Patients with type 2 diabetes (DM) have a higher risk of developing pulmonary tuberculosis (PTB); moreover, DM co-morbidity in PTB is associated with poor PTB treatment outcomes. Community based ...prevalence data on DM and prediabetes (pre-DM) among TB patients is lacking, particularly from the developing world. Therefore we conducted a prospective study to investigate the prevalence of DM and pre-DM and evaluated the risk factors for the presence of DM among newly detected PTB patients in rural areas of China.
In a prospective community based study carried out from 2010 to 2012, a representative sample of 6382 newly detected PTB patients from 7 TB clinics in Linyi were tested for DM. A population of 6674 non-TB controls from the same community was similarly tested as well. The prevalence of DM in TB patients (6.3%) was higher than that in non-TB controls (4.7%, p<0.05). PTB patients had a higher odds of DM than non-TB controls (adjusted OR 3.17, 95% CI 1.14-8.84). The prevalence of DM increased with age and was significantly higher in TB patients in the age categories above 30 years (p<0.05). Among TB patients, those with normal weight (BMI 18.5-23.9) had the lowest prevalence of DM (5.8%). Increasing age, family history of DM, positive sputum smear, cavity on chest X-ray and higher yearly income (≥10000 RMB yuan) were positively associated and frequent outdoor activity was negatively associated with DM in PTB patients.
The prevalence of DM in PTB patients was higher than in non-TB controls with a 3 fold higher adjusted odds ratio of having DM. Given the increasing DM prevalence and still high burden of TB in China, this association may represent a new public health challenge concerning the prevention and treatment of both diseases.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background Patients with type 2 diabetes (DM) have a higher risk of developing pulmonary tuberculosis (PTB); moreover, DM co-morbidity in PTB is associated with poor PTB treatment outcomes. Community ...based prevalence data on DM and prediabetes (pre-DM) among TB patients is lacking, particularly from the developing world. Therefore we conducted a prospective study to investigate the prevalence of DM and pre-DM and evaluated the risk factors for the presence of DM among newly detected PTB patients in rural areas of China. Methods and Findings In a prospective community based study carried out from 2010 to 2012, a representative sample of 6382 newly detected PTB patients from 7 TB clinics in Linyi were tested for DM. A population of 6674 non-TB controls from the same community was similarly tested as well. The prevalence of DM in TB patients (6.3%) was higher than that in non-TB controls (4.7%, p<0.05). PTB patients had a higher odds of DM than non-TB controls (adjusted OR 3.17, 95% CI 1.14-8.84). The prevalence of DM increased with age and was significantly higher in TB patients in the age categories above 30 years (p<0.05). Among TB patients, those with normal weight (BMI 18.5-23.9) had the lowest prevalence of DM (5.8%). Increasing age, family history of DM, positive sputum smear, cavity on chest X-ray and higher yearly income ( greater than or equal to 10000 RMB yuan) were positively associated and frequent outdoor activity was negatively associated with DM in PTB patients. Conclusions The prevalence of DM in PTB patients was higher than in non-TB controls with a 3 fold higher adjusted odds ratio of having DM. Given the increasing DM prevalence and still high burden of TB in China, this association may represent a new public health challenge concerning the prevention and treatment of both diseases.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Convolutional Neural Networks (CNN) are widely applied to Ground Penetrating Radar (GPR) inversion because they have strong data-driven capabilities and are suitable for the data structure form of ...GPR. For CNN, the computation increases with the distance that the convolutional block moves from one region to another when it calculates the relationship between two regions. For GPR data, the target reflection exists in the surrounding traces and full time-window of the target, which leads to high degree of remote relationship. In this paper, we propose GPR-TransUNet, a deep-learning based inversion network which use self-attention mechanism. According to the characteristics of GPR data, regression network and GPR-Loss mechanism were used. Both numerical and model experiments were arranged to test the performance of the network, and the result as well as comparative analysis demonstrate the superiority of GPR-TransUNet. Finally, we applied this method to the field GPR data of Guangxi as an attempt.
