Photonic crystal fiber half-taper probe based refractometer

A compact singlemode - photonic crystal fiber - singlemode fiber tip (SPST) refractive index sensor is demonstrated in this paper. A CO2 laser cleaving technique is utilised to provide a clean-cut fiber tip which is then coated by a layer of gold to increase reflection. An average sensitivity of 39.1 nm/RIU and a resolvable index change of 2.56 x 10-4 are obtained experimentally with a ~3.2 µm diameter SPST. The temperature dependence of this fiber optic sensor probe is presented. The proposed SPST refractometer is also significantly less sensitive to temperature and an experimental demonstration of this reduced sensitivity is presented in the paper. Because of its compactness, ease of fabrication, linear response, low temperature dependency, easy connectivity to other fiberized optical components and low cost, this refractometer could find various applications in chemical and biological sensing

Spatial Expansion and Soil Organic Carbon Storage Changes of Croplands in the Sanjiang Plain, China

Soil is the largest pool of terrestrial organic carbon in the biosphere and interacts strongly with the atmosphere, climate and land cover. Remote sensing (RS) and geographic information systems (GIS) were used to study the spatio-temporal dynamics of croplands and soil organic carbon density (SOCD) in the Sanjiang Plain, to estimate soil organic carbon (SOC) storage. Results show that croplands increased with 10,600.68 km2 from 1992 to 2012 in the Sanjiang Plain. Area of 13,959.43 km2 of dry farmlands were converted into paddy fields. Cropland SOC storage is estimated to be 1.29 ± 0.27 Pg C (1 Pg = 103 Tg = 1015 g) in 2012. Although the mean value of SOCD for croplands decreased from 1992 to 2012, the SOC storage of croplands in the top 1 m in the Sanjiang Plain increased by 70 Tg C (1220 to 1290). This is attributed to the area increases of cropland. The SOCD of paddy fields was higher and decreased more slowly than that of dry farmlands from 1992 to 2012. Conversion between dry farmlands and paddy fields and the agricultural reclamation from natural land-use types significantly affect the spatio-temporal patterns of cropland SOCD in the Sanjiang Plain. Regions with higher and lower SOCD values move northeast and westward, respectively, which is almost consistent with the movement direction of centroids for paddy fields and dry farmlands in the study area. Therefore, these results were verified. SOC storages in dry farmlands decreased by 17.5 Tg·year−1 from 1992 to 2012, whilst paddy fields increased by 21.0 Tg·C·year−1

Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization

Pathological speech usually refers to speech distortion resulting from illness or other biological insults. The assessment of pathological speech plays an important role in assisting the experts, while automatic evaluation of speech intelligibility is difficult because it is usually nonstationary and mutational. In this paper, we carry out an independent innovation of feature extraction and reduction, and we describe a multigranularity combined feature scheme which is optimized by the hierarchical visual method. A novel method of generating feature set based on S-transform and chaotic analysis is proposed. There are BAFS (430, basic acoustics feature), local spectral characteristics MSCC (84, Mel S-transform cepstrum coefficients), and chaotic features (12). Finally, radar chart and F-score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96 dimensions based on NKI-CCRT corpus and 104 dimensions based on SVD corpus. The experimental results denote that new features by support vector machine (SVM) have the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus and 78.7% on SVD corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation

Mapping Forest Cover in Northeast China from Chinese HJ-1 Satellite Data Using an Object-Based Algorithm

Forest plays a significant role in the global carbon budget and ecological processes. The precise mapping of forest cover can help significantly reduce uncertainties in the estimation of terrestrial carbon balance. A reliable and operational method is necessary for a rapid regional forest mapping. In this study, the goal relies on mapping forest and subcategories in Northeast China through the use of high spatio-temporal resolution HJ-1 imagery and time series vegetation indices within the context of an object-based image analysis and decision tree classification. Multi-temporal HJ-1 images obtained in a single year provide an opportunity to acquire phenology information. By analyzing the difference of spectral and phenology information between forest and non-forest, forest subcategories, decision trees using threshold values were finally proposed. The resultant forest map has a high overall accuracy of 0.91 ± 0.01 with a 95% confidence interval, based on the validation using ground truth data from field surveys. The forest map extracted from HJ-1 imagery was compared with two existing global land cover datasets: GlobCover 2009 and MCD12Q1 2009. The HJ-1-based forest area is larger than that of MCD12Q1 and GlobCover and more closely resembles the national statistics data on forest area, which accounts for more than 40% of the total area of the Northeast China. The spatial disagreement primarily occurs in the northern part of the Daxing'an Mountains, Sanjiang Plain and the southwestern part of the Songliao Plain. The compared result also indicated that the forest subcategories information from global land cover products may introduce large uncertainties for ecological modeling and these should be cautiously used in various ecological models. Given the higher spatial and temporal resolution, HJ-1-based forest products could be very useful as input to biogeochemical models (particularly carbon cycle models) that require accurate and updated estimates of forest area and type

