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Several epidemiological studies have investigated the association between sugar intake, the levels of systolic blood pressure (SBP) and diastolic blood pressure (DBP) and the risk of hypertension, but findings have been inconsistent. We carried out a systematic review and meta-analysis of observational studies to examine the associations between sugar intake, hypertension risk, and BP levels. Articles published up to February 2, 2021 were sourced through PubMed, EMBASE and Web of Science. Pooled relative risks (RRs) and 95% confidence intervals (CIs) were estimated using a fixed- or random-effects model. Restricted cubic splines were used to evaluate dose-response associations. Overall, 35 studies were included in the present meta-analysis (23 for hypertension and 12 for BP). Sugar-sweetened beverages (SSBs) and artificially sweetened beverages (ASBs) were positively associated with hypertension risk: 1.26 (95% CI, 1.15–1.37) and 1.10 (1.07–1.13) per 250-g/day increment, respectively. For SBP, only SSBs were significant with a pooled β value of 0.24 mmHg (95% CI, 0.12–0.36) per 250 g increase. Fructose, sucrose, and added sugar, however, were shown to be associated with elevated DBP with 0.83 mmHg (0.07–1.59), 1.10 mmHg (0.12–2.08), and 5.15 mmHg (0.09–10.21), respectively. Current evidence supports the harmful effects of sugar intake for hypertension and BP level, especially SSBs, ASBs, and total sugar intake. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop clinical scores to predict the risk of intensive care unit (ICU) admission in patients with COVID-19 and end stage kidney disease (ESKD). This was a prospective study in which 100 patients with ESKD were enrolled and divided into two groups: the ICU group and the non-ICU group. We utilized univariate logistic regression and nonparametric statistics to analyze the clinical characteristics and liver function changes of both groups. By plotting receiver operating characteristic curves, we identified clinical scores that could predict the risk of ICU admission. Out of the 100 patients with Omicron infection, 12 patients were transferred to the ICU due to disease aggravation, with an average of 9.08 days from hospitalization to ICU transfer. Patients transferred to the ICU more commonly experienced shortness of breath, orthopnea, and gastrointestinal bleeding. The peak liver function and changes from baseline in the ICU group were significantly higher, with p values <.05. We found that the baseline platelet-albumin-bilirubin score (PALBI) and neutrophil-to-lymphocyte ratio (NLR) were good predictors of ICU admission risk, with area under curve values of 0.713 and 0.770, respectively. These scores were comparable to the classic Acute Physiology and Chronic Health Evaluation II (APACHE-II) score (p >.05). Patients with ESKD and Omicron infection who are transferred to the ICU are more likely to have abnormal liver function. The baseline PALBI and NLR scores can better predict the risk of clinical deterioration and early transfer to the ICU for treatment. [ABSTRACT FROM AUTHOR]

Aquilaria sinensis (Lour.) Gilg (ASG) has been used as traditional medicine for centuries. However, the active ingredients from leaves and their anti-inflammatory mechanism are rarely reported. The network pharmacology and molecular docking strategies were applied to explore the potential mechanisms of Benzophenone compounds from the leaves of ASG (BLASG) against inflammation. BLASG-related targets were obtained from the SwissTargetPrediction and PharmMapper databases. Inflammation-associated targets were retrieved from GeneGards, DisGeNET, and CTD databases. Cytoscape software was used to draw a network diagram of BLASG and its corresponding targets. DAVID database was applied for enrichment analyses. A protein-protein interaction (PPI) network was constructed to identify the hub targets of BLASG. Molecular docking analyses were performed by AutoDockTools 1.5.6. Moreover, we used ELISA and qRT-PCR assays to validate the anti-inflammatory effects of BLASG by cell experiments. Four BLASG were extracted from ASG, and corresponding 225 potential targets were identified. PPI network analysis indicated that SRC, PIK3R1, AKT1, and other targets were the core therapeutic targets. Enrichment analyses revealed that the effects of BLASG are regulated by targets associated with apoptosis and inflammation-related pathways. In addition, molecular docking revealed that BLASG combined well with PI3K and AKT1. Furthermore, BLASG significantly decreased the inflammatory cytokines levels and down-regulated PIK3R1 and AKT1 gene expression in RAW264.7 cells. Our study predicted the potential targets and pathways of BLASG against inflammation, which offered a promising strategy to reveal the therapeutic mechanism of natural active components in the treatment of diseases. [ABSTRACT FROM AUTHOR]

