Objective Neurophysiological Indices for the Assessment of Chronic Tinnitus Based on EEG Microstate Parameters.

Chronic tinnitus is highly prevalent but lacks precise diagnostic or effective therapeutic standards. Its onset and treatment mechanisms remain unclear, and there is a shortage of objective assessment methods. We aim to identify abnormal neural activity and reorganization in tinnitus patients and reveal potential neurophysiological markers for objectively evaluating tinnitus. By way of analyzing EEG microstates, comparing metrics under three resting states (OE, CE, and OECEm) between tinnitus sufferers and controls, and correlating them with tinnitus symptoms. This study reflected specific changes in the EEG microstates of tinnitus patients across multiple resting states, as well as inconsistent correlations with tinnitus symptoms. Microstate parameters were significantly different when patients were in OE and CE states. Specifically, the occurrence of Microstate A and the transition probabilities (TP) from other Microstates to A increased significantly, particularly in the CE state (32-37%, p ≤ 0.05 ); and both correlated positively with the tinnitus intensity. Nevertheless, under the OECEm state, increases were mainly observed in the duration, coverage, and occurrence of Microstate B (15-47%, ), which negatively correlated with intensity ( [Formula: see text]-0.513, ). Additionally, TPx between Microstates C and D were significantly reduced and positively correlated with HDAS levels ( [Formula: see text] 0.548, ). Furthermore, parameters of Microstate D also correlated with THI grades ( [Formula: see text]-0.576, ). The findings of this study could offer compelling evidence for central neural reorganization associated with chronic tinnitus. EEG microstate parameters that correlate with tinnitus symptoms could serve as neurophysiological markers, contributing to future research on the objective assessment of tinnitus.

Land Fragmentation, Technology Adoption and Chemical Fertilizer Application: Evidence from China.

Although it has been widely recognized that land fragmentation has increased chemical fertilizer application, little is known about the role of technology adoption in mitigating these adverse effects. To empirically examine the relationship between land fragmentation, technology adoption and chemical fertilizer application, we developed a mediation model. We applied our analysis to a survey data set encompassing 1388 farm-level samples collected in 14 Chinese provinces in 2019. Our study demonstrated that land fragmentation can not only directly increase chemical fertilizer application but also indirectly increase it by hindering the adoption of agricultural mechanization technologies (AMT's) and soil testing fertilization technologies (STFT's). Both are recognized as potent drivers of fertilizer use reductions. Moreover, the adoption of information and communications technologies (ICT's) can help mitigate the negative effects of land fragmentation on technology adoption, thus reducing chemical fertilizer application intensity (CFAI). However, the direct effects of land fragmentation on CAFI was unaffected by ICT's. Our findings suggest that ICT's have revolutionized farmer recognition, promotion and adoption of agricultural technologies by increasing awareness and diffusion of agricultural technology information.

Gap-enhance Raman tags (GERTs) competitive immunoassay based Raman imaging for the quantitative detection of trace florfenicol in milk.

Surface Enhanced Raman Spectroscopy (SERS) is a highly sensitive detection method, however, it is still challenging to directly detect florfenicol due to its low Raman activity. To solve this problem, we present a novel sensor for selective detection of florfenicol residue in milk using gap-enhance Raman tags (GERTs) competitive immunoassay. In this nanosensor, Raman reporters 4-mercaptobenzoic acid (4MBA) were embedded in the 1 nm gap of Au core-gap-Au shell nanoparticles, which can protect 4MBA molecules from unwanted external environments and provide a high enhancement. The parameters affecting the detection sensitivity were discussed and optimized. The limit detection of florfenicol in milk can reach 0.1 µg/kg. To evaluate the performance of GERTs immunoassay, Ag@4MBA tags immunoassay was conducted and the results were compared. Moreover, Raman mapping was investigated to improve the quantitative prediction model with R2 of 0.981 achieved, by eliminating the signal fluctuations caused by the inhomogeneous distribution of GERTs.

Spatiotemporal Dynamics of Wetland in Dongting Lake Based on Multi-Source Satellite Observation Data during Last Two Decades.

