncogenic and oxidative stress by Seladin-1.

Expression of multiple oncogenes and inactivation of tumour suppressors is required to transform primary mammalian cells into cancer cells. Activated Ha-RasV12 (Ras) is usually associated with cancer, but it also produces paradoxical premature senescence in primary cells by inducing reactive oxygen species followed by accumulation of tumour suppressors p53 and p16(INK4a) (ref. 4). Here we identify, using a direct genetic screen, Seladin-1 (also known as Dhcr24) as a key mediator of Ras-induced senescence. Following oncogenic and oxidative stress, Seladin-1 binds p53 amino terminus and displaces E3 ubiquitin ligase Mdm2 from p53, thus resulting in p53 accumulation. Additionally, Seladin-1 associates with Mdm2 independently of p53, potentially affecting other Mdm2 targets. Ablation of Seladin-1 causes the bypass of Ras-induced senescence in rodent and human fibroblasts, and allows Ras to transform these cells. Wild-type Seladin-1, but not mutants that disrupt its association with either p53 or Mdm2, suppresses the transformed phenotype. The same mutants are also inactive in directing p53-dependent oxidative stress response. These results show an unanticipated role for Seladin-1, previously implicated in Alzheimer's disease and cholesterol metabolism, in integrating cellular response to oncogenic and oxidative stress.

Internal Residual Strain Measurements in Carbon Fiber-Reinforced Polymer Laminates Curing Process Using Embedded Tilted Fiber Bragg Grating Sensor.

Carbon fiber reinforced plastics (CFRP) have many mechanical properties that are superior to those of conventional structural materials and are becoming more and more widely used. Monitoring the curing process used to produce such composite material is important to ensure the quality of the process, especially for the characterization of residual strains after the material has been manufactured. In this study, we present a tilted fiber Bragg grating (TFBG) sensor used to monitor the curing of CFRP composite materials. The TFBG sensor was embedded into the layers of CFRP laminates to study the curing residual strain of the laminates. The experimental results showed that the curing residual stress was about -22.25 MPa, the axial residual strain was -281.351 µÎµ, and lateral residual strain of 89.91 µÎµ. The TFBG sensor was found to be sensitive to the curing residual strain of the CFRP, meaning that it has potential for use in applications involving composite curing processes. Moreover, it is indeed possible to improve the properties of composite materials via the optimization and monitoring of their curing parameters.

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The bay is the most susceptible area in the marine to human interference. It is of significance for maintaining ecological security of the bay to build an assessment framework of losses of bay ecosystem services caused by the C9 leakage event and evaluate it quantitatively. This study used market value, alternative cost, carbon tax and emergy analysis methods to construct a monetary value evaluation model for the lossses of key ecosystem services (food production, gas regulation, climate regulation, waste treatment, human health, nutrient cycling, species diversity maintenance, and recreation entertainment) caused by C9 leakage accident, and analyzed the losses of x-Bay ecosystem services. The results showed that total value of the losses of ecosystem services caused by C9 spill was 1.93×108 yuan, and the monetary value of loss per unit area was 1.19×108 yuan·km-2, which was more than 2800 times of the general marine oil spill events. Among all the components, the loss of food production services accounted for 77.1% of the total, being much higher than the impact of the general marine oil spills on human production and life. Our results could provide references to the assessment of ecosystem services loss caused by toxic substances like C9, and to the government decision-making and national territory spatial planning.

Multimodal classification of drug-naïve first-episode schizophrenia combining anatomical, diffusion and resting state functional resonance imaging.

The accurate diagnosis in the early stage of schizophrenia (SZ) is of great importance yet remains challenging. The classification between SZ and control groups based on magnetic resonance imaging (MRI) data using machine learning method could be helpful for SZ diagnosis. Increasing evidence showed that the combination of multimodal MRI data might further improve the classification performance However, medication effect has a profound influence on patients' anatomical and functional features and may reduce the classification efficiency. In this paper, we proposed a multimodal classification method to discriminate drug-naïve first-episode schizophrenia patients from healthy controls (HCs) by a combined structural MRI, diffusion tensor imaging (DTI) and resting state-functional MRI data. To reduce the feature dimension of multimodal data, we applied sparse coding (SC) for feature selection and multi-kernel support vector machine (SVM) for feature combination and classification. The best classification performance with the classification accuracy of 84.29% and area under the receiver operating characteristic (ROC) curve (AUC) of 81.64% was achieved when all modality data were combined. Interestingly, the identified functional markers were mainly found in default mode network (DMN) and cerebellar connections, while the structural markers were within limbic system and prefrontal-thalamo-hippocampal circuit.

Alterations in Brain Structural Connectivity After Unilateral Upper-Limb Amputation.

Previous studies have indicated that amputation induces reorganization of functional brain network. However, the influence of amputation on structural brain network remains unclear. In this study, using diffusion tensor imaging (DTI), we aimed to investigate the alterations in fractional anisotropy (FA) network after unilateral upper-limb amputation. We acquired DTI from twenty-two upper-limb amputees (15 dominant-side and 7 nondominant-side amputees) as well as fifteen healthy controls. Using DTI tractography and graph theoretical approaches, we examined the topological changes in FA network of amputees. Compared with healthy controls, dominant-side amputees showed reduced global mean strength, increased characteristic path length, and decreased nodal strength in the contralateral sensorimotor system and visual areas. In particular, the nodal strength of the contralateral postcentral gyrus was negatively correlated with residual limb usage, representing a use-dependent reorganization. In addition, the nodal strength of the contralateral middle temporal gyrus was positively correlated with the magnitude of phantom limb sensation. Our results suggested a degeneration of FA network after dominant-side upper-limb amputation.