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Deficiency of the interleukin-36 receptor antagonist (DITRA) is an autosomal recessive disease caused by mutations of the IL36RN gene. DITRA is characterized by acute generalized pustular psoriasis (GPP), fever, systemic inflammation, and leukocytosis. We report the case of a 5-year-old girl with severe scalp and nail involvement with a rapid response to secukinumab.

In order to solve the problem that the assumption of the straight shaft is inconsistent with the actual situation of marine water-lubricated journal bearings, the journal bending curve was identifi...

Objective: To compare the efficacy and safety of hyperthermic intravesical chemotherapy (HIVEC) and intravesical chemotherapy (IVEC) in patients with intermediate and high risk nonmuscle-invasive bladder cancer (NMIBC) after transurethral resection. Methods: We included 560 patients diagnosed with primary or recurrent NMIBC between April 2009 and December 2015 at 1 of 6 tertiary centers. We matched 364 intermediate or high risk cases and divided them into 2 groups: the HIVEC+IVEC group [chemohyperthermia (CHT) composed of 3 consecutive sessions followed by intravesical instillation without hyperthermia] and the IVEC group (intravesical instillation without hyperthermia). The data were recorded in the database. The primary endpoint was 2-year recurrence-free survival (RFS) in all NMIBC patients (n = 364), whereas the secondary endpoints were the assessment of radical cystectomy (RC) and 5-year overall survival (OS). Results: There was a significant difference in the 2-year RFS between the two groups in all patients (n = 364; HIVEC+IVEC: 82.42% vs. IVEC: 74.18%, P = 0.038). Compared with the IVEC group, the HIVEC+IVEC group had a lower incidence of RC (P = 0.0274). However, the 5-year OS was the same between the 2 groups (P = 0.1434). Adverse events (AEs) occurred in 32.7% of all patients, but none of the events was serious (grades 3–4). No difference in the incidence or severity of AEs between each treatment modality was observed. Conclusions: This retrospective study showed that HIVEC+IVEC had a higher 2-year RFS and a lower incidence of RC than IVEC therapy in intermediate and high risk NMIBC patients. Both treatments were well-tolerated in a similar manner.

Background Specific factors correlated with hypothyroidism in systemic lupus erythematosus (SLE) patients remain unclear. Therefore, we aim to evaluate the prevalence of thyroid dysfunction in Chinese patients with SLE and the relationship between clinical hypothyroidism and SLE. Methods We conducted a cross sectional study of the prevalence of thyroid dysfunction in 672 patients with SLE and 605 age- and sex-matched healthy controls. Demographic, clinical, and biochemical data were compared between 58 patients with SLE with hypothyroidism and 197 patients with SLE with euthyroidism. Multivariate analysis was performed using binomial logistic regression analysis. Spearman's rank correlation was used to identify an association between thyroid function and disease activity. Results The prevalence of thyroid dysfunction was significantly higher in patients with SLE than in controls (70.7% vs 19.7%). SLE was associated with higher rates of hypothyroidism (9.6%, P ≤ 0.001) and euthyroid sick syndrome (49.6%, P ≤ 0.001) compared with control subjects. Further analyses showed that hypothyroidism in patients with SLE was associated with high blood pressure, renal disorder, high serum creatinine, high uric acid, hyperlipidaemia, low C3 and C4, positive anti-dsDNA antibodies, and high SLE disease activity index (SLEDAI) score. In multiple logistic regression models, albumin, platelet count, serum creatinine, and anti-dsDNA antibodies were associated with hypothyroidism. Finally, free tri-iodothyronine was significantly negatively correlated with SLEDAI score. Conclusions Hypothyroidism was more prevalent in patients with SLE. There was a relationship between hypothyroidism with renal disorder and lupus activity. Albumin, platelet count, serum creatinine, and anti-dsDNA antibodies were correlated with hypothyroidism.