Scientists are faced with a formidable problem in improving the smart properties of multi-stimuli responsive fluorescent materials. Here, we integrated two kinds of stimuli-responsive materials: aggregation-induced emission (AIE) and liquid crystals (LCs) into one system. An AIE-active fluorescence liquid crystal compound of α-cyanodistyrene derivate Py-CN-LBM8 was synthesised using knoevenagel condensation reaction. Its AIE effect, LC behaviour, multi-stimuli responsive properties and mechanisms were investigated by polarised optical microscopes (POM), differential scanning calorimetry (DSC), X-ray diffraction (XRD), UV-visible (UV-vis) absorption spectra and photoluminescence (PL) spectra. The results showed that Py-CN-LBM8 exhibited significant physical response to external stimuli including temperature, mechanical force, solvent, pH and trace water. This work provided a strategy for designing new multi-stimuli responsive materials and made it possible to apply in the multifunction anti-counterfeit, sensor and information storage fields. [ABSTRACT FROM AUTHOR]

Satellite remote sensing provides effective methods for fog detection at large scales. At dawn or dusk, however, the separation of fog from the surface remains a great challenge because of the minor radiation difference between fog and the surface. This study develops a novel algorithm for fog detection suitable for dawn and dusk from high-frequency data of the Himawari-8 satellite. Methodologically, the area was divided into three subregions according to the distance of the terminator line. In regions at daytime that are far from the terminator line, a fog detection index of the low solar altitude angle (FDILSAA) was built to isolate the fog through the relationship between the mid-infrared (MIR) brightness temperature (IMIR) and its difference with the thermal infrared (TIR) brightness temperature (BTD = IMIR – ITIR, where IMIR and ITIR are the brightness temperatures at the MIR and TIR bands). In regions near the terminator line, the fog was extracted from the surface by a Gaussian mixture model (FD-GMMNTL) by the differences in MIR and TIR radiation changes. After that, the fog was further extracted from the low and mid-high clouds by the enhanced low cloud detection index (ELCDI) and by the difference in BTD and TIR characteristics. In regions at night, a BTD threshold value was adopted to detect fog. Validation results demonstrated that the algorithm in this study could precisely detect large-region radiation fog including dawn and dusk, with the overall probability of detection (POD) accuracies range from 76.3% to 89.2% at dawn and 67.3% to 77.0% at dusk. The relatively low accuracy at dusk was mainly attributed to desert in the study area, which has a similar BTD with fog after a day of solar radiation. Results of this study provide new insight for fog detection at dawn and dusk, which enhances the application of remote sensing in meteorology forecasting. [ABSTRACT FROM AUTHOR]

Even though many studies have shown the usefulness of clustering for the exploration of spatio-temporal patterns, until now there is no systematic description of clustering methods for geo-referenced time series (GTS) classified as one-way clustering, co-clustering and tri-clustering methods. Moreover, the selection of a suitable clustering method for a given dataset and task remains to be a challenge. Therefore, we present an overview of existing clustering methods for GTS, using the aforementioned classification, and compare different methods to provide suggestions for the selection of appropriate methods. For this purpose, we define a taxonomy of clustering-related geographical questions and compare the clustering methods by using representative algorithms and a case study dataset. Our results indicate that tri-clustering methods are more powerful in exploring complex patterns at the cost of additional computational effort, whereas one-way clustering and co-clustering methods yield less complex patterns and require less running time. However, the selection of the most suitable method should depend on the data type, research questions, computational complexity, and the availability of the methods. Finally, the described classification can include novel clustering methods, thereby enabling the exploration of more complex spatio-temporal patterns. [ABSTRACT FROM AUTHOR]

