Breast cancer, ranking first among women's cancers worldwide, develops from the breast tissue. Study of the breast tissue is, therefore of great significance to the diagnosis and treatment of breast ...cancer. Exosomes, acting as an effective communicator between cells, are in the ascendant in recent years. One of the most important cargoes contained in the exosomes is microRNAs, belonging to the non-coding RNA family. When the exosomal microRNAs are absorbed into the intracellular location, most of the microRNAs will act as tumor promoters or suppressors by inhibiting the translation process of the target mRNA, thus affecting the behavior of other stromal cells in the tumor microenvironment. At present, growing research focuses on the different types of donor cell sources, their contribution to cancer, miRNA profiling, their biomarker potential, etc. This review aims to state the function of diverse miRNAs in exosomes medicated cell-cell communication and the potency of some specific enriched miRNAs as molecular markers in clinical trials. We also describe the mechanism of anti-cancer compounds through exosomes and the exploration of artificially engineered techniques that lead miRNA-inhibitors into exosomes for therapeutic use.
For high-precision positioning applications, various GNSS errors need to be mitigated, including the tropospheric error, which remains a significant error source as it can reach up to a few meters. ...Although some commercial GNSS correction data providers, such as the Quasi-Zenith Satellite System (QZSS) Centimeter Level Augmentation Service (CLAS), have developed real-time precise regional troposphere products, the service is available only in limited regional areas. The International GNSS Service (IGS) has provided precise troposphere correction data in TRO format post-mission, but its long latency of 1 to 2 weeks makes it unable to support real-time applications. In this work, a real-time troposphere prediction method based on the IGS post-processing products was developed using machine learning techniques to eliminate the long latency problem. The test results from tropospheric predictions over a year using the proposed method indicate that the new method can achieve a prediction accuracy (RMSE) of 2 cm, making it suitable for real-time applications.
To establish and validate an individualized nomogram to predict the probability of death within 30 days in patients with sepsis-induced blood pressure drop would help clinical physicians to pay ...attention to those with higher risk of death after admission to wards.
A total of 1023 patients who were admitted to the Dongyang People's Hospital, China, enrolled in this study. They were divided into model group (717 patients) and validation group (306 patients). The study included 13 variables. The independent risk factors leading to death within 30 days were screened by univariate analyses and multivariate logistic regression analyses and used for Nomogram. The discrimination and correction of the prediction model were assessed by the area under the Receiver Operating Characteristic (ROC) curve and the calibration chart. The clinical effectiveness of the prediction model was assessed by the Decision Curve Analysis (DCA).
Seven variables were independent risk factors, included peritonitis, respiratory failure, cardiac insufficiency, consciousness disturbance, tumor history, albumin level, and creatinine level at the time of admission. The area under the ROC curve of the model group and validation group was 0.834 and 0.836. The P value of the two sets of calibration charts was 0.702 and 0.866. The DCA curves of the model group and validation group were above the two extreme (insignificant) curves.
The model described in this study could effectively predict the death of patients with sepsis-induced blood pressure drop.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Mining activities, and especially open-pit mines, have a significant impact on the Earth’s surface. They influence vegetation cover, soil properties, and hydrological conditions, both during mining ...and for many years after the mines have been deactivated. Exploring a fast, accurate, and low-cost method to monitor changes, through years, in such an anthropogenic environment is, therefore, an open challenge for the Earth Science community. We selected a case study located in the northeast of Beijing, to assess geomorphic changes related to mining activities. In 2014 and 2016, an unmanned aerial vehicle (UAV) collected two series of high-resolution images. Through the structure-from-motion photogrammetric technique, the images were used to generate high-resolution digital elevation models (DEMs). The assessment of geomorphic changes was carried out by two methodologies. At first, we quantitatively estimated the detectable area, volumetric changes, and the mined tonnage by using the DEM of difference (DoD), which calculated the differences between two DEMs on a cells-by-cells basis. Secondly, the slope local length of autocorrelation (SLLAC) allowed determining the surface covered by open-pit mining by using an empirical model extracting the extent of the open-pit. The analysis of the DoD allows estimating the areal changes and the volumetric changes. The analysis of the SLLAC and its derived parameter allows for the accurate depiction of terraces and the extent of changes within the open-pit mine. Our results underlined how UAVs equipped with high-resolution cameras can be fast, precise, and low-cost instruments for obtaining multi-temporal topographic information, especially when combined with suitable methodologies to analyze the surface geomorphology, for dynamic monitoring of open-pit mines.