What is known and objective The frequent tenofovir disoproxil fumarate (TDF)-related adverse drug reactions (ADRs) are nephrotoxicity and bone toxicity; however, tooth-related ADRs of TDF have not been reported. We describe the case of a 41-year-old Han Chinese man with chronic hepatitis B with TDF-associated tooth loss. Case summary He presented with halitosis, gingival swelling and tooth loss after TDF use. After excluding the possibility of other drug-related ADRs, TDF was considered a possible cause and switched with tenofovir alafenamide fumarate (TAF). After 6 months, the oral symptoms disappeared, with no additional tooth loss. What is new and conclusion This is the first report of such ADRs. The ADR score was 7, indicating tooth loss as a potential TDF-related ADR.

The recognition of partial discharge mode is an important indicator of the insulation condition in transformers, based on which maintenance can be arranged. Discharge feature extraction is the key to recognize discharge mode. To solve the problem of poor stability and low recognition rate of partial discharge mode, this paper proposes a feature extraction method based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy. First, the four partial discharge signals collected under laboratory conditions are decomposed by synchrosqueezed windowed Fourier transform, then a number of band-limited intrinsic mode type functions are obtained, and the original feature quantities of partial discharge signals are obtained by calculating the multi-scale dispersion entropies of each intrinsic mode type function. Based on that, original feature quantity is optimized by using the maximum relevance and minimum redundancy criteria. Finally, the classification is implemented by the support vector machine. Experimental results show that in the case of noise interference, the proposed synchrosqueezed windowed Fourier transform–multi-scale dispersion entropy method can still accurately describe the feature of different discharge signals and has a higher recognition rate than both the empirical mode decomposition–multi-scale dispersion entropy method and the direct multi-scale dispersion entropy method.

Background/Objective: To establish and validate an individualized nomogram to predict the probability of death within 30 days in patients with sepsis would help clinical physicians to make correct decision. Methods We collected data of 1,205 patients with sepsis. These included 16 indexes like age and blood, randomly assigned to the modeling and verification groups. In the modeling group, the independent risk factors related to death within 30 days were analyzed. Besides, a nomogram was established to draw the receiver-operating characteristic (ROC) curve of the subjects. Subsequently, the discriminant ability of the model was evaluated by the area under the ROC curve (AUC). Then, a calibration chart and Hosmer-Lemeshow test were employed to evaluate the calibration degree of the model, and the Decline Curve Analysis (DCA) test was used to evaluate the clinical effect of the model. Results The different independent risk factors related to the death of sepsis patients within 30 days included pro-brain natriuretic peptide (pro.bnp), albumin, lactic acid (lac), oxygenation index, mean arterial pressure (map), and hematocrit (hct). The AUC of the modeling and verification groups were 0.815 and 0.806, respectively. Moreover, the P-values of the Hosmer-Lemeshow test in the two groups were 0.391 and 0.100, respectively, and the DCA curves of the two groups were both above the two extreme curves. Conclusion Our model presents good significance for predicting the death of sepsis patients within 30 days. Therefore, there is a need to implement this model in clinical practice, as prompt prediction could help tailor treatment regimens and enhance survival outcomes.

Videos grow to be one of the largest mediums on the Internet. E-commerce platforms like Alibaba need to process millions of video data across multimedia (e.g., visual, audio, image, and text) and on a variety of tasks (e.g., retrieval, tagging, and summary) every day. In this work, we aim to develop a once and for all pretraining technique for diverse modalities and downstream tasks. To achieve this, we make the following contributions: (1) We propose a self-supervised multi-modal co-training framework. It takes cross-modal pseudo-label consistency as the supervision and can jointly learn representations of multiple modalities. (2) We introduce several novel techniques (e.g., sliding-window subset sampling, coarse-to-fine clustering, fast spatial-temporal convolution and parallel data transmission and processing) to optimize the training process, making billion-scale stable training feasible. (3) We construct a large-scale multi-modal dataset consisting of 1.4 billion videos (~0.5 PB) and train our framework on it. The training takes only 4.6 days on an in-house 256 GPUs cluster, and it simultaneously produces pretrained video, audio, image, motion, and text networks. (4) Finetuning from our pretrained models, we obtain significant performance gains and faster convergence on diverse multimedia tasks at Alibaba. Furthermore, we also validate the learned representation on public datasets. Despite the domain gap between our commodity-centric pretraining and the action-centric evaluation data, we show superior results against state-of-the-arts.