In this work, alkyd resin and 3-glycidyloxypropyl-polyhedral oligomeric silsesquioxanes were synthesized from castor oil and siloxane, respectively, and used to as the modifier of the epoxy resins. Laminate of fiberglass-reinforced composite were prepared from alkyd-epoxy resin with different 3-glycidyloxypropyl-polyhedral oligomeric silsesquioxanes content. The properties of nanocomposite were characterized. The results showed that the 3-glycidyloxypropyl-polyhedral oligomeric silsesquioxanes could increase the impact strength of nanocomposite. When the content of 3-glycidyloxypropyl-polyhedral oligomeric silsesquioxanes is 6 wt% of the alkyd resin, theTgenhances to 5.2°C, the impact strength increases to 53.01 kJ/m2(64%), but the tensile strength decreases 15.03 MPa (7.6%). Nanocomposite has good electrical properties and insulating properties. [ABSTRACT FROM AUTHOR]

Clustering is often used to explore patterns in georeferenced time series (GTS). Most clustering studies, however, only analyze GTS from one or two dimension(s) and are not capable of the simultaneous analysis of the data from three dimensions: spatial, temporal, and any third (e.g., attribute) dimension. Here we develop a novel clustering algorithm called the Bregman cuboid average triclustering algorithm with I-divergence (BCAT_I), which enables the complete partitional analysis of 3D GTS. BCAT_I simultaneously groups the data along its dimensions to form regular triclusters. These triclusters are subsequently refined using k means to fully capture spatiotemporal patterns in the data. By applying BCAT_I to time series of daily average temperature in The Netherlands (twenty-eight weather stations from 1992 to 2011), we identified the refined triclusters with similar temperature values along the spatial dimension (weather stations that represent locations) and two nested temporal dimensions (year and day). Geovisualization techniques were then used to display the patterns of intra-annual variability in temperature. Our results show that in the last two thirds of the study period, there is an intense variability of spring and winter temperatures in the northeast and center of The Netherlands. For the same period, an intense variability of spring temperatures is also visible in the southeast of the country. Our results also show that summer temperatures are homogenous across the country for most of the study period. This particular application demonstrates that BCAT_I enables a complete analysis of 3D GTS and, as such, it contributes to a better understanding of complex patterns in spatiotemporal data. [ABSTRACT FROM AUTHOR]

A proper characterization of land-use types is critical for constructing generalization constraints to guide and control landuse data generalization. This paper focused on identification and utilization of their importance based upon land-use distributions and application themes. First, this importance was identified using a three-step method that links a diversity index, a multiple attribute decision model and a spatial association analysis. Second, with the importance, a mathematical function was designed to determine minimum area thresholds of land-use polygons as an example of generalization constraints. Third, the importance was used to assist in the selection of generalization operators and evaluation of generalization outcomes. Fourth, a land-use dataset at 1:10 000, describing the land use of a typical rural area in Hubei province of China, was generalized towards a 1:50 000 dataset to verify the effects of the presented method and function. Three additional tests were implemented to analyze the sensitivity of the importance of land-use types on setting the minimum area threshold and generalization operations. The outcome showed that the proposed methods and functions make land-use data generalization more adaptable for in-use datasets and applications. [ABSTRACT FROM PUBLISHER]