•Geochemical behaviors of n-alkanes were studied during hydrocarbon expulsion.•Higher hydrocarbon production potential can result in earlier expulsion of hydrocarbons.•The < C17n-alkanes were ...expelled preferentially than > C18n-alkanes.•The < C17n-alkanes might have been expelled earlier by molecule diffusion.•The > C18n-alkanes were likely expelled in a bulk oil flow mode.
Lipid biomarker parameters and molecular carbon isotope compositions of n-alkanes were used as tracers to determine geochemical behavior of aliphatic hydrocarbons during their migration from two sequences of source rock-type mudstones (4050.8–4062.2 and 4067.4–4073 m depth, respectively) to the interbedded reservoir sandstones (4062.6–4066.3 m depth) in the Qikou Sag, Bohai Bay Basin, eastern China. Abnormally high values of the production index (0.69–0.90) and total extract/total organic carbon (TOC) ratios (0.53–1.21) in the sandstone samples indicated that the hydrocarbons in the sandstones were not produced in situ but migrated from the adjoining mudstones. A comparison of values in lipid biomarker parameters (oleanane index, gammacerane index, C23 tricyclic terpane/C30 αβ-hopane, and C29/C27 ααα20R sterane ratios) in the sandstone with the mudstone samples indicated steroids and hopanoids in the upper section of the sandstone layer (4062.6–4065.2 m depth) were transported from the overlying mudstones, while those in the lower sandstone layer (4066.3 m depth) were transported from the underlying mudstones. Molecular carbon isotope data suggest that all n-alkanes in the upper section of the sandstone layer were transported from the overlying mudstones. However, in the lower sandstone layer, most of the C15–C17n-alkanes were transported from the overlying mudstones, while the C18–C31n-alkanes were transported mainly from the underlying mudstones. These combined results suggest the short-chain n-alkanes (<C17) were expelled preferentially relative to other compounds (probably via molecule diffusion) while the long-chain n-alkanes (>C18) and steroids/hopanoids may have been expelled in a separate oil phase.
G-protein-coupled receptors (GPCRs) have been tractable drug targets for decades with over one-third of currently marketed drugs targeting GPCRs. Of these, the class A GPCR superfamily is highly ...represented, and continued drug discovery for this family of receptors may provide novel therapeutics for a vast range of diseases. GPCR allosteric modulation is an innovative targeting approach that broadens the available small molecule toolbox and is proving to be a viable drug discovery strategy, as evidenced by recent FDA approvals and clinical trials. Numerous class A GPCR allosteric modulators have been discovered recently, and emerging trends such as the availability of GPCR crystal structures, diverse functional assays, and structure-based computational approaches are improving optimization and development. This Perspective provides an update on allosterically targeted class A GPCRs and their disease indications and the medicinal chemistry approaches toward novel allosteric modulators and highlights emerging trends and opportunities in the field.
The purpose of this study is to produce a landslide susceptibility map of Southeastern Helong City, Jilin Province, Northeastern China. According to the geological hazard survey (1:50,000) project of ...Helong city, a total of 83 landslides were mapped in the study area. The slope unit, which is classified based on the curvature watershed method, is selected as the mapping unit. Based on field investigations and previous studies, three groups of influencing Factors—Lithological factors, topographic factors, and geological environment factors (including ten influencing factors)—are selected as the influencing factors. Artificial neural networks (ANN’s) and support vector machines (SVM’s) are introduced to build the landslide susceptibility model. Five-fold cross-validation, the receiver operating characteristic curve, and statistical parameters are used to optimize model. The results show that the SVM model is the optimal model. The landslide susceptibility maps produced using the SVM model are classified into five grades—very high, high, moderate, low, and very low—and the areas of the five grades were 127.43, 151.60, 198.77, 491.19, and 506.91 km2, respectively. The very high and high susceptibility areas included 79.52% of the total landslides, demonstrating that the landslide susceptibility map produced in this paper is reasonable. Consequently, this study can serve as a guide for landslide prevention and for future land planning in the southeast of Helong city.