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•GPR-TransUNet based on Regressive Self-Attention Mechanism and U-net is proposed.•GPR-TransUNet is successfully used to reconstruct the permittivity map of complex subsurface defects.•GPR-TransUNet is verified by synthetic and field data.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In ground-penetrating radar (GPR) data, clutter and noise are commonly observed in B-scan images, which can seriously affect the interpretability of the GPR data. In this article, we propose ...wavelet-GAN, a deep-learning network that integrates generative adversarial network (GAN) and discrete wavelet transform (DWT). Wavelet-GAN could decompose the GPR image into multiple frequency subimages and remove clutter. Additionally, we solve the problem of error handling when there are no features in the dataset through micro datasets, dataset fine-tuning, high-speed training, and multiple feature generalization. Our method decomposes GPR image by DWT, then convolutional neural network (CNN) and GAN are, respectively, used to reconstruct low-frequency and high-frequency target signal information. Finally, the information of different frequency bands is combined into a new GPR image by inverse DWT (IDWT). Wavelet-GAN uses a small-scale dataset for training, which enables it to make rapid adjustments to process new target types, even if we only have one typical target data. We compare our method with traditional methods and other deep-learning based methods and demonstrate that our wavelet-GAN performs better in real data processing. Finally, we apply this method as a data-preprocessing tool for machine learning inversion and tested its feasibility.
Water hazards threat the safety of underground mining. A detailed characterization of hydraulic parameters is helpful for the design of engineering projects to prevent underground water hazards. ...Utilizing aquifer’s natural artesian condition, water releasing tests are often performed in China’s underground mines. In this research, we propose to interpret water releasing test data with hydraulic tomography (HT) technique. A 216-hour duration test was performed to localize water hazard areas in Ningtiaota underground coal mine. Groundwater was released with four sequential stages from 30 boreholes. HT analysis clearly shows that the study area has a potential water hazard region in southern part by only using data from the first two stages, which coincides with existing water inrush incident location. With the addition of more pressure responses from stages 3 and 4, it discovers low transmissivity and storativity values in northern part. Our numerical examples further demonstrate that sequential water releasing tests produce comparable hydraulic parameter distributions to tomographic tests. In the end, we conclude that water releasing tests aided by HT technique can leads to a high resolution characterization of water hazard aquifers.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract Quantification of non‐uniqueness and uncertainty is important for transient electromagnetism (TEM). To address this issue, we develop a trans‐dimensional Bayesian inversion schema for TEM ...data interpretation. The trans‐dimensional posterior probability density (PPD) offers a solution to model selection and quantifies parameter uncertainty resulting from the model selection from all possible models rather than determining a single model. We use the reversible‐jump Markov chain Monte Carlo sampler to draw ensembles of models to approximate PPD. In addition to providing reasonable model selection, we address the reliability of the inversion results for uncertainty analysis. This strategy offers reasonable guidance when interpreting the inversion results. We make the following improvements in this paper. First, in terms of algorithmic acceleration, we use the nonlinear optimization inversion results as the initial model and implement the multi‐chain parallel method. Second, we develop double factors to control the sampling step size of the proposed distribution, so that the sampling models cover the high‐probability region of the parameter space as much as possible. Finally, we provide the potential scale reduction factor‐ η convergence criteria to assess the convergence of the samples and ensure the rationality of the output models. The proposed methodology is first tested on synthetic data and subsequently applied to a field dataset. The TEM inversion results show that probability inversion can provide reliable references for data interpretation through uncertainty analysis.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Highlights • Nuclear overexpression of YAP1 was linked to poor prognosis with GBC. • This study examined the mechanistic roles of YAP1 in GBC. • YAP1 promotes GBC cell growth via activation of the ...AXL/MAPK pathway. • This work provided a rationale for YAP1 as a therapeutic target for GBC.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Objective:
Transcription elongation factor 1 (
TCERG1
) is a nuclear protein consisted of multiple protein structural domains that plays an important role in regulating the transcription, extension, ...and splicing regulation of RNA polymerase II. However, the prognostic and immunological role of
TCERG
1 in human cancer remains unknown. In this study, we analyzed the expression of
TCERG1
gene in hepatocellular carcinoma (HCC) patients, its clinical significance, and its possible prognostic value by bioinformatics.
