THE CONSTRUCTION OF SMALL TOWN INFORMATION PORTAL USING OPEN SOURCE SOFTWARE

Abstract: Along with the development of small towns, traditional or common methods of urban informatization construction are not fit for small towns. Therefore it's essential to bring forward an appropriate way. By studying on the latest open source portal software uPortal, the paper discussed the application of personalized service, portal technology and information integration technology in informatization construction of small towns. Finally, the design and realization of the information portal and a portal website of small towns, which achieve the management and sharing of information in small towns, were presented

Staphylococcus cohnii infection diagnosed by metagenomic next generation sequencing in a patient on hemodialysis with cirrhotic ascites: a case report

BackgroundPatients with spontaneous bacterial peritonitis (SBP) often just receive empirical antibiotic therapy, as pathogens can be identified in only few patients using the techniques of conventional culture. Metagenomic next generation sequencing (mNGS) is a useful tool for diagnosis of infectious diseases. However, clinical application of mNGS in diagnosis of infected ascites of cirrhotic patients is rarely reported.Case presentationA 53-year-old male with cirrhosis on regular hemodialysis presented with continuous abdominal pain. After treatment with empiric antibiotics, his inflammatory parameters decreased without significant relief of abdominal pain. Finally, based on ascites mNGS detection, he was diagnosed as infection of Staphylococcus cohnii (S.cohnii), a gram-positive opportunistic pathogen. With targeted antibiotic treatment, the bacterial peritonitis was greatly improved and the patient’s abdominal pain was significantly alleviated.ConclusionsWhen conventional laboratory diagnostic methods and empirical antibiotic therapy fail, proper application of mNGS can help identify pathogens and significantly improve prognosis and patients’ symptoms

Re-analysis of gene mutations found in pituitary stalk interruption syndrome and a new hypothesis on the etiology

BackgroundPituitary stalk interruption syndrome (PSIS) is a complex clinical syndrome characterized by varied pituitary hormone deficiencies, leading to severe manifestations across multiple systems. These include lifelong infertility, short stature, mental retardation, and potentially life-threatening pituitary crises if not promptly diagnosed and treated. Despite extensive research, the precise pathogenesis of PSIS remains unclear. Currently, there are two proposed theories regarding the pathogenic mechanisms: the genetic defect theory and the perinatal injury theory.MethodsWe systematically searched English databases (PubMed, Web of Science, Embase) and Chinese databases (CNKI, WanFang Med Online, Sinomed) up to February 24, 2023, to summarize studies on gene sequencing in PSIS patients. Enrichment analyses of reported mutated genes were subsequently performed using the Metascape platform.ResultsOur study included 37 articles. KEGG enrichment analysis revealed mutated genes were enriched in the Notch signaling pathway, Wnt signaling pathway, and Hedgehog signaling pathway. GO enrichment analysis demonstrated mutated genes were enriched in biological processes such as embryonic development, brain development, axon development and guidance, and development of other organs.ConclusionBased on our summary and analyses, we propose a new hypothesis: disruptions in normal embryonic development, partially stemming from the genetic background and/or specific gene mutations in individuals, may increase the likelihood of abnormal fetal deliveries, where different degrees of traction during delivery may lead to different levels of pituitary stalk interruption and posterior lobe ectopia. The clinical diversity observed in PSIS patients may result from a combination of genetic background, specific mutations, and variable degrees of traction during delivery

Real-world observations and impacts of Chinese herbal medicine for migraine: results of a registry-based cohort study