Monitoring the dynamics of wetland resources has practical value for wetland protection, restoration and sustainable utilization. Dongting Lake wetland reserves are well known for both their intra-annual and inter-annual dynamic changes due to the effects of natural or human factors. However, most wetland monitoring research has failed to consider the seasonal wetlands, which is the most fragile wetland type, requiring more attention. In this study, we used multi-source time series remote sensing data to monitor three Dongting Lake wetland reserves between 2000 and 2020, and the seasonal wetlands were separated from permanent wetlands. Multispectral and indices time series were generated at 30 m resolution using a two-month composition strategy; the optimal features were then selected using the extension of the Jeffries-Matusita distance (JBh) and random forest (RF) importance score; yearly wetland maps were identified using the optimal features and the RF classifier. Results showed that (1) the yearly wetland maps had good accuracy, and the overall accuracy and kappa coefficients of all wetland maps from 2000 to 2020 were above 89.6% and 0.86, respectively. Optimal features selected by JBh can improve both computational efficiency and classification accuracy. (2) The acreage of seasonal wetlands varies greatly among multiple years due to inter-annual differences in precipitation and evaporation. (3) Although the total wetland area of the three Dongting Lake wetland reserves remained relatively stable between 2000 and 2020, the acreage of the natural wetland types still decreased by 197.0 km2, and the change from natural wetland to human-made wetland (paddy field) contributed the most to this decrease. From the perspective of the ecological community, the human-made wetland has lower ecological function value than natural wetlands, so the balance between economic development and ecological protection in the three Dongting Lake wetland reserves requires further evaluation. The outcomes of this study could improve the understanding of the trends and driving mechanisms of wetland dynamics, which has important scientific significance and application value for the protection and restoration of Dongting Lake wetland reserves.

[Development and test of a wheat chlorophyll, nitrogen and water content meter].

A portable meter was developed which can detect chlorophyll, nitrogen and moisture content of wheat leaf simultaneously, and can supply enough data for guiding fertilization and irrigation. This meter is composed of light path and electronic circuit. And this meter uses 660, 940 and 1450 nm LED together with narrow band filters as the active light source. The hardware circuit consists of micro-controller, LED drive circuit, detector, communication circuit, keyboard and LCD circuit. The meter was tested in the field and performed well with good repeatability and accuracy. The relative errors of chlorophyll and nitrogen test were about 10%, relative error for water content was 4%. The coefficients of variation of the three indices were all below 1.5%. All of these prove that the meter can be applied under the field condition to guide the wheat production.

Knowledge-Guided "Community Network" Analysis Reveals the Functional Modules and Candidate Targets in Non-Small-Cell Lung Cancer.

Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies that are the leading cause of cancer-related death worldwide. Although many NSCLC-related genes and pathways have been identified, there remains an urgent need to mechanistically understand how these genes and pathways drive NSCLC. Here, we propose a knowledge-guided and network-based integration method, called the node and edge Prioritization-based Community Analysis, to identify functional modules and their candidate targets in NSCLC. The protein-protein interaction network was prioritized by performing a random walk with restart algorithm based on NSCLC seed genes and the integrating edge weights, and then a "community network" was constructed by combining Girvan-Newman and Label Propagation algorithms. This systems biology analysis revealed that the CCNB1-mediated network in the largest community provides a modular biomarker, the second community serves as a drug regulatory module, and the two are connected by some contextual signaling motifs. Moreover, integrating structural information into the signaling network suggested novel protein-protein interactions with therapeutic significance, such as interactions between GNG11 and CXCR2, CXCL3, and PPBP. This study provides new mechanistic insights into the landscape of cellular functions in the context of modular networks and will help in developing therapeutic targets for NSCLC.

Next-Generation Sequencing and Proteomics of Cerebrospinal Fluid From COVID-19 Patients With Neurological Manifestations.

The SARS-CoV-2 and its variants are still hitting the world. Ever since the outbreak, neurological involvements as headache, ageusia, and anosmia in COVID-19 patients have been emphasized and reported. But the pathogenesis of these new-onset neurological manifestations in COVID-19 patients is still obscure and controversial. As difficulty always lay in the diagnosis of neurological infection, current reports to validate the presence of SARS-CoV-2 in cerebrospinal fluid (CSF) almost relied on the basic methods and warranted improvement. Here we reported a case series of 8 patients with prominent new-onset neurological manifestations, who were screened out from a patch of 304 COVID-19 confirmed patients. Next-generation sequencing (NGS) and proteomics were conducted in the simultaneously obtained CSF and serum samples of the selected patients, with three non-COVID-19 patients with matched demographic features used as the controls for proteomic analysis. SARS-CoV-2 RNA was detected in the CSF of four COVID-19 patients and was suspicious in the rest four remaining patients by NGS, but was negative in all serum samples. Proteomic analysis revealed that 185 and 59 proteins were differentially expressed in CSF and serum samples, respectively, and that only 20 proteins were shared, indicating that the proteomic changes in CSF were highly specific. Further proteomic annotation highlighted the involvement of complement system, PI3K-Akt signaling pathway, enhanced cellular interaction, and macrophages in the CSF proteomic alterations. This study, equipped with NGS and proteomics, reported a high detection rate of SARS-CoV-2 in the CSF of COVID-19 patients and the proteomic alteration of CSF, which would provide insights into understanding the pathological mechanism of SARS-CoV-2 CNS infection.

Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling.

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular mechanisms of PDAC at both the systems and molecular levels. Herein, we developed a computational method of predicting cancer genes and anticancer drug targets that combined three independent expression microarray datasets of PDAC patients and protein-protein interaction data. First, Support Vector Machine-Recursive Feature Elimination was applied to the gene expression data to rank the differentially expressed genes (DEGs) between PDAC patients and controls. Then, protein-protein interaction networks were constructed based on the DEGs, and a new score comprising gene expression and network topological information was proposed to identify cancer genes. Finally, these genes were validated by "druggability" prediction, survival and common network analysis, and functional enrichment analysis. Furthermore, two integrins were screened to investigate their structures and dynamics as potential drug targets for PDAC. Collectively, 17 disease genes and some stroma-related pathways including extracellular matrix-receptor interactions were predicted to be potential drug targets and important pathways for treating PDAC. The protein-drug interactions and hinge sites predication of ITGAV and ITGA2 suggest potential drug binding residues in the Thigh domain. These findings provide new possibilities for targeted therapeutic interventions in PDAC, which may have further applications in other cancer types.

Effects of drinking water with high iodine concentration on the intelligence of children in Tianjin, China.

OBJECTIVE: This study aimed to investigate the effects of drinking water with high concentrations of iodine on the intelligence of children in Tianjin, China. METHODS: It was a population-based health survey utilizing a random cluster sampling design conducted in June 2005. Participants were recruited from the total population of primary school children attending years 1-4 with ages ranging from 8 to 10 years. Intelligence quotient (IQ) was assessed using the combined Raven's test, second edition. Linear regression analyses were applied to test for any association between water iodine concentration and IQ. RESULTS: A total of 1229 students were recruited with a mean IQ of 105.8 (95% CI: 104.2-107.3). Water analyses indicated iodine concentrations were high in one rural region and exceedingly high in another with median values of 137.5 and 234.7 microg/l, respectively. There was a significant association between residing in the very high water iodine region and a reduction of IQ by an average of about nine points (P = 0.022), after adjusting for the potential confounding factors. CONCLUSION: Exposure to high iodine concentrations in drinking water has detrimental effects on the intelligence of children.

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra.

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models' performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

[Effect of high iodine concentration in drinking water on thyroid function of elementary school student in Tianjin].

OBJECTIVE: To study the effect of high iodine concentration in drinking water on thyroid function of elementary school student in Tianjin. METHODS: According to the water monitoring results and determination standard of iodine concentration excess area, urine iodine level and thyroid condition of elementary students were tested in the area with high and low water iodine concentration. Meanwhile the condition of iodized salt intaked students' family was inspected. RESULTS: The content of urine iodine and goiter rate of students in the area with high water iodine concentration were significantly higher than those with low water iodine concentration. But the iodine concentration of edible salt in students' family in the area with high water iodine concentration were significantly lower than those of students' family with low water iodine concentration. CONCLUSION: High water iodine concentration could impact thyroid function of elementary students in Tianjin.

[Health damage of indoor air pollution by decoration on residents].

OBJECTIVE: To explore the health damage of indoor air pollution on residents by detecting the major indoor air pollutants and surveying the health indexes of residents. METHODS: 159 decorated houses were divided into 2 groups by whether the concentration of formaldehyde exceeded national standard. To analyze the health damage of formaldehyde and TVOC on residents, respectively. RESULTS: The concentrations of formaldehyde of the over standard group and the corresponding under standard group were (0.170 +/- 0.075) mg/m3 and (0.064 +/- 0.022) mg/m3, the concentrations of TVOC of the over standard group and the corresponding under standard group were (2.033 +/- 1.161) mg/m3 and (0.271 +/- 0.142) mg/m3. The incidence of the fatigue, nausea, eye symptoms, symptoms of nose, throat dessication, skin dessication, skin itching sensation and red swelling of the skin of the over standard group was significantly increased than that of the corresponding under standard group (P < 0.05). The incidence of the nausea, skin itching sensation and breathe hard of the TVOC over standard group was significantly increased than that of the corresponding under standard group (P < 0.05). CONCLUSION: Formaldehyde or TVOC will cause severe health damage.