A floor rock roadway under an oblique straddle working face is a typical dynamic pressure roadway. Under the complex disturbance of excavation engineering works, the roadway often undergoes stress concentration and severe deformation and damage. To solve the problem of surrounding rock stability control for this roadway type, this study considered the East Forth main transport roadway in the floor strata of the 1762(3) working face of the Pansan coal mine. In situ ground pressure monitoring and numerical simulation calculation using the FLAC2D software were carried out. The influence laws of the surrounding rock lithology, the vertical and horizontal distance between the roadway and overlying working face, the positional relationship between the roadway and the overlying working face, and the support form and strength of the rock surrounding an oblique straddle roadway were obtained. Within the range of mining influence, the properties of the rock surrounding the roof and floor were very different, and the deformation of the rock surrounding the two sides exhibited regional difference. The influence range of the mining working face on the rock floor of the roadway was approximately 30–40 m, and that of horizontal mining was approximately 50–60 m. The mining influence on the rock surrounding the side roadway of the working face is large, but the mining influence on the roadway below is small. Using FLAC2D, the stress and displacement characteristics of the rock surrounding the obliquely straddle roadway were compared and analyzed when the bolt support, combined bolt and shed support, and bolt–shotcreting–grouting support were adopted, the proposed support scheme of bolting and shotcreting was successfully applied. The deformation of the rock surrounding the roadway was satisfactorily controlled, and the results were useful as a reference for similar roadway maintenance projects.

In the present study, promoter hypermethylation of cysteine dioxygenase type 1 (CDO1) was evaluated in non-small cell lung cancer (NSCLC) tissues to assess the value of CDO1 as a novel biomarker to improve the diagnosis of NSCLC. Tumor tissue samples and corresponding normal lung tissue samples from 42 patients with NSCLC were obtained at the Department of Thoracic Surgery, The Second Xiangya Hospital (Changsha, China). Conventional methylation-specific PCR (cMSP) and methylation-on-beads followed by quantitative methylation-specific PCR (MOB-qMSP) were used to analyze the tumor and normal lung tissue samples. Using these two methods, promoter DNA hypermethylation of the CDO1 gene was detected in 59.4 and 71.0% of tumor tissues of patients with NSCLC and in 9.4 and 0% of normal lung tissue, respectively. Compared with the rate of methylation in the well-differentiated NSCLC tissues (15.4 and 55.6%, respectively), the rate of CDO1 gene promoter methylation was higher in the poorly differentiated tissues (89.5 and 92.3%, respectively). Overall, it was demonstrated that the MOB-qMSP method had a higher positive detection rate for CDO1 hypermethylation compared with the cMSP method. In conclusion, CDO1 gene promoter hypermethylation was more frequently observed in NSCLC tissues compared with in normal lung tissues, and a high methylation frequency of the CDO1 gene in biopsy specimens of NSCLC was associated with the degree of differentiation.

Purpose: To determine the protective effect of ginkgolide B (GB) against isoproterenol (ISO)-induced chronic heart failure in a rat model. Methods: A total of 32 male Wistar rats were randomly divided into 4 groups. Rats in control group received only saline, while rats in GB alone group were injected with GB at a dose of 20 mg/kg body weight (bwt) intraperitoneally (i.p). Another group of rats was injected with ISO subcutaneously (s.c.) at a dose of 85 mg/kg for 2 days (ISO group). Rats in the GB+ISO group were administered GB at a dose of 20 mg/kg, i.p., for 7 days prior to exposure to ISO s.c. at a dose of 85 mg/kg. Results: Rats pre-treated with GB for 7 days prior to ISO exposure showed a significant decrease in cardiac infarct size, and marked decreases in the levels of cardiac biomarkers, inflammatory and apoptotic biomarkers, and lipid peroxidation (p < 0.05), but significant improvement in the levels ofendogenous antioxidants (p < 0.05). In addition, GB administration resulted in marked increases in the protein expression levels of heme oxygenase-1 (HO-1) and Nrf2 in cardiac tissue (p < 0.05). Conclusion: These results indicate that pre-treatment of chronic heart failure rats with GB for 7 consecutive days considerably lowered inflammatory and apoptotic markers via upregulation of Nrf2/HO-1 signaling pathway. Thus, GB has cardioprotective potential in humans. Keywords: Ginkgolide B, Nrf2/HO-1, Inflammatory markers, Apoptotic markers, Antioxidants