Background: Migraine is a prevalent, recurrent condition with substantial disease burden. Chinese herbal medicine (CHM) has been used frequently for migraine in controlled clinical settings. This study is to summarise the characteristics of patients who seek clinical care in a tertiary Chinese medicine hospital in China; to gather their preferences and values of using CHM; to explore the effect of CHM for migraine and its comorbidities in a real-world setting, and to collect first-hand expertise of clinicians’ practice pattern in prescribing CHM for migraine.Methods: This registry-based cohort study was prospectively conducted at Guangdong Provincial Hospital of Chinese Medicine from December 2020 to May 2022. Adult migraine patients seeking their initial anti-migraine clinical care at the hospital were consecutively recruited and followed up for 12 weeks. Practitioners specialised in headache management prescribed individualised treatments without research interference. Standardised case report forms were employed to gather information on patients’ preferences and perspective of seeking clinical care, as well as to assess participants’ migraine severity, comorbidities, and quality of life, at 4-weeks intervals. Various analytical methods were utilised based on the computed data.Results: In this study, we observed 248 participants. Of these, 73 received CHM treatment for 28 days or longer. Notably, these participants exhibited a greater disease severity, compared to those treated with CHM for less than 28 days. Of the 248 participants, 83.47% of them expected CHM would effectively reduce the severity of their migraine, around 50% expected effects for migraine-associated comorbidities, while 51.61% expressing concerns about potential side effects. CHM appeared to be effective in reducing monthly migraine days and pain intensity, improving patients’ quality of life, and potentially reducing comorbid anxiety, with a minimum of 28 days CHM treatment. Herbs such as gan cao, gui zhi, chuan xiong, fu ling, bai zhu, yan hu suo, etc. were frequently prescribed for migraine, based on patients’ specific symptoms.Conclusion: CHM appeared to be beneficial for migraine and comorbid anxiety in real-world clinical practice when used continuously for 28 days or more.Clinical Trial Registration:clinicaltrials.gov, identifier ChiCTR2000041003

Analysis and Prediction of Changes in Coastline Morphology in the Bohai Sea, China, Using Remote Sensing

Coastline change reflects the dynamics of natural processes and human activity, and influences the ecology and environment of the coastal strip. This study researched the change in coastline and sea area of the Bohai Sea, China, over a 30-year period using Landsat TM and OLI remote sensing data. The total change in coastline length, sea area, and the centroid of the sea surface were quantified. Variations in the coastline morphology were measured using four shape indexes: fractal dimension, compact ratio, circularity, and square degree. Equations describing fit of the shape index, coastline length, and marine area were built. Then the marine area 10 years later was predicted using the model that had the highest prediction accuracy. The results showed that the highest prediction accuracy for the coastline length was obtained using a compound function. When a cubic function was used to predict the compact ratio, then the highest prediction accuracy was obtained using this compact ratio and a quadratic function to predict sea area. This study can provide theoretical support for the coastal development planning and ecological environment protection around the Bohai Sea

Effects of Varying Particle Sizes and Different Types of LDH-Modified Anthracite in Simulated Test Columns for Phosphorous Removal

A comparative study was carried out for the removal of phosphorus in simulated unplanted vertical-flow constructed wetlands with different layered double hydroxide (LDHs) coated anthracite substrates. Three particle sizes of anthracites were selected and modified separately with nine kinds of LDH coating. The simulated substrates test columns loaded with the original and modified anthracites were constructed to treat the contaminated water. For the medium and large particle size modified anthracite substrates, the purification effects of total phosphorus, total dissolved phosphorus and phosphate were improved by various degrees, and the purification effect of the medium particle size anthracite is better than that of the large size one. The medium size anthracite modified by ZnCo-LDHs had optimal performance with average removal efficiencies of total phosphorus, total dissolved phosphorus and phosphate reaching 95%, 95% and 98%, respectively. The maximum adsorption capacity on ZnCo-LDHs and ZnAl-LDHs modified medium sizes anthracites were 65.79 (mg/kg) and 48.78 (mg/kg), respectively. In comparison, the small size anthracite is not suitable for LDHs modification

Remote Sensing Inversion of Suspended Matter Concentration Using a Neural Network Model Optimized by the Partial Least Squares and Particle Swarm Optimization Algorithms

Suspended matter concentration is an important index for the assessment of a water environment and it is also one of the core parameters for remote sensing inversion of water color. Due to the optical complexity of a water body and the interaction between different water quality parameters, the remote sensing inversion accuracy of suspended matter concentration is currently limited. To solve this problem, based on the remote sensing images from Gaofen-2 (GF-2) and the field-measured suspended matter concentration, taking a section of the Haihe River as the study area, this study establishes a remote sensing inversion model. The model combines the partial least squares (PLS) algorithm and the particle swarm optimization (PSO) algorithm to optimize the back-propagation neural network (BPNN) model, i.e., the PLS-PSO-BPNN model. The partial least squares algorithm is involved in screening the input values of the neural network model. The particle swarm optimization algorithm optimizes the weights and thresholds of the neural network model and it thus effectively overcomes the over-fitting of the neural network. The inversion accuracy of the optimized neural network model is compared with that of the partial least squares model and the traditional neural network model by determining the coefficient, the mean absolute error, the root mean square error, the correlation coefficient and the relative root mean square error. The results indicate that the root mean squared error of the PLS-PSO-BPNN inversion model was 3.05 mg/L, which is higher than the accuracy of the statistical regression model. The developed PLS-PSO-BPNN model could be widely applied in other areas to better invert the water quality parameters of surface water