With the ever-growing number of people who work at visual display terminals, the work-related musculoskeletal disorders of the upper body are believed to be an important problem all over the world. The forearm support, which can keep the forearm and wrist in biomechanical posture, is a possible protective factor of the development of upper body syndrome. This meta-analysis examines the efficacy of forearm support in reducing upper body syndrome. The Cochrane Library, EMBASE, Ovid, ScienceDirect, SpringerLink, Google Scholar, CNKI database, and Wanfang database were searched from inception until May 29, 2013. Relevant studies were included after the screening of title, abstract, and the full text. Impact of bias was assessed independently by 2 authors. Four studies that met all the inclusion criteria were included finally. The combined results based on all studies suggested that statistically the forearm support had a nonsignificant effect on upper body syndrome (odds ratio [OR] = 0.70, 95% confidence interval [CI]: 0.49, 1.02). The result of subgroup analysis suggested that forearm support has a significant effect on neck or shoulder syndrome (OR = 0.70, 95% CI: 0.43, 1.14) and the effect on upper extremity syndrome (OR = 0.76, 95% CI: 0.49, 1.19) is not significant. This meta-analysis suggested that the forearm support had statistically nonsignificant effect on preventing upper body syndrome on the whole. [ABSTRACT FROM PUBLISHER]

Clustering allows considering groups of similar data elements at a higher level of abstraction. This facilitates the extraction of patterns and useful information from large amounts of spatio-temporal data. Till now, most studies have focused on the extraction of patterns from a spatial or a temporal aspect. Here we use the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) to enable the simultaneous analysis of spatial and temporal patterns in geo-referenced time series (time evolving values of a property observed at fixed geographical locations). In addition, we present three geovisualization techniques to fully explore the co-clustering results: heatmaps offer a straightforward overview of the results; small multiples display the spatial and temporal patterns in geographic maps; ringmaps illustrate the temporal patterns associated to cyclic timestamps. To illustrate this study, we used Dutch daily average temperature data collected at 28 weather stations from 1992 to 2011. The co-clustering algorithm was applied hierarchically to understand the spatio-temporal patterns found in the data at the yearly, monthly and daily resolutions. Results pointed out that there is a transition in temperature patterns from northeast to southwest and from ‘cold’ to ‘hot’ years/months/days with only 3 years belonging to ‘cool’ or ‘cold’ years. Because of its characteristics, this newly introduced algorithm can concurrently analyse spatial and temporal patterns by identifying location-timestamp co-clusters that contain values that are similar along both the spatial and the temporal dimensions. [ABSTRACT FROM PUBLISHER]

Sea fog is a problematic weather phenomenon for marine transportation and navigation. Lacking ground observations, sea fog monitoring mainly depends on meteorological and environmental satellites because they provide large swaths of data with good spatial and temporal resolution. The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites provides several new features for sea fog detection because of the more available channels. In addition to the traditional variable-dual channel difference (DCDIR), which is widely used to detect sea fog and stratus clouds at night, this study uses and analyses several other variables including NDSI (normalized difference snow index), BTDback(brightness temperature difference in the thermal infrared channel between a sea fog/stratus cloud pixel and nearby clear-sky ocean surface), NWVI (normalized difference near-infrared water vapour index) and D_NWVI (NWVI difference between a possible sea fog/stratus cloud pixel and nearby clear-sky ocean surface), for all seasons. BTDback, NWVI, and D_NWVI show outstanding ability to discriminate between sea fog and stratus clouds. Automatic sea fog detection algorithms are developed using these variables for both daytime and night time with Terra/MODIS data based on a threshold scheme. During development of the algorithms, a series of data processes are also considered to maintain stable performance of the algorithms over wide areas and in all seasons. The algorithms are applied to Terra/MODIS data at a semi-operational mode from 2007 to 2013 and show promising results. Validation with data from field campaigns, one buoy station with good maintenance, 18 weather stations, and CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization)/CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) demonstrates the accuracy of the algorithms. The sea fog detection results are highly consistent with fog observations from the field campaign data. The validation with the buoy station data shows an overall accuracy of 90% under all weather conditions, an accuracy of 86% during foggy weather condition, and a KSS (Hanssen–Kuiper Skill Score) of 0.81. The validation with two-year data from 18 weather stations and CALIOP/CALIPSO over the Bohai Sea and Yellow Sea shows accuracy of 76.3% and 77.9%, respectively. The promising results indicate high probability of applying the algorithms in operational systems over the oceans adjacent to China or even wider oceans using Terra/MODIS data. [ABSTRACT FROM PUBLISHER]