Methods:
RNA sequencing data and clinicopathological characteristics of patients with HCC were collected from TCGA and CCLE databases. The Wilcoxon rank-sum test was used to analyze the expression of
TCERG1
in HCC tissues and normal tissues. The protein levels of
TCERG1
between normal and liver cancer tissues were analyzed by the Human Protein Atlas Database (HPA) (
www.proteinatlas.org
). Validation was performed using the Gene Expression Omnibus (GEO) dataset of 167 samples. The expression of
TCERG1
in HCC cells were verified by qRT-PCR, and CCK-8, scratch assay and Transwell assay were performed to detect cell proliferation, migration and invasion ability. According to the median value of
TCERG1
expression, patients were divided into high and low subgroups. Logistic regression, GSEA enrichment, TME, and single-sample set gene enrichment analysis (ssGSEA) were performed to explore the effects of
TCERG1
on liver cancer biological function and immune infiltrates.
TCERG1
co-expression networks were studied through the CCLE database and the LinkedOmics database to analyze genes that interact with
TCERG1
.
Results:
The expression levels of
TCERG1
in HCC patient tissues were significantly higher than in normal tissues. Survival analysis showed that high levels of
TCERG1
expression were significantly associated with low survival rates in HCC patients. Multifactorial analysis showed that high
TCERG1
expression was an independent risk factor affecting tumor prognosis. This result was also verified in the GEO database. Cellular experiments demonstrated that cell proliferation, migration and invasion were inhibited after silencing of
TCERG1
gene expression. Co-expression analysis revealed that
CPSF6
and
MAML1
expression were positively correlated with
TCERG1
. GSEA showed that in samples with high
TCERG1
expression, relevant signaling pathways associated with cell cycle, apoptosis, pathways in cancer and enriched in known tumors included Wnt signaling pathway, Vegf signaling pathway, Notch signaling pathway, MAPK signaling pathway and MTOR pathways. The expression of
TCERG1
was positively correlated with tumor immune infiltrating cells (T helper two cells, T helper cells).
Conclusion:
TCERG1
gene is highly expressed in hepatocellular carcinoma tissues, which is associated with the poor prognosis of liver cancer, and may be one of the markers for the diagnosis and screening of liver cancer and the prediction of prognosis effect. At the same time,
TCERG1
may also become a new target for tumor immunotherapy.
When analyzing a block cipher, the first step is to search for some valid distinguishers, for example, the differential trails in the differential cryptanalysis and the linear trails in the linear ...cryptanalysis. A distinguisher is advantageous if it can be utilized to attack more rounds and the amount of the involved key bits during the key-recovery process is small, as this leads to a long attack with a low complexity. In this article, we propose a two-step strategy to search for such advantageous distinguishers. This strategy is inspired by the intuition that if a differential is advantageous only when some properties are satisfied, then we can predefine some constraints describing these properties and search for the differentials in the small set.As applications, our strategy is used to analyze GIFT-128, which was proposed in CHES 2017. Based on some 20-round differentials, we give the first 27-round differential attack on GIFT-128, which covers one more round than the best previous result. Also, based on two 17-round linear trails, we give the first linear hull attack on GIFT-128, which covers 22 rounds. In addition, we also give some results on two GIFT-128 based AEADs GIFT-COFB and SUNDAE-GIFT.
When tunneling in karst mountainous areas, some adverse geological conditions such as karst caves and water-rich strata are often encountered, which may induce unexpected geological hazards and ...represent major hidden threats to construction safety. This paper describes the successful use of integrated investigation techniques in Qinlan-1 tunnel to predict geological hazards in Guangxi, China. The study area is located in limestone strata which are significantly affected by the fault and developed karst. The comprehensive investigation technique mainly included the geological survey, semi-airborne transient electromagnetic method (SATEM), seismic ahead prospecting (SAP), and ground penetrating radar (GPR). First, SATEM was applied to delineate the distribution scope and depth of the anomalous zones of apparent resistivity in the study area before the tunnel excavation. Then, SAP and GPR were used during excavation to investigate the detailed geological condition, such as the lithological change, the fracture zone’s interface, and the karst cave’s location. Finally, a fracture zone and a mud-filled karst cave were successfully detected, proving the effectiveness of the comprehensive investigation system. The comprehensive investigation system improves the exploration efficiency and accuracy of the adverse geological structure of the tunnel.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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