Mapping Spatial Variations of Structure and Function Parameters for Forest Condition Assessment of the Changbai Mountain National Nature Reserve

Forest condition is the baseline information for ecological evaluation and management. The National Forest Inventory of China contains structural parameters, such as canopy closure, stand density and forest age, and functional parameters, such as stand volume and soil fertility. Conventionally forest conditions are assessed through parameters collected from field observations, which could be costly and spatially limited. It is crucial to develop modeling approaches in mapping forest assessment parameters from satellite remote sensing. This study mapped structure and function parameters for forest condition assessment in the Changbai Mountain National Nature Reserve (CMNNR). The mapping algorithms, including statistical regression, random forests, and random forest kriging, were employed with predictors from Advanced Land Observing Satellite (ALOS)-2, Sentinel-1, Sentinel-2 satellite sensors, digital surface model of ALOS, and 1803 field sampled forest plots. Combined predicted parameters and weights from principal component analysis, forest conditions were assessed. The models explained spatial dynamics and characteristics of forest parameters based on an independent validation with all r values above 0.75. The root mean square error (RMSE) values of canopy closure, stand density, stand volume, forest age and soil fertility were 4.6%, 33.8%, 29.4%, 20.5%, and 14.3%, respectively. The mean assessment score suggested that forest conditions in the CMNNR are mainly resulted from spatial variations of function parameters such as stand volume and soil fertility. This study provides a methodology on forest condition assessment at regional scales, as well as the up-to-date information for the forest ecosystem in the CMNNR

Extracellular Vesicles: “Stealth Transport Aircrafts” for Drugs

Extracellular vesicles (EVs) can deliver many types of drugs with their natural source material transport properties, inherent long-term blood circulation capabilities and excellent biocompatibility, and have great potential in the field of drug carrier. Modification of the content and surface of EVs according to the purpose of treatment has become a research focus to improve the drug load and the targeting of EVs. EVs can maximize the stability of the drugs, prevent immune clearance and achieve accurate delivery. Therefore, EVs can be described as “stealth transport aircrafts” for drugs. This chapter will respectively introduce the application of natural EVs as cell substitutes in cell therapy and engineered EVs as carriers of nucleic acids, proteins, small molecule drugs and therapeutic viral particles in disease treatment. It will also explain the drug loading and modification strategies of EVs, the source and characteristics of EVs. In addition, the commercialization progress of EVs drugs will be mentioned here, and the problems in their applications will be discussed in conjunction with the application of EVs in the treatment of COVID-19

Glassy carbon electrode modified with 7,7,8,8-tetracyanoquinodimethane and graphene oxide triggered a synergistic effect: low-potential amperometric detection of reduced glutathione.

A sensitive electrochemical sensor based on the synergistic effect of 7,7,8,8-tetracyanoquinodimethane (TCNQ) and graphene oxide (GO) for low-potential amperometric detection of reduced glutathione (GSH) in pH 7.2 phosphate buffer solution (PBS) has been reported. This is the first time that the combination of GO and TCNQ have been successfully employed to construct an electrochemical sensor for the detection of glutathione. The surface of the glassy carbon electrode (GCE) was modified by a drop casting using TCNQ and GO. Cyclic voltammetric measurements showed that TCNQ and GO triggered a synergistic effect and exhibited an unexpected electrocatalytic activity towards GSH oxidation, compared to GCE modified with only GO, TCNQ or TCNQ/electrochemically reduced GO. Three oxidation waves for GSH were found at −0.05, 0.1 and 0.5 V, respectively. Amperometric techniques were employed to detect GSH sensitively using a GCE modified with TCNQ/GO at −0.05 V. The electrochemical sensor showed a wide linear range from 0.25 to 124.3 μM and 124.3 μM to 1.67 mM with a limit of detection of 0.15 μM. The electroanalytical sensor was successfully applied towards the detection of GSH in an eye drop solution

Rapid Invasion of Spartina alterniflora in the Coastal Zone of Mainland China: New Observations from Landsat OLI Images