With geological big data becoming a focus of geoscience research, the vast amount of textual geoscience data provides both opportunities and challenges for data analysis and data mining. In fact, it ...does not seem possible to meet the demands of the big data age through the traditional manual reading for information extraction and gaining knowledge. In this paper, a workflow is proposed to extract prospecting information by text mining based on convolutional neural networks (CNNs). The aim is to classify the text data and extract the prospecting information automatically. The procedure involves three parts: 1) text data acquisition; 2) text classification based on CNN; and 3) statistics and visualization. First, the large amount of available text data was acquired based on geoscience big data acquisition methodologies. After text preprocessing, the CNN was used to classify the geoscience text data into four categories (geology, geophysics, geochemistry, and remote sensing), with each category consisting of three levels of text scales (word, sentence, and paragraph). Second, the word frequency statistics, co-occurrence matrix statistics, and term frequency-inverse document frequency (TF-IDF) statistics were for words, sentences, and paragraphs, respectively, which aimed to obtain the key nodes and links derived from the content-words. Finally, the deep semantic information of the big data mining of relevant geoscience texts was visualized by word clouds, knowledge graphs (e.g., the chord and bigram graphs), and TF-IDF statistical graphs. The Lala copper deposit in Sichuan province was taken as a test case, for which the prospecting information was extracted successfully by the developed text mining methodologies. This paper provides a strong basis for research into establishing mineral deposits prospecting models based on logical knowledge trees. In addition, it shows the great potential of this method for intelligent information extraction within geoscience big data.
There are three main factors controlling the formation of debris flow; of these, the ability to evaluate the volume of source materials in a catchment is the most significant. Source materials come ...from channel bed sediment, nearby landslides and rilling and surface erosion of slopes. The objective of this study was to develop a multi-source method–including field surveys, optical remote sensing interpretation, and interferometric synthetic aperture radar (InSAR) technology–to estimate the volume of source materials in the debris flow in the Xulong Gully (XLG), China. The qualitative degree of stability of the source materials was estimated with volume of approximately 91.9 × 104 m3. Considering sediment connectivity, landslides debris were interpreted using optical remote sensing, and their volume was calculated, using an empirical formula, to be about 191.01 × 104 m3. Continuous monitoring using InSAR could help to obtain the large-scope precise process of ground surface deformation. Estimated erosion rate ranges from 1633 m3/(km2·year) to 4552 m3/(km2·year) and annual volume of erosion was 9.08 × 104 m3/year–25.31 × 104 m3/year. Higher elevation with good vegetation coverage showed the sedimentation process, while lower elevation area with little vegetation showed erosion process. The highest degree of erosion occurred in the summer, followed by spring, autumn, and winter. The trend of the degree of erosion was consistent with that of the monthly rainfall in the XLG in 2018. Verification results demonstrated that the proposed approach could improve the efficiency and accuracy of the estimates of source material volume in debris flows and assess hazards.
•A quantitative approach estimating debris flow source material is established.•Proposing an improved volume statistical formula considering connectivity.•InSAR is introduced to estimate annual changing volume of erosion.•Analyzing slope erosion and sedimentation process in dry-hot valley area
To better understand the dynamic process of rock avalanches blocking rivers, a novel numerical approach based on the coupled Eulerian-finite-discrete element method (CEFDEM) is proposed. The ...Samaoding paleolandslide blocking river event, which occurred at the upstream of the Jinsha River was used as a case study to further validate the new numerical approach. Field investigations, thermoluminescence dating, and geomorphological analysis were conducted to determine basic geological conditions and provide data for the numerical simulations. Then a calibrated 3D landslide blocking river simulation based on the CEFDEM was conducted. The landslide blocking river lasted for 70 s and landslide scale from the numerical simulation agrees well with the field investigation. The maximum overall feature speed of the entire sliding mass is 35 m/s, while part of the sliding mass can reach 69 m/s. Dynamic fragmentation of the rock slide is stratified such that the bottom of the sliding body has higher fragmentation degree than the top. The variation of kinetic energy, accumulated friction dissipation, and fracture energy of the sliding mass are also shown. The impulsive water wave is triggered immediately after sliding mass runs into river, and its maximum height is 132 m, while part of the wave can reach a speed of 64 m/s. The river water will be pushed by the subsequent sliding mass movement. A comparison of the CEFDEM model and particle flow code (PFC) model in landslide blocking river was conducted, and the advantages and limitations of CEFDEM model were discussed in detail.
•A novel numerical approach of CEFDEM is proposed.•Continuity of rock and its fragmentation characteristics are considered and reflected well.•Dynamic process of a high-level rock slide blocking river in the deep valley is shown.
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