Remote Sensing Inversion of Suspended Matter Concentration Using a Neural Network Model Optimized by the Partial Least Squares and Particle Swarm Optimization Algorithms

Suspended matter concentration is an important index for the assessment of a water environment and it is also one of the core parameters for remote sensing inversion of water color. Due to the optical complexity of a water body and the interaction between different water quality parameters, the remote sensing inversion accuracy of suspended matter concentration is currently limited. To solve this problem, based on the remote sensing images from Gaofen-2 (GF-2) and the field-measured suspended matter concentration, taking a section of the Haihe River as the study area, this study establishes a remote sensing inversion model. The model combines the partial least squares (PLS) algorithm and the particle swarm optimization (PSO) algorithm to optimize the back-propagation neural network (BPNN) model, i.e., the PLS-PSO-BPNN model. The partial least squares algorithm is involved in screening the input values of the neural network model. The particle swarm optimization algorithm optimizes the weights and thresholds of the neural network model and it thus effectively overcomes the over-fitting of the neural network. The inversion accuracy of the optimized neural network model is compared with that of the partial least squares model and the traditional neural network model by determining the coefficient, the mean absolute error, the root mean square error, the correlation coefficient and the relative root mean square error. The results indicate that the root mean squared error of the PLS-PSO-BPNN inversion model was 3.05 mg/L, which is higher than the accuracy of the statistical regression model. The developed PLS-PSO-BPNN model could be widely applied in other areas to better invert the water quality parameters of surface water

Estimation of Fuel Water Content in the Forest Ecotone of Guangzhou Based on the PROSAIL Model

Fuel moisture content (FMC), which is the ratio of equivalent water thickness (EWT) to dry matter content (DMC), plays a crucial role in the estimation of vegetation ignition probability and the fire propagation rate. The PROSAIL model can adequately simulate the canopy reflectance of vegetation, with the input of field-measured data into the model ensuring conformity with the ecological rules. If the EWT and DMC can be estimated by an empirical statistical method according to the reflectance spectrum, the universality of the physical model and the efficiency of the empirical statistical method can be considered. In this study, a fast and versatile method is established for estimating FMC based on the EWT, DMC, leaf area index measured data, and the PROSAIL model. The Normalized Difference Infrared Index (NDII) and Normalized Dry Matter Index (NDMI) were obtained from the spectral curves, with the results showing an obvious linear relationship between the NDII and EWT, NDMI, and DMC. Therefore, EWT and DMC can be estimated using the NDII and NDMI. The accuracy of the estimation results is verified to be high. The estimation model can be extended to Landsat 8 data to estimate FMC. The estimated FMC data verified by the measured data showed that R² was 0.743 and the RMSE was 34.2%. The model accuracy was reliable owing to large dynamic changes in the FMC. However, the estimated value of the FMC shifted significantly to the left during this study. The reasons for this are as follows: 1) The field-measured points are not sufficient to support the analysis according to different vegetation types, and the physical and chemical properties of different types are varied, leading to altered simulated spectral curves; 2) The vegetation spectrum is sensitive to the DMC at 1,650 nm, 1,720 nm, and 2,270 nm bands, and the sensitivity near the 1,650 nm and 1,720 nm bands is greater than that at 2,270 nm. However, because the Landsat 8 image does not have a 1,720 nm band, the 2,270 nm band was selected to calculate the NDMI, making the value of the estimated DMC too large, resulting in a small value of the estimated FMC and a significant shift to the left; 3) 1,650 nm and 2,270 nm are not in the central wavelength of the two bands of Landsat 8; therefore, the estimated DMC and FMC are shifted. In addition, the fast and versatile method, which is established based on the EWT, DMC, leaf area index measured data and the PROSAIL model, can realize large-scale and high-precision monitoring of the FMC, providing a scientific reference for forest fire prevention