As an optical molecular imaging modality, fluorescence molecular tomography (FMT) can monitor the activities of organisms in vivo at the molecular and cellular levels. However, the recovered image quality is affected by mesh voxel when the finite element method is utilized to recover the fluorescence probe. The target localization is likely to deviate from the actual target under the coarse mesh, but using the fine mesh will increase the number of unknowns, which makes the computational burden heavier and further aggravate the ill-posedness. To solve the problem, a reconstruction strategy using a non-uniform mesh for FMT is developed in this paper. The numerical experiment and physical experiment validated that the strategy is capable and effective for FMT.

The most aggressive form of brain tumor is gliomas, which leads to concise life when high grade. The early detection of glioma is important to save the life of patients. MRI is a commonly used approach for brain tumors evaluation. However, the massive amount of data provided by MRI prevents manual segmentation in a reasonable time, restricting the use of accurate quantitative measurements in clinical practice. An automatic and reliable method is required that can segment tumors accurately. To achieve end-to-end brain tumor segmentation, a hybrid deep learning model RMU-Net is proposed. The architecture of MobileNetV2 is modified by adding residual blocks to learn in-depth features. This modified Mobile Net V2 is used as an encoder in the proposed network, and upsampling layers of U-Net are used as the decoder part. The proposed model has been validated on BraTS 2020, BraTS 2019, and BraTS 2018 datasets. The RMU-Net achieved the dice coefficient scores for WT, TC, and ET of 91.35%, 88.13%, and 83.26% on the BraTS 2020 dataset, 91.76%, 91.23%, and 83.19% on the BraTS 2019 dataset, and 90.80%, 86.75%, and 79.36% on the BraTS 2018 dataset, respectively. The performance of the proposed method outperforms with less computational cost and time as compared to previous methods.

In order to better understand the developmental conditions and trends of the biodiversity at agricultural heritage sites, the structure and function of the ecosystem and ecological environment in Qingtian Rice-Fish Culture System is examined in the decade since the implementation of the GIAHS project. Through the collection of historical data and sample-plot survey, this study collates the data from 2005 to 2013 related to the heritage site to evaluate the ecological benefit of the Agricultural Heritage System conservation. The results show that: (1) Since the implementation of heritage conservation, the variety of traditional rice grown at the heritage sites basically remains stable, the varieties of other crops basically remain unchanged and the varieties of fruit trees and medicinal plants have developed at faster rates. As the ecological environment at the heritage sites gradually improved, the species and richness of the biodiversity in the ecosystem have increased, especially the increasing growth of egrets and boars. (2) From 2005 to 2013 in the Fangshan Town of Qingtian County, the ecosystem areas identified as river, forest, urban and bare land have increased. The increased area of the urban ecosystem is the largest one of them, which is up to 20.30 ha; while the area of the forest ecosystem has increased to 7.29 ha. The areas of wetland, reservoir, farmland and grassland have been reduced, and the area of grassland ecosystem has been reduced the most with a reduction of 28.87 ha. From the changes in the values of the ecosystem services of different ecosystem types, the values of forest ecosystem services have achieved the most growth, reaching up to 92000 yuan yr-1 and the growth of the river and bare land ecosystem service values are not obvious. (3) Over nearly a decade, the soil nutrients and the water quality of rice fields at the heritage site have not obviously changed and the plant diseases and insect pests in the rice fields have not been aggravated. The monitoring indexes are far better than the standard values. With the great support of the local government, the production and living conditions and the living environments of farmers in the villages of the heritage sites have been greatly improved through road reconstruction, water improvement, toilet enhancements and the greening, brightness and beautification of the villages.