Plant invasion imposes significant threats to biodiversity and ecosystem function. Thus, monitoring the spatial pattern of invasive plants is vital for effective ecosystem management. Spartina alterniflora (S. alterniflora) has been one of the most prevalent invasive plants along the China coast, and its spread has had severe ecological consequences. Here, we provide new observation from Landsat operational land imager (OLI) images. Specifically, 43 Landsat-8 OLI images from 2014 to 2016, a combination of object-based image analysis (OBIA) and support vector machine (SVM) methods, and field surveys covering the whole coast were used to construct an up-to-date dataset for 2015 and investigate the spatial variability of S. alterniflora in the coastal zone of mainland China. The classification results achieved good estimation, with a kappa coefficient of 0.86 and 96% overall accuracy. Our results revealed that there was approximately 545.80 km2 of S. alterniflora distributed in the coastal zone of mainland China in 2015, from Hebei to Guangxi provinces. Nearly 92% of the total area of S. alterniflora was distributed within four provinces: Jiangsu, Shanghai, Zhejiang, and Fujian. Seven national nature reserves invaded by S. alterniflora encompassed approximately one-third (174.35 km2) of the total area of S. alterniflora over mainland China. The Yancheng National Nature Reserve exhibited the largest area of S. alterniflora (115.62 km2) among the reserves. Given the rapid and extensive expansion of S. alterniflora in the 40 years since its introduction and its various ecological effects, geospatially varied responding decisions are needed to promote sustainable coastal ecosystems

Determination of Total Iron in Carbon-Containing Iron Ore by Perchloric Acid-Assisted Digestion and Potassium Dichromate Titration

Rapid and accurate determination of total iron content in iron ore is crucial for mineral processing research and improving iron-making efficiency. Iron ore needs to undergo beneficiation treatment before iron-making. Solid reducing agents, such as coal and brown coal, are commonly used in magnetic separation technology. However, the high carbon component in iron ore samples is difficult to completely remove during the dissolution process, resulting in cloudy or even black solutions. When SnCl2, TiCl3 reduction and potassium dichromate titration are used to determine total iron in iron ore containing solid reducing agents, direct determination of total iron becomes impossible. Although the traditional method of roasting to remove carbon before analysis can be used to determine the total iron content in iron ore, this method has large errors, a long analysis cycle (2−4h), and high energy consumption. A method is proposed here for removing carbon from the sample by adding perchloric acid during the dissolution process of sulfur phosphorus mixed acid, which has achieved good results. Through conditional experiments, the optimal condition for carbon removal was determined to be the drop-wise addition of perchloric acid when sulfuric acid smoke just left the liquid surface. This method was used to determine samples prepared from iron ore standard samples and bituminous coal standard samples in different proportions, with relative standard deviation (RSD) of 0.07%−0.43%, and relative error of 0.10%−0.28%. For samples prepared with solid reducing agents of lignite, coke, anthracite, and graphite in a 1∶1 ratio with iron ore standard samples, this method had good decarbonization effects, with RSD of 0.25%−0.33%, and relative error of 0.04%−0.19%. Using this method and the traditional method to determine actual samples with different carbon contents, the RSD ranged from 0.08%−0.40% and 0.14%−0.68%, respectively, and the analysis times for a single sample were 30min and 2−4h, respectively. Compared with the traditional roasting method, this method eliminates the step of removing carbon from roasted samples. It reduces the error of analysis results, improves work efficiency and reduces energy consumption with its simple operation process

Automated detection of MRI-negative temporal lobe epilepsy with ROI-based morphometric features and machine learning

Objective: Temporal lobe epilepsy (TLE) predominantly originates from the anteromedial basal region of the temporal lobe, and its prognosis is generally favorable following surgical intervention. However, TLE often appears negative in magnetic resonance imaging (MRI), making it difficult to quantitatively diagnose the condition solely based on clinical symptoms. There is a pressing need for a quantitative, automated method for detecting TLE.Methods: This study employed MRI scans and clinical data from 51 retrospective epilepsy cases, dividing them into two groups: 34 patients in TLE group and 17 patients in non-TLE group. The criteria for defining the TLE group were successful surgical removal of the epileptogenic zone in the temporal lobe and a favorable postoperative prognosis. A standard procedure was used for normalization, brain extraction, tissue segmentation, regional brain partitioning, and cortical reconstruction of T1 structural MRI images. Morphometric features such as gray matter volume, cortical thickness, and surface area were extracted from a total of 20 temporal lobe regions in both hemispheres. Support vector machine (SVM), extreme learning machine (ELM), and cmcRVFL+ classifiers were employed for model training and validated using 10-fold cross-validation.Results: The results demonstrated that employing ELM classifiers in conjunction with specific temporal lobe gray matter volume features led to a better identification of TLE. The classification accuracy was 92.79%, with an area under the curve (AUC) value of 0.8019.Conclusion: The method proposed in this study can significantly assist in the preoperative identification of TLE patients. By employing this method, TLE can be included in surgical criteria, which could alleviate patient symptoms and improve prognosis, thereby bearing substantial clinical significance