Objective: In order to evaluate the clinical and pathological characteristics of diuretics associated acute kidney injury (AKI) and its management. Methods: We performed a retrospective study including 131 cases that diagnosed as diuretics associated AKI from 1 January 1999 to 1 January 2010 in Ruijin Hospital affiliated to Shanghai Jiao Tong University. Drug applications and its related clinical, laboratory and histological data were collected. Results: The male to female ratio was 2:1. The proportion of ages <20 years, 20-40 years, 40-60 years and ≥60 years were 6.9%, 17.6%, 27.5% and 48.1% respectively. Most patients (96.2%) had at least one complication of which chronic kidney disease (CKD) occurred most frequently (72 in 131, 55.0%). We divided all the patients to diuretic group ( N = 131) and non-diuretic group ( N = 185) based on diuretics history. We found patients in diuretic group had higher rates of CVD (38.9% vs. 18.4%), hypertension (42.0% vs. 29.2%), CKD (55.0% vs. 27.0%) and DM (17.6% vs. 4.3%) than non-diuretic group. Of 131 diuretics associated AKI, 36 cases (27.5%) were caused by diuretics only, 39 cases (29.8%) were caused by the combination of diuretics and other drugs like antibiotics, contrast media, ACEI or NSAIDs, and 56 cases (42.7%) had other AKI risk factors such as operation, infection, acute heart failure or hepatorenal syndrome. In addition, our data suggested the severity of RIFLE classification and pathological injury of glomerular basement membrane was higher in large-dosage furosemide group (> = 120 mg/d) than in low-dosage group (<120 mg/d). The most common lesion induced by diuretics was vacuolar degeneration of tubular epithelial cell. Logistic regression analysis showed predictors of all-cause mortality were age, gender, RIFLE classification when AKI onset. Age and RIFLE classification were predictive factor of non-complete recovery. Conclusion: This article firstly focuses on diuretics associated AKI, whose onset was related to aging, primary diseases and diuretic dosage. The combination of diuretics with other drugs such as antibiotics, contrast media, ACEI, NSAIDs, etc. would synergistically induced AKI. The pathological lesion of diuretics associated AKI may be mostly manifested vacuolar degeneration of tubular epithelial cell. Aging, gender, severity of RIFLE classification may be predictive factors of all-cause mortality of diuretics associated AKI. [ABSTRACT FROM AUTHOR]

Analysing spatio-temporal weather patterns is fundamental to better understand the system Earth. Such patterns depend on the spatial and temporal resolution of the available data. Here, we study a particular spatio-temporal pattern, namely, synchronisation, and how this is affected by different temporal resolutions and temporal heterogeneity. Twenty years of daily temperature data collected in 28 Dutch meteorological stations are used as case study. Given the complexity of the analysis, we propose a geovisual analytic approach based on self-organizing maps (SOMs). This approach allows exploring the data from two perspectives: (1) station-based, in which spatially synchronous weather stations are grouped into clusters; and (2) year-based, in which temporal synchronisation is analysed using a calendar year as basic unit and similar years are clustered. Clusters are identified using the SOM U-matrices and maps. Next, the spatial distribution of synchronous stations is displayed in the geographic space. Trend plots are used to illustrate trends in every cluster and the temperatures of stations and years are compared with the corresponding cluster representative values to identify anomalies in the temperature records. The analysis is repeated at daily, weekly and monthly resolutions to study the effects of different temporal resolutions on synchronisation. Also daily spatial synchronisation results for all years with those for groups of daily synchronous years are analysed to study the effects of temporal heterogeneity. Results show that synchronisation results are different at different temporal resolutions. Monthly results are the most stable ones both in station-based and year-based. It is also observed that spatial synchronisation results are simplified when considering temporal heterogeneity. [ABSTRACT FROM AUTHOR]