Based on degradable pH-responsive hydrogel, we report on an enhanced three-dimensional data encryption security technique in which a pH value is used for information manipulation. Featuring three types of states upon the pH value variation, namely, shrinkage, expansion and degradation, the hydrogel renders a limited pH value window as the “key” for information decryption. The pH-dependent shrinkage-to-expansion conversion of the hydrogel leads to a threshold pH value for retrieving the recorded data, whilst the degradability of the hydrogel, which can be tuned by adjusting the composition ratio of PEGDA/AAc, gives rise to a second threshold pH value for irreversibly sabotaging the retrieved data. Pre-doping silver ions in the hydrogel facilitates explicit recording and reading of binary data in forms of three-dimensional silver patterns through photoreduction and scattering, respectively, with a femtosecond laser. By accurately matching the vertical spacing of the encoded silver nanopatterns with the diffraction-limited focal depth of the decryption microscope, we can tune the pH value to encrypt and retrieve information recorded in layers and set a critical pH value to smash encoded information, which proves a highly secured 3D data encoding protocol. This strategy can effectively enrich data encryption techniques, vastly enhancing data security within unattained chemical dimensions.

Background. Various studies reported that the prognosis of patients with cervical cancer (CC) was significantly associated with immunity, whereas limited studies have explored whether immune-associated genes could be classifiers for recurrence-free survival (RFS) of stage I CC. Thus, an improved immune-related gene signature for stage I CC patients’ prognosis is urgently required. Materials and Methods. We retrospectively analyzed the gene expression profiles of stage I CC patients in the GSE44001 set from the Gene Expression Omnibus (GEO) database. The stage I CC patients were randomly divided into the training group and the internal validation group. The training patients were adopted to develop a prognostic immune gene-based signature; meanwhile, the internal validation patients were used to validate the power of the selected immune gene-related signature using univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. The accuracy and reliability of the immune gene-related signature were evaluated based on Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves. Results. High power of the 8-immune gene signature was found on the basis of ROC analysis (AUC at 1, 3, and 5 years were exhibited in the internal validation group (0.702, 0.715, and 0.728, respectively), external validation group (0.702, 0.825, and 0.842, respectively), and entire GEO dataset (0.840, 0.894, and 0.852, respectively)). Besides, C -index, ROC, calibration plots, and decision curve analysis (DCA) also acted well in our nomogram, suggestive of a high ability of the nomogram to elevate the prognostic prediction of stage I CC patients. Conclusions. In this study, we successfully constructed an integrated 8-immune gene-based signature which could accurately identify patients with low prognostic risk from those with high prognostic risk. In addition, we developed an immune-related nomogram which can elevate the prognostic prediction of stage I CC patients.

A key challenge in self-supervised video representation learning is how to effectively capture motion information besides context bias. While most existing works implicitly achieve this with video-specific pretext tasks (e.g., predicting clip orders, time arrows, and paces), we develop a method that explicitly decouples motion supervision from context bias through a carefully designed pretext task. Specifically, we take the keyframes and motion vectors in compressed videos (e.g., in H.264 format) as the supervision sources for context and motion, respectively, which can be efficiently extracted at over 500 fps on the CPU. Then we design two pretext tasks that are jointly optimized: a context matching task where a pairwise contrastive loss is cast between video clip and keyframe features; and a motion prediction task where clip features, passed through an encoder-decoder network, are used to estimate motion features in a near future. These two tasks use a shared video backbone and separate MLP heads. Experiments show that our approach improves the quality of the learned video representation over previous works, where we obtain absolute gains of 16.0% and 11.1% in video retrieval recall on UCF101 and HMDB51, respectively. Moreover, we find the motion prediction to be a strong regularization for video networks, where using it as an auxiliary task improves the accuracy of action recognition with a margin of 7.4%~13.8%., Comment: Accepted by CVPR2021